Large-scale expansion of no-take closures within the Great Barrier Reef has not enhanced fishery production.

A rare opportunity to test hypotheses about potential fishery benefits of large-scale closures was initiated in July 2004 when an additional 28.4% of the 348 000 km2 Great Barrier Reef (GBR) region of Queensland, Australia was closed to all fishing. Advice to the Australian and Queensland governments that supported this initiative predicted these additional closures would generate minimal (10%) initial reductions in both catch and landed value within the GBR area, with recovery of catches becoming apparent after three years. To test these predictions, commercial fisheries data from the GBR area and from the two adjacent (non-GBR) areas of Queensland were compared for the periods immediately before and after the closures were implemented. The observed means for total annual catch and value within the GBR declined from preclosure (2000-2003) levels of 12780 Mg and Australian $160 million, to initial post-closure (2005-2008) levels of 8143 Mg and $102 million; decreases of 35% and 36% respectively. Because the reference areas in the non-GBR had minimal changes in catch and value, the beyond-BACI (before, after, control, impact) analyses estimated initial net reductions within the GBR of 35% for both total catch and value. There was no evidence of recovery in total catch levels or any comparative improvement in catch rates within the GBR nine years after implementation. These results are not consistent with the advice to governments that the closures would have minimal initial impacts and rapidly generate benefits to fisheries in the GBR through increased juvenile recruitment and adult spillovers. Instead, the absence of evidence of recovery in catches to date currently supports an alternative hypothesis that where there is already effective fisheries management, the closing of areas to all fishing will generate reductions in overall catches similar to the percentage of the fished area that is closed.

Automated analysis of respiratory behavior in extremely preterm infants and extubation readiness.

BACKGROUND: Rates of extubation failure of extremely preterm infants remain high. Analysis of breathing patterns variability during spontaneous breathing under endotracheal tube continuous positive airway pressure (ETT-CPAP) is a potential tool to predict extubation readiness. OBJECTIVE: To investigate if automated analysis of respiratory signals would reveal differences in respiratory behavior between infants that were successfully extubated or not. METHODS: Respiratory Inductive Plethysmography (RIP) signals were recorded during ETT-CPAP just prior to extubation. Signals were digitized, and analyzed using an Automated Unsupervised Respiratory Event Analysis (AUREA). Extubation failure was defined as reintubation within 72 hr. Statistical differences between infants who were successfully extubated or failed were calculated. RESULTS: A total of 56 infants were enrolled and one was excluded due to instability during the ETT-CPAP; 11 out of 55 infants studied failed extubation (20%). No differences in demographics were observed between the success and failure groups. Significant differences on the variability of some respiratory parameters or 'metrics' estimated by AUREA were observed between the 2 groups. Indeed, a simple classification using the variability of two metrics of respiratory behavior predicted extubation failure with high accuracy. CONCLUSION: Automated analysis of respiratory behavior during a short ETT-CPAP period may help in the prediction of extubation readiness in extremely preterm infants.

A new movement artifact detector for photoplethysmographic signals.

Oximeters are commonly used in abbreviated cardiorespiratory studies (ACS) to monitor blood oxygen saturation and heart rate using the photoplethysmography (PPG) signal. These data are prone to movement artifacts, especially in infants who move or need to be handled often. Therefore segments of PPG data contaminated by movement artifact must be detected as a first stage of analysis. In ACS this identification is generally done manually, by having an expert visually assess the quality of the signal. This is subjective and very time consuming, especially for long data records. For this reason we present a novel detector of PPG movement artifacts that uses moving average filters to remove trends, reduce the effect of white noise, and notch filter pulse-related information. The normalized root mean square of the filtered signal is then used as a detection statistic. We demonstrate its detection properties using a data set from infants recovering from anesthesia, and show that it performs better than other automated methods based on entropy or higher-order statistics. Furthermore, the new method is more robust than the other methods in the presence of large noise.

Subspace identification of Hammerstein systems using B-splines.

This paper presents an algorithm for the identification of Hammerstein cascades with hard nonlinearities. The nonlinearity of the cascade is described using a B-spline basis with fixed knot locations; the linear dynamics are described using a state-space model. The algorithm automatically estimates both the order of the linear system and the number and locations of the knots used to characterize the nonlinearity. Therefore, it significantly reduces the a priori knowledge about the underlying system required for identification. A simulation study on a model of reflex stiffness shows that the new method estimates the nonlinearity accurately in the presence of output noise.

Detection of breathing segments in respiratory signals.

The typical approach for analysis of respiratory records consists of detection of respiratory pauses and elimination of segments corrupted by movement artifacts. This is motivated by established rules used for manual scoring of respiratory events, which focus on pause segmentation and do not define criteria to identify breathing segments. With this strategy, breathing segments can only be inferred indirectly from the absence of abnormalities, yielding an unclear and ambiguous definition. In this work we present novel detectors for synchronous and asynchronous breathing, and compare them with AUREA, a novel system for Automated Unsupervised Respiratory Event Analysis, which performs indirect classification of breathing. Results from analysis of real infant respiratory data show an improvement in the identification of synchronous and asynchronous breathing of 9% and 27% respectively, demonstrating that direct detection of breathing enhances the classification performance.

Subspace methods for identification of human ankle joint stiffness.

Joint stiffness, the dynamic relationship between the angular position of a joint and the torque acting about it, describes the dynamic, mechanical behavior of a joint during posture and movement. Joint stiffness arises from both intrinsic and reflex mechanisms, but the torques due to these mechanisms cannot be measured separately experimentally, since they appear and change together. Therefore, the direct estimation of the intrinsic and reflex stiffnesses is difficult. In this paper, we present a new, two-step procedure to estimate the intrinsic and reflex components of ankle stiffness. In the first step, a discrete-time, subspace-based method is used to estimate a state-space model for overall stiffness from the measured overall torque and then predict the intrinsic and reflex torques. In the second step, continuous-time models for the intrinsic and reflex stiffnesses are estimated from the predicted intrinsic and reflex torques. Simulations and experimental results demonstrate that the algorithm estimates the intrinsic and reflex stiffnesses accurately. The new subspace-based algorithm has three advantages over previous algorithms: 1) It does not require iteration, and therefore, will always converge to an optimal solution; 2) it provides better estimates for data with high noise or short sample lengths; and 3) it provides much more accurate results for data acquired under the closed-loop conditions, that prevail when subjects interact with compliant loads.

System identification of biomedical systems from short transients using space methods.

System identification technology has been a popular tool to analysis the dynamic behavior of biomedical systems. A segment of data with enough information must be collected in order to obtain unbiased estimate of the systems. However, some biomedical systems have short transients, such as Vestibulo-Ocular Reflex (VOR). Systems often cannot be identified from the short transients collected in one experiment. Thus, the experiments must be repeated, and an ensemble of input and output recorded. This paper presents a subspace method to identify Multiple Input and Multiple Output (MIMO) state space models for biomedical systems from short transients using ensemble data. A simulating ankle joint stiffness experiment demonstrates that the algorithm provides accurate results.

Closed-loop system identification of ankle dynamics with compliant loads.

Joint stiffness, defined as the relation between the angular position of a joint and the torque acting about it, can be used to describe the dynamic behavior of the human ankle during posture and movement. Joint stiffness can be separated into intrinsic stiffness and reflex stiffness, which are modeled as a linear system and a LNL system, respectively. With a compliant load, joint stiffness can be viewed as being operated in closed-loop because the torque is fed back through the load to change the position. In this paper, we present a new method to estimate the intrinsic and reflex stiffness from the total torque measurement. An EIV (Errors-In-Variables) subspace system identification method is used to estimate the dynamics of each pathway directly from measured data. Simulation and experiment studies demonstrate that the method produces accurate results.

Nondestructive online in vitro monitoring of pre-osteoblast cell proliferation within microporous polymer scaffolds.

We present a system for the online, in vitro, nondestructive monitoring of tissue growth within microporous polymer scaffolds. The system is based on measuring the admittance of the sample over a frequency range of 10-200 MHz using an open-ended coaxial probe and impedance analyzer. The sample admittance is related to the sample complex permittivity (CP) by a quasi-static model of the probe's aperture admittance. A modified effective medium approximation is then used to relate the CP to the cell volume fraction. The change of cell volume fraction is used as a measure of tissue growth inside the scaffold. The system detected relative cell concentration differences between microporous polymer scaffolds seeded with 0.4, 0.45, 0.5, and 0.6 x 10(6) pre-osteoblast cells. In addition, the pre-osteoblast proliferation within 56 scaffolds over 14 days was recorded by the system and a concurrent DNA assay. Both techniques produced cell proliferation curves that corresponded to those found in literature. Thus, our data confirmed that the new system can assess relative cell concentration differences in microporous scaffolds enabling online nondestructive tissue growth monitoring.

Fetal heart rate deceleration detection using a discrete cosine transform implementation of singular spectrum analysis.

OBJECTIVES: To develop a singular-spectrum analysis (SSA) based change-point detection algorithm applicable to fetal heart rate (FHR) monitoring to improve the detection of deceleration events. METHODS: We present a method for decomposing a signal into near-orthogonal components via the discrete cosine transform (DCT) and apply this in a novel online manner to change-point detection based on SSA. The SSA technique forms models of the underlying signal that can be compared over time; models that are sufficiently different indicate signal change points. To adapt the algorithm to deceleration detection where many successive similar change events can occur, we modify the standard SSA algorithm to hold the reference model constant under such conditions, an approach that we term "base-hold SSA". The algorithm is applied to a database of 15 FHR tracings that have been preprocessed to locate candidate decelerations and is compared to the markings of an expert obstetrician. RESULTS: Of the 528 true and 1285 false decelerations presented to the algorithm, the base-hold approach improved on standard SSA, reducing the number of missed decelerations from 64 to 49 (21.9%) while maintaining the same reduction in false-positives (278). CONCLUSIONS: The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series.

Power-based segmentation of respiratory signals using forward-backward bank filtering.

We present an automated method for the segmentation of ribcage and abdominal signals measured by noninvasive respiratory inductance plethysmography (RIP) into quiet breathing and artifact-corrupted segments. This procedure, which involves forward-backward filtering, is applicable to the automated off-line analysis of long records of respiratory signals. Examples of applications include home and sleep laboratory studies of cardiorespiratory data. The new procedure was successfully applied to the segmentation of cardiorespiratory signals acquired post-operatively from infants in the recovery room of the Montreal Children's Hospital (MCH).

Decomposition of a parallel cascade model of ankle stiffness using subspace methods.

Joint stiffness, defined as the relation between the angular position of a joint and the torque acting about it, can be used to describe the dynamical behavior of the human ankle during posture and movement. Joint stiffness can be separated into intrinsic stiffness and reflex stiffness, which are modeled as a linear system and a Hammerstein system, respectively. A two-pathway parallel cascade model, with the intrinsic stiffness on one pathway and the reflex stiffness on the other, can be used to describe the joint stiffness. In this paper, we present a new method to separate the torque from each pathway from the total torque measurement. A subspace based system identification method is used to estimate the dynamics of each pathway directly from measured data without iteration. Simulation studies demonstrate that the method produces accurate results without the need of iteration.

Identification of time-varying intrinsic and reflex joint stiffness.

We have developed a time-varying, parallel- cascade system identification algorithm to separate joint stiffness into intrinsic and reflex components at each point in time throughout rapid movements. The components are identified using an iterative algorithm in which intrinsic and reflex dynamics are identified using separate time-varying (TV) techniques based on ensemble methods. An ensemble of input-output records having the same TV behavior is acquired and used to identify the system dynamics as impulse response functions at time increments corresponding to the sampling interval. Simulation studies showed that the time-varying, parallel-cascade algorithm performed well under realistic conditions with 99.9% VAF between simulated and predicted torque. To evaluate the performance of the algorithm under realistic conditions we applied it to an ensemble of experimental data acquired under stationary conditions. Results demonstrated that the TV estimates converged to those of the established time-invariant algorithm and allowed us to determine how variance of the TV estimates varied with the number of realizations in the ensemble.

A Highly Responsive System for On-line in vitro Assessment of Tissue Growth within MicroPorous Polymer Scaffolds.

We have proposed a highly responsive system for the on-line in vitro assessment of tissue growth within microporous polymer scaffolds that obviates any compromise of sample integrity. The system's function is based on the sample's loss factor: the imaginary part of the complex permittivity. Reflection measurements were performed using an open-ended coaxial probe and impedance analyzer; they were then related to the sample's complex permittivity by a quasi-static model of the probe's aperture admittance. Measurements of saline solutions showed that the real part of permittivity was corrupted by apparent polarization effects. Consequently, we developed a simplified formulation of the imaginary part of the Hanai-Wagner effective medium approximation to eliminate its dependence on the real part of complex permittivity measurement. This formulation allows the sample's cell concentration to be determined. The variation of a sample's cell concentration over time was used as a measure of tissue growth. Measurements in the frequency range of 10-200 MHz were performed on micro-porous polymer scaffolds seeded with progressively greater number of cells. Results demonstrated that the system detected concentration differences between cell-seeded scaffolds.

Experimental evaluation of computerised tomography point spread function variability within the field of view: parametric models.

The objective of the paper was to validate non-linear parametric models of computerised tomography point spread function (PSF), to investigate the role of model parameters and to verify the effect of different imaging conditions on estimated parameters. These models were then to be used experimentally to estimate the variation of PSF shape within the field of view of a scanner. Two parametric models of the PSF are presented. The Gaussian model is appropriate when PSF values are positive, and the damped cosine model can account for negative values. These models are non-linear and fully two-dimensional and do not assume radial symmetry. The models were fitted to images of a point source. The models accounted for over 99% of the variance in the PSF signal. Errors in modulation transfer function were limited to 5% when the appropriate model was selected. The difference in the blurring characteristics of three image reconstruction filters was well quantified by shape parameters, and position parameters located the PSF with subpixel accuracy. With a point source located 50mm directly above the centre of the field of view, the PSF was found to be anisotropic.

Modulation of stretch reflex excitability during quiet human standing.

Stretch reflex excitability was measured during quiet standing by using a bilateral electro-hydraulic actuator to apply perturbations of angular position to the ankle. Subjects were instructed to stand quietly while pulse displacements were applied at random time intervals. Position, torque, gastrocnemius soleus EMG, tibialis anterior EMG, heel position, tibia angle, femur angle, and sacrum angle were measured. Activation level and reflex excitability varied substantially from trial to trial - reflex torque decreased as the background torque level increased; while reflex EMG increased when background torque increased. This behavior is consistent with previous findings in prone subjects. Reflex torque for a given activation level was found to vary with the initial torque derivative. Negative torque derivatives produced greater reflex excitation then their positive counterparts. These findings suggest that reflex excitability in quiet human standing is modulated to optimize balance.

Time-varying parallel-cascade system identification of ankle stiffness from ensemble data.

Measurement of joint dynamic stiffness during time-varying conditions is crucial to understand the role of joint mechanics during movement. Stiffness can be separated into intrinsic and reflex components, and are modeled as linear dynamic and Hammerstein systems, respectively. Time-varying identification methods using ensemble data have been developed previously for both pathways and were tested separately on simulated data. In this study, these algorithms were integrated into the time-varying, parallel-cascade identification method. Ankle dynamics were modeled during a ramp input and simulated impulse response functions (IRFs) were generated. Gaussian white noise was low-pass filtered and was convolved with the simulated systems over 500 realizations. The ensemble data was used to evaluate the new identification technique. The mean variances accounted for (VAFs) between the true and identified IRFs for the intrinsic and reflex pathways were 99.9% and 97.7%, respectively, demonstrating the technique's strong ability to predict the system's dynamics.

Detection of movement artifacts in respiratory inductance plethysmography: performance analysis of a Neyman-Pearson energy-based detector.

In [3] we developed a method for the automated estimation of the phase relation between thoracic and abdominal signals measured by noninvasive respiratory inductance plethysmography (RIP). In the present paper, we improve on the phase estimator by including an automated procedure for the detection of periods of gross body movements. We assume that the number of sleep obstructive events during periods of gross body movements is zero in probability. We hope that combining the phase estimator with the gross body movement detector should yield improved diagnostic tools for the automated classification of obstructive hypopnea events.

Determination of the systematic and random measurement error in an LC-FTICR mass spectrometry analysis of a partially characterized complex peptide mixture.

In high-throughput proteomics, a promising approach presently being explored is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) to provide measurements of the masses of tryptic peptides in complex mixtures, which can then be used to identify the proteins which gave rise to those peptides. In order to apply this method, it is necessary to account for any systematic measurement error, and it is useful to have an estimate of the random error in measured masses. In this investigation, a complex mixture of peptides derived from a partially characterized sample was analyzed by LC-FTICR-MS. Through the application of a Bayesian probability model of the data, partial knowledge of the composition of the sample is sufficient both to determine any systematic error and to estimate the random error in measured masses.