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At least 1,261 records · Page 70Linked to original sources
PubMed2004

Verbal fluency output in children aged 7-16 as a function of the production criterion: qualitative analysis of clustering, switching processes, and semantic network exploitation.

Developmental changes in children's verbal fluency were explored in this study. One hundred and forty children aged from 7 to 16 completed four verbal fluency tasks, each with a different the production criterion (letter, sound, semantic, and free). The age differences were analyzed both in terms of number of words produced, and clustering, switching, and semantic network exploration. Analysis of the number of words produced showed a larger difference between the 7-8- and the 9-10-year-olds in semantic than in letter fluency, but this difference gradually disappeared with increasing age for semantic fluency while remaining constant for letter fluency. In letter fluency production, age modified both the number of switches and clusters formed whereas in semantic fluency tasks, only cluster size changed with age. Concerning the semantic network exploration indicators derived from the supermarket fluency task, the number of categories sampled increased from 11 to 12 years, but efficient semantic exploitation occurred only after the age of 13-14 years. These results are discussed in terms of the development of strategic retrieval components and categorical knowledge.

Adolescent↗
PubMed1999

Dynamical analysis of gene networks requires both mRNA and protein expression information.

One of the important goals of biology is to understand the relationship between DNA sequence information and nonlinear cellular responses. This relationship is central to the ability to effectively engineer cellular phenotypes, pathways, and characteristics. Expression arrays for monitoring total gene expression based on mRNA can provide quantitative insight into which gene or genes are on or off; but this information is insufficient to fully predict dynamic biological phenomena. Using nonlinear stability analysis we show that a combination of gene expression information at the message level and at the protein level is required to describe even simple models of gene networks. To help illustrate the need for such information we consider a mechanistic model for circadian rhythmicity which shows agreement with experimental observations when protein and mRNA information are included and we propose a framework for acquiring and analyzing experimental and mathematically derived information about gene networks.

Animals↗
PubMed1980

Task force report: social networks as mediators of social support: an analysis of the effects and determinants of social networks.

The intent of this paper is to present a representative, though not exhaustive, overview of the current literature on social networks, with an emphasis on research linking social networks to psychological adaptation. This overview includes a review of social network concepts; and analysis of the multiple determinants of social networks; an analysis of the varied effects of social networks; and the implications for policies and practices of community mental health centers. This paper adopts the view that the concept of social network is a useful tool in examining both the functional and the dysfunctional influences of one's primary group on individual adaption.

Adaptation, Psychological↗
PubMed2006

Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data.

BACKGROUND: Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process. RESULTS: Artificial Neural Networks were applied to biochemical and 16S rDNA data derived from 282 strains of Enterobacteriaceae, including 189 E. sakazakii isolates, in order to identify key characteristics which could improve the identification of E. sakazakii. The models developed resulted in a predictive performance for blind (validation) data of 99.3 % correct discrimination between E. sakazakii and closely related species for both phenotypic and genotypic data. Three main regions of the partial rDNA sequence were found to be key in discriminating the species. Comparison between E. sakazakii and other strains also constitutively positive for expression of the enzyme alpha-glucosidase resulted in a predictive performance of 98.7 % for 16S rDNA sequence data and 100% for phenotypic data. CONCLUSION: The computationally based methods developed here show a remarkable ability in reducing data dimensionality and complexity, in order to eliminate noise from the system in order to facilitate the speed and reliability of a potential strain identification system. Furthermore, the approaches described are also able to provide valuable information regarding the population structure and distribution of individual species thus providing the foundations for novel assays and diagnostic tests for rapid identification of pathogens.

Cronobacter sakazakii↗
PubMed2025

GSK3B inhibition partially reverses brain ethanol-induced transcriptomic changes in C57BL/6J mice: Expression network co-analysis with human genome-wide association studies.

Alcohol use disorder (AUD) is a chronic behavioral disease with greater than 50% of its risk due to complex genetic contributions. Existing pharmacological and behavioral treatments for AUD are minimally effective and underutilized. Animal model behavioral genetics and human genome-wide association studies have begun to identify individual genes contributing to the progressive compulsive consumption of ethanol that occurs with AUD, promising possible new therapeutic targets. Our laboratory has previously identified Gsk3b as a central member in a network of ethanol-responsive genes in mouse prefrontal cortex, which altered ethanol consumption with genetic manipulation and was also significantly associated with risk for alcohol dependence in human genome-wide association studies. Here we perform detailed brain RNA sequencing transcriptomic studies to characterize a highly specific and clinically available GSK3B pharmacological inhibitor, tideglusib, as a possible therapeutic for clinical trials on treatment of AUD. A model of chronic intermittent ethanol consumption was used to study gene expression changes in prefrontal cortex and nucleus accumbens in the presence or absence of tideglusib treatment. Multivariate analysis of differentially expressed genes showed that tideglusib largely reversed ethanol- induced expression changes for two prominent clusters of genes in both prefrontal cortex and nucleus accumbens. Bioinformatic analysis showed these genes to have prominent roles in neuronal functioning and synaptic activity. Additionally, mouse brain differential gene expression data was analyzed together with human protein-protein interaction and genome-wide association studies on AUD to derive networks responding to tideglusib and relevant to human genetic risk for alcohol dependence. These studies identified discrete networks significantly enriched with genes provisionally associated with AUD, and provide key information on central hubs of such networks. Together these studies document tideglusib as a major modulator of chronic ethanol consumption-evoked brain gene expression signatures, and identify possible new targets for therapeutic modulation of AUD.

Journal Article↗
PubMed2005

gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation.

Network Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to classical approaches such as principal component analysis or independent component analysis, NCA makes use of the connectivity structure from transcriptional regulatory networks to restrict the decomposition to a unique solution. However, the existing version of NCA cannot incorporate information beyond the network topology such as information obtained from regulatory gene knockouts that constrain the dynamics of regulatory signals. The ability of incorporating such information enables a more accurate and self-consistent analysis over different experiments and extends NCA to systems that may not satisfy the identifiability criteria of NCA. In this paper, we derive a generalized form of NCA, gNCA, which significantly expands the capability of transcription network analysis by incorporating regulatory signal constraints arising from genetic knockouts. The theoretical bases including criteria for uniqueness of solution and distinguishability between networks are derived. In addition, numerical techniques for robust decomposition are discussed. gNCA is then demonstrated using an Escherichia coli wild-type strain and an isogenic arcA deletion mutant during a carbon source transition.

Bacterial Outer Membrane Proteins↗
PubMed2003

The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks.

Recent analysis of the structure of transcription regulation networks revealed several "network motifs": regulatory circuit patterns that occur much more frequently than in randomized networks. It is important to understand whether these network motifs have specific functions. One of the most significant network motifs is the coherent feedforward loop, in which transcription factor X regulates transcription factor Y, and both jointly regulate gene Z. On the basis of mathematical modeling and simulations, it was suggested that the coherent feedforward loop could serve as a sign-sensitive delay element: a circuit that responds rapidly to step-like stimuli in one direction (e.g. ON to OFF), and at a delay to steps in the opposite direction (OFF to ON). Is this function actually carried out by feedforward loops in living cells? Here, we address this experimentally, using a system with feedforward loop connectivity, the L-arabinose utilization system of Escherichia coli. We measured responses to step-like cAMP stimuli at high temporal resolution and accuracy by means of green fluorescent protein reporters. We show that the arabinose system displays sign-sensitive delay kinetics. This type of kinetics is important for making decisions based on noisy inputs by filtering out fluctuations in input stimuli, yet allowing rapid response. This information-processing function may be performed by the feedforward loop regulation modules that are found in diverse systems from bacteria to humans.

Arabinose↗
PubMed2005

[A new linear neural network multi-component analysis method and its application in the analysis of VC yinqiao tablets quantitative analysis].

We measured NIR spectrum of VC yinqiao tablets with spectral instrument, analyzed the contents of acetaminophen and vitamin C in the VC yinqiao tablets with principal component analysis (PCA) and Linear Neural Network, and discussed the choice of principal component number and ANN's parameters affecting the network. To compare arithmetic performance, the authors also processed the spectral data with partial least squares and PCA-BP neural network. Compared with other two data process methods, the experiment and the result of data process showed that the PCA-linear neural network possess the best forecasting precision.

Acetaminophen↗
PubMed1995

Motor cortex activity and predicting side of movement: neural network and dipole analysis of pre-movement magnetic fields.

Neuromagnetic fields were recorded from human subjects during the performance of left and right voluntary finger movements. Modeling of current dipole sources indicated symmetric activation of both motor cortices beginning 600 ms prior to movement onset. This activity became lateralized to the contralateral hemisphere 200-300 ms prior to movement onset, the period during which an artificial neural network showed increased ability to predict side of movement within single trials. The results describe the mechanism of lateralization of cortical brain activity preceding voluntary movement and provide further evidence of the involvement of ipsilateral motor cortex in unilateral movements.

Contingent Negative Variation↗
PubMedDate not supplied

Morphometric analysis of collagen network and plasma perfused capillary bed in the myocardium of rats during evolution of cardiac hypertrophy.

In order to investigate the consequences of different types of cardiac hypertrophy on myocardial capillary and fibrosis density in rats we describe here, in the same hearts, the pattern of capillary bed density visualized by fluorescein isothiocyanate dextran (FITC-dextran) and the pattern of fibrosis density as determined by automated image analysis. Pressure overload was induced by clipping one renal artery in rats (one-clip, two-kidney Goldblatt hypertension, RHV). Volume overload was induced by creation of an arteriovenous shunt between the abdominal aorta and the vena cava (aorto-caval fistula model ACF). Animals were sacrified at 1, 3 and 6 months following surgical procedure. Immediately prior to sacrifice, FITC-dextran (MW 150,000) was injected with the animal under ether anesthesia. Five minutes later, cardiac diastolic arrest was induced by the i.v. injection of potassium chloride. The heart was rapidly excised and placed in a formaldehyde solution. The degree of cardiac hypertrophy was calculated after measurement of cardiac weight. Left ventricular wall thickness and cavity area were measured by microscopic methods. Capillary density and geometry were determined by morphometric methods, under ultraviolet light microscopy, using a graphic tablet connected to a microcomputer. The degree of myocardial fibrosis, visualized with Sirius Red, was estimated by the use of automated image analysis using light microscopy. In renovascular hypertension, cardiac hypertrophy was maximum at one month (36%) and persisted through the six months of the study. This increase in cardiac mass was concentric, due to a significant increase in ventricular wall thickness and was associated with a marked increase in fibrosis and a significant decrease in subendocardial capillary density. These effects existed already one month and did not change with time. In the aorto-caval fistula model, cardiac hypertrophy was also maximum at one month (+56%), but this eccentric increase in cardiac mass was associated with no significant change in left ventricular wall thickness, but rather with a significant increase in the surface area of the left ventricular cavity. This volume overload hypertrophy was associated with a decrease in subendocardial capillary density which was negatively correlated with time. In contrast to concentric hypertrophy there was no increase in the fibrosis density compared to the sham-operated groups. Despite the identical degrees of hypertrophy, pressure and volume cardiac overload differed in a significant manner in both left ventricular wall thickness and cavity surface area.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed1987

Birth weight and gestational age standards based on regional perinatal network data: an analysis of risk factors.

The most frequently used set of gestational age-birth weight curves in the United States is the Colorado (C) standard published in 1963. To investigate the usefulness of this standard in an urban population at sea level, we examined the birth weight vs gestational age data from 56,675 singleton liveborn infants born between 1982 and 1985 in the University of Illinois (UI) perinatal network of 13 hospitals. Between 32 and 42 weeks, the UI 10th, median, and 90th percentile weights were significantly higher than those of Colorado. At term gestations the Colorado 10th and 90th percentile weights were the same as the UI 3rd and 80th percentile weights, respectively. Using the UI and Colorado standards for 10th and 90th percentile weights, the study sample was divided into five subgroups. To evaluate the risk prevalence, we examined the frequency of neonatal death, low, and very low Apgar scores (below seven and three, respectively), respiratory distress, maternal hypertension, and diabetes in the five subgroups. The highest frequency of adverse factors was seen in infants classified as small for gestational age (SGA) by both standards, but overall, the size-for-gestation grouping was better accomplished using the UI rather than the C standard. In 9188 infants (16.2%) classified into wrong weight-gestation subgroups using the Colorado standard, the prevalence of actual risk factors was at variance with the group to which they were assigned. This included 3632 (6.4%) SGA infants who were grouped as appropriate for gestational age (AGA), and 5556 (9.8%) AGA infants grouped as large for gestational age.(ABSTRACT TRUNCATED AT 250 WORDS)

Birth Weight↗
PubMed2001

Identifying regulatory networks by combinatorial analysis of promoter elements.

Several computational methods based on microarray data are currently used to study genome-wide transcriptional regulation. Few studies, however, address the combinatorial nature of transcription, a well-established phenomenon in eukaryotes. Here we describe a new approach using microarray data to uncover novel functional motif combinations in the promoters of Saccharomyces cerevisiae. In addition to identifying novel motif combinations that affect expression patterns during the cell cycle, sporulation and various stress responses, we observed regulatory cross-talk among several of these processes. We have also generated motif-association maps that provide a global view of transcription networks. The maps are highly connected, suggesting that a small number of transcription factors are responsible for a complex set of expression patterns in diverse conditions. This approach may be useful for modeling transcriptional regulatory networks in more complex eukaryotes.

Cell Cycle↗
PubMed1997

Natural Microbial Community Compositions Compared by a Back-Propagating Neural Network and Cluster Analysis of 5S rRNA.

The community compositions of free-living and particle-associated bacteria in the Chesapeake Bay estuary were analyzed by comparing banding patterns of stable low-molecular-weight RNA (SLMW RNA) which include 5S rRNA and tRNA molecules. By analyzing images of autoradiographs of SLMW RNAs on polyacrylamide gels, band intensities of 5S rRNA were converted to binary format for transmission to a back-propagating neural network (NN). The NN was trained to relate binary input to sample stations, collection times, positions in the water column, and sample types (e.g., particle-associated versus free-living communities). Dendrograms produced by using Euclidean distance and average and Ward's linkage methods on data of three independently trained NNs yielded the following results. (i) Community compositions of Chesapeake Bay water samples varied both seasonally and spatially. (ii) Although there was no difference in the compositions of free-living and particle-associated bacteria in the summer, these community types differed significantly in the winter. (iii) In the summer, most bay samples had a common 121-nucleotide 5S rRNA molecule. Although this band occurred in the top water of midbay samples, it did not occur in particle-associated communities of bottom-water samples. (iv) Regardless of the season, midbay samples had the greatest variety of 5S rRNA sizes. The utility of NNs for interpreting complex banding patterns in electrophoresis gels was demonstrated.

Journal Article↗
PubMed1997

Recognition of patient anaesthetic levels: neural network systems, principal components analysis, and canonical discriminant variates.

The goal of this study was to examine the ability of Neural Networks to recognise the levels of anaesthetic state of a patient. Data obtained under different levels of anaesthesia have been modelled for the purpose. It is shown that inferential parameters can be used to recognise the levels of anaesthesia. In addition to demonstrating the ability of neural networks for classification we were interested in understanding the classification strategy discovered by the neural networks. Multivariate data analysis techniques, namely Principal Components Analysis and Canonical Discriminant Variates, were applied to analyse the resultant networks.

Anesthesia↗
PubMedDate not supplied

Neural networks for the analysis of small pulmonary nodules.

PURPOSE: Small pulmonary nodules can be readily detected by computed tomography (CT). The goal of this detection is to diagnose early lung cancer as the five year survival at this early stage is over 70% in contradistinction to the overall 5-year survival of around 10%. Critical to the efficacy of CT for early lung cancer detection is the ability to distinguish between benign and malignant nodules. We explored the usefulness of neural networks (NNs) to help in this differentiation. METHODS: CT images of 28 pulmonary nodules, 14 benign and 14 malignant, each having a diameter less than 3 cm were selected. All were sufficiently malignant in appearance to require needle biopsy and surgery. The statistical-multiple object detection and location system (S-MODALS) NN technique developed for automatic target recognition (ATR) was used to differentiate between these benign and malignant nodules. RESULTS: S-MODALS was able to correctly identify all but three benign nodules. S-MODALS classified a nodule as malignant because it looked similar to other malignant nodules. It identified the most similar nodules to display them to the radiologist. The specific features of the nodule that determined its classification were also shown, so that S-MODALS is not simply a "black box" technique but gives insight into the NN diagnostics. CONCLUSION: This initial evaluation of S-MODALS NNs using pulmonary nodules whose CT features were very suspicious for lung cancer demonstrated the potential to reduce the number of biopsies without missing malignant nodules. S-MODALS performed well, but additional optimization of the techniques specifically for CT images would further enhance its performance.

Biopsy↗
PubMedDate not supplied

Artificial neural networks and linear discriminant analysis: a valuable combination in the selection of new antibacterial compounds.

A set of topological descriptors has been used to discriminate between antibacterial and nonantibacterial drugs. Topological descriptors are simple integers calculated from the molecular structure represented in SMILES format. The methods used for antibacterial activity discrimination were linear discriminant analysis (LDA) and artificial neural networks of a multilayer perceptron (MLP) type. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval of the discriminant function and the output value of the neural network versus these values. Pharmacological distribution diagrams (PDD) were used as a visualizing technique for the identification of antibacterial agents. The results confirmed the discriminative capacity of the topological descriptors proposed. The combined use of LDA and MLP in the guided search and the selection of new structures with theoretical antibacterial activity proved highly effective, as shown by the in vitro activity and toxicity assays conducted.

Anti-Bacterial Agents↗
PubMed1995

Assessment of neural network models using prediction analysis.

Examination of classification or confusion matrices in neural network models is often advocated as a way to investigate the effects of network architecture on classification rules and to understand the topography of the classification space. In addition, for model comparison purposes it is useful to be able to make statements concerning the statistical reliability of these patterns of hit and error cells. Prediction analysis provides a number of descriptive and inferential measures for the evaluation of hypotheses about patterns of hit and error cells. The present paper applied recent advances in prediction analysis to the examination of neural network classification matrices. Results of the application of prediction analysis to the generalized XOR problem and to data from classification of cardiovascular responses of human subjects were used to illustrate the determination of absolute and relative fit of omnibus models as well as the evaluation of specific hypotheses within these models.

Cardiovascular System↗