mdpi-ai/citelens huggingface dataset:
The Carcinogenic Potential of Bisphenol A in the Liver Based on Transcriptomic Studies
Field: ONCOLOGY

Bisphenol A (BPA) is an environmental toxin widely used in the production of polycarbonate plastics. A correlation exists between BPA tissue contamination and the occurrence of pathological conditions, including cancer. First-passage detoxification of high BPA amounts in the liver promotes hepatotoxicity and morphological alterations of this organ, but there is a lack of knowledge about the molecular mechanisms underlying these phenomena. This prompted us to investigate changes in the liver transcriptomics of 3-month-old female mice exposed to BPA (50 mg/kg) in drinking water for 3 months. Five female mice served as controls. The animals were euthanized, the livers were collected, and RNA was extracted to perform RNA-seq analysis. The multistep transcriptomic bioinformatics revealed 120 differentially expressed genes (DEGs) in the BPA-exposed samples. Gene Ontology (GO) annotations indicated that DEGs have been assigned to many biological processes, including “macromolecule modification” and “protein metabolic process”. Several of the revealed DEGs have been linked to the pathogenesis of severe metabolic liver disorders and malignant tumors, in particular hepatocellular carcinoma. Data from this study suggest that BPA has a significant impact on gene expression in the liver, which is predictive of the carcinogenic potential of this compound in this organ.
A Multiclustering Evolutionary Hyperrectangle-Based Algorithm
Field: AI

Clustering is a grouping technique that has long been used to relate data homogeneously. With the huge growth of complex datasets from different sources in the last decade, new paradigms have emerged. Multiclustering is a new concept within clustering that attempts to simultaneously generate multiple clusters that are bound to be different from each other, allowing to analyze and discover hidden patterns in the dataset compared to single clustering methods. This paper presents a hybrid methodology based on an evolutionary approach with the concepts of hyperrectangle for multiclustering, called MultiCHCClust. The algorithm is applied in a post-processing stage and it improves the results obtained for a clustering algorithm with respect to the partitioning of the dataset and the optimization of the number of partitions, achieving a high degree of compactness and separation of the partitioned dataset as can be observed in a complete experimental study.
Deep Learning Performance Characterization on GPUs for Various Quantization Frameworks
Field: AI

Deep learning is employed in many applications, such as computer vision, natural language processing, robotics, and recommender systems. Large and complex neural networks lead to high accuracy; however, they adversely affect many aspects of deep learning performance, such as training time, latency, throughput, energy consumption, and memory usage in the training and inference stages. To solve these challenges, various optimization techniques and frameworks have been developed for the efficient performance of deep learning models in the training and inference stages. Although optimization techniques such as quantization have been studied thoroughly in the past, less work has been done to study the performance of frameworks that provide quantization techniques. In this paper, we have used different performance metrics to study the performance of various quantization frameworks, including TensorFlow automatic mixed precision and TensorRT. These performance metrics include training time and memory utilization in the training stage along with latency and throughput for graphics processing units (GPUs) in the inference stage. We have applied the automatic mixed precision (AMP) technique during the training stage using the TensorFlow framework, while for inference we have utilized the TensorRT framework for the post-training quantization technique using the TensorFlow TensorRT (TF-TRT) application programming interface (API).We performed model profiling for different deep learning models, datasets, image sizes, and batch sizes for both the training and inference stages, the results of which can help developers and researchers to devise and deploy efficient deep learning models for GPUs.
The Contribution of Educational Psychology to South African Preservice Teacher Training and Learner Support
Field: EDUCATION

Teacher education programmes are developed around the theoretical and practical understanding of child development, learning, assessment, behaviour management and motivation, which are areas of expertise in educational psychology. This paper aims to (a) critically investigate the contribution of educational psychology in the training of preservice teachers at South African universities and (b) understand the distribution of educational psychologists in public schools to support teaching and learning. A narrative literature review and email requests for unpublished documents from four educational psychologists were used as methods to collect literature in order to answer the following questions: What contribution does educational psychology make to training preservice teachers at public universities in South Africa? What contributions do educational psychologists make to support learners in South African public schools? Analysis was carried out by identifying recurring patterns in the literature reviewed. This study found that of the 26 public universities in South Africa, there are only 6 universities that offer educational psychology programmes. Educational psychology programmes in higher education institutions are in decline, leading to a decrease in the number of qualified educational psychologists. This decline negatively affects the involvement of educational psychologists in training preservice teachers in educational psychology modules or courses. Therefore, the inclusion of educational psychology as a core or fundamental module in the curriculum of preservice teachers to avoid dependence on the decreasing number of educational psychologists in higher education institutions is key. An increase in teacher training programmes in higher education should be merged with an equal increase in educational psychology core or fundamental courses in the curriculum of preservice teachers.
Structural modeling of EFL/ESL teachers’ physical activity, mental health, psychological well-being, and self-efficacy
Field: PSYCHOLOGY

Background: Physical activity (PA) is known to positively affect individuals’ mental and physical health, especially those who experience high levels of stress, such as teachers. Previous studies have examined the relationship between teachers’ PA, mental health, and well-being. Still, there is a lack of research on the direct and indirect effects of PA and self-efficacy. Purpose: This study aimed to investigate the structural relationship between teachers’ PA, mental health, well-being, and self-efficacy among ESL/EFL teachers. A total of 364 Chinese English language teachers were selected through convenience sampling. Mental health, physical activity, psychological well-being, and self-efficacy instruments were used. Methodology: The data was analyzed using Smart PLS software, and the hypothesized model was evaluated. The results indicated an acceptable level of divergent and convergent validity and goodness of fit. Results: The findings revealed that PA directly predicts teachers’ mental health and well-being, but the direct structural relationship between teachers’ PA and self-efficacy was not confirmed. However, the results showed that teachers’ PA contributes to their self-efficacy by enhancing their mental and psychological well-being. The total effect of teachers’ PA on their self-efficacy was significant. Additionally, mental health and psychological well-being strongly influenced teachers’ self-efficacy. Conclusion: In conclusion, regular weekly physical activity can help EFL/ESL teachers foster their mental health, psychological well-being, and self-efficacy. These findings have theoretical and practical significance for teachers, trainers, and educational psychologists.
Earning Your Way into General Education: Perceptions about Autism Influence Classroom Placement
Field: EDUCATION

The language used by teachers, school staff, and parents to talk about autistic students can send either positive or negative messages to other school staff, parents, and all students—with or without autism—about autistic students. Ultimately, these messages also extend to autistic people. Using qualitative focus group methods, we talked to parents, teachers, administrators, and other school staff to better understand how people speak about the inclusion of autistic students in general education classrooms in public schools. Overall, we found that many of our participants thought (1) autistic students need to earn their way into general education classrooms, unlike their peers without disabilities, (2) segregating students with disabilities away from their peers without disabilities is acceptable, and sometimes preferable, in school settings, and (3) there is power in inclusive education opportunities for students with and without disabilities in school settings. The findings from this study suggest that inclusive opportunities for autistic students were largely driven by stakeholder mindsets. These results should encourage school staff to think about and reflect on how they talk about autistic students in inclusive settings with the ultimate goal of creating more welcoming inclusive environments for autistic students.
“I’d Rather Do It Single-Handed”—Nursing Students’ Struggles with Group Assignments: A Qualitative Study
Field: EDUCATION

This study, framed by the GRPI (Goal, Role, Process, and Interaction) teamwork model, explores team dynamics among nursing students in performing group assignments, utilizing a qualitative research design. Twenty-three nursing students from Year 1 to Year 4 at a nursing school in Macau were invited. Semi-structured personal interviews were carried out. In addition, three teachers who were involved in instructing and assessing group assignments of nursing students were also interviewed. Data were analyzed using inductive and deductive approaches. The study found that although the barriers to accomplishing effective teamwork were embedded into the four dimensions of the DRPI model, they were interplayed. Communication was fundamental for teamwork, thus leading to a modified DRPI model. Teammates did not equally share the workload. Despite interpersonal conflicts among teammates, nursing students managed to stay in superficial harmony with their peers. They became more familiar with teamwork while advancing into their senior years but with decreased group communications. This study highlights various factors preventing students from transferring individualism to team players. Teaming is not an equal learning opportunity for teammates. Culturally upheld value of harmony prevails in the interpersonal relationships of the team members, which may compromise the teamwork spirit cultivation expectations from the teachers.
Integrating artificial intelligence into science lessons: teachers’ experiences and views
Field: EDUCATION

Background: In the midst of digital transformation, schools are transforming their classrooms as they prepare students for a world increasingly automated by new technologies, including artificial intelligence (AI). During curricular implementation, it has not made sense to teachers to teach AI as a stand-alone subject as it is not a traditional discipline in schools. As such, subject matter teachers may need to take on the responsibility of integrating AI content into discipline-based lessons to help students make connections and see its relevance rather than present AI as separate content. This paper reports on a study that piloted a new lesson package in science classrooms to introduce students to the idea of AI. Specifically, the AI-integrated science lesson package, designed by the research team, provided an extended activity that used the same context as an existing lesson activity. Three science teachers from different schools piloted the lesson package with small groups of students and provided feedback on the materials and implementation. Findings: The findings revealed the teachers’ perceptions of integrating AI into science lessons in terms of the connection between AI and science, challenges when implementing the AI lesson package and recommendations on improvements. First, the teachers perceived that AI and science have similarities in developing accurate models with quality data and using simplified reasoning, while they thought that AI and science play complementary roles when solving scientific problems. Second, the teachers thought that the biggest challenge in implementing the lesson package was a lack of confidence in content mastery, while the package would be challenging to get buy-in from teachers regarding curriculum adaptation and targeting the appropriate audience. Considering these challenges, they recommended that comprehensive AI resources be provided to teachers, while this package can be employed for science enrichment programs after-school. Conclusions: The study has implications for curriculum writers who design lesson packages that introduce AI in science classrooms and for science teachers who wish to contribute to the development of AI literacy for teachers and the extension of the range of school science and STEM to students.
The Past, the Present, and the Future of the Evolution of Mixed Reality in Teacher Education
Field: EDUCATION

The authors in this article provide a historical view (past) on the development of mixed reality (MR) simulation in teacher education as well as a brief history of simulation from other fields along with foundational knowledge on the evolution of simulation. The authors provide a systematic review of the current state (present) of the research in MR for teacher education within the past 5 years aligned with the research question “What are the uses, practices, and outcomes of MR simulation in teacher preparation?”. Three themes were identified, i.e., simulation to this point is designed by teacher educators, feedback matters in impacting outcomes, and practice is safe and reflective for those who prepare teachers in these environments. A summary is provided of these key articles and the findings. The authors conclude the article by sharing the potential evolution (future) of aspects of the model of MR, focusing on the use of AI agents and multi-modal data collection, including biometric signals, providing insights into simulation in teacher education.
Examining specific and non-specific symptoms of the best-fitting posttraumatic stress disorder model in conflict-exposed adolescents
Field: PSYCHOLOGY

Background: The 5th revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) construes PTSD symptoms into 4 clusters (intrusion, avoidance, negative alterations in cognitions and mood, alterations in arousal and reactivity; Model 1). However, recent literature has shown that this symptom structure does not best represent PTSD. Unfortunately, the findings of studies investigating the proposed alternative models are from consensus. Adding to the complexity of the issue of symptom-grouping models is the identification of specific and non-specific symptoms of PTSD. The present study aims to address these gaps by identifying the best-fitting PTSD model and subsequently examining what symptoms are considered specific and non-specific to PTSD in adolescent-survivors of armed political conflict and violence. Methods: The study utilized a sample of 641 adolescent victim survivors. We conducted CFA analyses and compared nested models through the scaled χ2 difference test, while comparison of non-nested models was done using the Bayesian information criterion (BIC). The best-fitted model was used in the consequent analysis, where we statistically controlled for the effect of non-specific psychological distress on PTSD by comparing the factor loadings and factor correlations before and after accounting for distress using the Aroian z-test. Results: The results provide support for the 7-factor hybrid model of PTSD over other proposed models for the current sample. Moreover, the data reveal that only 7 items could be construed as core symptoms, while the rest of the symptoms can be considered non-PTSD specific. Conclusions: Overall, the findings provide support for the validity of the hybrid PTSD model among political conflict-exposed adolescents. The results also show that the DSM-5 PTSD has both specific and non-specific features in the present sample of conflict-exposed adolescents. This has potential implications for theory, practice, and treatment of the disorder.
Diagnostic accuracy of contrast-enhanced computed tomography in assessing cervical lymph node status in patients with oral squamous cell carcinoma
Field: ONCOLOGY

Objective: Accurate preoperative prediction of lymph node (LN) status plays a pivotal role in determining the extension of neck dissection (ND) required for patients with oral squamous cell carcinoma (OSCC). This study aims to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting LN metastases (LNMs) and to explore clinicopathological factors associated with its reliability. Methods: Data from 239 patients with primary OSCC who underwent preoperative CT and subsequent radical surgery involving ND were retrospectively reviewed. Suspicious LNs were categorized into three groups: accentuated (< 10 mm), enlarged (≥ 10 mm), and melted. Statistical analysis encompassing correlation and comparative analysis, and determination of sensitivity, specificity, PPV, and NPV were performed. Results: Overall, sensitivity was significantly higher in the accentuated LNs group (83.54%) compared to the melted LNs group (39.24%, p < 0.05, t test). Conversely, specificity was significantly higher in the melted LNs group (98.19%) compared to the accentuated LNs group (55.15%, p < 0.05, t test). Accentuated LNs exhibited a false negative rate of 13.00%. False positive rates were 51.80%, 30.26% and 8.82%, respectively. Diagnostic accuracy for detecting LNMs in level IIa and IIb exceeded that of level III. Patients with solely accentuated LNs were more likely to have a small, well-differentiated tumor. However, no distinctions emerged in terms of the occurrence of T4 tumors among the three groups. Conclusion: CT proves sufficient to predict LNMs in patients with OSCC. Looking ahead, the potential integration of artificial intelligence and deep learning holds promise to further enhance the reliability of CT in LNMs detection. However, this prospect necessitates further investigation.
Attitude Construction toward Invasive Species through an Eco-Humanist Approach: A Case Study of the Lesser Kestrel and the Myna
Field: EDUCATION

The green school in northern Israel has embraced an eco-humanist approach with the aim of mitigating the animosity displayed by fifth-grade students towards invasive species. This antipathy arose due to the negative impact of these invasive species on a local species that the students were monitoring as a component of their environmental education curriculum. Therefore, the purpose of this study was to examine to what extent, if at all, there is a difference in the ethical attitude of the 188 fifth-grade students (10–11 years old) towards the conflict between the Lesser Kestrel (local species) and the Myna (invader species) and the solution to this conflict following the change to an eco-humanist teaching approach. The study, based on content analysis methodology of written self-reflection, and thematic analysis indicated that the fifth-graders’ knowledge was not adversely affected, but the solutions they proposed for resolving the conflict between the Myna and the Lesser Kestrel were more holistic, ethical, and moral after adopting the eco-humanist approach. Eco-humanism encourages an ethical attitude and environmental responsibility toward nature’s fate, including invasive species.
Motivational Influences on Health, Well-Being, and Lifestyle: Validation of the Spanish Version of the Treatment Self-Regulation Questionnaire in Four Health Domains
Field: PSYCHOLOGY

Background: Motivation is a central concept in self-determination theory (SDT). The Treatment Self-Regulation Questionnaire (TSRQ), which assesses motivation (autonomous, controlled, etc.), has been widely used. However, less is known about its applicability to samples such as college students, who may be at risk of having unhealthy behavior in many areas (including smoking, poor dietary habits, alcohol, or tobacco consumption). As this population is transitioning to adulthood, research is needed to understand motivation and changing health patterns. In addition, the lack of instruments for this population in Spain has made the measurement validation process a priority. The purpose of this psychometric study was to adapt the TSRQ to Spanish college students and to examine its structural and validity across four health domains. Methods: Two samples of Spanish college students (n = 347 and n = 244) agreed to participate in the study. Participants completed a booklet containing measures of motivation, well-being, general health, anxiety, depression, and lifestyle. Results: CFA supported a five-dimensional structure in each domain. Reliability values were also adequate for each questionnaire. Regarding other sources of validity, statistically significant relationships between self-determination, health, and well-being were clearly confirmed, and autonomy was a significant predictor of lifestyle. Conclusions: The Spanish version of the TSRQ showed adequate psychometric properties (dimensionality and internal structure, reliability, and validity evidence regarding its relationships with other constructs) in college students. The Spanish TSRQ will provide future research aimed to understand the motivational role in college students’ health behavior and well-being.