BAYESIAN INTEGRATIVE ANALYSIS OF OMICS DATA

Technological innovations have produced large multi-modal datasets that range in multiplatform genomic data, pathway data, proteomic data, imaging data and clinical data. Integrative analysis of such data sets have potentiality in revealing important biological and clinical insights into complex diseases like cancer. This dissertation focuses on Bayesian methodology establishment in integrative analysis of radiogenomics and pathway driver detection applied in cancer applications. We initially present Radio-iBAG that utilizes Bayesian approaches in analyzing radiological imaging and multi-platform genomic data, which we establish a multi-scale Bayesian hierarchical model that simultaneously identifies genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. Our method is motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, wherein it identifies important magnetic resonance imaging features and the associated genomic platforms that are also significantly related with patient survival times. For another aspect of integrative analysis, we then present pathDrive that aims to detect key genetic and epigenetic upstream drivers that influence pathway activity. The method is applied into colorectal cancer incorporated with its four molecular subtypes. For each of the pathways that significantly differentiates subgroups, we detect important genomic drivers that can be viewed as “switches” for the pathway activity. To extend the analysis, finally, we develop proteomic based pathway driver analysis for multiple cancer types wherein we simultaneously detect genomic upstream factors that influence a specific pathway for each cancer type within the cancer group. With Bayesian hierarchical model, we detect signals borrowing strength from common cancer type to rare cancer type, and simultaneously estimate their selection similarity. Through simulation study, our method is demonstrated in providing many advantages, including increased power and lower false discovery rates. We then apply the method into the analysis of multiple cancer groups, wherein we detect key genomic upstream drivers with proper biological interpretation. The overall framework and methodologies established in this dissertation illustrate further investigation in the field of integrative analysis of omics data, provide more comprehensive insight into biological mechanisms and processes, cancer development and progression

From impediment to adaptation: Chinese investments in Myanmar's new regulatory environment

Myanmar's political transition of 2011 was followed by changes in the political and economic realms of society. The transition emboldened social activism, expressed as protests regarding the injustices suffered by people under the military regime. Many of these protests were related to large-scale extractive investments that had little regard for local communities and the environment. After the West lifted most of its sanctions, transnational capital actors who had been absent for the previous two decades returned to the country, many of them offering higher investment standards. In response to the "push" of public pressure and the "pull" of new investments, reformists in the Government of Myanmar (GoM) are now attempting to implement a stronger investment regulatory framework. The GoM's new demands on foreign investments to comply with higher investment standards are strengthened by Chinese state reformers' own nascent efforts to curtail the excesses of that country's state-owned enterprises globally. As a result, prominent SOEs are being pressured to adapt to the new operating environment, resulting in observable changes in investment behaviour. We conclude that reform efforts are challenged by limitations on reformist state actors' autonomy and capacity to regulate investments

DeepSketchHair: Deep Sketch-based 3D Hair Modeling

We present sketchhair, a deep learning based tool for interactive modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a user-drawn sketch (consisting of hair contour and a few strokes indicating the hair growing direction within a hair region), and automatically generates a 3D hair model, which matches the input sketch both globally and locally. The key enablers of our system are two carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D vector field. Our system also supports hair editing with additional sketches in new views. This is enabled by another deep neural network, V2VNet, which updates the 3D vector field with respect to the new sketches. All the three networks are trained with synthetic data generated from a 3D hairstyle database. We demonstrate the effectiveness and expressiveness of our tool using a variety of hairstyles and also compare our method with prior art

Self-Domain Adaptation for Face Anti-Spoofing

Although current face anti-spoofing methods achieve promising results under intra-dataset testing, they suffer from poor generalization to unseen attacks. Most existing works adopt domain adaptation (DA) or domain generalization (DG) techniques to address this problem. However, the target domain is often unknown during training which limits the utilization of DA methods. DG methods can conquer this by learning domain invariant features without seeing any target data. However, they fail in utilizing the information of target data. In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference. Specifically, a domain adaptor is designed to adapt the model for test domain. In order to learn a better adaptor, a meta-learning based adaptor learning algorithm is proposed using the data of multiple source domains at the training step. At test time, the adaptor is updated using only the test domain data according to the proposed unsupervised adaptor loss to further improve the performance. Extensive experiments on four public datasets validate the effectiveness of the proposed method.Comment: Camera Ready, AAAI 202

Assembly of Silver Nanoparticles into Hollow Spheres Using Eu(III) Compound based on Trifluorothenoyl-Acetone

The preparation of luminescent silver hollow spheres using Eu(III) compound based on trifluorothenoyl-acetone is described. The structure and size of silver hollow spheres were determined by TEM images. The result shows the formation of hollow structure and average size of the silver hollow spheres (0.9 μm). The silver hollow spheres were further characterized by UV absorption spectrum, SNOM and SEM images, suggesting them to be formed by self-assemble of some isolated silver nanoparticles. The luminescent properties of them were also investigated and they are shown to be high emission strength; moreover, they offer the distinct advantage of a lower packing density compared with other commercial luminescent products

A Demographic Profile of Independently Incorporated Native American Foundations and Selected Funds in the United States

This report gives basic demographic information on 60 grantmaking entities grouped into three categories: 1) Native foundations that are independently incorporated; 2) 501c3 Native organizations; and 3) tribal funds. These categories capture the variety of Native controlled approaches currently at work in the field

First determination of Pu isotopes (239Pu, 240Pu and 241Pu) in radioactive particles derived from Fukushima Daiichi Nuclear Power Plant accident

Radioactive particles were released into the environment during the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident. Many studies have been conducted to elucidate the chemical composition of released radioactive particles in order to understand their formation process. However, whether radioactive particles contain nuclear fuel radionuclides remains to be investigated. Here, we report the first determination of Pu isotopes in radioactive particles. To determine the Pu isotopes (239Pu, 240Pu and 241Pu) in radioactive particles derived from the FDNPP accident which were free from the influence of global fallout, radiochemical analysis and inductively coupled plasma-mass spectrometry measurements were conducted. Radioactive particles derived from unit 1 and unit 2 or 3 were analyzed. For the radioactive particles derived from unit 1, activities of 239+240Pu and 241Pu were (1.70-7.06)×10-5 Bq and (4.10-8.10)×10-3 Bq, respectively and atom ratios of 240Pu/239Pu and 241Pu/239Pu were 0.330-0.415 and 0.162-0.178, respectively. These ratios were consistent with the simulation results from ORIGEN code and measurements from various environmental samples. In contrast, Pu was not detected in the radioactive particles derived from unit 2 or 3. The difference in Pu contents is clear evidence towards different formation processes of radioactive particles, and detailed formation processes can be investigated from Pu analysis