Monthly Archives: February 2024

Three AI-synthesized Mind-sets of Patients Talking to Their Surgeon about a Prospective Operation for Different Types of Diagnosed Cancer

DOI: 10.31038/CST.2024912

Abstract

The study shows what can be done when AI is built into a platform that is easy to use, with the topic being how the surgeon should communicate with a person who is about to have an operation for cancer. The approach posits three mind-sets for each type of cancer patient. Embedded AI in the program (SCAS; Socrates as a Service) is then instructed to answer a set of seven standard questions for each of the three AI-suggested mind-sets for that specific cancer. The study shows the power of AI to simulate the interaction between surgeon and patient, with the approach showing promise as an easy-to-customize method for teaching how to become sensitive to the emotional needs of people.

Introduction

Communicating with patients is a crucial aspect of a surgeon’s role, helping build trust and alleviate any fears or concerns the patient may have. Patients want to know about the risks and benefits of the surgery, as well as alternative treatment options available. It is important for the surgeon to explain the procedure in a way that the patient can understand, using non-medical jargon and providing visual aids if necessary. Patients also want to know about the recovery process, including how long it will take and what restrictions they may have post-op. Additionally, patients appreciate surgeons who are upfront about the potential complications of the surgery, as this shows transparency and honesty. It is important for the surgeon to listen to the patient’s concerns and address them in a compassionate and empathetic manner. Patients want to feel like their surgeon cares about their well-being and is invested in their successful outcome. Providing emotional support during the pre-op and post-op period can go a long way in helping the patient feel more at ease. Surgeons should also take the time to explain the anesthesia process to patients, as this can be a source of anxiety for many. Patients want to know what to expect during the surgery and how they will be monitored throughout the procedure. It is important for the surgeon to reassure the patient that they will be well cared for and that their safety is the top priority. Empathy and active listening are key components of effective communication with patients.. Furthermore, patients appreciate it when surgeons involve them in the decision-making process and take their preferences into account. This can help empower the patient and make them feel more in control of their healthcare. Surgeons should encourage patients to ask questions and express any concerns they may have, creating an open and transparent dialogue. A well-informed patient is more likely to have a positive outcome and adhere to post-operative instructions. Thus, effective communication between surgeons and patients is essential to build trust, alleviate fears, and ensure a successful surgical outcome. By addressing the patient’s concerns, providing clear and concise information, and showing empathy and compassion, surgeons can establish a strong rapport with their patients. To summarize, a well-informed patient is a confident patient, and a confident patient is more likely to have a positive surgical experience [1-4].

The Contribution of Mind Genomics to Understanding People, and In Turn Communicating with Patients

The field of mind genomics tries to explain how and why people act and make choices. It uses psychology, neuroscience, and marketing research to find out what people’s unconscious thoughts are that make them make the choices and preferences they do. Mind Genomics can separate people into groups based on the way they think by using advanced data analysis methods [5-7]. One of the most important things that Mind Genomics has shown is that people have different mental models. Within these mindsets are specific ways of thinking that affect how people see the world, decide what to do, and form opinions. Researchers can make products, services, and interventions that better meet the needs and preferences of different groups of people by finding and understanding these modes of thought. It’s possible that this personalized approach will make customers happier, help patients do better, and help the business grow [8,9]. The implications of discovering mind-sets extend far beyond the realm of marketing and consumer behavior. Within the medical field, knowing how patients think and feel can help create more effective treatment plans that make patients happier and improve their health. By making healthcare interventions fit the way patients think, providers can get patients to follow through more often, get them more involved, and ultimately improve the quality of care overall. If this personalized approach works, it could completely change how healthcare is provided and experienced [10-12]. Mind Genomics can help doctors and nurses better understand their patients’ unique behaviors and thoughts in clinics and hospitals. Finding the thought patterns that cause patients to act out can help doctors come up with better ways to talk to them, treat them, and help them in other ways. This individualized approach can help patients and providers trust each other more, work together better, and have a better relationship. Healthcare professionals can make a more supportive and empowering care environment for patients by recognizing and respecting their unique mental states [13-15]. In order to help patients in the clinic and hospital, healthcare professionals must first understand that people have different mental states. Clinical professionals can better meet the needs and preferences of each patient by recognizing and understanding the unique ways that each person thinks and acts. This personalized approach can help doctors and nurses get to know their patients, earn their trust, and provide better care overall. Health care professionals can make care more patient-centered and interesting by using Mind Genomics principles [16-19]. Mind Genomics’ discovery of mindsets could change the way healthcare is provided and experienced in a big way. Healthcare providers can improve outcomes, make patients happier, and lead to better health results by understanding and adapting interventions to each patient’s unique cognitive patterns. This personalized approach has the potential to change the relationship between the patient and the provider, get the patient more involved, and ultimately improve the quality of care as a whole. Healthcare professionals can make the care environment more caring, supportive, and effective by recognizing and embracing the different ways people think. To sum up, Mind Genomics is a revolutionary way to study how people act and make choices [20]. Researchers can make interventions, products, and services better fit the needs and wants of different groups of people by figuring out the different ways people think. The discovery of mindsets has huge implications for medicine, because tailoring care to each patient’s unique ways of thinking can lead to better outcomes, higher patient engagement, and higher patient satisfaction. Healthcare professionals can make care more compassionate and patient-centered by using Mind Genomics principles in their work. This can lead to more collaboration, trust, and better health outcomes..

How can AI Help Mind Genomics Discover Mind-Sets

Mind Genomics and AI can work together to learn about mindsets by combining them through data analysis and pattern recognition. AI can find patterns and themes that are unique to each mind-set by gathering and analyzing data from people with those mindsets. Because of this method, the AI can give each mindset a name or label based on the most important traits found in the data. This process of giving people names helps to group and tell the difference between different ways of thinking, which makes it possible to use more targeted and personalized communication methods. AI can not only name the mindset, but also guess what kinds of people are in it by looking at things like age, gender, and socioeconomic status. AI can find trends and correlations in the data collected from people with similar mental states. These can help us understand the demographic profile of each mental state. This information can be used to make sure that communication and intervention strategies work better with people who are in a certain demographic group and have a certain mindset. When trying to guess what questions a cancer patient might have before surgery, AI can use what it knows about different mindsets to guess the most common worries and doubts that people with that mindset might have. AI can find common themes and topics that are usually talked about before surgery by looking at data from past interactions between surgeons and cancer patients. This ability to predict the future lets AI know ahead of time what questions and concerns are most likely to come up during pre-operation consultations and prepares to answer them. If everything goes well with the surgery, AI can use what it knows about how people think to send the patient personalized and caring messages. AI can figure out the best ways to share good news with people of different personalities by looking at data on successful outcomes and patient feedback. This personalized way of talking to the patient helps to build trust and a relationship with them, which makes the recovery process more positive and helpful. If, on the other hand, things don’t go as planned and problems arise, AI can use its knowledge of how people think to send the patient messages that are both kind and helpful. AI can figure out the best and most understanding ways to share difficult information with people of all mentalities by looking at data on failed outcomes and patient experiences. This personalized way of talking to the patient helps to manage their expectations and support them during a tough and uncertain time.

Putting AI to the Test

The original Mind Genomics approach was to require the user to state the problem and provide four questions which tell a story. To each question the user would be requested to provide four answers, the answers called ‘elements,’ and being stand-alone phrases which conveyed a single idea. While seeming straightforward to an experience user, the ‘task’ of providing questions, and then answers to the questions soon emerged as a roadblock. The consequence was that many prospective users ‘froze; at the prospect of asking and answering questions, with the statement that they were not sufficiently conversant with the topic. The sad outcome was that many prospective users aborted their efforts as soon as they were requested to participate. The solution to the problems emerged from the introduction of Chat GPT by Open AI, Inc. The user simply had to ask the AI about the topic, and the AI would return a paragraph or two, from which the user would create the four questions. Later on, the system to create questions was codified into SCAS (Socrates as a Service). The strategy was to create 15 questions for each input ‘squib’ or background statement about the problem. The user was able to select questions, edit them if desired, provide their own questions if desired, and then ‘drop the questions’ into the study. Once the four questions were selected after one or several iterations, the user would then move to the next section, where the ‘squib’ was the question previously selected. The process would return 15 answers to each question. The entire process allowed for iteration after iteration, each iteration taking 10-15 seconds. The ‘hard part’ evolved to editing and polishing the squib to introduce the topic, the questions that were selected so that they would generate the correct answers, and finally the answers so that they would be meaningful as well as simple. What took days and weeks now took less than an hour and required relatively little familiarity with the topic. The fortunate ‘byway’ leading to this project on communication between surgeon and patient before the cancer operation occurred when the ‘squib’ to introduce the project was expanded, so that the squib contained within it statements to the effect that there were a certain number of (not-yet-named) mind-sets, and the answers to certain questions had to be created by AI once the mind-sets were established, also by AI. Table 1 shows the ‘squib’ or orientation to AI. The same squib was used for each of the nine cancers ‘studied’ using AI to provide the answers. Each table will present the three mind-sets for the particular cancer. It is important to keep in mind that the AI does not keep the information generated. Rather, each iteration is separate. The answers provided by AI attempt to conform to the format prescribed in Tables 1-9.

Table 1: Most of the time the answers do fall into the format. Occasionally, the introductory words might change, but the answer itself is appropriate to the question.

tab 1

Table 2: AI exploration of three mind-sets for liver cancer

tab 2

Table 3: AI exploration of three mind-sets for Myeloma

tab 3

Table 4: AI exploration of three mind-sets for stomach cancer

tab 4(1)

tab 4(2)

Table 5: AI exploration of three mind-sets for breast cancer

tab 5

Table 6: AI exploration of three mind-sets for colon cancer

tab 6

Table 7: AI exploration of three mind-sets for pancreatic cancer

tab 7(1)

tab 7(2)

Table 8: AI exploration of three mind-sets for stomach cancer

tab 8

Table 9: AI exploration of three mind-sets for breast cancer

tab 9

Dealing with Different Results from AI-Implications, Problems, Hidden Benefits

When artificial intelligence generates various synthesized mindsets for a patient with lung cancer, it can be both a problem and a learning opportunity. The different mindsets could be the result of AI processing and interpreting information in different ways, leading to contradictory conclusions. This discrepancy can be confusing for healthcare providers, making it difficult to determine the best course of action for the patient. However, the presence of various mindsets can be viewed as a positive aspect of using AI in healthcare education. It enables a more thorough examination of various perspectives and approaches to patient care, potentially improving nurses’ and doctors’ knowledge and skills. By examining and considering various synthesized mindsets, healthcare providers can gain a better understanding of the complexities involved in treating lung cancer patients. When using AI to teach nurses and doctors, encountering different mindsets for a patient with lung cancer emphasizes the value of critical thinking and evidence-based practice. It emphasizes the importance of healthcare providers critically evaluating AI-generated information and considering multiple perspectives when making clinical decisions. It also emphasizes the importance of continuing education and training to stay current on the latest advancements in healthcare technology and AI algorithms. Overall, the presence of various synthesized mindsets in AI-generated recommendations for patient care serves as a reminder that healthcare is a dynamic and ever-changing industry. It requires healthcare providers to think critically, be adaptable, and constantly seek new knowledge and insights in order to (Table 10).

Table 10: AI exploration of two iterations to create three mind-sets for lung cancer

tab 10(1)

tab 10(2)

tab 10(3)

Questions Posed by AI in the Output Stated as Facts, and Elaboration by AIs

The standard output of SCAS (Socrates as a Service) comprises both questions/answers as well as additional questions that should be answered. These are questions generated for every iteration, no matter what the input. That is, SCAS ends up creating additional ‘questions for further thought and study.’ Here the questions are, put to AI as statements of ‘fact,’ and with a request to AI to elaborate on the ‘fact’ (Table 11).

Table 11: Additional ‘insights’ provided by AI as elaborations of information put to AI as ‘facts’

tab 11

Discussion and Conclusions

AI synthesis of patients’ thoughts before surgery has the potential to completely change the medical field by giving personalized insights and suggestions based on patient data. With this technology, surgeons can better understand their patients’ feelings, hopes, and fears, which leads to better communication and better surgical outcomes. AI can also help doctors understand the psychological aspects of surgery by making them smarter and more empathetic. AI synthesis can be used to find possible risks and complications before surgery. This can lead to better results and happier patients. There could be problems with relying only on AI to combine mindsets. It’s possible that the algorithms used don’t always understand how complicated human emotions and experiences are, which could lead to assessments that are too simple or wrong. Additionally, relying too much on AI in medical practice may take the place of human connection and empathy, which could make the relationship between the patient and surgeon less human. In addition, using AI in this way might make intuition and personal judgment less important when making medical decisions, which could stop medical professionals from developing these important skills. However, AI for mindset synthesis in medicine may have advanced significantly in ten years. New technologies and algorithms may help surgeons understand and interpret patient emotions more accurately and nuancedly. AI mind-set synthesis could transform patient care and decision-making in healthcare in the next decade. AI may become part of preoperative assessments and treatment planning as technology advances, providing more personalized and efficient care. However, a renewed emphasis on human intuition and compassion in patient care may counteract AI’s overuse in medicine. Medicine may also face ethical issues related to AI use in sensitive medical settings, including privacy, consent, and technology limits. To ensure AI algorithms complement medical judgment and expertise, they must be constantly evaluated and refined. At the end of the day, the use of artificial intelligence (AI) in decision-making must be balanced with the need to provide patients with individualized attention. Over-reliance on AI synthesis could impede medical professionals’ ability to develop intuition and empathy. Care quality and patient outcomes could be jeopardized if patients and healthcare providers become emotionally distant due to an over-reliance on AI.

References

  1. Ertürk EB, Ünlü H (2018) Effects of pre-operative individualized education on anxiety and pain severity in patients following open-heart surgery. International Journal of Health Sciences 12: 26. [crossref]
  2. Grocott MPW, Ball JAS (2000) Consensus meeting: management of the high-risk surgical patient. Clinical Intensive Care 11: 263-281.
  3. Phillips J, Perriman C (2016) Pre-operative and post-operative care. Clinical Skills for Nursing Practice 423-446.
  4. Zeynep T, Gozde TS, Ikbal C, Emel S (2020) Pre-operative comfort levels of patients undergoing surgical intervention. International Journal of Caring Science 13: 1339-1345.
  5. Buss DM (ed.) (2005) The Handbook of Evolutionary Psychology. John Wiley & Sons.
  6. Milutinovic V, Salom J (2016) Mind Genomics: A Guide to Data-Driven Marketing Strategy. Springer.
  7. Moskowitz HR, Gofman A Beckley J, Ashman H (2006) Founding a new science: Mind Genomics. Journal of Sensory Studies 21: 266-307.
  8. Prahalad CK, Ramaswamy V (2000) Co-opting customer competence. Harvard Business Review 78: 79-90.
  9. Swift RS (2001) Accelerating customer relationships: Using CRM and relationship technologies. Prentice Hall Professional.
  10. Bombard Y, Baker GR, Orlando E, Fancott C, Bhatia P, Casalino S, Onate K, Denis JL, Pomey MP, et al. (2018) Engaging patients to improve quality of care: a systematic review. Implementation Science 13: 1-22.
  11. Koh HK, Brach C, Harris LM, Parchman ML (2013) A proposed ‘health literate care model’ would constitute a systems approach to improving patients’ engagement in care. Health Affairs 32: 357-367. [crossref]
  12. Zapka JG, Lemon SC (2004) Interventions for patients, providers, and health care organizations. Cancer: Interdisciplinary International Journal of the American Cancer Society 101: 1165-1187.
  13. Castro EM, Van Regenmortel T, Vanhaecht K, Sermeus W, Van Hecke A (2016) Patient empowerment, patient participation and patient-centeredness in hospital care: A concept analysis based on a literature review. Patient education and counseling 99: 1923-1939. [crossref]
  14. Holmström I, Röing M (2010) The relation between patient-centeredness and patient empowerment: a discussion on concepts. Patient Education and Counseling 79: 167-172.[crossref]
  15. Spence Laschinger HK, Gilbert S, Smith LM, Leslie K (2010) Towards a comprehensive theory of nurse/patient empowerment: applying Kanter’s empowerment theory to patient care. Journal of Nursing Management 18: 4-13. [crossref]
  16. Benner PE, Hooper-Kyriakidis PL, Stannard D (2011) Clinical wisdom and interventions in acute and critical care: A thinking-in-action approach. Springer Publishing Company.
  17. Charles C, Gafni A, Whelan T (1999) Decision-making in the physician-patient encounter: revisiting the shared treatment decision-making model. Social Science & Medicine 49: 651-661. [crossref]
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  19. Gerteis M, Edgman-Levitan S, Daley J, Delbanco TL (2002) Through the Patient’s Eyes: Understanding and Promoting Patient-Centered Care. John Wiley & Sons.
  20. Wallace LM (1986) Communication variables in the design of pre-surgical preparatory information. British Journal of Clinical Psychology 25: 111-118. [crossref]
FIG 2

Another Fluid Inclusion Type in Pegmatite Quartz: Complex Organic Compounds

DOI: 10.31038/GEMS.2024611

Abstract

Besides typical fluid and melt inclusions specific for miarolitic pegmatites, unusual fluid inclusions filled with complex solid or liquid hydrocarbons were described. Raman spectroscopy was used as the principal method for first characterizing these inclusions. We briefly discuss the meaning of the hydrocarbons in inclusions in pegmatite quartz and think that Oparin’s thoughts on the origin of life became new impulses.

Keywords

Volyn pegmatites, Raman spectroscopy, Organic compounds in fluid inclusions

Introduction

An illustrative description and a short genetic interpretation of the mostly isometric to lens-shaped miarolitic Volyn pegmatites within the Korosten Pluton in Ukraine is in Pavlishin and Dovgyi [1]. Generally, the pegmatite bodies are often gigantic: dimensions of 20 x 20 x 15 m are usual. Melt inclusion in quartz and topaz trace the magmatic stage of the Volyn chamber pegmatites [2] as a pseudo-binary solvus curve with the solvus crest at 732°C and 27.4% water down to temperatures of about 550°C. Besides melt inclusions, representing the magmatic state, the quartz from the giant miarolitic pegmatites often contains fluid inclusion with homogenization temperatures around 375 and 400°C, meaning the hydrothermal-metasomatic stage in the mineralization [1,3]. According to Pavlishin and Dovgyi [1], the postmagmatic stage is characterized by an intense dissolution of quartz at about 300-400°C. At this stage, or slightly lower temperatures, the inclusions with relatively pure organic liquids were probably trapped. The hard bitumoid kerite [C491H386O87(S)N] formed under the same conditions [4]. Kerite was found in the center of a pegmatite body and comprises heavy black aggregates of up to 3 kg of abiogenic origin (according to Ginsburg et al., [5], cited in Gorlenko et al. Last time, an intense and controversial discussion started on the 1.5-billion-year-old Volyn ‘biota’ by Franz et al. [5] and Head et al. [6].

Methods

We used for all microscopic and Raman spectrometric studies a petrographic polarization microscope with a rotating stage coupled with the RamMics R532 Raman spectrometer working in the spectral range of 0-4000 cm-1 using a 50 mW single mode 532nm laser. Details are in Thomas et al. 2022a [7] and 2022b [2]. For the Raman spectroscopic routine measurements, we used the Olympus long-distance LMPLN100x as a 100x objective. For the identification of the organic compounds, we applied the Spectral Database for Organic Compounds [8].

Sample

The studied sample, a polished quartz plate about 1.5 mm thick, is from the root of a giant quartz crystal (~1000 kg) from Volyn pegmatite body № 402. The original sample is in the Mineralogical Museum of the Taras Ševčenko National University Kyiv, and the studied plate is from D.K. Voznyak (Kyiv, Ukraine). A description of the sample cross-section is in Voznyak (2007). The inclusions in question are between the crystal zones IV and V. According to Voznyak [3], the fluid inclusions beside the new type of inclusions homogenize at 390-395°C in the vapor phase. Also, other quartz samples of the same pegmatite from different, unclear positions contain inclusions with organic liquids.

Results

The presence of graphite and diamonds in the pegmatite quartz shows mantle components participate in pegmatite formation. By the spheric form, they are intruded fast by supercritical fluids. Table 1 shows Raman spectroscopic data on diamonds and graphite. According to Zaitsev [9], the spectral position of the diamond Raman line can vary considerably. Values between 1331 and 1346 cm-1 are possible depending on crystal perfection.

Table 1: Raman data of spherical diamond and graphite inclusions in pegmatite quartz from Volyn (11 different aggregates).

Phase

Line position (cm-1) FWHM

(cm-1)

Line position (cm-1)

FWHM

(cm-1)

Diamond

1336.4 ± 6.4

83 ± 5.0

Graphite G

1580.6 ± 5.9

65.0 ± 7.0

Graphite D2

1609.7 ± 1.0

39.0 ± 10.0

FWHM-full width at half maximum

Because we studied only diamonds about 100µm deep under the sample surface, we used a laser power of 50 mW on the sample and a counting time of 200 seconds for the Raman spectra of diamond and graphite. The main concern of the present contribution is the description and composition of the inclusion type filled with hydrocarbon compounds. Most inclusions contain a small black ball-like carbon aggregate and small colorless to pale yellow strip-shaped crystals. Rare are tiny vapor bubbles. They form primarily at the Raman measurements. Their movement inside such inclusion demonstrates their liquid consistency. However, there are also inclusions with solid organic compounds with low melting temperatures, around 30°C, trapped as liquid droplets in the quartz. The following compounds found Thomas and Voznyak (2023) [10] in the inclusions: isoprene [C5H8], dimethyl cyclohexane [C8H16], 2,3-xylenol [C8H10O], and diisobutyl phthalate [C16H22O4]. Figure 1 shows the new fluid inclusions type filled with organic liquids, sometimes tiny crystals of organic composition, and mainly with a small black aggregate of complex hydrocarbons.

FIG 1

Figure 1: Typical fluid inclusion in pegmatite quartz from Volyn. The fluid is a liquid organic compound or a mixture (OL). C-hydrocarbon, V1, and V2 are the vapor bubbles at positions 1 and 2. The vapor bubble forms at the Raman measurement and disappears after the measurement. The change in the bubble position demonstrates the liquid state of the inclusion content.

The fluid inclusions, composed of organic material in the pegmatite quartz, are mostly isometric and have diameters between 20 and 100 µm. Figure 2 shows typical inclusions with organic material in quartz.

FIG 2

Figure 2: Typical organic fluid inclusions in Volyn pegmatite quartz. C with arrow shows small spherical hydrocarbon material. XXX-pale yellow colored crystals of organic material, similar to the liquid phase.

Clear indications are missing that the new kind with organic compounds-filled inclusions are impurities. However, up to now, the trapping conditions are not clear. The relatively isolated position far from the sample surfaces, microcracks, and the local distribution of these inclusions clearly show that they are not contaminations-they formed during the pegmatite crystallization. Generally, the small black ball-like aggregates inside the fluid inclusions are complex hydrocarbon material, not graphite or simple carbonaceous material, with the characteristic D1, D2, D3, and G bands in the Raman first-order region [11].

The origin of the organic material is related directly to the pegmatite crystallization, which follows from Figure 3, a melt inclusion in pegmatite quartz with a drop of organic material separated now from the volatile phase (V + L). Figures 2a and 2d show a faint meniscus in the liquid phases. In Thomas and Vosnyak (2023) [10], the meniscus is seen very well for inclusion 2a and shows three different organic liquid phases: dimethyl cyclohexane, 2,3-xylenol, and isoprene. That means at least the composition of the inclusions is not homogenous-from inclusion to inclusion, we see with Raman an inevitable variability by mixing different compounds. Mixing also happens by the laser light heating used for the Raman measurements. A surprise was the finding of DL-2-aminobutyric acid in fluid inclusions. α-Aminobutyric acid (AABA) is a non-proteinogenic amino acid with the structural formula H2N-C(CH3)2-COOH. It is rare and found in nature only in meteorites [12]. Table 2 lists the measured Raman data (532 nm laser) against the reference SDBS No. 1076 [8] using the 488 nm laser.

FIG 3

Figure 3: Melt inclusion in pegmatite quartz from Volyn. Gl-silicate glass, L-water-rich fluid phase. This phase also contains a tiny droplet of organic material (OL). OL-drop of a liquid organic compound. V-vapor phase, XXX-crystal.

Table 2: Comparison of the Raman data between natural inclusion in pegmatite quartz from Volyn and the SDBS database reference No. 1076: DL-2-amino-n-butyric acid.

DL-2-amino-n-butyric acid Volyn inclusions

DL-2-amino-n-butyric acid C4H9NO2 SDBS No. 1076
Band position in cm-1 Relative Intensity Band position in cm-1

Relative Intensity

2964

48 2979 87
2944 67 2943

96

2872

98 2881 55
1454 55 1455

39

1436

59 1426 31
1384 10 1382

20

1346

11 1337 23
1319 16 1319

32

1274

9 1274 11
1124 7 1120

10

1070

21 1069 29
1046 14 1046

28

987

4  985 15
 919 16  926

17

 863

11  871 28
 839 26  847

21

 808

27  811 29
 770 7  769

15

 648

6  643 32
 420 5  424

22

 n.d.

 226 24
 n.d.  186

49

 n.d.

 166

48

n.d.: not detected: by the strong background of the host (quartz), an unambiguous Raman measurement was impossible.

Figure 4 shows the structure formula of DL-2-amino-n-butyric acid [C4H9NO2] according to the database [8]. The linear formula is [C2H5CH(NH2)CO2H]. The melting point of DL-2-amino-n-butyric acid, a white solid, is 295°C. That means that the trapping of this substance must be higher than this temperature.

FIG 4

Figure 4: Structure formula of DL-2-amino-n-butyric acid according to the database (Author Collective, 1987)

The differences (relative intensity) in both spectra (Table 2) result mainly from the laser wavelengths used-532 vs. 488nm (database) and by mixing with small amounts of other organic components in the natural sample.

The unambiguous determination of the inclusion content alone with Raman spectroscopy is complicated by varying composition. The complex Raman band between 2800 and 3020 cm-1 is typical for all inclusions. A strong Raman band sometimes appears at 3079 cm-1 (e.g., at diisobytyl phthalate and butyl cyclohexane).

Another exceptional compound (tetrabutylgermane) is present as a liquid phase in the fluid inclusion (Figure 2e). Tetrabutylgermane is [C16H36Ge] or as a linear formula [CH3(CH2)3]4Ge. This compound is a colorless to light yellow liquid.

Table 3 lists the Raman data of the measured and given tetrabutylgermane. Differences result from the applied Raman stimulation light (532 vs. 488 nm) and possible impurities.

Table 3: Comparison of the Raman data between natural inclusion in pegmatite quartz from Volyn and the SDBS database reference No. 18433 (see Author collective, 1987): tetrabutylgermane.

Tetrabutylgermane in Volyn inclusions

Tetrabutylgermane C16H36Ge SDBS No. 18433
Band position in cm-1 Relative Intensity Band position in cm-1

Relative Intensity

2961

 48 2964 34
2930  90 2938

67

2895

 99 2895 95
2875  99 2877

72

2856

 99 2869 60
1450  49 1449

29

1437

 55 1425 13
1305  12 1311

12

1286

 12 1295 12
1060  14 1067

19

 888

 10  889 29
 845  13  853

16

628

 5  626 27
236  41  231

33

The occurrence of tetrabutylgermane, a metalorganic compound, is a surprise. However, Ge can enriched during pegmatite-forming processes in considerable amounts. The Raman bands’ slight deviation can also be caused by substituting Ge by Si contribution. The Raman Spectrum is presented in Figure 5 [8].

FIG 5

Figure 5: Part of the tetrabutylgermane Raman spectrum in a fluid inclusion in quartz of the Volyn pegmatite (Figure 2e).

Besides the compounds called here, there are a lot of others, often simpler in composition, for example, p-nitrophenol [C6H5NO3] or butyl cyclohexane [C10H20].

Discussion

The age of the Volyn pegmatites is, according to Popov [13], about 1760 ± 3 Ma. During the crystallization of the chamber pegmatites, starting at about 750°C [2], organic material is formed, enriched, and suspended in the late liquid phase. For the origin, we think of a catalytic formation from methane, CO2, or carbon coming from the mantle region, indicated by the finding of nanodiamonds and graphite in pegmatite quartz [10]. Freund [14] states that highly active carbon is present near all minerals and fluids coming from mantle depths and can form in water abiotic hydrocarbons. Carbonic material is often current during experimental work at high pressure and temperature due to contamination using carbon-bearing solutions such as acetone or CO2 [15]. So strong Raman bands between 2800 and 3000 cm-1 are, according to our Raman work, characteristic of hydrocarbons in synthetic stishovite, coesite, and buddingtonite [NH4AlSi3O8]. Carbon is often present in nature, particularly in supercritical fluids [16]. The transition from the supercritical into the critical and under-critical fluid is related to unusual processes, such as heterogeneous catalyzing [17], which can be responsible for the direct formation of the liquid organic material, including metalorganic compounds. The appearance of the ball-like carbon in almost all inclusions with organic material is unusual because, generally, carbon is graphite or a graphite-like material in fluid and melt inclusions. Unlike graphite, that black material is also unstable under the Raman laser light. Maybe this stuff forms the kerite aggregates found in the chamber center of Volyn pegmatites [1,4].

Freund [14] stated that the ancient ocean’s formation of appreciable amounts of biologically relevant essential molecules was ineffective. Alternatively, forming such molecules in a closed room like the Volyn pegmatites (as a natural laboratory) is possible. There are also significant older pegmatites with ages of 2900 Ma. Here forces the question: Is forming organic compounds also possible outside pegmatite bodies by the change of supercritical fluids coming from mantle regions into non-critical conditions in the ancient ocean? Oparin’s [18] thoughts obtain a new impulse here.

Acknowledgment

The present author thanks Dmytro K. Voznyak for providing the pegmatite samples

References

  1. Pavlishin VI, Dovgyi SA (2007) Mineralogy of the Volynian Chamber Pegmatites, Ukraine. Volodarsk- Mineralogical Almanac 12.
  2. Thomas R, Davidson P, Rericha A, Voznyak DK (2022b) Water-rich melt inclusions as “frozen” samples of the supercritical state in granites and pegmatites reveal extreme element enrichment resulting under non-equilibrium conditions. J. (Ukraine) 44: 3-15.
  3. Voznyak DK (2007) Microinclusions and reconstruction of conditions of endogenous mineral formation. Dumka, Kyiv, U.A. [in Ukrainian].
  4. Gorlenko VM, Zhmur SI, Duda VI, Suzina NE, Osipov GA, et. al. (2000) Fine structure of fossilized bacteria in Volyn kerite. Origins of Life and Evolution of the Biosphere 30: 567-577.
  5. Franz G, Lyckberg P, Khomenki V, Chournousenko V, Schulz HM, et al. (2022) Fossilization of Precambrian microfossils in the Volyn pegmatite, Ukraine. Biogeosciences 19: 1795-1811.
  6. Head MJ, Riding JB, O’Keefe JMK, Jeiter J, Gravendyck (2023) A reinterpretation of the 1.5 billion year old Voly’ biota’ of Ukraine, and discussion of the evolution of the eukaryotes. EGUsphere (Preprint repository).
  7. Thomas R, Davidson P, Rericha A, Recknagel U (2022a) Discovery of stishovite in the prismatine-bearing granulite from Waldheim, Germany: A possible role of supercritical fluids of ultrahigh-pressure origin. Geosciences 12: 1-13.
  8. Author Collective (1987) Spectral Database for Organic Compounds, SDBS, National Institute of Advanced Industrial Science and Technology (AIST), Japan.
  9. Zaitsev AM (2001) Optical Properties of Diamond. Data Handbook. Springer-Verlag Berlin, Heidelberg GmbH, 502.
  10. Thomas R, Voznyak DK (2023) Volyn Pegmatite: Unusual hydrocarbon-bearing fluid inclusions in pegmatite quartz. Aspects in Mining and Mineral Sciences 12: 1353-1360.
  11. Beyssac O, Coffee B, Chopin C, Rouzaud (2002) Raman spectra of carbonaceous material in metasediments: a new geothermometer. metamorphic Geol. 20: 859-871.
  12. Kvenvolden K, Lawless J, Pering K, Peterson E, Flores J, et al. (1970) Evidence for Extraterrestrial amino-acids and hydrocarbons in the Murchison meteorite. Nature 228: 923-926. [crossref]
  13. Popov DV (2023) Do pegmatites crystallise fast? A perspective from petrologically-constrained isotopic dating. Geosciences 13: 1-12.
  14. Freund F (1983) Atomarer Kohlenstoff in gesteinsbildenden Mineralen. Naturwissenschaftliche Rundschau. 36: 439-441.
  15. Mustart DA (1972) Phase relations in the peralkaline portion of the system Na2O-Al2O3-SiO2-H2 Dissertation, Stanford University, pg: 187 .
  16. Thomas R (2023) Growth of SiC whiskers in Beryl by a natural supercritical VLS process. Aspects in Mining & Mineral Science 11: 1292-1297.
  17. Thomas R, Rericha A (2023) The function of supercritical fluids for the solvus formation and enrichment of critical elements. (2023) Geology, Earth and Marine Sciences 5: 1-4.
  18. Oparin AI (1957) Die Enstehung des Lebens auf der Erde. VEB Deutscher Verlag der Wissenschaften Berlin 411.

The Role of Endocrine Mediators in the Neurodegeneration and Synaptic Dysfunction of Depressive Illness

DOI: 10.31038/EDMJ.2024811

 
 

Major depression is the 2nd greatest cause of disability worldwide, and the first greatest cause in individuals under 45 years of age. It is a lifetime disorder with multiple depressive episodes interspersed by remissions. Its principal manifestations include anxiety, especially directed at the self, feelings of worthlessness, inability to anticipate or experience pleasure, significant changes in appetite and sleep, hypersecretion of cortisol and norepinephrine [1], and a highly significant increase in the incidences of complex medical illness such as coronary artery disease, stroke [2], diabetes [3], and osteoporosis [4]. Specific central nervous system loci have been connected to these stigmata. There is a. loss of 40% in the volume of the subgenual prefrontal cortex [5,6], making depression a neurodegenerative disease. This site estimates the likelihood of punishment or reward, helps set the tone for the level of self-esteem, ordinarily restrains the amygdala in its generation of fear, accentuates the activity of the nucleus accumbens reward and pleasure center, and restrains the activity of the CRH-cortisol system and the sympathetic nervous system [1]. Its deficits in depression contribute to the majority of its principal clinical manifestations. As noted, these include anxiety and feelings of worthlessness, the decreased activities of the nucleus accumbens and ventral striatal system reward and pleasure centers, as well as hypercortisolism and a hypernoradrenergic state [1].

Hormonal mediators play large roles in these clinical and biochemical manifestations [7]. Corticotropin releasing hormone plays a significant role in the overall biological stigmata of depressive illness. We showed that CRH was hypersecreted in depression [8]. It is predominantly located in the hypothalamus to stimulate the pituitary-adrenal axis and in the amygdala to activate the locus-ceruleus norepinephrine system. It is by itself neurotoxic, and one of its principal effects, the inducement of hypercortisolism also promotes neurodegeneration and maladaptive central nervous system activity. It is also a potent stimulus to inflammation, and its actions include promoting the degranulation of mast cells. Either psychological or emotional stress activates both the hypothalamic and amygdala components of the CRH system [8].

Stress also activates inflammation in the brain and periphery independent of CRH. Circulating inflammatory mediators also damage neural tissue and function and play a significant role in the stigmata of depression [9,10]. In our early evolutionary history, the primary stressors were serious: either competition for territory or competitions for mates. In these instances, even the perception of danger stimulated neuroinflammation, in part, as a premonitory response to support tissue repair in the face of a flight or fight situation.

Norepinephrine excess in depression [11,12] also has deleterious effects. It not only produces anxiety, but also increases heart rate and blood pressure, production of a proinflammatory state (synergistic with CRH) increased coagulation, and insulin resistance.

Insulin in brain derives from the periphery to activate a host of insulin receptors in sites that are involved in depressive pathophysiology. Insulin resistances in the periphery [9] associated with increased plasma insulin levels decrease insulin transport into the CNS by downregulating blood brain barrier insulin receptors. Insulin in brain supports the density of synapses, maintain synaptic itegrity, and synaptic density and integrity decrease when insulin receptors are removed or dysfunctional. Depression is associated with significant synaptic dysfunction in multiple ways [13].

Estrogen in females and androgens in male are often reduced in depression. Estrogen is neuroprotective, anti-inflammatory, reduces anxiety and depression, promote cognition, and modulate synaptic plasticity in rodents. Androgen deficiency is associated with depression which is corrected by restoring androgen levels to normal.

Thyroid hormones are often low in patients with depressive illness [14]. Thyroid hormones suppress the activity of the amygdala and thyroid deficiency is likely to promote anxiety. Adult hypothyroidism is associated with an increase in glucocorticoid actions in the amygdala, which we have shown promotes anxiety, is associated with fear memory enhancement, and deficits in the extinction of fear memories. These deficits are reversed by thyroid hormone replacement.

Concluding Remarks

Endocrine abnormalities contribute to the neurogenerative aspects of depressive illness, to synaptic abnormalities, and can adversely affect sites such as the amygdala and the ventral striatum. CRH, noradrenergic, and glucocorticoid antagonists and anti-inflammatory treatment can all have therapeutic potential for treating depression. Agents that are neuroprotective can also have therapeutic potential in treating depression, and multiple neuroprotective compounds are currently in active trials in antidepressant protocols.

References

  1. PW G (2015) The organization of the stress system and its dysregulation in depressive illness. Mol Psychiat 20: 32-47. [crossref]
  2. Dong JY, Zhang YH, Tong J, Qin LQ (2012) Depression and risk of stroke: a meta-analysis of prospective studies. Stroke 43: 32-37. [crossref]
  3. MJ K, JWR T, ATF B, RJ H, FJ S, F P (2005) Depression as a risk factor for the onset of type II diabetes. Diabetologia 49: 837-845. [crossref]
  4. Michelson D, Stratakis C, Hill L, Reynolds J, Galliven E, et al. (1996) Bone mineral density in women with depression. N Engl J Med 335: 1176-1181. [crossref]
  5. Drevets WC, Price JL, Simpson JR, Todd RD, Reich T, et al. (1997) Subgenual prefrontal cortex abnormalities in mood disorders. Nature 386: 824-827. [crossref]
  6. Drevets WC, Savitz J, Trimble M (2008) The subgenual anterior cingulate cortex in mood disorders. CNS spectrums 13: 663-681. [crossref]
  7. PW G (2023) Breaking Through Depression: A Guide to the Next Generation of Promising Research and Revolutionary New Treatments. New York, NY: 12 Hachette Book Group; 2023.
  8. Gold PW LL, Roy A, Kling MA, Calabrese JR, et al. (1986) Responses to corticotropin-releasing hormone iushing’s disease. Pathophysiologic and diagnostic implications. Eng. J. Med 1329-1335. [crossref]
  9. Miller AH, Raison CL (2016) The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol 16: 22-34. [crossref]
  10. Miller AH, Maletic V, Raison CL (2009) Inflammation and its discontents: the role of cytokines in the pathophysiology of major depression. Biol Psychiatry 65: 732-741. [crossref]
  11. Gold PW, Wong ML, Goldstein DS, Gold HK, Ronsaville DS, et al. (2005) Cardiac implications of increased arterial entry and reversible 24-h central and peripheral norepinephrine levels in melancholia. Proc Natl Acad Sci U S A 102: 8303-8308. [crossref]
  12. Wong ML, Kling MA, Munson PJ, Listwak S, Licinio J, et al. (2000) Pronounced and sustained central hypernoradrenergic function in major depression with melancholic features: relation to hypercortisolism and corticotropin-releasing hormone. Proc Natl Acad Sci U S A 97: 325-330. [crossref]
  13. Gold PW, Licinio J, Pavlatou MG (2013) Pathological parainflammation and endoplasmic reticulum stress in depression: potential translational targets through the CNS insulin, klotho and PPAR-γ systems. Molecular Psychiatry 18: 154-165. [crossref]
  14. Bode H, Ivens B, Bschor T, Schwarzer G, Henssler J, et al. (2022) Hyperthyroidism and clinical depression: a systematic review and meta-analysis. Transl Psychiatry 12: 362. [crossref]
fig 3 new

Understanding Life Through Structured AI-Illustration Using PTSD

DOI: 10.31038/ASMHS.2024811

Abstract

The paper introduces a new approach for understanding aspects life, based upon the emerging science of Mind Genomics. The approach presents a internet-based technology (www.bimileap), which enables the interested person to use an AI-empowered system, Idea Coach, to ask ideas. Originally developed as a way to provide novices with a way to learn structured, critical thinking, Idea Coach has been expanded, allowing the user to describe a topic, posit the existence of mind-sets, and instruct the AI to answer a set of user-defined questions about these mind-sets. The Idea Coach system can be iterated to provide new sets of answers, each iteration requiring about 15-30 seconds. The Idea Coach request to AI can be changed ‘mid-stream’, to provide different types of information. The approach is illustrated with a deep analysis by Idea Coach of what might be in the mind of a person suffering from post-traumatic stress. The position of the paper is that AI can now be used to launch critical thinking about a topic, doing by posing ‘what if’ types of questions to promote discussion and experimentation.

Introduction

It is commonly recognized that a vast number of internet searches are done to understand situations, events, things which affect one personally [1-3]. The foregoing sentence seems so simple, so realistic, so obvious. The point is made cogently when one experiences a life-impacting situation, e.g., a disease to which one is newly diagnosed. What was an intellectual topic before may often evolve to an obsession with knowing as much about this disease or other problem [4,5], often to the point that the individual’s entire focus and conversation revolves around the different aspects of the disease, the origins, diagnoses, prognoses, and so forth [6].

One consequence of the desire to ‘know’ about things most important is the use of the Internet as a source of information. When the topic is health and medical most websites are careful to emphasize in one way or another that the reader should consult a medical professional for guidance, and that the information presented is for popular, informal consumption. Typically, the material presented to the reader is couched in an interesting, easy to understanding, and engaging fashion. The information is usually superficial, a level which makes sense because the typical reader wants a quick and superficial overview.

The origin of this paper was the request to provide a deeper level of information about a medical topic, that level not being one that a medical student might learn, but certain more structured and deeper than one might receive from a cursory search through Google.

The choice of PTSD was dictated by other reasons, namely a growing interest to deal with social issues intertwined with medical issues. One of the important ones was the providing deeper information about PTSD, a psychiatric disorder of interest to local town officials coping with the effects of exposures to violence among many of the town’s poorer citizens. Could Mind Genomics, and its AI components, Idea Coach, provide the user with new types of information.

The Historical Background

The approach presented here evolved over the 30-year span from the early 1990’s. At that time author Howard Moskowitz and colleague Derek Martin had been expanding the scope of concept testing by creating what was then called IdeaMap [7], later to evolve to Mind Genomics [8]. The ingoing vision was to democratize the acquisition of insights into two ways, one by a new way of thinking about insights, the other by the vision of DIY, first at one’s own computer, and then on the internet.

Up to the introduction of IdeaMap researchers tested new ideas by one of two ways. The first was called ‘promise’ testing, or some variation thereof. The ingoing notion was that the researcher would come up with a number of different ideas, and test these among prospective consumers. The ideas could be alternative execution of a basic idea or proposition’ (viz., rate each of these ideas for a new medicine), or the ideas could be even more basic (viz., rate the importance of each of these benefits of a new medicine, such as speed of relief, degree of relief, safety, etc.). These ideas could be rated in a study, but the basic notion is that the research could take a simple situation and have people rate facets of that situation, or take a simple offering, and have people rate ways of expressing what the offering could do. The methods were easy, the respondent in the study had no problem, but the test stimuli lacked context, depth, and the richness of everyday life. Nonetheless, the researchers working in the field followed through on these, and often were quite successful because the study with real consumers gave the developers and marketers a great deal of insight.

A second approach, also widely done, and complementing the promise test was to create an execution, a stand-alone ‘concept’ of the problem or offering. The test stimulus in this case was a concept ‘board’, usually presenting a picture, product or service name, description, and so forth. The execution of the basic idea was as important to the corporation as the basic idea itself. The ingoing assumption was that people needed to be convinced, by having something which appealed to them, presented in a way that would be more typical more ‘ecologically valid.’ The executions, the test concepts, demanded a great deal of judgment about what to put in, what to exclude, how to talk about the product or service, along with work to create the actual concept, e.g., what visuals were needed, and so forth. The studies were called concept screens when the concepts were rough, barebones descriptions, or called concept tests when the concepts were finished, and the testing was necessary to measure the expected performance in the marketplace.

The Contribution of Mind Genomics

When Mind Genomics appeared on the scene it is evolved form, beginning in the first years of this 21st century [8] the ingoing notion was that people should be able to respond easily to combinations of messages, even when the messages were not necessarily connected, but rather thrown together. Unpublished work with respondents beginning in 1980 with The Colgate Palmolive Company in Toronto, Canada evaluating new ideas for Colgate Dental Cream uncovered the surprising observation respondents reported NO DIFFICULTIES when they were exposed to combinations of messages in this sparse framework. There were, however, ‘concerns’ from the advertising agency.

This study is about PTSD, post-traumatic stress disorder [9], but the reality is that the approach presented here could be used virtually for any topic. Figure 1 shows the structure of a concept, or ‘vignette’ in the language of Mind Genomics. The figure shows a spare structure comprising a short introductory instruction, a small collection of unconnected phrases, albeit all dealing with PTSD, and then the rating scale. After an initial shock of seeing such a spare format, most respondents adjust quickly, going through the vignettes by ‘skimming’, and then making a quick decision about what rating to assign,. The reality was that in most studies with concepts the respondent skims the concept, grazing for information, rather than reading the material.

fig 1

Figure 1: Example of a vignette about PTSD (post-traumatic stress disorder)

The important advance of Mind Genomics was the focus on the material, not on th execution. It was the questions and the answers that were important. In the actual execution, the Mind Genomics platform, www.bimileap.com, would combine the answers into vignettes, according to an underlying experimental design. The questions would not appear in the vignettes, only the answers. Respondents participating found this format easy, perhaps slightly boring, but not intimidating at all. The people who were intimidated turned out to be the users wanting to use Mind Genomics. These prospective users ended up having to provide questions and answers, a task that seemed to be so easy to do when Mind Genomics was first developed, but which turned out to be intimidating as real people were exposed to the task. It had been unclear until that point how difficult people found the job of thinking about a topic, coming up with questions which tell a story, and then coming up with answers to those questions.

The Mind Genomics approach ended up disposing of the believed requirement that the vignettes presented to the respondent be complete vignettes. Rather, it was simpler to create a template that the user could complete. Rather than using statistical jargon, and talking about underlying experimental design, everything was put into the template, requiring the user simply to think of ideas. In the most recent format, the user would be instructed to give the project a name (Figure 2, Panel A), and then provide the template with four questions telling a story (Figure 2, Panel B), and then for each question, four answers (not shown).

fig 2

Figure 2: Set up screens for a Mind Genomics study. Panel A shows the first step, to name the study. Panel B shows the instructions to provide four questions.

Mind Genomics requires that the user think critically. Figure 1, Panel B instructs the user to create a set of four questions which ‘tell a story’.’ For many neophytes, individuals who wanted to experience what Mind Genomics could do for them, the task seemed overwhelming. In order to ameliorate this problem, Mind Genomics evolved to incorporate AI, through Idea Coach. Figure 2, Panel A shows the ‘squib’, which allows the user write a request to Mind Genomics, and in turn have AI use that request to create the questions, as well as create the answers (not shown). Panel B shows the four questions which were chosen (Figure 3).

The actual output of Idea Coach appears in Tables 1 and 2, respectively. Table 1 shows the request made to Idea Coach to provide it with 15 questions. From this, the user selected four questions, shown in Figure 3, Panel B. Table 2 shows the 15 answers to each question provided by Idea Coach.

Table 1: Output of 15 questions from Idea Coach, with the output emerging immediately after the request was given to Idea Coach.

tab 1

Table 2: The four sets of 15 answers, one set for each question selected (see Figure 2, Panel B)

tab 2
 
 

fig 3 new

Figure 3: Panel A shows a request to Idea Coach to provide relevant questions for the study. Panel B shows four questions which emerged from Idea Coach.

Moving Beyond the Question-and-Answer Format to the Tutoring/Coaching System

The success of Idea Coach as a way to provide questions and answers gave rise to another discovery, one which motivates this paper among others. That discovery is what emerges when the user asks more of Idea Coach than simply providing questions to address a simple squib (Table 1), or answers to a simple question (Table 2). Table 3 shows the squib for PTSD, but a far more elaborate request. The request ‘assumes’ without specification that PTSD has three different mind-sets. The squib does not specify anything about these mind-sets, but rather requests Idea Coach to answer 11 different questions.

Table 3: A more elaborate squib to both get answers about PTSD and to teach about PTSD

tab 3

In turn, Tables 4-6 show three sequential runs of Idea Coach, each taking about 30 seconds. Idea Coach uses the same squib shown in Table 3, and returning back each time with a unique set of answers, albeit answers with substantial commonality.

Table 4: The first set of answers to the squib shown in Table 3. Idea Coach attempts to provide answers to each question, viz., to each request.

tab 4(1)

tab 4(2)

Table 5: The second set of answers to the squib shown in Table 3. Idea Coach attempts to provide answers to each question, viz., to each request.

tab 5(1)

tab 5(2)

tab 5(3)

Table 6: The third set of answers to the squib shown in Table 3. Idea Coach attempts to provide answers to each question, viz., to each request.

tab 6(1)

tab 6(2)

Discussion and Conclusions

During the course of the research and writing, an effort requiring less and less time and effort, AI has been both lauded and lambasted, lauded because of the possibilities it has, lambasted because it is far from being omniscient and fair [10]. The world of academics is struggling with the impact of the widely available Chat GPT system and its clones [11], worrying about cheating [12], about the reliance of students on AI for their knowledge and even for writing the papers that they turn in for coursework [13].

The approach presented here may provide a different pattern of activities, one which incorporates early-stage learning with AI as a tutor, and then experimentation with real people, or eventually event with synthetic respondents, viz., survey takers constructed by AI. The paper began with the history of Mind Genomics, the problems with critical thinking, and the salutary effects introducing AI as an aid to creating questions, and then providing answers to those questions. The paper ‘ended’ with the benefits emerging from a more detailed introduction to the issue, that introduction created simply by expanding the nature of the squib, the introduction to the problem. Rather than a simple instruction to produce questions and answers, the paper shows how a more detailed request to AI could produce a wealth of information.

The suggestion made here is quite simple: REVERSE THE PROCESS. That is, for a designated topic topic, e.g., PTSD, begin the process by a tutorial with detailed request, such as that shown in Table 2. Once the tutorial has finished, the Idea Coach, acting as a true coach, has done its work, producing sufficient information about PTSD. It is now entirely in the hands of the user to do the Mind Genomics experiment with real people, using the information conveyed to the user by the expanded squib. Whether the user must use their own questions and answers or can once again use idea Coach to request questions and answers is a policy decision, one beyond the scope of this paper.

References

  1. Ellery PJ, Vaughn W, Ellery J, Bott J, Ritchey K, et al. (2008) Understanding internet health search patterns: An early exploration into the usefulness of Google Trends. Journal of Communication in Healthcare 1: 441-456.
  2. Mellon J (2014) Internet search data and issue salience: The properties of Google Trends as a measure of issue salience. Journal of Elections, Public Opinion & Parties 24: 45-72.
  3. Ripberger JT (2011) Capturing curiosity: Using internet search trends to measure public attentiveness.” Policy Studies Journal 39: 239-259.
  4. Svenstrup D, Jørgensen HL, Winther O (2015) Rare disease diagnosis: a review of web search, social media and large-scale data-mining approaches. Rare Diseases 3: p.e1083145.
  5. Zuccon G, Koopman B, Palotti J (2015) Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information. In: Advances in Information Retrieval: 37th European Conference on IR Research, ECIR 2015, Vienna, Austria, March 29-April 2, 2015. Proceedings 37 562-567. Springer International Publishing.
  6. Endicott NA, Endicott J (1963) Objective measures of somatic preoccupation. The Journal of Nervous and Mental Disease 137: 427-437.
  7. Moskowitz HR, Martin D (1993) How computer aided design and presentation of concepts speeds up the product development process. In: ESOMAR Marketing Research Congress 405.
  8. Moskowitz HR, Gofman A, Beckley J, Ashman H (2006) Founding a new science: Mind genomics. Journal of Sensory Studies 21: 266-307.
  9. Shalev AY (2001) What is posttraumatic stress disorder? Journal of Clinical Psychiatry 62: 4-10.
  10. Suchman L (2023) Imaginaries of omniscience: Automating intelligence in the US Department of Defense. Social Studies of Science 53: 761-786.
  11. Guleria A, Krishan K, Sharma V, Kanchan T (2023) ChatGPT: ethical concerns and challenges in academics and research. The Journal of Infection in Developing Countries 17: 1292-1299. [crossref]
  12. Cotton DR, Cotton PA, Shipway JR (2023) Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 1-12.
  13. Chan CKY (2023) A comprehensive AI policy education framework for university teaching and learning. Int J Educ Technol High Educ 20: 38.
FIG 1

Open Innovation in the World Order through AI: Gaza, Israel and Beckoning Opportunities

DOI: 10.31038/PSYJ.2024621

Abstract

The paper focuses on the use of AI to summarize and hypothesize mind-sets, motives, and strategies for peace, all involving the current conflict between Israel and Hamas in Gaza. The embedded AI in the BimiLeap program (Idea Coach), originally developed to facilitate critical thinking, has been expanded to allow users to understand social situations and the motives of people more profoundly. The Mind Genomics platform thus moves from its origin as an attitude research platform into an easy-to-use, rapid, affordable component of the world of Open Innovation, accessible to all, and producing useful, testable suggestions. The paper shows easy-to-create inquiries provided to AI, and the type of output immediately available, and then more output summaries returned a few minutes later.

Introduction

The sociology, psychology, and other areas of social science are filled with studies of conflict, of conflict resolution, and so forth. Conflict appears to be inborn. The purpose of this paper is to move from a profound study of the nature of conflicts to the use of easily deployed artificial intelligence to deal with conflicts. Conflict, disagreements, take many forms, each form the focus of a scientific literature going back decades, and for political conflict, going back centuries and millennia. The continuing question is how to resolve a conflict and can there be new ways of discovering resolutions.

The approach presented in this paper, AI-Enhanced Mind Genomics, emerged from decades of research on understanding how people make decisions. The objective of Mind Genomics is to study the decisions that people make in their ordinary lives, not so much by asking them what is important but instead by showing people different ‘vignettes’, combinations of messages about people, and asking people to select how they feel when they read these vignettes. Table 1 shows an example of a vignette and the rating question.

The important thing about the vignettes is that they represent slices of life. Although the vignette is simply a phrase, like those in Table 1 below, when these phrases are put into a combination, either by a person who is thinking or by a machine following a prescribed set of combinations, they paint a picture of situation that has some semblance of reality. People who read the vignette get a feeling of the situation and make a judgment about the situation.

Before proceeding with the AI enhancements, it is important to contrast the approach presented here with the conventional approaches. Conventional wisdom works with simple ideas, general concepts. These ideas often cover a wide range of topics. The ideas themselves are rarely ‘fleshed out’ with specific, depending rather on the mind of the respondent to fill in the specifics. In this way the researcher is able to identify the general point of view of the respondent, e.g., the respondent is interested in economic opportunities, or the respondent is interested in safety and security, etc. One need only take one of the seemingly omnipresent surveys on a business transaction or a medical visit to see the focus from the top down, from the general topics such as efficiency, politeness and competency. This is no focus on specifics, on the granularity of the experience.

Table 1: The vignette and the rating question

TAB 1

The Original Mind Genomics Approach

Mind Genomics emerged about thirty years ago, when author Moskowitz and colleague Derek Martin presented the approach at the annual congress of ESOMAR (World Society of Market Research), held in Copenhagen [1]. The approach, then called IdeaMap, featured the notion of evaluation of systematically varied vignettes, and then the deconstruction of the rating into the part-worth contribution made by each element. The approach was an advancement of earlier efforts called ‘conjoint measurement’, based on foundational work by Luce and Tukey [2], and continued by Wharton School professors Paul Green, Jerry Wind, and Abba Krieger [3,4]. Their focus was the complexity of decision making by assuming that people were presented with options, and that they had to trade-off one option for another. They could not have everything. The experiments run by Green and Wind were done to identify the rules of the mind, viz., what was important to a person. Their sets of experiments were augment by modeling and segmentation

In the end, these early contributions set the stage for the emergence of IdeaMap, morphing into RDE (Rule Developing Experimentation [5], and finally the emerging science of Mind Genomics [6] The Mind Genomics science facilitated by a computer platform, www.bimileap.com. The platform guided the user, first to define the study, then to develop four questions which told a story about the topic (Figure 1, Panel A). After some years of experience it became clear that the stumbling block was the development of meaningful questions. The education system taught people how to give answers, but not how to think critically, and clearly not how to formulate a series of questions which would tell a relevant story.

The introduction of AI in the form of ChatGPT) solved the problem [7]. The Mind Genomics platform was augmented with an AI capability called Idea Coach (Figure 1 Panel B). The user simply typed in a request for questions about a topic, and Idea Coach returned with 15 suggestions, as shown in Table 2. The user then selected four questions or developed questions without the AI, or even took the questions suggested by AI, and edited the questions. The output became the four questions. The same approach is used to provide answers to the questions selected. The user could select questions and edit them, obtain 15 answers to each question, and finally select four answers. This was done four times, once for each question. This second step, obtaining answers to a selected question using Idea Coach, appears in Table 3. The entire process from start to finish typically requires about 20 minutes once the user has understood the steps.

FIG 1

Figure 1: Panel A shows the request for four questions. Panel B shows the input to Idea Coach, which permits the user to invoke AI to help create the questions.

Table 2: The 15 questions generated by AI embedded in Idea Coach

TAB 2

Table 3: The 15 answers to one of the selected questions generated by AI embedded in Idea Coach

TAB 3

Using Mind Genomics and Idea Coach to Spur Open Innovation in Public Policy

The term open innovation has been presented as a new way to drive innovation [8-10]. The notion is that one can innovate in many ways, and need not follow the typical, perhaps exaggerated pattern of innovation following the ‘inspiration’ given to the highest-ranking member in the group or in the company. The number of articles on open innovation (Number?) is testament to the fact the concept is alive and well. The application of open innovation to public policy is also testament to the acceptance of the concept in a world where competition, profit and lost, and very survival take a different shape.

The focus of this paper is to present how a new way of thinking with Idea Coach and Mind Genomics can contribute to open innovation in public policy. The effort presented here shows just one exercise in what became a short set of exercises in public policy to formulate what to do in the current war between Israel and Hamas being fought in Gaza. The presentation shows directly what can be obtained in a matter of less than a few minutes and suggests how to iterate the inputs to suggest new ideas.

The remainder of this paper is the presentation of the results of the exercise. There is little need for comment. The results and the interpretation are self-evident. What is important to remember, however, are the immediacy, simplicity, affordability, and depth of the results. And, of course, the reality that these ideas must be quickly evaluated, whether using Mind Genomics with real people to identify the acceptable of the ideas, or some other method, such as in-depth interviews, focus groups, or even surveys.

Mind Genomics as Ideas Underlying Open Innovation in Policy: The Steps, the Results

Following the process shown in Figure 1, Panel B, the user defines the situation (Table 4). The squib is far more complicated, however, because now the user is going to engage the AI built into Idea Coach to provide much more information. The instructions are to provide questions, reasons underlying the questions, answers to those questions from Israel, answers to those questions from the UN, as well as making the answers come from an honest person, and making the questions and answers interesting.

Within a minute or so, the AI in Idea Coach returns with the request. The immediate results are shown in Table 5. The user can edit the request shown in Table 4 and re-run, or simply re-run for another attempt at providing the information. Each iteration can be done in 30 seconds or shorter. Within 10 minutes the motivated user can do 20 iteration, simply by pressing the repeat button. Some of the results will be the same in several iterations, but many of the iterations will surface new material. The process allows for instant iterations by giving the user the opportunity to modify the ‘squib’ or prompt to Idea Coach in real time, and then re-submit.

After about 20 minutes, and after the user has logged out correctly, the platform sends the user the entire booklet of iterations, doing so by email. In addition to one tab devoted to the iteration that the user saw, the platform further summarizes the results, and applies additional AI to the material it generated. Table 6 shows the summarized material.

Table 4: First set of inputs which frame the question

TAB 4

Table 5: Immediate output from Idea Coach to address the input squib (prompt) shown in Table 4

TAB 5

Table 6: Summarization and AI-based expansion of initial AI output shown in Table 5

TAB 6(1)

TAB 6(2)

TAB 6(3)

Moving the Process Entirely to AI to Explore Synthetized Mind-sets

The final demonstration in this paper emerges from the work in Mind Genomics which demonstrated the existence of ‘mind-sets’, operationally defined as people who responded the same way to specific messages in a granular topic (REF). These mind-sets may more profoundly differentiate people than do the simple patterns of response in questionnaires, primarily because the latter, the questionnaires, focus on the generalities. The different segments emerging from these questionnaires are more global. It is often very difficult to know what to say to a segment in these global segments when the requirement is to deal with a specific, localized issue. In contrast, mind-sets in Mind Genomics emerge from the granular. Mind-sets in Mind Genomics become general, global, only when they seem to emerge again and again in different topics as now-familiar ways in which the people differ.

What happens when the AI is told that there are three mind-sets, but is not told what the mind-sets are, or anything about them. Rather, the AI is asked to define the mind-sets, and then answer questions about these mind-sets. Table 7 shows one of these ‘exercises’, showing the input to Idea Coach at the top (section A), then the materials returned immediately in the middle (section B), and finally the AI summarization returned with the Idea Book at the boom (section C).

Table 7: Using A to synthesize and then understand possible mind-sets

TAB 7(1)

TAB 7(2)

TAB 7(3)

TAB 7(4)

TAB 7(5)

Discussion and Conclusions

There is no dearth of ideas in the world of public policy. As Mark Twain is presumed to have quipped about the weather, ‘while everybody talked about the weather, nobody seemed to do anything about it. The same may be said about the world order, although the proliferation of government organizations as well as NGO’s suggest that there ought to be a way to do things about conflicts, rather than watch the conflict continue to fester, with the loss of lives, property, and the destruction of hope.

The approach presented here was founded on an emerging science, Mind Genomics. Mind Genomics does not seek to fill holes in the literature, nor answer calls from the literature to create a nice, tidy, coherent whole piece of knowledge. Rather, having descended both from the abstract mathematical psychology of Luce, the elegance of the nascent field of consumer psychology by Wharton Marketing Professors Green and Wind, and finally battle tested in the commercial world, Mind Genomics presents a way to deal with these problems of public policy. Moving one step beyond, however, Mind Genomics incorporates AI in its Idea Coach, first to create questions and answers for those challenged to think critically about a topic, and in this second instantiation to do a lot of the thinking and suggest strategies.

What then will the literature look like in a generation when the science of Mind Genomics blends with the informational and idea generating capabilities of AI. Will there be a science of public policy? What will happen to issues with conflicts of all types, the ‘stuff of life’ which reduces its quality. It is possible that the conflicts of the world will each be addressed in a Mind Genomics ‘Idea Book’, such as the Gaza conflict addressed here, each of the Idea Books for the specific conflict requesting the same sets of choreographed suggestions, ranging from negotiations to suggestions for motives, the understanding of basic mind-sets, and finally activities to reduce the conflict and work towards a long-lasting peace. The immediate availability (minutes and hours) of Idea Books created for each situation, each conflict, makes it possible to make that conjecture, that dream, into a reality.

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FIG 3

Magnetism-Inspired Quantum-Mechanical Model of Gender Fluidity

DOI: 10.31038/PSYJ.2024614

Abstract

Quantum-mechanical models of human cognition, opinion formation and decision-making have changed the way we understand and predict human behaviour in many practical situations, including political elections, financial decisions and international affairs. Yet, at present, such models overlook certain essential social aspects of human behaviour and self-identification. In this paper, we introduce a magnetism-inspired quantum-mechanical model of gender fluidity, a concept that challenges social norms across the globe. Addressing a number of independent suggestions made by members of the general public concerning a potential analogy between quantum superposition and non-binary self-identification, we explore new territories, demonstrating that physic of magnetism can help explain gender fluidity and similar social phenomena better than the traditional quantum-mechanical models of human cognition and perception. We anticipate that the proposed model can be used to analyse experimental datasets aimed to develop sexual orientation and gender identity legal definitions as well as to create artificial intelligence systems that can sensibly identify both binary and non-binary genders.

Introduction

To survive and evolve in this world, humans have always strove to understand who and what they are. This continuous process has led to the creation of certain norms, acceptable social roles and behaviour patterns. One of such norms is the concept of gender, a set of socially constructed characteristics of women, men, girls and boys that varies from society to society and can change over time [1-7], playing an important role in popular culture and literature [8,9].The concept of gender fluidity challenges social norms [10-17]. Adopting a binary point of view, being gender fluid can be defined as having a different gender identity at different times [18]. For example, at one moment an individual may identify themselves as female and at another moment they may identify themselves as male. Yet, an individual may also identify themselves as both male and female at the same time or none. Such identity shifts can happen at different timescale: several times a day, weekly, monthly or yearly [19]. In fact, gender fluidity is more complex. However, the extent of its complexity has not been established yet despite the attempts to understand it using the methods adopted in the domain of complex systems [20].

Complex systems is an umbrella term applied to a methodological approach used in physics, engineering, life and social sciences, management and health to reveal how relationships between parts give rise to a collective behaviours of the entire system, also explaining how the system interacts with the environment [21,22]. The human brain is also a complex system [23] (this explains why neuroscience can also help understand the origins of gender fluidity [13]). Moreover, the brain is a nonlinear dynamical system since its behaviour changes over time [24]. Thus, due to complexity of gender fluidity and a dynamical nature of the brain and social processes relevant to gender fluidity, physical and mathematical principles underpinning the nonlinear dynamics [25] could be used to create a viable model of gender fluidity.

Indeed, using the fundamental principles of physics we can comprehend and challenge the common binary view of male and female through the prism of our basic understanding of nature. For example, let us consider a traditional digital computer and a quantum computer [26]. Similarly to an on/off light switch, a bit of a digital computer is always in one of two physical states corresponding to the binary values ‘0’ and ‘1’. However, a quantum computer uses a quantum bit (qubit) that can be in states |0=[1 0] and |1=[0 1]. These states are analogous to the ‘0’ and ‘1’ binary states of the classical digital computer. However, a qubit also exists in a continuum of states between |0 and |1, i.e. its states are a superposition =α|0 + β|1 with |α|2 + |β|2=1.

Computational algorithms based on measurements of the states of a qubit are exponentially faster than any possible deterministic classical algorithm [26]. Subsequently, it has been demonstrated that quantum mechanics can model human mental states better than any existing classical model [27-30]. In particular, it was suggested that a quantum-like superposition of human mental states can explain human preference, anomalies of decision-making and perception of optical illusions [31-41]. It is noteworthy that the models proposed in the cited papers are mostly phenomenological, i.e. they describe the psychological and behavioural science phenomena without necessarily conforming to the existing theories. Despite perceived limitations that are yet to be investigated in more detail, this approach has proven useful for analysis of complex experimental data, serving as a valuable tool for researchers working across several disciplines [42-44].

Importantly, quantum-mechanical models go hand in hand with the general public interest in gender fluidity. Indeed, it was suggested that, since the quantum physics that describes the universe is not binary, the gender must also be non-binary [45-47]. Often referring to the famous book [48], several authors elaborated this idea and tried to establish a stronger link between science and gender (see, e.g., [49,50]).

From the physical point of view, the ideas expressed in the works cited above may be illustrated using the concept of the Bloch sphere (Figure 1). When a quantum measurement is done [26,51], a closed qubit system interacts in a controlled way with an external system, thereby revealing the state of the qubit under measurement. Using the projective measurement operators M0=|0⟩⟨0| and M1=|1⟩⟨1| [26], the measurement probabilities for =α|0 + β|1 are P|0=|α|2 and P|1=|β|2, which means that the qubit will be in one of its basis states. Visually, the measurement procedure means that the qubit is projected on one of the coordinate axes (e.g., z-axis in Figure 1). Thus, we may assume that the state corresponds to the non-binary gender and that the quantum measurement makes a probabilistic prediction of whether the non-binary gender state is closer to the purely binary male or female gender.

FIG 1

Figure 1: Illustration of a projective measurement of a qubit using the Bloch sphere

However, while this theoretical approach serves as a formal model of the intuitive suggestions made in [45-47] and the relevant works, measuring a quantum system results in a collapse of the superposition quantum state that describes that system into one definite state, which is an essential feature of quantum mechanics [51]. Of course, on the level of the model, this peculiarity of quantum theory does not mean that the actual gender state is destroyed by the measurement. Nevertheless, despite the fact that quantum physics has achieved experimental success and wide-range applicability, the debate about the interpretation of the quantum measurement continues on a more philosophical level [52], complicating the comprehension of social quantum-mechanical models by non-experts in physics.

In this paper, we propose a more intuitive quantum-physical model of gender fluidity that helps avoid using the concept of quantum measurement. Figure 2a and 2b schematically illustrates how gender fluidity can be described using a combination of the concepts of binarity and magnetisation. While the depiction of the binary and non-binary genders in Figure 2a is used rather for illustration purposes (the readers interested in a more rigorous picture are referred to [19,53,54]), the arrows in Figure 2b, despite their schematic character, have a clear physical meaning—they correspond to the macrospins. The direction of the leftmost and rightmost arrows are assigned to the binary definitions of the gender. The directions of the in-between arrows phenomenologically describe non-binary gender identities.

FIG 2

Figure 2: (a, b) Illustration of how gender fluidity can be described using a combination of the concepts of binarity and the physical process of magnetisation. The arrows correspond to the macrospins. The direction of the leftmost and rightmost arrows denote the binary gender. The directions of the in-between arrows describe non-binary gender identities. (c) Result of a numerical simulation of magnetisation reversal for the static magnetic field H0 = 8 kOe. The left-and right-most macrospin arrows in Panel (b) correspond to the values 1 and 1, respectively.

Spin is a quantum-mechanical concept that is pivotal for magnetism [51,55]. Spin cannot be explained using the principles of classical physics. However, a theoretical relationship between spin and classical rotation can be established [56] (see Figure 3a and 3b). Yet, one can create a feasible classical physical model of magnetism using the concept of macrospin [57]. When in a substance the number of spins pointing in different directions is equal, the magnetic properties of this substance are cancelled. However, when most of the spins point in the same direction forming a macrospin, the substance becomes magnetic. This substance can be further magnetised using a static magnetic field to form a magnet.

The direction of the macrospin can be forced to change when the magnetised substance is affected by a dynamical (time-varying) magnetic field or an electric current. When the strength of the forcing is low, the macrospin just slightly deviates from its original orientation and then quickly returns to the equilibrium position (Figure 3c.i). As the strength of the forcing increases, the direction of macrospin significantly deviates from the equilibrium position and the macrospin starts to precess around the direction of the applied static magnetic field (Figure 3c.ii). We can visualise this precession as the motion of a spinning top, as illustrated in Figure 3b. Importantly, the macrospin can continue precessing for an indefinitely long time due to a balance between the forcing and damping processes in the substance. Furthermore, when a strong dynamic magnetic field or electric current is applied, the amplitude of the precession becomes high enough for the spin to permanently change its original direction for the opposite one via the process of magnetisation reversal (Figure 3c.iii).

FIG 3

Figure 3: (a) Illustration of precession of the magnetisation vector M representing a macrospin. (b) The movement associated with the precession of M can be compared with the wobbling motion of a spinning top. Simulated results: (c.i) M spirals back toward the direction of Heff due to the damping, (c.ii) stable precession of M around Heff and (c.iii) reversal of the direction of M. In the model, the applied field is H0 = 8 kOe. The coloured sphere is used for visualisation only.

Methods: Magnetisation Reversal Model

To demonstrate the reversal of the macrospin direction and represent the non-binary gender states illustrated in Figure 2, we create a rigorous physical model of magnetisation dynamics in a ferromagnetic metal (FM) nanostructure [58]. The readers not interested in these specific physical aspects of the model can skip reading this section without affecting their understanding of the mainstream discussion. It is known that an electric current is unpolarised since it involves electrons with a random polarisation of spins. However, when a current passes through a thin FM film with a fixed magnetisation direction it can become spin-polarised since spins become oriented predominantly in the same direction [59].

This physical effect is exploited in a spin transfer torque nano-oscillator (STNO) device that consists of a layer with a fixed direction of magnetisation M separated from a thinner FM layer by a non-magnetic metal layer [60]. When a spin-polarised current flows from the “fixed” magnetisation layer to the “free” magnetisation layer, the equilibrium orientation of magnetisation in the “free” layer becomes destabilised. Depending on the strength of the electric current, the destabilisation can lead to either stable precession of magnetisation of the “free” layer about the direction of the effective magnetic field or to a complete reversal of the magnetisation direction (see Figure 3c and the discussion around it).

We solve the Landau-Lifshitz-Gilbert equation to model the dynamics of magnetisation [61]:

M/∂t=γ [Heff × M] + TG + TSB, (1)

where γ is the gyromagnetic ratio. The first term of the right-hand side of Eq. (1) governs the precession of M (Figure 3a) about the direction of the effective magnetic field Heff=H0ez + H + Hex, where H0 is the external static magnetic field orientated along the z-axis, H is the dynamic field due to currents and magnetic sources such as demagnetising field and eddy current fields and Hex is the exchange field [62]. The dissipative torque is [61]

TGGMS1 [M × M/∂t],                                                                                      (2)

where Ms is the saturation magnetisation of the “free” magnetisation layer and αG is Gilbert damping parameter [61,62]. The Slonczewski-Berger torque is

TSB0IMS1 [M × [M × êp]],                                                                                    (3)

where I is the strength and êp is the direction of the spin polarisation of the current. The parameter σ0 accounts for the fundamental physical constants and the thickness of the “free” magnetisation layer [61]. Equation (1) is numerically solved using a finite-difference time-domain method and a typical set of material parameters used to model in STNO devices [62].

Results

We first demonstrate that the rigorous model of magnetisation reversal validates our earlier discussion of the microspin direction variation (Figure 2b). In Figure 2c we plot the dependence of the direction of M in the “free” layer as a function of the electric current strength for the applied magnetic field H0=8 kOe (from the physical point of view, we plot the dynamics of the Mz component of the magnetisation vector normalised to the saturation magnetisation Ms; H0=8 kOe is a typical field strength achievable with a laboratory electromagnet).

The trajectories of M for the static applied magnetic field H0=8 kOe computed at the simulated time of 1 ns are shown in Figure 3c. In Panel (c.i), since the current strength I=1.5 µA is low, the macrospin M spirals back toward the direction of the effective magnetic field Heff due to the effect of damping. In Panel (c.ii), the application of a stronger current I=2.25 µA results in a stable precession of M around Heff achieved when the spin transfer torque compensates the damping. Finally, in Panel (c.iii) the complete reversal of the direction of M is observed at I=3 µA.

Thus, we can see that the leftmost and rightmost macrospin arrows in Figure 2b correspond to the values 1 and 1 in Figure 2c, respectively. We can assign these states to the basis binary states, defined in terms of both |0 and |1 states in Figure 1 and binary gender roles illustrated in Figure 2a. Furthermore, we assign the values of magnetisation in between 1 and 1 to non-binary gender identifications, noting that, unlike the arrow-based representation in Figure 2a, the variation of the magnetisation from 1 and 1 is a gradual and continuous process.

It is noteworthy that by changing the value of H0 in a technically feasible range we can control the shape of the magnetisation reversal curve. This property can be used to account for the fact that the gender fluidity picture shown in Figure 2 is an idealisation but the real-life picture is more complex. Moreover, while it has not been established yet which curve would better fit a real-life gender fluidity scenario, we can reasonably assume that that curve would have step-like features. The proposed model can capture step-like features provided that the values of both H0 and electric current I are continuously varied in the modelling process. However, for the sake of clarity, in the following we will focus only on a gradually varying shape of the magnetisation reversal curve, demonstrating that a gradual variation has important implications.

Discussion

The gradual variation of the magnitude and direction of the magnetisation results in the formation of a sigmoid-like curve shown in Figure 2c. Although the illustration of gender fluidity in Figure 2a and the macrospin picture Figure 2b were intuitively designed following a sigmoid-like trajectory, a sigmoidal character of the result presented in Figure 2c has a solid scientific meaning. Indeed, it is well-established that the sigmoid function and its modifications play a central role virtually in all areas of fundamental and applied research [63-65], including the studies aimed to reveal how languages [66] and culture [67] change with time as well as how new ideas are born and spread in the society [68]. Yet, this function also fits many theoretical intuitions [69] and experimental datasets [69-73] obtained in the fields of psychology, economics, human behaviour and decision-making.

Another potential link between the physical model proposed in this paper and gender fluidity can be established based on a recent experiment that demonstrated that a perceptual illusion of having an opposite-sex body can be associated with gender fluidity [14]. Intriguingly, optical illusions have also been associated with quantum-mechanical effects [28,31,36,40,74] and they can be both modelled using the concept of qubit (Figure 1) and the process of magnetisation reversal introduced above (for details we refer the interested reader to [74] and references therein).

Yet, gender fluidity might be understood in the framework of the concept of quantum Darwinism, a theory that explains the emergence of the classical world from the quantum world following a process of Darwinian natural selection [75,76]. The quantum Darwinism theory clarifies the nature of quantum-classical correspondence and its postulates have been confirmed in a recent experiment [77]. These findings correlate with the works that have established a link between Darwinian natural selection, evolution and gender diversity [78-80].

On a general note, any model is an approximation that is valid mostly in one specific situation and can be applied, with certain limitations, to a number of similar situations. Yet, the sole purpose of a model can be to create a precedent for using certain theoretical approaches in a new area, motivating further research and development that, in turn, would produce a more practicable model. To a large extent the model proposed in this paper intends to create such a precedent, also urging all experts in the interdisciplinary field of gender studies to embrace novel approaches to the conduct of their research work.

Conclusions

To summarise, we proposed a magnetism-inspired quantum-mechanical model of gender fluidity that has the following characteristics that differentiate it from the existing classical and quantum models of human cognition, perception and decision-making:

  • Compared with the quantum models of human cognition that exploit the physical and mathematical properties of a qubit, the magnetism-inspired model is more intuitive and easier to use by experts who want to abstract from complex physical aspects. The accessibility of the model to non-experts is essential as evidenced by the previous examples, including a successful prediction of the result of major elections by sociologists who used a physical model as a black box [81,82];
  • A sigmoid-like output of the model fundamentally fits many processes that govern the dynamics of natural phenomena, societal changes and human cognition. Significant experimental evidence speaking in favour of sigmoidal approximation of human beliefs and actions has been recently produced in [83];
  • The model is linked to the quantum models of perception of optical illusions, which, intriguingly, connect it to a hypothesis claiming that gender fluidity may be explained by illusory body-sex changes.

Thus, the proposed model can be used to analyse experimental datasets such as those used to develop a sexual orientation and gender identity legal index [84]. It can also lay the foundation for advanced computer algorithms that would judiciously combine physics, statistics and machine learning techniques [74]. In particular, such algorithms can be used to create a deep learning system that can correctly recognise individuals of binary and non-binary gender [85] within the framework of the law system in a democratic state [86]. Finally, the general aspects of the model can be incorporated into school instruction materials aimed to teach the student about gender diversity.

Acknowledgements

The author would like to thank Professor Ganna Pogrebna for valuable discussions of the magnetisation reversal model and the role of sigmoid-like functions in psychology and behavioural science.

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The Relationship between Children’s Theory of Mind and Social-Emotional Health

DOI: 10.31038/PSYJ.2024613

Abstract

Mental state understanding, or ‘ToM’ (ToM), appears essential for social-emotional wellbeing. Yet, most research on this relationship has relied on measures that tap only one aspect of ToM (e.g., false beliefs) using a method that has well-documented limitations. This research expands on earlier work by including a multi-faceted measure (the Children’s Social Understanding Scale; CSUS) to (a) test whether ToM as a whole predicts social-emotional functioning, and (b) examine which facets of social emotional functioning are best predicted by ToM. Across 2 studies (n=768 children aged 3 to 12) ToM as a global construct predicted multiple facets of children’s social-emotional functioning (measured by the Strengths and Difficulties Questionnaire in both studies and the Social Skills Improvement System in Study 2). These results provide a more comprehensive understanding of the relationships between children’s ToM and various aspects of social-emotional functioning and suggest that interventions aimed at fostering children’s ToM as a whole is a fruitful avenue for improving children’s social skills and minimizing their social-emotional difficulties.

Introduction

Overview and Rationale

Social perspective-taking or ‘Theory of Mind’ (hereafter referred to as ToM) refers to the ability, or abilities, involved in inferring and reasoning about the mental states of others, such as their knowledge, beliefs, intentions, desires, perceptions and emotions. ToM is argued to be critical for effective communication and social-emotional functioning (i.e., the processes through which children develop the capacity to understand, convey and manage their thoughts and emotions and develop meaningful relationships, more broadly referred to as social emotional health [1]. The importance of this ToM capacity has captivated researchers’ interest for decades. Yet, there are still major discrepancies in the literature on how to define and measure ToM. The majority of the research on ToM to date has continued to rely heavily on laboratory-based measures that tap only one aspect of ToM [2]. The most studied aspect of children’s ToM is their false belief understanding using a laboratory-based measure called the ‘classic false belief task’ [3] (also known as the Maxi task, change of location task, or Sally-Anne task). This task measures a child’s ability to understand that others can have beliefs that are different from reality. As of January 2024, the classic false belief task had been cited in the literature over 9,681 times and is still considered by many to be the litmus test or ‘gold standard’ for ToM understanding in children, for example [4,5]. Indeed, according to a recent review of the literature on children’s ToM from ages 0 to 5, the false belief task was used in the vast majority (approximately 75%) of the research [2]. Importantly, ToM is a comprehensive capacity that is much broader than simply reasoning about false beliefs. Mental state understanding encompasses a variety of mental states, including reasoning about knowledge states, desires, perceptions, emotions and intentions [6,7].

Laboratory-based research has examined the developmental trajectory of different facets of ToM through a variety of different measures. Indeed, Beaudoin and colleagues [2] identified 220 different measures to assess one or more aspects of a child’s ToM. Although these measures are useful in illustrating the developmental progression of ToM understanding, most are not wellsuited for capturing individual differences [6,8]. Early work suggested that individual differences in ToM are associated with a host of positive outcomes for children’s social and emotional functioning such as better interpersonal relationships and increased social competence [9-12]. Yet, given the reliance on measures that tap only one facet of ToM, it is an open question whether ToM, as a whole, is associated with children’s social-emotional functioning or whether those findings are specific to the particular aspect of ToM measured in any given study (most frequently the false belief task). Similarly, the previous work, which has largely focused on a single measure of ToM, tells us little about which aspect of ToM is most important for children’s social-emotional functioning. Like ToM, social-emotional functioning also encompasses many aspects. In the vast majority of earlier work different researchers have tended to focus on one particular facet of social-emotional functioning (e.g., prosocial behaviour or peer relationships) in relation to some measure of ToM. While this piecemeal approach has tremendous value in its own right, it does not identify which aspects of children’s social-emotional functioning are best predicted by their ToM. The overarching aim of the current research was to provide a more unified account of the relations between children’s ToM and their social-emotional functioning by using multifaceted measures. Specifically, to assess ToM globally we employ a comprehensive and ecologically valid measure of ToM – the Children’s Social Understanding Scale (CSUS [8]) that includes 6 different facets of mental states and encompasses both basic and complex mental state reasoning. We also employ two comprehensive and well-established measures of social-emotional functioning-the Strength and Difficulties Questionnaire (SDQ [13]) and the Social Skills Improvement System (SSIS [14]), which will give us a global measure of children’s social emotional functioning as well as a measure of 5 specific facets including Prosocial Behavior, Peer Relations, Emotional Functioning, Conduct Problems, and Hyperactivity/Impulsivity.

Background: Defining and Measuring ‘ToM’

Despite the fact that ToM had been intensely studied for over 40 years, see for example, [15] there are still many inconsistencies in the literature regarding what constitutes a ToM. Some researchers describe ToM as a single process or achievement-the recognition that the mind can misrepresent reality, which is often examined using a single paradigm assessing false belief understanding. Wimmer and Perner [3] developed what is now commonly referred to as the classic false belief task (also known as the Maxi task, change of location task or Sally-Anne task) to provide the first test of whether children had a ToM. This task measures a child’s ability to understand that others can have beliefs that are different from reality. In this task, children observe a scenario where a protagonist (e.g., Sally) hides an object in one location (e.g., basket) and then leaves the scene. Once Sally is gone, another character (e.g., Anne) hides the object in a different location (e.g., box). Children are then asked where Sally will look for the object when she returns. To pass, a child must indicate that Sally will look in the basket where she originally left it; this response would reveal a child’s understanding that the mind can misrepresent reality. Other variations of this task exist (e.g., the unexpected contents task, [16-18]). Across hundreds of experimental replications, 3-year-olds consistently fail, and it is not until around 4 or 5 years of age that children pass this task [3,16,19,20; see 21 for a meta-analysis]. However, several researchers have challenged the validity of the standard false belief tasks [22], proposed alternative explanations for the age-related changes observed between 3 and 5 years of age [23], and demonstrated that much younger children, perhaps even infants, can reason about false beliefs if the tasks are made easier by removing extraneous task demands [24].

Although the false belief test has been argued to be the best test of whether children have a concept that the mind represents the world, many researchers highlight that there is much more to ToM than reasoning about false beliefs. The mind is comprised of various mental states (e.g. goals, perceptions, knowledge, beliefs, intentions, and emotions). Throughout this manuscript when we use the term ToM, we are not referring to a singular capacity to reason about false beliefs, instead we are referring to a global construct comprised of a set of processes that involve inferring and reasoning about the mental states of others, including their desires, perceptions, knowledge, beliefs, intentions and emotions. Considerable developmental research has focused on assessing different aspects of ToM by designing a range of laboratory-based measures revealing that some aspects of ToM appear to develop quite early, while others develop quite late. For example, by 14-to 18-months of age infants show some early evidence of ToM in their ability to imitate others’ goals and intentions [25-28] and recognition of how desires are related to emotions [29]. By the age of 2 children can talk systematically about a small set of emotional states (e.g., happiness, sadness, anger, fear, surprise, disgust; [30]) and label emotional expressions [31,32]. By at least age 3, children understand that others differ in their knowledge states (e.g., [7,33-35]). And, as noted above, by age 4 or 5 children appear to understand that a person can hold a belief that is false or inconsistent with reality (e.g., [3,7,21]). Although false belief understanding is sometimes argued to be the last ‘major’ developmental milestone in a young child’s mental state understanding (i.e., at this point the child appears to have at least a basic understanding of intentions, preferences, desires, knowledge, beliefs, and emotions), ToM development does not stop at the age of 4 or 5 [9]. Rather, individuals continue to gain insight into the complexities of the mind throughout much of their life. Here, the foundational or conceptual basics of ToM are established fairly early in development but continue to grow in complexity [9,36,37]. For instance, despite the fact that children possess an understanding of emotion words by age 2, the ability to effectively infer, integrate and reason about the emotional states of others gets more complex with age (e.g., understanding mixed emotions, nostalgia). Complex ToM understanding is further exemplified by concepts such as sarcasm, humor, bluffing, double bluffing, misunderstanding and irony [36,37]. These types of non-literal situations are later developing skills that require a child to understand the unseen or hidden meaning of the message [38]. For instance, it is not until around 9 or 10 years of age that children begin to demonstrate an understanding of sarcasm and irony [39]; for additional examples of complex ToM tasks see [40-44].

Despite the wealth of evidence that ToM is a multi-faceted and complex concept that consists of much more than false belief understanding, the majority of the research on ToM and its relationship to social-emotional functioning, has continued to rely on false belief reasoning (for a review [2]). Furthermore, these measures were never intended to be used to measure individual differences, despite their widespread use as such, and are not well-suited for capturing individual differences (e.g. they are pass/fail). Another major limitation with the bulk of the previous literature relying on standard false belief tasks is that these measures unnecessarily involve overcoming a fundamental cognitive bias known as the ‘curse of knowledge’ bias, the tendency to be biased by one’s own knowledge when reasoning about a more naive perspective [33,45]. As such, the individual differences in children’s performance on this task may reflect more general cognitive abilities rather than, or in addition to, differences in ToM (for recent evidence [23,46], see also [6,47]).

Do Individual Differences in ToM Predict Social-Emotional Functioning?

Notwithstanding the aforementioned limitations in measuring ToM, a large body of research suggests that individual differences in ToM (or at least one or more aspects of it) predicts a range of outcomes for children’s social and emotional functioning with the bulk of the research focused on 5 key aspects of social emotional functioning, each reviewed below: Prosocial Behavior, Peer Relationships, Hyperactivity and Impulsivity, Conduct Problems, and Emotional Symptoms.

ToM and Prosocial Behavior

Individual differences in ToM have long been argued to predict both empathy and prosocial behaviour (e.g., [48,49]). The development of empathy is believed to be essential for social-emotional functioning because it enables one to understand others’ mental states, which helps to foster the development of prosocial (e.g., helping, sharing) behaviour. An abundance of the work on empathy and prosocial behaviour has relied heavily on standard false belief tasks. For example, researchers assessed 4-to 6-year-old children on measures of language ability, false belief understanding, emotion comprehension and prosocial orientation [50]. The results revealed that children’s language ability and false belief understanding significantly correlated with their emotion comprehension and prosocial orientation. These findings suggest that children’s ability to understand other people’s beliefs (specifically false beliefs) is related to their emotion comprehension skills (e.g., understanding the expression and cause of emotions) and inclination to act prosocially [50].

In another study, researchers assessed 4-year-olds’ ToM (using aggregated scores across seven tasks, including change in location false belief tasks, false beliefs tasks involving a nasty surprise or nice surprise and a deception game task) and found that more accurate ToM was associated with increased frequencies of cooperative planning, less conflict and increased communication with friends [51]. Performance across the ToM and deception game tasks was aggregated, making it unclear whether it is primarily false belief understanding that contributes to these social-emotional benefits or whether these findings apply to ToM more generally. Consistent with this, in another study researchers assessed the relationship between children’s prosocial behavior (based on teacher report and peer nominations) and children’s ToM (using an unexpected contents false belief task, change in location false belief task, second-order false belief task, belief-desire reasoning task and a mixed emotion understanding task). The results revealed that children with higher aggregate ToM scores tended to have greater prosocial behavior [52]. Although a mixed emotion understanding task was included as one of the 5 aggregated items it is again unclear whether it is false belief understanding specifically that predicts prosocial behaviour (or some third variable such as the curse of knowledge bias) or whether these findings indicate a relationship between one’s general ToM abilities and one’s tendency to engage in prosocial behavior.

Importantly, not all of the research on empathy and prosocial behaviour has relied so heavily on false belief understanding. As a notable exception, a meta-analysis of 76 studies [53] examining prosocial behavior in relation to affective ToM (using a hidden emotions task) and cognitive ToM (using deception, diverse desires, intention understanding and knowledge access tasks) found that children (ages 2-12) with higher composite ToM scores also had higher scores on measures of prosocial behaviour. As such, this meta-analysis provides compelling evidence that children with better ToM skills more generally are more likely to act prosocially. The current research aims to replicate this finding using alternative methods and extend this work by addressing whether ToM is a better predictor of prosocial behavior or other aspects of social-emotional functioning.

ToM and Peer Relationships

Not surprisingly given the links between ToM and prosocial behaviour, and prosocial behaviour and peer relationships, individual differences in ToM understanding has also been shown to predict individual differences in peer relationships. Again, a significant amount of this work has relied heavily on classic false beliefs tasks, in large part due to historical limitations in measurement options, nascent knowledge of the ToM construct, and ‘bandwagoning’, rather than due to researcher negligence. For example, a metaanalysis of 20 studies on children 2 to 10 years of age using standard first-and second-order false belief tasks and measures of peer popularity found that children with increased false belief understanding tend to be more well-liked [54]. Similarly, De Rosnay and colleagues [55] found that children’s false belief understanding was significantly related to their everyday use of mindful conversational skills in real-world social interactions with peers.

Although an overabundance of the research on ToM and peer relationships has utilized false belief tasks, some researchers have importantly used measures in addition to false belief understanding. For instance, Slomkowski and Dunn [56] looked at affective ToM in addition to false belief understanding at 40 months of age. Then, at 47 months of age these same children were examined on their connected communication with a friend. The results revealed that false belief understanding was significantly associated with more connected communication with peers, while affective ToM, although still significant, was less strongly related to connected communication [56]. Newer research has included tasks other than false belief tasks but created aggregated ToM scores (e.g., [57]), making it unclear how much of the relationship is driven by false belief reasoning versus ToM more generally.

ToM and Hyperactivity/Impulsivity

In a smaller body of literature, ToM understanding has also been shown to predict hyperactivity and impulsivity. Again, much of the work on hyperactivity and impulsivity has relied on false belief understanding. For example, research has shown that children with Attention Deficit Hyperactivity Disorder (i.e., ADHD) display impairments on first-and second-order false belief tasks (e.g., [58,59]. In other work with a nonclinical sample of 5-year-olds, researchers analyzed the relations among cognitive flexibility (using the dimension change card sort task), ToM (using aggregated scores across six tasks, including diverse desire, diverse belief, knowledge access, contents false belief, low verbal false belief and second-order false belief) and hyperactivity and inattention (using the SDQ). Those with higher aggregate ToM scores and scores of cognitive flexibility had lower levels of hyperactivity and inattention. The researchers suggested that children’s ToM understanding may mediate the adverse relationship between cognitive flexibility and hyperactivity and inattention early in development [60].

It is important to acknowledge that some research on hyperactivity and impulsivity has observed a connection between ToM and ADHD without including false belief tasks. For example, Maoz and colleagues [61] examined ToM in 10-year-old children with ADHD and healthy controls, using the social faux pas task and the self-reported Interpersonal Reactivity Index, revealing that children with ADHD demonstrated significantly lower levels of ToM compared with healthy controls. Consistently, research examining 7-to 11-year-old children with ADHD had difficulty in identifying others’ intentions and emotions (e.g., [62]). Similarly, research on ToM using emotion recognition tasks found that children with ADHD display deficits in recognizing certain facial expressions, such as anger and fear (e.g., [63-65]). It appears that at least some aspects of ToM are important predictors of hyperactivity and impulsivity. However, it remains unclear whether ToM as a whole predicts hyperactivity and impulsivity or only certain aspects of ToM.

ToM and Conduct Problems

Not surprisingly given the relationship between ToM and hyperactivity and impulsivity, ToM has also been shown to predict conduct problems (e.g., lying, cheating, aggression). Again, some work linking ToM with conduct problems has relied on false belief understanding. For example, one study revealed that 6-12-year-olds with conduct problems (but not their typically developing peers) tended to display deficits in false belief reasoning [66]. However, other work linking ToM to conduct problems suggests this link is not limited to false belief understanding. For instance, ToM was impaired in a sample of 9-11-year old children with conduct problems using the Reading the Mind in the Eyes Test, in which participants must identify cognitive and affective mental states from photos of the eye region of faces [67]. Similarly, in another study assessing executive function (i.e., inhibition, working memory, set shifting), affective ToM (using the ‘facial scale’ of the Cambridge Mindreading Face-Voice Battery), cognitive ToM (by assessing children’s reasoning about others’ beliefs and desires) and parent-reported conduct problems (using the Child Behaviour Checklist) in 9-to 13-year-olds found a small negative correlation between ToM, specifically in affective aspects, and conduct problems [68]. In contrast, researchers examining the longitudinal relation between executive function (i.e., flexibility, working memory, inhibition), ToM (using a cartoon task with both cognitive and affective stories) and parent-reported conduct problems (using the SDQ) in children ages 6 to 11 found that higher executive function and both cognitive and affective ToM abilities predicted less conduct problems 1 year later [69]. Again, it appears that at least some aspects of ToM are important predictors of conduct problems; however, it remains unclear whether ToM as a whole predicts conduct problems or whether specific aspects are better predictors.

ToM and Emotional Symptoms

Finally, ToM understanding also predicts emotional symptoms such as sadness and depression, at least in adults. A meta-analysis of 18 studies examining the relationship between ToM and Major Depressive Disorder (i.e., MDD) in adults revealed that deficits in ToM can be a risk factor for depression and accompanying psychosocial impairment, with the level of ToM impairment predicting the severity of the depressive symptoms [70,71]. Whether deficits in ToM as a global construct are the risk factor for depression or whether some aspects of ToM play a greater role remains unclear as the metaanalyses collapsed across various ToM measures, including various false belief tasks. Intriguingly, other research linking ToM to emotional symptoms, using novel variants of the standard false belief task, have found that adults with chronic depression showed difficulties with false belief tasks concerning emotion states but not ones involving visual-spatial representations [72]. Whereas research assessing ToM in adolescents with MDD and healthy controls found that those with MDD were able to solve a basic false belief task (e.g., unexpected contents false belief task), but had difficulty with more complex ToM (e.g., second-order false belief, the reading the mind in the eyes test, or the hinting or sarcasm tasks) compared to healthy controls [73]. This research highlights that in addition to investigating a global deficit in ToM versus a specific deficit in one type of ToM (e.g., false belief reasoning) it may be fruitful to assess individual differences in complex vs basic ToM. Unfortunately, literature examining ToM in young children with emotional symptoms is quite scarce but provides impetus for further research in this area. For instance, research following a natural disaster (i.e., the 2012 earthquake in Italy) suggests that better false belief understanding can act as a protective factor against Post-Traumatic Stress Disorder (i.e., PTSD; [74]). Finally, a promising inaugural study on the relationship between emotional symptoms and ToM (using the Strange Stories task assessing various complex mental states), revealed that children with higher ToM scores experienced fewer depressive symptoms and fewer symptoms of panic disorder and separation anxiety [75].

Motivation for the Current Research

Overall, the research reviewed above illustrates that there is little consistency in how ToM is measured in any given study (with the exception of the use of false belief tasks that historically dominated the field). As a result, it is unclear whether ToM, as a whole, is associated with children’s social-emotional functioning, or whether one or more aspect of ToM might be better predictors of social-emotional functioning. In juxtaposition, the literature showing ToM predicting social-emotional functioning is predominately piecemeal (i.e., examining only one social-emotional outcome such as peer relations or emotional symptoms or hyperactivity in a given study), making it impossible to compare the strength of the social-emotional correlates of ToM with one another. In other words, which aspects of children’s social-emotional functioning are best predicted by their ToM? A critical focus of the current research is to fill these aforementioned gaps: To better understand the nature and strength of the relationships between children’s ToM using multi-faceted global measure of ToM, and social-emotional functioning including measures of 5 key domains: emotional symptoms, conduct problems, hyperactivity and impulsivity, peer relationship problems and prosocial behaviours. This research approach also aims to provide the critical groundwork needed to establish whether it is false belief reasoning, or theory of mind as a whole, that should be targeted in prevention and intervention approaches to improve children’s social emotional. To measure ToM we chose the Children’s Social Understanding Scale (CSUS; [8], which provides: (a) a parent-report measure of individual differences in ToM, that is (b) ecologically valid because it captures everyday behaviour over long periods of time, (c) allows for greater variability (versus pass/fail measures), (d) provides excellent test-retest reliability and internal consistency (Cronbach’s alpha=0.89; [76]), and (e) importantly encompasses the multi-faceted nature of ToM (i.e., including items assessing 6 different facets of belief, knowledge, perception, desire, intention and emotion understanding and encompassing both basic and complex ToM understanding). Parent-report measures are incredibly informative as most young children spend the majority of their time with their parents who are able to observe their behaviour across different contexts over an extended period of time. As a result, parents are uniquely situated to assess their child’s ToM, and may do so better than laboratory-based measures of ToM alone [8].

We also chose a comprehensive, and well-established parent-report measure of socialemotional functioning: the Strengths and Difficulties Questionnaire (SDQ; [13]). The SDQ displays good test-retest reliability (Cronbach’s alpha=0.62) and internal consistency (Cronbach’s alpha=0.73; [77]) and, in addition to providing a composite score of children’s social-emotional difficulties, importantly distinguishes between five different aspects of socialemotional functioning: emotional symptoms, conduct problems, hyperactivity and impulsivity, peer relationship problems and prosocial behaviours. Study 1 examined whether ToM, as a global construct, predicts different aspects of children’s social-emotional functioning, and if so, which of the five facets of social-emotional functioning are best predicted by ToM. We hypothesized that global ToM abilities would reliably predict children’s overall social-emotional functioning as well as each individual facet. Specifically, we predicted that children’s global ToM would positively predict prosocial behaviour, and negatively predict the peer relationship problems, hyperactivity and impulsivity, conduct problems and emotional symptoms. Given the piecemeal nature of the extant literature, we did not make specific predictions about the magnitude of the relationships between ToM and the five facets of social-emotional functioning. Study 2 replicated and extended this work in a younger sample.

Method: Study 1

Participants

Our sample consisted of 376 parents of children ranging in age between 3 and 12 years (M=6;3, SD=2;0, range: 3;0-12;7) with 49.5% males (n=186). Eighty-eight percent (n=309) of the children in our sample were born in Canada. Of the 41.8% of parents who provided information on their child’s ethnicity, 67.5% indicated Caucasian, 20.4% indicated Asian, and 12.1% indicated another option or mixed. Participants were recruited through preschool programs, schools and childcare facilities, and a large database of families who expressed interest in participating in research. The majority of participants completed paper and pencil versions of the survey in person while their child participated in another research project. Parents of children recruited through preschools and schools had the option to complete the survey online. The majority of participants were the mothers who were the primary caregivers. Sample size was not predetermined but rather parent data was collected until the research projects their children were participating in were complete. To be included in the sample, parents needed to complete a minimum of 80% of the items for each measure. For included participants, missing data were handled according to the procedures outlined by Tahiroglu and colleagues [8] and Goodman [13].

Materials and Procedure

Parents were administered the Strength and Difficulties Questionnaire (SDQ; [13]) and the Children’s Social Understanding Scale (CSUS; [8]), as well as some demographic questions (e.g. ethnicity, sex). The SDQ consists of 25-items focused on five different components of social-emotional health: emotional symptoms (e.g., often unhappy, depressed, or tearful), conduct problems (e.g., generally well behaved, usually does what adults request; reverse coded), hyperactivity and impulsivity (e.g., constantly fidgeting or squirming), peer relationship problems (e.g., rather solitary, prefers to play alone), and prosocial behavior (e.g., considerate of other people’s feelings). Parents rated their child’s behavior over the previous 6 months on each item with answers ‘Not True’ (0), ‘Somewhat True’ (1) and ‘Certainly True’ (2). The prosocial behaviors comprise the Strengths subscale, whereas the other four components—emotional symptoms, conduct problems, hyperactivity and impulsivity and peer relationship problems—comprise the Difficulties subscale.

The CSUS is a parent-report measure of children’s ToM, their understanding of mental states, including beliefs, desires, emotions, intentions, perceptions and knowledge. The CSUS consists of 18-items rated on a 4-point scale, including ‘Definitely Untrue’ (1), ‘Somewhat Untrue’ (2), ‘Somewhat True’ (3) and ‘Definitely True’ (4). Items include: “My child talks about differences in what people like or want (e.g., “you like coffee but I like juice”)”, “My child has trouble figuring out whether you are being serious or just joking” (reverse coded), and “My child talks about the difference between the way things look and how they really are (e.g., “It looks like a snake but it’s really a lizard”). See Appendix A for a full list of items. In the initial CSUS work, Tahiroglu and colleagues [8] sought to broaden the repertoire of measures available for assessing developmental differences in ToM. In Study 1, the authors demonstrated that they were able to create a psychometrically sound measure of ToM based on parent-reports. The CSUS scale displayed excellent internal consistency (i.e., Cronbach’s alpha=0.89), test-retest reliability (r=0.88, p<0.001), and convergent validity with children’s performance on laboratory-based ToM tasks (e.g., contents false-belief tasks, knowledge-access tasks, level two perspective-taking tasks). In Study 2, Tahiroglu and colleagues [8] further validated the CSUS by collecting data from a new sample of children and parents with a different set of ToM tasks. Again, the results revealed high internal consistency and revealed that parents’ ratings significantly correlated with children’s ToM performance even after controlling for age. In Study 3, Tahiroglu and colleagues [8] collected data with a slightly older sample of children using a more sophisticated ToM measure (i.e., restricted view task), while controlling for several other cognitive abilities (i.e., prospective memory, working memory, planning). The results of Study 3 provide evidence for construct validity by demonstrating a relation between parent-reported ToM via the CSUS and children’s ToM performance even when other cognitive abilities were held constant.

Results and Discussion

Analysis of the SDQ

Parents’ responses on the SDQ were coded using the scoring system outlined by the measure’s creators [13]. Scores were summed to determine the Strengths (M=8.31, SD=1.73, max score=10) and Difficulties scores (M=8.67, SD=5.06, max score=40). The internal consistency of the Strengths (Cronbach’s alpha=0.73) and Difficulties (Cronbach’s alpha=0.76) subscales were good. The reliability of the Difficulties subscales in our sample was also acceptable given that each subscale is comprised of only 5-items: emotional symptoms (Cronbach’s alpha=0.61), conduct problems (Cronbach’s alpha=0.56), hyperactivity and impulsivity (Cronbach’s alpha=0.78) and peer relationship problems (Cronbach’s alpha=0.57). See Table 1 for Subscale Means and SDs.

Table 1: Study 1: Means and Standard Deviations of the SDQ subscales

Mean(Max=10)

SD

Emotional Symptoms

1.78

1.76

Conduct Problems

1.54

1.49

Hyperactivity and Impulsivity

3.72

2.52

Peer Relationship Problems

1.47

1.77

Prosocial Behaviour

8.31

1.73

A 2 (sex: female, male) x 2 (subscale: Difficulties, Strengths) ANOVA indicated a significant effect of child’s sex on SDQ scores, F(2, 373)=8.11, p<0.001. That is, significant differences emerged between females and males on both the Difficulties, F(1, 374)=5.95, p=0.015, and Strengths subscales, F(1, 374)=14.65, p<0.001. Specifically, females scored lower than males on the Difficulties subscale (females: M=8.09, SD=4.80; males: M=9.32, SD=5.01) and higher than males on the Strengths subscale (females: M=8.57, SD=1.66; males: M=7.88, SD=1.81). There were no differences in SDQ scores based on nationality (i.e., born in Canada or not) or by ethnicity, thus these variables were excluded from subsequent analyses. Next, we tested whether age predicted SDQ scores. After accounting for the significant effects of sex, age did not significantly predict scores on the Difficulties portion of the SDQ, t=-.018, p=0.99; however, age significantly predicted scores on the Strengths portion of the SDQ, ΔR2=0.015, β=0.133, t=2.65, p=0.008. That is, children’s prosocial behaviour increased with age, whereas their social-emotional difficulties remained stable across age.

Analysis of the CSUS

Parents’ responses on the CSUS were coded using the scoring system outlined by the measure’s creators [8]. Scores were averaged to determine the CSUS score (M=3.32, SD=0.37, max score=4). The internal consistency of the scale in the current sample was excellent (Cronbach’s alpha=0.83) and consistent with the validation of the CSUS. There were no differences in CSUS scores based on sex, nationality (i.e., born in Canada) orethnicity. Next, we tested whether age predicted CSUS scores and found that age positively predicted CSUS scores, ΔR2=0.097, β=0.31, t=6.41, p<0.001, consistent with earlier findings [8].

Predictive effects of CSUS on SDQ

Of particular interest in this study was whether children’s ToM relates to their social-emotional functioning. The CSUS positively predicted the Strengths subscale of the SDQ, r(376)=0.30, p<0.001 and negatively predicted the Difficulties subscale of the SDQ, r(376)=-.21, p<0.001. Even after controlling for the effects of sex and age, the CSUS positively predicted the Strengths subscale of the SDQ, ΔR2=0.07, β=0.28, t=5.48, p<0.001 and negatively predicted the Difficulties subscale of the SDQ, ΔR2=0.05, β=0.-23, t=-4.30, p<0.001. In other words, an increased understanding of mental states predicts a twofold pattern in social-emotional functioning; children with a greater understanding of mental states demonstrate increased social-emotional strengths, in terms of more prosocial behaviors, and fewer social-emotional difficulties.

To determine precisely which social-emotional difficulties decrease with increased mental state understanding, we examined the predictive effects of the CSUS on the four Difficulties subscales of the SDQ. As seen in Table 2, even after controlling for the effects of sex and age, both the conduct problems subscale and the hyperactivity and impulsivity subscale were negatively correlated with children’s mental state understanding. A small negative correlation (r=-.09, p=0.056, one-tailed) was also observed between peer relationship problems and ToM.

Table 2: Study 1: Partial Correlations controlling for the child’s sex and age: CSUS scores independently predict conduct problems and hyperactivity and impulsivity of the SDQ Difficulties subscales.

Emotional Symptoms

Conduct Problems Hyperactivity and Impulsivity Peer Relationship Problems

Emotional Symptoms

Conduct Problems

r=0.19

p<0.001

Hyperactivity and Impulsivity

r=0.18

p<0.001

r=0.43

p<0.001

Peer Relationship Problems

r=0.27

p<0.001

r=0.20

p<0.001

r=0.08

p=0.142

CSUS

r=0.02

p=0.755

r=-.19

p<0.001

r=-.24

p<0.001

r=-.09

p=0.102

The critical questions of Study 1 were: (1) whether ToM as a global construct predicts children’s social-emotional functioning, and (2) which of the five facets of social-emotional functioning are best predicted by ToM. As hypothesized, we found that even when controlling for the effects of sex and age, the CSUS was positively predictive of children’s social-emotional strengths (i.e., prosocial behaviour) and negatively predictive of social-emotional difficulties. In terms of the five facets of children’s social-emotional functioning the largest relationship was observed between children’s prosocial behaviour and ToM. In terms of children’s socialemotional difficulties, the largest effects were observed in hyperactivity and impulsivity, followed by conduct problems. A small negative relationship was also observed with peer relationship problems. The current research importantly expands upon our understanding of the critical relationship between children’s ToM and their social-emotional functioning by utilizing multifaceted measures that provide a more comprehensive and ecologically valid picture of children’s functioning. Importantly, this research expands upon earlier work by revealing that children’s ToM as a whole (i.e., their understanding of desire, perception, knowledge, belief, intention and emotion), not just performance on false belief tasks, predicts their social-emotional functioning.

One goal of Study 2 was to determine whether the results of Study 1 would replicate in another community sample with children of a narrower age-range (e.g., Study 1=3-to 12-yearolds; mean age=6;3 versus Study 2=3-to 7-year-olds; mean age=4;6). The CSUS was originally designed to tap social understanding in younger age ranges (i.e., 3-6 years), at a time where the most developmental change occurs in ToM [8]. Out of convenience, Study 1 included participants through age 12. We observed individual differences in performance on the CSUS, even in these older ages; however, we wanted to ensure these results would replicate in a similarly-sized sample with a younger mean age. A second objective of Study 2 was to examine the relation between children’s ToM and an even broader range of children’s social-emotional skills. To that end, we added the Social Skills Improvement System (SSIS; [14]), which has seven different subscales that capture additional aspects of children’s social-emotional skills (i.e., communication, cooperation, assertion, responsibility, engagement, empathy, and self-control). Again, our primary research question was whether ToM as a whole predicts children’s social-emotional functioning and if so, which aspects of their social emotional functioning are most strongly predicted by ToM. We hypothesized that, consistent with the results of Study 1, ToM would reliably predict young children’s overall social-emotional functioning as measured by the SDQ, as well as the broader range of social-emotional skills measured by the addition of the SSIS.

Method: Study 2

Participants

Our sample consisted of 392 parents of children ranging in age from 3 to 7 years (M=4;6, SD=1;2, range: 3;0-7;9) with 47.7% female (n=187). Of the 92.3% of parents who provided information on where their child was born, 89.8% (n=352) indicated Canada. Of the 89.5% of parents who provided information on their child’s ethnicity, 44.6% indicated Caucasian, 13.0% indicated Asian, and 31.9% indicated another option or mixed. Participants were recruited through the same means as Study 1. Missing data were handled the same way as in Study 1. A subset of parents of children (n=88; 56% female) ranging in age from 3 to 7 years (M=4;4, SD=1.1; range: 3;0-7;4) completed the SSIS at the end of the survey. To be included in the analyses on the SSIS, a parent needed to complete a minimum of 80% of the items. For included participants, missing data were handled according to the procedures outlined by the measures’ authors (i.e., [14]).

Materials and Procedure

The procedure and measures for the SDQ and CSUS were identical to Study 1. Parents were also administered the Social Skills Improvement System (SSIS; [14]). The SSIS consists of 46-items focused on seven different subscales to better capture different aspects of children’s social-emotional skills, beyond the simple 5-item prosocial subscale of the SDQ used in Study 1, including: communication (e.g., speaks in appropriate tone of voice), cooperation (e.g., follows classroom rules), assertion (e.g., asks for help from adults), responsibility (e.g., takes responsibility for part of a group activity), engagement (e.g., participates in games or group activities), empathy (e.g., shows concern for others), and self-control (e.g., uses appropriate language when upset). Parents rated their child’s behaviour using a 4-point scale on each item with answers ‘Never’ (0), ‘Seldom’ (1), ‘Often’ (2) and ‘Almost Always’ (3).

Results and Discussion

Analysis of the SDQ

Responses on the SDQ were coded using the scoring system outlined by the measure’s creators [13]. Scores were summed to determine the Strengths (M=7.95, SD=1.85, max score=10) and Difficulties scores (M=8.12, SD=4.65, max score=40). The internal consistency of the Strengths (Cronbach’s alpha=0.73) and Difficulties (Cronbach’s alpha=0.76) subscales were good and consistent with Study 1. The internal consistencies of the Difficulties subscales were also acceptable for the 5-item subscales: emotional symptoms (Cronbach’s alpha=0.63), conduct problems (Cronbach’s alpha=0.64), hyperactivity and impulsivity (Cronbach’s alpha=0.79) and peer relationship problems (Cronbach’s alpha=0.56). See Table 3 for subscale Means and SDs.

Table 3: Study 2: Means and Standard Deviations of the SDQ subscales

Mean(Max=10)

SD

Emotional Symptoms

1.62

1.73

Conduct Problems

1.41

1.47

Hyperactivity and Impulsivity

3.62

2.41

Peer Relationship Problems

1.47

1.58

Prosocial Behaviour

7.95

1.85

A 2 (sex: female, male) x 2 (subscale: Difficulties, Strengths) ANOVA indicated a marginally significant effect of the child’s sex on SDQ scores, F(2, 289)=2.86, p=0.058. That is, marginally significant differences emerged between females and males on the Strengths subscale, F(1, 390)=5.62, p=0.018, but not on the Difficulties subscale, F(1, 390)=1.34, p=0.249. Specifically, females scored higher than males on the Strengths subscale (females: M=8.18, SD=1.86; males: M=7.74, SD=1.81) and similar to males on the Difficulties subscale (females: M=7.84, SD=4.45; males: M=8.38, SD=4.82). Consistent with Study 1, there were no differences in SDQ scores based on nationality or by ethnicity (all ps > .10), thus these variables were excluded from subsequent analyses. As in Study 1, age did not significantly predict the Difficulties portion of the SDQ, t=-.41, p=0.682, but predicted scores on the Strengths portion of the SDQ, ΔR2=0.03, β=0.13, t=2.55, p=0.011. That is, children’s prosocial behaviour tended to increase with age, whereas their social-emotional difficulties remained relatively stable across age.

Analysis of the CSUS

Responses on the CSUS were coded using the scoring system outlined by the measure’s creators [8]. Scores were averaged to determine the CSUS score (M=3.25, SD=0.41, max=4). The internal consistency of the scale was excellent (Cronbach’s alpha=0.84) consistent with the validation of the CSUS. Consistent with Study 1, there were no differences in CSUS scores based on sex, nationality, or ethnicity, all ps > .10. Age positively predicted CSUS scores, ΔR2=0.18, β=0.42, t=9.28, p<0.001, consistent with Study 1 and earlier work [8].

Predictive effects of CSUS on SDQ

A central hypothesis of this research was that children’s ToM as a broad multi-faceted construct would predict their social-emotional functioning. Indeed, even after controlling for the effects of sex and age, the CSUS positively predicted the Strengths subscale of the SDQ, ΔR2=0.17, β=0.41, t=7.97, p<0.001 and negatively predicted the Difficulties subscale of the SDQ, ΔR2=0.16, β=-.44, t=-8.44, p<0.001. In other words, as in Study 1, an increased understanding of mental states predicted a two-fold pattern in social-emotional functioning: Children with a greater understanding of mental states exhibit significantly greater social-emotional strengths, in the form of prosocial behaviours such as helping and sharing, and simultaneously significantly fewer social-emotional difficulties.

Predictive effects of CSUS on SDQ Difficulties subscales

To determine which social emotional difficulties decrease with increased ToM, we examined the predictive effects of the CSUS on the four Difficulties subscales of the SDQ. As seen in Table 4, even after controlling for the effects of sex and age, all aspects of social emotional difficulties negatively correlated with children’s ToM (all ps<0.001). That is, poorer mental state understanding as measured by the CSUS uniquely, individually, predicted children’s conduct problems, peer relationship problems, hyperactivity and impulsivity, and emotional symptoms.

Table 4: Study 2: Partial Correlations controlling for the child’s sex and age: CSUS scores independently predict each of the SDQ Difficulties subscales.

Emotional Symptoms

Conduct Problems Hyperactivity and Impulsivity Peer Relationship

Problems

Emotional Symptoms

Conduct Problems

r=0.21

p<0.001

Hyperactivity and Impulsivity

r=0.10

p=0.049

r=0.44

p<0.001

Peer Relationship Problems

r=0.32

p<0.001

r=0.22

p<0.001

r=0.05

p=0.303

CSUS

r=-.16

p=0.001

r=-.36

p<0.001

r=-.30

p<0.001

r=-.20

p<0.001

The results of the analyses examining which facets of children’s social-emotional difficulties are uniquely predicted by mental state understanding were similar but notably not identical across the two studies. In Study 1, the relationship between the CSUS and peer relationships was very small and only trended toward significance, and the CSUS and emotional symptoms were not significantly correlated. In contrast, in Study 2 all aspects of children’s difficulties were negatively correlated with ToM, although the smallest relationship was observed in emotional symptoms, suggesting it is the least related to ToM of the facets of socialemotional health included here. An examination of the means and standard deviations of the SDQ subscales (refer to Tables 1 and 3) reveals remarkable similarities, except the subscale means are somewhat greater in Study 1; reflecting greater difficulties in the older sample. One possible explanation of the mixed results for emotional symptoms is that the variability in the emotional symptoms for the older children may be more heavily influenced by factors other than ToM (e.g., stress at school, biological changes). Overall, the results of these two large sample studies reveal highly consistent results demonstrating that children’s mental state understanding positively predicts children’s social-emotional strengths and negatively predicts their socialemotional difficulties. The largest effects were observed in conduct problems and hyperactivity and impulsivity, suggesting that interventions aimed at fostering ToM may prove especially beneficial for these types of social emotional challenges.

Analysis of the SSIS

Responses on the SSIS were coded using the scoring system outlined by the measure’s creators [14]. Scores were summed to determine the SSIS score (M=94.72, SD=15.34, max=138). The internal consistency of the scale was excellent (Cronbach’s alpha=0.94). The internal consistencies of the SSIS subscales were also generally adequate: Cooperation (Cronbach’s alpha=0.84), Self-Control (Cronbach’s alpha=0.80), Empathy (Cronbach’s alpha=0.79), Engagement (Cronbach’s alpha=0.71), Responsibility (Cronbach’s alpha=0.70), Assertion (Cronbach’s alpha=0.67) and Communication (Cronbach’s alpha=0.40). There were no significant differences in SSIS scores based on nationality or ethnicity, all ps > .10. Significant differences were revealed between females and males on the SSIS, F(1,86)=9.11, p=0.003, with females scoring higher than males (M=98.94, SD=14.48; M=89.44, SD=14.91, respectively). Age did not significantly predict scores on the SSIS, t=1.27, p=0.208.

Predictive effects of the CSUS on SSIS

After controlling for sex and age, the CSUS positively predicted scores on the SSIS, ΔR2=0.36, β=0.59, t=5.62, p<0.001 (see Table 5 for the partial correlations for the CSUS scores and the SSIS subscales).

Table 5: Study 2: Partial Correlations controlling for the child’s sex and age: CSUS scores independently predict each of the SSIS subscales.

SISS Subscale

(1) (2) (3) (4) (5) (6)

(7)

(1) Communication

(2) Cooperation

r=0.59

p<0.001

(3) Assertion

r=0.60

p<0.001

r=0.62

p<0.001

(4) Responsibility

r=0.40

p<0.001

r=0.56

p<0.001

r=0.56

p<0.001

(5) Engagement

r=0.49

p<0.001

r=0.56

p<0.001

r=0.78

p<0.001

r=0.59

p<0.001

(6) Empathy

r=0.39

p<0.001

r=0.62

p<0.001

r=0.55

p<0.001

r=0.67

p<0.001

r=0.67

p<0.001

(7) Self-Control

r=0.19

p<0.001

r=0.54

p<0.001

r=0.74

p<0.001

r=0.51

p<0.001

r=0.66

p<0.001

r=0.50

p<0.001

(8) CSUS

r=0.49

p<0.001

r=0.47

p<0.001

r=0.43

p<0.001

r=0.44

p<0.001

r=0.42

p<0.001

r=0.31

p=0.004

r=0.39

p<0.001

Discussion

The focus of Study 2 was to determine whether the results of Study 1 would replicate in a second, younger, sample and whether the findings linking ToM with social emotional functioning would extend to a broader range of facets of social emotional functioning. Similar to Study 1, ToM as a global construct measured by the CSUS was positively predictive of children’s social-emotional strengths (i.e., prosocial behaviour) and negatively predictive of social-emotional difficulties on the SDQ. In terms of social-emotional difficulties, the largest effects were observed for conduct problems, followed by hyperactivity and impulsivity, and smaller effects were observed for peer relationship problems and emotional symptoms. Moreover, individual differences in this global measure of ToM predicted all 7 facets of social skills with the largest relationships observed in communication and cooperation. That is, children with a greater understanding of mental states, even after controlling for age-related improvements, exhibit greater social-emotional skills in terms of communication, cooperation, assertion, responsibility, engagement, empathy and self-control.

Across two studies we provide evidence that ToM, as a whole, predicts several critical aspects of children’s social-emotional functioning. Importantly then, the results from the earlier body of literature do not appear to be specific to children’s false belief understanding (i.e., the most commonly used measure) since false belief understanding only represents a single item in the 18-item CSUS.

General Discussion

A primary objective of this research was to corroborate and expand on earlier work suggesting an important link between children’s mental state understanding, or ToM, and children’s social-emotional functioning, especially given the limitations with one of the most commonly used methods. Specifically, we aimed to determine whether theory of mind as a whole, rather than some specific aspect or correlate of theory of mind predicted social-emotional functioning. By including multi-dimensional measures of these constructs, we were able to capture the multi-faceted nature of both ToM and social-emotioning functioning. The majority of earlier work on ToM, and its relationship to social-emotional functioning had relied on laboratory-based measures that tapped one or more specific aspects of ToM, most often the false belief task. As a result, previous research was limited in its ability to capture the full scope of individual differences in children’s ToM. In addition, this work helps elucidate the specific facets of children’s social-emotional functioning that are predicted by individual differences in their mental state understanding. By including multiple facets of social-emotional functioning within the same studies (12 facets in total) we can compare the magnitude of each of their respective relationships to children’s mental state understanding. Finally, this work provides a novel contribution to the literature by extending the research on the validity of the still relatively new CSUS measure as a measure of individual differences.

Children’s ToM as a Global Construct Predicts Social-Emotional Functioning

“In Study 1 children’s mental state”… rather than “Based on our findings from…” Based on our findings from Study 1, children’s mental state understanding, as measured by the CSUS, was positively predictive of children’s social-emotional strengths on the SDQ, namely prosocial behaviour, and negatively predictive of children’s social-emotional difficulties on the SDQ. Specifically, modest negative correlations were observed between children’s mental state understanding and their hyperactivity and impulsivity and conduct problems. Study 2 replicated these findings showing again that children’s ToM, was positively predictive of prosocial behaviour, and negatively predictive of children’s social-emotional difficulties, as measured by the SDQ. This time, in a sample with a younger mean age, we observed a larger relationship between children’s ToM and their level of social-emotional difficulties. Here, we again observed moderate negative correlations between children’s ToM and their hyperactivity and impulsivity and conduct problems, and also observed smaller, but significant, correlations with peer relationship problems and emotional symptoms, with the weakest relationship in the emotional symptoms. Importantly, Study 2 also revealed that ToM was positively predictive of a broader range of social skills, including seven additional facets measured by the SSIS. Here, children with better ToM demonstrated increased social-emotional skills in terms of communication, cooperation, assertion, responsibility, engagement, empathy and self-control.

ToM and Prosocial Behavior

The findings of Study 1 and Study 2 between ToM and different facets of social emotional functioning lend support to earlier claims that ToM predicts these different facets and help to rule out concerns that these findings are limited to false belief understanding or correlates of false belief understanding such as cognitive abilities like working memory or executive functioning. For instance, the relationship between ToM and prosocial behaviour found in both Study 1 and Study 2 is consistent with the aforementioned meta-analysis of 76 studies, which found that children with more sophisticated ToM understanding are more likely to act prosocial [53]. Importantly, the current work expands on these findings by using a global measure to show that ToM as a whole, not just certain aspects of ToM, such as false belief reasoning, predict children’s prosocial behaviour.

ToM and Hyperactivity and Impulsivity

In terms of social-emotional difficulties, recall that a smaller body of literature examining the relationship between ToM and hyperactivity and impulsivity similarly relied on laboratory-based measures focused on the use of false belief tasks and/or emotion understanding. The relationship between ToM and hyperactivity and impulsivity found in both Study 1 and Study 2 is consistent with previous research suggesting that children with more developed ToM have lower levels of hyperactivity and inattention [61,78,79]. The current findings support and expand upon this earlier research to show that ToM as a whole predicts children’s hyperactivity and impulsivity.

ToM and Conduct Problems

Recall that for conduct problems and ToM the relationship was less clear in previous research (e.g., [66-69]). Again, the work that has been conducted relied heavily on the use of false belief tasks or another laboratory-based ToM measure (i.e., emotion understanding) making it unclear whether ToM, as a global construct, was an important predictor of conduct problems. The relationship observed between ToM and conduct problems in both Study 1 and Study 2 is consistent with research suggesting that children with Conduct Disorder tend to display deficits in mental state understanding (e.g., [66]). Again, the current findings expand upon this research to show that ToM as a whole predicts children’s conduct problems and may shed light on theories viewing deficits in ToM as a precursor for the difficulties children with conduct disorder can have in responding to others’ distress cues [67,80]; see also [81].

ToM and Peer Relationship Problems

As previously mentioned, the majority of previous research examining peer relationship problems and ToM also relied heavily on the use of false belief tasks, although some work has also examined other ToM measures (i.e., emotion understanding). Although we found somewhat mixed evidence of ToM as a predictor of the 5-item peer relationship problems subscale of the SDQ (i.e., a significant moderate relationship in the younger sample in Study 2 but a weak relationship in Study 1), we consistently observed strong positive relationships between ToM and the seven facets of children’s social skills measured in the SSIS. Arguably, communication, cooperation, assertion, responsibility, engagement, empathy and self-control are all important social skills that contribute to peer relationships. As such, the current findings are largely consistent with previous research showing that better ToM abilities predict better peer relationships. For instance, these findings support research showing that ToM predicts more peer cooperation in independent play [82], higher peer acceptance and lower peer rejection [83,84], and a lower likelihood of being a victim or perpetrator of bullying [85]. The current work expands on this earlier research by using a global measure of mental state understanding to show that ToM as a whole, not just one or two tasks or aspects, predicts peer relationships. As such general approaches to fostering theory of mind rather than targeting false belief understanding specifically would likely have the most potential for prevention and intervention techniques aimed at improving children’s interpersonal relations.

ToM and Emotional Symptoms

Finally, recall that previous research examining emotional symptoms and ToM is very limited, especially in children. Given the weak relationship between ToM and emotional symptoms observed in Study 2 and the null relationship observed in Study 1, we refrain from drawing any conclusions about the link between ToM and children’s emotional symptoms. It is possible that the scarcity of published research in this area stems from ‘the file drawer problem’ (i.e., null results tend to be reported less), but it is also possible this remains an understudied area. Either way, this seems like an important direction for future research given the meta-analytic evidence of a link between depression and ToM deficits in adults [63], also see [71]. Do the ToM deficits associated with emotional symptoms only manifest later in development, or is the research with children using the wrong research tools?

Is the Relationship between ToM and Children’s Social Emotional Functioning Causational?

Although we observe an association between ToM and children’s overall social emotional functioning, we acknowledge that the correlational design of our studies prevents us from establishing causality. It is possible that children’s social-emotional functioning predicts greater ToM understanding, rather than the other way around. Alternatively, or in addition, ‘third’ variables, including executive function (i.e., working memory and inhibitory control), IQ and language (e.g., verbal ability), or still other variables may account for the current results, in whole or in part. Importantly, the critical focus of this work was not to determine if a causal relationship existed between children’s ToM and social-emotional functioning, rather it was to test whether, and to what degree, ToM as a whole is associated with children’s social-emotional functioning or whether earlier findings were limited to specific ToM measures. Even without establishing causality, this work identifies key areas where deficits in ToM can serve as early warning signs or risk factors and help lead to earlier diagnosis or support. For example, children with deficits in ToM appear to be at risk for experiencing conduct problems even if their ToM is not playing a causal role. Moreover, establishing the relationship between theory of mind and various aspects of social emotional functioning lays the critical groundwork for future research aimed at testing prevention or intervention approaches to foster ToM to see if there are corresponding changes in children’s subsequent social-emotional functioning. Although this work is correlational in nature, we wish to highlight just a few of the many reasons, supported by previous research, to suspect that better ToM does play a causal role, or roles (both proximate and distal), in one’s social-emotional health and wellbeing. One contribution of mental state understanding in children’s social-emotional functioning is demonstrated by its role in fostering empathy and prosocial behaviour [48,49]. The capacity to think and reason about another person’s perspective is argued to be a requirement for the development of empathy [86]. The development of empathy is essential for social-emotional functioning because it enables one to understand and share the feelings of others, and promotes the development of prosocial (i.e., helping) behaviours. For example, research has found that actively imagining and inferring how another person would feel induces empathetic responses and prosociality in participants [48]. As a second example, experimental data from children reveal that they are selective in their prosocial behaviour towards specific others. That is, they fail to share with people who intentionally fail to cooperate, but not people who are unintentionally uncooperative or unable to help [26], demonstrating how their sensitivity to others’ mental states plays a causal role in how they interact with other individuals. A third example of the contribution of ToM in children’s social-emotional functioning is the vital role it plays in language development and communication, which are critical in virtually all social interactions. ToM is an important contributor to the acquisition of language (e.g., [22]), and deficits in ToM in children with autism have been implicated as causal contributors to the language deficits in children with autism [87]. For example, children with autism have deficits in ToM and experience difficulty when making inferences about a speaker’s intentions when learning a new word, leading to word learning errors when compared with typically developing children [88]; also see [89-91]. Likewise, the pragmatic aspects of language (e.g., sarcasm) can also be more challenging for individuals with autism, as it involves utilizing the social context to infer the speaker’s intentions [92,93]. As a final example, ToM also contributes to children’s social-emotional functioning via its role in selective social learning. Social learning is universal and plays a critical role in children’s cognitive and social development (e.g., [5,94,95]), as much of the information children acquire comes from other people. Importantly, children use their ToM to avoid falling prey to misinformation from people who have a history of being overconfident [96], or people who try to intentionally deceive (e.g., [97]). Taken together these separate lines of research are consistent with the claim that ToM plays a causal role, or several causal roles, in fostering better social-emotional functioning. Note that this is by no means an exhaustive review of the myriad of ways enhanced ToM promotes better social-emotional functioning, nor are we suggesting that it accounts for all of the relationships between ToM and facets of social-emotional health. For instance, there is reason to suspect that the deficits in ToM associated with hyperactivity and impulsivity (e.g., in those with ADHD) are a consequence of the hyperactivity or co-morbid neurological dysfunction [63,98]. Furthermore, there is most likely a bi-directional interplay between children’s ToM and their social-emotional functioning. For example, children with more developed ToM may have better social-emotional functioning and more friends, and through their greater number of social interactions those individuals can learn even more about others’ mental states. Conversely, children with deficits in ToM may have poorer social-emotional functioning, which may lead to fewer social interactions and correspondingly fewer opportunities to improve their ToM.

Limitations, Future Directions, and Concluding Remarks

Taken together, the current research advances our understanding of the nature of the relationship between children’s ToM and social-emotional functioning by utilizing multi-faceted measures to provide a more comprehensive account of children’s functioning. The current research also adds support for the validity and utility of using the relatively new CSUS parentreport measure to capture individual differences in ToM. This work shows that the CSUS was differentially predictive of children’s prosocial behaviour and social skills (i.e., positively correlated) and negatively predictive of problem behaviours such as conduct problems and hyperactivity and impulsivity. These findings extend the convergent validity of the CSUS by demonstrating its relationship to children’s social-emotional functioning (i.e., SDQ and SSIS) expanding the initial validation CSUS work examining its relation with laboratory-based ToM tasks (e.g., contents false-belief tasks, knowledge-access tasks, level two perspective-taking tasks [8]).

We recognize, however, that there are limits to the validity and utility of parent-report based questionnaires like the CSUS and urge some caution in interpreting the validity and utility of the results given that the current work relies solely on the use of parent-report measures. On the downside, parents may provide systematic error based on preconceived biases they have about their children (e.g., overly optimistic or positivity bias). Moreover, only one parent completed the parent reports, the vast majority were mothers, offering a singular parent perspective of their child. On the upside, parent-reports are hugely informative as children spend the majority of their time with their parents and parents observe their behaviour across many different contexts over an extended period of time. As a result, parents are uniquely situated to assess their child’s abilities, and as some work suggests may do so better than laboratory-based measures [99-101].

It is also important to note that as a first step we limited our sample to typically developing children. Future research that includes clinical samples of children (e.g. ADHD, conduct disorder, ASD, childhood depression and anxiety) may provide an even more comprehensive understanding of the relationship between ToM constructs and social-emotional functioning. Finally, future research should consider adding the CSUS as an additional measure of ToM, alongside other laboratory-based ToM measures. Our work is consistent with much of the earlier research using false beliefs tasks; however, our research suggests that there is a lot more variability in ToM and although ToM as a whole predicts social-emotional functioning it is important to also examine the role of each subcomponent of ToM. In a recent systematic review of ToM measures in children, Beaudoin and colleagues [2] concluded that there are over 39 different types of ToM sub-abilities.

In summary, the current research importantly expands upon our understanding of the critical relationships between children’s ToM and their social-emotional functioning by utilizing multi-faceted measures that provide a more comprehensive and ecologically valid picture of children’s functioning. The bulk of previous research has been limited to one or two facets of ToM or taken a piecemeal approach to social-emotional functioning. This research documents the important relationships between children’s ToM and several facets of social-emotional functioning, including several strengths (i.e., prosocial behaviour, communication, cooperation, assertion, responsibility, engagement, empathy and self-control) and difficulties (i.e., conduct problems, peer relationship problems, hyperactivity and impulsivity, and to a lesser extent emotional symptoms). Developing further techniques to improve mental state understanding in children will be an especially fruitful avenue given that early childhood is a sensitive time for the development of key cognitive skills, and a time when cognitive malleability is high. There are two potential avenues for fostering children’s social-emotional skills and minimizing difficulties: (a) interventions for children to promote their ToM, and (b) interventions for parents to foster their ToM. We anticipate that the earlier intervention techniques are employed the more likely they are to be successful at fostering the many positive social-emotional life outcomes associated with more accurate mental state understanding. Research in this area is vital as early interventions aimed at fostering mental state understanding show tremendous promise to enhance children’s social-emotional health and wellbeing.

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FIG 1

Zinc Oxide and Peroxide Nanoparticles – from Chemical Synthesis to Electrochemical Analysis and Wastewater Filtration

DOI: 10.31038/NAMS.2024712

Abstract

There is growing interest in the application of transition metal oxide and peroxide nanoparticles in materials science and industrial engineering. Transition metal oxide nanoparticles (TMONPs) can form materials with a wide range of unique chemical, electronic and physical properties for manufacturing various commercial products. Presently and for the foreseeable future, transition metal oxides are important constituents of energy production systems. They can be modified by various chemical, mineral, and polymeric substances, to produce nanocomposites that are suitable for the fabrication of more advanced materials. In this review article, discussion will focus on zinc oxide (ZnO) and zinc peroxide (ZnO2) which are chemically related but technologically different in their applications. The toxicity of biochemically active ZnO nanoparticles has led to the development of new analytical methods with a focus on capillary electrophoresis with UV absorption or molecular fluorescence detection. The importance of wastewater analysis is emphasized with an outlook on the future perspectives in this fast-advancing research field. Next, the unique chemical properties and physical characteristics of ZnO2 nanoparticles are introduced, followed by their latest applications in and modifications for scientific endeavors. Methods suitable for their incorporation in altering membrane properties to improve filtration performance are detailed. Major challenges and research endeavors are highlighted in the design of more effective membranes for wastewater filtration, hereby ameliorating the environmental toxicology of effluent contaminants. Polymeric membranes incorporated with composite zinc peroxide nanoparticles is proposed to be a good replacement of zinc oxide due to their strong oxidative properties involving a higher number of reactive oxygen atoms per molecule. The latest understanding of zinc oxide/peroxide nanomaterials toxicology is described, and best practices are crucial to control their environmental distribution. Last, the key research gaps that need to be addressed for future assessment of toxicological risks are unveiled.

Keywords

Biosensors, Electrochemical analysis, Engineered composites, Membrane filtration, Nanoparticles, Toxicology, Transition metal oxides, Ultrafine dust, Wastewater, Zinc oxide, Zinc peroxide

Introduction

In recent years, advanced technology with nanoparticles have greatly sparked the interest of the scientific community. Their small sizes and tunable functional properties make them appealing as unique structures for biomedical applications, ranging from bioimaging, biosensing, drug delivery and theranostics. Material scientists have focused their research on the synthesis of transition metal oxide nanoparticles (TMONPs). Transition metal oxides exhibit unique physicochemical properties including catalytic function, ferroelectricity, piezoelectricity, magnetism, and supercapacitor performance. These oxides are fascinating to work with because their electrons interact strongly with each other, giving rise to a range of phenomenal effects such as high-temperature superconductivity and magnetoresistance. A comprehensive review summarizes different methods for synthesizing (TMONPs) as catalysts in oxidation reactions, and the unique role of metal oxide substrates in anchoring metal atoms for photocatalysis is emphasized. Biosynthesis of these nanoparticles by bacteria, fungi and plants can yield desirable crystallinity, diameters and morphologies if all process parameters (concentration, pH, temperature, time, and calcination temperature) are well controlled. Different TMONPs can be combined with other transition metal oxides to form nanocomposites that offer multiple synergistic advantages. They represent an important class of semiconductors finding applications in various major industries from solar energy transformation, magnetic storage media and electronic devices to photocatalysis. The development of novel electrochemical biosensors using morphologically varied transition metal oxides and their composites has highlighted the significance of TMONPs as promising electrode modifiers for the fabrication of electrochemical and biosensors. Reactivity of the metal oxide-water interface can be understood from the viewpoint of coordination chemistry, while the reactivity of a metal ion at a nanoparticle surface is compared to the reactivity of the same metal ion dissolved in an aqueous solution [1-12].

Nanotechnology is introducing many advantages over conventional methods of food processing, extending the shelf life, reducing deterioration, maintaining quality, and adding food values. Nanoparticles and nanomaterials improve barrier properties, detect pathogens, and alert the status of food. They will reduce the wastage of post-harvest loss of agriculture and horticulture produces. A substantial review has recently been published on the incorporation of transition metal oxides in the development of intelligent food nano-packaging. Peroxides are inorganic chemicals that contain a bivalent O-O group. These compounds release nascent oxygen readily and their major industrial applications include oxidizing agents, bleaching agents, and polymerization initiators. Chemical oxidation is one of the environmental site remediation methods that have emerged lately as a better alternative to traditional technologies. Nanosized oxidizing agents increase the ratio of surface to volume and hence the biodegradation speed for contaminants in soil and ground water. Sodium peroxide (Na2O2), sodium perborate, and sodium persulfate are common inorganic salts that react with water to produce hydrogen peroxide (H2O2). Other metal peroxides, such as BaO2, CaO2, CdO2 and MgO2, are highly stable and they promote the oxidation of organic substances only at higher temperatures. BaO2, CaO2, MgO2, TiO2 and ZnO2 provide antibacterial applications in biomedicine. Tin peroxide (SnO2) transforms to SnO when exposed to orange peel extracts with reducing ability. Zinc peroxide (ZnO2) nanoparticles can be employed to prepare intelligent nano-packaging for better food preservation if they do not leach out from the packaging to the food [13-21].

Zinc Peroxide Nanoparticles

In 2021, zinc oxide and peroxide were the world’s 776th most traded product, with a total trade of US$1.87B. The top exporters of ZnO2 and ZnO were the Netherlands, Mexico, Canada, United States, and Peru. Both ZnO and ZnO2 nanoparticles can be prepared from aqueous solutions containing zinc nitrate or formate using UV irradiation. When ZnO is treated with H2O2, an interfacial ZnO2 layer forms to cover the nanoparticle surface. ZnO nanoparticles can be obtained by heat treatment of the peroxide above the transition temperature of 233°C, up to the decomposition temperature of 473°C. The weight loss due to the thermal decomposition of ZnO2 into ZnO and O2 at 250°C is considerably larger than the expected theoretical value of 16.4% just by oxygen release. ZnO2 is a stronger oxidizing agent than ZnO. Decomposition of ZnO2 to ZnO and O2 resulted in the decrease of the band gap energy from 3.75 to 3.30 eV [22-28].

ZnO2 nanoparticles were traditionally prepared by peptization of Zn(OH)2 with the aid of H2O2 aqueous solution. They could be formed by a simple oxidation-hydrolysis-precipitation procedure, using zinc acetate as a precursor, hydrogen peroxide as an oxidizer, water as a preparation medium for hydrolysis, and polyethylene glycol as a stabilizer. ZnO2 nanoparticles can facilely be synthesized from zinc acetate and H2O2 using a sol-gel method under ultrasound assistance [29-33]. Characterization by scanning electron microscopy and energy dispersive X-ray spectroscopy (Figure 1) shows an atomic ratio Zn/O of 2.01 and an average particle diameter of 304±5 nm. A green method based on the reaction between Zn5(CO3)2(OH)6 powder and H2O2 in aqueous solution at room temperature can synthesize ZnO2 nanoparticles. ZnO2 nanoparticles can be prepared by the laser ablation of zinc in 30% H2O2 as another green technique using the fundamental wavelength (1064 nm) of a pulsed Nd: YAG laser, at a repetition rate of 15 Hz and laser fluence of 22 J/cm2 for an ablation time of 10 min. Furthermore, the Leidenfrost dynamics occurring in an underwater overheated zone ensures eruption of nanoclusters towards a colder region, forming monodisperse nanoclusters of ZnO2. Nowadays, ZnO2 nanoparticles are commercially available, either bare or coated with an organic ligand shell of polyethylene glycol for stable dispersion in water, methanol, ethanol, acetone and dimethyl sulfoxide [34-38].

FIG 1

Figure 1: (a, b) Scanning electron microscopy, and (c, d) energy dispersive X-ray spectroscopy of ZnO2 nanoparticles facilely prepared in our lab by following Ramírez et al.’s sol-gel method under ultrasound assistance.

Unique Properties of Zinc Peroxide Nanoparticles

ZnO2 is much more stable in aqueous solutions (as compared to calcium and magnesium peroxides) and it retains its peroxide content down to pH 6. At lower pH levels, H2O2 release is predictable as its dissolution product, Zn2+, is highly soluble. Nanocrystalline ZnO2 can be passivated against further oxidation by the addition of sub-stoichiometric amounts of potassium permanganate, which also increases the thermal stability of ZnO2 [39,40].

ZnO2 possess unique anti-bacterial, anti-corrosion, anti-fouling, and photocatalytic properties that are considered cost effective and environment friendly. They have been studied, due to their semiconducting and oxidizing properties, for various applications in optoelectronics, photocatalysis, sensors, biomedicine, and theranostics. Due to their large nonlinear optical susceptibilities, which are enhanced by two-photon electronic resonance, metal oxides are efficient sources of coherent anti-Stokes Raman Scattering (CARS). The FTIR spectrum of ZnO2 nanoparticles shows a characteristic absorption peak at 435-445 cm−1; the Raman spectrum shows characteristic peaks at 830-840 and 420-440 cm−1. ZnO2 nanoparticles exhibit photoluminescence with one strong emission band at 400 nm, one very weak emission band at 474 nm, and at 520 nm originating from the band edge and the oxygen vacancy. Polyvinyl alcohol/ZnO2 nano-composite films have been engineered via casting. Their energy gaps decrease with increasing ZnO2 concentrations to reach 2.80 eV at 2 wt.%, which are promising in anti-ultraviolet, opto-electronic, and optical limiting applications. A correlation exists between oxygen vacancies and the magnetization for pure ZnO2 nanoparticles at room temperature. Coating of 15-20% ZnO2 nanoparticles over graphene enhances magnetization more than 30 times due to the exchange interaction between localized electron spin moments resulting from oxygen vacancies at the surface [41-47].

ZnO2 nanoparticles have reportedly oxidative stress mediated toxicity on various mammalian cell lines. Oxygen release from the biofunctionalized nanoparticles is tunable according to the solution pH. Antimicrobial tests at 37°C on bacterial species exhibiting different susceptibility to oxygen have confirmed the antimicrobial activity of ZnO2 nanoparticles against Enterococcus faecalis, Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis and Prevotella intermedia. Accordingly, ZnO2 showed effective antifungal activities, with a minimum inhibitory concentration (MIC) of 16 mg/L against Candida albicans. Histopathology assessment has confirmed the role of ZnO2 nanoparticles in healing skin wounds. They exhibit angiogenic activity, due to onsite production of H2O2, for rapid tissue healing. ZnO2 demonstrate antimicrobial, anti-elastase, anti-keratinase, and anti-inflammatory properties that are valuable for biomedical applications. They also inhibit bacterial biofilm formation and combat multi-drug resistant bacteria. A minimum concentration of ZnO2 nanoparticles of 1 μg/mL inhibits the production of interleukin-1-β and interleukin 6 by peripheral blood mononuclear cells in the presence of lipopolysaccharides. ZnO2 nanoparticles at a concentration of 2 μg/mL causes DNA damage in vitro; at a concentration of 5 μg/mL they promote protein aggregation and facilitate the production of protein complexes that may interfere with normal immune functions [48-54].

Applications of Zinc Peroxide Nanoparticles

Nanosized ZnO2 is an efficient oxidant for the oxidation of aromatic alcohols to the corresponding carbonyl compounds selectively in excellent yields, using dimethyl carbonate as an environmentally benign solvent. ZnO2 nanoparticles reactively adsorb chemical warfare agent surrogate of mustard gas, selectively oxidizing diethyl sulfide to diethyl sulfoxide and 2-chloroethyl ethyl sulfide to hydroxyethyl ethyl sulfide. Crosslinking of conventional/carboxylated nitrile rubber with ZnO2 achieved total peroxide decomposition at vulcanization temperatures as low as 190-200°C [55-57].

Semiconducting CuO nanoparticles, as a CO2 gas sensitive material, can with an organic binder and ZnO2 for improved gas sensitive layer quality. The Lewis acid-base reaction between oxide oxygen and CO2 has been proposed as sensing mechanism for the measurements in dry air, whereas the formation of surface barriers between nano-grains due to the reaction with CO2 has been suggested for the CO2 response under humid conditions [58].

ZnO2 is a promising adsorbent nanomaterial for the removal of Congo red dye from contaminated water. The adsorption capacity is 208 mg g-1 within 10 min at pH 2-10. The adsorbent has a unique property to adjust pH within the 6.5-7.5 range irrespective of the acidic or basic nature of water. It is highly efficient even in the absence of sunlight to remove Congo red dye from contaminated water down to the permissible limits set by the World Health Organization and the United States Environmental Protection Agency. Crystal violet dye in industrial wastewater can be removed using sodium docusate-modified ZnO2, attaining >99.5% adsorption efficiency in 5 min at pH ∼10 as the zeta potential of ZnO2 decreases from −15 mV at pH 3 to −60 mV at pH 9. The higher negative charge results in stronger electrostatic interaction with the dye. Synthetic graphite flakes can be treated with 3-mercaptopropionic acid, followed by functionalization with ZnO2 nanoparticles, to efficiently remove As(III) and As(V). The adsorption data are best fitted with pseudo second order kinetic model and Freundlich adsorption isotherm, indicating chemisorption and multilayer adsorption on heterogeneous surface respectively. ZnO2 nanoparticles, capped with polyvinyl-pyrrolidone to control the particle size, is an efficient material for the decontamination of cyanide from contaminated water by adsorption at pH 5.8-7.8 within 15 min [59-62].

As a catalyst for removal of reactive blue dye, a maximum degradation efficiency of 85% was achieved by ZnO2 nanoparticles with polyethylene glycol, and 81% without PEG, after 120 min of photocatalytic reaction. ZnO2 nanoparticles have excellent degradation efficiency of brilliant green dye, achieving 84-86% after 120 min of photocatalytic reaction at pH 6-7. Eco-friendly carbon quantum dots/ZnO2 nanocomposite has been successfully synthesized for photocatalysis applications. It has higher efficiency than carbon quantum dots/TiO2 for the removal of different dyes and high stability under UV-A light. Nitrobenzene photodegradation by ZnO2 under UV lamps of 254 nm is optimal at pH 2, reaching up to 90% degradation in 2 h at 25°C [63-66].

For dental implants the accumulation of anaerobic bacteria is a main reason for peri-implant inflammation that can lead to implant loss. Decorating ZnO2 by Glc-1P permits their uptake in the gram-negative oxygen-sensitive bacterial cells. ZnO2 nanoparticles can be decorated with glucose 1-phosphate (Glc-1P) due to specific interaction of the phosphate function of Glc-1P with the nanoparticle surface. The anchored glucose molecules are accessible for specific interactions with lectin concanavalin A. Generation of ROS including hydrogen peroxide, hydroxyl radical, and peroxide anion can enhance the membrane permeability, cell wall damage, internalization of nanoparticles, and uptake of toxic dissolved Zn2+ ions. A ZnO2-based theranostic nano-agent enhances oxidative damage to cancer cells by combining endogenous and exogenous reactive ROS. After uptake by cancer cells, the pH-responsive ZnO2 nanoparticles, in addition to releasing exogenous H2O2, also provide Zn2+ to facilitate the production of endogenous O2·and H2O2 from mitochondrial electron transport chain, enabling highly effective synergistic tumor therapy [67-69].

Zinc Oxide Nanoparticles

ZnO is a white powder that has two main lattice structures: hexagonal wurtzite and cubic zincblende. The hexagonal structure is commonly found, and both structures are insoluble in water with a solubility of 0.16 mg/100 mL at 30°C. The solubility of uncoated ZnO, as determined by the Zn2+ concentration in the aqueous solution, ranges between 20 and 47 mg/L. The solubility product constant Ksp is a useful parameter for calculating the aqueous solubility of sparingly soluble compounds. A comparison of dissolution rates shows that the ZnO nanoparticles have a higher dissolution rate than the bulk oxide. A new methodology for the in-silico assessment of the solubility of ZnO based on statistical thermodynamics, combined with density functional tight binding theory for the evaluation of the free energy exchange during the dissolution process. Complete ionic dissolution of ZnO is hindered by the formation of O2− anions in solution, which are highly unstable. The dissolution rate will depend critically on the matrix with Zn ions and the mechanisms for diffusion or active transport of Zn2+ and O2-ions in biological processes. Any mass fraction of Zn2+ ions removed or washed away will lead to further dissolution and eventually complete solubilization of the particulate fraction of ZnO. The fact that zinc-rich foods are mostly animal products suggests that vegetarians and vegans may have difficulty getting enough zinc in their diet. Zinc supplements are a great way to have the recommended levels of zinc in the body for stronger immune systems and improved muscle building. They are particularly useful for older adults (especially older men), who are more likely to have zinc deficiency [70-75].

Unique Properties of Zinc Oxide Nanoparticles

A variety of industries, including the automotive, concrete, cosmetic, pharmaceutical and textile, have used ZnO nanoparticles as a major material. The annual turnover of ZnO nanoparticles is over US$ 900,000/year, and the specific cost of their production is US$20/kg. Numerous synthetic techniques have been developed to meet the increasing demand for ZnO nanoparticles. These alternatives offer environmental and financial advantages associated with their commercial production. Biological synthesis uses plant extracts or microbes as green resources for the preparation of ZnO nanoparticles. Considerable investment to improve the performance of diverse nanocomposites allows rapid development of novel photocatalytic/photooxidizing degradation technologies for removing dyes in industrial wastewater [76-78].

Nowadays, ZnO nanoparticles continue to be a great attraction to researchers in various scientific endeavors based on their unique physicochemical properties. The natural bandgap (3.37 eV) and n-type conducting behavior of ZnO can be tuned by doping with metals/metal oxides and non-metals to replace Zn2+ and O2-in the ZnO lattice, for various applications (such as solar cells, photocatalysis, medicines, light-emitting diodes, laser diodes, chemical and biosensors) owing to the direct influence of dopants on their electronic and physiochemical properties. A wide range of energy bandgaps (3-4 eV) is attained by green synthesis, indicating that ZnO nanoparticles can be employed in metal oxide semiconductor-based systems. The effectiveness of dye-sensitive solar cells is attributable to improved dye adsorption onto the nanoparticle surfaces. By adding ZnO in various amounts to a solution of polyvinylidene fluoride in 2-butanone during the fabrication process, followed by removing ZnO in an HCl bath once the organic solvent is evaporated, porous sensors can be made with different piezoelectric chains to control the piezoelectric coefficient. ZnO nanoparticles synthesized using the coprecipitation method present the best performance in catalysis, biosensing, imaging, drug delivery, and pollution absorption owing to their highest purity and crystalline phase, large Brunauer-Emmett-Teller surface area (~23 m2g-1) and pore volume in the mesoporous-macroporous structure [79-82].

ZnO nanoparticles have strong antimicrobial activity against a broad spectrum of bacteria (P. aeruginosa, E. coli, A. baumannii, K. pneumoniae and Staphylococcus aureus) and are effective against Hyalomma ticks. However, all fungal strains (P. chrysogenum, A. niger, T. citrinoviride and A. fumigatus) are resistant to ZnO nanoparticles. ZnO nanoparticles (1.5 mg/L) are protective against the detrimental effects of Clostridium perfringes type A infection in aquaculture. Their potential mechanisms of action against various kinds of viruses were discussed in a comprehensive review. The adoption of novel bio-assisted synthesis methodologies tailors the properties of ZnO nanoparticles to suit biomedical applications, underscoring their potential in cancer treatment towards MCF-7 breast cancer cell lines [83-87].

Applications of Zinc Oxide Nanoparticles

ZnO is produced synthetically for use as an additive in adhesives, antibacterials, baby powder, batteries, cement, ceramics, cigarette filters, cosmetics, ferrites, fire retardants, first-aid tapes, foods, glass, laser diodes, light emitting diodes, lubricants, ointments, paints, pigments, plastics, rubbers, sealants, semiconductors, solar cells, sun blocks, and wood products. Traditional uses of ZnO products include treating wounds following surgery and applying salves inside the mouth to treat ulcers or sores. Over 50% of ZnO is used in the rubber industry along with stearic acid for the vulcanization of rubber to produce tires, shoe soles, and even hockey pucks. ZnO nanoparticles have been added in food-packaging materials to stop food from spoiling. For use as binary/ternary composite anodes in lithium-ion batteries, ZnO has a higher theoretical capacity (978 mA.h/g) than many other transition metal oxides such as CoO (715 mA.h/g), NiO (718 mA.h/g) and CuO (674 mA.h/g). ZnO nanoparticles are extensively used in healthcare and environmental remediation applications attributable to their biodegradability. ZnO possesses unique biological properties for various antibacterial, antiinflammation, antitumor, and antiviral) applications. Addition of ZnO nanoparticles into crystal violet dye induces an alternative photoredox pathway, resulting in more generation of reactive oxygen species lethal to bacterial cells. This technique could be used to transform a wide range of bactericidal surfaces and contribute to maintaining low pathogen levels on hospital surfaces related to healthcare-associated infection. Hybrid ZnO-SiO2 nanoparticles possess favorable characteristics for antifouling purposes. Self-cleaning and anti-fouling polymeric membranes for wastewater treatment are commercially fabricable with ZnO nanocomposites [88-100].

The formation and breaking of transition metal-carbon bonds plays a pivotal role in the catalytic oxidation of organic sulfides, alcohols, olefins, and alkanes. The textile industry is environment unfriendly due to the massive use of dyes and chemicals. Discharge of untreated textile wastewaters loaded with dyes not only contaminates the soil and water resources but also threatens the public health. ZnO nanorods can be used as a photocatalyst to degrade 65% of methylene blue in 50 min. Biochar-ZnO composites obtained by pyrolysis at 600°C can degrade 90% rhodamine B in 75 min, while ZnO can degrade only 38%. ZnO nanoparticles can be doped with Ni (3%), through combustion at 550°C, to improve the photocatalytic degradation of methylene blue and tetracycline [101-103].

Sodium (15%)-doped ZnO degrades 95% of methylene blue under visible light illumination in 180 min, with a rate constant of 1.7×10-2 min−1 and tenacious photostability. Green synthesis of ZnO nanoparticles is gaining huge attention via eco-friendly protocols that reduce the destructive effect of chemical synthesis. ZnO nanoparticles synthesized from Synadium grantii leaf extract with Cu dopant exhibit superior photocatalytic activity for indigo carmine, methylene blue and rhodamine B dyes. Gynostemma plant extract can be used in a co-precipitation method to synthesize ZnO nanoparticles for the photocatalytic decolorization of malachite green dye under UV illumination within 180 min. Biogenic ZnO nanoparticles can be synthesized by using Pseudochrobactrum sp. C5 for catalytic degradation of dyes in wastewater treatment. Valorization of banana peel waste extract as the reducing and capping agents produces ZnO nanoparticles that show superior reusability and photodegradation efficiency for the removal of hazardous basic blue 9, crystal violet and cresol red dyes at pH 12 over irradiation time 90 min. Degradation of congo red by orange-peel-extract-biosynthesized ZnO nanoparticles via photocatalysis can remove 96% of the dye Photocatalytic degradation of rhodamine B dye in waste water and inhibition of butyrylcholinesterase, acetylcholinesterase and α-glycosidase enzymes are afforded by cauliflower-shaped ZnO nanoparticles synthesized using Alchemilla vulgaris leaves. Maximum photocatalytic degradation of pharmaceutical wastewater with ZnO was 40% and with TiO2 is 33% at pH 9, following pseudo-first-order kinetics. Combined use of TiO2/H2O2 is more effective than ZnO and TiO2 alone, achieving 45% degradation. ZnO and TiO2 can be used as catalysts for the degradation of dyeing factory effluents by the advanced oxidative process under UV irradiation at pH 3 for 8 h. Interestingly, a deposit of CdS nanoparticles on ZnO nanosheets provides excellent piezocatalytic efficiency for rhodamine B degradation under ultrasonic vibration. The nanocomposite of ZnO with porous hydroxyapatite (prepared from phosphate rock) improves the photodegradation of antibiotics in water and traps the by-products. An artificial neural network model can estimate the effect of different variables on AB113 dye removing decolorizing acid blue dye from textile wastewater in a sonophotocatalytic process. Reaction time, pH, ZnO dosage, ultrasonic power and persulfate dosage are optimized for maximum dye removal. After photocatalysis, if the treated water is discharged to the surface water along with the catalyst nanoparticles and degradation products, a resulting toxicity exists in the medium that can influence the lipid peroxidation and reduced glutathione in the aquatic vertebrates. Hence filtration is recommended before discharging, for separation of the catalyst nanoparticles. However, the filtration of nanoparticles from the treated water is costly and might outweigh the savings of energy [104-116].

Toxicology of ZnO Nanoparticles

Increasing production and application of transition metal oxide nanoparticles has raised concerns in regard to their environmental accumulation and toxicity in natural ecosystems. Nanoparticles are extensively studied for their chemical toxicology in aquatic microorganisms, agricultural products, fish, wildlife and humans. The uptake and accumulation of ZnO nanoparticles by aquatic organisms have considered the release of Zn2+ ions as well as the toxic mechanisms shared with other nanoparticles such as immunotoxicity, inflammation, lysosomal/mitochondrial damage, oxidative stress, programmed cell death, and redox activity. The growing usage of ZnO nanoparticles increases their release in municipal wastewater treatment plants. At 50 mg/L ZnO nanoparticles, both the granular activated sludge performance and the extracellular polymeric substances content are significantly reduced. This leads to decreases in the activities of ammonia monooxygenase and nitrate reductase. In addition, ZnO nanoparticles disrupt the cell membrane integrity and lead to bacterial cell death via intracellular ROS generation. After exposure to the nanoparticles, the bacterial community composition shifts to be dominated by Gram-positive bacteria. Antibacterial activity of ZnO nanoparticles is more pronounced with Gram-positive than Gram-negative bacteria. ZnO nanoparticles are biocompatible and effective as a food preservative against Salmonella typhi, Klebsiella pneumoniae and Shigella flexneri. They demonstrated significant antibacterial effects on various pathogenic bacteria in terms of zone-of-inhibition measured by the disc-diffusion method. When treated with ZnO nanoparticles (100-300 mg/L), significant reductions in marine microalgae C. vulgaris viable cells, LDH level, and non-enzymatic antioxidant glutathione are noticed while the activity of antioxidant enzyme superoxide dismutase and the level of lipid peroxidation significantly increase. ZnO nanoparticles possess antibacterial and antioxidant properties towards the remediation of hospital wastewater; the ones fabricated using Eriobotrya japonica leaves extract exhibit DPPH scavenging activity and are highly active against S. aureus, P. multocida, E. coli and B. subtilis strains. Redox imbalance, lignification and cell death cause reduction of root growth in wheat seedlings exposed to ZnO nanoparticles. Dietary exposure of carp to ZnO nanoparticles increases the aspartate aminotransferase activity significantly and decreases the alanine transferase activity significantly. ZnO nanoparticles act as a potent antidiabetic agent and severely elicit oxidative stress particularly at higher doses in diabetic rats (10 mg/kg). Initial exposure of human bronchial and pancreatic epithelial cells to oxidative stress sensitizes their subsequent response to cytotoxic challenge with ZnO nanoparticles. As in vitro model species human erythrocytes can be used to evaluate cytotoxicity, and human lymphocytes can be used for genotoxic studies. ZnO and TiO2 nanoparticles result in 65% and 52% hemolysis at 250 ppm respectively, indicating cytotoxicity to human red blood cells. Both nanoparticles were found to generate ROS concomitant with depletion of glutathione and glutathione S-transferase levels. ZnO nanoparticles are significantly more genotoxic than TiO2 nanoparticles at concentrations higher than 250 ppm. The nanoparticles preferentially kill cancerous cells over normal human cells. They enhance ultrasound-induced lipid peroxidation in the liposomal membrane. Two mechanisms underly the toxicity of ZnO nanoparticles: (i) generation of ROS and (ii) induction of apoptosis. The chemical toxicology of ZnO nanoparticles in adult male Wistar rats were investigated. All levels of zinc oxide nanoparticles had a significant impact on sperm quality and quantity. Significant toxicity effects of ZnO nanoparticles appeared at concentrations above 50 mg/kg body weight of animals. 200 mg/kg body weight resulted in increased total oxidant status and decreased total antioxidant capacity significantly. On the contrary, dietary supplementation of Nile tilapia with Se nanoparticles and ZnO nanoparticles induces synergistic effects that improve growth performance, blood health, and intestinal histomorphology. Seed priming with ZnO nanoparticles demonstrates beneficial effects of mitigating the phytotoxicity induced by Co stress in maize, significantly improving the plant growth, biomass, and photosynthetic machinery. Freshwater fish O. mossambicus fed with a supplemented diet of ZnO and Se nanoparticles raises the antioxidant response, boosts the immunity, and reduces the chance of getting infected by A. Hydrophilia. The entrance of ZnO or ZnS nanoparticles into freshwater systems may significantly impact the sedimentary microbial community structure and nitrogen cycling. Furthermore, they showed a strong anti-termite activity against Heterotermes indicola with a 100% mortality rate in 24 h [117-136].

Biosensors Incorporating Zinc Oxide Nanoparticles

Among all the optical biosensing systems, ZnO nanoparticles formed directly atop 3-aminopropyl triethoxysilane-treated Si substrates are more adhesive. Smaller particle sizes of ZnO will increase the fluorescence emission, eliminate several emission peaks, yield higher fluorescence quantum efficiency, and require lower excitation energy for fluorescence sensing. N-doped ZnO nanoparticles exhibit fluorescence emission at 385 nm (corresponding to the exciton absorption band) under excitation of 340 nm, responding with high selectivity and a detection limit of 4.9 μM for urea in blood serum. Self-assembly of diphenylalanine nanostructures in the presence of ZnO nanoparticles display distinctive luminescent emission at 550 nm that affords sensitive detection of trypsin down to 0.1 ng mL−1. As a surface-enhanced Raman scattering substrate, ZnO tips can be decorated with gold nanoparticles to take advantage of the synergistic effect. Assay for nicotine demonstrates high sensitivity, reaching a lower detection limit of 8.9×10−12 mol/L and offering a linear dynamic range of 10−10-10−6 mol/L. A localized plasmon-based fiber optic sensor can be immobilized with ZnO nanoparticles along with Au nanoparticles for the detection of p-cresol (a water pollutant) as low as 57 μM. A field effect transistor device consisting of ZnO nanoparticles and glutathione-S-transferase in the composite channel can successfully detect and quantifies glutathione in solution and in cancerous cells. The glucose content in food samples can be determined using ZnO nanoparticles, with a correlation coefficient of 0.9812 at 3.5 mM-27.8 mM concentrations [137-143].

A novel electrochemical sensor made by drop casting zinc oxide nanoparticles and electropolymerizing glutamic acid can detect sodium dodecyl sulfate with excellent selectivity via molecular imprinting. ZnO was overlaid on the interdigitated electrode of an electrochemical DNA biosensor to detect sequence complementation from Ganoderma boninense. ZnO nanoparticles prove to be excellent for doping carbon dots in electrochemical biosensor applications. Chemical vapor deposition of ZnO nanoparticles on an aluminum foil working electrode successfully sensed cysteine electrochemically. Smartphones can be combined with screen-printed electrodes or interdigital electrodes for in-situ electrochemical detection. The electrodes are often modified with biomaterials, chemical materials, and nanomaterials (such as ZnO) for biosensing to monitor ascorbic acid, dopamine, glucose, levodopa, and uric acid in point-of-care testing. Aluminum doping can be attained by radio frequency magnetron sputtering of ZnO nanoparticles deposited on a glass substrate for biosensor applications. Four different H2O2 biosensors have been designed using ZnO nanoparticles, multiwalled carbon nanotubes, Prussian blue, ionic liquid and horseradish peroxidase. The best analytical performance offers a linear dynamic range of 9.99×10-8‒7.55×10-4 M, detection limit of 1.37×10-8 M, and sensitivity of 17.00 µA mM-1. A laser scribed graphene-ZnFe2O4 electrochemical aptasensor for acute myocardial infarction screening has been developed for detecting the cardiac troponin-I biomarker, with a limit of detection of 0.001 ng/mL and a sensitivity of 19.3 µA/(ng/mL). The Ag-ZnO-graphene oxide/glassy carbon electrode exhibits high sensitivity, detection limit of 0.02 μM, and fast response within 3 s owing to the efficient oxidation of diclofenac sodium at 0.25 V. Trimetallic Ni/Ag/Zn oxide composite-modified glass carbon electrode has good sensor sensitivity of 0.96 μA/μM cm2 and detection limit of 0.3 μM for dopamine. ZnO-reduced graphene oxide-Au nanoparticles can modify a screen-printed electrode for fast electrochemical detection of dopamine in biological samples. A uric acid biosensor was constructed with nafion/uricase/ZnO nanorods-ZnO nanoparticles on a fluorine-doped tin oxide electrode. Differential pulse voltammetry demonstrated linearity over a wide concentration range (0.01-1.5 mM) with a high sensitivity (345 μA mM−1cm−2) and low limit of detection (2.5 μM). A glassy carbon electrode modified with carbon nanotubes, cytochrome C and ZnO nanoparticles has good sensitivity for the detection of streptomycin in pharmaceutical samples. The highly sensitive interface of penicillinase@CHIT/PtNP-ZnO/ZnHCF/FTO electrode shows a linear response and good limit of detection (0.1 μM) in antibiotics in forensic samples. Biosensors based on ZnO and NiO nanostructures decorated with Au nanoparticles have opened the doors to detect volatile organic compounds using electrochemical methods. Biomass carbon derived from cassava and its composites with ZnO nanoparticles can be synthesized for biosensing due to their low cost and resource availability [144-159]. In our lab, screen-printed electrodes are modified by a deposit of ZnO nanoparticles from aqueous suspension onto the graphite working electrode surface. After drying, a sample solution containing sodium metabisulfite analyte in 1 M KCl can be placed on top for chronoamperometry using the Homianze μEA 160C electrochemical analyzer. A typical current-time curve is obtained as shown in Figure 2a, which is ready for data analysis in accordance with the Cottrell equation as shown in Figure 2b.

FIG 2

Figure 2: (a) Screen shot of current-time curve obtained in our lab from sodium metabisulfite with ZnO nanoparticles deposited on graphite electrode. (b) Plot of current vs. inverse square root of time for Cottrell analysis.

Self-cleaning and Anti-fouling Polymeric Membranes for Wastewater Treatment and Analytical Separations

A recent trend in nanotechnology shows the application of nano-based materials, such as nano-adsorbents, nano-metals, nano-membranes, and photocatalysts, in water treatment processes. Nanomaterials typically have high reactivity and a high degree of functionalization, large specific surface area, and size-dependent properties which makes them suitable for applications in wastewater treatment and for water purification. Nanostructured catalytic membranes, nanosorbents and nanophotocatalyst-based approaches to remove pollutants from wastewater are eco-friendly and efficient, but they require more energy and more investment in order to purify the wastewater. Current and potential applications of nanoparticles and nanotechnologies in wastewater treatment as well as challenges have been reviewed on the basis of bibliometric results [160-165]. Self-cleaning surfaces have attracted significant attention in both the scientific and industrial communities [166]. In the past decade, transition metal oxide nanoparticles have extensively been incorporated with polymeric membranes for water treatment. Special emphasis is given here to their anti-fouling and self-cleaning properties when used also in the preparation of wastewater samples before chemical composition analysis. Various forms of copper, titanium dioxide and zinc peroxide were tested against microbial fouling and microbiologically influenced corrosion. Their incorporation into polyethylene (high density) and fiber-reinforced plastic provides surface protection. Wastewater treatment is currently a crucial topic worldwide due to global human population growth (83 million annually), industrial downstream contamination, and weathering degradation of polymers [167-170]. Various water treatment techniques are being advanced due to the rising concern of drinking water scarcity and safety. Besides conventional water treatments, the pressure-driven water purification technology has attracted attention due to its efficiency and received substantial applications. Pressure-driven membranes can be classified into microfiltration, ultrafiltration, nanofiltration, and reverse-osmosis. These membranes are used to separate ions, macromolecules, suspended particles and nanomaterials from water. Organic polymeric membranes are extensively used for commercial purposes due to their excellent physical, chemical, and mechanical characteristics. However, membrane fouling occurs due to their hydrophobic nature plus bacterial accumulation and is limiting their sustained operation over time. Regarding membrane fouling, a combination of polymer and nanoparticles is suggested to be a practical strategy for enhancing membrane hydrophilicity. Incorporating nanoparticles into polymeric membranes is becoming a trend in membrane technology. Polymers and metal oxides are becoming popular membrane filtration materials for wastewater treatment due to their surface functionality, large surface area, and unique optical/paramagnetic properties. Under visible light conditions, the polymer-metal oxide nanocomposite membrane affords superior photodegradation activity toward organic pollutants. Transition metal oxides have been evaluated by many researchers during the last decade for wastewater reclamation, as self-cleaning and anti-fouling agents, to utilize their surface mobility, magnetic and optical properties. Recently, a review on polymer nanocomposite membranes based on metal oxide nanoparticles was published in the field of ultrafiltration membrane technology [171-194].

ZnO nanoparticles have been extensively used by scientists and researchers, known to be inorganic, hydrophilic, low-cost, and green (environment-friendly) material. Fluoride contamination of water is a serious problem in the world, and zinc oxide nanoparticles are the best adsorbent for the removal of fluoride from water and wastewater, with an adsorption capacity of 100 mg/g. In wastewater treatment processes, ZnO nanoparticles exert a negative impact on the sludge flocculation performance but do not significantly impact the sludge sedimentation behavior. A decrease of the tyrosine protein-like substance level is probably the key reason for the decreased ζ potential in the loosely bound extracellular polymeric substances, which eventually induces a decline of the sludge flocculation performance under the ZnO stress. A novel deflocculant ZnO/chitosan nanocomposite film in disperser pretreatment enhances the energy efficiency of anaerobic digestion by achieving 99% solubilization of organics. In addition to the anti-fouling performance, ZnO nanoparticles also provide photocatalytic self-cleaning ability to the polymeric membranes. Hence, ZnO-incorporated composite membranes were considered an emerging topic in membrane technology. Modification of polyvinyl chloride membrane using ZnO nanoparticles is very effective for municipal wastewater treatment in the presence of ferric chloride coagulant. The nanocomposite membrane did not adsorb the sludge inside the pores, hence substantially limiting the membrane fouling. Polyvinylidene fluoride membrane with high hydrophilicity was reported to be developed through the conglomeration of ZnO and graphene oxide. Anti-fouling properties, porosity, water flux and wettability were improved to attain a stable effluent quality (0.6 NTU). Combination of graphene oxide and ZnO nanoparticles on polysulfone membrane surface improves the membrane performances to treat petroleum refinery wastewater in terms of higher porosity, increased hydrophilicity, better mechanical strength, reduced water contact angle, increased water uptake ability, higher permeate flux, rejection of total dissolved solids, and improved antifouling properties. Unfortunately, no sustainable membrane systems are yet fully established due to their huge energy requirements for partial removal or degradation of trace organic compounds. Impregnation of ZnO-graphene also reduces polyethersulfone membrane-solute and membrane-foulant hydrophobic interactions. ZnO incorporation enhances the hydrophilicity and improves the anti-fouling property of polyether sulfone membranes. A mean pore size of 0.64 nm and good humic acid rejection make the hybrid membrane well suited for nanofiltration in wastewater treatment and water reclamation. Multifunctional nanofibrous membranes with sunlight-driven self-cleaning performance for complex oily wastewater remediation can be constructed with an Ag/ZnO layer on the porous polyacrylonitrile nanofiber substrate. The membranes demonstrate excellent mechanical strength, superhydrophilic (water contact angle = 0°), underwater superoleophobic (contact angle = 154°) properties, high permeation flux (>619 Lm-2h-1) and separation efficiency (>99.7%) for various oil-in-water emulsions [195-204].

The effect of photoactive semiconductor catalyst (TiO2 and ZnO) on the anti-fouling and self-cleaning properties of polyether sulfone composite membranes (14% by weight) was studied with different concentrations of graphene oxide. The hydrophilicity of composite membranes improved as compared to neat membrane; however, graphene oxide-TiO2 functionalized membranes showed the lowest flux. Incorporation of CuO nanoparticles in polymeric membranes for water treatment is a potential solution for biofouling formation. Promising results have been reported for antibacterial/antifouling effects, increased hydrophilicity, water flux improvement, contaminant rejection capacity, structural membrane parameters, and reduction of concentration polarization. TiO2 nanoparticles have been added to improve the self-cleaning and anti-fouling ability of ultrafiltration polymer membranes through their photocatalytic activity. Immobilization of TiO2 nanoparticles on membrane surfaces was investigated to reduce organic fouling effects in a bioreactor by increasing the membrane hydrophilicity. Cajanus cajan seed extract and carbon nanoparticles reformed the hydrophobic PVDF membrane to hydrophilic. Introduction of TiO2 (0.02% by weight) into the membrane rendered it bi-functional, thus achieving 85% rejection of Cr(VI) and 92% reduction to Cr(III) in tannery wastewater. Ag2O, Fe2O3 and ZrO2 nanoparticles can be incorporated to improve the performance of polymeric filtration membranes due to their effects on permeability, selectivity, hydrophilicity, conductivity, mechanical strength, thermal stability, antiviral, and antibacterial properties. However, they might cause membrane deterioration. Thus, careful selection is required to choose the best composition of metal oxide nanoparticles for individual polymeric membranes. The advantages and disadvantages of Ag2O, CuO, Fe2O3, TiO2, ZnO and ZrO2-incorporated polymeric membranes for water purification have been compared in a new review. Their characteristics (antibacterial property, anti-viral property, conductivity, contaminants rejection, flux permeation, hydrophilicity, mechanical strength, permeability, surface charge, and thermal stability as shown in Table 1) can help decide on the best modification towards achieving sustainable and cost-effective treatment operations. Bimetallic transition metal oxide nanoparticles have attracted many researchers due to their salient features and characteristics over mono metallic oxide nanoparticles. PES ultrafiltration membranes were fabricated using the phase inversion technique (most commonly used technique to fabricate polymeric porous membranes with a large form of structure) with a composite of Fe2O3-Mn2O3 nanoparticles as modifier. Those membranes showed an excellent porosity (74%), high water flux (398 L/m2h), and better antifouling ability. Protein-based filtration tests showed an improved flux recovery ratio in protein separation and water treatment applications [205-214].

Table 1: Transition metal oxide nanoparticles-incorporated polymeric membranes

Transition Metal Oxides

Polymeric Membrane Advantages

Limitations

ZnO Polysulfone, polyurethane, polyvinylidene difluoride Antibacterial, anti-corrosion, anti-fouling, environment-friendly, hydrophilic, low-cost, mechanical strength, self-cleaning (photocatalytic activity). Not stable (photocatalytic property), mildly toxic.
TiO2 Antibacterial, anti-corrosion, anti-fouling, hydrophilic self-cleaning (photocatalytic activity). High doses may induce cytotoxicity, not stable (photocatalytic property).
Fe2O3 Abundantly available, can remove heavy metals, magnetic properties, mechanical strength, non-toxic. Nanoparticles tend to agglomerate easily.
CuO Antibacterial, anti-corrosion, anti-fouling, compound rejection capacity, hydrophilic, improving water flux, mechanical strength. Low-quality nanoparticles are produced via physical synthesis, toxic chemicals are used if produced through chemical synthesis.
Ag2O Pressure retarded osmosis membranes Almost non-toxic, anti-fouling, antimicrobial, resistant to corrosion, stable. Membranes are sensitive to nanoparticle concentration.
ZrO2 Novel membranes Capable of treating saline water, high-temperature stability, high water retention capacity. Fouling susceptibility, expensive raw materials.
Fe2O3–Mn2O3 Polyether sulfone Antifouling, minimal irreversible fouling, excellent water flux, improved recovery for protein separation. Agglomeration of nanoparticles.
TiO2-ZnO composite membrane Increased hydrophilicity, anti-fouling, self-cleaning properties, photocatalytic activity. Low flux.

Fabrication of transition metal oxide nanoparticles-modified polymeric membranes to make them operation-sustainable cost-efficient is challenging. Transition metal oxide nanoparticles have many interesting functional properties. However, integrating nanoparticles into a membrane remains a challenge. Atomic layer deposition and sequential infiltration synthesis were explored for the modification of polymeric membranes and fabrication of novel mesoporous structures. Fouling is a major problem that hinders the operation of membrane filtration processes. Bio-fouling causes performance degradation and elevates energy consumption due to blockage of membrane pores. In addition, it increases the frequency of membrane cleaning and reduces the membrane life span, thereby leading to higher maintenance and operation costs. Antibacterial membranes are considered an attractive strategy to retard biofouling. ZnO is reported to be useful as an anti-fouling agent in polymeric nanofiltration and reverse-osmosis membranes. Instead of ZnO, ZnO2 nanoparticles can be incorporated to make nanofiltration and reverse-osmosis polymeric membranes for better retardation of fouling since ZnO2 is a stronger oxidizing agent than ZnO and can produce free radicals and other reactive oxygen species to inhibit growth of microorganisms. The operating temperature of nanofiltration and reverse-osmosis is typically within 25-65°C, which is far below the transition temperature (233°C) of ZnO2. Hence, ZnO2 nanoparticles embedded in the polymeric membrane are completely stable during wastewater treatment. ZnO2 has photocatalytic self-cleaning property that would make it a strong modifier over ZnO in the fabrication of polymeric membranes. Importantly, these membranes can greatly facilitate the preparation of samples for instrumental analysis of emerging contaminants by removing microplastics (plastic particles smaller than 5 mm) that exist in wastewater and marine environments, including pharmaceuticals, personal care products, perfluoroalkyl substances, organophosphate flame retardants, illicit drugs, and isoprostanes in wastewater as biomarkers of oxidative stress during COVID-19 pandemic. A complete summary of recent advances and latest studies in the fabrication, modification, and industrial application of ZnO photocatalysts is available for further reading. Black TiO2 nanotube array can be employed as both photocatalyst and electrocatalyst to degrade dissolved organic matter in coking wastewater [215-227].

Analytical Methods for Transition Metal Oxide Nanoparticles

New developments have recently been reported concerning the chemical analysis of transition metal oxide nanoparticles in environmental water due to their biochemical toxicity. A unifying methodology for the selective detection of transition metal oxide nanoparticles in water, as well as sensitive determination of environmentally toxic and biochemically active contaminants that are bound on them, is urgently needed. In our lab, new analytical methods have undergone intensive development in the last ten years with a focus on capillary electrophoresis with UV and molecular fluorescence detection (Figure 3). The toxic effects of these emerging contaminants have already been verified by Health Canada and Environment Canada using bioassays. The methodology under intensive development in our research lab begins with a sample treatment step that encapsulates all waterborne nanoparticles/nanomaterials into lecithin liposomes. Centrifugation concentrates the loaded liposomes, and the supernatant water is withdrawn for instrumental analysis by liquid chromatography with detection by tandem mass spectrometry. Next a surfactant disintegrates the liposomes and isolates lecithin from the nanoparticles. Contaminants are desorbed from the nanoparticle surfaces using coordination chemistry, biochemical interaction, laser photo/photothermal chemistry, aerosol nebulization, and electrospray ionization. The desorbed contaminants can be analyzed, either immediately or after separation by capillary electrophoretic/gel filtration/liquid chromatography, by spectrofluorometric and mass spectrometric detection. Optical incoherent scattering and electrochemical chemistry can be adapted to detect the nanoparticles and desorbed contaminants at trace to ultratrace levels [228-238].

FIG 3

Figure 3: Development of new analytical methods for transition metal oxide nanoparticles in our lab.

Transition metal oxide nanoparticles are increasingly used as a solid carrier in the formulation of numerous drug products. They end up in waste streams, consequently infiltrating the aquatic environments and drinking water resources. Detection of nanoparticles in wastewater requires more advanced analytical methods than conventional water analysis, to prevent the ecosystem of plants and animals from unintended exposure to the released pharmaceuticals. Determination of nanoparticles in drinking water has important implications for protecting the public health sustainability. Unfortunately, many emerging contaminants are not yet stipulated in water quality regulations due to a lack of monitoring technology. Hence, there is an urgent need to develop new analytical methods that can monitor emerging contaminants in water resources. The interplay of nanoparticles, environmental pollution, and health risks is key to all industrial, environmental, and drinking water treatment regulations. A unifying analytical methodology will help scientists and engineers strengthen their control of nanoparticles in freshwater sources for drinking water treatment plants. New endeavors must challenge the traditional notion that environmental toxicological events involve only dissolved contaminants. Rather, environmental toxicology can involve a complex assortment of nanoparticles and associated contaminants whose combined effects on biological and mammalian cells are continuously evolving. The precise toxico-pathogenic effects of ZnO nanoparticles on the cardiovascular system under normal and cardiovascular disease risk factor milieu include down regulation of vascular development and elevation of oxidative stress in the heart tissue. Both endothelial nitric oxide generation and cardiac Ca2+-ATPase activity are significantly suppressed; the cardiac mitochondrial swelling is enhanced [239].

Wastewater Analysis

The nanoparticles released from different nanomaterials used in our household and industrial commodities find their way through waste disposal routes into the wastewater treatment facilities and end up in wastewater sludge. Further escape of these nanoparticles into the effluent will contaminate the aquatic and soil environment. Polyacrylic acid nanomembranes can be used as nano-filters to isolate and remove Ag and TiO2 nanoparticles in aqueous environmental samples using pressure-driven flow, with a filtration efficiency of >99%. The phytoremediation potential of Myriophyllum spicatum L. for removal of ZnO nanoparticles in tap water ranges between 29% and 70%, and slightly higher in pond water. Wastewater treatment plants are a primary source of many contaminants to the environment. Processing complex mixtures of waste, they can result in the continuous discharge of bioactive and endogenous compounds into sensitive aquatic ecosystems. Wastewater analysis has been demonstrated to be a cumulative approach for assessing the overall patterns of alcohol, drugs, tobacco and xenobiotic use by a population at the community level. Hospital wastewater, for one, is regarded as a very important source of fluoroquinolone antibiotics (ciprofloxacin, norfloxacin, and ofloxacin) in the aquatic environment. The development of analytical methods is crucial for the detection of oxidative stress biomarkers in wastewater, using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry and solid phase extraction. Mixed liquor can be collected from the secondary aeration tank while effluent wastewater is collected after the secondary settling tank in a wastewater treatment plant. Mixed liquor is the wastewater which leaves the aeration tank after biological treatment before going into the secondary settling tank for the suspended solids to sediment, while effluent wastewater is the ultimate treated wastewater which is discharged to the river from the treatment plant. Obviously, mixed liquor has much higher levels of suspended solids and relatively higher dissolved carbon content compared to effluent wastewater. Nanoparticles from thirteen different elements were determined, throughout the full-scale wastewater treatment process, by using single particle inductively coupled plasma mass spectrometry. Samples of the influent, post-primary treatment, effluent of the activated sludge process, as well as reclaimed water were analyzed. The incidence of metal-based nanoparticles decreases significantly after the conventional wastewater treatment train, and they are smaller in the effluent (<180 nm) than in the influent (<300 nm). However, anaerobic digesters store high nanoparticle concentrations. Hence, the disposal of sludge needs to take this into account to evaluate the risk of nanoparticles release to the environment [240-248].

The potential use of 8-iso-PGF2α as a sewage biomarker for assessing the status of community health was investigated by liquid chromatography-high resolution mass spectrometry coupled to immunoaffinity clean-up and β-glucuronidase treatment. Urinary excretion provides a mechanism for the entry of isoprostanes to wastewater treatment plants and subsequently the wider environment, where they may initiate a cycle of oxidative stress in aquatic biota. Additional isoprostanes may be produced within these organisms, further perpetuating this cycle of toxicity. An analytical method for their detection in wastewater, based on solid phase extraction and gas chromatography mass spectroscopy, involves a deconjugation treatment with -glucuronidase to increase the concentration of isoprostanes available for detection. The low ng/L range of concentrations of human metabolic biomarkers and the complex matrix composition pose bioanalytical challenges related to sample preparation, detection and quantification. A sensitive liquid chromatography-mass spectrometry method for the detection and analysis of opioid biomarkers has been validated according to the European Medicines Agency guidelines; Oasis HLB cartridges are useful for sample concentration. Ion pairing liquid chromatography with alkanesulfonates coupled to tandem mass spectrometry is valid for the analysis of aminoglycosides (veterinary antibiotics) in wastewater samples after addition of the ion paring salt directly into the raw or treated wastewater samples. Surface-enhanced Raman spectroscopy is good for the detection of methamphetamine based upon the assembly of Au@Ag core-shell nanoparticles on a disposable glassy nanofibrous electrospun paper matrix that gives strong scattering signals. Microplastics are generated while polishing eyeglass lenses and a huge amount of nanoplastics (<1 µm) passes through the conventional wastewater treatment process in considerable amounts. Microplastics (with adsorbed heavy metals) can be quantified in the wastewater by mass balance measurements using membrane filtration with polyaluminum chloride coagulation. The transport of nanoparticles in various wastewater treatment processes is fully discussed in another review [249-255].

Air Pollution Remediation and Quality Monitoring

One of the most favorable environmental applications of nanotechnology has been in air pollution remediation in which different nanomaterials are used. Nanoparticles have initiated the advancement in new and low-cost techniques for environmental pollution control including air pollution. Metal oxide nanofibers have demonstrated to be effective for air pollution remediation in the form of filter, catalyst, catalyst support, and photocatalyst. Fibrous metal oxide has several advantages including surface area, mechanic strength, chemical stability, thermal stability, and photocatalytic ability. In the field of selective reduction of nitrogen oxide, a catalyst with low cost, low toxicity, high activity, and good selectivity for N2 is needed to replace the high-cost and high-toxicity vanadium catalyst. Low-cost spinels MFe2O4 (M = Cu, Mn, and Zn) can be synthesized for this application, and MnFe2O4 exhibits the best activity (99.9%) and selectivity (95.7%) at 100°C [256-258].

Nanocomposites have distinctive physical and chemical properties that result in their use in the construction industry as innovative materials. Addition of nanoparticles can bring many important properties to the bulk construction and insulation materials. Unfortunately, release of ultrafine dust to the air environment has harmful impacts on human health. Nanoparticles can enter the human body through the skin, inhalation, and ingestion. Exposure to nanoparticles can cause serious respiratory, cardiovascular, skin, and nerve related diseases. It can pass through various mammalian membranes, or be absorbed in them, to cause various inflammatory reactions and fibrosis. Pneumoconiosis refers to a class of interstitial lung diseases caused by the inhalation of airborne dust and fibers. Engineered nanoparticles, owing to their high reactivity, can initiate inflammatory responses that trigger metastasis. Human exposure to nanoparticles can cause various health implications such as DNA damage and cell death. A global regulatory policy needs to be framed to assess the toxicity, risk and approval of nanoparticles used in the construction industry. The current OSHA standard for ZnO fume is 5 mg/m3 of air averaged over an eight-hour work shift. NIOSH recommends that the permissible exposure limit be changed to 5 mg/m3 averaged over a work shift of up to 10 hours per day, 40 hours per week, with a short-term exposure limit of 10 mg/m3 averaged over a 15-minute period. It would be scientifically interesting to investigate the percutaneous absorption of transition metal oxide nanoparticles following exposure to road dust powder [259-264].

Semiconducting metal oxide gas sensors have been developed for environmental gases including CO2, O2, O3 and NH3; highly toxic gases including CO, H2S and NO2; combustible gases such as CH4, H2, and liquefied petroleum gas; and volatile organic compound gases. Nanomaterial enabled sensors are applied for the detection of harmful gases such as H2S, SO2, and NO2 . An ultrafast sensor has been developed for trace-level detection of NH3 gas using ZnO nanoparticles, with ultra-fast response (5 sec) and recovery time (8 sec) at 5 ppm. Dopants can enhance the performance of semiconductor metal oxides for gas sensing applications by changing their microstructure morphology, activation energy, electronic structure, and band gap of the metal oxides. In some cases, dopants create defects in semiconductor metal oxides by generating oxygen vacancy or by forming solid solutions. To date, very little is known about the magnitude, patterns, and associated risks of human exposure to microplastics, particularly in the indoor environment. This is a significant research gap given that people spend most of their time indoors, which is exacerbated over the past year by COVID-19 lockdown measures [265-269].

Conclusion

This scientific field possesses immense potential that may provide incredible technological advances soon. The research findings covered in this review article could open many doors to new endeavors. Having more reactive oxygen atoms per molecule, ZnO2 can be considered as a stronger oxidizing agent than ZnO. This unique property makes its nanoparticles an excellent candidate for potential breakthroughs in analytical, biomolecular, food, material, and separation sciences. In addition, mono-dispersed ZnO2 nanoparticles could be coupled with a magnetic core to produce nanocarriers for in-situ disruption of cancer cells. High-performance nano-filtration and reverse-osmosis could be developed with ZnO2 for fouling remediation with self-cleaning feature. For future applications, intelligent antibacterial food nano-packaging could undergo new developments through incorporation of ZnO2 nanoparticles to the packaging film. It will become more and more important that the presence of ZnO/ZnO2 in wastewater is detected and quantitated for the protection of environmental sustainability and public health. The knowledge gap in this dynamic field, as highlighted in this review, will require novel research work.

Acknowledgement

Financial support from NSERC Canada (grant number RGPIN-2018-05320) is gratefully acknowledged.

Competing Interest Statement

The authors have no competing interests to declare.

Data Availability Statement

The raw/processed data required to reproduce our findings in Figure 2a/2b cannot be shared at this time as the data also forms part of an ongoing study.

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FIG 3

AI-eEmpowered Problem-Solving for Civic Issues: Considering New York in 2024

DOI: 10.31038/PEP.2024412

Abstract

Idea Coach, an AI-empowered program, was instructed to provide suggestions and innovations for dealing with eight problems plaguing the state of New York. These problems reflect different areas of civic distress (e.g., crime-plagued streets), economics (e.g., flight of the middle class from NY), and so forth. Idea Coach was often able to deliver meaningful suggestions about how specifically to ameliorate the specific problem, and recommend intuitively meaningful innovations to consider for the future. This exercise suggests the promise of using AI in the form of Chat GPT3.5, as a way to create a quick overview of problems and their solutions. The paper highlights the benefit of AI as providing an education about a problem, with the education accomplished quickly (a matter of minutes), inexpensively (low cost for the use of the platform), and iteratively (many alternatives proposed for consideration across successive and easy repetitions of the exercise).

Introduction

As the century progresses, or in truth as history progresses, often the bright hope for the future turns into a drab, unpleasant, and of course disappointing present, and over time that present becomes a past to mourn. As poet John Greenleaf Whittier wrote so touching in ‘Maude Muller’, ‘of all sad words of tongue and pen, the saddest are it might have been.

Government is presumed to be at the service of people. In free countries people are presumed to elect those who can give them a better life. And yet, repeatedly we are treated to noble visions and hopes turned into shattered dreams. Is it possible to create lists of things to improve society without having to spend extraordinary amounts of money on experts, consultants, consumer research ranging from qualitative groups (in-depth interviews, focus groups) to massive surveys, done quickly, over the internet, but covering little and all too often over-analyzed?

The introduction of the Internet in the 1990’s was hailed as a new way for people to connect with each other. Solving civic problems was part of the hope. Instead of the expensive interactions between people and the slow-moving efforts, it was often thought that this ‘new wave of communicating’ would generate a better society. People could surface issues better, with the presumption that once enough people and especially ‘experts’ heard about the issue, thought about, discussed ramifications and solutions, that somehow the magic of many minds working together would guide a solution to the problem, and all would be well.

The literature of suggested problem solving for civic problems is enormous. One need only look at Google Scholar to get a sense of the vast literature. Figure 1 shows a screen shot taken January 15, 2024. The topic is ‘solving social problems.’ Google Scholar emerges with more than six million hits (6,230,000 to be exact).

FIG 1

Figure 1: Google Scholar results from searching for papers on ‘solving social problems’.

The number of Google Scholar ‘hits’ drops down by more than half when the topic is more specific, ‘using AI to suggest solutions to social problems.’ The number is 2,810,000 and a longer search time, viz., from 0.10 seconds to 0.22 seconds. Figure 2 shows this screen shot.

FIG 2

Figure 2: Google Scholar results after ‘using AI to suggest solutions to social problems.’

Many of the papers talk about the promise of AI, and in turn the way AI should be considered, used, and interpreted. The paper in the literature emerge from the careful consideration of a new technology applied to the problem [1,2]. Some of the literature also pertains to the intersection of AI and societal goals, such as those put forward by the United Nations, for the year 2030 [3,4].

This paper moves from a general consideration of AI and social problems to an exploration of specifics, using the AI-empowered Idea Coach found in SaaS (Socrates as a Service, and in Mind Genomics. Both systems use AI in the form of Chat GPT3.5 [5]. They can be accessed directly: Mind Genomics a: www.bimileap.com Socrates as a Service at https://socratesasaservice.com/u/dashboard.

The original reason for the incorporation of AI into these two platforms comes from the frustrations encountered by researchers when investigating a topic using Mind Genomics. Mind Genomics focused on the creation of a set of questions for a topic, and then for each question required the user to create a set of four answers (elements). These ‘elements’ would then be mixed together to create small, easy to read combinations, vignettes. The ratings assigned to these vignettes would be then deconstructed into the contribution of the individual elements as a driver of the vignette. The benefit of this seemingly circuitous approach, replacing simple questions, is that the Mind Genomics projects focused on the ‘granular,’ where reality manifests itself. It was impossible to ‘game’ a Mind Genomics study. The results showed how people made decisions with real-world issues, rather than rating abstract concepts as is often the case with surveys focusing on a topic or focusing on a recent personal experience [6,7].

Within this carefully crafted system the key weakness was that many users felt intimidated by having to ask meaningful questions. To this end, AI was introduced as a guide, appearing to the user to be a seamless add-on to the Mind Genomics process, and given the name ‘Idea Coach’ to encourage the user. Figure 3 shows the initial format of the Mind Genomics effort. Panel A shows the request that the user provide four questions. It was here that the user became discouraged. Panel B shows the rectangle wherein the user writes a squib to describe the need to the AI-empowered Idea Coach, in this case a request to provide questions relevant to the issue of crime in the streets. Panel C shows the output form Idea Coach, comprising four of the 15 questions returned by the Idea Coach.

FIG 3

Figure 3: The sequence of screens showing the request for four questions (Panel A), the use of Idea Coach (Panel B), the output from an iteration of Idea Coach (Panel C), and the request for four answers to Question 1 of 4.

The approach shown in Figure 3 has been used for several two years, as of this writing. The introduction of the Idea Coach substantially enhanced the usability of the Mind Genomics paradigm allowing the researcher to develop questions and answers rapidly. The next steps for Mind Genomics studies were to combine the answers from the four questions into small, easy to read vignettes, combinations of answers (aka element). The vignettes comprised a minimum of two elements, and a maximum of four elements, the vignettes created according to an underlying experimental design. No vignette ever contained two or more elements or answers from a question, but often a vignette failed to contain an element from one, or even from two different questions.

The foregoing approach for Mind Genomics in general, and for Idea Coach in particular, focused on simplifying the creation of vignettes. What was not apparent at the point was the possibility that the AI embedded in Idea Coach could do far more, when properly instructed.

It is the extension of Idea Coach into new areas, specifically into new types of requests that one can make for the AI, and the presentation of the results for this initial foray beyond the simplistic question/answer paradigm.

Method

The approach here uses Idea Coach and thus AI is a new way, doing so in the spirit of Mind Genomics, but pushing the AI to provide all the information when the problem is described. That is, the squib describing the problem also requests alternative solutions, and reasons for the solutions. The squib is far more detailed than the original squibs written to generate questions, and then subsequent squibs which generated alternative sets of answers from the squib.

The paper begins with the first advance, viz., requesting questions and answers to be provided at the outset through Idea Coach. Table 1 shows a slightly expanded squib provided to Idea Coach, and thus constituting the instructions to the embedded AI. The squib comprises several logical sections, as follows:

  1. The general introduction. Here the introduction talks about New York, specifying the market, and the issue. There is nothing here which can guide Idea Coach about solving any problem.
  2. Instructions to Idea Coach to make sure that the results are specific, and the solutions are realistic, i.e., one can actually act on them.
  3. The statemen of the problem, in modest detail, providing some specification
  4. The request to AI to provide solutions to a specified problem.

It is important to keep in mind that this modification to the use of Idea Coach does not require two squibs, the first being the request for questions, and the second being the request for answers to specific questions. Figure 3 shows that traditional two-step process. Table 1 attempts to collapse the process.

Table 1: The revised process for Idea Coach, attempting to collapse the process into one step to give to Idea Coach.

TAB 1

The remainder of this paper comprises a demonstration of the solutions and innovation for each of xxx problems plaguing New York. When reading the tables one should keep in mind that the problems presented to the AI were very general. Each particular table was created with the same instructions. Only the problem differed The answers, however, were requested to be realistic. The AI was requested to give 10 solutions, a request that was sometimes honored, and sometimes not honored.

The Idea Coach was developed to be iterative (see Figure 3, bottom of Panel C). That is, the user could revise the request to Idea Coach, changing the nature of the problem, the number of requested solutions, and so forth. It is this ‘iterative’ nature of Idea Coach which leads to the format of Table 1. With a simple set of keystrokes the user can re-run the request to create a new set of solutions, or the user can modify the problem, e.g., from street crime to education, and immediately receive an additional set of solutions. Each iteration takes approximately 15-30 seconds, so that a set of 10 problems can be addressed in about five minutes.

After the initial solutions were presented to the user, and after the Mind Genomics program was closed by having the user log-off, a second module of the Mind Genomics platform went to work on the question and the answers, to provide a summarization. The summarization provided this additional information, shown in Table 2. This AI summarization was emailed to the user about 20 minutes after the user logged-off. The result is that within 30 minutes or so, the enterprising user of Idea Coach can investigate ten issues, or one issue ten times, receive results from AI, and then at the end of the process receive a summarized, deeply re-analyzed booklet, with each iteration deeply explored anew by AI.

Table 2: The summarization material provided in the Idea Book, provided to the user after the user logged off from the project.

TAB 2

Table 3 shows the first iteration to deal with reducing street crime. Table 4 shows the first iteration to improve education in New York State. Each table is divided into two parts, solutions and innovations, respectively.

Table 3: Solutions to reduce street crime, provided by Idea Coach. The top half of the table shows what is provided at the time of use. Both the top and bottom halves of the table are provided in the Idea Book, sent to the user.

TAB 3

Table 4: Solutions to stop the decline in educational performance in public schools in New York State, provided by Idea Coach. The top half of the table shows what is provided at the time of use. Both the top and bottom halves of the table are provided in the Idea Book, sent to the user.

TAB 4

The top part (solutions) presents the information that was returned immediately upon presenting Idea Coach with the squib. This information can be immediately scanned by the user, who can re-run the request to get more solutions, or modify the squib and then re-run it, thus changing the focus of the request to Idea Coach.

The bottom part (innovations) actually comes from the summarization of this information, provided to the user in the Idea Book . Recall that the Idea Book is send to the user about 20 minutes after the user logged-off. The embedded AI in Idea Coach reviews the solutions, and comes up a set of new suggestions, expanding the scope of the already-offered solutions. In this respect, one might say that AI is producing ‘new knowledge.’

Appendices 1-6 to this paper shows the solutions and innovations relevant to each of the remaining problems facing New York State. Appendix 7 shows an example of what the Idea Book returns to the user at post-processing. Appendix 7 presents the full Idea Book page for the second iteration dealing with reducing street crime. The sections of the page are shown in Table 2. Each iteration is separate, so Appendix 7 presents new data, albeit for the same topic of reducing street crime.

Moving from Solutions to Implementation, Assessment, and Expected Public Reaction

Tables 3 and 4 suggest that the AI embedded in Idea Coach can move beyond regurgitating information, and perhaps provide new ideas, or at least ideas that were not originally present in the request. Whether or not the innovations truly represent ‘new information’ is not relevant. What is relevant is the recognition that the AI can be ask to hypothesize about outcomes.

Tables 5 and 6 show the same topics, street crime and education, respectively, this time explored by AI in a deeper way, namely by asking AI to suggest a set of 10 solutions for each general problem. Each solution is to comprise five parts, the solution to be considered as a whole comprising those four parts. For each of the 10 solutions, Idea Coach is instructed to estimate four factors, each on its own unique scale: Effectiveness, Cost, Time, Public Reaction.

Tables 5 and 6 show that Idea Coach can do this task. There is no cautionary statement of impossibility, or that the task is beyond the program. Rather, Idea Coach and thus the embedded AI, returns dutifully with the larger solution, the four components, and then the evaluations.

The results shown by Tables 5 and 6 suggest that the incorporation of AI into the planning, using a simple platform such as Mind Genomics (www.BimiLeap.com) or Socrates as a Service (https://socratesasaservice.com/u/dashboard) present a new opportunity to systematically explore problems and their solutions, in a way which is feasible, fast, relatively complete, and perhaps enlightening and educational for those without deep experience in the topic.

Table 5: Expanded squib for Idea Coach, dealing with street crime. The squib requests 10 solutions, each with several steps, and then the evaluation of the solution on effectiveness, cost, timeline, and expected public reaction.

TAB 5(1)

TAB 5(2)

Table 6: Expanded squib for Idea Coach, dealing with street crime. The squib requests 10 solutions, each with several steps, and then the evaluation of the solution on effectiveness, cost, timeline, and expected public reaction.

TAB 6(1)

TAB 6(2)

TAB 6(3)

Discussion and Conclusions

This paper began as an effort to demonstrate to senior government officials in New York that the contribution of AI in an easy-to-use format, and even by a novice new to the issues, could be helpful. The initial efforts focused on the traditional use of Mind Genomics, namely, create questions, answers, mix the answers into vignettes, test the vignettes, and discover how people think. That has been the approach, one set up following the dictates of science (e.g., experimental psychology), and especially psychophysics), as well as one using rigorous experimental design to create the test stimuli [8] and analyze the results [9].

As in many efforts in science, that which is planned often evolves into that which ends up being discovered. The role of accidental discovery cannot be overemphasized here. No one was thinking that the Idea Coach squib should be written with a request both to list problems and list answers, and evaluations of those answers in terms of feasibility, cost, timing, and public reception. These were all steps to be taken with caution, slowly, after having studied the issue thoroughly, done one’s so-called ‘homework’, and then suggesting an educated opinion after having been immersed in the problem.

Nothing could have predicted either the initial results, nor the reactions to efforts and finally to the expansion of the request made by AI. The results shown in Tables 5 and 6, as well as in Appendices 1-6 confirm the dramatic ease with which new ideas can be quickly investigated, perhaps for themselves, or perhaps as steps prior to consumer research. Certainly, it is to be expected that researchers who have done the type of pre-work shown in Tables 5 and 6, as well as in Appendices 1-6, are likely to recognize good answers when they appear, simply because of their easy-to-obtain experience with the Mind Genomics world.

The approach presented here touches on a variety of topics, ranging from the application of Mind Genomics and Idea Coach real world issues, and the implication for philosophical issues brought up by the readable results and judgments provided by AI (Tables 5 and 6; Appendix 7). It is clear that the results suggest practical suggestions that can be tested empirically to determine the degree to which the AI appears able to prescribe solutions for problems ‘picked at random’.. More puzzling, however, is the ‘seeming capability’ of AI to judge the difficulty and the outcome of courses of actions. Perhaps the machines really do have so-called ‘tacit knowledge’, but from where would this deep knowledge come from, a knowledge which makes the answers so realistic? [10-12]. Despite these issues, however, and no matter what the deeper reality may be, demonstrations of the ability of AI to help in the formulation of policy for social issues seem destined to drive an increasing acceptance of AI as a collaborator to create a better society.

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Dealing with 21st Century Accusations of Genocide Levelled Against Israel: The Contribution of AIenhanced Mind Genomics to Debunk a Myth

DOI: 10.31038/MGSPE.2024413

Abstract

Using AI embedded in a user-friend platform (www.bimileap.com) it became possible to request new knowledge rapidly and easily about mind-sets dealing with accusations of genocide against Israel. Given only requests to identify mind-sets and suggest their beliefs and relevant communications, the AI embedded in the platform provide concrete, testable recommendations in a user-friendly form easily understood, as well as AI-summarizations of its own recommendations. The process, taking only minutes, is recommended as a new thrust for understanding conflicts, and improving the current world order.

Introduction

The tragic events of October 7, 2023, in the southern part of Israel culminated in the deaths of more than 1200+ Israelis and others attending an outdoor concert/festival. The military response of Israel against Hamas resulted in a widespread destruction of much of Gaza and the deaths of many people. The response of many students around the world was to support Gaza, and to claim that a genocide was being committed. Many university students around the world protested, wearing the black and gray keffiyehs which have come to symbolize the Palestinian fight against Israel. During this time, the increasing level of accusation reached unheard of proportions in the modern world as antisemitic tropes and canards emerged about Jews and their so-called activities. The remarkable ferocity of the accusation requires an understanding of how to deal with the hostility.

The Contribution of Mind Genomics

The approach here comes from the emerging science of Mind Genomics. Mind Genomics studies the way people make decisions about the issues of the everyday, such as what they will purchase in a store, what kind of service they want from a physician or lawyer or other professional, and so forth. Rather than doing experiments to understand specific things such as the way people process information, Mind Genomics works at the level of granular, everyday experience, attempting to understand how people think about the specifics of their lives. Mind Genomics can thus be thought of as the science of everyday experience [1-3].

Moving to Detailed Instructions Given to AI

The AI embedded in Idea Coach allows the user to pose more complicated questions. The questions move from simply questions to statements, actually hypotheses, and then pose questions based upon the hypotheses. Table 1 shows the instructions provided to Idea Coach in the www.bimileap platform. The instructions posit the existence of three mind-sets, although the instructions could have been changed to posit fewer or more. Previous work investigations with Mind Genomics often revealed that three mind-sets generated both a comprehensive coverage of different points of view on the topic but at the same time generated a feeling of parsimony. That is, three mind-sets satisfied both the need to cover the different points of view, but the desire to do with as little hypothesizing as possible. After introducing the topic, the request asked eight specific questions, all of which required the AI to hypothesize about what might be the case for each mind-set. The final piece of information was the group about whom the request was being made, specifically college students.

Table 1: The request as given to Idea Coach

TAB 1

As requested, Idea Coach returned with three mind-sets, shown in Table 2. These mind-sets are:

  1. Human rights advocates for Palestine
  2. Anti-colonialist perspective
  3. Anti-Zionist activists

Table 2: Specifics of the three mind-sets returned by the AI in Idea Coach

tab 2

It is instructive to keep in mind that there is nothing provided to the AI to suggest these three groups. Rather, it is the AI which generates the three mind-sets. It is also interesting to note that repeated efforts with AI will return with different sets of mind-sets. That is, for whatever reason, the answers may not be repeatable. That is, it appears almost the a similar but not the same person is thinking through the problem, so that the general patterns are repeated, but not specifics. The AI returns with sets of answers which appear to be internally consistent and agree with the name of the mind-set. There are two disclaimers.

Question #7: What is the likelihood that they will go from discussing the situation with Israel to acts of vandalism, violence, and overt antisemitism?

Answer to Question #7: It is difficult to determine the likelihood of individuals engaging in vandalism, violence, or overt anti-Semitism based solely on discussions. While strong emotions and disagreements may exist, it is important to promote respectful dialogue and understanding.

General disclaimer: Note: It is important to approach these discussions with nuance, empathy, and respect for differing opinions. The aim should not be to forcefully convince individuals but to promote understanding and the exploration of different perspectives.

Table 2 presents the specifics for the three mind-sets returned by the AI in Idea Coach. The table shows the specifics for these three mind-sets. It is important to emphasize here that mind-sets presented in Table 2 are strictly those ‘conceptualized by the AI’. What is important here is that AI returns results that are similar to what a human being might provide, or at least are plausible answers. In a Turing Test one might assume that these answers would defy the attempt of a person to know whether results are generated by machine or generated by people, especially if one were to ignore the language and focus just on the ‘information’ provided.

The initial ‘success’ in the process encouraged the use of AI to move further into the possibilities of looking at the topic in deeper granularity. Rather than instructing Idea Coach and its AI to assume three different mind-sets, the second part of effort ‘pushed the envelope’ to an arbitrary set of 12 different mind-sets. Table 3 shows the instructions to Idea Coach, positing the existence of these 12 mind-sets of students. The task assigned to Idea Coach was made simpler because initial efforts to have the AI answer seven questions for each of 12 mind-sets proved too difficult. The AI returned with a polite refusal each time the large request was made. It was easier simply to ask three questions, comprising name of mind-set, belief about Israel by mind-set, and finally the action step of ‘what does one say to convince this mind-set that Israel is doing the right thing?’ These steps were immediately acted upon by the AI in Idea Coach.

Table 3: The questions asked in the second phase, where 12 mind-sets were posited

tab 3

Table 4 presents the 12 mind-sets hypothesized by AI and returned to the user. The interesting points to note are that the mind-sets are plausible, seemingly different from each other, and that the AI attempted to fulfill the requests about belief regarding Israel and suggesting what to be said to justify Israel’s action. The underlying AI provides its answer in a form that is diplomatic and gentle.

Table 4: The 12 mind-sets, their hypothesized beliefs, and the suggested statements that could be used with them to convince them that Israel is doing the right thing.

tab 4

AI Summarization and the Creation of ‘New Knowledge’

The creation of ‘new knowledge’ using AI has emerged as a hot topic [4-6]. The issue is whether AI produces new knowledge in the way that believe a person produces new knowledge, although the reality is this question has not really been adequately answered. What has been suggested, however, is the power of AI to drive innovation, whether that means new knowledge or simply new applications of current knowledge [7,8]. It is clear that whatever the philosophical issue may be, AI seems to be on the cusp of producing something ‘new’, something unexpected, tangible and useful [9-11] Whatever the philosophical issue may be, can the Idea Coach be said to produce something which resembles new knowledge? The results suggested by Tables 2 and 4 hint at new knowledge because the AI seems to have generated clearly delineated mind-sets which ‘make sense’, as well as mind-sets that have not been widely articulated. A further effort to produce new knowledge beyond the simple answers to requests posed to AI in Tables 1 and 3 comprises the request to AI to summarize its own contributions, viz., to take the answers that AI had previously provided, and then provide additional summarizations. Table 5 shows eight sets of ‘summarizations’ based on the limited information provided in Table 4. After the Idea Coach provided the information in Table 4, it applied a secondary set of instructions called the ‘summarizer.’ Without any interference from people, the summarizer asked eight questions shown as numbers topics in Table 4. These topics range from key ideas to themes, to interested audiences, and finally to what’s missing and to innovations.

Table 5: Eight summarizations of the key ideas presented in Table 4. The summarization is returned automatically in the ‘Idea Book’ and is provided automatically to the user.

tab 5(1)

tab 5(2)

tab 5(3)

Discussion and Conclusions

The advances in AI on the one hand, and in consumer research on the other, have been melded into a user-friend tool, known by the rubric of Mind Genomics, and available in a user-friendly platform (www.bimileap.com). What began as a tool to drive knowledge by having people evaluate combinations of messages has evolved to an AI-driven tool to generate these combinations of messages (Idea Coach). That evolution began with the effort to address the problems that novice users and perfectionists alike experience, viz., the sheer emotional difficulty of having to develop ideas. It was this emergent ‘block’ to using Mind Genomics which promoted the use of AI to suggest these ideas to the user. The results were positive, and within 18 months the use of AI was so easy that even grade-school children could become published researchers, with quite relevant topics [12]. Indeed, the experience with Idea Coach was so positive to some that it actually became fun to do. The next step was to move beyond suggesting single ideas or messages to ‘test’. Rather than requesting single ideas to be provided as answers to a question, the evolution was to provide a deeper question, to provide complete structures of knowledge, such as the request to list mind-sets for a topic, and then to define many of the properties of the mind-set. It was this breakthrough which revealed the power of AI to provide what might be called deep knowledge, or at least deep synthesis of ideas. At the practical level, the effort involved in the creation of the knowledge should be a motivator for further exploration. The entire effort to create the information presented here was less than 10 minutes. The effort involved formulating a request to AI, incorporating the request into a simple squib, and then receiving the information within 30 seconds. The Mind Genomics program, automatically storing the results and allowing ‘on-the-fly’ re-runs (iterations) of either the same request or an edited one, made it possible to explore different aspects. The final results of just two of what turned out to be dozens of easy-to-do iterations appear here. The reality is that the platform enable the exploration of many ideas having to do with the ‘genocide’ accusation, these results not shown here. Some of the other explorations involved the exploration of mind-sets in different universities (viz., Harvard, Columbia, City University of New York), as well as explorations of different types of people specified as participants (e.g., college students versus non-students, of the same age). The sheer speed with which the results emerges combined with the depth of information available immediately and in summary form suggest the opportunity to create a reference book of hundreds of pages about any topic for which the mind of people may be an important feature.

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