26 research outputs found

    Tracking and analysis of movement at different scales: from endosomes to humans

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    Movement is apparent across all spatio-temporal scales in biology and can have a significant effect on the survival of the individual. For this reason, it has been the object of study in a wide range of research fields, i.e. in molecular biology, pharmaceutics, medical research but also in behavioural biology and ecology. The aim of the thesis was to provide methodologies and insight on the movement patterns seen at different spatio-temporal scales in biology; the intra-cellular, the cellular and the organism level. At the intra-cellular level, current thesis studied the compartmental inheritance in Human Osteosarcoma (U2-OS) cells. The inheritance pattern of the endosomal quantum dot fluorescence across two consecutive generations was for first time empirically revealed. In addition, a in silico model was developed to predict the inheritance across multiple generations. At the cellular level, a semi-automated routine was developed that can realize long-term nuclei tracking in U2-OS cell populations labeled with a cell cycle marker in their cytoplasm. A method to extract cell cycle information without the need to explicitly segment the cells was proposed. The movement behaviour of the cellular population and their possible inter-individual differences was also studied. Lastly, at the organism level, the focus of the thesis was to study the emergence of coordination in unfamiliar free-swimming stickleback fish shoals. It was demonstrated that there exist two different phases, the uncoordinated and the coordinated. In addition, the significance of uncoordinated phase to the establishment of the group’s social network was for first time evinced. The adaptation of the stickleback collectives was also studied over time, i.e. the effect of group’s repeated interactions on the emergence of coordination. Findings at the intra-cellular and cellular level can have significant implications on medical and pharmaceutical research. Findings at the organism level can also contribute to the understanding of how social interactions are formed and maintained in animal collectives

    Land Surface Monitoring Based on Satellite Imagery

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    This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

    Combining landscape genetics and movement ecology to assess functional connectivity for red deer (Cervus elaphus) in Schleswig-Holstein, Germany

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    Die anthropogen bedingte Zerschneidung der Landschaft stellt eine wichtige Herausforderung für den Natur- und Artenschutz dar. Große Säugetiere, wie zum Beispiel der Rothirsch (Cervus elaphus) sind durch die Fragmentierung einer Verkleinerung und zunehmenden Isolierung der Lebensräume ausgesetzt. Dies kann weitreichende Folgen wie einen verringerten Austausch an Individuen und damit langfristig an Genen mit sich ziehen. Um diesen Folgen entgegenzuwirken und den genetischen Austausch zu verbessern sind objektive Beurteilungsverfahren über die Konnektivität der Landschaft notwendig. Die Erfassung und Modellierung der funktionellen Landschaftskonnektivität für eine Zielart basiert häufig auf Grundlagen wie Expertenwissen, Habitatmodellen oder Bewegungsdaten. Allerdings werden diese Methoden hinsichtlich ihrer Repräsentativität für tatsächliche Abwanderungen oder effektivem Genfluss diskutiert. Im Rahmen von landschaftsgenetischen Analysen werden Informationen über den genetischen Austausch zwischen Populationen oder einzelnen Individuen mit entsprechenden Ausprägungen der Landschaft korreliert. Genetische Daten haben dabei den Vorteil, dass sie sowohl eine erfolgreiche Wanderung zwischen Verbreitungsgebieten als auch die anschließende Reproduktion mit anderen Individuen, widerspiegeln können. Daher stellt die Landschaftsgenetik eine innovative Ansatzmöglichkeit zur Beurteilung der funktionellen Landschaftskonnektivität dar. Ziel der Dissertation ist die Konzipierung und Evaluierung von artspezifischen Modellen der Landschaftskonnektivität mit Hilfe von Gendaten und Telemetrie-Ergebnissen. Der Rothirsch in Schleswig-Holstein dient dabei als Beispielart, mit der die Unterschiede bezüglich der methodischen und konzeptionellen Herangehensweisen demonstriert werden sollen. Insbesondere für die naturschutzfachliche Praxis und Korridorplanung ist dies von grundlegender Bedeutung. 8 Im ersten Kapitel wird zunächst eine generelle Einleitung in die Problematik der Landschaftszerschneidung gegeben und anhand des Rothirschs in Schleswig-Holstein verdeutlicht. Anschließend werden die verschiedenen Ansatzmöglichkeiten der Landschaftsgenetik als auch der Bewegungsökologie zur Beurteilung der Landschaftskonnektivität dargestellt. Die Bewegungsökologie setzt sich unter anderem damit auseinander, welche Faktoren die Bewegungen von Organismen in ihrem Lebensraum beeinflussen. Durch die Verknüpfung von Bewegungsdaten mit Landschaftsvariablen lassen sich so wichtige Erkenntnisse über die Lebensraumansprüche einer Zielart gewinnen. Dabei können unter anderem die Habitatpräferenzen während unterschiedlicher Bewegungsmuster, wie zum Beispiel der Abwanderung in neue Gebiete, differenziert betrachtet werden. Das zweite Kapitel befasst sich mit der genetischen Diversität und Differenzierung der lokalen Rothirschvorkommen in Schleswig-Holstein. Anhand der genetischen Daten wird dabei verdeutlicht, dass die regionalen Managementeinheiten (Hegeringe) nicht immer in sich geschlossene Populationen darstellen. Die Rothirschpopulationen weisen vielmehr eine hierarchische Struktur auf. Zum Beispiel ist der Genfluss, je nach Dichte der benachbarten Populationen, unterschiedlich stark ausgeprägt. Insgesamt konnte für mehrere Populationen eine im europäischen Vergleich geringe genetische Diversität festgestellt werden. Dies unterstreicht, dass ein besseres Verständnis über die Auswirkungen der Landschaftszerschneidung sowie eine Bewertung der Landschaftskonnektivität aus Sicht des Rothirschs notwendig ist, um dem Verlust an genetischer Vielfalt entgegenzuwirken. Eine Möglichkeit die Landschaftskonnektivität zu bewerten stellt die Analyse von Telemetrie-Daten dar. Für die Auswertung von solchen Bewegungsdaten stehen eine Vielzahl an Methoden zur Verfügung. Im dritten Kapitel werden die verschiedenen Ansätze zur Differenzierung unterschiedlicher Bewegungsmuster aus Telemetrie-Daten zusammengestellt. Durch eine umfangreiche Methodenübersicht werden Entscheidungshilfen für die Anwendung solcher Pfad-Segmentierungen zur Beantwortung bestimmter Fragestellungen in der Bewegungsökologie gegeben. Das vierte Kapitel greift unter anderem auf eine solche Methode der Pfad-Segmentierung zurück, um potentielle Ausbreitungsbewegungen innerhalb der Telemetrie-Daten von besenderten Rothirschen zu ermitteln. Diese Bewegungsdaten 9 werden anschließend mit Landschaftsvariablen verknüpft und ein Modell abgeleitet, welches den Widerstand für Wanderbewegungen darstellt (Widerstandsmodell). Darüber hinaus werden in dieser Studie weitere methodische Ansätze zur Modellierung der funktionellen Landschaftskonnektivität verglichen. Diese basieren unter anderem auf Expertenwissen und Habitatmodellen sowie weiteren Auswertungsansätzen der Bewegungsdaten. Für den Vergleich der resultierenden Widerstandsmodelle wird die Landschaftsgenetik hinzugezogen. Dabei werden effektive Distanzen basierend auf den jeweiligen Modellen den genetischen Distanzmaßen gegenübergestellt. Die Modelle mit der höchsten Übereinstimmung werden ferner genutzt, um methodische Unterschiede in der Ausweisung von Korridoren darzustellen. Es zeigte sich, dass für weitreichende Abwanderungen die Rothirsche auf geeignete Habitatverhältnisse innerhalb der Landschaftsmatrix angewiesen sind. Die Auswertung der Bewegungsdaten ergab hingegen, dass für kürzere Distanzen auch suboptimale Gebiete durchquert werden können. Abschließend werden im fünften Kapitel die Ergebnisse zusammengefasst und diskutiert. Besonderer Schwerpunkt liegt dabei auf dem Beitrag der Anwendung von Landschaftsgenetik und Bewegungsökologie im angewandten Naturschutz und welche Erkenntnisse für die Ausweisung und Effektivität von Korridoren gewonnen werden können.Human-caused restrictions like the fragmentation of the landscape poses a major challenge to wildlife conservation. Large and mobile species such as red deer (Cervus elaphus) are subject to increasing effects of isolation and a decrease of primary habitats. This can result in a reduction of the exchange of individuals or even a long-term loss of gene flow. In order to counteract these negative effects and to promote genetic exchange, suitable approaches for estimating functional connectivity of the landscape are necessary. In most cases, landscape models of functional connectivity for a given study species are based on expert knowledge, habitat suitability, or movement data. However, there is an ongoing debate whether these methods are representative of actual dispersal or effective gene flow. Landscape genetic analyses correlate estimates of genetic differentiation between populations or individuals with landscape composition. The advantage of genetic data is that it reflects both successful dispersal between populations, as well as subsequent reproduction with other individuals. Therefore, landscape genetics represent an innovative approach for assessing functional connectivity of the landscape matrix. The aim of this dissertation is to compare different species-specific models of functional connectivity utilizing genetic and movement data. Using red deer in Northern Germany as an example, the methodological and conceptual differences of multiple approaches are demonstrated. Overall, the presented thesis provides important insights for applied conservation of wildlife and planning of corridors. The first chapter provides a general introduction to the issue of landscape fragmentation and illustrates the effects on red deer in the study area of Schleswig-Holstein. Furthermore, the potential applications of landscape genetics and movement ecology to assess landscape connectivity are presented. For example, movement ecology provides an integral framework to explore the potential factors shaping the movements of organisms and the ecological consequences of these movements such as gene flow. The second chapter comprises a study on the genetic diversity and structure of red deer populations in Northern Germany. The results indicate that local populations are best described as an hierarchical network of subpopulations with different levels of gene flow. Overall, genetic diversity of red deer from the study area is quite low compared to other populations from Central Europe. This underlines that a better understanding of the isolation effects caused by landscape fragmentation and species-specific assessment of landscape connectivity for red deer are needed to address the observed loss of genetic diversity. One possible approach for estimating functional connectivity is by linking telemetry data with landscape variables in order to gain insights into the habitat requirements of a target species. However, habitat preferences are very likely to change with different movement behaviors. This represents an important point to consider when studying the effects of landscape composition on actual dispersal movements. The third chapter of this thesis presents an extensive overview on different methods for identifying behavioral patterns from movement data. Furthermore, it provides guidelines for deciding among the available methods of path-segmentation and shows how they can be applied to answer research questions within the movement ecology paradigm. The study described in the fourth chapter utilizes such a path-segmentation method to detect potential dispersal movements from telemetry data of multiple red deer individuals. The observed movements are then linked to landscape variables in order to model functional connectivity based on landscape resistance towards dispersal of red deer throughout the study area. In addition, the study applies and compares different methodological approaches for modeling functional connectivity based on expert knowledge, habitat models and other analyses of movement data. A landscape genetic approach is used as a means to compare the resulting resistance models. Effective distances derived from the models are compared with estimates on genetic distance. The highest ranked models are further used to illustrate methodological differences in the designation of conservation corridors. The results show that for large scale dispersal red deer rely on primary habitat conditions within the landscape matrix. However, connectivity based on the identified dispersal movements showed that areas of poor habitat quality can be traversed by red deer at shorter distances. Finally, in the fifth chapter, the results of the presented studies are summarized and discussed. In particular, the contribution of landscape genetics and movement ecology to applied conservation and landscape planning are elaborated. The results of this thesis could ultimately increase the effectiveness of conservation measures such as the placement of corridors.2021-06-2

    IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

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    Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis

    Artificial Intelligence for Data Analysis and Signal Processing

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    Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases. It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways. In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods. A human gait identification system based on the analysis of inertial signals has been developed, leading to misclassification rates smaller than 0.15%. Advanced deep learning architectures have been also investigated to tackle the problem of atrial fibrillation detection from short length and noisy electrocardiographic signals. The results show a clear improvement provided by representation learning over a knowledge-based approach. Another important clinical challenge, both for the patient and on-board automatic alarm systems, is to detect with reasonable advance the patterns leading to risky situations, allowing the patient to take therapeutic decisions on the basis of future instead of current information. This problem has been specifically addressed for the prediction of critical hypo/hyperglycemic episodes from continuous glucose monitoring devices, carrying out a comparative analysis among the most successful methods for glucose event prediction. This dissertation also shows evidence of the benefits of learning algorithms for vehicular traffic anomaly detection, through the use of a statistical Bayesian framework, and for the optimization of video streaming user experience, implementing an intelligent adaptation engine for video streaming clients. The proposed solution explores the promising field of deep learning methods integrated with reinforcement learning schema, showing its benefits against other state of the art approaches. The great knowledge transfer capability of artificial intelligence methods and the benefits of representation learning systems stand out from this research, representing the common thread among all the presented research fields

    Classification and detection of Critical Transitions: from theory to data

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    From population collapses to cell-fate decision, critical phenomena are abundant in complex real-world systems. Among modelling theories to address them, the critical transitions framework gained traction for its purpose of determining classes of critical mechanisms and identifying generic indicators to detect and alert them (“early warning signals”). This thesis contributes to such research field by elucidating its relevance within the systems biology landscape, by providing a systematic classification of leading mechanisms for critical transitions, and by assessing the theoretical and empirical performance of early warning signals. The thesis thus bridges general results concerning the critical transitions field – possibly applicable to multidisciplinary contexts – and specific applications in biology and epidemiology, towards the development of sound risk monitoring system

    Advances in Bioengineering

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    The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques
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