A study of the disappointment model in decision making under uncertainty

Call number: LD2668 .R4 IE 1989 W36Master of ScienceIndustrial and Manufacturing Systems Engineerin

Prediction of B-cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%)

Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration

[[abstract]]Background Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. Results Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. Conclusions In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance for protein-ligand binding region prediction. Based on the same approach and rationale, this method can also be applied to predict carbohydrate-antibody interactions for further design and development of carbohydrate-based vaccines. PLB-SAVE is available at http://save.cs.ntou.edu.tw.[[booktype]]電子

Structural Characterization and Antioxidative Activity of Low-Molecular-Weights Beta-1,3-Glucan from the Residue of Extracted Ganoderma lucidum Fruiting Bodies

The major cell wall constituent of Ganoderma lucidum (G. lucidum) is β-1,3-glucan. This study examined the polysaccharide from the residues of alkaline-extracted fruiting bodies using high-performance anion-exchange chromatography (HPAEC), and it employed nuclear magnetic resonance (NMR) and mass spectrometry (MS) to confirm the structures. We have successfully isolated low-molecular-weight β-1,3-glucan (LMG), in high yields, from the waste residue of extracted fruiting bodies of G. lucidum. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay evaluated the capability of LMG to suppress H2O2-induced cell death in RAW264.7 cells, identifying that LMG protected cells from H2O2-induced damage. LMG treatment decreased H2O2-induced intracellular reactive oxygen species (ROS) production. LMG also influenced sphingomyelinase (SMase) activity, stimulated by cell death to induce ceramide formation, and then increase cell ROS production. Estimation of the activities of neutral and acid SMases in vitro showed that LMG suppressed the activities of both neutral and acid SMases in a concentration-dependent manner. These results suggest that LMG, a water-soluble β-1,3-glucan recycled from extracted residue of G. lucidum, possesses antioxidant capability against H2O2-induced cell death by attenuating intracellular ROS and inhibiting SMase activity

A Model to Predict Total Chlorine Residue in the Cooling Seawater of a Power Plant Using Iodine Colorimetric Method

A model experiment monitoring the fate of total residue oxidant (TRO) in water at a constant temperature and salinity indicated that it decayed exponentially with time, and with TRO decaying faster in seawater than in distilled water. The reduction of TRO by temperature (°K) was found to fit a curvilinear relationship in distilled water (r2 = 0.997) and a linear relationship in seawater (r2 = 0.996). Based on the decay rate, flow rate, and the length of cooling water flowing through at a given temperature, the TRO level in the cooling water of a power plant could be estimated using the equation developed in this study. This predictive model would provide a benchmark for power plant operators to adjust the addition of chlorine to levels necessary to control bio-fouling of cooling water intake pipelines, but without irritating ambient marine organisms

Gut microbiota from B-cell-specific TLR9-deficient NOD mice promote IL-10 + Breg cells and protect against T1D

Introduction: Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of insulin-producing β cells. Toll-like receptor 9 (TLR9) plays a role in autoimmune diseases, and B cell-specific TLR9 deficiency delays T1D development. Gut microbiota are implicated in T1D, although the relationship is complex. However, the impact of B cell-specific deficiency of TLR9 on intestinal microbiota and the impact of altered intestinal microbiota on the development of T1D are unclear. Objectives: This study investigated how gut microbiota and the intestinal barrier contribute to T1D development in B cell-specific TLR9-deficient NOD mice. Additionally, this study explored the role of microbiota in immune regulation and T1D onset. Methods: The study assessed gut permeability, gene expression related to gut barrier integrity, and gut microbiota composition. Antibiotics depleted gut microbiota, and fecal samples were transferred to germ-free mice. The study also examined IL-10 production, Breg cell differentiation, and their impact on T1D development. Results: B cell-specific TLR9-deficient NOD mice exhibited increased gut permeability and downregulated gut barrier-related gene expression. Antibiotics restored gut permeability, suggesting microbiota influence. Altered microbiota were enriched in Lachnospiraceae, known for mucin degradation. Transferring this microbiota to germ-free mice increased gut permeability and promoted IL-10-expressing Breg cells. Rag-/- mice transplanted with fecal samples from Tlr9 fl/fl Cd19-Cre+ mice showed delayed diabetes onset, indicating microbiota’s impact. Conclusion: B cell-specific TLR9 deficiency alters gut microbiota, increasing gut permeability and promoting IL-10-expressing Breg cells, which delay T1D. This study uncovers a link between TLR9, gut microbiota, and immune regulation in T1D, with implications for microbiota-targeted T1D therapies

Gut microbiota and therapy for obesity and type 2 diabetes

There has been a major increase in Type 2 diabetes and obesity in many countries, and this will lead to a global public health crisis, which not only impacts on the quality of life of individuals well but also places a substantial burden on healthcare systems and economies. Obesity is linked to not only to type 2 diabetes but also cardiovascular diseases, musculoskeletal disorders, and certain cancers, also resulting in increased medical costs and diminished quality of life. A number of studies have linked changes in gut in obesity development. Dysbiosis, a deleterious change in gut microbiota composition, leads to altered intestinal permeability, associated with obesity and Type 2 diabetes. Many factors affect the homeostasis of gut microbiota, including diet, genetics, circadian rhythms, medication, probiotics, and antibiotics. In addition, bariatric surgery induces changes in gut microbiota that contributes to the metabolic benefits observed post-surgery. Current obesity management strategies encompass dietary interventions, exercise, pharmacotherapy, and bariatric surgery, with emerging treatments including microbiota-altering approaches showing promising efficacy. While pharmacotherapy has demonstrated significant advancements in recent years, bariatric surgery remains one of the most effective treatments for sustainable weight loss. However, access to this is generally limited to those living with severe obesity. This underscores the need for non-surgical interventions, particularly for adolescents and mildly obese patients. In this comprehensive review, we assess longitudinal alterations in gut microbiota composition and functionality resulting from the two currently most effective anti-obesity treatments: pharmacotherapy and bariatric surgery. Additionally, we highlight the functions of gut microbiota, focusing on specific bacteria, their metabolites, and strategies for modulating gut microbiota to prevent and treat obesity. This review aims to provide insights into the evolving landscape of obesity management and the potential of microbiota-based approaches in addressing this pressing global health challenge