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Inspired by the immune theory and multi-agent systems, an immune multi-agent active defense model for network intrusion is established. The concept of immune agent is introduced， and its running mechanism is established. The method, which uses antibody concentration to quantitatively describe the degree of intrusion danger, is presented. This model implements the multi-layer and distributed active defense mechanism for network intrusion. The experiment results show that this model is a good solution to the network security defense.With the rapid development of information technology and Internet, many network intrusion technologies have continuously changed, which result in more and more serious damages. However, these intrusion detection technologies [1] , including statistics analysis, feature analysis, data mining, etc., all have some limitations, lack self-adaptation and can only detect the known attacks other than new attack models; The system also lacks robustness, which means that even the error in a local place can affect the whole works. So it is very urgent to build an active defense system, which has the features of self-adaptation, early warning and robustness. The biological immune system (BIS) can identify and kill the intrusion antigens. It can be seen as a distributed autonomous system. Artificial immune systems (AIS) [2][3][4] derived from BIS, and have become an important research direction in the realm of artificial intelligence technologies. In 1994, the negative selection algorithm and the concept of computer immune system were proposed by Forrest [5] . In 1999, Kim [3] proposed dynamic clone selection algorithm, which was used in the network intrusion detection. Agent [6,7] is an entity that has the ability of consciousness, solving problems and communication. With the cooperation and coordination during the isolated agents in a multi-agent system (MAS) [8] , the problems in the complex environment can be solved and it has the features of distribution, robustness.

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Objective To investigate the association between polymorphisms of α-adducin (ADD) gene Gly460Trp, angiotensin converting enzyme (ACE) gene I/D and kidney lesions in patients with essential hypertension respectively and jointly. Methods The case-control study was performed in 169 hospitalised hypertensive patients and 169 normal subjects (group A). On the basis of creatinine clearance calculated by Cockcroft-Gault equation, hypertensive patients were divided into two groups: normal renal function group (group B: Ccr≥80 ml/min) with 63 subjects and abnormal renal function group (group C: Ccr<80 ml/min) with 106 subjects. Basic clinical data such as sex, age, body mass, height, hierarchy and course of hypertension were collected; biochemical parameters such as serum creatinine, urea nitrogen, uric acid, fasting plasma glucose, lipids were measured; PCR was used to characterise ACE genotypes (ID, II, DD); PCR and restriction fragment length polymorphism (RFLP) were used to characterise α-adducin genotypes (GT, GG, TT). Both of them were confi rmed by DNA sequencing. Results (1) The levels of age, serum creatinine, urea nitrogen and uric acid were higher in group C by comparison with group A and B, and had signifi cant difference (p<0.05). There was no signifi cance of sex, body mass index, fasting plasma glucose and lipids among group A, B and C (p>0.05). (2) The expected frequencies of the ACE and ADD genotypes were under the assumption of the Hardy-Weinberg equilibrium in hypertensive group and normal group respectively (p>0.05). (3) Chi-square test demonstrated that the distributions of ACE -DD, ADD-TT homozygote and ACE-DD+ADD-TT genotype (25.47%, 30.19% and 13.21% respectively) were higher in group C than in A (the distribution was, 10.06%, 14.79% and 1.59%, respectively) and in B (the distribution was 7.94%, 11.11% and 1.59% respectively); and had signifi cant difference (p<0.05), while there was no signifi cance of ACE-DD, ADD-TT homozygote and ACE-DD+ADD-TT genotype between group A and B (p>0.05). (4) Binary logistic stepwise regression demonstrated that hypertensive course and ACE-DD+ADD-TT genotype entered the model at last, and was on 11 April 2019 by guest. Protected by copyright.

In order to reduce the non-technical loss and reduce the operating cost of the power company, an abnormal power consumption detection algorithm is proposed. The algorithm includes feature extraction, principal component analysis, grid processing, local outliers, and so on. Firstly, we extract several feature quantities that characterize the user's power consumption pattern, and map the X users to the two-dimensional plane by principal component analysis. Data visualization and easy to calculate local outliers, and grid processing techniques to filter out data points in low density regions. The algorithm is used to reduce the number of training samples in the power user data set, and to output the anomalies and probabilities of all users' behavior. The experimental results show that the use of the sorting only need to detect the anomaly of a few users can find a large number of abnormal users, significantly improve the efficiency of the algorithm.