Feeding Behaviors of Laying Hens with or without Beak Trimming

This study quantifies feeding behavior of the W-36 White Leghorn laying hen (77-80 weeks old) as influenced by the management practice of beak trimming. The feeding behavior is characterized by a newly developed measurement system and computational algorithm. Non-trimmed (NT) and beak trimmed (BT) birds showed similar meal size. BT birds spent longer time at the feeder, which is compatible to their slower ingestion rate of 0.9 g/min vs. 1.3 g/min of the NT type. Compared with NT bird, the BT bird had smaller time intervals between meals, 200 vs. 450 s. By scientifically characterizing the feeding behavior of laying hens, baseline information will result that may help better quantify the welfare of birds

Examining the Link between Crime and Unemployment: A Time Series Analysis for Canada

We use national and regional Canadian data to analyse the relationship between economic activity (as reflected by the unemployment rate) and crime rates. Given potential aggregation bias, we disaggregate the crime data and look at the relationship between six different types of crimes rates and unemployment rate; we also disaggregate the data by region. We employ an error correction model in our analysis to test for short-run and long-run dynamics. We find no evidence of long-run relationship between crime and unemployment, when we look at both disaggregation by type of crime and disaggregation by region. Lack of evidence of a long-run relationship indicates we have no evidence of the motivation hypothesis. For selected types of property crimes, we find some evidence of a significant negative short-run relationship between crime and unemployment, lending support to the opportunity hypothesis. Inclusion of control variables in the panel analysis does not alter the findings, qualitatively or quantitatively

The JANUS X-Ray Flash Monitor

JANUS is a NASA small explorer class mission which just completed phase A and was intended for a 2013 launch date. The primary science goals of JANUS are to use high redshift (6<z<12) gamma ray bursts and quasars to explore the formation history of the first stars in the early universe and to study contributions to reionization. The X-Ray Flash Monitor (XRFM) and the Near-IR Telescope (NIRT) are the two primary instruments on JANUS. XRFM has been designed to detect bright X-ray flashes (XRFs) and gamma ray bursts (GRBs) in the 1-20 keV energy band over a wide field of view (4 steradians), thus facilitating the detection of z>6 XRFs/GRBs, which can be further studied by other instruments. XRFM would use a coded mask aperture design with hybrid CMOS Si detectors. It would be sensitive to XRFs/GRBs with flux in excess of approximately 240 mCrab. The spacecraft is designed to rapidly slew to source positions following a GRB trigger from XRFM. XRFM instrument design parameters and science goals are presented in this paper.Comment: submitted to Proc. SPIE, Vol. 7435 (2009), 7 pages, 8 figure

Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants

Abstract: Cerebral small vessel disease is a major cause of stroke and dementia, but its genetic basis is incompletely understood. We perform a genetic study of three MRI markers of the disease in UK Biobank imaging data and other sources: white matter hyperintensities (N = 42,310), fractional anisotropy (N = 17,663) and mean diffusivity (N = 17,467). Our aim is to better understand the disease pathophysiology. Across the three traits, we identify 31 loci, of which 21 were previously unreported. We perform a transcriptome-wide association study to identify associations with gene expression in relevant tissues, identifying 66 associated genes across the three traits. This genetic study provides insights into the understanding of the biological mechanisms underlying small vessel disease

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics. Here we examine a large cohort (the INTERVAL study; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores