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At least 253 records · Page 14Linked to original sources
PubMed2006

Recognition of the physiological actions of the triphasic EMG pattern by a dynamic recurrent neural network.

Triphasic electromyographic (EMG) patterns with a sequence of activity in agonist (AG1), antagonist (ANT) and again in agonist (AG2) muscles are characteristic of ballistic movements. They have been studied in terms of rectangular pulse-width or pulse-height modulation. In order to take into account the complexity of the EMG signal within the bursts, we used a dynamic recurrent neural network (DRNN) for the identification of this pattern in subjects performing fast elbow flexion movements. Biceps and triceps EMGs were fed to all 35 fully-connected hidden units of the DRNN for mapping onto elbow angular acceleration signals. DRNN training was supervised, involving learning rule adaptations of synaptic weights and time constants of each unit. We demonstrated that the DRNN is able to perfectly reproduce the acceleration profile of the ballistic movements. Then we tested the physiological plausibility of all the networks that reached an error level below 0.001 by selectively increasing the amplitude of each burst of the triphasic pattern and evaluating the effects on the simulated accelerating profile. Nineteen percent of these simulations reproduced the physiological action classically attributed to the 3 EMG bursts: AG1 increase showed an increase of the first accelerating pulse, ANT an increase of the braking pulse and AG2 an increase of the clamping pulse. These networks also recognized the physiological function of the time interval between AG1 and ANT, reproducing the linear relationship between time interval and movement amplitude. This task-dynamics recognition has implications for the development of DRNN as diagnostic tools and prosthetic controllers.

Action Potentials↗
PubMed1992

[Mechanism of cytokine pleiotropy and mutual regulation].

Cytokines manifest overlapping effects on many cell types and their production is regulated by a cytokine network. This minireview summarizes the current understanding of how affinity converter molecules, shared by several cytokines, is related to cytokine pleiotropy, and how Th1/Th2 are differentially developed and regulated through a cytokine network, especially, negative loop. Finally, the importance of the temporal expression of cytokines, in relation to the physiological relevance of the network, is pointed out.

Animals↗
PubMed1992

A parallel implementation of the backward error propagation neural network training algorithm: experiments in event identification.

An artificial neural-network-based (ANN) event detection and alarm generation system has been developed to aid clinicians in the identification of critical events commonly occurring in the anesthesia breathing circuit. To detect breathing circuit problems, the system monitored CO2 gas concentration, gas flow, and airway pressure. Various parameters were extracted from each of these input waveforms and fed into an artificial neural network. To develop truly robust ANNs, investigators are required to train their networks on large training data sets, requiring enormous computing power. We implemented a parallel version of the backward error propagation neural network training algorithm in the widely portable parallel programming language C-Linda. A maximum speedup of 4.06 was obtained with six processors. This speedup represents a reduction in total run-time from 6.4 to 1.5 h. By reducing the total run time of the computation through parallelism, we were able to optimize many of the neural network's initial parameters. We conclude that use of the master-worker model of parallel computation is an excellent method for speeding up the backward error propagation neural network training algorithm.

Algorithms↗
PubMed1999

Regulatory networks controlling Candida albicans morphogenesis.

Candida albicans undergoes reversible morphogenetic transitions between budding, pseudohyphal and hyphal growth forms that promote the virulence of this pathogenic fungus. The regulatory networks that control morphogenesis are being elucidated; however, the primary signals that trigger morphogenesis remain obscure, and the physiological outputs of these networks are complex.

Animals↗
PubMed2001

Patient monitoring using personal area networks of wireless intelligent sensors.

A wearable device for monitoring multiple physiological signals (polysomnograph) usually includes multiple wires connecting sensors and the monitoring device. In order to integrate information from intelligent sensors, all devices must be connected to a Personal Area Network (PAN). This system organization is unsuitable for longer and continuous monitoring, particularly during the normal activity. For instance, monitoring of athletes and computer assisted rehabilitation commonly involve unwieldy wires to arms and legs that restrain normal activity. We propose a wireless PAN of intelligent sensors as a system architecture of choice, and present a new design of wireless personal area network with physiological sensors for medical applications. Intelligent wireless sensors perform data acquisition and limited processing. Individual sensors monitor specific physiological signals (such as EEG, ECG, GSR, etc.) and communicate with each other and the personal server. Personal server integrates information from different sensors and communicates with the rest of telemedical system as a standard mobile unit. We present our prototype implementation of Wireless Intelligent SEnsor (WISE) based on a very low power consumption microcontroller and a DSP-based personal server. In future we expect all components of WISE integrated in a single chip for use in a variety of new medical applications and sophisticated human computer interfaces. Existing growth of wireless infrastructure will allow a range of new telemedical applications that will significantly improve the quality of health care.

Computer Communication Networks↗
PubMed2026

Dynamic and non-additive gene regulation shapes maize responses to simultaneous salt and cold stress.

Salt and cold stresses often occur together in nature and severely impact crop productivity, yet their transcriptional regulation remains poorly understood. Here, we conducted a time-series transcriptomic analysis of maize under salt, cold, and their combination at 0, 6, 12, and 24 h. Differential expression analysis revealed dynamic, condition-specific gene responses grouped into eight distinct temporal patterns. Promoter motif analysis of genes within each pattern identified 5-39 significantly enriched motifs, with over 40% lacking known counterparts, suggesting the involvement of previously uncharacterized cis-regulatory elements in stress-responsive transcriptional regulation. By comparing combined stress responses to the sum of single-stress effects, we found that about 74% of DEGs showed non-additive patterns, suggesting that combined stress triggers a distinct transcriptional program. Evolutionary analysis showed that additive DEGs tend to be more recently evolved, subject to weaker purifying selection, and enriched in transposed duplications, contrasting with the stronger constraint observed in non-additive DEGs. WGCNA identified 24 co-expression modules, among which 65 hub DEGs were detected in modules significantly correlated with specific stress conditions. Furthermore, we reconstructed 228, 20, and 200 sequential transcription factor cascades spanning 6 h, 12 h, and 24 h under cold, salt, and combined stress, respectively, with no cascade shared across all three conditions. Together, these results reveal that maize responses to combined salt and cold stress are largely non-additive and temporally dynamic, with distinct evolutionary patterns underlying different response types, offering insights and candidate regulators for enhancing crop stress resilience.

Zea mays↗
PubMedDate not supplied

Real-time assessment of mental workload using psychophysiological measures and artificial neural networks.

The functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty. Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty, and high task difficulty conditions, respectively. The high levels of accuracy suggest that these procedures can be used to provide accurate estimates of operator functional state that can be used to provide adaptive aiding. The relative contribution of each of the 43 psychophysiological features was also determined. Actual or potential applications of this research include test and evaluation and adaptive aiding implementation.

Adult↗
PubMed2003

Constraints-based models: regulation of gene expression reduces the steady-state solution space.

Constraints-based models have been effectively used to analyse, interpret, and predict the function of reconstructed genome-scale metabolic models. The first generation of these models used "hard" non-adjustable constraints associated with network connectivity, irreversibility of metabolic reactions, and maximal flux capacities. These constraints restrict the allowable behaviors of a network to a convex mathematical solution space whose edges are extreme pathways that can be used to characterize the optimal performance of a network under a stated performance criterion. The development of a second generation of constraints-based models by incorporating constraints associated with regulation of gene expression was described in a companion paper published in this journal, using flux-balance analysis to generate time courses of growth and by-product secretion using a skeleton representation of core metabolism. The imposition of these additional restrictions prevents the use of a subset of the extreme pathways that are derived from the "hard" constraints, thus reducing the solution space and restricting allowable network functions. Here, we examine the reduction of the solution space due to regulatory constraints using extreme pathway analysis. The imposition of environmental conditions and regulatory mechanisms sharply reduces the number of active extreme pathways. This approach is demonstrated for the skeleton system mentioned above, which has 80 extreme pathways. As regulatory constraints are applied to the system, the number of feasible extreme pathways is reduced to between 26 and 2 extreme pathways, a reduction of between 67.5 and 97.5%. The method developed here provides a way to interpret how regulatory mechanisms are used to constrain network functions and produce a small range of physiologically meaningful behaviors from all allowable network functions.

Animals↗
PubMed2006

Intrinsic bursting of immature CA3 pyramidal neurons and consequent giant depolarizing potentials are driven by a persistent Na+ current and terminated by a slow Ca2+ -activated K+ current.

The CA3 area of the mature hippocampus is known for its ability to generate intermittent network activity both in physiological and in pathological conditions. We have recently shown that in the early postnatal period, the intrinsic bursting of interconnected CA3 pyramidal neurons generates network events, which were originally called giant depolarizing potentials (GDPs). The voltage-dependent burst activity of individual pyramidal neurons is promoted by the well-known depolarizing action of endogenous GABA on immature neurons. In the present work, we show that a persistent Na+ current, I-Nap, accounts for the slow regenerative depolarization that triggers the intrinsic bursts in the neonatal rat CA3 pyramidal neurons (postnatal day 3-6), while a slow Ca2+ -activated K+ current, sI-K(Ca), is primarily responsible for the postburst slow afterhyperpolarization and consequent burst termination. In addition, we exploited pharmacological data obtained from intracellular recordings to study the mechanisms involved in network events recorded with field potential recordings. The data as a whole indicate that I-Nap and sI-K(Ca) are involved in the initiation and termination, respectively, of the pyramidal bursts and consequent network events underlying GDPs.

Animals↗
PubMed2006

Mycorrhizal networks: des liaisons dangereuses?

Mycorrhizal associations, by which most land plants receive mineral nutrition, are diffuse and often non-specific. A common mycorrhizal network is formed when fungal mycelia colonize and link together the roots of two or more plants, sometimes of different species. Here, we discuss recent work showing how, under realistic ecological conditions, such networks can affect the physiology and ecology of plants by facilitating interplant nutrient exchange, acting as inoculum reservoirs for seedlings and altering plant competitive abilities. Although mechanisms for their evolutionary emergence remain unclear, investigating mycorrhizal networks profoundly modifies our understanding of plant communities.

Biological Evolution↗
PubMed2004

A first-draft human protein-interaction map.

BACKGROUND: Protein-interaction maps are powerful tools for suggesting the cellular functions of genes. Although large-scale protein-interaction maps have been generated for several invertebrate species, projects of a similar scale have not yet been described for any mammal. Because many physical interactions are conserved between species, it should be possible to infer information about human protein interactions (and hence protein function) using model organism protein-interaction datasets. RESULTS: Here we describe a network of over 70,000 predicted physical interactions between around 6,200 human proteins generated using the data from lower eukaryotic protein-interaction maps. The physiological relevance of this network is supported by its ability to preferentially connect human proteins that share the same functional annotations, and we show how the network can be used to successfully predict the functions of human proteins. We find that combining interaction datasets from a single organism (but generated using independent assays) and combining interaction datasets from two organisms (but generated using the same assay) are both very effective ways of further improving the accuracy of protein-interaction maps. CONCLUSIONS: The complete network predicts interactions for a third of human genes, including 448 human disease genes and 1,482 genes of unknown function, and so provides a rich framework for biomedical research.

Databases, Protein↗
PubMed1999

A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks.

The aim of this study was to compare the ability of artificial neural networks and the Acute Physiology and Chronic Health Evaluation II score to predict mortality in adult intensive care units. The same physiological variables were used in both predictive models to predict hospital mortality from a data set of 8796 patients collected from 26 adult intensive care units in the United Kingdom and Ireland as part of the Intensive Care Society study. The results from the two models were compared with the actual outcome. The overall prediction accuracy and the overall goodness-of-fit of all the models were assessed. Both predictive models showed similar goodness-of-fit and prediction discrimination. The overall predictive and classification performance of the artificial neural network developed matched and in some aspects was better than that of Acute Physiology and Chronic Health Evaluation II.

APACHE↗
PubMed1997

Use of neural networks in telemedical monitoring of divers.

An artificial neural network (ANN) has been developed to predict and classify the risk of medical disorders for certain dive profiles. The telemedical data applied to the ANN represent different physiological and physical parameters that are transmitted acoustically from a diver to a remote receiver. The telemetered data include dive time, maximum depth and decompression stop times and depths. Preliminary tests demonstrated the successful performance of the ANN where classical mathematical and statistical methods had failed due to the complex nature and variability of the parameters involved.

Diving↗
PubMed2006

The effects of time delays in a phosphorylation-dephosphorylation pathway.

Complex signaling cascades involve many interlocked positive and negative feedback loops which have inherent delays. Modeling these complex cascades often requires a large number of variables and parameters. Delay differential equation models have been helpful in describing inherent time lags and also in reducing the number of governing equations. However the consequences of model reduction via delay differential equations have not been fully explored. In this paper we systematically examine the effect of delays in a complex network of phosphorylation-dephosphorylation cycles (described by Gonze and Goldbeter, J. Theor. Biol., 210, (2001) 167-186), which commonly occur in many biochemical pathways. By introducing delays in the positive and negative regulatory interactions, we show that a delay differential model can indeed reduce the number of cycles actually required to describe the phosphorylation-dephosphorylation pathway. In addition, we find some of the unique properties of the network and a quantitative measure of the minimum number of delay variables required to model the network. These results can be extended for modeling complex signalling cascades.

Feedback, Physiological↗
PubMed1993

Hydra: a C-language environment for real-time DOS multitasking at the bedside.

Patient monitoring at the bedside is an inherently parallel job, best handled by multiple individual tasks running concurrently. Cost and diffusion considerations strongly favor the use of PC's at the bedside, but their most widespread operating system, DOS, is not built for multitasking. Hence, a software platform in C language has been prepared, allowing the intermediate programmer to easily write independent modules which will then run simultaneously without conflicts. Such a platform aims at allowing effortless sharing of data among concurrently running processes, while providing strong insulation between tasks, enough to allow multiple copies of any one task to run simultaneously unknown to each other. A cooperative, memory sharing multitasking paradigm has been chosen, which offers fine granularity of timeslicing and low execution overhead at the price of some loss in generality of design. Speed, data exchange capability and number of stackable windows are greater than with commercial packages like Windows or LabWindows. Dynamical reprioritization of tasks is built in, allowing the computerized monitor to focus its attention and resources on urgent tasks.

Algorithms↗
PubMed2003

Photoinitiated crosslinked degradable copolymer networks for tissue engineering applications.

Diethylene glycol was used to initiate the ring opening polymerization of D,L-lactide and epsilon -caprolactone, as well as combinations of the two monomers. Esterification of the oligomer end groups with methacryloyl chloride led to divinyl terminated macromers that were reacted via photoinitiated polymerizations to produce crosslinked networks. The lactic and/or caproic acid repeat units can be hydrolyzed under physiological conditions, leading to degradable networks that may be useful for tissue engineering applications. Specifically, methacryloyl terminated poly(lactic acid-co-caproic acid) diethylene glycol based oligomers were prepared and characterized by 1H NMR. The number of ester linkages was kept constant while the ratio of lactic:caproic acid segments was varied. These macromers were then photopolymerized at 450 nm using a visible light initiating system to produce crosslinked degradable networks. The kinetics of the polymerizations were followed by DSC, and the dynamic mechanical behavior was monitored as a function of temperature to obtain the T(g) for each network composition. 1mm thick disks were subjected to hydrolytic degradation in an aqueous phosphate buffer solution at a pH=7.4 and 37 degrees C. The changes in the compressive modulus, as well as the % mass loss as a function of time, were recorded. Cellular compatibility was determined by seeding primary rat calverial osteoblast cells onto the disks and characterizing the cell morphology using scanning electron microscopy.

Animals↗
PubMed1994

A breathing circuit alarm system based on neural networks.

OBJECTIVE: The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms. METHODS: We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm. RESULTS: In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring. CONCLUSION: Neural networks may be useful in creating intelligent anesthesia alarm systems.

Anesthesiology↗
PubMed2002

Unconscious amygdalar fear conditioning in a subset of chronic fatigue syndrome patients.

Here, a novel hypothesis for chronic fatigue syndrome (CFS) is proposed. CFS may be a neurophysiological disorder focussing on the amygdala. During a 'traumatic' neurological event often involving acute psychological stress combined with a viral infection or other chemical or physiological stressor, a conditioned network or 'cell assembly' may be created in the amygdala. The unconscious amygdala may become conditioned to be chronically sensitised to negative symptoms arising from the body. Negative signals from the viscera or physiological, chemical and dietary stressors, become conditioned stimuli and the conditioned response is a chronic sympathetic outpouring from the amygdala via various brain pathways including the hypothalamus. This cell assembly then produces the CFS vicious circle, where an unconscious negative reaction to symptoms causes immune reactivation/dysfunction, chronic sympathetic stimulation, leading to sympathetic dysfunction, mental and physical exhaustion, and a host of other distressing symptoms and secondary complications. And these are exactly the symptoms that the amygdala and associated limbic structures are trained to monitor and respond to, perpetuating a vicious circle. Recovery from CFS may involve projections from the medial prefrontal cortex to the amygdala, to control the amygdala's expressions. I shall firstly discuss predisposing, precipitating, and perpetuating factors involved in the possible etiology of chronic fatigue syndrome (CFS), followed by the patient's experience of the illness. Finally, I shall look at a suggested explanation for the symptoms of CFS.

Amygdala↗