Search Results (163)

Prader–Willi syndrome (PWS) is a rare imprinting-related neurodevelopmental disorder caused by loss of paternally expressed genes within the chromosome 15q11–q13 region, including SNORD116, MAGEL2, and NDN. It provides a natural model for examining how genomic imprinting disruptions shape neural development and psychiatric vulnerability. This review synthesizes current evidence to clarify the mechanistic pathways linking imprinting defects and epigenetic dysregulation to neuropsychiatric outcomes in PWS. Published studies—including patient-derived induced pluripotent stem cell (iPSC) models, animal knockout systems (e.g., Magel2-null models), transcriptomic and DNA methylation datasets, and human neuroimaging research—were identified through targeted searches of PubMed and Web of Science and integrated narratively rather than through systematic procedures. Across these data sources, deletion-type PWS is primarily associated with impaired neuronal maturation, altered serotonergic signaling, and locus-specific transcriptional dysregulation. Maternal uniparental disomy (mUPD) is characterized by broader epigenetic alterations within the imprinted domain, genome-wide transcriptional effects, dopaminergic pathway alterations, and disrupted prefrontal–limbic connectivity linked to increased psychosis risk. Importantly, available evidence supports substantial phenotypic and mechanistic overlap between PWS subtypes, with genotype–phenotype associations reflecting probabilistic tendencies rather than categorical distinctions. Collectively, convergent findings across molecular, neurochemical, and systems-level studies support a mechanistic continuum extending from imprinting defects to behavioral phenotypes. These insights position PWS as a translational model for understanding how epigenetic dysregulation contributes to psychiatric risk and highlight the need for genotype-informed, mechanistically grounded research to advance biomarker development and targeted therapeutic strategies. Full article

In recent years, there has been increasing investment in the deployment of massive commercial Low Earth Orbit (LEO) constellations to provide global Internet connectivity. These constellations, now equipped with inter-satellite links, can serve as low-latency Internet backbones, requiring LEO satellites to act not only as access nodes for ground stations, but also as in-orbit core routers. Due to their high velocity and the resulting frequent handovers of ground gateways, LEO networks highly stress mobility procedures at both the sender and receiver endpoints. On the other hand, a growing trend in networking is the use of technologies based on the Information Centric Networking (ICN) paradigm for servicing IoT networks and sensor networks in general, as its addressing, storage, and security mechanisms are usually a good match for IoT needs. Furthermore, ICN networks possess additional characteristics that are beneficial for the massive LEO scenario. For instance, the mobility of the receiver is helped by the inherent data-forwarding procedures in their architectures. However, the mobility of the senders remains an open problem. This paper proposes a comprehensive solution to the mobility problem for massive LEO constellations using the Named-Data Networking (NDN) architecture, as it is probably the most mature ICN proposal. Our solution includes a scalable method to relate content to ground gateways and a way to address traffic to the gateway that does not require cooperation from the network routing algorithm. Moreover, our solution works without requiring modifications to the actual NDN protocol itself, so it is easy to test and deploy. Our results indicate that, for long enough handover lengths, traffic losses are negligible even for ground stations with just one satellite in sight. Full article

Reusing cached data is a widely adopted technique for improving network and system performance. Future Internet architectures such as Named Data Networking (NDN) leverage intermediate nodes—such as proxy servers and routers—to cache and deliver data, reducing latency and alleviating load on original data sources. However, a fundamental challenge of this approach is the lack of trust in intermediate nodes, as users cannot reliably identify and verify them. To address this issue, many systems adopt data-oriented verification rather than sender authentication, using Merkle Hash Trees (MHTs) to enable users to verify both the integrity and authenticity of received data. Despite its advantages, MHT-based authentication incurs significant redundancy: identical hash values are often recomputed, and witness data are repeatedly transmitted for each segment. These redundancies lead to increased computational and communication overhead, particularly in large-scale data publishing scenarios. This paper proposes a novel scheme to reduce such inefficiencies by enabling the reuse of previously verified node values, especially transmitted witnesses. The proposed scheme improves both computational and transmission efficiency by eliminating redundant computation arising from repeated calculation of identical node values. To achieve this, it stores and reuses received witness values. As a result, when verifying 2ⁿ segments (n > 8), the proposed method achieves more than an 80% reduction in total hash operations compared to the standard MHT. Moreover, our method preserves the security guarantees of the MHT while significantly optimizing its performance in terms of both computation and transmission costs. Full article

Named Data Networking (NDN) is identified as a significant shift within the information-centric networking (ICN) perspective that avoids our current IP-based infrastructures by retrieving data based on its name rather than where the host is placed. This shift in paradigm is especially beneficial in Internet of Things (IoT) settings because information sharing is a critical challenge, as millions of IoT items create enormous traffic. Content caching in the network is another key characteristic of NDN used in IoT, which enables data storing within the network and provides IoT devices with the opportunity to address nearby caching nodes to gain the intended content, which, in its turn, will minimize latency as well as bandwidth consumption. However, effective caching solutions must be developed since cache management is made difficult by the constant shifting of IoT networks and the constrained capabilities of IoT devices. This paper gives an overview of cache strategies in NDN-based IoT systems. It emphasizes six strategy types: popularity-based, freshness-aware, collaborative, hybrid, probabilistic, and machine learning-based, evaluating their performances in terms of demands like content preference, cache update, and power consumption. By analyzing various caching policies and their performance characteristics, this paper provides valuable insights for researchers and practitioners developing caching strategies in NDN-based IoT networks. Full article

This study presents a four-year evaluation (2020–2024) of an integrated climate mitigation project on a pig farm in Ávila, Spain, at an elevation of over 1100 m above sea level with continental climate conditions. The project aimed to reduce greenhouse gas emissions (GHG) and nitrogen pollution by implementing solid–liquid filtration followed by biological treatment in a 625 m³ Sequencing Batch Reactor (SBR) operating under a nitrification–denitrification (N-DN) regime. The SBR carried out four daily cycles, alternating aerobic and anoxic phases, with 5 and 8 m³ inlets. Aeration intensity and redox potential were continuously monitored to optimize bacterial activity. Analytical parameters (pH, electrical conductivity, solids content, nitrogen, phosphorus, and potassium) were measured using ISO methods and tracked frequently. Annual emission reductions were 75% for N₂O, up to 97% for NH₃, and 80% for N₂. In the summer months, we observed higher efficiency reduction for N₂, NH₃, and NO₂. Additionally, there was a 75% average reduction for COD and up to 92% for total GHG emissions. This real-world case study highlights the effectiveness of SBR-based N-DN systems for nutrient removal and emission reduction in high-altitude, climate-sensitive regions, contributing to EU nitrate directive compliance and circular economy practices. Full article

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

This paper addresses the challenges of multicasting in Mobile Ad Hoc Networks (MANETs), where communication relies exclusively on direct interactions between mobile nodes without the support of fixed infrastructure. In such networks, efficient information dissemination is critical, particularly in scenarios where an event detected by one node must be reliably communicated to a designated subset of nodes. The highly dynamic nature of MANET, characterized by frequent topology changes and unpredictable connectivity, poses significant challenges to stable and efficient multicasting. To address these issues, we adopt a Publish/Subscribe (Pub/Sub) model that utilizes brokers as intermediaries for information dissemination. However, ensuring the robustness of broker-based multicasting in a highly mobile environment requires novel strategies to mitigate the effects of frequent disconnections and mobility-induced disruptions. To this end, we propose a framework based on three key principles: (1) leveraging the Disruption-Tolerant Networking Implementations of the Bundle Protocol 7 (DTN7) at the network layer to sustain message delivery even in the presence of intermittent connectivity and high node mobility; (2) dynamically generating broker replicas to ensure that broker functionality persists despite sudden node failures or disconnections; and (3) enabling brokers and their replicas to periodically broadcast advertisement packets to maintain communication paths and facilitate efficient data forwarding, drawing inspiration from Named Data Networking (NDN) techniques. To evaluate the effectiveness of our approach, we conduct extensive simulations using ns-3, examining its impact on message delivery reliability, latency, and overall network throughput. The results demonstrate that our method significantly reduces message delivery delays while improving delivery rates, particularly in high-mobility scenarios. Additionally, the integration of DTN7 at the bundle layer proves effective in mitigating performance degradation in environments where nodes frequently change their positions. Our findings highlight the potential of our approach in enhancing the resilience and efficiency of broker-assisted multicasting in MANET, making it a promising solution for real-world applications such as disaster response, military operations, and decentralized IoT networks. Full article

Beef cattle breed improvement holds strategic significance in the livestock industry. Pinan cattle, developed through years of selective breeding in Xinye County, Henan Province, exhibit superior traits including thin skin, fine bone structure, rapid growth, high dressing percentage, excellent meat yield, and superior feed efficiency. However, research on the genetic characteristics of Pinan cattle remains in its infancy. In this study, we investigated population genetic diversity and positive selection signals in Pinang cattle based on whole-genome resequencing data. Using a selective sweep approach, we identified 98 candidate genes associated with growth, reproduction, and immunity, along with 13 high-confidence missense mutations, which may underlie key traits in this population. Based on the critical roles of the NDN and PARVA genes in reproduction and muscle development, the predominant T allele at the NDN c.581T > A and PARVA c.893T > A loci in the Pinan cattle population may partially explain their advantages in sexual precocity and rapid growth compared to other breeds or populations. This study provides an important theoretical basis for the genetic improvement of native beef cattle and lays a scientific foundation for further investigation into the growth and development mechanisms of Pinan cattle. Full article

Named Data Networking (NDN) is highly susceptible to Distributed Denial of Service (DDoS) attacks, such as Interest Flooding Attack (IFA) and Cache Pollution Attack (CPA). These attacks exploit the inherent data retrieval and caching mechanisms of NDN, leading to severe disruptions in data availability and network efficiency, thereby undermining the overall performance and reliability of the system. In this paper, an attack detection method based on an improved XGBoost is proposed and applied to the hybrid attack pattern of IFA and CPA. Through experiments, the performance of the new attacks and the efficacy of the detection algorithm are analyzed. In comparison with other algorithms, the proposed method is demonstrated to have advantages in terms of the advanced nature of the proposed classifier, which is confirmed by the AUC-score. Full article

As a data-centric next-generation network architecture, Named Data Networking (NDN) exhibits inherent compatibility with the distributed nature of the Internet of Things (IoT) through its name-based routing mechanism. However, existing signature schemes for NDN-IoT face dual challenges: resource-constrained IoT terminals struggle with certificate management and computationally intensive bilinear pairings under traditional Public Key Infrastructure (PKI), while NDN routers require low-latency batch verification for high-speed data forwarding. To address these issues, this study proposes ECAE, an efficient certificateless aggregate signature scheme based on elliptic curve cryptography (ECC). ECAE introduces a partial private key distribution mechanism in key generation, enabling the authentication of identity by a Key Generation Center (KGC) for terminal devices. It leverages ECC and universal hash functions to construct an aggregate verification model that eliminates bilinear pairing operations and reduces communication overhead. Security analysis formally proves that ECAE resists forgery, replay, and man-in-the-middle attacks under the random oracle model. Experimental results demonstrate substantial efficiency gains: total computation overhead is reduced by up to 46.18%, and communication overhead is reduced by 55.56% compared to state-of-the-art schemes. This lightweight yet robust framework offers a trusted and scalable verification solution for NDN-IoT environments. Full article

Information-centric networking (ICN) is a promising approach to address the limitations of current host-centric IP-based networking. ICN models feature ubiquitous in-network caching to provide faster and more reliable content delivery, name-based routing to provide better scalability, and self-certifying contents to ensure better security. Due to the differences in the core architecture of ICN compared to existing IP-based networks, it requires special considerations to provide quality-of-service (QoS) or quality-of-experience (QoE) support for applications based on ICNs. This paper discusses the latest advances in QoS and QoE research for ICNs. First, an overview of ICN architectures is given, followed by a summary of different factors that influence QoS and QoE. Approaches for improving QoS and QoE in ICNs are then discussed in five main categories: in-network caching, name resolution and routing, transmission and flow control, software-defined networking, and media-streaming-based strategies. Finally, open research questions for providing QoS and QoE support in ICNs are outlined for future research. Full article

Background: Neutrophils are key innate immune cells in peripheral blood. In recent years, sub-populations of neutrophils have been identified. In addition to the normal-density neutrophils (NDNs) in both healthy subjects and patients, low-density neutrophils (LDNs) were described in chronic inflammation and cancer. In lung transplants (LTx), neutrophils play crucial roles in reperfusion injury, acute rejection, and chronic lung allograft dysfunction. Our pilot study examines neutrophil subsets and function in LTx recipients during the first post-transplant year. Methods: We collected blood from 11 LTx recipients at various intervals. LDNs and normal-density neutrophils (NDNs) were isolated. The production of reactive oxygen species (ROS) by NDNs was measured after PMA activation using a Luminol-HRP assay. Neutrophil phenotypic markers were analyzed with flow cytometry. Results: The LDN-to-NDN ratio increased at 3 and 6 months post-transplant. Expression levels of CD62-L (aging marker), PDL-1 (immune checkpoint), CD15 (maturation), and CXCR4 (homeostasis regulator) showed modulation. Interestingly, ROS production by NDNs was mildly elevated at baseline, reduced at 6 months, and returned to baseline levels by 9 months post-transplant. Conclusions: Neutrophils exhibit dynamic changes in the first post-LTx year. Investigating neutrophil plasticity could reveal clinically relevant biomarkers and facilitate the development of diagnostic and therapeutic tools in LTx. Full article

Named data networking (NDN) is a content-centric network architecture that requires efficient name lookup to forward packets based on hierarchical content names. Whereas IP lookup operates on fixed-length addresses, NDN name lookup must identify the longest matching prefix (LMP) from a variable-length name space, making it computationally challenging. Hash table-based approaches provide O (1) search complexity but often require multiple accesses due to the unknown LMP length. To mitigate excessive off-chip memory accesses, Bloom filter-assisted pre-checking is commonly employed. However, conventional Bloom filter-based approaches can only perform membership tests and do not provide information about the next search range, which limits their ability to effectively reduce Bloom filter accesses. This paper proposes a distance-encoded Bloom filter to improve name lookup efficiency. It encodes two distance values into the Bloom filter, enabling a more refined search range compared to binary search-based methods. By utilizing these encoded distances, the proposed scheme not only reduces the number of Bloom filter queries but also ensures that only prefix nodes need to be stored in the hash table. This helps reduce hash collisions and minimize off-chip memory accesses. Experimental evaluation using large-scale FIB datasets shows that the proposed approach reduces both Bloom filter accesses and hash table lookups, which contributes to improving overall lookup performance. Full article

Low Earth Orbit (LEO) satellite networks are promising for satellite-based cloud platforms. Due to frequent link switching and long transmission distances in LEO satellite networks, applying the TCP/IP architecture introduces challenges such as packet loss and significant transmission delays. These issues can trigger excessive retransmissions, leading to link congestion and increased data acquisition delay. Deploying Named Data Networking (NDN) with connectionless communication and link-switching tolerance can help address these problems. However, the existing congestion control methods in NDN lack support for congestion avoidance, lossless forwarding, and tiered traffic scheduling, which are crucial for achieving low-delay operations in satellite-based cloud platforms. In this paper, we propose a Congestion Control method with Lossless Forwarding (CCLF). Addressing the time-varying nature of satellite networks, CCLF implements zero packet loss forwarding by monitoring output queues, aggregating packets, and prioritizing packet scheduling. This approach overcomes traditional end-to-end bottleneck bandwidth limitations, enhances network throughput, and achieves low-delay forwarding for critical Data packets. Compared with the Practical Congestion Control Scheme (PCON), the CCLF method achieves lossless forwarding at the network layer, reduces the average flow completion time by up to 41%, and increases bandwidth utilization by up to 57%. Full article