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PubMed1996

Singular perturbation analysis of competitive neural networks with different time scales.

The dynamics of complex neural networks must include the aspects of long- and short-term memory. The behavior of the network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. The main idea of this paper is to apply a stability analysis method of fixed points of the combined activity and weight dynamics for a special class of competitive neural networks. We present a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables as a global stability method and a modality of detecting the local stability behavior around individual equilibrium points.

Algorithms↗
PubMed1997

Application of neural networks to population pharmacokinetic data analysis.

This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient-related demographic variables (age, weight, gender, and other underlying illness), the individual's dosing regimens (dose and dosing interval), time of blood drawn, and the resulting tobramycin concentration. Neural networks were trained with this information to capture the relationships between the plasma tobramycin levels and the following factors: patient-related demographic factors, dosing regimens, and time of blood drawn. The data were also analyzed using a standard population pharmacokinetic modeling program, NON-MEM. The observed vs predicted concentration relationships obtained from the neural network approach were similar to those from NONMEM. The residuals of the predictions from neural network analyses showed a positive correlation with that from NONMEM. Average absolute errors were 33.9 and 37.3% for neural networks and 39.9% for NONMEM. Average prediction errors were found to be 2.59 and -5.01% for neural networks and 17.7% for NONMEM. We concluded that neural networks were capable of capturing the relationships between plasma drug levels and patient-related prognostic factors from routinely collected sparse within-patient pharmacokinetic data. Neural networks can therefore be considered to have potential to become a useful analytical tool for population pharmacokinetic data analysis.

Anti-Bacterial Agents↗
PubMed2006

Functional holography analysis: simplifying the complexity of dynamical networks.

We present a novel functional holography (FH) analysis devised to study the dynamics of task-performing dynamical networks. The latter term refers to networks composed of dynamical systems or elements, like gene networks or neural networks. The new approach is based on the realization that task-performing networks follow some underlying principles that are reflected in their activity. Therefore, the analysis is designed to decipher the existence of simple causal motives that are expected to be embedded in the observed complex activity of the networks under study. First we evaluate the matrix of similarities (correlations) between the activities of the network's components. We then perform collective normalization of the similarities (or affinity transformation) to construct a matrix of functional correlations. Using dimension reduction algorithms on the affinity matrix, the matrix is projected onto a principal three-dimensional space of the leading eigenvectors computed by the algorithm. To retrieve back information that is lost in the dimension reduction, we connect the nodes by colored lines that represent the level of the similarities to construct a holographic network in the principal space. Next we calculate the activity propagation in the network (temporal ordering) using different methods like temporal center of mass and cross correlations. The causal information is superimposed on the holographic network by coloring the nodes locations according to the temporal ordering of their activities. First, we illustrate the analysis for simple, artificially constructed examples. Then we demonstrate that by applying the FH analysis to modeled and real neural networks as well as recorded brain activity, hidden causal manifolds with simple yet characteristic geometrical and topological features are deciphered in the complex activity. The term "functional holography" is used to indicate that the goal of the analysis is to extract the maximum amount of functional information about the dynamical network as a whole unit.

Algorithms↗
PubMed1992

CT image analysis of the vertebral trabecular network in vivo.

A method of computed tomography (CT) image analysis of lumbar vertebrae has been developed, providing a visualization of the trabecular network as it is represented in a 1.5 mm-thick CT image. We measured the length of the network and the number of discontinuities found in the image. The ratio of these measurements was called the "trabecular fragmentation index" (TFI). CT images from 71 women between the ages of 50 and 59, and 94 women between the ages of 60 and 69 were divided into three groups according to quantitative computed tomography (QCT) vertebral density and to the presence or absence of crushing and fractures. The measure of the network length versus the vertebral area was significantly higher in normal subjects than in osteoporotics. A TFI threshold at 0.195 could separate the normal subjects, regardless of the decade, from osteoporotic ones. In females between 50 and 69 years of age, TFI was 0.166 (SD = 0.031) for the normal group and 0.248 (SD = 0.082) for osteoporotics. The osteopenic group without fractures but low bone mineral density (BMD) showed an intermediate TFI of 0.195 (SD = 0.05), placing this population on both sides of the threshold. Correlation between TFI and BMD was only -0.60. TFI could provide new information in vivo about the state of trabecular structure, particularly in the osteopenic group.

Aged↗
PubMed2001

Communication and cooperation in networked environments: an experimental analysis.

Interpersonal communication and cooperation do not happen exclusively face to face. In work contexts, as in private life, there are more and more situations of mediated communication and cooperation in which new online tools are used. However, understanding how to use the Internet to support collaborative interaction presents a substantial challenge for the designers and users of this emerging technology. First, collaborative Internet environments are designed to serve a purpose, so must be designed with intended users' tasks and goals explicitly considered. Second, in cooperative activities the key content of communication is the interpretation of the situations in which actors are involved. So, the most effective way of clarifying the meaning of messages is to connect them to a shared context of meaning. However, this is more difficult in the Internet than in other computer-based activities. This paper tries to understand the characteristics of cooperative activities in networked environments--shared 3D virtual worlds--through two different studies. The first used the analysis of conversations to explore the characteristics of the interaction during the cooperative task; the second analyzed whether and how the level of immersion in the networked environments influenced the performance and the interactional process. The results are analyzed to identify the psychosocial roots used to support cooperation in a digital interactive communication.

Adult↗
PubMed2004

Performance analysis of an iSCSI-based unified storage network.

In this paper, we introduced a novel storage architecture "Unified Storage Network", which merges NAC(Network Attached Channel) and SAN(Storage Area Network), and provides the file I/O services as NAS devices and provides the block I/O services as SAN. To overcome the drawbacks from FC, we employ iSCSI to implement the USN(Unified Storage Network). To evaluate whether iSCSI is more suitable for implementing the USN, we analyze iSCSI protocol and compare it with FC protocol from several components of a network protocol which impact the performance of the network. From the analysis and comparison, we can conclude that the iSCSI is more suitable for implementing the storage network than the FC under condition of the wide-area network. At last, we designed two groups of experiments carefully.

Computer Communication Networks↗
PubMed2002

Protein-interaction networks: from experiments to analysis.

Functional proteomics approaches aim to characterize comprehensively the function of gene products, and provide a first-level understanding of cellular mechanisms. Here, we review recent techniques for the construction and prediction of large-scale protein-interaction networks, with a particular emphasis on computational processing steps and comparative assessment of the reliability and completeness of the various approaches. We also discuss the use of protein-interaction network information in functional annotation and in the generation of higher-level biological hypotheses on pathways.

Computational Biology↗
PubMed2000

A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks.

A set of linear pathways often does not capture the full range of behaviors of a metabolic network. The concept of 'elementary flux modes' provides a mathematical tool to define and comprehensively describe all metabolic routes that are both stoichiometrically and thermodynamically feasible for a group of enzymes. We have used this concept to analyze the interplay between the pentose phosphate pathway (PPP) and glycolysis. The set of elementary modes for this system involves conventional glycolysis, a futile cycle, all the modes of PPP function described in biochemistry textbooks, and additional modes that are a priori equally entitled to pathway status. Applications include maximizing product yield in amino acid and antibiotic synthesis, reconstruction and consistency checks of metabolism from genome data, analysis of enzyme deficiencies, and drug target identification in metabolic networks.

Algorithms↗
PubMed2003

Analysis of the United Network for Organ Sharing database comparing renal allografts and patient survival in combined liver-kidney transplantation with the contralateral allografts in kidney alone or kidney-pancreas transplantation.

BACKGROUND: Combined liver-kidney transplantation (LKT) is the accepted treatment for patients with liver failure and irreversible renal insufficiency. Controversy exists as to whether simultaneous LKT with organs from the same donor confers immunologic and graft survival benefit to the kidney allograft. This study compares the outcomes of simultaneous LKT with the contralateral kidneys used for kidney alone transplantation (KAT) or combined pancreas-kidney transplantation (PKT) to understand the factors that account for the differences in survival. METHODS: From October 1987 to October 2001, LKTs with organs from 899 cadaver donors were reported to the United Network for Organ Sharing; 800 contralateral kidneys from these donors were used in 628 KAT and 172 PKT recipients. These 800 paired control patients were the basis of this analysis. RESULTS: Graft and patient survival rates were lower among LKT recipients compared with KAT (P<0.001) and PKT recipients (P<0.001), because of a higher patient mortality rate during the first 3 months posttransplant. Among human leukocyte antigen-mismatched transplants, LKT recipients demonstrated the highest 1-year rejection-free survival rate (LKT 70%, KAT 61%, and PKT 57% ) (P=0.005 vs. KAT, P=0.005 vs. PKT). There was a lower incidence of renal graft loss resulting from chronic rejection among LKT recipients (LKT 2% vs. KAT 8% vs. PKT 6%, P<0.0001). CONCLUSIONS: Patients undergoing LKT exhibit a higher rate of mortality during the first year posttransplant compared with patients undergoing KAT and KPT. Analysis of the data indicates an allograft-enhancing effect of liver transplantation on the renal allograft.

Adult↗
PubMed2003

A genome-wide affected sibpair linkage analysis of hypertension: the HyperGEN network.

Results are reported here from a genome-wide linkage analysis of hypertension in a large sample of hypertensive (affected) sibpairs (650 African American and 915 white sibpairs) recruited by the HyperGEN Network of the National Heart, Lung and Blood Institute (NHLBI) Family Blood Pressure Program (FBPP). Analysis using MAPMAKER/SIBS suggests one interesting region with a LOD score of 2.08 at 63 cM from the p telomere on chromosome 2 in the African American sibpairs, which may harbor hypertension susceptibility genes.

Black or African American↗
PubMed1990

Neural networks applied to quantitative structure-activity relationship analysis.

An application of the neural network to quantitative structure-activity relationship (QSAR) analysis has been studied. The new method was compared with the linear multiregression analysis in various ways. It was found that the neural network can be a potential tool in the routine work of QSAR analysis. The mathematical relationship of operation between the neural network and the multiregression analysis was described. It was shown that the neural network can exceed the level of the linear multiregression analysis.

Animals↗
PubMed2006

A closed queueing network approach to the analysis of patient flow in health care systems.

OBJECTIVES: To model patient flow in health care systems with bed capacity constraints in order to provide a useful decision aid for health service managers. METHODS: We model the patient flow of health care systems using a closed queueing network framework with the assumption that the system is always full. Key performance measures of the health care system are also derived. RESULTS: Using parameters taken from a study of a geriatric department in the UK, we show that the model is useful in helping service managers to gain better understanding of the behaviour of the system. In addition, we demonstrate that the model could help improving decision-making by allowing managers to explore different options and evaluate their impacts on performance. Our findings highlight the importance of policy makers taking into account the interactions between different phases of care. CONCLUSIONS: We have developed a novel approach to modelling the flow of patients through health care systems with constrained bed capacity.

Efficiency, Organizational↗
PubMed1999

Image analysis of the distal radius trabecular network using computed tomography.

Bone texture analysis might provide information about bone structure in a noninvasive manner. In a prospective case-control cross-sectional study we investigated the value of computed tomography (CT) image analysis of the distal radius in the assessment of osteoporosis. Twenty patients suffering from postmenopausal osteoporosis were studied and compared with 21 age-matched controls. Eight slices were selected in each patient: four consecutive coronal slices and four consecutive cross-sectional slices. Bone texture analysis was performed using statistical, fractal and structural methods leading to the measurement of 32 features. Structural variables derived from histomorphometric parameters were measured after segmentation from a binary or a skeletonized image. Bone mineral density was measured by dual-energy X-ray absorptiometry both at the lumbar spine and the femoral neck. Eight of the 9 statistical features were significantly different in osteoporotic women as compared with controls (coronal slices, p < 0. 05). Seven structural variables were statistically different between the two groups on coronal slices (p < 0.05): valley surface area, bone volume/tissue volume, trabecular partition, Euler's number, trabecular bone pattern factor, node-to-node strut count and terminus-to-terminus strut count. The most significant results on coronal slices (p < 0.01) concerned 4 structural features: trabecular partition, Euler's number, trabecular bone pattern factor and terminus-to-terminus strut count. Three features were statistically different (p < 0.01) between the two groups on cross-sectional slices (skeletonization from gray levels). A few features yielded by texture analysis were correlated with both lumbar spine and femoral neck bone mineral density, but the level of these correlations was weak (r < 0.5). In conclusion, CT image analysis of the distal radius is a useful tool for characterizing bone texture alterations in osteoporotic women. These findings are in keeping with microarchitectural osteoporosis-related changes diagnosed on bone biopsies.

Aged↗
PubMed1996

Artificial neural networks: a prospective tool for the analysis of psychiatric disorders.

Artificial neural networks are computer simulations of biological parallel distributed processing systems. They are able to undertake complex pattern recognition tasks, including diagnostic classification, prediction of disease onset and prognosis, and identification of determinants of clinical decisions. These capabilities have been utilized in general medicine, but as yet there has been little application of artificial neural networks in psychiatric research. Artificial neural networks can also be used to create models of brain function, providing a paradigm for cognition and the organization of neural systems that demonstrates how changes at the cellular level can affect information processing. These models are able to encompass both the biological and the behavioral dimensions of psychiatric disorders.

Humans↗
PubMed1995

Analysis of tRNA gene sequences by neural network.

The quantitative similarity among tRNA gene sequences was acquired by analysis with an artificial neural network. The evolutionary relationship derived from our results was consistent with those from other methods. A new sequence was recognized to be a tRNA-like gene by a neural network on the analysis of similarity. All of our results showed the efficiency of the artificial neural network method in the sequence analysis for biological molecules.

Animals↗
PubMed1992

Potential language and attentional networks revealed through factor analysis of rCBF data measured with SPECT.

We used changes in regional cerebral blood flow (rCBF) to disclose regions involved in central auditory and language processing in the normal brain. rCBF was quantified with a fast-rotating, single-photon emission computerized tomograph (SPECT) and inhalation of 133Xe. rCBF data were obtained simultaneously from parallel, transverse slices of the brain. The lower slice was positioned to include both Broca's and Wernicke's areas. The upper slice included regions generally regarded by neurobehaviorists as less related to primary auditory or linguistic functions. We presented three types of auditory stimuli to ten healthy, young volunteers: (a) diotically presented Danish speech, (b) dichotic word stimulation, and (c) white noise. Wilcoxon's signed ranks sum test revealed increased rCBF in language-related areas of cortex, viz., Wernicke's area and its right-sided homologous area as well as in Broca's area (left hemisphere), when subjects listened to narrative speech, compared to white noise (baseline). No significant rCBF differences were detected with this test during dichotic stimulation vs. white noise. A more sophisticated statistical method (factor analysis) disclosed patterns of functionally intercorrelated regions. The factor analysis reduced the highly intercorrelated rCBF measures from 28 regions of interest to a set of three independent factors. These factors accounted for 77% of the total variation in rCBF values. These three factors appeared to represent statistical analogues of independent brain networks involved in (I) auditory/linguistic, (II) attentional, and (III) visual imaging activity.

Acoustic Stimulation↗
PubMed2003

A multitrial analysis for revealing significant corticocortical networks in magnetoencephalography and electroencephalography.

We present an MEG/EEG framework to reveal statistically significant brain areas engaged in the same cognitive process across trials without resort to averaging procedures. The variability of neuronal responses is assumed to take place only in the reconstructed time series of cortical sources and not in their positions. This hypothesis allows the use of the surrogate data method to detect recurrently active brain areas across trials adjusted with any cortically constrained focal MEEG inverse solution. Results obtained from synthetic data show that considering several trials enhances the accuracy of the source localisation. We apply this approach on MEG data recorded during a simple visual stimulation. The considered stimulus is frequency tagged in order to reveal the neural network correlated to its perception using phase synchronisation analysis. The results show consistent patterns of distributed synchronous networks centred on occipital areas.

Adult↗