The oncogenic role of histone chaperone ASF1 proteins in solid tumors

Chromatin is the essential medium connecting regulatory signals such as transcription factors and signaling pathways to the alteration of gene activity and cellular phenotypes. Aberrant chromatin (epigenetic) environment plays an important role in carcinogenesis. The fundamental unit of chromatin is the nucleosome which is composed of a histone core wrapped with 145-147 base pairs of DNA around. In the last decades, great efforts have been made to delineate the role of aberrant DNA methylation and chromatin/histone-remodeling factors in oncogenesis. However, recent evidence has merged that the dysregulation of histone chaperones also acts as a cancer-driver. Anti-silencing function 1 (ASF1) is the most conserved histone H3-H4 chaperone, regulating histone metabolism. ASF1 proteins include two paralogs ASF1A and ASF1B in mammals. ASF1A and ASF1B have been reported as oncogenes in human cancers. Data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases show that ASF1A and ASF1B are overexpressed in 20 and 24 different types of cancers, respectively. Thus, in this thesis, I explored the oncogenic role of histone chaperone ASF1 and underlying molecular mechanisms in several solid tumors. In Paper I, the role for ASF1A in gastrointestinal cancer (GIC) was investigated. We discovered that ASF1A interacted with the oncogenic transcription factor β-catenin and promoted the transcription of β-catenin target genes (c-MYC, cyclin D1, ZEB1 and LGR5). The increased expression of these genes stimulated proliferation, stemness and migration/invasion of GIC cells. Over-expression and knockdown of ASF1A boosts and inhibits in vivo tumor growth and/or metastasis in mouse models, respectively. Higher levels of ASF1A expression predict significantly shorter patient survival in colorectal cancer (CRC). Further analyses of the Gene Expression Omnibus dataset validate higher ASF1A expression predicting a poor prognosis in CRC patients. Taken together, this study reveals the novel function of ASF1A as a transcription co-factor independent of its canonical role and the potential value of ASF1A for outcome prediction and targeted treatment in GIC. In Paper II, we show that ASF1A overexpression is widespread in human malignancies and is required for the infinite proliferation of cancer cells. When ASF1A was knocked-down in wild-type (wt) p53 carrying cells derived from hepatocellular carcinoma (HCC) and prostate cancer (PCa), DNA damage response was activated and up-regulation of p53-p21cip1 expression consequently occurred. These cells eventually underwent cellular senescence. Higher ASF1A expression and/or lower p21cip1 expression predicts a poor outcome in HCC patients. Thus, ASF1A may be a therapeutic target and a prognostic factor in HCC and other cancers. In Paper III, we evaluated whether ASF1B has diagnostic and prognostic values in adrenocortical carcinoma (ACC) and regulates invasion and metastasis. We first analyzed TCGA and GTEx data and found that the ASF1B gene was amplified in two thirds of ACC tumors and associated with its overexpression. ASF1B expression correlates with the ACC diagnostic criteria of the Weiss scoring system. Higher ASF1B expression and ASF1B copy number predict a poor outcome in the TCGA cohort of ACC patients. Knockdown of ASF1B in ACC cells impairs migration and invasion ability by inhibiting expression of the transcription factor FOXM1; whereas ASF1B over-expression exhibits opposing effects. These findings suggest that ASF1B may be a useful factor for ACC diagnostics and prognostication, and potentially a novel target for ACC therapy as well. Collectively, the results presented in this thesis gain profound insights into the oncogenic role of ASF1 in several solid tumors and demonstrated novel activities of ASF1 proteins beyond their conserved histone chaperone function. These findings will inspire further exploration of both the clinical and biological roles of ASF1 in precision oncology

Sign changes of fourier coefficients of modular forms of half integral weight, 2

In this paper, we investigate the sign changes of Fourier coefficients of half-integral weight Hecke eigenforms and give two quantitative results on the number of sign changes

Rapid Detection and Prognosis of Lung Cancer

University of Technology Sydney. Faculty of Engineering and Information Technology.Lung cancer is the leading cause of cancer deaths worldwide. In this research, we undertook a pilot study by designing an electronic nose (e-nose) system to evaluate the feasibility of using the e-nose for aroma identification. This study demonstrated the e-nose’s exciting potential to discriminate between mixtures. However, to achieve reliable diagnostic results in clinical settings, the stability and robustness of the system needed to be enhanced. Accordingly, the ‘Rapid Disease Detection System (RDDS)’ was developed to distinguish the breath biomarkers of lung cancer patients. Specifically, RDDS addresses the limitations of the prior system and focuses on optimising system structure and control logic to ensure the stability and repeatability of experimental results under medical conditions. Furthermore, to improve the survival prediction accuracy and assist prognostic decision-making, we proposed a MultiModal Deep learning model for non-small cell lung cancer (NSCLC) Survival Analysis (DeepMMSA). This was trained with the data of 422 NSCLC patients from The Cancer Imaging Archive, and it achieved a 4% increase in the percentage of concordant pairs (correct predicted pairs) among the overall population. DeepMMSA is the first method ever to use CT images fusion with clinical data and it reveals considerable potential for accurate survival prediction

Efficient Attribute-Based Encryption with Privacy-Preserving Key Generation and Its Application in Industrial Cloud

Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data volumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use industrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud computing technology provides a good solution. It receives more and more attention and support due to its flexible configuration, on-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work and concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms of security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In order to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate. At present, more and more applications are using ABE to ensure data security. However, the privacy protection issues during the key generation phase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and corresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great damage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects user\u27s privacy during key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the KGC cannot know user\u27s attributes and that the attribute auditing center (AAC) cannot obtain the user\u27s secret key. This is ideal for many privacy-sensitive scenarios, such as industrial big data scenario

Ventilation Structure Improvement of Air-cooled Induction Motor Using Multiphysics Simulations

 Optimal design of large induction motor is a process that involves electrical and mechanical skills as well as thermal and fluid dynamic skills. For recent machine layouts, one cannot rely on standard analysis methods. In multiphysics simulations which are done by weak coupling finite-element method, rotation boundary values on interface between air gap and rotor cannot be applied directly for fluid-dynamical analysis. A novel multi-component fluid method is proposed to deal with the influence of rotor rotation upon the air convection. This paper investigates a 3-D multi-physics simulation used in simulation of temperature distribution in air-cooled induction motor. The temperature rise in motor is due to Joule’s losses in stator windings and the induced eddy current in squirrel cages, and heat dissipation by air convection and solid conduction. The Joule’s losses calculated by 3-D eddy-current field analysis are used as the input for the thermal field analysis, which deeply depends on accurate air fluid field analysis. Through the coupled-field calculation, we proposed a new ventilation structure of a 15-phase motor to improve the cooling performance

Dual Relation Alignment for Composed Image Retrieval

Composed image retrieval, a task involving the search for a target image using a reference image and a complementary text as the query, has witnessed significant advancements owing to the progress made in cross-modal modeling. Unlike the general image-text retrieval problem with only one alignment relation, i.e., image-text, we argue for the existence of two types of relations in composed image retrieval. The explicit relation pertains to the reference image & complementary text-target image, which is commonly exploited by existing methods. Besides this intuitive relation, the observations during our practice have uncovered another implicit yet crucial relation, i.e., reference image & target image-complementary text, since we found that the complementary text can be inferred by studying the relation between the target image and the reference image. Regrettably, existing methods largely focus on leveraging the explicit relation to learn their networks, while overlooking the implicit relation. In response to this weakness, We propose a new framework for composed image retrieval, termed dual relation alignment, which integrates both explicit and implicit relations to fully exploit the correlations among the triplets. Specifically, we design a vision compositor to fuse reference image and target image at first, then the resulted representation will serve two roles: (1) counterpart for semantic alignment with the complementary text and (2) compensation for the complementary text to boost the explicit relation modeling, thereby implant the implicit relation into the alignment learning. Our method is evaluated on two popular datasets, CIRR and FashionIQ, through extensive experiments. The results confirm the effectiveness of our dual-relation learning in substantially enhancing composed image retrieval performance

Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer

Image retrieval-based cross-view localization methods often lead to very coarse camera pose estimation, due to the limited sampling density of the database satellite images. In this paper, we propose a method to increase the accuracy of a ground camera's location and orientation by estimating the relative rotation and translation between the ground-level image and its matched/retrieved satellite image. Our approach designs a geometry-guided cross-view transformer that combines the benefits of conventional geometry and learnable cross-view transformers to map the ground-view observations to an overhead view. Given the synthesized overhead view and observed satellite feature maps, we construct a neural pose optimizer with strong global information embedding ability to estimate the relative rotation between them. After aligning their rotations, we develop an uncertainty-guided spatial correlation to generate a probability map of the vehicle locations, from which the relative translation can be determined. Experimental results demonstrate that our method significantly outperforms the state-of-the-art. Notably, the likelihood of restricting the vehicle lateral pose to be within 1m of its Ground Truth (GT) value on the cross-view KITTI dataset has been improved from $35.54\%$ to $76.44\%$, and the likelihood of restricting the vehicle orientation to be within $1^{\circ}$ of its GT value has been improved from $19.64\%$ to $99.10\%$.Comment: Accepted to ICCV 202

Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark

In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1,510,330 image-text pairs, which is about 37.5 times larger than prevailing CUHK-PEDES, and all images are annotated with 27 attributes. Considering the privacy concerns and annotation costs, we leverage the off-the-shelf diffusion models to generate the dataset. To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text. As the name implies, APTM contains an attribute prompt learning stream and a text matching learning stream. (1) The attribute prompt learning leverages the attribute prompts for image-attribute alignment, which enhances the text matching learning. (2) The text matching learning facilitates the representation learning on fine-grained details, and in turn, boosts the attribute prompt learning. Extensive experiments validate the effectiveness of the pre-training on MALS, achieving state-of-the-art retrieval performance via APTM on three challenging real-world benchmarks. In particular, APTM achieves a consistent improvement of +6.96%, +7.68%, and +16.95% Recall@1 accuracy on CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively