Research on the Motivation and Attitude of College students' Physical Education in Taiwan

College students' physical education plays an important role in physical activity and cultivates the concept of independent health management. At present, what kind of learning attitude do Taiwan college students face in physical education? What motivation does the student influence the attitude of the physical education? What is the relevance? All of the above are the purpose of this study. The research method adopts the questionnaire survey method, and the survey data adopts descriptive statistical analysis, independent sample t test, single factor variance analysis, LSD post hoc comparison method, and typical correlation analysis. Research results: 1. The different background variables of Taiwanese college students are that the main motivation factor of physical education is to obtain good health fitness for "physical health". 2. Taiwanese college students have different background variables. They all think that the "cognitive learning" of physical education is the main factor of attitude, that is, the knowledge about health care and sports skills. 3. There is a positive correlation between learning motivation and learning attitude (ρ=.90). Learning motivation is one of the important factors affecting learning attitude. Research conclusions: 1. The factors of Taiwanese male and female college students' motivation for learning in physical education are mainly based on "physical health". 2. Freshmen have higher motivations and learning attitudes in physical education than second-grade to fourth-grade. 3. Taiwan female college students average 1 or 2 times per week, male college students have the most athletes 2 to 3 times per week, more than 90% of college students like sports. 4. There is a positive correlation between learning motivation and learning attitude, indicating that the stronger the attribute of learning motivation "physical health", the higher the student's learning attitude. 5. Satisfying students' motivation for learning helps students to learn positively. 6. Another important task of the college physical education class is to prepare students for future lifelong sports

An Evolutionary Method for Financial Forecasting in Microscopic High-Speed Trading Environment

The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms. As a result, today’s markets experience intense activity in the highly dynamic environment where trading systems respond to others at a much faster pace than before. This new breed of technology involves the implementation of high-speed trading strategies which generate significant portion of activity in the financial markets and present researchers with a wealth of information not available in traditional low-speed trading environments. In this study, we aim at developing feasible computational intelligence methodologies, particularly genetic algorithms (GA), to shed light on high-speed trading research using price data of stocks on the microscopic level. Our empirical results show that the proposed GA-based system is able to improve the accuracy of the prediction significantly for price movement, and we expect this GA-based methodology to advance the current state of research for high-speed trading and other relevant financial applications

All-Trans Retinoic Acid Induces DU145 Cell Cycle Arrest through Cdk5 Activation

All-trans retinoic acid (ATRA), the active form of vitamin A, plays an important role in the growth arrest of numerous types of cancer cells. It has been indicated that cyclin-dependent kinase 5 (Cdk5) activity can be affected by ATRA treatment. Our previous results demonstrate the involvement of Cdk5 in the fate of prostate cancer cells. The purpose of this study is to examine whether Cdk5 is involved in ATRA-induced growth arrest of the castration-resistant cancer cell line DU145 through up-regulating Cdk inhibitor protein, p27

miRTar: an integrated system for identifying miRNA-target interactions in human

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small non-coding RNA molecules that are ~22-nt-long sequences capable of suppressing protein synthesis. Previous research has suggested that miRNAs regulate 30% or more of the human protein-coding genes. The aim of this work is to consider various analyzing scenarios in the identification of miRNA-target interactions, as well as to provide an integrated system that will aid in facilitating investigation on the influence of miRNA targets by alternative splicing and the biological function of miRNAs in biological pathways.</p> <p>Results</p> <p>This work presents an integrated system, miRTar, which adopts various analyzing scenarios to identify putative miRNA target sites of the gene transcripts and elucidates the biological functions of miRNAs toward their targets in biological pathways. The system has three major features. First, the prediction system is able to consider various analyzing scenarios (1 miRNA:1 gene, 1:N, N:1, N:M, all miRNAs:N genes, and N miRNAs: genes involved in a pathway) to easily identify the regulatory relationships between interesting miRNAs and their targets, in 3'UTR, 5'UTR and coding regions. Second, miRTar can analyze and highlight a group of miRNA-regulated genes that participate in particular KEGG pathways to elucidate the biological roles of miRNAs in biological pathways. Third, miRTar can provide further information for elucidating the miRNA regulation, i.e., miRNA-target interactions, affected by alternative splicing.</p> <p>Conclusions</p> <p>In this work, we developed an integrated resource, miRTar, to enable biologists to easily identify the biological functions and regulatory relationships between a group of known/putative miRNAs and protein coding genes. miRTar is now available at <url>http://miRTar.mbc.nctu.edu.tw/</url>.</p

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information

High-throughput Automated Muropeptide Analysis (HAMA) Reveals Peptidoglycan Composition of Gut Microbial Cell Walls

Peptidoglycan (PGN), a net-like polymer constituted by muropeptides, provides protection for microorganisms and has been a major target for antibiotics for decades. Researchers have explored host-microbiome interactions through PGN recognition systems and discovered key muropeptides modulating host responses. However, most common characterization techniques for muropeptides are labor-intensive and require manual analysis of mass spectra due to the complex cross-linked PGN structures. Each species has unique moiety modifications and inter-/intra-bridges, which further complicates the structural analysis of PGN. Here, we developed a high-throughput automated muropeptide analysis (HAMA) platform leveraging tandem mass spectrometry and in silico muropeptide MS/MS fragmentation matching to comprehensively identify muropeptide structures, quantify their abundance, and infer PGN cross-linking types. We demonstrated the effectiveness of HAMA platform using well-characterized PGNs from E. coli and S. aureus and further applied it to common gut bacteria including Bifidobacterium, Bacteroides, Lactobacillus, Enterococcus, and Akkermansia muciiniphila. Specifically, we found that the stiffness and strength of the cell envelopes may correspond to the lengths and compositions of interpeptide bridges within Bifidobacterium species. In summary, the HAMA framework exhibits an automated, intuitive, and accurate analysis of PGN compositions, which may serve as a potential tool to investigate the post-synthetic modifications of saccharides, the variation in interpeptide bridges, and the types of cross-linking within bacterial PGNs.</p