Obesity lowers the threshold of allergic sensitization and augments airway eosinophilia in a mouse model of asthma

Clinical and epidemiological studies show a close association between obesity and the risk of asthma development. The underlying cause-effect relationship between metabolism, innate and adaptive immunity, and inflammation remains to be elucidated.

Enhanced Atlantic subpolar gyre variability through baroclinic threshold in a coarse resolution model

Direct observations, satellite measurements and paleo records reveal strong variability in the Atlantic subpolar gyre on various time scales. Here we show that variations of comparable amplitude can only be simulated in a coupled climate model in the proximity of a dynamical threshold. The threshold and the associated dynamic response is due to a positive feedback involving increased salt transport in the subpolar gyre and enhanced deep convection in its centre. A series of sensitivity experiments is performed with a coarse resolution ocean general circulation model coupled to a statistical-dynamical atmosphere model which in itself does not produce atmospheric variability. To simulate the impact of atmospheric variability, the model system is perturbed with freshwater forcing of varying, but small amplitude and multi-decadal to centennial periodicities and observational variations in wind stress. While both freshwater and wind-stress-forcing have a small direct effect on the strength of the subpolar gyre, the magnitude of the gyre's response is strongly increased in the vicinity of the threshold. Our results indicate that baroclinic self-amplification in the North Atlantic ocean can play an important role in presently observed SPG variability and thereby North Atlantic climate variability on multi-decadal scales.

A Functional-Based Distribution Diagnostic for a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed modesl and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated marginal residual vector by the Cholesky decomposition of the inverse of the estimated marginal variance matrix. Linear functions or the resulting "rotated" residuals are used to construct an empirical cumulative distribution function (ECDF), whose stochastic limit is characterized. We describe a resampling technique that serves as a computationally efficient parametric bootstrap for generating representatives of the stochastic limit of the ECDF. Through functionals, such representatives are used to construct global tests for the hypothesis of normal margional errors. In addition, we demonstrate that the ECDF of the predicted random effects, as described by Lange and Ryan (1989), can be formulated as a special case of our approach. Thus, our method supports both omnibus and directed tests. Our method works well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series).

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP models include convergence and mixing difficulties. This paper proposes an algorithm based on the Pólya urn scheme that extends the Gibbs sampling algorithms to non-conjugate models with normal base measures and exponential family likelihoods. The algorithm proceeds by making Laplace approximations to the likelihood function, thereby reducing the procedure to that of conjugate normal MDP models. To ensure the validity of the stationary distribution in the non-conjugate case, the proposals are accepted or rejected by a Metropolis-Hastings step. In the special case where the data are normally distributed, the algorithm is identical to the Gibbs sampler.

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.

Accuracy of linear measurements using three imaging modalities: two lateral cephalograms and one 3D model from CBCT data

BACKGROUND The aim of this study was to evaluate the accuracy of linear measurements on three imaging modalities: lateral cephalograms from a cephalometric machine with a 3 m source-to-mid-sagittal-plane distance (SMD), from a machine with 1.5 m SMD and 3D models from cone-beam computed tomography (CBCT) data. METHODS Twenty-one dry human skulls were used. Lateral cephalograms were taken, using two cephalometric devices: one with a 3 m SMD and one with a 1.5 m SMD. CBCT scans were taken by 3D Accuitomo® 170, and 3D surface models were created in Maxilim® software. Thirteen linear measurements were completed twice by two observers with a 4 week interval. Direct physical measurements by a digital calliper were defined as the gold standard. Statistical analysis was performed. RESULTS Nasion-Point A was significantly different from the gold standard in all methods. More statistically significant differences were found on the measurements of the 3 m SMD cephalograms in comparison to the other methods. Intra- and inter-observer agreement based on 3D measurements was slightly better than others. LIMITATIONS Dry human skulls without soft tissues were used. Therefore, the results have to be interpreted with caution, as they do not fully represent clinical conditions. CONCLUSIONS 3D measurements resulted in a better observer agreement. The accuracy of the measurements based on CBCT and 1.5 m SMD cephalogram was better than a 3 m SMD cephalogram. These findings demonstrated the linear measurements accuracy and reliability of 3D measurements based on CBCT data when compared to 2D techniques. Future studies should focus on the implementation of 3D cephalometry in clinical practice.

Consumption asymmetry and the stock market: New evidence through a threshold adjustment model

This paper investigates whether stock market wealth affects real consumption asymmetrically through a threshold adjustment model. The empirical findings for the US show that wealth produces an asymmetric effect on real consumption, with negative 'news' affecting consumption less than positive 'news.' Thus, policy makers may want to focus more attention on preventing asset 'bubbles' than on responding to negative asset shocks.

Does the Federal Reserve Follow a Non-Linear Taylor Rule?

The Taylor rule has become one of the most studied strategies for monetary policy. Yet, little is known whether the Federal Reserve follows a non-linear Taylor rule. This paper employs the smooth transition regression model and asks the question: does the Federal Reserve change its policy-rule according to the level of inflation and/or the output gap? I find that the Federal Reserve does follow a non-linear Taylor rule and, more importantly, that the Federal Reserve followed a non-linear Taylor rule during the golden era of monetary policy, 1985-2005, and a linear Taylor rule throughout the dark age of monetary policy, 1960-1979. Thus, good monetary policy is associated with a non-linear Taylor rule: once inflation approaches a certain threshold, the Federal Reserve adjusts its policy-rule and begins to respond more forcefully to inflation.

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Application of the general linear model to assess the effect of missing data on bone marrow mononuclear cell therapy with infusion timing and follow-up MRI

With most clinical trials, missing data presents a statistical problem in evaluating a treatment's efficacy. There are many methods commonly used to assess missing data; however, these methods leave room for bias to enter the study. This thesis was a secondary analysis on data taken from TIME, a phase 2 randomized clinical trial conducted to evaluate the safety and effect of the administration timing of bone marrow mononuclear cells (BMMNC) for subjects with acute myocardial infarction (AMI).^ We evaluated the effect of missing data by comparing the variance inflation factor (VIF) of the effect of therapy between all subjects and only subjects with complete data. Through the general linear model, an unbiased solution was made for the VIF of the treatment's efficacy using the weighted least squares method to incorporate missing data. Two groups were identified from the TIME data: 1) all subjects and 2) subjects with complete data (baseline and follow-up measurements). After the general solution was found for the VIF, it was migrated Excel 2010 to evaluate data from TIME. The resulting numerical value from the two groups was compared to assess the effect of missing data.^ The VIF values from the TIME study were considerably less in the group with missing data. By design, we varied the correlation factor in order to evaluate the VIFs of both groups. As the correlation factor increased, the VIF values increased at a faster rate in the group with only complete data. Furthermore, while varying the correlation factor, the number of subjects with missing data was also varied to see how missing data affects the VIF. When subjects with only baseline data was increased, we saw a significant rate increase in VIF values in the group with only complete data while the group with missing data saw a steady and consistent increase in the VIF. The same was seen when we varied the group with follow-up only data. This essentially showed that the VIFs steadily increased when missing data is not ignored. When missing data is ignored as with our comparison group, the VIF values sharply increase as correlation increases.^