Adults with diabetes residing in "food swamps" have higher hospitalization rates.

ObjectiveTo examine the relationship between food swamps and hospitalization rates among adults with diabetes.Data sourcesBlue Cross Blue Shield Association Community Health Management Hub® 2014, AHRQ Health Care Cost and Utilization Project state inpatient databases 2014, and HHS Area Health Resources File 2010-2014.Study designCross-sectional analysis of 784 counties across 15 states. Food swamps were measured using a ratio of fast food outlets to grocers. Multivariate linear regression estimated the association of food swamp severity and hospitalization rates. Population-weighted models were controlled for comorbidities; Medicaid; emergency room utilization; percentage of population that is female, Black, Hispanic, and over age 65; and state fixed effects. Analyses were stratified by rural-urban category.Principal findingsAdults with diabetes residing in more severe food swamps had higher hospitalization rates. In adjusted analyses, a one unit higher food swamp score was significantly associated with 49.79 (95 percent confidence interval (CI) = 19.28, 80.29) additional all-cause hospitalizations and 19.12 (95 percent CI = 11.09, 27.15) additional ambulatory care-sensitive hospitalizations per 1000 adults with diabetes. The food swamp/all-cause hospitalization rate relationship was stronger in rural counties than urban counties.ConclusionsFood swamps are significantly associated with higher hospitalization rates among adults with diabetes. Improving the local food environment may help reduce this disparity

Muon diffusion and electronic magnetism in Y2_2Ti2_2O7_7

We report a $\mu$SR study in a Y$_2$Ti$_2$O$_7$ single crystal. We observe slow local field fluctuations at low temperature which become faster as the temperature is increased. Our analysis suggests that muon diffusion is present in this system and becomes small below 40 K and therefore incoherent. A surprisingly strong electronic magnetic signal is observed with features typical for muons thermally diffusing towards magnetic traps below $\approx 100$ K and released from them above this temperature. We attribute the traps to Ti$^{3+}$ defects in the diluted limit. Our observations are highly relevant to the persistent spin dynamics debate on $R_2$Ti$_2$O$_7$ pyrochlores and their crystal quality

Likelihood Ratios for Deep Neural Networks in Face Comparison

In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state-of-the-art free software was preferred over commercial software. Three different open-source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel density estimation, and isotonic regression with pool adjacent violators algorithm. The results show that with low-quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. The rank correlation between investigators and software is around 80%. We conclude that the software can assist in reporting officers as it can do faster and more reliable comparisons with full-frontal images, which can help the forensic expert in casework