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In this work, we propose a new design to enhance ion transport in pressure-driven nanofluidic systems for energy harvesting. The proposed system uses two counter-charged nanochannels, i.e., one of the channels is negatively charged while the other one is positively charged. Under a pressure gradient, cations and anions move through negatively and positively charged channels, respectively, in different directions and contribute to the streaming current. Molecular dynamics simulations are employed to study the effects of surface charge density, channel height, and pressure gradient on the streaming current. Compared with the traditional system, where the nanochannel is negatively charged, the streaming current in the proposed system can be enhanced by a factor up to 6.6. In addition to the involvement of both cations and anions, the enhanced current in the proposed system is caused by the strengthened ionic fluxes due to relatively low-energy barriers for ions entering the channels.

Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To this end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that treats each node as a sequence containing a series of tokens constructed by our proposed Hop2Token module. For each node, Hop2Token aggregates the neighborhood features from different hops into different representations, producing a sequence of token vectors as one input. In this way, NAGphormer could be trained in a mini-batch manner and thus could scale to large graphs. Moreover, we mathematically show that compared to a category of advanced Graph Neural Networks (GNNs), called decoupled Graph Convolutional Networks, NAGphormer could learn more informative node representations from multi-hop neighborhoods. In addition, we propose a new data augmentation method called Neighborhood Augmentation (NrAug) based on the output of Hop2Token that augments simultaneously the features of neighborhoods from global as well as local views to strengthen the training effect of NAGphormer. Extensive experiments on benchmark datasets from small to large demonstrate the superiority of NAGphormer against existing graph Transformers and mainstream GNNs, and the effectiveness of NrAug for further boosting NAGphormer., Comment: 14pages, 5 figures. arXiv admin note: text overlap with arXiv:2206.04910

Graph pooling methods have been widely used on downsampling graphs, achieving impressive results on multiple graph-level tasks like graph classification and graph generation. An important line called node dropping pooling aims at exploiting learnable scoring functions to drop nodes with comparatively lower significance scores. However, existing node dropping methods suffer from two limitations: (1) for each pooled node, these models struggle to capture long-range dependencies since they mainly take GNNs as the backbones; (2) pooling only the highest-scoring nodes tends to preserve similar nodes, thus discarding the affluent information of low-scoring nodes. To address these issues, we propose a Graph Transformer Pooling method termed GTPool, which introduces Transformer to node dropping pooling to efficiently capture long-range pairwise interactions and meanwhile sample nodes diversely. Specifically, we design a scoring module based on the self-attention mechanism that takes both global context and local context into consideration, measuring the importance of nodes more comprehensively. GTPool further utilizes a diversified sampling method named Roulette Wheel Sampling (RWS) that is able to flexibly preserve nodes across different scoring intervals instead of only higher scoring nodes. In this way, GTPool could effectively obtain long-range information and select more representative nodes. Extensive experiments on 11 benchmark datasets demonstrate the superiority of GTPool over existing popular graph pooling methods.

The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs). In this work, we regard the self-attention mechanism, the core module of graph Transformers, as a two-step aggregation operation on a fully connected graph. Due to the property of generating positive attention values, the self-attention mechanism is equal to conducting a smooth operation on all nodes, preserving the low-frequency information. However, only capturing the low-frequency information is inefficient in learning complex relations of nodes on diverse graphs, such as heterophily graphs where the high-frequency information is crucial. To this end, we propose a Signed Attention-based Graph Transformer (SignGT) to adaptively capture various frequency information from the graphs. Specifically, SignGT develops a new signed self-attention mechanism (SignSA) that produces signed attention values according to the semantic relevance of node pairs. Hence, the diverse frequency information between different node pairs could be carefully preserved. Besides, SignGT proposes a structure-aware feed-forward network (SFFN) that introduces the neighborhood bias to preserve the local topology information. In this way, SignGT could learn informative node representations from both long-range dependencies and local topology information. Extensive empirical results on both node-level and graph-level tasks indicate the superiority of SignGT against state-of-the-art graph Transformers as well as advanced GNNs., Comment: Submitted to a conference

The decoupled Graph Convolutional Network (GCN), a recent development of GCN that decouples the neighborhood aggregation and feature transformation in each convolutional layer, has shown promising performance for graph representation learning. Existing decoupled GCNs first utilize a simple neural network (e.g., MLP) to learn the hidden features of the nodes, then propagate the learned features on the graph with fixed steps to aggregate the information of multi-hop neighborhoods. Despite effectiveness, the aggregation operation, which requires the whole adjacency matrix as the input, is involved in the model training, causing high training cost that hinders its potential on larger graphs. On the other hand, due to the independence of node attributes as the input, the neural networks used in decoupled GCNs are very simple, and advanced techniques cannot be applied to the modeling. To this end, we further liberate the aggregation operation from the decoupled GCN and propose a new paradigm of GCN, termed Neighborhood Convolutional Network (NCN), that utilizes the neighborhood aggregation result as the input, followed by a special convolutional neural network tailored for extracting expressive node representations from the aggregation input. In this way, the model could inherit the merit of decoupled GCN for aggregating neighborhood information, at the same time, develop much more powerful feature learning modules. A training strategy called mask training is incorporated to further boost the model performance. Extensive results demonstrate the effectiveness of our model for the node classification task on diverse homophilic graphs and heterophilic graphs., Comment: 8 pages, 4 figures, 2 tables, submitted to a conference of 2023

By incorporating the graph structural information into Transformers, graph Transformers have exhibited promising performance for graph representation learning in recent years. Existing graph Transformers leverage specific strategies, such as Laplacian eigenvectors and shortest paths of the node pairs, to preserve the structural features of nodes and feed them into the vanilla Transformer to learn the representations of nodes. It is hard for such predefined rules to extract informative graph structural features for arbitrary graphs whose topology structure varies greatly, limiting the learning capacity of the models. To this end, we propose an adaptive graph Transformer, termed Multi-Neighborhood Attention based Graph Transformer (MNA-GT), which captures the graph structural information for each node from the multi-neighborhood attention mechanism adaptively. By defining the input to perform scaled-dot product as an attention kernel, MNA-GT constructs multiple attention kernels based on different hops of neighborhoods such that each attention kernel can capture specific graph structural information of the corresponding neighborhood for each node pair. In this way, MNA-GT can preserve the graph structural information efficiently by incorporating node representations learned by different attention kernels. MNA-GT further employs an attention layer to learn the importance of different attention kernels to enable the model to adaptively capture the graph structural information for different nodes. Extensive experiments are conducted on a variety of graph benchmarks, and the empirical results show that MNA-GT outperforms many strong baselines., Comment: 8 pages, 4 figures, 5 tables, submitted to a conference of 2023

Node classification on attributed networks is a semi-supervised task that is crucial for network analysis. By decoupling two critical operations in Graph Convolutional Networks (GCNs), namely feature transformation and neighborhood aggregation, some recent works of decoupled GCNs could support the information to propagate deeper and achieve advanced performance. However, they follow the traditional structure-aware propagation strategy of GCNs, making it hard to capture the attribute correlation of nodes and sensitive to the structure noise described by edges whose two endpoints belong to different categories. To address these issues, we propose a new method called the itshape Propagation with Adaptive Mask then Training (PAMT). The key idea is to integrate the attribute similarity mask into the structure-aware propagation process. In this way, PAMT could preserve the attribute correlation of adjacent nodes during the propagation and effectively reduce the influence of structure noise. Moreover, we develop an iterative refinement mechanism to update the similarity mask during the training process for improving the training performance. Extensive experiments on four real-world datasets demonstrate the superior performance and robustness of PAMT.

The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers treat nodes as independent tokens and construct a single long sequence composed of all node tokens so as to train the Transformer model, causing it hard to scale to large graphs due to the quadratic complexity on the number of nodes for the self-attention computation. To this end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that treats each node as a sequence containing a series of tokens constructed by our proposed Hop2Token module. For each node, Hop2Token aggregates the neighborhood features from different hops into different representations and thereby produces a sequence of token vectors as one input. In this way, NAGphormer could be trained in a mini-batch manner and thus could scale to large graphs. Moreover, we mathematically show that as compared to a category of advanced Graph Neural Networks (GNNs), the decoupled Graph Convolutional Network, NAGphormer could learn more informative node representations from the multi-hop neighborhoods. Extensive experiments on benchmark datasets from small to large are conducted to demonstrate that NAGphormer consistently outperforms existing graph Transformers and mainstream GNNs. Code is available at https://github.com/JHL-HUST/NAGphormer., Comment: Accepted by ICLR 2023

Background Evidence suggests that subjective (perceived) social status (SSS) may predict health outcomes more strongly than objective social status, but little is known about the relationship between SSS and cardiovascular health (CVH). This study focuses on this relationship among diverse Hispanic/Latino adults because while poor CVH profiles are prevalent in this population, immigration complicates attempts to measure their social status. Methods and Results We analyzed baseline HCHS/SOL (Hispanic Community Health Study/Study of Latinos) data on 15 374 Hispanic/Latino adults aged 18 to 74 years in 2008 to 2011. SSS was assessed using the McArthur Scale, a 10-rung "social ladder." CVH was based on levels of 7 metrics defined by the American Heart Association. Linear and logistic regressions were used to examine cross-sectional associations of SSS with CVH (overall and single metrics) after adjusting for objective social status, demographic, and health factors. Less than half of the population (46%) had Ideal scores in ≥4 metrics of CVH. In multivariable-adjusted models, an increase in SSS was associated with a higher overall CVH score (β=0.04; 95% CI, 0.01-0.06) and greater likelihood of Ideal levels of body mass index, physical activity, and fasting blood glucose levels. Nativity and time in the United States modified the association between SSS and Ideal smoking. Conclusions Subjective measures of social status can enhance an understanding of CVH among Hispanic/Latino people. Future studies should explore the stability of SSS over time in comparison with objective social status and the mechanisms through which SSS may influence CVH.

Rationale & objectiveRecent studies suggest that periodontal disease may be associated with incident chronic kidney disease (CKD). However, studies have focused on older populations, and US Hispanics/Latinos were not well represented.Study designObservational cohort.Setting & participantsWe analyzed data from the Hispanic Community Health Study/Study of Latinos who completed a baseline visit with a periodontal examination and a follow-up visit, and did not have CKD at baseline.PredictorsPredictors included ≥30% of sites with clinical attachment loss ≥3 mm, ≥30% of sites with probing depth ≥4 mm, percentage of sites with bleeding on probing, and absence of functional dentition (<21 permanent teeth present).OutcomesOutcomes were incident low estimated glomerular filtration rate (eGFR) (eGFR <60 mL/min/1.73 m2 and decline in eGFR ≥1 mL/min/year); incident albuminuria (urine albumin:creatinine ratio [ACR] ≥30 mg/g); and change in eGFR and ACR.Analytic approachPoisson and linear regression.ResultsFor the sample (n = 7.732), baseline mean age was 41.5 years, 45.2% were male, 11.7% had ≥30% of sites with clinical attachment loss ≥3 mm, 5.1% had ≥30% of sites with probing depth ≥4 mm, 30.7% had ≥50% of sites with bleeding on probing, and 16.2% had absent functional dentition. During a median follow-up of 5.9 years, 149 patients developed low eGFR and 415 patients developed albuminuria. On multivariable analysis, presence versus absence of ≥30% of sites with probing depth ≥4 mm and absence of functional dentition were each associated with increased risk for incident low eGFR (incident density ratio, 2.31; 95% CI, 1.14-4.65 and 1.65, 95% CI, 1.01-2.70, respectively). None of the other predictors were associated with outcomes.LimitationsOnly a single kidney function follow-up measure.ConclusionsIn this cohort of US Hispanics/Latinos, we found that select measures of periodontal disease wer

Contains fulltext : 221113.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 221113.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 221113.pdf (Publisher’s version ) (Open Access)

Multi-output Gaussian processes (MOGPs) are an extension of Gaussian Processes (GPs) for predicting multiple output variables (also called channels, tasks) simultaneously. In this paper we use the convolution theorem to design a new kernel for MOGPs, by modeling cross channel dependencies through cross convolution of time and phase delayed components in the spectral domain. The resulting kernel is called Multi-Output Convolution Spectral Mixture (MOCSM) kernel. Results of extensive experiments on synthetic and real-life datasets demonstrate the advantages of the proposed kernel and its state of the art performance. MOCSM enjoys the desirable property to reduce to the well known Spectral Mixture (SM) kernel when a single-channel is considered. A comparison with the recently introduced Multi-Output Spectral Mixture kernel reveals that this is not the case for the latter kernel, which contains quadratic terms that generate undesirable scale effects when the spectral densities of different channels are either very close or very far from each other in the frequency domain., Comment: 12 pages, 48 figures. arXiv admin note: text overlap with arXiv:1808.01132

This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity.

The Yellow River source region is located in the transition region between permafrost and seasonally frozen ground on the northeastern Qinghai-Tibet Plateau. The region has experienced severe climate change, especially air temperature increases, in past decades. In this study, we employed a geomorphology-based eco-hydrological model (GBEHM) to assess the impacts of climate change on the frozen ground and eco-hydrological processes in the region. Based on a long-term simulation from 1981 to 2015, we found that the areal mean maximum thickness of seasonally frozen ground ranged from 1.1-1.8m and decreased by 1.2cm per year. Additionally, the ratio of the permafrost area to the total area decreased by 1.1% per year. These decreasing trends are faster than the average in China because the study area is on the sensitive margin of the Qinghai-Tibet Plateau. The annual runoff exhibited variations similar to those of the annual precipitation (R2=0.85), although the annual evapotranspiration (ET) exhibited an increasing trend (14.3mm/10a) similar to that of the annual mean air temperature (0.66°C/10a). The runoff coefficient (annual runoff divided by annual precipitation) displayed a decreasing trend because of the increasing ET, and the vegetation responses to climate warming and permafrost degradation were manifested as increases in the leaf area index (LAI) and ET at the start of the growing season. Furthermore, the results showed that changes to the frozen ground depth affected vegetation growth. Notably, a rapid decrease in the frozen ground depth (< -3.0cm/a) decreased the topsoil moisture and then decreased the LAI. This study showed that the eco-hydrological processes in the headwater area of the Yellow River have changed because of permafrost degradation, and these changes could further influence the water resources availability in the middle and lower reaches of the basin.

This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity.

The Yellow River source region is located in the transition region between permafrost and seasonally frozen ground on the northeastern Qinghai-Tibet Plateau. The region has experienced severe climate change, especially air temperature increases, in past decades. In this study, we employed a geomorphology-based eco-hydrological model (GBEHM) to assess the impacts of climate change on the frozen ground and eco-hydrological processes in the region. Based on a long-term simulation from 1981 to 2015, we found that the areal mean maximum thickness of seasonally frozen ground ranged from 1.1-1.8m and decreased by 1.2cm per year. Additionally, the ratio of the permafrost area to the total area decreased by 1.1% per year. These decreasing trends are faster than the average in China because the study area is on the sensitive margin of the Qinghai-Tibet Plateau. The annual runoff exhibited variations similar to those of the annual precipitation (R2=0.85), although the annual evapotranspiration (ET) exhibited an increasing trend (14.3mm/10a) similar to that of the annual mean air temperature (0.66°C/10a). The runoff coefficient (annual runoff divided by annual precipitation) displayed a decreasing trend because of the increasing ET, and the vegetation responses to climate warming and permafrost degradation were manifested as increases in the leaf area index (LAI) and ET at the start of the growing season. Furthermore, the results showed that changes to the frozen ground depth affected vegetation growth. Notably, a rapid decrease in the frozen ground depth (< -3.0cm/a) decreased the topsoil moisture and then decreased the LAI. This study showed that the eco-hydrological processes in the headwater area of the Yellow River have changed because of permafrost degradation, and these changes could further influence the water resources availability in the middle and lower reaches of the basin.

BackgroundFavorable levels of all readily measurable major cardiovascular disease risk factors (ie, low risk [LR]) are associated with lower risks of cardiovascular disease morbidity and mortality. Data are not available on LR prevalence among Hispanic/Latino adults of diverse ethnic backgrounds. This study aimed to describe the prevalence of a low cardiovascular disease risk profile among Hispanic/Latino adults in the United States and to examine cross-sectional associations of LR with measures of acculturation.Methods and resultsThe multicenter, prospective, population-based Hispanic Community Health Study/Study of Latinos examined 16 415 men and women aged 18 to 74 years at baseline (2008-2011) with diverse Hispanic/Latino backgrounds. Analyses involved 14 757 adults (mean age 41.3 years; 60.6% women). LR was defined using national guidelines for favorable levels of serum cholesterol, blood pressure, and body mass index and by not having diabetes mellitus and not currently smoking. Age-adjusted LR prevalence was low (8.4% overall; 5.1% for men, 11.2% for women) and varied by background (4.2% in men of Mexican heritage versus 15.0% in women of Cuban heritage). Lower acculturation (assessed using proxy measures) was significantly associated with higher odds of a LR profile among women only: Age-adjusted odds ratios of having LR were 1.64 (95% CI 1.24-2.17) for foreign-born versus US-born women and 1.96 (95% CI 1.49-2.58) for women residing in the United States <10 versus ≥10 years.ConclusionsAmong diverse US Hispanic/Latino adults, the prevalence of a LR profile is low. Lower acculturation is associated with higher odds of a LR profile among women but not men. Comprehensive public health strategies are needed to improve the cardiovascular health of US Hispanic/Latino adults.

Favorable levels of all readily measurable major cardiovascular disease risk factors (ie, low risk [LR]) are associated with lower risks of cardiovascular disease morbidity and mortality. Data are not available on LR prevalence among Hispanic/Latino adults of diverse ethnic backgrounds. This study aimed to describe the prevalence of a low cardiovascular disease risk profile among Hispanic/Latino adults in the United States and to examine cross-sectional associations of LR with measures of acculturation. The multicenter, prospective, population-based Hispanic Community Health Study/Study of Latinos examined 16 415 men and women aged 18 to 74 years at baseline (2008-2011) with diverse Hispanic/Latino backgrounds. Analyses involved 14 757 adults (mean age 41.3 years; 60.6% women). LR was defined using national guidelines for favorable levels of serum cholesterol, blood pressure, and body mass index and by not having diabetes mellitus and not currently smoking. Age-adjusted LR prevalence was low (8.4% overall; 5.1% for men, 11.2% for women) and varied by background (4.2% in men of Mexican heritage versus 15.0% in women of Cuban heritage). Lower acculturation (assessed using proxy measures) was significantly associated with higher odds of a LR profile among women only: Age-adjusted odds ratios of having LR were 1.64 (95% CI 1.24-2.17) for foreign-born versus US-born women and 1.96 (95% CI 1.49-2.58) for women residing in the United States <10 versus ≥10 years. Among diverse US Hispanic/Latino adults, the prevalence of a LR profile is low. Lower acculturation is associated with higher odds of a LR profile among women but not men. Comprehensive public health strategies are needed to improve the cardiovascular health of US Hispanic/Latino adults.

This study investigates the effects of uncertainty in rockphysics models on estimates of reservoir parameters from joint inversion of seismic AVA and CSEM data. The reservoir parameters are related to electrical resistivity using Archie's law, and to seismic velocity and density using the Xu-White model. To account for errors in the rock-physics models, we use two methods to handle uncertainty: (1) the model outputs are random functions with modes or means given by the model predictions, and (2) the parameters of the models are themselves random variables. Using a stochastic framework and Markov Chain Monte Carlo methods, we obtain estimates of reservoir parameters as well as of the uncertainty in the estimates. Synthetic case studies show that uncertainties in both rock-physics models and their associated parameters can have significant effects on estimates of reservoir parameters. Our method provides a means of quantifying how the uncertainty in the estimated reservoir parameters increases with increasing uncertainty in the rock-physics model and in the model parameters. We find that in the example we present, the estimation of water saturation is relatively less affected than is the estimation of clay content and porosity.

This study investigates the effects of uncertainty in rockphysics models on estimates of reservoir parameters from joint inversion of seismic AVA and CSEM data. The reservoir parameters are related to electrical resistivity using Archie's law, and to seismic velocity and density using the Xu-White model. To account for errors in the rock-physics models, we use two methods to handle uncertainty: (1) the model outputs are random functions with modes or means given by the model predictions, and (2) the parameters of the models are themselves random variables. Using a stochastic framework and Markov Chain Monte Carlo methods, we obtain estimates of reservoir parameters as well as of the uncertainty in the estimates. Synthetic case studies show that uncertainties in both rock-physics models and their associated parameters can have significant effects on estimates of reservoir parameters. Our method provides a means of quantifying how the uncertainty in the estimated reservoir parameters increases with increasing uncertainty in the rock-physics model and in the model parameters. We find that in the example we present, the estimation of water saturation is relatively less affected than is the estimation of clay content and porosity.

Background & aimsNonalcoholic fatty liver disease (NAFLD) was shown to disproportionally affect Hispanic persons. We examined the prevalence of suspected NAFLD in Hispanic/Latino persons with diverse backgrounds.MethodsWe studied the prevalence of suspected NAFLD among 12,133 persons included in the Hispanic Community Health Study/Study of Latinos. We collected data on levels of aminotransferase, metabolic syndrome (defined by National Cholesterol Education Program-Adult Treatment Panel III guidelines), demographics, and health behaviors. Suspected NAFLD was defined on the basis of increased level of aminotransferase in the absence of serologic evidence for common causes of liver disease or excessive alcohol consumption. In multivariate analyses, data were adjusted for metabolic syndrome, age, acculturation, diet, physical activity, sleep, and levels of education and income.ResultsIn multivariate analysis, compared with persons of Mexican heritage, persons of Cuban (odds ratio [OR], 0.69; 95% confidence interval [CI], 0.57-0.85), Puerto Rican (OR, 0.67; 95% CI, 0.52-0.87), and Dominican backgrounds (OR, 0.71; 95% CI, 0.54-0.93) had lower rates of suspected NAFLD. Persons of Central American and South American heritage had a similar prevalence of suspected NAFLD compared with persons of Mexican heritage. NAFLD was less common in women than in men (OR, 0.49; 95% CI, 0.40-0.60). Suspected NAFLD associated with metabolic syndrome and all 5 of its components.ConclusionsOn the basis of an analysis of a large database of health in Latino populations, we found the prevalence of suspected NAFLD among Hispanic/Latino individuals to vary by region of heritage.

The generalized linear model (GLM) is a popular model in many research areas. In the GLM, each outcome of the dependent variable is assumed to be generated from a particular distribution function in the exponential family. The mean of the distribution depends on the independent variables. The link function provides the relationship between the linear predictor and the mean of the distribution function. In this dissertation, two semiparametric extensions of the GLM will be developed. In the first part of this dissertation, we have proposed a new model, called a semiparametric generalized linear model with a log-concave random component (SGLM-L). In this model, the estimate of the distribution of the random component has a nonparametric form while the estimate of the systematic part has a parametric form. In the second part of this dissertation, we have proposed a model, called a generalized semiparametric single-index mixed model (GSSIMM). A nonparametric component with a single index is incorporated into the mean function in the generalized linear mixed model (GLMM) assuming that the random component is following a parametric distribution. In the first part of this dissertation, since most of the literature on the GLM deals with the parametric random component, we relax the parametric distribution assumption for the random component of the GLM and impose a log-concave constraint on the distribution. An iterative numerical algorithm for computing the estimators in the SGLM-L is developed. We construct a log-likelihood ratio test for inference. In the second part of this dissertation, we use a single index model to generalize the GLMM to have a linear combination of covariates enter the model via a nonparametric mean function, because the linear model in the GLMM is not complex enough to capture the underlying relationship between the response and its associated covariates. The marginal likelihood is approximated using the Laplace method. A penalized quasi-likelihood

The generalized linear model (GLM) is a popular model in many research areas. In the GLM, each outcome of the dependent variable is assumed to be generated from a particular distribution function in the exponential family. The mean of the distribution depends on the independent variables. The link function provides the relationship between the linear predictor and the mean of the distribution function. In this dissertation, two semiparametric extensions of the GLM will be developed. In the first part of this dissertation, we have proposed a new model, called a semiparametric generalized linear model with a log-concave random component (SGLM-L). In this model, the estimate of the distribution of the random component has a nonparametric form while the estimate of the systematic part has a parametric form. In the second part of this dissertation, we have proposed a model, called a generalized semiparametric single-index mixed model (GSSIMM). A nonparametric component with a single index is incorporated into the mean function in the generalized linear mixed model (GLMM) assuming that the random component is following a parametric distribution. In the first part of this dissertation, since most of the literature on the GLM deals with the parametric random component, we relax the parametric distribution assumption for the random component of the GLM and impose a log-concave constraint on the distribution. An iterative numerical algorithm for computing the estimators in the SGLM-L is developed. We construct a log-likelihood ratio test for inference. In the second part of this dissertation, we use a single index model to generalize the GLMM to have a linear combination of covariates enter the model via a nonparametric mean function, because the linear model in the GLMM is not complex enough to capture the underlying relationship between the response and its associated covariates. The marginal likelihood is approximated using the Laplace method. A penalized quasi-likelihood

We consider the estimation of the k + 1-dimensional nonparametric component (t) of the varying-coefficient model Y (t) = XT (t)(t) + "(t) based on longitudinal observation (Yij ,Xi(tij), tij), i = 1, ..., n, j = 1, ..., ni, where tij is the jth observed design time point t of the ith subjects at tij . The subjects are independently selected, but the repeated measurements within subject are possibly correlated.A Monte Carlo Simulation was established, kernel smoothing method was used to estimate (t) that minimizes a local least square criterion. The distribution for "(t) was analyzed. The degree of freedom was investigated.

The potential benefits of including geophysical data in hydrogeological site characterization have been stated numerous times (e.g. Ezzedine et al., 1999; Hubbard et al., 1999; Chen et al., 2001; Hubbard and Rubin, 2005). The principle reason for the growing interest in using geophysical methods in hydrogeological studies is that geophysics may provide spatially distributed models of physical properties in regions that are difficult to sample using conventional hydrological wellbore methods (e.g. Butler, 2005). The geophysical models often reveal more details compared with hydrogeological models derived from hydrogeological data, such as pump tests and observations of hydraulic heads. Furthermore, geophysical methods are less invasive compared with hydrogeological methods and they are comparatively cheap. Therefore, geophysical surveys can improve hydrogeological characterization if we could relate the geophysical and hydrogeological properties in an appropriate way. The added value of including geophysics in hydrogeological characterization has become increasingly accepted and several published case studies clearly show the worth of including geophysics for different applications and data types (e.g. see reviews by Hyndman and Tronicke, 2005; Goldman et al., 2005; Daniels et al., 2005). However, the success of a given hydrogeophysical case-study is dependent on many different factors and it is often difficult to develop an opinion a priori about the applicability of a method at another site or for another application. Here, we discuss some of the choices that need to be considered in a characterization effort and point out similarities and fundamental differences between different hydrogeophysical parameter estimation approaches presented in the literature.

Understanding fluid distribution and movement in the subsurface is critical for a variety of subsurface applications, such as remediation of environmental contaminants, sequestration of nuclear waste and CO2, intrusion of saline water into fresh water aquifers, and the production of oil and gas. It is well recognized that characterizing the properties that control fluids in the subsurface with the accuracy and spatial coverage needed to parameterize flow and transport models is challenging using conventional borehole data alone. Integration of conventional borehole data with more spatially extensive geophysical data (obtained from the surface, between boreholes, and from surface to boreholes) shows promise for providing quantitative information about subsurface properties and processes. Typically, estimation of subsurface properties involves a two-step procedure in which geophysical data are first inverted and then integrated with direct measurements and petrophysical relationship information to estimate hydrological parameters. However, errors inherent to geophysical data acquisition and inversion approaches and errors associated with petrophysical relationships can decrease the value of geophysical data in the estimation procedure. In this paper, we illustrate using two examples how joint inversion approaches, or simultaneous inversion of geophysical and hydrological data, offer great potential for overcoming some of these limitations.

Understanding fluid distribution and movement in the subsurface is critical for a variety of subsurface applications, such as remediation of environmental contaminants, sequestration of nuclear waste and CO2, intrusion of saline water into fresh water aquifers, and the production of oil and gas. It is well recognized that characterizing the properties that control fluids in the subsurface with the accuracy and spatial coverage needed to parameterize flow and transport models is challenging using conventional borehole data alone. Integration of conventional borehole data with more spatially extensive geophysical data (obtained from the surface, between boreholes, and from surface to boreholes) shows promise for providing quantitative information about subsurface properties and processes. Typically, estimation of subsurface properties involves a two-step procedure in which geophysical data are first inverted and then integrated with direct measurements and petrophysical relationship information to estimate hydrological parameters. However, errors inherent to geophysical data acquisition and inversion approaches and errors associated with petrophysical relationships can decrease the value of geophysical data in the estimation procedure. In this paper, we illustrate using two examples how joint inversion approaches, or simultaneous inversion of geophysical and hydrological data, offer great potential for overcoming some of these limitations.

It is widely recognized that natural heterogeneity and the large spatial variability of hydraulic parameters control the spread of contaminants and water in the subsurface. Similarly, hydrological and biogeochemical processes are variable over a wide range of spatial scales. The inadequacy of conventional (wellbore) approaches for characterizing or monitoring the parameters and processes at over large enough areas yet with high enough resolution hinders our ability to optimally manage our natural water resources. GPR methods hold promise for improved and minimally invasive characterization and monitoring of the subsurface. This paper will review several case studies where we have successfully used GPR for a variety of environmental and precision agricultural investigations.

It is widely recognized that natural heterogeneity and the large spatial variability of hydraulic parameters control the spread of contaminants and water in the subsurface. Similarly, hydrological and biogeochemical processes are variable over a wide range of spatial scales. The inadequacy of conventional (wellbore) approaches for characterizing or monitoring the parameters and processes at over large enough areas yet with high enough resolution hinders our ability to optimally manage our natural water resources. GPR methods hold promise for improved and minimally invasive characterization and monitoring of the subsurface. This paper will review several case studies where we have successfully used GPR for a variety of environmental and precision agricultural investigations.