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To accommodate the demands of a data-driven society, we have expanded our ability to collect and store data, develop sophisticated algorithms, and generate elaborated visual representations of the data analysis process outcomes. However, data preprocessing, as the activity of transforming the raw data into an appropriate format for subsequent analysis, is still a challenging part of this process. Although we can find studies that address the use of visualization techniques to support the activities in the scope of preprocessing, the current Visual Analytics processes do not consider preprocessing an equally important phase in their processes. Hence, with this paper, we aim to contribute to the discussion of how we can incorporate the preprocessing as a prominent phase in the Visual Analytics process and promote better alternatives to assist the data analysts during the preprocessing activities. To achieve that, we are introducing the Preprocessing Profiling Approach for Visual Analytics (PrAVA), a conceptual Visual Analytics process that includes Preprocessing Profiling as a new phase. It also contemplates a set of guidelines to be considered by new solutions adopting PrAVA. Moreover, we analyze its applicability through use case scenarios that show resourceful methods for data understanding and evaluation of the preprocessing impacts. As a final contribution, we indicate a list of research opportunities in the scope of preprocessing combined with visualization and Visual Analytics to stimulate a shift to visual preprocessing.

Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance. Part of this trend, visualization techniques have been extensively used to support classification models' interpretability, with a significant focus on rule-based models. Despite the advances, the existing approaches present limitations in terms of visual scalability, and the visualization of large and complex models, such as the ones produced by the Random Forest (RF) technique, remains a challenge. In this paper, we propose Explainable Matrix (ExMatrix), a novel visualization method for RF interpretability that can handle models with massive quantities of rules. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates, enabling the analysis of entire models and auditing classification results. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability., Comment: IEEE VIS VAST 2020

As governments take measures against COVID-19, the epidemic situation is expected to improve, but public sentiment is likely to fluctuate during this process, potentially influencing the best course of action. Social media has become a prevalent way for the public to express emotions and opinions in recent times. So that, the sentiment analysis on top of it may detect and provide valuable evidence of public attitude and help governments subsequent formulation of measures and policies. We present Senti-COVID19, an interactive visual analytic system for reflecting and analyzing public sentiment and detecting sentiment fluctuation triggers on social media. Senti-COVID19 adopts lexicon-based sentiment analysis to divulge the public opinion to COVID-19 events, employing libraries to extract keywords and statistics for providing detailed information. In addition, it offers visualizations for presenting the analysis, allowing users to quickly discover relevant information. Our results show that Senti-COVID19 can be used effectively to analyze sentiment from social media text, allowing users to explore derived data and identify insights from the collected tweets.

The striking results of deep neural networks (DNN) have motivated its wide acceptance to tackle large datasets and complex tasks such as natural language processing, facial recognition, and artificial image generation. However, DNN parameters are often empirically selected on a trial-and-error approach without detailed information on convergence behavior. While some visualization techniques have been proposed to aid the comprehension of general-purpose neural networks, only a few explore the training process, lacking the ability to adequately display how abstract representations are formed and represent the influence of training parameters during this process. This paper describes neural network training fingerprint (NNTF), a visual analytics approach to investigate the training process of any neural network performing classification. NNTF allows understanding how classification decisions change along the training process, displaying information about convergence, oscillations, and training rates. We show its usefulness through case studies and demonstrate how it can support the analysis of training parameters.

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and downstream costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Here, we demonstrate the capability of internal standard triggered-parallel reaction monitoring (IS-PRM) to prioritize candidate biomarkers for validation studies. A 5,176-plex assay coupling immunodepletion and fractionation with IS-PRM was developed and implemented in human plasma to quantify peptides representing 1,314 breast cancer biomarker candidates. Compared to prior approaches using data-dependent analysis, IS-PRM showed improved sensitivity (912 vs 295 proteins quantified) and precision (CV 0.1 vs 0.27) enabling rank-ordering of candidate biomarkers for validation studies. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Immunotherapies are revolutionizing cancer care, producing durable responses and potentially cures in a subset of patients. However, response rates are low for most tumors, grade 3/4 toxicities are not uncommon, and our current understanding of tumor immunobiology is incomplete. While hundreds of immunomodulatory proteins in the tumor microenvironment shape the anti-tumor response, few of them can be reliably quantified. To address this need, we developed a multiplex panel of targeted proteomic assays targeting 52 peptides representing 46 proteins using peptide immunoaffinity enrichment coupled to multiple reaction monitoring-mass spectrometry. We validated the assays in tissue and plasma matrices, where performance figures of merit showed over 3 orders of dynamic range and median inter-day CVs of 5.2% (tissue) and 21% (plasma). A feasibility study in clinical biospecimens showed detection of 48/52 peptides in frozen tissue and 38/52 peptides in plasma. The assays are publicly available as a resource for the research community.

The concept of involving users in the loop of analytic workflows refers to the ability to replace heuristics with user input in machine learning and data mining tasks. For supervised tasks, user engagement generally occurs via the manipulation of training data. But for unsupervised tasks, user involvement is limited to changes in the algorithm parametrization or the input data representation, also known as features. Typically, different types of features can be extracted from raw data, and the careful selection of the extraction strategy allows users to have more control over unsupervised tasks. Nevertheless, since there is no perfect feature extractor, the combination of multiple sets of features has been explored through a process called feature fusion. Feature fusion can be readily performed when the machine learning or data mining algorithms have a cost function, such as accuracy for classification tasks. However, when such a function does not exist, user support needs to be provided, otherwise the process is impractical. In this article, we present a novel feature fusion approach that employs data samples and visualization to allow users to not only effortlessly control the combination of different feature sets but also understand the attained results. The effectiveness of our approach is confirmed by a comprehensive set of qualitative and quantitative experiments, opening up different possibilities for user-guided analytical scenarios. The ability of our approach to provide real-time feedback for feature fusion is exploited in the context of unsupervised clustering techniques, where users can perform an exploratory process to discover the best combination of features that reflects their individual perceptions about similarity.

Made available in DSpace on 2022-04-28T19:40:22Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-15 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) In exploratory tasks involving high-dimensional datasets, dimensionality reduction (DR) techniques help analysts to discover patterns and other useful information. Although scatter plot representations of DR results allow for cluster identification and similarity analysis, such a visual metaphor presents problems when the number of instances of the dataset increases, resulting in cluttered visualizations. In this work, we propose a scatter plot-based multilevel approach to display DR results and address clutter-related problems when visualizing large datasets, together with the definition of a methodology to use focus+context interaction on non-hierarchical embeddings. The proposed technique, called ExplorerTree, uses a sampling selection technique on scatter plots to reduce visual clutter and guide users through exploratory tasks. We demonstrate ExplorerTree's effectiveness through a use case, where we visually explore activation images of the convolutional layers of a neural network. Finally, we also conducted a user experiment to evaluate ExplorerTree's ability to convey embedding structures using different sampling strategies. Faculty of Sciences and Technology São Paulo State University (UNESP) Faculty of Computer Science Dalhousie University Institute of Mathematics and Computer Sciences University of São Paulo Faculty of Sciences and Technology São Paulo State University (UNESP) FAPESP: 2016/11707-6 FAPESP: 2017/17450-0 FAPESP: 2018/17881-3 FAPESP: 2018/25755-8

Made available in DSpace on 2021-06-25T11:13:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Objectives: The objectives of this study were to characterize the craniofacial and airway morphology of oculo-auriculo-vertebral spectrum (OAVS) individuals using computed tomography (CT) examination. Setting and Sample Population: This sample included individuals in the age range from 5 to 14 years, consisted of a group of 18 OAVS individuals (12 females and 6 males), Pruzansky-Kaban1 IIB and III and by a paired control group matched by age and sex for comparison of morphometric and airway variables. Materials and Methods: Through the CT examination, airway analysis was performed using Dolphin Imaging® Software, and seven morphometric measurements were performed to evaluate craniofacial morphology by Materialize Mimics® Software. To compare airway and morphometric variables, the control group was used. Student's t test and Mann-Whitney U test were performed to compare differences between the groups. Results: Statistically significant differences were showed between the control and OAVS groups for the variables: total airway (TA) area, volume and MAA, RP area, RP volume, RP MAA, RG volume, RG MAA, total posterior height diff, Md incl and y-axis asymmetry. Pearson and Spearman's correlation showed mostly moderate correlations between Mand Occlusal canting AS with TA area and RP volume, Ax-Gn with TA area and Hy-C3 with TA volume. Conclusions: The OAVS's airway was altered and worse than the control group. Our results suggest that the contralateral side of OAVS individuals is unaffected; however, longitudinal assessments are needed to confirm it. Hyoid bone and postural measures play an important role in interpreting airway features of individuals with and without OAVS. Department of Pediatric Dentistry School of Dentistry São Paulo State University (UNESP) Department of Orthodontics University of São Paulo (USP) Hospital for Rehabilitation of Craniofacial Anomalies Department of Orthodontics Dentistry School of Dentistry University of Michigan Department of Orthodontics University of the Pacific Arthur A. Dugoni School of Dentistry Department of Clinical Genetics University of São Paulo (USP) Hospital for Rehabilitation of Craniofacial Anomalies Hospital for Rehabilitation of Craniofacial Anomalies University of São Paulo (USP) Department of Pediatric Dentistry School of Dentistry São Paulo State University (UNESP)

Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models’ inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose Random Forest Similarity Map (RFMap), a scalable interactive visual analytics tool designed to analyze RF ensemble models. RFMap focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values. The interactive nature of RFMap allows users to visually interpret model errors and decisions, establishing the necessary confidence and user trust in RF models and improving performance.

The growing neural gas (GNG) is an unsupervised topology learning algorithm that models a data space through interconnected units that stand on the populated areas of that space. Its output is a graph that can be visually represented on a two-dimensional plane, and be used as means to disclose cluster patterns in datasets. GNG, however, creates highly connected graphs when trained on high dimensional data, which in turn leads to highly clutter representations that fail to disclose any meaningful patterns. Moreover, its sequential learning limits its potential for faster executions on local datasets, and, more importantly, its potential for training on distributed datasets while leveraging from the computational resources of the infrastructures in which they reside. This paper presents two methods that improve GNG for the visualization of cluster patterns in large and high-dimensional datasets. The first one focuses on providing more meaningful and accurate cluster pattern representations of high-dimensional datasets, by avoiding connections that lead to high-dimensional graphs in the modeled topology, which may, in turn, lead to visual cluttering in 2D representations. The second method presented in this paper enables the use of GNG on big and distributed datasets with faster execution times, by modeling and merging separate parts of a dataset using the MapReduce model. Quantitative and qualitative evaluations show that the first method leads to the creation of lower-dimensional graph structures, which in turn provide more accurate and meaningful cluster representations; and that the second method preserves the accuracy and meaning of the cluster representations while enabling its execution in distributed settings. CC BY 4.0

MotivationDirected gene/protein regulatory networks inferred by applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of clinical outcomes. However, there remain unsolved challenges in DAG learning to jointly model clinical outcome variables, which often take binary values, and biomarker measurements, which usually are continuous variables. Therefore, in this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models for continuous and binary variables, DAGBagM allows for either type of nodes to be parents or children nodes in the learned graph. DAGBagM also employs a bootstrap aggregating strategy to reduce false positives and achieve better estimation accuracy. Moreover, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges for DAG reconstruction.ResultsAs shown by simulation studies, DAGBagM performs better in identifying edges between continuous and binary nodes, as compared to commonly used strategies of either treating binary variables as continuous or discretizing continuous variables. Moreover, DAGBagM outperforms several popular DAG structure learning algorithms including the score-based hill climbing (HC) algorithm, constraint-based PC-algorithm (PC-alg), and the hybrid method max-min hill climbing (MMHC) even for constructing DAG with only continuous nodes. The HC implementation in the R package DAGBagM is much faster than that in a widely used DAG learning R package bnlearn. When applying DAGBagM to proteomics datasets from ovarian cancer studies, we identify potential prognostic protein biomarkers in ovarian cancer.Availability and implementationDAGBagMis made available as a github repositoryhttps://github.com/jie108/dagbagM.

Electrochemically aligned collagen (ELAC) threads fabricated by the isoelectric focusing (IF) of collagen have previously shown potential in tissue engineering and more recently in the fabrication of biohybrid robot structures. For applications in biohybrid robotics, ELAC structures are needed that are both robust and compliant enough to facilitate muscle actuation. However, studies on the effects of IF parameters, and the interactions of such fabrication parameters, on the mechanical and geometric properties of resulting ELAC threads have not been previously found in literature. Understanding the impact of these manufacturing parameters on the material properties is critical to facilitate biohybrid robot design. In this study, the effects of IF duration, IF voltage, and collagen solution concentration were investigated and showed statistically significant effects on adjusting ELAC properties via single-factor experiments. The interactions between parameters exhibited significant joint effects on ELAC property tuning through two-factor experiments. Scanning electron microscopy and 2,4,6-trinitrobenzenesulfonic (TNBS) assays revealed the correlation between high mechanical properties and a combination of low porosity and high degree of crosslinking. By simply tuning IF parameters without changing other fabrication steps, such as crosslinker concentration, ELAC threads with a wide range of mechanical and geometric properties were fabricated. The average tensile modulus of the resulting ELAC threads ranged from 198 ± 90 to 758 ± 138 MPa. The average cross-sectional area ranged from 7756 ± 1000 to 1775 ± 457 μm2. The resultant mapping between IF parameters and ELAC thread properties enabled the production of strong and flexible threads with customizable properties.

Early detection of Primary Immunodeficiencies Disorders (PIDDs) is of paramount importance for effective treatment and disease management. Many PIDDs would be strong candidates for newborn screening (NBS) if robust screening methods could identify patients from dried blood spots (DBS) during the neonatal period. As majority of congenital PIDDs result in the reduction or absence of specific proteins, direct quantification of these target proteins represents an attractive potential screening tool. Unfortunately, detection is often limited by the extremely low protein concentrations in blood cells and limited blood volume present in DBS. We have recently developed a robust novel method for quantification of low abundance proteins in DBS for PIDDs using peptide immunoaffinity enrichment coupled to selected reaction monitoring (immuno-SRM). Here, we further generated a multiplexed Immuno-SRM panel for simultaneous screening of eight signature peptides representing five PIDD-specific and two cell-type specific proteins from DBS. In samples from 28 PIDD patients including two carriers, representing X-Linked Agammaglobulinemia (XLA), Wiskott-Aldrich Syndrome (WAS), X-Linked Chronic Granulomatous Disease (XL-CGD), DOCK8 Deficiency and ADA deficiency, peptides representing each disease are significantly reduced relative to normal controls and patient identification had excellent agreement with clinical and molecular diagnosis. Also included in the multiplex panel are cell specific markers for platelets (CD42) and Natural Killer Cells (CD56). In patients with WAS, CD42 levels were found to be significantly reduced consistent with characteristic thrombocytopenia. A patient with WAS analyzed before and after bone marrow transplant showed normalized WAS protein and platelet CD42 after treatment highlighting the ability of immuno-SRM to monitor the effects of PIDD treatment. The assay was readily reproduced in two separate laboratories with similar analytical performance and complete agreement in patient diagnosis demonstrating the effective standardized methods. A high-throughput Immuno-SRM method screens PIDD-specific peptides in a 2.5-min runtime meeting high volume NBS workflow requirements was also demonstrated in this report. This high-throughput method returned identical results to the standard Immuno-SRM PIDD panel. Immuno-SRM peptide analysis represents a robust potential clinical diagnostic for identifying and studying PIDD patients from easily collected and shipped DBS and supports a significant potential for early PIDD diagnosis through newborn screening.

Background AZD0156 and AZD6738 are potent and selective inhibitors of ataxia-telangiectasia-kinase (ATM) and ataxia-telangiectasia-mutated and Rad3-related (ATR), respectively, important sensors/signallers of DNA damage. Methods We used multiplexed targeted-mass-spectrometry to select pRAD50(Ser635) as a pharmacodynamic biomarker for AZD0156-mediated ATM inhibition from a panel of 45 peptides, then developed and tested a clinically applicable immunohistochemistry assay for pRAD50(Ser635) detection in FFPE tissue. Results We found moderate pRAD50 baseline levels across cancer indications. pRAD50 was detectable in 100% gastric cancers (n = 23), 99% colorectal cancers (n = 102), 95% triple-negative-breast cancers (TNBC) (n = 40) and 87.5% glioblastoma-multiformes (n = 16). We demonstrated AZD0156 target inhibition in TNBC patient-derived xenograft models; where AZD0156 monotherapy or post olaparib treatment, resulted in a 34–72% reduction in pRAD50. Similar inhibition of pRAD50 (68%) was observed following ATM inhibitor treatment post irinotecan in a colorectal cancer xenograft model. ATR inhibition, using AZD6738, increased pRAD50 in the ATM-proficient models whilst in ATM-deficient models the opposite was observed, suggesting pRAD50 pharmacodynamics post ATR inhibition may be ATM-dependent and could be useful to determine ATM functionality in patients treated with ATR inhibitors. Conclusion Together these data support clinical utilisation of pRAD50 as a biomarker of AZD0156 and AZD6738 pharmacology to elucidate clinical pharmacokinetic/pharmacodynamic relationships, thereby informing recommended Phase 2 dose/schedule.

Abstract: Low output syndrome significantly increases morbidity and mortality of cardiac surgery and lengthens the durations of intensive care unit and hospital stays. Its treatment by catecholamines can lead to undesirable systemic and cardiac complications. Levosimendan is a calcium sensitiser and adenosine triphosphate (ATP)-sensitive potassium channel (IK,ATP) opener agent. Due to these effects, it improves myocardium performance, does not influence adversely the balance between O2 supply and demand, and possesses cardioprotective and organ protective properties as well. Based on the scientific literature and experts’ opinions, a European recommendation was published on the perioperative use of levosimendan in cardiac surgery in 2015. Along this line, and also taking into consideration cardiac surgeon, anaesthesiologist and cardiologist representatives of the seven Hungarian heart centres and the children heart centre, the Hungarian recommendation has been formulated that is based on two pillars: literature evidence and Hungarian expert opinions. The reviewed fields are: coronary and valvular surgery, assist device implantation, heart transplantation both in adult and pediatric cardiologic practice. Orv Hetil. 2018; 159(22): 870–877.

Following preclinical optimization of B7-H3-specific CAR T cells against pediatric brain tumor models, we opened BrainChild-03 (NCT04185038), a phase 1 clinical trial of repeatedly dosed, outpatient, locoregional B7-H3-specific CAR T cells for children with recurrent/refractory central nervous system (CNS) tumors or diffuse intrinsic pontine glioma (DIPG). Here, we report the interim findings from patients enrolled on Arm C, dedicated to DIPG. The primary endpoints are feasibility and safety, with secondary endpoints of disease response. We utilize second-generation CAR T cells with a 4-1BB costimulatory domain and a methotrexate-resistant human DHFR mutein (huDHFRFS; L22F,F31S), allowing for methotrexate selection. We do not deliver conditioning chemotherapy. The first three evaluable patients with DIPG all met feasibility for generating a balanced CD4:CD8 CAR T cell product, with 3.85x109 CAR T cells generated for S005, 4.29x109 for S008, and 2.45x109 for S012, allowing for greater than 6 months of biweekly dosing for each patient. All subjects were treated at Dose Level 1 (1x107 CAR T cells). S005 received 10 doses before clinical progression greater than 2 years from diagnosis, S008 has received 10 doses and continues on therapy with decreased tumor volume, and S012 has received 5 doses and continues on study with stable disease. There have been no dose limiting toxicities (DLT). 3/3 patients exhibited post infusion fever, headache, and elevated serum CRP but no evidence of cytokine release syndrome (CRS) or systemic CAR T cells. 0/3 patients required PICU admissions. In the cerebrospinal fluid (CSF), 2/3 patients have had elevations of cytokines such as CXCL10 and CCL2, as well as circulating CSF CAR T cells. Advanced serial patient CSF proteomic and transcriptomic profiling are underway. Ultimately, this report provides preliminary evidence that outpatient locoregional B7-H3 CAR T cells for children with DIPG may be feasible and tolerable.

Multidimensional projection techniques have become essential analytical tools. Typically, they map data from a high-dimensional space into a low-dimensional visual space, preserving distance or neighborhood structures on the produced layout. Despite the advances, with faster and highly precise techniques, existing methods still carry deficiencies that impair their use as exploratory tools. An example is the mismatching that can occur between what the user considers similar/dissimilar and what is conveyed by the visual representation. Recently, a class of projection techniques aims at addressing this limitation, allowing users to control the projection process by changing the distance relationships using small data samples. Among such methods, Local Affine Multidimensional Projection has proved to be the state-of-the-art regarding the effectiveness of user intervention. Although Local Affine Multidimensional Projection has attained a relative success, it is limited to certain application domains. Since it relies on feature vector representations with data instances described by vectors embedded into a Euclidean space, scenarios that offer only distance information or a distance function cannot be handled. In this article, we present a novel multidimensional projection technique, called User-assisted Projection Technique for Distance Information, which takes advantage of the solid mathematical framework provided by Local Affine Multidimensional Projection, adapting it to scenarios where only distance information or a distance function is available. The results show that User-assisted Projection Technique for Distance Information is as fast, accurate, robust, and flexible as the existing state-of-the-art techniques, enabling the application of the refined user control provided by Local Affine Multidimensional Projection on domains not previously covered. Its versatility is illustrated in an application that involves the organization of book collections that employs an external source of information for the recommendation of new readings.

Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients&rsquo, survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening.

Dimensionality Reduction (DR) techniques are widely used to analyze and make sense of high-dimensional data. Each method is geared towards preserving a different aspect of the data. For example, some techniques favor neighborhood preservation whereas others favor distance preservation. While these DR techniques help users to represent their data, it makes a complex task to select a suitable DR. Also, most DR techniques have additional parameters that affect the results, which make the task of choosing a technique more difficult. Existing methods compare DR techniques using some quality metrics, and some of them combine DR outputs by averaging projections. However, it does not yet provide enough mechanisms to create a new DR according to user requirements. In this paper, we present a way to analyze and compare different DR techniques. It is an interactive assessment method that allows a user to explore known DR techniques, identify the differences between them, and create a new DR technique that combines existing techniques to match user expectations.

Platinum-based DNA cross-linking agents are widely used anti-cancer drugs. Tumor resistance to platinum compounds is a major determinant of patient survival, including in high grade serous ovarian cancer (HGSOC). Remarkably, despite >30 years of literature on platinum responses in human cancer, none of these findings is used clinically as a predictive biomarker to stratify patients for platinum resistance, nor exploited therapeutically to treat platinum-resistant disease. Thus, understanding mechanisms of platinum resistance is an urgent goal, both to identify predictive biomarkers of platinum response (to spare patients with platinum-resistant tumors futile platinum therapy) and to develop efficacious therapies for platinum-resistant disease. To better understand mechanisms underlying platinum resistance in HGSOC, we performed comprehensive, dynamic (+/-carboplatin), multiomic profiling of DNA, RNA, protein and post-translational modifications (phosphorylation, ubiquitination, acetylation) to identify the cellular networks that respond to platinum treatment and associate with platinum resistance in three HGSOC intra-patient cell line pairs (PEA1S/PEA2R, PEO1S/PEO4R, PEO14S/PEO23R). The cell line pairs were derived from ascites or pleural effusions (Langdon et al., 1988) from three patients both before (PEA1S, PEO1S, PEO14S) and after (PEA2R, PEO4R, PEO23R, respectively) their tumors became clinically platinum resistant (i.e., in vivo development of resistance). The molecular profiles revealed extensive responses to carboplatin and differential responses between platinum-sensitive and platinum-resistant cells. Higher oxidative phosphorylation and fatty acid oxidation (FAO) pathway expression were observed in the platinum-resistant cells, which was further validated in patient-derived xenograft (PDX) models. We show that pharmacologic inhibition or CRISPR knockout of CPT1A, which represents a rate limiting step of FAO, sensitize HGSOC cells to platinum. Thus, FAO, and CPT1A in particular, represent a candidate therapeutic target to overcome platinum resistance in HGSOC. Citation Format: Hong Wang, Dongqing Hugang, Shrabanti Chowdhury, Sara Savage, Richard Ivey, Jacob Kennedy, Jeffrey Whiteaker, Chenwei Lin, Xiaonan Hou, Catherine Huntoon, Uliana Voytovich, Zahra Shire, Qing Yu, Steven Gygi, Andrew Hoofnagle, Zachary Herbert, Anna Calinawan, Larry Karnitz, S. John Weroha, Scott Kaufmann, Bing Zhang, Pei Wang, Michael Birrer, Amanda Paulovich. Multiomic analysis identifies CPT1A and fatty acid oxidation as a potential therapeutic target in platinum-refractory high grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1426.

Kernel-based methods have experienced a substantial progress in the last years, tuning out an essential mechanism for data classification, clustering and pattern recognition. The effectiveness of kernel-based techniques, though, depends largely on the capability of the underlying kernel to properly embed data in the feature space associated to the kernel. However, visualizing how a kernel embeds the data in a feature space is not so straightforward, as the embedding map and the feature space are implicitly defined by the kernel. In this work, we present a novel technique to visualize the action of a kernel, that is, how the kernel embeds data into a high-dimensional feature space. The proposed methodology relies on a solid mathematical formulation to map kernelized data onto a visual space. Our approach is faster and more accurate than most existing methods while still allowing interactive manipulation of the projection layout, a game-changing trait that other kernel-based projection techniques do not have.

We performed a comprehensive proteogenomic analysis across seven childhood brain tumors for a deeper understanding of their functional biology. Whole genome sequencing, RNAseq, quantitative proteomic profiling and phosphoproteomics were performed on 219 fresh frozen tumor samples representing the histologic diagnoses of: low grade astrocytoma (93), ependymoma (32), high grade astrocytoma (26), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16) and atypical teratoid rhabdoid tumor (12). Unsupervised clustering analysis based on proteomics data reveals eight clusters with distinct protein profiles and pathway activities. While some clusters coincide with histologic diagnoses, a couple of clusters appear to be a mixture of different diagnoses, including one cluster consisting of “aggressive” tumors characterized by poor survival and high stemness scores. By integrating proteomic data with RNAseq and WGS data, we characterize the impact of mutations (H3K27M, BRAFV600E, BRAF fusion) and CNVs upon the proteome across various diagnoses. Multiomics based kinase-substrate association analysis and co-expression network analysis reveal targetable active kinase networks within these tumors. Proteomic data reveals unique biology associated with H3K27M mutation status in HGG and BRAF aberrations in LGG. Characterization of the tumor microenvironment through deconvolution analyses based on multi-omics data reveals 5 distinct tumor clusters associated with different populations of infiltrating immune cells and the relative activity of the immune system based upon the expression of pro-inflammation or immunosuppressive markers. This study reports the first large-scale deep comprehensive proteogenomic analysis crossing traditional histologic boundaries to uncover foundational pediatric brain tumor biology including functional insight that helps drive translational efforts.

Although high-grade serous ovarian cancers (HGSOC) are highly chemosensitive with an 85% initial response rate to platinum-based chemotherapy, 15% of patients are “exceptional nonresponders,” with platinum-refractory tumors that remain stable or progress during treatment. Unfortunately, we have no predictive biomarkers to identify refractory patients up front, and they receive futile chemotherapy through which most patients become too ill to be eligible for clinical trials. Hence, no progress has been made in treating these deadly tumors. The goal of this study is to identify mechanisms of platinum refractoriness to: i) predict refractory HGSOCs up front and ii) identify potential new drug targets in refractory disease to point to desperately needed new therapeutic approaches. Of note, 80-90% of patients who are initially platinum responsive will relapse and develop platinum-resistant disease, and it is possible that findings in platinum-refractory tumors might also provide insights into platinum-resistant tumors. Our NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC)-funded approach combines genomic and proteomic (“proteogenomic”) analyses of both preclinical models (0, 8, and 24 hours post-platinum exposure) and treatment-naïve human tumors. For preclinical models, we studied a well-characterized collection of patient-derived xenograft (PDX) models (10 sensitive, 10 refractory), as well as intrapatient HGSOC cell line pairs derived from patients before and after the development of platinum resistance. For the PDX models, proteogenomic profiling included RNASeq, WES, global proteomics, and phosphoproteomics at all 3 time points (0, 8, 24 hours). For cell line models (3 sensitive, 3 resistant), proteogenomic profiling was performed at all 3 timepoints (0, 8, 24 hours), and experiments were performed in complete biologic triplicate. Analyses included RNASeq, WES, global proteomics, phosphoproteomics, ubiquitin proteome, acetylated proteome, and pTyr. A large collection of 275 human HGSOCs (an approximate equal balance of platinum sensitive and refractory tumors) is currently undergoing proteomic profiling, and genomic profiles (WGS, RNASeq) will be performed on a subset. In parallel, we have performed a comprehensive review of 31 years of published work on platinum responses of human cancers, identifying ~700 genes implicated in the response and scoring each gene with respect to strength of the published evidence. Using a Bayesian approach, we are integrating the curated candidates from the literature with our empirical proteogenomic datasets to identify a candidate signature for detecting platinum-refractory disease prior to chemotherapy. We are also performing gene-regulatory network analysis to identify potential drivers of chemo response. NextGen, targeted, multiplex, multiple reaction monitoring mass spectrometry-based assays are being developed to quantify proteins in the signature for validation studies, using independent patient cohorts. This abstract is also being presented as Poster A62. Citation Format: Jacob J. Kennedy, Shrabanti Chowdhury, Sara R. Savage, Xiaonan Hou, Catherine J. Huntoon, Richard G. Ivey, Qing Yu, Chenwei Lin, Dongqing Huang, Lei Zhao, Uliana J. Voytovich, Regine M. Schoenherr, Zahra Shire, Steven J. Skates, Jeffrey R. Whiteaker, Andrew N. Hoofnagle, Samuel C. Mok, Bing Zhang, Larry M. Karnitz, S. John Weroha, Steven P. Gygi, Scott H. Kaufmann, Pei Wang, Michael J. Birrer, Amanda G. Paulovich. Proteogenomic approach to identify mechanisms of platinum refractoriness in high-grade serous ovarian cancers [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr PR02.

Background: Primary immunodeficiency disorders (PIDD) comprise a group of life-threatening congenital diseases characterized by absent or impaired immune responses. Despite the fact that effective, curative treatments are available with optimal clinical outcomes when diagnosed early, newborn screening does not exist for the majority of these diseases due to the lack of detectable, specific biomarkers or validated methods for population-based screening. Peptide immunoaffinity enrichment coupled with selected reaction monitoring mass spectrometry (immuno-SRM) is a sensitive proteomic assay, involving antibody-mediated peptide capture, that allows for concurrent quantification of multiple analytes. This assay has promise for use in potential newborn screening of PIDDs that lead to diminished or absent target proteins in the majority of cases. Objective: To determine and evaluate if a multiplex assay based on immuno-SRM is able to reliably and precisely distinguish affected patients with X-linked agammaglobulinemia (XLA), Wiskott-Aldrich Syndrome (WAS), and CD3ϵ-associated severe combined immunodeficiency (SCID) from one another and from unaffected normal control dried blood spot (DBS) samples. Methods: We performed a blinded, multiplexed analysis of proteolytically-generated peptides from WASp, BTK, and CD3ϵ (for WAS, XLA, and SCID, respectively) in DBS samples from 42 PIDD patients, 40 normal adult controls, and 62 normal newborns. The peptide ATPase copper transporting protein (ATP7B) 1056 was simultaneously monitored for quality assurance purposes. Results: The immuno-SRM assays reliably quantified the target peptides in DBS and accurately distinguished affected patients from normal controls. Analysis of signature peptides found statistically significant reduction or absence of peptide levels in affected patients compared to control groups in each case (WASp and BTK: p = 0.0001, SCID: p = 0.05). Intra and inter-assay precision ranged from 11 to 22% and 11 to 43% respectively; linearity (1.39–2000 fmol peptide), and stability (≤ 0.09% difference in 72 h) showed high precision for the multiplexed assay. Inter-laboratory assay comparison showed high concordance for measured peptide concentrations, with R2 linearity ≥ 0.97 for the WASp 274, CD3ϵ 197, BTK 407, and ATP7B 1056 peptides. Conclusion: Immuno-SRM-based quantification of proteotypic peptides from WASp, BTK, and CD3ϵ in DBS distinguishes relevant PIDD cases from one another and from controls, raising the possibility of employing this approach for large-scale multiplexed newborn screening of selective PIDDs.

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

The development of multimedia and network technologies strongly increase the interest on Internet broadcasting or streaming services, especially for soccer games. An example is the 2014 World Cup soccer tournament that registered record-breaking audiences worldwide, providing attractive alternatives to traditional TV viewing. The confluence of video streaming and computational resources opens up many possibilities for applications such as the online detection of interesting events, strategy analysis, or statistics comparisons. While much research targets algorithms to detect match statistics, strategy, retrieval, and indexing, the problem of presenting such information to users is much less studied. This paper proposes a simple but effective visual metaphor to help users browse and get insight into sports matches, with a focus on soccer games. We extract video segments, based on audio and metadata, identifying the main events of a game. Next, we use such events to define a visual representation that preserves the time-order of the video sequence, highlighting the most important events. Our visual representation enables the quick finding of the main events, allowing users to improve navigation when exploring a match, and also provides a way to evaluate the quality of a game or entire tournaments. We demonstrate our approach by applying it to several matches of 2014 World Cup, analyzing its knockout stage and comparing the final match in six different languages.

Introduction: Since the celebration of the 20th anniversary of the first heart transplantation in Hungary in 2012 the emerging need for modern heart failure management via mechanical circulatory support has evolved. In May 2012 the opening of a new heart failure and transplant unit with 9 beds together with the procurement of necessary devices at Semmelweis University accomplished this need. Aim: The aim of the authors was to report their initial experience obtained in this new cardiac assist device program. Method: Since May, 2012, mechanical circulatory support system was applied in 89 cases in 72 patients. Indication for support were end stage heart failure refractory to medical treatment and acute left or right heart failure. Results: Treatment was initiated for acute graft failure after heart transplantation in 27 cases, for end stage heart failure in 24 cases, for acute myocardial infarction in 21 cases, for acute postcardiotomy heart failure in 14 cases, for severe respiratory insufficiency in 2 cases and for drug intoxication in one case. Among the 30 survivor of the whole program 13 patients were successfully transplanted. Conclusions: The available devices can cover all modalities of current bridge therapy from short term support through medium support to heart transplantation or long term support and destination therapy. These conditions made possible the successful start of a new cardiac assist device program. Orv. Hetil., 2015, 156(13), 521–527.

As the number and complexity of visualization techniques have grown, it has become progressively more difficult to make a decision as to which technique to employ for any given situation or application. A particular case is that of multidimensional data visualization utilizing projections, which have gained much attention lately and are being utilized in a growing number of applications. With their popularity, many new variations of multidimensional projections have been proposed in the literature. Numerical evaluations are varied and are useful, but do not reflect visual properties of projections accurately. In this paper we present Projection Inspector, an approach that contributes to the problem of understanding the difference amongst projections. It is an interactive assessment method that allows a user to explore a “space” of known projection techniques and view their results, as well as to identify the differences between them. In addition, it generates “on-the-fly” new projection techniques via interpolations of existing techniques as the user explores the projection space. We present the theoretical foundations of the projection exploration space and an interactive tool that implements a view of this space. We demonstrate the approach with case studies that demonstrate the need for projection assessment and the value of combining projections into new, better suited, projection alternatives.

In this paper, we discuss the relevance of sensing and biosensing for the ongoing revolution in science and technology as a product of the merging of machine learning and Big Data into affordable technologies and accessible everyday products. Possible scenarios for the next decades are described with examples of intelligent systems for various areas, most of which will rely on ubiquitous sensing. The technological and societal challenges for developing the full potential of such intelligent systems are also addressed.

Az epicardialis pacemakerek ritka, de akar eletet veszelyeztető szovődmenye a generatortelepnek a subcostalisan kialakitott zsebből a hasuregbe tortenő elvandorlasa. A szerzők 69 eves nőbeteguk esetet ismertetik, aki aspecifikus hasi panaszokkal jelentkezett a klinika surgőssegi osztalyan. Anamneziseből kiemelendő, hogy korabbi mitralisműbillentyű-műtetet kovetően kialakult III. foku atrioventricularis blokkja miatt epigastrialis pacemakert helyeztek be ugy, hogy a pacemakergeneratort a subcostalisan kialakitott intramuscularis zsebben helyeztek el. Mellkasatvilagitas igazolta, hogy a telep – eredeti helyetől messzire – a Douglas-uregbe vandorolt. A szokatlan helyre es tavolsagra vandorolt keszuleket hasi feltarasbol tavolitottak el, kozvetlenul azt kovetően, hogy uj endocardialis pacemakerrendszer kerult beultetesre. A beteg szovődmenymentesen, a 7. posztoperativ napot kovetően otthonaba tavozott. Orv. Hetil., 2015, 156(4), 154–157.

In recent years there has been a growing interest in developing new methods and systems that allow users to interactively explore large volumes of data, such as document collections, multimedia collections or biomedical datasets. There are various approaches to support users in this interactive environment ranging from the development of new algorithms through visualisation methods to specialised interfaces. The overarching goal of this workshop is to bring together a group of researchers spanning across multiple facets of exploratory search and data analytics to discuss, and outline research challenges for this novel area.

Successful treatment of type A acute aortic dissection depends on the promptness of diagnostic evaluation and therapy. Fast diagnosis can be challenged by numerous complications such as myocardial ischemia, acute aortic insufficiency, and disturbances in organ perfusion and pericardial tamponade. The authors report the case history of a 72-year-old woman, who was admitted after resuscitation with ST segment elevation. Echocardiography revealed acute type A aortic dissection with signs of pericardial tamponade. An emergency operation consisting of the resection of the ascending aorta and the reconstruction of the aortic root was performed, which took six hours from admission until the end of the operation. Follow-up examinations demonstrated good left ventricular function and competent aortic valve. The authors propose that with the development of diagnostic and therapeutic options, faster and less invasive interventions will be introduced in near future for the treatment of acute aortic dissection, which may reduce the morbidity and mortality rates of this lethal illness. Orv. Hetil., 2014, 155(44), 1763–1767.

Panorama is a web application for storing, sharing, analyzing, and reusing targeted assays created and refined with Skyline,1 an increasingly popular Windows client software tool for targeted proteomics experiments. Panorama allows laboratories to store and organize curated results contained in Skyline documents with fine-grained permissions, which facilitates distributed collaboration and secure sharing of published and unpublished data via a web-browser interface. It is fully integrated with the Skyline workflow and supports publishing a document directly to a Panorama server from the Skyline user interface. Panorama captures the complete Skyline document information content in a relational database schema. Curated results published to Panorama can be aggregated and exported as chromatogram libraries. These libraries can be used in Skyline to pick optimal targets in new experiments and to validate peak identification of target peptides. Panorama is open-source and freely available. It is distributed as part of LabKey Server,2 an open source biomedical research data management system. Laboratories and organizations can set up Panorama locally by downloading and installing the software on their own servers. They can also request freely hosted projects on https://panoramaweb.org , a Panorama server maintained by the Department of Genome Sciences at the University of Washington.

The Hungarian adult heart transplant program, which started in 1992, has changed gradually in the past 20 years. After the early enthusiasm of the first cases it changed significantly and it became an organized programme. However, low donation activity and moderate referral numbers to the national transplant waiting list slowed down the process therefore, heart transplant numbers did not fulfill expectations in the early years. After a moderate increase in 2007 transplant numbers have dropped again until recently when Hungary partially joined Eurotransplant network. Excess fundamental resources allocated to cardiac transplantation by health care professionals and reorganizing transplant coordination as well as logistics forced dramatic changes in clinical management. In 2011 and 2012 major structural changes had been made at Semmelweis University. The newly established transplant intensive care unit and the initiation of mechanical circulatory support and assist device programme increased transplant numbers by 131% compared to previous years, as well as it resulted an 86.63% 30-day survival rate, hence last year was the most successful year of cardiac transplantation ever. Orv. Hetil., 2013, 154, 863–867.

We present a case of a 41 year old African-American male with a history of thromboembolic events that was found to have a SSP as a result of pulmonary infarct. Patient underwent definitive surgical intervention, lobectomy, after failure of initial management with thoracostomy tube and video-assisted thoracoscopic (VATS) with wedge resection and pleurodesis.

The fast growth of celiac disease diagnosis has sparked the production of gluten-free food and the search for reliable methods to detect gluten in foodstuff. In this paper, we report on a microfluidic electronic tongue (e-tongue) capable of detecting trace amounts of gliadin, a protein of gluten, down to 0.005 mg kg–1 in ethanol solutions, and distinguishing between gluten-free and gluten-containing foodstuff. In some cases, it is even possible to determine whether gluten-free foodstuff has been contaminated with gliadin. That was made possible with an e-tongue comprising four sensing units, three of which made of layer-by-layer (LbL) films of semiconducting polymers deposited onto gold interdigitated electrodes placed inside microchannels. Impedance spectroscopy was employed as the principle of detection, and the electrical capacitance data collected with the e-tongue were treated with information visualization techniques with feature selection for optimizing performance. The sensing units are disposable...

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.

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.

In this paper we discuss the detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data. The sensing units contained either lipase or glucose oxidase immobilized in layer-by-layer (LbL) films deposited onto interdigitated electrodes. The optimization consisted in identifying which part of the electrical response and combination of sensing units yielded the best distinguishing ability. It is shown that complete separation can be obtained for a range of concentrations of glucose and triglyceride when the interactive document map (IDMAP) technique is used to project the data into a two-dimensional plot. Most importantly, the optimization procedure can be extended to other types of biosensors, thus increasing the versatility of analysis provided by tailored molecular architectures exploited with various detection principles.

Multidimensional projection techniques have experienced many improvements lately, mainly regarding computational times and accuracy. However, existing methods do not yet provide flexible enough mechanisms for visualization-oriented fully interactive applications. This work presents a new multidimensional projection technique designed to be more flexible and versatile than other methods. This novel approach, called Local Affine Multidimensional Projection (LAMP), relies on orthogonal mapping theory to build accurate local transformations that can be dynamically modified according to user knowledge. The accuracy, flexibility and computational efficiency of LAMP is confirmed by a comprehensive set of comparisons. LAMP's versatility is exploited in an application which seeks to correlate data that, in principle, has no connection as well as in visual exploration of textual documents.

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.

Participatory design is a democratic process that involves direct collaboration with end-users to achieve design outcomes. In healthcare, patients are often considered the most valuable group to recruit into participatory design activities. However, when designing in unpredictable health settings with end-users who may be marginalised, direct designer-patient interaction is not always advisable, and as a result, designers may perceive that they are neglecting the fundamental democratic principles of participatory design. This article presents a communication design study that was conducted in an Australian children's hospital where clinicians were used as designer-patient mediators. I argue that the use of mediators can open up participation to a much wider range of end-users, including those that a designer cannot access, aligning with participatory design's democratic philosophy.

Most multidimensional projection techniques rely on distance (dissimilarity) information between data instances to embed high-dimensional data into a visual space. When data are endowed with Cartesian coordinates, an extra computational effort is necessary to compute the needed distances, making multidimensional projection prohibitive in applications dealing with interactivity and massive data. The novel multidimensional projection technique proposed in this work, called Part-Linear Multidimensional Projection (PLMP), has been tailored to handle multivariate data represented in Cartesian high-dimensional spaces, requiring only distance information between pairs of representative samples. This characteristic renders PLMP faster than previous methods when processing large data sets while still being competitive in terms of precision. Moreover, knowing the range of variation for data instances in the high-dimensional space, we can make PLMP a truly streaming data projection technique, a trait absent in previous methods.

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image—Projection Explorer for Images—a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.