Point Mutation of Hoxd12 in Mice

Purpose: Genes of the HoxD cluster play a major role in vertebrate limb development, and changes that modify the Hoxd12 locus affect other genes also, suggesting that HoxD function is coordinated by a control mechanism involving multiple genes during limb morphogenesis. In this study, mutant phenotypes were produced by treatment of mice with chemical mutagen, N-ethyl-N-nitrosourea (ENU). We analyzed mutant mice exhibiting the specific microdactyly phenotype and examined the genes affected. Materials and Methods: We focused on phenotype characteristics including size, bone formation, and digit morphology of ENU-induced microdactyly mice. The expressions of several molecules were analyzed by genome-wide screening and quantitative real-time PCR to define the affected genes. Results: We report on limb phenotypes of an ENU-induced A-to-C mutation in the Hoxd12 gene, resulting in alanine-to-serine conversion. Microdactyly mice exhibited growth defects in the zeugopod and autopod, shortening of digits, a missing tip of digit I, limb growth affected, and dramatic increases in the expressions of Fgf4 and Lmx1b. However, the expression level of Shh was not changed Hoxd12 point mutated mice. Conclusion: These results suggest that point mutation rather than the entire deletion of Hoxd12, such as in knockout and transgenic mice, causes the abnormal limb phenotype in microdactyly mice. The precise nature of the spectrum of differences requires further investigation.link_to_subscribed_fulltex

Traveling While Black

The history of African American travel is one of the great untold American stories. We seek a Level I Start-Up Grant to support the collaboration between humanities scholars and interactive designers to develop a choice-driven, exploratory game that places players directly in the shoes of African American travelers of the past. Through the game mechanics, players will explore the nature of prejudice, how it manifests, and the discrimination African Americans had to endure during the pre-civil rights era. The game will engage students and allow them to make strategic decisions, developing problem solving and systems thinking skills. Players will gain a rich and complex understanding of this important period in our nation’s history that continues to have contemporary resonance. The learning experience within the game will be augmented by the other platforms--documentary film, web series and digital cultural mapping--that make up the Traveling While Black (TWB) transmedia project

Mediating the East Asian Era of the Olympic Games (2018–2022):Introduction to the Special Issue

In the span of four years from 2018 to 2022, three consecutive Olympic Games were held in East Asia – namely PyeongChang 2018 in South Korea, Tokyo 2020 in Japan and Beijing 2022 in China. Given the geographic concentration of global multi-sports mega-events in the Far East, this period has been referred to as the ‘East Asian era’ of the Olympic Games. This introduction to the special issue outlines two major themes of the changes during the East Asian era: (1) the shift of economic and geopolitical power from the West to the East; and (2) the changes and challenges offered by the Olympic Agenda 2020 reforms and theCOVID-19 pandemic within East Asia. After that, each contribution will be introduced and briefly described. Overall, by collecting contributions focusing on the 2018–2022 Olympic Games, this special issue critically analyses the state of play in the formations of dominant and emerging discourses during the East Asia era and offers its implications for a broader understanding of the continuity and changes to the economic, political, social, cultural and ecological dimensions of the Olympic Movement

Discriminative Feature Learning for Unsupervised Video Summarization

In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video. Specifically, we tackle two critical issues based on our empirical observations: (i) Ineffective feature learning due to flat distributions of output importance scores for each frame, and (ii) training difficulty when dealing with long-length video inputs. To alleviate the first problem, we propose a simple yet effective regularization loss term called variance loss. The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance. For the second problem, we design a novel two-stream network named Chunk and Stride Network (CSNet) that utilizes local (chunk) and global (stride) temporal view on the video features. Our CSNet gives better summarization results for long-length videos compared to the existing methods. In addition, we introduce an attention mechanism to handle the dynamic information in videos. We demonstrate the effectiveness of the proposed methods by conducting extensive ablation studies and show that our final model achieves new state-of-the-art results on two benchmark datasets.Comment: Accepted to AAAI 2019 !!