Image Segmentation of Bacterial Cells in Biofilms

Bacterial biofilms are three-dimensional cell communities that live embedded in a self-produced extracellular matrix. Due to the protective properties of the dense coexistence of microorganisms, single bacteria inside the communities are hard to eradicate by antibacterial agents and bacteriophages. This increased resilience gives rise to severe problems in medical and technological settings. To fight the bacterial cells, an in-detail understanding of the underlying mechanisms of biofilm formation and development is required. Due to spatio-temporal variances in environmental conditions inside a single biofilm, the mechanisms can only be investigated by probing single-cells at different locations over time. Currently, the mechanistic information is primarily encoded in volumetric image data gathered with confocal fluorescence microscopy. To quantify features of the single-cell behaviour, single objects need to be detected. This identification of objects inside biofilm image data is called segmentation and is a key step for the understanding of the biological processes inside biofilms. In the first part of this work, a user-friendly computer program is presented which simplifies the analysis of bacterial biofilms. It provides a comprehensive set of tools to segment, analyse, and visualize fluorescent microscopy data without writing a single line of analysis code. This allows for faster feedback loops between experiment and analysis, and allows fast insights into the gathered data. The single-cell segmentation accuracy of a recent segmentation algorithm is discussed in detail. In this discussion, points for improvements are identified and a new optimized segmentation approach presented. The improved algorithm achieves superior segmentation accuracy on bacterial biofilms when compared to the current state-of-the-art algorithms. Finally, the possibility of deep learning-based end-to-end segmentation of biofilm data is investigated. A method for the quick generation of training data is presented and the results of two single-cell segmentation approaches for eukaryotic cells are adapted for the segmentation of bacterial biofilm segmentation.Bakterielle Biofilme sind drei-dimensionale Zellcluster, welche ihre eigene Matrix produzieren. Die selbst-produzierte Matrix bietet den Zellen einen gemeinschaftlichen Schutz vor äußeren Stressfaktoren. Diese Stressfaktoren können abiotischer Natur sein wie z.B. Temperatur- und Nährstoff\- schwankungen, oder aber auch biotische Faktoren wie z.B. Antibiotikabehandlung oder Bakteriophageninfektionen. Dies führt dazu, dass einzelne Zelle innerhalb der mikrobiologischen Gemeinschaften eine erhöhte Widerstandsfähigkeit aufweisen und eine große Herausforderung für Medizin und technische Anwendungen darstellen. Um Biofilme wirksam zu bekämpfen, muss man die dem Wachstum und Entwicklung zugrundeliegenden Mechanismen entschlüsseln. Aufgrund der hohen Zelldichte innerhalb der Gemeinschaften sind die Mechanismen nicht räumlich und zeitlich invariant, sondern hängen z.B. von Metabolit-, Nährstoff- und Sauerstoffgradienten ab. Daher ist es für die Beschreibung unabdingbar Beobachtungen auf Einzelzellebene durchzuführen. Für die nicht-invasive Untersuchung von einzelnen Zellen innerhalb eines Biofilms ist man auf konfokale Fluoreszenzmikroskopie angewiesen. Um aus den gesammelten, drei-dimensionalen Bilddaten Zelleigenschaften zu extrahieren, ist die Erkennung von den jeweiligen Zellen erforderlich. Besonders die digitale Rekonstruktion der Zellmorphologie spielt dabei eine große Rolle. Diese erhält man über die Segmentierung der Bilddaten. Dabei werden einzelne Bildelemente den abgebildeten Objekten zugeordnet. Damit lassen sich die einzelnen Objekte voneinander unterscheiden und deren Eigenschaften extrahieren. Im ersten Teil dieser Arbeit wird ein benutzerfreundliches Computerprogramm vorgestellt, welches die Segmentierung und Analyse von Fluoreszenzmikroskopiedaten wesentlich vereinfacht. Es stellt eine umfangreiche Auswahl an traditionellen Segmentieralgorithmen, Parameterberechnungen und Visualisierungsmöglichkeiten zur Verfügung. Alle Funktionen sind ohne Programmierkenntnisse zugänglich, sodass sie einer großen Gruppe von Benutzern zur Verfügung stehen. Die implementierten Funktionen ermöglichen es die Zeit zwischen durchgeführtem Experiment und vollendeter Datenanalyse signifikant zu verkürzen. Durch eine schnelle Abfolge von stetig angepassten Experimenten können in kurzer Zeit schnell wissenschaftliche Einblicke in Biofilme gewonnen werden.\\ Als Ergänzung zu den bestehenden Verfahren zur Einzelzellsegmentierung in Biofilmen, wird eine Verbesserung vorgestellt, welche die Genauigkeit von bisherigen Filter-basierten Algorithmen übertrifft und einen weiteren Schritt in Richtung von zeitlich und räumlich aufgelöster Einzelzellverfolgung innerhalb bakteriellen Biofilme darstellt. Abschließend wird die Möglichkeit der Anwendung von Deep Learning Algorithmen für die Segmentierung in Biofilmen evaluiert. Dazu wird eine Methode vorgestellt welche den Annotationsaufwand von Trainingsdaten im Vergleich zu einer vollständig manuellen Annotation drastisch verkürzt. Die erstellten Daten werden für das Training von Algorithmen eingesetzt und die Genauigkeit der Segmentierung an experimentellen Daten untersucht

Method and System for Identification of Metabolites Using Mass Spectra

A method and system is provided for mass spectrometry for identification of a specific elemental formula for an unknown compound which includes but is not limited to a metabolite. The method includes calculating a natural abundance probability (NAP) of a given isotopologue for isotopes of non-labelling elements of an unknown compound. Molecular fragments for a subset of isotopes identified using the NAP are created and sorted into a requisite cache data structure to be subsequently searched. Peaks from raw spectrum data from mass spectrometry for an unknown compound. Sample-specific peaks of the unknown com- pound from various spectral artifacts in ultra-high resolution Fourier transform mass spectra are separated. A set of possible isotope-resolved molecular formula (IMF) are created by iteratively searching the molecular fragment caches and combining with additional isotopes and then statistically filtering the results based on NAP and mass-to-charge (m/2) matching probabilities. An unknown compound is identified and its corresponding elemental molecular formula (EMF) from statistically-significant caches of isotopologues with compatible IMFs

Data based system design and network analysis tools for chemical and biological processes

Ph.DDOCTOR OF PHILOSOPH

High-Performance Modelling and Simulation for Big Data Applications

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

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

High-Performance Modelling and Simulation for Big Data Applications

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

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Search-Based Software Maintenance and Testing

2012 - 2013In software engineering there are many expensive tasks that are performed during development and maintenance activities. Therefore, there has been a lot of e ort to try to automate these tasks in order to signi cantly reduce the development and maintenance cost of software, since the automation would require less human resources. One of the most used way to make such an automation is the Search-Based Software Engineering (SBSE), which reformulates traditional software engineering tasks as search problems. In SBSE the set of all candidate solutions to the problem de nes the search space while a tness function di erentiates between candidate solutions providing a guidance to the optimization process. After the reformulation of software engineering tasks as optimization problems, search algorithms are used to solve them. Several search algorithms have been used in literature, such as genetic algorithms, genetic programming, simulated annealing, hill climbing (gradient descent), greedy algorithms, particle swarm and ant colony. This thesis investigates and proposes the usage of search based approaches to reduce the e ort of software maintenance and software testing with particular attention to four main activities: (i) program comprehension; (ii) defect prediction; (iii) test data generation and (iv) test suite optimiza- tion for regression testing. For program comprehension and defect prediction, this thesis provided their rst formulations as optimization problems and then proposed the usage of genetic algorithms to solve them. More precisely, this thesis investigates the peculiarity of source code against textual documents written in natural language and proposes the usage of Genetic Algorithms (GAs) in order to calibrate and assemble IR-techniques for di erent software engineering tasks. This thesis also investigates and proposes the usage of Multi-Objective Genetic Algorithms (MOGAs) in or- der to build multi-objective defect prediction models that allows to identify defect-prone software components by taking into account multiple and practical software engineering criteria. Test data generation and test suite optimization have been extensively investigated as search- based problems in literature . However, despite the huge body of works on search algorithms applied to software testing, both (i) automatic test data generation and (ii) test suite optimization present several limitations and not always produce satisfying results. The success of evolutionary software testing techniques in general, and GAs in particular, depends on several factors. One of these factors is the level of diversity among the individuals in the population, which directly a ects the exploration ability of the search. For example, evolutionary test case generation techniques that employ GAs could be severely a ected by genetic drift, i.e., a loss of diversity between solutions, which lead to a premature convergence of GAs towards some local optima. For these reasons, this thesis investigate the role played by diversity preserving mechanisms on the performance of GAs and proposed a novel diversity mechanism based on Singular Value Decomposition and linear algebra. Then, this mechanism has been integrated within the standard GAs and evaluated for evolutionary test data generation. It has been also integrated within MOGAs and empirically evaluated for regression testing. [edited by author]XII n.s