Interaction of marijuana and alcohol on fatal motor vehicle crash risk: a case–control study

Background: Concurrent use of marijuana and alcohol in drivers is of increasing concern but its role in crash causation has not been well understood. Methods: Using a case–control design, we assessed the individual and joint effects of marijuana and alcohol use on fatal crash risk. Cases (n = 1944) were drivers fatally injured in motor vehicle crashes in the United States at specific times in 2006, 2007 and 2008. Controls (n = 7719) were drivers who participated in the 2007 National Roadside Survey of Alcohol and Drug Use by Drivers. Results: Overall, cases were significantly more likely than controls to test positive for marijuana (12.2% vs. 5.9%, p < 0.0001), alcohol (57.8% vs. 7.7%, p < 0.0001) and both marijuana and alcohol (8.9% vs. 0.8%, p < 0.0001). Compared to drivers testing negative for alcohol and marijuana, the adjusted odds ratios of fatal crash involvement were 16.33 [95% confidence interval (CI): 14.23, 18.75] for those testing positive for alcohol and negative for marijuana, 1.54 (95% CI: 1.16, 2.03) for those testing positive for marijuana and negative for alcohol, and 25.09 (95% CI: 17.97, 35.03) for those testing positive for both alcohol and marijuana. Conclusions: Alcohol use and marijuana use are each associated with significantly increased risks of fatal crash involvement. When alcohol and marijuana are used together, there exists a positive synergistic effect on fatal crash risk on the additive scale

Bayesian Predictive Inference for Three Topics in Survey Samples.

In this thesis, I study three problems in survey samples: inference for finite population quantities in unequal probability sampling, variable selection for multiply imputed data, and the application of the multiple imputation method to the problem of detection limits. In survey samples, design-based estimators are often used for inference about finite population quantities when sample sizes are large. However, design-based inference relies on asymptotic assumptions; mean square error can be very large and nominal confidence interval coverage relatively poor when the sample is small. When design information is available to modelers, it can be used to improve the efficiency of the estimators. In Chapters II and III, I provide Bayesian model-based estimators for finite population proportions and quantiles in unequal probability sampling settings by fitting the survey outcomes on the penalized splines of the selection probabilities. Simulation studies show that the robust Bayesian estimator for proportions is more efficient and its 95% CI provides better confidence coverage with shorter average width than the Hajek estimator or the generalized regression estimator. The Bayesian estimators for quantiles also outperform the design-based estimators, with smaller mean squared errors and shorter average width of 95% CIs. When sparse data are selected into samples, the Bayesian estimators yield better confidence coverage. The second part of the research is motivated by two statistical issues connected with the University of Michigan Dioxin Exposure Study which employs a complex survey design. In Chapter IV, I propose a “combine then select” variable selection method which calculates combined p-values using the multiple imputation combining rule and then selects variables based on the combined p-values in each step of the selection. I show through simulations and the dioxin study data that the “combine then select” method is less likely to incorrectly select variables into the model than competing methods currently used in epidemiological studies. In Chapter V, I employ a proper multiple imputation approach to impute the serum dioxin concentrations for those below the limit of detection. I then use the complete imputed data to predict the age- and sex- specific percentiles of serum dioxin concentrations among the U.S. population.Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64741/1/qixuan_1.pd

Validity of oral fluid test for Delta-9-tetrahydrocannabinol in drivers using the 2013 National Roadside Survey Data

Background Driving under the influence of marijuana is a serious traffic safety concern in the United States. Delta 9-tetrahydrocannabinol (THC) is the main active compound in marijuana. Although blood THC testing is a more accurate measure of THC-induced impairment, measuring THC in oral fluid is a less intrusive and less costly method of testing. Methods We examined whether the oral fluid THC test can be used as a valid alternative to the blood THC test using a sensitivity and specificity analysis and a logistic regression, and estimate the quantitative relationship between oral fluid THC concentration and blood THC concentration using a correlation analysis and a linear regression on the log-transformed THC concentrations. We used data from 4596 drivers who participated in the 2013 National Roadside Survey of Alcohol and Drug Use by Drivers and for whom THC testing results from both oral fluid and whole blood samples were available. Results Overall, 8.9% and 9.4% of the participants tested positive for THC in oral fluid and whole blood samples, respectively. Using blood test as the reference criterion, oral fluid test for THC positivity showed a sensitivity of 79.4% (95% CI: 75.2%, 83.1%) and a specificity of 98.3% (95% CI: 97.9%, 98.7%). The log-transformed oral fluid THC concentration accounted for about 29% of the variation in the log-transformed blood THC concentration. That is, there is still 71% of the variation in the log-transformed blood THC concentration unexplained by the log-transformed oral fluid THC concentration. Back-transforming to the original scale, we estimated that each 10% increase in the oral fluid THC concentration was associated with a 2.4% (95% CI: 2.1%, 2.8%) increase in the blood THC concentration. Conclusions The oral fluid test is a highly valid method for detecting the presence of THC in the blood but cannot be used to accurately measure the blood THC concentration

Dietary phytosterols and phytostanols decrease cholesterol levels but increase blood pressure in WKY inbred rats in the absence of salt-loading

<p>Abstract</p> <p>Background</p> <p>There are safety concerns regarding widespread consumption of phytosterol and phytostanol supplemented food products. The aim of this study was to determine, in the absence of excess dietary salt, the individual effects of excess accumulation of dietary phytosterols and phytostanols on blood pressure in Wistar Kyoto (WKY) <it>inbred </it>rats that have a mutation in the <it>Abcg5 </it>gene and thus over absorb phytosterols and phytostanols.</p> <p>Methods</p> <p>Thirty 35-day old male WKY <it>inbred </it>rats (10/group) were fed a control diet or a diet containing phytosterols or phytostanols (2.0 g/kg diet) for 5 weeks. The sterol composition of the diets, plasma and tissues were analysed by gas chromatography. Blood pressure was measured by the tail cuff method. mRNA levels of several renal blood pressure regulatory genes were measured by real-time quantitative PCR.</p> <p>Results</p> <p>Compared to the control diet, the phytosterol diet resulted in 3- to 4-fold increases in the levels of phytosterols in plasma, red blood cells, liver, aorta and kidney of WKY <it>inbred </it>rats (<it>P </it>< 0.05). The phytostanol diet dramatically increased (> 9-fold) the levels of phytostanols in plasma, red blood cells, liver, aorta and kidney of these rats (<it>P </it>< 0.05). The phytosterol diet decreased cholesterol levels by 40%, 31%, and 19% in liver, aorta and kidney, respectively (<it>P </it>< 0.05). The phytostanol diet decreased cholesterol levels by 15%, 16%, 20% and 14% in plasma, liver, aorta and kidney, respectively (<it>P </it>< 0.05). The phytostanol diet also decreased phytosterol levels by 29% to 54% in plasma and tissues (<it>P </it>< 0.05). Both the phytosterol and phytostanol diets produced significant decreases in the ratios of cholesterol to phytosterols and phytostanols in plasma, red blood cells, liver, aorta and kidney. Rats that consumed the phytosterol or phytostanol diets displayed significant increases in systolic and diastolic blood pressure compared to rats that consumed the control diet (<it>P </it>< 0.05). The phytosterol diet increased renal <it>angiotensinogen </it>mRNA levels of these rats.</p> <p>Conclusion</p> <p>These data suggest that excessive accumulation of dietary phytosterols and phytostanols in plasma and tissues may contribute to the increased blood pressure in WKY <it>inbred </it>rats in the absence of excess dietary salt. Therefore, even though phytosterols and phytostanols lower cholesterol levels, prospective clinical studies testing the net beneficial effects of dietary phytosterols and phytostanols on cardiovascular events for subgroups of individuals that have an increased incorporation of these substances are needed.</p

Relightable Neural Human Assets from Multi-view Gradient Illuminations

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2,000 high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks. The dataset is available at https://miaoing.github.io/RNHA.Comment: Project page: https://miaoing.github.io/RNH

Timing and effect of a safe routes to school program on child pedestrian injury risk during school travel hours: Bayesian changepoint and difference-in-differences analysis

Background: In 2005, the US Congress allocated $612 million for a national Safe Routes to School (SRTS) program to encourage walking and bicycling to schools. We evaluated the effectiveness of a Safe Routes to School Program (SRTS) in controlling pedestrian injuries among school-age children. Methods: Bayesian changepoint analysis of quarterly counts of pedestrian injuries among 5 to 19-year- old children in New York City between 2001 and 2010 during school-travel hours in census tracts with and without SRTS. Overdispersed Poisson modeling for difference in differences following the changepoint. Results: In SRTS-intervention census tracts, a change point in the quarterly counts of injuries was identified in the second quarter of 2008, which was consistent with the timing of the implementation of SRTS interventions. In census tracts with SRTS interventions, the estimated quarterly rates of pedestrian injury per 10,000 population among school-age children during school-travel hours were 3.47 (95% Credible Interval [CrI] 2.67, 4.39) prior to the changepoint, and 0.74 (95% CrI 0.30, 1.50) after the changepoint. There was no change in the average number of quarterly injuries in non-SRTS census tracts . Overdispersed Poisson modeling revealed that SRTS implementation was associated with a 44% reduction (95% Confidence Interval [CI] 87% decrease to 130% increase) in school-age pedestrian injury risk during school-travel hours. Conclusions: Bayesian changepoint analysis of quarterly counts of school-age pedestrian injuries correctly identified the timing of SRTS intervention in New York City. Implementation of the SRTS program in New York City appears to be effective in reducing school-age pedestrian injuries during school-travel hours

Application of seaweed polysaccharide in bone tissue regeneration

Regeneration is a complex process influenced by many independent or combined factors, including inflammation, proliferation, and tissue remodeling. The ocean, the most extensive resource on Earth, is rich in Seaweed. With increasing research in recent years, researchers have discovered that seaweed polysaccharides have various pharmacological effects, including a particular efficacy in promoting bone tissue regeneration. However, the application of this material in the field of bone tissue engineering is very limited. However, there are few studies on the polysaccharide at home and abroad, and little is known about its potential application value in bone repair. In addition, the bioavailability of the seaweed polysaccharide is also low, and there are still many problems to be solved. For example, the ease of solubility of fucoidan in water is a key issue that restricts its practical application. In this review, we summarize the applications and mechanisms of seaweed polysaccharides in bone healing. We also propose to combine seaweed polysaccharides with novel technologies through different types of preparations, hydrogels, scaffolds, and 3D printing to improve their use in tissue healing and regeneration

Dynamic twisting and imaging of moir\'e crystals

The electronic band structure is an intrinsic property of solid-state materials that is intimately connected to the crystalline arrangement of atoms. Moir\'e crystals, which emerge in twisted stacks of atomic layers, feature a band structure that can be continuously tuned by changing the twist angle between adjacent layers. This class of artificial materials blends the discrete nature of the moir\'e superlattice with intrinsic symmetries of the constituent materials, providing a versatile platform for investigation of correlated phenomena whose origins are rooted in the geometry of the superlattice, from insulating states at "magic angles" to flat bands in quasicrystals. Here we present a route to mechanically tune the twist angle of individual atomic layers with a precision of a fraction of a degree inside a scanning probe microscope, which enables continuous control of the electronic band structure in-situ. Using nanostructured rotor devices, we achieve the collective rotation of a single layer of atoms with minimal deformation of the crystalline lattice. In twisted bilayer graphene, we demonstrate nanoscale control of the moir\'e superlattice period via external rotations, as revealed using piezoresponse force microscopy. We also extend this methodology to create twistable boron nitride devices, which could enable dynamic control of the domain structure of moir\'e ferroelectrics. This approach provides a route for real-time manipulation of moir\'e materials, allowing for systematic exploration of the phase diagrams at multiple twist angles in a single device