The average amino acid identity (AAI) is an index of pairwise genomic relatedness, and multiple studies have proposed its application in prokaryotic taxonomy and related disciplines. AAI demonstrates better resolution in elucidating taxonomic structure beyond the species rank when compared with average nucleotide identity (ANI), which is a standard criterion in species delineation. However, an efficient and easy-to-use computational tool for AAI calculation in large-scale taxonomic studies is not yet available. Here, we introduce a bioinformatic pipeline, named EzAAI, which allows for rapid and accurate AAI calculation in prokaryote sequences. The EzAAI tool is based on the MMSeqs2 program and computes AAI values almost identical to those generated by the standard BLAST algorithm with significant improvements in the speed of these evaluations. Our pipeline also provides a function for hierarchical clustering to create dendrograms, which is an essential part of any taxonomic study. EzAAI is available for download as a standalone JAVA program at http://leb.snu.ac.kr/ezaai.
The gut microbiota modulates overall metabolism, the immune system and brain development of the host. The majority of mammalian gut microbiota consists of bacteria. Among various model animals, the mouse has been most widely used in pre-clinical biological experiments. The significant compositional differences in taxonomic profiles among different mouse strains due to gastrointestinal locations, genotypes and vendors have been well documented. However, details of such variations are yet to be elucidated. This study compiled and analyzed 16S rRNA gene-based taxonomic profiles of 554 healthy mouse samples from 14 different projects to construct a comprehensive database of the microbiome of a healthy mouse gastrointestinal tract. The database, named Murine Microbiome Database, should provide researchers with useful taxonomic information and better biological insight about how each taxon, such as genus and species, is associated with locations in the gastrointestinal tract, genotypes and vendors. The database is freely accessible over the Internet.
Background/AimsWe aim to evaluate the differences in the microbiome of responders and non-responders, as well as predict the response to probiotic therapy, based on fecal microbiome data in patients with diarrhea-predominant irritable bowel syndrome (IBS-D).
MethodsA multi-strain probiotics that contains Lactobacillus acidophilus (KCTC 11906BP), Lactobacillus plantarum (KCTC11867BP), Lactobacillus rhamnosus (KCTC 11868BP), Bifidobacterium breve (KCTC 11858BP), Bifidobacterium lactis (KCTC 11903BP), Bifidobacterium longum (KCTC 11860BP), and Streptococcus thermophilus (KCTC 11870BP) were used. Patients were categorized into probiotic and placebo groups, and fecal samples were collected from all patients before and at the end of 8 weeks of treatment. The probiotic group was further divided into responders and non-responders. Responders were defined as patients who experienced adequate relief of overall irritable bowel syndrome symptoms after probiotic therapy. Fecal microbiota were investigated using Illumina MiSeq and analyzed using the EzBioCloud 16S database and microbiome pipeline (https://www.EZbiocloud.net).
ResultsThere was no significant difference in the alpha and beta diversity between the responder and non-responder groups. The abundances of the phylum Proteobacteria and genus Bacteroides significantly decreased after probiotic treatment. Bifidobacterium bifidum, Pediococcus acidilactici, and Enterococcus faecium showed a significantly higher abundance in the probiotic group after treatment compared to the placebo group. Enterococcus faecalis and Lactococcus lactis were identified as biomarkers of non-response to probiotics. The abundance of Fusicatenibacter saccharivorans significantly increased in the responders after treatment.
ConclusionsProbiotic treatment changes some composition of fecal bacteria in patients with IBS-D. E. faecalis and L. lactis may be prediction biomarkers for non-response to probiotics. Increased abundance of F. sccharivorans is correlated to symptom improvement by probiotics in patients with IBS-D.
The optimization of neural networks in terms of computation cost and memory footprint is crucial for their practical deployment on edge devices. In this work, we propose a novel quantization-aware training (QAT) scheme called noise injection pseudo quantization (NIPQ). NIPQ is implemented based on pseudo quantization noise (PQN) and has several advantages. First, both activation and weight can be quantized based on a unified framework. Second, the hyper-parameters of quantization (e.g., layer-wise bit-width and quantization interval) are automatically tuned. Third, after QAT, the network has robustness against quantization, thereby making it easier to deploy in practice. To validate the superiority of the proposed algorithm, we provide extensive analysis and conduct diverse experiments for various vision applications. Our comprehensive experiments validate the outstanding performance of the proposed algorithm in several aspects.Preprint. Under review.
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