Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping.

Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, and zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek "difference boundaries" that separate neighboring regions with significantly different spatial effects. Matters are more challenging with multiple outcomes over each unit, where we capture dependence among diseases as well as across the areal units. Here, we address multivariate difference boundary detection for correlated diseases. We formulate the problem in terms of Bayesian pairwise multiple comparisons and seek the posterior probabilities of neighboring spatial effects being different. To achieve this, we endow the spatial random effects with a discrete probability law using a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence. We evaluate our method through simulation studies and detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.

Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping.

Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatial patterns. Such maps rely upon spatial models for regionally aggregated data, where neighboring regions tend to exhibit similar outcomes than those farther apart. We contribute to the literature on multivariate disease mapping, which deals with measurements on multiple (two or more) diseases in each region. We aim to disentangle associations among the multiple diseases from spatial autocorrelation in each disease. We develop multivariate directed acyclic graphical autoregression models to accommodate spatial and inter-disease dependence. The hierarchical construction imparts flexibility and richness, interpretability of spatial autocorrelation and inter-disease relationships, and computational ease, but depends upon the order in which the cancers are modeled. To obviate this, we demonstrate how Bayesian model selection and averaging across orders are easily achieved using bridge sampling. We compare our method with a competitor using simulation studies and present an application to multiple cancer mapping using data from the Surveillance, Epidemiology, and End Results program.

Two Birds with One Stone: Using Indium Oxide Surficial Modification to Tune Inner Helmholtz Plane and Regulate Nucleation for Dendrite-free Lithium Anode.

Lithium metal has been considered as the most promising anode material due to its distinguished specific capacity of 3860 mAh g-1 and the lowest reduction potential of -3.04 V versus the Standard Hydrogen Electrode. However, the practicalization of Li-metal batteries (LMBs) is still challenged by the dendritic growth of Li during cycling, which is governed by the surface properties of the electrodepositing substrate. Herein, a surface modification with indium oxide on the copper current collector via magnetron sputtering, which can be spontaneously lithiated to form a composite of lithium indium oxide and Li-In alloy, is proposed. Thus, the growth of Li dendrites is effectively suppressed via regulating the inner Helmholtz plane modified with LiInO2 to foster the desolvation of Li-ion and induce the nucleation of Li-metal in two-dimensions through electro-crystallization with Li-In alloy. Using the In2 O3 modification, the Li-metal anode exhibits outstanding cyclic stability, and LMBs with lithium cobalt oxide cathode present excellent capacity retention (above 80% over 600 cycles). Enlightening, the scalable magnetron sputtering method reported here paves a novel way to accelerate the practical application of the Li anode in LMBs to pursue higher energy density.

The association between Deferred Action for Childhood Arrivals, health access, and mental health: the role of discrimination, medical mistrust, and stigma.

OBJECTIVE: There are approximately 11 million undocumented immigrants in the US, including 1.3 million young adults who are eligible for the Deferred Action for Childhood Arrivals (DACA) program. It is unclear how DACA influences engagement in healthcare or depressive symptoms, and the role of discrimination, medical mistrust, and stigma in healthcare settings. This study assesses the association of DACA on undocumented young adults' engagement with health care and depressive symptoms. DESIGN: We conducted an internet-based survey examining the health-related experiences of undocumented Latino and Asians and Pacific Islander (API) young adults in California (n = 218) between June and August 2017. Multivariable logistic regressions were conducted to assess the influence of DACA, discrimination, medical mistrust, and stigma on healthcare engagement and depressive symptoms. RESULTS: Approximately 78% of respondents had a gap in healthcare, and about 31% reported high levels of depressive symptoms. Controlling for demographic characteristics, compared to those without DACA, DACA-recipients had lower odds of reporting gaps in healthcare engagement (aOR = 0.270, p < 0.05) and depressive symptoms (aOR = 0.115, p < 0.01). Those facing discrimination, medical mistrust, and stigma in healthcare settings were less likely to have a healthcare visit and more likely to have higher depressive symptoms. CONCLUSIONS: DACA is a potential strategy to improve healthcare access and address the mental health of undocumented populations. In particular, issues of discrimination, stigma by healthcare providers, and medical mistrust need to be addressed.

Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models.

Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Substantive simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

Enhanced resistive switching characteristics in Al2O3 memory devices by embedded Ag nanoparticles.

In this paper, Al2O3/Ag/Al2O3 sandwiched thin films were deposited by magnetron sputtering. Al2O3 thin films with embedded Ag nanoparticles (AgNPs) have been fabricated by adopting appropriate experimental parameters. The measurements on the resistive switching behaviors demonstrated that the embedded AgNPs could substantially enhance the local electric field, and effectively reduce the switching voltages, resulting in a sharply increased OFF/ON ratio up to 106 at 0.5 V. Furthermore, the cycling stability was considerably improved owing to the reduced randomness for the formation and rupture of conductive filaments (CFs). AgNPs could also contribute with movable Ag ions, and the Ti top electrode usually reacts with Al2O3 promoting the formation of oxygen vacancies. As a result, a hybrid CF with better high-temperature stability was induced. Comparatively, if the embedded Ag sublayer is smooth, the switching parameters become dispersive owing to the random formation and rupture of CFs, and the switching performance is deteriorated. A physical model was proposed to understand the effect of the embedded AgNPs.