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visual data quantitative information
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Edward R. Tufte
LAYERING AND SEPARATION
COLOR AND INFORMATION
NARRATIVES OF SPACE AND TIME
Edward Tufte is a professor at Yale University,
where he teaches courses in statistical evidence
and information design. His books include
Visual Explanations: Images and Quantities,
Evidence and Narrative, Envisioning Information,
The Visual Display of Quantitative Information,
Political Control of the Economy, Data Analysis
for Politics and Policy, and Size and Democracy
(with Robert A. Dahl).
He is a fellow of the American Statistical
Association, the American Academy of Arts
and Sciences, the Guggenheim Foundation,
and the Center for Advanced Study in the
Behavioral Sciences. He has received honorary
doctorates from The Cooper Union and
Connecticut College, the Phi Beta Kappa Award
in Science, and the Joseph Rigo Award for
contributions to software documentation from
the Association for Computing Machinery.
Envisioning Information has received 14 awards
for content and design, including the Phi Beta
Kappa Award in Science and "Best Graphic
Design of the Year" from International Design.
Two paintings on silk depicting Dejima
Island, a view from the Bay (top), a view
from Nagasaki (bottom), circa 1860.
Edward R. Tufte
Graphics Press • Cheshire, Connecticut
Copyright © 1990 by Edward Rolf Tufte
POST OFFICE Box 430, CHESHIRE, CONNECTICUT 06410
All rights to illustrations and text reserved by Edward Rolf Tufte. This work may not be copied, reproduced, or translated in whole or in
part without written permission of the publisher, except for brief excerpts in connection with reviews or scholarly analysis. Use "with any form
of information storage and retrieval, electronic adaptation or whatever, computer software, or by similar or dissimilar methods now known
or developed in the future is also strictly forbidden without written permission of the publisher. A number of illustrations are reproduced by
permission; those copyright-holders are credited on page 126.
Printed in the United States of America Sixth printing, February 1998
The LULC simulation data we utilized to create future EN maps was produced by X. Liu et al. (2017), which was conducted at the national level. The reason we apply national-level simulated data to a local area is as follows. Firstly, China has a top-down land use planning system (also known as spatial planning) with five levels. The quantitative objectives in national plans are handed down to county-level plans through provincial and prefectural level plans (Zhong et al., 2014). That means land use patterns of nine cities in WUA are required to reflect relevant upper-level plans, for example, to satisfy the land use quota made by Hubei provincial plans and the national plans. Secondly, there are interdependencies across places so what happens in one region produces effects not only on this location but on other regions (Overman et al., 2010). And the increase of construction land in one place will shift protection pressure on natural ecosystems elsewhere for a sustainable goal. The land use simulation at the national level allocated land resources from a top-down perspective and links land use changes in a region to events taking place in other locations through global simulation. However, the Kappa coefficient of the simulated data in WUA is 0.55 and the overall accuracy is 0.71, which is lower than the statistic value at the national-level data. Although the Kappa between 0.4~0.6 is moderate and at an acceptable level (Appiah et al., 2015; Ding et al., 2013; Ku, 2016), the simulated accuracy of the land use data needs to be improved. Future work on exploring the impact of LULC dynamics on EN will develop based on the high-accuracy simulated data and updating the initial simulated time to 2020, by integrating the impacts of socioeconomic factors, climate change, regional planning, land use policy, etc.
2型糖尿病发病是环境因素与遗传因素相互作用的结果，已被鉴定与其相关的基因超过400个，这些基因也与肥胖、脂代谢紊乱和心血管病等密切相关。然而，目前检测出来的许多基因突变是低频率、罕见的变异，它们对总体疾病发生风险的作用尚未完全阐明，也缺乏相对应的靶向药物。 本研究主要是在前期研究基础上，期望能通过对个体基因组、蛋白质组、代谢组和临床表型的信息等进行多组学数据深度挖掘 (OWAS-DM)，利用GWAS关联的孟德尔遗传-蛋白组量化性状基因座 (MR-pQTL)，MR-mQTL (代谢组)，MR-eQTL (表型组) 共定位分析及生物信息学分析等多组学分析策略，发现2型糖尿病发生发展的潜在机制及标志物。并利用cMAP及分子对接等人工智能虚拟筛选技术结合基于实验的亲和质谱、表面等离子共振及高内涵等筛选技术，发展针对2型糖尿病靶标的高通量药物筛选技术，为糖尿病患者的个体化精准诊断和治疗提供新的手段。翻译成英文
Real-world issues are complex, so I seek to prepare myself with both advanced technical knowledge and practical experience. I see the MSCM program as my next step. I could fill my skill gap in optimizing complex systems. Courses such as analytic methods, industrial engineering, operation research, and systems engineering are highly technical and analytical. I believe these courses are essential for decision-based problem-solving and digital transformation. The opportunity in taking the Capstone Project would be a perfect start for my long-run goal of running a supply chain optimization solution provider company. By solving the supply chain problems of companies with quantitative modeling, I hope I can differentiate and summarize several solutions applicable to most companies.
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