Vibrational properties of hexagonal LiBC: Infrared and Raman spectroscopy

The paper presents infrared reflectivity and micro-Raman scattering spectra of LiBC powder pellets. The experiment allowed assignment of frequencies of all infrared and Raman active zone center modes. Results are compared with available ab-initio calculations; prediction of large Born effective charges on the nodes of B-C graphene sheets is confirmed.Comment: 4 pages, 5 figures (change: Fig 2 replaced

MetAMOS: A modular and open source metagenomic assembly and analysis pipeline

© 2013 Treangen et al. We describe MetAMOS, an open source and modular metagenomic assembly and analysis pipeline. MetAMOS represents an important step towards fully automated metagenomic analysis, starting with next-generation sequencing reads and producing genomic scaffolds, open-reading frames and taxonomic or functional annotations. MetAMOS can aid in reducing assembly errors, commonly encountered when assembling metagenomic samples, and improves taxonomic assignment accuracy while also reducing computational cost. MetAMOS can be downloaded from: https://github.com/treangen/MetAMOS

STRONG: metagenomics strain resolution on assembly graphs

We introduce STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, from multiple metagenome samples. STRONG performs coassembly, and binning into metagenome assembled genomes (MAGs), and stores the coassembly graph prior to variant simplification. This enables the subgraphs and their unitig per-sample coverages, for individual single-copy core genes (SCGs) in each MAG, to be extracted. A Bayesian algorithm, BayesPaths, determines the number of strains present, their haplotypes or sequences on the SCGs, and abundances. STRONG is validated using synthetic communities and for a real anaerobic digestor time series generates haplotypes that match those observed from long Nanopore reads

Corrigendum to “Counting adolescents in: the development of an adolescent health indicator framework for population-based settings”

The authors were recently made aware of an oversight such that parts of the text in the Introduction and Methods sections, which describe shortcomings in the existing literature and the methods in this work to identify frameworks and indicators, were missing attribution to published work cited elsewhere in the manuscript. To clarify, we adjust the relevant sections to fully attribute the prior work in three areas, as described below. Underlined text is additional to the original: While both school- and community-based modalities can provide nationally representative data among eligible adolescents, several shortcomings in adolescent health measurement in LMICs were noted by the GAMA Advisory Group (Reference 13 as in the original paper). First, these measurements do not equally cover all adolescent subgroups, with evidence gaps being largest for males, younger adolescents aged 10–14 years, adolescents of diverse genders, ethnicities, and religions, as well as those out of school and migrants. Second, age-disaggregated data are often lacking—due in part to incomplete age coverage—limiting their use for program planning. Third, several aspects of adolescent health are inadequately covered including mental health, substance use, injury, sexual and reproductive health among unmarried adolescents, and positive aspects of adolescent health and well-being. Fourth, the definitions and assessment methods used across adolescent health indicator frameworks are inconsistent. For example, adolescent overweight and obesity—a major cause of non-communicable diseases and a public health risk for future and intergeneration health—is inconsistently captured across indicator frameworks and strikingly absent from the SDGs (Reference 13 as in the original paper). Additional shortcomings include, current adolescent health data systems often lack intersectoral coordination beyond health (e.g., with education, water and sanitation, and social protection systems) and suffer from irregularities in coverage and timing (Reference 6 as in the original paper). Broadly, these indicator frameworks and strategy documents captured disease burden, health risks, and prominent social determinants of health during adolescence. To be congruent with the existing global recommendations and guidelines (References 3–7 as in the original paper) and global measurement efforts (References 10 and 16 as in the original paper), the indicator framework documents had to meet three inclusion criteria, as laid out by the GAMA Advisory Group (Reference 14 as in the original paper): (1) provide recommendations about the measurement of adolescents' health and well-being; (2) include indicators for “adolescents” covering the adolescent age range (10–19 years) in the whole or part; and (3) be global or regional in scope. Using the GAMA's approach (Reference 13 as in the original paper), the recommendations of Lancet Adolescent Health Commission (Reference 6 as in the original paper), and several other guidelines (References 7, 9, 12, 17–19 as in the original paper), we selected adolescent health and well-being domains based on four key aspects of adolescents in LMICs: a) population trends; b) disease burden; c) drivers of health inequality; and d) opportunity for interventions

Counting adolescents in: the development of an adolescent health indicator framework for population-based settings

Changing realities in low- and middle-income countries (LMICs) in terms of inequalities, urbanization, globalization, migration, and economic adversity shape adolescent development and health, as well as successful transitions between adolescence and young adulthood. It is estimated that 90% of adolescents live in LMICs in 2019, but inadequate data exist to inform evidence-based and concerted policies and programs tailored to address the distinctive developmental and health needs of adolescents. Population-based data surveillance such as Health and Demographic Surveillance Systems (HDSS) and school-based surveys provide access to a well-defined population and provide cost-effective opportunities to fill in data gaps about adolescent health and well-being by collecting population-representative longitudinal data. The Africa Research Implementation Science and Education (ARISE) Network, therefore, systematically developed adolescent health and well-being indicators and a questionnaire for measuring these indicators that can be used in population-based LMIC settings. We conducted a multistage collaborative and iterative process led by network members alongside consultation with health-domain and adolescent health experts globally. Seven key domains emerged from this process: socio-demographics, health awareness and behaviors; nutrition; mental health; sexual and reproductive health; substance use; and healthcare utilization. For each domain, we generated a clear definition; rationale for inclusion; sub-domain descriptions, and a set of questions for measurement. The ARISE Network will implement the questionnaire longitudinally (i.e., at two time-points one year apart) at ten sites in seven countries in sub-Saharan Africa and two countries in Asia. Integrating the questionnaire within established population-based data collection platforms such as HDSS and school settings can provide measured experiences of young people to inform policy and program planning and evaluation in LMICs and improve adolescent health and well-being

Nitrogen fixation in a landrace of maize is supported by a mucilage-associated diazotrophic microbiota

© 2018 Van Deynze et al. http://creativecommons.org/licenses/by/4.0/. Plants are associated with a complex microbiota that contributes to nutrient acquisition, plant growth, and plant defense. Nitrogen-fixing microbial associations are efficient and well characterized in legumes but are limited in cereals, including maize. We studied an indigenous landrace of maize grown in nitrogen-depleted soils in the Sierra Mixe region of Oaxaca, Mexico. This landrace is characterized by the extensive development of aerial roots that secrete a carbohydrate-rich mucilage. Analysis of the mucilage microbiota indicated that it was enriched in taxa for which many known species are diazotrophic, was enriched for homologs of genes encoding nitrogenase subunits, and harbored active nitrogenase activity as assessed by acetylene reduction and 15 N 2 incorporation assays. Field experiments in Sierra Mixe using 15 N natural abundance or 15 N-enrichment assessments over 5 years indicated that atmospheric nitrogen fixation contributed 29%–82% of the nitrogen nutrition of Sierra Mixe maize

Study of evaluation of groundwater in Gadilam basin using hydrogeochemical and isotope data

Gadilam river basin has gained its importance due to the presence of Neyveli Lignite open cast mines and other industrial complexes. It is also due to extensive depressurization of Cuddalore aquifer, and bore wells for New Veeranam Scheme are constructed downstream of the basin. Geochemical indicators of groundwater were used to identify the chemical processes that control hydrogeochemistry. Chemical parameters of groundwater such as pH, electrical conductivity, total dissolved solids, sodium (Na+), potassium (K+), calcium (Ca+), magnesium (Mg+), bicarbonate (HCO-3 ), sulfate (SO-4 ),phosphate (PO-4 ), and silica (H4SiO4) were determined. Interpretation of hydrogeochemical data suggests that leaching of ions followed by weathering and anthropogenic impact controls the chemistry of the groundwater. Isotopic study reveals that recharge from meteoric source in sedimentary terrain and rock-water interaction with significant evaporation prevails in hard rock region

Comparative genomic analysis of toxin-negative strains of Clostridium difficile from humans and animals with symptoms of gastrointestinal disease

Background: Clostridium difficile infections (CDI) are a significant health problem to humans and food animals. Clostridial toxins ToxA and ToxB encoded by genes tcdA and tcdB are located on a pathogenicity locus known as the PaLoc and are the major virulence factors of C. difficile. While toxin-negative strains of C. difficile are often isolated from faeces of animals and patients suffering from CDI, they are not considered to play a role in disease. Toxin-negative strains of C. difficile have been used successfully to treat recurring CDI but their propensity to acquire the PaLoc via lateral gene transfer and express clinically relevant levels of toxins has reinforced the need to characterise them genetically. In addition, further studies that examine the pathogenic potential of toxin-negative strains of C. difficile and the frequency by which toxin-negative strains may acquire the PaLoc are needed. Results: We undertook a comparative genomic analysis of five Australian toxin-negative isolates of C. difficile that lack tcdA, tcdB and both binary toxin genes cdtA and cdtB that were recovered from humans and farm animals with symptoms of gastrointestinal disease. Our analyses show that the five C. difficile isolates cluster closely with virulent toxigenic strains of C. difficile belonging to the same sequence type (ST) and have virulence gene profiles akin to those in toxigenic strains. Furthermore, phage acquisition appears to have played a key role in the evolution of C. difficile. Conclusions: Our results are consistent with the C. difficile global population structure comprising six clades each containing both toxin-positive and toxin-negative strains. Our data also suggests that toxin-negative strains of C. difficile encode a repertoire of putative virulence factors that are similar to those found in toxigenic strains of C. difficile, raising the possibility that acquisition of PaLoc by toxin-negative strains poses a threat to human health. Studies in appropriate animal models are needed to examine the pathogenic potential of toxin-negative strains of C. difficile and to determine the frequency by which toxin-negative strains may acquire the PaLoc