Automated image analysis systems to quantify physical and behavioral attributes of biological entities

All life forms in nature have physical and behavioral attributes which help them survive and thrive in their environment. Technologies, both within the areas of hardware systems and data processing algorithms, have been developed to extract relevant information about these attributes. Understanding the complex interplay of physical and behavioral attributes is proving important towards identifying the phenotypic traits displayed by organisms. This thesis attempts to leverage the unique advantages of portable/mobile hardware systems and data processing algorithms for applications in three areas of bioengineering: skin cancer diagnostics, plant parasitic nematology, and neglected tropical disease. Chapter 1 discusses the challenges in developing image processing systems that meet the requirements of low cost, portability, high-throughput, and accuracy. The research motivation is inspired by these challenges within the areas of bioengineering that are still elusive to the technological advancements in hardware electronics and data processing algorithms. A literature review is provided on existing image analysis systems that highlight the limitations of current methods and provide scope for improvement. Chapter 2 is related to the area of skin cancer diagnostics where a novel smartphone-based method is presented for the early detection of melanoma in the comfort of a home setting. A smartphone application is developed along with imaging accessories to capture images of skin lesions and classify them as benign or cancerous. Information is extracted about the physical attributes of a skin lesion such as asymmetry, border irregularity, number of colors, and diameter. Machine learning is employed to train the smartphone application using both dermoscopic and digital lesion images. Chapter 3 is related to the area of plant parasitic nematology where automated methods are presented to provide the nematode egg count from soil samples. A new lensless imaging system is built to record holographic videos of soil particles flowing through microscale flow assays. Software algorithms are written to automatically identify the nematode eggs from low resolution holographic videos or images captured from a scanner. Deep learning algorithm was incorporated to improve the learning process and train the software model. Chapter 4 is related to the area of neglected tropical diseases where new worm tracking systems have been developed to characterize the phenotypic traits of Brugia malayi adult male worms and their microfilaria. The worm tracking algorithm recognizes behavioral attributes of these parasites by extracting a number of features related to their movement and body posture. An imaging platform is optimized to capture high-resolution videos with appropriate field of view of B. malayi. The relevance of each behavioral feature was evaluated through drug screening using three common antifilarial compounds. The abovementioned image analysis systems provide unique advantages to the current experimental methods. For example, the smartphone-based software application is a low-cost alternative to skin cancer diagnostics compared to standard dermoscopy available in skin clinics. The lensless imaging system is a low-cost and high-throughput alternative for obtaining egg count densities of plant parasitic nematodes compared with visual counting under a microscope by trained personnel. The B. malayi worm tracking system provides an alternative to available C. elegans tracking software with options to extract multiple parameters related to its body skeleton and posture

Desarrollo de técnicas avanzadas de seguimiento de posturas para reconocimiento de comportamientos de C. elegans

Tesis por compendio[ES] El objetivo principal de esta tesis es el desarrollo de técnicas avanzadas de seguimiento de posturas para reconocimiento de comportamientos del Caenorhabditis elegans o C. elegans. El C. elegans es una clase de nematodo utilizado como organismo modelo para el estudio y tratamientos de diferentes enfermedades patológicas así como neurodegenerativas. Su comportamiento ofrece información valiosa para la investigación de nuevos fármacos (o productos alimenticios y cosméticos saludables) en el estudio de lifespan y healthspan. Al día de hoy, muchos de los ensayos con C. elegans se realizan de forma manual, es decir, usando microscopios para seguirlos y observar sus comportamientos o en laboratorios más modernos utilizando programas específicos. Estos programas no son totalmente automáticos, requieren ajuste de parámetros. Y en otros casos, son programas para visualización de imágenes donde el operador debe etiquetar maualmente el comportamiento de cada C. elegans. Todo esto se traduce a muchas horas de trabajo, lo cual se puede automatizar utilizando técnicas de visión por computador. Además de poder estimar indicadores de movilidad con mayor precisión que un operador humano. El problema principal en el seguimiento de posturas de C. elegans en placas de Petri son las agregaciones entre nematodos o con ruido del entorno. La pérdida o cambios de identidad son muy comunes ya sea de forma manual o usando programas automáticos/semi-automáticos. Y este problema se vuelve más complicado aún en imágenes de baja resolución. Los programas que automatizan estas tareas de seguimiento de posturas trabajan con técnicas de visión por computador usando técnicas tradicionales de procesamiento de imágenes o técnicas de aprendizaje profundo. Ambas técnicas han demostrado excelentes resultados en la detección y seguimiento de posturas de C. elegan}. Por un lado, técnicas tradicionales utilizan algoritmos/optimizadores para obtener la mejor solución, mientras que las técnicas de aprendizaje profundo aprenden de forma automática características del conjunto de datos de entrenamiento. El problema con las técnicas de aprendizaje profundo es que necesitan un conjunto de datos dedicado y grande para entrenar los modelos. La metodología utilizada para el desarrollo de esta tesis (técnicas avanzadas de seguimiento de posturas) se encuadran dentro del área de investigación de la visión artificial. Y ha sido abordada explorando ambas ramas de visión por computador para resolver los problemas de seguimiento de posturas de C. elegans en imágenes de baja resolución. La primera parte, es decir, secciones 1 y 2, capítulo 2, utilizó técnicas tradicionales de procesamiento de imágenes para realizar la detección y seguimiento de posturas de los C. elegans. Para ello se propuso una nueva técnica de esqueletización y dos nuevos criterios de evaluación para obtener mejores resultados de seguimiento, detección, y segmentación de posturas. Las siguientes secciones del capítulo 2 utilizan técnicas de aprendizaje profundo, y simulación de imágenes sintéticas para entrenar modelos y mejorar los resultados de detección y predicción de posturas. Los resultados demostraron ser más rápidos y más precisos en comparación con técnicas tradicionales. También se demostró que los métodos de aprendizaje profundo son más robustos ante la presencia de ruido en la placa.[CA] L'objectiu principal d'aquesta tesi és el desenvolupament de tècniques avançades de seguiment de postures per a reconeixement de comportaments del Caenorhabditis elegans o C. elegans. El C. elegans és una classe de nematodo utilitzat com a organisme model per a l'estudi i tractaments de diferents malalties patològiques així com neurodegeneratives. El seu comportament ofereix informació valuosa per a la investigació de nous fàrmacs (o productes alimentosos i cosmètics saludables) en l'estudi de lifespan i healthspan. Al dia de hui, molts dels assajos amb C. elegans es realitzen de manera manual, és a dir, usant microscopis per a seguir-los i observar els seus comportaments o en laboratoris més moderns utilitzant programes específics. Aquests programes no són totalment automàtics, requereixen ajust de paràmetres. I en altres casos, són programes per a visualització d'imatges on l'operador ha d'etiquetar maualment el comportament de cada C. elegans. Tot això es tradueix a moltes hores de treball, la qual cosa es pot automatitzar utilitzant tècniques de visió per computador. A més de poder estimar indicadors de mobilitat amb major precisió que un operador humà. El problema principal en el seguiment de postures de C. elegans en plaques de Petri són les agregacions entre nematodes o amb soroll de l'entorn. La pèrdua o canvis d'identitat són molt comuns ja siga de manera manual o usant programes automàtics/semi-automàtics. I aquest problema es torna més complicat encara en imatges de baixa resolució. Els programes que automatitzen aquestes tasques de seguiment de postures treballen amb tècniques de visió per computador usant tècniques tradicionals de processament d'imatges o tècniques d'aprenentatge profund. Totes dues tècniques han demostrat excel·lents resultats en la detecció i seguiment de postures de C. elegans. D'una banda, tècniques tradicionals utilitzen algorismes/optimizadors per a obtindre la millor solució, mentre que les tècniques d'aprenentatge profund aprenen de manera automàtica característiques del conjunt de dades d'entrenament. El problema amb les tècniques d'aprenentatge profund és que necessiten un conjunt de dades dedicat i gran per a entrenar els models. La metodologia utilitzada per al desenvolupament d'aquesta tesi (tècniques avançades de seguiment de postures) s'enquadren dins de l'àrea d'investigació de la visió artificial. I ha sigut abordada explorant totes dues branques de visió per computador per a resoldre els problemes de seguiment de postures de C. elegans en imatges de baixa resolució. La primera part, és a dir, secció 1 i 2, capítol 2, va utilitzar tècniques tradicionals de processament d'imatges per a realitzar la detecció i seguiment de postures dels C. elegans. Per a això es va proposar una nova tècnica de esqueletizació i dos nous criteris d'avaluació per a obtindre millors resultats de seguiment, detecció i segmentació de postures. Les següents seccions del capítol 2 utilitzen tècniques d'aprenentatge profund i simulació d'imatges sintètiques per a entrenar models i millorar els resultats de detecció i predicció de postures. Els resultats van demostrar ser més ràpids i més precisos en comparació amb tècniques tradicionals. També es va demostrar que els mètodes d'aprenentatge profund són més robustos davant la presència de soroll en la placa.[EN] The main objective of this thesis is the development of advanced posture-tracking techniques for behavioural recognition of Caenorhabditis elegans or C. elegans. C. elegans is a kind of nematode used as a model organism for the study and treatment of different pathological and neurodegenerative diseases. Their behaviour provides valuable information for the research of new drugs (or healthy food and cosmetic products) in the study of lifespan and healthspan. Today, many of the tests on C. elegans are performed manually, i.e. using microscopes to track them and observe their behaviour, or in more modern laboratories using specific software. These programmes are not fully automatic, requiring parameter adjustment. And in other cases, they are programmes for image visualisation where the operator must label the behaviour of each C. elegans manually. All this translates into many hours of work, which can be automated using computer vision techniques. In addition to being able to estimate mobility indicators more accurately than a human operator. The main problem in tracking C. elegans postures in Petri dishes is aggregations between nematodes or with noise from the environment. Loss or changes of identity are very common either manually or using automatic/semi-automatic programs. And this problem becomes even more complicated in low-resolution images. Programs that automate these pose-tracking tasks work with computer vision techniques using either traditional image processing techniques or deep learning techniques. Both techniques have shown excellent results in the detection and tracking of C. elegans postures. On the one hand, traditional techniques use algorithms/optimizers to obtain the best solution, while deep learning techniques automatically learn features from the training dataset. The problem with deep learning techniques is that they need a dedicated and large dataset to train the models. The methodology used for the development of this thesis (advanced posture-tracking techniques) falls within the research area of computer vision. It has been approached by exploring both branches of computer vision to solve the posture-tracking problems of C. elegans in low-resolution images. The first part, i.e. sections 1 and 2, chapter 2, used traditional image processing techniques to perform posture detection and tracking of C. elegans. For this purpose, a new skeletonization technique and two new evaluation criteria were proposed to obtain better posture-tracking, detection, and segmentation results. The next sections of chapter 2 use deep learning techniques, and synthetic image simulation to train models and improve posture detection and prediction results. The results proved to be faster and more accurate compared to traditional techniques. Deep learning methods were also shown to be more robust in the presence of plate noise.This research was supported by Ministerio de Ciencia, Innovación y Universidades [RTI2018-094312-B-I00 (European FEDER funds); FPI PRE2019-088214], and also was supported by Universitat Politècnica de València [“Funding for open access charge: Uni- versitat Politècnica de València”]. The author received a scholarship from the grant: Ayudas para contratos predoctorales para la formación de doctores 2019.Layana Castro, PE. (2023). Desarrollo de técnicas avanzadas de seguimiento de posturas para reconocimiento de comportamientos de C. elegans [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/198879Compendi

A STOCHASTIC SHAPE AND ORIENTATION MODEL FOR FIBRES WITH AN APPLICATION TO CARBON NANOTUBES

Methods are introduced for analysing the shape and orientation of planar fibres from greyscale images of fibrous systems. The sequence of image processing techniques needed for segmentation of fibres is described. The identified fibres were interpreted as deformed line segments for which two shape and two orientation parameters are estimated by the maximum likelihood method. The methods introduced are shown to perform quite well for simulated systems of deformed line segments with known properties. They were applied to TEM images of carbon nanotubes embedded in polycarbonate

Behavioural motifs of larval Drosophila melanogaster and Caenorhabditis elegans

I present a novel method for the unsupervised discovery of behavioural motifs in larval Drosophila melanogaster and Caenorhabditis elegans. Most current approaches to behavioural annotation suffer from the requirement of training data. As a result, automated programs carry the same observational biases as the humans who have annotated the data. The key novel element of my work is that it does not require training data; rather, behavioural motifs are discovered from the data itself. The method is based on an eigenshape representation of posture. Hence, my approach is called the eigenshape annotator (ESA). First, I examine the annotation consistency for a specific behaviour, the Omega turn of C. elegans, and find significant inconsistency in both expert annotation and the various Omega turn detection algorithms. This finding highlights the need for unbiased tools to study behaviour. A behavioural motif is defined as a particular sequence of postures that recurs frequently. In ESA, posture is represented by an eigenshape time series, and motifs are discovered in this representation. To find motifs, the time series is segmented, and the resulting segments are then clustered. The result is a set of self-similar time series segments, i.e. motifs. The advantage of this novel framework over the popular sliding windows approaches is twofold. First, it does not rely on the ‘closest neighbours’ definition of motifs, by which every motif has exactly two instances. Second, it does not require the assumption of exactly equal length for motifs of the same class. Behavioural motifs discovered using the segmentation-clustering framework are used as the basis of the ESA annotator. ESA is fully probabilistic, therefore avoiding rigid threshold values and allowing classification uncertainty to be quantified. I apply eigenshape annotation to both larval Drosophila and C. elegans, and produce a close match to hand annotation of behavioural states. However, many behavioural events cannot be unambiguously classified. By comparing the results to eigenshape annotation of an artificial agent’s behaviour, I argue that the ambiguity is due to greater continuity between behavioural states than is generally assumed for these organisms

Exploring links between parasitism, plasticity, metabolism and microbiome in the European eel, Anguilla anguilla

Introduction: page 1

Whole-body integration of gene expression and single-cell morphology

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets

Pathophysiology of infections by the gastric trichostrongylid Obeliscoides in a rabbit model system

Thesis (Ph.D.) University of Alaska Fairbanks, 1991The gastric trichostrongylid parasite Obeliscoides sp. was isolated from Alaskan snowshoe hares (Lepus americanus) and passaged 3 times in laboratory rabbits (Oryctolagus cuniculus). Despite its low fertility, the isolate persisted, often as occult infections, for up to 45 weeks and produced physiologic effects in clinically normal rabbits. Prominent eosinophilic and hyperplastic lesions of the gastric mucosa occurred during post-inoculation weeks (PIW) 2-15, while mononuclear aggregations were seen in older infections. Gastric lesion severity was directly related to size of the Obeliscoides population, which declined over time and was smaller in secondary infections. Anorexia occurred within 3 weeks of infective larval inoculation in 12 (of 21) primary and 2 (of 10) secondary infections. Serum total protein, albumin, and the A/G ratio were significantly reduced in anorectic infected rabbits compared to fasted uninfected rabbits. Fecal N excretion was significantly increased between PIW 1 and 5 in rabbits with primary infections, and during PIW 1 and 2 for those with secondary infections. Nitrogen absorption was enhanced during PIW 5-15 of primary infection. Serum gastrin concentrations, determined for the first time in Obeliscoides-infected rabbits by radioimmunoassay, were significantly elevated in primary infections during PIW 6 and 7, while hypokalemia was apparent during PIW 5. Hypermagnesemia occurred in both primary and secondary infections between PIW 8 and 15. Other serum constituents and concentrations of N, Ca and P in the gastrointestinal tract and feces remained largely unchanged. Total mean retention time (TMRT), 31.8 h, and GI turnover time (GITT), 26.3 h, of the fiber component (determined with Ce-141-marked fiber $>$355 microns) were significantly prolonged in secondary infections during PIW 16 to 26. TMRT (53.0 h) and GITT (57.0 h) of the liquid component (using Cr-51 EDTA), were determined for the first time in rabbits, and were not significantly changed by Obeliscoides infection. Persisting populations of this Obeliscoides isolate caused physiologic and pathologic alterations in clinically healthy rabbits. Because these effects were similar to those seen in ruminant Ostertagia spp. infections, this laboratory model could be useful in understanding the pathophysiology of costly production losses that occur in parasitized commercial livestock

Marine Drug Research in China: Selected Papers from the 15-NASMD Conference

The Book covers this whole field, from the discovery of structurally new and bioactive natural products (including biomacromolecules), from marine macro-/micro-organisms, to the pharmacodynamics, pharmacokinetics, metabolisms, and mechanisms of marine-derived lead compounds, both in vitro and in vivo, along with the synthesis and/or structural optimization of marine-derived lead compounds and their structure–activity relationships. Taken together, this Special Issue reprint not only provides inspiration for the discovery of marine-derived novel bioactive compounds, but also sheds light on the further research and development of marine candidate drugs

Translation-dependent mRNA localization in the Caenorhabditis elegans embryo

Includes bibliographical references.2022 Fall.Though each animal cell contains the same genetic information, cell-specific gene expression is required for embryos to develop into mature organisms. Embryos rely on maternally inherited components during early development to guide cell fate specification. In animals, de novo transcription is paused after fertilization until zygotic genome activation. Consequently, early embryos rely on post-transcriptional regulation of maternal mRNA to spatially and temporally regulate protein production. Caenorhabditis elegans has emerged as a powerful developmental model for studying mRNA localization of maternally-inherited transcripts. We have identified subsets of maternal mRNAs with cell-specific and subcellular patterning in the early C. elegans embryo. Previous RNA localization studies in C. elegans focused on maternal transcripts that cluster in the posterior lineage and showed mRNA localization occurs in a translation-independent manner through localization sequence elements in the 3'UTR. However, little is known about the mechanisms directing RNA localization to other subcellular locales in early embryos. Therefore, we sought to understand the localization of maternal transcripts found enriched at the plasma membrane and nuclear periphery, erm-1 (Ezrin/Radixin/Moesin) and imb-2 (Importin Beta), respectively. In this thesis, I characterize two different translation-dependent pathways for mRNA localization of maternal transcripts at the plasma membrane and nuclear periphery. I identified the PIP2-membrane binding region of the ERM-1 proteins is necessary for erm-1 mRNA localization while identifying additional membrane localized maternal transcripts through the presence of encoded PIP2-membrane binding domains. Additionally, I observed that mRNA localization patterns can change over developmental time corresponding to changes in translation status. For imb-2 mRNA localization, I found localization to the nuclear periphery is also translation-dependent. Through recoding the imb-2 mRNA sequence while maintaining the translated peptide sequence using alternative codons, I found both localization and transcript stability additionally depends on mRNA sequence context. These findings represent the first report of a translation-dependent localization pathway for two maternally-inherited transcripts in C. elegans and demonstrate the utility of C. elegans as a model for studying translation-dependent mRNA localization during development

The Practice of Sheep Veterinary Medicine

This book is intended to be a reference text for veterinarians who provide clinical services to sheep producers. It is directed first and foremost at Australian sheep-raising systems, but the approaches described herein will have wide application in all countries where sheep are raised under extensive grazing conditions. Most of the important conditions of sheep in Australia are relatively straightforward to diagnose, but the establishment of effective and economically sound control strategies is often the most difficult part of health management, particularly for those who are less familiar with sheep production systems. With six initial chapters focusing on providing readers with a basic understanding of the business and science underpinning sheep production, this book focuses its remaining chapters on reproduction and disease conditions, ordered largely on a systems basis. The book provides details about the way disease processes develop and manifest in sheep flocks, with numerous references for those who wish to read further. Australian sheep production is a profitable and fulfilling agricultural pursuit for a large number of farm owners, and this book is intended to assist those who work in the industry to add to the profitability and efficiency of sheep production systems, the quality of sheep products and the welfare of the sheep in those systems