Model Construction and Research using System Compositional Approach on Natural Hierarchical Neural Networks. Development of Computer Toolbox

System compositional approach to model construction and research of informational processes, which take place in biological hierarchical neural networks, is being discussed. A computer toolbox has been successfully developed for solution of tasks from this scientific sphere. A series of computational experiments investigating the work of this toolbox on olfactory bulb model has been carried out. The well-known psychophysical phenomena have been reproduced in experiments.

Endothelin receptor B and neural crest derived melanocyte development

Neural Crest cells (NCC) constitute a unique embryonic cell population that arises between the prospective epidermis and the dorsal aspect of the neural tube of vertebrates. NCC migrate ventromedially and dorsolaterally throughout the developing embryo giving rise to the peripheral nervous system constituents and melanocytes that ultimately reside in the skin and hair follicles respectively. Mice and humans with mutations in the Endothelin receptor b (Ednrb) gene manifest strikingly similar phenotypes characterized by hypopigmentation, hearing loss and megacolon these are due to absence of melanocytes in the skin and inner ear and lack of enteric ganglia in the distal part of the gut, respectively. Piebald lethal mice and humans with Hirschsprung's disease or Waardenburg syndrome carry different mutations in the Ednrb gene. The major goals of this project were to determine whether the action of Ednrb in NCC is required prior to commitment of these cells to the melanocytic lineage and to investigate its potential participation in the actual process of commitment. In order to achieve these goals transgenic mice that express Ednrb under two different regulatory elements were created. The first, Dct-Ednrb, expresses Ednrb under the control of the DOPAchrome tautomerase (Dct) promoter to direct expression to already committed melanocyte precursors. The second, Nes-Ednrb, expresses Ednrb under the regulation of the human nestin gene second enhancer to direct expression to pre-migratory NCC. Crosses of the Dct-Ednrb mouse with piebald lethal showed that the transgene was capable of rescuing the hypopigmentation phenotype of the later. This result indicates that the action of Ednrb after NCC commit to the melanocytic lineage is sufficient for normal melanocyte development. The Dct-Ednrb was further crossed with two other hypopigmentation mutants that carry mutations in the transcription factors Sox10 and Pax3. The transgene rescued the phenotype of the Sox10 mutant only. This suggests that Ednrb interacts with Sox10 but not with Pax3 during melanocyte development. The Nes-Ednrb mice developed a hypopigmentation phenotype that was augmented when crossed with piebald lethal or lethal spotting (mutation in Edn3, the ligand for Ednrb) mice but was rescued by over expression of Edn3. These results suggest that alterations in Ednrb expression early in development affect melanocyte development. This study provides novel information necessary to better understand the early embryonic development of NCC, clarifies specific interactions between different melanogenic genes and, could eventually help in the implementation of therapies for human pigmentary genetic disorders. ^

Mus Musculus Neural Crest Development: Is Pax3 Regulating the Expression of Endothelin Receptor B?

The vertebrate Neural Crest (NC) is formed during early embryonic development at the neurulation stage. This group of multi potent cells gives rise to a variety of derivatives such as the skin's pigmented cells (Melanocytes), the peripheral nervous system with its associated components, and the endocrine cells of the adrenal medulla amongst others. There are several molecular mechanisms that underlie the development and migration of NC derived cells. For example, during melanocyte differentiation and migration the Endothelin Receptor B and its ligand Endothelin 3 (EdnrB/Edn3), the kit/ Steel factor and the FGF receptor I FGF pathways amongst others play important roles. Additionally, several transcription factors such as Pax3, SoxlO and Mitfalso intervene during the NC cells differentiation processes. In this work, the possible regulatory interaction of Pax3 and EdnrB was assessed by in situ hybridization methods with EdnrB, SoxlO and Dct riboprobes in Pax3 homozygous embryos. To further characterize this interaction, genetic crosses between Pax3 heterozygous mutants and EdnrB heterozygous animals were established. Coat pigmentation was used as an indicator of genetic interaction on the progeny. Experimental results indicated that Pax3 does not directly regulate the expression of EdnrB during neural crest development but interact to produce normal coat color. I propose two possible models to explain the epistatic relationship of Pax3 and EdnrB during normal melanocyte development.

Cardiac Neural Crest Cells Affect the Development of the Myocardium and Conduction System in the Mouse

Neural crest cells originate from the dorsal most region of the embryonic neural tube. These cells migrate into several embryonic locations and differentiate into a variety of cell types. Cardiac neural crest (CNC) cells are a set of neural crest progenitors that aid in the proper formation of the cardiac septum, which separates the pulmonary from the systemic circulation. We have used Splotch mice to investigate whether the murine CNC cells play a role during the development oft he myocardium and the conduction system. Splotch mice carry a mutation in the P AX3 transcription factor, and display a problem in CNC cell migration. A scanning-electron-microscopy analysis of Splotch mutant-embryonic-hearts reveals abnormalities in the interventricular septum. In addition, the right and left ventricular cavities appear dilated relative to a wild type heart. Hoechst nuclei staining of Splotch heart cryosections demonstrates a decreased number of cardiomyocytes and a corresponding thinner ventricular wall. The absence of Connexin 40 in the ventricles of Splotch mutants, suggests conduction system defects. These results support the evidence that CNC cell signaling plays a role in modulating the growth and development of murine cardiomyocytes and their differentiation into conductile cells.

Cardiac Neural Crest Cells and Their Role in Murine Purkinje Fiber Development

The heart beat is regulated by the cardiac conduction system (CCS), a specialized group of cells that transmit electrical impulses around the heart chambers. During development, ventricular CCS cells originate from embryonic cardiomyocytes and not from the neural crest. Nonetheless, discoveries in chick implied that the cardiac neural crest (CNC) cells contribute to proper development of the ventricular CCS. In this report, the Splotch mouse mutant (Pax3sp), in which the CNC cells do not migrate to the heart, was used to investigate whether these cells also affect proper CCS development in mammals. Homozygote mutants (Pax3Sp!Sp) are lethal on 111 Embryonic Day 13 (E13), and can be phenotyped by spina bifida and exencephaly. Pax3Spi+ mice were crossed to obtain wild type, Pax3 Spi+ and Pax3 Sp!Sp embryos. Comparison of hematoxylin and eosin stained histological sections showed less trabeculation in El2.5 cardiac ventricles of Pax3Sp!Sp. Furthermore, immunofluorescence analysis with the Purkinje fiber marker Cx40 showed a qualitative difference between wild type and mutant hearts. Quantitative analysis indicated that Pax3 Sp!Sp ventricles had fewer Cx40 expressing cells, as well as less Cx40 being expressed per cell when compared to wild type ventricles. Immunofluorescence with the H3 histome mitosis antibody showed fewer proliferating cells in the ventricles of mutant embryos when compared to controls. These results suggest that CNCC affect the morphogenesis of cardiac ventricles and the development of the ventricular CCS by contributing cellular proliferation.

Epigenetic regulation in neural crest development : role of DNA methyltransferases 3A and 3B

The neural crest is a group of migratory, multipotent stem cells that play a crucial role in many aspects of embryonic development. This uniquely vertebrate cell population forms within the dorsal neural tube but then emigrates out and migrates long distances to different regions of the body. These cells contribute to formation of many structures such as the peripheral nervous system, craniofacial skeleton, and pigmentation of the skin. Why some neural tube cells undergo a change from neural to neural crest cell fate is unknown as is the timing of both onset and cessation of their emigration from the neural tube. In recent years, growing evidence supports an important role for epigenetic regulation as a new mechanism for controlling aspects of neural crest development. In this thesis, I dissect the roles of the de novo DNA methyltransferases (DNMTs) 3A and 3B in neural crest specification, migration and differentiation. First, I show that DNMT3A limits the spatial boundary between neural crest versus neural tube progenitors within the neuroepithelium. DNMT3A promotes neural crest specification by directly mediating repression of neural genes, like Sox2 and Sox3. Its knockdown causes ectopic Sox2 and Sox3 expression at the expense of neural crest territory. Thus, DNMT3A functions as a molecular switch, repressing neural to favor neural crest cell fate. Second, I find that DNMT3B restricts the temporal window during which the neural crest cells emigrate from the dorsal neural tube. Knockdown of DNMT3B causes an excess of neural crest emigration, by extending the time that the neural tube is competent to generate emigrating neural crest cells. In older embryos, this resulted in premature neuronal differentiation. Thus, DNMT3B regulates the duration of neural crest production by the neural tube and the timing of their differentiation. My results in avian embryos suggest that de novo DNA methylation, exerted by both DNMT3A and DNMT3B, plays a dual role in neural crest development, with each individual paralogue apparently functioning during a distinct temporal window. The results suggest that de novo DNA methylation is a critical epigenetic mark used for cell fate restriction of progenitor cells during neural crest cell fate specification. Our discovery provides important insights into the mechanisms that determine whether a cell becomes part of the central nervous system or peripheral cell lineages.

A study of Chinese private passenger cars based on BP neural network

This study employs BP neural network to simulate the development of Chinese private passenger cars. Considering the uncertain and complex environment for the development of private passenger cars, indicators of economy, population, price, infrastructure, income, energy and some other fields which have major impacts on it are selected at first. The network is proved to be operable to simulate the progress of chinese private passenger cars after modeling, training and generalization test. Based on the BP neural network model, sensitivity analysis of each indicator is carried on and shows that the sensitivity coefficients of fuel price change suddenly. This special phenomenon reveals that the development of Chinese private passenger cars may be seriously affected by the recent high fuel price. This finding is also consistent with facts and figures

Predicting project performance through neural networks

Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Urban growth monitoring techniques for sustainable development

This paper discusses the role of advance techniques for monitoring urban growth and change for sustainable development of urban environment. It also presents results of a case study involving satellite data for land use/land cover classification of Lucknow city using IRS-1C multi-spectral features. Two classification algorithms have been used in the study. Experiments were conducted to see the level of improvement in digital classification of urban environment using Artificial Neural Network (ANN) technique.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.