Introduction: Cerebral ischemia-reperfusion (CI/R) injury is caused by blood flow recovery after ischemic stroke. Chlorogenic acid (CGA, 5-O-caffeoylquinic acid) is a major polyphenol component of Coffea canephora, Coffea arabica L. and Mate (Ilex paraguariensis A. StHil.). Previous studies have shown that CGA has a significant neuroprotective effect and can improve global CI/R injury. However, the underlying molecular mechanism of CGA in CI/R injury has not been fully revealed. Materials: In this study, CI/R rat model was constructed. The rats were randomly divided into nine groups with ten in each group: Control, CGA (500 mg•kg-1), CI/R, CI/R + CGA (20 mg•kg-1), CI/R + CGA (100 mg•kg-1), CI/R + CGA (500 mg•kg-1), ML385 (30 mg•kg-1), CI/R + ML385 (30 mg•kg-1), CI/R + CGA + ML385. Cerebral infarction volume was detected by TTC staining. Brain pathological damage was detected by H&E staining. Apoptosis of cortical cells was detected by TUNEL staining. The expression of related proteins was detected by RT-qPCR and Western blotting. Results: Step-down test and Y maze test showed that CGA dose-dependently mitigated CI/ R-induced brain damage and enhanced learning and spatial memory. Besides, CGA promoted the expression of BDNF and NGF in a dose-dependent manner and alleviated CI/R-induced nerve injury. Moreover, CGA increased the activity of SOD and the level of GSH, as well as decreased production of ROS and LDH and the accumulation of MDA. Notably, CGA attenuated oxidative stress-induced brain injury and apoptosis and inhibited the expression of apoptosis-related proteins (cleaved caspase 3 and caspase 9). Additionally, CGA reversed CI/R induced inactivation of Nrf2 pathway and promoted Nrf2, NQO-1 and HO-1 expression. Nrf2 pathway inhibitor ML385 destroyed this promotion. Discussion: All the data indicated that CGA had a neuroprotective effect on the CI/R rats by regulating oxidative stress-related Nrf2 pathway.

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Demining and unexploded ordnance (UXO) clearance are extremely tedious and dangerous tasks. The use of robots bypasses the hazards and potentially increases the efficiency of both tasks. A first crucial step towards robotic mine/UXO clearance is to locate all the targets. This requires a path planner that generates a path to pass a detector over all points of a mine/UXO field, i.e., a planner that is complete. The current state of the art in path planning for mine/UXO clearance is to move a robot randomly or use simple heuristics. These methods do not possess completeness guarantees which are vital for locating all of the mines/UXOs. Using such random approaches is akin to intentionally using imperfect detectors. In this paper, we first overview our prior complete coverage algorithm and compare it with randomized approaches. In addition to the provable guarantees, we demonstrate that complete coverage achieves coverage in shorter time than random coverage. We also show that the use of complete approaches enables the creation of a filter to reject bad sensor readings, which is necessary for successful deployment of robots. We propose a new approach to handle sensor uncertainty that uses geometrical and topological features rather than sensor uncertainty models. We have verified our results by performing experiments in unstructured indoor environments. Finally, for scenarios where some a priori information about a minefield is available, we expedite the demining process by introducing a probabilistic method so that a demining robot does not have to perform exhaustive coverage.

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