Automated mapping of social networks in wild birds

Growing interest in the structure and dynamics of animal social networks has stimulated major advances [1], [2] and [3], but recording reliable association data for wild populations has remained challenging. While animal-borne ‘proximity’ tags have been available for some time [4], earlier devices were comparatively heavy, had limited detection ranges and/or necessitated recovery for data retrieval. We have developed wireless digital transceiver technology (‘Encounternet') that enables automated mapping of social networks in wild birds, yielding datasets of unprecedented size, quality and spatio-temporal resolution. Miniature, animal-borne tags record the proximity and duration of bird encounters, and periodically transfer logs to a grid of fixed receiver stations, from which datasets can be downloaded remotely for real-time analysis. We used our system to chart social associations in New Caledonian crows Corvus moneduloides [5] and [6]. Analysis of ca. 28,000 encounter logs for 34 crows over a 7-day period reveals a substantial degree of close-range association between non-family birds, demonstrating the potential for horizontal and oblique information exchange

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Profile of patients with rheumatic diseases undergoing treatment with anti-TNF agents in the Brazilian Public Health System (SUS), Belo Horizonte - MG

The aim of this study was to describe the baseline demographic and clinical characteristics as well as the functional status of a prospective cohort of patients with rheumatic diseases assisted by the Brazilian Public Health System (SUS). Data for 302 patients receiving tumor necrosis factor α inhibitors (anti-TNF agents) was collected through a standard form. Among patients, 229 (75.8%) were female and 155 (51.3%) were Caucasian; the mean age was 50.3 ± 12.8 years, and the mean disease duration was 9.9 ± 8.7 years. Among them 214 patients (70.9%) received adalimumab, 72 (23.8%) etanercept, and 16 (5.3%) infliximab. Mean Health Assessment Questionnaire-Disability Index (HAQ-DI) was 1.37 ± 0.67 for all participants. Poor functional response was associated with female gender, married patients and with a score of < 0.6 on the EuroQoL-5 dimensions (EQ-5D). Significant correlation was found between the HAQ-DI values, disease activity and quality of life (QOL). The results obtained in this study contribute to a better understanding of the clinical and demographic characteristics of patients with rheumatic diseases at the beginning of anti-TNF-agent treatment by SUS. Furthermore, our findings are consistent with another Brazilian and foreign cross-sectional investigations. This knowledge can be of great importance for further studies evaluating the effectiveness of biological agents, as well as, to contribute to improve the well-being of the patients with rheumatic diseases