Lidocaine, an anesthetic drug, protects Neuro2A cells against cadmium toxicity

Purpose: To investigate the neuroprotective effect of lidocaine in Neuro2A cells Methods: Differentiated N2a cells were used in this study. Cell viability and neuroprotection were assessed using dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and trypan blue assays, while Bax/Bcl-2 expression was assayed by western blotting. Mitochondrial membrane potential, reactive oxygen species and calcium levels were measured using flow cytometry. Results: Lidocaine protected differentiated N2a cells against cadmium-induced toxicity, and also attenuated cadmium toxicity-induced changes in mitochondrial membrane potential (MMP), reactive oxygen species (ROS) and calcium (Ca2+) levels. Furthermore, Bax/Bcl-2 ratio, which was disrupted by cadmium, and cadmium-induced apoptosis, were reversed by lidocaine. Conclusion: Lidocaine protects differentiated N2a cells against cadmium-induced toxicity by reversing apoptosis. Thus, lidocaine is a potential neuroprotective agent

Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets

We propose a data-driven approach to modeling the spatio-temporal characteristics of NAND flash memory read voltages using conditional generative networks. The learned model reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the specified program/erase (P/E) cycling time stamp. We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. We conclude that the model accurately captures the spatial and temporal features of the flash memory channel

OpenInst: A Simple Query-Based Method for Open-World Instance Segmentation

Open-world instance segmentation has recently gained significant popularitydue to its importance in many real-world applications, such as autonomous driving, robot perception, and remote sensing. However, previous methods have either produced unsatisfactory results or relied on complex systems and paradigms. We wonder if there is a simple way to obtain state-of-the-art results. Fortunately, we have identified two observations that help us achieve the best of both worlds: 1) query-based methods demonstrate superiority over dense proposal-based methods in open-world instance segmentation, and 2) learning localization cues is sufficient for open world instance segmentation. Based on these observations, we propose a simple query-based method named OpenInst for open world instance segmentation. OpenInst leverages advanced query-based methods like QueryInst and focuses on learning localization cues. Notably, OpenInst is an extremely simple and straightforward framework without any auxiliary modules or post-processing, yet achieves state-of-the-art results on multiple benchmarks. Specifically, in the COCO$\to$UVO scenario, OpenInst achieves a mask AR of 53.3, outperforming the previous best methods by 2.0 AR with a simpler structure. We hope that OpenInst can serve as a solid baselines for future research in this area