We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.
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http://dx.doi.org/10.1098/rstb.2020.0525 | DOI Listing |
Eur Radiol
January 2025
Department of Radiology, Montpellier Research Center Institute, PINKCC Laboratory, Montpellier, France.
Objective: To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up of patients with ovarian cancer (OC).
Methods: Twenty-one experts, members of the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds of questionnaires regarding imaging techniques and structured reporting used for pre-treatment evaluation of OC patients. The results of the survey were presented to the other authors during the group's annual meeting.
J Comput Chem
January 2025
Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression of enzyme is associated with conditions such as cancer, arthritis, and Alzheimer's disease (AD), where it contributes to neuroinflammation. In silico virtual screening is pivotal in early-stage drug discovery; however, the absence of coding or machine learning expertise can impede the development of reliable computational models capable of accurately predicting inhibitor compounds based on their chemical structure.
View Article and Find Full Text PDFClin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFJ Clin Med
January 2025
Hospital Virgen de la Arrixaca, 30120 Murcia, Spain.
Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Musical, Plastic and Corporal Expression, University of Jaén, 23071 Jaén, Spain.
: Eye-foot coordination is essential in sports and daily life, enabling the synchronization of vision and movement for tasks like ball control or crossing obstacles. This study aimed to examine both the validity and reliability of an innovative eye-foot coordination (EFC) test in a dual-task paradigm in children aged 6-11 years and the capacity of this test to discriminate between sex and age. : A total of 440 schoolchildren aged 6-11 years participated in this cross-sectional study.
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