An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

Using state variables to model the response of tumour cells to radiation and heat: a novel multi-hit-repair approach

In order to overcome the limitations of the linear-quadratic model and include synergistic effects of heat and radiation, a novel radiobiological model is proposed. The model is based on a chain of cell populations which are characterized by the number of radiation induced damages (hits). Cells can shift downward along the chain by collecting hits and upward by a repair process. The repair process is governed by a repair probability which depends upon state variables used for a simplistic description of the impact of heat and radiation upon repair proteins. Based on the parameters used, populations up to 4-5 hits are relevant for the calculation of the survival. The model describes intuitively the mathematical behaviour of apoptotic and nonapoptotic cell death. Linear-quadratic-linear behaviour of the logarithmic cell survival, fractionation, and (with one exception) the dose rate dependencies are described correctly. The model covers the time gap dependence of the synergistic cell killing due to combined application of heat and radiation, but further validation of the proposed approach based on experimental data is needed. However, the model offers a work bench for testing different biological concepts of damage induction, repair, and statistical approaches for calculating the variables of state.

Preliminary Experience in Treatment of Papillary and Macular Retinoblastoma: Evaluation of Local Control and Local Complications After Treatment with Linear Accelerator-Based Stereotactic Radiotherapy with Micromultileaf Collimator as Second-Line or Salvage Treatment after chemotherapy

To determine the local control and complication rates for children with papillary and/or macular retinoblastoma progressing after chemotherapy and undergoing stereotactic radiotherapy (SRT) with a micromultileaf collimator.

Variations of the Atlantic meridional overturning circulation in control and transient simulations of the last millennium

The variability of the Atlantic meridional overturing circulation (AMOC) strength is investigated in control experiments and in transient simulations of up to the last millennium using the low-resolution Community Climate System Model version 3. In the transient simulations the AMOC exhibits enhanced low-frequency variability that is mainly caused by infrequent transitions between two semi-stable circulation states which amount to a 10 percent change of the maximum overturning. One transition is also found in a control experiment, but the time-varying external forcing significantly increases the probability of the occurrence of such events though not having a direct, linear impact on the AMOC. The transition from a high to a low AMOC state starts with a reduction of the convection in the Labrador and Irminger Seas and goes along with a changed barotropic circulation of both gyres in the North Atlantic and a gradual strengthening of the convection in the Greenland-Iceland-Norwegian (GIN) Seas. In contrast, the transition from a weak to a strong overturning is induced by decreased mixing in the GIN Seas. As a consequence of the transition, regional sea surface temperature (SST) anomalies are found in the midlatitude North Atlantic and in the convection regions with an amplitude of up to 3 K. The atmospheric response to the SST forcing associated with the transition indicates a significant impact on the Scandinavian surface air temperature (SAT) in the order of 1 K. Thus, the changes of the ocean circulation make a major contribution to the Scandinavian SAT variability in the last millennium.

Genetic diversity in an indigenous horse breed: implications for mating strategies and the control of future inbreeding

The Franches-Montagnes is an indigenous Swiss horse breed, with approximately 2500 foalings per year. The stud book is closed, and no introgression from other horse breeds was conducted since 1998. Since 2006, breeding values for 43 different traits (conformation, performance and coat colour) are estimated with a best linear unbiased prediction (BLUP) multiple trait animal model. In this study, we evaluated the genetic diversity for the breeding population, considering the years from 2003 to 2008. Only horses with at least one progeny during that time span were included. Results were obtained based on pedigree information as well as from molecular markers. A series of software packages were screened to combine best the best linear unbiased prediction (BLUP) methodology with optimal genetic contribution theory. We looked for stallions with highest breeding values and lowest average relationship to the dam population. Breeding with such stallions is expected to lead to a selection gain, while lowering the future increase in inbreeding within the breed.

On trend estimation under monotone Gaussian subordination with long-memory: application to fossil pollen series

Fossil pollen data from stratigraphic cores are irregularly spaced in time due to non-linear age-depth relations. Moreover, their marginal distributions may vary over time. We address these features in a nonparametric regression model with errors that are monotone transformations of a latent continuous-time Gaussian process Z(T). Although Z(T) is unobserved, due to monotonicity, under suitable regularity conditions, it can be recovered facilitating further computations such as estimation of the long-memory parameter and the Hermite coefficients. The estimation of Z(T) itself involves estimation of the marginal distribution function of the regression errors. These issues are considered in proposing a plug-in algorithm for optimal bandwidth selection and construction of confidence bands for the trend function. Some high-resolution time series of pollen records from Lago di Origlio in Switzerland, which go back ca. 20,000 years are used to illustrate the methods.

Statistical inference for the optimal approximating model

In the setting of high-dimensional linear models with Gaussian noise, we investigate the possibility of confidence statements connected to model selection. Although there exist numerous procedures for adaptive (point) estimation, the construction of adaptive confidence regions is severely limited (cf. Li in Ann Stat 17:1001–1008, 1989). The present paper sheds new light on this gap. We develop exact and adaptive confidence regions for the best approximating model in terms of risk. One of our constructions is based on a multiscale procedure and a particular coupling argument. Utilizing exponential inequalities for noncentral χ2-distributions, we show that the risk and quadratic loss of all models within our confidence region are uniformly bounded by the minimal risk times a factor close to one.

Risk of Cardiovascular Events and Blood Pressure Control in Hypertensive HIV-Infected Patients: HIV Cohort Study (SHCS)

Background: Prevalence of hypertension in HIV infection is high, and information on blood pressure control in HIV-infected individuals is insufficient. We modeled blood pressure over time and the risk of cardiovascular events in hypertensive HIV-infected individuals. Methods: All patients from the Swiss HIV Cohort Study with confirmed hypertension (systolic or diastolic blood pressure above 139 or 89 mm Hg on 2 consecutive visits and presence of at least 1 additional cardiovascular risk factor) between April 1, 2000 and March 31, 2011 were included. Patients with previous cardiovascular events, already on antihypertensive drugs, and pregnant women were excluded. Change in blood pressure over time was modeled using linear mixed models with repeated measurement. Results: Hypertension was diagnosed in 2595 of 10,361 eligible patients. Of those, 869 initiated antihypertensive treatment. For patients treated for hypertension, we found a mean (95% confidence interval) decrease in systolic and diastolic blood pressure of −0.82 (−1.06 to −0.58) mm Hg and −0.89 (−1.05 to −0.73) mm Hg/yr, respectively. Factors associated with a decline in systolic blood pressure were baseline blood pressure, presence of chronic kidney disease, cardiovascular events, and the typical risk factors for cardiovascular disease. In patients with hypertension, increase in systolic blood pressure [(hazard ratio 1.18 (1.06 to 1.32) per 10 mm Hg increase], total cholesterol, smoking, age, and cumulative exposure to protease inhibitor–based and triple nucleoside regimens were associated with cardiovascular events. Conclusions: Insufficient control of hypertension was associated with increased risk of cardiovascular events indicating the need for improved management of hypertension in HIV-infected individuals.

The additive and interactive roles of aggression, prosocial behavior, and social preference in predicting resource control in young children

Distinguishing between physical and social aggression, this study examined whether the predictive effect of aggression on resource control a) is moderated by prosocial behavior and b) corresponds to a linear or a curvilinear trend. Moderating effects of children’s social preference among peers and child sex in this context were also tested. Based on a sample of 682 kindergarten children (348 girls; average age 72.7 months, 3.6 SD), multilevel regressions revealed additive linear effects of social preference and prosociality on resource control. Moderate (but not high) levels of social aggression also facilitated resource control for disliked children. There was no such threshold effect for well liked children, who increasingly controlled the resource the more socially aggressive they were. In contrast, physical aggression hampered resource control unless used very modestly. The present study has a number of positive features. First, the distinction between physical and social aggression improves our understanding of the relation between aggression and social competence and sketches a more differentiated picture of the role of different forms of aggression in resource control. Second, this study combines the concept of resource control with the concept of social preference and investigates curvilinear effects of aggression. Third, the direct observation of resource control in the Movie Viewer increases the internal validity of this study.

On degeneracy and invariances of random fields paths with applications in Gaussian process modelling

We study pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling. The key idea is that a number of structural properties one may wish to impose a priori on functions boil down to degeneracy properties under well-chosen linear operators. We first show in a second order set-up that almost sure degeneracy of random field paths under some class of linear operators defined in terms of signed measures can be controlled through the two first moments. A special focus is then put on the Gaussian case, where these results are revisited and extended to further linear operators thanks to state-of-the-art representations. Several degeneracy properties are tackled, including random fields with symmetric paths, centred paths, harmonic paths, or sparse paths. The proposed approach delivers a number of promising results and perspectives in Gaussian process modelling. In a first numerical experiment, it is shown that dedicated kernels can be used to infer an axis of symmetry. Our second numerical experiment deals with conditional simulations of a solution to the heat equation, and it is found that adapted kernels notably enable improved predictions of non-linear functionals of the field such as its maximum.

Comparison of 2.5D and 3D Quantification of Femoral Head Coverage in Normal Control Subjects and Patients with Hip Dysplasia.

Hip dysplasia is characterized by insufficient femoral head coverage (FHC). Quantification of FHC is of importance as the underlying goal of the surgery to treat hip dysplasia is to restore a normal acetabular morphology and thereby to improve FHC. Unlike a pure 2D X-ray radiograph-based measurement method or a pure 3D CT-based measurement method, previously we presented a 2.5D method to quantify FHC from a single anteriorposterior (AP) pelvic radiograph. In this study, we first quantified and compared 3D FHC between a normal control group and a patient group using a CT-based measurement method. Taking the CT-based 3D measurements of FHC as the gold standard, we further quantified the bias, precision and correlation between the 2.5D measurements and the 3D measurements on both the control group and the patient group. Based on digitally reconstructed radiographs (DRRs), we investigated the influence of the pelvic tilt on the 2.5D measurements of FHC. The intraclass correlation coefficients (ICCs) for absolute agreement was used to quantify interobserver reliability and intraobserver reproducibility of the 2.5D measurement technique. The Pearson correlation coefficient, r, was used to determine the strength of the linear association between the 2.5D and the 3D measurements. Student's t-test was used to determine whether the differences between different measurements were statistically significant. Our experimental results demonstrated that both the interobserver reliability and the intraobserver reproducibility of the 2.5D measurement technique were very good (ICCs > 0.8). Regression analysis indicated that the correlation was very strong between the 2.5D and the 3D measurements (r = 0.89, p < 0.001). Student's t-test showed that there were no statistically significant differences between the 2.5D and the 3D measurements of FHC on the patient group (p > 0.05). The results of this study provided convincing evidence demonstrating the validity of the 2.5D measurements of FHC from a single AP pelvic radiograph and proved that it could serve as a surrogate for 3D CT-based measurements. Thus it may be possible to use this method to avoid a CT scan for the purpose of estimating 3D FHC in diagnosis and post-operative treatment evaluation of patients with hip dysplasia.

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths

The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.