Amylase (E.C.3.2.1.1) is one of the most important enzymes, of great significance for biotechnology. With the increasing number of studies of amylases, it is urgent to comprehensively gasp the future research trend of amylase, which promotes research and applications of amylase. Besides, the understand the past, present and future of amylase research helps scientists to further pursuit frontier of amylase research in global scale. Therefore, we used data-driven analysis method to present the hot points and further trends of amylase, which obtains data from Web of Science database. We comparatively analyzed the research hot points of amylases from several scales: Publication trends, Disciplinary trends, Interdisciplinary research tendency, and Collaborations trends. The results show that the research of amylase has good continuity on research contents, containing applications of amylases, the catalytic models of amylases, and the amylase-related nutrition and metabolism. Amylase research in terms of “Nutrition & dietetics” and “Agriculture” fields may be the further new disciplinary trends. Additionally, the intensive collaboration of amylases focuses on structural function, chemical, and physical properties of amylase.

The accurate assessment of the brain’s functional network is seen as crucial for the understanding of complex relationships between different brain regions. Hidden information within different frequency bands, which is often overlooked by traditional linear correlation-based methods such as Pearson correlation (PC) and partial correlation, fails to be revealed, leading to the neglect of more intricate nonlinear factors. These limitations were aimed to be overcome in this study by the combination of fast continuous wavelet transform and normalized mutual information (NMI) to develop a novel approach. Original time-domain signals from resting-state functional magnetic resonance imaging were decomposed into different frequency domains using fast continuous wavelet transform, and adjacency matrices were constructed to enhance feature separation across brain regions. Both linear and nonlinear aspects between brain regions were comprehensively considered through the integration of complex correlation coefficient and NMI. The construction of functional brain networks was enabled by the adaptive selection of optimal frequency band combinations. The construction of the model was facilitated by feature extraction using tree models with extreme gradient boosting. It was demonstrated through comparative analysis that the method outperformed baseline methods such as PC and NMI, achieving an area under the curve of 0.9054. The introduction of nonlinear factors was found to increase precision by 14.25% and recall by 17.14%. Importantly, the approach optimized the original data without significantly altering the feature topology. Overall, this innovation advances the understanding of brain function, offering more accurate potential for future research and clinical applications.

Traditional price movement is modeled by the machine learning methods and neural network methods. However, the prediction is often concerned with the correlations rather than the causality. In this paper, we do not only consider the correlation but also borrow the idea of the Neural Hawkes model to help build the decaying effects between the stock price dynamics. In the work, we evaluate the prediction quality of the results using the log likelihood. Results show that our methods are competitive, the Neural Hawkes model achieved log likelihood value of seq (combining the time and type) to -0.6358 and -2.3878 in five days prediction and ten days prediction respectively, better than -4.3243 and -4.5841 by Hawkes model and -11.353 and -24.8147 by Inhibition Hawkes model.