Telomerase in hematological and other malignancies : therapeutic implications

The Telomere is a nucleoprotein complex consisting of TTAGGG repeated sequences covered by specialized binding proteins termed shelterins masking the end of linear chromosomes and thereby protecting the end of the chromosome from genomic instability. Telomeres become progressively shortened during cellular replication and acts as a mitotic clock to confer a limited lifespan to normal cells. Telomerase reverse transcriptase (TERT) is responsible for lengthening telomeric DNA. The enzyme is silent in most normal cells due to the transcriptional repression of the TERT gene encoding the telomerase catalytic component. Numerous studies have demonstrated that the induction of TERT and subsequent activation of telomerase is prerequisite to malignant transformation of human cells through a telomere-lengthening mechanism. Moreover, evidence has recently shown that TERT or telomerase possesses many other biological activities contributing to tumor development and progression, therefore targeting TET/telomerase has been suggested as a novel anti-cancer strategy. In the present project we studied telomere-lengthening-dependent and independent activities of TERT in malignant cells and explored therapeutic implications of TERT inhibition combined with other anti-cancer strategies in acute myeloid leukemia (AML) and gastric cancer. Paper I is focused on whether TERT inhibition and telomere dysfunction is involved in the anti- tumor effect of the DNA methyltransferase inhibitor 5-azacytidine (5-AZA). We demonstrated that 5-AZA induced DNA damage and telomere dysfunction in AML cell lines coupled with diminished TERT expression, telomere attrition and cellular apoptosis. The results suggests that 5-AZA-mediated TERT inhibition and telomere dysfunction contributes to its anti-cancer activity. Paper II was aimed to define the relationship between the oncogenic cyclooxygenase (COX2) and TERT and to evaluate the synergetic anti-cancer action of simultaneous COX2 and TERT inhibition. We found that the depletion of TERT led to elevated COX2 expression by activating p38. The COX2 inhibitor celecoxib or TERT inhibition alone was insufficient to affect cell viability, however, the combination synergistically killed cancer cells both in vitro and in vivo. Thus, the combined application of COX2 and telomerase inhibitors may be more efficient in cancer treatment. About 30% of AML patients exhibit somatic mutations of FMS-like tyrosine kinase 3 (FLT3), the majority of which carry internal tandem duplication (ITD) in the juxtamembrane. FLT3 inhibitors have been developed for AML treatment. In paper III, we determined whether FLT3-ITD regulated TERT expression and whether TERT affected the therapeutic efficacy of FLT3 inhibitors. We found that the FLT3 inhibitor PKC412 down-regulated TERT expression in a MYC-dependent manner, while ectopic expression of TERT attenuated killing efficacy of PKC412 in AML cells.Altogether, our findings demonstrated that the interplay between TERT and FLT3ITD plays important roles in AML carcinogenesis and that FLT3 inhibitors, when combined with TERT inhibition, are more efficient in the induction of AML cell apoptosis. In paper IV, we determined the effect of bortezomib on telomere homeostasis and its functional consequences. Bortezomib treatment inhibited TERT and telomerase expression, dysregulated shelterin proteins and shortened telomeres in AML and gastric cancer cell lines. The disrupted telomere structure triggered DNA damage response and cellular apoptosis. TERT overexpression significantly decreased DNA damage and telomere dysfunction and attenuated apoptosis mediated by bortezomib. Our findings collectively reveal a profound impact of bortezomib on telomere homeostasis/function, and down-regulation of TERT expression and telomere dysfunction induced by bortezomib thus both contributing to its cancer cell killing actions. In summary, the present results provide novel insights of the biological functions of TERT/telomerase in malignant cells. In addition, the finding that the combination of telomerase inhibition with other anti-cancer agents induced a robust and synergistic anti-tumor effect may have future important clinical implications

Deep Learning-Based Spatio-Temporal Data Mining Using Multi-Source Geospatial Data

With the rapid development of various geospatial technologies including remote sensing, mobile devices, and Global Position System (GPS), spatio-temporal data are abundantly available nowadays. Extracting valuable knowledge from spatio-temporal data is of crucial importance for many real-world applications such as intelligent transportation, social services, and intelligent distribution. With the fast increase of the amount and resolution of spatio-temporal data, traditional data mining methods are becoming obsolete. In recent years, deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have made promising achievements in many fields based on the strong ability in automated feature extraction and have been broadly used in different spatio-temporal data mining tasks. Many methods have been developed, and more diverse data were collected in recent decades, however, the existing methods have faced challenges from multi-source geospatial data. This thesis investigates four efficient techniques in different scenarios for spatio-temporal data mining that take advantage of multi-source geospatial data to overcome the limitations of traditional data mining methods. This study investigates spatio-temporal data mining from four different perspectives. Firstly, a multi-elemental geolocation inference method is proposed to predict the location of tweets without geo-tags. Secondly, an optimization model is proposed to detect multiple Areas-of-Interest (AOIs) simultaneously and solve the multi-AOIs detection problem. Thirdly, a multi-task Res-U-Net model with attention mechanism is developed for the extraction of the building roofs and the whole building shapes from remote sensing images, then an offset vector method is used to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. Lastly, a novel decoder fusion model is introduced to extract interior road network from remote sensing images and GPS trajectory data. And this method is effective for multi-source data mining. The proposed four methods use different techniques for spatio-temporal data mining to improve the detection performance. Numerous experiments show that the techniques developed in this thesis can detect ground features efficiently and effectively and overcome the limitations of conventional algorithms. The studies demonstrate that exploiting spatial information from multi-source geospatial data can improve the detection accuracy in comparison with single-source geospatial data

Level Set Methods for MRE Image Processing and Analysis

Ph.DDOCTOR OF PHILOSOPH

Quantum algorithms for optimal effective theory of many-body systems

A common situation in quantum many-body physics is that the underlying theories are known but too complicated to solve efficiently. In such cases, one usually builds simpler effective theories as low-energy or large-scale alternatives to the original theories. Here the central tasks are finding the optimal effective theories among a large number of candidates and proving their equivalence to the original theories. Recently quantum computing has shown the potential of solving quantum many-body systems by exploiting its inherent parallelism. It is thus an interesting topic to discuss the emergence of effective theories and design efficient tools for finding them based on the results from quantum computing. As the first step towards this direction, in this paper, we propose two approaches that apply quantum computing to find the optimal effective theory of a quantum many-body system given its full Hamiltonian. The first algorithm searches the space of effective Hamiltonians by quantum phase estimation and amplitude amplification. The second algorithm is based on a variational approach that is promising for near-future applications.Comment: 8 pages, 4 figure

Assembly of lipase and P450 fatty acid decarboxylase to constitute a novel biosynthetic pathway for production of 1-alkenes from renewable triacylglycerols and oils

<p> Background: Biogenic hydrocarbons (biohydrocarbons) are broadly accepted to be the ideal &#39;drop-in&#39; biofuel alternative to petroleum-based fuels due to their highly similar chemical composition and physical characteristics. The biological production of aliphatic hydrocarbons is largely dependent on engineering of the complicated enzymatic network surrounding fatty acid biosynthesis.</p

College students' cyberloafing and the sense of meaning of life: The mediating role of state anxiety and the moderating role of psychological flexibility

BackgroundWith the gradual penetration of network media into various fields of people's life, the relationship between network behavior and the sense of meaning of life is bound to be closer and closer. The purpose of this study is to explore the mediating role of state anxiety between cyber loafing and the sense of meaning of life, and the moderating role of psychological flexibility in this mediating relationship.MethodologyWith 964 undergraduates recruited as subjects three-wave-time-lagged quantitative research design was conducted in China. All participants were required to complete a self-reported electronic questionnaire. Then, the mediating mechanism and moderating effect were explored with utilization of SPSS25.0.ResultsThe results showed that cyberloafing had significant negative correlation with the sense of meaning of life. Our analysis testing the mediating effect showed that state anxiety partially mediated the relationship between cyberloafing and the sense of meaning of life (indirect effect = −0.05, p &lt; 0.01,), while the mediating effect was 31.25% of the total effect. Our analysis testing the moderating effect showed that psychological flexibility significantly moderated the relationship between cyberloafing and state anxiety (interaction effect = −0.26, p &lt; 0.01). And our analysis testing the moderated mediating effect showed that psychological flexibility played a moderating role in the mediating effect of state anxiety.ConclusionBased on the findings of this study, college students' cyberloafing negatively affects their sense of meaning of life. Therefore, appropriate measures should be taken to supervise and restrict college students' Internet use and provide them with corresponding guidance; certain psychological adjustment measures should also be taken when necessary to help college students with low psychological flexibility in reducing their state anxiety and improving their sense of meaning of life

Data-driven team ranking and match performance analysis in Chinese Football Super League

Recent years have seen an increasing body of research into the evaluation of the team-level technical- tactical performance in association football using match events data. However, most studies used mono-dimensional approach and modeled the influence of each performance aspects on match result in iso- lation, which limited the interpretability of the results. The study was aimed to apply a state-of-the-art algorithm to the ranking of team performance and exploitation of key performance features in relation to match outcome based on massive match dataset. Data of all 1200 matches from 2014 to 2018 Chinese Football Super League (CSL) were used. From the original 164 match events, we extracted 22 features that were related to attacking, passing, and defending performance and most. A Linear Support Vector Classi- fier (LSVC) model was subsequently built with these 22 input features and trained in order to rank the teams by their performance and analyze the features that influence most match outcome (win/not win), with the dataset being divided into a ratio of 4:1 to train and validate the model. The results have shown that the data-driven LSVC model displayed a prediction accuracy of 0.83 and the ranking of teams’ match performance and prediction of teams’ league standings were highly correlated with their actual rank- ing. Saves, pass success and shot on target in penalty area were demonstrated as top positive features for winning whereas shots on target during open play, pass and bad shot% were three negative features most influential for the match result. The team ranks of all teams were highly correlated with their real final league rankings. In general, CSL winning teams build their success based on defensive ability and shooting accuracy, and high-ranked teams could always maintain better performance than their coun- terparts. The team-rank framework could provide a consolidated and complex approach to evaluate the match performance quality of the teams, refining decisions-making during match preparation and player transfer at different periods of the season

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries