Electroweak corrections to W-boson pair production at the LHC

Vector-boson pair production ranks among the most important Standard-Model benchmark processes at the LHC, not only in view of on-going Higgs analyses. These processes may also help to gain a deeper understanding of the electroweak interaction in general, and to test the validity of the Standard Model at highest energies. In this work, the first calculation of the full one-loop electroweak corrections to on-shell W-boson pair production at hadron colliders is presented. We discuss the impact of the corrections on the total cross section as well as on relevant differential distributions. We observe that corrections due to photon-induced channels can be amazingly large at energies accessible at the LHC, while radiation of additional massive vector bosons does not influence the results significantly.Comment: 29 pages, 15 figures, 4 tables; some references and comments on \gamma\gamma -> WW added; matches version published in JHE

Transcribed-ultra conserved region expression is associated with outcome in high-risk neuroblastoma

<p>Abstract</p> <p>Background</p> <p>Neuroblastoma is the most common, pediatric, extra-cranial, malignant solid tumor. Despite multimodal therapeutic protocols, outcome for children with a high-risk clinical phenotype remains poor, with long-term survival still less than 40%. Hereby, we evaluated the potential of non-coding RNA expression to predict outcome in high-risk, stage 4 neuroblastoma.</p> <p>Methods</p> <p>We analyzed expression of 481 Ultra Conserved Regions (UCRs) by reverse transcription-quantitative real-time PCR and of 723 microRNAs by microarrays in 34 high-risk, stage 4 neuroblastoma patients.</p> <p>Results</p> <p>First, the comparison of 8 short- versus 12 long-term survivors showed that 54 UCRs were significantly (<it>P </it>< 0.0491) over-expressed in the former group. For 48 Ultra Conserved Region (UCRs) the expression levels above the cut-off values defined by ROC curves were strongly associated with good-outcome (OS: 0.0001 <<it>P </it>< 0.0185, EFS: 0.0001 <<it>P </it>< 0.0491). Then we tested the Transcribed-UCR (T-UCR) threshold risk-prediction model on an independent cohort of 14 patients. The expression profile of 28 T-UCRs was significantly associated to prognosis and at least 15 up-regulated T-UCRs are needed to discriminate (<it>P </it>< 0.0001) short- from long-survivors at the highest sensitivity and specificity (94.12%). We also identified a signature of 13 microRNAs differently expressed between long- and short-surviving patients. The comparative analysis of the two classes of non-coding RNAs disclosed that 9 T-UCRs display their expression level that are inversely correlated with expression of 5 complementary microRNAs of the signature, indicating a negative regulation of T-UCRs by direct interaction with microRNAs. Moreover, 4 microRNAs down-regulated in tumors of long-survivors target 3 genes implicated in neuronal differentiation, that are known to be over-expressed in low-risk tumors.</p> <p>Conclusions</p> <p>Our pilot study suggests that a deregulation of the microRNA/T-UCR network may play an important role in the pathogenesis of neuroblastoma. After further validation on a larger independent set of samples, such findings may be applied as the first T-UCR prognostic signature for high-risk neuroblastoma patients.</p

Searching for Heavy Charged Higgs Boson with Jet Substructure at the LHC

We study the heavy charged Higgs boson (from 800 GeV to 1500 GeV in this study) in production associated with a top quark at the LHC with the collision energy $\sqrt{s}=14$ TeV. Such a heavy charged Higgs boson can dominantly decay into a top quark and a bottom quark due to its large Yukawa couplings, like in MSSM. To suppress background events and to confirm the signal, we reconstruct the mass bumps of the heavy charged Higgs boson and the associated top quark. For this purpose, we propose a hybrid-R reconstruction method which utilizes the top tagging technique, a jet substructure technique developed for highly boosted massive particles. By using the full hadronic mode of $p p \to H^{\pm} t \to t tb$ as a test field, we find that this method can greatly reduce the combinatorics in the full reconstruction and can successfully reduce background events down to a controlled level. The sensitivity of LHC to the heavy charged Higgs boson with two $b$ taggings is studied and a $9.5\sigma$ significance can be achieved when $m_{H^\pm} =1 \textrm{TeV}$.Comment: 27 pages, 10 figures, 7 tables; v2: some typos corrected and references added; v3: discussion added, Fig.10 and Table7 updated, version published in JHE

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations

BACKGROUND: The Mini Mental State Examination (MMSE) is a cognitive test that is commonly used as part of the evaluation for possible dementia. OBJECTIVES: To determine the diagnostic accuracy of the Mini‐Mental State Examination (MMSE) at various cut points for dementia in people aged 65 years and over in community and primary care settings who had not undergone prior testing for dementia. SEARCH METHODS: We searched the specialised register of the Cochrane Dementia and Cognitive Improvement Group, MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (OvidSP), LILACS (BIREME), ALOIS, BIOSIS previews (Thomson Reuters Web of Science), and Web of Science Core Collection, including the Science Citation Index and the Conference Proceedings Citation Index (Thomson Reuters Web of Science). We also searched specialised sources of diagnostic test accuracy studies and reviews: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). We attempted to locate possibly relevant but unpublished data by contacting researchers in this field. We first performed the searches in November 2012 and then fully updated them in May 2014. We did not apply any language or date restrictions to the electronic searches, and we did not use any methodological filters as a method to restrict the search overall. SELECTION CRITERIA: We included studies that compared the 11‐item (maximum score 30) MMSE test (at any cut point) in people who had not undergone prior testing versus a commonly accepted clinical reference standard for all‐cause dementia and subtypes (Alzheimer disease dementia, Lewy body dementia, vascular dementia, frontotemporal dementia). Clinical diagnosis included all‐cause (unspecified) dementia, as defined by any version of the Diagnostic and Statistical Manual of Mental Disorders (DSM); International Classification of Diseases (ICD) and the Clinical Dementia Rating. DATA COLLECTION AND ANALYSIS: At least three authors screened all citations.Two authors handled data extraction and quality assessment. We performed meta‐analysis using the hierarchical summary receiver‐operator curves (HSROC) method and the bivariate method. MAIN RESULTS: We retrieved 24,310 citations after removal of duplicates. We reviewed the full text of 317 full‐text articles and finally included 70 records, referring to 48 studies, in our synthesis. We were able to perform meta‐analysis on 28 studies in the community setting (44 articles) and on 6 studies in primary care (8 articles), but we could not extract usable 2 x 2 data for the remaining 14 community studies, which we did not include in the meta‐analysis. All of the studies in the community were in asymptomatic people, whereas two of the six studies in primary care were conducted in people who had symptoms of possible dementia. We judged two studies to be at high risk of bias in the patient selection domain, three studies to be at high risk of bias in the index test domain and nine studies to be at high risk of bias regarding flow and timing. We assessed most studies as being applicable to the review question though we had concerns about selection of participants in six studies and target condition in one study. The accuracy of the MMSE for diagnosing dementia was reported at 18 cut points in the community (MMSE score 10, 14‐30 inclusive) and 10 cut points in primary care (MMSE score 17‐26 inclusive). The total number of participants in studies included in the meta‐analyses ranged from 37 to 2727, median 314 (interquartile range (IQR) 160 to 647). In the community, the pooled accuracy at a cut point of 24 (15 studies) was sensitivity 0.85 (95% confidence interval (CI) 0.74 to 0.92), specificity 0.90 (95% CI 0.82 to 0.95); at a cut point of 25 (10 studies), sensitivity 0.87 (95% CI 0.78 to 0.93), specificity 0.82 (95% CI 0.65 to 0.92); and in seven studies that adjusted accuracy estimates for level of education, sensitivity 0.97 (95% CI 0.83 to 1.00), specificity 0.70 (95% CI 0.50 to 0.85). There was insufficient data to evaluate the accuracy of the MMSE for diagnosing dementia subtypes.We could not estimate summary diagnostic accuracy in primary care due to insufficient data. AUTHORS' CONCLUSIONS: The MMSE contributes to a diagnosis of dementia in low prevalence settings, but should not be used in isolation to confirm or exclude disease. We recommend that future work evaluates the diagnostic accuracy of tests in the context of the diagnostic pathway experienced by the patient and that investigators report how undergoing the MMSE changes patient‐relevant outcomes

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response