Zobrazení zdroje stránky Heinshort2817

Together, these results support the occurrence of general-domain motivational and cognitive behavioral modules in zebrafish, which have been co-opted for the social domain.Solasonine, a steroidal glycoalkaloid isolated from the herbal plant Solanum nigrum Linn., has shown active against multiple human cancers; however, there is little knowledge on the activity of solasonine against gastric cancer until now. This study aimed to examine the effect of solasonine on the biological behaviours of human gastric cancer SGC-7901 cells. The results showed that solasonine suppressed SGC-7901 cell proliferation in a dose-dependent manner. Solasonine treatment mainly induced the cell cycle arrest at G2 phase in SGC-7901 cells. Treatment with solasonine resulted in significant down-regulation of Bcl-2 and Caspase-3 protein expression and reduced Bax and Bcl-xL protein expression in SGC-7901 cells. Solasonine shows a comparable inhibitory effect on the proliferation of human gastric cancer SGC-7901 cells with cisplatin, and solasonine induces of SGC-7901 cell apoptosis through triggering the endoplasmic reticulum stress pathway and the mitochondrial pathway. Our data indicate that solasonine may be a promising agent for the treatment of gastric cancer.HLA-C*01212 differs from HLA-C*01020101 by two non-synonmous nucleotide changes at positions 368 and 379 in exon 3.Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). [https://www.selleckchem.com/products/BAY-73-4506.html selleck chemicals] GRaSPWeb is freely available at https//grasp.ufv.br.Nearest neighbor parameters for estimating the folding stability of RNA secondary structures are in widespread use. For helices, current parameters penalize terminal AU base pairs relative to terminal GC base pairs. We curated an expanded database of helix stabilities determined by optical melting experiments. Analysis of the updated database shows that terminal penalties depend on the sequence identity of the adjacent penultimate base pair. New nearest neighbor parameters that include this additional sequence dependence accurately predict the measured values of 271 helices in an updated database with a correlation coefficient of 0.982. This refined understanding of helix ends facilitates fitting terms for base pair stacks with GU pairs. Prior parameter sets treated 5'GGUC3' paired to 3'CUGG5' separately from other 5'GU3'/3'UG5' stacks. The improved understanding of helix end stability, however, makes the separate treatment unnecessary. Introduction of the additional terms was tested with three optical melting experiments. The average absolute difference between measured and predicted free energy changes at 37°C for these three duplexes containing terminal adjacent AU and GU pairs improved from 1.38 to 0.27 kcal/mol. This confirms the need for the additional sequence dependence in the model.Precision medicine needs precise phenotypes. The Human Phenotype Ontology (HPO) uses clinical signs instead of diagnoses and has become the standard annotation for patients' phenotypes when describing single gene disorders. Use of the HPO beyond human genetics is however still limited. With SAMS (Symptom Annotation Made Simple), we want to bring sign-based phenotyping to routine clinical care, to hospital patients as well as to outpatients. Our web-based application provides access to three widely used annotation systems HPO, OMIM, Orphanet. Whilst data can be stored in our database, phenotypes can also be imported and exported as Global Alliance for Genomics and Health (GA4GH) Phenopackets without using the database. The web interface can easily be integrated into local databases, e.g. clinical information systems. SAMS offers users to share their data with others, empowering patients to record their own signs and symptoms (or those of their children) and thus provide their doctors with additional information. We think that our approach will lead to better characterised patients which is not only helpful for finding disease mutations but also to better understand the pathophysiology of diseases and to recruit patients for studies and clinical trials. SAMS is freely available at https//www.genecascade.org/SAMS/.Many transcription factors (TFs) in animals bind to both DNA and mRNA, regulating transcription and mRNA turnover. However, whether plant TFs function at both the transcriptional and post-transcriptional levels remains unknown. The rice (Oryza sativa) bZIP TF AVRPIZ-T-INTERACTING PROTEIN 5 (APIP5) negatively regulates programmed cell death and blast resistance and is targeted by the effector AvrPiz-t of the blast fungus Magnaporthe oryzae. We demonstrate that the nuclear localization signal of APIP5 is essential for APIP5-mediated suppression of cell death and blast resistance. APIP5 directly targets two genes that positively regulate blast resistance the cell wall-associated kinase gene OsWAK5 and the cytochrome P450 gene CYP72A1. APIP5 inhibits OsWAK5 expression and thus limits lignin accumulation; moreover, APIP5 inhibits CYP72A1 expression and thus limits reactive oxygen species production and defense compounds accumulation. Remarkably, APIP5 acts as an RNA-binding protein to regulate mRNA turnover of the cell death- and defense-related genes OsLSD1 and OsRac1. Therefore, APIP5 plays dual roles, acting as TF to regulate gene expression in the nucleus and as an RNA-binding protein to regulate mRNA turnover in the cytoplasm, a previously unidentified regulatory mechanism of plant TFs at the transcriptional and post-transcriptional levels.Computational pipelines have become a crucial part of modern drug discovery campaigns. Setting up and maintaining such pipelines, however, can be challenging and time-consuming-especially for novice scientists in this domain. TeachOpenCADD is a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. We offer Python-based solutions for common tasks in cheminformatics and structural bioinformatics in the form of Jupyter notebooks, based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that cover both theoretical background as well as hands-on programming. To promote reproducible and reusable research, we apply software best practices to our notebooks such as testing with automated continuous integration and adhering to the idiomatic Python style. The new TeachOpenCADD website is available at https//projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.<br /><br /> Machine learning (ML) has been used to predict the gamma passing rate (GPR) of intensity-modulated radiation therapy (IMRT) QA results. In this work, we applied a novel neural architecture search to automatically tune and search for the best deep neural networks instead of using hand-designed deep learning architectures.<br /><br /> One hundred and eighty-two IMRT plans were created and delivered with portal dosimetry. A total of 1497 fields for multiple treatment sites were delivered and measured by portal imagers. Gamma criteria of 2%/2mm with a 5% threshold were used. Fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). Auto-Keras was implemented to search for the best CNN architecture for fluence image regression. The network morphism was adopted in the searching process, in which the base models were ResNet and DenseNet. The performance of this CNN approach was compared with tree-based ML models previously developed for this application, using the same dataset.<br /><br /> The deep-learning-based approach had 98.3% of predictions within 3% of the measured 2%/2-mm GPRs with a maximum error of 3.1% and a mean absolute error of less than 1%. Our results show that this novel architecture search approach achieves comparable performance to the machine-learning-based approaches with handcrafted features.<br /><br /> We implemented a novel CNN model using imaging-based neural architecture for IMRT QA prediction. The imaging-based deep-learning method does not require a manual extraction of relevant features and is able to automatically select the best network architecture.<br /><br />We implemented a novel CNN model using imaging-based neural architecture for IMRT QA prediction. The imaging-based deep-learning method does not require a manual extraction of relevant features and is able to automatically select the best network architecture.Argonaute (Ago) proteins are programmable nucleases found in eukaryotes and prokaryotes. Prokaryotic Agos (pAgos) share a high degree of structural homology with eukaryotic Agos (eAgos), and eAgos originate from pAgos. Although eAgos exclusively cleave RNA targets, most characterized pAgos cleave DNA targets. This study characterized a novel pAgo, MbpAgo, from the psychrotolerant bacterium Mucilaginibacter paludis which prefers to cleave RNA targets rather than DNA targets. Compared to previously studied Agos, MbpAgo can utilize both 5'phosphorylated(5'P) and 5'hydroxylated(5'OH) DNA guides (gDNAs) to efficiently cleave RNA targets at the canonical cleavage site if the guide is between 15 and 17 nt long. Furthermore, MbpAgo is active at a wide range of temperatures (4-65°C) and displays no obvious preference for the 5'-nucleotide of a guide. Single-nucleotide and most dinucleotide mismatches have no or little effects on cleavage efficiency, except for dinucleotide mismatches at positions 11-13 that dramatically reduce target cleavage. MbpAgo can efficiently cleave highly structured RNA targets using both 5'P and 5'OH gDNAs in the presence of Mg2+ or Mn2+. The biochemical characterization of MbpAgo paves the way for its use in RNA manipulations such as nucleic acid detection and clearance of RNA viruses.With the advent of single-cell RNA sequencing (scRNA-seq), one major challenging is the so-called 'dropout' events that distort gene expression and remarkably influence downstream analysis in single-cell transcriptome. To address this issue, much effort has been done and several scRNA-seq imputation methods were developed with two categories model-based and deep learning-based. However, comprehensively and systematically comparing existing methods are still lacking. In this work, we use six simulated and two real scRNA-seq datasets to comprehensively evaluate and compare a total of 12 available imputation methods from the following four aspects (i) gene expression recovering, (ii) cell clustering, (iii) gene differential expression, and (iv) cellular trajectory reconstruction. We demonstrate that deep learning-based approaches generally exhibit better overall performance than model-based approaches under major benchmarking comparison, indicating the power of deep learning for imputation. Importantly, we built scIMC (single-cell Imputation Methods Comparison platform), the first online platform that integrates all available state-of-the-art imputation methods for benchmarking comparison and visualization analysis, which is expected to be a convenient and useful tool for researchers of interest.