Measurement of charge and light yields for Xe 127 L -shell electron captures in liquid xenon

Dark matter searches using dual-phase xenon time-projection chambers (LXe-TPCs) rely on their ability to reject background electron recoils (ERs) while searching for signal-like nuclear recoils (NRs). ER response is typically calibrated using β-decay sources, such as tritium, but these calibrations do not characterize events accompanied by an atomic vacancy, as in solar neutrino scatters off inner-shell electrons. Such events lead to emission of x rays and Auger electrons, resulting in higher electron-ion recombination and thus a more NR-like response than inferred from β-decay calibration. We present a cross-calibration of tritium β-decays and Xe127 electron-capture decays (which produce inner-shell vacancies) in a small-scale LXe-TPC and give the most precise measurements to date of light and charge yields for the Xe127 L-shell electron-capture in liquid xenon. We observe a 6.9σ (9.2σ) discrepancy in the L-shell capture response relative to tritium β decays, measured at a drift field of 363±14 V/cm (258±13 V/cm), when compared to simulations tuned to reproduce the correct β-decay response. In dark matter searches, use of a background model that neglects this effect leads to overcoverage (higher limits) for background-only multi-kiloton-year exposures, but at a level much less than the 1-σ experiment-to-experiment variation of the 90% C.L. upper limit on the interaction rate of a 50 GeV/c2 dark matter particle

Convolutional Neural Network and Stochastic Variational Gaussian Process for Heating Load Forecasting

Heating load forecasting is a key task for operational planning in district heating networks. In this work we present two advanced models for this purpose, namely a Convolutional Neural Network (CNN) and a Stochastic Variational Gaussian Process (SVGP). Both models are extensions of an autoregressive linear model available in the literature. The CNN outperforms the linear model in terms of 48-h prediction accuracy and its parameters are interpretable. The SVGP has performance comparable to the linear model but it intrinsically deals with prediction uncertainty, hence it provides both load estimations and confidence intervals. Models and performance are analyzed and compared on a real dataset of heating load collected in an Italian network

E-cadherin and cell adhesion: a role in architecture and function in the pancreatic islet

Background/Aims: The efficient secretion of insulin from beta-cells requires extensive intra-islet communication. The cell surface adhesion protein epithelial (E)-cadherin (ECAD) establishes and maintains epithelial tissues such as the islets of Langerhans. In this study, the role of ECAD in regulating insulin secretion from pseudoislets was investigated. Methods: The effect of an immuno-neutralising ECAD on gross morphology, cytosolic calcium signalling, direct cell-to-cell communication and insulin secretion was assessed by fura-2 microfluorimetry, Lucifer Yellow dye injection and insulin ELISA in an insulin-secreting model system. Results: Antibody blockade of ECAD reduces glucose-evoked changes in [Ca2+](i) and insulin secretion. Neutralisation of ECAD causes a breakdown in the glucose-stimulated synchronicity of calcium oscillations between discrete regions within the pseudoislet, and the transfer of dye from an individual cell within a cell cluster is attenuated in the absence of ECAD ligation, demonstrating that gap junction communication is disrupted. The functional consequence of neutralising ECAD is a significant reduction in insulin secretion. Conclusion: Cell adhesion via ECAD has distinct roles in the regulation of intercellular communication between beta-cells within islets, with potential repercussions for insulin secretion. Copyright (C) 2007 S. Karger AG, Basel

Multisensory causal inference in the brain

At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions

A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer

A common characteristic of environmental epidemiology is the multi-dimensional aspect of exposure patterns, frequently reduced to a cumulative exposure for simplicity of analysis. By adopting a flexible Bayesian clustering approach, we explore the risk function linking exposure history to disease. This approach is applied here to study the relationship between different smoking characteristics and lung cancer in the framework of a population based case control study

Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

International audienceWe present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to infer the groups of users as well as their latent behavioral functions. We also propose a non-parametric version based on a Dirichlet Process to automatically infer the number of clusters. We test the properties and performance of the model on a synthetic dataset that represents the participation of users in the threads of an online forum. Experiments show that dual-view models outperform single-view ones when one of the views lacks information

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

BACKGROUND: We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox's proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. RESULTS: Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. CONCLUSIONS: The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers

Psychological interventions in asthma

Asthma is a multifactorial chronic respiratory disease characterised by recurrent episodes of airway obstruction. The current management of asthma focuses principally on pharmacological treatments, which have a strong evidence base underlying their use. However, in clinical practice, poor symptom control remains a common problem for patients with asthma. Living with asthma has been linked with psychological co-morbidity including anxiety, depression, panic attacks and behavioural factors such as poor adherence and suboptimal self-management. Psychological disorders have a higher-than-expected prevalence in patients with difficult-to-control asthma. As psychological considerations play an important role in the management of people with asthma, it is not surprising that many psychological therapies have been applied in the management of asthma. There are case reports which support their use as an adjunct to pharmacological therapy in selected individuals, and in some clinical trials, benefit is demonstrated, but the evidence is not consistent. When findings are quantitatively synthesised in meta-analyses, no firm conclusions are able to be drawn and no guidelines recommend psychological interventions. These inconsistencies in findings may in part be due to poor study design, the combining of results of studies using different interventions and the diversity of ways patient benefit is assessed. Despite this weak evidence base, the rationale for psychological therapies is plausible, and this therapeutic modality is appealing to both patients and their clinicians as an adjunct to conventional pharmacological treatments. What are urgently required are rigorous evaluations of psychological therapies in asthma, on a par to the quality of pharmaceutical trials. From this evidence base, we can then determine which interventions are beneficial for our patients with asthma management and more specifically which psychological therapy is best suited for each patient

Business experience and start-up size: buying more lottery tickets next time around?

This paper explores the determinants of start-up size by focusing on a cohort of 6247 businesses that started trading in 2004, using a unique dataset on customer records at Barclays Bank. Quantile regressions show that prior business experience is significantly related with start-up size, as are a number of other variables such as age, education and bank account activity. Quantile treatment effects (QTE) estimates show similar results, with the effect of business experience on (log) start-up size being roughly constant across the quantiles. Prior personal business experience leads to an increase in expected start-up size of about 50%. Instrumental variable QTE estimates are even higher, although there are concerns about the validity of the instrument

Hippocampal volume in early onset depression

BACKGROUND: Abnormalities in limbic structures have been implicated in major depressive disorder (MDD). Although MDD is as common in adolescence as in adulthood, few studies have examined youth near illness onset in order to determine the possible influence of atypical development on the pathophysiology of this disorder. METHODS: Hippocampal volumes were measured in 17 MDD subjects (age = 16.67 ± 1.83 years [mean ± SD]; range = 13 – 18 years) and 17 age- and sex-matched healthy controls (16.23 ± 1.61 years [mean ± SD]; 13 – 18 years) using magnetic resonance imaging (MRI). RESULTS: An analysis of covariance revealed a significant difference between MDD and control subjects (F = 8.66, df = 1, 29, P = 0.006). This was more strongly localized to the left hippocampus (P = 0.001) than the right hippocampus (P = 0.047). CONCLUSIONS: Our findings provide new evidence of abnormalities in the hippocampus in early onset depression. However, our results should be considered preliminary given the small sample size studied