Robinson Crusoe’s translation and spreading of marine spirit in pre-modern China

AbstractWestern marine literature classics were translated into Pre-modern China, and Robinson Crusoe is one of the most representative. Various Chinese versions were rendered with the political and educational push of the time. The translation and introduction of the noted classic played a key role in the spreading and formation of the Chinese marine spirit, thus profoundly inspiring Chinese readers. The adventure of Robinson on the wild island provided a powerful spiritual impetus for those Chinese with lofty ideals. It is without doubt that the translated novel conforms to the spirit and demand of the time, and voices the inner mind of the Chinese, which is the very reason why it has been loved and accepted by the massive Chinese readers and influenced them so much ever since

Discovery of Novel Tubulin Inhibitors and Selective Survivin Inhibitors for Advanced Melanoma and Total Synthesis of Bioactive 20S-hydroxyvitamin D3

According to the statistics from American Cancer Society, the 5-year survival rate for patients with advanced melanoma is as low as 5%. Treatment of advanced melanoma, therefore, represents an unmet medical need. In this dissertation, I will show the effort to develop new generations of bioavailable tubulin inhibitors targeting the colchicine binding site and selective small-molecule survivin inhibitors for treating advanced melanoma. Extensive structure-activity relationship (SAR) studies of lead molecules ABI-231 and UC-112 have been performed. Chapter 1 will introduce the current situation of advanced or metastatic melanoma, its clinical drug treatments, as well as problems in current drug treatments. Microtubule dynamics and survivin will be discussed as promising therapeutic targets for developing anticancer drugs. 20S-hydroxyvitamin D3 (20S-OH-D3) will be introduced as a promising anti-inflammatory scaffold. Chapter 2 will disclose the SAR study of ABI-231, a previously reported potent tubulin inhibitor from our lab. In this chapter, a new synthetic method was developed to enable the synthesis of ABI-231 analogues modifying the indole moiety. The novel synthetic method involved the synthesis of a key diamine intermediate and imidazoline formation. From the new synthetic method, thirty ABI-231 analogues were synthesized and tested for activities. Among all analogues, 10ab with a 4-methyl-3-indole moiety and 10bb with a 4-indole moiety showed the most potent antiproliferative activities against a panel of melanoma cell lines. 10ab and 10bb had IC50s of 2.2 and 3.0 nM, respectively. The SAR result revealed that modification of the indole moiety in ABI-231 was beneficial to activity. In Chapter 3, we will describe our effort to develop the SAR study of ABI-231 focusing on modification of the 3,4,5-TMP moiety. This is selected since it is one of the most common moieties in current tubulin inhibitors targeting the colchicine binding site. To circumvent the use of potentially explosive azide reported in Chapter 2, an alternative was established to efficiently generate ABI-231 analogues. This new synthetic method involved Suzuki coupling and Grignard reactions to modify the 3,4,5-TMP moiety and to produce target compounds in gram-scale. Among the eight analogues synthesized, the one containing an unique 3-methoxybenzo[4,5]-dioxene moiety had the strongest antiproliferative activity against a panel of melanoma cell lines with an average IC50 of 1.9 nM. To our best knowledge, it represents the most successful instance of isosterically modifying the 3,4,5-TMP moiety in CA-4 derivatives. Chapter 4 will highlight our effort to synthesize reverse ABI (RABI) analogues for SAR study. In this chapter, a novel and concise synthetic route was established toaccess RABI scaffold. RABI scaffold was constructed through a Grignard reaction/Suzuki-Miyaura coupling reaction strategy. From this new synthetic method, twelve novel RABI analogues were synthesized. Compared to MX-RABI (the previously reported most potent RABI), several new RABI analogues showed significantly improved cytotoxicities. In particular, analogue 15i with a 4-indazole moiety showed the most potent antiproliferative activity against a panel of melanoma cell lines and had an average IC50 of 0.8 nM. This is the first sub-nM anti-tubulin compound in the related scaffolds. Chapter 5 will reveal our latest SAR study of UC-112, a previously reported selective survivin inhibitor. Fourteen UC-112 analogues modifying the benzyloxy moiety of UC-112 were synthesized. Their corresponding SAR result demonstrated that indole moiety was the most favorable (analogue 12a). Subsequent structural optimization of 12a by introducing either mono-substituent or di-substituent to the indole moiety led to the synthesis of another twenty-four new UC-112 analogues. Several substituted indole analogues showed equipotency to that of UC-112 and MX-106. Importantly, new indole analogues exhibited significant abilities to overcome multidrug-resistance mediated by Pgp overexpression. Chapter 6 is characterized by the establishment of a total synthetic method of 20SOH-D3 which showed comparable antiproliferative activity to 1,25α-dihydroxyvitamin D3 without hypercalcemic toxic effect upto a concentration of 60 μg/kg in vivo. The total synthesis of 20S-OH-D3 involved parallel generation of key intermediates from ergocalciferol. The vitamin D3 core structure was constructed through Wittig-Horner coupling reaction. Deprotection of SEM and TBS was achieved in one step. 20S-OH-D3 was furnished in sixteen steps with an overall yield of 0.4%

Advancing Land Cover Mapping in Remote Sensing with Deep Learning

Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based solutions to land cover mapping challenges such as how to deal with intricate objects and imbalanced classes in multi-spectral and high-spatial resolution remote sensing data. The first work presents a novel model to learn richer multi-scale and global contextual representations in very high-resolution remote sensing images, namely the dense dilated convolutions' merging (DDCM) network. The proposed method is light-weighted, flexible and extendable, so that it can be used as a simple yet effective encoder and decoder module to address different classification and semantic mapping challenges. Intensive experiments on different benchmark remote sensing datasets demonstrate that the proposed method can achieve better performance but consume much fewer computation resources compared with other published methods. Next, a novel graph model is developed for capturing long-range pixel dependencies in remote sensing images to improve land cover mapping. One key component in the method is the self-constructing graph (SCG) module that can effectively construct global context relations (latent graph structure) without requiring prior knowledge graphs. The proposed SCG-based models achieved competitive performance on different representative remote sensing datasets with faster training and lower computational cost compared to strong baseline models. The third work introduces a new framework, namely the multi-view self-constructing graph (MSCG) network, to extend the vanilla SCG model to be able to capture multi-view context representations with rotation invariance to achieve improved segmentation performance. Meanwhile, a novel adaptive class weighting loss function is developed to alleviate the issue of class imbalance commonly found in EO datasets for semantic segmentation. Experiments on benchmark data demonstrate the proposed framework is computationally efficient and robust to produce improved segmentation results for imbalanced classes. To address the key challenges in multi-modal land cover mapping of remote sensing data, namely, 'what', 'how' and 'where' to effectively fuse multi-source features and to efficiently learn optimal joint representations of different modalities, the last work presents a compact and scalable multi-modal deep learning framework (MultiModNet) based on two novel modules: the pyramid attention fusion module and the gated fusion unit. The proposed MultiModNet outperforms the strong baselines on two representative remote sensing datasets with fewer parameters and at a lower computational cost. Extensive ablation studies also validate the effectiveness and flexibility of the framework