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In this study, we have analyzed the steady-state kinetics of B lymphocytes in mouse and rat bone marrow using previously published experimental data. Over many years, Prof D.G. Osmond and his colleagues have built up a scheme of B cell development in mouse bone marrow based on the sequential expression of markers associated with the B lineage. The earliest precursor B cells comprise three populations of proliferating pro-B cells, i.e. early, intermediate, and late pro-B cells. The subsequent populations comprise pre-B cells that give rise to nondividing B lymphocytes expressing surface IgM. / In our analysis, we have checked the available published data for consistency with the proliferation of precursor B cells and their death via apoptosis at certain stages of cell development. We made an extensive summary of the existing data on the various B cell precursors and organized it into a comprehensible framework. We built a mathematical model for the proliferation and differentiation of mammalian B lymphocytes in laboratory mice and rats and estimated all of the parameters to explain the existing steady state data. In this context, mathematical modeling acts as a useful tool to analyze hypotheses and experimental results concerning the steady state numbers of B lymphocytes.

Thesis--Ed. D. Columbia University, Teachers College, 1974.

Thesis--Ed. D. Columbia University, Teachers College, 1974.

Thesis (Ph. D.)--University of Virginia, 2008. / Title from title page. Includes bibliographical references. Also available online through Digital Dissertations.

微電列陣今已被廣泛用於各種生理和理的研究。通過把微電列陣連接到肌肉細胞，細胞外的電生理信號會被有效地記錄，我們進而對尖峰信號的傳播模式進行分析，以便了解肌肉收縮的模式。本文旨在對觀測到的電生理信號進行統計模型擬合，從而獲得對於肌肉收縮模式的統計推論。我們提出了三種方法用以提取尖峰信號的激活時間，分別為均值方差法、局部加權回歸法(LOWESS方法)和Butterworth濾波法。然後對抽取出來的尖峰信號應用隨機Hough轉換，識別出多個傳播的信號波，從而得到肌肉收縮的率。對於每個信號波，我們建立了兩個模型來描述信號的傳播模式，即圓形波陣面模型和線性波陣模型。通過這兩種模型擬合，表達信傳播特徵的參數可被估算，例如激發信號波的起源位和起始時間，信號的傳播方向以及速度等。利用根據兩種模型合成的模擬數據，我們證明了隨機霍夫轉換算法和模型擬合的有效性及準確性，並把文中提出的算法用於大鼠心肌培養細胞的一個數據集。由此數據集得出的結果可以用於監測細胞的電生理變化，從而闡明藥物或環條件對心肌細胞產生的影響。 / The microelectrode array (MEA) has been widely used in physiological and pharmacological research. By attaching the MEA system to muscle cells, extracellular electrophysiological signals can be recorded, and the spike-signal propagation pattern can be analyzed for understanding the muscle contraction pattern. This thesis aims at providing a statistical framework for analyzing the muscle contraction pattern from the observed electrophysiological signals. We first provides three methods for extracting the activation time of signal spikes: the mean-variance method, the LOWESS smoothing method, and the Butterworth filtering method. The randomized Hough transform is then applied to the signal spikes to identify the multiple propagating waves, which gives the rate of beating. For each propagating wave, we propose two models to describe the signal propagation pattern, namely the circular wavefront model and the linear wavefront model. By fitting these two models, parameters that characterize the signal propagation can be estimated, such as the origin and time of excitation, the direction of propagation, and the speed of propagation. We demonstrate the performances of the randomized Hough tranform algorithm and model fitting in two simulation studies, and apply these approaches to a real data set of cultured cardiac myocytes of rats. The result may be used to monitor the electrophysiological changes and thereby elucidate the drug effect or environmental condition on cardiomyocytes. / Detailed summary in vernacular field only. / Lu, Jiayi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 62-65). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivating problem --- p.1 / Chapter 1.2 --- An overview of MEA --- p.2 / Chapter 1.3 --- Electrophysiology of cardiac myocytes --- p.5 / Chapter 1.4 --- Organization --- p.5 / Chapter 2 --- A generative model for MEA data --- p.7 / Chapter 2.1 --- Circular wavefront model --- p.9 / Chapter 2.2 --- Linear wavefront model --- p.11 / Chapter 3 --- Computing method for MEA signals --- p.13 / Chapter 3.1 --- Preliminaries --- p.13 / Chapter 3.1.1 --- Locally weighted scatterplot smoothing(LOWESS) --- p.13 / Chapter 3.1.2 --- Butterworth filter --- p.16 / Chapter 3.1.3 --- Hough transform --- p.16 / Chapter 3.1.4 --- Nonlinear minimization --- p.21 / Chapter 3.2 --- Overall procedure for MEA data analysis --- p.24 / Chapter 3.3 --- Extract the spike activation time --- p.25 / Chapter 3.4 --- Identification of multiple propagating waves --- p.28 / Chapter 3.5 --- Model fitting --- p.29 / Chapter 3.5.1 --- Circular wavefront model --- p.29 / Chapter 3.5.2 --- Linear wavefront model --- p.33 / Chapter 4 --- Simulation study based on synthesized data --- p.35 / Chapter 4.1 --- Wave detection using Hough transform --- p.35 / Chapter 4.1.1 --- Data synthesis from linear wavefront model --- p.35 / Chapter 4.1.2 --- Performance of the randomized Hough transform --- p.38 / Chapter 4.2 --- Model fitting for signal propagating pattern --- p.38 / Chapter 4.2.1 --- Data Synthesis from circular wavefront model --- p.38 / Chapter 4.2.2 --- Performance of the model fitting algorithm --- p.42 / Chapter 5 --- Real data application --- p.47 / Chapter 5.1 --- Data set --- p.47 / Chapter 5.2 --- Extract the spike activation time --- p.49 / Chapter 5.3 --- Identify multiple propagating waves --- p.52 / Chapter 5.4 --- Model fitting --- p.52 / Chapter 5.4.1 --- Fitting the circular wavefront model --- p.52 / Chapter 5.4.2 --- Fitting the linear wavefront model --- p.55 / Chapter 5.4.3 --- Comparison of the two models --- p.56 / Chapter 6 --- Conclusions and future directions --- p.60 / Chapter 6.1 --- Conclusions --- p.60 / Chapter 6.2 --- Future directions --- p.61

Diss. Linköping : Univ., 2002.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 5 uppsatser.

Diss. (sammanfattning) Umeå : Umeå universitet, 2006. / Härtill 4 uppsatser.

Thesis (Ph.D.). / Written for the Dept. of BioMedical Engineering. Title from title page of PDF (viewed 2008/02/12). Includes bibliographical references.