Auditory deprivation modifies the expression of brain-derived neurotrophic factor and tropomyosin receptor kinase B in the rat auditory cortex

The development and plasticity of central auditory system can be influenced by the change of peripheral neuronal activity. However, the molecular mechanism participating in the process remains elusive. Brain-derived neurotrophic factor(BDNF) binding with its functional receptor tropomyosin receptor kinase B(TrkB) has multiple effects on neurons. Here we used a rat model of auditory deprivation by bilateral cochlear ablation, to investigate the changes in expression of BDNF and Trk B in the auditory cortex after auditory deprivation that occurred during the critical period for the development of central auditory system. Reverse transcription-quantitative polymerase chain reaction(RTqPCR) and immunohistochemistry methods were adopted to detect the m RNA and protein expression levels of BDNF and TrkB in the auditory cortex at 2, 4, 6 and 8 weeks after surgery, respectively. The change in the expression of BDNF and TrkB mRNAs and proteins followed similar trend. In the bilateral cochlear ablation groups, the BDNF-TrkB expression level initially decreased at 2 weeks but increased at 4 weeks followed by the reduction at 6 and 8 weeks after cochlear removal, as compared to the age-matched sham control groups. In conclusion, the BDNF-TrkB signaling is involved in the plasticity of auditory cortex in an activity-dependent manner.

A privacy-preserving vehicle trajectory clustering framework

As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles.However,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage.Therefore,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy.We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means algorithm.In the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the client.The IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle trajectory.IKV is compared to numerous clustering methods on three datasets.In the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the experiments.Furthermore,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations.These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.

LDformer:a parallel neural network model for long-term power forecasting

Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system’s reliable power supply and the grid economy’s reliable operation.However,most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of data.To address this challenge,we propose a parallel time-series prediction model called LDformer.First,we combine Informer with long short-term memory(LSTM)to obtain deep representation abilities in the time series.Then,we propose a parallel encoder module to improve the robustness of the model and combine convolutional layers with an attention mechanism to avoid value redundancy in the attention mechanism.Finally,we propose a probabilistic sparse(ProbSparse)self-attention mechanism combined with UniDrop to reduce the computational overhead and mitigate the risk of losing some key connections in the sequence.Experimental results on five datasets show that LDformer outperforms the state-of-the-art methods for most of the cases when handling the different long-time-series prediction tasks.