Didriksen Pereira (yearcoffee63)

W27 monoclonal immunoglobulin A (IgA) suppresses pathogenic Escherichia coli cell growth; however, its effect on the human intestine remains unclear. We aimed to determine how W27 IgA affects the human colonic microbiota using the in vitro microbiota model. This model was established using fecal samples collected from 12 healthy volunteers; after anaerobic cultivation, each model was found to retain the genera found in the original human fecal samples. After pre-incubating W27 IgA with the respective fecal sample under aerobic conditions, the mixture of W27 IgA (final concentration, 0.5 μg/mL) and each fecal sample was added to the in vitro microbiota model and cultured under anaerobic conditions. Next-generation sequencing of the bacterial 16S rRNA gene revealed that W27 IgA significantly decreased the relative abundance of bacteria related to the genus Escherichia in the model. Additionally, at a final concentration of 5 μg/mL, W27 IgA delayed growth in the pure culture of Escherichia coli isolated from human fecal samples. Our study thus revealed the suppressive effect of W27 IgA on the genus Escherichia at relatively low-concentrations and the usefulness of an in vitro microbiota model to evaluate the effect of IgA as a gut microbiota regulator.Earthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they often occur across large areas, landslide detection requires rapid and reliable automatic detection approaches. Currently, deep learning (DL) approaches, especially different convolutional neural network and fully convolutional network (FCN) algorithms, are reliably achieving cutting-edge accuracies in automatic landslide detection. However, these successful applications of various DL approaches have thus far been based on very high resolution satellite images (e.g., GeoEye and WorldView), making it easier to achieve such high detection performances. In this study, we use freely available Sentinel-2 data and ALOS digital elevation model to investigate the application of two well-known FCN algorithms, namely the U-Net and residual U-Net (or so-called ResU-Net), for landslide detection. To our knowledge, this is the first application of FCN for landslide detection only from freely available data. We adapt the algorithms to the specific aim of landslide detection, then train and test with data from three different case study areas located in Western Taitung County (Taiwan), Shuzheng Valley (China), and Eastern Iburi (Japan). We characterize three different window size sample patches to train the algorithms. Our results also contain a comprehensive transferability assessment achieved through different training and testing scenarios in the three case studies. The highest f1-score value of 73.32% was obtained by ResU-Net, trained with a dataset from Japan, and tested on China's holdout testing area using the sample patch size of 64 × 64 pixels.The emao, a traditional beer starter used in the North-East regions of India produces a high quality of beer from rice substrates; however, its microbial community structure and functional metabolic modules remain unknown. To address this gap, we have used shot-gun whole-metagenome sequencing technology; accordingly, we have detected several enzymes that are known to catalyze saccharification, lignocellulose degradation, and biofuel production indicating the presence of metabolic functionome in the emao. The abundance of eukaryotic microorganisms, specifically the members of Mucoromycota and Ascomycota, dominated over the prokaryotes in the emao compared to previous metagenomic studies on such traditional starters where the relative abundance of prokaryotes occurred higher than the eukaryotes. The family Rhizopodaceae (64.5%) and its genus Rhizopus (64%) were the most dominant ones, followed by Phaffomycetaceae (11.14%) and its genus Wickerhamomyces (10.03%). The family Leuconostocaceae (6.09%) represented by two genera (Leuconostoc and Weissella)