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| http://dx.doi.org/10.1016/j.tics.2022.12.001 | DOI Listing |
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Application of Deep Learning Algorithms Based on the Multilayer Y0L0v8 Neural Network to Identify Fungal Keratitis.
Sovrem Tekhnologii Med
January 2025
MD, PhD, Ophthalmologist; Privolzhsky District Medical Center of Federal Medico-Biologic Agency of Russia, 14 llyinskaya St., Nizhny Novgorod, 603000, Russia; Assistant, Department of Eye Diseases; Privolzhsky Research Medical University, 10/1 Minin and Pozharsky Square, Nizhny Novgorod, 603005, Russia.
Unlabelled: is to develop a method for diagnosing fungal keratitis based on the analysis of photographs of the anterior segment of the eye using deep learning algorithms with subsequent evaluation of sensitivity and specificity of the method on a test data set in comparison with the results of practicing ophthalmologists.
Materials And Methods: The study has included the stages of data acquisition, image pre-training and markup, selection of training approach and neural network architecture, training with input data augmentation, validation with hyperparameter correction, evaluation of algorithm performance on a test sample, and determination of sensitivity and specificity of fungal keratitis detection by practicing doctors. A total of 274 anterior segment images were used, including 130 photographs of the eyes affected by fungal keratitis and 144 photographs illustrating normal eyes, keratitis of other etiologies, and various anterior segment pathologies.
The mind, brain, and body study: A protocol for examining the effects of the gut-brain-immune axis on internalizing symptoms in youth exposed to caregiving-related early adversity.
Brain Behav Immun Health
December 2024
Department of Psychology, University of California, Los Angeles, 502 Portola Plaza, Los Angeles, CA, 90095, USA.
Experiences of caregiving-related adversity are common and one of the strongest predictors of internalizing psychopathology (i.e., anxiety and depression).
View Article and Find Full Text PDFTensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States.
Phys Rev Lett
December 2024
California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, USA.
We introduce a change of perspective on tensor network states that is defined by the computational graph of the contraction of an amplitude. The resulting class of states, which we refer to as tensor network functions, inherit the conceptual advantages of tensor network states while removing computational restrictions arising from the need to converge approximate contractions. We use tensor network functions to compute strict variational estimates of the energy on loopy graphs, analyze their expressive power for ground states, show that we can capture aspects of volume law time evolution, and provide a mapping of general feed-forward neural nets onto efficient tensor network functions.
View Article and Find Full Text PDFpLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides.
J Chem Inf Model
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Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas 66506, United States.
Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification.
View Article and Find Full Text PDFAltered hippocampal effective connectivity predicts BMI and food approach behavior in children with obesity.
Int J Clin Health Psychol
January 2025
Faculty of Psychology, Southwest University, Chongqing 400715, China.
Objective: The vicious circle model of obesity proposes that the hippocampus plays a crucial role in food reward processing and obesity. However, few studies focused on whether and how pediatric obesity influences the potential direction of information exchange between the hippocampus and key regions, as well as whether these alterations in neural interaction could predict future BMI and eating behaviors.
Methods: In this longitudinal study, a total of 39 children with excess weight (overweight/obesity) and 51 children with normal weight, aged 8 to 12, underwent resting-state fMRI.
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