Současná zdravotnická technika produkuje každým okamžikem velké objemy dat. Výsledkem je informační přetížení a nemožnost zvládnout tato enormní data, např. na odd. intenzivní péče. Nástroje pro vizualizaci dat mají za cíl zmenšit toto informační přetížení pomocí inteligentní abstrakce a vizualizace zajímavých atributů zpracovávaných dat. Nově vyvíjené soft - warové nástroje pro vizualizaci by měly podporovat rychlé porozumění složitým, rozsáhlým a dynamicky rostoucím datovým souborům ve všech oblastech medicíny. Jednou z takových oblastí je analýza a vyhodnocování dlouhodobých záznamů EEG. S vyhodnocováním EEG je spojena celá řada problémů. Jedním z nich je potřeba vizuální kontroly záznamu lékařem. V případě, že lékař musí kontrolovat a hodnotit dlouhodobý záznam EEG, je počítačová podpora analýzy a vizualizace velkou pomocí. Právě možnosti vizualizace EEG záznamů a procesu jejich analýzy jsou předmětem našeho příspěvku.
Healthcare technology produces today large sets of data every second. An information overload results from these enormous data volumes not manageable by physicians, e.g. in intensive care. Data visualization tools aim at reducing the information overload by intelligent abstraction and visualization of the features of interest in the current situation. Newly developed soft - ware tools for visualization should support fast comprehension of complex, large, and dynamically growing datasets in all fi elds of medicine. One of such fi elds is the analysis and evaluation of long–term EEG recordings. One of the problems that are connected with the evaluation of EEG signals is that it necessitates visual checking of such a recording performed by a physician. In case the physician has to check and evaluate long–term EEG recordings computer–aided data analysis and visualization might be of great help. Soft ware tools for visualization of EEG data and data analysis are presented in the paper.
- MeSH
- Algorithms MeSH
- Models, Anatomic MeSH
- Electroencephalography utilization MeSH
- Epilepsy diagnosis MeSH
- Financing, Organized MeSH
- Classification MeSH
- Coma diagnosis physiopathology MeSH
- Humans MeSH
- Brain Mapping methods instrumentation MeSH
- Models, Neurological MeSH
- Neural Networks, Computer MeSH
- Signal Processing, Computer-Assisted instrumentation MeSH
- Cluster Analysis MeSH
- Sleep physiology MeSH
- Imaging, Three-Dimensional MeSH
- Check Tag
- Humans MeSH
sv. : ill. ; 28 cm
- MeSH
- Computer Graphics * MeSH
- Computer Simulation MeSH
- Image Processing, Computer-Assisted MeSH
- Publication type
- Periodical MeSH
- Conspectus
- Počítačová věda. Výpočetní technika. Informační technologie
- NML Fields
- technika
For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.
Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.
LiteMol suite is an innovative solution that enables near-instant delivery of model and experimental biomacromolecular structural data, providing users with an interactive and responsive experience in all modern web browsers and mobile devices. LiteMol suite is a combination of data delivery services (CoordinateServer and DensityServer), compression format (BinaryCIF), and a molecular viewer (LiteMol Viewer). The LiteMol suite is integrated into Protein Data Bank in Europe (PDBe) and other life science web applications (e.g., UniProt, Ensemble, SIB, and CNRS services), it is freely available at https://litemol.org , and its source code is available via GitHub. LiteMol suite provides advanced functionality (annotations and their visualization, powerful selection features), and this chapter will describe their use for visual inspection of protein structures.
- MeSH
- Databases, Protein MeSH
- Internet MeSH
- Web Browser MeSH
- Protein Conformation * MeSH
- Proteins chemistry MeSH
- Software MeSH
- User-Computer Interface MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Europe MeSH
Protein structures contain highly complex systems of voids, making up specific features such as surface clefts or grooves, pockets, protrusions, cavities, pores or channels, and tunnels. Many of them are essential for the migration of solvents, ions and small molecules through proteins, and their binding to the functional sites. Analysis of these structural features is very important for understanding of structure-function relationships, for the design of potential inhibitors or proteins with improved functional properties. Here we critically review existing software tools specialized in rapid identification, visualization, analysis and design of protein tunnels and channels. The strengths and weaknesses of individual tools are reported together with examples of their applications for the analysis and engineering of various biological systems. This review can assist users with selecting a proper software tool for study of their biological problem as well as highlighting possible avenues for further development of existing tools. Development of novel descriptors representing not only geometry, but also electrostatics, hydrophobicity or dynamics, is needed for reliable identification of biologically relevant tunnels and channels.
SUMMARY: The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform's visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network. AVAILABILITY AND IMPLEMENTATION: Plugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.
- MeSH
- Software * MeSH
- Systems Biology * MeSH
- Publication type
- Journal Article MeSH
Fourierova analýza je metoda umožňující dokonalý popis povrchu rohovky. U souboru 50 náhodně vybraných pacientů, u kterých byla provedena PRK, jsme zjišťovali předoperační hodnoty a pooperační vývoj sférického ekvivalentu, cylindru, decentrace a nepravidelného astigmatismu získaných Fourierovou analýzou topografických dat 1, 3, 6 a 12 měsíců po operaci. Pooperační decentrace vrcholu rohovky vzhledem ke středu zornice je oproti předoperační hodnotě statisticky významně vyšší. Hodnota nepravidelného astigmatismu před a po operaci se statisticky významně neliší. Byl zjištěn silný stupeň korelace mezi hodnotami sférického ekvivalentu získanými Fourierovou analýzou a simulovanou keratometrií. Klinický význam má korelace mezi parametry decentrace a nepravidelného astigmatismu s výslednou pooperační nejlépe korigovanou zrakovou ostrostí. Pomocí Fourierova rozkladu můžeme získat informace, které by mohly vysvětlit horší výsledek některých operací.
Fourier analysis is a powerful method of evaluating the surface of the cornea. In 50 patients after photorefractive keratectomy included in our study we measured spherical equivalent, regular astigmatism, irregular astigmatism and decentration retrospectively acquired by Fourier series analysis of corneal topography data 1, 3, 6 and 12 months after operation. Postoperative decentration increased significantly froma mean preoperative value.The preoperative andpostoperative values are not significantly different. The Fourier spherical equivalent and the values of keratometric spherical (equivalent) are highly correlated. Correlation among decentration, irregular astigmatism and best corrected visual acuity is important for clinical practice. Due to Fourier analysis we obtain information, which could explain worse results of some surgeries.
- MeSH
- Astigmatism MeSH
- Photorefractive Keratectomy methods instrumentation MeSH
- Fourier Analysis MeSH
- Humans MeSH
- Postoperative Period MeSH
- Preoperative Care MeSH
- Cornea surgery MeSH
- Corneal Topography methods MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Review MeSH
- Comparative Study MeSH
BACKGROUND: The knowledge of cancer burden in the population, its time trends, and the possibility of international comparison is an important starting point for cancer programs. A reliable interactive tool describing cancer epidemiology in children and adolescents has been nonexistent in the Czech Republic until recently. OBJECTIVE: The goal of this study is to develop a new web portal entitled the Czech Childhood Cancer Information System (CCCIS), which would provide information on childhood cancer epidemiology in the Czech Republic. METHODS: Data on childhood cancers have been obtained from the Czech National Cancer Registry. These data were validated using the clinical database of childhood cancer patients and subsequently combined with data from the National Register of Hospitalised Patients and with data from death certificates. These validated data were then used to determine the incidence and survival rates of childhood cancer patients aged 0 to 19 years who were diagnosed in the period 1994 to 2016 (N=9435). Data from death certificates were used to monitor long-term mortality trends. The technical solution is based on the robust PHP development Symfony framework, with the PostgreSQL system used to accommodate the data basis. RESULTS: The web portal has been available for anyone since November 2019, providing basic information for experts (ie, analyses and publications) on individual diagnostic groups of childhood cancers. It involves an interactive tool for analytical reporting, which provides information on the following basic topics in the form of graphs or tables: incidence, mortality, and overall survival. Feedback was obtained and the accuracy of outputs published on the CCCIS portal was verified using the following methods: the validation of the theoretical background and the user testing. CONCLUSIONS: We developed software capable of processing data from multiple sources, which is freely available to all users and makes it possible to carry out automated analyses even for users without mathematical background; a simple selection of a topic to be analyzed is required from the user.
- MeSH
- Data Analysis * MeSH
- Child MeSH
- Incidence MeSH
- Information Systems MeSH
- Humans MeSH
- Adolescent MeSH
- Neoplasms * epidemiology MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic MeSH
