Characterization of Antigen-Presenting Cells in Chicken Peyer’s Patches by Immunohistochemical Staining

In the chicken, Peyer’s patches (PPs) represent a crucial gut-associated lymphoid tissue (GALT) responsible for antigen sampling and activation of T-cells and B-cells. This involves antigen presentation in the context of major histocompatibility complex class II (MHC-II) by professional antigen-presenting cells (APCs). This study aims at elucidating the microanatomical organization of the APCs in the PPs in order to better understand their role in initiating the response to orally administered vaccines. PPs can be most readily identified in young birds (3-12 weeks of age) as an ovoid white patch about 1-cm in length on the antimesenteric side of the mucosa in the distal ileum between the ceca and cephalic to the cecal tonsils. The hallmarks that make the PPs different from the adjacent intestinal tissue include thickened villi, heavy lymphocyte infiltration, and isolated follicles deeply embedded in the muscularis mucosae/submucosa of the intestine. In this study, PPs and the adjacent tissue were rolled up like a Swiss-roll, snap frozen in liquid nitrogen (vapor phase), cryosectioned at 5μm and 10μm and fixed by cold acetone and 80% methanol prior to immunofluorescent visualization of CD205 (DCs), CD40 and/or MHC-II (APCs), IgM (B-cells) and CD3 (T-cells). In the center of the PP follicle, CD40 and surface IgM were abundantly expressed, whereas the expression of MHC-II and CD205 was relatively scarce. CD3^+ cells were predominantly distributed in the peripheral zone of the PP follicle, the lamina propria of the adjacent villi, and localized intraepithelially. MHC-II^+ APCs were packed subepithelially throughout the lamina propria, with some penetrating the follicle-associated epithelium (FAE) towards the lumen. CD205^+ DCs appeared as single cells near the crypts and were occasionally found inside the follicles. CD40^+ APCs were clustered both inside and outside the follicles. These results show that, much like in mammalian PPs, naïve B-cells are the major cell type occupying the follicles of chicken PPs, while T-cells are found in the interfollicular areas

Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

Towards Smarter Fluorescence Microscopy: Enabling Adaptive Acquisition Strategies With Optimized Photon Budget

Fluorescence microscopy is an invaluable technique for studying the intricate process of organism development. The acquisition process, however, is associated with the fundamental trade-off between the quality and reliability of the acquired data. On one hand, the goal of capturing the development in its entirety, often times across multiple spatial and temporal scales, requires extended acquisition periods. On the other hand, high doses of light required for such experiments are harmful for living samples and can introduce non-physiological artifacts in the normal course of development. Conventionally, a single set of acquisition parameters is chosen in the beginning of the acquisition and constitutes the experimenter’s best guess of the overall optimal configuration within the aforementioned trade-off. In the paradigm of adaptive microscopy, in turn, one aims at achieving more efficient photon budget distribution by dynamically adjusting the acquisition parameters to the changing properties of the sample. In this thesis, I explore the principles of adaptive microscopy and propose a range of improvements for two real imaging scenarios. Chapter 2 summarizes the design and implementation of an adaptive pipeline for efficient observation of the asymmetrically dividing neurogenic progenitors in Zebrafish retina. In the described approach the fast and expensive acquisition mode is automatically activated only when the mitotic cells are present in the field of view. The method illustrates the benefits of the adaptive acquisition in the common scenario of the individual events of interest being sparsely distributed throughout the duration of the acquisition. Chapter 3 focuses on computational aspects of segmentation-based adaptive schemes for efficient acquisition of the developing Drosophila pupal wing. Fast sample segmentation is shown to provide a valuable output for the accurate evaluation of the sample morphology and dynamics in real time. This knowledge proves instrumental for adjusting the acquisition parameters to the current properties of the sample and reducing the required photon budget with minimal effects to the quality of the acquired data. Chapter 4 addresses the generation of synthetic training data for learning-based methods in bioimage analysis, making them more practical and accessible for smart microscopy pipelines. State-of-the-art deep learning models trained exclusively on the generated synthetic data are shown to yield powerful predictions when applied to the real microscopy images. In the end, in-depth evaluation of the segmentation quality of both real and synthetic data-based models illustrates the important practical aspects of the approach and outlines the directions for further research