Abstract Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast ...and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.
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•A semi-nested, heptaplex RT-PCR assay for SARS-CoV-2 detection has been developed.•The complex melting spectrum was interpreted by an artificial intelligence algorithm.•The developed ...assay enables 96-sample pooled testing for increase of testing capacity.•About 8,000 pre-amplified samples could be screened in one realtime PCR run.
Asymptomatic transmission was found to be the Achilles’ heel of the symptom-based screening strategy, necessitating the implementation of mass testing to efficiently contain the transmission of COVID-19 pandemic. However, the global shortage of molecular reagents and the low throughput of available realtime PCR facilities were major limiting factors.
A novel semi-nested and heptaplex (7-plex) RT-PCR assay with melting analysis for detection of SARS-CoV-2 RNA has been established for either individual testing or 96-sample pooled testing. The complex melting spectrum collected from the heptaplex RT-PCR amplicons was interpreted with the support of an artificial intelligence algorithm for the detection of SARS-CoV-2 RNA. The analytical and clinical performance of the semi-nested RT-PCR assay was evaluated using RNAs synthesized in-vitro and those isolated from nasopharyngeal samples.
The LOD of the assay for individual testing was estimated to be 7.2 copies/reaction. Clinical performance evaluation indicated a sensitivity of 100% (95% CI: 97.83–100) and a specificity of 99.87% (95% CI: 99.55–99.98). More importantly, the assay supports a breakthrough sample pooling method, which makes possible parallel screening of up to 96 samples in one real-time PCR well without loss of sensitivity. As a result, up to 8,820 individual pre-amplified samples could be screened for SARS-CoV-2 within each 96-well plate of realtime PCR using the pooled testing procedure.
The novel semi-nested RT-PCR assay provides a solution for highly multiplex (7-plex) detection of SARS-CoV-2 and enables 96-sample pooled detection for increase of testing capacity.
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•The failure modes of FRP rods are predicted based on the maximum stress criterion.•The mechanical properties of unidirectional composites are calculated by RVE models.•Cohesive zone models are ...employed to validate the effect of bonding interfaces.•The simulation models are employed to predict the load-capacity of FRP rods.•The effects of shear-lag, diameter, failure modes on FRP rods are investigated in detail.
The tensile properties of fiber-reinforced polymer (FRP) rods in adhesively bonded anchorages are expected to be studied in detail. Therefore, the study aims to predict the failure modes and load-bearing capacity (load-capacity) of FRP rods subjected to direct tensile force. The cohesive zone models were employed to evaluate the interfacial bond between materials. Firstly, two representative volume element models of fibers and matrix were proposed to predict engineering constants and strengths of the FRP material in three dimensions. Then, the main simulation, including the FRP rod, filling material, and steel tube, was carried out to analyze FRP rods under the variation of cohesive zone model parameters. The load-capacity, failure modes, shear-lag effect were predicted based on the maximum stress criterion. The results revealed that the FRP material strengths enforce the failure in two modes associated with the transverse and longitudinal directions of FRP rods. In addition, diameter is a significant factor that increases the shear-lag effect and reduces the tensile strength of the FRP rods. The numerical simulation provided a new method to predict the load-capacity of FRP rods.
Autosomal recessive hyper‐IgE syndrome (AR‐HIES) is a rare primary immunodeficiency disorder characterized by high serum IgE levels, recurrent viral skin infections, severe allergies, and early‐onset ...malignancies, associated with mutations in the gene encoding the dedicator of cytokinesis 8 protein (DOCK8). We report a rare case of AR‐HIES with DOCK8 deficiency in a young Japanese male with a past medical history of chronic atopic dermatitis, severe food allergies, and severe herpes simplex virus infection. Treatment was successfully based on infection management, skincare, and dietary elimination. In addition, anti‐IgE therapy with omalizumab was the target treatment for this syndrome.
We report a rare case of AR‐HIES with DOCK8 deficiency in a young male that was successfully treated by infection management, skincare, diet elimination, and omalizumab.
Multisystem inflammatory syndrome is associated with COVID-19 and can result in reduced food intake, increased muscle catabolism, and electrolyte imbalance. Therefore COVID-19 patients are at high ...risk of being malnourished and of refeeding syndrome. The present study aimed to determine the prevalence and correlates of malnutrition and refeeding syndrome (RS) among COVID-19 patients in Hanoi, Vietnam. This prospective cohort study analyzed data from 1207 patients who were treated at the COVID-19 hospital of Hanoi Medical University (HMUH COVID-19) between September 2021 and March 2022. Nutritional status was evaluated by the Global Leadership Initiative on Malnutrition (GLIM) and laboratory markers. GLIM-defined malnutrition was found in 614 (50.9%) patients. Among those with malnutrition, 380 (31.5%) and 234 (19.4%) had moderate and severe malnutrition, respectively. The prevalence of risk of RS was 346 (28.7%). Those with severe and critical COVID symptoms are more likely to be at risk of RS compared to those with mild or moderate COVID, and having severe and critical COVID-19 infection increased the incidence of RS by 2.47 times, compared to mild and moderate disease. There was an association between levels of COVID-19, older ages, comorbidities, the inability of eating independently, hypoalbuminemia and hyponatremia with malnutrition. The proportion of COVID-19 patients who suffered from malnutrition was high. These results underscore the importance of early nutritional screening and assessment in COVID-19 patients, especially those with severe and critical infection.
Active Learning: The Almost Silver Bullet Hicks, Eric; Tran, Quang; Malasri, Kriangsiri ...
2020 12th International Conference on Knowledge and Systems Engineering (KSE),
2020-Nov.-12
Conference Proceeding
Despite its clear benefits, Active Learning (AL) has gone widely unused in favor of traditional teaching models, due to AL's higher costs. With the increased prevalence of software based teaching ...aids, many of these barriers have been removed and new Active Learning strategies can be more easily implemented. We show that AL can be utilized in the classroom to improve work efficiency, better use TAs, and achieve positive gains in student performance and understanding using integrated lab work.
Next generation sequencing technologies have the capability to provide large numbers of short reads inexpensively and accurately. Researchers have proposed many different methods to align short reads ...to reference genomes. Nevertheless, long repeats, which are known to be abundant in eukaiyotic genomes, have caused considerable difficulty for genome assembly methods that rely on short-read alignment. Although a few researchers have studied sequence complexity of genomes in terms of repeats, none have quantitatively related such complexity to the difficulty of short read alignment and assembly. In this paper, we investigate several measures of genome sequence complexity with the goal of quantifying the difficulty of short read alignment Using genomic data from 17 different organisms and testing against 12 state-of-the-art short-read aligners, we found a very strong correlation between the performance of virtually all of these aligners and measures of genome sequence complexity. Further, we show how these measures might be used to analyze and predict the performance of aligners, and more importantly, select the best aligners for specific genomes.
Advances in single-cell technologies have shifted genomics research from the analysis of bulk tissues toward a comprehensive characterization of individual cells. This holds enormous opportunities ...for both basic biology and clinical research. As such, identification and characterization of shortlived progenitors, stem cells, cancer stem cells, or circulating tumor cells are essential to better understand both normal and diseased tissue biology. However, quantifying gene expression in each cell remains a significant challenge due to the low amount of mRNA available within individual cells. This leads to the excess amount of zero counts caused by dropout events. Here we introduce RIA, a regression-based approach, that is able to reliably recover the missing values in single-cell data and thus can effectively improve the performance of downstream analyses. We compare RIA with state-of-the-art methods using five scRNA-seq datasets with a total of 3,535 cells. In each dataset analyzed, RIA outperforms existing approaches in improving the identification of cell populations while preserving the biological landscape. We also demonstrate that RIA is able to infer temporal trajectories of embryonic development stages.
Advances in technologies currently produce more and more cost-effective, high-throughput, and large-scale biological data. As a result, there is an urgent need for developing efficient computational ...methods for analyzing these massive data. In this dissertation, we introduce methods to address several important issues in gene expression and genomic sequence analysis, two of the most important areas in bioinformatics. Firstly, we introduce a novel approach to predicting patterns of gene response to multiple treatments in case of small sample size. Researchers are increasingly interested in experiments with many treatments such as chemicals compounds or drug doses. However, due to cost, many experiments do not have large enough samples, making it difficult for conventional methods to predict patterns of gene response. Here we introduce an approach which exploited dependencies of pairwise comparisons outcomes and resampling techniques to predict true patterns of gene response in case of insufficient samples. This approach deduced more and better functionally enriched gene clusters than conventional methods. Our approach is therefore useful for multiple-treatment studies which have small sample size or contain highly variantly expressed genes. Secondly, we introduce a novel method for aligning short reads, which are DNA fragments extracted across genomes of individuals, to reference genomes. Results from short read alignment can be used for many studies such as measuring gene expression or detecting genetic variants. Here we introduce a method which employed an iterated randomized algorithm based on FM-index, an efficient data structure for full-text indexing, to align reads to the reference. This method improved alignment performance across a wide range of read lengths and error rates compared to several popular methods, making it a good choice for community to perform short read alignment. Finally, we introduce a novel approach to detecting genetic variants such as SNPs (single nucleotide polymorphisms) or INDELs (insertions/deletions). This study has great significance in a wide range of areas, from bioinformatics and genetic research to medical field. For example, one can predict how genomic changes are related to phenotype in their organism of interest, or associate genetic changes to disease risk or medical treatment efficacy. Here we introduce a method which leveraged known genetic variants existing in well-established databases to improve accuracy of detecting variants. This method had higher accuracy than several state-of-the-art methods in many cases, especially for detecting INDELs. Our method therefore has potential to be useful in research and clinical applications which rely on identifying genetic variants accurately.
