BackgroundThe genus Brachyspira currently encompasses seven valid species that colonize the intestines of mammals and birds. In a previous study a group of strongly haemolytic isolates from pigs and mallards was provisionally described as a new species within genus Brachyspira, “B. suanatina”, and enteropathogenic properties were demonstrated in a porcine challenge model.MethodsIn the current study characterization of B. suanatina was performed on the basis of cell morphology, growth characteristics, enzyme profiles, DNA-DNA hybridization (DDH) and whole genome comparisons. The draft genome sequence of B. suanatina strain AN4859/03 was determined and compared with the available genomes of all valid species of Brachyspira.ResultsAccording to morphological traits, growth characteristics and enzymatic profiles, B. suanatina was similar to the type strain of B. hyodysenteriae, but using the recommended threshold value of 70 % similarity by DDH it did not belong to any of the recognized Brachyspira species (range 16–64 % similarity). This was further supported by average nucleotide identity values. Phylogenetic analysis performed using housekeeping genes and core genomes of all valid Brachyspira sp. and “B. hampsonii” revealed that B. suanatina and B. intermedia formed a clade distinct from B. hyodysenteriae. By comparing the genomes of the three closely related species B. intermedia, B. hyodysenteriae and B. suanatina similar profiles of general genomic features and distribution of genes in different functional categories were obtained. However, the genome size of B. hyodysenteriae was smallest among the species, suggesting the possibility of reductive evolution in the divergence of this species. A bacteriophage region and a putative plasmid sequence were also found in the genome of B. suanatina strain AN4859/03.ConclusionsThe results of our study suggest that despite being similar to B. hyodysenteriae phenotypically, B. suanatina should be regarded as a separate species based on its genetic characteristics. Based on characteristics presented in this report we propose that strains AN4859/03, AN1681:1/04, AN2384/04 and Dk12570-2 from pigs in Sweden and Denmark, and strains AN3949:2/02 and AN1418:2/01 isolated from mallards in Sweden, represent a unique species within genus Brachyspira. For this new species we propose the name B. suanatina for which the type strain is AN4859/03T (=ATCC® BAA-2592™ = DSM 100974T).Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-015-0537-y) contains supplementary material, which is available to authorized users.

The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL), is proposed for effective COVID-19 detection in X-ray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVID-RENet-1 & 2 models is exploited individually through a machine learning (ML) classifier. In COVID-RENet models, Region and Edge-based operations are carefully applied to learn region homogeneity and extract boundaries features. While in the case of the proposed DBHL framework, COVID-RENet-1 & 2 are fine-tuned using transfer learning on the chest X-rays. Furthermore, deep feature spaces are generated from the penultimate layers of the two models and then concatenated to get a single enriched boosted feature space. A conventional ML classifier exploits the enriched feature space to achieve better COVID-19 detection performance. The proposed COVID-19 detection frameworks are evaluated on radiologist’s authenticated chest X-ray data, and their performance is compared with the well-established CNNs. It is observed through experiments that the proposed DBHL framework, which merges the two-deep CNN feature spaces, yields good performance (accuracy: 98.53%, sensitivity: 0.99, F-score: 0.98, and precision: 0.98). Furthermore, a web-based interface is developed, which takes only 5-10s to detect COVID-19 in each unseen chest X-ray image. This web-predictor is expected to help early diagnosis, save precious lives, and thus positively impact society.

We announce the complete genome sequence of Streptococcus agalactiae strain 09mas018883, isolated from the milk of a cow with clinical mastitis. The availability of this genome may allow identification of candidate genes, leading to discovery of antigens that might form the basis for development of a vaccine as an alternative means of mastitis control.

We report the genome of a Staphylococcus aureus strain (ILRI_Eymole1/1) isolated from a nasal swab of a dromedary camel (Camelus dromedarius) in North Kenya. The complete genome sequence of this strain consists of a circular chromosome of 2,874,302 bp with a GC-content of 32.88 %. In silico annotation predicted 2755 protein-encoding genes and 76 non-coding genes. This isolate belongs to MLST sequence type 30 (ST30). Phylogenetic analysis based on a subset of 283 core genes revealed that it falls within the human clonal complex 30 (CC30) S. aureus isolate cluster but is genetically distinct. About 79 % of the protein encoding genes are part of the CC30 core genome (genes common to all CC30 S. aureus isolates), ~18 % were within the variable genome (shared among multiple but not all isolates) and ~ 3 % were found only in the genome of the camel isolate. Among the 85 isolate-specific genes, 79 were located within putative phages and pathogenicity islands. Protein encoding genes associated with bacterial adhesion, and secretory proteins that are essential components of the type VII secretion system were also identified. The complete genome sequence of S. aureus strain ILRI_Eymole1/1 has been deposited in the European Nucleotide Archive under the accession no LN626917.1.Electronic supplementary materialThe online version of this article (doi:10.1186/s40793-015-0098-6) contains supplementary material, which is available to authorized users.

Streptococcus agalactiae causes a range of clinical syndromes in camels (Camelus dromedarius). We report the genome sequences of two S. agalactiae isolates that induce abscesses in Kenyan camels. These genomes provide novel data on the composition of the S. agalactiae “pan genome” and reveal the presence of multiple genomic islands.