A comparison of different theoretical models of positron lifetime spectra for polymers

The same reference positron lifetime spectra of polyurethane, measured in temperature range 112 to 390 K, were analyzed with three theoretical models: the \conventional" model with three exponential decay component of discrete values of lifetimes, a model with one discrete exponential component and two \packages" of exponentials with log-normal distribution of annihilation rates, and a model considering positronium slow localization, positronium internal relaxation as well as \delayed" formation of positronium. It turns out that the two latter models fit the experimental data with the same excellent quality, in spite of the fact that in both models the ratio of intensities related to para-positronium and ortho-positronium was constrained to be as 1:3

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes

We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns. The three distributions we consider are derived from the gamma-Poisson, gamma-negative binomial, and beta-negative binomial processes. Because the models lead to closed-form Gibbs sampling update equations, they are natural candidates for nonparametric Bayesian priors over count matrices. A key aspect of our analysis is the recognition that, although the random count matrices within the family are defined by a row-wise construction, their columns can be shown to be i.i.d. This fact is used to derive explicit formulas for drawing all the columns at once. Moreover, by analyzing these matrices' combinatorial structure, we describe how to sequentially construct a column-i.i.d. random count matrix one row at a time, and derive the predictive distribution of a new row count vector with previously unseen features. We describe the similarities and differences between the three priors, and argue that the greater flexibility of the gamma- and beta- negative binomial processes, especially their ability to model over-dispersed, heavy-tailed count data, makes these well suited to a wide variety of real-world applications. As an example of our framework, we construct a naive-Bayes text classifier to categorize a count vector to one of several existing random count matrices of different categories. The classifier supports an unbounded number of features, and unlike most existing methods, it does not require a predefined finite vocabulary to be shared by all the categories, and needs neither feature selection nor parameter tuning. Both the gamma- and beta- negative binomial processes are shown to significantly outperform the gamma-Poisson process for document categorization, with comparable performance to other state-of-the-art supervised text classification algorithms.Comment: To appear in Journal of the American Statistical Association (Theory and Methods). 31 pages + 11 page supplement, 5 figure

Investigation of age-related protein changes in the human lens by quasi-elastic light scattering

The health and viability of cells and tissues in the human body depend on the functional integrity of proteins. A small number of long-lived proteins, including the crystallins in the lens of the eye, evade protein turnover, a typical cellular mechanism for repair and regeneration, and remain extant throughout life. The cumulative effect of post-translational modifications on the structure, function, and conformation of these long-lived proteins records the history of molecular aging in an individual. Along with absence of protein turnover, the optical accessibility, transparency, and age-related spatial order make the lens an ideal target for in vivo assessment of molecular aging. Accordingly, this doctoral thesis investigated the hypothesis that age-related perturbations that alter the protein environment in the human lens can be detected and monitored as a quantitative biomarker of molecular aging detectable by quasi-elastic light scattering (QLS). To test this hypothesis, QLS was applied in vitro and in vivo to study time-dependent changes in lens proteins. Water-soluble human lens protein extract was used in vitro as a model system that mimics the lens fiber cell cytoplasm. The effects of long-term incubation (nearly one year, proxy for aging), oxidative stress, ionizing radiation, metal-protein and pathogenic protein-protein interactions were investigated by QLS as a function of time. In vitro results were validated by protein gel electrophoresis and transmission electron microscopy. In vivo, age-dependent changes in lens proteins were assessed in healthy subjects across a broad age-range (5–61 years of age). Pathogenic protein aggregation in the lens was examined in vivo using Down syndrome (DS) subjects, a common chromosomal disease associated with an age-related Alzheimer’s disease (AD)-linked lens phenotype. Results obtained from the in vitro studies noted, for the first time, QLS detection of long-term supramolecular changes in a complex lens protein model system. Our FDA-approved QLS device was successful in assessing age-dependent lens protein changes in a clinical study at Boston Children’s Hospital (BCH). In two landmark studies conducted at BCH, we detected statistically significant AD-related lens protein changes in DS subjects aged 10–20 years, when compared with age-matched controls. These studies are the first clinical application of QLS in DS, and demonstrate protein changes in DS earlier than any previously reported studies. Due to the discrepancy in chronological and biological age and the lack of an objective index for the latter, we propose the application of QLS in the human lens as a quantitative biomarker of molecular aging