Notes

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Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.
Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.
Coronavirus disease highly contagious, is prevalent in all age and sex groups infecting the respiratory system. The present study seeks to investigate the epidemiology and effective factors in mortality of patients with COVID-19 in Ardabil province, northwestern Iran.

In a retrospective study, the hospitalized patients with laboratory-diagnosed COVID-19 between February to August 2020 were enrolled. The data registration portal was designated according to Iranian Ministry of Health and Medical Education guidelines. In this portal, demographic information, clinical presentation, laboratory and imaging data were registered for patients in all hospitals in the same format. The Hosmer-Lemeshow strategy was used for variable selection in a multiple model.

Of the patients involved 2812(50.3%) were male and 150 (2.7%) had contact with a confirmed case of COVID-19 in the last 14 days. Pre-existing comorbidity was reported in 1310 (23.4%) patients. Of all patients, 477(8.5%) died due to COVID-19. the result of the multiple logistic regression model indicated that after adjusting for other factors, higher age (OR = 3.11), fever or chills (OR = 1.61), shortness of breath (OR = 1.82), fatigue (OR = 0.71), headache (OR = 0.64), runny nose (OR = 1.54), Skeletal muscle pain (OR = 1.53), hospitalization (OR = 5.66), and hospitalization in ICU (OR = 5.12) were associated with death.

Hospitalization had the strongest effect on mortality followed by hospitalization in ICU, and higher age. This study showed that having some extra-pulmonary symptoms in contrast with pulmonary symptoms can predict as good prognostic factors.
Hospitalization had the strongest effect on mortality followed by hospitalization in ICU, and higher age. This study showed that having some extra-pulmonary symptoms in contrast with pulmonary symptoms can predict as good prognostic factors.
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. buy TAK-715 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. For genomic region assigenomic coverage. The pipeline is available at GitHub via https//github.com/MaHaoran627/CNN_Virus .
Observational studies have identified various associations between neuroimaging alterations and neuropsychiatric disorders. However, whether such associations could truly reflect causal relations remains still unknown.

Here, we leveraged genome-wide association studies (GWAS) summary statistics for (1) 11 psychiatric disorders (sample sizes varied from n = 9,725 to 1,331,010); (2) 110 diffusion tensor imaging (DTI) measurement (sample size n = 17,706); (3) 101 region-of-interest (ROI) volumes, and investigate the causal relationship between brain structures and neuropsychiatric disorders by two-sample Mendelian randomization. Among all DTI-Disorder combinations, we observed a significant causal association between the superior longitudinal fasciculus (SLF) and the risk of Anorexia nervosa (AN) (Odds Ratio [OR] = 0.62, 95 % confidence interval 0.50 ~ 0.76, P = 6.4 × 10
). Similar significant associations were also observed between the body of the corpus callosum (fractional anisotropy) and Alzheimer's disuld be causally related to some neuropsychiatric disorders, such as BP and AN. Also, the white matter structure might have a larger impact on neuropsychiatric disorders than subregion volumes.
While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association.

We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to coRCE .
There is great need for development of feasible rehabilitation for older people with dementia. Increased understanding of this population's experiences of rehabilitation participation is therefore important. The aim of this study was to explore the experiences of community-dwelling older people with dementia participating in a person-centred multidimensional interdisciplinary rehabilitation program.

Sixteen older people with dementia were interviewed about their experiences of participation in a person-centred multidimensional interdisciplinary rehabilitation program. The program comprised assessments by a comprehensive team of rehabilitation professionals followed by a rehabilitation period of 16 weeks, including interventions based on individualized rehabilitation goals conducted with the support of the rehabilitation team. The rehabilitation was performed in the participants' homes, in the community and at an outpatient clinic, including exercise with social interaction in small groups offered twice a week to all participants.
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