Size-Segregated Particle Number Concentrations and Respiratory Emergency Room Visits in Beijing, China

BACKGROUND: The link between concentrations of particulate matter (PM) and respiratory morbidity has been investigated in numerous studies. OBJECTIVES: The aim of this study was to analyze the role of different particle size fractions with respect to respiratory health in Beijing, China. METHODS: Data on particle size distributions from 3 nm to 1 mu m; PM10 (PM <= 10 mu m), nitrogen dioxide (NO2), and sulfur dioxide concentrations; and meteorologic variables were collected daily from March 2004 to December 2006. Concurrently, daily counts of emergency room visits (ERV) for respiratory diseases were obtained from the Peking University Third Hospital. We estimated pollutant effects in single-and two-pollutant generalized additive models, controlling for meteorologic and other time-varying covariates. Time-delayed associations were estimated using polynomial distributed lag, cumulative effects, and single lag models. RESULTS: Associations of respiratory ERV with NO2 concentrations and 100-1,000 nm-particle number or surface area concentrations were of similar magnitude-that is, approximately 5% increase in respiratory ERV with an interquartile range increase in air pollution concentration. In general, particles <50 nm were not positively associated with ERV, whereas particles 50-100 nm were adversely associated with respiratory ERV, both being fractions of ultrafine particles. Effect estimates from two-pollutant models were most consistent for NO2. CONCLUSIONS: Present levels of air pollution in Beijing were adversely associated with respiratory ERV. NO2 concentrations seemed to be a better surrogate for evaluating overall respiratory health effects of ambient air pollution than PM10 or particle number concentrations in Beijing.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000289065900032&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Environmental SciencesPublic, Environmental & Occupational HealthToxicologySCI(E)37ARTICLE4508-51311

FADS1 FADS2 Gene Cluster, PUFA Intake and Blood Lipids in Children: Results from the GINIplus and LISAplus Studies

BACKGROUND: Elevated cholesterol levels in children can be a risk factor for cardiovascular diseases in later life. In adults, it has been shown that blood lipid levels are strongly influenced by polymorphisms in the fatty acid desaturase (FADS) gene cluster in addition to nutritional and other exogenous and endogenous determinants. Our aim was to investigate whether lipid levels are determined by the FADS genotype already in children and whether this association interacts with dietary intake of n-3 fatty acids. METHODS: The analysis was based on data of 2006 children from two German prospective birth cohort studies. Total cholesterol, HDL, LDL and triglycerides were measured at 10 years of age. Six single nucleotide polymorphisms (SNPs) of the FADS gene cluster were genotyped. Dietary n-3 fatty acid intake was assessed by food frequency questionnaire. Linear regression modeling was used to assess the association between lipid levels, n-3 fatty acid intake and FADS genotype. RESULTS: Individuals carrying the homozygous minor allele had lower levels of total cholesterol [means ratio (MR) ranging from 0.96 (p = 0.0093) to 0.98 (p = 0.2949), depending on SNPs] and LDL [MR between 0.94 (p = 0.0179) and 0.97 (p = 0.2963)] compared to homozygous major allele carriers. Carriers of the heterozygous allele showed lower HDL levels [β between -0.04 (p = 0.0074) to -0.01 (p = 0.3318)] and higher triglyceride levels [MR ranging from 1.06 (p = 0.0065) to 1.07 (p = 0.0028)] compared to homozygous major allele carriers. A higher n-3 PUFA intake was associated with higher concentrations of total cholesterol, LDL, HDL and lower triglyceride levels, but these associations did not interact with the FADS1 FADS2 genotype. CONCLUSION: Total cholesterol, HDL, LDL and triglyceride concentrations may be influenced by the FADS1 FADS2 genotype already in 10 year old children. Genetically determined blood lipid levels during childhood might differentially predispose individuals to the development of cardiovascular diseases later in life

Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation

This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech coefficients. The constraint imposed by dynamic features (i.e., the time derivatives of the speech coefficients) are used to enhance the smoothness of predicted coefficient trajectories in two ways. One is to obtain the enhanced speech coefficients with a least square estimation from the coefficients and dynamic features predicted by DNN. The other is to incorporate the constraint of dynamic features directly into the DNN training process using a sequential cost function. In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space. The transform is estimated to maximize the likelihood of the transformed coefficients given a model of clean speech coefficients. Unlike the DNN approach, no parallel corpus is used and no assumption on distortion types is made. The two approaches are evaluated on the REVERB Challenge 2014 tasks. Both speech enhancement and automatic speech recognition (ASR) results show that the DNN-based mappings significantly reduce the reverberation in speech and improve both speech quality and ASR performance. For the speech enhancement task, the proposed dynamic feature constraint help to improve cepstral distance, frequency-weighted segmental signal-to-noise ratio (SNR), and log likelihood ratio metrics while moderately degrades the speech-to-reverberation modulation energy ratio. In addition, the cross transform feature adaptation improves the ASR performance significantly for clean-condition trained acoustic models.Published versio