Estimation for the bivariate quantile varying coefficient model with application to diffusion tensor imaging data analysis.

Despite interest in the joint modeling of multiple functional responses such as diffusion properties in neuroimaging, robust statistical methods appropriate for this task are lacking. To address this need, we propose a varying coefficient quantile regression model able to handle bivariate functional responses. Our work supports innovative insights into biomedical data by modeling the joint distribution of functional variables over their domains and across clinical covariates. We propose an estimation procedure based on the alternating direction method of multipliers and propagation separation algorithms to estimate varying coefficients using a B-spline basis and an $L_2$ smoothness penalty that encourages interpretability. A simulation study and an application to a real-world neurodevelopmental data set demonstrates the performance of our model and the insights provided by modeling functional fractional anisotropy and mean diffusivity jointly and their association with gestational age and sex.

An investigation into the relationship between community engagement and maternal mental health in Calgary, Alberta using the All Our Families cohort.

INTRODUCTION: Existing literature shows that increased community engagement is associated with decreased depressive symptoms. To our knowledge, no existing studies have investigated the relationship between community engagement and adverse mental health among mothers in a Canadian context, nor has this relationship been studied over time. The current study aims to address these gaps by modelling the association between community engagement and anxiety and depression longitudinally using a cohort of prenatal and postnatal mothers living in Calgary, Alberta. METHODS: We used data from the All our Families (AOF) study, a prospective cohort study of expectant and new mothers in Calgary, Alberta from 2008 to 2017 across seven timepoints. We used three-level latent growth curves to model the relationship between individual-level community engagement and maternal depression and anxiety scores, while adjusting for both individual and neighborhood-level characteristics. RESULTS: The study sample consisted of 2129 mothers across 174 neighborhoods in Calgary. Adjusted latent growth curve models demonstrated that community engagement was associated with lower depression (b = - 0.28, 95% CI - 0.33, - 0.23) and anxiety (b = - 0.07, 95% CI - 0.12, - 0.02) scores among mothers over time. DISCUSSION: Adjusted results show that community engagement has a protective effect against depression and anxiety amongst mothers. The results of this study are in line with existing evidence suggesting that social cohesion, civic participation, and community engagement are protective against adverse mental health outcomes.

Fast Fusion Clustering via Double Random Projection.

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.

A method for generating synthetic longitudinal health data.

Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health's administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68% for adjusted hazard ratios among 5 key outcomes of interest, indicating synthetic data produces similar analytic results to real data. The privacy assessment concluded that the attribution disclosure risk associated with this synthetic dataset was substantially less than the typical 0.09 acceptable risk threshold. Based on these metrics our results show that our synthetic data is suitably similar to the real data and could be shared for research purposes thereby alleviating concerns associated with the sharing of real data in some circumstances.

Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach.

Despite the importance of maternal gestational weight gain, it is not yet conclusively understood how weight gain during different stages of pregnancy influences health outcomes for either mother or child. We partially attribute this to differences in and the validity of statistical methods for the analysis of longitudinal and scalar outcome data. In this paper, we propose a Bayesian joint regression model that estimates and uses trajectory parameters as predictors of a scalar response. Our model remedies notable issues with traditional linear regression approaches found in the clinical literature. In particular, our methodology accommodates nonprospective designs by correcting for bias in self-reported prestudy measures; truly accommodates sparse longitudinal observations and short-term variation without data aggregation or precomputation; and is more robust to the choice of model changepoints. We demonstrate these advantages through a real-world application to the Alberta Pregnancy Outcomes and Nutrition (APrON) dataset and a comparison to a linear regression approach from the clinical literature. Our methods extend naturally to other maternal and infant outcomes as well as to areas of research that employ similarly structured data.

Tailoring 3D Carbon Foam using CNTs and MnO2 to Fabricate Stable Lithium/Dissolved Lithium Polysulfide Batteries.

The lithium-sulfur (Li-S) battery is an ideal electrochemical energy storage system owing to the high theoretical energy density and acceptable cost of finance and the environment. However, some disadvantages, including low electrical conductivity, poor sulfur utilization, and rapid capacity fading, obstruct its practical application. In this work, 3D carbon foam from a melamine resin is synthesized via high-temperature calcination. Carbon nanotubes (CNTs) and MnO2 are utilized to tailor the properties of the 3D cathode collector in the liquid Li2S6-containing Li-S battery without additional conductive agents, binders, and aluminum foil. Herein, the decorated MnO2 on the carbon fiber foam prolongs the lifespan of the Li-S battery, and adding CNTs is beneficial to enhance the capacity and cyclic performance of the Li-S battery under high sulfur loading. The Li-S battery with a sulfur loading of 3 mg cm-2 possesses a reversible capacity of 437.9 mA h g-1 after 400 cycles at 0.1 C. The capacity could be maintained at 568 mA h g-1 at 0.1 C after 80 cycles when the sulfur loading increases to 6 mg cm-2.

Machine learning models reveal neurocognitive impairment type and prevalence are associated with distinct variables in HIV/AIDS.

Neurocognitive impairment (NCI) among HIV-infected patients is heterogeneous in its reported presentations and frequencies. To determine the prevalence of NCI and its associated subtypes as well as predictive variables, we investigated patients with HIV/AIDS receiving universal health care. Recruited adult HIV-infected subjects underwent a neuropsychological (NP) test battery with established normative (sex-, age-, and education-matched) values together with assessment of their demographic and clinical variables. Three patient groups were identified including neurocognitively normal (NN, n = 246), HIV-associated neurocognitive disorders (HAND, n = 78), and neurocognitively impaired-other disorders (NCI-OD, n = 46). Univariate, multiple logistic regression and machine learning analyses were applied. Univariate analyses showed variables differed significantly between groups including birth continent, quality of life, substance use, and PHQ-9. Multiple logistic regression models revealed groups again differed significantly for substance use, PHQ-9 score, VACS index, and head injury. Random forest (RF) models disclosed that classification algorithms distinguished HAND from NN and NCI-OD from NN with area under the curve (AUC) values of 0.87 and 0.77, respectively. Relative importance plots derived from the RF model exhibited distinct variable rankings that were predictive of NCI status for both NN versus HAND and NN versus NCI-OD comparisons. Thus, NCI was frequently detected (33.5%) although HAND prevalence (21%) was lower than in several earlier reports underscoring the potential contribution of other factors to NCI. Machine learning models uncovered variables related to individual NCI types that were not identified by univariate or multiple logistic regression analyses, highlighting the value of other approaches to understanding NCI in HIV/AIDS.

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions.

Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.

Aggregation-Morphology-Dependent Electrochemical Performance of Co3O4 Anode Materials for Lithium-Ion Batteries.

The aggregation morphology of anode materials plays a vital role in achieving high performance lithium-ion batteries. Herein, Co3O4 anode materials with different aggregation morphologies were successfully prepared by modulating the morphology of precursors with different cobalt sources by the mild coprecipitation method. The fabricated Co3O4 can be flower-like, spherical, irregular, and urchin-like. Detailed investigation on the electrochemical performance demonstrated that flower-like Co3O4 consisting of nanorods exhibited superior performance. The reversible capacity maintained 910.7 mAh·g-1 at 500 mA·g-1 and 717 mAh·g-1 at 1000 mA·g-1 after 500 cycles. The cyclic stability was greatly enhanced, with a capacity retention rate of 92.7% at 500 mA·g-1 and 78.27% at 1000 mA·g-1 after 500 cycles. Electrochemical performance in long-term storage and high temperature conditions was still excellent. The unique aggregation morphology of flower-like Co3O4 yielded a reduction of charge-transfer resistance and stabilization of electrode structure compared with other aggregation morphologies.

Nanocellulose-Reinforced Polyurethane for Waterborne Wood Coating.

With the enhancement of people's environmental awareness, waterborne polyurethane (PU) paint-with its advantages of low release of volatile organic compounds (VOCs), low temperature flexibility, acid and alkali resistance, excellent solvent resistance and superior weather resistance-has made its application for wood furniture favored by the industry. However, due to its lower solid content and weak intermolecular force, the mechanical properties of waterborne PU paint are normally less than those of the traditional solvent-based polyurethane paint, which has become the key bottleneck restricting its wide applications. To this end, this study explores nanocellulose derived from biomass resources by the 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidation method to reinforce and thus improve the mechanical properties of waterborne PU paint. Two methods of adding nanocellulose to waterborne PU-chemical addition and physical blending-are explored. Results show that, compared to the physical blending method, the chemical grafting method at 0.1 wt% nanocellulose addition results in the maximum improvement of the comprehensive properties of the PU coating. With this method, the tensile strength, elongation at break, hardness and abrasion resistance of the waterborne PU paint increase by up to 58.7%, ~55%, 6.9% and 3.45%, respectively, compared to the control PU; while the glossiness and surface drying time were hardly affected. Such exploration provides an effective way for wide applications of water PU in the wood industry and nanocellulose in waterborne wood coating.

The Harbin Medical University nephrectomy score: a quantitative system for evaluating the complexity of laparoscopic retroperitoneal simple nephrectomy.

BACKGROUND: Laparoscopic retroperitoneal simple nephrectomy (LRSN) has been widely accepted as a mainstay option for benign non-functioning kidney. The complexity of the procedure, however, differs and remains a subject of controversy. OBJECTIVE: To develop a standardised Harbin Medical University nephrectomy score (HMUNS) system for evaluating LRSN complexity. SUBJECTS AND METHODS: A total of 6 variables with different factors comprising primary diseases, history of upper urinary tract surgery, body mass index (BMI), surgeon's learning curve, kidney volume, and Mayo Adhesive Probability (MAP) scores were included in the HMUN score. 95 consecutive patients who underwent LRSN at our institution were divided into low (2 to 6 points) and high (7 to 17 points) complexity groups with HMUNS and investigated the differences of operative time (OT), estimated blood loss (EBL), postoperative hospitalisation time (PHT), rate of intraoperative conversion to open surgery, and the Clavien-Dindo classifi cation (CDC) between both groups. RESULTS: Longer mean operative times (193.2±69.3 min vs. 151.9±46.3 min, p <0.05), more median estimated blood loss (100.0mL vs. 50.0mL, p <0.05), and higher rates of conversion to open surgery (1.2% vs. 25%, p <0.05) were observed in the high-complexity group (n=12) than in the low-complexity group (n=83). However, there were no remarkable differences between the two groups related to the baseline characteristics, post-surgical hospitalisation times, and postoperative complications. CONCLUSIONS: The HMUNS can effectively reflect LRSN complexity, thus providing a quantitative system for risk estimation and treatment decisions. Because of some limitations, further well-designed studies are necessary to confirm our fi ndings. Patient summary: The HMUNS, including primary diseases, history of upper urinary tract surgery, BMI, surgeon's learning curve, kidney volume, and MAP score, can provide an effective quantitative tool to evaluate the complexity of LRSN.

A Review of Statistical Methods in Imaging Genetics.

With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention, and treatment of numerous complex brain-related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel-wise approaches, reduced rank regression, mixture models, and group sparse multi-task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.

Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits.

Genetic studies often involve quantitative traits. Identifying genetic features that influence quantitative traits can help to uncover the etiology of diseases. Quantile regression method considers the conditional quantiles of the response variable, and is able to characterize the underlying regression structure in a more comprehensive manner. On the other hand, genetic studies often involve high-dimensional genomic features, and the underlying regression structure may be heterogeneous in terms of both effect sizes and sparsity. To account for the potential genetic heterogeneity, including the heterogeneous sparsity, a regularized quantile regression method is introduced. The theoretical property of the proposed method is investigated, and its performance is examined through a series of simulation studies. A real dataset is analyzed to demonstrate the application of the proposed method.

Electrochemical Restructuring Driven Catalytic Cycle of Bi-Based Heterojunctions for High-Performance Lithium-Sulfur Batteries.

Restructuring is an important phenomenon in catalytic reactions. Conversion-type materials with suitable redox potential may undergo in situ electrochemically driven restructurings and induce highly active catalytic sites in a working lithium-sulfur battery. Herein, driven by the electrochemical conversion reaction of BiVO4, a reversible catalytic cycle of Bi/amorphous Li3VO4 (a-Li3VO4) and Bi2S3/a-Li3VO4 heterojunctions is constructed, which targets the oxidation of Li2S and the conversion of polysulfide, respectively. The heterostructures and electrochemically driven size confinement provide abundant sites for shuttle restraining and sulfur conversion. Especially, the p-block Bi and Bi2S3 could dramatically reduce the conversion energy barriers of Li2S and polysulfide by virtue of the p-p orbital hybridization, promoting bidirectional reactions of the sulfur cathode. As a result, the corresponding sulfur cathode possesses a high reversible capacity of 7.5 mAh cm-2 after 120 cycles under a high sulfur loading of 10.3 mg cm-2 with a current density of 0.38 mA cm-2. This study furnishes a feasible scheme to obtain highly effective catalysts for bidirectional sulfur redox by utilizing the electrochemically induced restructuring.

The HIV Restriction Factor Profile in the Brain Is Associated with the Clinical Status and Viral Quantities.

HIV-encoded DNA, RNA and proteins persist in the brain despite effective antiretroviral therapy (ART), with undetectable plasma and cerebrospinal fluid viral RNA levels, often in association with neurocognitive impairments. Although the determinants of HIV persistence have garnered attention, the expression and regulation of antiretroviral host restriction factors (RFs) in the brain for HIV and SIV remain unknown. We investigated the transcriptomic profile of antiretroviral RF genes by RNA-sequencing with confirmation by qRT-PCR in the cerebral cortex of people who are uninfected (HIV[-]), those who are HIV-infected without pre-mortem brain disease (HIV[+]), those who are HIV-infected with neurocognitive disorders (HIV[+]/HAND) and those with neurocognitive disorders with encephalitis (HIV[+]/HIVE). We observed significant increases in RF expression in the brains of HIV[+]/HIVE in association with the brain viral load. Machine learning techniques identified MAN1B1 as a key gene that distinguished the HIV[+] group from the HIV[+] groups with HAND. Analyses of SIV-associated RFs in brains from SIV-infected Chinese rhesus macaques with different ART regimens revealed diminished RF expression among ART-exposed SIV-infected animals, although ART interruption resulted in an induced expression of several RF genes including OAS3, RNASEL, MX2 and MAN1B1. Thus, the brain displays a distinct expression profile of RFs that is associated with the neurological status as well as the brain viral burden. Moreover, ART interruption can influence the brain's RF profile, which might contribute to disease outcomes.

MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES.

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.

High-Dimensional Spatial Quantile Function-on-Scalar Regression.

This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates on functional responses across different quantile levels and also gives a practical way to generate new images for given covariate values. Theoretically, we establish the minimax rates of convergence for estimating coefficient functions under both fixed and random designs. We further develop an efficient primal-dual algorithm to handle high-dimensional image data. Simulations and real data analysis are conducted to examine the finite-sample performance.

Measuring re-identification risk using a synthetic estimator to enable data sharing.

BACKGROUND: One common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population. OBJECTIVES: Develop an accurate risk estimator for the sample-to-population attack. METHODS: A type of estimator based on creating a synthetic variant of a population dataset was developed to estimate the re-identification risk for an adversary performing a sample-to-population attack. The accuracy of the estimator was evaluated through a simulation on four different datasets in terms of estimation error. Two estimators were considered, a Gaussian copula and a d-vine copula. They were compared against three other estimators proposed in the literature. RESULTS: Taking the average of the two copula estimates consistently had a median error below 0.05 across all sampling fractions and true risk values. This was significantly more accurate than existing methods. A sensitivity analysis of the estimator accuracy based on variation in input parameter accuracy provides further application guidance. The estimator was then used to assess re-identification risk and de-identify a large Ontario COVID-19 behavioral survey dataset. CONCLUSIONS: The average of two copula estimators consistently provides the most accurate re-identification risk estimate and can serve as a good basis for managing privacy risks when data are de-identified and shared.

Low-Rank Plus Sparse Decomposition of fMRI Data With Application to Alzheimer's Disease.

Studying functional brain connectivity plays an important role in understanding how human brain functions and neuropsychological diseases such as autism, attention-deficit hyperactivity disorder, and Alzheimer's disease (AD). Functional magnetic resonance imaging (fMRI) is one of the most popularly used tool to construct functional brain connectivity. However, the presence of noises and outliers in fMRI blood oxygen level dependent (BOLD) signals might lead to unreliable and unstable results in the construction of connectivity matrix. In this paper, we propose a pipeline that enables us to estimate robust and stable connectivity matrix, which increases the detectability of group differences. In particular, a low-rank plus sparse (L + S) matrix decomposition technique is adopted to decompose the original signals, where the low-rank matrix L recovers the essential common features from regions of interest, and the sparse matrix S catches the sparse individual variability and potential outliers. On the basis of decomposed signals, we construct connectivity matrix using the proposed novel concentration inequality-based sparse estimator. In order to facilitate the comparisons, we also consider correlation, partial correlation, and graphical Lasso-based methods. Hypothesis testing is then conducted to detect group differences. The proposed pipeline is applied to rs-fMRI data in Alzheimer's disease neuroimaging initiative to detect AD-related biomarkers, and we show that the proposed pipeline provides accurate yet more stable results than using the original BOLD signals.

Comparison of Prognostic Scoring Systems to Predict Durable Pain Relief After Microvascular Decompression for Trigeminal Neuralgia.

BACKGROUND: Microvascular decompression (MVD) is an effective treatment for trigeminal neuralgia, but pain recurs in a substantial minority of patients. Two recently published scoring systems by Hardaway et al. and Panczykowski et al. use simple preoperative clinical and imaging features to predict durable pain relief following MVD, but their predictive performance has not been independently validated. This study aimed to compare predictive performance of the Hardaway et al. score (HS) and Panczykowski et al. score (PS) for 1-year, 3-year, and long-term pain-free outcomes after MVD for trigeminal neuralgia. METHODS: HS and PS were computed for a retrospective, single-institution cohort of 68 patients with trigeminal neuralgia who underwent MVD. Primary outcome was pain recurrence after MVD. Predictive performance of HSs and PSs was evaluated with area under the curve sensitivity analysis and regression models for survival analyses at 1 year, 3 years, and last follow-up. RESULTS: Area under the curve for predicting pain-free outcome was higher for PS versus HS at 1 year (0.873 vs. 0.775) and 3 years (0.793 vs. 0.704). Cox proportional hazard models showed that PS better predicted long-term pain-free outcomes compared with HS (P < 0.05). One-year pain-free outcome was best predicted by pain type; longer-term outcomes were better predicted by presence and degree of neurovascular compression on preoperative imaging. CONCLUSIONS: PS is superior to HS in predicting pain-free outcomes after MVD, which may aid in patient selection and counseling. Overall, more significant neurovascular compression of the trigeminal nerve root, and to a lesser extent classical paroxysmal pain, are good predictors of durable pain relief after MVD.