299 research outputs found
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
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
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
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
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