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Drug target interaction energies by the kernel energy method in aminoglycoside drugs and ribosomal A site RNA targets

Huang, Lulu; Massa, Lou; Karle, Jerome
Tipo: Artigo de Revista Científica
Português
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It is possible to use the full power of ab initio quantum mechanics in application to the interaction of drugs and their molecular targets. This idea had barely been realized until recently, because of the well known growth in computational difficulty of the use of quantum mechanics, with the number of atoms in the molecule to be studied. Because the biochemical molecules of medicinal chemistry are so often large, containing thousands or even tens of thousands of atoms, the computational difficulty of the full quantum problem had been prohibitive. Two things have happened, however, that change this perspective: (i) the advances of parallel supercomputers, and (ii) the discovery of a quantum formalism called quantum crystallography and the use of quantum kernels, a method that is well suited for parallel computation. Such advances would allow the quantum mechanical ab initio calculation of the molecular energy of peptides, proteins, DNA, and RNA, obtaining results of high accuracy. In this approach the computational difficulty of representing a molecule increases only modestly with the number of atoms. The calculations are simplified by adopting an acceptable approximation that allows a full biological molecule to be represented by smaller “kernels” of atoms. These results suggest that problems of medicinal chemistry...

Pre-anthesis ovary development determines genotypic differences in potential kernel weight in sorghum

Yang, Zongjian; van Oosterom, Erik J.; Jordan, David R.; Hammer, Graeme L.
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Português
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Kernel weight is an important factor determining grain yield and nutritional quality in sorghum, yet the developmental processes underlying the genotypic differences in potential kernel weight remain unclear. The aim of this study was to determine the stage in development at which genetic effects on potential kernel weight were realized, and to investigate the developmental mechanisms by which potential kernel weight is controlled in sorghum. Kernel development was studied in two field experiments with five genotypes known to differ in kernel weight at maturity. Pre-fertilization floret and ovary development was examined and post-fertilization kernel-filling characteristics were analysed. Large kernels had a higher rate of kernel filling and contained more endosperm cells and starch granules than normal-sized kernels. Genotypic differences in kernel development appeared before stamen primordia initiation in the developing florets, with sessile spikelets of large-seeded genotypes having larger floret apical meristems than normal-seeded genotypes. At anthesis, the ovaries for large-sized kernels were larger in volume, with more cells per layer and more vascular bundles in the ovary wall. Across experiments and genotypes, there was a significant positive correlation between kernel dry weight at maturity and ovary volume at anthesis. Genotypic effects on meristem size...

Benzyl and Methyl Fatty Hydroxamic Acids Based on Palm Kernel Oil as Chelating Agent for Liquid-Liquid Iron(III) Extraction

Haron, Md Jelas; Jahangirian, Hossein; Silong, Sidik; Yusof, Nor Azah; Kassim, Anuar; Rafiee-Moghaddam, Roshanak; Mahdavi, Behnam; Peyda, Mazyar; Abdollahi, Yadollah; Amin, Jamileh
Fonte: Molecular Diversity Preservation International (MDPI) Publicador: Molecular Diversity Preservation International (MDPI)
Tipo: Artigo de Revista Científica
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26.775781%
Liquid-liquid iron(III) extraction was investigated using benzyl fatty hydroxamic acids (BFHAs) and methyl fatty hydroxamic acids (MFHAs) as chelating agents through the formation of iron(III) methyl fatty hydroxamate (Fe-MFHs) or iron(III) benzyl fatty hydroxamate (Fe-BFHs) in the organic phase. The results obtained under optimized conditions, showed that the chelating agents in hexane extract iron(III) at pH 1.9 were realized effectively with a high percentage of extraction (97.2% and 98.1% for MFHAs and BFHAs, respectively). The presence of a large amount of Mg(II), Ni(II), Al(III), Mn(II) and Co(II) ions did affect the iron(III) extraction. Finally stripping studies for recovering iron(III) from organic phase (Fe-MFHs or Fe-BFHs dissolved in hexane) were carried out at various concentrations of HCl, HNO3 and H2SO4. The results showed that the desired acid for recovery of iron(III) was 5 M HCl and quantitative recovery of iron(III) was achieved from Fe(III)-MFHs and Fe(III)-BFHs solutions in hexane containing 5 mg/L of Fe(III).

Evaluation of reduced-tillering (tin) wheat lines in managed, terminal water deficit environments

Mitchell, J.H.; Rebetzke, G.J.; Chapman, S.C.; Fukai, S.
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Português
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17.589703%
Small or shrivelled wheat kernels (screenings) that reduce crop value are commonly produced in terminal drought environments. The aim of this study was to establish whether the incorporation of the tiller inhibition (tin) gene would contribute to maintenance of kernel weight and reductions in screenings under terminal water deficit. Five Silverstar near-isogenic lines contrasting in high and low tiller potential and their recurrent Silverstar parent were established at two plant densities under managed terminal water deficit (mild and severe) and irrigated conditions. With irrigation (grain yield of 5.6 t ha–1), kernels of all lines weighed ~31mg, with restricted-tillering (R-tin) lines producing an average 15% lower grain yield. Under both mild and severe terminal water deficit (4.1 t ha–1 and 2.8 t ha–1), free-tillering lines had relatively high screenings ranging from 11.9% to 16.2%. Compared with free-tillering lines, R-tin lines maintained large kernel weight (~29mg kernel–1) and had 29% and 51% fewer screenings under the two stresses, and a significantly greater (+11%) grain yield under mild stress. Higher kernel weights in tin lines were realized even with the greater kernel number per spike. The higher kernel weight of the R-tin lines under stress conditions was associated with greater anthesis biomass and increased stem water-soluble carbohydrates...

Detecting Genomic Clustering of Risk Variants from Sequence Data: Cases vs. Controls

Schaid, Daniel J.; Sinnwell, Jason P.; McDonnell, Shannon K.; Thibodeau, Stephen N.
Tipo: Artigo de Revista Científica
Português
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As the ability to measure dense genetic markers approaches the limit of the DNA sequence itself, taking advantage of possible clustering of genetic variants in, and around, a gene would benefit genetic association analyses, and likely provide biological insights. The greatest benefit might be realized when multiple rare variants cluster in a functional region. Several statistical tests have been developed, one of which is based on the popular Kulldorff scan statistic for spatial clustering of disease. We extended another popular spatial clustering method – Tango’s statistic – to genomic sequence data. An advantage of Tango’s method is that it is rapid to compute, and when single test statistic is computed, its distribution is well approximated by a scaled chi-square distribution, making computation of p-values very rapid. We compared the Type-I error rates and power of several clustering statistics, as well as the omnibus sequence kernel association test (SKAT). Although our version of Tango’s statistic, which we call “Kernel Distance” statistic, took approximately half the time to compute than the Kulldorff scan statistic, it had slightly less power than the scan statistic. Our results showed that the Ionita-Laza version of Kulldorff’s scan statistic had the greatest power over a range of clustering scenarios.

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine

Pettersson-Yeo, William; Benetti, Stefania; Marquand, Andre F.; Joules, Richard; Catani, Marco; Williams, Steve C. R.; Allen, Paul; McGuire, Philip; Mechelli, Andrea
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
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In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%...

Avalia??o reprodutiva de touros bubalinos alimentados com subprodutos da agroind?stria na Amaz?nia Oriental

SANTOS, Alessandra Ximenes
Português
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Supporting the object-oriented database on the Kernel Database System

Kellett, Daniel A.; Kwon, Tae-Wook
Português
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27.305476%
If a single operating system can support multitudes of different programming languages and data structures, a database system can support a variety of data models and data languages. In this thesis, a Kernel Database System (KDS) supporting classical data models and data languages (i.e., hierarchical, network, relational, and functional) is used to support a demonstration object oriented data model and data language. This thesis extends previous research by accommodating an object-oriented-data-model-and-language interface in the KDS. Consequently, the research shows that it is feasible to use the KDS to support modem data models and languages as well as classical ones. This thesis details the KDS design, Insert operation, and Display function. This thesis also details how to implement modifications to the Test-Interface so that the KDS can support the object-oriented database. This thesis proves complex data structures in an object-oriented data model can be realized using an attribute-based data model which is the kernel data model of the KDS. Second, it details how the KDS is designed showing why no changes needed to be made to the KDS to implement the object-oriented toy database. Third, it argues the advantages of using a KDS in the database-system design. The KDS design produces savings in costs from compatability...

Efficient estimation using the characteristic function : theory and applications with high frequency data

Kotchoni, Rachidi
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Thèse ou Mémoire numérique / Electronic Thesis or Dissertation
Português
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Nous abordons deux sujets distincts dans cette thèse: l'estimation de la volatilité des prix d'actifs financiers à partir des données à haute fréquence, et l'estimation des paramétres d'un processus aléatoire à partir de sa fonction caractéristique. Le chapitre 1 s'intéresse à l'estimation de la volatilité des prix d'actifs. Nous supposons que les données à haute fréquence disponibles sont entachées de bruit de microstructure. Les propriétés que l'on prête au bruit sont déterminantes dans le choix de l'estimateur de la volatilité. Dans ce chapitre, nous spécifions un nouveau modèle dynamique pour le bruit de microstructure qui intègre trois propriétés importantes: (i) le bruit peut être autocorrélé, (ii) le retard maximal au delà duquel l'autocorrélation est nulle peut être une fonction croissante de la fréquence journalière d'observations; (iii) le bruit peut avoir une composante correlée avec le rendement efficient. Cette dernière composante est alors dite endogène. Ce modèle se différencie de ceux existant en ceci qu'il implique que l'autocorrélation d'ordre 1 du bruit converge vers 1 lorsque la fréquence journalière d'observation tend vers l'infini. Nous utilisons le cadre semi-paramétrique ainsi défini pour dériver un nouvel estimateur de la volatilité intégrée baptisée "estimateur shrinkage". Cet estimateur se présente sous la forme d'une combinaison linéaire optimale de deux estimateurs aux propriétés différentes...

Caracterização de cultivares brasileiras de trigo com indicação de aplicabilidade tecnológica

Scheuer, Patrícia Matos
Tipo: Dissertação de Mestrado Formato: 115 p.| il., grafs., tabs.
Português
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17.417527%

O sistema operacional de rede heterogêneo HetNOS; The HetNOS heterogeneous network operating system

Barcellos, Antonio Marinho Pilla
Tipo: Dissertação Formato: application/pdf
Português
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17.305476%

Acceleration of the shiftable O(1) algorithm for bilateral filtering and non-local means

Chaudhury, Kunal N.
Tipo: Artigo de Revista Científica
Português
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17.417527%
A direct implementation of the bilateral filter [1] requires O(\sigma_s^2) operations per pixel, where \sigma_s is the (effective) width of the spatial kernel. A fast implementation of the bilateral filter was recently proposed in [2] that required O(1) operations per pixel with respect to \sigma_s. This was done by using trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shiftability property. In particular, a fast implementation of the Gaussian bilateral filter was realized by approximating the Gaussian range kernel using raised cosines. Later, it was demonstrated in [3] that this idea could be extended to a larger class of filters, including the popular non-local means filter [4]. As already observed in [2], a flip side of this approach was that the run time depended on the width \sigma_r of the range kernel. For an image with (local) intensity variations in the range [0,T], the run time scaled as O(T^2/\sigma^2_r) with \sigma_r. This made it difficult to implement narrow range kernels, particularly for images with large dynamic range. We discuss this problem in this note, and propose some simple steps to accelerate the implementation in general, and for small \sigma_r in particular. [1] C. Tomasi and R. Manduchi...

A SVD accelerated kernel-independent fast multipole method and its application to BEM

Cao, Yanchuang; Wen, Lihua; Rong, Junjie
Tipo: Artigo de Revista Científica
Português
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27.123394%
The kernel-independent fast multipole method (KIFMM) proposed in [1] is of almost linear complexity. In the original KIFMM the time-consuming M2L translations are accelerated by FFT. However, when more equivalent points are used to achieve higher accuracy, the efficiency of the FFT approach tends to be lower because more auxiliary volume grid points have to be added. In this paper, all the translations of the KIFMM are accelerated by using the singular value decomposition (SVD) based on the low-rank property of the translating matrices. The acceleration of M2L is realized by first transforming the associated translating matrices into more compact form, and then using low-rank approximations. By using the transform matrices for M2L, the orders of the translating matrices in upward and downward passes are also reduced. The improved KIFMM is then applied to accelerate BEM. The performance of the proposed algorithms are demonstrated by three examples. Numerical results show that, compared with the original KIFMM, the present method can reduce about 40% of the iterating time and 25% of the memory requirement.; Comment: 19 pages, 4 figures

Positive Operator Valued Measures and Feller Markov Kernels

Beneduci, Roberto
Tipo: Artigo de Revista Científica
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A Positive Operator Valued Measure (POVM) is a map $F:\mathcal{B}(X)\to\mathcal{L}_s^+(\mathcal{H})$ from the Borel $\sigma$-algebra of a topological space $X$ to the space of positive self-adjoint operators on a Hilbert space $\mathcal{H}$. We assume $X$ to be Hausdorff, locally compact and second countable and prove that a POVM $F$ is commutative if and only if it is the smearing of a spectral measure $E$ by means of a Feller Markov kernel. Moreover, we prove that the smearing can be realized by means of a strong Feller Markov kernel if and only if $F$ is uniformly continuous. Finally, we prove that a POVM which is absolutely continuous with respect to a finite measure $\nu$ admits a strong Feller Markov kernel. That provides a characterization of the smearing which connects a commutative POVM $F$ to a spectral measure $E$ and is relevant both from the mathematical and the physical viewpoint since smearings of spectral measures form a large and very relevant subclass of POVMs: they are paradigmatic for the modeling of certain standard forms of noise in quantum measurement (Busch,Lahti, Werner), they provide optimal approximators as marginals in joint measurements of incompatible observables (Busch, Lahti, Werner), they are important for a range of quantum information processing protocols...

Local universality in biorthogonal Laguerre ensembles

Zhang, Lun
Tipo: Artigo de Revista Científica
Português
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Double blind deconvolution: the analysis of postsynaptic currents in nerve cells.

Poskitt, Donald S; Dogancay, K; Chung, Shin-Ho
Fonte: Aiden Press Publicador: Aiden Press
Tipo: Artigo de Revista Científica
Português
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This paper is concerned with the analysis of observations made on a system that is being stimulated at fixed time intervals but where the precise nature and effect of any individual stimulus is unknown. The realized values are modelled as a stochastic process consisting of a random signal embedded in noise. The aim of the analysis is to use the data to unravel the unknown structure of the system and to ascertain the probabilistic behaviour of the stimuli. A method of parameter estimation based on quasi-profile likelihood is presented and the statistical properties of the estimates are established while recognizing that there will be a discrepancy between the model and the true data-generating mechanism. A method of model validation and determination is also advanced and kernel smoothing techniques are proposed as a basis for identifying the amplitude distribution of the stimuli. The data processing techniques described have a direct application to the investigation of excitatory post-synaptic currents recorded from nerve cells in the central nervous system and their use in quantal analysis of such data is illustrated.