Runout Analyses Using 2014 Oso Landslide

Understanding the runout of slope failures is important for hazard identification, risk assessments, and disaster prevention. This study evaluates the accuracy of three runout software packages, DAN3D, Anura3D, and FLO-2D, for modeling the runout of the 2014 Oso Landslide and investigates the viability of predicting the runout of other landslides and slope failures under similar conditions. This study uses the geotechnical conditions and failure mechanism reported by Stark et al. (2017) to conduct the Oso Landslide runout analyses and assess the accuracy/applicability of the runout models using field observations. These analyses show the importance of: (1) using a digital terrain model in the runout analysis, (2) modeling field representative shear strength properties and failure mechanisms, and (3) predicting runout distance, splash height, and duration for risk assessments and to improve public safety for this and other slopes.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology

Because of the inconvenience of installing sensors in a buried pipeline, an acoustic emission sensor is initially proposed for collecting and analyzing leakage signals inside the pipeline. Four operating conditions of a fluid-filled pipeline are established and a support vector machine (SVM) method is used to accurately classify the leakage condition of the pipeline. Wavelet decomposition and empirical mode decomposition (EMD) methods are initially used in denoising these signals to address the problem in which original leakage acoustic emission signals contain too much noise. Signals with more information and energy are then reconstructed. The time-delay estimation method is finally used to accurately locate the leakage source in the pipeline. The results show that by using SVM, wavelet decomposition and EMD methods, leakage detection in a liquid-filled pipe with built-in acoustic emission sensors is effective and accurate and provides a reference value for real-time online monitoring of pipeline operational status with broad application prospects

D3Targets-2019-nCoV: A Web Server to Identify Potential Targets for Antivirals Against 2019-nCoV

2019-nCoV has caused more than 560 deaths as of 6 February 2020 worldwide, mostly in China. Although there are no effective drugs approved, many clinical trials are incoming or ongoing in China which utilize traditional chinese medicine or modern medicine. Moreover, many groups are working on the cytopathic effect assay to fight against 2019-nCoV, which will result in compounds with good activity yet unknown targets. Identifying potential drug targets will be of great importance to understand the underlying mechanism of how the drug works. Here, we compiled the 3D structures of 17 2019-nCoV proteins and 3 related human proteins, which resulted in 208 binding pockets. Each submitted compound will be docked to these binding pockets by the docking software smina and the docking results will be presented in ascending order of compound-target interaction energy (kcal/mol). We hope the computational tool will shed some light on the potential drug target for the identified antivirals. D3Targets-2019-nCoV is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Docking/index.php.</p

D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.</p

D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19

Computational study of the strong binding mechanism of SARS-CoV-2 spike and ACE2

The spike protein of SARS-CoV-2 (SARS-CoV-2-S) helps the virus attach to and infect human cells. With various computational methods applied in this work, the accessibility of its RBD to ACE2, its key residues for stronger binding to ACE2 than the SARS-CoV spike (SARS-CoV-S), the origin of the stronger binding, and its potential sites for drug and antibody design were explored. It was found that the SARS-CoV-2-S could bind ACE2 with an RBD-angle ranging from 52.2º to 84.8º, which demonstrated that the RBD does not need to fully open to bind ACE2. Free energy calculation by an MM/GBSA approach not only revealed much stronger binding of SARS-CoV-2-S to ACE2 (ΔG=-21.7~-29.9 kcal/mol) than SARS-CoV-S (ΔG=-10.2~-16.4 kcal/mol) at different RBD-angles but also demonstrated that the binding becomes increasingly stronger as the RBD-angle increases. In comparison with the experimental results, the free energy decomposition disclosed more key residues interacting strongly with ACE2 than with the SARS-CoV-S, among which the Q493 might be the decisive residue variation (-5.84 kcal/mol) to the strong binding. With the mutation of all 18 different residues of SARS-CoV-S on the spike-ACE2 interface to the corresponding residues of SARS-CoV-2-S, it was found that the mutated SARS-CoV-S has almost the same binding affinity as SARS-CoV-2-S to ACE2, demonstrating that the remaining mutations outside the spike-ACE2 interface have little effect on its binding affinity to ACE2. Simulation of the conformational change pathway from “down” to “up” states disclosed 5 potential ligand-binding pockets correlated to the conformational change. Taking together the key residues, accessible RBD-angle and pocket correlation, potential sites for drug and antibody design were proposed, which should be helpful for interpreting the high infectiousness of SARS-CoV-2 and for developing a cure.</p

Global Identification of Multiple OsGH9 Family Members and Their Involvement in Cellulose Crystallinity Modification in Rice

<div><p>Plant glycoside hydrolase family 9 (GH9) comprises typical endo-β-1,4-glucanase (EGases, EC3.2.1.4). Although <em>GH9A</em> (<em>KORRIGAN</em>) family genes have been reported to be involved in cellulose biosynthesis in plants, much remains unknown about other GH9 subclasses. In this study, we observed a global gene co-expression profiling and conducted a correlation analysis between <em>OsGH9</em> and <em>OsCESA</em> among 66 tissues covering most periods of life cycles in 2 rice varieties. Our results showed that <em>OsGH9A3</em> and <em>B5</em> possessed an extremely high co-expression with <em>OsCESA1</em>, <em>3</em>, and <em>8</em> typical for cellulose biosynthesis in rice. Using two distinct rice non-GH9 mutants and wild type, we performed integrative analysis of gene expression level by qRT-PCR, cellulase activities <em>in situ</em> and <em>in vitro</em>, and lignocellulose crystallinity index (CrI) in four internodes of stem tissues. For the first time, OsGH9B1, 3, and 16 were characterized with the potential role in lignocellulose crystallinity alteration in rice, whereas OsGH9A3 and B5 were suggested for cellulose biosynthesis. In addition, phylogenetic analysis and gene co-expression comparison revealed GH9 function similarity in <em>Arabidopsis</em> and rice. Hence, the data can provide insights into GH9 function in plants and offer the potential strategy for genetic manipulation of plant cell wall using the five aforementioned novel <em>OsGH9</em> genes.</p> </div