The seventh zinc finger motif of A20 is required for the suppression of TNF-α-induced apoptosis

AbstractThe ubiquitin-editing enzyme A20 suppresses nuclear factor-κB (NF-κB) activation and tumor necrosis factor-α (TNF-α)-induced apoptosis in a deubiquitinating and ubiquitin ligase activity-dependent manner. Although recent studies revealed that A20 regulates NF-κB independently of its enzymatic activity through its seventh zinc finger motif (ZnF7), the involvement of ZnF7 in TNF-α-induced apoptosis is not clear. In this study, ZnF7 was found to be important for A20-mediated suppression of TNF-α-induced apoptosis. We also found that the ubiquitin ligases cIAP1/2 are required for A20 to suppress TNF-α-induced apoptosis. Because A20 binds to cIAP1/2 through ZnF7, these results suggest that A20 may control cIAP1/2 when suppressing TNF-α-induced apoptosis

Granules for Association Rules and Decision Support in the getRNIA System

This paper proposes granules for association rules in Deterministic Information Systems (DISs) and Non-deterministic Information Systems (NISs). Granules for an association rule are defined for every implication, and give us a new methodology for knowledge discovery and decision support. We see that decision support based on a table under the condition P is to fix the decision Q by using the most proper association rule P〵Rightarrow Q. We recently implemented a system getRNIA powered by granules for association rules. This paper describes how the getRNIA system deals with decision support under uncertainty, and shows some results of the experiment

Zyxin is a novel interacting partner for SIRT1

<p>Abstract</p> <p>Background</p> <p>SIRT1 is a mammalian homologue of NAD+-dependent deacetylase sirtuin family. It regulates longevity in several model organisms and is involved with cell survival, differentiation, metabolism among other processes in mammalian cells. SIRT1 modulates functions of various key targets via deacetylation. Recent studies have revealed SIRT1 protects neurons from axonal degeneration or neurodegeneration. Further, SIRT1 null mice exhibit growth retardation and developmental defects, suggesting its critical roles in neurons and development.</p> <p>Results</p> <p>To identify novel binding partners for SIRT1 in the central nervous system, we performed yeast two-hybrid screening on human fetal brain cDNA library and found that zyxin is a possible binding partner. SIRT1 and zyxin transcript were both preferentially expressed in developmental mouse brain. Zyxin accumulates in the nucleus where it is co-localized with SIRT1 after treatment with leptomycin B in COS-7 cells. Furthermore, SIRT1 deacetylates zyxin, suggesting SIRT1 could interact with nuclear-accumulated zyxin and modulate its function through deacetylation.</p> <p>Conclusion</p> <p>Zyxin could be a novel interacting partner of SIRT1. Zyxin is an adaptor protein at focal adhesion plaque, regulating cytoskeletal dynamics and signal transduction to convey signal from the ECM (extracellular matrix) to the nucleus. Our results raise the possibility that SIRT1 regulates signal transmission from ECM to the nucleus by modulating the functions of zyxin via deacetylation.</p

Generative Colorization of Structured Mobile Web Pages

Color is a critical design factor for web pages, affecting important factors such as viewer emotions and the overall trust and satisfaction of a website. Effective coloring requires design knowledge and expertise, but if this process could be automated through data-driven modeling, efficient exploration and alternative workflows would be possible. However, this direction remains underexplored due to the lack of a formalization of the web page colorization problem, datasets, and evaluation protocols. In this work, we propose a new dataset consisting of e-commerce mobile web pages in a tractable format, which are created by simplifying the pages and extracting canonical color styles with a common web browser. The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements. We present several Transformer-based methods that are adapted to this task by prepending structural message passing to capture hierarchical relationships between elements. Experimental results, including a quantitative evaluation designed for this task, demonstrate the advantages of our methods over statistical and image colorization methods. The code is available at https://github.com/CyberAgentAILab/webcolor.Comment: Accepted to WACV 202

Towards Flexible Multi-modal Document Models

Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors. In this work, we attempt at building a holistic model that can jointly solve many different design tasks. Our model, which we denote by FlexDM, treats vector graphic documents as a set of multi-modal elements, and learns to predict masked fields such as element type, position, styling attributes, image, or text, using a unified architecture. Through the use of explicit multi-task learning and in-domain pre-training, our model can better capture the multi-modal relationships among the different document fields. Experimental results corroborate that our single FlexDM is able to successfully solve a multitude of different design tasks, while achieving performance that is competitive with task-specific and costly baselines.Comment: To be published in CVPR2023 (highlight), project page: https://cyberagentailab.github.io/flex-d

LayoutDM: Discrete Diffusion Model for Controllable Layout Generation

Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named LayoutDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our LayoutDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks.Comment: To be published in CVPR2023, project page: https://cyberagentailab.github.io/layout-dm

Suppression of cell cycle progression by Jun dimerization protein (JDP2) involves down-regulation of cyclin A2

We report here a novel role for Jun dimerization protein-2 (JDP2) as a regulator of the progression of normal cells through the cell cycle. To determine the role of JDP2 in vivo, we generated Jdp2 knock-out (Jdp2KO) mice by targeting exon 1 to disrupt the site of initiation of transcription. The healing of wounded skin of Jdp2KO mice proceeded more rapidly than that of control mice and more proliferating cells were found at wound margins. Fibroblasts derived from embryos of Jdp2KO mice proliferated more rapidly and formed more colonies than wild-type fibroblasts. JDP2 was recruited to the promoter of the gene for cyclin A2 (ccna2) at a previously unidentified AP-1 site. Cells lacking Jdp2 had elevated levels of cyclin A2 mRNA. Moreover, reintroduction of JDP2 resulted in repression of transcription of ccna2 and of cell cycle progression. Thus, transcription of the gene for cyclin A2 appears to be a direct target of JDP2 in the suppression of cell proliferation

Fabrication and Fracture Toughness of CNTs/Alumina Composites with Fine Microstructures

ArticleKey Engineering Materials. 617: 205-208 (2014)journal articl