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Ensuring safety and efficiency in the rapidly advancing autonomous vehicle (AV) industry presents a significant engineering challenge. Comprehensive performance evaluations and critical scenario testing are essential for identifying situations that AVs cannot handle. Thus, generating critical scenarios is a key problem in AV testing system design. This paper proposes a game-theoretic adversarial interaction method to efficiently generate critical scenarios that challenge AV systems. Initial motion prediction for adversarial and surrounding vehicles is based on kinematic models and road constraints, establishing interaction action spaces to determine possible driving domains. A novel evaluation approach combines reachability sets with adversarial intensity to assess collision risks and adversarial strength for any state, used to solve behavior values for each interaction action state. Further, equilibrium action strategies for the vehicles are derived using Stackelberg game theory, yielding optimal actions considering adversarial interactions in the current traffic environment. Simulation results show that the adversarial scenarios generated by this method significantly increase incident rates by 158% to 1313% compared to natural driving scenarios, while ride comfort and driving efficiency decrease, and risk significantly increases. These findings provide critical insights for model improvement and demonstrate the proposed method's suitability for assessing AV performance in dynamic traffic environments. [ABSTRACT FROM AUTHOR]

Recruitment and accumulation of reactive astrocytes around senile plaques are common pathological features of Alzheimer's disease (AD), with unclear mechanisms. Chemerin, an adipokine implicated in neuroinflammation, acts through its receptor, chemokine-like receptor 1 (CMKLR1), which also functions as a receptor for amyloid β (Aβ). The impact of the chemerin/CMKLR1 axis on astrocyte migration towards Aβ plaques is unknown. Here we investigated the effect of CMKLR1 on astrocyte migration around Aβ deposition in APP/PS1 mice with Cmklr1 knockout (APP/PS1-Cmklr1−/−). CMKLR1-expressed astrocytes were upregulated in the cortices and hippocampi of 9-month-old APP/PS1 mice. Chemerin mainly co-localized with neurons, and its expression was reduced in the brains of APP/PS1 mice, compared to WT mice. CMKLR1 deficiency decreased astrocyte colocalization with Aβ plaques in APP/PS1-Cmklr1−/− mice, compared to APP/PS1 mice. Activation of the chemerin/CMKLR1 axis promoted the migration of primary cultured astrocytes and U251 cells, and reduced astrocyte clustering induced by Aβ42. Mechanistic studies revealed that chemerin/CMKLR1 activation induced STING phosphorylation. Deletion of STING attenuated the promotion of the chemerin/CMKLR1 axis relative to astrocyte migration and abolished the inhibitory effect of chemerin on Aβ42-induced astrocyte clustering. These findings suggest the involvement of the chemerin/CMKLR1/STING pathway in the regulation of astrocyte migration and recruitment to Aβ plaques/Aβ42. [ABSTRACT FROM AUTHOR]

The novel conductive polyvinylidene fluoride (PVDF) fibrous membrane with high conductivity and sensitivity was successfully prepared via electrostatic spinning and efficient silver reduction technology. Based on the selective dissolution of porogen of polyvinylpyrrolidone (PVP), the porous PVDF fibrous membrane with excellent adsorbability and mechanical strength was obtained, providing a structure base for the preparation of conductive PVDF fibrous membrane with silver nanoparticles (AgNPs-PVDF). The Ag+ in the AgNO3 mixed solution with PVP was absorbed and maintained in the inner parts and surface of the porous structure. After the reducing action of ascorbic acid-mixed solution with PVP, silver nanoparticles were obtained tightly in an original porous PVDF fibrous membrane, realizing the maximum conductivity of 2500 S/m. With combined excellent conductivity and mechanical strength, the AgNPs-PVDF fibrous membrane effectively and sensitively detected strain signals of throat vocalization, elbow, wrist, finger, and knee (gauge factor of 23). The electrospun conductive AgNPs-PVDF combined the characteristics of low resistance, high mechanical strength, and soft breathability, which provided a new and effective preparation method of conductive fibers for practical application in wearable devices. [ABSTRACT FROM AUTHOR]

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can reflect signals and transmissive signals simultaneously and can extend the coverage of signals. A conventional RIS mainly focuses on the case where the signal source and the target are on the same side. In this paper, a STAR-RIS-assisted non-orthogonal multiple access (NOMA) downlink communication system is considered to maximize the achievable rate for users by jointly optimizing the power-allocation coefficients, active beamforming, and STAR-RIS beamforming under the mode-switching (MS) protocol. The critical information of the channel is first extracted using the Uniform Manifold Approximation and Projection (UMAP) method. Based on the key extracted channel features, STAR-RIS elements and users are clustered individually using the fuzzy C-mean clustering (FCM) method. The alternating optimization method decomposes the original optimization problem into three sub-optimization problems. Finally, the sub-problems are converted to unconstrained optimization methods using penalty functions for the solution. Simulation results show that when the number of elements of RIS is 60, the achievable rate of the STAR-RIS–NOMA system is about 18% higher than that of the RIS–NOMA system. [ABSTRACT FROM AUTHOR]

Lymph node metastasis is associated with poor prognosis of oral squamous cell carcinoma (OSCC), and few studies have explored the relevance of postoperative lymphatic drainage (PLD) in metastatic OSCC. Alpha-enolase (ENO1) is a metabolic enzyme, which is related to lymphatic metastasis of OSCC. However, the role of ENO1 in PLD in metastatic OSCC has not been elucidated. Herein, we collected lymphatic drainage after lymphadenectomy between metastatic and non-metastatic lymph nodes in OSCC patients to investigate the relationship between ENO1 expression and metastasis, and to identify the proteins which interacted with ENO1 in PLD of patients with metastatic OSCC by MS/GST pulldown assay. Results revealed that the metabolic protein apolipoprotein C-III (ApoC3) was a novel partner of ENO1. The ENO1 bound to ApoC3 in OSCC cells and elicited the production of interleukin (IL)-8, as demonstrated through a cytokine antibody assay. We also studied the function of IL-8 on Jurkat T cells co-cultured with OSCC cells in vitro. Western blot analysis was applied to quantitate STAT3 (signal transducer and activator of transcription 3) and p-STAT3 levels. Mechanistically, OSCC cells activated the STAT3 signaling pathway on Jurkat T cells through IL-8 secretion, promoted apoptosis, and inhibited the proliferation of Jurkat T cells. Collectively, these findings illuminate the molecular mechanisms underlying the function of ENO1 in metastasis OSCC and provide new strategies for targeting ENO1 for OSCC treatment. [ABSTRACT FROM AUTHOR]

The purpose of our study is to predict the occurrence and prognosis of diabetic foot ulcers (DFUs) by clinical and lower extremity computed tomography angiography (CTA) data of patients using the artificial neural networks (ANN) model. DFU is a common complication of diabetes that severely affects the quality of life of patients, leading to amputation and even death. There are a lack of valid predictive techniques for the prognosis of DFU. In clinical practice, the use of scales alone has a large subjective component, leading to significant bias and heterogeneity. Currently, there is a lack of evidence-based support for patients to develop clinical strategies before reaching end-stage outcomes. The present study provides a novel technical tool for predicting the prognosis of DFU. After screening the data, 203 patients with diabetic foot ulcers (DFUs) were analyzed and divided into two subgroups based on their Wagner Score (138 patients in the low Wagner Score group and 65 patients in the high Wagner Score group). Based on clinical and lower extremity CTA data, 10 predictive factors were selected for inclusion in the model. The total dataset was randomly divided into the training sample, testing sample and holdout sample in ratio of 3:1:1. After the training sample and testing sample developing the ANN model, the holdout sample was utilized to assess the accuracy of the model. ANN model analysis shows that the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of the overall ANN model were 92.3%, 93.5%, 87.0%, 94.2% and 0.955, respectively. We observed that the proposed model performed superbly on the prediction of DFU with a 91.6% accuracy. Evaluated with the holdout sample, the model accuracy, sensitivity, specificity, PPV and NPV were 88.9%, 90.0%, 88.5%, 75.0% and 95.8%, respectively. By contrast, the logistic regression model was inferior to the ANN model. The ANN model can accurately and reliably predict the occurrence and prognosis of a DFU according to clinical and lower extremity CTA data. We provided clinicians with a novel technical tool to develop clinical strategies before end-stage outcomes. [ABSTRACT FROM AUTHOR]

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes' results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate. [ABSTRACT FROM AUTHOR]