The Balance of Excitation and Inhibition and Its Influence on Cortical States and Rett Syndrome

Our brain consists of billions of neurons, properly coordinating to process information and realize brain functions. Among them, there are two types of neurons: excitatory neurons and inhibitory neurons. The firing of excitatory neurons increases the membrane potential of downstream neurons, and thus excites other neurons to fire. The firing of inhibitory neurons, in contrast, decreases the membrane potential of downstream neurons, and thus inhibits other neurons to fire. The interplay of excitatory and inhibitory neurons shape the spiking activity in the population. Thus, the `balance of excitation and inhibition\u27 plays an important role in cortical processing and brain functions. An imbalance towards either excitation or inhibition leads to dysfunctional circuit mechanisms, and is related to many brain disorders. Here, we explore the influence of the balance of excitation and inhibition on cortical states and a devastating neurodevelopmental disorder - Rett syndrome (RTT).In Chapter 1, we built a family of models where varying the ratio of inhibitory synaptic strength relative to excitatory synaptic strength tunes the network dynamics to two possible cortical states - criticality and asynchronous dynamics. And our work shows that the two distinct and competing scenarios can be generated in the same neural system, when the excitation and inhibition are properly balanced. In Chapter 2, we studied how the RTT-related imbalance of excitation and inhibition influences cortical dynamics and motor function in freely behaving rats by comparing normal rats with a transgenic rat model of RTT. Our results suggest that excessive inhibition in RTT gives rise to an excessive synchrony in primary motor cortex, which is related to stereotyped intracortical and cortex-body interactions and less complexity in movement

In vitro characterization of injectable collagen and collagen-genipin hydrogels for neural tissue engineering

Nervous system injury leads to the permanent loss of sensory and motor functions. Injectable hydrogel containing therapeutic agents can be directly injected to the injured cavity as a promising approach for minimally-invasive treatment of nerve injury. However, such injectable hydrogels have not been well developed and documented in the literature. As inspired, this project aims to develop injectable collagen-based gels for nerve injury repair and to characterize in vitro for supporting neurite outgrowth of dorsal root ganglia (DRG) explants and dissociated neurons. To develop collagen-based gels, collagen at varying concentrations (e.g. 1.5, 2 and 2.5 mg/mL) were used to form gels under physiological conditions and genipin (0.25-5 mM) were applied as the chemical crosslinker. Characterization studies showed that collagen-based hydrogels could form porous and fibrillary gels within a time period of 40 s at 37 °C and genipin could significantly improve the mechanical property of gels and the resistance to degradation. To evaluate the cytotoxicity of the injectable hydrogels and compare the cell behaviour in two-dimensional (2D) and three-dimensional (3D) environments, rat primary Schwann cells (PRSCs) were seeded onto and encapsulated within the gels, and the cell viability was examined at Day 3 by the Live/Dead assay. The results showed that collagen gels provide superior support for PRSCs survival in both 2D and 3D cultures, for example, with a cell viability of 96 % and 95 %, respectively, for the collagen gel with a concentration of 1.5 mg/mL. Collagen chemically crosslinked by genipin at 0.25 and 0.5 mM exhibited a permissive but less favorable environment to PRSCs comparing with pure collagen. Genipin over 1 mM inhibited the PRSCs survival significantly in both 2D and 3D cultures. DRG explant and dissociated neuron cultures were examined as in vitro cell models to evaluate the therapeutic efficacy of collagen and collagen-genipin gels for nerve injury repair and the cellular response was also characterized and compared to each other. Preliminary 2D cultures were shown to greatly support neurite extension and 2.5 mg/mL collagen gel supported the most neurite extension and branching development. It was shown that genipin had a significant effect on the neurite density but not neurite length of DRG explants, whereas the dissociated neurons were more sensitive to genipin. Enrichment of culture medium with nerve growth factor (NGF) could significantly enhance the neurite length and density. PRSCs as the supportive cells were co-cultured with DRG explants/dissociated neurons in 3D hydrogels. Confocal microscopy showed that the neurites of DRG explants and dissociated neurons could extend freely within the physical collagen gels, and dissociated neurons exhibited pseudo-unipolar phenotype in 3D environment indicating true axonal extension. Moreover, genipin had a significantly inhibitory effect on dissociated neurons whereas the explants were more tolerant to genipin possibly due to the preserved cellular components and interactions. It was also shown that hydrogels infiltrated with PRSCs could enhance the neurite elongation and branches dramatically. Our research has determined the therapeutic potency of injectable collagen-based gels containing the PRSCs for nerve injury repair and gained new insights into the use of the injectable gel as a delivery substrate in neural tissue engineering

Research on E-commerce Online to Offline Behavior Mechanism in Agricultural Products

Currently, online and offline channel integration as a successful business model is used in many industries, this paper aims at providing an insight into the factors affecting online channel (online shop) and offline channels (entity shop) in the agricultural product industry. Drawn from the extant literature, a consumer online and offline behavior model including trust, system quality, information quality , environment quality and service quality, online and offline satisfaction ,customer loyalty were provides. Data were obtained from 228 customers in china during 2014. Based on the data obtained, SPSS19.0 software is used to analyze reliability test and validity, descriptive statistics, correlation analysis, and AMOS17.0 were employed to calculate the path coefficient, and tests the proposed model. Data analysis shows that: The model describes the relationship among the online satisfaction, offline satisfaction and customer loyalty, and establishes a multi-relationship model that includes trust, system quality, information quality, environment quality and service quality. The factors impacting online satisfaction include trust, system quality and information quality; the factors impacting offline satisfaction include environment satisfaction and service quality. Meanwhile, we highlight the role of trust in online satisfaction, and prove that both online satisfaction and offline satisfaction have interaction on customer loyalty. Keywords

Experimental Study on Chinese Athletes’ Physical Distribution in Sanda Competition

Reasonable distribution of physical strength plays a very important role in adversary Sanda competition. According to the physical characteristics of Sanda athletes, this paper studied the physical distribution during Sanda competition. The results are as follows: a) If an athlete’s opponent is physically stronger, then he is not supposed to be recklessly but to take defense-oriented strategies. The athlete has to avoid the opponent’s heavy blow and seize the opportunity to accurately counterattack. b) If his physical strength is equal with the opponent’s, the athlete is supposed to maintain self-physical strength, and try to consume opponent physically. c) If the athlete is physically stronger, then he should actively attack, but not rampage. Two specific tactics are proposed: i) try to consume the opponent’s strength if the opponent is very easily excited or impetuous; ii) try to use defensive strategies if the opponent less attack

Competitive accretion in the protocluster G10.6-0.4?

We present the results of high spatial resolution observations at 1.1 mm waveband, with the Submillimetre Array (SMA), towards the protocluster G10.6-0.4. The 1.1 mm continuum emission reveals seven dense cores, towards which infall motions are all detected with the red-shifted absorption dips in HCN (3--2) line. This is the first time that infall is seen towards multiple sources in a protocluster. We also identified four infrared point sources in this region, which are most likely Class 0/I protostars. Two jet-like structures are also identified from Spitzer/IRAC image. The dense core located in the centre has much larger mass than the off-centre cores. The clump is in overall collapse and the infall motion is supersonic. The standard deviation of core velocities and the velocity differences between the cores and the cloud/clump are all larger than the thermal velocity dispersion. The picture of G10.6-0.4 seems to favor the "competitive accretion" model but needs to be tested by further observations.Comment: 9 pages, 9 figures, 2 tables, Submitted to MNRA

Deep Multimodal Speaker Naming

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

It is well believed that video captioning is a fundamental but challenging task in both computer vision and artificial intelligence fields. The prevalent approach is to map an input video to a variable-length output sentence in a sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless, the training of RNN still suffers to some degree from vanishing/exploding gradient problem, making the optimization difficult. Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations. In this paper, we present a novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TDConvED) that fully employ convolutions in both encoder and decoder networks for video captioning. Technically, we exploit convolutional block structures that compute intermediate states of a fixed number of inputs and stack several blocks to capture long-term relationships. The structure in encoder is further equipped with temporal deformable convolution to enable free-form deformation of temporal sampling. Our model also capitalizes on temporal attention mechanism for sentence generation. Extensive experiments are conducted on both MSVD and MSR-VTT video captioning datasets, and superior results are reported when comparing to conventional RNN-based encoder-decoder techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8% to 67.2% on MSVD.Comment: AAAI 201