Relation-driven Query of Multiple Time Series.

Querying time series based on their relations is a crucial part of multiple time series analysis. By retrieving and understanding time series relations, analysts can easily detect anomalies and validate hypotheses in complex time series datasets. However, current relation extraction approaches, including knowledge- and data-driven ones, tend to be laborious and do not support heterogeneous relations. By conducting a formative study with 11 experts, we concluded six time series relations, including correlation, causality, similarity, lag, arithmetic, and meta, and summarized three pain points in querying time series involving these relations. We proposed RelaQ, an interactive system that supports the time series query via relation specifications. RelaQ allows users to intuitively specify heterogeneous relations when querying multiple time series, understand the query results based on a scalable, multi-level visualization, and explore possible relations beyond the existing queries. RelaQ is evaluated with two cases and a user study with 12 participants, showing promising effectiveness and usability.

Visualizing Large-Scale Spatial Time Series with GeoChron.

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.

ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants.

Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.

A survey of urban visual analytics: Advances and future directions.

Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.

Multilevel Visual Analysis of Aggregate Geo-Networks.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.

Interactive Visual Exploration of Longitudinal Historical Career Mobility Data.

The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this article, we work closely with the constructors of a new dataset, CGED-Q (China Government Employee Database-Qing), that records the career trajectories of over 340,000 government officials in the Qing bureaucracy in China from 1760 to 1912. We use these data to study career mobility from a historical perspective and understand social mobility and inequality. However, existing statistical approaches are inadequate for analyzing career mobility in this historical dataset with its fine-grained attributes and long time span, since they are mostly hypothesis-driven and require substantial effort. We propose CareerLens, an interactive visual analytics system for assisting experts in exploring, understanding, and reasoning from historical career data. With CareerLens, experts examine mobility patterns in three levels-of-detail, namely, the macro-level providing a summary of overall mobility, the meso-level extracting latent group mobility patterns, and the micro-level revealing social relationships of individuals. We demonstrate the effectiveness and usability of CareerLens through two case studies and receive encouraging feedback from follow-up interviews with domain experts.

Visual Cascade Analytics of Large-Scale Spatiotemporal Data.

Many spatiotemporal events can be viewed as contagions. These events implicitly propagate across space and time by following cascading patterns, expanding their influence, and generating event cascades that involve multiple locations. Analyzing such cascading processes presents valuable implications in various urban applications, such as traffic planning and pollution diagnostics. Motivated by the limited capability of the existing approaches in mining and interpreting cascading patterns, we propose a visual analytics system called VisCas. VisCas combines an inference model with interactive visualizations and empowers analysts to infer and interpret the latent cascading patterns in the spatiotemporal context. To develop VisCas, we address three major challenges 1) generalized pattern inference; 2) implicit influence visualization; and 3) multifaceted cascade analysis. For the first challenge, we adapt the state-of-the-art cascading network inference technique to general urban scenarios, where cascading patterns can be reliably inferred from large-scale spatiotemporal data. For the second and third challenges, we assemble a set of effective visualizations to support location navigation, influence inspection, and cascading exploration, and facilitate the in-depth cascade analysis. We design a novel influence view based on a three-fold optimization strategy for analyzing the implicit influences of the inferred patterns. We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts.

Compass: Towards Better Causal Analysis of Urban Time Series.

The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts.

Access to Deuterated Unnatural α-Amino Acids and Peptides by Photochemical Acyl Radical Addition.

A visible-light-enabled, photocatalyst-free conjugate addition reaction of dehydroamino acids is disclosed. Employing 4-acyl-1,4-dihydropyridines as both a radical reservoir and reductant, various ß-acyl α-amino acids and their deuterated analogues were obtained in good results. Both late-stage peptide modification and stereoselective synthesis of chiral oxazolidinones are successfully achieved. The protocol is characterized by mild conditions and efficient derivatization, thus unlocking a novel blueprint to access unnatural amino acid derivatives, important building blocks with potential application in the peptidomimetic toolbox.

[CCL18 Promotes the Invasion of Lung Adenocarcinoma through ANXA2].

BACKGROUND: ANXA2 plays a very important role in cancer progression. chemokine ligand 18 (CCL18) is associated with the invasion, migration, metastasis and poor prognosis of lung adenocarcinoma (LUAD). In this study, we aimed to explore whether CCL18 promotes LUAD invasion through ANXA2, and its role and molecular mechanism in LUAD invasion. METHODS: Western blot was used to detect ANXA2 expression in LUAD tissues and adjacent non-tumor tissues, the transfection efficiency of SiANXA2#2 in cells and the role of ANXA2 as an upstream regulator in the AKT/cofilin signaling pathway. In vitro cytological experiments such as chemotaxis experiment and transwell invasion test was used to explore the mechanism of ANXA2 on LUAD metastasis. F-actin polymerization experiment and Western blot were used to detect whether invasion ability alteration of SiANXA2#2 A549 cells are related to F-actin. RESULTS: Western blot analysis showed that compared with adjacent non-tumor tissues, the protein expression level of ANXA2 in cancer tissues increased (P<0.05). In the chemotaxis experiment and invasion experiment, the chemotaxis and invasion ability induced by CCL18 decreased when ANXA2 knockdowned (P<0.05). Compared with the control group, F-actin polymerization was significantly lower in ANXA2 knockdown group, while phosphorylation of AKT at Ser473 and Thr308 and phosphorylation of Cofilin and LIMK were reduced in ANXA2 knockdown group (P<0.05). CONCLUSIONS: ANXA2 knockdown can reduce the invasive effect of CCL18 on LUAD cells by reducing phosphorylation of AKT and downstream pathways.

Towards Better Bus Networks: A Visual Analytics Approach.

Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics.

CCL18 promotes the invasion and metastasis of breast cancer through Annexin A2.

Chemokine (C­C motif) ligand 18 (CCL18) is derived from breast tumor­associated macrophages (TAMs), which are primarily a macrophage subpopulation with an M2 phenotype. CCL18 binds to its receptor, PYK2 N­terminal domain interacting receptor 1 (Nir1), and promotes tumor progression and metastasis by inducing epithelial­mesenchymal transition (EMT) via the PI3K/Akt/GSK3ß/Snail signaling pathway in breast cancer cells. Recent research shows that Annexin A2 (AnxA2) plays a significant role in the invasion, metastasis, angiogenesis, proliferation, F­actin polymerization and multidrug resistance to chemotherapy of breast cancer. The present study aimed to elucidate the molecular mechanisms by which CCL18 promotes breast cancer progression through AnxA2 which are not fully understood. Western blot analysis showed that the expression of AnxA2 was upregulated in highly invasive breast cancer cell lines and invasive ductal carcinoma. Furthermore, through chemotaxis, scratch, Matrigel invasion, and spontaneous metastasis assays, it was demonstrated that AnxA2 enhanced the invasion of breast cancer cells and the metastasis of human breast cancer cells to lungs of SCID mice with CCL18 stimulation. Cellular F­actin measurement assay showed that reduction of AnxA2 suppressed CCL18­induced F­actin polymerization though phosphorylation of integrin ß1 in breast cancer cells. Immunofluorescence and western blot analysis revealed that AnxA2 promoted CCL18­induced EMT via the PI3K/Akt/GSK3ß/Snail signaling pathway, and LY294002 inhibited the phosphorylation of AnxA2 in vitro. In brief, AnxA2, as a downstream molecule of Nir 1 binding to CCL18, promotes invasion and metastasis by EMT through the PI3K/Akt/GSK3ß/Snail signaling pathway in breast cancer. This study suggests that AnxA2 is a potential anti­invasion/metastasis target for therapeutic intervention in breast cancer.

AirVis: Visual Analytics of Air Pollution Propagation.

Air pollution has become a serious public health problem for many cities around the world. To find the causes of air pollution, the propagation processes of air pollutants must be studied at a large spatial scale. However, the complex and dynamic wind fields lead to highly uncertain pollutant transportation. The state-of-the-art data mining approaches cannot fully support the extensive analysis of such uncertain spatiotemporal propagation processes across multiple districts without the integration of domain knowledge. The limitation of these automated approaches motivates us to design and develop AirVis, a novel visual analytics system that assists domain experts in efficiently capturing and interpreting the uncertain propagation patterns of air pollution based on graph visualizations. Designing such a system poses three challenges: a) the extraction of propagation patterns; b) the scalability of pattern presentations; and c) the analysis of propagation processes. To address these challenges, we develop a novel pattern mining framework to model pollutant transportation and extract frequent propagation patterns efficiently from large-scale atmospheric data. Furthermore, we organize the extracted patterns hierarchically based on the minimum description length (MDL) principle and empower expert users to explore and analyze these patterns effectively on the basis of pattern topologies. We demonstrated the effectiveness of our approach through two case studies conducted with a real-world dataset and positive feedback from domain experts.

SRVis: Towards Better Spatial Integration in Ranking Visualization.

Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.