Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users.

Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC15). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario.

Ailanthone induces autophagy and ferroptosis in non­small cell lung cancer Lewis cells.

Ailanthone (AIL), a monomer derived from ailanthus in Chinese medicine, has been demonstrated to have antitumor effects, albeit the underlying mechanism is unknown. Autophagy and ferroptosis are two modes of cell death that have been championed as potential mechanisms implicated in the antitumor effects of various drugs. The present study demonstrated that AIL effectively suppresses the Lewis cell proliferation in non-small cell lung cancer using MTT and colony formation assays. Autophagy and ferroptosis were verified using western blotting, immunofluorescence and ferroptosis detection. Additionally, the findings revealed that regulating the AMPK/mTOR/p70S6k signaling pathway may be the underlying mechanism for the antitumor effect of AIL. The present study established a theoretical foundation for further research into the utilization of AIL as a novel antitumor approach.

Transcriptional Regulation of ING5 and its Suppressive Effects on Gastric Cancer.

ING5 targets histone acetyltransferase or histone deacetylase complexes for local chromatin remodeling. Its transcriptional regulation and suppressive effects on gastric cancer remain elusive. Luciferase assay, EMSA, and ChIP were used to identify the cis-acting elements and trans-acting factors of the ING5 gene. We analyzed the effects of SAHA on the aggressive phenotypes of ING5 transfectants, and the effects of different ING5 mutants on aggressive phenotypes in SGC-7901 cells. Finally, we observed the effects of ING5 abrogation on gastric carcinogenesis. EMSA and ChIP showed that both SRF (-717 to -678 bp) and YY1 (-48 to 25bp) interacted with the promoter of ING5 and up-regulated ING5 expression in gastric cancer via SRF-YY1-ING5-p53 complex formation. ING5, SRF, and YY1 were overexpressed in gastric cancer, (P<0.05), and associated with worse prognosis of gastric cancer patients (P<0.05). ING5 had positive relationships with SRF and YY1 expression in gastric cancer (P<0.05). SAHA treatment caused early arrest at S phase in ING5 transfectants of SGC-7901 (P<0.05), and either 0.5 or 1.0 µM SAHA enhanced their migration and invasion (P<0.05). The wild-type and mutant ING5 transfectants showed lower viability and invasion than the control (P<0.05) with low CDC25, VEGF, and MMP-9 expression. Gastric spontaneous adenocarcinoma was observed in Atp4b-cre; ING5f/f, Pdx1-cre; ING5f/f, and K19-cre; ING5f/f mice. ING5 deletion increased the sensitivity of MNU-induced gastric carcinogenesis. ING5 mRNA might be a good marker of gastric carcinogenesis, and poor prognosis. ING5 expression was positively regulated by the interaction of SRF-YY1-ING5-p53 complex within the ING5 promoter from -50 bp upstream to the transcription start site. ING5 deletion might contribute to the tumorigenesis and histogenesis of gastric cancer.