Collaborative research between clinicians and researchers: a multiple case study of implementation

<p>Abstract</p> <p>Background</p> <p>Bottom-up, clinician-conceived and directed clinical intervention research, coupled with collaboration from researcher experts, is conceptually endorsed by the participatory research movement. This report presents the findings of an evaluation of a program in the Veterans Health Administration meant to encourage clinician-driven research by providing resources believed to be critical. The evaluation focused on the extent to which funded projects: maintained integrity to their original proposals; were methodologically rigorous; were characterized by collaboration between partners; and resulted in sustained clinical impact.</p> <p>Methods</p> <p>Researchers used quantitative (survey and archival) and qualitative (focus group) data to evaluate the implementation, evaluation, and sustainability of four clinical demonstration projects at four sites. Fourteen research center mentors and seventeen clinician researchers evaluated the level of collaboration using a six-dimensional model of participatory research.</p> <p>Results</p> <p>Results yielded mixed findings. Qualitative and quantitative data suggested that although the process was collaborative, clinicians' prior research experience was critical to the quality of the projects. Several challenges were common across sites, including subject recruitment, administrative support and logistics, and subsequent dissemination. Only one intervention achieved lasting clinical effect beyond the active project period. Qualitative analyses identified barriers and facilitators and suggested areas to improve sustainability.</p> <p>Conclusions</p> <p>Evaluation results suggest that this participatory research venture was successful in achieving clinician-directed collaboration, but did not produce sustainable interventions due to such implementation problems as lack of resources and administrative support.</p

Regulator of G Protein Signaling 3 Modulates Wnt5b Calcium Dynamics and Somite Patterning

Vertebrate development requires communication among cells of the embryo in order to define the body axis, and the Wnt-signaling network plays a key role in axis formation as well as in a vast array of other cellular processes. One arm of the Wnt-signaling network, the non-canonical Wnt pathway, mediates intracellular calcium release via activation of heterotrimeric G proteins. Regulator of G protein Signaling (RGS) proteins can accelerate inactivation of G proteins by acting as G protein GTPase-activating proteins (GAPs), however, the possible role of RGS proteins in non-canonical Wnt signaling and development is not known. Here, we identify rgs3 as having an overlapping expression pattern with wnt5b in zebrafish and reveal that individual knockdown of either rgs3 or wnt5b gene function produces similar somite patterning defects. Additionally, we describe endogenous calcium release dynamics in developing zebrafish somites and determine that both rgs3 and wnt5b function are required for appropriate frequency and amplitude of calcium release activity. Using rescue of gene knockdown and in vivo calcium imaging assays, we demonstrate that the activity of Rgs3 requires its ability to interact with Gα subunits and function as a G protein GAP. Thus, Rgs3 function is necessary for appropriate frequency and amplitude of calcium release during somitogenesis and is downstream of Wnt5 activity. These results provide the first evidence for an essential developmental role of RGS proteins in modulating the duration of non-canonical Wnt signaling

Liquid-gas phase transition in nuclear multifragmentation

The equation of state of nuclear matter suggests that at suitable beam energies the disassembling hot system formed in heavy ion collisions will pass through a liquid-gas coexistence region. Searching for the signatures of the phase transition has been a very important focal point of experimental endeavours in heavy ion collisions, in the last fifteen years. Simultaneously theoretical models have been developed to provide information about the equation of state and reaction mechanisms consistent with the experimental observables. This article is a review of this endeavour.Comment: 63 pages, 27 figures, submitted to Adv. Nucl. Phys. Some typos corrected, minor text change

Discovering collectively informative descriptors from high-throughput experiments

<p>Abstract</p> <p>Background</p> <p>Improvements in high-throughput technology and its increasing use have led to the generation of many highly complex datasets that often address similar biological questions. Combining information from these studies can increase the reliability and generalizability of results and also yield new insights that guide future research.</p> <p>Results</p> <p>This paper describes a novel algorithm called BLANKET for symmetric analysis of two experiments that assess informativeness of descriptors. The experiments are required to be related only in that their descriptor sets intersect substantially and their definitions of case and control are consistent. From resulting lists of n descriptors ranked by informativeness, BLANKET determines <b>shortlists </b>of descriptors from each experiment, generally of different lengths p and q. For any pair of shortlists, four numbers are evident: the number of descriptors appearing in both shortlists, in exactly one shortlist, or in neither shortlist. From the associated contingency table, BLANKET computes Right Fisher Exact Test (RFET) values used as scores over a plane of possible pairs of shortlist lengths <abbrgrp><abbr bid="B1">1</abbr><abbr bid="B2">2</abbr></abbrgrp>. BLANKET then chooses a pair or pairs with RFET score less than a threshold; the threshold depends upon n and shortlist length limits and represents a quality of intersection achieved by less than 5% of random lists.</p> <p>Conclusions</p> <p>Researchers seek within a universe of descriptors some minimal subset that collectively and efficiently predicts experimental outcomes. Ideally, any smaller subset should be insufficient for reliable prediction and any larger subset should have little additional accuracy. As a method, BLANKET is easy to conceptualize and presents only moderate computational complexity. Many existing databases could be mined using BLANKET to suggest optimal sets of predictive descriptors.</p

A community based participatory approach to improving health in a Hispanic population

ABSTRACT: BACKGROUND: The Charlotte-Mecklenburg region has one of the fastest growing Hispanic communities in the country. This population has experienced disparities in health outcomes and diminished ability to access healthcare services. This city is home to an established practice-based research network (PBRN) that includes community representatives, health services researchers, and primary care providers. The aims of this project are: to use key principles of community-based participatory research (CBPR) within a practice-based research network (PBRN) to identify a single disease or condition that negatively affects the Charlotte Hispanic community; to develop a community-based intervention that positively impacts the chosen condition and improves overall community health; and to disseminate findings to all stakeholders. METHODS/DESIGN: This project is designed as CBPR. The CBPR process creates new social networks and connections between participants that can potentially alter patterns of healthcare utilization and other health-related behaviors. The first step is the development of equitable partnerships between community representatives, providers, and researchers. This process is central to the CBPR process and will occur at three levels -- community members trained as researchers and outreach workers, a community advisory board (CAB), and a community forum. Qualitative data on health issues facing the community -- and possible solutions -- will be collected at all three levels through focus groups, key informant interviews and surveys. The CAB will meet monthly to guide the project and oversee data collection, data analysis, participant recruitment, implementation of the community forum, and intervention deployment. The selection of the health condition and framework for the intervention will occur at the level of a community-wide forum. Outcomes of the study will be measured using indicators developed by the participants as well as geospatial modeling.On completion, this study will: determine the feasibility of the CBPR process to design interventions; demonstrate the feasibility of geographic models to monitor CBPR-derived interventions; and further establish mechanisms for implementation of the CBPR framework within a PBRN

SNP-SNP interactions in breast cancer susceptibility

BACKGROUND: Breast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination. METHODS: In an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle. RESULTS: None of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism (CCND1-[Pro241Pro] and COMT-[Met108/158Val]), cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism (GSTP1-[Ile105Val] and COMT-[Met108/158Val]) pathways. CONCLUSION: The importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management

Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe

We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median $z\sim 0.03$). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between $z\sim 0.6$ and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July

Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe

We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median $z\sim 0.03$). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between $z\sim 0.6$ and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July