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Emerging evidence demonstrates that the blockade of intracellular Ca2+ signals may protect pancreatic acinar cells against Ca2+ overload, intracellular protease activation, and necrosis. The activation of cannabinoid receptor subtype 2 (CB2R) prevents acinar cell pathogenesis in animal models of acute pancreatitis. However, whether CB2Rs modulate intracellular Ca2+ signals in pancreatic acinar cells is largely unknown. We evaluated the roles of CB2R agonist, GW405833 (GW) in agonist-induced Ca2+ oscillations in pancreatic acinar cells using multiple experimental approaches with acute dissociated pancreatic acinar cells prepared from wild type, CB1R-knockout (KO), and CB2R-KO mice. Immunohistochemical labeling revealed that CB2R protein was expressed in mouse pancreatic acinar cells. Electrophysiological experiments showed that activation of CB2Rs by GW reduced acetylcholine (ACh)-, but not cholecystokinin (CCK)-induced Ca2+ oscillations in a concentration-dependent manner; this inhibition was prevented by a selective CB2R antagonist, AM630, or was absent in CB2R-KO but not CB1R-KO mice. In addition, GW eliminated L-arginine-induced enhancement of Ca2+ oscillations, pancreatic amylase, and pulmonary myeloperoxidase. Collectively, we provide novel evidence that activation of CB2Rs eliminates ACh-induced Ca2+ oscillations and L-arginine-induced enhancement of Ca2+ signaling in mouse pancreatic acinar cells, which suggests a potential cellular mechanism of CB2R-mediated protection in acute pancreatitis.

Trajectory forecasting is a widely-studied problem for autonomous navigation. However, existing benchmarks evaluate forecasting based on independent snapshots of trajectories, which are not representative of real-world applications that operate on a continuous stream of data. To bridge this gap, we introduce a benchmark that continuously queries future trajectories on streaming data and we refer to it as "streaming forecasting." Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safetycritical problem yet overlooked by snapshot-based benchmarks. Moreover, forecasting in the context of continuous timestamps naturally asks for temporal coherence between predictions from adjacent timestamps. Based on this benchmark, we further provide solutions and analysis for streaming forecasting. We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster. Our algorithm estimates the states of occluded agents by propagating their positions with multimodal trajectories, and leverages differentiable filters to ensure temporal consistency. Both occlusion reasoning and temporal coherence strategies significantly improve forecasting quality, resulting in 25% smaller endpoint errors for occluded agents and 10-20% smaller fluctuations of trajectories. Our work is intended to generate interest within the community by highlighting the importance of addressing motion forecasting in its intrinsic streaming setting. Code is available at https://github.com/ziqipang/StreamingForecasting.