Null Distribution Of The Likelihood Ratio Statistic For Feed-Forward Neural Networks

Despite recent publications exploring model complexity with modern regression methods, their dimensionality is rarely quantified in practice and the distributions of related test statistics are not well characterized. Through a simulation study, we describe the null distribution of the likelihood ratio statistic for several different feed-forward neural network models

Bayesian model averaging approach in health effects studies: Sensitivity analyses using PM10 and cardiopulmonary hospital admissions in Allegheny County, Pennsylvania and simulated data

AbstractGeneralized Additive Models (GAMs) with natural cubic splines (NS) as smoothing functions have become a standard analytical tool in time series studies of health effects of air pollution. However, standard model selection procedures ignore the model uncertainty that may lead to biased estimates, in particular those of the lagged effects. We addressed this issue by Bayesian model averaging (BMA) approach which accounts for model uncertainty by combining information from all possible models where GAMs and NS were used. Firstly, we conducted a sensitivity analysis with simulation studies for Bayesian model averaging with different calibrated hyperparameters contained in the posterior model probabilities. Our results indicated the importance of selecting the optimum degree of lagging for variables, based not only on maximizing the likelihood, but also by considering the possible effects of concurvity, consistency of degree of lagging, and biological plausibility. This was illustrated by analyses of the Allegheny County Air Pollution Study (ACAPS) where the quantity of interest was the relative risk of cardiopulmonary hospital admissions for a 20 μg/m3 increase in PM10 values for the current day. Results showed that the posterior means of the relative risk and 95% posterior probability intervals were close to each other under different choices of the prior distributions. Simulation results were consistent with these findings. It was also found that using lag variables in the model when there is only same day effect, may underestimate the relative risk attributed to the same day effect

Relation of Income and Education Level with Cardiorespiratory Fitness

International Journal of Exercise Science 8(3): 265-276, 2015. While there is strong evidence measuring the association between leisure time physical activity (LTPA) and socioeconomic status (SES) there are limited data on the relationship between cardiorespiratory fitness (CRF) and SES. The purpose of this cross-sectional study was to examine differences in CRF and LTPA between household income and individual education in young adults. A sample of 171 (males n=98, female n=73) young adults participated in the University of Pittsburgh-Physical Activity Study. Participants completed CRF testing. Demographic characteristics were assessed via interviewer administered standardized survey and LTPA was assessed using the interviewer administered Modifiable Activity Questionnaire. Participants were grouped by income and education level. Analysis of variance and general linear modeling was used to compare LTPA and CRF between groups. There were no differences in CRF between income levels (p=0.126) or education levels (p=0.990) for the total sample. There were no differences in LTPA between income levels (p=0.936) or education level (p=0.182) for the total sample. Results suggest that neither income nor education levels are indicators of CRF in this sample of young adults. Other environmental, sociological, or familial health mediators may have a strong effect on CRF in young adult males and females

Estimation of Short-Term Effects of Air Pollution on Stroke Hospital Admissions in Wuhan, China

Background and Objective:High concentrations of air pollutants have been linked to increased incidence of stroke in North America and Europe but not yet assessed in mainland China. The aim of this study is to evaluate the association between stroke hospitalization and short-term elevation of air pollutants in Wuhan, China.Methods:Daily mean NO2, SO2 and PM10 levels, temperature and humidity were obtained from 2006 through 2008. Data on stroke hospitalizations (ICD 10: I60-I69) at four hospitals in Wuhan were obtained for the same period. A time-stratified case-crossover design was performed by season (April-September and October-March) to assess effects of pollutants on stroke hospital admissions.Results:Pollution levels were higher in October-March with averages of 136.1 μg/m3 for PM10, 63.6 μg/m3 for NO2 and 71.0 μg/m3 for SO2 than in April-September when averages were 102.0 μg/m3, 41.7 μg/m3 and 41.7 μg/m3, respectively (p<.001). During the cold season, every 10 μg/m3 increase in NO2 was associated with a 2.9% (95%C.I. 1.2%-4.6%) increase in stroke admissions on the same day. Every 10 ug/m3 increase in PM10 daily concentration was significantly associated with an approximate 1% (95% C.I. 0.1%-1.4%) increase in stroke hospitalization. A two-pollutant model indicated that NO2 was associated with stroke admissions when controlling for PM10. During the warm season, no significant associations were noted for any of the pollutants.Conclusions:Exposure to NO2 is significantly associated with stroke hospitalizations during the cold season in Wuhan, China when pollution levels are 50% greater than in the warm season. Larger and multi-center studies in Chinese cities are warranted to validate our findings. © 2013 Xiang et al

Characteristics of slow progression to diabetes in multiple islet autoantibody-positive individuals from five longitudinal cohorts:the SNAIL study

Aims/hypothesis Multiple islet autoimmunity increases risk of diabetes, but not all individuals positive for two or more islet autoantibodies progress to disease within a decade. Major islet autoantibodies recognise insulin (IAA), GAD (GADA), islet antigen-2 (IA-2A) and zinc transporter 8 (ZnT8A). Here we describe the baseline characteristics of a unique cohort of ‘slow progressors’ (n = 132) who were positive for multiple islet autoantibodies (IAA, GADA, IA-2A or ZnT8A) but did not progress to diabetes within 10 years. Methods Individuals were identified from five studies (BABYDIAB, Germany; Diabetes Autoimmunity Study in the Young [DAISY], USA; All Babies in Southeast Sweden [ABIS], Sweden; Bart’s Oxford Family Study [BOX], UK and the Pittsburgh Family Study, USA). Multiple islet autoantibody characteristics were determined using harmonised assays where possible. HLA class II risk was compared between slow progressors and rapid progressors (n = 348 diagnosed <5 years old from BOX) using the χ2 test. Results In the first available samples with detectable multiple antibodies, the most frequent autoantibodies were GADA (92%), followed by ZnT8A (62%), IAA (59%) and IA-2A (41%). High risk HLA class II genotypes were less frequent in slow (28%) than rapid progressors (42%, p = 0.011), but only two slow progressors carried the protective HLA DQ6 allele. Conclusion No distinguishing characteristics of slow progressors at first detection of multiple antibodies have yet been identified. Continued investigation of these individuals may provide insights into slow progression that will inform future efforts to slow or prevent progression to clinical diabetes

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Management of KPC-Producing Klebsiella pneumoniae Infections

Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae (KPC-KP) has become one of the most important contemporary pathogens, especially in endemic areas

Interpretative and predictive modelling of Joint European Torus collisionality scans

Transport modelling of Joint European Torus (JET) dimensionless collisionality scaling experiments in various operational scenarios is presented. Interpretative simulations at a fixed radial position are combined with predictive JETTO simulations of temperatures and densities, using the TGLF transport model. The model includes electromagnetic effects and collisions as well as □(→┬E ) X □(→┬B ) shear in Miller geometry. Focus is on particle transport and the role of the neutral beam injection (NBI) particle source for the density peaking. The experimental 3-point collisionality scans include L-mode, and H-mode (D and H and higher beta D plasma) plasmas in a total of 12 discharges. Experimental results presented in (Tala et al 2017 44th EPS Conf.) indicate that for the H-mode scans, the NBI particle source plays an important role for the density peaking, whereas for the L-mode scan, the influence of the particle source is small. In general, both the interpretative and predictive transport simulations support the experimental conclusions on the role of the NBI particle source for the 12 JET discharges

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century