Genetic variants in C1GALT1 are associated with gastric cancer risk by influencing immune infiltration

Core 1 synthase glycoprotein-N-acetylgalactosamine 3-β-galactosyltransferase 1(C1GALT1)is known to play a critical role in the development of gastric cancer,but few studies have elucidated associations between genetic variants in C1GALT1 and gastric cancer risk.By using the genome-wide association study data from the database of Genotype and Phenotype(dbGAP),we evaluated such associations with a multivariable logistic regression model and identified that the rs35999583 G>C in C1GALT1 was associated with gastric cancer risk(odds ratio,0.83;95% confidence interval[CI],0.75-0.92;P=3.95×10^(-4)).C1GALT1 mRNA expression levels were significantly higher in gastric tumor tissues than in normal tissues,and gastric cancer patients with higher C1GALT1 mRNA levels had worse overall survival rates(hazards ratio,1.33;95%CI,1.05-1.68;P_(log-rank)=1.90×10^(-2)).Furthermore,we found that C1GALT1 copy number differed in various immune cells and that C1GALT1 mRNA expression levels were positively correlated with the infiltrating levels of CD4^(+)T cells and macrophages.These results suggest that genetic variants of C1GALT1 may play an important role in gastric cancer risk and provide a new insight for C1GALT1 into a promising predictor of gastric cancer susceptibility and immune status.

Immune Genetic Algorithm for Optimal Design

A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.

Application of a Genetic Algorithm Based on the Immunity for Flow Shop under Uncertainty

The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se- quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov- ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an- tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.

Genetic variants of innate immune receptors and infections after liver transplantation

Infection is the leading cause of complication after liver transplantation, causing morbidity and mortality in the first months after surgery. Allograft rejection is mediated through adaptive immunological responses, and thus immunosuppressive therapy is necessary after transplantation. In this setting, the presence of genetic variants of innate immunity receptors may increase the risk of post-transplant infection, in comparison with patients carrying wild-type alleles. Numerous studies have investigated the role of genetic variants of innate immune receptors and the risk of complication after liver transplantation, but their results are discordant. Tolllike receptors and mannose-binding lectin are arguably the most important studied molecules; however, many other receptors could increase the risk of infection after transplantation. In this article, we review the published studies analyzing the impact of genetic variants in the innate immune system on the development of infectious complications after liver transplantation.

Intelligent optimization of the structure of the large section highway tunnel based on improved immune genetic algorithm

As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country,it has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying,expressing and disposing such kind of multiple variables and complicated non-linear relations. In this paper,a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm,thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory,the global search capability of the immune genetic algorithm is raised,thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel,the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency,the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationization design of the wall rock of the tunnel.

Optimization of Submarine Hydrodynamic Coefficients Based on Immune Genetic Algorithm

Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations.Some hydrodynamic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm.By using adaptive weight method to determine the weight and target function,the multi-objective optimization could be translated into single-objective optimization.For a certain kind of submarine,three typical maneuvers were chosen to be the objects of study:overshoot maneuver in horizontal plane,overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane.From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrodynamic coefficient,the efficiency of proposed method is proved.

A novel immune genetic algorithm based on quasi secondary response

Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also used in the process of IGA QSR. Theoretical analysis, numerical examples of three benchmark mathematical optimization problems and a trave ling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy （ SGA ES）. Besides, IGA QSR allows the designers to stop and restart the optimization process freely with out losing the best results that have already been obtained. These properties make IGA QSR be a fea sible, effective and robust search algorithm for complex engineering problems.

Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

Immunologic, metabolic and genetic factors in hepatitis C virus infection

The mechanisms that regulate disease progression during hepatitis C virus(HCV)infection and the response to treatment are not clearly identified.Numerous studies have demonstrated that a strong host immune response against HCV favors HCV clearance.In addition,genetic factors and metabolic machinery,particularly cholesterol modulation,are involved in HCV infection.It is likely that the interplay between all of these factors contributes to the outcome of HCV infection.In recent years,the world has experienced its largest epidemic of obesity.Mexico and the United States are the leading sufferers from this epidemic at the global level.Obesity is associated with the development ofnumerous pathologies including hypercholesterolemia which is one of the eight most important risk factors for mortality in Mexico.This may be related to the course of HCV infection in this population.Here,we focus on the urgent need to study the progression of HCV infection in relation to ethnic characteristics.Discoveries are discussed that hold promise in identifying immune,metabolic and genetic factors that,in conjunction,could be therapeutic targets or predictors of the progression of HCV infection.

Holistic paradigm in carcinogenesis:Genetics,epigenetics,immunity,inflammation and oral infections

Recent debate among the experts of cancer research regarding the main causes of carcinogenesis encouraged us to review the etiology of cancer pathogenesis. The somatic mutation theory attributes carcinogenesis to random errors in DNA multiplication while the tissue organization field theory ascribes causation to environmental factors. We recognize complexity in cancer pathogenesis and accept the premise of both DNA multiplication errors and environmental factors in cancer development. Furthermore, it should also be noted that the combination of these factors and the relative importance of the each differ in various types of cancers. For example, in some cancers, genetics plays a prominent role while in others environment such as obesity plays a much stronger role. Additionally, the cancer mitigating factors should also be considered. The balance of cancer-enhancing and cancer-suppressing forces determines the cancer incidence. Ultimately, identifying the lifestyle factors that revise somatic mutations or epigenetic alterations will lead to a clear understanding of pathogenic mechanisms of cancer and to the optimal preventive strategies. This narrative review evaluates the published evidence on carcinogenesis pertaining to the whole organism(thus, holistic) incorporating genetics, epigenetics, immunology, inflammation and infections with emphasis on oral infections.

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.

Immune Genetic Learning of Fuzzy Cognitive Map

This paper presents a hybrid methodology of automatically constructing fuzzy cognitive map (FCM). The method uses immune genetic algorithm to learn the connection matrix of FCM. In the algorithm, the DNA coding method is used and an immune operator based on immune mechanism is constructed. The characteristics of the system and the experts' knowledge are abstracted as vaccine for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.

Adaptive template filter method for image processing based on immune genetic algorithm

To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.

Adaptive immune-genetic algorithm for global optimization to multivariable function

An adaptive immune-genetic algorithm （AIGA） is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response （secondary response）, adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.

Immune and Genetic Algorithm Based Assembly Sequence Planning

In this paper an assembly sequence planning model inspired by natural immune and genetic algorithm (ASPIG) based on the part degrees of freedom matrix (PDFM) is proposed, and a proto system — DSFAS based on the ASPIG is introduced to solve assembly sequence problem. The concept and generation of PDFM and DSFAS are also discussed. DSFAS can prevent premature convergence, and promote population diversity, and can accelerate the learning and convergence speed in behavior evolution problem.

NOVEL QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON IMMUNITY

A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.

Pharmacogenetics of the systemic treatment in advanced hepatocellular carcinoma

Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers. To date, most patients with HCC are diagnosed at an advanced tumor stage, excluding them from potentially curative therapies (i.e., resection, liver transplantation, percutaneous ablation). Treatments with palliative intent include chemoembolization and systemic therapy. Among systemic treatments, the small-molecule multikinase inhibitor sorafenib has been the only systemic treatment available for advanced HCC over 10 years. More recently, other smallmolecule multikinase inhibitors (e.g., regorafenib, lenvatinib, cabozantinib) have been approved for HCC treatment. The promising immune checkpoint inhibitors (e.g., nivolumab, pembrolizumab) are still under investigation in Europe while in the US nivolumab has already been approved by FDA in sorafenib refractory or resistant patients. Other molecules, such as the selective CDK4/6inhibitors (e.g., palbociclib, ribociclib), are in earlier stages of clinical development, and the c- MET inhibitor tivantinib did not show positive results in a phase III study. However, even if the introduction of targeted agents has led to great advances in patient response and survival with an acceptable toxicity profile, a remarkable inter-individual heterogeneity in therapy outcome persists and constitutes a significant problem in disease management. Thus, the identification of biomarkers that predict which patients will benefit from a specific intervention could significantly affect decision-making and therapy planning. Germ-line variants have been suggested to play an important role in determining outcomes of HCC systemic therapy in terms of both toxicity and treatment efficacy. Particularly, a number of studies have focused on the role of genetic polymorphisms impacting the drug metabolic pathway and membrane translocation as well as the drug mechanism of action as predictive/prognostic markers of HCC treatment. The aim of this review is to summarize and critically discuss the pharmacogenetic literature evidences, with particular attention to sorafenib and regorafenib, which have been used longer than the others in HCC treatment.

A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.

Pathogenetic mechanisms of antiphospholipid antibody production in antiphospholipid syndrome

Antiphospholiipid syndrome(APS) is an autoimmune disease characterized by the pathological action of antiphospholipid antibodies(a PL),that leads to recurrent pregnancy loss and thrombosis.Despite limited evidence,it is clear that there are both inherited and acquired components of the ontogeny of these antibodies.Animal genetic studies and human familial and population studies highlight the influence of genetic factors in APS,particularly human leukocyte antigen associations.Similarly,both animal and human studies have reported the importance of acquired factors in APS development and infectious agents in particular have a great impact on a PL production.Bacterial and viral agents have been implicated in the induction of autoimmune responses by various mechanisms including molecular mimicry,cryptic autoantigens exposure and apoptosis.In this review we highlight the latest updates with regards to inherited and acquired factors leading to the manufacturing of pathogenic antibodies and APS.