Nepalese trekking guides: A quantitative study of sexual health knowledge and sexual behaviour

Background: Tourism, a global industry, brings with it a number of public health problems, one of which is the spread of sexually transmitted infections transmitted between travellers and hosts. Previous studies have largely focused on sex workers and sex tourists. This study assesses sexual behaviour, knowledge and condom use among male trekking guides in Nepal. Methods: A self-administered questionnaire survey (n=324) was conducted using snowball sampling amongst men working as mountain trekking guides in Nepal. Results: Most respondents (59%) had initiated sex before the age of 18. Most (84 %) reported sexual relations with a woman other than their partner, 46% reported foreign partners, 43% had Nepalese partners, and 28% had concurrent foreign and Nepalese partners. Most (70 %) reported ever having sex with a foreign woman and two-thirds had had sexual intercourse with foreign women in the previous 12 months. Participants’ age, education status, age of first sex, smoking and drinking habits and English proficiency were significant predictors of having sex with foreign women. About 60% reported condom use during their most recent occasion of extra-martial sex. A similar proportion had used a condom during last sexual intercourse with a foreign woman. The likelihood of condom use was associated with a guide’s age, educational level, ethnicity, age of first sex and work experience. Conclusions: Most trekking guides reported sexual relations with foreign women as well as irregular use of condoms. Although sexual health knowledge about among trekking guides is high, some misconceptions still result in unsafe sex. Hence there is an urgent need to revise the existing training for trekking guides and implement appropriate health promotion programmes

Structural basis of RNA processing by human mitochondrial RNase P

Human mitochondrial transcripts contain messenger and ribosomal RNAs flanked by transfer RNAs (tRNAs), which are excised by mitochondrial RNase (mtRNase) P and Z to liberate all RNA species. In contrast to nuclear or bacterial RNase P, mtRNase P is not a ribozyme but comprises three protein subunits that carry out RNA cleavage and methylation by unknown mechanisms. Here, we present the cryo-EM structure of human mtRNase P bound to precursor tRNA, which reveals a unique mechanism of substrate recognition and processing. Subunits TRMT10C and SDR5C1 form a subcomplex that binds conserved mitochondrial tRNA elements, including the anticodon loop, and positions the tRNA for methylation. The endonuclease PRORP is recruited and activated through interactions with its PPR and nuclease domains to ensure precise pre-tRNA cleavage. The structure provides the molecular basis for the first step of RNA processing in human mitochondria

Genome-Wide Association Study Reveals Novel Genomic Regions for Grain Yield and Yield-Related Traits in Drought-Stressed Synthetic Hexaploid Wheat

Synthetic hexaploid wheat (SHW; 2n = 6x = 42, AABBDD, Triticum aestivum L.) is produced from an interspecific cross between durum wheat (2n = 4x = 28, AABB, T. turgidum L.) and goat grass (2n = 2x = 14, DD, Aegilops tauschii Coss.) and is reported to have significant novel alleles-controlling biotic and abiotic stresses resistance. A genome-wide association study (GWAS) was conducted to unravel these loci [marker–trait associations (MTAs)] using 35,648 genotyping-by-sequencing-derived single nucleotide polymorphisms in 123 SHWs. We identified 90 novel MTAs (45, 11, and 34 on the A, B, and D genomes, respectively) and haplotype blocks associated with grain yield and yield-related traits including root traits under drought stress. The phenotypic variance explained by the MTAs ranged from 1.1% to 32.3%. Most of the MTAs (120 out of 194) identified were found in genes, and of these 45 MTAs were in genes annotated as having a potential role in drought stress. This result provides further evidence for the reliability of MTAs identified. The large number of MTAs (53) identified especially on the D-genome demonstrate the potential of SHWs for elucidating the genetic architecture of complex traits and provide an opportunity for further improvement of wheat under rapidly changing climatic conditions

Estimating global warming potential for agricultural landscapes with minimal field data and cost

Greenhouse gas (GHG) emissions from agriculture comprise 10-12% of anthropocentric global emissions; and 76% of the agricultural emissions are generated in the developing world. Landscape GHG accounting is an effective way to efficiently develop baseline emissions and appropriate mitigation approaches. In a 9,736-hectare case study area dominated by rice and wheat in the Karnal district of Haryana state, India, the authors used a low-cost landscape agricultural GHG accounting method with limited fieldwork, remote sensing, and biogeochemical modeling. We used the DeNitrification-DeComposition (DNDC) model software to simulate crop growth and carbon and nitrogen cycling to estimate net GHG emissions, with information based on the mapping of cropping patterns over time using multi- resolution and multi-temporal optical remote sensing imagery. We estimated a mean net emission of 78,620 tCO2e/yr (tons of carbon dioxide equivalents per year) with a 95% confidence interval of 51,212-106,028 tCO2e/yr based on uncertainties in our crop mapping and soil data. A modeling sensitivity analysis showed soil clay fraction, soil organic carbon fraction, soil density, and nitrogen amendments to be among the most sensitive factors, and therefore critical to capture in field surveys. We recommend a multi-phase approach to increase efficiency and reduce cost in GHG accounting. Field campaigns and aspects of remote sensing image characteristics can be optimized for targeted landscapes through solid background research. An appropriate modeling approach can be selected based on crop and soil characteristics. Soil data in developing world landscapes remain a significant source of uncertainty for studies like these and should remain a key research and data development effort

Effect of Graded Levels of Condensed Tannin (CT) from \u3cem\u3eMimosa pudica\u3c/em\u3e on \u3cem\u3ein-Vitro\u3c/em\u3e Methane Production

Livestock in the country are primarily being fed on fibrous feed resulted in high enteric methane (CH4) emission along with low nutrients availability to host animal. Rumen methano genesis is necessary for the host system as this process ensure the removal of fermentative H2 through the reduction of CO2 into CH4. At the same time this process is wasteful because the emission also represents a loss of dietary energy (6-12% of gross energy intake) apart from contributing to global warming. Worldwide livestock contribute around 90-95 Tg methane to the pool with a contribution of 12-13% from the Indian livestock. Various nutritional and other approaches have been attempted with highly variable success rate in the country and elsewhere for the enteric methane amelioration. The cost of the item used for the mitigation purpose, adaptation of ruminal microbes and toxicity to either host animal or inhabiting microbes are few important criteria for an economic, sustainable and effective amelioration approach (Malik et al. 2015). Herbal materials are being used by the peoples since ages; however, their anti-methanogenic effect is recently established. The anti-methanogenic effect of different herbal materials mainly lies in their secondary metabolites which are highly effective even at very low concentration (Bhatta et al., 2014). Keeping these facts in view, a study was carried to ascertain the effect of varying levels of CT on in vitro total gas and methane production

Unlocking the novel genetic diversity and population structure of synthetic Hexaploid wheat

Background: Synthetic hexaploid wheat (SHW) is a reconstitution of hexaploid wheat from its progenitors (Triticum turgidum ssp. durum L.; AABB x Aegilops tauschii Coss.; DD) and has novel sources of genetic diversity for broadening the genetic base of elite bread wheat (BW) germplasm (T. aestivum L). Understanding the diversity and population structure of SHWs will facilitate their use in wheat breeding programs. Our objectives were to understand the genetic diversity and population structure of SHWs and compare the genetic diversity of SHWs with elite BW cultivars and demonstrate the potential of SHWs to broaden the genetic base of modern wheat germplasm. Results: The genotyping-by-sequencing of SHW provided 35,939 high-quality single nucleotide polymorphisms (SNPs) that were distributed across the A (33%), B (36%), and D (31%) genomes. The percentage of SNPs on the D genome was nearly same as the other two genomes, unlike in BW cultivars where the D genome polymorphism is generally much lower than the A and B genomes. This indicates the presence of high variation in the D genome in the SHWs. The D genome gene diversity of SHWs was 88.2% higher than that found in a sample of elite BW cultivars. Population structure analysis revealed that SHWs could be separated into two subgroups, mainly differentiated by geographical location of durum parents and growth habit of the crop (spring and winter type). Further population structure analysis of durum and Ae. parents separately identified two subgroups, mainly based on type of parents used. Although Ae. tauschii parents were divided into two sub-species: Ae. tauschii ssp. tauschii and ssp. strangulate, they were not clearly distinguished in the diversity analysis outcome. Population differentiation between SHWs (Spring_SHW and Winter_SHW) samples using analysis of molecular variance indicated 17.43% of genetic variance between populations and the remainder within populations. Conclusions: SHWs were diverse and had a clearly distinguished population structure identified through GBS-derived SNPs. The results of this study will provide valuable information for wheat genetic improvement through inclusion of novel genetic variation and is a prerequisite for association mapping and genomic selection to unravel economically important marker-trait associations and for cultivar development

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing