Method of moments solution of volume integral equations using parametric geometry modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94709/1/rds4692.pd

Impact of the introduction of organised screening for cervical cancer in Turin, Italy: cancer incidence by screening history 1992–98

After an organised cervical screening programme was introduced in Turin in 1992, the age-adjusted cervical cancer incidence ratio in 1992–98 was 0.81 (95% confidence interval (CI) 0.59–1.09) for invited vs not invited women and 0.25 (95% CI 0.13–0.50) for attenders vs non attenders. An organised screening programme can further reduce cervical cancer incidence in an area where substantial spontaneous activity was previously present

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder

Assessing the distribution of volatile organic compounds using land use regression in Sarnia, "Chemical Valley", Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Land use regression (LUR) modelling is proposed as a promising approach to meet some of the challenges of assessing the intra-urban spatial variability of ambient air pollutants in urban and industrial settings. However, most of the LUR models to date have focused on nitrogen oxides and particulate matter. This study aimed at developing LUR models to predict BTEX (benzene, toluene, ethylbenzene, m/p-xylene and o-xylene) concentrations in Sarnia, 'Chemical Valley', Ontario, and model the intra-urban variability of BTEX compounds in the city for a community health study.</p> <p>Method</p> <p>Using Organic Vapour Monitors, pollutants were monitored at 39 locations across the city of Sarnia for 2 weeks in October 2005. LUR models were developed to generate predictor variables that best estimate BTEX concentrations.</p> <p>Results</p> <p>Industrial area, dwelling counts, and highways adequately explained most of the variability of BTEX concentrations (<it>R</it>²: 0.78 – 0.81). Correlations between measured BTEX compounds were high (> 0.75). Although most of the predictor variables (e.g. land use) were similar in all the models, their individual contributions to the models were different.</p> <p>Conclusion</p> <p>Yielding potentially different health effects than nitrogen oxides and particulate matter, modelling other air pollutants is essential for a better understanding of the link between air pollution and health. The LUR models developed in these analyses will be used for estimating outdoor exposure to BTEX for a larger community health study aimed at examining the determinants of health in Sarnia.</p

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch

spatially-explicit test of the refuge strategy for delaying insecticide resistance

The refuge strategy is used worldwide to delay the evolution of pest resistance to insecticides that are either sprayed or produced by transgenic Bacillus thuringiensis (Bt) crops. This strategy is based on the idea that refuges of host plants where pests are not exposed to an insecticide promote survival of susceptible pests. Despite widespread adoption of this approach, large-scale tests of the refuge strategy have been problematic. Here we tested the refuge strategy with 8 y of data on refuges and resistance to the insecticide pyriproxyfen in 84 populations of the sweetpotato whitefly (Bemisia tabaci) from cotton fields in central Arizona. We found that spatial variation in resistance to pyriproxyfen within each year was not affected by refuges of melons or alfalfa near cotton fields. However, resistance was negatively associated with the area of cotton refuges and positively associated with the area of cotton treated with pyriproxyfen. A statistical model based on the first 4 y of data, incorporating the spatial distribution of cotton treated and not treated with pyriproxyfen, adequately predicted the spatial variation in resistance observed in the last 4 y of the study, confirming that cotton refuges delayed resistance and treated cotton fields accelerated resistance. By providing a systematic assessment of the effectiveness of refuges and the scale of their effects, the spatially explicit approach applied here could be useful for testing and improving the refuge strategy in other crop-pest systems. pesticide resistance | predictive evolutionary models | pest management | resistance management P opulation growth will continue to favor agricultural intensification for decades. Because agricultural intensification is associated with increased pest pressure, pesticides generally help to increase yield (1-3). Although significant progress has been made to reduce reliance on pesticides (4, 5), an increasing number of insects and mites exhibit field-evolved resistance to synthetic pesticides, Bacillus thuringiensis (Bt) sprays, and transgenic Bt crops (6, 7). Negative consequences of resistance include increased pesticide use, disruption of food webs and ecosystem services, increased risk to human health, and loss of profits for farmers and industry (1, 3). One of the main strategies for delaying resistance promotes survival of susceptible pests by providing refuges, which are areas of host plants where pests are not exposed to an insecticide. Theory predicts that refuges will slow the evolution of resistance by reducing the fitness advantage of resistant individuals (7-9). Refuges can also reduce the heritability of resistance when susceptible individuals mate with resistant individuals surviving exposure to an insecticide (7). Empirical support for the refuge strategy was provided by short-term laboratory and greenhouse experiments (10, 11). Although these experiments test the hypothesis that mating between susceptible and resistant individuals delays the evolution of resistance, they do not consider several factors that affect resistance in the field (7-9), and thus only provide partial support for effectiveness of the refuge strategy in the field. Retrospective analyses of variation in resistance evolution in the field also suggest that refuges have been effective, but these previous tests have been based primarily on comparisons among species, or qualitative comparisons within species based on a limited number of widely separated geographic areas (12, 13). In such tests, factors that vary among species or geographic areas can confound the effects of refuges. Accordingly, large-scale field tests of the refuge strategy for a single species within a geographic area where factors affecting resistance are similar are needed to test the refuge strategy more rigorously. Moreover, tests of predictive refuge strategy models are required to determine if the refuge strategy can delay resistance (14). Furthermore, to improve our ability to develop efficient refuge strategies, empirical approaches are necessary to characterize effects of refuges on resistance evolution (7, 15). Here we tested the refuge strategy using 8 y of data on refuges and resistance to the insecticide pyriproxyfen in 84 populations of the sweetpotato whitefly (Bemisia tabaci) sampled in cotton fields of central Arizona. We studied the B biotype of B. tabaci, also known as the Asia Minor-Middle East 1 species, which is a key pest of cotton and other crops in Arizona and worldwide (16). The insect growth regulators pyriproxyfen (a juvenile hormone analog) and buprofezin (a chitin synthesis inhibitor) are selective insecticides that have been used for whitefly control in Arizona cotton (Gossypium spp.) since 1996 (17, 18). A single application of either insecticide on cotton when B. tabaci populations start to increase has substantially reduced sprays of broad-spectrum insecticides, helped to conserve natural enemies, and restored farmers &apos; profits (18, 19). To deter rapid evolution of resistance, farmers in Arizona generally have not used pyriproxyfen to control B. tabaci on crops other than cotton Although B. tabaci is polyphagous, few whitefly crops other than cotton are available in central Arizona from June to September, when pyriproxyfen is sprayed on cotton. In principle, crops that could act as refuges include spring melons (Citrullus lanatus and Cucumis melo), alfalfa (Medicago sativa) and cotton not treated with pyriproxyfen (referred to hereafter as untreated cotton). B. tabac

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe