Local power and size properties of the LR, Wald, score and gradient tests in dispersion models

We derive asymptotic expansions for the nonnull distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the class of dispersion models, under a sequence of Pitman alternatives. The asymptotic distributions of these statistics are obtained for testing a subset of regression parameters and for testing the precision parameter. Based on these nonnull asymptotic expansions, the power of all four tests, which are equivalent to first order, are compared. Furthermore, in order to compare the finite-sample performance of these tests in this class of models, Monte Carlo simulations are presented. An empirical application to a real data set is considered for illustrative purposes. (C) 2012 Elsevier B.V. All rights reserved.

A new class of bragg stacks and its principle application

This work is focused on the development of high quality nanoporous 1D photonic crystals –so called Bragg stacks – made by spin-coating of approximately 25 nm large SiO2 and TiO2 nanoparticles bearing interparticle voids large enough to infiltrate reactive species. Therefore, the first part of this work describes the synthesis of well-dispersed TiO2 nanoparticles in this size range (the corresponding SiO2 nanoparticles are commercially available). In the second part, a protocol was developed to prepare nanoporous Bragg stacks of up to 12 bilayers with high quality and precision. Tailor-made Bragg stacks were prepared for different applications such as (i) a surface emitting feedback laser with a FWHM of only 6 nm and (ii) an electrochromic device with absorption reversibly switchable by an external electrical bias independently of the Bragg reflection. In the last chapter, the approach to 1D photonic crystals is transferred to 1D phononic crystals. Contrast in the modulus is achieved by spin-coating SiO2 and PMMA as high and low moduli material. This system showed a band gap of fg = 12.6 GHz with a width of Dfg/fg = 4.5 GHz.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

Fixed block configuration GDDs with block size 6 and (3, r)-regular graphs

Chapter 1 is used to introduce the basic tools and mechanics used within this thesis. Most of the definitions used in the thesis will be defined, and we provide a basic survey of topics in graph theory and design theory pertinent to the topics studied in this thesis. In Chapter 2, we are concerned with the study of fixed block configuration group divisible designs, GDD(n; m; k; λ1; λ2). We study those GDDs in which each block has configuration (s; t), that is, GDDs in which each block has exactly s points from one of the two groups and t points from the other. Chapter 2 begins with an overview of previous results and constructions for small group size and block sizes 3, 4 and 5. Chapter 2 is largely devoted to presenting constructions and results about GDDs with two groups and block size 6. We show the necessary conditions are sufficient for the existence of GDD(n, 2, 6; λ1, λ2) with fixed block configuration (3; 3). For configuration (1; 5), we give minimal or nearminimal index constructions for all group sizes n ≥ 5 except n = 10, 15, 160, or 190. For configuration (2, 4), we provide constructions for several families ofGDD(n, 2, 6; λ1, λ2)s. Chapter 3 addresses characterizing (3, r)-regular graphs. We begin with providing previous results on the well studied class of (2, r)-regular graphs and some results on the structure of large (t; r)-regular graphs. In Chapter 3, we completely characterize all (3, 1)-regular and (3, 2)-regular graphs, as well has sharpen existing bounds on the order of large (3, r)- regular graphs of a certain form for r ≥ 3. Finally, the appendix gives computational data resulting from Sage and C programs used to generate (3, 3)-regular graphs on less than 10 vertices.

Size-dependent accumulation of particles in lysosomes modulates dendritic cell function through impaired antigen degradation.

INTRODUCTION Nanosized particles may enable therapeutic modulation of immune responses by targeting dendritic cell (DC) networks in accessible organs such as the lung. To date, however, the effects of nanoparticles on DC function and downstream immune responses remain poorly understood. METHODS Bone marrow-derived DCs (BMDCs) were exposed in vitro to 20 or 1,000 nm polystyrene (PS) particles. Particle uptake kinetics, cell surface marker expression, soluble protein antigen uptake and degradation, as well as in vitro CD4(+) T-cell proliferation and cytokine production were analyzed by flow cytometry. In addition, co-localization of particles within the lysosomal compartment, lysosomal permeability, and endoplasmic reticulum stress were analyzed. RESULTS The frequency of PS particle-positive CD11c(+)/CD11b(+) BMDCs reached an early plateau after 20 minutes and was significantly higher for 20 nm than for 1,000 nm PS particles at all time-points analyzed. PS particles did not alter cell viability or modify expression of the surface markers CD11b, CD11c, MHC class II, CD40, and CD86. Although particle exposure did not modulate antigen uptake, 20 nm PS particles decreased the capacity of BMDCs to degrade soluble antigen, without affecting their ability to induce antigen-specific CD4(+) T-cell proliferation. Co-localization studies between PS particles and lysosomes using laser scanning confocal microscopy detected a significantly higher frequency of co-localized 20 nm particles as compared with their 1,000 nm counterparts. Neither size of PS particle caused lysosomal leakage, expression of endoplasmic reticulum stress gene markers, or changes in cytokines profiles. CONCLUSION These data indicate that although supposedly inert PS nanoparticles did not induce DC activation or alteration in CD4(+) T-cell stimulating capacity, 20 nm (but not 1,000 nm) PS particles may reduce antigen degradation through interference in the lysosomal compartment. These findings emphasize the importance of performing in-depth analysis of DC function when developing novel approaches for immune modulation with nanoparticles.

Data base on heterotrophic dinoflagellates grazing and growth rate as a function of size, prey size and type compiled from the literature

Microzooplankton (the 20 to 200 µm size class of zooplankton) is recognised as an important part of marine pelagic ecosystems. In terms of biomass and abundance heterotrophic dinoflagellates are one of the important groups of organism in microzooplankton. However, their rates - grazing and growth - , feeding behaviour and prey preferences are poorly known and understood. A set of data was assembled in order to derive a better understanding of heterotrophic dinoflagellates rates, in response to parameters such as prey concentration, prey type (size and species), temperature and their own size. With these objectives, literature was searched for laboratory experiments with information on one or more of these parameters effect studied. The criteria for selection and inclusion in the database included: (i) controlled laboratory experiment with a known dinoflagellate feeding on a known prey; (ii) presence of ancillary information about experimental conditions, used organisms - cell volume, cell dimensions, and carbon content. Rates and ancillary information were measured in units that meet the experimenter need, creating a need to harmonize the data units after collection. In addition different units can link to different mechanisms (carbon to nutritive quality of the prey, volume to size limits). As a result, grazing rates are thus available as pg C dinoflagellate-1 h-1, µm3 dinoflagellate-1 h-1 and prey cell dinoflagellate-1 h-1; clearance rate was calculated if not given and growth rate is expressed as the growth rate per day.

Data base on pelagic ciliates grazing and growth rate as a function of size, prey size and type compiled from the literature

Microzooplankton (the 20 to 200 µm size class of zooplankton) is recognised as an important part of marine pelagic ecosystems. In terms of biomass and abundance pelagic ciliates are one of the important groups of organism in microzooplankton. However, their rates - grazing and growth - , feeding behaviour and prey preferences are poorly known and understood. A set of data was assembled in order to derive a better understanding of pelagic ciliates rates, in response to parameters such as prey concentration, prey type (size and species), temperature and their own size. With these objectives, literature was searched for laboratory experiments with information on one or more of these parameters effect studied. The criteria for selection and inclusion in the database included: (i) controlled laboratory experiment with a known ciliates feeding on a known prey; (ii) presence of ancillary information about experimental conditions, used organisms - cell volume, cell dimensions, and carbon content. Rates and ancillary information were measured in units that meet the experimenter need, creating a need to harmonize the data units after collection. In addition different units can link to different mechanisms (carbon to nutritive quality of the prey, volume to size limits). As a result, grazing rates are thus available as pg C/(ciliate*h), µm**3/(ciliate*h) and prey cell/(ciliate*h); clearance rate was calculated if not given and growth rate is expressed as the growth rate per day.

Efficient term size computation for granularity control

Knowing the size of the terms to which program variables are bound at run-time in logic programs is required in a class of optimizations which includes granularity control and recursion elimination. Such size is difficult to even approximate at compile time and is thus generally computed at run-time by using (possibly predeñned) predicates which traverse the terms involved. We propose a technique which has the potential of performing this computation much more efficiently. The technique is based on ñnding program procedures which are called before those in which knowledge regarding term sizes is needed and which traverse the terms whose size is to be determined, and transforming such procedures so that they compute term sizes "on the fly". We present a systematic way of determining whether a given program can be transformed in order to compute a given term size at a given program point without additional term traversal. Also, if several such transformations are possible our approach allows ñnding minimal transformations under certain criteria. We also discuss the advantages and applications of our technique (specifically in the task of granularity control) and present some performance results.

Towards dynamic term size computation via program transformation

Knowing the size of the terms to which program variables are bound at run-time in logic programs is required in a class of applications related to program optimization such as, for example, recursion elimination and granularity analysis. Such size is difficult to even approximate at compile time and is thus generally computed at run-time by using (possibly predefined) predicates which traverse the terms involved. We propose a technique based on program transformation which has the potential of performing this computation much more efficiently. The technique is based on finding program procedures which are called before those in which knowledge regarding term sizes is needed and which traverse the terms whose size is to be determined, and transforming such procedures so that they compute term sizes "on the fly". We present a systematic way of determining whether a given program can be transformed in order to compute a given term size at a given program point without additional term traversal. Also, if several such transformations are possible our approach allows finding minimal transformations under certain criteria. We also discuss the advantages and present some applications of our technique.