Design of groundwater level prediction system based on BP neural network

In order to understand the dynamic of groundwater level and master the earthquake precursor dynamic, we designed groundwater level prediction system based on BP neural network. According to the groundwater level of Deyang, Sichuan Province, SWY-II digital water level meter is used to collect the groundwater level data of Deyang. Based on the collected water level data in 2015, the BP neural network is used to predict the change of groundwater level, and the data collected for one year are trained and tested. The structure of BP neural network is designed with three input nodes and one output node. In order to further validate the proposal, the groundwater level from July 1 to October 26, 2017 is predicted. The experiment shows that the scheme can predict groundwater level effectively and provide reliable data for earthquake precursor work

Reduced intrinsic DNA curvature leads to increased mutation rate

Background: Mutation rates vary across the genome. Many trans factors that influence mutation rates have been identified, as have specific sequence motifs at the 1–7-bp scale, but cis elements remain poorly characterized. The lack of understanding regarding why different sequences have different mutation rates hampers our ability to identify positive selection in evolution and to identify driver mutations in tumorigenesis. Results: Here, we use a combination of synthetic genes and sequences of thousands of isolated yeast colonies to show that intrinsic DNA curvature is a major cis determinant of mutation rate. Mutation rate negatively correlates with DNA curvature within genes, and a 10% decrease in curvature results in a 70% increase in mutation rate. Consistently, both yeast and humans accumulate mutations in regions with small curvature. We further show that this effect is due to differences in the intrinsic mutation rate, likely due to differences in mutagen sensitivity and not due to differences in the local activity of DNA repair. Conclusions: Our study establishes a framework for understanding the cis properties of DNA sequence in modulating the local mutation rate and identifies a novel causal source of non-uniform mutation rates across the genome