Explore the vast range of titles available.

Abstract Context A whole food plant-based diet (WFPBD), minimally processed foods with limited consumption of animal products, is associated with improved health outcomes. The benefits of WFPBD are underexplored in individuals with type 1 diabetes (T1D). Objective The primary objective of this analysis is to evaluate the association between WFPBD on glycemia in individuals with T1D. Methods Utilizing prospectively collected meal events from the Type 1 Diabetes Exercise Initiative, we examined the effect of WFPBD intake on glycemia, determined by the plant-based diet index (PDI). The PDI calculates overall, healthful (hPDI), and unhealthy PDI (uPDI) to evaluate for degree of processed foods and animal products (ie, WFPBD). Mixed effects linear regression model assessed time in range (TIR), time above range, and time below range. Results We analyzed 7938 meals from 367 participants. TIR improved with increasing hPDI scores, conferring a 4% improvement in TIR between highest and lowest hPDI scores (high hPDI: 75%, low hPDI: 71%; P < .001). Compared with meals with low hPDI, meals with high hPDI had lower glucose excursion (high hPDI: 53 mg/dL, low hPDI: 62 mg/dL; P < .001) and less time >250 mg/dL (high hPDI: 8%, low hPDI: 14%; P < .001). These effects were present but less pronounced by PDI (high PDI: 74%, low PDI: 71%; P = .01). No differences in time below 70 mg/dL and 54 mg/dL were observed by PDI or hPDI. Conclusion Meal events with higher hPDI were associated with 4% postprandial TIR improvement. These benefits were seen primarily in WFPBD meals (captured by hPDI) and less pronounced plant-based meals (captured by PDI), emphasizing the benefit of increasing unprocessed food intake over limiting animal products alone.

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.