A performance evaluation of pruning effects on hybrid neural network

In this paper, we explore the pruning effects on a hybrid mode sequential learning algorithmnamely FuzzyARTMAP-prunable Radial Basis Function (FAM-PRBF) that utilizes FuzzyARTMAP to learn a training dataset and Radial Basis Function Network (RBFN) to performregression and classification. The pruning algorithm is used to optimize the hidden layer ofthe RBFN. The experimental results show that FAM-PRBF has successfully reduced thecomplexity and computation time of the neural network.Keywords: pruning; radial basis function network; fuzzy ARTMAP

Patterns of care and emergency presentations for people with non-small cell lung cancer in New South Wales, Australia: A population-based study

Introduction Little is known about population-wide emergency presentations and patterns of care for people diagnosed with non-small cell lung cancer (NSCLC) in Australia. We examined patients’ characteristics associated with presenting to an emergency department around the time of diagnosis (“emergency presenters”), and receiving anti-cancer treatment within 12 months of diagnosis. Materials and Methods Participants in the 45 and Up Study who were newly diagnosed with NSCLC during 2006–2010 were included. We used linked data from population-wide health databases including Medicare and pharmaceutical claims, inpatient hospitalisations and emergency department presentations to follow participants to June 2014. Patients’ characteristics associated with being an emergency presenter and receiving any anti-cancer treatment were examined. Results A total of 647 NSCLC cases were included (58.6% male, median age 73 years). Emergency presenters (34.5% of cases) were more likely to have a high Charlson comorbidity index score, be an ex-smoker who had quit in the past 15 years and to be diagnosed with distant metastases. Almost all patients had visited their general practitioner ≥3 times in the 6 months prior to diagnosis. Nearly one-third (29.5%) of patients did not receive any anti-cancer treatment, however, there were no differences between emergency and non-emergency presenters in the likelihood of receiving treatment. Those less likely to be treated were older, had no private health insurance, and had unknown stage disease recorded. Conclusion Our results indicate the difficulties in diagnosing lung cancer at an early stage and inequities in NSCLC treatment. Future research should address opportunities to diagnose lung cancer earlier and to optimise treatment pathways

Management and investigation of neonatal encephalopathy: 2017 update.

This review discusses an approach to determining the cause of neonatal encephalopathy, as well as current evidence on resuscitation and subsequent management of hypoxic-ischaemic encephalopathy (HIE). Encephalopathy in neonates can be due to varied aetiologies in addition to hypoxic-ischaemia. A combination of careful history, examination and the judicious use of investigations can help determine the cause. Over the last 7 years, infants with moderate to severe HIE have benefited from the introduction of routine therapeutic hypothermia; the number needed to treat for an additional beneficial outcome is 7 (95% CI 5 to 10). More recent research has focused on optimal resuscitation practices for babies with cardiorespiratory depression, such as delayed cord clamping after establishment of ventilation and resuscitation in air. Around a quarter of infants with asystole at 10 min after birth who are subsequently cooled have normal outcomes, suggesting that individualised decision making on stopping resuscitation is needed, based on access to intensive treatment unit and early cooling. The full benefit of cooling appears to have been exploited in our current treatment protocols of 72 hours at 33.5°C; deeper and longer cooling showed adverse outcome. The challenge over the next 5-10 years will be to assess which adjunct therapies are safe and optimise hypothermic brain protection in phase I and phase II trials. Optimal care may require tailoring treatments according to gender, genetic risk, injury severity and inflammatory status

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

Mapping of serotype-specific, immunodominant epitopes in the NS-4 region of hepatitis C virus (HCV):use of type-specific peptides to serologically differentiate infections with HCV types 1, 2, and 3

The effect of sequence variability between different types of hepatitis C virus (HCV) on the antigenicity of the NS-4 protein was investigated by epitope mapping and by enzyme-linked immunosorbent assay with branched oligopeptides. Epitope mapping of the region between amino acid residues 1679 and 1768 in the HCV polyprotein revealed two major antigenic regions (1961 to 1708 and 1710 to 1728) that were recognized by antibody elicited upon natural infection of HCV. The antigenic regions were highly variable between variants of HCV, with only 50 to 60% amino acid sequence similarity between types 1, 2, and 3. Although limited serological cross-reactivity between HCV types was detected between peptides, particularly in the first antigenic region of NS-4, type-specific reactivity formed the principal component of the natural humoral immune response to NS-4. Type-specific antibody to particular HCV types was detected in 89% of the samples from anti-HCV-positive blood donors and correlated almost exactly with genotypic analysis of HCV sequences amplified from the samples by polymerase chain reaction. Whereas almost all blood donors appeared to be infected with a single virus type (97%), a higher proportion of samples (40%) from hemophiliacs infected from transfusion of non-heat-inactivated clotting factor contained antibody to two or even all three HCV types, providing evidence that long-term exposure may lead to multiple infection with different variants of HCV

The effect of Bi promoter on vanadium phosphate catalysts synthesized via sesquihydrate route

A series of 1%, 3% and 5% Bi-doped vanadyl pyrophosphate catalysts were prepared via sesquihydrate route (VPOs method). These catalysts were denoted as VPOs-Bi1%, VPOs-Bi3% and VPOs-Bi5%. Bulk and Bi-promoted vanadyl pyrophosphate catalysts prepared via sesquihydrate route exhibited a well-crystallized (VO)2P2O7 phase. Two V5+ phases, i.e. β-VOPO4 and αII-VOPO4 were observed in all Bi-promoted VPO catalysts, which led to an increase in the specific surface area and average oxidation state of vanadium. Bi-promoted VPO catalysts showed six to nine times higher amounts of oxygen evolved than the bulk VPO catalyst in oxygen TPD and a significant shift in the reduction peaks to lower temperatures. Catalytic tests revealed that both activity and selectivity to maleic anhydride increased with the presence of bismuth promoter