Modeling and acceleration of content delivery in world wide web

Ph.DDOCTOR OF PHILOSOPH

Dietary Probiotic Bacillus licheniformis TC22 Increases Growth, Immunity, and Disease Resistance, against Vibrio splendidus Infection in Juvenile Sea Cucumbers Apostichopus japonicus

In this study we examined the effects of probiotic Bacillus licheniformis TC22 on growth, immunity, and disease resistance against Vibrio splendidus in juvenile sea cucumbers Apostichopus japonicus. For 30 days, sea cucumbers were fed diets with TC22 at 0 (control), 105, 107, and 109 CFU/g respectively. Results showed that dietary TC22 at 109 CFU/g significantly improved (P0.05). Dietary TC22 at 109 CFU/g significantly improved phagocytosis, and total nitric oxide synthase activity in sea cucumbers (P0.05). Respiratory burst in sea cucumbers fed dietary TC22 at 109 CFU/g was significantly higher than those fed dietary TC22 at 107 CFU/g (P<0.05). Cumulative mortality after V. splendidus challenge decreased significantly in the sea cucumbers fed with TC22 at 109 CFU/g (P<0.05). The present study confirmed dietary B. licheniformis TC22 at 109 CFU/g could significantly improve immunity and disease resistance in juvenile A. japonicus

Evaporative Enrichment of Oxygen-18 and Deuterium in Lake Waters on the Tibetan Plateau

Stable isotopes (δ18O and δD) are useful tracers for investigating hydrologic and climatic variability on a variety of temporal and spatial scales. Since the early isotopic studies on mountainous glaciers in the late 1960s, a great deal of information has been generated on the isotopic composition of rainfall, snow, ice, surface waters, and lake carbonate sediments across the Tibetan Plateau. However, measurements of δ18O and δD values of lake water are scarce. Here we present a new dataset of δ18O and δD values of lake waters collected from 27 lakes across the plateau during a reconnaissance survey in summer 2009. δ18O and δD values of lake water range from −19.9 to 6.6‰ and from −153 to −16‰, respectively. The average values of δ18O and δD are −6.4 and −72‰, considerably greater than those of precipitation observed in this region. The derived Tibetan lake water line, δD = 5.2δ18O − 38.9, is significantly different from the global meteoric water line. Most of the lakes, including some freshwater lakes, contain water with negative values of d-excess (d). There is a negative correlation between d and total dissolved solids (TDS). Each of these findings indicates that evaporation-induced isotopic enrichment prevails in Tibetan lakes. Moreover, we develop an isotope modeling scheme to calculate E/P ratios for Tibetan lakes, using a combination of existing isotopic fractionation equations and the Rayleigh distillation model. We use the intersection of the local evaporation line and GMWL as a first approximation of δ18O and δD values of lake water inputs to infer an E/P ratio for each lake. Our modeling calculations reveal that although variable from lake to lake, the water budget across the plateau is positive, with an average E/P of 0.52. This is in good agreement with other observational and model data that show varying degrees of increases in lake size from satellite imagery and significant decreases in lake salinity in many lakes on the plateau over the last several decades. Together with the new isotopic dataset, the proposed modeling framework can be used to examine and quantify past changes in a lake’s hydrologic balance from the isotopic record of downcore carbonate sediments in the region

A decision analysis model for KEGG pathway analysis

The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways. A decision analysis model is developed in this article that accounts for dependence among pathways in time-course experiments and multiple treatments experiments. This model introduces a decision coefficient—a designed index, to identify the most relevant pathways in a given experiment by taking into account not only the direct determination factor of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the direct and indirect determination factors of each pathway are employed to demonstrate the regulation mechanisms among KEGG pathways, and the sign of decision coefficient can be used to preliminarily estimate the impact direction of each KEGG pathway. The simulation study of decision analysis demonstrated the application of decision analysis model for KEGG pathway analysis. A microarray dataset from bovine mammary tissue over entire lactation cycle was used to further illustrate our strategy. The results showed that the decision analysis model can provide the promising and more biologically meaningful results. Therefore, the decision analysis model is an initial attempt of optimizing pathway analysis methodology.https://doi.org/10.1186/s12859-016-1285-

Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.Comment: MICCAI 202

Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification

Sepsis is a systemic inflammatory response syndrome caused by bacteria and other pathogenic microorganisms. Every year, approximately 31.5 million patients are diagnosed with sepsis, and approximately 5.3 million patients succumb to the disease. In this study, we identified biomarkers for diagnosing sepsis analyzed the relationships between genes and Immune cells that were differentially expressed in specimens from patients with sepsis compared to normal controls. Finally, We verified its effectiveness through animal experiments. Specifically, we analyzed datasets from four microarrays(GSE11755、GSE12624、GSE28750、GSE48080) that included 106 blood specimens from patients with sepsis and 69 normal human blood samples. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The composition of 22 immune cell components in patients with sepsis were also determined using CIBERSORT. The expression level of the biomarkers in Sepsis was examined by the use of qRT-PCR and Western Blot (WB). We identified 50 differentially expressed genes between the cohorts, including 2 significantly upregulated and 48 significantly downregulated genes, and KEGG pathway analysis identified Salmonella infection, human T cell leukemia virus 1 infection, Epstein−Barr virus infection, hepatitis B, lysosome and other pathways that were significantly enriched in blood from patients with sepsis. Ultimately, we identified COMMD9, CSF3R, and NUB1 as genes that could potentially be used as biomarkers to predict sepsis, which we confirmed by ROC analysis. Further, we identified a correlation between the expression of these three genes and immune infiltrate composition. Immune cell infiltration analysis revealed that COMMD9 was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. CSF3R was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. NUB1 was correlated with T cells regulatory (Tregs), T cells gamma delta, T cells follicular helper, et al. Taken together, our findings identify potential new diagnostic markers for sepsis that shed light on novel mechanisms of disease pathogenesis and, therefore, may offer opportunities for therapeutic intervention

Rnd3/RhoE Modulates HIF1α/VEGF Signaling by Stabilizing HIF1α and Regulates Responsive Cardiac Angiogenesis

The insufficiency of compensatory angiogenesis in the heart of patients with hypertension contributes to heart failure transition. The hypoxia-inducible factor 1α-vascular endothelial growth factor (HIF1α-VEGF) signaling cascade controls responsive angiogenesis. One of the challenges in reprograming the insufficient angiogenesis is to achieve a sustainable tissue exposure to the proangiogenic factors, such as HIF1α stabilization. In this study, we identified Rnd3, a small Rho GTPase, as a proangiogenic factor participating in the regulation of the HIF1α-VEGF signaling cascade. Rnd3 physically interacted with and stabilized HIF1α, and consequently promoted VEGFA expression and endothelial cell tube formation. To demonstrate this proangiogenic role of Rnd3 in vivo, we generated Rnd3 knockout mice. Rnd3 haploinsufficient (Rnd3(+/-)) mice were viable, yet developed dilated cardiomyopathy with heart failure after transverse aortic constriction stress. The poststress Rnd3(+/-) hearts showed significantly impaired angiogenesis and decreased HIF1α and VEGFA expression. The angiogenesis defect and heart failure phenotype were partially rescued by cobalt chloride treatment, a HIF1α stabilizer, confirming a critical role of Rnd3 in stress-responsive angiogenesis. Furthermore, we generated Rnd3 transgenic mice and demonstrated that Rnd3 overexpression in heart had a cardioprotective effect through reserved cardiac function and preserved responsive angiogenesis after pressure overload. Finally, we assessed the expression levels of Rnd3 in the human heart and detected significant downregulation of Rnd3 in patients with end-stage heart failure. We concluded that Rnd3 acted as a novel proangiogenic factor involved in cardiac responsive angiogenesis through HIF1α-VEGFA signaling promotion. Rnd3 downregulation observed in patients with heart failure may explain the insufficient compensatory angiogenesis involved in the transition to heart failure

Validation of the plasma-wall self-organization model for density limit in ECRH-assisted start-up of Ohmic discharges on J-TEXT

A recently developed plasma-wall self-organization (PWSO) model predicts a significantly enhanced density limit, which may be attainable in tokamaks with ECRH-assisted ohmic startup and sufficiently high initial neutral density. Experiments have been conducted on J-TEXT to validate such a density limit scenario based on this model. Experimental results demonstrate that increasing the pre-filled gas pressure or ECRH power during the startup phase can effectively enhance plasma purity and raise the density limit at the flat-top. Despite the dominant carbon fraction in the wall material, some discharges approach the edge of the density-free regime of the 1D model of PWSO.Comment: 17 pages, 8 figure

Guardian spirit of be (Shaman) and Culture tradition : For ongdin - taban - tnger ritual

This file provide the detailed impact direction data of selected KEGG pathway categories, subcategories and the secondary pathways from− 15 to 300 vs.− 30d in bovine mammary tissue during lactation in Table S2 (a) and (b), respectively. The numbers colored in red color are the filled data by the average of all the other impact values in this pathway, which is the missing data originally. (DOCX 35 kb