White-tailed deer hunting and habitat use in Robert Allerton Park

Using Robert Allerton Park (RAP) and the immediately surrounding properties in east-central Illinois as a study site, my objectives were to investigate changes in deer habitat use and spatial clustering following 10 years of deer removal in RAP. I evaluated changes in annual deer counts within RAP’s three main habitat types, dry mesic upland forest, wet mesic floodplain forest and developed land using a generalized linear mixed model. Annual counts were categorized into two periods: no deer removal (1988-2004) and deer removal (2005-2015). Second, I used Moran’s I spatial autocorrelation measures to evaluate annual changes in deer clustering. To evaluate changes in spatial clustering as a result of deer removal, I used Getis-Ord General Gi* hot spot analysis to compare spatial clustering between periods of no removal and removal. As expected, my results indicate that the number of deer removed annually decreased deer count in RAP. When analyzed by period, deer removal affected deer count, however, this impact varied between habitat types. Wet mesic floodplain forest and developed areas experienced insignificant reductions in deer count between periods whereas dry mesic upland forested habitats experienced significant reductions. Moreover, I detected an increasing trend in annual deer clustering across the study area prior to deer removal. Once the removal program was implemented, I observed a decrease in deer clustering across years. Changes were evident in both cluster location and size across the study site between periods. In conclusion, more deer were observed in wet mesic floodplain forest and developed land following removal which could be explained by the deer removal program, preferential habitat selection, temporal changes in understory quality or a combination of these factors

The atypical 'hippocampal' glutamate receptor coupled to phospholipase D that controls stretch-sensitivity in primary mechanosensory nerve endings is homomeric purely metabotropic GluK2

ACKNOWLEDGEMENTS We would like to thank: Prof. Christophe Mulle, University of Bordeaux, France for the generous donation of the GluK2-Neo mice; Prof. Roberto Pellicciari and Prof. Maura Marinozzi, University of Perugia, Italy for the generous gift of PCCG-13; the Microscopy and Histology core facility at the Institute of Medical Sciences, University of Aberdeen for their support and assistance in some of the imaging in this work. We would also like to thank Prof. Gernot Riedel, University of Aberdeen UK and Prof. David Jane, University of Bristol UK for helpful comments during the work and discussion about drafts of this manuscript.Peer reviewedPublisher PD

Understanding the Securitization of Subprime Mortgage Credit

In this paper, we provide an overview of the subprime mortgage securitization process and the seven key informational frictions that arise. We discuss the ways that market participants work to minimize these frictions and speculate on how this process broke down. We continue with a complete picture of the subprime borrower and the subprime loan, discussing both predatory borrowing and predatory lending. We present the key structural features of a typical subprime securitization, document how rating agencies assign credit ratings to mortgage-backed securities, and outline how these agencies monitor the performance of mortgage pools over time. Throughout the paper, we draw upon the example of a mortgage pool securitized by New Century Financial during 2006

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

The More Friends, the Less Political Talk? Predictors of Facebook Discussions Among College Students

Although previous research has indicated that Facebook users, especially young adults, can cultivate their civic values by talking about public matters with their Facebook friends, little research has examined the predictors of political discussion on Facebook. Using survey data from 442 college students in the United States, this study finds that individual characteristics and network size influence college students' expressive behavior on Facebook related to two controversial topics: gay rights issues and politics. In line with previous studies about offline political discussion, the results show that conflict avoidance and ambivalence about target issues are negatively associated with Facebook discussions. Perhaps the most interesting finding is that users who have a large number of Facebook friends are less likely to talk about politics and gay rights issues on Facebook despite having access to increasing human and information resources. Theoretical implications of these findings and future directions are addressed.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140346/1/cyber.2013.0477.pd