48,895 research outputs found
The SSDC contribution to the improvement of knowledge by means of 3D data projections of minor bodies
The latest developments of planetary exploration missions devoted to minor
bodies required new solutions to correctly visualize and analyse data acquired
over irregularly shaped bodies. ASI Space Science Data Center (SSDC-ASI,
formerly ASDC-ASI Science Data Center) worked on this task since early 2013,
when started developing the web tool MATISSE (Multi-purpose Advanced Tool for
the Instruments of the Solar System Exploration) mainly focused on the
Rosetta/ESA space mission data. In order to visualize very high-resolution
shape models, MATISSE uses a Python module (vtpMaker), which can also be
launched as a stand-alone command-line software. MATISSE and vtpMaker are part
of the SSDC contribution to the new challenges imposed by the "orbital
exploration" of minor bodies: 1) MATISSE allows to search for specific
observations inside datasets and then analyse them in parallel, providing
high-level outputs; 2) the 3D capabilities of both tools are critical in
inferring information otherwise difficult to retrieve for non-spherical targets
and, as in the case for the GIADA instrument onboard Rosetta, to visualize data
related to the coma. New tasks and features adding valuable capabilities to the
minor bodies SSDC tools are planned for the near future thanks to new
collaborations
Visualizing genetic constraints
Principal Components Analysis (PCA) is a common way to study the sources of
variation in a high-dimensional data set. Typically, the leading principal
components are used to understand the variation in the data or to reduce the
dimension of the data for subsequent analysis. The remaining principal
components are ignored since they explain little of the variation in the data.
However, evolutionary biologists gain important insights from these low
variation directions. Specifically, they are interested in directions of low
genetic variability that are biologically interpretable. These directions are
called genetic constraints and indicate directions in which a trait cannot
evolve through selection. Here, we propose studying the subspace spanned by low
variance principal components by determining vectors in this subspace that are
simplest. Our method and accompanying graphical displays enhance the
biologist's ability to visualize the subspace and identify interpretable
directions of low genetic variability that align with simple directions.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS603 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Fuzzy Fibers: Uncertainty in dMRI Tractography
Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI)
allows for noninvasive reconstruction of fiber bundles in the human brain. In
this chapter, we discuss sources of error and uncertainty in this technique,
and review strategies that afford a more reliable interpretation of the
results. This includes methods for computing and rendering probabilistic
tractograms, which estimate precision in the face of measurement noise and
artifacts. However, we also address aspects that have received less attention
so far, such as model selection, partial voluming, and the impact of
parameters, both in preprocessing and in fiber tracking itself. We conclude by
giving impulses for future research
PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.
MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online
Requirements for building information modeling based lean production management systems for construction
Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last
Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face
Uncertainty in phylogenetic tree estimates
Estimating phylogenetic trees is an important problem in evolutionary
biology, environmental policy and medicine. Although trees are estimated, their
uncertainties are discarded by mathematicians working in tree space. Here we
explicitly model the multivariate uncertainty of tree estimates. We consider
both the cases where uncertainty information arises extrinsically (through
covariate information) and intrinsically (through the tree estimates
themselves). The importance of accounting for tree uncertainty in tree space is
demonstrated in two case studies. In the first instance, differences between
gene trees are small relative to their uncertainties, while in the second, the
differences are relatively large. Our main goal is visualization of tree
uncertainty, and we demonstrate advantages of our method with respect to
reproducibility, speed and preservation of topological differences compared to
visualization based on multidimensional scaling. The proposal highlights that
phylogenetic trees are estimated in an extremely high-dimensional space,
resulting in uncertainty information that cannot be discarded. Most
importantly, it is a method that allows biologists to diagnose whether
differences between gene trees are biologically meaningful, or due to
uncertainty in estimation.Comment: Final version accepted to Journal of Computational and Graphical
Statistic
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