Set-valued Data: Regression, Design and Outliers

The focus of this dissertation is to study set‐valued data from three aspects, namely regression, optimal design and outlier identification. This dissertation consists of three peer‐reviewed published articles, each of them addressing one aspect. Their titles and abstracts are listed below: 1. Local regression smoothers with set‐valued outcome data: This paper proposes a method to conduct local linear regression smoothing in the presence of set‐valued outcome data. The proposed estimator is shown to be consistent, and its mean squared error and asymptotic distribution are derived. A method to build error tubes around the estimator is provided, and a small Monte Carlo exercise is conducted to confirm the good finite sample properties of the estimator. The usefulness of the method is illustrated on a novel dataset from a clinical trial to assess the effect of certain genes’ expressions on different lung cancer treatments outcomes. 2. Optimal design for multivariate multiple linear regression with set‐identified response: We consider the partially identified regression model with set‐identified responses, where the estimator is the set of the least square estimators obtained for all possible choices of points sampled from set‐identified observations. We address the issue of determining the optimal design for this case and show that, for objective functions mimicking those for several classical optimal designs, their set‐identified analogues coincide with the optimal designs for point‐identified real‐valued responses. 3. Depth and outliers for samples of sets and random sets distributions: We suggest several constructions suitable to define the depth of set‐valued observations with respect to a sample of convex sets or with respect to the distribution of a random closed convex set. With the concept of a depth, it is possible to determine if a given convex set should be regarded an outlier with respect to a sample of convex closed sets. Some of our constructions are motivated by the known concepts of half‐space depth and band depth for function‐valued data. A novel construction derives the depth from a family of non‐linear expectations of random sets. Furthermore, we address the role of positions of sets for evaluation of their depth. Two case studies concern interval regression for Greek wine data and detection of outliers in a sample of particles

Structure Diagram Recognition in Financial Announcements

Accurately extracting structured data from structure diagrams in financial announcements is of great practical importance for building financial knowledge graphs and further improving the efficiency of various financial applications. First, we proposed a new method for recognizing structure diagrams in financial announcements, which can better detect and extract different types of connecting lines, including straight lines, curves, and polylines of different orientations and angles. Second, we developed a two-stage method to efficiently generate the industry's first benchmark of structure diagrams from Chinese financial announcements, where a large number of diagrams were synthesized and annotated using an automated tool to train a preliminary recognition model with fairly good performance, and then a high-quality benchmark can be obtained by automatically annotating the real-world structure diagrams using the preliminary model and then making few manual corrections. Finally, we experimentally verified the significant performance advantage of our structure diagram recognition method over previous methods

The conformation change of Bcl-2 is involved in arsenic trioxide-induced apoptosis and inhibition of proliferation in SGC7901 human gastric cancer cells

<p>Abstract</p> <p>Background</p> <p>Arsenic trioxide has been established as a first-line agent for treating acute promyelocytic leukemia. Experimental data suggest that arsenic trioxide also can have a potential use as chemotherapeutic agent for other malignancies. The precise mechanisms of action of arsenic trioxide have though not been elucidated. As the role of Bcl-2 in arsenic trioxide-mediated cell apoptosis and conformation change of Bcl-2 in response to arsenic trioxide treatment has not been studied. The aim of the present study was to determine whether conformation change of Bcl-2 is involved in the action of arsenic trioxide.</p> <p>Methods</p> <p>Human gastric cancer SGC7901 cells were exposed to different concentrations of arsenic trioxide. Proliferation was measured by using the Kit-8 cell counting assay. Analysis of nuclear morphology was observed by DAPI staining. The apoptosis rates of cells treated with arsenic trioxide were analyzed by flow cytometry using Annexin V-FITC staining. The conformation change of Bcl-2 and Bax activation were detected by immunostaining and Western blot analysis. Total expression of Bcl-2 and Bax were examined by Western blot analysis.</p> <p>Results</p> <p>Arsenic trioxide inhibited the growth of human gastric cancer SGC7901 cells and induced apoptosis. There were two Bcl-2 phenotypes coexisting in SGC7901 cells and the Bcl-2 cytoprotective phenotype could change into a cytodestructive phenotype following conformational change of Bcl-2, triggered by arsenic trioxide exposure. Bax activation might also be involved in arsenic trioxide-induced Bcl-2 conformational change. Arsenic trioxide did not change levels of total Bcl-2 expression, but up-regulated total Bax expression for the treatment time ranging from 3 to 24 hours.</p> <p>Conclusion</p> <p>Arsenic trioxide induces apoptosis through induction of Bcl-2 conformational change, Bax activation and up-regulation of total Bax expression rather than affecting total Bcl-2 expression in human gastric cancer SGC7901 cells. The conformational change of Bcl-2 may be a novel described mechanism of arsenic trioxide-induced apoptosis in cancer cells.</p