Corynoline Alleviates Osteoarthritis Development via the Nrf2/NF-κB Pathway.

Purpose: OA is a multifactorial joint disease in which inflammation plays a substantial role in the destruction of joints. Corynoline (COR), a component of Corydalis bungeana Turcz., has anti-inflammatory effects. Materials and Methods: We evaluated the significance and potential mechanisms of COR in OA development. The viabilities of chondrocytic cells upon COR exposure were assessed by CCK-8 assays. Western blot, qPCR, and ELISA were used to assess extracellular matrix (ECM) degeneration and inflammation. The NF-κB pathway was evaluated by western blot and immunofluorescence (IF). Prediction of the interacting proteins of COR was done by molecular docking, while Nrf2 knockdown by siRNAs was performed to ascertain its significance. Micro-CT, H&E, Safranin O-Fast Green (S-O), toluidine blue staining, and immunohistochemical examination were conducted to assess the therapeutic effects of COR on OA in destabilization of medial meniscus (DMM) models. Results: COR inhibited ECM degeneration and proinflammatory factor levels and modulated the NF-κB pathway in IL-1ß-treated chondrocytes. Mechanistically, COR bound Nrf2 to downregulate the NF-κB pathway. Moreover, COR ameliorated the OA process in DMM models. Conclusion: We suggest that COR ameliorates OA progress through the Nrf2/NF-κB axis, indicating COR may have a therapeutic potential for OA.

An Intelligent Self-Driving Truck System for Highway Transportation.

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.

Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven Optimization.

Interactive multi-agent simulation algorithms are used to compute the trajectories and behaviors of different entities in virtual reality scenarios. However, current methods involve considerable parameter tweaking to generate plausible behaviors. We introduce a novel approach (Heter-Sim) that combines physics-based simulation methods with data-driven techniques using an optimization-based formulation. Our approach is general and can simulate heterogeneous agents corresponding to human crowds, traffic, vehicles, or combinations of different agents with varying dynamics. We estimate motion states from real-world datasets that include information about position, velocity, and control direction. Our optimization algorithm considers several constraints, including velocity continuity, collision avoidance, attraction, direction control. Other constraints are implemented by introducing a novel energy function to control the motions of heterogeneous agents. To accelerate the computations, we reduce the search space for both collision avoidance and optimal solution computation. Heter-Sim can simulate tens or hundreds of agents at interactive rates and we compare its accuracy with real-world datasets and prior algorithms. We also perform user studies that evaluate the plausible behaviors generated by our algorithm and a user study that evaluates the plausibility of our algorithm via VR.

Realistic Data-Driven Traffic Flow Animation Using Texture Synthesis.

We present a novel data-driven approach to populate virtual road networks with realistic traffic flows. Specifically, given a limited set of vehicle trajectories as the input samples, our approach first synthesizes a large set of vehicle trajectories. By taking the spatio-temporal information of traffic flows as a 2D texture, the generation of new traffic flows can be formulated as a texture synthesis process, which is solved by minimizing a newly developed traffic texture energy. The synthesized output captures the spatio-temporal dynamics of the input traffic flows, and the vehicle interactions in it strictly follow traffic rules. After that, we position the synthesized vehicle trajectory data to virtual road networks using a cage-based registration scheme, where a few traffic-specific constraints are enforced to maintain each vehicle's original spatial location and synchronize its motion in concert with its neighboring vehicles. Our approach is intuitive to control and scalable to the complexity of virtual road networks. We validated our approach through many experiments and paired comparison user studies.

Simulating Flying Insects Using Dynamics and Data-Driven Noise Modeling to Generate Diverse Collective Behaviors.

We present a biologically plausible dynamics model to simulate swarms of flying insects. Our formulation, which is based on biological conclusions and experimental observations, is designed to simulate large insect swarms of varying densities. We use a force-based model that captures different interactions between the insects and the environment and computes collision-free trajectories for each individual insect. Furthermore, we model the noise as a constructive force at the collective level and present a technique to generate noise-induced insect movements in a large swarm that are similar to those observed in real-world trajectories. We use a data-driven formulation that is based on pre-recorded insect trajectories. We also present a novel evaluation metric and a statistical validation approach that takes into account various characteristics of insect motions. In practice, the combination of Curl noise function with our dynamics model is used to generate realistic swarm simulations and emergent behaviors. We highlight its performance for simulating large flying swarms of midges, fruit fly, locusts and moths and demonstrate many collective behaviors, including aggregation, migration, phase transition, and escape responses.