Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil.

Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose.

approach to identify microsatellite candidate biomarkers to differentiate the biovar of genomes.

is the causative bacterial agent of the zoonotic disease known as caseous lymphadenitis, and it presents several mechanisms of response to host defenses, including the presence of virulence factors (VFs). The genomes of these bacteria have several polymorphic markers known as microsatellites, or simple sequence repeats (SSRs), that can be used to characterize the genome, to study possible polymorphisms existing among strains, and to verify the effects of such polymorphic markers in coding regions and regions associated with VFs. In this study, several SSRs were identified within coding regions throughout the 54 genomes of this species, revealing possible polymorphisms associated with coding regions that could be used as strain-specific or serotype-specific identifiers of .

Text mining for identification of biological entities related to antibiotic resistant organisms.

Antimicrobial resistance is a significant public health problem worldwide. In recent years, the scientific community has been intensifying efforts to combat this problem; many experiments have been developed, and many articles are published in this area. However, the growing volume of biological literature increases the difficulty of the biocuration process due to the cost and time required.

Bacteriocin Producing Strains Isolated from Bovine Mastitis in Brazil.

Antibiotic resistance is one of the biggest health challenges of our time. We are now facing a post-antibiotic era in which microbial infections, currently treatable, could become fatal. In this scenario, antimicrobial peptides such as bacteriocins represent an alternative solution to traditional antibiotics because they are produced by many organisms and can inhibit bacteria, fungi, and/or viruses.

Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance.

Exiguobacterium antarcticum strain B7 is a psychrophilic Gram-positive bacterium that possesses enzymes that can be used for several biotechnological applications. However, many proteins from its genome are considered hypothetical proteins (HPs). These functionally unknown proteins may indicate important functions regarding the biological role of this bacterium, and the use of bioinformatics tools can assist in the biological understanding of this organism through functional annotation analysis.

GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data.

Downstream analysis of genomic and transcriptomic sequence data is often executed by functional annotation that can be performed by various bioinformatics tools and biological databases. However, a full fast integrated tool is not available for such analysis. Besides, the current available software is not able to produce analytic lists of annotations and graphs to help users in evaluating the output results.

Draft Genome Sequence of Strain PA07 Biovar , Isolated from a Sheep Udder in Amazonia.

In this work, we present the draft genome sequence of strain PA07 biovar , isolated from a caseous secretion from a sheep udder in Pará, Brazil. The genome contains 2,320,235 bp, 52.2% G+C content, 2,191 coding sequences (CDSs), five pseudogenes, 48 tRNAs, and three rRNAs.