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PubMed2002

An ensemble source spectra model for merchant ship-radiated noise.

This paper presents an evaluation of the classical model for determining an ensemble of the broadband source spectra of the sound generated by individual ships and proposes an alternate model to overcome the deficiencies in the classical model. The classical model, proposed by Ross [Mechanics of Underwater Noise (Pergamon, New York, 1976)] postulates that the source spectrum for an individual ship is proportional to a baseline spectrum with the constant of proportionality determined by a power-law relationship on the ship speed and length. The model evaluation, conducted on an ensemble of 54 source spectra over a 30-1200-Hz to 1200-Hz frequency band, shows that this assumption yields large rms errors in the broadband source level for the individual ships and significantly overestimates the variability in the source level across the ensemble of source spectra. These deficiencies are a consequence of the negligible correlation between the source level and the ship speed and the source level and the ship length. The alternate model proposed here represents the individual ship spectra by a modified rational spectrum where the poles and zeros are restricted to the real axis and the exponents of the terms are not restricted to integer values. An evaluation of this model on the source spectra ensemble indicates that the rms errors are significantly less than those obtained with any model where the frequency dependence is represented by a single baseline spectrum. Furthermore, at high frequencies (400 to 1200 Hz), a single-term rational spectrum model is sufficient to describe the frequency dependence and, at the low frequencies (30 to 400 Hz), there is only a modest reduction in the rms error for a higher order model. Finally, a joint probability density on the two parameters of the single term model based on the measured histograms of these parameters is proposed. This probability density provides a mechanism for generating an ensemble of ship spectra.

Journal Article↗

PubMed1985

Determination of cardiac output using ensemble-averaged impedance cardiograms.

Although impedance cardiography provides safe and reliable noninvasive estimates of stroke volume in humans, its usefulness is limited by the necessity for subjects to be apneic and motionless. In an effort to circumvent this restriction we studied the validity of ensemble-averaging of impedance data in exercising normal subjects and in intensive-care patients. The correlation coefficient (r value) between 128 ensemble-averaged and standard hand-digitized determinations of stroke volume index from the same records taken during rest and exercise in six normal male subjects was +0.97 (P less than 0.001). The r value for ensemble-averaged stroke volume indices during free breathing and breath hold in the same subjects was +0.92 (P less than 0.001), suggesting that breath hold did not significantly affect the stroke volume estimation. In 14 freely breathing hospital intensive-care patients the r value between simultaneous thermodilution cardiac output readings and ensemble-averaged impedance determinations was +0.87 (P less than 0.01). The results indicate that ensemble-averaging of transthoracic impedance data provides waveforms from which reliable estimates of cardiac output can be made during normal respiration in healthy human subjects at rest and exercise and in critically ill patients.

Adult↗

PubMed2006

EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences.

BACKGROUND: Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms. RESULTS: We proposed a novel clustering-based ensemble algorithm named EMD for de novo motif discovery by combining multiple predictions from multiple runs of one or more base component algorithms. The ensemble approach is applied to the motif discovery problem for the first time. The algorithm is tested on a benchmark dataset generated from E. coli RegulonDB. The EMD algorithm has achieved 22.4% improvement in terms of the nucleotide level prediction accuracy over the best stand-alone component algorithm. The advantage of the EMD algorithm is more significant for shorter input sequences, but most importantly, it always outperforms or at least stays at the same performance level of the stand-alone component algorithms even for longer sequences. CONCLUSION: We proposed an ensemble approach for the motif discovery problem by taking advantage of the availability of a large number of motif discovery programs. We have shown that the ensemble approach is an effective strategy for improving both sensitivity and specificity, thus the accuracy of the prediction. The advantage of the EMD algorithm is its flexibility in the sense that a new powerful algorithm can be easily added to the system.

Algorithms↗

PubMed2003

Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.

We have recently developed a closed-loop environment in which we can test the ability of primates to control the motion of a virtual device using ensembles of simultaneously recorded neurons /29/. Here we use a maximum likelihood method to assess the information about task performance contained in the neuronal ensemble. We trained two animals to control the motion of a computer cursor in three dimensions. Initially the animals controlled cursor motion using arm movements, but eventually they learned to drive the cursor directly from cortical activity. Using a population vector (PV) based upon the relation between cortical activity and arm motion, the animals were able to control the cursor directly from the brain in a closed-loop environment, but with difficulty. We added a supervised learning method that modified the parameters of the PV according to task performance (adaptive PV), and found that animals were able to exert much finer control over the cursor motion from brain signals. Here we describe a maximum likelihood method (ML) to assess the information about target contained in neuronal ensemble activity. Using this method, we compared the information about target contained in the ensemble during arm control, during brain control early in the adaptive PV, and during brain control after the adaptive PV had settled and the animal could drive the cursor reliably and with fine gradations. During the arm-control task, the ML was able to determine the target of the movement in as few as 10% of the trials, and as many as 75% of the trials, with an average of 65%. This average dropped when the animals used a population vector to control motion of the cursor. On average we could determine the target in around 35% of the trials. This low percentage was also reflected in poor control of the cursor, so that the animal was unable to reach the target in a large percentage of trials. Supervised adjustment of the population vector parameters produced new weighting coefficients and directional tuning parameters for many neurons. This produced a much better performance of the brain-controlled cursor motion. It was also reflected in the maximum likelihood measure of cell activity, producing the correct target based only on neuronal activity in over 80% of the trials on average. The changes in maximum likelihood estimates of target location based on ensemble firing show that an animal's ability to regulate the motion of a cortically controlled device is not crucially dependent on the experimenter's ability to estimate intention from neuronal activity.

Action Potentials↗

PubMed2004

Differences in hippocampal neuronal population responses to modifications of an environmental context: evidence for distinct, yet complementary, functions of CA3 and CA1 ensembles.

Understanding how the hippocampus processes information critical for establishing spatial and declarative memories will benefit greatly from determining not only what kind of information the hippocampus registers, but also how this information is processed across the different hippocampal subfields. We addressed this question using a novel immediate-early gene-based brain-imaging method (Arc/H1a catFISH) that allows comparisons of neuronal ensembles activated by two experiences separated by approximately 30 min. Rats exposed to the same environment twice activated CA3 and CA1 ensembles with a similarly high degree of overlap. Changing the identity or configuration of local cues, or changing distal cues, activated CA3 and CA1 ensembles with reduced overlap. Yet, the overlap was greater in CA3 than in CA1. In contrast, rats exposed to two completely different environments activated CA3 and CA1 ensembles with low overlap, and this overlap was even lower in CA3 compared with CA1. Thus, CA3 has a discontinuous, whereas CA1 has a graded, population response to alterations of an environment. Additionally, as indicated by the percentage of active neurons, the context representation was more sparse in CA3 (approximately 18%) than in CA1 (approximately 35%). Finally, CA3 and CA1 activity levels were not correlated within a session, arguing against a simple coactivation of these regions. Instead, the within-rat ratio of CA3/CA1 cell activity was correlated across sessions, suggesting that the balance of CA3/CA1 activity is individual specific. Taken together, these findings suggest that CA3 and CA1 neuronal ensembles perform distinct, yet complementary, functions in the processing of spatial and contextual information.

Animals↗

PubMed2006

Toward an accurate theoretical framework for describing ensembles for proteins under strongly denaturing conditions.

Our focus is on an appropriate theoretical framework for describing highly denatured proteins. In high concentrations of denaturants, proteins behave like polymers in a good solvent and ensembles for denatured proteins can be modeled by ignoring all interactions except excluded volume (EV) effects. To assay conformational preferences of highly denatured proteins, we quantify a variety of properties for EV-limit ensembles of 23 two-state proteins. We find that modeled denatured proteins can be best described as follows. Average shapes are consistent with prolate ellipsoids. Ensembles are characterized by large correlated fluctuations. Sequence-specific conformational preferences are restricted to local length scales that span five to nine residues. Beyond local length scales, chain properties follow well-defined power laws that are expected for generic polymers in the EV limit. The average available volume is filled inefficiently, and cavities of all sizes are found within the interiors of denatured proteins. All properties characterized from simulated ensembles match predictions from rigorous field theories. We use our results to resolve between conflicting proposals for structure in ensembles for highly denatured states.

Algorithms↗

PubMed2003

Large-scale ensemble averaging of ambulatory impedance cardiograms.

Impedance cardiography has been used increasingly to measure human physiological responses to emotional and mentally engaging stimuli. The validity of large-scale ensemble averaging of ambulatory impedance cardiograms was evaluated for preejection period (PEP), interbeat interval, and dZ/dt(min) amplitude. We tested whether the average of "classical" 60-sec ensemble averages across periods with fixed activity, posture, physical load, social situation, and location could be accurately estimated from a single large-scale ensemble average spanning these entire periods. Impedance and electrocardiograms were recorded for about 24-h from 21 subjects. Recordings were scored by seven raters, using both methods for each subject. Good agreement (average intraclass correlation coefficient was .91) between both ensemble averaging methods was found for all three cardiac function measures. The results indicate that for unambiguous ambulatory impedance cardiograms, large-scale ensemble averaging is valid, which makes measuring prolonged changes in cardiac sympathetic activity by measuring ambulatory PEP feasible even in large epidemiological samples.

Activity Cycles↗

PubMed1998

[Heat strain for personnel wearing three levels of protective ensembles at moderate and heavy workloads].

The effects of individual protective ensembles and workloads on personnel's physiological heat strain were experienced by personnel in a hot environment (35 degrees C, 55% relative humidity). Six young males performed treadmill tests at both moderate (ML: 1.11 m/s, 5% grade) and heavy (HL: 1.34 m/s, 5% grade) workloads. They wore three levels of protective ensembles: protective mask only (PM), protective clothing only (PC), and full protective ensembles (FP). Tests lasted 60 min and 45 min, respectively, for ML and HL. Physiological measurements included heart rate (HR), rectal (Tre) and skin (Tsk) temperature, sweat rate (SR), and sweat evaporative rate (ER). Differences between the ensembles and the workloads were significant for HR responses. The increase rate in Tre and Tsk, and SR, ER were significantly reduced from PM and PC to FP condition during ML and Hl tests. The sweat evaporation restriction of ensembles and the increase of heat production from exercise are the main factors of enhancing the heat strain.

Adult↗

PubMed2003

The effects of spontaneous activity, background noise, and the stimulus ensemble on information transfer in neurons.

Information theory is playing an increasingly important role in the analysis of neural data as it can precisely quantify the reliability of stimulus-response functions. Estimating the mutual information between a neural spike train and a time varying stimulus is, however, not trivial in practice and requires assumptions about the specific computations being performed by the neuron under study. Consequently, estimates of the mutual information depend on these assumptions and their validity must be ascertained in the particular physiological context in which experiments are carried out. Here we compare results obtained using different information measures that make different assumptions about the neural code (i.e. the way information is being encoded and decoded) and the stimulus ensemble (i.e. the set of stimuli that the animal can encounter in nature). Our comparisons are carried out in the context of spontaneously active neurons. However, some of our results are also applicable to neurons that are not spontaneously active. We first show conditions under which a single stimulus provides a good sample of the entire stimulus ensemble. Furthermore, we use a recently introduced information measure that is based on the spontaneous activity of the neuron rather than on the stimulus ensemble. This measure is compared to the Shannon information and it is shown that the two differ only by a constant. This constant is shown to represent the information that the neuron's spontaneous activity transmits about the fact that no stimulus is present in the animal's environment. As a consequence, the mutual information measure based on spontaneous activity is easily applied to stimuli that mimic those seen in nature, as it does not require a priori knowledge of the stimulus ensemble. Finally, we consider the effect of noise in the animal's environment on information transmission about sensory stimuli. Our results show that, as expected, such 'background' noise will increase the trial-to-trial variability of the neural response to repeated presentations of a stimulus. However, the same background noise can also increase the variability of the spike train and hence can lead to increased information transfer in the presence of background noise. Our study emphasizes how different assumptions can lead to different predictions for the information transmission of a neuron. Assumptions about the computations being performed by the system under study as well as the stimulus ensemble and background noise should therefore be carefully considered when applying information theory.

Action Potentials↗

PubMed1995

Effects of wearing two different clothing ensembles on endurance performance of handgrip exercise.

The experiment was done to investigate the effects of wearing two different clothing ensembles on endurance performance of handgrip exercise in 7 female subjects. Subjects walked on the ground for 1 h where air temperature ranged from 21 to 25 degrees C, glove temperature from 22 to 28 degrees C, relative humidity from 30 to 50% RH, air movement from 0.3 to 0.90 m/s, wearing the clothing ensemble HALF or LONG. The clothing ensemble HALF consisted of half-sleeved shirts, knee-length trousers, sandals, hat and LONG of long-sleeved shirts, long trousers, gloves, socks, walking shoes, hat. After 1 h of walking, the subjects exercised with a hand ergometer in a climatic chamber (25 +/- 1 degrees C, 50 +/- 10% RH) until volitional exhaustion. Rectal temperature, heart rate, body weight loss, number of contractions and strength of each contraction were measured during the experiment. Major findings were as follows: 1) Number of contractions in handgrip exercise after 1 h of walking was significantly greater in clothing ensemble HALF than in LONG, for which lower maintenance of rectal temperature during 1 h of walking on the ground in HALF might be responsible. 2) There was no significant difference in the strength of contraction between two clothing ensembles. Our present results suggest that how to wear clothing is of significance for the endurance performance of handgrip exercise.

Adult↗

PubMed1996

Hippocampal place fields: relationship between degree of field overlap and cross-correlations within ensembles of hippocampal neurons.

The capacity to record from multiple neurons in awake freely moving animals provides a means for characterizing organizational principles of place field encoding within ensembles of hippocampal neurons. In this study, cross-correlations between pairs of hippocampal place cells and degree of overlap between their respective place fields were analyzed during behavioral performance of delayed matching (DMS) or non-matching sample (DNMS) tasks, or while the same rats chased pellets in a different environment. The relationship between field overlap and cross-correlations of neural spike activity within ensembles was shown to be a positive, exponentially increasing, function. Place fields from the same neurons were markedly "remapped" between the Delay and Pellet-chasing tasks, with respect to physical location and size of fields. However individual pairs of place cells within each ensemble retained nearly the same degree of overlap and cross-correlation even though the spatial environment and the tasks differed markedly. This suggested that place cells were organized in functional "clusters" which exhibited the same inter-relations with respect to place field overlap and cross-correlations, irrespective of actual field of location. When cross-correlations between place cells were compared to placement of the array recording electrodes within the hippocampus, the strongest correlations were found along previously defined posterior-projecting fiber gradients between CA3 and CA1 subfields (Ishizuka et al. [1990], J Comp Neurol 295:580-623; Li et al. [1994] (J Comp Neurol 339:181-208). These findings suggest that the functional organization of place fields conforms to anatomical principles suspected to operate within hippocampal ensembles.

Animals↗

PubMed2005

Reconstruction of the postsubiculum head direction signal from neural ensembles.

Head direction cells change their firing rates as a function of the orientation of an animal within an environment. Typically, these cells display a unimodal tuning curve with maximal firing at the cell's preferred direction. As different cells have different preferred directions, the population of cells has been hypothesized to represent the orientation of the animal within the environment. Previous research has shown that pairs of simultaneously recorded head direction cells respond similarly to cue manipulations, suggesting that a population of head direction cells acts in concert to represent the animal's orientation within its environment. Ensembles of head direction cells were recorded from the postsubiculum from rats foraging in an open field. Directional responses of each cell were quantified by the nonparametric Watson's U2 statistic, a measure which makes no explicit assumptions of tuning curve shape. Directionally responsive cells were then used to reconstruct each animal's orientation within the open field using population vector, optimal-linear estimator, and Bayesian methods. The results indicated that postsubiculum contained a complete representation of the animal's orientation. The internal consistency of a neural ensemble can be assessed by comparing the ensemble activity to the expected activity given the reconstructed orientation. This has been termed the "coherency" of the neural ensemble. Reconstruction error decreased as the coherency of the orientation representation increased, indicating that coherency could be used to measure a level of confidence in the representation quality. Because coherency is a linear measure dependent only on internal variables, coherency may be a behaviorally relevant measure used to ascertain the animal's confidence in its representation of orientation.

Action Potentials↗

PubMed2004

Partial rigid-body dynamics in NPT, NPAT and NPgammaT ensembles for proteins and membranes.

A partial rigid-body method of molecular dynamics simulations for proteins and membranes is presented. In this method, the symplectic integrator for rigid bodies is combined with the equations of motion for the NPT ensemble. The standard NPT ensemble is extended to the membrane-specific ensembles, the NPAT (constant normal pressure and lateral surface area of membranes and constant temperature) and NPgammaT (constant normal pressure and lateral surface tension of membranes and constant temperature) ensembles. By more than 30-ns simulations of aqueous proteins and hydrated lipid bilayers, the results of the partial rigid-body method demonstrated excellent conservation of total energy and consistent behavior with the traditional constraint method in terms of structural distribution and fluctuation of proteins and lipids. The efficient implementation of the partial rigid-body method in parallel computation is presented, which is shown to work well in large-scale molecular dynamics simulations.

Algorithms↗

PubMed2004

N-Terminal myristoylation predictions by ensembles of neural networks.

N-terminal myristoylation is a post-translational modification that causes the addition of a myristate to a glycine in the N-terminal end of the amino acid chain. This work presents neural network (NN) models that learn to discriminate myristoylated and nonmyristoylated proteins. Ensembles of 25 NNs and decision trees were trained on 390 positive sequences and 327 negative sequences. Experiments showed that NN ensembles were more accurate than decision tree ensembles. Our NN predictor evaluated by the leave-one-out procedure, obtained a false positive error rate equal to 2.1%. That was better than the PROSITE pattern for myristoylation for which the false positive error rate was 22.3%. On a recent version of Swiss-Prot (41.2), the NN ensemble predicted 876 myristoylated proteins, while 1150 proteins were predicted by the PROSITE pattern for myristoylation. Finally, compared to the well-known NMT predictor, the NN predictor gave similar results. Our tool is available under http://www.expasy.org/tools/myristoylator/myristoylator.html.

Amino Acid Sequence↗

PubMed2003

Efficient RMSD measures for the comparison of two molecular ensembles. Root-mean-square deviation.

Quantitative measures are presented for comparing the conformations of two molecular ensembles. The measures are based on Kabsch's formula for the root-mean-square deviation (RMSD) and the covariance matrix of atomic positions of isotropically distributed ensembles (IDE). By using a Taylor series expansion, it is shown that the RMSD can be expressed solely in terms of the IDE matrices. A fast approximate method is introduced for the pairwise RMSD determination whose computational cost scales linearly with the number of structures. A similarity measure for two structural ensembles that is based on the trace metric of the differences of powers of the IDE matrices is presented. The measures are illustrated for conformational ensembles generated by a molecular dynamics computer simulation of a partially folded A-state analog of ubiquitin.

Computational Biology↗

PubMed1994

Predicting protein mutant energetics by self-consistent ensemble optimization.

In this paper we present a self-consistent ensemble optimization (SCEO) theory for efficient conformational search, which we have applied to predicting the effects of mutations on protein thermostability. This approach takes advantage of a statistical mechanical self-consistency condition to home in iteratively on the global minimum structure. We employ a fast potential of mean-force approximation to cut computation time to a few minutes for a typical protein mutation, with only linear time-dependence on the size of the prediction problem. Rather than seeking a single, static structure of minimum energy, the new method optimizes an ensemble of many conformations, seeking to predict the most likely ensemble for the native state at a desired temperature. Testing this approach with a simple physical model focusing entirely on steric interactions and side-chain rearrangement, we obtain robustly convergent prediction of core side-chain conformation, and of hydrophobic core mutations' effects on protein stability. Self-consistent ensemble optimization is superior to simulated annealing in its speed and convergence to the global minimum, and insensitive to starting conformation. In calculations on lambda repressor protein, structural predictions for an eight-residue molten-zone had side-chain r.m.s. error of 0.49 A for the wild-type protein. Evaluation of the method's mutant structure predictions should become possible, as structures of these mutant repressors are solved. Predicted energies for a series of nine hydrophobic core mutants correlated with measured free energies of unfolding with a coefficient of 0.82.

Amino Acid Sequence↗

PubMed2000

Transition-state ensemble in enzyme catalysis: possibility, reality, or necessity?

Proteins are not rigid structures; they are dynamic entities, with numerous conformational isomers (substates). The dynamic nature of protein structures amplifies the structural variation of the transition state for chemical reactions performed by proteins. This suggests that utilizing a transition state ensemble to describe chemical reactions involving proteins may be a useful representation. Here we re-examine the nature of the transition state of protein chemical reactions (enzyme catalysis), considering both recent developments in chemical reaction theory (Marcus theory for SN2 reactions), and protein dynamics effects. The classical theory of chemical reactions relies on the assumption that a reaction must pass through an obligatory transition-state structure. The widely accepted view of enzymatic catalysis holds that there is tight binding of the substrate to the transition-state structure, lowering the activation energy. This picture, may, however, be oversimplified. The real meaning of a transition state is a surface, not a single saddle point on the potential energy surface. In a reaction with a "loose" transition-state structure, the entire transition-state region, rather than a single saddle point, contributes to reaction kinetics. Consequently, here we explore the validity of such a model, namely, the enzymatic modulation of the transition-state surface. We examine its utility in explaining enzyme catalysis. We analyse the possibility that instead of optimizing binding to a well-defined transition-state structure, enzymes are optimized by evolution to bind efficiently with a transition-state ensemble, with a broad range of activated conformations. For enzyme catalysis, the key issue is still transition state (ensemble) stabilization. The source of the catalytic power is the modulation of the transition state. However, our definition of the transition state is the entire transition-state surface rather just than a single well-defined structure. This view of the transition-state ensemble is consistent with the nature of the protein molecule, as embodied and depicted in the protein energy landscape of folding, and binding, funnels.

Animals↗

PubMedDate not supplied

Intercellular relationships in elementary neuronal ensembles.

The mechanisms of combination of cortical neurons into discrete modules or elementary ensembles, serving as functional units and forming a mosaic of activity in which incoming information is coded, have been investigated. Neurons activated primarily by terminals of specific afferents respond stably with a minimal latent period and become the centers of ensembles, whose periphery is formed by secondarily activated neurons, responding variably with a longer latent period. The stochastic participation of these neurons in the structure of the ensembles lies at the basis of plasticity of cortical mechanisms. Neurons inhibited intracortically, and possibly, via ascending inhibitory pathways, are concentrated at the periphery and near the output cells of the ensemble.

Action Potentials↗