SWE-SPHysics Simulation of Dam Break Flows at South-Gate Gorges Reservoir

This paper applied a Smoothed Particle Hydrodynamics (SPH) approach to solve Shallow Water Equations (SWEs) to study practical dam-break flows. The computational program is based on the open source code SWE-SPHysics, where a Monotone Upstream-centered Scheme for Conservation Laws (MUSCL) reconstruction method is used to improve the Riemann solution with Lax-Friedrichs flux. A virtual boundary particle method is applied to treat the solid boundary. The model is first tested on two benchmark collapses of water columns with the existence of downstream obstacle. Subsequently the model is applied to forecast a prototype dam-break flood, which might occur in South-Gate Gorges Reservoir area of Qinghai Province, China. It shows that the SWE-SPH modeling approach could provide a promising simulation tool for practical dam-break flows in engineering scale

SPHysics Simulation of Experimental Spillway Hydraulics

In this paper, we use the parallel open source code parallelSPHysics based on the weakly compressible Smoothed Particle Hydrodynamics (WCSPH) approach to study a spillway flow over stepped stairs. SPH is a robust mesh-free particle modelling technique and has great potential in treating the free surfaces in spillway hydraulics. A laboratory experiment is carried out for the different flow discharges and spillway step geometries. The physical model is constructed from a prototype reservoir dam in the practical field. During the experiment, flow discharge over the weir crest, free surface, velocity and pressure profiles along the spillway are measured. In the present SPH study, a straightforward push-paddle model is used to generate the steady inflow discharge in front of the weir. The parallelSPHysics model is first validated by a documented benchmark case of skimming flow over a stepped spillway. Subsequently, it is used to reproduce a laboratory experiment based on a prototype hydraulic dam project located in Qinghai Province, China. The detailed comparisons are made on the pressure profiles on the steps between the SPH results and experimental data. The energy dissipation features of the flows under different flow conditions are also discussed. It is shown that the pressure on the horizontal face of the steps demonstrates an S-shape, while on the vertical face it is negative on the upper part and positive on the lower part. The energy dissipation efficiency of the spillway could reach nearly 80%

Analysis of the Roles of the <i>ISLR2</i> Gene in Regulating the Toxicity of Zearalenone Exposure in Porcine Intestinal Epithelial Cells

Zearalenone (ZEN) is one of the mycotoxins that pose high risks for human and animal health, as well as food safety. However, the regulators involved in ZEN cellular toxicity remain largely unknown. Herein, we showed that cell viability of porcine intestinal epithelial cells (IPEC-J2) tended to decrease with increasing doses of ZEN by the cell counting kit-8 assay. Expression of the ISLR2 (immunoglobulin superfamily containing leucine-rich repeat 2) gene in IPEC-J2 cells was significantly downregulated upon ZEN exposure. Furthermore, we found the dose–effect of ZEN on ISLR2 expression. We then overexpressed the ISLR2 gene and observed that overexpression of ISLR2 obviously reduced the effects of ZEN on cell viability, apoptosis rate and oxidative stress level. In addition, ISLR2 overexpression significantly decreased the expression of TNF-α and IFN-α induced by ZEN. Our findings revealed the effects of ZEN on the ISLR2 gene expression and indicated the ISLR2 gene as a novel regulator of ZEN-induced cytotoxicity, which provides potential molecular targets against ZEN toxicity

Expression Analysis and the Roles of the <i>Sec1</i> Gene in Regulating the Composition of Mouse Gut Microbiota

The Sec1 gene encodes galactose 2-L-fucosyltransferase, whereas expression during development of the Sec1 gene mouse and its effect on the composition of the gut microbiota have rarely been reported. In this study, we examined Sec1 gene expression during mouse development, constructed Sec1 knockout mice, and sequenced their gut microbial composition. It was found that Sec1 was expressed at different stages of mouse development. Sec1 knockout mice have significantly higher intraperitoneal fat accumulation and body weight than wild-type mice. Analysis of gut microbial composition in Sec1 knockout mice revealed that at the phylum level, Bacteroidetes accounted for 68.8%and 68.3% of gut microbial composition in the Sec1−/− and Sec1+/+ groups, respectively, and Firmicutes accounted for 27.1% and 19.7%, respectively; while Firmicutes/Bacteroidetes were significantly higher in Sec1−/− mice than in Sec1+/+ mice (39.4% vs. 28.8%). In verucomicrobia, it was significantly higher in Sec1−/− mice than in Sec1+/+ group mice. At the family level, the dominant bacteria Prevotellaceae, Akkermansiaceae, Bacteroidaceae, and Lacilltobacaceae were found to be significantly reduced in the gut of Sec1−/− mice among Sec1+/+ gut microbes, while Lachnospiraceae, Ruminococcaceae, Rikenellaceae, Helicobacteraceae, and Tannerellaceae were significantly increased. Indicator prediction also revealed the dominant bacteria Akkermansiaceae and Lactobacillaceae in Sec1+/+ gut microorganisms, while the dominant bacteria Rikenellaceae, Marinifilaceae, ClostridialesvadinBB60aceae, Erysipelotrichaceae, Saccharimonadaceae, Clostridiaceae1, and Christensenellaceae in Sec1−/− group. This study revealed that the Sec1 gene was expressed in different tissues at different time periods in mice, and Sec1 knockout mice had significant weight gain, significant abdominal fat accumulation, and significant changes in gut microbial flora abundance and metabolic function, providing a theoretical basis and data support for the study of Sec1 gene function and effects on gut microbiota-related diseases

Protein–ligand binding affinity prediction with edge awareness and supervised attention

Summary: Accurate prediction of protein–ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug–Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization

Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes

Abstract Background Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4). Results The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo. Conclusions The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription

Isoindigo–Thiophene D–A–D–Type Conjugated Polymers: Electrosynthesis and Electrochromic Performances

Four novel isoindigo–thiophene D–A–D–type precursors are synthesized by Stille coupling and electrosynthesized to yield corresponding hybrid polymers with favorable electrochemical and electrochromic performances. Intrinsic structure–property relationships of precursors and corresponding polymers, including surface morphology, band gaps, electrochemical properties, and electrochromic behaviors, are systematically investigated. The resultant isoindigo–thiophene D–A–D–type polymer combines the merits of isoindigo and polythiophene, including the excellent stability of isoindigo–based polymers and the extraordinary electrochromic stability of polythiophene. The low onset oxidation potential of precursors ranges from 1.10 to 1.15 V vs. Ag/AgCl, contributing to the electrodeposition of high–quality polymer films. Further kinetic studies illustrate that isoindigo–thiophene D–A–D–type polymers possess favorable electrochromic performances, including high optical contrast (53%, 1000 nm), fast switching time (0.8 s), and high coloration efficiency (124 cm2 C−1). These features of isoindigo–thiophene D–A–D–type conjugated polymers could provide a possibility for rational design and application as electrochromic materials

Un service auxiliaire du tribunal pour enfants, la protection toulousaine de l'enfance : thèse pour le doctorat présentée... / par Germain Delpuech,... ; Université de Toulouse, Faculté de droit

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Trade-off between maximum energy production and firm output of 12-reservoir system in the Qinghai Power Grid.

<p>Trade-off between maximum energy production and firm output of 12-reservoir system in the Qinghai Power Grid.</p