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At least 1,765 records · Page 98Linked to original sources
PubMed2026

Cumulative microscopy reveals cellular states in fibroblasts from patients with genetic disorders.

Analysis of cellular states and signaling trajectories can provide insights into causes of disease. We developed cumulative microscopy, a method to perform cyclical imaging without elution or quenching steps. Cumulative microscopy computationally extracts individual signals from accumulating fluorescence during sequential imaging. We use cumulative microscopy to quantitatively assess cell cycle and stress markers in individual primary fibroblasts from patients with rare genetic proliferative disorders with increased cancer risk. Neural network-based analysis of cumulative microscopy data suggests that cells from patients with Cartilage-hair hypoplasia (CHH), but not Mulibrey Nanism (MUL), show replication stress. We analyze cell states and cell trajectories and find that a subset of cells from patients with CHH show spontaneous replication stress, followed by cell cycle exit in both G1 and G2 phases. We note that replication stress potentially could underlie both proliferative defects and increased cancer risk in CHH patients and conclude that cumulative microscopy is an efficient, quantitative, and generalizable approach to multiplex microscopy.

Humans↗
PubMed2007

Towards the plant metabolome and beyond.

Methods for network-wide analysis are increasingly showing that the textbook view of the regulation of plant metabolism is often incomplete and misleading. Recent innovations in small-molecule analysis have created the ability to rapidly identify and quantify numerous compounds, and these data are creating new opportunities for understanding plant metabolism and for plant metabolic engineering.

Gene Expression Regulation, Plant↗
PubMed2002

Food-web structure and network theory: The role of connectance and size.

Networks from a wide range of physical, biological, and social systems have been recently described as "small-world" and "scale-free." However, studies disagree whether ecological networks called food webs possess the characteristic path lengths, clustering coefficients, and degree distributions required for membership in these classes of networks. Our analysis suggests that the disagreements are based on selective use of relatively few food webs, as well as analytical decisions that obscure important variability in the data. We analyze a broad range of 16 high-quality food webs, with 25-172 nodes, from a variety of aquatic and terrestrial ecosystems. Food webs generally have much higher complexity, measured as connectance (the fraction of all possible links that are realized in a network), and much smaller size than other networks studied, which have important implications for network topology. Our results resolve prior conflicts by demonstrating that although some food webs have small-world and scale-free structure, most do not if they exceed a relatively low level of connectance. Although food-web degree distributions do not display a universal functional form, observed distributions are systematically related to network connectance and size. Also, although food webs often lack small-world structure because of low clustering, we identify a continuum of real-world networks including food webs whose ratios of observed to random clustering coefficients increase as a power-law function of network size over 7 orders of magnitude. Although food webs are generally not small-world, scale-free networks, food-web topology is consistent with patterns found within those classes of networks.

Ecology↗
PubMed2006

Understanding spatial diagram structure: an analysis of hierarchies, matrices, and networks.

Abstract diagrams are powerful tools for comprehension and problem solving in diverse contexts. Two studies examined the structural properties of (i.e., applicability conditions for) three interrelated spatial diagrams--hierarchies, matrices, and networks. College students from two groups with distinct educational backgrounds and learning histories--advanced computer science students and representative undergraduates--rated the diagnosticity of the hypothesized applicability conditions for each of the 3 diagrams. The results validated 24-26 of the 30 hypothesized applicability conditions and provided evidence regarding the relative importance, or diagnosticity, of the validated properties for each type of diagram. A different set of properties was identified as most highly diagnostic for each type of diagram, indicating that the three spatial diagrams are optimized to serve different representational functions: The matrix stores static information about the kind of relation that exists between pairs of items in different sets, the network conveys dynamic information by showing the local connections and global routes connecting the items being represented, and the hierarchy depicts a rigid structure of power or precedence relations among items. The quantitative and qualitative differences in representational knowledge due to educational background are discussed.

Analysis of Variance↗
PubMed2004

Indirect adaptive control for synchronous generator: comparison of MLP/RBF neural networks approach with Lyapunov stability analysis.

This paper compares two indirect adaptive neurocontrollers, namely a multilayer perceptron neurocontroller (MLPNC) and a radial basis function neurocontroller (RBFNC) to control a synchronous generator. The different damping and transient performances of two neurocontrollers are compared with those of conventional linear controllers, and analyzed based on the Lyapunov direct method.

Neural Networks, Computer↗
PubMed2004

[Living conditions of deer in the provinces of Western Pomerania and Lubuskie as revealed by mandibular content of fluoride, calcium and magnesium. II. Detailed statistical analysis with the statistica neural networks software].

Animals from areas contaminated by industrial emissions containing fluoride may accumulate it predominantly in hard tissues. The stable composition of the mandible makes it suitable for the study of accumulation of fluorides and other elements (Mg, Ca). It is also possible to determine the extent of pollution with fluorine compounds around industrial plants and to reveal some of the features of the natural habitat of wild animals. We applied the advanced technique of neural networks to investigate the simultaneous influence of multiple parameters on the accumulation of fluorine, magnesium, and calcium in the mandible of deer and possible interactions between these elements. The first step involved the generation of a neural network. In the present work, networks were created with the Intelligent Problem Solver (IPS). Input data were divided into three sets. One was used for training (Tr) of the network, another for validation (Ve) and control over the training process and the last one for testing (Te). Network quality was checked with the following parameters: Data Mean--average value of variable; Data SD--standard deviation of variable; Error Mean--average value of error; Abs. Mean--average error modulus; Error SD--standard deviation of error; SD Ratio--ratio of standard deviation of error to standard deviation of variable (Error SD/Data SD); Correlation--Pearson's correlation coefficient. For the model to be satisfactory, Pearson's correlation coefficient should be close to 1. In the case of complex problems with an unclear influence of parameters on the process, an analysis of parametric sensitivity must be performed. This task was accomplished automatically with the computer program. It was found that the effect of the same parameters on the accumulation of fluorine, magnesium and calcium varied. The results suggest interactions between the examined elements.

Animals↗
PubMed1991

Three-dimensional MR microscopy with large arrays.

MR microscopy of fixed specimens is described using large (256(3] arrays. Images are acquired at 7.0 T with voxels as small as 70 x 70 x 70 microns (3.4 x 10(-4) mm3), more than 25,000 times smaller than routine clinical body imaging. Separation of the acquisition, reconstruction, archival, and analysis onto networked workstations provides flexibility and efficiency in handling the large data sets. The isotropic data can be interactively displayed through any plane without loss of in-plane resolution. The potential for applications of MR microscopy in clinical pathology is addressed.

Animals↗
PubMed2002

Control analysis for autonomously oscillating biochemical networks.

It has hitherto not been possible to analyze the control of oscillatory dynamic cellular processes in other than qualitative ways. The control coefficients, used in metabolic control analyses of steady states, cannot be applied directly to dynamic systems. We here illustrate a way out of this limitation that uses Fourier transforms to convert the time domain into the stationary frequency domain, and then analyses the control of limit cycle oscillations. In addition to the already known summation theorems for frequency and amplitude, we reveal summation theorems that apply to the control of average value, waveform, and phase differences of the oscillations. The approach is made fully operational in an analysis of yeast glycolytic oscillations. It follows an experimental approach, sampling from the model output and using discrete Fourier transforms of this data set. It quantifies the control of various aspects of the oscillations by the external glucose concentration and by various internal molecular processes. We show that the control of various oscillatory properties is distributed over the system enzymes in ways that differ among those properties. The models that are described in this paper can be accessed on http://jjj.biochem.sun.ac.za.

Biochemistry↗
PubMed2005

A delayed neural network for solving linear projection equations and its analysis.

In this paper, we present a delayed neural network approach to solve linear projection equations. The Lyapunov-Krasovskii theory for functional differential equations and the linear matrix inequality (LMI) approach are employed to analyze the global asymptotic stability and global exponential stability of the delayed neural network. Compared with the existing linear projection neural network, theoretical results and illustrative examples show that the delayed neural network can effectively solve a class of linear projection equations and some quadratic programming problems.

Algorithms↗
PubMed2002

Dynamics of large proteins through hierarchical levels of coarse-grained structures.

Elastic network models have been successful in elucidating the largest scale collective motions of proteins. These models are based on a set of highly coupled springs, where only the close neighboring amino acids interact, without any residue specificity. Our objective here is to determine whether the equivalent cooperative motions can be obtained upon further coarse-graining of the protein structure along the backbone. The influenza virus hemagglutinin A (HA), composed of N = 1509 residues, is utilized for this analysis. Elastic network model calculations are performed for coarse-grained HA structures containing only N/2, N/10, N/20, and N/40 residues along the backbone. High correlations (>0.95) between residue fluctuations are obtained for the first dominant (slowest) mode of motion between the original model and the coarse-grained models. In the case of coarse-graining by a factor of 1/40, the slowest mode shape for HA is reconstructed for all residues by successively selecting different subsets of residues, shifting one residue at a time. The correlation for this reconstructed first mode shape with the original all-residue case is 0.73, while the computational time is reduced by about three orders of magnitude. The reduction in computational time will be much more significant for larger targeted structures. Thus, the dominant motions of protein structures are robust enough to be captured at extremely high levels of coarse-graining. And more importantly, the dynamics of extremely large complexes are now accessible with this new methodology.

Amino Acids↗
PubMedDate not supplied

Heuristic extraction of rules in pruned artificial neural networks models used for quantifying highly overlapping chromatographic peaks.

The suitability of an approach for extracting heuristic rules from trained artificial neural networks (ANNs) pruned by a regularization method and with architectures designed by evolutionary computation for quantifying highly overlapping chromatographic peaks is demonstrated. The ANN input data are estimated by the Levenberg-Marquardt method in the form of a four-parameter Weibull curve associated with the profile of the chromatographic band. To test this approach, two N-methylcarbamate pesticides, carbofuran and propoxur, were quantified using a classic peroxyoxalate chemiluminescence reaction as a detection system for chromatographic analysis. Straightforward network topologies (one and two outputs models) allow the analytes to be quantified in concentration ratios ranging from 1:7 to 5:1 with an average standard error of prediction for the generalization test of 2.7 and 2.3% for carbofuran and propoxur, respectively. The reduced dimensions of the selected ANN architectures, especially those obtained after using heuristic rules, allowed simple quantification equations to be developed that transform the input variables into output variables. These equations can be easily interpreted from a chemical point of view to attain quantitative analytical information regarding the effect of both analytes on the characteristics of chromatographic bands, namely profile, dispersion, peak height, and residence time.

Journal Article↗
PubMed2005

Determination of functional interactions among signalling pathways in Escherichia coli K-12.

Interaction among different signalling pathways has been noted repeatedly. However, no systematic method has been developed to identify and quantify such interactions. Here we reported that network component analysis (NCA) was able to determine interactions among various signalling pathways in Escherichia coli K-12 based on known transcription factor (TF)-promoter connectivity information and microarray data from genetic knockout strains. The TF activities determined from NCA allow the quantitation of functional interactions, barring gross errors in the connectivity and microarray data. By using a robust statistical test, 37 pairs of functional interactions were identified. Eighteen interaction pairs confirmed previous implications, while 19 others represent new predictions. These results demonstrate that the functional interactions among various signalling pathways may be rather significant. With reasonable TF-promoter connectivity, NCA coupled with genetic knockouts and microarray experiments provides a systematic way to elucidate interaction networks. As this approach cannot distinguish between cross-talks and unidentified direct regulation, the results should provide incentives for further experimental testing.

Escherichia coli K12↗
PubMed2006

Convergence properties of the degree distribution of some growing network models.

In this article we study a class of randomly grown graphs that includes some preferential attachment and uniform attachment models, as well as some evolving graph models that have been discussed previously in the literature. The degree distribution is assumed to form a Markov chain; this gives a particularly simple form for a stochastic recursion of the degree distribution. We show that for this class of models the empirical degree distribution tends almost surely and in norm to the expected degree distribution as the size of the graph grows to infinity and we provide a simple asymptotic expression for the expected degree distribution. Convergence of the empirical degree distribution has consequences for statistical analysis of network data in that it allows the full data to be summarized by the degree distribution of the nodes without losing the ability to obtain consistent estimates of parameters describing the network.

Markov Chains↗
PubMed1996

Anti-cancer drug characterisation using a human cell line panel representing defined types of drug resistance.

Differential drug response in a human cell line panel representing defined types of cytotoxic drug resistance was measured using the non-clonogenic fluorometric microculture cytotoxicity assay (FMCA). In total 37 drugs were analysed; eight topoisomerase II inhibitors, eight anti-metabolites, eight alkylating agents, eight tubulin-active agents and five compounds with other or unknown mechanisms of action, including one topoisomerase I inhibitor. Correlation analysis of log IC50 values obtained from the panel showed a high degree of similarity among the drugs with a similar mechanism of action. The mean percentage of mechanistically similar drugs included among the ten highest correlations, when each drug was compared with the remaining data set, was 100%, 92%, 88% and 52% for the topoisomerase II inhibitors, alkylators, tubulinactive agents and anti-metabolites respectively. Classification of drugs into the four categories representing different mechanisms of action using a probabilistic neural network (PNN) analysis resulted in 29 (91%) correct predictions. The results indicate the feasibility of using a limited number of cell lines for prediction of mechanism of action of anti-cancer drugs. The present approach may be well suited for initial classification and evaluation of novel anti-cancer drugs and as a potential tool to guide lead compound optimisation.

Antineoplastic Agents↗
PubMed1999

Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage.

BACKGROUND: Artificial neural network (ANN) analysis methods have led to more sensitive diagnosis of myocardial infarction and improved prediction of mortality in breast cancer, prostate cancer, and trauma patients. Prognostic studies have identified early clinical and radiographic predictors of mortality after intracerebral hemorrhage (ICH). To date, published models have not achieved the accuracy necessary for use in making decisions to limit medical interventions. We recently reported a logistic regression model that correctly classified 79% of patients who died and 90% of patients who survived. In an attempt to improve prediction of mortality we computed an ANN model with the same data. OBJECTIVE: To determine whether an ANN analysis would provide a more accurate prediction of mortality after ICH when compared with multiple logistic regression models computed using the same data. METHODS: Analyses were conducted on data collected prospectively on 81 patients with supratentorial ICH. Multiple logistic regression was used to predict hospital mortality, then an ANN analysis was applied to the same data set. Input variables were age, gender, race, hydrocephalus, mean arterial pressure, pulse pressure, Glasgow Coma Scale score, intraventricular hemorrhage, hydrocephalus, hematoma size, hematoma location (ganglionic, thalamic, or lobar), cisternal effacement, pineal shift, history of hypertension, history of diabetes, and age. RESULTS: The ANN model correctly classified all patients (100%) as alive or dead compared with 85% correct classification for the logistic regression model. A second ANN verification model was equally accurate. The ANN was superior to the logistic regression model on all objective measures of fit. CONCLUSIONS: ANN analysis more effectively uses information for prediction of mortality in this sample of patients with ICH. A well-validated ANN may have a role in the clinical management of ICH.

Adolescent↗
PubMed1997

Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network.

We investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.

Breast Diseases↗
PubMed1999

Fast global oscillations in networks of integrate-and-fire neurons with low firing rates.

We study analytically the dynamics of a network of sparsely connected inhibitory integrate-and-fire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the limit when the number of neurons --> infinity, the network exhibits a sharp transition between a stationary and an oscillatory global activity regime where neurons are weakly synchronized. The activity becomes oscillatory when the inhibitory feedback is strong enough. The period of the global oscillation is found to be mainly controlled by synaptic times but depends also on the characteristics of the external input. In large but finite networks, the analysis shows that global oscillations of finite coherence time generically exist both above and below the critical inhibition threshold. Their characteristics are determined as functions of systems parameters in these two different regions. The results are found to be in good agreement with numerical simulations.

Algorithms↗
PubMed2026

Comparative in silico analysis of Apis mellifera immune responses to Varroa destructor and Tropilaelaps mercedesae: Common and mite-specific molecular signatures.

Parasitic mites Varroa destructor and Tropilaelaps mercedesae represent major threats to global honey bee (Apis mellifera) health and productivity, yet comparative molecular insights into host responses remain limited. To address this, we systematically compiled published studies (2015-2025) reporting genes associated with honey bee interactions with V. destructor (11 studies, 87 genes), T. mercedesae (4 studies, 35 genes), and hygienic behavior (6 studies, 44 genes). Gene identifiers were harmonized to the Amel_HAv3.1 genome assembly, yielding three non-redundant sets: 64 Varroa-associated, 34 Tropilaelaps-associated, and 44 hygienic behavior-associated genes. Venn analysis identified 10 overlapping genes (including A0A088A8D5, A0A088ADL8, ABAE_APIME, Def1, Def2, Gapdh, HYTA_APIME, Imd, LOC726783, and Vg), suggesting conserved defense mechanisms, while 41 and 24 genes were uniquely associated with Varroa and Tropilaelaps, respectively. Enrichment analyses revealed Varroa-responsive genes were enriched in immune processes, chitin catabolism, and signaling pathways (Toll/Imd, MAPK, Wnt). Tropilaelaps-associated genes were enriched for antibacterial defense and stress response, with Toll/Imd signaling as the sole significantly enriched pathway. Overlapping genes reinforced core innate immunity activation. Protein-protein interaction network centrality analysis identified key hub genes: Def1, HYTA_APIME, ABAE_APIME, PPO, Imd, PGRP-LC, Vg for Varroa; and ACPH1_APIME, MRJP1, Vg, LOC726783 for Tropilaelaps. Results demonstrate that, despite differences in mite biology, honey bees show a conserved immune response against both parasites, centered on antibacterial defense, humoral immunity, and activation of the Toll/Imd pathway. Although limited by the in-silico nature and research asymmetries reflecting Tropilaelaps' emergence, this curated resource establishes a comprehensive framework for elucidating shared and distinct molecular defense mechanisms. Ultimately, this approach prioritizes diagnostic markers and candidate genes for functional validation and breeding strategies to enhance colony resilience against mite‑driven disease globally.

Animals↗