The Carcinogenicity of Aflatoxin B1

Aflatoxins are a class of carcinogenic mycotoxins, products of Aspergillus fungi, which are known contaminants in a large portion of the world’s food supply. Aflatoxin B1 (AFB1) is the most potent toxin, which has been strongly linked to the development of hepatocellular carcinoma (HCC), especially given coinfection with hepatitis B virus (HBV). AFB1 is catalyzed by cytochrome P450 (CYP450) into aflatoxin B1-8,9-exo-epoxide to form DNA adducts, which leads to carcinogenesis by disrupting DNA repair. AFB1-induced DNA damage is also caused by the production of excessive ROS, leading to the oxidation of DNA bases. The majority of AFB1-related to HCC carry G-to-T transversion of p53 gene. When the p53 gene is mutated, it shows a “gain of oncogenic function.” In addition, epigenetic alterations may potentially be beneficial for the treatment of HCC, because the epigenetic changes are reversible. This chapter will provide important information on the carcinogenicity of AFB1, including DNA damage checkpoint response and epigenetic alteration

Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling

High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work in image rescaling formulates downscaling and upscaling as a unified task and learns a bijective mapping between HR and LR via invertible networks. However, in real-world applications (e.g., social media), most images are compressed for transmission. Lossy compression will lead to irreversible information loss on LR images, hence damaging the inverse upscaling procedure and degrading the reconstruction accuracy. In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling. To tackle the distribution shift, we first develop an end-to-end asymmetric framework with two separate bijective mappings for high-quality and compressed LR images, respectively. Then, based on empirical analysis of this framework, we model the distribution of the lost information (including downscaling and compression) using isotropic Gaussian mixtures and propose the Enhanced Invertible Block to derive high-quality/compressed LR images in one forward pass. Besides, we design a set of losses to regularize the learned LR images and enhance the invertibility. Extensive experiments demonstrate the consistent improvements of SAIN across various image rescaling datasets in terms of both quantitative and qualitative evaluation under standard image compression formats (i.e., JPEG and WebP).Comment: Accepted by AAAI 2023. Code is available at https://github.com/yang-jin-hai/SAI

Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation

When humans perform contact-rich manipulation tasks, customized tools are often necessary and play an important role in simplifying the task. For instance, in our daily life, we use various utensils for handling food, such as knives, forks and spoons. Similarly, customized tools for robots may enable them to more easily perform a variety of tasks. Here, we present an end-to-end framework to automatically learn tool morphology for contact-rich manipulation tasks by leveraging differentiable physics simulators. Previous work approached this problem by introducing manually constructed priors that required detailed specification of object 3D model, grasp pose and task description to facilitate the search or optimization. In our approach, we instead only need to define the objective with respect to the task performance and enable learning a robust morphology by randomizing the task variations. The optimization is made tractable by casting this as a continual learning problem. We demonstrate the effectiveness of our method for designing new tools in several scenarios such as winding ropes, flipping a box and pushing peas onto a scoop in simulation. We also validate that the shapes discovered by our method help real robots succeed in these scenarios

Stab-GKnock: Controlled variable selection for partially linear models using generalized knockoffs

The recently proposed fixed-X knockoff is a powerful variable selection procedure that controls the false discovery rate (FDR) in any finite-sample setting, yet its theoretical insights are difficult to show beyond Gaussian linear models. In this paper, we make the first attempt to extend the fixed-X knockoff to partially linear models by using generalized knockoff features, and propose a new stability generalized knockoff (Stab-GKnock) procedure by incorporating selection probability as feature importance score. We provide FDR control and power guarantee under some regularity conditions. In addition, we propose a two-stage method under high dimensionality by introducing a new joint feature screening procedure, with guaranteed sure screening property. Extensive simulation studies are conducted to evaluate the finite-sample performance of the proposed method. A real data example is also provided for illustration.Comment: 40 pages, 11 figures, 4 table