Emotions, Partisanship, and Misperceptions: How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political Misinformation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112200/1/jcom12164.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/112200/2/jcom12164-sup-0001-AppendixS1.pd

Driving a Wedge Between Evidence and Beliefs: How Online Ideological News Exposure Promotes Political Misperceptions

This article has 2 goals: to provide additional evidence that exposure to ideological online news media contributes to political misperceptions, and to test 3 forms this media‐effect might take. Analyses are based on representative survey data collected during the 2012 U.S. presidential election (N = 1,004). Panel data offer persuasive evidence that biased news site use promotes inaccurate beliefs, while cross‐sectional data provide insight into the nature of these effects. There is no evidence that exposure to ideological media reduces awareness of politically unfavorable evidence, though in some circumstances biased media do promote misunderstandings of it. The strongest and most consistent influence of ideological media exposure is to encourage inaccurate beliefs regardless of what consumers know of the evidence.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134259/1/jcc412164_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134259/2/jcc412164.pd

Analysis of alcohol dependence phenotype in the COGA families using covariates to detect linkage

Linkage analysis methods that incorporate etiological heterogeneity of complex diseases are likely to demonstrate greater power than traditional linkage analysis methods. Several such methods use covariates to discriminate between linked and unlinked pedigrees with respect to a certain disease locus. Here we apply several such methods including two mixture models, ordered subset analysis, and a conditional logistic model to genome scan data on the DSM-IV alcohol dependence phenotype on the Collaborative Studies on Genetics of Alcoholism families, and compare the results to traditional nonparametric linkage analysis. In general, there was little agreement among the various covariate-based linkage statistics. Linkage signals with empirical p-values less than 0.001 were detected on chromosomes 3, 4, 7, 10, and 12, with the highest peak occurring at the GABRB1 gene using the ecb21 covariate

Incidental Exposure, Selective Exposure, and Political Information Sharing: Integrating Online Exposure Patterns and Expression on Social Media

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139929/1/jcc412199.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139929/2/jcc412199-sup-0001-FigureS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139929/3/jcc412199_am.pd

Shared morphological consequences of global warming in North American migratory birds

Increasing temperatures associated with climate change are predicted to cause reductions in body size, a key determinant of animal physiology and ecology. Using a four‐decade specimen series of 70 716 individuals of 52 North American migratory bird species, we demonstrate that increasing annual summer temperature over the 40‐year period predicts consistent reductions in body size across these diverse taxa. Concurrently, wing length – an index of body shape that impacts numerous aspects of avian ecology and behaviour – has consistently increased across species. Our findings suggest that warming‐induced body size reduction is a general response to climate change, and reveal a similarly consistent and unexpected shift in body shape. We hypothesise that increasing wing length represents a compensatory adaptation to maintain migration as reductions in body size have increased the metabolic cost of flight. An improved understanding of warming‐induced morphological changes is important for predicting biotic responses to global change.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153188/1/ele13434-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153188/2/ele13434.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153188/3/ele13434_am.pd

Final Report of the AFIT Quality Initiative Internal Discovery Committee

This document contains results of a study designed to document the key elements for student success at AFIT in our continuing education and graduate programs and discover to what degree they exist at AFIT. The effort represents an attempt to guide improvement of our graduate and continuing education programs through experience available from our faculty, staff and students. The process outlined herein was designed to achieve success by allowing the participants to define what it means to succeed and then self-assess the presence of these factors at AFIT. It’s therefore a true internal discovery process since its output reflects the state of our internal understanding of teaching and learning excellence. This inclusive approach, which garnered participation from 400 people across AFIT’s schools, will be used in conjunction with the external committee\u27s recommendations to determine a course of action to invest into AFIT\u27s instructional capabilities

Adjusting the melting point of a model system via Gibbs-Duhem integration: application to a model of Aluminum

Model interaction potentials for real materials are generally optimized with respect to only those experimental properties that are easily evaluated as mechanical averages (e.g., elastic constants (at T=0 K), static lattice energies and liquid structure). For such potentials, agreement with experiment for the non-mechanical properties, such as the melting point, is not guaranteed and such values can deviate significantly from experiment. We present a method for re-parameterizing any model interaction potential of a real material to adjust its melting temperature to a value that is closer to its experimental melting temperature. This is done without significantly affecting the mechanical properties for which the potential was modeled. This method is an application of Gibbs-Duhem integration [D. Kofke, Mol. Phys.78, 1331 (1993)]. As a test we apply the method to an embedded atom model of aluminum [J. Mei and J.W. Davenport, Phys. Rev. B 46, 21 (1992)] for which the melting temperature for the thermodynamic limit is 826.4 +/- 1.3K - somewhat below the experimental value of 933K. After re-parameterization, the melting temperature of the modified potential is found to be 931.5K +/- 1.5K.Comment: 9 pages, 5 figures, 4 table

Complementary genetic and genomic approaches help characterize the linkage group I seed protein QTL in soybean

Background: The nutritional and economic value of many crops is effectively a function of seed protein and oil content. Insight into the genetic and molecular control mechanisms involved in the deposition of these constituents in the developing seed is needed to guide crop improvement. A quantitative trait locus (QTL) on Linkage Group I (LG I) of soybean (Glycine max (L.) Merrill) has a striking effect on seed protein content. Results: A soybean near-isogenic line (NIL) pair contrasting in seed protein and differing in an introgressed genomic segment containing the LG I protein QTL was used as a resource to demarcate the QTL region and to study variation in transcript abundance in developing seed. The LG I QTL region was delineated to less than 8.4 Mbp of genomic sequence on chromosome 20. Using Affymetrix® Soy GeneChip and high-throughput Illumina® whole transcriptome sequencing platforms, 13 genes displaying significant seed transcript accumulation differences between NILs were identified that mapped to the 8.4 Mbp LG I protein QTL region. Conclusions: This study identifies gene candidates at the LG I protein QTL for potential involvement in the regulation of protein content in the soybean seed. The results demonstrate the power of complementary approaches to characterize contrasting NILs and provide genome-wide transcriptome insight towards understanding seed biology and the soybean genome

Effect of metformin on maternal and fetal outcomes in obese pregnant women (EMPOWaR):a randomised, double-blind, placebo-controlled trial

Background: Maternal obesity is associated with increased birthweight, and obesity and premature mortality in adult offspring. The mechanism by which maternal obesity leads to these outcomes is not well understood, but maternal hyperglycaemia and insulin resistance are both implicated. We aimed to establish whether the insulin sensitising drug metformin improves maternal and fetal outcomes in obese pregnant women without diabetes. Methods: We did this randomised, double-blind, placebo-controlled trial in antenatal clinics at 15 National Health Service hospitals in the UK. Pregnant women (aged ≥16 years) between 12 and 16 weeks' gestation who had a BMI of 30 kg/m2 or more and normal glucose tolerance were randomly assigned (1:1), via a web-based computer-generated block randomisation procedure (block size of two to four), to receive oral metformin 500 mg (increasing to a maximum of 2500 mg) or matched placebo daily from between 12 and 16 weeks' gestation until delivery of the baby. Randomisation was stratified by study site and BMI band (30–39 vs ≥40 kg/m2). Participants, caregivers, and study personnel were masked to treatment assignment. The primary outcome was Z score corresponding to the gestational age, parity, and sex-standardised birthweight percentile of liveborn babies delivered at 24 weeks or more of gestation. We did analysis by modified intention to treat. This trial is registered, ISRCTN number 51279843. Findings: Between Feb 3, 2011, and Jan 16, 2014, inclusive, we randomly assigned 449 women to either placebo (n=223) or metformin (n=226), of whom 434 (97%) were included in the final modified intention-to-treat analysis. Mean birthweight at delivery was 3463 g (SD 660) in the placebo group and 3462 g (548) in the metformin group. The estimated effect size of metformin on the primary outcome was non-significant (adjusted mean difference −0·029, 95% CI −0·217 to 0·158; p=0·7597). The difference in the number of women reporting the combined adverse outcome of miscarriage, termination of pregnancy, stillbirth, or neonatal death in the metformin group (n=7) versus the placebo group (n=2) was not significant (odds ratio 3·60, 95% CI 0·74–17·50; p=0·11). Interpretation: Metformin has no significant effect on birthweight percentile in obese pregnant women. Further follow-up of babies born to mothers in the EMPOWaR study will identify longer-term outcomes of metformin in this population; in the meantime, metformin should not be used to improve pregnancy outcomes in obese women without diabetes

A Framework For Detecting Noncoding Rare-Variant associations of Large-Scale Whole-Genome Sequencing Studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 toPMed samples. We also analyze five non-lipid toPMed traits