The role of hippocampal niche exosomes in rat hippocampal neurogenesis after fimbria-fornix transection.

Exosomes transfer signaling molecules such as proteins, lipids, and RNAs to facilitate cell-cell communication and play an important role in the stem cell microenvironment. In previous work, we demonstrated that rat fimbria-fornix transection (FFT) enhances neurogenesis from neural stem cells (NSCs) in the subgranular zone (SGZ). However, how neurogenesis is modulated after denervation remains unknown. Here, we investigated whether exosomes in a denervated hippocampal niche may affect neurogenesis. Using the FFT rat model, we extracted hippocampal exosomes and identified them using western blots, transmission electron microscopy (TEM), and nanoparticle size measurement. We also used RNA sequencing and bioinformatic analysis of exosomes to identify noncoding RNA expression profiles and neurogenesis-related miRNAs, respectively. RNA sequencing analysis demonstrated 9 upregulated and 15 downregulated miRNAs. miR-3559-3P and miR-6324 increased gradually after FFT. Thus, we investigated the function of miR-3559-3P and miR-6324 with NSC proliferation and differentiation assays. Transfection of miR-3559-3p and miR-6324 mimics inhibited the proliferation of NSCs and promoted the differentiation of NSCs into neurons, while miR-3559-3p and miR-6324 inhibitors promoted NSC proliferation and inhibited neuronal differentiation. Additionally, the exosome marker molecules CD9, CD63, and Alix were expressed in exosomes extracted from the hippocampal niche. Finally, TEM showed that exosomes were ∼100 nm in diameter and had a "saucer-like" bilayer membrane structure. Taken together, these findings suggest that differentially expressed exosomes and their related miRNAs in the denervated hippocampal niche can promote differentiation of NSCs into neurons.

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors.

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments.

BIDL: a brain-inspired deep learning framework for spatiotemporal processing.

Brain-inspired deep spiking neural network (DSNN) which emulates the function of the biological brain provides an effective approach for event-stream spatiotemporal perception (STP), especially for dynamic vision sensor (DVS) signals. However, there is a lack of generalized learning frameworks that can handle various spatiotemporal modalities beyond event-stream, such as video clips and 3D imaging data. To provide a unified design flow for generalized spatiotemporal processing (STP) and to investigate the capability of lightweight STP processing via brain-inspired neural dynamics, this study introduces a training platform called brain-inspired deep learning (BIDL). This framework constructs deep neural networks, which leverage neural dynamics for processing temporal information and ensures high-accuracy spatial processing via artificial neural network layers. We conducted experiments involving various types of data, including video information processing, DVS information processing, 3D medical imaging classification, and natural language processing. These experiments demonstrate the efficiency of the proposed method. Moreover, as a research framework for researchers in the fields of neuroscience and machine learning, BIDL facilitates the exploration of different neural models and enables global-local co-learning. For easily fitting to neuromorphic chips and GPUs, the framework incorporates several optimizations, including iteration representation, state-aware computational graph, and built-in neural functions. This study presents a user-friendly and efficient DSNN builder for lightweight STP applications and has the potential to drive future advancements in bio-inspired research.

Unsupervised domain adaptation of bearing fault diagnosis based on Join Sliced Wasserstein Distance.

Deep neural networks have been successfully utilized in the mechanical fault diagnosis, however, a large number of them have been based on the same assumption that training and test datasets followed the same distributions. Unfortunately, the mechanical systems are easily affected by environment noise interference, speed or load change. Consequently, the trained networks have poor generalization under various working conditions. Recently, unsupervised domain adaptation has been concentrated on more and more attention since it can handle different but related data. Sliced Wasserstein Distance has been successfully utilized in unsupervised domain adaptation and obtained excellent performances. However, most of the approaches have ignored the class conditional distribution. In this paper, a novel approach named Join Sliced Wasserstein Distance (JSWD) has been proposed to address the above issue. Four bearing datasets have been selected to validate the practicability and effectiveness of the JSWD framework. The experimental results have demonstrated that about 5% accuracy is improved by JSWD with consideration of the conditional probability than no the conditional probability, in addition, the other experimental results have indicated that JSWD could effectively capture the distinguishable and domain-invariant representations and have a has superior data distribution matching than the previous methods under various application scenarios.

Fault diagnosis of rotor based on Semi-supervised Multi-Graph Joint Embedding.

Traditional graph embedding methods only consider the pairwise relationship between fault data. But in practical applications, the relationship of high-dimensional fault data usually is multiple classes corresponding to multiple samples. Therefore, the hypergraph structure is introduced to fully portray the complex structural relationship of high-dimensional fault data. However, during the construction of the hypergraph, the hyperedge weight is usually set as the sum of the similarities between every two vertices contained within the hyperedge, and this "averaging effect" causes the relationship between data sample points with high similarity to be weakened, while the relationship between data sample points with low similarity to be strengthened. This phenomenon also leads to the hypergraph cannot accurately portray the relationship of high-dimensional data, which reduces the fault classification accuracy. To address this issue, a novel dimensionality reduction method named Semi-supervised Multi-Graph Joint Embedding (SMGJE) is proposed and applied to rotor fault diagnosis. SMGJE constructs simple graphs and hypergraphs with the same sample points and characterizes the structure of high-dimensional data in a multi-graph joint embedding. The edges of the simple graph are the direct description of the similarity between sample points so that SMGJE can overcome this "averaging effect" of the hypergraph. The effectiveness of the proposed method is verified by two different fault datasets.

Modeling learnable electrical synapse for high precision spatio-temporal recognition.

Bio-inspired recipes are being introduced to artificial neural networks for the efficient processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is the most remarkable one thanks to its temporal processing capability, lightweight model structure, and well investigated direct training methods. However, most learnable LIF networks generally take neurons as independent individuals that communicate via chemical synapses, leaving electrical synapses all behind. On the contrary, it has been well investigated in biological neural networks that the inter-neuron electrical synapse takes a great effect on the coordination and synchronization of generating action potentials. In this work, we are engaged in modeling such electrical synapses in artificial LIF neurons, where membrane potentials propagate to neighbor neurons via convolution operations, and the refined neural model ECLIF is proposed. We then build deep networks using ECLIF and trained them using a back-propagation-through-time algorithm. We found that the proposed network has great accuracy improvement over traditional LIF on five datasets and achieves high accuracy on them. In conclusion, it reveals that the introduction of the electrical synapse is an important factor for achieving high accuracy on realistic spatio-temporal tasks.

Learnable Heterogeneous Convolution: Learning both topology and strength.

Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficient way. Inspired by the biological plasticity of dendritic topology and synaptic strength, our method, Learnable Heterogeneous Convolution, realizes joint learning of kernel shape and weights, which unifies existing handcrafted convolution techniques in a data-driven way. A model based on our method can converge with structural sparse weights and then be accelerated by devices of high parallelism. In the experiments, our method either reduces VGG16/19 and ResNet34/50 computation by nearly 5× on CIFAR10 and 2× on ImageNet without harming the performance, where the weights are compressed by 10× and 4× respectively; or improves the accuracy by up to 1.0% on CIFAR10 and 0.5% on ImageNet with slightly higher efficiency. The code will be available on www.github.com/Genera1Z/LearnableHeterogeneousConvolution.

Drug-eluting stents vs. intracoronary brachytherapy for in-stent restenosis: a meta-analysis.

BACKGROUND: It has been reported that drug-eluting stents (DES) were superior to intracoronary brachytherapy (ICBT) in patients with in-stent restenosis (ISR). However, it is unknown whether there might be differences between DES and ICBT in terms of efficacy and safety in large sample size and long-term follow-up. HYPOTHESIS: The aim of this study was to determine whether DES implantation remains favorable in large sample size and long-term follow-up when compared with ICBT among patients with ISR. METHODS: We conducted a search in MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials without language restrictions. A meta-analysis of 1942 cases from 12 controlled trials of DES vs ICBT for ISR was performed. RESULTS: Drug-eluting stents were significantly more effective in reducing target-vessel revascularization (TVR) (odds ratio [OR]: 0.44, 95% confidence interval [CI]: 0.23-0.81, P = 0.009) and binary restenosis (OR: 0.34, 95% CI: 0.26-0.46, P<0.00001) compared with ICBT at midterm follow-up. There were no significant differences between DES and ICBT in cardiac death, myocardial infarction (MI), and late stent thrombosis at midterm follow-up. A statistical significance has been found between the 2 groups in TVR (OR: 0.61, 95% CI: 0.43-0.86, P = 0.005) at long-term follow-up. There were no significant differences in cardiac death and MI between the 2 groups at long-term follow-up. CONCLUSIONS: These findings provide evidence that DES is superior to ICBT for the treatment of ISR in TVR and binary restenosis reduction, but not in cardiac death, MI, and late stent thrombosis reduction. © 2011 Wiley Periodicals, Inc. Yong-Guang Lu, MD, and Yan-Mei Chen, MD, contributed equally to this work. The authors have no funding, financial relationships, or conflicts of interest to disclose.