Characterization of Systemic Disease Development and Paw Inflammation in a Susceptible Mouse Model of Mayaro Virus Infection and Validation Using X-ray Synchrotron Microtomography

Mayaro virus (MAYV) is an emerging arthropod-borne virus endemic in Latin America and the causative agent of arthritogenic febrile disease. Mayaro fever is poorly understood; thus, we established an in vivo model of infection in susceptible type-I interferon receptor-deficient mice (IFNAR−/−) to characterize the disease. MAYV inoculations in the hind paws of IFNAR−/− mice result in visible paw inflammation, evolve into a disseminated infection and involve the activation of immune responses and inflammation. The histological analysis of inflamed paws indicated edema at the dermis and between muscle fibers and ligaments. Paw edema affected multiple tissues and was associated with MAYV replication, the local production of CXCL1 and the recruitment of granulocytes and mononuclear leukocytes to muscle. We developed a semi-automated X-ray microtomography method to visualize both soft tissue and bone, allowing for the quantification of MAYV-induced paw edema in 3D with a voxel size of 69 µm3. The results confirmed early edema onset and spreading through multiple tissues in inoculated paws. In conclusion, we detailed features of MAYV-induced systemic disease and the manifestation of paw edema in a mouse model extensively used to study infection with alphaviruses. The participation of lymphocytes and neutrophils and expression of CXCL1 are key features in both systemic and local manifestations of MAYV disease

Nuclei segmentation using level set method and data fusion for the CIN classification

This paper deals with the automation of the detection of the cervical cancer through histology images. This process is divided into two parts, corresponding to segmentation and data fusion. The segmentation and classification of the cervical epithelium images is done using hybrid image processing techniques. The digitized histology images provided have a pre-cervical cancer condition called cervical intraepithelial neoplasia (CIN) by expert pathologists. Previously, image analysis studies focused on nuclei-level features to classify the epithelium into the CIN grades. The current study focuses on nuclei segmentation based on the level set segmentation and fuzzy c-means clustering methods. Morphological post-processing operations are used to smooth the image and to remove non-nuclei objects. This algorithm is evaluated on a 71-image dataset of digitized histology images for nuclei segmentation. Experimental results showed a nuclei detection accuracy of 99.53 percent. The second section of this thesis deals with the fusion of the 117 CIN features obtained after processing the input cervical images. Various data fusion techniques are tested using machine learning tools. For further research, the best algorithm from Weka is chosen --Abstract, page iv

The Neutrophil's Eye-View: Inference and Visualisation of the Chemoattractant Field Driving Cell Chemotaxis In Vivo

As we begin to understand the signals that drive chemotaxis in vivo, it is becoming clear that there is a complex interplay of chemotactic factors, which changes over time as the inflammatory response evolves. New animal models such as transgenic lines of zebrafish, which are near transparent and where the neutrophils express a green fluorescent protein, have the potential to greatly increase our understanding of the chemotactic process under conditions of wounding and infection from video microscopy data. Measurement of the chemoattractants over space (and their evolution over time) is a key objective for understanding the signals driving neutrophil chemotaxis. However, it is not possible to measure and visualise the most important contributors to in vivo chemotaxis, and in fact the understanding of the main contributors at any particular time is incomplete. The key insight that we make in this investigation is that the neutrophils themselves are sensing the underlying field that is driving their action and we can use the observations of neutrophil movement to infer the hidden net chemoattractant field by use of a novel computational framework. We apply the methodology to multiple in vivo neutrophil recruitment data sets to demonstrate this new technique and find that the method provides consistent estimates of the chemoattractant field across the majority of experiments. The framework that we derive represents an important new methodology for cell biologists investigating the signalling processes driving cell chemotaxis, which we label the neutrophils eye-view of the chemoattractant field

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Deep learning features encode interpretable morphologies within histological images.

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes ( mones ) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = [Formula: see text] for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = [Formula: see text]). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy

Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System

Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells\u27 features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting

A quantitative image analysis for the cellular cytoskeleton during in vitro tumor growth

The cellular cytoskeleton is a dynamic subcellular structure that can be a marker of key biological phenomena including cell division, organelle movement, shape changes and locomotion during the avascular tumor phase. Little attention is paid to quantify changes in the cytoskeleton while nuclei and cytoplasmic both are present in subcellular microscopic images. In this paper, we proposed a quantitative image analysis method to analyze subcellular cytoskeletal changes using a texture analysis method preceded by segmentation of nuclei, cytoplasm and ruffling regions (area except nuclei and cytoplasm). To test and validate this model we hypothesized that Mammary Serine Protease Inhibitor (maspin) acts as cytoskeleton regulator that mediates cell-extracellular matrix (ECM) adhesion in tumor. Maspin-a tumor suppressor gene shows multiple tumor suppressive properties such as increasing tumor cell apoptosis and reducing migration, proliferation, invasion, and overall tumor metastasis. The proposed method obtained separated ruffling regions from segmentation steps and then adopted gray–level histograms (GLH) and grey-level co-occurrence matrix (GLCM) texture analysis techniques. In order to verify the reliability, the proposed texture analysis method was used to compare the control and maspin expressing cells grown on different ECM components: plastic, collagen I, fibronectin and laminin. The results show that the texture parameters extracted reflect the different cytoskeletal changes. These changes indicate that maspin acts as a regulator of the cell-ECM enhancement process, while it reduces the cell migration. Overall, this paper not only presents a quantitative image analysis approach to analyze subcellular cytoskeletal architectures but also provides a comprehensive tool for the biologist, pathologist, cancer specialist, and computer scientist to understand cellular and subcellular organization of cells. In long term, this method can be extended to be used in live cell tracking in vivo, image informatics based point-of-care expert system and quantification of various complex architectures in organisms

Behavioral immune landscapes of inflammation.

Transcriptional or proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues. These approaches, however, fail to describe dynamic scenarios in which cells can change their biochemical properties and downstream “behavioral” outputs every few seconds or minutes. Here, we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamism of individual leukocytes at sites of active inflammation. By analyzing over 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioral descriptors of individual cells and used these high-dimensional datasets to build behavioral landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and inside blood vessels uncovered a continuum of neutrophil states, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioral in vivo screening of thousands of cells from 24 different mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and genetic or pharmacological interference of Fgr protected from inflammatory injury. Thus, behavioral landscapes report unique biological properties of dynamic environments at high cellular, spatial and temporal resolution.pre-print4302 K

Multi-parametric MRI Study of Brain Insults (Traumatic Brain Injury and Brain Tumor) in Animal Models

abstract: The objective of this small animal pre-clinical research project was to study quantitatively the long-term micro- and macro- structural brain changes employing multiparametric MRI (Magnetic Resonance Imaging) techniques. Two separate projects make up the basis of this thesis. The first part focuses on obtaining prognostic information at early stages in the case of Traumatic Brain Injury (TBI) in rat animal model using imaging data acquired at 24-hours and 7-days post injury. The obtained parametric T2 and diffusion values from DTI (Diffusion Tensor Imaging) showed significant deviations in the signal intensities from the control and were potentially useful as an early indicator of the severity of post-traumatic injury damage. DTI was especially critical in distinguishing between the cytotoxic and vasogenic edema and in identification of injury regions resolving to normal control values by day-7. These results indicate the potential of quantitative MRI as a clinical marker in predicting prognosis following TBI. The second part of this thesis focuses on studying the effect of novel therapeutic strategies employing dendritic cell (DC) based vaccinations in mice glioma model. The treatment cohorts included comparing a single dose of Azacytidine drug vs. mice getting three doses of drug per week. Another cohort was used as an untreated control group. The MRI results did not show any significant changes in between the two treated cohorts with no reduction in tumor volumes compared to the control group. The future studies would be focused on issues regarding the optimal dose for the application of DC vaccine. Together, the quantitative MRI plays an important role in the prognosis and diagnosis of the above mentioned pathologies, providing essential information about the anatomical location, micro-structural tissue environment, lesion volume and treatment response.Dissertation/ThesisMasters Thesis Bioengineering 201