Distribution of Bats in Bottomland Hardwood Forests of the Arkansas Delta Region

Bat distribution data is incomplete for the delta region of Arkansas. We extensively surveyed 16 counties within the Mississippi alluvial plain that comprises the delta from late spring to early fall 2004 using mist nets. We obtained 44 new county records for 9 species: Myotis lucifigus, M. austroriparius, Pipistrellus subflavus, Eptesicus fuscus, Lasiurus seminolus, L. borealis, L. cinereus, Nycticeius humeralis, and Corynorhinus rafinesquii. We generated updated distribution maps for these species and eastward Arkansas range expansions were documented for L.seminolus. Possible sampling concerns and research directions are discussed in relation to the needs of bats inhabiting bottomland forests of the delta, particularly M. austroriparius and C. rafinesquii

Biotic responses to climate extremes in terrestrial ecosystems

Anthropogenic climate change is increasing the incidence of climate extremes. Consequences of climate extremes on biodiversity can be highly detrimental, yet few studies also suggest beneficial effects of climate extremes on certain organisms. To obtain a general understanding of ecological responses to climate extremes, we present a review of how 16 major taxonomic/functional groups (including microorganisms, plants, invertebrates, and vertebrates) respond during extreme drought, precipitation, and temperature.Most taxonomic/functional groups respond negatively to extreme events, whereas groups such as mosses, legumes, trees, and vertebrate predators respond most negatively to climate extremes. We further highlight that ecological recovery after climate extremes is challenging to predict purely based on ecological responses during or immediately after climate extremes. By accounting for the characteristics of the recovering species, resource availability, and species interactions with neighboring competitors or facilitators, mutualists, and enemies, we outline a conceptual framework to better predict ecological recovery in terrestrial ecosystems

A study of patent thickets

Report analysing whether entry of UK enterprises into patenting in a technology area is affected by patent thickets in the technology area

Covariation of depressive symptoms, parkinsonism, and post-dexamethasone plasma cortisol levels in a bipolar patient: simultaneous response to ECT and lithium carbonate

: A patient presented with concurrent mood congruent delusions, parkinsonism, and elevated post-dexamethasone plasma cortisol levels. This triad could result from simultaneous development of cholinergic-monoaminergic dysfunction within critical limbic and extrapyramidal loci. The magnitude of each abnormality decreased in concert during a course of electroconvulsive therapy (ECT). Remaining abnormalities disappeared during treatment with lithium. Actions of ECT and lithium on muscarinic systems are reviewed, and a strategy for testing the hypothesis that dysfunction of cholinergic-monoaminergic mechanisms develops in parallel in different neural networks is considered.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66202/1/j.1600-0447.1986.tb06229.x.pd

“Forward Genetics” as a Method to Maximize Power and Cost-Efficiency in Studies of Human Complex Traits

There is increasing interest in methods to disentangle the relationship between genotype and (endo)phenotypes in human complex traits. We present a population-based method of increasing the power and cost-efficiency of studies by selecting random individuals with a particular genotype and then assessing the accompanying quantitative phenotypes. Using statistical derivations, power- and cost graphs we show that such a “forward genetics” approach can lead to a marked reduction in sample size and costs. This approach is particularly apt for implementing in epidemiological studies for which DNA is already available but the phenotyping costs are high

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer.

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.We thank the two anonymous reviewers for their constructive comments. This research was supported by the National Institutes of Health (NIH) grant R01CA169122; P.W. was supported by NIH grants R01HL116720 and R21HL126032. S.H.O. was supported by NIH grant P30CA008748. R.E.N. and the Queensland Pancreatic Cancer Study were funded by the Australian National Health and Medical Research Council. The authors thank Ms. Jessica Swann and the National Institute of Statistical Sciences writing workshop for editorial assistance and suggestions. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing computing resources. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. The authors declare that there is no conflict of interest

MiRNA-Related SNPs and Risk of Esophageal Adenocarcinoma and Barrett's Esophagus: Post Genome-Wide Association Analysis in the BEACON Consortium.

Incidence of esophageal adenocarcinoma (EA) has increased substantially in recent decades. Multiple risk factors have been identified for EA and its precursor, Barrett's esophagus (BE), such as reflux, European ancestry, male sex, obesity, and tobacco smoking, and several germline genetic variants were recently associated with disease risk. Using data from the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) genome-wide association study (GWAS) of 2,515 EA cases, 3,295 BE cases, and 3,207 controls, we examined single nucleotide polymorphisms (SNPs) that potentially affect the biogenesis or biological activity of microRNAs (miRNAs), small non-coding RNAs implicated in post-transcriptional gene regulation, and deregulated in many cancers, including EA. Polymorphisms in three classes of genes were examined for association with risk of EA or BE: miRNA biogenesis genes (157 SNPs, 21 genes); miRNA gene loci (234 SNPs, 210 genes); and miRNA-targeted mRNAs (177 SNPs, 158 genes). Nominal associations (P0.50), and we did not find evidence for interactions between variants analyzed and two risk factors for EA/BE (smoking and obesity). This analysis provides the most extensive assessment to date of miRNA-related SNPs in relation to risk of EA and BE. While common genetic variants within components of the miRNA biogenesis core pathway appear unlikely to modulate susceptibility to EA or BE, further studies may be warranted to examine potential associations between unassessed variants in miRNA genes and targets with disease risk.This work was supported by the National Institutes of Health [R01CA136725 to T.L.V. and D.C.W, T32CA009168 to T.L.V, and K05CA124911 to T.L.V.]. Additional funding sources for individual studies included in the BEACON GWAS, and for BEACON investigators, have been acknowledged previously (16).This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.012861