Extensive microbial and functional diversity within the chicken cecal microbiome

Chickens are major source of food and protein worldwide. Feed conversion and the health of chickens relies on the largely unexplored complex microbial community that inhabits the chicken gut, including the ceca. We have carried out deep microbial community profiling of the microbiota in twenty cecal samples via 16S rRNA gene sequences and an in-depth metagenomics analysis of a single cecal microbiota. We recovered 699 phylotypes, over half of which appear to represent previously unknown species. We obtained 648,251 environmental gene tags (EGTs), the majority of which represent new species. These were binned into over two-dozen draft genomes, which included Campylobacter jejuni and Helicobacter pullorum. We found numerous polysaccharide- and oligosaccharide-degrading enzymes encoding within the metagenome, some of which appeared to be part of polysaccharide utilization systems with genetic evidence for the co-ordination of polysaccharide degradation with sugar transport and utilization. The cecal metagenome encodes several fermentation pathways leading to the production of short-chain fatty acids, including some with novel features. We found a dozen uptake hydrogenases encoded in the metagenome and speculate that these provide major hydrogen sinks within this microbial community and might explain the high abundance of several genera within this microbiome, including Campylobacter, Helicobacter and Megamonas

STT-BNN: A Novel Energy-efficient and Scalable BNN Accelerator Based on STT-MRAM

IEEE ISCAS 2021United State

Effects of two types of smartphone-based stress management programmes on depressive and anxiety symptoms among hospital nurses in Vietnam: a protocol for three-arm randomised controlled trial

INTRODUCTION: Due to an increasing demand for healthcare in low-income and middle-income countries in Asia, it is important to develop a strategy to manage work-related stress in healthcare settings, particularly among nurses in these countries. The purpose of this three-arm randomised controlled trial (RCT) is to examine the effects of a newly developed smartphone-based multimodule stress management programme on reducing severity of depressive and anxiety symptoms as primary outcomes at 3-month and 7-month follow-ups among hospital nurses in Vietnam. METHODS AND ANALYSIS: The target study population will be registered nurses working in a large general hospital (which employs approximately about 2000 nurses) in Vietnam. They will be invited to participate in this study. Participants who fulfil the eligibility criteria will be randomly allocated to the free-choice, multimodule stress management (intervention group A, n=360), the internet cognitive behavioural therapy (iCBT), that is, fixed-order stress management (intervention group B, n=360), or a treatment as usual control group (n=360). Two types (free-choice and fixed sequential order) of smartphone-based six-module stress management programmes will be developed. Participants in the intervention groups will be required to complete one of the programmes within 10 weeks after the baseline survey. The primary outcomes are depressive and anxiety symptoms, measured by using the Depression Anxiety and Stress Scales (DASS) at 3-month and 7 month follow-ups. ETHICS AND DISSEMINATION: The study procedures have been approved by the Research Ethics Review Board of Graduate School of Medicine/Faculty of Medicine, the University of Tokyo (no 11991) and the Ethical Review Board for Biomedical Research of Hanoi University of Public Health (no 346/2018/YTCC-HD3). If a significant effect of the intervention programmes will be found in the RCT, the programmes will be made available to all nurses in the hospital including the control group. If the positive effects are found in this RCT, the e-stress management programmes will be disseminated to all nurses in Vietnam. TRIAL REGISTRATION NUMBER: UMIN000033139; Pre-results

Genetic variants of MICB and PLCE1 and associations with non-severe dengue

BACKGROUND: A recent genome-wide association study (GWAS) identified susceptibility loci for dengue shock syndrome (DSS) at MICB rs3132468 and PLCE1 rs3740360. The aim of this study was to define the extent to which MICB (rs3132468) and PLCE1 (rs3740360) were associated with less severe clinical phenotypes of pediatric and adult dengue. METHODS: 3961 laboratory-confirmed dengue cases and 5968 controls were genotyped at MICB rs3132468 and PLCE1 rs3740360. Per-allele odds ratios (OR) with 95% confidence intervals (CI) were calculated for each patient cohort. Pooled analyses were performed for adults and paediatrics respectively using a fixed effects model. RESULTS: Pooled analysis of the paediatric and adult cohorts indicated a significant association between MICB rs3132468 and dengue cases without shock (OR  =  1.15; 95%CI: 1.07 - 1.24; P  =  0.0012). Similarly, pooled analysis of pediatric and adult cohorts indicated a significant association between dengue cases without shock and PLCE1 rs3740360 (OR  =  0.92; 95%CI: 0.85 - 0.99; P  =  0.018). We also note significant association between both SNPs (OR  =  1.48; P  =  0.0075 for MICB rs3132468 and OR  =  0.75, P  =  0.041 for PLCE1 rs3740360) and dengue in infants. DISCUSSION: This study confirms that the MICB rs3132468 and PLCE1 rs3740360 risk genotypes are not only associated with DSS, but are also associated with less severe clinical phenotypes of dengue, as well as with dengue in infants. These findings have implications for our understanding of dengue pathogenesis

Sensitivity and specificity of a novel classifier for the early diagnosis of dengue.

BackgroundDengue is the commonest arboviral disease of humans. An early and accurate diagnosis of dengue can support clinical management, surveillance and disease control and is central to achieving the World Health Organisation target of a 50% reduction in dengue case mortality by 2020. Methods5729 children with fever of &lt;72 hrs duration were enrolled into this multicenter prospective study in southern Vietnam between 2010-2012. A composite of gold standard diagnostic tests identified 1692 dengue cases. Using statistical methods, a novel Early Dengue Classifier (EDC) was developed that used patient age, white blood cell count and platelet count to discriminate dengue cases from non-dengue cases. ResultsThe EDC had a sensitivity of 74.8% (95%CI: 73.0-76.8%) and specificity of 76.3% (95%CI: 75.2-77.6%) for the diagnosis of dengue. As an adjunctive test alongside NS1 rapid testing, sensitivity of the composite test was 91.6% (95%CI: 90.4-92.9%). ConclusionsWe demonstrate that the early diagnosis of dengue can be enhanced beyond the current standard of care using a simple evidence-based algorithm. The results should support patient management and clinical trials of specific therapies.</p

Sensitivity and specificity of a novel classifier for the early diagnosis of dengue.

BackgroundDengue is the commonest arboviral disease of humans. An early and accurate diagnosis of dengue can support clinical management, surveillance and disease control and is central to achieving the World Health Organisation target of a 50% reduction in dengue case mortality by 2020. Methods5729 children with fever of andlt;72 hrs duration were enrolled into this multicenter prospective study in southern Vietnam between 2010-2012. A composite of gold standard diagnostic tests identified 1692 dengue cases. Using statistical methods, a novel Early Dengue Classifier (EDC) was developed that used patient age, white blood cell count and platelet count to discriminate dengue cases from non-dengue cases. ResultsThe EDC had a sensitivity of 74.8% (95%CI: 73.0-76.8%) and specificity of 76.3% (95%CI: 75.2-77.6%) for the diagnosis of dengue. As an adjunctive test alongside NS1 rapid testing, sensitivity of the composite test was 91.6% (95%CI: 90.4-92.9%). ConclusionsWe demonstrate that the early diagnosis of dengue can be enhanced beyond the current standard of care using a simple evidence-based algorithm. The results should support patient management and clinical trials of specific therapies.</p

Modeling the impact on virus transmission of Wolbachia-mediated blocking of dengue virus infection of Aedes aegypti

Dengue is the most common arboviral infection of humans and is a public health burden in more than 100 countries. Aedes aegypti mosquitoes stably infected with strains of the intracellular bacterium Wolbachia are resistant to dengue virus (DENV) infection and are being tested in field trials. To mimic field conditions, we experimentally assessed the vector competence of A. aegypti carrying the Wolbachia strains wMel and wMelPop after challenge with viremic blood from dengue patients. We found that wMelPop conferred strong resistance to DENV infection of mosquito abdomen tissue and largely prevented disseminated infection. wMel conferred less resistance to infection of mosquito abdomen tissue, but it did reduce the prevalence of mosquitoes with infectious saliva. A mathematical model of DENV transmission incorporating the dynamics of viral infection in humans and mosquitoes was fitted to the data collected. Model predictions suggested that wMel would reduce the basic reproduction number, R0, of DENV transmission by 66 to 75%. Our results suggest that establishment of wMelPop-infected A. aegypti at a high frequency in a dengue-endemic setting would result in the complete abatement of DENV transmission. Establishment of wMel-infected A. aegypti is also predicted to have a substantial effect on transmission that would be sufficient to eliminate dengue in low or moderate transmission settings but may be insufficient to achieve complete control in settings where R0 is high. These findings develop a framework for selecting Wolbachia strains for field releases and for calculating their likely impact

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets

Objective: Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods: Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results: Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions: Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated

Applying machine learning to identify anti‐vaccination tweets during the covid‐19 pandemic

Anti‐vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti‐vaccination content widely available on social media, including Twitter. Being able to identify anti‐vaccination tweets could provide useful information for formulating strategies to reduce anti‐vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti‐vaccination tweets that were published during the COVID‐19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long shortterm memory networks with pre‐trained GLoVe embeddings (Bi‐LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi‐LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi‐LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti‐vaccination tweets in future studies