Texture-Based Segmentation and Finite Element Mesh Generation for Heterogeneous Biological Image Data

The design, analysis, and control of bio-systems remain an engineering challenge. This is mainly due to the material heterogeneity, boundary irregularity, and nonlinear dynamics associated with these systems. The recent developments in imaging techniques and stochastic upscaling methods provides a window of opportunity to more accurately assess these bio-systems than ever before. However, the use of image data directly in upscaled stochastic framework can only be realized by the development of certain intermediate steps. The goal of the research presented in this dissertation is to develop a texture-segmentation method and a unstructured mesh generation for heterogeneous image data. The following two new techniques are described and evaluated in this dissertation: 1. A new texture-based segmentation method, using the stochastic continuum concepts and wavelet multi-resolution analysis, is developed for characterization of heterogeneous materials in image data. The feature descriptors are developed to efficiently capture the micro-scale heterogeneity of macro-scale entities. The materials are then segmented at a representative elementary scale at which the statistics of the feature descriptor stabilize. 2. A new unstructured mesh generation technique for image data is developed using a hierarchical data structure. This representation allows for generating quality guaranteed finite element meshes. The framework for both the methods presented in this dissertation, as such, allows them for extending to higher dimensions. The experimental results using these methods conclude them to be promising tools for unifying data processing concepts within the upscaled stochastic framework across biological systems. These are targeted for inclusion in decision support systems where biological image data, simulation techniques and artificial intelligence will be used conjunctively and uniformly to assess bio-system quality and design effective and appropriate treatments that restore system health

Disease-specific gene repositioning in breast cancer

The nuclear repositioning of specific genes may be a novel diagnostic strategy to distinguish malignant from normal tissue

Feedforward control of acausal systems under broadband deterministic forcing

We present some discrete-time perspectives on the active vibration control of a simple acausal system subjected to deterministic but broadband excitation. Here, exact vibration cancellation is not possible with bounded control forces. However, on penalizing high control forces using a weighting factor, we obtain a one parameter family of finite-time optimal feedforward (FTOF) controllers that substantially reduce vibration levels through bounded control forces. These controllers require knowledge of the future of the forcing, and are thus acausal. However, only a finite amount of the future needs to be known for practical purposes. These future values are predicted using a neural network. The FTOF controllers perform better than a direct application of the standard LMS algorithm. An LMS algorithm which uses several future values of the forcing performs better still. Some recommendations are made for practical applications

Allele-specific nuclear positioning of the monoallelically expressed astrocyte marker GFAP

Chromosomes and genes are nonrandomly arranged within the mammalian cell nucleus. However, the functional significance of nuclear positioning in gene expression is unclear. Here we directly probed the relationship between nuclear positioning and gene activity by comparing the location of the active and inactive copies of a monoallelically expressed gene in single cell nuclei. We demonstrate that the astrocyte-specific marker GFAP (glial fibrillary acidic protein) is monoallelically expressed in cortical astrocytes. Selection of the active allele occurs in a stochastic manner and is generally maintained through cell division. Taking advantage of the monoallelic expression of GFAP, we show that the functionally distinct alleles occupy differential radial positions within the cell nucleus and differentially associate with intranuclear compartments. In addition, coordinately regulated astrocyte-specific genes on distinct chromosomes spatially associate in their inactive state and dissociate upon activation. These results provide direct evidence for function-related differential positioning of individual gene alleles within the interphase nucleus