Nuclear factor erythroid 2-related factor 2 (NRF2) is a basic leucine zipper protein that participates in a complex regulatory network in the body. The activation of NRF2 can prevent and treat colorectal cancer (CRC). A variety of natural compounds can activate NRF2 to inhibit oxidative stress and inflammation to prevent the occurrence and development of CRC, inhibit the proliferation of CRC cells and induce their apoptosis. However, some studies have also shown that it also has negative effects on CRC, such as overexpression of NRF2 can promote the growth of colorectal tumors and increase the drug resistance of chemotherapeutic drugs such as 5-fluorouracil and oxaliplatin. Therefore, inhibition of NRF2 can also be helpful in the treatment of CRC. In this study, we analyze the current research progress of NRF2 in CRC from various aspects to provide new ideas for prevention and treatment based on the NRF2 signaling pathway in CRC.

High-throughput studies of biological systems are rapidly generating a wealth of 'omics'-scale data. Many of these studies are temporal collecting proteomics and genomics data capturing dynamic observations. While temporal omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values due to technical and experimental reasons. Data prediction methods, i.e., imputation and forecasting, have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. In this paper, we propose a graph-based method for temporal omics data imputation and forecasting that handle omics data containing missing values at multiple time points. The method takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform temporal imputation and forecasting using a long short-term memory network. Evaluating the proposed method on two real-world datasets demonstrated a distinct advantage over existing data imputation and forecasting methods. On the omics dataset, the average mean square error of our method improved 11.3% for imputation and 6.4% for forecasting compared to the baseline methods.INDEX TERMS Temporal biological data, missing values, data prediction, graph neural networks

SOPC (System on Programmable Chip) is an on-chip programmable system based on large scale Field Programmable Arrays (FPGAs). This paper presented an implementation of an SOPC system with a custom hardware neural network using Altera FPGA chip-EP2C35F672C. The embedded Nios processor was used as the test bench. The test result showed that the SOPC Platform with hardware neural network is faster than the software implementation respectively and the accuracy of the design meets the requirement of system. The verified SOPC system can closely model real-world system, which will have wide applications in different areas such as pattern recognition, data mining and signal processing.