Mesenchymal stem/stromal cells （MSCs） possess some characteristics of immune cells, including a pro-inflammatory phenotype, an immunosuppressive phenotype, antibacterial properties and the expression of Toll-like re...Mesenchymal stem/stromal cells （MSCs） possess some characteristics of immune cells, including a pro-inflammatory phenotype, an immunosuppressive phenotype, antibacterial properties and the expression of Toll-like receptor proteins. Here we show that, similar to immune cells, MSCs retain information from danger signals or environmental stimuli for a period of time. When treated with the pro-inflammatory factors lipopolysaccharide （LPS） or tumor necrosis factor-a （TNF-a）, MSCs display increased expression of IL-6, IL-8 and MCP-1. Following re-plating and several rounds of cell division in the absence of stimulating factors, the expression of IL-6, IL-8 and MCP-1 remained higher than in untreated cells for over 7 days. A spike in cytokine secretion occurred when cells were exposed to a second round of stimulation. We primed MSCs with LPS and LPS-primed MSCs had better therapeutic efficacy at promoting skin flap survival in a diabetic rat model than did unprimed MSCs. Finally, we found that several microRNAs, including miR146a, miR150 and miR155, along with the modification of DNA by 5-hydroxymethylcytosine （5hmC）, mediate the MSC response to LPS and TNF-e stimulation. Collectively, our data suggest that MSCs have a short-term memory of environmental signals, which may impact their therapeutic potential.展开更多
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicabilit...Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.展开更多
Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning netwo...Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning network for hand gesture recognition.The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation.To learn short-term features,each video input is segmented into a fixed number of frame groups.A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot.These two entities are fused and fed into a convolutional neural network(Conv Net)for feature extraction.The Conv Nets for all groups share parameters.To learn longterm features,outputs from all Conv Nets are fed into a long short-term memory(LSTM)network,by which a final classification result is predicted.The new model has been tested with two popular hand gesture datasets,namely the Jester dataset and Nvidia dataset.Comparing with other models,our model produced very competitive results.The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.展开更多
文摘Mesenchymal stem/stromal cells （MSCs） possess some characteristics of immune cells, including a pro-inflammatory phenotype, an immunosuppressive phenotype, antibacterial properties and the expression of Toll-like receptor proteins. Here we show that, similar to immune cells, MSCs retain information from danger signals or environmental stimuli for a period of time. When treated with the pro-inflammatory factors lipopolysaccharide （LPS） or tumor necrosis factor-a （TNF-a）, MSCs display increased expression of IL-6, IL-8 and MCP-1. Following re-plating and several rounds of cell division in the absence of stimulating factors, the expression of IL-6, IL-8 and MCP-1 remained higher than in untreated cells for over 7 days. A spike in cytokine secretion occurred when cells were exposed to a second round of stimulation. We primed MSCs with LPS and LPS-primed MSCs had better therapeutic efficacy at promoting skin flap survival in a diabetic rat model than did unprimed MSCs. Finally, we found that several microRNAs, including miR146a, miR150 and miR155, along with the modification of DNA by 5-hydroxymethylcytosine （5hmC）, mediate the MSC response to LPS and TNF-e stimulation. Collectively, our data suggest that MSCs have a short-term memory of environmental signals, which may impact their therapeutic potential.
基金supported by the Natural Science Foundation of China(Grant Nos.51979158,51639008,51679135,and 51422905)the Program of Shanghai Academic Research Leader by Science and Technology Commission of Shanghai Municipality(Project No.19XD1421900)。
文摘Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.
文摘Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning network for hand gesture recognition.The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation.To learn short-term features,each video input is segmented into a fixed number of frame groups.A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot.These two entities are fused and fed into a convolutional neural network(Conv Net)for feature extraction.The Conv Nets for all groups share parameters.To learn longterm features,outputs from all Conv Nets are fed into a long short-term memory(LSTM)network,by which a final classification result is predicted.The new model has been tested with two popular hand gesture datasets,namely the Jester dataset and Nvidia dataset.Comparing with other models,our model produced very competitive results.The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.
