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Because hydrogen sulfide (H2S) is classified as a gaseous signaling molecule, protein S-sulfhydration is known to be one of the mechanisms by which H2S signals are conducted. PTP1B, a negative regulator in insulin signaling, has been found to be S-sulfhydrated at Cys215-SH to form Cys215-SSH in response to endoplasmic reticulum (ER) stress. Therefore, we aimed to understand the change in PTP1B S-sulfhydration and cellular redox homeostasis in response to insulin stimulation. We demonstrated a feasible PEG-switch method to determine the levels of PTP1B S-sulfhydration. According to the results obtained from HEK293T and MDA-MB-231 cells, insulin induced a change in PTP1B S-sulfhydration that was similar to the change in Insulin receptor substrate 1 (IRS1) phosphorylation in both cell lines. However, insulin-induced PTP1B S-sulfhydration and IRS1 phosphorylation were only significantly affected by metformin in HEK293T cells. Insulin also induced an increase in reactive oxygen species (ROS) in both cell lines. However, the level of H2S, GSH, and GSSG was only significantly affected by insulin and metformin in HEK293T cells. HEK293T cells maintained high levels of H2S and cysteine, but low levels of GSSG and GSH in general compared to MDA-MB-231 cells. From these findings, we suggest that PTP1B activity is modulated by H2S and redox-regulated S-sulfhydration during insulin signaling. [ABSTRACT FROM AUTHOR]

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots. [ABSTRACT FROM PUBLISHER]