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 full analytic potential energy curve for the a 3 Sigma+ state of KLi from a limited vibrational data set.

Fourier transform spectra of near-infrared laser-induced fluorescence in (39)K(6)Li show transitions to high vibrational levels of both the X (1)Sigma(+) and a (3)Sigma(+) electronic states. These include 147 transitions into six vibrational levels of the a (3)Sigma(+) state, which lie between 7 and 88 cm(-1) below the dissociation asymptote. Unfortunately, their energies span less than 30% of the well depth. However, fitting those data to eigenvalues of analytical model potential functions whose outer limbs incorporate the theoretically predicted long-range form, V(R) approximately D-C(6)R(6)-C(8)R(8), yields complete, plausible potential curves for this state. The best fits converge to remarkably similar solutions which indicate that D(e)=287(+/-4) cm(-1) and R(e)=4.99(+/-0.09) A for the a (3)Sigma(+) state of KLi, with omega(e)=47.3(+/-1.4) and 44.2(+/-1.5) cm(-1) for (39)K(6)Li and (39)K(7)Li, respectively. Properties of the resulting potential are similar to those of a published ab initio potential and are consistent with those of the analogous states of Li(2), K(2), Na(2), and NaK.

Isotope effects and Born-Oppenheimer breakdown in excited singlet states of the lithium dimer.

Observation of infrared electronic transitions involving the 1 (1)Deltag state of 7Li2 has instigated an investigation of Born-Oppenheimer breakdown in four singlet electronic states correlating with (2s+2s), (2s+2p), and (2p+2p) lithium atoms. The 1 (1)Deltag state, which correlates at long range with (2p+2p) atoms, has been observed in emission from the (5p) (1)Piu Rydberg state and in 1 (1)Deltag-B (1)Piu bands, in both instances following optical-optical double-resonance excitation. The latter transition was observed previously for the lighter isotopomer, 6Li2 [C. Linton, F. Martin, P. Crozet, A. J. Ross, and R. Bacis, J. Mol. Spectrosc. 158, 445 (1993)]. By analyzing multiple-isotopomer data for several electronic systems simultaneously, we have determined the electronic isotope shifts and the leading vibrational and/or rotational Born-Oppenheimer breakdown terms for the X (1)Sigmag+, A (1)Sigmau+, B (1)Piu, and 1 (1)Deltag states of the lithium dimer. This paper also reports Fourier transform measurements of the B-X absorption spectra of 6Li2 and 7Li2, which were required to better define the bottom portion of the B (1)Piu state potential.

Fourier transform infrared emission spectra of MgH and MgD.

High resolution Fourier transform infrared emission spectra of MgH and MgD have been recorded. The molecules were generated in an emission source that combines an electrical discharge with a high temperature furnace. Several vibration-rotation bands were observed for all six isotopomers in the X (2)Sigma(+) ground electronic state: v=1-->0 to 4-->3 for (24)MgH, v=1-->0 to 3-->2 for (25)MgH and (26)MgH, v=1-->0 to 5-->4 for (24)MgD, v=1-->0 to 4-->3 for (25)MgD and (26)MgD. The new data were combined with the previous ground state data, obtained from diode laser vibration-rotation measurements and pure rotation spectra, and spectroscopic constants were determined for the v=0 to 4 levels of (24)MgH and the v=0 to 5 levels of (24)MgD. In addition, Dunham constants and Born-Oppenheimer breakdown correction parameters were obtained in a combined fit of the six isotopomers. The equilibrium vibrational constants (omega(e)) for (24)MgH and (24)MgD were found to be 1492.776(7) cm(-1) and 1077.298(5) cm(-1), respectively, while the equilibrium rotational constants (B(e)) are 5.825 523(8) cm(-1) and 3.034 344(4) cm(-1). The associated equilibrium bond distances (r(e)) were determined to be 1.729 721(1) A for (24)MgH and 1.729 157(1) A for (24)MgD.

Discrimination of anesthetic states using mid-latency auditory evoked potential and artificial neural networks.

This study was undertaken to determine whether artificial neural network (ANN) processing of mid-latency auditory evoked potentials (MLAEPs) can identify different anesthetic states during propofol anesthesia, and to determine those parameters that are most useful in the identification process. Twenty-one patients undergoing elective abdominal surgery were studied. To maintain general anesthesia, the patients received propofol (3-5 mgkg(-1) h(-1) intravenously). Epidural analgesia at the level of T4-5 blocked painful stimuli. MLAEP was recorded continuously with patients awake, during induction, during maintenance of general anesthesia, and during emergence until the patients were recovered from anesthesia. Latencies of the 5 MLAEP peaks and three peak to peak amplitudes were measured, along with hemodynamic parameters (heart rate, systolic, and diastolic arterial blood pressure). Four-layer ANNs were used to model the relationship between the parameters of the MLAEP and the four different states (awake, adequate anesthesia, during/before intraoperative movement, and emergence from anesthesia). The best identification accuracy was obtained using only the five latencies. The combination of five latencies and three amplitudes did not improve the identification accuracy. Use of the only the three hemodynamic parameters produced a much poorer identification. This study suggests that the MLAEP has useful information for identifying different anesthetic states, especially in its latencies. A nonlinear discrimination approach, such as the ANN, can effectively capture the relation between the MLAEP patterns and the different states of anesthesia.

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.

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.

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.

Simultaneous regulation of hemodynamic and anesthetic states: a simulation study.

A model predictive control strategy to simultaneously regulate hemodynamic and anesthetic variables in critical care patients is presented. A nonlinear canine circulatory model, which has been used to study the effect of inotropic and vasoactive drugs on hemodynamic variables, has been extended to include propofol pharmacokinetics and pharmacodynamics. Propofol blood concentration is used as a measure for depth of anesthesia. The simulation model is used to design and test the control strategy. The optimization-based model predictive control strategy assures that constraints imposed on the drug infusion rates are met. The physician always remains "in the loop" and serves as the "primary controller" by making propofol blood concentration setpoint changes based on observations about anesthetic depth. Results are shown for three simulated cases: (i) congestive heart failure, (ii) postcoronary artery bypass, and (iii) acute changes in hemodynamic variables.

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.

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.

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.

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.

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.

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.

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.

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.

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 rat growth hormone proximal silencer contains a novel DNA-binding site for multiple nuclear proteins that represses basal promoter activity.

Cell-type-specific expression of the rat growth hormone (rGH) gene is determined by the interaction of both positive as well as negative regulatory proteins with cis-acting elements located upstream of the rGH mRNA start site. We have recently shown that the rat liver transcription factor NF1-L binds to the proximal rGH silencer (called silencer-1) to repress its transcriptional activity. However, this single factor proved to be insufficient by itself to confer cell-specific gene repression. We therefore attempted to identify other regulatory proteins interacting with silencer 1, which might be needed to achieve full cell-specific repression of that gene. A common recognition site for three yet uncharacterized nuclear proteins (designated as SBP1, SBP2 and SBP3) which bind a DNA sequence adjacent to the NF1-L-binding site in the rGH silencer-1 element were identified. UV crosslinking of DNA/protein complexes and nuclear protein fractionation/renaturation from SDS/polyacrylamide gels further indicated that the molecular masses for SBP1-3 are 41, 26 and 17 kDa respectively, the major species being the 26-kDa protein (SBP2) which account for 83% of the shifted SBP double-stranded oligonucleotide in gel mobility-shift assays. For this reason, most of this study focussed on the characterization of SBP2. We demonstrated that binding of NF1-L and SBP2 to their respective recognition sequence is a mutually exclusive event. Although an SBP-binding activity has been found in every non-pituitary tissue or cell line tested, no such activity could be detected in either rat pituitaries or rat pituitary GH4C1 cells. Insertion of the SBP element upstream of the basal promoter of the mouse p12 heterologous gene resulted in a consistent decrease in chloramphenicol acetyl transferase reporter gene expression following transient transfections in non-pituitary cells only, suggesting that the related SBP1-3 proteins might be involved in generally repressing gene transcription in a cell-specific manner.

Time-frequency spectral representation of the EEG as an aid in the detection of depth of anesthesia.

A time-frequency spectral representation (TFSR) has been used to study the nonstationary information in the EEG as an aid in determining the anesthetic depth. This paper uses a TFSR with an exponential weighting function for the purpose. Raw EEG data were collected form 10 mongrel dogs at various levels of halothane anesthesia. Depth of anesthesia was tested by observing the response to tail clamping, which is considered a supramaximal stimulus in dogs. A positive response was graded as awake (depth 0), and a negative response was graded as asleep (depth 1). The EEG obtained during a period of 30 sec tail clamp was processed into TFSRs. It was observed that at depth 0, the spectrum becomes localized in time and frequency. The percentage of energy in the delta (1-3.5 Hz) and theta (3.5-7.5 Hz) frequency bands increased. At depth 1, the spectrum remained unchanged throughout the period of tail clamp. The performance of the TFSR in detecting the patient's awareness was also compared with the power spectrum. It was concluded that under certain anesthetic conditions, the TFSR is superior to the power spectrum.