Search Results (4,226)

Background/Objectives: Alzheimer’s disease (AD) is characterized by progressive neurodegeneration driven by interconnected mechanisms, including oxidative stress, mitochondrial dysfunction, neuroinflammation, synaptic impairment, and abnormal protein aggregation. MicroRNAs (miRNAs) have emerged as post-transcriptional regulators of these complex pathways; however, efficient delivery remains a major limitation. Small extracellular vesicles (sEVs) have been proposed as biologically compatible carriers for miRNA delivery. Methods: In this study, milk-derived sEVs were isolated, characterized, and loaded with microRNA-137-5p (miR-137-5p). Their effects were evaluated in an amyloid-β (Aβ)-induced in vitro AD model using SH-SY5Y human neuroblastoma cells. Oxidative stress markers, including reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD), lactate dehydrogenase (LDH), and glutathione peroxidase 1 (GPX1), were assessed. Inflammation- and neuroprotection-related gene expression analyses included intercellular adhesion molecule 1 (ICAM1), tumor necrosis factor alpha (TNF-α), and brain-derived neurotrophic factor (BDNF). Cytoskeletal injury was evaluated using neurofilament light chain (NfL). Mitochondrial stress markers included cytochrome c (Cyt-c), 8-hydroxy-2′-deoxyguanosine (8-OHdG), PTEN-induced kinase 1 (PINK1), dynamin-1-like protein (DNM1L), and mitochondrial transcription factor A (TFAM). Synaptic and extracellular matrix-associated proteins, including complexin-2 (CPLX2), SPARC-related modular calcium-binding protein 1 (SMOC1), and receptor tyrosine kinase-like orphan receptor 1 (ROR1), as well as AD-related biomarkers, including total tau, phosphorylated tau at threonine 181 (pTau-181), phosphorylated tau at threonine 217 (pTau-217), and amyloid-β 1–40 (Aβ1–40), were evaluated using molecular and biochemical approaches. Results: Aβ exposure was associated with increased oxidative stress, inflammatory activation, mitochondrial and cytoskeletal alterations, synaptic-related disturbances, and elevations in tau- and amyloid-associated proteins. Treatment with unloaded sEVs was associated with partial modulation of several parameters, whereas miR-137-5p-loaded sEVs were consistently associated with normalization of multiple pathological markers toward control levels. Conclusions: These findings indicate that miR-137-5p-enriched sEVs may represent a useful experimental platform for multi-target modulation of AD-related cellular alterations. Further mechanistic and in vivo studies are required to clarify translational relevance. Full article

Deep-sea exploration increasingly relies on Autonomous Underwater Vehicles (AUVs) to enable persistent, wide-area surveying in harsh and uncertain environments. In practice, however, deployments are constrained by tight energy budgets and bandwidth-limited, intermittent acoustic links, which complicate mission-level coordination. Moreover, many existing systems treat perception and control as loosely coupled modules, often resulting in redundant sensing, inefficient communication, and degraded overall performance—particularly under heterogeneous sensing modalities and shifting geological conditions. To address these challenges, we propose a hierarchical Federated Meta-Transfer Learning (FMTL) framework that tightly integrates collaborative perception with adaptive control for swarm optimization. The framework operates at three levels: (1) Representation Learning aligns heterogeneous sensors in a shared latent space via a physics-informed contrastive objective, substantially reducing communication overhead; (2) Meta-Learning Adaptation enables rapid transfer and convergence in new environments with minimal data exchange; and (3) Energy-Aware Control realizes closed-loop exploration by coupling Federated Explainable AI (FXAI) with decentralized multi-agent reinforcement learning (MARL) for path planning under energy constraints. Validated in high-fidelity hardware-in-the-loop simulations and a digital-twin environment, FMTL outperforms state-of-the-art baselines, achieving an AUC of 0.94 for target identification. Furthermore, an energy–intelligence Pareto analysis demonstrates a 4.5× improvement in information gain per Joule. Overall, this work provides a physically consistent and communication-efficient blueprint for the optimization and control of next-generation intelligent marine swarms. Full article

Background/Objectives: Clostridioides difficile infection (CDI) is a major healthcare-associated infection associated with profound antibiotic-induced gut microbiome disruption that frequently persists after clinical resolution. This pilot study aimed to characterize early post-infectious gut microbiome recovery following an inaugural CDI episode and to descriptively assess microbiome remodeling during adjunctive multi-strain probiotic supplementation. Methods: Adult patients with mild-to-moderate CDI were prospectively enrolled after completing standard antimicrobial therapy and received a 30-day course of a high-potency, 10-strain probiotic formulation. Stool samples were collected before and after supplementation and analyzed using 16S rRNA gene sequencing with microbiome-inferred functional profiling, alongside targeted screening for enteric protozoa and yeasts. Results: Five patients completed paired analyses. At baseline, all patients exhibited severe dysbiosis characterized by markedly reduced microbial diversity, depletion of Actinobacteria and short-chain fatty acid-producing taxa, expansion of Proteobacteria, and unfavorable inferred metabolic signatures. After supplementation, four of five patients were observed to exhibit increased microbial diversity and partial improvement in global dysbiosis indices. Microbiome recovery was heterogeneous and non-linear, involving variable reductions in Proteobacteria, recovery of Actinobacteria, or both, with incomplete normalization of taxonomic balances and inferred functions. Enterotype shifts were observed in three patients, consistent with ecological reorganization rather than full restoration. Baseline protozoal colonization resolved in affected patients, while fungal dynamics showed clearance or species-level replacement. No early CDI recurrences were observed during follow-up. Conclusions: Interpretation is limited by the single-arm design without a control group, which precludes distinguishing supplementation-associated changes from natural post-antibiotic recovery. Even so, our findings highlight the complexity and inter-individual variability of early post-CDI microbiome recovery and support further investigation of integrative microbiome profiling to describe post-infectious dysbiosis dynamics. Full article

This study investigated high-speed air-atomized water-mist impingement cooling of 2219 aluminum alloy plates using a self-developed spray-quenching setup. Cooling intensity was controlled by varying the water loading fraction, and cooling curves were recorded using embedded thermocouples. Solution–aging treatments with conventional water quenching and mist quenching were performed, and multi-pass pre-deformation routes were applied before and/or after solution treatment. Tensile properties were evaluated at room temperature. Mist impingement cooling achieved markedly higher cooling rates than air cooling, with peak values in the order of 10³ °C/s. Higher cooling intensity improved quenching efficiency and increased strength after aging. Multi-pass pre-deformation enhanced yield strength, but reduced elongation at high deformation levels, revealing a strength–ductility trade-off. These results provide guidance for optimizing quenching and pre-deformation parameters in heat treatment of 2219 aluminum alloy components. Full article

Unconsolidated sandstone reservoirs, particularly in offshore and marine environments, are highly susceptible to sand production, which leads to flow-capacity degradation, plugging evolution, sand-retention instability, and erosion–corrosion damage in gravel-pack completion systems. To address the lack of system-level and quantitative integrity evaluation methods, a unified assessment framework is developed by coupling flow behavior, sand-retention mechanisms, and erosion–corrosion damage processes. The gravel-pack completion system is idealized as a concentric multilayer porous-medium structure under steady-state radial Darcy flow, and an equivalent radial permeability model is established to characterize flow capacity and anti-plugging performance, which enables consistent comparison of different completion schemes under identical plugging conditions. Based on sand-retention mechanisms, a sand-retention capacity index is proposed by integrating formation particle size distribution, screen aperture, gravel size, and sand-leakage risk. An erosion–corrosion coupled damage model is further developed to predict screen damage rates in CO₂-containing environments, and an integrity index is formulated to link damage evolution with long-term service performance. By integrating flow capacity, anti-plugging performance, sand-retention capacity, and structural integrity using a weighted geometric mean, a comprehensive evaluation index is established for overall system integrity assessment. Using the proposed framework, a representative formation sand with d₁₀ = 30  μm, d₅₀ = 180  μm, and d₉₀ = 500 μm  is evaluated. The optimal sand control design corresponds to a gravel median size of 971.53 μm (equivalent to a standard 16/20 mesh gravel) and an optimal screen aperture of 523.11 μm, with a screen porosity of 0.56. Under these conditions, the selected screen aperture and gravel size are well matched with the formation sand size, falling within recommended engineering ranges and achieving a favorable balance among sand retention, flow capacity, anti-plugging performance, and structural integrity. The proposed framework provides a quantitative and engineering-applicable basis for the optimization and integrity classification of offshore gravel-pack sand control completions under multi-constraint operating conditions. Full article

Histopathological image analysis remains the cornerstone of cancer diagnosis; however, manual assessment is challenged by stain variability, differences in imaging magnification, and complex morphological patterns. The proposed multi-pretrained deep learning fusion (MPDLF) approach combines two widely used CNN architectures: ResNet50, which captures deeper semantic representations, and VGG16, which extracts fine-grained details. This work differs from previous fusion studies by providing a controlled evaluation of early, intermediate, and late fusion for integrating two pretrained CNN backbones (ResNet50 and VGG16) under single-modality histopathology constraints. To isolate the fusion effect, identical training settings are used across three public H&E datasets. Early fusion achieved the best test performance for the two primary tasks reported here: breast cancer binary classification (accuracy = 0.9070, 95% CI: 0.8742–0.9404; AUC = 0.9707, 95% CI: 0.9541–0.9844) and renal clear cell carcinoma (RCCC) five-class grading (accuracy = 0.8792, 95% CI: 0.8529–0.9041; AUC (OvR, macro) = 0.9895, 95% CI: 0.9859–0.9927). Future work will extend these experiments to additional magnification levels (100×, 200×, and 400×) for breast cancer histopathology images and explore advanced hybrid fusion strategies across different histopathology datasets. Full article

Background: Rare cancers combine low incidence with high biological heterogeneity and multi-institutional care trajectories. These features make single-center learning structurally incomplete and render pathway fragmentation a dominant driver of preventable harm, variability, and waste. In this context, care quality is best understood as a property of pathway integrity across routing, diagnostics (imaging/biopsy planning), multidisciplinary intent-setting, definitive treatment, and surveillance—rather than as a department-level attribute. Objective: To define a pragmatic, transferable operating blueprint for a rare-cancer Learning Health System (LHS) that turns routine care into continuous, auditable learning under explicit governance, while maintaining claims discipline and protecting measurement validity. Approach: We synthesize an implementation-oriented operating model using the Swiss Sarcoma Network (SSN) as an exemplar. The blueprint couples clinical governance (Integrated Practice Unit logic, hub-and-spoke routing, auditable multidisciplinary team decision systems) with an interoperable real-world-time data backbone designed for benchmarking, pathway mapping, and feedback. The operating logic is expressed as a closed-loop control cycle: capture → harmonize → benchmark → learn → implement → re-measure, with explicit owners, minimum requirements, and failure modes. Results/Blueprint: (i) The model specifies a minimal set of data primitives—time-stamped and traceable decision points covering baseline and tumor characteristics, pathway timing, treatment exposure, outcomes and complications, and feasible longitudinal PROMs and PREMs; (ii) a VBHC-ready, multi-domain measurement backbone spanning outcomes, harms, timeliness, function, process fidelity, and resource stewardship; and (iii) two non-negotiable validity guardrails: explicit applicability (“N/A”) rules and mandatory case-mix/complexity stratification. Implementation is treated as a governed step with defined workflow levers, fidelity criteria, balancing measures, and escalation thresholds to prevent “dashboard medicine” and surrogate-driven optimization. Conclusions: This perspective contributes an operating model—not a platform or single intervention—that enables credible improvement science and establishes prerequisites for downstream causal learning and minimum viable digital twins. By distinguishing enabling infrastructure from the governed clinical system as the primary intervention, the blueprint supports scalable, learnable excellence in rare-cancer care while protecting against gaming, inequity, and inference drift. Distinct from generic LHS or VBHC frameworks, this blueprint specifies validity gates required for rare-cancer benchmarking—explicit applicability (“N/A”) rules, denominator integrity/capture completeness disclosure, anti-gaming safeguards, and escalation governance. These elements are critical in rare cancers because small denominators, high heterogeneity, and multi-institutional pathways otherwise make benchmarking prone to artifacts and unsafe inferences. Full article

In practice, village planning often suffers from an “emphasis on plan preparation but neglect of implementation”, a challenge that is especially evident in Sichuan Province, China, where highly diverse landforms and uneven development foundations make one-size-fits-all evaluation approaches difficult to apply. This study aims to develop a locally adaptable and operational method to quantify village planning implementation effectiveness control, enabling cross-type comparison and bottleneck diagnosis. We construct a three-level indicator system spanning eight domains—baseline control, land-use layout and construction, ecological protection and restoration, industrial development, infrastructure, public service facilities, living environment, and disaster prevention and mitigation—and determine indicator weights using the Analytic Hierarchy Process (AHP). To capture both compliance and progress, a dual-path scoring strategy is employed: constraint-based indicators are assessed using a threshold method by comparing current values (T1) with planning standards/thresholds (T2), while expectation-based indicators adopt a progress-ratio method incorporating baseline values before plan preparation (T0), current status (T1), and targets (T2). Three representative villages—Gaohuai (peri-urban integration), Sanlongchang (agglomeration and upgrading), and Lianmeng (characteristic protection)—are examined. Results show medium-to-high comprehensive scores (81–85) with pronounced type differences: Gaohuai ranks highest (85.37), whereas Sanlongchang is lowest (81.40), and Lianmeng is intermediate (83.71). Comparative diagnosis reveals shared bottlenecks driven by the superposition of “quota–space–ecological constraints”, alongside type-specific weaknesses requiring differentiated control strategies. The proposed framework offers a replicable, multi-source-data-oriented tool for implementation monitoring and adaptive policy adjustment. The novelty lies in reframing village plan implementation evaluation as implementation control effectiveness under a baseline-constrained planning system, while operationalizing a dual-path, unified-scale scoring scheme with a type-screenable indicator library for cross-type comparison and checklist-oriented diagnosis. Full article

This paper presents a multi-layer architecture designed to enhance the reliable autonomous flight of single and multiple quadcopters in simulation. The architecture leverages concepts inspired by the resilient spacecraft executive to hierarchically organize trajectory planning and flight control and integrates an extended Simplex framework that employs multiple candidate algorithms to provide safety assurance at each layer, with a supervisory program that adapts Simplex behavior based on system states and environmental conditions to enable high-level mission management. The approach is evaluated in bridge-inspection simulations under environmental uncertainties, including varying wind conditions and obstacles. Across multiple operating configurations and Monte Carlo simulation runs, the architecture achieves high coverage rates; notably, under high-wind conditions, it reduces average trajectory deviation by 66.2%. The results demonstrate proactive safety through graceful degradation in both trajectory planning and flight control under stress and off-nominal conditions. Full article

Driving style recognition plays a crucial role in improving the operational safety, fuel efficiency, and intelligent control of commercial vehicles. Under real-world driving conditions, Controller Area Network (CAN) bus data from commercial vehicles simultaneously contain rapid transient variations induced by pedal and braking operations, as well as long-term behavioral trends reflecting driving habits, exhibiting pronounced multi-temporal characteristics. In addition, such data are typically affected by high noise levels, high dimensionality, and highly variable operating conditions, which makes it difficult for methods relying on single-scale features or handcrafted rules difficult to maintain robust and stable performance in complex scenarios. To address these challenges, this paper proposes a driving style classification network, termed the Multi-Scale Convolution and Efficient Channel Attention Network (MSCA-Net). By employing parallel convolutional branches with different temporal receptive fields, the proposed network is able to capture fast driver responses, local temporal dependencies, and long-term behavioral evolution, enabling unified modeling of cross-scale temporal patterns in driving behavior. Meanwhile, the Efficient Channel Attention mechanism adaptively emphasizes CAN signal channels that are highly relevant to driving style discrimination, thereby enhancing the discriminative capability and robustness of the learned feature representations. Experiments conducted on real-world multi-dimensional CAN time-series data collected from commercial vehicles demonstrate that the proposed MSCA-Net achieves improved classification performance in driving style recognition. Furthermore, the potential application of the recognized driving styles in adaptive Automated Manual Transmission shift strategy adjustment is discussed, providing a feasible engineering pathway toward behavior-aware intelligent control of commercial vehicle powertrains. Full article

Multi-stack Polymer electrolyte Membrane Fuel Cell (PEMFC) systems are increasingly adopted in heavy-duty mobility to overcome the power limitations and thermal instability of single-stack configurations. However, the overall energy efficiency, hydrogen utilization, and thermal behavior of multi-stack fuel cell trucks are highly dependent on the applied Power Management System (PMS). In this study, high-fidelity, system-level dynamic model of multi-stack fuel cell truck was developed using Matlab/Simscape^TM, and three PMS approaches (rule-based control, state-machine control, and fuzzy logic control) were comparatively evaluated. The analysis includes coolant temperature regulation, hydrogen consumption, battery State of Charge (SoC) dynamics, and the parasitic power demand of Balance of Plant (BoP) components. Results show that the fuzzy logic PMS provides the most balanced operating profile by smoothing transient fuel cell loading and actively leveraging the battery during high-demand periods. In the thermal domain, the fuzzy logic PMS reduced temperature overshoot by up to 61.20%, demonstrating the most stable thermal control among the three strategies. Hydrogen consumption decreased by 3.08% and 0.89% compared with the rule-based and state-machine PMS, respectively, while parasitic power consumption decreased by 7.12% and 3.32%, confirming improvements in overall energy efficiency. TOPSIS-based multi-criteria decision analysis further showed that the fuzzy logic PMS achieved the highest closeness coefficient (0.9112), indicating superior system-level performance. These findings highlight the importance of PMS design for achieving energy-optimal and thermally stable operation of multi-stack PEMFC trucks and provide practical guidance for future control strategies, heavy-duty mobility applications, and next-generation hydrogen powertrain optimization. Full article

Common-mode voltage (CMV) is a critical concern in motor drive applications employing multilevel inverters, as it can lead to significant issues such as high-frequency noise, electromagnetic interference, and motor bearing degradation. These effects can compromise the reliability, reduce the operational lifespan of electric machines, and introduce safety hazards. In this study, an enhanced Direct Torque Control (DTC) strategy incorporating Space Vector Modulation (SVM) is proposed to specifically address CMV-related challenges in induction motors (IM) driven by a three-level Neutral-Point-Clamped (NPC) inverter. The proposed DTC scheme utilizes a specialized modulation technique that effectively mitigates CMV while also minimizing current harmonic content, and torque and flux ripples with a constant switching frequency. The developed SVM algorithm simplifies the three-level space vector representation into six equivalent two-level diagrams, enabling more efficient control. The zero-voltage vector is synthesized virtually by combining two active vectors within a two-level hexagonal structure. The effectiveness of the proposed DTC approach is validated through both simulation and Hardware-In-the-Loop (HIL) testing. Compared to the conventional DTC method, the proposed solution demonstrates superior performance in CMV minimization and leakage current reduction. Notably, it limits the CMV amplitude to V_dc/6, a significant improvement over the V_dc/2 typically observed with the standard DTC approach. Full article

Dysarthria in patients post-stroke is often accompanied by central facial paralysis, which impairs facial motor control and emotional expression. Current assessments rely on acoustic modalities, overlooking facial pathological cues and their correlation with emotional expression, which hinders comprehensive disease assessment. To address this issue, we propose a multimodal severity classification framework that integrates facial and acoustic features. Firstly, a multi-level annotation algorithm based on a pre-trained model and motion amplitude was designed to overcome the problem of data scarcity. Secondly, facial topology was modeled using Delaunay triangulation, with spatial relationships captured via graph convolutional networks (GCNs), while abnormal muscle coordination is quantified using facial action units (AUs). Finally, we proposed a multimodal feature set fusion technology framework to achieve the compensation of facial visual features for acoustic modalities and the analysis of disease classification. Our experimental results using the THE-POSSD dataset demonstrate an accuracy of 92.0% and an F1 score of 91.6%, significantly outperforming single-modality baselines. This study reveals the changes in facial movements and sensitive areas of patients under different emotional states, verifies the compensatory ability of visual patterns for auditory patterns, and demonstrates the potential of this multimodal framework for objective assessment and future clinical applications in speech disorders. Full article

China possesses 400 million hectares of grasslands that provide regulating ecosystem services (ESs), including wind erosion control, water conservation, and carbon sequestration. The central government implemented the Grassland Ecological Protection Subsidy and Reward Policy (GERCP) in 2011, allocating 150 billion yuan (approximately $23 billion) through 2020, while national vegetation coverage increased from 51.0% in 2011 to 56.1% in 2020. Existing valuation studies emphasize total economic value but rarely quantify the concentration of ES values across space or their alignment with fiscal allocation. We compiled 734 grassland ES valuation observations from 186 studies published between 2000 and 2024, and estimated a multi-level mixed-effects meta-regression model for benefit transfer. We projected standardized county-level ES values, decomposed spatial inequality using the Gini coefficient and Theil index, and assessed the mismatch between value-informed allocation weights and observed GERCP transfers. Predicted values exhibit high concentration (Gini coefficient = 0.58), and between-zone differences explain 52% of total Theil inequality. The mismatch analysis identifies 94 high-value and low-compensation counties concentrated in southern Qinghai and northern Tibet, where per-hectare values are 180 to 240% above national medians, and compensation is 35 to 55% below the median. The results support value-informed targeting and redistribution of fiscal weights across regions, while payment levels require pricing benchmarks based on opportunity cost or conservation cost rather than total economic value. We propose calibrating compensation rates through a tiered schedule based on ESV quantiles or standardized ecosystem-service bundles, and implementing county-level differentiated payments with periodic updating tied to monitoring and evaluation. As a minimum viable step, we recommend piloting this scheme in counties with high ESV yet low current compensation, and integrating it into existing ecological compensation funding channels to reduce administrative frictions. Full article

Peptide-based vaccines offer a modular and readily manufacturable platform for both prophylactic and therapeutic immunization. However, their broader translation has been constrained by the limited capacity to predict protective immunity directly from sequence-level features. Recent advances in systems vaccinology and high-throughput immune profiling have substantially expanded the experimental evidence, while generative artificial intelligence now enables de novo design of peptide immunogens and multi-epitope antigens under precisely controlled constraints. This review approaches how these complementary developments are transforming peptide vaccine research, moving beyond classical reverse vaccinology and conventional epitope prediction toward integrated, data-driven design frameworks. We discuss key generative model architectures and conditioning strategies aligned with vaccine objectives, including approaches that account for structural presentation, antigen processing and population-level human leukocyte antigen (HLA) diversity. Central to this perspective is the requirement for rigorous experimental validation and for strengthening the computational–experimental feedback loop through iterative in vitro and in vivo testing informed by systems-level immune readouts. We highlight representative applications spanning infectious diseases, cancer immunotherapy and vector-borne vaccinology, and we outline major technical and translational challenges that must be addressed to enable robust real-world deployment. Finally, we propose future directions for precision peptide vaccinology, emphasizing standardized functional benchmarks, the development of richer curated datasets linking sequence space to immune outcomes, and the early incorporation of formulation and delivery constraints into generative design pipelines. Full article