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We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically developed for this application, include Gaussian mixture models, Euler characteristic curves and texture analysis. Using HC features outperforms the CNN for smaller sample sizes and with increased interpretability. On the other hand, with enough training data, the combined classifier outperforms the models trained with HC features or CNN features alone. These results illustrate the added value of HC features with respect to CNNs, especially when insufficient data is available, as is often found in clinical studies.Carboranes are a class of carbon-boron molecular clusters with three-dimensional aromaticity, and inherent robustness. These endowments enable carboranes as valuable building blocks for applications ranging from functional materials to pharmaceuticals. Thus, the chemistry of carboranes has received tremendous research interest, and significant progress has been made in the past decades. However, many attempts to the synthesis of carboranes with more than 14 vertices had been unsuccessful since the report of a 14-vertex carborane in 2005. The question arises as to whether these long sought-after molecules exist. We describe in this article the synthesis and structural characterization of 15- and 16-vertex closo-carboranes as well as 16-vertex ruthenacarborane. Such a success relies on the introduction of silyl groups to both cage carbons, stabilizing the corresponding nido-carborane dianions and promoting the capitation reaction with HBBr2·SMe2. This work would shed some light on the preparation of carboranes with 17 vertices or more, and open the door for studying supercarborane chemistry.To function, biomolecules require sufficient specificity of interaction as well as stability to live in the cell while still being able to move. Thermodynamic stability of only a limited number of specific structures is important so as to prevent promiscuous interactions. The individual interactions in proteins, therefore, have evolved collectively to give funneled minimally frustrated landscapes but some strategic parts of biomolecular sequences located at specific sites in the structure have been selected to be frustrated in order to allow both motion and interaction with partners. We describe a framework efficiently to quantify and localize biomolecular frustration at atomic resolution by examining the statistics of the energy changes that occur when the local environment of a site is changed. The location of patches of highly frustrated interactions correlates with key biological locations needed for physiological function. At atomic resolution, it becomes possible to extend frustration analysis to protein-ligand complexes. At this resolution one sees that drug specificity is correlated with there being a minimally frustrated binding pocket leading to a funneled binding landscape. Atomistic frustration analysis provides a route for screening for more specific compounds for drug discovery.Continuous cropping lowers the production and quality of ramie (Boehmeria nivea L. Gaud). This study aimed to reveal the metagenomic and metabolomic changes between the healthy- and obstacle-plant after a long period of continuous cropping. After 10 years of continuous cropping, ramie planted in some portions of the land exhibited weak growth and low yield (Obstacle-group), whereas, ramie planted in the other portion of the land grew healthy (Health-group). We collected rhizosphere soil and root samples from which measurements of soil chemical and plant physiochemical properties were taken. All samples were subjected to non-targeted gas chromatograph-mass spectrometer (GS/MS) metabolome analysis. click here Further, metagenomics was performed to analyze the functional genes in rhizospheric soil organisms. Based on the findings, ramie in Obstacle-group were characterized by shorter plant height, smaller stem diameter, and lower fiber production than that in Health-group. Besides, the Obstacle-group showed a lower relative abundance of Rhizobiaceae, Lysobacter antibioticus, and Bradyrhizobium japonicum, but a higher relative abundance of Azospirillum lipoferum and A. brasilense compared to the Health-group. Metabolomic analysis results implicated cysteinylglycine (Cys-Gly), uracil, malonate, and glycerol as the key differential metabolites between the Health- and Obstacle-group. Notably, this work revealed that bacteria such as Rhizobia potentially synthesize IAA and are likely to reduce the biotic stress of ramie. L. antibioticus also exerts a positive effect on plants in the fight against biotic stress and is mediated by metabolites including orthophosphate, uracil, and Cys-Gly, which may serve as markers for disease risk. These bacterial effects can play a key role in plant resistance to biotic stress via metabolic and methionine metabolism pathways.Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogenicity. ClassifyCNV uses genomic coordinates and CNV type as input and reports a clinical classification for each variant, a classification score breakdown, and a list of genes of potential importance for variant interpretation. We validate ClassifyCNV’s performance using a set of known clinical CNVs and a set of manually evaluated variants. ClassifyCNV matches the pathogenicity category for 81% of manually evaluated variants with the significance of the remaining pathogenic and benign variants automatically determined as uncertain, requiring a further evaluation by a clinician. ClassifyCNV facilitates the implementation of the latest ACMG guidelines in high-throughput CNV analysis, is suitable for integration into NGS analysis pipelines, and can decrease time to diagnosis. The tool is available at https//github.com/Genotek/ClassifyCNV .