21,692 research outputs found

    Development of method of matched morphological filtering of biomedical signals and images

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    Formalized approach to the analysis of biomedical signals and images with locally concentrated features is developed on the basis of matched morphological filtering taking into account the useful signal models that allowed generalizing the existing methods of digital processing and analysis of biomedical signals and images with locally concentrated features. The proposed matched morphological filter has been adapted to solve such problems as localization of the searched structural elements on biomedical signals with locally concentrated features, estimation of the irregular background aimed at the visualization quality improving of biological objects on X-ray biomedical images, pathologic structures selection on mammogram. The efficiency of the proposed methods of matched morphological filtration of biomedical signals and images with locally concentrated features is proved by experiments

    The Phyre2 web portal for protein modeling, prediction and analysis

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    Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission

    Solving the tasks of subsurface resources management in GIS RAPID environment

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    Purpose. Solving the tasks of subsurface resources management based on the created GIS RAPID geoinformation technology. Methods. Close spatial relationships of lineament network characteristics and earthquake epicenters were detected in 3 seismically active areas located in the mountainous regions of Central Europe. Digital elevation models (DEM) based on ASTER satellite surveys and earthquake epicenter data were used. The nature of spatial relationship of lineament network and vein ore objects was studied in the territory of Congo DR, in the Lake Kivu area using space imagery. Gold ore objects were searched and forecasted in Uzbekistan in the site of Jamansai Mountains. High- resolution imagery from QuickBird 2 satellite, geophysical field surveys, geological and geochemical data were used. Findings. It was found that a significant number of epicenters are located in areas of high concentration of “non-standard” azimuths lineaments – from 27 to 34% of the total number of lineaments. It was revealed that 59.6% of the epicenters are located within 10% of sites with the highest values of complex deformation maps; 50% of the areas with the highest values of these maps contain, on average, 89% of all earthquake epicenters. It was found that satellite image lineament concentration maps with “non-standard” azimuths reflect the spatial relationship with known deposits much better than the concentration map of all lineaments. It was detected that the total area of gold ore objects perspective sites is about 20 km2. Originality. The use of GIS RAPID in a number of earth’s crust areas has allowed to establish new regularities linking the networks of physical field and landscape lineament characteristics with ore bodies and earthquake epicenters localization. Practical implications. A new technology has been developed for solving geological forecasting and prospecting problems. The technology can be used to solve a wide range of practical problems, especially in difficult geological conditions when searching for deep objects weakly presented in external fields and landscape.ĐœĐ”Ń‚Đ°. Đ Ń–ŃˆĐ”ĐœĐœŃ заЎач ĐœĐ°ĐŽŃ€ĐŸĐșĐŸŃ€ĐžŃŃ‚ŃƒĐČĐ°ĐœĐœŃ ĐœĐ° базі стĐČĐŸŃ€Đ”ĐœĐŸŃ–Ìˆ ĐłĐ”ĐŸŃ–ĐœŃ„ĐŸŃ€ĐŒĐ°Ń†Ń–ĐžÌ†ĐœĐŸŃ–Ìˆ Ń‚Đ”Ń…ĐœĐŸĐ»ĐŸĐłŃ–Ń–Ìˆ ГІС РАПІД. ĐœĐ”Ń‚ĐŸĐŽĐžĐșĐ°. ВояĐČĐ»Đ”ĐœĐœŃ Ń‚Ń–ŃĐœĐžŃ… ĐżŃ€ĐŸŃŃ‚ĐŸŃ€ĐŸĐČох ĐČĐ·Đ°Ń”ĐŒĐŸĐ·ĐČâ€™ŃĐ·ĐșіĐČ Ń€Ń–Đ·ĐœĐŸĐŒĐ°ĐœŃ–Ń‚ĐœĐžŃ… хараĐșтДрОстОĐș ĐŒĐ”Ń€Đ”Đ¶ Đ»Ń–ĐœĐ”Đ°ĐŒĐ”ĐœŃ‚Ń–ĐČ Ń– Đ”ĐżŃ–Ń†Đ”ĐœŃ‚Ń€Ń–ĐČ Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃƒŃŃ–ĐČ ĐżŃ€ĐŸĐČĐŸĐŽĐžĐ»ĐŸŃŃ у 3 ŃĐ”ĐžÌ†ŃĐŒĐŸĐ°ĐșтоĐČĐœĐžŃ… ĐŽŃ–Đ»ŃĐœĐșах, Ń€ĐŸĐ·Ń‚Đ°ŃˆĐŸĐČĐ°ĐœĐžŃ… ĐČ ĐłŃ–Ń€ŃŃŒĐșох Ń€Đ°ĐžÌ†ĐŸĐœĐ°Ń… ĐŠĐ”ĐœŃ‚Ń€Đ°Đ»ŃŒĐœĐŸŃ–Ìˆ ЄĐČŃ€ĐŸĐżĐž. ВоĐșĐŸŃ€ĐžŃŃ‚ĐŸĐČуĐČĐ°Đ»ĐžŃŃ Ń†ĐžŃ„Ń€ĐŸĐČі ĐŒĐŸĐŽĐ”Đ»Ń– Ń€Đ”Đ»ŃŒŃ”Ń„Ńƒ (DEM), ĐżĐŸĐ±ŃƒĐŽĐŸĐČĐ°ĐœŃ– Đ·Đ° Đ·ĐžÌ†ĐŸĐŒĐșĐ°ĐŒĐž Đ·Ń– ŃŃƒĐżŃƒŃ‚ĐœĐžĐșĐ° ASTER і ĐŽĐ°ĐœŃ– ĐżĐŸ Đ”ĐżŃ–Ń†Đ”ĐœŃ‚Ń€Đ°Ń… Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃƒŃŃ–ĐČ. Đ”ĐŸŃĐ»Ń–ĐŽĐ¶Đ”ĐœĐœŃ хараĐșŃ‚Đ”Ń€Ńƒ ĐżŃ€ĐŸŃŃ‚ĐŸŃ€ĐŸĐČĐŸĐłĐŸ ĐČĐ·Đ°Ń”ĐŒĐŸĐ·ĐČâ€™ŃĐ·Đșу ĐŒĐ”Ń€Đ”Đ¶Ń– Đ»Ń–ĐœĐ”Đ°ĐŒĐ”ĐœŃ‚Ń–ĐČ Ń– Đ¶ĐžĐ»ŃŒĐœĐžŃ… Ń€ŃƒĐŽĐœĐžŃ… ĐŸĐ±â€™Ń”ĐșтіĐČ ĐżŃ€ĐŸĐČĐŸĐŽĐžĐ»ĐžŃŃ ĐœĐ° Ń‚Đ”Ń€ĐžŃ‚ĐŸŃ€Ń–Ń–Ìˆ Đ”Đ”ĐŒĐŸĐșŃ€Đ°Ń‚ĐžŃ‡ĐœĐŸŃ–Ìˆ Đ Đ”ŃĐżŃƒĐ±Đ»Ń–ĐșĐž ĐšĐŸĐœĐłĐŸ, ĐČ Ń€Đ°ĐžÌ†ĐŸĐœŃ– ĐŸĐ·Đ”Ń€Đ° КіĐČу Ń–Đ· ĐČĐžĐșĐŸŃ€ĐžŃŃ‚Đ°ĐœĐœŃĐŒ ĐșĐŸŃĐŒŃ–Ń‡ĐœĐžŃ… Đ·ĐžÌ†ĐŸĐŒĐŸĐș. Đ”ĐŸŃĐ»Ń–ĐŽĐ¶Đ”ĐœĐœŃ ĐżĐŸŃˆŃƒĐșу та ĐżŃ€ĐŸĐłĐœĐŸĐ·Ńƒ Đ·ĐŸĐ»ĐŸŃ‚ĐŸŃ€ŃƒĐŽĐœĐžŃ… ĐŸĐ±â€™Ń”ĐșтіĐČ ĐČĐžĐșĐŸĐœŃƒĐČĐ°Đ»ĐžŃŃ ĐČ ĐŁĐ·Đ±Đ”ĐșĐžŃŃ‚Đ°ĐœŃ– ĐœĐ° ĐŽŃ–Đ»ŃĐœŃ†Ń– Đ”Đ¶Đ°ĐŒĐ°ĐœŃĐ°ĐžÌ†ŃĐșіх гір. ВоĐșĐŸŃ€ĐžŃŃ‚ĐŸĐČуĐČĐ°Đ»ĐžŃŃ ĐČĐžŃĐŸĐșĐŸŃ‚ĐŸŃ‡ĐœŃ– ĐșĐŸŃĐŒŃ–Ń‡ĐœŃ– Đ·ĐžÌ†ĐŸĐŒĐșĐž Đ·Ń– ŃŃƒĐżŃƒŃ‚ĐœĐžĐșĐ° QuickBird 2, Đ·ĐžÌ†ĐŸĐŒĐșĐž ĐłĐ”ĐŸŃ„Ń–Đ·ĐžŃ‡ĐœĐžŃ… ĐżĐŸĐ»Ń–ĐČ, ĐłĐ”ĐŸĐ»ĐŸĐłŃ–Ń‡ĐœŃ– та ĐłĐ”ĐŸŃ…Ń–ĐŒŃ–Ń‡ĐœŃ– ĐŽĐ°ĐœŃ–. Đ Đ”Đ·ŃƒĐ»ŃŒŃ‚Đ°Ń‚Đž. ВояĐČĐ»Đ”ĐœĐŸ, Ń‰ĐŸ Đ·ĐœĐ°Ń‡ĐœĐ° Ń‡Đ°ŃŃ‚ĐžĐœĐ° Đ”ĐżŃ–Ń†Đ”ĐœŃ‚Ń€Ń–ĐČ ĐżŃ€ĐžŃƒŃ€ĐŸŃ‡Đ”ĐœĐ° ŃĐ°ĐŒĐ” ĐŽĐŸ ĐŽŃ–Đ»ŃĐœĐŸĐș піЮĐČĐžŃ‰Đ”ĐœĐŸŃ–Ìˆ ĐșĐŸĐœŃ†Đ”ĐœŃ‚Ń€Đ°Ń†Ń–Ń–Ìˆ Đ»Ń–ĐœĐ”Đ°ĐŒĐ”ĐœŃ‚Ń–ĐČ â€œĐœĐ”ŃŃ‚Đ°ĐœĐŽĐ°Ń€Ń‚ĐœĐžŃ…â€ Đ°Đ·ĐžĐŒŃƒŃ‚Ń–ĐČ, сĐșлаЎаючО ĐČіЮ 27 ĐŽĐŸ 34% Đ·Đ°ĐłĐ°Đ»ŃŒĐœĐŸĐłĐŸ чОсла Đ»Ń–ĐœĐ”Đ°ĐŒĐ”ĐœŃ‚Ń–ĐČ. Đ’ŃŃ‚Đ°ĐœĐŸĐČĐ»Đ”ĐœĐŸ, Ń‰ĐŸ 59.6% Đ”ĐżŃ–Ń†Đ”ĐœŃ‚Ń€Ń–ĐČ Đ·ĐœĐ°Ń…ĐŸĐŽŃŃ‚ŃŒŃŃ ĐČŃĐ”Ń€Đ”ĐŽĐžĐœŃ– 10% Ń‚Đ”Ń€ĐžŃ‚ĐŸŃ€Ń–Ń–Ìˆ ĐŽŃ–Đ»ŃĐœĐŸĐș, Ń‰ĐŸ ĐČĐŸĐ»ĐŸĐŽŃ–ŃŽŃ‚ŃŒ ĐœĐ°ĐžÌ†ĐČĐžŃ‰ĐžĐŒĐž Đ·ĐœĐ°Ń‡Đ”ĐœĐœŃĐŒĐž ĐșĐŸĐŒĐżĐ»Đ”ĐșŃĐœĐžŃ… Đșарт ĐŽĐ”Ń„ĐŸŃ€ĐŒĐ°Ń†Ń–ĐžÌ†; 50% Ń‚Đ”Ń€ĐžŃ‚ĐŸŃ€Ń–Ń–Ìˆ Đ· ĐœĐ°ĐžÌ†ĐČĐžŃ‰ĐžĐŒĐž Đ·ĐœĐ°Ń‡Đ”ĐœĐœŃĐŒĐž цох Đșарт ĐČĐŒŃ–Ń‰Đ°ŃŽŃ‚ŃŒ, ĐČ ŃĐ”Ń€Đ”ĐŽĐœŃŒĐŸĐŒŃƒ, 89% усіх Đ”ĐżŃ–Ń†Đ”ĐœŃ‚Ń€Ń–ĐČ Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃƒŃŃ–ĐČ. Đ’ĐžĐ·ĐœĐ°Ń‡Đ”ĐœĐŸ, Ń‰ĐŸ Đșарто ĐșĐŸĐœŃ†Đ”ĐœŃ‚Ń€Đ°Ń†Ń–Ń–Ìˆ Đ»Ń–ĐœĐ”Đ°ĐŒĐ”ĐœŃ‚Ń–ĐČ ĐșĐŸŃĐŒĐŸĐ·ĐœŃ–ĐŒĐșіĐČ Đ· â€œĐœĐ”ŃŃ‚Đ°ĐœĐ°Ń€Ń‚ĐœĐžĐŒĐžâ€ Đ°Đ·ĐžĐŒŃƒŃ‚Đ°ĐŒĐž Đ·ĐœĐ°Ń‡ĐœĐŸ ĐșращД ĐČŃ–ĐŽĐŸĐ±Ń€Đ°Đ¶Đ°ŃŽŃ‚ŃŒ ĐżŃ€ĐŸŃŃ‚ĐŸŃ€ĐŸĐČĐžĐžÌ† ĐČĐ·Đ°Ń”ĐŒĐŸĐ·ĐČâ€™ŃĐ·ĐŸĐș Đ· ĐČŃ–ĐŽĐŸĐŒĐžĐŒĐž Ń€ĐŸĐŽĐŸĐČĐžŃ‰Đ°ĐŒĐž у ĐżĐŸŃ€Ń–ĐČĐœŃĐœĐœŃ– Đ· ĐșĐ°Ń€Ń‚ĐŸŃŽ ĐșĐŸĐœŃ†Đ”ĐœŃ‚Ń€Đ°Ń†Ń–Ń–Ìˆ ĐČсіх Đ»Ń–ĐœĐ”Đ°ĐŒĐ”ĐœŃ‚Ń–ĐČ. Đ’ŃŃ‚Đ°ĐœĐŸĐČĐ»Đ”ĐœĐŸ, Ń‰ĐŸ ŃŃƒĐŒĐ°Ń€ĐœĐ° ĐżĐ»ĐŸŃ‰Đ° пДрспДĐșтоĐČĐœĐžŃ… ĐŽŃ–Đ»ŃĐœĐŸĐș Đ·ĐŸĐ»ĐŸŃ‚ĐŸŃ€ŃƒĐŽĐœĐžŃ… ĐŸĐ±â€™Ń”ĐșтіĐČ ŃĐșлала Đ±Đ»ĐžĐ·ŃŒĐșĐŸ 20 ĐșĐŒ2. НауĐșĐŸĐČĐ° ĐœĐŸĐČĐžĐ·ĐœĐ°. Đ—Đ°ŃŃ‚ĐŸŃŃƒĐČĐ°ĐœĐœŃ ГІС РАПІД ĐœĐ° ряЮі ĐŽŃ–Đ»ŃĐœĐŸĐș Đ·Đ”ĐŒĐœĐŸŃ–Ìˆ ĐșĐŸŃ€Đž ĐŽĐŸĐ·ĐČĐŸĐ»ĐžĐ»ĐŸ ĐČŃŃ‚Đ°ĐœĐŸĐČото ĐœĐŸĐČі Đ·Đ°ĐșĐŸĐœĐŸĐŒŃ–Ń€ĐœĐŸŃŃ‚Ń–, Ń‰ĐŸ Đ·ĐČâ€™ŃĐ·ŃƒŃŽŃ‚ŃŒ хараĐșтДрОстОĐșĐž ĐŒĐ”Ń€Đ”Đ¶Ń– Đ»Ń–ĐœĐ”Đ°ĐŒĐ”ĐœŃ‚Ń–ĐČ Ń„Ń–Đ·ĐžŃ‡ĐœĐžŃ… ĐżĐŸĐ»Ń–ĐČ Ń– Đ»Đ°ĐœĐŽŃˆĐ°Ń„Ń‚Ńƒ Đ· Đ»ĐŸĐșалізацією Ń€ŃƒĐŽĐœĐžŃ… Ń‚Ń–Đ» та Đ”ĐżŃ–Ń†Đ”ĐœŃ‚Ń€Ń–ĐČ Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃƒŃŃ–ĐČ. ПраĐșŃ‚ĐžŃ‡ĐœĐ° Đ·ĐœĐ°Ń‡ĐžĐŒŃ–ŃŃ‚ŃŒ. Đ ĐŸĐ·Ń€ĐŸĐ±Đ»Đ”ĐœĐŸ ĐœĐŸĐČу Ń‚Đ”Ń…ĐœĐŸĐ»ĐŸĐłŃ–ŃŽ Ń€Ń–ŃˆĐ”ĐœĐœŃ ĐżŃ€ĐŸĐłĐœĐŸĐ·ĐœĐžŃ… і ĐżĐŸŃˆŃƒĐșĐŸĐČох ĐłĐ”ĐŸĐ»ĐŸĐłŃ–Ń‡ĐœĐžŃ… Đ·Đ°ĐČĐŽĐ°ĐœŃŒ, яĐșĐ° ĐŒĐŸĐ¶Đ” Đ·Đ°ŃŃ‚ĐŸŃĐŸĐČуĐČатося ĐŽĐ»Ń ĐČĐžŃ€Ń–ŃˆĐ”ĐœĐœŃ ŃˆĐžŃ€ĐŸĐșĐŸĐłĐŸ ĐșĐŸĐ»Đ° праĐșŃ‚ĐžŃ‡ĐœĐžŃ… заЎач, ĐŸŃĐŸĐ±Đ»ĐžĐČĐŸ у сĐșĐ»Đ°ĐŽĐœĐžŃ… ĐłĐ”ĐŸĐ»ĐŸĐłŃ–Ń‡ĐœĐžŃ… ŃƒĐŒĐŸĐČах про ĐżĐŸŃˆŃƒĐșах ĐłĐ»ĐžĐ±ĐŸĐșĐŸĐ·Đ°Đ»ŃĐłĐ°ŃŽŃ‡ĐžŃ… ĐŸĐ±â€™Ń”ĐșтіĐČ, Ń‰ĐŸ ŃĐ»Đ°Đ±ĐŸ ĐČояĐČĐ»ŃŃŽŃ‚ŃŒŃŃ ĐČ Đ·ĐŸĐČĐœŃ–ŃˆĐœŃ–Ń… ĐżĐŸĐ»ŃŃ… і Đ»Đ°ĐœĐŽŃˆĐ°Ń„Ń‚Ń–.ĐŠĐ”Đ»ŃŒ. Đ Đ”ŃˆĐ”ĐœĐžŃ заЎач ĐœĐ”ĐŽŃ€ĐŸĐżĐŸĐ»ŃŒĐ·ĐŸĐČĐ°ĐœĐžŃ ĐœĐ° базД ŃĐŸĐ·ĐŽĐ°ĐœĐœĐŸĐžÌ† ĐłĐ”ĐŸĐžĐœŃ„ĐŸŃ€ĐŒĐ°Ń†ĐžĐŸĐœĐœĐŸĐžÌ† Ń‚Đ”Ń…ĐœĐŸĐ»ĐŸĐłĐžĐž ГИС РАПИД. ĐœĐ”Ń‚ĐŸĐŽĐžĐșĐ°. ВыяĐČĐ»Đ”ĐœĐžĐ” Ń‚Đ”ŃĐœŃ‹Ń… ĐżŃ€ĐŸŃŃ‚Ń€Đ°ĐœŃŃ‚ĐČĐ”ĐœĐœŃ‹Ń… ĐČĐ·Đ°ĐžĐŒĐŸŃĐČŃĐ·Đ”ĐžÌ† Ń€Đ°Đ·ĐœĐŸĐŸĐ±Ń€Đ°Đ·ĐœŃ‹Ń… хараĐșтДрОстОĐș ŃĐ”Ń‚Đ”ĐžÌ† Đ»ĐžĐœĐ”Đ°ĐŒĐ”ĐœŃ‚ĐŸĐČ Đž ŃĐżĐžŃ†Đ”ĐœŃ‚Ń€ĐŸĐČ Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃŃĐ”ĐœĐžĐžÌ† ĐżŃ€ĐŸĐČĐŸĐŽĐžĐ»ĐŸŃŃŒ ĐČ 3 ŃĐ”ĐžÌ†ŃĐŒĐŸĐ°ĐșтоĐČĐœŃ‹Ń… участĐșах, Ń€Đ°ŃĐżĐŸĐ»ĐŸĐ¶Đ”ĐœĐœŃ‹Ń… ĐČ ĐłĐŸŃ€ĐœŃ‹Ń… Ń€Đ°ĐžÌ†ĐŸĐœĐ°Ń… ĐŠĐ”ĐœŃ‚Ń€Đ°Đ»ŃŒĐœĐŸĐžÌ† ЕĐČŃ€ĐŸĐżŃ‹. Đ˜ŃĐżĐŸĐ»ŃŒĐ·ĐŸĐČĐ°Đ»ĐžŃŃŒ Ń†ĐžŃ„Ń€ĐŸĐČŃ‹Đ” ĐŒĐŸĐŽĐ”Đ»Đž Ń€Đ”Đ»ŃŒĐ”Ń„Đ° (DEM), ĐżĐŸŃŃ‚Ń€ĐŸĐ”ĐœĐœŃ‹Đ” ĐżĐŸ ŃŃŠĐ”ĐŒĐșĐ°ĐŒ ŃĐŸ ŃĐżŃƒŃ‚ĐœĐžĐșĐ° ASTER, Đž ĐŽĐ°ĐœĐœŃ‹Đ” ĐŸĐ± ŃĐżĐžŃ†Đ”ĐœŃ‚Ń€Đ°Ń… Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃŃĐ”ĐœĐžĐžÌ†. Đ˜ŃŃĐ»Đ”ĐŽĐŸĐČĐ°ĐœĐžŃ хараĐșтДра ĐżŃ€ĐŸŃŃ‚Ń€Đ°ĐœŃŃ‚ĐČĐ”ĐœĐœĐŸĐžÌ† ĐČĐ·Đ°ĐžĐŒĐŸŃĐČŃĐ·Đž сДтО Đ»ĐžĐœĐ”Đ°ĐŒĐ”ĐœŃ‚ĐŸĐČ Đž Đ¶ĐžĐ»ŃŒĐœŃ‹Ń… Ń€ŃƒĐŽĐœŃ‹Ń… ĐŸĐ±ŃŠĐ”ĐșŃ‚ĐŸĐČ ĐżŃ€ĐŸĐČĐŸĐŽĐžĐ»ĐžŃŃŒ ĐœĐ° Ń‚Đ”Ń€Ń€ĐžŃ‚ĐŸŃ€ĐžĐž Đ”Đ”ĐŒĐŸĐșратОчДсĐșĐŸĐžÌ† РДспублОĐșĐž ĐšĐŸĐœĐłĐŸ, ĐČ Ń€Đ°ĐžÌ†ĐŸĐœĐ” ĐŸĐ·Đ”Ń€Đ° КоĐČу с ĐžŃĐżĐŸĐ»ŃŒĐ·ĐŸĐČĐ°ĐœĐžĐ”ĐŒ ĐșĐŸŃĐŒĐžŃ‡Đ”ŃĐșох ŃŃŠĐ”ĐŒĐŸĐș. Đ˜ŃŃĐ»Đ”ĐŽĐŸĐČĐ°ĐœĐžŃ ĐżĐŸĐžŃĐșĐ° Đž ĐżŃ€ĐŸĐłĐœĐŸĐ·Đ° Đ·ĐŸĐ»ĐŸŃ‚ĐŸŃ€ŃƒĐŽĐœŃ‹Ń… ĐŸĐ±ŃŠĐ”ĐșŃ‚ĐŸĐČ ĐČŃ‹ĐżĐŸĐ»ĐœŃĐ»ĐžŃŃŒ ĐČ ĐŁĐ·Đ±Đ”ĐșĐžŃŃ‚Đ°ĐœĐ” ĐœĐ° участĐșĐ” Đ”Đ¶Đ°ĐŒĐ°ĐœŃĐ°ĐžÌ†ŃĐșох ĐłĐŸŃ€. Đ˜ŃĐżĐŸĐ»ŃŒĐ·ĐŸĐČĐ°Đ»ĐžŃŃŒ ĐČŃ‹ŃĐŸĐșĐŸŃ‚ĐŸŃ‡ĐœŃ‹Đ” ĐșĐŸŃĐŒĐžŃ‡Đ”ŃĐșОД ŃŃŠĐ”ĐŒĐșĐž ŃĐŸ ŃĐżŃƒŃ‚ĐœĐžĐșĐ° QuickBird 2, ŃŃŠĐ”ĐŒĐșĐž ĐłĐ”ĐŸŃ„ĐžĐ·ĐžŃ‡Đ”ŃĐșох ĐżĐŸĐ»Đ”ĐžÌ†, ĐłĐ”ĐŸĐ»ĐŸĐłĐžŃ‡Đ”ŃĐșОД Đž ĐłĐ”ĐŸŃ…ĐžĐŒĐžŃ‡Đ”ŃĐșОД ĐŽĐ°ĐœĐœŃ‹Đ”. Đ Đ”Đ·ŃƒĐ»ŃŒŃ‚Đ°Ń‚Ń‹. ВыяĐČĐ»Đ”ĐœĐŸ, Ń‡Ń‚ĐŸ Đ·ĐœĐ°Ń‡ĐžŃ‚Đ”Đ»ŃŒĐœĐ°Ń часть ŃĐżĐžŃ†Đ”ĐœŃ‚Ń€ĐŸĐČ ĐżŃ€ĐžŃƒŃ€ĐŸŃ‡Đ”ĐœĐ° ĐžĐŒĐ”ĐœĐœĐŸ Đș участĐșĐ°ĐŒ ĐżĐŸĐČŃ‹ŃˆĐ”ĐœĐœĐŸĐžÌ† ĐșĐŸĐœŃ†Đ”ĐœŃ‚Ń€Đ°Ń†ĐžĐž Đ»ĐžĐœĐ”Đ°ĐŒĐ”ĐœŃ‚ĐŸĐČ â€œĐœĐ”ŃŃ‚Đ°ĐœĐŽĐ°Ń€Ń‚ĐœŃ‹Ń…â€ Đ°Đ·ĐžĐŒŃƒŃ‚ĐŸĐČ, ŃĐŸŃŃ‚Đ°ĐČĐ»ŃŃ ĐŸŃ‚ 27 ĐŽĐŸ 34% ĐŸĐ±Ń‰Đ”ĐłĐŸ чОсла Đ»ĐžĐœĐ”Đ°ĐŒĐ”ĐœŃ‚ĐŸĐČ. ĐŁŃŃ‚Đ°ĐœĐŸĐČĐ»Đ”ĐœĐŸ, Ń‡Ń‚ĐŸ 59.6% ŃĐżĐžŃ†Đ”ĐœŃ‚Ń€ĐŸĐČ ĐœĐ°Ń…ĐŸĐŽŃŃ‚ŃŃ ĐČĐœŃƒŃ‚Ń€Đž 10% Ń‚Đ”Ń€Ń€ĐžŃ‚ĐŸŃ€ĐžĐž участĐșĐŸĐČ, ĐŸĐ±Đ»Đ°ĐŽĐ°ŃŽŃ‰ĐžŃ… ĐœĐ°ĐžĐČŃ‹ŃŃˆĐžĐŒĐž Đ·ĐœĐ°Ń‡Đ”ĐœĐžŃĐŒĐž ĐșĐŸĐŒĐżĐ»Đ”ĐșŃĐœŃ‹Ń… Đșарт ĐŽĐ”Ń„ĐŸŃ€ĐŒĐ°Ń†ĐžĐžÌ†; 50% Ń‚Đ”Ń€Ń€ĐžŃ‚ĐŸŃ€ĐžĐž с ĐœĐ°ĐžĐČŃ‹ŃŃˆĐžĐŒĐž Đ·ĐœĐ°Ń‡Đ”ĐœĐžŃĐŒĐž этох Đșарт ĐČĐŒĐ”Ń‰Đ°ŃŽŃ‚, ĐČ ŃŃ€Đ”ĐŽĐœĐ”ĐŒ, 89% ĐČсДх ŃĐżĐžŃ†Đ”ĐœŃ‚Ń€ĐŸĐČ Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃŃĐ”ĐœĐžĐžÌ†. ĐžĐżŃ€Đ”ĐŽĐ”Đ»Đ”ĐœĐŸ, Ń‡Ń‚ĐŸ Đșарты ĐșĐŸĐœŃ†Đ”ĐœŃ‚Ń€Đ°Ń†ĐžĐž Đ»ĐžĐœĐ”Đ°ĐŒĐ”ĐœŃ‚ĐŸĐČ ĐșĐŸŃĐŒĐŸŃĐœĐžĐŒĐșĐŸĐČ Ń â€œĐœĐ”ŃŃ‚Đ°ĐœĐ°Ń€Ń‚ĐœŃ‹ĐŒĐžâ€ Đ°Đ·ĐžĐŒŃƒŃ‚Đ°ĐŒĐž Đ·ĐœĐ°Ń‡ĐžŃ‚Đ”Đ»ŃŒĐœĐŸ Đ»ŃƒŃ‡ŃˆĐ” ĐŸŃ‚Ń€Đ°Đ¶Đ°ŃŽŃ‚ ĐżŃ€ĐŸŃŃ‚Ń€Đ°ĐœŃŃ‚ĐČĐ”ĐœĐœŃƒŃŽ ĐČĐ·Đ°ĐžĐŒĐŸŃĐČŃĐ·ŃŒ с ОзĐČĐ”ŃŃ‚ĐœŃ‹ĐŒĐž ĐŒĐ”ŃŃ‚ĐŸŃ€ĐŸĐ¶ĐŽĐ”ĐœĐžŃĐŒĐž ĐżĐŸ сраĐČĐœĐ”ĐœĐžŃŽ с ĐșĐ°Ń€Ń‚ĐŸĐžÌ† ĐșĐŸĐœŃ†Đ”ĐœŃ‚Ń€Đ°Ń†ĐžĐž ĐČсДх Đ»ĐžĐœĐ”Đ°ĐŒĐ”ĐœŃ‚ĐŸĐČ. ĐŁŃŃ‚Đ°ĐœĐŸĐČĐ»Đ”ĐœĐŸ, Ń‡Ń‚ĐŸ ŃŃƒĐŒĐŒĐ°Ń€ĐœĐ°Ń ĐżĐ»ĐŸŃ‰Đ°ĐŽŃŒ пДрспДĐșтоĐČĐœŃ‹Ń… участĐșĐŸĐČ Đ·ĐŸĐ»ĐŸŃ‚ĐŸŃ€ŃƒĐŽĐœŃ‹Ń… ĐŸĐ±ŃŠĐ”ĐșŃ‚ĐŸĐČ ŃĐŸŃŃ‚Đ°ĐČОла ĐŸĐșĐŸĐ»ĐŸ 20 ĐșĐŒ2. ĐĐ°ŃƒŃ‡ĐœĐ°Ń ĐœĐŸĐČĐžĐ·ĐœĐ°. ĐŸŃ€ĐžĐŒĐ”ĐœĐ”ĐœĐžĐ” ГИС РАПИД ĐœĐ° Ń€ŃĐŽĐ” участĐșĐŸĐČ Đ·Đ”ĐŒĐœĐŸĐžÌ† ĐșĐŸŃ€Ń‹ ĐżĐŸĐ·ĐČĐŸĐ»ĐžĐ»ĐŸ ŃƒŃŃ‚Đ°ĐœĐŸĐČоть ĐœĐŸĐČŃ‹Đ” Đ·Đ°ĐșĐŸĐœĐŸĐŒĐ”Ń€ĐœĐŸŃŃ‚Đž, сĐČŃĐ·Ń‹ĐČающОД хараĐșтДрОстОĐșĐž сДтО Đ»ĐžĐœĐ”Đ°ĐŒĐ”ĐœŃ‚ĐŸĐČ Ń„ĐžĐ·ĐžŃ‡Đ”ŃĐșох ĐżĐŸĐ»Đ”ĐžÌ† Đž Đ»Đ°ĐœĐŽŃˆĐ°Ń„Ń‚Đ° с Đ»ĐŸĐșĐ°Đ»ĐžĐ·Đ°Ń†ĐžĐ”ĐžÌ† Ń€ŃƒĐŽĐœŃ‹Ń… тДл Đž ŃĐżĐžŃ†Đ”ĐœŃ‚Ń€ĐŸĐČ Đ·Đ”ĐŒĐ»Đ”Ń‚Ń€ŃŃĐ”ĐœĐžĐžÌ†. ПраĐșтОчДсĐșая Đ·ĐœĐ°Ń‡ĐžĐŒĐŸŃŃ‚ŃŒ. Đ Đ°Đ·Ń€Đ°Đ±ĐŸŃ‚Đ°ĐœĐ° ĐœĐŸĐČая Ń‚Đ”Ń…ĐœĐŸĐ»ĐŸĐłĐžŃ Ń€Đ”ŃˆĐ”ĐœĐžŃ ĐżŃ€ĐŸĐłĐœĐŸĐ·ĐœŃ‹Ń… Đž ĐżĐŸĐžŃĐșĐŸĐČых ĐłĐ”ĐŸĐ»ĐŸĐłĐžŃ‡Đ”ŃĐșох заЎач, ĐșĐŸŃ‚ĐŸŃ€Đ°Ń ĐŒĐŸĐ¶Đ”Ń‚ ĐżŃ€ĐžĐŒĐ”ĐœŃŃ‚ŃŒŃŃ ĐŽĐ»Ń Ń€Đ”ŃˆĐ”ĐœĐžŃ ŃˆĐžŃ€ĐŸĐșĐŸĐłĐŸ Đșруга праĐșтОчДсĐșох заЎач, ĐŸŃĐŸĐ±Đ”ĐœĐœĐŸ ĐČ ŃĐ»ĐŸĐ¶ĐœŃ‹Ń… ĐłĐ”ĐŸĐ»ĐŸĐłĐžŃ‡Đ”ŃĐșох ŃƒŃĐ»ĐŸĐČоях про ĐżĐŸĐžŃĐșах ĐłĐ»ŃƒĐ±ĐŸĐșĐŸĐ·Đ°Đ»Đ”ĐłĐ°ŃŽŃ‰ĐžŃ… ĐŸĐ±ŃŠĐ”ĐșŃ‚ĐŸĐČ, ŃĐ»Đ°Đ±ĐŸ ĐżŃ€ĐŸŃĐČĐ»ŃŃŽŃ‰ĐžŃ…ŃŃ ĐČĐŸ ĐČĐœĐ”ŃˆĐœĐžŃ… ĐżĐŸĐ»ŃŃ… Đž Đ»Đ°ĐœĐŽŃˆĐ°Ń„Ń‚Đ”.The work is performed as a part of planned research of the geoinformation systems department of the Dnipro University of Technology. The results are obtained without any financial support of grants and research projects. The authors express appreciation to reviewers and editors for their valuable comments, recommendations, and attention to the work

    Multimedia information technology and the annotation of video

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    The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

    Structural alphabets derived from attractors in conformational space

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    Background: The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.Results: A Structural Alphabet has been derived by clustering all four-residue fragments of a high-resolution subset of the protein data bank and extracting the high-density states as representative conformational states. Each fragment is uniquely defined by a set of three independent angles corresponding to its degrees of freedom, capturing in simple and intuitive terms the properties of the conformational space. The fragments of the Structural Alphabet are equivalent to the conformational attractors and therefore yield a most informative encoding of proteins. Proteins can be reconstructed within the experimental uncertainty in structure determination and ensembles of structures can be encoded with accuracy and robustness.Conclusions: The density-based Structural Alphabet provides a novel tool to describe local conformations and it is specifically suitable for application in studies of protein dynamics. © 2010 Pandini et al; licensee BioMed Central Ltd

    How to perform RT-qPCR accurately in plant species?: a case study on flower colour gene expression in an azalea (Rhododendron simsii hybrids) mapping population

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    Background: Flower colour variation is one of the most crucial selection criteria in the breeding of a flowering pot plant, as is also the case for azalea (Rhododendron simsii hybrids). Flavonoid biosynthesis was studied intensively in several species. In azalea, flower colour can be described by means of a 3-gene model. However, this model does not clarify pink-coloration. The last decade gene expression studies have been implemented widely for studying flower colour. However, the methods used were often only semi-quantitative or quantification was not done according to the MIQE-guidelines. We aimed to develop an accurate protocol for RT-qPCR and to validate the protocol to study flower colour in an azalea mapping population. Results: An accurate RT-qPCR protocol had to be established. RNA quality was evaluated in a combined approach by means of different techniques e.g. SPUD-assay and Experion-analysis. We demonstrated the importance of testing noRT-samples for all genes under study to detect contaminating DNA. In spite of the limited sequence information available, we prepared a set of 11 reference genes which was validated in flower petals; a combination of three reference genes was most optimal. Finally we also used plasmids for the construction of standard curves. This allowed us to calculate gene-specific PCR efficiencies for every gene to assure an accurate quantification. The validity of the protocol was demonstrated by means of the study of six genes of the flavonoid biosynthesis pathway. No correlations were found between flower colour and the individual expression profiles. However, the combination of early pathway genes (CHS, F3H, F3'H and FLS) is clearly related to co-pigmentation with flavonols. The late pathway genes DFR and ANS are to a minor extent involved in differentiating between coloured and white flowers. Concerning pink coloration, we could demonstrate that the lower intensity in this type of flowers is correlated to the expression of F3'H. Conclusions: Currently in plant research, validated and qualitative RT-qPCR protocols are still rare. The protocol in this study can be implemented on all plant species to assure accurate quantification of gene expression. We have been able to correlate flower colour to the combined regulation of structural genes, both in the early and late branch of the pathway. This allowed us to differentiate between flower colours in a broader genetic background as was done so far in flower colour studies. These data will now be used for eQTL mapping to comprehend even more the regulation of this pathway

    Advancing transcriptome platforms

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    During the last decade of years, remarkable technological innovations have emerged that allow the direct or indirect determination of the transcriptome at unprecedented scale and speed. Studies using these methods have already altered our view of the extent and complexity of transcript profiling, which has advanced from one-gene-at-a-time to a holistic view of the genome. Here, we outline the major technical advances in transcriptome characterization, including the most popular used hybridization-based platform, the well accepted tag-based sequencing platform, and the recently developed RNA-Seq (RNA sequencing) based platform. Importantly, these next-generation technologies revolutionize assessing the entire transcriptome via the recent RNA-Seq technology

    Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications.

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    Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression
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