Assessment of Functioning of Village Health and Sanitation Committees (VHSCs) of Indore District

Background: The NRHM framework of implementation mentions provision of Village Health and Sanitation Committee (VHSC) in each revenue village that has to be formed within the overall framework of Panchayati Raj Institution (PRI). Objective: To review the current status of formation, training and functioning of VHSCs in Indore district and mechanism of utilization of united funds in these VHSCs. Materials and Methods: A cross sectional study was carried out in 32 villages, of four blocks of Indore district. Different stakeholders of VHSCs of these 32 villages were included purposively as study subjects. Data was collected using predesigned, pretested semi structured questionnaires and checklist. Total of 133 interviews of different stakeholders and 32 record reviews were carried out. The quantitative data collected by interviews and record reviews was analyzed by SPSS software and qualitative data was analyzed manually using qualifier. Results: Significant association between knowledge and awareness about any aspect of VHSC and type of stakeholder has been observed. PRI members and Self Help Group (SHG) members have been found to be totally ignorant about many aspects of VHSC. No formal training has ever been imparted to the members of VHSCs regarding functioning of VHSC at village level. None of the functionaries were found to be aware of village health plan. Conclusion: The efficiency and impact of VHSCs have been found to be very limited

Design Considerations for Massively Parallel Sequencing Studies of Complex Human Disease

Massively Parallel Sequencing (MPS) allows sequencing of entire exomes and genomes to now be done at reasonable cost, and its utility for identifying genes responsible for rare Mendelian disorders has been demonstrated. However, for a complex disease, study designs need to accommodate substantial degrees of locus, allelic, and phenotypic heterogeneity, as well as complex relationships between genotype and phenotype. Such considerations include careful selection of samples for sequencing and a well-developed strategy for identifying the few “true” disease susceptibility genes from among the many irrelevant genes that will be found to harbor rare variants. To examine these issues we have performed simulation-based analyses in order to compare several strategies for MPS sequencing in complex disease. Factors examined include genetic architecture, sample size, number and relationship of individuals selected for sequencing, and a variety of filters based on variant type, multiple observations of genes and concordance of genetic variants within pedigrees. A two-stage design was assumed where genes from the MPS analysis of high-risk families are evaluated in a secondary screening phase of a larger set of probands with more modest family histories. Designs were evaluated using a cost function that assumes the cost of sequencing the whole exome is 400 times that of sequencing a single candidate gene. Results indicate that while requiring variants to be identified in multiple pedigrees and/or in multiple individuals in the same pedigree are effective strategies for reducing false positives, there is a danger of over-filtering so that most true susceptibility genes are missed. In most cases, sequencing more than two individuals per pedigree results in reduced power without any benefit in terms of reduced overall cost. Further, our results suggest that although no single strategy is optimal, simulations can provide important guidelines for study design

Statistical Guidance for Experimental Design and Data Analysis of Mutation Detection in Rare Monogenic Mendelian Diseases by Exome Sequencing

Recently, whole-genome sequencing, especially exome sequencing, has successfully led to the identification of causal mutations for rare monogenic Mendelian diseases. However, it is unclear whether this approach can be generalized and effectively applied to other Mendelian diseases with high locus heterogeneity. Moreover, the current exome sequencing approach has limitations such as false positive and false negative rates of mutation detection due to sequencing errors and other artifacts, but the impact of these limitations on experimental design has not been systematically analyzed. To address these questions, we present a statistical modeling framework to calculate the power, the probability of identifying truly disease-causing genes, under various inheritance models and experimental conditions, providing guidance for both proper experimental design and data analysis. Based on our model, we found that the exome sequencing approach is well-powered for mutation detection in recessive, but not dominant, Mendelian diseases with high locus heterogeneity. A disease gene responsible for as low as 5% of the disease population can be readily identified by sequencing just 200 unrelated patients. Based on these results, for identifying rare Mendelian disease genes, we propose that a viable approach is to combine, sequence, and analyze patients with the same disease together, leveraging the statistical framework presented in this work

A measure of individual role in collective dynamics

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures describe a node's importance by its position in a network. The key issue obviated is that the contribution of a node to the collective behavior is not uniquely determined by the structure of the system but it is a result of the interplay between dynamics and network structure. We show that dynamical influence measures explicitly how strongly a node's dynamical state affects collective behavior. For critical spreading, dynamical influence targets nodes according to their spreading capabilities. For diffusive processes it quantifies how efficiently real systems may be controlled by manipulating a single node.Comment: accepted for publication in Scientific Report

Identification of Sequence Variants in Genetic Disease-Causing Genes Using Targeted Next-Generation Sequencing

Identification of gene variants plays an important role in research on and diagnosis of genetic diseases. A combination of enrichment of targeted genes and next-generation sequencing (targeted DNA-HiSeq) results in both high efficiency and low cost for targeted sequencing of genes of interest.To identify mutations associated with genetic diseases, we designed an array-based gene chip to capture all of the exons of 193 genes involved in 103 genetic diseases. To evaluate this technology, we selected 7 samples from seven patients with six different genetic diseases resulting from six disease-causing genes and 100 samples from normal human adults as controls. The data obtained showed that on average, 99.14% of 3,382 exons with more than 30-fold coverage were successfully detected using Targeted DNA-HiSeq technology, and we found six known variants in four disease-causing genes and two novel mutations in two other disease-causing genes (the STS gene for XLI and the FBN1 gene for MFS) as well as one exon deletion mutation in the DMD gene. These results were confirmed in their entirety using either the Sanger sequencing method or real-time PCR.Targeted DNA-HiSeq combines next-generation sequencing with the capture of sequences from a relevant subset of high-interest genes. This method was tested by capturing sequences from a DNA library through hybridization to oligonucleotide probes specific for genetic disorder-related genes and was found to show high selectivity, improve the detection of mutations, enabling the discovery of novel variants, and provide additional indel data. Thus, targeted DNA-HiSeq can be used to analyze the gene variant profiles of monogenic diseases with high sensitivity, fidelity, throughput and speed

Managing Carbon Aspirations: The Influence of Corporate Climate Change Targets on Environmental Performance

Addressing climate change is among the most challenging ethical issues facing contemporary business and society. Unsustainable business activities are causing significant distributional and procedural injustices in areas such as public health and vulnerability to extreme weather events, primarily because of a distinction between primary emitters and those already experiencing the impacts of climate change. Business, as a significant contributor to climate change and beneficiary of externalizing environmental costs, has an obligation to address its environmental impacts. In this paper, we explore the role of firms’ climate change targets in shaping their emissions trends in the context of a large multi-country sample of companies. We contrast two intentions for setting emissions reductions targets: symbolic attempts to manage external stakeholder perceptions via “greenwashing” and substantive commitments to reducing environmental impacts. We argue that the attributes of firms’ climate change targets (their extent, form, and time horizon) are diagnostic of firms’ underlying intentions. Consistent with our hypotheses, while we find no overall effect of setting climate change targets on emissions, we show that targets characterized by a commitment to more ambitious emissions reductions, a longer target time frame, and absolute reductions in emissions are associated with significant reductions in firms’ emissions. Our evidence suggests the need for vigilance among policy-makers and environmental campaigners regarding the underlying intentions that accompany environmental management practices and shows that these can to some extent be diagnosed analytically

Age groups and spread of influenza: implications for vaccination strategy

<p>Abstract</p> <p>Background</p> <p>The unpredictable nature of the potentially devastating impact of 2009 pH1N1 influenza pandemic highlights the need for pandemic preparedness planning, where modeling studies could be most useful for simulations of possible future scenarios.</p> <p>Methods</p> <p>A compartmental model with pre-symptomatic and asymptomatic influenza infections is proposed which incorporates age groups as well as intervention measures such as age-specific vaccination, in order to study spread of influenza in a community.</p> <p>Results</p> <p>We derive the basic reproduction number and other effective reproduction numbers under various intervention measures. For illustration, we make use of the Pneumonia and Influenza (P&I) mortality data and vaccination data of the very young (age 0-2) and the very old (age >64) during 2004-2005 Taiwan winter influenza season to fit our model and to compute the relevant reproduction numbers. The reproduction number for this winter flu season is estimated to be slightly above one (~1.0001).</p> <p>Conclusions</p> <p>Comparatively large errors in fitting the P&I mortality data of the elderly (>64) were observed shortly after winter school closings in January, which may indicate the impact of younger, more active age groups transmitting influenza to other age groups outside of the school settings; in particular, to the elderly in the households. Pre-symptomatic infections seemed to have little effect on the model fit, while asymptomatic infection by asymptomatic infectives has a more pronounced impact on the model fit for the elderly mortality, perhaps indicating a larger role in disease transmission by asymptomatic infection. Simulations indicate that the impact of vaccination on the disease incidence might not be fully revealed in the change (or the lack thereof) in the effective reproduction number with interventions, but could still be substantial. The estimated per contact transmission probability for susceptible elderly is significantly higher than that of any other age group, perhaps highlighting the vulnerability of the elderly due to close contacts with their caretakers from other age groups. The relative impact of targeting the very young and the very old for vaccination was weakened by their relative inactivity, thus giving evidence of the lack of impact of vaccinating these two groups on the overall transmissibility of the disease in the community. This further underscores the need for morbidity-based strategy to prevent elderly mortality.</p

FGF2 regulates melanocytes viability through the STAT3-transactivated PAX3 transcription

PAX3 (paired box 3) is known to have an important role in melanocyte development through modulation of microphthalmia-associated transcription factor transcription. Here we found that PAX3 transcriptional activity could be regulated through FGF2 (basic fibroblast growth factor)-STAT3 (signal transducer and activator of transcription 3) signaling in the pigment cells. To study its function in vivo, we have generated a transgenic mouse model expressing PAX3 driven by tyrosinase promoter in a tissue-specific fashion. These animals exhibit hyperpigmentation in the epidermis, evident in the skin color of their ears and tails. We showed that the darker skin color results from both increased melanocyte numbers and melanin synthesis. Together, our study delineated a novel pathway in the melanocyte lineage, linking FGF2-STAT3 signaling to increased PAX3 transcription. Moreover, our results suggest that this pathway might contribute to the regulation of melanocyte numbers and melanin levels, and thereby provide an alternative strategy to induce pigmentation

Rare Variants in Ischemic Stroke: An Exome Pilot Study

The genetic architecture of ischemic stroke is complex and is likely to include rare or low frequency variants with high penetrance and large effect sizes. Such variants are likely to provide important insights into disease pathogenesis compared to common variants with small effect sizes. Because a significant portion of human functional variation may derive from the protein-coding portion of genes we undertook a pilot study to identify variation across the human exome (i.e., the coding exons across the entire human genome) in 10 ischemic stroke cases. Our efforts focused on evaluating the feasibility and identifying the difficulties in this type of research as it applies to ischemic stroke. The cases included 8 African-Americans and 2 Caucasians selected on the basis of similar stroke subtypes and by implementing a case selection algorithm that emphasized the genetic contribution of stroke risk. Following construction of paired-end sequencing libraries, all predicted human exons in each sample were captured and sequenced. Sequencing generated an average of 25.5 million read pairs (75 bp×2) and 3.8 Gbp per sample. After passing quality filters, screening the exomes against dbSNP demonstrated an average of 2839 novel SNPs among African-Americans and 1105 among Caucasians. In an aggregate analysis, 48 genes were identified to have at least one rare variant across all stroke cases. One gene, CSN3, identified by screening our prior GWAS results in conjunction with our exome results, was found to contain an interesting coding polymorphism as well as containing excess rare variation as compared with the other genes evaluated. In conclusion, while rare coding variants may predispose to the risk of ischemic stroke, this fact has yet to be definitively proven. Our study demonstrates the complexities of such research and highlights that while exome data can be obtained, the optimal analytical methods have yet to be determined

High performance computing for haplotyping: Models and platforms

\u3cp\u3eThe reconstruction of the haplotype pair for each chromosome is a hot topic in Bioinformatics and Genome Analysis. In Haplotype Assembly (HA), all heterozygous Single Nucleotide Polymorphisms (SNPs) have to be assigned to exactly one of the two chromosomes. In this work, we outline the state-of-the-art on HA approaches and present an in-depth analysis of the computational performance of GenHap, a recent method based on Genetic Algorithms. GenHap was designed to tackle the computational complexity of the HA problem by means of a divide-et-impera strategy that effectively leverages multi-core architectures. In order to evaluate GenHap’s performance, we generated different instances of synthetic (yet realistic) data exploiting empirical error models of four different sequencing platforms (namely, Illumina NovaSeq, Roche/454, PacBio RS II and Oxford Nanopore Technologies MinION). Our results show that the processing time generally decreases along with the read length, involving a lower number of sub-problems to be distributed on multiple cores.\u3c/p\u3