Jiguo Cao

FLS simulator performance predicts intraoperative laparoscopic skill

IntroductionSimulators are being used more and more for teaching and testing laparoscopic skills. However, it has yet to be firmly established that simulator performance reflects operative laparoscopic skill. The study reported here was designed to test the hypothesis that laparoscopic simulator performance predicts intraoperative laparoscopic skill.MethodsA review of our prospectively maintained database identified 40 subjects who underwent Fundamentals of Lapraoscopic Surgery (FLS) skills testing and objective intraoperative assessments within the same 6-month period. Subjects consisted of 22 novice (postgraduate year [PGY] 1–2), 10 intermediate (PGY 3–4), and 8 experienced (PGY 5, fellows, and attendings) laparoscopic surgeons. Laparoscopic performance was objectively assessed in the operating room using the previously validated Global Operative Assessment of Laparoscopic Skill (GOALS). Analysis of variance (ANOVA) was used to compare mean FLS scores and mean GOALS scores across experience levels. The relationship between individual FLS scores and GOALS scores was assessed with linear regression analysis. A multivariate analysis evaluated FLS score and surgeon experience as predictors of intraoperative GOALS score. A receiver-operator curve (ROC) was constructed in order to define an FLS cutoff score that predicts intraoperative performance at or above the level of experienced surgeons. Significance was defined as p < 0.05.ResultsMean FLS scores and mean GOALS scores increased with increasing experience. Individual FLS scores correlated significantly with intraoperative GOALS scores (0.77, p < 0.001). Multivariate analysis confirmed that FLS score is an independent predictor of intraoperative GOALS scores. The ROC identified an FLS cutoff score of 70 with optimal sensitivity (91%) and specificity (86%) for predicting a GOALS score at or above the level of experienced surgeons.ConclusionsIn this study sample, FLS simulator scores were independently predictive of intraoperative laparoscopic performance as measured by GOALS. More precisely, an FLS cutoff score of 70 optimized sensitivity and specificity for expert intraoperative performance. A larger prospective study is justified to validate these findings.

Arterial versus capillary blood gases: a meta-analysis.

A meta-analysis determined whether capillary blood gases accurately reflect arterial blood samples. A mixed effects model was used on 29 relevant studies obtained from a PubMed/Medline search. From 664 and 222 paired samples obtained from the earlobe and fingertip, respectively, earlobe compared to fingertip sampling shows that the standard deviation of the difference is about 2.5x less (or the precision is 2.5x better) in resembling arterial PO(2) over a wide range of arterial PO(2)s (21-155 mm Hg ). The lower the arterial PO(2), the more accurate it is when predicting arterial PO(2) from any capillary sample (p<0.05). However, while earlobe sampling predicts arterial PO(2) (adjusted r(2)=0.88, mean bias=3.8 mm Hg compared to arterial), fingertip sampling does not (adjusted r(2)=0.48, mean bias=11.5 mm Hg compared to arterial). Earlobe sampling is slightly more accurate compared to fingertip sampling in resembling arterial PCO(2) (arterial versus earlobe, adjusted r(2)=0.94, mean bias=1.9 mm Hg ; arterial versus fingertip, adjusted r(2)=0.95, mean bias=2.2 mm Hg compared to arterial) but both sites can closely reflect arterial PCO(2) (880 total paired samples, range 10-114 mm Hg ). No real difference between sampling from the earlobe or fingertip were found for pH as both sites accurately reflect arterial pH over a wide range of pH (587 total paired samples, range 6.77-7.74, adjusted r(2)=0.90-0.94, mean bias=0.02). In conclusion, sampling blood from the fingertip or earlobe (preferably) accurately reflects arterial PCO(2) and pH over a wide range of values. Sampling blood, too, from earlobe (but never the fingertip) may be appropriate as a replacement for arterial PO(2), unless precision is required as the residual standard error is 6 mm Hg when predicting arterial PO(2) from an earlobe capillary sample.

Automated Load Curve Data Cleansing in Power Systems

Load curve data refers to the electric energy consumption recorded by meters at certain time intervals at delivery points or end user points, and contains vital information for day-to-day operations, system analysis, system visualization, system reliability performance, energy saving and adequacy in system planning. Unfortunately, it is unavoidable that load curves contain corrupted data and missing data due to various random failure factors in meters and transfer processes. This paper presents the B-Spline smoothing and Kernel smoothing based techniques to automatically cleanse corrupted and missing data. In implementation, a man-machine dialogue procedure is proposed to enhance the performance. The experiment results on the real British Columbia Transmission Corporation (BCTC) load curve data demonstrated the effectiveness of the presented solution.

Estimating dynamic models for gene regulation networks

MOTIVATION Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data. RESULTS In this article, we introduce the generalized profiling method to estimate ODE parameters in a gene regulation network from microarray gene expression data which can be rather noisy. Because numerically solving ODEs is computationally expensive, we apply the penalized smoothing technique, a fast and stable computational method to approximate ODE solutions. The ODE solutions with our parameter estimates fit the data well. A goodness-of-fit test of dynamic models is developed to identify gene regulation networks.

Parameter Estimation of Partial Differential Equation Models

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online.

Standardisation and application of the single-breath determination of nitric oxide uptake in the lung

Diffusing capacity of the lung for nitric oxide (DLNO), otherwise known as the transfer factor, was first measured in 1983. This document standardises the technique and application of single-breath DLNO. This panel agrees that 1) pulmonary function systems should allow for mixing and measurement of both nitric oxide (NO) and carbon monoxide (CO) gases directly from an inspiratory reservoir just before use, with expired concentrations measured from an alveolar “collection” or continuously sampled via rapid gas analysers; 2) breath-hold time should be 10 s with chemiluminescence NO analysers, or 4–6 s to accommodate the smaller detection range of the NO electrochemical cell; 3) inspired NO and oxygen concentrations should be 40–60 ppm and close to 21%, respectively; 4) the alveolar oxygen tension (PAO2) should be measured by sampling the expired gas; 5) a finite specific conductance in the blood for NO (θNO) should be assumed as 4.5 mL·min-1·mmHg-1·mL-1 of blood; 6) the equation for 1/θCO should be (0.0062·PAO2+1.16)·(ideal haemoglobin/measured haemoglobin) based on breath-holding PAO2 and adjusted to an average haemoglobin concentration (male 14.6 g·dL−1, female 13.4 g·dL−1); 7) a membrane diffusing capacity ratio (DMNO/DMCO) should be 1.97, based on tissue diffusivity. Pulmonary diffusing capacity for nitric oxide is standardised by a panel of experts for use around the world http://ow.ly/TpV1306Yhji

Robust Estimation for Ordinary Differential Equation Models

Applied scientists often like to use ordinary differential equations (ODEs) to model complex dynamic processes that arise in biology, engineering, medicine, and many other areas. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. We propose a robust method to address this problem. The dynamic process is represented with a nonparametric function, which is a linear combination of basis functions. The nonparametric function is estimated by a robust penalized smoothing method. The penalty term is defined with the parametric ODE model, which controls the roughness of the nonparametric function and maintains the fidelity of the nonparametric function to the ODE model. The basis coefficients and ODE parameters are estimated in two nested levels of optimization. The coefficient estimates are treated as an implicit function of ODE parameters, which enables one to derive the analytic gradients for optimization using the implicit function theorem. Simulation studies show that the robust method gives satisfactory estimates for the ODE parameters from noisy data with outliers. The robust method is demonstrated by estimating a predator-prey ODE model from real ecological data.

Blockwise empirical likelihood for time series of counts

Time series of counts have a wide variety of applications in real life. Analyzing time series of counts requires accommodations for serial dependence, discreteness, and overdispersion of data. In this paper, we extend blockwise empirical likelihood (Kitamura, 1997 [15]) to the analysis of time series of counts under a regression setting. In particular, our contribution is the extension of Kitamuras (1997) [15] method to the analysis of nonstationary time series. Serial dependence among observations is treated nonparametrically using a blocking technique; and overdispersion in count data is accommodated by the specification of a variance-mean relationship. We establish consistency and asymptotic normality of the maximum blockwise empirical likelihood estimator. Simulation studies show that our method has a good finite sample performance. The method is also illustrated by analyzing two real data sets: monthly counts of poliomyelitis cases in the USA and daily counts of non-accidental deaths in Toronto, Canada.

Linear Mixed-Effects Modeling by Parameter Cascading

A linear mixed-effects model (LME) is a familiar example of a multilevel parameter structure involving nuisance and structural parameters, as well as parameters that essentially control the model’s complexity. Marginalization over nuisance parameters, such as the restricted maximization likelihood method, has been the usual estimation strategy, but it can involve onerous and complex algorithms to achieve the integrations involved. Parameter cascading is described as a multicriterion optimization algorithm that is relatively simple to program and leads to fast and stable computation. The method is applied to LME, where well-developed marginalization methods are already available. Our results suggest that parameter cascading is at least as good as, if not better than, the available methods. We also extend the LME model to multicurve data smoothing by introducing a basis partitioning scheme and defining roughness penalty terms for both functional fixed effect and random effects. The results are substantially better than those obtained by using the previous LME methods. A supplemental document is available online.

Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations

Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online.