Notes

Id associated with monoclonal antibodies in opposition to human being kidney glomerular endothelial tissues in lupus nephritis that induce endothelial interferon-alpha generation.
The Arthrobacter group is a known set of bacteria from cold regions, the species of which are highly likely to play diverse roles at low temperatures. However, their survival mechanisms in cold regions such as Antarctica are not yet fully understood. In this study, we compared the genomes of 16 strains within the Arthrobacter group, including strain PAMC25564, to identify genomic features that help it to survive in the cold environment.

Using 16S rRNA sequence analysis, we found and identified a species of Arthrobacter isolated from cryoconite. We designated it as strain PAMC25564 and elucidated its complete genome sequence. The genome of PAMC25564 is composed of a circular chromosome of 4,170,970bp with a GC content of 66.74 % and is predicted to include 3,829 genes of which 3,613 are protein coding, 147 are pseudogenes, 15 are rRNA coding, and 51 are tRNA coding. In addition, we provide insight into the redundancy of the genes using comparative genomics and suggest that PAMC25564 has glycogen and trehalthe complete Arthrobacter sp. PAMC25564 genome and used comparative analysis to provide insight into the redundancy of its CAZymes for potential cold adaptation. This provides a foundation to understanding how the Arthrobacter strain produces energy in an extreme environment, which is by way of CAZymes, consistent with reports on the use of these specialized enzymes in cold environments. Knowledge of glycogen metabolism and cold adaptation mechanisms in Arthrobacter species may promote in-depth research and subsequent application in low-temperature biotechnology.
Coxiella burnetii is the Gram-negative bacterium responsible for Q fever in humans and coxiellosis in domesticated agricultural animals. Previous vaccination efforts with whole cell inactivated bacteria or surface isolated proteins confer protection but can produce a reactogenic immune responses. Thereby a protective vaccine that does not cause aberrant immune reactions is required. The critical role of T-cell immunity in control of C. burnetii has been made clear, since either CD8
or CD4
T cells can empower clearance. The purpose of this study was to identify C. burnetii proteins bearing epitopes that interact with major histocompatibility complexes (MHC) from multiple host species (human, mouse, and cattle).

Of the annotated 1815 proteins from the Nine Mile Phase I (RSA 493) assembly, 402 proteins were removed from analysis due to a lack of inter-isolate conservation. An additional 391 proteins were eliminated from assessment to avoid potential autoimmune responses due to the presence of host homoloportant model organism. This work provides new vaccine targets for future vaccination efforts and enhances opportunities for selecting multiple T-cell epitope types to include within a vaccine.
These data represent the first proteome-wide evaluation of C. burnetii peptide epitopes. Furthermore, the inclusion of human, mouse, and bovine data capture a range of hosts for this zoonotic pathogen plus an important model organism. This work provides new vaccine targets for future vaccination efforts and enhances opportunities for selecting multiple T-cell epitope types to include within a vaccine.
Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them.

To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation.

Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results.

We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.
We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.
Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample.

In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. Vismodegib in vivo For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data.
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