In presynaptic nerve terminals, complexin regulates spontaneous "mini" neurotransmitter release and activates Ca 2+ -triggered synchronized neurotransmitter release. We studied the role of the C-terminal domain of mammalian complexin in these processes using singleparticle optical imaging and electrophysiology. The C-terminal domain is important for regulating spontaneous release in neuronal cultures and suppressing Ca 2+ -independent fusion in vitro, but it is not essential for evoked release in neuronal cultures and in vitro. This domain interacts with membranes in a curvature-dependent fashion similar to a previous study with worm complexin [Snead D, Wragg RT, Dittman JS, Eliezer D (2014) Membrane curvature sensing by the C-terminal domain of complexin. Nat Commun 5:4955]. The curvature-sensing value of the C-terminal domain is comparable to that of α-synuclein. Upon replacement of the C-terminal domain with membrane-localizing elements, preferential localization to the synaptic vesicle membrane, but not to the plasma membrane, results in suppression of spontaneous release in neurons. Membrane localization had no measurable effect on evoked postsynaptic currents of AMPA-type glutamate receptors, but mislocalization to the plasma membrane increases both the variability and the mean of the synchronous decay time constant of NMDA-type glutamate receptor evoked postsynaptic currents.

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution (SCE) and the particle swarm optimization (PSO) framework. But it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex engineering problems. To overcome the short-comings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of 7 benchmark function with the original SFLA, other improved SFLAs, genetic algorithm (GA), PSO, artificial bee colony (ABC) algorithm, and the grasshopper optimization algorithm with invasive weed optimization (IWGOA). The testing results showed that the modified algorithms can effectively improve the solution accuracies and convergence properties, exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

Bearing is an important part of rotating machinery, and its early fault diagnosis and accurate classification have always been difficult in engineering application. At present, the models based on the fusion of various optimization algorithms and neural networks have become one of the emerging techniques for accurate fault identification. Firstly, an improved antlion optimizer (ALO) algorithm based on estimation of distribution algorithm (EDA) and variable-step Lévy flight strategy, abbreviated as ELALO, is proposed as a new bionic intelligence. During the initialization of population, the individuals with poor fitness are redistributed by the Gaussian probability model. In view of the stagnation of iteration, Lévy flight strategy is introduced and the adaptive change of disturbance step length is controlled. Experimental results on 4 benchmark functions show that the novel ELALO can effectively improve the solution accuracy and convergence speed, compared with the original ALO. Secondly, in order to solve the disadvantage that extreme learning machine (ELM) network is easy to fall into local optimization, this ELALO algorithm is used to initialize the weights and thresholds of its network and to form the new pattern recognition model, ELALO-ELM. Finally, the bearing data of 8 patterns from Western Reserve University are decomposed by local mean decomposition (LMD), and then the symbolic entropy (SE) of the first three product function (PF) components signals is extracted and used as the input eigenvectors. Compared with the standard ELM and ALO-ELM models, the ELALO-ELM model has better generalization and stronger robustness and it can effectively improve the efficiency of network training and the accuracy of early fault pattern classification in bearing fault diagnosis. The new ELALO-ELM model can also be used for other difficult classification problems.