Multivariate analyses of social-behavioral factors with health insurance coverage among Asian Americans in California

This study aimed to estimate the prevalence of uninsurance among California adults and Asian Americans, and to examine the associations of social-behavioral variables with uninsurance. A total of 24,136 adults (aged 18–64) including 2,060 Asian Americans were selected from the combined 2013–2014 California Health Interview Survey. Weighted univariate and multivariate logistic regression analyses were used to estimate the associations of potential factors with uninsurance. To evaluate the relationship of independent variables, the oblique principal component cluster analysis (OPCCA) was used to classify 9 variables into disjoint clusters. For Whites, African Americans, Latinos, and Asians, the prevalence of uninsurance was 8.5%, 10.3%, 24.7%, and 12.6%, respectively. Among Asians, the prevalence of uninsurance was 15.5%, 9.2%, 6.2%, 20.8% and 12.1% for Chinese, Filipinos, Japanese, Koreans, and Vietnamese, respectively. In the whole sample, multivariate logistic regression analysis revealed that being male, non-citizen, lower education, higher poverty, and current smoking were associated with uninsurance. Among Asians, compared to Koreans, being Filipinos and Vietnamese were associated with lower odds of being uninsured; meanwhile being male, non-citizen, lower education, and higher poverty were significantly associated with increased odds of uninsurance. Elder age groups and current smoking were significantly associated with increased odds of uninsurance in bivariate analysis; however, such associations disappeared after adjusting for other factors. Nine independent variables were divided into 2 clusters, where the variables in the same cluster were strongly correlated but had weak correlations with the variables in the other cluster. In conclusion, there are differences in the prevalence of uninsurance between Asians and Whites, and among Asian subgroups. Being male, non-citizen, lower education, higher poverty and current smoking were positively significantly associated with uninsurance

Principal Component Regression Analysis of Nutrition Factors and Physical Activities with Diabetes

The associations of nutrition factors and physical activities with adult diabetes are inconsistent; while most of these factors are inter correlated. The aims of this study are to overcome the disturbance of the multicollinearity of the risk factors and examine the associations of these factors with diabetes using the principal component analysis (PCA) and regression analysis with principal component scores (PCS). Totally, 659 adults with diabetes and 2827 non-diabetic were selected from the 2012 Health Information National Trends Survey (HINTS 4, Cycle 2). PCA was utilized to deal with multicollinearity of the risk factors. Weighted univariate and multiple logistic regression analyses were used to estimate the associations of potential factors and PCS with diabetes. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The first 3 PCs for nutrition factors and physical activities could explain 70% variances. The first principal component (PC1) is a measure of nutrition factors (fruit and vegetables consumption), PC2 is a measure for physical activities (moderate exercise and strength training), and PC3 is about calorie information use and soda use. Weighted multiple logistic regression showed that African Americans, middle aged adults (45-64 years), elderly (65+), never married, and with lower education were associated with increased odds of diabetes. After adjusting for others factors, the PC1 showed marginal association with diabetes (OR=0.84, 95% CI=0.70-1.01); while PC2 and PC3 revealed significant associations with diabetes (OR=0.73, 95% CI=0.61-0.86 and OR=0.85, 95% CI=0.74-0.99, respectively). In conclusion, PCA can be used to reduce the indicators in complex survey data. The first 3 PCs of nutrition factors and physical activities were associated with diabetes. Promotion of health food and physical activities should be encouraged to help decrease the prevalence of diabetes

Recent Intensified Winter Coldness in the Mid-High Latitudes of Eurasia and Its Relationship with Daily Extreme Low Temperature Variability

Observational records in recent decades show a large-scale decrease in the cold-season temperature variance in the Northern Hemisphere midlatitudes under continuous global warming. However, severe low temperature events in winter frequently occurred in midlatitude Eurasia (MEA) in the last decade. Here, we define a new coldness intensity (CI) index for the near-surface based on the amplitude of daily anomalously cold temperatures in winter to demonstrate the CI of the variability of low temperature extremes. The results show that a sign-consistent mode dominates the CI variation in MEA, with a marked intensification during the last decade via empirical orthogonal function (EOF) analysis. This leading mode is significantly related to the frequency of winter extreme events. The associated circulations are characterized by a remarkable anomalous anticyclone in Northwest Eurasia, which induced substantial cold advection in MEA. The widespread intensified CI in MEA is closely linked with strong surface anticyclones and synoptic blocking in the mid-high latitudes (25 ∘ E-85 ∘ E). Coincidently, positive phase shifts of the first two leading modes of the extratropical circulation, which feature similar blocking-like anomalies in the northwestern Eurasian subarctic, jointly play an important role in the recent frequency of severe winters

Systematically characterizing dysfunctional long intergenic noncoding RNAs in multiple brain regions of major psychosis

Alzheimer\u27s disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and age at onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 single nucleotide polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8×10-3) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The hazards ratio (HR) with its 95% confidence interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR=1.33, 95%CI=1.13-1.57, p=5.0×10-4). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR=1.34, 95%CI=1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p=0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p=2.27×10-3 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD

Bayesian Cox Proportional Hazards Model in Survival Analysis of HACE1 Gene with Age at Onset of Alzheimer’s Disease

Alzheimer’s Disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and Age at Onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 Single Nucleotide Polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8 × 10-3) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The Hazards Ratio (HR) with its 95% Confidence Interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR = 1.33, 95% CI = 1.13-1.57, p = 5.0 × 10-4). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR = 1.34, 95% CI = 1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p = 0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p = 2.27 × 10-3 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD

Reconstruction of Typhoon Structure Using 3-Dimensional Doppler Radar Radial Velocity Data with the Multigrid Analysis: A Case Study in an Idealized Simulation Context

Extracting multiple-scale observational information is critical for accurately reconstructing the structure of mesoscale circulation systems such as typhoon. The Space and Time Mesoscale Analysis System (STMAS) with multigrid data assimilation developed in Earth System Research Laboratory (ESRL) in National Oceanic and Atmospheric Administration (NOAA) has addressed this issue. Previous studies have shown the capability of STMAS to retrieve multiscale information in 2-dimensional Doppler radar radial velocity observations. This study explores the application of 3-dimensional (3D) Doppler radar radial velocities with STMAS for reconstructing a 3D typhoon structure. As for the first step, here, we use an idealized simulation framework. A two-scale simulated “typhoon” field is constructed and referred to as “truth,” from which randomly distributed conventional wind data and 3D Doppler radar radial wind data are generated. These data are used to reconstruct the synthetic 3D “typhoon” structure by the STMAS and the traditional 3D variational (3D-Var) analysis. The degree by which the “truth” 3D typhoon structure is recovered is an assessment of the impact of the data type or analysis scheme being evaluated. We also examine the effects of weak constraint and strong constraint on STMAS analyses. Results show that while the STMAS is superior to the traditional 3D-Var for reconstructing the 3D typhoon structure, the strong constraint STMAS can produce better analyses on both horizontal and vertical velocities

Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning

Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic scenes and limited visual cues, it is of great challenge to predict how long there will be an accident from early observed frames. Most existing approaches are developed to learn features of accident-relevant agents for accident anticipation, while ignoring the features of their spatial and temporal relations. Besides, current deterministic deep neural networks could be overconfident in false predictions, leading to high risk of traffic accidents caused by self-driving systems. In this paper, we propose an uncertainty-based accident anticipation model with spatio-temporal relational learning. It sequentially predicts the probability of traffic accident occurrence with dashcam videos. Specifically, we propose to take advantage of graph convolution and recurrent networks for relational feature learning, and leverage Bayesian neural networks to address the intrinsic variability of latent relational representations. The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features. In addition, we collect a new Car Crash Dataset (CCD) for traffic accident anticipation which contains environmental attributes and accident reasons annotations. Experimental results on both public and the newly-compiled datasets show state-of-the-art performance of our model. Our code and CCD dataset are available at https://github.com/Cogito2012/UString.Comment: Accepted by ACM MM 202