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Polyphenols have attracted attention due to their antioxidant capacity and beneficial effects to health. Therefore, fast, inexpensive, and efficient methods to discriminate and to quantify polyphenols are of interest for food industry. In this paper, Layer-by-Layer films of poly(allylamine hydrochloride) and iron tetrasulfonated phthalocyanine were employed as sensor for determination of polyphenols in green tea (camellia sinensis), and green and roasted mate teas (ilex paraguariensis). The polyphenol sensor was tested in catechol standard solution by differential pulse voltammetry (DPV), reaching a limit of detection of 1.76 × 10-7 mol/L. The determination of polyphenols in the tea samples was obtained by analytical curve and catechol standard addition using electrochemical techniques. Projection techniques (information visualization) were applied to the DPV results of the tea samples and a pattern of separation following the phenolic content was obtained. The results support the application of the sensor in fast classification of beverages according to their polyphenol content.

Immunoaffinity enrichment of peptides coupled to multiple reaction monitoring-mass spectrometry (immuno-MRM) enables highly specific, sensitive, and precise quantification of peptides and post-translational modifications. Major obstacles to developing a large number of immuno-MRM assays are the poor availability of monoclonal antibodies (mAbs) validated for immunoaffinity enrichment of peptides and the cost and lead time of developing the antibodies de novo. Although many thousands of mAbs are commercially offered, few have been tested for application to immunoaffinity enrichment of peptides. In this study we tested the success rate of using commercially available mAbs for peptide immuno-MRM assays. We selected 105 commercial mAbs (76 targeting non-modified “pan” epitopes, 29 targeting phosphorylation) to proteins associated with the DNA damage response network. We found that 8 of the 76 pan (11%) and 5 of the 29 phospho-specific mAbs (17%) captured tryptic peptides (detected by LC-MS/MS) of their protein targets from human cell lysates. Seven of these mAbs were successfully used to configure and analytically characterize immuno-MRM assays. By applying selection criteria upfront, the results indicate that a screening success rate of up to 24% is possible, establishing the feasibility of screening a large number of catalog antibodies to provide readily-available assay reagents.

Interactive visualization systems for exploring and manipulating high-dimensional feature spaces have experienced a substantial progress in the last few years. State-of-art methods rely on solid mathematical and computational foundations that enable sophisticated and flexible interactive tools. Current methods are even capable of modifying data attributes during interaction, highlighting regions of potential interest in the feature space, and building visualizations that bring out the relevance of attributes. However, those methodologies rely on complex and non-intuitive interfaces that hamper the free handling of the feature spaces. Moreover, visualizing how neighborhood structures are affected during the space manipulation is also an issue for existing methods. This paper presents a novel visualization-assisted methodology for interacting and transforming data attributes embedded in feature spaces. The proposed approach relies on a combination of multidimensional projections and local transformations to provide an interactive mechanism for modifying attributes. Besides enabling a simple and intuitive visual layout, our approach allows the user to easily observe the changes in neighborhood structures during interaction. The usefulness of our methodology is shown in an application geared to image retrieval.

We review recent visualization techniques aimed at supporting tasks that require the analysis of text documents, from approaches targeted at visually summarizing the relevant content of a single document to those aimed at assisting exploratory investigation of whole collections of documents.Techniques are organized considering their target input material—either single texts or collections of texts—and their focus, which may be at displaying content, emphasizing relevant relationships, highlighting the temporal evolution of a document or collection, or helping users to handle results from a query posed to a search engine.We describe the approaches adopted by distinct techniques and briefly review the strategies they employ to obtain meaningful text models, discuss how they extract the information required to produce representative visualizations, the tasks they intend to support and the interaction issues involved, and strengths and limitations. Finally, we show a summary of techniques, highlighting their goals and distinguishing characteristics. We also briefly discuss some open problems and research directions in the fields of visual text mining and text analytics. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Word clouds have become one of the most widely accepted visual resources for document analysis and visualization, motivating the development of several methods for building layouts of keywords extracted from textual data. Existing methods are effective to demonstrate content, but are not capable of preserving semantic relationships among keywords while still linking the word cloud to the underlying document groups that generated them. Such representation is highly desirable for exploratory analysis of document collections. In this paper we present a novel approach to build document clouds, named ProjCloud that aim at solving both semantical layouts and linking with document sets. ProjCloud generates a semantically consistent layout from a set of documents. Through a multidimensional projection, it is possible to visualize the neighborhood relationship between highly related documents and their corresponding word clouds simultaneously. Additionally, we propose a new algorithm for building word clouds inside polygons, which employs spectral sorting to maintain the semantic relationship among words. The effectiveness and flexibility of our methodology is confirmed when comparisons are made to existing methods. The technique automatically constructs projection based layouts the user may choose to examine in the form of the point clouds or corresponding word clouds, allowing a high degree of control over the exploratory process. © 2012 Wiley Periodicals, Inc.

Fiber tracts detection is an increasingly common technology for diagnosis and also understanding of brain function. Although tools for tracing and presenting brain fibers are advanced, it is still difficult for physicians or students to explore the dataset in 3D due to their intricate topology. In this work we present a visual exploration approach for fiber tracts data aimed at supporting exploration of such data. The work employs a local, precise and fast 2D multidimensional projection technique that allows a large number of fibers to be handled simultaneously and to select groups of bundled fibers for further exploration. In this approach, a DTI feature dataset, including curvature as well as spatial features, is projected on a 2D or 3D view. By handling groups formed in this view, exploration is linked to corresponding brain fibers in object space. The link exists in both directions and fibers selected in object space are also mapped to feature space. Our approach also allows users to modify the projection, controlling and improving, if necessary, the definition of groups of fibers for small and large datasets, due to the local nature of the projection. Compared to other related work, the method presented here is faster for creating visual representations, making it possible to explore complete sets of fibers tracts up to 250K fibers, which was not possible previously. Additionally, the ability to change configuration of the feature space representation adds a high degree of flexibility to the process. © 2012 Wiley Periodicals, Inc.

Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e.g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework's applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.

An overview is presented of the many opportunities and challenges for using “Big Data” concepts to treat data from chemical sensors. Issues discussed include the need to make data machine readable ...