Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate

Acknowledgments: This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. Furthermore, this work has been financially supported by the Leibniz Society (project ECONS), and the Stordalen Foundation (JFD). For certain calculations, the software packages pyunicorn (Donges et al. 2013a) and igraph (Csa´rdi and Nepusz 2006) were used. The authors would like to thank Manoel F. Cardoso, Niklas Boers, and the reviewers for helpful comments on the manuscript. Open Access: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Peer reviewedPostprin

Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe

Analysis of observations indicates that there was a rapid increase in summer (June-August, JJA) mean surface air temperature (SAT) since the mid-1990s over Western Europe. Accompanying this rapid warming are significant increases in summer mean daily maximum temperature, daily minimum temperature, annual hottest day temperature and warmest night temperature, and an increase in frequency of summer days and tropical nights, while the change in the diurnal temperature range (DTR) is small. This study focuses on understanding causes of the rapid summer warming and associated temperature extreme changes. A set of experiments using the atmospheric component of the state-of-the-art HadGEM3 global climate model have been carried out to quantify relative roles of changes in sea surface temperature (SST)/sea ice extent (SIE), anthropogenic greenhouse gases (GHGs), and anthropogenic aerosols (AAer). Results indicate that the model forced by changes in all forcings reproduces many of the observed changes since the mid-1990s over Western Europe. Changes in SST/SIE explain 62.2% ± 13.0% of the area averaged seasonal mean warming signal over Western Europe, with the remaining 37.8% ± 13.6% of the warming explained by the direct impact of changes in GHGs and AAer. Results further indicate that the direct impact of the reduction of AAer precursor emissions over Europe, mainly through aerosol-radiation interaction with additional contributions from aerosol-cloud interaction and coupled atmosphere-land surface feedbacks, is a key factor for increases in annual hottest day temperature and in frequency of summer days. It explains 45.5% ± 17.6% and 40.9% ± 18.4% of area averaged signals for these temperature extremes. The direct impact of the reduction of AAer precursor emissions over Europe acts to increase DTR locally, but the change in DTR is countered by the direct impact of GHGs forcing. In the next few decades, greenhouse gas concentrations will continue to rise and AAer precursor emissions over Europe and North America will continue to decline. Our results suggest that the changes in summer seasonal mean SAT and temperature extremes over Western Europe since the mid-1990s are most likely to be sustained or amplified in the near term, unless other factors intervene

Artificial intelligence in biological activity prediction

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.This study was supported by the European Commission through project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408), and by the Portuguese FCT under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020.info:eu-repo/semantics/publishedVersio

Recent advances in the understanding of Chlamydophila pecorum infections, sixteen years after it was named as the fourth species of the Chlamydiaceae family

Chlamydophila pecorum found in the intestine and vaginal mucus of asymptomatic ruminants has also been associated with different pathological conditions in ruminants, swine and koalas. Some endangered species such as water buffalos and bandicoots have also been found to be infected by C. pecorum. The persistence of C. pecorum strains in the intestine and vaginal mucus of ruminants could cause long-term sub-clinical infection affecting the animal’s health. C. pecorum strains present many genetic and antigenic variations, but coding tandem repeats have recently been found in some C. pecorum genes, allowing C. pecorum strains isolated from sick animals to be differentiated from those isolated from asymptomatic animals. This review provides an update on C. pecorum infections in different animal hosts and the implications for animal health. The taxonomy, typing and genetic aspects of C. pecorum are also reviewed

Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

<p>Abstract</p> <p>Background</p> <p>While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species.</p> <p>Results</p> <p>We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (<it>MLH1</it>, <it>PMS2</it>, <it>EPHB4 </it>) and could confirm more than 73% of them based on evidence in the literature.</p> <p>Conclusions</p> <p>The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.</p

Wing pathology of white-nose syndrome in bats suggests life-threatening disruption of physiology

White-nose syndrome (WNS) is causing unprecedented declines in several species of North American bats. The characteristic lesions of WNS are caused by the fungus Geomyces destructans, which erodes and replaces the living skin of bats while they hibernate. It is unknown how this infection kills the bats. We review here the unique physiological importance of wings to hibernating bats in relation to the damage caused by G. destructans and propose that mortality is caused by catastrophic disruption of wing-dependent physiological functions. Mechanisms of disease associated with G. destructans seem specific to hibernating bats and are most analogous to disease caused by chytrid fungus in amphibians

A Common CNR1 (Cannabinoid Receptor 1) Haplotype Attenuates the Decrease in HDL Cholesterol That Typically Accompanies Weight Gain

We have previously shown that genetic variability in CNR1 is associated with low HDL dyslipidemia in a multigenerational obesity study cohort of Northern European descent (209 families, median  = 10 individuals per pedigree). In order to assess the impact of CNR1 variability on the development of dyslipidemia in the community, we genotyped this locus in all subjects with class III obesity (body mass index >40 kg/m2) participating in a population-based biobank of similar ancestry. Twenty-two haplotype tagging SNPs, capturing the entire CNR1 gene locus plus 15 kb upstream and 5 kb downstream, were genotyped and tested for association with clinical lipid data. This biobank contains data from 645 morbidly obese study subjects. In these subjects, a common CNR1 haplotype (H3, frequency 21.1%) is associated with fasting TG and HDL cholesterol levels (p = 0.031 for logTG; p = 0.038 for HDL-C; p = 0.00376 for log[TG/HDL-C]). The strength of this relationship increases when the data are adjusted for age, gender, body mass index, diet and physical activity. Mean TG levels were 160±70, 155±70, and 120±60 mg/dL for subjects with 0, 1, and 2 copies of the H3 haplotype. Mean HDL-C levels were 45±10, 47±10, and 48±9 mg/dL, respectively. The H3 CNR1 haplotype appears to exert a protective effect against development of obesity-related dyslipidemia

Cytokine Plasma Levels: Reliable Predictors for Radiation Pneumonitis?

BACKGROUND: Radiotherapy (RT) is the primary treatment modality for inoperable, locally advanced non-small-cell lung cancer (NSCLC), but even with highly conformal treatment planning, radiation pneumonitis (RP) remains the most serious, dose-limiting complication. Previous clinical reports proposed that cytokine plasma levels measured during RT allow to estimate the individual risk of patients to develop RP. The identification of such cytokine risk profiles would facilitate tailoring radiotherapy to maximize treatment efficacy and to minimize radiation toxicity. However, cytokines are produced not only in normal lung tissue after irradiation, but are also over-expressed in tumour cells of NSCLC specimens. This tumour-derived cytokine production may influence circulating plasma levels in NSCLC patients. The aim of the present study was to investigate the prognostic value of TNF-alpha, IL-1beta, IL-6 and TGF-beta1 plasma levels to predict radiation pneumonitis and to evaluate the impact of tumour-derived cytokine production on circulating plasma levels in patients irradiated for NSCLC. METHODOLOGY/PRINCIPAL FINDINGS: In 52 NSCLC patients (stage I-III) cytokine plasma levels were investigated by ELISA before and weekly during RT, during follow-up (1/3/6/9 months after RT), and at the onset of RP. Tumour biopsies were immunohistochemically stained for IL-6 and TGF-beta1, and immunoreactivity was quantified (grade 1-4). RP was evaluated according to LENT-SOMA scale. Tumour response was assessed according to RECIST criteria by chest-CT during follow-up. In our clinical study 21 out of 52 patients developed RP (grade I/II/III/IV: 11/3/6/1 patients). Unexpectedly, cytokine plasma levels measured before and during RT did not correlate with RP incidence. In most patients IL-6 and TGF-beta1 plasma levels were already elevated before RT and correlated significantly with the IL-6 and TGF-beta1 production in corresponding tumour biopsies. Moreover, IL-6 and TGF-beta1 plasma levels measured during follow-up were significantly associated with the individual tumour responses of these patients. CONCLUSIONS/SIGNIFICANCE: The results of this study did not confirm that cytokine plasma levels, neither their absolute nor any relative values, may identify patients at risk for RP. In contrast, the clear correlations of IL-6 and TGF-beta1 plasma levels with the cytokine production in corresponding tumour biopsies and with the individual tumour responses suggest that the tumour is the major source of circulating cytokines in patients receiving RT for advanced NSCLC