The 7th International Conference on Plant Vascular Biology 2025 (PVB2025) took place at the KKR Hotel Osaka from July 7th to July 12th. The conference attracted 169 participants from 20 countries, including 105 attendees from outside Japan. PVB2025 featured 12 plenary scientific sessions, 23 invited talks, 31 contributed talks selected from submitted abstracts, and 89 poster presentations. Plant vascular biology is a crucial area of plant science, as vascular systems transport essential resources that plants need for survival. These systems also function as a communication network, facilitating internal and external information flow at the whole-body level. Participants at PVB2025 aimed to share their latest findings in this field. Additionally, the conference emphasized the importance of inspiring and encouraging young scientists who will become the next generation of community leaders. To support this initiative, we offered five travel awards and five Best Poster Awards to undergraduate students, graduate students, and early-career professionals. Overall, PVB2025 successfully fostered discussions and collaborations in plant vascular biology research while allowing participants to experience the unique and vibrant culture of Osaka through "eating and talking till you drop."

HCGN: A Hierarchical Causal-Graph Network for sustainable communication and coordination in edge–fog systems

The study examined assessing the awareness and use of ICT tools among university students in Nigeria: Implications for digital literacy. ICT tools in the case of higher education denote a vast array of technological tools utilized in communication, creation, dissemination, storage, and management of information. The research design used was a descriptive survey research. The study population consisted of undergraduate students in the Nigerian universities. Currently, according to the National Universities Commission (NUC) data, more than 2.3 million undergraduates study at 43 federal universities, 48 state universities, and 79 private universities of the country. A sample population of 585 respondents was picked out of an estimated population of 656 qualified students who had undergone or had access to institutional ICT facilities in the chosen universities, or participated in ICT-related courses. The findings indicate that ICT use in communication, internet use, and social networking were highly used by students and academic ICT use was less prevalent (data analysis, reference management, and e-library access). The results have wide implications on the development of digital-literacy in universities in Nigeria. The study concluded that ICT awareness among the Nigerian university students is high, but not evenly distributed among the institutions. Most students are conversant with the general ICT tools yet they have inadequate skills on academic-specific technologies that are vital in academics interaction. It was recommended that ICT cannot be isolated to be taught in computer science departments but it must be mainstreamed to all courses. The course evaluation may incorporate the Internet-based elements, online portfolios or e-projects submission to promote active ICT use.

Network analysis of proactive health behaviors, parent-adolescent communication, and depressive symptoms among adolescents in China.

Inference-Subgraph Driven Multi-Agent DRL for Joint Resource Orchestration in Communication and Computing Power Network

Dynamic memory event-triggered attitude consensus optimization control for multi-spacecraft systems with a dynamic communication network

Collapse of quorum sensing networks underlies low-temperature failure of ammonia oxidation.

zDHHC-mediated palmitoylation modification patterns and tumor immune microenvironment infiltration characterization in pancreatic cancer.

When Learning Meets Dynamics: Distributed User Connectivity Maximization in AAV-Based Communication Networks

Hierarchical fault diagnosis of train communication networks based on cross-dimensional information fusion and mixture-of-head attention mechanism

Quantum queue-channels arise naturally in the context of buffering in quantum networks, wherein the noise suffered by the quantum states depends on the time spent waiting in the buffer. In [1], a simple upper-bound on the classical capacity of an additive queue-channel was derived and was shown to be achievable for the erasure and depolarizing channels. In this paper, we characterise the classical capacity for the class of unital qubit queue-channels, and show that a simple product (non-entangled) decoding strategy is capacity-achieving. As an intermediate step, we present a simpler derivation of an explicit capacity achieving product decoding strategy for any i.i.d. unital qubit channel, which may be of interest. As an important special case, we also derive the capacity and optimal decoding strategies for a symmetric generalized amplitude damping (GAD) queue-channel. Our results provide useful insights towards designing practical quantum communication networks, and highlight the need to explicitly model the impact of buffering.

Hpv-driven rewiring of the tumor immune microenvironment through single-cell profiling informs prognosis and therapy in HNSCC.

Multiomics data reveal microglia's promotion for choroidal neovascularization in endothelial cells.

Measurement-Device-Independent Quantum Key Distribution (MDIQKD) protocols can effectively resist all possible attacks targeting the measurement devices in a Quantum Key Distribution (QKD) system, thus exhibiting high security. However, due to the protocol's high sensitivity to channel attenuation, its key generation rate and transmission distance are significantly limited in practical applications.<br>To improve the performance of MDI-QKD, researchers have proposed quantum memory (QM) assisted MDI-QKD protocols, which have enhanced the protocol's performance to a certain extent. Nevertheless, under finite-size conditions where the total number of transmitted pulses is limited, accurately estimating the relevant statistical parameters remains a challenge. As a result, existing QM-assisted MDI-QKD schemes still suffer from issues such as low key rates and limited secure transmission distances.<br>To address these problems, this paper proposes a novel improved finite-size QM-assisted MDI-QKD protocol. By utilizing quantum memories to temporarily store early-arriving pulses and release them synchronously, the protocol effectively reduces the impact caused by channel asymmetry. Additionally, the protocol introduces a four-intensity decoy-state method to improve the estimation accuracy of single-photon components. Meanwhile, to mitigate the impact of finite-length effects on QM schemes, the proposed protocol incorporates a collective constraint model and a double-scanning algorithm to jointly estimate scanning error counts and vacuum-related counts. This approach enhances the estimation accuracy of the single-photon detection rate and phase error rate under finite-size conditions, thereby significantly improving the secure key rate of the MDI-QKD system.<br>Simulation results demonstrate that under the same experimental conditions, compared with the existing QM-assisted three-intensity decoystate MDI-QKD protocol and the four-intensity decoy-state MDI-QKD protocol based on Heralded Single-photon Source, (HSPS), the proposed protocol extends the secure transmission distance by more than 30 kilometers and 100 kilometers, respectively. This proves that under the same parameter settings, the proposed scheme exhibits significant advantages in both key rate and secure transmission distance. Therefore, this research provides important theoretical references and valuable benchmarks for the development of long-distance, high-security quantum communication networks.

Topology optimization of AUV relays for layered deep-sea communication networks

Spectrum Allocation and Optimisation of Wireless Communication Networks

The muscle-brain axis in type 2 diabetes: Molecular pathways linking sarcopenia and cognitive decline.

Traditional street lighting systems are inadequate in performing basic functions such as monitoring, control, and centralized management. The traditional lighting technologies used in these systems do not allow for the optimization of energy consumption and maintenance activities. A significant portion of the existing infrastructure is designed with older generation lamps and analog control units, which imposes major limitations in terms of energy efficiency and sustainability. However, rapid technological advances in recent years and increased industrial activity focused on smart lighting solutions have enabled the integration of sensor-based, communication-enabled, and remotely accessible Internet of Things (IoT) and LED-based systems into street lighting systems. This study examines IoT and LED-based systems developed to increase energy efficiency and reduce operating costs in street lighting applications. The study describes current technological developments in IoT applications by bringing together smart poles equipped with LED lamp technology, smart sensors, communication networks, and monitoring unit components. Furthermore, LED-based systems used in lighting systems are evaluated in comparison with traditional high-pressure sodium (HPS) and mercury vapor (HPM) lamps. Technical parameters such as power consumption, light efficiency, color rendering index (CRI), and lifespan are analyzed. Furthermore, this study demonstrates the applicability of these systems in smart city infrastructures in line with Turkey's National Energy Efficiency Action Plan and contributes to future applications.

ABSTRACT Vehicular Communication Networks (VCNs) are essential for autonomous vehicles and Intelligent Transportation Systems but face challenges in security vulnerabilities and data sparsity. Traditional attack models inadequately represent VCN dynamics, weakening threat detection, while existing datasets lack real-world mobility and spatiotemporal details. This study addresses these gaps by developing a comprehensive attack simulation framework, enhancing critical network attacks i.e. position spoofing, Sybil, and wormhole through realistic mobility patterns, positional dynamics, and temporal interactions. The resulting dataset contains legitimate and malicious instances: Spoofing (45,975 legitimate, 589 malicious), Wormhole (52,237 legitimate, 5,219 malicious), and Sybil (14,829 legitimate, 1,753 malicious). It includes essential vehicular-specific features such as mobility dynamics, inter-vehicle distances, and end-to-end communication patterns. For validation, machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Logistic Regression were employed. Detection performance was evaluated using accuracy, precision, recall, and two F1-score variants (standard and macro). Results indicate high detection efficacy, with Random Forest achieving accuracy between 93.6% and 99.8% and F1-macro scores from 88.5% to 97.7%. Compared to previous studies lacking spatiotemporal considerations, our dataset’s enhanced realism demonstrates significant potential in advancing data-driven anomaly detection and real-world threat mitigation in dynamic vehicular environments.

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is increasingly understood as a multifactorial condition influenced by systemic and environmental factors beyond the central nervous system. A growing body of evidence shows that the gut-brain-microbiome axis (GBMA), a complex bidirectional communication network, is involved in neural, endocrine, immune, and metabolic pathways in AD pathogenesis. This narrative review synthesizes emerging insights into the role of gut microbiota dysbiosis in promoting neuroinflammation, amyloid-β aggregation, blood-brain barrier disruption, and cognitive decline. We explored recent advancements in metagenomics and metabolomics for profiling microbial communities and their functional metabolites linked to AD. Alterations in microbe-derived compounds, such as short-chain fatty acids and tryptophan metabolites, influence neurodevelopment, glial activation, and mitochondrial dysfunction. Multi-omics integration, enhanced by artificial intelligence (AI), enables precise biomarker discovery, patient stratification, and the development of personalized therapeutic strategies. Translational opportunities include microbiome-based diagnostics, probiotic therapy, and stratified interventions. However, clinical translation faces challenges such as methodological heterogeneity, inter-individual microbiome variation, data governance issues, and algorithmic bias. We emphasize the need for diverse reference panels, longitudinal multimodal cohorts, and shared AI-ready datasets to enhance the reproducibility and global equity of research. Strategic investment in integrative, ethically governed, and interdisciplinary approaches is essential to unlock the full therapeutic and diagnostic potential of GBMA in AD.