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Enteric viruses (EVs) are the largest contributors to foodborne illnesses and outbreaks globally. Their ability to persist in the environment, coupled with the challenges experienced in environmental monitoring, creates a critical aperture through which agricultural crops may become contaminated. This study involved a 17-month investigation of select human EVs and viral indicators in nontraditional irrigation water sources (surface and reclaimed waters) in the Mid-Atlantic region of the United States. Real-time quantitative PCR was used for detection of Aichi virus, hepatitis A virus, and norovirus genotypes I and II (GI and GII, respectively). Pepper mild mottle virus (PMMoV), a common viral indicator of human fecal contamination, was also evaluated, along with atmospheric (air and water temperature, cloud cover, and precipitation 24 h, 7 days, and 14 days prior to sample collection) and physicochemical (dissolved oxygen, pH, salinity, and turbidity) data, to determine whether there were any associations between EVs and measured parameters. EVs were detected more frequently in reclaimed waters (32% [n = 22]) than in surface waters (4% [n = 49]), similar to PMMoV detection frequency in surface (33% [n = 42]) and reclaimed (67% [n = 21]) waters. Our data show a significant correlation between EV and PMMoV (R2 = 0.628, P

As climate change continues to stress freshwater resources, we have a pressing need to identify alternative (nontraditional) sources of microbially safe water for irrigation of fresh produce. This study is part of the center CONSERVE, which aims to facilitate the adoption of adequate agricultural water sources. A 26-month longitudinal study was conducted at 11 sites to assess the prevalence of bacteria indicating water quality, fecal contamination, and crop contamination risk (Escherichia coli, total coliforms [TC], Enterococcus, and Aeromonas). Sites included nontidal freshwater rivers/creeks (NF), a tidal brackish river (TB), irrigation ponds (PW), and reclaimed water sites (RW). Water samples were filtered for bacterial quantification. E. coli, TC, enterococci (∼86%, 98%, and 90% positive, respectively; n = 333), and Aeromonas (∼98% positive; n = 133) were widespread in water samples tested. Highest E. coli counts were in rivers, TC counts in TB, and enterococci in rivers and ponds (P

BACKGROUND: Maternal exposures to traffic-related air pollution have been associated with adverse pregnancy outcomes. Exposures to traffic-related air pollutants are strongly influenced by time spent near traffic. However, little is known about womens travel activities during pregnancy and whether questionnaire-based data can provide reliable information on travel patterns during pregnancy. OBJECTIVES: Examine womens in-vehicle travel behavior during pregnancy and examine the difference in travel data collected by questionnaire and global positioning system (GPS) and their potential for exposure error. METHODS: We measured work-related travel patterns in 56 pregnant women using a questionnaire and one-week GPS tracking three times during pregnancy (30 weeks of gestation). We compared self-reported activities with GPS-derived trip distance and duration, and examined potentially influential factors that may contribute to differences. We also described in-vehicle travel behavior by pregnancy periods and influences of demographic and personal factors on daily travel times. Finally, we estimated personal exposure to particle-bound polycyclic aromatic hydrocarbon (PB-PAH) and examined the magnitude of exposure misclassification using self-reported vs. GPS travel data. RESULTS: Subjects overestimated both trip duration and trip distance compared to the GPS data. We observed moderately high correlations between self-reported and GPS-recorded travel distance (home to work trips: r = 0.88; work to home trips: r = 0.80). Better agreement was observed between the GPS and the self-reported travel time for home to work trips (r = 0.77) than work to home trips (r = 0.64). The subjects on average spent 69 and 93 minutes traveling in vehicles daily based on the GPS and self-reported data, respectively. Longer daily travel time was observed among participants in early pregnancy, and during certain pregnancy periods in women with higher education attainment, higher income, and no children. When comparing self-reported vs. GPS data, we found that estimated personal exposure to PB-PAH did not differ remarkably at the population level, but the difference was large at an individual level. CONCLUSION: Self-reported home-to-work data overestimated both trip duration and trip distance compared to GPS data. Significant differences in PAH exposure estimates were observed at individual level using self-reported vs. GPS data, which has important implications in air pollution epidemiological studies.

BackgroundMaternal exposures to traffic-related air pollution have been associated with adverse pregnancy outcomes. Exposures to traffic-related air pollutants are strongly influenced by time spent near traffic. However, little is known about women’s travel activities during pregnancy and whether questionnaire-based data can provide reliable information on travel patterns during pregnancy.ObjectivesExamine women’s in-vehicle travel behavior during pregnancy and examine the difference in travel data collected by questionnaire and global positioning system (GPS) and their potential for exposure error.MethodsWe measured work-related travel patterns in 56 pregnant women using a questionnaire and one-week GPS tracking three times during pregnancy (30 weeks of gestation). We compared self-reported activities with GPS-derived trip distance and duration, and examined potentially influential factors that may contribute to differences. We also described in-vehicle travel behavior by pregnancy periods and influences of demographic and personal factors on daily travel times. Finally, we estimated personal exposure to particle-bound polycyclic aromatic hydrocarbon (PB-PAH) and examined the magnitude of exposure misclassification using self-reported vs. GPS travel data.ResultsSubjects overestimated both trip duration and trip distance compared to the GPS data. We observed moderately high correlations between self-reported and GPS-recorded travel distance (home to work trips: r = 0.88; work to home trips: r = 0.80). Better agreement was observed between the GPS and the self-reported travel time for home to work trips (r = 0.77) than work to home trips (r = 0.64). The subjects on average spent 69 and 93 minutes traveling in vehicles daily based on the GPS and self-reported data, respectively. Longer daily travel time was observed among participants in early pregnancy, and during certain pregnancy periods in women with higher education attainment, higher income, and no children. When comparing self-reported vs. GPS data, we found that estimated personal exposure to PB-PAH did not differ remarkably at the population level, but the difference was large at an individual level.ConclusionSelf-reported home-to-work data overestimated both trip duration and trip distance compared to GPS data. Significant differences in PAH exposure estimates were observed at individual level using self-reported vs. GPS data, which has important implications in air pollution epidemiological studies.

BackgroundAir pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data.MethodsWe developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model.ResultsIndoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data.ConclusionsOur models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

BackgroundPeople's time-location patterns are important in air pollution exposure assessment because pollution levels may vary considerably by location. A growing number of studies are using global positioning systems (GPS) to track people's time-location patterns. Many portable GPS units that archive location are commercially available at a cost that makes their use feasible for epidemiological studies.MethodsWe evaluated the performance of five portable GPS data loggers and two GPS cell phones by examining positional accuracy in typical locations (indoor, outdoor, in-vehicle) and factors that influence satellite reception (building material, building type), acquisition time (cold and warm start), battery life, and adequacy of memory for data storage. We examined stationary locations (eg, indoor, outdoor) and mobile environments (eg, walking, traveling by vehicle or bus) and compared GPS locations to highly-resolved US Geological Survey (USGS) and Digital Orthophoto Quarter Quadrangle (DOQQ) maps.ResultsThe battery life of our tested instruments ranged from 20 minutes and varied significantly by building structure type and by cold or warm start. No GPS device was found to have consistently superior performance with regard to spatial accuracy and signal loss. At fixed outdoor locations, 65%-95% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices. At fixed indoor locations, 50%-80% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices except one. Most of the GPS devices performed well during commuting on a freeway, with >80% of points within 10-m of the DOQQ route, but the performance was significantly impacted by surrounding structures on surface streets in highly urbanized areas.ConclusionsAll the tested GPS devices had limitations, but we identified several devices which showed promising performance for tracking subjects' time location patterns in epidemiological studies.

Real‐world data derived from electronic health records often exhibit high levels of missingness in variables, such as laboratory results, presenting a challenge for statistical analyses. We developed a systematic workflow for gathering evidence of different missingness mechanisms and performing subsequent statistical analyses. We quantify evidence for missing completely at random (MCAR) or missing at random (MAR), mechanisms using Hotelling's multivariate t‐test, and random forest classifiers, respectively. We further illustrate how to apply sensitivity analyses using the not at random fully conditional specification procedure to examine changes in parameter estimates under missing not at random (MNAR) mechanisms. In simulation studies, we validated these diagnostics and compared analytic bias under different mechanisms. To demonstrate the application of this workflow, we applied it to two exemplary case studies with an advanced non‐small cell lung cancer and a multiple myeloma cohort derived from a real‐world oncology database. Here, we found strong evidence against MCAR, and some evidence of MAR, implying that imputation approaches that attempt to predict missing values by fitting a model to observed data may be suitable for use. Sensitivity analyses did not suggest meaningful departures of our analytic results under potential MNAR mechanisms; these results were also in line with results reported in clinical trials. [ABSTRACT FROM AUTHOR]

A translational- torsional coupling dynamics model of planetary gear transmission on the low speed shaft in 2. 5 MW wind turbine gearbox is built by using lumped- parameter method. Through comparing the inherent frequency of the three planetary gears under the conditions of the mesh stiffness are equal and taking the same average mesh stiffness with the conditions of the different mesh stiffness which are influenced by meshing phase difference,the conclusion that the influence of different combination of mesh stiffness under the influence of meshing phase difference on the low inherent frequency is less,but have much more influence on the high inherent frequency is obained. This analysis of inherent frequencies laid a foundation for dynamics analysis of the planetary gear transmission system in 2. 5MW wind turbine.