Nonlinear models and small sample performance of the generalized method of moments

In this paper I explore the issue of nonlinearity (both in the datageneration process and in the functional form that establishes therelationship between the parameters and the data) regarding the poorperformance of the Generalized Method of Moments (GMM) in small samples.To this purpose I build a sequence of models starting with a simple linearmodel and enlarging it progressively until I approximate a standard (nonlinear)neoclassical growth model. I then use simulation techniques to find the smallsample distribution of the GMM estimators in each of the models.

A retail benchmarking approach to efficient two-way access pricing

We study a retail benchmarking approach to determine access prices for interconnected networks. Instead of considering fixed access charges as in the existing literature, we study access pricing rules that determine the access price that network i pays to network j as a linear function of the marginal costs and the retail prices set by both networks. In the case of competition in linear prices, we show that there is a unique linear rule that implements the Ramsey outcome as the unique equilibrium, independently of the underlying demand conditions. In the case of competition in two-part tariffs, we consider a class of access pricing rules, similar to the optimal one under linear prices but based on average retail prices. We show that firms choose the variable price equal to the marginal cost under this class of rules. Therefore, the regulator (or the competition authority) can choose one among the rules to pursue additional objectives such as consumer surplus, network coverage or investment: for instance, we show that both static and dynamic e±ciency can be achieved at the same time.

Effects of prior repeated-sprints with different recovery duration on oxygen uptake kinetics during subsequent heavy-exercise

Introduction: Prior repeated-sprints (6) has become an interesting method to resolve the debate surrounding the principal factors that limits the oxygen uptake (V'O2) kinetics at the onset of exercise [i.e., muscle O2 delivery (5) or metabolic inertia (3)]. The aim of this study was to compare the effects of two repeated-sprints sets of 6x6s separated by different recovery duration between the sprints on V'O2 and muscular de-oxygenation [HHb] kinetics during a subsequent heavy-intensity exercise. Methods: 10 male subjects performed a 6-min constant-load cycling test (T50) at intensity corresponding to half of the difference between V'O2max and the ventilatory threshold. Then, they performed two repeated-sprints sets of 6x6s all-out separated by different recovery duration between the sprints (S1:30s and S2:3min) followed, after 7-min-recovery, by the T50 (S1T50 and S2T50, respectively). V'O2, [HHb] of the vastus lateralis (VL) and surface electromyography activity [i.e., root-mean-square (RMS) and the median frequency of the power density spectrum (MDF)] from VL and vastus medialis (VM) were recorded throughout T50. Models using a bi-exponential function for the overall T50 and a mono-exponential for the first 90s of T50 were used to define V'O2 and [HHb] kinetics respectively. Results: V'O2 mean value was higher in S1 (2.9±0.3l.min-1) than in S2 (1.2±0.3l.min-1); (p<0.001). The peripheral blood flow was increased after sprints as attested by a higher basal heart rate (HRbaseline) (S1T50: +22%; S2T50: +17%; p≤0.008). Time delay [HHb] was shorter for S1T50 and S2T50 than for T50 (-22% for both; p≤0.007) whereas the mean response time of V'O2 was accelerated only after S1 (S1T50: 32.3±2.5s; S2T50: 34.4±2.6s; T50: 35.7±5.4s; p=0.031). There were no significant differences in RMS between the three conditions (p>0.05). MDF of VM was higher during the first 3-min in S1T50 than in T50 (+6%; p≤0.05). Conclusion: The study show that V'O2 kinetics was speeded by prior repeated-sprints with a short (30s) but not a long (3min) inter-sprints-recovery even though the [HHb] kinetics was accelerated and the peripheral blood flow was enhanced after both sprints. S1, inducing a greater PCr depletion (1) and change in the pattern of the fibres recruitment (increase in MDF) compared with S2, may decrease metabolic inertia (2), stimulate the oxidative phosphorylation activation (4) and accelerate V'O2 kinetics at the beginning of the subsequent high-intensity exercise.

One-sided tolerance interval in a two-way balanced nested model with mixed effects

In many research areas (such as public health, environmental contamination, and others) one deals with the necessity of using data to infer whether some proportion (%) of a population of interest is (or one wants it to be) below and/or over some threshold, through the computation of tolerance interval. The idea is, once a threshold is given, one computes the tolerance interval or limit (which might be one or two - sided bounded) and then to check if it satisfies the given threshold. Since in this work we deal with the computation of one - sided tolerance interval, for the two-sided case we recomend, for instance, Krishnamoorthy and Mathew [5]. Krishnamoorthy and Mathew [4] performed the computation of upper tolerance limit in balanced and unbalanced one-way random effects models, whereas Fonseca et al [3] performed it based in a similar ideas but in a tow-way nested mixed or random effects model. In case of random effects model, Fonseca et al [3] performed the computation of such interval only for the balanced data, whereas in the mixed effects case they dit it only for the unbalanced data. For the computation of twosided tolerance interval in models with mixed and/or random effects we recomend, for instance, Sharma and Mathew [7]. The purpose of this paper is the computation of upper and lower tolerance interval in a two-way nested mixed effects models in balanced data. For the case of unbalanced data, as mentioned above, Fonseca et al [3] have already computed upper tolerance interval. Hence, using the notions persented in Fonseca et al [3] and Krishnamoorthy and Mathew [4], we present some results on the construction of one-sided tolerance interval for the balanced case. Thus, in order to do so at first instance we perform the construction for the upper case, and then the construction for the lower case.

Atrial septal pacing for the termination of atrial fibrillation: study in a biophysical model of human atria.

AIMS: While successful termination by pacing of organized atrial tachycardias has been observed in patients, single site rapid pacing has not yet led to conclusive results for the termination of atrial fibrillation (AF). The purpose of this study was to evaluate a novel atrial septal pacing algorithm for the termination of AF in a biophysical model of the human atria. METHODS AND RESULTS: Sustained AF was generated in a model based on human magnetic resonance images and membrane kinetics. Rapid pacing was applied from the septal area following a dual-stage scheme: (i) rapid pacing for 10-30 s at pacing intervals 62-70% of AF cycle length (AFCL), (ii) slow pacing for 1.5 s at 180% AFCL, initiated by a single stimulus at 130% AFCL. Atrial fibrillation termination success rates were computed. A mean success rate for AF termination of 10.2% was obtained for rapid septal pacing only. The addition of the slow pacing phase increased this rate to 20.2%. At an optimal pacing cycle length (64% AFCL) up to 29% of AF termination was observed. CONCLUSION: The proposed septal pacing algorithm could suppress AF reentries in a more robust way than classical single site rapid pacing. Experimental studies are now needed to determine whether similar termination mechanisms and rates can be observed in animals or humans, and in which types of AF this pacing strategy might be most effective.

A wearable system for multi-segment foot kinetics measurement.

This study aims to design a wearable system for kinetics measurement of multi-segment foot joints in long-distance walking and to investigate its suitability for clinical evaluations. The wearable system consisted of inertial sensors (3D gyroscopes and 3D accelerometers) on toes, forefoot, hindfoot, and shank, and a plantar pressure insole. After calibration in a laboratory, 10 healthy elderly subjects and 12 patients with ankle osteoarthritis walked 50m twice wearing this system. Using inverse dynamics, 3D forces, moments, and power were calculated in the joint sections among toes, forefoot, hindfoot, and shank. Compared to those we previously estimated for a one-segment foot model, the sagittal and transverse moments and power in the ankle joint, as measured via multi-segment foot model, showed a normalized RMS difference of less than 11%, 14%, and 13%, respectively, for healthy subjects, and 13%, 15%, and 14%, for patients. Similar to our previous study, the coronal moments were not analyzed. Maxima-minima values of anterior-posterior and vertical force, sagittal moment, and power in shank-hindfoot and hindfoot-forefoot joints were significantly different between patients and healthy subjects. Except for power, the inter-subject repeatability of these parameters was CMC>0.90 for healthy subjects and CMC>0.70 for patients. Repeatability of these parameters was lower for the forefoot-toes joint. The proposed measurement system estimated multi-segment foot joints kinetics with acceptable repeatability but showed difference, compared to those previously estimated for the one-segment foot model. These parameters also could distinguish patients from healthy subjects. Thus, this system is suggested for outcome evaluations of foot treatments.

A Bowl of Strawberries Can Go A Long WAY..., 2009

Strawberries are one of the healthiest fruits you can eat. They are a great source of Vitamin C. One serving of just eight strawberries will provide 140 % of the US Recommended Daily Allowance of Vitamin C. In a recent study, strawberries ranked second among the top ten fruits in antioxidant capacity (TAC), which is one reason why they may help prevent cancer and heart disease. This brochure produced by The Department of Agriculture and Land Stewardship.

Interplay between T cell receptor binding kinetics and the level of cognate peptide presented by major histocompatibility complexes governs CD8+ T cell responsiveness.

Through a rational design approach, we generated a panel of HLA-A*0201/NY-ESO-1(157-165)-specific T cell receptors (TCR) with increasing affinities of up to 150-fold from the wild-type TCR. Using these TCR variants which extend just beyond the natural affinity range, along with an extreme supraphysiologic one having 1400-fold enhanced affinity, and a low-binding one, we sought to determine the effect of TCR binding properties along with cognate peptide concentration on CD8(+) T cell responsiveness. Major histocompatibility complexes (MHC) expressed on the surface of various antigen presenting cells were peptide-pulsed and used to stimulate human CD8(+) T cells expressing the different TCR via lentiviral transduction. At intermediate peptide concentration we measured maximum cytokine/chemokine secretion, cytotoxicity, and Ca(2+) flux for CD8(+) T cells expressing TCR within a dissociation constant (K(D)) range of ∼1-5 μM. Under these same conditions there was a gradual attenuation in activity for supraphysiologic affinity TCR with K(D) < ∼1 μM, irrespective of CD8 co-engagement and of half-life (t(1/2) = ln 2/k(off)) values. With increased peptide concentration, however, the activity levels of CD8(+) T cells expressing supraphysiologic affinity TCR were gradually restored. Together our data support the productive hit rate model of T cell activation arguing that it is not the absolute number of TCR/pMHC complexes formed at equilibrium, but rather their productive turnover, that controls levels of biological activity. Our findings have important implications for various immunotherapies under development such as adoptive cell transfer of TCR-engineered CD8(+) T cells, as well as for peptide vaccination strategies.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Effects of aerobic fitness on oxygen uptake kinetics in heavy intensity swimming.

This study aimed to characterise both the [Formula: see text] kinetics within constant heavy-intensity swimming exercise, and to assess the relationships between [Formula: see text] kinetics and other parameters of aerobic fitness, in well-trained swimmers. On separate days, 21 male swimmers completed: (1) an incremental swimming test to determine their maximal oxygen uptake [Formula: see text], first ventilatory threshold (VT), and the velocity associated with [Formula: see text] [Formula: see text] and (2) two square-wave transitions from rest to heavy-intensity exercise, to determine their [Formula: see text] kinetics. All the tests involved breath-by-breath analysis of freestyle swimming using a swimming snorkel. [Formula: see text] kinetics was modelled with two exponential functions. The mean values for the incremental test were 56.0 ± 6.0 ml min(-1) kg(-1), 1.45 ± 0.08 m s(-1); and 42.1 ± 5.7 ml min(-1) kg(-1) for [Formula: see text], [Formula: see text] and VT, respectively. For the square-wave transition, the time constant of the primary phase (τ(p)) averaged 17.3 ± 5.4 s and the relevant slow component (A'(sc)) averaged 4.8 ± 2.9 ml min(-1) kg(-1) [representing 8.9% of the end-exercise [Formula: see text] (%A'(sc))]. τ(p) was correlated with [Formula: see text] (r = -0.55, P = 0.01), but not with either [Formula: see text] (r = 0.05, ns) or VT (r = 0.14, ns). The %A'(sc) did not correlate with either [Formula: see text] (r = -0.14, ns) or [Formula: see text] (r = 0.06, ns), but was inversely related with VT (r = -0.61, P < 0.01). This study was the first to describe the [Formula: see text] kinetics in heavy-intensity swimming using specific swimming exercise and appropriate methods. As has been demonstrated in cycling, faster [Formula: see text] kinetics allow higher aerobic power outputs to be attained. The slow component seems to be reduced in swimmers with higher ventilatory thresholds.

Fat oxidation kinetics during exercise in obese individuals

Introduction An impaired ability to oxidize fat may be a factor in the obesity's aetiology (3). Moreover, the exercise intensity (Fatmax) eliciting the maximal fat oxidation rate (MFO) was lower in obese (O) compared with lean (L) individuals (4). However, difference in fat oxidation rate (FOR) during exercise between O and L remains equivocal and little is known about FORs during high intensities (>60% ) in O compared with L. This study aimed to characterize fat oxidation kinetics over a large range of intensities in L and O. Methods 12 healthy L [body mass index (BMI): 22.8±0.4] and 16 healthy O men (BMI: 38.9±1.4) performed submaximal incremental test (Incr) to determine whole-body fat oxidation kinetics using indirect calorimetry. After a 15-min resting period (Rest) and 10-min warm-up at 20% of maximal power output (MPO, determined by a maximal incremental test), the power output was increased by 7.5% MPO every 6-min until respiratory exchange ratio reached 1.0. Venous lactate and glucose and plasma concentration of epinephrine (E), norepinephrine (NE), insulin and non-esterified fatty acid (NEFA) were assessed at each step. A mathematical model (SIN) (1), including three variables (dilatation, symmetry, translation), was used to characterize fat oxidation (normalized by fat-free mass) kinetics and to determine Fatmax and MFO. Results FOR at Rest and MFO were not significantly different between groups (p≥0.1). FORs were similar from 20-60% (p≥0.1) and significantly lower from 65-85% in O than in L (p≤0.04). Fatmax was significantly lower in O than in L (46.5±2.5 vs 56.7±1.9 % respectively; p=0.005). Fat oxidation kinetics was characterized by similar translation (p=0.2), significantly lower dilatation (p=0.001) and tended to a left-shift symmetry in O compared with L (p=0.09). Plasma E, insulin and NEFA were significantly higher in L compared to O (p≤0.04). There were no significant differences in glucose, lactate and plasma NE between groups (p≥0.2). Conclusion The study showed that O presented a lower Fatmax and a lower reliance on fat oxidation at high, but not at moderate, intensities. This may be linked to a: i) higher levels of insulin and lower E concentrations in O, which may induce blunted lipolysis; ii) higher percentage of type II and a lower percentage of type I fibres (5), and iii) decreased mitochondrial content (2), which may reduce FORs at high intensities and Fatmax. These findings may have implications for an appropriate exercise intensity prescription for optimize fat oxidation in O. References 1. Cheneviere et al. Med Sci Sports Exerc. 2009 2. Holloway et al. Am J Clin Nutr. 2009 3. Kelley et al. Am J Physiol. 1999 4. Perez-Martin et al. Diabetes Metab. 2001 5. Tanner et al. Am J Physiol Endocrinol Metab. 2002

A fully adaptive numerical approximation for a two-dimensional epidemic model with nonlinear cross-diffusion

An epidemic model is formulated by a reactionâeuro"diffusion system where the spatial pattern formation is driven by cross-diffusion. The reaction terms describe the local dynamics of susceptible and infected species, whereas the diffusion terms account for the spatial distribution dynamics. For both self-diffusion and cross-diffusion, nonlinear constitutive assumptions are suggested. To simulate the pattern formation two finite volume formulations are proposed, which employ a conservative and a non-conservative discretization, respectively. An efficient simulation is obtained by a fully adaptive multiresolution strategy. Numerical examples illustrate the impact of the cross-diffusion on the pattern formation.