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Phylogenetic trees from real-world data often include short edges with very few substitutions per site, which can lead to partially resolved trees and poor accuracy. Theory indicates that the number of sites needed to accurately reconstruct a fully resolved tree grows at a rate proportional to the inverse square of the length of the shortest edge. However, when inferred trees are partially resolved due to short edges, “accuracy” should be defined as the rate of discovering false splits (clades on a rooted tree) relative to the actual number found. Thus, accuracy can be high even if short edges are common. Specifically, in a “near-perfect” parameter space in which trees are large, the tree length (the sum of all edge lengths) is small, and rate variation is minimal, the expected false positive rate is less than /3; the exact value depends on tree shape and sequence length. This expected false positive rate is far belowthe false negative rate for small and oftenwell below5%even when some assumptions are relaxed. We show this result analytically for maximum parsimony and explore its extension to maximum likelihood using theory and simulations. For hypothesis testing, we show that measures of split “support” that rely on bootstrap resampling consistently imply weaker support than that implied by the false positive rates in nearperfect trees. The near-perfect parameter space closely fits several empirical studies of human virus diversification during outbreaks and epidemics, including Ebolavirus, Zika virus, and SARS-CoV-2, reflecting low substitution rates relative to high transmission/sampling rates in these viruses.

Circular RNAs have been implicated as critical regulators in the initiation and progression of colorectal cancer (CRC). This study was intended to elucidate the functional significance of the circ₀089761/miR‐27b‐3p/programmed cell death ligand 1 (PD‐L1) axis in CRC. Our findings indicated that circ₀089761 expression was significantly elevated in CRC tissues and cell lines. Furthermore, the high expression of circ₀089761 was correlated with TNM stage and tumor size. Silencing circ₀089761 inhibited CRC cell proliferation, migration, and invasion, and increased apoptosis. Mechanistically, circ₀089761 facilitated its biological function by binding to miR‐27b‐3p to upregulate PD‐L1 expression in CRC. Coculture experiments confirmed that low expression of circ₀089761 impeded CD8 + T cell apoptosis and depletion, activated CD8 + T cell function, and increased secretion of the immune effector cytokines IFN‐γ, TNF‐α, perforin, and granzyme‐B. MiR‐27b‐3p inhibition or PD‐L1 overexpression partially impeded CD8 + T cell function. The circ₀089761/miR‐27b‐3p/PD‐L1 axis is postulated to exert pivotal functions in the mechanistic progression of CRC. Furthermore, it holds promising prospects as a feasible biomarker and therapeutic target for CRC.

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The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one‐dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state‐of‐the‐art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT‐BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT‐BIH Arrhythmia dataset.

The overall objective was to determine how no extracellular glucose and/or low glycogen content affect fatigue kinetics in mouse flexor digitorum brevis (FDB) single muscle fibers. High glycogen content (Hi GLY), near normal in situ level, was obtained by incubating fibers in culture medium containing glucose and insulin while low glycogen content (Lo GLY), at about 19% of normal in situ level, was achieved by incubating fibers without glucose. Neither Lo GLY nor the absence of extracellular glucose (0GLU) affected tetanic [Ca2+]i prior to fatigue. The number of contracting unfatigued fibers versus stimulus strength relationship of Lo GLY‐0GLU fibers was shifted to higher voltages compared to Hi GLY fibers exposed to 5.5 mM glucose (5GLU). The relationship for Lo GLY‐0GLU fibers was shifted back toward that of Hi GLY‐5GLU fibers when glucose was reintroduced, whereas the removal of glucose from Hi GLY‐5GLU fibers had no effect. Fatigue was elicited with one 200 ms long tetanic contraction every s for 3 min. Both Lo GLY and 0GLU increased the rate at which intracellular tetanic concentration ([Ca2+]i) declined and unstimulated [Ca2+]i increased during fatigue in the order of the least fatigue resistant > mid fatigue resistant > the most fatigue resistant fibers.

Modern wearable devices report several heart rate‐based nocturnal health metrics, including resting heart rate (RHR) and heart rate variability (HRV). The purpose of this study was to assess the validity of nocturnal RHR and HRV from five wearable devices (Garmin Fenix 6, Oura Generation 3, Oura Generation 4, Polar Grit X Pro, & Whoop 4.0) against an electrocardiogram (ECG) reference. Thirteen healthy adults (6 females) wore an ECG reference and multiple wearables simultaneously during sleep, totaling 536 nights. Interdevice accuracy varied significantly (p < 0.05). For RHR, Oura Gen 3 (Lin's Concordance [CCC] = 0.97, mean absolute percentage error [MAPE] = 1.67 ± 1.54%) and Gen 4 (CCC = 0.98, MAPE = 1.94 ± 2.51%) demonstrated the highest accuracy, outperforming Polar's poor (CCC = 0.86, MAPE = 2.71 ± 2.75%) and WHOOP's moderate agreement (CCC = 0.91, MAPE = 3.00 ± 2.15%). Garmin was excluded from RHR analyses due to methodological inconsistencies. For HRV, Oura devices provided the highest accuracy; Oura Gen 4 (CCC = 0.99, MAPE = 5.96 ± 5.12%), Oura Gen 3 (CCC = 0.97, MAPE = 7.15 ± 5.48%). WHOOP showed moderate accuracy (CCC = 0.94, MAPE = 8.17 ± 10.49%), followed by poor agreement from both Garmin (CCC = 0.87, MAPE = 10.52 ± 8.63%) and Polar (CCC = 0.82, MAPE = 16.32 ± 24.39%). Oura devices showed the highest agreement for RHR and HRV, and WHOOP showed acceptable agreement, whereas Garmin Fenix and Polar demonstrated lower concordance, highlighting the importance of continuous validation and providing valuable benchmarks for clinicians, researchers, and consumers.

The mechanisms underlying blood flow restriction with low‐load exercise (BFR‐exercise)‐mediated muscle hypertrophy are not well understood. Lack of standardized rodent models of BFR‐exercise likely contributes to this gap. We demonstrate in male rats a comprehensive, clinically relevant protocol that generates a muscle loss state via bilateral anterior cruciate ligament reconstruction, achieves targeted, external blood flow occlusion, and performs weighted hind‐limb isolating knee extension exercise with BFR. These methods can be applied to mechanistic and physiologic studies of BFR‐exercise.

Although basketball is not an endurance sport, a high level of peak oxygen consumption (peak V̇O2) and ventilatory threshold (VT) are beneficial. Currently, no validated cardiopulmonary exercise test (CPET) protocols exist for basketball. This study developed and validated a basketball‐specific CPET protocol and describes the fitness measures. Sixty treadmill CPET's were performed on NCAA Division 1 basketball players. The first 30 CPET's created a prediction equation for peak V̇O2, and the second 30 tested the validity of the equation. The equation, y = 2.70x + 24.84; r2 = 0.995, predicted peak V̇O2. Mean relative peak V̇O2 was 54.1 ± 4.6 mL min−1 kg−1. Mean relative V̇O2 at VT was 37.8 ± 5.8 mL min−1 kg−1, and heart rate (HR) at VT was 155.1 ± 14.2 bpm. These corresponded to 70.1 ± 10.7% of peak V̇O2, and 81.4 ± 6.3% of peak HR values. This equation can accurately determine oxygen consumption when direct metabolic testing is not available. The measures of peak V̇O2, VT, and HRs can be used to assess player fitness, design training protocols, and potentially help explain in‐game performance. Additional research is needed to fully establish the relationship between CPET values and basketball performance.

The gastrointestinal (GI) tract plays a critical role in nutrient absorption, immune responses, and overall health. Traditional models such as two‐dimensional cell cultures have provided valuable insights but fail to replicate the dynamic and complex microenvironment of the human gut. Gut‐on‐a‐chip platforms, which incorporate cells located in the gut into microfluidic devices that simulate peristaltic motion and fluid flow, represent a significant advancement in modeling GI physiology and diseases. This review discusses the evolution of gut‐on‐a‐chip technology, from simple cellular mono‐cultures models to more sophisticated systems incorporating bi‐cultures and tri‐cultures that enable studies of drug metabolism, disease modeling, and gut–microbiome interactions. Although challenges remain, including maintaining long‐term cell viability and replicating immune responses, these platforms hold great potential for advancing personalized medicine and improving drug discovery efforts targeting gastrointestinal disorders.