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LoopGrafter: Visual Support for the Grafting Workflow of Protein Loops. LoopGrafter:为蛋白质环的嫁接工作流程提供可视化支持。
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-09-29 DOI: 10.1109/TVCG.2021.3114755
Filip Opaleny, Pavol Ulbrich, Joan Planas-Iglesias, Jan Byska, Gaspar P Pinto, David Bednar, Katarina FurmanovA, Barbora KozlikovA

In the process of understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on the exploration of regions in proteins called loops. Analyzing various characteristics of these regions helps the experts to design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. As this process requires extensive manual treatment and currently there is no proper visual support for it, we designed LoopGrafter: a web-based tool that provides experts with visual support through all the loop grafting pipeline steps. The tool is logically divided into several phases, starting with the definition of two input proteins and ending with a set of grafted proteins. Each phase is supported by a specific set of abstracted 2D visual representations of loaded proteins and their loops that are interactively linked with the 3D view onto proteins. By sequentially passing through the individual phases, the user is shaping the list of loops that are potential candidates for loop grafting. In the end, the actual in-silico insertion of the loop candidates from one protein to the other is performed and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. LoopGrafter was designed in tight collaboration with protein engineers, and its final appearance reflects many testing iterations. We showcase the contribution of LoopGrafter on a real case scenario and provide the readers with the experts' feedback, confirming the usefulness of our tool.

在现代生物化学理解和重新设计蛋白质功能的过程中,蛋白质工程师越来越重视探索蛋白质中被称为环的区域。分析这些区域的各种特征有助于专家设计将所需功能从一种蛋白质转移到另一种蛋白质。这一过程被称为环路嫁接。由于这一过程需要大量人工处理,而且目前还没有适当的可视化支持,因此我们设计了 LoopGrafter:一种基于网络的工具,为专家提供可视化支持,帮助他们完成所有环路嫁接流水线步骤。该工具在逻辑上分为几个阶段,从定义两个输入蛋白质开始,到一组嫁接蛋白质结束。每个阶段都有一套特定的抽象二维可视化载入蛋白质及其环路,这些二维可视化载入蛋白质及其环路与蛋白质的三维视图交互连接。通过依次经过各个阶段,用户可以形成可能进行环路嫁接的环路列表。最后,将候选环路从一个蛋白质实际插入另一个蛋白质,并将结果直观地呈现给用户。这样,通过对蛋白质及其环路进行完全计算合理设计,就能得到新设计的蛋白质结构,并可通过体外实验进一步组装和测试。LoopGrafter 是与蛋白质工程师密切合作设计的,其最终外观反映了多次测试迭代的结果。我们展示了 LoopGrafter 在实际案例中的贡献,并向读者提供了专家的反馈意见,证实了我们工具的实用性。
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引用次数: 0
Examining Effort in 1D Uncertainty Communication Using Individual Differences in Working Memory and NASA-TLX 使用工作记忆和NASA-TLX的个体差异研究一维不确定性沟通的努力
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-08-10 DOI: 10.31234/osf.io/wpz8b
Spencer C. Castro, P. S. Quinan, Helia Hosseinpour, Lace M. K. Padilla
As uncertainty visualizations for general audiences become increasingly common, designers must understand the full impact of uncertainty communication techniques on viewers' decision processes. Prior work demonstrates mixed performance outcomes with respect to how individuals make decisions using various visual and textual depictions of uncertainty. Part of the inconsistency across findings may be due to an over-reliance on task accuracy, which cannot, on its own, provide a comprehensive understanding of how uncertainty visualization techniques support reasoning processes. In this work, we advance the debate surrounding the efficacy of modern 1D uncertainty visualizations by conducting converging quantitative and qualitative analyses of both the effort and strategies used by individuals when provided with quantile dotplots, density plots, interval plots, mean plots, and textual descriptions of uncertainty. We utilize two approaches for examining effort across uncertainty communication techniques: a measure of individual differences in working-memory capacity known as an operation span (OSPAN) task and self-reports of perceived workload via the NASA-TLX. The results reveal that both visualization methods and working-memory capacity impact participants' decisions. Specifically, quantile dotplots and density plots (i.e., distributional annotations) result in more accurate judgments than interval plots, textual descriptions of uncertainty, and mean plots (i.e., summary annotations). Additionally, participants' open-ended responses suggest that individuals viewing distributional annotations are more likely to employ a strategy that explicitly incorporates uncertainty into their judgments than those viewing summary annotations. When comparing quantile dotplots to density plots, this work finds that both methods are equally effective for low-working-memory individuals. However, for individuals with high-working-memory capacity, quantile dotplots evoke more accurate responses with less perceived effort. Given these results, we advocate for the inclusion of converging behavioral and subjective workload metrics in addition to accuracy performance to further disambiguate meaningful differences among visualization techniques.
随着面向普通观众的不确定性可视化变得越来越普遍,设计师必须了解不确定性沟通技术对观众决策过程的全面影响。先前的工作证明了个人如何使用各种视觉和文本描述不确定性来做出决策的混合绩效结果。结果之间的不一致部分可能是由于对任务准确性的过度依赖,它本身不能提供对不确定性可视化技术如何支持推理过程的全面理解。在这项工作中,我们通过对个人在提供分位数点图、密度图、间隔图、平均图和不确定性文本描述时所使用的努力和策略进行收敛的定量和定性分析,推进了围绕现代一维不确定性可视化效果的辩论。我们利用两种方法来检查不确定性沟通技术的努力:工作记忆容量的个体差异测量,即操作跨度(osspan)任务和通过NASA-TLX感知工作量的自我报告。结果表明,可视化方法和工作记忆容量都影响被试的决策。具体来说,分位数点图和密度图(即分布注释)比区间图、不确定性的文本描述和平均图(即摘要注释)产生更准确的判断。此外,参与者的开放式回答表明,与查看摘要注释的人相比,查看分布式注释的人更有可能采用一种明确地将不确定性纳入其判断的策略。当比较分位数点图和密度图时,这项工作发现这两种方法对低工作记忆个体同样有效。然而,对于具有高工作记忆容量的个体,分位数点图用较少的感知努力唤起更准确的反应。鉴于这些结果,我们提倡除了准确性性能之外,还包括收敛的行为和主观工作负载指标,以进一步消除可视化技术之间有意义的差异。
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引用次数: 18
Rethinking the Ranks of Visual Channels 对视觉频道排名的再思考
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-07-23 DOI: 10.31219/osf.io/n7kxu
Caitlyn M. McColeman, Fumeng Yang, S. Franconeri, Timothy F. Brady
Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values. To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or ‘wind map’ (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands).
数据可以使用视觉通道(如位置、长度或亮度)进行视觉表示。这些视觉通道的现有排名是基于参与者报告两个描述值之间的比率的准确程度。有一种假设是,这种排名应该适用于不同的任务和不同数量的分数。然而,令人惊讶的是,很少有现有的工作来检验这一假设,特别是考虑到视觉计算比率在现实世界的可视化中相对不重要,与观看、记忆和比较趋势和主题相比,在几乎普遍描绘两个以上值的显示中。为了模拟从可视化的一瞥中提取的信息,我们要求参与者在看到可视化后立即从内存中重现一组值。这些值可以显示在条形图(位置(条形))、折线图(位置)、热图(亮度)、气泡图(面积)、未对齐的条形图(长度)或“风图”(角度)中。使用贝叶斯多级建模方法,我们展示了视觉通道的等级位置如何在不同数量的标记(2、4或8)之间移动,以及偏差、精度和误差测量。即使是只有2个分数的复制品,该排名也不成立,而且新的概率排名对不同分数的复制物极不一致。除了通道选择之外,其他因素对性能的影响更大,如序列中的值的数量(例如,更多的标记导致更大的误差),或每个标记的值(例如,小的值被系统地高估)。8分的显示器的每个视觉通道都比4分差,这与视觉记忆的既定限制一致。这些结果表明,需要进行一系列实证研究,超越两个价值比判断,将其作为可靠排名视觉通道质量的基线,包括测试新任务(趋势或主题的检测)、时间尺度(即时计算或稍后的比较)和值的数量(从少数到数千)。
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引用次数: 13
Loon: Using Exemplars to Visualize Large-Scale Microscopy Data Loon:使用示例将大规模显微镜数据可视化
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-05-04 DOI: 10.31219/osf.io/dfajc
Devin Lange, Edward R. Polanco, R. Judson-Torres, T. Zangle, A. Lex
Which drug is most promising for a cancer patient? A new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer this question in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, adjusting filters, removing noise, and analyzing the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both derived data such as growth rates and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach for choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.
哪种药物对癌症患者最有希望?一种新的基于显微镜的方法,可以测量用不同药物治疗的单个癌细胞的质量,有望在几个小时内回答这个问题。然而,从这些图像中提取数据的分析管道仍远未实现完全自动化:在分割、调整滤波器、去除噪声和分析结果等预处理步骤中,需要人工干预来进行质量控制。为了解决这个工作流程,我们开发了Loon,这是一个基于定量相显微镜成像分析药物筛选数据的可视化工具。Loon将增长率和成像数据等衍生数据可视化。由于图像是大规模自动采集的,对图像进行人工检测和分割是不可行的。然而,对于质量控制和数据分析来说,审查具有代表性的细胞样本是必不可少的。我们引入了一种新的方法来选择和可视化具有代表性的样本单元,这些样本单元与低层数据保持着密切的联系。通过将衍生数据可视化功能与新型范例可视化功能紧密集成,并提供选择和过滤功能,Loon非常适合决定哪些药物适合特定患者。
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引用次数: 5
Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings 低维嵌入中关系与结构的视觉探索
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-04-08 DOI: 10.31219/osf.io/ujbrs
K. Eckelt, A. Hinterreiter, Patrick Adelberger, C. Walchshofer, V. Dhanoa, C. Humer, Moritz Heckmann, C. Steinparz, M. Streit
In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups is accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.
在这项工作中,我们提出了一种交互式可视化方法来探索和形成高维数据嵌入中的结构关系。这些结构关系,如项目序列、项目与组的关联以及项目组之间的层次结构,定义了许多真实世界数据集的属性。然而,大多数现有的嵌入视觉探索方法将这些结构视为二等公民,或者根本不考虑它们。在我们提出的分析工作流程中,用户探索嵌入的丰富散点图,其中项目和/或组之间的关系在视觉上突出显示。单个项目、项目组或连接的项目和组之间的差异的原始高维数据可以通过附加的摘要可视化来访问。我们仔细地为不同的数据类型和语义上下文定制了这些摘要和差异可视化。在探索性分析期间,用户可以通过在项目和/或组之间设置额外的组和关系来具体化他们的见解。我们通过来自不同领域的两个用例和多个示例来演示我们的方法的实用性和潜在影响。
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引用次数: 6
INFOVIS 2020 Program Committee INFOVIS 2020计划委员会
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-02-01 DOI: 10.1109/tvcg.2020.3033686
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引用次数: 0
Copyright notice 版权声明
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-02-01 DOI: 10.1109/tvcg.2020.3035922
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引用次数: 0
Contents 内容
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-02-01 DOI: 10.1109/tvcg.2020.3033677
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引用次数: 0
VIS 2020 Steering Committees VIS 2020指导委员会
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-02-01 DOI: 10.1109/tvcg.2020.3033716
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引用次数: 0
Info Vis Reviewers Info-Vis审查员
IF 5.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-02-01 DOI: 10.1109/tvcg.2020.3033652
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引用次数: 0
期刊
IEEE Transactions on Visualization and Computer Graphics
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