Abstract Background Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data.While artificial intelligence and Led Emergency Blade machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to mis
Energy-dependent protein folding: modeling how a protein folding machine may work [version 1; peer review: 1 approved, 2 approved with reservations]
Background: Proteins fold robustly and reproducibly in vivo, but many cannot fold in vitro in isolation from cellular components.Despite the remarkable progress that has been achieved by the artificial intelligence approaches in predicting the protein native conformations, the pathways that lead to such conformations, either in vitro or in vivo, re
An evidence base to optimise methods for involving patient and public contributors in clinical trials: a mixed-methods study
Background: In comparison with other study designs, randomised trials are regarded as particularly likely to benefit from patient and public involvement (PPI).Using mixed-methods research we investigated PPI from the perspectives of researchers and PPI contributors.Methods: Randomised trials in receipt of funding from the Health Technology Assessme
Divergence and Similarity Characteristics for Two Fuzzy Measures Based on Associated Probabilities
The article deals with the definitions of the distance, divergence, and similarity characteristics between two finite fuzzy measures, which are generalizations of the same definitions between two finite probability distributions.As is known, a fuzzy measure can be uniquely represented by the so-called its associated probability class (APC).The idea