Biological cell tracking and lineage inference via random finite sets

Automatic cell tracking has long been a challenging problem due to the uncertainty of cell dynamic and observation process, where detection probability and clutter rate are unknown and time-varying. This is compounded when cell lineages are also to be inferred. In this paper, we propose a novel biological cell tracking method based on the Labeled Random Finite Set (RFS) approach to study cell migration patterns. Our method tracks cells with lineage by using a Generalised Label Multi-Bernoulli (GLMB) filter with objects spawning, and a robust Cardinalised Probability Hypothesis Density (CPHD) to address unknown and time-varying detection probability and clutter rate. The proposed method is capable of quantifying the certainty level of the tracking solutions. The capability of the algorithm on population dynamic inference is demonstrated on a migration sequence of breast cancer cells

Multiple Object Tracking in Light Microscopy Images Using Graph-based and Deep Learning Methods

Multi-Objekt-Tracking (MOT) ist ein Problem der Bildanalyse, welches die Lokalisierung und Verknüpfung von Objekten in einer Bildsequenz über die Zeit umfasst, mit zahlreichen Anwendungen in Bereichen wie autonomes Fahren, Robotik oder Überwachung. Neben technischen Anwendungsgebieten besteht auch ein großer Bedarf an MOT in biomedizinischen Anwendungen. So können beispielsweise Experimente, die mittels Lichtmikroskopie über mehrere Stunden oder Tage hinweg erfasst wurden, Hunderte oder sogar Tausende von ähnlich aussehenden Objekten enthalten, was eine manuelle Analyse unmöglich macht. Um jedoch zuverlässige Schlussfolgerungen aus den verfolgten Objekten abzuleiten, ist eine hohe Qualität der prädizierten Trajektorien erforderlich. Daher werden domänenspezifische MOT-Ansätze benötigt, die in der Lage sind, die Besonderheiten von lichtmikroskopischen Daten zu berücksichtigen. In dieser Arbeit werden daher zwei neuartige Methoden für das MOT-Problem in Lichtmikroskopie-Bildern erarbeitet sowie Ansätze zum Vergleich der Tracking-Methoden vorgestellt. Um die Performanz der Tracking-Methode von der Qualität der Segmentierung zu unterscheiden, wird ein Ansatz vorgeschlagen, der es ermöglicht die Tracking-Methode getrennt von der Segmentierung zu analysieren, was auch eine Untersuchung der Robustheit von Tracking-Methoden gegeben verschlechterter Segmentierungsdaten erlaubt. Des Weiteren wird eine graphbasierte Tracking-Methode vorgeschlagen, welche eine Brücke zwischen einfach anzuwendenden, aber weniger performanten Tracking-Methoden und performanten Tracking-Methoden mit vielen schwer einstellbaren Parametern schlägt. Die vorgeschlagene Tracking-Methode hat nur wenige manuell einstellbare Parameter und ist einfach auf 2D- und 3D-Datensätze anwendbar. Durch die Modellierung von Vorwissen über die Form des Tracking-Graphen ist die vorgeschlagene Tracking-Methode außerdem in der Lage, bestimmte Arten von Segmentierungsfehlern automatisch zu korrigieren. Darüber hinaus wird ein auf Deep Learning basierender Ansatz vorgeschlagen, der die Aufgabe der Instanzsegmentierung und Objektverfolgung gleichzeitig in einem einzigen neuronalen Netzwerk erlernt. Außerdem lernt der vorgeschlagene Ansatz Repräsentationen zu prädizieren, die für den Menschen verständlich sind. Um die Performanz der beiden vorgeschlagenen Tracking-Methoden im Vergleich zu anderen aktuellen, domänenspezifischen Tracking-Ansätzen zu zeigen, werden sie auf einen domänenspezifischen Benchmark angewendet. Darüber hinaus werden weitere Bewertungskriterien für Tracking-Methoden eingeführt, welche zum Vergleich der beiden vorgeschlagenen Tracking-Methoden herangezogen werden

Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach

The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources

Proceedings of the Sixth Hydraulics Conference

https://ir.uiowa.edu/uisie/1036/thumbnail.jp

NASA thesaurus. Volume 3: Definitions

Publication of NASA Thesaurus definitions began with Supplement 1 to the 1985 NASA Thesaurus. The definitions given here represent the complete file of over 3,200 definitions, complimented by nearly 1,000 use references. Definitions of more common or general scientific terms are given a NASA slant if one exists. Certain terms are not defined as a matter of policy: common names, chemical elements, specific models of computers, and nontechnical terms. The NASA Thesaurus predates by a number of years the systematic effort to define terms, therefore not all Thesaurus terms have been defined. Nevertheless, definitions of older terms are continually being added. The following data are provided for each entry: term in uppercase/lowercase form, definition, source, and year the term (not the definition) was added to the NASA Thesaurus. The NASA History Office is the authority for capitalization in satellite and spacecraft names. Definitions with no source given were constructed by lexicographers at the NASA Scientific and Technical Information (STI) Facility who rely on the following sources for their information: experts in the field, literature searches from the NASA STI database, and specialized references

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Hybrid bio-robotics: from the nanoscale to the macroscale

[eng] Hybrid bio-robotics is a discipline that aims at integrating biological entities with synthetic materials to incorporate features from biological systems that have been optimized through millions of years of evolution and are difficult to replicate in current robotic systems. We can find examples of this integration at the nanoscale, in the field of catalytic nano- and micromotors, which are particles able to self-propel due to catalytic reactions happening in their surface. By using enzymes, these nanomotors can achieve motion in a biocompatible manner, finding their main applications in active drug delivery. At the microscale, we can find single-cell bio-swimmers that use the motion capabilities of organisms like bacteria or spermatozoa to transport microparticles or microtubes for targeted therapeutics or bio-film removal. At the macroscale, cardiac or skeletal muscle tissue are used to power small robotic devices that can perform simple actions like crawling, swimming, or gripping, due to the contractions of the muscle cells. This dissertation covers several aspects of these kinds of devices from the nanoscale to the macro-scale, focusing on enzymatically propelled nano- and micromotors and skeletal muscle tissue bio-actuators and bio-robots. On the field of enzymatic nanomotors, there is a need for a better description of their dynamics that, consequently, might help understand their motion mechanisms. Here, we focus on several examples of nano- and micromotors that show complex dynamics and we propose different strategies to analyze their motion. We develop a theoretical framework for the particular case of enzymatic motors with exponentially decreasing speed, which break the assumptions of constant speed of current methods of analysis and need different strategies to characterize their motion. Finally, we consider the case of enzymatic nanomotors moving in complex biological matrices, such as hyaluronic acid, and we study their interactions and the effects of the catalytic reaction using dynamic light scattering, showing that nanomotors with negative surface charge and urease-powered motion present enhanced parameters of diffusion in hyaluronic acid. Moving towards muscle-based robotics, we investigate the application of 3D bioprinting for the bioengineering of skeletal muscle tissue. We demonstrate that this technique can yield well-aligned and functional muscle fibers that can be stimulated with electric pulses. Moreover, we develop and apply a novel co-axial approach to obtain thin and individual muscle fibers that resemble the bundle-like organization of native skeletal muscle tissue. We further exploit the versatility of this technique to print several types of materials in the same process and we fabricate bio-actuators based on skeletal muscle tissue with two soft posts. Due to the deflection of these cantilevers when the tissue contracts upon stimulation, we can measure the generated forces, therefore obtaining a force measurement platform that could be useful for muscle development studies or drug testing. With these applications in mind, we study the adaptability of muscle tissue after applying various exercise protocols based on different stimulation frequencies and different post stiffness, finding an increase of the force generation, especially at medium frequencies, that resembles the response of native tissue. Moreover, we adapt the force measurement platform to be used with human-derived myoblasts and we bioengineer two models of young and aged muscle tissue that could be used for drug testing purposes. As a proof of concept, we analyze the effects of a cosmetic peptide ingredient under development, focusing on the kinematics of high stimulation contractions. Finally, we present the fabrication of a muscle-based bio-robot able to swim by inertial strokes in a liquid interface and a nanocomposite-laden bio-robot that can crawl on a surface. The first bio-robot is thoroughly characterized through mechanical simulations, allowing us to optimize the skeleton, based on a serpentine or spring-like structure. Moreover, we compare the motion of symmetric and asymmetric designs, demonstrating that, although symmetric bio-robots can achieve some motion due to spontaneous symmetry breaking during its self-assembly, asymmetric bio-robots are faster and more consistent in their directionality. The nanocomposite-laden crawling bio-robot consisted of embedded piezoelectric boron nitride nanotubes that improved the differentiation of the muscle tissue due to a feedback loop of piezoelectric effect activated by the same spontaneous contractions of the tissue. We find that bio-robots with those nanocomposites achieve faster motion and stronger force outputs, demonstrating the beneficial effects in their differentiation. This research presented in this thesis contributes to the development of the field of bio-hybrid robotic devices. On enzymatically propelled nano- and micromotors, the novel theoretical framework and the results regarding the interaction of nanomotors with complex media might offer useful fundamental knowledge for future biomedical applications of these systems. The bioengineering approaches developed to fabricate murine- or human-based bio-actuators might find applications in drug screening or to model heterogeneous muscle diseases in biomedicine using the patient’s own cells. Finally, the fabrication of bio-hybrid swimmers and nanocomposite crawlers will help understand and improve the swimming motion of these devices, as well as pave the way towards the use of nanocomposite to enhance the performance of future actuators.[spa] La bio-robótica híbrida es una disciplina cuyo objetivo es la integración de entidades biológicas con materiales sintéticos para superar los desafíos existentes en el campo de la robótica blanda, incorporando características de los sistemas biológicos que han sido optimizadas durante millones de años de evolución natural y no son fáciles de reproducir artificialmente. Esta tesis cubre varios aspectos de este tipo de dispositivos desde la nanoescala a la macroescala, enfocándose en nano- y micromotores propulsados enzimáticamente y bio-actuadores y bio-robots basados en tejido muscular esquelético. En el campo de nanomotores enzimáticos, existe la necesidad de encontrar mejores modelos que puedan describir la dinámica de su movimiento para llegar a entender sus mecanismos de propulsión subyacentes. Aquí, nos enfocamos en diversos ejemplos de nano- y micromotores que muestran dinámicas de movimiento complejas y proponemos diferentes estrategias que se pueden utilizar para analizar y caracterizar este movimiento. Moviéndonos hacia robots basados en células musculares, investigamos la aplicación de la técnica de bioimpresión en 3D para la biofabricación de músculo esquelético. Demostramos que esta técnica puede producir fibras musculares funcionales y bien alineadas que puede ser estimuladas y contraerse con pulsos eléctricos. Investigamos la versatilidad de esta técnica para imprimir varios tipos de materiales en el mismo proceso y fabricamos bio-actuadores basados en músculo esquelético. Debido a los movimientos de unos postes gracias a las contracciones musculares, podemos obtener medidas de la fuerza ejercida, obteniendo una plataforma de medición de fuerzas que podría ser de utilidad para estudios sobre el desarrollo del músculo o para testeo de fármacos. Finalmente, presentamos la fabricación de un bio-robot basado en músculo esquelético capaz de nadar en la superficie de un líquido y un bio-robot con nanocompuestos incrustados que puede arrastrarse por una superficie sólida. El primer de ellos es minuciosamente caracterizado a través de simulaciones mecánicas, permitiéndonos optimizar su esqueleto, basado en una estructura tipo serpentina o muelle. El segundo bio-robot contiene nanotubos piezoeléctricos incrustados en su tejido, los cuales ayudan en la diferenciación del músculo debido a una retroalimentación basada en su efecto piezoeléctrico y activada por las contracciones espontáneas del tejido. Mostramos que estos bio-robots pueden generar un movimiento más rápido y una mayor generación de fuerza, demostrando los efectos beneficiales en la diferenciación del tejido

A generalized labeled multi-Bernoulli tracker for time lapse cell migration

© 2017 IEEE. Tracking is a means to accomplish the more fundamental task of extracting relevant information about cell behavior from time-lapse microscopy data. Hence, characterizing uncertainty or confidence in the information inferred from the data is as important as the tracking of the cells. In this paper, we show that in addition to being a principled Bayesian multi-object tracking approach, the Random Finite Set (RFS) framework is capable of providing consistent characterization of uncertainty for the information inferred from the data. In particular, we use an efficient implementation of the Generalized Labeled Multi-Bernoulli (GLMB) filter to track a large number of cells in a cell migration experiment and demonstrate how to characterize uncertainty on variables inferred from the data such as cell counts, survival rate, birth rate, mean position, mean velocity using standard constructs from RFS theory

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications