The mammary gland is a branched epithelial organ comprised of myoepithelial, ductal and alveolar cells that are derived from resident stem and progenitor cells. The progression from mammary gland stem cell(s) to the differentiated mammary gland cell types is poorly understood. Here, I describe the identification and characterization of two luminal progenitor cell populations in the mouse mammary gland, and investigate the role of the transcription factor C/EBPβ in their development. In Chapter 2, I describe the isolation of two luminal progenitor cell populations (Sca1+ and Sca1- luminal cells) and show that they are differentially primed in their gene expression towards ductal and alveolar cell fates, respectively. Furthermore, I show that in vivo genetic priming affects the in vitro differentiation potential of Sca1+ and Sca1- luminal cells. In Chapter 3, I show that C/EBPβ is required for the appropriate specification of ductal and alveolar lineages, and in its absence, alveolar lineage priming is lost, and ductal lineage priming is up-regulated in both Sca1+ and Sca1- cells. Preliminary data also shows that in addition to severe proliferation defects, the changes in in vivo lineage priming in Cebpb-/- mice also affect the in vitro differentiation potential of Cebpb-/- Sca1+ and Sca1- luminal progenitors. Lastly, in Chapter 4, I describe the genome-wide binding characteristics of C/EBPβ in Sca1+, Sca1- and P16.5 alveolar cells. These experiments reveal that genome-wide C/EBPβ occupancy is correlated with alveolar cells fate, and that C/EBPβ target genes perform distinct cellular functions in alveolar cells (Sca1- cells and P16.5). Furthermore, I show that Elf5 is directly regulated by C/EBPβ, and posit that direct regulation of Elf5 by C/EBPβ may be one mechanism through which C/EBPβ exerts its alveolar cell fate programming.

潜变量模型作为处理多元数据的一种有效的方法，在行为学、教育学、社会心理学以及医学等各个领域都受到了广泛关注。在分析潜变量模型时，大多数现有的统计方法和软件都是基于响应变量为正态分布的假设。尽管一些最近发展的方法可以处理部分的非正态数据，但在分析高度非正态的数据时依然存在问题。此外，在实际研究中还经常会遇到不完全数据，如缺失数据和删失数据。简单地忽略或错误地处理不完全数据可能会严重扭曲统计结果。在本文中，我们发展了贝叶斯惩罚样条方法，同时采用马尔科夫链蒙特卡洛方法，用以分析存有高度非正态和不完全数据的变换潜变量模型。我们在变换潜变量模型中讨论了不同类型的不完全数据，如完全随机缺失数据、随机缺失数据、不可忽略的缺失数据以及删失数据。我们还利用离差信息准则来选择正确的模型和数据缺失机制。我们通过许多模拟研究论证了我们提出的方法。此方法被应用于关于工作满意度、家庭生活、工作态度的研究，以及香港地区2 型糖尿病患者心血管疾病的研究。 / Latent variable models (LVMs), as useful multivariate techniques, have attracted significant attention from various fields, including the behavioral, educational, social-psychological, and medical sciences. In the analysis of LVMs, most existing statistical methods and software have been developed under the normal assumption of response variables. While some recent developments can partially address the non-normality of data, they are still problematic in dealing with highly non-normal data. Moreover, the presence of incomplete data, such as missing data and censoring data, is a practical issue in substantive research. Simply ignoring incomplete data or wrongly managing incomplete data might seriously distort statistical influence results. In this thesis, we develop a Bayesian P-spline approach, coupled with Markov chain Monte Carlo (MCMC) methods, to analyze transformation LVMs with highly non-normal and incomplete data. Different types of incomplete data, such as missing completely at random data, missing at random data, nonignorable missing data, as well as censored data, are discussed in the context of transformation LVMs. The deviance information criterion is proposed to conduct model comparison and select an appropriate missing mechanism. The empirical performance of the proposed methodologies is examined via many simulation studies. Applications to a study concerning people's job satisfaction, home life, and work attitude, as well as a study on cardiovascular diseases for type 2 diabetic patients in Hong Kong are presented. / Detailed summary in vernacular field only. / Liu, Pengfei. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 115-127). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Latent Variable Models --- p.1 / Chapter 1.2 --- Missing Data --- p.4 / Chapter 1.3 --- Censoring Data --- p.5 / Chapter 1.4 --- Penalized B-splines --- p.6 / Chapter 1.5 --- Bayesian Methods --- p.7 / Chapter 1.6 --- Outline of the Thesis --- p.8 / Chapter 2 --- Transformation Structural Equation Models --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.2 --- Model Description --- p.11 / Chapter 2.3 --- Bayesian Estimation --- p.12 / Chapter 2.3.1 --- Bayesian P-splines --- p.12 / Chapter 2.3.2 --- Identifiability Constraints --- p.15 / Chapter 2.3.3 --- Prior Distributions --- p.16 / Chapter 2.3.4 --- Posterior Inference --- p.18 / Chapter 2.4 --- Bayesian Model Selection via DIC --- p.20 / Chapter 2.5 --- Simulation Studies --- p.23 / Chapter 2.5.1 --- Simulation 1 --- p.23 / Chapter 2.5.2 --- Simulation 2 --- p.26 / Chapter 2.5.3 --- Simulation 3 --- p.27 / Chapter 2.6 --- Conclusion --- p.28 / Chapter 3 --- Transformation SEMs with Missing Data that are Missing At Random --- p.43 / Chapter 3.1 --- Introduction --- p.43 / Chapter 3.2 --- Model Description --- p.45 / Chapter 3.3 --- Bayesian Estimation and Model Selection --- p.46 / Chapter 3.3.1 --- Modeling Transformation Functions --- p.46 / Chapter 3.3.2 --- Identifiability Constraints --- p.47 / Chapter 3.3.3 --- Prior Distributions --- p.48 / Chapter 3.3.4 --- Bayesian Estimation --- p.49 / Chapter 3.3.5 --- Model Selection via DIC --- p.52 / Chapter 3.4 --- Simulation Studies --- p.53 / Chapter 3.4.1 --- Simulation 1 --- p.54 / Chapter 3.4.2 --- Simulation 2 --- p.56 / Chapter 3.5 --- Conclusion --- p.57 / Chapter 4 --- Transformation SEMs with Nonignorable Missing Data --- p.65 / Chapter 4.1 --- Introduction --- p.65 / Chapter 4.2 --- Model Description --- p.67 / Chapter 4.3 --- Bayesian Inference --- p.68 / Chapter 4.3.1 --- Model Identification and Prior Distributions --- p.68 / Chapter 4.3.2 --- Posterior Inference --- p.69 / Chapter 4.4 --- Selection of Missing Mechanisms --- p.71 / Chapter 4.5 --- Simulation studies --- p.73 / Chapter 4.5.1 --- Simulation 1 --- p.73 / Chapter 4.5.2 --- Simulation 2 --- p.76 / Chapter 4.6 --- A Real Example --- p.77 / Chapter 4.7 --- Conclusion --- p.79 / Chapter 5 --- Transformation Latent Variable Models with Multivariate Censored Data --- p.86 / Chapter 5.1 --- Introduction --- p.86 / Chapter 5.2 --- Model Description --- p.88 / Chapter 5.3 --- Bayesian Inference --- p.90 / Chapter 5.3.1 --- Model Identification and Bayesian P-splines --- p.90 / Chapter 5.3.2 --- Prior Distributions --- p.91 / Chapter 5.3.3 --- Posterior Inference --- p.93 / Chapter 5.4 --- Simulation Studies --- p.96 / Chapter 5.4.1 --- Simulation 1 --- p.96 / Chapter 5.4.2 --- Simulation 2 --- p.99 / Chapter 5.5 --- A Real Example --- p.100 / Chapter 5.6 --- Conclusion --- p.103 / Chapter 6 --- Conclusion and Further Development --- p.113 / Bibliography --- p.115

高維資料的隱含結構挖掘是機器學習、模式識別和生物資訊學等領域中的重要問題。本論文從實踐和理論上研究了具有不同隱含結構模式的非高斯因數分析(Non-Gaussian Factor Analysis)模型。本文既從兩步法又從自動法的角度重點研究確定隱因數個數的模型選擇問題，及其在模式識別和生物資訊學上的實際應用。 / 非高斯因數分析在單高斯因數的情況下退化為傳統的因數分析(Factor Analysis)模型。我們發展了一套系統地比較模型選擇方法性能的工具，比較研究了經典的模型選擇準則(比如AIC 等)，及近年來基於隨機矩陣理論的統計檢驗方法，還有貝葉斯陰陽(Bayesian Ying-Yang)和諧學習理論。同時，我們也對四個經典準則提供了一個適用於小樣本的低估因數數目傾向的相對排序的理論結果。 / 基於傳統的因數分析模型，我們還研究了參數化形式對模型選擇方法的性能的影響，一個重要的但被忽略或很少研究的問題，因為似然函數等價的參數化形式在傳統的模型選擇準則像AIC 下不會有性能差異。但是，我們通過大量的模擬資料和實際資料上的結果發現，在兩個常用的似然函數等價的因數分析參數化形式中，其中一個更加有利於在變分貝葉斯(Variational Bayes)和貝葉斯陰陽理論框架下做模型選擇。 進一步地，該兩個參數化形式被作為兩端拓展成一系列具有等價似然函數的參數化形式。實驗結果更加可靠地揭示了參數化形式的逐漸變化對模型選擇的影響。同時，實驗結果也顯示參數先驗分佈的引入可以提高模型選擇的準確度，並給出了相應的新的學習演算法。系統比較表明，不僅是兩步法還是自動法，貝葉斯陰陽學習理論都比變分貝葉斯的模型選擇的性能更佳，並且能在有利的參數化形式中獲得更大的提高。 / 二元因數分析(Binary FA)也是一種非高斯因數分析模型，它用伯努利因數去解釋隱含結構。首先，我們引入一種叫做正則對偶(canonical dual)的方法去解決在二元因數分析學習演算法中遇到的一個計算複雜度很大的二值二次規劃(Binary Quadratic Programming)問題。雖然它不能準確找到二值二次規劃的全域最優，它卻提高了整個學習演算法的計算速度和自動模型選擇的準確性。由此表明，局部嵌套的子優化問題的解不需要太精確反而能對整個學習演算法的性能更有利。然後，先驗分佈的引入進一步提高了模型選擇的性能，並且貝葉斯陰陽學習理論被系統的實驗結果證實要優於變分貝葉斯。接著，我們進一步發展了一個適用於二值資料的二元矩陣分解演算法。該演算法有理論的結果保證它的性能，並且在實際應用中，能以比其他相關演算法更優的性能從大規模的蛋白相互作用網路中檢測出蛋白功能複合物。 / 進一步，我們在一個半盲(semi-blind)的框架下研究了非高斯因數分析的演算法及其在系統生物學中的應用。非高斯因數分析模型被用於基因轉錄調控建模，並引入稀疏約束到連接矩陣，從而提出一個能有效估計轉錄因數調控信號的方法，而不需要像網路分量分析(Network Component Analysis)方法那樣預先給定轉錄因數調控基因的拓撲網路結構。特別地，借助二元因數分析，調控信號中的二元特徵能被直接捕捉。這種似開關的模式在很多生物過程的調控機制裡面起著重要作用。 / 最後，基於半盲非高斯因數分析學習演算法，我們提出了一套分析外顯子測序數據的方法，能有效地找出與疾病關聯的易感基因，提供了一個可能的方向去解決傳統的全基因組關聯分析(GWAS)方法在低頻高雜訊的外顯子測序數據上失效的問題。在一個1457 個樣本的大規模外顯子測序數據的初步結果顯示，我們的方法既能確認很多已經被認為是與疾病相關的基因，又能找到新的被重複驗證有顯著性的易感基因。相關的表達譜資料進一步顯示所找到的新基因在疾病和對照上有顯著的上下調的表達差異。 / Mining the underlying structure from high dimensional observations is of critical importance in machine learning, pattern recognition and bioinformatics. In this thesis, we, empirically or theoretically, investigate non-Gaussian Factor Analysis (NFA) models with different underlying structures. We focus on the problem of determining the number of latent factors of NFA, from two-stage approach model selection to automatic model selection, with real applications in pattern recognition and bioinformatics. / We start by a degenerate case of NFA, the conventional Factor Analysis (FA) with latent Gaussian factors. Many model selection methods have been proposed and used for FA, and it is important to examine their relative strengths and weaknesses. We develop an empirical analysis tool, to facilitate a systematic comparison on model selection performances of not only classical criteria (e.g., Akaike’s information criterion or shortly AIC) but also recently developed methods (e.g., Kritchman & Nadler’s hypothesis tests), as well as the Bayesian Ying-Yang (BYY) harmony learning. Also, we prove a theoretical relative order of underestimation tendency of four classical criteria. / Then, we investigate how parameterizations affect model selection performance, an issue that has been ignored or seldom studied since traditional model selection criteria, like AIC, perform equivalently on different parameterizations that have equivalent likelihood functions. Focusing on two typical parameterizations of FA, one of which is found to be better than the other under both Variational Bayes (VB) and BYY via extensive experiments on synthetic and real data. Moreover, a family of FA parameterizations that have equivalent likelihood functions are presented, where each one is featured by an integer r, with the two known parameterizations being both ends as r varies from zero to its upper bound. Investigations on this FA family not only confirm the significant difference between the two parameterizations in terms of model selection performance, but also provide insights into what makes a better parameterization. With a Bayesian treatment to the new FA family, alternative VB algorithms on FA are derived, and also BYY algorithms on FA are extended to be equipped with prior distributions on the parameters. A systematic comparison shows that BYY generally outperforms VB under various scenarios including varying simulation configurations and incrementally adding priors to parameters, as well as automatic model selection. / To describe binary latent features, we proceed to binary factor analysis (BFA), which considers Bernoulli factors. First, we introduce a canonical dual approach to tackling a difficult Binary Quadratic Programming (BQP) problem encountered as a computational bottleneck in BFA learning. Although it is not an exact BQP solver, it improves the learning speed and model selection accuracy, which indicates that some amount of error in solving the BQP, a problem nested in the hierarchy of the whole learning process, brings gain on both computational efficiency and model selection performance. The results also imply that optimization is important in learning, but learning is not just a simple optimization. Second, we develop BFA algorithms under VB and BYY to incorporate Bayesian priors on the parameters to improve the automatic model selection performance, and also show that BYY is superior to VB under a systematic comparison. Third, for binary observations, we propose a Bayesian Binary Matrix Factorization (BMF) algorithm under the BYY framework. The performance of the BMF algorithm is guaranteed with theoretical proofs and verified by experiments. We apply it to discovering protein complexes from protein-protein interaction (PPI) networks, an important problem in bioinformatics, with outperformance comparing to other related methods. / Furthermore, we investigate NFA under a semi-blind learning framework. In practice, there exist many scenarios of knowing partially either or both of the system and the input. Here, we modify Network Component Analysis (NCA) to model gene transcriptional regulation in system biology by NFA. The previous hardcut NFA algorithm is extended here as sparse BYY-NFA by considering either or both of a priori connectivity and a priori sparse constraint. Therefore, the a priori knowledge about the connection topology of the TF-gene regulatory network required by NCA is not necessary for our NFA algorithm. The sparse BYY-NFA can be further modified to get a sparse BYY-BFA algorithm, which directly models the switching patterns of latent transcription factor (TF) activities in gene regulation, e.g., whether or not a TF is activated. Mining switching patterns provides insights into exploring regulation mechanism of many biological processes. / Finally, the semi-blind NFA learning is applied to identify those single nucleotide polymorphisms (SNPs) that are significantly associated with a disease or a complex trait from exome sequencing data. By encoding each exon/gene (which may contain multiple SNPs) as a vector, an NFA classifier, obtained in a supervised way on a training set, is used for prediction on a testing set. The genes are selected according to the p-values of Fisher’s exact test on the confusion tables collected from prediction results. The selected genes on a real dataset from an exome sequencing project on psoriasis are consistent in part with published results, and some of them are probably novel susceptible genes of the disease according to the validation results. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Tu, Shikui. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 196-212). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Motivations --- p.1 / Chapter 1.1.2 --- Independent Factor Analysis (IFA) --- p.2 / Chapter 1.1.3 --- Learning Methods --- p.6 / Chapter 1.2 --- Related Work --- p.14 / Chapter 1.2.1 --- Learning Gaussian FA --- p.14 / Chapter 1.2.2 --- Learning NFA --- p.16 / Chapter 1.2.3 --- Learning Semi-blind NFA --- p.18 / Chapter 1.3 --- Main Contribution of the Thesis --- p.18 / Chapter 1.4 --- Thesis Organization --- p.25 / Chapter 1.5 --- Publication List --- p.27 / Chapter 2 --- FA comparative analysis --- p.31 / Chapter 2.1 --- Determining the factor number --- p.32 / Chapter 2.2 --- Model Selection Methods --- p.34 / Chapter 2.2.1 --- Two-Stage Procedure and Classical Model Selection Criteria --- p.34 / Chapter 2.2.2 --- Kritchman&Nadler's Hypothesis Test (KN) --- p.35 / Chapter 2.2.3 --- Minimax Rank Estimation (MM) --- p.37 / Chapter 2.2.4 --- Minka's Criterion (MK) for PCA --- p.38 / Chapter 2.2.5 --- Bayesian Ying-Yang (BYY) Harmony Learning --- p.39 / Chapter 2.3 --- Empirical Analysis --- p.42 / Chapter 2.3.1 --- A New Tool for Empirical Comparison --- p.42 / Chapter 2.3.2 --- Investigation On Model Selection Performance --- p.44 / Chapter 2.4 --- A Theoretic Underestimation Partial Order --- p.49 / Chapter 2.4.1 --- Events of Estimating the Hidden Dimensionality --- p.49 / Chapter 2.4.2 --- The Structural Property of the Criterion Function --- p.49 / Chapter 2.4.3 --- Experimental Justification --- p.54 / Chapter 2.5 --- Concluding Remarks --- p.58 / Chapter 3 --- FA parameterizations affect model selection --- p.70 / Chapter 3.1 --- Parameterization Issue in Model Selection --- p.71 / Chapter 3.2 --- FAr: ML-equivalent Parameterizations of FA --- p.72 / Chapter 3.3 --- Variational Bayes on FAr --- p.74 / Chapter 3.4 --- Bayesian Ying-Yang Harmony Learning on FAr --- p.77 / Chapter 3.5 --- Empirical Analysis --- p.82 / Chapter 3.5.1 --- Three levels of investigations --- p.82 / Chapter 3.5.2 --- FA-a vs FA-b: performances of BYY, VB, AIC, BIC, and DNLL --- p.84 / Chapter 3.5.3 --- FA-r: performances of VB versus BYY --- p.87 / Chapter 3.5.4 --- FA-a vs FA-b: automatic model selection performance of BYYandVB --- p.90 / Chapter 3.5.5 --- Classification Performance on Real World Data Sets --- p.92 / Chapter 3.6 --- Concluding remarks --- p.93 / Chapter 4 --- BFA learning versus optimization --- p.104 / Chapter 4.1 --- Binary Factor Analysis --- p.105 / Chapter 4.2 --- BYY Harmony Learning on BFA --- p.107 / Chapter 4.3 --- Empirical Analysis --- p.108 / Chapter 4.3.1 --- BIC and Variational Bayes (VB) on BFA --- p.108 / Chapter 4.3.2 --- Error in solving BQP affects model selection --- p.110 / Chapter 4.3.3 --- Priors over parameters affect model selection --- p.114 / Chapter 4.3.4 --- Comparisons among BYY, VB, and BIC --- p.115 / Chapter 4.3.5 --- Applications in recovering binary images --- p.116 / Chapter 4.4 --- Concluding Remarks --- p.117 / Chapter 5 --- BMF for PPI network analysis --- p.124 / Chapter 5.1 --- The problem of protein complex prediction --- p.125 / Chapter 5.2 --- A novel binary matrix factorization (BMF) algorithm --- p.126 / Chapter 5.3 --- Experimental Results --- p.130 / Chapter 5.3.1 --- Other methods in comparison --- p.130 / Chapter 5.3.2 --- Data sets --- p.131 / Chapter 5.3.3 --- Evaluation criteria --- p.131 / Chapter 5.3.4 --- On altered graphs by randomly adding and deleting edges --- p.132 / Chapter 5.3.5 --- On real PPI data sets --- p.137 / Chapter 5.3.6 --- On gene expression data for biclustering --- p.137 / Chapter 5.4 --- A Theoretical Analysis on BYY-BMF --- p.138 / Chapter 5.4.1 --- Main results --- p.138 / Chapter 5.4.2 --- Experimental justification --- p.140 / Chapter 5.4.3 --- Proofs --- p.143 / Chapter 5.5 --- Concluding Remarks --- p.147 / Chapter 6 --- Semi-blind NFA: algorithms and applications --- p.148 / Chapter 6.1 --- Determining transcription factor activity --- p.148 / Chapter 6.1.1 --- A brief review on NCA --- p.149 / Chapter 6.1.2 --- Sparse NFA --- p.150 / Chapter 6.1.3 --- Sparse BFA --- p.156 / Chapter 6.1.4 --- On Yeast cell-cycle data --- p.160 / Chapter 6.1.5 --- On E. coli carbon source transition data --- p.166 / Chapter 6.2 --- Concluding Remarks --- p.170 / Chapter 7 --- Applications on Exome Sequencing Data Analysis --- p.172 / Chapter 7.1 --- From GWAS to Exome Sequencing --- p.172 / Chapter 7.2 --- Encoding An Exon/Gene --- p.173 / Chapter 7.3 --- An NFA Classifier --- p.175 / Chapter 7.4 --- Results --- p.176 / Chapter 7.4.1 --- Simulation --- p.176 / Chapter 7.4.2 --- On a real exome sequencing data set: AHMUe --- p.177 / Chapter 7.5 --- Concluding Remarks --- p.186 / Chapter 8 --- Conclusion and FutureWork --- p.187 / Chapter A --- Derivations of the learning algorithms on FA-r --- p.190 / Chapter A.1 --- The VB learning algorithm on FA-r --- p.190 / Chapter A.2 --- The BYY learning algorithm on FA-r --- p.193 / Bibliography --- p.195

Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide. Current treatments are subpar, with late stage diagnosis and poor prognosis contributing to limited treatment options. The evolutionarily conserved, ubiquitously expressed transcription factor LSF is overexpressed in HCC, and its expression is positively correlated with disease severity. Certain small molecules, known as Factor Quinolinone Inhibitors (FQIs), specifically inhibit LSF DNA-binding activity, inhibit HCC cell proliferation in vitro and prevent tumor growth in an endogenous mouse liver cancer model without apparent toxicity. The targeting of transcription factors by small molecule inhibitors has been historically difficult (Dunker and Uversky, 2010), warranting further molecular investigation into the requirement for LSF in HCC to confirm that the anti-tumor effects of FQIs are the consequence of LSF inhibition. This body of work investigates a dual approach for inhibiting LSF function in order to determine the molecular consequences for HCC cells. To identify the specific point of the cell cycle where LSF is required for HCC proliferation, synchronous HCC cells were treated with FQI or with short interfering RNA to reduce levels of LSF. The results indicate that LSF is required for proper mitotic progression in HCC cells. Specifically, these data show a reduction of key mitotic regulators Aurora Kinase B and Cdc20, at the level of mRNA and protein expression. Time-lapse microscopy also demonstrated an increase in the time for progression through mitosis, with a prometaphase/metaphase delay. Immunofluorescence analysis revealed a prometaphase delay plus aberrant cell division and generation of multi-nucleated cells. These findings were consistent with both FQI1 treatment and RNA interference. Additionally, shorter incubation with FQI1 surprisingly revealed a distinct, non-transcriptional regulation of mitosis in HCC cells, suggesting that mitotic regulation by LSF is multi-faceted. As a targeted therapy for use in the clinic, the in vivo toxicity of FQIs is critical to investigate. Whole blood provides populations of rapidly dividing normal cells that can test susceptibility to anti-mitotic compounds. When mice were treated with FQI1, the blood analysis showed no toxicity. Taken together, these findings indicate that LSF is a mitotic regulator in HCC, further supporting the therapeutic promise of molecular therapies targeting LSF. / 2019-03-04T00:00:00Z

"During heart valve remodeling, and in many disease states, valvular interstitial cells (VICs) shift to an activated myofibroblast phenotype which is characterized by enhanced synthetic and contractile activity. Pronounced alpha smooth muscle actin (alpha-SMA)-containing stress fibers, the hallmark of activated myofibroblasts, are also observed when VICs are placed under tension due to altered mechanical loading in vivo or during in vitro culture on stiff substrates or under high mechanical loads and in the presence of transforming growth factor-beta 1 (TGF-beta 1). The work presented herein describes three distinct model systems for application of controlled mechanical environment to VICs cultured in vitro. The first system uses polyacrylamide (PA) gels of defined stiffness to evaluate the response of VICs over a large range of stiffness levels and TGF-beta 1 concentration. The second system controls the boundary stiffness of cell-populated gels using springs of defined stiffness. The third system cyclically stretches soft or stiff two-dimensional (2D) gels while cells are cultured on the gel surface as it is deformed. Through the use of these model systems, we have found that the level of 2D stiffness required to maintain the quiescent VIC phenotype is potentially too low for a material to both act as matrix to support cell growth in the non-activated state and also to withstand the mechanical loading that occurs during the cardiac cycle. Further, we found that increasing the boundary stiffness on a three-dimensional (3D) cell populated collagen gel resulted in increased cellular contractile forces, alpha-SMA expression, and collagen gel (material)stiffness. Finally, VIC morphology is significantly altered in response to stiffness and stretch. On soft 2D substrates, VICs cultured statically exhibit a small rounded morphology, significantly smaller than on stiff substrates. Following equibiaxial cyclic stretch, VICs spread to the extent of cells cultured on stiff substrates, but did not reorient in response to uniaxial stretch to the extent of cells stretched on stiff substrates. These studies provide critical information for characterizing how VICs respond to mechanical stimuli. Characterization of these responses is important for the development of tissue engineered heart valves and contributes to the understanding of the role of mechanical cues on valve pathology and disease onset and progression. While this work is focused on valvular interstitial cells, the culture conditions and methods for applying mechanical stimulation could be applied to numerous other adherent cell types providing information on the response to mechanical stimuli relevant for optimizing cell culture, engineered tissues or fundamental research of disease states."

The objective of this research was to develop and verify a mathematical model and the necessary material database that allow predicting the physical and material property changes that occur in aluminum casting alloys in response to precipitation-hardening heat treatment. The model accounts for all three steps of the typical precipitation hardening heat treatment; i.e., the solutionizing, quenching, and aging steps; and it allows predicting the local hardness and tensile strength, and the local residual stresses, distortion and dimensional changes that develop in the cast component during each step of the heat treatment process. The model uses commercially available finite element software and an extensive database that was developed specifically for the aluminum alloy under consideration - namely A356.2 casting alloy. The database includes the mechanical, physical, and thermal properties of the alloy all as functions of temperature. The model predictions were compared to measurements made on commercial cast components that were heat treated according to standard heat treatment protocols and the model predictions were found to be in good agreement with the measurements.

Entry resistance in smoke detectors was investigated using experimental and analytical approaches. The experimental work consisted of measuring velocity inside the sensing chamber of smoke detectors with a two-component Laser Doppler Velocimeter and exposing addressable smoke detectors to four different aerosol sources. The velocity measurements and exposure tests were performed in NIST's Fire Emulator / Detector Evaluator under steady state flow conditions in the range of 0.08 to 0.52 m/s. The addressable detectors were a photoelectric and an ionization detector. A specially constructed rectangular detector model was also used for the interior velocity measurements in order to have geometry compatible with numerical approaches, such as computational fluid dynamics modeling or a two-dimensional analytical solution. The experimental data was used to investigate the fluid mechanics and mass transport processes in the entry resistance problem. An inlet velocity boundary condition was developed for the smoke detectors evaluated in this study by relating the external velocity and detector geometry to the internal velocity by way of a resistance factor. Data from the exposure tests was then used to characterize the nature of aerosol entry lag and sensor response. The time to alarm for specific alarm points was determined in addition to performing an exponential curve fit to obtain a characteristic response time. A mass transport model for smoke detector response was developed and solved numerically. The mass transport model was used to simulate the response time data collected in the experimental portion of this study and was found, in general, to underestimate the measured response time by up to 20 seconds. However, in the context of wastebasket fire scenario the amount of underprediction in the model is 5 seconds or less which is within the typically polling interval time of 5 to 10 seconds for an addressable system. Therefore, the mass transport model results developed using this proposed engineering framework show promise and are within the expected uncertainty of practical fire protection engineering design situations.

The finite difference method was used to calculate the variable heat transfer coefficient required to maximize mechanical properties of heat treated wrought 7075 aluminum alloy without causing residual stress. Quench simulation enabled determination of maximum surface heat flux bordering on inducing plastic flow in the work piece. Quench Factor Analysis was used to correlate cylinder diameter to yield strength in the T73 condition. It was found that the maximum bar diameter capable of being quenched without residual stress while meeting military mechanical design minimums is 2". It was also found that the cooling rate must increase exponentially and that the maximum cooling rate needed to achieve minimum mechanical properties is well within the capability of metals heat treatment industry.

MEMS (Microelectromechanical Systems) acoustic sensors are a promising platform for Point-of-Care biosensing. In particular, piezoelectrically driven acoustic sensors can provide fast results with high sensitivity, can be miniaturized and mass produced, and have the potential to be fully integrated with sample handling and electronics in handheld devices. Furthermore, they can be designed as multiplexed arrays to detect multiple biomarkers of interest in parallel. In order to develop a microfabricated biosensing platform, a specific and high affinity biodetection platform must be optimized, and the microfabricated sensors must be designed to have high sensitivity and maintain good performance in a liquid environment. A biomolecular sensing system that uses high affinity peptide aptamers and a passivation layer has been optimized for the detection of proteins of interest using the quartz crystal microbalance with dissipation monitoring (QCM-D). The resulting system is highly specific to target proteins, differentiating between target IgG molecules and other closely related IgG subclasses, even in complex environments such as serum. Piezoelectrically actuated MEMS resonators are designed to operate in flexural microplate modes, with several modes shown to be ideally suited for fluid based biosensing due to improved performance in the liquid environment. The increase in quality factor of these MEMS microplate devices in liquid, as compared to air, is further investigated through the analytical and finite element modeling of MEMS fluid damping mechanisms, with a focus on acoustic radiation losses for circular microplate devices. It is found that the impedance mismatch at the air-water interface of a droplet is a key contributor to reduced acoustic radiation losses and thus improved device performance in water. Microplate acoustic sensors operating in flexural plate wave and microplate flexural modes are then integrated with a fluidic cell to facilitate protein sensing from fluid samples. Flexural plate wave devices are used to measure protein mass adsorbed to the sensor surface and initial results toward microplate flexural mode protein sensing are presented. Finally, challenges and areas of future research are discussed to outline the path towards finalization of a sensing platform taking advantage of the combination of the sensitive MEMS acoustic sensor capable of operating in a liquid environment and the specific and high affinity biomolecular detection system. Together, these form the potential basis of a novel Point-of-Care platform for simple and rapid monitoring of protein levels in complex samples.

Technological advances have enabled profiling gene expression variability, both at the RNA and the protein level, with ever increasing throughput. In addition, miniaturisation has enabled quantifying gene expression from small volumes of the input material and most recently at the level of single cells. Increasingly these technologies also preserve context information, such as assaying tissues with high spatial resolution. A second example of contextual information is multi-omics protocols, for example to assay gene expression and DNA methylation from the same cells or samples. Although such contextual gene expression datasets are increasingly available for both popu- lation and single-cell variation studies, methods for their analysis are not established. In this thesis, we propose two modelling approaches for the analysis of gene expression variation in specific biological contexts. The first contribution of this thesis is a statistical method for analysing single cell expression data in a spatial context. Our method identifies the sources of gene expression variability by decomposing it into different components, each attributable to a different source. These sources include aspects of spatial variation such as cell-cell interactions. In applications to data across different technologies, we show that cell-cell interactions are indeed a major determinant of the expression level of specific genes with a relevant link to their function. The second contribution is a latent variable model for the unsupervised analysis of gene expression data, while accounting for structured prior knowledge on experimental context. The proposed method enables the joint analysis of gene expression data and other omics data profiled in the same samples, and the model can be used to account for the grouping structure of samples, e.g. samples from individuals with different clinical covariates or from distinct experimental batches. Our model constitutes a principled framework to compare the molecular identities of these distinct groups.