Perceived neighborhood disorder and type 2 diabetes disparities in Hispanic, Black, and White Americans.

Introduction: Approximately 32 million Americans have type 2 diabetes, and that number continues to grow. Higher prevalence rates are observed among certain subgroups, including members of marginalized racial/ethnic groups as well as residents of disordered neighborhoods (i.e., those with more trash and vandalism). Institutionalized discriminatory practices have resulted in disproportionate representation of marginalized racial/ethnic groups in disordered neighborhoods compared to non-Hispanic Whites. These neighborhood disparities may partially contribute to health disparities, given that signs of neighborhood disorder often relate to a general withdrawal from the neighborhood, minimizing opportunities for both physical and social engagement. Yet, research suggests variability across racial/ethnic groups both in reporting rates of neighborhood disorder and in the extent to which neighborhood disorder is interpreted as posing a threat to health and well-being. Methods: Using 2016-2018 Health and Retirement Study data (n = 10,419, mean age = 67 years), a representative sample of older US adults, this study examined the possibility of racial/ethnic differences in associations between perceived neighborhood disorder and type 2 diabetes risk. Participants reported their perceptions of neighborhood disorder and type 2 diabetes status. Weighted logistic regression models predicted type 2 diabetes risk by perceived neighborhood disorder, race/ethnicity, and their interaction. Results: Non-Hispanic Blacks and Hispanics had higher type 2 diabetes risk; these two groups also reported more disorder in their neighborhoods compared to non-Hispanic Whites. Perceiving more neighborhood disorder was associated with increased type 2 diabetes risk, but the interaction between race/ethnicity and disorder was not significant. Discussion: Findings from the current study suggest that the negative effects of perceiving neighborhood disorder, a neighborhood-level stressor, extend to increased type 2 diabetes risk.

[Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini].

OBJECTIVE: To introduce Back-propagation (BP) neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini. METHOD: BP neural network was established and optimized with uniform design. Genetic algotithm was used for multi-objective optimization of extraction technology of cortex fraxini. RESULT: the optimization of extraction was as follows: extraction temperature was 99 degrees C, concentration of EtOH was 50%, liquid-solid ratio was 7, extraction time was 94 min. The proportional error between predictive value and practical measured value was just -1.16% and -5.14%. CONCLUSION: Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of cortex fraxini is advisable.