Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group

Sulfonylureas, a commonly used class of medication used to treat type 2 diabetes, have been associated with an increased risk of cardiovascular disease. Their effects on QT interval duration and related electrocardiographic phenotypes are potential mechanisms for this adverse effect. In 11 ethnically diverse cohorts that included 71 857 European, African-American and Hispanic/Latino ancestry individuals with repeated measures of medication use and electrocardiogram (ECG) measurements, we conducted a pharmacogenomic genome-wide association study of sulfonylurea use and three ECG phenotypes: QT, JT and QRS intervals. In ancestry-specific meta-analyses, eight novel pharmacogenomic loci met the threshold for genome-wide significance (P<5 × 10−8), and a pharmacokinetic variant in CYP2C9 (rs1057910) that has been associated with sulfonylurea-related treatment effects and other adverse drug reactions in previous studies was replicated. Additional research is needed to replicate the novel findings and to understand their biological basis

Cost effectiveness of silent discharge plasma for point-of-use VOC emissions control in semiconductor fabrication

Extensive research into the treatment and control of Volatile Organic Compounds (VOCs) from semiconductor industry manufacturing processes has identified the need for alternatives to existing combustion devices. Specifically, semiconductor manufacturing design is moving toward the application of effective, small-scale, abatement control technologies for specific point-of-use (POU) waste streams associated with a particular component or manufacturing tool. The consortium of companies involved in semiconductor precompetitive research and development known collectively as SEMATECH recently evaluated eleven emerging environmental technologies designed to treat POU process emissions of VOCs specific to the semiconductor industry. After rigorous technical review only one technology, the Silent Discharge Plasma (SDP) developed at Low Alamos National Laboratory, was considered to successfully meet the required technical performance standards and potential cost effectiveness necessary for continued consideration by SEMATECH in their point-of-use emissions control plans

Scintillation Counters for the D0 Muon Upgrade

We present the results of an upgrade to the D0 muon system. Scintillating counters have been added to the existing central D0 muon system to provide rejection for cosmic ray muons and out-of-time background, and to provide additional fast timing information for muons in an upgraded Tevatron. Performance and results from the 1994-1996 Tevatron run are presented.Comment: 30 pages, 25 postscript figure

75th Anniversary of ‘Existence of Electromagnetic-Hydrodynamic Waves’

We have recently passed the 75th anniversary of one of the most important results in solar and space physics: Hannes Alfv\'en's discovery of Alfv\'en waves and the Alfv\'en speed. To celebrate the anniversary, this article recounts some major episodes in the history of MHD waves. Following an initially cool reception, Alfv\'en's ideas were propelled into the spotlight by Fermi's work on cosmic rays, the new mystery of coronal heating and, as scientific perception of interplanetary space shifted dramatically and the space race started, detection of Alfv\'en waves in the solar wind. From then on, interest in MHD waves boomed, laying the foundations for modern remote observations of MHD waves in the Sun, coronal seismology and some of today's leading theories of coronal heating and solar wind acceleration. In 1970, Alfv\'en received the Nobel Prize for his work in MHD, including these discoveries. The article concludes with some reflection about what the history implies about the way we do science, especially the advantages and pitfalls of idealised mathematical models.Comment: 10 pages, accepted by Solar Physic

Drug-gene interactions of antihypertensive medications and risk of incident cardiovascular disease: A pharmacogenomics study from the CHARGE consortium

Background Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals. Methods Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk ofmajor cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regressionmodels to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases). Results Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10-8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genom

A pooled analysis of fall incidence from placebo‐controlled trials of denosumab

Recent studies suggest that the RANK/RANKL system impacts muscle function and/or mass. In the pivotal placebo‐controlled fracture trial of the RANKL inhibitor denosumab in women with postmenopausal osteoporosis, treatment was associated with a lower incidence of non‐fracture‐related falls (p = 0.02). This ad hoc exploratory analysis pooled data from five placebo‐controlled trials of denosumab to determine consistency across trials, if any, of the reduction of fall incidence. The analysis included trials in women with postmenopausal osteoporosis and low bone mass, men with osteoporosis, women receiving adjuvant aromatase inhibitors for breast cancer, and men receiving androgen deprivation therapy for prostate cancer. The analysis was stratified by trial, and only included data from the placebo‐controlled period of each trial. A time‐to‐event analysis of first fall and exposure‐adjusted subject incidence rates of falls were analyzed. Falls were reported and captured as adverse events. The analysis comprised 10,036 individuals; 5030 received denosumab 60 mg subcutaneously once every 6 months for 12 to 36 months and 5006 received placebo. Kaplan–Meier estimates showed an occurrence of falls in 6.5% of subjects in the placebo group compared with 5.2% of subjects in the denosumab group (hazard ratio = 0.79; 95% confidence interval 0.66–0.93; p = 0.0061). Heterogeneity in study designs did not permit overall assessment of association with fracture outcomes. In conclusion, denosumab may reduce the risk of falls in addition to its established fracture risk reduction by reducing bone resorption and increasing bone mass. These observations require further exploration and confirmation in studies with muscle function or falls as the primary outcome

Deep learning with electronic health records for short-term fracture risk identification : crystal bone algorithm development and validation

Background: Fractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years. Objective: The goal of this study was to develop and evaluate an algorithm for the identification of patients at risk of fracture in a subsequent 1- to 2-year period. In order to address the aforementioned limitations of current prediction tools, this approach focused on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions. Methods: Using retrospective electronic health record data from over 1,000,000 patients, we developed Crystal Bone, an algorithm that applies machine learning techniques from natural language processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient’s future trajectory might contain a fracture event, or whether the signature of the patient’s journey is similar to that of a typical future fracture patient. A holdout set with 192,590 patients was used to validate accuracy. Experimental baseline models and human-level performance were used for comparison. Results: The model accurately predicted 1- to 2-year fracture risk for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC] 0.81). These algorithms outperformed the experimental baselines (AUROC 0.67) and showed meaningful improvements when compared to retrospective approximation of human-level performance by correctly identifying 9649 of 13,765 (70%) at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians. Conclusions: These findings indicate that it is possible to use a patient’s unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the health care system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture

Search for electroweak production of single top quarks in ppˉp\bar{p} collisions.

We present a search for electroweak production of single top quarks in the electron+jets and muon+jets decay channels. The measurements use ~90 pb^-1 of data from Run 1 of the Fermilab Tevatron collider, collected at 1.8 TeV with the DZero detector between 1992 and 1995. We use events that include a tagging muon, implying the presence of a b jet, to set an upper limit at the 95% confidence level on the cross section for the s-channel process ppbar->tb+X of 39 pb. The upper limit for the t-channel process ppbar->tqb+X is 58 pb. (arXiv

Search for new particles in the two-jet decay channel with the D0 detector

We present the results of a search for the production of new particles decaying into two jets in pbarp collisions at sqrt{s} = 1.8 TeV, using the DZero 1992-1995 data set corresponding to 109 pb^-1. We exclude at the 95% confidence level the production of excited quarks (q*) with masses below 775 GeV/c^2, the most restictive limit to date. We also exclude standard-model-like W' (Z') bosons with masses between 300 and 800 GeV/c^2 (400 and 640 GeV/c^2). A W' boson with mass $<$ 300 GeV/c^2 has been excluded by previous measurements, and our lower limit is therefore the most stringent to date