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PubMedDate not supplied

Light microscopic analysis of cellular networks in the pineal gland of the golden hamster as revealed by methylene blue labeling.

Pineal glands of golden hamsters were stained transcardially with methylene blue and studied in paraffin sections by light microscopy. In the superficial portion of the pineal complexes, a selective labeling of a subpopulation of polymorphic cells with processes of different length was achieved. It could not be clarified, whether these cells form a syncytium or are only in contact with each other. Although functional interpretations of the morphological results have to be drawn with great care, the investigation might be of special interest for a better understanding of the well-known inhibiting effects of the dye to soluble guanylyl cyclase and nitric oxide synthase within this neuroendocrine gland.

Animals↗
PubMed2005

STEPP--Search Tool for Exploration of Petri net Paths: a new tool for Petri net-based path analysis in biochemical networks.

To understand biochemical processes caused by, e. g., mutations or deletions in the genome, the knowledge of possible alternative paths between two arbitrary chemical compounds is of increasing interest for biotechnology, pharmacology, medicine, and drug design. With the steadily increasing amount of data from high-throughput experiments new biochemical networks can be constructed and existing ones can be extended, which results in many large metabolic, signal transduction, and gene regulatory networks. The search for alternative paths within these complex and large networks can provide a huge amount of solutions, which can not be handled manually. Moreover, not all of the alternative paths are generally of interest. Therefore, we have developed and implemented a method, which allows us to define constraints to reduce the set of all structurally possible paths to the truly interesting path set. The paper describes the search algorithm and the constraints definition language. We give examples for path searches using this dedicated special language for a Petri net model of the sucrose-to-starch breakdown in the potato tuber.

Algorithms↗
PubMed1990

Angiotensin II (AII)-related idiotypic network. III. Comparative analysis of idiotopes and paratopes borne by monoclonal antibodies raised against AII (AB1) and its internal image (AB3).

We have previously produced mAb against angiotensin II (AII), a phylogenetically conserved vasopressive octapeptide, and shown that they identify four distinct epitopes on the AII molecule. In addition we used internal image bearing polyclonal antiidiotypic antibodies raised against rabbit anti AII to produce mAb3. In this study we analyze the segregation of the idiotypic and paratopic repertoires of the mAb1 and mAb3. Analysis of mAb1 carried out with polyclonal Ab2 raised against the four distinct paratopes permitted classification of the mAb1 into four categories: (p+, id+) comprises antibodies with shared paratopic and idiotypic specificities: (p+, id-) is made up of antibodies that fail to express the Id defined by Ab2 raised against other antibodies pertaining to the same paratopic group; (p-, id+) includes antibodies that express cross-reactive Id on distinct paratopes; (p-, id-) refers to antibodies unrelated by their paratopes and Id mAb2 confirmed these results and showed expression of identical or closely related Id on clearly distinct paratopes. At the Ab3 level, using polyclonal Ab4, there was a higher degree of Id cross-reactivity between the two paratopes available. These data suggest that the parallel set concept may apply to the immune response to a natural peptidic Ag and its internal image. Comparison of idiotypic repertoires of mAb1 and mAb3 (using Ab2 and Ab4 antibodies) confirmed the lack of public Id and showed the predominance on mAb3 of "new" idiotypes absent from mAb1 molecules, as expected for internal image-induced antibodies. Cross-reactive idiotypes defined on mAb1 and conserved on mAb3 were expressed on the two paratopes defined at the Ab3 level. They were located on the H chain of the homologous paratope and required the association of H and L chains on the heterologous paratope. Our analysis suggests that, in the AII system, the idiotypic and paratopic repertoires segregate at least in part independently. The paratopic repertoire is limited to a small number of phylogenetically conserved specificities and may be encoded by germline genes. In contrast, the idiotypic repertoire is broader with respect to specificities, species, and localization on H and L chains. This extended diversity may be generated by somatic mutations or use of various combinations of H and L chains and/or V, D, J segments.

Angiotensin II↗
PubMed1997

Classification and staging of dementia of the Alzheimer type: a comparison between neural networks and linear discriminant analysis.

OBJECTIVE: To examine the utility of artificial neural networks (ANNs) for differentiating patients with Alzheimer disease from healthy control subjects and for staging the degree of dementia. DESIGN: Comparison of the classification abilities of ANNs with the statistical technique of linear discriminant analysis (LDA) using the results of 11 neuropsychological tests as predictors. PARTICIPANTS: Ninety-two patients with a diagnosis of probable Alzheimer disease (referred from a geriatric clinic) and 43 elderly control subjects (independently solicited). The patients met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria for probable dementia, with clinical ratings of dementia severity derived from the Cambridge Examination for Mental Disorders of the Elderly (CAMDEX). MAIN OUTCOME MEASURES: Classifications between and within groups were determined by using LDA and ANNs, and more detailed comparisons of the 2 methods were performed by using chi2 analyses and unweighted and weighted kappa statistics. RESULTS: Linear discriminant analysis correctly identified 71.9% of cases. Artificial neural networks, trained to classify the subjects using the same data, correctly classified 91.1% of the cases. Subsidiary analyses showed that although both techniques effectively discriminated between the control subjects and patients with dementia, the ANNs were more powerful in discriminating severity levels within the dementia population. The analyses for goodness of fit revealed that the ANN classification produced a better fit to the actual data. A comparison of the weighted proportion of agreement between the criterion and predictor variables also showed that the ANNs clearly outperformed LDA in classification accuracy for the full data set and patients-only data set. CONCLUSION: The results demonstrate the utility of ANNs for group classification of patients with Alzheimer disease and elderly controls and for staging dementia severity using neuropsychological data.

Aged↗
PubMed1979

Network thermodynamic approach compartmental analysis. Na+ transients in frog skin.

We introduce a general network thermodynamic method for compartmental analysis which uses a compartmental model of sodium flows through frog skin as an illustrative example (Huf and Howell, 1974a). We use network thermodynamics (Mikulecky et al., 1977b) to formulate the problem, and a circuit simulation program (ASTEC 2, SPICE2, or PCAP) for computation. In this way, the compartment concentrations and net fluxes between compartments are readily obtained for a set of experimental conditions involving a square-wave pulse of labeled sodium at the outer surface of the skin. Qualitative features of the influx at the outer surface correlate very well with those observed for the short circuit current under another similar set of conditions by Morel and LeBlanc (1975). In related work, the compartmental model is used as a basis for simulation of the short circuit current and sodium flows simultaneously using a two-port network (Mikulecky et al., 1977a, and Mikulecky et al., A network thermodynamic model for short circuit current transients in frog skin. Manuscript in preparation; Gary-Bobo et al., 1978). The network approach lends itself to computation of classic compartmental problems in a simple manner using circuit simulation programs (Chua and Lin, 1975), and it further extends the compartmental models to more complicated situations involving coupled flows and non-linearities such as concentration dependencies, chemical reaction kinetics, etc.

Animals↗
PubMed1994

Experimental analysis of large belief networks for medical diagnosis.

We present an experimental analysis of two parameters that are important in knowledge engineering for large belief networks. We conducted the experiments on a network derived from the Internist-1 medical knowledge base. In this network, a generalization of the noisy-OR gate is used to model causal independence for the multivalued variables, and leak probabilities are used to represent the nonspecified causes of intermediate states and findings. We study two network parameters, (1) the parameter governing the assignment of probability values to the network, and (2) the parameter denoting whether the network nodes represent variables with two or more than two values. The experimental results demonstrate that the binary simplification computes diagnoses with similar accuracy to the full multivalued network. We discuss the implications of these parameters, as well other network parameters, for knowledge engineering for medical applications.

Artificial Intelligence↗
PubMed2005

Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli.

MOTIVATION: The modeling and simulation of genetic regulatory networks have created the need for tools for model validation. The main challenges of model validation are the achievement of a match between the precision of model predictions and experimental data, as well as the efficient and reliable comparison of the predictions and observations. RESULTS: We present an approach towards the validation of models of genetic regulatory networks addressing the above challenges. It combines a method for qualitative modeling and simulation with techniques for model checking, and is supported by a new version of the computer tool Genetic Network Analyzer (GNA). The model-validation approach has been applied to the analysis of the network controlling the nutritional stress response in Escherichia coli. AVAILABILITY: GNA and the model of the stress response network are available at http://www-helix.inrialpes.fr/gna.

Bacterial Physiological Phenomena↗
PubMed2006

Dynamical advantages of scale-free networks.

A dynamical analysis of common network topologies is given and it is reported that a scale-free structure has two vital and distinctive features. First, complex but nevertheless reproducible states exist and, second, single-site induced state switching reminiscent of gene-expression control exists also. This indicates that scale-free networks have key dynamical advantages over other network topologies that could have contributed to their evolutionary success and thus may provide another reason for their prevalence in nature.

Models, Biological↗
PubMed2000

Microarray analysis of the transcriptional network controlled by the photoreceptor homeobox gene Crx.

BACKGROUND: Terminal differentiation of many cell types is controlled and maintained by tissue- or cell-specific transcription factors. Little is known, however, of the transcriptional networks controlled by such factors and how they regulate differentiation. The paired-type homeobox transcription factor, Crx, has a pivotal role in the terminal differentiation of vertebrate photoreceptors. Mutations in the human CRX gene result in either congenital blindness or photoreceptor degeneration and targeted mutation of the mouse Crx results in failure of development of the light-detecting outer segment of photoreceptors. RESULTS: We have characterized the transcriptional network controlled by Crx by microarray analysis of gene expression in developing retinal tissue from Crx(+/+) and Crx(-/-) mice. These data were combined with analyses of gene expression in developing and adult retina, as well as adult brain. The most abundant elements of this network are ten photoreceptor-specific or -enriched genes, including six phototransduction genes. All of the available 5' regulatory regions of the putative Crx targets contain a novel motif that is composed of a head-to-tail arrangement of two Crx-binding-element-like sequences. Analysis of the 5' regions of a set of mouse and human genes suggests that this motif is specific to Crx targets. CONCLUSIONS: This study demonstrates that cDNA microarrays can be successfully used to define the transcriptional networks controlled by transcription factors in vertebrate tissue in vivo.

Animals↗
PubMed1999

Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis.

The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.

Anesthesia↗
PubMed2002

Predicting the presence of acute pulmonary embolism: a comparative analysis of the artificial neural network, logistic regression, and threshold models.

OBJECTIVE: The objective of this study was to determine whether an artificial neural network, a new data analysis method, offers increased performance over conventional logistic regression in predicting the presence of a pulmonary embolism for patients in a well-known data set. MATERIALS AND METHODS: Data from the 1064 patients who received an angiographically based diagnosis of pulmonary embolism in the Prospective Investigation of Pulmonary Embolism Diagnosis study were encoded using a previously described method. The 21 input variables represented abnormalities identified on each patient's ventilation-perfusion scan and chest radiograph. Two methods-an artificial neural network with one hidden layer and a multivariate logistic regression-were compared for accuracy in predicting the presence or absence of pulmonary embolism on subsequent pulmonary arteriography. RESULTS: No significant difference was observed between the two methods. Areas under the receiver operating characteristic curves +/- standard deviation were 0.78 +/- 0.02 for the artificial neural network model and 0.79 +/- 0.02 for the logistic regression model. Furthermore, use of these two methods resulted in no more diagnostic accuracy than did the use of a simple threshold model based only on the number of subsegmental perfusion defects, which was the dominant input variable. CONCLUSION: In the study population, the usefulness of data from ventilation-perfusion scans as predictors of the presence of a pulmonary embolism was similar for the three analytic methods, a finding that reinforces the importance of making comparisons to simpler or more established methods when performing studies involving complex analytic models, such as artificial neural networks.

Acute Disease↗