Endocrine Responses to Heated Resistance Exercise in Men and Women.

ABSTRACT: Pryor, JL, Sweet, DK, Rosbrook, P, Qiao, J, Looney, DP, Mahmood, S, and Rideout, T. Endocrine responses to heated resistance exercise in men and women. J Strength Cond Res XX(X): 000-000, 2024-We examined the endocrine responses of 16 (female = 8) resistance trained volunteers to a single bout of whole-body high-volume load resistance exercise in hot (HOT; 40° C) and temperate (TEMP; 20° C) environmental conditions. Thermoregulatory and heart rate (HR) data were recorded, and venous blood was acquired before and after resistance exercise to assess serum anabolic and catabolic hormones. In men, testosterone increased after resistance exercise in HOT and TEMP (p < 0.01), but postexercise testosterone was not different between condition (p = 0.51). In women, human growth hormone was different between condition at pre-exercise (p = 0.02) and postexercise (p = 0.03). After controlling for pre-exercise values, the between-condition postexercise difference was abolished (p = 0.16). There were no differences in insulin-like growth factor-1 for either sex (p ≥ 0.06). In women, cortisol increased from pre-exercise to postexercise in HOT (p = 0.04) but not TEMP (p = 0.19), generating a between-condition difference at postexercise (p < 0.01). In men, cortisol increased from pre-exercise to postexercise in HOT only (p < 0.01). Rectal temperature increased to a greater extent in HOT compared with TEMP in both men (p = 0.01) and women (p = 0.02). Heart rate increased after exercise under both conditions in men and women (p = 0.01), but only women experience greater postexercise HR in HOT vs. TEMP (p = 0.04). The addition of heat stress to resistance exercise session did not overtly shift the endocrine response toward an anabolic or catabolic response. When acute program variables are prescribed to increase postresistance exercise anabolic hormones, adding heat stress is not synergistic but does increase physiologic strain (i.e., elevated HR and rectal temperature).

Plateau in Core Temperature during Shorter but Not Longer Work/Rest Cycles in Heat.

This study compared physiological responses to two work/rest cycles of a 2:1 work-to-rest ratio in a hot environment. In a randomized crossover design, fourteen participants completed 120 min of walking and rest in the heat (36.3 ± 0.6 °C, 30.2 ± 4.0% relative humidity). Work/rest cycles were (1) 40 min work/20 min rest [40/20], or (2) 20 min work/10 min rest [20/10], both completing identical work. Core temperature (Tc), skin temperature (Tsk), heart rate (HR), nude body mass, and perception of work were collected. Comparisons were made between trials at equal durations of work using three-way mixed model ANOVA. Tc plateaued in [20/10] during the second hour of work (p = 0.93), while Tc increased in [40/20] (p < 0.01). There was no difference in maximum Tc ([40/20]: 38.08 ± 0.35 °C, [20/10]: 37.99 ± 0.27 °C, p = 0.22) or end-of-work Tsk ([40/20]: 36.1 ± 0.8 °C, [20/10]: 36.0 ± 0.7 °C, p = 0.45). End-of-work HR was greater in [40/20] (145 ± 25 b·min-1) compared to [20/10] (141 ± 27 b·min-1, p = 0.04). Shorter work/rest cycles caused a plateau in Tc while longer work/rest cycles resulted in a continued increase in Tc throughout the work, indicating that either work structure could be used during shorter work tasks, while work greater than 2 h in duration may benefit from shorter work/rest cycles to mitigate hyperthermia.

NanoCon: contrastive learning-based deep hybrid network for nanopore methylation detection.

MOTIVATION: 5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios. RESULTS: Here, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offering a more accurate and robust detection of 5mC sites. Evaluation results demonstrate that NanoCon outperforms existing methods, highlighting its potential as a valuable tool in genomic sequencing and methylation prediction. In addition, we also verified the effectiveness of our representation learning ability on two datasets by visualizing the dimension reduction of the features of methylation and nonmethylation sites from our NanoCon. Furthermore, cross-species and cross-5mC methylation motifs experiments indicated the robustness and the ability to perform transfer learning of our model. We hope this work can contribute to the community by providing a powerful and reliable solution for 5mC site detection in genomic studies. AVAILABILITY AND IMPLEMENTATION: The project code is available at https://github.com/Challis-yin/NanoCon.

Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design.

Antimicrobial peptides are peptides that are effective against bacteria and viruses, and the discovery of new antimicrobial peptides is of great importance to human life and health. Although the design of antimicrobial peptides using machine learning methods has achieved good results in recent years, it remains a challenge to learn and design novel antimicrobial peptides with multiple properties of interest from peptide data with certain property labels. To this end, we propose Multi-CGAN, a deep generative model-based architecture that can learn from single-attribute peptide data and generate antimicrobial peptide sequences with multiple attributes that we need, which may have a potentially wide range of uses in drug discovery. In particular, we verified that our Multi-CGAN generated peptides with the desired properties have good performance in terms of generation rate. Moreover, a comprehensive statistical analysis demonstrated that our generated peptides are diverse and have a low probability of being homologous to the training data. Interestingly, we found that the performance of many popular deep learning methods on the antimicrobial peptide prediction task can be improved by using Multi-CGAN to expand the data on the training set of the original task, indicating the high quality of our generated peptides and the robust ability of our method. In addition, we also investigated whether it is possible to directionally generate peptide sequences with specified properties by controlling the input noise sampling for our model.

Deep Generative Models in De Novo Drug Molecule Generation.

The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence has exhibited promising and efficient performance for drug-like molecule generation. In particular, deep generative models achieve great success in de novo generation of drug-like molecules with desired properties, showing massive potential for novel drug discovery. In this study, we review the recent progress of molecule generation using deep generative models, mainly focusing on molecule representations, public databases, data processing tools, and advanced artificial intelligence based molecule generation frameworks. In particular, we present a comprehensive comparison of state-of-the-art deep generative models for molecule generation and a summary of commonly used molecular design strategies. We identify research gaps and challenges of molecule generation such as the need for better databases, missing 3D information in molecular representation, and the lack of high-precision evaluation metrics. We suggest future directions for molecular generation and drug discovery.

Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications.

Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.