126 research outputs found

    Decision rules, trees and tests for tables with many-valued decisions : comparative study

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    In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches

    Optimization of approximate inhibitory rules relative to number of misclassifications

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    In this work, we consider so-called nonredundant inhibitory rules, containing an expression "attribute≠ value" on the right-hand side, for which the number of misclassifications is at most a threshold y. We study a dynamic programming approach for description of the considered set of rules. This approach allows also the optimization of nonredundant inhibitory rules relative to the length and coverage [1, 2]. The aim of this paper is to investigate an additional possibility of optimization relative to the number of misclassifications. The results of experiments with decision tables from the UCI Machine Learning Repository [3] show this additional optimization achieves a fewer misclassifications. Thus, the proposed optimization procedure is promising

    Dynamic programming algorithm for generation of optimal elimination trees for multi-frontal direct solver over h-refined grids

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    In this paper we present a dynamic programming algorithm for finding optimal elimination trees for computational grids refined towards point or edge singularities. The elimination tree is utilized to guide the multi-frontal direct solver algorithm. Thus, the criterion for the optimization of the elimination tree is the computational cost associated with the multi-frontal solver algorithm executed over such tree. We illustrate the paper with several examples of optimal trees found for grids with point, isotropic edge and anisotropic edge mixed with point singularity. We show the comparison of the execution time of the multi-frontal solver algorithm with results of MUMPS solver with METIS library, implementing the nested dissection algorithm. © The Authors. Published by Elsevier B.V

    Quasi-optimal elimination trees for 2D grids with singularities

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    We construct quasi-optimal elimination trees for 2D finite element meshes with singularities.These trees minimize the complexity of the solution of the discrete system. The computational cost estimates of the elimination process model the execution of the multifrontal algorithms in serial and in parallel shared-memory executions. Since the meshes considered are a subspace of all possible mesh partitions, we call these minimizers quasi-optimal.We minimize the cost functionals using dynamic programming. Finding these minimizers is more computationally expensive than solving the original algebraic system. Nevertheless, from the insights provided by the analysis of the dynamic programming minima, we propose a heuristic construction of the elimination trees that has cost O(log(Ne log(Ne)), where N e is the number of elements in the mesh.We show that this heuristic ordering has similar computational cost to the quasi-optimal elimination trees found with dynamic programming and outperforms state-of-the-art alternatives in our numerical experiments

    Тихая эпидемия: Исследование роста проблем психического здоровья в современном обществе

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    Психическое здоровье — это тема, которая долгое время часто замалчивалась. В этой статье мы рассмотрим основные причины роста психических заболеваний в современном обществе и их влияние на отдельных людей и общество в целом. Мы также рассмотрим некоторые из наиболее эффективных способов решения этой проблемы и обеспечения того, чтобы люди получали помощь, необходимую им для счастливой и здоровой жизни.Mental health is a topic that has often been hushed up for a long time. In this article, we will look at the main causes of the growth of mental illness in modern society and their impact on individuals and society as a whole. We will also look at some of the most effective ways to address this problem and ensure that people receive the help they need to live a happy and healthy life

    Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

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    Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a nonfunctional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20 % better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a give

    Scanning ion conductance microscopy: a convergent high-resolution technology for multi-parametric analysis of living cardiovascular cells

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    Cardiovascular diseases are complex pathologies that include alterations of various cell functions at the levels of intact tissue, single cells and subcellular signalling compartments. Conventional techniques to study these processes are extremely divergent and rely on a combination of individual methods, which usually provide spatially and temporally limited information on single parameters of interest. This review describes scanning ion conductance microscopy (SICM) as a novel versatile technique capable of simultaneously reporting various structural and functional parameters at nanometre resolution in living cardiovascular cells at the level of the whole tissue, single cells and at the subcellular level, to investigate the mechanisms of cardiovascular disease. SICM is a multimodal imaging technology that allows concurrent and dynamic analysis of membrane morphology and various functional parameters (cell volume, membrane potentials, cellular contraction, single ion-channel currents and some parameters of intracellular signalling) in intact living cardiovascular cells and tissues with nanometre resolution at different levels of organization (tissue, cellular and subcellular levels). Using this technique, we showed that at the tissue level, cell orientation in the inner and outer aortic arch distinguishes atheroprone and atheroprotected regions. At the cellular level, heart failure leads to a pronounced loss of T-tubules in cardiac myocytes accompanied by a reduction in Z-groove ratio. We also demonstrated the capability of SICM to measure the entire cell volume as an index of cellular hypertrophy. This method can be further combined with fluorescence to simultaneously measure cardiomyocyte contraction and intracellular calcium transients or to map subcellular localization of membrane receptors coupled to cyclic adenosine monophosphate production. The SICM pipette can be used for patch-clamp recordings of membrane potential and single channel currents. In conclusion, SICM provides a highly informative multimodal imaging platform for functional analysis of the mechanisms of cardiovascular diseases, which should facilitate identification of novel therapeutic strategies

    Eukaryotic-like Ser/Thr Protein Kinases SpkC/F/K Are Involved in Phosphorylation of GroES in the Cyanobacterium Synechocystis

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    Serine/threonine protein kinases (STPKs) are the major participants in intracellular signal transduction in eukaryotes, such as yeasts, fungi, plants, and animals. Genome sequences indicate that these kinases are also present in prokaryotes, such as cyanobacteria. However, their roles in signal transduction in prokaryotes remain poorly understood. We have attempted to identify the roles of STPKs in response to heat stress in the prokaryotic cyanobacterium Synechocystis sp. PCC 6803, which has 12 genes for STPKs. Each gene was individually inactivated to generate a gene-knockout library of STPKs. We applied in vitro Ser/Thr protein phosphorylation and phosphoproteomics and identified the methionyl-tRNA synthetase, large subunit of RuBisCO, 6-phosphogluconate dehydrogenase, translation elongation factor Tu, heat-shock protein GrpE, and small chaperonin GroES as the putative targets for Ser/Thr phosphorylation. The expressed and purified GroES was used as an external substrate to screen the protein extracts of the individual mutants for their Ser/Thr kinase activities. The mutants that lack one of the three protein kinases, SpkC, SpkF, and SpkK, were unable to phosphorylate GroES in vitro, suggesting possible interactions between them towards their substrate. Complementation of the mutated SpkC, SpkF, and SpkK leads to the restoration of the ability of cells to phosphorylate the GroES. This suggests that these three STPKs are organized in a sequential order or a cascade and they work one after another to finally phosphorylate the GroES

    Improvement of regeneration in pepper: a recalcitrant species

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    [EN] Organogenesis is influenced by factors like genotype, type of explant, culture medium components, and incubation conditions. The influence of ethylene, which can be produced in the culture process, can also be a limiting factor in recalcitrant species like pepper. In this work, bud induction was achieved from cotyledons and hypocotyls-from eight pepper cultivars-on Murashige and Skoog (MS) medium supplemented with 22.2 mu M 6-benzyladenine (6BA) and 5.71 mu M indole-3-acetic acid (IAA), in media with or without silver nitrate (SN) (58.86 mu M), a suppressor of ethylene action. In the SN-supplemented medium, the frequencies of explants with buds and with callus formation were lower in both kinds of explant, but higher numbers of developed shoots were isolated from explants cultured on SN. Bud elongation was better in medium with gibberellic acid (GA(3)) (2.88 mu M) than in medium free of growth regulators or supplemented with 1-aminocyclopropane-1-carboxylic acid (ACC) at 34.5 mu M. However, isolation of shoots was difficult and few plants were recovered. The effect of adding SN following bud induction (at 7 d) and that of dark incubation (the first 7 d of culture) was also assessed in order to improve the previous results. When SN was added after bud induction, similar percentages of bud induction were found for cotyledons (average frequency 89.37% without SN and 94.37% with SN) whereas they doubled in hypocotyls (50% without SN and 87.7% with SN). In addition, in both kinds of explant, the number of developed plants able to be transferred to soil (developed and rooted) was greatly increased by SN. 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