Gustavo Saluste (124505), Maria I. Albarran (124508), Rosa M. Alvarez (124511) et al. · Figshare · 2015
<p>a) Imidazopyridazine ligand <b>2</b> bound to PIM-1 in the crystal structure, where explicit hydrogen bonds involved in the binding are highlighted; b) Bidimensional (2D) interaction map for the complex PIM-1<b>:</b>compound <b>2</b> extracted from 2C3I.pdb; c) 3-point pharmacophore deduced from the compound <b>2</b> bioactive conformation, using the CHD scheme, as implemented in MOE software (the hydrogen-bond acceptor is light-blue and aromatic ring pharmacophoric features are in green).</p>
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Jeffrey Alan Johnson · Public administration and information technology · 2018
Cited by 4
Dmitry Tkachenko, Chou Bentler, ; -Bentler et al. · European Journal of Technology and Design · 2024
The article explores information structural modeling. Information structural modeling includes two types. The first type of structural modeling is used when studying the surrounding world. The second type of structural modeling is used when constructing new structures and modifying known structures. Information structural modeling is based on the features of data used in computer science. Structural modeling is figurative modeling. It has two features. The first feature of the modeling is that all types of data are treated as areal data. The second feature of structural modeling is that models have a dual formal and graphic or visual form. Structural modeling uses a set-theoretic and systems approach. Structural modeling is applied to data, technologies and systems. These types of structures are different and require different modeling techniques. Many objects have a hierarchical structure. This is due to the hierarchy of the surrounding world and the nesting of objects. Information structural modeling is a complex type of modeling. It is much more complex than formal modeling or symbolic modeling. Information structural modeling has two forms: figurative and formal. When constructing a figurative or visual form, it is necessary to solve the problem of the information content of the image. There is always complete information correspondence between the formal structural model and the modeling object. Between the figurative structural model and the modeling object there is either a complete information correspondence or a partial information correspondence. The article shows an example of reducing a complex set to a hierarchical structure. This example shows that structural modeling reduces the complexity of systems and configurations.
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I.I. BREKHMAN · Trends in Pharmacological Sciences · 1981
Cited by 12
Ludwig Krippahl · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2003
Dissertação apresentada para obtenção de Grau de Doutor em Bioquímica,Bioquímica Estrutural, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
Cited by 2Open access
Greet De Baets (216809), Loic Van Doorn (792215), Frederic Rousseau (5872) et al. · Figshare · 2015
<p>(A-C) Boxplot of the average A) sidechain burial, B) mainchain burial and C) stability (dG) of the APR calculated by FoldX. A negative dG indicates that the residue contributes to the thermodynamic stability of the protein. (D) The minimum distance in structural space between the mutation and the strongest APR present in the domain.</p>
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Marina V. Karsanina, Efim V. Lavrukhin, D. S. Fomin et al. · 2020
&lt;p&gt;The ability of correlation functions to describe structure (Karsanina et al., 2015; Karsanina et al., 2018) and provide means to reconstruct the structure based on correlation functions (Gerke and Karsanina, 2015; Karsanina and Gerke, 2018) alone was proposed as means to effectively compress and store structural information (Gerke et al., 2015). This is especially appealing considering the fact that truly multi-scale digital 3D soil structure model for a single genetic horizon even with the resolution not finer than 1 &amp;#181;m will contain enormous amount (approx., up to 10^15 voxels or even more) of data. Effective management and pore-scale simulations based on such datasets does not seem feasible at the moment. Another approach would be to retrieve only a relevant part of the dataset and operate on it indirectly, in particular based on correlation functions or stochastic reconstructions. The main aim of this work was to investigate the possibility to compress soil structural data, as resulted from X-ray microtomography data and directional correlation functions computation (Gerke et al., 2014), into a very limited number of parameters, potentially with minimal information content loss. We show that with the help of the proposed technique it is possible to compress a 3D image of 900^3-1300^3 voxels into a set of correlation functions, that with the help of fitting of an analytical function in the form of the superposition of three different basis functions may help to map all these correlation functions in a vector of six parameters. We apply the proposed methodology to 16 different soil 3D images and discuss numerous important implications that can help to achieve the ultimate goal of building 3D multi-scale soil structure model from meter to nm. Such model would help in establishing a fully multi-scale hydrological model operating from first principles as opposed to coarse continuum scale models.&lt;/p&gt;&lt;p&gt;This work was supported by Russian Science Foundation grant 19-72-10082 (correlation functions) and Russian Foundation for Basic Research grant 18-34-20131 &amp;#1084;&amp;#1086;&amp;#1083;_&amp;#1072;_&amp;#1074;&amp;#1077;&amp;#1076; (soil data).&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;Karsanina, M. V., Gerke, K. M., Skvortsova, E. B., Ivanov, A. L., &amp; Mallants, D. (2018). Enhancing image resolution of soils by stochastic multiscale image fusion. Geoderma, 314, 138-145.&lt;/p&gt;&lt;p&gt;Gerke, K. M., Karsanina, M. V., &amp; Mallants, D. (2015). Universal stochastic multiscale image fusion: an example application for shale rock. Scientific reports, 5, 15880.&lt;/p&gt;&lt;p&gt;Karsanina, M. V., &amp; Gerke, K. M. (2018). Hierarchical Optimization: Fast and Robust Multiscale Stochastic Reconstructions with Rescaled Correlation Functions. Physical Review Letters, 121(26), 265501.&lt;/p&gt;&lt;p&gt;Gerke, K. M., &amp; Karsanina, M. V. (2015). Improving stochastic reconstructions by weighting correlation functions in an objective function. EPL (Europhysics Letters), 111(5), 56002.&lt;/p&gt;&lt;p&gt;Gerke, K. M., Karsanina, M. V., Vasilyev, R. V., &amp; Mallants, D. (2014). Improving pattern reconstruction using directional correlation functions. EPL (Europhysics Letters), 106(6), 66002.&lt;/p&gt;&lt;p&gt;Karsanina, M. V., Gerke, K. M., Skvortsova, E. B., &amp; Mallants, D. (2015). Universal spatial correlation functions for describing and reconstructing soil microstructure. PLoS ONE, 10(5), e0126515.&lt;/p&gt;
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A. Ghali, Mohammad F. Daemi, M. Mansour · Pattern Recognition Letters · 1998
Cited by 5
Wang, Hui · Figshare · 2022
Structural information of LaH9.63
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Mark Burgin, Rainer Feistel · 2017
Structures play a crucial role in the world. They define things and thoughts, processes and functions, images and ideas. The goal of this research is to study structural information, which is intrinsically connected to structures. Here we present two approaches. In one of them, structural information is information in structures. In the other one, structural information is information about structures.
Cited by 2Open access
Peter Paul Kirschenmann · Sovietica · 1970
Cited by 0
Hibbler R.C · 2020
Cited by 0
Richard Ward, Olav Schiemann · Structure and bonding · 2012
Cited by 14
Davies, Eric, Cardiff EDC · ORCA Online Research @Cardiff (Cardiff University) · 2016
A guide to the European Union's structural policy, with hyperlinks to sources of information within European Sources Online and on external websites.
Cited by 0Open access
Christophe Rivière · Zenodo (CERN European Organization for Nuclear Research) · 2025
This technical disclosure introduces a method for encoding information within a carrier signal by exploiting global internal relations between distributed signal components rather than localized signal values. Unlike conventional modulation or cryptographic schemes, where information is associated with identifiable symbols or blocks, the proposed approach embeds the payload in non-local correlations spanning the entire signal window. Decoding requires a shared structural key that defines how the signal is decomposed, which internal relations are observed, and how these relations are projected back into information states. Without this structural knowledge, the signal remains fully observable but carries no locally identifiable information. The method is applicable to both analog and digital signals and is independent of the physical transmission medium. It is intended as a complementary, structure-based layer for information obfuscation and secure transport, orthogonal to classical cryptographic assumptions. This document serves as a technical disclosure to establish prior art and outline the conceptual framework of structural information encoding.
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Wang, Hui · Figshare · 2022
Structural information of LaH9.63
Cited by 0Open access
Roman Krzanowski · Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce) · 2017
This paper presents Minimal Information Structural Realism (MISR), that claims that information (signified by I) is an ontologically and epistemologically objective entity (signified by R) and is apprehended as, but not identical to structures perceived in nature (signified by S). Two informal arguments are presented in support of this claim. One argument is based on the conclusions from models of quantum mechanics (QM) and cosmology, while the other argument is referred to as incompleteness of epistemic definitions of information. MISR is not associated directly with the structural realism (SR) of the ontic or epistemic kinds, and is only remotely related to the concept of information structural realism (ISR) defined by Floridi.
Cited by 3Open access
Cardiff EDC, . · Archive of European Integration (AEI) (University of Pittsburgh) · 2013
A guide to the European Union’s Structural Policy, with hyperlinks to sources of information within European Sources Online and on external websites
Cited by 0Open access
Christophe Rivière · Zenodo (CERN European Organization for Nuclear Research) · 2025
This technical disclosure introduces a method for encoding information within a carrier signal by exploiting global internal relations between distributed signal components rather than localized signal values. Unlike conventional modulation or cryptographic schemes, where information is associated with identifiable symbols or blocks, the proposed approach embeds the payload in non-local correlations spanning the entire signal window. Decoding requires a shared structural key that defines how the signal is decomposed, which internal relations are observed, and how these relations are projected back into information states. Without this structural knowledge, the signal remains fully observable but carries no locally identifiable information. The method is applicable to both analog and digital signals and is independent of the physical transmission medium. It is intended as a complementary, structure-based layer for information obfuscation and secure transport, orthogonal to classical cryptographic assumptions. This document serves as a technical disclosure to establish prior art and outline the conceptual framework of structural information encoding.
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Steven R. Brown · Policy Sciences · 2002
Cited by 27