mdpi-ai/citelens huggingface dataset:
Parental conflict and adolescents’ socially adverse emotions: the mediating role of family functioning
Field: PSYCHOLOGY

Objective: To examine the process of how parental conflict and family functioning influence adolescents’ socially adverse emotions (shyness and loneliness).Methods: Stratified cluster sampling was used to conduct a questionnaire survey among 1,100 junior high school students from three junior high schools in Beijing, Chongqing, and Shijiazhuang, China.Results: (1) The overall experience of adolescents’ socially adverse emotions was at the moderate level; boys’ experience of shyness and loneliness was significantly higher than that of girls; the experience of shyness and loneliness in the second grade was significantly higher than that in the first grade; (2) Parental conflict was significantly negatively correlated with family functioning and significantly positively correlated with adolescents’ socially adverse emotions, while family functioning was significantly negatively correlated with adolescents’ socially adverse emotions; (3) Family functioning partially mediates the relationship between parental conflict and adolescents’ shyness and completely mediates the relationship between parental conflict and adolescents’ loneliness.Conclusion: Compared to adolescents’ shyness, family functioning plays a more important mediating role in the relationship between parental conflict and adolescents’ loneliness.
Electrolyte prognosis scoring system can predict overall survival in patients with osteosarcoma
Field: ONCOLOGY

Osteosarcoma stands as the most prevalent bone tumor, characterized by a heightened tendency for local recurrence and distant metastasis, resulting in a bleak prognosis. Presently, there exists a shortage of novel markers to effectively determine the prognosis of osteosarcoma patients. Recent research indicates that hematological markers partially mirror an individual’s microenvironment, offering potential insights into predicting patient prognosis. However, prior studies predominantly focused on the prognostic significance of singular hematological indices, failing to comprehensively represent the tumor microenvironment of patients. In our investigation, we meticulously gathered data on 22 hematological and electrolyte markers, utilizing LASSO Cox regression analysis to devise an Electrolyte Prognostic Scoring System (EPSS). The EPSS encompasses various indicators, including immunity, inflammation, coagulation, and electrolyte levels. Our findings indicate that the EPSS stands as an independent prognostic factor for overall survival among osteosarcoma patients. It serves as a valuable addition to clinical characteristics, adept at discerning high-risk patients from those deemed clinically low-risk. Furthermore, EPSS-based nomograms demonstrate commendable predictive capabilities.
Who gets to be an ELT course book author? Native speakerism in English for specific purposes and business English course books
Field: EDUCATION

Introduction: Native speakerism has a profound influence on many aspects of ELT, for example negatively affecting job opportunities of those perceived as ‘non-native speakers’. Nevertheless, little is known about the effect of native speakerism on the recruitment of course book authors (CBAs).Methods: Therefore, this study analysed the linguistic and ethnic representation of 161 CBAs of 77 business English business English and English for specific purposes English for Specific Purposes course books (CBs) published globally by Pearson, OUP, CUP, Macmillan and NGL.Results: The data clearly show that publishers tend to hire white ‘native speakers’ from the UK as CBAs. More specifically, 90% of all CBA slots were taken by ‘native speakers’, 95% by white CBAs, and 78% by CBAs from the UK.Discussion: This indicates a profound native speakerist bias among publishers against not only ‘non-native speakers’, but also those ‘native speakers’ who are not white or do not come from the UK. It is thus suggested that business English and English for Specific Purposes publishers pay greater attention to the diversity of the author teams they hire.
Causal Economic Machine Learning (CEML): “Human AI”
Field: AI

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly “human AI”. This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing.
STEM career expectations across four diverse countries: motivation to learn mathematics mediates the effects of gender and math classroom environments
Field: EDUCATION

We tested the broad generality of a model for predicting 9th–10th grade students’ STEM career expectations by age 30, focusing on hard science, mathematics and engineering professions only, known for driving innovation, research and development. The model’s predictors included motivation to learn mathematics, gender, and math classroom environments (disciplinary climate, teacher support and instructional strategies fostering conceptual understanding). We used data from the Programme for International Student Assessment (PISA) 2022. Four countries were selected based on the percentage of students expecting STEM careers, representing high vs. low groups (Qatar and Morocco vs. Czech Republic and Lithuania, respectively). Analysis began with computing correlations between the variables, followed by path analyses for each country to determine both direct and indirect effects of the predictors on students’ STEM career expectations. We found that motivation to learn mathematics not only directly predicted STEM career expectations but also mediated the influence of the remaining variables: gender (boys show higher motivation to learn math), and math classroom environments (students in well-disciplined math classes with supportive teachers who employ instructional strategies fostering math reasoning also demonstrate higher motivation to learn math). Remarkably, our model consistently demonstrated robustness across all four countries, despite their significant economic, ethnic, and religious diversity. Theoretically, the model reveals that 9th–10th grade students’ transitory long-term STEM career expectations are shaped by their interest in mathematics, their perceived importance of the subject, confidence in their self-efficacy to succeed in math tasks, perceptions of classroom disciplinary climate, teacher support, and their exposure to instructional strategies aimed at enhancing math reasoning. Practically, it suggests widespread potential for informing interventions aimed at increasing student motivation to pursue STEM careers through improved mathematics education practices.
Evaluation of the Implementation of Project-Based-Learning in Engineering Programs: A Review of the Literature
Field: EDUCATION

Project-Based Learning (PBL), as an experiential methodology, improves learning outcomes and competencies (technical and non-technical) in engineering students. The Conceive–Design–Implement–Operate (CDIO) approach, adopted globally in engineering education, is based on PBL but expands the curriculum framework. Developed by MIT and the Royal Institute of Technology (KTH) in Sweden, CDIO focuses on the entire life cycle of engineering projects to train engineers so that they are capable of applying knowledge in real-life situations. Integrating CDIO and PBL into engineering curricula requires changes in teaching methodologies, teacher training and workspaces. The literature has explored their combination, highlighting shared values and mutual reinforcements. An assessment model is crucial for implementing PBL and evidencing improvement in student and course skills. Only through assessment and the cycle of continuous improvement will the adoption of PBL in engineering programs be advanced. A systematic review of the literature is proposed to identify effective methods in the evaluation of educational programs based on PBL, analyzing related research areas and evaluations according to the CDIO approach.
Comparison of the diagnostic efficacy between imaging features and iodine density values for predicting microvascular invasion in hepatocellular carcinoma
Field: ONCOLOGY

Background: In recent years, studies have confirmed the predictive capability of spectral computer tomography (CT) in determining microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Discrepancies in the predicted MVI values between conventional CT imaging features and spectral CT parameters necessitate additional validation.Methods: In this retrospective study, 105 cases of small HCC were reviewed, and participants were divided into MVI-negative (n=53, Male:48 (90.57%); mean age, 59.40 ± 11.79 years) and MVI-positive (n=52, Male:50(96.15%); mean age, 58.74 ± 9.21 years) groups using pathological results. Imaging features and iodine density (ID) obtained from three-phase enhancement spectral CT scans were gathered from all participants. The study evaluated differences in imaging features and ID values of HCC between two groups, assessing their diagnostic accuracy in predicting MVI occurrence in HCC patients. Furthermore, the diagnostic efficacy of imaging features and ID in predicting MVI was compared.Results: Significant differences were noted in the presence of mosaic architecture, nodule-in-nodule architecture, and corona enhancement between the groups, all with p-values < 0.001. There were also notable disparities in IDs between the two groups across the arterial phase, portal phase, and delayed phases, all with p-values < 0.001. The imaging features of nodule-in-nodule architecture, corona enhancement, and nonsmooth tumor margin demonstrate significant diagnostic efficacy, with area under the curve (AUC) of 0.761, 0.742, and 0.752, respectively. In spectral CT analysis, the arterial, portal, and delayed phase IDs exhibit remarkable diagnostic accuracy in detecting MVI, with AUCs of 0.821, 0.832, and 0.802, respectively. Furthermore, the combined models of imaging features, ID, and imaging features with ID reveal substantial predictive capabilities, with AUCs of 0.846, 0.872, and 0.904, respectively. DeLong test results indicated no statistically significant differences between imaging features and IDs.Conclusions: Substantial differences were noted in imaging features and ID between the MVI-negative and MVI-positive groups in this study. The ID and imaging features exhibited a robust diagnostic precision in predicting MVI. Additionally, our results suggest that both imaging features and ID showed similar predictive efficacy for MVI.
AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem
Field: AI

This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem’s unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED’s integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.
Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis
Field: AI

Background: Nonsurgical treatment of uncomplicated appendicitis is a reasonable option in many cases despite the sparsity of robust, easy access, externally validated, and multimodally informed clinical decision support systems (CDSSs). Developed by OpenAI, the Generative Pre-trained Transformer 3.5 model (GPT-3) may provide enhanced decision support for surgeons in less certain appendicitis cases or those posing a higher risk for (relative) operative contra-indications. Our objective was to determine whether GPT-3.5, when provided high-throughput clinical, laboratory, and radiological text-based information, will come to clinical decisions similar to those of a machine learning model and a board-certified surgeon (reference standard) in decision-making for appendectomy versus conservative treatment. Methods: In this cohort study, we randomly collected patients presenting at the emergency department (ED) of two German hospitals (GFO, Troisdorf, and University Hospital Cologne) with right abdominal pain between October 2022 and October 2023. Statistical analysis was performed using R, version 3.6.2, on RStudio, version 2023.03.0 + 386. Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. Statistical significance was defined as p < 0.05. Results: There was agreement between the surgeon’s decision and GPT-3.5 in 102 of 113 cases, and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT-3.5. The estimated model training accuracy was 83.3% (95% CI: 74.0, 90.4), while the validation accuracy for the model was 87.0% (95% CI: 66.4, 97.2). This is in comparison to the GPT-3.5 accuracy of 90.3% (95% CI: 83.2, 95.0), which did not perform significantly better in comparison to the machine learning model (p = 0.21). Conclusions: This study, the first study of the “intended use” of GPT-3.5 for surgical treatment to our knowledge, comparing surgical decision-making versus an algorithm found a high degree of agreement between board-certified surgeons and GPT-3.5 for surgical decision-making in patients presenting to the emergency department with lower abdominal pain.
FOBism Unveiled: Quantifying Assimilative Racism within Asians in the United States
Field: PSYCHOLOGY

FOB (fresh-off-the-boat) is a term used to refer to unassimilated immigrants or sojourners, which has created a divide within the Asian community. In this study, we coined the term FOBism, a form of internalized racism (or appropriated racial oppression) that intersects with assimilation, and we developed a measure of FOBism. We created a 14-item, 3-factor FOBism Scale and evaluated its psychometric properties among a sample of 296 Asians in the United States. Exploratory structural equation modeling (ESEM) was utilized to select items and evaluate the factorial validity. Results yielded a strong factor structure, internal consistency reliability, and construct validity. Construct validity was demonstrated through FOBism scores’ positive correlations with measures of within-group discrimination and internalized racism, and negative associations with an Asian cultural orientation. The FOBism Scale is a promising measure that could be used as an assessment tool and to raise awareness of the phenomenon.
Large language models for whole-learner support: opportunities and challenges
Field: AI

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the “whole learner” by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs.
Targeting the Leloir Pathway with Galactose-Based Antimetabolites in Glioblastoma
Field: ONCOLOGY

Background: Glioblastoma (GBM) uses Glut3 and/or Glut14 and the Leloir pathway to catabolize D-Galactose (Gal). UDP-4-deoxy-4-fluorogalactose (UDP-4DFG) is a potent inhibitor of the two key enzymes, UDP-galactose-4-epimerase (GALE) and UDP-Glucose 6-dehydrogenase (UGDH), involved in Gal metabolism and in glycan synthesis. The Gal antimetabolite 4-deoxy-4-fluorogalactose (4DFG) is a good substrate for Glut3/Glut14 and acts as a potent glioma chemotherapeutic. Methods: Primary GBM cell cultures were used to examine toxicity and alterations in glycan composition via lectin binding in fixed cells and by Western blots. Toxicity/efficacy in vivo data was performed in mouse flank and intracranial models. The effect of 4DFG on D-glucose (Glc) metabolism in GBM cells was assessed by using 13C NMR-based tracer studies. Results: 4DFG is moderately potent against GBM cells (IC50: 125–300 µM). GBM glycosylation is disrupted by 4DFG. Survival analysis in an intracranial mouse model showed that treatment with 4DFG (6 × 25 mg/kg of 4DFG, intravenously) improved outcomes by three-fold (p < 0.01). Metabolic flux analysis revealed that both glycolytic and mitochondrial metabolic fluxes of [U-13C]Glc were significantly decreased in the presence of 4DFG in GBM cells. Conclusion: A functional Gal-scavenging pathway in GBM allows Gal-based antimetabolites to act as chemotherapeutics. 4DFG is metabolized by GBM in vitro and in vivo, is lethal to GBM tumors, and is well tolerated in mice.
The Integration of Mixed Reality Simulation into Reading Literacy Modules
Field: EDUCATION

The reading literacy crisis, among learners, in countries throughout the world and in South Africa seems to be reaching pandemic levels. Hence, the quality of teaching and the preparation that pre-service teachers receive at initial teacher education institutions is under the spotlight. A proactive action research design is used to integrate mixed reality simulation into reading literacy modules. Our data collection methods included professional conversations, WhatsApp voice notes and video calls, reflective journal entries and reflections on observing video recordings of lesson segments in the MRS environment. The data was analyzed using content analysis. The main themes emanating from the data included: lack of focus on high leverage teaching practices, limited use of pedagogies of enactment, add-on to existing content, experimentation, perceptions, planning and preparation, content-method integration, pedagogies of enactment, assessment, resources and feedback. Grounded in a Community of Practice framework, we narrate our experiences of re-imagining mixed reality simulation as a core component of initial teacher education programs. The authors conclude by sharing insights and recommendations for policymakers, faculty leaders, and curriculum designers, contributing to informed decisions regarding integrating and potentially upscaling mixed reality simulation within reading literacy modules in initial teacher education programs.