No abstract

Live cells precisely control their temporal pattern in energy dissipative processes such as catalysis and assembly. Here, we demonstrate a DNA-based artificial dissipative nonequilibrium system where the transient state is controlled by the processive digestion of λ-exonuclease (λ Exo). This enzyme reaction serves as an orthogonal and independent molecular timer allowing for the programmable regulation of the transient-state lifetime. This dissipation system is concatenated to enzyme catalysis and nanostructure assembly networks. Dynamic activation of enzyme catalysis and dynamic disassembly of DNA nanotubes (DNT) are realized, and the state lifetimes of these systems are accurately encoded by the DNA timer. This work demonstrates nontrivial dissipation systems with built-in molecular timers, which can be a useful tool for developing artificial reaction networks and nanostructures with enhanced complexities and intelligence.

No abstract

Financial distress prediction is a major issue in the burgeoning fintech field. Given the importance of the reliability of the prediction results, there is an urgent need for the explanatory ability of the financial distress prediction model. From the modeling and explanation point of view, this study employs four prevailing tree‐based gradient boosting models, namely, gradient boosting decision tree, extreme gradient boosting, light gradient boosting machine, and categorical boosting, to build financial distress prediction models by using financial data of listed companies in China from 1998 to 2014 and five different prediction time spans. We observe that tree‐based gradient boosting models have better prediction performance than other prediction models. To explore the reasons for the prediction results, we deploy TreeSHAP. Then, we use Shapley regression to examine the statistically significant relationships between financial indicators and financial distress. We discover that financial indicators, such as net asset value per share and ratio of operating profits to current liabilities, are significantly related to financial distress. There is usually a nonlinear relationship between the financial predictors and prediction target. Thus, this study provides an effective method for financial distress prediction and an explanation of the results for listed companies in China.

No abstract