Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.

COVID-19 vaccines administered in the United States are safe and effective at lowering COVID-19 incidence, death, and hospitalization rates.¹ The federal and local governments have put forward various strategies to increase the vaccination rate, including making the vaccine available to the public free of cost. Nevertheless, the vaccination rate in the US is less than the suggested threshold for herd immunity^2,3 and is falling. Many states have launched incentive programs to boost vaccination, including vaccine lotteries in which a vaccinated individual is eligible to win a big cash prize.

The question of the optimal nutrient application in agricultural production continues to gather attention because of its relevance in increasing the profitability of crop production and protecting the environment. Economically optimal nutrient application decision rules are commonly derived by fitting crop yield functions to nutrients. The choice of the appropriate yield functional form thus is critical for estimating unbiased and more precise nutrient recommendations. This review explores recent developments in yield response functions to the nutrient application. We first introduce the concept of the economic theory of production and yield response functions. We then explain, with mathematical equations, some commonly used functional forms: quadratic response, Mitscherlich–Baule, deterministic, and stochastic plateau functions. We also summarize the strengths and weaknesses of each functional form and its implications for nutrient management strategies from an economic perspective. In recent studies, researchers have suggested that stochastic plateau functional forms, both linear and quadratic, could explain yield response to the nutrient application in a better way. Finally, we recommend fitting stochastic plateau functions in conjunction with carryover information in a dynamic optimization setting may yield more accurate optimal nutrient levels. Future research should continue to investigate whether these functional forms perform better in different crop and soil environments.

This paper explores new frontiers in agricultural natural language processing (NLP) by investigating the effectiveness of food-related text corpora for pretraining transformer-based language models. Specifically, we focus on semantic matching, establishing mappings between food descriptions and nutrition data through fine-tuning AgriBERT with the FoodOn ontology. Our work introduces an expanded comparison with state-of-the-art language models such as GPT-4, Mistral-large, Claude 3 Sonnet, and Gemini 1.0 Ultra. This exploratory investigation, rather than a direct comparison, aims to understand how AgriBERT, a domain-specific, fine-tuned, open-source model, complements the broad knowledge and generative abilities of these advanced LLMs in addressing the unique challenges of the agricultural sector. We also experiment with other applications, such as cuisine prediction from ingredients, expanding our research to include various NLP tasks beyond semantic matching. Overall, this paper underscores the potential of integrating domain-specific models like AgriBERT with advanced LLMs to enhance the performance and applicability of agricultural NLP applications.