Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

This paper takes China’s 2014–2019 provincial data as the observation sample to explore the dynamic coupling law of the digital economy and high-quality economic development. First, using the coupling coordination model, it is found that the coupling coordination degree of the digital economy and high-quality economic growth is on an upward trend, and the coupling coordination degree in the eastern region is higher than that in other regions; then, using Markov chain algorithm, it is found that the coupling coordination degree in the east of region achieves a two-level leap of “antagonism stage-running-in stage-coordination stage”, while the central and western regions accomplish a single level of “antagonism stage-running-in stage” leap. Finally, using the Dagum Gini coefficient decomposition method, it was found that the mean values of inter-regional, intra-regional, and supervariate density differences in coupling coordination contributed 67.60%, 24.03%, and 8.36% to the overall differences, respectively, with highly moderate fluctuations. The general, inter-regional and intra-regional differences all show a decreasing trend, but there is heterogeneity in their corresponding variation characteristics. This paper provides substantial empirical evidence for exploring the inherent laws and provides an essential guarantee for China’s regional economy’s comprehensive, coordinated, and sustainable development.

No abstract

Ribonucleic acids (RNAs) involve in various physiological/pathological processes by interacting with proteins, compounds, and other RNAs. A variety of powerful computational methods have been developed to predict such valuable interactions. However, all these methods rely heavily on the ‘digitalization’ (also known as ‘encoding’) of RNA-associated interacting pairs into a computer-recognizable descriptor. In other words, it is urgently needed to have a powerful tool that can not only represent each interacting partner but also integrate both partners into a computer-recognizable interaction. Herein, RNAincoder (deep learning-based encoder for RNA-associated interactions) was therefore proposed to (a) provide a comprehensive collection of RNA encoding features, (b) realize the representation of any RNA-associated interaction based on a well-established deep learning-based embedding strategy and (c) enable large-scale scanning of all possible feature combinations to identify the one of optimal performance in RNA-associated interaction prediction. The effectiveness of RNAincoder was extensively validated by case studies on benchmark datasets. All in all, RNAincoder is distinguished for its capability in providing a more accurate representation of RNA-associated interactions, which makes it an indispensable complement to other available tools. RNAincoder can be accessed at https://idrblab.org/rnaincoder/

As one of the effective market instruments in carbon emission reduction policy, carbon trading is capable of promoting the smooth implementation of the “dual carbon” goal. Based on the path evolutionary game method of information economics, this paper constructs a dynamic game model of the evolution and development of government and enterprise carbon emission reduction. It also analyzes the evolution and development law of government and enterprise carbon emission reduction. We used the carbon market trading data of Guangdong Province to simulate the evolutionary game path of government and enterprise carbon emission reduction under the “double carbon” target and then selected strategies. Results show that (1) Scientific adjustment of carbon quota can effectively shorten the realization time of carbon emission reduction probability of high-pollution enterprises, obtain additional surplus carbon quota, and win extra carbon emission reduction income; (2) Increasing financial subsidies can improve the probability of carbon emission reduction of high-pollution enterprises but cannot prevent the periodic change in carbon emission reduction probability, which in turn helps prolong the “window period” of government regulation on carbon emission reduction; (3) Increasing carbon emission penalties will help high-pollution enterprises actively reduce emissions and improve the motivation of government supervision; (4) The government can introduce a dynamic reward and punishment mechanism. If the government properly chooses the reward and punishment strategy, it may not necessarily pay additional subsidies, so that the government and enterprises can cooperate in tacit agreement to achieve the goal of carbon emission reduction; (5) If the price of carbon emission permits is adjusted, high-pollution enterprises will actively reduce carbon emissions and gain greater benefits no matter what regulatory measures the government takes. Results of this study have profound significance for carbon emission reduction strategies and government regulation of high-pollution enterprises and will help China achieve its “dual carbon” development goal.