155,382 research outputs found
Morphological and karyotypical characterization of four biotypes of red rice (Oryza sativa subsp. Japonica var. sylvatica)
No full textThe present work relates to a morphological and karyotypical analysis of four phenotypically different weedy red rice biotypes in comparison with the cultivar Loto, used as control. A preliminary analysis, by means of a computerized chromosome image method (CHIA – EA), showed that in the red rice various traslocations occur which always involve a chromosome of the first pair together with other elements of the set (Sparacino et al., 2004). The results presented here clearly indicate that each red rice biotype is characterized by a specific traslocation, showing a relationship between different phenotypes and karyotypes
Estimation of endogenous glucose production after a glucose perturbation by nonparametric stochastic deconvolution.
No full textThe knowledge of the time course of endogenous glucose production (EGP) after a glucose perturbation is crucially important for understanding the glucose regulation system in both healthy and disease (e.g. diabetes) states. EGP is not directly accessible, and thus an indirect measurement approach is required. The estimation of EGP during an intravenous glucose tolerance test (IVGTT) can be posed as an input estimation problem solvable as a Fredholm integral equation of the first kind (A. Caumo and C. Cobelli, Am. J. Physiol., 264 (1993) E829-E841). The time-varying model of the kernel of the glucose system was identified from a concomitant tracer experiment, and EGP was reconstructed by employing the Phillips-Tikhonov regularization (deconvolution) algorithm. However, the proposed deconvolution approach left some issues open, e.g. how to choose the amount of regularization and how to deal with nonuniform/infrequent sampling. Here, a solution to these problems is provided by resorting to a new deconvolution algorithm. Thanks to the stochastic embedding into which the new deconvolution method is stated, the amount of regularization is determined in a statistically sound manner. In addition, in face of infrequent sampling, a time continuous profile of EGP is obtained. The method is shown to work reliably for reconstructing EGP in different IVGTT experimental protocols, both in normal and disease states
Bayesian identification of a population compartmental model of C-peptide kinetics
No full textWhen models are used to measure or predict physiological variables and parameters in a given individual, the experiments needed are often complex and costly. A valuable solution for improving their cost effectiveness is represented by population models. A widely used population model in insulin secretion studies is the one proposed by Van Cauter et al. (Diabetes 41:368-377, 1992), which determines the parameters of the two compartment model of C-peptide kinetics in a given individual from the knowledge of his/her age, sex, body surface area, and health condition (i.e., normal, obese, diabetic). This population model was identified from the data of a large training set (more than 200 subjects) via a deterministic approach. This approach, while sound in terms of providing a point estimate of C-peptide kinetic parameters in a given individual, does not provide a measure of their precision. In this paper, by employing the same training set of Van Cauter et al., we show that the identification of the population model into a Bayesian framework (by using Markov chain Monte Carlo) allows, at the individual level, the estimation of point values of the C-peptide kinetic parameters together with their precision. A successful application of the methodology is illustrated in the estimation of C-peptide kinetic parameters of seven subjects (not belonging to the training set used for the identification of the population model) for which reference values were available thanks to an independent identification experiment
Identification and Simulation of a Spatial Ecological Model in a Lake with Fractal Boundary
No full textWe propose a 2D ecological model of phytoplankton dynamics accounting for the distribution and the evolution of algae in a large basin located in the Amazonian region. The model is described by a set of reaction-drift-diffusion equations and is driven by several exogenous inputs, such as wind velocity and direction, water temperature and solar radiation. Due to the roughness of the domain, a preliminary boundary extraction with a curvelet algorithm is performed. Then, the model is simulated in an approximated domain, where the contour has been reconstructed by estimating a set of Recurrent Fractal Interpolation Functions, aimed at preserving its fractal structure. Simulations are combined with time and space chlorophyll-a data in order to estimate the parameters of the model. The proposed algorithm is based on an iterative two-step identification procedure, where reaction parameters are recovered first and then used for estimating diffusion and transport parameters. Comparison results at different accuracy approximations and before and after the algorithm implementation are presented and discussed
A new dynamic index of insulin sensitivity
No full textInsulin sensitivity is a crucial parameter of glucose metabolism. The standard measures of insulin sensitivity obtained by an euglycaemic hyperinsulinaemic clamp, SI(clamp), or by the minimal model (MM), SI, do not account for the dynamics of insulin action, i.e., how fast or slow insulin action reaches its plateau value. This is an important physiological information. In this paper we formally define a new insulin sensitivity index which also incorporates information on the dynamics of insulin action, SID, show its properties, and exemplify how it can be measured both with the clamp and the MM method. Then, by resorting to real and synthetic data, we show both in IVGTT MM and clamp studies why this new index SID offers, in comparison with SI, a more comprehensive picture of the control of insulin on glucose
Maximum Likelihood vs Maximum a Posteriori Parameter Estimation of Physiological System Models: The C-peptide Impulse Response Case Study
No full textMaximum-likelihood (ML), also given its connection to least squares (LS), is widely adopted in parameter estimation of physiological system models, i.e., assigning numerical values to the unknown model parameters from the experimental data. A more sophisticated but less used approach is maximum a posteriori (MAP) estimation. Conceptually, while ML adopts a Fisherian approach, i.e., only experimental measurements are supplied to the estimator, MAP estimation is a Bayesian approach, i.e., a priori available statistical information on the unknown parameters is also exploited for their estimation. Here, after a brief review of the theory behind ML and MAP estimators, the authors compare their performance in the solution of a case study concerning the determination of the parameters of a sum of exponential model which describes the impulse response of C-peptide (CP), a key substance for reconstructing insulin secretion. The results show that MAP estimation always leads to parameter estimates with a precision (sometimes significantly) higher than that obtained through ML, at the cost of only a slightly worse fit. Thus, a 3 exponential model can be adopted to describe the CP impulse response model in place of the two exponential model usually identified in the literature by the ML/LS approach. Simulated case studies are also reported to evidence the importance of taking into account a priori information in a data poor situation, e.g., when a few or too noisy measurements are available. In conclusion, the authors' results show that, when a priori information on the unknown model parameters is available, Bayes estimation can be of relevant interest, since it can significantly improve the precision of parameter estimates with respect to Fisher estimation. This may also allow the adoption of more complex models than those determinable by a Fisherian approach
Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.
No full textBackground and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data.
Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay.
Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay
Erratum: Enhanced accuracy of continuous glucose monitoring by online extended kalman filtering (Diabetes Technology & Therapeutics (2010) 12:5 (353-363))
No full textReconstruction of insulin secretion by deconvolution: Domain of validity of a monoexponential impulse response model.
No full textInsulin secretion rate (ISR) in vivo is reconstructed by deconvolution from plasma concentration of C-peptide (CP), a peptide with linear kinetics which is co-secreted with insulin but is not extracted by the liver. Deconvolution requires the knowledge of the CP impulse response. A two-exponential (2E) model is usually chosen to describe the CP impulse response but a one-exponential (1E) model is also used in the literature. The purpose here is to discuss the domain of validity of the 1E model in reconstructing the ISR by deconvolution. In particular, we show that the 1E model can be reliably used only if the ISR spectrum is concentrated in a narrow frequency band and a suitable input is designed for its identification
A wavelet methodology for EEG time-frequency analysis in a time discrimination task
No full textEEG signals recorded by surface electrodes placed on the scalp can be thought as nonstationary
stochastic processes in both time and space, especially in response to external stimuli.
Cognitive tasks, in particular, are reflected by changes in EEG dynamics concerning both rhythms
energy and connectivity across different brain regions. In the frequency-domain, EEG analysis is
complicated and time-frequency methodologies are needed. The Wavelet Transform, in particular,
represents a powerful tool for analysing, within a time-frequency embedding, the EEG. In this study
we applied a wavelet-based methodology to extract quantitative time-frequency parameters from EEG
signals recorded during a time discrimination task in 12 subjects. We used a continous wavelet
transform with a complex Morlet as mother function. In order to improve the time-frequency resolution
and to make it satisfactory, each of the four standard EEG rhythms (i.e. theta, alpha, beta, gamma) was
studied with Morlet wavelet parameters tuned ad hoc on the basis of both the width of the specific
frequency band and the particular type of activity under examination. The numerical values of the
estimated time-frequency indexes were then compared, evidencing statistically significant differences
in the brain response between experimental conditions
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