The impact of alcohol and drug use on employment: A labor market study using the National Longitudinal Survey of Youth

The purpose of this study was, first, to estimate of the impact of alcohol and drug use on the employment status of men and women, and second, to examine whether a history of past use, as opposed to current use, adversely affects the propensity to be employed. Using data from the National Longitudinal Survey of Youth we conducted a cross-sectional and a longitudinal analysis with logistic regression estimation to model the probability that a person was employed in 1992. In addition to usual regressors, interactions between substance use measures, between substance use measures and human capital variables, and between substance use measures and race dummies were included in the equation. The longitudinal analysis utilized a conditional likelihood method based on employment data in 1992 and 1988 and included the difference between 1992 regressors and their 1988 counterparts. A comparison was made between the prediction accuracy of the logit choice model, linear discriminant analysis, k-nearest neighbor analysis, and three modern classification methods that are used extensively in the area of machine learning. Results showed that the logit model performs relatively well in classifying individuals into employed and unemployed categories based on individual attributes. Results of the cross-sectional and longitudinal analysis were mixed, but not inconsistent with our prior expectations that use of alcohol or drug has a negative impact on a person's propensity to be employed. Cross-sectional results show a clear negative impact of past substance use on a person's employment probability among all demographic groups examined (by gender: all persons, blacks, Hispanics, families with income below the poverty line, and high users of alcohol or drugs). However, when current and past use are considered together, only women seem to experience negative impacts. The results of the longitudinal analysis are less clear, although they do indicate that negative impacts are associated with the interaction between substance use measures and human capital variables. Limitations of the study are pointed out and suggestions are made for future research.

An Integer GARCH model for a Poisson process with time varying zero-inflation

A time-varying zero-inflated serially dependent Poisson process is proposed. The model assumes that the intensity of the Poisson Process evolves according to a generalized autoregressive conditional heteroscedastic (GARCH) formulation. The proposed model is a generalization of the zero-inflated Poisson Integer GARCH model proposed by Fukang Zhu in 2012, which in return is a generalization of the Integer GARCH (INGARCH) model introduced by Ferland, Latour, and Oraichi in 2006. The proposed model builds on previous work by allowing the zero-inflation parameter to vary over time, governed by a deterministic function or by an exogenous variable. Both the Expectation Maximization (EM) and the Maximum Likelihood Estimation (MLE) approaches are presented as possible estimation methods. A simulation study shows that both parameter estimation methods provide good estimates. Applications to two real-life data sets show that the proposed INGARCH model provides a better fit than the traditional zero-inflated INGARCH model in the cases considered

A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Bigdata

In this paper, a multi-step dimension-reduction approach is proposed for addressing nonlinear relationships within attributes. In this work, the attributes in the data are first organized into groups. In each group, the dimensions are reduced via a parametric mapping that takes into account nonlinear relationships. Mapping parameters are estimated using a low rank singular value decomposition (SVD) of distance covariance. Subsequently, the attributes are reorganized into groups based on the magnitude of their respective singular values. The group-wise organization and the subsequent reduction process is performed for multiple steps until a singular value-based user-defined criterion is satisfied. Simulation analysis is utilized to investigate the performance with five big data-sets

Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata

In this paper, generalization error for traditional learning regimes-based classification is demonstrated to increase in the presence of bigdata challenges such as noise and heterogeneity. To reduce this error while mitigating vanishing gradients, a deep neural network (NN)-based framework with a direct error-driven learning scheme is proposed. To reduce the impact of heterogeneity, an overall cost comprised of the learning error and approximate generalization error is defined where two NNs are utilized to estimate the costs respectively. To mitigate the issue of vanishing gradients, a direct error-driven learning regime is proposed where the error is directly utilized for learning. It is demonstrated that the proposed approach improves accuracy by 7 % over traditional learning regimes. The proposed approach mitigated the vanishing gradient problem and improved generalization by 6%

A Statistical Solution to a Text Decoding Challenge Problem

Given an encoded unknown text message in the form of a three dimensional spatial series generated by the use of four smooth nonlinear functions, we use a method based on simple statistical reasoning to pick up samples for rebuilding the four functions. The estimated functions are then used to decode the sequence. The experimental results show that our method gives a nearly perfect decoding, enabling us to submit a 100% accurate solution to the IJCNN challenge proble

Phytoforensics: Trees as bioindicators of potential indoor exposure via vapor intrusion

Human exposure to volatile organic compounds (VOCs) via vapor intrusion (VI) is an emerging public health concern with notable detrimental impacts on public health. Phytoforensics, plant sampling to semi-quantitatively delineate subsurface contamination, provides a potential non-invasive screening approach to detect VI potential, and plant sampling is effective and also time- and cost-efficient. Existing VI assessment methods are time- and resourceintensive, invasive, and require access into residential and commercial buildings to drill holes through basement slabs to install sampling ports or require substantial equipment to install groundwater or soil vapor sampling outside the home. Tree-core samples collected in 2 days at the PCE Southeast Contamination Site in York, Nebraska were analyzed for tetrachloroethene (PCE) and results demonstrated positive correlations with groundwater, soil, soil-gas, sub-slab, and indoor-air samples collected over a 2-year period. Because treecore samples were not collocated with other samples, interpolated surfaces of PCE concentrations were estimated so that comparisons could be made between pairs of data. Results indicate moderate to high correlation with average indoor-air and sub-slab PCE concentrations over long periods of time (months to years) to an interpolated tree-core PCE concentration surface, with Spearman\u27s correlation coefficients (ρ) ranging from 0.31 to 0.53 that are comparable to the pairwise correlation between sub-slab and indoor-air PCE concentrations (ρ = 0.55, n = 89). Strong correlations between soil-gas, sub-slab, and indoor-air PCE concentrations and an interpolated tree-core PCE concentration surface indicate that trees are valid indicators of potential VI and human exposure to subsurface environment pollutants. The rapid and non-invasive nature of tree sampling are notable advantages: even with less than 60 trees in the vicinity of the source area, roughly 12 hours of tree-core sampling with minimal equipment at the PCE Southeast Contamination Site was sufficient to delineate vapor intrusion potential in the study area and offered comparable delineation to traditional sub-slab sampling performed at 140 properties over a period of approximately 2 years

MODELING SLEEP AND WAKE BOUTS IN DROSOPHILA MELANOGASTER

Adequate sleep restores vital processes required for health and well-being; but the function and regulation of sleep is not well understood. Unfortunately, a definition of adequate sleep is unclear. On an hours-long timescale, consolidated and cycling sleep results in better health and performance outcomes. At shorter timescales, older studies report conflicting results regarding the relationship between sleep and wake bout durations. One approach to this problem has been to simply analyze the distribution of bout durations. While informative, this method eliminates the time relationship between bouts, which may be important. Here, we develop a model that describes the relationship between sleep and wake bout durations using the model organism, Drosophila melanogaster, which exhibits behavioral and molecular homology to human sleep. We present an exploratory analysis of the data to gain a better understanding of the sleep bout duration distribution by considering a broader range of potential distributions than considered in previous studies. We use the results of the distribution analysis to develop a model for sleep bout durations in the fly based upon their past sleep and wake history and find that this relationship should not be ignored

Identification of differential pharyngeal cytokine profiles during HIV infection

Significantly higher pharyngeal shedding of Epstein-Barr virus (EBV) is observed during HIV infection. Increased EBV shedding in pharynx is not affected even during highly active antiretroviral theyrapy (HAART). EBV positive monocyte populations have been shown to carry EBV to pharyngeal mucosa. Human cytokine profiles are often altered to facilitate herpes virus infection. Thus pharyngeal cytokine profiles may influence EBV reactivation and shedding during HIV infection. Our objective was to compare 37 pharyngeal cytokine profiles of HIV-seropositive patients who were or were not receiving HAART therapy

Fluconazole resistance in Candida albicans is induced by Pseudomonas aeruginosa quorum sensing

Microorganisms employ quorum sensing (QS) mechanisms to communicate with each other within microbial ecosystems. Emerging evidence suggests that intraspecies and interspecies QS plays an important role in antimicrobial resistance in microbial communities. However, the relationship between interkingdom QS and antimicrobial resistance is largely unknown. Here, we demonstrate that interkingdom QS interactions between a bacterium, Pseudomonas aeruginosa and a yeast, Candida albicans, induce the resistance of the latter to a widely used antifungal fluconazole. Phenotypic, transcriptomic, and proteomic analyses reveal that P. aeruginosa’s main QS molecule, N-(3-Oxododecanoyl)-L-homoserine lactone, induces candidal resistance to fluconazole by reversing the antifungal’s effect on the ergosterol biosynthesis pathway. Accessory resistance mechanisms including upregulation of C. albicans drug-efflux, regulation of oxidative stress response, and maintenance of cell membrane integrity, further confirm this phenomenon. These findings demonstrate that P. aeruginosa QS molecules may confer protection to neighboring yeasts against azoles, in turn strengthening their co-existence in hostile polymicrobial infection sites