Diffusion theory and knowledge dissemination, utilization and integration

Many accomplishments of public health have been distributed unevenly among populations. This article reviews the concepts of applying evidence-based practice in public health in the face of the varied cultures and circumstances of practice in these varied populations. Key components of EBPH include: making decisions based on the best available scientific evidence, using data and information systems systematically, applying program planning frameworks, engaging the community and practitioners in decision making, conducting sound evaluation, and disseminating what is learned. The usual application of these principles has overemphasized the scientific evidence as the starting point, whereas this review suggests engaging the community and practitioners as an equally important starting point to assess their needs, assets and circumstances, which can be facilitated with program planning frameworks and use of local assessment and surveillance data

Forward-Biased Thermal Cycling: A New Module Qualification Test

Following a proposal by BP Solarex to modify the standard module qualification sequence, we performed a forward-biased themal cycling on three types of commercial modules to evaluate the procedure. The total number of thermal cycles was doubled to 400 and maximum power measurements were made every 50 cycles

Coherent States of SU(l,1)SU(l,1) groups

This work can be considered as a continuation of our previous one (J.Phys., 26 (1993) 313), in which an explicit form of coherent states (CS) for all SU(N) groups was constructed by means of representations on polynomials. Here we extend that approach to any SU(l,1) group and construct explicitly corresponding CS. The CS are parametrized by dots of a coset space, which is, in that particular case, the open complex ball $CD^{l}$. This space together with the projective space $CP^{l}$, which parametrizes CS of the SU(l+1) group, exhausts all complex spaces of constant curvature. Thus, both sets of CS provide a possibility for an explicit analysis of the quantization problem on all the spaces of constant curvature.Comment: 22 pages, to be published in "Journal of Physics A

PV Cell and Module Calibration Activities at NREL

The performance of PV cells and modules with respect to standard reference conditions is a key indicator of progress of a given technology. This task provides the U.S. terrestrial PV community with the most accurate measurements that are technically possible in a timely fashion. The international module certification and accreditation program PVGap requires certification laboratories to maintain their calibration traceability path to groups like this one. The politics of a "world record" efficiency requires that an independent laboratory perform these measurements for credibility. Most manufacturers base their module peak watt rating upon standards and reference cells calibrated under this task. This task has been involved in reconciling disputes between manufacturers and their cell suppliers in terms of expected versus actual performance. This task has also served as a resource to the PV community for consultation on solar simulation, current versus voltage measurement instrumentation, measurement procedures and measurement artifacts

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.Peer reviewe

Piezo is essential for amiloride-sensitive stretch-activated mechanotransduction in larval Drosophila dorsal bipolar dendritic sensory neurons

Date of Acceptance: 05/06/2015Peer reviewedPublisher PD

Conceptual frameworks and empirical approaches used to assess the impact of health research: an overview of reviews

<p>Abstract</p> <p>Background</p> <p>How to assess the impact of research is of growing interest to funders, policy makers and researchers mainly to understand the value of investments and to increase accountability. Broadly speaking the term "research impact" refers to the contribution of research activities to achieve desired societal outcomes. The aim of this overview is to identify the most common approaches to research impact assessment, categories of impact and their respective indicators.</p> <p>Methods</p> <p>We systematically searched the relevant literature (PubMed, The Cochrane Library (1990-2009)) and funding agency websites. We included systematic reviews, theoretical and methodological papers, and empirical case-studies on how to evaluate research impact. We qualitatively summarised the included reports, as well the conceptual frameworks.</p> <p>Results</p> <p>We identified twenty-two reports belonging to four systematic reviews and 14 primary studies. These publications reported several theoretical frameworks and methodological approaches (bibliometrics, econometrics, ad hoc case studies). The "payback model" emerged as the most frequently used. Five broad categories of impact were identified: a) advancing knowledge, b) capacity building, c) informing decision-making, d) health benefits, e) broad socio-economic benefits. For each proposed category of impact we summarized a set of indicators whose pros and cons are presented and briefly discussed.</p> <p>Conclusions</p> <p>This overview is a comprehensive, yet descriptive, contribution to summarize the conceptual framework and taxonomy of an heterogeneous and evolving area of research. A shared and comprehensive conceptual framework does not seem to be available yet and its single components (epidemiologic, economic, and social) are often valued differently in different models.</p

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

dentification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 x 10(-8)) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.Peer reviewe