Modification Of 4,5- Aminoglycosides To Overcome Drug Resistance Bacteria And Toxic Side Effect

ABSTRACT MODIFICATION OF 4,5- AMINOGLYCOSIDES TO OVERCOME DRUG RESISTANCE BACTERIA AND TOXIC SIDE EFFECT by Guanyu Yang September 2018 Advisor: Professor David Crich Major: Chemistry Degree: Doctor of Philosophy Infectious diseases causing by antibiotic resistant pathogen are one of the major threat to human health and society today. Many researchers tried to develop next generation of antibiotics by reinvesting the existing antibacterial drugs. Aminoglycosides have long been used as highly potent and broad-spectrum antibiotics for treating bacterial infections. But their side effect, especially the irreversible ototoxicity, and the fast-growing resistant problem limit their application. The goal of this research was to develop next generation of AGAs that are less toxic and resistance-proof by modifying known aminoglycosides. Chapter one briefly explains the MDR bacterial infection problem and its influence. Aminoglycosides are also well discussed in this chapter, including their history, classifications, mechanism of action, toxicity and resistance problems, as well as the recent research advances. Chapter two discusses the synthesis and biological evaluation of 6’-deshydroxymethyl paromomycin The loss of activity shows in the biological test suggested that the 6\u27-deshydroxymethyl modification was not an effective modification. Chapter three discusses the 3’-deoxy modification on different 4,5-AGAs. A novel synthetic method utilizing samarium iodide reduction to achieve 3’-deoxygenation modification is introduced. This new method shows good substrate compatibility and avoids the tedious scheme in the traditional method. The 3’-deoxy 4,5-AGAs retain their antibacterial activity and exhibit activity against some AGA resistance strains. But they still suffers from APH(3’,5’’) resistance mechanism. Chapter four describes the synthesis and biological test results of the 3’,5’’-dideoxy-5’’-formamido paromomycin. The synthesis of this doubly modified compound demonstrates the wide application potency of the samarium iodide reduction for 3’-deoxy modification. The biological experiment results show that the doubly modified compound has good antibacterial activity even in the presence of some common AMEs. Chapter five discussed the synthesis and biological evaluation of a triply modified paromomycin derivative. The combination of 3’-deoxy, 4’-deoxy-4’-C-propyl and 5’’-deoxy-5’’-formamido modification into paromomycin leads to unexpected loss of antiribosomal and antibacterial activity. Finally, chapter six documents the experiment procedure and characterization data for the synthesized compounds and chapter seven presents the overall conclusion

Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning

Coronary artery segmentation on coronary-computed tomography angiography (CCTA) images is crucial for clinical use. Due to the expertise-required and labor-intensive annotation process, there is a growing demand for the relevant label-efficient learning algorithms. To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary artery segmentation and clinical diagnostic characteristics. Further, we propose a progressive weakly supervised learning framework to achieve accurate segmentation under PVA. First, our proposed framework learns the local features of vessels to propagate the knowledge to unlabeled regions. Subsequently, it learns the global structure by utilizing the propagated knowledge, and corrects the errors introduced in the propagation process. Finally, it leverages the similarity between feature embeddings and the feature prototype to enhance testing outputs. Experiments on clinical data reveals that our proposed framework outperforms the competing methods under PVA (24.29% vessels), and achieves comparable performance in trunk continuity with the baseline model using full annotation (100% vessels).Comment: Accepted at MICCAI 202

Visible and Near Infrared Image Fusion Based on Texture Information

Multi-sensor fusion is widely used in the environment perception system of the autonomous vehicle. It solves the interference caused by environmental changes and makes the whole driving system safer and more reliable. In this paper, a novel visible and near-infrared fusion method based on texture information is proposed to enhance unstructured environmental images. It aims at the problems of artifact, information loss and noise in traditional visible and near infrared image fusion methods. Firstly, the structure information of the visible image (RGB) and the near infrared image (NIR) after texture removal is obtained by relative total variation (RTV) calculation as the base layer of the fused image; secondly, a Bayesian classification model is established to calculate the noise weight and the noise information and the noise information in the visible image is adaptively filtered by joint bilateral filter; finally, the fused image is acquired by color space conversion. The experimental results demonstrate that the proposed algorithm can preserve the spectral characteristics and the unique information of visible and near-infrared images without artifacts and color distortion, and has good robustness as well as preserving the unique texture.Comment: 10 pages,11 figure

Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation

Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures and variable global morphologies. In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint. First, we propose a dynamic snake convolution to accurately capture the features of tubular structures by adaptively focusing on slender and tortuous local structures. Subsequently, we propose a multi-view feature fusion strategy to complement the attention to features from multiple perspectives during feature fusion, ensuring the retention of important information from different global morphologies. Finally, a continuity constraint loss function, based on persistent homology, is proposed to constrain the topological continuity of the segmentation better. Experiments on 2D and 3D datasets show that our DSCNet provides better accuracy and continuity on the tubular structure segmentation task compared with several methods. Our codes will be publicly available.Comment: Accepted by ICCV 202

NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering

Attributed graph clustering is one of the most fundamental tasks among graph learning field, the goal of which is to group nodes with similar representations into the same cluster without human annotations. Recent studies based on graph contrastive learning method have achieved remarkable results when exploit graph-structured data. However, most existing methods 1) do not directly address the clustering task, since the representation learning and clustering process are separated; 2) depend too much on data augmentation, which greatly limits the capability of contrastive learning; 3) ignore the contrastive message for clustering tasks, which adversely degenerate the clustering results. In this paper, we propose a Neighborhood Contrast Framework for Attributed Graph Clustering, namely NCAGC, seeking for conquering the aforementioned limitations. Specifically, by leveraging the Neighborhood Contrast Module, the representation of neighbor nodes will be 'push closer' and become clustering-oriented with the neighborhood contrast loss. Moreover, a Contrastive Self-Expression Module is built by minimizing the node representation before and after the self-expression layer to constraint the learning of self-expression matrix. All the modules of NCAGC are optimized in a unified framework, so the learned node representation contains clustering-oriented messages. Extensive experimental results on four attributed graph datasets demonstrate the promising performance of NCAGC compared with 16 state-of-the-art clustering methods. The code is available at https://github.com/wangtong627/NCAGC