Combining Smart Material Platforms and New Computational Tools to Investigate Cell Motility Behavior and Control

Cell-extracellular matrix (ECM) interactions play a critical role in regulating important biological phenomena, including morphogenesis, tissue repair, and disease states. In vivo, cells are subjected to various mechanical, chemical, and electrical cues to collectively guide their functionality within a specific microenvironment. To better understand the mechanisms regulating cell adhesive, differentiation, and motility dynamics, researchers have developed in vitro platforms to synthetically mimic native tissue responses. While important information about cell-ECM interactions have been revealed using these systems, a knowledge gap currently exists regarding how cell responses in static environments relate to the dynamic cell-ECM interaction behaviors observed in vivo. Advances at the intersection of materials science, biophysics, and cell biology have recently enabled the production of dynamic ECM mimics where cells can be exposed to controlled mechanical, electrical or chemical cues to directly decouple cell-ECM related behaviors from cell-cell or cell-environmental factors. Utilization of these dynamic synthetic biomaterials will enable discovery of novel mechanisms fundamental in tissue development, homeostasis, repair, and disease. In this dissertation, the primary goal was to evaluate how mechanical changes in the ECM regulate cell motility and polarization responses. This was accomplished through two major aims: 1) by developing a modular image processing tool that could be applied in complex synthetic in vitro microenvironments to asses cell motility dynamics, and 2) to utilize that tool to advance understanding of mechanobiology and mechanotransduction processes associated with development, wound healing, and disease progression. Therefore, the first portion of this thesis (Chapters 2 and 3) dealt with proof of concept for our newly developed automated cell tracking system, termed ACTIVE (automated contour-based tracking for in vitro environments), while the second portion of this thesis (Chapter 4-7) addressed applying this system in multiple experimental designs to synthesize new knowledge regarding cell-ECM or cell-cell interactions. In Chapter 1, we introduced why cell-ECM interactions are essential for in vivo processes and highlighted the current state of the literature. In Chapter 2, we demonstrated that ACTIVE could achieve greater than 95% segmentation accuracy at multiple cell densities, while improving two-body cell-cell interaction error by up to 43%. In Chapter 3 we showed that ACTIVE could be applied to reveal subtle differences in fibroblast motility atop static wrinkled or static non-wrinkled surfaces at multiple cell densities. In Chapters 4 and 5, we characterized fibroblast motility and intracellular reorganization atop a dynamic shape memory polymer biomaterial, focusing on the role of the Rho-mediated pathway in the observed responses. We then utilized ACTIVE to identify differences in subpopulation dynamics of monoculture versus co-culture endothelial and smooth muscle cells (Chapter 6). In Chapter 7, we applied ACTIVE to investigate E. coli biofilm formation atop poly(dimethylsiloxane) surfaces with varying stiffness and line patterns. Finally, we presented a summary and future work in Chapter 8. Collectively, this work highlights the capabilities of the newly developed ACTIVE tracking system and demonstrates how to synthesize new information about mechanobiology and mechanotransduction processes using dynamic biomaterial platforms

A speech envelope landmark for syllable encoding in human superior temporal gyrus.

The most salient acoustic features in speech are the modulations in its intensity, captured by the amplitude envelope. Perceptually, the envelope is necessary for speech comprehension. Yet, the neural computations that represent the envelope and their linguistic implications are heavily debated. We used high-density intracranial recordings, while participants listened to speech, to determine how the envelope is represented in human speech cortical areas on the superior temporal gyrus (STG). We found that a well-defined zone in middle STG detects acoustic onset edges (local maxima in the envelope rate of change). Acoustic analyses demonstrated that timing of acoustic onset edges cues syllabic nucleus onsets, while their slope cues syllabic stress. Synthesized amplitude-modulated tone stimuli showed that steeper slopes elicited greater responses, confirming cortical encoding of amplitude change, not absolute amplitude. Overall, STG encoding of the timing and magnitude of acoustic onset edges underlies the perception of speech temporal structure

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

Application of advanced surface patterning techniques to study cellular behavior

Surface manipulation for the fabrication of chemical or topographic micro- and nanopatterns, has been central to the evolution of in vitro biology research. A high variety of surface patterning methods have been implemented in a wide spectrum of applications, including fundamental cell biology studies, development of diagnostic tools, biosensors and drug delivery systems, as well as implant design. Surface engineering has increased our understanding of cell functions such as cell adhesion and cell-cell interaction mechanics, cell proliferation, cell spreading and migration. From a plethora of existing surface engineering techniques, we use standard microcontact printing methods followed by click chemistry to study the role of intercellular contacts in collective cancer cell migration. Cell dispersion from a confined area is fundamental in a number of biological processes, including cancer metastasis. To date, a quantitative understanding of the interplay of single cell motility, cell proliferation, and intercellular contacts remains elusive. In particular, the role of E- and N-Cadherin junctions, central components of intercellular contacts, is still controversial. Combining theoretical modeling with in vitro observations, we investigate the collective spreading behavior of colonies of human cancer cells (T24). The spreading of these colonies is driven by stochastic single-cell migration with frequent transient cell-cell contacts. We find that inhibition of E- and N-Cadherin junctions decreases colony spreading and average spreading velocities, without affecting the strength of correlations in spreading velocities of neighboring cells. Based on a biophysical simulation model for cell migration, we show that the behavioral changes upon disruption of these junctions can be explained by reduced repulsive excluded volume interactions between cells. This suggests that in cancer cell migration, cadherin-based intercellular contacts sharpen cell boundaries leading to repulsive rather than cohesive interactions between cells, thereby promoting efficient cell spreading during collective migration. Despite the remarkable progress in surface engineering technology and its applications, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photo-immobilization technique, comprising a light-dose dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable pattering step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes, and that our innovative approach has a great potential for further applications in cell science. In summary, this work introduces two novel and versatile paradigms of surface patterning for studying different aspects of cell behaviour in different cell types. The reliability of both setups is experimentally confirmed, providing new insight into the role of cell-cell contacts during collective cancer cell migration as well as the tip/stalk switch behaviour during angiogenesis

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas