https://www.selleckchem.com/mTOR.html aegypti larvae. This synergistic activity is among the most powerful described so far with Bt toxins and is comparable to that reported for Cyt1A when interacting with Cry4Aa, Cry4Ba or Cry11Aa. Synergistic mosquitocidal activity was also observed between the recombinant proteins Cyt2Ba and Cry4Aa, but in this case, the synergistic potentiation was 4.6-fold. In conclusion, although Cry10Aa and Cyt2Ba are rarely detectable or appear as minor components in the crystals of Bti strains, they represent toxicity factors with a high potential for the control of mosquito populations.Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets MIT-