Changes of Radial Diffusivity and Fractional Anisotopy in the Optic Nerve and Optic Radiation of Glaucoma Patients

Purpose of this study was to evaluate with diffusion-tensor imaging (DTI) changes of radial diffusivity (RD) and fractional anisotropy (FA) in the optic nerve (ON) and optic radiation (OR) in glaucoma and to determine whether changes in RD and FA correlate with disease severity. Therefore, glaucoma patients and controls were examined using 3T. Regions of interest were positioned on RD and FA maps, and mean values were calculated for ON and OR and correlated with optic nerve atrophy and reduced spatial-temporal contrast sensitivity (STCS) of the retina. We found, that RD in glaucoma patients was significantly higher in the ON (0.74 ± 0.21 versus 0.58 ± 0.17·10−3 mm2 s−1; P < 0.05) and OR (0.79 ± 0.23 versus 0.62 ± 0.14·10−3 mm2 s−1; P < 0.05) compared to controls. Aside, FA was significantly decreased (0.48 ± 0.15 versus 0.66 ± 0.12 and 0.50 ± 0.20 versus 0.66 ± 0.11; P < 0.05). Hereby, correlation between changes in RD/FA and optic nerve atrophy/STCS was observed (r > 0.77). In conclusion, DTI at 3 Tesla allows robust RD and FA measurements in the ON and OR. Hereby, the extent of RD increase and FA decrease in glaucoma correlate with established ophthalmological examinations

Analyse der Sehbahn zur Glaukomerkennung unter Verwendung der Diffusions-Tensor-Bildgebung

Glaukomerkrankungen sind eine Optikusneuropathie, die das gesamte visuelle System beeinflusst. Die weltweite Prävalenz des Glaukoms wird auf 60,5 Millionen Menschen geschätzt. Unbehandelt kann die durch ein Glaukom verursachte visuelle Beeinträchtigung bis zu völliger Blindheit führen. Eine Erkennung der Krankheit im Frühstadium kann dies verhindern. Glaukomerkrankungen werden jedoch meist zu spät identifiziert, da die Erkrankung langsam fortschreitet und kaum eindeutige Symptome aufweist. Darüber hinaus sind die Pathophysiologie des Glaukoms und seine biologischen Grundlagen und Faktoren bisher noch nicht vollständig ermittelt und verstanden. Deshalb müssen neue Wege in der Forschung und Diagnostik eingeschlagen werden, um das Verständnis der zugrunde liegenden Mechanismen und letztendlich die Behandlung zu verbessern. Der Großteil der aktuell eingesetzten Methoden zur Glaukomdiagnose analysiert schwerpunktmäßig die Netzhaut des Auges, trotz der transsynaptischen Natur der Faserdegeneration, die ein Glaukom verursacht. Diese Ansätze ignorieren einen erheblichen Teil des visuellen Systems, nämlich die Sehbahn im Gehirn. Die Fortschritte in der Bildgebung, insbesondere des Diffusion Tensor Imaging (DTI), ermöglichen die Identifizierung und Charakterisierung von Fasern der weißen Gehirnsubstanz. Untersuchungen haben ergeben, dass eine Glaukomerkrankung sich auf verschiedene Teile des visuellen Systems auswirkt. Parameter, die aus dem DTI abgeleitet wurden, zeigten bei Glaukompatienten Abweichungen für den Sehnerv und die Sehstrahlung. Dies ist ein starker Indikator für die diagnostische Relevanz der Sehbahnanalyse. In dieser Arbeit werden Methoden zur Analyse der Sehbahn mittels DTI vorgestellt, mit denen bestehende netzhautbasierte Techniken zur Glaukomuntersuchung ergänzt werden können. Ein System zur automatischen Identifizierung der Sehstrahlung auf Basis des DTI wird präsentiert. Die Segmentierung wurde auf gesunde Personen und Glaukompatienten angewendet und zeigt eine hohe Genauigkeit bei der Segmentierung dieser komplizierten Faserstruktur. Die Automatisierung eliminiert die Notwendigkeit, medizinische Gutachten von Experten erstellen zu lassen und erleichtert Studien mit einer großen Anzahl von Patienten. Dieser Algorithmus ist die Grundlage eines Frameworks für die Bestimmung durch Glaukom verursachter lokaler Veränderungen der Sehstrahlung mittles DTI. Das Framework kann für weitere Studien und das Verständnis der Pathophysiologie des Glaukoms genutzt werden. Darüber hinaus wurde das Framework auf Gesunde und Glaukompatienten angewendet, um eine Kartographierung des Glaukomeffekts in der Sehstrahlung zu ermöglichen. Schließlich wird ein System zur Erkennung und Unterscheidung verschiedener Glaukomformen vorgeschlagen, das auf einer DTI-Analyse der Sehbahn-Fasern basiert. Die Klassifikationsergebnisse zeigen die hohe Genauigkeit des Systems im Vergleich zu vielen aktuellen netzhautbasierten Glaukom-Erkennungsystemen. Der vorgeschlagene Ansatz nutzt die Sehbahn-Analyse, die einen neuen Trend in der Glaukom-Diagnose darstellt, anstelle der üblichen Augenanalyse. Eine Analyse des gesamten visuellen Systems kann wichtige Informationen ergeben, die den Ablauf der Glaukomuntersuchung und die Behandlung verbessern.Glaucoma is an optic neuropathy affecting the entire visual system. The worldwide prevalence of glaucoma is estimated to be 60.5 million people. The visual disorder caused by glaucoma can reach complete blindness if untreated. Various treatment approaches exist that can largely prevent the visual disability and limit the vision loss due to glaucoma if the disease is diagnosed in its early phases. Nevertheless, the slow progression of the disease along with the lack of clear symptoms results in the late identification of glaucoma. Moreover, the pathophysiology of glaucoma, and its biological foundation and factors are not yet fully determined or understood. Therefore, novel directions are essential for improving the diagnostic flow and the understanding of the glaucoma mechanism. Most of the glaucoma diagnostic methods analyze the eye with a main focus on the retina, despite the transsynaptic nature of the fiber degeneration caused by glaucoma. Thus, they ignore a significant part of the visual system represented by the visual pathway in the brain. The advances in neuroimaging, especially diffusion tensor imaging (DTI), enable the identification and characterization of white matter fibers. It has been reported that glaucoma affects different parts of the visual system. Optic nerve and optic radiation were shown to have abnormalities measured by DTI-derived parameters in the presence of glaucoma. These outcomes suggest the significance of visual pathway analysis in the diagnosis. In this work, we propose visual pathway analysis using DTI in glaucoma diagnosis to complement the existing retina-based techniques. A system is proposed to automatically identify the optic radiation on the DTI-images. The segmentation algorithm is applied to healthy and glaucoma subjects and showed high accuracy in segmenting such a complicated fiber structure. The automation eliminates the necessity of medical experts’ intervention and facilitates studies with large number of subjects. This algorithm was incorporated in a framework for the determination of the local changes of the optic radiation due to glaucoma using DTI. The framework can aid further studies and understanding of the pathophysiology of glaucoma. Moreover, the framework is applied to normal and glaucoma groups to provide localization maps of the glaucoma effect on the optic radiation. Finally, we propose a system that extracts different aspects of the visual pathway fibers from the diffusion tensor images for detecting and discriminating different glaucoma entities. The classification results indicate the superior performance of the system compared to many state of the art retina-based glaucoma detection systems. The proposed approach utilizes visual pathway analysis rather than the conventional eye analysis which presents a new trend in glaucoma diagnosis. Analyzing the entire visual system could provide significant information that can improve the glaucoma examination flow and treatment

Genistein Loaded Nanofibers Protect Spinal Cord Tissue Following Experimental Injury in Rats

Innovative drug-delivery systems offer a unique approach to effectively provide therapeutic drug dose over the needed time to achieve better tissue protection and enhanced recovery. The hypothesis of the current study was to test the antioxidant and anti-inflammatory effects of genistein and nanofibers on the spinal cord tissue following experimental spinal cord injury (SCI). Rats were treated post SCI with genistein that is loaded on chitosan/polyvinyl alcohol (CS/PVA) nanofibers as an implantable drug-delivery system. SCI caused marked oxidative damage and inflammation, as is evident by the reduction in the super oxide dismutase (SOD) activity and the level of interleukin-10 (IL-10) in injured spinal cord tissue, as well as the significant increase in the levels of nitric oxide (NO), malondialdehyde (MDA), and tumor necrosis factor-alpha (TNF-&alpha;). Treatment of rats post SCI with genistein and CS/PVA nanofibers improved most of the above-mentioned biochemical parameters and shifted them toward the control group values. Genistein induced an increase in the activity of SOD and the level of IL-10, while causing a decrease in NO, MDA, and TNF-&alpha; in injured spinal cord tissue. Genistein and CS/PVA nanofibers provide a novel combination for treating inflammatory nervous tissue conditions, especially when combined as an implantable drug-delivery system

A New Time-Varying Feedback RISE Control of PKMs: Theory and Application

In this paper, we propose a novel time-varying feedback control strategy based on the Robust Integral of the Sign of the Error (RISE). The main motivation is to enhance the tracking performance of RISE controller at high dynamic operating conditions. RISE control law ensures a semi-global asymptotic tracking without introducing severe restrictions on the uncertain and nonlinearly parametrized systems. More nonlinearities are added to the original RISE control law by replacing the static feedback gains with nonlinear ones which depend on the system state variables. The proposed contribution is implemented in real-time experiments on a non-redundant three-degrees-of-freedom parallel manipulator named Delta. Comparing to the standard RISE controller, experimental results show better tracking performances of the proposed time-varying feedback RISE controller

Ultra-Local Model-Based Intelligent Robust Control of PKMs: Theory and Simulations

International audienceIn this paper, we propose a novel Intelligent Robust Control (IRC) suitable for controlling highly nonlinear Multiple-Input-Multiple-Output (MIMO) systems. The proposed IRC scheme takes advantage of the Robust Integral of the Sign of the Error (RISE) control law and Model-Free Control (MFC). The MFC scheme is mainly composed of: (i) a nonlinear function estimated from an ultralocal model representing the input-output behavior of the system, (ii) the ν th derivative of the reference trajectory as a feedforward term, and (iii) a feedback control term. MFC is characterized by its simple concept and its ability to compensate for the modeled and unmodeled system dynamics through its nonlinear compensation term. The proposed IRC approach consists of redesigning the feedback term of MFC scheme based on RISE feedback law to further improve its robustness against external disturbances and to guarantee a semi-global asymptotic tracking despite the presence of disturbances and uncertainties. Numerical simulations under different operating conditions have been conducted on T3KR parallel manipulator, in a pick-and-throw task, to validate the relevance of the proposed IRC strategy. The comparison with a model-based feedforward RISE and a feedforward super-twisting sliding mode control, by exploiting different performance indices, confirms the superiority of the proposed IRC approach

Time-Optimal Pick-and-Throw S-Curve Trajectories for Fast Parallel Robots

International audienceIn suitable robotic applications, throwing an object instead of placing it has the potential of improving the cycle time. In this context, a challenge is to generate time-optimal Pick-and-Throw (P&T) trajectories in order to further increase productivity. This paper introduces a methodology to determine a minimum-time throwing motion. This methodology consists essentially in determining an optimal release configuration (i.e. position and velocity) allowing an object to be thrown towards a desired target while minimizing the travel time of the throwing motion of the robot. To validate the potential of the proposed P&T approach, a comparison with the standard Pick-and-Place (P&P) process and an existing P&T method is made using the Delta-like parallel robot T3KR under different operating conditions. The obtained experimental results demonstrate the superiority and efficiency of the proposed P&T approach over the usual P&P and the existing P&T methods in terms of picking speed and cycle time

RISE Feedback Control of Cable-Driven Parallel Robots: Design and Real-Time Experiments

International audienceControl of Cable-Driven Parallel Robots (CDPRs) is considered as a challenging task due to their highly nonlinear dynamic behavior, abundant uncertainties, low-stiff cables, parameters variation, cable tensions, and actuation redundancy. Hence, a robust controller is needed to obtain higher performance despite the above mentioned issues. In this paper, we propose a Robust Integral of the Sign of the Error (RISE) control scheme to solve the problem of reference trajectory tracking. RISE feedback control is a robust nonlinear continuous controller which can guarantee a semi-global asymptotic tracking under limited assumptions on the system's structure. RISE ensures the closed-loop system robustness towards parametric uncertainties and external disturbances. The proposed control solution is designed and implemented in real-time experiments on a fully constrained 4-DOF Cable-Driven Parallel Robot (CDPR) named PICKABLE. The obtained experimental results show that the proposed controller outperforms the classical PID controller and the first-order Sliding Mode Control (SMC) in terms of tracking performances and robustness towards payload variations

Scenario-Based Network Reconfiguration and Renewable Energy Resources Integration in Large-Scale Distribution Systems Considering Parameters Uncertainty

Renewable energy integration has been recently promoted by many countries as a cleaner alternative to fossil fuels. In many research works, the optimal allocation of distributed generations (DGs) has been modeled mathematically as a DG injecting power without considering its intermittent nature. In this work, a novel probabilistic bilevel multi-objective nonlinear programming optimization problem is formulated to maximize the penetration of renewable distributed generations via distribution network reconfiguration while ensuring the thermal line and voltage limits. Moreover, solar, wind, and load uncertainties are considered in this paper to provide a more realistic mathematical programming model for the optimization problem under study. Case studies are conducted on the 16-, 59-, 69-, 83-, 415-, and 880-node distribution networks, where the 59- and 83-node distribution networks are real distribution networks in Cairo and Taiwan, respectively. The obtained results validate the effectiveness of the proposed optimization approach in maximizing the hosting capacity of DGs and power loss reduction by greater than 17% and 74%, respectively, for the studied distribution networks