Ab initio determination of the lifetime of the 62P3/26^2P_{3/2} state f or 207Pb+^{207}Pb^+ by relativistic many-body theory

Relativistic coupled-cluster(RCC) theory has been employed to calculate the life time of the $6 ^2P_{3/2}$ state of single ionized lead($^{207}Pb$) to an accurac y of 3% and compared with the corresponding value obtained using second order r elativistic many-body perturbation theory(RMBPT). This is one of the very few ap plications of this theory to excited state properties of heavy atomic systems. C ontributions from the different electron correlation effects are given explicitl y

Two new rapid SNP-typing methods for classifying Mycobacterium tuberculosis complex into the main phylogenetic lineages

There is increasing evidence that strain variation in Mycobacterium tuberculosis complex (MTBC) might influence the outcome of tuberculosis infection and disease. To assess genotype-phenotype associations, phylogenetically robust molecular markers and appropriate genotyping tools are required. Most current genotyping methods for MTBC are based on mobile or repetitive DNA elements. Because these elements are prone to convergent evolution, the corresponding genotyping techniques are suboptimal for phylogenetic studies and strain classification. By contrast, single nucleotide polymorphisms (SNP) are ideal markers for classifying MTBC into phylogenetic lineages, as they exhibit very low degrees of homoplasy. In this study, we developed two complementary SNP-based genotyping methods to classify strains into the six main human-associated lineages of MTBC, the 'Beijing' sublineage, and the clade comprising Mycobacterium bovis and Mycobacterium caprae. Phylogenetically informative SNPs were obtained from 22 MTBC whole-genome sequences. The first assay, referred to as MOL-PCR, is a ligation-dependent PCR with signal detection by fluorescent microspheres and a Luminex flow cytometer, which simultaneously interrogates eight SNPs. The second assay is based on six individual TaqMan real-time PCR assays for singleplex SNP-typing. We compared MOL-PCR and TaqMan results in two panels of clinical MTBC isolates. Both methods agreed fully when assigning 36 well-characterized strains into the main phylogenetic lineages. The sensitivity in allele-calling was 98.6% and 98.8% for MOL-PCR and TaqMan, respectively. Typing of an additional panel of 78 unknown clinical isolates revealed 99.2% and 100% sensitivity in allele-calling, respectively, and 100% agreement in lineage assignment between both methods. While MOL-PCR and TaqMan are both highly sensitive and specific, MOL-PCR is ideal for classification of isolates with no previous information, whereas TaqMan is faster for confirmation. Furthermore, both methods are rapid, flexible and comparably inexpensive

First insights into the phylogenetic diversity of Mycobacterium tuberculosis in Nepal

BACKGROUND: Tuberculosis (TB) is a major public health problem in Nepal. Strain variation in Mycobacterium tuberculosis may influence the outcome of TB infection and disease. To date, the phylogenetic diversity of M. tuberculosis in Nepal is unknown. METHODS AND FINDINGS: We analyzed 261 M. tuberculosis isolates recovered from pulmonary TB patients recruited between August 2009 and August 2010 in Nepal. M. tuberculosis lineages were determined by single nucleotide polymorphisms (SNP) typing and spoligotyping. Drug resistance was determined by sequencing the hot spot regions of the relevant target genes. Overall, 164 (62.8%) TB patients were new, and 97 (37.2%) were previously treated. Any drug resistance was detected in 50 (19.2%) isolates, and 16 (6.1%) were multidrug-resistant. The most frequent M. tuberculosis lineage was Lineage 3 (CAS/Delhi) with 106 isolates (40.6%), followed by Lineage 2 (East-Asian lineage, includes Beijing genotype) with 84 isolates (32.2%), Lineage 4 (Euro-American lineage) with 41 (15.7%) isolates, and Lineage 1 (Indo-Oceanic lineage) with 30 isolates (11.5%). Based on spoligotyping, we found 45 different spoligotyping patterns that were previously described. The Beijing (83 isolates, 31.8%) and CAS spoligotype (52, 19.9%) were the dominant spoligotypes. A total of 36 (13.8%) isolates could not be assigned to any known spoligotyping pattern. Lineage 2 was associated with female sex (adjusted odds ratio [aOR] 2.58, 95% confidence interval [95% CI] 1.42-4.67, p = 0.002), and any drug resistance (aOR 2.79; 95% CI 1.43-5.45; p = 0.002). We found no evidence for an association of Lineage 2 with age or BCG vaccination status. CONCLUSIONS: We found a large genetic diversity of M. tuberculosis in Nepal with representation of all four major lineages. Lineages 3 and 2 were dominating. Lineage 2 was associated with clinical characteristics. This study fills an important gap on the map of the M. tuberculosis genetic diversity in the Asian reg

CRISPR Inhibition of Prophage Acquisition in Streptococcus pyogenes

Streptococcus pyogenes, one of the major human pathogens, is a unique species since it has acquired diverse strain-specific virulence properties mainly through the acquisition of streptococcal prophages. In addition, S. pyogenes possesses clustered regularly interspaced short palindromic repeats (CRISPR)/Cas systems that can restrict horizontal gene transfer (HGT) including phage insertion. Therefore, it was of interest to examine the relationship between CRISPR and acquisition of prophages in S. pyogenes. Although two distinct CRISPR loci were found in S. pyogenes, some strains lacked CRISPR and these strains possess significantly more prophages than CRISPR harboring strains. We also found that the number of spacers of S. pyogenes CRISPR was less than for other streptococci. The demonstrated spacer contents, however, suggested that the CRISPR appear to limit phage insertions. In addition, we found a significant inverse correlation between the number of spacers and prophages in S. pyogenes. It was therefore suggested that S. pyogenes CRISPR have permitted phage insertion by lacking its own spacers. Interestingly, in two closely related S. pyogenes strains (SSI-1 and MGAS315), CRISPR activity appeared to be impaired following the insertion of phage genomes into the repeat sequences. Detailed analysis of this prophage insertion site suggested that MGAS315 is the ancestral strain of SSI-1. As a result of analysis of 35 additional streptococcal genomes, it was suggested that the influences of the CRISPR on the phage insertion vary among species even within the same genus. Our results suggested that limitations in CRISPR content could explain the characteristic acquisition of prophages and might contribute to strain-specific pathogenesis in S. pyogenes

Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages

Generalist and specialist species differ in the breadth of their ecological niches. Little is known about the niche width of obligate human pathogens. Here we analyzed a global collection of Mycobacterium tuberculosis lineage 4 clinical isolates, the most geographically widespread cause of human tuberculosis. We show that lineage 4 comprises globally distributed and geographically restricted sublineages, suggesting a distinction between generalists and specialists. Population genomic analyses showed that, whereas the majority of human T cell epitopes were conserved in all sublineages, the proportion of variable epitopes was higher in generalists. Our data further support a European origin for the most common generalist sublineage. Hence, the global success of lineage 4 reflects distinct strategies adopted by different sublineages and the influence of human migration.We thank S. Lecher, S. Li and J. Zallet for technical support. Calculations were performed at the sciCORE scientific computing core facility at the University of Basel. This work was supported by the Swiss National Science Foundation (grants 310030_166687 (S.G.) and 320030_153442 (M.E.) and Swiss HIV Cohort Study grant 740 to L.F.), the European Research Council (309540-EVODRTB to S.G.), TB-PAN-NET (FP7-223681 to S.N.), PathoNgenTrace projects (FP7-278864-2 to S.N.), SystemsX.ch (S.G.), the German Center for Infection Research (DZIF; S.N.), the Novartis Foundation (S.G.), the Natural Science Foundation of China (91631301 to Q.G.), and the National Institute of Allergy and Infectious Diseases (5U01-AI069924-05) of the US National Institutes of Health (M.E.)

The Human Serum Metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

MULTIDIMENSIONAL POVERTY IN BAJHANG DISTRICT OF NEPAL

Multidimensional poverty is currently at the heart of many theoretical, empirical and institutional debates. South Asia has the world's highest levels of poverty where 49% of people are multidimensionally poor. Poor and vulnerable households are predominantly in rural and mountainous areas of Nepal. This is a case study conducted in Bajhang district of Nepal, where we have selected five Village Development Committees to conduct our research. Well-structured questionnaires were entertained as face to face interview. This paper applies Alkire-Foster Methodology 2011 for measuring the multidimensional poverty. At poverty cut off, k=3, it was found that 49.7% of people are multidimensionally poor. Dimension wise breakdown shows that cooking fuel, flooring, nutrition, electricity, child mortality and schooling have major contributors among overall multidimensional poverty. This research is important for policy makers to provide much clearer guidance for anti-poverty policy on the basis of different dimensions, climatic zones, areas and demographic distributions