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At least 1,369 records · Page 76Linked to original sources
PubMed2000

Improving the oral bioavailability of the iron chelator HBED by breaking the symmetry of the intramolecular H-bond network.

Physicochemical analysis and Monte Carlo simulations were used to identify structural features which prevent oral absorption of HBED, a potent iron chelator. In water the dominant conformations of HBED involve the hydrophobic collapse of the two aromatic rings. These conformations are favored in polar media because they expose the polar phenolic hydroxy groups to the solvent and partially shield the nonpolar aromatic rings. In a less polar solvent such as chloroform, a symmetrical H-bond network between the carboxylates and the amines dominates the conformational space. This leads to the exposure of the phenolic hydroxy groups to the solvent, which is unfavorable for solvation. The low solubility of HBED in nonpolar solvents was confirmed experimentally by determination of the partition coefficients in octanol, chloroform, and cyclohexane and may explain the poor membrane permeability of this compound. The high conformational stability which disfavors partitioning into phospholipids is mainly due to the symmetrical H-bond network. Potentiometric titrations of a monoester of HBED in MeOH/water indicate that the protonation sequence was changed compared to that of the parent compound, suggesting that the symmetrical H-bond network was disrupted. Conformational analysis in chloroform confirmed that, in contrast to HBED, no symmetric interaction between the carboxylate and the nitrogen amines is possible in the half-ester and a variety of conformations which allow partial shielding of the polar phenolic OH groups are energetically possible. This theoretical model predicting a better solubility of the half-esters in nonpolar solvents was supported by the large increase in the partition coefficients in octanol, chloroform, and cyclohexane measured experimentally. The high absorbability predicted by physicochemical and computer simulation methods was corroborated by in vivo experiments in marmoset monkeys where the monoethyl ester derivative of HBED was well-absorbed orally while the parent compound was nearly ineffective in the same model.

Administration, Oral↗
PubMed1993

Neural network model: application to automatic analysis of human sleep.

We describe an approach to automatic all-night sleep analysis based on neural network models and simulated on a digital computer. First, automatic sleep stage scoring was performed using a multilayer feedforward network. Second, supervision of the automatic decision was achieved using ambiguity rejection and artifact rejection. Then, numerical analysis of sleep was carried out using all-night spectral analysis for the background activity of the EEG and sleep pattern detectors for the transient activity. Computerized analysis of sleep recordings may be considered as an essential tool to describe the sleep process and to reflect the dynamical organization of human sleep.

Computer Simulation↗
PubMed2005

Quantitative analysis of keratin filament networks in scanning electron microscopy images of cancer cells.

The keratin filament network is an important part of the cytoskeleton. It is involved in the regulation of shape and viscoelasticity of epithelial cells. The morphology of keratin networks depends on post-translational modifications of keratin monomers. In-vitro studies indicated that network characteristics, such as filament crosslink density, determines the biophysical properties of the filament network. This report presents a quantitative method for the morphological analysis of keratin filament networks. Visualization of filaments was based on prefixation extraction of epithelial cells and scanning electron microscopy (SEM). SEM images were processed by a skeletonization algorithm to obtain a graph structure that represents individual filaments as well as their connections. This method was applied to investigate the effects of transforming growth factor alpha (TGFalpha) on the morphology of keratin networks in pancreatic cancer cells. TGFalpha contributes to pancreatic cancer progression and activates signalling pathways phosphorylating keratin monomers. Using this new method, a significant alteration to the keratin network morphology could be detected in response to TGFalpha.

Cell Transformation, Neoplastic↗
PubMedDate not supplied

Antimicrobial activity characterization in a heterogeneous group of compounds.

In this work we carry out a study of pattern recognition to detect the microbiological activity in a group of heterogeneous compounds. The structural descriptors utilized are the topological connectivity indexes. The methods followed are stepwise linear discriminant analysis (linear analysis) and artificial neural network (nonlinear analysis). Although both methods are appropriate to differentiate between active and inactive compounds, the artificial neural network is, in this case, more adequate, since it shows in a test set a prediction success of 98%, versus 92% obtained with linear discriminant analysis.

Anti-Infective Agents↗
PubMed2005

Conservation analysis of large biochemical networks.

MOTIVATION: Large biochemical networks pose a unique challenge from the point of view of evaluating conservation laws. The computational problem in most cases exceeds the capability of available software tools, often resulting in inaccurate computation of the number and form of conserved cycles. Such errors have profound effects on subsequent calculations, particularly in the evaluation of the Jacobian which is a critical quantity in many other calculations. The goal of this paper is to outline a new algorithm that is computationally efficient and robust at extracting the correct conservation laws for very large biochemical networks. RESULTS: We show that our algorithm can perform the conservation analysis of large biochemical networks, and can evaluate the correct conserved cycles when compared with other similar software tools. Biochemical simulators such as Jarnac and COPASI are successful at extracting only a subset of the conservation laws that our algorithm can. This is illustrated with examples for some large networks which show the advantages of our method.

Algorithms↗
PubMed2006

A stable learning algorithm for block-diagonal recurrent neural networks: application to the analysis of lung sounds.

A novel learning algorithm, the Recurrent Neural Network Constrained Optimization Method (RENNCOM) is suggested in this paper, for training block-diagonal recurrent neural networks. The training task is formulated as a constrained optimization problem, whose objective is twofold: (1) minimization of an error measure, leading to successful approximation of the input/output mapping and (2) optimization of an additional functional, the payoff function, which aims at ensuring network stability throughout the learning process. Having assured the network and training stability conditions, the payoff function is switched to an alternative form with the scope to accelerate learning. Simulation results on a benchmark identification problem demonstrate that, compared to other learning schemes with stabilizing attributes, the RENNCOM algorithm has enhanced qualities, including, improved speed of convergence, accuracy and robustness. The proposed algorithm is also applied to the problem of the analysis of lung sounds. Particularly, a filter based on block-diagonal recurrent neural networks is developed, trained with the RENNCOM method. Extensive experimental results are given and performance comparisons with a series of other models are conducted, underlining the effectiveness of the proposed filter.

Algorithms↗
PubMed2006

[Automated recognition of VOCs using artificial neural networks].

Quantitative analysis of FTIR spectra, which are seriously overlapped in the spectral bands, was studied by artificial neural networks. The optimum network was chosen by a new criterion, i. e. the degree of approximation. After the network was established, two kinds of spectra were resolved. It was demonstrated that accurate results could be obtained when two components were both included. In addition, the unknown spectrum could be identified and quantified. It was showed that the artificial neural network has excellent non-linear ability of solution. Meanwhile, the method provides an efficient approach to the identification and quantification of the unknown samples.

English Abstract↗
PubMed2002

Evaluation of calibration data in capillary electrophoresis using artificial neural networks to increase precision of analysis.

Increase of precision in capillary electrophoresis can be achieved applying suitable markers and evaluating calibration curves and data analysis with artificial neural networks. They are able to account for errors in both x- and y-axes, nonlinear response of detector and non-linearity of calibration curves eventually. A comparison of the artificial neural networks approach with ordinary least-squares (OLS) and bivariate least-squares regression (BLS) was done. While OLS and BLS give similar results, the method proposed and tested in analysis of several pharmaceutical products yields lower prediction errors than traditional linear least-squares methods and the precision of analysis was found in the range 0.5-1.5% relative.

Calibration↗
PubMed1994

Discrimination between demented patients and normals based on topographic EEG slow wave activity: comparison between z statistics, discriminant analysis and artificial neural network classifiers.

The topographic distributions of absolute delta and theta powers were used to classify demented patients and normals by means of z statistics, discriminant analysis and artificial neural networks (NN). The data were taken from two psychopharmacological studies in mildly to moderately demented patients (111 and 96 patients for studies I and II, respectively) and from 56 normal healthy controls. All patients were diagnosed according to DSM-III criteria and were free of medication for at least 2 weeks. The NN used was a strictly layered feed-forward network with complete connections. The z-transformed absolute power values in the combined delta and theta frequency range at 17 electrodes, recorded in a 3 min vigilance-controlled EEG with eyes closed, were used as input. After having trained the NN successfully by backpropagating of errors, the generalization test with independent data results in a classification performance of 90% determined by "relative operating characteristic" analysis. The NN out-performed z statistics and discriminant analysis. This high percentage of correct classifications may justify the development of further application of NNs based on topographic EEG data.

Aged↗
PubMed2003

Multistability analysis for recurrent neural networks with unsaturating piecewise linear transfer functions.

Multistability is a property necessary in neural networks in order to enable certain applications (e.g., decision making), where monostable networks can be computationally restrictive. This article focuses on the analysis of multistability for a class of recurrent neural networks with unsaturating piecewise linear transfer functions. It deals fully with the three basic properties of a multistable network: boundedness, global attractivity, and complete convergence. This article makes the following contributions: conditions based on local inhibition are derived that guarantee boundedness of some multistable networks, conditions are established for global attractivity, bounds on global attractive sets are obtained, complete convergence conditions for the network are developed using novel energy-like functions, and simulation examples are employed to illustrate the theory thus developed.

Linear Models↗
PubMed2002

UV index experimental values during the years 2000 and 2001 from the Spanish broadband UV-B radiometric network.

An analysis is made of experimental ultraviolet erythemal solar radiation data measured during the years 2000 and 2001 by the Spanish UV-B radiation evaluation and prediction network. This network consists of 16 Robertson-Berger type pyranometers for evaluating solar erythemal radiation and five Brewer spectroradiometers for evaluating the stratospheric ozone. On the basis of these data the Ultraviolet Index (UVI) was evaluated for the measuring stations that are located either in coastal regions or in the more densely populated regions inland on the Iberian Peninsula. It has been checked that in most cases the maximum irradiance values corresponded to solar noon, although there were exceptions that could be explained by cloudiness. The maximum experimental values of the UVI were around 9 during the summer, though frequently passing this value at the inland measurement stations. The annual accumulated dose of irradiation on a horizontal plane has also been studied, as well as the evolution through the year in units of energy, standard erythemal doses and minimum erythemal doses, according to different phototypes.

Erythema↗
PubMed1999

Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems.

The last few years have brought tremendous progress in experimental methods for metabolic flux determination by carbon-labeling experiments. A significant enlargement of the available measurement data set has been achieved, especially when isotopomer fractions within intracellular metabolite pools are quantitated. This information can be used to improve the statistical quality of flux estimates. Furthermore, several assumptions on bidirectional intracellular reaction steps that were hitherto indispensable may now become obsolete. To make full use of the complete measurement information a general mathematical model for isotopomer systems is established in this contribution. Then, by introducing the important new concept of cumomers and cumomer fractions, it is shown that the arising nonlinear isotopomer balance equations can be solved analytically in all cases. In particular, the solution of the metabolite flux balances and the positional carbon-labeling balances presented in part I of this series turn out to be just the first two steps of the general solution procedure for isotopomer balances. A detailed analysis of the isotopomer network structure then opens up new insights into the intrinsic structure of isotopomer systems. In particular, it turns out that isotopomer systems are not as complex as they appear at first glance. This enables some far-reaching conclusions to be drawn on the information potential of isotopomer experiments with respect to flux identification. Finally, some illustrative examples are examined to show that an information increase is not guaranteed when isotopomer measurements are used in addition to positional enrichment data.

Algorithms↗
PubMed2005

Identification of dynamical correlations within the myosin motor domain by the normal mode analysis of an elastic network model.

In order to systematically analyze functionally relevant dynamical correlations within macromolecular complexes, we have developed computational methods based on the normal mode analysis of an elastic network model. First, we define two types of dynamical correlations (fluctuation-based and density-based), which are computed by summing up contributions from all low-frequency normal modes up to a given cutoff. Then we use them to select dynamically important "hinge residues" whose elastic distortion affects the fluctuations of a large number of residues. Second, in order to clarify long-range dynamical correlations, we decompose the dynamical correlations to individual normal modes to identify the most relevant modes. We have applied these methods to the analysis of the motor domain of Dictyostelium myosin and have obtained the following three interesting results that shed light on its mechanism of force generation: first, we find the hinge residues are distributed over several key inter-subdomain joints (including the nucleotide-binding pocket, the relay helix, the SH1 helix, the strut between the upper 50 kDa and the lower 50 kDa subdomains), which is consistent with their hypothesized roles in modulating functionally relevant inter-subdomain conformational changes; second, a single mode 7 (for structure 1VOM) is found to dominate the fluctuation-based correlations between the converter/strut and the nucleotide-binding pocket, revealing a surprising simplicity for their intriguing roles in the force generation mechanism; finally, we find a negative density-based correlation between the strut and the nucleotide-binding pocket, which is consistent with the hypothesized signaling pathway that links the actin-binding site's opening/closing with the nucleotide-binding pocket's closing/opening.

Animals↗
PubMed2006

Analysis of artificial neural networks for pattern-based adaptive control.

Adaptive pattern-based control strategies adapt their parameters from an analysis of response patterns exhibited by the system. This work presents an analysis of a class of artificial neural network (ANN) pattern-based adaptive control. It provides conditions under which the adaptive algorithm will converge, and it also characterizes the closed-loop stability properties. In addition, a method for monitoring the adaptation is also proposed. Several simulation examples illustrate our findings.

Algorithms↗
PubMed2006

Measuring multiple fluxes through plant metabolic networks.

Fluxes through metabolic networks are crucial for cell function, and a knowledge of these fluxes is essential for understanding and manipulating metabolic phenotypes. Labeling provides the key to flux measurement, and in network flux analysis the measurement of multiple fluxes allows a flux map to be superimposed on the metabolic network. The principles and practice of two complementary methods, dynamic and steady-state labeling, are described, emphasizing best practice and illustrating their contribution to network flux analysis with examples taken from the plant and microbial literature. The principal analytical methods for the detection of stable isotopes are also described, as well as the procedures for obtaining flux maps from labeling data. A series of boxes summarizing the key concepts of network flux analysis is provided for convenience.

Chromatography, Gas↗
PubMed2026

Circular RNAs orchestrate integrated post-transcriptional responses to combined heat and drought stress in rice.

Circular RNAs (circRNAs) are emerging post-transcriptional regulators, yet their landscape and functional roles in rice under combined abiotic stress remain largely unexplored. Here, we systematically reanalyzed strand-specific RNA-seq data to characterize circRNAs responsive to simultaneous heat and drought stress. Following quality control, read mapping, and dual-algorithm prediction using CIRI2 and CIRCexplorer2, we identified 208 high-confidence circRNAs distributed across all 12 chromosomes. Comparative profiling revealed 83 circRNAs uniquely expressed in control samples, 51 in stressed samples, and 74 shared between conditions, indicating stress-dependent circularization. Junction-read analysis highlighted a spectrum of circularization strength, ranging from highly abundant circRNAs with dominant junction reads to low-confidence candidates masked by linear transcript background. Genomic annotation showed that circRNAs primarily originated from exonic and intergenic regions, with a pronounced negative-strand bias; several genes generated multiple circRNA isoforms via alternative back-splicing. Functional enrichment of host genes suggested involvement in protein folding, nutrient reservoir activity, RNA degradation, and branched-chain amino acid catabolism, implicating roles in stress adaptation and metabolic regulation. Differential expression analysis identified seven circRNAs specifically induced under combined stress conditions. Network topology analysis pinpointed key miRNAs-including osa-miR414, osa-miR1439, and osa-miR2919-as candidate topological hubs within the predicted network. Their predicted target genes, such as those encoding stress-responsive transcription factors and signaling proteins, suggest potential roles in coordinating post-transcriptional responses to combined stress. Network topology analysis pinpointed key miRNAs-including osa-miR414, osa-miR1439, and osa-miR2919-as candidate topological hubs within the predicted network. Their predicted target genes, such as those encoding stress-responsive transcription factors and signaling proteins, suggest potential roles in coordinating post-transcriptional responses to combined stress. Overall, this study provides a comprehensive map of circRNAs in rice under combined heat and drought stress, suggests their potential as ceRNAs based on predictive analysis, and lays a foundation for future experimental validation of circRNA-mediated regulation.

Oryza↗
PubMed2006

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Abscisic Acid↗
PubMed2002

Energy balance for analysis of complex metabolic networks.

Predicting behavior of large-scale biochemical networks represents one of the greatest challenges of bioinformatics and computational biology. Computational tools for predicting fluxes in biochemical networks are applied in the fields of integrated and systems biology, bioinformatics, and genomics, and to aid in drug discovery and identification of potential drug targets. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are promising tools for the analysis of large complex networks. Here we introduce energy balance analysis (EBA)--the theory and methodology for enforcing the laws of thermodynamics in such simulations--making the results more physically realistic and revealing greater insight into the regulatory and control mechanisms operating in complex large-scale systems. We show that EBA eliminates thermodynamically infeasible results associated with FBA.

Biophysics↗