Three Ways of Combining Genotyping and Resequencing in Case-Control Association Studies

We describe three statistical results that we have found to be useful in case-control genetic association testing. All three involve combining the discovery of novel genetic variants, usually by sequencing, with genotyping methods that recognize previously discovered variants. We first consider expanding the list of known variants by concentrating variant-discovery in cases. Although the naive inclusion of cases-only sequencing data would create a bias, we show that some sequencing data may be retained, even if controls are not sequenced. Furthermore, for alleles of intermediate frequency, cases-only sequencing with bias-correction entails little if any loss of power, compared to dividing the same sequencing effort among cases and controls. Secondly, we investigate more strongly focused variant discovery to obtain a greater enrichment for disease-related variants. We show how case status, family history, and marker sharing enrich the discovery set by increments that are multiplicative with penetrance, enabling the preferential discovery of high-penetrance variants. A third result applies when sequencing is the primary means of counting alleles in both cases and controls, but a supplementary pooled genotyping sample is used to identify the variants that are very rare. We show that this raises no validity issues, and we evaluate a less expensive and more adaptive approach to judging rarity, based on group-specific variants. We demonstrate the important and unusual caveat that this method requires equal sample sizes for validity. These three results can be used to more efficiently detect the association of rare genetic variants with disease

Landscape structure, human disturbance and crop management affect foraging ground selection by migrating geese

It is well known that agricultural intensification has caused severe population declines among bird species which use farmland for breeding and overwintering, while migrating bird species may benefit from intensive farming, but in turn damage crops. Knowledge of the habitat selection of migrating birds is important from both a conservation and agro-economic point of view. We investigated the habitat preferences of three common migrating goose species: White-fronted Goose Anser albifrons, Bean Goose A. fabalis and Greylag Goose A. anser during the autumn of 2009 in western Poland. A total of 24 flocks of these species were identified. Geese preferred large, elevated fields that were remote from forests and human settlements but in close proximity to a lake. Geese selected maize stubbles and avoided winter cereals. They selected sites in landscapes with a lower diversity of crops. Flock size was negatively correlated with the proportion of pastures in the landscape, but it increased with field size, distance to forest and distance to town. Our results are in contrast with the paradigm that less intensive farmland positively influences habitat use by birds during foraging. We advise the delayed ploughing of stubbles with the aim of creating appropriate foraging habitats for geese and minimizing damage to cereal crops

Short-term wind power prediction based on extreme learning machine with error correction

Introduction: Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind. The fluctuation of the wind generation has a great impact on the unit commitment. Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way. Methods: In this paper, a combined approach based on Extreme Learning Machine (ELM) and an error correction model is proposed to predict wind power in the short-term time scale. Firstly an ELM is utilized to forecast the short-term wind power. Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method. Results: For short-term forecasting, the Extreme Learning Machine (ELM) doesn’t perform well. The overall NRMSE (Normalized Root Mean Square Error) of forecasting results for 66 days is 21.09 %. For the ultra-short term forecasting after error correction, most of forecasting errors lie in the interval of [−10 MW, 10 MW]. The error distribution is concentrated and almost unbiased. The overall NRMSE is 5.76 %. Conclusion: The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error (NRMSE)

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at √s = 7 TeV with the ATLAS detector

This paper presents a measurement of the cross-section for high transverse momentum W and Z bosons produced in pp collisions and decaying to all-hadronic final states. The data used in the analysis were recorded by the ATLAS detector at the CERN Large Hadron Collider at a centre-of-mass energy of √s = 7 TeV;{\rm Te}{\rm V}$and correspond to an integrated luminosity of$4.6\;{\rm f}{{{\rm b}}^{-1}}$. The measurement is performed by reconstructing the boosted W or Z bosons in single jets. The reconstructed jet mass is used to identify the W and Z bosons, and a jet substructure method based on energy cluster information in the jet centre-of-mass frame is used to suppress the large multi-jet background. The cross-section for events with a hadronically decaying W or Z boson, with transverse momentum${{p}_{{\rm T}}}\gt 320\;{\rm Ge}{\rm V}$and pseudorapidity$|\eta |\lt 1.9$, is measured to be${{\sigma }_{W+Z}}=8.5\pm 1.7$ pb and is compared to next-to-leading-order calculations. The selected events are further used to study jet grooming techniques

Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at s√=8 TeV with the ATLAS detector

The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1fb−1 of proton–proton collision data at √s = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via t˜→tχ˜01 or t˜→ bχ˜±1 →bW(∗)χ˜01 , where χ˜01 (χ˜±1 ) denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of t˜ → tχ˜01 . For a branching fraction of 100%, top squark masses in the range 270–645 GeV are excluded for χ˜01 masses below 30 GeV. For a branching fraction of 50% to either t˜ → tχ˜01 or t˜ → bχ˜±1 , and assuming the χ˜±1 mass to be twice the χ˜01 mass, top squark masses in the range 250–550 GeV are excluded for χ˜01 masses below 60 GeV

Search for pair-produced long-lived neutral particles decaying to jets in the ATLAS hadronic calorimeter in ppcollisions at √s=8TeV

The ATLAS detector at the Large Hadron Collider at CERN is used to search for the decay of a scalar boson to a pair of long-lived particles, neutral under the Standard Model gauge group, in 20.3fb−1of data collected in proton–proton collisions at √s=8TeV. This search is sensitive to long-lived particles that decay to Standard Model particles producing jets at the outer edge of the ATLAS electromagnetic calorimeter or inside the hadronic calorimeter. No significant excess of events is observed. Limits are reported on the product of the scalar boson production cross section times branching ratio into long-lived neutral particles as a function of the proper lifetime of the particles. Limits are reported for boson masses from 100 GeVto 900 GeV, and a long-lived neutral particle mass from 10 GeVto 150 GeV

Mindfulness-based cognitive therapy for psychological distress in pregnancy: study protocol for a randomized controlled trial

BACKGROUND: Clinically significant psychological distress in pregnancy is common, with epidemiological research suggesting that between 15 and 25 % of pregnant women experience elevated symptoms of stress, anxiety, and depression. Untreated psychological distress in pregnancy is associated with poor obstetrical outcomes, changes in maternal physiology, elevated incidence of child physical and psychological disorders, and is predictive of maternal postpartum mood disorders. Despite the wide-ranging impact of antenatal psychological distress on mothers and their children, there is a gap in our knowledge about the most effective treatments that are available for psychological distress experienced in pregnancy. Additionally, no trials have focused on potential physiological changes that may occur as a result of receiving mindfulness training in pregnancy. The proposed trial will determine the effectiveness of an 8-week modified Mindfulness-based Cognitive Therapy (MBCT) intervention delivered during pregnancy. METHODS: A randomized controlled trial (RCT) design with repeated measures will be used to evaluate the effectiveness of MBCT to treat psychological distress in pregnancy. A sample of 60 consenting pregnant women aged 18 years and above will be enrolled and randomized to the experimental (MBCT) or control (treatment as usual) condition. Primary (e.g., symptoms of stress, depression, and anxiety), secondary (cortisol, blood pressure (BP), heart rate variability (HRV), and sleep) and other outcome data (e.g., psychological diagnoses) will be collected via a combination of laboratory visits and at-home assessments from both groups at baseline (T(1)), immediately following the intervention (T(2)), and at 3 months postpartum (T(3)). Descriptive statistics will be used to describe sample characteristics. Data will be analyzed using an intention-to-treat approach. Hierarchical linear models will be used to test intervention effects on primary and secondary outcomes. DISCUSSION: The trial is expected to improve knowledge about evidence-based treatments for psychological distress experienced in pregnancy and to evaluate the potential impact of mindfulness-based interventions on maternal physiology. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02214732, registered on 7 August 2014. Protocol Version 2.0., 5 September 2016. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13063-016-1601-0) contains supplementary material, which is available to authorized users