Bmc Genomics
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Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as transcriptomes and metabolomes, have long existed as a valuable resource for facilitating diagnosis of patient statuses/stages. It is timely imperative to compare deep learning neural networks against classical machine learning methods in the setting of matrix-formed omics data in terms of classification accuracy and robustness. ⋯ Our results concluded that shallow MLPs (of one or two hidden layers) with ample hidden neurons are sufficient to achieve superior and robust classification performance in exploiting numerical matrix-formed omics data for diagnosis purpose. Specific observations regarding optimal network width, class imbalance tolerance, and inaccurate labeling tolerance will inform future improvement of neural network applications on functional genomics data.
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Corynebacterium diphtheriae is the main etiological agent of diphtheria, a global disease causing life-threatening infections, particularly in infants and children. Vaccination with diphtheria toxoid protects against infection with potent toxin producing strains. However a growing number of apparently non-toxigenic but potentially invasive C. diphtheriae strains are identified in countries with low prevalence of diphtheria, raising key questions about genomic structures and population dynamics of the species. This study examined genomic diversity among 48 C. diphtheriae isolates collected in Australia over a 12-year period using whole genome sequencing. Phylogeny was determined using SNP-based mapping and genome wide analysis. ⋯ The genomic diversity of toxigenic and non-toxigenic strains of C. diphtheriae in Australia suggests multiple sources of infection and colonisation. Genomic surveillance of co-circulating toxigenic and non-toxigenic C. diphtheriae offer new insights into the evolution and virulence of pathogenic clones and can inform targeted public health actions and policy. The genomes presented in this investigation will contribute to the global surveillance of C. diphtheriae both for the monitoring of antibiotic resistance genes and virulent strains such as those belonging to ST32.
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Members of the genus Naegleria are free-living eukaryotes with the capability to transform from the amoeboid form into resting cysts or moving flagellates in response to environmental conditions. More than 40 species have been characterized, but only Naegleria fowleri (N. fowleri) is known as a human pathogen causing primary amoebic meningoencephalitis (PAM), a fast progressing and mostly fatal disease of the central nervous system. Several studies report an involvement of phospholipases and other molecular factors, but the mechanisms involved in pathogenesis are still poorly understood. To gain a better understanding of the relationships within the genus of Naegleria and to investigate pathogenicity factors of N. fowleri, we characterized the genome of its closest non-pathogenic relative N. lovaniensis. ⋯ In this study, we characterize the hitherto unknown genome of N. lovaniensis. With the description of the 30 Mb genome, a further piece is added to reveal the complex taxonomic relationship of Naegleria. Further, the whole genome sequencing data confirms the hypothesis of the close relationship between N. fowleri and N. lovaniensis. Therefore, the genome of N. lovaniensis provides the basis for further comparative approaches on the molecular and genomic level to unravel pathogenicity factors of its closest human pathogenic relative N. fowleri and possible treatment options for the rare but mostly fatal primary meningoencephalitis.
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Following publication of the original article [1], the authors reported that one of the authors' names was erroneously changed during proofing and published incorrectly. In this Correction the incorrect and correct author name are shown. The original publication of this article has been corrected.
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Plant-parasitic nematodes cause severe damage to a wide range of crop and forest species worldwide. The migratory endoparasitic nematode, Bursaphelenchus xylophilus, (pinewood nematode) is a quarantine pathogen that infects pine trees and has a hugely detrimental economic impact on the forestry industry. Under certain environmental conditions large areas of infected trees can be destroyed, leading to damage on an ecological scale. The interactions of B. xylophilus with plants are mediated by secreted effector proteins produced in the pharyngeal gland cells. Identification of effectors is important to understand mechanisms of parasitism and to develop new control measures for the pathogens. ⋯ We provide a major scientific advance in the area of effector regulation. We identify a novel promoter motif (STATAWAARS) associated with expression in the pharyngeal gland cells. Our data, coupled with those from previous studies, suggest that lineage-specific promoter motifs are a theme of effector regulation in the phylum Nematoda.