Search Results (815)

Innate immunity predictors of HIV-1 risk and pathogenesis vary with reproductive hormones, pregnancy, and lactation, yet the underlying mechanisms remain unclear. We hypothesized that pregnancy-associated physiological adaptations alter systemic microRNA (miRNA) expression, thereby regulating immunity, pathogenesis and susceptibility to infection. We analyzed 174 serum samples from 88 participants in a longitudinal cohort from Uganda and Zimbabwe across pre-pregnancy (PP), pregnancy (P), and postpartum breastfeeding (BF). Cell-free peripheral blood miRNAs (n = 2083) were profiled using HTG EdgeSeq. Pregnancy-specific miRNAs were identified by intersecting differentially expressed (DE) miRNAs from P vs. PP and P vs. BF comparisons. miRNA targets and pathways were analyzed using miRWalk, Cytoscape/ClueGO, and cytoHubba. Pregnancy was associated with DE miRNAs (29 upregulated and 131 downregulated) targeting 2733 validated genes. Enriched pathways (FDR < 0.05) included adaptive immune response, Hippo Signaling, Cellular Senescence, HSV-1 infection, and two cancer-related pathways. Pregnancy-enriched targets within each pathway overlapped with the HIV–host interactome by 37–88%. Network analysis identified 47 hub genes interacting with 18 HIV-1 proteins, with Tat and gp120 being most connected viral and HLA-A being the most connected host protein. These findings indicate that pregnancy-driven systemic miRNAs target the HIV–host interactome and specifically identify pregnancy-enriched central hub genes involved in cell cycle control, viral immune evasion and replication to be further investigated for their predictive value in HIV acquisition and pathogenesis in longitudinal cohorts and experimental settings. Full article

Cloud-dependent irrigation platforms can support remote monitoring, but their use in precision fertigation is limited when local decisions must be made quickly and reliably. Network delay, temporary disconnection, and the use of single-point measurements may all reduce the ability of a system to respond to spatial variation in soil moisture and nutrient demand. In this work, an edge-controlled precision fertigation system was developed by combining multi-parameter soil sensing, spatial task scheduling, and a 6-DOF robotic manipulator. The ESP32 controller runs a preemptive FreeRTOS scheduler, allowing sensor acquisition, inverse-kinematics calculation, and pump actuation to be handled as separate tasks. A Kalman filter was used to smooth soil moisture measurements, and a hysteresis-based control strategy was adopted to reduce false triggering and repeated pump switching. To improve fertigation safety, a hardware–software interlock was added so that fertilizer delivery is always accompanied by water delivery. Hardware-in-the-Loop simulation and a 14-day field deployment were used to evaluate the system. The controller achieved an end-to-end latency of less than 38 ms and maintained operation during network interruptions through cached local parameters. After calibration, the robotic end-effector positioning error was reduced to ±2.4 mm. The hysteresis strategy lowered daily pump cycling by 71%. Based on prototype duty-cycle data and seasonal extrapolation, the projected seasonal water use and fertilizer demand were 44% and 38% lower, respectively, than those estimated for a uniform application. These values should be interpreted as model-based projections rather than direct season-long measurements. During 72 h of continuous operation, no Modbus faults were observed, and RTOS heap fragmentation remained stable. Overall, the results suggest that edge-based deterministic control can provide a practical route for precision fertigation where both spatial variability and intermittent connectivity must be considered. Full article

Chillers serve as the core high-energy-consuming equipment in heating, ventilation, and air conditioning (HVAC) systems. During long-term continuous operation, they tend to suffer gradual subtle degradation, with a performance deviation less than 5%. Conventional fault diagnosis methods rely on manual threshold judgment or labeled fault data, which fail to realize accurate early warning signals. In addition, existing algorithms lack multi-dimensional baseline comparisons to verify their practical engineering performance. To address these limitations, this paper proposes an unsupervised health assessment method combining an LSTM autoencoder and Euclidean distance (LSTM-AE-ED). A multi-gradient fault time-series dataset is generated via a MATLAB R2022b/Simscape mechanism model verified by both summer field measurements and refrigeration pressure-enthalpy cycles, which resolves the practical engineering challenges of scarce on-site fault samples and potential equipment damage caused by actual fault tests. The proposed model is trained solely on healthy time-series data. It extracts dynamic coupling characteristics of chillers through LSTM, constructs a dimensionless health index based on Euclidean distance in feature space, and introduces the standard deviation of health index to improve evaluation stability. Baseline comparisons with vanilla AE and single-layer LSTM are carried out. Experimental results demonstrate that the proposed method achieves an identification accuracy of 96.3% and exhibits high sensitivity to mild degradation of four typical faults, adapting to dynamic multi-working-condition scenarios. This approach requires no additional acquisition devices for derived parameters such as power consumption and COP; online assessment can be realized merely with standard temperature, pressure, and flow sensors equipped on chillers. With lightweight inference performance, it is suitable for edge monitoring terminals of chillers in data centers, providing a low-cost and practical quantitative technical scheme for predictive maintenance and hierarchical early warning signals of refrigeration equipment. Full article

High-resolution seismic reconstruction is a challenging inverse problem because field seismic traces are inherently band-limited and their high-frequency components are further degraded by source bandwidth limitations, acquisition conditions, random noise, and attenuation during wave propagation. Classical resolution enhancement methods can partially sharpen seismic events, but they usually rely on restrictive assumptions about stationarity, minimum-phase wavelets, or accurate attenuation models. In this study, we propose a structure-preserving bidirectional bandwidth translation network for seismic resolution enhancement. Instead of formulating the task as a one-way paired regression problem, the proposed approach interprets resolution enhancement as unpaired translation between low-bandwidth and high-bandwidth seismic domains. A cycle-consistent adversarial objective is combined with an SSIM-based structural constraint so that the model simultaneously improves spectral recovery, waveform fidelity, and reflector continuity. To reduce the domain gap between synthetic and field data, we further construct a hybrid training corpus by combining field-extracted wavelets with synthetic reflectivity sequences and train a lightweight one-dimensional residual generator–discriminator architecture tailored to oscillatory seismic traces. Comprehensive experiments are conducted on synthetic data, a field seismic profile, and the public SEG Open Data benchmark. In addition to comparisons with conventional deconvolution and time-varying frequency deconvolution, the manuscript reports quantitative comparisons with representative learning-based baselines, together with ablation studies, parameter sensitivity analysis, robustness evaluation under different noise levels and bandwidth settings, and computational cost analysis. The results show that the proposed method consistently achieves a favorable balance between spectral extension and structural preservation, demonstrating its potential as a practical data-driven solution for seismic resolution enhancement. Full article

Real-time, free-breathing, ungated cardiac magnetic resonance imaging (CMR) is a clinically valuable alternative to conventional breath-held, ECG-gated cine imaging for patients who cannot sustain breath holds or produce reliable cardiac rhythms, including pediatric, arrhythmic, and respiratory-compromised populations. Achieving diagnostic image quality in this setting requires aggressive k-space undersampling and sophisticated reconstruction. Because no fully sampled reference exists for such acquisitions, supervised deep learning is not directly applicable, motivating unsupervised, subject-specific methods. Existing approaches typically rely on low-dimensional continuous latent spaces, which can limit their capacity to represent concurrent cardiac and respiratory motions as distinct states and may suffer from posterior collapse. We introduce VQ-SToRM (Vector-Quantized Smoothness Regularization on Manifolds), an unsupervised framework that adapts the Vector-Quantized Variational Autoencoder to real-time CMR by replacing the continuous latent manifold of prior existing methods with a learned discrete codebook. The encoder, decoder, and codebook are trained jointly on the undersampled non-Cartesian k-t space data of a single subject. On free-breathing, ungated spiral acquisitions from healthy volunteers, VQ-SToRM accurately resolved cardiac and respiratory motion across all phases of the cardiac cycle. A systematic ablation study identified a compact configuration—a codebook of only five embeddings of dimension ten—as optimal, indicating that a small discrete codebook is sufficient to represent the dominant cardiac and respiratory motion content. Compared with V-SToRM and Time-DIP, VQ-SToRM achieved smoother frame-to-frame transitions and comparable or superior signal-to-noise and contrast-to-noise ratios with lower variance across frames and datasets, offering a promising path toward clinically practical real-time CMR. Full article

Background: Longitudinal menstrual cycle monitoring requires digital health systems capable of handling individual variability, irregular sampling, and incomplete real-world observations. Most consumer-focused menstrual-tracking applications depend on simplified calendar-based logic, offering limited support for transparent longitudinal data handling, interoperability, and the management of irregular real-world observations. Objective: This study presents the design, implementation, and formative evaluation of a non-clinical digital health infrastructure for longitudinal menstrual cycle monitoring, with an emphasis on modular system architecture, longitudinal data processing, and user-perceived usability. Methods: A modular digital health system was developed in accordance with separation-of-concerns and privacy-by-design principles, combining a backend analytical infrastructure with a mobile application interface. The architecture was designed to support longitudinal data acquisition, variability-aware processing, and extensibility while remaining independent of proprietary analytical services. System evaluation included technical and functional verification, formative usability assessment, and quality evaluation using the user version of the Mobile App Rating Scale (uMARS). Results: In the uMARS evaluation (N = 63), the mean total score across core domains was 3.11 ± 0.76. Information quality (3.44 ± 0.85) and functionality (3.27 ± 0.88) received the highest ratings, whereas engagement (2.83 ± 0.84) received the lowest, consistent with the system’s prototype character. Internal consistency was high (Cronbach’s α = 0.91), and sensitivity analysis restricted to female participants yielded results comparable to those of the full sample. Conclusions: The proposed system demonstrates the technical and functional feasibility of a modular digital health architecture for longitudinal menstrual cycle monitoring under heterogeneous real-world data conditions. The findings support the use of variability-aware and extensible monitoring infrastructures as a foundation for future applied research and iterative development of women’s digital health systems without making diagnostic or predictive clinical claims. Full article

This study aims to systematically evaluate the balancing mechanism between road performance, carbon emissions, and economic cost when selecting asphalt materials for severe cold regions, filling the gap in multi-criteria decision-making for composite chemical modifications. To address alternating temperatures, heavy traffic, and modified asphalt transport difficulties, this study presents a novel evaluation framework focusing on the performance–environmental–cost nexus of a desulfurized rubber powder composite SBS-modified asphalt mixture, which provides a clear technological breakthrough for high-ratio scrap tire recycling in seasonal frost zones. Two reference mixtures serve as comparisons: a conventional rubber powder composite SBS (styrene–butadiene–styrene triblock)-modified asphalt mixture (CR-SBS) and an SBS-modified asphalt mixture (SBS). A comparative experiment was conducted between the two materials and the SBS-modified asphalt mixture (ACR-SBS) compounded with desulfurized rubber powder. High-temperature stability was tested by the rutting test, low-temperature crack resistance by the beam bending test, and water stability by the immersion Marshall and freeze–thaw splitting tests. Life cycle carbon emissions and economic costs were quantified from raw material acquisition to construction. The results show that desulfurized rubber powder composite with ACR-SBS delivers the most superior overall road performance. However, it also generates the highest life cycle carbon footprint. Its total carbon emission reaches 162,800 kgCO₂eq, which is 13.7% (19,600 kgCO₂eq) higher than SBS (143,200 kgCO₂eq) and 7.7% (11,600 kgCO₂eq) higher than CR-SBS (151,200 kgCO₂eq). The total cost of ACR-SBS is 391,000 CNY, which is 1.5% (6000 CNY) higher than SBS (385,000 CNY) and 1.3% (5000 CNY) lower than CR-SBS (396,000 CNY). These findings provide a basis for the selection of high-performance, low-carbon, and economical composite-modified asphalt in severe cold regions. Full article

Hydrotherapy is widely used in rehabilitation because it reduces mechanical loading while preserving neuromuscular and cardiovascular stimulation. However, the biomechanical characterization of deep-water running remains limited, particularly when using accessible wearable systems for cycle-based movement analysis. This study aimed to evaluate the concurrent validity and agreement of a low-cost accelerometry device for cycle-based analysis of deep-water running, using a commercial accelerometry system as the reference measurement system. Twenty-one healthy participants performed a 25 m deep-water running task with simultaneous data acquisition from mechanically coupled sensors to ensure alignment. A total of 75 synchronized cycles were processed using a standardized pipeline that included filtering, synchronization, cycle detection, and parameter extraction. Statistical analysis was conducted using the Wilcoxon signed-rank test, intraclass correlation coefficient, Spearman’s correlation, Bland–Altman analysis, and error metrics. The results showed good agreement for temporal and volumetric variables, including cycle duration (ICC = 0.84), cumulative acceleration (ICC = 0.82), and area under the curve (ICC = 0.68). However, lower agreement and systematic bias were observed for intensity-related variables, particularly RMS and peak acceleration, despite more than 92% of cycles falling within the 95% limits of agreement (LoA). These findings suggest that the proposed device provides acceptable agreement for temporal and volumetric variables during deep-water running and may represent a low-cost alternative for movement monitoring in aquatic environments. However, intensity-related variables should be interpreted with caution due to the systematic differences observed between systems. Full article

Cellular senescence is a state of stable cell cycle arrest triggered by various internal and external stressors. It represents an important tumor-suppressive mechanism that effectively prevents the proliferation of damaged cells. During tumor initiation and progression, cellular senescence plays a dual and paradoxical role. On one hand, it induces cell cycle arrest to inhibit the development of tumors in potentially malignant cells. On the other hand, it can promote tumor progression through the senescence-associated secretory phenotype (SASP), which enhances inflammation and extracellular matrix remodeling. This review outlines the definition and key characteristics of cellular senescence and analyzes different senescence-inducing stimuli along with their underlying molecular mechanisms. It further discusses the molecular basis for the maintenance of stable senescence, mechanisms to escape growth arrest, and how these cells contribute to tumor recurrence through dedifferentiation and acquisition of stemness properties. Additionally, the dual regulatory role of SASP in tumor progression is examined. In terms of cancer therapy, with a deeper understanding of the mechanisms of senescent cells, treatment strategies are gradually shifting from single senescence-inducing approaches to more comprehensive combinatorial strategies. Meanwhile, the integration of single-cell omics technologies with artificial intelligence and machine learning offers new prospects for personalized therapy. Full article

Background: Recent technology improvements have enabled desorption electrospray ionisation (DESI) mass spectrometry imaging to achieve down to 5 µm (pixel) image resolution. However, operating at this resolution introduces challenges, particularly regarding increased total analysis time and the need for sufficient instrument sensitivity to detect analytes from very small tissue areas. Methods: High mass and image resolution DESI imaging was performed on rat brain tissue using a Xevo™ MRT benchtop mass spectrometer equipped with a multi-reflecting time-of-flight mass analyser and a DESI XS source. Data acquisition was conducted at speeds of up to 100 Hz. Sensitivity was assessed using a dilution series of five Active Pharmaceutical Ingredients (APIs) spotted onto porcine liver tissue. Signal detection limits were evaluated using extracted ion chromatograms (XICs) with signal-to-noise (S/N) calculations against blank samples. Additionally, enhanced duty cycle (EDC) was applied to evaluate improvements in analyte signal intensity across specific mass ranges in both positive and negative ionisation modes. Results: At acquisition speeds of up to 100 Hz, excellent data quality was achieved, with signal intensity remaining suitable for analytical applications. All five tested APIs were detectable at concentrations of 25 pg/mm². Three of the five compounds were further detected at concentrations as low as 2.5 pg/mm², with signal-to-noise ratios greater than 5. The application of EDC resulted in a significant increase in analyte signal intensity within the targeted mass ranges, particularly for small molecule endogenous metabolites and lipids, in both ionisation modes. Furthermore, the system demonstrated substantially improved spectral quality, achieving mass resolution up to 100,000 FWHM. This enabled the resolution of previously indistinguishable analytes with significantly improved mass accuracy compared to systems operating at approximately 30,000 FWHM. Conclusions: The Xevo™ MRT mass spectrometer with DESI XS source enables high-resolution DESI imaging at speeds up to 100 Hz without compromising data quality or sensitivity. The system demonstrates excellent detection limits for pharmaceutical compounds and improved performance through enhanced duty cycle operation. Overall, the combination of high spatial resolution, increased mass resolution, and improved spectral quality allows for more accurate analyte differentiation, representing a significant advancement over lower-resolution systems. Full article

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of $92.80\pm 2.03\%$, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions. Full article

Seasonal fluctuations in open solid-state fermentation drive batch-to-batch variability in Chinese Baijiu Daqu; however, how environmental shifts reshape microbial functional expression and non-volatile flavour precursors in medium-high-temperature Daqu remains poorly resolved. In this study, data-independent acquisition (DIA)-based quantitative metaproteomics and untargeted liquid chromatography–mass spectrometry (LC-MS) metabolomics were integrated to characterise winter and summer Daqu from Luzhou, Sichuan. Among 2904 annotated non-volatile metabolites, orthogonal partial least squares discriminant analysis (OPLS-DA) revealed clear seasonal separation; 1472 differential metabolites (560 up- and 912 downregulated in winter vs. summer; variable importance in projection [VIP] > 1, p < 0.05) were enriched in glycolysis/gluconeogenesis, the tricarboxylic acid (TCA) cycle, amino acid biosynthesis, and starch/sucrose metabolism. DIA-based quantitative metaproteomics further resolved season-specific enzyme expression: summer Daqu exhibited elevated saccharolytic, glycolytic and amino-acid-converting enzymes (β-glucosidase, 6-phosphofructokinase, pyruvate dehydrogenase), whereas winter Daqu was enriched in glucose oxidase, phosphoenolpyruvate carboxykinase and aldehyde dehydrogenase, consistent with a pattern suggestive of carbon-storage prioritisation. Proteome–metabolome integration established a coherent “enzyme protein abundance–inferred metabolic tendency–metabolite accumulation” correlative framework axis: higher hydrolytic and central-carbon enzyme abundance in summer corresponded to increased maltose, lactate, acetate, L-glutamate and L-aspartate. Therefore, production season reshapes Daqu quality chiefly by corresponding to distinct patterns of in situ enzyme protein abundance, providing a DIA quantitative metaproteome-anchored mechanistic framework for screening high-expression starters and stabilising seasonal Daqu quality. Full article

Plant functional traits are central to regulating ecosystem multifunctionality (EMF), yet how coordinated above- and below-ground resource acquisition strategies mediate the effects of forest structural diversity on EMF remain insufficiently understood, particularly in typical south subtropical forests. Here, we applied a trait-based framework to disentangle the pathways linking forest structural diversity to EMF through plant resource acquisition strategies. Typical south subtropical forests were sampled for community-level leaf and root traits, including leaf total nitrogen and total phosphorus content, specific leaf area, leaf dry matter content, root diameter, specific root length, root tissue density, root total nitrogen and root total phosphorus content. EMF was quantified using 13 indicators associated with carbon storage, litter decomposition, primary productivity, and nutrient cycling, evaluated using both averaging and multi-threshold approaches. Principal component analysis was used to summarize trait variation along major functional axes representing the leaf and root economics spectra, and structural equation modeling was employed to quantify direct and trait-mediated pathways linking forest structural diversity to EMF. We found pronounced variation in EMF among forest types, with multifunctionality increasing along the classical fast-slow plant economics spectrum. Communities dominated by fast-growing species exhibited consistently higher EMF than those dominated by slow-growing species, with below-ground traits showing stronger associations with EMF than above-ground traits. In contrast, EMF was unrelated to the root collaboration gradient, suggesting that alternative below-ground foraging strategies contributed little to multifunctionality. Moreover, the positive effects of structural diversity on EMF were indirectly mediated through both leaf and root conservation gradients. Notably, the relative importance of these trait-mediated pathways was threshold-dependent. Root conservation gradients dominated EMF at low multifunctionality thresholds, whereas leaf conservation gradients became increasingly important at higher thresholds. Our findings show that forest structural diversity enhances ecosystem multifunctionality through coordinated leaf and root strategies. By revealing trait-mediated links between biodiversity and EMF, this study clarifies how community composition and species turnover shape multifunctionality in typical south subtropical forests. Full article

In response to the current limitation where conventional constant volume combustion apparatuses are generally confined to pressure ratings of 5–20 MPa, insufficient for the demands of ultra-high-pressure combustion fundamental research, this study designs and verifies a high-pressure-resistant constant volume combustion apparatus with a rated working pressure of 250 MPa. The strength design and safety factor calculation for the combustion chamber main body were conducted based on the Lame thick-walled cylinder elastic theory. A finite element numerical simulation method was systematically employed to perform static analysis, transient impact response analysis, and high-cycle fatigue-life assessment of the key components of the apparatus. The results indicate that under a 250 MPa design internal pressure load, the maximum circumferential stress at the inner wall of the combustion chamber main body is 328.0 MPa, with a safety factor greater than 1.5, complying with relevant safety codes for high-pressure vessels. Under transient loading simulating combustion impact, the maximum equivalent stress of all structural components is below the material yield strength, with a maximum elastic deformation of less than 0.06 mm, demonstrating excellent structural stiffness and impact resistance. Fatigue assessment with a design-life target of 1.0 × 10⁶ pressure cycles shows that the cumulative damage values for all components are significantly less than 1.0, meeting the reliability requirements for long-term cyclic service. This apparatus integrates functional modules such as high-pressure precision gas mixing, high-energy reliable ignition, high-speed transient parameter acquisition, and safe product collection, providing a stable, controllable, and safe experimental platform for in-depth research on the combustion mechanisms of gaseous fuels under ultra-high-pressure conditions. Full article

Generative artificial intelligence (AI) is increasingly present in medical education, yet its indiscriminate use risks impairing the acquisition of foundational clinical competencies, including clinical reasoning, hypothesis generation, and patient-centered communication, through processes of never-skilling, mis-skilling, and deskilling. This paper presents M3RGE-AI (Responsible, Reliable, and Reflexive use of Generative AI in Medical Education), a conceptual framework for the purposeful integration of AI as a cognitive scaffold in medical training. Drawing on established learning theories, zone of proximal development, deliberate practice, and peer learning, the framework assigns progressively expanding AI functions across training stages, prioritizes Socratic over directive interactions, requires transparent and verifiable sourcing of AI-generated content, and incorporates peer moderation and AI-off assessment checkpoints to mitigate over-reliance. The framework is operationalized through alternating AI-on and AI-off cycles, governance processes, and educator training protocols. Applied within these constraints, AI can shorten feedback loops and broaden clinical exposure while preserving independent reasoning and authentic patient communication. M3RGE-AI offers a theoretically grounded and institutionally implementable model for integrating generative AI into medical curricula without sacrificing the essential human competencies that underpin safe clinical practice. Full article