Performance Assessment of Kernel Density Clustering for Gene Expression Profile Data

PubMed Central

Zeng, Beiyan; Chen, Yiping P.; Smith, Oscar H.

2003-01-01

Kernel density smoothing techniques have been used in classification or supervised learning of gene expression profile (GEP) data, but their applications to clustering or unsupervised learning of those data have not been explored and assessed. Here we report a kernel density clustering method for analysing GEP data and compare its performance with the three most widely-used clustering methods: hierarchical clustering, K-means clustering, and multivariate mixture model-based clustering. Using several methods to measure agreement, between-cluster isolation, and withincluster coherence, such as the Adjusted Rand Index, the Pseudo F test, the r2 test, and the profile plot, we have assessed the effectiveness of kernel density clustering for recovering clusters, and its robustness against noise on clustering both simulated and real GEP data. Our results show that the kernel density clustering method has excellent performance in recovering clusters from simulated data and in grouping large real expression profile data sets into compact and well-isolated clusters, and that it is the most robust clustering method for analysing noisy expression profile data compared to the other three methods assessed. PMID:18629292

Investigation of various energy deposition kernel refinements for the convolution/superposition method

PubMed Central

Huang, Jessie Y.; Eklund, David; Childress, Nathan L.; Howell, Rebecca M.; Mirkovic, Dragan; Followill, David S.; Kry, Stephen F.

2013-01-01

Purpose: Several simplifications used in clinical implementations of the convolution/superposition (C/S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C/S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C/S algorithm. Methods: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C/S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C/S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels. Results: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found that depth was the most dominant factor affecting the pattern of energy deposition; however, the effects of field size and off-axis distance were not negligible. For the material-specific kernels, we found that as the density of the material increased, more energy was deposited laterally by charged particles, as opposed to in the forward direction. Thus, density scaling of water kernels becomes a worse approximation as the density and the effective atomic number of the material differ more from water. Implementation of spatially variant, polyenergetic kernels increased the percent depth dose value at 25 cm depth by 2.1%–5.8% depending on the field size, while implementation of titanium kernels gave 4.9% higher dose upstream of the metal cavity (i.e., higher backscatter dose) and 8.2% lower dose downstream of the cavity. Conclusions: Of the various kernel refinements investigated, inclusion of depth-dependent and metal-specific kernels into the C/S method has the greatest potential to improve dose calculation accuracy. Implementation of spatially variant polyenergetic kernels resulted in a harder depth dose curve and thus has the potential to affect beam modeling parameters obtained in the commissioning process. For metal implants, the C/S algorithms generally underestimate the dose upstream and overestimate the dose downstream of the implant. Implementation of a metal-specific kernel mitigated both of these errors. PMID:24320507

Urban Transmission of American Cutaneous Leishmaniasis in Argentina: Spatial Analysis Study

PubMed Central

Gil, José F.; Nasser, Julio R.; Cajal, Silvana P.; Juarez, Marisa; Acosta, Norma; Cimino, Rubén O.; Diosque, Patricio; Krolewiecki, Alejandro J.

2010-01-01

We used kernel density and scan statistics to examine the spatial distribution of cases of pediatric and adult American cutaneous leishmaniasis in an urban disease-endemic area in Salta Province, Argentina. Spatial analysis was used for the whole population and stratified by women > 14 years of age (n = 159), men > 14 years of age (n = 667), and children < 15 years of age (n = 213). Although kernel density for adults encompassed nearly the entire city, distribution in children was most prevalent in the peripheral areas of the city. Scan statistic analysis for adult males, adult females, and children found 11, 2, and 8 clusters, respectively. Clusters for children had the highest odds ratios (P < 0.05) and were located in proximity of plantations and secondary vegetation. The data from this study provide further evidence of the potential urban transmission of American cutaneous leishmaniasis in northern Argentina. PMID:20207869

Resolvability of regional density structure and the road to direct density inversion - a principal-component approach to resolution analysis

NASA Astrophysics Data System (ADS)

Płonka, Agnieszka; Fichtner, Andreas

2017-04-01

Lateral density variations are the source of mass transport in the Earth at all scales, acting as drivers of convective motion. However, the density structure of the Earth remains largely unknown since classic seismic observables and gravity provide only weak constraints with strong trade-offs. Current density models are therefore often based on velocity scaling, making strong assumptions on the origin of structural heterogeneities, which may not necessarily be correct. Our goal is to assess if 3D density structure may be resolvable with emerging full-waveform inversion techniques. We have previously quantified the impact of regional-scale crustal density structure on seismic waveforms with the conclusion that reasonably sized density variations within the crust can leave a strong imprint on both travel times and amplitudes, and, while this can produce significant biases in velocity and Q estimates, the seismic waveform inversion for density may become feasible. In this study we perform principal component analyses of sensitivity kernels for P velocity, S velocity, and density. This is intended to establish the extent to which these kernels are linearly independent, i.e. the extent to which the different parameters may be constrained independently. We apply the method to data from 81 events around the Iberian Penninsula, registered in total by 492 stations. The objective is to find a principal kernel which would maximize the sensitivity to density, potentially allowing for as independent as possible density resolution. We find that surface (mosty Rayleigh) waves have significant sensitivity to density, and that the trade-off with velocity is negligible. We also show the preliminary results of the inversion.

Characterization of a maximum-likelihood nonparametric density estimator of kernel type

NASA Technical Reports Server (NTRS)

Geman, S.; Mcclure, D. E.

1982-01-01

Kernel type density estimators calculated by the method of sieves. Proofs are presented for the characterization theorem: Let x(1), x(2),...x(n) be a random sample from a population with density f(0). Let sigma 0 and consider estimators f of f(0) defined by (1).

Automated skin lesion segmentation with kernel density estimation

NASA Astrophysics Data System (ADS)

Pardo, A.; Real, E.; Fernandez-Barreras, G.; Madruga, F. J.; López-Higuera, J. M.; Conde, O. M.

2017-07-01

Skin lesion segmentation is a complex step for dermoscopy pathological diagnosis. Kernel density estimation is proposed as a segmentation technique based on the statistic distribution of color intensities in the lesion and non-lesion regions.

Nonparametric entropy estimation using kernel densities.

PubMed

Lake, Douglas E

2009-01-01

The entropy of experimental data from the biological and medical sciences provides additional information over summary statistics. Calculating entropy involves estimates of probability density functions, which can be effectively accomplished using kernel density methods. Kernel density estimation has been widely studied and a univariate implementation is readily available in MATLAB. The traditional definition of Shannon entropy is part of a larger family of statistics, called Renyi entropy, which are useful in applications that require a measure of the Gaussianity of data. Of particular note is the quadratic entropy which is related to the Friedman-Tukey (FT) index, a widely used measure in the statistical community. One application where quadratic entropy is very useful is the detection of abnormal cardiac rhythms, such as atrial fibrillation (AF). Asymptotic and exact small-sample results for optimal bandwidth and kernel selection to estimate the FT index are presented and lead to improved methods for entropy estimation.

Discontinuous functional for linear-response time-dependent density-functional theory: The exact-exchange kernel and approximate forms

NASA Astrophysics Data System (ADS)

Hellgren, Maria; Gross, E. K. U.

2013-11-01

We present a detailed study of the exact-exchange (EXX) kernel of time-dependent density-functional theory with an emphasis on its discontinuity at integer particle numbers. It was recently found that this exact property leads to sharp peaks and step features in the kernel that diverge in the dissociation limit of diatomic systems [Hellgren and Gross, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.85.022514 85, 022514 (2012)]. To further analyze the discontinuity of the kernel, we here make use of two different approximations to the EXX kernel: the Petersilka Gossmann Gross (PGG) approximation and a common energy denominator approximation (CEDA). It is demonstrated that whereas the PGG approximation neglects the discontinuity, the CEDA includes it explicitly. By studying model molecular systems it is shown that the so-called field-counteracting effect in the density-functional description of molecular chains can be viewed in terms of the discontinuity of the static kernel. The role of the frequency dependence is also investigated, highlighting its importance for long-range charge-transfer excitations as well as inner-shell excitations.

Analysis of Drude model using fractional derivatives without singular kernels

NASA Astrophysics Data System (ADS)

Jiménez, Leonardo Martínez; García, J. Juan Rosales; Contreras, Abraham Ortega; Baleanu, Dumitru

2017-11-01

We report study exploring the fractional Drude model in the time domain, using fractional derivatives without singular kernels, Caputo-Fabrizio (CF), and fractional derivatives with a stretched Mittag-Leffler function. It is shown that the velocity and current density of electrons moving through a metal depend on both the time and the fractional order 0 < γ ≤ 1. Due to non-singular fractional kernels, it is possible to consider complete memory effects in the model, which appear neither in the ordinary model, nor in the fractional Drude model with Caputo fractional derivative. A comparison is also made between these two representations of the fractional derivatives, resulting a considered difference when γ < 0.8.

Putting Priors in Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2004-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Abiotic stress growth conditions induce different responses in kernel iron concentration across genotypically distinct maize inbred varieties

PubMed Central

Kandianis, Catherine B.; Michenfelder, Abigail S.; Simmons, Susan J.; Grusak, Michael A.; Stapleton, Ann E.

2013-01-01

The improvement of grain nutrient profiles for essential minerals and vitamins through breeding strategies is a target important for agricultural regions where nutrient poor crops like maize contribute a large proportion of the daily caloric intake. Kernel iron concentration in maize exhibits a broad range. However, the magnitude of genotype by environment (GxE) effects on this trait reduces the efficacy and predictability of selection programs, particularly when challenged with abiotic stress such as water and nitrogen limitations. Selection has also been limited by an inverse correlation between kernel iron concentration and the yield component of kernel size in target environments. Using 25 maize inbred lines for which extensive genome sequence data is publicly available, we evaluated the response of kernel iron density and kernel mass to water and nitrogen limitation in a managed field stress experiment using a factorial design. To further understand GxE interactions we used partition analysis to characterize response of kernel iron and weight to abiotic stressors among all genotypes, and observed two patterns: one characterized by higher kernel iron concentrations in control over stress conditions, and another with higher kernel iron concentration under drought and combined stress conditions. Breeding efforts for this nutritional trait could exploit these complementary responses through combinations of favorable allelic variation from these already well-characterized genetic stocks. PMID:24363659

NAIplot: An opensource web tool to visualize neuraminidase inhibitor (NAI) phenotypic susceptibility results using kernel density plots.

PubMed

Lytras, Theodore; Kossyvakis, Athanasios; Mentis, Andreas

2016-02-01

The results of neuraminidase inhibitor (NAI) enzyme inhibition assays are commonly expressed as 50% inhibitory concentration (IC50) fold-change values and presented graphically in box plots (box-and-whisker plots). An alternative and more informative type of graph is the kernel density plot, which we propose should be the preferred one for this purpose. In this paper we discuss the limitations of box plots and the advantages of the kernel density plot, and we present NAIplot, an opensource web application that allows convenient creation of density plots specifically for visualizing the results of NAI enzyme inhibition assays, as well as for general purposes. Copyright © 2015 Elsevier B.V. All rights reserved.

Improvement of density models of geological structures by fusion of gravity data and cosmic muon radiographies

NASA Astrophysics Data System (ADS)

Jourde, K.; Gibert, D.; Marteau, J.

2015-04-01

This paper examines how the resolution of small-scale geological density models is improved through the fusion of information provided by gravity measurements and density muon radiographies. Muon radiography aims at determining the density of geological bodies by measuring their screening effect on the natural flux of cosmic muons. Muon radiography essentially works like medical X-ray scan and integrates density information along elongated narrow conical volumes. Gravity measurements are linked to density by a 3-D integration encompassing the whole studied domain. We establish the mathematical expressions of these integration formulas - called acquisition kernels - and derive the resolving kernels that are spatial filters relating the true unknown density structure to the density distribution actually recovered from the available data. The resolving kernels approach allows to quantitatively describe the improvement of the resolution of the density models achieved by merging gravity data and muon radiographies. The method developed in this paper may be used to optimally design the geometry of the field measurements to perform in order to obtain a given spatial resolution pattern of the density model to construct. The resolving kernels derived in the joined muon/gravimetry case indicate that gravity data are almost useless to constrain the density structure in regions sampled by more than two muon tomography acquisitions. Interestingly the resolution in deeper regions not sampled by muon tomography is significantly improved by joining the two techniques. The method is illustrated with examples for La Soufrière of Guadeloupe volcano.

Spatiotemporal characteristics of elderly population’s traffic accidents in Seoul using space-time cube and space-time kernel density estimation

PubMed Central

Cho, Nahye; Son, Serin

2018-01-01

The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people’s traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people’s traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people’s traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers’ traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims’ hotspots are more sporadic than elderly drivers’ hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future. PMID:29768453

Spatiotemporal characteristics of elderly population's traffic accidents in Seoul using space-time cube and space-time kernel density estimation.

PubMed

Kang, Youngok; Cho, Nahye; Son, Serin

2018-01-01

The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people's traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people's traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people's traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers' traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims' hotspots are more sporadic than elderly drivers' hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future.

Accuracy of lung nodule density on HRCT: analysis by PSF-based image simulation.

PubMed

Ohno, Ken; Ohkubo, Masaki; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Wada, Shinichi

2012-11-08

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT-based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high-resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF-based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT-based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.

[Spatial analysis of road traffic accidents with fatalities in Spain, 2008-2011].

PubMed

Gómez-Barroso, Diana; López-Cuadrado, Teresa; Llácer, Alicia; Palmera Suárez, Rocío; Fernández-Cuenca, Rafael

2015-09-01

To estimate the areas of greatest density of road traffic accidents with fatalities at 24 hours per km(2)/year in Spain from 2008 to 2011, using a geographic information system. Accidents were geocodified using the road and kilometer points where they occurred. The average nearest neighbor was calculated to detect possible clusters and to obtain the bandwidth for kernel density estimation. A total of 4775 accidents were analyzed, of which 73.3% occurred on conventional roads. The estimated average distance between accidents was 1,242 meters, and the average expected distance was 10,738 meters. The nearest neighbor index was 0.11, indicating that there were aggregations of accidents in space. A map showing the kernel density was obtained with a resolution of 1 km(2), which identified the areas of highest density. This methodology allowed a better approximation to locating accident risks by taking into account kilometer points. The map shows areas where there was a greater density of accidents. This could be an advantage in decision-making by the relevant authorities. Copyright © 2014 SESPAS. Published by Elsevier Espana. All rights reserved.

A fast and objective multidimensional kernel density estimation method: fastKDE

DOE PAGES

O'Brien, Travis A.; Kashinath, Karthik; Cavanaugh, Nicholas R.; ...

2016-03-07

Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a kernel bandwidth. While techniques exist for choosing the kernel bandwidth optimally and objectively, they are computationally intensive, since they require repeated calculation of the KDE. A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchiamore » and Pigolotti (2011). While this solution theoretically applies to multidimensional KDEs, it has not been clear how to practically do so. A method for practically extending the Bernacchia-Pigolotti KDE to multidimensions is introduced. This multidimensional extension is combined with a recently-developed computational improvement to their method that makes it computationally efficient: a 2D KDE on 10 5 samples only takes 1 s on a modern workstation. This fast and objective KDE method, called the fastKDE method, retains the excellent statistical convergence properties that have been demonstrated for univariate samples. The fastKDE method exhibits statistical accuracy that is comparable to state-of-the-science KDE methods publicly available in R, and it produces kernel density estimates several orders of magnitude faster. The fastKDE method does an excellent job of encoding covariance information for bivariate samples. This property allows for direct calculation of conditional PDFs with fastKDE. It is demonstrated how this capability might be leveraged for detecting non-trivial relationships between quantities in physical systems, such as transitional behavior.« less

Improvement of density models of geological structures by fusion of gravity data and cosmic muon radiographies

NASA Astrophysics Data System (ADS)

Jourde, K.; Gibert, D.; Marteau, J.

2015-08-01

This paper examines how the resolution of small-scale geological density models is improved through the fusion of information provided by gravity measurements and density muon radiographies. Muon radiography aims at determining the density of geological bodies by measuring their screening effect on the natural flux of cosmic muons. Muon radiography essentially works like a medical X-ray scan and integrates density information along elongated narrow conical volumes. Gravity measurements are linked to density by a 3-D integration encompassing the whole studied domain. We establish the mathematical expressions of these integration formulas - called acquisition kernels - and derive the resolving kernels that are spatial filters relating the true unknown density structure to the density distribution actually recovered from the available data. The resolving kernel approach allows one to quantitatively describe the improvement of the resolution of the density models achieved by merging gravity data and muon radiographies. The method developed in this paper may be used to optimally design the geometry of the field measurements to be performed in order to obtain a given spatial resolution pattern of the density model to be constructed. The resolving kernels derived in the joined muon-gravimetry case indicate that gravity data are almost useless for constraining the density structure in regions sampled by more than two muon tomography acquisitions. Interestingly, the resolution in deeper regions not sampled by muon tomography is significantly improved by joining the two techniques. The method is illustrated with examples for the La Soufrière volcano of Guadeloupe.

MODELING LEACHING OF VIRUSES BY THE MONTE CARLO METHOD

EPA Science Inventory

A predictive screening model was developed for fate and transport
of viruses in the unsaturated zone. A database of input parameters
allowed Monte Carlo analysis with the model. The resulting kernel
densities of predicted attenuation during percolation indicated very ...

Dissection of Genetic Factors underlying Wheat Kernel Shape and Size in an Elite × Nonadapted Cross using a High Density SNP Linkage Map.

PubMed

Kumar, Ajay; Mantovani, E E; Seetan, R; Soltani, A; Echeverry-Solarte, M; Jain, S; Simsek, S; Doehlert, D; Alamri, M S; Elias, E M; Kianian, S F; Mergoum, M

2016-03-01

Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL), developed using an elite (ND 705) and a nonadapted genotype (PI 414566), was extensively phenotyped in replicated field trials and genotyped using Infinium iSelect 90K assay to gain insight into the genetic architecture of kernel shape and size. A high density genetic map consisting of 10,172 single nucleotide polymorphism (SNP) markers, with an average marker density of 0.39 cM/marker, identified a total of 29 genomic regions associated with six grain shape and size traits; ∼80% of these regions were associated with multiple traits. The analyses showed that kernel length (KL) and width (KW) are genetically independent, while a large number (∼59%) of the quantitative trait loci (QTL) for kernel shape traits were in common with genomic regions associated with kernel size traits. The most significant QTL was identified on chromosome 4B, and could be an ortholog of major rice grain size and shape gene or . Major and stable loci also were identified on the homeologous regions of Group 5 chromosomes, and in the regions of (6A) and (7A) genes. Both parental genotypes contributed equivalent positive QTL alleles, suggesting that the nonadapted germplasm has a great potential for enhancing the gene pool for grain shape and size. This study provides new knowledge on the genetic dissection of kernel morphology, with a much higher resolution, which may aid further improvement in wheat yield and quality using genomic tools. Copyright © 2016 Crop Science Society of America.

Using kernel density estimation to understand the influence of neighbourhood destinations on BMI

PubMed Central

King, Tania L; Bentley, Rebecca J; Thornton, Lukar E; Kavanagh, Anne M

2016-01-01

Objectives Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. Study design and setting A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Methods Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)–Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable. Results For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β −0.86, 95% CI −1.58 to −0.13, p=0.022; Q5, β −1.03 95% CI −1.65 to −0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β −0.77 95% CI −1.52, −0.02, p=0.045. Conclusions This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity. PMID:26883235

Segmentation of the Speaker's Face Region with Audiovisual Correlation

NASA Astrophysics Data System (ADS)

Liu, Yuyu; Sato, Yoichi

The ability to find the speaker's face region in a video is useful for various applications. In this work, we develop a novel technique to find this region within different time windows, which is robust against the changes of view, scale, and background. The main thrust of our technique is to integrate audiovisual correlation analysis into a video segmentation framework. We analyze the audiovisual correlation locally by computing quadratic mutual information between our audiovisual features. The computation of quadratic mutual information is based on the probability density functions estimated by kernel density estimation with adaptive kernel bandwidth. The results of this audiovisual correlation analysis are incorporated into graph cut-based video segmentation to resolve a globally optimum extraction of the speaker's face region. The setting of any heuristic threshold in this segmentation is avoided by learning the correlation distributions of speaker and background by expectation maximization. Experimental results demonstrate that our method can detect the speaker's face region accurately and robustly for different views, scales, and backgrounds.

A comparison of spatial analysis methods for the construction of topographic maps of retinal cell density.

PubMed

Garza-Gisholt, Eduardo; Hemmi, Jan M; Hart, Nathan S; Collin, Shaun P

2014-01-01

Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed 'by eye'. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation 'respects' the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the 'noise' caused by artefacts and permits a clearer representation of the dominant, 'real' distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome.

A density-adaptive SPH method with kernel gradient correction for modeling explosive welding

NASA Astrophysics Data System (ADS)

Liu, M. B.; Zhang, Z. L.; Feng, D. L.

2017-09-01

Explosive welding involves processes like the detonation of explosive, impact of metal structures and strong fluid-structure interaction, while the whole process of explosive welding has not been well modeled before. In this paper, a novel smoothed particle hydrodynamics (SPH) model is developed to simulate explosive welding. In the SPH model, a kernel gradient correction algorithm is used to achieve better computational accuracy. A density adapting technique which can effectively treat large density ratio is also proposed. The developed SPH model is firstly validated by simulating a benchmark problem of one-dimensional TNT detonation and an impact welding problem. The SPH model is then successfully applied to simulate the whole process of explosive welding. It is demonstrated that the presented SPH method can capture typical physics in explosive welding including explosion wave, welding surface morphology, jet flow and acceleration of the flyer plate. The welding angle obtained from the SPH simulation agrees well with that from a kinematic analysis.

Characteristics of uranium carbonitride microparticles synthesized using different reaction conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Silva, Chinthaka M; Lindemer, Terrence; Voit, Stewart L

2014-11-01

Three sets of different experimental conditions by changing the cover gases during the sample preparation were tested to synthesize uranium carbonitride (UC1-xNx) microparticles. In the first two sets of experiments using (N2 to N2-4%H2 to Ar) and (Ar to N2 to Ar) environments, single phase UC1-xNx was synthesized. When reducing environments (Ar-4%H2 to N2-4%H2 to Ar-4%H2) were utilized, theoretical densities up to 97% of single phase UC1-xNx kernels were obtained. Physical and chemical characteristics such as density, phase purity, and chemical compositions of the synthesized UC1-xNx materials for the diferent experimental conditions used are provided. In-depth analysis of the microstruturesmore » of UC1-xNx has been carried out and is discussed with the objective of large batch fabrication of high density UC1-xNx kernels.« less

StreamMap: Smooth Dynamic Visualization of High-Density Streaming Points.

PubMed

Li, Chenhui; Baciu, George; Han, Yu

2018-03-01

Interactive visualization of streaming points for real-time scatterplots and linear blending of correlation patterns is increasingly becoming the dominant mode of visual analytics for both big data and streaming data from active sensors and broadcasting media. To better visualize and interact with inter-stream patterns, it is generally necessary to smooth out gaps or distortions in the streaming data. Previous approaches either animate the points directly or present a sampled static heat-map. We propose a new approach, called StreamMap, to smoothly blend high-density streaming points and create a visual flow that emphasizes the density pattern distributions. In essence, we present three new contributions for the visualization of high-density streaming points. The first contribution is a density-based method called super kernel density estimation that aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robust density morphing algorithm that generates several smooth intermediate frames for a given pair of frames. The third contribution is a trend representation design that can help convey the flow directions of the streaming points. The experimental results on three datasets demonstrate the effectiveness of StreamMap when dynamic visualization and visual analysis of trend patterns on streaming points are required.

Evaluation of various carbon blacks and dispersing agents for use in the preparation of uranium microspheres with carbon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunt, Rodney Dale; Johnson, Jared A.; Collins, Jack Lee

A comparison study on carbon blacks and dispersing agents was performed to determine their impacts on the final properties of uranium fuel kernels with carbon. The main target compositions in this internal gelation study were 10 and 20 mol % uranium dicarbide (UC 2), which is UC 1.86, with the balance uranium dioxide. After heat treatment at 1900 K in flowing carbon monoxide in argon for 12 h, the density of the kernels produced using a X-energy proprietary carbon suspension, which is commercially available, ranged from 96% to 100% of theoretical density (TD), with full conversion of UC to UCmore » 2 at both carbon concentrations. However, higher carbon concentrations such as a 2.5 mol ratio of carbon to uranium in the feed solutions failed to produce gel spheres with the proprietary carbon suspension. The kernels using our former baseline of Mogul L carbon black and Tamol SN were 90–92% of TD with full conversion of UC to UC 2 at a variety of carbon levels. Raven 5000 carbon black and Tamol SN were used to produce 10 mol % UC2 kernels with 95% of TD. However, an increase in the Raven 5000 concentration led to a kernel density below 90% of TD. Raven 3500 carbon black and Tamol SN were used to make very dense kernels without complete conversion to UC 2. Lastly, the selection of the carbon black and dispersing agent is highly dependent on the desired final properties of the target kernels.« less

Evaluation of various carbon blacks and dispersing agents for use in the preparation of uranium microspheres with carbon

NASA Astrophysics Data System (ADS)

Hunt, R. D.; Johnson, J. A.; Collins, J. L.; McMurray, J. W.; Reif, T. J.; Brown, D. R.

2018-01-01

A comparison study on carbon blacks and dispersing agents was performed to determine their impacts on the final properties of uranium fuel kernels with carbon. The main target compositions in this internal gelation study were 10 and 20 mol % uranium dicarbide (UC2), which is UC1.86, with the balance uranium dioxide. After heat treatment at 1900 K in flowing carbon monoxide in argon for 12 h, the density of the kernels produced using a X-energy proprietary carbon suspension, which is commercially available, ranged from 96% to 100% of theoretical density (TD), with full conversion of UC to UC2 at both carbon concentrations. However, higher carbon concentrations such as a 2.5 mol ratio of carbon to uranium in the feed solutions failed to produce gel spheres with the proprietary carbon suspension. The kernels using our former baseline of Mogul L carbon black and Tamol SN were 90-92% of TD with full conversion of UC to UC2 at a variety of carbon levels. Raven 5000 carbon black and Tamol SN were used to produce 10 mol % UC2 kernels with 95% of TD. However, an increase in the Raven 5000 concentration led to a kernel density below 90% of TD. Raven 3500 carbon black and Tamol SN were used to make very dense kernels without complete conversion to UC2. The selection of the carbon black and dispersing agent is highly dependent on the desired final properties of the target kernels.

Evaluation of various carbon blacks and dispersing agents for use in the preparation of uranium microspheres with carbon

DOE PAGES

Hunt, Rodney Dale; Johnson, Jared A.; Collins, Jack Lee; ...

2017-10-12

A comparison study on carbon blacks and dispersing agents was performed to determine their impacts on the final properties of uranium fuel kernels with carbon. The main target compositions in this internal gelation study were 10 and 20 mol % uranium dicarbide (UC 2), which is UC 1.86, with the balance uranium dioxide. After heat treatment at 1900 K in flowing carbon monoxide in argon for 12 h, the density of the kernels produced using a X-energy proprietary carbon suspension, which is commercially available, ranged from 96% to 100% of theoretical density (TD), with full conversion of UC to UCmore » 2 at both carbon concentrations. However, higher carbon concentrations such as a 2.5 mol ratio of carbon to uranium in the feed solutions failed to produce gel spheres with the proprietary carbon suspension. The kernels using our former baseline of Mogul L carbon black and Tamol SN were 90–92% of TD with full conversion of UC to UC 2 at a variety of carbon levels. Raven 5000 carbon black and Tamol SN were used to produce 10 mol % UC2 kernels with 95% of TD. However, an increase in the Raven 5000 concentration led to a kernel density below 90% of TD. Raven 3500 carbon black and Tamol SN were used to make very dense kernels without complete conversion to UC 2. Lastly, the selection of the carbon black and dispersing agent is highly dependent on the desired final properties of the target kernels.« less

Kernel machine SNP set analysis provides new insight into the association between obesity and polymorphisms located on the chromosomal 16q.12.2 region: Tehran Lipid and Glucose Study.

PubMed

Javanrouh, Niloufar; Daneshpour, Maryam S; Soltanian, Ali Reza; Tapak, Leili

2018-06-05

Obesity is a serious health problem that leads to low quality of life and early mortality. To the purpose of prevention and gene therapy for such a worldwide disease, genome wide association study is a powerful tool for finding SNPs associated with increased risk of obesity. To conduct an association analysis, kernel machine regression is a generalized regression method, has an advantage of considering the epistasis effects as well as the correlation between individuals due to unknown factors. In this study, information of the people who participated in Tehran cardio-metabolic genetic study was used. They were genotyped for the chromosomal region, evaluation 986 variations located at 16q12.2; build 38hg. Kernel machine regression and single SNP analysis were used to assess the association between obesity and SNPs genotyped data. We found that associated SNP sets with obesity, were almost in the FTO (P = 0.01), AIKTIP (P = 0.02) and MMP2 (P = 0.02) genes. Moreover, two SNPs, i.e., rs10521296 and rs11647470, showed significant association with obesity using kernel regression (P = 0.02). In conclusion, significant sets were randomly distributed throughout the region with more density around the FTO, AIKTIP and MMP2 genes. Furthermore, two intergenic SNPs showed significant association after using kernel machine regression. Therefore, more studies have to be conducted to assess their functionality or precise mechanism. Copyright © 2018 Elsevier B.V. All rights reserved.

Fracture analysis of a transversely isotropic high temperature superconductor strip based on real fundamental solutions

NASA Astrophysics Data System (ADS)

Gao, Zhiwen; Zhou, Youhe

2015-04-01

Real fundamental solution for fracture problem of transversely isotropic high temperature superconductor (HTS) strip is obtained. The superconductor E-J constitutive law is characterized by the Bean model where the critical current density is independent of the flux density. Fracture analysis is performed by the methods of singular integral equations which are solved numerically by Gauss-Lobatto-Chybeshev (GSL) collocation method. To guarantee a satisfactory accuracy, the convergence behavior of the kernel function is investigated. Numerical results of fracture parameters are obtained and the effects of the geometric characteristics, applied magnetic field and critical current density on the stress intensity factors (SIF) are discussed.

New Families of Skewed Higher-Order Kernel Estimators to Solve the BSS/ICA Problem for Multimodal Sources Mixtures.

PubMed

Jabbar, Ahmed Najah

2018-04-13

This letter suggests two new types of asymmetrical higher-order kernels (HOK) that are generated using the orthogonal polynomials Laguerre (positive or right skew) and Bessel (negative or left skew). These skewed HOK are implemented in the blind source separation/independent component analysis (BSS/ICA) algorithm. The tests for these proposed HOK are accomplished using three scenarios to simulate a real environment using actual sound sources, an environment of mixtures of multimodal fast-changing probability density function (pdf) sources that represent a challenge to the symmetrical HOK, and an environment of an adverse case (near gaussian). The separation is performed by minimizing the mutual information (MI) among the mixed sources. The performance of the skewed kernels is compared to the performance of the standard kernels such as Epanechnikov, bisquare, trisquare, and gaussian and the performance of the symmetrical HOK generated using the polynomials Chebyshev1, Chebyshev2, Gegenbauer, Jacobi, and Legendre to the tenth order. The gaussian HOK are generated using the Hermite polynomial and the Wand and Schucany procedure. The comparison among the 96 kernels is based on the average intersymbol interference ratio (AISIR) and the time needed to complete the separation. In terms of AISIR, the skewed kernels' performance is better than that of the standard kernels and rivals most of the symmetrical kernels' performance. The importance of these new skewed HOK is manifested in the environment of the multimodal pdf mixtures. In such an environment, the skewed HOK come in first place compared with the symmetrical HOK. These new families can substitute for symmetrical HOKs in such applications.

Resolvability of regional density structure

NASA Astrophysics Data System (ADS)

Plonka, A.; Fichtner, A.

2016-12-01

Lateral density variations are the source of mass transport in the Earth at all scales, acting as drivers of convectivemotion. However, the density structure of the Earth remains largely unknown since classic seismic observables and gravityprovide only weak constraints with strong trade-offs. Current density models are therefore often based on velocity scaling,making strong assumptions on the origin of structural heterogeneities, which may not necessarily be correct. Our goal is to assessif 3D density structure may be resolvable with emerging full-waveform inversion techniques. We have previously quantified the impact of regional-scale crustal density structure on seismic waveforms with the conclusion that reasonably sized density variations within thecrust can leave a strong imprint on both travel times and amplitudes, and, while this can produce significant biases in velocity and Q estimates, the seismic waveform inversion for density may become feasible. In this study we performprincipal component analyses of sensitivity kernels for P velocity, S velocity, and density. This is intended to establish theextent to which these kernels are linearly independent, i.e. the extent to which the different parameters may be constrainedindependently. Since the density imprint we observe is not exclusively linked to travel times and amplitudes of specific phases,we consider waveform differences between complete seismograms. We test the method using a known smooth model of the crust and seismograms with clear Love and Rayleigh waves, showing that - as expected - the first principal kernel maximizes sensitivity to SH and SV velocity structure, respectively, and that the leakage between S velocity, P velocity and density parameter spaces is minimal in the chosen setup. Next, we apply the method to data from 81 events around the Iberian Penninsula, registered in total by 492 stations. The objective is to find a principal kernel which would maximize the sensitivity to density, potentially allowing for independent density resolution, and, as the final goal, for direct density inversion.

Analysis of the spatial distribution of dengue cases in the city of Rio de Janeiro, 2011 and 2012.

PubMed

Carvalho, Silvia; Magalhães, Mônica de Avelar Figueiredo Mafra; Medronho, Roberto de Andrade

2017-08-17

Analyze the spatial distribution of classical dengue and severe dengue cases in the city of Rio de Janeiro. Exploratory study, considering cases of classical dengue and severe dengue with laboratory confirmation of the infection in the city of Rio de Janeiro during the years 2011/2012. The georeferencing technique was applied for the cases notified in the Notification Increase Information System in the period of 2011 and 2012. For this process, the fields "street" and "number" were used. The ArcGis10 program's Geocoding tool's automatic process was performed. The spatial analysis was done through the kernel density estimator. Kernel density pointed out hotspots for classic dengue that did not coincide geographically with severe dengue and were in or near favelas. The kernel ratio did not show a notable change in the spatial distribution pattern observed in the kernel density analysis. The georeferencing process showed a loss of 41% of classic dengue registries and 17% of severe dengue registries due to the address in the Notification Increase Information System form. The hotspots near the favelas suggest that the social vulnerability of these localities can be an influencing factor for the occurrence of this aggravation since there is a deficiency of the supply and access to essential goods and services for the population. To reduce this vulnerability, interventions must be related to macroeconomic policies. Analisar a distribuição espacial dos casos de dengue clássico e dengue grave no município do Rio de Janeiro. Estudo exploratório, considerando casos de dengue clássico e de dengue grave com comprovação laboratorial da infecção, ocorridos no município do Rio de Janeiro nos anos de 2011/2012. Foi aplicada a técnica de georreferenciamento dos casos notificados no Sistema de Informação de Agravos de Notificação, no período de 2011 e 2012. Para esse processo, utilizaram-se os campos "logradouro" e "número". Foi realizado o processo automático da ferramenta Geocoding do programa ArcGis10. A análise espacial foi feita a partir do estimador de densidade Kernel. A densidade de Kernel apontou áreas quentes para dengue clássico não coincidente geograficamente a dengue grave, estando localizadas dentro ou próximas de favelas. O cálculo da razão de Kernel não apresentou modificação significativa no padrão de distribuição espacial observados na análise da densidade de Kernel. O processo de georreferenciamento mostrou perda de 41% dos registros de dengue clássico e 17% de dengue grave devido ao endereçamento da ficha do Sistema de Informação de Agravos de Notificação. As áreas quentes próximas às favelas sugerem que a vulnerabilidade social existente nessas localidades pode ser um fator de influência para a ocorrência desse agravo, uma vez que há deficiência da oferta e acesso a bens e serviços essenciais para a população. Para diminuir essa vulnerabilidade, as intervenções devem estar relacionadas a políticas macroeconômicas.

Dissection of genetic factors underlying wheat kernel shape and size in an elite x nonadapted cross using a high density SNP linkage map

USDA-ARS?s Scientific Manuscript database

Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL), developed using an elite (ND 705) and a nonadapted genotype (PI 414...

An introduction to kernel-based learning algorithms.

PubMed

Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

2001-01-01

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

Treesearch

Rafal Podlaski; Francis A. Roesch

2014-01-01

Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

Zika virus disease, microcephaly and Guillain-Barré syndrome in Colombia: epidemiological situation during 21 months of the Zika virus outbreak, 2015-2017.

PubMed

Méndez, Nelson; Oviedo-Pastrana, Misael; Mattar, Salim; Caicedo-Castro, Isaac; Arrieta, German

2017-01-01

The Zika virus disease (ZVD) has had a huge impact on public health in Colombia for the numbers of people affected and the presentation of Guillain-Barre syndrome (GBS) and microcephaly cases associated to ZVD. A retrospective descriptive study was carried out, we analyze the epidemiological situation of ZVD and its association with microcephaly and GBS during a 21-month period, from October 2015 to June 2017. The variables studied were: (i) ZVD cases, (ii) ZVD cases in pregnant women, (iii) laboratory-confirmed ZVD in pregnant women, (iv) ZVD cases associated with microcephaly, (v) laboratory-confirmed ZVD associated with microcephaly, and (vi) ZVD associated to GBS cases. Average number of cases, attack rates (AR) and proportions were also calculated. The studied variables were plotted by epidemiological weeks and months. The distribution of ZVD cases in Colombia was mapped across the time using Kernel density estimator and QGIS software; we adopted Kernel Ridge Regression (KRR) and the Gaussian Kernel to estimate the number of Guillain Barre cases given the number of ZVD cases. One hundred eight thousand eighty-seven ZVD cases had been reported in Colombia, including 19,963 (18.5%) in pregnant women, 710 (0.66%) associated with microcephaly (AR, 4.87 cases per 10,000 live births) and 453 (0.42%) ZVD associated to GBS cases (AR, 41.9 GBS cases per 10,000 ZVD cases). It appears the cases of GBS increased in parallel with the cases of ZVD, cases of microcephaly appeared 5 months after recognition of the outbreak. The kernel density map shows that throughout the study period, the states most affected by the Zika outbreak in Colombia were mainly San Andrés and Providencia islands, Casanare, Norte de Santander, Arauca and Huila. The KRR shows that there is no proportional relationship between the number of GBS and ZVD cases. During the cross validation, the RMSE achieved for the second order polynomial kernel, the linear kernel, the sigmoid kernel, and the Gaussian kernel are 9.15, 9.2, 10.7, and 7.2 respectively. This study updates the epidemiological analysis of the ZVD situation in Colombia describes the geographical distribution of ZVD and shows the functional relationship between ZVD cases and GBS.

An atomistic fingerprint algorithm for learning ab initio molecular force fields

NASA Astrophysics Data System (ADS)

Tang, Yu-Hang; Zhang, Dongkun; Karniadakis, George Em

2018-01-01

Molecular fingerprints, i.e., feature vectors describing atomistic neighborhood configurations, is an important abstraction and a key ingredient for data-driven modeling of potential energy surface and interatomic force. In this paper, we present the density-encoded canonically aligned fingerprint algorithm, which is robust and efficient, for fitting per-atom scalar and vector quantities. The fingerprint is essentially a continuous density field formed through the superimposition of smoothing kernels centered on the atoms. Rotational invariance of the fingerprint is achieved by aligning, for each fingerprint instance, the neighboring atoms onto a local canonical coordinate frame computed from a kernel minisum optimization procedure. We show that this approach is superior over principal components analysis-based methods especially when the atomistic neighborhood is sparse and/or contains symmetry. We propose that the "distance" between the density fields be measured using a volume integral of their pointwise difference. This can be efficiently computed using optimal quadrature rules, which only require discrete sampling at a small number of grid points. We also experiment on the choice of weight functions for constructing the density fields and characterize their performance for fitting interatomic potentials. The applicability of the fingerprint is demonstrated through a set of benchmark problems.

Nonparametric probability density estimation by optimization theoretic techniques

NASA Technical Reports Server (NTRS)

Scott, D. W.

1976-01-01

Two nonparametric probability density estimators are considered. The first is the kernel estimator. The problem of choosing the kernel scaling factor based solely on a random sample is addressed. An interactive mode is discussed and an algorithm proposed to choose the scaling factor automatically. The second nonparametric probability estimate uses penalty function techniques with the maximum likelihood criterion. A discrete maximum penalized likelihood estimator is proposed and is shown to be consistent in the mean square error. A numerical implementation technique for the discrete solution is discussed and examples displayed. An extensive simulation study compares the integrated mean square error of the discrete and kernel estimators. The robustness of the discrete estimator is demonstrated graphically.

Some physical properties of ginkgo nuts and kernels

NASA Astrophysics Data System (ADS)

Ch'ng, P. E.; Abdullah, M. H. R. O.; Mathai, E. J.; Yunus, N. A.

2013-12-01

Some data of the physical properties of ginkgo nuts at a moisture content of 45.53% (±2.07) (wet basis) and of their kernels at 60.13% (± 2.00) (wet basis) are presented in this paper. It consists of the estimation of the mean length, width, thickness, the geometric mean diameter, sphericity, aspect ratio, unit mass, surface area, volume, true density, bulk density, and porosity measures. The coefficient of static friction for nuts and kernels was determined by using plywood, glass, rubber, and galvanized steel sheet. The data are essential in the field of food engineering especially dealing with design and development of machines, and equipment for processing and handling agriculture products.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jolly, Brian C.; Helmreich, Grant; Cooley, Kevin M.

In support of fully ceramic microencapsulated (FCM) fuel development, coating development work is ongoing at Oak Ridge National Laboratory (ORNL) to produce tri-structural isotropic (TRISO) coated fuel particles with both UN kernels and surrogate (uranium-free) kernels. The nitride kernels are used to increase fissile density in these SiC-matrix fuel pellets with details described elsewhere. The surrogate TRISO particles are necessary for separate effects testing and for utilization in the consolidation process development. This report focuses on the fabrication and characterization of surrogate TRISO particles which use 800μm in diameter ZrO 2 microspheres as the kernel.

A shock-capturing SPH scheme based on adaptive kernel estimation

NASA Astrophysics Data System (ADS)

Sigalotti, Leonardo Di G.; López, Hender; Donoso, Arnaldo; Sira, Eloy; Klapp, Jaime

2006-02-01

Here we report a method that converts standard smoothed particle hydrodynamics (SPH) into a working shock-capturing scheme without relying on solutions to the Riemann problem. Unlike existing adaptive SPH simulations, the present scheme is based on an adaptive kernel estimation of the density, which combines intrinsic features of both the kernel and nearest neighbor approaches in a way that the amount of smoothing required in low-density regions is effectively controlled. Symmetrized SPH representations of the gas dynamic equations along with the usual kernel summation for the density are used to guarantee variational consistency. Implementation of the adaptive kernel estimation involves a very simple procedure and allows for a unique scheme that handles strong shocks and rarefactions the same way. Since it represents a general improvement of the integral interpolation on scattered data, it is also applicable to other fluid-dynamic models. When the method is applied to supersonic compressible flows with sharp discontinuities, as in the classical one-dimensional shock-tube problem and its variants, the accuracy of the results is comparable, and in most cases superior, to that obtained from high quality Godunov-type methods and SPH formulations based on Riemann solutions. The extension of the method to two- and three-space dimensions is straightforward. In particular, for the two-dimensional cylindrical Noh's shock implosion and Sedov point explosion problems the present scheme produces much better results than those obtained with conventional SPH codes.

Adiabatic-connection fluctuation-dissipation DFT for the structural properties of solids—The renormalized ALDA and electron gas kernels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patrick, Christopher E., E-mail: chripa@fysik.dtu.dk; Thygesen, Kristian S., E-mail: thygesen@fysik.dtu.dk

2015-09-14

We present calculations of the correlation energies of crystalline solids and isolated systems within the adiabatic-connection fluctuation-dissipation formulation of density-functional theory. We perform a quantitative comparison of a set of model exchange-correlation kernels originally derived for the homogeneous electron gas (HEG), including the recently introduced renormalized adiabatic local-density approximation (rALDA) and also kernels which (a) satisfy known exact limits of the HEG, (b) carry a frequency dependence, or (c) display a 1/k{sup 2} divergence for small wavevectors. After generalizing the kernels to inhomogeneous systems through a reciprocal-space averaging procedure, we calculate the lattice constants and bulk moduli of a testmore » set of 10 solids consisting of tetrahedrally bonded semiconductors (C, Si, SiC), ionic compounds (MgO, LiCl, LiF), and metals (Al, Na, Cu, Pd). We also consider the atomization energy of the H{sub 2} molecule. We compare the results calculated with different kernels to those obtained from the random-phase approximation (RPA) and to experimental measurements. We demonstrate that the model kernels correct the RPA’s tendency to overestimate the magnitude of the correlation energy whilst maintaining a high-accuracy description of structural properties.« less

Triso coating development progress for uranium nitride kernels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jolly, Brian C.; Lindemer, Terrence; Terrani, Kurt A.

2015-08-01

In support of fully ceramic matrix (FCM) fuel development [1-2], coating development work is ongoing at the Oak Ridge National Laboratory (ORNL) to produce tri-structural isotropic (TRISO) coated fuel particles with UN kernels [3]. The nitride kernels are used to increase fissile density in these SiC-matrix fuel pellets with details described elsewhere [4]. The advanced gas reactor (AGR) program at ORNL used fluidized bed chemical vapor deposition (FBCVD) techniques for TRISO coating of UCO (two phase mixture of UO2 and UCx) kernels [5]. Similar techniques were employed for coating of the UN kernels, however significant changes in processing conditions weremore » required to maintain acceptable coating properties due to physical property and dimensional differences between the UCO and UN kernels (Table 1).« less

The quantitative properties of three soft X-ray flare kernels observed with the AS&E X-ray telescope on Skylab

NASA Technical Reports Server (NTRS)

Kahler, S. W.; Petrasso, R. D.; Kane, S. R.

1976-01-01

The physical parameters for the kernels of three solar X-ray flare events have been deduced using photographic data from the S-054 X-ray telescope on Skylab as the primary data source and 1-8 and 8-20 A fluxes from Solrad 9 as the secondary data source. The kernels had diameters of about 5-7 seconds of arc and in two cases electron densities at least as high as 0.3 trillion per cu cm. The lifetimes of the kernels were 5-10 min. The presence of thermal conduction during the decay phases is used to argue: (1) that kernels are entire, not small portions of, coronal loop structures, and (2) that flare heating must continue during the decay phase. We suggest a simple geometric model to explain the role of kernels in flares in which kernels are identified with emerging flux regions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jolly, Brian C.; Lindemer, Terrence; Terrani, Kurt A.

In support of fully ceramic matrix (FCM) fuel development, coating development work has begun at the Oak Ridge National Laboratory (ORNL) to produce tri-isotropic (TRISO) coated fuel particles with UN kernels. The nitride kernels are used to increase heavy metal density in these SiC-matrix fuel pellets with details described elsewhere. The advanced gas reactor (AGR) program at ORNL used fluidized bed chemical vapor deposition (FBCVD) techniques for TRISO coating of UCO (two phase mixture of UO 2 and UC x) kernels. Similar techniques were employed for coating of the UN kernels, however significant changes in processing conditions were required tomore » maintain acceptable coating properties due to physical property and dimensional differences between the UCO and UN kernels.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pavlou, A. T.; Betzler, B. R.; Burke, T. P.

Uncertainties in the composition and fabrication of fuel compacts for the Fort St. Vrain (FSV) high temperature gas reactor have been studied by performing eigenvalue sensitivity studies that represent the key uncertainties for the FSV neutronic analysis. The uncertainties for the TRISO fuel kernels were addressed by developing a suite of models for an 'average' FSV fuel compact that models the fuel as (1) a mixture of two different TRISO fuel particles representing fissile and fertile kernels, (2) a mixture of four different TRISO fuel particles representing small and large fissile kernels and small and large fertile kernels and (3)more » a stochastic mixture of the four types of fuel particles where every kernel has its diameter sampled from a continuous probability density function. All of the discrete diameter and continuous diameter fuel models were constrained to have the same fuel loadings and packing fractions. For the non-stochastic discrete diameter cases, the MCNP compact model arranged the TRISO fuel particles on a hexagonal honeycomb lattice. This lattice-based fuel compact was compared to a stochastic compact where the locations (and kernel diameters for the continuous diameter cases) of the fuel particles were randomly sampled. Partial core configurations were modeled by stacking compacts into fuel columns containing graphite. The differences in eigenvalues between the lattice-based and stochastic models were small but the runtime of the lattice-based fuel model was roughly 20 times shorter than with the stochastic-based fuel model. (authors)« less

A Comparison of Spatial Analysis Methods for the Construction of Topographic Maps of Retinal Cell Density

PubMed Central

Garza-Gisholt, Eduardo; Hemmi, Jan M.; Hart, Nathan S.; Collin, Shaun P.

2014-01-01

Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed ‘by eye’. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation ‘respects’ the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the ‘noise’ caused by artefacts and permits a clearer representation of the dominant, ‘real’ distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome. PMID:24747568

Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party

NASA Astrophysics Data System (ADS)

Su, Chunhua; Bao, Feng; Zhou, Jianying; Takagi, Tsuyoshi; Sakurai, Kouichi

The rapid growth of the Internet provides people with tremendous opportunities for data collection, knowledge discovery and cooperative computation. However, it also brings the problem of sensitive information leakage. Both individuals and enterprises may suffer from the massive data collection and the information retrieval by distrusted parties. In this paper, we propose a privacy-preserving protocol for the distributed kernel density estimation-based clustering. Our scheme applies random data perturbation (RDP) technique and the verifiable secret sharing to solve the security problem of distributed kernel density estimation in [4] which assumed a mediate party to help in the computation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Brien, Travis A.; Kashinath, Karthik; Cavanaugh, Nicholas R.

Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a kernel bandwidth. While techniques exist for choosing the kernel bandwidth optimally and objectively, they are computationally intensive, since they require repeated calculation of the KDE. A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchiamore » and Pigolotti (2011). While this solution theoretically applies to multidimensional KDEs, it has not been clear how to practically do so. A method for practically extending the Bernacchia-Pigolotti KDE to multidimensions is introduced. This multidimensional extension is combined with a recently-developed computational improvement to their method that makes it computationally efficient: a 2D KDE on 10 5 samples only takes 1 s on a modern workstation. This fast and objective KDE method, called the fastKDE method, retains the excellent statistical convergence properties that have been demonstrated for univariate samples. The fastKDE method exhibits statistical accuracy that is comparable to state-of-the-science KDE methods publicly available in R, and it produces kernel density estimates several orders of magnitude faster. The fastKDE method does an excellent job of encoding covariance information for bivariate samples. This property allows for direct calculation of conditional PDFs with fastKDE. It is demonstrated how this capability might be leveraged for detecting non-trivial relationships between quantities in physical systems, such as transitional behavior.« less

Handling Density Conversion in TPS.

PubMed

Isobe, Tomonori; Mori, Yutaro; Takei, Hideyuki; Sato, Eisuke; Tadano, Kiichi; Kobayashi, Daisuke; Tomita, Tetsuya; Sakae, Takeji

2016-01-01

Conversion from CT value to density is essential to a radiation treatment planning system. Generally CT value is converted to the electron density in photon therapy. In the energy range of therapeutic photon, interactions between photons and materials are dominated with Compton scattering which the cross-section depends on the electron density. The dose distribution is obtained by calculating TERMA and kernel using electron density where TERMA is the energy transferred from primary photons and kernel is a volume considering spread electrons. Recently, a new method was introduced which uses the physical density. This method is expected to be faster and more accurate than that using the electron density. As for particle therapy, dose can be calculated with CT-to-stopping power conversion since the stopping power depends on the electron density. CT-to-stopping power conversion table is also called as CT-to-water-equivalent range and is an essential concept for the particle therapy.

GPU Acceleration of Mean Free Path Based Kernel Density Estimators for Monte Carlo Neutronics Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burke, TImothy P.; Kiedrowski, Brian C.; Martin, William R.

Kernel Density Estimators (KDEs) are a non-parametric density estimation technique that has recently been applied to Monte Carlo radiation transport simulations. Kernel density estimators are an alternative to histogram tallies for obtaining global solutions in Monte Carlo tallies. With KDEs, a single event, either a collision or particle track, can contribute to the score at multiple tally points with the uncertainty at those points being independent of the desired resolution of the solution. Thus, KDEs show potential for obtaining estimates of a global solution with reduced variance when compared to a histogram. Previously, KDEs have been applied to neutronics formore » one-group reactor physics problems and fixed source shielding applications. However, little work was done to obtain reaction rates using KDEs. This paper introduces a new form of the MFP KDE that is capable of handling general geometries. Furthermore, extending the MFP KDE to 2-D problems in continuous energy introduces inaccuracies to the solution. An ad-hoc solution to these inaccuracies is introduced that produces errors smaller than 4% at material interfaces.« less

Three-dimensional holoscopic image coding scheme using high-efficiency video coding with kernel-based minimum mean-square-error estimation

NASA Astrophysics Data System (ADS)

Liu, Deyang; An, Ping; Ma, Ran; Yang, Chao; Shen, Liquan; Li, Kai

2016-07-01

Three-dimensional (3-D) holoscopic imaging, also known as integral imaging, light field imaging, or plenoptic imaging, can provide natural and fatigue-free 3-D visualization. However, a large amount of data is required to represent the 3-D holoscopic content. Therefore, efficient coding schemes for this particular type of image are needed. A 3-D holoscopic image coding scheme with kernel-based minimum mean square error (MMSE) estimation is proposed. In the proposed scheme, the coding block is predicted by an MMSE estimator under statistical modeling. In order to obtain the signal statistical behavior, kernel density estimation (KDE) is utilized to estimate the probability density function of the statistical modeling. As bandwidth estimation (BE) is a key issue in the KDE problem, we also propose a BE method based on kernel trick. The experimental results demonstrate that the proposed scheme can achieve a better rate-distortion performance and a better visual rendering quality.

Optimization of soxhlet extraction and physicochemical analysis of crop oil from seed kernel of Feun Kase (Thevetia peruviana)

NASA Astrophysics Data System (ADS)

Suwari, Kotta, Herry Z.; Buang, Yohanes

2017-12-01

Optimizing the soxhlet extraction of oil from seed kernel of Feun Kase (Thevetia peruviana) for biodiesel production was carried out in this study. The solvent used was petroleum ether and methanol, as well as their combinations. The effect of three factors namely different solvent combinations (polarity), extraction time and extraction temperature were investigated for achieving maximum oil yield. Each experiment was conducted in 250 mL soxhlet apparatus. The physicochemical properties of the oil yield (density, kinematic viscosity, acid value, iodine value, saponification value, and water content) were also analyzed. The optimum conditions were found after 4.5 h with extraction time, extraction temperature at 65 oC and petroleum ether to methanol ratio of 90 : 10 (polarity index 0.6). The oil extract was found to be 51.88 ± 3.18%. These results revealed that the crop oil from seed kernel of Feun Kase (Thevetia peruviana) is a potential feedstock for biodiesel production.

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-06-01

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less

Detonability of turbulent white dwarf plasma: Hydrodynamical models at low densities

NASA Astrophysics Data System (ADS)

Fenn, Daniel

The origins of Type Ia supernovae (SNe Ia) remain an unsolved problem of contemporary astrophysics. Decades of research indicate that these supernovae arise from thermonuclear runaway in the degenerate material of white dwarf stars; however, the mechanism of these explosions is unknown. Also, it is unclear what are the progenitors of these objects. These missing elements are vital components of the initial conditions of supernova explosions, and are essential to understanding these events. A requirement of any successful SN Ia model is that a sufficient portion of the white dwarf plasma must be brought under conditions conducive to explosive burning. Our aim is to identify the conditions required to trigger detonations in turbulent, carbon-rich degenerate plasma at low densities. We study this problem by modeling the hydrodynamic evolution of a turbulent region filled with a carbon/oxygen mixture at a density, temperature, and Mach number characteristic of conditions found in the 0.8+1.2 solar mass (CO0812) model discussed by Fenn et al. (2016). We probe the ignition conditions for different degrees of compressibility in turbulent driving. We assess the probability of successful detonations based on characteristics of the identified ignition kernels, using Eulerian and Lagrangian statistics of turbulent flow. We found that material with very short ignition times is abundant in the case that turbulence is driven compressively. This material forms contiguous structures that persist over many ignition time scales, and that we identify as prospective detonation kernels. Detailed analysis of the kernels revealed that their central regions are densely filled with material characterized by short ignition times and contain the minimum mass required for self-sustained detonations to form. It is conceivable that ignition kernels will be formed for lower compressibility in the turbulent driving. However, we found no detonation kernels in models driven 87.5 percent compressively. We indirectly confirmed the existence of the lower limit of the degree of compressibility of the turbulent drive for the formation of detonation kernels by analyzing simulation results of the He0609 model of Fenn et al. (2016), which produces a detonation in a helium-rich boundary layer. We found that the amount of energy in the compressible component of the kinetic energy in this model corresponds to about 96 percent compressibility in the turbulent drive. The fact that no detonation was found in the original CO0812 model for nominally the same problem conditions suggests that models with carbon-rich boundary layers may require higher resolution in order to adequately represent the mass distributions in terms of ignition times.

Nonlocal kinetic energy functional from the jellium-with-gap model: Applications to orbital-free density functional theory

NASA Astrophysics Data System (ADS)

Constantin, Lucian A.; Fabiano, Eduardo; Della Sala, Fabio

2018-05-01

Orbital-free density functional theory (OF-DFT) promises to describe the electronic structure of very large quantum systems, being its computational cost linear with the system size. However, the OF-DFT accuracy strongly depends on the approximation made for the kinetic energy (KE) functional. To date, the most accurate KE functionals are nonlocal functionals based on the linear-response kernel of the homogeneous electron gas, i.e., the jellium model. Here, we use the linear-response kernel of the jellium-with-gap model to construct a simple nonlocal KE functional (named KGAP) which depends on the band-gap energy. In the limit of vanishing energy gap (i.e., in the case of metals), the KGAP is equivalent to the Smargiassi-Madden (SM) functional, which is accurate for metals. For a series of semiconductors (with different energy gaps), the KGAP performs much better than SM, and results are close to the state-of-the-art functionals with sophisticated density-dependent kernels.

Quantitative volcanic susceptibility analysis of Lanzarote and Chinijo Islands based on kernel density estimation via a linear diffusion process

PubMed Central

Galindo, I.; Romero, M. C.; Sánchez, N.; Morales, J. M.

2016-01-01

Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures. PMID:27265878

Quantitative volcanic susceptibility analysis of Lanzarote and Chinijo Islands based on kernel density estimation via a linear diffusion process.

PubMed

Galindo, I; Romero, M C; Sánchez, N; Morales, J M

2016-06-06

Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures.

Quantitative volcanic susceptibility analysis of Lanzarote and Chinijo Islands based on kernel density estimation via a linear diffusion process

NASA Astrophysics Data System (ADS)

Galindo, I.; Romero, M. C.; Sánchez, N.; Morales, J. M.

2016-06-01

Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures.

Snake River Plain Geothermal Play Fairway Analysis - Phase 1 Raster Files

DOE Data Explorer

John Shervais

2015-10-09

Snake River Plain Play Fairway Analysis - Phase 1 CRS Raster Files. This dataset contains raster files created in ArcGIS. These raster images depict Common Risk Segment (CRS) maps for HEAT, PERMEABILITY, AND SEAL, as well as selected maps of Evidence Layers. These evidence layers consist of either Bayesian krige functions or kernel density functions, and include: (1) HEAT: Heat flow (Bayesian krige map), Heat flow standard error on the krige function (data confidence), volcanic vent distribution as function of age and size, groundwater temperature (equivalue interval and natural breaks bins), and groundwater T standard error. (2) PERMEABILTY: Fault and lineament maps, both as mapped and as kernel density functions, processed for both dilational tendency (TD) and slip tendency (ST), along with data confidence maps for each data type. Data types include mapped surface faults from USGS and Idaho Geological Survey data bases, as well as unpublished mapping; lineations derived from maximum gradients in magnetic, deep gravity, and intermediate depth gravity anomalies. (3) SEAL: Seal maps based on presence and thickness of lacustrine sediments and base of SRP aquifer. Raster size is 2 km. All files generated in ArcGIS.

Novel near-infrared sampling apparatus for single kernel analysis of oil content in maize.

PubMed

Janni, James; Weinstock, B André; Hagen, Lisa; Wright, Steve

2008-04-01

A method of rapid, nondestructive chemical and physical analysis of individual maize (Zea mays L.) kernels is needed for the development of high value food, feed, and fuel traits. Near-infrared (NIR) spectroscopy offers a robust nondestructive method of trait determination. However, traditional NIR bulk sampling techniques cannot be applied successfully to individual kernels. Obtaining optimized single kernel NIR spectra for applied chemometric predictive analysis requires a novel sampling technique that can account for the heterogeneous forms, morphologies, and opacities exhibited in individual maize kernels. In this study such a novel technique is described and compared to less effective means of single kernel NIR analysis. Results of the application of a partial least squares (PLS) derived model for predictive determination of percent oil content per individual kernel are shown.

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

PubMed

Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

2018-02-13

Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Linked-cluster formulation of electron-hole interaction kernel in real-space representation without using unoccupied states.

PubMed

Bayne, Michael G; Scher, Jeremy A; Ellis, Benjamin H; Chakraborty, Arindam

2018-05-21

Electron-hole or quasiparticle representation plays a central role in describing electronic excitations in many-electron systems. For charge-neutral excitation, the electron-hole interaction kernel is the quantity of interest for calculating important excitation properties such as optical gap, optical spectra, electron-hole recombination and electron-hole binding energies. The electron-hole interaction kernel can be formally derived from the density-density correlation function using both Green's function and TDDFT formalism. The accurate determination of the electron-hole interaction kernel remains a significant challenge for precise calculations of optical properties in the GW+BSE formalism. From the TDDFT perspective, the electron-hole interaction kernel has been viewed as a path to systematic development of frequency-dependent exchange-correlation functionals. Traditional approaches, such as MBPT formalism, use unoccupied states (which are defined with respect to Fermi vacuum) to construct the electron-hole interaction kernel. However, the inclusion of unoccupied states has long been recognized as the leading computational bottleneck that limits the application of this approach for larger finite systems. In this work, an alternative derivation that avoids using unoccupied states to construct the electron-hole interaction kernel is presented. The central idea of this approach is to use explicitly correlated geminal functions for treating electron-electron correlation for both ground and excited state wave functions. Using this ansatz, it is derived using both diagrammatic and algebraic techniques that the electron-hole interaction kernel can be expressed only in terms of linked closed-loop diagrams. It is proved that the cancellation of unlinked diagrams is a consequence of linked-cluster theorem in real-space representation. The electron-hole interaction kernel derived in this work was used to calculate excitation energies in many-electron systems and results were found to be in good agreement with the EOM-CCSD and GW+BSE methods. The numerical results highlight the effectiveness of the developed method for overcoming the computational barrier of accurately determining the electron-hole interaction kernel to applications of large finite systems such as quantum dots and nanorods.

Pattern formation of microtubules and motors: inelastic interaction of polar rods.

PubMed

Aranson, Igor S; Tsimring, Lev S

2005-05-01

We derive a model describing spatiotemporal organization of an array of microtubules interacting via molecular motors. Starting from a stochastic model of inelastic polar rods with a generic anisotropic interaction kernel we obtain a set of equations for the local rods concentration and orientation. At large enough mean density of rods and concentration of motors, the model describes orientational instability. We demonstrate that the orientational instability leads to the formation of vortices and (for large density and/or kernel anisotropy) asters seen in recent experiments.

Differential metabolome analysis of field-grown maize kernels in response to drought stress

USDA-ARS?s Scientific Manuscript database

Drought stress constrains maize kernel development and can exacerbate aflatoxin contamination. In order to identify drought responsive metabolites and explore pathways involved in kernel responses, a metabolomics analysis was conducted on kernels from a drought tolerant line, Lo964, and a sensitive ...

Do we really need a large number of particles to simulate bimolecular reactive transport with random walk methods? A kernel density estimation approach

NASA Astrophysics Data System (ADS)

Rahbaralam, Maryam; Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier

2015-12-01

Random walk particle tracking methods are a computationally efficient family of methods to solve reactive transport problems. While the number of particles in most realistic applications is in the order of 106-109, the number of reactive molecules even in diluted systems might be in the order of fractions of the Avogadro number. Thus, each particle actually represents a group of potentially reactive molecules. The use of a low number of particles may result not only in loss of accuracy, but also may lead to an improper reproduction of the mixing process, limited by diffusion. Recent works have used this effect as a proxy to model incomplete mixing in porous media. In this work, we propose using a Kernel Density Estimation (KDE) of the concentrations that allows getting the expected results for a well-mixed solution with a limited number of particles. The idea consists of treating each particle as a sample drawn from the pool of molecules that it represents; this way, the actual location of a tracked particle is seen as a sample drawn from the density function of the location of molecules represented by that given particle, rigorously represented by a kernel density function. The probability of reaction can be obtained by combining the kernels associated to two potentially reactive particles. We demonstrate that the observed deviation in the reaction vs time curves in numerical experiments reported in the literature could be attributed to the statistical method used to reconstruct concentrations (fixed particle support) from discrete particle distributions, and not to the occurrence of true incomplete mixing. We further explore the evolution of the kernel size with time, linking it to the diffusion process. Our results show that KDEs are powerful tools to improve computational efficiency and robustness in reactive transport simulations, and indicates that incomplete mixing in diluted systems should be modeled based on alternative mechanistic models and not on a limited number of particles.

Genome-wide Association Analysis of Kernel Weight in Hard Winter Wheat

USDA-ARS?s Scientific Manuscript database

Wheat kernel weight is an important and heritable component of wheat grain yield and a key predictor of flour extraction. Genome-wide association analysis was conducted to identify genomic regions associated with kernel weight and kernel weight environmental response in 8 trials of 299 hard winter ...

Using Adjoint Methods to Improve 3-D Velocity Models of Southern California

NASA Astrophysics Data System (ADS)

Liu, Q.; Tape, C.; Maggi, A.; Tromp, J.

2006-12-01

We use adjoint methods popular in climate and ocean dynamics to calculate Fréchet derivatives for tomographic inversions in southern California. The Fréchet derivative of an objective function χ(m), where m denotes the Earth model, may be written in the generic form δχ=int Km(x) δln m(x) d3x, where δln m=δ m/m denotes the relative model perturbation. For illustrative purposes, we construct the 3-D finite-frequency banana-doughnut kernel Km, corresponding to the misfit of a single traveltime measurement, by simultaneously computing the 'adjoint' wave field s† forward in time and reconstructing the regular wave field s backward in time. The adjoint wave field is produced by using the time-reversed velocity at the receiver as a fictitious source, while the regular wave field is reconstructed on the fly by propagating the last frame of the wave field saved by a previous forward simulation backward in time. The approach is based upon the spectral-element method, and only two simulations are needed to produce density, shear-wave, and compressional-wave sensitivity kernels. This method is applied to the SCEC southern California velocity model. Various density, shear-wave, and compressional-wave sensitivity kernels are presented for different phases in the seismograms. We also generate 'event' kernels for Pnl, S and surface waves, which are the Fréchet kernels of misfit functions that measure the P, S or surface wave traveltime residuals at all the receivers simultaneously for one particular event. Effectively, an event kernel is a sum of weighted Fréchet kernels, with weights determined by the associated traveltime anomalies. By the nature of the 3-D simulation, every event kernel is also computed based upon just two simulations, i.e., its construction costs the same amount of computation time as an individual banana-doughnut kernel. One can think of the sum of the event kernels for all available earthquakes, called the 'misfit' kernel, as a graphical representation of the gradient of the misfit function. With the capability of computing both the value of the misfit function and its gradient, which assimilates the traveltime anomalies, we are ready to use a non-linear conjugate gradient algorithm to iteratively improve velocity models of southern California.

New Fukui, dual and hyper-dual kernels as bond reactivity descriptors.

PubMed

Franco-Pérez, Marco; Polanco-Ramírez, Carlos-A; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-06-21

We define three new linear response indices with promising applications for bond reactivity using the mathematical framework of τ-CRT (finite temperature chemical reactivity theory). The τ-Fukui kernel is defined as the ratio between the fluctuations of the average electron density at two different points in the space and the fluctuations in the average electron number and is designed to integrate to the finite-temperature definition of the electronic Fukui function. When this kernel is condensed, it can be interpreted as a site-reactivity descriptor of the boundary region between two atoms. The τ-dual kernel corresponds to the first order response of the Fukui kernel and is designed to integrate to the finite temperature definition of the dual descriptor; it indicates the ambiphilic reactivity of a specific bond and enriches the traditional dual descriptor by allowing one to distinguish between the electron-accepting and electron-donating processes. Finally, the τ-hyper dual kernel is defined as the second-order derivative of the Fukui kernel and is proposed as a measure of the strength of ambiphilic bonding interactions. Although these quantities have never been proposed, our results for the τ-Fukui kernel and for τ-dual kernel can be derived in zero-temperature formulation of the chemical reactivity theory with, among other things, the widely-used parabolic interpolation model.

Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels.

PubMed

Cid, Jaime A; von Davier, Alina A

2015-05-01

Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.

Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.

Magnetic field influences on the lateral dose response functions of photon-beam detectors: MC study of wall-less water-filled detectors with various densities.

PubMed

Looe, Hui Khee; Delfs, Björn; Poppinga, Daniela; Harder, Dietrich; Poppe, Björn

2017-06-21

The distortion of detector reading profiles across photon beams in the presence of magnetic fields is a developing subject of clinical photon-beam dosimetry. The underlying modification by the Lorentz force of a detector's lateral dose response function-the convolution kernel transforming the true cross-beam dose profile in water into the detector reading profile-is here studied for the first time. The three basic convolution kernels, the photon fluence response function, the dose deposition kernel, and the lateral dose response function, of wall-less cylindrical detectors filled with water of low, normal and enhanced density are shown by Monte Carlo simulation to be distorted in the prevailing direction of the Lorentz force. The asymmetric shape changes of these convolution kernels in a water medium and in magnetic fields of up to 1.5 T are confined to the lower millimetre range, and they depend on the photon beam quality, the magnetic flux density and the detector's density. The impact of this distortion on detector reading profiles is demonstrated using a narrow photon beam profile. For clinical applications it appears as favourable that the magnetic flux density dependent distortion of the lateral dose response function, as far as secondary electron transport is concerned, vanishes in the case of water-equivalent detectors of normal water density. By means of secondary electron history backtracing, the spatial distribution of the photon interactions giving rise either directly to secondary electrons or to scattered photons further downstream producing secondary electrons which contribute to the detector's signal, and their lateral shift due to the Lorentz force is elucidated. Electron history backtracing also serves to illustrate the correct treatment of the influences of the Lorentz force in the EGSnrc Monte Carlo code applied in this study.

Reproductive sink of sweet corn in response to plant density and hybrid

USDA-ARS?s Scientific Manuscript database

Improvements in plant density tolerance have played an essential role in grain corn yield gains for ~80 years; however, plant density effects on sweet corn biomass allocation to the ear (the reproductive ‘sink’) is poorly quantified. Moreover, optimal plant densities for modern white-kernel shrunke...

Reservoir area of influence and implications for fisheries management

USGS Publications Warehouse

Martin, Dustin R.; Chizinski, Christopher J.; Pope, Kevin L.

2015-01-01

Understanding the spatial area that a reservoir draws anglers from, defined as the reservoir's area of influence, and the potential overlap of that area of influence between reservoirs is important for fishery managers. Our objective was to define the area of influence for reservoirs of the Salt Valley regional fishery in southeastern Nebraska using kernel density estimation. We used angler survey data obtained from in-person interviews at 17 reservoirs during 2009–2012. The area of influence, defined by the 95% kernel density, for reservoirs within the Salt Valley regional fishery varied, indicating that anglers use reservoirs differently across the regional fishery. Areas of influence reveal angler preferences in a regional context, indicating preferred reservoirs with a greater area of influence. Further, differences in areas of influences across time and among reservoirs can be used as an assessment following management changes on an individual reservoir or within a regional fishery. Kernel density estimation provided a clear method for creating spatial maps of areas of influence and provided a two-dimensional view of angler travel, as opposed to the traditional mean travel distance assessment.

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

PubMed

Kwak, Nojun

2016-05-20

Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

A heat kernel proof of the index theorem for deformation quantization

NASA Astrophysics Data System (ADS)

Karabegov, Alexander

2017-11-01

We give a heat kernel proof of the algebraic index theorem for deformation quantization with separation of variables on a pseudo-Kähler manifold. We use normalizations of the canonical trace density of a star product and of the characteristic classes involved in the index formula for which this formula contains no extra constant factors.

Diversity of maize kernels from a breeding program for protein quality III: Ionome profiling

USDA-ARS?s Scientific Manuscript database

Densities of single and multiple macro- and micronutrients have been estimated in mature kernels of 1,348 accessions in 13 maize genotypes. The germplasm belonged to stiff stalk (SS) and non-stiff stalk (NS) heterotic groups (HG) with one (S1) to four (S4) years of inbreeding (IB), or open pollinati...

Left ventricle segmentation via graph cut distribution matching.

PubMed

Ben Ayed, Ismail; Punithakumar, Kumaradevan; Li, Shuo; Islam, Ali; Chong, Jaron

2009-01-01

We present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. The energy and its graph cut optimization based on an original first-order approximation of the Bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. The algorithm seeks a region within each frame by optimization of two priors, one geometric (distance-based) and the other photometric, each measuring a distribution similarity between the region and a model learned from the first frame. Based on global rather than pixelwise information, the proposed algorithm does not require complex training and optimization with respect to geometric transformations. Unlike related active contour methods, it does not compute iterative updates of computationally expensive kernel densities. Furthermore, the proposed first-order analysis can be used for other intractable energies and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of graph cuts. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert.

Visualization of spatial patterns and temporal trends for aerial surveillance of illegal oil discharges in western Canadian marine waters.

PubMed

Serra-Sogas, Norma; O'Hara, Patrick D; Canessa, Rosaline; Keller, Peter; Pelot, Ronald

2008-05-01

This paper examines the use of exploratory spatial analysis for identifying hotspots of shipping-based oil pollution in the Pacific Region of Canada's Exclusive Economic Zone. It makes use of data collected from fiscal years 1997/1998 to 2005/2006 by the National Aerial Surveillance Program, the primary tool for monitoring and enforcing the provisions imposed by MARPOL 73/78. First, we present oil spill data as points in a "dot map" relative to coastlines, harbors and the aerial surveillance distribution. Then, we explore the intensity of oil spill events using the Quadrat Count method, and the Kernel Density Estimation methods with both fixed and adaptive bandwidths. We found that oil spill hotspots where more clearly defined using Kernel Density Estimation with an adaptive bandwidth, probably because of the "clustered" distribution of oil spill occurrences. Finally, we discuss the importance of standardizing oil spill data by controlling for surveillance effort to provide a better understanding of the distribution of illegal oil spills, and how these results can ultimately benefit a monitoring program.

Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

NASA Astrophysics Data System (ADS)

Zhu, Fengle; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert; Bhatnagar, Deepak; Cleveland, Thomas

2015-05-01

Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.

Efficient 3D movement-based kernel density estimator and application to wildlife ecology

USGS Publications Warehouse

Tracey-PR, Jeff; Sheppard, James K.; Lockwood, Glenn K.; Chourasia, Amit; Tatineni, Mahidhar; Fisher, Robert N.; Sinkovits, Robert S.

2014-01-01

We describe an efficient implementation of a 3D movement-based kernel density estimator for determining animal space use from discrete GPS measurements. This new method provides more accurate results, particularly for species that make large excursions in the vertical dimension. The downside of this approach is that it is much more computationally expensive than simpler, lower-dimensional models. Through a combination of code restructuring, parallelization and performance optimization, we were able to reduce the time to solution by up to a factor of 1000x, thereby greatly improving the applicability of the method.

Calculation of the time resolution of the J-PET tomograph using kernel density estimation

NASA Astrophysics Data System (ADS)

Raczyński, L.; Wiślicki, W.; Krzemień, W.; Kowalski, P.; Alfs, D.; Bednarski, T.; Białas, P.; Curceanu, C.; Czerwiński, E.; Dulski, K.; Gajos, A.; Głowacz, B.; Gorgol, M.; Hiesmayr, B.; Jasińska, B.; Kamińska, D.; Korcyl, G.; Kozik, T.; Krawczyk, N.; Kubicz, E.; Mohammed, M.; Pawlik-Niedźwiecka, M.; Niedźwiecki, S.; Pałka, M.; Rudy, Z.; Rundel, O.; Sharma, N. G.; Silarski, M.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Zgardzińska, B.; Zieliński, M.; Moskal, P.

2017-06-01

In this paper we estimate the time resolution of the J-PET scanner built from plastic scintillators. We incorporate the method of signal processing using the Tikhonov regularization framework and the kernel density estimation method. We obtain simple, closed-form analytical formulae for time resolution. The proposed method is validated using signals registered by means of the single detection unit of the J-PET tomograph built from a 30 cm long plastic scintillator strip. It is shown that the experimental and theoretical results obtained for the J-PET scanner equipped with vacuum tube photomultipliers are consistent.

Performance Characteristics of a Kernel-Space Packet Capture Module

DTIC Science & Technology

2010-03-01

Defense, or the United States Government . AFIT/GCO/ENG/10-03 PERFORMANCE CHARACTERISTICS OF A KERNEL-SPACE PACKET CAPTURE MODULE THESIS Presented to the...3.1.2.3 Prototype. The proof of concept for this research is the design, development, and comparative performance analysis of a kernel level N2d capture...changes to kernel code 5. Can be used for both user-space and kernel-space capture applications in order to control comparative performance analysis to

Kernel analysis of partial least squares (PLS) regression models.

PubMed

Shinzawa, Hideyuki; Ritthiruangdej, Pitiporn; Ozaki, Yukihiro

2011-05-01

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Using kernel density estimates to investigate lymphatic filariasis in northeast Brazil

PubMed Central

Medeiros, Zulma; Bonfim, Cristine; Brandão, Eduardo; Netto, Maria José Evangelista; Vasconcellos, Lucia; Ribeiro, Liany; Portugal, José Luiz

2012-01-01

After more than 10 years of the Global Program to Eliminate Lymphatic Filariasis (GPELF) in Brazil, advances have been seen, but the endemic disease persists as a public health problem. The aim of this study was to describe the spatial distribution of lymphatic filariasis in the municipality of Jaboatão dos Guararapes, Pernambuco, Brazil. An epidemiological survey was conducted in the municipality, and positive filariasis cases identified in this survey were georeferenced in point form, using the GPS. A kernel intensity estimator was applied to identify clusters with greater intensity of cases. We examined 23 673 individuals and 323 individuals with microfilaremia were identified, representing a mean prevalence rate of 1.4%. Around 88% of the districts surveyed presented cases of filarial infection, with prevalences of 0–5.6%. The male population was more affected by the infection, with 63.8% of the cases (P<0.005). Positive cases were found in all age groups examined. The kernel intensity estimator identified the areas of greatest intensity and least intensity of filarial infection cases. The case distribution was heterogeneous across the municipality. The kernel estimator identified spatial clusters of cases, thus indicating locations with greater intensity of transmission. The main advantage of this type of analysis lies in its ability to rapidly and easily show areas with the highest concentration of cases, thereby contributing towards planning, monitoring, and surveillance of filariasis elimination actions. Incorporation of geoprocessing and spatial analysis techniques constitutes an important tool for use within the GPELF. PMID:22943547

Space Use and Movement of a Neotropical Top Predator: The Endangered Jaguar

PubMed Central

Stabach, Jared A.; Fleming, Chris H.; Calabrese, Justin M.; De Paula, Rogério C.; Ferraz, Kátia M. P. M.; Kantek, Daniel L. Z.; Miyazaki, Selma S.; Pereira, Thadeu D. C.; Araujo, Gediendson R.; Paviolo, Agustin; De Angelo, Carlos; Di Bitetti, Mario S.; Cruz, Paula; Lima, Fernando; Cullen, Laury; Sana, Denis A.; Ramalho, Emiliano E.; Carvalho, Marina M.; Soares, Fábio H. S.; Zimbres, Barbara; Silva, Marina X.; Moraes, Marcela D. F.; Vogliotti, Alexandre; May, Joares A.; Haberfeld, Mario; Rampim, Lilian; Sartorello, Leonardo; Ribeiro, Milton C.; Leimgruber, Peter

2016-01-01

Accurately estimating home range and understanding movement behavior can provide important information on ecological processes. Advances in data collection and analysis have improved our ability to estimate home range and movement parameters, both of which have the potential to impact species conservation. Fitting continuous-time movement model to data and incorporating the autocorrelated kernel density estimator (AKDE), we investigated range residency of forty-four jaguars fit with GPS collars across five biomes in Brazil and Argentina. We assessed home range and movement parameters of range resident animals and compared AKDE estimates with kernel density estimates (KDE). We accounted for differential space use and movement among individuals, sex, region, and habitat quality. Thirty-three (80%) of collared jaguars were range resident. Home range estimates using AKDE were 1.02 to 4.80 times larger than KDE estimates that did not consider autocorrelation. Males exhibited larger home ranges, more directional movement paths, and a trend towards larger distances traveled per day. Jaguars with the largest home ranges occupied the Atlantic Forest, a biome with high levels of deforestation and high human population density. Our results fill a gap in the knowledge of the species’ ecology with an aim towards better conservation of this endangered/critically endangered carnivore—the top predator in the Neotropics. PMID:28030568

Space Use and Movement of a Neotropical Top Predator: The Endangered Jaguar.

PubMed

Morato, Ronaldo G; Stabach, Jared A; Fleming, Chris H; Calabrese, Justin M; De Paula, Rogério C; Ferraz, Kátia M P M; Kantek, Daniel L Z; Miyazaki, Selma S; Pereira, Thadeu D C; Araujo, Gediendson R; Paviolo, Agustin; De Angelo, Carlos; Di Bitetti, Mario S; Cruz, Paula; Lima, Fernando; Cullen, Laury; Sana, Denis A; Ramalho, Emiliano E; Carvalho, Marina M; Soares, Fábio H S; Zimbres, Barbara; Silva, Marina X; Moraes, Marcela D F; Vogliotti, Alexandre; May, Joares A; Haberfeld, Mario; Rampim, Lilian; Sartorello, Leonardo; Ribeiro, Milton C; Leimgruber, Peter

2016-01-01

Accurately estimating home range and understanding movement behavior can provide important information on ecological processes. Advances in data collection and analysis have improved our ability to estimate home range and movement parameters, both of which have the potential to impact species conservation. Fitting continuous-time movement model to data and incorporating the autocorrelated kernel density estimator (AKDE), we investigated range residency of forty-four jaguars fit with GPS collars across five biomes in Brazil and Argentina. We assessed home range and movement parameters of range resident animals and compared AKDE estimates with kernel density estimates (KDE). We accounted for differential space use and movement among individuals, sex, region, and habitat quality. Thirty-three (80%) of collared jaguars were range resident. Home range estimates using AKDE were 1.02 to 4.80 times larger than KDE estimates that did not consider autocorrelation. Males exhibited larger home ranges, more directional movement paths, and a trend towards larger distances traveled per day. Jaguars with the largest home ranges occupied the Atlantic Forest, a biome with high levels of deforestation and high human population density. Our results fill a gap in the knowledge of the species' ecology with an aim towards better conservation of this endangered/critically endangered carnivore-the top predator in the Neotropics.

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Nonlocal and Nonadiabatic Effects in the Charge-Density Response of Solids: A Time-Dependent Density-Functional Approach

NASA Astrophysics Data System (ADS)

Panholzer, Martin; Gatti, Matteo; Reining, Lucia

2018-04-01

The charge-density response of extended materials is usually dominated by the collective oscillation of electrons, the plasmons. Beyond this feature, however, intriguing many-body effects are observed. They cannot be described by one of the most widely used approaches for the calculation of dielectric functions, which is time-dependent density functional theory (TDDFT) in the adiabatic local density approximation (ALDA). Here, we propose an approximation to the TDDFT exchange-correlation kernel which is nonadiabatic and nonlocal. It is extracted from correlated calculations in the homogeneous electron gas, where we have tabulated it for a wide range of wave vectors and frequencies. A simple mean density approximation allows one to use it in inhomogeneous materials where the density varies on a scale of 1.6 rs or faster. This kernel contains effects that are completely absent in the ALDA; in particular, it correctly describes the double plasmon in the dynamic structure factor of sodium, and it shows the characteristic low-energy peak that appears in systems with low electronic density. It also leads to an overall quantitative improvement of spectra.

Nonlocal and Nonadiabatic Effects in the Charge-Density Response of Solids: A Time-Dependent Density-Functional Approach.

PubMed

Panholzer, Martin; Gatti, Matteo; Reining, Lucia

2018-04-20

The charge-density response of extended materials is usually dominated by the collective oscillation of electrons, the plasmons. Beyond this feature, however, intriguing many-body effects are observed. They cannot be described by one of the most widely used approaches for the calculation of dielectric functions, which is time-dependent density functional theory (TDDFT) in the adiabatic local density approximation (ALDA). Here, we propose an approximation to the TDDFT exchange-correlation kernel which is nonadiabatic and nonlocal. It is extracted from correlated calculations in the homogeneous electron gas, where we have tabulated it for a wide range of wave vectors and frequencies. A simple mean density approximation allows one to use it in inhomogeneous materials where the density varies on a scale of 1.6 r_{s} or faster. This kernel contains effects that are completely absent in the ALDA; in particular, it correctly describes the double plasmon in the dynamic structure factor of sodium, and it shows the characteristic low-energy peak that appears in systems with low electronic density. It also leads to an overall quantitative improvement of spectra.

SOME ENGINEERING PROPERTIES OF SHELLED AND KERNEL TEA (Camellia sinensis) SEEDS.

PubMed

Altuntas, Ebubekir; Yildiz, Merve

2017-01-01

Camellia sinensis is the source of tea leaves and it is an economic crop now grown around the World. Tea seed oil has been used for cooking in China and other Asian countries for more than a thousand years. Tea is the most widely consumed beverages after water in the world. It is mainly produced in Asia, central Africa, and exported throughout the World. Some engineering properties (size dimensions, sphericity, volume, bulk and true densities, friction coefficient, colour characteristics and mechanical behaviour as rupture force of shelled and kernel tea ( Camellia sinensis ) seeds were determined in this study. This research was carried out for shelled and kernel tea seeds. The shelled tea seeds used in this study were obtained from East-Black Sea Tea Cooperative Institution in Rize city of Turkey. Shelled and kernel tea seeds were characterized as large and small sizes. The average geometric mean diameter and seed mass of the shelled tea seeds were 15.8 mm, 10.7 mm (large size); 1.47 g, 0.49 g (small size); while the average geometric mean diameter and seed mass of the kernel tea seeds were 11.8 mm, 8 mm for large size; 0.97 g, 0.31 g for small size, respectively. The sphericity, surface area and volume values were found to be higher in a larger size than small size for the shelled and kernel tea samples. The shelled tea seed's colour intensity (Chroma) were found between 59.31 and 64.22 for large size, while the kernel tea seed's chroma values were found between 56.04 68.34 for large size, respectively. The rupture force values of kernel tea seeds were higher than shelled tea seeds for the large size along X axis; whereas, the rupture force values of along X axis were higher than Y axis for large size of shelled tea seeds. The static coefficients of friction of shelled and kernel tea seeds for the large and small sizes higher values for rubber than the other friction surfaces. Some engineering properties, such as geometric mean diameter, sphericity, volume, bulk and true densities, the coefficient of friction, L*, a*, b* colour characteristics and rupture force of shelled and kernel tea ( Camellia sinensis ) seeds will serve to design the equipment used in postharvest treatments.

A Non-Local, Energy-Optimized Kernel: Recovering Second-Order Exchange and Beyond in Extended Systems

NASA Astrophysics Data System (ADS)

Bates, Jefferson; Laricchia, Savio; Ruzsinszky, Adrienn

The Random Phase Approximation (RPA) is quickly becoming a standard method beyond semi-local Density Functional Theory that naturally incorporates weak interactions and eliminates self-interaction error. RPA is not perfect, however, and suffers from self-correlation error as well as an incorrect description of short-ranged correlation typically leading to underbinding. To improve upon RPA we introduce a short-ranged, exchange-like kernel that is one-electron self-correlation free for one and two electron systems in the high-density limit. By tuning the one free parameter in our model to recover an exact limit of the homogeneous electron gas correlation energy we obtain a non-local, energy-optimized kernel that reduces the errors of RPA for both homogeneous and inhomogeneous solids. To reduce the computational cost of the standard kernel-corrected RPA, we also implement RPA renormalized perturbation theory for extended systems, and demonstrate its capability to describe the dominant correlation effects with a low-order expansion in both metallic and non-metallic systems. Furthermore we stress that for norm-conserving implementations the accuracy of RPA and beyond RPA structural properties compared to experiment is inherently limited by the choice of pseudopotential. Current affiliation: King's College London.

Spatial Variability of Organic Carbon in a Fractured Mudstone and Its Effect on the Retention and Release of Trichloroethene (TCE)

NASA Astrophysics Data System (ADS)

Sole-Mari, G.; Fernandez-Garcia, D.

2016-12-01

Random Walk Particle Tracking (RWPT) coupled with Kernel Density Estimation (KDE) has been recently proposed to simulate reactive transport in porous media. KDE provides an optimal estimation of the area of influence of particles which is a key element to simulate nonlinear chemical reactions. However, several important drawbacks can be identified: (1) the optimal KDE method is computationally intensive and thereby cannot be used at each time step of the simulation; (2) it does not take advantage of the prior information about the physical system and the previous history of the solute plume; (3) even if the kernel is optimal, the relative error in RWPT simulations typically increases over time as the particle density diminishes by dilution. To overcome these problems, we propose an adaptive branching random walk methodology that incorporates the physics, the particle history and maintains accuracy with time. The method allows particles to efficiently split and merge when necessary as well as to optimally adapt their local kernel shape without having to recalculate the kernel size. We illustrate the advantage of the method by simulating complex reactive transport problems in randomly heterogeneous porous media.

Screening of the aerodynamic and biophysical properties of barley malt

NASA Astrophysics Data System (ADS)

Ghodsvali, Alireza; Farzaneh, Vahid; Bakhshabadi, Hamid; Zare, Zahra; Karami, Zahra; Mokhtarian, Mohsen; Carvalho, Isabel. S.

2016-10-01

An understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; germination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the dependent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process.

Locally adaptive methods for KDE-based random walk models of reactive transport in porous media

NASA Astrophysics Data System (ADS)

Sole-Mari, G.; Fernandez-Garcia, D.

2017-12-01

Random Walk Particle Tracking (RWPT) coupled with Kernel Density Estimation (KDE) has been recently proposed to simulate reactive transport in porous media. KDE provides an optimal estimation of the area of influence of particles which is a key element to simulate nonlinear chemical reactions. However, several important drawbacks can be identified: (1) the optimal KDE method is computationally intensive and thereby cannot be used at each time step of the simulation; (2) it does not take advantage of the prior information about the physical system and the previous history of the solute plume; (3) even if the kernel is optimal, the relative error in RWPT simulations typically increases over time as the particle density diminishes by dilution. To overcome these problems, we propose an adaptive branching random walk methodology that incorporates the physics, the particle history and maintains accuracy with time. The method allows particles to efficiently split and merge when necessary as well as to optimally adapt their local kernel shape without having to recalculate the kernel size. We illustrate the advantage of the method by simulating complex reactive transport problems in randomly heterogeneous porous media.

Kernel and divergence techniques in high energy physics separations

NASA Astrophysics Data System (ADS)

Bouř, Petr; Kůs, Václav; Franc, Jiří

2017-10-01

Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at DØ experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82 % AUC while using the restricted feature selection entering the signal separation procedure.

On supervised graph Laplacian embedding CA model & kernel construction and its application

NASA Astrophysics Data System (ADS)

Zeng, Junwei; Qian, Yongsheng; Wang, Min; Yang, Yongzhong

2017-01-01

There are many methods to construct kernel with given data attribute information. Gaussian radial basis function (RBF) kernel is one of the most popular ways to construct a kernel. The key observation is that in real-world data, besides the data attribute information, data label information also exists, which indicates the data class. In order to make use of both data attribute information and data label information, in this work, we propose a supervised kernel construction method. Supervised information from training data is integrated into standard kernel construction process to improve the discriminative property of resulting kernel. A supervised Laplacian embedding cellular automaton model is another key application developed for two-lane heterogeneous traffic flow with the safe distance and large-scale truck. Based on the properties of traffic flow in China, we re-calibrate the cell length, velocity, random slowing mechanism and lane-change conditions and use simulation tests to study the relationships among the speed, density and flux. The numerical results show that the large-scale trucks will have great effects on the traffic flow, which are relevant to the proportion of the large-scale trucks, random slowing rate and the times of the lane space change.

Bell nozzle kernel analysis program

NASA Technical Reports Server (NTRS)

Elliot, J. J.; Stromstra, R. R.

1969-01-01

Bell Nozzle Kernel Analysis Program computes and analyzes the supersonic flowfield in the kernel, or initial expansion region, of a bell or conical nozzle. It analyzes both plane and axisymmetric geometrices for specified gas properties, nozzle throat geometry and input line.

Subsonic and Supersonic Flutter Analysis of a Highly Tapered Swept-Wing Planform, Including Effects of Density Variation and Finite Wing Thickness, and Comparison with Experiments

NASA Technical Reports Server (NTRS)

Yates, Carson, Jr.

1967-01-01

The flutter characteristics of several wings with an aspect-ratio of 4.0, a taper ratio of 0.2, and a quarter-chord sweepback of 45 deg. have been investigated analytically for Mach numbers up to 2.0. The calculations were based on the modified-strip-analysis method, the subsonic-kernel-function method, piston theory, and quasi-steady second-order theory. Results of t h e analysis and comparisons with experiment indicated that: (1) Flutter speeds were accurately predicted by the modified strip analysis, although accuracy at t h e highest Mach numbers required the use of nonlinear aerodynamic theory (which accounts for effects of wing thickness) for the calculation of the aerodynamic parameters. (2) An abrupt increase of flutter-speed coefficient with increasing Mach number, observed experimentally in the transonic range, was also indicated by the modified strip analysis. (3) In the low supersonic range for some densities, a discontinuous variation of flutter frequency with Mach number was indicated by the modified strip analysis. An abrupt change of frequency appeared experimentally in the transonic range. (4) Differences in flutter-speed-coefficient levels obtained from tests at low supersonic Mach numbers in two wind tunnels were also predicted by the modified strip analysis and were shown to be caused primarily by differences in mass ratio. (5) Flutter speeds calculated by the subsonic-kernel-function method were in good agreement with experiment and with the results of the modified strip analysis. (6) Flutter speed obtained from piston theory and from quasi-steady second-order theory were higher than experimental values by at least 38 percent.

Credit scoring analysis using kernel discriminant

NASA Astrophysics Data System (ADS)

Widiharih, T.; Mukid, M. A.; Mustafid

2018-05-01

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

NASA Astrophysics Data System (ADS)

Troudi, Molka; Alimi, Adel M.; Saoudi, Samir

2008-12-01

The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs). Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE) depends directly upon [InlineEquation not available: see fulltext.] which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of [InlineEquation not available: see fulltext.], the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

Production of near-full density uranium nitride microspheres with a hot isostatic press

DOE Office of Scientific and Technical Information (OSTI.GOV)

McMurray, Jacob W.; Kiggans, Jr., Jim O.; Helmreich, Grant W.

Depleted uranium nitride (UN) kernels with diameters ranging from 420 to 858 microns and theoretical densities (TD) between 87 and 91 percent were postprocessed using a hot isostatic press (HIP) in an argon gas media. This treatment was shown to increase the TD up to above 97%. Uranium nitride is highly reactive with oxygen. Therefore, a novel crucible design was implemented to remove impurities in the argon gas via in situ gettering to avoid oxidation of the UN kernels. The density before and after each HIP procedure was calculated from average weight, volume, and ellipticity determined with established characterization techniquesmore » for particle. Furthermore, micrographs confirmed the nearly full densification of the particles using the gettering approach and HIP processing parameters investigated in this work.« less

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2012 CFR

2012-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2011 CFR

2011-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2013 CFR

2013-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2014 CFR

2014-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

A Kernel-Free Particle-Finite Element Method for Hypervelocity Impact Simulation. Chapter 4

NASA Technical Reports Server (NTRS)

Park, Young-Keun; Fahrenthold, Eric P.

2004-01-01

An improved hybrid particle-finite element method has been developed for the simulation of hypervelocity impact problems. Unlike alternative methods, the revised formulation computes the density without reference to any kernel or interpolation functions, for either the density or the rate of dilatation. This simplifies the state space model and leads to a significant reduction in computational cost. The improved method introduces internal energy variables as generalized coordinates in a new formulation of the thermomechanical Lagrange equations. Example problems show good agreement with exact solutions in one dimension and good agreement with experimental data in a three dimensional simulation.

Modeling utilization distributions in space and time

USGS Publications Warehouse

Keating, K.A.; Cherry, S.

2009-01-01

W. Van Winkle defined the utilization distribution (UD) as a probability density that gives an animal's relative frequency of occurrence in a two-dimensional (x, y) plane. We extend Van Winkle's work by redefining the UD as the relative frequency distribution of an animal's occurrence in all four dimensions of space and time. We then describe a product kernel model estimation method, devising a novel kernel from the wrapped Cauchy distribution to handle circularly distributed temporal covariates, such as day of year. Using Monte Carlo simulations of animal movements in space and time, we assess estimator performance. Although not unbiased, the product kernel method yields models highly correlated (Pearson's r - 0.975) with true probabilities of occurrence and successfully captures temporal variations in density of occurrence. In an empirical example, we estimate the expected UD in three dimensions (x, y, and t) for animals belonging to each of two distinct bighorn sheep {Ovis canadensis) social groups in Glacier National Park, Montana, USA. Results show the method can yield ecologically informative models that successfully depict temporal variations in density of occurrence for a seasonally migratory species. Some implications of this new approach to UD modeling are discussed. ?? 2009 by the Ecological Society of America.

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

On- and off-axis spectral emission features from laser-produced gas breakdown plasmas

NASA Astrophysics Data System (ADS)

Harilal, S. S.; Skrodzki, P. J.; Miloshevsky, A.; Brumfield, B. E.; Phillips, M. C.; Miloshevsky, G.

2017-06-01

Laser-heated gas breakdown plasmas or sparks emit profoundly in the ultraviolet and visible region of the electromagnetic spectrum with contributions from ionic, atomic, and molecular species. Laser created kernels expand into a cold ambient with high velocities during their early lifetime followed by confinement of the plasma kernel and eventually collapse. However, the plasma kernels produced during laser breakdown of gases are also capable of exciting and ionizing the surrounding ambient medium. Two mechanisms can be responsible for excitation and ionization of the surrounding ambient: photoexcitation and ionization by intense ultraviolet emission from the sparks produced during the early times of their creation and/or heating by strong shocks generated by the kernel during its expansion into the ambient. In this study, an investigation is made on the spectral features of on- and off-axis emission of laser-induced plasma breakdown kernels generated in atmospheric pressure conditions with an aim to elucidate the mechanisms leading to ambient excitation and emission. Pulses from an Nd:YAG laser emitting at 1064 nm with a pulse duration of 6 ns are used to generate plasma kernels. Laser sparks were generated in air, argon, and helium gases to provide different physical properties of expansion dynamics and plasma chemistry considering the differences in laser absorption properties, mass density, and speciation. Point shadowgraphy and time-resolved imaging were used to evaluate the shock wave and spark self-emission morphology at early and late times, while space and time resolved spectroscopy is used for evaluating the emission features and for inferring plasma physical conditions at on- and off-axis positions. The structure and dynamics of the plasma kernel obtained using imaging techniques are also compared to numerical simulations using the computational fluid dynamics code. The emission from the kernel showed that spectral features from ions, atoms, and molecules are separated in time with early time temperatures and densities in excess of 35 000 K and 4 × 1018/cm3 with an existence of thermal equilibrium. However, the emission from the off-kernel positions from the breakdown plasmas showed enhanced ultraviolet radiation with the presence of N2 bands and is represented by non-local thermodynamic equilibrium (non-LTE) conditions. Our results also highlight that the ultraviolet radiation emitted during the early time of spark evolution is the predominant source of the photo-excitation of the surrounding medium.

On- and off-axis spectral emission features from laser-produced gas breakdown plasmas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harilal, S. S.; Skrodzki, P. J.; Miloshevsky, A.

Laser-heated gas breakdown plasmas or sparks emit profoundly in the ultraviolet and visible region of the electromagnetic spectrum with contributions from ionic, atomic, and molecular species. Laser created kernels expand into a cold ambient with high velocities during its early lifetime followed by confinement of the plasma kernel and eventually collapse. However, the plasma kernels produced during laser breakdown of gases are also capable of exciting and ionizing the surrounding ambient medium. Two mechanisms can be responsible for excitation and ionization of surrounding ambient: viz. photoexcitation and ionization by intense ultraviolet emission from the sparks produced during the early timesmore » of its creation and/or heating by strong shocks generated by the kernel during its expansion into the ambient. In this study, an investigation is made on the spectral features of on- and off-axis emission features of laser-induced plasma breakdown kernels generated in atmospheric pressure conditions with an aim to elucidate the mechanisms leading to ambient excitation and emission. Pulses from an Nd:YAG laser emitting at 1064 nm with 6 ns pulse duration are used to generate plasma kernels. Laser sparks were generated in air, argon, and helium gases to provide different physical properties of expansion dynamics and plasma chemistry considering the differences in laser absorption properties, mass density and speciation. Point shadowgraphy and time-resolved imaging were used to evaluate the shock wave and spark self-emission morphology at early and late times while space and time resolved spectroscopy is used for evaluating the emission features as well as for inferring plasma fundaments at on- and off-axis. Structure and dynamics of the plasma kernel obtained using imaging techniques are also compared to numerical simulations using computational fluid dynamics code. The emission from the kernel showed that spectral features from ions, atoms and molecules are separated in time with an early time temperatures and densities in excess of 35000 K and 4×10 18 /cm 3 with an existence of thermal equilibrium. However, the emission from the off-kernel positions from the breakdown plasmas showed enhanced ultraviolet radiation with the presence of N 2 bands and represented by non-LTE conditions. Finally, our results also highlight that the ultraviolet radiation emitted during early time of spark evolution is the predominant source of the photo-excitation of the surrounding medium.« less

On- and off-axis spectral emission features from laser-produced gas breakdown plasmas

DOE PAGES

Harilal, S. S.; Skrodzki, P. J.; Miloshevsky, A.; ...

2017-06-01

Laser-heated gas breakdown plasmas or sparks emit profoundly in the ultraviolet and visible region of the electromagnetic spectrum with contributions from ionic, atomic, and molecular species. Laser created kernels expand into a cold ambient with high velocities during its early lifetime followed by confinement of the plasma kernel and eventually collapse. However, the plasma kernels produced during laser breakdown of gases are also capable of exciting and ionizing the surrounding ambient medium. Two mechanisms can be responsible for excitation and ionization of surrounding ambient: viz. photoexcitation and ionization by intense ultraviolet emission from the sparks produced during the early timesmore » of its creation and/or heating by strong shocks generated by the kernel during its expansion into the ambient. In this study, an investigation is made on the spectral features of on- and off-axis emission features of laser-induced plasma breakdown kernels generated in atmospheric pressure conditions with an aim to elucidate the mechanisms leading to ambient excitation and emission. Pulses from an Nd:YAG laser emitting at 1064 nm with 6 ns pulse duration are used to generate plasma kernels. Laser sparks were generated in air, argon, and helium gases to provide different physical properties of expansion dynamics and plasma chemistry considering the differences in laser absorption properties, mass density and speciation. Point shadowgraphy and time-resolved imaging were used to evaluate the shock wave and spark self-emission morphology at early and late times while space and time resolved spectroscopy is used for evaluating the emission features as well as for inferring plasma fundaments at on- and off-axis. Structure and dynamics of the plasma kernel obtained using imaging techniques are also compared to numerical simulations using computational fluid dynamics code. The emission from the kernel showed that spectral features from ions, atoms and molecules are separated in time with an early time temperatures and densities in excess of 35000 K and 4×1018 /cm3 with an existence of thermal equilibrium. However, the emission from the off-kernel positions from the breakdown plasmas showed enhanced ultraviolet radiation with the presence of N2 bands and represented by non-LTE conditions. Our results also highlight that the ultraviolet radiation emitted during early time of spark evolution is the predominant source of the photo-excitation of the surrounding medium.« less

Prioritizing individual genetic variants after kernel machine testing using variable selection.

PubMed

He, Qianchuan; Cai, Tianxi; Liu, Yang; Zhao, Ni; Harmon, Quaker E; Almli, Lynn M; Binder, Elisabeth B; Engel, Stephanie M; Ressler, Kerry J; Conneely, Karen N; Lin, Xihong; Wu, Michael C

2016-12-01

Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations. A recently proposed procedure, KerNel Iterative Feature Extraction (KNIFE), provides a general framework for incorporating variable selection into kernel machine methods. In this article, we focus on quantitative traits and relatively common SNPs, and adapt the KNIFE procedure to genetic association studies and propose an approach to identify driver SNPs after the application of SKAT to gene set analysis. Our approach accommodates several kernels that are widely used in SNP analysis, such as the linear kernel and the Identity by State (IBS) kernel. The proposed approach provides practically useful utilities to prioritize SNPs, and fills the gap between SNP set analysis and biological functional studies. Both simulation studies and real data application are used to demonstrate the proposed approach. © 2016 WILEY PERIODICALS, INC.

A method of smoothed particle hydrodynamics using spheroidal kernels

NASA Technical Reports Server (NTRS)

Fulbright, Michael S.; Benz, Willy; Davies, Melvyn B.

1995-01-01

We present a new method of three-dimensional smoothed particle hydrodynamics (SPH) designed to model systems dominated by deformation along a preferential axis. These systems cause severe problems for SPH codes using spherical kernels, which are best suited for modeling systems which retain rough spherical symmetry. Our method allows the smoothing length in the direction of the deformation to evolve independently of the smoothing length in the perpendicular plane, resulting in a kernel with a spheroidal shape. As a result the spatial resolution in the direction of deformation is significantly improved. As a test case we present the one-dimensional homologous collapse of a zero-temperature, uniform-density cloud, which serves to demonstrate the advantages of spheroidal kernels. We also present new results on the problem of the tidal disruption of a star by a massive black hole.

An Ensemble Approach to Building Mercer Kernels with Prior Information

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2005-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Transient and asymptotic behaviour of the binary breakage problem

NASA Astrophysics Data System (ADS)

Mantzaris, Nikos V.

2005-06-01

The general binary breakage problem with power-law breakage functions and two families of symmetric and asymmetric breakage kernels is studied in this work. A useful transformation leads to an equation that predicts self-similar solutions in its asymptotic limit and offers explicit knowledge of the mean size and particle density at each point in dimensionless time. A novel moving boundary algorithm in the transformed coordinate system is developed, allowing the accurate prediction of the full transient behaviour of the system from the initial condition up to the point where self-similarity is achieved, and beyond if necessary. The numerical algorithm is very rapid and its results are in excellent agreement with known analytical solutions. In the case of the symmetric breakage kernels only unimodal, self-similar number density functions are obtained asymptotically for all parameter values and independent of the initial conditions, while in the case of asymmetric breakage kernels, bimodality appears for high degrees of asymmetry and sharp breakage functions. For symmetric and discrete breakage kernels, self-similarity is not achieved. The solution exhibits sustained oscillations with amplitude that depends on the initial condition and the sharpness of the breakage mechanism, while the period is always fixed and equal to ln 2 with respect to dimensionless time.

Quantification of process variables for carbothermic synthesis of UC 1-xN x fuel microspheres

DOE PAGES

Lindemer, Terrance B.; Silva, Chinthaka M.; Henry, Jr, John James; ...

2016-11-05

This report details the continued investigation of process variables involved in converting sol-gel-derived, urania-carbon microspheres to ~820-μm-dia. UC 1-xN x fuel kernels in flow-through, vertical Mo and W crucibles at temperatures up to 2123 K. Experiments included calcining of air-dried UO 3-H 2O-C microspheres in Ar and H 2-containing gases, conversion of the resulting UO 2-C kernels to dense UO2:2UC in the same gases and vacuum, and its conversion in N 2 to UC 1-xN x (x = ~0.85). The thermodynamics of the relevant reactions were applied extensively to interpret and control the process variables. Producing the precursor UO 2:2UCmore » kernel of ~96% theoretical density was required, but its subsequent conversion to UC 1-xN x at 2123 K was not accompanied by sintering and resulted in ~83-86% of theoretical density. Increasing the UC 1-xN x kernel nitride component to ~0.98 in flowing N 2-H 2 mixtures to evolve HCN was shown to be quantitatively consistent with present and past experiments and the only useful application of H 2 in the entire process.« less

Quantification of process variables for carbothermic synthesis of UC1-xNx fuel microspheres

NASA Astrophysics Data System (ADS)

Lindemer, T. B.; Silva, C. M.; Henry, J. J.; McMurray, J. W.; Voit, S. L.; Collins, J. L.; Hunt, R. D.

2017-01-01

This report details the continued investigation of process variables involved in converting sol-gel-derived, urania-carbon microspheres to ∼820-μm-dia. UC1-xNx fuel kernels in flow-through, vertical Mo and W crucibles at temperatures up to 2123 K. Experiments included calcining of air-dried UO3-H2O-C microspheres in Ar and H2-containing gases, conversion of the resulting UO2-C kernels to dense UO2:2UC in the same gases and vacuum, and its conversion in N2 to UC1-xNx (x = ∼0.85). The thermodynamics of the relevant reactions were applied extensively to interpret and control the process variables. Producing the precursor UO2:2UC kernel of ∼96% theoretical density was required, but its subsequent conversion to UC1-xNx at 2123 K was not accompanied by sintering and resulted in ∼83-86% of theoretical density. Increasing the UC1-xNx kernel nitride component to ∼0.98 in flowing N2-H2 mixtures to evolve HCN was shown to be quantitatively consistent with present and past experiments and the only useful application of H2 in the entire process.

Evidence-based Kernels: Fundamental Units of Behavioral Influence

PubMed Central

Biglan, Anthony

2008-01-01

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior. PMID:18712600

EFFECTS OF FLUID AND COMPUTED TOMOGRAPHIC TECHNICAL FACTORS ON CONSPICUITY OF CANINE AND FELINE NASAL TURBINATES

PubMed Central

Uosyte, Raimonda; Shaw, Darren J; Gunn-Moore, Danielle A; Fraga-Manteiga, Eduardo; Schwarz, Tobias

2015-01-01

Turbinate destruction is an important diagnostic criterion in canine and feline nasal computed tomography (CT). However decreased turbinate visibility may also be caused by technical CT settings and nasal fluid. The purpose of this experimental, crossover study was to determine whether fluid reduces conspicuity of canine and feline nasal turbinates in CT and if so, whether CT settings can maximize conspicuity. Three canine and three feline cadaver heads were used. Nasal slabs were CT-scanned before and after submerging them in a water bath; using sequential, helical, and ultrahigh resolution modes; with images in low, medium, and high frequency image reconstruction kernels; and with application of additional posterior fossa optimization and high contrast enhancing filters. Visible turbinate length was measured by a single observer using manual tracing. Nasal density heterogeneity was measured using the standard deviation (SD) of mean nasal density from a region of interest in each nasal cavity. Linear mixed-effect models using the R package ‘nlme’, multivariable models and standard post hoc Tukey pair-wise comparisons were performed to investigate the effect of several variables (nasal content, scanning mode, image reconstruction kernel, application of post reconstruction filters) on measured visible total turbinate length and SD of mean nasal density. All canine and feline water-filled nasal slabs showed significantly decreased visibility of nasal turbinates (P < 0.001). High frequency kernels provided the best turbinate visibility and highest SD of aerated nasal slabs, whereas medium frequency kernels were optimal for water-filled nasal slabs. Scanning mode and filter application had no effect on turbinate visibility. PMID:25867935

SOMKE: kernel density estimation over data streams by sequences of self-organizing maps.

PubMed

Cao, Yuan; He, Haibo; Man, Hong

2012-08-01

In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over data streams based on sequences of self-organizing map (SOM). In many stream data mining applications, the traditional KDE methods are infeasible because of the high computational cost, processing time, and memory requirement. To reduce the time and space complexity, we propose a SOM structure in this paper to obtain well-defined data clusters to estimate the underlying probability distributions of incoming data streams. The main idea of this paper is to build a series of SOMs over the data streams via two operations, that is, creating and merging the SOM sequences. The creation phase produces the SOM sequence entries for windows of the data, which obtains clustering information of the incoming data streams. The size of the SOM sequences can be further reduced by combining the consecutive entries in the sequence based on the measure of Kullback-Leibler divergence. Finally, the probability density functions over arbitrary time periods along the data streams can be estimated using such SOM sequences. We compare SOMKE with two other KDE methods for data streams, the M-kernel approach and the cluster kernel approach, in terms of accuracy and processing time for various stationary data streams. Furthermore, we also investigate the use of SOMKE over nonstationary (evolving) data streams, including a synthetic nonstationary data stream, a real-world financial data stream and a group of network traffic data streams. The simulation results illustrate the effectiveness and efficiency of the proposed approach.

Testosterone and androstanediol glucuronide among men in NHANES III.

PubMed

Duan, Chuan Wei; Xu, Lin

2018-03-09

Most of the androgen replacement therapies were based on serum testosterone and without measurements of total androgen activities. Whether those with low testosterone also have low levels of androgen activity is largely unknown. We hence examined the association between testosterone and androstanediol glucuronide (AG), a reliable measure of androgen activity, in a nationally representative sample of US men. Cross-sectional analysis was based on 1493 men from the Third National Health and Nutrition examination Survey (NHANES III) conducted from 1988 to 1991. Serum testosterone and AG were measured by immunoassay. Kernel density was used to estimate the average density of serum AG concentrations by quartiles of testosterone. Testosterone was weakly and positively correlated with AG (correlation coefficient = 0.18). The kernel density estimates show that the distributions are quite similar between the quartiles of testosterone. After adjustment for age, the distributions of AG in quartiles of testosterone did not change. The correlation between testosterone and AG was stronger in men with younger age, lower body mass index, non-smoking and good self-rated health and health status. Serum testosterone is weakly correlated with total androgen activities, and the correlation is even weaker for those with poor self-rated health. Our results suggest that measurement of total androgen activity in addition to testosterone is necessary in clinical practice, especially before administration of androgen replacement therapy.

Verification of forecast ensembles in complex terrain including observation uncertainty

NASA Astrophysics Data System (ADS)

Dorninger, Manfred; Kloiber, Simon

2017-04-01

Traditionally, verification means to verify a forecast (ensemble) with the truth represented by observations. The observation errors are quite often neglected arguing that they are small when compared to the forecast error. In this study as part of the MesoVICT (Mesoscale Verification Inter-comparison over Complex Terrain) project it will be shown, that observation errors have to be taken into account for verification purposes. The observation uncertainty is estimated from the VERA (Vienna Enhanced Resolution Analysis) and represented via two analysis ensembles which are compared to the forecast ensemble. For the whole study results from COSMO-LEPS provided by Arpae-SIMC Emilia-Romagna are used as forecast ensemble. The time period covers the MesoVICT core case from 20-22 June 2007. In a first step, all ensembles are investigated concerning their distribution. Several tests have been executed (Kolmogorov-Smirnov-Test, Finkelstein-Schafer Test, Chi-Square Test etc.) showing no exact mathematical distribution. So the main focus is on non-parametric statistics (e.g. Kernel density estimation, Boxplots etc.) and also the deviation between "forced" normal distributed data and the kernel density estimations. In a next step the observational deviations due to the analysis ensembles are analysed. In a first approach scores are multiple times calculated with every single ensemble member from the analysis ensemble regarded as "true" observation. The results are presented as boxplots for the different scores and parameters. Additionally, the bootstrapping method is also applied to the ensembles. These possible approaches to incorporating observational uncertainty into the computation of statistics will be discussed in the talk.

Survival analysis for the missing censoring indicator model using kernel density estimation techniques

PubMed Central

Subramanian, Sundarraman

2008-01-01

This article concerns asymptotic theory for a new estimator of a survival function in the missing censoring indicator model of random censorship. Specifically, the large sample results for an inverse probability-of-non-missingness weighted estimator of the cumulative hazard function, so far not available, are derived, including an almost sure representation with rate for a remainder term, and uniform strong consistency with rate of convergence. The estimator is based on a kernel estimate for the conditional probability of non-missingness of the censoring indicator. Expressions for its bias and variance, in turn leading to an expression for the mean squared error as a function of the bandwidth, are also obtained. The corresponding estimator of the survival function, whose weak convergence is derived, is asymptotically efficient. A numerical study, comparing the performances of the proposed and two other currently existing efficient estimators, is presented. PMID:18953423

Survival analysis for the missing censoring indicator model using kernel density estimation techniques.

PubMed

Subramanian, Sundarraman

2006-01-01

This article concerns asymptotic theory for a new estimator of a survival function in the missing censoring indicator model of random censorship. Specifically, the large sample results for an inverse probability-of-non-missingness weighted estimator of the cumulative hazard function, so far not available, are derived, including an almost sure representation with rate for a remainder term, and uniform strong consistency with rate of convergence. The estimator is based on a kernel estimate for the conditional probability of non-missingness of the censoring indicator. Expressions for its bias and variance, in turn leading to an expression for the mean squared error as a function of the bandwidth, are also obtained. The corresponding estimator of the survival function, whose weak convergence is derived, is asymptotically efficient. A numerical study, comparing the performances of the proposed and two other currently existing efficient estimators, is presented.

Fine-mapping of qGW4.05, a major QTL for kernel weight and size in maize.

PubMed

Chen, Lin; Li, Yong-xiang; Li, Chunhui; Wu, Xun; Qin, Weiwei; Li, Xin; Jiao, Fuchao; Zhang, Xiaojing; Zhang, Dengfeng; Shi, Yunsu; Song, Yanchun; Li, Yu; Wang, Tianyu

2016-04-12

Kernel weight and size are important components of grain yield in cereals. Although some information is available concerning the map positions of quantitative trait loci (QTL) for kernel weight and size in maize, little is known about the molecular mechanisms of these QTLs. qGW4.05 is a major QTL that is associated with kernel weight and size in maize. We combined linkage analysis and association mapping to fine-map and identify candidate gene(s) at qGW4.05. QTL qGW4.05 was fine-mapped to a 279.6-kb interval in a segregating population derived from a cross of Huangzaosi with LV28. By combining the results of regional association mapping and linkage analysis, we identified GRMZM2G039934 as a candidate gene responsible for qGW4.05. Candidate gene-based association mapping was conducted using a panel of 184 inbred lines with variable kernel weights and kernel sizes. Six polymorphic sites in the gene GRMZM2G039934 were significantly associated with kernel weight and kernel size. The results of linkage analysis and association mapping revealed that GRMZM2G039934 is the most likely candidate gene for qGW4.05. These results will improve our understanding of the genetic architecture and molecular mechanisms underlying kernel development in maize.

An alternative covariance estimator to investigate genetic heterogeneity in populations.

PubMed

Heslot, Nicolas; Jannink, Jean-Luc

2015-11-26

For genomic prediction and genome-wide association studies (GWAS) using mixed models, covariance between individuals is estimated using molecular markers. Based on the properties of mixed models, using available molecular data for prediction is optimal if this covariance is known. Under this assumption, adding individuals to the analysis should never be detrimental. However, some empirical studies showed that increasing training population size decreased prediction accuracy. Recently, results from theoretical models indicated that even if marker density is high and the genetic architecture of traits is controlled by many loci with small additive effects, the covariance between individuals, which depends on relationships at causal loci, is not always well estimated by the whole-genome kinship. We propose an alternative covariance estimator named K-kernel, to account for potential genetic heterogeneity between populations that is characterized by a lack of genetic correlation, and to limit the information flow between a priori unknown populations in a trait-specific manner. This is similar to a multi-trait model and parameters are estimated by REML and, in extreme cases, it can allow for an independent genetic architecture between populations. As such, K-kernel is useful to study the problem of the design of training populations. K-kernel was compared to other covariance estimators or kernels to examine its fit to the data, cross-validated accuracy and suitability for GWAS on several datasets. It provides a significantly better fit to the data than the genomic best linear unbiased prediction model and, in some cases it performs better than other kernels such as the Gaussian kernel, as shown by an empirical null distribution. In GWAS simulations, alternative kernels control type I errors as well as or better than the classical whole-genome kinship and increase statistical power. No or small gains were observed in cross-validated prediction accuracy. This alternative covariance estimator can be used to gain insight into trait-specific genetic heterogeneity by identifying relevant sub-populations that lack genetic correlation between them. Genetic correlation can be 0 between identified sub-populations by performing automatic selection of relevant sets of individuals to be included in the training population. It may also increase statistical power in GWAS.

Boundary conditions for gas flow problems from anisotropic scattering kernels

NASA Astrophysics Data System (ADS)

To, Quy-Dong; Vu, Van-Huyen; Lauriat, Guy; Léonard, Céline

2015-10-01

The paper presents an interface model for gas flowing through a channel constituted of anisotropic wall surfaces. Using anisotropic scattering kernels and Chapman Enskog phase density, the boundary conditions (BCs) for velocity, temperature, and discontinuities including velocity slip and temperature jump at the wall are obtained. Two scattering kernels, Dadzie and Méolans (DM) kernel, and generalized anisotropic Cercignani-Lampis (ACL) are examined in the present paper, yielding simple BCs at the wall fluid interface. With these two kernels, we rigorously recover the analytical expression for orientation dependent slip shown in our previous works [Pham et al., Phys. Rev. E 86, 051201 (2012) and To et al., J. Heat Transfer 137, 091002 (2015)] which is in good agreement with molecular dynamics simulation results. More important, our models include both thermal transpiration effect and new equations for the temperature jump. While the same expression depending on the two tangential accommodation coefficients is obtained for slip velocity, the DM and ACL temperature equations are significantly different. The derived BC equations associated with these two kernels are of interest for the gas simulations since they are able to capture the direction dependent slip behavior of anisotropic interfaces.

Three-Dimensional Sensitivity Kernels of Z/H Amplitude Ratios of Surface and Body Waves

NASA Astrophysics Data System (ADS)

Bao, X.; Shen, Y.

2017-12-01

The ellipticity of Rayleigh wave particle motion, or Z/H amplitude ratio, has received increasing attention in inversion for shallow Earth structures. Previous studies of the Z/H ratio assumed one-dimensional (1D) velocity structures beneath the receiver, ignoring the effects of three-dimensional (3D) heterogeneities on wave amplitudes. This simplification may introduce bias in the resulting models. Here we present 3D sensitivity kernels of the Z/H ratio to Vs, Vp, and density perturbations, based on finite-difference modeling of wave propagation in 3D structures and the scattering-integral method. Our full-wave approach overcomes two main issues in previous studies of Rayleigh wave ellipticity: (1) the finite-frequency effects of wave propagation in 3D Earth structures, and (2) isolation of the fundamental mode Rayleigh waves from Rayleigh wave overtones and converted Love waves. In contrast to the 1D depth sensitivity kernels in previous studies, our 3D sensitivity kernels exhibit patterns that vary with azimuths and distances to the receiver. The laterally-summed 3D sensitivity kernels and 1D depth sensitivity kernels, based on the same homogeneous reference model, are nearly identical with small differences that are attributable to the single period of the 1D kernels and a finite period range of the 3D kernels. We further verify the 3D sensitivity kernels by comparing the predictions from the kernels with the measurements from numerical simulations of wave propagation for models with various small-scale perturbations. We also calculate and verify the amplitude kernels for P waves. This study shows that both Rayleigh and body wave Z/H ratios provide vertical and lateral constraints on the structure near the receiver. With seismic arrays, the 3D kernels afford a powerful tool to use the Z/H ratios to obtain accurate and high-resolution Earth models.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

NASA Astrophysics Data System (ADS)

Kimuli, Daniel; Wang, Wei; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

2018-03-01

A short-wave infrared (SWIR) hyperspectral imaging system (1000-2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

Comparative analysis of genetic architectures for nine developmental traits of rye.

PubMed

Masojć, Piotr; Milczarski, P; Kruszona, P

2017-08-01

Genetic architectures of plant height, stem thickness, spike length, awn length, heading date, thousand-kernel weight, kernel length, leaf area and chlorophyll content were aligned on the DArT-based high-density map of the 541 × Ot1-3 RILs population of rye using the genes interaction assorting by divergent selection (GIABDS) method. Complex sets of QTL for particular traits contained 1-5 loci of the epistatic D class and 10-28 loci of the hypostatic, mostly R and E classes controlling traits variation through D-E or D-R types of two-loci interactions. QTL were distributed on each of the seven rye chromosomes in unique positions or as a coinciding loci for 2-8 traits. Detection of considerable numbers of the reversed (D', E' and R') classes of QTL might be attributed to the transgression effects observed for most of the studied traits. First examples of E* and F QTL classes, defined in the model, are reported for awn length, leaf area, thousand-kernel weight and kernel length. The results of this study extend experimental data to 11 quantitative traits (together with pre-harvest sprouting and alpha-amylase activity) for which genetic architectures fit the model of mechanism underlying alleles distribution within tails of bi-parental populations. They are also a valuable starting point for map-based search of genes underlying detected QTL and for planning advanced marker-assisted multi-trait breeding strategies.

Classification accuracy on the family planning participation status using kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Kurniawan, Dian; Suparti; Sugito

2018-05-01

Population growth in Indonesia has increased every year. According to the population census conducted by the Central Bureau of Statistics (BPS) in 2010, the population of Indonesia has reached 237.6 million people. Therefore, to control the population growth rate, the government hold Family Planning or Keluarga Berencana (KB) program for couples of childbearing age. The purpose of this program is to improve the health of mothers and children in order to manifest prosperous society by controlling births while ensuring control of population growth. The data used in this study is the updated family data of Semarang city in 2016 that conducted by National Family Planning Coordinating Board (BKKBN). From these data, classifiers with kernel discriminant analysis will be obtained, and also classification accuracy will be obtained from that method. The result of the analysis showed that normal kernel discriminant analysis gives 71.05 % classification accuracy with 28.95 % classification error. Whereas triweight kernel discriminant analysis gives 73.68 % classification accuracy with 26.32 % classification error. Using triweight kernel discriminant for data preprocessing of family planning participation of childbearing age couples in Semarang City of 2016 can be stated better than with normal kernel discriminant.

Mapping and validation of major quantitative trait loci for kernel length in wild barley (Hordeum vulgare ssp. spontaneum).

PubMed

Zhou, Hong; Liu, Shihang; Liu, Yujiao; Liu, Yaxi; You, Jing; Deng, Mei; Ma, Jian; Chen, Guangdeng; Wei, Yuming; Liu, Chunji; Zheng, Youliang

2016-09-13

Kernel length is an important target trait in barley (Hordeum vulgare L.) breeding programs. However, the number of known quantitative trait loci (QTLs) controlling kernel length is limited. In the present study, we aimed to identify major QTLs for kernel length, as well as putative candidate genes that might influence kernel length in wild barley. A recombinant inbred line (RIL) population derived from the barley cultivar Baudin (H. vulgare ssp. vulgare) and the long-kernel wild barley genotype Awcs276 (H.vulgare ssp. spontaneum) was evaluated at one location over three years. A high-density genetic linkage map was constructed using 1,832 genome-wide diversity array technology (DArT) markers, spanning a total of 927.07 cM with an average interval of approximately 0.49 cM. Two major QTLs for kernel length, LEN-3H and LEN-4H, were detected across environments and further validated in a second RIL population derived from Fleet (H. vulgare ssp. vulgare) and Awcs276. In addition, a systematic search of public databases identified four candidate genes and four categories of proteins related to LEN-3H and LEN-4H. This study establishes a fundamental research platform for genomic studies and marker-assisted selection, since LEN-3H and LEN-4H could be used for accelerating progress in barley breeding programs that aim to improve kernel length.

Influence of wheat kernel physical properties on the pulverizing process.

PubMed

Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula

2014-10-01

The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

Sensitivity Kernels for the Cross-Convolution Measure: Eliminate the Source in Waveform Tomography

NASA Astrophysics Data System (ADS)

Menke, W. H.

2017-12-01

We use the adjoint method to derive sensitivity kernels for the cross-convolution measure, a goodness-of-fit criterion that is applicable to seismic data containing closely-spaced multiple arrivals, such as reverberating compressional waves and split shear waves. In addition to a general formulation, specific expressions for sensitivity with respect to density, Lamé parameter and shear modulus are derived for a isotropic elastic solid. As is typical of adjoint methods, the kernels depend upon an adjoint field, the source of which, in this case, is the reference displacement field, pre-multiplied by a matrix of cross-correlations of components of the observed field. We use a numerical simulation to evaluate the resolving power of a topographic inversion that employs the cross-convolution measure. The estimated resolving kernel shows is point-like, indicating that the cross-convolution measure will perform well in waveform tomography settings.

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

PubMed

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

The construction of a two-dimensional reproducing kernel function and its application in a biomedical model.

PubMed

Guo, Qi; Shen, Shu-Ting

2016-04-29

There are two major classes of cardiac tissue models: the ionic model and the FitzHugh-Nagumo model. During computer simulation, each model entails solving a system of complex ordinary differential equations and a partial differential equation with non-flux boundary conditions. The reproducing kernel method possesses significant applications in solving partial differential equations. The derivative of the reproducing kernel function is a wavelet function, which has local properties and sensitivities to singularity. Therefore, study on the application of reproducing kernel would be advantageous. Applying new mathematical theory to the numerical solution of the ventricular muscle model so as to improve its precision in comparison with other methods at present. A two-dimensional reproducing kernel function inspace is constructed and applied in computing the solution of two-dimensional cardiac tissue model by means of the difference method through time and the reproducing kernel method through space. Compared with other methods, this method holds several advantages such as high accuracy in computing solutions, insensitivity to different time steps and a slow propagation speed of error. It is suitable for disorderly scattered node systems without meshing, and can arbitrarily change the location and density of the solution on different time layers. The reproducing kernel method has higher solution accuracy and stability in the solutions of the two-dimensional cardiac tissue model.

Volterra series truncation and kernel estimation of nonlinear systems in the frequency domain

NASA Astrophysics Data System (ADS)

Zhang, B.; Billings, S. A.

2017-02-01

The Volterra series model is a direct generalisation of the linear convolution integral and is capable of displaying the intrinsic features of a nonlinear system in a simple and easy to apply way. Nonlinear system analysis using Volterra series is normally based on the analysis of its frequency-domain kernels and a truncated description. But the estimation of Volterra kernels and the truncation of Volterra series are coupled with each other. In this paper, a novel complex-valued orthogonal least squares algorithm is developed. The new algorithm provides a powerful tool to determine which terms should be included in the Volterra series expansion and to estimate the kernels and thus solves the two problems all together. The estimated results are compared with those determined using the analytical expressions of the kernels to validate the method. To further evaluate the effectiveness of the method, the physical parameters of the system are also extracted from the measured kernels. Simulation studies demonstrates that the new approach not only can truncate the Volterra series expansion and estimate the kernels of a weakly nonlinear system, but also can indicate the applicability of the Volterra series analysis in a severely nonlinear system case.

Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale.

PubMed

Buck, Christoph; Kneib, Thomas; Tkaczick, Tobias; Konstabel, Kenn; Pigeot, Iris

2015-12-22

Built environment studies provide broad evidence that urban characteristics influence physical activity (PA). However, findings are still difficult to compare, due to inconsistent measures assessing urban point characteristics and varying definitions of spatial scale. Both were found to influence the strength of the association between the built environment and PA. We simultaneously evaluated the effect of kernel approaches and network-distances to investigate the association between urban characteristics and physical activity depending on spatial scale and intensity measure. We assessed urban measures of point characteristics such as intersections, public transit stations, and public open spaces in ego-centered network-dependent neighborhoods based on geographical data of one German study region of the IDEFICS study. We calculated point intensities using the simple intensity and kernel approaches based on fixed bandwidths, cross-validated bandwidths including isotropic and anisotropic kernel functions and considering adaptive bandwidths that adjust for residential density. We distinguished six network-distances from 500 m up to 2 km to calculate each intensity measure. A log-gamma regression model was used to investigate the effect of each urban measure on moderate-to-vigorous physical activity (MVPA) of 400 2- to 9.9-year old children who participated in the IDEFICS study. Models were stratified by sex and age groups, i.e. pre-school children (2 to <6 years) and school children (6-9.9 years), and were adjusted for age, body mass index (BMI), education and safety concerns of parents, season and valid weartime of accelerometers. Association between intensity measures and MVPA strongly differed by network-distance, with stronger effects found for larger network-distances. Simple intensity revealed smaller effect estimates and smaller goodness-of-fit compared to kernel approaches. Smallest variation in effect estimates over network-distances was found for kernel intensity measures based on isotropic and anisotropic cross-validated bandwidth selection. We found a strong variation in the association between the built environment and PA of children based on the choice of intensity measure and network-distance. Kernel intensity measures provided stable results over various scales and improved the assessment compared to the simple intensity measure. Considering different spatial scales and kernel intensity methods might reduce methodological limitations in assessing opportunities for PA in the built environment.

Kernel analysis in TeV gamma-ray selection

NASA Astrophysics Data System (ADS)

Moriarty, P.; Samuelson, F. W.

2000-06-01

We discuss the use of kernel analysis as a technique for selecting gamma-ray candidates in Atmospheric Cherenkov astronomy. The method is applied to observations of the Crab Nebula and Markarian 501 recorded with the Whipple 10 m Atmospheric Cherenkov imaging system, and the results are compared with the standard Supercuts analysis. Since kernel analysis is computationally intensive, we examine approaches to reducing the computational load. Extension of the technique to estimate the energy of the gamma-ray primary is considered. .

Permissible Home Range Estimation (PHRE) in restricted habitats: A new algorithm and an evaluation for sea otters

USGS Publications Warehouse

Tarjan, Lily M; Tinker, M. Tim

2016-01-01

Parametric and nonparametric kernel methods dominate studies of animal home ranges and space use. Most existing methods are unable to incorporate information about the underlying physical environment, leading to poor performance in excluding areas that are not used. Using radio-telemetry data from sea otters, we developed and evaluated a new algorithm for estimating home ranges (hereafter Permissible Home Range Estimation, or “PHRE”) that reflects habitat suitability. We began by transforming sighting locations into relevant landscape features (for sea otters, coastal position and distance from shore). Then, we generated a bivariate kernel probability density function in landscape space and back-transformed this to geographic space in order to define a permissible home range. Compared to two commonly used home range estimation methods, kernel densities and local convex hulls, PHRE better excluded unused areas and required a smaller sample size. Our PHRE method is applicable to species whose ranges are restricted by complex physical boundaries or environmental gradients and will improve understanding of habitat-use requirements and, ultimately, aid in conservation efforts.

Resummed memory kernels in generalized system-bath master equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mavros, Michael G.; Van Voorhis, Troy, E-mail: tvan@mit.edu

2014-08-07

Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between themore » two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics.« less

Proteome analysis of the almond kernel (Prunus dulcis).

PubMed

Li, Shugang; Geng, Fang; Wang, Ping; Lu, Jiankang; Ma, Meihu

2016-08-01

Almond (Prunus dulcis) is a popular tree nut worldwide and offers many benefits to human health. However, the importance of almond kernel proteins in the nutrition and function in human health requires further evaluation. The present study presents a systematic evaluation of the proteins in the almond kernel using proteomic analysis. The nutrient and amino acid content in almond kernels from Xinjiang is similar to that of American varieties; however, Xinjiang varieties have a higher protein content. Two-dimensional electrophoresis analysis demonstrated a wide distribution of molecular weights and isoelectric points of almond kernel proteins. A total of 434 proteins were identified by LC-MS/MS, and most were proteins that were experimentally confirmed for the first time. Gene ontology (GO) analysis of the 434 proteins indicated that proteins involved in primary biological processes including metabolic processes (67.5%), cellular processes (54.1%), and single-organism processes (43.4%), the main molecular function of almond kernel proteins are in catalytic activity (48.0%), binding (45.4%) and structural molecule activity (11.9%), and proteins are primarily distributed in cell (59.9%), organelle (44.9%), and membrane (22.8%). Almond kernel is a source of a wide variety of proteins. This study provides important information contributing to the screening and identification of almond proteins, the understanding of almond protein function, and the development of almond protein products. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Microwave sensing of moisture content and bulk density in flowing grain

USDA-ARS?s Scientific Manuscript database

Moisture content and bulk density were determined from measurement of the dielectric properties of flowing wheat kernels at a single microwave frequency (5.8 GHz). The measuring system consisted of two high-gain microwave patch antennas mounted on opposite sides of rectangular chute and connected to...

Shell cracking strength in almond (Prunus dulcis [Mill.] D.A. Webb.) and its implication in uses as a value-added product.

PubMed

Ledbetter, C A

2008-09-01

Researchers are currently developing new value-added uses for almond shells, an abundant agricultural by-product. Almond varieties are distinguished by processors as being either hard or soft shelled, but these two broad classes of almond also exhibit varietal diversity in shell morphology and physical characters. By defining more precisely the physical and chemical characteristics of almond shells from different varieties, researchers will better understand which specific shell types are best suited for specific industrial processes. Eight diverse almond accessions were evaluated in two consecutive harvest seasons for nut and kernel weight, kernel percentage and shell cracking strength. Shell bulk density was evaluated in a separate year. Harvest year by almond accession interactions were highly significant (p0.01) for each of the analyzed variables. Significant (p0.01) correlations were noted for average nut weight with kernel weight, kernel percentage and shell cracking strength. A significant (p0.01) negative correlation for shell cracking strength with kernel percentage was noted. In some cases shell cracking strength was independent of the kernel percentage which suggests that either variety compositional differences or shell morphology affect the shell cracking strength. The varietal characterization of almond shell materials will assist in determining the best value-added uses for this abundant agricultural by-product.

Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

PubMed

Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab; Zhao, Ni; Shen, Judong; Li, Yun; Wu, Michael C

Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

A strategy for analysis of (molecular) equilibrium simulations: Configuration space density estimation, clustering, and visualization

NASA Astrophysics Data System (ADS)

Hamprecht, Fred A.; Peter, Christine; Daura, Xavier; Thiel, Walter; van Gunsteren, Wilfred F.

2001-02-01

We propose an approach for summarizing the output of long simulations of complex systems, affording a rapid overview and interpretation. First, multidimensional scaling techniques are used in conjunction with dimension reduction methods to obtain a low-dimensional representation of the configuration space explored by the system. A nonparametric estimate of the density of states in this subspace is then obtained using kernel methods. The free energy surface is calculated from that density, and the configurations produced in the simulation are then clustered according to the topography of that surface, such that all configurations belonging to one local free energy minimum form one class. This topographical cluster analysis is performed using basin spanning trees which we introduce as subgraphs of Delaunay triangulations. Free energy surfaces obtained in dimensions lower than four can be visualized directly using iso-contours and -surfaces. Basin spanning trees also afford a glimpse of higher-dimensional topographies. The procedure is illustrated using molecular dynamics simulations on the reversible folding of peptide analoga. Finally, we emphasize the intimate relation of density estimation techniques to modern enhanced sampling algorithms.

Generalization of the subsonic kernel function in the s-plane, with applications to flutter analysis

NASA Technical Reports Server (NTRS)

Cunningham, H. J.; Desmarais, R. N.

1984-01-01

A generalized subsonic unsteady aerodynamic kernel function, valid for both growing and decaying oscillatory motions, is developed and applied in a modified flutter analysis computer program to solve the boundaries of constant damping ratio as well as the flutter boundary. Rates of change of damping ratios with respect to dynamic pressure near flutter are substantially lower from the generalized-kernel-function calculations than from the conventional velocity-damping (V-g) calculation. A rational function approximation for aerodynamic forces used in control theory for s-plane analysis gave rather good agreement with kernel-function results, except for strongly damped motion at combinations of high (subsonic) Mach number and reduced frequency.

Design of CT reconstruction kernel specifically for clinical lung imaging

NASA Astrophysics Data System (ADS)

Cody, Dianna D.; Hsieh, Jiang; Gladish, Gregory W.

2005-04-01

In this study we developed a new reconstruction kernel specifically for chest CT imaging. An experimental flat-panel CT scanner was used on large dogs to produce 'ground-truth" reference chest CT images. These dogs were also examined using a clinical 16-slice CT scanner. We concluded from the dog images acquired on the clinical scanner that the loss of subtle lung structures was due mostly to the presence of the background noise texture when using currently available reconstruction kernels. This qualitative evaluation of the dog CT images prompted the design of a new recon kernel. This new kernel consisted of the combination of a low-pass and a high-pass kernel to produce a new reconstruction kernel, called the 'Hybrid" kernel. The performance of this Hybrid kernel fell between the two kernels on which it was based, as expected. This Hybrid kernel was also applied to a set of 50 patient data sets; the analysis of these clinical images is underway. We are hopeful that this Hybrid kernel will produce clinical images with an acceptable tradeoff of lung detail, reliable HU, and image noise.

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

New durum wheat with soft kernel texture: end-use quality analysis of the Hardness locus in Triticum turgidum ssp. durum

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture dictates U.S. wheat market class. Durum wheat has limited demand and culinary end-uses compared to bread wheat because of its extremely hard kernel texture which precludes conventional milling. ‘Soft Svevo’, a new durum cultivar with soft kernel texture comparable to a soft whit...

Unconventional protein sources: apricot seed kernels.

PubMed

Gabrial, G N; El-Nahry, F I; Awadalla, M Z; Girgis, S M

1981-09-01

Hamawy apricot seed kernels (sweet), Amar apricot seed kernels (bitter) and treated Amar apricot kernels (bitterness removed) were evaluated biochemically. All kernels were found to be high in fat (42.2--50.91%), protein (23.74--25.70%) and fiber (15.08--18.02%). Phosphorus, calcium, and iron were determined in all experimental samples. The three different apricot seed kernels were used for extensive study including the qualitative determination of the amino acid constituents by acid hydrolysis, quantitative determination of some amino acids, and biological evaluation of the kernel proteins in order to use them as new protein sources. Weanling albino rats failed to grow on diets containing the Amar apricot seed kernels due to low food consumption because of its bitterness. There was no loss in weight in that case. The Protein Efficiency Ratio data and blood analysis results showed the Hamawy apricot seed kernels to be higher in biological value than treated apricot seed kernels. The Net Protein Ratio data which accounts for both weight, maintenance and growth showed the treated apricot seed kernels to be higher in biological value than both Hamawy and Amar kernels. The Net Protein Ratio for the last two kernels were nearly equal.

Earth Structure, Ice Mass Changes, and the Local Dynamic Geoid

NASA Astrophysics Data System (ADS)

Harig, C.; Simons, F. J.

2014-12-01

Spherical Slepian localization functions are a useful method for studying regional mass changes observed by satellite gravimetry. By projecting data onto a sparse basis set, the local field can be estimated more easily than with the full spherical harmonic basis. We have used this method previously to estimate the ice mass change in Greenland from GRACE data, and it can also be applied to other planetary problems such as global magnetic fields. Earth's static geoid, in contrast to the time-variable field, is in large part related to the internal density and rheological structure of the Earth. Past studies have used dynamic geoid kernels to relate this density structure and the internal deformation it induces to the surface geopotential at large scales. These now classical studies of the eighties and nineties were able to estimate the mantle's radial rheological profile, placing constraints on the ratio between upper and lower mantle viscosity. By combining these two methods, spherical Slepian localization and dynamic geoid kernels, we have created local dynamic geoid kernels which are sensitive only to density variations within an area of interest. With these kernels we can estimate the approximate local radial rheological structure that best explains the locally observed geoid on a regional basis. First-order differences of the regional mantle viscosity structure are accessible to this technique. In this contribution we present our latest, as yet unpublished results on the geographical and temporal pattern of ice mass changes in Antarctica over the past decade, and we introduce a new approach to extract regional information about the internal structure of the Earth from the static global gravity field. Both sets of results are linked in terms of the relevant physics, but also in being developed from the marriage of Slepian functions and geoid kernels. We make predictions on the utility of our approach to derive fully three-dimensional rheological Earth models, to be used for corrections for glacio-isostatic adjustment, as necessary for the interpretation of time-variable gravity observations in terms of ice sheet mass-balance studies.

An Experimental Study of Briquetting Process of Torrefied Rubber Seed Kernel and Palm Oil Shell.

PubMed

Hamid, M Fadzli; Idroas, M Yusof; Ishak, M Zulfikar; Zainal Alauddin, Z Alimuddin; Miskam, M Azman; Abdullah, M Khalil

2016-01-01

Torrefaction process of biomass material is essential in converting them into biofuel with improved calorific value and physical strength. However, the production of torrefied biomass is loose, powdery, and nonuniform. One method of upgrading this material to improve their handling and combustion properties is by densification into briquettes of higher density than the original bulk density of the material. The effects of critical parameters of briquetting process that includes the type of biomass material used for torrefaction and briquetting, densification temperature, and composition of binder for torrefied biomass are studied and characterized. Starch is used as a binder in the study. The results showed that the briquette of torrefied rubber seed kernel (RSK) is better than torrefied palm oil shell (POS) in both calorific value and compressive strength. The best quality of briquettes is yielded from torrefied RSK at the ambient temperature of briquetting process with the composition of 60% water and 5% binder. The maximum compressive load for the briquettes of torrefied RSK is 141 N and the calorific value is 16 MJ/kg. Based on the economic evaluation analysis, the return of investment (ROI) for the mass production of both RSK and POS briquettes is estimated in 2-year period and the annual profit after payback was approximately 107,428.6 USD.

Food Environment and Weight Change: Does Residential Mobility Matter?

PubMed Central

Laraia, Barbara A.; Downing, Janelle M.; Zhang, Y. Tara; Dow, William H.; Kelly, Maggi; Blanchard, Samuel D.; Adler, Nancy; Schillinger, Dean; Moffet, Howard; Warton, E. Margaret; Karter, Andrew J.

2017-01-01

Abstract Associations between neighborhood food environment and adult body mass index (BMI; weight (kg)/height (m)2) derived using cross-sectional or longitudinal random-effects models may be biased due to unmeasured confounding and measurement and methodological limitations. In this study, we assessed the within-individual association between change in food environment from 2006 to 2011 and change in BMI among adults with type 2 diabetes using clinical data from the Kaiser Permanente Diabetes Registry collected from 2007 to 2011. Healthy food environment was measured using the kernel density of healthful food venues. Fixed-effects models with a 1-year-lagged BMI were estimated. Separate models were fitted for persons who moved and those who did not. Sensitivity analysis using different lag times and kernel density bandwidths were tested to establish the consistency of findings. On average, patients lost 1 pound (0.45 kg) for each standard-deviation improvement in their food environment. This relationship held for persons who remained in the same location throughout the 5-year study period but not among persons who moved. Proximity to food venues that promote nutritious foods alone may not translate into clinically meaningful diet-related health changes. Community-level policies for improving the food environment need multifaceted strategies to invoke clinically meaningful change in BMI among adult patients with diabetes. PMID:28387785

Image quality of mixed convolution kernel in thoracic computed tomography.

PubMed

Neubauer, Jakob; Spira, Eva Maria; Strube, Juliane; Langer, Mathias; Voss, Christian; Kotter, Elmar

2016-11-01

The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test.Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall (P < 0.03). Compared to the hard convolution kernel, the mixed convolution kernel was rated with a higher image quality for aorta, anterior mediastinal structures, paratracheal soft tissue, hilar lymph nodes, esophagus, pleuromediastinal border, large and medium sized pulmonary vessels and abdomen (P < 0.004) but a lower image quality for trachea, segmental bronchi, lung parenchyma, and skeleton (P < 0.001).The mixed convolution kernel cannot fully substitute the standard CT reconstructions. Hard and soft convolution kernel reconstructions still seem to be mandatory for thoracic CT.

Fast clustering using adaptive density peak detection.

PubMed

Wang, Xiao-Feng; Xu, Yifan

2017-12-01

Common limitations of clustering methods include the slow algorithm convergence, the instability of the pre-specification on a number of intrinsic parameters, and the lack of robustness to outliers. A recent clustering approach proposed a fast search algorithm of cluster centers based on their local densities. However, the selection of the key intrinsic parameters in the algorithm was not systematically investigated. It is relatively difficult to estimate the "optimal" parameters since the original definition of the local density in the algorithm is based on a truncated counting measure. In this paper, we propose a clustering procedure with adaptive density peak detection, where the local density is estimated through the nonparametric multivariate kernel estimation. The model parameter is then able to be calculated from the equations with statistical theoretical justification. We also develop an automatic cluster centroid selection method through maximizing an average silhouette index. The advantage and flexibility of the proposed method are demonstrated through simulation studies and the analysis of a few benchmark gene expression data sets. The method only needs to perform in one single step without any iteration and thus is fast and has a great potential to apply on big data analysis. A user-friendly R package ADPclust is developed for public use.

Seismic Imaging of VTI, HTI and TTI based on Adjoint Methods

NASA Astrophysics Data System (ADS)

Rusmanugroho, H.; Tromp, J.

2014-12-01

Recent studies show that isotropic seismic imaging based on adjoint method reduces low-frequency artifact caused by diving waves, which commonly occur in two-wave wave-equation migration, such as Reverse Time Migration (RTM). Here, we derive new expressions of sensitivity kernels for Vertical Transverse Isotropy (VTI) using the Thomsen parameters (ɛ, δ, γ) plus the P-, and S-wave speeds (α, β) as well as via the Chen & Tromp (GJI 2005) parameters (A, C, N, L, F). For Horizontal Transverse Isotropy (HTI), these parameters depend on an azimuthal angle φ, where the tilt angle θ is equivalent to 90°, and for Tilted Transverse Isotropy (TTI), these parameters depend on both the azimuth and tilt angles. We calculate sensitivity kernels for each of these two approaches. Individual kernels ("images") are numerically constructed based on the interaction between the regular and adjoint wavefields in smoothed models which are in practice estimated through Full-Waveform Inversion (FWI). The final image is obtained as a result of summing all shots, which are well distributed to sample the target model properly. The impedance kernel, which is a sum of sensitivity kernels of density and the Thomsen or Chen & Tromp parameters, looks crisp and promising for seismic imaging. The other kernels suffer from low-frequency artifacts, similar to traditional seismic imaging conditions. However, all sensitivity kernels are important for estimating the gradient of the misfit function, which, in combination with a standard gradient-based inversion algorithm, is used to minimize the objective function in FWI.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

PubMed

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of a single kernel analysis method for detection of 2-acetyl-1-pyrroline in aromatic rice germplasm

USDA-ARS?s Scientific Manuscript database

Solid-phase microextraction (SPME) in conjunction with GC/MS was used to distinguish non-aromatic rice (Oryza sativa, L.) kernels from aromatic rice kernels. In this method, single kernels along with 10 µl of 0.1 ng 2,4,6-Trimethylpyridine (TMP) were placed in sealed vials and heated to 80oC for 18...

New durum wheat with soft kernel texture: milling performance and end-use quality analysis of the Hardness locus in Triticum turgidum ssp. durum

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture dictates U.S. wheat market class. Durum wheat has limited demand and culinary end-uses compared to bread wheat because of its extremely hard kernel texture which preclude conventional milling. ‘Soft Svevo’, a new durum cultivar with soft kernel texture comparable to a soft white...

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

The pre-image problem in kernel methods.

PubMed

Kwok, James Tin-yau; Tsang, Ivor Wai-hung

2004-11-01

In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.

Influence of moisture content on physical properties of minor millets.

PubMed

Balasubramanian, S; Viswanathan, R

2010-06-01

Physical properties including 1000 kernel weight, bulk density, true density, porosity, angle of repose, coefficient of static friction, coefficient of internal friction and grain hardness were determined for foxtail millet, little millet, kodo millet, common millet, barnyard millet and finger millet in the moisture content range of 11.1 to 25% db. Thousand kernel weight increased from 2.3 to 6.1 g and angle of repose increased from 25.0 to 38.2°. Bulk density decreased from 868.1 to 477.1 kg/m(3) and true density from 1988.7 to 884.4 kg/m(3) for all minor millets when observed in the moisture range of 11.1 to 25%. Porosity decreased from 63.7 to 32.5%. Coefficient of static friction of minor millets against mild steel surface increased from 0.253 to 0.728 and coefficient of internal friction was in the range of 1.217 and 1.964 in the moisture range studied. Grain hardness decreased from 30.7 to 12.4 for all minor millets when moisture content was increased from 11.1 to 25% db.

Carbothermic Synthesis of ~820- m UN Kernels. Investigation of Process Variables

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lindemer, Terrence; Silva, Chinthaka M; Henry, Jr, John James

2015-06-01

This report details the continued investigation of process variables involved in converting sol-gel-derived, urainia-carbon microspheres to ~820-μm-dia. UN fuel kernels in flow-through, vertical refractory-metal crucibles at temperatures up to 2123 K. Experiments included calcining of air-dried UO 3-H 2O-C microspheres in Ar and H 2-containing gases, conversion of the resulting UO 2-C kernels to dense UO 2:2UC in the same gases and vacuum, and its conversion in N 2 to in UC 1-xN x. The thermodynamics of the relevant reactions were applied extensively to interpret and control the process variables. Producing the precursor UO 2:2UC kernel of ~96% theoretical densitymore » was required, but its subsequent conversion to UC 1-xN x at 2123 K was not accompanied by sintering and resulted in ~83-86% of theoretical density. Decreasing the UC 1-xN x kernel carbide component via HCN evolution was shown to be quantitatively consistent with present and past experiments and the only useful application of H2 in the entire process.« less

A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images.

PubMed

Miller, Nathan D; Haase, Nicholas J; Lee, Jonghyun; Kaeppler, Shawn M; de Leon, Natalia; Spalding, Edgar P

2017-01-01

Grain yield of the maize plant depends on the sizes, shapes, and numbers of ears and the kernels they bear. An automated pipeline that can measure these components of yield from easily-obtained digital images is needed to advance our understanding of this globally important crop. Here we present three custom algorithms designed to compute such yield components automatically from digital images acquired by a low-cost platform. One algorithm determines the average space each kernel occupies along the cob axis using a sliding-window Fourier transform analysis of image intensity features. A second counts individual kernels removed from ears, including those in clusters. A third measures each kernel's major and minor axis after a Bayesian analysis of contour points identifies the kernel tip. Dimensionless ear and kernel shape traits that may interrelate yield components are measured by principal components analysis of contour point sets. Increased objectivity and speed compared to typical manual methods are achieved without loss of accuracy as evidenced by high correlations with ground truth measurements and simulated data. Millimeter-scale differences among ear, cob, and kernel traits that ranged more than 2.5-fold across a diverse group of inbred maize lines were resolved. This system for measuring maize ear, cob, and kernel attributes is being used by multiple research groups as an automated Web service running on community high-throughput computing and distributed data storage infrastructure. Users may create their own workflow using the source code that is staged for download on a public repository. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F2 Population

PubMed Central

Hu, Yanmin; Li, Weihua; Fu, Zhiyuan; Ding, Dong; Li, Haochuan; Qiao, Mengmeng; Tang, Jihua

2014-01-01

Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-related traits, including kernel weight (KW), volume (KV), length (KL), thickness (KT), and width (KWI), were collected from an immortalized F2 (IF2) maize population comprising of 243 crosses performed at two separate locations over a span of two years. A total of 54 unconditional main QTL for these five kernel-related traits were identified, many of which were clustered in chromosomal bins 6.04–6.06, 7.02–7.03, and 10.06–10.07. In addition, qKL3, qKWI6, qKV10a, qKV10b, qKW10a, and qKW7a were detected across multiple environments. Sixteen main QTL were identified for KW conditioned on the other four kernel traits (KL, KWI, KT, and KV). Thirteen main QTL were identified for KV conditioned on three kernel-shape traits. Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level, followed by KT, and then KL; KV was mostly strongly influenced by KT, followed by KWI, and was least impacted by KL. Digenic epistatic analysis identified 18 digenic interactions involving 34 loci over the entire genome. However, only a small proportion of them were identical to the main QTL we detected. Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits. The main QTL identified in this study for determining kernel-related traits with high broad-sense heritability may play important roles during kernel development. Furthermore, digenic interactions were shown to exert relatively large effects on KL (the highest AA and DD effects were 4.6% and 6.7%, respectively) and KT (the highest AA effects were 4.3%). PMID:24586932

Crop Loss Relationships and Economic Injury Levels for Ferrisia gilli (Hemiptera: Pseudococcidae) Infesting Pistachio in California.

PubMed

Haviland, David R; Beede, Robert H; Daane, Kent M

2015-12-01

Ferrisia gilli Gullan (Hemiptera: Pseudococcidae) is a new pest in California pistachios, Pistacea vera L. We conducted a 3-yr field study to determine the type and amount of damage caused by F. gilli. Using pesticides, we established gradients of F. gilli densities in a commercial pistachio orchard near Tipton, CA, from 2005 to 2007. Each year, mealybug densities on pistachio clusters were recorded from May through September and cumulative mealybug-days were determined. At harvest time, nut yield per tree (5% dried weight) was determined, and subsamples of nuts were evaluated for market quality. Linear regression analysis of cumulative mealybug-days against fruit yield and nut quality measurements showed no relationships in 2005 and 2006, when mealybug densities were moderate. However, in 2007, when mealybug densities were very high, there was a negative correlation with yield (for every 1,000 mealybug-days, there was a decrease in total dry weight per tree of 0.105 kg) and percentage of split unstained nuts (for every 1,000 mealybug-days, there was a decrease in the percentage of split unstained of 0.560%), and a positive correlation between the percentage of closed kernel and closed blank nuts (for every 1,000 mealybug-days, there is an increase in the percentage of closed kernel and closed blank of 0.176 and 0.283%, respectively). The data were used to determine economic injury levels, showing that for each mealybug per cluster in May there was a 4.73% reduction in crop value associated with quality and a 0.866 kg reduction in yield per tree (4.75%). © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Excitation energies of dissociating H2: A problematic case for the adiabatic approximation of time-dependent density functional theory

NASA Astrophysics Data System (ADS)

Gritsenko, O. V.; van Gisbergen, S. J. A.; Görling, A.; Baerends, E. J.

2000-11-01

Time-dependent density functional theory (TDDFT) is applied for calculation of the excitation energies of the dissociating H2 molecule. The standard TDDFT method of adiabatic local density approximation (ALDA) totally fails to reproduce the potential curve for the lowest excited singlet 1Σu+ state of H2. Analysis of the eigenvalue problem for the excitation energies as well as direct derivation of the exchange-correlation (xc) kernel fxc(r,r',ω) shows that ALDA fails due to breakdown of its simple spatially local approximation for the kernel. The analysis indicates a complex structure of the function fxc(r,r',ω), which is revealed in a different behavior of the various matrix elements K1c,1cxc (between the highest occupied Kohn-Sham molecular orbital ψ1 and virtual MOs ψc) as a function of the bond distance R(H-H). The effect of nonlocality of fxc(r,r') is modeled by using different expressions for the corresponding matrix elements of different orbitals. Asymptotically corrected ALDA (ALDA-AC) expressions for the matrix elements K12,12xc(στ) are proposed, while for other matrix elements the standard ALDA expressions are retained. This approach provides substantial improvement over the standard ALDA. In particular, the ALDA-AC curve for the lowest singlet excitation qualitatively reproduces the shape of the exact curve. It displays a minimum and approaches a relatively large positive energy at large R(H-H). ALDA-AC also produces a substantial improvement for the calculated lowest triplet excitation, which is known to suffer from the triplet instability problem of the restricted KS ground state. Failure of the ALDA for the excitation energies is related to the failure of the local density as well as generalized gradient approximations to reproduce correctly the polarizability of dissociating H2. The expression for the response function χ is derived to show the origin of the field-counteracting term in the xc potential, which is lacking in the local density and generalized gradient approximations and which is required to obtain a correct polarizability.

Kolkhoung (Pistacia khinjuk) Hull Oil and Kernel Oil as Antioxidative Vegetable Oils with High Oxidative Stability 
and Nutritional Value.

PubMed

Asnaashari, Maryam; Hashemi, Seyed Mohammad Bagher; Mehr, Hamed Mahdavian; Yousefabad, Seyed Hossein Asadi

2015-03-01

In this study, in order to introduce natural antioxidative vegetable oil in food industry, the kolkhoung hull oil and kernel oil were extracted. To evaluate their antioxidant efficiency, gas chromatography analysis of the composition of kolkhoung hull and kernel oil fatty acids and high-performance liquid chromatography analysis of tocopherols were done. Also, the oxidative stability of the oil was considered based on the peroxide value and anisidine value during heating at 100, 110 and 120 °C. Gas chromatography analysis showed that oleic acid was the major fatty acid of both types of oil (hull and kernel) and based on a low content of saturated fatty acids, high content of monounsaturated fatty acids, and the ratio of ω-6 and ω-3 polyunsaturated fatty acids, they were nutritionally well--balanced. Moreover, both hull and kernel oil showed high oxidative stability during heating, which can be attributed to high content of tocotrienols. Based on the results, kolkhoung hull oil acted slightly better than its kernel oil. However, both of them can be added to oxidation-sensitive oils to improve their shelf life.

Immigration and geographic access to prenatal clinics in Brooklyn, NY: a geographic information systems analysis.

PubMed

McLafferty, Sara; Grady, Sue

2005-04-01

We compared levels of geographic access to prenatal clinics in Brooklyn, NY, between immigrant and US-born mothers and among immigrant groups by country of birth. We used birth data to characterize the spatial distribution of mothers and kernel estimation to measure clinic density within a 2-mile radius of each mother. Results showed that geographic access to clinics differs substantially by country of birth. Certain groups (e.g., Pakistani, Bangladeshi) have relatively poor geographic access despite a high need for prenatal care.

Effect of different ripening stages on walnut kernel quality: antioxidant activities, lipid characterization and antibacterial properties.

PubMed

Amin, Furheen; Masoodi, F A; Baba, Waqas N; Khan, Asma Ashraf; Ganie, Bashir Ahmad

2017-11-01

Packing tissue between and around the kernel halves just turning brown (PTB) is a phenological indicator of kernel ripening at harvest in walnuts. The effect of three ripening stages (Pre-PTB, PTB and Post-PTB) on kernel quality characteristics, mineral composition, lipid characterization, sensory analysis, antioxidant and antibacterial activity were investigated in fresh kernels of indigenous numbered walnut selection of Kashmir valley "SKAU-02". Proximate composition, physical properties and sensory analysis of walnut kernels showed better results for Pre-PTB and PTB while higher mineral content was seen for kernels at Post-PTB stage in comparison to other stages of ripening. Kernels showed significantly higher levels of Omega-3 PUFA (C18:3 n3 ) and low n6/n3 ratio when harvested at Pre-PTB and PTB stages. The highest phenolic content and antioxidant activity was observed at the first stage of ripening and a steady decrease was observed at later stages. TBARS values increased as ripening advanced but did not show any significant difference in malonaldehyde formation during early ripening stages whereas it showed marked increase in walnut kernels at post-PTB stage. Walnut extracts inhibited growth of Gram-positive bacteria ( B. cereus, B. subtilis, and S. aureus ) with respective MICs of 1, 1 and 5 mg/mL and gram negative bacteria ( E. coli, P. and K. pneumonia ) with MIC of 100 mg/mL. Zone of inhibition obtained against all the bacterial strains from walnut kernel extracts increased with increase in the stage of ripening. It is concluded that Pre-PTB harvest stage with higher antioxidant activities, better fatty acid profile and consumer acceptability could be preferred harvesting stage for obtaining functionally superior walnut kernels.

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.

PubMed

Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K

2016-03-01

Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically.

graphkernels: R and Python packages for graph comparison

PubMed Central

Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-01-01

Abstract Summary Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. Availability and implementation The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. Contact mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch Supplementary information Supplementary data are available online at Bioinformatics. PMID:29028902

graphkernels: R and Python packages for graph comparison.

PubMed

Sugiyama, Mahito; Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-02-01

Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch. Supplementary data are available online at Bioinformatics. © The Author(s) 2017. Published by Oxford University Press.

Comparative histological and transcriptional analysis of maize kernels infected with Aspergillus flavus and Fusarium verticillioides

USDA-ARS?s Scientific Manuscript database

Aspergillus flavus and Fusarium verticillioides infect maize kernels and contaminate them with the mycotoxins aflatoxin and fumonisin, respectively. Combined histological examination of fungal colonization and transcriptional changes in maize kernels at 4, 12, 24, 48, and 72 hours post inoculation (...

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Time-frequency analysis of electric motors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bentley, C.L.; Dunn, M.E.; Mattingly, J.K.

1995-12-31

Physical signals such as the current of an electric motor become nonstationary as a consequence of degraded operation and broken parts. In this instance, their power spectral densities become time dependent, and time-frequency analysis techniques become the appropriate tools for signal analysis. The first among these techniques, generally called the short-time Fourier transform (STFT) method, is the Gabor transform 2 (GT) of a signal S(t), which decomposes the signal into time-local frequency modes: where the window function, {Phi}(t-{tau}), is a normalized Gaussian. Alternatively, one can decompose the signal into its multi-resolution representation at different levels of magnification. This representation ismore » achieved by the continuous wavelet transform (CWT) where the function g(t) is a kernel of zero average belonging to a family of scaled and shifted wavelet kernels. The CWT can be interpreted as the action of a microscope that locates the signal by the shift parameter b and adjusts its magnification by changing the scale parameter a. The Fourier-transformed CWT, W,{sub g}(a, {omega}), acts as a filter that places the high-frequency content of a signal into the lower end of the scale spectrum and vice versa for the low frequencies. Signals from a motor in three different states were analyzed.« less

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evidence-Based Kernels: Fundamental Units of Behavioral Influence

ERIC Educational Resources Information Center

Embry, Dennis D.; Biglan, Anthony

2008-01-01

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior-influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of…

Considering causal genes in the genetic dissection of kernel traits in common wheat.

PubMed

Mohler, Volker; Albrecht, Theresa; Castell, Adelheid; Diethelm, Manuela; Schweizer, Günther; Hartl, Lorenz

2016-11-01

Genetic factors controlling thousand-kernel weight (TKW) were characterized for their association with other seed traits, including kernel width, kernel length, ratio of kernel width to kernel length (KW/KL), kernel area, and spike number per m 2 (SN). For this purpose, a genetic map was established utilizing a doubled haploid population derived from a cross between German winter wheat cultivars Pamier and Format. Association studies in a diversity panel of elite cultivars supplemented genetic analysis of kernel traits. In both populations, genomic signatures of 13 candidate genes for TKW and kernel size were analyzed. Major quantitative trait loci (QTL) for TKW were identified on chromosomes 1B, 2A, 2D, and 4D, and their locations coincided with major QTL for kernel size traits, supporting the common belief that TKW is a function of other kernel traits. The QTL on chromosome 2A was associated with TKW candidate gene TaCwi-A1 and the QTL on chromosome 4D was associated with dwarfing gene Rht-D1. A minor QTL for TKW on chromosome 6B coincided with TaGW2-6B. The QTL for kernel dimensions that did not affect TKW were detected on eight chromosomes. A major QTL for KW/KL located at the distal tip of chromosome arm 5AS is being reported for the first time. TaSus1-7A and TaSAP-A1, closely linked to each other on chromosome 7A, could be related to a minor QTL for KW/KL. Genetic analysis of SN confirmed its negative correlation with TKW in this cross. In the diversity panel, TaSus1-7A was associated with TKW. Compared to the Pamier/Format bi-parental population where TaCwi-A1a was associated with higher TKW, the same allele reduced grain yield in the diversity panel, suggesting opposite effects of TaCwi-A1 on these two traits.

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Efficient exact-exchange time-dependent density-functional theory methods and their relation to time-dependent Hartree-Fock.

PubMed

Hesselmann, Andreas; Görling, Andreas

2011-01-21

A recently introduced time-dependent exact-exchange (TDEXX) method, i.e., a response method based on time-dependent density-functional theory that treats the frequency-dependent exchange kernel exactly, is reformulated. In the reformulated version of the TDEXX method electronic excitation energies can be calculated by solving a linear generalized eigenvalue problem while in the original version of the TDEXX method a laborious frequency iteration is required in the calculation of each excitation energy. The lowest eigenvalues of the new TDEXX eigenvalue equation corresponding to the lowest excitation energies can be efficiently obtained by, e.g., a version of the Davidson algorithm appropriate for generalized eigenvalue problems. Alternatively, with the help of a series expansion of the new TDEXX eigenvalue equation, standard eigensolvers for large regular eigenvalue problems, e.g., the standard Davidson algorithm, can be used to efficiently calculate the lowest excitation energies. With the help of the series expansion as well, the relation between the TDEXX method and time-dependent Hartree-Fock is analyzed. Several ways to take into account correlation in addition to the exact treatment of exchange in the TDEXX method are discussed, e.g., a scaling of the Kohn-Sham eigenvalues, the inclusion of (semi)local approximate correlation potentials, or hybrids of the exact-exchange kernel with kernels within the adiabatic local density approximation. The lowest lying excitations of the molecules ethylene, acetaldehyde, and pyridine are considered as examples.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness.

Pollen flow in the wildservice tree, Sorbus torminalis (L.) Crantz. II. Pollen dispersal and heterogeneity in mating success inferred from parent-offspring analysis.

PubMed

Oddou-Muratorio, Sylvie; Klein, Etienne K; Austerlitz, Frédéric

2005-12-01

Knowing the extent of gene movements from parents to offspring is essential to understand the potential of a species to adapt rapidly to a changing environment, and to design appropriate conservation strategies. In this study, we develop a nonlinear statistical model to jointly estimate the pollen dispersal kernel and the heterogeneity in fecundity among phenotypically or environmentally defined groups of males. This model uses genotype data from a sample of fruiting plants, a sample of seeds harvested on each of these plants, and all males within a circumscribed area. We apply this model to a scattered, entomophilous woody species, Sorbus torminalis (L.) Crantz, within a natural population covering more than 470 ha. We estimate a high heterogeneity in male fecundity among ecological groups, both due to phenotype (size of the trees and flowering intensity) and landscape factors (stand density within the neighbourhood). We also show that fat-tailed kernels are the most appropriate to depict the important abilities of long-distance pollen dispersal for this species. Finally, our results reveal that the spatial position of a male with respect to females affects as much its mating success as ecological determinants of male fecundity. Our study thus stresses the interest to account for the dispersal kernel when estimating heterogeneity in male fecundity, and reciprocally.

Ultralow dose dentomaxillofacial CT imaging and iterative reconstruction techniques: variability of Hounsfield units and contrast-to-noise ratio

PubMed Central

Bischel, Alexander; Stratis, Andreas; Kakar, Apoorv; Bosmans, Hilde; Jacobs, Reinhilde; Gassner, Eva-Maria; Puelacher, Wolfgang; Pauwels, Ruben

2016-01-01

Objective: The aim of this study was to evaluate whether application of ultralow dose protocols and iterative reconstruction technology (IRT) influence quantitative Hounsfield units (HUs) and contrast-to-noise ratio (CNR) in dentomaxillofacial CT imaging. Methods: A phantom with inserts of five types of materials was scanned using protocols for (a) a clinical reference for navigated surgery (CT dose index volume 36.58 mGy), (b) low-dose sinus imaging (18.28 mGy) and (c) four ultralow dose imaging (4.14, 2.63, 0.99 and 0.53 mGy). All images were reconstructed using: (i) filtered back projection (FBP); (ii) IRT: adaptive statistical iterative reconstruction-50 (ASIR-50), ASIR-100 and model-based iterative reconstruction (MBIR); and (iii) standard (std) and bone kernel. Mean HU, CNR and average HU error after recalibration were determined. Each combination of protocols was compared using Friedman analysis of variance, followed by Dunn's multiple comparison test. Results: Pearson's sample correlation coefficients were all >0.99. Ultralow dose protocols using FBP showed errors of up to 273 HU. Std kernels had less HU variability than bone kernels. MBIR reduced the error value for the lowest dose protocol to 138 HU and retained the highest relative CNR. ASIR could not demonstrate significant advantages over FBP. Conclusions: Considering a potential dose reduction as low as 1.5% of a std protocol, ultralow dose protocols and IRT should be further tested for clinical dentomaxillofacial CT imaging. Advances in knowledge: HU as a surrogate for bone density may vary significantly in CT ultralow dose imaging. However, use of std kernels and MBIR technology reduce HU error values and may retain the highest CNR. PMID:26859336

UNVEILING THE NATURE OF THE UNIDENTIFIED GAMMA-RAY SOURCES. V. ANALYSIS OF THE RADIO CANDIDATES WITH THE KERNEL DENSITY ESTIMATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Massaro, F.; Funk, S.; D'Abrusco, R.

2013-11-01

Nearly one-third of the γ-ray sources detected by Fermi are still unidentified, despite significant recent progress in this area. However, all of the γ-ray extragalactic sources associated in the second Fermi-LAT catalog have a radio counterpart. Motivated by this observational evidence, we investigate all the radio sources of the major radio surveys that lie within the positional uncertainty region of the unidentified γ-ray sources (UGSs) at a 95% level of confidence. First, we search for their infrared counterparts in the all-sky survey performed by the Wide-field Infrared Survey Explorer (WISE) and then we analyze their IR colors in comparison withmore » those of the known γ-ray blazars. We propose a new approach, on the basis of a two-dimensional kernel density estimation technique in the single [3.4] – [4.6] – [12] μm WISE color-color plot, replacing the constraint imposed in our previous investigations on the detection at 22 μm of each potential IR counterpart of the UGSs with associated radio emission. The main goal of this analysis is to find distant γ-ray blazar candidates that, being too faint at 22 μm, are not detected by WISE and thus are not selected by our purely IR-based methods. We find 55 UGSs that likely correspond to radio sources with blazar-like IR signatures. An additional 11 UGSs that have blazar-like IR colors have been found within the sample of sources found with deep recent Australia Telescope Compact Array observations.« less

Identification of Damaged Wheat Kernels and Cracked-Shell Hazelnuts with Impact Acoustics Time-Frequency Patterns

USDA-ARS?s Scientific Manuscript database

A new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with ...

Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain

ERIC Educational Resources Information Center

Hannagan, Thomas; Grainger, Jonathan

2012-01-01

It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jakel, Scholkopf, & Wichmann, 2009). We point out that "String kernels," initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal…

Unsupervised multiple kernel learning for heterogeneous data integration.

PubMed

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Defect Analysis Of Quality Palm Kernel Meal Using Statistical Quality Control In Kernels Factory

NASA Astrophysics Data System (ADS)

Sembiring, M. T.; Marbun, N. J.

2018-04-01

The production quality has an important impact retain the totality of characteristics of a product or service to pay attention to its capabilities to meet the needs that have been established. Quality criteria Palm Kernel Meal (PKM) set Factory kernel is as follows: oil content: max 8.50%, water content: max 12,00% and impurity content: max 4.00% While the average quality of the oil content of 8.94%, the water content of 5.51%, and 8.45% impurity content. To identify the defective product quality PKM produced, then used a method of analysis using Statistical Quality Control (SQC). PKM Plant Quality Kernel shows the oil content was 0.44% excess of a predetermined maximum value, and 4.50% impurity content. With excessive PKM content of oil and dirt cause disability content of production for oil, amounted to 854.6078 kg PKM and 8643.193 kg impurity content of PKM. Analysis of the results of cause and effect diagram and SQC, the factors that lead to poor quality of PKM is Ampere second press oil expeller and hours second press oil expeller.

Analysis and fabrication of tungsten CERMET materials for ultra-high temperature reactor applications via pulsed electric current sintering

NASA Astrophysics Data System (ADS)

Webb, Jonathan A.

The optimized development path for the fabrication of ultra-high temperature W-UO2 CERMET fuel elements were explored within this dissertation. A robust literature search was conducted, which concluded that a W-UO 2 fuel element must contain a fine tungsten microstructure and spherical UO2 kernels throughout the entire consolidation process. Combined Monte Carlo and Computational Fluid Dynamics (CFD) analysis were used to determine the effects of rhenium and gadolinia additions on the performance of W-UO 2 fuel elements at refractory temperatures and in dry and water submerged environments. The computational analysis also led to the design of quasi-optimized fuel elements that can meet thermal-hydraulic and neutronic requirements A rigorous set of experiments were conducted to determine if Pulsed Electric Current Sintering (PECS) can fabricate tungsten and W-Ce02 specimens to the required geometries, densities and microstructures required for high temperature fuel elements as well as determine the mechanisms involved within the PECS consolidation process. The CeO2 acts as a surrogate for UO 2 fuel kernels in these experiments. The experiments seemed to confirm that PECS consolidation takes place via diffusional mass transfer methods; however, the densification process is rapidly accelerated due to the effects of current densities within the consolidating specimen. Fortunately the grain growth proceeds at a traditional rate and the PECS process can yield near fully dense W and W-Ce02 specimens with a finer microstructure than other sintering techniques. PECS consolidation techniques were also shown to be capable of producing W-UO2 segments at near-prototypic geometries; however, great care must be taken to coat the fuel particles with tungsten prior to sintering. Also, great care must be taken to ensure that the particles remain spherical in geometry under the influence of a uniaxial stress as applied during PECS, which involves mixing different fuel kernel sizes in order to reduce the porosity in the initial green compact. Particle mixing techniques were also shown to be capable of producing consolidated CERMETs, but with a less than desirable microstructure. The work presented herin will help in the development of very high temperature reactors for terrestrial and space missions in the future.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adelman, Jahred A.

A measurement of the top quark mass in pmore » $$\\bar{p}$$ collisions at √s = 1.96 TeV is presented. The analysis uses a template method, in which the overconstrained kinematics of the Lepton+Jets channel of the t$$\\bar{t}$$ system are used to measure a single quantity, the reconstructed top quark mass, that is strongly correlated with the true top quark mass. in addition, the dijet mass of the hadronically decaying W boson is used to constrain in situ the uncertain jet energy scale in the CDF detector. Two-dimensional probability density functions are derived using a kernel density estimate-based machinery. Using 1.9 fb -1 of data, the top quark mass is measured to be 171.8$$+1.9\\atop{-1.9}$$(stat.) ± 1.0(syst.)GeV/c 2.« less

Equalizing resolution in smoothed-particle hydrodynamics calculations using self-adaptive sinc kernels

NASA Astrophysics Data System (ADS)

García-Senz, Domingo; Cabezón, Rubén M.; Escartín, José A.; Ebinger, Kevin

2014-10-01

Context. The smoothed-particle hydrodynamics (SPH) technique is a numerical method for solving gas-dynamical problems. It has been applied to simulate the evolution of a wide variety of astrophysical systems. The method has a second-order accuracy, with a resolution that is usually much higher in the compressed regions than in the diluted zones of the fluid. Aims: We propose and check a method to balance and equalize the resolution of SPH between high- and low-density regions. This method relies on the versatility of a family of interpolators called sinc kernels, which allows increasing the interpolation quality by varying only a single parameter (the exponent of the sinc function). Methods: The proposed method was checked and validated through a number of numerical tests, from standard one-dimensional Riemann problems in shock tubes, to multidimensional simulations of explosions, hydrodynamic instabilities, and the collapse of a Sun-like polytrope. Results: The analysis of the hydrodynamical simulations suggests that the scheme devised to equalize the accuracy improves the treatment of the post-shock regions and, in general, of the rarefacted zones of fluids while causing no harm to the growth of hydrodynamic instabilities. The method is robust and easy to implement with a low computational overload. It conserves mass, energy, and momentum and reduces to the standard SPH scheme in regions of the fluid that have smooth density gradients.

A comparative study of outlier detection for large-scale traffic data by one-class SVM and kernel density estimation

NASA Astrophysics Data System (ADS)

Ngan, Henry Y. T.; Yung, Nelson H. C.; Yeh, Anthony G. O.

2015-02-01

This paper aims at presenting a comparative study of outlier detection (OD) for large-scale traffic data. The traffic data nowadays are massive in scale and collected in every second throughout any modern city. In this research, the traffic flow dynamic is collected from one of the busiest 4-armed junction in Hong Kong in a 31-day sampling period (with 764,027 vehicles in total). The traffic flow dynamic is expressed in a high dimension spatial-temporal (ST) signal format (i.e. 80 cycles) which has a high degree of similarities among the same signal and across different signals in one direction. A total of 19 traffic directions are identified in this junction and lots of ST signals are collected in the 31-day period (i.e. 874 signals). In order to reduce its dimension, the ST signals are firstly undergone a principal component analysis (PCA) to represent as (x,y)-coordinates. Then, these PCA (x,y)-coordinates are assumed to be conformed as Gaussian distributed. With this assumption, the data points are further to be evaluated by (a) a correlation study with three variant coefficients, (b) one-class support vector machine (SVM) and (c) kernel density estimation (KDE). The correlation study could not give any explicit OD result while the one-class SVM and KDE provide average 59.61% and 95.20% DSRs, respectively.

Graph wavelet alignment kernels for drug virtual screening.

PubMed

Smalter, Aaron; Huan, Jun; Lushington, Gerald

2009-06-01

In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.

Kernels, Degrees of Freedom, and Power Properties of Quadratic Distance Goodness-of-Fit Tests

PubMed Central

Lindsay, Bruce G.; Markatou, Marianthi; Ray, Surajit

2014-01-01

In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it. This leads to a method akin to the Neyman-Pearson lemma for constructing optimal kernels for specific alternatives. We then introduce a midpower analysis as a device for choosing optimal degrees of freedom for a family of alternatives of interest. Finally, we introduce a new diffusion kernel, called the Pearson-normal kernel, and study the extent to which the normal approximation to the power of tests based on this kernel is valid. Supplementary materials for this article are available online. PMID:24764609

Floating macro-litter along the Mediterranean French coast: Composition, density, distribution and overlap with cetacean range.

PubMed

Di-Méglio, Nathalie; Campana, Ilaria

2017-05-15

This study investigated the composition, density and distribution of floating macro-litter along the Liguro-Provençal basin with respect to cetaceans presence. Survey transects were performed in summer between 2006 and 2015 from sailing vessels with simultaneous cetaceans observations. During 5171km travelled, 1993 floating items were recorded, widespread in the whole study area. Plastics was the predominant category, with bags/packaging always representing >45% of total items. Overall mean density (14.98 items/km 2 ) was stable with significant increase reported only in 2010-2011; monthly analysis showed lower litter densities in July-September, suggesting possible seasonal patterns. Kernel density estimation for plastics revealed ubiquitous distribution rather than high accumulation areas, mainly due to the circulation dynamics of this area. The presence range of cetaceans (259 sightings, 6 species) corresponded by ~50% with plastic distribution, indicating high potential of interaction, especially in the eastern part of the area, but effective risks for marine species might be underrepresented. Copyright © 2017 Elsevier Ltd. All rights reserved.

A spatial analysis of health-related resources in three diverse metropolitan areas

PubMed Central

Smiley, Melissa J.; Diez Roux, Ana V.; Brines, Shannon J.; Brown, Daniel G.; Evenson, Kelly R.; Rodriguez, Daniel A.

2010-01-01

Few studies have investigated the spatial clustering of multiple health-related resources. We constructed 0.5-mile kernel densities of resources for census areas in New York City, NY (n=819 block groups), Baltimore, MD (n=737), and Winston-Salem, NC (n=169). Three of the four resource densities (supermarkets/produce stores, retail areas, and recreational facilities) tended to be correlated with each other, whereas park density was less consistently and sometimes negatively correlated with the others. Blacks were more likely to live in block groups with multiple low resource densities. Spatial regression models showed that block groups with higher proportions of black residents tended to have lower supermarket/produce, retail, and recreational facility densities, although these associations did not always achieve statistical significance. A measure that combined local and neighboring block group racial composition was often a stronger predictor of resources than the local measure alone. Overall, our results from three diverse U.S. cities show that health-related resources are not randomly distributed across space and that disadvantage in multiple domains often clusters with residential racial patterning. PMID:20478737

Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

Descriptive statistics and correlation analysis of agronomic traits in a maize recombinant inbred line population.

PubMed

Zhang, H M; Hui, G Q; Luo, Q; Sun, Y; Liu, X H

2014-01-21

Maize (Zea mays L.) is one of the most important crops in the world. In this study, 13 agronomic traits of a recombinant inbred line population that was derived from the cross between Mo17 and Huangzao4 were investigated in maize: ear diameter, ear length, ear axis diameter, ear weight, plant height, ear height, days to pollen shed (DPS), days to silking (DS), the interval between DPS and DS, 100-kernel weight, kernel test weight, ear kernel weight, and kernel rate. Furthermore, the descriptive statistics and correlation analysis of the 13 traits were performed using the SPSS 11.5 software. The results providing the phenotypic data here are needed for the quantitative trait locus mapping of these agronomic traits.

Recombination Analysis of Herpes Simplex Virus 1 Reveals a Bias toward GC Content and the Inverted Repeat Regions

PubMed Central

Lee, Kyubin; Kolb, Aaron W.; Sverchkov, Yuriy; Cuellar, Jacqueline A.; Craven, Mark

2015-01-01

ABSTRACT Herpes simplex virus 1 (HSV-1) causes recurrent mucocutaneous ulcers and is the leading cause of infectious blindness and sporadic encephalitis in the United States. HSV-1 has been shown to be highly recombinogenic; however, to date, there has been no genome-wide analysis of recombination. To address this, we generated 40 HSV-1 recombinants derived from two parental strains, OD4 and CJ994. The 40 OD4-CJ994 HSV-1 recombinants were sequenced using the Illumina sequencing system, and recombination breakpoints were determined for each of the recombinants using the Bootscan program. Breakpoints occurring in the terminal inverted repeats were excluded from analysis to prevent double counting, resulting in a total of 272 breakpoints in the data set. By placing windows around the 272 breakpoints followed by Monte Carlo analysis comparing actual data to simulated data, we identified a recombination bias toward both high GC content and intergenic regions. A Monte Carlo analysis also suggested that recombination did not appear to be responsible for the generation of the spontaneous nucleotide mutations detected following sequencing. Additionally, kernel density estimation analysis across the genome found that the large, inverted repeats comprise a recombination hot spot. IMPORTANCE Herpes simplex virus 1 (HSV-1) virus is the leading cause of sporadic encephalitis and blinding keratitis in developed countries. HSV-1 has been shown to be highly recombinogenic, and recombination itself appears to be a significant component of genome replication. To date, there has been no genome-wide analysis of recombination. Here we present the findings of the first genome-wide study of recombination performed by generating and sequencing 40 HSV-1 recombinants derived from the OD4 and CJ994 parental strains, followed by bioinformatics analysis. Recombination breakpoints were determined, yielding 272 breakpoints in the full data set. Kernel density analysis determined that the large inverted repeats constitute a recombination hot spot. Additionally, Monte Carlo analyses found biases toward high GC content and intergenic and repetitive regions. PMID:25926637

Modularized seismic full waveform inversion based on waveform sensitivity kernels - The software package ASKI

NASA Astrophysics Data System (ADS)

Schumacher, Florian; Friederich, Wolfgang; Lamara, Samir; Gutt, Phillip; Paffrath, Marcel

2015-04-01

We present a seismic full waveform inversion concept for applications ranging from seismological to enineering contexts, based on sensitivity kernels for full waveforms. The kernels are derived from Born scattering theory as the Fréchet derivatives of linearized frequency-domain full waveform data functionals, quantifying the influence of elastic earth model parameters and density on the data values. For a specific source-receiver combination, the kernel is computed from the displacement and strain field spectrum originating from the source evaluated throughout the inversion domain, as well as the Green function spectrum and its strains originating from the receiver. By storing the wavefield spectra of specific sources/receivers, they can be re-used for kernel computation for different specific source-receiver combinations, optimizing the total number of required forward simulations. In the iterative inversion procedure, the solution of the forward problem, the computation of sensitivity kernels and the derivation of a model update is held completely separate. In particular, the model description for the forward problem and the description of the inverted model update are kept independent. Hence, the resolution of the inverted model as well as the complexity of solving the forward problem can be iteratively increased (with increasing frequency content of the inverted data subset). This may regularize the overall inverse problem and optimizes the computational effort of both, solving the forward problem and computing the model update. The required interconnection of arbitrary unstructured volume and point grids is realized by generalized high-order integration rules and 3D-unstructured interpolation methods. The model update is inferred solving a minimization problem in a least-squares sense, resulting in Gauss-Newton convergence of the overall inversion process. The inversion method was implemented in the modularized software package ASKI (Analysis of Sensitivity and Kernel Inversion), which provides a generalized interface to arbitrary external forward modelling codes. So far, the 3D spectral-element code SPECFEM3D (Tromp, Komatitsch and Liu, 2008) and the 1D semi-analytical code GEMINI (Friederich and Dalkolmo, 1995) in both, Cartesian and spherical framework are supported. The creation of interfaces to further forward codes is planned in the near future. ASKI is freely available under the terms of the GPL at www.rub.de/aski . Since the independent modules of ASKI must communicate via file output/input, large storage capacities need to be accessible conveniently. Storing the complete sensitivity matrix to file, however, permits the scientist full manual control over each step in a customized procedure of sensitivity/resolution analysis and full waveform inversion. In the presentation, we will show some aspects of the theory behind the full waveform inversion method and its practical realization by the software package ASKI, as well as synthetic and real-data applications from different scales and geometries.

Nonparametric Fine Tuning of Mixtures: Application to Non-Life Insurance Claims Distribution Estimation

NASA Astrophysics Data System (ADS)

Sardet, Laure; Patilea, Valentin

When pricing a specific insurance premium, actuary needs to evaluate the claims cost distribution for the warranty. Traditional actuarial methods use parametric specifications to model claims distribution, like lognormal, Weibull and Pareto laws. Mixtures of such distributions allow to improve the flexibility of the parametric approach and seem to be quite well-adapted to capture the skewness, the long tails as well as the unobserved heterogeneity among the claims. In this paper, instead of looking for a finely tuned mixture with many components, we choose a parsimonious mixture modeling, typically a two or three-component mixture. Next, we use the mixture cumulative distribution function (CDF) to transform data into the unit interval where we apply a beta-kernel smoothing procedure. A bandwidth rule adapted to our methodology is proposed. Finally, the beta-kernel density estimate is back-transformed to recover an estimate of the original claims density. The beta-kernel smoothing provides an automatic fine-tuning of the parsimonious mixture and thus avoids inference in more complex mixture models with many parameters. We investigate the empirical performance of the new method in the estimation of the quantiles with simulated nonnegative data and the quantiles of the individual claims distribution in a non-life insurance application.

Correction of scatter in megavoltage cone-beam CT

NASA Astrophysics Data System (ADS)

Spies, L.; Ebert, M.; Groh, B. A.; Hesse, B. M.; Bortfeld, T.

2001-03-01

The role of scatter in a cone-beam computed tomography system using the therapeutic beam of a medical linear accelerator and a commercial electronic portal imaging device (EPID) is investigated. A scatter correction method is presented which is based on a superposition of Monte Carlo generated scatter kernels. The kernels are adapted to both the spectral response of the EPID and the dimensions of the phantom being scanned. The method is part of a calibration procedure which converts the measured transmission data acquired for each projection angle into water-equivalent thicknesses. Tomographic reconstruction of the projections then yields an estimate of the electron density distribution of the phantom. It is found that scatter produces cupping artefacts in the reconstructed tomograms. Furthermore, reconstructed electron densities deviate greatly (by about 30%) from their expected values. The scatter correction method removes the cupping artefacts and decreases the deviations from 30% down to about 8%.

KERNELHR: A program for estimating animal home ranges

USGS Publications Warehouse

Seaman, D.E.; Griffith, B.; Powell, R.A.

1998-01-01

Kernel methods are state of the art for estimating animal home-range area and utilization distribution (UD). The KERNELHR program was developed to provide researchers and managers a tool to implement this extremely flexible set of methods with many variants. KERNELHR runs interactively or from the command line on any personal computer (PC) running DOS. KERNELHR provides output of fixed and adaptive kernel home-range estimates, as well as density values in a format suitable for in-depth statistical and spatial analyses. An additional package of programs creates contour files for plotting in geographic information systems (GIS) and estimates core areas of ranges.

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Use of Subsonic Kernel Function in an Influence-Coefficient Method of Aeroelastic Analysis and some Comparisons with Experiment

NASA Technical Reports Server (NTRS)

Sewall, John L.; Herr, Robert W.; Watkins, Charles E.

1960-01-01

This paper illustrates the development and application of an influence-coefficient method of analysis for calculating the response of a flexible wing in an airstream to an oscillating disturbing force and for treating such aeroelastic instabilities as flutter and divergence. Aerodynamic coefficients are derived on the basis of lifting - surface theory for subsonic compressible flow by use of the method presented in NASA Technical Report R-48. Application of the analysis is made to a uniform cantilever wing- tip tank configuration for which responses to a sinusoidal disturbing force and flutter speeds were measured over a range of subsonic Mach numbers and densities. Calculated responses and flutter speeds based on flexibility influence coefficients measured at nine stations are in good agreement with experiment, provided the aerodynamic load is distributed over the wing so that local centers of pressure very nearly coincide with these nine influence stations. The use of experimental values of bending and torsional structural damping coefficients in the analysis generally improved the agreement between calculated and experimental responses. Some calculations were made to study the effects on density on responses near the flutter conditions, and linear response trends were obtained over a wide range of densities.

3D local feature BKD to extract road information from mobile laser scanning point clouds

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Liu, Yuan; Dong, Zhen; Liang, Fuxun; Li, Bijun; Peng, Xiangyang

2017-08-01

Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise.

Benchmarks for detecting 'breakthroughs' in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation.

PubMed

Miladinovic, Branko; Kumar, Ambuj; Mhaskar, Rahul; Djulbegovic, Benjamin

2014-10-21

To understand how often 'breakthroughs,' that is, treatments that significantly improve health outcomes, can be developed. We applied weighted adaptive kernel density estimation to construct the probability density function for observed treatment effects from five publicly funded cohorts and one privately funded group. 820 trials involving 1064 comparisons and enrolling 331,004 patients were conducted by five publicly funded cooperative groups. 40 cancer trials involving 50 comparisons and enrolling a total of 19,889 patients were conducted by GlaxoSmithKline. We calculated that the probability of detecting treatment with large effects is 10% (5-25%), and that the probability of detecting treatment with very large treatment effects is 2% (0.3-10%). Researchers themselves judged that they discovered a new, breakthrough intervention in 16% of trials. We propose these figures as the benchmarks against which future development of 'breakthrough' treatments should be measured. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Study of the convergence behavior of the complex kernel least mean square algorithm.

PubMed

Paul, Thomas K; Ogunfunmi, Tokunbo

2013-09-01

The complex kernel least mean square (CKLMS) algorithm is recently derived and allows for online kernel adaptive learning for complex data. Kernel adaptive methods can be used in finding solutions for neural network and machine learning applications. The derivation of CKLMS involved the development of a modified Wirtinger calculus for Hilbert spaces to obtain the cost function gradient. We analyze the convergence of the CKLMS with different kernel forms for complex data. The expressions obtained enable us to generate theory-predicted mean-square error curves considering the circularity of the complex input signals and their effect on nonlinear learning. Simulations are used for verifying the analysis results.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels.

PubMed

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Characterization and fine mapping of qkc7.03: a major locus for kernel cracking in maize.

PubMed

Yang, Mingtao; Chen, Lin; Wu, Xun; Gao, Xing; Li, Chunhui; Song, Yanchun; Zhang, Dengfeng; Shi, Yunsu; Li, Yu; Li, Yong-Xiang; Wang, Tianyu

2018-02-01

A major locus conferring kernel cracking in maize was characterized and fine mapped to an interval of 416.27 kb. Meanwhile, combining the results of transcriptomic analysis, the candidate gene was inferred. Seed development requires a proper structural and physiological balance between the maternal tissues and the internal structures of the seeds. In maize, kernel cracking is a disorder in this balance that seriously limits quality and yield and is characterized by a cracked pericarp at the kernel top and endosperm everting. This study elucidated the genetic basis and characterization of kernel cracking. Primarily, a near isogenic line (NIL) with a B73 background exhibited steady kernel cracking across environments. Therefore, deprived mapping populations were developed from this NIL and its recurrent parent B73. A major locus on chromosome 7, qkc7.03, was identified to be associated with the cracking performance. According to a progeny test of recombination events, qkc7.03 was fine mapped to a physical interval of 416.27 kb. In addition, obvious differences were observed in embryo development and starch granule arrangement within the endosperm between the NIL and its recurrent parent upon the occurrence of kernel cracking. Moreover, compared to its recurrent parent, the transcriptome of the NIL showed a significantly down-regulated expression of genes related to zeins, carbohydrate synthesis and MADS-domain transcription factors. The transcriptomic analysis revealed ten annotated genes within the target region of qkc7.03, and only GRMZM5G899476 was differently expressed between the NIL and its recurrent parent, indicating that this gene might be a candidate gene for kernel cracking. The results of this study facilitate the understanding of the potential mechanism underlying kernel cracking in maize.

On the solution of integral equations with a generalized cauchy kernel

NASA Technical Reports Server (NTRS)

Kaya, A. C.; Erdogan, F.

1986-01-01

In this paper a certain class of singular integral equations that may arise from the mixed boundary value problems in nonhomogeneous materials is considered. The distinguishing feature of these equations is that in addition to the Cauchy singularity, the kernels contain terms that are singular only at the end points. In the form of the singular integral equations adopted, the density function is a potential or a displacement and consequently the kernel has strong singularities of the form (t-x) sup-2, x sup n-2 (t+x) sup n, (n or = 2, 0x,tb). The complex function theory is used to determine the fundamental function of the problem for the general case and a simple numerical technique is described to solve the integral equation. Two examples from the theory of elasticity are then considered to show the application of the technique.

Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2016-02-03

A giant leap has been made in the past couple of decades with the introduction of kernel-based learning as a mainstay for designing effective nonlinear computational learning algorithms. In view of the geometric interpretation of conditional expectation and the ubiquity of multiscale characteristics in highly complex nonlinear dynamic systems [1]-[3], this paper presents a new orthogonal projection operator wavelet kernel, aiming at developing an efficient computational learning approach for nonlinear dynamical system identification. In the framework of multiresolution analysis, the proposed projection operator wavelet kernel can fulfill the multiscale, multidimensional learning to estimate complex dependencies. The special advantage of the projection operator wavelet kernel developed in this paper lies in the fact that it has a closed-form expression, which greatly facilitates its application in kernel learning. To the best of our knowledge, it is the first closed-form orthogonal projection wavelet kernel reported in the literature. It provides a link between grid-based wavelets and mesh-free kernel-based methods. Simulation studies for identifying the parallel models of two benchmark nonlinear dynamical systems confirm its superiority in model accuracy and sparsity.

A survey of kernel-type estimators for copula and their applications

NASA Astrophysics Data System (ADS)

Sumarjaya, I. W.

2017-10-01

Copulas have been widely used to model nonlinear dependence structure. Main applications of copulas include areas such as finance, insurance, hydrology, rainfall to name but a few. The flexibility of copula allows researchers to model dependence structure beyond Gaussian distribution. Basically, a copula is a function that couples multivariate distribution functions to their one-dimensional marginal distribution functions. In general, there are three methods to estimate copula. These are parametric, nonparametric, and semiparametric method. In this article we survey kernel-type estimators for copula such as mirror reflection kernel, beta kernel, transformation method and local likelihood transformation method. Then, we apply these kernel methods to three stock indexes in Asia. The results of our analysis suggest that, albeit variation in information criterion values, the local likelihood transformation method performs better than the other kernel methods.

Thermal Density Functional Theory: Time-Dependent Linear Response and Approximate Functionals from the Fluctuation-Dissipation Theorem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pribram-Jones, Aurora; Grabowski, Paul E.; Burke, Kieron

We present that the van Leeuwen proof of linear-response time-dependent density functional theory (TDDFT) is generalized to thermal ensembles. This allows generalization to finite temperatures of the Gross-Kohn relation, the exchange-correlation kernel of TDDFT, and fluctuation dissipation theorem for DFT. Finally, this produces a natural method for generating new thermal exchange-correlation approximations.

Thermal Density Functional Theory: Time-Dependent Linear Response and Approximate Functionals from the Fluctuation-Dissipation Theorem

DOE PAGES

Pribram-Jones, Aurora; Grabowski, Paul E.; Burke, Kieron

2016-06-08

We present that the van Leeuwen proof of linear-response time-dependent density functional theory (TDDFT) is generalized to thermal ensembles. This allows generalization to finite temperatures of the Gross-Kohn relation, the exchange-correlation kernel of TDDFT, and fluctuation dissipation theorem for DFT. Finally, this produces a natural method for generating new thermal exchange-correlation approximations.

Gaussian processes with optimal kernel construction for neuro-degenerative clinical onset prediction

NASA Astrophysics Data System (ADS)

Canas, Liane S.; Yvernault, Benjamin; Cash, David M.; Molteni, Erika; Veale, Tom; Benzinger, Tammie; Ourselin, Sébastien; Mead, Simon; Modat, Marc

2018-02-01

Gaussian Processes (GP) are a powerful tool to capture the complex time-variations of a dataset. In the context of medical imaging analysis, they allow a robust modelling even in case of highly uncertain or incomplete datasets. Predictions from GP are dependent of the covariance kernel function selected to explain the data variance. To overcome this limitation, we propose a framework to identify the optimal covariance kernel function to model the data.The optimal kernel is defined as a composition of base kernel functions used to identify correlation patterns between data points. Our approach includes a modified version of the Compositional Kernel Learning (CKL) algorithm, in which we score the kernel families using a new energy function that depends both the Bayesian Information Criterion (BIC) and the explained variance score. We applied the proposed framework to model the progression of neurodegenerative diseases over time, in particular the progression of autosomal dominantly-inherited Alzheimer's disease, and use it to predict the time to clinical onset of subjects carrying genetic mutation.

Achillea millefolium L. extract mediated green synthesis of waste peach kernel shell supported silver nanoparticles: Application of the nanoparticles for catalytic reduction of a variety of dyes in water.

PubMed

Khodadadi, Bahar; Bordbar, Maryam; Nasrollahzadeh, Mahmoud

2017-05-01

In this paper, silver nanoparticles (Ag NPs) are synthesized using Achillea millefolium L. extract as reducing and stabilizing agents and peach kernel shell as an environmentally benign support. FT-IR spectroscopy, UV-Vis spectroscopy, X-ray Diffraction (XRD), Field emission scanning electron microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDS), Thermo gravimetric-differential thermal analysis (TG-DTA) and Transmission Electron Microscopy (TEM) were used to characterize peach kernel shell, Ag NPs, and Ag NPs/peach kernel shell. The catalytic activity of the Ag NPs/peach kernel shell was investigated for the reduction of 4-nitrophenol (4-NP), Methyl Orange (MO), and Methylene Blue (MB) at room temperature. Ag NPs/peach kernel shell was found to be a highly active catalyst. In addition, Ag NPs/peach kernel shell can be recovered and reused several times with no significant loss of its catalytic activity. Copyright © 2017 Elsevier Inc. All rights reserved.

Selection and properties of alternative forming fluids for TRISO fuel kernel production

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, M. P.; King, J. C.; Gorman, B. P.

2013-01-01

Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardousmore » alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of ~10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1- bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 °C and 80 °C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory fuel kernels.« less

Selection and properties of alternative forming fluids for TRISO fuel kernel production

NASA Astrophysics Data System (ADS)

Baker, M. P.; King, J. C.; Gorman, B. P.; Marshall, D. W.

2013-01-01

Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardous alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of ˜10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1-bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 °C and 80 °C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory fuel kernels.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement in practice.

Exploiting induced variation to dissect quantitative traits in barley.

PubMed

Druka, Arnis; Franckowiak, Jerome; Lundqvist, Udda; Bonar, Nicola; Alexander, Jill; Guzy-Wrobelska, Justyna; Ramsay, Luke; Druka, Ilze; Grant, Iain; Macaulay, Malcolm; Vendramin, Vera; Shahinnia, Fahimeh; Radovic, Slobodanka; Houston, Kelly; Harrap, David; Cardle, Linda; Marshall, David; Morgante, Michele; Stein, Nils; Waugh, Robbie

2010-04-01

The identification of genes underlying complex quantitative traits such as grain yield by means of conventional genetic analysis (positional cloning) requires the development of several large mapping populations. However, it is possible that phenotypically related, but more extreme, allelic variants generated by mutational studies could provide a means for more efficient cloning of QTLs (quantitative trait loci). In barley (Hordeum vulgare), with the development of high-throughput genome analysis tools, efficient genome-wide identification of genetic loci harbouring mutant alleles has recently become possible. Genotypic data from NILs (near-isogenic lines) that carry induced or natural variants of genes that control aspects of plant development can be compared with the location of QTLs to potentially identify candidate genes for development--related traits such as grain yield. As yield itself can be divided into a number of allometric component traits such as tillers per plant, kernels per spike and kernel size, mutant alleles that both affect these traits and are located within the confidence intervals for major yield QTLs may represent extreme variants of the underlying genes. In addition, the development of detailed comparative genomic models based on the alignment of a high-density barley gene map with the rice and sorghum physical maps, has enabled an informed prioritization of 'known function' genes as candidates for both QTLs and induced mutant genes.

Reproducing Kernel Particle Method in Plasticity of Pressure-Sensitive Material with Reference to Powder Forming Process

NASA Astrophysics Data System (ADS)

Khoei, A. R.; Samimi, M.; Azami, A. R.

2007-02-01

In this paper, an application of the reproducing kernel particle method (RKPM) is presented in plasticity behavior of pressure-sensitive material. The RKPM technique is implemented in large deformation analysis of powder compaction process. The RKPM shape function and its derivatives are constructed by imposing the consistency conditions. The essential boundary conditions are enforced by the use of the penalty approach. The support of the RKPM shape function covers the same set of particles during powder compaction, hence no instability is encountered in the large deformation computation. A double-surface plasticity model is developed in numerical simulation of pressure-sensitive material. The plasticity model includes a failure surface and an elliptical cap, which closes the open space between the failure surface and hydrostatic axis. The moving cap expands in the stress space according to a specified hardening rule. The cap model is presented within the framework of large deformation RKPM analysis in order to predict the non-uniform relative density distribution during powder die pressing. Numerical computations are performed to demonstrate the applicability of the algorithm in modeling of powder forming processes and the results are compared to those obtained from finite element simulation to demonstrate the accuracy of the proposed model.

Quantification and classification of neuronal responses in kernel-smoothed peristimulus time histograms

PubMed Central

Fried, Itzhak; Koch, Christof

2014-01-01

Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron's actual response envelope. We here develop a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. We tested the efficacy of the h-coefficient in a large data set of Monte Carlo simulated smoothed peristimulus time histograms with varying response amplitudes, response durations, trial numbers, and baseline firing rates. Across all these conditions, the h-coefficient significantly outperformed more classical classifiers, with a mean false alarm rate of 0.004 and a mean hit rate of 0.494. We also tested the h-coefficient's performance in a set of neuronal responses recorded in humans. The algorithm behind the h-coefficient provides various opportunities for further adaptation and the flexibility to target specific parameters in a given data set. Our findings confirm that the h-coefficient can provide a conservative and powerful tool for the analysis of peristimulus time histograms with great potential for future development. PMID:25475352

LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

PubMed

Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

2018-05-07

Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

Incorporating temporal variation in seabird telemetry data: time variant kernel density models

USGS Publications Warehouse

Gilbert, Andrew; Adams, Evan M.; Anderson, Carl; Berlin, Alicia; Bowman, Timothy D.; Connelly, Emily; Gilliland, Scott; Gray, Carrie E.; Lepage, Christine; Meattey, Dustin; Montevecchi, William; Osenkowski, Jason; Savoy, Lucas; Stenhouse, Iain; Williams, Kathryn

2015-01-01

A key component of the Mid-Atlantic Baseline Studies project was tracking the individual movements of focal marine bird species (Red-throated Loon [Gavia stellata], Northern Gannet [Morus bassanus], and Surf Scoter [Melanitta perspicillata]) through the use of satellite telemetry. This element of the project was a collaborative effort with the Department of Energy (DOE), Bureau of Ocean Energy Management (BOEM), the U.S. Fish and Wildlife Service (USFWS), and Sea Duck Joint Venture (SDJV), among other organizations. Satellite telemetry is an effective and informative tool for understanding individual animal movement patterns, allowing researchers to mark an individual once, and thereafter follow the movements of the animal in space and time. Aggregating telemetry data from multiple individuals can provide information about the spatial use and temporal movements of populations. Tracking data is three dimensional, with the first two dimensions, X and Y, ordered along the third dimension, time. GIS software has many capabilities to store, analyze and visualize the location information, but little or no support for visualizing the temporal data, and tools for processing temporal data are lacking. We explored several ways of analyzing the movement patterns using the spatiotemporal data provided by satellite tags. Here, we present the results of one promising method: time-variant kernel density analysis (Keating and Cherry, 2009). The goal of this chapter is to demonstrate new methods in spatial analysis to visualize and interpret tracking data for a large number of individual birds across time in the mid-Atlantic study area and beyond. In this chapter, we placed greater emphasis on analytical methods than on the behavior and ecology of the animals tracked. For more detailed examinations of the ecology and wintering habitat use of the focal species in the midAtlantic, see Chapters 20-22.

Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - part A.

PubMed

Sinha, Shriprakash

2017-12-04

Ever since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials, augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues. This manuscript ∙ explores the strength of contributing factors in the signaling pathway, ∙ analyzes the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and ∙ investigates the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices. The results show the advantage of using density based indices over variance based indices mainly due to the former's employment of distance measures & the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis & representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs & reveal hidden biological information within colorectal cancer samples.

Mutual information estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rehfeld, K.; Marwan, N.; Heitzig, J.; Kurths, J.

2012-04-01

For the automated, objective and joint analysis of time series, similarity measures are crucial. Used in the analysis of climate records, they allow for a complimentary, unbiased view onto sparse datasets. The irregular sampling of many of these time series, however, makes it necessary to either perform signal reconstruction (e.g. interpolation) or to develop and use adapted measures. Standard linear interpolation comes with an inevitable loss of information and bias effects. We have recently developed a Gaussian kernel-based correlation algorithm with which the interpolation error can be substantially lowered, but this would not work should the functional relationship in a bivariate setting be non-linear. We therefore propose an algorithm to estimate lagged auto and cross mutual information from irregularly sampled time series. We have extended the standard and adaptive binning histogram estimators and use Gaussian distributed weights in the estimation of the (joint) probabilities. To test our method we have simulated linear and nonlinear auto-regressive processes with Gamma-distributed inter-sampling intervals. We have then performed a sensitivity analysis for the estimation of actual coupling length, the lag of coupling and the decorrelation time in the synthetic time series and contrast our results to the performance of a signal reconstruction scheme. Finally we applied our estimator to speleothem records. We compare the estimated memory (or decorrelation time) to that from a least-squares estimator based on fitting an auto-regressive process of order 1. The calculated (cross) mutual information results are compared for the different estimators (standard or adaptive binning) and contrasted with results from signal reconstruction. We find that the kernel-based estimator has a significantly lower root mean square error and less systematic sampling bias than the interpolation-based method. It is possible that these encouraging results could be further improved by using non-histogram mutual information estimators, like k-Nearest Neighbor or Kernel-Density estimators, but for short (<1000 points) and irregularly sampled datasets the proposed algorithm is already a great improvement.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

Evaluating and interpreting the chemical relevance of the linear response kernel for atoms II: open shell.

PubMed

Boisdenghien, Zino; Fias, Stijn; Van Alsenoy, Christian; De Proft, Frank; Geerlings, Paul

2014-07-28

Most of the work done on the linear response kernel χ(r,r') has focussed on its atom-atom condensed form χAB. Our previous work [Boisdenghien et al., J. Chem. Theory Comput., 2013, 9, 1007] was the first effort to truly focus on the non-condensed form of this function for closed (sub)shell atoms in a systematic fashion. In this work, we extend our method to the open shell case. To simplify the plotting of our results, we average our results to a symmetrical quantity χ(r,r'). This allows us to plot the linear response kernel for all elements up to and including argon and to investigate the periodicity throughout the first three rows in the periodic table and in the different representations of χ(r,r'). Within the context of Spin Polarized Conceptual Density Functional Theory, the first two-dimensional plots of spin polarized linear response functions are presented and commented on for some selected cases on the basis of the atomic ground state electronic configurations. Using the relation between the linear response kernel and the polarizability we compare the values of the polarizability tensor calculated using our method to high-level values.

Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

PubMed Central

Ruan, Peiying; Hayashida, Morihiro; Maruyama, Osamu; Akutsu, Tatsuya

2013-01-01

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes. PMID:23776458

Knowledge Driven Image Mining with Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Compound analysis via graph kernels incorporating chirality.

PubMed

Brown, J B; Urata, Takashi; Tamura, Takeyuki; Arai, Midori A; Kawabata, Takeo; Akutsu, Tatsuya

2010-12-01

High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit different biological characteristics. In this paper, we propose a new method that extends the recently developed tree pattern graph kernel to accommodate stereoisomers. We show that Support Vector Regression (SVR) with a chiral graph kernel is useful for target property prediction by demonstrating its application to a set of human vitamin D receptor ligands currently under consideration for their potential anti-cancer effects.

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

PubMed

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Enriched reproducing kernel particle method for fractional advection-diffusion equation

NASA Astrophysics Data System (ADS)

Ying, Yuping; Lian, Yanping; Tang, Shaoqiang; Liu, Wing Kam

2018-06-01

The reproducing kernel particle method (RKPM) has been efficiently applied to problems with large deformations, high gradients and high modal density. In this paper, it is extended to solve a nonlocal problem modeled by a fractional advection-diffusion equation (FADE), which exhibits a boundary layer with low regularity. We formulate this method on a moving least-square approach. Via the enrichment of fractional-order power functions to the traditional integer-order basis for RKPM, leading terms of the solution to the FADE can be exactly reproduced, which guarantees a good approximation to the boundary layer. Numerical tests are performed to verify the proposed approach.

A Si IV/O IV Electron Density Diagnostic for the Analysis of IRIS Solar Spectra

NASA Astrophysics Data System (ADS)

Young, P. R.; Keenan, F. P.; Milligan, R. O.; Peter, H.

2018-04-01

Solar spectra of ultraviolet bursts and flare ribbons from the Interface Region Imaging Spectrograph (IRIS) have suggested high electron densities of > {10}12 cm‑3 at transition region temperatures of 0.1 MK, based on large intensity ratios of Si IV λ1402.77 to O IV λ1401.16. In this work, a rare observation of the weak O IV λ1343.51 line is reported from an X-class flare that peaked at 21:41 UT on 2014 October 24. This line is used to develop a theoretical prediction of the Si IV λ1402.77 to O IV λ1401.16 ratio as a function of density that is recommended to be used in the high-density regime. The method makes use of new pressure-dependent ionization fractions that take account of the suppression of dielectronic recombination at high densities. It is applied to two sequences of flare kernel observations from the October 24 flare. The first shows densities that vary between 3× {10}12 and 3× {10}13 cm‑3 over a seven-minute period, while the second location shows stable density values of around 2× {10}12 cm‑3 over a three-minute period.

Determination of active ingredients in corn silk, leaf, and kernel by capillary electrophoresis with electrochemicaI detection.

PubMed

Lin, Miao; Chu, Qing-Cui; Tian, Xiu-Hui; Ye, Jian-Nong

2007-01-01

Corn has been known for its accumulation of flavones and phenolic acids. However, many parts of corn, except kernel, have not drawn much attention. In this work, a method based on capillary zone electrophoresis with electrochemical detection has been used for the separation and determination of epicatechin, rutin, ascorbic acid (Vc), kaempferol, chlorogenic acid, and quercetin in corn silk, leaf, and kernel. The distribution comparison of the ingredients among silk, leaf, and kernel is discussed. Several important factors--including running buffer acidity, separation voltage, and working electrode potential--were evaluated to acquire the optimum analysis conditions. Under the optimum conditions, the analytes could be well separated within 19 min in a 40-mmol/L borate buffer (pH 9.2). The response was linear over three orders of magnitude with detection limits (S/N = 3) ranging from 4.97 x 10(-8) to 9.75 x 10(-8) g/mL. The method has been successfully applied for the analysis of corn silk, leaf, and kernel with satisfactory results.

Calculation of plasma dielectric response in inhomogeneous magnetic field near electron cyclotron resonance

NASA Astrophysics Data System (ADS)

Evstatiev, Evstati; Svidzinski, Vladimir; Spencer, Andy; Galkin, Sergei

2014-10-01

Full wave 3-D modeling of RF fields in hot magnetized nonuniform plasma requires calculation of nonlocal conductivity kernel describing the dielectric response of such plasma to the RF field. In many cases, the conductivity kernel is a localized function near the test point which significantly simplifies numerical solution of the full wave 3-D problem. Preliminary results of feasibility analysis of numerical calculation of the conductivity kernel in a 3-D hot nonuniform magnetized plasma in the electron cyclotron frequency range will be reported. This case is relevant to modeling of ECRH in ITER. The kernel is calculated by integrating the linearized Vlasov equation along the unperturbed particle's orbits. Particle's orbits in the nonuniform equilibrium magnetic field are calculated numerically by one of the Runge-Kutta methods. RF electric field is interpolated on a specified grid on which the conductivity kernel is discretized. The resulting integrals in the particle's initial velocity and time are then calculated numerically. Different optimization approaches of the integration are tested in this feasibility analysis. Work is supported by the U.S. DOE SBIR program.

A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

PubMed

Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

2015-09-01

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.

Computed tomography coronary stent imaging with iterative reconstruction: a trade-off study between medium kernel and sharp kernel.

PubMed

Zhou, Qijing; Jiang, Biao; Dong, Fei; Huang, Peiyu; Liu, Hongtao; Zhang, Minming

2014-01-01

To evaluate the improvement of iterative reconstruction in image space (IRIS) technique in computed tomographic (CT) coronary stent imaging with sharp kernel, and to make a trade-off analysis. Fifty-six patients with 105 stents were examined by 128-slice dual-source CT coronary angiography (CTCA). Images were reconstructed using standard filtered back projection (FBP) and IRIS with both medium kernel and sharp kernel applied. Image noise and the stent diameter were investigated. Image noise was measured both in background vessel and in-stent lumen as objective image evaluation. Image noise score and stent score were performed as subjective image evaluation. The CTCA images reconstructed with IRIS were associated with significant noise reduction compared to that of CTCA images reconstructed using FBP technique in both of background vessel and in-stent lumen (the background noise decreased by approximately 25.4% ± 8.2% in medium kernel (P

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population.

PubMed

Cheng, Ruiru; Kong, Zhongxin; Zhang, Liwei; Xie, Quan; Jia, Haiyan; Yu, Dong; Huang, Yulong; Ma, Zhengqiang

2017-07-01

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement. Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419 × Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

Maternal and child mortality indicators across 187 countries of the world: converging or diverging.

PubMed

Goli, Srinivas; Arokiasamy, Perianayagam

2014-01-01

This study reassessed the progress achieved since 1990 in maternal and child mortality indicators to test whether the progress is converging or diverging across countries worldwide. The convergence process is examined using standard parametric and non-parametric econometric models of convergence. The results of absolute convergence estimates reveal that progress in maternal and child mortality indicators is diverging for the entire period of 1990-2010 [maternal mortality ratio (MMR) - β = .00033, p < .574; neonatal mortality rate (NNMR) - β = .04367, p < .000; post-neonatal mortality rate (PNMR) - β = .02677, p < .000; under-five mortality rate (U5MR) - β = .00828, p < .000)]. In the recent period, such divergence is replaced with convergence for MMR but diverged for all the child mortality indicators. The results of Kernel density estimate reveal considerable reduction in divergence of MMR for the recent period; however, the Kernel density distribution plots show more than one 'peak' which indicates the emergence of convergence clubs based on their mortality levels. For child mortality indicators, the Kernel estimates suggest that divergence is in progress across the countries worldwide but tended to converge for countries with low mortality levels. A mere progress in global averages of maternal and child mortality indicators among a global cross-section of countries does not warranty convergence unless there is a considerable reduction in variance, skewness and range of change.

SPHYNX: an accurate density-based SPH method for astrophysical applications

NASA Astrophysics Data System (ADS)

Cabezón, R. M.; García-Senz, D.; Figueira, J.

2017-10-01

Aims: Hydrodynamical instabilities and shocks are ubiquitous in astrophysical scenarios. Therefore, an accurate numerical simulation of these phenomena is mandatory to correctly model and understand many astrophysical events, such as supernovas, stellar collisions, or planetary formation. In this work, we attempt to address many of the problems that a commonly used technique, smoothed particle hydrodynamics (SPH), has when dealing with subsonic hydrodynamical instabilities or shocks. To that aim we built a new SPH code named SPHYNX, that includes many of the recent advances in the SPH technique and some other new ones, which we present here. Methods: SPHYNX is of Newtonian type and grounded in the Euler-Lagrange formulation of the smoothed-particle hydrodynamics technique. Its distinctive features are: the use of an integral approach to estimating the gradients; the use of a flexible family of interpolators called sinc kernels, which suppress pairing instability; and the incorporation of a new type of volume element which provides a better partition of the unity. Unlike other modern formulations, which consider volume elements linked to pressure, our volume element choice relies on density. SPHYNX is, therefore, a density-based SPH code. Results: A novel computational hydrodynamic code oriented to Astrophysical applications is described, discussed, and validated in the following pages. The ensuing code conserves mass, linear and angular momentum, energy, entropy, and preserves kernel normalization even in strong shocks. In our proposal, the estimation of gradients is enhanced using an integral approach. Additionally, we introduce a new family of volume elements which reduce the so-called tensile instability. Both features help to suppress the damp which often prevents the growth of hydrodynamic instabilities in regular SPH codes. Conclusions: On the whole, SPHYNX has passed the verification tests described below. For identical particle setting and initial conditions the results were similar (or better in some particular cases) than those obtained with other SPH schemes such as GADGET-2, PSPH or with the recent density-independent formulation (DISPH) and conservative reproducing kernel (CRKSPH) techniques.

An actuarial approach to retrofit savings in buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Subbarao, Krishnappa; Etingov, Pavel V.; Reddy, T. A.

An actuarial method has been developed for determining energy savings from retrofits from energy use data for a number of buildings. This method should be contrasted with the traditional method of using pre- and post-retrofit data on the same building. This method supports the U.S. Department of Energy Building Performance Database of real building performance data and related tools that enable engineering and financial practitioners to evaluate retrofits. The actuarial approach derives, from the database, probability density functions (PDFs) for energy savings from retrofits by creating peer groups for the user’s pre post buildings. From the energy use distribution ofmore » the two groups, the savings PDF is derived. This provides the basis for engineering analysis as well as financial risk analysis leading to investment decisions. Several technical issues are addressed: The savings PDF is obtained from the pre- and post-PDF through a convolution. Smoothing using kernel density estimation is applied to make the PDF more realistic. The low data density problem can be mitigated through a neighborhood methodology. Correlations between pre and post buildings are addressed to improve the savings PDF. Sample size effects are addressed through the Kolmogorov--Smirnov tests and quantile-quantile plots.« less

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

PubMed

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

EBSD and Nanoindentation-Correlated Study of Delamination Fracture in Al-Li Alloy 2090

NASA Technical Reports Server (NTRS)

Tayon, Wesley A.; Crooks, Roy E.; Domack, Marcia S.; Wagner, John A.; Elmustafa, A. A.

2008-01-01

Al-Li alloys offer attractive combinations of high strength and low density. However, a tendency for delamination fracture has limited their use. A better understanding of the delamination mechanisms may identify methods to control delaminations through processing modifications. A combination of new techniques has been used to evaluate delamination fracture in Al-Li alloys. Both high quality electron backscattered diffraction (EBSD) information and valid nanoindentation measurements were obtained from fractured test specimens. Correlations were drawn between nano-scale hardness variations and local texture along delaminating boundaries. Intriguing findings were observed for delamination fracture through the combined analysis of grain orientation, Taylor factor, and kernel average misorientation.

Suspended liquid particle disturbance on laser-induced blast wave and low density distribution

NASA Astrophysics Data System (ADS)

Ukai, Takahiro; Zare-Behtash, Hossein; Kontis, Konstantinos

2017-12-01

The impurity effect of suspended liquid particles on the laser-induced gas breakdown was experimentally investigated in quiescent gas. The focus of this study is the investigation of the influence of the impurities on the shock wave structure as well as the low density distribution. A 532 nm Nd:YAG laser beam with an 188 mJ/pulse was focused on the chamber filled with suspended liquid particles 0.9 ± 0.63 μm in diameter. Several shock waves are generated by multiple gas breakdowns along the beam path in the breakdown with particles. Four types of shock wave structures can be observed: (1) the dual blast waves with a similar shock radius, (2) the dual blast waves with a large shock radius at the lower breakdown, (3) the dual blast waves with a large shock radius at the upper breakdown, and (4) the triple blast waves. The independent blast waves interact with each other and enhance the shock strength behind the shock front in the lateral direction. The triple blast waves lead to the strongest shock wave in all cases. The shock wave front that propagates toward the opposite laser focal spot impinges on one another, and thereafter a transmitted shock wave (TSW) appears. The TSW interacts with the low density core called a kernel; the kernel then longitudinally expands quickly due to a Richtmyer-Meshkov-like instability. The laser-particle interaction causes an increase in the kernel volume which is approximately five times as large as that in the gas breakdown without particles. In addition, the laser-particle interaction can improve the laser energy efficiency.

The effect of CT technical factors on quantification of lung fissure integrity

NASA Astrophysics Data System (ADS)

Chong, D.; Brown, M. S.; Ochs, R.; Abtin, F.; Brown, M.; Ordookhani, A.; Shaw, G.; Kim, H. J.; Gjertson, D.; Goldin, J. G.

2009-02-01

A new emphysema treatment uses endobronchial valves to perform lobar volume reduction. The degree of fissure completeness may predict treatment efficacy. This study investigated the behavior of a semiautomated algorithm for quantifying lung fissure integrity in CT with respect to reconstruction kernel and dose. Raw CT data was obtained for six asymptomatic patients from a high-risk population for lung cancer. The patients were scanned on either a Siemens Sensation 16 or 64, using a low-dose protocol of 120 kVp, 25 mAs. Images were reconstructed using kernels ranging from smooth to sharp (B10f, B30f, B50f, B70f). Research software was used to simulate an even lower-dose acquisition of 15 mAs, and images were generated at the same kernels resulting in 8 series per patient. The left major fissure was manually contoured axially at regular intervals, yielding 37 contours across all patients. These contours were read into an image analysis and pattern classification system which computed a Fissure Integrity Score (FIS) for each kernel and dose. FIS values were analyzed using a mixed-effects model with kernel and dose as fixed effects and patient as random effect to test for difference due to kernel and dose. Analysis revealed no difference in FIS between the smooth kernels (B10f, B30f) nor between sharp kernels (B50f, B70f), but there was a significant difference between the sharp and smooth groups (p = 0.020). There was no significant difference in FIS between the two low-dose reconstructions (p = 0.882). Using a cutoff of 90%, the number of incomplete fissures increased from 5 to 10 when the imaging protocol changed from B50f to B30f. Reconstruction kernel has a significant effect on quantification of fissure integrity in CT. This has potential implications when selecting patients for endobronchial valve therapy.

Carbothermic Synthesis of 820 m UN Kernels: Literature Review, Thermodynamics, Analysis, and Related Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lindemer, Terrence; Voit, Stewart L; Silva, Chinthaka M

2014-01-01

The U.S. Department of Energy is considering a new nuclear fuel that would be less susceptible to ruptures during a loss-of-coolant accident. The fuel would consist of tristructural isotropic coated particles with large, dense uranium nitride (UN) kernels. This effort explores many factors involved in using gel-derived uranium oxide-carbon microspheres to make large UN kernels. Analysis of recent studies with sufficient experimental details is provided. Extensive thermodynamic calculations are used to predict carbon monoxide and other pressures for several different reactions that may be involved in conversion of uranium oxides and carbides to UN. Experimentally, the method for making themore » gel-derived microspheres is described. These were used in a microbalance with an attached mass spectrometer to determine details of carbothermic conversion in argon, nitrogen, or vacuum. A quantitative model is derived from experiments for vacuum conversion to an uranium oxide-carbide kernel.« less

Phenolic constituents of shea (Vitellaria paradoxa) kernels.

PubMed

Maranz, Steven; Wiesman, Zeev; Garti, Nissim

2003-10-08

Analysis of the phenolic constituents of shea (Vitellaria paradoxa) kernels by LC-MS revealed eight catechin compounds-gallic acid, catechin, epicatechin, epicatechin gallate, gallocatechin, epigallocatechin, gallocatechin gallate, and epigallocatechin gallate-as well as quercetin and trans-cinnamic acid. The mean kernel content of the eight catechin compounds was 4000 ppm (0.4% of kernel dry weight), with a 2100-9500 ppm range. Comparison of the profiles of the six major catechins from 40 Vitellaria provenances from 10 African countries showed that the relative proportions of these compounds varied from region to region. Gallic acid was the major phenolic compound, comprising an average of 27% of the measured total phenols and exceeding 70% in some populations. Colorimetric analysis (101 samples) of total polyphenols extracted from shea butter into hexane gave an average of 97 ppm, with the values for different provenances varying between 62 and 135 ppm of total polyphenols.

Source Code Analysis Laboratory (SCALe)

DTIC Science & Technology

2012-04-01

Versus Flagged Nonconformities (FNC) Software System TP/FNC Ratio Mozilla Firefox version 2.0 6/12 50% Linux kernel version 2.6.15 10/126 8...is inappropriately tuned for analysis of the Linux kernel, which has anomalous results. Customizing SCALe to work with software for a particular...servers support a collection of virtual machines (VMs) that can be configured to support analysis in various environments, such as Windows XP and Linux . A

A comparison of explosive cyclone characteristics in recent reanalyses: NCEP CFSR, JRA-55, and ERA-Interim

NASA Astrophysics Data System (ADS)

Kita, Y.; Waseda, T.

2016-12-01

Explosive cyclones (EXPCs) were investigated in three recent reanalyses. Their tracking methods is diverse among researchers, and additionally reanalysis data they use are various. Reanalysis data are essential as initial conditions to implement a downscale simulation with high accuracy. In this study, characteristics of EXPCs in three recent reanalyses were investigated from several perspectives: track densities, minimum MSLP (Mean Sea Level Pressure), and radius of EXPCs. The tracking method of extratropical cyclones (ECs) is to track local minimum of MSLP. The domain is limited to Eastern Asia and the North Pacific Ocean (lat20°:70°, lon100°:200°), and target period is 2000-2014. Fig.1 shows that the frequencies of EXPCs, which is defined as ECs whose MSLP drops by over 12hPa in 12hours, are greatly different, noting that extracted EXPCs are those whose most deepening phases were located around Japan (lat20°:60°, lon110°:160°). In addition, they are dissimilar to those in a previous EXPCs database (Kawamura et al.) and results in weather map analyses. The differences between each frequency might be caused by MSLP at their centers: there were sometimes small gaps of a few hPa. The minimum MSLP and effective radius were also investigated, but distributions of effective radii of EXPCs did not show significant difference (Fig.2). Thus, the gaps of central MSLP just matter in the differences of their trends. To evaluate the path density of EXPCs, two-dimensional kernel density estimation was conducted. The kernel densities of EXPCs' tracks in three reanalyses seem similar: they accumulated apparently above ocean (not shown). Two-dimensional kernel densities of EXPCs' most deepening points accumulated above Sea of Japan, Kuroshio and Extension. Therefore, it is proved that there are considerable differences in numbers of EXPCs depending on reanalyses, while the general characteristics of EXPCs just have little difference. It is worthwhile to say that careful attention should be paid when researchers investigate an individual EXPC with reanalysis data.

Tobacco Retail Environments and Social Inequalities in Individual-Level Smoking and Cessation Among Scottish Adults.

PubMed

Pearce, Jamie; Rind, Esther; Shortt, Niamh; Tisch, Catherine; Mitchell, Richard

2016-02-01

Many neighborhood characteristics may constrain or enable smoking. This study investigated whether the neighborhood tobacco retail environment was associated with individual-level smoking and cessation in Scottish adults, and whether inequalities in smoking status were related to tobacco retailing. Tobacco outlet density measures were developed for neighborhoods across Scotland using the September 2012 Scottish Tobacco Retailers Register. The outlet data were cleaned and geocoded (n = 10,161) using a Geographic Information System. Kernel density estimation was used to calculate an outlet density measure for each postcode. The kernel density estimation measures were then appended to data on individuals included in the 2008-2011 Scottish Health Surveys (n = 28,751 adults aged ≥16), via their postcode. Two-level logistic regression models examined whether neighborhood density of tobacco retailing was associated with current smoking status and smoking cessation and whether there were differences in the relationship between household income and smoking status, by tobacco outlet density. After adjustment for individual- and area-level confounders, compared to residents of areas with the lowest outlet densities, those living in areas with the highest outlet densities had a 6% higher chance of being a current smoker, and a 5% lower chance of being an ex-smoker. There was little evidence to suggest that inequalities in either current smoking or cessation were narrower in areas with lower availability of tobacco retailing. The findings suggest that residents of environments with a greater availability of tobacco outlets are more likely to start and/or sustain smoking, and less likely to quit. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Non-Gaussian probabilistic MEG source localisation based on kernel density estimation☆

PubMed Central

Mohseni, Hamid R.; Kringelbach, Morten L.; Woolrich, Mark W.; Baker, Adam; Aziz, Tipu Z.; Probert-Smith, Penny

2014-01-01

There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate. PMID:24055702

Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores.

PubMed

Yao, H; Hruska, Z; Kincaid, R; Brown, R; Cleveland, T; Bhatnagar, D

2010-05-01

The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r(2), was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1-20, 20-100, and >or=100 ng g(-1) (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to 0.91 when a threshold of either 20 or 100 ng g(-1) was used. Overall, the results indicate that fluorescence hyperspectral imaging may be applicable in estimating aflatoxin content in individual corn kernels.

Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration

NASA Astrophysics Data System (ADS)

Chen, Guoxiong; Cheng, Qiuming

2016-02-01

Multi-resolution and scale-invariance have been increasingly recognized as two closely related intrinsic properties endowed in geofields such as geochemical and geophysical anomalies, and they are commonly investigated by using multiscale- and scaling-analysis methods. In this paper, the wavelet-based multiscale decomposition (WMD) method was proposed to investigate the multiscale natures of geochemical pattern from large scale to small scale. In the light of the wavelet transformation of fractal measures, we demonstrated that the wavelet approximation operator provides a generalization of box-counting method for scaling analysis of geochemical patterns. Specifically, the approximation coefficient acts as the generalized density-value in density-area fractal modeling of singular geochemical distributions. Accordingly, we presented a novel local singularity analysis (LSA) using the WMD algorithm which extends the conventional moving averaging to a kernel-based operator for implementing LSA. Finally, the novel LSA was validated using a case study dealing with geochemical data (Fe2O3) in stream sediments for mineral exploration in Inner Mongolia, China. In comparison with the LSA implemented using the moving averaging method the novel LSA using WMD identified improved weak geochemical anomalies associated with mineralization in covered area.

Urban Rain Gauge Siting Selection Based on Gis-Multicriteria Analysis

NASA Astrophysics Data System (ADS)

Fu, Yanli; Jing, Changfeng; Du, Mingyi

2016-06-01

With the increasingly rapid growth of urbanization and climate change, urban rainfall monitoring as well as urban waterlogging has widely been paid attention. In the light of conventional siting selection methods do not take into consideration of geographic surroundings and spatial-temporal scale for the urban rain gauge site selection, this paper primarily aims at finding the appropriate siting selection rules and methods for rain gauge in urban area. Additionally, for optimization gauge location, a spatial decision support system (DSS) aided by geographical information system (GIS) has been developed. In terms of a series of criteria, the rain gauge optimal site-search problem can be addressed by a multicriteria decision analysis (MCDA). A series of spatial analytical techniques are required for MCDA to identify the prospective sites. With the platform of GIS, using spatial kernel density analysis can reflect the population density; GIS buffer analysis is used to optimize the location with the rain gauge signal transmission character. Experiment results show that the rules and the proposed method are proper for the rain gauge site selection in urban areas, which is significant for the siting selection of urban hydrological facilities and infrastructure, such as water gauge.

Classification and quantification analysis of peach kernel from different origins with near-infrared diffuse reflection spectroscopy

PubMed Central

Liu, Wei; Wang, Zhen-Zhong; Qing, Jian-Ping; Li, Hong-Juan; Xiao, Wei

2014-01-01

Background: Peach kernels which contain kinds of fatty acids play an important role in the regulation of a variety of physiological and biological functions. Objective: To establish an innovative and rapid diffuse reflectance near-infrared spectroscopy (DR-NIR) analysis method along with chemometric techniques for the qualitative and quantitative determination of a peach kernel. Materials and Methods: Peach kernel samples from nine different origins were analyzed with high-performance liquid chromatography (HPLC) as a reference method. DR-NIR is in the spectral range 1100-2300 nm. Principal component analysis (PCA) and partial least squares regression (PLSR) algorithm were applied to obtain prediction models, The Savitzky-Golay derivative and first derivative were adopted for the spectral pre-processing, PCA was applied to classify the varieties of those samples. For the quantitative calibration, the models of linoleic and oleinic acids were established with the PLSR algorithm and the optimal principal component (PC) numbers were selected with leave-one-out (LOO) cross-validation. The established models were evaluated with the root mean square error of deviation (RMSED) and corresponding correlation coefficients (R2). Results: The PCA results of DR-NIR spectra yield clear classification of the two varieties of peach kernel. PLSR had a better predictive ability. The correlation coefficients of the two calibration models were above 0.99, and the RMSED of linoleic and oleinic acids were 1.266% and 1.412%, respectively. Conclusion: The DR-NIR combined with PCA and PLSR algorithm could be used efficiently to identify and quantify peach kernels and also help to solve variety problem. PMID:25422544

Computational investigation of intense short-wavelength laser interaction with rare gas clusters

NASA Astrophysics Data System (ADS)

Bigaouette, Nicolas

Current Very High Temperature Reactor designs incorporate TRi-structural ISOtropic (TRISO) particle fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel by dropping a cold precursor solution into a column of hot trichloroethylene (TCE). The temperature difference drives the liquid precursor solution to precipitate the metal solution into gel spheres before reaching the bottom of a production column. Over time, gelation byproducts inhibit complete gelation and the TCE must be purified or discarded. The resulting mixed-waste stream is expensive to dispose of or recycle, and changing the forming fluid to a non-hazardous alternative could greatly improve the economics of kernel production. Selection criteria for a replacement forming fluid narrowed a list of ~10,800 chemicals to yield ten potential replacements. The physical properties of the alternatives were measured as a function of temperature between 25 °C and 80 °C. Calculated terminal velocities and heat transfer rates provided an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane were selected for further testing, and surrogate yttria-stabilized zirconia (YSZ) kernels were produced using these selected fluids. The kernels were characterized for density, geometry, composition, and crystallinity and compared to a control group of kernels produced in silicone oil. Production in 1-bromotetradecane showed positive results, producing dense (93.8 %TD) and spherical (1.03 aspect ratio) kernels, but proper gelation did not occur in the other alternative forming fluids. With many of the YSZ kernels not properly gelling within the length of the column, this project further investigated the heat transfer properties of the forming fluids and precursor solution. A sensitivity study revealed that the heat transfer properties of the precursor solution have the strongest impact on gelation time. A COMSOL heat transfer model estimated an effective thermal diffusivity range for the YSZ precursor solution as 1.13x10 -8 m2/s to 3.35x10-8 m 2/s, which is an order of magnitude smaller than the value used in previous studies. 1-bromotetradecane is recommended for further investigation with the production of uranium-based kernels.

The Feasibility of Palm Kernel Shell as a Replacement for Coarse Aggregate in Lightweight Concrete

NASA Astrophysics Data System (ADS)

Itam, Zarina; Beddu, Salmia; Liyana Mohd Kamal, Nur; Ashraful Alam, Md; Issa Ayash, Usama

2016-03-01

Implementing sustainable materials into the construction industry is fast becoming a trend nowadays. Palm Kernel Shell is a by-product of Malaysia’s palm oil industry, generating waste as much as 4 million tons per annum. As a means of producing a sustainable, environmental-friendly, and affordable alternative in the lightweight concrete industry, the exploration of the potential of Palm Kernel Shell to be used as an aggregate replacement was conducted which may give a positive impact to the Malaysian construction industry as well as worldwide concrete usage. This research investigates the feasibility of PKS as an aggregate replacement in lightweight concrete in terms of compressive strength, slump test, water absorption, and density. Results indicate that by using PKS for aggregate replacement, it increases the water absorption but decreases the concrete workability and strength. Results however, fall into the range acceptable for lightweight aggregates, hence it can be concluded that there is potential to use PKS as aggregate replacement for lightweight concrete.

Lyman continuum observations of solar flares

NASA Technical Reports Server (NTRS)

Machado, M. E.; Noyes, R. W.

1978-01-01

A study is made of Lyman continuum observations of solar flares, using data obtained by the EUV spectroheliometer on the Apollo Telescope Mount. It is found that there are two main types of flare regions: an overall 'mean' flare coincident with the H-alpha flare region, and transient Lyman continuum kernels which can be identified with the H-alpha and X-ray kernels observed by other authors. It is found that the ground level hydrogen population in flares is closer to LTE than in the quiet sun and active regions, and that the level of Lyman continuum formation is lowered in the atmosphere from a mass column density .000005 g/sq cm in the quiet sun to .0003 g/sq cm in the mean flare, and to .001 g/sq cm in kernels. From these results the amount of chromospheric material 'evaporated' into the high temperature region is derived, which is found to be approximately 10 to the 15th g, in agreement with observations of X-ray emission measures.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations.

PubMed

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D; Flint-Garcia, Sherry A

2016-08-09

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. Copyright © 2016 Liu et al.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

PubMed Central

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D.; Flint-Garcia, Sherry A.

2016-01-01

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. PMID:27317774

Detecting peanuts inoculated with toxigenic and atoxienic Aspergillus flavus strains with fluorescence hyperspectral imagery

NASA Astrophysics Data System (ADS)

Xing, Fuguo; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Zhu, Fengle; Brown, Robert L.; Bhatnagar, Deepak; Liu, Yang

2017-05-01

Aflatoxin contamination in peanut products has been an important and long-standing problem around the world. Produced mainly by Aspergillus flavus and Aspergillus parasiticus, aflatoxins are the most toxic and carcinogenic compounds among toxins. This study investigated the application of fluorescence visible near-infrared (VNIR) hyperspectral images to assess the spectral difference between peanut kernels inoculated with toxigenic and atoxigenic inocula of A. flavus and healthy kernels. Peanut kernels were inoculated with NRRL3357, a toxigenic strain of A. flavus, and AF36, an atoxigenic strain of A. flavus, respectively. Fluorescence hyperspectral images under ultraviolet (UV) excitation were recorded on peanut kernels with and without skin. Contaminated kernels exhibited different fluorescence features compared with healthy kernels. For the kernels without skin, the inoculated kernels had a fluorescence peaks shifted to longer wavelengths with lower intensity than healthy kernels. In addition, the fluorescence intensity of peanuts without skin was higher than that of peanuts with skin (10 times). The fluorescence spectra of kernels with skin are significantly different from that of the control group (p<0.001). Furthermore, the fluorescence intensity of the toxigenic, AF3357 peanuts with skin was lower than that of the atoxigenic AF36 group. Discriminate analysis showed that the inoculation group can be separated from the controls with 100% accuracy. However, the two inoculation groups (AF3357 vis AF36) can be separated with only ∼80% accuracy. This study demonstrated the potential of fluorescence hyperspectral imaging techniques for screening of peanut kernels contaminated with A. flavus, which could potentially lead to the production of rapid and non-destructive scanning-based detection technology for the peanut industry.

Dielectric relaxation measurement and analysis of restricted water structure in rice kernels

NASA Astrophysics Data System (ADS)

Yagihara, Shin; Oyama, Mikio; Inoue, Akio; Asano, Megumi; Sudo, Seiichi; Shinyashiki, Naoki

2007-04-01

Dielectric relaxation measurements were performed for rice kernels by time domain reflectometry (TDR) with flat-end coaxial electrodes. Difficulties in good contact between the surfaces of the electrodes and the kernels are eliminated by a TDR set-up with a sample holder for a kernel, and the water content could be evaluated from relaxation curves. Dielectric measurements were performed for rice kernels, rice flour and boiled rice with various water contents, and the water amount and dynamic behaviour of water molecules were explained from restricted dynamics of water molecules and also from the τ-β (relaxation time versus the relaxation-time distribution parameter of the Cole-Cole equation) diagram. In comparison with other aqueous systems, the dynamic structure of water in moist rice is more similar to aqueous dispersion systems than to aqueous solutions.

Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery.

PubMed

Speicher, Nora K; Pfeifer, Nico

2015-06-15

Despite ongoing cancer research, available therapies are still limited in quantity and effectiveness, and making treatment decisions for individual patients remains a hard problem. Established subtypes, which help guide these decisions, are mainly based on individual data types. However, the analysis of multidimensional patient data involving the measurements of various molecular features could reveal intrinsic characteristics of the tumor. Large-scale projects accumulate this kind of data for various cancer types, but we still lack the computational methods to reliably integrate this information in a meaningful manner. Therefore, we apply and extend current multiple kernel learning for dimensionality reduction approaches. On the one hand, we add a regularization term to avoid overfitting during the optimization procedure, and on the other hand, we show that one can even use several kernels per data type and thereby alleviate the user from having to choose the best kernel functions and kernel parameters for each data type beforehand. We have identified biologically meaningful subgroups for five different cancer types. Survival analysis has revealed significant differences between the survival times of the identified subtypes, with P values comparable or even better than state-of-the-art methods. Moreover, our resulting subtypes reflect combined patterns from the different data sources, and we demonstrate that input kernel matrices with only little information have less impact on the integrated kernel matrix. Our subtypes show different responses to specific therapies, which could eventually assist in treatment decision making. An executable is available upon request. © The Author 2015. Published by Oxford University Press.

Providing Observation Context via Kernel Visualization and Informatics for Planning and Data Analysis

NASA Astrophysics Data System (ADS)

Kidd, J. N.; Selznick, S.; Hergenrother, C. W.

2018-04-01

From our lessons learned and SPICE expertise, we lay out the features and capabilities of a new web-based tool to provide an accessible platform to obtain context and informatics from a planetary mission's SPICE kernels.

An Optimal Bahadur-Efficient Method in Detection of Sparse Signals with Applications to Pathway Analysis in Sequencing Association Studies.

PubMed

Dai, Hongying; Wu, Guodong; Wu, Michael; Zhi, Degui

2016-01-01

Next-generation sequencing data pose a severe curse of dimensionality, complicating traditional "single marker-single trait" analysis. We propose a two-stage combined p-value method for pathway analysis. The first stage is at the gene level, where we integrate effects within a gene using the Sequence Kernel Association Test (SKAT). The second stage is at the pathway level, where we perform a correlated Lancaster procedure to detect joint effects from multiple genes within a pathway. We show that the Lancaster procedure is optimal in Bahadur efficiency among all combined p-value methods. The Bahadur efficiency,[Formula: see text], compares sample sizes among different statistical tests when signals become sparse in sequencing data, i.e. ε →0. The optimal Bahadur efficiency ensures that the Lancaster procedure asymptotically requires a minimal sample size to detect sparse signals ([Formula: see text]). The Lancaster procedure can also be applied to meta-analysis. Extensive empirical assessments of exome sequencing data show that the proposed method outperforms Gene Set Enrichment Analysis (GSEA). We applied the competitive Lancaster procedure to meta-analysis data generated by the Global Lipids Genetics Consortium to identify pathways significantly associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, and total cholesterol.

Geoboard and Balance Activities for the Gifted Child.

ERIC Educational Resources Information Center

Bondy, Kay W.

1979-01-01

The author describes mathematics activities for gifted children which make use of the geoboard and balance. The problem, solutions, and theoretical backing are provided for determining areas of squares, areas of irregular shapes, the weight of popped and unpopped popcorn, kernels, and liquid mass and density. (SBH)

Beyond electronegativity and local hardness: Higher-order equalization criteria for determination of a ground-state electron density.

PubMed

Ayers, Paul W; Parr, Robert G

2008-08-07

Higher-order global softnesses, local softnesses, and softness kernels are defined along with their hardness inverses. The local hardness equalization principle recently derived by the authors is extended to arbitrary order. The resulting hierarchy of equalization principles indicates that the electronegativity/chemical potential, local hardness, and local hyperhardnesses all are constant when evaluated for the ground-state electron density. The new equalization principles can be used to test whether a trial electron density is an accurate approximation to the true ground-state density and to discover molecules with desired reactive properties, as encapsulated by their chemical reactivity indicators.

Multiple kernel learning using single stage function approximation for binary classification problems

NASA Astrophysics Data System (ADS)

Shiju, S.; Sumitra, S.

2017-12-01

In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model.

Granger causality revisited

PubMed Central

Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

2014-01-01

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

Model for interevent times with long tails and multifractality in human communications: An application to financial trading

NASA Astrophysics Data System (ADS)

Perelló, Josep; Masoliver, Jaume; Kasprzak, Andrzej; Kutner, Ryszard

2008-09-01

Social, technological, and economic time series are divided by events which are usually assumed to be random, albeit with some hierarchical structure. It is well known that the interevent statistics observed in these contexts differs from the Poissonian profile by being long-tailed distributed with resting and active periods interwoven. Understanding mechanisms generating consistent statistics has therefore become a central issue. The approach we present is taken from the continuous-time random-walk formalism and represents an analytical alternative to models of nontrivial priority that have been recently proposed. Our analysis also goes one step further by looking at the multifractal structure of the interevent times of human decisions. We here analyze the intertransaction time intervals of several financial markets. We observe that empirical data describe a subtle multifractal behavior. Our model explains this structure by taking the pausing-time density in the form of a superstatistics where the integral kernel quantifies the heterogeneous nature of the executed tasks. A stretched exponential kernel provides a multifractal profile valid for a certain limited range. A suggested heuristic analytical profile is capable of covering a broader region.

Neutronics Studies of Uranium-bearing Fully Ceramic Micro-encapsulated Fuel for PWRs

DOE PAGES

George, Nathan M.; Maldonado, G. Ivan; Terrani, Kurt A.; ...

2014-12-01

Our study evaluated the neutronics and some of the fuel cycle characteristics of using uranium-based fully ceramic microencapsulated (FCM) fuel in a pressurized water reactor (PWR). Specific PWR lattice designs with FCM fuel have been developed that are expected to achieve higher specific burnup levels in the fuel while also increasing the tolerance to reactor accidents. The SCALE software system was the primary analysis tool used to model the lattice designs. A parametric study was performed by varying tristructural isotropic particle design features (e.g., kernel diameter, coating layer thicknesses, and packing fraction) to understand the impact on reactivity and resultingmore » operating cycle length. Moreover, to match the lifetime of an 18-month PWR cycle, the FCM particle fuel design required roughly 10% additional fissile material at beginning of life compared with that of a standard uranium dioxide (UO 2) rod. Uranium mononitride proved to be a favorable fuel for the fuel kernel due to its higher heavy metal loading density compared with UO 2. The FCM fuel designs evaluated maintain acceptable neutronics design features for fuel lifetime, lattice peaking factors, and nonproliferation figure of merit.« less

Improving KPCA Online Extraction by Orthonormalization in the Feature Space.

PubMed

Souza Filho, Joao B O; Diniz, Paulo S R

2018-04-01

Recently, some online kernel principal component analysis (KPCA) techniques based on the generalized Hebbian algorithm (GHA) were proposed for use in large data sets, defining kernel components using concise dictionaries automatically extracted from data. This brief proposes two new online KPCA extraction algorithms, exploiting orthogonalized versions of the GHA rule. In both the cases, the orthogonalization of kernel components is achieved by the inclusion of some low complexity additional steps to the kernel Hebbian algorithm, thus not substantially affecting the computational cost of the algorithm. Results show improved convergence speed and accuracy of components extracted by the proposed methods, as compared with the state-of-the-art online KPCA extraction algorithms.

Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis.

PubMed

Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen

2016-08-18

The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

Two-stage autoignition and edge flames in a high pressure turbulent jet

DOE PAGES

Krisman, Alex; Hawkes, Evatt R.; Chen, Jacqueline H.

2017-07-04

A three-dimensional direct numerical simulation is conducted for a temporally evolving planar jet of n-heptane at a pressure of 40 atmospheres and in a coflow of air at 1100 K. At these conditions, n-heptane exhibits a two-stage ignition due to low- and high-temperature chemistry, which is reproduced by the global chemical model used in this study. The results show that ignition occurs in several overlapping stages and multiple modes of combustion are present. Low-temperature chemistry precedes the formation of multiple spatially localised high-temperature chemistry autoignition events, referred to as ‘kernels’. These kernels form within the shear layer and core ofmore » the jet at compositions with short homogeneous ignition delay times and in locations experiencing low scalar dissipation rates. An analysis of the kernel histories shows that the ignition delay time is correlated with the mixing rate history and that the ignition kernels tend to form in vortically dominated regions of the domain, as corroborated by an analysis of the topology of the velocity gradient tensor. Once ignited, the kernels grow rapidly and establish edge flames where they envelop the stoichiometric isosurface. A combination of kernel formation (autoignition) and the growth of existing burning surface (via edge-flame propagation) contributes to the overall ignition process. In conclusion, an analysis of propagation speeds evaluated on the burning surface suggests that although the edge-flame speed is promoted by the autoignitive conditions due to an increase in the local laminar flame speed, edge-flame propagation of existing burning surfaces (triggered initially by isolated autoignition kernels) is the dominant ignition mode in the present configuration.« less

Antioxidant capacity and phenolics content of apricot (Prunus armeniaca L.) kernel as a function of genotype.

PubMed

Korekar, Girish; Stobdan, Tsering; Arora, Richa; Yadav, Ashish; Singh, Shashi Bala

2011-11-01

Fourteen apricot genotypes grown under similar cultural practices in Trans-Himalayan Ladakh region were studied to find out the influence of genotype on antioxidant capacity and total phenolic content (TPC) of apricot kernel. The kernels were found to be rich in TPC ranging from 92.2 to 162.1 mg gallic acid equivalent/100 g. The free radical-scavenging activity in terms of inhibitory concentration (IC(50)) ranged from 43.8 to 123.4 mg/ml and ferric reducing antioxidant potential (FRAP) from 154.1 to 243.6 FeSO(4).7H(2)O μg/ml. A variation of 1-1.7 fold in total phenolic content, 1-2.8 fold in IC(50) by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and 1-1.6 fold in ferric reducing antioxidant potential among the examined kernels underlines the important role played by genetic background for determining the phenolic content and antioxidant potential of apricot kernel. A positive significant correlation between TPC and FRAP (r=0.671) was found. No significant correlation was found between TPC and IC(50); FRAP and IC(50); TPC and physical properties of kernel. Principal component analysis demonstrated that genotypic effect is more pronounced towards TPC and total antioxidant capacity (TAC) content in apricot kernel while the contribution of seed and kernel physical properties are not highly significant.

Estimating peer density effects on oral health for community-based older adults.

PubMed

Chakraborty, Bibhas; Widener, Michael J; Mirzaei Salehabadi, Sedigheh; Northridge, Mary E; Kum, Susan S; Jin, Zhu; Kunzel, Carol; Palmer, Harvey D; Metcalf, Sara S

2017-12-29

As part of a long-standing line of research regarding how peer density affects health, researchers have sought to understand the multifaceted ways that the density of contemporaries living and interacting in proximity to one another influence social networks and knowledge diffusion, and subsequently health and well-being. This study examined peer density effects on oral health for racial/ethnic minority older adults living in northern Manhattan and the Bronx, New York, NY. Peer age-group density was estimated by smoothing US Census data with 4 kernel bandwidths ranging from 0.25 to 1.50 mile. Logistic regression models were developed using these spatial measures and data from the ElderSmile oral and general health screening program that serves predominantly racial/ethnic minority older adults at community centers in northern Manhattan and the Bronx. The oral health outcomes modeled as dependent variables were ordinal dentition status and binary self-rated oral health. After construction of kernel density surfaces and multiple imputation of missing data, logistic regression analyses were performed to estimate the effects of peer density and other sociodemographic characteristics on the oral health outcomes of dentition status and self-rated oral health. Overall, higher peer density was associated with better oral health for older adults when estimated using smaller bandwidths (0.25 and 0.50 mile). That is, statistically significant relationships (p < 0.01) between peer density and improved dentition status were found when peer density was measured assuming a more local social network. As with dentition status, a positive significant association was found between peer density and fair or better self-rated oral health when peer density was measured assuming a more local social network. This study provides novel evidence that the oral health of community-based older adults is affected by peer density in an urban environment. To the extent that peer density signifies the potential for social interaction and support, the positive significant effects of peer density on improved oral health point to the importance of place in promoting social interaction as a component of healthy aging. Proximity to peers and their knowledge of local resources may facilitate utilization of community-based oral health care.

Effect of mixing scanner types and reconstruction kernels on the characterization of lung parenchymal pathologies: emphysema, interstitial pulmonary fibrosis and normal non-smokers

NASA Astrophysics Data System (ADS)

Xu, Ye; van Beek, Edwin J.; McLennan, Geoffrey; Guo, Junfeng; Sonka, Milan; Hoffman, Eric

2006-03-01

In this study we utilize our texture characterization software (3-D AMFM) to characterize interstitial lung diseases (including emphysema) based on MDCT generated volumetric data using 3-dimensional texture features. We have sought to test whether the scanner and reconstruction filter (kernel) type affect the classification of lung diseases using the 3-D AMFM. We collected MDCT images in three subject groups: emphysema (n=9), interstitial pulmonary fibrosis (IPF) (n=10), and normal non-smokers (n=9). In each group, images were scanned either on a Siemens Sensation 16 or 64-slice scanner, (B50f or B30 recon. kernel) or a Philips 4-slice scanner (B recon. kernel). A total of 1516 volumes of interest (VOIs; 21x21 pixels in plane) were marked by two chest imaging experts using the Iowa Pulmonary Analysis Software Suite (PASS). We calculated 24 volumetric features. Bayesian methods were used for classification. Images from different scanners/kernels were combined in all possible combinations to test how robust the tissue classification was relative to the differences in image characteristics. We used 10-fold cross validation for testing the result. Sensitivity, specificity and accuracy were calculated. One-way Analysis of Variances (ANOVA) was used to compare the classification result between the various combinations of scanner and reconstruction kernel types. This study yielded a sensitivity of 94%, 91%, 97%, and 93% for emphysema, ground-glass, honeycombing, and normal non-smoker patterns respectively using a mixture of all three subject groups. The specificity for these characterizations was 97%, 99%, 99%, and 98%, respectively. The F test result of ANOVA shows there is no significant difference (p <0.05) between different combinations of data with respect to scanner and convolution kernel type. Since different MDCT and reconstruction kernel types did not show significant differences in regards to the classification result, this study suggests that the 3-D AMFM can be generally introduced.

Error and Complexity Analysis for a Collocation-Grid-Projection Plus Precorrected-FFT Algorithm for Solving Potential Integral Equations with LaPlace or Helmholtz Kernels

NASA Technical Reports Server (NTRS)

Phillips, J. R.

1996-01-01

In this paper we derive error bounds for a collocation-grid-projection scheme tuned for use in multilevel methods for solving boundary-element discretizations of potential integral equations. The grid-projection scheme is then combined with a precorrected FFT style multilevel method for solving potential integral equations with 1/r and e(sup ikr)/r kernels. A complexity analysis of this combined method is given to show that for homogeneous problems, the method is order n natural log n nearly independent of the kernel. In addition, it is shown analytically and experimentally that for an inhomogeneity generated by a very finely discretized surface, the combined method slows to order n(sup 4/3). Finally, examples are given to show that the collocation-based grid-projection plus precorrected-FFT scheme is competitive with fast-multipole algorithms when considering realistic problems and 1/r kernels, but can be used over a range of spatial frequencies with only a small performance penalty.

Morphometric evaluation of the Afşin-Elbistan lignite basin using kernel density estimation and Getis-Ord's statistics of DEM derived indices, SE Turkey

NASA Astrophysics Data System (ADS)

Sarp, Gulcan; Duzgun, Sebnem

2015-11-01

A morphometric analysis of river network, basins and relief using geomorphic indices and geostatistical analyses of Digital Elevation Model (DEM) are useful tools for discussing the morphometric evolution of the basin area. In this study, three different indices including valley floor width to height ratio (Vf), stream gradient (SL), and stream sinuosity were applied to Afşin-Elbistan lignite basin to test the imprints of tectonic activity. Perturbations of these indices are usually indicative of differences in the resistance of outcropping lithological units to erosion and active faulting. To map the clusters of high and low indices values, the Kernel density estimation (K) and the Getis-Ord Gi∗ statistics were applied to the DEM-derived indices. The K method and Gi∗ statistic highlighting hot spots and cold spots of the SL index, the stream sinuosity and the Vf index values helped to identify the relative tectonic activity of the basin area. The results indicated that the estimation by the K and Gi∗ including three conceptualization of spatial relationships (CSR) for hot spots (percent volume contours 50 and 95 categorized as high and low respectively) yielded almost similar results in regions of high tectonic activity and low tectonic activity. According to the K and Getis-Ord Gi∗ statistics, the northern, northwestern and southern parts of the basin indicates a high tectonic activity. On the other hand, low elevation plain in the central part of the basin area shows a relatively low tectonic activity.

The use of kernel density estimators in breakthrough curve reconstruction and advantages in risk analysis

NASA Astrophysics Data System (ADS)

Siirila, E. R.; Fernandez-Garcia, D.; Sanchez-Vila, X.

2014-12-01

Particle tracking (PT) techniques, often considered favorable over Eulerian techniques due to artificial smoothening in breakthrough curves (BTCs), are evaluated in a risk-driven framework. Recent work has shown that given a relatively few number of particles (np), PT methods can yield well-constructed BTCs with kernel density estimators (KDEs). This work compares KDE and non-KDE BTCs simulated as a function of np (102-108) and averaged as a function of the exposure duration, ED. Results show that regardless of BTC shape complexity, un-averaged PT BTCs show a large bias over several orders of magnitude in concentration (C) when compared to the KDE results, remarkably even when np is as low as 102. With the KDE, several orders of magnitude less np are required to obtain the same global error in BTC shape as the PT technique. PT and KDE BTCs are averaged as a function of the ED with standard and new methods incorporating the optimal h (ANA). The lowest error curve is obtained through the ANA method, especially for smaller EDs. Percent error of peak of averaged-BTCs, important in a risk framework, is approximately zero for all scenarios and all methods for np ≥105, but vary between the ANA and PT methods, when np is lower. For fewer np, the ANA solution provides a lower error fit except when C oscillations are present during a short time frame. We show that obtaining a representative average exposure concentration is reliant on an accurate representation of the BTC, especially when data is scarce.

Spatial distribution of deaths due to Alzheimer's disease in the state of São Paulo, Brazil.

PubMed

Almeida, Milena Cristina da Silva; Gomes, Camila de Moraes Santos; Nascimento, Luiz Fernando Costa

2014-01-01

Alzheimer's disease is a common cause of dementia and identifying possible spatial patterns of mortality due to this disease may enable preventive actions. The objective of this study was to identify spatial distribution patterns of mortality due to Alzheimer's disease in the state of São Paulo. Ecological and exploratory study conducted in all municipalities in the state of São Paulo. Data on Alzheimer's disease mortality in the state of São Paulo between 2004 and 2009 were obtained from DATASUS (the Department of Informatics in the Brazilian Ministry of Health). Death rates per 100,000 inhabitants were then calculated and spatial analysis was performed by constructing a death rate map, global Moran index and local Moran index, which were used to obtain the Moran map. The kernel technique was also applied. The Terra View 4.0.0 software was used. 13,030 deaths due to Alzheimer were reported in the state of São Paulo (rate of 5.33 deaths/100,000 inhabitants). São José do Rio Preto, Ribeirão Preto, Bauru and Araçatuba had higher rates. The Moran index was I = 0.085 (P < 0.002). The Moran map identified 42 municipalities that merit intervention and the kernel estimator identified a high density of deaths in the northwestern region of the state. Higher densities of deaths due to Alzheimer were concentrated more to the north and northwest of the state of São Paulo. It was possible to identify municipalities that have priority for interventions to reduce the death rates due to this disease.

Food Environment and Weight Change: Does Residential Mobility Matter?: The Diabetes Study of Northern California (DISTANCE).

PubMed

Laraia, Barbara A; Downing, Janelle M; Zhang, Y Tara; Dow, William H; Kelly, Maggi; Blanchard, Samuel D; Adler, Nancy; Schillinger, Dean; Moffet, Howard; Warton, E Margaret; Karter, Andrew J

2017-05-01

Associations between neighborhood food environment and adult body mass index (BMI; weight (kg)/height (m)2) derived using cross-sectional or longitudinal random-effects models may be biased due to unmeasured confounding and measurement and methodological limitations. In this study, we assessed the within-individual association between change in food environment from 2006 to 2011 and change in BMI among adults with type 2 diabetes using clinical data from the Kaiser Permanente Diabetes Registry collected from 2007 to 2011. Healthy food environment was measured using the kernel density of healthful food venues. Fixed-effects models with a 1-year-lagged BMI were estimated. Separate models were fitted for persons who moved and those who did not. Sensitivity analysis using different lag times and kernel density bandwidths were tested to establish the consistency of findings. On average, patients lost 1 pound (0.45 kg) for each standard-deviation improvement in their food environment. This relationship held for persons who remained in the same location throughout the 5-year study period but not among persons who moved. Proximity to food venues that promote nutritious foods alone may not translate into clinically meaningful diet-related health changes. Community-level policies for improving the food environment need multifaceted strategies to invoke clinically meaningful change in BMI among adult patients with diabetes. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image

NASA Astrophysics Data System (ADS)

Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.

2010-04-01

Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.

Nonparametric model validations for hidden Markov models with applications in financial econometrics.

PubMed

Zhao, Zhibiao

2011-06-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice1[OPEN

PubMed Central

Lan, Liu; Wang, Hongze; Xu, Yuancheng; Yang, Xiaohong; Li, Wenqiang; Tong, Hao; Xiao, Yingjie; Pan, Qingchun; Qiao, Feng; Raihan, Mohammad Sharif; Liu, Haijun; Yang, Ning; Wang, Xiaqing; Deng, Min; Jin, Minliang; Zhao, Lijun; Luo, Xin; Zhan, Wei; Liu, Nannan; Wang, Hong; Chen, Gengshen

2017-01-01

Maize (Zea mays) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice (Oryza sativa) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1, a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis (Arabidopsis thaliana) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis. PMID:28811335

The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice.

PubMed

Liu, Jie; Huang, Juan; Guo, Huan; Lan, Liu; Wang, Hongze; Xu, Yuancheng; Yang, Xiaohong; Li, Wenqiang; Tong, Hao; Xiao, Yingjie; Pan, Qingchun; Qiao, Feng; Raihan, Mohammad Sharif; Liu, Haijun; Zhang, Xuehai; Yang, Ning; Wang, Xiaqing; Deng, Min; Jin, Minliang; Zhao, Lijun; Luo, Xin; Zhou, Yang; Li, Xiang; Zhan, Wei; Liu, Nannan; Wang, Hong; Chen, Gengshen; Li, Qing; Yan, Jianbing

2017-10-01

Maize ( Zea mays ) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice ( Oryza sativa ) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1 , a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis ( Arabidopsis thaliana ) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1 ). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis. © 2017 American Society of Plant Biologists. All Rights Reserved.

Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers.

PubMed

Li, Faji; Wen, Weie; He, Zhonghu; Liu, Jindong; Jin, Hui; Cao, Shuanghe; Geng, Hongwei; Yan, Jun; Zhang, Pingzhi; Wan, Yingxiu; Xia, Xianchun

2018-06-01

We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay. Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai × Shi 4185 (D × S), Gaocheng 8901 × Zhoumai 16 (G × Z) and Linmai 2 × Zhong 892 (L × Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5-32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY.

Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm

NASA Astrophysics Data System (ADS)

Hu, Yan-Yan; Li, Dong-Sheng

2016-01-01

The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

Acid soil infertility effects on peanut yields and yield components

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blamey, F.P.C.

1983-01-01

The interpretation of soil amelioration experiments with peanuts is made difficult by the unpredictibility of the crop and by the many factors altered when ameliorating acid soils. The present study was conducted to investigate the effects of lime and gypsum applications on peanut kernel yield via the three first order yield components, pods per ha, kernels per pod, and kernel mass. On an acid medium sandy loam soil (typic Plinthustult), liming resulted in a highly significant kernel yield increase of 117% whereas gypsum applications were of no significant benefit. As indicated by path coefficient analysis, an increase in the numbermore » of pods per ha was markedly more important in increasing yield than an increase in either the number of kernels per pod or kernel mass. Furthermore, exch. Al was found to be particularly detrimental to pod number. It was postulated that poor peanut yields resulting from acid soil infertility were mainly due to the depressive effect of exch. Al on pod number. Exch. Ca appeared to play a secondary role by ameliorating the adverse effects of exch. Al.« less

Performance of fly ash based geopolymer incorporating palm kernel shell for lightweight concrete

NASA Astrophysics Data System (ADS)

Razak, Rafiza Abd; Abdullah, Mohd Mustafa Al Bakri; Yahya, Zarina; Jian, Ang Zhi; Nasri, Armia

2017-09-01

A concrete which cement is totally replaced by source material such as fly ash and activated by highly alkaline solutions is known as geopolymer concrete. Fly ash is the most common source material for geopolymer because it is a by-product material, so it can get easily from all around the world. An investigation has been carried out to select the most suitable ingredients of geopolymer concrete so that the geopolymer concrete can achieve the desire compressive strength. The samples were prepared to determine the suitable percentage of palm kernel shell used in geopolymer concrete and cured for 7 days in oven. After that, other samples were prepared by using the suitable percentage of palm kernel shell and cured for 3, 14, 21 and 28 days in oven. The control sample consisting of ordinary Portland cement and palm kernel shell and cured for 28 days were prepared too. The NaOH concentration of 12M, ratio Na2SiO3 to NaOH of 2.5, ratio fly ash to alkaline activator solution of 2.0 and ratio water to geopolymer of 0.35 were fixed throughout the research. The density obtained for the samples were 1.78 kg/m3, water absorption of 20.41% and the compressive strength of 14.20 MPa. The compressive strength of geopolymer concrete is still acceptable as lightweight concrete although the compressive strength is lower than OPC concrete. Therefore, the proposed method by using fly ash mixed with 10% of palm kernel shell can be used to design geopolymer concrete.

Green synthesis of Pd nanoparticles at Apricot kernel shell substrate using Salvia hydrangea extract: Catalytic activity for reduction of organic dyes.

PubMed

Khodadadi, Bahar; Bordbar, Maryam; Nasrollahzadeh, Mahmoud

2017-03-15

For the first time the extract of the plant of Salvia hydrangea was used to green synthesis of Pd nanoparticles (NPs) supported on Apricot kernel shell as an environmentally benign support. The Pd NPs/Apricot kernel shell as an effective catalyst was prepared through reduction of Pd 2+ ions using Salvia hydrangea extract as the reducing and capping agent and Pd NPs immobilization on Apricot kernel shell surface in the absence of any stabilizer or surfactant. According to FT-IR analysis, the hydroxyl groups of phenolics in Salvia hydrangea extract as bioreductant agents are directly responsible for the reduction of Pd 2+ ions and formation of Pd NPs. The as-prepared catalyst was characterized by Fourier transform infrared (FT-IR) and UV-Vis spectroscopy, field emission scanning electron microscopy (FESEM) equipped with an energy dispersive X-ray spectroscopy (EDS), Elemental mapping, X-ray diffraction analysis (XRD) and transmittance electron microscopy (TEM). The synthesized catalyst was used in the reduction of 4-nitrophenol (4-NP), Methyl Orange (MO), Methylene Blue (MB), Rhodamine B (RhB), and Congo Red (CR) at room temperature. The Pd NPs/Apricot kernel shell showed excellent catalytic activity in the reduction of these organic dyes. In addition, it was found that Pd NPs/Apricot kernel shell can be recovered and reused several times without significant loss of catalytic activity. Copyright © 2016 Elsevier Inc. All rights reserved.

A New Monte Carlo Method for Estimating Marginal Likelihoods.

PubMed

Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn; Lewis, Paul O

2018-06-01

Evaluating the marginal likelihood in Bayesian analysis is essential for model selection. Estimators based on a single Markov chain Monte Carlo sample from the posterior distribution include the harmonic mean estimator and the inflated density ratio estimator. We propose a new class of Monte Carlo estimators based on this single Markov chain Monte Carlo sample. This class can be thought of as a generalization of the harmonic mean and inflated density ratio estimators using a partition weighted kernel (likelihood times prior). We show that our estimator is consistent and has better theoretical properties than the harmonic mean and inflated density ratio estimators. In addition, we provide guidelines on choosing optimal weights. Simulation studies were conducted to examine the empirical performance of the proposed estimator. We further demonstrate the desirable features of the proposed estimator with two real data sets: one is from a prostate cancer study using an ordinal probit regression model with latent variables; the other is for the power prior construction from two Eastern Cooperative Oncology Group phase III clinical trials using the cure rate survival model with similar objectives.

Electron correlation in Hooke's law atom in the high-density limit.

PubMed

Gill, P M W; O'Neill, D P

2005-03-01

Closed-form expressions for the first three terms in the perturbation expansion of the exact energy and Hartree-Fock energy of the lowest singlet and triplet states of the Hooke's law atom are found. These yield elementary formulas for the exact correlation energies (-49.7028 and -5.807 65 mE(h)) of the two states in the high-density limit and lead to a pair of necessary conditions on the exact correlation kernel G(w) in Hartree-Fock-Wigner theory.

Viscozyme L pretreatment on palm kernels improved the aroma of palm kernel oil after kernel roasting.

PubMed

Zhang, Wencan; Leong, Siew Mun; Zhao, Feifei; Zhao, Fangju; Yang, Tiankui; Liu, Shaoquan

2018-05-01

With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. Copyright © 2018 Elsevier Ltd. All rights reserved.

An improved numerical method for the kernel density functional estimation of disperse flow

NASA Astrophysics Data System (ADS)

Smith, Timothy; Ranjan, Reetesh; Pantano, Carlos

2014-11-01

We present an improved numerical method to solve the transport equation for the one-point particle density function (pdf), which can be used to model disperse flows. The transport equation, a hyperbolic partial differential equation (PDE) with a source term, is derived from the Lagrangian equations for a dilute particle system by treating position and velocity as state-space variables. The method approximates the pdf by a discrete mixture of kernel density functions (KDFs) with space and time varying parameters and performs a global Rayleigh-Ritz like least-square minimization on the state-space of velocity. Such an approximation leads to a hyperbolic system of PDEs for the KDF parameters that cannot be written completely in conservation form. This system is solved using a numerical method that is path-consistent, according to the theory of non-conservative hyperbolic equations. The resulting formulation is a Roe-like update that utilizes the local eigensystem information of the linearized system of PDEs. We will present the formulation of the base method, its higher-order extension and further regularization to demonstrate that the method can predict statistics of disperse flows in an accurate, consistent and efficient manner. This project was funded by NSF Project NSF-DMS 1318161.

Evaluation of human exposure to single electromagnetic pulses of arbitrary shape.

PubMed

Jelínek, Lukás; Pekárek, Ludĕk

2006-03-01

Transient current density J(t) induced in the body of a person exposed to a single magnetic pulse of arbitrary shape or to a magnetic jump is filtered by a convolution integral containing in its kernel the frequency and phase dependence of the basic limit value adopted in a way similar to that used for reference values in the International Commission on Non-lonising Radiation Protection statement. From the obtained time-dependent dimensionless impact function W(J)(t) can immediately be determined whether the exposure to the analysed single event complies with the basic limit. For very slowly varying field, the integral kernel is extended to include the softened ICNIRP basic limit for frequencies lower than 4 Hz.

Frequency Domain Analysis of Narx Neural Networks

NASA Astrophysics Data System (ADS)

Chance, J. E.; Worden, K.; Tomlinson, G. R.

1998-06-01

A method is proposed for interpreting the behaviour of NARX neural networks. The correspondence between time-delay neural networks and Volterra series is extended to the NARX class of networks. The Volterra kernels, or rather, their Fourier transforms, are obtained via harmonic probing. In the same way that the Volterra kernels generalize the impulse response to non-linear systems, the Volterra kernel transforms can be viewed as higher-order analogues of the Frequency Response Functions commonly used in Engineering dynamics; they can be interpreted in much the same way.

On the Asymptotic Behavior of the Kernel Function in the Generalized Langevin Equation: A One-Dimensional Lattice Model

NASA Astrophysics Data System (ADS)

Chu, Weiqi; Li, Xiantao

2018-01-01

We present some estimates for the memory kernel function in the generalized Langevin equation, derived using the Mori-Zwanzig formalism from a one-dimensional lattice model, in which the particles interactions are through nearest and second nearest neighbors. The kernel function can be explicitly expressed in a matrix form. The analysis focuses on the decay properties, both spatially and temporally, revealing a power-law behavior in both cases. The dependence on the level of coarse-graining is also studied.

Data-based diffraction kernels for surface waves from convolution and correlation processes through active seismic interferometry

NASA Astrophysics Data System (ADS)

Chmiel, Malgorzata; Roux, Philippe; Herrmann, Philippe; Rondeleux, Baptiste; Wathelet, Marc

2018-05-01

We investigated the construction of diffraction kernels for surface waves using two-point convolution and/or correlation from land active seismic data recorded in the context of exploration geophysics. The high density of controlled sources and receivers, combined with the application of the reciprocity principle, allows us to retrieve two-dimensional phase-oscillation diffraction kernels (DKs) of surface waves between any two source or receiver points in the medium at each frequency (up to 15 Hz, at least). These DKs are purely data-based as no model calculations and no synthetic data are needed. They naturally emerge from the interference patterns of the recorded wavefields projected on the dense array of sources and/or receivers. The DKs are used to obtain multi-mode dispersion relations of Rayleigh waves, from which near-surface shear velocity can be extracted. Using convolution versus correlation with a grid of active sources is an important step in understanding the physics of the retrieval of surface wave Green's functions. This provides the foundation for future studies based on noise sources or active sources with a sparse spatial distribution.

Spectral-element simulations of wave propagation in complex exploration-industry models: Imaging and adjoint tomography

NASA Astrophysics Data System (ADS)

Luo, Y.; Nissen-Meyer, T.; Morency, C.; Tromp, J.

2008-12-01

Seismic imaging in the exploration industry is often based upon ray-theoretical migration techniques (e.g., Kirchhoff) or other ideas which neglect some fraction of the seismic wavefield (e.g., wavefield continuation for acoustic-wave first arrivals) in the inversion process. In a companion paper we discuss the possibility of solving the full physical forward problem (i.e., including visco- and poroelastic, anisotropic media) using the spectral-element method. With such a tool at hand, we can readily apply the adjoint method to tomographic inversions, i.e., iteratively improving an initial 3D background model to fit the data. In the context of this inversion process, we draw connections between kernels in adjoint tomography and basic imaging principles in migration. We show that the images obtained by migration are nothing but particular kinds of adjoint kernels (mainly density kernels). Migration is basically a first step in the iterative inversion process of adjoint tomography. We apply the approach to basic 2D problems involving layered structures, overthrusting faults, topography, salt domes, and poroelastic regions.

Spatial Relative Risk Patterns of Autism Spectrum Disorders in Utah

ERIC Educational Resources Information Center

Bakian, Amanda V.; Bilder, Deborah A.; Coon, Hilary; McMahon, William M.

2015-01-01

Heightened areas of spatial relative risk for autism spectrum disorders (ASD), or ASD hotspots, in Utah were identified using adaptive kernel density functions. Children ages four, six, and eight with ASD from multiple birth cohorts were identified by the Utah Registry of Autism and Developmental Disabilities. Each ASD case was gender-matched to…

Boundary Kernel Estimation of the Two Sample Comparison Density Function

DTIC Science & Technology

1989-05-01

not for the understand- ing, love, and steadfast support of my wife, Catheryn . She supported my move to statistics a mere fortnight after we were...school one learns things of a narrow and technical nature; 0 Catheryn has shown me much of what is fundamentally true and important in this world. To her

Source Code Analysis Laboratory (SCALe) for Energy Delivery Systems

DTIC Science & Technology

2010-12-01

the software for reevaluation. Once the ree- valuation process is completed, CERT provides the client a report detailing the software’s con - formance...Flagged Nonconformities (FNC) Software System TP/FNC Ratio Mozilla Firefox version 2.0 6/12 50% Linux kernel version 2.6.15 10/126 8% Wine...inappropriately tuned for analysis of the Linux kernel, which has anomalous results. Customizing SCALe to work with energy system software will help

Tobacco outlet density and tobacco knowledge, beliefs, purchasing behaviours and price among adolescents in Scotland.

PubMed

Tunstall, Helena; Shortt, Niamh K; Niedzwiedz, Claire L; Richardson, Elizabeth A; Mitchell, Richard J; Pearce, Jamie R

2018-06-01

Despite long-term falls in global adult smoking prevalence and over 50 years of tobacco control policies, adolescent smoking persists. Research suggests greater densities of tobacco retail outlets in residential neighbourhoods are associated with higher adolescent smoking rates. Policies to reduce retail outlets have therefore been identified by public health researchers as a potential 'new frontier' in tobacco control. Better understanding of the pathways linking density of tobacco retailers and smoking behaviour could support these policies. In this study we use path analysis to assess how outlet density in the home environment is related to adolescent tobacco knowledge, beliefs, retail purchases and price in Scotland. We assessed 22,049 13 and 15 year old respondents to the nationally representative cross-sectional 2010 Scottish School Adolescent Lifestyle and Substance Use Survey. Outlet density was based on Scottish Tobacco Retailers Register, 2012, data. A spatially-weighted Kernel Density Estimation measure of outlet density within 400 m of respondents' home postcode was grouped into tertiles. The analysis considered whether outlet density was associated with the number of cigarette brands adolescents could name, positive beliefs about smoking, whether smokers purchased cigarettes from shops themselves or through adult proxies and perceived cost of cigarettes. Models were stratified by adolescent smoking status. The path analyses indicated that outlet density was not associated with most outcomes, but small, significant direct effects on knowledge of cigarette brands among those who had never smoked were observed. With each increase in outlet density tertile the mean number of brands adolescents could name rose by 0.07 (mean = 1.60; SD = 1.18; range = 4). This suggests greater outlet densities may have affected adolescents' knowledge of cigarette brands but did not encourage positive attitudes to smoking, purchases from shops or lower cigarette prices. Exposure to tobacco outlets may influence adolescents' awareness of tobacco products, a potential pathway to smoking behaviour. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quantitative analysis and comparative study of four cities green pattern in API system on the background of big data

NASA Astrophysics Data System (ADS)

Xin, YANG; Si-qi, WU; Qi, ZHANG

2018-05-01

Beijing, London, Paris, New York are typical cities in the world, so comparative study of four cities green pattern is very important to find out gap and advantage and to learn from each other. The paper will provide basis and new ideas for development of metropolises in China. On the background of big data, API (Application Programming Interface) system can provide extensive and accurate basic data to study urban green pattern in different geographical environment in domestic and foreign. On the basis of this, Average nearest neighbor tool, Kernel density tool and Standard Ellipse tool in ArcGIS platform can process and summarize data and realize quantitative analysis of green pattern. The paper summarized uniqueness of four cities green pattern and reasons of formation on basis of numerical comparison.

Predicting spatial patterns of plant recruitment using animal-displacement kernels.

PubMed

Santamaría, Luis; Rodríguez-Pérez, Javier; Larrinaga, Asier R; Pias, Beatriz

2007-10-10

For plants dispersed by frugivores, spatial patterns of recruitment are primarily influenced by the spatial arrangement and characteristics of parent plants, the digestive characteristics, feeding behaviour and movement patterns of animal dispersers, and the structure of the habitat matrix. We used an individual-based, spatially-explicit framework to characterize seed dispersal and seedling fate in an endangered, insular plant-disperser system: the endemic shrub Daphne rodriguezii and its exclusive disperser, the endemic lizard Podarcis lilfordi. Plant recruitment kernels were chiefly determined by the disperser's patterns of space utilization (i.e. the lizard's displacement kernels), the position of the various plant individuals in relation to them, and habitat structure (vegetation cover vs. bare soil). In contrast to our expectations, seed gut-passage rate and its effects on germination, and lizard speed-of-movement, habitat choice and activity rhythm were of minor importance. Predicted plant recruitment kernels were strongly anisotropic and fine-grained, preventing their description using one-dimensional, frequency-distance curves. We found a general trade-off between recruitment probability and dispersal distance; however, optimal recruitment sites were not necessarily associated to sites of maximal adult-plant density. Conservation efforts aimed at enhancing the regeneration of endangered plant-disperser systems may gain in efficacy by manipulating the spatial distribution of dispersers (e.g. through the creation of refuges and feeding sites) to create areas favourable to plant recruitment.

Interparameter trade-off quantification and reduction in isotropic-elastic full-waveform inversion: synthetic experiments and Hussar land data set application

NASA Astrophysics Data System (ADS)

Pan, Wenyong; Geng, Yu; Innanen, Kristopher A.

2018-05-01

The problem of inverting for multiple physical parameters in the subsurface using seismic full-waveform inversion (FWI) is complicated by interparameter trade-off arising from inherent ambiguities between different physical parameters. Parameter resolution is often characterized using scattering radiation patterns, but these neglect some important aspects of interparameter trade-off. More general analysis and mitigation of interparameter trade-off in isotropic-elastic FWI is possible through judiciously chosen multiparameter Hessian matrix-vector products. We show that products of multiparameter Hessian off-diagonal blocks with model perturbation vectors, referred to as interparameter contamination kernels, are central to the approach. We apply the multiparameter Hessian to various vectors designed to provide information regarding the strengths and characteristics of interparameter contamination, both locally and within the whole volume. With numerical experiments, we observe that S-wave velocity perturbations introduce strong contaminations into density and phase-reversed contaminations into P-wave velocity, but themselves experience only limited contaminations from other parameters. Based on these findings, we introduce a novel strategy to mitigate the influence of interparameter trade-off with approximate contamination kernels. Furthermore, we recommend that the local spatial and interparameter trade-off of the inverted models be quantified using extended multiparameter point spread functions (EMPSFs) obtained with pre-conditioned conjugate-gradient algorithm. Compared to traditional point spread functions, the EMPSFs appear to provide more accurate measurements for resolution analysis, by de-blurring the estimations, scaling magnitudes and mitigating interparameter contamination. Approximate eigenvalue volumes constructed with stochastic probing approach are proposed to evaluate the resolution of the inverted models within the whole model. With a synthetic Marmousi model example and a land seismic field data set from Hussar, Alberta, Canada, we confirm that the new inversion strategy suppresses the interparameter contamination effectively and provides more reliable density estimations in isotropic-elastic FWI as compared to standard simultaneous inversion approach.

X-ray photoelectron spectroscopic analysis of rice kernels and flours: Measurement of surface chemical composition.

PubMed

Nawaz, Malik A; Gaiani, Claire; Fukai, Shu; Bhandari, Bhesh

2016-12-01

The objectives of this study were to evaluate the ability of X-ray photoelectron spectroscopy (XPS) to differentiate rice macromolecules and to calculate the surface composition of rice kernels and flours. The uncooked kernels and flours surface composition of the two selected rice varieties, Thadokkham-11 (TDK11) and Doongara (DG) demonstrated an over-expression of lipids and proteins and an under-expression of starch compared to the bulk composition. The results of the study showed that XPS was able to differentiate rice polysaccharides (mainly starch), proteins and lipids in uncooked rice kernels and flours. Nevertheless, it was unable to distinguish components in cooked rice samples possibly due to complex interactions between gelatinized starch, denatured proteins and lipids. High resolution imaging methods (Scanning Electron Microscopy and Confocal Laser Scanning Microscopy) were employed to obtain complementary information about the properties and location of starch, proteins and lipids in rice kernels and flours. Copyright © 2016. Published by Elsevier Ltd.

Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement

PubMed Central

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes. PMID:21765890

Should I stay or should I go? A habitat-dependent dispersal kernel improves prediction of movement.

PubMed

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes.

Optimized data fusion for K-means Laplacian clustering

PubMed Central

Yu, Shi; Liu, Xinhai; Tranchevent, Léon-Charles; Glänzel, Wolfgang; Suykens, Johan A. K.; De Moor, Bart; Moreau, Yves

2011-01-01

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20980271

Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

Li, Quanbao; Wei, Fajie; Zhou, Shenghan

2017-05-01

The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

Analysis and Development of A Robust Fuel for Gas-Cooled Fast Reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Knight, Travis W.

2010-01-31

The focus of this effort was on the development of an advanced fuel for gas-cooled fast reactor (GFR) applications. This composite design is based on carbide fuel kernels dispersed in a ZrC matrix. The choice of ZrC is based on its high temperature properties and good thermal conductivity and improved retention of fission products to temperatures beyond that of traditional SiC based coated particle fuels. A key component of this study was the development and understanding of advanced fabrication techniques for GFR fuels that have potential to reduce minor actinide (MA) losses during fabrication owing to their higher vapor pressuresmore » and greater volatility. The major accomplishments of this work were the study of combustion synthesis methods for fabrication of the ZrC matrix, fabrication of high density UC electrodes for use in the rotating electrode process, production of UC particles by rotating electrode method, integration of UC kernels in the ZrC matrix, and the full characterization of each component. Major accomplishments in the near-term have been the greater characterization of the UC kernels produced by the rotating electrode method and their condition following the integration in the composite (ZrC matrix) following the short time but high temperature combustion synthesis process. This work has generated four journal publications, one conference proceeding paper, and one additional journal paper submitted for publication (under review). The greater significance of the work can be understood in that it achieved an objective of the DOE Generation IV (GenIV) roadmap for GFR Fuel—namely the demonstration of a composite carbide fuel with 30% volume fuel. This near-term accomplishment is even more significant given the expected or possible time frame for implementation of the GFR in the years 2030 -2050 or beyond.« less

Exchange and correlation effects on plasmon dispersions and Coulomb drag in low-density electron bilayers

NASA Astrophysics Data System (ADS)

Badalyan, S. M.; Kim, C. S.; Vignale, G.; Senatore, G.

2007-03-01

We investigate the effect of exchange and correlation (XC) on the plasmon spectrum and the Coulomb drag between spatially separated low-density two-dimensional electron layers. We adopt a different approach, which employs dynamic XC kernels in the calculation of the bilayer plasmon spectra and of the plasmon-mediated drag, and static many-body local field factors in the calculation of the particle-hole contribution to the drag. The spectrum of bilayer plasmons and the drag resistivity are calculated in a broad range of temperatures taking into account both intra- and interlayer correlation effects. We observe that both plasmon modes are strongly affected by XC corrections. After the inclusion of the complex dynamic XC kernels, a decrease of the electron density induces shifts of the plasmon branches in opposite directions. This is in stark contrast with the tendency observed within random phase approximation that both optical and acoustical plasmons move away from the boundary of the particle-hole continuum with a decrease in the electron density. We find that the introduction of XC corrections results in a significant enhancement of the transresistivity and qualitative changes in its temperature dependence. In particular, the large high-temperature plasmon peak that is present in the random phase approximation is found to disappear when the XC corrections are included. Our numerical results at low temperatures are in good agreement with the results of recent experiments by Kellogg [Solid State Commun. 123, 515 (2002)].

Nonparametric model validations for hidden Markov models with applications in financial econometrics

PubMed Central

Zhao, Zhibiao

2011-01-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise. PMID:21750601

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

PubMed

Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K

2010-06-01

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.

Anthocyanin composition and oxygen radical scavenging capacity (ORAC) of milled and pearled purple, black, and common barley.

PubMed

Bellido, Guillermo G; Beta, Trust

2009-02-11

The importance of anthocyanins to the total antioxidant capacity of various fruits and vegetables has been well established, but less attention has been focused on cereal grains. This study investigated the antioxidant capacity and anthocyanin composition of a bran-rich pearling fraction (10% outer kernel layers) and whole kernel flour of purple (CI-1248), black (PERU-35), and yellow (EX-83) barley genotypes. HPLC analysis showed that as much as 6 times more anthocyanin per unit weight (microg/g) was present in the bran-rich fractions of yellow and purple barley (1587 and 3534, respectively) than in their corresponding whole kernel flours (210 and 573, respectively). Delphinidin 3-glucoside, delphinidin 3-rutinoside, cyanidin 3-glucoside, petunidin 3-glucoside, and cyanidin chloride were positively identified in barley, with as many as 9 and 15 anthocyanins being detected in yellow and purple barley, respectively. Antioxidant activity analysis showed that the ORAC values for the bran-rich fractions were significantly (p < 0.05) higher than for the whole kernel flour.

Measurement of vascular wall attenuation: comparison of CT angiography using model-based iterative reconstruction with standard filtered back-projection algorithm CT in vitro.

PubMed

Suzuki, Shigeru; Machida, Haruhiko; Tanaka, Isao; Ueno, Eiko

2012-11-01

To compare the performance of model-based iterative reconstruction (MBIR) with that of standard filtered back projection (FBP) for measuring vascular wall attenuation. After subjecting 9 vascular models (actual attenuation value of wall, 89 HU) with wall thickness of 0.5, 1.0, or 1.5 mm that we filled with contrast material of 275, 396, or 542 HU to scanning using 64-detector computed tomography (CT), we reconstructed images using MBIR and FBP (Bone, Detail kernels) and measured wall attenuation at the center of the wall for each model. We performed attenuation measurements for each model and additional supportive measurements by a differentiation curve. We analyzed statistics using analyzes of variance with repeated measures. Using the Bone kernel, standard deviation of the measurement exceeded 30 HU in most conditions. In measurements at the wall center, the attenuation values obtained using MBIR were comparable to or significantly closer to the actual wall attenuation than those acquired using Detail kernel. Using differentiation curves, we could measure attenuation for models with walls of 1.0- or 1.5-mm thickness using MBIR but only those of 1.5-mm thickness using Detail kernel. We detected no significant differences among the attenuation values of the vascular walls of either thickness (MBIR, P=0.1606) or among the 3 densities of intravascular contrast material (MBIR, P=0.8185; Detail kernel, P=0.0802). Compared with FBP, MBIR reduces both reconstruction blur and image noise simultaneously, facilitates recognition of vascular wall boundaries, and can improve accuracy in measuring wall attenuation. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Home range and space use patterns of flathead catfish during the summer-fall period in two Missouri streams

USGS Publications Warehouse

Vokoun, Jason C.; Rabeni, Charles F.

2005-01-01

Flathead catfish Pylodictis olivaris were radio-tracked in the Grand River and Cuivre River, Missouri, from late July until they moved to overwintering habitats in late October. Fish moved within a definable area, and although occasional long-distance movements occurred, the fish typically returned to the previously occupied area. Seasonal home range was calculated with the use of kernel density estimation, which can be interpreted as a probabilistic utilization distribution that documents the internal structure of the estimate by delineating portions of the range that was used a specified percentage of the time. A traditional linear range also was reported. Most flathead catfish (89%) had one 50% kernel-estimated core area, whereas 11% of the fish split their time between two core areas. Core areas were typically in the middle of the 90% kernel-estimated home range (58%), although several had core areas in upstream (26%) and downstream (16%) portions of the home range. Home-range size did not differ based on river, sex, or size and was highly variable among individuals. The median 95% kernel estimate was 1,085 m (range, 70– 69,090 m) for all fish. The median 50% kernel-estimated core area was 135 m (10–2,260 m). The median linear range was 3,510 m (150–50,400 m). Fish pairs with core areas in the same and neighboring pools had static joint space use values of up to 49% (area of intersection index), indicating substantial overlap and use of the same area. However, all fish pairs had low dynamic joint space use values (<0.07; coefficient of association), indicating that fish pairs were temporally segregated, rarely occurring in the same location at the same time.

Evaluating the intersection of a regional wildlife connectivity network with highways.

PubMed

Cushman, Samuel A; Lewis, Jesse S; Landguth, Erin L

2013-01-01

Reliable predictions of regional-scale population connectivity are needed to prioritize conservation actions. However, there have been few examples of regional connectivity models that are empirically derived and validated. The central goals of this paper were to (1) evaluate the effectiveness of factorial least cost path corridor mapping on an empirical resistance surface in reflecting the frequency of highway crossings by American black bear, (2) predict the location and predicted intensity of use of movement corridors for American black bear, and (3) identify where these corridors cross major highways and rank the intensity of these crossings. We used factorial least cost path modeling coupled with resistant kernel analysis to predict a network of movement corridors across a 30.2 million hectare analysis area in Montana and Idaho, USA. Factorial least cost path corridor mapping was associated with the locations of actual bear highway crossings. We identified corridor-highway intersections and ranked these based on corridor strength. We found that a major wildlife crossing overpass structure was located close to one of the most intense predicted corridors, and that the vast majority of the predicted corridor network was "protected" under federal management. However, narrow, linear corridors connecting the Greater Yellowstone Ecosystem to the rest of the analysis area had limited protection by federal ownership, making these additionally vulnerable to habitat loss and fragmentation. Factorial least cost path modeling coupled with resistant kernel analysis provides detailed, synoptic information about connectivity across populations that vary in distribution and density in complex landscapes. Specifically, our results could be used to quantify the structure of the connectivity network, identify critical linkage nodes and core areas, map potential barriers and fracture zones, and prioritize locations for mitigation, restoration and conservation actions.

Assessment of the microbiological safety of edible roasted nut kernels on retail sale in England, with a focus on Salmonella.

PubMed

Little, C L; Jemmott, W; Surman-Lee, S; Hucklesby, L; de Pinnal, E

2009-04-01

There is little published information on the prevalence of Salmonella in edible nut kernels. A study in early 2008 of edible roasted nut kernels on retail sale in England was undertaken to assess the microbiological safety of this product. A total of 727 nut kernel samples of different varieties were examined. Overall, Salmonella and Escherichia coli were detected from 0.2 and 0.4% of edible roasted nut kernels. Of the nut varieties examined, Salmonella Havana was detected from 1 (4.0%) sample of pistachio nuts, indicating a risk to health. The United Kingdom Food Standards Agency was immediately informed, and full investigations were undertaken. Further examination established the contamination to be associated with the pistachio kernels and not the partly opened shells. Salmonella was not detected in other varieties tested (almonds, Brazils, cashews, hazelnuts, macadamia, peanuts, pecans, pine nuts, and walnuts). E. coli was found at low levels (range of 3.6 to 4/g) in walnuts (1.4%), almonds (1.2%), and Brazils (0.5%). The presence of Salmonella is unacceptable in edible nut kernels. Prevention of microbial contamination in these products lies in the application of good agricultural, manufacturing, and storage practices together with a hazard analysis and critical control points system that encompass all stages of production, processing, and distribution.

Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

PubMed

Echinaka, Yuki; Ozeki, Yukiyasu

2016-10-01

The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

A dual-input nonlinear system analysis of autonomic modulation of heart rate

NASA Technical Reports Server (NTRS)

Chon, K. H.; Mullen, T. J.; Cohen, R. J.

1996-01-01

Linear analyses of fluctuations in heart rate and other hemodynamic variables have been used to elucidate cardiovascular regulatory mechanisms. The role of nonlinear contributions to fluctuations in hemodynamic variables has not been fully explored. This paper presents a nonlinear system analysis of the effect of fluctuations in instantaneous lung volume (ILV) and arterial blood pressure (ABP) on heart rate (HR) fluctuations. To successfully employ a nonlinear analysis based on the Laguerre expansion technique (LET), we introduce an efficient procedure for broadening the spectral content of the ILV and ABP inputs to the model by adding white noise. Results from computer simulations demonstrate the effectiveness of broadening the spectral band of input signals to obtain consistent and stable kernel estimates with the use of the LET. Without broadening the band of the ILV and ABP inputs, the LET did not provide stable kernel estimates. Moreover, we extend the LET to the case of multiple inputs in order to accommodate the analysis of the combined effect of ILV and ABP effect on heart rate. Analyzes of data based on the second-order Volterra-Wiener model reveal an important contribution of the second-order kernels to the description of the effect of lung volume and arterial blood pressure on heart rate. Furthermore, physiological effects of the autonomic blocking agents propranolol and atropine on changes in the first- and second-order kernels are also discussed.

Effect of solvent on the extraction of phenolic compounds and antioxidant capacity of hazelnut kernel.

PubMed

Fanali, Chiara; Tripodo, Giusy; Russo, Marina; Della Posta, Susanna; Pasqualetti, Valentina; De Gara, Laura

2018-03-22

Hazelnut kernel phenolic compounds were recovered applying two different extraction approaches, namely ultrasound-assisted solid/liquid extraction (UA-SLE) and solid-phase extraction (SPE). Different solvents were tested evaluating total phenolic compounds and total flavonoids contents together to antioxidant activity. The optimum extraction conditions, in terms of the highest value of total phenolic compounds extracted together to other parameters like simplicity and cost were selected for method validation and individual phenolic compounds analysis. The UA-SLE protocol performed using 0.1 g of defatted sample and 15 mL of extraction solvent (1 mL methanol/1 mL water/8 mL methanol 0.1% formic acid/5 mL acetonitrile) was selected. The analysis of hazelnut kernel individual phenolic compounds was obtained by HPLC coupled with DAD and MS detections. Quantitative analysis was performed using a mixture of six phenolic compounds belonging to phenolic classes' representative of hazelnut. Then, the method was fully validated and the resulting RSD% values for retention time repeatability were below 1%. A good linearity was obtained giving R 2 no lower than 0.997.The accuracy of the extraction method was also assessed. Finally, the method was applied to the analysis of phenolic compounds in three different hazelnut kernel varieties observing a similar qualitative profile with differences in the quantity of detected compounds. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Kernel-Phase Interferometry for Super-Resolution Detection of Faint Companions

NASA Astrophysics Data System (ADS)

Factor, Samuel M.; Kraus, Adam L.

2017-01-01

Direct detection of close in companions (exoplanets or binary systems) is notoriously difficult. While coronagraphs and point spread function (PSF) subtraction can be used to reduce contrast and dig out signals of companions under the PSF, there are still significant limitations in separation and contrast. Non-redundant aperture masking (NRM) interferometry can be used to detect companions well inside the PSF of a diffraction limited image, though the mask discards ˜95% of the light gathered by the telescope and thus the technique is severely flux limited. Kernel-phase analysis applies interferometric techniques similar to NRM to a diffraction limited image utilizing the full aperture. Instead of non-redundant closure-phases, kernel-phases are constructed from a grid of points on the full aperture, simulating a redundant interferometer. I have developed my own faint companion detection pipeline which utilizes an Bayesian analysis of kernel-phases. I have used this pipeline to search for new companions in archival images from HST/NICMOS in order to constrain planet and binary formation models at separations inaccessible to previous techniques. Using this method, it is possible to detect a companion well within the classical λ/D Rayleigh diffraction limit using a fraction of the telescope time as NRM. This technique can easily be applied to archival data as no mask is needed and will thus make the detection of close in companions cheap and simple as no additional observations are needed. Since the James Webb Space Telescope (JWST) will be able to perform NRM observations, further development and characterization of kernel-phase analysis will allow efficient use of highly competitive JWST telescope time.

Kernel-Phase Interferometry for Super-Resolution Detection of Faint Companions

NASA Astrophysics Data System (ADS)

Factor, Samuel

2016-10-01

Direct detection of close in companions (binary systems or exoplanets) is notoriously difficult. While chronagraphs and point spread function (PSF) subtraction can be used to reduce contrast and dig out signals of companions under the PSF, there are still significant limitations in separation and contrast. While non-redundant aperture masking (NRM) interferometry can be used to detect companions well inside the PSF of a diffraction limited image, the mask discards 95% of the light gathered by the telescope and thus the technique is severely flux limited. Kernel-phase analysis applies interferometric techniques similar to NRM though utilizing the full aperture. Instead of closure-phases, kernel-phases are constructed from a grid of points on the full aperture, simulating a redundant interferometer. I propose to develop my own faint companion detection pipeline which utilizes an MCMC analysis of kernel-phases. I will search for new companions in archival images from NIC1 and ACS/HRC in order to constrain binary and planet formation models at separations inaccessible to previous techniques. Using this method, it is possible to detect a companion well within the classical l/D Rayleigh diffraction limit using a fraction of the telescope time as NRM. This technique can easily be applied to archival data as no mask is needed and will thus make the detection of close in companions cheap and simple as no additional observations are needed. Since the James Webb Space Telescope (JWST) will be able to perform NRM observations, further development and characterization of kernel-phase analysis will allow efficient use of highly competitive JWST telescope time.

Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.

PubMed

Zhang, Hanyuan; Tian, Xuemin; Deng, Xiaogang; Cao, Yuping

2018-05-16

As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Experiences in autotuning matrix multiplication for energy minimization on GPUs

DOE PAGES

Anzt, Hartwig; Haugen, Blake; Kurzak, Jakub; ...

2015-05-20

In this study, we report extensive results and analysis of autotuning the computationally intensive graphics processing units kernel for dense matrix–matrix multiplication in double precision. In contrast to traditional autotuning and/or optimization for runtime performance only, we also take the energy efficiency into account. For kernels achieving equal performance, we show significant differences in their energy balance. We also identify the memory throughput as the most influential metric that trades off performance and energy efficiency. Finally, as a result, the performance optimal case ends up not being the most efficient kernel in overall resource use.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

NASA Astrophysics Data System (ADS)

Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

2014-05-01

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies.

Spatial and temporal analysis of Aids cases in Brazil, 1996-2011: increased risk areas over time.

PubMed

Sousa, Artur Iuri Alves de; Pinto, Vitor Laerte

2016-01-01

to identify areas with greater risk of AIDS transmission in Brazil. this is an ecological study involving georeference of AIDS cases incidence, prevalence and density in Brazilian municipalities using the Kernel method for the periods 1996-1999, 2000-2003, 2004-2007 and 2008-2011. 633,512 AIDS cases were reported between 1996-2011; between 2008-2011, there was increased risk of AIDS transmission in Recife-João Pessoa region, the emergence of areas with average density in the regions of Belém, São Luís, Maceió, Aracaju and Salvador, and a decline in the intensity of risk in São Paulo, Campinas and Ribeirão Preto; prevalence rates were most concentrated in the Southeast, South and Midwest regions of the country. overall, AIDS incidence in Brazil showed successive increases in the periods analyzed; case prevalence indicates spatial clusters, with high concentrations in the Southeast, South and Midwest regions.

The temperature and density structures of an X-ray flare during the decay phase. [Skylab observations

NASA Technical Reports Server (NTRS)

Silk, J. K.; Kahler, S. W.; Krieger, A. S.; Vaiana, G. S.

1976-01-01

The X-ray flare of 9 August 1973 was characterized by a spatially small kernel structure which persisted throughout its duration. The decay phase of this flare was observed in the objective grating mode of the X-ray telescope aboard the Skylab. Data analysis was carried out by scanning the images with a microdensitometer, converting the density arrays to energy using laboratory film calibration data and taking cross sections of the energy images. The 9 August flare shows two distinct periods in its decay phase, involving both cooling and material loss. The objective grating observations reveal that the two phenomena are separated in time. During the earlier phase of the flare decay, the distribution of emission measure as a function of temperature is changing, the high temperature component of the distribution being depleted relative to the cooler body of plasma. As the decay continues, the emission measure distribution stabilizes and the flux diminishes as the amount of material at X-ray emitting temperatures decreases.

The density matrix renormalization group algorithm on kilo-processor architectures: Implementation and trade-offs

NASA Astrophysics Data System (ADS)

Nemes, Csaba; Barcza, Gergely; Nagy, Zoltán; Legeza, Örs; Szolgay, Péter

2014-06-01

In the numerical analysis of strongly correlated quantum lattice models one of the leading algorithms developed to balance the size of the effective Hilbert space and the accuracy of the simulation is the density matrix renormalization group (DMRG) algorithm, in which the run-time is dominated by the iterative diagonalization of the Hamilton operator. As the most time-dominant step of the diagonalization can be expressed as a list of dense matrix operations, the DMRG is an appealing candidate to fully utilize the computing power residing in novel kilo-processor architectures. In the paper a smart hybrid CPU-GPU implementation is presented, which exploits the power of both CPU and GPU and tolerates problems exceeding the GPU memory size. Furthermore, a new CUDA kernel has been designed for asymmetric matrix-vector multiplication to accelerate the rest of the diagonalization. Besides the evaluation of the GPU implementation, the practical limits of an FPGA implementation are also discussed.

Superstatistical generalised Langevin equation: non-Gaussian viscoelastic anomalous diffusion

NASA Astrophysics Data System (ADS)

Ślęzak, Jakub; Metzler, Ralf; Magdziarz, Marcin

2018-02-01

Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations.

Infrared microspectroscopic imaging of plant tissues: spectral visualization of Triticum aestivum kernel and Arabidopsis leaf microstructure

PubMed Central

Warren, Frederick J; Perston, Benjamin B; Galindez-Najera, Silvia P; Edwards, Cathrina H; Powell, Prudence O; Mandalari, Giusy; Campbell, Grant M; Butterworth, Peter J; Ellis, Peter R

2015-01-01

Infrared microspectroscopy is a tool with potential for studies of the microstructure, chemical composition and functionality of plants at a subcellular level. Here we present the use of high-resolution bench top-based infrared microspectroscopy to investigate the microstructure of Triticum aestivum L. (wheat) kernels and Arabidopsis leaves. Images of isolated wheat kernel tissues and whole wheat kernels following hydrothermal processing and simulated gastric and duodenal digestion were generated, as well as images of Arabidopsis leaves at different points during a diurnal cycle. Individual cells and cell walls were resolved, and large structures within cells, such as starch granules and protein bodies, were clearly identified. Contrast was provided by converting the hyperspectral image cubes into false-colour images using either principal component analysis (PCA) overlays or by correlation analysis. The unsupervised PCA approach provided a clear view of the sample microstructure, whereas the correlation analysis was used to confirm the identity of different anatomical structures using the spectra from isolated components. It was then demonstrated that gelatinized and native starch within cells could be distinguished, and that the loss of starch during wheat digestion could be observed, as well as the accumulation of starch in leaves during a diurnal period. PMID:26400058

Variation of biometric parameters in corn cobs under the influence of nitrogen fertilization

NASA Astrophysics Data System (ADS)

Gigel, Prisecaru; Florin, Sala

2017-07-01

Biometric parameters as elements of productivity on corn cobs, along with plant density per unit area (ha) are essential in achieving production. The influence of differentiated fertilization with nitrogen was evaluated at the level of productivity elements on corn cobs, Andreea hybrid. Biometric parameters of the corn cobs (total length - L; usable length - l; uncoated length with corn kernels - lu; diameter at the base - Db, middle - Dm, and top of the corn cobs - Dt; corn cob weight - Cw, grain weight - Gw) were directly influenced by the doses of nitrogen. Regression analysis has facilitated the prediction of grain weight as the main element of productivity under different statistical certainty based on nitrogen doses (R2 = 0.962, p<0.01), on the total length of corn cobs (R2 = 0.985, p<0.01), on the usable length of corn cobs (R2 = 0.996, p<<0.001), on the diameter at the base of corn cobs (R2 = 0.824, p<0.01), on the diameter at the middle of corn cobs (R2 = 0.807, p<0.01), on uncoated length with corn kernels (R2 = 0.624, p<0.01) and on the diameter at the top of the corn cobs (R2 = 0.384, p=0.015).

Building integral projection models: a user's guide

PubMed Central

Rees, Mark; Childs, Dylan Z; Ellner, Stephen P; Coulson, Tim

2014-01-01

In order to understand how changes in individual performance (growth, survival or reproduction) influence population dynamics and evolution, ecologists are increasingly using parameterized mathematical models. For continuously structured populations, where some continuous measure of individual state influences growth, survival or reproduction, integral projection models (IPMs) are commonly used. We provide a detailed description of the steps involved in constructing an IPM, explaining how to: (i) translate your study system into an IPM; (ii) implement your IPM; and (iii) diagnose potential problems with your IPM. We emphasize how the study organism's life cycle, and the timing of censuses, together determine the structure of the IPM kernel and important aspects of the statistical analysis used to parameterize an IPM using data on marked individuals. An IPM based on population studies of Soay sheep is used to illustrate the complete process of constructing, implementing and evaluating an IPM fitted to sample data. We then look at very general approaches to parameterizing an IPM, using a wide range of statistical techniques (e.g. maximum likelihood methods, generalized additive models, nonparametric kernel density estimators). Methods for selecting models for parameterizing IPMs are briefly discussed. We conclude with key recommendations and a brief overview of applications that extend the basic model. The online Supporting Information provides commented R code for all our analyses. PMID:24219157

A Daily Snack Containing Leafy Green Vegetables, Fruit, and Milk before and during Pregnancy Prevents Gestational Diabetes in a Randomized, Controlled Trial in Mumbai, India.

PubMed

Sahariah, Sirazul A; Potdar, Ramesh D; Gandhi, Meera; Kehoe, Sarah H; Brown, Nick; Sane, Harshad; Coakley, Patsy J; Marley-Zagar, Ella; Chopra, Harsha; Shivshankaran, Devi; Cox, Vanessa A; Jackson, Alan A; Margetts, Barrie M; Fall, Caroline Hd

2016-07-01

Prospective observational studies suggest that maternal diets rich in leafy green vegetables and fruit may help prevent gestational diabetes mellitus (GDM). Our objective was to test whether increasing women's dietary intake of leafy green vegetables, fruit, and milk before conception and throughout pregnancy reduced their risk of GDM. Project SARAS ("excellent") (2006-2012) was a nonblinded, individually randomized, controlled trial in women living in slums in the city of Mumbai, India. The interventions included a daily snack made from leafy green vegetables, fruit, and milk for the treatment group or low-micronutrient vegetables (e.g., potato and onion) for the control group, in addition to the usual diet. Results for the primary outcome, birth weight, have been reported. Women were invited to take an oral-glucose-tolerance test (OGTT) at 28-32 wk gestation to screen for GDM (WHO 1999 criteria). The prevalence of GDM was compared between the intervention and control groups, and Kernel density analysis was used to compare distributions of 120-min plasma glucose concentrations between groups. Of 6513 women randomly assigned, 2291 became pregnant; of these, 2028 reached a gestation of 28 wk, 1008 (50%) attended for an OGTT, and 100 (9.9%) had GDM. In an intention-to-treat analysis, the prevalence of GDM was reduced in the treatment group (7.3% compared with 12.4% in controls; OR: 0.56; 95% CI: 0.36, 0.86; P = 0.008). The reduction in GDM remained significant after adjusting for prepregnancy adiposity and fat or weight gain during pregnancy. Kernel density analysis showed that this was explained by the fact that fewer women in the treatment group had a 2-h glucose concentration in the range 7.5-10.0 mmol/L. In low-income settings, in which women have a low intake of micronutrient-rich foods, improving dietary micronutrient quality by increasing intake of leafy green vegetables, fruit, and/or milk may have an important protective effect against the development of GDM. This trial was registered at www.controlled-trials.com as ISRCTN62811278.

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

PubMed Central

Vehtari, Aki; Mäkinen, Ville-Petteri; Soininen, Pasi; Ingman, Petri; Mäkelä, Sanna M; Savolainen, Markku J; Hannuksela, Minna L; Kaski, Kimmo; Ala-Korpela, Mika

2007-01-01

Background A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by 1H NMR spectroscopy of serum. Results A Bayesian methodology, with a biochemical motivation, is presented for a real 1H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the 1H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the 1H NMR spectra. Conclusion The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics. PMID:17493257

Exact analytic solution for non-linear density fluctuation in a ΛCDM universe

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Jaiyul; Gong, Jinn-Ouk, E-mail: jyoo@physik.uzh.ch, E-mail: jinn-ouk.gong@apctp.org

We derive the exact third-order analytic solution of the matter density fluctuation in the proper-time hypersurface in a ΛCDM universe, accounting for the explicit time-dependence and clarifying the relation to the initial condition. Furthermore, we compare our analytic solution to the previous calculation in the comoving gauge, and to the standard Newtonian perturbation theory by providing Fourier kernels for the relativistic effects. Our results provide an essential ingredient for a complete description of galaxy bias in the relativistic context.

Joint and collaborative representation with local Volterra kernels convolution feature for face recognition

NASA Astrophysics Data System (ADS)

Feng, Guang; Li, Hengjian; Dong, Jiwen; Chen, Xi; Yang, Huiru

2018-04-01

In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

Genetic analysis of grain attributes, milling performance, and end-use quality traits in hard red spring wheat (Triticum aestivum L.)

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture dictates U.S. wheat market class and culinary end-uses. Of interest to wheat breeders is to identify quantitative trait loci (QTL) for wheat kernel texture, milling performance, or end-use quality because it is imperative for wheat breeders to ascertain the genetic architecture ...

A sparse matrix-vector multiplication based algorithm for accurate density matrix computations on systems of millions of atoms

NASA Astrophysics Data System (ADS)

Ghale, Purnima; Johnson, Harley T.

2018-06-01

We present an efficient sparse matrix-vector (SpMV) based method to compute the density matrix P from a given Hamiltonian in electronic structure computations. Our method is a hybrid approach based on Chebyshev-Jackson approximation theory and matrix purification methods like the second order spectral projection purification (SP2). Recent methods to compute the density matrix scale as O(N) in the number of floating point operations but are accompanied by large memory and communication overhead, and they are based on iterative use of the sparse matrix-matrix multiplication kernel (SpGEMM), which is known to be computationally irregular. In addition to irregularity in the sparse Hamiltonian H, the nonzero structure of intermediate estimates of P depends on products of H and evolves over the course of computation. On the other hand, an expansion of the density matrix P in terms of Chebyshev polynomials is straightforward and SpMV based; however, the resulting density matrix may not satisfy the required constraints exactly. In this paper, we analyze the strengths and weaknesses of the Chebyshev-Jackson polynomials and the second order spectral projection purification (SP2) method, and propose to combine them so that the accurate density matrix can be computed using the SpMV computational kernel only, and without having to store the density matrix P. Our method accomplishes these objectives by using the Chebyshev polynomial estimate as the initial guess for SP2, which is followed by using sparse matrix-vector multiplications (SpMVs) to replicate the behavior of the SP2 algorithm for purification. We demonstrate the method on a tight-binding model system of an oxide material containing more than 3 million atoms. In addition, we also present the predicted behavior of our method when applied to near-metallic Hamiltonians with a wide energy spectrum.

High-Throughput Sequencing of Small RNA Transcriptomes in Maize Kernel Identifies miRNAs Involved in Embryo and Endosperm Development.

PubMed

Xing, Lijuan; Zhu, Ming; Zhang, Min; Li, Wenzong; Jiang, Haiyang; Zou, Junjie; Wang, Lei; Xu, Miaoyun

2017-12-14

Maize kernel development is a complex biological process that involves the temporal and spatial expression of many genes and fine gene regulation at a transcriptional and post-transcriptional level, and microRNAs (miRNAs) play vital roles during this process. To gain insight into miRNA-mediated regulation of maize kernel development, a deep-sequencing technique was used to investigate the dynamic expression of miRNAs in the embryo and endosperm at three developmental stages in B73. By miRNA transcriptomic analysis, we characterized 132 known miRNAs and six novel miRNAs in developing maize kernel, among which, 15 and 14 miRNAs were commonly differentially expressed between the embryo and endosperm at 9 days after pollination (DAP), 15 DAP and 20 DAP respectively. Conserved miRNA families such as miR159, miR160, miR166, miR390, miR319, miR528 and miR529 were highly expressed in developing embryos; miR164, miR171, miR393 and miR2118 were highly expressed in developing endosperm. Genes targeted by those highly expressed miRNAs were found to be largely related to a regulation category, including the transcription, macromolecule biosynthetic and metabolic process in the embryo as well as the vitamin biosynthetic and metabolic process in the endosperm. Quantitative reverse transcription-PCR (qRT-PCR) analysis showed that these miRNAs displayed a negative correlation with the levels of their corresponding target genes. Importantly, our findings revealed that members of the miR169 family were highly and dynamically expressed in the developing kernel, which will help to exploit new players functioning in maize kernel development.

Individual detection of genetically modified maize varieties in non-identity-preserved maize samples.

PubMed

Akiyama, Hiroshi; Sakata, Kozue; Kondo, Kazunari; Tanaka, Asako; Liu, Ming S; Oguchi, Taichi; Furui, Satoshi; Kitta, Kazumi; Hino, Akihiro; Teshima, Reiko

2008-03-26

In many countries, the labeling of grains and feed- and foodstuffs is mandatory if the genetically modified organism (GMO) content exceeds a certain level of approved GM varieties. The GMO content in a maize sample containing the combined-trait (stacked) GM maize as determined by the currently available methodology is likely to be overestimated. However, there has been little information in the literature on the mixing level and varieties of stacked GM maize in real sample grains. For the first time, the GMO content of non-identity-preserved (non-IP) maize samples imported from the United States has been successfully determined by using a previously developed individual kernel detection system coupled to a multiplex qualitative PCR method followed by multichannel capillary gel electrophoresis system analysis. To clarify the GMO content in the maize samples imported from the United States, determine how many stacked GM traits are contained therein, and which GM trait varieties frequently appeared in 2005, the GMO content (percent) on a kernel basis and the varieties of the GM kernels in the non-IP maize samples imported from the United States were investigated using the individual kernel analysis system. The average (+/-standard deviation) of the GMO contents on a kernel basis in five non-IP sample lots was determined to be 51.0+/-21.6%, the percentage of a single GM trait grains was 39%, and the percentage of the stacked GM trait grains was 12%. The MON810 grains and NK603 grains were the most frequent varieties in the single GM traits. The most frequent stacked GM traits were the MON810xNK603 grains. In addition, the present study would provide the answer and impact for the quantification of GM maize content in the GM maize kernels on labeling regulation.

The Nature of Turbulence in the LITTLE THINGS Dwarf Irregular Galaxies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maier, Erin; Chien, Li-Hsin; Hollyday, Gigja

We present probability density functions and higher order (skewness and kurtosis) analyses of the galaxy-wide and spatially resolved distributions of H i column density in the LITTLE THINGS sample of dwarf irregular galaxies. This analysis follows that of Burkhart et al. for the Small Magellanic Cloud (SMC). About 60% of our sample have galaxy-wide values of kurtosis that are similar to that found for the SMC, with a range up to much higher values, and kurtosis increases with integrated star formation rate. Kurtosis and skewness were calculated for radial annuli and for a grid of 32 pixel × 32 pixel kernels acrossmore » each galaxy. For most galaxies, kurtosis correlates with skewness. For about half of the galaxies, there is a trend of increasing kurtosis with radius. The range of kurtosis and skewness values is modeled by small variations in the Mach number close to the sonic limit and by conversion of H i to molecules at high column density. The maximum H i column densities decrease with increasing radius in a way that suggests molecules are forming in the weak-field limit, where H{sub 2} formation balances photodissociation in optically thin gas at the edges of clouds.« less

Congested Aggregation via Newtonian Interaction

NASA Astrophysics Data System (ADS)

Craig, Katy; Kim, Inwon; Yao, Yao

2018-01-01

We consider a congested aggregation model that describes the evolution of a density through the competing effects of nonlocal Newtonian attraction and a hard height constraint. This provides a counterpoint to existing literature on repulsive-attractive nonlocal interaction models, where the repulsive effects instead arise from an interaction kernel or the addition of diffusion. We formulate our model as the Wasserstein gradient flow of an interaction energy, with a penalization to enforce the constraint on the height of the density. From this perspective, the problem can be seen as a singular limit of the Keller-Segel equation with degenerate diffusion. Two key properties distinguish our problem from previous work on height constrained equations: nonconvexity of the interaction kernel (which places the model outside the scope of classical gradient flow theory) and nonlocal dependence of the velocity field on the density (which causes the problem to lack a comparison principle). To overcome these obstacles, we combine recent results on gradient flows of nonconvex energies with viscosity solution theory. We characterize the dynamics of patch solutions in terms of a Hele-Shaw type free boundary problem and, using this characterization, show that in two dimensions patch solutions converge to a characteristic function of a disk in the long-time limit, with an explicit rate on the decay of the energy. We believe that a key contribution of the present work is our blended approach, combining energy methods with viscosity solution theory.

Habitat characteristics of adult frosted elfins (Callophrys irus) in sandplain communities of southeastern Massachusetts, USA

USGS Publications Warehouse

Albanese, G.; Vickery, P.D.; Sievert, P.R.

2007-01-01

Changes to land use and disturbance frequency threaten disturbance-dependent Lepidoptera within sandplain habitats of the northeastern United States. The frosted elfin (Callophrys irus) is a rare and declining monophagous butterfly that is found in xeric open habitats maintained by disturbance. We surveyed potential habitat for adult frosted elfins at four sites containing frosted elfin populations in southeastern Massachusetts, United States. Based on the survey data, we used kernel density estimation to establish separate adult frosted elfin density classes, and then used regression tree analysis to describe the relationship between density and habitat features. Adult frosted elfin density was greatest when the host plant, wild indigo (Baptisia tinctoria), density was >2.6 plants/m2 and tree canopy cover was <29%. Frosted elfin density was inversely related to tree cover and declined when the density of wild indigo was <2.6 plants/m2 and shrub cover was ???16%. Even small quantities of non-native shrub cover negatively affected elfin densities. This effect was more pronounced when native herbaceous cover was <36%. Our results indicate that management for frosted elfins should aim to increase both wild indigo density and native herbaceous cover and limit native tree and shrub cover in open sandplain habitats. Elimination of non-native shrub cover is also recommended because of the negative effects of even low non-native shrub cover on frosted elfin densities. The maintenance of patches of early successional sandplain habitat with the combination of low tree and shrub cover, high host plant densities, and the absence of non-native shrubs appears essential for frosted elfin persistence, but may also be beneficial for a number of other rare sandplain insects and plant species. ?? 2006 Elsevier Ltd. All rights reserved.

Interlaboratory study of DNA extraction from multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for individual kernel detection system of genetically modified maize.

PubMed

Akiyama, Hiroshi; Sakata, Kozue; Makiyma, Daiki; Nakamura, Kosuke; Teshima, Reiko; Nakashima, Akie; Ogawa, Asako; Yamagishi, Toru; Futo, Satoshi; Oguchi, Taichi; Mano, Junichi; Kitta, Kazumi

2011-01-01

In many countries, the labeling of grains, feed, and foodstuff is mandatory if the genetically modified (GM) organism content exceeds a certain level of approved GM varieties. We previously developed an individual kernel detection system consisting of grinding individual kernels, DNA extraction from the individually ground kernels, GM detection using multiplex real-time PCR, and GM event detection using multiplex qualitative PCR to analyze the precise commingling level and varieties of GM maize in real sample grains. We performed the interlaboratory study of the DNA extraction with multiple ground samples, multiplex real-time PCR detection, and multiplex qualitative PCR detection to evaluate its applicability, practicality, and ruggedness for the individual kernel detection system of GM maize. DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR were evaluated by five laboratories in Japan, and all results from these laboratories were consistent with the expected results in terms of the commingling level and event analysis. Thus, the DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for the individual kernel detection system is applicable and practicable in a laboratory to regulate the commingling level of GM maize grain for GM samples, including stacked GM maize.

Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions.

PubMed

Wong, Stephen; Hargreaves, Eric L; Baltuch, Gordon H; Jaggi, Jurg L; Danish, Shabbar F

2012-01-01

Microelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery. We evaluated two methods of interpolation (standard vs. kernel) that generated spatial profiles from temporal trends. We compared interpolated spatial profiles to true spatial profiles that were calculated with depth windows, using correlation coefficient analysis. Excellent approximation of true spatial profiles is achieved by interpolation. Kernel-interpolated spatial profiles produced superior correlation coefficient values at optimal kernel widths (r = 0.932-0.940) compared to standard interpolation (r = 0.891). The choice of kernel function and kernel width resulted in trade-offs in smoothing and resolution. Interpolation of feature activity to create spatial profiles from temporal trends is accurate and can standardize and facilitate MER functional localization of subcortical structures. The methods are computationally efficient, enhancing localization without imposing additional constraints on the MER clinical procedure during DBS surgery. Copyright © 2012 S. Karger AG, Basel.

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-04

Cassava ( Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant. Copyright © 2018 Elias et al.

Performance analysis and kernel size study of the Lynx real-time operating system

NASA Technical Reports Server (NTRS)

Liu, Yuan-Kwei; Gibson, James S.; Fernquist, Alan R.

1993-01-01

This paper analyzes the Lynx real-time operating system (LynxOS), which has been selected as the operating system for the Space Station Freedom Data Management System (DMS). The features of LynxOS are compared to other Unix-based operating system (OS). The tools for measuring the performance of LynxOS, which include a high-speed digital timer/counter board, a device driver program, and an application program, are analyzed. The timings for interrupt response, process creation and deletion, threads, semaphores, shared memory, and signals are measured. The memory size of the DMS Embedded Data Processor (EDP) is limited. Besides, virtual memory is not suitable for real-time applications because page swap timing may not be deterministic. Therefore, the DMS software, including LynxOS, has to fit in the main memory of an EDP. To reduce the LynxOS kernel size, the following steps are taken: analyzing the factors that influence the kernel size; identifying the modules of LynxOS that may not be needed in an EDP; adjusting the system parameters of LynxOS; reconfiguring the device drivers used in the LynxOS; and analyzing the symbol table. The reductions in kernel disk size, kernel memory size and total kernel size reduction from each step mentioned above are listed and analyzed.

Moisture Adsorption Isotherm and Storability of Hazelnut Inshells and Kernels Produced in Oregon, USA.

PubMed

Jung, Jooyeoun; Wang, Wenjie; McGorrin, Robert J; Zhao, Yanyun

2018-02-01

Moisture adsorption isotherms and storability of dried hazelnut inshells and kernels produced in Oregon were evaluated and compared among cultivars, including Barcelona, Yamhill, and Jefferson. Experimental moisture adsorption data fitted to Guggenheim-Anderson-de Boer (GAB) model, showing less hygroscopic properties in Yamhill than other cultivars of inshells and kernels due to lower content of carbohydrate and protein, but higher content of fat. The safe levels of moisture content (MC, dry basis) of dried inshells and kernels for reaching kernel water activity (a w ) ≤0.65 were estimated using the GAB model as 11.3% and 5.0% for Barcelona, 9.4% and 4.2% for Yamhill, and 10.7% and 4.9% for Jefferson, respectively. Storage conditions (2 °C at 85% to 95% relative humidity [RH], 10 °C at 65% to 75% RH, and 27 °C at 35% to 45% RH), times (0, 4, 8, or 12 mo), and packaging methods (atmosphere vs. vacuum) affected MC, a w , bioactive compounds, lipid oxidation, and enzyme activity of dried hazelnut inshells or kernels. For inshells packaged at woven polypropylene bag, MC and a w of inshells and kernels (inside shells) increased at 2 and 10 °C, but decreased at 27 °C during storage. For kernels, lipid oxidation and polyphenol oxidase activity also increased with extended storage time (P < 0.05), and MC and a w of vacuum packaged samples were more stable during storage than those atmospherically packaged ones. Principal component analysis showed correlation of kernel qualities with storage condition, time, and packaging method. This study demonstrated that the ideal storage condition or packaging method varied among cultivars due to their different moisture adsorption and physicochemical and enzymatic stability during storage. Moisture adsorption isotherm of hazelnut inshells and kernels is useful for predicting the storability of nuts. This study found that water adsorption and storability varied among the different cultivars of nuts, in which Yamhill was less hygroscopic than Barcelona and Jefferson, thus more stable during storage. For ensuring food safety and quality of nuts during storage, each cultivar of kernels should be dried to a certain level of MC. Lipid oxidation and enzyme activity of kernel could be increased with extended storage time. Vacuum packaging was recommended to kernels for reducing moisture adsorption during storage. © 2018 Institute of Food Technologists®.

The social evaluation of faces: a meta-analysis of functional neuroimaging studies

PubMed Central

Mende-Siedlecki, Peter; Said, Christopher P.

2013-01-01

Neuroscience research on the social evaluation of faces has accumulated over the last decade, yielding divergent results. We used a meta-analytic technique, multi-level kernel density analysis (MKDA), to analyze 29 neuroimaging studies on face evaluation. Across negative face evaluations, we observed the most consistent activations in bilateral amygdala. Across positive face evaluations, we observed the most consistent activations in medial prefrontal cortex, pregenual anterior cingulate cortex (pgACC), medial orbitofrontal cortex (mOFC), left caudate and nucleus accumbens (NAcc). Based on additional analyses comparing linear and non-linear responses, we propose a ventral/dorsal dissociation within the amygdala, wherein separate populations of neurons code for face valence and intensity, respectively. Finally, we argue that some of the differences between studies are attributable to differences in the typicality of face stimuli. Specifically, extremely attractive faces are more likely to elicit responses in NAcc/caudate and mOFC. PMID:22287188

Design and Analysis of Architectures for Structural Health Monitoring Systems

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi; Sixto, S. L. (Technical Monitor)

2002-01-01

During the two-year project period, we have worked on several aspects of Health Usage and Monitoring Systems for structural health monitoring. In particular, we have made contributions in the following areas. 1. Reference HUMS architecture: We developed a high-level architecture for health monitoring and usage systems (HUMS). The proposed reference architecture is shown. It is compatible with the Generic Open Architecture (GOA) proposed as a standard for avionics systems. 2. HUMS kernel: One of the critical layers of HUMS reference architecture is the HUMS kernel. We developed a detailed design of a kernel to implement the high level architecture.3. Prototype implementation of HUMS kernel: We have implemented a preliminary version of the HUMS kernel on a Unix platform.We have implemented both a centralized system version and a distributed version. 4. SCRAMNet and HUMS: SCRAMNet (Shared Common Random Access Memory Network) is a system that is found to be suitable to implement HUMS. For this reason, we have conducted a simulation study to determine its stability in handling the input data rates in HUMS. 5. Architectural specification.

[Ttextual research of Cannabis sativa varieties and medicinal part].

PubMed

Wei, Yingfang; Wang, Huadong; Guo, Shanshan; Yan, Jie; Long, Fei

2010-07-01

To determine the medicinal part and varieties of Cannabis Sativa through herbal textual research to Provide bibliographic reference for clinical application. Herbal textual research of C. Sativa from ancient herbal works and modern data analysis. Through the herbal textual research, the plant of the C. sativa, for Fructus Cannabis used now is identical with that described in ancient herbal literatures. People did not make a sharp distinction on medicinal part of C. sativa in the early stage literatures, female inflorescence and unripe fruit, fruit and kernel of seed were all used. Since Taohongjing realized the toxicity ofpericarp, all the herbal and prescription works indicate that the pericarp shall be removed before usage and only the kernel can be used. However, in modem literatures, both fruit and kernel can be used as medicinal part. The plants for Fructus Cannabis described in modern and ancient literatures are identical. The base of the original plant is the same either in ancient or modern. And the toxicity of the fruit is more than that of the kernel. The kernel is the exact medicinal part of C. Sativa.

Characterization of Mesocarp and Kernel Lipids from Elaeis guineensis Jacq., Elaeis oleifera [Kunth] Cortés, and Their Interspecific Hybrids.

PubMed

Lieb, Veronika M; Kerfers, Margarete R; Kronmüller, Amrei; Esquivel, Patricia; Alvarado, Amancio; Jiménez, Víctor M; Schmarr, Hans-Georg; Carle, Reinhold; Schweiggert, Ralf M; Steingass, Christof B

2017-05-10

Morphological traits, total lipid contents, and fatty acid profiles were assessed in fruits of several accessions of Elaeis oleifera [Kunth] Cortés, Elaeis guineensis Jacq., and their interspecific hybrids. The latter featured the highest mesocarp-to-fruit ratios (77.9-78.2%). The total lipid contents of both E. guineensis mesocarp and kernel were significantly higher than for E. oleifera accessions. Main fatty acids comprised C16:0, C18:1n9, and C18:2n6 in mesocarp and C12:0, C14:0, and C18:1n9 in kernels. E. oleifera samples were characterized by higher proportions of unsaturated long-chain fatty acids. Saturated medium-chain fatty acids supported the clustering of E. guineensis kernels in multivariate statistics. Hybrid mesocarp lipids had an intermediate fatty acid composition, whereas their kernel lipids resembled those of E. oleifera genotypes. Principal component analysis based on lipid contents and proportions of individual fatty acids permitted clear-cut distinction of E. oleifera, E. guineensis, and their hybrids.

Production of LEU Fully Ceramic Microencapsulated Fuel for Irradiation Testing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Terrani, Kurt A; Kiggans Jr, James O; McMurray, Jake W

2016-01-01

Fully Ceramic Microencapsulated (FCM) fuel consists of tristructural isotropic (TRISO) fuel particles embedded inside a SiC matrix. This fuel inherently possesses multiple barriers to fission product release, namely the various coating layers in the TRISO fuel particle as well as the dense SiC matrix that hosts these particles. This coupled with the excellent oxidation resistance of the SiC matrix and the SiC coating layer in the TRISO particle designate this concept as an accident tolerant fuel (ATF). The FCM fuel takes advantage of uranium nitride kernels instead of oxide or oxide-carbide kernels used in high temperature gas reactors to enhancemore » heavy metal loading in the highly moderated LWRs. Production of these kernels with appropriate density, coating layer development to produce UN TRISO particles, and consolidation of these particles inside a SiC matrix have been codified thanks to significant R&D supported by US DOE Fuel Cycle R&D program. Also, surrogate FCM pellets (pellets with zirconia instead of uranium-bearing kernels) have been neutron irradiated and the stability of the matrix and coating layer under LWR irradiation conditions have been established. Currently the focus is on production of LEU (7.3% U-235 enrichment) FCM pellets to be utilized for irradiation testing. The irradiation is planned at INL s Advanced Test Reactor (ATR). This is a critical step in development of this fuel concept to establish the ability of this fuel to retain fission products under prototypical irradiation conditions.« less

QVAST: a new Quantum GIS plugin for estimating volcanic susceptibility

NASA Astrophysics Data System (ADS)

Bartolini, S.; Cappello, A.; Martí, J.; Del Negro, C.

2013-08-01

One of the most important tasks of modern volcanology is the construction of hazard maps simulating different eruptive scenarios that can be used in risk-based decision-making in land-use planning and emergency management. The first step in the quantitative assessment of volcanic hazards is the development of susceptibility maps, i.e. the spatial probability of a future vent opening given the past eruptive activity of a volcano. This challenging issue is generally tackled using probabilistic methods that use the calculation of a kernel function at each data location to estimate probability density functions (PDFs). The smoothness and the modeling ability of the kernel function are controlled by the smoothing parameter, also known as the bandwidth. Here we present a new tool, QVAST, part of the open-source Geographic Information System Quantum GIS, that is designed to create user-friendly quantitative assessments of volcanic susceptibility. QVAST allows to select an appropriate method for evaluating the bandwidth for the kernel function on the basis of the input parameters and the shapefile geometry, and can also evaluate the PDF with the Gaussian kernel. When different input datasets are available for the area, the total susceptibility map is obtained by assigning different weights to each of the PDFs, which are then combined via a weighted summation and modeled in a non-homogeneous Poisson process. The potential of QVAST, developed in a free and user-friendly environment, is here shown through its application in the volcanic fields of Lanzarote (Canary Islands) and La Garrotxa (NE Spain).

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

Single-kernel analysis of fumonisins and other fungal metabolites in maize from South African subsistence farmers.

PubMed

Mogensen, J M; Sørensen, S M; Sulyok, M; van der Westhuizen, L; Shephard, G S; Frisvad, J C; Thrane, U; Krska, R; Nielsen, K F

2011-12-01

Fumonisins are important Fusarium mycotoxins mainly found in maize and derived products. This study analysed maize from five subsistence farmers in the former Transkei region of South Africa. Farmers had sorted kernels into good and mouldy quality. A total of 400 kernels from 10 batches were analysed; of these 100 were visually characterised as uninfected and 300 as infected. Of the 400 kernels, 15% were contaminated with 1.84-1428 mg kg(-1) fumonisins, and 4% (n=15) had a fumonisin content above 100 mg kg(-1). None of the visually uninfected maize had detectable amounts of fumonisins. The total fumonisin concentration was 0.28-1.1 mg kg(-1) for good-quality batches and 0.03-6.2 mg kg(-1) for mouldy-quality batches. The high fumonisin content in the batches was apparently caused by a small number (4%) of highly contaminated kernels, and removal of these reduced the average fumonisin content by 71%. Of the 400 kernels, 80 were screened for 186 microbial metabolites by liquid chromatography-tandem mass spectrometry, detecting 17 other fungal metabolites, including fusaric acid, equisetin, fusaproliferin, beauvericin, cyclosporins, agroclavine, chanoclavine, rugulosin and emodin. Fusaric acid in samples without fumonisins indicated the possibility of using non-toxinogenic Fusaria as biocontrol agents to reduce fumonisin exposure, as done for Aspergillus flavus. This is the first report of mycotoxin profiling in single naturally infected maize kernels. © 2011 Taylor & Francis

Scalable and Power Efficient Data Analytics for Hybrid Exascale Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choudhary, Alok; Samatova, Nagiza; Wu, Kesheng

This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high performance analytical pipelines. As the architectures of emerging HPC systems become inherently heterogeneous, there is a need to design algorithms for data analysis kernels accelerated on hybrid multi-node, multi-core HPC architectures comprised of a mix of CPUs, GPUs, and SSDs. Furthermore, the power-aware trend drives the advances in our performance-energy tradeoff analysis framework which enables our data analysis kernels algorithms and software to be parameterized so that users can choose the right power-performance optimizations.

The multifacet graphically contracted function method. I. Formulation and implementation

NASA Astrophysics Data System (ADS)

Shepard, Ron; Gidofalvi, Gergely; Brozell, Scott R.

2014-08-01

The basic formulation for the multifacet generalization of the graphically contracted function (MFGCF) electronic structure method is presented. The analysis includes the discussion of linear dependency and redundancy of the arc factor parameters, the computation of reduced density matrices, Hamiltonian matrix construction, spin-density matrix construction, the computation of optimization gradients for single-state and state-averaged calculations, graphical wave function analysis, and the efficient computation of configuration state function and Slater determinant expansion coefficients. Timings are given for Hamiltonian matrix element and analytic optimization gradient computations for a range of model problems for full-CI Shavitt graphs, and it is observed that both the energy and the gradient computation scale as O(N2n4) for N electrons and n orbitals. The important arithmetic operations are within dense matrix-matrix product computational kernels, resulting in a computationally efficient procedure. An initial implementation of the method is used to present applications to several challenging chemical systems, including N2 dissociation, cubic H8 dissociation, the symmetric dissociation of H2O, and the insertion of Be into H2. The results are compared to the exact full-CI values and also to those of the previous single-facet GCF expansion form.

The multifacet graphically contracted function method. I. Formulation and implementation.

PubMed

Shepard, Ron; Gidofalvi, Gergely; Brozell, Scott R

2014-08-14

The basic formulation for the multifacet generalization of the graphically contracted function (MFGCF) electronic structure method is presented. The analysis includes the discussion of linear dependency and redundancy of the arc factor parameters, the computation of reduced density matrices, Hamiltonian matrix construction, spin-density matrix construction, the computation of optimization gradients for single-state and state-averaged calculations, graphical wave function analysis, and the efficient computation of configuration state function and Slater determinant expansion coefficients. Timings are given for Hamiltonian matrix element and analytic optimization gradient computations for a range of model problems for full-CI Shavitt graphs, and it is observed that both the energy and the gradient computation scale as O(N(2)n(4)) for N electrons and n orbitals. The important arithmetic operations are within dense matrix-matrix product computational kernels, resulting in a computationally efficient procedure. An initial implementation of the method is used to present applications to several challenging chemical systems, including N2 dissociation, cubic H8 dissociation, the symmetric dissociation of H2O, and the insertion of Be into H2. The results are compared to the exact full-CI values and also to those of the previous single-facet GCF expansion form.

Aflatoxin B1 levels in groundnut products from local markets in Zambia.

PubMed

Njoroge, Samuel M C; Matumba, Limbikani; Kanenga, Kennedy; Siambi, Moses; Waliyar, Farid; Maruwo, Joseph; Machinjiri, Norah; Monyo, Emmanuel S

2017-05-01

In Zambia, groundnut products (milled groundnut powder, groundnut kernels) are mostly sold in under-regulated markets. Coupled with the lack of quality enforcement in such markets, consumers may be at risk to aflatoxin exposure. However, the level of aflatoxin contamination in these products is not known. Compared to groundnut kernels, milled groundnut powder obscures visual indicators of aflatoxin contamination in groundnuts such as moldiness, discoloration, insect damage or kernel damage. A survey was therefore conducted from 2012 to 2014, to estimate and compare aflatoxin levels in these products (n = 202), purchased from markets in important groundnut growing districts and in urban areas. Samples of whole groundnut kernels (n = 163) and milled groundnut powder (n = 39) were analysed for aflatoxin B 1 (AFB 1 ) by competitive enzyme-linked immunosorbent assay (cELISA). Results showed substantial AFB 1 contamination levels in both types of groundnut products with maximum AFB 1 levels of 11,100 μg/kg (groundnut kernels) and 3000 μg/kg (milled groundnut powder). However, paired t test analysis showed that AFB 1 contamination levels in milled groundnut powder were not always significantly higher (P > 0.05) than those in groundnut kernels. Even for products from the same vendor, AFB 1 levels were not consistently higher in milled groundnut powder than in whole groundnut kernels. This suggests that vendors do not systematically sort out whole groundnut kernels of visually poor quality for milling. However, the overall contamination levels of groundnut products with AFB 1 were found to be alarmingly high in all years and locations. Therefore, solutions are needed to reduce aflatoxin levels in such under-regulated markets.

An effective fuzzy kernel clustering analysis approach for gene expression data.

PubMed

Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

2015-01-01

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.

An Iterative Interplanetary Scintillation (IPS) Analysis Using Time-dependent 3-D MHD Models as Kernels

NASA Astrophysics Data System (ADS)

Jackson, B. V.; Yu, H. S.; Hick, P. P.; Buffington, A.; Odstrcil, D.; Kim, T. K.; Pogorelov, N. V.; Tokumaru, M.; Bisi, M. M.; Kim, J.; Yun, J.

2017-12-01

The University of California, San Diego has developed an iterative remote-sensing time-dependent three-dimensional (3-D) reconstruction technique which provides volumetric maps of density, velocity, and magnetic field. We have applied this technique in near real time for over 15 years with a kinematic model approximation to fit data from ground-based interplanetary scintillation (IPS) observations. Our modeling concept extends volumetric data from an inner boundary placed above the Alfvén surface out to the inner heliosphere. We now use this technique to drive 3-D MHD models at their inner boundary and generate output 3-D data files that are fit to remotely-sensed observations (in this case IPS observations), and iterated. These analyses are also iteratively fit to in-situ spacecraft measurements near Earth. To facilitate this process, we have developed a traceback from input 3-D MHD volumes to yield an updated boundary in density, temperature, and velocity, which also includes magnetic-field components. Here we will show examples of this analysis using the ENLIL 3D-MHD and the University of Alabama Multi-Scale Fluid-Kinetic Simulation Suite (MS-FLUKSS) heliospheric codes. These examples help refine poorly-known 3-D MHD variables (i.e., density, temperature), and parameters (gamma) by fitting heliospheric remotely-sensed data between the region near the solar surface and in-situ measurements near Earth.

A Projection and Density Estimation Method for Knowledge Discovery

PubMed Central

Stanski, Adam; Hellwich, Olaf

2012-01-01

A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features. PMID:23049675

Optimal focal-plane restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1989-01-01

Image restoration can be implemented efficiently by calculating the convolution of the digital image and a small kernel during image acquisition. Processing the image in the focal-plane in this way requires less computation than traditional Fourier-transform-based techniques such as the Wiener filter and constrained least-squares filter. Here, the values of the convolution kernel that yield the restoration with minimum expected mean-square error are determined using a frequency analysis of the end-to-end imaging system. This development accounts for constraints on the size and shape of the spatial kernel and all the components of the imaging system. Simulation results indicate the technique is effective and efficient.

Small-scale modification to the lensing kernel

NASA Astrophysics Data System (ADS)

Hadzhiyska, Boryana; Spergel, David; Dunkley, Joanna

2018-02-01

Calculations of the cosmic microwave background (CMB) lensing power implemented into the standard cosmological codes such as camb and class usually treat the surface of last scatter as an infinitely thin screen. However, since the CMB anisotropies are smoothed out on scales smaller than the diffusion length due to the effect of Silk damping, the photons which carry information about the small-scale density distribution come from slightly earlier times than the standard recombination time. The dominant effect is the scale dependence of the mean redshift associated with the fluctuations during recombination. We find that fluctuations at k =0.01 Mpc-1 come from a characteristic redshift of z ≈1090 , while fluctuations at k =0.3 Mpc-1 come from a characteristic redshift of z ≈1130 . We then estimate the corrections to the lensing kernel and the related power spectra due to this effect. We conclude that neglecting it would result in a deviation from the true value of the lensing kernel at the half percent level at small CMB scales. For an all-sky, noise-free experiment, this corresponds to a ˜0.1 σ shift in the observed temperature power spectrum on small scales (2500 ≲l ≲4000 ).

Knowledge Driven Image Mining with Mixture Density Mercer Kernals

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper we present the theory of Mercer Kernels; describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Genetic analysis of kernel texture (grain hardness) in a hard red spring wheat (Triticum aestivum L.) bi-parental population

USDA-ARS?s Scientific Manuscript database

Grain hardness is a very important trait in determining wheat market class and also influences milling and baking traits. At the grain Hardness (Ha) locus on chromosome 5DS, there are two primary mutations responsible for conveying a harder kernel texture among U.S. hard red spring wheats: (1) the P...

Hyperspectral imaging for detection of black tip damage in wheat kernels

NASA Astrophysics Data System (ADS)

Delwiche, Stephen R.; Yang, I.-Chang; Kim, Moon S.

2009-05-01

A feasibility study was conducted on the use of hyperspectral imaging to differentiate sound wheat kernels from those with the fungal condition called black point or black tip. Individual kernels of hard red spring wheat were loaded in indented slots on a blackened machined aluminum plate. Damage conditions, determined by official (USDA) inspection, were either sound (no damage) or damaged by the black tip condition alone. Hyperspectral imaging was separately performed under modes of reflectance from white light illumination and fluorescence from UV light (~380 nm) illumination. By cursory inspection of wavelength images, one fluorescence wavelength (531 nm) was selected for image processing and classification analysis. Results indicated that with this one wavelength alone, classification accuracy can be as high as 95% when kernels are oriented with their dorsal side toward the camera. It is suggested that improvement in classification can be made through the inclusion of multiple wavelength images.

RTOS kernel in portable electrocardiograph

NASA Astrophysics Data System (ADS)

Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.

2011-12-01

This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

PubMed

Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

2012-01-01

Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.

Geospatial analysis of cyclist injury trends: an investigation in Melbourne, Australia.

PubMed

Lawrence, Brendan M; Stevenson, Mark R; Oxley, Jennifer A; Logan, David B

2015-01-01

This study applied geospatial analysis to explore spatial trends in cycling-related injury in Melbourne, Australia, in order to identify an area where injury density was reducing against expectation. The crash characteristics and cycling environment of the identified area were examined to better understand factors related to cycling safety. Two methods were used to examine spatial trends in cycling-related injury. Firstly, cycling injury density was calculated using a kernel density estimation method for the years 2000 to 2011. This was used to examine patterns in injury density across Melbourne over an extended time period. Secondly, absolute change in injury density was calculated between 2005 and 2011. From this, a geographical area presenting a reduced injury density was selected for a case study, and crash characteristics of the area were obtained for the observational period. This led to discussion on which changes to the cycling environment, if any, may be associated with the reduced injury rate. Injury density in Melbourne had been progressively increasing between 2000 and 2011, with a nearly 3-fold increase in the peak injury density over that period. Decreases were observed in some locations between 2005 and 2011, and a geographical area to the southeast of Melbourne experienced a more significant decrease than others. This appeared to be associated with a combination of behavior and road infrastructure change, although a lack of data to verify change in cycling exposure prevented more definitive associations from being established. The apparent positive response of the injury rate to behavior and road infrastructure interventions is promising, yet the injury rate is unlikely to achieve the government's road safety target of 30% reduction in serious injuries by 2022. Moreover, the number of injuries sustained at the most common crash location appears to be increasing. Further research is necessary to discern which specific features of the urban road infrastructure have an effect on the risk of injury to a cyclist and which combination of features is consistent with a safe cycling environment.

Applications of the line-of-response probability density function resolution model in PET list mode reconstruction.

PubMed

Jian, Y; Yao, R; Mulnix, T; Jin, X; Carson, R E

2015-01-07

Resolution degradation in PET image reconstruction can be caused by inaccurate modeling of the physical factors in the acquisition process. Resolution modeling (RM) is a common technique that takes into account the resolution degrading factors in the system matrix. Our previous work has introduced a probability density function (PDF) method of deriving the resolution kernels from Monte Carlo simulation and parameterizing the LORs to reduce the number of kernels needed for image reconstruction. In addition, LOR-PDF allows different PDFs to be applied to LORs from different crystal layer pairs of the HRRT. In this study, a thorough test was performed with this new model (LOR-PDF) applied to two PET scanners-the HRRT and Focus-220. A more uniform resolution distribution was observed in point source reconstructions by replacing the spatially-invariant kernels with the spatially-variant LOR-PDF. Specifically, from the center to the edge of radial field of view (FOV) of the HRRT, the measured in-plane FWHMs of point sources in a warm background varied slightly from 1.7 mm to 1.9 mm in LOR-PDF reconstructions. In Minihot and contrast phantom reconstructions, LOR-PDF resulted in up to 9% higher contrast at any given noise level than image-space resolution model. LOR-PDF also has the advantage in performing crystal-layer-dependent resolution modeling. The contrast improvement by using LOR-PDF was verified statistically by replicate reconstructions. In addition, [(11)C]AFM rats imaged on the HRRT and [(11)C]PHNO rats imaged on the Focus-220 were utilized to demonstrated the advantage of the new model. Higher contrast between high-uptake regions of only a few millimeter diameter and the background was observed in LOR-PDF reconstruction than in other methods.

Applications of the line-of-response probability density function resolution model in PET list mode reconstruction

PubMed Central

Jian, Y; Yao, R; Mulnix, T; Jin, X; Carson, R E

2016-01-01

Resolution degradation in PET image reconstruction can be caused by inaccurate modeling of the physical factors in the acquisition process. Resolution modeling (RM) is a common technique that takes into account the resolution degrading factors in the system matrix. Our previous work has introduced a probability density function (PDF) method of deriving the resolution kernels from Monte Carlo simulation and parameterizing the LORs to reduce the number of kernels needed for image reconstruction. In addition, LOR-PDF allows different PDFs to be applied to LORs from different crystal layer pairs of the HRRT. In this study, a thorough test was performed with this new model (LOR-PDF) applied to two PET scanners - the HRRT and Focus-220. A more uniform resolution distribution was observed in point source reconstructions by replacing the spatially-invariant kernels with the spatially-variant LOR-PDF. Specifically, from the center to the edge of radial field of view (FOV) of the HRRT, the measured in-plane FWHMs of point sources in a warm background varied slightly from 1.7 mm to 1.9 mm in LOR-PDF reconstructions. In Minihot and contrast phantom reconstructions, LOR-PDF resulted in up to 9% higher contrast at any given noise level than image-space resolution model. LOR-PDF also has the advantage in performing crystal-layer-dependent resolution modeling. The contrast improvement by using LOR-PDF was verified statistically by replicate reconstructions. In addition, [11C]AFM rats imaged on the HRRT and [11C]PHNO rats imaged on the Focus-220 were utilized to demonstrated the advantage of the new model. Higher contrast between high-uptake regions of only a few millimeter diameter and the background was observed in LOR-PDF reconstruction than in other methods. PMID:25490063

Infrared microspectroscopic imaging of plant tissues: spectral visualization of Triticum aestivum kernel and Arabidopsis leaf microstructure.

PubMed

Warren, Frederick J; Perston, Benjamin B; Galindez-Najera, Silvia P; Edwards, Cathrina H; Powell, Prudence O; Mandalari, Giusy; Campbell, Grant M; Butterworth, Peter J; Ellis, Peter R

2015-11-01

Infrared microspectroscopy is a tool with potential for studies of the microstructure, chemical composition and functionality of plants at a subcellular level. Here we present the use of high-resolution bench top-based infrared microspectroscopy to investigate the microstructure of Triticum aestivum L. (wheat) kernels and Arabidopsis leaves. Images of isolated wheat kernel tissues and whole wheat kernels following hydrothermal processing and simulated gastric and duodenal digestion were generated, as well as images of Arabidopsis leaves at different points during a diurnal cycle. Individual cells and cell walls were resolved, and large structures within cells, such as starch granules and protein bodies, were clearly identified. Contrast was provided by converting the hyperspectral image cubes into false-colour images using either principal component analysis (PCA) overlays or by correlation analysis. The unsupervised PCA approach provided a clear view of the sample microstructure, whereas the correlation analysis was used to confirm the identity of different anatomical structures using the spectra from isolated components. It was then demonstrated that gelatinized and native starch within cells could be distinguished, and that the loss of starch during wheat digestion could be observed, as well as the accumulation of starch in leaves during a diurnal period. © 2015 The Authors The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

PubMed Central

Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German

2017-01-01

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897

Sensory and instrumental texture assessment of roasted pistachio nut/kernel by partial least square (PLS) regression analysis: effect of roasting conditions.

PubMed

Mohammadi Moghaddam, Toktam; Razavi, Seyed M A; Taghizadeh, Masoud; Sazgarnia, Ameneh

2016-01-01

Roasting is an important step in the processing of pistachio nuts. The effect of hot air roasting temperature (90, 120 and 150 °C), time (20, 35 and 50 min) and air velocity (0.5, 1.5 and 2.5 m/s) on textural and sensory characteristics of pistachio nuts and kernels were investigated. The results showed that increasing the roasting temperature decreased the fracture force (82-25.54 N), instrumental hardness (82.76-37.59 N), apparent modulus of elasticity (47-21.22 N/s), compressive energy (280.73-101.18 N.s) and increased amount of bitterness (1-2.5) and the hardness score (6-8.40) of pistachio kernels. Higher roasting time improved the flavor of samples. The results of the consumer test showed that the roasted pistachio kernels have good acceptability for flavor (score 5.83-8.40), color (score 7.20-8.40) and hardness (score 6-8.40) acceptance. Moreover, Partial Least Square (PLS) analysis of instrumental and sensory data provided important information for the correlation of objective and subjective properties. The univariate analysis showed that over 93.87 % of the variation in sensory hardness and almost 87 % of the variation in sensory acceptability could be explained by instrumental texture properties.

Pure endmember extraction using robust kernel archetypoid analysis for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Yang, Gang; Wu, Ke; Li, Weiyue; Zhang, Dianfa

2017-09-01

A robust kernel archetypoid analysis (RKADA) method is proposed to extract pure endmembers from hyperspectral imagery (HSI). The RKADA assumes that each pixel is a sparse linear mixture of all endmembers and each endmember corresponds to a real pixel in the image scene. First, it improves the re8gular archetypal analysis with a new binary sparse constraint, and the adoption of the kernel function constructs the principal convex hull in an infinite Hilbert space and enlarges the divergences between pairwise pixels. Second, the RKADA transfers the pure endmember extraction problem into an optimization problem by minimizing residual errors with the Huber loss function. The Huber loss function reduces the effects from big noises and outliers in the convergence procedure of RKADA and enhances the robustness of the optimization function. Third, the random kernel sinks for fast kernel matrix approximation and the two-stage algorithm for optimizing initial pure endmembers are utilized to improve its computational efficiency in realistic implementations of RKADA, respectively. The optimization equation of RKADA is solved by using the block coordinate descend scheme and the desired pure endmembers are finally obtained. Six state-of-the-art pure endmember extraction methods are employed to make comparisons with the RKADA on both synthetic and real Cuprite HSI datasets, including three geometrical algorithms vertex component analysis (VCA), alternative volume maximization (AVMAX) and orthogonal subspace projection (OSP), and three matrix factorization algorithms the preconditioning for successive projection algorithm (PreSPA), hierarchical clustering based on rank-two nonnegative matrix factorization (H2NMF) and self-dictionary multiple measurement vector (SDMMV). Experimental results show that the RKADA outperforms all the six methods in terms of spectral angle distance (SAD) and root-mean-square-error (RMSE). Moreover, the RKADA has short computational times in offline operations and shows significant improvement in identifying pure endmembers for ground objects with smaller spectrum differences. Therefore, the RKADA could be an alternative for pure endmember extraction from hyperspectral images.

On processed splitting methods and high-order actions in path-integral Monte Carlo simulations.

PubMed

Casas, Fernando

2010-10-21

Processed splitting methods are particularly well adapted to carry out path-integral Monte Carlo (PIMC) simulations: since one is mainly interested in estimating traces of operators, only the kernel of the method is necessary to approximate the thermal density matrix. Unfortunately, they suffer the same drawback as standard, nonprocessed integrators: kernels of effective order greater than two necessarily involve some negative coefficients. This problem can be circumvented, however, by incorporating modified potentials into the composition, thus rendering schemes of higher effective order. In this work we analyze a family of fourth-order schemes recently proposed in the PIMC setting, paying special attention to their linear stability properties, and justify their observed behavior in practice. We also propose a new fourth-order scheme requiring the same computational cost but with an enlarged stability interval.

Power Series Approximation for the Correlation Kernel Leading to Kohn-Sham Methods Combining Accuracy, Computational Efficiency, and General Applicability

NASA Astrophysics Data System (ADS)

Erhard, Jannis; Bleiziffer, Patrick; Görling, Andreas

2016-09-01

A power series approximation for the correlation kernel of time-dependent density-functional theory is presented. Using this approximation in the adiabatic-connection fluctuation-dissipation (ACFD) theorem leads to a new family of Kohn-Sham methods. The new methods yield reaction energies and barriers of unprecedented accuracy and enable a treatment of static (strong) correlation with an accuracy of high-level multireference configuration interaction methods but are single-reference methods allowing for a black-box-like handling of static correlation. The new methods exhibit a better scaling of the computational effort with the system size than rivaling wave-function-based electronic structure methods. Moreover, the new methods do not suffer from the problem of singularities in response functions plaguing previous ACFD methods and therefore are applicable to any type of electronic system.

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Kernel abortion in maize : I. Carbohydrate concentration patterns and Acid invertase activity of maize kernels induced to abort in vitro.

PubMed

Hanft, J M; Jones, R J

1986-06-01

Kernels cultured in vitro were induced to abort by high temperature (35 degrees C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35 degrees C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth.

Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Zheng, Yuan F.

2017-01-01

Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches. PMID:24808345

Kernel Tuning and Nonuniform Influence on Optical and Electrochemical Gaps of Bimetal Nanoclusters.

PubMed

He, Lizhong; Yuan, Jinyun; Xia, Nan; Liao, Lingwen; Liu, Xu; Gan, Zibao; Wang, Chengming; Yang, Jinlong; Wu, Zhikun

2018-03-14

Fine tuning nanoparticles with atomic precision is exciting and challenging and is critical for tuning the properties, understanding the structure-property correlation and determining the practical applications of nanoparticles. Some ultrasmall thiolated metal nanoparticles (metal nanoclusters) have been shown to be precisely doped, and even the protecting staple metal atom could be precisely reduced. However, the precise addition or reduction of the kernel atom while the other metal atoms in the nanocluster remain the same has not been successful until now, to the best of our knowledge. Here, by carefully selecting the protecting ligand with adequate steric hindrance, we synthesized a novel nanocluster in which the kernel can be regarded as that formed by the addition of two silver atoms to both ends of the Pt@Ag 12 icosohedral kernel of the Ag 24 Pt(SR) 18 (SR: thiolate) nanocluster, as revealed by single crystal X-ray crystallography. Interestingly, compared with the previously reported Ag 24 Pt(SR) 18 nanocluster, the as-obtained novel bimetal nanocluster exhibits a similar absorption but a different electrochemical gap. One possible explanation for this result is that the kernel tuning does not essentially change the electronic structure, but obviously influences the charge on the Pt@Ag 12 kernel, as demonstrated by natural population analysis, thus possibly resulting in the large electrochemical gap difference between the two nanoclusters. This work not only provides a novel strategy to tune metal nanoclusters but also reveals that the kernel change does not necessarily alter the optical and electrochemical gaps in a uniform manner, which has important implications for the structure-property correlation of nanoparticles.

7 CFR 810.602 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of flaxseed kernels that are badly ground-damaged, badly weather... instructions. Also, underdeveloped, shriveled, and small pieces of flaxseed kernels removed in properly... recleaning. (c) Heat-damaged kernels. Kernels and pieces of flaxseed kernels that are materially discolored...

Credit scoring analysis using weighted k nearest neighbor

NASA Astrophysics Data System (ADS)

Mukid, M. A.; Widiharih, T.; Rusgiyono, A.; Prahutama, A.

2018-05-01

Credit scoring is a quatitative method to evaluate the credit risk of loan applications. Both statistical methods and artificial intelligence are often used by credit analysts to help them decide whether the applicants are worthy of credit. These methods aim to predict future behavior in terms of credit risk based on past experience of customers with similar characteristics. This paper reviews the weighted k nearest neighbor (WKNN) method for credit assessment by considering the use of some kernels. We use credit data from a private bank in Indonesia. The result shows that the Gaussian kernel and rectangular kernel have a better performance based on the value of percentage corrected classified whose value is 82.4% respectively.

Registering Cortical Surfaces Based on Whole-Brain Structural Connectivity and Continuous Connectivity Analysis

PubMed Central

Gutman, Boris; Leonardo, Cassandra; Jahanshad, Neda; Hibar, Derrek; Eschen-burg, Kristian; Nir, Talia; Villalon, Julio; Thompson, Paul

2014-01-01

We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains’ continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer’s patients and controls. Our measures show significant differences between the two groups. PMID:25320795

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

Kernels cultured in vitro were induced to abort by high temperature (35°C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35°C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth. PMID:16664846

QCDNUM: Fast QCD evolution and convolution

NASA Astrophysics Data System (ADS)

Botje, M.

2011-02-01

The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline coefficients by solving (coupled) triangular matrix equations with a forward substitution algorithm. Fast computation of convolution integrals as weighted sums of spline coefficients, with weights derived from user-given convolution kernels. Restrictions: Accuracy and speed are determined by the density of the evolution grid. Running time: Less than 10 ms on a 2 GHz Intel Core 2 Duo processor to evolve the gluon density and 12 quark densities at next-to-next-to-leading order over a large kinematic range.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

PubMed

Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

PubMed Central

Yuan, Xianfeng; Song, Mumin; Chen, Zhumin; Li, Yan

2015-01-01

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

Out-of-Sample Extensions for Non-Parametric Kernel Methods.

PubMed

Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

2017-02-01

Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.

7 CFR 810.1202 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

... kernels. Kernels, pieces of rye kernels, and other grains that are badly ground-damaged, badly weather.... Also, underdeveloped, shriveled, and small pieces of rye kernels removed in properly separating the...-damaged kernels. Kernels, pieces of rye kernels, and other grains that are materially discolored and...

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize.

PubMed

Chen, Jiafa; Zhang, Luyan; Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize

PubMed Central

Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed. PMID:27070143

Statistics of primordial density perturbations from discrete seed masses

NASA Technical Reports Server (NTRS)

Scherrer, Robert J.; Bertschinger, Edmund

1991-01-01

The statistics of density perturbations for general distributions of seed masses with arbitrary matter accretion is examined. Formal expressions for the power spectrum, the N-point correlation functions, and the density distribution function are derived. These results are applied to the case of uncorrelated seed masses, and power spectra are derived for accretion of both hot and cold dark matter plus baryons. The reduced moments (cumulants) of the density distribution are computed and used to obtain a series expansion for the density distribution function. Analytic results are obtained for the density distribution function in the case of a distribution of seed masses with a spherical top-hat accretion pattern. More generally, the formalism makes it possible to give a complete characterization of the statistical properties of any random field generated from a discrete linear superposition of kernels. In particular, the results can be applied to density fields derived by smoothing a discrete set of points with a window function.

Two-Component Noncollinear Time-Dependent Spin Density Functional Theory for Excited State Calculations.

PubMed

Egidi, Franco; Sun, Shichao; Goings, Joshua J; Scalmani, Giovanni; Frisch, Michael J; Li, Xiaosong

2017-06-13

We present a linear response formalism for the description of the electronic excitations of a noncollinear reference defined via Kohn-Sham spin density functional methods. A set of auxiliary variables, defined using the density and noncollinear magnetization density vector, allows the generalization of spin density functional kernels commonly used in collinear DFT to noncollinear cases, including local density, GGA, meta-GGA and hybrid functionals. Working equations and derivations of functional second derivatives with respect to the noncollinear density, required in the linear response noncollinear TDDFT formalism, are presented in this work. This formalism takes all components of the spin magnetization into account independent of the type of reference state (open or closed shell). As a result, the method introduced here is able to afford a nonzero local xc torque on the spin magnetization while still satisfying the zero-torque theorem globally. The formalism is applied to a few test cases using the variational exact-two-component reference including spin-orbit coupling to illustrate the capabilities of the method.

gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels.

PubMed

Larson, Nicholas B; McDonnell, Shannon; Cannon Albright, Lisa; Teerlink, Craig; Stanford, Janet; Ostrander, Elaine A; Isaacs, William B; Xu, Jianfeng; Cooney, Kathleen A; Lange, Ethan; Schleutker, Johanna; Carpten, John D; Powell, Isaac; Bailey-Wilson, Joan E; Cussenot, Olivier; Cancel-Tassin, Geraldine; Giles, Graham G; MacInnis, Robert J; Maier, Christiane; Whittemore, Alice S; Hsieh, Chih-Lin; Wiklund, Fredrik; Catalona, William J; Foulkes, William; Mandal, Diptasri; Eeles, Rosalind; Kote-Jarai, Zsofia; Ackerman, Michael J; Olson, Timothy M; Klein, Christopher J; Thibodeau, Stephen N; Schaid, Daniel J

2017-05-01

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results. © 2017 WILEY PERIODICALS, INC.

Density Deconvolution With EPI Splines

DTIC Science & Technology

2015-09-01

effects of various substances on test subjects [11], [12]. Whereas in geophysics, a shot may be fired into the ground, in pharmacokinetics, a signal is...be significant, including medicine, bioinformatics, chemistry, as- tronomy, and econometrics , as well as an extensive review of kernel based methods...demonstrate the effectiveness of our model in simulations motivated by test instances in [32]. We consider an additive measurement model scenario where

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rao, N.S.V.

The classical Nadaraya-Watson estimator is shown to solve a generic sensor fusion problem where the underlying sensor error densities are not known but a sample is available. By employing Haar kernels this estimator is shown to yield finite sample guarantees and also to be efficiently computable. Two simulation examples, and a robotics example involving the detection of a door using arrays of ultrasonic and infrared sensors, are presented to illustrate the performance.

Nowcasting Cloud Fields for U.S. Air Force Special Operations

DTIC Science & Technology

2017-03-01

application of Bayes’ Rule offers many advantages over Kernel Density Estimation (KDE) and other commonly used statistical post-processing methods...reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past...nowcasting, low cloud forecasting, cloud reflectance, ISR, Bayesian estimation, statistical post-processing, machine learning 15. NUMBER OF PAGES

QVAST: a new Quantum GIS plugin for estimating volcanic susceptibility

NASA Astrophysics Data System (ADS)

Bartolini, S.; Cappello, A.; Martí, J.; Del Negro, C.

2013-11-01

One of the most important tasks of modern volcanology is the construction of hazard maps simulating different eruptive scenarios that can be used in risk-based decision making in land-use planning and emergency management. The first step in the quantitative assessment of volcanic hazards is the development of susceptibility maps (i.e., the spatial probability of a future vent opening given the past eruptive activity of a volcano). This challenging issue is generally tackled using probabilistic methods that use the calculation of a kernel function at each data location to estimate probability density functions (PDFs). The smoothness and the modeling ability of the kernel function are controlled by the smoothing parameter, also known as the bandwidth. Here we present a new tool, QVAST, part of the open-source geographic information system Quantum GIS, which is designed to create user-friendly quantitative assessments of volcanic susceptibility. QVAST allows the selection of an appropriate method for evaluating the bandwidth for the kernel function on the basis of the input parameters and the shapefile geometry, and can also evaluate the PDF with the Gaussian kernel. When different input data sets are available for the area, the total susceptibility map is obtained by assigning different weights to each of the PDFs, which are then combined via a weighted summation and modeled in a non-homogeneous Poisson process. The potential of QVAST, developed in a free and user-friendly environment, is here shown through its application in the volcanic fields of Lanzarote (Canary Islands) and La Garrotxa (NE Spain).

Reduced expression of selected FASCICLIN-LIKE ARABINOGALACTAN PROTEIN genes associates with the abortion of kernels in field crops of Zea mays (maize) and of Arabidopsis seeds.

PubMed

Cagnola, Juan Ignacio; Dumont de Chassart, Gonzalo Javier; Ibarra, Silvia Elizabeth; Chimenti, Claudio; Ricardi, Martiniano María; Delzer, Brent; Ghiglione, Hernán; Zhu, Tong; Otegui, María Elena; Estevez, José Manuel; Casal, Jorge José

2018-03-01

Abortion of fertilized ovaries at the tip of the ear can generate significant yield losses in maize crops. To investigate the mechanisms involved in this process, 2 maize hybrids were grown in field crops at 2 sowing densities and under 3 irrigation regimes (well-watered control, drought before pollination, and drought during pollination), in all possible combinations. Samples of ear tips were taken 2-6 days after synchronous hand pollination and used for the analysis of gene expression and sugars. Glucose and fructose levels increased in kernels with high abortion risk. Several FASCICLIN-LIKE ARABINOGALACTAN PROTEIN (FLA) genes showed negative correlation with abortion. The expression of ZmFLA7 responded to drought only at the tip of the ear. The abundance of arabinogalactan protein (AGP) glycan epitopes decreased with drought and pharmacological treatments that reduce AGP activity enhanced the abortion of fertilized ovaries. Drought also reduced the expression of AthFLA9 in the siliques of Arabidopsis thaliana. Gain- and loss-of-function mutants of Arabidopsis showed a negative correlation between AthFLA9 and seed abortion. On the basis of gene expression patterns, pharmacological, and genetic evidence, we propose that stress-induced reductions in the expression of selected FLA genes enhance abortion of fertilized ovaries in maize and Arabidopsis. © 2018 John Wiley & Sons Ltd.

Building integral projection models: a user's guide.

PubMed

Rees, Mark; Childs, Dylan Z; Ellner, Stephen P

2014-05-01

In order to understand how changes in individual performance (growth, survival or reproduction) influence population dynamics and evolution, ecologists are increasingly using parameterized mathematical models. For continuously structured populations, where some continuous measure of individual state influences growth, survival or reproduction, integral projection models (IPMs) are commonly used. We provide a detailed description of the steps involved in constructing an IPM, explaining how to: (i) translate your study system into an IPM; (ii) implement your IPM; and (iii) diagnose potential problems with your IPM. We emphasize how the study organism's life cycle, and the timing of censuses, together determine the structure of the IPM kernel and important aspects of the statistical analysis used to parameterize an IPM using data on marked individuals. An IPM based on population studies of Soay sheep is used to illustrate the complete process of constructing, implementing and evaluating an IPM fitted to sample data. We then look at very general approaches to parameterizing an IPM, using a wide range of statistical techniques (e.g. maximum likelihood methods, generalized additive models, nonparametric kernel density estimators). Methods for selecting models for parameterizing IPMs are briefly discussed. We conclude with key recommendations and a brief overview of applications that extend the basic model. The online Supporting Information provides commented R code for all our analyses. © 2014 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

MO-G-17A-06: Kernel Based Dosimetry for 90Y Microsphere Liver Therapy Using 90Y Bremsstrahlung SPECT/CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mikell, J; Siman, W; Kappadath, S

2014-06-15

Purpose: 90Y microsphere therapy in liver presents a situation where beta transport is dominant and the tissue is relatively homogenous. We compare voxel-based absorbed doses from a 90Y kernel to Monte Carlo (MC) using quantitative 90Y bremsstrahlung SPECT/CT as source distribution. Methods: Liver, normal liver, and tumors were delineated by an interventional radiologist using contrast-enhanced CT registered with 90Y SPECT/CT scans for 14 therapies. Right lung was segmented via region growing. The kernel was generated with 1.04 g/cc soft tissue for 4.8 mm voxel matching the SPECT. MC simulation materials included air, lung, soft tissue, and bone with varying densities.more » We report percent difference between kernel and MC (%Δ(K,MC)) for mean absorbed dose, D70, and V20Gy in total liver, normal liver, tumors, and right lung. We also report %Δ(K,MC) for heterogeneity metrics: coefficient of variation (COV) and D10/D90. The impact of spatial resolution (0, 10, 20 mm FWHM) and lung shunt fraction (LSF) (1,5,10,20%) on the accuracy of MC and kernel doses near the liver-lung interface was modeled in 1D. We report the distance from the interface where errors become <10% of unblurred MC as d10(side of interface, dose calculation, FWHM blurring, LSF). Results: The %Δ(K,MC) for mean, D70, and V20Gy in tumor and liver was <7% while right lung differences varied from 60–90%. The %Δ(K,MC) for COV was <4.8% for tumor and liver and <54% for the right lung. The %Δ(K,MC) for D10/D90 was <5% for 22/23 tumors. d10(liver,MC,10,1–20) awere <9mm and d10(liver,MC,20,1–20) awere <15mm; both agreed within 3mm to the kernel. d10(lung,MC,10,20), d10(lung,MC,10,1), d10(lung,MC,20,20), and d10(lung,MC,20,1) awere 6, 25, 15, and 34mm, respectively. Kernel calculations on blurred distributions in lung had errors > 10%. Conclusions: Liver and tumor voxel doses with 90Y kernel and MC agree within 7%. Large differences exist between the two methods in right lung. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA138986. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.« less

Inferring probabilistic stellar rotation periods using Gaussian processes

NASA Astrophysics Data System (ADS)

Angus, Ruth; Morton, Timothy; Aigrain, Suzanne; Foreman-Mackey, Daniel; Rajpaul, Vinesh

2018-02-01

Variability in the light curves of spotted, rotating stars is often non-sinusoidal and quasi-periodic - spots move on the stellar surface and have finite lifetimes, causing stellar flux variations to slowly shift in phase. A strictly periodic sinusoid therefore cannot accurately model a rotationally modulated stellar light curve. Physical models of stellar surfaces have many drawbacks preventing effective inference, such as highly degenerate or high-dimensional parameter spaces. In this work, we test an appropriate effective model: a Gaussian Process with a quasi-periodic covariance kernel function. This highly flexible model allows sampling of the posterior probability density function of the periodic parameter, marginalizing over the other kernel hyperparameters using a Markov Chain Monte Carlo approach. To test the effectiveness of this method, we infer rotation periods from 333 simulated stellar light curves, demonstrating that the Gaussian process method produces periods that are more accurate than both a sine-fitting periodogram and an autocorrelation function method. We also demonstrate that it works well on real data, by inferring rotation periods for 275 Kepler stars with previously measured periods. We provide a table of rotation periods for these and many more, altogether 1102 Kepler objects of interest, and their posterior probability density function samples. Because this method delivers posterior probability density functions, it will enable hierarchical studies involving stellar rotation, particularly those involving population modelling, such as inferring stellar ages, obliquities in exoplanet systems, or characterizing star-planet interactions. The code used to implement this method is available online.

THREE-POINT PHASE CORRELATIONS: A NEW MEASURE OF NONLINEAR LARGE-SCALE STRUCTURE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolstenhulme, Richard; Bonvin, Camille; Obreschkow, Danail

2015-05-10

We derive an analytical expression for a novel large-scale structure observable: the line correlation function. The line correlation function, which is constructed from the three-point correlation function of the phase of the density field, is a robust statistical measure allowing the extraction of information in the nonlinear and non-Gaussian regime. We show that, in perturbation theory, the line correlation is sensitive to the coupling kernel F{sub 2}, which governs the nonlinear gravitational evolution of the density field. We compare our analytical expression with results from numerical simulations and find a 1σ agreement for separations r ≳ 30 h{sup −1} Mpc.more » Fitting formulae for the power spectrum and the nonlinear coupling kernel at small scales allow us to extend our prediction into the strongly nonlinear regime, where we find a 1σ agreement with the simulations for r ≳ 2 h{sup −1} Mpc. We discuss the advantages of the line correlation relative to standard statistical measures like the bispectrum. Unlike the latter, the line correlation is independent of the bias, in the regime where the bias is local and linear. Furthermore, the variance of the line correlation is independent of the Gaussian variance on the modulus of the density field. This suggests that the line correlation can probe more precisely the nonlinear regime of gravity, with less contamination from the power spectrum variance.« less

7 CFR 810.802 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of grain kernels for which standards have been established under the.... (d) Heat-damaged kernels. Kernels and pieces of grain kernels for which standards have been...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2011 CFR

2011-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2012 CFR

2012-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2013 CFR

2013-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

Does the choice of neighbourhood supermarket access measure influence associations with individual-level fruit and vegetable consumption? A case study from Glasgow.

PubMed

Thornton, Lukar E; Pearce, Jamie R; Macdonald, Laura; Lamb, Karen E; Ellaway, Anne

2012-07-27

Previous studies have provided mixed evidence with regards to associations between food store access and dietary outcomes. This study examines the most commonly applied measures of locational access to assess whether associations between supermarket access and fruit and vegetable consumption are affected by the choice of access measure and scale. Supermarket location data from Glasgow, UK (n = 119), and fruit and vegetable intake data from the 'Health and Well-Being' Survey (n = 1041) were used to compare various measures of locational access. These exposure variables included proximity estimates (with different points-of-origin used to vary levels of aggregation) and density measures using three approaches (Euclidean and road network buffers and Kernel density estimation) at distances ranging from 0.4 km to 5 km. Further analysis was conducted to assess the impact of using smaller buffer sizes for individuals who did not own a car. Associations between these multiple access measures and fruit and vegetable consumption were estimated using linear regression models. Levels of spatial aggregation did not impact on the proximity estimates. Counts of supermarkets within Euclidean buffers were associated with fruit and vegetable consumption at 1 km, 2 km and 3 km, and for our road network buffers at 2 km, 3 km, and 4 km. Kernel density estimates provided the strongest associations and were significant at a distance of 2 km, 3 km, 4 km and 5 km. Presence of a supermarket within 0.4 km of road network distance from where people lived was positively associated with fruit consumption amongst those without a car (coef. 0.657; s.e. 0.247; p0.008). The associations between locational access to supermarkets and individual-level dietary behaviour are sensitive to the method by which the food environment variable is captured. Care needs to be taken to ensure robust and conceptually appropriate measures of access are used and these should be grounded in a clear a priori reasoning.

Does the choice of neighbourhood supermarket access measure influence associations with individual-level fruit and vegetable consumption? A case study from Glasgow

PubMed Central

2012-01-01

Background Previous studies have provided mixed evidence with regards to associations between food store access and dietary outcomes. This study examines the most commonly applied measures of locational access to assess whether associations between supermarket access and fruit and vegetable consumption are affected by the choice of access measure and scale. Method Supermarket location data from Glasgow, UK (n = 119), and fruit and vegetable intake data from the ‘Health and Well-Being’ Survey (n = 1041) were used to compare various measures of locational access. These exposure variables included proximity estimates (with different points-of-origin used to vary levels of aggregation) and density measures using three approaches (Euclidean and road network buffers and Kernel density estimation) at distances ranging from 0.4 km to 5 km. Further analysis was conducted to assess the impact of using smaller buffer sizes for individuals who did not own a car. Associations between these multiple access measures and fruit and vegetable consumption were estimated using linear regression models. Results Levels of spatial aggregation did not impact on the proximity estimates. Counts of supermarkets within Euclidean buffers were associated with fruit and vegetable consumption at 1 km, 2 km and 3 km, and for our road network buffers at 2 km, 3 km, and 4 km. Kernel density estimates provided the strongest associations and were significant at a distance of 2 km, 3 km, 4 km and 5 km. Presence of a supermarket within 0.4 km of road network distance from where people lived was positively associated with fruit consumption amongst those without a car (coef. 0.657; s.e. 0.247; p0.008). Conclusions The associations between locational access to supermarkets and individual-level dietary behaviour are sensitive to the method by which the food environment variable is captured. Care needs to be taken to ensure robust and conceptually appropriate measures of access are used and these should be grounded in a clear a priori reasoning. PMID:22839742

Probability Distribution Extraction from TEC Estimates based on Kernel Density Estimation

NASA Astrophysics Data System (ADS)

Demir, Uygar; Toker, Cenk; Çenet, Duygu

2016-07-01

Statistical analysis of the ionosphere, specifically the Total Electron Content (TEC), may reveal important information about its temporal and spatial characteristics. One of the core metrics that express the statistical properties of a stochastic process is its Probability Density Function (pdf). Furthermore, statistical parameters such as mean, variance and kurtosis, which can be derived from the pdf, may provide information about the spatial uniformity or clustering of the electron content. For example, the variance differentiates between a quiet ionosphere and a disturbed one, whereas kurtosis differentiates between a geomagnetic storm and an earthquake. Therefore, valuable information about the state of the ionosphere (and the natural phenomena that cause the disturbance) can be obtained by looking at the statistical parameters. In the literature, there are publications which try to fit the histogram of TEC estimates to some well-known pdf.s such as Gaussian, Exponential, etc. However, constraining a histogram to fit to a function with a fixed shape will increase estimation error, and all the information extracted from such pdf will continue to contain this error. In such techniques, it is highly likely to observe some artificial characteristics in the estimated pdf which is not present in the original data. In the present study, we use the Kernel Density Estimation (KDE) technique to estimate the pdf of the TEC. KDE is a non-parametric approach which does not impose a specific form on the TEC. As a result, better pdf estimates that almost perfectly fit to the observed TEC values can be obtained as compared to the techniques mentioned above. KDE is particularly good at representing the tail probabilities, and outliers. We also calculate the mean, variance and kurtosis of the measured TEC values. The technique is applied to the ionosphere over Turkey where the TEC values are estimated from the GNSS measurement from the TNPGN-Active (Turkish National Permanent GNSS Network) network. This study is supported by by TUBITAK 115E915 and Joint TUBITAK 114E092 and AS CR14/001 projects.

L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction.

PubMed

Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

2014-04-01

A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.

Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies.

PubMed

Zhao, Ni; Zhan, Xiang; Huang, Yen-Tsung; Almli, Lynn M; Smith, Alicia; Epstein, Michael P; Conneely, Karen; Wu, Michael C

2018-03-01

Many large GWAS consortia are expanding to simultaneously examine the joint role of DNA methylation in addition to genotype in the same subjects. However, integrating information from both data types is challenging. In this paper, we propose a composite kernel machine regression model to test the joint epigenetic and genetic effect. Our approach works at the gene level, which allows for a common unit of analysis across different data types. The model compares the pairwise similarities in the phenotype to the pairwise similarities in the genotype and methylation values; and high correspondence is suggestive of association. A composite kernel is constructed to measure the similarities in the genotype and methylation values between pairs of samples. We demonstrate through simulations and real data applications that the proposed approach can correctly control type I error, and is more robust and powerful than using only the genotype or methylation data in detecting trait-associated genes. We applied our method to investigate the genetic and epigenetic regulation of gene expression in response to stressful life events using data that are collected from the Grady Trauma Project. Within the kernel machine testing framework, our methods allow for heterogeneity in effect sizes, nonlinear, and interactive effects, as well as rapid P-value computation. © 2017 WILEY PERIODICALS, INC.

Analysis of the power flow in nonlinear oscillators driven by random excitation using the first Wiener kernel

NASA Astrophysics Data System (ADS)

Hawes, D. H.; Langley, R. S.

2018-01-01

Random excitation of mechanical systems occurs in a wide variety of structures and, in some applications, calculation of the power dissipated by such a system will be of interest. In this paper, using the Wiener series, a general methodology is developed for calculating the power dissipated by a general nonlinear multi-degree-of freedom oscillatory system excited by random Gaussian base motion of any spectrum. The Wiener series method is most commonly applied to systems with white noise inputs, but can be extended to encompass a general non-white input. From the extended series a simple expression for the power dissipated can be derived in terms of the first term, or kernel, of the series and the spectrum of the input. Calculation of the first kernel can be performed either via numerical simulations or from experimental data and a useful property of the kernel, namely that the integral over its frequency domain representation is proportional to the oscillating mass, is derived. The resulting equations offer a simple conceptual analysis of the power flow in nonlinear randomly excited systems and hence assist the design of any system where power dissipation is a consideration. The results are validated both numerically and experimentally using a base-excited cantilever beam with a nonlinear restoring force produced by magnets.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Gaussianization for fast and accurate inference from cosmological data

NASA Astrophysics Data System (ADS)

Schuhmann, Robert L.; Joachimi, Benjamin; Peiris, Hiranya V.

2016-06-01

We present a method to transform multivariate unimodal non-Gaussian posterior probability densities into approximately Gaussian ones via non-linear mappings, such as Box-Cox transformations and generalizations thereof. This permits an analytical reconstruction of the posterior from a point sample, like a Markov chain, and simplifies the subsequent joint analysis with other experiments. This way, a multivariate posterior density can be reported efficiently, by compressing the information contained in Markov Chain Monte Carlo samples. Further, the model evidence integral (I.e. the marginal likelihood) can be computed analytically. This method is analogous to the search for normal parameters in the cosmic microwave background, but is more general. The search for the optimally Gaussianizing transformation is performed computationally through a maximum-likelihood formalism; its quality can be judged by how well the credible regions of the posterior are reproduced. We demonstrate that our method outperforms kernel density estimates in this objective. Further, we select marginal posterior samples from Planck data with several distinct strongly non-Gaussian features, and verify the reproduction of the marginal contours. To demonstrate evidence computation, we Gaussianize the joint distribution of data from weak lensing and baryon acoustic oscillations, for different cosmological models, and find a preference for flat Λcold dark matter. Comparing to values computed with the Savage-Dickey density ratio, and Population Monte Carlo, we find good agreement of our method within the spread of the other two.

Classification With Truncated Distance Kernel.

PubMed

Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

2018-05-01

This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts.

PubMed

Qiao, Xiaojun; Jiang, Jinbao; Qi, Xiaotong; Guo, Haiqiang; Yuan, Deshuai

2017-04-01

It's well-known fungi-contaminated peanuts contain potent carcinogen. Efficiently identifying and separating the contaminated can help prevent aflatoxin entering in food chain. In this study, shortwave infrared (SWIR) hyperspectral images for identifying the prepared contaminated kernels. Feature selection method of analysis of variance (ANOVA) and feature extraction method of nonparametric weighted feature extraction (NWFE) were used to concentrate spectral information into a subspace where contaminated and healthy peanuts can have favorable separability. Then, peanut pixels were classified using SVM. Moreover, image segmentation method of region growing was applied to segment the image as kernel-scale patches and meanwhile to number the kernels. The result shows that pixel-wise classification accuracies are 99.13% for breed A, 96.72% for B and 99.73% for C in learning images, and are 96.32%, 94.2% and 97.51% in validation images. Contaminated peanuts were correctly marked as aberrant kernels in both learning images and validation images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determining the minimum required uranium carbide content for HTGR UCO fuel kernels

DOE PAGES

McMurray, Jacob W.; Lindemer, Terrence B.; Brown, Nicholas R.; ...

2017-03-10

There are three important failure mechanisms that must be controlled in high-temperature gas-cooled reactor (HTGR) fuel for certain higher burnup applications are SiC layer rupture, SiC corrosion by CO, and coating compromise from kernel migration. All are related to high CO pressures stemming from free O generated when uranium present as UO 2 fissions and the O is not subsequently bound by other elements. Furthermore, in the HTGR UCO kernel design, CO buildup from excess O is controlled by the inclusion of additional uranium in the form of a carbide, UC x. An approach for determining the minimum UC xmore » content to ensure negligible CO formation was developed and demonstrated using CALPHAD models and the Serpent 2 reactor physics and depletion analysis tool. Our results are intended to be more accurate than previous estimates by including more nuclear and chemical factors, in particular the effect of transmutation products on the oxygen distribution as the fuel kernel composition evolves with burnup.« less

Analysis Monthly Import of Palm Oil Products Using Box-Jenkins Model

NASA Astrophysics Data System (ADS)

Ahmad, Nurul F. Y.; Khalid, Kamil; Saifullah Rusiman, Mohd; Ghazali Kamardan, M.; Roslan, Rozaini; Che-Him, Norziha

2018-04-01

The palm oil industry has been an important component of the national economy especially the agriculture sector. The aim of this study is to identify the pattern of import of palm oil products, to model the time series using Box-Jenkins model and to forecast the monthly import of palm oil products. The method approach is included in the statistical test for verifying the equivalence model and statistical measurement of three models, namely Autoregressive (AR) model, Moving Average (MA) model and Autoregressive Moving Average (ARMA) model. The model identification of all product import palm oil is different in which the AR(1) was found to be the best model for product import palm oil while MA(3) was found to be the best model for products import palm kernel oil. For the palm kernel, MA(4) was found to be the best model. The results forecast for the next four months for products import palm oil, palm kernel oil and palm kernel showed the most significant decrease compared to the actual data.

Comparative efficacy of storage bags, storability and damage potential of bruchid beetle.

PubMed

Harish, G; Nataraja, M V; Ajay, B C; Holajjer, Prasanna; Savaliya, S D; Gedia, M V

2014-12-01

Groundnut during storage is attacked by number of stored grain pests and management of these insect pests particularly bruchid beetle, Caryedon serratus (Oliver) is of prime importance as they directly damage the pod and kernels. In this regard different storage bags that could be used and duration up to which we can store groundnut has been studied. Super grain bag recorded minimum number of eggs laid and less damage and minimum weight loss in pods and kernels in comparison to other storage bags. Analysis of variance for multiple regression models were found to be significant in all bags for variables viz, number of eggs laid, damage in pods and kernels, weight loss in pods and kernels throughout the season. Multiple comparison results showed that there was a high probability of eggs laid and pod damage in lino bag, fertilizer bag and gunny bag, whereas super grain bag was found to be more effective in managing the C. serratus owing to very low air circulation.

Thermochemical Assessment of Oxygen Gettering by SiC or ZrC in PuO2-x TRISO Fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Besmann, Theodore M

2010-01-01

Particulate nuclear fuel in a modular helium reactor is being considered for the consumption of excess plutonium and related transuranics. In particular, efforts to largely consume transuranics in a single-pass will require the fuel to undergo very high burnup. This deep burn concept will thus make the proposed plutonia TRISO fuel particularly likely to suffer kernel migration where carbon in the buffer layer and inner pyrolytic carbon layer is transported from the high temperature side of the particle to the low temperature side. This phenomenon is oberved to cause particle failure and therefore must be mitigated. The addition of SiCmore » or ZrC in the oxide kernel or in a layer in communication with the kernel will lower the oxygen potential and therefore prevent kernel migration, and this has been demonstrated with SiC. In this work a thermochemical analysis was performed to predict oxygen potential behavior in the plutonia TRISO fuel to burnups of 50% FIMA with and without the presence of oxygen gettering SiC and ZrC. Kernel migration is believed to be controlled by CO gas transporting carbon from the hot side to the cool side, and CO pressure is governed by the oxygen potential in the presence of carbon. The gettering phases significantly reduce the oxygen potential and thus CO pressure in an otherwise PuO2-x kernel, and prevent kernel migration by limiting CO gas diffusion through the buffer layer. The reduction in CO pressure can also reduce the peak pressure within the particles by ~50%, thus reducing the likelihood of pressure-induced particle failure. A model for kernel migration was used to semi-quantitatively assess the effect of controlling oxygen potential with SiC or ZrC and did demonstrated the dramatic effect of the addition of these phases on carbon transport.« less

Research on offense and defense technology for iOS kernel security mechanism

NASA Astrophysics Data System (ADS)

Chu, Sijun; Wu, Hao

2018-04-01

iOS is a strong and widely used mobile device system. It's annual profits make up about 90% of the total profits of all mobile phone brands. Though it is famous for its security, there have been many attacks on the iOS operating system, such as the Trident apt attack in 2016. So it is important to research the iOS security mechanism and understand its weaknesses and put forward targeted protection and security check framework. By studying these attacks and previous jailbreak tools, we can see that an attacker could only run a ROP code and gain kernel read and write permissions based on the ROP after exploiting kernel and user layer vulnerabilities. However, the iOS operating system is still protected by the code signing mechanism, the sandbox mechanism, and the not-writable mechanism of the system's disk area. This is far from the steady, long-lasting control that attackers expect. Before iOS 9, breaking these security mechanisms was usually done by modifying the kernel's important data structures and security mechanism code logic. However, after iOS 9, the kernel integrity protection mechanism was added to the 64-bit operating system and none of the previous methods were adapted to the new versions of iOS [1]. But this does not mean that attackers can not break through. Therefore, based on the analysis of the vulnerability of KPP security mechanism, this paper implements two possible breakthrough methods for kernel security mechanism for iOS9 and iOS10. Meanwhile, we propose a defense method based on kernel integrity detection and sensitive API call detection to defense breakthrough method mentioned above. And we make experiments to prove that this method can prevent and detect attack attempts or invaders effectively and timely.

Production of Low Enriched Uranium Nitride Kernels for TRISO Particle Irradiation Testing

DOE Office of Scientific and Technical Information (OSTI.GOV)

McMurray, J. W.; Silva, C. M.; Helmreich, G. W.

2016-06-01

A large batch of UN microspheres to be used as kernels for TRISO particle fuel was produced using carbothermic reduction and nitriding of a sol-gel feedstock bearing tailored amounts of low-enriched uranium (LEU) oxide and carbon. The process parameters, established in a previous study, produced phasepure NaCl structure UN with dissolved C on the N sublattice. The composition, calculated by refinement of the lattice parameter from X-ray diffraction, was determined to be UC 0.27N 0.73. The final accepted product weighed 197.4 g. The microspheres had an average diameter of 797±1.35 μm and a composite mean theoretical density of 89.9±0.5% formore » a solid solution of UC and UN with the same atomic ratio; both values are reported with their corresponding calculated standard error.« less

Is there a single best estimator? selection of home range estimators using area- under- the-curve

USGS Publications Warehouse

Walter, W. David; Onorato, Dave P.; Fischer, Justin W.

2015-01-01

Comparisons of fit of home range contours with locations collected would suggest that use of VHF technology is not as accurate as GPS technology to estimate size of home range for large mammals. Estimators of home range collected with GPS technology performed better than those estimated with VHF technology regardless of estimator used. Furthermore, estimators that incorporate a temporal component (third-generation estimators) appeared to be the most reliable regardless of whether kernel-based or Brownian bridge-based algorithms were used and in comparison to first- and second-generation estimators. We defined third-generation estimators of home range as any estimator that incorporates time, space, animal-specific parameters, and habitat. Such estimators would include movement-based kernel density, Brownian bridge movement models, and dynamic Brownian bridge movement models among others that have yet to be evaluated.

Prioritizing landscapes for longleaf pine conservation

USGS Publications Warehouse

Grand, James B.; Kleiner, Kevin J.

2016-01-01

We developed a spatially explicit model and map, as a decision support tool (DST), to aid conservation agencies creating or maintaining open pine ecosystems. The tool identified areas that are likely to provide the greatest benefit to focal bird populations based on a comprehensive landscape analysis. We used NLCD 2011, SSURGO, and SEGAP data to map the density of desired resources for open pine ecosystems and six focal species of birds and 2 reptiles within the historic range of longleaf pine east of the Mississippi River. Binary rasters were created of sites with desired characteristics such as land form, hydrology, land use and land cover, soils, potential habitat for focal species, and putative source populations of focal species. Each raster was smoothed using a kernel density estimator. Rasters were combined and scaled to map priority locations for the management of each focal species. Species’ rasters were combined and scaled to provide maps of overall priority for birds and for birds and reptiles. The spatial data can be used to identify high priority areas for conservation or to compare areas under consideration for maintenance or creation of open pine ecosystems.

SEM-EDX analysis of an unknown "known" white powder found in a shipping container from Peru

NASA Astrophysics Data System (ADS)

Albright, Douglas C.

2009-05-01

In 2008, an unknown white powder was discovered spilled inside of a shipping container of whole kernel corn during an inspection by federal inspectors in the port of Baltimore, Maryland. The container was detained and quarantined while a sample of the powder was collected and sent to a federal laboratory where it was screened using chromatography for the presence of specific poisons and pesticides with negative results. Samples of the corn kernels and the white powder were forwarded to the Food and Drug Administration, Forensic Chemistry Center for further analysis. Stereoscopic Light Microscopy (SLM), Scanning Electron Microscopy/Energy Dispersive X-ray Spectrometry (SEM/EDX), and Polarized Light Microscopy/Infrared Spectroscopy (PLM-IR) were used in the analysis of the kernels and the unknown powder. Based on the unique particle analysis by SLM and SEM as well as the detection of the presence of aluminum and phosphorous by EDX, the unknown was determined to be consistent with reacted aluminum phosphide (AlP). While commonly known in the agricultural industry, aluminum phosphide is relatively unknown in the forensic community. A history of the use and acute toxicity of this compound along with some very unique SEM/EDX analysis characteristics of aluminum phosphide will be discussed.

Physico-chemical properties of instant ogbono (Irvingia gabonensis) mix powder

PubMed Central

Bamidele, Oluwaseun P; Ojedokun, Omotayo S; Fasogbon, Beatrice M

2015-01-01

The main objective of the research is to develop a recipe of instant dry soup mix for easy preparation of ogbono soup. Instant ogbono mix powder was processed using common locally ingredients. Dika kernel powder, dried ugwu leaf, crayfish, stock fish, and a mixture of locust bean, onion, seasoning and Cameroon powder were formulated at different ratios to find the best acceptable ogbono mix powder. The samples were subjected to proximate, functional, vitamin, mineral, and sensory analyses. The formulated sample D with the highest ratio of crayfish and stock fish had the highest value of protein and carbohydrate (24.13 and 35.61%, respectively). The control sample (100% dika kernel powder) was low in moisture content (6.20%) but high in crude fat, other samples followed in this order (control > A > B > C > D) for crude fat. Ash, crude fiber, and carbohydrate showed a significant difference (P < 0.05) in all the samples. The functional properties of the sample showed a significant difference (P < 0.05) in all the samples with the control having the highest value for the water absorption, swelling capacity, and bulk density which may be due to the high crude fiber and low moisture content recorded for the control sample in the proximate analysis. The mineral content of all the samples were higher than the control with phosphorous having the highest value and iron the least value. Vitamin C was the main dominating vitamin in the sample followed by vitamin B2, vitamin A, and vitamin B3. The sensory evaluation revealed that 100% dika kernel powder gave a good attribute of the soup but with less nutritional composition, while some formulated samples showed a similar attribute with higher nutritional value. Sample A with the highest overall acceptability had the best attribute of ogbono soup. Instant ogbono mix powder has higher nutritional value and easy to cook. PMID:26288723

Effect of different CT scanners and settings on femoral failure loads calculated by finite element models.

PubMed

Eggermont, Florieke; Derikx, Loes C; Free, Jeffrey; van Leeuwen, Ruud; van der Linden, Yvette M; Verdonschot, Nico; Tanck, Esther

2018-03-06

In a multi-center patient study, using different CT scanners, CT-based finite element (FE) models are utilized to calculate failure loads of femora with metastases. Previous studies showed that using different CT scanners can result in different outcomes. This study aims to quantify the effects of (i) different CT scanners; (ii) different CT protocols with variations in slice thickness, field of view (FOV), and reconstruction kernel; and (iii) air between calibration phantom and patient, on Hounsfield Units (HU), bone mineral density (BMD), and FE failure load. Six cadaveric femora were scanned on four CT scanners. Scans were made with multiple CT protocols and with or without an air gap between the body model and calibration phantom. HU and calibrated BMD were determined in cortical and trabecular regions of interest. Non-linear isotropic FE models were constructed to calculate failure load. Mean differences between CT scanners varied up to 7% in cortical HU, 6% in trabecular HU, 6% in cortical BMD, 12% in trabecular BMD, and 17% in failure load. Changes in slice thickness and FOV had little effect (≤4%), while reconstruction kernels had a larger effect on HU (16%), BMD (17%), and failure load (9%). Air between the body model and calibration phantom slightly decreased the HU, BMD, and failure loads (≤8%). In conclusion, this study showed that quantitative analysis of CT images acquired with different CT scanners, and particularly reconstruction kernels, can induce relatively large differences in HU, BMD, and failure loads. Additionally, if possible, air artifacts should be avoided. © 2018 Orthopaedic Research Society. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society. J Orthop Res. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of the Orthopaedic Research Society.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Linkage disequilibrium, SNP frequency change due to selection, and association mapping in popcorn chromosome regions containing QTLs for quality traits

PubMed Central

Paes, Geísa Pinheiro; Viana, José Marcelo Soriano; Silva, Fabyano Fonseca e; Mundim, Gabriel Borges

2016-01-01

Abstract The objectives of this study were to assess linkage disequilibrium (LD) and selection-induced changes in single nucleotide polymorphism (SNP) frequency, and to perform association mapping in popcorn chromosome regions containing quantitative trait loci (QTLs) for quality traits. Seven tropical and two temperate popcorn populations were genotyped for 96 SNPs chosen in chromosome regions containing QTLs for quality traits. The populations were phenotyped for expansion volume, 100-kernel weight, kernel sphericity, and kernel density. The LD statistics were the difference between the observed and expected haplotype frequencies (D), the proportion of D relative to the expected maximum value in the population, and the square of the correlation between the values of alleles at two loci. Association mapping was based on least squares and Bayesian approaches. In the tropical populations, D-values greater than 0.10 were observed for SNPs separated by 100-150 Mb, while most of the D-values in the temperate populations were less than 0.05. Selection for expansion volume indirectly led to increase in LD values, population differentiation, and significant changes in SNP frequency. Some associations were observed for expansion volume and the other quality traits. The candidate genes are involved with starch, storage protein, lipid, and cell wall polysaccharides synthesis. PMID:27007903

Linkage disequilibrium, SNP frequency change due to selection, and association mapping in popcorn chromosome regions containing QTLs for quality traits.

PubMed

Paes, Geísa Pinheiro; Viana, José Marcelo Soriano; Silva, Fabyano Fonseca E; Mundim, Gabriel Borges

2016-03-01

The objectives of this study were to assess linkage disequilibrium (LD) and selection-induced changes in single nucleotide polymorphism (SNP) frequency, and to perform association mapping in popcorn chromosome regions containing quantitative trait loci (QTLs) for quality traits. Seven tropical and two temperate popcorn populations were genotyped for 96 SNPs chosen in chromosome regions containing QTLs for quality traits. The populations were phenotyped for expansion volume, 100-kernel weight, kernel sphericity, and kernel density. The LD statistics were the difference between the observed and expected haplotype frequencies (D), the proportion of D relative to the expected maximum value in the population, and the square of the correlation between the values of alleles at two loci. Association mapping was based on least squares and Bayesian approaches. In the tropical populations, D-values greater than 0.10 were observed for SNPs separated by 100-150 Mb, while most of the D-values in the temperate populations were less than 0.05. Selection for expansion volume indirectly led to increase in LD values, population differentiation, and significant changes in SNP frequency. Some associations were observed for expansion volume and the other quality traits. The candidate genes are involved with starch, storage protein, lipid, and cell wall polysaccharides synthesis.

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Factorization and the synthesis of optimal feedback kernels for differential-delay systems

NASA Technical Reports Server (NTRS)

Milman, Mark M.; Scheid, Robert E.

1987-01-01

A combination of ideas from the theories of operator Riccati equations and Volterra factorizations leads to the derivation of a novel, relatively simple set of hyperbolic equations which characterize the optimal feedback kernel for the finite-time regulator problem for autonomous differential-delay systems. Analysis of these equations elucidates the underlying structure of the feedback kernel and leads to the development of fast and accurate numerical methods for its computation. Unlike traditional formulations based on the operator Riccati equation, the gain is characterized by means of classical solutions of the derived set of equations. This leads to the development of approximation schemes which are analogous to what has been accomplished for systems of ordinary differential equations with given initial conditions.

Generalization Analysis of Fredholm Kernel Regularized Classifiers.

PubMed

Gong, Tieliang; Xu, Zongben; Chen, Hong

2017-07-01

Recently, a new framework, Fredholm learning, was proposed for semisupervised learning problems based on solving a regularized Fredholm integral equation. It allows a natural way to incorporate unlabeled data into learning algorithms to improve their prediction performance. Despite rapid progress on implementable algorithms with theoretical guarantees, the generalization ability of Fredholm kernel learning has not been studied. In this letter, we focus on investigating the generalization performance of a family of classification algorithms, referred to as Fredholm kernel regularized classifiers. We prove that the corresponding learning rate can achieve [Formula: see text] ([Formula: see text] is the number of labeled samples) in a limiting case. In addition, a representer theorem is provided for the proposed regularized scheme, which underlies its applications.

Functional and numerical responses of shrews to competition vary with mouse density.

PubMed

Eckrich, Carolyn A; Flaherty, Elizabeth A; Ben-David, Merav

2018-01-01

For decades, ecologists have debated the importance of biotic interactions (e.g., competition) and abiotic factors in regulating populations. Competition can influence patterns of distribution, abundance, and resource use in many systems but remains difficult to measure. We quantified competition between two sympatric small mammals, Keen's mice (Peromyscus keeni) and dusky shrews (Sorex monticolus), in four habitat types on Prince of Wales Island in Southeast Alaska. We related shrew density to that of mice using standardized regression models while accounting for habitat variables in each year from 2010-2012, during which mice populations peaked (2011) and then crashed (2012). Additionally, we measured dietary overlap and segregation using stable isotope analysis and kernel utilization densities and estimated the change in whole community energy consumption among years. We observed an increase in densities of dusky shrews after mice populations crashed in 2012 as expected under competitive release. In addition, competition coefficients revealed that the influence of Keen's mice was dependent on their density. Also in 2012, shrew diets shifted, indicating that they were able to exploit resources previously used by mice. Nonetheless, increases in shrew numbers only partially compensated for the community energy consumption because, as insectivores, they are unlikely to utilize all food types consumed by their competitors. In pre-commercially thinned stands, which exhibit higher diversity of resources compared to other habitat types, shrew populations were less affected by changes in mice densities. These spatially and temporally variable interactions between unlikely competitors, observed in a relatively simple, high-latitude island ecosystem, highlight the difficulty in assessing the role of biotic factors in structuring communities.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

7 CFR 981.7 - Edible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

Kernel K-Means Sampling for Nyström Approximation.

PubMed

He, Li; Zhang, Hong

2018-05-01

A fundamental problem in Nyström-based kernel matrix approximation is the sampling method by which training set is built. In this paper, we suggest to use kernel -means sampling, which is shown in our works to minimize the upper bound of a matrix approximation error. We first propose a unified kernel matrix approximation framework, which is able to describe most existing Nyström approximations under many popular kernels, including Gaussian kernel and polynomial kernel. We then show that, the matrix approximation error upper bound, in terms of the Frobenius norm, is equal to the -means error of data points in kernel space plus a constant. Thus, the -means centers of data in kernel space, or the kernel -means centers, are the optimal representative points with respect to the Frobenius norm error upper bound. Experimental results, with both Gaussian kernel and polynomial kernel, on real-world data sets and image segmentation tasks show the superiority of the proposed method over the state-of-the-art methods.

Resource-Constrained Spatial Hot Spot Identification

DTIC Science & Technology

2011-01-01

into three categories ( Cameron and Leitner, 2005):2 Thematic Mapping. Concentrations of events are color-coded in discrete geo- graphic areas that...of Boston burglary events in 1999 and provided by Cameron and Leitner (2005). The first map reflects burglary rates per 100,000 residents by Census...Burglary Rates, 1999 RAND A8567-22 1 0 1 2 Miles Thematic mapping Kernel density interpolation Hierarchical clustering Source: Cameron and Leitner, 2005. For

The multifacet graphically contracted function method. I. Formulation and implementation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shepard, Ron; Brozell, Scott R.; Gidofalvi, Gergely

2014-08-14

The basic formulation for the multifacet generalization of the graphically contracted function (MFGCF) electronic structure method is presented. The analysis includes the discussion of linear dependency and redundancy of the arc factor parameters, the computation of reduced density matrices, Hamiltonian matrix construction, spin-density matrix construction, the computation of optimization gradients for single-state and state-averaged calculations, graphical wave function analysis, and the efficient computation of configuration state function and Slater determinant expansion coefficients. Timings are given for Hamiltonian matrix element and analytic optimization gradient computations for a range of model problems for full-CI Shavitt graphs, and it is observed that bothmore » the energy and the gradient computation scale as O(N{sup 2}n{sup 4}) for N electrons and n orbitals. The important arithmetic operations are within dense matrix-matrix product computational kernels, resulting in a computationally efficient procedure. An initial implementation of the method is used to present applications to several challenging chemical systems, including N{sub 2} dissociation, cubic H{sub 8} dissociation, the symmetric dissociation of H{sub 2}O, and the insertion of Be into H{sub 2}. The results are compared to the exact full-CI values and also to those of the previous single-facet GCF expansion form.« less

Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python.

PubMed

Irvine, Michael A; Hollingsworth, T Déirdre

2018-05-26

Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Effect of Aspergillus niger xylanase on dough characteristics and bread quality attributes.

PubMed

Ahmad, Zulfiqar; Butt, Masood Sadiq; Ahmed, Anwaar; Riaz, Muhammad; Sabir, Syed Mubashar; Farooq, Umar; Rehman, Fazal Ur

2014-10-01

The present study was conducted to investigate the impact of various treatments of xylanase produced by Aspergillus niger applied in bread making processes like during tempering of wheat kernels and dough mixing on the dough quality characteristics i.e. dryness, stiffness, elasticity, extensibility, coherency and bread quality parameters i.e. volume, specific volume, density, moisture retention and sensory attributes. Different doses (200, 400, 600, 800 and 1,000 IU) of purified enzyme were applied to 1 kg of wheat grains during tempering and 1 kg of flour (straight grade flour) during mixing of dough in parallel. The samples of wheat kernels were agitated at different intervals for uniformity in tempering. After milling and dough making of both types of flour (having enzyme treatment during tempering and flour mixing) showed improved dough characteristics but the improvement was more prominent in the samples receiving enzyme treatment during tempering. Moreover, xylanase decreased dryness and stiffness of the dough whereas, resulted in increased elasticity, extensibility and coherency and increase in volume & decrease in bread density. Xylanase treatments also resulted in higher moisture retention and improvement of sensory attributes of bread. From the results, it is concluded that dough characteristics and bread quality improved significantly in response to enzyme treatments during tempering as compared to application during mixing.

Spatial analysis of crime incidence and adolescent physical activity.

PubMed

Robinson, Alyssa I; Carnes, Fei; Oreskovic, Nicolas M

2016-04-01

Adolescents do not achieve recommended levels of physical activity. Crime is believed to be a barrier to physical activity among youth, but findings are inconsistent. This study compares the spatial distribution of crime incidences and moderate-to-vigorous physical activity (MVPA) among adolescents in Massachusetts between 2011 and 2012, and examines the correlation between crime and MVPA. Eighty adolescents provided objective physical activity (accelerometer) and location (Global Positioning Systems) data. Crime report data were obtained from the city police department. Data were mapped using geographic information systems, and crime and MVPA densities were calculated using kernel density estimations. Spearman's correlation tested for associations between crime and MVPA. Overall, 1694 reported crimes and 16,702min of MVPA were included in analyses. A strong positive correlation was present between crime and adolescent MVPA (ρ=0.72, p<0.0001). Crime remained positively associated with MVPA in locations falling within the lowest quartile (ρ=0.43, p<0.0001) and highest quartile (ρ=0.32, p<0.0001) of crime density. This study found a strong positive association between crime and adolescent MVPA, despite research suggesting the opposite relationship. This counterintuitive finding may be explained by the logic of a common destination: neighborhood spaces which are desirable destinations and promote physical activity may likewise attract crime. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatial Analysis of Crime Incidence and Adolescent Physical Activity

PubMed Central

Robinson, Alyssa I.; Carnes, Fei

2016-01-01

Adolescents do not achieve recommended levels of physical activity. Crime is believed to be a barrier to physical activity among youth, but findings are inconsistent. This study compares the spatial distribution of crime incidences and moderate-to-vigorous physical activity (MVPA) among adolescents in Massachusetts between 2011 and 2012, and examines the correlation between crime and MVPA. Eighty adolescents provided objective physical activity (accelerometer) and location (Global Positioning Systems) data. Crime report data were obtained from the city police department. Data were mapped using geographic information systems, and crime and MVPA densities were calculated using kernel density estimations. Spearman’s correlation tested for associations between crime and MVPA. Overall, 1,694 reported crimes and 16,702 minutes of MVPA were included in analyses. A strong positive correlation was present between crime and adolescent MVPA (ρ=0.72, p<0.0001). Crime remained positively associated with MVPA in locations falling within the lowest quartile (ρ=0.43, p<0.0001) and highest quartile (ρ=0.32, p<0.0001) of crime density. This study found a strong positive association between crime and adolescent MVPA, despite research suggesting the opposite relationship. This counterintuitive finding may be explained by the logic of a common destination: neighborhood spaces which are desirable destinations and promote physical activity may likewise attract crime. PMID:26820115

Exploiting graph kernels for high performance biomedical relation extraction.

PubMed

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures. We demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Brownian motion of a nano-colloidal particle: the role of the solvent.

PubMed

Torres-Carbajal, Alexis; Herrera-Velarde, Salvador; Castañeda-Priego, Ramón

2015-07-15

Brownian motion is a feature of colloidal particles immersed in a liquid-like environment. Usually, it can be described by means of the generalised Langevin equation (GLE) within the framework of the Mori theory. In principle, all quantities that appear in the GLE can be calculated from the molecular information of the whole system, i.e., colloids and solvent molecules. In this work, by means of extensive Molecular Dynamics simulations, we study the effects of the microscopic details and the thermodynamic state of the solvent on the movement of a single nano-colloid. In particular, we consider a two-dimensional model system in which the mass and size of the colloid are two and one orders of magnitude, respectively, larger than the ones associated with the solvent molecules. The latter ones interact via a Lennard-Jones-type potential to tune the nature of the solvent, i.e., it can be either repulsive or attractive. We choose the linear momentum of the Brownian particle as the observable of interest in order to fully describe the Brownian motion within the Mori framework. We particularly focus on the colloid diffusion at different solvent densities and two temperature regimes: high and low (near the critical point) temperatures. To reach our goal, we have rewritten the GLE as a second kind Volterra integral in order to compute the memory kernel in real space. With this kernel, we evaluate the momentum-fluctuating force correlation function, which is of particular relevance since it allows us to establish when the stationarity condition has been reached. Our findings show that even at high temperatures, the details of the attractive interaction potential among solvent molecules induce important changes in the colloid dynamics. Additionally, near the critical point, the dynamical scenario becomes more complex; all the correlation functions decay slowly in an extended time window, however, the memory kernel seems to be only a function of the solvent density. Thus, the explicit inclusion of the solvent in the description of Brownian motion allows us to better understand the behaviour of the memory kernel at those thermodynamic states near the critical region without any further approximation. This information is useful to elaborate more realistic descriptions of Brownian motion that take into account the particular details of the host medium.

7 CFR 810.2202 - Definition of other terms.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernels, foreign material, and shrunken and broken kernels. The sum of these three factors may not exceed... the removal of dockage and shrunken and broken kernels. (g) Heat-damaged kernels. Kernels, pieces of... sample after the removal of dockage and shrunken and broken kernels. (h) Other grains. Barley, corn...

7 CFR 981.8 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or...

7 CFR 51.1415 - Inedible kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Inedible kernels. 51.1415 Section 51.1415 Agriculture... Standards for Grades of Pecans in the Shell 1 Definitions § 51.1415 Inedible kernels. Inedible kernels means that the kernel or pieces of kernels are rancid, moldy, decayed, injured by insects or otherwise...

An Approximate Approach to Automatic Kernel Selection.

PubMed

Ding, Lizhong; Liao, Shizhong

2016-02-02

Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.