bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quadruple‐editing of the MAPK and PI3K pathways effectively blocks the progression of KRAS‐mutated colorectal cancer cells
Authors: Wang Zaozao , Kang Bin , Gao Qianqian , Huang Lei , Di Jiabo , Fan Yingcong , Yu Jianhong , Jiang Beihai , Gao Feng , Wang Dan , Sun Haixi , Gu Ying , Li Jian , Su Xiangqian 
Published: June 2021

AbstractMutated KRAS promotes the activation of the MAPK pathway and the progression of colorectal cancer (CRC) cells. Aberrant activation of the PI3K pathway strongly attenuates the efficacy of MAPK suppression in KRAS‐mutated CRC. The development of a novel strategy targeting a dual pathway is therefore highly essential for the therapy of KRAS‐mutated CRC. In this study, a quadruple‐depleting system for the KRAS, MEK1, PIK3CA, and MTOR genes based on CRISPR/SaCas9 was developed. Adenovirus serotype 5 (ADV5) was integrated with two engineered proteins, an adaptor and a protector, to form ADV‐protein complex (APC) for systemic delivery of the CRISPR system. Quadruple‐editing could significantly inhibit the MAPK and PI3K pathways in CRC cells with oncogenic mutations of KRAS and PIK3CA or with KRAS mutation and compensated PI3K activation. Compared with MEK and PI3K/MTOR inhibitors, quadruple‐editing induced more significant survival inhibition on primary CRC cells with oncogenic mutations of KRAS and PIK3CA. The adaptor specifically targeting EpCAM and the hexon‐shielding protector could dramatically enhance ADV5 infection efficiency to CRC cells and significantly reduce off‐targeting tropisms to many organs except the colon. Moreover, quadruple‐editing intravenously delivered by APC significantly blocked the dual pathway and tumor growth of KRAS‐mutated CRC cells, without influencing normal tissues in cell‐ and patient‐derived xenograft models. Therefore, APC‐delivered quadruple‐editing of the MAPK and PI3K pathways shows a promising therapeutic potential for KRAS‐mutated CRC.
Aqueous seeding in intraocular retinoblastoma: A review
Authors: Kaliki Swathi 
Published: June 2021

AbstractThe management of retinoblastoma is complex. With increasing treatment modalities and increasing experience with each treatment modality, the globe salvage rates have drastically improved with time. Aqueous seeding in an eye with retinoblastoma is classified as group E based on International Classification of Intraocular Retinoblastoma, and most group E eyes were enucleated a decade earlier. Newer modalities of treatment have improved the globe salvage rates in group E eyes including those with aqueous seeding. Various globe salvage treatment modalities attempted for the management of aqueous seeds include external beam radiotherapy, plaque radiotherapy, intra‐arterial chemotherapy, periocular chemotherapy, intravitreal chemotherapy and intracameral chemotherapy. Of all treatment modalities, intracameral chemotherapy holds promise for the management of aqueous seeds in selected patients, but it should be done with the utmost care by a trained ocular oncologist. Enucleation is still the preferred modality of treatment for eyes with advanced retinoblastoma and anterior segment invasion. This review focuses on the current evidence on aqueous seeding, highlighting the pathogenesis, classification, clinical presentation, treatment and outcomes.
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
Authors: Fischer M. T. , Seebacher D. , Sevastjanova R. , Keim D. A. , El‐Assady M. 
Published: June 2021

AbstractCommunication consists of both meta‐information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text‐mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta‐information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user‐adaptable and problem‐specific machine learning‐based retrieval system. An interactive multi‐level matrix‐based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross‐checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real‐world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.
Exploring Multi‐dimensional Data via Subset Embedding
Authors: Xie Peng , Tao Wenyuan , Li Jie , Huang Wentao , Chen Siming 
Published: June 2021

AbstractMulti‐dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly‐formatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully‐connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3‐step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.
Parameterized Splitting of Summed Volume Tables
Authors: Reinbold Christian , Westermann Rüdiger 
Published: June 2021

AbstractSummed Volume Tables (SVTs) allow one to compute integrals over the data values in any cubical area of a three‐dimensional orthogonal grid in constant time, and they are especially interesting for building spatial search structures for sparse volumes. However, SVTs become extremely memory consuming due to the large values they need to store; for a dataset of n values an SVT requires 𝒪(n log n) bits. The 3D Fenwick tree allows recovering the integral values in 𝒪(log3 n) time, at a memory consumption of 𝒪(n) bits. We propose an algorithm that generates SVT representations that can flexibly trade speed for memory: From similar characteristics as SVTs, over equal memory consumption as 3D Fenwick trees at significantly lower computational complexity, to even further reduced memory consumption at the cost of raising computational complexity. For a 641 × 9601 × 9601 binary dataset, the algorithm can generate an SVT representation that requires 27.0GB and 46 · 8 data fetch operations to retrieve an integral value, compared to 27.5GB and 1521·8 fetches by 3D Fenwick trees, a decrease in fetches of 97%. A full SVT requires 247.6GB and 8 fetches per integral value. We present a novel hierarchical approach to compute and store intermediate prefix sums of SVTs, so that any prescribed memory consumption between 𝒪(n) bits and 𝒪(n log n) bits is achieved. We evaluate the performance of the proposed algorithm in a number of examples considering large volume data, and we perform comparisons to existing alternatives.
Learning Contextualized User Preferences for Co‐Adaptive Guidance in Mixed‐Initiative Topic Model Refinement
Authors: Sperrle F. , Schäfer H. , Keim D. , El‐Assady M. 
Published: June 2021

AbstractMixed‐initiative visual analytics systems support collaborative human‐machine decision‐making processes. However, many multi‐objective optimization tasks, such as topic model refinement, are highly subjective and context‐dependent. Hence, systems need to adapt their optimization suggestions throughout the interactive refinement process to provide efficient guidance. To tackle this challenge, we present a technique for learning context‐dependent user preferences and demonstrate its applicability to topic model refinement. We deploy agents with distinct associated optimization strategies that compete for the user's acceptance of their suggestions. To decide when to provide guidance, each agent maintains an intelligible, rule‐based classifier over context vectorizations that captures the development of quality metrics between distinct analysis states. By observing implicit and explicit user feedback, agents learn in which contexts to provide their specific guidance operation. An agent in topic model refinement might, for example, learn to react to declining model coherence by suggesting to split a topic. Our results confirm that the rules learned by agents capture contextual user preferences. Further, we show that the learned rules are transferable between similar datasets, avoiding common cold‐start problems and enabling a continuous refinement of agents across corpora.
Daisen: A Framework for Visualizing Detailed GPU Execution
Authors: Sun Yifan , Zhang Yixuan , Mosallaei Ali , Shah Michael D. , Dunne Cody , Kaeli David 
Published: June 2021

AbstractGraphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator‐generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator‐generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator‐generated GPU execution traces. Daisen also includes a web‐based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open‐sourced implementation of Daisen can be found at gitlab.com/akita/vis. Supplemental materials including a demo video, survey questions, evaluation study guide, and post‐study evaluation survey are available at osf.io/j5ghq.
Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion
Authors: McDonald T. , Shrestha R. , Yi X. , Bhatia H. , Chen D. , Goswami D. , Pascucci V. , Turbyville T. , Bremer P.‐T. 
Published: June 2021

AbstractSingle particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high‐resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events – merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time‐steps of simulated and in‐vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.
SumRe: Design and Evaluation of a Gist‐based Summary Visualization for Incident Reports Triage
Authors: Kakar T. , Qin X. , La T. , Sahoo S. K. , De S. , Rundensteiner E. A. , Harrison L. 
Published: June 2021

AbstractIncident report triage is a common endeavor in many industry sectors, often coupled with serious public safety implications. For example, at the US Food and Drug Administration (FDA), analysts triage an influx of incident reports to identify previously undiscovered drug safety problems. However, these analysts currently conduct this critical yet error‐prone incident report triage using a generic table‐based interface, with no formal support. Visualization design, task‐characterization methodologies, and evaluation models offer several possibilities for better supporting triage workflows, including those dealing with drug safety and beyond. In this work, we aim to elevate the work of triage through a task‐abstraction activity with FDA analysts. Second, we design an alternative gist‐based summary of text documents used in triage (SumRe). Third, we conduct a crowdsourced evaluation of SumRe with medical experts. Results of the crowdsourced study with medical experts (n = 20) suggest that SumRe better supports accuracy in understanding the gist of a given report, and in identifying important reports for followup activities. We discuss implications of these results, including design considerations for triage workflows beyond the drug domain, as well as methodologies for comparing visualization‐enabled text summaries.
Design Space of Origin‐Destination Data Visualization
Authors: Tennekes Martijn , Chen Min 
Published: June 2021

AbstractVisualization is an essential tool for observing and analyzing origin‐destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin‐destination data visualization (ODDV). We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.
Boundary Objects in Design Studies: Reflections on the Collaborative Creation of Isochrone Maps
Authors: Vuillemot R. , Rivière P. , Beignon A. , Tabard A. 
Published: June 2021

AbstractWe propose to take an artifact‐centric approach to design studies by leveraging the concept of boundary object. Design studies typically focus on processes and articulate design decisions in a project‐specific context with a goal of transferability. We argue that design studies could benefit from paying attention to the material conditions in which teams collaborate to reach design outcomes. We report on a design study of isochrone maps following cartographic generalization principles. Focusing on boundary objects enables us to characterize five categories of artifacts and tools that facilitated collaboration between actors involved in the design process (structured collections, structuring artifacts, process‐centric artifacts, generative artifacts, and bridging artifacts). We found that artifacts such as layered maps and map collections played a unifying role for our inter‐disciplinary team. We discuss how such artifacts can be pivotal in the design process. Finally, we discuss how considering boundary objects could improve the transferability of design study results, and support reflection on inter‐disciplinary collaboration in the domain of Information Visualization.
Design Patterns and Trade‐Offs in Responsive Visualization for Communication
Authors: Kim Hyeok , Moritz Dominik , Hullman Jessica 
Published: June 2021

AbstractIncreased access to mobile devices motivates the need to design communicative visualizations that are responsive to varying screen sizes. However, relatively little design guidance or tooling is currently available to authors. We contribute a detailed characterization of responsive visualization strategies in communication‐oriented visualizations, identifying 76 total strategies by analyzing 378 pairs of large screen (LS) and small screen (SS) visualizations from online articles and reports. Our analysis distinguishes between the Targets of responsive visualization, referring to what elements of a design are changed and Actions representing how targets are changed. We identify key trade‐offs related to authors' need to maintain graphical density, referring to the amount of information per pixel, while also maintaining the “message” or intended takeaways for users of a visualization. We discuss implications of our findings for future visualization tool design to support responsive transformation of visualization designs, including requirements for automated recommenders for communication‐oriented responsive visualizations.
Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth
Authors: Lan Fangfei , Young Michael , Anderson Lauren , Ynnerman Anders , Bock Alexander , Borkin Michelle A. , Forbes Angus G. , Kollmeier Juna A. , Wang Bei 
Published: June 2021

AbstractWe present a state‐of‐the‐art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.
Genetic and ecological evidence of long‐term translocation success of the federally endangered Stephens' kangaroo rat
Authors: Shier Debra M. , Navarro Asako Y. , Tobler Mathias , Thomas Steven M. , King Shauna N. D. , Mullaney Claire B. , Ryder Oliver A. 
Published: June 2021

AbstractThe efficacy of translocation as a method for conserving species in peril has not been fully evaluated. Post‐release monitoring rarely involves long‐term assessments and initial success metrics may not translate to population viability. In particular, genetic factors may play a critical role in fitness following release as founder effect, genetic drift, inbreeding depression and outbreeding depression can influence extinction risk. As part of a multi‐year recovery effort, we translocated populations of the endangered Stephens' kangaroo rat (Dipodomys stephensi; SKR), in order to restore population connectivity. Here we evaluate the success of the translocation efforts through long‐term population estimation and genetic analyses. We conducted post‐release assessments for 9 years, and used Bayesian clustering analysis and principal components analysis (PCA) of 24 microsatellite markers developed for this species to assess genetic admixture between source and translocated populations. Demographic results indicate long‐term translocation success, as evidenced by founder survival and establishment, and population growth and regulation. Genetic results show admixture at the release site between genetically discrete source populations, provide definitive evidence that translocated individuals, not immigrants, produced offspring at the release site, that these offspring successfully reproduced and that even with moderate to great genetic differentiation of source populations, outbreeding depression was not detected. Despite the historical failure of heteromyid translocation efforts, our long‐term study illustrates how the development of species‐specific best practices led to an effective strategy for SKR recovery. More broadly, our results suggest efforts that use a systematic hypothesis‐testing framework alongside long‐term monitoring offer great promise in improving translocation outcomes.