The association of women's empowerment with stillbirths in Nepal.

INTRODUCTION: Globally, 2.6 million stillbirths occur each year. Empowering women can improve their overall reproductive health and help reduce stillbirths. Women empowerment has been defined as women's ability to make choices in economic decision-making, household and health care decision-making. In this paper, we aimed to evaluate if women's empowerment is associated with stillbirths. METHODS: Data from 2016 Nepal Demographic Health Surveys (NDHS) were analysed to evaluate the association between women's empowerment and stillbirths. Equiplots were generated to assess the distribution of stillbirths by wealth quintile, place of residence and level of maternal education using data from NHDS 1996, 2001, 2006, 2011 and 2016 data. For the association of women empowerment factors and stillbirths, univariate and multivariate analyses were conducted. RESULTS: A total of 88 stillbirths were reported during the survey. Univariate analysis showed age of mother, education of mother, age of husband, wealth index, head of household, decision on healthcare and decision on household purchases had significant association with stillbirths (p < 0.05). In multivariate analysis, only maternal age 35 years and above was significant (aOR 2.42; 1.22-4.80). Education of mother (aOR 1.48; 0.94-2.33), age of husband (aOR 1.54; 0.86-2.76), household head (aOR 1.51; 0.88-2.59), poor wealth index (aOR 1.62; 0.98-2.68), middle wealth index (aOR 1.37; 0.76-2.47), decision making for healthcare (aOR 1.36; 0.84-2.21) and household purchases (aOR 1.01; 0.61-1.66) had no any significant association with stillbirths. CONCLUSIONS: There are various factors linked with stillbirths. It is important to track stillbirths to improve health outcomes of mothers and newborn. Further studies are necessary to analyse women empowerment factors to understand the linkages between empowerment and stillbirths

Nemo-like kinase regulates the expression of vascular endothelial growth factor (VEGF) lein alveolar epithelial cells

The canonical Wnt signaling can be silenced either through β-catenin-mediated ubiquitination and degradation or through phosphorylation of Tcf and Lef by nemo-like kinase (NLK). In the present study, we generated NLK deficient animals and found that these mice become cyanotic shortly before death because of lung maturation defects. NLK-/- lungs exhibited smaller and compressed alveoli and the mesenchyme remained thick and hyperplastic. This phenotype was caused by epithelial activation of vascular endothelial growth factor (VEGF) via recruitment of Lef1 to the promoter of VEGF. Elevated expression of VEGF and activation of the VEGF receptor through phosphorylation promoted an increase in the proliferation rate of epithelial and endothelial cells. In summary, our study identifies NLK as a novel signaling molecule for proper lung development through the interconnection between epithelial and endothelial cells during lung morphogenesis

Agrobiodiversity and Its Conservation in Nepal

Nepal is a part of the world\u27s biodiversity hotspot and ranks the 49th in the world for biodiversity. Agrobiodiversity and its conservation status were studied through literature review, field survey, key informant survey and focus group discussion. Results of field implementation of some good practices and action research were also documented. Among 24,300 total species in the country, 28% are agricultural genetic resources (AGRs), termed as agrobiodiversity. Agrobiodiversity has six components (crops, forages, livestock, aquatic, insects and microorganisms) and four sub-components (domesticated, semi-domesticated, wild relatives and wild edible) in Nepal. Agrobiodiversity on each component exists at agroecosystem, species, variety/breed/biotype/race/strain, genotype and allele levels, within an altitude range from 60 to 5,000 masl. There are 12 agroecosystems supporting 1026 species under crop component, 510 under forage, 35 under livestock, 250 under the aquatic animal, 17 under aquatic plant, 3,500 under insect and 800 under microorganism. An estimated loss of agrobiodiversity is 40%, however, farmers have reported up to 100% loss of AGRs in some areas for a particular species. Conservation of agrobiodiversity has been initiated since 1986. Four strategies namely ex-situ, on-farm, in-situ and breeding have been adopted for conservation and sustainable utilization of AGRs. Eighty good practices including process, methods and actions for managing agrobiodiversity have been in practice and these practices come under five conservation components (sensitization, method and approach, accelerator, value and enabling environment). Within the country, 18,765 accessions of AGRs have been conserved in different kinds of banks. A total of 24,683 accessions of Nepalese crops, forages and microbes have been conserved in different International and foreign genebanks. Some collections are conserved as safety duplication and safety backup in different CGIARs\u27 banks and World Seed Vault, Korea. Two global databases (GENESYS and EURISCO) have maintained 19,200 Nepalese accessions. Geographical Information System, Climate Analog Tool and biotechnological tools have been applied for better managing AGRs. Many stakeholders need to further concentrate on the conservation and utilization of AGRs. Global marketing of some native AGRs is necessary for sustaining agriculture and attracting young generations as well as conserving them through use

Diversity Analysis and Physico-Morphlogical Characteritics of Indigenous Germplasm of Lablab Bean

Germplasm characterization is an important component of crop breeding program. In characterizing indigenous beans lablab which is used for vegetables as well pulses in Nepal. Twenty three lablab beans germplasm were evaluated for different qualitative and quantitive physico-morphological charecteristics for two years during 2011 and 2012 at Horticulture Research Station, Malepatan, Pokhara. The germplasm showed considerable variations in most of the qualitative and quantitative traits. Leaf size, vine color, flower color, pod color, pod shape, pod type and seed color varied among the genotypes. Variation was also observed in yield attributing characters eg, pod length and width, 10 fresh pod weight, seeds per pod and 100-seed weight. Days to 50% flowering ranged from 81 to 130 days indicating the presence of early varieties. Fresh pod weight of 10 pods was ranged from 45.0 g to 162.5 g. Multivariate analysis indicated four groups in these genotypes, among with ML-02 and ML-10 were distinct in comparioson with other genotypes. Simple selection may be considered to develop high yielding, early type varieties from these gentopypes

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age

Management of Root Knot Nematode on Tomato Through Grafting Root Stock of Solanum Sisymbriifolium

The root-knot nematodes (Meloidogyne spp) are difficult to manage once established in the field because of their wide host range, and soil-borne nature. Thus, the aim of the present study was to examine the use of resistant root stock of wild brinjal (Solanum sisymbriifolium) to reduce the loss caused by the nematodes on tomato. For the management of root-knot nematodes, grafted plant with resistant root stock of the wild brinjal was tested under farmers\u27 field conditions at Hemza of Kaski district. Grafted and non-grafted plants were produced in root-knot nematode-free soil. Around three week-old grafted and non-grafted tomato plants were transplanted in four different plastic tunnels where root-knot nematodes had been reported previously. The plants were planted in diagonal position to each other as a pair plot in 80 × 60 cm2 spacing in an average of 20 × 7 m2 plastic tunnels. Galling Index (GI) was recorded three times in five randomly selected plants in each plot at 60 days intervals. The first observation was recorded two months after transplanting. Total fruit yield was recorded from same plants. In the grafted plants, the root system was totally free from gall whereas in an average of 7.5 GI in 0-10 scale was recorded in the non-grafted plants. Fruits were harvested from time to time and cumulated after final harvest to calculate the total fruit yield. It was estimated that on an average tomato fruit yield was significantly (P&gt;0.05) increased by 37 percent in the grafted plants compared with the non-grafted plants. Grafting technology could be used effectively for cultivation of commonly grown varieties, which are susceptible to root-knot nematodes in disease prone areas. This can be used as an alternative technology for reducing the use of hazardous pesticides for enhancing commercial organic tomato production.Journal of Nepal Agricultural Research Council Vol.3 2017: 27-3

Containment and equivalence of weighted automata: Probabilistic and max-plus cases

This paper surveys some results regarding decision problems for probabilistic and max-plus automata, such as containment and equivalence. Probabilistic and max-plus automata are part of the general family of weighted automata, whose semantics are maps from words to real values. Given two weighted automata, the equivalence problem asks whether their semantics are the same, and the containment problem whether one is point-wise smaller than the other one. These problems have been studied intensively and this paper will review some techniques used to show (un)decidability and state a list of open questions that still remain

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p