Features extraction using random matrix theory.

Representing the complex data in a concise and accurate way is a special stage in data mining methodology. Redundant and noisy data affects generalization power of any classification algorithm, undermines the results of any clustering algorithm and finally encumbers the monitoring of large dynamic systems. This work provides several efficient approaches to all aforementioned sides of the analysis. We established, that notable difference can be made, if the results from the theory of ensembles of random matrices are employed. Particularly important result of our study is a discovered family of methods based on projecting the data set on different subsets of the correlation spectrum. Generally, we start with traditional correlation matrix of a given data set. We perform singular value decomposition, and establish boundaries between essential and unimportant eigen-components of the spectrum. Then, depending on the nature of the problem at hand we either use former or later part for the projection purpose. Projecting the spectrum of interest is a common technique in linear and non-linear spectral methods such as Principal Component Analysis, Independent Component Analysis and Kernel Principal Component Analysis. Usually the part of the spectrum to project is defined by the amount of variance of overall data or feature space in non-linear case. The applicability of these spectral methods is limited by the assumption that larger variance has important dynamics, i.e. if the data has a high signal-to-noise ratio. If it is true, projection of principal components targets two problems in data mining, reduction in the number of features and selection of more important features. Our methodology does not make an assumption of high signal-to-noise ratio, instead, using the rigorous instruments of Random Matrix Theory (RNIT) it identifies the presence of noise and establishes its boundaries. The knowledge of the structure of the spectrum gives us possibility to make more insightful projections. For instance, in the application to router network traffic, the reconstruction error procedure for anomaly detection is based on the projection of noisy part of the spectrum. Whereas, in bioinformatics application of clustering the different types of leukemia, implicit denoising of the correlation matrix is achieved by decomposing the spectrum to random and non-random parts. For temporal high dimensional data, spectrum and eigenvectors of its correlation matrix is another representation of the data. Thus, eigenvalues, components of the eigenvectors, inverse participation ratio of eigenvector components and other operators of eigen analysis are spectral features of dynamic system. In our work we proposed to extract spectral features using the RMT. We demonstrated that with extracted spectral features we can monitor the changing dynamics of network traffic. Experimenting with the delayed correlation matrices of network traffic and extracting its spectral features, we visualized the delayed processes in the system. We demonstrated in our work that broad range of applications in feature extraction can benefit from the novel RMT based approach to the spectral representation of the data

The IIASA-LUC Project Georeferenced Database of the Former USSR. Volume 5: Land Categories

The IIASA/LUC georeferenced database for the former U.S.S.R. was created within the framework of the project ``Modeling Land-Use and Land Cover Changes in Europe and Northern Asia" (LUC). For Russia, essential information on relief, soil, vegetation, land cover and use, etc. for routine environmental analysis was lacking when the LUC project started developing the database. In addition, the environmental data on the former U.S.S.R. which was available occurred in formats (papers, tables, etc.) that in general could not be used with modern information technology, and in particular in model building. In creating the LUC project database, we have achieved three objectives: 1) to obtain relevant information for the LUC project modeling exercises; 2) to develop data which are usable with modern information technology; 3) to contribute a series of digital databases which could be applied for various other analyses by the national and international scientific community. In defining the tasks it was agreed to create a set of digital databases which could be handled by geographic information systems (GIS). The full set of georeferenced digital databases was combined into the LUC project's GIS, using ARC/INFO. However, each individual item (physiography, soil, vegetation, etc.) was created as a unique specific digital database, allowing each item to be used separately, depending on users' needs. The complete series of these unique georeferenced digital databases for the territory of the former U.S.S.R. is described in the IIASA/LUC volumes: Volume 1 -- Physiography (landforms, slope conditions, elevations); Volume 2 -- Soil; Volume 3 -- Soil degradation status (Russia); Volume 4 -- Vegetation; Volume 5 -- Land categories; Volume 6 -- Agricultural regionalization

The IIASA-LUC Project Georeferenced Database of the former USSR. Volume 3: Soil Degradation Status in Russia

The IIASA/LUC georeferenced database for the former USSR (in part only for Russia), was created within the framework of the project "Modeling of Land Use and Land Cover Changes in Europe and Northern Asia" (LUC). For Russia, essential information on relief, soil, vegetation, land cover and use, etc. for routine environmental analysis was lacking when the LUC project first started developing the database. In addition, the environmental data on the former USSR which was available occurred in formats (papers, tables, etc.) that in general could not be used with modern information technology, and in particular in model building. In creating the LUC project database, we have established a threefold task: (1) to obtain the relevant information for the LUC project modeling exercises; (2) to develop data which is applicable to modern information technology; (3) to contribute a series of digital databases which could be applied for a number of other specific analysis by the national and international scientific community. In defining the tasks it was agreed to create a set of digital databases which could be handled by a geographic information systems (GIS). This required that the data had to be georeferenced. The complete set of georeferenced digital databases was combined into the LUC project's GIS, using ARC/INFO. However, each individual item (physiography, soil, vegetation, etc.) was created as an unique specific digital database, allowing to be used separately, depending on user's needs. The complete series of the unique georeferenced digital databases is described in several IIASA/LUC volumes: Volume 1 -- Physiography (land forms, slope conditions, elevations); Volume 2 -- Soil; Volume 3 -- Soil degradation status (Russia); Volume 4 -- Vegetation; Volume 5 -- Land categories; Volume 6 -- Agricultural regionalization

The IIASA-LUC Project Georeferenced Database of the Former U.S.S.R., Volume 4: Vegetation

The IIASA/LUC georeferenced database for the former U.S.S.R. was created within the framework of the project "Modeling Land Use and Land Cover Changes in Europe and Northern Asia" (LUC). For Russia, essential information on relief, soil, vegetation, land cover and use, etc., for routine environmental analysis was lacking when the LUC project started developing the database. In addition, the environmental data on the former U.S.S.R. which were available, occurred in formats (papers, tables, etc.) that in general could not be used with modern information technology, and in particular in model building. In creating the LUC project database, we have established a threefold task: (1) to obtain the relevant information for the LUC project modeling exercises; (2) to develop data which is applicable to modem information technology; (3) to contribute a series of digital databases which could be applied for a number of other specific analyses by the national and international scientific community. In defining the tasks it was agreed to create a set of digital databases which could be handled by geographic information systems (GIS). The full set of georeferenced digital databases was combined into the LUC project's GIS, using ARC/INFO. However, each individual item (physiography, soil, vegetation, etc.) was created as a separate digital database, allowing each item to be used independently, according to users' needs. The complete series of the unique georeferenced digital databases for the territory of the former U.S.S.R. is described in the IIASA/LUC volumes: Volume 1: Physiography (landforms, slope conditions, elevations); Volume 2: Soil; Volume 3: Soil degradation status (Russia); Volume 4: Vegetation; Volume 5: Land categories

Enhancement of methodology for protection of structures in contradiction of thermal effects

The research paper is devoted to protection of structures against heat and temperature effects. The necessity of improving the calculation of multilayered fence structures is shown. The solution of a one-dimensional unsteady heat conduction equation with constant and variable coefficients allowing to use of inhomogeneous and anisotropic materials as the fence material is given. An example of the solution of a fence made of inhomogeneous and anisotropic material is given. Solution of heat conduction equation is obtained by the recurrence-operator method. The solution of one-dimensional unsteady heat conduction equation with variable coefficients is obtained using the recurrence-operator method. The possibility of using the solution of the equation for multilayered inhomogeneous anisotropic fence materials is indicated

Bacterial formate dehydrogenase. Increasing the enzyme thermal stability by hydrophobization of alpha-helices

AbstractNAD+-dependent formate dehydrogenase (EC 1.2.1.2, FDH) from methylotrophic bacterium Pseudomonas sp.101 exhibits the highest stability among the similar type enzymes studied. To obtain further increase in the thermal stability of FDH we used one of general approaches based on hydrophobization of protein α-helices. Five serine residues in positions 131, 160, 168, 184 and 228 were selected for mutagenesis on the basis of (i) comparative studies of nine FDH amino acid sequences from different sources and (ii) with the analysis of the ternary structure of the enzyme from Pseudomonas sp.101. Residues Ser-131 and Ser-160 were replaced by Ala, Val and Leu. Residues Ser-168, Ser-184 and Ser-228 were changed into Ala. Only Ser/Ala mutations in positions 131, 160, 184 and 228 resulted in an increase of the FDH stability. Mutant S168A was 1.7 times less stable than the wild-type FDH. Double mutants S(131,160)A and S(184,228)A and the four-point mutant S(131,160,184,228)A were also prepared and studied. All FDH mutants with a positive stabilization effect had the same kinetic parameters as wild-type enzyme. Depending on the position of the replaced residue, the single point mutation Ser/Ala increased the FDH stability by 5–24%. Combination of mutations shows near additive effect of each mutation to the total FDH stabilization. Four-point mutant S(131,160,184,228)A FDH had 1.5 times higher thermal stability compared to the wild-type enzyme

A precision medicine initiative for Alzheimer's disease: the road ahead to biomarker-guided integrative disease modeling

After intense scientific exploration and more than a decade of failed trials, Alzheimer’s disease (AD) remains a fatal global epidemic. A traditional research and drug development paradigm continues to target heterogeneous late-stage clinically phenotyped patients with single 'magic bullet' drugs. Here, we propose that it is time for a paradigm shift towards the implementation of precision medicine (PM) for enhanced risk screening, detection, treatment, and prevention of AD. The overarching structure of how PM for AD can be achieved will be provided through the convergence of breakthrough technological advances, including big data science, systems biology, genomic sequencing, blood-based biomarkers, integrated disease modeling and P4 medicine. It is hypothesized that deconstructing AD into multiple genetic and biological subsets existing within this heterogeneous target population will provide an effective PM strategy for treating individual patients with the specific agent(s) that are likely to work best based on the specific individual biological make-up. The Alzheimer’s Precision Medicine Initiative (APMI) is an international collaboration of leading interdisciplinary clinicians and scientists devoted towards the implementation of PM in Neurology, Psychiatry and Neuroscience. It is hypothesized that successful realization of PM in AD and other neurodegenerative diseases will result in breakthrough therapies, such as in oncology, with optimized safety profiles, better responder rates and treatment responses, particularly through biomarker-guided early preclinical disease-stage clinical trials

Anosognosia for hemiplegia as a tripartite disconnection syndrome

© 2019 Pacella et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.The syndrome of Anosognosia for Hemiplegia (AHP) can provide unique insights into the neurocognitive processes of motor awareness. Yet, prior studies have only explored predominately discreet lesions. Using advanced structural neuroimaging methods in 174 patients with a right-hemisphere stroke, we were able to identify three neural systems that contribute to AHP, when disconnected or directly damaged: the (i) premotor loop (ii) limbic system, and (iii) ventral attentional network. Our results suggest that human motor awareness is contingent on the joint contribution of these three systems.Peer reviewedFinal Published versio

Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.

There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects