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The (liquid + liquid) coexistences and the critical behavior of isobaric heat capacity per unit volume for critical binary solutions {dimethyl carbonate + n-undecane, or n-tridecane} have been studied. The critical exponents β and α were deduced and found to be consistent with the 3D-Ising values. The critical amplitudes were determined and used to test the asymmetric criticality of coexistence curves. It was found that the heat capacity does play an important role in describing the asymmetric criticality of the coexistence curves.

Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research.

Densities of binary mixtures {x dimethyl adipate + (1 − x) n-heptane} and {x dimethyl adipate + (1 − x) n-octane} were measured at various temperatures. The excess molar volumes were calculated from the density data and fitted with the Redlich–Kister equation. The critical anomaly of density was detected for {x dimethyl adipate + (1 − x) n-octane} binary solutions with the critical compositions in a narrow temperature range of the near-critical one-phase region. However, the density anomaly and the excess properties were taken into account in a test of the complete scaling theory and found to be negligible. The isobaric heat capacities per unit volume of the {x dimethyl adipate + (1 − x) n-heptane} and {x dimethyl adipate + (1 − x) n-octane} binary solutions at the critical compositions were also determined over the temperature range both near and far away from the critical points. It was found that the heat capacity plays an important role in describing the asymmetry of the coexistence curves.

Visual analytics employs interactive visualizations to integrate users’ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business. The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field. This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications. More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions. We explore and discuss the analytics space to add the current understanding and better understand research trends in the field.

Community structure is an important characteristic of many real networks, which shows high concentrations of edges within special groups of vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships among communities, identifying attribute-structure relationships, and selecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks. The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated, uncluttered visualizations, and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.

In this paper, we propose a novel constrained coclustering method to achieve two goals. First, we combine information-theoretic coclustering and constrained clustering to improve clustering performance. Second, we adopt both supervised and unsupervised constraints to demonstrate the effectiveness of our algorithm. The unsupervised constraints are automatically derived from existing knowledge sources, thus saving the effort and cost of using manually labeled constraints. To achieve our first goal, we develop a two-sided hidden Markov random field (HMRF) model to represent both document and word constraints. We then use an alternating expectation maximization (EM) algorithm to optimize the model. We also propose two novel methods to automatically construct and incorporate document and word constraints to support unsupervised constrained clustering: 1) automatically construct document constraints based on overlapping named entities (NE) extracted by an NE extractor; 2) automatically construct word constraints based on their semantic distance inferred from WordNet. The results of our evaluation over two benchmark data sets demonstrate the superiority of our approaches against a number of existing approaches.

Turbidity in the one-phase region and the isobaric heat capacity per unit volume in both one-phase and two-phase regions for the critical solutions of {benzonitrile + n-alkane} were measured, from which the values of the system-dependent critical amplitudes for the correlation length, the osmotic compressibility, the heat capacity and the correction-to-scaling term of the heat capacity were deduced. The previously reported data of the coexistence curves for {benzonitrile + n-alkane} were also reanalyzed with the crossover model to obtain the crossover parameters. Subsequently, these results together with the values of the critical amplitude related to the coexistence curve reported previously were used to calculate some universal critical amplitude ratios, which showed reasonable agreement with the theoretical predictions.

Sponsored and organized by School of Software Engineering, Tianjin University, and co-organized by Yanshan University and Northeast University at Qinhuangdao, 2012 Workshop on Information Visualization was successfully held in Beidaihe, China, 9-10 June 2012. This article reports and summarizes the discussion details, including the challenges and current major research directions in information visualization and visual analytics. The article also sets the future directions and proposes various action items for promoting and advancing the cutting-edge research in information visualization and visual analytics.

The Ethylene-Responsive Factors (ERFs) comprise a large family of transcriptional factors that play critical roles in plant immunity. Gray mold disease caused by Botrytis cinerea, a typical necrotrophic fungal pathogen, is the serious disease that threatens tomato production worldwide. However, littler is known about the molecular mechanism regulating the immunity to B. cinerea in tomato. In the present study, virus-induced gene silencing (VIGS)-based functional analyses of 18 members of B3 group (also called Group IX) in tomato ERF family were performed to identify putative ERFs that are involved in disease resistance against Botrytis cinerea. VIGS-based silencing of either SlERF.B1 or SlERF.C2 had lethal effect while silencing of SlERF.A3 (Pit4) significantly suppressed vegetative growth of tomato plants. Importantly, silencing of SlERF.A1, SlERF.A3, SlERF.B4 or SlERF.C3 resulted in increased susceptibility to B. cinerea, attenuated the B. cinerea-induced expression of jasmonic acid/ethylene-mediated signaling responsive defense genes and promoted the B. cinerea-induced H2O2 accumulation. However, silencing of SlERF.A3 also decreased the resistance against Pseudomonas syringae pv. tomato (Pst) DC3000 but silencing of SlERF.A1, SlERF.B4 or SlERF.C3 did not affect the resistance to this bacterial pathogen. Expression of SlERF.A1, SlERF.A3, SlERF.B4 or SlERF.C3 was induced by B. cinerea and by defense signaling hormones such as salicylic acid, methyl jasmonate and 1-aminocyclopropane-1-carboxylic acid (an ethylene precursor). SlERF.A1, SlERF.B4, SlERF.C3 and SlERF.A3 proteins were found to localize in nucleus of cells and possess transactivation activity in yeasts. These data suggest that SlERF.A1, SlERF.B4 and SlERF.C3, three previously uncharacterized ERFs in B3 group, and SlERF.A3, a previously identified ERF with function in immunity to Pst DC3000, play important roles in resistance against B. cinerea in tomato.

CCR4-Not complex is a multifunctional regulator that plays important roles in multiple cellular processes in eukaryotes. In the present study, the biological function of FonNot2, a core subunit of the CCR4-Not complex, was explored in Fusarium oxysporum f. sp. niveum (Fon), the causal agent of watermelon wilt disease. FonNot2 was expressed at higher levels in conidia and germinating conidia and during infection in Fon-inoculated watermelon roots than in mycelia. Targeted disruption of FonNot2 resulted in retarded vegetative growth, reduced conidia production, abnormal conidial morphology, and reduced virulence on watermelon. Scanning electron microscopy observation of infection behaviors and qRT-PCR analysis of in planta fungal growth revealed that the ΔFonNot2 mutant was defective in the ability to penetrate watermelon roots and showed reduced fungal biomass in root and stem of the inoculated plants. Phenotypic and biochemical analyses indicated that the ΔFonNot2 mutant displayed hypersensitivity to cell wall perturbing agents (e.g., Congo Red and Calcofluor White) and oxidative stress (e.g., H2O2 and paraquat), decreased fusaric acid content, and reduced reactive oxygen species (ROS) production during spore germination. Our data demonstrate that FonNot2 plays critical roles in regulating vegetable growth, conidiogenesis and conidia morphology, and virulence on watermelon via modulating cell wall integrity, oxidative stress response, ROS production and FA biosynthesis through the regulation of transcription of genes involved in multiple pathways.

Trehalose and its metabolism have been demonstrated to play important roles in control of plant growth, development, and stress responses. However, direct genetic evidence supporting the functions of trehalose and its metabolism in defense response against pathogens is lacking. In the present study, genome-wide characterization of putative trehalose-related genes identified 11 SlTPSs for trehalose-6-phosphate synthase, 8 SlTPPs for trehalose-6-phosphate phosphatase and one SlTRE1 for trehalase in tomato genome. Nine SlTPSs, 4 SlTPPs, and SlTRE1 were selected for functional analyses to explore their involvement in tomato disease resistance. Some selected SlTPSs, SlTPPs, and SlTRE1 responded with distinct expression induction patterns to Botrytis cinerea and Pseudomonas syringae pv. tomato (Pst) DC3000 as well as to defense signaling hormones (e.g., salicylic acid, jasmonic acid, and a precursor of ethylene). Virus-induced gene silencing-mediated silencing of SlTPS3, SlTPS4, or SlTPS7 led to deregulation of ROS accumulation and attenuated the expression of defense-related genes upon pathogen infection and thus deteriorated the resistance against B. cinerea or Pst DC3000. By contrast, silencing of SlTPS5 or SlTPP2 led to an increased expression of the defense-related genes upon pathogen infection and conferred an increased resistance against Pst DC3000. Silencing of SlTPS3, SlTPS4, SlTPS5, SlTPS7, or SlTPP2 affected trehalose level in tomato plants with or without infection of B. cinerea or Pst DC3000. These results demonstrate that SlTPS3, SlTPS4, SlTPS5, SlTPS7, and SlTPP2 play roles in resistance against B. cinerea and Pst DC3000, implying the importance of trehalose and tis metabolism in regulation of defense response against pathogens in tomato.

The Ethylene-Responsive Factors (ERFs) comprise a large family of transcriptional factors that play critical roles in plant immunity. Gray mold disease caused by Botrytis cinerea, a typical necrotrophic fungal pathogen, is the serious disease that threatens tomato production worldwide. However, littler is known about the molecular mechanism regulating the immunity to B. cinerea in tomato. In the present study, virus-induced gene silencing (VIGS)-based functional analyses of 18 members of B3 group (also called Group IX) in tomato ERF family were performed to identify putative ERFs that are involved in disease resistance against B. cinerea. VIGS-based silencing of either SlERF.B1 or SlERF.C2 had lethal effect while silencing of SlERF.A3 (Pit4) significantly suppressed vegetative growth of tomato plants. Importantly, silencing of SlERF.A1, SlERF.A3, SlERF.B4, or SlERF.C3 resulted in increased susceptibility to B. cinerea, attenuated the B. cinerea-induced expression of jasmonic acid/ethylene-mediated signaling responsive defense genes and promoted the B. cinerea-induced H2O2 accumulation. However, silencing of SlERF.A3 also decreased the resistance against Pseudomonas syringae pv. tomato (Pst) DC3000 but silencing of SlERF.A1, SlERF.B4 or SlERF.C3 did not affect the resistance to this bacterial pathogen. Expression of SlERF.A1, SlERF.A3, SlERF.B4, or SlERF.C3 was induced by B. cinerea and by defense signaling hormones such as salicylic acid, methyl jasmonate, and 1-aminocyclopropane-1-carboxylic acid (an ethylene precursor). SlERF.A1, SlERF.B4, SlERF.C3, and SlERF.A3 proteins were found to localize in nucleus of cells and possess transactivation activity in yeasts. These data suggest that SlERF.A1, SlERF.B4, and SlERF.C3, three previously uncharacterized ERFs in B3 group, and SlERF.A3, a previously identified ERF with function in immunity to Pst DC3000, play important roles in resistance against B. cinerea in tomato.

Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable., Submitted to VIS 2016

Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods are all batch algorithms that scan the full dataset before each update of the model and make inexact variational approximations with mean-field assumptions. Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics. This paper fills this research void, and presents a fast and parallelizable inference algorithm using Gibbs Sampling with Stochastic Gradient Langevin Dynamics that does not make any unwarranted assumptions. We also present a Metropolis-Hastings based $O(1)$ sampler for topic assignments for each word token. In a distributed environment, our algorithm requires very little communication between workers during sampling (almost embarrassingly parallel) and scales up to large-scale applications. We are able to learn the largest Dynamic Topic Model to our knowledge, and learned the dynamics of 1,000 topics from 2.6 million documents in less than half an hour, and our empirical results show that our algorithm is not only orders of magnitude faster than the baselines but also achieves lower perplexity., 10 pages, 8 figures, to appear in WWW 2016

Zoomable user interfaces are widely used in static visualizations and have many benefits. However, they are not well supported in animated visualizations due to problems such as change blindness and information overload. We propose the spot-tracking lens, a new zoomable user interface for animated bubble charts, to tackle these problems. It couples zooming with automatic panning and provides a rich set of auxiliary techniques to enhance its effectiveness. Our preliminary user studies suggested that, besides allowing users to examine detail information, it can be an engaging approach to exploratory analysis for dynamic data.

Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic-preserving word clouds by leveraging tailored seam carving, a well-established content-aware image resizing operator. The method can optimize a word cloud layout by removing a left-to-right or top-to-bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian-based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques.

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.

Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result: the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data.

It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow.

When and where is an idea dispersed? Social media, like Twitter, has been increasingly used for exchanging information, opinions and emotions about events that are happening across the world. Here we propose a novel visualization design, “Whisper”, for tracing the process of information diffusion in social media in real time. Our design highlights three major characteristics of diffusion processes in social media: the temporal trend, social-spatial extent, and community response of a topic of interest. Such social, spatiotemporal processes are conveyed based on a sunflower metaphor whose seeds are often dispersed far away. In Whisper, we summarize the collective responses of communities on a given topic based on how tweets were retweeted by groups of users, through representing the sentiments extracted from the tweets, and tracing the pathways of retweets on a spatial hierarchical layout. We use an efficient flux line-drawing algorithm to trace multiple pathways so the temporal and spatial patterns can be identified even for a bursty event. A focused diffusion series highlights key roles such as opinion leaders in the diffusion process. We demonstrate how our design facilitates the understanding of when and where a piece of information is dispersed and what are the social responses of the crowd, for large-scale events including political campaigns and natural disasters. Initial feedback from domain experts suggests promising use for today's information consumption and dispersion in the wild.

Upon pathogen infection, activation of immune response requires effective transcriptional reprogramming that regulates inducible expression of a large set of defense genes. A number of ethylene-responsive factor transcription factors have been shown to play critical roles in regulating immune responses in plants. In the present study, we explored the functions of Arabidopsis AtERF15 in immune responses against Pseudomonas syringae pv. tomato (Pst) DC3000, a (hemi)biotrophic bacterial pathogen, and Botrytis cinerea, a necrotrophic fungal pathogen. Expression of AtERF15 was induced by infection of Pst DC3000 and B. cinerea and by treatments with salicylic acid (SA) and methyl jasmonate. Biochemical assays demonstrated that AtERF15 is a nucleus-localized transcription activator. The AtERF15-overexpressing (AtERF15-OE) plants displayed enhanced resistance while the AtERF15-RNAi plants exhibited decreased resistance against Pst DC3000 and B. cinerea. Meanwhile, Pst DC3000- or B. cinerea-induced expression of defense genes was upregulated in AtERF15-OE plants but downregulated in AtERF15-RNAi plants, as compared to the expression in wild type plants. In response to infection with B. cinerea, the AtERF15-OE plants accumulated less reactive oxygen species (ROS) while the AtERF15-RNAi plants accumulated more ROS. The flg22- and chitin-induced oxidative burst was abolished and expression levels of the pattern-triggered immunity-responsive genes AtFRK1 and AtWRKY53 were suppressed in AtER15-RNAi plants upon treatment with flg22 or chitin. Furthermore, SA-induced defense response was also partially impaired in the AtERF15-RNAi plants. These data demonstrate that AtERF15 is a positive regulator of multiple layers of the immune responses in Arabidopsis.

NADPH oxidases (also known as respiratory burst oxidase homologs, Rbohs) are key enzymes that catalyze the generation of reactive oxygen species (ROS) in plants. In the present study, eight SlRboh genes were identified in tomato and their possible involvement in resistance to Botrytis cinerea and drought tolerance was examined. Expression of SlRbohs was induced by B. cinerea and Pseudomonas syringae pv. tomato but displayed distinct patterns. Virus-induced gene silencing based silencing of SlRbohB resulted in reduced resistance to B. cinerea but silencing of other SlRbohs did not affect the resistance. Compared to non-silenced plants, the SlRbohB-silenced plants accumulated more ROS and displayed attenuated expression of defense genes after infection with B. cinerea. Silencing of SlRbohB also suppressed flg22-induced ROS burst and the expression of SlLrr22, a marker gene related to PAMP-triggered immunity (PTI). Transient expression of SlRbohB in Nicotiana benthamiana led to enhanced resistance to B. cinerea. Furthermore, silencing of SlRbohB resulted in decreased drought tolerance, accelerated water loss in leaves and the altered expression of drought-responsive genes. Our data demonstrate that SlRbohB positively regulates the resistance to B. cinerea, flg22-induced PTI, and drought tolerance in tomato.

Background Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases. MMPs have been characterized in detail in mammals and shown to play key roles in many physiological and pathological processes. Although MMPs in some plant species have been identified, the function of MMPs in biotic stress responses remains elusive. Results A total of five MMP genes were identified in tomato genome. qRT-PCR analysis revealed that expression of Sl-MMP genes was induced with distinct patterns by infection of Botrytis cinerea and Pseudomonas syringae pv. tomato (Pst) DC3000 and by treatment with defense-related hormones such as salicylic acid, jasmonic acid and ethylene precursor 1-amino cyclopropane-1-carboxylic acid. Virus-induced gene silencing (VIGS)-based knockdown of individual Sl-MMPs and disease assays indicated that silencing of Sl3-MMP resulted in reduced resistance to B. cinerea and Pst DC3000, whereas silencing of other four Sl-MMPs did not affect the disease resistance against these two pathogens. The Sl3-MMP-silenced tomato plants responded with increased accumulation of reactive oxygen species and alerted expression of defense genes after infection of B. cinerea. Transient expression of Sl3-MMP in leaves of Nicotiana benthamiana led to an enhanced resistance to B. cinerea and upregulated expression of defense-related genes. Biochemical assays revealed that the recombinant mature Sl3-MMP protein had proteolytic activities in vitro with distinct preferences for specificity of cleavage sites. The Sl3-MMP protein was targeted onto the plasma membrane of plant cells when transiently expressed in onion epidermal cells. Conclusion VIGS-based knockdown of Sl3-MMP expression in tomato and gain-of-function transient expression of Sl3-MMP in N. benthamiana demonstrate that Sl3-MMP functions as a positive regulator of defense response against B. cinerea and Pst DC3000. Electronic supplementary material The online version of this article (doi:10.1186/s12870-015-0536-z) contains supplementary material, which is available to authorized users.

S-adenosylhomocysteine hydrolase (SAHH), catalyzing the reversible hydrolysis of S-adenosylhomocysteine (SAH) to adenosine and homocysteine, is a key enzyme that maintain the cellular methylation potential in all organisms. We report here the biological functions of tomato SlSAHHs in stress response. The tomato genome contains three SlSAHH genes that encode SlSAHH proteins with high level of sequence identity. qRT-PCR analysis revealed that SlSAHHs responded with distinct expression induction patterns to Pseudomonas syringae pv. tomato (Pst) DC3000 and Botrytis cinerea as well as to defense signaling hormones such as salicylic acid, jasmonic acid and a precursor of ethylene. Virus-induced gene silencing-based knockdown of individual SlSAHH gene did not affect the growth performance and the response to Pst DC3000. However, co-silencing of three SlSAHH genes using a conserved sequence led to significant inhibition of vegetable growth. The SlSAHH-co-silenced plants displayed increased resistance to Pst DC3000 but did not alter the resistance to B. cinerea. Co-silencing of SlSAHHs resulted in constitutively activated defense responses including elevated SA level, upregulated expression of defense-related and PAMP-triggered immunity marker genes and increased callose deposition and H2O2 accumulation. Furthermore, the SlSAHH-co-silenced plants also exhibited enhanced drought stress tolerance although they had relatively small roots. These data demonstrate that, in addition to the functions in growth and development, SAHHs also play important roles in regulating biotic and abiotic stress responses in plants.

A high-quality label layout is critical for effective information understanding and consumption. Existing labeling methods fail to help users quickly gain an overview of visualized data when the number of labels is large. Visual clutter is a major challenge preventing these methods from being applied to real-world applications. To address this, we propose a context-aware label layout that can measure and reduce visual clutter during the layout process. Our method formulates the clutter model using four factors: confusion, visual connection, distance, and intersection. Based on this clutter model, an effective clutter-aware labeling method has been developed that can generate clear and legible label layouts in different visualizations. We have applied our method to several types of visualizations and the results show promise, especially in support of an uncluttered and informative label layout.

Primers used in this study for different purposes. (DOC 136 kb)

Silencing efficiency in pTRV2- SlPDS -infiltrated tomato plants. Two-week-old seedlings were infiltrated with agrobacteria carrying pTRV2- SlPDS or pTRV2- GUS and leaf samples were collected 3Â weeks after VIGS treatment. (A) Phenotype of pTRV2-SlPDS- or pTRV2-GUS-infiltrated tomato plants. (B) The transcript of SlPDS was analysed by qRT-PCR. (PDF 3931 kb)

SlMED21 is an orthologue of AtMED21. (A) Comparisons of tomato SlMED21 protein with Arabidopsis AtMED21 protein. (B) Neighbor-joining tree of SlMED21 from tomato and other eukaryotic organisms. The MED21 amino acid sequences from various organisms were used for building the phylogenetic tree. Sequence alignment was carried out by the ClustalX program. Phylogenetic trees were constructed using the neighbor-joining (NJ) method of the MEGA6 program with the p-distance and complete deletion option parameters. The reliability of the obtained trees was tested using a bootstrapping method with 1000 replicates. The MED21 sequences are from Sorgum bicolor (SbMED21, AAL73528), Oryza Sative (OsMED21, BAD03057), Arabidopsis (AtMED21, AAD03443, At4g04780), Solanum lycopersicum (SlMED21, AK328398), Mus Musculus (MmMED21, AAH12286), H. sapiens (HsMED21, NP_004255), Danio rerio (DrMED21, NP_998588), S. cervisae (ScMED21, P47822), Schizosaccharomyces pombe (SpMED21, CAA22343), Physcomitrella patens (PpMED21, XP_001763794) and Anopheles gambiae (AgMED21, XP_307937). The AtMED21 and SlMED21 were marked in red. (PDF 3007 kb)

Primers used in this study for different purposes. (PDF 50 kb)

The papers in this special section were presenteda at the 2015 IEEE Pacific Visualization Symposium (PacificVis’15) that was held in Hangzhou from April 14 to 17, 2015.

The proposed method uses context-preserving, dynamic word clouds to illustrate content evolution. It generates a sequence of word clouds in which related words are grouped together. This sequence is then coupled with a trend chart that summarizes content changes so that users can better explore large collections of documents.

ImageHive communicates information about an image collection by generating a summary image that preserves the relationships between images and avoids occluding their salient parts. It uses a constrained graph-layout algorithm first, to preserve image similarities and keep important parts visible, and then a constrained Voronoi tessellation algorithm to locally refine the layout and tile the image plane.

Streamit lets users explore visualizations of text streams without prior knowledge of the data. It incorporates incoming documents from a continuous source into an existing visualization context with automatic grouping and separation based on document similarities. A powerful user interface allows in-depth data analysis.

We present a system to analyze user interests by analyzing their online behaviors from large-scale usage logs. We surmise that user interests can be characterized by a large collection of features we call the behavioral genes that can be deduced from both their explicit and implicit online behaviors. It is the goal of this research to sequence the entire behavioral genome for online population, namely, to identify the pertinent behavioral genes and uncover their relationships in explaining and predicting user behaviors, so that high quality user profiles can be created and the online services can be better customized using these profiles. Within the scope of this paper, we demonstrate the work using the partial genome derived from web search logs. Our demo system is supported by an open access web service we are releasing and sharing with the research community. The main functions of the web service are: (1) calculating query similarities based on their lexical, temporal and semantic scores, (2) clustering a group of user queries into tasks with the same search and browse intent, and (3) inferring user topical interests by providing a probability distribution over a search taxonomy.

Biotic and abiotic stresses are major unfavorable factors that affect crop productivity worldwide. NAC proteins comprise a large family of transcription factors that play important roles in plant growth and development as well as in responses to biotic and abiotic stresses. In a virus-induced gene silencing-based screening to identify genes that are involved in defense response against Botrytis cinerea, we identified a tomato NAC gene SlSRN1 (Solanum lycopersicum Stress-related NAC1). SlSRN1 is a plasma membrane-localized protein with transactivation activity in yeast. Expression of SlSRN1 was significantly induced by infection with B. cinerea or Pseudomonas syringae pv. tomato (Pst) DC3000, leading to 6–8 folds higher than that in the mock-inoculated plants. Expression of SlSRN1 was also induced by salicylic acid, jasmonic acid and 1-amino cyclopropane-1-carboxylic acid and by drought stress. Silencing of SlSRN1 resulted in increased severity of diseases caused by B. cinerea and Pst DC3000. However, silencing of SlSRN1 resulted in increased tolerance against oxidative and drought stresses. Furthermore, silencing of SlSRN1 accelerated accumulation of reactive oxygen species but attenuated expression of defense genes after infection by B. cinerea. Our results demonstrate that SlSRN1 is a positive regulator of defense response against B. cinerea and Pst DC3000 but is a negative regulator for oxidative and drought stress response in tomato.

Research into social network analysis has shown that graph metrics, such as degree and closeness, are often used to summarize structural changes in a dynamic graph. However there have been few visual analytics approaches that have been proposed to help analysts study graph evolutions in the context of graph metrics. In this paper, we present a novel approach, called GraphFlow, to visualize dynamic graphs. In contrast to previous approaches that provide users with an animated visualization, GraphFlow offers a static flow visualization that summarizes the graph metrics of the entire graph and its evolution over time. Our solution supports the discovery of high-level patterns that are difficult to identify in an animation or in individual static representations. In addition, GraphFlow provides users with a set of interactions to create filtered views. These views allow users to investigate why a particular pattern has occurred. We showcase the versatility of GraphFlow using two different datasets and describe how it can help users gain insights into complex dynamic graphs.

This paper presents a new approach for reconstructing solids with planar, quadric and toroidal surfaces from three-view engineering drawings. By applying geometric theory to 3-D reconstruction, our method is able to remove restrictions placed on the axes of curved surfaces by existing methods. The main feature of our algorithm is that it combines the geometric properties of conics with affine properties to recover a wider range of 3-D edges. First, the algorithm determines the type of each 3-D candidate conic edge based on its projections in three orthographic views, and then generates that candidate edge using the conjugate diameter method. This step produces a wire-frame model that contains all candidate vertices and candidate edges. Next, a maximum turning angle method is developed to find all the candidate faces in the wire-frame model. Finally, a general and efficient searching technique is proposed for finding valid solids from the candidate faces; the technique greatly reduces the searching space and the backtracking incidents. Several examples are given to demonstrate the efficiency and capability of the proposed algorithm.

The solid powder and crystal complexes of thorium(IV) nitrate with N,N,N′,N′-tetraphenyl-3,6-dioxaoctanediamide (L), having the formula [Th(NO3)4(L)] and [Th(NO3)4(L)]·2MeCN, respectively, have been prepared and characterized by elemental analysis, molar conductance, IR and 1H NMR spectra. The latter complex crystallizes in the monoclinic space group P21/c, with a 14.156(3), b= 14.595(2), c= 20.723(3)A, β= 97.27(1)° and Z= 4. The final R= 0.0376 for 1857 independent observed [l 3σ(l)] reflections. The thorium atom is twelve-co-ordinated by oxygen atoms, eight from four bidentate nitrate groups and four from L, forming a distorted icosahedron. The compound L acts as a tetradentate ligand forming a half-ring co-ordination structure. The Th–O bonds average 2.515(5)(carbonyl oxygen), 2.565(5)(nitrate oxygen) and 2.653(5)A(ether oxygen).

Storyline visualizations, which are useful in many applications, aim to illustrate the dynamic relationships between entities in a story. However, the growing complexity and scalability of stories pose great challenges for existing approaches. In this paper, we propose an efficient optimization approach to generating an aesthetically appealing storyline visualization, which effectively handles the hierarchical relationships between entities over time. The approach formulates the storyline layout as a novel hybrid optimization approach that combines discrete and continuous optimization. The discrete method generates an initial layout through the ordering and alignment of entities, and the continuous method optimizes the initial layout to produce the optimal one. The efficient approach makes real-time interactions (e.g., bundling and straightening) possible, thus enabling users to better understand and track how the story evolves. Experiments and case studies are conducted to demonstrate the effectiveness and usefulness of the optimization approach.

Understanding topic hierarchies in text streams and their evolution patterns over time is very important in many applications. In this paper, we propose an evolutionary multi-branch tree clustering method for streaming text data. We build evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which considers both the likelihood of the current tree and conditional prior given the previous tree. We also introduce a constraint model to compute the conditional prior of a tree in the multi-branch setting. Experiments on real world news data demonstrate that our algorithm can better incorporate historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm.

Heat capacities in the critical and the non-critical regions for {benzonitrile + tridecane} and {benzonitrile + pentadecane}, and light scattering for {benzonitrile + undecane}, {benzonitrile + dodecane}, {benzonitrile + tridecane}, {benzonitrile + tetradecane}, {benzonitrile + pentadecane}, and {benzonitrile + hexadecane} in the critical two-phase region were measured. Light scattering measurements confirmed the existence of the asymmetry for the osmotic compressibility while no such asymmetry was observed for the correlation length. An analysis of the osmotic compressibility asymmetry suggested the dominance of the singular term |ΔT[circumflex]|(β), which supports the complete scaling theory. The consistency of the complete scaling theory in descriptions of different asymmetry behaviors was also discussed. Moreover, it was found that the contribution of the heat capacity-related term is also important in describing the asymmetry of the osmotic compressibility as it was observed in studies of the diameters of the coexistence curves.

Correlated topical trend detection is very useful in analyzing public and social media influence. In this paper, we propose an algorithm that can both detect the correlation and discover the corresponding keywords that trigger the correlation. To detect the correlation, we use a projection vector to project two text streams onto the same space, and then use a least square cost function to regress one text stream over the other with different time lags. To extract the corresponding keywords, we impose the non-negative sparsity constraints over the projection parameters. In addition, we present an accelerated algorithm based on Nesterov's method to efficiently solve the optimization problem. In our experiments, we use both syntehtic and real data sets to demonstrate the advantages and capabilities of the proposed algorithm over CCA on the follower link prediction problem.

After the news of Osama Bin Laden's death leaked through Twitter, many people wondered if Twitter would fundamentally change the way we produce, spread, and consume news. In this paper we provide an in-depth analysis of how the news broke and spread on Twitter. We confirm the claim that Twitter broke the news first, and find evidence that Twitter had convinced a large number of its audience before mainstream media confirmed the news. We also discover that attention on Twitter was highly concentrated on a small number of "opinion leaders" and identify three groups of opinion leaders who played key roles in spreading the news: individuals affiliated with media played a large part in breaking the news, mass media brought the news to a wider audience and provided eager Twitter users with content on external sites, and celebrities helped to spread the news and stimulate conversation. Our findings suggest Twitter has great potential as a news medium.

Visualization resizing is useful for many applications where users may use different display devices. General resizing techniques (e.g., uniform scaling) and image-resizing techniques suffer from several drawbacks, as they do not consider the content of the visualizations. This work introduces ViSizer, a perception-based framework for automatically resizing a visualization to fit any display. We formulate an energy function based on a perception model (feature congestion), which aims to determine the optimal deformation for every local region. We subsequently transform the problem into an optimization problem by the energy function. An efficient algorithm is introduced to iteratively solve the problem, allowing for automatic visualization resizing.

Much research has been devoted to topic detection from text, but one major challenge has not been addressed: revealing the rich relationships that exist among the detected topics. Finding such relationships is important since many applications are interested in how topics come into being, how they develop, grow, disintegrate, and finally disappear. In this paper, we present a novel method that reveals the connections between topics discovered from the text data. Specifically, our method focuses on how one topic splits into multiple topics, and how multiple topics merge into one topic. We adopt the hierarchical Dirichlet process (HDP) model, and propose an incremental Gibbs sampling algorithm to incrementally derive and refine the labels of clusters. We then characterize the splitting and merging patterns among clusters based on how labels change. We propose a global analysis process that focuses on cluster splitting and merging, and a finer granularity analysis process that helps users to better understand the content of the clusters and the evolution patterns. We also develop a visualization process to present the results.

Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data.

Text visualization becomes an increasingly more important research topic as the need to understand massive-scale textual information is proven to be imperative for many people and businesses. However, it is still very challenging to design effective visual metaphors to represent large corpora of text due to the unstructured and high-dimensional nature of text. In this paper, we propose a data model that can be used to represent most of the text corpora. Such a data model contains four basic types of facets: time, category, content (unstructured), and structured facet. To understand the corpus with such a data model, we develop a hybrid visualization by combining the trend graph with tag-clouds. We encode the four types of data facets with four separate visual dimensions. To help people discover evolutionary and correlation patterns, we also develop several visual interaction methods that allow people to interactively analyze text by one or more facets. Finally, we present two case studies to demonstrate the effectiveness of our solution in support of multi-faceted visual analysis of text corpora.