Induced pseudoscalar coupling of the proton weak interaction

The induced pseudoscalar coupling $g_p$ is the least well known of the weak coupling constants of the proton's charged--current interaction. Its size is dictated by chiral symmetry arguments, and its measurement represents an important test of quantum chromodynamics at low energies. During the past decade a large body of new data relevant to the coupling $g_p$ has been accumulated. This data includes measurements of radiative and non radiative muon capture on targets ranging from hydrogen and few--nucleon systems to complex nuclei. Herein the authors review the theoretical underpinnings of $g_p$, the experimental studies of $g_p$, and the procedures and uncertainties in extracting the coupling from data. Current puzzles are highlighted and future opportunities are discussed.Comment: 58 pages, Latex, Revtex4, prepared for Reviews of Modern Physic

First Case of Bioterrorism-Related Inhalational Anthrax in the United States, Palm Beach County, Florida, 2001

On October 4, 2001, we confirmed the first bioterrorism-related anthrax case identified in the United States in a resident of Palm Beach County, Florida. Epidemiologic investigation indicated that exposure occurred at the workplace through intentionally contaminated mail. One additional case of inhalational anthrax was identified from the index patient’s workplace. Among 1,076 nasal cultures performed to assess exposure, Bacillus anthracis was isolated from a co-worker later confirmed as being infected, as well as from an asymptomatic mail-handler in the same workplace. Environmental cultures for B. anthracis showed contamination at the workplace and six county postal facilities. Environmental and nasal swab cultures were useful epidemiologic tools that helped direct the investigation towards the infection source and transmission vehicle. We identified 1,114 persons at risk and offered antimicrobial prophylaxis

Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer.

Nearly 50% of cancer patients undergo radiotherapy. Late radiotherapy toxicity affects quality-of-life in long-term cancer survivors and risk of side-effects in a minority limits doses prescribed to the majority of patients. Development of a test predicting risk of toxicity could benefit many cancer patients. We aimed to meta-analyze individual level data from four genome-wide association studies from prostate cancer radiotherapy cohorts including 1564 men to identify genetic markers of toxicity. Prospectively assessed two-year toxicity endpoints (urinary frequency, decreased urine stream, rectal bleeding, overall toxicity) and single nucleotide polymorphism (SNP) associations were tested using multivariable regression, adjusting for clinical and patient-related risk factors. A fixed-effects meta-analysis identified two SNPs: rs17599026 on 5q31.2 with urinary frequency (odds ratio [OR] 3.12, 95% confidence interval [CI] 2.08-4.69, p-value 4.16×10(-8)) and rs7720298 on 5p15.2 with decreased urine stream (OR 2.71, 95% CI 1.90-3.86, p-value=3.21×10(-8)). These SNPs lie within genes that are expressed in tissues adversely affected by pelvic radiotherapy including bladder, kidney, rectum and small intestine. The results show that heterogeneous radiotherapy cohorts can be combined to identify new moderate-penetrance genetic variants associated with radiotherapy toxicity. The work provides a basis for larger collaborative efforts to identify enough variants for a future test involving polygenic risk profiling.This work was supported by Cancer Research UK (C1094/A11728 to CMLW and NGB for the RAPPER study, C26900/A8740 to GCB, and C8197/A10865 to AMD), the Royal College of Radiologists (C26900/ A8740 to GCB), the National Institute for Health Research (GCB; no grant number), Addenbrooke's Charitable Trust (GCB; no grant number), Institute of Cancer Research (National Institute for Health Research) Biomedical Research Centre (C46/A2131 to DPD and SG), the National Institute for Health Research Cambridge Biomedical Research Centre (NGB; no grant number), UK Medical Research Council (RG70550 to LD), the Joseph Mitchell Trust (AMD; no grant number), the Experimental Cancer Medicine Centre (CMLW; no grant number), Cancer Research UK Program grant Section of Radiotherapy (C33589/ A19727 to SLG), the United States National Institutes of Health (1R01CA134444 to BSR and HO, 2P30CA014520-34 to SB, and 1K07CA187546-01A1 to SLK), the American Cancer Society (RSGT-05- 200-01-CCE to BSR), the U.S. Department of Defense (PC074201 to BSR and HO), Mount Sinai Tisch Cancer Institute Developmental Fund Award (BSR; no grant number), the Instituto de Salud Carlos III (FIS PI10/00164 and PI13/02030 to AV and PI13/01136 to AC), Fondo Europeo de Desarrollo Regional (FEDER 2007–2013 to AV and AC; no grant number), Instituto de Salud Carlos III (FIS PI10/00164 and PI13/ 02030 to AV and PI13/01136 to AC), Xunta de Galicia and the European Social Fund (POS-A/2013/034 to LF), and the Alberta Cancer Board Research Initiative Program (103.0393.71760001404 to MP). AMD receives support from the REQUITE study, which is funded by the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 601826. Laboratory infrastructure for the RAPPER study was funded by Cancer Research UK [C8197/A10123] and the Manchester Experimental Cancer Medicine Centre. The RAPPER cohort comprises individuals and data recruited into the RT01 and CHHiP UK radiotherapy trials. The RT01 trial was supported by the UK Medical Research Council. The CHHiP trial (CRUK/06/016) was supported by the Department of Health and Cancer Research UK (C8262/A7253); trial recruitment was facilitated within centers by the National Institute for Health Research Cancer Research Network. DPD and SLG acknowledge NHS funding to the NIHR Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and Institute of Cancer Research.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ebiom.2016.07.02

Nonlinear Time Series Analysis of Nodulation Factor Induced Calcium Oscillations: Evidence for Deterministic Chaos?

Legume plants form beneficial symbiotic interactions with nitrogen fixing bacteria (called rhizobia), with the rhizobia being accommodated in unique structures on the roots of the host plant. The legume/rhizobial symbiosis is responsible for a significant proportion of the global biologically available nitrogen. The initiation of this symbiosis is governed by a characteristic calcium oscillation within the plant root hair cells and this signal is activated by the rhizobia. Recent analyses on calcium time series data have suggested that stochastic effects have a large role to play in defining the nature of the oscillations. The use of multiple nonlinear time series techniques, however, suggests an alternative interpretation, namely deterministic chaos. We provide an extensive, nonlinear time series analysis on the nature of this calcium oscillation response. We build up evidence through a series of techniques that test for determinism, quantify linear and nonlinear components, and measure the local divergence of the system. Chaos is common in nature and it seems plausible that properties of chaotic dynamics might be exploited by biological systems to control processes within the cell. Systems possessing chaotic control mechanisms are more robust in the sense that the enhanced flexibility allows more rapid response to environmental changes with less energetic costs. The desired behaviour could be most efficiently targeted in this manner, supporting some intriguing speculations about nonlinear mechanisms in biological signaling

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning

Patient Safety in the Cardiac Operating Room: Human Factors and Teamwork: A Scientific Study from the American Heart Association

The cardiac surgical operating room (OR) is a complex environment in which highly trained subspecialists interact with each other using sophisticated equipment to care for patients with severe cardiac disease and significant comorbidities. Thousands of patient lives have been saved or significantly improved with the advent of modern cardiac surgery. Indeed, both mortality and morbidity for coronary artery bypass surgery have decreased during the past decade. Nonetheless, the highly skilled and dedicated personnel in cardiac ORs are human and will make errors. Refined techniques, advanced technologies, and enhanced coordination of care have led to significant improvements in cardiac surgery outcomes