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PubMed2004

An application of a genetic algorithm in conjunction with other data mining methods for estimating outcome after hospitalization in cancer patients.

BACKGROUND: We investigated which factors predicted the risk of in-hospital mortality in a general population of cancer patients with non-terminal disease and whether employing the genetic algorithm technique would be useful in this regard. MATERIAL/METHODS: A total of 201 cancer patients, including all cases of in-hospital mortality over a 2-year period, as well as a control group of subjects discharged during the same period, all having an Eastern Cooperative Oncology Group (ECOG) performance status of of < or =3 at the time of admission, were retrospectively evaluated. Indicators of in-hospital mortality were determined by multivariate logistic regression, recursive partitioning analysis, neural network, and genetic algorithm (GA) techniques. The performance of the different techniques were compared by a number of measures, including receiver operating curve (ROC) analysis. RESULTS: All four analysis methods selected a combination of six explanatory variables to explain the risk of in-hospital mortality: lactate dehydrogenase (LDH), alanine transaminase (ALT), hemoglobin (Hb), white blood cell counts (Wbc), type of cancer, and reason for admission. Compared with the other 3 methods, GA selected the least number of explanatory variables, i.e. LDH and reason for admission, with similar fraction of cases explained (78.6%), and yielded a fitness score of 0.52. CONCLUSIONS: LDH is an important indicator of in-hospital mortality for hospitalized cancer patients not in terminal stage. GA reliably predicted in-hospital mortality and was shown to be as efficient as the other data mining techniques employed in this study. Its use in a clinical setting for prognostication in oncology appears promising.

Adolescent↗
PubMed2006

Towards industrial application of quasi real-time metabolic flux analysis for mammalian cell culture.

Cellular physiology and metabolism were monitored using a quasi real-time combination of on-line and off-line data to estimate metabolic fluxes in an established bioreaction network. The utility of this approach towards optimizing bioreactor operation was demonstrated for CHO cells cultivated in 15 L perfusion reactors at 20 x 10(6) cells/mL. Medium composition and dilution rates were changed to obtain several steady states with varying glucose and glutamine concentrations. When cells were restored to initial culture medium and perfusion rate conditions after being exposed to lower glucose and glutamine concentrations, the pyruvate flux into the TCA cycle was increased 30% while the pyruvate flux through lactate was decreased 30%, suggesting steady-state multiplicity. By appropriately altering cellular metabolism, perfusion bioreactors can operate at lower perfusion rates without significant accumulation of inhibitory metabolites such as lactate. Changes in glucose, lactate and glutamine uptake/production rates had significant effects on the calculation of other fluxes in the network. Sensitivity analysis of these key metabolic fluxes highlighted the need for accurate and reliable real-time sensors. Overall, rapid observation of metabolic fluxes can be a valuable tool for bioprocess development, monitoring and control. The framework presented in this study offers a convenient means for quasi real-time estimation of metabolic fluxes and represents a step towards realizing the potential of metabolic flux analysis for accelerated bioprocess optimization.

Animals↗
PubMed2007

BOLD coherence reveals segregated functional neural interactions when adapting to distinct torque perturbations.

In the natural world, we experience and adapt to multiple extrinsic perturbations. This poses a challenge to neural circuits in discriminating between different context-appropriate responses. Using event-related fMRI, we characterized the neural dynamics involved in this process by randomly delivering a position- or velocity-dependent torque perturbation to subjects' arms during a target-capture task. Each perturbation was color-cued during movement preparation to provide contextual information. Although trajectories differed between perturbations, subjects significantly reduced error under both conditions. This was paralleled by reduced BOLD signal in the right dentate nucleus, the left sensorimotor cortex, and the left intraparietal sulcus. Trials included "NoGo" conditions to dissociate activity related to preparation from execution and adaptation. Subsequent analysis identified perturbation-specific neural processes underlying preparation ("NoGo") and adaptation ("Go") early and late into learning. Between-perturbation comparisons of BOLD magnitude revealed negligible differences for both preparation and adaptation trials. However, a network-level analysis of BOLD coherence revealed that by late learning, response preparation ("NoGo") was attributed to a relative focusing of coherence within cortical and basal ganglia networks in both perturbation conditions, demonstrating a common network interaction for establishing arbitrary visuomotor associations. Conversely, late-learning adaptation ("Go") was attributed to a focusing of BOLD coherence between a cortical-basal ganglia network in the viscous condition and between a cortical-cerebellar network in the positional condition. Our findings demonstrate that trial-to-trial acquisition of two distinct adaptive responses is attributed not to anatomically segregated regions, but to differential functional interactions within common sensorimotor circuits.

Adaptation, Physiological↗
PubMed2026

Continuous theta-burst stimulation over the right DLPFC modulates central executive network connectivity in depression: exploratory analysis of a randomized clinical trial.

Previous studies suggest that transcranial magnetic stimulation exerts antidepressant effects and is associated with alterations in functional connectivity (FC), but the neural correlates remain unclear. This exploratory sham-controlled trial investigated the effect of continuous theta-burst stimulation (cTBS) over the right dorsolateral prefrontal cortex (DLPFC) on FC in major depressive disorder (MDD). Seventy MDD patients were randomized to receive two-week treatment of personalized cTBS or sham stimulation. Resting-state fMRI was performed at baseline and post-treatment. Ultimately, 31 patients in the active cTBS group and 28 patients in the sham group passed imaging quality control and were included in the final analysis. To identify the FC that may have been influenced by cTBS treatment, two complementary FC analyses were conducted: (1) voxel-wise degree centrality (DC) followed by seed-based FC, and (2) an individual FC analysis based on the stimulation targets. Furthermore, correlations between FC changes and clinical symptoms improvement were examined. Both groups exhibited reductions of depression scores, with greater improvement in the active group. Compared to the sham group, active cTBS showed increased DC in the precuneus and elevated FC between the precuneus (within the para-cingulate network) and the right inferior parietal lobule (IPL) and DLPFC. Further stimulation target-based analysis revealed increased FC between stimulation targets and both the precuneus and visual regions following treatment. Our findings reveal neural changes associated with cTBS over the right DLPFC in MDD, notably involving the precuneus and its connectivity with the right IPL/DLPFC, suggesting alterations within the central executive network. TRIAL REGISTRATION: chictr.org.cn; ChiCTR2300068273.

Humans↗
PubMed2004

A neural network approach to movement pattern analysis.

Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, where the sequence of clusters to which the time-depending configurations belong, characterizes the process as a 2-dimensional trajectory. The advantages of this method are that: the high dimensionality of the original processes is reduced to two dimensional trajectories, the clusters are automatically determined by the network, and all data for further analyses can automatically be transferred into a data base. Thus, the processes can either be visualized and analysed by an expert or again processed by further automatic analysing tools, as has been done with similarity matrices. The disadvantage is that a KFM-training needs a huge amount of information, which normally is not available from experiments. However, the Dynamically Controlled Network DyCoN (a special type of KFM) makes it possible to reduce the amount of original training data substantially--e.g., by adding stochastically generated ones. Currently, DyCoN is used in several projects in order to generally support analyses of processes in sport. It should be emphasized that the presented approach is not meant to improve the understanding or to develop models of human movement but to give a survey of the advantages and methodological aspects of net-based movement analysis.

Algorithms↗
PubMed1999

Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks.

PURPOSE: Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data. METHODS: Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.6 to 3.4 parts per million. In the first network construction, the ANN had to differentiate between tumours from infections, while the second network had to differentiate between all five histological classes. RESULTS: ANN analysis gave a histologically correct diagnosis for low- and high-grade gliomas with an accuracy of 73% and 98% respectively. None of the 62 tumours was diagnosed as an infectious lesion. Among the non-neoplastic lesions, ANN classification was correct in 89% of tuberculomas and in 83% of brain abscesses. The specificity of ANN diagnosis was 98%, 92%, 99%, and 100% for low-grade gliomas, high-grade gliomas, tuberculomas and abscesses respectively. CONCLUSION: The present data show the clinical utility of non-invasive 1H MRS by automated ANN analysis in the diagnosis of tumour and non-tumour cerebral disorders.

Adolescent↗
PubMed2005

Cost-minimization analysis of a wide-area teleradiology network in a French region.

OBJECTIVE: The objective of our study was to perform a cost-minimization analysis of a wide-area teleradiology network. DESIGN: A prospective analysis of all transmissions over 1 year (data transmitted at the time of the remote consultation, and health outcomes of patients from medical record). INTERVENTION: The inter-hospital teleradiology network of the Aquitaine area (RIHRA) is a telemedicine system enabling the management of remote emergencies and elective radiology consultations. MAIN OUTCOME MEASURE: A cost-minimization study enabled a comparison of care procedures following the use of the network with those which would have been implemented without the network. The outcome measures of effectiveness were the transfers, hospitalizations, and consultations avoided or added. Fixed and variable costs were estimated. RESULTS: Among the 664 transmissions included in the study, 562 (85%) were performed in emergency and 102 (15%) for elective (non-emergency) cases. In emergency, 48% of transfers were avoided. For elective teleconsultations, a transfer was avoided for 37% of the patients and hospitalization for 12%. An extra consultation occurred after remote consultation for 2% of the patients. Annual saving can be estimated at 102,779 EUR for the Aquitaine area. CONCLUSIONS: This study underlines the efficiency of an inter-hospital teleradiology network. A qualitative evaluation of the impact of the use of the system should be carried out to improve technical and organizational operations.

Cost Control↗
PubMed1984

Social networks and psychosocial support among disabled people.

Analysis of the network characteristics and nature of social ties among physically disabled people living in an inner London borough showed network size rather than network type was related to the availability of psychosocial support, reflecting the important role of both related and non-related people in the provision of this form of support. The study also questioned three commonly held assumptions. Firstly, in contrast to the image of physically disabled people as lacking social ties, those with a high level of disability, although having a lower level of social contact outside the household than other groups, did not differ significantly in other aspects of their network structures and support. Secondly, in contrast to the characterisation of inner city areas as relatively homogeneous and as lacking locally based ties, the inner London area studied comprised a variety of network types with a large proportion of respondents deriving support from people living in close proximity. Thirdly, whereas the presence of household members, and especially a spouse, tend to be equated with the availability of strong emotional support, considerable numbers of married people lacked such support. This suggests measures to promote psychosocial support need to be fairly broadly based and cautions against using marital status as a proxy measure of support.

Adolescent↗
PubMed2026

Partial enumeration of extreme rays in metabolic networks using bit pattern trees.

Extreme ray analysis of metabolic networks, even though very powerful, is currently limited to smaller metabolic networks. Some approaches to generating partial sets of extreme rays exist, but the computational efficiency of the so-called double-description method is yet to be exploited. Previous work highlighted the possibility of sampling within its iterations, enabling partial enumeration for double-description based methods. However, these approaches severely lack computational efficiency to be a suitable alternative. In this work, the highly efficient bit pattern trees are used within the sampling framework to significantly enhance its output and speed. Combined with the recent revision of the Canonical Basis Approach (CBA), our approach outperforms the other tested methods under the reported benchmark conditions even for a full enumeration study, requiring only half the computation time. In addition, a filter setting allows the memory demand to be scaled down while retaining high efficiency. However, some issues with the combinatorial explosion of candidates still persist and are further investigated. This study therefore puts forward a novel, double description-based alternative to partial enumeration of extreme rays. Further improvements in memory efficiency would allow this promising approach to scale powerful extreme ray-based analyses to genome-scale metabolic networks.

constraint-based modelling↗
PubMedDate not supplied

Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices.

A new method, ALOGPS v 2.0 (http://www.lnh.unil.ch/~itetko/logp/), for the assessment of n-octanol/water partition coefficient, log P, was developed on the basis of neural network ensemble analysis of 12 908 organic compounds available from PHYSPROP database of Syracuse Research Corporation. The atom and bond-type E-state indices as well as the number of hydrogen and non-hydrogen atoms were used to represent the molecular structures. A preliminary selection of indices was performed by multiple linear regression analysis, and 75 input parameters were chosen. Some of the parameters combined several atom-type or bond-type indices with similar physicochemical properties. The neural network ensemble training was performed by efficient partition algorithm developed by the authors. The ensemble contained 50 neural networks, and each neural network had 10 neurons in one hidden layer. The prediction ability of the developed approach was estimated using both leave-one-out (LOO) technique and training/test protocol. In case of interseries predictions, i.e., when molecules in the test and in the training subsets were selected by chance from the same set of compounds, both approaches provided similar results. ALOGPS performance was significantly better than the results obtained by other tested methods. For a subset of 12 777 molecules the LOO results, namely correlation coefficient r(2)= 0.95, root mean squared error, RMSE = 0.39, and an absolute mean error, MAE = 0.29, were calculated. For two cross-series predictions, i.e., when molecules in the training and in the test sets belong to different series of compounds, all analyzed methods performed less efficiently. The decrease in the performance could be explained by a different diversity of molecules in the training and in the test sets. However, even for such difficult cases the ALOGPS method provided better prediction ability than the other tested methods. We have shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data. A comparative performance of the methods as well as a dependence on the number of non-hydrogen atoms in a molecule is also presented.

Journal Article↗
PubMed1997

[Temporary medicosocial admission: alternative to hospitalization of elderly persons?].

The orientation of elderly patients, temporarily disabled, to acute care beds is inappropriate because of its adverse effects on functional status and its costs. The creation of short-stay units (SSU) in nursing homes provides an alternative to acute care hospitalization. The aim of this retrospective study, involving the first 64 patients oriented to the SSU from the emergency center, was to evaluate this new health care network. The analysis was focused on the rate of appropriate orientation (site of living at four month; subsequent medical events), as well as the functional quality of this health care network. Information were collected from medical records of the 64 patients oriented to SSU and 64 sex- and age-matched patients admitted during the same period, and the opinion of the network's partners. The mean age of patients and controls was 82 years. Four months after admission to the SSU, the orientation was considered appropriate in 58% of the cases (living at home without subsequent hospitalization), doubtful in 8%, and inappropriate in 33%; 27% of patients and 13% of controls were living definitely in nursing homes (p < 0.1). No medical or social characteristics was correlated to inappropriate orientations. In conclusion, the creation of SSU may be considered as an improvement in the care of elderly patients. The main problem of this orientation was the high percentage of patients living permanently in a nursing home four months later. Accurate assessment's tools capable to predict the subsequent decrease of the functional status should be used in the daily practice in order to improve the orientation of elderly patients.

Aged↗
PubMed2005

Recursive principal components analysis.

A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by the network are adapted to the temporal statistics of the input. Moreover, sequences stored in the network may be retrieved explicitly, in the reverse order of presentation, thus providing a straight-forward neural implementation of a logical stack.

Humans↗
PubMed2006

Dynamics analysis and analog associative memory of networks with LT neurons.

The additive recurrent network structure of linear threshold neurons represents a class of biologically-motivated models, where nonsaturating transfer functions are necessary for representing neuronal activities, such as that of cortical neurons. This paper extends the existing results of dynamics analysis of such linear threshold networks by establishing new and milder conditions for boundedness and asymptotical stability, while allowing for multistability. As a condition for asymptotical stability, it is found that boundedness does not require a deterministic matrix to be symmetric or possess positive off-diagonal entries. The conditions put forward an explicit way to design and analyze such networks. Based on the established theory, an alternate approach to study such networks is through permitted and forbidden sets. An application of the linear threshold (LT) network is analog associative memory, for which a simple design method describing the associative memory is suggested in this paper. The proposed design method is similar to a generalized Hebbian approach, but with distinctions of additional network parameters for normalization, excitation and inhibition, both on a global and local scale. The computational abilities of the network are dependent on its nonlinear dynamics, which in turn is reliant upon the sparsity of the memory vectors.

Algorithms↗
PubMed2006

The role of PI3K/AKT, MAPK/ERK and NFkappabeta signalling in the maintenance of human embryonic stem cell pluripotency and viability highlighted by transcriptional profiling and functional analysis.

Understanding the molecular mechanism by which pluripotency is maintained in human embryonic stem cells (hESC) is important for the development of improved methods to derive, culture and differentiate these into cells of potential therapeutic use. Large-scale transcriptional comparison of the hES-NCL1 line derived from a day 8 embryo with H1 line derived from a day 5 embryo (WiCell Inc.) showed that only 0.52% of the transcripts analysed varied significantly between the two cell lines. This is within the variability range that has been reported when hESC derived from days 5-6 embryos have been compared with each other. This implies that transcriptional differences between the cell lines are likely to reflect their genetic profile rather than the embryonic stage from which they were derived. Bioinformatic analysis of expression changes observed when these cells were induced to differentiate as embryoid bodies suggested that quite a few of the downregulated genes were components of signal transduction networks. Subsequent analysis using western blotting, flow cytometry and antibody arrays implicated components of the PI3K/AKT kinase, MAPK/ERK and NFkappabeta pathways and confirmed that these components are decreased upon differentiation. Disruption of these pathways in isolation using specific inhibitors resulted in loss of pluripotency and/or loss of viability suggesting the importance of such signalling pathways in embryonic stem cell maintenance.

Cell Survival↗
PubMed2002

New computational approaches for analysis of cis-regulatory networks.

The investigation and modeling of gene regulatory networks requires computational tools specific to the task. We present several locally developed software tools that have been used in support of our ongoing research into the embryogenesis of the sea urchin. These tools are especially well suited to iterative refinement of models through experimental and computational investigation. They include: BioArray, a macroarray spot processing program; SUGAR, a system to display and correlate large-BAC sequence analyses; SeqComp and FamilyRelations, programs for comparative sequence analysis; and NetBuilder, an environment for creating and analyzing models of gene networks. We also present an overview of the process used to build our model of the Strongylocentrotus purpuratus endomesoderm gene network. Several of the tools discussed in this paper are still in active development and some are available as open source.

Chromosomes, Artificial, Bacterial↗
PubMed2003

Release dynamics of tetracycline from a loaded semi-interpenetrating polymeric material of polyvinyl alcohol and poly(acrylamide-co-styrene).

A semi-interpenetrating polymer network (IPN) of polyvinyl alcohol (PVA) and poly(acrylamide-co-styrene) (PAMS) was prepared and its potential for controlled release of tetracycline was assessed. The IPN was characterized by IR spectral analysis and network parameters such as the average molecular weight between crosslinks (M(c)), crosslink density (q) and number of elastically effective chains (V(e)) were evaluated. The influence of various experimental conditions such as different percent loadings, composition of the IPNs, thickness of the loaded device, pH and nature of the release medium were investigated on the release profiles of the drug. Various kinetic constants such as the diffusional exponent (n), diffusion constant (D) and penetration velocity (v) were evaluated for different release processes and based on the dynamic release data, an analysis of transport mechanisms of tetracycline was made using Fick's equations.

Journal Article↗
PubMed1992

Desmosome assembly in MDCK epithelial cells does not require the presence of functional microtubules.

Desmosomes, complex multisubunit structures that assemble at sites of cell-cell contact, are important components of the epithelial junctional complex. Desmosome assembly requires the coordinated interaction at the plasma membrane of at least 8 cytoplasmic and integral membrane proteins organized into two structurally and functionally distinct domains, the cytoplasmic plaque and membrane core. Previous studies (Pasdar et al., J. Cell Biol., 113:645-655) provided evidence that cytokeratin filaments and microtubules may regulate transfer and assembly of cytoplasmic plaque and membrane core proteins, respectively. To determine directly the role of microtubules in these processes, Madin-Darby canine kidney (MDCK) cells were treated with nocodazole or colchicine to disrupt the microtubular network. Biochemical analysis of the different components of the cytoplasmic plaque and membrane core domains revealed little or no effect of nocodazole or colchicine on the kinetics of synthesis, post-translational modifications, transfer of proteins to the plasma membrane or their metabolic stability in the presence or absence of cell-cell contact. Likewise, immunofluorescence analysis of desmosome formation demonstrated an apparently normal desmosome assembly in the presence of nocodazole or colchicine upon induction of cell-cell contact. These results indicate that an intact microtubular network is not necessary for the processing or transport of the desmosomal membrane core glycoproteins to the plasma membrane in the absence or presence of cell-cell contact. Furthermore, the integration of the cytoplasmic plaque and membrane core domains induced by cell-cell contact at the plasma membranes of adjacent cells does not require the presence of functional microtubules.

Animals↗
PubMed2006

Using the topology of metabolic networks to predict viability of mutant strains.

Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly disputed hypothesis. Although structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g., flux balance analysis, are more successful in predicting phenotypes of knockout strains. We reconcile these seemingly conflicting results by showing that the topology of the metabolic networks of both Escherichia coli and Saccharomyces cerevisiae are, in fact, sufficient to predict the viability of knockout strains with accuracy comparable to flux balance analysis on large, unbiased mutant data sets. This surprising result is obtained by introducing a novel topology-based measure of network transport: synthetic accessibility. We also show that other popular topology-based characteristics such as node degree, graph diameter, and node usage (betweenness) fail to predict the viability of E. coli mutant strains. The success of synthetic accessibility demonstrates its ability to capture the essential properties of the metabolic network, such as the branching of chemical reactions and the directed transport of material from inputs to outputs. Our results strongly support a link between the topology and function of biological networks and, in agreement with recent genetic studies, emphasize the minimal role of flux rerouting in providing robustness of mutant strains.

Algorithms↗