ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites.

RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various diseases. Accurate identification of ac4C modification sites is essential for unraveling its functional implications.

Does a priming warm-up influence the incidence of [Formula: see text] during a ramp test and verification phase?

Objective: This study compared the effects of two different warm-up protocols (normal vs. priming) on the oxygen plateau ([Formula: see text]) incidence rate during a ramp test. It also compared the cardiopulmonary responses during the ramp test and subsequent verification phase.

Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction.

N-7methylguanosine (m7G) modification plays a crucial role in various biological processes and is closely associated with the development and progression of many cancers. Accurate identification of m7G modification sites is essential for understanding their regulatory mechanisms and advancing cancer therapy. Previous studies often suffered from insufficient research data, underutilization of motif information, and lack of interpretability.

Heat stress increases carbohydrate oxidation rates and oxygen uptake during prolonged load carriage exercise.

Military missions are conducted in a multitude of environments including heat and may involve walking under load following severe exertion, the metabolic demands of which may have nutritional implications for fueling and recovery planning. Ten males equipped a military pack loaded to 30% of their body mass and walked in 20°C/40% relative humidity (RH) (TEMP) or 37°C/20% RH (HOT) either continuously (CW) for 90 min at the first ventilatory threshold or mixed walking (MW) with unloaded running intervals above the second ventilatory threshold between min 35 and 55 of the 90 min bout. Pulmonary gas, thermoregulatory, and cardiovascular variables were analyzed following running intervals.

Load-Velocity Profiles Before and After Heated Resistance Exercise.

Sweet, DK, Qiao, J, Rosbrook, P, and Pryor, JL. Load-velocity profiles before and after heated resistance exercise. J Strength Cond Res 38(6): 1019-1024, 2024-This study examined neuromuscular performance using load-velocity (L-V) profiles in men and women before and after resistance exercise (RE) in hot (HOT; 40° C) and temperate (TEMP; 21° C) environments.

Resistance Training in the Heat: Mechanisms of Hypertrophy and Performance Enhancement.

Pryor, JL, Sweet, D, Rosbrook, P, Qiao, J, Hess, HW, and Looney, DP. Resistance training in the heat: Mechanisms of hypertrophy and performance enhancement. J Strength Cond Res 38(7): 1350-1357, 2024-The addition of heat stress to resistance exercise or heated resistance exercise (HRE) is growing in popularity as emerging evidence indicates altered neuromuscular function and an amplification of several mechanistic targets of protein synthesis.

Endocrine Responses to Heated Resistance Exercise in Men and Women.

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 38(7): 1248-1255, 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.

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.

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.

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.

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.