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At least 415 records · Page 23Linked to original sources
PubMed2006

Gene expression profiling in lung tissues from mice exposed to cigarette smoke, lipopolysaccharide, or smoke plus lipopolysaccharide by inhalation.

The purpose of this study was to investigate whether coexposure to lipopolysacchride (LPS) will heighten the inflammatory response and other pulmonary lesions in mice exposed to cigarette smoke, and thus to evaluate the potential use of this LPS-compromised mouse model as a model for chronic obstructive pulmonary disease (COPD) investigation. AKR/J male mice were exposed to HEPA-filtered air (sham control group), cigarette smoke (smoke group), LPS (LPS group), or smoke plus LPS (smoke-LPS group) by nose-only inhalation. Lungs were collected at the end of the 3-wk exposure and processed for microarray analysis. Clustering and network analysis showed decreased heat-shock response and chaperone activity, increased immune and inflammatory response, and increased mitosis in all three exposed groups. Two networks/function modules were exclusively found in the smoke-LPS group, that is, the downregulated muscle development/muscle contraction process and the upregulated reactive oxygen species production process. Notably, the number of genes and function modules/networks associated with inflammation was reduced in the smoke-LPS group compared to the LPS group. The most upregulated gene in the smoke group, MMP12, is a matrix metalloproteinase that preferentially degrades elastin and has been implicated in COPD development. NOXO1, which was upregulated in all three treatment groups, positively regulates the expression of a subunit of NADPH oxidase (NOX1), a major source of reactive oxygen species, and may play an important role in the pathogenesis of COPD. Serum amyloid A1, which is an acute-phase systemic inflammation marker and can be induced by LPS exposure, was significantly upregulated in the LPS and smoke-LPS groups. MARCO, a scavenger receptor expressed in macrophages that may play a significant role in LPS-induced inflammatory response, was upregulated in the LPS group and the smoke-LPS group, but not in the smoke group. In conclusion, gene expression profiling identified genes and function modules that may be related to COPD pathogenesis and may be useful as biomarkers to monitor COPD progression. In addition, an LPS-compromised mouse model showed potential as a useful tool for studying cigarette smoke-associated COPD.

Administration, Inhalation↗
PubMed1992

Network range and health.

The inclusion of network concepts in the stress-distress model of health represents a major theoretical advance. Most researchers use the dyadic approach of social network analysis to construct network measures of social support. Working from the argument that network structure and social support are conceptually and empirically distinct, we extend the stress-distress model to include measures of network structure (network range) as predictors of exposure to stress, access to social support, and distress. We find that the density, diversity, and size dimensions of network range affect exposure to stress, access to social support, and distress differentially and that, in each case, their effects are gender-specific.

Adaptation, Psychological↗
PubMed2006

[Mass-balance ecopath model of Belbu Gulf ecosystem].

Based on the investigation of fishery resources and eco-environment in the Beibu Gulf of northern South China Sea from October 1997 to May 1999, and with EwE software, a mass-balance ecopath model of Beibu Gulf ecosystem was constructed, which consisted of 16 functional groups (boxes) including marine mammals and seabirds, each representing the organisms with similar roles in the food web, and covered the main trophic flow in Beibu Gulf ecosystem. The food web in Beibu Gulf ecosystem was dominated by detrital path, and benthic invertebrate played a significant role in transferring energy from detritus to higher trophic levels. Phytoplankton was the primary producer, and the fractional trophic levels ranged from 1.00 to 4.04, with marine mammals occupying the highest trophic level. By using network analysis, the system network was mapped into a linear food chain, and six discrete trophic levels were found, with a mean transfer efficiency of 12.3% from detritus, and 12.2% from primary producer within the ecosystem. The biomass density of commercially utilized species estimated by the model was 8.7 t x km(-2), and the bioproduction only accounted for 1.81% of the net primary production, which indicated that the system was still in developing status and instable.

Animals↗
PubMed2003

Sensitivity analysis of stoichiometric networks: an extension of metabolic control analysis to non-steady state trajectories.

A sensitivity analysis of general stoichiometric networks is considered. The results are presented as a generalization of Metabolic Control Analysis, which has been concerned primarily with system sensitivities at steady state. An expression for time-varying sensitivity coefficients is given and the Summation and Connectivity Theorems are generalized. The results are compared to previous treatments. The analysis is accompanied by a discussion of the computation of the sensitivity coefficients and an application to a model of phototransduction.

Animals↗
PubMed1998

Neural Network Computer Analysis of Fetal Heart Rate.

> Objective: A nonsubjective evaluation of intrapartum fetal heart rate (FHR) with a neural network (NNW) computer system and its clinical application. Methods: Eight simple FHR data were input into the NNW computer after 16-step normalizations. The computer was composed of 40 units in the input layer, 30 in intermediate layer, and 3 in the output layer, and the probabilities to be normal, suspicious, and pathological were obtained at the output. Before use, the computer was trained 10,000 times by 50-min teacher FHR data of 20 cases with known outcomes. The trained NNW computer was tested by FHRs of another 29 cases. The outcome probabilities in 15 min were calculated every 5 min in another 10 cases, and the bar graphs of the probabilities were displayed in sequence in the trendgrams. Results: The trained NNW computer was 100% accurate in the internal check; in the external check 86% of the results were evaluated correctly with the cardiotocogram, Apgar score, and umbilical arterial pH of the 29 test cases. The FHR scores of our conventional computer FHR analysis were higher in the suspicious and pathological groups than the normal group, and the fetal distress index was high in the pathological group. The trendgrams were simply accurate in typically normal or abnormal cases, transitory abnormal probabilities were shown in intermediate cases, and mixed suspicious and pathological probabilities suggested pathological outcome. Conclusions: The outcome probabilities and their trendgrams in the NNW FHR analysis are promising in objective decision making in the intrapartum stage.

Journal Article↗
PubMed2006

A network approach for distinguishing ethical issues in research and development.

In this paper we report on our experiences with using network analysis to discern and analyse ethical issues in research into, and the development of, a new wastewater treatment technology. Using network analysis, we preliminarily interpreted some of our observations in a Group Decision Room (GDR) session where we invited important stakeholders to think about the risks of this new technology. We show how a network approach is useful for understanding the observations, and suggests some relevant ethical issues. We argue that a network approach is also useful for ethical analysis of issues in other fields of research and development. The abandoning of the overarching rationality assumption, which is central to network approaches, does not have to lead to ethical relativism.

Community Participation↗
PubMed2004

A neural network model analysis to identify victims of intimate partner violence.

The objective of this study was to determine if a neural network model can identify victims of intimate partner violence (IPV). A custom neural network model was constructed and trained using the 1995 ED databases at Truman Medical Center of all female visits. The input vector developed was an array of 100 binary elements containing, in coded form, the patient's age, day of week, primary diagnosis (excluding 995.81), disposition, race, time, and E-code. The trained network was then presented with a series of 19,830 female patients from the 1996 ED database to determine if it could discriminate cases from control subjects. The neural network identified 231 of 297 known IPV victims (sensitivity 78%) in the 1996 database. It also categorized 2234 false-positive patients out of 19,533 IPV-negative patients (specificity 89%). A computer-based neural network model, when supplied with information commonly available in the ED medical record, can identify victims of IPV.

Case-Control Studies↗
PubMed2005

Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis.

MOTIVATION: Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced and to generate generalized models which are applicable to 'real world' scenarios. One such scenario is the classification of bacterial species. These vary immensely, some remaining remarkably stable whereas others are extremely labile showing rapid mutation and change. Such variation makes clinical diagnosis difficult and pathogens may be easily misidentified. RESULTS: We applied artificial neural networks (Neuroshell 2) in parallel with cluster analysis and principal components analysis to surface enhanced laser desorption/ionization (SELDI)-TOF mass spectrometry data with the aim of accurately identifying the bacterium Neisseria meningitidis from species within this genus and other closely related taxa. A subset of ions were identified that allowed for the consistent identification of species, classifying >97% of a separate validation subset of samples into their respective groups. AVAILABILITY: Neuroshell 2 is commercially available from Ward Systems.

Algorithms↗
PubMed2026

NAViFluX: a visualization‑centric platform for interactive analysis, refinement and design of genome‑scale metabolic networks.

MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, architectural complexity, and limited visualization support impede its adoption by the broader scientific community. Existing tools exclusively specialize in GSMN analyses or visualization while lacking important features such as pathway-specific views, database-integrated refinement, and comprehensive enrichment and perturbation analyses. RESULTS: Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based tool that unifies native pathway/subsystem map generation, interactive model refinement via KEGG/BiGG, pathway merging and modules for flux computations, topology, and functional enrichment all within network views. Using three independent case studies on Escherichia coli, the utility of NAViFluX for characterization of nutrient-specific metabolic adaptations, enhancing gene essentiality predictions and interpretability, and rational design of an optimized carbon-fixing metabolic state is demonstrated. AVAILABILITY AND IMPLEMENTATION: All source code and supplementary files associated with the case studies are publicly available via Zenodo at https://zenodo.org/records/19107831. NAViFluX can be easily installed as a standalone software through https://github.com/bnsb-lab-iith/NAViFluX.

Metabolic Networks and Pathways↗
PubMed2005

Graph theoretic modeling of large-scale semantic networks.

During the past several years, social network analysis methods have been used to model many complex real-world phenomena, including social networks, transportation networks, and the Internet. Graph theoretic methods, based on an elegant representation of entities and relationships, have been used in computational biology to study biological networks; however they have not yet been adopted widely by the greater informatics community. The graphs produced are generally large, sparse, and complex, and share common global topological properties. In this review of research (1998-2005) on large-scale semantic networks, we used a tailored search strategy to identify articles involving both a graph theoretic perspective and semantic information. Thirty-one relevant articles were retrieved. The majority (28, 90.3%) involved an investigation of a real-world network. These included corpora, thesauri, dictionaries, large computer programs, biological neuronal networks, word association networks, and files on the Internet. Twenty-two of the 28 (78.6%) involved a graph comprised of words or phrases. Fifteen of the 28 (53.6%) mentioned evidence of small-world characteristics in the network investigated. Eleven (39.3%) reported a scale-free topology, which tends to have a similar appearance when examined at varying scales. The results of this review indicate that networks generated from natural language have topological properties common to other natural phenomena. It has not yet been determined whether artificial human-curated terminology systems in biomedicine share these properties. Large network analysis methods have potential application in a variety of areas of informatics, such as in development of controlled vocabularies and for characterizing a given domain.

Algorithms↗
PubMedDate not supplied

Neural-network-assisted analysis and microscopic rescreening in presumed negative cervical cytologic smears. A comparison.

OBJECTIVE: To compare cytologists' detection of abnormalities when using neural network-assisted (NNA) review, as employed by the PAPNET Testing System and to compare the effectiveness of this mode of review to that of unassisted, conventional rescreening of cervical smears initially diagnosed as negative. STUDY DESIGN: The study was undertaken as part of a multicenter clinical trial involving over 10,000 smears from 10 investigation sites (9 academic institutions and 1 private laboratory). Using a subset of "negative" control smears from three university laboratories, the false negative detection yields of NNA review (performed using the PAPNET System) and conventional microscopic rescreening (performed as part of routine quality control practice) were compared. The false negative detection yield was defined as the percentage of rescreened negatives reclassified as abnormal. RESULTS: The results demonstrate that using NNA review, the detection yield of false negative smears, as a proportion of negative smears reexamined, is statistically significantly greater than that obtained using conventional quality control rescreening. The false negative yield generated using NNA analysis was 6.2% (142/2293) versus 0.6% (82/13761) for conventional rescreening. A statistically significant improvement in identification of abnormality is observed for NNA review as opposed to unassisted rescreening despite constraining the comparison in the following ways: (1) comparing the yields of rescreening of negative smears obtained from the same time intervals for both methods, (2) comparing the yields of rescreening of negative smears obtained from the years after the Clinical Laboratory Improvement Act (1990 and 1991) for both methods, and (3) disregarding the identification of atypical squamous cells of undetermined significance/atypical glandular cells of undetermined significance cases and comparing only the identification of squamous intraepithelial lesions using the two methods. CONCLUSION: Using neural network-assisted review, cytologists uncovered a significantly higher proportion of previously undetected cervical abnormalities per smear reexamined than they did using unassisted, conventional rescreening.

Automation↗
PubMed1994

Network interactions among limbic cortices, basal forebrain, and cerebellum differentiate a tone conditioned as a Pavlovian excitor or inhibitor: fluorodeoxyglucose mapping and covariance structural modeling.

1. The objective was to examine how opposite learned behavioral responses to the same physical tone were differentiated by the pattern of interactions between extraauditory neural regions. This was pursued using a new approach combining behavior, neuroimaging, and network analysis to integrate information about differences in regional activity with differences in the covariance relationships between brain areas. 2. A tone was used as either a Pavlovian conditioned excitor or inhibitor. Rats were conditioned with reinforced trials of a conditioned excitor (A+) intermixed with nonreinforced trials of a tone-light compound (AX-). The tone was the excitor (A+) for the tone-excitor group and was the inhibitor (X-) for the tone-inhibitor group. After conditioning, all rats were injected with [14C(U)]2-fluoro-2-deoxyglucose (FDG) and presented with the same tone. 3. FDG autoradiography was used to measure regional activity and to generate interregional correlations of activity resulting from the presentation of the tone. A stepwise discriminant analysis was used to select brain regions that differentiated the excitor from the inhibitor effects. 4. Network analysis consisted of constructing an anatomic model of the brain regions, selected by the discriminant analysis, linking the regions with their known anatomical connections. Then, functional models for the tone-excitor and -inhibitor groups were constructed using structural equation modeling. Correlations of activity between regions were decomposed to calculate numerical weights, or path coefficients, for each anatomic path. These path coefficients were used to compare the interactions for the tone-excitor and -inhibitor models. 5. Regional differences in FDG uptake were found in the sulcal frontal cortex (SFC), lateral septum (LS), medial septum/diagonal band (MS/DB), retrosplenial cortex (RS), and dentate-interpositus nuclei of the cerebellum (DEN). Discriminant analysis selected three other regions that significantly discriminated the tone-excitor and -inhibitor groups: perirhinal cortex (PRh), nucleus accumbens (ACB), and the anteroventral nucleus of the thalamus (AVN). 6. Structural equation modeling identified two functional circuits that differentiated the groups. One involved the basal forebrain regions (LS, MS/DB, ACB) and the other limbic thalamocortical structures (SFC, RS, PRh, AVN). Differences in the interactions within these circuits were mainly in sign of the covariance relationships between regions, from positive for the tone-excitor model to negative path coefficients for the tone-inhibitor model. The path coefficient between the basal forebrain circuit and the limbic thalamocortical circuit showed the largest magnitude difference. This quantitative difference was mediated by a path from the MS/DB to PRh.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed1988

Differentiating the effects of three benzodiazepines on non-REM sleep EEG spectra. A neural-network pattern classification analysis.

Neural-network pattern classifiers were used to study the effects of long half-life flurazepam (30 mg) and quazepam (15 mg), and short half-life triazolam (0.5 mg) on non-REM sleep. We measured the magnitude of effect, time course, and EEG spectral signature of the three benzodiazepines as a function of third-of-night. Of the three benzodiazepines studied, flurazepam had the largest effect and quazepam had the most stable time course. The effects of triazolam were similar to those of quazepam. These EEG differences may prove to be more clinically useful markers than the usual measurement of plasma levels, and may be used to guide the therapy of sleep disorders.

Adult↗
PubMed2005

Phenotype analysis using network motifs derived from changes in regulatory network dynamics.

The intrinsic dynamic response of a transcriptional regulatory network depends directly on molecular interactions in the cellular transcription, translation, and degradation machineries. These interactions can be incorporated into dynamic mathematical models of the biochemical system using the biophysical relationship with the model parameters. Modifications of such interactions bring changes to the biological behavior of the cells, and therefore, many normal and pathological cellular states depend on them. It is important for analysis, prediction, diagnosis, and treatment of cellular function to have an experimentally derived model with parameters that adequately represent the molecular interactions of interest. Finding the model and parameters of a transcriptional regulatory network is a difficult task that has been approached at different levels and with different techniques. We develop here a new analysis method (based on previous work on network inference, modeling, and parameter identification) that finds the most changed parameters from yeast oligonucleotide microarray expression patterns in cases where a phenotype difference exists between two samples. We then relate and examine the changed parameters with their associated genes, corresponding genetic functional categories, and particular subnetworks and connectivities. The biophysical bases for these changes are also identified by studying the relationship of the changed parameters with the transcription, translation, and degradation mechanisms. The method is improved to cases where there are two or more transcription factors influencing transcription, and a statistical analysis is performed to give a measurement of the uniqueness and robustness of the parameter fit.

Algorithms↗
PubMed2005

The geometry of the flux cone of a metabolic network.

The analysis of metabolic networks has become a major topic in biotechnology in recent years. Applications range from the enhanced production of selected outputs to the prediction of genotype-phenotype relationships. The concepts used are based on the assumption of a pseudo steady-state of the network, so that for each metabolite inputs and outputs are balanced. The stoichiometric network analysis expands the steady state into a combination of nonredundant subnetworks with positive coefficients called extremal currents. Based on the unidirectional representation of the system these subnetworks form a convex cone in the flux-space. A modification of this approach allowing for reversible reactions led to the definition of elementary modes. Extreme pathways are obtained with the same method but splitting up internal reactions into forward and backward rates. In this study, we explore the relationship between these concepts. Due to the combinatorial explosion of the number of elementary modes in large networks, we promote a further set of metabolic routes, which we call the minimal generating set. It is the smallest subset of elementary modes required to describe all steady states of the system. For large-scale networks, the size of this set is of several magnitudes smaller than that of elementary modes and of extreme pathways.

Animals↗
PubMed2006

'Mind control' experiences on the internet: implications for the psychiatric diagnosis of delusions.

BACKGROUND: The DSM criteria for a delusion indicate that it should not include any beliefs held by a person's 'culture or subculture'. The internet has many examples of people reporting 'mind control experiences' (MCEs) on self-published web pages, many of which suggest a community based around such beliefs and experiences. It was hypothesized that some of these reports are likely to reflect delusional beliefs and the hyperlinks between web reports were likely to show evidence of social structure, demonstrating the 'culture or subculture' exemption to be increasingly redundant in light of new technology. SAMPLING AND METHODS: Texts from web sites reporting MCEs (n = 10), experience of cancer (n = 10), depression (n = 10) and being stalked (n = 10) were identified, and were blind-rated by three independent psychiatrists for the presence of delusions. Hyperlinks from web sites reporting MCEs were used to create a network structure; this was compared with a size-matched, randomly generated network and known social networks from the literature using social network analysis. CONCLUSIONS: The sampled web-published accounts of MCEs are highly likely to be influenced by delusional beliefs. Social network analysis suggests there is significant evidence of an online community based around these beliefs. The fact that individuals can form a community based on the content of a potentially delusional belief presents a paradox for the DSM diagnostic criteria for a delusion, and suggests the need to revise and revisit the original operational definition in the light of these new technological developments.

Culture↗