Cellular reprogramming can manipulate the identity of cells to generate the desired cell types1– 3. The use of cell intrinsic components, including oocyte cytoplasm and transcription factors, can enforce somatic cell reprogramming to pluripotent stem cells4– 7. By contrast, chemical stimulation by exposure to small molecules offers an alternative approach that can manipulate cell fate in a simple and highly controllable manner8– 10. However, human somatic cells are refractory to chemical stimulation owing to their stable epigenome2,11,12 and reduced plasticity13,14; it (...) is therefore challenging to induce human pluripotent stem cells by chemical reprogramming. Here we demonstrate, by creating an intermediate plastic state, the chemical reprogramming of human somatic cells to human chemically induced pluripotent stem cells that exhibit key features of embryonic stem cells. The whole chemical reprogramming trajectory analysis delineated the induction of the intermediate plastic state at the early stage, during which chemical-induced dedifferentiation occurred, and this process was similar to the dedifferentiation process that occurs in axolotl limb regeneration. Moreover, we identified the JNK pathway as a major barrier to chemical reprogramming, the inhibition of which was indispensable for inducing cell plasticity and a regeneration-like program by suppressing pro-inflammatory pathways. Our chemical approach provides a platform for the generation and application of human pluripotent stem cells in biomedicine. This study lays foundations for developing regenerative therapeutic strategies that use well-defined chemicals to change cell fates in humans. (shrink)

養尊處優的生活，養成有稜有角的嬌性；披著一襲刺蝟花衣的她，穿梭於人生的追尋。直到無意搭上慈濟列車，她發揮勇於承擔的力量，不但改變自己，更影響了別人。在精進蛻變的菩薩道上，她的生命如彩蝶般蛹變而璀璨，她 就是慈濟委員---紀媽咪。.

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data (...) utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data. (shrink)

With the rapid development of science and technology, UAVs have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects (...) of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm, Gradient Boosting Decision Tree algorithm, K Nearest Neighbor algorithm, and Decision Tree algorithm. Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield. (shrink)

As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory improved on bidirectional long short-term memory adds (...) 1 − tanh function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network, and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron, recurrent neural network, long short-term memory, BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error, root-mean-squared error, and R-square evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks. (shrink)