Search Results (946)

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

In this study, the effects of post-weld heat treatment (PWHT) on residual stress distribution and fatigue crack propagation (FCP) behavior in linear friction welded (LFW) Ti-6Al-4V joints were investigated. Microstructural evolution in the weld center zone (WCZ), thermomechanically affected zone (TMAZ), heat-affected zone (HAZ), and base metal (BM) was characterized using scanning electron microscropy (SEM) and electron backscatter diffraction (EBSD). Mechanical properties were evaluated via Vickers hardness testing and digital image correlation (DIC)-based tensile testing. Residual stresses before and after PWHT were measured using the contour method. The LFW process introduced significant residual stresses, with tensile stresses up to 709.2 MPa in the WCZ, resulting in non-uniform fatigue crack growth behavior. PWHT at 650 °C and 750 °C effectively reduced these stresses. After PWHT, fatigue cracks propagated uniformly across the weld region, enabling reliable determination of crack growth rates. The average crack growth rates of the heat-treated specimens were comparable to those of the base metal, confirming that PWHT, particularly at 750 °C, stabilizes the fatigue crack path and relieves internal stress. Full article

To elucidate the modification mechanism of waterborne polyurethane (WPU) on emulsified asphalt, anionic and cationic WPUs are utilized as modifiers. As well, their effects on physical properties, microstructure, and compatibility are characterized using basic performance tests, Fourier transform infrared spectroscopy (FTIR), and atomic force microscopy (AFM). The results show that WPU-modified emulsified asphalt exhibited a higher softening point, reduced penetration, and decreased ductility, suggesting enhanced high-temperature stability but diminished low-temperature flexibility. Among all samples, the combination of cationic WPU with cationic emulsified asphalt shows the highest softening point (54.1 °C), whereas cationic emulsified asphalt alone exhibits the lowest one (52.9 °C). Anionic emulsified asphalt demonstrates the highest penetration (79 mm), while non-ionic WPU combined with cationic emulsified asphalt shows the lowest one (59.3 mm). The ductility decreases from 90.3 cm to 28.7 cm. The storage stability varies with WPU ion type. Cationic WPU-modified samples showed the poorest storage stability (0.7% residue), while anionic-modified samples exhibit the best one (0.4% residue). FTIR analysis confirms the presence of characteristic WPU absorption peaks, indicating that physical blending occurs, and chemical interaction is limited. AFM observations reveal that anionic WPUs provide superior compatibility, forming fine, uniformly distributed particles with the lowest surface roughness (5.655 nm). In contrast, cationic WPUs form chain-like structures that cure effectively but exhibit poor dispersion. This study provides a basis for the development of high-performance WPU-modified emulsified asphalt. Full article

Serverless computing has gained significant attention in recent years. However, the cold start problem remains a major challenge, not only because of the substantial latency it introduces to function execution time, but also because frequent cold starts lead to poor resource utilization, especially during workload fluctuations. To address these issues, we propose a multi-level scheduling solution: the Hybrid Model. This model is designed to reduce the frequency of cold starts while maximizing container utilization. At the global level (across invokers), the Hybrid Model employs a skewness-aware scheduling strategy to select the most appropriate invoker for each request. Within each invoker, we introduce a greedy buffer-aware scheduling method that leverages the available slack (remaining buffer) of warm containers to aggressively encourage their reuse. Both the global and the local schedule are tightly integrated with a prediction component- The Hybrid Predictor- that combines Auto-Regressive Integrated Moving Average ARIMA (linear trends) and Random Forest (non-linear residuals + environment-aware features) for 5-min workload forecasts. The Hybrid Model is implemented on Apache OpenWhisk and evaluated using Azure-like traces and real FaaS applications. The evaluations show that the Hybrid Model achieves up to 34% SLA violation reductions compared to three state-of-the-art approaches and maintains the container utilization to be more than 80%. Full article

In Global Navigation Satellite System (GNSS)-denied environments, opportunistic positioning using non-cooperative Low Earth Orbit (LEO) satellite signals has shown strong potential. However, dynamic platforms face challenges in maintaining sufficient satellite counts and favorable geometric distributions due to limited signal quality and short observation windows. To address this, we propose a fast satellite selection algorithm based on the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) for dynamic, multi-constellation LEO opportunistic navigation. By introducing Pareto non-dominated solutions, the algorithm balances Doppler Geometric Dilution of Precision (DGDOP), signal strength, residual visibility time, and receiver sensitivity. Through iterative optimization, it constructs a subset of satellites with minimal DGDOP while reducing computational burden, enabling real-time fusion and switching at the receiver end. We validate the algorithm through UAV-based experiments in dynamic scenarios. Compared to GWO, PSO, and NSGA-II, the proposed method achieves computation time reductions of 27.06%, 27.05%, and 68.57%, respectively. It also reduces the overall navigation solution time to 54.96% of that required when using all visible satellites, significantly enhancing real-time responsiveness and system robustness. These results demonstrate that the NSWOA-based satellite selection algorithm outperforms existing intelligent methods in both computational efficiency and optimization accuracy, making it well-suited for real-time, multi-constellation LEO dynamic opportunistic navigation. Full article

The processing of metallurgical slags is an urgent task, as they contain residual amounts of precious and non-ferrous metals such as gold, silver, copper and zinc. The efficiency of extraction of these metals directly depends on the granulometric composition of the processed material, which determines the need for its detailed analysis. The purpose of this study is to analyze the effect of the granulometric composition of slags on the efficiency of extraction of non-ferrous metals using the flotation method. For this purpose, studies were carried out, including granulometric analysis, chemical composition analysis and flotation tests using Na₂S, KAX and 3418A reagents. The analysis showed that the main part of the slag consisted of particles less than 3.36 mm, while the content of copper was 0.60%, zinc was 2.37%, gold was 0.1 g/t and silver was 7.2 g/t. Flotation experiments confirmed that the use of Na₂S and 3418A increased the recoverability of copper and zinc, and reducing the particle size to d80 <10 microns increased the efficiency of copper extraction by 7%. Thus, the optimization of flotation processes and the control of granulometric composition make it possible to increase the efficiency of metallurgical waste processing, reduce losses of valuable metals and reduce the environmental burden. Full article

Bananas, one of the most widely consumed tropical fruits in the world, are susceptible to attack by the anthracnose fungus Colletotrichum musae during the post-harvest period. Currently, fungus control is generally based on the use of chemical products, often applied a few days before harvest, which could lead to a risk of residues in the fruit, thus creating a high demand for fresh and organic fruits. Therefore, essential oils present an emerging alternative for the treatment of anthracnose. Here, we evaluated the chemical composition and potential of Morinda citrifolia essential oil as a preventive and curative measure to control C. musae in bananas, also considering the quality of the fruit. In addition, computational docking analysis was conducted to predict potential molecular interactions between octanoic and butanoic acids and the enzyme Tyrosine tRNA, as a potential target for the M. citrifolia essential oil fungicide actions. We also evaluated the essential oil’s safety for beneficial organisms such as the fungus Trichoderma asperellum and the ladybugs Eriopis connexa Germar and Coleomegilla maculata DeGeer. Initially, in vitro growth inhibition tests were performed with doses of 10.0, 30.0, and 50.0 µL/mL of M. citrifolia essential oil, as well as an assessment of the phytotoxic effects on the fruit. Subsequently, using non-phytotoxic doses, we evaluated the effect of the essential oil as a preventive and curative measure against anthracnose and its impact on fruit quality. Our results showed that octanoic, butanoic, and hexanoic acids were the major compounds in M. citrifolia essential oil, inhibiting the growth of C. musae by interacting with the Tyrosine tRNA enzyme of C. musae. The non-phytotoxic dose on the fruit was 10 µL/mL of noni essential oil, which reduced C. musae growth by 30% when applied preventively and by approximately 25% when applied as a curative measure. This significantly reduced the Area Under the Disease Progress Curve without affecting the fruit weight, although there was a slight reduction in °Brix. The growth of non-target organisms, such as T. asperellum and the insect predators Co. maculata and E. connexa, was not affected. Collectively, our findings suggest that M. citrifolia essential oil is a promising alternative for the prevention and control of anthracnose in banana fruit caused by C. musae, without adversely affecting its organoleptic characteristics or non-target organisms. Full article

The global population is rising at an alarming rate and is projected to reach 10 billion by 2050, necessitating a substantial increase in food production. However, the overuse of chemical pesticides, including antibacterial agents and synthetic fertilizers, poses a major threat to sustainable agriculture. This review examines the ecological and health impacts of antibacterial agents (e.g., streptomycin, oxytetracycline, etc.) in horticultural crops, focusing on their effects on non-target organisms such as beneficial microbes involved in plant growth promotion and resistance development. Certain agents (e.g., triclosan, sulfonamides, and fluoroquinolones) leach into water systems, degrading water quality, while others leave toxic residues in crops, leading to human health risks like dysbiosis and antibiotic resistance. To mitigate these hazards, sustainable alternatives such as integrated plant disease management (IPDM) and biotechnological solutions are essential. Advances in genetic engineering including resistance-conferring genes like EFR1/EFR2 (Arabidopsis), Bs2 (pepper), and Pto (tomato) help combat pathogens such as Ralstonia solanacearum and Xanthomonas campestris. Additionally, CRISPR-Cas9 enables precise genome editing to enhance inherent disease resistance in crops. Emerging strategies like biological control, plant-growth-promoting rhizobacteria (PGPRs), and nanotechnology further reduce dependency on chemical antibacterial agents. This review highlights the urgent need for sustainable disease management to safeguard ecosystem and human health while ensuring food security. Full article

Carbonaceous–siliceous–argillaceous rock-type uranium deposits, a major uranium resource in China, pose significant environmental risks due to heavy metal contamination. Geochemical investigations in the former Zoige uranium mine revealed elevated As, Cd, Cr, Cu, Ni, U, and Zn concentrations in soils and sediments, particularly at river confluences and downstream regions, attributed to leachate migration from ore bodies and tailings ponds. Surface samples exhibited high Cd bioavailability. The integrated BCR and mineral analysis reveals that Acid-soluble and reducible fractions of Ni, Cu, Zn, As, and Pb are governed by carbonate dissolution and Fe-Mn oxide dynamics via silicate weathering, while residual and oxidizable fractions show weak mineral-phase dependencies. Positive Matrix Factorization identified natural lithogenic, anthropogenic–natural composite, mining-related sources. Pollution assessments using geo-accumulation index and contamination factor demonstrated severe contamination disparities: soils showed extreme Cd pollution, moderate U, As, Zn contamination, and no Cr, Pb pollution (overall moderate risk); sediments exhibited extreme Cd pollution, moderate Ni, Zn, U levels, and negligible Cr, Pb impacts (overall extreme risk). USEPA health risk models indicated notable non-carcinogenic (higher in adults) and carcinogenic risks (higher in children) for both age groups. Ecological risk assessments categorized As, Cr, Cu, Ni, Pb, and Zn as low risk, contrasting with Cd (extremely high risk) and sediment-bound U (high risk). These findings underscore mining legacy as a critical environmental stressor and highlight the necessity for multi-source pollution mitigation strategies. Full article

We present a semi-automatic framework for transcribing foreign personal names into Lithuanian, aimed at reducing pronunciation errors in text-to-speech systems. Focusing on noisy, web-crawled data, the pipeline combines rule-based filtering, morphological normalization, and manual stress annotation—the only non-automated step—to generate training data for character-level transcription models. We evaluate three approaches: a weighted finite-state transducer (WFST), an LSTM-based sequence-to-sequence model with attention, and a Transformer model optimized for character transduction. Results show that word-pair models outperform single-word models, with the Transformer achieving the best performance (19.04% WER) on a cleaned and augmented dataset. Data augmentation via word order reversal proved effective, while combining single-word and word-pair training offered limited gains. Despite filtering, residual noise persists, with 54% of outputs showing some error, though only 11% were perceptually significant. Full article

To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

Horse iris recognition is a non-invasive identification method with great potential for precise management in intelligent horse farms. However, horses’ natural vigilance often leads to stress and resistance when exposed to close-range infrared cameras. This behavior makes it challenging to capture clear iris images, thereby reducing recognition performance. This paper addresses the challenge of generating high-resolution iris images from existing low-resolution counterparts. To this end, we propose a novel hybrid-architecture image super-resolution (SR) network. Central to our approach is the design of Paired Asymmetric Transformer Block (PATB), which incorporates Contextual Query Generator (CQG) to efficiently capture contextual information and model global feature interactions. Furthermore, we introduce an Efficient Residual Dense Block (ERDB), specifically engineered to effectively extract finer-grained local features inherent in the image data. By integrating PATB and ERDB, our network achieves superior fusion of global contextual awareness and local detail information, thereby significantly enhancing the reconstruction quality of horse iris images. Experimental evaluations on our self-constructed dataset of horse irises demonstrate the effectiveness of the proposed method. In terms of standard image quality metrics, it achieves the PSNR of 30.5988 dB and SSIM of 0.8552. Moreover, in terms of identity-recognition performance, the method achieves Precision, Recall, and F1-Score of 81.48%, 74.38%, and 77.77%, respectively. This study provides a useful contribution to digital horse farm management and supports the ongoing development of smart animal husbandry. Full article

Background/Objectives: Post-keratoplasty astigmatism can limit visual recovery even after successful corneal transplantation. Femtosecond laser-assisted arcuate keratotomy (FSAK) has emerged as a method to reduce high residual astigmatism and enhance visual outcomes. This study aimed to evaluate the outcome of FSAK in treating astigmatism following keratoplasty. Methods: This retrospective study included 32 eyes from 31 patients who underwent FSAK after keratoplasty. Inclusion required complete suture removal, regular corneal topography, and the absence of additional ocular pathology or prior intraocular surgery. Data collected included uncorrected (UCVA) and best-spectacle-corrected visual acuity (BSCVA), manifest refraction, and tomographic parameters. The primary outcomes were changes in visual, refractive, and tomographic measures across the entire cohort, with further subgroup analysis between penetrating keratoplasty (PKP) and lamellar keratoplasty (LKP) eyes. Secondary outcomes were documentation of complications. Results: UCVA improved significantly from 0.92 ± 0.33 to 0.58 ± 0.39 LogMAR (p < 0.001). BSCVA showed a non-significant trend toward improvement from 0.32 ± 0.21 to 0.26 ± 0.22 LogMAR (p = 0.158). The manifest cylinder reduced significantly from −6.15 ± 2.75 D to −4.49 ± 2.92 D (p = 0.037). Corneal topography revealed significant postoperative steepening in keratometric values. While overall outcomes were comparable between the subgroups, LKP eyes demonstrated a greater myopic shift and a higher rate of overcorrection, whereas PKP eyes tended toward undercorrection. Conclusions: FSAK appears to be an effective approach for reducing post-keratoplasty astigmatism and improving uncorrected visual acuity. Given the biomechanical differences between graft types, individualized treatment planning based on graft characteristics may enhance surgical predictability and optimize outcomes. Full article

Sparse borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. We propose an adaptive graph convolutional network with spatial autocorrelation (ASI-GCN) model to overcome this challenge. The ASI-GCN model effectively constrains pollutant concentration transfer while capturing subtle spatial variations, improving soil pollution characterization accuracy. We tested our model at a coking plant using 215 soil samples from 15 boreholes, evaluating its robustness with three pollutants of varying volatility: arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). Leave-one-out cross-validation demonstrates that the ASI-GCN_RC_G model (ASI-GCN with residual connections) achieves the highest prediction accuracy. Specifically, the R for As, BaP, and Ben are 0.728, 0.825, and 0.781, respectively, outperforming traditional models by 58.8% (vs. IDW), 45.82% (vs. OK), and 53.78% (vs. IDW). Meanwhile, their RMSE drop by 36.56% (vs. Bayesian_K), 38.02% (vs. Bayesian_K), and 35.96% (vs. IDW), further confirming the model’s superior precision. Beyond accuracy, Monte Carlo uncertainty analysis reveals that most predicted areas exhibit low uncertainty, with only a few high-pollution hotspots exhibiting relatively high uncertainty. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, and semi-volatile BaP concentrated in the silty sand layer. At the same time, volatile Ben was predominantly found in the clay and fine sand layers. By integrating spatial autocorrelation with deep graph representation, ASI-GCN redefines sparse data 3D mapping, offering a transformative tool for precise environmental governance and human health assessment. Full article