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.

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.

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.

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.

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.

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.

Enhancing the Mechanical Properties of Waterborne Polyurethane Paint by Graphene Oxide for Wood Products.

Water-based polyurethane paint is widely used for wood furniture by virtue of the eco-friendliness, rich gloss, and flexible tailorability of its mechanical properties. However, its low solution (water or alcohol) resistance and poor hardness and wear resistance limit its application. The emerging graphene oxide has a high specific surface area and abundant functional groups with excellent mechanical properties, endowing it with great potential to modify waterborne polyurethane as a nanofiller. In this study, graphene oxide prepared by Hummers' method is introduced in the chemosynthetic waterborne polyurethane through physical blending. The testing results demonstrate that the appropriate usage of graphene oxide at 0.1 wt% could obviously improve water absorption resistance and alcohol resistance, significantly enhancing the mechanical properties of waterborne polyurethane paint. The corresponding tensile strength, abrasion resistance, and pendulum hardness of the graphene oxide-modified paint film increase by 62.23%, 14.76%, and 12.7%, respectively, compared with the pristine paint film. Meanwhile, the composite paint film containing graphene oxide possesses superiority, including gloss, abrasion resistance, pendulum hardness, and tensile strength in contrast with the commercial paint. The use of graphene oxide to enhance the waterborne polyurethane possesses strong operability and practical value, and could provide useful reference for the modification of waterborne wood paint.

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.

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation.

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications.

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.

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.

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.

Discovery of Highly Potent and Selective IRAK1 Degraders to Probe Scaffolding Functions of IRAK1 in ABC DLBCL.

MyD88 gene mutation has been identified as one of the most prevalent driver mutations in the activated B-cell-like diffuse large B-cell lymphoma (ABC DLBCL). The published literature suggests that interleukin-1 receptor-associated kinase 1 (IRAK1) is an essential gene for ABC DLBCL harboring MyD88 mutation. Importantly, the scaffolding function of IRAK1, rather than its kinase activity, is required for tumor cell survival. Herein, we present our design, synthesis, and biological evaluation of a novel series of potent and selective IRAK1 degraders. One of the most potent compounds, Degrader-3 (JNJ-1013), effectively degraded cellular IRAK1 protein with a DC50 of 3 nM in HBL-1 cells. Furthermore, JNJ-1013 potently inhibited IRAK1 downstream signaling pathways and demonstrated strong anti-proliferative effects in ABC DLBCL cells with MyD88 mutation. This work suggests that IRAK1 degraders have the potential for treating cancers that are dependent on the IRAK1 scaffolding function.

Predictive variables for peripheral neuropathy in treated HIV type 1 infection revealed by machine learning.

OBJECTIVE: Peripheral neuropathies (PNPs) in HIV-infected patients are highly debilitating because of neuropathic pain and physical disabilities. We defined prevalence and associated predictive variables for PNP subtypes in a cohort of persons living with HIV. DESIGN: Adult persons living with HIV in clinical care were recruited to a longitudinal study examining neurological complications. METHODS: Each patient was assessed for symptoms and signs of PNP with demographic, laboratory, and clinical variables. Univariate, multiple logistic regression and machine learning analyses were performed by comparing patients with and without PNP. RESULTS: Three patient groups were identified: PNP (n = 111) that included HIV-associated distal sensory polyneuropathy (n = 90) or mononeuropathy (n = 21), and non-neuropathy (n = 408). Univariate analyses showed multiple variables differed significantly between the non-neuropathy and PNP groups including age, estimated HIV type 1 (HIV-1) duration, education, employment, neuropathic pain, peak viral load, polypharmacy, diabetes, cardiovascular disorders, AIDS, and prior neurotoxic nucleoside antiretroviral drug exposure. Classification algorithms distinguished those with PNP, all with area under the receiver operating characteristic curve values of more than 0.80. Random forest models showed greater accuracy and area under the receiver operating characteristic curve values compared with the multiple logistic regression analysis. Relative importance plots showed that the foremost predictive variables of PNP were HIV-1 duration, peak plasma viral load, age, and low CD4+ T-cell levels. CONCLUSION: PNP in HIV-1 infection remains common affecting 21.4% of patients in care. Machine-learning models uncovered variables related to PNP that were undetected by conventional analyses, emphasizing the importance of statistical algorithmic approaches to understanding complex neurological syndromes.

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.

Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain.

Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters-R2* and quantitative susceptibility (QS)-to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L 1 (lasso), total variation (TV), and L 2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.

Model-robust designs for nonlinear quantile regression.

We construct robust designs for nonlinear quantile regression, in the presence of both a possibly misspecified nonlinear quantile function and heteroscedasticity of an unknown form. The asymptotic mean-squared error of the quantile estimate is evaluated and maximized over a neighbourhood of the fitted quantile regression model. This maximum depends on the scale function and on the design. We entertain two methods to find designs that minimize the maximum loss. The first is local - we minimize for given values of the parameters and the scale function, using a sequential approach, whereby each new design point minimizes the subsequent loss, given the current design. The second is adaptive - at each stage, the maximized loss is evaluated at quantile estimates of the parameters, and a kernel estimate of scale, and then the next design point is obtained as in the sequential method. In the context of a Michaelis-Menten response model for an estrogen/hormone study, and a variety of scale functions, we demonstrate that the adaptive approach performs as well, in large study sizes, as if the parameter values and scale function were known beforehand and the sequential method applied. When the sequential method uses an incorrectly specified scale function, the adaptive method yields an, often substantial, improvement. The performance of the adaptive designs for smaller study sizes is assessed and seen to still be very favourable, especially so since the prior information required to design sequentially is rarely available.