京都大学 / 0048 / 新制・課程博士 / 博士(薬科学) / 甲第20310号 / 薬科博第79号 / 新制||薬科||9(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 金子 周司, 教授 竹島 浩, 教授 中山 和久 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

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Thesis advisor: Eranthie Weerapana / Reactive oxygen species (ROS) modulate protein function through cysteine oxidation. Identifying protein targets of ROS can provide insight into uncharacterized ROS-regulated pathways especially within ROS generating organelles such as the mitochondria. There are several known examples of mitochondrial cysteine targets that alter protein and pathway activity resulting in pathological effects. Several chemoproteomic workflows, including ABPP and OxICAT, can be used to identify sites of cysteine oxidation. However, determining ROS targets localized within subcellular regions and ROS hotspots remains challenging with existing workflows. Here, we present combined cysteine- monitoring chemoproteomic platforms (isoTOP-ABPP and OxICAT) with mitochondrial enrichment (organelle isolation and proximity labeling) to monitor cysteine oxidation events within the mitochondria. First, we profile redox- sensitive cysteines under exogenous and endogenous peroxide in isolated mitochondria using isoTOP-ABPP and OxICAT. Next, we introduce PL-OxICAT which combines enzymatic proximity labeling (PL) (TurboID/APEX) and OxICAT to monitor localized cysteine oxidation events within subcellular compartments such as the mitochondrial matrix and intermembrane space as well as ROS hotspots. Together, these platforms further hone our ability to monitor cysteine oxidation events within specific subcellular locations and ROS hotspots and provide a deeper understanding of the protein targets of endogenous and exogenous ROS. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

Die mobile Robotik bietet erhebliches Potenzial zur Verbesserung intralogistischer Prozes- se, insbesondere durch den effektiven Einsatz mobiler Roboter. Eine zentrale Rolle spielt dabei das Flottenmanagement, welches ausschlaggebend f¨ur den effizienten Betrieb dieser Roboter ist. Aktuelle Flottenmanagementsysteme bestehen h¨aufig aus propriet¨aren Softwaresystemen, was die Interoperabilit¨at zwischen verschiedenen Robotern sowie technischen Geb¨aude- systemen erheblich erschwert. In dieser Arbeit wird eine L¨osung f¨ur dieses Problem vor- gestellt: Open-RMF, ein Open-Source Robotik Flottenmanagementsystem. Der Fokus der Arbeit liegt auf der Anwendbarkeit von Open-RMF in realen Szenarien. Daher wurde im Rahmen der Arbeit ein Demonstrator aufgebaut, der genutzt wird, um dem mobilen Roboter eine kontrollierte Umgebung zu schaffen. Zudem wurde eine T¨ur- und Liftsteuerung entwickelt, die in der Lage ist, mit Open-RMF zu kommunizieren, um einen nahtlosen Betrieb sicherzustellen. Diese Entwicklung zeigt das Potenzial von Open- RMF als universelle Plattform zur Integration und Steuerung unterschiedlicher Roboter und Systeme in der Intralogistik.:Abbildungsverzeichnis VI 1 Einleitung 1 2 Einsatz von mobilen Robotern in der Intralogistik 3 2.1 Roboter in der Industrie 3 2.1.1 Industrie im Wandel 3 2.1.2 Intralogistik 5 2.1.3 Einsatz von Fahrerlosen Transportsystemen in der Industrie 8 2.1.4 Fahrerlose Transportsysteme und technische Gebäudeausrüstung 10 2.2 Roboter-Orchestrierungssystem 13 2.2.1 Grundlagen eines Orchestrierungssystems 13 2.2.2 Anforderungen an ein Flottenmanagementsystem 13 2.2.3 Aktuelle Lösungen 15 2.2.4 Interoperabilit¨atsstandards 15 2.2.5 Nachrichtenprotokolle 18 3 Open-RMF 19 3.1 Überblick 19 3.2 ROS 2-Grundlagen 20 3.2.1 Allgemeines 20 3.2.2 Konzepte 20 3.2.3 Workspace 22 3.2.4 ROS 2-Pakete 23 3.2.5 Werkzeuge 24 3.3 Demos 28 3.4 Flottenadapter 29 3.5 Pfadplanung und Konfliktmanagement 32 4 Demonstrator 33 4.1 Aufbau des Demonstrators 33 4.1.1 Überblick 33 4.1.2 Allgemeiner Aufbau des Demonstrators 35 4.1.3 Überblick über die Steuerungen 39 4.1.4 Türen 42 4.1.5 Aufzug 47 4.2 Verwendeter mobiler Roboter 49 4.3 Einrichtung des Systems 51 4.3.1 Einrichtung von Open-RMF 51 4.3.2 Einrichtung des Turtlebot 4 54 5 Auswertung und Diskussion 56 6 Fazit und Ausblick 63 7 Anhang 70 7.1 RMF Flottenadapter 70 7.2 Free Fleet Server 73 7.3 Free Fleet Client 74 7.4 Protokoll 76 Literaturverzeichnis 78 Eidesstattliche Erklärung 84

Hypertension and diabetes have long been closely linked. As such, the major player in the renin, angiotensin system, angiotensin II, has recently been investigated for its effects on metabolism and diabetes. Since skeletal muscle is one of the most metabolically active tissues, this study investigates the effects of angiotensin II specifically on skeletal muscle. In this study, L6 skeletal muscle cells were treated with angiotensin II for either 3 or 24 hours and a number of effects were investigated. Fatty acid oxidation and lipid synthesis was measured using [1-14C]-palmitate, glucose oxidation and glycogen synthesis were measured using 14C-glucose. In addition,mitochondrial oxidative capacity was measured using an XF 24 Flux Analyzer (Seahorse Bioscience) and reactive oxygen species measured using confocal microscopy. The clinical study involving the drug Benicar ® investigated the metabolic effects of blocking angiotensin II on skeletal muscle fatty acid oxidation, glucose oxidation, and oxidative and glycolytic enzyme activity. In L6 cells, angiotensin II significantly reduced fatty acid oxidation after 24 hours (p<0.01) and 3 hours (p<0.001) if angiotensin II was present during oxidation experiments. It also significantly reduced mitochondrial oxidative capacity (p<0.05) after 24 hours and significantly increased reactive oxygen species production (p<0.05) over 3 hours. The clinical study showed no significant effects of Benicar® on fatty acid or glucose oxidation or any enzyme activities. / Master of Science

Im vorliegenden Beitrag wird ein Labordemonstrator zur Erprobung von Methoden des Imitationslernens für autonome Arbeitsmaschinen vorgestellt. Der Einsatz miniaturisierter Modelle ermöglicht dabei eine kosteneffiziente und flexible Testplattform, mit der sich realitätsnahe Szenarien im Kleinmaßstab abbilden lassen. Nach einer Einführung in das Thema wird der hard- und softwaretechnische Aufbau des Demonstrators erläutert und auf den aktuellen Realisierungsstand eingegangen. Die Funktionsfähigkeit wird anhand eines einführenden Anwendungsbeispiels zur Positionsregelung eines Traktormodells dargelegt. Das vorgestellte System eignet sich als Grundlage für die Erprobung weiterer Szenarien und Methoden sowie deren möglichen Transfer auf reale Anwendungen.

Dieser Artikel beschreibt die Entwicklung und Anwendung eines flexiblen Demonstrators für die mobile Robotik, der insbesondere das Flottenmanagement in der Intralogistik adressiert. Der Demonstrator nutzt Open-Source-Software wie ROS 2, um eine skalierbare und anpassbare Testplattform zu schaffen, die nicht nur für die Forschung, sondern auch für den Lehrbetrieb geeignet ist. Der Demonstrator zeigt, wie durch den Einsatz von Micro-ROS und einer ESP32-basierten Steuerung eine drahtlose Kommunikation zwischen mobilen Robotern und technischer Gebäudeausstattung wie Türen und Aufzügen ermöglicht wird. Als Anwendungsbeispiel wurde die Integration von Open-RMF [2] gewählt, einer quelloffenen Plattform zur Orchestrierung heterogener Roboterflotten. Open-RMF ermöglicht die Verwaltung mobiler Roboter und deren Integration mit technischer Gebäudeausstattung wie Türen und Aufzügen. Mit seiner flexiblen Architektur und der Nutzung von ROS 2 bietet es eine innovative Lösung für die komplexen Anforderungen der Intralogistik. Diese Arbeit entstand im Rahmen einer Bachelorarbeit [1] und leistet einen praxisorientierten Beitrag zur Entwicklung und Implementierung moderner Robotiksysteme. Der Schwerpunkt lag auf der Integration realer Roboter in ein Open-RMFSystem. Dieser Prozess wurde ausführlich dokumentiert und in einem GitHub-Repository (Github [3]) zusammengefasst, um zukünftige Arbeiten zu unterstützen und die Weiterentwicklung zu fördern. Ergänzend dazu wurde ein Video auf YouTube [4] veröffentlicht, das die wichtigsten Funktionen des Systems anschaulich zeigt.

O projeto de mestrado, denominado de forma abreviada como GPUServices, se insere no contexto da pesquisa e do desenvolvimento de métodos de processamento de dados de sensores tridimensionais aplicados a robótica móvel. Tais métodos serão chamados de serviços neste projeto e incluem algoritmos de pré-processamento de nuvens de pontos 3D com segmentação dos dados, a separação e identificação de zonas planares (chão, vias), e detecção de elementos de interesse (bordas, obstáculos). Devido à grande quantidade de dados a serem tratados em um curto espaço de tempo, esses serviços utilizam processamento paralelo por GPU para realizar o processamento parcial ou completo destes dados. A área de aplicação em foco neste projeto visa prover serviços para um sistema ADAS: veículos autônomos e inteligentes, forçando-os a se aproximarem de um sistema de processamento em tempo real devido ao contexto de direção autônoma. Os serviços são divididos em etapas de acordo com a metodologia do projeto, mas sempre buscando a aceleração com o uso de paralelismo inerente: O pré-projeto consiste de organizar um ambiente que seja capaz de coordenar todas as tecnologias utilizadas e que explore o paralelismo; O primeiro serviço tem a responsabilidade de extrair inteligentemente os dados do sensor que foi usado pelo projeto (Sensor laser Velodyne de múltiplos feixes), que se mostra necessário devido à diversos erros de leitura e ao formato de recebimento, fornecendo os dados em uma estrutura matricial; O segundo serviço em cooperação com o anterior corrige a desestabilidade espacial do sensor devido à base de fixação não estar perfeitamente paralela ao chão e devido aos amortecimentos do veículo; O terceiro serviço separa as zonas semânticas do ambiente, como plano do chão, regiões abaixo e acima do chão; O quarto serviço, similar ao anterior, realiza uma pré-segmentação das guias da rua; O quinto serviço realiza uma segmentação de objetos do ambiente, separando-os em blobs; E o sexto serviço utiliza de todos os anteriores para a detecção e segmentação das guias da rua. Os dados recebidos pelo sensor são na forma de uma nuvem de pontos 3D com grande potencial de exploração do paralelismo baseado na localidade das informações. Porém, sua grande dificuldade é a grande taxa de dados recebidos do sensor (em torno de 700.000 pontos/seg.), sendo esta a motivação deste projeto: usar todo o potencial do sensor de forma eficiente ao usar o paralelismo de programação GPU, disponibilizando assim ao usuário serviços de tratamento destes dados. / The master\'s project, abbreviated hence forth as GPUServices, fits in the context of research and development of three-dimensional sensor data processing methods applied to mobile robotics. Such methods will be called services in this project, which include a 3D point cloud preprocessing algorithms with data segmentation, separation and identification of planar areas (ground track), and also detecting elements of interest (borders, barriers). Due to the large amount of data to be processed in a short time, these services should use parallel processing, using the GPU to perform partial or complete processing of these data. The application area in focus in this project aims to provide services for an ADAS system: autonomous and intelligent vehicles, forcing them to get close to a real-time processing system due to the autonomous direction of context.The services are divided into stages according to the project methodology, but always striving for acceleration using inherent parallelism: The pre-project consists of organizing an environment for development that is able to coordinate all used technologies, to exploit parallelism and to be integrated to the system already used by the autonomous car; The first service has a responsibility to intelligently extract sensor data that will be used by the project (Laser sensor Velodyne multi-beam), it appears necessary because of the many reading errors and the receiving data format, hence providing data in a matrix structure; The second service, in cooperation with the above, corrects the spatial destabilization due to the sensor fixing base not perfectly parallel to the ground and due to the damping of the vehicle; The third service separates the environment into semantics areas such as ground plane and regions below and above the ground; The fourth service, similar to the above, performs a pre-segmentation of street cruds; The fifth service performs an environmental objects segmentation, separating them into blobs; The sixth service uses all prior to detection and segmentation of street guides.The received sensor data is structured in the form of a cloud of points. They allow processing with great potential for exploitation of parallelism based on the location of the information. However, its major difficulty is the high rate of data received from the sensor (around 700,000 points/sec), and this gives the motivation of this project: to use the full potential of sensor to efficiently use the parallelism of GPU programming, therefore providing data processing services to the user, providing services that helps and make the implementation of ADAS systems easier and/or faster.

The daVinci Research Kit (dVRK) is a research platform that consists of the clinical daVinci surgical robot, provided by Intuitive Surgical to Academic Institutions. It provides an open source software and hardware platform for researchers to study and analyze the current architecture and expand the capabilities of the existing technology. The line between general purpose robotics and medical robotics has segregated the two fields. A significant part of the segregation lies at the software end, where new tools and methods developed in general purpose robotics cannot make it to medical robotics in a short amount of time. This research focuses on the integration of a widely used software architecture for general purpose robotics with the dVRK with the hope of utilizing the research and development from one field to the other. As a first step towards this bridging, a motion planning framework and a dynamic simulator has been developed for the dVRK using ROS. The motion planning framework is aimed to assist the surgeon in performing task with additional safety and machine intelligence. A few use cases have been proposed as well. Lastly, a Matlab Interface has been developed that is standalone in terms of usage and provides capabilities to interact with dVRK.