The provided texts offer a comprehensive overview of enteral nutrition (EN) in hospitalized adults, synthesizing 15 years of research and clinical practice leading up to April 2025. They define EN as a method to prevent or treat disease-related malnutrition (DRM) in patients unable to eat orally, emphasizing its low utilization despite high malnutrition prevalence. The sources discuss evolving guidelines, the pathophysiology of malnutrition and inflammation, and practical considerations for EN administration, including access methods and safety advancements. Finally, a forthcoming review article by Leah Gramlich and Peggi Guenter, both prominent figures in the field, is highlighted as a summary of these advancements and their implications for future practice.

The provided texts detail the American Statistical Association's (ASA) landmark 2016 statement on p-values and statistical significance, driven by widespread misuse and misinterpretation within the scientific community. They outline a historical timeline of concerns regarding p-values, highlighting a "reproducibility crisis" and "circular logic" in their application. The sources explain the six key principles of the ASA statement, clarifying what p-values do and do not measure, and advocating for full transparency and contextual interpretation rather than rigid thresholds. Finally, they introduce alternative statistical approaches while emphasizing that no single index should replace scientific reasoning.

The provided texts comprehensively outline problem-solving agents and search algorithms in Artificial Intelligence. They explain how these agents formulate problems by defining states, initial states, goal states, actions, and action costs to create an abstract model of the environment. The sources detail various uninformed search strategies like Breadth-First Search, Uniform-Cost Search, Depth-First Search, Iterative Deepening Search, and Bidirectional Search, evaluating them based on completeness, optimal cost, time complexity, and space complexity. Furthermore, the texts explore informed (heuristic) search strategies such as Greedy Best-First Search and A* Search, emphasizing the critical role of heuristic functions derived through methods like problem relaxation, pattern databases, and landmark points, or even learned using machine learning.