Intrinsic control of axon regeneration

Spinal cord injury disrupts the connections between the brain and spinal cord, often resulting in the loss of sensory and motor function below the lesion site. The most important reason for such permanent functional deficits is the failure of injured axons to regenerate after injury. In principle, the functional recovery could be achieved by two forms of axonal regrowth: the regeneration of lesioned axons which will reconnect with their original targets and the sprouting of spared axons that form new circuits and compensate for the lost function. Our recent studies reveal the activity of the mammalian target of rapamycin (mTOR) pathway, a major regulator of new protein synthesis, as a critical determinant of axon regrowth in the adult retinal ganglion neurons[1]. In this review, I summarize current understanding of the cellular and molecular mechanisms that control the intrinsic regenerative ability of mature neurons

Neuropilin Is a Receptor for the Axonal Chemorepellent Semaphorin III

AbstractExtending axons in the developing nervous system are guided to their targets through the coordinate actions of attractive and repulsive guidance cues. The semaphorin family of guidance cues comprises several members that can function as diffusible axonal chemorepellents. To begin to elucidate the mechanisms that mediate the repulsive actions of Collapsin-1/Semaphorin III/D (Sema III), we searched for Sema III–binding proteins in embryonic rat sensory neurons by expression cloning. We report that Sema III binds with high affinity to the transmembrane protein neuropilin, and that antibodies to neuropilin block the ability of Sema III to repel sensory axons and to induce collapse of their growth cones. These results provide evidence that neuropilin is a receptor or a component of a receptor complex that mediates the effects of Sema III on these axons

Theory of the special Smith-Purcell radiation from a rectangular grating

The recently uncovered special Smith-Purcell radiation (S-SPR) from the rectangular grating has significantly higher intensity than the ordinary Smith-Purcell radiation (SPR). Its monochromaticity and directivity are also much better. Here we explored the mechanism of the S-SPR by applying the fundamental electromagnetic theory and simulations. We have confirmed that the S-SPR is exactly from the radiating eigen modes of the grating. Its frequency and direction are well correlated with the beam velocity and structure parameters, which indicates its promising applications in tunable wave generation and beam diagnostic

Analysis and Design of Adaptive Synchronization of a Complex Dynamical Network with Time-Delayed Nodes and Coupling Delays

This paper is devoted to the study of synchronization problems in uncertain dynamical networks with time-delayed nodes and coupling delays. First, a complex dynamical network model with time-delayed nodes and coupling delays is given. Second, for a complex dynamical network with known or unknown but bounded nonlinear couplings, an adaptive controller is designed, which can ensure that the state of a dynamical network asymptotically synchronizes at the individual node state locally or globally in an arbitrary specified network. Then, the Lyapunov-Krasovskii stability theory is employed to estimate the network coupling parameters. The main results provide sufficient conditions for synchronization under local or global circumstances, respectively. Finally, two typical examples are given, using the M-G system as the nodes of the ring dynamical network and second-order nodes in the dynamical network with time-varying communication delays and switching communication topologies, which illustrate the effectiveness of the proposed controller design methods

Do stocking densities affect the gut microbiota of gibel carp (Carassius auratus gibelio) cultured in ponds?

The aim of the present study was to evaluate the intestinal microbial communities of gibel carp (Carassius auratus gibelio) cultivated in two beach ponds at different stocking densities. The two ponds were both ~3.33 hm2 in acreage and ~1.5 m in depth. The stocking densities included one intensive with 2 fish m–3 while the other treated as semi-intensive with 1 fish m–3. The gut microbiota (both allochthonous and autochthonous) were sampled after 135 days of feeding. Denaturing gradient gel electrophoresis (DGGE) of PCR-amplified 16S rRNA gene segments was used to evaluate the bacterial community. Actinobacteria, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria and some unclassified_bacteria taxa were identified in gut samples and feed. Similar bacterial communities (Cs=0.83) were observed with respect to the autochthonous and allochthonous gut microbiota of gibel carp cultured in the intensive culture pond. In contrast to these results, some difference (Cs=0.61) was observed in the gut microbiota of fish reared in the semi-intensive culture pond. Our results indicated that the difference in the bacterial communities between allochthonous bacteria and gut associated bacteria of gibel carp was not constant and was modulated by the stocking density

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.Comment: Accepted by IEEE Transactions on Intelligent Vehicle