Tmed10 deficiency results in impaired exocrine pancreatic differentiation in zebrafish larvae.

Transmembrane p24 trafficking protein 10 (TMED10) is a conserved vesicle trafficking protein. It is dysregulated in Alzheimer disease and plays a pivotal role in the pathogenesis of Alzheimer disease. In addition to the brain, TMED10 is highly expressed in the exocrine pancreas; however, its biological functions and underlying mechanisms remain largely unknown. We studied reduced Tmed10 in zebrafish embryos by morpholino oligonucleotide knockdown and CRISPR-Cas9 mutagenesis. Tmed10-deficient embryos showed extensive loss of acinar mass and impaired acinar differentiation. TMED10 has been reported to have an inhibitory effect on γ-secretase. As one of the substrates of γ-secretase, membrane-bound ß-catenin was significantly reduced in Tmed10-deficient embryos. Increased γ-secretase activity in wild-type embryos resulted in a phenotype similar to that of tmed10 mutants. And the mutant phenotype could be rescued by treatment with the γ-secretase inhibitor, N-[N-(3, 5-difluorophenacetyl)-l-alanyl]-s-phenylglycinet-butyl ester (DAPT). In addition, the reduced membrane-bound ß-catenin was accompanied with up-regulated ß-catenin target genes in Tmed10-deficient embryos. Overexpression of ß-catenin signaling inhibitor Dickkopf-1 (DKK-1) could rescue the exocrine pancreas defects. Taken together, our study reveals that Tmed10 regulates exocrine pancreatic differentiation through γ-secretase. Reduced membrane-bound ß-catenin, accompanied with hyperactivation of ß-catenin signaling, is an important cause of exocrine pancreas defects in Tmed10-deficient embryos. Our study reaffirms the importance of appropriate ß-catenin signaling in exocrine pancreas development. These findings may provide a theoretical basis for the development of treatment strategies for TMED10-related diseases.

Adaptive neural network projection analytical fault-tolerant control of underwater salvage robot with event trigger.

Introduction: To solve the problem of control failure caused by system failure of deep-water salvage equipment under severe sea conditions, an event-triggered fault-tolerant control method (PEFC) based on proportional logarithmic projection analysis is proposed innovatively. Methods: First, taking the claw-type underwater salvage robot as the research object, amore universal thruster fault model was established to describe the fault state of equipment failure, interruption, stuck, and poor contact. Second, the controller was designed by the proportional logarithmic projection analytical method. The system input signal was amplified and projected as a virtual input, which replaces the original input to isolate and learn the fault factor online by the analytical algorithm. The terminal sliding mode observer was used to compensate for the external disturbance of the system, and the adaptive neural network was used to fit the dynamic uncertainty of the system. The system input was introduced into the event-triggered mechanism to reduce the output regulation frequency of the fault thruster. Results: Finally, the simulation results showed that the method adopted in this study reduced the power output by 28.95% and the update frequency of power output by 75% compared with the traditional adaptive overdrive fault-tolerant control (AOFC) method and realized accurate pose tracking under external disturbance and system dynamic uncertain disturbance. Discussion: It has been proven that the algorithm used in this research can still reasonably allocate power to reduce the load of a fault thruster and complete the tracking task under fault conditions.