The role of dwelling type when estimating the effect of magnetic fields on childhood leukemia in the California Power Line Study (CAPS).

PurposeThe type of dwelling where a child lives is an important factor when considering residential exposure to environmental agents. In this paper, we explore its role when estimating the potential effects of magnetic fields (MF) on leukemia using data from the California Power Line Study (CAPS). In this context, dwelling type could be a risk factor, a proxy for other risk factors, a cause of MF exposure, a confounder, an effect-measure modifier, or some combination.MethodsWe obtained information on type of dwelling at birth on over 2,000 subjects. Using multivariable-adjusted logistic regression, we assessed whether dwelling type was a risk factor for childhood leukemia, which covariates and MF exposures were associated with dwelling type, and whether dwelling type was a potential confounder or an effect-measure modifier in the MF-leukemia relationship under the assumption of no-uncontrolled confounding.ResultsA majority of children lived in single-family homes or duplexes (70%). Dwelling type was associated with race/ethnicity and socioeconomic status but not with childhood leukemia risk, after other adjustments, and did not alter the MF-leukemia relationship upon adjustment as a potential confounder. Stratification revealed potential effect-measure modification by dwelling type on the multiplicative scale.ConclusionDwelling type does not appear to play a significant role in the MF-leukemia relationship in the CAPS dataset as a leukemia risk factor or confounder. Future research should explore the role of dwelling as an effect-measure modifier of the MF-leukemia association

Why Spiking Neural Networks Are Efficient: A Theorem

Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of such features is the fact that in biological neural networks, signals are represented by a train of spikes. Researchers have tried adding this spikiness to machine learning and indeed got very good results, especially when processing time series (and, more generally, spatio-temporal data). In this paper, we provide a theoretical explanation for this empirical success

Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification

Classification of different gases is important, and it is possible to use different gas sensors for this purpose. Electronic noses, for example, combine separated gas sensors into an array for detecting different gases. However, the use of separated sensors in an array suffers from being bulky, high-energy consumption and complex fabrication processes. Generally, gas sensing properties, including gas selectivity, of semiconductor gas sensors are strongly dependent on their working temperature. It is therefore feasible to use a single device composed of identical sensors arranged in a temperature gradient for classification of multiple gases. Herein, we introduce a design for simple fabrication of gas sensor array based on bilayer Pt/SnO2 for real-time monitoring and classification of multiple gases. The study includes design simulation of the sensor array to find an effective gradient temperature, fabrication of the sensors and test of their performance. The array, composed of five sensors, was fabricated on a glass substrate without the need of backside etching to reduce heat loss. A SnO2 thin film sensitized with Pt on top deposited by sputtering was used as sensing material. The sensor array was tested against different gases including ethanol, methanol, isopropanol, acetone, ammonia, and hydrogen. Radar plots and principal component analysis were used to visualize the distinction of the tested gases and to enable effective classification

Prototype edge-grown nanowire sensor array for the real-time monitoring and classification of multiple gases

The monitoring and classification of different gases using a single resistive semiconductor sensor are challenging because of the similar response characteristics. An array of separated sensors can be used as an electronic nose, but such arrays have a bulky structure and complex fabrication processes. Herein, we easily fabricated a gas-sensor array based on edge-grown SnO2 nanowires for the real-time monitoring and classification of multiple gases. The array comprised four sensors and was designed on a glass substrate. SnO2 nanowires were grown on-chip from the edge of electrodes, made contact together, and acted as sensing elements. This method was advantageous over the post-synthesis technique because the SnO2 nanowires were directly grown from the edge of the electrodes rather than on the surface. Accordingly, damage to the electrode was avoided by alloying Sn with Pt at a high growth temperature. The sensing characteristics of the sensor array were further examined for different gases, including methanol, isopropanol, ethanol, ammonia, hydrogen sulphide and hydrogen. Radar plots were used to improve the selective detection of different gases and enable effective classification

What to expect from a non-suspicious prostate MRI? A review = Que peut-on attendre d’une IRM prostatique non suspecte ? Une revue de la littérature

BACKGROUND: Many guidelines now recommend multiparametric MRI (mpMRI) prior to an initial or repeat prostate biopsy. However, clinical decision making for men with a non-suspicious mpMRI (Likert or PIRADS score 1-2) varies. OBJECTIVES: To review the most recent literature to answer three questions. (1) Should we consider systematic biopsy if mpMRI is not suspicious? (2) Are there additional predictive factors that can help decide which patient should have a biopsy? (3) Can the low visibility of some cancers be explained and what are the implications? SOURCES: A narrative review was performed in Medline databases using two searches with the terms "MRI" and "prostate cancer" and ("diagnosis" or "biopsy") and ("non-suspicious" or "negative" or "invisible"); "prostate cancer MRI visible". References of the selected articles were screened for additional articles. STUDY SELECTION: Studies published in the last 5 years in English language were assessed for eligibility and selected if data was available to answer one of the three study questions. RESULTS: Considering clinically significant cancer as ISUP grade≥2, the negative predictive value (NPV) of mpMRI in various settings and populations ranges from 76% to 99%, depending on cancer prevalence and the type of confirmatory reference test used. NPV is higher among patients with prior negative biopsy (88-96%), and lower for active surveillance patients (85-90%). The PSA density (PSAd) with a threshold of PSAd<0.15ng/ml/ml was the most studied and relevant predictive factor used in combination with mpMRI to rule out clinically significant cancer. Finally, mpMRI-invisible tumours appear to differ from a histopathological and genetic point of view, conferring clinical advantage to invisibility. LIMITATIONS: Most published data come from expert centres and results may not be reproducible in all settings. CONCLUSION: mpMRI has high diagnostic accuracy and in cases of negative mpMRI, PSA density can be used to determine which patient should have a biopsy. Growing knowledge of the mechanisms and genetics underlying MRI visibility will help develop more accurate risk calculators and biomarkers

On Optimization Modulo Theories, MaxSMT and Sorting Networks

Optimization Modulo Theories (OMT) is an extension of SMT which allows for finding models that optimize given objectives. (Partial weighted) MaxSMT --or equivalently OMT with Pseudo-Boolean objective functions, OMT+PB-- is a very-relevant strict subcase of OMT. We classify existing approaches for MaxSMT or OMT+PB in two groups: MaxSAT-based approaches exploit the efficiency of state-of-the-art MAXSAT solvers, but they are specific-purpose and not always applicable; OMT-based approaches are general-purpose, but they suffer from intrinsic inefficiencies on MaxSMT/OMT+PB problems. We identify a major source of such inefficiencies, and we address it by enhancing OMT by means of bidirectional sorting networks. We implemented this idea on top of the OptiMathSAT OMT solver. We run an extensive empirical evaluation on a variety of problems, comparing MaxSAT-based and OMT-based techniques, with and without sorting networks, implemented on top of OptiMathSAT and {\nu}Z. The results support the effectiveness of this idea, and provide interesting insights about the different approaches.Comment: 17 pages, submitted at Tacas 1

Expanding HIV Testing Efforts in Concentrated Epidemic Settings: A Population-Based Survey from Rural Vietnam

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Anchoring of proteins to lactic acid bacteria

The anchoring of proteins to the cell surface of lactic acid bacteria (LAB) using genetic techniques is an exciting and emerging research area that holds great promise for a wide variety of biotechnological applications. This paper reviews five different types of anchoring domains that have been explored for their efficiency in attaching hybrid proteins to the cell membrane or cell wall of LAB. The most exploited anchoring regions are those with the LPXTG box that bind the proteins in a covalent way to the cell wall. In recent years, two new modes of cell wall protein anchoring have been studied and these may provide new approaches in surface display. The important progress that is being made with cell surface display of chimaeric proteins in the areas of vaccine development and enzyme- or whole-cell immobilisation is highlighted.

Thermophilic anaerobic digestion of model organic wastes: Evaluation of biomethane production and multiple kinetic models analysis

© 2019 Elsevier Ltd The main aim of this work was to test various organic wastes, i.e. from a livestock farm, a cattle slaughterhouse and agricultural waste streams, for its ability to produce methane under thermophilic anaerobic digestion (AD) conditions. The stability of the digestion, potential biomethane production and biomethane production rate for each waste were assessed. The highest methane yield (110.83 mL CH4/g VSadded day) was found in the AD of crushed animal carcasses on day 4. The experimental results were analyzed using four kinetic models and it was observed that the Cone model described the biomethane yield as well as the methane production rate of each substrate. The results from this study showed the good potential of model organic wastes to produce biomethane

Dynamic Limits on Planar Libration-Orbit Coupling Around an Oblate Primary

This paper explores the dynamic properties of the planar system of an ellipsoidal satellite in an equatorial orbit about an oblate primary. In particular, we investigate the conditions for which the satellite is bound in librational motion or when the satellite will circulate with respect to the primary. We find the existence of stable equilibrium points about which the satellite can librate, and explore both the linearized and non-linear dynamics around these points. Absolute bounds are placed on the phase space of the libration-orbit coupling through the use of zero-velocity curves that exist in the system. These zero-velocity curves are used to derive a sufficient condition for when the satellite's libration is bound to less than 90 degrees. When this condition is not satisfied so that circulation of the satellite is possible, the initial conditions at zero libration angle are determined which lead to circulation of the satellite. Exact analytical conditions for circulation and the maximum libration angle are derived for the case of a small satellite in orbits of any eccentricity.Comment: Submitted to Celestial Mechanics and Dynamical Astronom