Artificial Immune System based on Hybrid and External Memory for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be further improved because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. Thus, a hybrid PSO-AIS and a new external memory CSA based scheme called EMCSA are proposed. In hybrid PSO-AIS, the good features of PSO and AIS are combined in order to reduce any limitation. Alternatively, EMCSA captures all the best antibodies into the memory in order to enhance global searching capability. In this preliminary study, the results show that the performance of hybrid PSO-AIS compares favourably with other algorithms while EMCSA produced moderate results in most of the simulations

Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hypermutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions

Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solution for each exposure (iteration) namely Single Best Remainder (SBR) CSA. In this study, the results show that the performance of the proposed algorithm (SBR-CSA) compares favourably with other algorithms while Half Best Insertion (HBI) CSA produced moderate results in most of the simulations

Novel point mutations attenuate autotaxin activity

BACKGROUND: The secreted enzyme autotaxin (ATX) stimulates tumor cell migration, tumorigenesis, angiogenesis, and metastasis. ATX hydrolyzes nucleotides, but its hydrolysis of lysophospholipids to produce lysophosphatidic acid (LPA) accounts for its biological activities. ATX has been identified only as a constitutively active enzyme, and regulation of its activity is largely unexplored. In spite of its presence in plasma along with abundant putative substrate LPC, the product LPA is found in plasma at unexpectedly low concentrations. It is plausible that the LPA-producing activity of ATX is regulated by its expression and by access to substrate(s). For this reason studying the interaction of enzyme with substrate is paramount to understanding the regulation of LPA production. RESULTS: In this study we determine ATX hydrolytic activities toward several artificial and natural substrates. Two novel point mutations near the enzyme active site (H226Q and H434Q) confer attenuated activity toward all substrates tested. The Vmax for LPC compounds depends upon chain length and saturation; but this order does not differ among wild type and mutants. However the mutant forms show disproportionately low activity toward two artificial substrates, pNpTMP and FS-3. The mutant forms did not significantly stimulate migration responses at concentrations that produced a maximum response for WT-ATX, but this defect could be rescued by inclusion of exogenous LPC. CONCLUSION: H226Q-ATX and H434Q-ATX are the first point mutations of ATX/NPP2 demonstrated to differentially impair substrate hydrolysis, with hydrolysis of artificial substrates being disproportionately lower than that of LPC. This implies that H226 and H434 are important for substrate interaction. Assays that rely on hydrolyses of artificial substrates (FS-3 and pNpTMP), or that rely on hydrolysis of cell-derived substrate, might fail to detect certain mutated forms of ATX that are nonetheless capable of producing LPA in the presence of sufficient exogenous substrate. H420Q-ATX could not be differentiated from WT-ATX, indicating that histidine at position 420 is not required for any of the activities of ATX tested in this studyope

A secretory phospholipase A2-mediated neuroprotection and anti-apoptosis

BACKGROUND: Phospholipase A2 liberates free fatty acids and lysophospholipids upon hydrolysis of phospholipids and these products are often associated with detrimental effects such as inflammation and cerebral ischemia. The neuroprotective effect of neutral phospholipase from snake venom has been investigated. RESULTS: A neutral anticoagulant secretory phospholipase A2 (nPLA) from the venom of Naja sputatrix (Malayan spitting cobra) has been found to reduce infarct volume in rats subjected to focal transient cerebral ischemia and to alleviate the neuronal damage in organotypic hippocampal slices subjected to oxygen-glucose deprivation (OGD). Real-time PCR based gene expression analysis showed that anti-apoptotic and pro-survival genes have been up-regulated in both in vivo and in vitro models. Staurosporine or OGD mediated apoptotic cell death in astrocytoma cells has also been found to be reduced by nPLA with a corresponding reduction in caspase 3 activity. CONCLUSION: We have found that a secretory phospholipase (nPLA) purified from snake venom could reduce infarct volume in rodent stroke model. nPLA, has also been found to reduce neuronal cell death, apoptosis and promote cell survival in vitro ischemic conditions. In all conditions, the protective effects could be seen at sub-lethal concentrations of the protein

Mathematical function optimization using AIS antibody remainder method

Artificial immune system (AIS) is one of the metaheuristics used for solving combinatorial optimization problems. In AIS, clonal selection algorithm (CSA) has good global searching capability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single and multi objective functions

A comparison of mantle versus involved-field radiotherapy for Hodgkin's lymphoma: reduction in normal tissue dose and second cancer risk

BACKGROUND: Hodgkin's lymphoma (HL) survivors who undergo radiotherapy experience increased risks of second cancers (SC) and cardiac sequelae. To reduce such risks, extended-field radiotherapy (RT) for HL has largely been replaced by involved field radiotherapy (IFRT). While it has generally been assumed that IFRT will reduce SC risks, there are few data that quantify the reduction in dose to normal tissues associated with modern RT practice for patients with mediastinal HL, and no estimates of the expected reduction in SC risk. METHODS: Organ-specific dose-volume histograms (DVH) were generated for 41 patients receiving 35 Gy mantle RT, 35 Gy IFRT, or 20 Gy IFRT, and integrated organ mean doses were compared for the three protocols. Organ-specific SC risk estimates were estimated using a dosimetric risk-modeling approach, analyzing DVH data with quantitative, mechanistic models of radiation-induced cancer. RESULTS: Dose reductions resulted in corresponding reductions in predicted excess relative risks (ERR) for SC induction. Moving from 35 Gy mantle RT to 35 Gy IFRT reduces predicted ERR for female breast and lung cancer by approximately 65%, and for male lung cancer by approximately 35%; moving from 35 Gy IFRT to 20 Gy IFRT reduces predicted ERRs approximately 40% more. The median reduction in integral dose to the whole heart with the transition to 35 Gy IFRT was 35%, with a smaller (2%) reduction in dose to proximal coronary arteries. There was no significant reduction in thyroid dose. CONCLUSION: The significant decreases estimated for radiation-induced SC risks associated with modern IFRT provide strong support for the use of IFRT to reduce the late effects of treatment. The approach employed here can provide new insight into the risks associated with contemporary IFRT for HL, and may facilitate the counseling of patients regarding the risks associated with this treatment

An Improved Artificial Immune System Based On Antibody Reminder Method For Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hyper mutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAS) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. I n this study, the CSA is modified using the best solutions for each exposure (iteration) namely Remainder-CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single objective functions

Particle Swarm based Artificial Immune System for Multimodal Function Optimization and Engineering Application Problem

Artificial Immune Systems (AIS) has generated great interest among researchers as the algorithm is able to improve local searching ability and efficiency. However, the rate of convergence for AIS in finding the global minima is rather slow as compare to other Evolutionary Algorithms. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used effectively in solving complicated optimization problems, but they tend to converge prematurely at the local minima. In this study, the hybrid AIS (HAIS) is proposed by combining the good features of AIS and PSO in order to reduce this shortcoming. By comparing the optimization results of the mathematical functions and the engineering problem using GA, AIS and HAIS, it is observed that HAIS achieved better performances in terms of accuracy, convergence rate and stability