Search Results (367)

Tomato yellow leaf curl virus (TYLCV) is a highly destructive pathogen of global tomato crops. The open reading frame (ORF) of TYLCV V2 contains two initiation codons (ATG1/V2-1 and ATG2/V2-2), producing distinct protein isoforms. Using custom antibodies, we confirmed V2-1 and V2-2 expression in infected Nicotiana benthamiana and tomato plants. Deletion mutants revealed their specialized roles: V2-1 was indispensable for viral replication and systemic spread—its loss severely reduced pathogenicity and genome accumulation. V2-2 acted as an auxiliary factor, and its deletion attenuated symptoms but kept the virus infection. Host-specific effects were observed—V2-1 deletion led to lower viral DNA/coat protein levels in N. benthamiana than in tomato, suggesting host-dependent regulation. Mutant viruses declined progressively in tomato, indicating host defense clearance. Heterologous co-expression of both isoforms via potato virus X induced systemic necrosis in N. benthamiana, demonstrating functional synergy between isoforms. Both initiation codons were essential for V2-mediated suppression of transcriptional gene silencing (TGS) and post-transcriptional gene silencing (PTGS). This study uncovers the mechanistic divergence of V2 isoforms in TYLCV infection, highlighting their collaborative roles in virulence and host manipulation. The findings advance understanding of geminivirus coding complexity and offer potential targets for resistance strategies. Full article

Livestock farming is a vital component of global food security, yet it remains a major contributor to greenhouse gas (GHG) emissions, particularly methane (CH₄), which has a global warming potential 28 times greater than carbon dioxide (CO₂). This review provides a comprehensive synthesis of current knowledge surrounding the sources, biological mechanisms, and mitigation strategies related to CH₄ emissions from ruminant livestock. We first explore the process of methanogenesis within the rumen, detailing the role of methanogenic archaea and the environmental factors influencing CH₄ production. A thorough assessment of both direct and indirect methods used to quantify CH₄ emissions is presented, including in vitro techniques (e.g., syringe method, batch culture, RUSITEC), in vivo techniques (e.g., respiration chambers, Greenfeed, laser CH₄ detectors), and statistical modeling approaches. The advantages and limitations of each method are critically analyzed in terms of accuracy, cost, feasibility, and applicability to different farming systems. We then examine a wide range of mitigation strategies, organized into four core pillars: (1) animal and feed management (e.g., genetic selection, pasture quality improvement), (2) diet formulation (e.g., feed additives such as oils, tannins, saponins, and seaweed), (3) rumen manipulation (e.g., probiotics, ionophores, defaunation, vaccination), and (4) manure management practices and policy-level interventions. These strategies are evaluated not only for their environmental impact but also for their economic and practical viability in diverse livestock systems. By integrating technological innovations with sustainable agricultural practices, this review highlights pathways to reduce CH₄ emissions while maintaining animal productivity. It aims to support decision-makers, researchers, and livestock producers in the global effort to transition toward climate-smart, low-emission livestock farming. Full article

This study investigated the effects of macronutrient type and concentration on the biomass yield and biochemical composition of hydroponically grown wheat sprouts (HWS), with the aim of identifying fertilization strategies that optimize both productivity and feed quality. HWS were cultivated using a nutrient film technique over a 7-day period under controlled environmental conditions, with treatments including calcium nitrate (CN1–CN3), potassium phosphate (CP1–CP3), potassium sulfate (CK1–CK2), and a balanced NPK 20–20–20 fertilizer (NPK1–NPK3), each applied at three increasing concentrations. The quantitative parameters assessed included biomass yield per unit of dry seed (DP, kg kg⁻¹) and dry matter content (DM, %), while qualitative traits included crude protein (CP), ether extract (EE), crude fiber (CF), and ash content. Results indicated that balanced NPK fertilization significantly enhanced performance, with NPK3 achieving the highest biomass yield (6.39 kg kg⁻¹), CP (24.26%), CF (5.63%), and ash (16.0%) content. In contrast, CN3 treatments reduced yield (4.84 kg kg⁻¹) despite increasing CP (19.65%), indicating trade-offs between nitrogen enrichment and vegetative expansion. Phosphorus-based treatments (CP2–CP3) improved nutrient density without suppressing yield. Regression analyses revealed strong correlations between DM and both CF (R² = 0.81) and ash (R² = 0.71), supporting their utility as indirect indicators of feed quality. EE content remained stable (2.07–2.67%) across all treatments, suggesting its limited responsiveness to macronutrient manipulation. These findings highlight the importance of nutrient synergy in hydroponic systems and provide a practical framework for tailoring fertilization regimes to meet specific agronomic and nutritional objectives in precision livestock feeding and provide practical guidance for optimizing hydroponic livestock feed production. Full article

In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion. Full article

The BCR-ABL1 chimeric oncoprotein plays a pivotal role in the pathogenesis of Chronic Myeloid Leukemia (CML) as its constitutive kinase activity transforms the hematopoietic stem cell, promoting pro-survival signaling. We and others have previously shown that the manipulation of BCR-ABL1 catalytic activity modulates its intracellular localization, thereby transforming the culprit of CML into a pro-apoptotic protein that selectively kills leukemic cells. Here, we investigated the role of the BCR coiled-coil and C2 domains on BCR-ABL1 intracellular localization and leukemogenic potential. We performed a bioinformatic analysis that identified two putative nuclear localization signals (NLSs) in BCR. Using recombinant DNA strategies, we generated multiple BCR and BCR-ABL1 mutants that were ectopically expressed in human cells. The intracellular localization of each construct was analyzed by immunofluorescence, while their biological activity was investigated employing proliferation and transforming assays. We show that BCR displays two nuclear localization signals functionally inactivated by the coiled-coil and C2 domains. The removal of these regions reactivated the nuclear migration of both BCR and BCR-ABL1 mutants. Moreover, BCR-ABL1 constructs devoid of the coiled-coil and C2 domains displayed reduced transforming potential in Ba/F3 cells and in primary human CD34+ progenitors. Finally, we demonstrate that the deletion of the C2 domain compromises TKI efficacy. Our findings identify two nuclear localization signals in the BCR sequence that are functionally suppressed by the coiled-coil and C2 domains. Targeting these regions may provide additional therapeutic strategies to manipulate both BCR-ABL1 intracellular localization and kinase activity. Full article

Yeast genetic improvement is entering a transformative phase, driven by the integration of artificial intelligence (AI), big data analytics, and synthetic microbial communities with conventional methods such as sexual breeding and random mutagenesis. These advancements have substantially expanded the potential for innovative re-engineering of yeast, ranging from single-strain cultures to complex polymicrobial consortia. This review compares traditional genetic manipulation techniques with cutting-edge approaches, highlighting recent breakthroughs in their application to beer and wine fermentation. Among the innovative strategies, adaptive laboratory evolution (ALE) stands out as a non-GMO method capable of rewiring complex fitness-related phenotypes through iterative selection. In contrast, GMO-based synthetic biology approaches, including the most recent developments in CRISPR/Cas9 technologies, enable efficient and scalable genome editing, including multiplexed modifications. These innovations are expected to accelerate product development, reduce costs, and enhance the environmental sustainability of brewing and winemaking. However, despite their technological potential, GMO-based strategies continue to face significant regulatory and market challenges, which limit their widespread adoption in the fermentation industry. Full article

The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) attacks, anomalous network behaviors, and data manipulation, which threaten the security and reliability of IoT ecosystems. New methods based on machine learning have been reported in the literature, addressing topics such as intrusion detection and prevention. This paper proposes an advanced anomaly detection framework for IoT networks expressed in several phases. In the first phase, data preprocessing is conducted using techniques like the Median-KS Test to remove noise, handle missing values, and balance datasets, ensuring a clean and structured input for subsequent phases. The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. This approach identifies the most significant features, reduces dimensionality, and enhances computational efficiency without sacrificing accuracy. In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. This multi-step methodology ensures adaptability and scalability in handling diverse IoT scenarios. The evaluation results demonstrate the superiority of the proposed framework over existing methods. It achieves a 12.5% improvement in accuracy (98%), a 14% increase in detection rate (95%), a 9.3% reduction in false positive rate (10%), and a 10.8% decrease in false negative rate (5%). These results underscore the framework’s effectiveness, reliability, and scalability for securing real-world IoT networks against evolving cyber threats. Full article

Background: Even with advanced management involving pharmacologic and ventilatory strategies, respiratory dysfunction increases morbidity and reduces the quality of life. This narrative review examines how craniofacial and cervical manipulative interventions—including nasomaxillary skeletal expansion, breathing re-education, and structural techniques—may holistically optimize airway function by enhancing neurological and lymphatic dynamics, modulating vagal tone, reducing pharyngeal collapsibility, and supporting immune regulation across diverse clinical settings. Objectives: To explore manual techniques that influence respiratory and autonomic function and to evaluate their reported clinical efficacy and supporting evidence, particularly in the context of airway disorders such as asthma and pneumonia. Methods: A narrative review of the literature from PubMed and Google Scholar was conducted using search terms related to airway function and osteopathic manipulative techniques (OMTs). The inclusion criteria spanned 2010–2025 English-language peer-reviewed full-text articles on airway function, OMT, and emergency airway maneuvers. Clinical trials, observational studies, and reviews were included; non-peer-reviewed content and animal studies (unless mechanistically relevant) were excluded. Chapman’s reflexes related to respiratory function were incorporated to highlight somatic–visceral correlations. Key Findings: The techniques reviewed included frontal lift, vomer manipulation, maxillary and zygomatic balancing, and cervical adjustments. Thoracic OMT methods, such as diaphragm doming and lymphatic pump techniques, were also addressed. Emergency techniques, such as the BURP and Larson maneuvers, prone positioning, and high-frequency chest wall oscillation, were presented as comparative strategies to OMTs for acute airway management. Conclusions: Craniofacial and cervical manipulations can be a promising adjunct for enhancing airway function. However, the current literature displays heterogeneity and lack of large-scale randomized trials, which emphasize the necessity for standardized research and the establishment of clinical guidelines with the collected evidence. Full article

The primary objective of this study is to investigate the intricate relationship between different ownership structures, such as family, institutional, managerial, and foreign ownership, and tax avoidance practices. It also seeks to explore the moderating influence of female board members in shaping these relationships. This study utilizes balanced panel data from 72 industrial and service firms listed on the Amman Stock Exchange during the period of 2018 to 2023. The Generalized Method of Moments (GMM) was employed to estimate the results. The results indicate that family and foreign ownership positively influence tax avoidance practices, suggesting that families may engage in tax avoidance to benefit from rent extraction, while foreign investors may pressure managers to manipulate tax liabilities or shift profits across countries to minimize taxes. In contrast, the presence of female directors as well as institutional and managerial ownership is associated with a reduction in tax avoidance. Female directors play a moderating role in the relationship between ownership structure and tax avoidance. Their presence in interaction with institutional ownership reduces tax avoidance by focusing on tax compliance strategies. However, this effect changes in family and foreign-owned firms, where control over decision-making lies with the families or foreign shareholders, limiting the impact of female directors in promoting compliance and aligning their role with the tax avoidance strategies preferred by the controlling owners. Full article

Mesenchymal stem cells (MSCs) have a broad clinical potential, but their selection and expansion on plastic cause unknown purity and phenotypic alterations, reducing therapy efficiency. Furthermore, their behavior in non-adherent conditions during systemic transplantation remains poorly understood. The sphere formation from single cells is commonly used to assess stemness, but MSCs lack this ability, raising questions about their anchorage dependence for proliferation. We investigated whether bone marrow-derived MSCs can complete cytokinesis in non-adherent environments. Primary human and mouse bone marrow-derived MSCs were synchronized in early mitosis using nocodazole and were cultured on soft, rigid, or non-adherent surfaces. Both human and mouse MSCs displayed an ALIX (abscission licensor) recruitment to the midbody 40–90 min post-nocodazole release, regardless of the substrate adherence. Cells maintained for 4hr in the suspension remained viable, and daughter cells rapidly migrated apart upon the re-adhesion to fibronectin-coated surfaces, demonstrating cytokinesis completion in suspension. These findings distinguish MSCs from fibroblasts (which require adhesion for division), provide a more general stemness feature, and suggest that adhesion-independent cytokinesis is a trait relevant to the post-transplantation survival and tissue homing. This property may offer strategies to expand MSCs with an improved purity and functionality and to enhance engraftment by leveraging cell cycle manipulation to promote an early extracellular matrix deposition at target sites. Full article

Large pre-trained models (PLMs) have provided tremendous opportunities and potentialities for multimodal fake news detection. However, existing multimodal fake news detection methods never manipulate the token-wise hierarchical semantics of news yielded from PLMs and extremely rely on contrastive learning but ignore the symmetry between text and image in terms of the abstract level. This paper proposes a novel multimodal fake news detection method that helps to balance the understanding between text and image via (1) designing a global-token across-attention mechanism to capture the correlations between global text and tokenwise image representations (or tokenwise text and global image representations) obtained from BERT and ViT; (2) proposing a QK-sharing strategy within cross-attention to enforce model symmetry that reduces information redundancy and accelerates fusion without sacrificing representational power; (3) deploying a semantic augmentation module that systematically extracts token-wise multilayered text semantics from stacked BERT blocks via CNN and Bi-LSTM layers, thereby rebalancing abstract-level disparities by symmetrically enriching shallow and deep textual signals. We also prove the effectiveness of our approach by comparing it with four state-of-the-art baselines. All the comparisons were conducted using three widely adopted multimodal fake news datasets. The results show that our approach outperforms the benchmarks by 0.8% in accuracy and 2.2% in F1-score on average across the three datasets, which demonstrates a symmetric, token-centric fusion of fine-grained semantic fusion, thereby driving more robust fake news detection. Full article

Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. Unfortunately, they still represent a persistent challenge in digital image forensics, underlining the importance of ensuring the integrity of digital visual content. In this study, we present a systematic evaluation of the performance of a convolutional neural network (CNN) specifically designed for copy-move manipulation detection, applied to three datasets widely used in the literature in the context of digital forensics: CoMoFoD, Coverage, and CASIA v2. Our experimental analysis highlighted a significant variability of the results, with an accuracy ranging from 95.90% on CoMoFoD to 27.50% on Coverage. This inhomogeneity has been attributed to specific structural factors of the datasets used, such as the sample size, the degree of imbalance between classes, and the intrinsic complexity of the manipulations. We also investigated different regularization techniques and data augmentation strategies to understand their impact on the network performance, finding that adopting the L2 penalty and reducing the learning rate led to an accuracy increase of up to 2.5% for CASIA v2, while on CoMoFoD we recorded a much more modest impact (1.3%). Similarly, we observed that data augmentation was able to improve performance on large datasets but was ineffective on smaller ones. Our results challenge the idea of universal generalizability of CNN architectures in the context of copy-move forgery detection, highlighting instead how performance is strictly dependent on the intrinsic characteristics of the dataset under consideration. Finally, we propose a series of operational recommendations for optimizing the training process, the choice of the dataset, and the definition of robust evaluation protocols aimed at guiding the development of detection systems that are more reliable and generalizable. Full article

The use of multi-joint arms in a high-spindle environment can solve complex problems, but the singularity problem of the manipulator related to the structure of the serial manipulator is prominent. Therefore, based on the general mathematical model of fruit spatial distribution in high-spindle apple orchards, this study proposes two harvesting system architecture schemes that can meet the constraints of fruit spatial distribution and reduce the singularity of harvesting robot operation, which are four-arm dual-module independent moving scheme (Scheme A) and four-arm single-module parallel moving scheme (Scheme B). Based on the link-joint method, the analytical expression of the singular configuration of the redundant degree of freedom arm group system under the two schemes is obtained. Then, the inverse kinematics solution method of the redundant arm group and the singularity avoidance picking trajectory planning strategy are proposed to realize the judgment and solution of the singular configuration in the complex working environment of the high-spindle. The singularity rate of Scheme A in the simulation environment is 17.098%, and the singularity rate of Scheme B is only 6.74%. In the field experiment, the singularity rate of Scheme A is 26.18%, while the singularity rate of Scheme B is 13.22%. The success rate of Schemes A and B are 80.49% and 72.33%, respectively. Through experimental comparison and analysis, Scheme B is more prominent in solving singular problems but still needs to improve the success rate in future research. This paper can provide a reference for solving the singular problems in the complex working environment of high spindles. Full article

This paper presents a secure control framework enhanced with an event-triggered mechanism to ensure resilient and resource-efficient operation under false data injection (FDI) attacks on sensor measurements. The proposed method integrates a Kalman filter and a neural network (NN) to construct a hybrid observer capable of detecting and compensating for malicious anomalies in sensor measurements in real time. Lyapunov-based update laws are developed for the neural network weights to ensure closed-loop system stability. To efficiently manage system resources and minimize unnecessary control actions, an event-triggered control (ETC) strategy is incorporated, updating the control input only when a predefined triggering condition is violated. A Lyapunov-based stability analysis is conducted, and linear matrix inequality (LMI) conditions are formulated to guarantee the boundedness of estimation and system errors, as well as to determine the triggering threshold used in the event-triggered mechanism. Simulation studies on a two-degree-of-freedom (2-DOF) robot manipulator validate the effectiveness of the proposed scheme in mitigating various FDI attack scenarios while reducing control redundancy and computational overhead. The results demonstrate the framework’s suitability for secure and resource-aware control in safety-critical applications. Full article

Rising global temperatures challenge poultry production by disrupting the cecal microbiota, which is essential for chicken health. Thermal manipulation (TM) during embryogenesis is a potential strategy to enhance thermotolerance in broilers. This study examined TM’s effects on the cecal microbiome, body weight (BW), and body temperature (BT) under chronic heat stress (CHS). Fertile Indian River eggs (n = 800) were incubated under control (37.8 °C, 56% RH) or TM conditions (39 °C, 65% RH for 18 h per day from embryonic day 10 to 18). On post-hatch day 18, male chicks were assigned to either CHS (35 ± 0.5 °C for five days) or thermoneutral conditions (24 ± 0.5 °C). The CHS-TM group showed a significantly higher BW than the CHS-CON group (p < 0.05). Under thermoneutral conditions, TM chicks had a lower BT on day 1 (p < 0.05), while the CHS-TM group exhibited a non-significant BT reduction compared to the CHS-CON group under heat stress (p > 0.05). An analysis of the gut microbiome showed that the beta diversity analysis (PERMANOVA, p < 0.05) indicated distinct microbial shifts. Firmicutes and Bacteroidota dominated the phylum level, with CHS increased Bacilli and Lactobacillus while reducing Lachnospirales in the CHS-TM group. These findings suggest that TM modulates gut microbiota and mitigates BW loss, offering a potential strategy to enhance broilers’ resilience to heat stress. Full article