Search Results (3,073)

Two-pore channels (TPCs) are evolutionarily conserved intracellular cation channels found in both plants and animals, where they mediate ion fluxes across endomembrane compartments. While historically the plant channel was among the first plant ion channels to be characterized, thanks to the relative ease of applying the patch-clamp technique to isolated plant vacuoles, where it is localized, the functional properties of the two main human isoforms, HsTPC1 and HsTPC2, expressed in endosomal and lysosomal membranes, were elucidated much later. In plants, TPCs are typically represented by a single isoform, exemplified by AtTPC1 in the model plant Arabidopsis thaliana, which functions as a voltage-dependent, Ca²⁺-regulated channel. The physiological role of plant TPCs is not yet fully clarified, although evidence suggests that they may contribute to systemic signaling and stress responses. In humans, two main isoforms, HsTPC1 and HsTPC2, are expressed in endosomal and lysosomal membranes. Human TPCs are primarily regulated by the phosphoinositide PI(3,5)P₂ and display a high selectivity for Na⁺. However, these channels also appear as a non-selective cationic conductance when activated by the potent Ca²⁺-mobilizing messenger NAADP, likely through interaction with an accessory protein. Functionally, human TPCs are involved in endolysosomal trafficking, membrane fusion, and intracellular signaling, with emerging roles in immunity, metabolism, and disease. Overall, TPCs represent key components of intracellular ion homeostasis and cellular physiology; however, their precise regulatory mechanisms and integrated physiological roles remain only partially understood and, in several respects, are still elusive. Full article

Overhauser magnetometer (OVM) is a proton precession magnetometer (PM) enhanced by electron resonance, and it is widely used in earthquake prediction, UXO detection, geological exploration, etc. For fast measurement, high cycling rate is necessary for OVM to enhance spatial resolution. Due to the impossibility to receive Larmor signal during the polarization process, traditional intermittent measurement is limited in fast mobile measurement applications owing to the long polarization time. Since it is difficult for proton magnetization to align rapidly for the long longitudinal relaxation time of liquid proton, we combined RF continuous excitation with a series 90° pulse to achieve fast measurement. To achieve the best alignment, a dynamic equation of Larmor precession is constructed and calculated, and the influences such as pulse waveform, pulse strength, and pulse duration on the proton magnetization alignment were investigated. The influence of different waveform pulses on the Larmor signal was studied experimentally, and the experimental results verified that the polarization time can be significantly shortened and fast measurement can be achieved by optimizing the waveform, strength, and duration of the 90° pulse. By using the optimized 90° pulse, the proton magnetization can be saturated within 3 ms, and 0.02 nT sensitivity was observed at 1 Hz cycling rate. Consistency between theory and the experiment indicates that the dynamic equation of Larmor motion can provide theoretical guidance for the investigation of fast measurement. Full article

Background: Magnesium sulfate (MgSO₄) has been investigated as an adjuvant in perioperative analgesia because of its antagonistic effects on the N-methyl-D-aspartate receptor (NMDA receptor) and its potential to attenuate central sensitization. However, clinical findings regarding its analgesic efficacy remain inconsistent across surgical procedures. Lumbar microdiscectomy is a common spinal procedure in which effective early postoperative pain control is important for patient comfort and early mobilization. This study aimed to evaluate the effect of intraoperative MgSO₄ administration on early postoperative analgesia and perioperative outcomes in patients undergoing lumbar microdiscectomy. Methods: This retrospective single-center cohort study included thirty-eight patients with American Society of Anesthesiologists (ASA) physical status I–II who underwent elective single-level lumbar microdiscectomy under general anesthesia. Patients were divided into two groups according to intraoperative magnesium administration: a control group receiving standard anesthesia without MgSO₄ (n = 19) and an MgSO₄ group receiving an intravenous MgSO₄ bolus of 30 mg/kg followed by a continuous infusion of 10 mg/kg/h until skin closure (n = 19). Postoperative pain intensity was assessed using the Numeric Rating Scale (NRS) at 0, 5, 10, 15, and 30 min after admission to the post-anesthesia care unit. Secondary outcomes included intraoperative remifentanil consumption, extubation time, and time to first mobilization. Complementary in silico analyses included molecular docking and protein–protein interaction (PPI) network analysis. Results: Postoperative NRS scores were numerically lower in the MgSO₄ group; however, between-group differences were not statistically significant. Mean intraoperative remifentanil consumption was numerically lower in the MgSO₄ group (236 ± 166 µg) compared with the control group (319 ± 298 µg), without statistical significance (p = 0.27). Repeated-measures analysis demonstrated the significant effect of time on postoperative NRS scores, whereas the overall group effect was not significant. Molecular analyses indicated stable morphine binding to opioid receptors and highlighted glutamatergic signaling components as central nodes within the interaction network. Conclusions: Intraoperative MgSO₄ administration was not associated with significant improvements in early postoperative pain scores or perioperative recovery parameters following lumbar microdiscectomy. Molecular analyses provide exploratory in silico insights and should be interpreted cautiously given the retrospective design and the in silico nature of these findings. Full article

Background/Objectives: Portable and wearable ECG technologies are increasingly used in adult cardiac monitoring. However, evidence supporting their feasibility and diagnostic performance in pediatric populations remains limited. This systematic review evaluates the diagnostic accuracy, usability, artifact susceptibility, and user acceptance of mobile ECG technologies in pediatric cardiology. Methods: A systematic literature search was performed in the Embase, PubMed, Scopus, and Web of Science databases. The review was conducted in accordance with the PRISMA 2020 guidelines and was registered in the PROSPERO database. Results: A total of 30 publications were included in the final analysis. Portable ECG devices demonstrated good feasibility diagnostic utility in children. Handheld systems provided high-quality tracings with strong agreement with standard 12-lead ECGs and higher adherence, as well as user satisfaction compared with conventional event recorders. However, automated rhythm classification frequently misidentified pediatric arrhythmias. Smartwatch-based ECG recordings showed high diagnostic accuracy when manually interpreted, but automated algorithms were unreliable, particularly for tachyarrhythmias and conduction abnormalities. Alternative electrode placement strategies improved smartwatch performance, and patient acceptance was consistently high. ECG patch monitoring, particularly with extended-wear devices, achieved the highest diagnostic yield, detecting arrhythmias often missed by short-duration Holter monitoring while maintaining comparable signal quality. Conclusions: Mobile ECG technologies represent a promising adjunct for pediatric rhythm surveillance, offering diagnostic performance comparable to standard modalities when interpreted by clinicians and improved usability and patient acceptance. Persistent limitations include the poor reliability of adult-oriented automated algorithms and the underrepresentation of younger children and the predominantly off-label use of these devices in pediatric populations, underscoring the need for pediatric-specific algorithm development and age-adapted device design. Full article

Periodontitis represents a primary etiological factor in tooth mobility, with oxidative stress contributing critically to periodontal tissue destruction. Evodiamine (EVO), a quinazolinocarboline alkaloid, exhibits multiple biological activities; however, its antioxidant effects and mechanism in periodontitis have not been elucidated. The aim of this study was to investigate the regulatory effect of EVO on oxidative stress in periodontitis and to explore the associated molecular mechanism. The results indicate that EVO exhibits potent antimicrobial activity against key periodontal pathogens and suppresses pathogen-induced ROS generation as well as the release of pro-inflammatory cytokines (IL-1β, IL-6, TNF-α) under periodontitis conditions. EVO binds specifically to the Kelch domain of KEAP1 with a strong binding energy (−11.67 kcal/mol), inhibits KEAP1–NRF2 interaction, and consequently upregulates the expression of antioxidant enzymes (HO-1, NQO1, GCLC, and SOD2), while downregulating the expression of iNOS, COX2, and NOX2. Furthermore, EVO inhibits the pro-apoptotic effect of the JAK2/STAT3 signaling axis and mitigates inflammation, alleviates cell cycle arrest, and promotes the migration and repair of periodontal ligament cells. Collectively, these findings suggest that EVO acts as a potential binder of KEAP1 that alleviates periodontal inflammation through modulation of oxidative stress and regulation of the JAK2/STAT3 pathway. Full article

Signal synchronization is one of the core aspects of communication, ensuring that the receiver accurately decodes the signals transmitted by the sender. However, in the diverse application scenarios and broad spectrum range of 5G new radio (NR), the performance of traditional estimation algorithms often deteriorates as frequency offset increases and noise interference intensifies. This work focuses on the estimation of time offset, cell sector identifier (ID), and frequency offset in 5G mobile communication systems. We leverage the advanced learning capabilities and adaptability of a convolutional neural network (CNN) to optimize the estimation process. Additionally, we incorporate a long short-term memory (LSTM) network to capture the dynamic variations in time-varying channels. The results demonstrate that the proposed neural network exhibits significant advantages in estimation performance. Full article

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Smart-city programmes in emerging economies often produce climate-risk registers, dashboards, and narrative reports that do not lead to real changes in technical specifications or budget decisions. This study examines how the internal audit function can transform such symbolic compliance into enforceable climate-governance practices within Egypt’s state-led smart-city developments. This paper applies an interpretive single-case study design, drawing on interviews, documents, and field observations to analyse how climate-risk signals move from operational systems into governance, procurement, and reporting routines. A unified risk-and-control framework is introduced that integrates enterprise risk management, internal control over sustainability information, and the requirements of the international climate-disclosure standards. The findings show that an internal audit provides the enforcement mechanism that converts climate-scenario breaches into mandatory amendments to design clauses, acceptance tests, and operating and capital expenditure decisions across critical assets such as coastal protection, water systems, district cooling, mobility, and data-centre infrastructure. This study offers a practical governance architecture—such as threshold-to-specification tables, climate-weighted procurement gates, quarterly compliance certifications, and verifiable data-lineage controls—that enables public managers to embed accountable and transparent climate resilience within smart-city programmes. This research contributes to sustainability governance by demonstrating how an internal audit moves climate-risk management from narrative reporting toward enforceable, auditable action. Full article

Bone formation requires a substantial energy supply to sustain extracellular matrix production and mineralization, yet the temporal contribution of lipid metabolism during osteoblast maturation remains incompletely characterized. This study investigated the molecular and transcriptional remodeling of lipid metabolism. Intracellular lipid distribution was analyzed by confocal microscopy using Nile Red staining. Transcriptional modulation of lipid synthesis, storage, lipolysis, genes associated with mitochondrial fatty acid oxidation, and osteogenic markers were assessed by quantitative real-time PCR, and the biochemical composition was evaluated by Raman spectroscopy. Early stages of spheroid development showed higher expression of genes involved in lipid synthesis and storage (FASN, DGAT2, and PLIN2) together with intracellular lipid accumulation, whereas later stages displayed increased expression of lipolytic and β-oxidation markers (PNPLA2/ATGL, CPT1A, and HADHA), accompanied by the redistribution of lipid droplets. The Raman analysis revealed a time-dependent variation of lipid-associated CH₂/CH₃ bands and modulation of protein-related Amide I–III signals, consistent with biochemical remodeling during maturation. Overall, the data indicate a coordinated transcriptional shift from lipid accumulation-associated pathways toward lipid mobilization during osteogenic progression in a 3D culture. This model provides a controlled experimental platform for investigating metabolic regulation during bone formation and for studying metabolic alterations associated with skeletal disorders. Full article

Mini-roundabouts are increasingly implemented as compact, low-cost alternatives to conventional roundabouts and signalized intersections, especially at low-speed, space-constrained urban locations where safety is a concern. Their design emphasizes speed management, reduced conflict severity, and operational simplicity, contributing to safer mobility for all road users. This paper reviews U.S., German, and UK design guidelines and synthesizes empirical safety evidence from before-and-after studies of mini-roundabout conversions. In terms of design, the U.S. practice typically relies on a single large design vehicle and more permissive geometry, whereas the German guidance adopts a multi-vehicle approach with tighter curvature and stronger compactness to enforce lower speeds, affecting crash risk and driver behavior. The UK guidance is distinguished by its flush or slightly domed central marking and flexible application approach. Conversions from two-way stop-controlled (TWSC) or one-way stop-controlled (OWSC) intersections yield substantial reductions in injury and severe crashes, with total crash reductions of 17–42%. Conversions from all-way stop-controlled (AWSC) intersections present more variable outcomes, including increases in total crashes, because drivers are still reacting based on the previous control and may not adjust their expectations quickly. Modular roundabouts are also examined as alternative compact interventions for constrained or high-risk sites, with early evidence indicating reductions in severe crashes and improved speed control while minimizing construction costs and right-of-way impacts. Full article

With the rapid advancement of location-based services, next Point-of-Interest (POI) recommendation has emerged as a critical task in personalized mobility modeling and recommendation systems. It aims to predict users’ future locations based on their historical trajectories, thereby enhancing the personalization and intelligence of recommendation systems. Despite the promising progress, two key challenges remain insufficiently addressed. First, many existing methods overlook the dynamic evolution of user trajectories across multiple perspectives, resulting in entangled representations that fail to capture user intent accurately. Second, they often ignore the latent synergy across diverse perspectives, which limits the effective utilization of complementary information for recommendation. To address these issues, we propose a novel framework called MRHL. MRHL constructs multiple hypergraphs to represent distinct views of user behavior, including interaction frequency, time decay, and geographical proximity. An enhanced hypergraph convolutional network is employed to effectively model the high-order relationships within them. We propose a cascaded enhancement fusion mechanism that progressively integrates multi-view hypergraph representations to enrich the semantic information of user representations. In addition, a multi-relational contrastive learning strategy is developed to capture the consistent signals across different views, thereby enhancing the robustness and discriminative capability of user and POI representations. Extensive experiments on three public datasets consistently demonstrate that MRHL outperforms a range of strong baselines. Full article

Cadmium (Cd) is widely dispersed in the environment and has emerged as a major environmental contaminant. Although Salix viminalis shows potential for phytoremediation of Cd pollution, the defence mechanism of its roots against heavy metals remains unclear. This study explores the adaptive response of S. viminalis roots to Cd stress from physiological, transcriptomic, and metabolomic perspectives. The results suggest that Cd stress exerts inhibitory effects on root growth and development. Compared with the control (Cd-free), the root volume and dry weight of S. viminalis exposed to Cd decreased by 26% and 29%, respectively. After exposure to Cd stress for 14 and 21 days, the Cd content in the roots increased by 117-fold and 134-fold, the hydrogen peroxide content increased by 89% and 110%, and the malondialdehyde content increased by 82% and 88%, respectively. This phenomenon can be attributed to the fact that the continuous accumulation of Cd in the roots may have aggravated the degree of lipid peroxidation. A total of 9171 differentially expressed genes (DEGs) and 169 differential metabolites (DIMs) were identified through transcriptomic and metabolomic analyses. Further combined analyses revealed the potential roles of several pathways in the defensive response of S. viminalis roots against Cd stress, including plant hormone signal transduction, thiamine metabolism, glycolysis, glycerophospholipid metabolism, and other pathways. Notably, the feedback regulatory effects formed by thiamine metabolism and hormone signal transduction related to auxin, jasmonic acid, and salicylic acid play a crucial role in the early stage when roots are exposed to Cd stress. These effects mobilized osmotic adjustment in roots by enhancing saccharide metabolism and activated the Cd detoxification process by altering lipid metabolism, thereby contributing positively to the defence of willow roots against Cd stress. These findings provide insights into the adaptive mechanism of S. viminalis roots in response to Cd and the application of fast-growing woody plants in heavy metal phytoremediation. Full article

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

Urban traffic signal control heavily impacts vehicle emissions, yet most reinforcement learning models falsely assume flat terrain, ignoring the energy penalties of uphill stop-and-go driving. This omission creates a structural misalignment between generic, delay-focused rewards and the energetic realities of hilly corridors. In this work, we propose a topography-aware deep reinforcement learning framework that mitigates this hidden ecological cost. Our Context-Specific Reward Design procedure selects, normalizes, and calibrates reward terms based on physical conditions and traffic composition. The controller was trained using a microscopic simulation calibrated from video-derived traffic data, featuring a 3.8-degree uphill approach, 14,800 vehicles over 9 h, and a 20% heavy-vehicle fleet. In the uphill setting, the specialized controller reduced total CO₂ emissions to 256.97 million milligrams, corresponding to 8.6% and 4.7% reductions relative to a pressure-based and a standard deep Q-learning controller, respectively. The proposed method also achieved the lowest mean trip duration of 72.09 s and a queue length of 1.31 vehicles. Welch’s t-tests confirmed that these CO₂, duration, and queue improvements were significant. Overall, treating topography as a foundational design variable is crucial for sustainable urban mobility. Full article

Seamless localization across indoor and outdoor environments remains a fundamental challenge for wearable navigation systems, particularly those intended to assist visually impaired individuals. This challenge arises from the unreliability of GNSS signals in indoor and transitional spaces and the cumulative drift inherent to IMU–based dead reckoning. To address these limitations, this paper proposes a physics-informed GNSS–IMU sensor fusion framework that enables robust, real-time wearable navigation across heterogeneous environments. The proposed system dynamically adapts to environmental context, employing GNSS dominant localization in outdoor settings and PINN enhanced IMU-based dead reckoning during GNSS denied indoor operation. At the core of the framework is a tightly coupled Physics-Informed Neural Network (PINN) and Extended Kalman Filter (EKF), where the PINN embeds kinematic motion constraints to correct inertial drift and suppress sensor noise, while the EKF performs probabilistic state estimation and sensor fusion. The framework is implemented on a compact, energy-efficient wearable platform and evaluated using real-world indoor–outdoor pedestrian trajectories. Experimental results demonstrate improved localization accuracy, significantly reduced drift during indoor navigation, and stable indoor–outdoor transitions compared to conventional GNSS–IMU fusion methods. The proposed approach offers a practical and reliable solution for wearable assistive navigation and has broader applicability in smart mobility and autonomous wearable systems. Full article