Fourier analysis of wave turbulence in a thin elastic plate

The spatio-temporal dynamics of the deformation of a vibrated plate is measured by a high speed Fourier transform profilometry technique. The space-time Fourier spectrum is analyzed. It displays a behavior consistent with the premises of the Weak Turbulence theory. A isotropic continuous spectrum of waves is excited with a non linear dispersion relation slightly shifted from the linear dispersion relation. The spectral width of the dispersion relation is also measured. The non linearity of this system is weak as expected from the theory. Finite size effects are discussed. Despite a qualitative agreement with the theory, a quantitative mismatch is observed which origin may be due to the dissipation that ultimately absorbs the energy flux of the Kolmogorov-Zakharov casade.Comment: accepted for publication in European Physical Journal B see http://www.epj.or

Artificial Pancreas: In Silico Study Shows No Need of Meal Announcement and Improved Time in Range of Glucose with Intraperitoneal vs. Subcutaneous Insulin Delivery

Contemporary Artificial Pancreas (AP) consists of a subcutaneous (SC) glucose sensor, a SC insulin pump and a control algorithm. Even the most advanced systems are far from optimal, in particular due to the non-physiologic nature of SC route. While SC insulin delivery is convenient and minimally invasive, it introduces delays to insulin action that make tight control difficult, particularly during meals. In addition frequent patient interventions are needed, e.g., at mealtime. The intraperitoneal (IP) insulin delivery could address this major challenge since it exhibits a faster pharmacokinetics/pharmacodynamics, hence making easier to quickly respond to glycemic disturbances. A 1-day hospital closed-loop study has shown significant improvements of IP glucose control vs SC AP, and that meal announcement is not necessary. However, the IP AP has not been tested in more realistic everyday life conditions. In this work we have performed an in silico study of 14 days of an IP AP by using the UVA/Padova simulator which includes intra- and inter-day variability of insulin sensitivity and several real life scenarios. We show superiority of IP AP vs SC AP in terms of quality of glucose control (time in range 87% IP vs 80% SC) without the need of a meal announcement

A nonparametric approach for model individualization in an artificial pancreas

The identification of patient-tailored linear time invariant glucose-insulin models is investigated for type 1 diabetic patients, that are characterized by a substantial inter-subject variability. The individualized linear models are identified by considering a novel kernel-based nonparametric approach and are compared with a linear time invariant average model in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean squared error. Model identification and validation are based on in-silico data collected from the adult virtual population of the UVA/Padova simulator. The data generation involves a protocol designed to produce a sufficient input excitation without compromising patient safety, compatible also with real life scenarios. The identified models are exploited to synthesize an individualized Model Predictive Controller (MPC) for each patient, which is used in an Artificial Pancreas to maintain the blood glucose concentration within an euglycemic range. The MPC used in several clinical studies, synthesized on the basis of a non-individualized average linear time invariant model, is also considered as reference. The closed-loop control performance is evaluated in an in-silico study on the adult virtual population of the UVA/Padova simulator in a perturbed scenario, in which the MPC is blind to random variations of insulin sensitivity in each virtual patient. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background 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

The hot IVGTT two-compartment minimal model : indexes of glucose effectiveness and insulin sensitivity

A two-compartment minimal model (2CMM) has been proposed [A. Caumo and C. Cobelli. Am. J. Physiol. 264 (Endocrinol. Metab. 27): E829-E841, 1993] to describe intravenous glucose tolerance test (IVGTT) labeled (hereafter hot) glucose kinetics. This model, at variance with the one-compartment minimal model (1CMM), allows the estimation of a plausible profile of glucose production. The aim of this study is to show that the 2CMM also allows the assessment of insulin sensitivity (SI2*), glucose effectiveness (SG2*), and plasma clearance rate (PCR). The 2CMM was identified on stable-isotope IVGTTs performed in normal subjects (n = 14). Results were (means +/- SE) SG2* = 0.85 +/- 0.14 ml.kg-1.min-1, PCR = 2.02 +/- 0.14 ml.kg-1.min-1, and SI2* = 13.83 +/- 2.54 x 10(-2) ml.kg-1.min-1.microU-1.ml. The 1CMM was also identified; glucose effectiveness and insulin sensitivity indexes were SG*V = 1.36 +/- 0.08 ml.kg-1.min-1 and SI*V = 12.98 +/- 2.21 x 10(-2) ml.kg-1.min-1.microU-1.ml, respectively, where V is the 1CMM glucose distribution volume. SG*V was lower than PCR and higher than SG2* and did not correlate with either [r = 0.45 (NS) and r = 0.50 (NS), respectively], whereas SI*V was not different from and was correlated with SI2* (r = 0.95; P < 0.001). SG* compares well (r = 0.78; P < 0.001) with PCR normalized by the 2CMM total glucose distribution volume. In conclusion, the 2CMM is a powerful tool to assess glucose metabolism in vivo

MARKERLESS ANALYSIS OF SWIMMERS’ MOTION: A PILOT STUDY

Regular laboratory-based motion analysis with skin surface markers is not always feasible. In particular, when studying swimmers kinematics, traditional motion capture techniques cannot be adopted. Although video recordings from swimmers often exist, current methods for biomechanical analysis of these are inadequate. They usually rely on manual digitization of joints’ position on a single sagittal view of the subject. Therefore, in this study a method for three dimensional (3D) markerless motion capture of swimmers is presented. The method adopts the markerless motion capture system developed at Stanford University