Tensor interaction constraints from beta decay recoil spin asymmetry of trapped atoms

We have measured the angular distribution of recoiling daughter nuclei emitted from the Gamow-Teller $\beta$ decay of spin-polarized $^{80}$Rb. The asymmetry of this distribution vanishes to lowest order in the Standard Model (SM) in pure Gamow-Teller decays, producing an observable very sensitive to new interactions. We measure the non-SM contribution to the asymmetry to be $A_{T}$= 0.015 $\pm$ 0.029 (stat) $\pm$ 0.019 (syst), consistent with the SM prediction. We constrain higher-order SM corrections using the measured momentum dependence of the asymmetry, and their remaining uncertainty dominates the systematic error. Future progress in determining the weak magnetism term theoretically or experimentally would reduce the final errors. We describe the resulting constraints on fundamental 4-Fermi tensor interactions.Comment: 11 pages, 13 figures; v2 published in Phys. Rev. C, with referee clarifications and figures improved for black-and-whit

A short leucocyte telomere length is associated with development of insulin resistance

International audienceAIMS/HYPOTHESIS:A number of studies have shown that leucocyte telomere length (LTL) is inversely associated with insulin resistance and type 2 diabetes mellitus. The aim of the present longitudinal cohort study, utilising a twin design, was to assess whether shorter LTL predicts insulin resistance or is a consequence thereof.METHODS:Participants were recruited between 1997 and 2000 through the population-based national Danish Twin Registry to participate in the GEMINAKAR study, a longitudinal evaluation of metabolic disorders and cardiovascular risk factors. Baseline and follow-up measurements of LTL and insulin resistance over an average of 12 years were performed in a subset of the Registry consisting of 338 (184 monozygotic and 154 dizygotic) same-sex twin pairs.RESULTS:Age at baseline examination was 37.4 ± 9.6 (mean ± SD) years. Baseline insulin resistance was not associated with age-dependent changes in LTL (attrition) over the follow-up period, whereas baseline LTL was associated with changes in insulin resistance during this period. The shorter the LTL at baseline, the more pronounced was the increase in insulin resistance over the follow-up period (p < 0.001); this effect was additive to that of BMI. The co-twin with the shorter baseline LTL displayed higher insulin resistance at follow-up than the co-twin with the longer LTL.CONCLUSIONS/INTERPRETATION:These findings suggest that individuals with short LTL are more likely to develop insulin resistance later in life. By contrast, presence of insulin resistance does not accelerate LTL attrition

Identification of RNA recognition elements in the Saccharomyces cerevisiae transcriptome

Post-transcriptional regulation of gene expression, including mRNA localization, translation and decay, is ubiquitous yet still largely unexplored. How is the post-transcriptional regulatory program of each mRNA encoded in its sequence? Hundreds of specific RNA-binding proteins (RBPs) appear to play roles in mediating the post-transcriptional regulatory program, akin to the roles of specific DNA-binding proteins in transcription. As a step toward decoding the regulatory programs encoded in each mRNA, we focused on specific mRNA–protein interactions. We computationally analyzed the sequences of Saccharomyces cerevisiae mRNAs bound in vivo by 29 specific RBPs, identifying eight novel candidate motifs and confirming or extending six earlier reported recognition elements. Biochemical selections for RNA sequences selectively recognized by 12 yeast RBPs yielded novel motifs bound by Pin4, Nsr1, Hrb1, Gbp2, Sgn1 and Mrn1, and recovered the known recognition elements for Puf3, She2, Vts1 and Whi3. Most of the RNA elements we uncovered were associated with coherent mRNA expression changes and were significantly conserved in related yeasts, supporting their functional importance and suggesting that the corresponding RNA–protein interactions are evolutionarily conserved

An empirical investigation of dance addiction

Although recreational dancing is associated with increased physical and psychological well-being, little is known about the harmful effects of excessive dancing. The aim of the present study was to explore the psychopathological factors associated with dance addiction. The sample comprised 447 salsa and ballroom dancers (68% female, mean age: 32.8 years) who danced recreationally at least once a week. The Exercise Addiction Inventory (Terry, Szabo, & Griffiths, 2004) was adapted for dance (Dance Addiction Inventory, DAI). Motivation, general mental health (BSI-GSI, and Mental Health Continuum), borderline personality disorder, eating disorder symptoms, and dance motives were also assessed. Five latent classes were explored based on addiction symptoms with 11% of participants belonging to the most problematic class. DAI was positively associated with psychiatric distress, borderline personality and eating disorder symptoms. Hierarchical linear regression model indicated that Intensity (ß=0.22), borderline (ß=0.08), eating disorder (ß=0.11) symptoms, as well as Escapism (ß=0.47) and Mood Enhancement (ß=0.15) (as motivational factors) together explained 42% of DAI scores. Dance addiction as assessed with the Dance Addiction Inventory is associated with indicators of mild psychopathology and therefore warrants further research

Semiconductor-based DNA sequencing of histone modification states

The recent development of a semiconductor-based, non-optical DNA sequencing technology promises scalable, low-cost and rapid sequence data production. The technology has previously been applied mainly to genomic sequencing and targeted re-sequencing. Here we demonstrate the utility of Ion Torrent semiconductor-based sequencing for sensitive, efficient and rapid chromatin immunoprecipitation followed by sequencing (ChIP-seq) through the application of sample preparation methods that are optimized for ChIP-seq on the Ion Torrent platform. We leverage this method for epigenetic profiling of tumour tissues

Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes

available in PMC 2016 November 07The fungal kingdom is the source of almost all industrial enzymes in use for lignocellulose bioprocessing. We developed a systems-level approach that integrates transcriptomic sequencing, proteomics, phenotype, and biochemical studies of relatively unexplored basal fungi. Anaerobic gut fungi isolated from herbivores produce a large array of biomass-degrading enzymes that synergistically degrade crude, untreated plant biomass and are competitive with optimized commercial preparations from Aspergillus and Trichoderma. Compared to these model platforms, gut fungal enzymes are unbiased in substrate preference due to a wealth of xylan-degrading enzymes. These enzymes are universally catabolite-repressed and are further regulated by a rich landscape of noncoding regulatory RNAs. Additionally, we identified several promising sequence-divergent enzyme candidates for lignocellulosic bioprocessing.United States. Dept. of Energy. Office of Science (Biological and Environmental Research (BER) program)United States. Department of Energy (DOE Grant DE-SC0010352)United States. Department of Agriculture (Award 2011-67017-20459)Institute for Collaborative Biotechnologies (grant W911NF-09-0001

Study of γπ→ππ\gamma\pi \to \pi\pi below 1 GeV using Integral Equation Approach

The scattering of $\gamma \pi \to \pi \pi$ is studied using the axial anomaly, elastic unitarity, analyticity and crossing symmetry. Using the technique to derive the Roy's equation, an integral equation for the P-wave amplitude is obtained in terms of the strong P-wave pion pion phase shifts. Its solution is obtained numerically by an iteration procedure using the starting point as the solution of the integral equation of the Muskelshsvilli-Omnes type. It is, however, ambiguous and depends sensitively on the second derivative of the P-wave amplitude at $s=m_\pi^2$ which cannot directly be measured.Comment: 26 pages, 10 figure

A Prediction Model to Prioritize Individuals for a SARS-CoV-2 Test Built from National Symptom Surveys

Background: The gold standard for COVID-19 diagnosis is detection of viral RNA through PCR. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. Methods: We devised a model estimating the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions that have been associated with SARS-CoV-2 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel in its first 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. Findings: Our model was validated on a held-out set of individuals from Israel where it achieved an auROC of 0.737 (CI: 0.712–0.759) and auPR of 0.144 (CI: 0.119–0.177) and demonstrated its applicability outside of Israel in an independently collected symptom survey dataset from the US, UK, and Sweden. Our analyses revealed interactions between several symptoms and age, suggesting variation in the clinical manifestation of the disease in different age groups. Conclusions: Our tool can be used online and without exposure to suspected patients, thus suggesting worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified. Moreover, individuals at high risk for a positive test result can be isolated prior to testing. Funding: E.S. is supported by the Crown Human Genome Center, Larson Charitable Foundation New Scientist Fund, Else Kroener Fresenius Foundation, White Rose International Foundation, Ben B. and Joyce E. Eisenberg Foundation, Nissenbaum Family, Marcos Pinheiro de Andrade and Vanessa Buchheim, Lady Michelle Michels, and Aliza Moussaieff and grants funded by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation. H.R. is supported by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and by a research grant from Madame Olga Klein – Astrachan