We have previously shown that Stu2p is a microtubule-binding protein and a component of the Saccharomyces cerevisiae spindle pole body (SPB). Here we report the identification of Spc72p, a protein that interacts with Stu2p. Stu2p and Spc72p associate in the two-hybrid system and can be coimmunoprecipitated from yeast extracts. Stu2p and Spc72p also interact with themselves, suggesting the possibility of a multimeric Stu2p-Spc72p complex. Spc72p is an essential component of the SPB and is able to associate with a preexisting SPB, indicating that there is a dynamic exchange between soluble and SPB forms of Spc72p. Unlike Stu2p, Spc72p does not bind microtubules in vitro, and was not observed to localize along microtubules in vivo. A temperature-sensitive spc72 mutation causes defects in SPB morphology. In addition, most spc72 mutant cells lack cytoplasmic microtubules; the few cytoplasmic microtubules that are observed are excessively long, and some of these are unattached to the SPB. spc72 cells are able to duplicate and separate their SPBs to form a bipolar spindle, but spindle elongation and chromosome segregation rarely occur. The chromosome segregation block does not arrest the cell cycle; instead, spc72 cells undergo cytokinesis, producing aploid cells and polyploid cells that contain multiple SPBs. 相似文献
Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily. 相似文献
In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.