Diminishing cognitive capacities in an ever hotter world: evidence from an applicable power-law description

Objective: Modeling and evaluating a series of power law descriptions for boundary conditions of undiminished cognitive capacities under thermal stress. Background: Thermal stress degrades cognition, but precisely which components are affected, and to what degree, has yet to be fully determined. With increasing global temperatures, this need is becoming urgent. Power-law distributions have proven their utility in describing differing natural mechanisms, including certain orders of human performance, but never as a rationalization of stress-altered states of attention. Method: From a survey of extant empirical data, absolute thresholds for thermal tolerance for varying forms of cognition were identified. These thresholds were then modeled using a rational power-law description. The implications of the veracity of that description were then identified and analyzed. Results: Cognitive performance thresholds under thermal stress are advanced as power-law relationships, t = f(T) = c[(T – Tref)/Tref]-α. Coherent scaling parameters for diverse cognitive functionalities are specified that are consistent with increases in deep (core) body temperature. Therefore, scale invariance provides a “universal constant,” viz, 20% detriment in mental performance per 10% increase in T deviation, from a comfortable reference temperature Tref. Conclusion: We know the thermal range within which humans can survive is quite narrow. The presented power-law descriptions imply that if making correct decisions is critical for our future existence, then our functional thermal limits could be much more restricted than previously thought. Application: We provide our present findings, such that others can both assess and mitigate the effects of adverse thermal loads on cognition, in whatever human scenario they occur

Correction: D-pinitol, a highly valuable product from carob pods: Healthpromoting effects and metabolic pathways of this natural super-food ingredient and its derivatives

The purpose of this note is to give some corrections for our published article in [1]. © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)-based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI-based methodologies for the fulfillment of regulatory-related toxicological issues. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning