INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Reduced mesoporous and microporous titanium oxide composites as novel hydrogen storage materials

In this work, mesoporous and microporous titanium oxides were reduced and/or impregnated by a variety of reducing agents, such as alkali metals, organometallic sandwich compounds of Ti, V, and Cr, as well as alkali fullerides. These new composite materials were characterized by nitrogen adsorption, powder X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy and elemental analysis. The hydrogen sorption properties were investigated as a function of surface area, pore size, and reducing agent for these new composite materials at 77 K. Unlike MOFs and porous carbons, the hydrogen sorption performance of these new composite materials does not depend greatly on surface area; however, the reduction in the surface Ti species seems to be the crucial factor in determining hydrogen sorption capacities. For example, microporous Ti oxide reduced with bis(toluene) Ti possesses a surface area of 208 m2/g, but exhibits an overall volumetric storage capacity of 40.46 kg/m3 at 77 K and 100 atm. This volumetric storage capacity is higher than that of pristine material, which has a surface area of 942 m2/g. The improved performance for these reduced composite materials relative to the untreated sample was attributed to the increased reduction level of the metal centers in the framework of the structure, which allows for more facile π-back donation to the H-H σ bond, a factor known to strengthen hydrogen binding to metals. Another surprising feature in these reduced materials is the unusual trend in enthalpies, which show an unprecedented increase in binding strength as the surface coverage increase. The binding enthalpies also increase on progressive reduction, from 4.21 to 8.35 kJ/mol. This highly unusual behavior reflects a different mechanism of surface binding than simple physisorption, and indicates that further efforts are required to find a suitable reducing reagent in order to reach even higher volumetric storage densities and tune the hydrogen binding enthalpies to over 20 kJ/mol, which is proposed to be ideal value for porous samples operating at ambient temperature

A Review Research of Washback in Language Testing

Washback is an important concept in language testing. It reflects the effect of the testing. Based on washback, modification and optimization can be made to face the need of teachers and learners. This article aims to make a review research on washback at home and abroad to find a new direction for future research. The research is based on proper journal articles on both core international journals and CNKI. In line with all related articles, three main aspects of washback research - theoretical models, positive and negative washback effects - are illustrated. Theoretical models contribute to empirical research and help to better understand washback. Negative washback is a common phenomenon in testing so that possible reasons causing the phenomenon will be mainly discussed. Positive washback is usually generated by making some updates. This research provides an insight into a multi-method study with the other field - test ethics - in the educational system

Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization

In this paper, we study a class of stochastic optimization problems, referred to as the \emph{Conditional Stochastic Optimization} (CSO), in the form of \min_{x \in \mathcal{X}} \EE_{\xi}f_\xi\Big({\EE_{\eta|\xi}[g_\eta(x,\xi)]}\Big), which finds a wide spectrum of applications including portfolio selection, reinforcement learning, robust learning, causal inference and so on. Assuming availability of samples from the distribution \PP(\xi) and samples from the conditional distribution \PP(\eta|\xi), we establish the sample complexity of the sample average approximation (SAA) for CSO, under a variety of structural assumptions, such as Lipschitz continuity, smoothness, and error bound conditions. We show that the total sample complexity improves from \cO(d/\eps^4) to \cO(d/\eps^3) when assuming smoothness of the outer function, and further to \cO(1/\eps^2) when the empirical function satisfies the quadratic growth condition. We also establish the sample complexity of a modified SAA, when $\xi$ and $\eta$ are independent. Several numerical experiments further support our theoretical findings. Keywords: stochastic optimization, sample average approximation, large deviations theoryComment: Typo corrected. Reference added. Revision comments handle