Search Results (133)

Accumulating studies have identified the pivotal role of long non-coding RNAs (lncRNAs) in participating in host–virus interactions during virus infections. However, the regulatory roles of lncRNAs in influenza A virus (IAV) infection are still not fully elucidated. In this study, using high-throughput sequencing, we comprehensively compared the expression profiles of lncRNAs and mRNAs in mouse lungs infected either with the nonpathogenic parental (SDL124) H7N9 virus or its moderately pathogenic mouse-adapted (S8) variant. A total of 7636 significantly differentially expressed (SDE) lncRNAs were obtained in the S8-infected group compared to the mock group. As for the SDL124 group, 1042 SDE lncRNAs were identified. Subsequently, the mRNAs co-expressed with SDE lncRNAs were subjected to functional annotation and pathway enrichment analysis. The results indicated that the target mRNAs regulated by the S8 virus were mainly enriched in various immunological processes and exhibited a strong correlation with inflammatory-related signaling pathways. Moreover, 12 lncRNAs and 10 mRNAs co-expressed with SDE lncRNAs were selected and successfully verified by RT-qPCR. Among these lncRNAs, NONMMUG032982.2 and NONMMUG032328.2 exhibited strong antiviral activity against IAV. Additionally, these two lncRNAs were chosen for further in-depth bioinformatics analysis, including transcription factor prediction, coding capacity assessment, genomic location, construction of secondary structure, and prediction of potential interacting proteins. Taken together, these findings provide a cluster of lncRNAs probably associated with the virulence of IAV in mice and shed light on the anti-IAV effects of two functional lncRNAs, establishing a molecular foundation for further exploring the regulatory mechanisms of lncRNAs in IAV infection. Full article

This study used the QuEChERS method in combination with liquid chromatography–quadrupole-time-of-flight mass spectrometry (LC-Q-TOF/MS) to develop a method for simultaneous detection of 187 pesticides and 10 mycotoxins in Astragalus. The samples were extracted using an acetonitrile–water solution containing 5% formic acid, and the amount of purification materials was optimized through response surface methodology. The results show that 197 compounds exhibit good linear relationships within their respective linear ranges (R² > 0.995). The screening detection limits (SDLs) and the limits of quantification (LOQs) ranged from 0.001 to 0.02 mg/kg and 0.002 to 0.02 mg/kg, respectively. At the spiked levels of 1, 2, and 10 times LOQ, compound recoveries ranged from 61.5% to 118.9%, 67.1% to 119.6%, and 72.0% to 119.3%, respectively, with relative standard deviations (RSDs) all less than 20.0%. The intra-day precision and inter-day precision are less than 10% and 20%, respectively. This method was applied to detect 20 batches of commercially available Astragalus samples. Six compounds (three pesticides and three mycotoxins) were detected; the residues of aflatoxin and ochratoxin A in two batches exceeded the maximum residue limits and required attention. The established method is simple, rapid, and highly sensitive. It is also reproducible and meets the requirements for the accurate quantitative analysis of multiple pesticide residues and mycotoxins in Astragalus. Full article

This study examines how digital transformation (DX) unfolds in Small and Medium-sized Enterprises (SMEs) through an analytical integration of dynamic capabilities (DCs) and service-dominant logic (SDL). While DX research is abundant, existing studies tend to discuss internal organizational capabilities (DCs) and external value co-creation (SDL) in isolation, offering limited insight into how resource-constrained SMEs execute transformation in practice. Employing a multiple case study approach based on Japanese SMEs, this paper uses the micro-foundations of DC (sensing, seizing, and transforming) as an analytical lens to examine how the resource integration processes emphasized in SDL are operationalized through phased organizational decision-making. The findings illustrate that while DC provides the organizational process logic for change, SDL offers the perspective through which SMEs overcome internal resource scarcity by engaging in external collaboration. By bridging internal capability-based and external co-creation perspectives, this study contributes to a more granular and contextually grounded understanding of transformation processes under resource constraints. From a practical perspective, the findings highlight the importance of fostering dialogue and building external relationships as conditions for activating dynamic capabilities and mitigating organizational rigidity, offering practically relevant implications for SME managers and policymakers. Full article

Remote sensing image scene classification holds significant application value and has long been a research hotspot in remote sensing. However, remote sensing images contain diverse objects and complex backgrounds. Reducing background interference while focusing on key target regions in the images remains a challenge, which limits the potential improvement of classification accuracy. In this paper, a local image generation module (LIGM) is proposed to generate weights for the original images. The resulting local images, generated by weighting the original images, effectively focus on key target regions while suppressing background regions. Based on the LIGM, a two-stream architecture with self-distillation and local streams (SDLS) is proposed. The self-distillation stream extracts features from the original images using a convolutional neural network (CNN) and two MobileNetV2 networks. Furthermore, a multiplex-guided attention (MGA) module is introduced into this stream to facilitate cross-network attention-guided learning between the CNN and MobileNetV2 features. In the local stream, a MobileNetV2 network is employed to extract features from the local images. The classification logits produced by the two streams are fused, resulting in the final SDLS classification score. Experimental results demonstrate that SDLS achieves competitive performance on multiple datasets. Full article

Detection of hazardous substances in feed is important for ensuring human health. A method based on liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was developed and validated for the screening and confirmation of 420 hazardous substances, including pesticides, veterinary drugs, and mycotoxins commonly found in feed. The screening phase employed less stringent criteria to minimize false negatives caused by matrix effects. Subsequently, stricter identification criteria were applied for confirmation to avoid false positives from interfering compounds. The performance of the proposed method was verified by limit of detection (LOD, 5~500 μg/L), screening detection limits (SDL, 50~500 μg/L), matrix effect (ME, 36.12~121.16%), precision (0.02~14.98%), stability, and accuracy. The method was successfully applied to real feed samples, demonstrating its capability to detect the presence of the 420 target hazardous substances. We believe our method provides strong technical support for ensuring the quality and safety of feed. Full article

Local scour around suction caisson foundations has emerged as a significant geotechnical hazard for offshore wind turbines as developments extend into deeper waters. This study quantitatively evaluates the scour-induced degradation of the bearing capacity of suction buckets in sand using a three-dimensional finite element model incorporating the Hardening Soil (HS) constitutive model. The HS framework enables realistic representation of stress-dependent stiffness, dilatancy, and plastic hardening, which are essential for simulating stress redistribution caused by scour. Parametric analyses covering a broad range of relative scour depths show that scour depth is the primary factor governing capacity loss. Increasing scour leads to systematic reductions in horizontal and moment capacities, evident stiffness softening, and a downward migration of plastic zones. A critical threshold is identified at S_d/L = 0.3, beyond which the rate of capacity deterioration increases significantly. The H–M failure envelopes contract progressively and exhibit increasing flattening with scour depth while maintaining nearly constant eccentricity. Empirical relationships between scour depth and key envelope parameters are further proposed to support engineering prediction. The results highlight the necessity of integrating scour effects into design and assessment procedures for suction bucket foundations to ensure the long-term performance and safety of offshore wind turbines. Full article

Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures. Full article

Value co-creation is essential for the success and sustainability of Small and Medium Enterprises (SMEs), enabling them to integrate resources and knowledge from multiple stakeholders, such as customers, suppliers, and universities, to develop innovative offerings. However, research drawing on Service-Dominant Logic (SDL) and Resource-Based View (RBV) has devoted limited attention to how entrepreneurial ecosystem cooperation and marketing innovation jointly shape SME value co-creation, particularly in smaller and peripheral economies. This study examines the impact of entrepreneurial ecosystems (EEs) on value co-creation in SMEs, focusing on the moderating role of marketing innovation. EEs provide SMEs with access to new knowledge, technologies, and financial resources, which support innovation and enhance their competitiveness. Using microdata from the Portuguese Community Innovation Survey (CIS) 2020 and logistic regression models, we investigate how cooperation with key stakeholders (universities, customers, suppliers, consultants, competitors and government agencies) affects the likelihood that SMEs engage in value co-creation with users. The results show that ecosystem cooperation significantly contributes to value co-creation, with cooperation with universities, customers and suppliers exerting the strongest effects. Marketing innovation further strengthens the association between ecosystem cooperation and value co-creation, especially for knowledge-intensive and market-oriented partners. Theoretically, the study extends SDL by applying its multi-actor value co-creation perspective to entrepreneurial ecosystem configurations and specifying how cooperation with distinct actors activates co-creation mechanisms in SMEs. It extends RBV by conceptualising ecosystem cooperation as an externally orchestrated bundle of strategic resources and by positioning marketing innovation as a dynamic capability that shapes the returns to such cooperation. The findings also provide practical guidance for SMEs and policymakers seeking to design ecosystems and marketing strategies that support collaborative innovation in the knowledge economy. Full article

High carbon dioxide (CO₂) emissions from industrial processes have intensified the need for large-scale, sustainable, and low-energy-consumption carbon capture technologies. Amine-based chemical absorption is a promising method for large-scale CO₂ reduction, but it faces challenges like high regeneration energy consumption, technical limitations, and commercialization difficulties. To reduce energy consumption in regeneration, this paper reviews low-energy regeneration methods, including absorbent optimization, catalytic regeneration, process waste heat recovery, and calcium-based chemical desorption, and explains the energy-saving mechanisms of each approach. Focusing on technical development bottlenecks, this paper provides a comprehensive review of the technical advantages, application limitations, and key challenges associated with various methods. Based on commercialization needs, this paper thoroughly investigates the development process and industrialization status of carbon capture technology in the iron and steel industry. Research has revealed that optimized absorbent designs reduce regeneration heat loads, catalytic acid sites promote proton transfer and lower desorption temperature, utilization of waste heat reduce additional energy consumption, and calcium-based compounds offer both low energy consumption and economic advantages in desorption. This article constructs a theoretical framework for low-energy regeneration technology, identifies innovation priorities, and analyzes scalability challenges and development pathways, providing theoretical support and technical guidance for industrial implementation. Full article

Understanding the impact of hydric stress on medicinal plants in the context of climate change is becoming increasingly important. This study aimed to assess the quality of a seed lot of Agastache mexicana subsp. mexicana (Amm) through a novel calculation of the Vigour Index on time basis (${VI}_T$). The evaluation was based on relationships among plant height, leaf number, survival time, and plant density across six irrigation regimes, referred to as stages, which differed in the timing and quantity of water, designed to impose water stress from seedling emergence until plant death. To maximise growth and survival time, we utilised two input factors: Artificial Shade Levels (ASLs) of 38%, 87%, and 94%, as well as Silicon Dioxide Levels (SDLs) of 0.0%, 0.2%, 0.4%, and 0.8%. The effects of these treatments were measured using the Survival Index (SI) and the ${VI}_T$. The plants achieved their highest SI and ${VI}_T$ values influenced by minimum mortality and maximum height and leaf number in stage three. This behaviour aligned with the field capacity of the substrate, supporting the evaluation of stages one and two as waterlogging stress, while the remaining stages were classified as drought stress. The ${VI}_T$ results showed statistically significant effects from ASL, particularly at 94%. However, the ${VI}_T$ in relation to SDL was not statistically significant. The ${VI}_T$ measurements were visualised using spline interpolation, a method that provides an effective approach to quantify adverse conditions affecting Amm’s development and that it can support to identify the hydric stresses type. Full article

To investigate the influence of surface films on the material removal mechanism of single-crystal silicon during nanogrinding, molecular dynamics (MD) simulations were performed under different surface-film conditions. The simulations examined atomic displacements, grinding forces, radial distribution functions (RDF), phase transformations, temperature distributions, and residual stress distributions to elucidate the damage mechanisms at the surface and subsurface on the nanoscale. In this study, boron nitride (BN) and graphene films were applied to the surface of single-crystal silicon workpieces for nanogrinding simulations. The results reveal that both BN and graphene films effectively suppress chip formation, thereby improving the surface quality of the workpiece, with graphene showing a stronger inhibitory effect on atomic displacements. Both films reduce tangential forces and mitigate grinding force fluctuations, while increasing normal forces; the increase in normal force is smaller with BN. Although both films enlarge the subsurface damage layer (SDL) thickness and exhibit limited suppression of crystalline phase transformations, they help to alleviate surface stress release. In addition, the films reduce the surface and subsurface temperatures, with graphene yielding a lower temperature. Residual stresses beneath the abrasive grain are also reduced when either film is applied. Overall, BN and graphene films can enhance the machined surface quality, but further optimization is required to minimize subsurface damage (SSD), providing useful insights for the optimization of single-crystal silicon nanogrinding processes. Full article

Traditional lecture-based teaching often struggles to foster student engagement, active participation, and deep learning in large-class general education courses. As class sizes grow, students may become passive learners, limiting their ability to develop essential skills such as self-directed learning and teamwork. Innovative instructional strategies are needed to address these challenges and create a more interactive, student-centered learning environment. Team-Based Learning (TBL) has emerged as a practical pedagogical approach that promotes collaboration, critical thinking, and student accountability. This study investigates the influence of TBL on Self-Directed Learning (SDL) and Team Dynamics (TD) through a quasi-experimental design. One class was classified as the experimental group (TBL), while the other was classified as the control group (traditional lecture-based teaching). Data were analyzed using independent-samples one-way ANCOVA and the Johnson–Neyman method to examine the impacts of TBL on SDL and TD. The results indicate that the experimental group adopting TBL outperformed the control group in both SDL and TD. The ANCOVA results revealed that TBL had a significant positive impact on the self-monitoring factor of SDL after controlling for pre-test scores. Furthermore, the Johnson–Neyman analysis demonstrated that the effect of TBL varied across different pre-test levels, suggesting that the influence of TBL on SDL and TD was more pronounced under certain conditions. Overall, this study supports the effectiveness of TBL as a pedagogical strategy in large-class general education courses, highlighting its potential to enhance students’ SDL and TD. These findings provide valuable insights for future teaching practices and curriculum design, emphasizing the need for more interactive, student-centered learning approaches in higher education. Full article

Retinal vessel segmentation is critical for early diagnosis of diabetic retinopathy, yet existing deep models often compromise accuracy for complexity. We propose DSAE-Net, a lightweight dual-stage network that addresses this challenge by (1) introducing a Parameterized Cascaded W-shaped Architecture enabling progressive feature refinement with only 1% of the parameters of a standard U-Net; (2) designing a novel Skeleton Distance Loss (SDL) that overcomes boundary loss limitations by leveraging vessel skeletons to handle severe class imbalance; (3) developing a Cross-modal Fusion Attention (CMFA) module combining group convolutions and dynamic weighting to effectively expand receptive fields; and (4) proposing Coordinate Attention Gates (CAGs) to optimize skip connections via directional feature reweighting. Evaluated extensively on DRIVE, CHASE_DB1, HRF, and STARE datasets, DSAE-Net significantly reduces computational complexity while outperforming state-of-the-art lightweight models in segmentation accuracy. Its efficiency and robustness make DSAE-Net particularly suitable for real-time diagnostics in resource-constrained clinical settings. Full article

Pistachio (Pistacia vera) is widely consumed among tree nuts but capable of triggering severe IgE-mediated reactions in allergic individuals. Due to the similarity of cashew-borne and pistachio-borne allergen proteins and DNA, traditional detection methods, such as ELISA and PCR, often suffer from cross-reactivity, limiting their ability to discriminate between these two allergens. This study presents a sensitive LC-MS/MS method for the simultaneous detection of pistachio and cashew allergens in processed food with a screening detection limit (SDL) equal to 1 mg/kg. The method was validated for specificity, SDL, β error, precision, and ruggedness, and applied to various matrices (cereals, chocolate, sauces, and meat products). Ruggedness testing showed that all considered parameters must be carefully monitored by the operator, and sample preparation must be carried out without any modification in parameter values, under strictly controlled conditions. Good reproducibility was achieved for pistachio detection, while ongoing investigations should be carried out to overcome existing constraints for cashew. The LC-MS/MS method described in this work is a discriminatory method suitable for official food allergen control to selectively differentiate pistachio from cashew allergens, overcoming the limitations of PCR and ELISA when cross-reactivity occurs. It represents a validated tool for pistachio detection and a promising approach toward improving cashew allergen analysis. Full article

As digital transformation continues to redefine education, understanding how emerging technologies can enhance self-directed learning (SDL) becomes essential for learners, educators, instructional designers, and policymakers, as this approach supports personalized learning, strengthens student autonomy, and responds to the demands of more flexible and dynamic educational environments. This systematic review examines how artificial intelligence (AI) tools enhance SDL by offering personalized, adaptive, and real-time support for learners in online environments. Following the PRISMA 2020 methodology, a literature search was conducted to identify relevant studies published between 2020 and 2025. After applying inclusion, exclusion, and quality criteria, 77 studies were selected for in-depth analysis. The findings indicate that AI-powered tools such as intelligent tutoring systems, chatbots, conversational agents, and natural language processing applications promote learner autonomy, enable self-regulation, provide real-time feedback, and support individualized learning paths. However, several challenges persist, including overreliance on technology, cognitive overload, and diminished human interaction. These insights suggest that, while AI plays a transformative role in the evolution of education, its integration must be guided by thoughtful pedagogical design, ethical considerations, and a learner-centered approach to fully support the future of education through the internet. Full article