Monthly Archives: December 2020

Smaller and Small: Strategies to Iterate to Knowledge about the Granular Aspects of Donations

DOI: 10.31038/CST.2020543

Abstract

The paper presents the use of an emerging science, Mind Genomics, to understand a practical aspect of daily life: what motivates a person to donate to a specific charity. Beyond the knowledge of specific messages which are deemed to be potentially effective as a stimulus to donation, the paper shows how knowledge of a specific end-use can inform us about the mind of a person for a more general problem—how understanding the messages for donation drives a deeper understanding of human motivation. The paper moves from inexpensive pilot tests, through an affordable experiment, and onto the creation of a tool to assign new people worldwide to the proper groups, so they can receive the appropriately targeted messages.

Introduction

Knowing What to Say to Donors to Encourage Giving

In today’s world, departments of development for various organizations have become increasingly important and active. One is inundated daily by requests for donations for all sorts of causes, ranging from simple letters from individuals to sophisticated outreach including brochures and other presentations with information intended to tap one’s emotions and open one’s wallets. Most appeals from organizations appear to be ‘on point,’ with the proper phrases, the proper images, and so forth [1-3].

Approaches to Science – Idiographic versus Nomothetic

Today’s culture of science drives research towards large samples and well-defined stimuli. Despite the fact that a great deal of science is exploratory, the majority of studies published would have us believe that the studies are following the hallowed dicta of philosopher of science Karl Popper, invoking the hypothetico-deductive system, creating a hypothesis, and then falsifying it. The editors of major journals look for breakthrough work, combining a robust combination of novelty and familiarity. Such work is not common, although it occasionally surfaces. The evolving culture of science focuses on extensions of today’s state of knowledge as represented in the existing scientific literature. The typical phrase is ‘plugging holes in the literature,’ or ‘answer a call from the literature.’ Scientific rigor is as much rigorous statistics as rigorous thinking. The published work must convince by virtue of statistical differences, not by daring challenges which advance science. Despite what is promoted as scientific ‘doctrine,’ today’s scientific world frowns upon these new directions, however, when the content of journals and the reactions of reviewers are studied in detail. A quandary arises when the research is meant to explore a topic rigorously with good underlying design but with affordable samples, with the goal to be used for practical ends while truly adding to knowledge of a topic. Can this effort be called science? Typically, these problems emerge in the social and behavioral sciences, but less frequently in the harder sciences.

The focus of this paper is how one can quickly, inexpensively, and rigorously uncover the nature of the donor’s mind for a specific end recipient, that recipient being Children’s Cancer Center (name disguised to preserve confidentiality). The objective is to support children with cancer by addressing their medical, social, and psychological needs, as well as their family’s challenges. The problem is to discover what type of messages are likely to drive a person to donate. The problem is a practical one with a limited scope, specifically Children’s Cancer Center’s donations, but the learning which emerges from the study is relevant to an understanding of other communications driving support for a given charity. The empirical part of this paper shows the two steps followed to discover what to say to potential donors about Children’s Cancer Center. The combination of the two studies may be viewed as a discussion of ‘method,’ so-called methodological research. The specific findings of the second study, which is larger, but still small in terms of general practice, show what can be discovered for practical use.

About Children’s Cancer Center

Data from the World Health Organization (WHO) and the National Cancer Institute reveal that, in the United States, cancer is the leading cause of death by disease past infancy and will lead to the deaths of approximately 1,190 children in the U.S. in 2021. Further, As of January, 2015, the most recent data readily available, The National Cancer Institute reports that there are 429,000 survivors of childhood and adolescent cancer (diagnosed at ages 0 to 19 years) alive in the United States, and these survivors face serious medical problems during and after the acute phase of their disease (National Cancer Institute, 2018, 2020) [4,5].

Childhood cancer is a global issue. According to St. Jude Children’s Research Hospital’s website, cancer is diagnosed each year in about 175,000 children ages 14. The World Health Organization reports that more than 300,000 new cases are diagnosed annually in children ages 0-19. The number of actual cases is probably greater, because children in low-income countries are not likely to be included as part of the count. As Alex’s Lemonade website points out, “globally, cancer stole 11.5 million years of healthy life away from children in 2017.” This is because of life years taken away from kids who die, as opposed to a 90-year-old adult who dies of cancer and has very few life years left (Alex’s Lemonade, 2020) [6].

Despite the global prevalence of childhood cancer and the death rates associated with it, now 80% survival rates for US children but only 20% globally, only 4% of US government funding in the cancer sector is directed towards pediatric cancers. This has been challenged by pediatric cancer activists for years. A 2015 article by Kristin Connor in the Washington Examiner sheds light on the logic behind why our government doesn’t increase funding for childhood cancer:

“…cancer research funds are driven by the number of people — of any age — who have the disease. And, of course, adults, with decades of exposures and behaviors, experience cancer in much greater numbers than young children. This approach therefore seems like the “democratic” way to distribute federal money. Yet it doesn’t do much for the more than 15,700 children diagnosed each year with cancer, and the more than 40,000 children undergoing cancer treatment each year all across the United States. But instead of looking at the number of annual diagnoses, perhaps we should consider the number of life-years potentially saved. For each child with cancer, on average, as many as 71 potential life years might be saved. That’s an important factor that is not being considered when funding allocation decisions are made.” [7].

Despite great progress in US survival rates (84% of children diagnosed with cancer are alive at least five years after diagnosis), 16% are still dying AND those who do survive for five years are not necessarily cured, and many of them suffer from long-term side effects from their illness and associated treatments. According to Alex’s Lemonade, “Children who were treated for cancer are twice as likely to suffer chronic health conditions later in life versus children without a history of cancer.”

Some reason for optimism comes from the WHO: “most childhood cancers can be cured with generic medicines and other forms of treatments including surgery and radiotherapy. Treatment of childhood cancer can be cost-effective in all income settings.” Early intervention is critical to improving pediatric cancer outcomes. The WHO also calls for childhood cancer data systems, which are “needed to drive continuous improvements in the quality of care, and to drive policy decisions.” Donating to organizations like Children’s Cancer Center supports those factors that will lead to improved outcomes around the world. With the WHO’s statement that “the most effective strategy to reduce the burden of cancer in children is to focus on a prompt, correct diagnosis followed by effective therapy,” supporting an organization like Children’s Cancer Center is critical to reducing death rates of children worldwide [8].

The Mind Genomics Approach

Mind Genomics is an emerging science with roots in experimental psychology, sociology, consumer research, and statistics, respectively. The objective of a Mind Genomics study is to understand the messages for a topic which drive a specific response, such as ‘Dislike/Like,’ ‘Not interested/Interested,’ ‘Will Not donate/ will donate,’ ‘Will pay a certain amount,’ ‘expected to feel a certain way,’ and so forth. The purview of Mind Genomics is everyday life and the expected decisions that people make when they are presented with messages about a specific, granular, situation, of the type that would confront them daily. The process of Mind Genomics, the intellectual underpinnings, the statistics, and business-relevant patents have been documented extensively, and need not be repeated in their specifics. The reader is directed to a representative list [9-11]. Mind Genomics grew out of the need to create a new vision of science, one studying the behavior of the everyday, from the viewpoint of experimentation, rather than observation. Anthropology already studied individual cultures and behaviors in depth, with recent efforts attempting to move from purely descriptive to quantitative [12]. Sociology already studies everyday behavior but does not conduct experiments, and looks for general rules in everyday behavior, rules which are ‘nomothetic,’ dealing with generalities. Social psychology moves more closely into the world of the mind but again deals with issues of nomos. Social psychology is not experimental, and while it may deal with ordinary daily behavior, it attempts to provide a broad sweep of the behavior of people, rather than focusing on the topic itself. The topic of the study is only a means to understand the person. In the above disciplines, researchers focus on the person, using the normal situation to understand the person.

In contrast to other disciplines, Mind Genomics focuses on the specifics of the situation, using the person and the rules of judgment to understand. Thus, the learning is about the specifics of daily life, and less so about the person himself or herself. Indeed, one might use the metaphor that Mind Genomics focuses on the situation, with the situation ‘illuminated’ through the lights of different sources. These ‘lights,’ these different forms of illumination, are the people. The ultimate objective of Mind Genomics is to create a ‘Wiki of daily experience,’ a virtual encyclopedia of daily life and the different aspects of that daily life, dimensionalized into specifics, with the data being the aspect and numbers representing the way the ordinary person feels about that specific, on some type of scale. The problem for the ‘project of science’ is what type of information is acceptable for science? That is, the project discussed has a specific objective. Does the fact that there is such an objective invalidate the science, simply because the results pertain to a specific end-user, the Children’s Cancer Center charity? Furthermore, are the results not ‘valid’ because the base size is low? Finally, what is the status of the preparatory study—a small preliminary study to identify whether there are messages which resonate? Do preparatory studies deserve a place in the research report, because they illustrate the way towards making the larger discovery, by one or a set of small, ‘trial’ experiments?

Illustrating the Process of Mind Genomics Applied to Donations

Mind Genomics has already been used to study the nature of effective communication for donations [10,13]. The objective of the study is to understand the most productive and effective way to communicate to a prospective donor to Children’s Cancer Center. The relevance of the topic, donation, and the relevance of Children’s Cancer Center in the world of charity organizations for children with cancer will become obvious from the review of today’s information about children and cancer. Thus, anything helping to understand WHAT to communicate, and to WHOM, can play a major role in the world of health care and fundraising. The ordinary process for understanding what to communicate does not invoke science, nor does it invoke foundational experiments bridging the world of science and application. The ordinary process might be either to select previous messages that ‘worked’ to drive donations, or perhaps to classify the prospective donors into different groups, based upon WHO they are, WHAT they have done in the past, or how they THINK about general topics. The short case presented here shows how a rigorous scientific approach to understand how the mind of the donor can be applied to situations where guidance is needed, rather than where one wishes to establish for a scientific proposition with reasonable certainty. The underlying world view is that even within the world of application, one can create knowledge which informs the greater science. In the case study presented here, we show how a small pair of studies, one with four respondents and a succeeding one with 50 respondents, informs the world of charitable donations, establishing patterns that can used later on as springboards either for more application or for theory building. We now move to the science of Mind Genomics, following the process, not so much to establish general rules, but rather to investigate a specific, defined situation: donation to Children’s Cancer Center Hospital. We follow a series of steps, whether the Mind Genomics study is designed to understand charitable donations in general, or to understand charitable donations to a specific cause.

Our presentation of the process shows the results from two iterations. The first iteration, with the very small base size of four respondents (n=4), will show how Mind Genomics extracts information at virtually the level of one or a few individuals, in a manner similar to the way the anthropologist or the consumer researcher extracts information from in-depth interviews with one or two people or from focus groups of three or more people. The second iteration will move on with a more quantitative study of the responses from 50 individuals, after building on the learning from the first iteration, changing some of the material, and then testing. It is important to note that the process need not be restricted to one small study followed by one larger one, but might comprise several small studies, until these sequences of ‘iterations’ provide the information which seem to be most appropriate to answer the applied question, and to provide the structured knowledge for a ‘wiki of the mind’ with respect to the topic.

Step 1: Choose a Topic

This step sounds simple, but it requires the researcher to focus on a specific topic. Choosing the specific topic is the start of critical thinking required by Mind Genomics, whether the topic is a general one of daily behavior (what makes a person donate to a charity?) or a specific one (what makes a person want to donate to Children’s Cancer Center Hospital?).

Step 2: Create Four Questions Which ‘Tell a Story,’ Pertaining to the Topic

The iterative nature of Mind Genomics ensures that the researcher need not worry that the questions are correct. Indeed, part of the underlying world view of Mind Genomics is that science should be exploratory.

Step 3: Create Four Answers to Each Question

Again, these answers need not be the correct answers. The ability to iterate, to run a number of these small experiments, generate data which guide the researcher to better questions and better answers.

Step 4: Select a Rating Scale

The rating scale can be 5, 7 or 9 points. The actual number of scale points is left to the discretion of the respondents, as is the rating scale. There is no right or wrong scale. The topic of questions and scales has been a focus of researchers for a century. The pragmatic side of Mind Genomics is that the scale should be simple. The scale for this type of question (not donate vs. donate) should be simple to understand, anchored at both ends. An odd number of scale points is easier to work with when there is the possibility of a neutral point.

Step 5: Launch the Study and Get the Results Fully Analyzed within 90 Minutes

The process obtains respondents through a panel service (Luc.id), with the Mind Genomics platform automatically analyzing the data and returning a complete report, the entire process typically taking less than one to one and a half hours.

Step 6: Present the Appropriate Vignettes to the Respondent, Vignettes Created for That Respondent by the Permuted Experimental Design

Record the rating on the anchored 1-9 scale, and record the response time (consideration time), operationally defined as the number of seconds from the appearance of the vignette on the screen to the actual rating assigned by the respondent. Each respondent evaluates the appropriate set of vignettes to constitute an experimental design, allowing subsequent powerful analyses. Each respondent evaluates a unique set of combinations of messages, so that across the set of respondents the evaluations cover many of the possible combinations, rather than covering a few combinations, but with precision. The learning will be in the stimuli, not in the precision of the measurement.

Step 7: Obtain Data Analyzable Both at the Level of the Individual and at the Level of the Group, Respectively

Each respondent evaluates a full experimental design, analyzable at the level of the individual respondent. For the design comprising four questions and four answers (elements), the design prescribes 24 combinations (vignettes). Each vignette comprises 2-4 elements, no more than one element or answer from any question. The design ensures that each element appears 5x, uncorrelated with any other element; The experimental design is maintained, but the combinations are changed according to a permutation scheme [14,15]. Thus, the combinations cover more of the ‘design space’ than the usual approach using experimental design. The underlying rationale is that it is more productive to test many possible combinations with underlying variability (noise) in each measurement than to limit oneself to a few combinations, measuring each point in the design with many replicate measures to average out the variation. In short, the argument by Mind Genomics is that knowledge emerges from scope with modest precision at each point (the big pattern emerges), rather than from precision with narrow scope. This is the key tenet of Mind Genomics: scope is better than precision, at least in the early explorations of a topic.

Step 8: Convert the Rating Scale in Two Ways

The first transformation is ‘Top 3’ defined as a transformed value of 0 when the original rating was 1-6, defined as 100 when the original was 7-9. The first transformation focuses on what ‘drives’ a person to select ‘donate.’ The second transformation is ‘Bot 3’ defined as a transformed value of 0 when the original rating was 4-9, defined as a transformed value of 100 when the original rating was 1-3. The second transformation focuses on what ‘drives’ a person to select ‘will not donate.’ To all transformed ratings a small random number was added (<10-5) to ensure variation in the transformed rating, and thus to ensure that the OLS (ordinary least-squares regression) will ‘work,’ and not ‘crash.’ OLS regression requires variation in the dependent variable. The addition of the small random number ensures that variation without materially affecting the results.

Step 9: Cluster the Individual Respondents Based Upon the Pattern of Their 16 Coefficients for Top3

The clustering is done using k-means clustering with the measure of distance being (1-Pearson Correlation), viz., [16]. The Pearson correlation coefficient shows the strength of a linear relation between two sets of measures. When the relation is perfectly linear, increases in one measure correspond to precise increase in the other measure. There is no scatter, the Pearson correlation is +1, and the distance is 0 (1-1=0). In contrast, when the relation is perfectly inverse, increases in one measure correspond to precise decreases in the other measure. Again, there is no scatter, the Pearson correlation is -1, and the distance is 2 (1–1=2). The clustering program generates two, and then three groups, called mind-sets, because the clusters represent groups who attend to the elements or messages in different ways. We select that cluster solution (the array of mind-sets) which tells the most obvious story (interpretable), and which comprises the smallest number of segments (mind-sets). For the data in this study, the three-mind-set solution was easier to understand.

Step 10: Create the Model for All Appropriate Data from the Respondents from Each Key Subgroup

Each group (Total, three Mind-Sets) generates three models or equation; Top3 (drivers of positive response), Bot3 (drivers of negative response), and RT (response time, or consideration time, measure of engagement with the material, whether the response to the element was positive or negative).

The model is a simple weighted, linear equation of the form:

Top3 (or Bot3) = ko + k1(A1) + k2(A2) … k16 (D4)

Response Time = k1(A1) + k2(A2) …. K16 (D4)

The additive constant k0, shows the estimated Top3 (or Bot3) response in the absence of elements. The additive constant can be thought of as a baseline response, or the underlying, fundamental likelihood of the respondent to ‘donate’ (Top3) or ‘not donate’ (Bot3). The additive constant is not meaningful for response time RT, since in the absence of elements there is nothing to which one can respond.

Step 11: Assign a New Person to One of the Mind-sets by Means of a Short Questionnaire, the PVI, Personal Viewpoint Identifier

The PVI assigns a NEW person to one of the mind-sets, and by so doing expands the scope of the small-scale studies to practical use, whether to create a more effective campaign (application), or to understand the distribution and possibly nature of the people in the different mind-sets. This adds to our general knowledge of the minds of people regarding messages relevant to donations (science).

Results

The Two Studies

To illustrate the value of small studies and what can be learned with a sequential approach requiring 2-3 days, we present the results of two studies designed to understand what messages may work for a campaign. The project deals with messaging to drive donations for Children’s Cancer Center, a hospital devoted to pediatric cancer (name of actual hospital disguised to maintain confidentiality). To make the topic general, the actual study was conducted among the general population to uncover the messages which would appeal the general population, not simply appeal to previous donors to Children’s Cancer Center.

The knowledge development was done in two phases. The first phase, or experiment, can be considered a pilot study with 10 respondents, sufficient to provide deep insights. The key difference between a pilot study of 10 respondents and a larger scale study of 40-50 respondents, or even a much larger scale study of 100-200 respondents, is simply the ability to identify different groups in the population and study the pattern of their responses.

Study 1: Preliminary Learning through a ‘Mini-Study’

As noted above, the Mind Genomics project begins with the topic (donating to Children’s Cancer Center specifically, or a cancer hospital for children in general). The next step requires the researcher to formulate the four questions that ‘tell a story.’ The questions emerging from the initial discussion tell such a story. They may not be the only questions, but in an exploratory study the objective is to learn just ‘what works.’ The four questions are:

A. Question A: What is it like to be a pediatric cancer patient?

B. Question B: Why is it important to support Children’s Cancer Center?

C. Question C: What are the outcomes for children when you donate?

D. Question D: How do you give your donation?

The study was executed with 10 respondents chosen from a large group of panel participants recruited by Luc.id, Inc., based in Louisiana, a provider of panel participants for on-line studies. Each respondent evaluated a different set of 24 vignettes, constructed according to Step 6 above. With as few as one respondent the Mind Genomics study generates meaningful data, readable at the level of that single respondent. With 10 respondents, and occasionally even as few as three or four, patterns rapidly emerge, patterns which are relevant for the respondents, but which may or may not be projectible to the population at large.

Table 1 shows the coefficients for the response time, the positive coefficients for the positive responses (I will donate), and the positive coefficients for the negative responses (I will not donate).

Table 1: The four questions, four answers to each question and the coefficients from the grand model relating the 16 elements to the binary transformed rating. To help the underlying patterns emerge, only the positive, non-zero, coefficients are shown for Top3 and for Bot3. The response times are shown for all elements, but only the response times of 1.1 seconds are shown, those driving ‘engagement’.

table 1

The response time gives a sense of the elements which most engage the respondent. Even with a small base size of 10 respondents, the data from the deconstruction of the ratings into the contribution of the elements gives a sense of the nature of elements that effectively engage. Those elements talk about the children, and survival. The elements may not ‘drive expected donations,’ but they do engage as shown by the long response time (consideration time). Moving on to the ratings, or more specifically the transformed ratings, Table 1 suggests that the additive constant, the proclivity to donate or not to donate ranges between 40 and 50. In the absence of elements which provide specific information, there is no dramatic drive to donate or not to donate, at least for these randomly chosen respondents. What is more important, however, is that among the 16 elements or, the messages, only two reach significance (coefficients of 8 or higher).

You can donate online with the click of your mouse!

Children’s Cancer Center freely shares their research and treatment protocols with hospitals around the world.

The two elements have little in common, suggesting that there is probably no single strong message. It is the nature of researchers to continue looking. These data suggest no ‘magic bullet.’ They do suggest that if there are any ‘magic bullets,’ they may be found in different mind-sets in the population, if such mind-sets can be identified. It is at this point that one can begin to formulate hypotheses about the psychology of donations. The hypothesis emerging here is that ‘painting a graphic word picture’ of the child will engage attention. The science of Mind Genomics has now enriched our thinking about the psychology of donations and generosity, suggesting that graphic design of the recipient is something to consider. The data suggest a further opportunity to understand the nature of the portrait being painted.

Study 2: Identifying the Underlying Structure of What Works for Donating to the Hospital

Study 1 constituted the first foray into the topic, executed with 10 respondents. Although 10 respondents are not often considered to be sufficient to establish results, a base of 10 respondents from one or two focus groups is acceptable when considered to be an exploratory step. Thus, we considered Study 1 to be exploratory, providing information in a disciplined way, but with simply too few respondents.

Here again are the questions from Study 1

Question A: What is it like to be a pediatric cancer patient?

Question B: Why is it important to support Children’s Cancer Center?

Question C: What are the outcomes for children when you donate?

Question D: How do you give your donation?

Based upon the patterns of responses, here are the four revised questions. The key change is Question D, and of course the text of the elements themselves.

Question A: What is it like to be a pediatric cancer patient (why would you want to help)?

Question B: Why is it important to support Children’s Cancer Center?

Question C: What are the outcomes for children when you donate?

Question D: What would inspire you to give?

The second study was conducted with 50 respondents, of which the data from 48 respondents were retained. The remaining two respondents did not provide age or gender, and so their data were eliminated.

Table 2 shows the same type of data as does Table 1, this time for the new set of respondents, and the new set of elements. Once again, each respondent evaluated a unique set of 24 vignettes, constructed by experimental design, so that the data can be analyzed down to the level of the individual respondent. This strategy, so-called ‘within-subjects design’ ensures that the data can be further deconstructed into subgroups called ‘mind-sets,’ based upon the patterns of the coefficients for each respondent. At first glance, the data reaffirm the previous finding from Study #1 that there are no elements which strongly driving expected donations, when the topic is associated with Children’s Cancer Center. One might consider this a failure when the objective is to discover the so-called ‘magic bullet,’ the message that will work for everyone. The result might be the continued search for this ‘magic bullet’ in successive efforts, only to realize in the end that there is no ‘magic bullet,’ or if there is, no one has any idea about what specifically it is  and how to express it. At the practical level, the effort will be seen to have been wasted. There will be no science of communication about charitable contributions and how people feel. The unsatisfactory conclusion, soon to be discarded is ‘no business results, no contributions to the science of people, no additional knowledge for science of charity communications.’

Table 2: Results from the total panel from Study #2.

table 2

The picture changes, and significant learning for practical application and for foundational knowledge emerges, when one dives more deeply into Mind Genomics and discovers mind-sets. In Mind Genomics, the continuing data suggest the existence of what could be called mind-sets, different patterns of ideas which are interpretable in the form of a ‘story,’ patterns that seem to attach themselves to people. In the world of Mind-Genomics, considering people simply as ‘protoplasm which responds,’ there emerge groups of ideas which separately drive strong responses. People are the carriers of these ideas. People allow these groups of ideas to emerge. A person typically falls into a specific mind-set for a topic and not into the other mind-sets for the same topic. When we look at the data through the lens of mind-sets, using the computational process outlined in Steps 9 and 10 above and doing the computation on the Top3 (positive responses), we emerge with three new-to-the-world mind-sets, shown in Table 3 for Top3 (positive response – likely to donate), and in Table 4 for Bot3 (negative response – not likely to donate).

Table 3: Drivers of positive responses to messages, showing total panel, gender, and the three emergent mind-sets based on clustering using Top3.

table 3

To strengthen the scientific aspect of the results—the learning which is meant to be foundational rather than simply a direction for messaging to raise money—we include gender, as well, comparing the responses of males and females. Table 3 suggests three mind-sets. It is in the mind-sets that the strong elements emerge, elements with coefficients of +8 or higher.

a. There are some positive elements by gender, but no strong performers at all. Both male and female respondents are modestly interested in donating (additive constant=49)

b. Mind-Set 1 – Describe the professional services (modestly interested in donating at a basic level, additive constant=40)

c. Mind-Set 2 – Describe the person helped (modestly interested in donating at a basic level, additive constant=39)

d. Mind-Set 3 – Describe the institution’s performance (strongly interested in donating at a basic level, additive constant=69).

Table 4 shows the messages that should be avoided, the messages driving the response of ‘Not Donate.’ The coefficients emerge from considering only ratings of 1 and 2 as relevant on the 5-point scale, with ratings of 3-5 (neutral, will donate) as not relevant, and coded as 0. The additive constant is far lower for Not Donate than it is for Donate, meaning that people are more inclined to say that they will donate. Several messages to be used by the Center are likely to ‘backfire’, driving donors away. Mind-Set 2 is especially sensitive to the wrong messages.

Table 4: Drivers of negative responses to messages, showing total panel, gender, and the three emergent mind-sets based on clustering using Top3.

table 4

Finding these Respondents in the Population

A continuing topic in Mind Genomics is the value of a ‘next step’ beyond the already-important discovery of mind-sets. Mind-sets themselves provide a way of understanding daily life, through a new focus on the every-day and the way people differ in their typical behaviors. Yet, beyond the scientific contribution of knowledge is the remarkable potential of expanding the value of the learning, moving beyond the respondents tested in the study to the entire world. A simile is the colorimeter used to quantify colors of objects. The science of color can be developed in any location with any material. The real value of the science in terms of the ‘world outside’ is to measure the colors of new objects, not by repeating the study in which the colors were discovered, but rather by measuring the colors of the new objects using a machine in which the science has been already programmed [17-23]. The approach used in Mind Genomics is called the PVI, the personal viewpoint identifier. The objective is to use the data from Table 3 (MS1, MS2, MS3) to create a short questionnaire (six questions), on a simple to-use scale. The questions come from the actual study. The pattern of responses assigns a NEW PERSON to one of the three mind-sets. There are 64 possible patterns, each pattern mapping to one of the three mind-sets. Figure 1 shows the PVI, doing so in two parts. The left part is a short introduction, to introduce the person to the task, and to obtain optional background information. The right part is the actual PVI, including some basic questions about attitudes towards ‘giving’ and the six-question PVI. The results are forwarded to a database and can be sent to the respondents, as well.

fig 1

Figure 1: The PVI, personal viewpoint identifier, based upon the second study. The PVI is located at: https://www.pvi360.com/TypingToolPage.aspx?projectid=1261&userid=2018.

Discussion and Conclusion

The empirical results are simple to discover, just by looking at the table of elements and how the elements or messages drive interest in donating. The message used in requesting should be straightforward and focus on how the organization saves and changes lives for the better. The outcome of the organization’s work, and not the process, should be the main message. One should avoid directly focusing on needs and tax breaks, respectively. Although a minority of prospective donors will care about needs or tax advantages, most people say that they will contribute when they are suitably convinced by the cause and mission of the effort and in the vision detailing how their contribution can help. It is at this point that one can begin to formulate hypotheses about the psychology of donations. The hypothesis emerging here is that ‘painting a graphic word picture’ of the child will engage attention. The science of Mind Genomics has now enriched our thinking about the psychology of donations and generosity, suggesting that graphic design of the recipient is something to consider. The data suggest a further opportunity to understand the nature of the portrait being painted. It is not important to point out major ‘learnings’ from the results, learnings which confirm or disconfirm what is known in the literature, or what is hypothesized to be the case for the psychology of donors or the psychology of children with cancer. That information is, of course, important to know for science. What is more important, however, is the ability to have at one’s disposal a tool for small-scale, iterative experimentation: Mind Genomics, a tool which returns rich information even with remarkably small base sizes, such as n=10 or even fewer. In the world of science, Mind Genomics becomes a tool bridging the gap between the idiographic (individual) and the nomothetic (the general world). Just as important, Mind Genomics becomes both a practical tool to increase donations, as well as a tool for the development of systematized knowledge, both for the current generation and for those to come—a ‘wiki of the mind.’ Finally, viewed from the grand proscenium arch of civilization, Mind Genomics provides a record of how people of a certain time, in a specific environment, and faced with known needs, think about topics— a record of inestimable value to philosophy, psychology, history, sociology, anthropology, and economics, just to name a few disciplines where knowledge of the granular is important.

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  10. Galanter E, Moskowitz H, Silcher M (2011) The Price of Grace: Donations, Charities, and the Mind. People, Preferences and Prices: Sequencing the Economic Genome of the Consumer Mind pg:103.
  11. Moskowitz HR, Gofman A (2007) Selling blue elephants: How to make great products that people want before they even know they want them. Pearson Education.
  12. Williams LL, Quave K (2019) Quantitative Anthropology, A workbook. Elsevier.
  13. Gabay G, Moskowitz H, Gere A (2019) September. UNDERSTANDING THE DONATING MIND & OPTIMIZING MESSAGING–PUBLIC HOSPITALS. In 12th Annual Conference of the EuroMed Academy of Business.
  14. Gofman A, Moskowitz H (2010a) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127-145.
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  20. Importance of CEO involvement in creating a culture of philanthropy in hospitals.
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Differences between 5-Minute and 15-Minute Measurement Time Intervals of the CGM Sensor Glucose Device Using GH-Method: Math-Physical Medicine (No. 281)

Introduction

This paper describes the research results by comparing the glucose data from a Continuous Glucose Monitor (CGM) sensor device collecting glucose at 5-minute (5-min) and 15-minute (15-min) intervals during a period of 125 days, from 2/19/2020 to 6/23/2020, using the GH-Method: math-physical medicine approach. The purposes of this study are to compare the measurement differences and to uncover any possible useful information due to the different time intervals of the glucose collection.

Methods

Since 1/1/2012, the author measured his glucose values using the finger-piercing method: once for FPG and three times for PPG each day. On 5/5/2018, he applied a CGM sensor device (brand name: Libre) on his upper arm and checked his glucose measurements every 15 minutes, a total of ~80 times each day. After the first bite of his meal, he measured his Postprandial Plasma Glucose (PPG) level every 15 minutes for a total of 3-hours or 180 minutes. He maintained the same measurement pattern during all of his waking hours. However, during his sleeping hours (00:00-07:00), he measured his Fasting Plasma Glucose (FPG) in one-hour intervals.

With his academic background in mathematics, physics, computer science, and engineering including his working experience in the semiconductor high-tech industry, he was intrigued with the existence of “high frequency glucose component” which is defined as those lower glucose values (i.e. lower amplitude) but occurring frequently (i.e.. higher frequency). In addition, he was interested in identifying those energies associated with higher frequency glucose components such as the various diabetes complications that would contribute to the damage of human organs and to what degree of impact. For example, there are 13 data-points for the 15-minute PPG waveforms, while there are 37 data-points for the 5-minute PPG waveforms. These 24 additional data points would provide more information about the higher frequency PPG components.

Starting from 2/19/2020, he utilized a hardware device based on Bluetooth technology and embedded with customized application software to automatically transmit all of his CGM collected glucose data from the Libre sensor directly into his customized research program known as the eclaireMD system, but in a shorter time period for each data transfer. On the same day, he made a decision to transmit his glucose data at 5-minute time intervals continuously throughout the day; therefore, he is able to collect ~240 glucose data within 24 hours.

He chose the past 4-months from 2/19/2020 to 6/19/2020, as his investigation period for analyzing the glucose situation. The comparison study included the average glucose, high glucose, low glucose, waveforms (i.e. curves), correlation coefficients (similarity of curve patterns), and ADA-defined TAR/TIR/TBR analyses. This is his secondresearch report on the 5-minute glucose data. His first paper focused on the most rudimentary comparisons [1].

References 2 through 4 explained some example research using his developed GH-Method: math-physical medicine approach [2,3].

Results

The top diagram of Figure 1 shows that, for 125 days from 2/19/2020 – 6/23/2020, he has an average of 259 glucose measurements per day using 5-minute intervals and an average of 85 measurements per day using 15-minute intervals. Due to the signal stability of using Bluetooth technology, for the 5-min, it actually has 259 data instead of the 240 data per day.

IMROJ-5-3-516-g001

Figure 1. Daily glucose, 30-days & 90-days moving average glucose of both 15-minutes and 5-minutes.

The middle diagram of Figure 1 illustrates the 30-days moving average of the same dataset as the “daily” glucose curve. Therefore, after ignoring the curves during the first 30 days, we focus on the remaining three months and can detect the trend of glucose movement easier than “daily” glucose data chart. There are two facts that can be observed from this middle diagram. First, the gap between 5-min and 15-min is wider in the second month, while the gap becomes smaller during the third and fourth month. This means that the 5-min results are converging with the 15-min results.Secondly, both curves of 5-min and 15-min are much higher than the finger glucose (blue line). This indicates that the Libre sensor provides a higher glucose reading than the finger glucose. From the listed data below, the CGM sensor daily average glucoses are about 8% to 10% higher than the finger glucose.

5-min sensor: 118 mg/dL (108%)

15-min sensor: 120 mg/dL (110%)

Finger glucose: 109 mg/dL (100%).

The bottom diagram of Figure 1 is the 90-days moving average glucose. Unfortunately, his present dataset only covers 4 months due to late start of collecting his 5-min data; however, the data trend of the last month, from 5/19-6/23/2020, can still provide a meaningful trend indication. As time goes by, additional data will continue to be collected, his 5-min glucose’s 90-days moving trend will be seen more clearly.

Figure 2 shows the synthesized views of his daily glucose, PPG, and FPG.Here, “synthesized” is defined as the average data of 125 days.For example, the PPG curve is calculated based on his 125×3=375 meals. Listed below is a summary of his primary glucose data (mg/dL) in the format of “average glucose/extreme glucose”. Extreme means either maximum or minimum, where the maximum for both daily glucose and PPG due to his concerns of hyperglycemic situation, and the minimum for FPG due to his concerns of insulin shock. The percentage number in prentice is the correlation coefficients between the curves of 15-min and 5-min.

Daily (24 hours):15-min vs. 5-min

117/143vs. 119/144(99%)

PPG (3 hours):15-min vs. 5-min

126/135vs. 125/134(98%)

FPG (7 hours):15-min vs. 5-min

102/95 vs. 105/99 (89%).

Those primary glucose values between 15-min and 5-min are close to each other in the glucose categories. It is evident that the author’s diabetes conditions are under well control for these 4 months. However, by looking at Figure 2 and three correlation coefficients %, we can see that daily glucose and PPG have higher similarity of curve patterns (high correlation coefficients of 98% and 99%) between 15-min and 5-min, but FPG curves have a higher degree of mismatch in patterns (lower correlation coefficient of 89%). This signifies that his FPG values during sleeping hours have a bigger difference between 15-min and 5-min.

IMROJ-5-3-516-g002

Figure 2. Synthesized daily glucose, PPG, and FPG of both 15-minutes and 5-minutes.

Figure 3 are the results using candlestick model [4,5]. The top diagram is the 15-min candlestick chart and the bottom diagram is the 5-min candlestick chart. Candlestick chart, also known as the K-Line chart, includes five primary values of glucoses during a particular time period; “day” is used in this study. These five primary glucose data are:

Start: beginning of the day.

Close: end of the day.

Minimum: lowest glucose.

Maximum: highest glucose.

Average: average for the day.

Listed below are five primary glucose values of both 15-min and 5-min.

15-min: 108/116/86/170/120.

5-min: 111/116/84/173/118.

IMROJ-5-3-516-g003

Figure 3. Candlestick charts of both 15-minutes and 5-minutes.

By ignoring the first two glucoses, start and close, let us focus on the last three glucoses: minimum, maximum, and average. The 5-min method has a lower minimum and a higher maximum than the 15-min method. This is due to the 5-min method capturing more glucose data; therefore, it is easier to catch the lowest and highest glucoses during the day. The difference of 2mg/dL between 15-min’s average 120 mg/dL and 5-min’s average 118 mg/dL is only a negligible 1.7%.

Again, it is also obvious from these candlestick charts that the author’s diabetes conditions are under well control for these 4 months.

Conclusion

In summary, the glucose differences between 5-min and 15-min based on simple arithmetic and statistical calculations are not significant enough to draw any conclusion or make any suggestion on which are the “suitable” or better measurement time intervals. However, the author will continue his research to pursue this investigation of energy associated with higher-frequency glucose components in order to determine the glucose energy’s impact or damage on human organs (i.e. diabetes complications).

The author has read many medical papers about diabetes. The majority of them are related to the medication effects on glucose symptoms control, not so much on investigating and understanding “glucose” itself. This situation is similar to taming and training a horse without a good understanding of the temperament and behaviors of the animal. Medication is like giving the horse a tranquilizer to calm it down. Without a deep understanding of glucose behaviors, how can we truly control the root cause of diabetes disease by only managing the symptoms of hyperglycemia?

References

  1. Hsu, Gerald C. eclaireMD Foundation, USA (2020) Analyzing CGM sensor glucoses at 5-minute intervals using GH-Method: math-physical medicine (No. 278).
  2. Hsu, Gerald C. eclaireMD Foundation, USA(2020) Predicting Finger PPG by using Sensor PPG waveform and data via regression analysis with three different methods using GH-Method: math-physical medicine (No. 249).
  3. Hsu, Gerald C. eclaireMD Foundation, USA (2019) Applying segmentation pattern analysis to investigate postprandial plasma glucose characteristics and behaviors of the carbs/sugar intake amounts in different eating places using GH Method: math-physical medicine (No. 150).
  4. Hsu, Gerald C. eclaireMD Foundation, USA (2019) A case study of the impact on glucose, particularly postprandial plasma glucose based on the 14-day sensor device reliability using GH-Method: math-physical medicine (No. 124).
  5. Hsu, Gerald C. eclaireMD Foundation, USA. Comparison study of PPG characteristics from candlestick model using GH-Method: Math-Physical Medicine (No. 261).

Incivility from Undergraduate Nursing Students in the United Kingdom

DOI: 10.31038/IJNM.2020111

Abstract

There is growing evidence that under-graduate nursing students are demonstrating inappropriate and uncivil behaviour towards academics which is also reported as harassment and contra-power harassment. Harassment is unwanted behaviour which an individual finds offensive or which makes them feel intimidated or humiliated and unwanted behaviours include verbal or written words of abuse such as offensive emails, comments on social media network sites, stalking and sexually motivated behaviours. Contra-power harassment is defined as the harassment of individuals in formal positions of power and authority by those that are not. One of the most cited reasons for inappropriate behaviour by undergraduate students is related to grading of course work and course progression, but literature relating to what extent this is occurring towards nurse academics is nominal.

Aim: The aim of this study is to understand the extent to which nursing academics experience inappropriate, uncivil or harassing behaviour deemed as harassment from students.

Method: Nursing academics in Universities in the United Kingdom, which provided undergraduate nursing programmes, were invited to complete an online questionnaire; an introductory letter and participant information sheet was provided. A 41-item Likert scale (strongly agree-strongly disagree) was used to elicit academics’ experiences of contra-power harassment and their views regarding possible contributing factors.

Results: The responses from UK academics indicated that students were disrespectful and demanding in their written communications; that they challenging academic integrity; and they expected to be coached more to gain a higher degree classification. This mirrored the Australian responses [1] which indicated that inappropriate behaviour was related to consumerism of higher education and a sense of entitlement from students as they paid for a degree and that academics experiencing the highest levels of student harassment related to assessment grades.

Conclusions: Incivility, poor and demanding behaviour is becoming more common place in higher education and this is causing academics to question their own interactions and understanding of student psychology and culture and the need to develop coping strategies. Appreciation of the risk factors of poor behaviour can aid academics in ensuring that not only is there an appropriate harassment prevention policy but that the implementation of appropriate prevention strategies is in place.

Highlights

  • Students harass academics to try to gain a higher grade in their academic work.
  • Students demonstrate poor language skills in electronic communications.
  • Undergraduate students’ uncivil behaviour is affecting academics.

Keywords

Harassment, Incivility, Contra-power, Student nurses

Introduction

Research is showing that violence in society is increasing and can cause suffering and ruin lives. Socially aggressive behaviour can occur across the life span and is where individuals may be irritable, impulsive, angry and violent; accordingly, individuals will be more aggressive due to developmental transitions, a range of medical and / or psychiatric diagnoses [2]. Whilst not everyone may be subjected to violent behaviour literature suggesting that unsociable behaviour is increasing, and that it is shaped by society, led to the City University of London establishing The Violence and Society Centre (2019) [3], it’s aim to ‘produce the evidence to build the theory needed to inform policy, politics and practice to move towards zero violence’. One can suggest therefore that students entering higher education may have been on the receiving end of socially aggressive behaviour, and as such have the potential to demonstrate violent behaviour in the university. They may also demonstrate unacceptable behaviour because they are vulnerable to the newness of the university environment, and/or have a new stressful living environment, and the social pressures of belonging and the need to achieve is great [4]. Research exploring the behaviour of nursing students is showing that they are behaving in an uncivil, aggressive and harassing manner toward academics. Lee [5] and Christensen [1] suggest that poor behaviour is a result of the commercialisation of higher education with it being seen as an economic investment, pay-as-you-go access to a university education. Kopp and Finney [6] discuss how perceptions of academic entitlement has been theoretically linked with uncivil student behaviour. However, entitlement and the reality of higher education are too often incompatible as the effort needed to obtain a degree and the demands of the course are too high for some students to achieve and non-achievement is a threat to investment which manifests itself in poor uncivil behaviour [1,5,7].

Background

Inappropriate, uncivil contra-power harassing behaviour towards academics by students is becoming more common place. Research has focused on different types and potential causes. White [7-9], all identified that contra-power harassment was characterised by verbal, task, personal and isolationist attack. Verbal abuse is reported as being the most common form of incivility and consists of shouting, swearing, inappropriate language or verbally aggressive language, name calling or heckling. Nursing student incivility in the USA identified that the three major disruptive behaviours were inattentiveness in class, attendance problems and lateness [10]; over 40% (n: 409) of respondents identified that they had been subjected to verbal abuse and over 23% being subjected to offensive physical contact / violence which included hitting or slapping. White’s [7] UK research also found that malicious rumour mongering was rife and this is identified as social and emotional abuse. Blizard [8] discusses isolation attack and how this can be students using mobile phones or talking during lessons, or when individual students using the collective voice to air their displeasure and harass.  De Souza & Fansler’s [9] work on contra-power sexual harassment found that personal attack manifested in comments of a sexual nature being written in unit/module evaluations, stalking and in some cases sexual harassment. They found that over 50% of academics had experienced some form of sexual harassment or unwanted sexual attention from students. White’s [10] study described how female academics where the targets of sexual innuendo or seen as sexual objects by male students, and where male academics where offered sexually explicit picture texts as bribery for favourable assessment results. Lashley and DeMeneses [10-13] identified that incivility in nursing students was demonstrated by lateness, inattention, absenteeism, academic dishonesty, verbal abuse, aggressive behaviours (including use of mobile technology). Task harassment was also identified and this included contacting academics outside normal working hours, allegations of bias marking, and fabricating evidence against an academic and character assassination on social media. Other literature which focuses on non-nursing students found similarities [13-18]. Despite workplace bullying and harassment being unlawful in the UK (UK Equality Act 2010) it is still occurring. This is mirrored in the USA by the Workplace Bullying Institute [19] who estimates that one in three employees has been bullied. Lampman [9] found that women in academia reported significantly more negative outcomes as a result of harassment than men as they were more likely to receive threats, episodes of intimidation or bullying from students. It should be noted here that there is a prevalence of females in nurse academia because nursing in the UK is predominantly a female profession (in 2016 only 11.4% were male). Nurses are regulated by the Nursing and Midwifery Council (NMC) – [20] and must abide by the NMC Code of professional conduct (NMC 2018).  This states that nurses must ‘treat people with kindness, respect and compassion’ and ‘recognise diversity’ and ‘respect and uphold people’s human rights’ and as such nurses need to have exceptional communication and interpersonal skills and hold an empathetic disposition. However, there is a national and international scrutiny of healthcare which suggested that nurses, especially nursing students, do not hold the disposition necessary to be a nurse [21]. Phillips [22], and Rosser [23] longitudinal study however showed that student nurse did hold caring values; and Scammell [24] identified that higher education recruitment strategies in the UK upheld a values based selection and admission criteria. Yet Watson [25] suggest that service users, and their families and carers, are dissatisfied with healthcare and that worldwide political influences are impacting on healthcare provision [26-29] is causing discontent. Literature is suggesting that incivility towards academics is becoming a commonly occurring phenomenon and is causing academics to be concerned. Kopp and Finney [6], Lampman [9] and Christensen [1] suggest that part of the reasons for growing incivility is that there is a growing sense of entitlement and a shifting of cultural norms by the present generation of students accessing higher education. Alarmingly Christensen [1] found that students neither concerned nor cared about the consequences of harassing the academics. In Lee’s [5] UK work she highlighted how there is power imbalance in favour of the university student. Indeed, Keashly and Neuman [30] noted that for many academics, caught in the ‘cycle of abuse’, had very little recourse and feared repercussions if not being believed if they spoke out and this left them powerless. Indeed, academics’ being bullied by students is also being reported on in national press] and how the abuse is making staff extremely anxious [31].

Aim

The aim of this study is to better understand the extent to which nursing academics experience contra-power harassment from undergraduate nursing students.

Method, Setting and Sample

A convenience sample of 19 universities across the UK were invited to take part. Heads of nursing departments / Deans of faculties were asked to disseminate an online survey. A participation information sheet outlining the aim of the study, study protocol, ethical approval, what participation entailed and link to the study were emailed to the heads / Deans. Anonymity of the university and respondents was emphasised (Table 1).

Table 1: Participant Demographics (n=17).

Age 36-40 2
41-45 1
46-50 5
51-55 4
56-60 4
>60 1
Gender Female 12
Male 5
University Faculty Health 6
Science & Engineering 2
Business 4
Arts & Humanities 1
Other 3
Academic Level Associate Lecturer 1
Lecturer 2
Senior Lecturer 10
Principle Lecturer 3
Associate Professor 1
Years’ Experience 2-5 1
6-10 1
11-15 8
16-20 4
21-25 1
26-30 2
Current Work Status Full-Time 15
Part-Time 2
Teaching Space Undergraduate 13
Post Graduate 4

[NB: 1 participant did not follow through with completing the survey].

Ethics

Ethics approval was sought and granted by the ethics committees in the authors universities (Western Sydney University & Bournemouth University).

Data Collection

Data was collected from November 2018 to May 2019. The Likert scale statements were developed from the literature. For validity a draft survey was sent to five experienced research active nursing academics, after which refinements were made until consensus reached. The survey had three sections 1) demographics, 2) experiences of contra-power harassment and 3) possible contributing factors. Demographic data asked for age, gender, years of academic experience, majority of teaching practice (under-graduate or postgraduate), and academic level. A total of 41 Likert scale statements were included in sections two and three. Section two used a five-point scale ‘never-always’ scale and contained contra-power harassment statements (Table 2). Section three used a five-point scale ‘strongly disagree – strongly agree’ scale with pre-worded statements which focused on perceptions of contributing factors (Table 3).

Table 2: Academics Experiences of Contra-Power Harassment (n=16).

  Scoring: Never (1) – Always (5) Sometimes

N (%)

Often

N (%)

Always

N (%)

Median (Mean) Std. Dev
Q1 I feel that when a student complains, their word is believed, whereas I have to justify my actions 3 (18) 5 (31) 3 (18) 3 (3.31) 1.25
Q2 I receive criticism about my student feedback, that is not constructive 4 (25) 4 (25) 2 (2.56) 1.09
Q3 I feel my role is less about educating students, and more about me being a provider of marks/grades 6 (37) 5 (31) 2 (12) 3 (3.25) 1.18
Q4 I have had experiences of students being aggressive and disrespectful to me in their response to their marks and grades 10 (62) 2 (12) 3 (2.81) .75
Q5 Students do not take responsibility for their learning, and then insist it’s my fault for not teaching them well enough 8 (50) 6 (37) 3 (3.19) .83
Q6 I feel like retaliating against a student who has been unfairly critical of me, on a personal level 7 (43) 1 (6) 2 (2.25) 1.00
Q7 I find students challenge my authority, my experience and my expertise 5 (31) 3 (18) 2 (2.56) .96
Q8 I notice that some students’ expectations of their academic ability are too high or unachievable, and this is reflected in how they communicate with me 5 (31) 8 (50) 3 (3.25) .93
Q9 In my experience, as student expectations of their academic ability increase, so do complaints 5 (31) 7 (43) 1 (6) 3 (3.38) .89
Q10 I feel powerless to discipline a student who is harassing me 4 (25) 3 (18) 3 (18) 3 (3.00) 1.41
Q11 I have been ‘stalked’ by students when outside of the university physically and/or electronically 3 (18) 1 (1.56) .81
Q12 I have had students repeatedly contact me when outside of the normal classroom times, by email or phone messages 3 (18) 6 (37) 3 (2.69) 1.25
Q13 I have had students criticise the marks and /or feedback other academics have given them 10 (62) 6 (37) 3 (3.38) .50
Q14 I feel that the student harassment I experience is because students behave unprofessionally with university academics 4 (25) 7 (43) 1 (6) 3 (3.19) 1.17
Q15 I have had students argue about their marks simply because they want a higher grade 4 (25) 7 (43) 3 (3.13) .89
Q16 I have had students complaining about their mark when they have compared their work with other students because they want a higher grade 10 (62) 4 (25) 3 (3.13) .62
Q17 I feel I am being perceived by students not as a knowledgeable expert, but as one who provides a service 6 (37) 4 (25) 1 (6) 3 (2.94) 1.12
Q18 I have been the centre of unfounded student accusations of impropriety of a sexual nature
Q19 I sometimes engage in displaced aggression against other individuals as a result of student harassment 1 (6) 1 (1.38) .619
Q20 I feel angry when students harass me unnecessarily 5 (31) 2 (12) 3 (18) 3 (2.94) 1.39
Q21 I feel scared and fear for my physical safety when a student is verbally aggressive 3 (18) 1 (6) 0 2 (1.88) .96
Q22 I feel helpless and powerless when students personally attack me on social media 1 (6) 1 (6) 3 (18) 1 (2.27) 1.67
Q23 I am irritated when students actively engage with their electronic devices (e.g. mobile phones, tablets, laptops) in the lesson I’m teaching 4 (25) 5 (31) 2 (12) 3 (3.19) 1.17
Q24 I have been accused of being racist because students are not happy with the mark they have been awarded or don’t feel supported as they would expect 1 (6) 1 (6) 1 (1.44) .89
Q25 I am concerned for my professional reputation when I respond to a student who has harassed me 4 (25) 2 (12) 2 (2.31) 1.30

Note: Std Dev – Standard Deviation.

Table 3: Academics attitudes to the contributing factors associated with Contra-Power Harassment.

Scoring: Strongly Disagree (1) – Strongly Agree (5) Percentage % (n=16) Median (Mean) Std. Dev
Q1 There is a lot of pressure on academics to answer emails from students quickly 75 (12) 4 (4.19) .98
Q2 Some students write emails that can be misconstrued as abusive and disrespectful because they have poor written language skills 68 (11) 4 (3.63) .96
Q3 I am distressed when student emails attack me personally and when they are demanding or confrontational 75 (12) 4 (3.75) 1.07
Q4 I believe that consumerism in higher education leads some students to believe that they hold a greater balance of power than the academics 75 (12) 5 (4.31) 1.13
Q5 Sometimes, I am not sure whether it is in my best interests to report student harassment of me to the University 31 (5) 3 (2.88) 1.26
Q6 I feel that students harass academics because students do not have the ability to cope with academic and personal stressors 62 (10) 4 (3.75) 1.00
Q7 Sometimes I feel I have not received support from the University when I report a student’s harassment 24 (3) 3 (2.69) 1.19
Q8 It is usually when assignments or exams are due that I get the most unacceptable behaviour from students 55 (9) 4 (3.38) 1.09
Q9 I believe widening participation has led to increased levels of student harassment of academics 30 (5) 2 (2.81) 1.22
Q10 I believe students hold the view that academics owe them something because they are paying for their degree 81 (13) 5 (4.31) .94
Q11 The commercialisation of higher education has led to some students being self- absorbed and self-centred, and as a result they are quick to blame others rather than accept responsibility 81 (13) 4 (4.19) 1.05
Q12 The diversity of the student cohort has led to me being harassed more frequently 18 (3) 2 (2.31) 1.20
Q13 When students are unclear or unsure of the programme and/or university requirements, they display more aggressive and unacceptable behaviours 68 (11) 4 (3.88) .72
Q14 Students today use aggression to exert power over academics 43 (7) 3 (3.25) 1.07
Q15 I believe that there is often a cultural clash when students behave aggressively or inappropriately towards me 62 (10) 3 (2.75) 1.13
Q16 The way some students communicate with me is belittling 37 (6) 2 (2.81) 1.05

Note: The higher the mean the more negatively nursing academics responded; Percentage indicates those that responded either “Agree” or “Strongly Agree”; Std Dev=Standard Deviation.

Data Analysis

Non-parametric testing using Mann Whitney U was used to analyse the demographical data and experiences of and contributing factors associated with contra-power harassment. Cronbach’s-Alpha was also performed to assess internal consistency of the Likert scale statements. Inferential statistics, measures of central tendency and Cronbach’s-Alpha were used to assess consistency of Likert Scale statements. Inductive content analysis was used to identify patterns in the four open ended questions and generic themes identified.

Findings

There were 16 respondents – more females than male. Respondents were lecturer and senior lecturer grades with between 6 and 9 years’ experience of teaching undergraduate students predominantly in Southern England.

Responses to questions which focused on nursing academics experiences of contra-power harassment clearly showed that respondents had experienced harassment from nursing students. Analysis showed 3 main themes: – entitlement, desire for higher grade and societal culture. One of the main forms of harassment related to language skills in the form of poorly written and / or demanding emails from the nursing student and this being supported by harassing emails from their parents.

‘People sometimes forget to say please and thank you before and after a request and this makes the request read like a demand’; ‘Students use words/ comments such as “unfair” or “I am displeased with my mark”. On their own they don’t sound particularly abusive but when it is part of a longer email it all starts to build to feel more threatening’.

‘high achieving parents expect much from their children which can result in the children behaving in unacceptable ways due to the pressure and their parents undertake some bullying behaviour’.

‘More and more parents are getting involved and there can be some very bullying tones’

Another form of harassment related to teaching credibility and challenging academic judgement.

They [students] lash out, insult my credibility and teaching content’; ‘I’ve had students challenge my academic judgement (at the time feedback and marks are released) but my feedback is comprehensive and specific’; ‘stating they have not received help or guidance when what is required is covered in lectures, seminars, drop in sessions and 1:1 meetings, but these students are the ones who have not attended’.

‘A group of low performing students pursued a systematic but completely spurious complaint in a very rude and obnoxious manner; one male student was particularly aggressive’.

Responses to questions which asked nursing academics about the contributing factors associated with contra-power harassment clearly showed that most unacceptable behaviour occurred around assessments and the students desire to have a high class degree.

‘I WANT A BETTER MARK’ (capitals denoting shouting) or ‘I want a first’.

‘They are paying therefore they expect to get good marks’.

‘Students pay a lot of money and some believe they are buying a degree’.

‘We always had 60% as a trigger point, e.g. below 60% students were likely to challenge but this has now, over the last 5 years or so, moved to being 70% so now we get challenged is students aren’t given 70%+’.

‘When academic judgement is overturned it makes it appears that despite regulations the student will win’.

‘Grade grabbing has increased and the uni appeals procedure encourages personal attacks’.

The question asking whether widening participation had increased harassment tended to show that academic disagreed, although academics perceived that struggling students expected more help and school attainment had not helped with the independent study needed at university.

Schools let students resubmit work until they get a good mark. We don’t. They are frustrated by the lack of a second chance which they are used to’.

‘Students seek coaching rather than guidance’.

‘Often these students appear to have less social skills to cope with criticism – they take it personally and not about the piece of work submitted’.

Other comments indicating societal expectations included sexism and racism:

As a female I do feel that sometimes students from the Middle East do not always respect female academics’.

‘As a female and international academic, the wider cohort is more condemning and sceptical of my ability compare to a ‘white British male’ teaching the exact same content’.

‘Respect for academics seems to have gone out of the window with students swearing at academics telling them to ‘F’ off. This seems to happen to the much younger academics where the age differences are small’.

Discussion

The results from this study suggest that undergraduate student nurses are being uncivil towards academics and this takes form in a variety of ways. It is suggested that incivility is due to a societal culture because students feel entitled to more help and an expectation of a higher grade. Findings from this, like others, shows that students harass academics to give them higher grades. Indeed, in the UK universities have been exploring potential grade inflation. Statistics show that the increase has been part of a long term trend and in the early 1990’s about 8% of students achieved a first class degree whereas in 2018 it was 26%, a rise from 18% in 2012 – 2013 (Higher Education Statistics Agency 2018) – [32], and internal audit is subjecting academics to justify the grades given. Research is also highlighting that students are trying to increase their grades by what is now called ‘contract cheating’. It is suggested that as a university education is a commodity, rather than a development of thinking, learning and reasoning, students are buying essays, being dishonest in their essay writing (i.e. parents are writing the essays) and that they do not feel this is wrong [33-36]. The results from this study are not too dissimilar to other research which highlights that student aggression and contra-power harassment is exhibited in a variety of ways. However, what the research is not showing is that UK academics are subjected to constant assessment and one could suggest that tolerance of uncivil behaviour is lessened. In recent years, university managers, leaders and academics have been expected to be responsive to diverse student needs and expectations, a decline in funding, a competitive research environment together with an increase in fiscal accountability. Houston [37] state that ‘meeting challenges to deliver outputs and outcomes is a complex balancing act’ as academics are not only required to balance teaching commitments, income generate, meet research outputs and publishing requirements but they are constantly subjected to internal and external accountability and a number of national measurements’. There are three such measurements. One is the National Student Survey which was introduced in 2005 and is managed by the Office for Students (the independent regulator of higher education in England). This survey assesses undergraduate student’s opinions of the quality of their degree programmes and whilst the results have made institutions take student feedback seriously it has also been used by university managers to discipline staff if scores are low. Another tool is the Research Excellence Framework (REF) which was introduced in 2008 by the Higher Education Funding Council for England (initially called the Research Assessment Exercise and replaced by the REF in 2014). The aim being to produce UK-wide indicators of research excellence providing a quality international benchmark to drive funding and assesses the impact of academic’s research. The third tool, introduced in 2017, is the Teaching Excellence Framework (TEF). This measures excellence in three areas: teaching quality, learning environment and the educational and professional outcomes achieved by students. Consequently, academic are being assessed by internal and external measures and these are key matrices and important consideration for academics applying for promotion and career progression. Positive student feedback in NSS and high scores in TEF and REF are also important in the mandate for supporting university funding. At the same time that academics are being assessed via these national frameworks they are being subjected to excessive demands from students. Student expectations are high and a consumer identity which is being recognised by students are making them demand more from the university [38-40]. Not only is there a growing body of research which shows that academics are being harassed by undergraduates but there is a growing body of research that is showing that horizontal violence (an umbrella term used to describe a range of aggressive behaviours between colleagues) between nurses is as rife [41-48]. Student nurses in the UK spend half of the duration of their programme in practice (2300 hours over three years) and one could suggest that if they are subjected to horizontal violence, or witness to it, and as such they may assume it is ‘normal’ behaviour. For example, research identifies that nurses tolerate low level incivilities, such as condescending tone or gossip or eye-rolling, and consequently student nurses are socialized to accept these behaviours as part of the job and one could suggest that  they may transpose it into the university setting by being uncivil to academics. There is also evidence that academics, despite universities have anti-bullying policies, are being bullied by their employers and that victims pay a high price (such as job loss). The outcome of bullying is often hidden from the public and The Guardian [49] – a renowned British newspaper – reported that in two years UK universities have spent nearly £90m on payoffs to staff who have been subjected to bullying and that as many as 4,000 settlements occurred, some of which are thought to relate to allegations of bullying. However they reported that these payoffs came with “gagging orders”. The British Broadcasting Corporation also undertook an independent survey and identified that ‘Dozens of academics were made to sign Non-Disclosure Agreements after being “harassed” out of their jobs following the raising of’ complaints’ (BBC 2019) [50]. Reports such as this raise fear, stress and reduced motivation for work and one could suggest that this and the constant inspection of their work is preventing staff from achieving high levels of performance. Khan [51-53] systematic review clearly identified that academics that are exposed to the excessive demands of work are subject to burnout resulting in physical and psychological issues and a consequence of this is that universities less productive due to poorly performing academics who have a lower sense of commitment.

Conclusion

There is no doubt that undergraduate students are demonstrating uncivil behaviour that this is having an effect on academics and there are many studies that have looked at the potential causes of this behaviour and its effects on academics. This study has added to the body of knowledge because it specifically relates to undergraduate student nurses and their behaviour towards nurse academics. It is showing that nurse academics are experiencing harassment due to students demands for higher grades and when the students have not achieved they appear to have less social skills in order to cope with the feedback. What has also discussed is a controversial issue which is that academics are less able to manage student behaviour because they are facing constant assessment themselves from internal and external forces. Also this study has suggested that incivility in the nursing profession is acting as role model for student and this is manifesting itself in university.

Study Limitations

This survey was originally sent to academics in the UK, Australia and New Zealand. However, the UK responses were very few in comparison to Australia (n=82) [1] and although the overall findings were not too dissimilar one questioned why responses might have been so few. One might suggest that recent discourse in the UK universities had led to academics being fearful of completing the questionnaire or general disharmony with working life causing anxiety and fatigue, and high workloads do not allow time for participating in research such as this. Of course they may be also suffering from survey fatigue as they are expected to complete returns for REF, TEF and respond to NSS feedback.

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Unusual Sella Mass: Pituitary Abscess (PA)

Abstract

Pituitary abscess (PA) caused by an infectious process is a rare cause of Sellar mass. The clinical features and radiological appearance of PA as an intra- or supra-sellar mass are similar to many other pituitary lesions, and so they are often misdiagnosed as pituitary tumor.

70% of cases occur in a previously healthy pituitary gland. These are classified as primary pituitary abscesses, persumbly secondary to either hematogenous spread or as an extension from an adjacent infective focus such as meningitis, sphenoid sinusitis, Cavernous sinus thrombophlebitis or contaminated cerebrospinal fluid (CSF) fistula.

The rest are secondary abscesses, and arise from pre-existing lesions, such as an adenoma, apoplexy in a tumor, a craniopharyngioma, or a complicated Rathke’s cleft cyst and lymphoma. The risk factors are for PA are immunosuppression, previous irradiation or surgical procedures to the pituitary gland [1].

In almost 50% of cases, the pathogenic microorganism causing the infection is not isolated. A history of recent meningitis sinusitis or head surgery can be the source [2].

Correct diagnosis before surgery is difficult and is usually confirmed intra- or post-operatively. The early surgical intervention allows appropriate antibiotic therapy and hormone replacement resulting in reduced mortality and morbidity. A long term follow-up is recommended because of the high risk of recurrence and of postoperative hormone deficiencies.

Keywords

Pituitary abscess, Papilledema, Panhypopituitarism, Rathke’s cleft cyst, Propionibacterium acnes

Introduction

A pituitary abscess (PA) represents 0.2%-0.6% of all pituitary lesions and can be life threatening. It can have a prolonged disease course. The first case was reported by Heslop in 1848, and so far, <300 cases have been reported worldwide [3]. It is an infectious process that presents as a mass in the Sella. Clinical features and the radiological appearance of the PA as an intra or suprasellar mass are similar to many other pituitary lesions, so it is often misdiagnosed as a cystic pituitary tumor, craniopharyngioma, and Rathke’s cyst. It can be life-threatening if not appropriately diagnosed or treated, and the outcome is difficult to predict. Fortunately, the majority of the cases have a chronic course. The disease has a higher prevalence in females between the age of 12 to 76 years. The average period it takes to diagnose from the onset of symptoms is around 8 years.

PA can occur as a primary disease or can be secondary to infections caused by either hematogenous spread or as an extension from an adjacent infected tissue such as meningitis, sphenoid sinusitis, Cavernous sinus thrombophlebitis or contaminated cerebrospinal fluid fistula 70% of cases occur in a previously healthy pituitary gland. These are classified as primary pituitary abscesses, and the rest are secondary abscesses that arise from pre-existing lesions, such as an adenoma, apoplexy in a tumor, a craniopharyngioma or a complicated Rathke’s cleft cyst and lymphoma [4].

In almost 50% of cases, the pathogenic microorganism causing the infection cannot be isolated. A history of recent meningitis sinusitis or head surgery can be the source [2].

Correct diagnosis before surgery is difficult and is usually confirmed intra- or post-operatively. The early surgical intervention allows appropriate antibiotic therapy and hormone replacement resulting in reduced mortality and morbidity. A long term follow-up is recommended because of the high risk of recurrence and postoperative hormone deficiencies.

We present 2 cases of pituitary abscess in young women. One presented with bilateral papilledema and the other with panhypopituitarism. Both had a sellar mass on an MRI scan, and the diagnosis was made intra-operatively. Microbiological culture in both cases was positive for Propionibacterium acnes (P.acnes). P.acnes is a gram-positive organism, a part of the normal skin microbe. This organism is most commonly isolated from wounds following craniotomies after Staphylococcus aureus and streptococcus epidermidis. Low-grade infections can manifest between 3-36 months.

Case 1

A 14-year old South Asian girl presented with a one-month history of worsening frontal headaches that occurred daily, associated with vomiting, nausea, lethargy, photophobia, and sleep disturbance. Aside from well-controlled asthma, she has been previously healthy. There was no recent travel history or infectious contacts. On examination, she appeared alert and active. She had bilateral papilledema, suggesting raised intracranial pressure (ICP). She was apyrexial and systemically well. Her cerebral magnetic resonance imaging (MRI) scan revealed a soft tissue mass in the pituitary fossa extending up towards the optic chiasm, with mild edema in the optic nerve and tracts. The scan also showed an enlarged pituitary gland and thickened stalk. The findings suggest an inflammatory process like hypophysitis, particularly Langerhans cell histiocytosis (LCH) because of her age. There were no other features of LCH. She had a normal liver US and skeletal survey. She had no symptoms of Diabetes insipidus. Her pituitary hormones were normal, including the stimulated cortisol. Her Prolactin was elevated. Her serum sodium and osmolality were normal. Her ESR was slightly raised, but autoantibodies, serum tumor markers, ACE, and the Quantiferon tuberculosis test were negative. Her IgG4 subclass was normal (Table 1). The formal ophthalmology review did not show evidence of bilateral papilledema. Her symptoms improved with oral analgesics, and steroid treatment was not initiated.

Table 1: Results at initial presentation.

                    Short Synacthen test

Time T=0 T-30 T=60
Cortisol (nmol/L) 186 452 594

                   Baseline tests

Test Result Normal range
IGF-1(nmol/L) 47.9 18.3 to 63.5
TSH (mU/L) 1.62 0.51-4.3
T4 (pmol/L) 13.3 10.8-19
LH (IU/L) 4.3 Follicular phase 2-13

Mid cycle 14-6

Luteal phase 1-11

FSH (IU/L) 1.8 Follicular phase 4-13

Mid cycle 5-22

Luteal phase 2-8

Postmenopause>25

ACTH (ng/L) <3 0-50
Prolactin (mU/L) 806 102-496
Serum Na mmol/L 144 133-146
Serum Osmolality mOsmo/Kg 293 282-300
Random Urine Osmolality mOsmo/Kg 475 100-1400
LDH (u/L) 188 120 to 300
HCG (IU/L) <1 0-1
alpha Fetoprotein (kU/L) 1 0-10
C-Reactive protein (mg/L) 1.5 0-5
ESR (mm/h) 28 1-12
Complement C3 (g/l) 1.1 0.75-1.65
Complement C4 (g/l) 0.29 0.14-0.54
Antinuclear antibodies Negative
Angiotensin convert enzyme (U/L) 42 16-85
IgG4 (g/L) 0.04 0-1.3

 

A repeat MRI scan 3 months later discussed in a multidisciplinary meeting was reported to suggest Rathke’s cleft cyst abscess/ Pituitary abscess (Figures 1 and 2). She underwent a trans-sphenoidal endoscopic pituitary biopsy for diagnosis. The appearances suggested a Rathke’s left cyst and a pituitary abscess. Immunostaining for ACTH, FSH, LH, growth hormone, TSH and Prolactin, chromogranin, synaptophysin, and collagen IV was consistent with anterior pituitary tissue. Microbiological culture on prolonged incubation was positive for Propionibacterium with no acid-fast bacilli growth. TB culture was also negative. She received a 6-week course of antibiotics, including 2 weeks of intravenous ceftriaxone and oral metronidazole followed by 4 weeks of oral co-amoxiclav. Her headaches and vomiting deteriorated after biopsy with a peak CRP of 218 mg/L, which resolved following medical treatment. Imaging with MRI and baseline pituitary function blood tests has since been repeated following the 6 weeks to assess the management’s effectiveness, which showed normal results. The patient reported the resolution of headaches and able to resume full-time schooling.

fig 1 414

Figure 1: MRI at presentation.

fig 2 414

Figure 2: MRI 3 months after transphenoidal surgery.

Case 2

29 years old Caucasian fine arts student presented to the emergency department with fever, headaches, profuse sweating, tiredness, and blurring of vision. Her symptoms, particularly headaches, had worsened over the last 12 months. She had noticed polydipsia and polyuria. She also had amenorrhoea for twelve months. She was treated at her local hospital twice in the preceding 3 years with symptoms of headaches, fever, weight loss, and vomiting. She had a lumbar puncture 3 times to rule out a possibility of central nervous system infection. On both occasions, she was discharged home after empirical treatment with antibiotics for suspected meningitis. There was no other past medical history. There was no recent travel history or infectious contacts. She was not on any regular medications.

The initial pituitary MRI and contrast-enhanced MRI scan revealed the absence of the posterior pituitary bright spot and a thickened pituitary stalk with a deviation of infundibulum to the right. There was a homogenous hyperintense area within the pituitary gland with no discernable pituitary tissue. This area was hypointense on T2 (Figures 3-5). The differential diagnosis was apoplexy, hypophysitis or a proteinaceous cystic lesion replacing or compressing the pituitary gland. The optic nerves and the chiasm appeared normal. Her investigations confirmed her to have hypopituitarism with Diabetes insipidus (Table 2). Her lumbar puncture showed no CSF abnormality. Her tumor markers and Quantiferon for tuberculosis were negative. The case was discussed in multidisciplinary meeting (MDT) and with empirical diagnosis of hypophysitis, she was started on prednisolone with the replacement of deficient hormones, including Desmopressin. She showed no improvement in her clinical symptoms. A 3 month interval scan showed an increase in the size of the pituitary gland with further thickening of the stalk and optic chiasm displaced superiorly. After the second discussion in MDT, she had a pituitary biopsy. During surgery, soft yellow-white pus-like material was drained after dural incision. The microscopy showed necrotic material with a little amount of compressed anterior pituitary gland, chronic inflammation, and no evidence of adenoma or granuloma or giant cells was found. No acid-fast bacilli or organisms were seen on gram staining, and the culture for TB was negative. There was scanty growth of Propionibacterium acneformis. Her interval scan 3 months later showed complete resolution of the non-enhancing T1 hypertense pituitary tissue with a further decrease in the size of the pituitary gland. She remains on full hormones replacement. She had an insulin tolerance test that confirmed her growth hormone deficiency, and she is now on growth hormone replacement. She remains on hydrocortisone, Thyroxine, female hormone replacement, and Desmopressin.

fig 3 414

Figure 3: MRI at presentation.

fig 4 414

Figure 4: MRI 3 months later.

fig 5 414

Figure 5: MRI post-surgery.

Table 2: Results at initial presentation.

                   Short Synacthen test

Time T=0 T-30
Cortisol (nmol/L) 148 169

                   Baseline tests

Test Result Normal range
IGF-1(nmol/L) 12.7 11.9-40.7
TSH (mU/L) 1.35 0.27-4.20
T4 (pmol/L) 5.3 10.8-25.5
LH (IU/L) 3.1 Follicular phase 2-13

Mid cycle 14-96

Luteal phase 1-11

FSH (IU/L) 5.1 Follicular phase 4-13

Mid cycle 5-22

Luteal phase 2-8

Postmenopause>25

Oestradiol (pmol/L) <92 92-1462
Prolactin (mU/L) 577 102-496
Serum Na mmol/L 142 133-146
Serum Osmolality mOsmo/Kg 301 275-295
Random Urine Osmolality mOsmo/Kg 154 100-1400
CSF-b HCG (IU/L) <2 <2
CSF-alpha fetoprotein (µg/L) <1 <1
C-reactive protein (mg/L) 1.4 0-5
ESR (mm/h) 3 1-12
Antinuclear antibodies Negative
IgG4 (g/L) <0.01 0-1.3

Discussion

A pituitary abscess is an infectious process characterized by the accumulation of purulent material in the sella turcica. It is rare, and can be a life-threatening condition unless promptly diagnosed and treated. We report 2 cases of secondary pituitary abscess in young women. The first case was due to abscess in the Rahtke’s cleft cyst (RCC), and the second was Pituitary gland abscess with a history of otitis media and repeated lumbar punctures for presumed meningitis.

The clinical presentation of PA is nonspecific, such as headaches, pituitary hypofunction, and visual disturbances, whereas the infection can be discreet and inconstant [5,6]. Symptoms can be acute, subacute, or chronic, explaining the late diagnosis; in some cases. Visual disturbance, including hemianopia, can be present in 50% of cases. Headache without a particular pattern is a regular feature (70-90%) and can be debilitating. Anterior pituitary hypofunction due to destruction and necrosis of the gland is the commonest presentation resulting in fatigue and amenorrhoea (54-85%). In one series, 28 out of the 33 patients had anterior pituitary hypofunction. Pituitary hormone deficiencies persist in the majority of patients following treatment Up to 70% of patients with PA can have central Diabetes insipidus. In contrast, fever with signs of meningeal irritation is reported in 25% of cases [5].

MRI is the imaging of choice for the pituitary lesions. PA can present as a suprasellar mass (65%) or as an intrasellar mass (35%). A typical PA appears as a single cystic or partially cystic mass that is hypointense on T1-weighted image and hyperintense in T2-weighted image. It can show a rim of enhancement after contrast gadolinium. The posterior pituitary bright spot is mostly absent in majority of the cases (Wang et al.). The lesion’s signal depends on protein, water, lipid content, and whether there is hemorrhage. Imaging can also show the invasion of an adjacent anatomical structure, peripheral meningeal enhancement, thickening of the pituitary stalk, and paranasal sinus enhancement [6].

Diffusion-weighted magnetic resonance imaging (DWI) is widely used to differentiate cerebral abscess from other necrotic masses. Brain abscesses typically show high intensity on DWI with decreased apparent diffusion coefficient (ADC) value in their central region. The high intensity on DWI is useful but not specific to PA because pituitary apoplexy can also exhibit high intensity on DWI [7]. The accuracy of DWI in PA remains controversial. In the Wang et al. case series, PA was misdiagnosed in one-third of the case [6]. The radiological differential diagnosis includes, Rathke’s cleft cyst, cystic pituitary adenoma, arachnoid, and dermoid cysts, metastases, glioblastoma multiforme, chronic hematoma, and multiple sclerosis [8]. Rathke’s cleft cyst mainly can mimic a pituitary abscess [9]. RCC is the second most common incidentaloma after adenomas and accounts for 20% of incidental pituitary lesions at autopsy. The incidence of RCCs in children was reported to be much lower than in adults. However, the prevalence is now believed to be much higher, especially among those with the endocrine-related disorder [10]. Gunes et al. reported the radiological appearance of RCC on MRI in 13.5% of the children who underwent MRI for the investigation of endocrine-related disorders. Patients with RCC are usually asymptomatic, but symptomatic RCC is more common in females in both adult and pediatric populations [11]. RCC can cause significant morbidity such as headache, visual disturbances, chemical meningitis, endocrine dysfunction (hypothyroidism, menstrual abnormalities, diabetes insipidus, adrenal dysfunction, and very rarely apoplexy). Short stature, growth deceleration, delayed puberty are also reported in children and adolescents.

The diagnosis of PA in most cases can only be confirmed after surgical exploration, due to overlapping of clinical signs, symptoms, imaging, and laboratory findings with other sellar lesions. Signs of inflammation are present in less than a third of the patients. The PA should be included in the differential diagnosis of patients with headaches or signs of pituitary dysfunction and patients with pituitary mass who develop signs of meningeal inflammation.

The main treatment for PA in patients with mass effect is Transsphenoidal excision (TSS) with decompression of sella and antibiotic therapy. This can result in the resolution of visual abnormalities. Treatment is effective for typical symptoms such as fever, headache, and visual changes. Patients with shorter duration of symptoms and those with primary abscess have better improvement in their pituitary dysfunction. Majority of the patients remain with pituitary dysfunction even after the treatment.

Antibiotic therapy should to started promptly even in the patients who are waiting for microbiology and histological confirmation for about 4–6 weeks [1,12]. Empirical treatment with ceftriaxone is indicated until the results are available. Hormone replacement is commenced depending on the hormone deficits including stress dose glucocorticoid therapy. Hypocortisolemia should be recognized among patients presenting with sellar masses, as early diagnosis and treatment improve survival and endocrinological outcome. Patients who suffer from the pituitary abscess may eventually have a good quality of life if they are diagnosed and treated early. A craniotomy is reserved for larger lesions with the suprasellar extension or where transsphenoidal surgery is ineffective [13]. In a series published with 66 patients, 81.8% of patients recovered completely, 12.1% of patients had at least one operation for recurrence, and only one patient had died [14].

There are widespread pathogenic microorganisms in abscesses. These include Gram-positive bacteria, Gram-negative bacteria, anaerobes, and fungi [8,11]. Streptococcus and Staphylococcus are the most predominant Grampositive bacteria, whereas Escherichia coli, Mycobacterium, and Neisseria have also been reported [3,10,11]. Aspergillus fumigatus is mostly isolated in cases of secondary PA. Immunosuppressed patients mostly have Candida and Histoplasma. Cultures are positive only in 50% of cases; therefore, broad-spectrum antibiotics are given as empirical treatment. The pathogen identification is important for the therapeutic management [15].

Both patients had culture-positive for Propionibacterium acnes (P. acnes). This organism is seated deeply in the pilosebaceous glands, mainly in the scalp and face. It is a slow-growing, pleomorphic, non-spore-forming gram-positive anaerobic bacillus that is a universal component of the normal skin microbiota. It is usually considered a contaminant of blood cultures but occasionally can cause serious infections, including postoperative central nervous system (CNS) infections. P. acnes are the most commonly isolated organism after Staphylococcus aureus and Staphylococcus epidermidis following craniotomies. In the presence of heavy infiltrates, the Gram stain is not reliable. Gram stain is only positive in about 10.5% of clinically significant infections with moderate growth. P. acnes behave in a less aggressive manner than other postsurgical organisms and only accounts for a small fraction of CNS infections [16]. P. acnes abscesses typically follow craniotomy, shunts, access to reservoirs, trauma, and foreign bodies. Granulomatous responses have been documented in the CNS following P. acnes infections.

P. acnes grow slowly in the laboratory. This can cause in a delay in diagnosis, missed diagnosis, or delay in treatment if specimens are not cultured for an extended period. Cultures may not grow for as long as 14 days, so samples should be held beyond the usual 5 to 7 days. Gram stain may not be a reliable technique for the rapid diagnosis of P. acnes infections. When there is evidence of an abundant inflammatory response in the Gram-stained smear, a more careful evaluation of cultures must be performed. Polymerase chain reaction for the 16S rRNA or mass spectrometry can be a useful tool for rapid identification and typing of P. acnes following recovery in culture. Propionibacterium is susceptible to antibiotics used for the treatment of anaerobic infections, including penicillin, erythromycin, lincomycin, and clindamycin, but not metronidazole, which is notably ineffective against P. acnes [17].

Patients with PA should be followed up with serial MRI of the pituitary, hormonal profile and visual fields at 3, 6, and 12 months after surgery. The recurrence rate is variable and depends on the nature of the abscess (primary or secondary. The majority of relapses are associated with either an immunological defect or previous pituitary surgery [12,18].

Conclusion

We presented 2 cases of unusual sellar mass from an abscess in an adolescent and a young adult due to P. acnes, both responded well to treatment.

The pituitary abscess should be included as the differential diagnosis of patients with a sellar or a suprasellar mass, headaches, pituitary dysfunction, and meningeal inflammation.

The diagnosis is difficult before surgery because of overlapping clinical signs, radiological and laboratory findings with other sellar lesions.

Broad-spectrum antibiotics should be started empirically even before the culture results are available.

Culture is positive only in 50% of cases, and in case of unusual bacteria like P. acnes, an extended culture is required for the confirmation of the diagnosis.

Pituitary dysfunction should be recognized and appropriately treated particularly glucocorticoid replacement.

Transsphenoidal surgery is the treatment of choice and this is followed by pronged 4-6 weeks of broad-spectrum antibiotic therapy.

Early and efficient surgical and medical management results in lower mortality and higher recovery of pituitary hormone function.

Patients should be followed up with MRI imaging, assessment of the hormone replacement if required, and visual field assessment because of a chance of recurrence.

References

    1. Lin Y, Lin F, Liang Q, Li Y, Wnag Z, et al. (2017) Pituitary abscess: report of two cases and review of the literature. Neuropsychiatr Dis Treat 13: 1521-1526. [crossref]
    2. Furnica RM, Lelotte J, Duprez T, Maiter D, Alexopoulou O, et al. (2018) Recurrent pituitary abscess: case report and review of the literature. Endocrinol Diabetes Metab Case Rep 17-0162. [crossref]
    3. Kummaraganti S, Bachuwar R, Hundia V, et al. (2013) Pituitary abscess: A rare cause of pituitary mass lesion. Endocrine Abstracts 31: 1. [crossref]
    4. Al Salamn JM, Al Agha RAMB, Helmy M, et al. (2017) Pituitary abscess. BMJ Case Rep 2016-217912. [crossref]
    5. Nordjoe YE, Igombe SRA, Laamrani FZ, Jroundi L, et al. (2019) Pituitary abscess: two case reports. J Med Case Rep 13: 342.
    6. Wang Z, Gao L, Zhou X, Guo X, Wang Q, Lian W, Wang R, Xing B, et al. (2018) Magnetic resonance imaging characteristics of pituitary abscess: a review of 51 cases. World Neurosurg 114: e900-e902. [crossref]
    7. Xu XX, Li B, Yang HF, Du Y, Li Y, Wang WX, et al. (2014) Can diffusion-weighted imaging be used to differentiate brain abscess from other ring-enhancing brain lesions? A meta-analysis. Clin Radiol 69: 909-915. [crossref]
    8. Corsello SM, Paragliola1 RM, et al. (2017) Differential diagnosis of pituitary masses at magnetic resonance Imaging. Endocrine 58: 1-2.
    9. Coulter IC, Mahmood S, Scoones D, Bradey N, Kane PJ, et al. (2014) Abscess formation within a Rathke’s cleft cyst. J Surg Case Rep 11: 105. [crossref]
    10. Vasilev V, Rostomyan1 L, Daly AF, Potorac J, Zacharieva S, et al. (2016) Bonneville JF and Becker A. Pituitary ‘incidentaloma’: neuroradiological assessment and differential diagnosis. European Journal of Endocrinology 175: R171-R18. [crossref]
    11. Güneş A, Güneş SO (2020) The neuroimaging features of Rathke’s cleft cysts in children with endocrine-related diseases. Diagn Interv Radiol 1: 61-67. [crossref]
    12. Vates GE, Berger MS, Wilson CB, et al. (2001) Diagnosis and management of pituitary abscess: a review of twenty-four cases. J Neurosurg 95: 233-241. [crossref]
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    14. Ling X, Zhu T, Luo Z, Zhang Y, Chen Y, Zhao P, Si Y (2017) A review of pituitary abscess: our experience with surgical resection and nursing care. Transl Cancer Res 6(4): 852-859.
    15. Achermann Y, Goldstein EJC, Coenye T, Shirtliff ME, et al. (2014) Propionibacterium acnes: from Commensal to Opportunistic Biofilm-Associated Implant Pathogen. Clin Microbiol Rev 27: 419-440. [crossref]
    16. Chung S, Kim JS, Seo SW, Ra EK S, Joo SI, Kim SY, Park SS, Kim EC, et al. (2011) A Case of Brain Abscess Caused by Propionibacterium acnes 13 Months after Neurosurgery and Confirmed by 16S rRNA Gene Sequencing. Korean J Lab Med 31(2): 122-126. [crossref]
    17. Yacoub AT, Khwaja S, Daniel L, et al. (2015) Propionibacterium acnes Causing Central Nervous System Infections: A Case Report and Review of Literature. Infectious Diseases in Clinical Practice 23: 60-65. [crossref]
    18. Batool SM, Mubarak F, Enam SA, et al. (2019) Diffusion-weighted magnetic resonance imaging may be useful in differentiating fungal abscess from malignant intracranial lesion: Case report. Surg Neurol Int 10: 13. [crossref]

A Safety Signal’s Significance with the COVID-19 Coronavirus

Introduction

The global pandemic involving COVID-19 (coronavirus) has produced unprecedented challenges for the medical, healthcare providers and our world community. The World Health Organization (WHO 2020) initially declared COVID-19 a pandemic, pointing to the over numerous cases of the coronavirus illness in over a hundred countries and territories around the world and the sustained risk of further global spread [1,2]. The term pandemic is most often applied to new influenza strains, and the Centers for Disease Control and Prevention (CDC) use it to refer to strains of virus that are able to infect people easily and spread from person to person in an efficient and sustained manner. Such a declaration refers to the spread of a disease, rather than the severity of the illness it causes. A pandemic declaration can result in increased levels of stress, anxiety, panic and levels of functional depression for some individuals [3]. Recognized is the realization that these unusual circumstances create significant uncertainty and unease in the professional and personal lives of health care professionals and their patients.

Definition of a Safety Signal

“Safety signals” are learned cues that predict the nonoccurrence of an aversive event. As such, safety signals are potent inhibitors of fear and stress responses. Investigations of safety signal learning have increased over the last few years due in part to the finding that traumatized persons are unable to use safety cues to inhibit fear, making it a clinically relevant phenotype.

The coronavirus has traumatized some which has been recognized as a state of heightened fear or anxiety in environments globally. This symptom has been conceptualized as a generalization of the fear conditioned during the traumatic experience that becomes resistant to extinction. As opposed to danger learning where a cue is paired with aversive stimulation, safety learning involves associating distinct environmental stimuli also known as safety signals that can be used an applied when aversive events occur as in a global pandemic.

During periods of high stress such as during this Covid-19 pandemic, fear often permeates the lives of many because if the unknown nature of this illness. This occurs because of the absence of a learned safety signal. Such safety signals can inhibit fear responses to cues in the environment. As such, safety signals are only learned when the subject expects danger but it does not necessarily occur. More fundamental to the clinical importance of a safety signal is the distinction between safe and dangerous circumstances. Thus, identifying the mechanisms of safety learning represents a significant goal for basic neuroscience that should inform future prevention and treatment of trauma and other anxiety disorders.

With COVID-19 global pandemic, the World Health Organization (2020) continues to ask countries to “take urgent and aggressive action.” World leaders continue holding international teleconferences with health officials to address the most effective way to protect the public and develop public health policy for the coronavirus that has caused multiple illnesses and deaths worldwide.

Transitioning the Pandemic

The urgency has created stressful life experiences for all ages that pose the potential for illness resulting for some in disabling fear, a hallmark of anxiety and stress-related disorders [4]. Researchers at Yale University and Weill Cornell Medicine report on a novel way that could help combat such anxiety experienced at times like these. When life events as the spread of the Corvid 19 triggers excessive fear and the absence of a safety signal. In humans, a symbol or a sound that is never associated with adverse events can relieve anxiety through an entirely different brain network than that activated by fear and worry. Each individual must find their own “safety signal” whether that is a mantra, song, a person, or even an item like a stuffed animal that represents the presence of safety and security.

The Centers for Disease Control and Prevention (CDC), the World Health Organization (WHO), and other reputable agencies have advocated on how to address the coronavirus by washing hands frequently, avoid sharing personal items, and maintaining social distance from others beyond immediate family.

While it’s still unclear exactly how much of the current coronavirus outbreak has been fueled by asymptomatic, mildly symptomatic, or pre-symptomatic individuals, the risk of contagion exists. A yet to be published article in the CDC journal “Emerging Infectious Disease” (CDC 2020) reports that the time between cases in a chain of transmission is less than a week, with more than 10% of patients being infected by someone who has the virus but does not yet have symptoms according to Dr. Luren Meyers, a professor of integrative biology at UT Austin, who was part of a team of scientists from the United States, France, China and Hong Kong examining this viral threat.

Earlier this year, researchers in China published a research letter in the Journal of the American Medical Association, outlining a case of an asymptomatic woman in Wuhan, China who reportedly spread the virus to five family members while traveling to Anyang, China-all of whom developed COVID-19 pneumonia. The sequence of events suggests that the coronavirus may have been transmitted by the asymptomatic carrier,” [5].

Prevention Interventions

Coordinated regional efforts are underway under the direction of the Centers for Disease Control and Prevention (CDC) that provides guidelines aimed at prevention intervention. Each individual should make the effort to create one’s own “safety signal” by following the recommendations of the CDC (2020). Know how it spreads and that there is currently no vaccine to prevent coronavirus disease (COVID-19). Critical for prevention is avoided exposing the virus. The virus is thought to spread mainly from person-to-person. Between people who are in close contact with one another. Through respiratory droplets produced when an infected person coughs or sneezes. These droplets can land in the mouths or noses of people who are nearby or possibly be inhaled into the lungs.

Disinfecting by washing hands often with soap and water for at least twenty seconds especially after you have been in a public place or after blowing your nose, coughing, or sneezing. If soap and water are not readily available, use a hand sanitizer that contains at least 60% alcohol. Cover all surfaces of your hands and rub them together until they feel dry. Avoid touching the eyes, nose, and mouth with unwashed hands Put distance between yourself and other people if COVID-19 is spreading in your community. This is especially important for people who are at higher risk of getting immune compromised illness.

Health care calls for “sheltering in place” are effort to provide primary prevention it’s important to stay home to slow the spread of COVID-19, and if you must go out, practice personal quarantine. While we stay home, don’t let fear and anxiety about the COVID-19 pandemic become overwhelming. Managing mental health issues can be aided by taking breaks from watching, reading, or listening to news stories and social media. It remains important to take the time to connect with others. Networking with friends and loved ones over the phone or via video chat about the thoughts and feelings experienced during this pandemic is very important to maintain mental health daring three times. Employ the use mindful meditation, eating healthy meals, exercising regularly, and getting plenty of sleep.

Take steps to protect yourself and others. Stay sheltered in place especially when you’re sick. Shelter in place means to seek safety within the building one already occupies, rather than to evacuate the area or seek a community emergency shelter. The American Red Cross says the warning is issued when “chemical, biological, or radiological contaminants which would include exposure to the coronavirus.

Efforts must be made to cover one’s mouth and nose with a tissue when you cough or sneeze or use the inside of your elbow. Throw used tissues in the trash. Immediately wash your hands with soap and water for at least 20 seconds. If soap and water are not readily available, clean your hands with a hand sanitizer that contains at least 60% alcohol.

It is important to wear a facemask for your own health as well as the health of others. Everyone should wear a facemask when they are around other people (e.g., sharing a room or vehicle) and before entering a healthcare provider’s office. If someone is not able to wear a facemask due to breathing difficulties, then these individuals should cover all coughs and sneezes, and people who are caring for theme should wear a facemask when they enter ones room. Wear a facemask when caring for someone who is showing any signs or symptoms of respiratory infection and fever.

When considering the anxiety and apprehension individuals may experience with the vulnerabilities of the present pandemic and future epidemics of this proportion, patient medical education can provide a buffer against the Prevention interventions that include cleaning and disinfecting objects and surfaces that are touched regularly. This includes tables, doorknobs, light switches, countertops, handles, desks, phones, keyboards, toilets, faucets, and sinks. If surfaces are dirty, clean them: Use detergent or soap and water prior to disinfection. With first signs of symptoms, take advantage of Virtual Care in an effort to minimize unnecessary visits to an emergency room or health care provider’s office, which can also decrease the spread of illness and/or infection of many conditions, including COVID-19. Finally, each individual is encouraged to establish one’s own “safety signal” by adhering to the multiple precautions that include the guidelines developed and promoted by the World Health organization and the Centers for Disease Control and Prevention (CDC 2020).

References

  1. Centers for Disease Control (2020) Coronavirus Disease 2019 (COVID-19).
  2. World Health Organization (2020) Coronavirus disease 2019 (COVID-19): Situation Report-38.
  3. Miller TW (2015) Problem Epidemics in Recent Times. Health & Wellness. Lexington Kentucky: Rock point Publisher Incorporated.
  4. Miller TW (2010) Handbook of Stressful Transitions across the Life Span. New York: Springer Publishers Incorporated.
  5. Huang C, Wang Y, Li X, et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395: 497-506.

Understanding the Algebra of the Restaurant Patron: A Cartography Using Cognitive Economics and Mind Genomics

DOI: 10.31038/NRFSJ.2020311

Abstract

The studies reported here extended the range of Mind Genomics beyond considering how people feel about a situation (homo emotionalis) to what type of economic impact would be occasioned by that situation (homo economicus). The topic here is the familiar experience of observing the behavior of the staff with each other, and with the customer, in a restaurant. Respondents rated the expected price of the check using a relative scale (25% less vs. 25% more). Shifting the focus to economic consideration revealed fewer strong performing messages, and fewer, and less clear mind-sets, based upon the pattern of individual respondents. Confirming previous unpublished observations, the data from the three studies suggest that shifting the attention of the respondent to economics rather than emotions forces the respondent into a conservative stance. Studies on pricing must take this emergent conservatism into account when attempting to understand how people actually ‘feel’ about a situation.

Introduction – From The Outside Looking In

Today’s social sciences for the most part deal with normative behavior, behavior that is typical in situations. A term for this behavior is nomothetic, from the Greek word nomos, meaning general rule or normative rule. The focus on the nomothetic can be seen from studies of how patrons think of restaurants in a general sort of way, and from the unbelievable omnipresence of customer satisfaction surveys focusing on the food, the service, the décor, and so forth [1-4]. Customer satisfaction is a growing business. The hospitality industry is one of the biggest users, in order to understand the experience, from what happens, to how it happens. For the most part, researchers use a set number of questions about the experience, breaking down the questions into responses about the décor, the server’s attitudes, the food, and so forth. In a typical survey the objective is to obtain a quick measure of the subjective impression of the restaurant, an impression which is tallied with many others to generate a profile of performance, or a set of composite scores [1]. The end result is knowledge about what is important to the customer, information relevant for journals and the science, as well as how did a specific establishment perform on a certain day, information important for business. The typical questions focus on the person’s feelings, attempting to link feelings to economic implications, such as the increase or decrease of the business.

When researchers try to understand a situation, they can avail themselves of a variety of techniques. Anthropological observation, depth interviews, focus groups, and surveys are the major tools. Most of these tools are used within the context of understanding the business as a social entity (anthropology, sociology), or as a money-making enterprise that can be analyzed and fine-tuned to increase the revenues and the profitability, as well as increase both employee satisfaction and customer satisfaction. The world of the restaurant is of continuing interest to researchers. The restaurant is a microcosm, of interest to businesspeople, organization psychologists, those in the world of food service, and so forth. There are no lack of papers and journals devoted to the world of restaurants in general, and to the world of food service in particular. Most of the papers look at the restaurant from the ‘outside,’ observing either the behavior, or asking the customer to evaluate the experience. There have been some papers looking at the mind of the restaurant consumer in some depth, moving beyond the standard surface questions [5,6]. Most of these deeper-focused papers deal with the topic from the point of view of the profession hospitality, and not from the point of view of psychology.

The Contribution of Mind Genomics to Understanding the Perception of the Restaurant Experience

Mind Genomics is a newly developing science, dealing with the nature of how we make decisions in our daily lives. Rather than focusing on unusual and artificial situations to propose or disprove a hypothesis, Mind Genomics can be better considered to be a cartography, a study of the landscape, with the goal to uncover patterns in everyday life, specifically patterns involved in the way people take in information, and make decisions. Mind Genomics differs from social psychology which observes behavior and hypothesizes inner structures of the mind and differs from experimental psychology which sets up artificial situations, measures responses, and develops hypotheses about mental processes. In contrast, Mind Genomics creates mixture of communication elements about the specifics of a topic, measures the responses to meaningful combinations of messages created according to an experimental design, and deduces the ‘algebra’ of the mind regarding how the person weights the information. Mind Genomics thus combines the methods of market research (concept evaluation), statistical design (systematic variations of combinations of messages), and experimental psychology (evaluating and deconstructing the patterns of response of respondents, viz., ‘subjects’ who participate in an experiment disguised as a simple survey).

In previous studies using the methods of Mind Genomics, the focus has been on the emotional or affective response to the test messages. These responses may either be ratings (e.g., dislike/like, not buy/buy; not believe/believer), the selection of a usage occasion or even the selection of an emotion [7]. The approach of instructing a respondent to give an opinion may be described as investigating ‘homo emotionalis,’ emotional man. In recent years, researcher have begun to consider economic aspects. In concept testing and in conjoint measurement, for example, researchers have mixed price with other features, and instructed the respondent to select the preferred combination of price + features (pairwise trade-off) or rate interest in a selling proposition about a product or a service, with price being one of the features in the proposition (concept testing). During the past two decades, author Moskowitz has occasionally explored the potential of using price as a dependent variable. The respondent is instructed to read a test concept, and instead of (or in addition to) rating the product on liking, the respondent is instructed to select a price. The analysis re-codes the rating, replacing each rating by the price attached to it. The price may be presented in irregular order so that the respondent has to search for the price in a set of price. That approach ensures that the price is not simply used as a Likert scale of magnitude [8].

The integrated set of three studies here, dealing with the response to customers observing the behavior of managers and servers in a restaurant extends the use of Mind Genomics and economics. Author Rappaport has coined the term ‘cognitive economics’ for the extension, where economic considerations, rather than ratings of emotions, serve as the dependent variable [9].

Attribution Instead of Rating

The new direction in Mind Genomics, Attribution, will follow the approach pioneered with the direct estimation of price. In the latter studies, where price was the rating variable, the respondent evaluated different combinations of product features and benefits, selecting a price that might be appropriate for a product or service described by the concept or test vignette. The terms vignette, concept and test combination are used interchangeably. The analysis by OLS (ordinary least-squares) regression revealed the part-worth value of each benefit or feature, or even brand name and tag line. Since the rating was expressed in terms of dollars and cents, the equation uncovered the dollar value of each element. The OLS equation was expressed as: Dollar Value = k1(A1) + k2(A2) … k16(D16), as an example. The equation shows the dollar value selected by the respondent deconstructed into 16 smaller dollar values, k1 – k16, for 16 elements (features, benefits, brand names, tag lines, etc.). Attribution in Mind Genomics moves the focus from the evaluation of price for a specific item whose components are known to the estimated price that would be paid for a situation to be described, where there are no features, but rather actions. One might call this the ‘dollar value of a smile.’ The undergirding hypothesis is that one can present vignettes about situations, such as staff behavior in a restaurant, and ask respondents to judge the relative magnitude of the check for a meal, the relative magnitude from more expensive to the same to less expensive.

The notion of attribution is new, without any exploratory data to be found. There is a well-developed science for the dollar value of product and service features, but the dollar value pertains to what is being purchased. There is an expectation that the dollar value will change with the different features. We are accustomed to paying more or less for certain benefits, features, and even brands. The act of judging is straightforward, at least at a subjective level. Whether the judgments are correct or not can be determined through experiment. In contrast, attribution explores a potentially tenuous relation, if any, between money and the perception of behavior, in a world where the two may not be linked at all. The process of measuring this variable we call ‘attribution’ will become clear as we move through three studies dealing with the estimated size of the ‘check’ for a meal, based upon a description of the behavior of the server and the manager. Each experiment begins with four questions about the situation, and four answers to each question. The role of the question is to set up the structure of information, and to create a structure for the answer. The respondent never sees the question but rather sees only a set of combinations of answers. The respondent 24 different combinations of answers, viz., 24 ‘vignettes’ or test concepts, and rates each vignette on the expected size of the check that the meal would cost. The study does not ask the respondent what she or he would pay for the meal, but rather instructs the respondent to guess about the size of the check to be given by the server. There is no direct cue about price, since the source of the size of the check is unknown, and the respondent is being told that the check is simply delivered.

The origin of these studies emerges from ongoing discussions about the lack of knowledge about the mind of the customer, other than the sociological and market research studies of the type cited above. That is, there is little known about the everyday formation of impressions about the restaurant by customers who walk into a restaurant, are seated, and observe what is going on. The information of interest to most people is the restaurant itself, and the criteria for judgment as to whether one wants to return to the restaurant. The standard knowledge emerging from the experiments is surface. It should be noted that this set of three exploratory studies is both novel and routine. The novelty is the use of pricing as a dependent measure to assess a subjective impression. The dollar value of an experience is not new [9,10], just as the dollar value of product quality is not new [11]. What is new is the use of a seemingly unrelated measure, the dollar value of the check or bill for the meal. There is no clear or necessary or ‘right’ relationship between the dollar value of the check and the description of the restaurant.

The Three Experiments – Mind Genomics Applied to Cognitive Economic Attribution

Mind Genomics works according to a systematized process, following a user-friend path. The software makes the set up straightforward. The set-up system is simple, shown by Figure 1, which represents the different steps that the researcher follows, and at each point types in the relevant information onto a computerized form. Figure 1 is meant to be schematic, showing an actual sequence of completed forms, in the sequence presented to the user. The user is led through a series of forms to complete. The process is virtually self-explanatory but is absent bells and whistles. The format is simple, to the point, and guides the researcher through the process, step by step, beginning with the selection of the topic, the requirement to create four questions, the requirement to create four answers for each question, and finishing with the introduction to the experiment, the rating scale, and the anchor points for the rating scale (highest and lowest).

FIG 1

Figure 1: The set-up system for the Mind Genomics project.

It is worth noting that the ‘difficulties’ encountered in these studies are not from the study itself, but typically because people think in an undisciplined fashion. The form in Figure 1 forces the respondent to think in a systematical fashion, beginning with the topic, then proceeding to the questions, and finally moving to creating four answers for each question. After the first one or two experiences, the thinking of the typical researcher changes, as the respondent begins to follow the disciplined path demanded by the computer program. We illustrate the set up with the first of the three studies, traits of the server. We deal with the results in detail, and then follow up with a cursory analysis of the key findings for the other two studies, the interaction among the staff (Study 2) and the interaction with the customer (Study 3).

Step 1 (Panel A)

Select the name of the topic. This first step requires the researcher to give a name to the project. As simple and as direct as it sounds, Step 1 requires the researcher to focus on the topic as a coherent ‘whole,’ rather than thinking about the topic in a diffuse way. The research then records the name on the proper screen. The study here is Traits of Servers.

Step 2 (Panel B)

Select four questions which tell a story about the topic. It is at Step 2 that the topic should crystallize in the mind of the respondent. The text is typed onto the computer form, one question after another. The questions are never seen directly by the respondent, but simply used as an aid to help generate the creation of the four answers to each question. It is relevant to note here that Step 2 is the most difficult step in the entire process. Most people do not approach problems and knowledge acquisition in a structured, disciplined fashion. Two or three experiences suffice.

Step 3 (Panels C1-C4)

Repeat each question (automatically done by the computer), and instruct the respondent to type in the four different answers to the question in exactly the language and format that the respondent will see it. It is straightforward here to copy text from other languages and other alphabets, and then paste into the computer form. Each of the four panels corresponds to one of the questions. Table 1 shows the four questions, and the four answers to each question.

Table 1: The four questions and the four answers to each question for Study #1.

Study 1: Traits of server and manager
Question A: what personality traits does a server possess?
A1 the server’s personality: consistent smile; high energy; competent in customer service
A2 the server’s personality: insensitive to people with different personalities who show up
A3 the server’s personality: easily communicates with people similar to themselves regarding their specific needs like special dietary requests
A4 the server’s personality: sensitive to customer’s/coworker’s cultural differences; understands we are all different
Question B: what personality traits does a manager possess?
B1 the manager’s personality: stern disposition; takes on an authoritative role
B2 the manager’s personality: knows their customers likes and dislikes the
B3 the manager’s personality: knows their staff’s strengths and weaknesses
B4 the manager’s personality: knows their staff’s weaknesses and strengths
Question C: how does a server assist his/her manager?
C1 server assists manager: shows up to work on time on a consistent basis
C2 server assists manager: shows up with a can-do, team player attitude
C3 server assists manager: friendly to coworkers and customers alike
C4 server assists manager: shows up late
Question D: how does a manager assist his/her wait staff?
D1 the manager assists: stern disposition; takes on an authoritative role
D2 the manager assists: knowledge in all aspects of restaurant tasks
D3 the manager assists: deals with confrontations between staff and customers in a bias manner
D4 the manager assists: shows favoritism amongst staff and customers; generally disrespectful

The rationale for four questions and four answers per question comes from the vision of the researchers to create an easy-to-use system to answers questions about specific topics, such as products, political candidates, and social situations. The original goal was to make the number of possible messages about a topic virtually unlimited. With repeated experience, it became clear that most issues could be satisfied with 36 elements, such as four questions with nine answers (36 elements, 60 vignettes), or six questions with six answers each (36 elements, 48 vignettes). Over time it was the design comprises four questions with four different alternatives (16 elements, 24 vignettes) which emerged as the most practical. Note that elements B3 and B4 are the same, except for a reversal of the order of elements. B3 began with strengths and finished with weaknesses. B4 began with weaknesses and finished with strengths. The Mind Genomics process lets us explore these side issues of order, and study different ways of expressing the same idea, whether these be minor differences (e.g., order of ideas) or major differences (different tonality of language.)

Step 4 (Panels D1 and D2)

Orient the respondent (D1) and then create the rating questions, selecting the number of points, and the rating scale (D2). There are three sequential steps to create the rating scale, comprising the text, the number of scale points, and the anchor points for the low end of the scale and the high end of the scale. Only two scale anchors are allowed in the current version. For other formats, the actual scale points and their anchors are typed out.  For this study, the rating scale is:

Please read the vignette below. How much would you expect the price to be for your meal
1= 25% lower  …   9= 25% higher

Step 5 (Panel E)

Show the actual vignette. This is not part of the set up, but is what the vignette looks like on a computer tablet or a PC. There is a slightly different ‘look’ for a smartphone, due to the difference in size and dimension. Each respondent evaluates a unique set of 24 vignettes, comprising either two, threeor four elements, viz., answers. A vignette can contain a maximum of one answer from a question, never two or more answers, This simple bookkeeping device ensures that the respondent will never be presented with a vignette comprising mutually contradictory elements. at least contradictory by presenting different altenratives to the same question.

Each respondent evaluated a different set of 24 vignettes, created by permuting the basic experimental design [12]. This strategy maintains  the power of an experimental design even at the level of the individual respondent, but ensures that each respondent evlauated a unique set of 24 vignettes. Two benefits emerge, the first beiug the ability to analyze the data by creating a model at the level of the individual (important for clustering), and the second ensuring that the study covers a wide range of possible combinations and thus needs absolutely no knowledge about the most promising combinations to test.

Step 6

Create the database (Table 2). The project generates 24 rows of data for each person. An example of the database appears in Table 2, with the table transpose for presentation purpooses. . The data are set up for immediately stastical anaysis.

Table 2: Example of the database prepared for analysis. The actual matrix format for data analysis is transposed 90 degrees.

Panelist Each respondent has a unique identification number (UID) 1 2 3 4 5 6
Row in database The 30 respondents generate 24 rows of data each, one for each vignette 246 407 478 583 642 678
Gender Male or Female, obtained from an up-front classification question Fem Male Fem Male Fem Male
Age The respondent gives year of birth 36 23 19 24 23 20
Age Group After-the fact grouping into two ages Old Old Young Old Old Young
Self-Profiling (Answer one only) Who do you relate to most in a restaurant setting?

1= Wait staff ( food )

2= Owner

3= Bus ( drinks, setup, cleanup )

4=Cashier/host

1 1 1 1 3 1
Test Order The computer records the order of trial 12 23 17 6 18 22
A1 Each element in the study us coded 1 when appearing in the vignette, and 0 when absent from the vignette 0 0 0 0 0 0
A2 0 1 0 0 0 0
A3 0 0 0 0 0 0
A4 0 0 0 0 0 1
B1 0 0 1 0 0 0
B2 0 0 0 0 0 0
B3 1 0 0 1 1 0
B4 0 0 0 0 0 1
C1 0 0 0 0 0 1
C2 1 0 0 0 1 0
C3 0 0 1 1 0 0
C4 0 0 0 0 0 0
D1 0 1 1 0 0 0
D2 0 0 0 0 0 0
D3 1 0 0 0 1 0
D4 0 0 0 1 0 0
Rating The 9-point rating scale anchored at 1 (25% lower) and 9 (25% higher) 8 4 8 6 7 2
Price The percentage departure from 0 19 -6 19 6 13 -19
Rtseconds Response time to the vignette in the nearest 10th of a second 1.2 0.8 5.0 4.9 1.1 4.0
Clusters2 Membership in one of the two clusters 1 2 1 1 2 2
Clusters3 Membership one of the three cluster 1 3 1 3 2 2

Step 7: Convert the Data to Percent

The nine ratings of price are transformed to relative price, with a rating of 9 transformed to +25 (25% higher), a rating of 5 transformed to 0 (same expected price), and a 1 transformed to -25 (25% lower).

Step 8

Build separate equations for the predefined groups (total, gender, age) The data from each of the self-defined groups, total, gender, and age, were analyzed to create an equation of the form:

Percent Departure of Check from Typical (+25 to – 25) = k1(A1) + k2(A2) … k16(D4)

The coefficient for an element is relative change (percent) is size of the check when the element is inserted into the vignette: (increase in expected check when positive, decrease in expected check when negative).

Table 3 shows the coefficients for the different groups. We highlight only those elements which generate positive or negative changes of 8% or higher in the check. The interesting finding from the first group of respondents is that there are no elements which drive up the value of the check, or drive it down, as least strongly. No coefficient is 8 or higher, viz., no element can be attributed to be a major driver of the check price.

Step 9

Create new to the world mind-sets by dividing the respondents into groups based upon the patterns of their coefficients. Each respondent generates an equation with 16 coefficients, the equation relating the presence/absence of the 16 elements to the percent change expected for the check. The percent is shown as a whole number. A 25% increase in the check is shown as +25; 25% decrease in the check is shown as -25). The pattern of coefficients allows the use of k-means clustering [7]. The clustering program computes a measure of ‘distance’ between pairs of respondents, the measure D defined as (1-Pearson Correlation, viz. 1-R.) The Pearson Correlation, R, measures the strength of a linear relation between two variables, based upon the different observations. There are 16 observations for each respondent. The Pearson Correlation varies from a high of +1 when two variables are perfectly linearly related to each other, to 0 when two variables are not related to each other, to -1 when two variables are inversely related to each other.

Step 10

Create the models for two and three clusters emerging from the clustering. The segmentation or clustering does not know anything about the ‘meaning’ of the elements, but simply works with the coefficients, and the distance values. The clustering yields five new models, two for two-mindsets, and three for three-mind sets. It is the task of the researcher to name these mind-sets, based upon the pattern of strong performing positive elements. We will only present the results for the three mind-sets.

Results – Study #1 (Traits of Servers and Manager)

The first analysis comprises the deconstruction of relative price based on the traits of the staff. Table 3 shows that nearly all elements increase the expected bill, but each element increases the expected size of the check to a small degree. There are no elements which stand out as strong contributors of the magnitude of the check, at least when we deal with respondents classified by gender or by age, respectively.

Table 3: Study #1: How the traits and behaviors of the server and the manager drives the relative size of the check. Numbers in the cells are the increment or decrement of the size of the check, expressed as percent, attributable to the element.

table 3

The respondent who is instructed to assign monetary value to a situation (so-called homo economicus) often is more conservative than the respondent who is instructed to assign a rating of a feeling. These data suggest a conservative response. For the Total Panel, the highest contribution to the checks only 3.6% (server assists manager: shows up to work on time on a consistent basis.) For the Total Panel, the lowest contribution to the check is -1.9% (server assists manager: shows up with a can-do, team player attitude.) There are similar, small contributions for the subgroups defined by gender and by age. At least for the total panel and for the key subgroups defined by age and gender, there is no clear relation between the positive behavior of the staff, their interaction, and the price of the check. We see a clearer set of contributions when we divide the respondents into ‘mind-sets’ based upon the pattern of their coefficients for the relative price, rather than by who they are (mind-sets versus conventional geo-demographic subgroups). Yet, as both Table 3 shows for all the data, and Table 4 shows for the strong-performing elements by mind-set, there are still very few elements which drive an expectation of a large increment or decrement of the check.

Table 4: Study #1: How the traits of the server and the manager drives the relative size of the check. Data from the strongest elements for the three mind-sets.

table 4

The division of respondents into three mind-sets suggests that:

Mind-Set 1

No clear elements drive change in size of the check

Mind-Set 2

Associates warm service with a higher check, associates manager involvement with a lower check. It may be that these respondents feel that any focus on the server’s personality will increase the check.

Mind-Set 3

Expects to pay more for a server who does the job. Expects to pay less for a server who is friendly, and with whom the customer identifies.

Study 1 on the Traits of the Server and Manager suggests that,, in contrast to homo emotionalis who can be shown to have expansive feelings, these patterns emerging when the mind-sets are separated, homo economicus still shows a constrained range of feelings, even when the different mind-sets are identified by the same clustering method, k-means.

Study #2 (Behavior of Staff as the Customer Enters the Restaurant)

The second study moves to what the customer might observe when walking into the restaurant, but before the customer has been seated. We see no clear relation between the incremental or decremental size of the check and staff behavior at the entrance to the restaurant (Table 5)

Table 5: Study #2: How the behavior of the staff at the time of customer entrance to the restaurant drives the relative size of the check. Numbers in the cells are the increment or decrement of the size of the check, expressed as percent, attributable to the element.

table 5

The key differences which emerge come from the three mind-sets (Table 6).

Table 6: Study #2: How the behavior of the staff at the time of customer entrance to the restaurant drives the relative size of the check. Data from the strongest elements for the three mind-sets.

table 6

Mind-Set 1 appears to expect to pay more for staff which look busy, whether they are harmoniously busy or not. Mind-Set appears to expect to pay less for staff seemingly eager to wait on the customer.

Mind-Set 2 expects to pay more when the staff look busy.

Mind-Set 3 expects to pay more when the staff look competent and resolve a problem. Mind-Set 3 expects to pay less for incompetent service.

Study #2 reaffirms that when the respondent is asked to use economics, specifically money as a measure of something that is not usually appraised in economic terms, viz., behavior and service, homo economicus takes over, and forces the respondent in a conservative, judgmental stance. No elements emerge as dramatically strong drivers of the magnitude of the check.

Study # 3 – Dollar Value of Description of the Interaction Between Server and Customer

Study #3 was run exactly as studies 1 and 2.This time, however, the topic was the interaction of the server and the customer. Once again, no patterns emerge for the total panel and for the key subgroups of gender and age (Table 7). The key results emerge for the mind-sets (Table 8)

Table 7: Study #3: How the interaction of the server with the customer drives the relative size of the check. Numbers in the cells are the increment or decrement of the size of the check, expressed as percent, attributable to the element.

table 7

Table 8: Study #3: How the interaction of the server with the customer drives the relative size of the check. Data by mind-set

table 8

Mind-Set 1

Focus on the customer generates an expectation of a higher check. Focus on the server generates an expectation of a lower check.

Mind-Set 2

Weak effects. No strong expectations either direction.

Mind-Set 3

Focus on incompetence drives the expectation of a slightly higher check.

Again, in contrast to homo emotionalis, we see homo economicus is far more conservative, especially when there is attribution without clear linkage, rather than evaluation with clear linkage. An example of the evaluation would be the expectation of the price of the check when the messages deal with the actual food, rather than the service.

Beyond Cognitive Responses of Homo Economicus to A Focus on Engagement Time (Response Time)

The second aspect of the analysis involves the amount of time that a respondent spends making a decision. The data from the deconstruction suggests that the respondent is conservative, at least at a conscious level. At the level of the unconscious, however, can we discover anything more about homo economics and attribution? That is, if we are able to measure the time needed to make a decision, do we learn anything more? Or, in fact, is attribution more elusive? One of the features of the Mind Genomics system is the ability to measure response times, defined as the number of seconds between the time the vignette appears o the screen and the time that the respondent assigns a rating. The response time shortens and reaches a steady stage after 2-3 experiences with the task. Since each respondent evaluated all of the elements in different combinations, and each element appeared many times in each position, one need not eliminate the first 1-3 vignettes. They can simply be included because the slow response should distribute itself approximately equally across all respondents and all elements.

The respondents could not have known their own response times for each element, for three reasons:

  1. The respondent was not aware that the response time was being measured
  2. There was too much to do when evaluating 24 vignettes
  3. Each vignette comprised 2-4 elements.

The response times are measured as a totality. Any response time of 9 seconds or longer was defined as 9 seconds. The randomization of experimental designs ensured that the vignettes requiring 9 seconds or longer would most likely comprise similar elements.

Figure 2 shows the distribution of the response times for each element. The three histograms are plotted in a vertical fashion, allowing the eye to compare the shape of the histograms. It is clear that the response times tend to be longest when the task is to attribute relative price of the check to the traits of the server and the manager. It is clear that the response times tend to be shortest when the task is to attribute relative price of the check to the interaction of the server with the customer. These patterns make intuitive sense, because the respondent can identify with the situation of the server interacting with the respondent. There is little to think about. The reaction is quick because the situation is familiar.

FIG 2

Figure 2: Histograms of the frequencies of the response times for the vignettes. Each graph pertains to one study.

A deeper look into the data reveals the number of seconds that can be ascribed to each element. The analysis is similar to the previous analysis linking the presence/absence of the element to the relative magnitude of the check (1=25% less to 9=25% more). This time, the dependent variable is the response time to the nearest tenth of a second. The equation showing the deconstruction of the response time once again has no additive constant: Response Time (Seconds) = k1(A1) + k2(A2) … k16(D4)

Table 9 shows the combination of element and subgroup for elements defined to ‘engage the respondent.’ In this study, engagement is operationally defined as an element whose deconstructed value of response time is 1.4 seconds or longer. The number 1.4 seconds is an operational definition of engagement, emerging from the analysis of hundreds of studies of this type. The typical engagement times for elements are generally 0.3 to 0.7 seconds, but the engagement times vary by seriousness of topic. Thus, 1.4 seconds for estimated response time is a safe estimate for an element which engages, albeit an estimate of convenience since there is no agreed-upon definition of engagement vs. response time.

Table 9: Response times (engagement) to individual elements by respondents in key subgroups. Only those elements generating response times of 1.4 seconds or more are shown in the table.

table 9

Table 9 suggests that there are some elements which engage the respondent in for dramatically longer times.

The total panel shows no long engagement times.

Males engage with the elements about assisting, whether server assists manager or manager assists server. In contrast, females engage in the element talking about a negative end to the meal.

Younger respondents engage with assistance as well, whether positive or negative. They also respond to elements talking about the nature of the service. Older respondents do not engage with any element.

Mind-Set 1 engages with all types of elements, positive and negative, and at all stages of the staff-customer interaction.

Mind-Set 2 engages with speed of service (‘beeline’).

Mind-Set 3 engages most with the staff being busy.

The use of response time reveals a somewhat more detailed story, suggesting that the attribution of dollar value to staff behavior may not reveal itself as much in the conscious evaluation of ‘how much money will change hands’ but rather in the unconscious variation in engagement time (response time to individual elements).

Discussion and Conclusion

The emerging science of Mind Genomics has been previously used to understand how people respond in an emotional fashion to the description of features and attributes of products and situations [13], as well as understand the dollar value of features and products [10,11]. The approach here moves from the evaluation of concrete descriptions of products and situations to the attribution of value to situations which have no intrinsic value in an of themselves. The introductory studies here are the atmosphere and behavior of service and managerial staff in a restaurant, and the attributed value of such service to one economic indicator, the magnitude of the check.

The data suggest that it is difficult to link economics (e.g., value of the check) to behavior which is not directly related to the product. The Mind Genomics experiment works, at least in practice. What emerges, however is a greatly constricted pattern, a conservatism which does show itself dramatically when one is rating the concrete situation based on feelings, or when one is rating the dollar value of a tangible item or clearly defined service for which one will pay. The implications of this study are great. We live in an economic society where the focus is on customer satisfaction, and the expected economic returns of customer satisfaction. These data suggest that such efforts may be more difficult than one might think. It is all well and good to measure the satisfaction of customers, but just how does that translate into what people will pay. The data from this study suggests that the results of a Mind Genomics study might not be very clear, whether the study deals with the evaluation of a situation without a customer (Study #1: Traits of Server and Manager), the evaluation of a situation where the customer is being introduced into the situation (Study #2: Staff Behavior as Customer Walks In), or even the evaluation of a situation describing the interaction with the staff (Study #3: Interaction of Server and Customer). Or to summarize, how then do we measure the dollar value of customer satisfaction? What have we missed?

References

  1. Han H, Ryu K (2009) The Roles of the Physical Environment, Price Perception, and Customer Satisfaction in Determining Customer Loyalty in the Restaurant Industry. Journal of Hospitality & Tourism Research 33: 487-510.
  2. Namkung Y, Jang S (2007) Does Food Quality Really Matter in Restaurants? Its Impact On Customer Satisfaction and Behavioral Intentions. Journal of Hospitality & Tourism Research 31: 387-409.
  3. Qin H, Prybutok VR (2009) Service quality, customer satisfaction, and behavioral intentions in fast-food restaurants. International Journal of Quality and Service Science 1: 78-95.
  4. Ryu K, Han H (2010) Influence of the Quality of Food, Service, and Physical Environment on Customer Satisfaction and Behavioral Intention in Quick-Casual Restaurants: Moderating Role of Perceived Price. Journal of Hospitality & Tourism Research 34: 310-329.
  5. Jang S, Liu Y, Namkung Y (2011) Effects of authentic atmospherics in ethnic restaurants: investigating Chinese restaurants. International Journal of Contemporary Hospitality Management 23: 662-680.
  6. Teng CC (2011) Commercial hospitality in restaurants and tourist accommodation: Perspectives from international consumer experience in Scotland. International Journal of Hospitality Management 30: 866-874.
  7. Zemel R, Choudhuri SG, Gere A, Upreti H, Deite Y, et al. (2019) Mind, consumers, and dairy: Applying artificial intelligence, mind genomics, and predictive viewpoint typing.
  8. Moskowitz H, Baum E, Rappaport S, Gere A (2019) Estimated Stock Price Based on Company Communications: Mind Genomics and Cognitive Economics as Knowledge-Creation Tools for Behavioral Finance. Edelweiss Applied Science and Technology 4: 60-69.
  9. Moskowitz H, Rappaport S, Moskowitz D, Porretta S, Velema B, et al. (2017) Chapter 14 – Product design for bread through mind genomics and cognitive economics. In D. Bagchi & S. Nair (Eds.), Developing New Functional Food and Nutraceutical Products 249-278.
  10. Moskowitz HR (2012) ‘Mind genomics’: the experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiol Behav 107: 606-613.
  11. Moskowitz HR (1995) The dollar value of product quality: The effect of pricing versus overall liking on consumer stated purchase intent for pizza. Journal of Sensory Studies 10: 239-247.
  12. Gofman A, Moskowitz H (2010) Isomorphic Permuted Experimental Designs and Their Application in Conjoint Analysis. Journal of Sensory Studies 25: 127-145.
  13. Gere A, Harizi A, Bellissimo N, Roberts D, Moskowitz H (2020) Creating a mind genomics wiki for non-meat analogs. Sustainability 12: 5352.

Self-Recovery of Pancreatic Beta Cell’s Insulin Secretion Based on 10+ Years Annualized Data of Food, Exercise, Weight, and Glucose Using GHMethod: Math-Physical Medicine (No. 339)

Abstract

The author was inspired from reading two recently published medical papers regarding pancreatic beta cells insulin secretion or diabetes reversal via weight reduction. The weight reduction is directly related to the patient’s lifestyle improvement through diet and exercise. He has published six medical papers on beta cells based on different stages in observations of his continuous glucose improvements; therefore, in this article, he will investigate food ingredients, meal portions, weight, and glucose improvement based on his 10+ years of collected big data.

Here is the summary of his findings:

  1. His successful weight reduction, from 220 lbs. in 2010 to 171 lbs. in 2020, comes from his food portion reduction and exercise increase.
  2. His lower carbs/sugar intake amount, from 40 grams in 2010 to 12 grams in 2020, is resulted from his learned food nutrition knowledge and meal portion reduction, from 150% in 2010 to 67% in 2020.
  3. His weight reduction contributes to his FPG reduction, from 220 mg/dL in 2010 to 104 mg/dL in 2020. His carbs/sugar control and increased walking steps, from 2,000 steps in 2010 to ~16,000 steps in 202, have contributed to his PPG reduction, from 300 mg/dL in 2010 to 109 mg/dL in 2020. When both FPG and PPG are reduced, his daily glucose is decreased as well, from 280 mg/dL in 2010 to 108 mg/dL in 2020.
  4. His damaged beta cell’s insulin production and functionality, most likely, have been repaired about 16% for the past 6 years or 27% in the past 10 years at a self-repair rate of 2.7% per year.

The conclusion from this paper is a 2.7% annual beta cells self-repair rate which is similar to his previously published papers regarding his range of pancreatic beta cells self-recovery of insulin secretion with an annual rate between 2.3% to 3.2%.

To date, the author has written seven papers discussing his pancreatic beta cell’s self-recovery of insulin secretion. In his first six papers [1-7], he used several different “cutting angles” or “analysis approaches” to delve deeper into this complex biomedical subject and achieved consistent results within the range of 2.3% to 3.2% of annual self-recovery rate.

He used a quantitative approach with precision to discover and reconfirm his pancreatic beta cell’s health state by linking it backwards step-by-step with his collected data of glucose, weight, diet, and exercise. He has produced another dataset for a self-repair rate of 2.7% which is located right in the middle between 2.3% and 3.2% from his previous findings.

In his opinion, type 2 diabetes (T2D) is no longer a non-reversible or non-curable disease. Diabetes is not only “controllable” but it is also “self-repairable”, even though at a rather slow rate. He would like to share his research findings and his persistent efforts from the past decade with his medical research colleagues and to provide encouragement to motivate other T2D patients like himself to reverse their diabetes conditions.

Introduction

The author was inspired from reading two recently published medical papers regarding pancreatic beta cells insulin secretion or diabetes reversal via weight reduction. The weight reduction is directly related to the patient’s lifestyle improvement through diet and exercise. He has published six medical papers on beta cells based on different stages in observations of his continuous glucose improvements; therefore, in this article, he will investigate food ingredients, meal portions, weight, and glucose improvement based on his 10+ years of collected big data.

Methods

Background

To learn more about his developed GH-Method: math-physical medicine (MPM) research methodology, readers can review his article, Biomedical research methodology based on GH-Method: math-physical medicine (No. 54 and No. 310), in Reference [1] to understand his MPM analysis method.

Diabetes History

In 1995, the author was diagnosed with severe type 2 diabetes (T2D). His daily average glucose reached 280 mg/dL with a peak glucose at 398 mg/dL and his HbA1C was at 10% in 2010. Since 2005, he has suffered many kinds of diabetes complications, including five cardiac episodes (without having a stroke), foot ulcer, renal complications, bladder infection, diabetic retinopathy, and hypothyroidism.

As of 9/30/2020, his daily average glucose is approximately 106 mg/dL and HbA1C at 6.1%. It should be mentioned that he started to reduce the dosage of his three different diabetes medications (maximum dosages) in early 2013 and finally stop taking them on 12/8/2015. In other words, his glucose record since 2016 to the present is totally “medication-free”.

Beginning on 1/1/2012, he started to collect his weight value in the early morning and his glucose values four times a day: FPG x1 in the early morning and PPG x3 at two hours after the first bite of each meal. Since 1/1/2014, he also started to collect his carbs/sugar amount in grams and post-meal walking steps. Prior to these two dates, especially during the period of 2010 to 2012, the manually collected biomarkers and lifestyle details were scattered and unorganized. Therefore, those annualized data from 2010 to 2012 or 2014 were guesstimated values with his best effort. It should be further mentioned that on 1/1/2013, he began to reduce his dosages of three diabetes educations step by step. By 1/1/2015, he was only taking 500 mg of Metformin for controlling his diabetes conditions. Finally, he completely ceased taking Metformin on 12/8/2015; therefore, since 1/1/2016, his body has been completely free of any diabetes medications.

Other Research Results

Recently, a Danish medical research team has published an article on JAMA which emphasizes a strengthen lifestyle program can reverse” T2D. This program includes a weekly exercise (5-6 times and 30-60 minutes each time), daily walking more than 10,000 steps using smart phone to keep a record, personalized diet and nutritional guidance by healthcare professionals, etc. The observed results from this Danish report are patientsoverall HbA1C reduction of 0.31%, and their diabetes medication dosage reduction from 73% to 26%.

DiRECT research report from UK also indicated that an aggressive weight reduction program can induce improvement on diabetes conditions. This UK program includes low-calories diet for 3-5 months with 825-853 K-calories per day, plus daily walking of 15,000 steps per day. The observed results from this UK report are patientsoverall HbA1C reduction of 0.9%, weight reduction of 10 kg (or 22 lbs.), and reduced diabetes medication dosage as well.

The Author’s Approach

Inspired by the results from the two European studies and based on his own collected big data over the past 10+ years, from 2010 to 2020, he decided to conduct a similar research on his own. He has separated his 10+ years data into two periods. The first period of 5 years, from 2010 to 2014, with partially collected and partially guesstimated data under different degrees of medication influence, and the second period of 6 years, from 2015 to 2020, with a complete set of collected raw data stored in software and severs without any medication influence.

His trend of thoughts include a sequence from cause to consequence as listed below from top to bottom:

  • Food and meal’s portion %
  • K-calories per day
  • Weight (lbs.)
  • FPG (mg/dL)
  • Carbs/sugar intake (grams)
  • Walking
  • PPG (mg/dL)
  • Daily glucose (mg/dL)

He has further conducted nine calculations of correlation coefficient based on the above parameters to examine the degree of connections between any 2 elements of these total 8 parameters. It should be mentioned that the correlation coefficients can only be done between two data sets, or two curves.

More importantly, in addition to examining the raw data, he also placing an emphasis on the annual change rate percentage, its trend, and their comparisons of these 8 parameters.

Results

Figure 1 shows his background data table which includes his calculated annual averages of the 8 parameters plus proteins, fat, and daily K-calories, based on his daily data collected during 2010 to 2020.

fig 1

Figure 1: Background data table.

Figure 2 depicts the annual change rate percentage of his food (meal portion %, K-calories, and carbs/sugar) and his weight. In this figure, meal portion and weight have similar change rates which means the less he eats, the lighter his weight. Also, carbs/sugar amount and K-calories have similar change rates which means the less his K-calories, the less his carbs/sugar intake amount.

fig 2

Figure 2: Annual change rates of Weight and Food (meal portion, K-calories, and carbs/sugar).

Figure 3 illustrates the similar trend of annual data of his weight and three food components (meal portion, K-calories, and carbs/sugar amount).

fig 3

Figure 3: Annual change rates of Weight and Food (meal portion, K-calories, and carbs/sugar).

Exercise is a missing component from this figure which is also essential on weight reduction. The more he eats, the higher intake amounts of his K-calories and his carbs/sugar as well. During the past decade on his effort for weight reduction, he has focused on reducing both of his meal portion percentage and carb/sugar intake amount. As a result, he was able to reduce his weight from 220 lbs (100 kg) and his average glucose from 280 mg/dL in 2010 to 171 lbs. (78 kg) and 106 mg/dL in 2020 (without any medication).

Figure 4 reflects the annual change rate percentage of his daily glucose, weight and carbs/sugar amount. In this figure, the change rates of his glucose and weight are remarkably similar, almost a mirror image, which indicates the lower his weight, the lower his glucose. This finding matches the two European studies and the common knowledge possessed by healthcare professionals. The reason for the obviously mismatched change rates between carbs/sugar and glucose or weight is due to the missing component of exercise which is equally important on glucose reduction.

fig 4

Figure 4: Annual change rates of Weight, Glucose, and Carbs/sugar.

Figure 5 focuses exclusively on the relationships among data of glucose, carbs/sugar, and exercise. The positive correlation coefficient between glucose and carbs/sugar is expressed by these two similar moving trends. On the other hand, the negative correlation coefficient between glucose and exercise (walking) is expressed by these two opposite moving trends.

fig 5

Figure 5: Annual data of Weight, Glucose, and Carbs/sugar.

Figures 6-8 collectively collective together to show the 9 sets of calculated correlation coefficients among those 8 listed elements in above section of Methods. A better illustration of these three figures can be found in a table, where all of the calculated correlations are above 90%, which means they are highly connected to each other (Figure 9). Even the correlation of -89% between glucose and walking exercise is also extremely high in a negative manner.

fig 6

Figure 6: Correlation coefficients among Weight, K-calories, meal portion.

fig 7

Figure 7: Correlation coefficients among Weight, Glucose, Carbs/sugar.

fig 8

Figure 8: Correlation coefficients among PPG, Carb/sugar, Walking, FPG, Weight.

fig 9

Figure 9: A combined data table of 9 correlation coefficients among 8 elements.

Figure 10 reveals the detailed annual change rates of 8 elements for a 10+ year period from 2010 to 2020. It should be pointed out that his average change rates within 6 years from 2015 through 2020 are 2.7% per year for both FPG and PPG, and 3.4% for daily glucose. This conclusion is similar to his six previously published papers regarding his pancreatic beta cell’s self-recovery rate of insulin secretion. Most likely, his beta cells insulin production and functionality have been repaired about 16% during the past 6 years or 27% during the past 10 years at a self-repair rate of 2.7% per year.

fig 10

Figure 10: A combined data table of annual change rates of 7 elements, especially glucose change rates of 2.7%.

Here is the summary of his findings:

  1. His successful weight reduction, from 220 lbs. in 2010 to 171 lbs. in 2020, comes from his food portion reduction and exercise increase.
  2. His lower carbs/sugar intake amount, from 40 grams in 2010 to 12 grams in 2020, is resulted from his learned food nutrition knowledge and meal portion reduction, from 150% in 2010 to 67% in 2020.
  3. His weight reduction contributes to his FPG reduction, from 220 mg/dL in 2010 to 104 mg/dL in 2020. His carbs/sugar control and increased walking steps, from 2,000 steps in 2010 to ~16,000 steps in 202, have contributed to his PPG reduction, from 300 mg/dL in 2010 to 109 mg/dL in 2020. When both FPG and PPG are reduced, his daily glucose is decreased as well, from 280 mg/dL in 2010 to 108 mg/dL in 2020.
  4. His damaged beta cell’s insulin production and functionality, most likely, have been repaired about 16% for the past 6 years or 27% in the past 10 years at a self-repair rate of 2.7% per year.

Summary

To date, the author has written seven papers discussing his pancreatic beta cell’s self-recovery of insulin secretion. In his first six papers [2-7], he used several different “cutting angles” or “analysis approaches” to delve deeper into this complex biomedical subject and achieved consistent results within the range of 2.3% to 3.2% of annual self-recovery rate.

He used a quantitative approach with precision to discover and reconfirm his pancreatic beta cell’s health state by linking it backwards step-by-step with his collected data of glucose, weight, diet, and exercise. He has produced another dataset for a self-repair rate of 2.7% which is located right in the middle between 2.3% and 3.2% from his previous findings.

In his opinion, type 2 diabetes (T2D) is no longer a non-reversible or non-curable disease. Diabetes is not only “controllable” but it is also “self-repairable”, even though at a rather slow rate. He would like to share his research findings and his persistent efforts from the past decade with his medical research colleagues and to provide encouragement to motivate other T2D patients like himself to reverse their diabetes conditions.

References

  1. Hsu, Gerald C. eclaireMD Foundation, USA. “GH-Method: Methodology of math-physical medicine, No. 54 and No. 310.”
  2. Hsu, Gerald C. eclaireMD Foundation, USA. “Changes in relative health state of pancreas beta cells over eleven years using GH-Method: math-physical medicine (No. 112).”
  3. Hsu, Gerald C. eclaireMD Foundation, USA. “Probable partial recovery of pancreatic beta cells insulin regeneration using annualized fasting plasma glucose via GH-Method: math-physical medicine (No. 133).”
  4. Hsu, Gerald C. eclaireMD Foundation, USA. “Probable partial self-recovery of pancreatic beta cells using calculations of annualized fasting plasma glucose using GH-Method: math-physical medicine (No. 138).”
  5. Hsu, Gerald C. eclaireMD Foundation, USA. “Guesstimate probable partial self-recovery of pancreatic beta cells using calculations of annualized glucose data using GH-Method: math-physical medicine (No. 139).”
  6. Hsu, Gerald C. eclaireMD Foundation, USA. “Relationship between metabolism and risk of cardiovascular disease and stroke, risk of chronic kidney disease, and probability of pancreatic beta cells self-recovery using GH-Method: Math-Physical Medicine (No. 259).”
  7. Hsu, Gerald C. eclaireMD Foundation, USA. “Self-recovery of pancreatic beta cell’s insulin secretion based on annualized fasting plasma glucose, baseline postprandial plasma glucose, and baseline daily glucose data using GH-Method: math-physical medicine (No. 297).”

Quantification of Tooth Wear by Selected Desensitizing Polishing Pastes Using White Light Profilometry

DOI: 10.31038/JDMR.2020344

Abstract

Objectives: To analyse tooth wear using white light non-contact profilometry following the polishing of the tooth surface with selected polishing pastes.

Methods: Three polishing pastes containing a range of particles sizes and different coarseness (extra-fine, medium, course) were compared with commercially available prophylaxis pastes (Nupro with Novamin® and Nupro with Fluoride) as controls. Particle size distribution was analysed using a using particle size analyser and quantified using Masterizer software. Teeth were in 70% ethanol prior to evaluation. 25 extracted human premolar teeth were distributed in five groups (n=5), and the teeth were mounted in a silicone putty matrix leaving an exposed buccal surface. White light profilometry with Proscan 2000 software was used to scan each tooth surface before and after polishing. Scantron ProForm software was used to superimpose images and measure surface loss and analyse the difference between the two surfaces-scans by the Proscan 2000 software.

Results: Particle size analysis indicated that all samples consisted of a wide distribution of particles’ sizes (DX 10, 50, and 90). The course polishing paste had the largest DX 90 whereas Nupro with Fluoride had the lowest DX 90. The extra-fine pumice had the lowest DX 90, although this paste had larger values for DX 10 and DX 50 compared to the medium paste. The volume tooth loss analysis demonstrated that the course pumice had the most tooth surface loss compared to the extra-fine pumice which had the least amount of tooth surface loss. The average volume loss per group was 0.808, 0.022, 0.014, 0.022, 0.026 (course, medium, extra-fine, Nupro with Fluoride, and Nupro with Novamin®) respectively.

Conclusions: The results indicated that the larger the DX 90 within the paste, the more tooth surface loss occurred due to the abrasivity of the paste. There was however minimal or no significant difference in the amount of tooth loss between the control polishing pastes.

Keywords

Prophylaxis polishing pastes, Abrasion, White light profilometry, Particle size analysis

Introduction

Dental materials are frequently used in polishing procedures during periodontal procedures in daily dental practice and the abrasives in these materials may subsequently have an impact on tooth surface loss and wear. Several factors are indicated in the aetiology of tooth wear with or without Dentine Hypersensitivity (DH) such as erosion, attrition and, abrasion. Furthermore, different materials other than a tooth can cause tooth contact when it contacts a tooth (so-called two-body or three- body contact [Tribology]) [1]. The term wear is, therefore, a better descriptive term to define the loss of tooth structure [2]. Tooth wear can be defined as the net loss of tooth structure when it is under function [1]. Previous studies have reported a growing interest in quantifying tooth structure loss which is called ‘wear quantification’ both in vivo and in vitro in three dimensions. Volume and mean height are the most clinically relevant parameters that can be used to analyse tooth loss [3]. It is essential to have a systematic, reliable and, repeatable data using a wear quantification method. The method itself is time consuming, which requires an experienced operator to apply the different software packages that are available commercially for wear quantification [4]. It is, however, a useful method to compare and evaluate the effect of different new materials, which may cause tooth wear in vitro. An accurate surface topographic representation of a tooth both pre- and post-wear testing is essential for any in vitro wear qualification to be valid. There are three main types of sensors that are used for scanning and subsequently quantifying the wear namely: 1) contact sensors [5], 2) non-contact sensors [6] and 3) white light [7] which are all suitable for systematic studies [4]. Investigators have previously utilised white light non-contact profilometric techniques as a quantifiable measure of tooth loss/abrasive wear and/or erosion [7-9]. White light profilometry uses effective sensors to measure the distance in which they can split the white light beam into its constituent wavelength [10]. Each wavelength matches to its corresponding distance which creates its monochromatic image point. Therefore, the image reflects the surface topography of a scanned specimen which it can provide a quantitative measure of shape, texture, microtopography, microform and roughness [10].

Aim

The aim of this in vitro study was to analyse tooth wear on extracted human teeth using contactless white light profilometry following professional polishing with selected polishing pastes with different types of pumice used in the polishing of teeth during periodontal procedures.

Material and Method

This exploratory study was based on two procedures. The first part described in this paper was to quantify tooth wear using a white light profilometry following polishing of the teeth to choose the ideal abrasivity of the pumice that would be incorporated into future prophy-paste formulations. The second part of the study was the evaluation of selected pastes to determine their effectiveness in tubular occlusion and this will be reported in a subsequent paper.

Particle Size Analysis

The same weight (50 mg) of the polishing prophylaxis paste samples were dissolved separately in 50 mL deionised water. Once the solid particles were dispersed, the diluted solution was transferred into a system that uses the MASTERSIZER 3000E (MALVERN software) to initiate the measurements and analysing the particle sizes through the laser diffraction method using a dispersion of particles in a liquid, wet, Hydro EV, deionised water with a 1.33 refractive index. The Mastersizer E used is designed to obtain values for a wide particle size range of 0.1 to 3500 µm. The setting of the software was pre-set manually to a duration of 15 seconds background measurement(s) and 10 seconds sample measurements. The diluted solution was added in small quantities until the obstruction range of 5-20% was achieved. A speed of 2000 rpm for the hydro pump speed was used for all tested samples. Four different measurements for each sample were automatically reported, analysed, and averaged by the software. The median for different volume distributions DX 10, 50, and 90 were recorded and the data was subsequently exported into an Excel file for analysis.

Preparation of Materials

A total of 30 extracted, caries free human premolars were collected from the walk-in dental polyclinics from Kuwait in 2017 after obtaining verbal consent from patients for the use of their teeth in research. The teeth were stored in a small container of Listerine mouthwash (Johnson and Johnson, UK) and brought to the UK by HFH under QMUL guidelines UK. The teeth were transferred and stored in a 70% Ethanol solution in a specimen container at room temperature within the Department of Physical Sciences Unit at Mile End, London in accordance with HTA regulations. The extracted premolars were distributed into five groups (n=5) and teeth were mounted in a silicon putty matrix (Zetaplus plus mixed with an indurent gel (Zhermack SpA, Italy), leaving an exposed buccal surface to evaluate. The groups were numbered from 1 to 5 and they were stored in the 70% Ethanol solution at room temperature within the Department.

Prior to scanning the samples were prepared by placing three divots using a ½ round bur at high speed on the flattest buccal (facial) surface of each tooth. Three polishing pumices with a range of particle sizes and different coarseness of pumice (extra-fine, medium, course) (Kemdent Works, Swindon UK) were compared to commercially available prophylaxis pastes namely, Nupro with Novamin®, Nupro with Fluoride (Dentsply International, USA)(Controls). A battery-operated dental polisher portable handpiece (Dentitex model number TP-01; 8000 rpm motor), was used as a polishing carrier device instead of a slow speed handpiece for practical purposes. Its cup has the same size as a dental office polishing cup. To avoid any contamination of the materials, each cup was dedicated for a specific prophylaxis paste. Three different pumice powders (course, medium and extra-fine) (Kemdent; Swindon, UK) were characterized in terms of their particle size distribution (Masterizer software). The exact weight measure of the samples was dissolved separately in 50 mg deionised water. The diluted solution was transferred to initiate the measurements and analysing the particle sizes through a laser diffraction method using a dispersion of particles in a liquid, Hydro EV, deionised water with 1.33 refractive index (Mastersizer 3000E from Malvern software).

Quantification of Tooth Surface Loss

White Light Profilometry (WLP)

Two software programmes were used for analysing the tooth surface loss; namely: a Proscan 2000 and a Scantron ProForm. The Proscan 2000 software is designed for shape analysis, object digitisation and accurate surface analysis. The Scantron ProForm software is designed for analysing the differences between two surfaces-scans made by the Proscan 2000 software accuracy.

Three divots on the buccal surface of every tooth were placed to define reference points and the surfaces scanned. The pumice was used with water only, and the tooth was polished for two minutes using the portable polishing handpiece (Dentitex). The tooth was gently rinsed with water until all pumice particles were no longer observed on the tooth surface. A second scan was undertaken using white light profilometry. The two scans were then superimposed in a different software Scantron ProForm to measure any surface volume loss and analyse the difference between the two surfaces-scans. An area of 0.4 x 0.4 µm² was randomly selected between the three divots as a standard dimension for all samples (Figure 1).

fig 1

Figure 1: shows a) superimposing of pre-treated and post-treated tooth surface using course pumice. b) Random selection of area 0.4×0.4 µm² between the three created divots on the tooth.

Results and Discussion

The particle size analysis showed that all samples consisted of a wide distribution of particle sizes (DX 10, 50, and 90). Table 1 and Figure 2 show the particle size distribution for each sample. The course pumice sample had the largest amount of DX 90 particle size whereas Nupro with Fluoride had the smallest DX 90. The extra-fine pumice sample had the smallest DX 90 for the pumice powders but had larger values for DX 10 and DX 50 than the medium pumice sample.

Table 1: Distribution of DX 10, 50, and 90 µm particle sizes of the five groups.

DX 10 (µm)

DX 50 (µm)

DX 90 (µm)

Course Pumice

53.4

119

253

Medium Pumice

4.04

21.0

75.1

Extra-Fine Pumice

4.33

23.7

62.0

Nupro with F

5.27

19.1

53.8

Nupro with NovaMin®

13.8

44.4

121

fig 2

Figure 2: Particle Size Distribution DX 10, 50, and 90 particle sizes of the five materials.

The tooth surface loss volume was analysed using white light non-contact profilometry following the polishing of the tooth surface with the selected polishing pastes. The results demonstrated that the course pumice had the most tooth surface loss compared to the extra-fine pumice which had the least amount of tooth surface loss. The average volume loss per group was 0.808, 0.022, 0.014, 0.022, 0.026 mm3 (course, medium, extra-fine, Nupro with Fluoride, and Nupro with Novamin®) respectively (Table 2 and Figure 3). The t-test between the Medium vs. Extra-Fine samples was 0.0098 which indicated a significant difference in surface loss. Based on this result an extra-fine pumice was recommended to be incorporated in the prophy-paste formulation in subsequent studies. The results indicated that the larger the DX 90 value of the paste, the more tooth surface loss occurred due to the abrasivity of the paste. Thus, it seems that the coarse particles in the particle size distribution close to D90 dominate the tooth loss. There were no significant differences in the amount of tooth loss between the two control samples.

Table 2: The average of tooth surface loss in (mm3) for the different materials analysed where T is the tooth sample that was used.

Sample/Material

Course

Medium Extra-Fine Nupro with Fluoride

Nupro with Novamin®

T1

1.074

0.019 0.013 0.0303

0.021

T2

0.708

0.021 0.009 0.0196

0.017

T3

0.633

0.017 0.015 0.008

0.026

T4

0.877

0.029 0.017 0.0236

0.039

T5

0.749

0.025 0.018 0.0294

0.028

Average

0.8082

0.0222 0.0144 0.0222

0.0262

Standard Deviation

0.1729

0.0048 0.0036 0.0091

0.0083

fig 3

Figure 3: The average of tooth surface loss (mm3) between the selected prophy-pastes after removing the course particle sample: T is the tooth sample that was used.

Table 2 shows the average of tooth surface loss in (mm3) for the different materials analysed where T is the tooth sample that was used.

Conclusion

The results from this exploratory study on the effect of the particle size distribution on tooth surface loss indicated that the larger the DX 90 particle size of the pumice samples, the more tooth surface loss and wear. The extra-fine pumice sample should be incorporated into a prophylaxis paste to reduce any potential tooth surface loss.

References

  1. Addy M (2000) Dentine hypersensitivity: Definition, prevalence, distribution and etiology. In: Addy M, Embery G, Edgar WM, Orchardson R, editors. Tooth wear and sensitivity: Clinical advances in restorative dentistry. London: Martin Dunitz 2000: 239-248.
  2. Smith RG (1997) Gingival recession. Reappraisal of an enigmatic condition and a new index for monitoring. J Clin Periodontol 24: 201-205. [crossref]
  3. Pintado MR, Anderson GC, DeLong R, Douglas WH (1997) Variation in tooth wear in young adults over a two-year period. J Prosthet Dent 77: 313-320. [crossref]
  4. Heintze SD, Cavalleri A, Forjanic M, Zellweger G, Rousson V (2006) A comparison of three different methods for the quantification of the in vitro wear of dental materials. Dent Mater 22: 1051-1062. [crossref]
  5. Magne P Oh WS, Pintado MR, DeLong R (1999) Wear of enamel and veneering ceramics after laboratory and chairside finishing procedures. J Prosthet Dent 82: 669-679. [crossref]
  6. Mehl A, Gloger W, Kunzelmann KH, Hickel R (1997) A new optical 3-D device for the detection of wear. J Dent Res 76: 1799-1807. [crossref]
  7. Vieira A, Overweg E, Ruben JL, Huysmans MC (2006) Toothbrush abrasion, simulated tongue friction and attrition of eroded bovine enamel in vitro. J Dent 34: 336-342. [crossref]
  8. Hara AT, Zero DT (2008) Analysis of the erosive potential of calcium-containing acidic beverages. Eur J Oral Sci 116: 60-65. [crossref]
  9. Theocharopoulos A, Zou L, Hill R, Cattell MJ (2010) Wear quantification of human enamel and dental glass-ceramics using white light profilometry. Wear 269: 930-993.
  10. Litwin D, Galas J, Blocki N (2006) Variable wavelength profilometry, in: Proceedings of the Symposium on Photonics Technologies for 7th Framework Program (Wroclaw, 2006). 476-479.

PI3K Signaling Pathway Regulates the Caspase-9 in Renal Tubular Epithelial Cells

DOI: 10.31038/JPPR.2020331

Abstract

Background: To evaluate how resveratrol regulates the IL-6 signaling in renal cell, pCMV6-IL-6 was overexpressed in the renal tubular epithelial cell line NRK-52E.

Methods: IHC and TUNEL assay were used to identify the localization and apoptosis detection of the overexpression of IL-6 in NRK-52E cells. To identify the effect of overexpressed IL-6, the mitochondrial fraction was isolated and caspase activities and western blotting were performed.

Results: Our results revealed that pCMV6-IL-6 was overexpressed in the nucleus and around the nuclear membrane of the cells. Moreover, the cell membrane showed no IL-6 overexpression, which may be suggest absence of the IL-6/IL-6R binding effect on the cell membrane. Furthermore, the results of the TUNEL assay demonstrated that pCMV6-IL-6-transfected cells showed features of apoptotic cells. The results of the caspase activity assay revealed that resveratrol significantly attenuated IL-6-induced caspase-3 activity but not attenuated IL-6-induced caspase-9 activity, indicating the antiapoptotic ability of resveratrol response on caspase-3 activity. The PI3K inhibitor could decrease the caspase-9 level, suggesting that the reduction of the caspase-9 level mediate by through the PI3K signaling pathway.

Conclusions: Taken together, our results demonstrated that IL-6 expression not only in the cytosol but also in the nucleus of renal tubular epithelial cells. The PI3K signaling pathway regulates the caspase-9 in renal tubular epithelial cells.

Keywords

IL-6, p-STAT3, Caspase-3, Caspase-9, PI3K, Resveratrol

Introduction

Resveratrol is a naturally occurring stilbene that has been used in anticancer, antiaging, and anti-inflammatory treatment; it has also been applied for cardioprotection and nephroprotection in oxonate-induced hyperuricemic mice and for central nervous system protection [1-3]. Resveratrol provides protection against both acute and chronic kidney injury because of its antioxidant properties and ability to activate sirtuin [4]. Therefore, resveratrol is a useful alternative treatment for renal injury. A previous study suggested that resveratrol acts as an antihyperuricemic and nephroprotective agent in hyperuricemic mice; however, whether resveratrol has beneficial effects on kidney disease in humans and other hyperuricemic animal models remains unclear. Interleukin-6 (IL-6) is a multifunctional cytokine that regulates numerous biological processes including organ development, acute-phase responses, inflammation, and immune responses [5]. The role of IL-6 is not restricted to the immune system; it is also involved in the regulation of metabolic processes. Although IL-6 is a proinflammatory cytokine that promotes inflammation under various pathological conditions involving trans-signaling, its anti-inflammatory and regenerative properties mediated by classic signaling have increasingly been recognized [6-8].The local activation of IL-6 classic and trans-signaling pathways is implicated in renal autoimmune and inflammatory diseases, indicating the importance of IL-6 regulation in renal disease [9]. All kidney resident cells can secrete IL-6 in certain milieu, but only podocytes express the IL-6 receptor (IL-6R); however, other kidney resident cells do not express IL-6R and use classic IL-6 signaling. Moreover, IL-6 is a well-known activator of signal transducer and activator of transcription 3 (STAT3) [10]. STAT3 has been studied as a transcription factor; a small pool of STAT3 was localized in the mitochondria, where it functioned as a positive regulator of the mitochondrial electron transport chain [11,12]. To clarify the regulation of IL-6 in renal cells, recombinant pCMV6-IL-6 was overexpressed in the renal tubular epithelial cell line NRK-52E. Our studies demonstrated that IL-6 expression both in the cytosol and nucleus of renal cells. Resveratrol attenuated IL-6-induced caspase-3 and caspase-9 activities. The PI3K inhibitor could decrease the caspase-9 level, suggesting that the PI3K signaling pathway regulates the caspase-9 in renal tubular epithelial cells.

Method

Preparation of NRK-52E Cells and Transfection

A normal rat kidney tubular epithelial cell line, NRK-52E (BCRC60086), was purchased from the Food Industry Research and Development Institute, Taiwan. The cells were cultured in Dulbecco’s Modified Eagle Medium containing 4.5 g/L glucose, 4 mM L-glutamine, and 5% bovine calf serum (Thermo Fisher Scientific Inc., Waltham, MA, USA) and were grown at 37°C in a humidified environment with 5% CO2. pCMV6 and pCMV6-IL-6 cDNA plasmid were purchased from OriGene. The cells were transfected with 2 μg of pCMV6 or pCMV6-IL-6 in each well by using lipofectamine (Thermo Fisher Scientific Inc., Waltham, MA, USA), according to the manufacturer’s instructions.

Treatment with Resveratrol

After NRK-52E cells reached 50-70% confluence, 10 mM of resveratrol (Sigma-Aldrich Corp. MO, USA) were added to the culture medium, and the culture was incubated for 24 h. Control cells were maintained at 37°C in a humidified environment with 5% CO2.

Immunohistochemistry

The cells were fixed in 10% phosphate-buffered formalin, blocked with antibody diluent buffer (Dako, Agilent Technologies, Santa Clara, CA, USA), and incubated with anti-DDK antibody (OriGenen Technologies, Inc., Rockville MD, USA) diluted at 1:500 for 60 min at room temperature. Subsequently, the cells were incubated with secondary antibodies conjugated with horseradish peroxidase (HRP) polymer for 30 min at room temperature. The cells were then treated with a chromogen, 3,3ʹ-diaminobenzidine tetrahydrochloride (Vector Laboratories, CA, USA), for 10 min. Images were captured using an inverted Nikon ECLIPSE TE2000-S (Nikon Instruments Inc., Melville, NY, USA).

DeadEnd™ Colorimetric Apoptosis Detection

The apoptotic cells were assayed using the terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling (TUNEL) colorimetric method according to the manufacturer’s protocol (Promega Corporation, Madison WI, USA). Briefly, fixed cells were washed, permeabilized, and then incubated with 100 μL of TdT end-labeling cocktail for 60 min at 37°C in a humidified chamber. The cells were blocked with 0.3% hydrogen peroxide and bound with streptavidin HRP. After washing with PBS, and the cells were incubated with 100 μL of 3,3′-diaminobenzidine substrate solution for 10 min at 25°C. Images were captured using an inverted Nikon ECLIPSE TE2000-S (Nikon Instruments Inc., Melville, NY, USA).

Caspase-3/CPP32 and Caspase-9 Colorimetric Assay

Caspase-3 and caspase-9 activities were determined using the caspase-3/CPP32 and caspase-9 colorimetric assay kit (BioVision Inc., Milpitas CA, USA), respectively. The cells were washed in cold PBS, resuspended in 50 mL cell lysis buffer, and incubated on ice for 10 min. Cell lysates were pelleted, followed by the transfer of supernatants to microcentrifuge tubes. Subsequently, 50 mL of cell lysates and 50 mL of the reaction buffer was added to microplate wells; 5 mL of 4mM DEVD-pNA substrate for caspase-3 and 4mM LEHD-pNA substrate for caspase-9 were added and then incubated at 37°C for 2 h. A control reaction of treated cells without DEVD-pNA or LEHD-pNA was included. The absorbance was measured at 405 nm using the BioTek Synergy H1 ELISA reader (BioTek Instruments Inc., Winooski VT, USA).

Mitochondrial Fraction Isolation

The cells were lysed in cytosol extraction buffer containing DTT and protease inhibitors. The samples were maintained on ice for 10 min and then centrifuged at 700xg for 10 min at 4°C. The supernatant was then transferred to a fresh microcentrifuge tube and centrifuged at 10,000xg for 30 min at 4°C. The supernatant was collected as cytosolic fraction, and the pellet was resuspended in mitochondria extraction buffer containing DTT and protease inhibitors as mitochondrial fraction.

SDS-PAGE and Western Blotting

The protein concentration of supernatants was measured using a BCA kit (Pierce Biotechnology, Inc., USA). For each sample, 10 μg or 50 μg of the protein lysate was separated on 10% or 15% polyacrylamide gels and then transferred to PVDF membranes by using a semidry transfer apparatus (Bio-Rad, Hercules, CA, USA). The membranes were blocked in 5% nonfat dry milk in TBST buffer (25 mM of Tris at pH 7.5, 135 mM of NaCl, and 0.15% Tween-20) for 1 h and then incubated with anti-β-Actin (Santa Cruz Biotechnology, Inc.) for 1 h or with anti-DDK (FLAG) antibody (OriGnen Technologies, Inc., Rockville, MD, USA), anti-p-STAT3-Tyr705 (Santa Cruz Biotechnology, Inc.), or anti-caspase-3, anti-caspase-9, anti-COX IV, anti-p-Akt-Ser473, and anti-p-p38 MAP kinase-Thr180/Tyr182 (Cell Signaling Technology, Inc., Danvers, MA,USA) at 4°C for overnight. The blots were washed using TBST and then incubated for 50 min with secondary antibodies conjugated with horseradish peroxidase (Invitrogen, Thermo Fisher Scientific Inc., Waltham, MA, USA). The immunoreactive proteins were detected using an enhanced chemiluminescence detection system (GE Healthcare Bio-Sciences, Marlborough, MA, USA) according to the manufacturer’s instructions.

Statistical Analysis

All data in this study are presented as mean ± standard error of the mean (SEM) from triplicate measurements. The stained blots were scanned and quantified using ImageJ 1.52a software (NIH, USA). A p value of <0.05, <0.01, or <0.001 (one-way ANOVA) was considered significant. All statistical analyses were performed using SigmaPlot, Version 13.0 (Systat Software Inc., San Jose, CA, USA).

Results

IL-6 Overexpression was found in the Nucleus and Around the Nuclear Membrane

As shown in Figure 1c, pCMV6-IL-6 was overexpressed in the nucleus and around the nuclear membrane of the cells. Moreover, the cell membrane showed no IL-6 overexpression, which may be suggest absence of the IL-6/IL-6R binding effect on the cell membrane. Besides, IL-6-overexpressing cells showed the presence of apoptotic bodies, as detected using DeadEndTM colorimetric apoptosis detection assay (Figure 2d), suggesting that IL-6 regulation may be related to apoptosis.

fig 1

Figure 1: Immunohistochemistry staining of overexpressed IL-6 in NRK-52E cells. (a) The immunohistochemistry staining of the control group. (b) The cells transfected through a pCMV6 vector. (c) The cells transfected through pCMV6-IL-6. The arrow indicates the cell overexpressed IL-6.

Resveratrol Attenuated IL-6-induced Caspase-3 Activities in NRK-52E Cells

As shown in Figure 3a, IL-6 overexpression significantly promoted caspase-3 activity in NRK-52E cells (p = 0.014). Moreover, resveratrol significantly attenuated caspase-3 activity not only in treatment alone (Figure 2b, p = 0.002) but also in pCMV6 transfection cells (Figure 2b, p = 0.046) and in pCMV6-IL-6 transfection cells (Figure 2b, p = 0.002). Furthermore, IL-6 overexpression could not regulate caspase-9 activity and resveratrol also could not regulate overexpressed-IL-6-induced caspase-9 activity (Figure 3c).

fig 2

Figure 2: DeadEnd colorimetric apoptosis staining of overexpressed IL-6 in NRK-52E cells. Colorimetric staining of overexpressed IL-6 in NRK-52E cells. (a) The negative control of colorimetric staining. (b) The cells transfected through the pCMV6 vector. (c) Cells transfected through the pCMV6-IL-6 vector. (d) The amplified apoptotic nuclei cells (brown).

Resveratrol Attenuated IL-6-induced p-STAT3 Expression

As shown in Figure 4b, pCMV6-IL-6 expression was found in both cytosol and mitochondrial fractions, and high pCMV6-IL-6 expressed was found in the mitochondrial fraction, indicating the IL-6 may be involved in the mitochondria regulation of cell function. Moreover, IL-6 overexpression significantly promoted both cytosol and mitochondrial p-STAT3 levels, and resveratrol significantly attenuated the IL-6-induced p-STAT3 level in both cytosol and mitochondrial fractions (Figure 4c and 4d, p < 0.001). Furthermore, IL-6 overexpression increased STAT3 mRNA expression, and resveratrol attenuated IL-6-induced STAT3 mRNA expression (Figure 4d). Our results showing that IL-6 could activate STAT3 activity in NRK-52E cells.

Overexpressed IL-6 Promoted the Caspase-9 Protein Level in Mitochondrial Fraction

As shown in Figure 4b and 4g, resveratrol significantly promoted the IL-6-induced caspase-9 level only in the mitochondrial fraction (Figure 4g, p < 0.001). Moreover, IL-6 overexpression significantly reduced the caspase-9 level in the cytosol fraction (Figure 4f, p < 0.001) but increased the level in the mitochondrial fraction (Figure 4g, p < 0.001). Interestingly, resveratrol promoted the IL-6-induced caspase-9 level (Figure 4g, p < 0.001) in the mitochondrial fraction but attenuated IL-6-induced caspase-9 activity in both the cytosol (Figure 3c, p = 0.036) and mitochondria (Figure 4h, p = 0.024) fractions, suggesting that resveratrol attenuated IL-6-induced caspase-9 activity not only in the cytosol fraction but also in the mitochondrial fraction.

Caspase-9 was Mediated by PI3K Signaling Pathway in NRK-52E Cells

As shown in Figure 5, the caspase-9 level decreased after the PI3K inhibitor LY294002 was added to the culture medium. However, the p38 MAPK inhibitor SB203580 and the STAT3 inhibitor stattic did not influence the caspase-9 level (data not shown), suggesting that the regulation of the caspase-9 may mediate by through the PI3K signaling pathway. Moreover, LY294002 and SB203580 did not affect the p-STAT3 level or the caspase-3 level, suggesting p-STAT3 and caspase-3 levels are mediated through other pathways.

Discussion

Resveratrol affects multiple cellular processes and is an excellent candidate for use in human disorders. Numerous experimental studies and clinical trials have been conducted to analyze the systemic anti-inflammatory, antioxidative, multiorgan protective effects of resveratrol [13-15]. Our previous study explored the resveratrol is a potentially therapeutic strategy for hyperuricemia rats and disclosed immunoreactivity of IL-6 in renal cortex [16]. In this study, to evaluate how resveratrol regulates IL-6 in renal cells, pCMV6-IL-6 was overexpressed in the rat tubular epithelial cell line NRK-52E. The IL-6 mRNA level in NRK-52E cells was upregulated after treatment with uric acid and was downregulated after treatment with resveratrol, suggesting the anti-inflammatory property of resveratrol (data not shown). It has been demonstrated that the IL-6 exerts proapoptotic effects through the IL-6 trans-signaling pathway and exerts antiapoptotic effects through the classic pathway [9,17,18]. IL-6 signaling through the membrane-bound IL-6R is mostly regenerative and anti-inflammatory, and the signaling of IL-6/sIL-6R has been termed IL-6 trans-signaling, which induces the proinflammatory properties of IL-6. As demonstrated by Nechemia-Arbely et al., IL-6 trans-signaling mediates a protective response to renal injury [19]. In Nechemia-Arbely’s study, the administration of an IL-6/sIL-6R fusion protein prevented the onset of acute kidney injury and significantly enhanced survival. Therefore, the role of IL-6 in the process of cell injury is still controversial. Our study showed that IL-6 was expressed in the cytosol and nuclear of the renal cells (Figure 1), and low IL-6R mRNA expression was found in the cells (data not shown), indicating the absence of the IL-6/IL-6R binding effect on the cell membrane. Moreover, IL-6-overexpression cells presented apoptotic bodies, as revealed in the DeadEndTM colorimetric apoptosis detection assay, suggesting that the regulation of IL-6 may be related to the apoptosis process (Figure 2). Apoptosis is regulated by two interrelated signaling pathways: the extrinsic or death-receptor pathway and the intrinsic or mitochondrial pathway; both pathways use the caspase cascade [20]. Caspase-9 is a key player in the intrinsic or mitochondrial pathway that is involved in various stimuli, including chemotherapy, stress agent, and radiation [21]. Cytochrome c is released from the mitochondria to the cytoplasm in cells in response to intrinsic stimuli and forms the apoptosome, which mediates caspase-9 activation [22]. Moreover, caspase-3 is a major executioner caspase that is cleaved and activated by both caspase-8 and caspase-9 initiator caspases [22]. In the present study, the results showed that IL-6 overexpression significantly promoted caspase-3 activity (Figure 3a) but did not affect caspase-9 activities (Figure 3c) suggesting the caspase-3 activity may regulate the overexpressed IL-6-induced apoptosis. However, IL-6 overexpression significantly reduced the cytosolic caspase-9 protein level (Figure 4f, p < 0.001) but significantly promoted the mitochondrial caspase-9 protein level (Figure 4g, p < 0.001). The possibility of caspase-9 shifting from the cytosol to mitochondria, induced by IL-6 overexpression, remains to be further studied. Moreover, resveratrol only significantly attenuated IL-6-induced caspase-3 activities (Figure 3b, p = 0.002) but no effect on caspase-9 activities or overexpressed-IL-6-induced caspase-9 activity (Figure 3c) indicating that resveratrol exerts antiapoptotic ability in attenuated caspase-3 activities induced by IL-6 overexpression in NRK-52E cells. STAT3 has been studied as a transcription factor, and a small pool of STAT3 was localized in the mitochondria, where it functioned as a positive regulator of mitochondrial electron transport chain [11-12]. IL-6 is a well-known activator of STAT3 [10]. In the present study, as shown in Figure 4b, pCMV6-IL-6 expression was found in both cytosol and mitochondrial fractions, and pCMV6-IL-6 expression was high in the mitochondrial fraction, indicating that IL-6 may be involved in the mitochondrial regulation of cell function. As expected, IL-6 overexpression significantly promoted both cytosol and mitochondria p-STAT3 levels (Figure 4c and 4d, p < 0.001), indicating that IL-6 could activate STAT3 activity in NRK-52E cells. Resveratrol can exert its anticancer effects by negative regulation of STAT3/5 signaling cascade [23]. Our results also showed that resveratrol not only significantly attenuated the IL-6-induced p-STAT3 level in both cytosol and mitochondrial fractions (Figure 4c and 4d, p < 0.001) but also significantly decreased IL-6-induced STAT3 mRNA expression (Figure 4e, p < 0.05), indicating that resveratrol may downregulate IL-6-induced STAT3 mRNA expression. Human caspase-9 is phosphorylated on Ser196 by Akt/PKB, resulting in the attenuation of its activity, which suggests that the PI3K signaling pathway plays a central role in antiapoptosis [24]. Moreover, the ERK MAPK pathway inhibits caspase-9 activity through direct phosphorylation at Thr 125 [25]. Past studies have suggested that at least one signaling pathway modulates caspase-9. Our study showed that the caspase-9 level was inhibited by the PI3K inhibitor (LY294002) (Figure 5) but could not be inhibited by the MAPK inhibitor (SB203580) or STAT3 inhibitor (stattic) (data not shown), indicating that in NRK-52E cells, the regulation of capase-9 may mediated by PI3K signaling.

fig 3

Figure 3: Caspase-3 and caspase-9 activity assays of IL-6 overexpressed NRK-52E cells. (a) Caspase-3 activity of overexpressed IL-6. (b) Caspase-3 activity of overexpressed IL-6 cells containing 10 mM resveratrol. (c) Caspase-9 activity of overexpressed IL-6 cells containing 10 mM resveratrol. Data are presented as mean ± standard error of the mean for the three measurements.

fig 4

Figure 4: Western blotting analysis of overexpressed IL-6 in cytosol and mitochondrial fractions. Cytosolic fraction corresponds to 50 μg of protein lysate prepared. Mitochondrial fraction corresponds to 10 μg of protein lysate prepared. (a) Overexpressed IL-6 in with or without resveratrol treatment. (b) Protein level of overexpressed IL-6, p-STAT3, STAT3, p-Akt, Akt, caspase-9, caspase-3, actin and COX IV. (c and f) p-STAT3 and caspase-9 expression in the cytosol, respectively. (d and g) p-STAT3 and caspase-9 expression in the mitochondria, respectively. (e) The mRNA expression of STAT3. (h) Mitochondrial caspase-9 activity. Anti-IV COX indicates the mitochondrial marker. Data are presented as mean ± standard error of the mean for the three measurements. *p < 0.05; **p < 0.01; ***p < 0.001.

fig 5

Figure 5: Western blotting analysis of NRK-52E cells treatment with protein inhibitors. Lanes correspond to 50 μg of protein lysate prepared. (a) Lysates were analyzed with corresponding antibodies against p-Akt (Ser473), p-P38 (Thr180/Tyr182), caspase-9, p-STAT3 (Tyr705), caspase-3, and β-Actin. (b) LY294002 containing lysates were analyzed with corresponding antibodies against p-Akt (Ser473), caspase-9, p-STAT3 (Tyr705) and β-Actin.

Taken together, our important finding in this study explored: (1) The overexpressed IL-6 both located in the cytosol and nucleus of renal cells. (2) IL-6 overexpression significantly reduced the caspase-9 level in the cytosol fraction but increased the level in the mitochondrial fraction. (3) The PI3K signaling pathway regulates the caspase-9 in renal tubular epithelial cells. Further research is particularly important on elucidate IL-6 overexpression how to mediate the reduction of the caspase-9 level and the possibility of caspase-9 shifting from the cytosol to mitochondria in response to IL-6.

Abbreviations

IL-6: Interleukin-6

IL-6R: IL-6 Receptor

STAT3: Signal Transducer and Activator of Transcription 3

p-STAT3: p-Signal Transducer and Activator of Transcription 3.

Acknowledgement

This study was funded by grant from Ministry of Science and Technology, Taiwan (MOST 106-2320-B-390-001) and Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan (ZBH106-08).

Conflict of Interest Statement

The authors have declared no conflict of interest.

Funding

This study was funded by grant from Ministry of Science and Technology, Taiwan (MOST 106-2320-B-390-001) and Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan (ZBH106-08).

Author Contributions

Wu, P.F. and Lee, C.T. conceived, designed and performed the experiments. Wu, P.F. analyzed the data and wrote the paper.

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Differentiation of Sediment Source Regions in the Southern Benue Trough and Anambra Basin, Nigeria: Insights from Geochemistry of Upper Cretaceous Strata

DOI: 10.31038/GEMS.2020224

Abstract

It is widely accepted that the lithic fill of the Anambra Basin, Southern Nigeria was sourced from the reworked pre-Santonian rocks of the Benue Trough. However, this hypothesis cannot account for the large sand volumes within the basin especially as the lithic fill of the Southern Benue Trough comprises mudstones, carbonates and subordinate sandstone units. In this study, we set out to investigate the provenance of the Mamu Formation as well as pre-Santonian Awgu and Eze-Aku groups by undertaking geochemical evaluation of cuttings from 5-wells spread across the Anambra Basin. The results of the well data, which was integrated with our previously generated data on the western margin of the Anambra basin as well as published data on the eastern margin reveal that the pre-Santonian units are characterized by a lower degree of chemical alteration and were sourced from basement complex rocks. By contrast, the more chemically altered Mamu Formation is sourced from recycled Southern Benue Trough strata, basement complex rocks as well as, anorogenic granites. In addition, the pre-Santonian units show spatio-temporal compositional variability, which is due to a large proportion of detrital contribution accruing from mafic rocks in the latest Cenomanian to early Turonian, whereas from middle Turonian to Coniacian the detrital contribution was more from felsic sources. Furthermore, the observed spatial geochemical variability of the Mamu Formation is adduced to be a consequence of detrital contribution from three source regions: the eastern, western and northern provenance regions. The eastern provenance region is characterized by a stronger mafic signature, low levels of Nb, Ta, Sn and Ti, high levels of W, Pb and Zn, strong Pb-Zn covariation as well as enrichment of Zn over Pb (Pb/Zn < 1), whereas the western and northern regions show higher levels of Nb, Ta, Sn and Ti. In addition, the western provenance is characterized by higher Pb over Zn (Pb/Zn >1) and lower W concentration, which is distinct from the northern provenance with Pb/Zn <1 and higher W concentration. Discriminant plots show clear evidence of mixing of provenance regions especially in the Idah-1 and Amansiodo-1 well whose sediments show secondary Pb, Sn and W mineral enrichment respectively.

Keywords

Chemical alteration, West African Rift system, mineral enrichment, Trans-Saharan Seaway

Introduction

Several hypotheses have been put forward to explain the provenance of the Anambra basin’s lithic fill. The leading hypothesis posits that the lithic fill of the Anambra Basin was sourced from reworked pre-Santonian rocks of the Benue [1-3]. This is preferred over sourcing from the basement complex in the eastern highlands (Oban Massif and Cameroun highlands) [3,4]. The main drawback of the former is its inability to account for the large sand volumes in the post-Santonian units [5,6], especially the dominantly sandy Ajali Formation since the pre-Santonian rocks in the Southern Benue Trough are predominantly made up of mudstone and limestone units. The latter hypothesis does not convincingly explain the clear evidence of sediment recycling inferred from the textural, mineralogical and geochemical characteristics, which has been observed in the post-Santonian units [3,7-9]. Besides the aforementioned hypotheses, further data and reports, support that more than one provenance region exists. Petters [10] opined that sediment contribution from the palaeo-River Niger and the Southern Benue Trough exists. This hypothesis has been somewhat reinforced by recent palaeogeographic and palaeo-drainage models of Bonne, Markwick and Edegbai [11-15]. Tijani [8], who undertook textural and geochemical analysis of the Ajali Formation, hypothesized a sediment provenance in the Adamawa-Oban Massif highlands as well as from the pre-Santonian strata of the Southern Benue Trough. Our previous findings in the western segment of the Anambra basin [9] using high resolution multidisciplinary techniques suggested some detrital contribution from basement complex rocks in the southwest (minor) as well as the pre-Santonian rocks (major). It is against this background that we undertook this study, which seeks to investigate the provenance of the Awgu and Eze-Aku groups, and the Mamu Formation as a basis for deciphering the provenance regions of the Anambra Basin’s lithic fill using geochemical data from outcrops in the western and eastern margin [9,13] as well as from 5 wells spread across the Anambra Basin. Furthermore, data from regional geochemical analysis of sediments from streams draining parts of southwestern and northcentral Nigeria, reports form Pb-Zn deposits in the Benue Trough [11,12] as well as reports from mineralized pegmatite [16] and biotite granite [17] domains in southwestern and north central Nigeria, respectively, complemented this study.

Geologic Overview

The Benue Trough is a NE-SW trending depression approximately 1000 km by 100 km in dimension, which comprises a suite of depocenters broadly grouped into Northern, Central and Southern Benue Trough (Figure 1) [18,19]. It is part of a much larger west and central rift system (WCARS) that formed due to stresses arising from the opening of the South Atlantic Ocean in the Barremian age [18-23]. The opening of the Equatorial Atlantic Ocean consequent upon the final separation of the African plate from the South American plate in the Albian [24] resulted in flooding of the Southern Benue Trough, leading to the deposition of the Asu-River Group. Due to global sea level rise, this flooding, which peaked in the Cenomanian-Turonian boundary con Turonian tinued into the Turonian [25,26], were floodwaters from the equatorial Atlantic Ocean connected with floodwaters from the Tethys Ocean to establish the Trans-Saharan seaway through the Benue Trough. This resulted in the deposition of the Eze-Aku and Awgu groups [10,27]. The Eze-Aku and Awgu groups belong to one depositional episode spanning latest Cenomanian to Coniacian [27]. Gebhardt [28] reported that these units could only be differentiated based on fossil content. In the southern Benue Trough, the Eze-Aku group comprises chiefly of highly fossiliferous calcareous mudstone intercalated with sands, and limestone units deposited in environments ranging from continental to deep marine environments [29-36]. The Awgu Group consists of limestone, mudstone interstratified with thin limestone and marl units [27], as well as subordinate sands. Coal units have been documented at the top of the stratigraphic succession. These units are interpreted to have been deposited in delta plain to marine conditions [10,19,28]. The Trans-Saharan Seaway was short lived and eventually broken in the Santonian primarily due to a change in stress regime, which brought about reactivation of NE-SW trending faults, folding, volcanism as well as exhumation of pre-Santonian strata of which the southern Benue trough was the most affected [37]. After the Santonian inversion, came a phase of renewed subsidence west of the Southern Benue Trough, which formed the Anambra Basin. The Anambra Basin (Figure 1) represents the sag phase of the Benue Trough evolution. The oldest and youngest parts of its lithic fill comprises the Nkporo Group whose facies is dominantly marine, but shows fluvial to fluvio-marine character at the marginal parts of the basin [6,19] and the brackish Nsukka respectively.

fig 1

Figure 1: Map of Nigeria showing areas underlain by sedimentary and basement rocks. Below is a W-E cross section showing lithostratigraphic packages of southern Nigeria ranging from Barremian to Ypresian (Edegbai et al., 2019b).

The Mamu Formation comprises mudstone, sand, limestone, carbonaceous and calcareous mudstone, as well as coal and minor ironstone units, which exhibit spatio-temporal variability with respect to thickness and facies [9,36,38,39] Simpson in [19,31,40]. In recent times, these units have been adduced to represent estuarine to shallow marine depositional conditions [9,36,39]. In addition, variable ages ranging from middle Maastrichtian in the North (Gebhardt, 1998) – 22 to late Campanian to middle Maastrichtian in the South implying later sedimentation of the Mamu in northern Anambra-Basin have been reported (Figure 2) [31,38,40,41].

Materials and Methods

Elemental Analysis

Ninety drill cuttings and core samples from the Nzam-1, Idah-1, Owan-1, Amansiodo-1 wells (Figure 3a-3g) representing the post-Santonian Mamu Formation as well as the pre-Santonian Eze-Aku and Awgu groups were obtained from the Nigerian Geological Survey Agency storage Core Repository at Kaduna, Nigeria. A combination of cluster and systematic sampling techniques (modified by sample availability and stratigraphic control based on well logs and original reports from oil companies) was employed. Sample preparation entailed homogenization and mechanical pulverization into powder, succeeded by near total multi-acid digestion and elemental analysis using ICP-MS at Activation Laboratories, Ontario, Canada.

fig 3

Figure 3: a-d, Lithology of the Mamu Formation penetrated by Owan-1, Idah-1, Nzam-1 and Amansiodo-1 wells respectively. e-f, Lithology of the Awgu Group penetrated by the Amansiodo-1 and Akukwa-II wells. g, Lithology of the Eze-Aku Group penetrated by the Akukwa-II well. See Edegbai, et al., (2019b) for the lithology of outcropping units of the Mamu Formation on the western margin.

Results

The results of major and trace element analysis were integrated before comparison with previously generated data from outcrops located at the western flank of the Anambra Basin by the authors [some of which have been published [9] as well as data from the eastern margin [13]. As observed [42], argillaceous sediments and fine sands better preserve the provenance signature of source units than coarser units. Consequently, for the purpose of our study, only data from the outcropping dark mudstone lithofacies in the western flank, which has been subdivided into marsh, bay and central basin subenvironments in order of proximality [9], was integrated with data from the drill cuttings. A summary of the elemental analysis results is presented in Appendix 1a-c.

Major Elements (Ca, Fe, Mg, Mn, Ti and Al)

Outcropping Mamu Formation

On the western margin, Al and Fe are the most abundant major elements in the sediment samples (Appendix 1a, Figure 4a-b). 65.2 %, 95.7 %, 96.1 % of samples from the marsh, bay and central basin subenvironments are above the average upper continental crust [43] limit for Al. All samples from the marsh subenvironment have concentrations below the UCC limit for Fe (UCC = 3.5 %), whereas 50 % and 91.3 % of the samples from the central basin and bay subenvironments, respectively fall below the UCC limit for Fe. While all samples have concentrations below the UCC limits for Ca, K, Mg, and Na (UCC = 3 %, 2.8 %, 1.33 %, and 2.89 % respectively), the Ti concentrations are above the UCC composition (UCC = 0.41 %) (excluding one outlier from the central basin subenvironment). Outcrop data [13] for the eastern margin suggests that Al and Fe are the most abundant major elements (Appendix 1a, Figure 4a-b). The concentrations of Ca, K, Mg and Na concentration in the samples are below the respective UCC limits. 88.9% and 66.7% of the samples have Al and Fe concentrations below the respective UCC, while all the samples have Ti concentration above the UCC for Ti (Appendix 1a, Figure 4a-b). In broad terms, the more distal and saline central basin subenvironment shows the highest concentration of Ca, Fe, K, Mg, Mn, Na and Al in all the dark mudstone samples (Appendix 1a, Figure 4a-b). These are very similar in their median values to those reported [13] (Appendix 1a, Figure 4a-b), whereas the lowest concentrations are recorded from the more proximal less saline marsh subenvironment (Appendix 1a, Figure 4a-b).

fig 4

Figure 4: Variograms showing the median concentrations of major and high field strength elements for all sample locations as well as regional data from western and northcentral Nigeria (Lapworth et al., 2012).

Well Data

Mamu Formation

Aluminium and Fe are the most abundant among the major elements (Appendix 1a, Figure 4a-b). In the Owan-1 well, all samples are below the UCC limits for Ca, Fe, K, Mg, and Na, while 71.4 % and 14.3 % of the samples have concentrations above the respective UCC limits for Ti and Al. All samples from the Amansiodo-1 well have Fe, Ti, Al, K, Mg, and Na concentrations below the respective UCC limits. In addition, 33.3% of the samples have Ca concentration above the UCC limit. All the samples from the Idah-1 and Nzam-1 wells have concentrations below the UCC limits for Ca, K, Mg and Na. Furthermore, all the samples from the Nzam-1 well have Ti concentrations above the UCC limit, as do bulk of the samples (90.5%) from the Idah-1 well. With respect to Fe and Al concentrations, 87.5% and 75% of samples from the Nzam-1 well, as well as 85.7% and 61.9% of samples from the Idah-1 well have Fe and Al concentrations greater than the respective UCC. The data from Amansiodo-1 (closest to the eastern boundary) and the Owan-1 (on the western margin) wells show very distinct major element distribution in comparison to results from the more central Nzam-1 and Idah-1 wells. The Amansiodo-1 samples possess the largest median concentrations of Ca as well as much lower concentrations of the other major elements. The median values of the major element data from Owan-1 are very comparable with the marsh outcrop samples, which are also depleted in Ca and Mg (Appendix 1a, Figure 4a-b). The samples from Idah-1and Nzam-1 wells show greater Ca, Fe, K, Mg, Mn, Na and Ti concentrations (Appendix 1a, Figure 4a-b), in comparison to data from the marginal wells. Furthermore, the Idah-1 well also shows subtle variation in major element concentration when compared to the southern Nzam-1 well. Greater concentrations of Ca, Mg, Mn and Ti abound in the Idah-1well in comparison to the Nzam-1 well, which shows greater concentrations of K, Na and Al (Appendix 1a, Figure 4a-b).

Pre-Santonian Units.

Aluminum and Fe are the most abundant major elements in the samples from the Awgu Group (Appendix 1a, Figure 4a-b). Whereas all samples show Fe, Ti and Al concentrations above the respective UCC limits (except an outlier from the Akukwa-II well), the concentrations of Ca, K, Mg and Na in the samples (except an outlier from the Amansiodo-1 well) remain below their respective UCC limits (Appendix 1a, Figure 4a-b). Furthermore, the major element distribution in the Awgu Group shows slight variability. Whereas samples from the Amansiodo-1 well are slightly more enriched in Fe, K, Ti and Al, the Akukwa-II well samples are slightly more enriched in Ca and Na (Appendix 1a, Figure 4a-b). In the Eze-Aku Group, Al and Fe are the most abundant major elements (Appendix 1a, Figure 4a-b). All the samples show K and Na concentrations below their respective UCC limits, while 85% and 95.2% of the samples have Ca and Mg concentrations below the respective UCC limits. In addition, the concentration of Fe and Al in the bulk of the samples is above the UCC limit. In general, samples from the Eze-Aku Group show slight enrichment in Na and Ca over the samples from the Awgu Group that reveal higher concentrations of Fe, K, Mg, Ti and Al. In addition, the major element distribution in the pre-Santonian units are quite comparable to those observed from the centrally positioned Nzam-1 and Idah-1wells (Appendix 1a, Figure 4a-b).

High Field Strength Elements (HFSE: Th, U, Ta, Nb, Zr, Y, Hf)

Outcropping Mamu Formation

All the dark mudstone samples on the western margin have U and Nb concentrations above the respective UCC limits (UCC for U and Nb = 2.8 ppm and 12.0 ppm respectively) (Appendix 1b, Figure 4c-d). The Th and Ta concentrations of all the samples from marsh and bay subenvironments, and the bulk of the samples (92.3% and 88.5% respectively) from the central basin subenvironment are above the respective UCC limits for Th (UCC = 10.7 ppm) and Ta (UCC = 1 ppm). In addition, a very large proportion of the dark mudstone samples have concentrations below the UCC for Zr and Hf (UCC for Zr and Hf = 190 and 5.8 ppm, respectively). Furthermore, 56.5%, 34.8%, and 38.5% of samples from the marsh, bay and central basin subenvironments respectively have Y concentration above the UCC limit (UCC = 22 ppm). On the eastern margin, data from Odoma et al. (2015), show that all the samples are enriched above the UCC concentration for Th, U, Nb, Zr and Hf (Appendix 1b, Figure 4c-d). In general, with the exception of Zr and Hf, which are much higher, the concentration of the other HFSE being discussed are more comparable to the outcrops at the Benin flank than the well data (Appendix 1b, Figure 4c-d).

Well Data

Mamu Formation

As observed in the major element distribution, the Amansiodo-1 well samples show very distinct geochemical distribution of the HFSE (Th, U, Ta, Nb, Zr, Y, and Hf) as indicated by very low concentrations that are at least one order lower than those obtained from the outcropping units (Appendix 1b, Figure 4c-d). The HFSE abundance from the Owan-1 well, though much higher than the data from the Amansiodo-1 well, is subordinate to the outcropping units (Appendix 1b, Figure 4c-d). In the more centrally located Nzam-1 and Idah-1wells, a very large proportion of the samples show enrichment in Th, U, Ta, and Nb above the respective UCC (Appendix 1b, Figure 4c-d). In the Idah-1 well, 57%, 85.7% and 28.6 % of the samples have concentration above the respective UCC for Zr, Y, and Hf (Appendix 1b, Figure 4c-d). The Zr, Y and Hf concentrations that are higher than the outcropping units on the western margin are subordinate to the Zr and Hf on the eastern margin [13]. By contrast, the outcropping units on the western margin show more enrichment in Th, U, Ta, and Nb than the sediments in the Nzam-1 and Idah-1wells (Appendix 1b, Figure 4c-d). Furthermore, with the exception of Th, the median concentrations of the HSFE being discussed decreases from samples from Idah-1 well location to the samples from Nzam-1 well (Appendix 1b, Figure 4c-d). In the Nzam-1 well, the bulk of the samples, which show enrichment in Th, U, Ta and Nb above the respective UCC limit, show depletion in Zr, Y, and Hf concentrations.

Pre-Santonian units

The Awgu Group samples from the Amansiodo-1 well show more enrichment in Th, U, Ta, Nb, Zr, Y and Hf in comparison to samples from the Akukwa-II well (Appendix 1b, Figure 4c-d). The median values of the HFSE are comparable to the Mamu Formation data from the Idah-1and Nzam-1 wells. In addition, a large proportion of the samples from the Amansiodo-1 well show enrichment above the respective UCC for Th, U, Ta and Nb and Y (Appendix 1b, Figure 4c-d). Conversely, the samples are depleted below the respective UCC composition for Zr and Hf (Appendix 1b, Figure 4c-d). A much lower proportion of the samples from the Akukwa- II well show enrichment above the respective UCC limits for U, Ta, Nb and Y. Furthermore, none of the samples are enriched above the UCC concentrations for Th, Zr and Hf (Appendix 1b, Figure 4c-d). In broad terms, when compared with the post-Santonian Mamu Formation (excluding the samples from Owan-1 well and the Amansiodo-1 well), the Awgu Group is depleted in Th, U, Ta, Nb, Zr and Hf concentrations (Appendix 1b, Figure 4c-d). In contrast, the concentration of La and Y is much higher than in post-Santonian units (Appendix 1b, Figure 4c-d). The bulk of the samples from the Eze-Aku Group show depletion in Th, U and Hf concentrations below the respective UCC composition (Appendix 1b, Figure 4c-d). In addition, none of the samples show enrichment in Zr and Hf above the respective UCC limits (Appendix 1b, Figure 4c-d). Conversely, a larger proportion of the samples are enriched in Ta and Nb above the respective UCC limits. The HFSE distribution within the Eze-Aku Group is very comparable to the data from the Awgu Group in the Akukwa II well, except that much lower Zr concentrations are present (Appendix 1b, Figure 4c-d).

Transition Trace Elements [(TTE) Ni, Co, V, Cr and Sc)]

Outcropping Units

A large proportion of the marsh and bay samples are depleted in Ni, Co and Sc content. Conversely, the bulk of the samples show enrichment in Cr (Appendix 1b, Figure 5a-b). There is a distinction in the V content of the marsh and bay samples. Whereas the bulk of the Marsh samples are enriched above the UCC limit for V (UCC = 107 ppm), only 8.7 % of the Bay samples show V enrichment above the UCC limit. In comparison to the marsh and bay units, the central basin samples are much more enriched in TTE, only subordinate to the marsh unit in V concentration (Appendix 1b, Figure 5a-b). On the eastern margin, data [13] shows depletion of Ni and Co, whereas a substantial proportion of the samples show enrichment above the Cr and Sc of the respective UCC composition (UCC = 83 ppm and 13.6 ppm, respectively) (Appendix 1b, Figure 5a-b). In addition, 44.4% of the samples are enriched above the UCC mean for V. In general, the central basin unit shows the most enrichment in TTE when compared with the other outcrop units (Appendix 1b, Figure 5a-b), which is perhaps due to the redox conditions prevailing. The V and Cr content in the eastern margin is much lower than the marsh and central basin units in the western margin are (Appendix 1b, Figure 5a-b). Furthermore, excluding the central basin unit, all other outcrop samples are depleted in Ni and Co concentrations (Appendix 1b, Figure 5a-b).

fig 5

Figure 5: Variograms showing the median concentrations of TTE as well as Pb, Sn, W, Zn, Mo, and Cu for all sample locations as well as regional data from western and northcentral Nigeria (Lapworth et al., 2012).

Well Data

Mamu Formation

The TTE distribution in samples from the Owan-1 and Amansiodo-1 wells are very distinct from the more centrally located wells due to their lower TTE concentrations. The samples from the Nzam-1 well show significant enrichment above the samples from the Idah-1well (Appendix 1b, Figure 5a-b). The Ni concentration in majority of the well samples are below the UCC limit (UCC = 44 ppm). In addition, while the V, Cr and Sc abundances of all samples from the Owan-1 and Amansiodo-1 wells as well as the majority of the samples from the Idah-1well fall below the respective UCC composition (Appendix 1b, Figure 5a-b), the Co content in the majority of the samples are above the UCC mean (UCC = 17 ppm). In general, the outcropping units on the western margin contain higher levels of V, Cr and Sc than their well counterparts (Appendix 1b, Figure 5a-b).

Pre-Santonian Units

Excluding the Cr concentration, which is depleted in the samples from Akukwa – II well, the TTE distribution in the Awgu Group is quite similar with a dominance of samples enriched above the respective UCC limits. Excluding the Ni concentration, which are much lower, the Eze-Aku unit shows similar distribution of TTE with those of the Awgu Group in the Akukwa –II well (Appendix 1b, Figure 5a-b). In broad terms, higher V, Co, Ni, and Sc concentrations persist in the pre-Santonian units when compared with the Mamu Formation, which is more enriched in Cr.

Pb, Sn, W, Zn, Mo and Cu Bivalent Metals

Outcropping Mamu Formation

The marsh unit contains significantly lower Pb, Sn, Zn and Cu concentration when compared with the bay and central basin units (Appendix 1a, Figure 5c-d). The bay unit shows more enrichment in Pb, Sn, Mo and W when compared with the central basin unit that has a much higher Zn concentration (Appendix 1a, Figure 5c-d). A large proportion of the central basin samples have Sn, W, Mo and Zn below the respective UCC limits (UCC = 5.5 ppm, 2 ppm, 1.5 ppm, and 71 ppm, respectively), whereas 58% of the samples show enrichment in Cu above the UCC limit (UCC = 25 ppm). In addition, sizeable proportions of the marsh samples have W, Zn, and Cu below the respective UCC limits, whereas a majority of the bay samples shows enrichment in W, Mo, and Cu as well as depletion of Zn when compared with the respective UCC means. Furthermore, all the samples show enrichment in Pb above the UCC limit (UCC = 17 ppm), whereas a sizeable proportion show enrichment in Mo above the UCC limit. On the eastern margin [13], all the samples show enrichment in Pb above the UCC limit (Appendix 1a, Figure 5c-d). In addition, 53% and 26% of the samples show enrichment in Zn and Cu, respectively, when compared with the UCC. In general, the outcropping units along the western margin show higher levels of Pb and Sn than the eastern margin, which shows more enrichment in Zn (Appendix 1a, Figure 5c-d).

Well Samples

Mamu Formation

All the well samples show enrichment at or above the UCC concentration of W, whereas the bulk of the well samples show depletion in Mo. Excluding a few samples from the Idah-1well, all others are depleted in Sn and Cu when compared with the respective UCC average (Appendix 1a, Figure 5c-d). A very large proportion of the samples from the Idah-1and Nzam-1 wells shows enrichment above the UCC limits for Pb and Zn (Appendix 1a, Figure 5c-d). The samples from Idah-1well in particular shows very high levels of Pb and Zn as well as Sn in some intervals. The Owan-1 and Amansiodo-1 wells show some distinction, as a large proportion of the samples from both wells is depleted in Zn when compared with the centrally located wells (Appendix 1a, Figure 5c-d). In addition, all the samples from the Amansiodo-1 well show enrichment above the UCC for Pb, whereas only 28.6% of samples from the Owan-1 well have Pb concentration above the UCC.

Pre-Santonian Units

All the samples from the Awgu Group across the wells are enriched in Pb, W and Zn above the respective UCC, whereas by contrast, are depleted in Sn (Appendix 1a, Figure 5c-d). The samples from Akukwa-II well show higher levels of Zn, W, Mo and Cu, thus contrasting with samples from the Amansiodo-1 well. In addition, nearly all the samples from the Akukwa-II well are enriched above the UCC limits for Mo and Cu, whereas a lower proportion of samples from the Amansiodo-1 well (60% and 53.3% respectively) are enriched above the respective UCC. A very large proportion of the samples from the Eze-Aku Group show enrichment in Pb, W, Zn, Mo, and Cu, whereas all the samples are depleted in Sn (Appendix 1a, Figure 5c-d). In addition, the Eze-Aku group is more enriched in Mo, W, and Zn when compared with samples from the Awgu Group. In general, the pre-Santonian units show enrichment in W, Zn, Mo, and Cu when compared with data from the post-Santonian Units (Appendix 1a, Figure 5c-d). There is significantly more enrichment of Pb in the Mamu Formation when compared with data from the pre-Santonian Awgu and Eze-Aku Groups.

Discussion

Degree of Chemical Alteration

The order of stability of major elements as suggested [44] implies that Si, Fe, Ti and Al are the most stable elements. Thus, the proportion of major elements can provide some clues as to the degree of chemical alteration in the source region. The most depleted elements are Na, Ca, and Mg indicative of a high degree of initial weathering, except in the case of the Eze-Aku Group in the Akukwa-II well and the Mamu Formation in Amansiodo-1 well that are enriched in non-silicate Ca. Na/K, Mg/K, K/Al and Na/Al, which reflects the proportion of less stable minerals like plagioclase, biotite, chlorite, smectite, vermiculite and illite relative to more stable K-feldspar, illite and Kaolinite has been shown to track the degree of weathering of crustal material [45]. A higher degree of chemical alteration is inferred for the outcropping units on the western and eastern margins as well as the samples from the Owan well based on the low Na/Al, K/Al, Mg/K and Na/K. This is illustrated further by the major element distribution [(Na, Ca, Mg) <K<Ti<Fe<Al] as well as low Mg/Ti (Appendix 1a, c Figure . 4a-b, 6a-c). In addition, higher Na/Al, K/Al, Mg/K (Appendix 1c, Figure 6c) recorded for the central basin mudstones as well as the samples from the eastern margin [13] suggests relatively lower degrees of chemical alteration. This is adduced to authigenic illite and smectite formation arising from an increase in salinity [9]. Furthermore, in comparison with the outcropping units, data from the Amansiodo-1 well as well as the more centrally placed Nzam-1 and Idah-1wells show much higher Na/Al, K/Al, Mg/K, Na/K, Mg/Ti values (Appendix 1c, Figure 6c). This indicates a lower degree of chemical alteration regardless of carbonate dilution (calcite cement) in the Amansiodo-1 well (Na<K<Mg<Ti<Al<Fe <Ca) that has modified the major element distribution pattern. We hypothesize that the higher salinities in these areas as suggested by early Maastrichtian paleogeographic reconstruction [6] may account for some increment in the Na/Al, K/Al, Mg/K, Na/K, Mg/Ti values as well as the extent of mixing from provenance regions (discussed in section 5.3). In addition, data from the eastern margin as well as the central basin mudstones, which show higher K relative to Ti (which increases with higher Mg/Ti) further illustrates this. The data from the Awgu Group is comparable to those observed in the Nzam-1 and Idah-1 wells) except in Mg/Ti, which is much higher (Appendix 1c, Figure 6c). The observed major element trend (Ca<Na<Ti<Mg<K<Fe<Al) at the Amansiodo-1 well is distinct from that of the Awgu (Ca<Ti<Na<Mg<K<Fe<Al) and Eze-Aku (Ti<Mg<Na<K<Ca<Fe<Al) groups observed at the Akukwa-II well, which have lower Ti relative to Na, Mg and K (Appendix 1a, Figure 4a-b, 6c). This implies a higher degree of chemical alteration of the Awgu Group in the Amansiodo well. In general, regardless of carbonate dilution in the samples from the Eze-Aku Group, we can infer that a much lower degree of chemical alteration and consequently mineralogical immaturity persists in the pre-Santonian units when compared with the Mamu Formation. This is based on the much higher Na/Al, K/Al, Mg/K, Na/K, Mg/Ti (Appendix 1c, Figure 6c), as well as higher percentages of smectite, illite and mixed layered clays reported for these units, in comparison to those reported for the Mamu Formation [7,9,27]. Furthermore, our findings are consistent with published results of petrographic analysis, which reported textural and mineralogical immaturity of the pre-Santonian units as distinct from the more texturally and mineralogically mature post-Santonian units of which the Mamu Formation subsists [3,5,7,46]. This is in spite of the humid equatorial climatic conditions that prevailed at during the Cenomanian-Turonian and Campanian- Maastrichtian stages [47].

fig 6

Figure 6: Ternary plots showing the distribution of K, Na, Mg, Ca, Na and Ti concentrations of all sample locations as well as a variogram of median values of Mg/Ti, Mg/K, Na/Al, and K/Al.

Source Rock Composition

Some trace elements common to felsic and mafic rocks have reduced mobility when subjected to weathering, erosion, transportation, and diagenesis [48-53]. Consequently, their concentrations in sedimentary rocks can give valuable insight in provenance studies [52]. To reduce the uncertainty regarding the accuracy of provenance determination using trace elements, we utilized trace elements whose concentrations are least affected by redox conditions. We assume that the concentration of these conservative trace elements in our samples preserve the geochemistry of the sediment provenance regions. The Th/Sc vs. La/Sc, TiO2 vs. Zr, Th/Sc vs. Sc, as well as Th/Sc vs. Zr/Sc discriminant plots [50,52] (Figure 7a-h) highlight intra- and interformational variation in the geochemical characteristics of the pre-Santonian units and the Mamu Formation, which are useful in determining the chemical composition of source units.

fig 7

Figure 7: TiO2 vs. Zr (a-b) (after Hayashi et al., 1997), Th/Sc vs. La/Sc (c-d) (after Cullers, 2000) and binary plots showing source composition of the pre-Santonian units as well as the Mamu Formation. Th/Sc vs. Sc (after McLennan and Taylor, 1991) (e-f) and Th/Sc vs. Zr/Sc (g-h) (McLennan et al., 1993) binary plots indicate variable basement sources for pre-Santonian strata as well as a combination of felsic basement rocks and recycled pre-Santonian strata sources for the Mamu Formation.

Pre-Santonian Units

Samples from the pre-Santonian units show a uniform Sc concentration (averaging ~ 15ppm) (Figure 7e-f), whereas the Th, Zr and La content of these units are highly variable (Figure 7a-h, Appendix 1b). The geochemical characteristics of the pre-Santonian units suggests a basement source rock with compositional variability as shown by the Th/Sc < 1 (Figure 7c-f) [50]. This is illustrated further by the Th/Sc vs. Zr/Sc binary plot [54] (Figure 7g-h), which indicate that these units were not sourced from reworked older sedimentary rocks. Intraformational compositional variability is visible in the Awgu Group (across the Amansiodo-1 and Akukwa-II wells) as well as the Eze-Aku Group. In the Akukwa-II well, a mafic to intermediate source rock composition is inferred due to the much lower Th, Zr, La and other HFSE concentrations, whereas in the Amansiodo-1 well, which has much higher concentration of HFSE, an intermediate to felsic source rock composition is inferred (Appendix 1b, Figure 4c-d, 7a-d). The observed spatial variation in degree of chemical alteration in the Awgu group (highlighted in section 5.1) is in part due to the more felsic nature of the source rocks for the sediments from the Amansiodo-1 well. The Eze-Aku unit shows source rock composition varying from (predominantly) mafic to felsic basement rocks owing to a range of Th, Zr, and La concentrations, which are the lowest among the pre-Santonian units (Appendix 1b, Figure 4c-d, 7a-d).

Mamu Formation

Samples from the pre-Santonian units show a non-uniform Sc concentration as well as variable Th, Zr, and La concentrations. This depicts a (predominant) felsic to intermediate source composition (Figure 7b, d, f) hypothesized to be derived from reworked pre-Santonian units as well as (predominantly) silica rich igneous and metamorphic rocks. Evidence for recycling of pre-Santonian units is illustrated by a higher degree of chemical alteration (see section 5.1), low index of compositional variability [9], a large proportion of the samples having Th/Sc > 1 (characteristic of recycled sedimentary rocks), as well as inferences from Th/Sc vs. Zr/Sc and Th/Sc vs. Sc (Figure 7f, h) discriminant plots [50,54]. In addition, the better textural and mineralogical maturity reported for the post-Santonian units [3,5,7,46] is attributable to a significant proportion of their provenance originating from reworked pre-Santonian units. Furthermore, Th/Sc < 1 reported for some samples (Appendix 1c), inferences from Th/Sc vs. Zr/Sc and Th/Sc vs. Sc (Figure 7f, h) discriminant plots [50,53], as well as variability in the degree of chemical alteration (discussed in section 5.1) provides evidence for detrital contribution from silica rich igneous and metamorphic rocks. This is further illustrated by the high concentration of W reported for the sediments (especially in the Owan-1, Amansiodo-1 and Idah-1 wells) (see section 5.3), which are much higher than those recorded for the pre-Santonian units points to detrital contribution from basement rocks, as W is not known to survive several weathering and sedimentation cycles [17].

Provenance

Leveraging on the reports of geochemical observations of the north central and southwestern basement complex, as well as Pb-Zn deposits in southern Benue trough [12,17], we attempted to work out the dominant source regions in different parts of the Anambra Basin during the late Campanian to early Maastrichtian time.

Three of the factors controlling element associations, which were identified by Lapworth, proved to be quite useful in this study. These are:

a) An iron-oxide/hydroxide and ilmenite factor, which explains the low to moderate positive covariation between Fe and Cu, Cr, Mo, V, Zn, Co, Sn, and Ti. The presence of ilmenite allows for a positive covariance between Fe and Ti or Sn;

b) A mafic factor, which explains the positive covariation between Fe, Mn, and Mg due to the presence of ferromagnesian minerals such as olivine, pyroxene, hornblende, and biotite;

c) A coltan factor. Coltan abundance covaries positively with Ta, Nb, Ti, Sn, and W.

Mamu Formation

On the western margin, the outcropping units show a broad Pb-Zn covariation (Figure 8a-c), as well as an enrichment of Pb over Zn (Pb/Zn > 1) (Appendix 1c). There is a moderate influence from moderate Fe-oxide/hydroxide factor, which is observed only in the more proximal marsh unit as well as a strong to moderate coltan influence for Sn and W as shown by the positive Sn and W covariation with Nb, Ta and Ti (Figure 8a-c). The Sn vs. Pb and W vs. Pb show a broad distribution in the bay unit, whereas a moderate positive covariation is observed in the central basin and marsh units (Figure 8a-c).

fig 8

Figure 8: Correlation matrix for major and element abundances in sediments of the Mamu Formation on the western and eastern margins as well as the Owan-1 well.

On the eastern margin, a weak positive Pb-Zn covariation exists with Pb/Zn < 1. In addition, there is a strong influence from the mafic factor as well as a minimal influence from the Fe-oxide/hydroxide/ilmenite factor. The absence of Sn and W data prevents a discussion of the coltan factor. However, a moderately positive Pb vs. Nb covariation (Figure 8d) suggests some potential influence by the coltan factor. There is a coltan source, which exerts a minor influence on the distribution of Ti, Sn, and W in samples from the Owan-1 well (Figure 8e). By contrast, the distributions of Sn and W are strongly controlled by the ilmenite factor as shown by the strong positive covariation of Ti with Sn, Fe and W (Figure 8e). There is also a moderate influence from a mafic source as well as a good Pb-Zn covariation (Pb/Zn < 1). In the Amansiodo-1 well, there is a moderate mafic factor influence, a broad Pb-Zn covariation (Pb/Zn <1), as well as a strong Fe-oxide/hydroxide/ilmenite factor (Figure 8e). In contrast to the sediments from the Owan-1 well wherein a moderate positive covariation of Pb vs. Sn is observed, the sediments from the Amansiodo-1 well show a broad Pb vs. Sn covariation as well as a moderate positive coltan influence for Ti and Sn (Figure 9a). Furthermore, in the Owan-1 well there is broad W vs. Pb covariation as well as good W vs. Zn covariation (Figure 8e), whereas the Amansiodo-1 well there is a good positive W vs. Pb covariation as well as a moderate positive W vs. Zn covariation (Figure 9a).

fig 9

Figure 9: Correlation matrix for major and element abundances in sediments of the Mamu Formation and pre-Santonian units.

The Pb vs. Zn shows a poor covariation in sediments from the Idah-1well (Pb/Zn >1), which becomes moderate in the Nzam-1 well (Pb/Zn>1) (Figure 8b-c). The influence of a coltan source for Sn and W improves from being weak in the Idah-1 well samples to moderate in the Nzam-1 well samples (Figure 8b-c). In addition, the influence of the mafic factor as well as the Fe-oxide/hydroxide factor is moderate in samples from these wells (Figure 8b-c).

Awgu Group

As observed earlier, this unit exhibits strong spatial geochemical variability. In sediments from the Amansiodo-1 well, the Fe-oxide/hydroxide/ilmenite influence is minimal to non-existent (Figure 9d). There is also a strong mafic component as well as good positive Pb-Zn covariation (Figure 9d) (median Pb/Zn = 0.23). Conversely, the sediments from Akukwa-II well show a moderate positive Pb-Zn covariation (median Pb/Zn = 0.17), as well as a fair to strong influence from the mafic and Fe-oxide/hydroxide/ilmenite factors (Figure 9e). Furthermore, whereas the sediments from the Akukwa-II well show a strong positive covariation of Ta with Sn as well as a strong negative covariation of W with Ta (Figure 9e), the sediments from the Amansiodo-1 well show the opposite. This is illustrated by the moderate covariation of Ta with W as well as a strong negative covariation of Sn with Ta (Figure 9d).

Eze-Aku Group

In the Eze-Aku Group, the influence of the coltan, mafic, as well as the Fe-oxide/hydroxide components are strong (Figure 9f). Sn moderately covaries positively with Nb, Ta, and Ti, whereas W shows a broad to moderately negative covariation with Nb, Ta and Ti (Figure 9f). There is a good positive Pb-Zn covariation (median Pb/ Zn = 0.22). In general, the pre-Santonian units show a stronger mafic influence as well as a stronger Pb-Zn covariation, which is a function of the composition of the source rocks (section 5.2).

Differentiation of Provenance Regions

Pre-Santonian Units

Based on field observations, petrographic studies and paleocurrent measurements, earlier studies favoured the granites, gneisses and metasediments in the eastern highlands and southwestern basement complex of Nigeria (Figure 2) [5,7,9,46] as the provenance sources for the pre-Santonian units. The identification of a dominant mafic provenance for the Eze-Aku unit from our data (Figure 7a, c, e), which is strengthened by the strong mafic factor influence as illustrated by the strong positive covariation between Fe vs. Mg, Fe vs. Mn as well as negative covariations of Fe vs. Pb and Fe vs. Sn (Figure 9f) Lapworth is quite an interesting find as this has only been advanced for the Asu-River Group [7]. The basement complex in the eastern highlands have been adduced to be the provenance for the Awgu and Eze-Aku groups in the eastern segment of the Anambra Basin [7,34]. However, we hypothesize a significant detrital contribution from the mineralized biotite granites as well the basement complex rocks of north central Nigeria (Figure 10a-b) due to the Nb, Ta and W that are above the respective UCC as well as Sn (Appendix 1a-b, Figures 4c and 5c). A strong detrital contribution from north central Nigeria is adduced to be responsible for the distinct geochemical character observed in the sediments from Amansiodo-1 well in comparison to the Akukwa-II well. This is illustrated by the more felsic character or the sediments, higher degree of chemical alteration, higher Th, U, Nb, Ta, Sn (Figures 4c and 5c, Appendix 1a-b), higher enrichment of Nb over Ta [16], as well as inference from the Nb/W vs. Nb/Ta bivariate plot (Figure 10a). Conversely, the sediments from Akukwa-II well, which show a higher W (Figure 5c) as well as the strong negative to broad Ta vs. W covariation (Figure 9e-f) strongly suggests a large proportion of detrital contribution from the eastern highlands (Figure 10a-b) whose pegmatites are enriched in W, but barren with respect to Sn, Ta and Nb [55,56].

fig 2

Figure 2: Conceptual early Maastrichtian paleogeographic model with sample locations, ore deposits and mineralized granites or pegmatites.

fig 10

Figure 10: a, Nb/W vs. Nb/Ta binary plot differentiating provenance regions of pre-Santonian units. b, conceptual early Turonian paleogeographic model showing contribution from eastern and northcentral highlands.

Furthermore, we hypothesize a spatio-temporal variation in detrital contribution from the various lithostratigraphic units that make up the eastern highlands and north central Nigeria. Detrital contribution was more from mafic rocks in the latest Cenomanian to early Turonian, whereas from middle Turonian to Coniacian the detrital contribution was more from felsic sources (Figure 7a-f). This is consistent with the findings [7].

Mamu Formation

From the geochemical characteristics highlighted above, we hypothesize that the Mamu Formation is sourced from basement complex rocks as well as recycled pre-Santonian strata. In addition, we can distinguish three broad provenance regions: a Northern provenance, Western provenance, and an Eastern provenance (Figure 11).

fig 11

Figure 11: Provenance regions of the Anambra Basin

Western Provenance Region

This region comprises the southwestern basement complex rocks as well as pre-Santonian units, relics of which exist as inliers within the basement complex rocks (Figure 11). In general, this provenance region is characterized by a strong coltan factor controlling the enrichment of Nb, Ta, Sn, W (and Pb to a certain extent), high levels of Th, U, Ta, Nb, Sn, Pb as well has higher Pb/Zn (Pb/Zn >1) when compared to the eastern province. Leveraging on published data [8], the main difference between the western provenance terrain from those of the southwestern portion of the north central province is the much higher Pb abundance, which is consistent with the findings of Lapworth. There is some variability in the element pattern of the western provenance, as a portion of it is not strongly influenced by the coltan factor as shown by much lower Pb, Sn, Nb, Ta, and Y concentrations, lower Pb/Zn (Pb/Zn < 1), as well as much higher W recorded from sediments of the Owan-1 well. The very weak positive covariation for Nb vs. Ti and Nb vs. Sn illustrate further evidence for this (Figure 8e). In addition, the good positive covariation between Ti vs. Sn suggests an alternative source for Ti instead of coltan, which is suspected to be ilmenite Lapworth as well as minerals in the ilmenite-geikielite (MgTiO3) and ilmenite-pyrophanite (MnTiO3) solid solution series due to good to moderate positive covariation of Ti vs. Fe, Mn, and Mg (Figure 8e). These Titanium bearing minerals have been documented to occur in the southwestern basement complex rocks [57-60].

Eastern Provenance Region

The eastern provenance region (Figure 8) comprises the pre-Santonian strata from the Southern Benue Trough as well as the basement complex rocks from the eastern highlands (Figure 11). In general, higher Zn, TTE, Cu, Mo, and major element (excluding Al and Ti) concentrations, much lower Pb/Zn ratios (Pb/Zn <1), a strong W enrichment [55-56], as well as lower levels of Nb, Ta and Sn in comparison with the northern and western provenance regions characterize the eastern provenance. In addition, there exists a good positive Pb vs. Zn covariation, as well as a less strong coltan influence for Sn, which in contrast with the western provenance region shows a broad or strong negative covariation with W. The much higher major element concentrations characteristic of this provenance region is a function of the strong mafic influence on the sediments.

Northern Provenance Region

The anorogenic biotite granites as well as the basement complex rocks in the north central provenance region is hypothesized to have contributed detritus for sediments in the northern segment of the basin, sediments close to the western margin, the area around the Amansiodo-1 well, as well as intervals within the Idah-1 well. We came to this conclusion because some intervals in the Idah-1 well have W concentration above 23.2 ppm (Fig. 12a), which is the highest W concentration reported for stream sediments draining the southwestern portion of the north central basement complex Lapworth. High levels of W concentration have been reported for the biotite granites in the Afu complex, north central Nigeria [17]. In addition, these units show high levels of Nb, Y, Th, Zn, Ti, and U, higher enrichment of Nb over Ta [16], as well as low V and Pb/Zn (Pb/Zn < 1).

Mixing of Provenance Regions

Our published data on the outcropping units on the western margin posit that the marsh samples are the most proximal units of the dark mudstone lithofacies [9]. This implies that the geochemistry of this unit is the least influenced by mixing from the northern and eastern provenance regions. The bay samples are the most affected by mixing as illustrated by higher median concentrations of HFSE as well as Pb, Sn, and W recorded from the bay samples (Figure 12a-d) when compared with the marsh and central basin samples. This is due to contribution from multiple source regions as depicted by the broad distributions of Pb vs. Sn and W vs. Pb (Figure 8b), the fractionation (concentration gradient) of Pb, Nb, W, and Sn between the outcropping Patti Formation (Bida Basin), sediments from the Idah-1 and Owan-1 wells, as well as the outcropping Mamu Formation on the western margin (Figure 12a-d).

fig 12

Figure 12: Evidence of mixing of provenance regions deduced from median concentrations of Pb, W, Nb, and Sn from spatial units of the Mamu and Patti formations.

The sediments of the more centrally located Idah-1 and Nzam-1 wells also show clear evidence of mixing of source terrains. This is clearly illustrated by the Pb/Nb vs. Pb/Sn as well as the Pb vs. Sn bivariate plots (Figure 13a-b). We hypothesize that the high Pb values associated with sediments from the Idah-1 well is due to mixing of detritus from all three-provenance regions, as concentrations well above the lower thresholds for Pb and Zn (100 ppm and 200 ppm respectively) in Pb-Zn mineralized regions of the eastern provenance [11,12] abound. In addition, the high levels of W recorded in some intervals in the Idah-1 well as well as the Amansiodo-1 well (Appendix 1a) are within the range reported for the Sn-Nb-Ta mineralized biotite granites of the Afu complex [17] located in the Northcentral provenance region.

fig 13

Figure 13: Pb/Nb vs. Pb/Sn (a) and Pb vs. Sn (b) binary plots showing further evidence of mixing of source regions.

Conclusion

This study reports the following findings:

  • The pre-Santonian units are sourced from compositionally variable basement complex rocks, ranging from felsic to mafic in composition.
  • There is evidence for spatio-temporal variability in the detrital contribution from the basement complex rocks. Detrital contribution was more from mafic rocks in the latest Cenomanian to early Turonian, whereas from middle Turonian to Coniacian the detrital contribution shifted to more felsic sources.
  • The provenance of the Mamu Formation is from felsic source rocks comprising of basement complex rocks as well as recycled pre-Santonian rocks. The significant detrital contribution from basement complex rocks provides clear insight regarding to the origin of large sand volumes in the post-Santonian Anambra basin. These hitherto could not be accounted for, due to the predominance of argillaceous and carbonate rocks in the Southern Benue Trough
  • Three provenance regions comprising the northern, western, eastern sectors contributed detritus during the Campano-Maastrichtian with evidence of mixing of provenance sources.
  • The Mamu Formation shows evidence of secondary Pb, Sn, and W mineral accumulation.

Acknowledgement

This research received support from University of Benin Research and Publications Committee, the Fulbright Commission (15160892), the Niger Delta Development Commission, Nigeria (NDDC/DEHSS/2015PGFS/EDS/011), and DAAD (ST32 – PKZ: 91559388). Julius Imarhiagbe and Reuben Okoliko assisted with fieldwork and sampling of cuttings and core at the Nigerian Geological Survey Agency respectively. In addition, the first author wishes to acknowledge the motivation, guidance and instruction provided by Prof. W.O. Emofurieta and Mr. Sam Coker during the early phase of this research (Table 1a-1c).

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Table 1a-1c: Summary table showing the results of elemental analysis as well as elemental ratios.

Appendix 1a

S/N Lithostratigraphic Unit Location Ca Fe K Mg Mn Na Ti Al TiO2 Pb Sn W Zn Mo Cu
U1 IA Mamu

Formation

Western margin

(Marsh

subenvironment)

0.021 0.94 0.32 0.07 0.004 0.015 0.91 8.05 1.68 27.8 3.6 2.1 14 3 7.1
U1 1C 0.014 0.57 0.26 0.05 0.003 0.015 0.83 6.83 1.58 21.9 3.2 1.7 14 2.6 8.7
U1 2A 0.014 1.19 0.30 0.05 0.003 0.015 0.89 7.89 1.68 23.4 3.8 2 9 2.2 7.7
U1 2B 0.014 0.72 0.23 0.04 0.003 0.015 0.80 6.46 1.48 26.5 3 1.7 18 0.9 4.6
U1 2C 0.014 0.57 0.31 0.06 0.003 0.015 0.94 7.89 1.79 32.4 3.5 1.9 9 2.3 4.2
U1 3A 0.014 1.09 0.32 0.06 0.004 0.015 0.93 7.73 1.75 25.6 3.7 2.2 15 1.1 6
U1 3B 0.014 0.86 0.38 0.07 0.003 0.022 0.95 9.10 1.81 28.6 4.3 2.2 15 1.9 5.8
U1 5A 0.021 0.80 0.36 0.07 0.004 0.022 0.90 11.70 1.63 29.1 4.1 2 15 2.5 6.5
U1 5B 0.021 0.92 0.36 0.07 0.004 0.022 0.85 12.54 1.56 27.7 4 2 12 2 5.1
U1 6A 0.021 0.87 0.37 0.07 0.003 0.015 0.87 11.75 1.60 25.6 4.1 2.2 13 1.1 6.6
U1 7A 0.029 1.84 0.25 0.08 0.006 0.022 0.76 10.69 1.47 23.8 3.2 1.7 211 2.1 27.4
U1 7B 0.014 0.60 0.27 0.07 0.004 0.015 0.87 9.84 1.60 25.9 3.7 1.9 83 1 11.3
U1 8A 0.014 1.45 0.23 0.07 0.004 0.007 0.78 10.50 1.36 23.9 3.6 1.5 139 0.8 15.6
U1 8B 0.057 1.27 0.25 0.08 0.008 0.022 0.87 7.62 1.64 26.2 3.5 2 132 1.4 10.6
U1 8C 0.014 0.49 0.17 0.05 0.008 0.015 0.80 5.72 1.46 20.1 3.1 1.8 241 3.8 6.8
U1 8D 0.014 0.66 0.22 0.05 0.004 0.022 0.82 8.36 1.55 27.2 3.7 1.9 155 0.7 6.8
U1 9B 0.014 1.67 0.27 0.05 0.007 0.015 0.93 10.22 1.69 31.4 3.9 2.2 38 2.5 16.3
U1 9C 0.014 0.83 0.29 0.05 0.005 0.022 0.96 11.06 1.74 30.9 4.5 2.4 33 1.3 14.2
U1 10 0.014 0.87 0.20 0.04 0.002 0.015 0.96 14.08 1.77 36.7 4.8 2.4 11 2 14
U1 18 0.014 1.76 0.26 0.04 0.003 0.015 0.97 13.07 1.82 31 4.9 2.4 10 1.5 31.4
U1 19 0.007 0.73 0.19 0.04 0.003 0.007 0.95 11.61 1.73 26.1 4.8 1.9 10 1.1 33.1
AU-1a 0.021 1.07 0.70 0.16 0.004 0.022 0.80 10.27 1.51 27.9 3.9 1.8 21 1.9 36.2
AU 2 0.021 1.55 0.88 0.21 0.003 0.022 0.81 12.13 1.49 26.2 4.2 1.8 27 1 23.1
Mean 0.02 1.0 0.3 0.07 0.004 0.017 0.9 9.8 1.6 27.2 3.8 1.9 54 1.8 13.4
Median 0.01 0.9 0.3 0.06 0.004 0.015 0.9 10.2 1.6 26.5 3.8 2.0 15 1.9 8.7
SD 0.01 0.4 0.2 0.04 0.002 0.005 0.1 2.3 0.1 3.7 0.5 0.3 70 0.8 9.9
IM 2B Mamu

Formation

Western margin

(Central Basin

subenvironment)

0.04 2.84 0.99 0.22 0.006 0.02 0.86 14.77 1.59 39.6 5.6 2.4 43 1.8 25.9
1M 2C 0.03 2.63 1.00 0.23 0.006 0.03 0.97 13.44 1.72 41.9 5.5 2.5 57 1.5 19.6
1M 2D 0.03 2.90 1.01 0.24 0.006 0.03 0.87 13.44 1.62 47.2 5.5 2 71 0.6 28.7
1M 2E 0.064 4.57 0.98 0.30 0.016 0.03 0.84 13.60 1.50 36.7 5.1 2.2 96 1.3 22.7
IM 4A 0.021 4.59 0.71 0.18 0.007 0.03 0.71 10.48 1.24 32 4.3 1.7 43 1 14.7
IM 11A 0.06 7.20 0.60 0.22 0.008 0.02 0.66 12.60 1.13 46.2 6.3 1.6 127 1 30.5
IM 11B 0.06 4.02 1.00 0.29 0.010 0.03 0.69 16.62 1.12 30.7 5.6 1.8 67 1 32.1
IM 11C 0.19 5.01 1.14 0.34 0.014 0.03 0.66 12.54 1.13 39.5 5.3 1.6 125 0.6 28.1
IM 13A 0.11 7.41 1.00 0.41 0.092 0.03 0.60 13.44 1.05 26.8 4.7 1.6 106 1.2 31.1
1M 13B 0.043 3.30 1.06 0.30 0.008 0.03 0.76 15.35 1.30 29.2 5.4 2.2 94 1.1 35.1
IM 14A 0.54 5.32 1.08 0.32 0.021 0.04 0.64 12.60 1.11 38.8 4.3 1.7 191 2 32.2
IM 16A 0.26 12.52 1.20 0.42 0.253 0.02 0.39 11.86 0.70 25.9 3.8 1.3 128 2.2 28.9
IM 16B 0.19 7.13 1.36 0.41 0.084 0.03 0.46 13.23 0.82 31 4 1.6 122 1.7 24.3
1M 16C 0.09 3.97 1.45 0.35 0.021 0.03 0.54 13.92 0.96 33.7 4.7 1.5 69 1.5 19.2
1M 16D 0.06 2.91 1.35 0.30 0.008 0.03 0.57 13.23 1.02 23.4 4.4 1.7 38 0.5 17.9
IM 18a 0.79 0.93 1.06 0.13 0.004 0.03 0.42 11.63 0.72 28.5 2.75 0.9 47 1.1 9.75
IM 18C 0.13 1.41 0.81 0.18 0.004 0.02 0.66 17.78 1.12 47 5.9 2.1 34 1.5 24.2
IM 19A 0.10 1.96 1.10 0.24 0.004 0.02 0.61 17.25 1.05 41.9 5.4 1.9 37 2 36.5
IM 19B 0.07 2.34 1.10 0.24 0.003 0.02 0.57 16.99 0.98 34 5.3 1.7 32 1 43.1
IM 19D 0.05 3.43 0.92 0.19 0.007 0.03 0.61 16.53 1.08 46.3 5.1 2.2 33 1 209.1
IM 19E 0.04 1.88 1.05 0.22 0.005 0.03 0.71 17.36 1.22 38.1 5.8 2.3 30 1.9 19.3
IM 2A 0.04 3.18 0.74 0.16 0.004 0.02 0.76 13.50 1.34 33.7 4.6 1.9 30 1.7 11.3
IM 4B 0.014 4.12 0.48 0.11 0.007 0.01 0.65 7.55 1.10 29.5 3.5 1.3 32 1.4 22.8
IM 14C 0.24 6.67 1.15 0.39 0.020 0.04 0.52 13.60 0.91 37.7 4.5 1.5 128 3.3 44
IM 18B 0.16 1.50 0.70 0.15 0.004 0.02 0.64 16.68 1.01 41.1 5.9 1.7 36 2.2 111.1
IM 19C 0.07 3.81 0.96 0.21 0.004 0.03 0.58 16.20 0.96 31.1 5 1.7 29 1.3 147.4
Mean 0.13 4.1 1.0 0.3 0.02 0.03 0.7 14.1 1.1 35.8 4.9 1.8 71.0 1.4 41.1
Median 0.07 3.6 1.0 0.2 0.01 0.03 0.7 13.6 1.1 35.4 5.1 1.7 52.0 1.4 28.4
SD 0.17 2.5 0.2 0.1 0.05 0.01 0.1 2.4 0.3 6.9 0.8 0.4 44.2 0.6 45.3
OK 7A Mamu

Formation

Western margin

(Bay

subenvironment)

0.014 3.28 0.46 0.12 0.004 0.015 0.86 12.01 1.51 46.6 5.7 2.3 80 3.2 18.3
OK 7B 0.014 2.71 0.23 0.06 0.008 0.015 0.63 6.93 1.09 23.7 4.9 1.4 174 6.4 13
OK 7C 0.014 6.76 0.46 0.12 0.018 0.030 0.50 15.61 0.90 71 5.5 1.7 96 7.9 40.7
OK 7D 0.014 5.38 0.42 0.10 0.005 0.022 0.60 15.88 1.06 52.7 6.2 1.7 118 2.9 38.8
OK 7E 0.014 2.42 0.40 0.08 0.002 0.022 0.94 14.98 1.76 48.7 6.5 2.8 145 2.5 25.7
OK 7F 0.014 2.02 0.42 0.08 0.005 0.022 0.72 17.04 1.31 34.6 5.5 2.1 149 1.5 26.7
OK 7G 0.021 1.96 0.47 0.07 0.003 0.030 0.73 11.54 1.34 35 6.5 2 29 1.8 18
OK 7H 0.014 1.19 0.37 0.07 0.005 0.015 0.83 12.54 1.53 29.3 6.1 2 30 2.6 15.7
OK 7I 0.021 1.43 0.39 0.07 0.003 0.022 0.87 15.24 1.51 46.9 7 2.1 37 2.8 30
OK 7J 0.021 1.60 0.44 0.07 0.005 0.022 1.03 13.81 1.88 47.7 7.5 2.6 31 4.3 29.9
OK 9 0.021 0.80 0.42 0.08 0.002 0.015 0.76 16.88 1.37 41.1 5.6 2 33 1 19.1
OK 11A 0.014 1.63 0.64 0.12 0.005 0.022 0.99 15.40 1.71 36.4 7 2.7 31 3.3 17.8
OK 11B 0.021 1.57 0.88 0.16 0.003 0.022 1.05 15.88 1.84 42.9 7.3 2.9 28 2.1 13.8
OK 13A 0.014 1.29 0.45 0.07 0.004 0.022 0.88 13.07 1.56 38.4 7.2 2.2 30 3.5 27.1
OK 13B 0.014 1.14 0.50 0.08 0.003 0.022 0.99 15.61 1.74 49 7.7 2.5 37 1.8 29.9
OK 15 0.021 0.62 0.51 0.05 0.004 0.030 0.94 8.10 1.70 36.8 7.3 2.3 18 1.5 13.7
OK 17 0.021 0.75 0.47 0.05 0.005 0.022 0.90 11.75 1.64 34.9 6.4 2.1 23 1.8 36.7
OK 19A 0.014 0.68 0.41 0.06 0.003 0.022 0.93 13.92 1.66 37.7 7.2 2.2 31 1.1 59.3
OK 19B 0.014 1.04 0.47 0.08 0.003 0.022 1.04 15.72 1.78 47.5 7.6 2.7 37 1.9 32.7
OK 21A 0.029 1.20 0.60 0.09 0.003 0.022 1.14 10.43 2.00 41.6 8 2.9 23 1 20.3
OK 21B 0.021 1.55 0.62 0.10 0.003 0.022 1.11 11.91 2.01 40.7 7.9 2.7 23 1.7 17.8
OK 24A 0.014 0.62 0.42 0.06 0.002 0.022 0.96 13.87 1.65 42.5 7.5 2.3 30 1.5 63.6
Ok 24B 0.014 0.64 0.41 0.05 0.004 0.022 0.88 10.06 1.53 43.4 6.2 1.9 19 1.9 26.4
Mean 0.017 1.8 0.5 0.08 0.004 0.022 0.9 13.4 1.6 42.1 6.7 2.3 54.4 2.6 27.6
Median 0.014 1.5 0.5 0.08 0.004 0.022 0.9 13.9 1.7 41.6 7 2.2 31.0 1.9 26.4
SD 0.004 1.5 0.1 0.03 0.003 0.004 0.2 2.7 0.3 9.3 0.7 0.4 47.4 1.7 13.4
Nz-16 Mamu

Formation

Nzam-1 Well 0.74 4.35 1.36 0.65 0.07 0.514 0.57 7.74 0.96 25.9 2.1 7.5 89 1.6 15
Nz-17 0.51 4.96 1.17 0.58 0.04 0.625 0.65 9.34 1.09 25 2.9 16.8 126 2.3 18.7
Nz-18 0.47 6.31 1.06 0.81 0.02 0.619 0.54 > 20 0.90 27.2 3.4 3.7 155 2 27.3
Nz-19 0.17 3.61 2.02 0.39 0.03 0.404 0.81 10.00 1.36 24.8 3.4 81.9 119 1.3 22.2
Nz-20 0.32 3.97 0.79 0.46 0.03 0.485 0.44 6.95 0.74 17 2 2 65 2.5 21.1
Nz-21 0.34 4.29 1.29 0.50 0.05 0.566 0.66 8.60 1.11 23.3 2.8 2 94 1.4 17.3
Nz-22 0.34 3.13 1.16 0.32 0.03 0.564 0.61 7.89 1.02 23.8 2.5 3.4 64 1.2 13.7
Nz-39 0.36 6.69 1.13 0.65 0.18 0.467 0.81 7.73 1.36 20.9 3.1 36.6 95 1.6 18.7
Mean 0.4 4.7 1.3 0.6 0.056 0.531 0.6 8.3 1.1 23.5 2.8 19.2 100.9 1.7 19.3
Median 0.4 4.3 1.2 0.5 0.034 0.539 0.6 7.9 1.1 24.3 2.9 5.6 94.5 1.6 18.7
SD 0.2 1.3 0.4 0.2 0.051 0.077 0.1 1.1 0.2 3.22 0.5 27.9 31.1 0.5 4.3
ID-3 Mamu

Formation

Idah-1 Well 0.07 0.25 0.02 0.01 0.005 0.014 0.28 0.15 0.46 126 0.7 7.9 9 0.3 2.3
ID-4 0.02 1.07 1.59 0.06 0.006 0.074 0.57 4.80 0.94 22 1.5 103 31 1.6 8.9
ID-5 0.23 2.45 0.61 0.37 0.040 0.126 0.33 3.66 0.54 179 2.6 3.2 52 0.6 9.8
ID-6 0.37 4.80 1.26 0.80 0.046 0.462 0.81 8.28 1.34 75.4 3.3 3.2 88 1.4 20.3
ID-7 0.48 5.27 1.34 0.71 0.050 0.469 1.09 6.70 1.82 148 3.8 4.9 92 1.9 24.2
ID-8 0.43 5.78 1.19 0.84 0.079 0.432 0.71 7.56 1.18 2290 19.1 23.2 186 1.4 21.4
ID-9 0.48 5.00 1.05 0.71 0.065 0.454 0.63 7.24 1.06 2400 18 51.6 241 1.6 19.9
ID-10 0.73 6.82 0.80 1.09 0.083 0.314 0.62 4.87 1.04 325 4.7 25.1 152 1.7 14
ID-11 0.43 4.74 0.83 0.77 0.066 0.402 0.84 5.78 1.40 222 3.7 4.8 95 1.3 19.7
ID-12 0.73 7.05 0.93 0.83 0.080 0.280 0.64 6.19 1.07 450 6.1 57.2 138 1.7 21.8
ID-13 0.58 5.81 1.09 0.55 0.051 0.315 0.77 7.74 1.29 126 3.8 16.6 187 1.5 20.7
ID-14 0.39 6.47 1.17 0.64 0.051 0.377 0.82 9.23 1.37 1250 11.3 13.1 146 1.4 23.4
ID-15 0.40 7.13 1.08 0.66 0.063 0.341 0.77 8.47 1.28 249 5 3.1 139 1.5 20.8
ID-16 0.25 4.82 1.11 0.48 0.018 0.366 1.23 9.59 2.05 31.5 4.6 17.1 114 1.1 22.8
ID-17 0.16 4.81 1.02 0.44 0.029 0.229 0.90 10.70 1.49 163 6.2 76.1 150 1.4 21.4
ID-18 0.35 7.09 1.05 0.53 0.047 0.256 0.71 10.00 1.19 666 8.3 2.4 136 0.8 24.3
ID-19 0.44 5.97 0.88 0.54 0.031 0.231 0.67 9.47 1.12 334 6.6 2.9 214 1.3 20.4
ID-20 0.36 5.94 1.07 0.68 0.047 0.334 0.69 9.92 1.15 222 4.8 2.3 160 1.7 21.7
ID-21 0.69 5.34 1.30 1.06 0.024 0.452 0.57 10.70 0.95 146 4.4 2.2 208 0.7 25.4
ID-22 0.53 5.27 1.40 1.09 0.031 0.446 0.57 10.80 0.95 97.1 3.6 2.1 151 0.6 23.5
ID-23 0.71 5.23 1.20 0.98 0.050 0.431 0.63 9.37 1.05 97.7 4.1 5.5 203 0.9 22.4
Mean 0.4 5.1 1.1 0.7 0.05 0.32 0.7 7.7 1.2 458.1 6.0 20.4 137.7 1.3 19.5
Median 0.4 5.3 1.1 0.7 0.05 0.34 0.7 8.3 1.2 179 4.6 5.5 146 1.4 21.4
SD 0.2 1.8 0.3 0.3 0.02 0.13 0.2 2.7 0.4 683.5 4.7 28.1 60.8 0.4 5.9
OW-10 Mamu

Formation

Owan-1 Well 0.02 0.76 0.03 0.02 0.004 0.010 0.46 1.54 0.76 11.3 1.1 > 200 30 1.3 1.4
OW-11 0.03 0.70 0.32 0.06 0.005 0.018 0.74 9.72 1.23 30.2 3.5 2.9 100 0.2 29.8
OW-12 0.01 0.34 0.07 0.02 0.004 0.010 0.13 3.54 0.22 14 0.5 4.4 26 < 0.1 4.7
OW-13 0.01 0.20 0.07 0.02 0.003 0.009 0.30 2.93 0.50 11.8 0.5 15.4 18 0.2 3.9
OW-14 0.04 1.66 0.19 0.08 0.011 0.012 0.73 7.37 1.21 34.1 2.5 42.1 66 3.9 13.6
OW-15 0.12 1.47 0.21 0.05 0.008 0.016 0.92 6.45 1.54 24 2.5 169 40 1.3 11.2
OW-16 0.03 0.76 0.10 0.02 0.004 0.008 0.45 2.44 0.75 11.1 1.1 185 50 0.6 5.2
Mean 0.04 0.8 0.1 0.04 0.006 0.012 0.5 4.9 0.9 19.5 1.7 69.8 47.1 1.3 10
Median 0.03 0.8 0.1 0.02 0.004 0.010 0.5 3.5 0.8 14 1.1 28.8 40 1 5.2
SD 0.04 0.5 0.1 0.03 0.003 0.004 0.3 3.0 0.5 9.8 1.2 84.4 28.3 1.4 9.7
Am-3 Mamu

Formation

Amansiodo-1

Well

4.82 0.62 0.04 0.07 0.007 0.024 0.06 0.65 0.10 28.5 1.3 > 200 61 1.5 20.9
Am-4 6.28 0.69 0.03 0.09 0.009 0.022 0.06 0.72 0.11 27.7 1.5 > 200 78 1.6 28.1
Am-5 3.95 1.67 0.05 0.12 0.014 0.050 0.08 0.87 0.14 42.2 1.2 > 200 111 1.8 21.1
Am-6 1.16 0.52 0.03 0.03 0.008 0.015 0.06 0.54 0.11 19 0.5 191 37 0.9 13.5
Am-7 0.51 0.62 0.04 0.02 0.005 0.014 0.06 0.62 0.11 42.2 0.7 > 200 59 1.2 6.5
Am-8 0.38 0.54 0.04 0.02 0.005 0.014 0.07 0.66 0.12 36.4 0.4 195 55 1 4.7
Am-9 0.10 0.43 0.03 0.02 0.003 0.010 0.05 0.51 0.08 24.1 0.4 > 200 34 1 3.9
Am-10 0.50 0.57 0.02 0.02 0.004 0.007 0.03 0.26 0.05 31.2 0.4 195 31 2.5 5.3
Am-11 0.85 2.50 0.03 0.04 0.012 0.011 0.07 0.62 0.12 86.5 0.6 196 58 1.4 8.2
Mean 2.1 0.9 0.03 0.05 0.007 0.019 0.06 0.6 0.1 37.5 0.8 194.3 58.2 1.4 12.5
Median 0.9 0.6 0.03 0.03 0.007 0.014 0.06 0.6 0.1 31.2 0.6 195 58 1.4 8.2
SD 2.1 0.6 0.01 0.03 0.003 0.012 0.01 0.15 0.02 20.0 0.4 2.2 24.9 0.5 8.9
Enu 1.1 Mamu

Formation

Eastern margin 0.04 4.16 0.95 0.36 0.04 0.16 0.86 10.24 0.86 35 122 30
Enu 1.2 (Odoma et al.,

2015)

0.03 3.11 1.11 0.28 0.02 0.24 0.97 8.03 0.97 30 53 18
Enu 1.3 0.03 1.90 1.19 0.30 0.01 0.27 1.01 8.46 1.01 29 53 11
Enu 1.4 0.03 3.68 1.04 0.22 0.03 0.23 0.93 8.91 0.93 32 69 25
Enu 1.5 0.03 3.64 1.02 0.28 0.03 0.19 0.88 8.94 0.88 29 78 27
Enu 2.2 0.02 8.25 0.85 0.25 0.01 0.13 0.85 10.76 0.85 25 51 22
Enu2.3 0.03 5.71 0.87 0.25 0.01 0.14 1.02 10.56 1.02 24 39 26
Enu2.4 0.03 2.58 1.46 0.37 0.01 0.16 0.92 11.64 0.92 23 51 31
Enu2.5 0.04 4.71 0.96 0.27 0.01 0.17 1.21 10.37 1.21 27 152 27
mean 0.03 4.2 1.1 0.3 0.02 0.19 1.0 9.8 1.0 28.2 74.2 24.1
median 0.03 3.7 1.0 0.3 0.01 0.17 0.9 10.2 0.9 29 53 26
SD 0.01 1.9 0.2 0.1 0.01 0.05 0.1 1.2 0.1 3.9 38.1 6.3
Mamu Formation

average

0.05 5.1 1.4 0.9 0.05 1.06 0.7 9.3 1.1 29.6 3.8 5.5 52.5 1.4 20.1
Pre-Santonian Units
Am-23 Awgu

Group

Amansiodo-1

Well

0.54 8.56 1.18 0.78 0.121 0.453 1.05 10.30 1.75 24.5 3.8 5.9 106 1.7 26.7
Am-24 0.34 5.56 1.96 0.83 0.046 0.528 0.99 12.0 1.64 27.8 4 8 125 2.6 31.4
Am-25 0.27 5.64 1.62 0.74 0.039 0.526 0.98 11.60 1.64 27.2 4 9.7 107 1.5 29
Am-26 0.46 6.18 2.25 1.03 0.076 0.610 0.87 11.40 1.45 25.6 3.7 12.4 119 1.3 28.5
Am-27 0.31 5.41 1.93 1.16 0.042 0.646 0.77 11.90 1.28 28.7 4 5.3 123 1.5 23.7
Am-28 0.29 5.24 1.51 0.95 0.042 0.590 0.84 11.60 1.41 25.6 3.8 4.2 110 1.6 24.6
Am-29 0.33 5.89 1.48 1.01 0.056 0.640 0.77 12.50 1.28 27.1 3.9 3.8 109 0.9 23.2
Am-30 0.31 5.98 1.27 0.99 0.047 0.649 0.76 11.40 1.27 30.8 4.4 6.1 194 1.3 25.4
Am-31 0.29 5.79 1.43 1.04 0.052 0.706 0.76 12.90 1.27 25.4 4 2.4 132 1.5 24.9
Am-32 0.34 5.74 1.44 1.01 0.048 0.723 0.86 12.90 1.43 24.8 4 5.5 104 2.4 23.3
Am-33 0.28 5.73 1.77 1.01 0.045 0.654 0.83 13.0 1.39 25.9 4 4.9 103 3.8 33.6
Am-34 0.27 5.76 1.70 1.06 0.055 0.721 0.77 12.70 1.29 26.6 3.9 2.6 89 3.7 42.1
Am-35 1.10 4.74 3.75 1.08 0.073 0.569 0.65 12.70 1.08 32.6 4.4 12 160 5.3 30.5
Am-36 1.28 8.86 1.16 1.56 0.110 0.743 0.56 9.69 0.93 23.4 3.6 9.3 88 2.7 17.7
Am-37 0.78 5.99 1.65 1.21 0.075 0.763 0.65 10.90 1.08 23.4 4.2 12 87 2.1 22.7
Mean 0.5 6.1 1.7 1.0 0.062 0.635 0.8 11.8 1.4 26.6 4 6.9 117.1 2.3 27.2
Median 0.3 5.8 1.6 1.0 0.052 0.646 0.8 11.9 1.3 25.9 4 5.9 109 1.7 25.4
SD 0.3 1.1 0.6 0.2 0.025 0.090 0.1 1.0 0.2 2.6 0.2 3.4 28.4 1.2 5.8
Ak-3 Awgu

Group

Akukwa-II Well 0.66 6.87 1.15 1.20 0.07 0.742 0.517 10.20 0.86 22.3 3.1 5.9 147 2.3 35
Ak-4 0.53 5.73 1.25 0.74 0.024 0.733 0.551 10.90 0.92 24.9 3.3 6.8 130 2.8 49.8
Ak-5 0.54 5.38 1.09 0.76 0.027 0.729 0.711 11.80 1.19 28 3.8 5.7 161 3.3 34.8
Ak-6 0.53 4.95 1.11 0.92 0.025 0.719 0.614 10.90 1.02 21.6 3.3 11.7 125 2.2 40.1
Ak-7 0.38 5.58 0.95 1.12 0.091 0.778 0.708 10.60 1.18 25.5 3.2 10.4 122 2.7 32.7
Ak-8 0.29 5.03 1.29 1.03 0.028 0.814 0.710 11.70 1.18 28.6 3.6 9.2 174 4.1 33.3
Ak-9 0.46 5.91 1.27 1.14 0.075 0.697 0.674 11.20 1.12 26.8 3.3 9.7 158 2.6 32.9
Ak-10 0.42 3.19 0.86 0.44 0.041 1.340 0.281 6.60 0.47 26.4 1.7 68.1 82 1.9 14.4
Ak-11 0.31 5.47 1.45 0.79 0.045 0.689 0.800 11.0 1.33 24.4 3.6 8.8 128 2.3 30.5
Mean 0.5 5.4 1.2 0.9 0.047 0.805 0.618 10.5 1.0 25.4 3.2 15.1 136.3 2.7 33.7
Median 0.5 5.5 1.2 0.9 0.041 0.733 0.674 10.9 1.1 25.5 3.3 9.2 130 2.6 33.3
SD 0.1 1.0 0.2 0.3 0.025 0.204 0.154 1.6 0.3 2.4 0.6 20 27.4 0.7 9.3
Ak-12 Eze-Aku

Group

Akukwa-II Well 0.30 5.30 1.22 0.84 0.052 0.725 0.767 11.80 1.28 24.2 3.6 7.6 225 2.2 34.3
Ak-13 0.42 5.43 1.25 0.92 0.066 0.737 0.702 11.30 1.17 18.9 3.3 15.3 133 3.1 31.8
Ak-14 0.73 12.80 1.28 2.53 0.470 0.430 0.431 7.65 0.72 24.4 2.3 2.5 55 0.7 18.2
Ak-15 0.36 4.84 1.76 0.86 0.060 0.659 0.694 10.30 1.16 23.3 3.6 15.4 134 2.4 29.7
Ak-16 0.38 5.11 1.28 0.94 0.042 0.760 0.673 9.68 1.12 22.2 3.3 13.6 147 2.6 30
Ak-17 0.27 5.46 1.19 0.97 0.057 0.829 0.762 11.60 1.27 29.2 3.9 9.4 134 2.1 32.8
Ak-18 0.39 4.64 1.38 0.87 0.043 1.060 0.633 10.0 1.06 21.3 3.6 13.4 190 3 28.3
Ak-19 6.77 4.31 1.24 0.85 0.053 0.925 0.519 7.90 0.87 19.6 3.1 11 153 12.7 28.7
Ak-20 1.62 4.82 1.47 0.96 0.049 1.070 0.659 9.43 1.10 16.8 3.6 14.1 137 3.2 27.9
Ak-21 1.55 4.57 1.44 0.89 0.052 1.040 0.663 9.13 1.11 24.6 3.6 13.8 150 3.1 26.6
Ak-22 1.82 4.08 1.43 0.71 0.037 1.430 0.647 9.25 1.08 24 3.4 16.7 119 2.8 21.9
Ak-23 12.90 5.60 0.99 0.75 0.102 0.664 0.369 6.56 0.62 113 4 21.3 383 11.3 177
Ak-24 2.43 5.47 2.04 1.15 0.074 1.430 0.757 10.50 1.26 38.5 4.4 18.1 135 2.9 30.2
Ak-25 2.11 5.76 1.89 1.12 0.072 1.530 0.771 10.50 1.29 44.8 4.6 15.5 145 3.8 34.3
Ak-26 2.07 5.77 1.95 1.15 0.080 1.520 0.781 9.98 1.30 35.6 4.5 18.5 111 3.8 21.9
Ak-27 1.49 5.06 1.52 0.92 0.055 1.230 0.655 8.64 1.09 39.1 3.9 13.2 100 2.9 31.9
Ak-28 2.14 5.07 1.76 0.94 0.050 1.420 0.724 8.62 1.21 30.8 4 22.2 88 1.7 20.2
Ak-29 1.49 4.33 1.29 0.81 0.042 1.040 0.524 7.86 0.87 27.4 3.5 24.2 92 3.1 30.8
Ak-30 2.13 5.01 1.64 0.90 0.045 1.140 0.661 8.67 1.10 24 4.2 8.1 109 5.1 29.9
Ak-31 3.56 4.13 1.41 0.52 0.043 1.280 0.451 8.18 0.75 32.4 3.5 36.5 98 3.8 28.3
Ak-32 3.12 4.41 1.48 0.78 0.047 1.210 0.529 8.21 0.88 32.8 3.5 26.7 131 4.2 25.9
Mean 2.3 5.3 1.5 1.0 0.076 1.054 0.637 9.3 1.1 31.8 3.7 16.1 141 3.8 35.3
Median 1.6 5.1 1.4 0.9 0.052 1.060 0.661 9.3 1.1 24.6 3.6 15.3 134 3.1 29.7
SD 2.9 1.8 0.3 0.4 0.092 0.319 0.121 1.4 0.2 20.0 0.5 7.4 66.0 2.9 32.8
UCC 3.0 3.5 2.8 1.33 0.06 2.89 0.41 8.04 0.68 17 5.5 2.0 71 1.5 25

Appendix 1b

S/N Lithostratigraphic Unit Location Ni Co V Cr Sc Th U Ta Nb Zr Y Hf La
U1 IA Mamu

Formation

Western margin

(Marsh

subenvironment)

14.7 3.5 110 108 9 14.8 5 2.1 30.3 173.2 24.4 4.6 50.2
U1 1C 11.3 2.1 90 96 6 11.8 4.3 2 28 157 20.6 4.2 43.9
U1 2A 11.4 2.1 115 100 7 13.1 4.5 2.1 31.8 171.8 22.5 4.4 47.2
U1 2B 8 1.7 102 65 7 11.2 3.8 1.8 26.7 155.8 20.6 3.9 43
U1 2C 10.6 2.2 113 97 9 12.6 4.6 2.1 31.4 176.6 22.7 4.6 47.8
U1 3A 10.3 2.2 110 76 9 12.4 4.6 2.2 33.6 186.1 23.8 5 48.7
U1 3B 16.3 2.9 101 97 10 12.3 4.6 2.5 34.9 193.3 25.8 5.2 49.6
U1 5A 15.1 3.1 159 131 13 14.8 5.1 2.2 30.5 160.7 26.8 4.3 42.8
U1 5B 15.3 2.9 166 128 13 13.4 4.5 2 27.6 149.9 22.6 4.4 35.9
U1 6A 14.3 3.6 166 104 12 13.3 4.4 2.1 29.4 155 20.9 4.7 43.3
U1 7A 50 34.5 148 124 15 16.3 4.5 1.8 24.4 133.1 22 4 47.2
U1 7B 26.2 15 145 66 12 12.6 4.4 1.9 29.1 146 22.5 4 46.1
U1 8A 37.5 23.9 149 85 13 15.1 4.1 1.8 25.5 126.5 19.7 3.6 38.8
U1 8B 31.9 17.9 128 80 13 14.4 4.5 2 28.9 154.9 24 4.1 52.2
U1 8C 31.9 16.5 103 86 10 13.8 4.3 1.8 25.4 142.2 19.6 3.9 42.1
U1 8D 62.2 27.5 172 66 12 11.9 5.4 1.9 28.8 161.5 23.1 4 39.4
U1 9B 37 25.9 157 111 13 15.3 5.5 2.2 32 173.1 22.1 4.4 50.7
U1 9C 31.2 20.4 151 91 13 12.4 5.2 2.4 33.3 178.6 17.3 4.5 40.1
U1 10 22.4 7.4 184 142 12 17.7 4.1 2.3 33.2 182 20.9 4.9 63.5
U1 18 10.6 2 154 128 17 16.7 5.1 2.4 33.2 198.7 20.4 5.8 56.4
U1 19 9.9 1.9 137 93 13 14.6 4.7 2.1 31.4 180.1 19.5 4.7 47.1
AU-1a 15.9 3.5 113 102 13 14.3 4.7 1.8 24.3 138.3 30.6 4 47.2
AU 2 15.6 3.9 127 109 16 13.4 4.1 1.7 25 134 25 3.8 30.1
Mean 22.2 9.9 134.8 99.4 11.6 13.8 4.6 2.1 29.5 162.1 22.5 4.4 45.8
Median 15.6 3.5 137.0 97.0 12.0 13.4 4.5 2.1 29.4 160.7 22.5 4.4 47.1
SD 14.2 10.3 26.9 21.6 2.8 1.7 0.4 0.2 3.2 20.1 2.9 0.5 6.9
IM 2B Mamu

Formation

Western margin

(Central Basin

subenvironment)

42.7 23 159 103 17 18.1 6.8 2.3 30.6 111.8 20.6 3.4 31.5
1M 2C 32.9 13.2 161 105 16 19.9 7.2 2.4 32.8 123.4 30.5 3.8 40.6
1M 2D 32.1 16.1 150 99 16 20.9 7 2.2 30.7 112.6 26.8 3.4 41.8
1M 2E 46.7 22.5 151 109 16 18.6 5.9 2.1 28.4 102.4 25.9 3.2 42
IM 4A 12 123 75 11 19.5 5.4 1.7 24.4 98.2 19.9 2.8 50.4
IM 11A 53.5 30.7 139 95 15 30.9 9.8 1.8 25 151.4 20.5 4.9 61.3
IM 11B 50.3 21 144 114 21 16.1 7 1.6 24.3 78.2 11.4 2.3 23.6
IM 11C 54.7 31.2 140 125 24 30.3 8.5 1.6 23 111.3 54.8 3.6 87.2
IM 13A 58.8 32 133 131 16 23.8 6.4 1.4 20.3 74.2 21.3 2.3 50
1M 13B 47.7 22.5 146 113 21 18.2 6 1.8 26.6 89.4 14.4 3 28.9
IM 14A 61.9 29.9 131 111 20 23.1 8.3 1.5 21.2 100.5 64.8 2.9 72.1
IM 16A 59.1 26.1 104 104 17 13.4 4.5 0.9 13.1 63.9 30.6 1.9 37.5
IM 16B 60.9 25.1 103 99 18 13.2 4.7 1 15.9 80.8 62.2 2.4 34.8
1M 16C 46.1 15.1 118 101 16 13.3 4.4 1.3 18.2 91.9 31.9 2.8 27.8
1M 16D 39.2 19.2 115 97 17 17.9 4.8 1.3 19.5 108 18.1 3.3 40.2
IM 18a 45.9 3.15 68.5 67.5 19 20.2 12.2 0.9 13.5 93.3 57.6 2.7 89
IM 18C 32.9 6.3 94 94 21 15.3 10.8 1.5 22.5 78.5 14.7 2.3 26.3
IM 19A 28.2 7.6 111 107 20 14.2 8.6 1.5 20.2 98.3 18.3 3.1 27.9
IM 19B 43.2 13.4 109 100 13 13 7.5 1.3 19.9 93 11.9 2.8 20
IM 19D 54.5 36.8 102 105 14 14.2 5.6 1.3 20.6 99.9 13.9 2.7 31.7
IM 19E 26.9 7.4 128 109 20 10.1 5.9 1.7 23.7 114 7.9 3.3 13.3
IM 2A 29.2 9.5 131 101 13 14 6 1.8 27.3 92.9 16.3 2.7 37.2
IM 4B 24.9 11.8 99 68 9 18.6 4.6 1.6 23.6 100.6 19.9 2.9 50.6
IM 14C 75.4 27.5 110 99 19 16.2 7.7 1.2 17.6 70.6 36.7 2 46.8
IM 18B 33.3 8.8 91 89 11 9.6 8.5 1.5 22.1 66.4 10.5 2 11.5
IM 19C 62.6 34.8 98 108 14 16.1 7.7 1.4 18.4 91.8 18.8 2.8 36.1
Mean 45.7 19.5 121.5 101.1 16.7 17.6 7.0 1.6 22.4 96.1 26.2 2.9 40.8
Median 46.1 20.1 120.5 102 16.5 17.5 6.9 1.5 22.3 95.7 20.2 2.8 37.4
SD 13.5 9.8 23.5 14.6 3.6 5.2 2.0 0.4 5.0 19.1 16.3 0.6 19.5
OK 7A Mamu

Formation

Western margin

(Bay

subenvironment)

33.8 16.2 95 92 13 26.4 7.2 2.5 32.7 120.2 36.9 3.6 62.6
OK 7B 21.6 7.8 69 118 10 24.5 5.6 2 23.5 115.3 32.1 3.8 60
OK 7C 65.7 36.9 114 102 17 19.2 9 1.5 19.9 46.6 39.8 1.3 40.5
OK 7D 57.1 28.8 93 104 17 20.5 11.6 1.8 24 59 43.3 1.9 39.4
OK 7E 43.9 22.9 101 91 16 19.3 8.7 2.9 37.8 110.1 32.7 3.5 42.9
OK 7F 88.4 29.1 98 72 15 11.9 8.5 2.2 28 98.1 14 2.7 16.9
OK 7G 52.5 12.5 105 70 15 26 7.9 2.6 30.4 184.4 21.4 5.8 60
OK 7H 26.4 6.2 67 97 11 20 8.2 2.6 34.1 135.2 12.8 4.3 36.4
OK 7I 26.9 8.9 83 86 14 17.3 12 2.5 34.2 108.5 10.1 3.2 27.1
OK 7J 29.4 6.2 104 119 17 25.7 13.7 3.2 43.3 169.2 17.2 5.2 45.4
OK 9 47.2 6 88 88 14 19.6 10.8 2.5 31.7 111.5 13.2 3.5 35.4
OK 11A 25.3 5.6 103 105 15 14.8 10.3 2.7 37.8 150.7 13.9 4.6 20.1
OK 11B 22.2 5 110 93 15 14.4 8 2.9 39.2 168.1 14.1 5.1 22.6
OK 13A 25.4 5.2 86 99 16 20.5 10.5 2.8 35.1 112 16.7 3.4 40.5
OK 13B 29.2 6.6 96 94 18 13.3 14 3.1 41.2 120.8 13.2 3.7 25.1
OK 15 27.3 3.1 54 56 12 36.4 8.3 3.4 39.1 255.4 24.1 8.1 73.6
OK 17 42.7 4.6 61 84 24 35.1 11.8 3 36.4 185.3 21.3 5.7 64.7
OK 19A 29.8 6.1 76 96 27 23.8 19.5 3.1 38.2 157.2 18.9 5 43.2
OK 19B 35.3 6.5 88 100 18 19.7 12.1 3.1 43.1 125.9 14.6 3.8 34.3
OK 21A 24.1 3.3 94 72 18 35.3 12.4 3.7 45.6 245.8 27.9 7.8 77.7
OK 21B 22.3 4.2 97 90 16 28 10.8 3.5 44.6 219.1 23.1 6.5 71
OK 24A 33.9 5.3 76 86 16 15.9 31.2 3 40 119.4 20.6 3.6 51.9
Ok 24B 25.9 4.2 73 76 15 25.2 11.5 2.9 34.9 156 20.5 5.1 64
Mean 36.4 10.5 88.3 90.9 16.0 22.3 11.5 2.8 35.4 142.3 21.8 4.4 45.9
Median 29.4 6.2 93 92 16 20.5 10.8 2.9 36.4 125.9 20.5 3.8 42.9
SD 16.5 9.6 16.1 15.0 3.7 6.9 5.2 0.5 6.8 52.0 9.4 1.7 18
Nz-16 Mamu

Formation

Nzam-1 Well 32.3 14.9 88 100 11 14.4 2.7 1.1 16.8 171 20.9 4.5 39.9
Nz-17 36.3 17.5 120 74 14 16.3 3.1 1.3 20.8 144 21.7 3.8 45.5
Nz-18 50.8 20.2 183 89 17 13.7 2.5 1.1 17.4 95.8 21.5 2.5 39.2
Nz-19 38.5 33.5 126 78 16 18.5 5.9 1.7 25.2 187 26.4 5.2 54.6
Nz-20 26 12.1 89 92 11 13.3 2.4 0.7 12.5 119 16.3 3.2 36.1
Nz-21 33.6 17.7 117 95 13 15.6 3.6 1.4 21.2 152 23 4.1 43
Nz-22 26.1 13 89 89 11 15.9 3 1.3 19.3 156 19.1 4.2 40.1
Nz-39 36.3 25.7 121 86 14 11.8 4.2 1.5 20.6 173 29.1 4.4 42.5
Mean 35 19.3 116.6 87.9 13.4 14.9 3.4 1.26 19.2 149.7 22.25 3.99 42.61
Median 35 17.6 118.5 89 13.5 15 3.05 1.3 19.95 154 21.6 4.15 41.3
SD 7.9 7.2 31.3 8.5 2.3 2.08 1.16 0.30 3.75 30.03 4.01 0.83 5.61
ID-3 Mamu

Formation

Idah-1 Well 3.6 4.9 13 19 2 8.2 1.1 0.1 1.8 23.4 5.9 0.3 18.6
ID-4 14 26.4 43 53 6 12.9 3.3 0.5 11.6 215 17 6.4 38
ID-5 14.5 7.8 53 49 7 7.9 2.1 0.4 8.2 70.7 12.7 2 22.1
ID-6 36 22.4 109 71 14 12.2 6.6 1.8 26 204 28.5 5.5 41
ID-7 35.9 25 103 66 13 13.1 7.8 1.9 31.2 258 35.4 6.6 47.5
ID-8 32.7 21.7 98 78 13 12.5 4.9 1.5 22.1 185 27.2 5.1 40.7
ID-9 33.3 17.4 87 67 12 11.7 4.5 1.4 19.9 179 24.6 5 37.1
ID-10 19.8 16.4 62 54 9 10.5 4.6 1 19.5 194 24.7 5.1 34.4
ID-11 27.6 16.1 76 68 11 11.9 5.4 1.4 24.9 220 27.1 6 38.5
ID-12 29.6 24.1 72 83 11 12.3 4.7 1 20.2 172 27.7 4.6 39.5
ID-13 28.8 21.3 90 76 13 14.4 6.1 1.8 25.6 226 30.5 6.3 45.9
ID-14 34 23.8 103 67 15 15.2 6 1.9 26.9 201 29.6 5.7 45.9
ID-15 33.7 21.9 96 77 14 15.5 5.8 1.8 26.2 206 29.6 5.8 47.6
ID-16 33.5 26.8 121 83 16 17.3 7.1 2.2 38 293 37.2 7.6 58.6
ID-17 36.8 35 92 64 15 19.7 7.2 2.3 32.5 237 30.7 7.3 56.8
ID-18 41 23.4 113 67 16 17.6 7.9 0.6 18.2 194 33.4 5.6 53.1
ID-19 33.6 23.8 84 54 14 18.5 6.3 1.9 26.3 188 27.5 5.6 52.3
ID-20 43.3 29.6 98 62 15 16.6 6.5 1.8 24.6 199 27.6 5.8 47.1
ID-21 45.7 21.5 118 84 16 13.3 4.6 1.4 18.7 128 28.2 3.8 42.8
ID-22 41.3 20.5 113 78 17 17.4 4.8 1.4 19.1 138 27.3 4 47.1
ID-23 43 22.4 113 71 15 13 6.3 1.5 22.6 157 39.2 4.4 47.9
Mean 31.51 21.5 88.43 66.24 12.57 13.89 5.41 1.41 22.1 185.2 27.22 5.17 42.97
Median 33.6 22.4 96 67 14 13.1 5.8 1.5 22.6 194 27.7 5.6 45.9
SD 10.7 6.60 27.39 14.90 3.80 3.19 1.74 0.604 8.09 59.39 7.64 1.67 9.86
OW-10 Mamu

Formation

Owan-1 Well 7.3 32.6 37 35 3 13 3 0.5 9.5 196 10.3 6.2 27.2
OW-11 35.9 30.9 50 82 18 24 8.5 < 0.1 1.5 114 40.4 4.5 66.4
OW-12 4.6 11.6 10 36 5 13.7 3 < 0.1 0.4 120 14.6 3.6 27.9
OW-13 4.8 23 19 29 4 9 1.9 < 0.1 1 96.1 9.8 2 21.3
OW-14 21 15.4 91 82 11 18.8 4.2 1.3 21.7 228 24.2 6.8 47.8
OW-15 18.7 41.7 71 82 9 18.6 4.4 0.2 7.8 228 27.3 6.1 49.3
OW-16 12.2 29.4 43 38 5 10.6 3.2 0.1 4.1 144 12.4 4.1 29.1
Mean 14.93 26.4 45.86 54.86 7.86 15.38 4.03 0.53 6.57 160.9 19.86 4.76 38.43
Median 12.2 29.4 43 38 5 13.7 3.2 0.35 4.1 144 14.6 4.5 29.1
SD 11.29 10.4 28.22 25.54 5.30 5.30 2.14 0.54 7.53 55.67 11.34 1.71 16.36
Am-3 Mamu

Formation

Amansiodo-1

Well

4.5 35.9 12 11 1 1.8 0.6 < 0.1 1.2 11.5 4 0.4 6.8
Am-4 5.3 35.7 14 16 1 1.6 0.6 < 0.1 1.5 11.5 4.6 0.3 7.1
Am-5 6.4 51.3 22 21 2 2.3 0.8 < 0.1 1.6 24.6 4.9 0.7 7.9
Am-6 2.5 35.1 10 10 1 1.8 0.8 < 0.1 2 50.5 3.3 0.6 5.8
Am-7 3 45.5 10 11 1 1.6 0.6 < 0.1 1.4 13.3 3.4 0.3 5.6
Am-8 2.4 39.2 10 13 1 1.7 0.7 < 0.1 1.9 13.3 3.4 0.3 5.7
Am-9 2.1 40.6 9 12 < 1 1.3 0.5 < 0.1 1 22.8 2.8 0.5 4.6
Am-10 1.9 40.4 6 10 < 1 1.1 0.5 < 0.1 0.4 11 2.2 0.3 3.6
Am-11 3.5 50.2 21 24 2 2.3 0.9 < 0.1 1.5 10.7 3.8 0.3 7.2
Mean 3.51 41.54 12.67 14.22 1.29 1.72 0.67 1.39 18.8 3.6 0.41 6.03
Median 3 40.4 10 12 1 1.7 0.6 1.5 13.3 3.4 0.3 5.8
SD 1.57 6.13 5.45 5.09 0.49 0.40 0.14 0.48 12.98 0.84 0.15 1.36
Enu 1.1 Mamu

Formation

Eastern margin

(Odoma et al.,

2015)

42 34 119 100 22 21 6 31 296 9
Enu 1.2 22 16 95 88 12 22 6 33 717 21
Enu 1.3 19 14 103 90 10 19 8 34 700 18
Enu 1.4 31 23 101 81 23 19 6 31 395 17
Enu 1.5 35 28 103 86 17 18 7 31 375 8
Enu 2.2 20 6 120 86 16 18 5 34 363 14
Enu2.3 18 5 101 83 10 17 8 38 409 10
Enu2.4 21 9 120 92 14 21 6 34 287 6
Enu2.5 27 17 125 96 22 23 6 43 491 14
mean 26.11 16.89 109.67 89.11 16.22 19.78 6.44 34.33 448.1 13.0
median 22 16 103 88 16 19 6 34 395 14
SD 8.31 9.91 11.12 6.11 5.17 2.05 1.01 3.94 159.5 5.07
Mamu Formation

average

25.45 18.85 99.5 88 13.75 14.35 5.15 1.5 22.45 149 20.5 4.28 41.3
Pre-Santonian

Units

Am-23 Awgu

Group

Amansiodo-1

Well

42.8 29.4 140 81 19 16.6 7.6 1.8 28.6 222 59.4 5.1 60.5
Am-24 46.6 28 154 87 20 15.6 5.4 1.7 27.1 208 37.5 4.8 52.1
Am-25 46.7 28.7 145 83 19 16.5 5.9 1.7 27.4 208 35.4 4.7 53.6
Am-26 45.1 29.5 172 101 20 14.5 4.2 1.5 25 179 32.2 4.1 47.4
Am-27 47.3 25.3 162 113 20 14.1 3.5 1 22 174 27.8 4.2 46.3
Am-28 45.8 23.9 165 102 19 15.7 4.3 0.7 19.9 189 30.8 4.3 51.1
Am-29 45.9 24 151 93 19 16.1 4.1 0.2 14 155 30.7 3.8 52.6
Am-30 46.4 24.5 150 93 18 15.9 4 0.2 17 161 34.7 4.5 48.7
Am-31 46.2 21.9 171 120 19 15.9 3.7 0.8 19 132 28.8 3.4 49.2
Am-32 46.1 23.4 174 121 19 15.6 4.1 1.4 24.5 151 32.3 3.6 49.9
Am-33 45.3 22.3 174 118 19 15.8 4 1.2 23.1 143 29.3 3.6 51.5
Am-34 45.5 21.4 173 120 20 15.9 3.7 0.9 19.7 133 25.3 3.2 48.5
Am-35 42.3 21.7 145 89 17 20.7 2.9 1.6 23.3 82.2 25.7 2.4 60.1
Am-36 42.9 19 119 152 16 15.1 2.4 0.9 14.9 55.6 59.9 1.5 51.6
Am-37 43.8 19.9 142 77 17 17.1 2.8 1.3 19.7 71.7 23.6 2 51.4
Mean 45.25 24.2 155.8 103.3 18.73 16.07 4.17 1.13 21.68 151 34.23 3.68 51.63
Median 45.8 23.9 154 101 19 15.9 4 1.2 22 155 30.8 3.8 51.4
SD 1.56 3.38 16.17 20.43 1.22 1.49 1.31 0.51 4.46 50.20 1.05 4.05
Ak-3 Awgu

Group

Akukwa-II Well 48.1 16.5 168 89 15 6.6 2.3 1 16.1 121 21.6 2.9 14.6
Ak-4 56.6 22.3 193 110 16 4.1 2.9 0.9 15.4 97.3 21.7 2.4 6.5
Ak-5 50.6 20 159 109 17 7.3 2.9 1.3 22 146 28.9 3.5 14.5
Ak-6 53.5 22.7 171 60 16 3.7 2.6 1 16.5 104 24.5 2.5 8.8
Ak-7 47.2 21.8 137 53 15 4 3 1.1 18.6 137 25.5 3.3 12.2
Ak-8 55.6 25.3 182 66 17 3 3.2 1.1 19.3 124 24 3 13
Ak-9 47.9 19.8 160 65 16 5.7 2.8 1.1 17.5 117 24.8 2.9 17.4
Ak-10 20.9 17.8 87 42 8 7.4 1.6 0.6 9.6 66.8 12.4 1.6 23.5
Ak-11 46.6 19.8 157 69 17 3.8 3.4 1.3 21.7 129 24.5 3.2 14.7
Mean 47.44 20.7 157.1 73.67 15.22 5.07 2.74 1.04 17.41 115.8 23.1 2.81 13.91
Median 48.1 20 160 66 16 4.1 2.9 1.1 17.5 121 24.5 2.9 14.5
SD 10.62 2.67 30.72 23.92 2.82 1.7 0.53 0.21 3.74 23.75 4.55 0.58 4.88
Ak-12 Eze-Aku

Group

Akukwa-II Well 53.7 22 165 62 17 2.8 3.3 1.2 20.3 129 25.3 3.1 12.2
Ak-13 46.6 20.6 167 64 16 2.4 3.1 1.1 19.3 117 27.8 2.8 10.8
Ak-14 29.1 20.1 107 81 16 11.7 2.6 0.1 7.8 82.8 17.7 2.2 37.7
Ak-15 43.9 17.1 161 58 15 1.8 2.8 1.2 21.4 132 25.1 3.2 11.1
Ak-16 39.8 17.7 157 63 14 2.7 2.6 1.1 18.4 105 24.2 2.6 10.3
Ak-17 45.1 22.1 179 101 16 9.7 3.5 1.3 21.2 108 23.1 2.8 21.8
Ak-18 40.2 17.6 132 65 14 3.2 2.5 1.1 18.6 79.7 20.8 2.1 10.8
Ak-19 66.5 28.5 226 77 13 5.7 4.2 1 16 73.2 21.6 1.9 17.7
Ak-20 45.2 20.3 168 67 14 4.2 2.6 1.1 18.7 69.4 20.9 1.8 13.1
Ak-21 44.1 22 145 65 13 5.2 2.7 1.1 19.2 83.3 20.8 2.2 14.9
Ak-22 39.1 17.5 157 81 13 8 2.9 1.2 18.9 77 20.7 2 20
Ak-23 43.9 19 96 65 10 5.2 1.8 0.7 12.8 59.9 23.9 1.3 12.2
Ak-24 43.9 27.3 149 102 16 15.9 2.6 1.3 22 45.6 22.3 1.2 39.2
Ak-25 43.7 20.7 141 101 16 17.1 2.6 1.4 21.2 43.7 21 1.2 50.2
Ak-26 43.5 22.2 142 102 16 15.4 2.6 1.3 21.2 41.1 21.6 1.1 50.2
Ak-27 43.2 19.8 119 80 14 3.2 2.3 1 17.9 37.4 19.6 1.1 11.3
Ak-28 41.7 21.8 138 81 14 5.6 2.6 1.3 20.1 41 18.5 1.1 17.8
Ak-29 37.3 30.5 118 67 13 2.7 2.3 0.9 16.5 35.7 19.8 1 9.5
Ak-30 48.9 16.9 134 89 14 3 2.5 1.1 17.8 36.7 20.3 1 8.5
Ak-31 37.7 17 115 68 13 3 2.4 1 15.3 33.2 18.1 0.9 9.9
Ak-32 44.6 23.4 128 67 13 2.9 2.5 1 16.2 32.3 19 0.9 9.6
Mean 43.89 21.1 145 76.48 14.29 6.26 2.71 1.07 18.13 69.67 21.53 1.79 18.99
Median 43.9 20.6 142 68 14 4.2 2.6 1.1 18.7 69.4 20.9 1.8 12.2
SD 7.08 3.78 28.72 14.77 1.65 4.84 0.49 0.27 3.32 33.08 2.60 0.78 13.35
UCC 44 17 107 83 13.6 10.7 2.8 1.0 12 190 22 5.8 30

Appendix 1c

S/N Lithostratigraphic Unit Location Ni Co V Cr Sc Th U Ta Nb Zr Y Hf La
U1 IA Mamu

Formation

Western margin

(Marsh

subenvironment)

14.7 3.5 110 108 9 14.8 5 2.1 30.3 173.2 24.4 4.6 50.2
U1 1C 11.3 2.1 90 96 6 11.8 4.3 2 28 157 20.6 4.2 43.9
U1 2A 11.4 2.1 115 100 7 13.1 4.5 2.1 31.8 171.8 22.5 4.4 47.2
U1 2B 8 1.7 102 65 7 11.2 3.8 1.8 26.7 155.8 20.6 3.9 43
U1 2C 10.6 2.2 113 97 9 12.6 4.6 2.1 31.4 176.6 22.7 4.6 47.8
U1 3A 10.3 2.2 110 76 9 12.4 4.6 2.2 33.6 186.1 23.8 5 48.7
U1 3B 16.3 2.9 101 97 10 12.3 4.6 2.5 34.9 193.3 25.8 5.2 49.6
U1 5A 15.1 3.1 159 131 13 14.8 5.1 2.2 30.5 160.7 26.8 4.3 42.8
U1 5B 15.3 2.9 166 128 13 13.4 4.5 2 27.6 149.9 22.6 4.4 35.9
U1 6A 14.3 3.6 166 104 12 13.3 4.4 2.1 29.4 155 20.9 4.7 43.3
U1 7A 50 34.5 148 124 15 16.3 4.5 1.8 24.4 133.1 22 4 47.2
U1 7B 26.2 15 145 66 12 12.6 4.4 1.9 29.1 146 22.5 4 46.1
U1 8A 37.5 23.9 149 85 13 15.1 4.1 1.8 25.5 126.5 19.7 3.6 38.8
U1 8B 31.9 17.9 128 80 13 14.4 4.5 2 28.9 154.9 24 4.1 52.2
U1 8C 31.9 16.5 103 86 10 13.8 4.3 1.8 25.4 142.2 19.6 3.9 42.1
U1 8D 62.2 27.5 172 66 12 11.9 5.4 1.9 28.8 161.5 23.1 4 39.4
U1 9B 37 25.9 157 111 13 15.3 5.5 2.2 32 173.1 22.1 4.4 50.7
U1 9C 31.2 20.4 151 91 13 12.4 5.2 2.4 33.3 178.6 17.3 4.5 40.1
U1 10 22.4 7.4 184 142 12 17.7 4.1 2.3 33.2 182 20.9 4.9 63.5
U1 18 10.6 2 154 128 17 16.7 5.1 2.4 33.2 198.7 20.4 5.8 56.4
U1 19 9.9 1.9 137 93 13 14.6 4.7 2.1 31.4 180.1 19.5 4.7 47.1
AU-1a 15.9 3.5 113 102 13 14.3 4.7 1.8 24.3 138.3 30.6 4 47.2
AU 2 15.6 3.9 127 109 16 13.4 4.1 1.7 25 134 25 3.8 30.1
Mean 22.2 9.9 134.8 99.4 11.6 13.8 4.6 2.1 29.5 162.1 22.5 4.4 45.8
Median 15.6 3.5 137.0 97.0 12.0 13.4 4.5 2.1 29.4 160.7 22.5 4.4 47.1
SD 14.2 10.3 26.9 21.6 2.8 1.7 0.4 0.2 3.2 20.1 2.9 0.5 6.9
IM 2B Mamu

Formation

Western margin

(Central Basin

subenvironment)

42.7 23 159 103 17 18.1 6.8 2.3 30.6 111.8 20.6 3.4 31.5
1M 2C 32.9 13.2 161 105 16 19.9 7.2 2.4 32.8 123.4 30.5 3.8 40.6
1M 2D 32.1 16.1 150 99 16 20.9 7 2.2 30.7 112.6 26.8 3.4 41.8
1M 2E 46.7 22.5 151 109 16 18.6 5.9 2.1 28.4 102.4 25.9 3.2 42
IM 4A 12 123 75 11 19.5 5.4 1.7 24.4 98.2 19.9 2.8 50.4
IM 11A 53.5 30.7 139 95 15 30.9 9.8 1.8 25 151.4 20.5 4.9 61.3
IM 11B 50.3 21 144 114 21 16.1 7 1.6 24.3 78.2 11.4 2.3 23.6
IM 11C 54.7 31.2 140 125 24 30.3 8.5 1.6 23 111.3 54.8 3.6 87.2
IM 13A 58.8 32 133 131 16 23.8 6.4 1.4 20.3 74.2 21.3 2.3 50
1M 13B 47.7 22.5 146 113 21 18.2 6 1.8 26.6 89.4 14.4 3 28.9
IM 14A 61.9 29.9 131 111 20 23.1 8.3 1.5 21.2 100.5 64.8 2.9 72.1
IM 16A 59.1 26.1 104 104 17 13.4 4.5 0.9 13.1 63.9 30.6 1.9 37.5
IM 16B 60.9 25.1 103 99 18 13.2 4.7 1 15.9 80.8 62.2 2.4 34.8
1M 16C 46.1 15.1 118 101 16 13.3 4.4 1.3 18.2 91.9 31.9 2.8 27.8
1M 16D 39.2 19.2 115 97 17 17.9 4.8 1.3 19.5 108 18.1 3.3 40.2
IM 18a 45.9 3.15 68.5 67.5 19 20.2 12.2 0.9 13.5 93.3 57.6 2.7 89
IM 18C 32.9 6.3 94 94 21 15.3 10.8 1.5 22.5 78.5 14.7 2.3 26.3
IM 19A 28.2 7.6 111 107 20 14.2 8.6 1.5 20.2 98.3 18.3 3.1 27.9
IM 19B 43.2 13.4 109 100 13 13 7.5 1.3 19.9 93 11.9 2.8 20
IM 19D 54.5 36.8 102 105 14 14.2 5.6 1.3 20.6 99.9 13.9 2.7 31.7
IM 19E 26.9 7.4 128 109 20 10.1 5.9 1.7 23.7 114 7.9 3.3 13.3
IM 2A 29.2 9.5 131 101 13 14 6 1.8 27.3 92.9 16.3 2.7 37.2
IM 4B 24.9 11.8 99 68 9 18.6 4.6 1.6 23.6 100.6 19.9 2.9 50.6
IM 14C 75.4 27.5 110 99 19 16.2 7.7 1.2 17.6 70.6 36.7 2 46.8
IM 18B 33.3 8.8 91 89 11 9.6 8.5 1.5 22.1 66.4 10.5 2 11.5
IM 19C 62.6 34.8 98 108 14 16.1 7.7 1.4 18.4 91.8 18.8 2.8 36.1
Mean 45.7 19.5 121.5 101.1 16.7 17.6 7.0 1.6 22.4 96.1 26.2 2.9 40.8
Median 46.1 20.1 120.5 102 16.5 17.5 6.9 1.5 22.3 95.7 20.2 2.8 37.4
SD 13.5 9.8 23.5 14.6 3.6 5.2 2.0 0.4 5.0 19.1 16.3 0.6 19.5
OK 7A Mamu

Formation

Western margin

(Bay

subenvironment)

33.8 16.2 95 92 13 26.4 7.2 2.5 32.7 120.2 36.9 3.6 62.6
OK 7B 21.6 7.8 69 118 10 24.5 5.6 2 23.5 115.3 32.1 3.8 60
OK 7C 65.7 36.9 114 102 17 19.2 9 1.5 19.9 46.6 39.8 1.3 40.5
OK 7D 57.1 28.8 93 104 17 20.5 11.6 1.8 24 59 43.3 1.9 39.4
OK 7E 43.9 22.9 101 91 16 19.3 8.7 2.9 37.8 110.1 32.7 3.5 42.9
OK 7F 88.4 29.1 98 72 15 11.9 8.5 2.2 28 98.1 14 2.7 16.9
OK 7G 52.5 12.5 105 70 15 26 7.9 2.6 30.4 184.4 21.4 5.8 60
OK 7H 26.4 6.2 67 97 11 20 8.2 2.6 34.1 135.2 12.8 4.3 36.4
OK 7I 26.9 8.9 83 86 14 17.3 12 2.5 34.2 108.5 10.1 3.2 27.1
OK 7J 29.4 6.2 104 119 17 25.7 13.7 3.2 43.3 169.2 17.2 5.2 45.4
OK 9 47.2 6 88 88 14 19.6 10.8 2.5 31.7 111.5 13.2 3.5 35.4
OK 11A 25.3 5.6 103 105 15 14.8 10.3 2.7 37.8 150.7 13.9 4.6 20.1
OK 11B 22.2 5 110 93 15 14.4 8 2.9 39.2 168.1 14.1 5.1 22.6
OK 13A 25.4 5.2 86 99 16 20.5 10.5 2.8 35.1 112 16.7 3.4 40.5
OK 13B 29.2 6.6 96 94 18 13.3 14 3.1 41.2 120.8 13.2 3.7 25.1
OK 15 27.3 3.1 54 56 12 36.4 8.3 3.4 39.1 255.4 24.1 8.1 73.6
OK 17 42.7 4.6 61 84 24 35.1 11.8 3 36.4 185.3 21.3 5.7 64.7
OK 19A 29.8 6.1 76 96 27 23.8 19.5 3.1 38.2 157.2 18.9 5 43.2
OK 19B 35.3 6.5 88 100 18 19.7 12.1 3.1 43.1 125.9 14.6 3.8 34.3
OK 21A 24.1 3.3 94 72 18 35.3 12.4 3.7 45.6 245.8 27.9 7.8 77.7
OK 21B 22.3 4.2 97 90 16 28 10.8 3.5 44.6 219.1 23.1 6.5 71
OK 24A 33.9 5.3 76 86 16 15.9 31.2 3 40 119.4 20.6 3.6 51.9
Ok 24B 25.9 4.2 73 76 15 25.2 11.5 2.9 34.9 156 20.5 5.1 64
Mean 36.4 10.5 88.3 90.9 16.0 22.3 11.5 2.8 35.4 142.3 21.8 4.4 45.9
Median 29.4 6.2 93 92 16 20.5 10.8 2.9 36.4 125.9 20.5 3.8 42.9
SD 16.5 9.6 16.1 15.0 3.7 6.9 5.2 0.5 6.8 52.0 9.4 1.7 18
Nz-16 Mamu

Formation

Nzam-1 Well 32.3 14.9 88 100 11 14.4 2.7 1.1 16.8 171 20.9 4.5 39.9
Nz-17 36.3 17.5 120 74 14 16.3 3.1 1.3 20.8 144 21.7 3.8 45.5
Nz-18 50.8 20.2 183 89 17 13.7 2.5 1.1 17.4 95.8 21.5 2.5 39.2
Nz-19 38.5 33.5 126 78 16 18.5 5.9 1.7 25.2 187 26.4 5.2 54.6
Nz-20 26 12.1 89 92 11 13.3 2.4 0.7 12.5 119 16.3 3.2 36.1
Nz-21 33.6 17.7 117 95 13 15.6 3.6 1.4 21.2 152 23 4.1 43
Nz-22 26.1 13 89 89 11 15.9 3 1.3 19.3 156 19.1 4.2 40.1
Nz-39 36.3 25.7 121 86 14 11.8 4.2 1.5 20.6 173 29.1 4.4 42.5
Mean 35 19.3 116.6 87.9 13.4 14.9 3.4 1.26 19.2 149.7 22.25 3.99 42.61
Median 35 17.6 118.5 89 13.5 15 3.05 1.3 19.95 154 21.6 4.15 41.3
SD 7.9 7.2 31.3 8.5 2.3 2.08 1.16 0.30 3.75 30.03 4.01 0.83 5.61
ID-3 Mamu

Formation

Idah-1 Well 3.6 4.9 13 19 2 8.2 1.1 0.1 1.8 23.4 5.9 0.3 18.6
ID-4 14 26.4 43 53 6 12.9 3.3 0.5 11.6 215 17 6.4 38
ID-5 14.5 7.8 53 49 7 7.9 2.1 0.4 8.2 70.7 12.7 2 22.1
ID-6 36 22.4 109 71 14 12.2 6.6 1.8 26 204 28.5 5.5 41
ID-7 35.9 25 103 66 13 13.1 7.8 1.9 31.2 258 35.4 6.6 47.5
ID-8 32.7 21.7 98 78 13 12.5 4.9 1.5 22.1 185 27.2 5.1 40.7
ID-9 33.3 17.4 87 67 12 11.7 4.5 1.4 19.9 179 24.6 5 37.1
ID-10 19.8 16.4 62 54 9 10.5 4.6 1 19.5 194 24.7 5.1 34.4
ID-11 27.6 16.1 76 68 11 11.9 5.4 1.4 24.9 220 27.1 6 38.5
ID-12 29.6 24.1 72 83 11 12.3 4.7 1 20.2 172 27.7 4.6 39.5
ID-13 28.8 21.3 90 76 13 14.4 6.1 1.8 25.6 226 30.5 6.3 45.9
ID-14 34 23.8 103 67 15 15.2 6 1.9 26.9 201 29.6 5.7 45.9
ID-15 33.7 21.9 96 77 14 15.5 5.8 1.8 26.2 206 29.6 5.8 47.6
ID-16 33.5 26.8 121 83 16 17.3 7.1 2.2 38 293 37.2 7.6 58.6
ID-17 36.8 35 92 64 15 19.7 7.2 2.3 32.5 237 30.7 7.3 56.8
ID-18 41 23.4 113 67 16 17.6 7.9 0.6 18.2 194 33.4 5.6 53.1
ID-19 33.6 23.8 84 54 14 18.5 6.3 1.9 26.3 188 27.5 5.6 52.3
ID-20 43.3 29.6 98 62 15 16.6 6.5 1.8 24.6 199 27.6 5.8 47.1
ID-21 45.7 21.5 118 84 16 13.3 4.6 1.4 18.7 128 28.2 3.8 42.8
ID-22 41.3 20.5 113 78 17 17.4 4.8 1.4 19.1 138 27.3 4 47.1
ID-23 43 22.4 113 71 15 13 6.3 1.5 22.6 157 39.2 4.4 47.9
Mean 31.51 21.5 88.43 66.24 12.57 13.89 5.41 1.41 22.1 185.2 27.22 5.17 42.97
Median 33.6 22.4 96 67 14 13.1 5.8 1.5 22.6 194 27.7 5.6 45.9
SD 10.7 6.60 27.39 14.90 3.80 3.19 1.74 0.604 8.09 59.39 7.64 1.67 9.86
OW-10 Mamu

Formation

Owan-1 Well 7.3 32.6 37 35 3 13 3 0.5 9.5 196 10.3 6.2 27.2
OW-11 35.9 30.9 50 82 18 24 8.5 < 0.1 1.5 114 40.4 4.5 66.4
OW-12 4.6 11.6 10 36 5 13.7 3 < 0.1 0.4 120 14.6 3.6 27.9
OW-13 4.8 23 19 29 4 9 1.9 < 0.1 1 96.1 9.8 2 21.3
OW-14 21 15.4 91 82 11 18.8 4.2 1.3 21.7 228 24.2 6.8 47.8
OW-15 18.7 41.7 71 82 9 18.6 4.4 0.2 7.8 228 27.3 6.1 49.3
OW-16 12.2 29.4 43 38 5 10.6 3.2 0.1 4.1 144 12.4 4.1 29.1
Mean 14.93 26.4 45.86 54.86 7.86 15.38 4.03 0.53 6.57 160.9 19.86 4.76 38.43
Median 12.2 29.4 43 38 5 13.7 3.2 0.35 4.1 144 14.6 4.5 29.1
SD 11.29 10.4 28.22 25.54 5.30 5.30 2.14 0.54 7.53 55.67 11.34 1.71 16.36
Am-3 Mamu

Formation

Amansiodo-1

Well

4.5 35.9 12 11 1 1.8 0.6 < 0.1 1.2 11.5 4 0.4 6.8
Am-4 5.3 35.7 14 16 1 1.6 0.6 < 0.1 1.5 11.5 4.6 0.3 7.1
Am-5 6.4 51.3 22 21 2 2.3 0.8 < 0.1 1.6 24.6 4.9 0.7 7.9
Am-6 2.5 35.1 10 10 1 1.8 0.8 < 0.1 2 50.5 3.3 0.6 5.8
Am-7 3 45.5 10 11 1 1.6 0.6 < 0.1 1.4 13.3 3.4 0.3 5.6
Am-8 2.4 39.2 10 13 1 1.7 0.7 < 0.1 1.9 13.3 3.4 0.3 5.7
Am-9 2.1 40.6 9 12 < 1 1.3 0.5 < 0.1 1 22.8 2.8 0.5 4.6
Am-10 1.9 40.4 6 10 < 1 1.1 0.5 < 0.1 0.4 11 2.2 0.3 3.6
Am-11 3.5 50.2 21 24 2 2.3 0.9 < 0.1 1.5 10.7 3.8 0.3 7.2
Mean 3.51 41.54 12.67 14.22 1.29 1.72 0.67 1.39 18.8 3.6 0.41 6.03
Median 3 40.4 10 12 1 1.7 0.6 1.5 13.3 3.4 0.3 5.8
SD 1.57 6.13 5.45 5.09 0.49 0.40 0.14 0.48 12.98 0.84 0.15 1.36
Enu 1.1 Mamu

Formation

Eastern margin

(Odoma et al.,

2015)

42 34 119 100 22 21 6 31 296 9
Enu 1.2 22 16 95 88 12 22 6 33 717 21
Enu 1.3 19 14 103 90 10 19 8 34 700 18
Enu 1.4 31 23 101 81 23 19 6 31 395 17
Enu 1.5 35 28 103 86 17 18 7 31 375 8
Enu 2.2 20 6 120 86 16 18 5 34 363 14
Enu2.3 18 5 101 83 10 17 8 38 409 10
Enu2.4 21 9 120 92 14 21 6 34 287 6
Enu2.5 27 17 125 96 22 23 6 43 491 14
mean 26.11 16.89 109.67 89.11 16.22 19.78 6.44 34.33 448.1 13.0
median 22 16 103 88 16 19 6 34 395 14
SD 8.31 9.91 11.12 6.11 5.17 2.05 1.01 3.94 159.5 5.07
Mamu Formation

average

25.45 18.85 99.5 88 13.75 14.35 5.15 1.5 22.45 149 20.5 4.28 41.3
Pre-Santonian

Units

Am-23 Awgu

Group

Amansiodo-1

Well

42.8 29.4 140 81 19 16.6 7.6 1.8 28.6 222 59.4 5.1 60.5
Am-24 46.6 28 154 87 20 15.6 5.4 1.7 27.1 208 37.5 4.8 52.1
Am-25 46.7 28.7 145 83 19 16.5 5.9 1.7 27.4 208 35.4 4.7 53.6
Am-26 45.1 29.5 172 101 20 14.5 4.2 1.5 25 179 32.2 4.1 47.4
Am-27 47.3 25.3 162 113 20 14.1 3.5 1 22 174 27.8 4.2 46.3
Am-28 45.8 23.9 165 102 19 15.7 4.3 0.7 19.9 189 30.8 4.3 51.1
Am-29 45.9 24 151 93 19 16.1 4.1 0.2 14 155 30.7 3.8 52.6
Am-30 46.4 24.5 150 93 18 15.9 4 0.2 17 161 34.7 4.5 48.7
Am-31 46.2 21.9 171 120 19 15.9 3.7 0.8 19 132 28.8 3.4 49.2
Am-32 46.1 23.4 174 121 19 15.6 4.1 1.4 24.5 151 32.3 3.6 49.9
Am-33 45.3 22.3 174 118 19 15.8 4 1.2 23.1 143 29.3 3.6 51.5
Am-34 45.5 21.4 173 120 20 15.9 3.7 0.9 19.7 133 25.3 3.2 48.5
Am-35 42.3 21.7 145 89 17 20.7 2.9 1.6 23.3 82.2 25.7 2.4 60.1
Am-36 42.9 19 119 152 16 15.1 2.4 0.9 14.9 55.6 59.9 1.5 51.6
Am-37 43.8 19.9 142 77 17 17.1 2.8 1.3 19.7 71.7 23.6 2 51.4
Mean 45.25 24.2 155.8 103.3 18.73 16.07 4.17 1.13 21.68 151 34.23 3.68 51.63
Median 45.8 23.9 154 101 19 15.9 4 1.2 22 155 30.8 3.8 51.4
SD 1.56 3.38 16.17 20.43 1.22 1.49 1.31 0.51 4.46 50.20 1.05 4.05
Ak-3 Awgu

Group

Akukwa-II Well 48.1 16.5 168 89 15 6.6 2.3 1 16.1 121 21.6 2.9 14.6
Ak-4 56.6 22.3 193 110 16 4.1 2.9 0.9 15.4 97.3 21.7 2.4 6.5
Ak-5 50.6 20 159 109 17 7.3 2.9 1.3 22 146 28.9 3.5 14.5
Ak-6 53.5 22.7 171 60 16 3.7 2.6 1 16.5 104 24.5 2.5 8.8
Ak-7 47.2 21.8 137 53 15 4 3 1.1 18.6 137 25.5 3.3 12.2
Ak-8 55.6 25.3 182 66 17 3 3.2 1.1 19.3 124 24 3 13
Ak-9 47.9 19.8 160 65 16 5.7 2.8 1.1 17.5 117 24.8 2.9 17.4
Ak-10 20.9 17.8 87 42 8 7.4 1.6 0.6 9.6 66.8 12.4 1.6 23.5
Ak-11 46.6 19.8 157 69 17 3.8 3.4 1.3 21.7 129 24.5 3.2 14.7
Mean 47.44 20.7 157.1 73.67 15.22 5.07 2.74 1.04 17.41 115.8 23.1 2.81 13.91
Median 48.1 20 160 66 16 4.1 2.9 1.1 17.5 121 24.5 2.9 14.5
SD 10.62 2.67 30.72 23.92 2.82 1.7 0.53 0.21 3.74 23.75 4.55 0.58 4.88
Ak-12 Eze-Aku

Group

Akukwa-II Well 53.7 22 165 62 17 2.8 3.3 1.2 20.3 129 25.3 3.1 12.2
Ak-13 46.6 20.6 167 64 16 2.4 3.1 1.1 19.3 117 27.8 2.8 10.8
Ak-14 29.1 20.1 107 81 16 11.7 2.6 0.1 7.8 82.8 17.7 2.2 37.7
Ak-15 43.9 17.1 161 58 15 1.8 2.8 1.2 21.4 132 25.1 3.2 11.1
Ak-16 39.8 17.7 157 63 14 2.7 2.6 1.1 18.4 105 24.2 2.6 10.3
Ak-17 45.1 22.1 179 101 16 9.7 3.5 1.3 21.2 108 23.1 2.8 21.8
Ak-18 40.2 17.6 132 65 14 3.2 2.5 1.1 18.6 79.7 20.8 2.1 10.8
Ak-19 66.5 28.5 226 77 13 5.7 4.2 1 16 73.2 21.6 1.9 17.7
Ak-20 45.2 20.3 168 67 14 4.2 2.6 1.1 18.7 69.4 20.9 1.8 13.1
Ak-21 44.1 22 145 65 13 5.2 2.7 1.1 19.2 83.3 20.8 2.2 14.9
Ak-22 39.1 17.5 157 81 13 8 2.9 1.2 18.9 77 20.7 2 20
Ak-23 43.9 19 96 65 10 5.2 1.8 0.7 12.8 59.9 23.9 1.3 12.2
Ak-24 43.9 27.3 149 102 16 15.9 2.6 1.3 22 45.6 22.3 1.2 39.2
Ak-25 43.7 20.7 141 101 16 17.1 2.6 1.4 21.2 43.7 21 1.2 50.2
Ak-26 43.5 22.2 142 102 16 15.4 2.6 1.3 21.2 41.1 21.6 1.1 50.2
Ak-27 43.2 19.8 119 80 14 3.2 2.3 1 17.9 37.4 19.6 1.1 11.3
Ak-28 41.7 21.8 138 81 14 5.6 2.6 1.3 20.1 41 18.5 1.1 17.8
Ak-29 37.3 30.5 118 67 13 2.7 2.3 0.9 16.5 35.7 19.8 1 9.5
Ak-30 48.9 16.9 134 89 14 3 2.5 1.1 17.8 36.7 20.3 1 8.5
Ak-31 37.7 17 115 68 13 3 2.4 1 15.3 33.2 18.1 0.9 9.9
Ak-32 44.6 23.4 128 67 13 2.9 2.5 1 16.2 32.3 19 0.9 9.6
Mean 43.89 21.1 145 76.48 14.29 6.26 2.71 1.07 18.13 69.67 21.53 1.79 18.99
Median 43.9 20.6 142 68 14 4.2 2.6 1.1 18.7 69.4 20.9 1.8 12.2
SD 7.08 3.78 28.72 14.77 1.65 4.84 0.49 0.27 3.32 33.08 2.60 0.78 13.35
UCC 44 17 107 83 13.6 10.7 2.8 1.0 12 190 22 5.8 30

Appendix 1c

S/N Lithostratigraphic Unit Location Th/Sc Zr/Sc La/Sc Pb/Zn K/Al Mg/K Mg/Ti Pb/Nb Pb/Sn Na/Al Na/K Nb/Ta Nb/W
U1 IA Mamu

Formation

Western margin

(Marsh

subenvironment)

1.64 19.24 5.58 1.99 0.04 0.21 0.07 0.92 7.72 0.002 0.05 14.4 14.4
U1 1C 1.97 26.17 7.32 1.56 0.04 0.21 0.07 0.78 6.84 0.002 0.06 14.0 16.5
U1 2A 1.87 24.54 6.74 2.6 0.04 0.18 0.06 0.74 6.16 0.002 0.05 15.1 15.9
U1 2B 1.60 22.26 6.14 1.47 0.04 0.18 0.05 0.99 8.83 0.002 0.06 14.8 15.7
U1 2C 1.40 19.62 5.31 3.6 0.04 0.2 0.06 1.03 9.26 0.002 0.05 15.0 16.5
U1 3A 1.38 20.68 5.41 1.71 0.04 0.19 0.07 0.76 6.92 0.002 0.05 15.3 15.3
U1 3B 1.23 19.33 4.96 1.91 0.04 0.19 0.08 0.82 6.65 0.002 0.06 14.0 15.9
U1 5A 1.14 12.36 3.29 1.94 0.03 0.2 0.08 0.95 7.1 0.002 0.06 13.9 15.3
U1 5B 1.03 11.53 2.76 2.31 0.03 0.2 0.09 1.0 6.93 0.002 0.06 13.8 13.8
U1 6A 1.11 12.92 3.61 1.97 0.03 0.2 0.08 0.87 6.24 0.001 0.04 14.0 13.4
U1 7A 1.09 8.87 3.15 0.11 0.02 0.32 0.1 0.98 7.44 0.002 0.09 13.6 14.4
U1 7B 1.05 12.17 3.84 0.31 0.03 0.24 0.08 0.89 7.00 0.002 0.05 15.3 15.3
U1 8A 1.16 9.73 2.99 0.17 0.02 0.29 0.09 0.94 6.64 0.001 0.03 14.2 17.0
U1 8B 1.11 11.92 4.02 0.2 0.03 0.32 0.09 0.91 7.49 0.003 0.09 14.5 14.5
U1 8C 1.38 14.22 4.21 0.08 0.03 0.31 0.07 0.79 6.48 0.003 0.09 14.1 14.1
U1 8D 0.99 13.46 3.28 0.18 0.03 0.22 0.06 0.94 7.35 0.003 0.10 15.2 15.2
U1 9B 1.18 13.32 3.90 0.83 0.03 0.18 0.05 0.98 8.05 0.001 0.05 14.5 14.5
U1 9C 0.95 13.74 3.09 0.94 0.03 0.19 0.06 0.93 6.87 0.002 0.08 13.9 13.9
U1 10 1.48 15.17 5.29 3.34 0.01 0.21 0.04 1.11 7.65 0.001 0.07 14.4 13.8
U1 18 0.98 11.69 3.32 3.10 0.02 0.16 0.04 0.93 6.33 0.001 0.06 13.8 13.8
U1 19 1.12 13.85 3.62 2.61 0.02 0.19 0.04 0.83 5.44 0.001 0.04 15.0 16.5
AU-1a 1.1 10.64 3.63 1.33 0.07 0.23 0.2 1.15 7.15 0.002 0.03 13.5 13.5
AU 2 0.84 8.38 1.88 0.97 0.07 0.23 0.25 1.05 6.24 0.002 0.03 14.7 13.9
Mean 1.25 15.03 4.23 1.53 0.03 0.22 0.08 0.93 7.08 0.002 0.06 14.4 14.9
Median 1.14 13.46 3.84 1.56 0.03 0.2 0.07 0.93 6.93 0.002 0.06 14.4 14.5
SD 0.29 4.99 1.37 1.09 0.01 0.05 0.05 0.11 0.86 0.001 0.02 0.6 1.1
IM 2B Mamu

Formation

Western margin

(Central Basin

subenvironment)

1.07 6.58 1.85 0.92 0.07 0.22 0.25 1.29 7.07 0.002 0.02 13.3 12.8
1M 2C 1.24 7.71 2.54 0.74 0.08 0.23 0.24 1.28 7.62 0.002 0.03 13.7 13.1
1M 2D 1.31 7.04 2.61 0.67 0.08 0.24 0.28 1.54 8.58 0.002 0.03 14.0 15.4
1M 2E 1.16 6.40 2.63 0.38 0.07 0.31 0.36 1.29 7.20 0.002 0.03 13.5 12.9
IM 4A 1.77 8.93 4.58 0.74 0.07 0.25 0.25 1.31 7.44 0.003 0.04 14.4 14.4
IM 11A 2.06 10.09 4.09 0.36 0.05 0.37 0.34 1.85 7.33 0.002 0.04 13.9 15.6
IM 11B 0.77 3.72 1.12 0.46 0.06 0.29 0.42 1.26 5.48 0.002 0.03 15.2 13.5
IM 11C 1.26 4.64 3.63 0.32 0.09 0.30 0.51 1.72 7.45 0.002 0.03 14.4 14.4
IM 13A 1.49 4.64 3.13 0.25 0.07 0.41 0.68 1.32 5.70 0.002 0.03 14.5 12.7
1M 13B 0.87 4.26 1.38 0.31 0.07 0.28 0.39 1.10 5.41 0.002 0.03 14.8 12.1
IM 14A 1.16 5.03 3.61 0.20 0.09 0.30 0.50 1.83 9.02 0.003 0.03 14.1 12.5
IM 16A 0.79 3.76 2.21 0.20 0.10 0.35 1.08 1.98 6.82 0.002 0.02 14.6 10.1
IM 16B 0.73 4.49 1.93 0.25 0.10 0.30 0.89 1.95 7.75 0.002 0.02 15.9 9.9
1M 16C 0.83 5.74 1.74 0.49 0.10 0.24 0.65 1.85 7.17 0.002 0.02 14.0 12.1
1M 16D 1.05 6.35 2.37 0.62 0.10 0.22 0.53 1.20 5.32 0.002 0.02 15.0 11.5
IM 18a 1.06 4.91 4.68 0.61 0.09 0.12 0.30 2.11 10.36 0.003 0.03 15.0 15.0
IM 18C 0.73 3.74 1.25 1.38 0.05 0.22 0.27 2.09 7.97 0.001 0.03 15.0 10.7
IM 19A 0.71 4.92 1.40 1.13 0.06 0.22 0.39 2.07 7.76 0.001 0.02 13.5 10.6
IM 19B 1.00 7.15 1.54 1.06 0.07 0.22 0.41 1.71 6.42 0.001 0.02 15.3 11.7
IM 19D 1.01 7.14 2.26 1.40 0.06 0.21 0.32 2.25 9.08 0.002 0.03 15.8 9.4
IM 19E 0.51 5.70 0.67 1.27 0.06 0.21 0.31 1.61 6.57 0.002 0.03 13.9 10.3
IM 2A 1.08 7.15 2.86 1.12 0.06 0.22 0.22 1.23 7.33 0.002 0.03 15.2 14.4
IM 4B 2.07 11.18 5.62 0.92 0.06 0.23 0.17 1.25 8.43 0.002 0.03 14.8 18.2
IM 14C 0.85 3.72 2.46 0.30 0.09 0.33 0.74 2.14 8.38 0.003 0.03 14.7 11.7
IM 18B 0.87 6.04 1.05 1.14 0.04 0.21 0.23 1.86 6.97 0.001 0.02 14.7 13.0
IM 19C 1.15 6.56 2.58 1.07 0.06 0.21 0.35 1.69 6.22 0.002 0.03 13.1 10.8
Mean 1.10 6.06 2.53 0.71 0.07 0.26 0.43 1.65 7.34 0.002 0.03 14.5 12.6
Median 1.06 5.89 2.41 0.64 0.07 0.23 0.36 1.70 7.33 0.002 0.03 14.5 12.6
SD 0.39 1.94 1.24 0.40 0.02 0.06 0.22 0.36 1.21 0.001 0.01 0.7 2.1
OK 7A Mamu

Formation

Western margin

(Bay

subenvironment)

2.03 9.25 4.82 0.58 0.04 0.26 0.14 1.43 8.18 0.001 0.03 13.1 14.2
OK 7B 2.45 11.53 6.0 0.14 0.03 0.26 0.10 1.01 4.84 0.002 0.06 11.8 16.8
OK 7C 1.13 2.74 2.38 0.74 0.03 0.25 0.23 3.57 12.91 0.002 0.07 13.3 11.7
OK 7D 1.21 3.47 2.32 0.45 0.03 0.23 0.16 2.20 8.50 0.001 0.05 13.3 14.1
OK 7E 1.21 6.88 2.68 0.34 0.03 0.20 0.08 1.29 7.49 0.001 0.06 13.0 13.5
OK 7F 0.79 6.54 1.13 0.23 0.02 0.20 0.12 1.24 6.29 0.001 0.05 12.7 13.3
OK 7G 1.73 12.29 4.0 1.21 0.04 0.14 0.09 1.15 5.39 0.003 0.06 11.7 15.2
OK 7H 1.82 12.29 3.31 0.98 0.03 0.18 0.08 0.86 4.80 0.001 0.04 13.1 17.1
OK 7I 1.24 7.75 1.94 1.27 0.03 0.19 0.08 1.37 6.70 0.001 0.06 13.7 16.3
OK 7J 1.51 9.95 2.67 1.54 0.03 0.16 0.07 1.10 6.36 0.002 0.05 13.5 16.7
OK 9 1.40 7.96 2.53 1.25 0.03 0.19 0.10 1.30 7.34 0.001 0.04 12.7 15.9
OK 11A 0.99 10.05 1.34 1.17 0.04 0.18 0.12 0.96 5.20 0.001 0.04 14.0 14.0
OK 11B 0.96 11.21 1.51 1.53 0.06 0.18 0.15 1.09 5.88 0.001 0.03 13.5 13.5
OK 13A 1.28 7.0 2.53 1.28 0.03 0.16 0.08 1.09 5.33 0.002 0.05 12.5 16.0
OK 13B 0.74 6.71 1.39 1.32 0.03 0.16 0.08 1.19 6.36 0.001 0.05 13.3 16.5
OK 15 3.03 21.28 6.13 2.04 0.06 0.11 0.06 0.94 5.04 0.004 0.06 11.5 17.0
OK 17 1.46 7.72 2.70 1.52 0.04 0.10 0.05 0.96 5.45 0.002 0.05 12.1 17.3
OK 19A 0.88 5.82 1.60 1.22 0.03 0.15 0.07 0.99 5.24 0.002 0.06 12.3 17.4
OK 19B 1.09 6.99 1.91 1.28 0.03 0.17 0.08 1.10 6.25 0.001 0.05 13.9 16.0
OK 21A 1.96 13.66 4.32 1.81 0.06 0.15 0.08 0.91 5.20 0.002 0.04 12.3 15.7
OK 21B 1.75 13.69 4.44 1.77 0.05 0.17 0.09 0.91 5.15 0.002 0.04 12.7 16.5
OK 24A 0.99 7.46 3.24 1.42 0.03 0.14 0.06 1.06 5.67 0.002 0.05 13.3 17.4
Ok 24B 1.68 10.4 4.27 2.28 0.04 0.12 0.06 1.24 7.00 0.002 0.06 12.0 18.4
Mean 1.45 9.25 3.01 1.19 0.04 0.18 0.10 1.26 6.37 0.002 0.05 12.8 15.7
Median 1.26 7.86 2.60 1.26 0.03 0.17 0.08 1.10 5.77 0.002 0.05 13.1 16.0
SD 0.56 3.93 1.44 0.57 0.01 0.04 0.04 0.57 1.77 0.001 0.01 0.7 1.7
Nz-16 Mamu

Formation

Nzam-1 Well 1.31 15.55 3.63 0.29 0.18 0.48 1.13 1.54 12.33 0.066 0.38 15.27 2.24
Nz-17 1.16 10.29 3.25 0.20 0.13 0.50 0.89 1.20 8.62 0.067 0.53 16.0 1.24
Nz-18 0.81 5.64 2.31 0.18 0.76 1.49 1.56 8.0 0.58 15.82 4.70
Nz-19 1.16 11.69 3.41 0.21 0.20 0.19 0.48 0.98 7.29 0.040 0.20 14.82 0.31
Nz-20 1.21 10.82 3.28 0.26 0.11 0.58 1.04 1.36 8.50 0.070 0.61 17.86 6.25
Nz-21 1.20 11.69 3.31 0.25 0.15 0.39 0.75 1.10 8.32 0.066 0.44 15.14 10.60
Nz-22 1.45 14.18 3.65 0.37 0.15 0.28 0.53 1.23 9.52 0.071 0.49 14.85 5.68
Nz-39 0.84 12.36 3.04 0.22 0.15 0.58 0.80 1.02 6.74 0.060 0.41 13.73 0.56
Mean 1.14 11.53 3.23 0.25 0.15 0.47 0.89 1.25 8.67 0.063 0.46 15.44 3.95
Median 1.18 11.69 3.30 0.23 0.15 0.49 0.85 1.22 8.41 0.066 0.46 15.21 3.47
SD 0.22 2.95 0.43 0.06 0.03 0.18 0.33 0.22 1.71 0.011 0.13 1.20 3.55
ID-3 Mamu

Formation

Idah-1 Well 4.10 11.70 9.30 14.00 0.13 0.50 0.04 70.0 180 0.093 0.70 18.0 0.23
ID-4 2.15 35.83 6.33 0.71 0.33 0.04 0.11 1.90 14.67 0.015 0.05 23.20 0.11
ID-5 1.13 10.10 3.16 3.44 0.17 0.61 1.14 21.83 68.85 0.034 0.21 20.50 2.56
ID-6 0.87 14.57 2.93 0.86 0.15 0.64 0.99 2.90 22.85 0.056 0.37 14.44 8.13
ID-7 1.01 19.85 3.65 1.61 0.20 0.53 0.65 4.74 38.95 0.070 0.35 16.42 6.37
ID-8 0.96 14.23 3.13 12.31 0.16 0.71 1.19 103.6 119.9 0.057 0.36 14.73 0.95
ID-9 0.98 14.92 3.09 9.96 0.15 0.68 1.12 120.6 133.3 0.063 0.43 14.21 0.39
ID-10 1.17 21.56 3.82 2.14 0.16 1.36 1.76 16.67 69.15 0.064 0.39 19.50 0.78
ID-11 1.08 20.00 3.50 2.34 0.14 0.93 0.92 8.92 60 0.070 0.48 17.79 5.19
ID-12 1.12 15.64 3.59 3.26 0.15 0.89 1.29 22.28 73.77 0.045 0.30 20.20 0.35
ID-13 1.11 17.39 3.53 0.67 0.14 0.51 0.71 4.92 33.16 0.041 0.29 14.22 1.54
ID-14 1.01 13.40 3.06 8.56 0.13 0.55 0.78 46.47 110.6 0.041 0.32 14.16 2.05
ID-15 1.11 14.71 3.40 1.79 0.13 0.61 0.86 9.50 49.8 0.040 0.32 14.56 8.45
ID-16 1.08 18.31 3.66 0.28 0.12 0.43 0.39 0.83 6.85 0.038 0.33 17.27 2.22
ID-17 1.31 15.80 3.79 1.09 0.10 0.43 0.49 5.02 26.29 0.021 0.23 14.13 0.43
ID-18 1.10 12.13 3.32 4.90 0.11 0.51 0.74 36.59 80.24 0.026 0.24 30.33 7.58
ID-19 1.32 13.43 3.74 1.56 0.09 0.61 0.80 12.70 50.61 0.024 0.26 13.84 9.07
ID-20 1.11 13.27 3.14 1.39 0.11 0.64 0.99 9.02 46.25 0.034 0.31 13.67 10.70
ID-21 0.83 8.00 2.68 0.70 0.12 0.82 1.87 7.81 33.18 0.042 0.35 13.36 8.50
ID-22 1.02 8.12 2.77 0.64 0.13 0.78 1.92 5.08 26.97 0.041 0.32 13.64 9.10
ID-23 0.87 10.47 3.19 0.48 0.13 0.82 1.56 4.32 23.83 0.046 0.36 15.07 4.11
Mean 1.26 15.40 3.75 3.46 0.14 0.65 0.97 24.56 60.44 0.05 0.33 16.82 4.23
Median 1.10 14.57 3.40 1.61 0.13 0.61 0.92 9.02 49.80 0.04 0.32 14.73 2.56
SD 0.70 5.95 1.47 4.12 0.05 0.25 0.52 33.81 44.01 0.02 0.12 4.15 3.71
OW-10 Mamu

Formation

Owan-1 Well 4.33 65.33 9.07 0.38 0.02 0.67 0.04 1.19 10.27 0.006 0.33 19
OW-11 1.33 6.33 3.69 0.30 0.03 0.19 0.08 20.13 8.63 0.002 0.06 0.52
OW-12 2.74 24.0 5.58 0.54 0.02 0.29 0.16 35.0 28.0 0.003 0.14 0.09
OW-13 2.25 24.03 5.33 0.66 0.02 0.29 0.07 11.80 23.60 0.003 0.13 0.06
OW-14 1.71 20.73 4.35 0.52 0.03 0.42 0.11 1.57 13.64 0.002 0.06 16.69 0.52
OW-15 2.07 25.33 5.48 0.60 0.03 0.24 0.05 3.08 9.60 0.002 0.08 39.0 0.05
OW-16 2.12 28.80 5.82 0.22 0.04 0.20 0.05 2.71 10.09 0.003 0.08 41.0 0.02
Mean 2.37 27.79 5.62 0.46 0.03 0.33 0.08 10.78 14.83 0.003 0.13 28.92 0.21
Median 2.12 24.03 5.48 0.52 0.03 0.29 0.07 3.08 10.27 0.003 0.08 29.00 0.08
SD 0.97 18.05 1.70 0.16 0.01 0.17 0.04 12.76 7.76 0.002 0.10 12.85 0.24
Am-3 Mamu

Formation

Amansiodo-1

Well

1.80 11.50 6.80 0.47 0.06 1.75 1.23 23.75 21.92 0.037 0.60
Am-4 1.60 11.50 7.10 0.36 0.04 3.0 1.43 18.47 18.47 0.031 0.733
Am-5 1.15 12.30 3.95 0.38 0.06 2.40 1.48 26.38 35.17 0.057 1.0
Am-6 1.80 50.50 5.80 0.51 0.06 1.0 0.48 9.50 38.0 0.028 0.50 0.01
Am-7 1.60 13.30 5.60 0.72 0.07 0.50 0.31 30.14 60.29 0.023 0.35
Am-8 1.70 13.30 5.70 0.66 0.06 0.50 0.29 19.16 91.0 0.021 0.35 0.01
Am-9 0.71 0.06 0.67 0.42 24.10 60.25 0.020 0.333
Am-10 1.01 0.08 1.0 0.71 78.0 78.0 0.027 0.35 0.002
Am-11 1.15 5.35 3.60 1.49 0.05 1.33 0.58 57.67 144.20 0.018 0.37 0.008
Mean 1.54 16.82 5.51 0.70 0.06 1.35 0.77 31.91 60.81 0.029 0.51 0.007
Median 1.60 12.30 5.70 0.66 0.06 1.00 0.58 24.10 60.25 0.027 0.37 0.009
SD 0.24 13.16 1.14 0.32 0.01 0.79 0.43 19.62 35.55 0.011 0.21 0.003
Enu 1.1 Mamu

Formation

Eastern margin

(Odoma et al.,

2015)

0.95 3.27 0.29 0.09 0.38 0.42 1.13 0.02 0.16
Enu 1.2 1.83 6.50 0.57 0.14 0.25 0.29 0.91 0.03 0.22
Enu 1.3 1.90 8.20 0.55 0.14 0.25 0.30 0.85 0.03 0.22
Enu 1.4 0.83 2.91 0.46 0.12 0.21 0.24 1.03 0.03 0.22
Enu 1.5 1.06 3.65 0.37 0.11 0.27 0.32 0.94 0.02 0.18
Enu 2.2 1.13 3.38 0.49 0.08 0.30 0.30 0.74 0.01 0.15
Enu2.3 1.70 7.60 0.62 0.08 0.28 0.24 0.63 0.01 0.16
Enu2.4 1.50 5.36 0.45 0.13 0.25 0.40 0.68 0.01 0.11
Enu2.5 1.05 3.60 0.18 0.09 0.28 0.22 0.63 0.02 0.18
mean 1.33 4.94 0.44 0.11 0.28 0.30 0.84 0.02 0.18
median 1.13 3.65 0.46 0.11 0.27 0.30 0.85 0.02 0.18
SD 0.41 2.03 0.14 0.02 0.05 0.07 0.18 0.01 0.04
Mamu Formation

average

1.04 10.84 3.00 0.18 0.15 0.63 1.36 1.32 6.83 0.11 0.74 14.97
Pre-Santonian

Units

Am-23 Awgu

Group

Amansiodo-1

Well

0.87 11.68 3.18 0.23 0.12 0.66 0.74 0.86 6.45 0.044 0.38 15.89 4.85
Am-24 0.78 10.40 2.61 0.22 0.16 0.42 0.84 1.03 6.95 0.044 0.27 15.94 3.39
Am-25 0.87 10.95 2.82 0.25 0.14 0.46 0.75 0.99 6.80 0.045 0.33 16.12 2.82
Am-26 0.73 8.95 2.37 0.22 0.20 0.46 1.19 1.02 6.92 0.054 0.27 16.67 2.02
Am-27 0.71 8.70 2.32 0.23 0.16 0.60 1.51 1.31 7.18 0.054 0.34 22.0 4.15
Am-28 0.83 9.95 2.69 0.23 0.13 0.63 1.13 1.29 6.74 0.051 0.39 28.43 4.74
Am-29 0.85 8.16 2.77 0.25 0.12 0.68 1.32 1.94 6.95 0.051 0.43 70.0 3.68
Am-30 0.88 8.94 2.71 0.16 0.11 0.78 1.30 1.81 7.00 0.057 0.51 85.0 2.79
Am-31 0.84 6.95 2.59 0.19 0.11 0.73 1.37 1.34 6.35 0.055 0.49 23.75 7.92
Am-32 0.82 7.95 2.63 0.24 0.11 0.70 1.18 1.01 6.20 0.056 0.50 17.50 4.45
Am-33 0.83 7.53 2.71 0.25 0.14 0.57 1.21 1.12 6.48 0.050 0.37 19.25 4.71
Am-34 0.80 6.65 2.43 0.30 0.13 0.62 1.37 1.35 6.82 0.057 0.42 21.89 7.58
Am-35 1.22 4.84 3.54 0.20 0.30 0.29 1.67 1.40 7.41 0.045 0.15 14.56 1.94
Am-36 0.94 3.48 3.23 0.27 0.12 1.35 2.79 1.57 6.50 0.077 0.64 16.56 1.60
Am-37 1.01 4.22 3.02 0.27 0.15 0.73 1.87 1.19 5.57 0.070 0.46 15.15 1.64
Mean 0.86 7.96 2.77 0.23 0.15 0.65 1.35 1.28 6.69 0.054 0.4 26.58 3.89
Median 0.84 8.16 2.71 0.23 0.13 0.63 1.30 1.29 6.80 0.054 0.39 17.50 3.68
SD 0.12 2.42 0.34 0.03 0.05 0.24 0.51 0.31 0.45 0.009 0.12 21.21 1.94
Ak-3 Awgu

Group

Akukwa-II Well 0.44 8.07 0.97 0.15 0.11 1.04 2.32 1.39 7.19 0.073 0.65 16.10 2.73
Ak-4 0.26 6.08 0.41 0.19 0.12 0.59 1.34 1.62 7.55 0.067 0.59 17.11 2.26
Ak-5 0.43 8.59 0.85 0.17 0.09 0.70 1.07 1.27 7.37 0.062 0.67 16.92 3.86
Ak-6 0.23 6.50 0.55 0.17 0.10 0.83 1.50 1.31 6.55 0.066 0.65 16.50 1.41
Ak-7 0.27 9.13 0.81 0.21 0.09 1.18 1.58 1.37 7.97 0.073 0.82 16.91 1.79
Ak-8 0.18 7.29 0.77 0.16 0.11 0.80 1.45 1.48 7.94 0.070 0.63 17.55 2.10
Ak-9 0.36 7.31 1.09 0.17 0.11 0.90 1.69 1.53 8.12 0.062 0.55 15.91 1.80
Ak-10 0.93 8.35 2.94 0.32 0.13 0.51 1.57 2.75 15.53 0.203 1.56 16.00 0.14
Ak-11 0.22 7.59 0.87 0.19 0.13 0.55 0.99 1.12 6.78 0.063 0.48 16.69 2.47
Mean 0.37 7.66 1.03 0.19 0.11 0.79 1.50 1.54 8.33 0.082 0.73 16.63 2.06
Median 0.27 7.59 0.85 0.17 0.11 0.80 1.50 1.39 7.55 0.067 0.65 16.69 2.10
SD 0.23 0.99 0.75 0.05 0.02 0.23 0.39 0.48 2.75 0.046 0.32 0.55 1.01
Ak-12 Eze-Aku

Group

Akukwa-II Well 0.17 7.59 0.72 0.11 0.10 0.69 1.10 1.19 6.72 0.061 0.59 16.92 2.67
Ak-13 0.15 7.31 0.68 0.14 0.11 0.74 1.31 0.98 5.73 0.065 0.59 17.55 1.26
Ak-14 0.73 5.18 2.36 0.44 0.17 1.98 5.87 3.13 10.61 0.056 0.34 78.00 3.12
Ak-15 0.12 8.80 0.74 0.17 0.17 0.49 1.24 1.09 6.47 0.064 0.37 17.83 1.39
Ak-16 0.19 7.50 0.74 0.15 0.13 0.73 1.40 1.21 6.73 0.079 0.59 16.73 1.35
Ak-17 0.61 6.75 1.36 0.22 0.10 0.82 1.27 1.38 7.49 0.071 0.7 16.31 2.26
Ak-18 0.23 5.69 0.77 0.11 0.14 0.63 1.37 1.15 5.92 0.106 0.77 16.91 1.39
Ak-19 0.44 5.63 1.36 0.13 0.16 0.69 1.64 1.23 6.32 0.117 0.75 16.00 1.45
Ak-20 0.30 4.96 0.94 0.12 0.16 0.65 1.46 0.90 4.67 0.113 0.73 17.00 1.33
Ak-21 0.40 6.41 1.15 0.16 0.16 0.62 1.34 1.28 6.83 0.114 0.72 17.45 1.39
Ak-22 0.62 5.92 1.54 0.20 0.16 0.50 1.10 1.27 7.06 0.155 1.0 15.75 1.13
Ak-23 0.52 5.99 1.22 0.30 0.15 0.76 2.03 8.83 28.25 0.101 0.67 18.29 0.60
Ak-24 0.99 2.85 2.45 0.29 0.19 0.56 1.52 1.75 8.75 0.136 0.70 16.92 1.22
Ak-25 1.07 2.73 3.14 0.31 0.18 0.59 1.45 2.11 9.74 0.146 0.81 15.14 1.37
Ak-26 0.96 2.57 3.14 0.32 0.20 0.59 1.47 1.68 7.91 0.152 0.78 16.31 1.15
Ak-27 0.23 2.67 0.81 0.39 0.18 0.61 1.41 2.18 10.03 0.142 0.81 17.90 1.36
Ak-28 0.40 2.93 1.27 0.35 0.20 0.53 1.30 1.53 7.70 0.165 0.81 15.46 0.91
Ak-29 0.21 2.75 0.73 0.30 0.16 0.63 1.55 1.66 7.83 0.132 0.81 18.33 0.68
Ak-30 0.21 2.62 0.61 0.22 0.19 0.55 1.36 1.35 5.71 0.131 0.7 16.18 2.20
Ak-31 0.23 2.55 0.76 0.33 0.17 0.37 1.15 2.12 9.26 0.156 0.91 15.30 0.42
Ak-32 0.22 2.49 0.74 0.25 0.18 0.53 1.47 2.03 9.37 0.147 0.82 16.20 0.61
Mean 0.43 4.85 1.30 0.24 0.16 0.68 1.61 1.91 8.53 0.115 0.71 19.64 1.39
Median 0.30 5.18 0.94 0.22 0.16 0.62 1.40 1.38 7.49 0.117 0.73 16.91 1.35
SD 0.30 2.10 0.80 0.10 0.03 0.32 1.00 1.67 4.79 0.036 0.16 13.40 0.68