65 research outputs found

    Analysis of Cellular and Subcellular Morphology using Machine Learning in Microscopy Images

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    Human cells undergo various morphological changes due to progression in the cell-cycle or environmental factors. Classification of these morphological states is vital for effective clinical decisions. Automated classification systems based on machine learning models are data-driven and efficient and help to avoid subjective outcomes. However, the efficacy of these models is highly dependent on the feature description along with the amount and nature of the training data. This thesis presents three studies of automated image-based classification of cellular and subcellular morphologies. The first study presents 3D Sorted Random Projections (SRP) which includes the proposed approach to compute 3D plane information for texture description of 3D nuclear images. The proposed 3D SRP is used to classify nuclear morphology and measure changes in heterochromatin, which in turn helps to characterise cellular states. Classification performance evaluated on 3D images of the human fibroblast and prostate cancer cell lines shows that 3D SRP provides better classification than other feature descriptors. The second study is on imbalanced multiclass and single-label classification of blood cell images. The scarcity of minority sam ples causes a drop in classification performance on minority classes. This study proposes oversampling of minority samples us ing data augmentation approaches, namely mixup, WGAN-div and novel nonlinear mixup, along with a minority class focussed sampling strategy. Classification performance evaluated using F1-score shows that the proposed deep learning framework out performs state-of-the art approaches on publicly available images of human T-lymphocyte cells and red blood cells. The third study is on protein subcellular localisation, which is an imbalanced multiclass and multilabel classification problem. In order to handle data imbalance, this study proposes an oversampling method which includes synthetic images constructed using nonlinear mixup and geometric/colour transformations. The regularisation capability of nonlinear mixup is further improved for protein images. In addition, an imbalance aware sampling strategy is proposed to identify minority and medium classes in the dataset and include them during training. Classification performance evaluated on the Human Protein Atlas Kaggle challenge dataset using F1-score shows that the proposed deep learning framework achieves better predictions than existing methods

    Histopathology-selective spatial oncogenic phenotypes in non-small cell lung cancer

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    Non-small cell lung cancer (NSCLC) constitutes over 85% of lung cancer. Histologically, NSCLC can be broadly classified into adenocarcinoma (AC), squamous cell carcinoma (SCC), large cell carcinoma (LCC), and adenosquamous carcinoma (ASC). AC represents about 65% of all NSCLC cases, and it can be further subdivided based on tumor size and primary growth patterns, such as papillary, acinar, and mucinous. The formation of NSCLC histotypes is orchestrated by cells of origin, genetic alterations, and microenvironmental properties. Although NSCLC carries significant heterogeneity, some genetic mutations, functional phenotypes, and therapeutic responses are associated with specific NSCLC histotypes. Therefore, understanding histotype-selective etiology becomes essential for mechanistic studies and therapeutic applications in the NSCLC research field. Image-based tissue phenotyping has been commonly used for histological classification. It also allows the direct visualization of the distribution and expression of functional molecules. Quantifying such in situ phenotypes can be applied to hypothesis-based functional studies or data-driven correlative analyses. The first part of this thesis developed a spatial image analysis tool package. The making of Spa-RQ, an open-source tool package for image registration and quantification, reflected on the need to perform spatial phenotyping using serial tissue sections in a standardized laboratory workflow. Subsequently, we applied Spa-RQ to identify the histotype-selective, rather than genetically defined activation of MAPK, AKT, and mTOR signaling pathways in murine and human NSCLC samples. The diverse co-activation patterns between these pathways in different tissue compartments, measured by marker expression overlapping using Spa-RQ, may associate with heterogeneous responses towards combinatorial targeted therapies. The second part of this thesis work investigated the histotype-selective functions of a potential therapeutic target. The lung developmental transcription factor SOX9 is silenced in normal adult lung epithelia while it is re-expressed in NSCLC tissues. Its oncogenicity is widely acknowledged but has thus far not been confirmed in NSCLC subtypes. Analyzing the correlation between SOX9 expression and histotype-specific clinical staging, survival, and invasiveness revealed a clinical significance for increased SOX9 expression only in non-mucinous ACs, despite its broad expression in ASC, SCC, and mucinous AC. Supporting this, by comparing the histotype spectra in mouse models following Sox9 loss, we identified a critical role of SOX9 in promoting lung papillary AC progression. On the other hand, its expression was not required for developing squamous and mucinous structure tissues. Finally, using spatial phenotyping, we explained such opposing roles of SOX9 in NSCLC subtypes by the different cells of origin and microenvironmental properties: SOX9 expression was required to form advanced AC from the lung alveolar progenitor cells; on the contrary, its expression was dispensable for SCC development and even interfered with squamous metastasis. Therefore, this work exposed SOX9 as a potential drug target specific to a subgroup of lung AC. In summary, the identification of histotype-selective functional oncogenic phenotypes, as achieved in this thesis, contributes to understanding the heterogeneous nature of tumorigenesis, cancer progression, and drug sensitivities.Non-small cell lung cancer (NSCLC) constitutes over 85% of lung cancer. Histologically, NSCLC can be broadly classified into adenocarcinoma (AC), squamous cell carcinoma (SCC), large cell carcinoma (LCC), and adenosquamous carcinoma (ASC). AC represents about 65% of all NSCLC cases, and it can be further subdivided based on tumor size and primary growth patterns, such as papillary, acinar, and mucinous. The formation of NSCLC histotypes is orchestrated by cells of origin, genetic alterations, and microenvironmental properties. Although NSCLC carries significant heterogeneity, some genetic mutations, functional phenotypes, and therapeutic responses are associated with specific NSCLC histotypes. Therefore, understanding histotype-selective etiology becomes essential for mechanistic studies and therapeutic applications in the NSCLC research field. Image-based tissue phenotyping has been commonly used for histological classification. It also allows the direct visualization of the distribution and expression of functional molecules. Quantifying such in situ phenotypes can be applied to hypothesis-based functional studies or data-driven correlative analyses. The first part of this thesis developed a spatial image analysis tool package. The making of Spa-RQ, an open-source tool package for image registration and quantification, reflected on the need to perform spatial phenotyping using serial tissue sections in a standardized laboratory workflow. Subsequently, we applied Spa-RQ to identify the histotype-selective, rather than genetically defined activation of MAPK, AKT, and mTOR signaling pathways in murine and human NSCLC samples. The diverse co-activation patterns between these pathways in different tissue compartments, measured by marker expression overlapping using Spa-RQ, may associate with heterogeneous responses towards combinatorial targeted therapies. The second part of this thesis work investigated the histotype-selective functions of a potential therapeutic target. The lung developmental transcription factor SOX9 is silenced in normal adult lung epithelia while it is re-expressed in NSCLC tissues. Its oncogenicity is widely acknowledged but has thus far not been confirmed in NSCLC subtypes. Analyzing the correlation between SOX9 expression and histotype-specific clinical staging, survival, and invasiveness revealed a clinical significance for increased SOX9 expression only in non-mucinous ACs, despite its broad expression in ASC, SCC, and mucinous AC. Supporting this, by comparing the histotype spectra in mouse models following Sox9 loss, we identified a critical role of SOX9 in promoting lung papillary AC progression. On the other hand, its expression was not required for developing squamous and mucinous structure tissues. Finally, using spatial phenotyping, we explained such opposing roles of SOX9 in NSCLC subtypes by the different cells of origin and microenvironmental properties: SOX9 expression was required to form advanced AC from the lung alveolar progenitor cells; on the contrary, its expression was dispensable for SCC development and even interfered with squamous metastasis. Therefore, this work exposed SOX9 as a potential drug target specific to a subgroup of lung AC. In summary, the identification of histotype-selective functional oncogenic phenotypes, as achieved in this thesis, contributes to understanding the heterogeneous nature of tumorigenesis, cancer progression, and drug sensitivity

    Advances in Image Processing, Analysis and Recognition Technology

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    For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

    Undergraduate and graduate catalog 2022-2023.

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    The PDF for the 2022-2023 undergraduate and graduate catalog for Texas Tech University is 476 pages long

    Microscopy and Analysis

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    Microscopes represent tools of the utmost importance for a wide range of disciplines. Without them, it would have been impossible to stand where we stand today in terms of understanding the structure and functions of organelles and cells, tissue composition and metabolism, or the causes behind various pathologies and their progression. Our knowledge on basic and advanced materials is also intimately intertwined to the realm of microscopy, and progress in key fields of micro- and nanotechnologies critically depends on high-resolution imaging systems. This volume includes a series of chapters that address highly significant scientific subjects from diverse areas of microscopy and analysis. Authoritative voices in their fields present in this volume their work or review recent trends, concepts, and applications, in a manner that is accessible to a broad readership audience from both within and outside their specialist area

    Undergraduate and graduate catalog 2021-2022.

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    The PDF for the 2021-2022 undergraduate and graduate catalog for Texas Tech University is 460 pages long

    Visual Cortex

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    The neurosciences have experienced tremendous and wonderful progress in many areas, and the spectrum encompassing the neurosciences is expansive. Suffice it to mention a few classical fields: electrophysiology, genetics, physics, computer sciences, and more recently, social and marketing neurosciences. Of course, this large growth resulted in the production of many books. Perhaps the visual system and the visual cortex were in the vanguard because most animals do not produce their own light and offer thus the invaluable advantage of allowing investigators to conduct experiments in full control of the stimulus. In addition, the fascinating evolution of scientific techniques, the immense productivity of recent research, and the ensuing literature make it virtually impossible to publish in a single volume all worthwhile work accomplished throughout the scientific world. The days when a single individual, as Diderot, could undertake the production of an encyclopedia are gone forever. Indeed most approaches to studying the nervous system are valid and neuroscientists produce an almost astronomical number of interesting data accompanied by extremely worthy hypotheses which in turn generate new ventures in search of brain functions. Yet, it is fully justified to make an encore and to publish a book dedicated to visual cortex and beyond. Many reasons validate a book assembling chapters written by active researchers. Each has the opportunity to bind together data and explore original ideas whose fate will not fall into the hands of uncompromising reviewers of traditional journals. This book focuses on the cerebral cortex with a large emphasis on vision. Yet it offers the reader diverse approaches employed to investigate the brain, for instance, computer simulation, cellular responses, or rivalry between various targets and goal directed actions. This volume thus covers a large spectrum of research even though it is impossible to include all topics in the extremely diverse field of neurosciences

    High-Performance Modelling and Simulation for Big Data Applications

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    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
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