mdpi-ai/citelens huggingface dataset:
Academic writing strategies in university students from three disciplinary areas: design and validation of an instrument
Field: EDUCATION

It is acknowledged that writing strategies are highly important not only for their usefulness in the process of text production but also for their value as a learning technique, as they involve the use of cognitive and metacognitive operations. Various international studies have validated scales to measure their use and have helped characterize expert and non-expert university writers. However, regarding specific strategies in Spanish as a mother tongue, further research is still needed in the context of higher education students. This study aimed to develop a standardized measurement instrument to identify university students' use of academic writing strategies according to disciplinary area. The sample consisted of 290 students from the Humanities and Social Sciences, Health Sciences, and Engineering from a regional university in Chile. A Likert-type scale was applied whose design was based on a thorough review of successful instruments from different parts of the world. The results indicate that the developed instrument is appropriate for higher education contexts and across different disciplinary areas.
Neuroscience Exposure as a Predictor of Teaching Self-Efficacy
Field: PSYCHOLOGY

Teaching self-efficacy refers to a teacher’s confidence in their ability to engage students and foster learning, directly influencing their instructional planning, strategies, and student assessment practices. Neuroscience education for teachers has been shown to increase enthusiasm and support professional growth by introducing essential brain-related principles. This study investigated whether prior exposure to neuroscience predicts teaching self-efficacy among Brazilian basic education teachers. A total of 1120 teachers completed online surveys, providing sociodemographic information, educational background, teaching experience, and data regarding their previous neuroscience exposure. Participants’ neuroscience knowledge was assessed through a questionnaire designed to measure familiarity with fundamental neuroscience concepts, and teaching self-efficacy was evaluated using the Teacher Sense of Efficacy Scale (TSES). The results indicated that teachers with prior exposure to extracurricular neuroscience courses demonstrated significantly higher neuroscience knowledge. Additionally, those with previous neuroscience exposure exhibited a marginally significant increase in self-efficacy for instructional strategies and a significant increase in classroom management, while no significant differences were observed in student engagement. Regression analyses confirmed that neuroscience exposure significantly predicted self-efficacy in instructional strategies and classroom management. These findings reinforce the connection between neuroscience education and enhanced teaching self-efficacy, underscoring the importance of neuroeducation programs as valuable tools for supporting teachers’ professional development and well-being.
Classification of Exoplanetary Light Curves Using Artificial Intelligence
Field: AI

In this article, we propose a robust star classification methodology leveraging light curves collected from 15 datasets within the Kepler field in the visible optical spectrum. By employing a Bagging neural network ensemble approach, specifically an Bagging-Performance Approach Neural Network (BAPANN), which integrates three supervised neural network architectures, we successfully classified 760 samples of curves which represent 9 type of stars. Our method demonstrated a high classification accuracy of up to 97% using light curve datasets containing 13, 20, 50, 150, and 450 points per star. The BAPANN achieved a minimum error rate of 0.1559 and exhibited efficient learning, requiring an average of 29 epochs. Additionally, nine types of stellar variability were classified through 45 conducted tests, taking into account error margins of 0, 5, and 10 for the light curve samples. These results highlight the BAPANN model’s robustness against uncertainty and ability to converge quickly in terms of iterations needed for learning, training, and validation.
Can chatbots teach us how to behave? Examining assumptions about user interactions with AI assistants and their social implications
Field: AI

In this article we examine the issue of AI assistants, and the way they respond to insults and sexually explicit requests. Public concern over these responses, particularly because AI assistants are usually female-voiced, prompted tech companies to make them more assertive. Researchers have explored whether these female-voiced AI assistants could encourage abusive behavior and reinforce societal sexism. However, the extent and nature of the problem are unclear due to a lack of data on user interactions. By combining psychological and socio-cultural perspectives, we problematize these assumptions and outline a number of research questions for leveraging AI assistants to promote gender inclusivity more effectively.
A Novel Human Action Recognition Model by Grad-CAM Visualization with Multi-level Feature Extraction Using Global Average Pooling with Sequence Modeling by Bidirectional Gated Recurrent Units
Field: AI

Human action recognition is essential in many real-world scenarios, such as video surveillance, human–computer interaction, and behavior analysis. Despite the progress in deep learning, issues such as occlusion, distraction from the background, and motion pattern variability still exist, thus restricting the generalization ability of current models. Most methods are based only on spatial or temporal features and cannot efficiently capture both in one framework, causing lower accuracy in realistic situations. In response to these shortcomings, a multilevel feature extraction approach was proposed by integrating spatial and temporal features to improve the action recognition precision. The method captures RGB frames, optical flow, spatial saliency maps, and temporal saliency maps to enable an overall inspection of video streams. Efficient feature extraction was achieved by applying a pre-trained Inception V3 model and then bidirectional gated recurrent units (Bi-GRUs) to include sequential modeling. An attention mechanism was also included to boost the classification process by focusing on key temporal segments. UCF101 and HMDB51 benchmark datasets evaluated the efficiency of the strategy. The model’s accuracy was 98.13% on UCF101 and 81.45% on HMDB51, which validated the superior discrimination ability of the model in processing heterogeneous human actions. These results confirm that the provided framework is an efficient and discriminative action recognition approach, thus suitable for applications requiring extensive motion analysis and real-time deployment.
Persistence patterns among secondary STEM teachers: a comparative study of Noyce scholar cohorts in face-to-face and blended learning environments amid the pandemic
Field: EDUCATION

Noyce scholars were provided funding to compete teaching certification in STEM and earn a master’s degree. Then, they were required to teach for 2 years in a Title I school setting. All cohorts were impacted by the pandemic (e.g., university coursework, student teaching and/or teaching was converted to blended learning). This study highlights the differences in teaching persistence across the three cohorts of scholars (n = 24) regarding continuance in earning their degrees and completing their two-year teaching obligation. Descriptive case study methodology was used in this comparative study across three cohorts. The primary research question explored how different modalities of initial teaching experiences impact early persistence among secondary STEM teachers. The supplemental research question explored scholars’ intention to remain in the teaching profession. Results indicated that the cohort with blended first year teaching experiences had the lowest persistence rate. Generally, scholars intend on persisting in the profession for 6 years or more. Recommendations for practice include the need for more traditional, face-to-face initial teaching experiences and a cohort model for new teachers. Recommendations for research include continued evaluation of Noyce projects, longitudinal studies to track STEM teachers’ persistence, and a comprehensive analysis of teacher preparation programs’ effectiveness in promoting teacher retention.
Change theory and theory of change: what’s the difference anyway?
Field: EDUCATION

This commentary focuses on the difference between a theory of change and change theory, as it relates to systemic change projects in STEM higher education. A theory of change is project-specific and related to evaluation. It makes the underlying rationale of a project explicit, which supports planning, implementation, and assessment of the project. In addition, a theory of change is often required by funding agencies as part of grant proposals. In contrast, change theories represent theoretical and empirically grounded knowledge about how change occurs that goes beyond any one project. Ideally, a theory of change is informed by change theories. This essay describes the connections between a theory of change and change theory and provides examples of how change theory can inform a project’s theory of change. Grounding projects in change theory allows change agents to draw on existing knowledge and to better contribute to our collective knowledge about how to achieve meaningful change in STEM higher education.
Development and application of the Action Taxonomy for Learning Assistants (ATLAs)
Field: EDUCATION

Background: The success of the learning assistant (LA) model has largely been attributed to LA facilitation of active learning tasks. A deeper understanding of how LAs facilitate these tasks would inform LA training and support successful adoption of the LA model. Our investigation of LA actions during their interaction with students in the classroom contributes to that understanding. We present and discuss the development of the action taxonomy for learning assistants (ATLAs), as well as illustrate its applicability by presenting some analyses that were conducted on sample data. Results: The LAs carried out several different actions that we categorized broadly as LA-Directed Facilitation, LA-Guided Facilitation, Advice, Feedback, Course-Related Talk, and Non-Course-Related Talk. LA-Directed Facilitation and LA-Guided Facilitation were the most common types of actions observed. We found that LA actions varied by course. Conclusions: ATLAs is a tool that can be used to examine LA actions. In our sample data set, LAs undertook many different actions during interactions with students which indicates that LAs play several different roles in the classroom. These findings have practical implications not only for faculty seeking to implement a peer instruction model such as the LA model, but also for instructors wanting to utilize LAs in their courses more effectively. Understanding what the LAs are doing during interactions with students can provide us insight into the different roles that LAs undertake. Knowledge of these roles will guide effective training, feedback, and direction of LAs, particularly during the pedagogy course.
Resistance to MET/VEGFR2 Inhibition by Cabozantinib Is Mediated by YAP/TBX5-Dependent Induction of FGFR1 in Castration-Resistant Prostate Cancer
Field: ONCOLOGY

The overall goal of this study was to elucidate the role of FGFR1 induction in acquired resistance to MET and VEGFR2 inhibition by cabozantinib in prostate cancer (PCa) and leverage this understanding to improve therapy outcomes. The response to cabozantinib was examined in mice bearing patient-derived xenografts in which FGFR1 was overexpressed. Using a variety of cell models that reflect different PCa disease states, the mechanism underpinning FGFR1 signaling activation by cabozantinib was investigated. We performed parallel investigations in specimens from cabozantinib-treated patients to confirm our in vitro and in vivo data. FGFR1 overexpression was sufficient to confer resistance to cabozantinib. Our results demonstrate transcriptional activation of FGF/FGFR1 expression in cabozantinib-resistant models. Further analysis of molecular pathways identified a YAP/TBX5-driven mechanism of FGFR1 and FGF overexpression induced by MET inhibition. Importantly, knockdown of YAP and TBX5 led to decreased FGFR1 protein expression and decreased mRNA levels of FGFR1, FGF1, and FGF2. This association was confirmed in a cohort of hormone-naïve patients with PCa receiving androgen deprivation therapy and cabozantinib, further validating our findings. These findings reveal that the molecular basis of resistance to MET inhibition in PCa is FGFR1 activation through a YAP/TBX5-dependent mechanism. YAP and its downstream target TBX5 represent a crucial mediator in acquired resistance to MET inhibitors. Thus, our studies provide insight into the mechanism of acquired resistance and will guide future development of clinical trials with MET inhibitors.
Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression
Field: AI

Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, we describe methods for analyzing deep learning models of clinical and demographic psychiatric data, using our recent work on a deep learning model of STAR*D and CO-MED remission prediction. Methods: Our deep learning analysis with STAR*D and CO-MED yielded four models that predicted response to the four treatments used across the two datasets. Here, we use classical statistics and simple data representations to improve interpretability of the features output by our deep learning model and provide finer grained understanding of their clinical and etiological significance. Specifically, we use representations derived from our model to yield features predicting both treatment non-response and differential treatment response to four standard antidepressants, and use linear regression and t-tests to address questions about the contribution of trauma, education, and somatic symptoms to our models. Results: Traditional statistics were able to probe the input features of our deep learning models, reproducing results from previous research, while providing novel insights into depression causes and treatments. We found that specific features were predictive of treatment response, and were able to break these down by treatment and non-response categories; that specific trauma indices were differentially predictive of baseline depression severity; that somatic symptoms were significantly different between males and females, and that education and low income proved important psycho-social stressors associated with depression. Conclusion: Traditional statistics can augment interpretation of deep learning models. Such interpretation can lend us new hypotheses about depression and contribute to building causal models of etiology and prognosis. We discuss dataset-specific effects and ideal clinical samples for machine learning analysis aimed at improving tools to assist in optimizing treatment.
Emotion Recognition and Aging. Comparing a Labeling Task With a Categorization Task Using Facial Representations
Field: PSYCHOLOGY

Research suggests that aging comes with a decline in the ability to identify emotional expressions. In previous studies on emotion recognition and aging, participants were typically instructed to classify images of facial expressions using sets of lexical emotion labels. Yet, in daily life, when exposed to facial expressions by others, people match these with their conceptual knowledge of how emotions are visually presented (i.e., a smile for “happiness”), rather than recalling lexical labels (i.e., the word “happy”). By comparing performances of young adults and older adults on an emotion sorting task based on visual categorization and a traditional labeling task based on lexical categorization, this research aimed to explore a different way of studying emotion recognition abilities over the lifespan. In line with earlier research, results of the labeling task showed that our older participants (Mage = 71.9) were less accurate in labeling emotions than participants in a young age group (Mage = 23.8), especially for expressions of sadness, fear, anger and contempt. Outcomes of the categorization task suggest that older adults have difficulties separating distinctive meanings of emotions more than young adults do. Results of this study indeed shows a decline in emotion recognition using both tasks, and suggests future studies to examine possible changes in conceptual knowledge of emotions, rather than the inability to perceive certain facial cues.
Improved Prediction of Aqueous Solubility of Novel Compounds by Going Deeper With Deep Learning
Field: ONCOLOGY

Aqueous solubility is an important physicochemical property of compounds in anti-cancer drug discovery. Artificial intelligence solubility prediction tools have scored impressive performances by employing regression, machine learning, and deep learning methods. The reported performances vary significantly partly because of the different datasets used. Solubility prediction on novel compounds needs to be improved, which may be achieved by going deeper with deep learning. We constructed deeper-net models of ~20-layer modified ResNet convolutional neural network architecture, which were trained and tested with 9,943 compounds encoded by molecular fingerprints. Retrospectively tested by 62 recently-published novel compounds, one deeper-net model outperformed four established tools, shallow-net models, and four human experts. Deeper-net models also outperformed others in predicting the solubility values of a series of novel compounds newly-synthesized for anti-cancer drug discovery. Solubility prediction may be improved by going deeper with deep learning. Our deeper-net models are accessible at
Misregulation of ELK1, AP1, and E12 Transcription Factor Networks Is Associated with Melanoma Progression
Field: ONCOLOGY

Melanoma is among the most malignant cutaneous cancers and when metastasized results in dramatically high mortality. Despite advances in high-throughput gene expression profiling in cancer transcriptomic studies, our understanding of mechanisms driving melanoma progression is still limited. We present here an in-depth bioinformatic analysis of the melanoma RNAseq, chromatin immunoprecipitation (ChIP)seq, and single-cell (sc)RNA seq data to understand cancer progression. Specifically, we have performed a consensus network analysis of RNA-seq data from clinically re-grouped melanoma samples to identify gene co-expression networks that are conserved in early (stage 1) and late (stage 4/invasive) stage melanoma. Overlaying the fold-change information on co-expression networks revealed several coordinately up or down-regulated subnetworks that may play a critical role in melanoma progression. Furthermore, by incorporating histone lysine-27 acetylation information and highly expressed genes identified from the single-cell RNA data from melanoma patient samples, we present a comprehensive list of pathways, putative protein-protein interactions (PPIs) and transcription factor (TF) networks that are driving cancer progression. From this analysis, we have identified Elk1, AP1 and E12 TF networks that coordinately change expression in late melanoma when compared to early melanoma, implicating these TFs in melanoma progression. Additionally, the sumoylation-associated interactome is upregulated in invasive melanoma. Together, this bioinformatic analysis potentially implicates a combination of TF networks and PPIs in melanoma progression, which if confirmed in the experimental systems, could be used as targets for drug intervention in melanoma.
Does physiological arousal lead to increased catastrophic misinterpretation? An experiment based on the concept of a fear memory
Field: PSYCHOLOGY

Background: While there has been research on catastrophic misinterpretation of ambiguous situations and on the effects of the induction of physiological arousal, there has been no experimental research on the relationship between them. Based on the concept of a fear memory, we aimed to investigate if the induction of physiological arousal leads to catastrophic misinterpretations. Methods: Participants were shown either a suspenseful film clip to induce physiological arousal (EG, n = 43) or a calm film clip with no specific effect on arousal levels (CG, n = 40) before completing a measure of catastrophic misinterpretation (BSIQ-FR). To assess the specific predictive value of physiological arousal, measurements of other known predictors were included (BSI, BDI-II, ACQ, BSQ, STAI-T, ASI-3). Results: The film manipulation led to a significant increase in physiological arousal in the EG but not in the CG. The EG did not report more catastrophic misinterpretations than the CG – however, more participants in the EG reported at least one catastrophic misinterpretation. The increase in physiological arousal due to the film manipulation predicted catastrophic misinterpretation in the open response format in the EG, but not in the CG, even when controlling for other known predictors. Conclusions: Our study provides evidence that experimentally induced physiological arousal can predict catastrophic misinterpretation. The findings support the concept of a fear memory. With the BSIQ-FR, a German questionnaire measuring catastrophic misinterpretation was introduced. Further research on the relationship between physiological arousal and catastrophic misinterpretation with clinical samples is recommended.
Integrated Analysis of RNA-Binding Proteins in Glioma
Field: ONCOLOGY

RNA-binding proteins (RBPs) play important roles in many cancer types. However, RBPs have not been thoroughly and systematically studied in gliomas. Global analysis of the functional impact of RBPs will provide a better understanding of gliomagenesis and new insights into glioma therapy. In this study, we integrated a list of the human RBPs from six sources—Gerstberger, SONAR, Gene Ontology project, Poly(A) binding protein, CARIC, and XRNAX—which covered 4127 proteins with RNA-binding activity. The RNA sequencing data were downloaded from The Cancer Genome Atlas (TCGA) (n = 699) and Chinese Glioma Genome Atlas (CGGA) (n = 325 + 693). We examined the differentially expressed genes (DEGs) using the R package DESeq2, and constructed a weighted gene co-expression network analysis (WGCNA) of RBPs. Furthermore, survival analysis was also performed based on the univariate and multivariate Cox proportional hazards regression models. In the WGCNA analysis, we identified a key module involved in the overall survival (OS) of glioblastomas. Survival analysis revealed eight RBPs (PTRF, FNDC3B, SLC25A43, ZC3H12A, LRRFIP1, HSP90B1, HSPA5, and BNC2) are significantly associated with the survival of glioblastoma patients. Another 693 patients within the CGGA database were used to validate the findings. Additionally, 3564 RBPs were classified into canonical and non-canonical RBPs depending on the domains that they contain, and non-canonical RBPs account for the majority (72.95%). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that some non-canonical RBPs may have functions in glioma. Finally, we found that the knockdown of non-canonical RBPs, PTRF, or FNDC3B can alone significantly inhibit the proliferation of LN229 and U251 cells. Simultaneously, RNA Immunoprecipitation (RIP) analysis indicated that PTRF may regulate cell growth and death- related pathways to maintain tumor cell growth. In conclusion, our findings presented an integrated view to assess the potential death risks of glioblastoma at a molecular level, based on the expression of RBPs. More importantly, we identified non-canonical RNA-binding proteins PTRF and FNDC3B, showing them to be potential prognostic biomarkers for glioblastoma.