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Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to predict the pathogenicity of deletions, insertions and duplications structural variations that occur in exons. We constructed three types of annotation features for each structural variation event in the ClinVar database. First, we treated complex structural variations as multiple consecutive single nucleotide polymorphisms events, and annotated them with correlation scores based on single nucleic acid substitutions, such as the impact on protein function. Second, we determined which genes the variation occurred in, and constructed gene-based annotation features for each structural variation. Third, we also calculated related features based on the transcriptome, such as histone signal, the overlap ratio of variation and genomic element definitions, etc. Finally, we employed a gradient boosting decision tree machine learning method, and used the deletions, insertions and duplications in the ClinVar database to train a structural variation pathogenicity prediction model SVPath. These structural variations are clearly indicated as pathogenic or benign. Experimental results show that our SVPath has achieved excellent predictive performance and outperforms existing state-of-the-art tools. SVPath is very promising in evaluating the clinical pathogenicity of structural variants. SVPath can be used in clinical research to predict the clinical significance of unknown pathogenicity and new structural variation, so as to explore the relationship between diseases and structural variations in a computational way.

 

Surface gravity waves are formed by kinetic energy transfer across the air‐sea interface. Significant improvements have been made in understanding the influence of wind on the energy input of waves. However, the impact of gustiness remains underexplored, and little is known about the mechanisms through which gusts alter the wavy surface. In this study, we utilize three‐months observational data collected during the 2010 Impact of Typhoons on the Ocean in the Pacific experiment to investigate the relationship between waves and gustiness. Results show that gustiness increases sea roughness and wave energy, leading to wave development beyond the theoretical peak of Pierson‐Moskowitz spectrum. We further find that the Hasselmann enhancement factor linearly increases with gustiness, indicating gustiness alters the wave development. We suggest that this occurs via two pathways, first, through direct coupling between swell and gustiness which increases wave‐wave interaction, and second through instantaneous reduction in wave age.

 

Although the increased expression of members of the chondroitin sulfate proteoglycan family, such as neuron-glial antigen 2 (NG2), have been well documented after an injury to the spinal cord, a complete picture as to the cellular origins and function of this NG2 expression has yet to be made. Using a spinal cord injury (SCI) mouse model, we describe that some infiltrated bone marrow-derived macrophages (BMDMΦ) are early contributors to NG2/CSPG4 expression and secretion after SCI. We demonstrate for the first time that a lesion-related form of cellular debris generated from damaged myelin sheaths can increase NG2/CSPG4 expression in BMDMΦ, which then exhibit enhanced proliferation and decreased phagocytic capacity. These results suggest that BMDMΦ may play a much more nuanced role in secondary spinal cord injury than previously thought, including acting as early contributors to the NG2 component of the glial scar.