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Alzheimer's disease (AD), a progressive neurodegenerative condition, notably impacts cognitive functions and daily activity. One method of detecting dementia involves a task where participants describe a given picture, and extensive research has been conducted using the participants' speech and transcribed text. However, very few studies have explored the modality of the image itself. In this work, we propose a method that predicts dementia automatically by representing the relationship between images and texts as a graph. First, we transcribe the participants' speech into text using an automatic speech recognition system. Then, we employ a vision language model to represent the relationship between the parts of the image and the corresponding descriptive sentences as a bipartite graph. Finally, we use a graph convolutional network (GCN), considering each subject as an individual graph, to classify AD patients through a graph-level classification task. In experiments conducted on the ADReSSo Challenge datasets, our model surpassed the existing state-of-the-art performance by achieving an accuracy of 88.73%. Additionally, ablation studies that removed the relationship between images and texts demonstrated the critical role of graphs in improving performance. Furthermore, by utilizing the sentence representations learned through the GCN, we identified the sentences and keywords critical for AD classification. [ABSTRACT FROM AUTHOR]

The ongoing global pandemic caused by the SARS-CoV-2 virus has demanded the urgent search for effective therapeutic interventions. In response, our research aimed at identifying natural products (NPs) with potential inhibitory effects on the entry of the SARS-CoV-2 spike (S) protein into host cells. Utilizing the Protein Data Bank Japan (PDBJ) and BindingDB databases, we isolated 204 S-glycoprotein sequences and conducted a clustering analysis to identify similarities and differences among them. We subsequently identified 33,722 binding molecules (BMs) by matching them with the sequences of 204 S-glycoproteins and compared them with 52,107 secondary metabolites (SMs) from the KNApSAcK database to identify potential inhibitors. We conducted docking and drug-likeness property analyses to identify several SMs with potential as drug candidates based on binding energy (BE), no Lipinski's rule violation (LV), psychochemical properties within the pink area of the bioavailability radar, and a bioavailability score (BAS) not less than 0.55. Fourteen SMs were predicted through computational analysis as potential candidates for inhibiting the three major types of S proteins. Our study provides a foundation for further experimental validation of these compounds as potential therapeutic agents against SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Every node in a network is said to be resolved if it can be uniquely identified by a vector of distances to a specific set of nodes. The metric dimension is equivalent to the least possible cardinal number of a resolving set. Conditional resolving sets are obtained by imposing various constraints on resolving set. It is a fundamental parameter that provides insights into the structural properties and navigability of graphs, with diverse applications across different fields. This article focuses on identifying the metric dimension for a new network, star fan graph. [ABSTRACT FROM AUTHOR]

Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. This study aims to improve fraud detection accuracy using machine learning techniques. Our approach consists of three key stages: data preprocessing, model training and integration, and result analysis with feature interpretation. Initially, we examined the dataset's characteristics and employed embedded and permutation methods to test the performance and runtime of single models under different feature sets, selecting the minimal number of features that could still achieve high performance. We then applied ensemble techniques, including Voting, Weighted, and Stacking methods, to combine different models and compare their performances. Feature interpretation was achieved through partial dependence plots (PDP), SHAP, and LIME, allowing us to understand each feature's impact on the predictions. Finally, we benchmarked our approach against existing studies to evaluate its advantages and limitations. The findings demonstrate improved fraud detection accuracy and offer insights into the interpretability of machine learning models in this context. [ABSTRACT FROM AUTHOR]