Persistent molecular superfluid response in doped para-hydrogen clusters.

Direct observation of superfluid response in para-hydrogen (p-H(2)) remains a challenge because of the need for a probe that would not induce localization and a resultant reduction in superfluid fraction. Earlier work [H. Li, R. J. Le Roy, P.-N. Roy, and A. R. W. McKellar, Phys. Rev. Lett. 105, 133401 (2010)] has shown that carbon dioxide can probe the effective inertia of p-H(2) although larger clusters show a lower superfluid response due to localization. It is shown here that the lighter carbon monoxide probe molecule allows one to measure the effective inertia of p-H(2) clusters while maintaining a maximum superfluid response with respect to dopant rotation. Microwave spectroscopy and a theoretical analysis based on Feynman path-integral simulations are used to support this conclusion.

A short protocol for micro-purification of nuclear proteins from whole animal tissue.

Although a number of small-scale procedures have been described for the preparation of crude nuclear extracts from established cell lines, none were provided for the preparation of similar extracts from small amounts of animal tissue. In addition, no small-scale procedures contain enrichment steps that render the detection of low-abundant DNA-binding proteins easier. Here we describe a simple, efficient procedure for the rapid preparation of high-quality nuclear extracts from either whole animal tissue or established cell lines. It is based on a rapid isolation of the nuclei followed by a KCl extraction and a further micro-enrichment of the DNA binding proteins on heparin Sepharose CL-6B. Extracts prepared in such a way are suitable for the analysis of specific DNA/protein interactions by the use of gel shift assays or by DNaseI and dimethylsulfate footprinting techniques. Most importantly, the entire process can be fulfilled at minimal cost within a day on as little as one gram of fresh tissue, which renders this procedure extremely attractive for the analysis of DNA binding proteins involved in the control of gene expression.

Issues in the design of a multirate model-based controller for a nonlinear drug infusion system.

Multivariable controller design for the regulation of mean arterial pressure (MAP) and cardiac output (CO) in congestive heart failure patients is restricted by the limited frequency of CO sampling. Performance criteria for the controller specify maximum allowable transient settling times for both variables, and the design should account for the inherent multirate nature of the process in order to satisfy these criteria. We present a multirate model predictive control (MPC) design for MAP and CO regulation by combined infusion of sodium nitroprusside and dopamine, based on a comprehensive nonlinear model of the system. The multirate MPC algorithm is based on nonlinear quadratic dynamic matrix control. To reduce computation time, we introduce a selective linearization technique that linearizes the model on the basis of trends in the plant-model mismatch. The problem is complicated by restrictions on initial dopamine infusion, prescribed to avoid extremely slow responses. We present a novel rule-based override (RBO) to the MPC controller that uses a set of heuristics to initialize dopamine. The performance of the MPC/RBO controller is illustrated using simulation results.

Control of a nonsquare drug infusion system: A simulation study.

A model predictive control strategy was developed and tested on a nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Different patient conditions such as congestive heart failure, post-operative hypertension, and sepsis shock were studied in closed-loop simulations. The model predictive controller, which uses a different linear model depending on the patient condition, allowed constraints to be explicitly enforced. The controller was initially tuned on the basis of a linear plant model, then tested on the nonlinear physiological model; the simulations demonstrated the ability to handle constraints, such as drug dosage specifications, commonly desired by critical care physicians.

Adaptive control of closed-circuit anesthesia.

Closed-circuit anesthesia (CCA) is more economical and ecologically safer than open circuit anesthesia. However, gas concentrations are more difficult to control. Computer control of CCA has been proposed to facilitate its use. Past efforts have either been limited to the control of anesthetic gas concentrations or apply only to a small group of patients. This paper describes a comprehensive control system applicable to a large class of patients. This system controls the end-tidal oxygen and anesthetic gas concentrations, and the circuit volume. The CCA process was modeled by writing mass balance equations. Simplifying assumptions yielded a bilinear single-input-single-output model for the anesthetic gas concentration and a bilinear multiple-input-multiple-output model for the circuit volume and oxygen concentration. One-step-ahead controllers were used to control these two subsystems. Simulations showed that the control performance was most sensitive to the gas uptakes. Three independent, least-mean-squares estimation schemes were implemented to estimate the uptakes of oxygen, nitrous oxide, and anesthetic gas. These estimates were used in the control law and resulted in explicit adaptive control. The performance of the adaptive controller was compared to that of a fixed controller (with precalculated gas uptakes) in five animal experiments. The adaptive controller performed better than the fixed controller in all cases. The most significant difference was in the anesthetic gas response time 3.6 +/- 0.70 min for adaptive control and 7.04 +/- 5.62 min for fixed control. The adaptive controller was also robust with respect to variations in the system parameters such as the functional residual capacity, leak, deadspace and gas uptakes.(ABSTRACT TRUNCATED AT 250 WORDS)

Multiple drug hemodynamic control by means of a supervisory-fuzzy rule-based adaptive control system: validation on a model.

A control device that uses an expert system approach for a two input-two output system has been developed and evaluated using a mathematical model of the hemodynamic response of a dog. The two inputs are the infusion rates of two drugs: sodium nitroprusside (SNP) and dopamine (DPM). The two controlled variables are the mean arterial pressure and the cardiac output. The control structure is dual mode, i.e., it has two levels: a critical conditions (coarse) control mode and a noncritical conditions (fine) control mode. The system switches from one to the other when threshold conditions are met. Different "controller parameters sets"-including the values for the threshold conditions-can be given to the system which will lead to different controller outputs. Both control modes are rule-based, and supervisory capabilities are added to ensure adequate drug delivery. The noncritical control mode is a fuzzy logic controller. The system includes heuristic features typically considered by anesthesiologists, like waiting periods and the observance of a "forbidden dosage range" for DPM infusion when used as an inotrope. An adaptation algorithm copes with the wide range of sensitivities to SNP found among different individuals, as well as the time varying sensitivity frequently observed in a single patient. The control device is eventually tested on a nonlinear model, designed to mimic the conditions of congestive heart failure in a dog. The test runs show a highest overshoot of 3 mmHg with nominal SNP sensitivity. When tested with different simulated SNP sensitivities, the controller adaptation produces a faster response to lower sensitivities, and reduced oscillations to higher sensitivities. The simulations seem to show that the system is able to drive and adequately keep the two hemodynamic variables within prescribed limits.

The use of fuzzy integrals and bispectral analysis of the electroencephalogram to predict movement under anesthesia.

The objective of this study was to design and evaluate a methodology for estimating the depth of anesthesia in a canine model that integrates electroencephalogram (EEG)-derived autoregressive (AR) parameters, hemodynamic parameters, and the alveolar anesthetic concentration. Using a parameters, and the alveolar anesthetic concentration. Using a parametric approach, two separate AR models of order ten were derived for the EEG, one from the third-order cumulant sequence and the other from the autocorrelation lags of the EEG. Since the anesthetic dose versus depth of anesthesia curve is highly nonlinear, a neural network (NN) was chosen as the basic estimator and a multiple NN approach was conceived which took hemodynamic parameters, EEG derived parameters, and anesthetic concentration as input feature vectors. Since the estimation of the depth of anesthesia involves cognitive as well as statistical uncertainties, a fuzzy integral was used to integrate the individual estimates of the various networks and to arrive at the final estimate of the depth of anesthesia. Data from 11 experiments were used to train the NN's which were then tested on nine other experiments. The fuzzy integral of the individual NN estimates (when tested on 43 feature vectors from seven of the nine test experiments) classified 40 (93%) of them correctly, offering a substantial improvement over the individual NN estimates.

Modeling the hemodynamic response to dopamine in acute heart failure.

A descriptive incremental nonlinear single-input-multiple-output (SIMO) model of the hemodynamic response [cardiac output (CO) and mean aortic pressure (MAP)] to the inotropic drug dopamine in acute ischemic heart failure was constructed to facilitate the design of closed-loop control systems. The structure of the CO component of the model is a first-order system with a sigmoidal relationship. The MAP component is a first-order system with a threshold. Parameter identification was performed on data collected during positive step (drug on) and negative step (drug off) testing using multiple levels (2-6 mcg/kg/min) of infusion of dopamine in a canine model of acute ischemic heart failure. Parameter estimation utilized a least squares objective function and a linearized form of the step response of the model in the time domain. The model provides good approximations to the mean empirical responses.

Adaptive control of multiplexed closed-circuit anesthesia.

This paper describes the design of an adaptive closed-circuit anesthesia controller based on a multiplexed mass spectrometer system. The controller deals with measurement deterioration caused by measurement delay and rise time through a long catheter as well as long sampling times due to the multiplexed measurements. Measurement data are extrapolated between sampling periods to increase the estimation convergence rate. A multiple-step-ahead predictive control algorithm is used to calculate intermediate control inputs between sampling intervals. Simulations are used to validate the designed controller.

Derived fuzzy knowledge model for estimating the depth of anesthesia.

Reliable and noninvasive monitoring of the depth of anesthesia (DOA) is highly desirable. Based on adaptive network-based fuzzy inference system (ANFIS) modeling, a derived fuzzy knowledge model is proposed for quantitatively estimating the DOA and validate it by 30 experiments using 15 dogs undergoing anesthesia with three different anesthetic regimens (propofol, isoflurane, and halothane). By eliciting fuzzy if-then rules, the model provides a way to address the DOA estimation problem by using electroencephalogram-derived parameters. The parameters include two new measures (complexity and regularity) extracted by nonlinear quantitative analyses, as well as spectral entropy. The model demonstrates good performance in discriminating awake and asleep states for three common anesthetic regimens (accuracy 90.3 % for propofol, 92.7 % for isoflurane, and 89.1% for halothane), real-time feasibility, and generalization ability (accuracy 85.9% across the three regimens). The proposed fuzzy knowledge model is a promising candidate as an effective tool for continuous assessment of the DOA.

Multiple-drug hemodynamic control using fuzzy decision theory.

A fuzzy-logic-based, automated drug-delivery system has been developed and validated on a nonlinear canine circulatory model for managing hemodynamic states. This controller features: 1) a fuzzy decision analysis module for patient status determination by assessing cardiac index, systemic vascular resistance index, and pulmonary vascular resistance index and 2) a fuzzy hemodynamic management module utilizing dopamine, phenylephrine, nitroprusside, and nitroglycerin for regulating mean arterial pressure, mean pulmonary arterial pressure, and cardiac output. A rule-based drug delivery scheduling program has been devised and incorporated to execute the therapeutic strategy as recommended by the decision analysis module. Compared to the existing controllers, this system is able to achieve a faster response time with a more secured and effective regulation. The simulation results have demonstrated the feasibility of the decision analysis process for automated management of the arterial and venous circulation with an expanded arsenal of pharmacological agents.

Anesthesia control using midlatency auditory evoked potentials.

This paper shows the development of a system to control inhalation anesthetic concentration delivered to a patient based upon that patient's midlatency auditory evoked potentials (MLAEP's). It was developed and tested in dogs by determining response to the supramaximal stimulus of tail clamping. Prior to tail clamp, the MLAEP was recorded along with inhalational anesthetic concentration and classified as responders or nonresponders as determined by tail clamping. This was performed at a number of different anesthetic levels to obtain a data training set. The MLAEP's were compacted by means of discrete time wavelet transform (DTWT), and together with anesthetic concentration value, a stepwise discriminant analysis (SDA) was performed to determine those features which could separate responders from nonresponders. It was determined that only three features were necessary for this recognition. These features were then used to train a four-layer artificial neural network (ANN) to separate the responders from nonresponders. The network was tested using a separate set of data, resulting in a 93% recognition rate in the anesthetic transition zone between responders and nonresponders, and 100% recognition rate outside this zone. The anesthetic controller used this ANN combined with fuzzy logic and rule-based control. A set of ten animal experiments were performed to test the robustness of this controller. Acceptable clinical performance was obtained, showing the feasibility of this approach.

Design of a recognition system to predict movement during anesthesia.

The need for a reliable method of predicting movement during anesthesia has existed since the introduction of anesthesia. This paper proposes a recognition system, based on the autoregressive (AR) modeling and neural network analysis of the electroencephalograph (EEG) signals, to predict movement following surgical stimulation. The input to the neural network will be the AR parameters, the hemodynamic parameters blood pressure (BP) and heart rate (HR), and the anesthetic concentration in terms of the minimum alveolar concentration (MAC). The output will be the prediction of movement. Design of the system and results from the preliminary tests on dogs are presented in this paper. The experiments were carried out on 13 dogs at different levels of halothane. Movement prediction was tested by monitoring the response to tail clamping, which is considered to be a supramaximal stimulus in dogs. The EEG data obtained prior to tail clamping was processed using a tenth-order AR model and the parameters obtained were used as input to a three-layer perceptron feedforward neural network. Using only AR parameters the network was able to correctly classify subsequent movement in 85% of the cases as compared to 65% when only hemodynamic parameters were used as the input to the network. When both the measures were combined, the recognition rate rose to greater than 92%. When the anesthetic concentration was added as an input the network could be considerably simplified without sacrificing classification accuracy. This recognition system shows the feasibility of using the EEG signals for movement during anesthesia.

Hemodynamic management of congestive heart failure by means of a multiple mode rule-based control system using fuzzy logic.

A rule-based system was designed to control the mean arterial pressure (MAP) and the cardiac output (CO) of a patient with congestive heart failure (CHF), using two vasoactive drugs: sodium nitroprusside (SNP) and dopamine (DPM). The controller has three different modes, that engage according to the hemodynamic state. The critical conditions control mode (CCC) determines the initial infusion rates, and continues active if the MAP or the CO fall outside of the defined criticality thresholds: an upper and a lower boundary for the MAP and a lower boundary for the CO. Inside the boundaries the control is performed by noncritical conditions control modes (NCC's), which are fuzzy logic controllers. If the CO is within normal range and the MAP is close to the goal range, then the MAP is driven using only SNP, in a single-input-single-output mode (NCC-SISO). Otherwise the NCC multiple-input-multiple-output is active (NCC-MIMO). The goal values for the controlled variables are defined as a band of 5 mmHg for the MAP and 5 mL/kg/min for the CO, but there is little concern for this application if the CO is too high (i.e., in practical terms the CO only needs to achieve a necessary minimum rate). The NCC-MIMO includes a gain adaptation algorithm to cope with the wide variety in sensitivities to SNP. Supervisory capabilities to ensure adequate drug delivery complete the controller scheme. After extensive testing and tuning on a CHF-hemodynamics nonlinear model, the control system was applied in dog experiments, which led to further enhancements. The results show an adequate control, presenting a fast response to setpoint changes with an acceptable overshoot.

Depth of anesthesia estimation and control.

A fully automated system was developed for the depth of anesthesia estimation and control with the intravenous anesthetic, Propofol. The system determines the anesthesia depth by assessing the characteristics of the mid-latency auditory evoked potentials (MLAEP). The discrete time wavelet transformation was used for compacting the MLAEP which localizes the time and the frequency of the waveform. Feature reduction utilizing step discriminant analysis selected those wavelet coefficients which best distinguish the waveforms of those responders from the nonresponders. A total of four features chosen by such analysis coupled with the Propofol effect-site concentration were used to train a four-layer artificial neural network for classifying between the responders and the nonresponders. The Propofol is delivered by a mechanical syringe infusion pump controlled by Stanpump which also estimates the Propofol effect-site and plasma concentrations using a three-compartment pharmacokinetic model with the Tackley parameter set. In the animal experiments on dogs, the system achieved a 89.2% accuracy rate for classifying anesthesia depth. This result was further improved when running in real-time with a confidence level estimator which evaluates the reliability of each neural network output. The anesthesia level is adjusted by scheduled incrementation and a fuzzy-logic based controller which assesses the mean arterial pressure and/or the heart rate for decrementation as necessary. Various safety mechanisms are implemented to safeguard the patient from erratic controller actions caused by external disturbances. This system completed with a friendly interface has shown satisfactory performance in estimating and controlling the depth of anesthesia.

EEG complexity as a measure of depth of anesthesia for patients.

A new approach for quantifying the relationship between brain activity patterns and depth of anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the electroencephalogram (EEG) using Lempel-Ziv complexity analysis. Twenty-seven patients undergoing vascular surgery were studied under general anesthesia with sevoflurane, isoflurane, propofol, or desflurane. The EEG was recorded continuously during the procedure and patients' anesthesia states were assessed according to the responsiveness component of the observer's assessment of alertness/sedation (OAA/S) score. An OAA/S score of zero or one was considered asleep and two or greater was considered awake. Complexity of the EEG was quantitatively estimated by the measure C(n), whose performance in discriminating awake and asleep states was analyzed by statistics for different anesthetic techniques and different patient populations. Compared with other measures, such as approximate entropy, spectral entropy, and median frequency, C(n) not only demonstrates better performance (93% accuracy) across all of the patients, but also is an easier algorithm to implement for real-time use. The study shows that C(n) is a very useful and promising EEG-derived parameter for characterizing the (DOA) under clinical situations.

Multiple-model adaptive predictive control of mean arterial pressure and cardiac output.

A multiple-model adaptive predictive controller has been designed to simultaneously regulate mean arterial pressure and cardiac output in congestive heart failure subjects by adjusting the infusion rates of nitroprusside and dopamine. The algorithm is based on the multiple-model adaptive controller and utilizes model predictive controllers to provide reliable control in each model subspace. A total of 36 linear small-signal models were needed to span the entire space of anticipated responses. To reduce computation time, only the six models with the highest probabilities were used in the control calculations. The controller was evaluated on laboratory animals that were either surgically or pharmacologically altered to exhibit symptoms of congestive heart failure. During trials, the controller performance was robust with respect to excessive switching between models and nonconvergence to a single dominant model. A comparison is also made with a previous multiple-drug controller design.

Predicting movement during anaesthesia by complexity analysis of electroencephalograms.

A new approach to predicting movement during anaesthesia by using complexity analysis of electroencephalograms (EEG) signals is presented. The raw EEG signal is first decomposed into six consecutive different scaling components by wavelet transform on the basis of its self-similarity. The Lempel-Ziv complexity measures C(n) are extracted from the raw EEG and its corresponding components by complexity analysis. Prediction of movement during anaesthesia is then made by a four-layer artificial neural network (ANN) using the C(n)s. The combination of these three different approaches enables the system to address the non-analytical, non-stationary, non-linear and dynamical properties of the EEG. From 20 dog experiments, 109 distinct EEG recordings are collected under isoflurane anaesthesia. Testing the ANN using the 'drop one dog' method, the performance obtained for the system in detecting movement is: sensitivity 88%, specificity 97% and accuracy 92%. Comparisons with other methods, such as spectral edge frequency, median frequency and principal component analysis, show that the proposed system has a certain advantage. This new method is computationally fast and well suited for realtime clinical implementation.