Deer-Vehicle Crash Patterns Across Ecoregions in Michigan

Deer-vehicle collisions (DVCs) impact the economic and social well being of humans. We examined large-scale patterns behind DVCs across 3 ecoregions: Southern Lower Peninsula (SLP), Northern Lower Peninsula (NLP), and Upper Peninsula (UP) in Michigan. A 3 component conceptual model of DVCs with drivers, deer, and a landscape was the framework of analysis. The conceptual model was parameterized into a parsimonious mathematical model. The dependent variable was DVCs by county by ecoregion and the independent variables were percent forest cover, percent crop cover, mean annual vehicle miles traveled (VMT), and mean deer density index (DDI) by county. A discriminant function analysis of the 4 independent variables by counties by ecoregion indicated low misclassification, and provided support to the groupings by ecoregions. The global model and all sub-models were run for the 3 ecoregions and evaluated using information-theoretic approaches. Adjusted R2 values for the global model increased substantially from the SLP (0.21) to the NLP (0.54) to the UP (0.72). VMT and DDI were important variables across all 3 ecoregions. Percent crop cover played an important role in DVCs in the SLP and UP. The scale at which causal factors of DVCs operate appear to be finer in southern Michigan than in northern Michigan. Reduction of DVCs will likely occur only through a reduction in deer density, a reduction in traffic volume, or in modification of sitespecific factors, such as driver behavior, sight distance, highway features, or speed limits.

Reliability, Validity and Classification Accuracy of the South Oaks Gambling Screen in a Brazilian Sample

The purpose of this study was to examine the reliability, validity and classification accuracy of the South Oaks Gambling Screen (SOGS) in a sample of the Brazilian population. Participants in this study were drawn from three sources: 71 men and women from the general population interviewed at a metropolitan train station; 116 men and women encountered at a bingo venue; and 54 men and women undergoing treatment for gambling. The SOGS and a DSM-IV-based instrument were applied by trained researchers. The internal consistency of the SOGS was 0.75 according to the Cronbach`s alpha model, and construct validity was good. A significant difference among groups was demonstrated by ANOVA (F ((2.238)) = 221.3, P < 0.001). The SOGS items and DSM-IV symptoms were highly correlated (r = 0.854, P < 0.01). The SOGS also presented satisfactory psychometric properties: sensitivity (100), specificity (74.7), positive predictive rate (60.7), negative predictive rate (100) and misclassification rate (0.18). However, a cut-off score of eight improved classification accuracy and reduced the rate of false positives: sensitivity (95.4), specificity (89.8), positive predictive rate (78.5), negative predictive rate (98) and misclassification rate (0.09). Thus, the SOGS was found to be reliable and valid in the Brazilian population.

Comparative performance evaluation of hepatitis C virus genotyping based on the 5' untranslated region versus partial sequencing of the NS5B region of brazilian patients with chronic hepatitis C

Background Genotyping of hepatitis C virus (HCV) has become an essential tool for prognosis and prediction of treatment duration. The aim of this study was to compare two HCV genotyping methods: reverse hybridization line probe assay (LiPA v.1) and partial sequencing of the NS5B region. Methods Plasma of 171 patients with chronic hepatitis C were screened using both a commercial method (LiPA HCV Versant, Siemens, Tarrytown, NY, USA) and different primers targeting the NS5B region for PCR amplification and sequencing analysis. Results Comparison of the HCV genotyping methods showed no difference in the classification at the genotype level. However, a total of 82/171 samples (47.9%) including misclassification, non-subtypable, discrepant and inconclusive results were not classified by LiPA at the subtype level but could be discriminated by NS5B sequencing. Of these samples, 34 samples of genotype 1a and 6 samples of genotype 1b were classified at the subtype level using sequencing of NS5B. Conclusions Sequence analysis of NS5B for genotyping HCV provides precise genotype and subtype identification and an accurate epidemiological representation of circulating viral strains.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.

Effects of cardiology review of the electrocardiogram in patients with suspected acute coronary syndromes

Misclassification of the electrocardiogram (ECG) contributes to treatment errors in patients with acute coronary syndrome. We hypothesized that cardiology ECG review could reduce these errors.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models

Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All the proposals are illustrated with two real examples and their finite sample properties are evaluated via a simulation study.

Development and testing of a procedure for systematically assigning gestational age for clinical and research use

A graphing method was developed and tested to estimate gestational ages pre-and postnatally in a consistent manner for epidemiological research and clinical purposes on feti/infants of women with few consistent prenatal estimators of gestational age. Each patient's available data was plotted on a single page graph to give a comprehensive overview of that patient. A hierarchical classification of gestational age determination was then applied in a systematic manner, and reasonable gestational age estimates were produced. The method was tested for validity and reliability on 50 women who had known dates for their last menstrual period or dates of conception, and multiple ultrasound examinations and other gestational age estimating measures. The feasibility of the procedure was then tested on 1223 low income women with few gestational age estimators. The graphing method proved to have high inter- and intrarater reliability. It was quick, easy to use, inexpensive, and did not require special equipment. The graphing method estimate of gestational age for each infant was tested against the last menstrual period gestational age estimate using paired t-Tests, F tests and the Kolmogorov-Smirnov test of similar populations, producing a 98 percent probability or better that the means and data populations were the same. Less than 5 percent of the infants' gestational ages were misclassified using the graphing method, much lower than the amount of misclassification produced by ultrasound or neonatal examination estimates. ^

Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

Daytime Low Stratiform Cloud Detection on AVHRR Imagery

The near-real time retrieval of low stratiform cloud (LSC) coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite scene. The algorithm is suited for the 1 × 1 km Advanced Very High Resolution Radiometer (AVHRR) data and was trained and validated against collocated SYNOP observations. Utilisation of these two combined data sources requires a formulation of constraints in order to discriminate cases where the LSC is overlaid by higher clouds. The LSC classification process is based on six features which are first converted to the integer form by step functions and combined by means of bitwise operations. Consequently, a set of values reflecting a unique combination of those features is derived which is further employed to extract the LSC occurrence probability estimates from the precomputed look-up vectors (LUV). Although the validation analyses confirmed good performance of the algorithm, some inevitable misclassification with other optically thick clouds were reported. Moreover, the comparison against Polar Platform System (PPS) cloud-type product revealed superior classification accuracy. From the temporal perspective, the acquired results reported a presence of diurnal and annual LSC probability cycles over Europe.

Dementia in patients with atrial fibrillation and the value of the Hachinski ischemic score.

AIM To assess the prevalence of vascular dementia, mixed dementia and Alzheimer's disease in patients with atrial fibrillation, and to evaluate the accuracy of the Hachinski ischemic score for these subtypes of dementia. METHODS A nested case-control study was carried out. A total of 103 of 784 consecutive patients evaluated for cognitive status at the Ambulatory Geriatric Clinic had a diagnosis of atrial fibrillation. Controls without atrial fibrillation were randomly selected from the remaining 681 patients using a 1:2 matching for sex, age and education. RESULTS The prevalence of vascular dementia was twofold in patients with atrial fibrillation compared with controls (21.4% vs 10.7%, P = 0.024). Alzheimer's disease was also more frequent in the group with atrial fibrillation (12.6% vs 7.3%, P = 0.046), whereas mixed dementia had a similar distribution. The Hachinski ischemic score poorly discriminated between dementia subtypes, with misclassification rates between 46% (95% CI 28-66) and 70% (95% CI 55-83). In patients with atrial fibrillation, these rates ranged from 55% (95% CI 32-77) to 69% (95% CI 39-91%). In patients in whom the diagnosis of dementia was excluded, the Hachinski ischemic score suggested the presence of vascular dementia in 11% and mixed dementia in 30%. CONCLUSIONS Vascular dementia and Alzheimer's disease, but not mixed dementia, are more prevalent in patients with atrial fibrillation. The discriminative accuracy of the Hachinski ischemic score for dementia subtypes in atrial fibrillation is poor, with a significant proportion of misclassifications.

Hazardous air pollutants and childhood lymphohematopoietic cancer in Southeast Texas, 1995--2004

Southeast Texas, including Houston, has a large presence of industrial facilities and has been documented to have poorer air quality and significantly higher cancer rates than the remainder of Texas. Given citizens’ concerns in this 4th largest city in the U.S., Mayor Bill White recently partnered with the UT School of Public Health to determine methods to evaluate the health risks of hazardous air pollutants (HAPs). Sexton et al. (2007) published a report that strongly encouraged analytic studies linking these pollutants with health outcomes. In response, we set out to complete the following aims: 1. determine the optimal exposure assessment strategy to assess the association between childhood cancer rates and increased ambient levels of benzene and 1,3-butadiene (in an ecologic setting) and 2. evaluate whether census tracts with the highest levels of benzene or 1,3-butadiene have higher incidence of childhood lymphohematopoietic cancer compared with census tracts with the lowest levels of benzene or 1,3-butadiene, using Poisson regression. The first aim was achieved by evaluating the usefulness of four data sources: geographic information systems (GIS) to identify proximity to point sources of industrial air pollution, industrial emission data from the U.S. EPA’s Toxic Release Inventory (TRI), routine monitoring data from the U.S. EPA Air Quality System (AQS) from 1999-2000 and modeled ambient air levels from the U.S. EPA’s 1999 National Air Toxic Assessment Project (NATA) ASPEN model. Further, once these four data sources were evaluated, we narrowed them down to two: the routine monitoring data from the AQS for the years 1998-2000 and the 1999 U.S. EPA NATA ASPEN modeled data. We applied kriging (spatial interpolation) methodology to the monitoring data and compared the kriged values to the ASPEN modeled data. Our results indicated poor agreement between the two methods. Relative to the U.S. EPA ASPEN modeled estimates, relying on kriging to classify census tracts into exposure groups would have caused a great deal of misclassification. To address the second aim, we additionally obtained childhood lymphohematopoietic cancer data for 1995-2004 from the Texas Cancer Registry. The U.S. EPA ASPEN modeled data were used to estimate ambient levels of benzene and 1,3-butadiene in separate Poisson regression analyses. All data were analyzed at the census tract level. We found that census tracts with the highest benzene levels had elevated rates of all leukemia (rate ratio (RR) = 1.37; 95% confidence interval (CI), 1.05-1.78). Among census tracts with the highest 1,3-butadiene levels, we observed RRs of 1.40 (95% CI, 1.07-1.81) for all leukemia. We detected no associations between benzene or 1,3-butadiene levels and childhood lymphoma incidence. This study is the first to examine this association in Harris and surrounding counties in Texas and is among the first to correlate monitored levels of HAPs with childhood lymphohematopoietic cancer incidence, evaluating several analytic methods in an effort to determine the most appropriate approach to test this association. Despite recognized weakness of ecologic analyses, our analysis suggests an association between childhood leukemia and hazardous air pollution.^

The application of latent variable models to the assessment of determinants of HIV risk behavior

Studies on the relationship between psychosocial determinants and HIV risk behaviors have produced little evidence to support hypotheses based on theoretical relationships. One limitation inherent in many articles in the literature is the method of measurement of the determinants and the analytic approach selected. ^ To reduce the misclassification associated with unit scaling of measures specific to internalized homonegativity, I evaluated the psychometric properties of the Reactions to Homosexuality scale in a confirmatory factor analytic framework. In addition, I assessed the measurement invariance of the scale across racial/ethnic classifications in a sample of men who have sex with men. The resulting measure contained eight items loading on three first-order factors. Invariance assessment identified metric and partial strong invariance between racial/ethnic groups in the sample. ^ Application of the updated measure to a structural model allowed for the exploration of direct and indirect effects of internalized homonegativity on unprotected anal intercourse. Pathways identified in the model show that drug and alcohol use at last sexual encounter, the number of sexual partners in the previous three months and sexual compulsivity all contribute directly to risk behavior. Internalized homonegativity reduced the likelihood of exposure to drugs, alcohol or higher numbers of partners. For men who developed compulsive sexual behavior as a coping strategy for internalized homonegativity, there was an increase in the prevalence odds of risk behavior. ^ In the final stage of the analysis, I conducted a latent profile analysis of the items in the updated Reactions to Homosexuality scale. This analysis identified five distinct profiles, which suggested that the construct was not homogeneous in samples of men who have sex with men. Lack of prior consideration of these distinct manifestations of internalized homonegativity may have contributed to the analytic difficulty in identifying a relationship between the trait and high-risk sexual practices. ^

Determining associations and assessing methodological issues in a case-control study of hazardous air pollutants and neural tube defects

Recent studies have reported positive associations between maternal exposures to air pollutants and several adverse birth outcomes. However, there have been no assessments of the association between environmental hazardous air pollutants (HAPs) such as benzene, toluene, ethylbenzene, and xylene (BTEX) and neural tube defects (NTDs) a common and serious group of congenital malformations. Before examining this association, two important methodological questions must be addressed: (1) is maternal residential movement likely to result in exposure misclassification and (2) is it appropriate to lump defects of the neural tube, such as anencephaly and spina bifida, into a composite disease endpoint (i.e., NTDs). ^ Data from the National Birth Defects Prevention Study and Texas Birth Defects Registry were used to: (1) assess the extent to which change of residence may result in exposure misclassification when exposure is based on the address at delivery; (2) formally assess heterogeneity of the associations between known risk factors for NTDs, using polytomous logistic regression; and (3) conduct a case-control study assessing the association between ambient air levels of BTEX and the risk of NTDs among offspring. ^ Regarding maternal residential mobility, this study suggests address at delivery was not significantly different from using address at conception when assigning quartile of benzene exposure (OR 1.0, 95% CI 0.9, 1.3). On the question of effect heterogeneity among NTDs, the effect estimates for infant sex P = 0.017), maternal body mass index P = 0.016), and folate supplementation P = 0.050) were significantly different for anencephaly and spina bifida, suggesting it is often more appropriate to assess potential risk factors among subgroups of NTDs. For the main study question on the association between environmental HAPs and NTDs, mothers who have offspring with isolated spina bifida are 2.4 times likely to live in areas with the highest benzene levels (95% CI 1.1, 5.0). However, no other significant associations were observed.^ This project is the first to include not only an assessment of the relationship between environmental levels of BTEX and NTDs, but also two separate studies addressing important methodological issues associated with this question. Our results contribute to the growing body of evidence regarding air pollutant exposure and adverse birth outcomes. ^