Metabolomics-Enhanced Gas Chromatography Mass Spectrometry (GC-MS) For The Quality Standardisation Of Clinacanthus Nutans

Clinacanthus nutans, a widely used medicinal plant, is extensively grown in tropical Asia and Southest Asian countries. C. nutans with its broad spectrum of pharmacological activities has been traditionally used to treat cancer, inflammatory disorders, diabetes, insect bites and skin problems Clinacanthus nutans, ubatan herba yang digunakan secara meluas, berkembang pesat di Asia tropika dan negara-negara Asia Tenggara. Aktiviti farmakologi C. nutans yang berkembang luas telah digunakan secara tradisional untuk merawat kanser, gangguan keradangan, kencing manis, gigitan serangga dan masalah kuli

포도나무 갈색무늬병과 줄기혹병에 감염된 포도나무의 대사물질 유형

학위논문 (박사)-- 서울대학교 대학원 : 농생명공학부, 2017. 2. 김영호.In this study, profilings of disease-related metabolites were assessed for characterizing two major grapevine diseases, leaf spot caused by Pseudocercospora vitis and crown gall caused by Agrobacterium vitis, respectively, for both of which GC-MS and multivariate data analysis were applied as the analytical instrumentation and statistical validation. For the leaf spot, a total of 20 metabolites were determined to have their relations with lesion symptoms and the periphery of the leaf spot symptoms, most of which were increased in their contents in the periphery of the symptoms compared with the necrotic lesions. For the crown gall, 13 metabolites increased significantly in relation to the response types, mostly at post-inoculation stages, more prevalently (8 metabolites) at two days after inoculation than other stages, and more related to the susceptible response type (SS) (7 metabolites) than resistant (RR) (3 metabolites) or moderately resistant (SR) (one metabolite) response type. This suggests that most of the disease-related metabolites may be induced by the pathogen infection largely for facilitating gall development except resveratrol, a phytoalexin involved in the resistance response (RR). A total of six metabolites were identified from the comparison of the metabolite profiling between the leaf tissues with and without leaf spot symptoms, including maltotriose, glucoheptonic acid and tartronic acid, a phenolic compound 1-hydroxyanthraquinone and two amino acids aspartic acid and L-threonine. This suggests these may be the disease-related metabolites occurring widely around the infection sites of the leaf tissues with the leaf spot. For the crown gall, most numerous metabolites related to the infection or wound significantly occurred at 2 days after inoculation (DAI) (post-1) (8 for infection in SS, 4 for wound in RR), second-most at 7 DAI (post-2) (3 for infection in SS, 6 and 1 for wounding in SS and RR, respectively), and least at 30 DAI (post-3) (all 3 for infection in RR or SR), suggesting that the responses to the pathogen infection mostly occur in the susceptible grapevines at post-1, and those to wounding (wound healing) occur earlier in the grapevines with RR than those with SS or SR. It also suggests the responses to wounding should be completed and no more wound-related metabolites are detected at post-3. All of the results suggest that plant resistance and wound healing responses are inter-related, enhancing the other responses to increase resistance to the pathogen infection and to speed up the wound healing processes. These disease characteristics revealed by metabolite profiling would provide valuable information and new insights on the understandings of the diseases so as to be used for the development of grapevine disease management strategies.GENERAL INTRODUCTION 1 LITERATURE CITED 8 CHAPTER 1. Metabolic profiles of four different tissues located in grape leaf spot disease caused by Pseudocercospora vitis 14 ABSTRACT 15 INTRODUCTION 16 MATERIALS AND METHODS 19 I. Plant materials 19 II. Sample extraction 19 III. GC-MS analysis 21 IV. Multivariate analysis 22 RESULTS 23 I. Leaf metabolites differences with unsupervised analysis (PCA) 23 II. Leaf metabolites differences with supervised analysis (OPLS-DA) 25 DISCUSSION. 29 LITERATURE CITED 34 CHAPTER 2. Metabolic profiles of grapevine internode tissues infected with Agrobacterium vitis 39 ABSTRACT 40 INTRODUCTION 41 MATERIALS AND METHODS 44 I. Grapevine, pathogen, and pathogen inoculation 44 II. Evaluation for response of Vitis species to Agrobacterium vitis K306 infection 45 III. Tissue sampling 45 IV. Extraction of total metabolites from the sampled tissue 46 V. GC-MS analysis 46 VI. Multivariate data analysis 47 RESULTS 49 I. Responses of grapevine species to Agrobacterium vitis K306 infection in the inoculation test 49 II. GC-MS analysis 49 III. Multivariate data analysis 52 DISCUSSION 58 LITERATURE CITED 65 CHAPTER 3. Metadata analysis of GC-MS metabolites profiling of grapevine infected with Pseudocercospora vitis and Agrobacterium vitis 72 ABSTRACT 73 INTRODUCTION 75 MATERIALS AND METHODS 77 I. Plant materials 77 II. GC-MS analysis and multivariate data analysis 78 RESULTS AND DISCUSSION. 79 I. Leaf spot caused by Psuedocercospora vitis 79 II. The crown gall caused by Agrobacterium vitis 85 LITERATURE CITED 98 ABSTRACT IN KOREAN 105Docto

Real-Time Automatic Linear Feature Detection in Images

Linear feature detection in digital images is an important low-level operation in computer vision that has many applications. In remote sensing tasks, it can be used to extract roads, railroads, and rivers from satellite or low-resolution aerial images, which can be used for the capture or update of data for geographic information and navigation systems. In addition, it is useful in medical imaging for the extraction of blood vessels from an X-ray angiography or the bones in the skull from a CT or MR image. It also can be applied in horticulture for underground plant root detection in minirhizotron images. In this dissertation, a fast and automatic algorithm for linear feature extraction from images is presented. Under the assumption that linear feature is a sequence of contiguous pixels where the image intensity is locally maximal in the direction of the gradient, linear features are extracted as non-overlapping connected line segments consisting of these contiguous pixels. To perform this task, point process is used to model line segments network in images. Specific properties of line segments in an image are described by an intensity energy model. Aligned segments are favored while superposition is penalized. These constraints are enforced by an interaction energy model. Linear features are extracted from the line segments network by minimizing a modified Candy model energy function using a greedy algorithm whose parameters are determined in a data-driven manner. Experimental results from a collection of different types of linear features (underground plant roots, blood vessels and urban roads) in images demonstrate the effectiveness of the approach

A Robotic System for Learning Visually-Driven Grasp Planning (Dissertation Proposal)

We use findings in machine learning, developmental psychology, and neurophysiology to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually-driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a gripper, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up such important issues as observation sparsity in high-dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well-established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for projections of high-dimensional data sets that capture task invariants. We also pursue the following problem: how can we use human expertise and insight into grasping to train a system to select both appropriate hand preshapes and approaches for a wide variety of objects, and then have it verify and refine its skills through trial and error. To accomplish this learning we propose a new class of Density Adaptive reinforcement learning algorithms. These algorithms use statistical tests to identify possibly interesting regions of the attribute space in which the dynamics of the task change. They automatically concentrate the building of high resolution descriptions of the reinforcement in those areas, and build low resolution representations in regions that are either not populated in the given task or are highly uniform in outcome. Additionally, the use of any learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not damage itself. We address this by the use of an instrumented, compliant robot wrist that controls impact forces