Search Results (977)

The Ca_v1.3 L-type calcium (Ca²⁺) channel plays a critical role in cardiac excitation-contraction coupling, regulating heart rate, contractility, and gene expression. The C-terminus of Ca_v1.3 has recently been shown to translocate to the nucleus and act as a transcriptional factor to modulate the function of Ca²⁺-activated K⁺ channels in atrial cardiomyocytes. However, the role of the Ca_v1.3-C-terminus in the regulation of transcription of its own Ca_v1.3 gene remains unknown. We evaluated the impact of the nuclear translocation of the Ca_v1.3-C-terminus on the transcription of the Ca_v1.3 gene and Ca_v1.3 promoter activity in vitro using cultured neonate rat ventricular myocytes (NRVMs), and mouse atrial cardiomyocytes (HL-1). Lentiviral infection of NRVMs demonstrated that the cleaved Ca_v1.3-C-terminus translocates to the nucleus where it acts as a trans-regulator. The C-terminus of Ca_v1.3 increased transcription of Ca_v1.3 in vitro in NRVMs and in vivo in mice ventricles. Additionally, MEF2 transcription factor binding sites within the Ca_v1.3 promoter may contribute to the regulatory effect of the Ca_v1.3-C-terminus. These data are the first to demonstrate unique upregulation of Ca_v1.3 transcription by its own mobile Ca_v1.3-C-terminus both in vitro and in vivo. These findings suggest that the Ca_v1.3-C-terminus has intrinsic properties as a trans-regulator of gene expression and may contribute to the modulation of cardiac function. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

Background: Chronic stress is a major risk factor for cognitive decline and blood–brain barrier (BBB) disruption, yet the underlying molecular mechanisms remain elusive. This study aimed to investigate the specific role of the metabolic intermediate homocysteine (Hcy) in chronic stress-induced BBB dysfunction and cognitive impairment. Methods: We utilized a male Sprague-Dawley rat model of chronic unpredictable mild stress (CUMS) and administered vitamin B complex to lower Hcy levels in vivo. Regional Hcy accumulation, BBB permeability, and cognitive behaviors were assessed. In vitro, primary rat brain microvascular endothelial cells (BMECs) were exposed to Hcy to evaluate barrier-forming function, transcriptomic alterations, DNA methylation patterns, Cav1.2 expression, and reactive oxygen species (ROS) production. Results: CUMS selectively induced BBB hyperpermeability and significant Hcy accumulation predominantly within the rat hippocampus, which correlated intimately with cognitive deficits. Lowering Hcy levels via vitamin B supplementation successfully restored hippocampal BBB integrity and alleviated cognitive impairment. In addition, elevated Hcy severely impaired the barrier function of BMECs. Mechanistically, Hcy reduced global DNA methylation in BMECs and specifically induced targeted DNA hypomethylation at the intro region of Cacna1c. This epigenetic shift caused the transcriptional derepression and overexpression of the Cav1.2 calcium channel. Upregulated Cav1.2 subsequently triggered a robust ROS burst, leading to tight junction degradation. Conclusions: Our findings unveil a novel metabolic–epigenetic axis where Hcy-driven Cacna1c hypomethylation directly disrupts BMECs function to dismantle the hippocampal BBB. Lowering Hcy or targeting this Hcy-Cav1.2 pathway establishes a promising therapeutic strategy for mitigating stress-related neurovascular damage and cognitive disorders. Full article

Latrophilin 1 (LPHN1/ADGRL1), an adhesion G-protein-coupled receptor (GPCR), is the principal receptor for α-latrotoxin (αLTX), a toxin that triggers massive neurotransmitter release. However, its endogenous signaling mechanism remains elusive. Here, we dissect the LPHN1 signaling pathway at the vertebrate neuromuscular junction, using the pore-deficient αLTX mutant LTX^N4C as a selective agonist. Combining electrophysiological recordings from LPHN1 knockout mice with pharmacological inhibitors, calcium imaging, and biochemical assays, we delineate the cascade from receptor activation to spontaneous quantal acetylcholine release. We demonstrate that LPHN1 is specifically localized to the presynaptic membrane and mediates LTX^N4C-evoked release. Upon activation, LPHN1 engages the G_αq–phospholipase C pathway to generate inositol 1,4,5-trisphosphate (IP₃), triggering Ca²⁺ release from intracellular stores via IP₃ receptors. This store depletion activates store-operated Ca²⁺ entry (SOCE), providing sustained Ca²⁺ required for LTX^N4C-induced burst-like exocytosis. We uncover distinct roles for Ca_V2.1 and Ca_V1 channels in initiating and sustaining this response. These findings establish LPHN1 as a GPCR that harnesses intracellular stores and SOCE to drive spontaneous neurotransmission, revealing a novel signaling paradigm for adhesion GPCRs in presynaptic function. Full article

Enteric viral infections represent a major concern for poultry production, causing growth retardation, impaired feed conversion, and increased mortality, particularly in young birds. To investigate the involvement of RNA and DNA enteric viruses in flocks exhibiting growth problems, seven poultry farms in southern Germany, including broiler, pullet, and breeder operations, were examined for the presence of chicken astrovirus (CAstV), avian reovirus (ARV), and fowl adenovirus-1 (FAdV-1) by means of RT-PCR. All farms exhibited growth retardation, diarrhea, and enteritis-associated lesions. Histopathology revealed features of runting–stunting syndrome in most of the broiler farms and depletion of lymphatic tissue in most of the pullet farms. CAstV was detected in all flocks, ARV in six, and FAdV-1 in four farms. To further characterize the viral agents, metagenomic sequencing of cecal tonsils from one severely affected broiler flock confirmed the presence of a CAstV strain identical (100%) to CAV/Belgium/4134_001/2019. In addition, the complete genome of avian Sicinivirus was assembled for the first time in Germany, showing 96.8% nucleotide identity with a Dutch strain (Chicken/NLD/2019/V_M_056_picorna_2). These findings demonstrate the widespread circulation and co-infection of enteric viruses on German poultry farms and underline the transboundary nature of these infections, emphasizing the need for enhanced surveillance and biosecurity measures to mitigate their impact on poultry health and productivity. Full article

The planning of trajectories for Connected Autonomous Vehicles (CAVs) represents a pivotal aspect of autonomous driving technologies, enabling secure navigation within traffic environments. Traditional models for trajectory control primarily focus on the efficiency and safety of individual vehicles but often overlook the dynamics involved in vehicle-to-vehicle and vehicle-to-infrastructure interactions. This study introduces a novel concept, the “driving risk field,” which imposes constraints on vehicular movement within designated road spaces to enhance safety. A vehicle dynamics model is developed, employing a non-linear fifth-degree polynomial to approximate the trajectory curves, with optimization performed using the Sequential Quadratic Programming (SQP) method. The efficacy of the optimized model is validated through simulations on the Prescan/Simulink plat Full article

Highway vehicle–road collaborative systems rely on the effective deployment of roadside equipment (RSE) to support both traffic sensing and communication. In mixed connected and automated vehicle (CAV) and human-driven vehicle (HDV) traffic environments, existing studies on hybrid RSE deployment have mainly focused on unilateral deployment or scenarios with a high CAV penetration rate, whereas bilateral deployment under a low-to-medium CAV penetration rate has received limited attention. To address this gap, this study proposes a bilateral hybrid deployment framework for highways, in which sensing and communication RSE (scRSE) and communication RSE (cRSE) are jointly allocated based on data sensing accuracy and communication connection probability. The proposed method is validated through a case study on the Qinglan Expressway in Shandong Province, China. The results show that the bilateral hybrid deployment method outperforms the benchmark deployment methods in both sensing and communication performance. In a representative scenario, the mean symmetric mean absolute percentage error (SMAPE) decreases from 2.36% under bilateral uniform deployment to 0.94% under bilateral hybrid deployment, while the mean communication connection probability (MCCP) increases from 82.20% to 86.29%. Moreover, the proposed method performs better than unilateral deployment strategies under the same deployment conditions. These findings indicate that the proposed bilateral hybrid deployment framework offers a practical and cost-effective solution for highway RSE allocation in mixed traffic environments, particularly under low-CAV-penetration conditions. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

Skeletal muscle differentiation is tightly regulated by membrane potential dynamics and voltage-dependent ion channel activity. Potassium (K⁺) and calcium (Ca²⁺) currents cooperate to orchestrate the transition of myoblasts into fusion-competent myotubes, and alterations in this process are associated with dystrophic phenotypes. Here, we investigated the electrophysiological remodeling accompanying C2C12 myogenesis and the modulatory effects of the polyphenol resveratrol (RES) on calcium voltage-gated channel subunit alpha 1 S (CACNA1S, Cav1.1, L-type) currents. Whole-cell patch-clamp recordings were performed in proliferating and differentiating C2C12 cells to characterize the temporal expression of K⁺ currents and voltage-dependent Ca²⁺ channels (VDCCs). During differentiation, three electrophysiological subpopulations were identified according to K⁺ current profiles: SK4+/EAG−/Kir−, SK4−/EAG+/Kir−, and SK4−/EAG+/Kir+. This sequence paralleled a progressive membrane hyperpolarization from −20 mV to −70 mV, consistent with the physiological maturation of myogenic cells. In C2C12 myocytes, nimodipine-sensitive L-type currents were the only Ca²⁺ conductance observed. Their activation threshold (~−30 mV) and half-activation voltage (V/2 ≈ −12 mV) indicated the co-expression of embryonic and adult Cav1.1 isoforms. Exposure to RES (30 µM, 48 h) produced a depolarizing shift in activation (ΔV/2 ≈ +9 mV) and a reduction in current amplitude across all voltages, consistent with a transition toward the adult splice variant of Cav1.1. These findings suggest that RES promotes electrophysiological maturation of skeletal muscle cells by modulating calcium channel expression and gating behavior. Given its known ability to correct splicing abnormalities in CACNA1S and related genes, resveratrol emerges as a promising pharmacological agent for restoring calcium homeostasis in neuromuscular disorders such as myotonic dystrophy type 1 (DM1). Full article

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) often exhibits limited clinical response to targeted therapies, such as Cetuximab. Identifying key drivers of tumor progression and elucidating the factors that modulate therapeutic sensitivity are essential for improving clinical outcomes. In this study, we aimed to investigate the role of CAV2 in HNSCC proliferation and its impact on Cetuximab sensitivity. Methods: Prognosis-associated genes in HNSCC were screened using the The Cancer Genome Atlas (TCGA) database. The functional role of Caveolin-2 (CAV2) in cell proliferation and apoptosis was assessed via Cell Counting Kit-8 (CCK-8), colony formation, and flow cytometry assays. Mechanistic insights were obtained through co-immunoprecipitation, ubiquitination assays, and proteomic analysis. The impact of CAV2 on Cetuximab sensitivity was evaluated both in vitro and in a xenograft mouse model. Results: Clinical analysis of 43 pairs of HNSCC tumor and adjacent normal tissues revealed that elevated CAV2 expression was significantly associated with poor prognosis in HNSCC patients (95%CI: 1.197–1.7518, p = 1.33 × 10⁻¹³). In vitro, knockdown of CAV2 suppressed cell proliferation and significantly increased apoptosis rates (from 5.1% to 10.8%, p = 0.004). Mechanistically, CAV2 interacted with the PACT protein and disrupted the PACT-PKR axis via the ubiquitin–proteasome pathway. Notably, CAV2 deficiency synergized with Cetuximab treatment, reducing the the half maximal inhibitory concentration (IC₅₀) value by 6-fold compared with control cells and suppressing tumor growth by 48.41% in xenograft models compared to Cetuximab monotherapy (p < 0.0001). Conclusions: In conclusion, these findings establish CAV2 as a critical regulator of HNSCC progression and Cetuximab sensitivity via post-translational modulation of the PACT–PKR axis. Targeting the CAV2/PACT/PKR axis may therefore represent a promising therapeutic strategy to potentiate the efficacy of EGFR-targeted therapy in patients with HNSCC. Full article

In high-density expressway environments, lane-changing (LC) maneuvers act as stochastic perturbations that compromise the hydrodynamic stability of traffic flow, leading to safety hazards and operational delays. While existing literature has extensively modeled crash severity in static complex environments (e.g., tunnels and mountainous terrains), there remains a critical deficiency in quantifying the dynamic, systemic risks induced by LC maneuvers under saturation conditions. To address this gap, this study proposes a novel Systemic Risk Assessment Framework. First, a Hidden Markov Model (HMM) is employed to decode the latent state transitions of following vehicles, quantifying the systemic consequence of LC maneuvers as “operational delay” based on traffic wave theory. Second, a Bayesian Network (BN) is constructed to infer the causal probability of risk, integrating geometric proxies such as insertion angle with kinematic variables. Validated with real-world trajectory data, the model achieves high accuracy in identifying risk accumulation precursors. This research contributes to the field of transportation systems by shifting the risk paradigm from static collision prediction to dynamic system reliability analysis, offering theoretical support for Connected and Autonomous Vehicle (CAV) decision logic. Full article

To investigate the impact mechanism of different platoon control strategies on mixed traffic flow, this paper evaluates the overall performance of different heterogeneous platoon control strategies in smoothing small traffic disturbances and improving traffic safety. First, this paper derives the stability conditions for homogeneous and mixed traffic flow based on transfer function theory. Second, by simulating small disturbance experiments, the trend of speed under different traffic densities and the penetration rate of CAVs are analyzed. The characteristics of speed change coefficients under different platoon control strategies are comparatively analyzed based on the results in part 1. Finally, numerical simulation experiments were designed to analyze the safety performance of traffic flow under each strategy. The results show that (1) the combination of a variable time gap strategy with vehicle speed has the strongest ability to suppress disturbances. Among the combination spacing strategies, the combination of the variable time gap strategy with vehicle speed and the constant time gap strategy performs best in smoothing small disturbances. (2) At low penetration rates, incorporating CAVs may increase the instability of the traffic flow, while at high rates, CAVs effectively enhance the stability. These findings provide important guidance for selecting platoon control strategies in mixed traffic flow environments from the perspective of stability and safety. Full article

Connected autonomous vehicles (CAVs) rely on deep neural network-based perception systems to operate safely in complex driving environments. However, these systems remain vulnerable to adversarial perturbations that can induce misclassification without perceptible changes to human observers. Explainable artificial intelligence (XAI) has been proposed as a potential adversarial detection mechanism by exposing inconsistencies in model attention. This study evaluated the effectiveness of NoiseCAM-based explanation-space detection on the German Traffic Sign Recognition Benchmark (GTSRB) using a single 32 × 32 CNN architecture. Adversarial examples were generated using FGSM under perturbation budgets $ϵ$ = 0.01–0.10, and detection performance was evaluated using accuracy, precision, recall, F1-score, and ROC–AUC. Results show that NoiseCAM achieves detection accuracies between 51.8% and 52.9% with ROC–AUC values of 0.52–0.53, only marginally above random discrimination (0.5). Class-wise analysis further reveals substantial variability in detection reliability across traffic sign categories, with visually structured regulatory signs exhibiting higher separability than complex warning signs. These findings suggest that explanation-space inconsistencies alone provide limited adversarial detection capability in low-resolution, safety-critical perception pipelines. The study contributes to the understanding of the operational limits of explanation-based adversarial detection and highlights the need to integrate XAI signals with complementary robustness or uncertainty-aware mechanisms for reliable deployment in autonomous driving systems. Full article

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management. Full article

Soil and water bioengineering (SWBE) is increasingly used as a nature-based solution for riverbank stabilization and riparian restoration, yet its effectiveness in tropical environments remains constrained by limited field-based evidence of species performance under hydrological disturbance. This study evaluated the establishment success and ecological effectiveness of four native riparian species (Croton urucurana Baill., Sesbania virgata (Cav.) Pers., Iochroma arborescens (L.) J.M.H.Shaw, and Gymnanthes schottiana Müll.Arg.) installed as live cuttings on a riprap structure exposed to recurrent flooding along the Paraopeba River, Brazil. A total of 160 live cuttings were monitored over a 33-month establishment period to assess survival, structural development, spontaneous vegetation recruitment, and changes in soil chemical properties and soil organic carbon stocks. Flooding acted as a dominant ecological filter, causing substantial early mortality, with overall survival declining sharply during a 70-day inundation period that included 58 consecutive days of submergence. Croton urucurana exhibited the highest survival and structural development, reaching median heights exceeding 5 m and cumulative shoot diameters greater than 100 mm after 33 months, whereas Gymnanthes schottiana showed complete mortality within the first year. Vegetation establishment facilitated spontaneous recruitment of native woody species, with 22 individuals recorded in planted sections compared to only 3 in adjacent non-planted areas. Soil organic carbon stocks increased from 38.9 to 60.6 Mg C ha⁻¹ in the 0–40 cm soil profile, indicating rapid soil development. These results demonstrate that SWBE interventions can simultaneously promote riverbank stabilization, vegetation recovery, and soil carbon accumulation. By providing quantitative field-based evidence under realistic hydrological disturbance conditions, this study advances the understanding of species selection and the ecological effectiveness of SWBE interventions in tropical riparian ecosystems. Full article