Structure, scale and emergence

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Scale in the history of medicine

This is a history of medicine that takes its point of departure in the specimens of human bodily material used to produce medical knowledge. An ordering principle of scale prompts a material and epistemic history of 18th-21st century medicine that highlights shifts in interest towards smaller and smaller units of study: from organs in pathological collections, over microscope slides, to samples in biobanks. The account reveals a set of connected scales of the site of disease, time of diagnosis, size of cohorts, number of disease categories, and technologies of investigation. Moreover, the principle of following the scale of specimens demonstrates the continued importance of physical specimens in medicine, it synthesizes studies of important epistemic objects of medicine such as the organ specimen, the microscope slide and the blood sample, and it draws new historical connections from pathological collections to biobanks.