Search Results (144)

Sugar beet (Beta vulgaris ssp. vulgaris) is a vital crop, contributing to nearly a quarter of global sugar production, but faces significant challenges from biotic stressors, particularly aphids, which transmit damaging yellowing viruses such as Beet Yellow Virus (BYV) and Beet Mild Yellowing Virus (BMYV). Following the partial ban of neonicotinoids in Europe, viral infections in sugar beet have surged, highlighting the need for a deeper understanding of aphid-mediated virus transmission mechanisms. This study aims to evaluate the transmission efficiency of BYV and BMYV through different clones of the aphid vector Myzus persicae from sugar beet seed companies across Europe, and to analyze the feeding behaviors of efficient clones to identify factors influencing virus transmission. The transmission rates of yellowing viruses by M. persicae clones ranged from 52% to 79% for BMYV (mean 65%) and 7% to 96% for BYV (mean 47%). While no significant differences in BMYV transmission efficiency were observed among clones, a significant difference was detected between two BYV-carrying clones. Moreover, the BYV-carrying clone exhibited prolonged penetration activities during its feeding phase compared to the BMYV-carrying clone, suggesting a potential behavioral influence on transmission efficiency. This study highlights the importance of considering aphid clone influence in the development of sugar beet resistance. Full article

Target tracking is a representative task in multi-agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that integrates behavior cloning with temporal difference (BCTD) and multi-agent proximal policy optimization (MAPPO). Expert trajectories are generated using the artificial potential field (APF), followed by policy pre-training via behavior cloning and TD-based value optimization. MAPPO is then employed for dynamic fine-tuning, enhancing robustness and coordination. Experiments in a simulated environment show that the proposed MAPPO+BCTD framework outperforms MAPPO, QMIX, and MADDPG in success rate, convergence speed, and tracking efficiency. The proposed method effectively alleviates the local optimization problem of APF and the training inefficiency problem of RL, offering a scalable and reliable solution for dynamic multi-agent coordination. Full article

Snail and Zeb1 have been suggested as markers for the hybrid/mixed epithelial (E)/mesenchymal (M) state of cancer cells. Such cancer cells co-express E- and M-specific transcripts and possess cancer stem cell properties. M13HS-2/-8 tumor hybrid clones derived from human M13SV1-EGFP-Neo breast epithelial cells and human HS578T-Hyg breast cancer cells exhibited co-expression of Snail and Zeb1. To explore the impact of Snail on stemness/epithelial-to-mesenchymal transition (EMT)-related properties in M13HS-2/-8 tumor hybrid clones, Snail was knocked out (KO) using CRISPR/Cas9. Mammosphere formation, colony formation, Western blot analyses, cell migration, and invasion assays were conducted for the characterization of Snail knockout cells. Interestingly, Snail-KO in M13SV1-EGFP-Neo cells resulted in the up-regulation of vimentin and N-cadherin, suggesting EMT induction, which was associated with a significantly enhanced colony formation capacity. In contrast, EMT marker pattern and colony formation capacities of M13HS-2/-8 Snail-KO tumor hybrid clones remained unchanged. Notably, the mammosphere formation capacities of M13HS-2/-8 Snail-KO tumor hybrid clones were significantly reduced. The migratory behavior of all Snail-KO cells was not altered compared with their wild-type counterparts. In contrast, M13HS-2 hybrids and their M13HS-2 Snail-KO variant exhibited a markedly enhanced invasive capacity. Therefore, Snail plays a role as a mediator of stemness properties rather than mediating EMT. Full article

In this article, two generalized net models (GNMs) are proposed to study the behavior and effectiveness of intelligent virtual agents (IVA) working in a risky environment under different scenarios and training algorithms. The proposed GNMs allow for the selection of machine learning algorithms such as intensity of characteristics Q-learning (InCh-Q), as well as the modification of multi-plan reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), the generative adversarial imitation learning (GAIL) algorithm, and behavioral cloning (CB). The choice of action, the change in priorities, and the achievement of goals by the IVA are studied under different scenarios, such as fire extinguishing, rescue operations, evacuation, patrolling, and training. Transitions in the GNMs represent the scenarios and learning algorithms. The tokens that pass through the GNMs can be the GNMs of the IVA architecture or the IVA memory model, which are enriched with knowledge and experience during the experiments, as the scenarios develop. The proposed GNMs are formally correct and, at the same time, understandable, practically applicable, and convenient for interpretation. Achieving GNMs that meet these requirements is a complex problem. Therefore, issues related to the design and use of GNMs for the reliable modeling and analysis of the behavior and effectiveness of IVAs operating in a dynamic and risky environment are discussed. Some advantages and challenges in using GNMs compared to other classical models used to study IVA behavior are considered. Full article

Fibronectin (FN), a primary component of the extracellular matrix (ECM), features multiple structural domains closely linked to various cellular behaviors, including migration, spreading, adhesion, and proliferation. The FN3 domain, which contains the RGD sequence, is critical in tissue repair because it enables interaction with integrin receptors on the cell surface. However, the large molecular weight of wild-type FN presents challenges for its large-scale production through heterologous expression. Therefore, this study focused on cloning the FN3 functional domain of full-length FN for expression and validation. This study selected Bacillus subtilis as the expression host due to its prominent advantages, including efficient protein secretion, absence of endotoxins, and minimal codon bias. The recombinant vector pHT43-FN3 was successfully constructed through homologous recombination technology and transformed into Bacillus subtilis WB800N. The FN3 protein was successfully expressed after induction with IPTG. Following purification of the recombinant FN protein using a His-tag nickel column, SDS-PAGE analysis showed that the molecular weight of FN3 was approximately 27.3 kDa. Western blot analysis confirmed the correct expression of FN3, and the BCA protein assay kit determined a protein yield of 5.4 mg/L. CCK8 testing demonstrated the good biocompatibility of FN3. In vitro cell experiments showed that FN3 significantly promoted cell migration at a 20 μg/mL concentration and enhanced cell adhesion at 10 μg/mL. In summary, this study successfully utilized Bacillus subtilis to express the FN3 functional domain peptide from FN protein and has validated its ability to promote cell migration and adhesion. These findings not only provide a strategy for the expression of FN protein in B. subtilis, but also establish an experimental foundation for the potential application of FN3 protein in tissue repair fields such as cutaneous wound healing and cartilage regeneration. Full article

The rapid development of sixth-generation mobile communication systems has brought about significant advancements in both Quality of Service (QoS) and Quality of Experience (QoE) for users, largely due to the extremely high data rates and a diverse range of service offerings. However, these advancements have also introduced challenges, especially concerning the growing demand for a wireless spectrum and the limited availability of resources. Various efforts have been made and research has attempted to tackle this issue such as the use of Cognitive Radio Networks (CRNs), which allows opportunistic spectrum access and intelligent resource management. This work demonstrate a new method in the optimization of allocation resource in CRNs based on the Snake Optimizer (SO) along with reinforcement learning (RL), which is an effective meta-heuristic algorithm that simulates snake cloning behavior. SO is tested over three different scenarios with varying numbers of secondary users (SUs), primary users (PUs), and frequency bands available. The obtained results reveal that the proposed approach is able to largely satisfy the aforementioned requirements and ensures high spectrum utilization efficiency and low collision rates, which eventually lead to the maximum possible spectral capacity. The study also demonstrates that SO is versatile and resilient and thus indicates its capability of serving as an effective method for augmenting resource management in next-generation wireless communication systems. Full article

Circulating tumor cells (CTCs) are multifaceted biomarkers with significant potential for precision oncology, offering opportunities to refine diagnoses and personalize treatments across various cancer types, including colorectal and breast cancer. CTC assays serve as reliable prognostic indicators, even during chemotherapy and/or molecularly targeted therapies. Notably, CTCs exhibit heterogeneity that gradually develops during carcinogenesis and becomes more pronounced in advanced disease stages. These intra- and intertumoral heterogeneities pose challenges, particularly when drug-resistant clones emerge following therapy. The dynamic behavior of CTCs provides valuable insights into treatment response and prognosis. Extensive efforts have led to the development of technologies for effective CTC isolation, accelerating their clinical implementation. While both CTC and circulating tumor DNA (ctDNA) tests offer prognostic value, they reflect different aspects of tumor biology: CTC counts indicate tumor progression, while ctDNA levels correlate with tumor burden. The combined analysis is expected to yield complementary insights. CTC tests are feasible in general hospitals and may serve as tumor markers comparable to, or even superior to, conventional markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) for colorectal cancer, and CA15-3 for breast cancer. Early incorporation of CTC tests into routine blood panels appears to be a rational and promising approach. Full article

User-centered design (UCD) commonly requires direct player participation, yet budget limitations or restricted access to users can impede this goal. To address these challenges, this research explores a transformer-based approach coupled with a diffusion process to replicate real player behavior in a 2D side-scrolling action–adventure environment that emphasizes exploration. By collecting an extensive set of gameplay data from real participants in an open-source game, “A Robot Named Fight!”, this study gathered comprehensive state and input information for training. A transformer model was then adapted to generate button-press sequences from encoded game states, while the diffusion mechanism iteratively introduced and removed noise to refine its predictions. The results indicate a high degree of replication of the participant’s actions in contexts similar to the training data, as well as reasonable adaptation to previously unseen scenarios. Observational analysis further confirmed that the model mirrored essential aspects of the user’s style, including navigation strategies, the avoidance of unnecessary combat, and selective obstacle clearance. Despite hardware constraints and reliance on a single observer’s feedback, these findings suggest that a transformer–diffusion methodology can robustly approximate user behavior. This approach holds promise not only for automated playtesting and level design assistance in similar action–adventure games but also for broader domains where simulating user interaction can streamline iterative design and enhance player-centric outcomes. Full article

Amidst the large-scale integration of renewable energy, power grid operations are increasingly characterized by higher levels of uncertainty, challenging the system’s safety and stability. Traditional model-driven dispatch methods are computationally intensive, and recent Reinforcement Learning (RL) techniques struggle with slow training times due to high-dimensional state spaces, while the inability to fully utilize the system’s topology information affects scheduling accuracy. This paper introduces a novel Behavioral Cloning of Grid Expert Strategy with Integrated Graph Neural Networks (GES-GNNBC) method for efficient and highly accurate real-time dispatch. The approach integrates grid expert strategies with graph theory-based modeling and Behavioral Cloning (BC), capturing the topological information of the power grid through Graph Neural Networks (GNN) to improve scheduling accuracy. Tested on a modified IEEE 33-bus model rich in renewable sources, GES-GNNBC outperforms both traditional and RL methods in stability and efficiency of computing optimization schemes and power balance strategies, markedly improving dispatch decision-making speed and effectiveness. Full article

Drought stress is the most common threat to plant growth, while physiological integration can significantly enhance the drought tolerance of clonal plants, making it essential to research the behavior of clones under drought conditions and explore the potential applications of clonal plants. This study applied polyethylene-glycol-6000-induced stress to proximal, middle and distal clonal ramets of Kentucky bluegrass (Poa pratensis L.) and used an isotope labeling technique to evaluate the water physiological integration and photosynthetic capacity. When the proximal ramet was subjected to drought stress treatment, the decrease in ²H isotopes in the roots from 4 h to 6 h was significantly smaller than the increase in ²H isotopes in their own leaves. Additionally, the reductions in δ²H values of middle and distal ramets roots were 4.14 and 2.6 times greater, respectively, than the increases in their respective leaf δ²H values. The results indicate that under drought stress, water physiological integration was observed among different clonal ramets. In addition, drought stress inhibits the photosynthetic-related indicators in clonal ramets, with varying degrees of response and trends in photosynthetic characteristics among different clonal ramets. The proximal ramet treatment group, treated with polyethylene glycol 6000, was most affected by drought stress, while the distal ramet treatment group was least affected. The proximal ramet treatment group, treated with polyethylene glycol 6000, showed a decrease in water use efficiency after 6 h of drought treatment, while the other groups exhibited some increase. This indicates differences in water utilization and regulation among the different clonal ramets under drought stress. This study holds significant theoretical importance for exploring the characteristics of physiological integration and the photosynthetic mechanisms of Kentucky bluegrass clones under drought stress. Full article

The improved control performance of the buoy and winch collaborative control system can enhance the stability of the connection between underwater robots and ground industrial control equipment. To overcome the challenge of mathematical modeling of this control system, this research introduces reinforcement learning and transformer models in the design process. The main contributions include the development of two simulation environments for training DRL agents, designing a reward function to guide the exploration process, proposing a buoy control algorithm based on the discrete soft actor-critic (SAC) algorithm, and proposing a winch cable length prediction algorithm based on a lightweight transformer model. The experiment results demonstrated significant improvements in rewards diagrams, buoy control trajectories, and winch model performance, showcasing the effectiveness of our proposed system. The average error of the buoy tracking trajectories induced by different policies trained in the two environments is less than 0.05, and the evaluation error of the behavior cloning lightweight transformer model is less than 0.03. Full article

The poplar bark beetle Trypophloeus binodulus (Coleoptera: Scolytidae) is a key pest of poplar trees (Malpighiales: Salicaceae, genus Populus) across northern Spain. However, among the more than 200 poplar clones available on the market, the clone USA 184-411 has the highest susceptibility to T. binodulus attacks. We tested the hypothesis that compounds released by the most susceptible poplar clone chemically mediate Trypophloeus binodulus behavior. The leaf and bark volatile chemical profile composition of host poplar Populus nigra L. (Salicaceae) clones were analyzed and tested on adult T. binodulus by electroantennography (EAG) and by monitoring their populations in baited traps in northern Spain. The collection of volatiles released by the leaves and bark of both clones by SPME revealed the emission of 53 components from different chemical classes. Salicylaldehyde dominated these collections of leaf volatiles and was more abundant in aerations of the more susceptible poplar clone (USA 184-411). The EAG response of adult beetles to salicylaldehyde was higher than that of any other plant odorants. In field trials, traps baited with salicylaldehyde + ethanol significantly captured more adults than all other treatments, irrespective of other lure components. The present study could aid in developing semiochemical-based management tactics against this important pest. Full article

The target of breeding red-fleshed apples is to increase their potential health benefits related to red flesh coloration and consumer acceptance. The objective of this study was to determine the usefulness of four clones (90, 120, 156, and 158) of red-fleshed apples for freeze-drying compared to the cultivar ‘Trinity’. Red-fleshed apples were dried in the form of slices using a laboratory freeze-dryer. The changes in color features and image texture parameters after drying and the sensory quality of freeze-dried samples were assessed. Trends of increase in the value of the L* parameter and decrease in the a* and b* parameters after freeze-drying were observed. Furthermore, freeze-drying caused statistically significant changes in analyzed image textures named XHMean, RHMean, SHMean, VHMean, LHMean, and UHMean. Machine learning models developed based on the color parameters L*, a*, and b* distinguished raw and freeze-dried red-fleshed apples with an average accuracy of 84% for clone 90 up to 99.0% for clone 156, and models based on twenty selected image textures exhibited an accuracy of 98.5% for clone 156 to 100% for clones 90 and 158 and the cultivar ‘Trinity’. The very attractive external appearance, medium-intense fruity smell, crunchiness, and intense fruity taste of all the apple slices were revealed. The innovative aspect of this study included the comparison of the drying behavior and sensory quality of the new clones and a standard cultivar of red-fleshed apples. Moreover, innovative methods and results were used to determine the effect of freeze-drying on red-fleshed apple quality, considering novel models involving thousands of image textures and machine learning algorithms. Full article

The integrity of global elections is increasingly under threat from artificial intelligence (AI) technologies. As AI continues to permeate various aspects of society, its influence on political processes and elections has become a critical area of concern. This is because AI language models are far from neutral or objective; they inherit biases from their training data and the individuals who design and utilize them, which can sway voter decisions and affect global elections and democracy. In this research paper, we explore how AI can directly impact election outcomes through various techniques. These include the use of generative AI for disseminating false political information, favoring certain parties over others, and creating fake narratives, content, images, videos, and voice clones to undermine opposition. We highlight how AI threats can influence voter behavior and election outcomes, focusing on critical areas, including political polarization, deepfakes, disinformation, propaganda, and biased campaigns. In response to these challenges, we propose a Blockchain-based Deepfake Authenticity Verification Framework (B-DAVF) designed to detect and authenticate deepfake content in real time. It leverages the transparency of blockchain technology to reinforce electoral integrity. Finally, we also propose comprehensive countermeasures, including enhanced legislation, technological solutions, and public education initiatives, to mitigate the risks associated with AI in electoral contexts, proactively safeguard democracy, and promote fair elections. Full article

The study of tick olfaction is relatively new compared to that of insects, and the molecular mechanisms involved remain poorly understood. Despite several potential chemosensory genes identified in multiple tick species, these are yet to be validated through independent functional experiments. In this research, we cloned and analyzed a microplusin-like gene, HlonML-1, and investigated its role in the chemosensory activities of H. longicornis. The results showed that this gene’s amino acid sequences lack histidine residues essential for antimicrobial activity, and it is evolutionarily linked to putative chemosensory microplusins in ticks. Gene expression analyses indicated that HlonML-1 was significantly more abundant in ticks exposed to potential attractants and in the forelegs of H. longicornis than in non-exposed ticks and the hindlegs, respectively. Tick forelegs support the Haller’s organ, which is a sensory structure mostly specialized for chemosensation. Furthermore, Y-tube olfactometer assays indicated that silencing HlonML-1 significantly impaired adult ticks’ ability to detect selected odors, while their gustatory-related behavior remained unaffected compared to the control groups. Given its unique sequences, relative abundance in chemosensory tissues, and impact on odor detection, HlonML-1 is likely involved in the olfactory chemosensation of H. longicornis. Future research validating putative chemosensory microplusins in the genomes of various tick species may enhance our understanding of their olfactory functions in tick and lead to the identification of new molecular targets for developing tick repellents. Full article