Search Results (17,249)

The mixed lymphocyte reaction (MLR) is a classic functional assay that models in vitro interactions between responder T cells and allogeneic antigen-presenting cells (APCs). It quantifies the magnitude and quality of alloreactivity, integrating signals from allorecognition, co-stimulation, inflammatory context, and minor histocompatibility antigens that may not be captured by molecular matching alone. This review is narrative in nature and is intended as a practical, non-systematic synthesis of the field. To provide a modern, practice-oriented synthesis of MLR designs, readouts, and translational uses, highlighting how new technologies have expanded MLR from bulk proliferation into multidimensional immune profiling.We summarize why MLR remains valuable as a functional compatibility probe beyond HLA typing, including the high baseline frequency of alloreactive T cells that produces robust signals without priming. We then review key design options (one-way vs. two-way formats; stimulator inactivation; responder definition; APC source and maturation) and how these choices affect interpretation for rejection and graft-versus-host disease risk modeling, tolerance-focused studies, and immunomodulatory screening. Next, we outline major readouts—radiometric and flow cytometric proliferation (dye dilution, Ki-67), cytokine/chemokine profiling, cytotoxicity adaptations, and next-generation add-ons (e.g., scRNA-seq, TCR sequencing)—emphasizing complementary strengths and common pitfalls. Finally, we consolidate practical quality and reproducibility controls (donor variability, dynamic range, timing, batch effects, and acceptance criteria) to improve cross-study comparability and translational readiness. Modern MLR platforms combine controllable allogeneic stimulation with scalable, high-resolution readouts for mechanistic discovery, immune monitoring and translational immune profiling. Standardized modular design and rigorous quality control can improve reproducibility and support broader adoption across transplantation, immunotherapy, and immune-modulation research. Full article

Additive Manufacturing (AM) and Wearable Technologies (WT) have rapidly evolved over the past decade. AM offers highly customisable fabrication, while WT enables minimally invasive health monitoring. The intersection of these fields presents emerging opportunities in biomedical and engineering domains. This study aims to map the scientific landscape of AM–WT research between 2015 and 2025 through a comprehensive bibliometric analysis. A total of 718 peer-reviewed publications were extracted from Web of Science (WoS), Scopus, and PubMed, following PRISMA-ScR guidelines. Using RStudio and the Bibliometrix package, analyses included co-authorship, citation trends, keyword co-occurrence, and thematic mapping. Custom author disambiguation scripts enhanced data quality and reliability. An annual publication growth of 24.89% was observed, with notable increases after 2020. Core themes included 3D printing, biosensors, microfluidics, and organ-on-a-chip devices. A shift from manufacturing-oriented research to biomedical integration is evident. Research output is dominated by the US, China, and South Korea, with moderate but not yet highly internationalised collaboration. The field of AM–WT research is undergoing a decisive transition from fabrication-focused studies to interdisciplinary, application-driven innovations. This shift is marked by increasing integration in healthcare and bioelectronics, yet hindered by regional imbalances and thematic gaps. Addressing these will be critical to advancing global impact. This study offers a cross-database bibliometric overview of AM–WT research. By combining three major data sources, it provides enhanced coverage and introduces novel analytical dimensions to guide future interdisciplinary efforts in personalised healthcare and wearable device innovation. Full article

Multi-point array thin-film thermocouples have strong potential for high-precision, wide-range temperature monitoring in applications such as aircraft engine thermal condition assessment and industrial process control. However, conventional single-point thin-film thermocouples cannot satisfy the distributed measurement requirements of large-area temperature fields, and the accuracy of multi-point arrays is often degraded by coupling effects among sensing nodes, which hinders their engineering deployment. In this work, a multi-point array thin-film thermocouple is fabricated via precision welding, and an insulating layer is deposited on the sensor surface using electrospray atomization to establish a multi-point temperature-sensing hardware system. To compensate for coupling-induced deviations, a deep learning–based calibration method is developed: measurements from the array and reference thermocouples are synchronously collected to build the dataset, outliers are removed using the interquartile range (IQR) method, and a three-hidden-layer multilayer perceptron (MLP) is trained for each node independently using the Adam optimizer (learning rate 0.001) with an 8:2 train–test split. Performance is quantified by MAE, MSE, and R², and the results show that the proposed approach markedly reduces measurement errors and improves the accuracy of the array thermocouples, demonstrating reliable performance and practical applicability for precise large-area temperature-field monitoring. Full article

Background/Objectives: FLASH radiotherapy (RT) has shown potential to reduce normal tissue toxicity compared with conventional (CONV) RT while maintaining tumor control. FLASH RT is characterized by ultra-high dose rate delivery, commonly using mean dose rates ≥ 40 Gy/s and sub-second delivery times. Most preclinical studies have used single-fraction regimens, leaving the feasibility and normal tissue impact of clinically relevant fractionation largely unexplored. We evaluated electron FLASH RT given in a standard five-fraction regimen to a porcine skin model, simulating adjuvant treatment workflow for high-risk cutaneous melanoma. Method: Three Yorkshire–Landrace swine received paired five-fraction electron irradiations to dorsolateral skin using either FLASH RT (mean dose rates 175–246 Gy/s) or CONV RT (8 Gy/min). Radiation was delivered with a 9-MeV electron beam; field diameters of 4, 7, or 10 cm; and doses of 5 × 6, 5 × 7, or 5 × 8 Gy. Dosimetry was validated with several dosimeters and real-time beam monitoring, confirming dose accuracy within 3%. Skin toxicity was assessed over 22–24 weeks using clinical grading, erythema spectrophotometry, and histopathologic evaluation. Results: FLASH RT was well tolerated at 5 × 6 Gy and 5 × 7 Gy, with no significant differences in peak radiation dermatitis, erythema index, or histologic damage compared with CONV RT. At 5 × 8 Gy, both modalities caused unacceptable toxicity, including moist desquamation and necrosis. No volume-dependent effects were observed. Conclusions: Although a FLASH-specific normal tissue sparing effect was not observed, this study demonstrates the technical feasibility and safety of delivering fractionated electron FLASH RT in a large animal model using a clinically relevant workflow. These findings support further investigation of physical beam parameters and biological modifiers, such as tissue oxygenation, and inform the clinical translation of fractionated FLASH RT for cutaneous malignancies. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

Metal additive manufacturing (AM) offers unprecedented opportunities to fabricate complex, lightweight metallic components, yet its practical deployment remains fundamentally constrained by defects arising from rapid melting and solidification. Cyclic thermal transients generate cracks, pores, residual stresses, and lack-of-fusion regions, undermining mechanical performance and reliability. Ultrasonic field-assisted laser-based additive manufacturing (UF-LBAM) has emerged as a powerful approach to manipulate melt pool dynamics and suppress defect formation. Nevertheless, the governing physical mechanisms remain poorly understood, particularly under highly non-equilibrium ultrasonic excitation, where acoustic pressure oscillations, melt convection, cavitation, and solidification are intricately coupled across multiple temporal and spatial scales. Here, we provide a systematic review of X-ray based fundamental studies in UF-LBAM and the diverse applications of machine learning (ML), detailing the literature selection criteria and methodology. We highlight advances spanning synchrotron X-ray revealed physical phenomena, ML-driven real-time monitoring and defect prediction, and pathways toward industrial implementation. Critical challenges persist, including fundamental physics gaps, transferability of ML models across alloy systems, and real-time control limitations. We further identify promising directions for the field, such as physics-informed models, multimodal diagnostics, and closed-loop control, which together promise to unlock the full potential of UF-LBAM for high-performance metal component fabrication. Full article

Multistep-ahead prediction of critical states in the iron ore sintering process is essential for maintaining production stability, enhancing energy efficiency, and reducing industrial emissions. However, large time delays, strong coupling, and condition drifts challenge existing spatiotemporal graph neural networks (STGNNs). This paper proposes Process-aware Mixed Dynamic Graph Network (ProMix-DGNet), which integrates a Decoupled Two-Stream Topology Learning mechanism—fusing Adaptive Static Graph with a Radial Basis Function (RBF)-driven Dynamic Graph Constructor—to ensure robust spatial modeling under high-noise conditions. Furthermore, Process-View Global Mixer explicitly captures long-range process coupling across the entire sintering strand, overcoming the receptive field limitations of traditional graph convolutions. In the decoding phase, a future control-informed module utilizes a bidirectional Long Short-Term Memory (BiLSTM) and a global mixer to align known future control setpoints with the system’s spatial topology. These features are integrated via a gated residual mechanism that dynamically modulates the interaction between control intents and historical representations. Extensive experiments conducted on two real-world industrial datasets, Sinter-A and Sinter-B, demonstrate that ProMix-DGNet consistently outperforms mainstream baselines across multiple metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results verify the model’s higher accuracy and robustness in complex large-time-delay systems, offering a reliable framework for the intelligent monitoring and closed-loop optimization of sintering process. Full article

In the field of orthopedic surgery, particularly distraction osteogenesis (DO), the mechanical environment plays a decisive role in the quality of bone regeneration and the safety of the soft tissue envelope. The continuous monitoring and accurate prediction of distraction resisting forces (DRF) are critical for preventing soft tissue complications such as nerve ischemia, joint contractures, and mechanical failure of the lengthening device. However, current clinical practice relies heavily on subjective assessment or passive monitoring tools that lack predictive capabilities. To address this gap, this study proposes a comprehensive solution combining a custom mechanical acquisition system with a high-fidelity finite element (FE) prediction method. The system design features a novel “double-ring” sensor interface specifically engineered to decouple axial distraction forces from parasitic bending moments generated by asymmetric muscle tension. Furthermore, a patient-specific FE model utilizing the Ogden hyperelastic constitutive law was derived, explicitly based on the patient’s muscle volume from preoperative CT imaging, to predict the non-linear force evolution. The feasibility and accuracy of the system were validated in a pilot in vivo study using a single ovine model ($N=1$). To isolate the soft tissue resistance from callus formation, distraction was performed immediately postoperatively up to a total length of 4 cm. Experimental results demonstrated the system’s high linearity ($R^2>0.999$) and its ability to capture the characteristic viscoelastic relaxation of living tissues. The FE model successfully predicted the peak distraction forces, showing improved agreement with experimental data at larger distraction magnitudes. By integrating mechanical sensing with predictive modeling, this framework lays the foundation for future closed-loop, patient-specific control in distraction osteogenesis. Full article

Standing tile inlets are commonly used to drain unwanted surface water from croplands but can exacerbate pollution by facilitating the transport of nutrients to waterways. Blind inlets have increasingly been viewed as a beneficial alternative to standing inlets since they control erosion and capture particulate nutrients. However, conventional blind inlets do little to limit dissolved nutrient transport, and modified blind inlet (MBI) designs have been proposed that incorporate woodchips—a medium that facilitates denitrification. While initial investigations have highlighted MBIs’ remediation potential, their ability to meet prescribed drainage standards has not been well-documented. In this study, we designed and installed MBIs composed of pea gravel and woodchips in two eastern Iowa fields under row crop cultivation. Flow and nitrate were continuously monitored using in situ equipment directly downstream of the MBIs (February 2023–June 2025). Observed flows were very ephemeral, consisting of ~25 distinct events at both sites, with no flow recorded in between. During several wet weather events, flow rates exceeded the MBIs’ design requirements, confirming their sufficient drainage capacity to prevent in-field ponding. Nitrate concentrations varied considerably, with long-term averages of 11.6 and 19.1 mg/L and overall loadings of 4.94 and 7.10 kg during our 28-month study. We also measured phosphate and sulfate during select wet weather events, and discrepancies in concentrations between inlets and outlets suggested that groundwater was often present alongside surficial drainage in our monitoring setup. We believe our results argue for increased adoption of MBIs in conservation and further quantification of their remediation capabilities. Full article

Background/Objectives: Intravitreal injections (IVIs) are increasingly performed in outpatient settings, raising concerns regarding how to guarantee operating-theatre-level environmental safety. Mobile laminar airflow (LAF) units may create an ultraclean instrument field, but microbiological evidence from real-world IVI clinics is limited. Methods: We performed environmental monitoring during three IVI sessions, each including approximately 20 injections per session, in an outpatient procedure room equipped with a mobile LAF device (Operio Toul Mobile). Airborne microbial contamination was measured with a SAS Super 100 impactor (1 m³ per sample) at two locations, the procedure-room air and the LAF field, across seven predefined time points (T₋₁to T₅). Surface contamination of the instrument-covering drape was assessed at mid- and end-session using 24 cm² contact plates on four culture media. Colonies were expressed as CFU/m³ or CFU/24 cm² and analysed using a two-way repeated-measures ANOVA (location × time), with Holm-adjusted within-session paired post hoc comparisons at each time point. Results: During LAF operation (T₀–T₄), mean airborne load was 89.8 ± 10.8 CFU/m³ in room air versus 10.9 ± 4.6 CFU/m³ under LAF, corresponding to an 87.9% mean reduction (Holm-adjusted p < 0.01). At T₋₁ and T₅ (LAF off), counts were not significantly different between locations. Airborne microbial species consisted mainly of skin/oral commensals; no obligate pathogens were detected. All 24 drape samples showed 0 CFU. Conclusions: In this high-throughput outpatient IVI clinic, the mobile LAF device maintained a stable ultraclean microenvironment at the instrument field despite moderate background room contamination, supporting its use as an adjunct to standard aseptic measures, without the need to change the covering drape during the session. Full article

Accurate flood detection is critical for disaster response, yet the scarcity of diverse annotated datasets hinders robust model development. Existing resources typically suffer from limited geographic scope and insufficient annotation granularity, restricting the generalization capabilities of computer vision methods. To bridge this gap, we introduce AIFloodSense, a comprehensive evaluation benchmark designed to advance domain-generalized Artificial Intelligence for climate resilience. The dataset comprises 470 high-resolution aerial images capturing 230 distinct flood events across 64 countries and six continents. Unlike prior benchmarks, AIFloodSense ensures exceptional global diversity and temporal relevance (2022–2024), supporting three complementary tasks: (i) Image Classification, featuring novel sub-tasks for environment type, camera angle, and continent recognition; (ii) Semantic Segmentation, providing precise pixel-level masks for flood, sky, buildings, and background; and (iii) Visual Question Answering (VQA), enabling natural language reasoning for disaster assessment. We provide baseline benchmarks for all tasks using state-of-the-art architectures, demonstrating the dataset’s complexity and its utility in fostering robust AI tools for environmental monitoring. Crucially, we show that despite its compact size, AIFloodSense enables better generalization on external test sets than much larger alternatives, validating the premise that rigorous diversity is more effective than scale for training robust flood detection models, and is made publicly available to accelerate further research in the field. Full article

Climate change is increasingly affecting groundwater resources in the Carpathian Basin, while rising temperatures are likely to increase irrigation demand and pressure on aquifers. We assessed climate- and pumping-driven impacts on the Nyírség recharge–discharge system (north-eastern Hungary) by combining shallow groundwater monitoring (1970–2022) with hydroclimate indicators from CHIRPS precipitation and ERA5-Land air temperature and snow depth (1981–2024). Using these datasets, we developed and calibrated a MODFLOW groundwater-flow model for representative wet (2010) and dry (2022) conditions, incorporating permitted abstraction and scenario-based estimates of unregistered pumping. We then ran scenario simulations to evaluate mid-century (2050) conditions and managed aquifer recharge (MAR) options. Precipitation exhibits strong interannual variability, but the region shows marked warming and a pronounced decline in snow storage, implying reduced cold-season buffering and higher evaporative demand. Simulations reproduce the observed post-2010 decline in shallow groundwater, with the largest decreases in higher-elevation recharge areas, whereas increased pumping mainly intensifies localized drawdown near major well fields. Scenario results indicate that climate-driven reductions in recharge dominate basin-scale declines by 2050, while MAR provides primarily local benefits; direct subsurface injection performs best among the tested options. These findings support practical groundwater management by prioritizing measurable and enforceable abstraction (including unregistered withdrawals), demand-side irrigation efficiency and adaptive caps in recharge areas, and targeted subsurface MAR where source water and infrastructure are available. Full article

Shot-peening treatments improve the fatigue performance of mechanical components thanks to the surface modifications introduced and mainly due to the residual compressive stresses present in the layer of material near the shot-peened surface. There is no unanimous agreement in scientific literature regarding the kinetics of the damage process. However, it is generally accepted that, due to morphological and microstructural changes in the shot-peened layer, the material is more prone to early crack initiation, the propagation of which is then significantly slowed down or even stopped by the local stress field. This work focuses on applying the acoustic emission (AE) technique to detect fatigue crack initiation and propagation in shot-peened Al-alloy components. The analysis is conducted on Al-7075-T6 alloy, subjected to different shot-peening conditions and fatigue tested under alternating four-point bending. The results from the AE analyses are then correlated with a fractographic analysis. For all shot-peening conditions investigated, acoustic emission consistently indicated probable crack nucleation at approximately two-thirds of the total fatigue life, followed by a significant damage accumulation phase prior to dominant crack propagation. The final increase in acoustic activity coincided with the measurable loss of stiffness, confirming the onset of accelerated crack growth leading to fracture. The results demonstrate that, despite some experimental challenges, AE monitoring has the potential for the early detection of damage initiation. Full article

Current studies on fatigue crack initiation in pipelines remain relatively limited. Existing frameworks are confronted with issues including difficulties in crack monitoring and limited consideration of intragranular short-crack propagation. To address these issues, a framework was proposed for predicting fatigue crack initiation life in pipelines with girth welds. The proposed framework incorporates full-scale testing, temperature field simulation and microstructural evolution analysis to overcome limitations in crack measurement and microstructural characterization. In addition, intragranular short-crack propagation has been taken into account in the proposed framework. The proposed framework predicts the fatigue crack initiation life through multiscale coupling. Agreement between the prediction and experimental results supports the validity of the proposed framework. The framework provides reliable predictions of fatigue crack initiation life for pipelines with girth welds under high-cycle fatigue (HCF) conditions. Full article

To address challenges related to complex background interference and insufficient multi-scale target feature extraction in lettuce growth stage detection. The lightweight YOLOv10 detection model and the specific characteristics of lettuce field data were used. The CNCM channel non-local mixture mechanism and ASF adaptive spatial frequency attention mechanism were incorporated to optimize lightweight modules, including DownSample, Zoom_cat, and ScalSeq, within the original model. Consequently, an improved CCASF-YOLOv10 model was constructed, integrating multi-scale feature fusion and enhanced target feature extraction. Experimental results demonstrate that, in an NVIDIA A40 GPU testing environment, the model achieves an accuracy rate of 91.9%, a recall rate of 91.6%, mAP@0.5 of 95.3%, and mAP@0.5:0.95 of 72.9%. The parameter size is 11.9 M, and the single-frame inference speed is 24.76 ms, indicating a favorable balance between detection precision, model efficiency, and real-time inference. Furthermore, an intelligent machine vision detection system for lettuce growth-stage monitoring and precise field control was developed using the CCASF-YOLOv10 model. This system facilitates the industrial advancement of lettuce cultivation. Full article