Tatiyana V. Apanasovich

Global gas flux from mud volcanoes: A significant source of fossil methane in the atmosphere and the ocean

[1] There are yet unidentified sources of fossil methane (CH 4 ) in the atmosphere. Mud volcanoes (MVs) are a potentially significant but poorly quantified geologic source of fossil hydrocarbon gases and CO 2 to the atmosphere and the ocean not included in the current models of sources and sinks. Our statistical analysis of 36 previous measurements and estimates of gas flux from individual MVs suggests that the global gas flux may be as high as ∼33 Tg yr -1 (∼15.9 Tg yr during quiescent periods plus ∼17.1 Tg yr -1 during eruptions). Onshore and shallow offshore MVs are estimated to contribute ∼6 Tg yr -1 of greenhouse gases directly to the atmosphere. MVs may contribute 9% of fossil CH 4 missing in the modern atmospheric CH 4 budget, and ∼12% in the preindustrial budget. Large volumes (∼27 Tg yr -1 ) of gas may escape from deep-water MVs, suggesting that global gas flux from the seafloor may be underestimated.

Additive partial linear models with measurement errors

We consider statistical inference for additive partial linear models when the linear covariate is measured with error. We propose attenuation-to-correction and SIMEX estimators of the parameter of interest. It is shown that the first resulting estimator is asymptotically normal and requires no undersmoothing. This is an advantage of our estimator over existing backfitting-based estimators for semiparametric additive models which require undersmoothing of the nonparametric component in order for the estimator of the parametric component be root-n consistent. This feature stems from a decrease of the bias of the resulting estimator which is appropriately derived using a profile procedure. A similar characteristic in semiparametric partially linear models was obtained by Wang et al. (2005). We also discuss the asymptotics of the proposed SIMEX approach. Finite-sample performance of the proposed estimators is assessed by simulation experiments. The proposed methods are applied to a dataset from a semen study.

A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components

We introduce a valid parametric family of cross-covariance functions for multivariate spatial random fields where each component has a covariance function from a well-celebrated Matérn class. Unlike previous attempts, our model indeed allows for various smoothnesses and rates of correlation decay for any number of vector components. We present the conditions on the parameter space that result in valid models with varying degrees of complexity. We discuss practical implementations, including reparameterizations to reflect the conditions on the parameter space and an iterative algorithm to increase the computational efficiency. We perform various Monte Carlo simulation experiments to explore the performances of our approach in terms of estimation and cokriging. The application of the proposed multivariate Matérn model is illustrated on two meteorological datasets: temperature/pressure over the Pacific Northwest (bivariate) and wind/temperature/pressure in Oklahoma (trivariate). In the latter case, our flexible trivariate Matérn model is valid and yields better predictive scores compared with a parsimonious model with common scale parameters.

Dietary n‐3 PUFA affect TcR‐mediated activation of purified murine T cells and accessory cell function in co‐cultures

Diets enriched in n‐3 polyunsaturated fatty acids (PUFA) suppress several functions of murine splenic T cells by acting directly on the T cells and/or indirectly on accessory cells. In this study, the relative contribution of highly purified populations of the two cell types to the dietary suppression of T cell function was examined. Mice were fed diets containing different levels of n‐3 PUFA; safflower oil (SAF; control containing no n‐3 PUFA), fish oil (FO) at 2% and 4%, or 1% purified docosahexaenoic acid (DHA) for 2 weeks. Purified (>90%) T cells were obtained from the spleen, and accessory cells (>95% adherent, esterase‐positive) were obtained by peritoneal lavage. Purified T cells or accessory cells from each diet group were co‐cultured with the alternative cell type from every other diet group, yielding a total of 16 different co‐culture combinations. The T cells were stimulated with either concanavalin A (ConA) or antibodies to the T cell receptor (TcR)/CD3 complex and the costimulatory molecule CD28 (αCD3/αCD28), and proliferation was measured after four days. Suppression of T cell proliferation in the co‐cultures was dependent upon the dose of dietary n‐3 PUFA fed to mice from which the T cells were derived, irrespective of the dietary treatment of accessory cell donors. The greatest dietary effect was seen in mice consuming the DHA diet (P = 0·034 in the anova; P=0·0053 in the Trend Test), and was observed with direct stimulation of the T cell receptor and CD28 costimulatory ligand, but not with ConA. A significant dietary effect was also contributed accessory cells (P = 0·033 in the Trend Test). We conclude that dietary n‐3 PUFA affect TcR‐mediated by T cell activation by both direct and indirect (accessory cell) mechanisms.

On estimation in binary autologistic spatial models

There is a large and increasing literature in methods of estimation for spatial data with binary responses. The goal of this article is to describe some of these methods for the autologistic spatial model, and to discuss computational issues associated with them. The main way we do this is via illustration using a spatial epidemiology data set involving liver cancer. We first demonstrate why maximum likelihood is not currently feasible as a method of estimation in the spatial setting with binary data using the autologistic model. We then discuss alternative methods, including pseudo likelihood, generalized pseudo likelihood, and Monte Carlo maximum likelihood estimators. We describe their asymptotic efficiencies and the computational effort required to compute them. These three methods are applied to the data set and compared in a simulation experiment.

SIMEX and standard error estimation in semiparametric measurement error models

SIMEX is a general-purpose technique for measurement error correction. There is a substantial literature on the application and theory of SIMEX for purely parametric problems, as well as for purely non-parametric regression problems, but there is neither application nor theory for semiparametric problems. Motivated by an example involving radiation dosimetry, we develop the basic theory for SIMEX in semiparametric problems using kernel-based estimation methods. This includes situations that the mismeasured variable is modeled purely parametrically, purely non-parametrically, or that the mismeasured variable has components that are modeled both parametrically and nonparametrically. Using our asymptotic expansions, easily computed standard error formulae are derived, as are the bias properties of the nonparametric estimator. The standard error method represents a new method for estimating variability of nonparametric estimators in semiparametric problems, and we show in both simulations and in our example that it improves dramatically on first order methods.We find that for estimating the parametric part of the model, standard bandwidth choices of order O(n(-1/5)) are sufficient to ensure asymptotic normality, and undersmoothing is not required. SIMEX has the property that it fits misspecified models, namely ones that ignore the measurement error. Our work thus also more generally describes the behavior of kernel-based methods in misspecified semiparametric problems.

Weighted least squares methods for prediction in the functional data linear model

The problem of prediction in functional linear regression is conventionally addressed by reducing dimension via the standard principal component basis. In this paper we show that an alternative basis chosen through weighted least-squares, or weighted least-squares itself, can be more effective when the experimental errors are heteroscedastic. We give a concise theoretical result which demonstrates the effectiveness of this approach, even when the model for the variance is inaccurate, and we explore the numerical properties of the method. We show too that the advantages of the suggested adaptive techniques are not found only in low-dimensional aspects of the problem; rather, they accrue almost equally among all dimensions.

Human Airway Measurement from CT Images

A wide range of pulmonary diseases, including common ones such as COPD, affect the airways. If the dimensions of airway can be measured with high confidence, the clinicians will be able to better diagnose diseases as well as monitor progression and response to treatment. In this paper, we introduce a method to assess the airway dimensions from CT scans, including the airway segments that are not oriented axially. First, the airway lumen is segmented and skeletonized, and subsequently each airway segment is identified. We then represent each airway segment using a segment-centric generalized cylinder model and assess airway lumen diameter (LD) and wall thickness (WT) for each segment by determining inner and outer wall boundaries. The method was evaluated on 14 healthy patients from a Weill Cornell database who had two scans within a 2 month interval. The corresponding airway segments were located in two scans and measured using the automated method. The total number of segments identified in both scans was 131. When 131 segments were considered altogether, the average absolute change over two scans was 0.31 mm for LD and 0.12 mm for WT, with 95% limits of agreement of [-0.85, 0.83] for LD and [-0.32, 0.26] for WT. The results were also analyzed on per-patient basis, and the average absolute change was 0.19 mm for LD and 0.05 mm for WT. 95% limits of agreement for per-patient changes were [-0.57, 0.47] for LD and [-0.16, 0.10] for WT.

Characterization of pulmonary nodules: effects of size and feature type on reported performance

Differences in the size distribution of malignant and benign pulmonary nodules in databases used for training and testing characterization systems have a significant impact on the measured performance. The magnitude of this effect and methods to provide more relevant performance results are explored in this paper. Two- and three-dimensional features, both including and excluding size, and two classifiers, logistic regression and distance-weighted nearest-neighbors (dwNN), were evaluated on a database of 178 pulmonary nodules. For the full database, the area under the ROC curve (AUC) of the logistic regression classifier for 2D features with and without size was 0.721 and 0.614 respectively, and for 3D features with and without size, 0.773 and 0.737 respectively. In comparison, the performance using a simple size-threshold classifier was 0.675. In the second part of the study, the performance was measured on a subset of 46 nodules from the entire subset selected to have a similar size-distribution of malignant and benign nodules. For this subset, performance of the size-threshold was 0.504. For logistic regression, the performance for 2D, with and without size, were 0.578 and 0.478, and for 3D, with and without size, 0.671 and 0.767. Over all the databases, logistic regression exhibited better performance using 3D features than 2D features. This study suggests that in systems for nodule classification, size is responsible for a large part of the reported performance. To address this, system performance should be reported with respect to the performance of a size-threshold classifier.

PULMONARY NODULE CLASSIFICATION: SIZE DISTRIBUTION ISSUES

Automated nodule classification systems determine a model based on features extracted from documented databases of nodules. These databases cover a large size range and have an unequal distribution of malignant and benign nodules, leading to a high correlation between malignancy and size. For two recent studies in the literature, much of the reported performance of the system may be derived from size based on analysis of their size distributions. We performed experiments to determine the effect of unequal size distribution on a nodule classification systems performance. Preliminary results indicate that the performance across the entire dataset (a sensitivity/specificity of 0.85/0.80) does not generalize to a subset of nodules (0.50/0.80), but performance can be improved by specifically training on that subset (0.60/0.80). Additional testing with larger datasets needs to be performed, but results reported in this area are overly optimistic.