Search Results (804)

Background: It is still challenging to develop effective vaccines against tumorigenic human gammaherpesviruses such as Epstein–Barr virus (EBV). A major obstacle is the lack of a small animal model that reproduces the natural infection course of human gammaherpesviruses to allow for proper assessment of vaccine efficacy. Murine gammaherpesvirus 68 (MHV68) is a natural pathogen of wild rodents and laboratory mice and therefore can be used as a surrogate for human gammaherpesviruses to evaluate vaccination strategies. Methods: In this study, two mRNA vaccine candidates were generated, one encoding a fusion protein of the MHV68 gH with the gL (gHgL-mRNA) and the other expressing the MHV68 gB protein (gB-mRNA). The immunogenicity and protective efficacy of the mRNA vaccine candidates were evaluated in a mouse model of MHV68 infection. Results: The gHgL-mRNA but not the gB-mRNA candidate vaccine was able to induce neutralizing antibodies in mice, whereas both vaccines could elicit antigen-specific T-cell responses. Following MHV68 intranasal inoculation, complete blocking of the establishment of viral latency was observed in some mice immunized with individual gHgL-mRNA or gB-mRNA vaccines. Notably, co-immunization with the two mRNA vaccines appeared to be more effective than individual vaccines, achieving sterile immunity in 50% of the vaccinated mice. Conclusions: This study demonstrates that immunization with mRNA platform-based subunit vaccines is indeed capable of preventing MHV68 latent infection, thus validating a safe and efficacious vaccination strategy that may be applicable to human gammaherpesviruses. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

Space-based gravitational wave detectors, such as the Laser Interferometer Space Antenna (LISA) and Taiji, will observe GWs from $O\left({10}^8\right)$ galactic binary systems, allowing a completely unobscured view of the Milky Way structure. While previous studies have established theoretical expectations based on idealized data-analysis methods that use the true catalog of sources, we present an end-to-end analysis pipeline for inferring galactic structure parameters based on the detector output alone. We employ the GBSIEVER algorithm to extract GB signals from LISA Data Challenge data and develop a maximum likelihood approach to estimate a bulge-disk galactic model using the resolved GBs. We introduce a two-tiered selection methodology, combining frequency derivative thresholding and proximity criteria, to address the systematic overestimation of frequency derivatives that compromises distance measurements. We quantify the performance of our pipeline in recovering key Galactic structure parameters and the potential biases introduced by neglecting the errors in estimating the parameters of individual GBs. Our methodology represents a step forward in developing practical techniques that bridge the gap between theoretical possibilities and observational implementation. Full article

Blasting is an effective technique for fragmenting rock in open-pit mining operations. Blasting operations produce either boulders or fine fragments, both of which increase costs and pose environmental risks. As a result, predicting the mean fragmentation size (MFS) distribution of rock is critical for assessing blasting operations’ quality and mitigating risks. Due to the limitations of empirical and statistical models, several researchers are turning to artificial intelligence (AI)-based techniques to predict the MFS distribution of rock. Thus, this study uses three AI tree-based algorithms—extra trees (ET), gradient boosting (GB), and random forest (RF)—to predict the MFS distribution of rock. The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. The dataset used in this study was collected from the published literature and through the data augmentation of a large-scale dataset of 3740 blast samples. Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. Lastly, an interactive web application has been developed to assist engineers with the timely prediction of MFS. The predictive model developed in this study is a reliable intelligent model because it combines high accuracy with a strong, explainable AI tool for predicting MFS. Full article

Accurate and real-time detection of rice shelling rate (SR) and brown rice breakage rate (BR) is crucial for intelligent hulling sorting but remains challenging because of small grain size, dense adhesion, and uneven illumination causing missed detections and blurred boundaries in traditional YOLOv8n. This paper proposes a high-precision, lightweight solution based on an enhanced YOLOv8n with improvements in network architecture, feature fusion, and attention mechanism. The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. A bidirectional feature pyramid network (BiFPN) weights multiscale feature fusion to improve edge positioning accuracy of dense grains. Attention mechanism for fine-grained classification (AFGC) is embedded to focus on texture and damage details, enhancing adaptability to light fluctuations. The Detect_Rice lightweight head compresses parameters via group normalization and dynamic convolution sharing, optimizing small-target response. The improved model achieved 96.8% precision and 96.2% mAP. Combined with a quantity–mass model, SR/BR detection errors reduced to 1.11% and 1.24%, meeting national standard (GB/T 29898-2013) requirements, providing an effective real-time solution for intelligent hulling sorting. Full article

Encryption and decryption are essential components in signal processing and optical communication systems, providing data confidentiality, integrity, and secure high-speed transmission. We present a novel design and simulation of an all-optical encryption and decryption system operating at 120 Gb/s using carrier reservoir semiconductor optical amplifiers (CR-SOAs) embedded in Mach–Zehnder interferometers (MZIs). The architecture relies on two consecutive exclusive-OR (XOR) logic gates, implemented through phase-sensitive interference in the CR-SOA-MZI structure. The first XOR gate performs encryption by combining the input data signal with a secure optical key, while the second gate decrypts the encoded signal using the same key. The fast gain recovery and efficient carrier dynamics of CR-SOAs enable a high-speed, low-latency operation suitable for modern photonic networks. The system is modeled and simulated using Mathematica Wolfram, and the output quality factors of the encrypted and decrypted signals are found to be 28.57 and 14.48, respectively, confirming excellent signal integrity and logic performance. The influence of key operating parameters, including the impact of amplified spontaneous emission noise, on system behavior is also examined. This work highlights the potential of CR-SOA-MZI-based designs for scalable, ultrafast, and energy-efficient all-optical security applications. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

To enhance the environmental sustainability and structural performance of concrete-filled steel tubes (CFSTs), this study experimentally investigates the eccentric compression behavior of short CFST columns incorporating alkali-activated concrete (AAC) and internal stiffeners. Fifteen specimens were tested, varying in steel tube thickness, stiffener thickness, and eccentricity. The results show that increasing eccentricity reduces load-bearing capacity and stiffness, while stiffeners delay local buckling and improve stability. Based on the experimental findings, the applicability of the GB 50936-2014, Design of Steel and Composite Structures Specification, and the American AISC-LRFD specification to the design of ACFST columns is further evaluated. Corresponding design recommendations are proposed, and a regression-based predictive model for eccentric bearing capacity is developed, showing good agreement with the test results, with prediction errors within 10%.providing technical references for the development of low-carbon, high-performance CFST members. Full article

Forecasting future solar power plant production is essential to continue the development of photovoltaic energy and increase its share in the energy mix for a more sustainable future. Accurate solar radiation forecasting greatly improves the balance maintenance between energy supply and demand and grid management performance. This study assesses the influence of input selection on short-term global horizontal irradiance (GHI) forecasting across two contrasting Algerian climates: arid Ghardaïa and coastal Algiers. Eight feature selection methods (Pearson, Spearman, Mutual Information (MI), LASSO, SHAP (GB and RF), and RFE (GB and RF)) are evaluated using a Gradient Boosting model over horizons from one to six hours ahead. Input relevance depends on both the location and forecast horizon. At t+1, MI achieves the best results in Ghardaïa (nMAE = 6.44%), while LASSO performs best in Algiers (nMAE = 10.82%). At t+6, SHAP- and RFE-based methods yield the lowest errors in Ghardaïa (nMAE = 17.17%), and RFE-GB leads in Algiers (nMAE = 28.13%). Although performance gaps between methods remain moderate, relative improvements reach up to 30.28% in Ghardaïa and 12.86% in Algiers. These findings confirm that feature selection significantly enhances accuracy (especially at extended horizons) and suggest that simpler methods such as MI or LASSO can remain effective, depending on the climate context and forecast horizon. Full article

This study introduces and validates a novel instrument to measure aggressive gender backlash (AGB), a distinct and underexplored dimension of gender backlash (GB) within intimate partner relationships. Based on the General Aggression Model, a multidimensional scale was developed and tested using data from 513 Peruvian female microentrepreneurs. Results demonstrate solid evidence of reliability, discriminant validity, and predictive validity across five dimensions: hostility, the withdrawal of support, sabotage/coercion, gender stereotyping, and masculine victimization. The findings reveal that AGB is more prevalent than intimate partner violence against women (IPVAW) and often precedes it. AGB encompasses covert, non-violent behaviors that aim to resist female empowerment, such as emotional sabotage, manipulation, and disqualification, often normalized within relationships. This construct is significantly associated with lower levels of empowerment, increased subordination, emotional morbidity, and decreased work productivity. This study redefines GB as an interpersonal process measurable at the individual level and provides the first validated tool for its assessment. By conceptualizing AGB as a persistent, harmful, and functionally equivalent mechanism to IPVAW, though not necessarily physically violent, this research fills a key gap in gender violence literature. It offers practical implications for early detection and prevention strategies. Full article

Capsicum (pepper) is an economically vital genus in the Solanaceae family, with most species possessing about 3 Gb genomes. However, the recently sequenced Capsicum rhomboideum (~1.7 Gb) represents the first reported case of an extremely compact genome in Capsicum, providing a unique and ideal model for studying genome size evolution. To elucidate the mechanisms driving this variation, we performed comparative genomic analyses between the compact Capsicum rhomboideum and the reference Capsicum annuum cv. CM334 (~2.9 Gb). Although their genome size differences initially suggested whole-genome duplication (WGD) as a potential driver, both species shared two ancient WGD events with identical timing, predating their divergence and thus ruling out WGD as a direct contributor to their size difference. Instead, transposable elements (TEs), particularly long terminal repeat retrotransposons (LTR-RTs), emerged as the dominant force shaping genome size variation. Genome size strongly correlated with LTR-RT abundance, and multiple LTR-RT burst events aligned with major phases of genome expansion. Notably, the integrity and transcriptional activity of LTR-RTs decline over evolutionary time; older insertions exhibit greater structural degradation and reduced activity, reflecting their dynamic nature. This study systematically delineated the evolutionary trajectory of LTR-RTs—from insertion and proliferation to decay–uncovering their pivotal role in driving Capsicum genome size evolution. Our findings advance the understanding of plant genome dynamics and provide a framework for studying genome size variation across diverse plant lineages. Full article

This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (p < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones. Full article

In the present study, the mechanical properties of high-strength steel rebar with different crossrib spacing that affect the bond behavior between steel rebar and concrete is investigated. To reveal the effects of crossrib spacing on the bond behavior of 630 MPa high-strength steel rebar (T63) in concrete, 42 bonding specimens were designed using T63 rebars and T63 rebars with increased crossrib spacing (TB63). The bond properties of two kinds of steel rebar with concrete were investigated by pull-out test and the failure modes, bond strengths, relative slippages, and bond-slip curves were obtained. Based on analysis of bond-slip curves, the applicability of the existing bond-slip constitutive model to describe T63 and TB63 rebars was discussed. It was found that 30–50% increase in crossrib spacing had little effect on the bond failure mode and bond strength of T63 rebar. The bond-slip curves of the two types of bonding specimens were similar and there is a 1.3 to 1.5-fold increase in peak slippage with TB63. The calculation method of critical bond length in Chinese code (GB 50010-2010) is applicable to T63 and TB63 rebars, and the bond-slip characteristics of T63 rebar with different crossrib spacings was reliably described by the bond-slip constitutive model. The research results can be used as the basis for the application of T63 reinforcement and can also be used as a reference for optimizing of rebar ribs outline. Full article

Financial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic indicators are also combined to forecast the Standard & Poor’s (S&P) 500 index. Initially, contextual data are scored using TextBlob and pre-trained DistilBERT-base-uncased models, and then a combined dataset is formed. Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). LR and MLP show robust results with high R² scores, close to 0.998, and low error MSE and MAE rates, averaging at 350 and 13 points, respectively, across both training and test datasets, with technical indicators contributing the most to the prediction. While other models also perform very well under different dataset combinations, overfitting challenges are evident in the results, even after additional hyperparameter tuning. Potential limitations are highlighted, motivating further exploration and adaptation techniques in financial modeling that enhance predictive capabilities. Full article

Glioblastoma (GB) is the most common malignant primary CNS tumor with a fast-growing and invasive profile. As a result of the poor prognosis and limited therapy available, glioblastoma shows a high mortality rate. Given the scarcity of effective chemotherapy options, multiple studies have explored the potential of tyrosine kinase inhibitors. To mitigate resistance and improve potency and selectivity, we proposed the combination of a potent irreversible epidermal growth factor receptor inhibitor—LASSBio-1971—and a potent phosphatidylinositol-3-kinase/mammalian target of rapamycin dual inhibitor—Gedatolisib—through an in vitro phenotypic study using five human GB lines. Here, we aimed to establish the cytotoxic potency, selectivity, and effect on proliferation, apoptosis, migration, and the cell cycle. Our data showed the cytotoxic potency of Gedatolisib and LASSBio-1971 and improved selectivity in the GB cell lines. They highlighted the synergistic response from their combination and its impact on migration reduction, G0/G1 cell cycle arrest, GB cytotoxicity, and apoptosis-inducing effects for different GB cell lines. The drug combination studies in phenotypic in vitro models made it possible to suggest a new potential treatment for glioblastoma that justifies further safety in in vivo phases of preclinical trials with the combination. Full article