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Additional file 1: Text S1. Detailed methods of transcriptome sequencing, data analysis and verification.

Additional file 1: Text S1. Detailed methods of transcriptome sequencing, data analysis and verification.

The polluted coastal water of Shenzhen Bay was treated with composites of active materials, bacteria and algae as well as combinations of two or three of these composites. The water quality was then examined after 10 days with a focus on 8 parameters, including dissolved oxygen (DO), suspended substances (SS), turbidity (Turbidity), ammonia nitrogen (NH4+-N), total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD) and harmful marine vibrio. The results showed that the water quality was dramatically improved and the levels of harmful marine vibrio were significantly reduced after the treatment with the combination of active materials, bacteria and algae. The three composites can improve the rate of DO by 89.08% and had the highest removal rates of NH4+- N, TN, TP, COD and harmful marine vibrio (HMV), at 92.59%, 94.05%, 90.05%, 94.12% and 98.21%, respectively. However, the removal rates of SS and turbidity were the highest for the treatment with active materials compound with bacteria, at 95% and 92.69%, respectively. The combination of the three composites clearly had the best treatment effect for the restoration of polluted coastal waters. This study established solid theoretical and practical foundations to control coastal pollution and restore the coastal ecology.

An automatic identification system for the OLED's black-spot detection was designed in this paper. Complete approaches based on machine vision technology are proposed to get the sub-pixel location of black-spots. In this method, feature area is recognized based on the identification of spots' edge characteristics. The method of circle fitting is used to get positions of the spots' center. The experiments show that this system can realize the automatic identification for black spot's feature and center in sub-pixel. The accuracy of algorithm is better, and its self-adaptability is high. It provides an necessary condition for further research of OLED defect detection.

An immune-inspired danger theory model based on fuzzy set was proposed in this article. In the model the concept of fuzzy set and degree of membership are presented in the foundation of the "danger" definition in artificial immune system. By carrying on the computation to the degree of danger, it makes the organism to apperceive the danger and simultaneously to make a strategy efficiently. It improves the organism to recognize the "danger" effectively, and then enhances the rate of intrusion detection based on the artificial immune model, and reduces the false alarm.