Search Results (100)

Understanding the intimate interactions between plants and their microbiota at the cellular level is essential for unlocking the full potential of plant holobionts in agricultural systems. Traditional bulk and microbial community-level sequencing approaches reveal broad community patterns but fail to resolve how distinct plant cell types interact with or regulate microbial colonization, as well as the diverse antagonistic and synergistic interactions and responses existing between various microbial populations. Recent advances in single-cell and spatial multi-omics have transformed our understanding of plant cell identities as well as gene regulatory programs and their dynamic regulation in response to environmental stresses and plant development. In this review, we highlight the single-cell discoveries that uncover the plant cell-type-specific microbial perception, immune activation, and symbiotic differentiation, particularly in roots, nodules, and leaves. We further discuss how integrating transcriptomic, epigenomic, and spatial data can reconstruct multilayered interaction networks that connect plant cell-type-specific regulatory states with microbial spatial niches and inter-kingdom signaling (e.g., ligand–receptor and metabolite exchange), providing a foundation for developing new strategies to engineer crop–microbiome interactions to support sustainable agriculture. We conclude by outlining key methodological challenges and future research priorities that point toward building a fully integrated cellular interactome of the plant holobiont. Full article

The global maritime and logistics sectors are undergoing rapid digital transformation driven by emerging technologies such as automation, the Internet of Things (IoT), and blockchain. Artificial Intelligence (AI), with its ability to analyze complex datasets, predict operational patterns, and optimize resource allocation, offers a transformative potential beyond the capabilities of conventional technologies. However, mixed results are shown in its implementation. This study examines the current state of AI applications to unlock higher levels of efficiency and competitiveness in logistics firms. A mixed-methods approach was employed, combining surveys from logistics companies with in-depth interviews from key stakeholders in ports and logistics firms to triangulate insights and enhance the validity of the findings. Our results reveal that while technologies such as automation and digital tracking are increasingly utilized to improve operational transparency and cargo management, AI applications remain limited and largely experimental. Where implemented, AI contributes to strategic decision-making, predictive maintenance, customer service enhancement, and cargo flow optimization. Nonetheless, financial conditions, data integration challenges, and a shortage of AI-skilled professionals continue to impede its wider adoption. To overcome these challenges, this study recommends targeted investments in AI infrastructure, the establishment of collaborative frameworks between public authorities, financial institutions, and technology-driven Higher Education Institutions (HEIs), and the development of human capital capable of sustaining AI-enabled transformation. By strategically leveraging AI, Oman can position its ports and logistics sector as a regional leader in efficiency, innovation, and sustainable growth. Full article

In the context of global climate governance and China’s ‘Dual Carbon’ strategy, enhancing carbon emission efficiency (CEE) is a critical pathway toward high-quality development. Urban–rural integration (URI), reshaping urban–rural structures and resource allocation, has significant environmental implications. However, the mechanisms through which URI influences city-level CEE remain underexplored. Using panel data from 283 Chinese prefecture-level cities (2005–2022), we employ a Spatial Durbin Model to investigate URI’s direct and spatial spillover effects. First, spatiotemporally, URI demonstrates an imbalanced pattern, with higher levels in eastern coastal regions and lower levels in central and western areas. Conversely, CEE exhibits a north–south divide, with higher efficiency in the south. URI advancement has been sluggish with persisting imbalances, whereas CEE has demonstrated a consistent upward trend. Second, the relationship between URI and CEE is characterized by nonlinearity and spatial dependence. The direct effect follows a U-shaped curve, initially inhibiting but later promoting local CEE once a threshold is surpassed (URI = 0.103). The spatial spillover effect follows an inverted U-shaped trajectory (threshold URI = 0.179), suggesting that inter-regional dynamics evolve from synergistic promotion to potential competition. These findings underscore the necessity of phased, adaptive policies to unlock the potential between URI and CEE, providing a scientific basis for coordinating urban–rural development with carbon neutrality objectives. Full article

Duckweed has attracted considerable attention for its high protein content, rapid growth, and broad potential in biotechnological applications. Understanding key phenotypic traits is crucial for unlocking and maximizing this potential. While most studies on duckweed growth have been conducted under natural or non-sterile conditions, here we minimize environmental influences and focus on the genetic component of growth by assessing growth performance under axenic culture. In this study, we measured relative growth rate (RGR) in four duckweed species, Landoltia punctata (G. Mey.) Les & D. J. Crawford, Lemna aequinoctialis Welw., Spirodela polyrhiza (L.) Schleid., and Wolffia globosa (Roxb.) Hartog & Plas. collected from various natural locations across Thailand. A total of six to seven strains were tested for each species. The relative growth rates of studied species ranged from 0.012 day⁻¹ in S. polyrhiza to 0.162 day⁻¹ in W. globosa. Significant intraspecific variation was observed in L. punctata, S. polyrhiza, and W. globosa, with the coefficients of variation between 9.6 to 109.9 percent. Each strain showed distinct growth characteristics: Most displayed a steady growth pattern, whereas W. globosa showed exponential growth at Day 35 after the start of experiment. The results provide the first systematic comparisons of baseline growth rate data for duckweed species in Thailand. These findings advance the understanding of strain-specific growth traits in duckweed and establish a standardized protocol for evaluating growth traits under axenic conditions, providing a basis for future research and applications. Full article

The evolution towards 6G is set to transform Vehicle-to-Everything (V2X) networks by introducing advanced technologies such as Six-Dimensional Movable Antenna (6DMA). This technology endows Roadside Units (RSUs) with dynamic beam-steering capabilities, enabling adaptive coverage. However, traditional RSU deployment strategies, optimized for static coverage, are fundamentally mismatched with these new dynamic capabilities, leading to a critical deployment–optimization mismatch. This paper addresses this challenge by proposing DyDO, a novel Dynamic Deployment and Optimization framework for the utilization of 6DMA-RSUs. Our framework strategically decouples the problem into two modules operating on distinct timescales. On a slow timescale, an offline deployment module analyzes long-term historical traffic data to identify optimal RSU locations. This is guided by a newly proposed metric, the Dynamic Potential Score (DPS), which quantifies a location’s intrinsic value for dynamic adaptation by integrating spatial concentration, temporal volatility, and traffic magnitude. On a fast timescale, an online control module employs an efficient Sequential Angular Search (SAS) algorithm to perform real-time, adaptive beam steering based on immediate traffic patterns. Extensive experiments on a large-scale, real-world trajectory dataset demonstrate that DyDO outperforms conventional static deployment methodologies. This work highlights the necessity of dynamic-aware deployment to fully unlock the potential of 6DMA in future V2X systems. Full article

To mitigate the environmental burden of sugar industry filter mud in Guangxi and unlock its resource potential, this study introduces a novel approach leveraging the unique microbial resources of mangrove ecosystems to enhance composting efficiency. Microbial strains were isolated from rhizosphere sediments of mangroves in the Beilun River in Fangchenggang and inoculated into a composting system using sugar filter mud. The results demonstrated that inoculation with a mangrove-derived microbial consortium—represented by the nitrogen-fixing strain P1N2—significantly accelerated and prolonged the thermophilic phase (≥53.6 °C for 12 days), leading to greater organic matter degradation and a reduced carbon-to-nitrogen ratio (C/N = 15.2). High-throughput sequencing revealed distinct microbial succession patterns during composting. It confirmed that the exogenous inoculant reshaped the indigenous microbial community, promoting the dominance of functional taxa, including Ochrobactrum, Bacillus, and Nocardiopsis, at key stages, thereby facilitating efficient humus synthesis. Pot experiments further verified that the resulting compost improved soil structure, stabilized nutrient availability, and markedly increased the yield and quality of Chinese flowering cabbage (Brassica parachinensis). These findings demonstrate that mangrove-derived microbial inoculants serve as potent bio-enhancers, providing an environmentally sustainable and technically feasible pathway for the high-value reutilization of sugar industry filter mud. Full article

The plant plasma membrane serves as the primary interface for perceiving extracellular signals, a function largely mediated by plasma membrane receptors (PMRs). In wheat (Triticum aestivum), the functional characterization of these receptors is impeded by the species’ large, hexaploid genome, which results in extensive gene duplication and functional redundancy. This review synthesizes current knowledge on wheat PMRs, covering their diversity, classification, and signaling mechanisms, with a particular emphasis on their central role in plant immunity. We highlight the remarkable structural and functional diversification of PMR families, which range in size from 10 members, as seen in the case of wheat leaf rust kinase (WLRK), to over 3424 members in the receptor-like kinase (RLK) family. Furthermore, we reviewed the role of PMRs in being critical for detecting a wide array of biotic stimuli, including pathogen-associated molecular patterns (PAMPs), herbivore-associated molecular patterns (HAMPs), and symbiotic signals. Upon perception, PMRs initiate downstream signaling cascades that orchestrate defense responses, including transcriptional reprogramming, cell wall reinforcement, and metabolic changes. The review also examines the complex cross-talk between immune receptors and other signaling pathways, such as those mediated by brassinosteroid and jasmonic acid receptors, which underpin the delicate balance between growth and defense. Finally, we bridge these fundamental insights to applications in crop improvement, delineating strategies like marker-assisted selection, gene stacking, and receptor engineering to enhance disease resistance. After identifying key obstacles such as genetic redundancy and pleiotropic effects, we propose future research directions that leverage multi-omics, systems biology, and synthetic biology to fully unlock the potential of wheat PMRs for sustainable agriculture. Full article

This paper examines whether financial development affects economic growth across different levels of carbon emissions in 30 Chinese provinces from 1990 to 2022. We employ a novel partially linear functional-coefficient model with latent factor structure. This approach relaxes the traditional assumptions of linearity and cross-sectional independence, allowing us to capture more flexible growth patterns. Our empirical findings reveal three key insights: (i) the positive effect of financial development on economic growth follows a nonlinear pattern—it initially strengthens as carbon emissions increase but declines rapidly after emissions reach a threshold; (ii) innovation and openness show limited impacts on economic growth; (iii) regional variations exist based on resource endowment. These findings offer important policy implications. Promoting green financial products could extend the beneficial range of carbon emissions for economic growth. Optimizing innovation structures and supervising foreign enterprises may help unlock growth potential while preventing pollution transfer. Regional strategies would benefit from accounting for resource disparities. Full article

Leber congenital amaurosis (LCA) is a severe inherited retinal disorder manifesting at birth or in early infancy, with a subset of cases linked to mutations in the aryl hydrocarbon receptor-interacting protein-like 1 (AIPL1) gene. Initially identified as the disease locus for LCA4, AIPL1 exhibits a retina-specific expression pattern. Its protein product is a unique member of the FKBP family, distinguished by its specific structural features and specialized functions. A wide spectrum of mutations in AIPL1 is associated with varying severities of retinal degeneration, implicating diverse pathogenic mechanisms. While the early onset and rapid progression of AIPL1-related disorders pose a therapeutic challenge, significant progress in gene therapy has unlocked promising avenues for effective treatment. This comprehensive review summarizes current findings to spark interest and pave the way for further studies in the therapy of AIPL1-caused retinal diseases. Full article

Osteosarcoma (OS) remains the most common primary malignant bone tumor in adolescents, with conventional treatments yielding only modest improvements in long-term survival. Immunotherapy has emerged as a promising strategy to overcome these limitations. B7-H3 (CD276) stands apart from other potential targets due to its high expression in tumors cells, as well as its strong association with tumor aggressiveness and poor prognosis. This review provides a comprehensive overview of B7-H3, covering its molecular structure, regulatory mechanisms, biological functions, and expression patterns in tumor tissues. We emphasize the dual roles of B7-H3—both immunoregulatory and non-immunoregulatory—in shaping the tumor microenvironment (TME) and facilitating immune evasion. Building on these insights, we summarize current immunotherapeutic strategies targeting B7-H3 in OS, including monoclonal antibodies (mAbs), chimeric antigen receptor T cells (CAR-T), antibody-drug conjugates (ADCs), and bispecific antibodies (bsAbs). These four strategies have their own advantages and deficiencies. Excitingly, rapid advances in nanoparticle-based systems offer promising solutions to overcome the limitations, especially to develop more effective drug delivery systems and to reshape the TME by targeting immune cells. Despite promising progress, significant challenges remain. These include the absence of an identified B7-H3 receptor, the immunosuppressive and heterogeneous nature of the OS TME, and the need for improved targeting specificity and safety. Addressing these challenges through optimization of delivery systems, combination strategies, and the integration of nanotechnology may unlock the full potential of B7-H3-based immunotherapy in the treatment of OS. Full article

In this age of consumerism, most companies are doing their utmost to convince their customers of their products and to attract new customers. The IT development we see today is a perfect solution for strengthening the relationship between companies and their customers, giving them the opportunity to expand their customer base. The Internet of Things refers to an inter-connected system of smart devices that communicate and exchange data and big data analytics over the internet. As this involves the process of the treating data to unlock hidden information, patterns, and insights, the combination of both tools creates a revolution in customer relations and gives us the opportunity to understand our customers’ needs before they do themselves. This article presents an exploratory literature review of studies analyzing the relationship between IOT and big data in marketing. It provides a deep analysis of various scholars’ works that examine the methodology used by these tools to reinforce customer relations and acquire new ones. This review provides an overview of the most interesting research on this topic and the methods and techniques employed as well as an analysis of the obstacles and challenges involved. The results of this research show that IOT and big data analytics are key factors for an efficient analysis of clients’ needs. Full article

The autonomous coordination of multi-robot systems in complex, environments remains a fundamental challenge. Current Multi-Agent Reinforcement Learning (MARL) methods often struggle to reason effectively about the dynamic, causal relationships between agents and their surroundings. To address this, we introduce the Graph-Gated Transformer (GGT), a novel neural architecture designed to inject explicit relational priors directly into the self-attention mechanism for multi-robot coordination. The core mechanism of the GGT involves dynamically constructing a Tactical Relational Graph that encodes high-priority relationships like collision risk and cooperative intent. This graph is then used to generate an explicit attention mask, compelling the Transformer to focus its reasoning exclusively on entities rather than engaging in brute-force pattern matching across all perceived objects. Integrated into a Centralized Training with Decentralized Execution (CTDE) framework with QMIX, our approach demonstrates substantial improvements in high-fidelity simulations. In complex scenarios with dynamic obstacles and sensor noise, our GGT-based system achieves 95.3% coverage area efficiency with only 0.4 collisions per episode, a stark contrast to the 60.3% coverage and 20.7 collisions of standard QMIX. Ablation studies confirm that this structured, gated attention mechanism—not merely the presence of attention—is the key to unlocking robust collective autonomy. This work establishes that explicitly constraining the Transformer’s attention space with dynamic, domain-aware relational graphs is a powerful and effective architectural solution for engineering safe and intelligent multi-robot systems. Full article

Smart Electromagnetic Skins (SESs) provide a cost-effective and efficient alternative to increasing the number of Base Stations (BSs) for improving the performance of next-generation communication networks and contribute to the implementation of Smart Radio Environments (SREs). SESs generalize the concept of ReflectArrays (RAs) because they redirect the incident field in a non-specular direction. However, as the difference between the pointing and specular directions increases, specular and parasitic effects arise, which affect the radiation pattern, energy efficiency, and pointing direction. The techniques generally adopted for SES design, using homogenized-effective-medium model, are unable to overcome this drawback efficiently. Starting with initial SES design based on the Phase-Gradient (PG) approach, the suppression of the higher order modes has been achieved by incorporating volumetric charge-current distributions when defining radiation modes, using theory of electromagnetic-multipoles. This approach reveals formation of anapoles in single-layer SESs/RAs for first time ever. By combining both local and non-local approaches in super-cell design, higher-order symmetry-breaking of unit cells is utilized to exploit anapole formation as a parasitic mode suppression method. Numerical analysis of SESs with increasing size confirms the effectiveness of the proposed approach, which allows for a drastic reduction in parasitic modes while leaving the performance of the desired mode unchanged. Adopting a multipole perspective enhances the understanding of SES radiation mechanisms, unlocks their unexploited performance potential, and opens new opportunities for multifunctional design. Full article

Conformal electronics are distinguished by their unique characteristics, such as the integration of structure and function and their conformability with complex geometries. These features unlock a broad spectrum of applications, including structural health monitoring and the creation of metasurfaces. However, the current landscape of large-scale curved electronic fabrication is characterized by a significant gap in specialized equipment and standardized strategies. In this context, we introduce a pioneering strategy that leverages robotized electrohydrodynamic (EHD) printing for the conformal fabrication of large-scale curved electronics on 3D surfaces. This comprehensive multi-robot EHD conformal printing strategy integrates several critical components, including plasma surface treatment, EHD conformal printing, and near-infrared (NIR) sintering processes. These are supported by enabling technologies such as 3D surface reconstruction and precise hybrid positioning. Notably, our strategy achieves 5 µm printing resolution via EHD lithography and 35 µm repeatable positioning accuracy. After plasma treatment, conductive patterns on FR4 substrates reach 5B-level adhesion strength. NIR sintering enables high-efficiency sintering within only 125 s. Seamless integration of these processes into multi-robot collaborative equipment enables the fabrication of large-area conformal electronics, such as 400 mm × 1000 mm unmanned aerial vehicle wings and 650 mm × 350 mm satellite shells, and supports multi-layer systems including wires, LED arrays, antennas, and sensors. This strategy possesses substantial potential to transcend the limitations inherent in traditional fabrication methods, paving the way for new frontiers in conformal electronics across a variety of applications, including smart wings and satellite surfaces. Full article

Heart failure with preserved ejection fraction (HFpEF) remains a major clinical challenge due to its heterogeneous presentation and limited therapeutic options. Accurate patient phenotyping is essential to improve diagnosis, prognostication, and treatment personalization. Machine learning (ML) has emerged as a powerful tool to identify clinically meaningful HFpEF subgroups by integrating diverse data sources, including clinical, imaging, biomarker, and physiological parameters. ML-based models can uncover subtle patterns not captured by traditional methods, offering improved risk stratification, earlier intervention, and guidance toward individualized therapy. Future progress will rely on standardized data collection, validation across populations, and incorporation into clinical decision support systems. Advancements in explainable artificial intelligence, federated learning, and multi-omics integration are expected to further refine phenotyping strategies and translate into improved patient outcomes. Continued interdisciplinary collaboration is essential to unlock the full potential of ML in transforming HFpEF management. Full article