INDIGO - INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles.

Background: The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes. Results: We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments. Conclusions: We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.IA and AAK were supported from the KAUST CBRC Base Fund of VBB. WBa and VBB were supported from the KAUST Base Funds of VBB. US was supported by the KAUST Base Fund of US. This study was partly supported by the Saudi Economic and Development Company (SEDCO) Research Excellence award to US and VBB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Identification of Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections among Healthcare Workers at Sultan Qaboos University Hospital, Oman

Objectives: This study aimed to describe the incidence and features of asymptomatic COVID-19 infections among HCWs at a tertiary hospital in Oman. Methods: This cross-sectional study was conducted between August 2020 and February 2021 among HCWs with no history of COVID-19 infection using an online questionnaire to collect sociodemographic and clinical data. COVID-19 infection was diagnosed using nasopharyngeal/throat swabs, which were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Analyses were performed using Chi-squared test, Fisher’s exact test, or univariate ordinary least squares regression, as appropriate. Results: A total of 583 HCWs participated in the study. Most were female (56.6%) and the mean age was 35 ± 8 years . Only 9.6% (95% confidence interval [CI]: 7.3–12.3%) of the HCWs were at high exposure risk as they were directly involved in the care of COVID-19-infected patients. Overall, 4.1% (95% CI: 2.7–6.1%) of the HCWs screened positive for SARS-CoV-2; of these, five (20.8%) developed symptoms within two weeks. The frequency of SARS-CoV-2 positivity among HCWs working in high, intermediate, low, and miscellaneous risk areas was 1.8% (95% CI: <0.1–9.6%), 2.6% (95% CI: <0.1–6.5%), 5.3% (95% CI: 0.3–9.3%), and 4.8% (95% CI: <0.1–69.3%), respectively. Working in high-risk areas was associated with increased compliance with various infection control strategies (P <0.001). Conclusion: There was a greater frequency of SARS-CoV-2 positivity among HCWs working in lower-risk areas, whereas HCWs who worked in high-risk areas were significantly more likely to report increased compliance with infection control strategies. Keywords: SARS-CoV-2; COVID-19 Nucleic Acid Testing; Asymptomatic Infections; Health Personnel; Occupational Exposure; Infection Control; Real-Time Polymerase Chain Reaction; Oman

The implications of model–informed drug discovery and development for tuberculosis

The research leading to these results received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115337, the resources of which comprise financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.Despite promising advances in the field and highly effective first-line treatment, an estimated 9.6 million people are still infected with tuberculosis (TB). Innovative methods are required to effectively transition the growing number of compounds into novel combination regimens. However, progression of compounds into patients occurs despite the lack of clear understanding of the pharmacokinetic-pharmacodynamic (PK/PD) relations. The PreDiCT-TB consortium was established in response to the existing gaps in TB drug development. The aim of the consortium is to develop new preclinical tools in concert with an in silico model-based approach, grounded in PKPD principles. Here, we highlight the potential impact of such an integrated framework on various stages in TB drug development and on the dose rationale for drug combinations.PostprintPeer reviewe

The Creation of the System of Heir-Apparent by Mu’awiyah bin Abu Sufian in (680 A.D) and Reactions against it

The study highlights the political issue that played a major role in shaping the system of government in the Islamic state. This is the creation of the system of “heir apparent” by Mu’awiyah bin Abu Sufian, the Umayyad Caliph. The study follows a descriptive approach based on historical analysis and comparison of accounts, and then follows the reactions against that system. The findings of the study show that the system of “heir apparent” was one of the causes of bloody conflicts and violent strife experienced by the Islamic State, and was, in fact, a major cause of the fall of the Umayyad State in the East, in 132 AH / 750 AD

DETECTION OF PNEUMONIA BY USING NINE PRE-TRAINED TRANSFER LEARNING MODELS BASED ON DEEP LEARNING TECHNIQUES

Pneumonia is a serious chest disease that affects the lungs. This disease has become an important issue that must be taken care of in the field of medicine due to its rapid and intense spread, especially among people who are addicted to smoking. This paper presents an efficient prediction system for detecting pneumonia using nine pre-trained transfer learning models based on deep learning technique (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, and SqueezeNet). The dataset in this study consisted of 5856 chest x-rays, which were divided into 5216 for training and 624 for the test. In the training phase, the images were pre-processed by resizing the input images to the same dimensions to reduce complexity and computation. The images are then forwarded to the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, SqueezeNet) to extract features and classify the images as normal or pneumonia. The results of the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121 DenseNet-169, AlexNet and SqueezeNet) give accuracies (98.72%, 98.94%, 98.88%, 98.72%, 96.2%, 94.69%, 96.29%, 95.01% and 96.10%) respectively. We found that the SeNet-154 model gave the best result with an accuracy of 98.94% with a validation loss (0.018103). When comparing our results with older studies, it should be noted that the proposed method is superior to other methods

Evaluation of statistical approaches for association testing in noisy drug screening data

Background Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. Results To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. Conclusions We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.Peer reviewe

Creating reproducible pharmacogenomic analysis pipelines

BSTRACT"/jats:title""jats:p"The field of Pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community. Document type: Preprin

Assessing the importance of car meanings and attitudes in consumer evaluations of electric vehicles

This paper reports findings from a research study which assesses the importance of attitudinal constructs related to general car attitudes and the meanings attached to car ownership over evaluations of electric vehicles (EVs). The data are assessed using principal component analysis to evaluate the structure of the underlying attitudinal constructs. The identified constructs are then entered into a hierarchical regression analysis which uses either positive or negative evaluations of the instrumental capabilities of EVs as the dependent variable. Results show that attitudinal constructs offer additional predictive power over socioeconomic characteristics and that the symbolic and emotive meanings of car ownership are as, if not more, effective in explaining the assessment of EV instrumental capability as compared to issues of cost and environmental concern. Additionally, the more important an individual considers their car to be in their everyday life, the more negative their evaluations are of EVs whilst individuals who claim to be knowledgeable about cars in general and EVs in particular have a lower propensity for negative EV attitudes. However, positive and negative EV attitudes are related to different attitudinal constructs suggesting that it is possible for someone to hold both negative and positive assessments at the same time

A multi-template multiplex PCR assay for hepatitis B virus and human β-globin

The Hepatitis B surface antigen (HBsAg) is the hallmark of HBV infection. Detection of antibodies to HBs and the core (i.e. HBsAg and HBcAb) are primary serological algorithms in the laboratory diagnosis of HBV. Detection of HBsAg DNA is an important supplement to serological diagnosis especially in clinical cases. Simultaneous amplification of internal cellular controls is a good indicator of sample quality. Human P-globin is a well characterised housekeeping gene (HKG) that is often applied as internal controls (IC) in molecular diagnosis. In this study, individual plasmid clones of the human P-globin and HBs genes were constructed. These plasmid constructs have been applied to characterise a multiplex PCR assays for HBs and P-globin genes. The findings suggest detection limits of less than 10 genome copies of either template In vitro using conventional and multiplex PCR conditions. Under the multiplex conditions, co-amplification of P-globin and HBsAg DNA had a resultant effect on assay sensitivity. This study further highlights the importance of molecular diagnosis in HBV infectious individuals. If fully optimised, this assay could provide a possible diagnostic complement to serological detection in developing countries

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe