Enhancing Nitrogen Use Efficiency Through Ai-Powered Image Analysis and Innovative N-Rich Spot Method

This study conducted in 2023 aimed to enhance nitrogen use efficiency (NUE) in wheat and corn grown in South Dakota. Based on dynamic weather conditions and other factor interactions, conventional nitrogen (N) recommendations need to be improved. Soil properties information, including electrical conductivity, was used to create management zones. In each zone, three N-rich spots were established as biosensors. Drones and satellites collected imagery data, and an AI-driven approach assessed the crop response to applied N. A dynamic N application approach, integrating aerial data with historical records, was developed and evaluated. Our methodology, at a 95% confidence level, resulted in a 12.4% higher yield in wheat and a potential 4.77% increase in corn yield compared to conventional approaches, with a 16.2% and 10% reduction in N application in wheat and corn fields, respectively. This led to cost savings and environmental benefits. The financial outcomes revealed cost savings of $7.87 per acre in wheat and$3.62 per acre in corn. The wheat yield increased to 75.09 bu/ac compared to 66.61 bu/ac in control plots, generating an additional revenue of $57.82 per acre. The corn yield increased to 173.75 bu/ac compared to 165.84 bu/ac, indicating a potential increase of 6.89 bu/ac and additional revenue of approximately$34.11 per acre. Moreover, there was a 16.2% increase in NUE in wheat and a 4.3% improvement in corn compared to traditional methods. The findings from this study will be applicable for farmers as a decision-making tool, providing a straightforward approach to enhance NUE while increasing their farm profit

Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models. Trained on large-scale dataset to bridge the gap between different modalities, foundation models facilitate contextual reasoning, generalization, and prompt capabilities at test time. The predictions of these models can be adjusted for new tasks by augmenting the model input with task-specific hints called prompts without requiring extensive labeled data and retraining. Capitalizing on the advances in computer vision, medical imaging has also marked a growing interest in these models. To assist researchers in navigating this direction, this survey intends to provide a comprehensive overview of foundation models in the domain of medical imaging. Specifically, we initiate our exploration by providing an exposition of the fundamental concepts forming the basis of foundation models. Subsequently, we offer a methodical taxonomy of foundation models within the medical domain, proposing a classification system primarily structured around training strategies, while also incorporating additional facets such as application domains, imaging modalities, specific organs of interest, and the algorithms integral to these models. Furthermore, we emphasize the practical use case of some selected approaches and then discuss the opportunities, applications, and future directions of these large-scale pre-trained models, for analyzing medical images. In the same vein, we address the prevailing challenges and research pathways associated with foundational models in medical imaging. These encompass the areas of interpretability, data management, computational requirements, and the nuanced issue of contextual comprehension.Comment: The paper is currently in the process of being prepared for submission to MI

Rickettsial Seroepidemiology among Farm Workers, Tianjin, People’s Republic of China

High seroprevalence rates for Anaplasma phagocytophilum (8.8%), Coxiella burnetii (6.4%), Bartonella henselae (9.6%), and Rickettsia typhi (4.1%) in 365 farm workers near Tianjin, People’s Republic of China, suggest that human infections with these zoonotic bacteria are frequent and largely unrecognized. Demographic features of seropositive persons suggest distinct epidemiology, ecology, and risks

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality