Explore the vast range of titles available.

Background and Objectives: Outcome data from wearable devices are increasingly used in both research and clinics. Traditionally, a dedicated device is chosen for a given study or clinical application to collect outcome data as soon as the patient is included in a study or undergoes a procedure. The current study introduces a new measurement strategy, whereby patients’ own devices are utilized, allowing for both a pre-injury baseline measure and ability to show achievable results. Materials and Methods: Patients with a pre-existing musculoskeletal injury of the upper and lower extremity were included in this exploratory, proof-of-concept study. They were followed up for a minimum of 6 weeks after injury, and their wearable outcome data (from a smartphone and/or a body-worn sensor) were continuously acquired during this period. A descriptive analysis of the screening characteristics and the observed and achievable outcome patterns was performed. Results: A total of 432 patients was continuously screened for the study, and their screening was analyzed. The highest success rate for successful inclusion was in younger patients. Forty-eight patients were included in the analysis. The most prevalent outcome was step count. Three distinctive activity data patterns were observed: patients recovering, patients with slow or no recovery, and patients needing additional measures to determine treatment outcomes. Conclusions: Measuring outcomes in trauma patients with the Bring Your Own Device (BYOD) strategy is feasible. With this approach, patients were able to provide continuous activity data without any dedicated equipment given to them. The measurement technique is especially suited to particular patient groups. Our study’s screening log and inclusion characteristics can help inform future studies wishing to employ the BYOD design.

The Yukon–Kuskokwim Delta of Alaska, USA is a globally important region for numerous avian species including millions of migrating and nesting waterbirds. However, data on the current spatial distribution of critical nesting areas and the importance of environmental variables in the selection of nest locations are generally lacking for waterbirds in this region. We modeled nest densities for 6 species of geese and eiders that commonly breed on the Yukon–Kuskokwim Delta, including cackling goose (Branta hutchinsii minima), emperor goose (Chen canagica), black brant (B. bernicla nigricans), greater white-fronted goose (Anser albifrons frontalis), spectacled eider (Somateria fischeri), and common eider (S. mollissima). The data used were from single-visit nest searches on 2,318 plots sampled during 29 years from 1985 to 2013. We modeled nest density for each species by combining data across years and using random forests methods and time-static landscape environmental variables. These models provide the first habitat-specific predictive distributions of nest density for these species breeding on the Yukon–Kuskokwim Delta of Alaska. Predictive performance of the random forests models varied among species, explaining 13–69% of the variance in nest density. For most species, nest density was greatest near the coast and within lowland habitats. Predicted nest densities mapped across the coastal zone of the Yukon–Kuskokwim Delta revealed areas of high and low nest densities that can be used to inform management and conservation decisions.

Single Nucleotide Polymorphisms (SNPs) in genes involved in the DNA Base Excision Repair (BER) pathway could be associated with cancer risk in carriers of mutations in the high-penetrance susceptibility genes BRCA1 and BRCA2, given the relation of synthetic lethality that exists between one of the components of the BER pathway, PARP1 (poly ADP ribose polymerase), and both BRCA1 and BRCA2. In the present study, we have performed a comprehensive analysis of 18 genes involved in BER using a tagging SNP approach in a large series of BRCA1 and BRCA2 mutation carriers. 144 SNPs were analyzed in a two stage study involving 23,463 carriers from the CIMBA consortium (the Consortium of Investigators of Modifiers of BRCA1 and BRCA2). Eleven SNPs showed evidence of association with breast and/or ovarian cancer at p<0.05 in the combined analysis. Four of the five genes for which strongest evidence of association was observed were DNA glycosylases. The strongest evidence was for rs1466785 in the NEIL2 (endonuclease VIII-like 2) gene (HR: 1.09, 95% CI (1.03-1.16), p = 2.7×10-3) for association with breast cancer risk in BRCA2 mutation carriers, and rs2304277 in the OGG1 (8-guanine DNA glycosylase) gene, with ovarian cancer risk in BRCA1 mutation carriers (HR: 1.12 95%CI: 1.03-1.21, p = 4.8×10-3). DNA glycosylases involved in the first steps of the BER pathway may be associated with cancer risk in BRCA1/2 mutation carriers and should be more comprehensively studied.