Turcotte, E. E. | Search bioRxiv

bioRxiv2025

Shivering, but not adipose tissue thermogenesis, increases as a function of mean skin temperature in cold-exposed men and women

Skin cooling results in the activation of heat generating mechanisms to counteract heat lost to the environment. Here, we aim to understand the extent that variations in cold-stimulated heat production may be driven by differences in the contribution of shivering and non-shivering thermogenesis (NST) and the interaction with biological sex. Using a novel mean skin temperature clamping technique in healthy men and women, our data shows that cold-stimulated heat production rises with increasing shivering and myocardial oxidative metabolism in a skin temperature-dependent fashion. Shivering and myocardial thermogenesis were also moderately associated. In contrast, adipose tissue NST did not increase in a linear manner to reductions in skin temperature. Men and women displayed similar thermoregulatory responses, although women presented more pronounced shivering through a greater recruitment of lower-body muscles and greater number of motor units recruited. Thus, shivering contributes proportionally to cold-induced thermogenesis whereas adipose tissue thermogenesis displays an all-or-none response.

physiology↗

bioRxiv2021

DeepImageTranslator: a free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis

ObjectivesThe advent of deep-learning has set new standards in an array of image translation applications. At present, the use of these methods often requires computer programming experience. Non-commercial programs with graphical interface usually do not allow users to fully customize their deep-learning pipeline. Therefore, our primary objective is to provide a simple graphical interface that allows students and researchers with no programming experience to easily create, train, and evaluate custom deep-learning models for image translation. We also aimed to test the applicability of our tool (the DeepImageTranslator) in two different tasks: semantic segmentation and noise reduction of CT images. MethodsThe DeepImageTranslator was implemented using the Tkinter library; backend computations were implemented using Pillow, Numpy, OpenCV, Augmentor, Tensorflow, and Keras libraries. Convolutional neural networks (CNNs) were trained using DeepImageTranslator and assessed with three-way cross-validation. The effects of data augmentation, deep-supervision, and sample size on model accuracy were also systematically assessed. ResultsThe DeepImageTranslator a simple tool that allows users to customize all aspects of their deep-learning pipeline, including the CNN, the training optimizer, the loss function, and the types of training image augmentation scheme. We showed that DeepImageTranslator can be used to achieve state-of-the-art accuracy and generalizability in semantic segmentation and noise reduction. Highly accurate 3D segmentation models for body composition can be obtained using training sample sizes as small as 17 images. Therefore, for studies with small datasets, researchers can randomly select a very small subset of images for manual labeling, which can then be used to train a specialized CNN model with DeepImageTranslator to fully automate segmentation of the entire dataset, thereby saving tremendous time and effort. ConclusionsAn open-source deep-learning tool for accurate image translation with a user-friendly graphical interface was presented and evaluated. This standalone software can be downloaded for Windows 10 at: https://sourceforge.net/projects/deepimagetranslator/

bioinformatics↗