Apoptotic Sphingolipid Ceramide in Cancer Therapy

Apoptosis, also called programmed cell death, is physiologically and pathologically involved in cellular homeostasis. Escape of apoptotic signaling is a critical strategy commonly used for cancer tumorigenesis. Ceramide, a derivative of sphingolipid breakdown products, acts as second messenger for multiple extracellular stimuli including growth factors, chemical agents, and environmental stresses, such as hypoxia, and heat stress as well as irradiation. Also, ceramide acts as tumor-suppressor lipid because a variety of stress stimuli cause apoptosis by increasing intracellular ceramide to initiate apoptotic signaling. Defects on ceramide generation and sphingolipid metabolism are developed for cancer cell survival and cancer therapy resistance. Alternatively, targeting ceramide metabolism to correct these defects might provide opportunities to overcome cancer therapy resistance

Sialolipoma of the Floor of the Mouth: A Case Report

Intra-oral lipoma is a well-known entity, but lipomatous tumors including salivary gland tissue containing clustered or peripherally located ducts and acinar cells are uncommon. They are a newly recognized entity of salivary gland lipoma, designated sialolipoma. We describe a case of sialolipoma arising in the floor of the mouth presenting with apparently normal salivary gland tissue, as demonstrated by both histologic and immunohistochemical findings, in a 67-year-old female. Complete surgical removal of the tumor with preservation of the sublingual gland was implemented after a careful examination confirming that the lesion did not originate from the sublingual gland

Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings

Reconstructing 3D human heads in low-view settings presents technical challenges, mainly due to the pronounced risk of overfitting with limited views and high-frequency signals. To address this, we propose geometry decomposition and adopt a two-stage, coarse-to-fine training strategy, allowing for progressively capturing high-frequency geometric details. We represent 3D human heads using the zero level-set of a combined signed distance field, comprising a smooth template, a non-rigid deformation, and a high-frequency displacement field. The template captures features that are independent of both identity and expression and is co-trained with the deformation network across multiple individuals with sparse and randomly selected views. The displacement field, capturing individual-specific details, undergoes separate training for each person. Our network training does not require 3D supervision or object masks. Experimental results demonstrate the effectiveness and robustness of our geometry decomposition and two-stage training strategy. Our method outperforms existing neural rendering approaches in terms of reconstruction accuracy and novel view synthesis under low-view settings. Moreover, the pre-trained template serves a good initialization for our model when encountering unseen individuals.Comment: Accepted by ICCV2023. Visit our project page at https://github.com/xubaixinxbx/3dhead

Urban Radiance Field Representation with Deformable Neural Mesh Primitives

Neural Radiance Fields (NeRFs) have achieved great success in the past few years. However, most current methods still require intensive resources due to ray marching-based rendering. To construct urban-level radiance fields efficiently, we design Deformable Neural Mesh Primitive~(DNMP), and propose to parameterize the entire scene with such primitives. The DNMP is a flexible and compact neural variant of classic mesh representation, which enjoys both the efficiency of rasterization-based rendering and the powerful neural representation capability for photo-realistic image synthesis. Specifically, a DNMP consists of a set of connected deformable mesh vertices with paired vertex features to parameterize the geometry and radiance information of a local area. To constrain the degree of freedom for optimization and lower the storage budgets, we enforce the shape of each primitive to be decoded from a relatively low-dimensional latent space. The rendering colors are decoded from the vertex features (interpolated with rasterization) by a view-dependent MLP. The DNMP provides a new paradigm for urban-level scene representation with appealing properties: $(1)$ High-quality rendering. Our method achieves leading performance for novel view synthesis in urban scenarios. $(2)$ Low computational costs. Our representation enables fast rendering (2.07ms/1k pixels) and low peak memory usage (110MB/1k pixels). We also present a lightweight version that can run 33$\times$ faster than vanilla NeRFs, and comparable to the highly-optimized Instant-NGP (0.61 vs 0.71ms/1k pixels). Project page: \href{https://dnmp.github.io/}{https://dnmp.github.io/}.Comment: Accepted to ICCV202