The human gut is one of the most densely populated microbial communities in the world. The interaction of microbes with human host cells is responsible for several disease conditions and of criticality to human health. It is imperative to understand the relationships between these microbial communities within the human gut and their roles in disease. In this study we analyse the microbial communities within the human gut and their role in Inflammatory Bowel Disease (IBD). The bacterial communities were interrogated using Length Heterogeneity PCR (LH-PCR) fingerprinting of mucosal and luminal associated microbial communities for a class of healthy and diseases patients.

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Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

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Background Low-coverage sequencing is a cost-effective way to obtain reads spanning an entire genome. However, read depth at each locus is low, making sequencing error difficult to separate from actual variation. Prior to variant calling, sequencer reads are aligned to a reference genome, with alignments stored in Sequence Alignment/Map (SAM) files. Each alignment has a mapping quality (MAPQ) score indicating the probability a read is incorrectly aligned. This study investigated the recalibration of probability estimates used to compute MAPQ scores for improving variant calling performance in single-sample, low-coverage settings. Materials and Methods Simulated tomato, hot pepper and rice genomes were implanted with known variants. From these, simulated paired-end reads were generated at low coverage and aligned to the original reference genomes. Features extracted from the SAM formatted alignment files for tomato were used to train machine learning models to detect incorrectly aligned reads and output estimates of the probability of misalignment for each read in all three data sets. MAPQ scores were then re-computed from these estimates. Next, the SAM files were updated with new MAPQ scores. Finally, Variant calling was performed on the original and recalibrated alignments and the results compared. Results Incorrectly aligned reads comprised only 0.16% of the reads in the training set. This severe class imbalance required special consideration for model training. The F1 score for detecting misaligned reads ranged from 0.76 to 0.82. The best performing model was used to compute new MAPQ scores. Single Nucleotide Polymorphism (SNP) detection was improved after mapping score recalibration. In rice, recall for called SNPs increased by 5.2%, while for tomato and pepper it increased by 3.1% and 1.5%, respectively. For all three data sets the precision of SNP calls ranged from 0.91 to 0.95, and was largely unchanged both before and after mapping score recalibration. Conclusion Recalibrating MAPQ scores delivers modest improvements in single-sample variant calling results. Some variant callers operate on multiple samples simultaneously. They exploit every sample’s reads to compensate for the low read-depth of individual samples. This improves polymorphism detection and genotype inference. It may be that small improvements in single-sample settings translate to larger gains in a multi-sample experiment. A study to investigate this is ongoing.