Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models

Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning

BackgroundDiabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms.MethodsIntegrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM ­ RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model’s efficiency was examined.ResultsWe identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM ­ RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698).ConclusionsAs a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease’s clinical symptoms and prognostic heterogeneity

Target of Rapamycin (TOR) Regulates the Expression of lncRNAs in Response to Abiotic Stresses in Cotton

TOR (Target of Rapamycin) kinase is an evolutionarily conserved protein kinase, which integrates stress-related cues with growth and metabolic outputs. Long non-coding RNAs (lncRNAs) play a vital role in the regulation of eukaryotic genes. However, little is known about TOR's function in regulating the expression of lncRNAs in plants. In this study, four putative homologous genes encoding the TOR protein were identified by utilizing the recently completed cotton genome. Pharmacological experiments with TOR inhibitor AZD8055 and on silencing GhTOR genes resulted in obvious cotton growth retardation, indicating the conserved role of TOR in plant growth. The expression pattern analyses in different tissues reveal that TOR may play a role in root development, and the transcript levels of TOR genes were changed under different stress conditions. Importantly, we found TOR may be a key player in regulating the expression of long non-coding RNAs (lncRNAs). A total of 10,315 lncRNAs were discovered in cotton seedlings, 90.7% of which were long intergenic ncRNAs. Moreover, we identified the differentially expressed lncRNAs, of which 296 were significantly upregulated and 105 were downregulated in TOR inactivated plants. GO and KEGG analyses of differentially expressed lncRNA neighboring genes reveal that these differentially expressed lncRNA-targeted genes are involved in many life processes, including stress response, glutathione, and ribosomes in cotton. A series of differentially expressed lncRNAs potentially involved in plant stress response was identified under TOR inhibition. Collectively, these results suggest that cotton TOR proteins may directly modulate the expression of putative stress-related lncRNAs and eventually play a potential role in the cotton stress response

A Novel Komodo Mlipir Algorithm and Its Application in PM2.5 Detection

The paper presents an improved Komodo Mlipir Algorithm (KMA) with variable inertia weight and chaos mapping (VWCKMA). In contrast to the original Komodo Mlipir Algorithm (KMA), the chaotic sequence initialization population generated by Tent mapping and Tent Chaos disturbance used in VWCKMA can effectively prevent the algorithm from falling into a local optimal solution and enhance population diversity. Individuals of different social classes can be controlled by the variable inertia weight, and the convergence speed and accuracy can be increased. For the purpose of evaluating the performance of the VWCKMA, function optimization and actual predictive optimization experiments are conducted. As a result of the simulation results, the convergence accuracy and convergence speed of the VWCKMA have been considerably enhanced for single-peak, multi-peak, and fixed-dimensional complex functions in different dimensions and even thousands of dimensions. To address the nonlinearity of PM2.5 prediction in practical problems, the weights and thresholds of the BP neural network were iteratively optimized using VWCKMA, and the BP neural network was then used to predict PM2.5 using the optimal parameters. Experimental results indicate that the accuracy of the VWCKMA-optimized BP neural network model is 85.085%, which is 19.85% higher than that of the BP neural network, indicating that the VWCKMA has a certain practical application

A Novel Komodo Mlipir Algorithm and Its Application in PM2.5 Detection

The paper presents an improved Komodo Mlipir Algorithm (KMA) with variable inertia weight and chaos mapping (VWCKMA). In contrast to the original Komodo Mlipir Algorithm (KMA), the chaotic sequence initialization population generated by Tent mapping and Tent Chaos disturbance used in VWCKMA can effectively prevent the algorithm from falling into a local optimal solution and enhance population diversity. Individuals of different social classes can be controlled by the variable inertia weight, and the convergence speed and accuracy can be increased. For the purpose of evaluating the performance of the VWCKMA, function optimization and actual predictive optimization experiments are conducted. As a result of the simulation results, the convergence accuracy and convergence speed of the VWCKMA have been considerably enhanced for single-peak, multi-peak, and fixed-dimensional complex functions in different dimensions and even thousands of dimensions. To address the nonlinearity of PM2.5 prediction in practical problems, the weights and thresholds of the BP neural network were iteratively optimized using VWCKMA, and the BP neural network was then used to predict PM2.5 using the optimal parameters. Experimental results indicate that the accuracy of the VWCKMA-optimized BP neural network model is 85.085%, which is 19.85% higher than that of the BP neural network, indicating that the VWCKMA has a certain practical application

TMIE Is an Essential Component of the Mechanotransduction Machinery of Cochlear Hair Cells

SummaryHair cells are the mechanosensory cells of the inner ear. Mechanotransduction channels in hair cells are gated by tip links. The molecules that connect tip links to transduction channels are not known. Here we show that the transmembrane protein TMIE forms a ternary complex with the tip-link component PCDH15 and its binding partner TMHS/LHFPL5. Alternative splicing of the PCDH15 cytoplasmic domain regulates formation of this ternary complex. Transducer currents are abolished by a homozygous Tmie-null mutation, and subtle Tmie mutations that disrupt interactions between TMIE and tip links affect transduction, suggesting that TMIE is an essential component of the hair cell’s mechanotransduction machinery that functionally couples the tip link to the transduction channel. The multisubunit composition of the transduction complex and the regulation of complex assembly by alternative splicing is likely critical for regulating channel properties in different hair cells and along the cochlea’s tonotopic axis

Advances in metabolic reprogramming of NK cells in the tumor microenvironment on the impact of NK therapy

Abstract Natural killer (NK) cells are unique from other immune cells in that they can rapidly kill multiple neighboring cells without the need for antigenic pre-sensitization once the cells display surface markers associated with oncogenic transformation. Given the dynamic role of NK cells in tumor surveillance, NK cell-based immunotherapy is rapidly becoming a "new force" in tumor immunotherapy. However, challenges remain in the use of NK cell immunotherapy in the treatment of solid tumors. Many metabolic features of the tumor microenvironment (TME) of solid tumors, including oxygen and nutrient (e.g., glucose, amino acids) deprivation, accumulation of specific metabolites (e.g., lactate, adenosine), and limited availability of signaling molecules that allow for metabolic reorganization, multifactorial shaping of the immune-suppressing TME impairs tumor-infiltrating NK cell function. This becomes a key barrier limiting the success of NK cell immunotherapy in solid tumors. Restoration of endogenous NK cells in the TME or overt transfer of functionally improved NK cells holds great promise in cancer therapy. In this paper, we summarize the metabolic biology of NK cells, discuss the effects of TME on NK cell metabolism and effector functions, and review emerging strategies for targeting metabolism-improved NK cell immunotherapy in the TME to circumvent these barriers to achieve superior efficacy of NK cell immunotherapy

Profiling chromatin accessibility in formalin-fixed paraffin-embedded samples

Archived formalin-fixed paraffin-embedded (FFPE) samples are the global standard format for preservation of the majority of biopsies in both basic research and translational cancer studies, and profiling chromatin accessibility in the archived FFPE tissues is fundamental to understanding gene regulation. Accurate mapping of chromatin accessibility from FFPE specimens is challenging because of the high degree of DNA damage. Here, we first showed that standard ATAC-seq can be applied to purified FFPE nuclei but yields lower library complexity and a smaller proportion of long DNA fragments. We then present FFPE-ATAC, the first highly sensitive method for decoding chromatin accessibility in FFPE tissues that combines Tn5-mediated transposition and T7 in vitro transcription. The FFPE-ATAC generates high-quality chromatin accessibility profiles with 500 nuclei from a single FFPE tissue section, enables the dissection of chromatin profiles from the regions of interest with the aid of hematoxylin and eosin (H&E) staining, and reveals disease-associated chromatin regulation from the human colorectal cancer FFPE tissue archived for >10 yr. In summary, the approach allows decoding of the chromatin states that regulate gene expression in archival FFPE tissues, thereby permitting investigators to better understand epigenetic regulation in cancer and precision medicine.