Host behaviour and exposure risk in an insect-pathogen interaction

1. Studies of variability in host resistance to disease generally emphasize variability in susceptibility given exposure, neglecting the possibility that hosts may vary in behaviours that affect the risk of exposure. 2. In many insects, horizontal transmission of baculoviruses occurs when larvae consume foliage contaminated by the cadavers of virus-infected conspecific larvae; so, host behaviour may have a strong effect on the risk of infection. 3. We studied variability in the behaviour of gypsy moth (Lymantria dispar) larvae, which are able to detect and avoid virus-contaminated foliage. 4. Our results show that detection ability can be affected by the family line that larvae originate from, even at some distance froma virus-infected cadaver, and suggest that cadaver-detection ability may be heritable. 5. There is thus the potential for natural selection to act on cadaver-detection ability, and thereby to affect the dynamics of pathogen-driven cycles in gypsy moth populations. 6. We argue that host behaviour is a neglected component in studies of variability in disease resistance. © 2010 The Authors. Journal compilation © 2010 British Ecological Society

Altitude dependence of atmospheric temperature trends: Climate models versus observation

As a consequence of greenhouse forcing, all state of the art general circulation models predict a positive temperature trend that is greater for the troposphere than the surface. This predicted positive trend increases in value with altitude until it reaches a maximum ratio with respect to the surface of as much as 1.5 to 2.0 at about 200 to 400 hPa. However, the temperature trends from several independent observational data sets show decreasing as well as mostly negative values. This disparity indicates that the three models examined here fail to account for the effects of greenhouse forcings.Comment: 9 pages, 3 figure

Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an integrative prior for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses

What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups

Scalar invariance is an unachievable ideal that in practice can only be approximated; often using potentially questionable approaches such as partial invariance based on a stepwise selection of parameter estimates with large modification indices. Study 1 demonstrates an extension of the power and flexibility of the alignment approach for comparing latent factor means in large-scale studies (30 OECD countries, 8 factors, 44 items, N = 249,840), for which scalar invariance is typically not supported in the traditional confirmatory factor analysis approach to measurement invariance(CFA-MI). Importantly, we introduce an alignment-within-CFA (AwC) approach, transforming alignment from a largely exploratory tool into a confirmatory tool, and enabling analyses that previously have not been possible with alignment (testing the invariance of uniquenesses and factor variances/covariances; multiple-group MIMIC models; contrasts on latent means) and structural equation models more generally. Specifically, it also allowed a comparison of gender differences in a 30-country MIMIC AwC (i.e., a SEM with gender as a covariate) and a 60-group AwC CFA (i.e., 30 countries × 2 genders) analysis. Study 2, a simulation study following up issues raised in Study 1, showed that latent means were more accurately estimated with alignment than with the scalar CFA-MI, and particularly with partial invariance scalar models based on the heavily criticized stepwise selection strategy. In summary, alignment augmented by AwC provides applied researchers from diverse disciplines considerable flexibility to address substantively important issues when the traditional CFA-MI scalar model does not fit the data

The discomforting rise of ' public geographies': a 'public' conversation.

In this innovative and provocative intervention, the authors explore the burgeoning ‘public turn’ visible across the social sciences to espouse the need to radically challenge and reshape dominant and orthodox visions of ‘the academy’, academic life, and the role and purpose of the academic

Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types

Background: Immune infiltration of the tumor microenvironment has been associated with improved survival for some patients with solid tumors. The precise makeup and prognostic relevance of immune infiltrates across a broad spectrum of tumors remain unclear

Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model

Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify high-quality (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data