Search Results (193)

Tumor necrosis factor (TNF) receptor-associated factor 7 (TRAF7) is a signal transducer in the TNF receptor superfamily. TRAF7 is unique among its superfamily in that it does not contain a TRAF-C domain but does contain WD-40 domains. TRAF7 interacts with mitogen-activated protein kinases (MAPK), which are known regulators of inflammation and shear stress response. Notably, these molecular interactions have profound implications for the function of brain endothelial cells (ECs), which are pivotal for sustaining the integrity of the blood–brain barrier (BBB), orchestrating neurovascular coupling (NVC), and modulating the vascular architecture. By directly influencing MAPK signaling pathways, particularly the shear stress-responsive MAPK kinase kinase 3 (MEKK3)–MAPK kinase 5 (MEK5)–extracellular-regulated protein kinase 5 (ERK5) cascade, TRAF7 contributes to vascular homeostasis, as exemplified by its role in phosphorylating ERK5. Such molecular events underpin the capacity of brain ECs to regulate substance exchange, adjust blood flow in response to neural activity, and maintain efficient cerebral perfusion, all of which are essential for preserving brain health and cognitive performance. By synthesizing the current evidence regarding TRAF7’s molecular functions and its impact on brain endothelial integrity, cerebrovascular aging, and exploring implications for therapeutic strategies targeting vascular dysfunction in the aging brain, this review fills a crucial gap in the literature. Given the limited number of original studies directly addressing these contexts, the review will integrate broader insights from related literature to provide a foundational overview for future research in this developing field. The culmination of this literature will provide a rationale for the development of novel TRAF7-targeted therapies to restore vascular integrity in the context of aging, which could maintain cognitive health. Although TRAF7 has been implicated in regulating endothelial permeability during inflammation, its precise functions in brain ECs and the subsequent effects on cerebrovascular structure and cognitive function remain to be fully elucidated. Full article

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures. Full article

The crustacean Daphnia magna produces genetically identical females and males by parthenogenesis. Males are produced in response to environmental cues including crowding and lack of food. For male development, the DM-domain containing transcription factor Doublesex1 (DSX1) is expressed spatiotemporally in male-specific traits and orchestrates male trait formation in both somatic and gonadal tissues. However, it remains unknown how the dsx1 gene is silenced in females to avoid male trait development. Heterochromatin Protein 1 (HP1) plays a crucial role in epigenetic gene silencing during developmental processes. Here we report the identification of four HP1 orthologs in D. magna. None of these orthologs exhibited sexually dimorphic expression, and among them, HP1-1 was most abundantly expressed during embryogenesis. The knock-down of HP1-1 in female embryos led to the derepression of dsx1 in the male-specific traits, resulting in the development of male characteristics, such as the elongation of the first antennae. These results suggest that HP1-1 silences dsx1 for female development while environmental cues unlock this silencing to induce male production. We infer the HP1-dependent formation of a sex-specific chromatin structure on the dsx1 locus is a key process in the environmental sex determination of D. magna. Full article

Industrial Control Systems (ICS) are increasingly targeted by sophisticated and evolving cyberattacks, while conventional static defense mechanisms and isolated intrusion detection models often lack the robustness required to cope with such dynamic threats. To overcome these limitations, we propose RHAD (Reinforced Heterogeneous Anomaly Detector), a resilient and adaptive anomaly detection framework specifically designed for ICS environments. RHAD combines a heterogeneous ensemble of detection models with a confidence-aware scheduling mechanism guided by reinforcement learning (RL), alongside a time-decaying sliding window voting strategy to enhance detection accuracy and temporal robustness. The proposed architecture establishes a modular collaborative framework that enables dynamic and fine-grained protection for industrial network traffic. At its core, the RL-based scheduler leverages the Proximal Policy Optimization (PPO) algorithm to dynamically assign model weights and orchestrate container-level executor replacement in real time, driven by network state observations and runtime performance feedback. We evaluate RHAD using two publicly available ICS datasets—SCADA and WDT—achieving 99.19% accuracy with an F1-score of 0.989 on SCADA, and 98.35% accuracy with an F1-score of 0.987 on WDT. These results significantly outperform state-of-the-art deep learning baselines, confirming RHAD’s robustness under class imbalance conditions. Thus, RHAD provides a promising foundation for resilient ICS security and shows strong potential for broader deployment in cyber-physical systems. Full article

Seminal plasma, traditionally regarded as a passive transport medium for sperm, has emerged as a sophisticated biofluid orchestrating critical dialogues in reproductive physiology. Contemporary research reveals its multifunctional role in modulating endometrial receptivity through molecular priming of the female reproductive tract, a process essential for successful embryo implantation. Notably, seminal plasma contains numerous extracellular vesicles (EVs) that serve as critical mediators of intercellular communication via the regulation of biological processes in target cells. Through this sophisticated vesicular communication system, seminal plasma extracellular vesicles (SPEVs) coordinate critical reproductive events. Thus, it will be important to elucidate the molecular mechanisms by which SPEVs mediate reproductive processes, to provide knowledge that may improve fertility outcomes. Herein, we elucidated the emerging potential of SPEVs as non-invasive biomarkers for male fertility assessment and infertility diagnosis. Furthermore, this review systematically summarized current advances in SPEVs, highlighting their multifaceted roles in mediating sperm maturation, regulating sperm capacitation, and modulating embryo implantation through targeted delivery of bioactive signaling molecules. Full article

In Supervisory Control and Data Acquisition (SCADA) systems, central to industrial automation and control systems, the Front-end Processor (FEP) facilitates seamless communication between field control devices and central management systems. As the Industrial Internet of Things (IIoT) and Industry 4.0 centered on the smart factory paradigm gain traction, conventional FEPs are increasingly showing limitations in various aspects. To address these issues, Data Distribution Service, a real-time communication middleware, and Kubernetes, a container orchestration platform, have garnered attention. However, the effective integration of conventional SCADA protocols, such as DNP3.0, IEC 61850, and Modbus with DDS, remains a key challenge. Therefore, this article proposes a Kubernetes-based real-time FEP for the modernization of SCADA systems. The proposed FEP ensures interoperability through an efficient translation mechanism between traditional SCADA protocols—DNP3.0, IEC 61850, and Modbus—and the Data Distribution Service protocol. In addition, the performance evaluation shows that the FEP achieves high throughput and sub-millisecond latency, confirming its suitability for real-time industrial control applications. This approach overcomes the limitations of conventional FEPs and enables the realization of more flexible and scalable industrial control systems. However, further research is needed to validate the system under large-scale deployment scenarios and enhance security capabilities. Future work will focus on performance evaluation in realistic conditions and the integration of quantum-resistant security mechanisms to strengthen resilience in critical infrastructure environments. Full article

Alzheimer’s disease (AD), a predominant neurodegenerative disorder, is clinically characterized by progressive cognitive deterioration and behavioral deficits. An in-depth understanding of the pathogenesis and neuropathology of AD is essential for the development of effective treatments and early diagnosis techniques. The neuropathological signature of AD involves two hallmark lesions: intraneuronal neurofibrillary tangles composed of hyperphosphorylated tau aggregates and extracellular senile plaques containing amyloid-β (Aβ) peptide depositions. Although Aβ-centric research has dominated AD investigations over the past three decades, pharmacological interventions targeting Aβ pathology have failed to demonstrate clinical efficacy. Tau, a microtubule-associated protein predominantly localized to neuronal axons, orchestrates microtubule stabilization and axonal transport through dynamic tubulin interactions under physiological conditions. In AD pathogenesis, however, tau undergoes pathogenic post-translational modifications (PTMs), encompassing hyperphosphorylation, lysine acetylation, methylation, ubiquitination, and glycosylation. These PTM-driven alterations induce microtubule network disintegration, mitochondrial dysfunction, synaptic impairment, and neuroinflammatory cascades, ultimately culminating in irreversible neurodegeneration and progressive cognitive decline. This review synthesizes contemporary advances in tau PTM research and delineates their mechanistic contributions to AD pathogenesis, thereby establishing a framework for biomarker discovery, targeted therapeutic development, and precision medicine approaches in tauopathies. This review synthesizes contemporary advances in tau PTM research and delineates their mechanistic contributions to AD pathogenesis, thereby establishing a solid theoretical and experimental basis for the early diagnosis of neurodegenerative diseases, the discovery of therapeutic targets, and the development of novel therapeutic strategies. Full article

Containerized applications are pivotal to contemporary cloud-native architectures, yet they present novel security challenges. Kubernetes, a prevalent open-source platform for container orchestration, provides robust automation but lacks inherent security measures. The intricate architecture and scattered security documentation may result in misconfigurations and vulnerabilities, jeopardizing system confidentiality, integrity, and availability. This paper analyzes the key aspects of Kubernetes security by combining theoretical examination with practical application, concentrating on architectural hardening, access control, image security, and compliance assessment. The text commences with a synopsis of Kubernetes architecture, networking, and storage, analyzing prevalent security issues in containerized environments. The emphasis transitions to practical methodologies for safeguarding clusters, encompassing image scanning, authentication and authorization, monitoring, and logging. The paper also examines recognized Kubernetes CVEs and illustrates vulnerability scanning on a local cluster. The objective is to deliver explicit, implementable recommendations for enhancing Kubernetes security, assisting organizations in constructing more robust containerized systems. Full article

Kubernetes has emerged as the industry standard for container orchestration in cloud environments, with its scheduler dynamically placing container instances across cluster nodes based on predefined rules and algorithms. Various efforts have been made to extend and improve upon the Kubernetes scheduler. However, as the majority of Kubernetes clusters operate on homogeneous hardware, most scheduling algorithms are also only developed for homogeneous systems. Heterogeneous infrastructures, which include IoT devices or specialized hardware, have become more widespread and require specialized tuning to optimize workload assignment, for which researchers and developers working on scheduling systems require access to heterogeneous hardware for development and testing; such data may not be available. While simulations such as CloudSim or K8sSim can provide insights, the level of detail they can offer to validate new schedulers is limited, as they are only simulations. To address this, we introduce Q8S, a tool for emulating heterogeneous Kubernetes clusters including x86_64 and ARM64 architectures on OpenStack using QEMU. Emulations created through Q8S provide a higher level of detail than simulations and can be used to train machine learning scheduling algorithms. By providing an environment capable of executing real workloads, Q8S enables researchers and developers to test and refine their scheduling algorithms, ultimately leading to more efficient and effective heterogeneous cluster management. We release our implementation of Q8S as open source. Full article

The ladybird beetle, Propylea japonica Thunberg (Coleoptera: Coccinellidae), is a widely distributed natural predator that is crucial in controlling various agricultural pests in China. Despite frequent references to its remarkable thermotolerance, the molecular mechanisms underlying its thermotolerance remain poorly understood. Here, we investigated metabolomic changes in P. japonica following exposure to acute heat stress (AHS) lasting 1 h at 39 °C and 43 °C in populations from Zhengzhou (ZZ, warm temperate climate zone) and Shenzhen (SZ, subtropical climate zone), representing distinct northern and southern Chinese ecosystems. A total of 4165 and 4151 metabolites were detected in positive and negative ion modes, respectively. The high proportion of lipid and lipid-like metabolites (35.5%) and the top 20 pathways containing the highest number of metabolites, implying membrane fluidity modulation and energy metabolism restructuring, served as the core adaptive mechanism in P. japonica populations confronting thermal stress. The SZ25 vs. SZ39 exhibited a significantly higher number of differentially expressed metabolites (DEMs), which were predominantly enriched in the purine and tryptophan metabolism pathways. This indicated that these pathways orchestrate thermal adaptation in the SZ population by coordinating energy metabolism reprogramming, orchestrating antioxidant defense mechanisms, and modulating neuroendocrine homeostasis dysregulation. Additionally, the starch and sucrose, arachidonic acid, and fructose and mannose metabolism pathways were also implicated. This study enhances our understanding of P. japonica thermotolerance and provides a valuable reference for thermotolerance mechanisms in other insects. Full article

Common in Fungal Extracellular Membrane (CFEM) effectors, a unique class of fungal-specific proteins, play critical roles in host-pathogen interactions. While CFEM proteins have been extensively characterized in phytopathogens, their presence and functions in Fusarium verticillioides remained unexplored. Here, we systematically identified 19 CFEM-containing proteins in F. verticillioides, among which FvCFEM12 exhibited secretory activity and plant infection-induced expression. Functional characterization revealed that FvCFEM12 suppressed Bax- and INF1-triggered cell death in Nicotiana benthamiana leaves. Furthermore, heterologous expression of FvCFEM12 in maize leaves using P. syringae strain D36E can compromise immune responses against bacterial pathogens. Deletion of FvCFEM12 impaired fungal virulence, altered hyphal morphology, and reduced cell wall stress tolerance. Interestingly, FvCFEM12 physically interacted with the maize wall-associated receptor kinase ZmWAK17ET, and targeted silencing of ZmWAK17 in maize enhanced susceptibility to F. verticillioides. Our findings revealed that FvCFEM12 is a dual-function effector that suppresses plant immunity and maintains fungal cell wall integrity, thereby orchestrating fungal pathogenicity at the host–pathogen interface. Full article

Open Radio Access Networks (ORANs) are transforming the traditional telecommunications landscape by offering more flexible, vendor-independent solutions. Unlike previous systems, which relied on rigid, vertical configurations, ORAN introduces network programmability that is AI-driven and horizontally scalable. This shift is facilitated by modern container orchestrators, such as Kubernetes and Red Hat OpenShift, which simplify the development and deployment of components such as gNB, CU/DU, and RAN Intelligent Controllers (RICs). While these advancements help reduce costs by enabling shared infrastructure, they also create new challenges in meeting ORAN’s stringent latency requirements, especially when managing large-scale xApp deployments. Near-RTRICs are responsible for controlling xApps that must adhere to tight latency constraints, often less than one second. Current orchestration methods fail to meet these demands, as they lack the required scalability and long latencies. Additionally, non-API-based E2AP (over SCTP) further complicates the scaling process. To address these challenges, we introduce ORAN-HAutoscaling, a framework designed to enable horizontal scaling through Kubernetes. This framework ensures that latency constraints are met while supporting large-scale xApp deployments with optimal resource utilization. ORAN-HAutoscaling dynamically allocates and distributes xApps into scalable pods, ensuring that central processing unit (CPU) utilization remains efficient and latency is minimized, thus improving overall performance. Full article

The liver is a pioneer internal organ that orchestrates major metabolic, detoxification, and endocrine roles. Acute factors like hepatitis and drug allergy and chronic causes like metabolic dysfunction-associated fatty liver disease (MASLD) and Hepatocellular carcinoma (HCC) drive hepatic wellness imbalances. Liver fibrosis is a reversible and curable anomaly, but the limited availability of safe and higher-specificity therapeutics is a challenging quest in hepatology. This study investigates the hepato-protective effect of Phyllanthus niruri compounds against liver fibrosis targets like lysyl oxidase-like 2 (LOXL2), heat shock protein 47 (HSP47), bromodomain-containing protein 4 (BRD4) and inhibitory kappa B kinase beta (IKKβ) and compare their anti-hepatic fibrosis activity against known inhibitors. Potential plant compounds from P. niruri were retrieved from the literature repositories, and the top 35 compounds were screened based on molecular weight, Lipinski’s rule of 5, and bioavailability score. The in silico molecular docking and in silico ADMET results provide valuable insights into hit compounds of P. niruri, namely quercitrin and hinokinin, to have good binding scores (BE) below −7 kcal/mol threshold and molecular interactions with many key residues of all the four liver fibrosis targets namely the BRD4, HSP47, LOLX2, and IKKB proteins explored in this research. Quercitrin has been identified to have BE values of −8.1, −8.3, −8.2, and −9.1 kcal/mol scores against the BRD4, HSP47, LOLX2, and IKKB proteins, respectively. Similarly, hinokinin also shows BE values of −8.8, −7.4, −6.7, and −9.0 kcal/mol scores with BRD4, HSP47, LOLX2, and IKKB proteins individually. Further, in vitro and animal model-based in vivo experimental analysis needs to be explored to validate the potential of quercitrin and hinokinin for anti-liver fibrosis in the future. Full article

The non-destructive geophysical testing method ground penetrating radar (GPR), along with satellite image and air photo assessment, a review of the existing literature sources, and Holocaust survivor testimony, was used to document the location of potential mass graves in Alytus, Lithuania. In World War II, six million Jews were murdered, as were as many as five million other victims of Nazi Germany’s orchestrated persecution. In the summer of 1941, 8030 Jews (4.70 percent of Lithuania’s Jewish population) lived in Alytus County, where the town of Alytus is located. It is estimated that over 8000 Jews were murdered in Alytus County, including nearly the entire Jewish population of the town of Alytus. The murder of Jews from Alytus County accounts for approximately 4.2% of the total number of Lithuanian Jews killed in the Holocaust. Survivor testimony indicates that several thousand Jews from both the town and county were murdered and buried in the Vidzgiris Forest about 1000 m from the town center. In 2022, field reconnaissance at locations in the forest, which appeared to be disturbed in a 1944 German Luftwaffe air photograph, indicated that these disturbances were associated with natural geomorphic processes and not the Holocaust. Analysis of GPR data that was collected using a pulseEKKO Pro 500-megahertz groundpenetrating radar (GPR) system in 2022 in the vicinity of monuments erected in the forest to memorialize mass graves indicates that no mass graves were directly associated with these monuments. The 1944 air photograph contained two roads that traversed through and abruptly ended in the forest, which was the impetus for detailed field reconnaissance in that area. A segment of a 150 m long linear surface feature found in the forest was assessed using GPR, and based on the profile that was generated, it was determined that this feature is possibly a segment of a much more extensive mass grave. Testimony of a Holocaust survivor stated that as many as three burial trenches exist in this portion of the forest. Additional research using non-destructive GPR technology, air photograph and satellite image assessment, and the existing literature and testimony-based data are required for the Vidzgiris Forest to better define these and other potential mass graves and other Holocaust-related features. Full article

Liver fibrosis, a consequence of chronic liver injury, represents a major global health burden and is the leading cause of liver failure, morbidity, and mortality. The pathological hallmark of this condition is excessive extracellular matrix deposition, driven primarily by integrin-mediated mechanotransduction. Integrins, transmembrane heterodimeric proteins that serve as primary ECM receptors, orchestrate complex mechanosignaling networks that regulate the activation, differentiation, and proliferation of hepatic stellate cells and other ECM-secreting myofibroblasts. These mechanical signals create self-reinforcing feedback loops that perpetuate the fibrotic response. Recent advances have provided insight into the roles of specific integrin subtypes in liver fibrosis and revealed their regulation of key downstream effectors—including transforming growth factor beta, focal adhesion kinase, RhoA/Rho-associated, coiled-coil containing protein kinase, and the mechanosensitive Hippo pathway. Understanding these mechanotransduction networks has opened new therapeutic possibilities through pharmacological manipulation of integrin-dependent signaling. Full article