Search Results (253)

Cancer cells, like yeast, use fermentation despite the presence of oxygen, a phenomenon called aerobic glycolysis. The advantage is that it maintains many C-C bonds of glucose, allowing highly proliferating cells to produce the biomolecules that are necessary for cytokinesis. However, aerobic glycolysis is less energy-efficient than respiration, and it must operate at high frequency and produces large amounts of lactate, which modifies and stimulates DNA repair enzymes via lysine lactylation. This makes cancer cells resistant to radiotherapy, which requires a combination with chemotherapy using drugs that inhibit DNA repair. However, this converts healthy cells to cancer cells, indicating that research is still required regarding the relationship between glycolysis and cancer. Using yeast as a model, we discovered that the glycolytic enzymes TPI and GAPDH (Tpi1p and Tdh1-3p in yeast) interact with the DNA damage-dependent Checkpoint Rad9p (53BP1/BRCA1/MDC1 in humans). We propose that Tpi1p and Tdh1-3p override Rad9p, allowing cells with damaged DNA to proliferate. We isolated tpi and gapdh mutant strains that are deficient in DNA repair. While the tpi mutant strain has lower enzymatic activity, the gapdh mutant strains have normal enzymatic activity, confirming previous reports that GAPDH moonlights in the DNA damage response. Full article

Precise respiration assessment is crucial for heart rate variability (HRV) interpretation as respiratory components—particularly respiratory sinus arrhythmia (RSA)—provide essential information on vagally mediated regulation. Conventional single-lead electrocardiogram-derived respiration (EDR) methods measure the amplitude modulation of the QRS-waveform caused by respiratory chest movements. This causes a displacement of the electrical heart axis in relation to the ECG lead axis, typically within the 2D frontal plane of the Einthoven electrode montage. Another approach is based on heartbeat acceleration and deceleration during respective inspiration and expiration causing RR interval modulation. However, interval-based methods depend on the complexity of sympathovagal factors that affect RSA. The present feasibility study accounts for the 3D rotational movement of the electrical heart axis during the respiratory cycle and avoids non-respiratory neuromodulatory confounds. The beat-to-beat cardiac rotation was extracted from Frank-XYZ coordinates reconstructed via a four-electrode EASI device. In a pilot study with data from 19 healthy adults performing acoustically paced breathing (6–18 bpm), three surrogates (${\mathrm{RR-Interval}}_{\mathrm{EDR}}$, ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$) were compared using a unified Python 3.11.13 pipeline (3D VCG R-peak detection, multivariate Mahalanobis artifact correction, wavelet-based analysis) against a synthetic reference derived from the instructed breathing schedule. The results demonstrated a consistently lower estimation error and higher reference-based signal-to-noise ratio (refSNR), measuring spectral alignment with the paced-breathing trajectory for ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and achieving a mean refSNR of 6.01 dB (vs. 4.62 dB for ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ and 3.20 dB for ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$) and a mean absolute estimation error of 0.016 Hz (vs. 0.050 Hz and 0.032 Hz, respectively). Notably, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$ performance slightly improved at higher heart rates, consistent with the interpretation that higher cardiac sampling density benefits spectral resolution for chest movement-based methods, whereas ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ showed no significant heart rate dependence. Furthermore, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ was compared with the EDR results obtained by applying the Kubios-HRV Premium software (version 3.5.0). Kubios-EDR yielded higher precision at elevated breathing frequencies, whereas ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ outperformed Kubios-EDR at breathing rates below 10 bpm—a range that is particularly relevant for vagally activating slow breathing protocols or treatments. Future work should validate this method using a direct respiration measurement under spontaneous natural breathing conditions. Full article

Wildfires are increasing in frequency and severity across Mediterranean ecosystems. However, the immediate soil biogeochemical responses that determine shortly post-fire resilience remain poorly understood. This study assessed how contrasting fire severity levels influence soil physicochemical, nutrient, and biochemical properties in ecologically relevant vegetation microsites—beneath Quercus ilex L. canopy, Stipa tenacissima L. tussock, and open interspaces—in a Mediterranean holm oak woodland in central Spain. Soils were sampled early after a wildfire and analyzed for organic matter, nutrient pools, water repellency, microbial respiration, nitrogen mineralization, and enzyme activities. Fire severity was the dominant driver of immediate post-fire soil responses. High-severity fire reduced soil organic matter, cation exchange capacity, total C and N, nitrate, microbial respiration, and all measured enzyme activities, with the most pronounced losses occurring beneath Q. ilex canopy. In contrast, ammonium, labile phosphorus, pH and soil water repellency increased under high severity, mainly in this microsite. Low-severity fire generally preserved biological functioning, with values comparable to unburned soils. Microsite identity modulated the magnitude of fire effects, with soils beneath Q. ilex cover microsite showing the greatest sensitivity, and open interspaces the least. The microsite × severity interaction detected for key nutrients and biochemical variables suggests that high-severity fire might destroy the microsite-specific fertility islands that constitute the functional core of Mediterranean woodland soils. These findings should be considered in management strategies prioritizing their monitoring and protection. Full article

Forest soils serve as critical terrestrial carbon sinks. While broad hydrothermal controls on soil respiration (R_s) are established, uncertainties persist regarding high-frequency temporal dynamics and moisture-dependent variations in temperature sensitivity (Q₁₀). Specifically, conventional reliance on discrete, clear-day sampling obscures how precipitation disrupts diurnal patterns. To address this, we continuously monitored R_s and environmental factors in two Northeast Chinese mixed forests (Korean pine, Pinus koraiensis (KP), and Dahurian larch, Larix gmelinii (DL)) to quantify weather-driven daily dynamics and carbon fluxes. Precipitation primarily drove daily variability, but more importantly, it reshaped day–night asymmetry. Under clear-day conditions, R_s exhibited a consistent daytime-dominant pattern, with daytime fluxes being significantly higher than nighttime fluxes (p < 0.05). However, precipitation events fundamentally neutralized this asymmetry, resulting in no significant day–night differences across most phenological stages. Annual R_s effluxes (759 and 965 g C m⁻² yr⁻¹ for KP and DL, respectively) lacked significant inter-stand or temporal variations. Seasonal emissions peaked unimodally in July, with the non-growing season contributing merely 5%–8%. Notably, spring freeze–thaw R_s in the KP stand surged interannually by 143%. While R_s correlated positively with temperature (p < 0.001), Q₁₀ was co-regulated by forest stand and moisture. Under moderate moisture, the KP stand’s Q₁₀ (2.72) was significantly lower than the DL stand’s (3.81); however, this divergence neutralized under low moisture. Consequently, soil moisture acts as both a direct R_s driver and a fundamental regulator of its temperature sensitivity. These empirical findings provide critical data to calibrate forest carbon models, improving predictions of soil carbon feedbacks under future climate scenarios. Full article

Falls among older adults are a major public health concern, yet collecting large-scale real fall data for radar-based detection is ethically and practically difficult. This study presents a controlled simulation-to-real feasibility study for trip-fall detection using continuous-wave (CW) Doppler radar. The method couples a physics-informed kinematic trip-fall model with a CW radar observation model to synthesize I/Q signals and Doppler spectrograms, while domain randomization varies body size, fall direction, initial velocity, sensor placement, aspect angle, amplitude, and noise. Synthetic walking and respiration data were also generated for controlled three-class classification among trip fall, walking, and seated quiet breathing. In Experiment I, the simulated spectrograms reproduced the dominant time–frequency characteristics of measured enacted trip-fall signals acquired with a 24 GHz CW radar; quantitative similarity analysis yielded a mean SSIM of 0.782 and a Doppler-ridge MAE of 24.6 Hz across five fall directions. In Experiment II, a ResNet-18 classifier trained only on simulated spectrograms achieved a macro-F1 score of 0.912 [95% CI: 0.883–0.936] on measured data from ten participants, three start locations, and eight directions. Under the present controlled evaluation, this exceeded the available real-data-trained baseline of 0.748 [95% CI: 0.691–0.805] (paired subject-level permutation test, $p=0.006$). These findings suggest that physics-informed simulation with domain randomization can reduce dependence on real trip-fall samples under limited-data conditions. The results do not establish robustness to other fall morphologies, fall-like activities of daily living, different environments, different radar devices, or embedded deployment. Full article

Bacterial persistence, a non-heritable high-antibiotic-tolerance phenotype, is a key driver of recurrent clinical infections and antibiotic treatment failure. The pheromone-responsive pCF10 plasmid in Enterococcus faecalis (E. faecalis) mediates antibiotic resistance gene dissemination, but its role in bacterial persister formation remains unclear. This study systematically investigated the regulatory role of pheromone cCF10 in the persister phenotype of pCF10-carrying E. faecalis and its underlying molecular mechanisms. We confirmed that cCF10 enhanced persistence against levofloxacin in OG1RF (pCF10), with the persister frequency increasing from 0.291% to 16.466% upon treatment. Transcriptomic analysis revealed that cCF10 activated the (p)ppGpp-mediated stringent response and downregulated the expression of genes associated with energy-intensive pathways, including those involved in DNA repair, protein folding, and respiration. Concurrently, cCF10 enhanced the expression of genes related to biofilm formation and cell lysis resistance and downregulated components of its own sensing and uptake systems. These findings demonstrate that cCF10 induces transcriptional reprogramming associated with increased persister formation in E. faecalis carrying the pCF10 plasmid and identify potential targets within the stringent response and associated metabolic pathways for the development of anti-persister strategies. Full article

Cardiovascular diseases are the primary causes of mortality worldwide, often characterized by subtle onset and acute progression. Traditional ECG electrodes may cause skin irritation, limiting routine monitoring and early risk assessment. Relying on the advantages of non-contact monitoring, millimeter-wave radar-based cardiac monitoring combined with deep learning has become a popular research direction recently. To overcome the poor generalization of methods trained from single-source datasets, this study designed seven experimental scenarios covering wakefulness and sleep. A novel deep learning network consisting of encoder and decoder structures named PMG-SATNet was proposed. The encoder comprises a parallel multi-scale feature extraction module and a global temporal relationship modeling module to capture fine-grained local patterns and long-range dependencies. The decoder employs a temporal convolutional network augmented with a spectral attention mechanism to emphasize clinically relevant ECG frequency bands and suppress respiration and body motion interference. After being validated on the self-built dataset, PMG-SATNet outperformed baseline models in terms of Pearson correlation coefficient and root mean square error, with an improvement of 3.3% and 3.8%, and 16.4% and 23.8%, respectively. The validation results imply that PMG-SATNet is capable of recovering ECG signals from millimeter-wave radar-derived chest vibrations with high fidelity and can potentially be implemented in real-life cardiac health monitoring. Full article

This experiment aimed to evaluate physiological and behavioral responses of crossbred Dehong dairy buffaloes to heat stress (HS) in comparison with those in a thermoneutral (TN) environment. Twelve crossbred dairy buffaloes at similar lactation stages were randomly allocated to two groups of six animals each. Six buffaloes were exposed to HS conditions and the other six to TN conditions in an open loose-housing barn without individual stalls. Respiration rates were manually recorded at 08:00 h, 13:00 h, and 18:00 h. Duration and frequency of behaviors (standing, lying, feeding, and drinking) were continuously monitored using digital cameras for 20 consecutive days. Compared with the TN group, HS-exposed buffaloes exhibited markedly higher respiration rates (p < 0.001) and feeding frequencies (p < 0.05), but significantly shorter feeding duration throughout the observation period (p < 0.05). No significant differences were observed in the time spent standing, lying, or drinking between the two groups (p ≥ 0.05). Under HS conditions, buffaloes preferred a vertical lying posture to reduce exposure to intense solar radiation. These results suggest that crossbred Dehong dairy buffaloes can adapt to heat stress by modulating their physiological and behavioral strategies. The observed changes in physiological indices and behavioral patterns provide fundamental data for further elucidating the heat stress adaptation mechanisms in dairy buffaloes. Full article

Dust pollution induced by blasting during tunnel construction via the drill-and-blast method poses a severe threat to workers’ health and construction safety. To address this issue, a wet chord grid dust removal and purification device adaptable to deep-buried long tunnels was developed in this study. The device integrates dust control and removal functions, featuring mobility, high purification efficiency, and water recycling capability. Through experimental tests, the optimal operating parameters of the system were determined: the dust removal efficiency reached a peak of 94.3% (laboratory optimal value from the basic parameter optimization test) when the frequency of the extraction axial flow fan was set to 30 Hz and the cross-sectional wind speed of the chord grid reached 3.34 m/s. The circulating water tank achieved the optimal water treatment performance under the conditions of a relative buried depth of 0.42 for the water inlet, a volume ratio of 1:2 for the sedimentation area to the clear water area, and a relative baffle height of 0.65. Numerical simulations based on CFD software (2021) revealed that the on-site dust removal efficiency of the device reached 79.86% and 87.9% under the working conditions where the tunnel face was 10 m and 100 m away from the connecting passage, respectively, which are in good agreement with the field measurement results. In the practical application at the Shierpo Tunnel of the Guangxi Tianba Expressway, the device achieved an average total dust removal efficiency of 78.4%, with 81.2% removal efficiency for PM₁₀ and 76.5% for PM_2.5, demonstrating excellent engineering applicability and dust removal performance for respirable dust. This study provides effective technical support and a theoretical basis for improving the construction environment of drill-and-blast tunnels. Full article

Acoustic divergence is widely recognized as a key driver of speciation and niche differentiation in vocal animals. In echolocating horseshoe bats (Rhinolophus), the larynx is specialized for producing high-duty-cycle signals used in foraging, navigation, and species recognition. While the ecological role of echolocation is established, the molecular mechanisms regulating laryngeal frequency remain unclear. We compared the laryngeal transcriptomes of three closely related, sympatric Rhinolophus species with distinct resting frequencies (RFs): R. episcopus (~46 kHz), R. siamensis (~66 kHz), and R. osgoodi (~85 kHz). This comparison identified 511 differentially expressed genes. High-frequency species upregulated genes involved in cytoskeletal dynamics and muscle contraction, such as cell adhesion molecules and motor proteins, while low-frequency species upregulated genes related to cellular homeostasis and metabolic maintenance. Weighted gene co-expression network analysis revealed two RF-correlated modules: a high-frequency module enriched in aerobic respiration and carbon metabolism and a low-frequency module enriched in lipid metabolism. Protein–protein interaction analysis identified ACTC1, vital for muscle contraction, as a hub gene. Evolutionary analysis showed that ACTC1 is highly conserved, with no significant positive selection, indicating that transcriptional regulation, rather than coding-sequence divergence, is the primary driver of the observed functional differences. These findings suggest that RF variation likely results from transcriptional remodeling in laryngeal superfast muscles. This study provides the first transcriptomic evidence linking laryngeal gene expression with acoustic divergence and offers new insights into the genetic basis of bat echolocation. Full article

Ultrasound imaging is a crucial tool for guiding radiation therapy, particularly for cancers such as pancreatic cancer, where tumors exhibit respiration-induced motion. While flexible ultrasound transducers offer improved anatomical conformity and reduced compression-induced distortion compared to rigid probes, their variable geometry presents significant challenges for conventional beamforming. In this study, we investigate a deep learning-based beamforming framework that directly predicts delayed RF data from raw RF input, bypassing explicit transducer shape estimation and traditional delay-and-sum computations. Building upon an artificial curvature simulation strategy, we systematically analyze the impact of curvature-induced variation and inherent RF noise on model performance and generalizability. We further introduce frequency-domain analysis to quantify RF-level signal variation that may not be apparent in spatial-domain image comparisons. Our results demonstrate that although noise-augmented training improves prediction consistency, reconstruction performance remains limited under the current prototype noise conditions. These findings highlight the importance of RF data diversity and noise characterization in developing clinically robust deep learning beamformers for flexible transducer-based ultrasound-guided radiation therapy. Full article

Emotion recognition from physiological signals has immense applications in healthcare and human–computer interaction. We developed an electrodermal activity (EDA)-graph signal processing pipeline that produces highly sensitive features for detecting the affective dimensions (arousal and valence) of emotions. Using the Continuously Annotated Signals of Emotion dataset, we compared our graph-based EDA features (EDA-graph) with traditional time- and frequency-domain EDA features and features derived from other signals (heart rate variability, pulse transit time, electromyography, skin temperature, and respiration) for detecting affective dimensions using machine learning regression models. The EDA-graph features showed superior performance in continuous affective dimension recognition compared to the most accurate state-of-the-art models, achieving RMSE values of 0.801 for arousal and 0.714 for valence. Furthermore, we used a variety of traditional and recently published datasets collected in laboratory and ambulatory settings to perform a comprehensive evaluation of the robust generalization capabilities of our approach across different emotional contexts. The models demonstrated exceptional performance in classifying emotional states across the datasets, achieving 98.2% accuracy in detecting positive, negative, and mixed emotions; 92.75% in discriminating between emotions (relaxed, amused, bored, scared, and neutral); and 86.54% in detecting stress vs. no stress. These results highlight the potential of a graph-based analysis of EDA in emotion recognition systems in different contexts, especially for real-world applications. Full article

In the context of climate change and increased frequency of droughts, summer supplementation of grazing cattle may improve productivity and resilience of pastoral systems. However, the provision of supplements may increase the risk of heat stress in cattle. This study aimed to evaluate the productive and physiological response of grazing steers supplemented during summer. Three independent studies were conducted over three summers (2020–2023). In each experiment, steers grazing native grasslands with access to shade were allotted to one of two treatments: control (CONT) and supplementation (SUPPL), in a completely randomized block design. SUPPL steers were group-fed in the morning three days per week with an energy–protein ration at a level of 1.98% body weight (BW) on days of feeding. Pasture attributes, animal performance, respiration rate (RR), and body temperature (BT) were analyzed using a mixed model. According to the temperature–humidity index, cattle were exposed to heat stress 32% of the time. Summer supplementation increased average daily gain and final body weight of steers. Although supplementation temporarily increased BT (morning) and RR (afternoon), daily average RR and BT were similar for both treatments. These findings show that summer supplementation improves animal performance of grazing steers without increased risk of severe heat stress. These results are aligned with the concept of sustainable livestock intensification, which aims to enhance animal source foods to feed a growing population without causing collateral animal welfare issues. Full article

Emotions and affective responses are core intervention targets in music therapy. Through acoustic elements, music can evoke emotional responses at physiological and neurological levels, influencing cognition and behavior while providing an important dimension for evaluating therapeutic efficacy. However, emotions are inherently abstract and difficult to represent directly. Artificial intelligence models therefore provide a promising tool for modeling and quantifying such abstract affective states from physiological signals. In this paper, we propose a structured and explicitly factorized multi-modal representation learning framework for joint affective state and preference inference. Instead of entangling heterogeneous dynamics within monolithic encoders, the framework decomposes representation learning into cross-channel interaction modeling and intra-channel temporal–spectral organization modeling. The framework integrates electroencephalography (EEG), peripheral physiological signals (GSR, BVP, EMG, respiration, and temperature), and eye-movement data (EOG) within a unified temporal modeling paradigm. At its core, a Dynamic Token Feature Extractor (DTFE) transforms raw time series into compact token representations and explicitly factorizes representation learning into (i) explicit channel-wise cross-series interaction modeling and (ii) temporal–spectral refinement via learnable frequency-domain gating. These complementary structural modules are implemented through Cross-Series Intersection (CSI) and Intra-Series Intersection (ISI), which perform low-rank channel dependency learning and adaptive spectral modulation, respectively. A hierarchical cross-modal fusion strategy integrates modality-level tokens in a representation-consistent and interaction-aware manner, enabling coordinated modeling of neural, autonomic, and attentional responses. The entire framework is optimized under a unified multi-task objective for valence, arousal, and liking prediction. Experiments on the DEAP dataset demonstrate consistent improvements over state-of-the-art methods. The model achieves 98.32% and 98.45% accuracy for valence and arousal prediction, 97.96% for quadrant classification in single-task evaluation, and 92.8%, 91.8%, and 93.6% accuracy for valence, arousal, and liking in joint multi-task settings. Overall, this work establishes a structure-aware and factorized multi-modal representation learning framework for robust affective decoding and intelligent music therapy systems. Full article

Global climate change has increased the frequency and intensity of heat waves, posing a significant threat to livestock production. During heat exposure, the disruption of intestinal barrier integrity is a pivotal event in the pathogenesis of heat stress-induced intestinal injury. Endoplasmic reticulum (ER) stress and mitochondrial dysfunction are key consequences of heat stress at the cellular level. However, direct causal evidence linking ER stress to mitochondrial dysfunction in heat-stressed enterocytes remains limited. To investigate this, we used an integrated transcriptomic, metabolomic, and functional validation strategy to assess mitochondrial bioenergetics and cellular ultrastructure in porcine intestinal epithelial (IPEC-J2) cells under acute heat stress. Transcriptomic analysis revealed extensive reprogramming, highlighting the significant enrichment of pathways related to protein processing in the endoplasmic reticulum, apoptosis, and MAPK signaling. Untargeted metabolomics identified significant perturbations in amino acid and energy metabolism, as well as altered bile acid profiles. Functional assessments confirmed that heat stress severely impaired mitochondrial bioenergetics, as evidenced by reduced maximal respiration and ATP production, and induced ultrastructural damage to mitochondria. The pharmacological inhibition of ER stress by 4-phenylbutyric acid (4-PBA) significantly attenuated the mitochondrial bioenergetic impairment and ultrastructural damage, whereas ER stress induction recapitulated these defects. We demonstrate that heat stress induces profound transcriptional and metabolic remodeling characterized by ER stress activation, which critically mediates subsequent mitochondrial bioenergetic dysfunction and ultrastructural damage. Our findings suggest that targeting ER stress may represent a promising therapeutic strategy to ameliorate enterocyte mitochondrial dysfunction and mitigate heat stress-induced intestinal injury in livestock. Full article