COMPUTATIONAL CHARACTERIZATION FOR GENOME INTEGRATION SITES OF HEPATITIS B VIRUS (HBV) AND GENE TARGETS OF HBV X PROTEIN

Master'sMASTER OF SCIENC

Validation data of parallel 3D surface-borehole electromagnetic forward modeling

Forward modeling of geophysical electromagnetic fields over large three-dimensional volumes is a heavy computational task that demands effective accelerating strategies. As a solution to this computational challenge, a hybrid parallel computing algorithm with multiple meshes has been previously proposed for 3D forward modeling of time-domain electromagnetic (TEM) fields by Liu et al. (2019). MPI and OpenMP were used for parallel computing and multiple meshes for optimizing the design of the geological model and frequencies used in forward modeling. The data presented in this paper offers complementary information on the calculation of the different components, such as the model discretization with regular or multiple meshes, the parallel computing with even or uneven modes, and an example of 3D TEM forward modeling through the proposed algorithm

The Functions of EP300 in Activated Pancreatic Stellate Cells and the Drug Resistance Problem in Pancreatic Cancer

Pancreatic stellate cells (PSCs) are generally quiescent in normal conditions, but during inflammation or cancer these cells are activated, differentiate to myofibroblast-like cells, proliferate, migrate and start secreting extracellular matrix protein, which are the main contributors to the stromal formation during the process of cancer. EP300 is an important transcription coactivator and plays an important role in the process of cell proliferation and differentiation. Thus, we hypothesize that targeting EP300 will affect the activation of PSCs and may influence the process of pancreatic cancer, especially for pancreatic ductal adenocarcinoma (PDAC). Transient specific small interfering RNA (SiRNA) knockdown of EP300 resulted in reduced expression of fibronectin (FN) and collagen I (Col-I) in activated PSCs. Stable knockdown of EP300 by CRISPR/Cas9 gRNA plasmid had the same effects. However, the migration of PSCs was increased. And we firstly showed that EP300 manipulated cell migration through ERK pathway. Furthermore, EP300 down regulation in PSCs increased the proliferation effect PSCs had on pancreatic cancer cells and PSCs protected tumor cells from chemotherapy more. Together, the evidences draw the conclusion that EP300 is a tumor suppressor gene, its downregulation increases the migration of PSCs and PSCs becomes more supportive for pancreatic cancer cells, but that reduces the extra cellular matrix production of PSCs. High resistance to chemotherapy is a frustrating issue in treating pancreatic ductal adenocarcinoma. It is one reason for a 5-year survival rate of PDAC patients lower than 5%. In recent years, researcher showed that the tumor microenvironment might make a great contribution to the drug resistance of pancreatic cancer. PSCs are important cells that exist in the tumor stroma of pancreatic cancer. Gemcitabine is a nucleoside analog, which is currently used as the best standard treatment for pancreatic cancer patients. In the present study, I analyzed how PSCs will affect the drug resistance of different drug sensitive pancreatic cancer cell lines. My results for the first time showed that conditioned medium from PSCs promotes chemo-resistance of Bxpc-3 cells by up regulating RRM1 and RRM2, but has no influence on the drug sensitivity of Panc-1 and Miapaca-2 cells. In addition, I could show that factors that are <100kDa and produced by pancreatic stellate cells are responsible for the effects. These factors are heat insensitive, trypsin and proteinase K insensitive and cannot be degraded by nucleases either, but the exact factor has yet to be determined

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior

We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation. Code available at https://github.com/deepseek-ai/DreamCraft3D.Comment: Project Page: https://mrtornado24.github.io/DreamCraft3D

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed to take better advantage of our hybrid dataset. In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc. Then in the fine module, a conditional StyleGAN architecture trained with high-fidelity scan models is introduced to enrich elaborate facial geometric and texture details. Note that different from previous methods, our base and detailed modules are both changeable, which enables an innovative application of adjusting both the basic attributes and the facial details of 3D face models. Furthermore, we propose a single-image fitting framework based on differentiable rendering. Rich experiments show that our method outperforms the state-of-the-art methods.Comment: https://github.com/LizhenWangT/FaceVers

Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars

3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Towards fine-grained control over facial attributes, recent efforts incorporate 3D Morphable Face Model (3DMM) to describe deformation in generative radiance fields either explicitly or implicitly. Explicit methods provide fine-grained expression control but cannot handle topological changes caused by hair and accessories, while implicit ones can model varied topologies but have limited generalization caused by the unconstrained deformation fields. We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images. To achieve both deformation accuracy and topological flexibility, we propose a 3D representation called Generative Texture-Rasterized Tri-planes. The proposed representation learns Generative Neural Textures on top of parametric mesh templates and then projects them into three orthogonal-viewed feature planes through rasterization, forming a tri-plane feature representation for volume rendering. In this way, we combine both fine-grained expression control of mesh-guided explicit deformation and the flexibility of implicit volumetric representation. We further propose specific modules for modeling mouth interior which is not taken into account by 3DMM. Our method demonstrates state-of-the-art 3D-aware synthesis quality and animation ability through extensive experiments. Furthermore, serving as 3D prior, our animatable 3D representation boosts multiple applications including one-shot facial avatars and 3D-aware stylization.Comment: Project page: https://mrtornado24.github.io/Next3D