Self-Affirmation Improves Problem-Solving under Stress

High levels of acute and chronic stress are known to impair problem-solving and creativity on a broad range of tasks. Despite this evidence, we know little about protective factors for mitigating the deleterious effects of stress on problem-solving. Building on previous research showing that self-affirmation can buffer stress, we tested whether an experimental manipulation of self-affirmation improves problem-solving performance in chronically stressed participants. Eighty undergraduates indicated their perceived chronic stress over the previous month and were randomly assigned to either a self-affirmation or control condition. They then completed 30 difficult remote associate problem-solving items under time pressure in front of an evaluator. Results showed that self-affirmation improved problem-solving performance in underperforming chronically stressed individuals. This research suggests a novel means for boosting problem-solving under stress and may have important implications for understanding how self-affirmation boosts academic achievement in school settings. © 2013 Creswell et al

A High Statistics Search for Ultra-High Energy Gamma-Ray Emission from Cygnus X-3 and Hercules X-1

We have carried out a high statistics (2 Billion events) search for ultra-high energy gamma-ray emission from the X-ray binary sources Cygnus X-3 and Hercules X-1. Using data taken with the CASA-MIA detector over a five year period (1990-1995), we find no evidence for steady emission from either source at energies above 115 TeV. The derived upper limits on such emission are more than two orders of magnitude lower than earlier claimed detections. We also find no evidence for neutral particle or gamma-ray emission from either source on time scales of one day and 0.5 hr. For Cygnus X-3, there is no evidence for emission correlated with the 4.8 hr X-ray periodicity or with the occurrence of large radio flares. Unless one postulates that these sources were very active earlier and are now dormant, the limits presented here put into question the earlier results, and highlight the difficulties that possible future experiments will have in detecting gamma-ray signals at ultra-high energies.Comment: 26 LaTeX pages, 16 PostScript figures, uses psfig.sty to be published in Physical Review

Movement of the human foot in 100 pain free individuals aged 18–45 : implications for understanding normal foot function

Background: Understanding motion in the normal healthy foot is a prerequisite for understanding the effects of pathology and thereafter setting targets for interventions. Quality foot kinematic data from healthy feet will also assist the development of high quality and research based clinical models of foot biomechanics. To address gaps in the current literature we aimed to describe 3D foot kinematics using a 5 segment foot model in a population of 100 pain free individuals. Methods: Kinematics of the leg, calcaneus, midfoot, medial and lateral forefoot and hallux were measured in 100 self reported healthy and pain free individuals during walking. Descriptive statistics were used to characterise foot movements. Contributions from different foot segments to the total motion in each plane were also derived to explore functional roles of different parts of the foot. Results: Foot segments demonstrated greatest motion in the sagittal plane, but large ranges of movement in all planes. All foot segments demonstrated movement throughout gait, though least motion was observed between the midfoot and calcaneus. There was inconsistent evidence of movement coupling between joints. There were clear differences in motion data compared to foot segment models reported in the literature. Conclusions: The data reveal the foot is a multiarticular structure, movements are complex, show incomplete evidence of coupling, and vary person to person. The data provide a useful reference data set against which future experimental data can be compared and may provide the basis for conceptual models of foot function based on data rather than anecdotal observations

Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×$10^{−9}$). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci

The Pennsylvania Environmental Resource Consortium: A State-Wide Collaborative Network for Sustainable, Outreach, Education, and Action

This paper explores the organizational theory, programs, and concomitant challenges faced by a state-level higher education consortium for sustainability in the United States, the Pennsylvania Environmental Resource Consortium (PERC). We provide insights for other institutions of higher education that may want to form consortia or consider changes to existing consortia. PERC members collaborate to advance sustainability on member campuses, in local communities, and across the Commonwealth. PERC envisions thriving, just communities on a healthy planet, and seeks to inspire higher education communities throughout the Commonwealth to lead transformational sustainability efforts through example, expertise, and collaboration. This chapter provides a brief theoretical background in PERC as a collaborative. It shares history and context for PERC’s mission and activities as well as an overview of its programs. It includes reflections on challenges to collaboration and coordination, including from COVID-19, changing digital technology, disparities among PERC institutions, accelerating sustainability challenges in the Anthropocene, anti-intellectualism and hyperpartisanship in the Commonwealth and the United States, and PERC’s own staffing, volunteerism, participation, and funding challenges. The chapter closes by revisiting the organization’s 2021–2025 Strategic Plan as an invitation to consider how cooperation, coordination, and collaboration among higher education institutions can positively impact sustainability across sectors

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

Abstracts from the 3rd Conference on Aneuploidy and Cancer: Clinical and Experimental Aspects

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