The role of smart sensor networks for voltage monitoring in smart grids

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, distributed voltage monitoring is one of the main issues to address. In this field, the application of traditional hierarchical monitoring paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To try to overcome these challenges, this paper proposes the concept of a decentralized non-hierarchal voltage monitoring architecture based on intelligent and cooperative smart entities. These devices employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables describing the global grid state

The Tumor Suprressor APC: Nuclear Functions and Regulation by Heat Shock Response

Because mutation of the tumor suppressor APC initiates ~80% of all colorectal cancers, understanding APC function is central for better diagnostic, preventive, and therapeutic strategies for the disease. In-vitro studies have indicated that APC shuttles between the cytoplasm and nucleus, using two nuclear localization signals (NLS) and five nuclear export signals (NES). To better understand the role of nuclear APC, our lab made a mouse model with mutations in both NLS (ApcmNLS) sequences. In this dissertation, I report higher Wnt signalling and increased proliferation in intestinal epithelial cells from ApcmNLS/mNLS mice, and observe that these mice are more susceptible to colitis-induced colon tumorigenesis. Furthermore, ApcMin mice, a well-characterized Apc mouse model that carries an Apc truncation mutation, have increased intestinal polyp multiplicity, size, and proliferation index when they also carry the ApcmNLS allele. Taken together, these data support a role for nuclear Apc in cell proliferation, inhibition of Wnt signalling, and tumor suppression. ApcmNLS/Min mice also display extra-intestinal phenotypes, including enhanced mammary tumorigenicity and more severe anaemia, than in ApcMin/+ mice, suggesting a role for nuclear Apc in other tissues. My studies also identified and characterized a polymorphism in the promoter of the Pla2ga2 (Mom-1) gene that might be responsible for the attenuated phenotype observed in ApcMin/+ mice in some genetic backgrounds. I developed a simple, reliable, PCR-based test for this polymorphic allele that will allow easy screening of mouse colonies. The mechanisms by which cellular APC levels are regulated are not completely understood. In this dissertation, I show that induction of the heat-shock response increases APC levels both in colon cancer cell lines and in mouse intestinal epithelial cells. I tested two compounds to induce the heat-shock response and found altered tumor multiplicity, size, and regional distribution in two mouse models with different germline mutations in Apc. I also showed that a novel non-toxic heat-shock response inducer, KU-32, protects against colitis-mediated tumorigencity in mice. I propose that regulation of APC levels via heat-shock response contributes to many aspects of APC and intestinal tumor biology, and can serve as a novel molecular target for prevention and treatment of colorectal cancer

Deep Multimodality Image-Guided System for Assisting Neurosurgery

Intrakranielle Hirntumoren gehören zu den zehn häufigsten bösartigen Krebsarten und sind für eine erhebliche Morbidität und Mortalität verantwortlich. Die größte histologische Kategorie der primären Hirntumoren sind die Gliome, die ein äußerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen Hirnläsionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung für neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden. Die Hirntumorchirurgie steht jedoch vor großen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschließlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen Kontrastverstärkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und Anästhesie, was den Nutzen präopera-tiver Bilddaten für die Steuerung des Eingriffs einschränkt. Bildgesteuerte Systeme bieten Ärzten einen unschätzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner Bildgebungsmodalitäten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsächlich um computergestützte Systeme, die mit Hilfe von Computer-Vision-Methoden die Durchführung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen müssen jedoch immer noch den Operationsplan aus präoperativen Bildern gedanklich mit Echtzeitinformationen zusammenführen, während sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung überwachen. Daher war die Notwendigkeit einer Bildführung während neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ärzte. Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems für die peri-operative bildgeführte Neurochirurgie (IGN), nämlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die Gesamtüberle-bensrate maximiert und die postoperative neurologische Morbidität minimiert wird. Im Rahmen dieser Arbeit werden zunächst neuartige Methoden für die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen präope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jüngs-ten Entwicklungen im Deep Learning vorgeschlagen. Anschließend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem für den Menschen verständliche, erklärbare Karten erstellt werden. Schließlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die für die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zuständig ist. Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. Für das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework für die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den Würfelkoeffizienten für das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-Ansätze wie 3D-Faltungen über alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet. Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, für die Registrierung präoperativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgeführt: BITE und RESECT. Zwei erfahrene Neurochirurgen führten eine zusätzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung überlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. Darüber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfähige Ergebnisse liefern, was seine Allgemeingültigkeit unter Beweis stellt und daher für die intraoperative neurochirurgische Führung von Nutzen sein kann. Für das Modul "Erklärbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben Erklärungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklärbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. Darüber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-Modalitäten zur end-gültigen Vorhersage zu verstehen. Der Erklärungsprozess könnte medizinischen Fachleu-ten zusätzliche Informationen über die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann. Außerdem wurde ein interaktives neurochirurgisches Display für die Eingriffsführung entwickelt, das die verfügbare kommerzielle Hardware wie iUS-Navigationsgeräte und Instrumentenverfolgungssysteme unterstützt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der Fähigkeit zur Integration von (1) präoperativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestätigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgeführt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt. In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jüngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen präzise zu führen und prä- und intraoperative Patientenbilddaten sowie interventionelle Geräte in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend für die Anwendung von Deep-Learning-Modellen zur Unterstützung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse

Understanding phenotypic variation in rodent models with germline Apc mutations

Adenomatous Polyposis Coli (APC) is best known for its crucial role in colorectal cancer suppression. Rodent models with various Apc mutations have enabled experimental validation of different Apc functions in tumors and normal tissues. Since the development of the first mouse model with a germline Apc mutation in the early 1990s, twenty other Apc mouse and rat models have been generated. This article compares and contrasts currently available Apc rodent models with particular emphasis on providing potential explanations for their reported variation in three areas: 1) intestinal polyp multiplicity, 2) intestinal polyp distribution, and 3) extra intestinal phenotypes

Bibliometric Analysis of Distributed Generation

This paper describes the application of data mining techniques for eludicating patterns and trends in technological innovation. Specifically, we focus on the use of bibliometric methods, viz techniques which focus on trends in the publication of text documents rather than the content of these documents. Of particular interest is the relationship between publication patterns, as characterized by term occurrence frequencies, and the underlying technological trends and developments which drive these trends. To focus the discussions and to provide a concrete example of their applicability, a detailed case study focussing on research in the area of Distributed Generation (DG) is also presented; however, the techniques and general approach devised here will be applicable to a broad range of industries, situations, and locations. Our results are promising and indicate that interesting information and conclusions can be derived from this line of analysis. The results obtained using data extraction techniques highlight and present the evolution of DG-related technology focus areas, and their relative importance within this field

Defining, Executing and Visualizing Representative Workflows in a Retail Domain

Our lives are filled with routine activities that we do more or less on auto-pilot such as driving to work and cooking. This thesis explores a workflow representation as a way to represent such activities of daily living. The domain of a retail store environment is used. Workflows are initially expressed in a structured English representation, then translated into a Petri net notation and implemented in mix of Petri nets, Lua, and C so that the resulting workflows can be displayed as the actions of collections of avatarbots (avatars controlled by programs) in a 3D virtual world, Second Life. One aim of the thesis is to explore a range of retail workflows to begin to understand the range of mundane workflows at least in one area of daily living, shopping. We observe that, at a leaf level, our workflows consist of observable behaviors (implemented by action verbs like chatting, face and body animations, and actions like go to, pick up, and carry). These workflows are brittle and exception handling is needed to handle common bugs in a workflow. A second aim is to add metrics to our workflows so that similar workflows can be compared with different initial conditions, e.g., when a store‟s floor layout is varied causing some avatars (representing customers) to walk farther and pass by more goods than other avatars (representing clerks). We conclude that representation of workflow scenarios in a virtual world platform can be helpful in illustrating, analyzing, and improving a real life environment, such as a retail store

Antimicrobial Effects on Swine Gastrointestinal Microbiota and Their Accompanying Antibiotic Resistome

Antimicrobials are the most commonly prescribed drugs in the swine industry. While antimicrobials are an effective treatment for serious bacterial infections, their use has been associated with major adverse effects on health. It has been shown that antimicrobials have substantial direct and indirect impacts on the swine gastrointestinal (GI) microbiota and their accompanying antimicrobial resistome. Antimicrobials have also been associated with a significant public health concern through selection of resistant opportunistic pathogens and increased emergence of antimicrobial resistance genes (ARGs). Since the mutualistic microbiota play a crucial role in host immune regulation and in providing colonization resistance against potential pathogens, the detrimental impacts of antimicrobial treatment on the microbiota structure and its metabolic activity may lead to further health complications later in life. In this review, we present an overview of antimicrobial use in the swine industry and their role in the emergence of antimicrobial resistance. Additionally, we review our current understanding of GI microbiota and their role in swine health. Finally, we investigate the effects of antimicrobial administration on the swine GI microbiota and their accompanying antibiotic resistome. The presented data is crucial for the development of robust non-antibiotic alternative strategies to restore the GI microbiota functionality and guarantee effective continued use of antimicrobials in the livestock production system

The role of antibiotics metaphylaxis on developmental dynamics of fecal microbiota and persistence of antimicrobial resistome in piglets

The swine gastrointestinal microbiota is comprised of a diverse and complex microbial population that coexists in a coordinated, complex mucosal ecosystem that contributes to host gastrointestinal and immunological development. While antimicrobial are cost-effective tools for prevention and treatment of infectious diseases, the impact of their use on potentially beneficent mucosal microbial communities has not been widely explored. Optimization of antimicrobial use in swine management systems requires full understanding of antimicrobial-induced changes on developmental dynamics of gut microbiota and prevalence of antimicrobial resistance genes (ARGs). While the antibiotic resistance profiles of pathogens have been characterized, the antimicrobial resistance bacteria and ARGs from the whole gut microbiota have received far less attention. The objective of this study was to characterize the impact of parenteral antibiotics administration on composition and diversity of the resident fecal microbiota in pigs. In commercial swine farm, five antimicrobial treatment groups, each consisting of four, eight-week old piglets, were administered one of the antimicrobial; Tulathromycin (TUL), Ceftiofur Crystalline free acid (CCFA), Ceftiofur hydrochloride (CHC), Oxytetracycline (OTC), and Procaine Penicillin G (PPG) at label dose and route. Individual fecal swabs were collected immediately before antimicrobial administration (control = day 0), and again on days 1, 3, 7, and 14 after dosing. Additionally, a randomized complete block design was used to study the impacts of early-life antimicrobial intervention on fecal microbiota structure, and their accompanying antimicrobial resistome in neonatal piglets. Forty-eight litters were blocked to one of six treatments; Control (CONT), TUL, CCFA, CHC, OTC and PPG. Two piglets per litter were individually identified and weights and deep fecal swabs were collected at days 0 (prior to treatment), 5, 10, 15 and 20. All fecal swabs were processed for DNA extraction. Sequencing analysis of the V3-V4 hypervariable region of 16S rRNA gene and the selected ARGs were performed using Illumina Miseq platform. Moreover, whole genome, metagenomics sequencing approach was performed on subset of samples from the CONT and TUL groups, to assess the fecal microbiota structure and their accompanying antimicrobial resistome. In growing piglets, the most abundant phyla were Firmicutes, Bacteroidetes, and Proteobacteria. Linear discriminant analysis, showed a pronounced, antimicrobial-dependent shift in the composition of fecal microbiota over time from day 0. By day 14, the fecal microbial compositions of the groups receiving CHC and TUL had returned to a distribution that closely resembled that observed on day 0, but differences were still evident. In contrast, animals that received PPG, OTC and CCFA, showed a tendency towards a normalized microbiota structure on day 7, but appeared to deviate away from the day 0 composition by day 14. In neonatal piglets, our results show that, while early-life antibiotics prophylaxis had no effect on individual weight gain, or mortality, it was associated with noticeable changes in the prevalence of selected ARGs, and minor shift in the composition of the fecal microbiota during this developmental stage. Relative to CONT, only TUL treated piglet exhibited significant decline in chao1 richness index at day 20. Compared to the CONT, the PPG treated piglets exhibited a significant increase in the prevalence of ermB and tetW at day 20 of life. Tulathromycin intervention was also resulted in significant increase in the abundance of tet W at days 10 and 20, and erm B at day 20. Using whole genome metagenomics sequencing on subset of samples from the CONT and TUL groups, a total of 127 antimicrobial resistome related to 19 different classes of antibiotics were identified across all samples. The majority of identified antimicrobial resistome were observed in both experimental groups and at all-time points. The magnitude and extent of differences in microbial composition, and antimicrobial resistome, between the TUL and CONT groups were statistically insignificant. However, both the fecal microbiota composition and antimicrobial resistome were changed significantly between the sampling days. Based on our results, the observed changes in fecal microbiota in growing piglets showed antimicrobial-specific variation in both duration and extent. While in the perinatal piglets, the shifts in fecal microbiota structure caused by perinatal antimicrobial intervention are modest and limited to particular groups of microbial taxa. However, early life PPG and TUL intervention could promote selection of ARGs in herds. While additional investigations are required to explore the consistency of these findings across larger populations, these results could open the door to new perspectives on the utility of early life antimicrobial administration to healthy neonates in swine management systems

A simple and accurate approach to solve the power flow for balanced islanded microgrids

Power flow studies are very important in the planning or expansion of power system. With the integration of distributed generation (DG), micro-grids are becoming attractive. So, it is important to study the power flow of micro-grids. In grid connected mode, the power flow of the system can be solved in a conventional manner. In islanded mode, the conventional method (like Gauss Seidel) cannot be applied to solve power flow analysis. Hence some modifications are required to implement the conventional Gauss Seidel method to islanded micro-grids. This paper proposes a Modified Gauss Seidel (MGS) method, which is an extension of the conventional Gauss Seidel (GS) method. The proposed method is simple, easy to implement and accurate in solving the power flow analysis for islanded microgrids. The MGS algorithm is implemented on a 6 bus test system. The results are compared against the simulations results obtained from PSCAD/EMTDC which proves the accuracy of the proposed MGS algorithm