Mol* web molecular graphics engine.

Glutathione-S-transferases (GSTs) are ubiquitous detoxification enzymes that catalyse the conjugation of electrophilic substrates to glutathione. Here, we present the crystal structures of Gtt2, a GST of Saccharomyces cerevisiae, in apo and two ligand-bound forms, at 2.23 A, 2.20 A and 2.10 A, respectively. Although Gtt2 has the overall structure of a GST, the absence of the classic catalytic essential residues--tyrosine, serine and cysteine--distinguishes it from all other cytosolic GSTs of known structure. Site-directed mutagenesis in combination with activity assays showed that instead of the classic catalytic residues, a water molecule stabilized by Ser129 and His123 acts as the deprotonator of the glutathione sulphur atom. Furthermore, only glycine and alanine are allowed at the amino-terminus of helix-alpha1 because of stereo-hindrance. Taken together, these results show that yeast Gtt2 is a novel atypical type of cytosolic GST.

BackgroundStructural information about epitopes, particularly the three-dimensional (3D) structures of antigens in complex with immune receptors, presents a valuable source of data for immunology. This information is available in the Protein Data Bank (PDB) and provided in curated form by the Immune Epitope Database and Analysis Resource (IEDB). With continued growth in these data and the importance in understanding molecular level interactions of immunological interest there is a need for new specialized molecular visualization and analysis tools.ResultsThe EpitopeViewer is a platform-independent Java application for the visualization of the three-dimensional structure and sequence of epitopes and analyses of their interactions with antigen-specific receptors of the immune system (antibodies, T cell receptors and MHC molecules). The viewer renders both 3D views and two-dimensional plots of intermolecular interactions between the antigen and receptor(s) by reading curated data from the IEDB and/or calculated on-the-fly from atom coordinates from the PDB. The 3D views and associated interactions can be saved for future use and publication. The EpitopeViewer can be accessed from the IEDB Web site through the quick link 'Browse Records by 3D Structure.'ConclusionThe EpitopeViewer is designed and been tested for use by immunologists with little or no training in molecular graphics. The EpitopeViewer can be launched from most popular Web browsers without user intervention. A Java Runtime Environment (RJE) 1.4.2 or higher is required.

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

ObjectiveTo design, develop and prototype clinical dashboards to integrate high-frequency health and wellness data streams using interactive and real-time data visualisation and analytics modalities.Materials and methodsWe developed a clinical dashboard...

Understanding protein conformation is key to understanding their function. Importantly, most proteins adopt multiple conformations with nontrivial ensemble distributions that change depending on their environment to perform functions like catalysis, signaling, and transport. Recently, machine learning techniques, especially deep generative models, have been employed to develop protein conformation generators. These models, known as unified protein ensemble samplers, are trained on the Protein Data Bank (PDB) dataset and can generate diverse protein conformation ensembles given a protein sequence. However, their reliance solely on structural data from the PDB, which primarily captures folded protein states, restricts the diversity of the generated ensembles and can result in physically unrealistic conformations. In this paper, we overcome these challenges by introducing ExEnDiff, an experiment-guided diffusion model for protein conformation generation. ExEnDiff integrates experimental measurements as a physical prior, enabling the generation of protein conformations with desired properties. Our experiments on a variety of fast-folding and intrinsically disordered proteins demonstrate that ExEnDiff significantly advances the capabilities of current unified protein ensemble samplers. With little computational cost, ExEnDiff can capture important proteins' configuration properties and the underlying Boltzmann distribution, paving the way for a next-generation molecular dynamics engine. We further demonstrate the effectiveness of ExEnDiff to capture conformational changes in the presence of mutations and as an efficient tool for determining a reasonable collective variable space for protein ensembles. With these results, ExEnDiff is well poised to push the study of protein ensembles into a data-rich regime currently available to few problems in biology. Published by the American Physical Society 2025

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.

The large spatial variability of hydraulic properties in natural geologic systems over a wide range of scales, and the difficulty of collecting representative and sufficient hydraulic property measurements using conventional sampling techniques, render estimation of spatial correlation parameters difficult. Further compounding the estimation problem is the observation that the integral scale estimate is a function of the measurement support scale. To mitigate these problems, we investigate the use of tomographic geophysical data in combination with hydrogeological data in the spatial correlation estimation procedure. Two synthetic case studies were investigated where the scale of the geophysical measurements were varied relative to the scale of the hydrogeological properties. The spatial correlation structure parameter estimation procedure was performed in the spectral domain, where analysis of data having different support scales and spatial sampling windows was facilitated. Comparison of the spatial correlation structure parameters estimated from measured data with those of the synthetic aquifers revealed which type of data (tomographic, hydrogeological, or a combination of both) was most effective for recovering spatial correlation statistics under different sampling/heterogeneity conditions. These synthetic case studies suggest that collection of a few tomographic profiles and interpretation of these profiles together with limited well bore data can yield correlation structure information that is otherwise obtainable only from extensive hydrological sampling.

Visualization, a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data visualization as a distinct field. As such, the number of visualization resources and the demand for those resources is increasing at a rapid pace. After a decades-equivalent long search process, we present a survey of open visualization resources for all those with an interest in interactive data visualization and visual analytics. Because the number of resources is so large, we focus on collections of resources, of which there are already many ranging from literature collections to collections of practitioner resources. Based on this, we develop a classification of visualization resource collections with a focus on the resource type, e.g. literature-based, web-based, developer focused and special topics. The result is an overview and details-on-demand of many useful resources. The collection offers a valuable jump-start for those seeking out data visualization resources from all backgrounds spanning from beginners such as students to teachers, practitioners, developers, and researchers wishing to create their own advanced or novel visual designs. This paper is a response to students and others who frequently ask for visualization resources available to them.

The purpose of this study was to develop an interactive data visualization platform for evaluating trabeculectomy outcomes and monitoring clinical care quality. This is a retrospective electronic health record study in a glaucoma division of an academic medical center. We included 633 patients who underwent trabeculectomy between July 2016 and December 2018. Data were collected for intraocular pressure (IOP), vision loss, number of follow-up visits, and return to the operating room within the initial 3 months. Regression analyses were used to assess factors associated with these study outcomes. Risk of IOP ≤5 mm Hg was assessed using survival analysis. An interactive data visualization platform was created. A total of 429 trabeculectomy and 204 combined surgeries were analyzed, with preoperative IOP of 21.9±9.4 and 19.4±6.9 mm Hg, respectively. Mean pressure declined by 53% and 41% (P=0.001) by 3 months. Risk of pressure ≤5 mm Hg was higher in white patients and those who underwent trabeculectomy alone. At 3 months, patients on average had 5 clinic visits; 5.5% required additional surgical procedures, and 18% experienced loss of at least 3 lines of best-recorded (not best-corrected) vision. Returning to the operating room was significantly associated with an increased number of clinic visits (coefficient=3.94) and higher odds of vision loss (odds ratio=11.12). Over the initial 3 months after trabeculectomy, additional surgeries are rare, while a significant proportion of patients lost vision, and this was strongly associated with a need to return to the operating room. Interactive electronic health record data visualization can be a useful tool for monitoring clinical outcomes.

In the present study, the changes in the groundwater quality in a Hungarian settlement, Báránd, were examined, nine years after the construction of a sewerage network. The sewerage network in the study area was completed in 2014, with a household connection rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37 dug groundwater wells. Changes in the water quality were assessed using three water quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and geographic information (GIS), data visualization systems, and artificial intelligence (AI). During the evaluation of the quality of the groundwater, eight water chemical parameters were used (pH, EC, NH4+, NO2−, NO3−, PO43−, COD, Na+). Based on interpolated maps and water quality indices, it was established that while an increasing portion of the area exhibits adequate or good water quality compared to the pre-sewerage period, a deterioration has occurred relative to recent years. Even nine years after the sewerage network construction, elevated concentrations of inorganic nitrogen forms and organic matter persist, indicating the continued presence of accumulated pollutants, as confirmed by all three water quality indicators to varying degrees and spatial patterns. The interactive data visualization and cloud-based sharing of the data of the water quality geodatabase were made freely available with the help of Tableau Public. A Feed-Forward Neural Network (FFNN) was developed to predict the groundwater quality, estimating the water quality statuses of three water quality indicators based on water chemistry parameters. The results showed that the applied training algorithms and activation functions proved to be the most effective in the case of different network structures. The most accurate prediction of the WQI and CCME WQI indicators was provided by the Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error (RMSEWQI = 0.1205, RMSECCME WQI = 0.1305) and the highest determination coefficient (R2WQI = 0.9916, R2CCME WQI = 0.9838). For the Cd index, the accuracy of the model was lower (RMSE = 0.1621, R2 = 0.9714), suggesting that this indicator is more difficult to predict. With regard to our study, it should be emphasized that data visualization is a particularly practical tool for the post-processing of spatial monitoring data, as it is suitable for displaying information in an intuitive, visual form, for discovering spatial patterns and relationships, and for performing real-time analyses. AI is expected to further increase visualization efficiency in the future, enabling the rapid processing of large amounts of data and spatial databases, as well as the identification of complex patterns.

Interactive 3D data visualization plays a key role in HEP experiments, as it is used in many tasks at different levels of the data chain. Outside HEP, for interactive 3D graphics, the game industry makes heavy use of so-called “game engines”, modern software frameworks offering an extensive set of powerful graphics tools and cross-platform deployment. Recently, a very strong support for Virtual Reality (VR) technology has been added to such engines. In this talk we explore the usage of game engines and VR for HEP data visualization, discussing the needs, the challenges and the issues of using such technologies. We will also make use of ATLASrift, a VR application developed by the ATLAS experiment, to discuss the lessons learned while developing it using the game engine Unreal Engine, and the feedback on the use of Virtual Reality we got from users while using it at many demonstrations and public events.

In order to improve understanding and working with data, visualizing information is without a doubt the best method to implement. Data visualization as a term unites the established field of scientific visualization and the more recent field of information visualization. The goal of data visualization is to provide the viewer an aggregated representation of available data by taking into account human’s visual system and its influence to comprehension. Spotting trends, seeing patterns and identifying outliers are some of the human’s visual system processes that are being manipulated in order to make data more accessible and appealing. This procedure of graphical representations creation helps engaging data exploration and even more, data extraction. Along with computer and graphical engineering, visualizations have grown and reached a very satisfactory level of variations and techniques, indulging even the most exacting data facilitators whether they are researchers, computer scientists, statisticians etc. A variety of data visualization software has been developed the last decades but Stanford University’s Protovis is by far the most distinguished tool to do the job. Below, a study is presented on data visualization’s purpose and prospects and how these became a necessity through time.

The curse of dimensionality, i.e., the fact that feature spaces of increasing dimensionality with finite sample sizes tend to be empty, has given incentive to a plethora of research activities in various disciplines and diverse application fields, e.g., statistics or neural networks. Three major application fields are multivariate data classification, data analysis and data visualization. In this contribution, methods for dimensionality reduction from three decades of interdisciplinary research are browsed and their applicability in the above application domains is briefly discussed. Complementing techniques for ensuing interactive data visualization, data navigation and visual exploratory data analysis are presented, which exploit the remarkable human perceptive and associative capabilities for interactive visual exploratory data analysis and systematic recognition system design. The main focus of this paper is on the comparison of feature selection and feature extraction techniques and the potential benefit of their combination. Further, the interesting implications of dimensionality reduction for VLSI design and related area and power consumption are pointed out.

While the visualization of data is critical for many scientific applications, the development of programs to render geometrical forms in real time, providing interactive control over viewpoint and angle, animation, and/or user interaction, can be complicated for a scientist. The need to master unfamiliar technology such as graphics engines, windowing software, and graphical user interfaces constitute major challenges. An introduction to the graphical display of data using the Coin3d toolkit for object modeling and rendering is given. The Qt library for graphical user interfacing and the SoQt package, which serves as “glue” between Qt and Coin3d are also presented. Applications to visualizing fractal geometries, three-dimensional objects, and random data are presented.