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PubMed2001

Networks and tuberculosis: an undetected community outbreak involving public places.

After decades of decline in developed countries, there was a resurgence of tuberculosis in the mid-1980s accompanied by increased recognition that this infectious disease has long remained a major public health problem at the global level. New methods from molecular biology, in particular DNA 'fingerprinting' (of Mycobacterium tuberculosis), made it clear that current transmission and recent infection (in contrast to reactivation of earlier, latent infection) were much more significant than previously believed. Studies of tuberculosis outbreaks using these new tools pointed to complex networks through which infection was spreading and highlighted the need for new approaches to outbreak investigation and disease control. In the study reported here a new approach--combining methods from molecular biology, epidemiology and network analysis--was used to examine an outbreak of tuberculosis in Houston, Texas. Initial investigation using conventional strategies revealed few contacts among 37 patients with identical (six-band) DNA (IS6110-based) fingerprints but subsequent research uncovered over 40 places (including many gay bars) to which patients in this outbreak could be linked. Network methods were used to reconstruct an outbreak network and to quantify the relative importance (here, 'betweenness' centrality) of different actors (persons and places) playing a role in the outbreak. The multidisciplinary work provides the basis for a new approach to outbreak investigation and disease control.

AIDS-Related Opportunistic Infections↗

PubMed1997

[Diagnostic process in internal medicine: decision analysis or intuition?].

The diagnostic process is a hypothesis-testing approach under conditions of uncertainty. Experience-based intuition plays an important role in complex clinical situations. Serious cognitive errors may be avoidable if possible heuristic problems are considered. Probabilistic reasoning according to Bayes' theorem, especially for determination of pretest probability, is crucial for coping with uncertainty and the myriad available diagnostic tests and procedures. Their proper interpretation depends on fundamental test variables such as sensitivity, specificity and likelihood ratio. Computers and neural networks cannot replace cognitive reasoning and probabilistic strategies in daily clinical practice. Decision analysis is pivotal, particularly when the aim is to maintain high-quality medical care at a time of limited resources.

Decision Making, Computer-Assisted↗

PubMed2000

Variable sensitivity in unsupervised clustering tasks with an n-tuple-based self-organising neural network.

This article investigates the application of the SOLNN (Self-Organising Logic Neural Network) n-tuple-based network to character recognition and image segmentation clustering tasks, where the classes consist of a large number of distinct sub-classes. It is shown that the SOLNN clustering performance and node utilisation are both improved by virtue of the distribution constraint mechanism. The clustering results are supported by means of a detailed analysis of the characteristics of each pattern space. This analysis, coupled with comparative results obtained using other self-organising models, illustrates that the SOLNN clusters the patterns in accordance to the pattern space characteristics and thus is well-suited to clustering complex datasets.

Algorithms↗

PubMed1998

Protein disorder and the evolution of molecular recognition: theory, predictions and observations.

Observations going back more than 20 years show that regions in proteins with disordered backbones can play roles in their binding to other molecules; typically, the disordered regions become ordered upon complex formation. Thought-experiments with Schulz Diagrams, which are defined herein, suggest that disorder-to-order transitions are required for natural selection to operate separately on affinity and specificity. Separation of affinity and specificity may be essential for fine-tuning the molecular interaction networks that comprise the living state. For low affinity, high specificity interactions, our analysis suggests that natural selection would parse the amino acids conferring flexibility in the unbound state from those conferring specificity in the bound state. For high affinity, low specificity or for high affinity, multiple specificity interactions, our analysis suggests that the disorder-to-order transitions enable alternative packing interactions between side chains to accommodate the different binding targets. Disorder-to-order transitions upon binding also have significant kinetic implications as well, by having complex effects on both on- and off-rates. Current data are insufficient to decide on these proposals, but sequence and structure analysis on two examples support further investigations of the role of disorder-to-order transitions upon binding.

Algorithms↗

PubMed2026

Emergence and phylogeography of the dengue vector Aedes aegypti in Southeastern Iran.

BACKGROUND: Aedes (Stegomyia) aegypti (Linnaeus) is the primary vector of dengue, chikungunya, Zika, and yellow fever viruses. Its recent detection in southeastern Iran raises public health concerns about arbovirus spread to new regions. This study provides the first genetic and phylogeographic analysis of Ae. aegypti populations from Sistan and Baluchistan Province (SBP), Iran, to infer their origin and invasion pathways. METHODS: Mitochondrial COI and ND4 genes were analysed in newly collected Ae. aegypti specimens from border areas, ports, and urban centres of SBP. Haplotype network analyses were constructed using the TCS method in PopART, and phylogenetic analyses were conducted using global reference sequences. RESULTS: Iranian specimens comprised 7 COI haplotypes (n = 18) and 10 ND4 haplotypes (n = 17). COI phylogeny placed Iranian specimens into two main clades, while ND4 analysis distributed them across several derived clades, mostly clustering with lineages from Latin America (Brazil, Mexico) or Africa. One Iranian specimen showed a close relationship with a Saudi Arabian sequence (bootstrap: 98%) near the basal region. Combined COI + ND4 analysis revealed a monophyletic clade of Iranian specimens with a Sri Lankan specimen, distinct from other global lineages. The global COI network (n = 47) showed a star-like topology with a dominant haplotype 1 shared among 10 Iranian specimens. The ND4 network (n = 31) revealed a complex topology with 18 haplotypes, where a Saudi Arabian and one Iranian specimen (~30 mutational steps) possibly represented the peripheral root. CONCLUSIONS: Detection of diverse Ae. aegypti clades confirm establishment of this vector in southeastern Iran. Results support multiple introductions and genetic connectivity with Latin America, Africa, and South Asia, pointing to an emerging invasion corridor. Continued genomic surveillance and integrated vector monitoring are urgently needed to guide prevention strategies.

Animals↗

PubMedDate not supplied

Prediction of plasma levels of aminoglycoside antibiotic in patients with severe illness by means of an artificial neural network simulator.

PURPOSE: The purpose of this work was to predict plasma peak and trough levels of an aminoglycoside antibiotic in patients with severe illness in an intensive care unit by a novel approach. Plasma levels were predicted based on the values of 15 physiological measurements using an artificial neural network (ANN) simulator. METHOD: A data set of 15 physiological measurements for 30 patients was used to develop the model. The ANN structure consisted of three layers: an input layer comprised of 15 processing elements, a hidden layer comprised of 10 processing elements with a sigmoid function as an activation function, and an output layer of two processing elements (peak and trough levels). The weight between neurons was trained according to the delta rule back-propagation of errors algorithm. Predicted values were obtained by "leave-one-out" experiments by both ANN and multiple linear regression analysis (MLRA). RESULTS: The correlation coefficients between observed and predicted values obtained by ANN prediction using standardized data sets were r=0.825 and r=0.854 for peak and trough levels, respectively. The correlation coefficients obtained by MLRA were r=0. 037 and r=0.276 for peak and trough levels, respectively. These results indicate that ANN shows better performance in prediction of aminoglycoside plasma levels from patients' physiological measurements than MLRA. CONCLUSIONS: Prediction of plasma levels of antibiotic in patients with severe illness by ANN was superior to the standard statistical method. Standardization of input data was found to be important for better prediction. ANN has some advantages over standard statistical methods, as it can recognize complex relationships in the data.

Aminoglycosides↗

PubMed2001

[DNA arrays: technological aspects and applications].

The Human Genome Project has allowed considerable progress in the construction of physical and genetic maps and the identification of genes involved in human sicknesses. The accelerated accumulation of biological information and knowledge is due in large part to the sequencing projects of other organisms, which in fact paved the way for the Human Genome Project. In parallel, recently developed techniques which take advantage of genomic sequences allow large scale molecular analyses resulting in the functional annotation of many of the proteins represented by these genes. This is the goal of functional genomics. These progresses are at the origin of the present revolution in biomedical research. DNA microarrays are playing a dominant role compared to the other developing technologies since they are relatively easy to make and use and are applicable to numerous scientific inquiries. They allow the simultaneous analysis of several thousands of genes in biological samples from sick or healthy tissues, at the genome or transcriptome level. The data obtained is expected to result in major advances in the health sciences. In addition to an improved understanding of the complex molecular interaction networks of healthy cells and tissues, a more precise genetic characterization of the molecular mechanisms involved in pathology should result in the identification of new therapeutic targets and the development of new medicines. The genetic profiles thus obtained should also permit the definition of new pathologic subclasses not recognizable by traditional clinical factors, as well as new markers for susceptibility to certain illnesses, and new prognostic markers or methods of predicting responses to treatment. In this article, we present the different approaches and potential applications of DNA microarray technology, in particular as applied to cancer research.

Chromosome Mapping↗

PubMed1995

High resolution crystal structures of recombinant human renin in complex with polyhydroxymonoamide inhibitors.

The crystal structures of recombinant glycosylated human renin in complex with several polyhydroxymonoamide inhibitors have been determined at up to 1.8 A resolution. The high resolution structures permit a detailed analysis of the conformation of renin, the interactions between the inhibitors and renin, and the network of ordered water molecules. The polyhydroxymonoamide inhibitors are bound with their backbones in an extended conformation, and with their side-chains occupying the S3 to S1 pockets. The inhibited renin molecules are shown to exist in both the closed and the open conformations. Inhibitors bound to the two distinct forms of renin can assume different conformations at the P3 position.

Amides↗

PubMed1996

NMR analysis of site-specific ligand binding in oligomeric proteins. Dynamic studies on the interaction of riboflavin synthase with trifluoromethyl-substituted intermediates.

The binding of small ligands to symmetrical oligomeric proteins may lead to a number of different partially ligated intermediates but should finally yield a symmetrical fully ligated enzyme/ligand complex. In the case of the trimeric protein, riboflavin synthase, some ligands form an unexpected protein/ligand complex, even in the presence of a large excess of ligand. Three different bound forms were observed by 19F NMR spectroscopy, and Scatchard-type analysis suggested binding sites of similar affinities. NOESY analysis of the kinetic network revealed that the three bound states exchange with free ligand, but not with each other, thus suggesting that the trimeric enzyme could be asymmetrical. This information permits appropriate precautions to be taken during X-ray structure analysis of riboflavin synthase, which is in progress. Quantitative analysis of the NOESY spectra yielded different rate constants for the different binding sites. For comparison, the monomeric lumazine protein was investigated as an example of a case with simple two-site exchange. For such systems, all kinetic parameters including kon and the dissociation constant can be determined from the NOESY spectrum. The data show that NMR spectroscopy can produce qualitative and quantitative information in cases of nonequivalent binding sites in oligomeric proteins if isolated NMR signals of the different forms can be observed. The technique is not limited to 19F as reporter nucleus.

Bacillus subtilis↗

PubMed1991

Application of neural computing in pharmaceutical product development.

Neural computing technology is capable of solving problems involving complex pattern recognition. This technology is applied here to pharmaceutical product development. The most commonly used computational algorithm, the delta back-propagation network, was utilized to recognize the complex relationship between the formulation variables and the in vitro drug release parameters for a hydrophilic matrix capsule system. This new computational technique was also compared with the response surface methodology (RSM). Artificial neural network (ANN) analysis was able to predict the response values for a series of validation experiments more precisely than RSM. ANN may offer an alternative to RSM because it allows for the development of a system that can incorporate literature and experimental data to solve common problems in the pharmaceutical industry.

Algorithms↗

PubMed1995

Molecular basis of cell integrity and morphogenesis in Saccharomyces cerevisiae.

In fungi and many other organisms, a thick outer cell wall is responsible for determining the shape of the cell and for maintaining its integrity. The budding yeast Saccharomyces cerevisiae has been a useful model organism for the study of cell wall synthesis, and over the past few decades, many aspects of the composition, structure, and enzymology of the cell wall have been elucidated. The cell wall of budding yeasts is a complex and dynamic structure; its arrangement alters as the cell grows, and its composition changes in response to different environmental conditions and at different times during the yeast life cycle. In the past few years, we have witnessed a profilic genetic and molecular characterization of some key aspects of cell wall polymer synthesis and hydrolysis in the budding yeast. Furthermore, this organism has been the target of numerous recent studies on the topic of morphogenesis, which have had an enormous impact on our understanding of the intracellular events that participate in directed cell wall synthesis. A number of components that direct polarized secretion, including those involved in assembly and organization of the actin cytoskeleton, secretory pathways, and a series of novel signal transduction systems and regulatory components have been identified. Analysis of these different components has suggested pathways by which polarized secretion is directed and controlled. Our aim is to offer an overall view of the current understanding of cell wall dynamics and of the complex network that controls polarized growth at particular stages of the budding yeast cell cycle and life cycle.

Cell Wall↗

PubMed2000

Comparison of human ictal, interictal and normal non-linear component analyses.

OBJECTIVES: The non-linear properties of EEG and filtered rhythms obtained from healthy subjects and epileptic patients with complex partial seizures were analyzed to investigate whether EEG in different neurological states can be generated by the mechanism that integrates several non-linear dynamic systems. METHODS: The control EEG (from 26 healthy subjects), interictal EEG and ictal EEG (from 25 patients) were digitally filtered into delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-40 Hz) components. The correlation dimension was calculated on each original signal and corresponding surrogate data. A new method was developed to accelerate the calculation of the correlation integral. Function P(m,r) was defined to visualize the meaning of the correlation dimension. The point critical to the estimation was determined by the P(m,r) function. RESULTS: The EEG in the control subjects and patients showed significantly lower correlation dimensions than the surrogate data. The delta, alpha, beta and gamma components from the control EEG exhibited similar complexity to the surrogate data, while only the alpha component from the interictal EEG presented the same dimension as the surrogate data. The correlation dimensions of the theta and alpha components remained the same when the neurological state changed from interictal EEG to ictal EEG. The complexity of the beta component was higher than the complexity of other components in both control subjects and patients. The correlation dimension of EEG was significantly correlated to the complexity of delta, theta, beta and gamma components. CONCLUSIONS: Our results suggest that EEG and filtered components in different neurological states demonstrate varied dynamic properties. The characteristics of neuronal networks can be differentiated by the dynamics of filtered components. Separating EEG into different dynamic systems may facilitate understanding of the mechanisms involved in the human EEG.

Adult↗

PubMed1975

Mathematical modeling of differentiation in Dictyostelium discoideum.

Methods for the dynamic analysis of biochemical differentiation are presented. These are demonstrated in the analysis of biochemical differentiation of the carbohydrate system in D. discoideum. Procedures for simplification which are presented are projection and contraction of the system trajectory in state space and the generation of reduced equivalent dynamic metabolic networks. The importance of the hierarchical structure of differentiating systems is discussed and the concept of a dynamic embedding diagram is introduced. It is shown that complex systems must be analyzed on an epoch by epoch basis, each epoch being a period of time characterized by a constant dynamic embedding diagram, and that widely different time scales and state space scales may be necessary in different epochs. In particular there is no a priori lower limit to the time scale which may be necessary during the analysis. Some problems in mathematically defining differentiation are discussed.

Carbohydrate Metabolism↗

PubMed2001

Recognition of environmental and genetic effects on barley phenolic fingerprints by neural networks.

Through computational analysis of high-performance liquid chromatography (HPLC) traces we find correlations between secondary metabolites and growth conditions of six varieties of barley. Using artificial neural networks, it was possible to classify chromatograms for which the varieties were fertilized by nitrogen and treated by fungicide. For each variety of barley we could also differentiate it from the others. Surprisingly, all these classification tasks could be solved successfully by a simple network with no hidden units. When adding to the methodology pruning of the network weights, we were able to reduce the set of peaks in the chromatograms and obtain a necessary subset from which the growth conditions and differentiation may be decided. In some instances, more complex networks with hidden units could lead to a further reduction of the number of peaks used. In most cases, far more than half of the peaks are redundant. We find that it requires fewer information-rich peaks to perform the variety differentiation tasks than to recognize any of the growth conditions. Analysis of the network weights reveals correlations between weighted combinations of peaks.

Chromatography↗

PubMed1993

Keratin 14 protein in cultured nonparenchymal rat hepatic epithelial cells: characterization of keratin 14 and keratin 19 as antigens for the commonly used mouse monoclonal antibody OV-6.

We have recently reported that cell lines of nonparenchymal origin isolated from rat liver and pancreas, which have been suggested to be the progeny of a facultative stem cell compartment in vivo, express an unusual combination of keratins (K). These cell lines express K8 and K14 but not K18 and K5, their normal partners in filament formation (Bisgaard HC, Thorgeirsson SS, J Cell Physiol 147:333-343, 1991). However, upon spontaneous transformation and differentiation toward a hepatoblastlike progeny, K14 expression is abrogated and replaced by expression of K18 (Wirth et al., Electrophoresis 13:305-332, 1992). In the study presented here, we confirmed by protein sequence analysis that K14 was a major component of the intermediate filaments in a nonparenchymal cell line of hepatic origin. Immunocytochemical analysis of the cells in monolayer demonstrated that K8 as well as K14 were incorporated in the cellular cytoskeleton. Further analysis by immunoprecipitation showed that filament complexes were formed between K8 and K14 as atypical partners. Thus, we concluded that in some nonparenchymal cell lines isolated from rat liver, K8 and K14 form a major intermediate filament network. Finally, we showed that an antibody widely used in studies of the cell lineages of hepatic and pancreatic tissues and their neoplasms, the mouse monoclonal antibody OV-6, recognizes a common epitope in K14 and K19.

Amino Acid Sequence↗

PubMed1990

Superposition of arteriolar vasomotion waves and regulation of blood flow in skeletal muscle microcirculation.

In skin muscle microcirculation of Syrian hamsters, rhythmic diameter changes were studied along the arteriolar network, under normoxic conditions, at rest. A teflon coated-aluminum chamber was implanted in the dorsum skin of animals. The microcirculation was investigated using intravital microscopy technique. Vessel diameters were determined by a computer-assisted method. Power spectrum analysis of vasomotion recordings was carried out with Fast Fourier Transform and Autoregressive modelling. To determine vasomotion waveform spreading, cross-spectral data (amplitude and phase) were computed, using the modified periodogram method (FFT). The arterioles were classified according to Strahler's method. Order 1 vessels (diameter: 7.50 +/- 1.16 microns) showed the highest frequency, 4-15 cycles per min, and percentage amplitude in the range 60-100%. Order 2 and 3 arterioles had intermediate frequencies, and amplitude in the range 50-100%, and 15-50%, respectively. The largest order 4 vessels (diameter: 28.97 +/- 9.55 microns) had the lowest frequency, 0.3-3 cpm, and amplitude in the range 5-20%. In most networks, cross-correlation analysis revealed two groups of frequency components. Low frequency group was propagated from order 4 and 3 vessels downstream. High frequency components were transmitted upstream from order 1 and 2 arterioles. Therefore, a complex superposition of waveforms resulted from the activity of discrete points along the microvasculature. In conclusion, rhythmic diameter changes of arterioles in skeletal muscle microcirculation regulate blood flow distribution in capillary units and control tissue oxygenation.

Animals↗

PubMed1995

Resolution of batch variations in pyrolysis mass spectrometry of bacteria by the use of artificial neural network analysis.

A simple, but stringent, three group model of bacterial interstrain identity (two cultures of the same strain of Escherichia coli) and difference (a culture of a serologically distinct strain) was used in multiple serial weekly subcultures for five weeks to demonstrate the effect of both growth-related (phenotypic) and machine-related variation on pyrolysis mass spectra. An aliquot of serum from a single sample was included in each pyrolysis batch to distinguish machine drift from culture drift. Conventional principal component (PC) canonical variate (CV) analysis was successful within each pyrolysis batch but the variations between batches precluded the use of data from more than one batch in successful PCCV analysis. In contrast, artificial neural networks (ANNs) trained with data from one batch could be successfully used to identify groups in data from non-contemporaneous pyrolysis batches. Although the ANN method will require validation in more complex settings than this simple model, it is a promising approach to the problem of batch constraint in pyrolysis mass spectrometry.

Bacterial Typing Techniques↗

PubMed2026

Beyond one-to-one mappings: Modelling distributed lesion-symptom relationships with multilayer networks.

Lesion-symptom mapping is widely used to identify causal relationships between brain structures and behaviour, and has played a central role in neuropsychologically informed network models of cognition. However, even recent approaches remain constrained by a one-to-one mapping framework, which oversimplifies the complex relationships between network-level damage and cognitive deficits. In addition, the non-orthogonality of cortical and white matter damage makes it difficult to disentangle their distinct contributions. Here, we used graph-based multilayer network analysis to address these limitations and evaluate clinical relevance. Using neuroanatomical and longitudinal neuropsychological data from 252 patients who underwent awake neurosurgery for low-grade glioma, we constructed interactive, three-layer networks for each hemisphere. Layer 1 comprised neuropsychological tasks (NT), layer 2 structural disconnections (SD), and layer 3 cortical damage (CD). Nodes represented tasks, white matter tracts, and cortical parcels, respectively, whereas within-layer edges captured correlations in performance or co-occurring damage patterns. Multilayer community detection identified domain- and hemisphere-specific brain-behaviour motifs linking executive, language, and spatial functions to distinct combinations of cortical and white matter disruption, a pattern confirmed by two spatial embedding approaches. Centrality analyses revealed a continuum of mapping relationships, ranging from one-to-one to one-to-many associations, indicating that tasks such as verbal fluency are better explained by multiple disconnection mechanisms. Additional analyses uncovered many-to-one and many-to-many relationships and highlighted tracts and cortical regions with domain-general relevance. Together, these findings support a neurobiologically grounded, network-oriented account of how structural brain damage gives rise to cognitive deficits, with implications for clinical care.

Humans↗