Search Results (64)

Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Furthermore, current classification approaches often fail to reflect subtype-specific transcriptional programs, underscoring the need for computational strategies that reduce dimensionality and identify discriminative molecular features. Methods: We designed a multi-stage feature selection and network analysis framework tailored for high-dimensional transcriptomic data. Starting with ~65,000 mRNA features, we applied unsupervised variance-based filtering and correlation pruning to eliminate low-information genes and reduce redundancy. The applied supervised Select-K Best filtering further refined the feature space. To enhance robustness, we implemented a hybrid selection strategy combining recursive feature elimination (RFE) with random forests and LASSO regression to identify discriminative mRNA features. Finally, these features were then used to construct a gene co-expression similarity network. Results: This pipeline reduced approximately 65,000 gene features to a subset of 83 discriminative transcripts, which were then used for network construction to reveal subtype-specific biology. The analysis identified four distinct groups. One group exhibited classical high-grade serous features defined by TP53 mutations and homologous recombination deficiency, while another was enriched for PI3K/AKT and ARID1A-associated signaling consistent with clear cell and endometrioid-like biology. A third group displayed drug resistance-associated transcriptional programs with receptor tyrosine kinase activation, and the fourth demonstrated a hybrid profile bridging serous and endometrioid expression modules. Conclusions: This pilot study shows that combining unsupervised and supervised feature selection with network modeling enables robust stratification of ovarian cancer subtypes. Full article

Grapevine viruses reduce harvests and degrade fruit quality, but their genetic diversity in Kazakhstan has remained unexplored. We collected symptomatic leaves from local vineyards and recovered eleven fragments of the coat-protein gene: one from grapevine fanleaf virus, five from grapevine virus A, and five from grapevine leafroll-associated virus 3. After Sanger sequencing, we compared these fragments with more than one thousand international counterparts to place the Kazakh strains on the global family tree. The results reveal a clear spectrum of genetic diversity that mirrors each virus’s route of spread. Grapevine virus A, which is moved both mechanically and by insects, proved the most variable; grapevine fanleaf virus, carried by dagger nematodes and pruning sap, had intermediate variability; and grapevine leafroll-associated virus 3, moved only by mealybugs and scales, was highly conserved. All Kazakh sequences fell inside established foreign lineages, showing that the viruses were imported multiple times rather than evolving locally. Grapevine virus A will require broad-coverage or multiplex PCR primers to avoid false negatives, whereas the stable leafroll virus can be monitored with a single high-sensitivity assay. Combined with vector management—mealybug control for leafroll, and nematode testing for fanleaf—these data lay the groundwork for a national clean-plant program and more resilient vineyards across Central Asia. Full article

Microglia and macrophages are critical immune cells within the central nervous system (CNS), with distinct roles in development, homeostasis, and disease. Once viewed as passive bystanders, these cells are now recognized for their dynamic phenotypic plasticity, which enables them to respond to a wide range of physiological and pathological stimuli. During homeostasis, microglia and CNS-resident macrophages actively participate in synaptic pruning, neuronal support, myelin regulation, and immune surveillance, contributing to CNS integrity. However, under pathological conditions, these cells can adopt neurotoxic phenotypes, exacerbating neuroinflammation, oxidative stress, and neuronal damage in diseases such as Alzheimer’s, Parkinson’s, multiple sclerosis, and glioblastoma. This review synthesizes emerging insights into the molecular, epigenetic, and metabolic mechanisms that govern the behavior of microglia and macrophages, highlighting their developmental origins, niche-specific programming, and interactions with other CNS cells. We also explore novel therapeutic strategies aimed at modulating these immune cells to restore CNS homeostasis, including nanotechnology-based approaches for selective targeting, reprogramming, and imaging. Understanding the complex roles of microglia and macrophages in both health and disease is crucial for the development of precise therapies targeting neuroimmune interfaces. Continued advances in single-cell technologies and nanomedicine are paving the way for future therapeutic interventions in neurological disorders. Full article

Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of adaptive autonomy for path planning. This approach allows the system to autonomously select the best path-planning strategy. The technique utilizes dynamic programming with an adaptive memory size, leveraging a cellular decomposition technique to divide the map into convex cells. The path is divided into three segments: the first segment connects the starting point to the center of the starting cell, the second segment connects the center of the goal cell to the goal point, and the third segment connects the center of the starting cell to the center of the goal cell. Since each cell is convex, internal path planning simply requires a straight line between two points within a cell. Path planning uses an improved $A^{\ast }$ (I-$A^{\ast }$) algorithm, which evaluates the feasibility of a direct path to the goal from the current position during execution. When a direct path is discovered, the algorithm promptly returns and saves it in memory. The memory size is proportional to the square of the total number of cells, and it stores paths between the centers of cells. By storing and reusing previously calculated paths, this method significantly reduces redundant computation and supports long-term sustainability in mobile robot deployments. The final phase of the path-planning process involves pruning, which eliminates unnecessary waypoints. This approach obviates the need for repetitive path planning across different scenarios thanks to its compact memory size. As a result, paths can be swiftly retrieved from memory when needed, enabling efficient and prompt navigation. Simulation results indicate that this algorithm consistently outperforms other algorithms in finding the shortest path quickly. Full article

Genomic prediction has emerged as a powerful tool in aquaculture breeding, but its effectiveness depends on the careful selection of informative single nucleotide polymorphisms (SNPs) and the application of appropriate prediction models. This study aimed to enhance genomic prediction accuracy in Russian sturgeon (Acipenser gueldenstaedtii) by optimizing SNP selection strategies and exploring the performance of linear and machine learning models. Three economically important traits—caviar yield, caviar color, and body weight—were selected due to their direct relevance to breeding goals and market value. Whole-genome sequencing (WGS) data were obtained from 971 individuals with an average sequencing depth of 13.52×. To reduce marker density and eliminate redundancy, three SNP selection strategies were applied: (1) genome-wide association study (GWAS)-based prioritization to select trait-associated SNPs; (2) linkage disequilibrium (LD) pruning to retain independent markers; and (3) random sampling as a control. Genomic prediction was conducted using both linear (e.g., GBLUP) and machine learning models (e.g., random forest) across varying SNP densities (1 K to 50 K). Results showed that GWAS-based SNP selection consistently outperformed other strategies, especially at moderate densities (≥10 K), improving prediction accuracy by up to 3.4% compared to the full WGS dataset. LD-based selection at higher densities (30 K and 50 K) achieved comparable performance to full WGS. Notably, machine learning models, particularly random forest, exceeded the performance of linear models, yielding an additional 2.0% increase in accuracy when combined with GWAS-selected SNPs. In conclusion, integrating WGS data with GWAS-informed SNP selection and advanced machine learning models offers a promising framework for improving genomic prediction in sturgeon and holds promise for broader applications in aquaculture breeding programs. Full article

In this work, we evaluated the silvicultural and ecological parameters of Populus bolleana Lauche trees growing in conditions of anthropogenic pollution, using the example of one of the largest megacities of the Donetsk ridge, the city of Donetsk. The objectives of this study included determining the level of anthropogenic load of the territory; conducting dendrological studies to assess morphometric and allometric parameters, age structure, and condition of P. bolleana stands under the influence of environmental factors; as well as completing biomechanical studies to assess and predict the mechanical stability of stands. A total of 1109 plants growing in areas with increased anthropogenic load and in the control areas were studied. The model territories of the study were located in the city of Donetsk on Fallen Communards Avenue (length of field routes: 2.6 km) and Ilyicha Avenue (length of field routes: 9.7 km). Control plantings grew on the territory of the Donetsk botanical garden and residential (dormitory) districts of the city. The age structure of P. bolleana plantations remained uniform throughout the city for 50–55 years due to the fact that the landscaping was under a single state program. In the steppe zone in the south of the East European Plain, with a high level of anthropogenic load and severe natural climatic factors, the critical age of P. bolleana (55 years) was determined. The condition of plantations and their morphometric indices correlate with the level of anthropogenic load of the city (H, Dbase, DBH). Under control conditions, the plants are in good condition with signs of weakening (2 points). Under conditions of increased anthropogenic load, the plants are in a severely weakened condition (3 points). A total of 25% of the plants in the sample are in critical condition (4–5 points). The main damages to the crowns and trunks of plants include core rot, mechanical damage to bark and tissues, the development of core rot through the affected skeletal branch, crown thinning, and drying. P. bolleana trees are valued for their crown area and ability to retain dust particles from the air. The analysis of experimentally obtained data on the crown area showed that in the initial phases of ontogenesis, the average deviation in the crown area of plants does not depend on the place of growth. Due to artificial narrowing and sanitary pruning of the crown, as well as skeletal branches dying along the busiest highways, the values do not exceed 22–23 m² on average, with an allometric coefficient of 0.35–0.37. When comparing this coefficient in the control areas, the crown area in areas with a high level of anthropogenic load is 36 ± 11% lower. For trees growing under the conditions of the anthropogenic load of an industrial city and having reached the critical age, mechanical resistance varied depending on the study area and load level. At sites with a high level of pollution of the territory, a significant decrease in indicators was revealed in comparison with the control (mcr—71%, EI—75%, RRB—43%). Having analyzed all the obtained data, we can conclude that, until the age of 50–55 years, P. bolleana retains good viability, mechanical resistance, and general allometric ratios, upon which the stability of the whole plant depends. Even with modern approaches and tendencies toward landscaping with exotic introductions, it is necessary to keep P. bolleana as the main species in dendrobanocenoses. Full article

Blueberry is a high-value fruit crop in the United States, with Georgia and Florida serving as important early-season production regions. In these areas, several thrips species (Thysanoptera: Thripidae), including Frankliniella tritici (Fitch), Frankliniella bispinosa (Morgan), and Scirtothrips dorsalis (Hood), have emerged as economically significant pests. While F. tritici and F. bispinosa primarily damage floral tissues, S. dorsalis targets young foliage. Their rapid reproduction, high mobility, and broad host range contribute to rapid population buildup and complicate the management programs. Species identification is often difficult due to overlapping morphological features and requires the use of molecular diagnostic tools for accurate identification. Although action thresholds, such as 2–6 F. tritici per flower cluster, are used to guide management decisions, robust economic thresholds based on yield loss remain undeveloped. Integrated pest management (IPM) practices include regular monitoring, cultural control (e.g., pruning, reflective mulch), biological control using Orius insidiosus (Say) and predatory mites, and chemical control. Reduced-risk insecticides like spinetoram and spinosad offer effective suppression while minimizing harm to pollinators and beneficial insects. However, the brief flowering period limits the establishment of biological control agents. Developing species-specific economic thresholds and phenology-based IPM strategies is critical for effective and sustainable thrips management in blueberry cropping systems. Full article

With the widespread application of the Go language, the demand for vulnerability detection in Go programs is increasing. Existing detection models and methods have deficiencies in extracting source code features of Go programs and mainly focus on detecting concurrency vulnerabilities. In response to these issues, we propose a Go program vulnerability detection method based on a graph neural network (GNN). The core of this approach is to utilize GraphSAGE to extract the global structure and deep semantic information of each concurrent function, maximizing the learning of concurrency vulnerability features. To capture contextual information of fine-grained code fragments in source code, we employ taint analysis to extract taint propagation chains and use a Transformer model with a multi-head attention mechanism, based on lexical analysis, to extract fine-grained vulnerability features. We integrate graph-level and token-level features to maximize the detection of various complex types of vulnerabilities in Go source code. Experimental results on a real-world vulnerability dataset demonstrate that our model outperforms existing detection methods and tools, achieving an F1-score of 91.35%. Furthermore, ablation experiments confirm that the proposed feature fusion method effectively extracts deep vulnerability features. Full article

Cold climate viticulture is challenged by climatic variability, including increased frost risk, shorter growing seasons, and unpredictable weather events that impact vine productivity and grape quality. Global warming is altering traditional viticulture zones, prompting the exploration of new regions for grape cultivation, the selection of climate-resilient cultivars, and the implementation of adaptive practices. This review synthesizes recent advances in adaptive viticulture practices and plant growth regulator applications, highlighting novel molecular and physiological insights on cold stress resilience and berry quality. Key strategies include delayed winter pruning to mitigate frost damage, osmoprotectant application to improve freeze tolerance, and canopy management techniques (cluster thinning and defoliation) to enhance berry ripening and wine composition. Their effectiveness depends on vineyard microclimate, soil properties and variety-specific physiological response. Cover cropping is examined for its role in vine vigor regulation, improving soil microbial diversity, and water retention, though its effectiveness depends on soil type, participation patterns, and vineyard management practices. Recent transcriptomic and metabolomic studies have provided new regulatory mechanisms in cold stress adaptation, highlighting the regulatory roles of abscisic acid, brassinosteroids, ethylene, and salicylic acid in dormancy induction, oxidative stress response, and osmotic regulation. Reflective mulch technologies are currently examined for their ability to enhance light interception, modulating secondary metabolite accumulation, improving technological maturity (soluble solids, pH, and titratable acidity) and enhancing phenolic compounds content. The effectiveness of these strategies remains highly site-specific, influenced by variety selection and pruning methods particularly due to their differences on sugar accumulation and berry weight. Future research should prioritize long-term vineyard trials to refine these adaptive strategies, integrate genetic and transcriptomic insights into breeding programs to improve cold hardiness, and develop precision viticulture tools tailored to cold climate vineyard management. Full article

Pruning waste (PW) and agricultural timber residue are rarely treated, creating environmental pollution issues. The lack of regulations and environmental control criteria has led to poor ecosystems. In this study, it is proposed to transform PW and turn it from a nuisance into a valuable energy source and other alternative resources under environmental constraints. Current reuse and recycling options include turning the waste into a food source or using it to generate energy, compost, soil fertilizer, and other products. A linear programming model with Boolean variables and a management model are defined and run for the reuse of PW. The management model defines the diverse options for PW reuse in terms of resource recovery. These options depend, to a considerable extent, on the country’s production capacity and the preferred applied alternatives. The country of Israel is split into separate regions, which are classified according to the preferred alternatives for PW treatment and reuse. These alternatives include factors such as the annual amounts of trash generated, transportation expenses, energy demands, and requirements based on annual and daily needs. An optimization model (based on operations research methods) is defined, solved, and subjected to a series of constraints. The goal of the study is to find out the best location for PW treatment facilities and optimal recycling product technology using linear programming software with Boolean variables. The results show that a net profit of approximately 3.5 million USD/year for a total community of close to 10 × 10⁶ residents could be derived from the amounts of waste, including improved environmental control, in addition to the additional energy source. This work raises an urgent need to control and regulate recycling policies for PW in various environmental regions worldwide. Full article

This study focused on improving the milk fat percentage for crossbred dairy cows in smallholder systems, using GEBV. The data were collected from 2507 animals between 2016 and 2023 under BAIF’s Enhanced Genetic Gains program in Pune, India. After refining the dataset, 33,845 records from 1896 animals were analyzed. The result showed that 75.54% of farms had either one or two animals. Prior to quality control, the mean milk fat percentage was 3.94%, but it decreased to 3.83% after data pruning, which necessitated removing the outliers. Genetic analysis involved 1478 animals genotyped for 49,911 SNPs after applying a rigorous quality control process, and imputation improved the accuracy of genomic data, boosting allele frequency correlation from 0.594 to 0.882. The study revealed that the additive genetic variance, phenotypic variance, and error variance were calculated as 0.012, 0.118, and 0.106, respectively. The heritability was estimated at 0.10, suggesting cautious use for breeding improvements. The GEBV ranged from 0.096 to 3.10%, which offers breeders a practical tool for selecting high-fat-producing cows. This research provides valuable insights into optimizing milk quality and advancing genetic improvement strategies in smallholder dairy systems. Full article

Unit commitment (UC) is a critical challenge in power system optimization, typically formulated as a high-dimensional mixed-integer linear programming (MILP) problem with non-deterministic polynomial-time hard (NP-hard) complexity. While the branch-and-bound (B&B) algorithm can determine optimal solutions, its computational cost increases exponentially with the number of units, which limits the practical application of UC. Machine learning (ML) has recently emerged as a promising tool for addressing UC, but its effectiveness relies on substantial training samples. Moreover, ML models suffer significant performance degradation when the number of units changes, a phenomenon known as the task mismatch problem. This paper introduces a novel method for Branching Acceleration for UC, aiming to reduce the computational complexity of the B&B algorithm while achieving near-optimal solutions. The method leverages invariant branching tree-related features and UC domain-specific features, employing imitation learning to develop an enhanced pruning policy for more precise node pruning. Numerical studies on both standard and practical testing systems demonstrate that the method significantly accelerates computation with few training samples and negligible accuracy loss. Furthermore, it exhibits robust generalization capability for handling task mismatches and can be seamlessly integrated with other B&B acceleration techniques, providing a practical and efficient solution for UC problems. Full article

The sustainable management of biowaste, mainly food and pruning waste, is currently a challenge due to the increase in its production. The CaMPuSTAJE program, which has been implemented on the campus of the Public University of Navarre (UPNA) since 2019, is an excellent example of how the institution is addressing its strategic interests in sustainable waste management. The principal aim of this program is to manage the biowastes generated by the campus canteens through a simple community composting facility, involving UPNA students and graduates. This program aims to promote experiential learning and applied research in sustainability and circular economy, managing their own waste in a circular and local way. Thus, four composting sets of the CaMPuSTAJE program were evaluated by monitoring the process and the main chemical properties of the composting samples. Also, final composts were fully characterized to ensure the process reproducibility and efficiency and the absence of any hazard in the end-products. The final composts showed a significant agronomic quality, had low content of potentially toxic elements, and were free from phytotoxicity, thus being able to be reintroduced as an organic amendment at the university campus itself. Full article

Efficient and accurate vision models are essential for real-world applications such as medical imaging and deepfake detection, where both performance and computational efficiency are critical. While recent vision models achieve high accuracy, they often come with the trade-off of increased size and computational demands. In this work, we propose MobileNet-HeX, a new ensemble model based on Heterogeneous MobileNet eXperts, designed to achieve top-tier performance while minimizing computational demands in real-world vision tasks. By utilizing a two-step Expand-and-Squeeze mechanism, MobileNet-HeX first expands a MobileNet population through diverse random training setups. It then squeezes the population through pruning, selecting the top-performing models based on heterogeneity and validation performance metrics. Finally, the selected Heterogeneous eXpert MobileNets are combined via sequential quadratic programming to form an efficient super-learner. MobileNet-HeX is benchmarked against state-of-the-art vision models in challenging case studies, such as skin cancer classification and deepfake detection. The results demonstrate that MobileNet-HeX not only surpasses these models in performance but also excels in speed and memory efficiency. By effectively leveraging a diverse set of MobileNet eXperts, we experimentally show that small, yet highly optimized, models can outperform even the most powerful vision networks in both accuracy and computational efficiency. Full article

Background: This study explores the use of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a crucial resource for sustainable energy and material production. Efficient biomass logistics play a key role in supporting circular bioeconomy principles by improving resource utilization and reducing operational costs. Methods: Two optimization approaches are evaluated: a base MILP model designed for scenarios with single processing points and an advanced model that incorporates intermediate processing steps to enhance logistical efficiency. The models were tested using synthetic datasets simulating vineyard regions to assess their performance. Results: The models demonstrated significant improvements, achieving cost reductions of up to 30% while enhancing operational efficiency and resource utilization. The study highlights the scalability and real-world applicability of the proposed models. Conclusions: The findings underscore the potential of MILP models in optimizing biomass supply chains and advancing circular bioeconomy goals. However, key limitations, such as computational complexity and adaptability to dynamic environments, are noted. Future research should focus on real-time data integration, dynamic updates, and multi-objective optimization to improve model robustness and applicability across diverse supply chain scenarios. Full article