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PubMed1989

[Morphometric analysis of the vascular network in the preoptic area of the brain in man].

Twenty brains of human beings of both sex aged 17-64 were used for morphometric analysis of the vascular network of preoptic area. Blood vessels of the brain were filled by the mixture od India ink and gelatine. Serial resections 200 microns thick were illuminated by the method of Spalteholz. Standard stereologic parameters--volume density, surface density and the average half diameter of blood vessels were used for the quantification of capillary blood network density. By the comparative test of obtained mean values of males and females no statistically significant differences pertaining to sex and in respect to the size and density of capillary network in preoptic area were confirmed. This fact supported the assumption that although there were no morphologic differences in the blood network changes in various functional states, accompanying neuroendocrine events in the body of adults were in all probability the main and responsible factors inducing greater or less vascularization of specific organs.

Adolescent↗
PubMed2004

Classification of individual lung cancer cell lines based on DNA methylation markers: use of linear discriminant analysis and artificial neural networks.

The classification of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) can pose diagnostic problems due to inter-observer variability and other limitations of histopathology. There is an interest in developing classificatory models of lung neoplasms based on the analysis of multivariate molecular data with statistical methods and/or neural networks. DNA methylation levels at 20 loci were measured in 41 SCLC and 46 NSCLC cell lines with the quantitative real-time PCR method MethyLight. The data were analyzed with artificial neural networks (ANN) and linear discriminant analysis (LDA) to classify the cell lines into SCLC or into NSCLC. Models used either data from all 20 loci, or from five significant DNA methylation loci that were selected by a step-wise back-propagation procedure (PTGS2, CALCA, MTHFR, ESR1, and CDKN2A). The data were sorted randomly by cell line into 10 different data sets, each with training and testing subsets composed of 71 and 16 of the cases, respectively. Ten ANN models were trained using the 10 data sets: five using 20 variables, and five using the five variables selected by step-wise back-propagation. The ANN models with 20 input variables correctly classified 100% of the cell lines, while the models with only five variables correctly classified 87 to 100% of cases. For comparison, 10 different LDA models were trained and tested using the same data sets with either the original data or with logarithmically transformed data. Again, half of the models used all 20 variables while the others used only the five significant variables. LDA models provided correct classifications in 62.5% to 87.5% of cases. The classifications provided by all of the different models were compared with kappa statistics, yielding kappa values ranging from 0.25 to 1.0. We conclude that ANN models based on DNA methylation profiles can objectively classify SCLC and NSCLC cells lines with substantial to perfect concordance, while LDA models based on DNA methylation profiles provide poor to substantial concordance. Our work supports the promise of ANN analysis of DNA methylation data as a powerful approach for the development of automated methods for lung cancer classification.

Carcinoma, Non-Small-Cell Lung↗
PubMed1976

Brain architecture and mechanisms that underlie language: an information-processing analysis.

A neural network model for naming visual objects and their attributes, and understanding certain simple types of sentences has been presented. The model is based on neural processes rather than linguistic or symbolic constructs. The following are the major structural features of the model: 1. Memory stores are associative networks that perform an analysis of their inputs in real time. This analysis converts the input pattern into a "recognition pattern" that depends on the specific stored information and its location in the store. 2. The naming store is an analyzer network that converts the sensory encoding of a sentence into a "sentence pattern". The sentence pattern is obtained by averaging the recognition signals from several memory locations in the naming store. Because of the organization of words in the naming store, sentence patterns are an encoding of the structure of the sentences. 3. The sentence store is an associative memory store that recognizes sentence patterns and associates with each one an "instruction sequence" or "program" that specifies how the system should respond to the sentence. It is the enabling of access to this "program" that constitutes "understanding." Sentences are understood in real time, without the explicit grammatical analysis that is usual in sentence-understanding systems. Understanding a sentence involves two functionally distinct processes: (1) associating with its sentence pattern an instruction sequence that specifices what to do in order to generate an appropriate response to the sentence matching that pattern: for example, to look in the visual field for an object, determine its location, and so on, and (2) analysis of the tokens in the sentence that indicate specific attributes or entities to look for or determine.

Association↗
PubMedDate not supplied

Discrimination between migraine patients and normal subjects based on steady state visual evoked potentials: discriminant analysis and artificial neural network classifiers.

Fifty-one migraine patients and 19 control subjects were examined by steady state visual evoked potentials (SSVEPs) procedure. The aim of this study was to develop a discriminant analysis and an artificial neural network (NN) classifier in order to discriminate between migraneurs during attack-free periods and normal subjects. Discriminant analysis correctly classified 72.5% of migraine patients with a false positive rate of 36.8%. The NN method had a sensitivity of 100% with a false positive rate of 15%. The results of this study confirm SSVEP pattern as a marker of migraine and demonstrate that NNs could be a useful method in the statistical analysis of topographic EEG data.

Adult↗
PubMed1983

Boolean analysis of cell regulation networks.

A comparison is made between the predictions of the Boolean and continuous analysis of a regulation model when the formation of two mediators interacting by cross-inhibition is stimulated by one or two specific signals. For such a system, the Boolean analysis reproduces the characteristics of behaviour previously predicted by continuous analysis (multiple stable states of opposite type, discontinuous transition, and associated hysteresis phenomenon). The qualitative agreement between the two methods allows a qualitative but rigorous treatment of regulation systems in which the Boolean analysis is applicable. From a general schematic representation of interaction in bidirectional control systems, we analyse by the Boolean method a large range of possible systems of increasing complexities which could theoretically apply. Previously unforeseen consequences of some systems are described. After that, we give a logical analysis of a well-known system (negative loop grafted with additional external controls) and discuss the application of such a system to explain certain oscillatory phenomena in the cell, showing the disrupting role of an additional control on the expected behaviour. Thus, when the analysis of a model including a negative loop does not indicate the possibility of experimentally suggested oscillations, we propose other simple logical structures which can predict this behaviour. Finally, we show a logical analysis of an opposite type of example of cell regulation where the biochemical observations can be accounted for simply by a negative loop grafted with one input variable.

Animals↗
PubMed2006

'Five-parameter' analysis of grain boundary networks by electron backscatter diffraction.

This paper describes state-of-the-art analysis of grain boundary populations by EBSD, with particular emphasis on advanced, nonstandard analysis. Data processing based both on misorientation alone and customised additions which include the boundary planes are reviewed. Although commercial EBSD packages offer comprehensive data processing options for interfaces, it is clear that there is a wealth of more in-depth data that can be gleaned from further analysis. In particular, determination of all five degrees of freedom of the boundary population provides an exciting opportunity to study grain boundaries by EBSD in a depth that was hitherto impossible. In this presentation we show 'five-parameter' data from 50 000 boundary segments in grain boundary engineered brass. This is the first time that the distribution of boundary planes has been revealed in a grain boundary engineered material.

Journal Article↗
PubMed2000

Convergence analysis of cascade error projection--an efficient learning algorithm for hardware implementation.

In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural network. Our analysis also shows that the convergence of the cascade architecture neural network is assured because it satisfies Liapunov criteria, in an added hidden unit domain rather than in the time domain. From this analysis, a mathematical foundation for the cascade correlation learning algorithm can be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case from mathematical analysis in which an efficient hardware learning algorithm called Cascade Error Projection(CEP) is proposed. The CEP provides efficient learning in hardware and it is faster to train, because part of the weights are deterministically obtained, and the learning of the remaining weights from the inputs to the hidden unit is performed as a single-layer perceptron learning with previously determined weights kept frozen. In addition, one can start out with zero weight values (rather than random finite weight values) when the learning of each layer is commenced. Further, unlike cascade correlation algorithm (where a pool of candidate hidden units is added), only a single hidden unit is added at a time. Therefore, the simplicity in hardware implementation is also achieved. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.

Algorithms↗
PubMed1996

The comparison of geometric and electronic properties of molecular surfaces by neural networks: application to the analysis of corticosteroid-binding globulin activity of steroids.

It is shown how a self-organizing neural network such as the one introduced by Kohonen can be used to analyze features of molecular surfaces, such as shape and the molecular electrostatic potential. On the one hand, two-dimensional maps of molecular surface properties can be generated and used for the comparison of a set of molecules. On the other hand, the surface geometry of one molecule can be stored in a network and this network can be used as a template for the analysis of the shape of various other molecules. The application of these techniques to a series of steroids exhibiting a range of binding activities to the corticosteroid-binding globulin receptor allows one to pinpoint the essential features necessary for biological activity.

Drug Design↗
PubMed2002

Modular response analysis of cellular regulatory networks.

The sheer complexity of intracellular regulatory networks, which involve signal transducing, metabolic, and genetic circuits, hampers our ability to carry out a quantitative analysis of their functions. Here, we describe an approach that greatly simplifies this type of analysis by capitalizing on the modular organization of such networks. Steady-state responses of the network as a whole are accounted for in terms of intermodular interactions between the modules alone; processes operating solely within modules need not be considered when analysing signal transfer through the entire network. The intermodular interactions are quantified through (local) response coefficients which populate an interaction map (matrix). This matrix can be derived from a biochemical or molecular biological analysis of (macro) molecular interactions that constitute the regulatory network. The approach is illustrated by two examples: (i) mitogenic signalling through the mitogen-activated protein kinase cascade in the epidermal growth factor receptor network and (ii) regulation of ammonium assimilation in Escherichia coli.

Animals↗
PubMed2006

Analysis of gene regulatory network models with graded and binary transcriptional responses.

The steep sigmoid framework developed by Plahte and Kjøglum [Plahte, E., Kjøglum, S., 2005. Analysis and generic properties of gene regulatory networks with graded response functions. Phys. D 201, 150-176, doi:10.1016/j.physd.2004.11.014] provides a uniform description of gene regulatory networks in which there may be both graded and binary transcriptional responses, as well as a method for analysing the models developed. Here we extend this framework. We show that there is a relation between the location of steady states and the feedback structure of a system, thus generalising existing results for Boolean type models. In addition, we justify underlying assumptions and generic features of the modelling framework in terms of biology and generalise the overall approach to take into account that each transcription factor only regulates one gene at a given threshold. By this assumption, the analysis of the models are greatly simplified.

Animals↗
PubMed1998

Molecular epidemiological analysis of HIV in sexual networks in Uganda.

OBJECTIVE: To investigate the suitability of HIV sequence analysis, based on the p17 region of the gag gene, to characterize the sexual networks in and around a trading town in south-west Uganda. METHODS: Blood samples were obtained from 54 HIV-seropositive members of three distinct sexual networks and phylogenetic analysis carried out on proviral DNA sequences obtained from the p17 region of gag from 53 individuals. RESULTS: Despite documented evidence of very little sexual mixing between residents of the trading town, fishing village and surrounding rural area, there was no evidence of clustering of sequences associated with place of residence. More strikingly, known sexual partners failed to show significantly related sequences, and the two pairs of sequences that did show significant similarity came from individuals who had no known social or sexual contact. CONCLUSIONS: Sequence analyses such as those described here have proved effective in confirming or identifying epidemiological links not only following single transmission events but also within risk groups. However, the results from Uganda contrast markedly with those from Europe and the United States. The length of time that the community has been infected, the number of occasions when the virus has been introduced and the high degree of partner change may contribute to the lack of supportive evidence for sociological studies of sexual networks in Uganda.

DNA, Viral↗
PubMed2000

Novel approach to acoustical voice analysis using artificial neural networks.

Perceptual rating scales are widely used for the assessment of voice quality. These ratings may be influenced by the individual experience of the listener. Thus, researchers have turned to acoustical measures which may eventually correlate with voice quality. In this study we tested whether multivariate statistics, combined with artificial neural networks, could identify patterns of acoustic voice parameters corresponding to a widely used perceptual rating scale. In a multicenter study with 31 raters, voice samples of 117 individuals with or without voice disorders were perceptually rated. The RBH index, consisting of a 4-point scale of roughness, breathiness, and hoarseness, was used. Voice samples were then analyzed with an acoustical feature extraction and classified using amultivariate regression tree analysis with the perceptual ratings as a priori information. Artificial neural networks were trained to selected acoustic parameters having high "relative importance" in the regression trees. Mean classification accuracies were around 30% with topographic feature maps (trained with Learning Vector Quantization algorithm) and 65-85% with feedforward networks (trained with RProp algorithm). Based on the best-fitting results with feedforward networks, a classification system (computer program) consisting of 50 simultaneous working networks was developed. Using this program, the classification matched 40% of the a prori values in both R and B domains. In 65% they matched at least in one domain. These accuracies are within the range reported by other authors using artificial neural networks in biology and clinical medicine. Thus, the results encourage further research of feedforward networks for acoustic voice analysis.

Humans↗
PubMed1996

Classification of species in the genus Penicillium by Curie point pyrolysis/mass spectrometry followed by multivariate analysis and artificial neural networks.

Curie point pyrolysis/mass spectrometry of Penicillium species was performed with 530 degrees C Curie point foils. The mass spectra were submitted to principal component analysis, canonical variates analysis and hierarchical cluster analysis, producing a final dendrogram by the use of average linkage clustering. By this approach a successful classification of the species Penicillium italicum, P. expansum and P. digitatum originating from fruits was obtained. Isolates of the same species grouped together in the dendrogram, while the different species were distinguished. Also when grown on two different agar media, replicates of the same species grouped together. Likewise, a satisfactory classification was achieved by multivariate analysis of the data for various isolates of the cheese-associated fungi Aspergillus versicolor, P. discolor, P. roqueforti, P. solitum, P. verrucosum, P. commune and P. palitans. However, some difficulties appeared in distinguishing the closely related species P. commune and P. palitans. Such difficulties became greater on including more isolates and limiting the analysis to five of the species. The use of back-propagation artificial neural networks, in contrast, resulted in a correct classification in all cases. Thus, it is concluded that Curie point pyrolysis/mass spectrometry is useful in chemotaxonomic studies of the closely related species in the genus Penicillium.

Cheese↗
PubMed2005

Applying a neural network to predict the thermodynamic parameters for an expanded nearest-neighbor model.

Predicting the secondary and tertiary structure of RNAs largely depends on our capabilities in estimating the thermodynamics of RNA duplexes. In this work, an expanded nearest-neighbor model, designated INN-48, is established. The thermodynamic parameters of this model are predicted using both multiple linear regression analysis and neural network analysis. It is suggested that due to the increase in the number of parameters and the insufficiency of the existing data, neural network analysis results in more reliable predictions. Furthermore, it is suggested that INN-48 can be used to estimate the thermodynamics of RNA duplex formation for longer sequences, whereas INN-HB, the previous model on which INN-48 is based, can be used for short sequences.

Animals↗
PubMed2002

Aroma quality differentiation of pyrazine derivatives using self-organizing molecular field analysis and artificial neural network.

The encoding of various aroma impressions and the distinction between different aroma qualities are unsolved problems, as differences between aroma impressions can be described only in a qualitative but not in a quantitative manner. As a consequence, classifications of various aroma qualities cannot easily be performed by standard QSAR methods. To find a proper way to encode aroma impressions for SAR studies, a total of 50 pyrazine-based aroma compounds showing the aroma quality of earthy, green-earthy, or green are analyzed. Special attention is thereby turned on the mixed aroma impression green-earthy. Classifications on the whole data set as well as on smaller subsets are calculated using self-organizing molecular field analysis (SOMFA) and artificial neural networks (ANNs). SOMFA classifies between two or three aroma impressions, leading to models satisfying in predictive power. ANN analysis using multilayer perceptron network architecture with one hidden layer and nominal output as well as genetic regression neural network) with two hidden layers and numerical output both lead to a rather good performance rate of 94%.

Neural Networks, Computer↗
PubMed2002

Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria.

Metabolite balancing has turned out to be a powerful computational tool in metabolic engineering. However, the linear equation systems occurring in this analysis are often underdetermined. If it is difficult or impossible to find the missing constraints, it is nevertheless feasible in some cases to determine the values of a subset of the unknown rates. Here, a procedure for finding out which reaction rates can be uniquely calculated in underdetermined metabolic networks and computing these rates is given. The method is based on the null space to the stoichiometry matrix corresponding to the reactions with unknown rates. It is shown that this method is considerably easier to handle than an algorithm given previously (Van der Heijden et al., 1994a). Furthermore, a useful elementary representation of the null space is presented which is closely related with the elementary flux modes. This unique representation is central to a more general approach to observability/calculability analysis. In particular, it allows one to find, in an easy way, those sets of measurable rates that enable a calculation of a certain unknown rate. Besides, rates which are never calculable by metabolite balancing may be easily detected by this method. The applicability of these methods is illustrated by a model of the central metabolism in purple nonsulfur bacteria. The photoheterotrophic growth of these representatives of anoxygenic photosynthetic bacteria is stoichiometrically analyzed. Interesting metabolic constraints caused by the necessary balancing of NADPH can be detected in a highly underdetermined system. This is, to our knowledge, the first application of stoichiometric analysis to the metabolic network in this bacteria group using metabolite balancing techniques. A new software tool, the FluxAnalyzer, is introduced. It allows quantitative and structural analysis of metabolic networks in a graphical user interface.

Algorithms↗
PubMed2003

The European phenology network.

The analysis of changes in the timing of life cycle-events of organisms (phenology) has been able to contribute significantly to the assessment of potential impacts of climate change on ecology. These phenological responses of species to changes in climate are likely to have significant relevance for socio-economic issues such as agriculture, forestry and human health and have proven able to play a role in raising environmental awareness and education on climate change. This paper presents the European Phenology Network (EPN), which aims to increase the efficiency, added value and use of phenological monitoring and research, and to promote the practical use of phenological data in assessing the impact of global (climate) change and possible adaptation measures. The paper demonstrates that many disciplines have to deal with changes in the timing of life-cycle events in response to climate change and that many different user groups are involved. Furthermore, it shows how EPN addresses issues such as (1) raising public awareness and education, (2) the integration and co-operation of existing observing systems, (3) integration and access to phenological information and (4) communication.

Adaptation, Physiological↗
PubMed1981

Analysis of a schizophrenic psychosocial network.

The amorphous concept of social support systems merits construction of a conceptually coherent theoretical model linked to social theory and amenable to empirical investigation. The social network paradigm is presented as such a model. The model is further defined in terms of the intimate psychosocial network, which has been empirically studied with the Pattison Psychosocial Kinship Inventory. The characteristics of the normal network are shown to differ substantially in the schizophrenic network. The structure and functions of the schizophrenic network are illustrated in a case study analysis. The schizophrenic network is shown to exhibit dynamics that generate and perpetuate psychotic behavior. A strategy for network intervention is described, based on the model of structural change in the network social system.

Adult↗