ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

Objectives:Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods:We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results:ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion:ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu)

Upland Bare Ground and Riparian Vegetative Cover Under Strategic Grazing Management, Continuous Stocking, and Multiyear Rest in New Mexico Mid-grass Prairie

On the Ground • We compared land cover attributes on rangeland pastures with strategically managed ranches (SGM), continuously stocked (CS), and rested pastures. • SGM pastures had less upland bare ground and more riparian vegetative cover than adjoining CS pastures, and SGM pastures had bare ground cover comparable to pastures rested from grazing for three or more years. • Differences in riparian cover between management types were greatest in years of near-average precipitation and lower in years of high precipitation or drought. • Remote sensing technologyprovided a means of quantifying range condition and comparing management effectiveness on large landscapes in a constantly changing environment

Upland Bare Ground and Riparian Vegetative Cover Under Strategic Grazing Management, Continuous Stocking, and Multiyear Rest in New Mexico Mid-grass Prairie

On the Ground • We compared land cover attributes on rangeland pastures with strategically managed ranches (SGM), continuously stocked (CS), and rested pastures. • SGM pastures had less upland bare ground and more riparian vegetative cover than adjoining CS pastures, and SGM pastures had bare ground cover comparable to pastures rested from grazing for three or more years. • Differences in riparian cover between management types were greatest in years of near-average precipitation and lower in years of high precipitation or drought. • Remote sensing technologyprovided a means of quantifying range condition and comparing management effectiveness on large landscapes in a constantly changing environment

Effects of Home Exercise on Immediate and Delayed Affect and Mood Among Rural Individuals at Risk for Type 2 Diabetes

Physical activity is important for reducing overweight and obesity and related health consequences. This study examined changes in mood following 16 weeks of exercise in a sample of 29 individuals residing in a rural area and at risk for developing Type 2 diabetes mellitus (T2DM). Significant positive mood changes were detected, with moderate to large effect sizes. Assessments also revealed significant delayed postexercise positive emotion changes. These findings extend research on the mood benefits of exercise to individuals residing in rural settings and at risk for T2DM and suggest that to gain a full understanding of the exercise-affect relation, investigators need to assess affect at delayed intervals following exercise

The Effects of Electric Power Lines on the Breeding Ecology of Greater Sage-Grouse

Anthropogenic infrastructure can negatively affect wildlife through direct mortality and/or displacement behaviors. Some tetranoids (grouse spp.) species are particularly vulnerable to tall anthropogenic structures because they evolved in ecosystems void of vertical structures. In western North America, electric power transmission and distribution lines (power lines) occur in sagebrush (Artemisia spp.) landscapes within the range of the greater sage-grouse (Centrocercus urophasianus; sage-grouse). The U.S. Fish and Wildlife Service recommended using buffer zones near leks to mitigate the potential impacts of power lines on sage-grouse. However, recommended buffer distances are inconsistent across state and federal agencies because data are lacking. To address this, we evaluated the effects of power lines on sage-grouse breeding ecology within Utah, portions of southeastern Idaho, and southwestern Wyoming from 1998–2013. Overall, power lines negatively affected lek trends up to a distance of 2.7 and 2.8 km, respectively. Power lines died not affect lek persistence. Female sage-grouse avoided transmission lines during the nesting and brooding seasons at distances up to 1.1 and 0.8 km, respectively. Nest and brood success were negatively affected by transmission lines up to distances of 2.6 and 1.1 km, respectively. Distribution lines did not appear to affect sage-grouse habitat selection or reproductive fitness. Our analyses demonstrated the value of sagebrush cover in mitigating potential power line impacts. Managers can minimize the effects of new transmission power lines by placing them in existing anthropogenic corridors and/or incorporating buffers at least 2.8 km from active leks. Given the uncertainty we observed in our analyses regarding sage-grouse response to distribution lines coupled with their role in providing electric power service directly to individual consumers, we recommend that buffers for these power lines be considered on a case-by-case basis. Micrositing to avoid important habitats and habitat reclamation may reduce the potential impacts of new power line construction

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes

The release of the 1000(th) complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms

Global human footprint on the linkage between biodiversity and ecosystem functioning in reef fishes

Copyright: © 2011 Mora et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Difficulties in scaling up theoretical and experimental results have raised controversy over the consequences of biodiversity loss for the functioning of natural ecosystems. Using a global survey of reef fish assemblages, we show that in contrast to previous theoretical and experimental studies, ecosystem functioning (as measured by standing biomass) scales in a non-saturating manner with biodiversity (as measured by species and functional richness) in this ecosystem. Our field study also shows a significant and negative interaction between human population density and biodiversity on ecosystem functioning (i.e., for the same human density there were larger reductions in standing biomass at more diverse reefs). Human effects were found to be related to fishing, coastal development, and land use stressors, and currently affect over 75% of the world's coral reefs. Our results indicate that the consequences of biodiversity loss in coral reefs have been considerably underestimated based on existing knowledge and that reef fish assemblages, particularly the most diverse, are greatly vulnerable to the expansion and intensity of anthropogenic stressors in coastal areas

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity

Collaborative case management to aid return to work after long-term sickness absence: a pilot randomised controlled trial

Background: Despite high levels of employment among working-age adults in the UK, there is still a significant minority who are off work with ill health at any one time (so-called ‘sickness absence’). Long-term sickness absence results in significant costs to the individual, to the employer and to wider society. Objective: The overall objective of the intervention was to improve employee well-being with a view to aiding return to work. To meet this aim, a collaborative case management intervention was adapted to the needs of UK employees who were entering or experiencing long-term sickness absence. Design: A pilot randomised controlled trial, using permuted block randomisation. Recruitment of patients with long-term conditions in settings such as primary care was achieved by screening of routine records, followed by mass mailing of invitations to participants. However, the proportion of patients responding to such invitations can be low, raising concerns about external validity. Recruitment in the Case Management to Enhance Occupational Support (CAMEOS) study used this method to test whether or not it would transfer to a population with long-term sickness absence in the context of occupational health (OH). Participants: Employed people on long-term sickness absence (between 4 weeks and 12 months). The pilot was run with two different collaborators: a large organisation that provided OH services for a number of clients and a non-profit community-based organisation. Intervention: Collaborative case management was delivered by specially trained case managers from the host organisations. Sessions were delivered by telephone and supported use of a self-help handbook. The comparator was usual care as provided by participants’ general practitioner (GP) or OH provider. This varied for participants according to the services available to them. Neither participants nor the research team were blind to randomisation