Doctor of Philosophy

dissertationExcessive synthesis and storage of lipids is a prominent feature of the current epidemic of metabolic disorders, including obesity, diabetes and nonalcoholic fatty liver disease (NAFLD). Upon feeding, fatty acids and triglycerides are synthesized primarily in the liver in response to insulin signaling. This process is mediated by the sterol regulatory element binding protein 1c (SREBP-1c) transcription factor, a principal regulator of lipogenesis. Upon activation, SREBP- 1c stimulates the transcription of the key lipogenic enzymes that catalyze the synthesis of fatty acids and their esterification to triacylglycerides. Hyperactivation of SREBP-1c has been implicated in promoting pathologic fat synthesis and driving features of the metabolic syndrome, including hepatic lipid accumulation (steatosis), dyslipidemia and insulin resistance. PAS kinase (PASK) is an evolutionarily conserved serine/threonine kinase that has been proposed to function as a nutrient-responsive metabolic regulator. Pask-/- mice are resistant to high fat diet-induced metabolic disorders. Interestingly, Pask-/- mice exhibited almost complete protection from hepatic steatosis, but the mechanism underlying this phenotype was unknown. Here, we show that PASK promotes hepatic lipogenesis by activating SREBP-1c. This regulation occurs at the proteolytic maturation step of SREBP-1c, where the endoplasmic reticulum-bound precursor SREBP-1c undergoes proteolytic iv cleavages to liberate the transcriptionally active fragment of the protein. SREBP- 1c maturation is strongly induced by feeding and insulin signaling, a condition that also stimulates the hepatic expression of PASK. Using genetic and pharmacological approaches, we demonstrate that PASK is required for SREBP- 1c maturation in response to feeding and insulin stimulation. Inhibition of PASK results in decreased expression of the lipogenic SREBP-1c target genes and reduced lipid production in cultured cells and in the mouse and rat liver. Importantly, administration of a PASK inhibitor not only improves hepatic steatosis and whole-body dyslipidemia, but also partially reverses insulin resistance in animal models of diet-induced obesity and dyslipidemia, indicating that PASK is a potential therapeutic target for metabolic diseases. These studies not only further our understanding of the physiological functions of PASK, but also provide new insight into the pathogenesis and treatment of NAFLD and other metabolic disorders

Semantics and efficient evaluation of partial tree-pattern queries on XML

Current applications export and exchange XML data on the web. Usually, XML data are queried using keyword queries or using the standard structured query language XQuery the core of which consists of the navigational query language XPath. In this context, one major challenge is the querying of the data when the structure of the data sources is complex or not fully known to the user. Another challenge is the integration of multiple data sources that export data with structural differences and irregularities. In this dissertation, a query language for XML called Partial Tree-Pattern Query (PTPQ) language is considered. PTPQs generalize and strictly contain Tree-Pattern Queries (TPQs) and can express a broad structural fragment of XPath. Because of their expressive power and flexibility, they are useful for querying XML documents the structure of which is complex or not fully known to the user, and for integrating XML data sources with different structures. The dissertation focuses on three issues. The first one is the design of efficient non-main-memory evaluation methods for PTPQs. The second one is the assignment of semantics to PTPQs so that they return meaningful answers. The third one is the development of techniques for answering TPQs using materialized views. Non-main-memory XML query evaluation can be done in two modes (which also define two evaluation models). In the first mode, data is preprocessed and indexes, called inverted lists, are built for it. In the second mode, data are unindexed and arrives continuously in the form of a stream. Existing algorithms cannot be used directly or indirectly to efficiently compute PTPQs in either mode. Initially, the problem of efficiently evaluating partial path queries in the inverted lists model has been addressed. Partial path queries form a subclass of PTPQs which is not contained in the class of TPQs. Three novel algorithms for evaluating partial path queries including a holistic one have been designed. The analytical and experimental results show that the holistic algorithm outperforms the other two. These results have been extended into holistic and non-holistic approaches for PTPQs in the inverted lists model. The experiments show again the superiority of the holistic approach. The dissertation has also addressed the problem of evaluating PTPQs in the streaming model, and two original efficient streaming algorithms for PTPQs have been designed. Compared to the only known streaming algorithm that supports an extension of TPQs, the experimental results show that the proposed algorithms perform better by orders of magnitude while consuming a much smaller fraction of memory space. An original approach for assigning semantics to PTPQs has also been devised. The novel semantics seamlessly applies to keyword queries and to queries with structural restrictions. In contrast to previous approaches that operate locally on data, the proposed approach operates globally on structural summaries of data to extract tree patterns. Compared to previous approaches, an experimental evaluation shows that our approach has a perfect recall both for XML documents with complete and with incomplete data. It also shows better precision compared to approaches with similar recall. Finally, the dissertation has addressed the problem of answering XML queries using exclusively materialized views. An original approach for materializing views in the context of the inverted lists model has been suggested. Necessary and sufficient conditions have been provided for tree-pattern query answerability in terms of view-to-query homomorphisms. A time and space efficient algorithm was designed for deciding query answerability and a technique for computing queries over view materializations using stack- based holistic algorithms was developed. Further, optimizations were developed which (a) minimize the storage space and avoid redundancy by materializing views as bitmaps, and (b) optimize the evaluation of the queries over the views by applying bitwise operations on view materializations. The experimental results show that the proposed approach obtains largely higher hit rates than previous approaches, speeds up significantly the evaluation of queries without using views, and scales very smoothly in terms of storage space and computational overhead

Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles

In this study, we proposed and validated a multi-atlas guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions of interest (ROIs) from structural magnetic resonance images (MRIs). One major limitation of existing state-of-the-art 3D FCN segmentation models is that they often apply image patches of fixed size throughout training and testing, which may miss some complex tissue appearance patterns of different brain ROIs. To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns. In addition, with an introduction of multi-atlas based guidance in M-FCN, our segmentation was generated by combining the information of images and labels, which is highly robust. To reduce over-fitting of the FCN model on the training data, we adopted an ensemble strategy in the learning procedure. Evaluation was performed on two brain MRI datasets, aiming respectively at segmenting 14 subcortical and ventricular structures and 54 brain ROIs. The segmentation results of the proposed method were compared with those of a state-of-the-art multi-atlas based segmentation method and an existing 3D FCN segmentation model. Our results suggested that the proposed method had a superior segmentation performance

Synergetic pyrolysis of LiNi1/3Co1/3Mn1/3O2 and PET plastics shed light on highly efficient and energy saving strategies for battery recovery and regeneration

With the increasing consumption of lithium-ion batteries (LIBs), it is highly desirable to develop efficient and energy saving strategies for battery material recovery and regeneration. In this study, a synergetic pyrolysis strategy was developed to recover valuable metals by thermal treatment of LiNi1/3Co1/3Mn1/3O2 (NCM) cathode materials with the addition of polyethylene terephthalate (PET) plastics. It is the first time that PET plastics served as reaction additives to accelerate the lattice decay and thermal decomposition of NCM materials. With the assistance of PET synergetic pyrolysis, NCM started to decompose at only 400°C, and was completely converted to Li2CO3, MnO and Ni-Co alloy after thermal reaction at 550°C for 30 min with the NCM:PET mass ratio of 1.0:0.3. The thermal degradation of PET was retarded with various free radicals and reductive gases released. Furthermore, a density functional theory (DFT) calculation verified the combination preference of O-Li bonding between horizontal PET and the Li terminated NCM (001) surface. The surface adsorption caused atom capture and the free radical/gaseous reduction reactions explained the synergetic effect of PET on promoting the lattice destruction of NCM cathode materials. Moreover, the complete decomposition of NCM well benefited the post treatment, and the subsequent 2 separation of Li and transition metals (TM: Ni, Co and Mn) could be efficiently achieved by water washing method. Regenerated NCM was also synthesized by using the recovered Li- and TM- containing products as feedstocks. As a result, this study provided a novel NCM recovery strategy with significant privileges of chemical free, energy saving, highly efficient and scalable. Meanwhile, this strategy proposed an ideal solution for the minimization and utilization of PET plastics. In addition, the mechanism study provided a theoretical guidance on the industrialization and broaden application of PET plastic for effective metal recovery from spent LIBs by this synergetic pyrolysis strategy

Predicaments and Countermeasures of Network Supervision in the Government of Anti-Corruption in China

Network supervision has become a new way of administrative supervision. Because of the failure of traditional administrative supervision, the network supervision is promoted to the the forefront of anti-corruption campaigns. However, network supervision, which is like a double-edged sword, plays a positive role and negative effects in administrative supervision. The network supervision can mobilize the enthusiasm of citizen participation, and it can decrease the cost of supervision. Openness and effectiveness of network can promote the development of network supervision, and also can avoid its dilemmas. Therefore, with the help of laws and regulations and autonomous network, network supervision may exert its particular advantages. It will improve efficiency of administrative supervision