PeerJ Computer Science Preprints: Graphicshttps://peerj.com/preprints/index.atom?journal=cs&subject=10200Graphics articles published in PeerJ Computer Science PreprintsIdentification of protein pockets and cavities by Euclidean Distance Transformhttps://peerj.com/preprints/273142018-11-012018-11-01Sebastian Daberdaku
Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.
Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.Crossing SSH and STEM approaches in a MapDesign course using open data and softwarehttps://peerj.com/preprints/272372018-09-262018-09-26Massimiliano CannataGiovanni ProfetaMichela VoegeliManuel LüscherLaura Morandi
This paper presents the design, realization and evaluation of a Map Design course conducted using an open source GIS (QGIS) to students of the bachelor in Visual Communication. The specific challenge was teaching approaches from Social Science and Humanities (SSH) and Science, Technology, Engineering and Mathematics (STEM) disciplines to integrate rigorous cartographic methodologies for map production with aesthetic visual aspects. This was successfully addressed with an hybridization approach that discuss themes from the two disciplines point of view and a goal-oriented course organization that produced as an output real map products. The general evaluation of this new course by students and teachers was positive. Despite the main criticism was related to the complexity of the used tools with respect to the course duration, the quality of the outputs demonstrated a very good capacity of students in learning and fusing of STEM and SSH concepts.
This paper presents the design, realization and evaluation of a Map Design course conducted using an open source GIS (QGIS) to students of the bachelor in Visual Communication. The specific challenge was teaching approaches from Social Science and Humanities (SSH) and Science, Technology, Engineering and Mathematics (STEM) disciplines to integrate rigorous cartographic methodologies for map production with aesthetic visual aspects. This was successfully addressed with an hybridization approach that discuss themes from the two disciplines point of view and a goal-oriented course organization that produced as an output real map products. The general evaluation of this new course by students and teachers was positive. Despite the main criticism was related to the complexity of the used tools with respect to the course duration, the quality of the outputs demonstrated a very good capacity of students in learning and fusing of STEM and SSH concepts.An interactive tool for teaching map projectionshttps://peerj.com/preprints/272182018-09-162018-09-16Magnus HeitzlerHans-Rudolf BärRoland SchenkelLorenz Hurni
Map projections are one of the fundamental concepts of geographic information science and cartography. An understanding of the different variants and properties is critical when creating maps or carrying out geospatial analyses. To support learning about map projections, we present an online tool that allows to interactively explore the construction process of map projections. A central 3D view shows the three main building blocks for perspective map projections: the globe, the projection surface (cone, cylinder, plane) and the projection center. Interactively adjusting these objects allows to create a multitude of arrangements forming the basis for common map projections. Further insights can be gained by adding supplementary information, such as projection lines and Tissot’s indicatrices. Once all objects have been arranged in a desired way, the projection surface can be unrolled to form the final flat map. Currently, the tool is limited to visualize the construction of true perspective map projections. In the future, prime concerns are to increase the genericity of the application to support more map projections and to integrate it into the GITTA (Geographic Information Technology Training Alliance) platform.
Map projections are one of the fundamental concepts of geographic information science and cartography. An understanding of the different variants and properties is critical when creating maps or carrying out geospatial analyses. To support learning about map projections, we present an online tool that allows to interactively explore the construction process of map projections. A central 3D view shows the three main building blocks for perspective map projections: the globe, the projection surface (cone, cylinder, plane) and the projection center. Interactively adjusting these objects allows to create a multitude of arrangements forming the basis for common map projections. Further insights can be gained by adding supplementary information, such as projection lines and Tissot’s indicatrices. Once all objects have been arranged in a desired way, the projection surface can be unrolled to form the final flat map. Currently, the tool is limited to visualize the construction of true perspective map projections. In the future, prime concerns are to increase the genericity of the application to support more map projections and to integrate it into the GITTA (Geographic Information Technology Training Alliance) platform.A semi-automatic tool to georeference historical landscape imageshttps://peerj.com/preprints/272042018-09-142018-09-14Nicolas BlancTimothée ProduitJens Ingensand
Smapshot is a web-based participatory virtual globe where users can georeference historical images of the landscape by clicking a minimum of six well identifiable correspondence points between the image and a 3D virtual globe. The images database is expected to grow exponentially. In a near future, the work of the web users will no longer be enough.
To tackle this issue, we developed a semi-automatic process to georeference images. The volunteers will be shown only images having a maximum number of neighbour images in the matching graph. These neighbour images are the ones with which they share some overlay. This overlap is detected using the SIFT algorithm in a pairewise matching process.
For an image pair made of a reference image with a known pose and a query image we want to georeference, we extracted the 3D world coordinates of the tie points from a digital elevation model.
Then, by running a perspective-n-point algorithm after having geometrically tested the resulting homography between the two images, we compute the 6 degree of freedom pose, i.e. the position (X,Y,Z) and orientation (azimuth, tilt and roll angles) of the query image. The query image then becomes a reference and the georeference computation can be propagated more deeply in the graph structure.
Smapshot is a web-based participatory virtual globe where users can georeference historical images of the landscape by clicking a minimum of six well identifiable correspondence points between the image and a 3D virtual globe. The images database is expected to grow exponentially. In a near future, the work of the web users will no longer be enough.To tackle this issue, we developed a semi-automatic process to georeference images. The volunteers will be shown only images having a maximum number of neighbour images in the matching graph. These neighbour images are the ones with which they share some overlay. This overlap is detected using the SIFT algorithm in a pairewise matching process.For an image pair made of a reference image with a known pose and a query image we want to georeference, we extracted the 3D world coordinates of the tie points from a digital elevation model.Then, by running a perspective-n-point algorithm after having geometrically tested the resulting homography between the two images, we compute the 6 degree of freedom pose, i.e. the position (X,Y,Z) and orientation (azimuth, tilt and roll angles) of the query image. The query image then becomes a reference and the georeference computation can be propagated more deeply in the graph structure.Open educational resources for cartography: the Thematic Mapping Tutorhttps://peerj.com/preprints/272032018-09-142018-09-14Barend Köbben
At the ITC faculty of the University of Twente, we have been teaching cartography for more then 60 years. Throughout this period, the technology of mapping has undergone spectacular changes and nowadays most students do not draw their maps any more, but use software instead. However, for maps to be effective in communication, their design still has to follow the same rules as before. Ideally, one wants to teach these design rules independently from the tools, such that the students understand how a good map works, not just which buttons to click to create it.
For this purpose, we created the Thematic Mapping Tutor. It is an open, web-based system that provides a structured way of constructing thematic maps out of selected data. The system uses the input of the student to construct a map in the Vega-Lite grammar, which is transformed to web-graphics.
In this paper we describe the educational philosophy behind the system, as well as technical details about its functionality. We report on first tests, and reflect on the possibilities and pitfalls of the system.
At the ITC faculty of the University of Twente, we have been teaching cartography for more then 60 years. Throughout this period, the technology of mapping has undergone spectacular changes and nowadays most students do not draw their maps any more, but use software instead. However, for maps to be effective in communication, their design still has to follow the same rules as before. Ideally, one wants to teach these design rules independently from the tools, such that the students understand how a good map works, not just which buttons to click to create it.For this purpose, we created the Thematic Mapping Tutor. It is an open, web-based system that provides a structured way of constructing thematic maps out of selected data. The system uses the input of the student to construct a map in the Vega-Lite grammar, which is transformed to web-graphics.In this paper we describe the educational philosophy behind the system, as well as technical details about its functionality. We report on first tests, and reflect on the possibilities and pitfalls of the system.Raincloud plots: a multi-platform tool for robust data visualizationhttps://peerj.com/preprints/271372018-08-232018-08-23Micah AllenDavide PoggialiKirstie WhitakerTom R MarshallRogier Kievit
Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complimentary to this, many scientists have realized the need for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots ). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.
Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complimentary to this, many scientists have realized the need for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots ). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.SAS macros for longitudinal IRT modelshttps://peerj.com/preprints/267402018-03-202018-03-20Maja OlsbjergKarl Bang Christensen
IRT models are often applied when observed items are used to measure a unidimensional latent variable. Originally used in educational research, IRT models are now widely used when focus is on physical functioning or psychological well-being. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a collection of SAS macros that can be used for fitting data to, simulating from, and visualizing longitudinal IRT models. The macros encompass dichotomous as well as polytomous item response formats and are sufficiently flexible to accommodate changes in item parameters across time points and local dependence between responses at different time points.
IRT models are often applied when observed items are used to measure a unidimensional latent variable. Originally used in educational research, IRT models are now widely used when focus is on physical functioning or psychological well-being. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a collection of SAS macros that can be used for fitting data to, simulating from, and visualizing longitudinal IRT models. The macros encompass dichotomous as well as polytomous item response formats and are sufficiently flexible to accommodate changes in item parameters across time points and local dependence between responses at different time points.Emulation of surgical fluid interactions in real-timehttps://peerj.com/preprints/33342018-02-232018-02-23Donald StredneyBradley HittleHector Medina-FettermanThomas KerwinGregory Wiet
The surgical skills required to successfully maintain hemostasis, the control of operative blood, requires considerable deliberate practice. Hemostasis requires the deft orchestration of bi-dexterous tool manipulation. We present our approach to computationally emulate both irrigation and bleeding associated with neurotologic surgical technique. The overall objective is to provide a visually plausible, three dimensional, real-time simulation of bleeding and irrigation in a virtual otologic simulator system. The results present a unique simulation environment for deliberate study and practice.
The surgical skills required to successfully maintain hemostasis, the control of operative blood, requires considerable deliberate practice. Hemostasis requires the deft orchestration of bi-dexterous tool manipulation. We present our approach to computationally emulate both irrigation and bleeding associated with neurotologic surgical technique. The overall objective is to provide a visually plausible, three dimensional, real-time simulation of bleeding and irrigation in a virtual otologic simulator system. The results present a unique simulation environment for deliberate study and practice.Infrastructure and tools for teaching computing throughout the statistical curriculumhttps://peerj.com/preprints/31812017-08-242017-08-24Mine Cetinkaya-RundelColin W Rundel
Modern statistics is fundamentally a computational discipline, but too often this fact is not reflected in our statistics curricula. With the rise of big data and data science it has become increasingly clear that students both want, expect, and need explicit training in this area of the discipline. Additionally, recent curricular guidelines clearly state that working with data requires extensive computing skills and that statistics students should be fluent in accessing, manipulating, analyzing, and modeling with professional statistical analysis software. Much has been written in the statistics education literature about pedagogical tools and approaches to provide a practical computational foundation for students. This article discusses the computational infrastructure and toolkit choices to allow for these pedagogical innovations while minimizing frustration and improving adoption for both our students and instructors.
Modern statistics is fundamentally a computational discipline, but too often this fact is not reflected in our statistics curricula. With the rise of big data and data science it has become increasingly clear that students both want, expect, and need explicit training in this area of the discipline. Additionally, recent curricular guidelines clearly state that working with data requires extensive computing skills and that statistics students should be fluent in accessing, manipulating, analyzing, and modeling with professional statistical analysis software. Much has been written in the statistics education literature about pedagogical tools and approaches to provide a practical computational foundation for students. This article discusses the computational infrastructure and toolkit choices to allow for these pedagogical innovations while minimizing frustration and improving adoption for both our students and instructors.Visualising higher-dimensional space-time and space-scale objects as projections to \(\mathbb{R}^3\)https://peerj.com/preprints/28442017-03-022017-03-02Ken Arroyo OhoriHugo LedouxJantien Stoter
Objects of more than three dimensions can be used to model geographic phenomena that occur in space, time and scale. For instance, a single 4D object can be used to represent the changes in a 3D object's shape across time or all its optimal representations at various levels of detail. In this paper, we look at how such higher-dimensional space-time and space-scale objects can be visualised as projections from \(\mathbb{R}^4\) to \(\mathbb{R}^3\). We present three projections that we believe are particularly intuitive for this purpose: (i) a simple `long axis' projection that puts 3D objects side by side; (ii) the well-known orthographic and perspective projections; and (iii) a projection to a 3-sphere (\(S^3\)) followed by a stereographic projection to \(\mathbb{R}^3\), which results in an inwards-outwards fourth axis. Our focus is in using these projections from \(\mathbb{R}^4\) to \(\mathbb{R}^3\), but they are formulated from \(\mathbb{R}^n\) to \(\mathbb{R}^{n-1}\) so as to be easily extensible and to incorporate other non-spatial characteristics. We present a prototype interactive visualiser that applies these projections from 4D to 3D in real-time using the programmable pipeline and compute shaders of the Metal graphics API.
Objects of more than three dimensions can be used to model geographic phenomena that occur in space, time and scale. For instance, a single 4D object can be used to represent the changes in a 3D object's shape across time or all its optimal representations at various levels of detail. In this paper, we look at how such higher-dimensional space-time and space-scale objects can be visualised as projections from \(\mathbb{R}^4\) to \(\mathbb{R}^3\). We present three projections that we believe are particularly intuitive for this purpose: (i) a simple `long axis' projection that puts 3D objects side by side; (ii) the well-known orthographic and perspective projections; and (iii) a projection to a 3-sphere (\(S^3\)) followed by a stereographic projection to \(\mathbb{R}^3\), which results in an inwards-outwards fourth axis. Our focus is in using these projections from \(\mathbb{R}^4\) to \(\mathbb{R}^3\), but they are formulated from \(\mathbb{R}^n\) to \(\mathbb{R}^{n-1}\) so as to be easily extensible and to incorporate other non-spatial characteristics. We present a prototype interactive visualiser that applies these projections from 4D to 3D in real-time using the programmable pipeline and compute shaders of the Metal graphics API.