How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. "$131 million is about the cost to employ everyone in Texas over a lunch period". First, we collect a dataset of numeric mentions in news articles, where each mention is labeled with a set of rated perspectives. We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation. In construction, we compose formulae from numeric facts in a knowledge base and rank the resulting formulas based on familiarity, numeric proximity and semantic compatibility. In generation, we convert a formula into natural language using a sequence-to-sequence recurrent neural network. Our system obtains a 15.2% F1 improvement over a non-compositional baseline at formula construction and a 12.5 BLEU point improvement over a baseline description generation

REROUTING PRE-EXISTING HOST VACCINE-INDUCED IMMUNITY TOWARDS BREAST CANCER

For decades, investigators have attempted to activate the immune system to prevent cancer metastasis or recurrence; however, owing to host immune tolerance to cancer antigens and the immunosuppressive environment at tumor sites, many such attempts have failed. The recent success of anti-CTLA4, PD-L1 and PD-1 antibodies targeting immune checkpoint pathways and HPV vaccines has renewed hope that patient survival can be increased through enhancing T-cell responses. We propose to test a novel approach that may bypass host immune tolerance to cancer cells. We hypothesize that host T-cell immunity acquired through vaccination against or natural infection with infectious diseases—e.g., influenza—can be re-routed to breast cancer cells if the cancer cells also express the vaccine antigens and present the antigens in complex with MHC on the cell surface. We chose HER2-breast cancer as a model for proof-of-principle. In our study, we first examined MHC-I expression, which is required for mediating T cell-mediated response, in a panel of breast cancer cell lines with low or high levels of HER2. A previous study in literature reported an inverse correlation between the levels of HER2 and MHC-I expression in breast cancer cells. In contrast to that finding, we found no significant direct inverse correlation between the levels of HER2 and MHC-I expression. In the presence of peripheral blood mononuclear cells (PBMC), trastuzumab treatment resulted in a significant increase not only in MHC-I expression but also CD86 expression in the panel of breast cell lines. We demonstrated that this increase in MHC-I expression was correlated with an increase in IFN-γ in the co-culture of breast cancer cells and PMBC through trastuzumab-engaged PBMC. We further showed that trastuzumab treatment enhanced MHC-I expression in vivo in 4T1 mouse mammary tumors engineered to overexpress human HER2. To test our hypothesis of therapeutically redirecting preexisting non-cancer immunity developed through vaccination or contract with an infectious disease to cancer in vivo, we first immunized BALB/c mice with influenza PR8 virus to mimic flu vaccination, and then we challenged the mice with the highly aggressive 4T1 mouse mammary tumor cells or 4T1 cells lentivirally transduced to express hemagglutinin (HA) and nucleoprotein (NP) antigens of PR8 influenza virus, termed 4T1-HA+NP cells. We found a 70% rejection of 4T1-HA+NP tumors by day 12 and a significant reduction in tumor size and metastasis compared to mock (PBS) immunized mice. We also found that the anti-tumor responses in the influenza immunized group are associated with high percentage of memory CD8+ T cells, NK cells, mature DC’s, and low percentage of Treg cells and MDSC infiltration to the 4T1-HANP tumors. We next developed a trastuzumab-based immunoliposome to test our hypothesis of redirecting host influenza-induced immunity to cancer by therapeutic delivering influenza antigens to HER2-overexpressing breast cancer cells. The HER2-targeting immunoliposome was confirmed to retain its high affinity binding to HER2 in HER2-overexpressing breast cancer cells in vitro and in vivo. The immunoliposome effectively delivered labelled antigenic MHC-I influenza antigens in vivo and induce tumor regression in HER2-overexpressing tumors in influenza pre-immunized mice but not in naïve mice. Our data confirm that pre-existing non-cancer immunity can be rerouted to cancer cells through therapeutic delivery of relevant antigens using an immunoliposome approach

Multivariate Distributions of Correlated Binary Variables Generated by Pair-Copulas

Correlated binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. The generalized estimating equations (GEEs) and the multivariate probit (MP) model are two of the popular methods for analyzing such data. However, both methods have some significant drawbacks. The GEEs may not have an underlying likelihood and the MP model may fail to generate a multivariate binary distribution with specified marginals and bivariate correlations. In this paper, we study multivariate binary distributions that are based on D-vine pair-copula models as a superior alternative to these methods. We elucidate the construction of these binary distributions in two and three dimensions with numerical examples. For higher dimensions, we provide a method of constructing a multidimensional binary distribution with specified marginals and equicorrelated correlation matrix. We present a real-life data analysis to illustrate the application of our results

EM Estimation for Zero- and \u3ci\u3ek\u3c/i\u3e-Inflated Poisson Regression Model

Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count k tends to be higher in the data. The zero- and k-inflated Poisson distribution model (ZkIP) is appropriate in such situations The ZkIP distribution essentially is a mixture distribution of Poisson and degenerate distributions at points zero and k. In this article, we study the fundamental properties of this mixture distribution. Using stochastic representation, we provide details for obtaining parameter estimates of the ZkIP regression model using the Expectation-Maximization (EM) algorithm for a given data. We derive the standard errors of the EM estimates by computing the complete, missing, and observed data information matrices. We present the analysis of two real-life data using the methods outlined in the paper

Limit Theorems in the Area of Large Deviations for Some Dependent Random Variables

A magnetic body can be considered to consist of n sites, where n is large. The magnetic spins at these n sites, whose sum is the total magnetization present in the body, can be modelled by a triangular array of random variables (X(n) 1,..., X(n) n). Standard theory of physics would dictate that the joint distribution of the spins can be modelled by dQn(x) = zn-1 exp[ -Hn(x)]Π dP(xj), where x = (x1,..., xn) ∈ Rn, where Hn is the Hamiltonian, zn is a normalizing constant and P is a probability measure on R. For certain forms of the Hamiltonian Hn, Ellis and Newman (1978b) showed that under appropriate conditions on P, there exists an integer r ≥ 1 such that Sn/n1-1/2r converges in distribution to a random variable. This limiting random variable is Gaussian if r = 1 and non-Gaussian if r ≥ 2. In this article, utilizing the large deviation local limit theorems for arbitrary sequences of random variables of Chaganty and Sethuraman (1985), we obtain similar limit theorems for a wider class of Hamiltonians Hn, which are functions of moment generating functions of suitable random variables. We also present a number of examples to illustrate our theorems

D-Vine Copula Model For Dependent Binary Data

High-dimensional dependent binary data are prevalent in a wide range of scientific disciplines. A popular method for analyzing such data is the Multivariate Probit (MP) model. But the MP model sometimes fails even within a feasible range of binary correlations, because the underlying correlation matrix of the latent variables may not be positive definite. In this research, we proposed pair copula models, assuming the dependence between the binary variables is first order autoregressive (AR(1))or equicorrelated structure. Also, when Archimediean copula is used, most paper converted Kendall Tau to corresponding copula parameter, there is no explicit function of Pearson’s correlation coefficient with copula parameter. Therefore, we obtain the relationship between binary variable coefficient with copula parameter in the study as well. The outline of this poster presentation is as follows: we start with the definition of the copula and pictorially illustrate the relation between the copula parameter and the binary correlation. We illustrate pair copula constructions of multivariate binary distributions using D-vines and C-vines. We show the application of our method on a real life data. Finally, we briefly discuss our ongoing research.https://digitalcommons.odu.edu/gradposters2020_sciences/1003/thumbnail.jp