Search

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN. [ABSTRACT FROM AUTHOR]

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. [ABSTRACT FROM AUTHOR]

Abstract: The World Health Organization (WHO) has estimated that there are about 8 million new cases annually of active Tuberculosis (TB). Despite its irregular effectiveness (0–89%), the Bacillus Calmette-Guérin) BCG is the only vaccine available worldwide for prevention of TB; thus, the design is important of novel and more efficient vaccination strategies. Considering that β-defensin-2 is an antimicrobial peptide that induces dendritic cell maturation through the TLR-4 receptor and that both ESAT-6 and Ag85B are immunodominant mycobacterial antigens and efficient activators of the protective immune response, we constructed two DNA vaccines by the fusion of the gene encoding β-defensin-2 and antigens ESAT6 (pDE) and 85B (pDA). After confirming efficient local antigen expression that induced high and stable Interferon gamma (IFN-γ) production in intramuscular (i.m.) vaccinated Balb/c mice, groups of mice were vaccinated with DNA vaccines in a prime-boost regimen with BCG and with BCG alone, and 2 months later were challenged with the mild virulence reference strain H37Rv and the highly virulent clinical isolate LAM 5186. The level of protection was evaluated by survival, lung bacilli burdens, and extension of tissue damage (pneumonia). Vaccination with both DNA vaccines showed similar protection to that of BCG. After the challenge with the highly virulent Mycobacterium tuberculosis strain, animals that were prime-boosted with BCG and then boosted with both DNA vaccines showed significant higher survival and less tissue damage than mice vaccinated only with BCG. These results suggest that improvement of BCG vaccination, such as the prime-boost DNA vaccine, represents a more efficient vaccination scheme against TB. [Copyright &y& Elsevier]