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

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

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

NFAT5 Is Activated by Hypoxia: Role in Ischemia and Reperfusion in the Rat Kidney

The current hypothesis postulates that NFAT5 activation in the kidney's inner medulla is due to hypertonicity, resulting in cell protection. Additionally, the renal medulla is hypoxic (10–18 mmHg); however there is no information about the effect of hypoxia on NFAT5. Using in vivo and in vitro models, we evaluated the effect of reducing the partial pressure of oxygen (PO2) on NFAT5 activity. We found that 1) Anoxia increased NFAT5 expression and nuclear translocation in primary cultures of IMCD cells from rat kidney. 2) Anoxia increased transcriptional activity and nuclear translocation of NFAT5 in HEK293 cells. 3) The dose-response curve demonstrated that HIF-1α peaked at 2.5% and NFAT5 at 1% of O2. 4) At 2.5% of O2, the time-course curve of hypoxia demonstrated earlier induction of HIF-1α gene expression than NFAT5. 5) siRNA knockdown of NFAT5 increased the hypoxia-induced cell death. 6) siRNA knockdown of HIF-1α did not affect the NFAT5 induction by hypoxia. Additionally, HIF-1α was still induced by hypoxia even when NFAT5 was knocked down. 7) NFAT5 and HIF-1α expression were increased in kidney (cortex and medulla) from rats subjected to an experimental model of ischemia and reperfusion (I/R). 7) Experimental I/R increased the NFAT5-target gene aldose reductase (AR). 8) NFAT5 activators (ATM and PI3K) were induced in vitro (HEK293 cells) and in vivo (I/R kidneys) with the same timing of NFAT5. 8) Wortmannin, which inhibits ATM and PI3K, reduces hypoxia-induced NFAT5 transcriptional activation in HEK293 cells. These results demonstrate for the first time that NFAT5 is induced by hypoxia and could be a protective factor against ischemic damage

Projected WIMP sensitivity of the LUX-ZEPLIN dark matter experiment

LUX-ZEPLIN (LZ) is a next-generation dark matter direct detection experiment that will operate 4850 feet underground at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, USA. Using a two-phase xenon detector with an active mass of 7 tonnes, LZ will search primarily for low-energy interactions with weakly interacting massive particles (WIMPs), which are hypothesized to make up the dark matter in our galactic halo. In this paper, the projected WIMP sensitivity of LZ is presented based on the latest background estimates and simulations of the detector. For a 1000 live day run using a 5.6-tonne fiducial mass, LZ is projected to exclude at 90% confidence level spin-independent WIMP-nucleon cross sections above 1.4×10-48 cm2 for a 40 GeV/c2 mass WIMP. Additionally, a 5σ discovery potential is projected, reaching cross sections below the exclusion limits of recent experiments. For spin-dependent WIMP-neutron(-proton) scattering, a sensitivity of 2.3×10-43 cm2 (7.1×10-42 cm2) for a 40 GeV/c2 mass WIMP is expected. With underground installation well underway, LZ is on track for commissioning at SURF in 2020