Revised QUICKI provides a strong surrogate estimate of insulin sensitivity when compared with the minimal model

OBJECTIVE: To compare insulin sensitivity (Si) from a frequently sampled intravenous glucose tolerance test (FSIGT) and subsequent minimal model analyses with surrogate measures of insulin sensitivity and resistance and to compare features of the metabolic syndrome between Caucasians and Indian Asians living in the UK. SUBJECTS: In all, 27 healthy male volunteers (14 UK Caucasians and 13 UK Indian Asians), with a mean age of 51.2 +/- 1.5 y, BMI of 25.8 +/- 0.6 kg/m(2) and Si of 2.85 +/- 0.37. MEASUREMENTS: Si was determined from an FSIGT with subsequent minimal model analysis. The concentrations of insulin, glucose and nonesterified fatty acids (NEFA) were analysed in fasting plasma and used to calculate surrogate measure of insulin sensitivity (quantitative insulin sensitivity check index (QUICKI), revised QUICKI) and resistance (homeostasis for insulin resistance (HOMA IR), fasting insulin resistance index (FIRI), Bennetts index, fasting insulin, insulin-to-glucose ratio). Plasma concentrations of triacylglycerol (TAG), total cholesterol, high density cholesterol, (HDL-C) and low density cholesterol, (LDL-C) were also measured in the fasted state. Anthropometric measurements were conducted to determine body-fat distribution. RESULTS: Correlation analysis identified the strongest relationship between Si and the revised QUICKI (r = 0.67; P = 0.000). Significant associations were also observed between Si and QUICKI (r = 0.51; P = 0.007), HOMA IR (r = -0.50; P = 0.009), FIRI and fasting insulin. The Indian Asian group had lower HDL-C (P = 0.001), a higher waist-hip ratio (P = 0.01) and were significantly less insulin sensitive (Si) than the Caucasian group (P = 0.02). CONCLUSION: The revised QUICKI demonstrated a statistically strong relationship with the minimal model. However, it was unable to differentiate between insulin-sensitive and -resistant groups in this study. Future larger studies in population groups with varying degrees of insulin sensitivity are recommended to investigate the general applicability of the revised QUICKI surrogate technique.

Analytical solutions of a minimal model of species migration in a bounded domain

A minimal model of species migration is presented which takes the form of a parabolic equation with boundary conditions and initial data. Solutions to the differential problem are obtained that can be used to describe the small- and large-time evolution of a species distribution within a bounded domain. These expressions are compared with the results of numerical simulations and are found to be satisfactory within appropriate temporal regimes. The solutions presented can be used to describe existing observations of nematode distributions, can be used as the basis for further work on nematode migration, and may also be interpreted more generally.

Transport of nanoparticles into polymersomes : a minimal model system of particles passage through biological membranes

This thesis focuses on the design and characterization of a novel, artificial minimal model membrane system with chosen physical parameters to mimic a nanoparticle uptake process driven exclusively by adhesion and softness of the bilayer. The realization is based on polymersomes composed of poly(dimethylsiloxane)-b-poly(2-methyloxazoline) (PMDS-b-PMOXA) and nanoscopic colloidal particles (polystyrene, silica), and the utilization of powerful characterization techniques. rnPDMS-b-PMOXA polymersomes with a radius, Rh ~100 nm, a size polydispersity, PD = 1.1 and a membrane thickness, h = 16 nm, were prepared using the film rehydratation method. Due to the suitable mechanical properties (Young’s modulus of ~17 MPa and a bending modulus of ~7⋅10-8 J) along with the long-term stability and the modifiability, these kind of polymersomes can be used as model membranes to study physical and physicochemical aspects of transmembrane transport of nanoparticles. A combination of photon (PCS) and fluorescence (FCS) correlation spectroscopies optimizes species selectivity, necessary for a unique internalization study encompassing two main efforts. rnFor the proof of concepts, the first effort focused on the interaction of nanoparticles (Rh NP SiO2 = 14 nm, Rh NP PS = 16 nm; cNP = 0.1 gL-1) and polymersomes (Rh P = 112 nm; cP = 0.045 gL-1) with fixed size and concentration. Identification of a modified form factor of the polymersome entities, selectively seen in the PCS experiment, enabled a precise monitor and quantitative description of the incorporation process. Combining PCS and FCS led to the estimation of the incorporated particles per polymersome (about 8 in the examined system) and the development of an appropriate methodology for the kinetics and dynamics of the internalization process. rnThe second effort aimed at the establishment of the necessary phenomenology to facilitate comparison with theories. The size and concentration of the nanoparticles were chosen as the most important system variables (Rh NP = 14 - 57 nm; cNP = 0.05 - 0.2 gL-1). It was revealed that the incorporation process could be controlled to a significant extent by changing the nanoparticles size and concentration. Average number of 7 up to 11 NPs with Rh NP = 14 nm and 3 up to 6 NPs with Rh NP = 25 nm can be internalized into the present polymersomes by changing initial nanoparticles concentration in the range 0.1- 0.2 gL-1. Rapid internalization of the particles by polymersomes is observed only above a critical threshold particles concentration, dependent on the nanoparticle size. rnWith regard possible pathways for the particle uptake, cryogenic transmission electron microscopy (cryo-TEM) has revealed two different incorporation mechanisms depending on the size of the involved nanoparticles: cooperative incorporation of nanoparticles groups or single nanoparticles incorporation. Conditions for nanoparticle uptake and controlled filling of polymersomes were presented. rnIn the framework of this thesis, the experimental observation of transmembrane transport of spherical PS and SiO2 NPs into polymersomes via an internalization process was reported and examined quantitatively for the first time. rnIn a summary the work performed in frames of this thesis might have significant impact on cell model systems’ development and thus improved understanding of transmembrane transport processes. The present experimental findings help create the missing phenomenology necessary for a detailed understanding of a phenomenon with great relevance in transmembrane transport. The fact that transmembrane transport of nanoparticles can be performed by artificial model system without any additional stimuli has a fundamental impact on the understanding, not only of the nanoparticle invagination process but also of the interaction of nanoparticles with biological as well as polymeric membranes. rn

Dynamics of a minimal model of interlocked positive and negative feedback loops of transcriptional regulation by cAMP-response element binding proteins.

cAMP-response element binding (CREB) proteins are involved in transcriptional regulation in a number of cellular processes (e.g., neural plasticity and circadian rhythms). The CREB family contains activators and repressors that may interact through positive and negative feedback loops. These loops can be generated by auto- and cross-regulation of expression of CREB proteins, via CRE elements in or near their genes. Experiments suggest that such feedback loops may operate in several systems (e.g., Aplysia and rat). To understand the functional implications of such feedback loops, which are interlocked via cross-regulation of transcription, a minimal model with a positive and negative loop was developed and investigated using bifurcation analysis. Bifurcation analysis revealed diverse nonlinear dynamics (e.g., bistability and oscillations). The stability of steady states or oscillations could be changed by time delays in the synthesis of the activator (CREB1) or the repressor (CREB2). Investigation of stochastic fluctuations due to small numbers of molecules of CREB1 and CREB2 revealed a bimodal distribution of CREB molecules in the bistability region. The robustness of the stable HIGH and LOW states of CREB expression to stochastic noise differs, and a critical number of molecules was required to sustain the HIGH state for days or longer. Increasing positive feedback or decreasing negative feedback also increased the lifetime of the HIGH state, and persistence of this state may correlate with long-term memory formation. A critical number of molecules was also required to sustain robust oscillations of CREB expression. If a steady state was near a deterministic Hopf bifurcation point, stochastic resonance could induce oscillations. This comparative analysis of deterministic and stochastic dynamics not only provides insights into the possible dynamics of CREB regulatory motifs, but also demonstrates a framework for understanding other regulatory processes with similar network architecture.

Dynamics of a minimal model of interlocked positive and negative feedback loops of transcriptional regulation by cAMP-response element binding proteins.

cAMP-response element binding (CREB) proteins are involved in transcriptional regulation in a number of cellular processes (e.g., neural plasticity and circadian rhythms). The CREB family contains activators and repressors that may interact through positive and negative feedback loops. These loops can be generated by auto- and cross-regulation of expression of CREB proteins, via CRE elements in or near their genes. Experiments suggest that such feedback loops may operate in several systems (e.g., Aplysia and rat). To understand the functional implications of such feedback loops, which are interlocked via cross-regulation of transcription, a minimal model with a positive and negative loop was developed and investigated using bifurcation analysis. Bifurcation analysis revealed diverse nonlinear dynamics (e.g., bistability and oscillations). The stability of steady states or oscillations could be changed by time delays in the synthesis of the activator (CREB1) or the repressor (CREB2). Investigation of stochastic fluctuations due to small numbers of molecules of CREB1 and CREB2 revealed a bimodal distribution of CREB molecules in the bistability region. The robustness of the stable HIGH and LOW states of CREB expression to stochastic noise differs, and a critical number of molecules was required to sustain the HIGH state for days or longer. Increasing positive feedback or decreasing negative feedback also increased the lifetime of the HIGH state, and persistence of this state may correlate with long-term memory formation. A critical number of molecules was also required to sustain robust oscillations of CREB expression. If a steady state was near a deterministic Hopf bifurcation point, stochastic resonance could induce oscillations. This comparative analysis of deterministic and stochastic dynamics not only provides insights into the possible dynamics of CREB regulatory motifs, but also demonstrates a framework for understanding other regulatory processes with similar network architecture.

A minimal model of partial synchrony

Acknowledgement We wish to acknowledge A. Pikovsky and M. Zaks for useful discussions. This work has been financially supported by the EU project COSMOS (642563).

A minimal model of partial synchrony

We wish to acknowledge A Pikovsky and M Zaks for useful discussions. This work has been financially supported by the EU project COSMOS (642563).

Memory-induced anomalous dynamics in a minimal random walk model

A discrete-time dynamics of a non-Markovian random walker is analyzed using a minimal model where memory of the past drives the present dynamics. In recent work N. Kumar et al., Phys. Rev. E 82, 021101 (2010)] we proposed a model that exhibits asymptotic superdiffusion, normal diffusion, and subdiffusion with the sweep of a single parameter. Here we propose an even simpler model, with minimal options for the walker: either move forward or stay at rest. We show that this model can also give rise to diffusive, subdiffusive, and superdiffusive dynamics at long times as a single parameter is varied. We show that in order to have subdiffusive dynamics, the memory of the rest states must be perfectly correlated with the present dynamics. We show explicitly that if this condition is not satisfied in a unidirectional walk, the dynamics is only either diffusive or superdiffusive (but not subdiffusive) at long times.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Scalar bosons in minimal and ultraminimal technicolor: Masses, trilinear couplings, and widths

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The effect of oral metformin on insulin sensitivity in insulin-resistant ponies

Metformin may be an effective therapeutic option for insulin-resistant (I-R) horses/ponies because, in humans, it reportedly enhances insulin sensitivity (SI) of peripheral tissues without stimulating insulin secretion. To determine the effect of metformin on insulin and glucose dynamics in I-R ponies, six ponies were studied in a cross-over design by Minimal Model analysis of a frequently-sampled intravenous glucose tolerance test (FSIGT). Metformin was administered at 15. mg/kg bodyweight (BW), orally, twice-daily, for 21. days to the metformin-treated group. The control group received a placebo. A FSIGT was conducted before and after treatment. The Minimal Model of glucose and insulin dynamics rendered indices describing SI, glucose effectiveness (Sg), acute insulin response to glucose (AIRg) and the disposition index (DI). The body condition score (BCS), BW and cresty neck score (CNS) were also assessed. There was no significant change in SI, Sg, AIRg, DI, BW, BCS or CNS in response to metformin, or over time in the control group. There were no measurable benefits of metformin on SI, consistent with recent work showing that the bioavailability of metformin in horses is poor, and chronic dosing may not achieve therapeutic blood concentrations. Alternatively, metformin may only be effective in obese ponies losing weight or with hyperglycaemia.

Indexes of insulin resistance and secretion in obese children and adolescents : a validation study

OBJECTIVE To assess the concurrent validity of fasting indexes of insulin sensitivity and secretion in - obese prepubertal (Tanner stage 1) children and pubertal (Tanner stages 2-5) glucose tolerance test (FSIVGTT) as a criterion measure. RESEARCH DESIGN AND METHODS Eighteen obese children and adolescents (11 girls and 7 boys, mean age 12.2 +/- 2.4 years, mean BMI 35.4 +/- 6.2 kg/m(2), mean BMI-SDS 3.5 +/- 0.5, 7 prepubertal and I I pubertal) participated in the study. All participants underwent an insulin-modified FSIVGTT on two occasions, and 15 repeated this test a third time (mean 12.9 and 12.0 weeks apart). S-i measured by the FSIVGTT was compared with homeostasis model assessment (HOMA) of insulin resistance (HOMA-IR), quantitative insulin-sensitivity check index (QUICKI), fasting glucose-to-insulin ratio (FGIR), and fasting insulin (estimates of insulin sensitivity derived from fasting samples). The acute insulin response (AIR) measured by the FSIVGTT was compared with HOMA of percent beta-cell function (HOMA-beta%), FGIR, and fasting insulin (estimates of insulin secretion derived from fasting samples). RESULTS There was a significant negative correlation between HOMA-IR and S-i (r = -0.89, r = -0.90, and r = -0.81, P < 0.01) and a significant positive correlation between QUICKI and S-i (r = 0.89, r = 0.90, and r = 0.81, P < 0.01) at each time point. There was a significant positive correlation between FGIR and S-i (r = 0.91, r = 0.91, and r = 0.82, P < 0.01) and a significant negative correlation between fasting insulin and S-i (r = -90, r = -0.90, and r = -0.88, P < 0.01). HOMA-beta% was not as strongly correlated with AIR (r = 0.60, r = 0.54, and r = 0.61, P < 0.05). CONCLUSIONS HOMA-IR, QUICKI, FGIR, and fasting insulin correlate strongly with S-i assessed by the FSIVGTT in obese children and adolescents. Correlations between HOMA-β% FGIR and fasting insulin, and AIR were not as strong. Indexes derived from fasting samples are a valid tool for assessing insulin sensitivity in prepubertal and pubertal obese children.