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PubMed2005

Directed molecular evolution by machine learning and the influence of nonlinear interactions.

Alternative search strategies for the directed evolution of proteins are presented and compared with each other. In particular, two different machine learning strategies based on partial least-squares regression are developed: the first contains only linear terms that represent a given residue's independent contribution to fitness, the second contains additional nonlinear terms to account for potential epistatic coupling between residues. The nonlinear modeling strategy is further divided into two types, one that contains all possible nonlinear terms and another that makes use of a genetic algorithm to select a subset of important interaction terms. The performance of each modeling type as a function of training set size is analysed. Simulated molecular evolution on a synthetic protein landscape shows the use of machine learning techniques to guide library design can be a powerful addition to library generation methods such as DNA shuffling.

Algorithms↗

PubMed2004

Configural representations in transverse patterning with a hippocampal model.

The hippocampus is necessary in both humans and rats for learning configural representations in tasks such as transverse patterning. The transverse patterning task, (A+B-, B+C-, C+A-), requires representing individual stimuli in the context of other stimuli. This paper extends a previous application to rat data [INNS World Congress on Neural Networks, 1995; Biol Cybern 6 (1998a) 203] by applying a model of the CA3 region of the hippocampus to human data. A decision function is also added that enables the system to choose among training items. Analysis of the simulations show that configural representations are formed by unique neural codes that depend on temporal and stimuli context. Based on the simulations, we hypothesize that configural representations in biological networks depend on a proper balance of input and context representations. Furthermore, the division of labor between functions in the model is a specific working hypothesis that in learning this task the hippocampus specializes in sequence prediction and the decision function evaluates the predictions.

Hippocampus↗

PubMed2000

Identifying three-phase induction motor faults using artificial neural networks

This paper presents an artificial neural network (ANN) based technique to identify faults in a three-phase induction motor. The main types of faults considered are overload, single phasing, unbalanced supply voltage, locked rotor, ground fault, over-voltage and under-voltage. Three-phase currents and voltages from the induction motor are used in the proposed approach. A feedforward layered neural network structure is used. The network is trained using the backpropagation algorithm. The trained network is tested with simulated fault current and voltage data. Fault detection is attempted in the no fault to fault transition period. Off-line testing results on a 3 HP induction motor model show that the proposed ANN based method is effective in identifying various types of faults.

Journal Article↗

PubMed2001

Research and prediction of coordination reactions between CPA-mA and some metal ions using artificial neural networks.

The complex relationship between maximum absorption wavelength (lambda(max)), molar absorptivity (epsilon) of the coordination compounds formed from m-acetyl-chlorophosphonazo (CPA-mA) and the metal ions, the acidity of coordination reaction, some properties of metal ions and the properties of more than 20 coordination compounds were studied using artificial neural networks with extended delta-bar-delta EDBD back learning algorithms in this paper. Six parameters: the pH of coordination reactions, metal ion radius (R), relative atomic weight (Wt), ionic electronic energy (E), metal ion standard Gibbs' free energy (deltaG0) and hard-soft acid-base dual scale (f) were used as input parameters, to predict the lambda(max) and epsilon of the coordination compounds. The structures of networks and the learning times were optimized. The best networks structure is 6-7-2. The optimum number of learning times is about 160,196. It is shown that the maximum relative error is no more than 6% in the testing set. The trained networks are used to simulate the complicated relations between the metal ion properties, coordination reaction conditions and the properties of coordination compounds. This optimized networks have been used for the prediction of the lembda(max) and epsilon of coordination compounds formed from Tb3+, Ho3+ with CPA-mA separately and with satisfactory results.

Journal Article↗

PubMed1998

Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL.

Predicting conditional probability densities with neural networks requires complex (at least two-hidden-layer) architectures, which normally leads to rather long training times. By adopting the RVFL concept and constraining a subset of the parameters to randomly chosen initial values (such that the EM-algorithm can be applied), the training process can be accelerated by about two orders of magnitude. This allows training of a whole ensemble of networks at the same computational costs as would be required otherwise for training a single model. The simulations performed suggest that in this way a significant improvement of the generalization performance can be achieved. Copyright 1997 Elsevier Science Ltd.

Journal Article↗

PubMed2005

Sleep in athletes undertaking protocols of exposure to nocturnal simulated altitude at 2650 m.

A popular method to attempt to enhance performance is for athletes to sleep at natural or simulated moderate altitude (SMA) when training daily near sea level. Based on our previous observation of periodic breathing in athletes sleeping at SMA, we hypothesised that athletes' sleep quality would also suffer with hypoxia. Using two typical protocols of nocturnal SMA (2650 m), we examined the effect on the sleep physiology of 14 male endurance-trained athletes. The selected protocols were Consecutive (15 successive exposure nights) and Intermittent (3x 5 successive exposure nights, interspersed with 2 normoxic nights) and athletes were randomly assigned to follow either one. We monitored sleep for two successive nights under baseline conditions (B; normoxia, 600 m) and then at weekly intervals (nights 1, 8 and 15 (N1, N8 and N15, respectively)) of the protocols. Since there was no significant difference in response between the protocols being followed (based on n=7, for each group) we are unable to support a preference for either one, although the likelihood of a Type II error must be acknowledged. For all athletes (n=14), respiratory disturbance and arousal responses between B and N1, although large in magnitude, were highly individual and not statistically significant. However, SpO2 decreased at N1 versus B (p<0.001) and remained lower on N8 (p<0.001) and N15 (p<0.001), not returning to baseline level. Compared to B, arousals were more frequent on N8 (p=0.02) and N15 (p=0.01). The percent of rapid eye movement sleep (REM) increased from N1 to N8 (p=0.03) and N15 (p=0.01). Overall, sleeping at 2650 m causes sleep disturbance in susceptible athletes, yet there was some improvement in REM sleep over the study duration.

Adult↗

PubMed2006

Surface acoustic wave (SAW) microsensor array for measuring VOCs in drinking water.

Exposure to volatile organic chemicals (VOCs) in drinking water has been linked to a number of adverse health effects including cancer, liver, and kidney damage. However, the large number of potential contaminants and the cost and complexity of existing analytical methods limits the extent to which water quality is routinely characterized. This project focused on the laboratory development and evaluation of an instrument for field analysis of VOCs in drinking water. The instrument is based on an array of six polymer-coated surface-acoustic-wave microsensors. A test-set consisting of dichloromethane, chloroform, 1,1,1-trichloroethane, perchloroethylene, and m-xylene was used in a series of experiments designed to optimize the purge-trap preconcentration system, calibrate the instrument over the concentration range of 0.2-2 times the USEPA maximum contaminant levels (MCLs), and compare results to those of a reference laboratory. The primary goal was to develop a cost-effective alternative for on-site evaluation of VOCs in water. Calibration and evaluation test results for spiked water samples demonstrate adequate sensitivity for 19 of the 21 regulated VOCs considered using a ten minute sampling and analysis cycle. Monte Carlo simulations characterized the performance of trained artificial neural networks (ANNs) which had correct classification rates of 99%, 90%, and 80% for the five individual test-set vapors and their binary and ternary mixtures, respectively. These results demonstrate the excellent potential of this technology for addressing the need for improved VOC field-screening methods for water supplies.

Acoustics↗

PubMed1998

Effect of GABA on the processing of interaural time differences in nucleus laminaris neurons in the chick.

Neurons in the avian nucleus laminaris (NL) are the first to receive binaural information and are presumed to play a role in encoding interaural time differences (ITD). NL not only receives excitatory projections from the ipsi- and contralateral nucleus magnocellularis, but also receives inhibitory (GABAergic) input. This study investigates how GABA (gamma-aminobutyric acid) influences ITD coding in NL. Intracellular responses of chick NL neurons were studied in a brain slice preparation. Both excitatory inputs to NL were electrically activated and the delay between trains of bilateral stimuli (simulated-interaural time difference [s-ITD]) was varied. The resulting s-ITD functions were recorded in the presence of 0-75 microM GABA. The discharge rate of NL neurons varied with s-ITD. Cells responded maximally using s-ITDs at which the peak of the ipsi- and contralateral excitatory postsynaptic potentials occurred simultaneously (favourable s-ITD). At unfavourable s-ITDs, the discharge rates usually fell below unilateral levels. GABA had contrary effects on the s-ITD functions depending on the drug concentration. A low GABA dose enhanced excitability at favourable s-ITD, but not at unfavourable s-ITDs. In contrast, higher GABA concentrations diminished excitability. Moderate GABA concentrations had no consistent effect. These results suggest that the GABAergic input to NL will either increase or decrease the excitability of the NL neuron depending on the degree to which this GABAergic input is activated. A gain control hypothesis is presented in which the GABAergic input makes ITD processing in NL independent of the stimulus intensity by adjusting the excitability of NL neurons.

Animals↗

PubMed2001

Creatine loading does not impact on stroke performance in tennis.

The effect of acute creatine supplementation on stroke quality was investigated during simulated match play. Well-trained tennis players reported to the test center on two occasions. On each occasion they performed the Leuven Tennis Performance Test (LTPT) and a 70 m shuttle run (SHR). During 5 days prior to each test session they received in random order and according to a double-blind cross-over study design either oral creatine supplements (4 x 5 g per day) or placebo. The two experimental periods were separated by a 5-week washout period. Stroke quality was evaluated during the LTPT by means of registration of error rate and measurement of ball velocity and precision of lateral and longitudinal ball placement. Compared with placebo, creatine supplementation did not significantly impact on either power or precision of first and second services, baseline strokes in neutral and defensive rallies, and volleys. Shuttle run time was 19.87 +/- 0.30 sec during placebo versus 19.85 +/- 0.27 sec during creatine treatment. Acute creatine supplementation does not enhance stroke performance or sprint power in match-like conditions in elite tennis players.

Adult↗

PubMed1996

[The Ulm Emergency Training Course. 2: Realization and acceptance of the emergency medicine practicum].

The first part of this publication described the concept of the student course in emergency medicine at the University Hospital Ulm as part of a "vertical curriculum" of education in emergency medicine. METHODS. A training circuit (Fig. 1) was conceptualised using training manikins (i.e. BLS, intubation, megacode training, ATLS training), computer programmes (ECG-simulation) and other means of instruction to simulate twelve realistic situations. These practical skill sessions were linked with case presentation and lectures. Its topics were chosen depending on the prevalence, type and urgency. Separate guidelines for teachers and students were edited prior to the beginning of the course in 1994. RESULTS. Evaluation showed good acceptance by the students for most parts of the course except the ATLS skill station and CPR computer simulations (Table 5). The score of the national board examination (multiple-choice test) of the students from Ulm was nevertheless not better than the mean of all german examinees (72.1 to 71.7%). CONCLUSION. The concept of the course in emergency medicine is well accepted by our students. Results of the national examination were not improved, since the course aims at performance, skills and competence in emergency medicine and not at teaching factual knowledge that is measured by the MCQ examinations.

Attitude of Health Personnel↗

PubMed2005

Prediction of molecular-dynamics simulation results using feedforward neural networks: reaction of a C2 dimer with an activated diamond (100) surface.

A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5.

Journal Article↗

PubMedDate not supplied

Prosodic and lexical-syntactic aspects of the therapeutic register.

The current study examined features of speech addressed to younger versus older and impaired versus normal listeners and explored factors that might motivate speech modification in the therapeutic register. Ten speech-language clinicians in training were engaged in a simulated storytelling task and produced narrations to four imaginary listener groups. Results revealed that clinicians modified their speech at multiple levels according to both the age and the diagnostic status of their listeners. As a group, the clinicians demonstrated decreased speaking rate, elongation of pauses, and use of shorter and less complex sentences when speaking to young children and individuals with language impairments. Despite these group trends there was notable individual variability between clinicians. Findings from this constrained experimental context may be extended to real life clinician-client interactions and have implications for therapeutic success.

Acoustics↗

PubMed2005

Suspended sediment removal by vegetative filter strip treating highway runoff.

Structural best management practices (BMPs) are often used to mitigate the impact of storm water runoff on receiving waters. Vegetative filter strips (VFS) are an example of a structural BMP that has been used to treat storm water and highway runoff. Physical factors affecting the performance of VFS include pollutant characteristics, vegetation composition and density, soil properties, and the physical dimensions of the filter strip. In this study, field-suspended sediment data were collected from an experimental VFS treating highway runoff in eastern North Carolina. Field data were used to test the design concepts of the VFS treatment train and to validate a simulation model for evaluating the impact of these physical factors on sediment removal as a function of filter strip length. It was concluded that the experimental filter strip was effective in removing more than 85% of the incoming total suspended sediment (TSS). Simulation results support field observations that a 10-m or longer filter strip can retain most of the medium and large particles (> 8 microm) transported in runoff. Simulations also indicate infiltration loss is largely responsible for the retention of small-size sediment particles (< 8 microm). Saturated hydraulic conductivity and initial water contents have little effects on TSS removal. The condition of vegetative coverage, in particular vegetation density, is another factor affecting the performance of filter strip.

Biodegradation, Environmental↗

PubMed2005

Neural network based approach for anomaly detection in the lungs region by electrical impedance tomography.

In this paper, we have shown a simple procedure to detect anomalies in the lungs region by electrical impedance tomography. The main aim of the present study is to investigate the possibility of anomaly detection by using neural networks. Radial basis function neural networks are used as classifiers to classify the anomaly as belonging to the anterior or posterior region of the left lung or the right lung. The neural networks are trained and tested with the simulated data obtained by solving the mathematical model equation governing current flow through the simulated thoracic region. The equation solution and model simulation are done with FEMLAB. The effect of adding a higher number of neurons to the hidden layer can be clearly seen by the reduction in classification error. The study shows that there is interaction between the size (radius) and conductivity of anomalies and for some combination of these two factors the classification error of neural networks will be very small.

Computer Simulation↗

PubMed2001

Genetic adaptive neural network to predict biochemical failure after radical prostatectomy: a multi-institutional study.

BACKGROUND AND PURPOSE: Despite many new procedures, radical prostatectomy remains one of the commonest methods of treating clinically localized prostate cancer. Both from the physician's and the patient's point of view, it is important to have objective estimation of the likelihood of recurrence, which forms the foundation for treatment selection for an individual patient. Currently, it is difficult to predict the probability of biochemical recurrence (rising serum prostate specific antigen [PSA] concentration) in an individual patient, and approximately 30% of the patients do experience recurrence. Tools predicting the recurrence will be of immense practical utility in the treatment selection and planning follow up. We have utilized preoperative parameters through a computer based genetic adaptive neural network model to predict recurrence in such patients, which can help primary care physicians and urologists in making management recommendations. PATIENTS AND METHODS: Fourteen hundred patients who underwent radical prostatectomy at participating institutions form the subjects of this study. Demographic data such as age, race, preoperative PSA, systemic biopsy based staging and Gleason scores were used to construct a neural network model. This model simulated the functioning of a trained human mind and learned from the database. Once trained, it was used to predict the outcomes in new patients. RESULTS: The patients in this comprehensive database were representative of the average prostate cancer patients as seen in USA. Their mean age was 68.4 years, the mean PSA concentration before surgery was 11.6 ng/mL, and 67% patients had a Gleason sum of 5 to 7. The mean length of follow-up was 41.5 months. Eighty percent of the cancers were clinical stage T2 and 5% T3. In our series, 64% of patients had pathologically organ-confined cancer, 33% positive margins, and 14% had seminal vesicle invasion. Lymph node positive patients were not included in this series. Progression as judged by serum PSA was noted in 30.6%. With entry of a few routinely used parameters, the model could correctly predict recurrence in 76% of the patients in the validation set. The area under the curve was 0.831. The sensitivity was 85%, the specificity 74%, the positive predictive value 77%, and the negative predictive value of 83%. CONCLUSION: It was possible to predict PSA recurrence with a high accuracy (76%). Physicians desiring objective treatment counseling can use this model, and significant cost savings are anticipated because of appropriate treatment selection and patient-specific follow-up protocols. This technology can be extended to other treatments such as watchful waiting, external-beam radiation, and brachytherapy.

Aged↗

PubMed2004

A spline function approach for detecting differentially expressed genes in microarray data analysis.

MOTIVATION: A primary objective of microarray studies is to determine genes which are differentially expressed under various conditions. Parametric tests, such as two-sample t-tests, may be used to identify differentially expressed genes, but they require some assumptions that are not realistic for many practical problems. Non-parametric tests, such as empirical Bayes methods and mixture normal approaches, have been proposed, but the inferences are complicated and the tests may not have as much power as parametric models. RESULTS: We propose a weakly parametric method to model the distributions of summary statistics that are used to detect differentially expressed genes. Standard maximum likelihood methods can be employed to make inferences. For illustration purposes the proposed method is applied to the leukemia data (training part) discussed elsewhere. A simulation study is conducted to evaluate the performance of the proposed method.

Algorithms↗

PubMed2005

Less is more: towards an optimal universal description of protein folds.

MOTIVATION: Identification and characterization of protein structure regularities can reveal the mechanisms governing protein structure, function and evolution. Here we focus on an intermediate level of regularity. We have developed automated methods to systematically construct a dictionary of supersecondary structures that can be used as 'protein parts' to describe fold-sized structures. RESULTS: The dictionary was constructed by aligning representative structures of all known folds, clustering similar substructures and selecting the most descriptive substructures in a minimum description length fashion. We show that the dictionary is compact and descriptive, capable of describing a substantial fraction of all known protein folds. We performed simulations using independent sets of training and testing folds. Dictionaries generated using the training set had high coverage over the folds in the testing set, suggesting that dictionary entries reflect general features of protein structures and should be capable of describing novel protein folds.

Algorithms↗

PubMedDate not supplied

Chemical weapon functional exercise--Cincinnati: observations and lessons learned from a "typical medium-sized" city's response to simulated terrorism utilizing Weapons of Mass Destruction.

In the wake of the September 11, 2001, attacks and the subsequent anthrax scare, there is growing concern about the United States' vulnerability to terrorist use of Weapons of Mass Destruction (WMD). As part of ongoing preparation for this terrible reality, many jurisdictions have been conducting simulated terrorist incidents to provide training for the public safety community, hospitals, and public health departments. As an example of this national effort to improve domestic preparedness for such events, a large scale, multi-jurisdictional chemical weapons drill was conducted in Cincinnati, Ohio, on May 20, 2000. This drill depicted the components of the early warning system for hospitals and public health departments, the prehospital medical response to terrorism. Over the course of the exercise, emergency medical services personnel decontaminated, triaged, treated, and transported eighty-five patients. Several important lessons were learned that day that have widespread applicability to health care delivery systems nationwide, especially in the areas of decontamination, triage, on-scene medical care, and victim transportation. As this training exercise helped Cincinnati to prepare for dealing with future large scale WMD incidents, such drills are invaluable preparation for all communities in a world increasingly at risk from terrorist attacks.

Ambulances↗