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PubMed2003

Measurement of fluorophore concentrations and fluorescence quantum yield in tissue-simulating phantoms using three diffusion models of steady-state spatially resolved fluorescence.

Steady-state diffusion theory models of fluorescence in tissue have been investigated for recovering fluorophore concentrations and fluorescence quantum yield. Spatially resolved fluorescence, excitation and emission reflectance Carlo simulations, and measured using a multi-fibre probe on tissue-simulating phantoms containing either aluminium phthalocyanine tetrasulfonate (AlPcS4), Photofrin meso-tetra-(4-sulfonatophenyl)-porphine dihydrochloride The accuracy of the fluorophore concentration and fluorescence quantum yield recovered by three different models of spatially resolved fluorescence were compared. The models were based on: (a) weighted difference of the excitation and emission reflectance, (b) fluorescence due to a point excitation source or (c) fluorescence due to a pencil beam excitation source. When literature values for the fluorescence quantum yield were used for each of the fluorophores, the fluorophore absorption coefficient (and hence concentration) at the excitation wavelength (mu(a,x,f)) was recovered with a root-mean-square accuracy of 11.4% using the point source model of fluorescence and 8.0% using the more complicated pencil beam excitation model. The accuracy was calculated over a broad range of optical properties and fluorophore concentrations. The weighted difference of reflectance model performed poorly, with a root-mean-square error in concentration of about 50%. Monte Carlo simulations suggest that there are some situations where the weighted difference of reflectance is as accurate as the other two models, although this was not confirmed experimentally. Estimates of the fluorescence quantum yield in multiple scattering media were also made by determining mu(a,x,f) independently from the fitted absorption spectrum and applying the various diffusion theory models. The fluorescence quantum yields for AlPcS4 and TPPS4 were calculated to be 0.59 +/- 0.03 and 0.121 +/- 0.001 respectively using the point source model, and 0.63 +/- 0.03 and 0.129 +/- 0.002 using the pencil beam excitation model. These results are consistent with published values.

Algorithms↗

PubMed2000

Risk assessment and standardized nosocomial infection rate in critically ill children.

OBJECTIVES: To develop and validate a pediatric nosocomial infection risk (PNIR) assessment model, and to compare the daily trends in risk factors between patients with nosocomial infection (cases) and without nosocomial infection (controls) in the pediatric intensive care unit (ICU). DESIGN: Prospective cohort. SETTING: A 16-bed pediatric ICU in an urban, university-affiliated, multidisciplinary, regional referral center. PATIENTS: Patients available for study included consecutive admissions to the unit between May 1, 1992, and April 30, 1993, and between May 9, 1995, and December 11, 1995. Patients from both data collection periods were pooled and randomly divided into training (70%) and validation (30%) samples. MEASUREMENTS AND MAIN RESULTS: In the logistic regression analysis using admission day data, three factors were shown to remain as independent risk factors. Invasive device use, parenteral nutrition, and the interaction between severity of illness-modified Pediatric Risk of Mortality III-24 score and postoperative care were associated with 2, 6, and 1.5 times the risk of developing nosocomial infection, respectively. This PNIR model performed well in both the training and validation samples as indicated by the goodness-of-fit test, which evaluated standardized nosocomial infection rates (observed vs. predicted nosocomial infection rates). The internal validity of the PNIR model was good. In trend analysis, severity of illness and invasive device use appear to have similar trend patterns, during the first week of pediatric ICU stay. There was no difference in any of these risk factors between cases and controls after 7 days of pediatric ICU stay. CONCLUSIONS: The PNIR assessment model incorporates intrinsic factors, such as patient severity of illness, and extrinsic factors contributing to the development of nosocomial infection in this high-risk population. The methodology using intrinsic and extrinsic factors to adjust for nosocomial infections should be taken into consideration when evaluating interhospital comparison of nosocomial infection rates, quality assessment, intervention strategies, and use of treatment modalities.

Child, Preschool↗

PubMed2003

A nonlinear rheological assessment of muscle recovery from eccentric stretch injury.

PURPOSE: To better understand the mechanical behavior of healing skeletal muscle; specifically the tissue's response after acute eccentric stretch injury. METHODS: Rabbit tibialis anterior (TA) muscle tendon units were subjected to an in vivo single stretch (eccentric) injury and mechanically evaluated (constant rate elongation to failure) at 1, 3, and 7 d postinjury. In addition to a traditional linear analysis (linear stiffness and failure load), an existing nonlinear rheological model was modified to interpret the experimental load-to-failure data. The models' performance were evaluated and discussed. RESULTS: No significant injury effect was observed, either within or between groups, across the 7-d healing interval, using the linear analysis. However, interpretation of the data using our nonlinear phenomenological model identified significant changes in mechanical behavior that went undetected by linear analyses. Percent differences, between injured and contralateral control limbs, of model parameter estimates were analyzed. Nonparametric statistical analysis illustrated significant changes in the first-order stiffness (k1) throughout the 7-d healing interval. Model simulations using mean values of each parameter revealed increased low-load tissue compliance after injury, with a decrease in linear slope that recovered steadily toward control values by day 7. At 7 d postinjury, virtually no differences were observed between injured and sham control tissues. CONCLUSIONS: Our findings suggest that acute eccentric injury increases the muscle's compliance 24 h after injury, with a steady recovery to uninjured values by the 7th day, yet these changes went undetected by linear analysis. Therefore, nonlinear analysis is necessary to recognize valuable information contained in the low-load region and to quantify important biomechanical phenomena of stretch-injured healing skeletal muscle.

Animals↗

PubMed2006

Impact of data quality and model complexity on prediction of pesticide leaching.

Accurate input data for leaching models are expensive and difficult to obtain which may lead to the use of "general" non-site-specific input data. This study investigated the effect of using different quality data on model outputs. Three models of varying complexity, GLEAMS, LEACHM, and HYDRUS-2D, were used to simulate pesticide leaching at a field trial near Hamilton, New Zealand, on an allophanic silt loam using input data of varying quality. Each model was run for four different pesticides (hexazinone, procymidone, picloram and triclopyr); three different sets of pesticide sorption and degradation parameters (i.e., site optimized, laboratory derived, and sourced from the USDA Pesticide Properties Database); and three different sets of soil physical data of varying quality (i.e., site specific, regional database, and particle size distribution data). We found that the selection of site-optimized pesticide sorption (Koc) and degradation parameters (half-life), compared to the use of more general database derived values, had significantly more impact than the quality of the soil input data used, but interestingly also more impact than the choice of the models. Models run with pesticide sorption and degradation parameters derived from observed solute concentrations data provided simulation outputs with goodness-of-fit values closest to optimum, followed by laboratory-derived parameters, with the USDA parameters providing the least accurate simulations. In general, when using pesticide sorption and degradation parameters optimized from site solute concentrations, the more complex models (LEACHM and HYDRUS-2D) were more accurate. However, when using USDA database derived parameters, all models performed about equally.

Adsorption↗

PubMed2006

The accurate QSPR models for the prediction of nonionic surfactant cloud point.

Quantitative structure-property relationship models were developed to predict cloud points and study the cloud phenomena of nonionic surfactants in aqueous solution. Four descriptors were selected by the heuristic method as the inputs of multiplier linear regression and support vector machine (SVM) models. Very satisfactory results were obtained. SVM models performed better both in fitness and in prediction capacity. For the test set, they gave a predictive correlation coefficient (R) of 0.9882, root mean squared error of 4.2727, and absolute average relative deviation of 9.5490, respectively. The proposed models can identify and provide some insight into what structural features are related to the cloud points of compounds, i.e., the molecular size, structure, and isomerism of the hydrocarbon moiety and the degree of oxyethylation. They can also help to understand the cloud phenomena of nonionic surfactants in aqueous solution. Additionally, this paper provides two simple, practical, and effective methods for analytical chemists to predict the cloud points of nonionic surfactants in aqueous solution.

Journal Article↗

PubMed2003

Modification of ASM No.1 for a submerged membrane bioreactor system: including the effects of soluble microbial products on membrane fouling.

In this study, a mathematical model for the submerged membrane bioreactor (SMBR) was developed by combining the activated sludge model (ASM) with a membrane resistance-in-series model. Some modifications were introduced to make ASM to be suitable for describing the characteristics of SMBR. A set of the 1st-order differential equations was established for 13 dependent variables relevant to particles and soluble matters. Performing model simulations for various conditions, the time when a membrane would be fouled could be predicted as well as the effluent quality. From simulation results, F/M ratio and SRT can be considered as major factors of the soluble microbial products (SMP) concentration in a reactor and it is clear that SMP can play an important role in membrane fouling and water quality simultaneously. The model would be very helpful in optimizing operation conditions as well as in designing an optimal SMBR system.

Bioreactors↗

PubMed1978

Predictive model of dynamic response of the human head/neck system to -Gx impact acceleration.

This paper describes the mathematical framework, underlying an empirical model, that predicts human head response using only the motion present at vertebra T1. Based on this framework, a model for --Gx impact acceleration was developed from data obtained on six volunteer subjects participating in the NAMRL impact acceleration experiments. Model performance was evaluated by comparing the errors in the predicted head responses with the normal variations observed between the responses of different subjects under identical impact accelerations. Independent sets of data were used for building and testing the model. The results of the evaluation indicate that the model will be useful in subsequent studies of human response to impact acceleration.

Acceleration↗

PubMed1995

Limited sampling models for simultaneous estimation of the pharmacokinetics of irinotecan and its active metabolite SN-38.

Irinotecan (CPT-11) is a novel topoisomerase I inhibitor with clinical activity in human malignancies. The objective of this study was to develop efficient limited sampling models (LSMs) to estimate simulataneously the area under the plasma concentration versus time curves (AUC) for both CPT-11 and its active metabolite SN-38. A total of 64 pharmacokinetic sets (> or = 24-h sampling) were obtained in phase I studies at doses ranging from 50 to 750 mg/m2 (0.5-h i.v. infusion). The patients were randomly assigned to a training data set (n = 32) and a test set (n = 32). Multiple linear regression analyses were used to determine the optimal LSMs based on the correlation coefficient (r), bias (MPE%, percentage of mean prediction error), and precision (RMSE%, percentage of root mean squared prediction error). Of these LSMs, the ones including maximal concentrations of CPT-11 (0.5 h, the end of the i.v. infusion) and metabolite SN-38 (approximately 1 h) were favored along with predictive precision and clinical constraints. Several bivariate models including a 6-h time point as the last sampling time (or 7 h) were found to be highly predictive of either the CPT-11 AUC or the SN-38 AUC. The chosen sampling time points were the ones that allowed the best compromise between the accurate determination of either compound alone with the same sampling times. The simultaneously best prediction of both CPT-11 and SN-38 AUCs was obtained with sampling time points harvested at 0.5, 1, and 6 h (or 7 h). With these sampling time points a trivariate model was selected for the determination of CPT-11 AUC namely, CPT-11 AUC (ng h ml-1) = 0.820 x C0.5h + 0.402 x C1h + 15.47 x C6h + 928, and a corresponding model was selected for the determination of metabolite AUC, i.e., SN-38 AUC (ng h ml-1) = 4.05 x C0.5h -0.81 x C1h + 23.01 x C6h - 69.78, where C(t) is the concentration in nanograms per milliliter of either compound at a given time t. These models performed well with the test data sets for CPT-11 AUC (r = 0.98, MPE% = -1.4, RMSE% = 13.9) and for SN-38 AUC (r = 0.95, MPE% = -6.5, RMSE% = 37.7). In addition to the determination of AUCs (and hence clearance), these models also allow the determination of the maximal concentrations of both compounds, which might be needed for pharmacodynamics studies.(ABSTRACT TRUNCATED AT 400 WORDS)

Antineoplastic Agents, Phytogenic↗

PubMed2005

Validation and refinement of mortality risk models for heart valve surgery.

BACKGROUND: The Northern New England Cardiovascular Disease Study Group (NNE) recently published risk models for hospital mortality after heart valve surgery. The Providence Health System Cardiovascular Study Group (PHS) has been collecting similar heart valve data for 8 years, providing an ideal opportunity to both validate the NNE risk models and attempt to produce an improved model, by using some different modeling techniques. METHODS: From 1997 to 2004, 3,324 patients aged 30 to 95 years underwent aortic valve replacement (AVR), and 1,596 underwent mitral valve replacement or repair (MVRR) at one of nine PHS medical centers. We used area under the receiver operating characteristic curve (c-index) to measure model discrimination, and Hosmer-Lemeshow statistic (H-L) to measure calibration. We modified the NNE models by ungrouping continuous variables, seeking optimal transformations of continuous variables, and imputing missing values by multiple regression. RESULTS: The prevalence and the lethality of risk factors were similar in PHS and NNE patients. The NNE models fit PHS patients well: c-index (95% confidence interval) = 0.75 (0.70 to 0.80) for AVR and 0.81 (0.76 to 0.86) for MVRR; H-L = 3.95 (p = 0.861) for AVR and 7.10 (p = 0.526) for MVRR. A single PHS model performed slightly better for both positions: c-index = 0.79 (0.75 to 0.83) for AVR and 0.84 (0.80 to 0.88) for MVRR; H-L = 2.75 (p = 0.949) for AVR and 12.21 (p = 0.142) for MVRR. CONCLUSIONS: The NNE models for aortic and mitral valve surgery were successfully validated using PHS patients. Using some different statistical approaches to modeling, we produced a new, unified model for both positions.

Adult↗

PubMedDate not supplied

Comparison of structure extraction methods for in vivo trabecular bone measurements.

In vivo trabecular bone structure measurements have become available recently using high resolution quantitative computed tomography (3D-QCT) or magnetic resonance imaging. In this work different structure extraction and morphometric evaluation techniques are compared, which are of potential use for in vivo assessment of human cancellous bone structure. Given the spatial resolution of actual in vivo examination procedures, best results are obtained by extracting first the skeleton of the structure and applying model independent 3D techniques to calculate trabecular number, then deriving mean trabecular thickness and separation from densitometric bone volume fraction and Tb.N*. Traditional histomorphometric methods based on bone surface and volume ratios and assuming a plate model performed less well.

Bone Density↗

PubMed2002

A standardized boundary element method volume conductor model.

OBJECTIVES: We used a 3-compartment boundary element method (BEM) model from an averaged magnetic resonance image (MRI) data set (Montreal Neurological Institute) in order to provide simple access to realistically shaped volume conductor models for source reconstruction, as compared to individually derived models. The electrode positions were transformed into the model's coordinate system, and the best fit dipole results were transformed back to the original coordinate system. The localization accuracy of the new approach was tested in a comparison with simulated data and with individual BEM models of epileptic spike data from several patients. METHODS: The standard BEM model consisted of a total of 4770 nodes, which describe the smoothed cortical envelope, the outside of the skull, and the outside of the skin. The electrode positions were transformed to the model coordinate system by using 3-5 fiducials (nasion, left and right preauricular points, vertex, and inion). The transformation consisted of an averaged scaling factor and a rigid transformation (translation and rotation). The potential values at the transformed electrode positions were calculated by linear interpolation from the stored transfer matrix of the outer BEM compartment triangle net. After source reconstruction the best fit dipole results were transformed back into the original coordinate system by applying the inverse of the first transformation matrix. RESULTS: Test-dipoles at random locations and with random orientations inside of a highly refined reference BEM model were used to simulate noise-free data. Source reconstruction results using a spherical and the standardized BEM volume conductor model were compared to the known dipole positions. Spherical head models resulted in mislocation errors at the base of the brain. The standardized BEM model was applied to averaged and unaveraged epileptic spike data from 7 patients. Source reconstruction results were compared to those achieved by 3 spherical shell models and individual BEM models derived from the individual MRI data sets. Similar errors to that evident with simulations were noted with spherical head models. Standardized and individualized BEM models were comparable. CONCLUSIONS: This new approach to head modeling performed significantly better than a simple spherical shell approximation, especially in basal brain areas, including the temporal lobe. By using a standardized head for the BEM setup, it offered an easier and faster access to realistically shaped volume conductor models as compared to deriving specific models from individual 3-dimensional MRI data.

Brain↗

PubMed2001

Sensitivity analysis and evaluation of microFacCO: a microscale motor vehicle emission factor model for CO emissions.

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting real-time site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by approximately 67% at ambient temperatures of 45 degrees F and > or = 95 degrees F in comparison with an ambient temperature of 75 degrees F. The input variable "emission failure standard rate" is more sensitive to estimating emission rates in the 1990s than in the 2000s. The estimation of emission rates is not very sensitive to relative humidity. MicroFacCO can also be applied to examine the contribution of emission rates per vehicle class and model year. The model evaluation is presented for tunnel studies at five locations. In general, this evaluation study found good agreement between the measured and the modeled emissions. These analyses and evaluations have identified the need for additional studies to update the high-speed (>35 mph) air conditioning (A/C) correction factor and to add effects due to road grades. MicroFacCO emission estimates are very sensitive to the emission standard failure rate. Therefore, the model performance can be greatly improved by using a local emission standard failure rate.

Algorithms↗

PubMed2026

Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.

Recent advances in large language models have extended to genomic applications, yet model robustness relative to context is unclear. Here, we demonstrate two intrinsic biases (input sequence length and nucleotide position) affecting SegmentNT results, a model included with the Nucleotide Transformer that provides nucleotide-level predictions of biological features. We demonstrate that nucleotide position within the input sequence (beginning, middle, or end) alters the nature of SegmentNT's raw prediction probabilities, which can be standardized to improve prediction consistency. While longer input sequence length improves model performance, diminishing returns suggest a surprisingly small input length of ∼3072 nucleotides might be sufficient for many applications. We further identify a 24-nucleotide periodic oscillation in SegmentNT's prediction probabilities, revealing an intrinsic bias potentially linked to the model's training tokenization (6-mers) and architecture. We identify potential approaches to account for these biases and provide generalizable insights for utilizing nucleotide-resolution functional prediction models.

Nucleotides↗

PubMed1992

External ear transfer function modeling: a beamforming approach.

In this article, a beamformer is proposed as a functional model for the spatial and temporal filtering characteristics of the external ear. The output of a beamformer is a weighted combination of the data received at an array of spatially distributed sensors. The beamformer weights and array geometry determine its spatial and temporal filtering characteristics. A procedure is described for choosing the weights to minimize the mean-squared error between the beamformer response and the measured response of the external ear. The effectiveness of the model is demonstrated by designing a beamformer of several hundred weights that duplicates and interpolates the measured external ear response of a cat over broad ranges of frequency and direction. A limited investigation of modeling performance as a function of array geometry is reported.

Animals↗

PubMed1994

Improving the outcomes of coronary artery bypass surgery in New York State.

OBJECTIVE: To assess changes in outcomes of coronary artery bypass graft (CABG) surgery in New York since 1989, when the State Department of Health began collecting, analyzing, and disseminating information regarding risk factors, mortality, and complications of CABG surgery. These new data stimulated specific quality improvement activities at hospitals throughout the state. DESIGN: A clinical database was used to identify significant independent risk factors and to assess risk-adjusted provider mortality rates. SETTING: All 30 hospitals performing CABG surgery in New York during the period 1989 through 1992. PATIENTS: All 57,187 patients undergoing isolated CABG surgery who were discharged from New York State hospitals in 1989 through 1992. MAIN OUTCOME MEASURES: Actual, expected (from a logistic regression model), and risk-adjusted in-hospital mortality. RESULTS: Actual mortality decreased from 3.52% in 1989 to 2.78% in 1992. Because average patient severity of illness increased, risk-adjusted mortality decreased even more--a decrease of 41% from 4.17% in 1989 to 2.45% in 1992. The risk-adjustment model performed well; there were no clinically or statistically significant differences between actual and predicted numbers of deaths at any of 10 levels of patient severity. CONCLUSIONS: We believe that this quality improvement program, based on the collection and dissemination of risk-adjusted mortality data for CABG surgery, played a significant role in the observed decline in the death rate from this procedure. Quality improvement programs based on similar principles for other procedures and conditions should be undertaken.

Actuarial Analysis↗

PubMed2006

Development and evaluation of a training module for the clinical introduction of the da Vinci robotic system in visceral and vascular surgery.

BACKGROUND: With the increasing use of the surgical robotic system in the clinical arena, appropriate training programs and assessment systems need to be established for mastery of this new technology. The authors aimed to design and evaluate a clinic-like training program for the clinical introduction of the da Vinci robotic system in visceral and vascular surgery. METHODS: Four trainees with different surgical levels of experience participated in this study using the da Vinci telemanipulator. Each participant started with an initial evaluation stage composed of standardized visceral and vascular operations (cholecystectomy, gastrotomy, anastomosis of the small intestine, and anastomosis of the aorta) in a porcine model. Then the participants went on to the training stage with the rat model, performing standardized visceral and vascular operations (gastrotomy, anastomosis of the large and small intestines, and anastomosis of the aorta) four times in four rats. The final evaluation stage was again identical to the initial stage. The operative times, the number of complications, and the performance quality of the participants were compared between the two evaluation stages to assess the impact of the training stage on the results. RESULTS: The operative times in the final evaluation stage were considerably shorter than in the initial evaluation stage and, except for cholecystectomies, all the differences reached statistical significance. Also, significantly fewer complications and improved quality for each operation in the final evaluation stage were documented, as compared with their counterparts in the initial evaluation stage. These improvements were recorded at each level of experience. CONCLUSIONS: The presented experimental small and large animal model is a standardized and reproducible training method for robotic surgery that allows evaluation of the surgical performance while shortening and optimizing the learning-curve.

Animals↗

PubMedDate not supplied

Concurrent visualization in a production supercomputing environment.

We describe a concurrent visualization pipeline designed for operation in a production supercomputing environment. The facility was initially developed on the NASA Ames "Columbia" supercomputer for a massively parallel forecast model (GEOS4). During the 2005 Atlantic hurricane season, GEOS4 was run 4 times a day under tight time constraints so that its output could be included in an ensemble prediction that was made available to forecasters at the National Hurricane Center. Given this time-critical context, we designed a configurable concurrent pipeline to visualize multiple global fields without significantly affecting the runtime model performance or reliability. We use MPEG compression of the accruing images to facilitate live low-bandwidth distribution of multiple visualization streams to remote sites. We also describe the use of our concurrent visualization framework with a global ocean circulation model, which provides a 864-fold increase in the temporal resolution of practically achievable animations. In both the atmospheric and oceanic circulation models, the application scientists gained new insights into their model dynamics, due to the high temporal resolution animations attainable.

Journal Article↗

PubMedDate not supplied

Polychotomous multivariate models for coronary heart disease simulation. IV. The impact of physiological aging.

This is an extension of a series of papers dealing with certain models used in the simulation of coronary heart disease. The current study investigates implications of including age as a risk factor in the models discussed in the preceding papers. The effects of using age as a risk factor were investigated in two ways. In one of these, age is interpreted as age of entry into the study; it is similar to the other risk factors in that it is assumed to be constant throughout the study. In the other, age is interpreted as the actual age; thus it increases during the course of simulations. Two polychotomous, multivariate risk functions developed in previous studies, the logistic risk and the Neyman exponential risk, were used to explore the effects of including age as a risk factor. The estimated risk coefficient for age was found to be statistically significant for both functions. The model performance was evaluated by comparing the observational data with outcomes simulated using Monte Carlo techniques. It was found that the logistic risk function failed to describe the observations either with age as a constant or with aging during the simulations. The models including the Neyman exponential risk avoidance fit the data well. The evaluation of the results indicates that aging during the simulations is better than using only the age as the constant value at entry to the study.

Adult↗