Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.

PubMed

Poon, Art F Y

2015-09-01

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

FastSKAT: Sequence kernel association tests for very large sets of markers.

PubMed

Lumley, Thomas; Brody, Jennifer; Peloso, Gina; Morrison, Alanna; Rice, Kenneth

2018-06-22

The sequence kernel association test (SKAT) is widely used to test for associations between a phenotype and a set of genetic variants that are usually rare. Evaluating tail probabilities or quantiles of the null distribution for SKAT requires computing the eigenvalues of a matrix related to the genotype covariance between markers. Extracting the full set of eigenvalues of this matrix (an n×n matrix, for n subjects) has computational complexity proportional to n 3 . As SKAT is often used when n>104, this step becomes a major bottleneck in its use in practice. We therefore propose fastSKAT, a new computationally inexpensive but accurate approximations to the tail probabilities, in which the k largest eigenvalues of a weighted genotype covariance matrix or the largest singular values of a weighted genotype matrix are extracted, and a single term based on the Satterthwaite approximation is used for the remaining eigenvalues. While the method is not particularly sensitive to the choice of k, we also describe how to choose its value, and show how fastSKAT can automatically alert users to the rare cases where the choice may affect results. As well as providing faster implementation of SKAT, the new method also enables entirely new applications of SKAT that were not possible before; we give examples grouping variants by topologically associating domains, and comparing chromosome-wide association by class of histone marker. © 2018 WILEY PERIODICALS, INC.

Expression of Fungal diacylglycerol acyltransferase2 Genes to Increase Kernel Oil in Maize[OA

PubMed Central

Oakes, Janette; Brackenridge, Doug; Colletti, Ron; Daley, Maureen; Hawkins, Deborah J.; Xiong, Hui; Mai, Jennifer; Screen, Steve E.; Val, Dale; Lardizabal, Kathryn; Gruys, Ken; Deikman, Jill

2011-01-01

Maize (Zea mays) oil has high value but is only about 4% of the grain by weight. To increase kernel oil content, fungal diacylglycerol acyltransferase2 (DGAT2) genes from Umbelopsis (formerly Mortierella) ramanniana and Neurospora crassa were introduced into maize using an embryo-enhanced promoter. The protein encoded by the N. crassa gene was longer than that of U. ramanniana. It included 353 amino acids that aligned to the U. ramanniana DGAT2A protein and a 243-amino acid sequence at the amino terminus that was unique to the N. crassa DGAT2 protein. Two forms of N. crassa DGAT2 were tested: the predicted full-length protein (L-NcDGAT2) and a shorter form (S-NcDGAT2) that encoded just the sequences that share homology with the U. ramanniana protein. Expression of all three transgenes in maize resulted in small but statistically significant increases in kernel oil. S-NcDGAT2 had the biggest impact on kernel oil, with a 26% (relative) increase in oil in kernels of the best events (inbred). Increases in kernel oil were also obtained in both conventional and high-oil hybrids, and grain yield was not affected by expression of these fungal DGAT2 transgenes. PMID:21245192

A weighted U-statistic for genetic association analyses of sequencing data.

PubMed

Wei, Changshuai; Li, Ming; He, Zihuai; Vsevolozhskaya, Olga; Schaid, Daniel J; Lu, Qing

2014-12-01

With advancements in next-generation sequencing technology, a massive amount of sequencing data is generated, which offers a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, the high-dimensional sequencing data poses a great challenge for statistical analysis. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a Weighted U Sequencing test, referred to as WU-SEQ, for the high-dimensional association analysis of sequencing data. Based on a nonparametric U-statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used sequence kernel association test (SKAT) method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-SEQ to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol. © 2014 WILEY PERIODICALS, INC.

Detection of Splice Sites Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

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.

Quantitative trait loci mapping for Gibberella ear rot resistance and associated agronomic traits using genotyping-by-sequencing in maize.

PubMed

Kebede, Aida Z; Woldemariam, Tsegaye; Reid, Lana M; Harris, Linda J

2016-01-01

Unique and co-localized chromosomal regions affecting Gibberella ear rot disease resistance and correlated agronomic traits were identified in maize. Dissecting the mechanisms underlying resistance to Gibberella ear rot (GER) disease in maize provides insight towards more informed breeding. To this goal, we evaluated 410 recombinant inbred lines (RIL) for GER resistance over three testing years using silk channel and kernel inoculation techniques. RILs were also evaluated for agronomic traits like days to silking, husk cover, and kernel drydown rate. The RILs showed significant genotypic differences for all traits with above average to high heritability estimates. Significant (P < 0.01) but weak genotypic correlations were observed between disease severity and agronomic traits, indicating the involvement of agronomic traits in disease resistance. Common QTLs were detected for GER resistance and kernel drydown rate, suggesting the existence of pleiotropic genes that could be exploited to improve both traits at the same time. The QTLs identified for silk and kernel resistance shared some common regions on chromosomes 1, 2, and 8 and also had some regions specific to each tissue on chromosomes 9 and 10. Thus, effective GER resistance breeding could be achieved by considering screening methods that allow exploitation of tissue-specific disease resistance mechanisms and include kernel drydown rate either in an index or as indirect selection criterion.

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 ...

The HAP Complex Governs Fumonisin Biosynthesis and Maize Kernel Pathogenesis in Fusarium verticillioides.

PubMed

Ridenour, John B; Smith, Jonathon E; Bluhm, Burton H

2016-09-01

Contamination of maize ( Zea mays ) with fumonisins produced by the fungus Fusarium verticillioides is a global concern for food safety. Fumonisins are a group of polyketide-derived secondary metabolites linked to esophageal cancer and neural tube birth defects in humans and numerous toxicoses in livestock. Despite the importance of fumonisins in global maize production, the regulation of fumonisin biosynthesis during kernel pathogenesis is poorly understood. The HAP complex is a conserved, heterotrimeric transcriptional regulator that binds the consensus sequence CCAAT to modulate gene expression. Recently, functional characterization of the Hap3 subunit linked the HAP complex to the regulation of secondary metabolism and stalk rot pathogenesis in F. verticillioides . Here, we determine the involvement of HAP3 in fumonisin biosynthesis and kernel pathogenesis. Deletion of HAP3 suppressed fumonisin biosynthesis on both nonviable and live maize kernels and impaired pathogenesis in living kernels. Transcriptional profiling via RNA sequencing indicated that the HAP complex regulates at least 1,223 genes in F. verticillioides , representing nearly 10% of all predicted genes. Disruption of the HAP complex caused the misregulation of biosynthetic gene clusters underlying the production of secondary metabolites, including fusarins. Taken together, these results reveal that the HAP complex is a central regulator of fumonisin biosynthesis and kernel pathogenesis and works as both a positive and negative regulator of secondary metabolism in F. verticillioides .

Whole-genome sequence-based analysis of thyroid function.

PubMed

Taylor, Peter N; Porcu, Eleonora; Chew, Shelby; Campbell, Purdey J; Traglia, Michela; Brown, Suzanne J; Mullin, Benjamin H; Shihab, Hashem A; Min, Josine; Walter, Klaudia; Memari, Yasin; Huang, Jie; Barnes, Michael R; Beilby, John P; Charoen, Pimphen; Danecek, Petr; Dudbridge, Frank; Forgetta, Vincenzo; Greenwood, Celia; Grundberg, Elin; Johnson, Andrew D; Hui, Jennie; Lim, Ee M; McCarthy, Shane; Muddyman, Dawn; Panicker, Vijay; Perry, John R B; Bell, Jordana T; Yuan, Wei; Relton, Caroline; Gaunt, Tom; Schlessinger, David; Abecasis, Goncalo; Cucca, Francesco; Surdulescu, Gabriela L; Woltersdorf, Wolfram; Zeggini, Eleftheria; Zheng, Hou-Feng; Toniolo, Daniela; Dayan, Colin M; Naitza, Silvia; Walsh, John P; Spector, Tim; Davey Smith, George; Durbin, Richard; Richards, J Brent; Sanna, Serena; Soranzo, Nicole; Timpson, Nicholas J; Wilson, Scott G

2015-03-06

Normal thyroid function is essential for health, but its genetic architecture remains poorly understood. Here, for the heritable thyroid traits thyrotropin (TSH) and free thyroxine (FT4), we analyse whole-genome sequence data from the UK10K project (N=2,287). Using additional whole-genome sequence and deeply imputed data sets, we report meta-analysis results for common variants (MAF≥1%) associated with TSH and FT4 (N=16,335). For TSH, we identify a novel variant in SYN2 (MAF=23.5%, P=6.15 × 10(-9)) and a new independent variant in PDE8B (MAF=10.4%, P=5.94 × 10(-14)). For FT4, we report a low-frequency variant near B4GALT6/SLC25A52 (MAF=3.2%, P=1.27 × 10(-9)) tagging a rare TTR variant (MAF=0.4%, P=2.14 × 10(-11)). All common variants explain ≥20% of the variance in TSH and FT4. Analysis of rare variants (MAF<1%) using sequence kernel association testing reveals a novel association with FT4 in NRG1. Our results demonstrate that increased coverage in whole-genome sequence association studies identifies novel variants associated with thyroid function.

A non-synonymous SNP within the isopentenyl transferase 2 locus is associated with kernel weight in Chinese maize inbreds (Zea mays L.).

PubMed

Weng, Jianfeng; Li, Bo; Liu, Changlin; Yang, Xiaoyan; Wang, Hongwei; Hao, Zhuanfang; Li, Mingshun; Zhang, Degui; Ci, Xiaoke; Li, Xinhai; Zhang, Shihuang

2013-07-05

Kernel weight, controlled by quantitative trait loci (QTL), is an important component of grain yield in maize. Cytokinins (CKs) participate in determining grain morphology and final grain yield in crops. ZmIPT2, which is expressed mainly in the basal transfer cell layer, endosperm, and embryo during maize kernel development, encodes an isopentenyl transferase (IPT) that is involved in CK biosynthesis. The coding region of ZmIPT2 was sequenced across a panel of 175 maize inbred lines that are currently used in Chinese maize breeding programs. Only 16 single nucleotide polymorphisms (SNPs) and seven haplotypes were detected among these inbred lines. Nucleotide diversity (π) within the ZmIPT2 window and coding region were 0.347 and 0.0047, respectively, and they were significantly lower than the mean nucleotide diversity value of 0.372 for maize Chromosome 2 (P < 0.01). Association mapping revealed that a single nucleotide change from cytosine (C) to thymine (T) in the ZmIPT2 coding region, which converted a proline residue into a serine residue, was significantly associated with hundred kernel weight (HKW) in three environments (P <0.05), and explained 4.76% of the total phenotypic variation. In vitro characterization suggests that the dimethylallyl diphospate (DMAPP) IPT activity of ZmIPT2-T is higher than that of ZmIPT2-C, as the amounts of adenosine triphosphate (ATP), adenosine diphosphate (ADP), and adenosine monophosphate (AMP) consumed by ZmIPT2-T were 5.48-, 2.70-, and 1.87-fold, respectively, greater than those consumed by ZmIPT2-C. The effects of artificial selection on the ZmIPT2 coding region were evaluated using Tajima's D tests across six subgroups of Chinese maize germplasm, with the most frequent favorable allele identified in subgroup PB (Partner B). These results showed that ZmIPT2, which is associated with kernel weight, was subjected to artificial selection during the maize breeding process. ZmIPT2-T had higher IPT activity than ZmIPT2-C, and this favorable allele for kernel weight could be used in molecular marker-assisted selection for improvement of grain yield components in Chinese maize breeding programs.

A Decision-Making Method with Grey Multi-Source Heterogeneous Data and Its Application in Green Supplier Selection

PubMed Central

Dang, Yaoguo; Mao, Wenxin

2018-01-01

In view of the multi-attribute decision-making problem that the attribute values are grey multi-source heterogeneous data, a decision-making method based on kernel and greyness degree is proposed. The definitions of kernel and greyness degree of an extended grey number in a grey multi-source heterogeneous data sequence are given. On this basis, we construct the kernel vector and greyness degree vector of the sequence to whiten the multi-source heterogeneous information, then a grey relational bi-directional projection ranking method is presented. Considering the multi-attribute multi-level decision structure and the causalities between attributes in decision-making problem, the HG-DEMATEL method is proposed to determine the hierarchical attribute weights. A green supplier selection example is provided to demonstrate the rationality and validity of the proposed method. PMID:29510521

A Decision-Making Method with Grey Multi-Source Heterogeneous Data and Its Application in Green Supplier Selection.

PubMed

Sun, Huifang; Dang, Yaoguo; Mao, Wenxin

2018-03-03

In view of the multi-attribute decision-making problem that the attribute values are grey multi-source heterogeneous data, a decision-making method based on kernel and greyness degree is proposed. The definitions of kernel and greyness degree of an extended grey number in a grey multi-source heterogeneous data sequence are given. On this basis, we construct the kernel vector and greyness degree vector of the sequence to whiten the multi-source heterogeneous information, then a grey relational bi-directional projection ranking method is presented. Considering the multi-attribute multi-level decision structure and the causalities between attributes in decision-making problem, the HG-DEMATEL method is proposed to determine the hierarchical attribute weights. A green supplier selection example is provided to demonstrate the rationality and validity of the proposed method.

Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters

PubMed Central

Jeong, Soowoong; Kim, Guisik; Lee, Sangkeun

2017-01-01

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved. PMID:28241475

Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters.

PubMed

Jeong, Soowoong; Kim, Guisik; Lee, Sangkeun

2017-02-23

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved.

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.

Lung dynamic MRI deblurring using low-rank decomposition and dictionary learning.

PubMed

Gou, Shuiping; Wang, Yueyue; Wu, Jiaolong; Lee, Percy; Sheng, Ke

2015-04-01

Lung dynamic MRI (dMRI) has emerged to be an appealing tool to quantify lung motion for both planning and treatment guidance purposes. However, this modality can result in blurry images due to intrinsically low signal-to-noise ratio in the lung and spatial/temporal interpolation. The image blurring could adversely affect the image processing that depends on the availability of fine landmarks. The purpose of this study is to reduce dMRI blurring using image postprocessing. To enhance the image quality and exploit the spatiotemporal continuity of dMRI sequences, a low-rank decomposition and dictionary learning (LDDL) method was employed to deblur lung dMRI and enhance the conspicuity of lung blood vessels. Fifty frames of continuous 2D coronal dMRI frames using a steady state free precession sequence were obtained from five subjects including two healthy volunteer and three lung cancer patients. In LDDL, the lung dMRI was decomposed into sparse and low-rank components. Dictionary learning was employed to estimate the blurring kernel based on the whole image, low-rank or sparse component of the first image in the lung MRI sequence. Deblurring was performed on the whole image sequences using deconvolution based on the estimated blur kernel. The deblurring results were quantified using an automated blood vessel extraction method based on the classification of Hessian matrix filtered images. Accuracy of automated extraction was calculated using manual segmentation of the blood vessels as the ground truth. In the pilot study, LDDL based on the blurring kernel estimated from the sparse component led to performance superior to the other ways of kernel estimation. LDDL consistently improved image contrast and fine feature conspicuity of the original MRI without introducing artifacts. The accuracy of automated blood vessel extraction was on average increased by 16% using manual segmentation as the ground truth. Image blurring in dMRI images can be effectively reduced using a low-rank decomposition and dictionary learning method using kernels estimated by the sparse component.

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.

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.

Higher criticism approach to detect rare variants using whole genome sequencing data

PubMed Central

2014-01-01

Because of low statistical power of single-variant tests for whole genome sequencing (WGS) data, the association test for variant groups is a key approach for genetic mapping. To address the features of sparse and weak genetic effects to be detected, the higher criticism (HC) approach has been proposed and theoretically has proven optimal for detecting sparse and weak genetic effects. Here we develop a strategy to apply the HC approach to WGS data that contains rare variants as the majority. By using Genetic Analysis Workshop 18 "dose" genetic data with simulated phenotypes, we assess the performance of HC under a variety of strategies for grouping variants and collapsing rare variants. The HC approach is compared with the minimal p-value method and the sequence kernel association test. The results show that the HC approach is preferred for detecting weak genetic effects. PMID:25519367

Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data.

PubMed

Wu, Hongle; Kato, Takafumi; Yamada, Tomomi; Numao, Masayuki; Fukui, Ken-Ichi

2017-07-01

We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns. In addition, by visualizing the transition of cluster dynamics, sleep-related sound events were found to relate to the various stages of sleep. Therefore, these results empirically warrant future study into the assessment of personal sleep quality using sound data. Copyright © 2017 Elsevier B.V. All rights reserved.

Isolation and Structural Characterization of Antioxidant Peptides from Degreased Apricot Seed Kernels.

PubMed

Zhang, Haisheng; Xue, Jing; Zhao, Huanxia; Zhao, Xinshuai; Xue, Huanhuan; Sun, Yuhan; Xue, Wanrui

2018-05-03

Background : The composition and sequence of amino acids have a prominent influence on theantioxidant activities of peptides. Objective : A series of isolation and purification experiments was conducted to explore the amino acid sequence of antioxidant peptides, which led to its antioxidation causes. Methods : The degreased apricot seed kernels were hydrolyzed by compound proteases of alkaline protease and flavor protease (3:2, u/u) to prepare apricot seed kernel hydrolysates (ASKH). ASKH were separated into ASKH-A and ASKH-B by dialysis bag. ASKH-B (MW < 3.5 kDa) was further separated into fractions by Sephadex G-25 and G-15 gel-filtration chromatography. Reversed-phase HPLC (RP-HPLC) was performed to separate fraction B4b into two antioxidant peptides (peptide B4b-4 and B4b-6). Results : The amino acid sequences were Val-Leu-Tyr-Ile-Trp and Ser-Val-Pro-Tyr-Glu, respectively. Conclusions : The results suggested that ASKH antioxidant peptides may have potential utility as healthy ingredients and as food preservatives due to their antioxidant activity. Highlights : Materials with regional characteristics were selected to explore, and hydrolysates were identified by RP-HPLC and matrix-assisted laser desorption ionization-time-of-flight-MS to obtain amino acid sequences.

A self-calibrated angularly continuous 2D GRAPPA kernel for propeller trajectories

PubMed Central

Skare, Stefan; Newbould, Rexford D; Nordell, Anders; Holdsworth, Samantha J; Bammer, Roland

2008-01-01

The k-space readout of propeller-type sequences may be accelerated by the use of parallel imaging (PI). For PROPELLER, the main benefits are reduced blurring due to T2 decay and SAR reduction, while for EPI-based propeller acquisitions such as Turbo-PROP and SAP-EPI, the faster k-space traversal alleviates geometric distortions. In this work, the feasibility of calculating a 2D GRAPPA kernel on only the undersampled propeller blades themselves is explored, using the matching orthogonal undersampled blade. It is shown that the GRAPPA kernel varies slowly across blades, therefore an angularly continuous 2D GRAPPA kernel is proposed, in which the angular variation of the weights is parameterized. This new angularly continuous kernel formulation greatly increases the numerical stability of the GRAPPA weight estimation, allowing the generation of fully sampled diagnostic quality images using only the undersampled propeller data. PMID:19025911

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.

Scalable Kernel Methods and Algorithms for General Sequence Analysis

ERIC Educational Resources Information Center

Kuksa, Pavel

2011-01-01

Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…

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

Multidimensional NMR inversion without Kronecker products: Multilinear inversion

NASA Astrophysics Data System (ADS)

Medellín, David; Ravi, Vivek R.; Torres-Verdín, Carlos

2016-08-01

Multidimensional NMR inversion using Kronecker products poses several challenges. First, kernel compression is only possible when the kernel matrices are separable, and in recent years, there has been an increasing interest in NMR sequences with non-separable kernels. Second, in three or more dimensions, the singular value decomposition is not unique; therefore kernel compression is not well-defined for higher dimensions. Without kernel compression, the Kronecker product yields matrices that require large amounts of memory, making the inversion intractable for personal computers. Finally, incorporating arbitrary regularization terms is not possible using the Lawson-Hanson (LH) or the Butler-Reeds-Dawson (BRD) algorithms. We develop a minimization-based inversion method that circumvents the above problems by using multilinear forms to perform multidimensional NMR inversion without using kernel compression or Kronecker products. The new method is memory efficient, requiring less than 0.1% of the memory required by the LH or BRD methods. It can also be extended to arbitrary dimensions and adapted to include non-separable kernels, linear constraints, and arbitrary regularization terms. Additionally, it is easy to implement because only a cost function and its first derivative are required to perform the inversion.

Detection of QTLs controlling fast kernel dehydration in maize (Zea mays L.).

PubMed

Qian, Y L; Zhang, X Q; Wang, L F; Chen, J; Chen, B R; Lv, G H; Wu, Z C; Guo, J; Wang, J; Qi, Y C; Li, T C; Zhang, W; Ruan, L; Zuo, X L

2016-08-19

In order to understand the effect of grain moisture of inbred lines at the silking and physiological maturity stages on kernel dehydration rate, 59 maize inbred lines from six subgroups were selected. Grain moisture was measured and QTLs associated with kernel dehydration were mapped. A rapid dehydration evaluation and association analysis revealed eight inbred lines with faster dehydration rate, including Yuanwu 02, K36, Zhonger/O2, Lo1125, Han 49, Qi 319, Hua 160, and PH4CV. A single sequence repeat analysis using 85 pairs detected five QTLs with phenotypic variation contribution ≥10% in the permanent F2 generation populations Zheng 58 x S1776 and Chang 7-2 x K1131, which had LOD threshold values ≥ 3 in both 2013 and 2014. The chromosome region of qFkdr7b had not previously been reported and is preliminarily identified as a new major QTL. A false positive field verification of grain dehydration rate of 53 inbred lines indicated that the screening result of the rapid dehydration inbred lines by specific amplification with marker Phi114 was most similar to the field assessment result, followed by markers Phi127 and Phi029. The rapid dehydration lines selected based on primer Phi114 amplification were also similar to the field dehydration rate and can thus be used for molecular marker-assisted selection. A significant effort is needed to improve stress resistance and shorten the growth period via fast kernel dehydration in intermediate materials of the inbred lines K36, Zhonger/ O2, Lo1125, Han 49, Hua 160, and PH4CV, and further using the selected lines for new combinations.

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.

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.

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

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.

Rare coding variation in paraoxonase-1 is associated with ischemic stroke in the NHLBI Exome Sequencing Project.

PubMed

Kim, Daniel Seung; Crosslin, David R; Auer, Paul L; Suzuki, Stephanie M; Marsillach, Judit; Burt, Amber A; Gordon, Adam S; Meschia, James F; Nalls, Mike A; Worrall, Bradford B; Longstreth, W T; Gottesman, Rebecca F; Furlong, Clement E; Peters, Ulrike; Rich, Stephen S; Nickerson, Deborah A; Jarvik, Gail P

2014-06-01

HDL-associated paraoxonase-1 (PON1) is an enzyme whose activity is associated with cerebrovascular disease. Common PON1 genetic variants have not been consistently associated with cerebrovascular disease. Rare coding variation that likely alters PON1 enzyme function may be more strongly associated with stroke. The National Heart, Lung, and Blood Institute Exome Sequencing Project sequenced the coding regions (exomes) of the genome for heart, lung, and blood-related phenotypes (including ischemic stroke). In this sample of 4,204 unrelated participants, 496 had verified, noncardioembolic ischemic stroke. After filtering, 28 nonsynonymous PON1 variants were identified. Analysis with the sequence kernel association test, adjusted for covariates, identified significant associations between PON1 variants and ischemic stroke (P = 3.01 × 10(-3)). Stratified analyses demonstrated a stronger association of PON1 variants with ischemic stroke in African ancestry (AA) participants (P = 5.03 × 10(-3)). Ethnic differences in the association between PON1 variants with stroke could be due to the effects of PON1Val109Ile (overall P = 7.88 × 10(-3); AA P = 6.52 × 10(-4)), found at higher frequency in AA participants (1.16% vs. 0.02%) and whose protein is less stable than the common allele. In summary, rare genetic variation in PON1 was associated with ischemic stroke, with stronger associations identified in those of AA. Increased focus on PON1 enzyme function and its role in cerebrovascular disease is warranted.

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

Hanft, J.M.; Jones, R.J.

This study was designed to compare the uptake and distribution of /sup 14/C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 309 and 35/sup 0/C were transferred to (/sup 14/C)sucrose media 10 days after pollination. Kernels cultured at 35/sup 0/C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected aftermore » 24 hours in culture on atlageled media. After 8 days in culture on (/sup 14/C)sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35/sup 0/C, respectively. Of the total carbohydrates, a higher percentage of label was associated with sucrose and lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35/sup 0/C compared to kernels cultured at 30/sup 0/C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35/sup 0/C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30/sup 0/C (89%). Kernels cultured at 35/sup 0/C had a correspondingly higher proportion of /sup 14/C in endosperm fructose, glucose, and sucrose.« less

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

This study was designed to compare the uptake and distribution of 14C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 30 and 35°C were transferred to [14C]sucrose media 10 days after pollination. Kernels cultured at 35°C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on labeled media. After 8 days in culture on [14C]sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35°C, respectively. This indicates that some of the sucrose taken up by the cob tissue was cleaved to fructose and glucose in the cob. Of the total carbohydrates, a higher percentage of label was associated with sucrose and a lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35°C compared to kernels cultured at 30°C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35°C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30°C (89%). Kernels cultured at 35°C had a correspondingly higher proportion of 14C in endosperm fructose, glucose, and sucrose. These results indicate that starch synthesis in the endosperm is strongly inhibited in kernels induced to abort by high temperature even though there is an adequate supply of sugar. PMID:16664847

A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

PubMed Central

Freytag, Saskia; Manitz, Juliane; Schlather, Martin; Kneib, Thomas; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Heinrich, Joachim; Bickeböller, Heike

2014-01-01

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). Here, the kernel converts genomic information of two individuals to a quantitative value reflecting their genetic similarity. With the selection of the kernel one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms. PMID:24434848

Efficient use of unlabeled data for protein sequence classification: a comparative study.

PubMed

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-04-29

Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags-the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably.

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.

Rare coding variation in paraoxonase-1 is associated with ischemic stroke in the NHLBI Exome Sequencing Project[S

PubMed Central

Kim, Daniel Seung; Crosslin, David R.; Auer, Paul L.; Suzuki, Stephanie M.; Marsillach, Judit; Burt, Amber A.; Gordon, Adam S.; Meschia, James F.; Nalls, Mike A.; Worrall, Bradford B.; Longstreth, W. T.; Gottesman, Rebecca F.; Furlong, Clement E.; Peters, Ulrike; Rich, Stephen S.; Nickerson, Deborah A.; Jarvik, Gail P.

2014-01-01

HDL-associated paraoxonase-1 (PON1) is an enzyme whose activity is associated with cerebrovascular disease. Common PON1 genetic variants have not been consistently associated with cerebrovascular disease. Rare coding variation that likely alters PON1 enzyme function may be more strongly associated with stroke. The National Heart, Lung, and Blood Institute Exome Sequencing Project sequenced the coding regions (exomes) of the genome for heart, lung, and blood-related phenotypes (including ischemic stroke). In this sample of 4,204 unrelated participants, 496 had verified, noncardioembolic ischemic stroke. After filtering, 28 nonsynonymous PON1 variants were identified. Analysis with the sequence kernel association test, adjusted for covariates, identified significant associations between PON1 variants and ischemic stroke (P = 3.01 × 10−3). Stratified analyses demonstrated a stronger association of PON1 variants with ischemic stroke in African ancestry (AA) participants (P = 5.03 × 10−3). Ethnic differences in the association between PON1 variants with stroke could be due to the effects of PON1Val109Ile (overall P = 7.88 × 10−3; AA P = 6.52 × 10−4), found at higher frequency in AA participants (1.16% vs. 0.02%) and whose protein is less stable than the common allele. In summary, rare genetic variation in PON1 was associated with ischemic stroke, with stronger associations identified in those of AA. Increased focus on PON1 enzyme function and its role in cerebrovascular disease is warranted. PMID:24711634

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.

Proteomic analysis of sweet algerian apricot kernels (Prunus armeniaca L.) by combinatorial peptide ligand libraries and LC-MS/MS.

PubMed

Ghorab, Hamida; Lammi, Carmen; Arnoldi, Anna; Kabouche, Zahia; Aiello, Gilda

2018-01-15

An investigation on the proteome of the sweet kernel of apricot, based on equalisation with combinatorial peptide ligand libraries (CPLLs), SDS-PAGE, nLC-ESI-MS/MS, and database search, permitted identifying 175 proteins. Gene ontology analysis indicated that their main molecular functions are in nucleotide binding (20.9%), hydrolase activities (10.6%), kinase activities (7%), and catalytic activity (5.6%). A protein-protein association network analysis using STRING software permitted to build an interactomic map of all detected proteins, characterised by 34 interactions. In order to forecast the potential health benefits deriving from the consumption of these proteins, the two most abundant, i.e. Prunin 1 and 2, were enzymatically digested in silico predicting 10 and 14 peptides, respectively. Searching their sequences in the database BIOPEP, it was possible to suggest a variety of bioactivities, including dipeptidyl peptidase-IV (DPP-IV) and angiotensin converting enzyme I (ACE) inhibition, glucose uptake stimulation and antioxidant properties. Copyright © 2017 Elsevier Ltd. All rights reserved.

Molecular and functional characterization of two drought-induced zinc finger proteins, ZmZnF1 and ZmZnF2 from maize kernels

USDA-ARS?s Scientific Manuscript database

We have isolated two cDNA clones encoding Zinc Finger proteins, designated as ZmZnF1 and ZmZnF2, from water-stressed maize kernels. Sequence analyses indicates that ZmZnF1 is homologous to the A20/AN1-type zinc finger protein and contains the zinc finger motif of Cx2–Cx10–CxCx4Cx2Hx5HxC. Whereas ZmZ...

Burrower bugs (Heteroptera: Cydnidae) in peanut: seasonal species abundance, tillage effects, grade reduction effects, insecticide efficacy, and management.

PubMed

Chapin, Jay W; Thomas, James S

2003-08-01

Pitfall traps placed in South Carolina peanut, Arachis hypogaea (L.), fields collected three species of burrower bugs (Cydnidae): Cyrtomenus ciliatus (Palisot de Beauvois), Sehirus cinctus cinctus (Palisot de Beauvois), and Pangaeus bilineatus (Say). Cyrtomenus ciliatus was rarely collected. Sehirus cinctus produced a nymphal cohort in peanut during May and June, probably because of abundant henbit seeds, Lamium amplexicaule L., in strip-till production systems. No S. cinctus were present during peanut pod formation. Pangaeus bilineatus was the most abundant species collected and the only species associated with peanut kernel feeding injury. Overwintering P. bilineatus adults were present in a conservation tillage peanut field before planting and two to three subsequent generations were observed. Few nymphs were collected until the R6 (full seed) growth stage. Tillage and choice of cover crop affected P. bilineatus populations. Peanuts strip-tilled into corn or wheat residue had greater P. bilineatus populations and kernel-feeding than conventional tillage or strip-tillage into rye residue. Fall tillage before planting a wheat cover crop also reduced burrower bug feeding on peanut. At-pegging (early July) granular chlorpyrifos treatments were most consistent in suppressing kernel feeding. Kernels fed on by P. bilineatus were on average 10% lighter than unfed on kernels. Pangaeus bilineatus feeding reduced peanut grade by reducing individual kernel weight, and increasing the percentage damaged kernels. Each 10% increase in kernels fed on by P. bilineatus was associated with a 1.7% decrease in total sound mature kernels, and kernel feeding levels above 30% increase the risk of damaged kernel grade penalties.

Effects of Amygdaline from Apricot Kernel on Transplanted Tumors in Mice.

PubMed

Yamshanov, V A; Kovan'ko, E G; Pustovalov, Yu I

2016-03-01

The effects of amygdaline from apricot kernel added to fodder on the growth of transplanted LYO-1 and Ehrlich carcinoma were studied in mice. Apricot kernels inhibited the growth of both tumors. Apricot kernels, raw and after thermal processing, given 2 days before transplantation produced a pronounced antitumor effect. Heat-processed apricot kernels given in 3 days after transplantation modified the tumor growth and prolonged animal lifespan. Thermal treatment did not considerably reduce the antitumor effect of apricot kernels. It was hypothesized that the antitumor effect of amygdaline on Ehrlich carcinoma and LYO-1 lymphosarcoma was associated with the presence of bacterial genome in the tumor.

Efficient use of unlabeled data for protein sequence classification: a comparative study

PubMed Central

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-01-01

Background Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags–the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Results Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. Conclusion The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably. PMID:19426450

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

A generalized association test based on U statistics.

PubMed

Wei, Changshuai; Lu, Qing

2017-07-01

Second generation sequencing technologies are being increasingly used for genetic association studies, where the main research interest is to identify sets of genetic variants that contribute to various phenotypes. The phenotype can be univariate disease status, multivariate responses and even high-dimensional outcomes. Considering the genotype and phenotype as two complex objects, this also poses a general statistical problem of testing association between complex objects. We here proposed a similarity-based test, generalized similarity U (GSU), that can test the association between complex objects. We first studied the theoretical properties of the test in a general setting and then focused on the application of the test to sequencing association studies. Based on theoretical analysis, we proposed to use Laplacian Kernel-based similarity for GSU to boost power and enhance robustness. Through simulation, we found that GSU did have advantages over existing methods in terms of power and robustness. We further performed a whole genome sequencing (WGS) scan for Alzherimer's disease neuroimaging initiative data, identifying three genes, APOE , APOC1 and TOMM40 , associated with imaging phenotype. We developed a C ++ package for analysis of WGS data using GSU. The source codes can be downloaded at https://github.com/changshuaiwei/gsu . weichangshuai@gmail.com ; qlu@epi.msu.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A high performance parallel algorithm for 1-D FFT

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

Agarwal, R.C.; Gustavson, F.G.; Zubair, M.

1994-12-31

In this paper the authors propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. They use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. They show that the multi-dimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. They implementedmore » this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.« less

Sliding Window Generalized Kernel Affine Projection Algorithm Using Projection Mappings

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Theodoridis, Sergios

2008-12-01

Very recently, a solution to the kernel-based online classification problem has been given by the adaptive projected subgradient method (APSM). The developed algorithm can be considered as a generalization of a kernel affine projection algorithm (APA) and the kernel normalized least mean squares (NLMS). Furthermore, sparsification of the resulting kernel series expansion was achieved by imposing a closed ball (convex set) constraint on the norm of the classifiers. This paper presents another sparsification method for the APSM approach to the online classification task by generating a sequence of linear subspaces in a reproducing kernel Hilbert space (RKHS). To cope with the inherent memory limitations of online systems and to embed tracking capabilities to the design, an upper bound on the dimension of the linear subspaces is imposed. The underlying principle of the design is the notion of projection mappings. Classification is performed by metric projection mappings, sparsification is achieved by orthogonal projections, while the online system's memory requirements and tracking are attained by oblique projections. The resulting sparsification scheme shows strong similarities with the classical sliding window adaptive schemes. The proposed design is validated by the adaptive equalization problem of a nonlinear communication channel, and is compared with classical and recent stochastic gradient descent techniques, as well as with the APSM's solution where sparsification is performed by a closed ball constraint on the norm of the classifiers.

Kernel-based whole-genome prediction of complex traits: a review.

PubMed

Morota, Gota; Gianola, Daniel

2014-01-01

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Functional linear models for association analysis of quantitative traits.

PubMed

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY PERIODICALS, INC.

Deblurring sequential ocular images from multi-spectral imaging (MSI) via mutual information.

PubMed

Lian, Jian; Zheng, Yuanjie; Jiao, Wanzhen; Yan, Fang; Zhao, Bojun

2018-06-01

Multi-spectral imaging (MSI) produces a sequence of spectral images to capture the inner structure of different species, which was recently introduced into ocular disease diagnosis. However, the quality of MSI images can be significantly degraded by motion blur caused by the inevitable saccades and exposure time required for maintaining a sufficiently high signal-to-noise ratio. This degradation may confuse an ophthalmologist, reduce the examination quality, or defeat various image analysis algorithms. We propose an early work specially on deblurring sequential MSI images, which is distinguished from many of the current image deblurring techniques by resolving the blur kernel simultaneously for all the images in an MSI sequence. It is accomplished by incorporating several a priori constraints including the sharpness of the latent clear image, the spatial and temporal smoothness of the blur kernel and the similarity between temporally-neighboring images in MSI sequence. Specifically, we model the similarity between MSI images with mutual information considering the different wavelengths used for capturing different images in MSI sequence. The optimization of the proposed approach is based on a multi-scale framework and stepwise optimization strategy. Experimental results from 22 MSI sequences validate that our approach outperforms several state-of-the-art techniques in natural image deblurring.

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.

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.

Genome-Wide Association Study Identifies Candidate Genes for Starch Content Regulation in Maize Kernels

PubMed Central

Liu, Na; Xue, Yadong; Guo, Zhanyong; Li, Weihua; Tang, Jihua

2016-01-01

Kernel starch content is an important trait in maize (Zea mays L.) as it accounts for 65–75% of the dry kernel weight and positively correlates with seed yield. A number of starch synthesis-related genes have been identified in maize in recent years. However, many loci underlying variation in starch content among maize inbred lines still remain to be identified. The current study is a genome-wide association study that used a set of 263 maize inbred lines. In this panel, the average kernel starch content was 66.99%, ranging from 60.60 to 71.58% over the three study years. These inbred lines were genotyped with the SNP50 BeadChip maize array, which is comprised of 56,110 evenly spaced, random SNPs. Population structure was controlled by a mixed linear model (MLM) as implemented in the software package TASSEL. After the statistical analyses, four SNPs were identified as significantly associated with starch content (P ≤ 0.0001), among which one each are located on chromosomes 1 and 5 and two are on chromosome 2. Furthermore, 77 candidate genes associated with starch synthesis were found within the 100-kb intervals containing these four QTLs, and four highly associated genes were within 20-kb intervals of the associated SNPs. Among the four genes, Glucose-1-phosphate adenylyltransferase (APS1; Gene ID GRMZM2G163437) is known as an important regulator of kernel starch content. The identified SNPs, QTLs, and candidate genes may not only be readily used for germplasm improvement by marker-assisted selection in breeding, but can also elucidate the genetic basis of starch content. Further studies on these identified candidate genes may help determine the molecular mechanisms regulating kernel starch content in maize and other important cereal crops. PMID:27512395

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.

Characteristics of uranium carbonitride microparticles synthesized using different reaction conditions

NASA Astrophysics Data System (ADS)

Silva, Chinthaka M.; Lindemer, Terrence B.; Voit, Stewart R.; Hunt, Rodney D.; Besmann, Theodore M.; Terrani, Kurt A.; Snead, Lance L.

2014-11-01

Three sets of experimental conditions were tested to synthesize uranium carbonitride (UC1-xNx) kernels from gel-derived urania-carbon microspheres. Primarily, three sequences of gases were used, N2 to N2-4%H2 to Ar, Ar to N2 to Ar, and Ar-4%H2 to N2-4%H2 to Ar-4%H2. Physical and chemical characteristics such as geometrical density, phase purity, and chemical compositions of the synthesized UC1-xNx were measured. Single-phase kernels were commonly obtained with densities generally ranging from 85% to 93% TD and values of x as high as 0.99. In-depth analysis of the microstrutures of UC1-xNx has been carried out and is discussed with the objective of large batch fabrication of high density UC1-xNx kernels.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Straight-chain halocarbon forming fluids for TRISO fuel kernel production - Tests with yttria-stabilized zirconia microspheres

NASA Astrophysics Data System (ADS)

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

2015-03-01

Current methods of TRISO fuel kernel production in the United States use a sol-gel process with trichloroethylene (TCE) as the forming fluid. After contact with radioactive materials, the spent TCE becomes a mixed hazardous waste, and high costs are associated with its recycling or disposal. Reducing or eliminating this mixed waste stream would not only benefit the environment, but would also enhance the economics of kernel production. Previous research yielded three candidates for testing as alternatives to TCE: 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane. This study considers the production of yttria-stabilized zirconia (YSZ) kernels in silicone oil and the three chosen alternative formation fluids, with subsequent characterization of the produced kernels and used forming fluid. Kernels formed in silicone oil and bromotetradecane were comparable to those produced by previous kernel production efforts, while those produced in chlorooctadecane and iodododecane experienced gelation issues leading to poor kernel formation and geometry.

The site, size, spatial stability, and energetics of an X-ray flare kernel

NASA Technical Reports Server (NTRS)

Petrasso, R.; Gerassimenko, M.; Nolte, J.

1979-01-01

The site, size evolution, and energetics of an X-ray kernel that dominated a solar flare during its rise and somewhat during its peak are investigated. The position of the kernel remained stationary to within about 3 arc sec over the 30-min interval of observations, despite pulsations in the kernel X-ray brightness in excess of a factor of 10. This suggests a tightly bound, deeply rooted magnetic structure, more plausibly associated with the near chromosphere or low corona rather than with the high corona. The H-alpha flare onset coincided with the appearance of the kernel, again suggesting a close spatial and temporal coupling between the chromospheric H-alpha event and the X-ray kernel. At the first kernel brightness peak its size was no larger than about 2 arc sec, when it accounted for about 40% of the total flare flux. In the second rise phase of the kernel, a source power input of order 2 times 10 to the 24th ergs/sec is minimally required.

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.

Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.

PubMed

Lado, Bettina; Matus, Ivan; Rodríguez, Alejandra; Inostroza, Luis; Poland, Jesse; Belzile, François; del Pozo, Alejandro; Quincke, Martín; Castro, Marina; von Zitzewitz, Jarislav

2013-12-09

In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

Estimation of biological parameters of marine organisms using linear and nonlinear acoustic scattering model-based inversion methods.

PubMed

Chu, Dezhang; Lawson, Gareth L; Wiebe, Peter H

2016-05-01

The linear inversion commonly used in fisheries and zooplankton acoustics assumes a constant inversion kernel and ignores the uncertainties associated with the shape and behavior of the scattering targets, as well as other relevant animal parameters. Here, errors of the linear inversion due to uncertainty associated with the inversion kernel are quantified. A scattering model-based nonlinear inversion method is presented that takes into account the nonlinearity of the inverse problem and is able to estimate simultaneously animal abundance and the parameters associated with the scattering model inherent to the kernel. It uses sophisticated scattering models to estimate first, the abundance, and second, the relevant shape and behavioral parameters of the target organisms. Numerical simulations demonstrate that the abundance, size, and behavior (tilt angle) parameters of marine animals (fish or zooplankton) can be accurately inferred from the inversion by using multi-frequency acoustic data. The influence of the singularity and uncertainty in the inversion kernel on the inversion results can be mitigated by examining the singular values for linear inverse problems and employing a non-linear inversion involving a scattering model-based kernel.

Effects of kernel vitreousness and protein level on protein molecular weight distribution, milling quality, and breadmaking quality in hard red spring wheat

USDA-ARS?s Scientific Manuscript database

Dark, hard, and vitreous kernel content is an important grading characteristic for hard red spring (HRS) wheat in the U.S. This research aimed to determine the associations of kernel vitreousness (KV) with protein molecular weight distribution (MWD) and quality traits that were not biased by quanti...

Kernel functions and Baecklund transformations for relativistic Calogero-Moser and Toda systems

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

Hallnaes, Martin; Ruijsenaars, Simon

We obtain kernel functions associated with the quantum relativistic Toda systems, both for the periodic version and for the nonperiodic version with its dual. This involves taking limits of previously known results concerning kernel functions for the elliptic and hyperbolic relativistic Calogero-Moser systems. We show that the special kernel functions at issue admit a limit that yields generating functions of Baecklund transformations for the classical relativistic Calogero-Moser and Toda systems. We also obtain the nonrelativistic counterparts of our results, which tie in with previous results in the literature.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Local Renyi entropic profiles of DNA sequences.

PubMed

Vinga, Susana; Almeida, Jonas S

2007-10-16

In a recent report the authors presented a new measure of continuous entropy for DNA sequences, which allows the estimation of their randomness level. The definition therein explored was based on the Rényi entropy of probability density estimation (pdf) using the Parzen's window method and applied to Chaos Game Representation/Universal Sequence Maps (CGR/USM). Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation. This report extends the concepts of continuous entropy by defining DNA sequence entropic profiles using the new pdf estimations to refine the density estimation of motifs. The new methodology enables two results. On the one hand it shows that the entropic profiles are directly related with the statistical significance of motifs, allowing the study of under and over-representation of segments. On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region. The computational applications, developed in Matlab m-code, the corresponding binary executables and additional material and examples are made publicly available at http://kdbio.inesc-id.pt/~svinga/ep/. The ability to detect local conservation from a scale-independent representation of symbolic sequences is particularly relevant for biological applications where conserved motifs occur in multiple, overlapping scales, with significant future applications in the recognition of foreign genomic material and inference of motif structures.

Local Renyi entropic profiles of DNA sequences

PubMed Central

Vinga, Susana; Almeida, Jonas S

2007-01-01

Background In a recent report the authors presented a new measure of continuous entropy for DNA sequences, which allows the estimation of their randomness level. The definition therein explored was based on the Rényi entropy of probability density estimation (pdf) using the Parzen's window method and applied to Chaos Game Representation/Universal Sequence Maps (CGR/USM). Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation. This report extends the concepts of continuous entropy by defining DNA sequence entropic profiles using the new pdf estimations to refine the density estimation of motifs. Results The new methodology enables two results. On the one hand it shows that the entropic profiles are directly related with the statistical significance of motifs, allowing the study of under and over-representation of segments. On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region. The computational applications, developed in Matlab m-code, the corresponding binary executables and additional material and examples are made publicly available at . Conclusion The ability to detect local conservation from a scale-independent representation of symbolic sequences is particularly relevant for biological applications where conserved motifs occur in multiple, overlapping scales, with significant future applications in the recognition of foreign genomic material and inference of motif structures. PMID:17939871

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.

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.

Rare Variant Association Test with Multiple Phenotypes

PubMed Central

Lee, Selyeong; Won, Sungho; Kim, Young Jin; Kim, Yongkang; Kim, Bong-Jo; Park, Taesung

2016-01-01

Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of “missing heritability,” likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiply correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multi-variant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used Sequence Kernel Association Test (SKAT) for a single phenotype. We applied MAAUSS to Whole Exome Sequencing (WES) data from a Korean population of 1,058 subjects, to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSS's performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases, had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability. PMID:28039885

Adaptive kernel regression for freehand 3D ultrasound reconstruction

NASA Astrophysics Data System (ADS)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

An Adaptive Genetic Association Test Using Double Kernel Machines.

PubMed

Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis

2015-10-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.

Diversity of plant oil seed-associated fungi isolated from seven oil-bearing seeds and their potential for the production of lipolytic enzymes.

PubMed

Venkatesagowda, Balaji; Ponugupaty, Ebenezer; Barbosa, Aneli M; Dekker, Robert F H

2012-01-01

Commercial oil-yielding seeds (castor, coconut, neem, peanut, pongamia, rubber and sesame) were collected from different places in the state of Tamil Nadu (India) from which 1279 endophytic fungi were isolated. The oil-bearing seeds exhibited rich fungal diversity. High Shannon-Index H' was observed with pongamia seeds (2.847) while a low Index occurred for coconut kernel-associated mycoflora (1.018). Maximum Colonization Frequency (%) was observed for Lasiodiplodia theobromae (176). Dominance Index (expressed in terms of the Simpson's Index D) was high (0.581) for coconut kernel-associated fungi, and low for pongamia seed-borne fungi. Species Richness (Chao) of the fungal isolates was high (47.09) in the case of neem seeds, and low (16.6) for peanut seeds. All 1279 fungal isolates were screened for lipolytic activity employing a zymogram method using Tween-20 in agar. Forty isolates showed strong lipolytic activity, and were morphologically identified as belonging to 19 taxa (Alternaria, Aspergillus, Chalaropsis, Cladosporium, Colletotrichum, Curvularia, Drechslera, Fusarium, Lasiodiplodia, Mucor, Penicillium, Pestalotiopsis, Phoma, Phomopsis, Phyllosticta, Rhizopus, Sclerotinia, Stachybotrys and Trichoderma). These isolates also exhibited amylolytic, proteolytic and cellulolytic activities. Five fungal isolates (Aspergillus niger, Chalaropsis thielavioides, Colletotrichum gloeosporioides, Lasiodiplodia theobromae and Phoma glomerata) exhibited highest lipase activities, and the best producer was Lasiodiplodia theobromae (108 U/mL), which was characterized by genomic sequence analysis of the ITS region of 18S rDNA.

General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.

PubMed

Marmarelis, V Z; Berger, T W

2005-07-01

This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process inputs. It shows that only the Wiener kernels can be estimated via cross-correlation, but must be defined as zero along the diagonals. The Volterra kernels can be estimated far more accurately (and from shorter data-records) by use of the Laguerre expansion technique adapted to point-process inputs, and they are independent of the mean rate of stimulation (unlike their P-W counterparts that depend on it). The Volterra kernels can also be estimated for broadband point-process inputs that are not Poisson. Useful applications of this modeling approach include cases where we seek to determine (model) the transfer characteristics between one neuronal axon (a point-process 'input') and another axon (a point-process 'output') or some other measure of neuronal activity (a continuous 'output', such as population activity) with which a causal link exists.

Learning a peptide-protein binding affinity predictor with kernel ridge regression

PubMed Central

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/. PMID:23497081

CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

PubMed Central

Porto, William F.; Pires, Állan S.; Franco, Octavio L.

2012-01-01

The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine. PMID:23240023

Increased Genomic Prediction Accuracy in Wheat Breeding Through Spatial Adjustment of Field Trial Data

PubMed Central

Lado, Bettina; Matus, Ivan; Rodríguez, Alejandra; Inostroza, Luis; Poland, Jesse; Belzile, François; del Pozo, Alejandro; Quincke, Martín; Castro, Marina; von Zitzewitz, Jarislav

2013-01-01

In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models. PMID:24082033

Regulation of Embryo Dormancy by Manipulation of Abscisic Acid in Kernels and Associated Cob Tissue of Zea mays L. Cultured in Vitro1

PubMed Central

Hole, David J.; Smith, J. D.; Cobb, B. Greg

1989-01-01

Sectors of Zea mays cobs, with and without kernels were cultured in vitro in the presence and absence of fluridone. Cultured kernels, cob tissue, and embryos developed similarly to those grown in the field. Abscisic acid (ABA) levels in the embryos were evaluated by enzyme-linked immunosorbant assay. ABA levels in intact embryos cultured in the presence of fluridone were extremely low and indicate an inhibition of ABA synthesis. ABA levels in isolated cob tissue indicate that ABA can be produced by cob tissue. Sections containing kernels cultured in the presence of fluridone were transferred to medium containing fluridone and ABA. Dormancy was induced in more than 50% of the kernels transferred from 13 to 15 days after pollination, but all of the kernels transferred at 16 days after pollination or later were viviparous. ABA recovered from kernels that were placed in medium containing fluridone and ABA suggest that ABA can be transported through the cob tissue into developing embryos and that ABA is required for induction of dormancy in intact embryos. PMID:16666978

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.

Long-term scale adaptive tracking with kernel correlation filters

NASA Astrophysics Data System (ADS)

Wang, Yueren; Zhang, Hong; Zhang, Lei; Yang, Yifan; Sun, Mingui

2018-04-01

Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.

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.

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.

Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

PubMed

Zhang, Zhanhui; Wu, Xiangyuan; Shi, Chaonan; Wang, Rongna; Li, Shengfei; Wang, Zhaohui; Liu, Zonghua; Xue, Yadong; Tang, Guiliang; Tang, Jihua

2016-02-01

Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.

Maize early endosperm growth and development: from fertilization through cell type differentiation.

PubMed

Leroux, Brian M; Goodyke, Austin J; Schumacher, Katelyn I; Abbott, Chelsi P; Clore, Amy M; Yadegari, Ramin; Larkins, Brian A; Dannenhoffer, Joanne M

2014-08-01

• Given the worldwide economic importance of maize endosperm, it is surprising that its development is not the most comprehensively studied of the cereals. We present detailed morphometric and cytological descriptions of endosperm development in the maize inbred line B73, for which the genome has been sequenced, and compare its growth with four diverse Nested Association Mapping (NAM) founder lines.• The first 12 d of B73 endosperm development were described using semithin sections of plastic-embedded kernels and confocal microscopy. Longitudinal sections were used to compare endosperm length, thickness, and area.• Morphometric comparison between Arizona- and Michigan-grown B73 showed a common pattern. Early endosperm development was divided into four stages: coenocytic, cellularization through alveolation, cellularization through partitioning, and differentiation. We observed tightly synchronous nuclear divisions in the coenocyte, elucidated that the onset of cellularization was coincident with endosperm size, and identified a previously undefined cell type (basal intermediate zone, BIZ). NAM founders with small mature kernels had larger endosperms (0-6 d after pollination) than lines with large mature kernels.• Our B73-specific model of early endosperm growth links developmental events to relative endosperm size, while accounting for diverse growing conditions. Maize endosperm cellularizes through alveolation, then random partitioning of the central vacuole. This unique cellularization feature of maize contrasts with the smaller endosperms of Arabidopsis, barley, and rice that strictly cellularize through repeated alveolation. NAM analysis revealed differences in endosperm size during early development, which potentially relates to differences in timing of cellularization across diverse lines of maize. © 2014 Botanical Society of America, Inc.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Agricultural factors affecting Fusarium communities in wheat kernels.

PubMed

Karlsson, Ida; Friberg, Hanna; Kolseth, Anna-Karin; Steinberg, Christian; Persson, Paula

2017-07-03

Fusarium head blight (FHB) is a devastating disease of cereals caused by Fusarium fungi. The disease is of great economic importance especially owing to reduced grain quality due to contamination by a range of mycotoxins produced by Fusarium. Disease control and prediction is difficult because of the many Fusarium species associated with FHB. Different species may respond differently to control methods and can have both competitive and synergistic interactions. Therefore, it is important to understand how agricultural practices affect Fusarium at the community level. Lower levels of Fusarium mycotoxin contamination of organically produced cereals compared with conventionally produced have been reported, but the causes of these differences are not well understood. The aim of our study was to investigate the effect of agricultural factors on Fusarium abundance and community composition in different cropping systems. Winter wheat kernels were collected from 18 organically and conventionally cultivated fields in Sweden, paired based on their geographical distance and the wheat cultivar grown. We characterised the Fusarium community in harvested wheat kernels using 454 sequencing of translation elongation factor 1-α amplicons. In addition, we quantified Fusarium spp. using real-time PCR to reveal differences in biomass between fields. We identified 12 Fusarium operational taxonomic units (OTUs) with a median of 4.5 OTUs per field. Fusarium graminearum was the most abundant species, while F. avenaceum had the highest occurrence. The abundance of Fusarium spp. ranged two orders of magnitude between fields. Two pairs of Fusarium species co-occurred between fields: F. poae with F. tricinctum and F. culmorum with F. sporotrichoides. We could not detect any difference in Fusarium communities between the organic and conventional systems. However, agricultural intensity, measured as the number of pesticide applications and the amount of nitrogen fertiliser applied, had an impact on Fusarium communities, specifically increasing the abundance of F. tricinctum. There were geographical differences in the Fusarium community composition where F. graminearum was more abundant in the western part of Sweden. The application of amplicon sequencing provided a comprehensive view of the Fusarium community in cereals. This gives us better opportunities to understand the ecology of Fusarium spp., which is important in order to limit FHB and mycotoxin contamination in cereals.

Non-parametric wall model and methods of identifying boundary conditions for moments in gas flow equations

NASA Astrophysics Data System (ADS)

Liao, Meng; To, Quy-Dong; Léonard, Céline; Monchiet, Vincent

2018-03-01

In this paper, we use the molecular dynamics simulation method to study gas-wall boundary conditions. Discrete scattering information of gas molecules at the wall surface is obtained from collision simulations. The collision data can be used to identify the accommodation coefficients for parametric wall models such as Maxwell and Cercignani-Lampis scattering kernels. Since these scattering kernels are based on a limited number of accommodation coefficients, we adopt non-parametric statistical methods to construct the kernel to overcome these issues. Different from parametric kernels, the non-parametric kernels require no parameter (i.e. accommodation coefficients) and no predefined distribution. We also propose approaches to derive directly the Navier friction and Kapitza thermal resistance coefficients as well as other interface coefficients associated with moment equations from the non-parametric kernels. The methods are applied successfully to systems composed of CH4 or CO2 and graphite, which are of interest to the petroleum industry.

Kernel Methods for Mining Instance Data in Ontologies

NASA Astrophysics Data System (ADS)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

An accurate and efficient method for evaluating the kernel of the integral equation relating pressure to normalwash in unsteady potential flow

NASA Technical Reports Server (NTRS)

Desmarais, R. N.

1982-01-01

This paper describes an accurate economical method for generating approximations to the kernel of the integral equation relating unsteady pressure to normalwash in nonplanar flow. The method is capable of generating approximations of arbitrary accuracy. It is based on approximating the algebraic part of the non elementary integrals in the kernel by exponential approximations and then integrating termwise. The exponent spacing in the approximation is a geometric sequence. The coefficients and exponent multiplier of the exponential approximation are computed by least squares so the method is completely automated. Exponential approximates generated in this manner are two orders of magnitude more accurate than the exponential approximation that is currently most often used for this purpose. Coefficients for 8, 12, 24, and 72 term approximations are tabulated in the report. Also, since the method is automated, it can be used to generate approximations to attain any desired trade-off between accuracy and computing cost.

Influence of the Host Contact Sequence on the Outcome of Competition among Aspergillus flavus Isolates during Host Tissue Invasion▿

PubMed Central

Mehl, H. L.; Cotty, P. J.

2011-01-01

Biological control of aflatoxin contamination by Aspergillus flavus is achieved through competitive exclusion of aflatoxin producers by atoxigenic strains. Factors dictating the extent to which competitive displacement occurs during host infection are unknown. The role of initial host contact in competition between pairs of A. flavus isolates coinfecting maize kernels was examined. Isolate success during tissue invasion and reproduction was assessed by quantification of isolate-specific single nucleotide polymorphisms using pyrosequencing. Isolates were inoculated either simultaneously or 1 h apart. Increased success during competition was conferred to the first isolate to contact the host independent of that isolate's innate competitive ability. The first-isolate advantage decreased with the conidial concentration, suggesting capture of limited resources on kernel surfaces contributes to competitive exclusion. Attempts to modify access to putative attachment sites by either coating kernels with dead conidia or washing kernels with solvents did not influence the success of the first isolate, suggesting competition for limited attachment sites on kernel surfaces does not mediate first-isolate advantage. The current study is the first to demonstrate an immediate competitive advantage conferred to A. flavus isolates upon host contact and prior to either germ tube emergence or host colonization. This suggests the timing of host contact is as important to competition during disease cycles as innate competitive ability. Early dispersal to susceptible crop components may allow maintenance within A. flavus populations of genetic types with low competitive ability during host tissue invasion. PMID:21216896

An Adaptive Genetic Association Test Using Double Kernel Machines

PubMed Central

Zhan, Xiang; Epstein, Michael P.; Ghosh, Debashis

2014-01-01

Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study. PMID:26640602

Excessive burden of lysosomal storage disorder gene variants in Parkinson's disease.

PubMed

Robak, Laurie A; Jansen, Iris E; van Rooij, Jeroen; Uitterlinden, André G; Kraaij, Robert; Jankovic, Joseph; Heutink, Peter; Shulman, Joshua M

2017-12-01

Mutations in the glucocerebrosidase gene (GBA), which cause Gaucher disease, are also potent risk factors for Parkinson's disease. We examined whether a genetic burden of variants in other lysosomal storage disorder genes is more broadly associated with Parkinson's disease susceptibility. The sequence kernel association test was used to interrogate variant burden among 54 lysosomal storage disorder genes, leveraging whole exome sequencing data from 1156 Parkinson's disease cases and 1679 control subjects. We discovered a significant burden of rare, likely damaging lysosomal storage disorder gene variants in association with Parkinson's disease risk. The association signal was robust to the exclusion of GBA, and consistent results were obtained in two independent replication cohorts, including 436 cases and 169 controls with whole exome sequencing and an additional 6713 cases and 5964 controls with exome-wide genotyping. In secondary analyses designed to highlight the specific genes driving the aggregate signal, we confirmed associations at the GBA and SMPD1 loci and newly implicate CTSD, SLC17A5, and ASAH1 as candidate Parkinson's disease susceptibility genes. In our discovery cohort, the majority of Parkinson's disease cases (56%) have at least one putative damaging variant in a lysosomal storage disorder gene, and 21% carry multiple alleles. Our results highlight several promising new susceptibility loci and reinforce the importance of lysosomal mechanisms in Parkinson's disease pathogenesis. We suggest that multiple genetic hits may act in combination to degrade lysosomal function, enhancing Parkinson's disease susceptibility. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An accurate method for evaluating the kernel of the integral equation relating lift to downwash in unsteady potential flow

NASA Technical Reports Server (NTRS)

Desmarais, R. N.

1982-01-01

The method is capable of generating approximations of arbitrary accuracy. It is based on approximating the algebraic part of the nonelementary integrals in the kernel by exponential functions and then integrating termwise. The exponent spacing in the approximation is a geometric sequence. The coefficients and exponent multiplier of the exponential approximation are computed by least squares so the method is completely automated. Exponential approximates generated in this manner are two orders of magnitude more accurate than the exponential approximation that is currently most often used for this purpose. The method can be used to generate approximations to attain any desired trade-off between accuracy and computing cost.

Small-target leak detection for a closed vessel via infrared image sequences

NASA Astrophysics Data System (ADS)

Zhao, Ling; Yang, Hongjiu

2017-03-01

This paper focus on a leak diagnosis and localization method based on infrared image sequences. Some problems on high probability of false warning and negative affect for marginal information are solved by leak detection. An experimental model is established for leak diagnosis and localization on infrared image sequences. The differential background prediction is presented to eliminate the negative affect of marginal information on test vessel based on a kernel regression method. A pipeline filter based on layering voting is designed to reduce probability of leak point false warning. A synthesize leak diagnosis and localization algorithm is proposed based on infrared image sequences. The effectiveness and potential are shown for developed techniques through experimental results.

Age-related change in fast adaptation mechanisms measured with the scotopic full-field ERG.

PubMed

Tillman, Megan A; Panorgias, Athanasios; Werner, John S

2016-06-01

To quantify the response dynamics of fast adaptation mechanisms of the scotopic ERG in younger and older adults using full-field m-sequence flash stimulation. Scotopic ERGs were measured for a series of flashes separated by 65 ms over a range of 260 ms in 16 younger (20-26, 22.2 ± 2.1; range mean ±1 SD) and 16 older (65-85, 71.2 ± 7) observers without retinal pathology. A short-wavelength (λ peak = 442 nm) LED was used for scotopic stimulation, and the flashes ranged from 0.0001 to 0.01 cd s m(-2). The complete binary kernel series was derived from the responses to the m-sequence flash stimulation, and the first- and second-order kernel responses were analyzed. The first-order kernel represented the response to a single, isolated flash, while the second-order kernels reflected the adapted flash responses that followed a single flash by one or more base intervals. B-wave amplitudes of the adapted flash responses were measured and plotted as a function of interstimulus interval to describe the recovery of the scotopic ERG. A linear function was fitted to the linear portion of the recovery curve, and the slope of the line was used to estimate the rate of fast adaptation recovery. The amplitudes of the isolated flash responses and rates of scotopic fast adaptation recovery were compared between the younger and older participants using a two-way ANOVA. The isolated flash responses and rates of recovery were found to be significantly lower in the older adults. However, there was no difference between the two age groups in response amplitude or recovery rate after correcting for age-related changes in the density of the ocular media. These results demonstrated that the rate of scotopic fast adaptation recovery of normal younger and older adults is similar when stimuli are equated for retinal illuminance.

FRIT characterized hierarchical kernel memory arrangement for multiband palmprint recognition

NASA Astrophysics Data System (ADS)

Kisku, Dakshina R.; Gupta, Phalguni; Sing, Jamuna K.

2015-10-01

In this paper, we present a hierarchical kernel associative memory (H-KAM) based computational model with Finite Ridgelet Transform (FRIT) representation for multispectral palmprint recognition. To characterize a multispectral palmprint image, the Finite Ridgelet Transform is used to achieve a very compact and distinctive representation of linear singularities while it also captures the singularities along lines and edges. The proposed system makes use of Finite Ridgelet Transform to represent multispectral palmprint image and it is then modeled by Kernel Associative Memories. Finally, the recognition scheme is thoroughly tested with a benchmarking multispectral palmprint database CASIA. For recognition purpose a Bayesian classifier is used. The experimental results exhibit robustness of the proposed system under different wavelengths of palm image.

A Protein Domain and Family Based Approach to Rare Variant Association Analysis.

PubMed

Richardson, Tom G; Shihab, Hashem A; Rivas, Manuel A; McCarthy, Mark I; Campbell, Colin; Timpson, Nicholas J; Gaunt, Tom R

2016-01-01

It has become common practice to analyse large scale sequencing data with statistical approaches based around the aggregation of rare variants within the same gene. We applied a novel approach to rare variant analysis by collapsing variants together using protein domain and family coordinates, regarded to be a more discrete definition of a biologically functional unit. Using Pfam definitions, we collapsed rare variants (Minor Allele Frequency ≤ 1%) together in three different ways 1) variants within single genomic regions which map to individual protein domains 2) variants within two individual protein domain regions which are predicted to be responsible for a protein-protein interaction 3) all variants within combined regions from multiple genes responsible for coding the same protein domain (i.e. protein families). A conventional collapsing analysis using gene coordinates was also undertaken for comparison. We used UK10K sequence data and investigated associations between regions of variants and lipid traits using the sequence kernel association test (SKAT). We observed no strong evidence of association between regions of variants based on Pfam domain definitions and lipid traits. Quantile-Quantile plots illustrated that the overall distributions of p-values from the protein domain analyses were comparable to that of a conventional gene-based approach. Deviations from this distribution suggested that collapsing by either protein domain or gene definitions may be favourable depending on the trait analysed. We have collapsed rare variants together using protein domain and family coordinates to present an alternative approach over collapsing across conventionally used gene-based regions. Although no strong evidence of association was detected in these analyses, future studies may still find value in adopting these approaches to detect previously unidentified association signals.

The Weighting Is The Hardest Part: On The Behavior of the Likelihood Ratio Test and the Score Test Under a Data-Driven Weighting Scheme in Sequenced Samples

PubMed Central

Minică, Camelia C.; Genovese, Giulio; Hultman, Christina M.; Pool, René; Vink, Jacqueline M.; Neale, Michael C.; Dolan, Conor V.; Neale, Benjamin M.

2017-01-01

Sequence-based association studies are at a critical inflexion point with the increasing availability of exome-sequencing data. A popular test of association is the sequence kernel association test (SKAT). Weights are embedded within SKAT to reflect the hypothesized contribution of the variants to the trait variance. Because the true weights are generally unknown, and so are subject to misspecification, we examined the efficiency of a data-driven weighting scheme. We propose the use of a set of theoretically defensible weighting schemes, of which, we assume, the one that gives the largest test statistic is likely to capture best the allele frequency-functional effect relationship. We show that the use of alternative weights obviates the need to impose arbitrary frequency thresholds in sequence data association analyses. As both the score test and the likelihood ratio test (LRT) may be used in this context, and may differ in power, we characterize the behavior of both tests. We found that the two tests have equal power if the set of weights resembled the correct ones. However, if the weights are badly specified, the LRT shows superior power (due to its robustness to misspecification). With this data-driven weighting procedure the LRT detected significant signal in genes located in regions already confirmed as associated with schizophrenia – the PRRC2A (P=1.020E-06) and the VARS2 (P=2.383E-06) – in the Swedish schizophrenia case-control cohort of 11,040 individuals with exome-sequencing data. The score test is currently preferred for its computational efficiency and power. Indeed, assuming correct specification, in some circumstances the score test is the most powerful. However, LRT has the advantageous properties of being generally more robust and more powerful under weight misspecification. This is an important result given that, arguably, misspecified models are likely to be the rule rather than the exception in weighting-based approaches. PMID:28238293

Reformulation of Possio's kernel with application to unsteady wind tunnel interference

NASA Technical Reports Server (NTRS)

Fromme, J. A.; Golberg, M. A.

1980-01-01

An efficient method for computing the Possio kernel has remained elusive up to the present time. In this paper the Possio is reformulated so that it can be computed accurately using existing high precision numerical quadrature techniques. Convergence to the correct values is demonstrated and optimization of the integration procedures is discussed. Since more general kernels such as those associated with unsteady flows in ventilated wind tunnels are analytic perturbations of the Possio free air kernel, a more accurate evaluation of their collocation matrices results with an exponential improvement in convergence. An application to predicting frequency response of an airfoil-trailing edge control system in a wind tunnel compared with that in free air is given showing strong interference effects.

Chlorogenic acid and maize ear rot resistance: a dynamic study investigating Fusarium graminearum development, deoxynivalenol production, and phenolic acid accumulation.

PubMed

Atanasova-Penichon, Vessela; Pons, Sebastien; Pinson-Gadais, Laetitia; Picot, Adeline; Marchegay, Gisèle; Bonnin-Verdal, Marie-Noelle; Ducos, Christine; Barreau, Christian; Roucolle, Joel; Sehabiague, Pierre; Carolo, Pierre; Richard-Forget, Florence

2012-12-01

Fusarium graminearum is the causal agent of Gibberella ear rot and produces trichothecene mycotoxins. Basic questions remain unanswered regarding the kernel stages associated with trichothecene biosynthesis and the kernel metabolites potentially involved in the regulation of trichothecene production in planta. In a two-year field study, F. graminearum growth, trichothecene accumulation, and phenolic acid composition were monitored in developing maize kernels of a susceptible and a moderately resistant variety using quantitative polymerase chain reaction and liquid chromatography coupled with photodiode array or mass spectrometry detection. Infection started as early as the blister stage and proceeded slowly until the dough stage. Then, a peak of trichothecene accumulation occurred and infection progressed exponentially until the final harvest time. Both F. graminearum growth and trichothecene production were drastically reduced in the moderately resistant variety. We found that chlorogenic acid is more abundant in the moderately resistant variety, with levels spiking in the earliest kernel stages induced by Fusarium infection. This is the first report that precisely describes the kernel stage associated with the initiation of trichothecene production and provides in planta evidence that chlorogenic acid may play a role in maize resistance to Gibberella ear rot and trichothecene accumulation.

Coronary Stent Artifact Reduction with an Edge-Enhancing Reconstruction Kernel - A Prospective Cross-Sectional Study with 256-Slice CT.

PubMed

Tan, Stéphanie; Soulez, Gilles; Diez Martinez, Patricia; Larrivée, Sandra; Stevens, Louis-Mathieu; Goussard, Yves; Mansour, Samer; Chartrand-Lefebvre, Carl

2016-01-01

Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The objective of this study is to assess in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel. This is a prospective cross-sectional study involving the assessment of 71 coronary stents (24 patients), with blinded observers. After 256-slice CT angiography, image reconstruction was done with medium-smooth and edge-enhancing kernels. Stent wall thickness was measured with both orthogonal and circumference methods, averaging thickness from diameter and circumference measurements, respectively. Image quality was assessed quantitatively using objective parameters (noise, signal to noise (SNR) and contrast to noise (CNR) ratios), as well as visually using a 5-point Likert scale. Stent wall thickness was decreased with the edge-enhancing kernel in comparison to the standard kernel, either with the orthogonal (0.97 ± 0.02 versus 1.09 ± 0.03 mm, respectively; p<0.001) or the circumference method (1.13 ± 0.02 versus 1.21 ± 0.02 mm, respectively; p = 0.001). The edge-enhancing kernel generated less overestimation from nominal thickness compared to the standard kernel, both with the orthogonal (0.89 ± 0.19 versus 1.00 ± 0.26 mm, respectively; p<0.001) and the circumference (1.06 ± 0.26 versus 1.13 ± 0.31 mm, respectively; p = 0.005) methods. The edge-enhancing kernel was associated with lower SNR and CNR, as well as higher background noise (all p < 0.001), in comparison to the medium-smooth kernel. Stent visual scores were higher with the edge-enhancing kernel (p<0.001). In vivo 256-slice CT assessment of coronary stents shows that the edge-enhancing CT reconstruction kernel generates thinner stent walls, less overestimation from nominal thickness, and better image quality scores than the standard kernel.

A Unified Mixed-Effects Model for Rare-Variant Association in Sequencing Studies

PubMed Central

Sun, Jianping; Zheng, Yingye; Hsu, Li

2013-01-01

For rare-variant association analysis, due to extreme low frequencies of these variants, it is necessary to aggregate them by a prior set (e.g., genes and pathways) in order to achieve adequate power. In this paper, we consider hierarchical models to relate a set of rare variants to phenotype by modeling the effects of variants as a function of variant characteristics while allowing for variant-specific effect (heterogeneity). We derive a set of two score statistics, testing the group effect by variant characteristics and the heterogeneity effect. We make a novel modification to these score statistics so that they are independent under the null hypothesis and their asymptotic distributions can be derived. As a result, the computational burden is greatly reduced compared with permutation-based tests. Our approach provides a general testing framework for rare variants association, which includes many commonly used tests, such as the burden test [Li and Leal, 2008] and the sequence kernel association test [Wu et al., 2011], as special cases. Furthermore, in contrast to these tests, our proposed test has an added capacity to identify which components of variant characteristics and heterogeneity contribute to the association. Simulations under a wide range of scenarios show that the proposed test is valid, robust and powerful. An application to the Dallas Heart Study illustrates that apart from identifying genes with significant associations, the new method also provides additional information regarding the source of the association. Such information may be useful for generating hypothesis in future studies. PMID:23483651

Deletion mutagenesis identifies a haploinsufficient role for gamma-zein in opaque-2 endosperm modification

USDA-ARS?s Scientific Manuscript database

Quality Protein Maize (QPM) is a hard kernel variant of the high-lysine mutant, opaque-2. Using gamma irradiation, we created opaque QPM variants to identify opaque-2 modifier genes and to investigate deletion mutagenesis combined with Illumina sequencing as a maize functional genomics tool. A K0326...

Isolation of a cDNA Encoding a Granule-Bound 152-Kilodalton Starch-Branching Enzyme in Wheat1

PubMed Central

Båga, Monica; Nair, Ramesh B.; Repellin, Anne; Scoles, Graham J.; Chibbar, Ravindra N.

2000-01-01

Screening of a wheat (Triticum aestivum) cDNA library for starch-branching enzyme I (SBEI) genes combined with 5′-rapid amplification of cDNA ends resulted in isolation of a 4,563-bp composite cDNA, Sbe1c. Based on sequence alignment to characterized SBEI cDNA clones isolated from plants, the SBEIc predicted from the cDNA sequence was produced with a transit peptide directing the polypeptide into plastids. Furthermore, the predicted mature form of SBEIc was much larger (152 kD) than previously characterized plant SBEI (80–100 kD) and contained a partial duplication of SBEI sequences. The first SBEI domain showed high amino acid similarity to a 74-kD wheat SBEI-like protein that is inactive as a branching enzyme when expressed in Escherichia coli. The second SBEI domain on SBEIc was identical in sequence to a functional 87-kD SBEI produced in the wheat endosperm. Immunoblot analysis of proteins produced in developing wheat kernels demonstrated that the 152-kD SBEIc was, in contrast to the 87- to 88-kD SBEI, preferentially associated with the starch granules. Proteins similar in size and recognized by wheat SBEI antibodies were also present in Triticum monococcum, Triticum tauschii, and Triticum turgidum subsp. durum. PMID:10982440

Determination of aflatoxin risk components for in-shell Brazil nuts.

PubMed

Vargas, E A; dos Santos, E A; Whitaker, T B; Slate, A B

2011-09-01

A study was conducted on the risk from aflatoxins associated with the kernels and shells of Brazil nuts. Samples were collected from processing plants in Amazonia, Brazil. A total of 54 test samples (40 kg) were taken from 13 in-shell Brazil nut lots ready for market. Each in-shell sample was shelled and the kernels and shells were sorted in five fractions: good kernels, rotten kernels, good shells with kernel residue, good shells without kernel residue, and rotten shells, and analysed for aflatoxins. The kernel:shell ratio mass (w/w) was 50.2/49.8%. The Brazil nut shell was found to be contaminated with aflatoxin. Rotten nuts were found to be a high-risk fraction for aflatoxin in in-shell Brazil nut lots. Rotten nuts contributed only 4.2% of the sample mass (kg), but contributed 76.6% of the total aflatoxin mass (µg) in the in-shell test sample. The highest correlations were found between the aflatoxin concentration in in-shell Brazil nuts samples and the aflatoxin concentration in all defective fractions (R(2)=0.97). The aflatoxin mass of all defective fractions (R(2)=0.90) as well as that of the rotten nut (R(2)=0.88) were also strongly correlated with the aflatoxin concentration of the in-shell test samples. Process factors of 0.17, 0.16 and 0.24 were respectively calculated to estimate the aflatoxin concentration in the good kernels (edible) and good nuts by measuring the aflatoxin concentration in the in-shell test sample and in all kernels, respectively. © 2011 Taylor & Francis

The Dent Stage of Maize Kernels Is the Most Conducive for Fumonisin Biosynthesis under Field Conditions ▿

PubMed Central

Picot, Adeline; Barreau, Christian; Pinson-Gadais, Laëtitia; Piraux, François; Caron, Daniel; Lannou, Christian; Richard-Forget, Florence

2011-01-01

The fungal pathogen Fusarium verticillioides infects maize ears and produces fumonisins, known for their adverse effects on human and animal health. Basic questions remain unanswered regarding the kernel stage(s) associated with fumonisin biosynthesis and the kernel components involved in fumonisin regulation during F. verticillioides-maize interaction under field conditions. In this 2-year field study, the time course of F. verticillioides growth and fumonisin accumulation in developing maize kernels, along with the variations in kernel pH and amylopectin content, were monitored using relevant and accurate analytical tools. In all experiments, the most significant increase in fumonisin accumulation or in fumonisin productivity (i.e., fumonisin production per unit of fungus) was shown to occur within a very short period of time, between 22/32 and 42 days after inoculation and corresponding to the dent stage. This stage was also characterized by acidification in the kernel pH and a maximum level of amylopectin content. Our data clearly support published results based on in vitro experiments suggesting that the physiological stages of the maize kernel play a major role in regulating fumonisin production. Here we have validated this result for in planta and field conditions, and we demonstrate that under such conditions the dent stage is the most conducive for fumonisin accumulation. PMID:21984235

Temporal Effects on Internal Fluorescence Emissions Associated with Aflatoxin Contamination from Corn Kernel Cross-Sections Inoculated with Toxigenic and Atoxigenic Aspergillus flavus.

PubMed

Hruska, Zuzana; Yao, Haibo; Kincaid, Russell; Brown, Robert L; Bhatnagar, Deepak; Cleveland, Thomas E

2017-01-01

Non-invasive, easy to use and cost-effective technology offers a valuable alternative for rapid detection of carcinogenic fungal metabolites, namely aflatoxins, in commodities. One relatively recent development in this area is the use of spectral technology. Fluorescence hyperspectral imaging, in particular, offers a potential rapid and non-invasive method for detecting the presence of aflatoxins in maize infected with the toxigenic fungus Aspergillus flavus . Earlier studies have shown that whole maize kernels contaminated with aflatoxins exhibit different spectral signatures from uncontaminated kernels based on the external fluorescence emission of the whole kernels. Here, the effect of time on the internal fluorescence spectral emissions from cross-sections of kernels infected with toxigenic and atoxigenic A. flavus , were examined in order to elucidate the interaction between the fluorescence signals emitted by some aflatoxin contaminated maize kernels and the fungal invasion resulting in the production of aflatoxins. First, the difference in internal fluorescence emissions between cross-sections of kernels incubated in toxigenic and atoxigenic inoculum was assessed. Kernels were inoculated with each strain for 5, 7, and 9 days before cross-sectioning and imaging. There were 270 kernels (540 halves) imaged, including controls. Second, in a different set of kernels (15 kernels/group; 135 total), the germ of each kernel was separated from the endosperm to determine the major areas of aflatoxin accumulation and progression over nine growth days. Kernels were inoculated with toxigenic and atoxigenic fungal strains for 5, 7, and 9 days before the endosperm and germ were separated, followed by fluorescence hyperspectral imaging and chemical aflatoxin determination. A marked difference in fluorescence intensity was shown between the toxigenic and atoxigenic strains on day nine post-inoculation, which may be a useful indicator of the location of aflatoxin contamination. This finding suggests that both, the fluorescence peak shift and intensity as well as timing, may be essential in distinguishing toxigenic and atoxigenic fungi based on spectral features. Results also reveal a possible preferential difference in the internal colonization of maize kernels between the toxigenic and atoxigenic strains of A. flavus suggesting a potential window for differentiating the strains based on fluorescence spectra at specific time points.

Temporal Effects on Internal Fluorescence Emissions Associated with Aflatoxin Contamination from Corn Kernel Cross-Sections Inoculated with Toxigenic and Atoxigenic Aspergillus flavus

PubMed Central

Hruska, Zuzana; Yao, Haibo; Kincaid, Russell; Brown, Robert L.; Bhatnagar, Deepak; Cleveland, Thomas E.

2017-01-01

Non-invasive, easy to use and cost-effective technology offers a valuable alternative for rapid detection of carcinogenic fungal metabolites, namely aflatoxins, in commodities. One relatively recent development in this area is the use of spectral technology. Fluorescence hyperspectral imaging, in particular, offers a potential rapid and non-invasive method for detecting the presence of aflatoxins in maize infected with the toxigenic fungus Aspergillus flavus. Earlier studies have shown that whole maize kernels contaminated with aflatoxins exhibit different spectral signatures from uncontaminated kernels based on the external fluorescence emission of the whole kernels. Here, the effect of time on the internal fluorescence spectral emissions from cross-sections of kernels infected with toxigenic and atoxigenic A. flavus, were examined in order to elucidate the interaction between the fluorescence signals emitted by some aflatoxin contaminated maize kernels and the fungal invasion resulting in the production of aflatoxins. First, the difference in internal fluorescence emissions between cross-sections of kernels incubated in toxigenic and atoxigenic inoculum was assessed. Kernels were inoculated with each strain for 5, 7, and 9 days before cross-sectioning and imaging. There were 270 kernels (540 halves) imaged, including controls. Second, in a different set of kernels (15 kernels/group; 135 total), the germ of each kernel was separated from the endosperm to determine the major areas of aflatoxin accumulation and progression over nine growth days. Kernels were inoculated with toxigenic and atoxigenic fungal strains for 5, 7, and 9 days before the endosperm and germ were separated, followed by fluorescence hyperspectral imaging and chemical aflatoxin determination. A marked difference in fluorescence intensity was shown between the toxigenic and atoxigenic strains on day nine post-inoculation, which may be a useful indicator of the location of aflatoxin contamination. This finding suggests that both, the fluorescence peak shift and intensity as well as timing, may be essential in distinguishing toxigenic and atoxigenic fungi based on spectral features. Results also reveal a possible preferential difference in the internal colonization of maize kernels between the toxigenic and atoxigenic strains of A. flavus suggesting a potential window for differentiating the strains based on fluorescence spectra at specific time points. PMID:28966606

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.

PubMed

Dias Junior, G S; Ferraretto, L F; Salvati, G G S; de Resende, L C; Hoffman, P C; Pereira, M N; Shaver, R D

2016-04-01

Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the kernel fraction was redried at 60°C for 48 h in a forced-air oven and dry sieved to determine GMPS and surface area. Linear relationships between CSPS from WPCS (n=80) and kernel fraction GMPS, surface area, and proportion passing through the 4.75-mm screen were poor. Strong quadratic relationships between proportion of kernel fraction passing through the 4.75-mm screen and kernel fraction GMPS and surface area were observed. These findings suggest that hydrodynamic separation and dry sieving of the kernel fraction may provide a better assessment of kernel breakage in WPCS than CSPS. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Effect of Protein Molecular Weight Distribution on Kernel and Baking Characteristics and Intra-varietal Variation in Hard Spring Wheats

USDA-ARS?s Scientific Manuscript database

Specific wheat protein fractions are known to have distinct associations with wheat quality traits. Research was conducted on 10 hard spring wheat cultivars grown at two North Dakota locations to identify protein fractions that affected wheat kernel characteristics and breadmaking quality. SDS ext...

On-Plant Larval Movement and Feeding Behavior of Fall Armyworm (Lepidoptera: Noctuidae) on Reproductive Corn Stages.

PubMed

Pannuti, L E R; Baldin, E L L; Hunt, T E; Paula-Moraes, S V

2016-02-01

Spodoptera frugiperda J.E. Smith (fall armyworm) is considered one of the most destructive pests of corn throughout the Americas. Although this pest has been extensively studied, little is known about its larval movement and feeding behavior on reproductive compared to vegetative corn stages. Thus, we conducted studies with two corn stages (R1 and R3) and four corn plant zones (tassel, above ear, ear zone, and below ear) in the field at Concord, NE (USA), and in the field and greenhouse at Botucatu, SP (Brazil), to investigate on-plant larval movement. The effects of different corn tissues (opened tassel, closed tassel, silk, kernel, and leaf), two feeding sequence scenarios (closed tassel-leaf-silk-kernel and leaf-silk-kernel), and artificial diet (positive control) on larval survival and development were also evaluated in the laboratory. Ear zone has a strong effect on feeding choice and survival of fall armyworm larvae regardless of reproductive corn stage. Feeding site choice is made by first-instar. Corn leaves of reproductive plants were not suitable for early instar development, but silk and kernel tissues had a positive effect on survival and development of fall armyworm larvae on reproductive stage corn. © 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.

Whole exome sequencing of rare variants in EIF4G1 and VPS35 in Parkinson disease

PubMed Central

Nuytemans, Karen; Bademci, Guney; Inchausti, Vanessa; Dressen, Amy; Kinnamon, Daniel D.; Mehta, Arpit; Wang, Liyong; Züchner, Stephan; Beecham, Gary W.; Martin, Eden R.; Scott, William K.

2013-01-01

Objective: Recently, vacuolar protein sorting 35 (VPS35) and eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) have been identified as 2 causal Parkinson disease (PD) genes. We used whole exome sequencing for rapid, parallel analysis of variations in these 2 genes. Methods: We performed whole exome sequencing in 213 patients with PD and 272 control individuals. Those rare variants (RVs) with <5% frequency in the exome variant server database and our own control data were considered for analysis. We performed joint gene-based tests for association using RVASSOC and SKAT (Sequence Kernel Association Test) as well as single-variant test statistics. Results: We identified 3 novel VPS35 variations that changed the coded amino acid (nonsynonymous) in 3 cases. Two variations were in multiplex families and neither segregated with PD. In EIF4G1, we identified 11 (9 nonsynonymous and 2 small indels) RVs including the reported pathogenic mutation p.R1205H, which segregated in all affected members of a large family, but also in 1 unaffected 86-year-old family member. Two additional RVs were found in isolated patients only. Whereas initial association studies suggested an association (p = 0.04) with all RVs in EIF4G1, subsequent testing in a second dataset for the driving variant (p.F1461) suggested no association between RVs in the gene and PD. Conclusions: We confirm that the specific EIF4G1 variation p.R1205H seems to be a strong PD risk factor, but is nonpenetrant in at least one 86-year-old. A few other select RVs in both genes could not be ruled out as causal. However, there was no evidence for an overall contribution of genetic variability in VPS35 or EIF4G1 to PD development in our dataset. PMID:23408866

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.

Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression

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

Edwards, Richard E; Zhang, Hao; Parker, Lynne Edwards

2013-01-01

Kernel methods have difficulties scaling to large modern data sets. The scalability issues are based on computational and memory requirements for working with a large matrix. These requirements have been addressed over the years by using low-rank kernel approximations or by improving the solvers scalability. However, Least Squares Support VectorMachines (LS-SVM), a popular SVM variant, and Kernel Ridge Regression still have several scalability issues. In particular, the O(n^3) computational complexity for solving a single model, and the overall computational complexity associated with tuning hyperparameters are still major problems. We address these problems by introducing an O(n log n) approximate l-foldmore » cross-validation method that uses a multi-level circulant matrix to approximate the kernel. In addition, we prove our algorithm s computational complexity and present empirical runtimes on data sets with approximately 1 million data points. We also validate our approximate method s effectiveness at selecting hyperparameters on real world and standard benchmark data sets. Lastly, we provide experimental results on using a multi-level circulant kernel approximation to solve LS-SVM problems with hyperparameters selected using our method.« less

Insights into the spurious long-range nature of local rs-dependent non-local exchange-correlation kernels

DOE PAGES

Lu, Deyu

2016-08-05

A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, r s(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace r s(r) by a global, average r s in the kernel.more » Exemplary studies with the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average r s significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.« less

A variational Bayes discrete mixture test for rare variant association

PubMed Central

Logsdon, Benjamin A.; Dai, James Y.; Auer, Paul L.; Johnsen, Jill M.; Ganesh, Santhi K.; Smith, Nicholas L.; Wilson, James G.; Tracy, Russell P.; Lange, Leslie A.; Jiao, Shuo; Rich, Stephen S.; Lettre, Guillaume; Carlson, Christopher S.; Jackson, Rebecca D.; O’Donnell, Christopher J.; Wurfel, Mark M.; Nickerson, Deborah A.; Tang, Hua; Reiner, Alexander P.; Kooperberg, Charles

2014-01-01

Recently, many statistical methods have been proposed to test for associations between rare genetic variants and complex traits. Most of these methods test for association by aggregating genetic variations within a predefined region, such as a gene. Although there is evidence that “aggregate” tests are more powerful than the single marker test, these tests generally ignore neutral variants and therefore are unable to identify specific variants driving the association with phenotype. We propose a novel aggregate rare-variant test that explicitly models a fraction of variants as neutral, tests associations at the gene-level, and infers the rare-variants driving the association. Simulations show that in the practical scenario where there are many variants within a given region of the genome with only a fraction causal our approach has greater power compared to other popular tests such as the Sequence Kernel Association Test (SKAT), the Weighted Sum Statistic (WSS), and the collapsing method of Morris and Zeggini (MZ). Our algorithm leverages a fast variational Bayes approximate inference methodology to scale to exome-wide analyses, a significant computational advantage over exact inference model selection methodologies. To demonstrate the efficacy of our methodology we test for associations between von Willebrand Factor (VWF) levels and VWF missense rare-variants imputed from the National Heart, Lung, and Blood Institute’s Exome Sequencing project into 2,487 African Americans within the VWF gene. Our method suggests that a relatively small fraction (~10%) of the imputed rare missense variants within VWF are strongly associated with lower VWF levels in African Americans. PMID:24482836

A variational Bayes discrete mixture test for rare variant association.

PubMed

Logsdon, Benjamin A; Dai, James Y; Auer, Paul L; Johnsen, Jill M; Ganesh, Santhi K; Smith, Nicholas L; Wilson, James G; Tracy, Russell P; Lange, Leslie A; Jiao, Shuo; Rich, Stephen S; Lettre, Guillaume; Carlson, Christopher S; Jackson, Rebecca D; O'Donnell, Christopher J; Wurfel, Mark M; Nickerson, Deborah A; Tang, Hua; Reiner, Alexander P; Kooperberg, Charles

2014-01-01

Recently, many statistical methods have been proposed to test for associations between rare genetic variants and complex traits. Most of these methods test for association by aggregating genetic variations within a predefined region, such as a gene. Although there is evidence that "aggregate" tests are more powerful than the single marker test, these tests generally ignore neutral variants and therefore are unable to identify specific variants driving the association with phenotype. We propose a novel aggregate rare-variant test that explicitly models a fraction of variants as neutral, tests associations at the gene-level, and infers the rare-variants driving the association. Simulations show that in the practical scenario where there are many variants within a given region of the genome with only a fraction causal our approach has greater power compared to other popular tests such as the Sequence Kernel Association Test (SKAT), the Weighted Sum Statistic (WSS), and the collapsing method of Morris and Zeggini (MZ). Our algorithm leverages a fast variational Bayes approximate inference methodology to scale to exome-wide analyses, a significant computational advantage over exact inference model selection methodologies. To demonstrate the efficacy of our methodology we test for associations between von Willebrand Factor (VWF) levels and VWF missense rare-variants imputed from the National Heart, Lung, and Blood Institute's Exome Sequencing project into 2,487 African Americans within the VWF gene. Our method suggests that a relatively small fraction (~10%) of the imputed rare missense variants within VWF are strongly associated with lower VWF levels in African Americans.

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.

Rare TREM2 variants associated with Alzheimer's disease display reduced cell surface expression.

PubMed

Sirkis, Daniel W; Bonham, Luke W; Aparicio, Renan E; Geier, Ethan G; Ramos, Eliana Marisa; Wang, Qing; Karydas, Anna; Miller, Zachary A; Miller, Bruce L; Coppola, Giovanni; Yokoyama, Jennifer S

2016-09-02

Rare variation in TREM2 has been associated with greater risk for Alzheimer's disease (AD). TREM2 encodes a cell surface receptor expressed on microglia and related cells, and the R47H variant associated with AD appears to affect the ability of TREM2 to bind extracellular ligands. In addition, other rare TREM2 mutations causing early-onset neurodegeneration are thought to impair cell surface expression. Using a sequence kernel association (SKAT) analysis in two independent AD cohorts, we found significant enrichment of rare TREM2 variants not previously characterized at the protein level. Heterologous expression of the identified variants showed that novel variants S31F and R47C displayed significantly reduced cell surface expression. In addition, we identified rare variant R136Q in a patient with language-predominant AD that also showed impaired surface expression. The results suggest rare TREM2 variants enriched in AD may be associated with altered TREM2 function and that AD risk may be conferred, in part, from altered TREM2 surface expression.

Bioinformatic as a tool to highlight and characterize extragenomic sequences within Fusarium verticillioides strains isolated from Italian Zea mays kernels

USDA-ARS?s Scientific Manuscript database

Fusarium Link is a genus including ubiquitous plant-pathogenic fungi that may cause severe crop losses. The Fusarium genus is divided in species complexes; the species are grouped by physiological, biological, ecological and genetic similarity. The Fusarium fujikuroi species complex (FFSC) is one of...

On nonsingular potentials of Cox-Thompson inversion scheme

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

Palmai, Tamas; Apagyi, Barnabas

2010-02-15

We establish a condition for obtaining nonsingular potentials using the Cox-Thompson inverse scattering method with one phase shift. The anomalous singularities of the potentials are avoided by maintaining unique solutions of the underlying Regge-Newton integral equation for the transformation kernel. As a by-product, new inequality sequences of zeros of Bessel functions are discovered.

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.

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.

Increased risk of pneumonia in residents living near poultry farms: does the upper respiratory tract microbiota play a role?

PubMed

Smit, Lidwien A M; Boender, Gert Jan; de Steenhuijsen Piters, Wouter A A; Hagenaars, Thomas J; Huijskens, Elisabeth G W; Rossen, John W A; Koopmans, Marion; Nodelijk, Gonnie; Sanders, Elisabeth A M; Yzermans, Joris; Bogaert, Debby; Heederik, Dick

2017-01-01

Air pollution has been shown to increase the susceptibility to community-acquired pneumonia (CAP). Previously, we observed an increased incidence of CAP in adults living within 1 km from poultry farms, potentially related to particulate matter and endotoxin emissions. We aim to confirm the increased risk of CAP near poultry farms by refined spatial analyses, and we hypothesize that the oropharyngeal microbiota composition in CAP patients may be associated with residential proximity to poultry farms. A spatial kernel model was used to analyze the association between proximity to poultry farms and CAP diagnosis, obtained from electronic medical records of 92,548 GP patients. The oropharyngeal microbiota composition was determined in 126 hospitalized CAP patients using 16S-rRNA-based sequencing, and analyzed in relation to residential proximity to poultry farms. Kernel analysis confirmed a significantly increased risk of CAP when living near poultry farms, suggesting an excess risk up to 1.15 km, followed by a sharp decline. Overall, the oropharyngeal microbiota composition differed borderline significantly between patients living <1 km and ≥1 km from poultry farms (PERMANOVA p  = 0.075). Results suggested a higher abundance of Streptococcus pneumoniae (mean relative abundance 34.9% vs. 22.5%, p  = 0.058) in patients living near poultry farms, which was verified by unsupervised clustering analysis, showing overrepresentation of a S. pneumoniae cluster near poultry farms ( p  = 0.049). Living near poultry farms is associated with an 11% increased risk of CAP, possibly resulting from changes in the upper respiratory tract microbiota composition in susceptible individuals. The abundance of S. pneumoniae near farms needs to be replicated in larger, independent studies.

Association analysis of rare variants near the APOE region with CSF and neuroimaging biomarkers of Alzheimer's disease.

PubMed

Nho, Kwangsik; Kim, Sungeun; Horgusluoglu, Emrin; Risacher, Shannon L; Shen, Li; Kim, Dokyoon; Lee, Seunggeun; Foroud, Tatiana; Shaw, Leslie M; Trojanowski, John Q; Aisen, Paul S; Petersen, Ronald C; Jack, Clifford R; Weiner, Michael W; Green, Robert C; Toga, Arthur W; Saykin, Andrew J

2017-05-24

The APOE ε4 allele is the most significant common genetic risk factor for late-onset Alzheimer's disease (LOAD). The region surrounding APOE on chromosome 19 has also shown consistent association with LOAD. However, no common variants in the region remain significant after adjusting for APOE genotype. We report a rare variant association analysis of genes in the vicinity of APOE with cerebrospinal fluid (CSF) and neuroimaging biomarkers of LOAD. Whole genome sequencing (WGS) was performed on 817 blood DNA samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sequence data from 757 non-Hispanic Caucasian participants was used in the present analysis. We extracted all rare variants (MAF (minor allele frequency) < 0.05) within a 312 kb window in APOE's vicinity encompassing 12 genes. We assessed CSF and neuroimaging (MRI and PET) biomarkers as LOAD-related quantitative endophenotypes. Gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O). A total of 3,334 rare variants (MAF < 0.05) were found within the APOE region. Among them, 72 rare non-synonymous variants were observed. Eight genes spanning the APOE region were significantly associated with CSF Aβ 1-42 (p < 1.0 × 10 -3 ). After controlling for APOE genotype and adjusting for multiple comparisons, 4 genes (CBLC, BCAM, APOE, and RELB) remained significant. Whole-brain surface-based analysis identified highly significant clusters associated with rare variants of CBLC in the temporal lobe region including the entorhinal cortex, as well as frontal lobe regions. Whole-brain voxel-wise analysis of amyloid PET identified significant clusters in the bilateral frontal and parietal lobes showing associations of rare variants of RELB with cortical amyloid burden. Rare variants within genes spanning the APOE region are significantly associated with LOAD-related CSF Aβ 1-42 and neuroimaging biomarkers after adjusting for APOE genotype. These findings warrant further investigation and illustrate the role of next generation sequencing and quantitative endophenotypes in assessing rare variants which may help explain missing heritability in AD and other complex diseases.

Gene function prediction with gene interaction networks: a context graph kernel approach.

PubMed

Li, Xin; Chen, Hsinchun; Li, Jiexun; Zhang, Zhu

2010-01-01

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

Field incidence of mycotoxins in commercial popcorn and potential environmental influences.

PubMed

Dowd, Patrick F; Johnson, Eric T

2010-02-01

Popcorn ear damage by insects and mycotoxin levels in kernels were monitored in several commercial popcorn fields in central Illinois over a 4-year period. Aflatoxin was rare, but fumonisin and deoxynivalenol (DON) were commonly encountered each year, and occurred at mean levels in fields up to 1.7 mg/kg (sample max. 2.77 mg/kg) and 1.9 mg/kg (sample max. 2.66 mg/kg), respectively. Neither fumonisin nor DON levels were significantly correlated with the percent of ears with visibly moldy insect-damaged kernels. Significant correlations were noted for the percent of ears with early caterpillar damage and both fumonisin and DON levels overall for some years and at specific sites in other years. Fumonisin levels were generally more highly correlated with insect damage than DON levels. Insect damaged kernels had 100- to 500-fold or greater levels of fumonisin compared to noninsect-damaged kernels, while DON levels were closer to 10- to 30-fold higher in insect damaged versus nondamaged kernels. A high percentage of DON-contaminated kernels were not insect damaged in 2007 and 2008. In some cases, differing mycotoxin levels for the same hybrid and same year planted at different locations appeared to be due to the prior crop. Higher DON levels in 2008 than other years were most likely associated with higher levels of rainfall and cooler temperatures than average during ear fill. While kernel sorters are reported to remove mycotoxin-contaminated popcorn kernels to acceptible levels, consideration of environmental factors that promote mycotoxins in popcorn should result in more effective control measures in the field.

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.

Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies.

PubMed

Tao, Chenyang; Feng, Jianfeng

2016-03-15

Quantifying associations in neuroscience (and many other scientific disciplines) is often challenged by high-dimensionality, nonlinearity and noisy observations. Many classic methods have either poor power or poor scalability on data sets of the same or different scales such as genetical, physiological and image data. Based on the framework of reproducing kernel Hilbert spaces we proposed a new nonlinear association criteria (NAC) with an efficient numerical algorithm and p-value approximation scheme. We also presented mathematical justification that links the proposed method to related methods such as kernel generalized variance, kernel canonical correlation analysis and Hilbert-Schmidt independence criteria. NAC allows the detection of association between arbitrary input domain as long as a characteristic kernel is defined. A MATLAB package was provided to facilitate applications. Extensive simulation examples and four real world neuroscience examples including functional MRI causality, Calcium imaging and imaging genetic studies on autism [Brain, 138(5):13821393 (2015)] and alcohol addiction [PNAS, 112(30):E4085-E4093 (2015)] are used to benchmark NAC. It demonstrates the superior performance over the existing procedures we tested and also yields biologically significant results for the real world examples. NAC beats its linear counterparts when nonlinearity is presented in the data. It also shows more robustness against different experimental setups compared with its nonlinear counterparts. In this work we presented a new and robust statistical approach NAC for measuring associations. It could serve as an interesting alternative to the existing methods for datasets where nonlinearity and other confounding factors are present. Copyright © 2016 Elsevier B.V. All rights reserved.

Association of yield-related traits in founder genotypes and derivatives of common wheat (Triticum aestivum L.).

PubMed

Guo, Jie; Shi, Weiping; Zhang, Zheng; Cheng, Jingye; Sun, Daizhen; Yu, Jin; Li, Xinlei; Guo, Pingyi; Hao, Chenyang

2018-02-20

Yield improvement is an ever-important objective of wheat breeding. Studying and understanding the phenotypes and genotypes of yield-related traits has potential for genetic improvement of crops. The genotypes of 215 wheat cultivars including 11 founder parents and 106 derivatives were analyzed by the 9 K wheat SNP iSelect assay. A total of 4138 polymorphic single nucleotide polymorphism (SNP) loci were detected on 21 chromosomes, of which 3792 were mapped to single chromosome locations. All genotypes were phenotyped for six yield-related traits including plant height (PH), spike length (SL), spikelet number per spike (SNPS), kernel number per spike (KNPS), kernel weight per spike (KWPS), and thousand kernel weight (TKW) in six irrigated environments. Genome-wide association analysis detected 117 significant associations of 76 SNPs on 15 chromosomes with phenotypic explanation rates (R 2 ) ranging from 2.03 to 12.76%. In comparing allelic variation between founder parents and their derivatives (106) and other cultivars (98) using the 76 associated SNPs, we found that the region 116.0-133.2 cM on chromosome 5A in founder parents and derivatives carried alleles positively influencing kernel weight per spike (KWPS), rarely found in other cultivars. The identified favorable alleles could mark important chromosome regions in derivatives that were inherited from founder parents. Our results unravel the genetic of yield in founder genotypes, and provide tools for marker-assisted selection for yield improvement.

Bayesian Correlation Analysis for Sequence Count Data

PubMed Central

Lau, Nelson; Perkins, Theodore J.

2016-01-01

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities’ measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low—especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities’ signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset. PMID:27701449

Genotyping-by-sequencing highlights original diversity patterns within a European collection of 1191 maize flint lines, as compared to the maize USDA genebank.

PubMed

Gouesnard, Brigitte; Negro, Sandra; Laffray, Amélie; Glaubitz, Jeff; Melchinger, Albrecht; Revilla, Pedro; Moreno-Gonzalez, Jesus; Madur, Delphine; Combes, Valérie; Tollon-Cordet, Christine; Laborde, Jacques; Kermarrec, Dominique; Bauland, Cyril; Moreau, Laurence; Charcosset, Alain; Nicolas, Stéphane

2017-10-01

Genotyping by sequencing is suitable for analysis of global diversity in maize. We showed the distinctiveness of flint maize inbred lines of interest to enrich the diversity of breeding programs. Genotyping-by-sequencing (GBS) is a highly cost-effective procedure that permits the analysis of large collections of inbred lines. We used it to characterize diversity in 1191 maize flint inbred lines from the INRA collection, the European Cornfed association panel, and lines recently derived from landraces. We analyzed the properties of GBS data obtained with different imputation methods, through comparison with a 50 K SNP array. We identified seven ancestral groups within the Flint collection (dent, Northern flint, Italy, Pyrenees-Galicia, Argentina, Lacaune, Popcorn) in agreement with breeding knowledge. Analysis highlighted many crosses between different origins and the improvement of flint germplasm with dent germplasm. We performed association studies on different agronomic traits, revealing SNPs associated with cob color, kernel color, and male flowering time variation. We compared the diversity of both our collection and the USDA collection which has been previously analyzed by GBS. The population structure of the 4001 inbred lines confirmed the influence of the historical inbred lines (B73, A632, Oh43, Mo17, W182E, PH207, and Wf9) within the dent group. It showed distinctly different tropical and popcorn groups, a sweet-Northern flint group and a flint group sub-structured in Italian and European flint (Pyrenees-Galicia and Lacaune) groups. Interestingly, we identified several selective sweeps between dent, flint, and tropical inbred lines that co-localized with SNPs associated with flowering time variation. The joint analysis of collections by GBS offers opportunities for a global diversity analysis of maize inbred lines.

The distal portion of the short arm of wheat (Triticum aestivum L.) chromosome 5D controls endosperm vitreosity and grain hardness.

PubMed

Morris, Craig F; Beecher, Brian S

2012-07-01

Kernel vitreosity is an important trait of wheat grain, but its developmental control is not completely known. We developed back-cross seven (BC(7)) near-isogenic lines in the soft white spring wheat cultivar Alpowa that lack the distal portion of chromosome 5D short arm. From the final back-cross, 46 BC(7)F(2) plants were isolated. These plants exhibited a complete and perfect association between kernel vitreosity (i.e. vitreous, non-vitreous or mixed) and Single Kernel Characterization System (SKCS) hardness. Observed segregation of 10:28:7 fit a 1:2:1 Chi-square. BC(7)F(2) plants classified as heterozygous for both SKCS hardness and kernel vitreosity (n = 29) were selected and a single vitreous and non-vitreous kernel were selected, and grown to maturity and subjected to SKCS analysis. The resultant phenotypic ratios were, from non-vitreous kernels, 23:6:0, and from vitreous kernels, 0:1:28, soft:heterozygous:hard, respectively. Three of these BC(7)F(2) heterozygous plants were selected and 40 kernels each drawn at random, grown to maturity and subjected to SKCS analysis. Phenotypic segregation ratios were 7:27:6, 11:20:9, and 3:28:9, soft:heterozygous:hard. Chi-square analysis supported a 1:2:1 segregation for one plant but not the other two, in which cases the two homozygous classes were under-represented. Twenty-two paired BC(7)F(2):F(3) full sibs were compared for kernel hardness, weight, size, density and protein content. SKCS hardness index differed markedly, 29.4 for the lines with a complete 5DS, and 88.6 for the lines possessing the deletion. The soft non-vitreous kernels were on average significantly heavier, by nearly 20%, and were slightly larger. Density and protein contents were similar, however. The results provide strong genetic evidence that gene(s) on distal 5DS control not only kernel hardness but also the manner in which the endosperm develops, viz. whether it is vitreous or non-vitreous.

Fixed and Data Adaptive Kernels in Cohen’s Class of Time-Frequency Distributions

DTIC Science & Technology

1992-09-01

translated into its associated analytic signal by using the techniques discussed in Chapter Four. 1. Wigner - Ville Distribution function PS = wvd (data,winlen...step,begin,theend) % PS = wvd (data,winlen,step,begin,theend) % ’wvd.ml returns the Wigner - Ville time-frequency distribution % for the input data...12 IV. FIXED KERNEL DISTRIBUTIONS .................................................................. 19 A. WIGNER - VILLE DISTRIBUTION

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

Lu, Deyu

A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, r s(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace r s(r) by a global, average r s in the kernel.more » Exemplary studies with the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average r s significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.« less

Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling

PubMed Central

Sinnott, Jennifer A.; Cai, Tianxi

2013-01-01

Summary Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai et al., 2011). In this paper, we derive testing and prediction methods for KM regression under the accelerated failure time model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. PMID:24328713

Kernel Smoothing Methods for Non-Poissonian Seismic Hazard Analysis

NASA Astrophysics Data System (ADS)

Woo, Gordon

2017-04-01

For almost fifty years, the mainstay of probabilistic seismic hazard analysis has been the methodology developed by Cornell, which assumes that earthquake occurrence is a Poisson process, and that the spatial distribution of epicentres can be represented by a set of polygonal source zones, within which seismicity is uniform. Based on Vere-Jones' use of kernel smoothing methods for earthquake forecasting, these methods were adapted in 1994 by the author for application to probabilistic seismic hazard analysis. There is no need for ambiguous boundaries of polygonal source zones, nor for the hypothesis of time independence of earthquake sequences. In Europe, there are many regions where seismotectonic zones are not well delineated, and where there is a dynamic stress interaction between events, so that they cannot be described as independent. From the Amatrice earthquake of 24 August, 2016, the subsequent damaging earthquakes in Central Italy over months were not independent events. Removing foreshocks and aftershocks is not only an ill-defined task, it has a material effect on seismic hazard computation. Because of the spatial dispersion of epicentres, and the clustering of magnitudes for the largest events in a sequence, which might all be around magnitude 6, the specific event causing the highest ground motion can vary from one site location to another. Where significant active faults have been clearly identified geologically, they should be modelled as individual seismic sources. The remaining background seismicity should be modelled as non-Poissonian using statistical kernel smoothing methods. This approach was first applied for seismic hazard analysis at a UK nuclear power plant two decades ago, and should be included within logic-trees for future probabilistic seismic hazard at critical installations within Europe. In this paper, various salient European applications are given.

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.

Variation in fungal microbiome (mycobiome) and aflatoxins during simulated storage of in-shell peanuts and peanut kernels.

PubMed

Xing, Fuguo; Ding, Ning; Liu, Xiao; Selvaraj, Jonathan Nimal; Wang, Limin; Zhou, Lu; Zhao, Yueju; Wang, Yan; Liu, Yang

2016-05-16

Internal transcribed spacer 2 (ITS2) sequencing was used to characterize the peanut mycobiome during 90 days storage at five conditions. The fungal diversity in in-shell peanuts was higher with 110 operational taxonomic units (OTUs) and 41 genera than peanut kernels (91 OTUs and 37 genera). This means that the micro-environment in shell is more suitable for maintaining fungal diversity. At 20-30 d, Rhizopus, Eurotium and Wallemia were predominant in in-shell peanuts. In peanut kernels, Rhizopus (>30%) and Eurotium (>20%) were predominant at 10-20 d and 30 d, respectively. The relative abundances of Rhizopus, Eurotium and Wallemia were higher than Aspergillus, because they were xerophilic and grew well on substrates with low water activity (aw). During growth, they released metabolic water, thereby favoring the growth of Aspergillus. Therefore, from 30 to 90 d, the relative abundance of Aspergillus increased while that of Rhizopus, Eurotium and Wallemia decreased. Principal Coordinate Analysis (PCoA) revealed that peanuts stored for 60-90 days and for 10-30 days clustered differently from each other. Due to low aw values (0.34-0.72) and low levels of A. flavus, nine of 51 samples were contaminated with aflatoxins.

Off-resonance artifacts correction with convolution in k-space (ORACLE).

PubMed

Lin, Wei; Huang, Feng; Simonotto, Enrico; Duensing, George R; Reykowski, Arne

2012-06-01

Off-resonance artifacts hinder the wider applicability of echo-planar imaging and non-Cartesian MRI methods such as radial and spiral. In this work, a general and rapid method is proposed for off-resonance artifacts correction based on data convolution in k-space. The acquired k-space is divided into multiple segments based on their acquisition times. Off-resonance-induced artifact within each segment is removed by applying a convolution kernel, which is the Fourier transform of an off-resonance correcting spatial phase modulation term. The field map is determined from the inverse Fourier transform of a basis kernel, which is calibrated from data fitting in k-space. The technique was demonstrated in phantom and in vivo studies for radial, spiral and echo-planar imaging datasets. For radial acquisitions, the proposed method allows the self-calibration of the field map from the imaging data, when an alternating view-angle ordering scheme is used. An additional advantage for off-resonance artifacts correction based on data convolution in k-space is the reusability of convolution kernels to images acquired with the same sequence but different contrasts. Copyright © 2011 Wiley-Liss, Inc.

Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study.

PubMed

Lima, Clodoaldo A M; Coelho, André L V

2011-10-01

We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely, Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). Copyright © 2011 Elsevier B.V. All rights reserved.

Chromatin accessibility prediction via a hybrid deep convolutional neural network.

PubMed

Liu, Qiao; Xia, Fei; Yin, Qijin; Jiang, Rui

2018-03-01

A majority of known genetic variants associated with human-inherited diseases lie in non-coding regions that lack adequate interpretation, making it indispensable to systematically discover functional sites at the whole genome level and precisely decipher their implications in a comprehensive manner. Although computational approaches have been complementing high-throughput biological experiments towards the annotation of the human genome, it still remains a big challenge to accurately annotate regulatory elements in the context of a specific cell type via automatic learning of the DNA sequence code from large-scale sequencing data. Indeed, the development of an accurate and interpretable model to learn the DNA sequence signature and further enable the identification of causative genetic variants has become essential in both genomic and genetic studies. We proposed Deopen, a hybrid framework mainly based on a deep convolutional neural network, to automatically learn the regulatory code of DNA sequences and predict chromatin accessibility. In a series of comparison with existing methods, we show the superior performance of our model in not only the classification of accessible regions against background sequences sampled at random, but also the regression of DNase-seq signals. Besides, we further visualize the convolutional kernels and show the match of identified sequence signatures and known motifs. We finally demonstrate the sensitivity of our model in finding causative noncoding variants in the analysis of a breast cancer dataset. We expect to see wide applications of Deopen with either public or in-house chromatin accessibility data in the annotation of the human genome and the identification of non-coding variants associated with diseases. Deopen is freely available at https://github.com/kimmo1019/Deopen. ruijiang@tsinghua.edu.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Interrelationship and path coefficient analysis of yield components in F4 progenies of tef (Eragrostis tef).

PubMed

Debebe, Abel; Singh, Harijat; Tefera, Hailu

2014-01-01

This experiment was conducted at Debre Zeit and Akaki during 2004-2005 cropping season on F2-derived F4 bulk families of three crosses, viz, DZ-01-974 x DZ-01-2786, DZ-01-974 x DZ-Cr-37 and Alba x Kaye Murri. To estimate the correlations and path coefficients between yield and yield components, 63 F4 families were taken randomly from each of the three crosses. The 189 F4 families, five parents and two checks were space planted following in 14 x 14 simple lattice design. Study of associations among traits indicated that yield was positively associated with shoot biomass, harvest index, lodging index and panicle kernel weight at phenotypic level at Debre Zeit. At Akaki, yield had significant positive correlation with shoot biomass, harvest index, plant height, panicle length and panicle weight. At genotypic level, grain yield per plot exhibited positive association with harvest index, shoot biomass, lodging index and panicle kernel weight at Debre Zeit. By contrast, days to heading, days to maturity, plant height and panicle length showed negative association with yield. At Akaki, kernel yield per plot was positively correlated at genotypic level with all the traits considered where lodging index had the highest correlation followed by shoot biomass, panicle kernel weight and harvest index. Path coefficient analysis at both phenotypic and genotypic levels for both the locations suggested those shoot biomass and harvest indexes are the two important yield determining traits. These two traits might be useful in indirect selection for yield improvement in the material generated from the three crosses under consideration.

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

Identification of a maize (Zea mays) chitinase allele sequence suitable for a role in ear rot fungal resistance

USDA-ARS?s Scientific Manuscript database

Chitinases are thought to play a role in plant resistance to pathogens, but the extent of this role is unknown. The gene for a maize chitinase “chitinase 2” previously reported to be induced by two ear rot pathogens in one maize inbred, was cloned from mRNA isolated from milk stage kernels of severa...

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.

Omnibus risk assessment via accelerated failure time kernel machine modeling.

PubMed

Sinnott, Jennifer A; Cai, Tianxi

2013-12-01

Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

Genome-wide association analysis identifies loci governing mercury accumulation in maize.

PubMed

Zhao, Zhan; Fu, Zhongjun; Lin, Yanan; Chen, Hao; Liu, Kun; Xing, Xiaolong; Liu, Zonghua; Li, Weihua; Tang, Jihua

2017-03-21

Owing to the rapid development of urbanisation and industrialisation, heavy metal pollution has become a widespread environmental problem. Maize planted on mercury (Hg)-polluted soil can absorb and accumulate Hg in its edible parts, posing a potential threat to human health. To understand the genetic mechanism of Hg accumulation in maize, we performed a genome-wide association study using a mixed linear model on an association population consisting of 230 maize inbred lines with abundant genetic variation. The order of relative Hg concentrations in different maize tissues was as follows: leaves > bracts > stems > axes > kernels. Combined two locations, a total of 37 significant single-nucleotide polymorphisms (SNPs) associated with kernels, 12 with axes, 13 with stems, 27 with bracts and 23 with leaves were detected with p < 0.0001. Each significant SNP was calculated and the SNPs significant associated with kernels, axes, stems, bracts and leaves explained 6.96%-10.56%, 7.19%-15.87%, 7.11%-10.19%, 7.16%-8.71% and 6.91%-9.17% of the phenotypic variation, respectively. Among the significant SNPs, nine co-localised with previously detected quantitative trait loci. This study will aid in the selection of Hg-accumulation inbred lines that satisfy the needs for pollution-safe cultivars and maintaining maize production.

Investigating the Impact of Aerosol Deposition on Snow Melt over the Greenland Ice Sheet Using a New Kernel

NASA Astrophysics Data System (ADS)

Li, Y.; Flanner, M.

2017-12-01

Accelerating surface melt on the Greenland Ice Sheet (GrIS) has led to a doubling of Greenland's contribution to global sea level rise during recent decades. The darkening effect due to black carbon (BC), dust, and other light absorbing impurities (LAI) enhances snow melt by boosting its absorption of solar energy. It is therefore important for coupled aerosol-climate and ice sheet models to include snow darkening effects from LAI, and yet most do not. In this study, we develop an aerosol deposition—snow melt kernel based on the Community Earth System Model (CESM) to investigate changes in melt flux due to variations in the amount and timing of aerosol deposition on the GrIS. The Community Land Model (CLM) component of CESM is driven with a large range of aerosol deposition fluxes to determine non-linear relationships between melt perturbation and deposition amount occurring in different months and location (thereby capturing variations in base state associated with elevation and latitude). The kernel product will include climatological-mean effects and standard deviations associated with interannual variability. Finally, the kernel will allow aerosol deposition fluxes from any global or regional aerosol model to be translated into surface melt perturbations of the GrIS, thus extending the utility of state-of-the-art aerosol models.

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.

Rényi continuous entropy of DNA sequences.

PubMed

Vinga, Susana; Almeida, Jonas S

2004-12-07

Entropy measures of DNA sequences estimate their randomness or, inversely, their repeatability. L-block Shannon discrete entropy accounts for the empirical distribution of all length-L words and has convergence problems for finite sequences. A new entropy measure that extends Shannon's formalism is proposed. Renyi's quadratic entropy calculated with Parzen window density estimation method applied to CGR/USM continuous maps of DNA sequences constitute a novel technique to evaluate sequence global randomness without some of the former method drawbacks. The asymptotic behaviour of this new measure was analytically deduced and the calculation of entropies for several synthetic and experimental biological sequences was performed. The results obtained were compared with the distributions of the null model of randomness obtained by simulation. The biological sequences have shown a different p-value according to the kernel resolution of Parzen's method, which might indicate an unknown level of organization of their patterns. This new technique can be very useful in the study of DNA sequence complexity and provide additional tools for DNA entropy estimation. The main MATLAB applications developed and additional material are available at the webpage . Specialized functions can be obtained from the authors.

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Song, Jiangning; Burrage, Kevin; Yuan, Zheng; Huber, Thomas

2006-03-09

The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Determining the multi-scale hedge ratios of stock index futures using the lower partial moments method

NASA Astrophysics Data System (ADS)

Dai, Jun; Zhou, Haigang; Zhao, Shaoquan

2017-01-01

This paper considers a multi-scale future hedge strategy that minimizes lower partial moments (LPM). To do this, wavelet analysis is adopted to decompose time series data into different components. Next, different parametric estimation methods with known distributions are applied to calculate the LPM of hedged portfolios, which is the key to determining multi-scale hedge ratios over different time scales. Then these parametric methods are compared with the prevailing nonparametric kernel metric method. Empirical results indicate that in the China Securities Index 300 (CSI 300) index futures and spot markets, hedge ratios and hedge efficiency estimated by the nonparametric kernel metric method are inferior to those estimated by parametric hedging model based on the features of sequence distributions. In addition, if minimum-LPM is selected as a hedge target, the hedging periods, degree of risk aversion, and target returns can affect the multi-scale hedge ratios and hedge efficiency, respectively.

Whole-Exome Sequencing to Identify Rare Variants and Gene Networks that Increase Susceptibility to Scleroderma in African Americans.

PubMed

Gourh, Pravitt; Remmers, Elaine F; Boyden, Steven E; Alexander, Theresa; Morgan, Nadia D; Shah, Ami A; Mayes, Maureen D; Doumatey, Ayo; Bentley, Amy R; Shriner, Daniel; Domsic, Robyn T; Medsger, Thomas A; Steen, Virginia D; Ramos, Paula S; Silver, Richard M; Korman, Benjamin; Varga, John; Schiopu, Elena; Khanna, Dinesh; Hsu, Vivien; Gordon, Jessica K; Saketkoo, Lesley Ann; Gladue, Heather; Kron, Brynn; Criswell, Lindsey A; Derk, Chris T; Bridges, S Louis; Shanmugam, Victoria K; Kolstad, Kathleen D; Chung, Lorinda; Jan, Reem; Bernstein, Elana J; Goldberg, Avram; Trojanowski, Marcin; Kafaja, Suzanne; Maksimowicz-McKinnon, Kathleen M; Mullikin, James C; Adeyemo, Adebowale; Rotimi, Charles; Boin, Francesco; Kastner, Daniel L; Wigley, Fredrick M

2018-05-06

Whole-exome sequencing (WES) studies in systemic sclerosis (SSc) patients of European American (EA) ancestry have identified variants in the ATP8B4 gene and enrichment of variants in genes in the extracellular matrix (ECM)-related pathway increasing SSc susceptibility. Our goal was to evaluate the association of the ATP8B4 gene and the ECM-related pathway with SSc in a cohort of African Americans (AA). SSc patients of AA ancestry were enrolled from 23 academic centers across the United States under the Genome Research in African American Scleroderma Patients (GRASP) consortium. Unrelated AA individuals without serological evidence of autoimmunity enrolled in the Howard University Family Study were used as unaffected controls. Functional variants in genes reported in the two WES studies in EA SSc were selected for gene association testing using the optimized sequence kernel association test (SKAT-O) and pathway analysis by Ingenuity pathway analysis in 379 patients and 411 controls. Principal components analysis demonstrated that the patients and controls had similar ancestral backgrounds with about equal proportions of mean European admixture. Using SKAT-O, we examined the association of individual genes that were previously reported in EAs, and none remained significant including ATP8B4 (P U nCorr =0.98). However, we confirm the previously reported association of the ECM-related pathway with enrichment of variants within the COL13A1, COL18A1, COL22A1, COL4A3, COL4A4, COL5A2, PROK1, and SERPINE1 genes (P C orr =1.95×10 -4 ). This is the largest genetic study in AAs with SSc to date, corroborating the role of functional variants aggregating in a fibrotic pathway and increasing SSc susceptibility. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Biocontrol of Aspergillus Species on Peanut Kernels by Antifungal Diketopiperazine Producing Bacillus cereus Associated with Entomopathogenic Nematode

PubMed Central

Kumar, Sasidharan Nishanth; Sreekala, Sreerag Ravikumar; Chandrasekaran, Dileep; Nambisan, Bala; Anto, Ruby John

2014-01-01

The rhabditid entomopathogenic nematode associated Bacillus cereus and the antifungal compounds produced by this bacterium were evaluated for their activity in reducing postharvest decay of peanut kernels caused by Aspergillus species in in vitro and in vivo tests. The results showed that B. cereus had a significant effect on biocontrol effectiveness in in vitro and in vivo conditions. The antifungal compounds produced by the B. cereus were purified using silica gel column chromatography and their structure was elucidated using extensive spectral analyses. The compounds were identified as diketopiperazines (DKPs) [cyclo-(L-Pro-Gly), cyclo(L-Tyr-L-Tyr), cyclo-(L-Phe-Gly) and cyclo(4-hydroxy-L-Pro-L-Trp)]. The antifungal activities of diketopiperazines were studied against five Aspergillus species and best MIC of 2 µg/ml was recorded against A. flavus by cyclo(4-hydroxy-L-Pro-L-Trp). To investigate the potential application of cyclo(4-hydroxy-L-Pro-L-Trp) to eliminate fungal spoilage in food and feed, peanut kernels was used as a food model system. White mycelia and dark/pale green spores of Aspergillus species were observed in the control peanut kernels after 2 days incubation. However the fungal growth was not observed in peanut kernels treated with cyclo(4-hydroxy-L-Pro-L-Trp). The cyclo(4-hydroxy-L-Pro-L-Trp) was nontoxic to two normal cell lines [fore skin (FS) normal fibroblast and African green monkey kidney (VERO)] up to 200 µg/ml in MTT assay. Thus the cyclo(4-hydroxy-L-Pro-L-Trp) identified in this study may be a promising alternative to chemical preservatives as a potential biopreservative agent which prevent fungal growth in food and feed. To the best of our knowledge, this is the first report demonstrating that the entomopathogenic nematode associated B. cereus and cyclo(4-hydroxy-L-Pro-L-Trp) could be used as a biocontrol agents against postharvest fungal disease caused by Aspergillus species. PMID:25157831

Framework for analyzing ecological trait-based models in multidimensional niche spaces

NASA Astrophysics Data System (ADS)

Biancalani, Tommaso; DeVille, Lee; Goldenfeld, Nigel

2015-05-01

We develop a theoretical framework for analyzing ecological models with a multidimensional niche space. Our approach relies on the fact that ecological niches are described by sequences of symbols, which allows us to include multiple phenotypic traits. Ecological drivers, such as competitive exclusion, are modeled by introducing the Hamming distance between two sequences. We show that a suitable transform diagonalizes the community interaction matrix of these models, making it possible to predict the conditions for niche differentiation and, close to the instability onset, the asymptotically long time population distributions of niches. We exemplify our method using the Lotka-Volterra equations with an exponential competition kernel.

Genetic manipulation of the γ-aminobutyric acid (GABA) shunt in rice: overexpression of truncated glutamate decarboxylase (GAD2) and knockdown of γ-aminobutyric acid transaminase (GABA-T) lead to sustained and high levels of GABA accumulation in rice kernels.

PubMed

Shimajiri, Yasuka; Oonishi, Takayuki; Ozaki, Kae; Kainou, Kumiko; Akama, Kazuhito

2013-06-01

Gamma-aminobutyric acid (GABA) is a non-protein amino acid commonly present in all organisms. Because cellular levels of GABA in plants are mainly regulated by synthesis (glutamate decarboxylase, GAD) and catabolism (GABA-transaminase, GABA-T), we attempted seed-specific manipulation of the GABA shunt to achieve stable GABA accumulation in rice. A truncated GAD2 sequence, one of five GAD genes, controlled by the glutelin (GluB-1) or rice embryo globulin promoters (REG) and GABA-T-based trigger sequences in RNA interference (RNAi) cassettes controlled by one of these promoters as well, was introduced into rice (cv. Koshihikari) to establish stable transgenic lines under herbicide selection using pyriminobac. T₁ and T₂ generations of rice lines displayed high GABA concentrations (2-100 mg/100 g grain). In analyses of two selected lines from the T₃ generation, there was a strong correlation between GABA level and the expression of truncated GAD2, whereas the inhibitory effect of GABA-T expression was relatively weak. In these two lines both with two T-DNA copies, their starch, amylose, and protein levels were slightly lower than non-transformed cv. Koshihikari. Free amino acid analysis of mature kernels of these lines demonstrated elevated levels of GABA (75-350 mg/100 g polished rice) and also high levels of several amino acids, such as Ala, Ser, and Val. Because these lines of seeds could sustain their GABA content after harvest (up to 6 months), the strategy in this study could lead to the accumulation GABA and for these to be sustained in the edible parts. © 2013 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

On the logarithmic-singularity correction in the kernel function method of subsonic lifting-surface theory

NASA Technical Reports Server (NTRS)

Lan, C. E.; Lamar, J. E.

1977-01-01

A logarithmic-singularity correction factor is derived for use in kernel function methods associated with Multhopp's subsonic lifting-surface theory. Because of the form of the factor, a relation was formulated between the numbers of chordwise and spanwise control points needed for good accuracy. This formulation is developed and discussed. Numerical results are given to show the improvement of the computation with the new correction factor.

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.

Deciphering drought-induced metabolic responses and regulation in developing maize kernels.

PubMed

Yang, Liming; Fountain, Jake C; Ji, Pingsheng; Ni, Xinzhi; Chen, Sixue; Lee, Robert D; Kemerait, Robert C; Guo, Baozhu

2018-02-12

Drought stress conditions decrease maize growth and yield, and aggravate preharvest aflatoxin contamination. While several studies have been performed on mature kernels responding to drought stress, the metabolic profiles of developing kernels are not as well characterized, particularly in germplasm with contrasting resistance to both drought and mycotoxin contamination. Here, following screening for drought tolerance, a drought-sensitive line, B73, and a drought-tolerant line, Lo964, were selected and stressed beginning at 14 days after pollination. Developing kernels were sampled 7 and 14 days after drought induction (DAI) from both stressed and irrigated plants. Comparative biochemical and metabolomic analyses profiled 409 differentially accumulated metabolites. Multivariate statistics and pathway analyses showed that drought stress induced an accumulation of simple sugars and polyunsaturated fatty acids and a decrease in amines, polyamines and dipeptides in B73. Conversely, sphingolipid, sterol, phenylpropanoid and dipeptide metabolites accumulated in Lo964 under drought stress. Drought stress also resulted in the greater accumulation of reactive oxygen species (ROS) and aflatoxin in kernels of B73 in comparison with Lo964 implying a correlation in their production. Overall, field drought treatments disordered a cascade of normal metabolic programming during development of maize kernels and subsequently caused oxidative stress. The glutathione and urea cycles along with the metabolism of carbohydrates and lipids for osmoprotection, membrane maintenance and antioxidant protection were central among the drought stress responses observed in developing kernels. These results also provide novel targets to enhance host drought tolerance and disease resistance through the use of biotechnologies such as transgenics and genome editing. © 2018 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

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.

Classification and recognition of dynamical models: the role of phase, independent components, kernels and optimal transport.

PubMed

Bissacco, Alessandro; Chiuso, Alessandro; Soatto, Stefano

2007-11-01

We address the problem of performing decision tasks, and in particular classification and recognition, in the space of dynamical models in order to compare time series of data. Motivated by the application of recognition of human motion in image sequences, we consider a class of models that include linear dynamics, both stable and marginally stable (periodic), both minimum and non-minimum phase, driven by non-Gaussian processes. This requires extending existing learning and system identification algorithms to handle periodic modes and nonminimum phase behavior, while taking into account higher-order statistics of the data. Once a model is identified, we define a kernel-based cord distance between models that includes their dynamics, their initial conditions as well as input distribution. This is made possible by a novel kernel defined between two arbitrary (non-Gaussian) distributions, which is computed by efficiently solving an optimal transport problem. We validate our choice of models, inference algorithm, and distance on the tasks of human motion synthesis (sample paths of the learned models), and recognition (nearest-neighbor classification in the computed distance). However, our work can be applied more broadly where one needs to compare historical data while taking into account periodic trends, non-minimum phase behavior, and non-Gaussian input distributions.

Study of the mechanisms for flame stabilization in gas turbine model combustors using kHz laser diagnostics

NASA Astrophysics Data System (ADS)

Boxx, Isaac; Carter, Campbell D.; Stöhr, Michael; Meier, Wolfgang

2013-05-01

An image-processing routine was developed to autonomously identify and statistically characterize flame-kernel events, wherein OH (from a planar laser-induced fluorescence, PLIF, measurement) appears in the probe region away from the contiguous OH layer. This routine was applied to datasets from two gas turbine model combustors that consist of thousands of joint OH-velocity images from kHz framerate OH-PLIF and particle image velocimetry (PIV). Phase sorting of the kernel centroids with respect to the dominant fluid-dynamic structure of the combustors (a helical precessing vortex core, PVC) indicates through-plane transport of reacting fluid best explains their sudden appearance in the PLIF images. The concentration of flame-kernel events around the periphery of the mean location of the PVC indicates they are likely the result of wrinkling and/or breakup of the primary flame sheet associated with the passage of the PVC as it circumscribes the burner centerline. The prevailing through-plane velocity of the swirling flow-field transports these fragments into the imaging plane of the OH-PLIF system. The lack of flame-kernel events near the center of the PVC (in which there is lower strain and longer fluid-dynamic residence times) indicates that auto-ignition is not a likely explanation for these flame kernels in a majority of cases. The lack of flame-kernel centroid variation in one flame in which there is no PVC further supports this explanation.

Dielectric properties of almond kernels associated with radio frequency and microwave pasteurization

NASA Astrophysics Data System (ADS)

Li, Rui; Zhang, Shuang; Kou, Xiaoxi; Ling, Bo; Wang, Shaojin

2017-02-01

To develop advanced pasteurization treatments based on radio frequency (RF) or microwave (MW) energy, dielectric properties of almond kernels were measured by using an open-ended coaxial-line probe and impedance analyzer at frequencies between 10 and 3000 MHz, moisture contents between 4.2% to 19.6% w.b. and temperatures between 20 and 90 °C. The results showed that both dielectric constant and loss factor of the almond kernels decreased sharply with increasing frequency over the RF range (10-300 MHz), but gradually over the measured MW range (300-3000 MHz). Both dielectric constant and loss factor of almond kernels increased with increasing temperature and moisture content, and largely enhanced at higher temperature and moisture levels. Quadratic polynomial equations were developed to best fit the relationship between dielectric constant or loss factor at 27, 40, 915 or 2450 MHz and sample temperature/moisture content with R2 greater than 0.967. Penetration depth of electromagnetic wave into samples decreased with increasing frequency (27-2450 MHz), moisture content (4.2-19.6% w.b.) and temperature (20-90 °C). The temperature profiles of RF heated almond kernels under three moisture levels were made using experiment and computer simulation based on measured dielectric properties. Based on the result of this study, RF treatment has potential to be practically used for pasteurization of almond kernels with acceptable heating uniformity.

Dielectric properties of almond kernels associated with radio frequency and microwave pasteurization.

PubMed

Li, Rui; Zhang, Shuang; Kou, Xiaoxi; Ling, Bo; Wang, Shaojin

2017-02-10

To develop advanced pasteurization treatments based on radio frequency (RF) or microwave (MW) energy, dielectric properties of almond kernels were measured by using an open-ended coaxial-line probe and impedance analyzer at frequencies between 10 and 3000 MHz, moisture contents between 4.2% to 19.6% w.b. and temperatures between 20 and 90 °C. The results showed that both dielectric constant and loss factor of the almond kernels decreased sharply with increasing frequency over the RF range (10-300 MHz), but gradually over the measured MW range (300-3000 MHz). Both dielectric constant and loss factor of almond kernels increased with increasing temperature and moisture content, and largely enhanced at higher temperature and moisture levels. Quadratic polynomial equations were developed to best fit the relationship between dielectric constant or loss factor at 27, 40, 915 or 2450 MHz and sample temperature/moisture content with R 2 greater than 0.967. Penetration depth of electromagnetic wave into samples decreased with increasing frequency (27-2450 MHz), moisture content (4.2-19.6% w.b.) and temperature (20-90 °C). The temperature profiles of RF heated almond kernels under three moisture levels were made using experiment and computer simulation based on measured dielectric properties. Based on the result of this study, RF treatment has potential to be practically used for pasteurization of almond kernels with acceptable heating uniformity.

Impact of rare variants in ARHGAP29 to the etiology of oral clefts: role of loss-of-function vs missense variants.

PubMed

Savastano, C P; Brito, L A; Faria, Á C; Setó-Salvia, N; Peskett, E; Musso, C M; Alvizi, L; Ezquina, S A M; James, C; GOSgene; Beales, P; Lees, M; Moore, G E; Stanier, P; Passos-Bueno, M R

2017-05-01

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is a prevalent, complex congenital malformation. Genome-wide association studies (GWAS) on NSCL/P have consistently identified association for the 1p22 region, in which ARHGAP29 has emerged as the main candidate gene. ARHGAP29 re-sequencing studies in NSCL/P patients have identified rare variants; however, their clinical impact is still unclear. In this study we identified 10 rare variants in ARHGAP29, including five missense, one in-frame deletion, and four loss-of-function (LoF) variants, in a cohort of 188 familial NSCL/P cases. A significant mutational burden was found for LoF (Sequence Kernel Association Test, p = 0.0005) but not for missense variants in ARHGAP29, suggesting that only LoF variants contribute to the etiology of NSCL/P. Penetrance was estimated as 59%, indicating that heterozygous LoF variants in ARHGAP29 confer a moderate risk to NSCL/P. The GWAS hits in IRF6 (rs642961) and 1p22 (rs560426 and rs4147811) do not seem to contribute to the penetrance of the phenotype, based on co-segregation analysis. Our data show that rare variants leading to haploinsufficiency of ARHGAP29 represent an important etiological clefting mechanism, and genetic testing for this gene might be taken into consideration in genetic counseling of familial cases. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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...

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.

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

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

Establishing the role of rare coding variants in known Parkinson's disease risk loci.

PubMed

Jansen, Iris E; Gibbs, J Raphael; Nalls, Mike A; Price, T Ryan; Lubbe, Steven; van Rooij, Jeroen; Uitterlinden, André G; Kraaij, Robert; Williams, Nigel M; Brice, Alexis; Hardy, John; Wood, Nicholas W; Morris, Huw R; Gasser, Thomas; Singleton, Andrew B; Heutink, Peter; Sharma, Manu

2017-11-01

Many common genetic factors have been identified to contribute to Parkinson's disease (PD) susceptibility, improving our understanding of the related underlying biological mechanisms. The involvement of rarer variants in these loci has been poorly studied. Using International Parkinson's Disease Genomics Consortium data sets, we performed a comprehensive study to determine the impact of rare variants in 23 previously published genome-wide association studies (GWAS) loci in PD. We applied Prix fixe to select the putative causal genes underneath the GWAS peaks, which was based on underlying functional similarities. The Sequence Kernel Association Test was used to analyze the joint effect of rare, common, or both types of variants on PD susceptibility. All genes were tested simultaneously as a gene set and each gene individually. We observed a moderate association of common variants, confirming the involvement of the known PD risk loci within our genetic data sets. Focusing on rare variants, we identified additional association signals for LRRK2, STBD1, and SPATA19. Our study suggests an involvement of rare variants within several putatively causal genes underneath previously identified PD GWAS peaks. Copyright © 2017 Elsevier Inc. All rights reserved.

A Novel Method to Predict Highly Expressed Genes Based on Radius Clustering and Relative Synonymous Codon Usage.

PubMed

Tran, Tuan-Anh; Vo, Nam Tri; Nguyen, Hoang Duc; Pham, Bao The

2015-12-01

Recombinant proteins play an important role in many aspects of life and have generated a huge income, notably in the industrial enzyme business. A gene is introduced into a vector and expressed in a host organism-for example, E. coli-to obtain a high productivity of target protein. However, transferred genes from particular organisms are not usually compatible with the host's expression system because of various reasons, for example, codon usage bias, GC content, repetitive sequences, and secondary structure. The solution is developing programs to optimize for designing a nucleotide sequence whose origin is from peptide sequences using properties of highly expressed genes (HEGs) of the host organism. Existing data of HEGs determined by practical and computer-based methods do not satisfy for qualifying and quantifying. Therefore, the demand for developing a new HEG prediction method is critical. We proposed a new method for predicting HEGs and criteria to evaluate gene optimization. Codon usage bias was weighted by amplifying the difference between HEGs and non-highly expressed genes (non-HEGs). The number of predicted HEGs is 5% of the genome. In comparison with Puigbò's method, the result is twice as good as Puigbò's one, in kernel ratio and kernel sensitivity. Concerning transcription/translation factor proteins (TF), the proposed method gives low TF sensitivity, while Puigbò's method gives moderate one. In summary, the results indicated that the proposed method can be a good optional applying method to predict optimized genes for particular organisms, and we generated an HEG database for further researches in gene design.

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...

Proteomic analysis of amphiphilic proteins of hexaploid wheat kernels.

PubMed

Amiour, Nardjis; Merlino, Marielle; Leroy, Philippe; Branlard, Gérard

2002-06-01

Wheat proteins and specially gluten proteins have been well studied and are closely associated with baking products. Amphiphilic proteins (proteins that are soluble using nonionic detergent Triton X-114 ) also play an important role in wheat quality. Some of them, like puroindolines, are lipid binding proteins, and are strongly linked to dough foaming properties and to fine crumb texture. However many amphiphilic proteins are still unknown and both their physiological and technological functions remain to be analysed. In order to explore these proteins, proteomic analysis was carried out using 81 F9 lines, progeny obtained from an interspecific cross "W7984"x"Opata", and already used to built a map of more than 2000 molecular markers (International Triticeae Mapping Initiative, ITMImap). Two-dimensional electrophoresis (immobilized pH gradient (pH 6-11)x sodium dodecyl sulfate-polyacrylamide gel electrophoresis) was performed on amphiphilic proteins with three to five replicates for each line. Silver stained gels were analysed using Melanie 3 software. Genetic determinism was carried out on 170 spots segregating between the two parental hexaploïd wheats. Many of these spots were mapped on different chromosomes of the ITMImap. Spots of interest were identified using matrix-assisted laser desorption/ionization-time of flight and some of them were partly sequenced using electrospray ionization-tandem mass spectrometry. This proteomic approach provided some very useful information about some proteic components linked to bread wheat quality and particularly to kernel hardness.

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.

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.

Genomic estimation of complex traits reveals ancient maize adaptation to temperate North America.

PubMed

Swarts, Kelly; Gutaker, Rafal M; Benz, Bruce; Blake, Michael; Bukowski, Robert; Holland, James; Kruse-Peeples, Melissa; Lepak, Nicholas; Prim, Lynda; Romay, M Cinta; Ross-Ibarra, Jeffrey; Sanchez-Gonzalez, Jose de Jesus; Schmidt, Chris; Schuenemann, Verena J; Krause, Johannes; Matson, R G; Weigel, Detlef; Buckler, Edward S; Burbano, Hernán A

2017-08-04

By 4000 years ago, people had introduced maize to the southwestern United States; full agriculture was established quickly in the lowland deserts but delayed in the temperate highlands for 2000 years. We test if the earliest upland maize was adapted for early flowering, a characteristic of modern temperate maize. We sequenced fifteen 1900-year-old maize cobs from Turkey Pen Shelter in the temperate Southwest. Indirectly validated genomic models predicted that Turkey Pen maize was marginally adapted with respect to flowering, as well as short, tillering, and segregating for yellow kernel color. Temperate adaptation drove modern population differentiation and was selected in situ from ancient standing variation. Validated prediction of polygenic traits improves our understanding of ancient phenotypes and the dynamics of environmental adaptation. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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

The NAS parallel benchmarks

NASA Technical Reports Server (NTRS)

Bailey, David (Editor); Barton, John (Editor); Lasinski, Thomas (Editor); Simon, Horst (Editor)

1993-01-01

A new set of benchmarks was developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of a set of kernels, the 'Parallel Kernels,' and a simulated application benchmark. Together they mimic the computation and data movement characteristics of large scale computational fluid dynamics (CFD) applications. The principal distinguishing feature of these benchmarks is their 'pencil and paper' specification - all details of these benchmarks are specified only algorithmically. In this way many of the difficulties associated with conventional benchmarking approaches on highly parallel systems are avoided.

[Crop geometry identification based on inversion of semiempirical BRDF models].

PubMed

Huang, Wen-jiang; Wang, Jin-di; Mu, Xi-han; Wang, Ji-hua; Liu, Liang-yun; Liu, Qiang; Niu, Zheng

2007-10-01

Investigations have been made on identification of erective and horizontal varieties by bidirectional canopy reflected spectrum and semi-empirical bidirectional reflectance distribution function (BRDF) models. The qualitative effect of leaf area index (LAI) and average leaf angle (ALA) on crop canopy reflected spectrum was studied. The structure parameter sensitive index (SPEI) based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso), was defined in the present study for crop geometry identification. However, the weights associated with the kernels of semi-empirical BRDF model do not have a direct relationship with measurable biophysical parameters. Therefore, efforts have focused on trying to find the relation between these semi-empirical BRDF kernel weights and various vegetation structures. SPEI was proved to be more sensitive to identify crop geometry structures than structural scattering index (SSI) and normalized difference f-index (NDFI), SPEI could be used to distinguish erective and horizontal geometry varieties. So, it is feasible to identify horizontal and erective varieties of wheat by bidirectional canopy reflected spectrum.

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

ZOOM Lite: next-generation sequencing data mapping and visualization software

PubMed Central

Zhang, Zefeng; Lin, Hao; Ma, Bin

2010-01-01

High-throughput next-generation sequencing technologies pose increasing demands on the efficiency, accuracy and usability of data analysis software. In this article, we present ZOOM Lite, a software for efficient reads mapping and result visualization. With a kernel capable of mapping tens of millions of Illumina or AB SOLiD sequencing reads efficiently and accurately, and an intuitive graphical user interface, ZOOM Lite integrates reads mapping and result visualization into a easy to use pipeline on desktop PC. The software handles both single-end and paired-end reads, and can output both the unique mapping result or the top N mapping results for each read. Additionally, the software takes a variety of input file formats and outputs to several commonly used result formats. The software is freely available at http://bioinfor.com/zoom/lite/. PMID:20530531

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.

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.

New adaptive statistical iterative reconstruction ASiR-V: Assessment of noise performance in comparison to ASiR.

PubMed

De Marco, Paolo; Origgi, Daniela

2018-03-01

To assess the noise characteristics of the new adaptive statistical iterative reconstruction (ASiR-V) in comparison to ASiR. A water phantom was acquired with common clinical scanning parameters, at five different levels of CTDI vol . Images were reconstructed with different kernels (STD, SOFT, and BONE), different IR levels (40%, 60%, and 100%) and different slice thickness (ST) (0.625 and 2.5 mm), both for ASiR-V and ASiR. Noise properties were investigated and noise power spectrum (NPS) was evaluated. ASiR-V significantly reduced noise relative to FBP: noise reduction was in the range 23%-60% for a 0.625 mm ST and 12%-64% for the 2.5 mm ST. Above 2 mGy, noise reduction for ASiR-V had no dependence on dose. Noise reduction for ASIR-V has dependence on ST, being greater for STD and SOFT kernels at 2.5 mm. For the STD kernel ASiR-V has greater noise reduction for both ST, if compared to ASiR. For the SOFT kernel, results varies according to dose and ST, while for BONE kernel ASIR-V shows less noise reduction. NPS for CT Revolution has dose dependent behavior at lower doses. NPS for ASIR-V and ASiR is similar, showing a shift toward lower frequencies as the IR level increases for STD and SOFT kernels. The NPS is different between ASiR-V and ASIR with BONE kernel. NPS for ASiR-V appears to be ST dependent, having a shift toward lower frequencies for 2.5 mm ST. ASiR-V showed greater noise reduction than ASiR for STD and SOFT kernels, while keeping the same NPS. For the BONE kernel, ASiR-V presents a completely different behavior, with less noise reduction and modified NPS. Noise properties of the ASiR-V are dependent on reconstruction slice thickness. The noise properties of ASiR-V suggest the need for further measurements and efforts to establish new CT protocols to optimize clinical imaging. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

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.

Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

NASA Astrophysics Data System (ADS)

Poletti, Enea; Veronese, Elisa; Calabrese, Massimiliano; Bertoldo, Alessandra; Grisan, Enrico

2012-02-01

The automatic segmentation of brain tissues in magnetic resonance (MR) is usually performed on T1-weighted images, due to their high spatial resolution. T1w sequence, however, has some major downsides when brain lesions are present: the altered appearance of diseased tissues causes errors in tissues classification. In order to overcome these drawbacks, we employed two different MR sequences: fluid attenuated inversion recovery (FLAIR) and double inversion recovery (DIR). The former highlights both gray matter (GM) and white matter (WM), the latter highlights GM alone. We propose here a supervised classification scheme that does not require any anatomical a priori information to identify the 3 classes, "GM", "WM", and "background". Features are extracted by means of a local multi-scale texture analysis, computed for each pixel of the DIR and FLAIR sequences. The 9 textures considered are average, standard deviation, kurtosis, entropy, contrast, correlation, energy, homogeneity, and skewness, evaluated on a neighborhood of 3x3, 5x5, and 7x7 pixels. Hence, the total number of features associated to a pixel is 56 (9 textures x3 scales x2 sequences +2 original pixel values). The classifier employed is a Support Vector Machine with Radial Basis Function as kernel. From each of the 4 brain volumes evaluated, a DIR and a FLAIR slice have been selected and manually segmented by 2 expert neurologists, providing 1st and 2nd human reference observations which agree with an average accuracy of 99.03%. SVM performances have been assessed with a 4-fold cross-validation, yielding an average classification accuracy of 98.79%.

ZmDof3, a maize endosperm-specific Dof protein gene, regulates starch accumulation and aleurone development in maize endosperm.

PubMed

Qi, Xin; Li, Shixue; Zhu, Yaxi; Zhao, Qian; Zhu, Dengyun; Yu, Jingjuan

2017-01-01

To explore the function of Dof transcription factors during kernel development in maize, we first identified Dof genes in the maize genome. We found that ZmDof3 was exclusively expressed in the endosperm of maize kernel and had the features of a Dof transcription factor. Suppression of ZmDof3 resulted in a defective kernel phenotype with reduced starch content and a partially patchy aleurone layer. The expression levels of starch synthesis-related genes and aleurone differentiation-associated genes were down-regulated in ZmDof3 knockdown kernels, indicating that ZmDof3 plays an important role in maize endosperm development. The maize endosperm, occupying a large proportion of the kernel, plays an important role in seed development and germination. Current knowledge regarding the regulation of endosperm development is limited. Dof proteins, a family of plant-specific transcription factors, play critical roles in diverse biological processes. In this study, an endosperm-specific Dof protein gene, ZmDof3, was identified in maize through genome-wide screening. Suppression of ZmDof3 resulted in a defective kernel phenotype. The endosperm of ZmDof3 knockdown kernels was loosely packed with irregular starch granules observed by electronic microscope. Through genome-wide expression profiling, we found that down-regulated genes were enriched in GO terms related to carbohydrate metabolism. Moreover, ZmDof3 could bind to the Dof core element in the promoter of starch biosynthesis genes Du1 and Su2 in vitro and in vivo. In addition, the aleurone at local position in mature ZmDof3 knockdown kernels varied from one to three layers, which consisted of smaller and irregular cells. Further analyses showed that knockdown of ZmDof3 reduced the expression of Nkd1, which is involved in aleurone cell differentiation, and that ZmDof3 could bind to the Dof core element in the Nkd1 promoter. Our study reveals that ZmDof3 functions in maize endosperm development as a positive regulator in the signaling system controlling starch accumulation and aleurone development.

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.

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.

Assessing pigmented pericarp of maize kernels as possible source of resistance to fusarium ear rot, Fusarium spp. infection and fumonisin accumulation.

PubMed

Venturini, Giovanni; Babazadeh, Laleh; Casati, Paola; Pilu, Roberto; Salomoni, Daiana; Toffolatti, Silvia L

2016-06-16

One of the purposes of maize genetic improvement is the research of genotypes resistant to fusarium ear rot (FER) and fumonisin accumulation. Flavonoids in the pericarp of the kernels are considered particularly able to reduce the fumonisin accumulation (FUM). The aim of this field study was to assess the effect of flavonoids, associated with anti-insect protection and Fusarium verticillioides inoculation, on FER symptoms and fumonisin contamination in maize kernels. Two isogenic hybrids, one having pigmentation in the pericarp (P1-rr) and the other without it (P1-wr), were compared. P1-rr showed lower values of FER symptoms and FUM contamination than P1-wr only if the anti-insect protection and the F. verticillioides inoculations were applied in combination. Fusarium spp. kernel infection was not influenced by the presence of flavonoids in the pericarp. Artificial F. verticillioides inoculation was more effective than anti-insect protection in enhancing the inhibition activity of flavonoids toward FUM contamination. The interactions between FUM contamination levels and FER ratings were better modeled in the pigmented hybrid than in the unpigmented one. The variable role that the pigment played in kernel defense against FER and FUM indicates that flavonoids alone may not be completely effective in the resistance of fumonisin contamination in maize. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Genus-Specific Primers for Study of Fusarium Communities in Field Samples

PubMed Central

Edel-Hermann, Véronique; Gautheron, Nadine; Durling, Mikael Brandström; Kolseth, Anna-Karin; Steinberg, Christian; Persson, Paula; Friberg, Hanna

2015-01-01

Fusarium is a large and diverse genus of fungi of great agricultural and economic importance, containing many plant pathogens and mycotoxin producers. To date, high-throughput sequencing of Fusarium communities has been limited by the lack of genus-specific primers targeting regions with high discriminatory power at the species level. In the present study, we evaluated two Fusarium-specific primer pairs targeting translation elongation factor 1 (TEF1). We also present the new primer pair Fa+7/Ra+6. Mock Fusarium communities reflecting phylogenetic diversity were used to evaluate the accuracy of the primers in reflecting the relative abundance of the species. TEF1 amplicons were subjected to 454 high-throughput sequencing to characterize Fusarium communities. Field samples from soil and wheat kernels were included to test the method on more-complex material. For kernel samples, a single PCR was sufficient, while for soil samples, nested PCR was necessary. The newly developed primer pairs Fa+7/Ra+6 and Fa/Ra accurately reflected Fusarium species composition in mock DNA communities. In field samples, 47 Fusarium operational taxonomic units were identified, with the highest Fusarium diversity in soil. The Fusarium community in soil was dominated by members of the Fusarium incarnatum-Fusarium equiseti species complex, contradicting findings in previous studies. The method was successfully applied to analyze Fusarium communities in soil and plant material and can facilitate further studies of Fusarium ecology. PMID:26519387

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as...

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.

Quality changes in macadamia kernel between harvest and farm-gate.

PubMed

Walton, David A; Wallace, Helen M

2011-02-01

Macadamia integrifolia, Macadamia tetraphylla and their hybrids are cultivated for their edible kernels. After harvest, nuts-in-shell are partially dried on-farm and sorted to eliminate poor-quality kernels before consignment to a processor. During these operations, kernel quality may be lost. In this study, macadamia nuts-in-shell were sampled at five points of an on-farm postharvest handling chain from dehusking to the final storage silo to assess quality loss prior to consignment. Shoulder damage, weight of pieces and unsound kernel were assessed for raw kernels, and colour, mottled colour and surface damage for roasted kernels. Shoulder damage, weight of pieces and unsound kernel for raw kernels increased significantly between the dehusker and the final silo. Roasted kernels displayed a significant increase in dark colour, mottled colour and surface damage during on-farm handling. Significant loss of macadamia kernel quality occurred on a commercial farm during sorting and storage of nuts-in-shell before nuts were consigned to a processor. Nuts-in-shell should be dried as quickly as possible and on-farm handling minimised to maintain optimum kernel quality. 2010 Society of Chemical Industry.

Whole exome sequence-based association analyses of plasma amyloid-β in African and European Americans; the Atherosclerosis Risk in Communities-Neurocognitive Study.

PubMed

Simino, Jeannette; Wang, Zhiying; Bressler, Jan; Chouraki, Vincent; Yang, Qiong; Younkin, Steven G; Seshadri, Sudha; Fornage, Myriam; Boerwinkle, Eric; Mosley, Thomas H

2017-01-01

We performed single-variant and gene-based association analyses of plasma amyloid-β (aβ) concentrations using whole exome sequence from 1,414 African and European Americans. Our goal was to identify genes that influence plasma aβ42 concentrations and aβ42:aβ40 ratios in late middle age (mean = 59 years), old age (mean = 77 years), or change over time (mean = 18 years). Plasma aβ measures were linearly regressed onto age, gender, APOE ε4 carrier status, and time elapsed between visits (fold-changes only) separately by race. Following inverse normal transformation of the residuals, seqMeta was used to conduct race-specific single-variant and gene-based association tests while adjusting for population structure. Linear regression models were fit on autosomal variants with minor allele frequencies (MAF)≥1%. T5 burden and Sequence Kernel Association (SKAT) gene-based tests assessed functional variants with MAF≤5%. Cross-race fixed effects meta-analyses were Bonferroni-corrected for the number of variants or genes tested. Seven genes were associated with aβ in late middle age or change over time; no associations were identified in old age. Single variants in KLKB1 (rs3733402; p = 4.33x10-10) and F12 (rs1801020; p = 3.89x10-8) were significantly associated with midlife aβ42 levels through cross-race meta-analysis; the KLKB1 variant replicated internally using 1,014 additional participants with exome chip. ITPRIP, PLIN2, and TSPAN18 were associated with the midlife aβ42:aβ40 ratio via the T5 test; TSPAN18 was significant via the cross-race meta-analysis, whereas ITPRIP and PLIN2 were European American-specific. NCOA1 and NT5C3B were associated with the midlife aβ42:aβ40 ratio and the fold-change in aβ42, respectively, via SKAT in African Americans. No associations replicated externally (N = 725). We discovered age-dependent genetic effects, established associations between vascular-related genes (KLKB1, F12, PLIN2) and midlife plasma aβ levels, and identified a plausible Alzheimer's Disease candidate gene (ITPRIP) influencing cell death. Plasma aβ concentrations may have dynamic biological determinants across the lifespan; plasma aβ study designs or analyses must consider age.

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.

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 generalized least-squares framework for rare-variant analysis in family data.

PubMed

Li, Dalin; Rotter, Jerome I; Guo, Xiuqing

2014-01-01

Rare variants may, in part, explain some of the hereditability missing in current genome-wide association studies. Many gene-based rare-variant analysis approaches proposed in recent years are aimed at population-based samples, although analysis strategies for family-based samples are clearly warranted since the family-based design has the potential to enhance our ability to enrich for rare causal variants. We have recently developed the generalized least squares, sequence kernel association test, or GLS-SKAT, approach for the rare-variant analyses in family samples, in which the kinship matrix that was computed from the high dimension genetic data was used to decorrelate the family structure. We then applied the SKAT-O approach for gene-/region-based inference in the decorrelated data. In this study, we applied this GLS-SKAT method to the systolic blood pressure data in the simulated family sample distributed by the Genetic Analysis Workshop 18. We compared the GLS-SKAT approach to the rare-variant analysis approach implemented in family-based association test-v1 and demonstrated that the GLS-SKAT approach provides superior power and good control of type I error rate.

Impact of gluten-friendly™ technology on wheat kernel endosperm and gluten protein structure in seeds by light and electron microscopy.

PubMed

Landriscina, L; D'Agnello, P; Bevilacqua, A; Corbo, M R; Sinigaglia, M; Lamacchia, C

2017-04-15

The main aim of this paper was to assess the impact of Gluten-Friendly™ (GF) technology (Italian priority patent n° 102015000084813 filed on 17th December 2015) on wheat kernel endosperm morphology and gluten protein structure, using SEM, light and immunofluorescent microscopy. Microscopy was combined with immunodetection with specific antibodies for gliadins, γ-gliadins, LMW subunits and antigenic epitopes to gain a better understanding of the technology at a molecular level. The results showed significant changes to gluten proteins after GF treatment; cross-reactivity towards the antibodies recognizing almost the entire range of gluten proteins as well as the antigenic epitopes through the sequences QQSF, QQSY, PEQPFPQGC and QQPFP was significantly reduced. The present study confirms the results from our previous work and shows, for the first time, the mechanism by which a chemical-physical treatment abolishes the antigenic capacity of gluten. Copyright © 2016 Elsevier Ltd. All rights reserved.

Protein Kinase Classification with 2866 Hidden Markov Models and One Support Vector Machine

NASA Technical Reports Server (NTRS)

Weber, Ryan; New, Michael H.; Fonda, Mark (Technical Monitor)

2002-01-01

The main application considered in this paper is predicting true kinases from randomly permuted kinases that share the same length and amino acid distributions as the true kinases. Numerous methods already exist for this classification task, such as HMMs, motif-matchers, and sequence comparison algorithms. We build on some of these efforts by creating a vector from the output of thousands of structurally based HMMs, created offline with Pfam-A seed alignments using SAM-T99, which then must be combined into an overall classification for the protein. Then we use a Support Vector Machine for classifying this large ensemble Pfam-Vector, with a polynomial and chisquared kernel. In particular, the chi-squared kernel SVM performs better than the HMMs and better than the BLAST pairwise comparisons, when predicting true from false kinases in some respects, but no one algorithm is best for all purposes or in all instances so we consider the particular strengths and weaknesses of each.

Increasing accuracy of dispersal kernels in grid-based population models

USGS Publications Warehouse

Slone, D.H.

2011-01-01

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.

Anthraquinones isolated from the browned Chinese chestnut kernels (Castanea mollissima blume)

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Qi, J. H.; Qin, L.; Wang, F.; Pang, M. X.

2016-08-01

Anthraquinones (AQS) represent a group of secondary metallic products in plants. AQS are often naturally occurring in plants and microorganisms. In a previous study, we found that AQS were produced by enzymatic browning reaction in Chinese chestnut kernels. To find out whether non-enzymatic browning reaction in the kernels could produce AQS too, AQS were extracted from three groups of chestnut kernels: fresh kernels, non-enzymatic browned kernels, and browned kernels, and the contents of AQS were determined. High performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) methods were used to identify two compounds of AQS, rehein(1) and emodin(2). AQS were barely exists in the fresh kernels, while both browned kernel groups sample contained a high amount of AQS. Thus, we comfirmed that AQS could be produced during both enzymatic and non-enzymatic browning process. Rhein and emodin were the main components of AQS in the browned kernels.

Visual Evoked Cortical Potential (VECP) Elicited by Sinusoidal Gratings Controlled by Pseudo-Random Stimulation

PubMed Central

Araújo, Carolina S.; Souza, Givago S.; Gomes, Bruno D.; Silveira, Luiz Carlos L.

2013-01-01

The contributions of contrast detection mechanisms to the visual cortical evoked potential (VECP) have been investigated studying the contrast-response and spatial frequency-response functions. Previously, the use of m-sequences for stimulus control has been almost restricted to multifocal electrophysiology stimulation and, in some aspects, it substantially differs from conventional VECPs. Single stimulation with spatial contrast temporally controlled by m-sequences has not been extensively tested or compared to multifocal techniques. Our purpose was to evaluate the influence of spatial frequency and contrast of sinusoidal gratings on the VECP elicited by pseudo-random stimulation. Nine normal subjects were stimulated by achromatic sinusoidal gratings driven by pseudo random binary m-sequence at seven spatial frequencies (0.4–10 cpd) and three stimulus sizes (4°, 8°, and 16° of visual angle). At 8° subtence, six contrast levels were used (3.12–99%). The first order kernel (K1) did not provide a consistent measurable signal across spatial frequencies and contrasts that were tested–signal was very small or absent–while the second order kernel first (K2.1) and second (K2.2) slices exhibited reliable responses for the stimulus range. The main differences between results obtained with the K2.1 and K2.2 were in the contrast gain as measured in the amplitude versus contrast and amplitude versus spatial frequency functions. The results indicated that K2.1 was dominated by M-pathway, but for some stimulus condition some P-pathway contribution could be found, while the second slice reflected the P-pathway contribution. The present work extended previous findings of the visual pathways contribution to VECP elicited by pseudorandom stimulation for a wider range of spatial frequencies. PMID:23940546

Broken rice kernels and the kinetics of rice hydration and texture during cooking.

PubMed

Saleh, Mohammed; Meullenet, Jean-Francois

2013-05-01

During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P < 0.05) but the unbroken kernels became significantly harder. Moisture content and moisture uptake rate were positively correlated, and cooked rice hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.

Redox changes accompanying storage protein mobilization in moist chilled and warm incubated walnut kernels prior to germination.

PubMed

Shahmoradi, Zeynab; Tamaskani, Fatemeh; Sadeghipour, Hamid Reza; Abdolzadeh, Ahmad

2013-01-01

Alterations in the redox state of storage proteins and the associated proteolytic processes were investigated in moist-chilled and warm-incubated walnut (Juglans regia L.) kernels prior to germination. The kernel total protein labeling with a thiol-specific fluorochrome i.e. monobromobimane (mBBr) revealed more reduction of 29-32 kDa putative glutelins, while in the soluble proteins, both putative glutelins and 41, 55 and 58 kDa globulins contained reduced disulfide bonds during mobilization. Thus, the in vivo more reduced disulfide bonds of storage proteins corresponds to greater solubility. After the in vitro reduction of walnut kernel proteins pre-treated by N-ethyl maleimide (NEM) with dithioerythrethiol (DTT) and bacterial thioredoxin, the 58 kDa putative globulin and a 6 kDa putative albumin were identified as disulfide proteins. Thioredoxin stimulated the reduction of the H(2)O(2)-oxidized 6 kDa polypeptide, but not the 58 kDa polypeptide by DTT. The solubility of 6 kDa putative albumin, 58 and 19-24 kDa putative globulins and glutelins, respectively, were increased by DTT. The in vitro specific mobilization of the 58 kDa polypeptide that occurred at pH 5.0 by the kernel endogenous protease was sensitive to the serine-protease inhibitor phenylmethylsulfonyl fluoride (PMSF) and stimulated by DTT. The specific degradation of the 58 kDa polypeptide might be achieved through thioredoxin-mediated activation of a serine protease and/or reductive unfolding of its 58 kDa polypeptide substrate. As redox changes in storage proteins occurred equally in both moist chilled and warm incubated walnut kernels, the regulatory functions of thioredoxins in promoting seed germination may be due to other germination related processes. Copyright © 2012 Elsevier GmbH. All rights reserved.

Static and dynamic behaviour of nonlocal elastic bar using integral strain-based and peridynamic models

NASA Astrophysics Data System (ADS)

Challamel, Noël

2018-04-01

The static and dynamic behaviour of a nonlocal bar of finite length is studied in this paper. The nonlocal integral models considered in this paper are strain-based and relative displacement-based nonlocal models; the latter one is also labelled as a peridynamic model. For infinite media, and for sufficiently smooth displacement fields, both integral nonlocal models can be equivalent, assuming some kernel correspondence rules. For infinite media (or finite media with extended reflection rules), it is also shown that Eringen's differential model can be reformulated into a consistent strain-based integral nonlocal model with exponential kernel, or into a relative displacement-based integral nonlocal model with a modified exponential kernel. A finite bar in uniform tension is considered as a paradigmatic static case. The strain-based nonlocal behaviour of this bar in tension is analyzed for different kernels available in the literature. It is shown that the kernel has to fulfil some normalization and end compatibility conditions in order to preserve the uniform strain field associated with this homogeneous stress state. Such a kernel can be built by combining a local and a nonlocal strain measure with compatible boundary conditions, or by extending the domain outside its finite size while preserving some kinematic compatibility conditions. The same results are shown for the nonlocal peridynamic bar where a homogeneous strain field is also analytically obtained in the elastic bar for consistent compatible kinematic boundary conditions at the vicinity of the end conditions. The results are extended to the vibration of a fixed-fixed finite bar where the natural frequencies are calculated for both the strain-based and the peridynamic models.

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.

Study on Energy Productivity Ratio (EPR) at palm kernel oil processing factory: case study on PT-X at Sumatera Utara Plantation

NASA Astrophysics Data System (ADS)

Haryanto, B.; Bukit, R. Br; Situmeang, E. M.; Christina, E. P.; Pandiangan, F.

2018-02-01

The purpose of this study was to determine the performance, productivity and feasibility of the operation of palm kernel processing plant based on Energy Productivity Ratio (EPR). EPR is expressed as the ratio of output to input energy and by-product. Palm Kernel plan is process in palm kernel to become palm kernel oil. The procedure started from collecting data needed as energy input such as: palm kernel prices, energy demand and depreciation of the factory. The energy output and its by-product comprise the whole production price such as: palm kernel oil price and the remaining products such as shells and pulp price. Calculation the equality of energy of palm kernel oil is to analyze the value of Energy Productivity Ratio (EPR) bases on processing capacity per year. The investigation has been done in Kernel Oil Processing Plant PT-X at Sumatera Utara plantation. The value of EPR was 1.54 (EPR > 1), which indicated that the processing of palm kernel into palm kernel oil is feasible to be operated based on the energy productivity.

In vitro lipolytic, antioxidant and anti-inflammatory activities of roasted pistachio kernel and skin constituents.

PubMed

Grace, Mary H; Esposito, Debora; Timmers, Michael A; Xiong, Jia; Yousef, Gad; Komarnytsky, Slavko; Lila, Mary Ann

2016-10-12

A comprehensive phytochemical analysis was conducted on pistachios to identify the differential contributions of skin and kernel phytochemicals to in vitro bioactivity. Qualitative and quantitative analyses of skin and kernel non-polar extracts (SNP and KNP, respectively) indicated that the major components are fatty acids (696.36 and 879.70 mg g -1 ), phytosterols (16.08 and 4.28 mg g -1 ), and γ-tocopherol (304.17 and 397.10 μg g -1 ). Analysis of the skin and kernel polar extracts (SP and KP, respectively) showed that skin accumulated higher levels of phenolic compounds, especially flavan-3-ols, compared to the kernel. An (epi)catechin hexoside was the major component in SP and KP (9.8 mg g -1 and 3.3 mg g -1 , respectively). Flavan-3-ols with different degrees of polymerization were detected in SP, but only the monomers were identified in the KP. Quercetin glycosides were the major flavonols present in both SP and KP. Bioassays with 3T3L1 mouse adipocytes demonstrated that all extracts decreased lipid accumulation, with SNP demonstrating the highest activity (17% inhibition). Bioassay guided fractionation of SNP indicated that the lipolytic activity was highest in the fraction consisting of linoleic acid (20%), linolenic acid (10%), and β-sitosterol (50%). Radical scavenging assays indicated that all pistachio extracts significantly inhibited ROS, while SP was the most inhibiting to NO production in LPS-stimulated RAW 264.7 macrophages. Gene expression profiles associated with inflammation (IL6, iNOS, and COX2) were characterized in the LPS-stimulated RAW264.7 macrophages after treatment with pistachio extracts. SP and KP were the most potent to inhibit the expression of COX2. The SNP had the strongest effect in decreasing non-mitochondrial oxidative burst associated with inflammatory response in macrophages.

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

PubMed Central

2013-01-01

Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755

fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

DTIC Science & Technology

2007-04-04

machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel

Baker-Akhiezer Spinor Kernel and Tau-functions on Moduli Spaces of Meromorphic Differentials

NASA Astrophysics Data System (ADS)

Kalla, C.; Korotkin, D.

2014-11-01

In this paper we study the Baker-Akhiezer spinor kernel on moduli spaces of meromorphic differentials on Riemann surfaces. We introduce the Baker-Akhiezer tau-function which is related to both the Bergman tau-function (which was studied before in the context of Hurwitz spaces and spaces of holomorphic Abelian and quadratic differentials) and the KP tau-function on such spaces. In particular, we derive variational formulas of Rauch-Ahlfors type on moduli spaces of meromorphic differentials with prescribed singularities: we use the system of homological coordinates, consisting of absolute and relative periods of the meromorphic differential, and show how to vary the fundamental objects associated to a Riemann surface (the matrix of b-periods, normalized Abelian differentials, the Bergman bidifferential, the Szegö kernel and the Baker-Akhiezer spinor kernel) with respect to these coordinates. The variational formulas encode dependence both on the moduli of the Riemann surface and on the choice of meromorphic differential (variation of the meromorphic differential while keeping the Riemann surface fixed corresponds to flows of KP type). Analyzing the global properties of the Bergman and Baker-Akhiezer tau-functions, we establish relationships between various divisor classes on the moduli spaces.

The Swift-Hohenberg equation with a nonlocal nonlinearity

NASA Astrophysics Data System (ADS)

Morgan, David; Dawes, Jonathan H. P.

2014-03-01

It is well known that aspects of the formation of localised states in a one-dimensional Swift-Hohenberg equation can be described by Ginzburg-Landau-type envelope equations. This paper extends these multiple scales analyses to cases where an additional nonlinear integral term, in the form of a convolution, is present. The presence of a kernel function introduces a new lengthscale into the problem, and this results in additional complexity in both the derivation of envelope equations and in the bifurcation structure. When the kernel is short-range, weakly nonlinear analysis results in envelope equations of standard type but whose coefficients are modified in complicated ways by the nonlinear nonlocal term. Nevertheless, these computations can be formulated quite generally in terms of properties of the Fourier transform of the kernel function. When the lengthscale associated with the kernel is longer, our method leads naturally to the derivation of two different, novel, envelope equations that describe aspects of the dynamics in these new regimes. The first of these contains additional bifurcations, and unexpected loops in the bifurcation diagram. The second of these captures the stretched-out nature of the homoclinic snaking curves that arises due to the nonlocal term.

7 CFR 981.9 - Kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including...

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

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.

Multiple kernels learning-based biological entity relationship extraction method.

PubMed

Dongliang, Xu; Jingchang, Pan; Bailing, Wang

2017-09-20

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

7 CFR 51.2295 - Half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off. ...

7 CFR 810.206 - Grades and grade requirements for barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

... weight per bushel (pounds) Sound barley (percent) Maximum Limits of— Damaged kernels 1 (percent) Heat damaged kernels (percent) Foreign material (percent) Broken kernels (percent) Thin barley (percent) U.S... or otherwise of distinctly low quality. 1 Includes heat-damaged kernels. Injured-by-frost kernels and...

Independent genetic control of maize (Zea mays L.) kernel weight determination and its phenotypic plasticity.

PubMed

Alvarez Prado, Santiago; Sadras, Víctor O; Borrás, Lucas

2014-08-01

Maize kernel weight (KW) is associated with the duration of the grain-filling period (GFD) and the rate of kernel biomass accumulation (KGR). It is also related to the dynamics of water and hence is physiologically linked to the maximum kernel water content (MWC), kernel desiccation rate (KDR), and moisture concentration at physiological maturity (MCPM). This work proposed that principles of phenotypic plasticity can help to consolidated the understanding of the environmental modulation and genetic control of these traits. For that purpose, a maize population of 245 recombinant inbred lines (RILs) was grown under different environmental conditions. Trait plasticity was calculated as the ratio of the variance of each RIL to the overall phenotypic variance of the population of RILs. This work found a hierarchy of plasticities: KDR ≈ GFD > MCPM > KGR > KW > MWC. There was no phenotypic and genetic correlation between traits per se and trait plasticities. MWC, the trait with the lowest plasticity, was the exception because common quantitative trait loci were found for the trait and its plasticity. Independent genetic control of a trait per se and genetic control of its plasticity is a condition for the independent evolution of traits and their plasticities. This allows breeders potentially to select for high or low plasticity in combination with high or low values of economically relevant traits. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A Comparison Study of Multivariate Fixed Models and Gene Association with Multiple Traits (GAMuT) for Next-Generation Sequencing

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Wang, Yifan; Weeks, Daniel E.; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Amos, Christopher I.; Mills, James L.; Boehnke, Michael; Xiong, Momiao; Fan, Ruzong

2016-01-01

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for: (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models which perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods. PMID:27917525

Diversity, distribution of Puroindoline genes and their effect on kernel hardness in a diverse panel of Chinese wheat germplasm.

PubMed

Ma, Xiaoling; Sajjad, Muhammad; Wang, Jing; Yang, Wenlong; Sun, Jiazhu; Li, Xin; Zhang, Aimin; Liu, Dongcheng

2017-09-20

Kernel hardness, which has great influence on the end-use properties of common wheat, is mainly controlled by Puroindoline genes, Pina and Pinb. Using EcoTILLING platform, we herein investigated the allelic variations of Pina and Pinb genes and their association with the Single Kernel Characterization System (SKCS) hardness index in a diverse panel of wheat germplasm. The kernel hardness varied from 1.4 to 102.7, displaying a wide range of hardness index. In total, six Pina and nine Pinb alleles resulting in 15 genotypes were detected in 1787 accessions. The most common alleles are the wild type Pina-D1a (90.4%) and Pina-D1b (7.4%) for Pina, and Pinb-D1b (43.6%), Pinb-D1a (41.1%) and Pinb-D1p (12.8%) for Pinb. All the genotypes have hard type kernel hardness of SKCS index (>60.0), except the wild types of Pina and Pinb combination (Pina-D1a/Pinb-D1a). The most frequent genotypes in Chinese and foreign cultivars was Pina-D1a/Pinb-D1b (46.3 and 39.0%, respectively) and in Chinese landraces was Pina-D1a/Pinb-D1a (54.2%). The frequencies of hard type accessions are increasing from 35.5% in the region IV, to 40.6 and 61.4% in the regions III and II, and then to 77.0% in the region I, while those of soft type are accordingly decreasing along with the increase of latitude. Varieties released after 2000 in Beijing, Hebei, Shandong and Henan have higher average kernel hardness index than that released before 2000. The kernel hardness in a diverse panel of Chinese wheat germplasm revealed an increasing of kernel hardness generally along with the latitude across China. The wild type Pina-D1a and Pinb-D1a, and one Pinb mutant (Pinb-D1b) are the most common alleles of six Pina and nine Pinb alleles, and a new double null genotype (Pina-D1x/Pinb-D1ah) possessed relatively high SKCS hardness index. More hard type varieties were released in recent years with different prevalence of Pin-D1 combinations in different regions. This work would benefit the understanding of the selection and molecular processes of kernel hardness across China and different breeding stages, and provide useful information for the improvement of wheat quality in China.

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.2125 - Split or broken kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will not...

7 CFR 51.2296 - Three-fourths half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

The Classification of Diabetes Mellitus Using Kernel k-means

NASA Astrophysics Data System (ADS)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

UNICOS Kernel Internals Application Development

NASA Technical Reports Server (NTRS)

Caredo, Nicholas; Craw, James M. (Technical Monitor)

1995-01-01

Having an understanding of UNICOS Kernel Internals is valuable information. However, having the knowledge is only half the value. The second half comes with knowing how to use this information and apply it to the development of tools. The kernel contains vast amounts of useful information that can be utilized. This paper discusses the intricacies of developing utilities that utilize kernel information. In addition, algorithms, logic, and code will be discussed for accessing kernel information. Code segments will be provided that demonstrate how to locate and read kernel structures. Types of applications that can utilize kernel information will also be discussed.

Detection of maize kernels breakage rate based on K-means clustering

NASA Astrophysics Data System (ADS)

Yang, Liang; Wang, Zhuo; Gao, Lei; Bai, Xiaoping

2017-04-01

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

Aflatoxin and nutrient contents of peanut collected from local market and their processed foods

NASA Astrophysics Data System (ADS)

Ginting, E.; Rahmianna, A. A.; Yusnawan, E.

2018-01-01

Peanut is succeptable to aflatoxin contamination and the sources of peanut as well as processing methods considerably affect aflatoxin content of the products. Therefore, the study on aflatoxin and nutrient contents of peanut collected from local market and their processed foods were performed. Good kernels of peanut were prepared into fried peanut, pressed-fried peanut, peanut sauce, peanut press cake, fermented peanut press cake (tempe) and fried tempe, while blended kernels (good and poor kernels) were processed into peanut sauce and tempe and poor kernels were only processed into tempe. The results showed that good and blended kernels which had high number of sound/intact kernels (82,46% and 62,09%), contained 9.8-9.9 ppb of aflatoxin B1, while slightly higher level was seen in poor kernels (12.1 ppb). However, the moisture, ash, protein, and fat contents of the kernels were similar as well as the products. Peanut tempe and fried tempe showed the highest increase in protein content, while decreased fat contents were seen in all products. The increase in aflatoxin B1 of peanut tempe prepared from poor kernels > blended kernels > good kernels. However, it averagely decreased by 61.2% after deep-fried. Excluding peanut tempe and fried tempe, aflatoxin B1 levels in all products derived from good kernels were below the permitted level (15 ppb). This suggests that sorting peanut kernels as ingredients and followed by heat processing would decrease the aflatoxin content in the products.

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

Identification of Kernel Proteins Associated with the Resistance to Fusarium Head Blight in Winter Wheat (Triticum aestivum L.)

PubMed Central

Góral, Tomasz; Kwiatek, Michał; Majka, Maciej; Kosmala, Arkadiusz

2014-01-01

Numerous potential components involved in the resistance to Fusarium head blight (FHB) in cereals have been indicated, however, our knowledge regarding this process is still limited and further work is required. Two winter wheat (Triticum aestivum L.) lines differing in their levels of resistance to FHB were analyzed to identify the most crucial proteins associated with resistance in this species. The presented work involved analysis of protein abundance in the kernel bulks of more resistant and more susceptible wheat lines using two-dimensional gel electrophoresis and mass spectrometry identification of proteins, which were differentially accumulated between the analyzed lines, after inoculation with F. culmorum under field conditions. All the obtained two-dimensional patterns were demonstrated to be well-resolved protein maps of kernel proteomes. Although, 11 proteins were shown to have significantly different abundance between these two groups of plants, only two are likely to be crucial and have a potential role in resistance to FHB. Monomeric alpha-amylase and dimeric alpha-amylase inhibitors, both highly accumulated in the more resistant line, after inoculation and in the control conditions. Fusarium pathogens can use hydrolytic enzymes, including amylases to colonize kernels and acquire nitrogen and carbon from the endosperm and we suggest that the inhibition of pathogen amylase activity could be one of the most crucial mechanisms to prevent infection progress in the analyzed wheat line with a higher resistance. Alpha-amylase activity assays confirmed this suggestion as it revealed the highest level of enzyme activity, after F. culmorum infection, in the line more susceptible to FHB. PMID:25340555

Identification of kernel proteins associated with the resistance to fusarium head blight in winter wheat (Triticum aestivum L.).

PubMed

Perlikowski, Dawid; Wiśniewska, Halina; Góral, Tomasz; Kwiatek, Michał; Majka, Maciej; Kosmala, Arkadiusz

2014-01-01

Numerous potential components involved in the resistance to Fusarium head blight (FHB) in cereals have been indicated, however, our knowledge regarding this process is still limited and further work is required. Two winter wheat (Triticum aestivum L.) lines differing in their levels of resistance to FHB were analyzed to identify the most crucial proteins associated with resistance in this species. The presented work involved analysis of protein abundance in the kernel bulks of more resistant and more susceptible wheat lines using two-dimensional gel electrophoresis and mass spectrometry identification of proteins, which were differentially accumulated between the analyzed lines, after inoculation with F. culmorum under field conditions. All the obtained two-dimensional patterns were demonstrated to be well-resolved protein maps of kernel proteomes. Although, 11 proteins were shown to have significantly different abundance between these two groups of plants, only two are likely to be crucial and have a potential role in resistance to FHB. Monomeric alpha-amylase and dimeric alpha-amylase inhibitors, both highly accumulated in the more resistant line, after inoculation and in the control conditions. Fusarium pathogens can use hydrolytic enzymes, including amylases to colonize kernels and acquire nitrogen and carbon from the endosperm and we suggest that the inhibition of pathogen amylase activity could be one of the most crucial mechanisms to prevent infection progress in the analyzed wheat line with a higher resistance. Alpha-amylase activity assays confirmed this suggestion as it revealed the highest level of enzyme activity, after F. culmorum infection, in the line more susceptible to FHB.

POPISK: T-cell reactivity prediction using support vector machines and string kernels

PubMed Central

2011-01-01

Background Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. Results This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. Conclusions A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK. PMID:22085524

POPISK: T-cell reactivity prediction using support vector machines and string kernels.

PubMed

Tung, Chun-Wei; Ziehm, Matthias; Kämper, Andreas; Kohlbacher, Oliver; Ho, Shinn-Ying

2011-11-15

Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK.

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...

7 CFR 51.1441 - Half-kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2011 CFR

2011-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2012 CFR

2012-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

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.

A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

PubMed

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

PubMed

Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

Identification and Characterization of microRNAs during Maize Grain Filling

PubMed Central

Lv, Panqing; Peng, Qian; Ding, Dong; Li, Weihua; Tang, Jihua

2015-01-01

The grain filling rate is closely associated with final grain yield of maize during the period of maize grain filling. To identify the key microRNAs (miRNAs) and miRNA-dependent gene regulation networks of grain filling in maize, a deep-sequencing technique was used to research the dynamic expression patternsof miRNAs at four distinct developmental grain filling stages in Zhengdan 958, which is an elite hybrid and cultivated widely in China. The sequencing result showed that the expression amount of almost all miRNAs was changing with the development of the grain filling and formed in seven groups. After normalization, 77 conserved miRNAs and 74 novel miRNAs were co-detected in these four samples. Eighty-one out of 162 targets of the conserved miRNAs belonged to transcriptional regulation (81, 50%), followed by oxidoreductase activity (18, 11%), signal transduction (16, 10%) and development (15, 9%). The result showed that miRNA 156, 393, 396 and 397, with their respective targets, might play key roles in the grain filling rate by regulating maize growth, development and environment stress response. The result also offered novel insights into the dynamic change of miRNAs during the developing process of maize kernels and assistedin the understanding of how miRNAs are functioning about the grain filling rate. PMID:25951054

Identification and Characterization of microRNAs during Maize Grain Filling.

PubMed

Jin, Xining; Fu, Zhiyuan; Lv, Panqing; Peng, Qian; Ding, Dong; Li, Weihua; Tang, Jihua

2015-01-01

The grain filling rate is closely associated with final grain yield of maize during the period of maize grain filling. To identify the key microRNAs (miRNAs) and miRNA-dependent gene regulation networks of grain filling in maize, a deep-sequencing technique was used to research the dynamic expression patterns of miRNAs at four distinct developmental grain filling stages in Zhengdan 958, which is an elite hybrid and cultivated widely in China. The sequencing result showed that the expression amount of almost all miRNAs was changing with the development of the grain filling and formed in seven groups. After normalization, 77 conserved miRNAs and 74 novel miRNAs were co-detected in these four samples. Eighty-one out of 162 targets of the conserved miRNAs belonged to transcriptional regulation (81, 50%), followed by oxidoreductase activity (18, 11%), signal transduction (16, 10%) and development (15, 9%). The result showed that miRNA 156, 393, 396 and 397, with their respective targets, might play key roles in the grain filling rate by regulating maize growth, development and environment stress response. The result also offered novel insights into the dynamic change of miRNAs during the developing process of maize kernels and assisted in the understanding of how miRNAs are functioning about the grain filling rate.

SOS2 and ACP1 Loci Identified through Large-Scale Exome Chip Analysis Regulate Kidney Development and Function.

PubMed

Li, Man; Li, Yong; Weeks, Olivia; Mijatovic, Vladan; Teumer, Alexander; Huffman, Jennifer E; Tromp, Gerard; Fuchsberger, Christian; Gorski, Mathias; Lyytikäinen, Leo-Pekka; Nutile, Teresa; Sedaghat, Sanaz; Sorice, Rossella; Tin, Adrienne; Yang, Qiong; Ahluwalia, Tarunveer S; Arking, Dan E; Bihlmeyer, Nathan A; Böger, Carsten A; Carroll, Robert J; Chasman, Daniel I; Cornelis, Marilyn C; Dehghan, Abbas; Faul, Jessica D; Feitosa, Mary F; Gambaro, Giovanni; Gasparini, Paolo; Giulianini, Franco; Heid, Iris; Huang, Jinyan; Imboden, Medea; Jackson, Anne U; Jeff, Janina; Jhun, Min A; Katz, Ronit; Kifley, Annette; Kilpeläinen, Tuomas O; Kumar, Ashish; Laakso, Markku; Li-Gao, Ruifang; Lohman, Kurt; Lu, Yingchang; Mägi, Reedik; Malerba, Giovanni; Mihailov, Evelin; Mohlke, Karen L; Mook-Kanamori, Dennis O; Robino, Antonietta; Ruderfer, Douglas; Salvi, Erika; Schick, Ursula M; Schulz, Christina-Alexandra; Smith, Albert V; Smith, Jennifer A; Traglia, Michela; Yerges-Armstrong, Laura M; Zhao, Wei; Goodarzi, Mark O; Kraja, Aldi T; Liu, Chunyu; Wessel, Jennifer; Boerwinkle, Eric; Borecki, Ingrid B; Bork-Jensen, Jette; Bottinger, Erwin P; Braga, Daniele; Brandslund, Ivan; Brody, Jennifer A; Campbell, Archie; Carey, David J; Christensen, Cramer; Coresh, Josef; Crook, Errol; Curhan, Gary C; Cusi, Daniele; de Boer, Ian H; de Vries, Aiko P J; Denny, Joshua C; Devuyst, Olivier; Dreisbach, Albert W; Endlich, Karlhans; Esko, Tõnu; Franco, Oscar H; Fulop, Tibor; Gerhard, Glenn S; Glümer, Charlotte; Gottesman, Omri; Grarup, Niels; Gudnason, Vilmundur; Hansen, Torben; Harris, Tamara B; Hayward, Caroline; Hocking, Lynne; Hofman, Albert; Hu, Frank B; Husemoen, Lise Lotte N; Jackson, Rebecca D; Jørgensen, Torben; Jørgensen, Marit E; Kähönen, Mika; Kardia, Sharon L R; König, Wolfgang; Kooperberg, Charles; Kriebel, Jennifer; Launer, Lenore J; Lauritzen, Torsten; Lehtimäki, Terho; Levy, Daniel; Linksted, Pamela; Linneberg, Allan; Liu, Yongmei; Loos, Ruth J F; Lupo, Antonio; Meisinger, Christine; Melander, Olle; Metspalu, Andres; Mitchell, Paul; Nauck, Matthias; Nürnberg, Peter; Orho-Melander, Marju; Parsa, Afshin; Pedersen, Oluf; Peters, Annette; Peters, Ulrike; Polasek, Ozren; Porteous, David; Probst-Hensch, Nicole M; Psaty, Bruce M; Qi, Lu; Raitakari, Olli T; Reiner, Alex P; Rettig, Rainer; Ridker, Paul M; Rivadeneira, Fernando; Rossouw, Jacques E; Schmidt, Frank; Siscovick, David; Soranzo, Nicole; Strauch, Konstantin; Toniolo, Daniela; Turner, Stephen T; Uitterlinden, André G; Ulivi, Sheila; Velayutham, Dinesh; Völker, Uwe; Völzke, Henry; Waldenberger, Melanie; Wang, Jie Jin; Weir, David R; Witte, Daniel; Kuivaniemi, Helena; Fox, Caroline S; Franceschini, Nora; Goessling, Wolfram; Köttgen, Anna; Chu, Audrey Y

2017-03-01

Genome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium ( n Stage1 : 111,666; n Stage2 : 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea ( PPM1J , EDEM3, ACP1, SPEG, EYA4, CYP1A1 , and ATXN2L ; P Stage1 <3.7×10 -7 ), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 ( P =5.4×10 -8 by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2 -knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation. Copyright © 2017 by the American Society of Nephrology.

Evaluating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Wilton, Donald R.; Champagne, Nathan J.

2008-01-01

Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.

Genetic mapping of new seed-expressed polyphenol oxidase genes in wheat (Triticum aestivum L.).

PubMed

Beecher, Brian S; Carter, Arron H; See, Deven R

2012-05-01

Polyphenol oxidase (PPO) enzymatic activity is a major cause in time-dependent discoloration in wheat dough products. The PPO-A1 and PPO-D1 genes have been shown to contribute to wheat kernel PPO activity. Recently a novel PPO gene family consisting of the PPO-A2, PPO-B2, and PPO-D2 genes has been identified and shown to be expressed in wheat kernels. In this study, the sequences of these five kernel PPO genes were determined for the spring wheat cultivars Louise and Penawawa. The two cultivars were found to be polymorphic at each of the PPO loci. Three novel alleles were isolated from Louise. The Louise X Penawawa mapping population was used to genetically map all five PPO genes. All map to the long arm of homeologous group 2 chromosomes. PPO-A2 was found to be located 8.9 cM proximal to PPO-A1 on the long arm of chromosome 2A. Similarly, PPO-D1 and PPO-D2 were separated by 10.7 cM on the long arm of chromosome 2D. PPO-B2 mapped to the long arm of chromosome 2B and was the site of a novel QTL for polyphenol oxidase activity. Five other PPO QTL were identified in this study. One QTL corresponds to the previously described PPO-D1 locus, one QTL corresponds to the PPO-D2 locus, whereas the remaining three are located on chromosome 2B.

Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone.

PubMed

Takagi, Satoshi; Nagase, Hiroyuki; Hayashi, Tatsuya; Kita, Tamotsu; Hayashi, Katsumi; Sanada, Shigeru; Koike, Masayuki

2014-01-01

The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings. Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images. Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images. The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients. This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2014 CFR

2014-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2013 CFR

2013-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2012 CFR

2012-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

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

Aflatoxin variability in pistachios.

PubMed Central

Mahoney, N E; Rodriguez, S B

1996-01-01

Pistachio fruit components, including hulls (mesocarps and epicarps), seed coats (testas), and kernels (seeds), all contribute to variable aflatoxin content in pistachios. Fresh pistachio kernels were individually inoculated with Aspergillus flavus and incubated 7 or 10 days. Hulled, shelled kernels were either left intact or wounded prior to inoculation. Wounded kernels, with or without the seed coat, were readily colonized by A. flavus and after 10 days of incubation contained 37 times more aflatoxin than similarly treated unwounded kernels. The aflatoxin levels in the individual wounded pistachios were highly variable. Neither fungal colonization nor aflatoxin was detected in intact kernels without seed coats. Intact kernels with seed coats had limited fungal colonization and low aflatoxin concentrations compared with their wounded counterparts. Despite substantial fungal colonization of wounded hulls, aflatoxin was not detected in hulls. Aflatoxin levels were significantly lower in wounded kernels with hulls than in kernels of hulled pistachios. Both the seed coat and a water-soluble extract of hulls suppressed aflatoxin production by A. flavus. PMID:8919781

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.

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

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.

Evolutionary distances in the twilight zone--a rational kernel approach.

PubMed

Schwarz, Roland F; Fletcher, William; Förster, Frank; Merget, Benjamin; Wolf, Matthias; Schultz, Jörg; Markowetz, Florian

2010-12-31

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

PubMed

Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

2015-05-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

Estimating average growth trajectories in shape-space using kernel smoothing.

PubMed

Hutton, Tim J; Buxton, Bernard F; Hammond, Peter; Potts, Henry W W

2003-06-01

In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.

Kernel methods for large-scale genomic data analysis

PubMed Central

Xing, Eric P.; Schaid, Daniel J.

2015-01-01

Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today’s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. PMID:25053743

Comparing Alternative Kernels for the Kernel Method of Test Equating: Gaussian, Logistic, and Uniform Kernels. Research Report. ETS RR-08-12

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; von Davier, Alina A.

2008-01-01

The kernel equating method (von Davier, Holland, & Thayer, 2004) is based on a flexible family of equipercentile-like equating functions that use a Gaussian kernel to continuize the discrete score distributions. While the classical equipercentile, or percentile-rank, equating method carries out the continuization step by linear interpolation,…

7 CFR 810.204 - Grades and grade requirements for Six-rowed Malting barley and Six-rowed Blue Malting barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

...— Damaged kernels 1 (percent) Foreign material (percent) Other grains (percent) Skinned and broken kernels....0 10.0 15.0 1 Injured-by-frost kernels and injured-by-mold kernels are not considered damaged kernels or considered against sound barley. Notes: Malting barley shall not be infested in accordance with...

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

PubMed

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei

2016-02-01

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. © 2016 WILEY PERIODICALS, INC.

Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

PubMed Central

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E.; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y.; Chen, Wei

2015-01-01

Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979

7 CFR 51.1413 - Damage.

Code of Federal Regulations, 2013 CFR

2013-01-01

... well cured; (e) Poorly developed kernels; (f) Kernels which are dark amber in color; (g) Kernel spots when more than one dark spot is present on either half of the kernel, or when any such spot is more...

7 CFR 51.1413 - Damage.

Code of Federal Regulations, 2014 CFR

2014-01-01

... well cured; (e) Poorly developed kernels; (f) Kernels which are dark amber in color; (g) Kernel spots when more than one dark spot is present on either half of the kernel, or when any such spot is more...

7 CFR 810.205 - Grades and grade requirements for Two-rowed Malting barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

... (percent) Maximum limits of— Wild oats (percent) Foreign material (percent) Skinned and broken kernels... Injured-by-frost kernels and injured-by-mold kernels are not considered damaged kernels or considered...

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

PubMed

Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo

2014-06-01

In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both local and global learning strategies, able to exploit the overall topology of the network. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

PubMed

Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-12

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

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.

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.

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

PubMed Central

Chung, Moo K.; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K.

2014-01-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435

Absorbed dose kernel and self-shielding calculations for a novel radiopaque glass microsphere for transarterial radioembolization.

PubMed

Church, Cody; Mawko, George; Archambault, John Paul; Lewandowski, Robert; Liu, David; Kehoe, Sharon; Boyd, Daniel; Abraham, Robert; Syme, Alasdair

2018-02-01

Radiopaque microspheres may provide intraprocedural and postprocedural feedback during transarterial radioembolization (TARE). Furthermore, the potential to use higher resolution x-ray imaging techniques as opposed to nuclear medicine imaging suggests that significant improvements in the accuracy and precision of radiation dosimetry calculations could be realized for this type of therapy. This study investigates the absorbed dose kernel for novel radiopaque microspheres including contributions of both short and long-lived contaminant radionuclides while concurrently quantifying the self-shielding of the glass network. Monte Carlo simulations using EGSnrc were performed to determine the dose kernels for all monoenergetic electron emissions and all beta spectra for radionuclides reported in a neutron activation study of the microspheres. Simulations were benchmarked against an accepted 90 Y dose point kernel. Self-shielding was quantified for the microspheres by simulating an isotropically emitting, uniformly distributed source, in glass and in water. The ratio of the absorbed doses was scored as a function of distance from a microsphere. The absorbed dose kernel for the microspheres was calculated for (a) two bead formulations following (b) two different durations of neutron activation, at (c) various time points following activation. Self-shielding varies with time postremoval from the reactor. At early time points, it is less pronounced due to the higher energies of the emissions. It is on the order of 0.4-2.8% at a radial distance of 5.43 mm with increased size from 10 to 50 μm in diameter during the time that the microspheres would be administered to a patient. At long time points, self-shielding is more pronounced and can reach values in excess of 20% near the end of the range of the emissions. Absorbed dose kernels for 90 Y, 90m Y, 85m Sr, 85 Sr, 87m Sr, 89 Sr, 70 Ga, 72 Ga, and 31 Si are presented and used to determine an overall kernel for the microspheres based on weighted activities. The shapes of the absorbed dose kernels are dominated at short times postactivation by the contributions of 70 Ga and 72 Ga. Following decay of the short-lived contaminants, the absorbed dose kernel is effectively that of 90 Y. After approximately 1000 h postactivation, the contributions of 85 Sr and 89 Sr become increasingly dominant, though the absorbed dose-rate around the beads drops by roughly four orders of magnitude. The introduction of high atomic number elements for the purpose of increasing radiopacity necessarily leads to the production of radionuclides other than 90 Y in the microspheres. Most of the radionuclides in this study are short-lived and are likely not of any significant concern for this therapeutic agent. The presence of small quantities of longer lived radionuclides will change the shape of the absorbed dose kernel around a microsphere at long time points postadministration when activity levels are significantly reduced. © 2017 American Association of Physicists in Medicine.

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.

What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization.

PubMed

Kwon, Oh-Hyun; Crnovrsanin, Tarik; Ma, Kwan-Liu

2018-01-01

Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a "good" layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.

Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data

USGS Publications Warehouse

Dawson, Andria; Paciorek, Christopher J.; McLachlan, Jason S.; Goring, Simon; Williams, John W.; Jackson, Stephen T.

2016-01-01

Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.

Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data

NASA Astrophysics Data System (ADS)

Dawson, Andria; Paciorek, Christopher J.; McLachlan, Jason S.; Goring, Simon; Williams, John W.; Jackson, Stephen T.

2016-04-01

Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.

Multi-PSF fusion in image restoration of range-gated systems

NASA Astrophysics Data System (ADS)

Wang, Canjin; Sun, Tao; Wang, Tingfeng; Miao, Xikui; Wang, Rui

2018-07-01

For the task of image restoration, an accurate estimation of degrading PSF/kernel is the premise of recovering a visually superior image. The imaging process of range-gated imaging system in atmosphere associates with lots of factors, such as back scattering, background radiation, diffraction limit and the vibration of the platform. On one hand, due to the difficulty of constructing models for all factors, the kernels from physical-model based methods are not strictly accurate and practical. On the other hand, there are few strong edges in images, which brings significant errors to most of image-feature-based methods. Since different methods focus on different formation factors of the kernel, their results often complement each other. Therefore, we propose an approach which combines physical model with image features. With an fusion strategy using GCRF (Gaussian Conditional Random Fields) framework, we get a final kernel which is closer to the actual one. Aiming at the problem that ground-truth image is difficult to obtain, we then propose a semi data-driven fusion method in which different data sets are used to train fusion parameters. Finally, a semi blind restoration strategy based on EM (Expectation Maximization) and RL (Richardson-Lucy) algorithm is proposed. Our methods not only models how the lasers transfer in the atmosphere and imaging in the ICCD (Intensified CCD) plane, but also quantifies other unknown degraded factors using image-based methods, revealing how multiple kernel elements interact with each other. The experimental results demonstrate that our method achieves better performance than state-of-the-art restoration approaches.

Classification of Phylogenetic Profiles for Protein Function Prediction: An SVM Approach

NASA Astrophysics Data System (ADS)

Kotaru, Appala Raju; Joshi, Ramesh C.

Predicting the function of an uncharacterized protein is a major challenge in post-genomic era due to problems complexity and scale. Having knowledge of protein function is a crucial link in the development of new drugs, better crops, and even the development of biochemicals such as biofuels. Recently numerous high-throughput experimental procedures have been invented to investigate the mechanisms leading to the accomplishment of a protein’s function and Phylogenetic profile is one of them. Phylogenetic profile is a way of representing a protein which encodes evolutionary history of proteins. In this paper we proposed a method for classification of phylogenetic profiles using supervised machine learning method, support vector machine classification along with radial basis function as kernel for identifying functionally linked proteins. We experimentally evaluated the performance of the classifier with the linear kernel, polynomial kernel and compared the results with the existing tree kernel. In our study we have used proteins of the budding yeast saccharomyces cerevisiae genome. We generated the phylogenetic profiles of 2465 yeast genes and for our study we used the functional annotations that are available in the MIPS database. Our experiments show that the performance of the radial basis kernel is similar to polynomial kernel is some functional classes together are better than linear, tree kernel and over all radial basis kernel outperformed the polynomial kernel, linear kernel and tree kernel. In analyzing these results we show that it will be feasible to make use of SVM classifier with radial basis function as kernel to predict the gene functionality using phylogenetic profiles.

Intraear Compensation of Field Corn, Zea mays, from Simulated and Naturally Occurring Injury by Ear-Feeding Larvae.

PubMed

Steckel, S; Stewart, S D

2015-06-01

Ear-feeding larvae, such as corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), can be important insect pests of field corn, Zea mays L., by feeding on kernels. Recently introduced, stacked Bacillus thuringiensis (Bt) traits provide improved protection from ear-feeding larvae. Thus, our objective was to evaluate how injury to kernels in the ear tip might affect yield when this injury was inflicted at the blister and milk stages. In 2010, simulated corn earworm injury reduced total kernel weight (i.e., yield) at both the blister and milk stage. In 2011, injury to ear tips at the milk stage affected total kernel weight. No differences in total kernel weight were found in 2013, regardless of when or how much injury was inflicted. Our data suggested that kernels within the same ear could compensate for injury to ear tips by increasing in size, but this increase was not always statistically significant or sufficient to overcome high levels of kernel injury. For naturally occurring injury observed on multiple corn hybrids during 2011 and 2012, our analyses showed either no or a minimal relationship between number of kernels injured by ear-feeding larvae and the total number of kernels per ear, total kernel weight, or the size of individual kernels. The results indicate that intraear compensation for kernel injury to ear tips can occur under at least some conditions. © 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.

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

Integrating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Champagne, Nathan J.; Wilton, Donald R.

2008-01-01

A formulation for integrating the gradient of the thin wire kernel is presented. This approach employs a new expression for the gradient of the thin wire kernel derived from a recent technique for numerically evaluating the exact thin wire kernel. This approach should provide essentially arbitrary accuracy and may be used with higher-order elements and basis functions using the procedure described in [4].When the source and observation points are close, the potential integrals over wire segments involving the wire kernel are split into parts to handle the singular behavior of the integrand [1]. The singularity characteristics of the gradient of the wire kernel are different than those of the wire kernel, and the axial and radial components have different singularities. The characteristics of the gradient of the wire kernel are discussed in [2]. To evaluate the near electric and magnetic fields of a wire, the integration of the gradient of the wire kernel needs to be calculated over the source wire. Since the vector bases for current have constant direction on linear wire segments, these integrals reduce to integrals of the form

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

SU-E-T-510: Calculation of High Resolution and Material-Specific Photon Energy Deposition Kernels.

PubMed

Huang, J; Childress, N; Kry, S

2012-06-01

To calculate photon energy deposition kernels (EDKs) used for convolution/superposition dose calculation at a higher resolution than the original Mackie et al. 1988 kernels and to calculate material-specific kernels that describe how energy is transported and deposited by secondary particles when the incident photon interacts in a material other than water. The high resolution EDKs for various incident photon energies were generated using the EGSnrc user-code EDKnrc, which forces incident photons to interact at the center of a 60 cm radius sphere of water. The simulation geometry is essentially the same as the original Mackie calculation but with a greater number of scoring voxels (48 radial, 144 angular bins). For the material-specific EDKs, incident photons were forced to interact at the center of a 1 mm radius sphere of material (lung, cortical bone, silver, or titanium) surrounded by a 60 cm radius water sphere, using the original scoring voxel geometry implemented by Mackie et al. 1988 (24 radial, 48 angular bins). Our Monte Carlo-calculated high resolution EDKs showed excellent agreement with the Mackie kernels, with our kernels providing more information about energy deposition close to the interaction site. Furthermore, our EDKs resulted in smoother dose deposition functions due to the finer resolution and greater number of simulation histories. The material-specific EDK results show that the angular distribution of energy deposition is different for incident photons interacting in different materials. Calculated from the angular dose distribution for 300 keV incident photons, the expected polar angle for dose deposition () is 28.6° for water, 33.3° for lung, 36.0° for cortical bone, 44.6° for titanium, and 58.1° for silver, showing a dependence on the material in which the primary photon interacts. These high resolution and material-specific EDKs have implications for convolution/superposition dose calculations in heterogeneous patient geometries, especially at material interfaces. © 2012 American Association of Physicists in Medicine.

Antidiabetic and antioxidant functionality associated with phenolic constituents from fruit parts of indigenous black jamun (Syzygium cumini L.) landraces.

PubMed

Gajera, H P; Gevariya, Shila N; Hirpara, Darshna G; Patel, S V; Golakiya, B A

2017-09-01

Fruit phenolics are important dietary antioxidant and antidiabetic constituents. The fruit parts (pulp, seed, seed coat, kernel) of underutilized indigenous six black jamun landraces ( Syzygium cumini L.), found in Gir forest region of India and differed in their fruit size, shape and weight, are evaluated and correlated with antidiabetic, DPPH radical scavenging and phenolic constituents. The α-amylase inhibitors propose an efficient antidiabetic strategy and the levels of postprandial hyperglycemia were lowered by restraining starch breakdown. The sequential solvent systems with ascending polarity-petroleum ether, ethyl acetate, methanol and water were performed for soxhlet extraction by hot percolation method and extractive yield was found maximum with methanolic fruit part extracts of six landraces. The methanolic extracts of fruit parts also evidenced higher antidiabetic activity and hence utilized for further characterization. Among the six landraces, pulp and kernel of BJLR-6 (very small, oblong fruits) evidenced maximum 53.8 and 98.2% inhibition of α-amylase activity, respectively. The seed attained inhibitory activity mostly contributed by the kernel fraction. The inhibition of DPPH radical scavenging activity was positively correlated with phenol constituents. An HPLC-PDA technique was used to quantify the seven individual phenolics. The seed and kernel of BJLR-6 exhibited higher individual phenolics-gallic, catechin, ellagic, ferulic acids and quercetin, whereas pulp evidenced higher with gallic acid and catechin as α-amylase inhibitors. The IC 50 value indicates concentration of fruit extracts exhibiting ≥50% inhibition on porcine pancreatic α-amylase (PPA) activity. The kernel fraction of BJLR6 evidenced lowest (8.3 µg ml -1 ) IC 50 value followed by seed (12.9 µg ml -1 ), seed coat (50.8 µg ml -1 ) and pulp (270 µg ml -1 ). The seed and kernel of BJLR-6 inhibited PPA at much lower concentrations than standard acarbose (24.7 µg ml -1 ) considering good candidates for antidiabetic herbal formulations.

Experimental pencil beam kernels derivation for 3D dose calculation in flattening filter free modulated fields

NASA Astrophysics Data System (ADS)

Diego Azcona, Juan; Barbés, Benigno; Wang, Lilie; Burguete, Javier

2016-01-01

This paper presents a method to obtain the pencil-beam kernels that characterize a megavoltage photon beam generated in a flattening filter free (FFF) linear accelerator (linac) by deconvolution from experimental measurements at different depths. The formalism is applied to perform independent dose calculations in modulated fields. In our previous work a formalism was developed for ideal flat fluences exiting the linac’s head. That framework could not deal with spatially varying energy fluences, so any deviation from the ideal flat fluence was treated as a perturbation. The present work addresses the necessity of implementing an exact analysis where any spatially varying fluence can be used such as those encountered in FFF beams. A major improvement introduced here is to handle the actual fluence in the deconvolution procedure. We studied the uncertainties associated to the kernel derivation with this method. Several Kodak EDR2 radiographic films were irradiated with a 10 MV FFF photon beam from two linacs from different vendors, at the depths of 5, 10, 15, and 20cm in polystyrene (RW3 water-equivalent phantom, PTW Freiburg, Germany). The irradiation field was a 50mm diameter circular field, collimated with a lead block. The 3D kernel for a FFF beam was obtained by deconvolution using the Hankel transform. A correction on the low dose part of the kernel was performed to reproduce accurately the experimental output factors. Error uncertainty in the kernel derivation procedure was estimated to be within 0.2%. Eighteen modulated fields used clinically in different treatment localizations were irradiated at four measurement depths (total of fifty-four film measurements). Comparison through the gamma-index to their corresponding calculated absolute dose distributions showed a number of passing points (3%, 3mm) mostly above 99%. This new procedure is more reliable and robust than the previous one. Its ability to perform accurate independent dose calculations was demonstrated.

Pearson correlation estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

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

2012-04-01

Many applications in the geosciences call for the joint and objective analysis of irregular time series. For automated processing, robust measures of linear and nonlinear association are needed. Up to now, the standard approach would have been to reconstruct the time series on a regular grid, using linear or spline interpolation. Interpolation, however, comes with systematic side-effects, as it increases the auto-correlation in the time series. We have searched for the best method to estimate Pearson correlation for irregular time series, i.e. the one with the lowest estimation bias and variance. We adapted a kernel-based approach, using Gaussian weights. Pearson correlation is calculated, in principle, as a mean over products of previously centralized observations. In the regularly sampled case, observations in both time series were observed at the same time and thus the allocation of measurement values into pairs of products is straightforward. In the irregularly sampled case, however, measurements were not necessarily observed at the same time. Now, the key idea of the kernel-based method is to calculate weighted means of products, with the weight depending on the time separation between the observations. If the lagged correlation function is desired, the weights depend on the absolute difference between observation time separation and the estimation lag. To assess the applicability of the approach we used extensive simulations to determine the extent of interpolation side-effects with increasing irregularity of time series. We compared different approaches, based on (linear) interpolation, the Lomb-Scargle Fourier Transform, the sinc kernel and the Gaussian kernel. We investigated the role of kernel bandwidth and signal-to-noise ratio in the simulations. We found that the Gaussian kernel approach offers significant advantages and low Root-Mean Square Errors for regular, slightly irregular and very irregular time series. We therefore conclude that it is a good (linear) similarity measure that is appropriate for irregular time series with skewed inter-sampling time distributions.

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.

Simultaneous determination of fumonisins B1 and B2 in different types of maize by matrix solid phase dispersion and HPLC-MS/MS.

PubMed

de Oliveira, Gabriel Barros; de Castro Gomes Vieira, Carolyne Menezes; Orlando, Ricardo Mathias; Faria, Adriana Ferreira

2017-10-15

This work involved the optimization and validation of a method, according to Directive 2002/657/EC and the Analytical Quality Assurance Manual of Ministério da Agricultura, Pecuária e Abastecimento, Brazil, for simultaneous extraction and determination of fumonisins B1 and B2 in maize. The extraction procedure was based on a matrix solid phase dispersion approach, the optimization of which employed a sequence of different factorial designs. A liquid chromatography-tandem mass spectrometry method was developed for determining these analytes using the selected reaction monitoring mode. The optimized method employed only 1g of silica gel for dispersion and elution with 70% ammonium formate aqueous buffer (50mmolL -1 , pH 9), representing a simple, cheap and chemically friendly sample preparation method. Trueness (recoveries: 86-106%), precision (RSD ≤19%), decision limits, detection capabilities and measurement uncertainties were calculated for the validated method. The method scope was expanded to popcorn kernels, white maize kernels and yellow maize grits. Copyright © 2017 Elsevier Ltd. All rights reserved.

JANUS: A Compilation System for Balancing Parallelism and Performance in OpenVX

NASA Astrophysics Data System (ADS)

Omidian, Hossein; Lemieux, Guy G. F.

2018-04-01

Embedded systems typically do not have enough on-chip memory for entire an image buffer. Programming systems like OpenCV operate on entire image frames at each step, making them use excessive memory bandwidth and power. In contrast, the paradigm used by OpenVX is much more efficient; it uses image tiling, and the compilation system is allowed to analyze and optimize the operation sequence, specified as a compute graph, before doing any pixel processing. In this work, we are building a compilation system for OpenVX that can analyze and optimize the compute graph to take advantage of parallel resources in many-core systems or FPGAs. Using a database of prewritten OpenVX kernels, it automatically adjusts the image tile size as well as using kernel duplication and coalescing to meet a defined area (resource) target, or to meet a specified throughput target. This allows a single compute graph to target implementations with a wide range of performance needs or capabilities, e.g. from handheld to datacenter, that use minimal resources and power to reach the performance target.

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2011 CFR

2011-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2013 CFR

2013-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2012 CFR

2012-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

Wigner functions defined with Laplace transform kernels.

PubMed

Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George

2011-10-24

We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America

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.

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.

Environmental effects on resistance gene expression in milk stage popcorn kernels and associations with mycotoxin production.

PubMed

Dowd, Patrick F; Johnson, Eric T

2015-05-01

Like other forms of maize, popcorn is subject to increased levels of contamination by a variety of different mycotoxins under stress conditions, although levels generally are less than dent maize under comparable stress. Gene array analysis was used to determine expression differences of disease resistance-associated genes in milk stage kernels from commercial popcorn fields over 3 years. Relatively lower expression of resistance gene types was noted in years with higher temperatures and lower rainfall, which was consistent with prior results for many previously identified resistance response-associated genes. The lower rates of expression occurred for genes such as chitinases, protease inhibitors, and peroxidases; enzymes involved in the synthesis of cell wall barriers and secondary metabolites; and regulatory proteins. However, expression of several specific resistance genes previously associated with mycotoxins, such as aflatoxin in dent maize, was not affected. Insect damage altered the spectrum of resistance gene expression differences compared to undamaged ears. Correlation analyses showed expression differences of some previously reported resistance genes that were highly associated with mycotoxin levels and included glucanases, protease inhibitors, peroxidases, and thionins.

Exome Array Analysis of Susceptibility to Pneumococcal Meningitis

PubMed Central

Kloek, Anne T.; van Setten, Jessica; van der Ende, Arie; Bots, Michiel L.; Asselbergs, Folkert W.; Serón, Mercedes Valls; Brouwer, Matthijs C.; van de Beek, Diederik; Ferwerda, Bart

2016-01-01

Host genetic variability may contribute to susceptibility of bacterial meningitis, but which genes contribute to the susceptibility to this complex disease remains undefined. We performed a genetic association study in 469 community-acquired pneumococcal meningitis cases and 2072 population-based controls from the Utrecht Health Project in order to find genetic variants associated with pneumococcal meningitis susceptibility. A HumanExome BeadChip was used to genotype 102,097 SNPs in the collected DNA samples. Associations were tested with the Fisher exact test. None of the genetic variants tested reached Bonferroni corrected significance (p-value <5 × 10−7). Our strongest signals associated with susceptibility to pneumococcal meningitis were rs139064549 on chromosome 1 in the COL11A1 gene (p = 1.51 × 10−6; G allele OR 3.21 [95% CI 2.05–5.02]) and rs9309464 in the EXOC6B gene on chromosome 2 (p = 6.01 × 10−5; G allele OR 0.66 [95% CI 0.54–0.81]). The sequence kernel association test (SKAT) tests for associations between multiple variants in a gene region and pneumococcal meningitis susceptibility yielded one significant associated gene namely COL11A1 (p = 1.03 × 10−7). Replication studies are needed to validate these results. If replicated, the functionality of these genetic variations should be further studied to identify by which means they influence the pathophysiology of pneumococcal meningitis. PMID:27389768

Hermite polynomials and quasi-classical asymptotics

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

Ali, S. Twareque, E-mail: twareque.ali@concordia.ca; Engliš, Miroslav, E-mail: englis@math.cas.cz

2014-04-15

We study an unorthodox variant of the Berezin-Toeplitz type of quantization scheme, on a reproducing kernel Hilbert space generated by the real Hermite polynomials and work out the associated quasi-classical asymptotics.

Online Pairwise Learning Algorithms.

PubMed

Ying, Yiming; Zhou, Ding-Xuan

2016-04-01

Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

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.

Influence of Kernel Age on Fumonisin B1 Production in Maize by Fusarium moniliforme

PubMed Central

Warfield, Colleen Y.; Gilchrist, David G.

1999-01-01

Production of fumonisins by Fusarium moniliforme on naturally infected maize ears is an important food safety concern due to the toxic nature of this class of mycotoxins. Assessing the potential risk of fumonisin production in developing maize ears prior to harvest requires an understanding of the regulation of toxin biosynthesis during kernel maturation. We investigated the developmental-stage-dependent relationship between maize kernels and fumonisin B1 production by using kernels collected at the blister (R2), milk (R3), dough (R4), and dent (R5) stages following inoculation in culture at their respective field moisture contents with F. moniliforme. Highly significant differences (P ≤ 0.001) in fumonisin B1 production were found among kernels at the different developmental stages. The highest levels of fumonisin B1 were produced on the dent stage kernels, and the lowest levels were produced on the blister stage kernels. The differences in fumonisin B1 production among kernels at the different developmental stages remained significant (P ≤ 0.001) when the moisture contents of the kernels were adjusted to the same level prior to inoculation. We concluded that toxin production is affected by substrate composition as well as by moisture content. Our study also demonstrated that fumonisin B1 biosynthesis on maize kernels is influenced by factors which vary with the developmental age of the tissue. The risk of fumonisin contamination may begin early in maize ear development and increases as the kernels reach physiological maturity. PMID:10388675

A comparison of skyshine computational methods.

PubMed

Hertel, Nolan E; Sweezy, Jeremy E; Shultis, J Kenneth; Warkentin, J Karl; Rose, Zachary J

2005-01-01

A variety of methods employing radiation transport and point-kernel codes have been used to model two skyshine problems. The first problem is a 1 MeV point source of photons on the surface of the earth inside a 2 m tall and 1 m radius silo having black walls. The skyshine radiation downfield from the point source was estimated with and without a 30-cm-thick concrete lid on the silo. The second benchmark problem is to estimate the skyshine radiation downfield from 12 cylindrical canisters emplaced in a low-level radioactive waste trench. The canisters are filled with ion-exchange resin with a representative radionuclide loading, largely 60Co, 134Cs and 137Cs. The solution methods include use of the MCNP code to solve the problem by directly employing variance reduction techniques, the single-scatter point kernel code GGG-GP, the QADMOD-GP point kernel code, the COHORT Monte Carlo code, the NAC International version of the SKYSHINE-III code, the KSU hybrid method and the associated KSU skyshine codes.

Starch-Branching Enzymes Preferentially Associated with A-Type Starch Granules in Wheat Endosperm1

PubMed Central

Peng, Mingsheng; Gao, Ming; Båga, Monica; Hucl, Pierre; Chibbar, Ravindra N.

2000-01-01

Two starch granule-bound proteins (SGP), SGP-140 and SGP-145, were preferentially associated with A-type starch granules (>10 μm) in developing and mature wheat (Triticum aestivum) kernels. Immunoblotting and N-terminal sequencing suggested that the two proteins were different variants of SBEIc, a 152-kD isoform of wheat starch-branching enzyme. Both SGP-140 and SGP-145 were localized to the endosperm starch granules but were not found in the endosperm soluble fraction or pericarp starch granules younger than 15 d post anthesis (DPA). Small-size starch granules (<10 μm) initiated before 15 DPA incorporated SGP-140 and SGP-145 throughout endosperm development and grew into full-size A-type starch granules (>10 μm). In contrast, small-size starch granules harvested after 15 DPA contained only low amounts of SGP-140 and SGP-145 and developed mainly into B-type starch granules (<10 μm). Polypeptides of similar mass and immunologically related to SGP-140 and/or SGP-145 were also preferentially incorporated into A-type starch granules of barley (Hordeum vulgare), rye (Secale cereale), and triticale (× Triticosecale Wittmack) endosperm, which like wheat endosperm have a bimodal starch granule size distribution. PMID:10982441

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

High-throughput sequence alignment using Graphics Processing Units

PubMed Central

Schatz, Michael C; Trapnell, Cole; Delcher, Arthur L; Varshney, Amitabh

2007-01-01

Background The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies. Results This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. Conclusion MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU. PMID:18070356

Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

2016-07-07

Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adaptive kernel function using line transect sampling

NASA Astrophysics Data System (ADS)

Albadareen, Baker; Ismail, Noriszura

2018-04-01

The estimation of f(0) is crucial in the line transect method which is used for estimating population abundance in wildlife survey's. The classical kernel estimator of f(0) has a high negative bias. Our study proposes an adaptation in the kernel function which is shown to be more efficient than the usual kernel estimator. A simulation study is adopted to compare the performance of the proposed estimators with the classical kernel estimators.

Kernel Partial Least Squares for Nonlinear Regression and Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Clancy, Daniel (Technical Monitor)

2002-01-01

This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

Automated classification of LV regional wall motion based on spatio-temporal profiles from cardiac cine magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Mantilla, Juan; Garreau, Mireille; Bellanger, Jean-Jacques; Paredes, José Luis

2013-11-01

Assessment of the cardiac Left Ventricle (LV) wall motion is generally based on visual inspection or quantitative analysis of 2D+t sequences acquired in short-axis cardiac cine-Magnetic Resonance Imaging (MRI). Most often, cardiac dynamic is globally analized from two particular phases of the cardiac cycle. In this paper, we propose an automated method to classify regional wall motion in LV function based on spatio-temporal pro les and Support Vector Machines (SVM). This approach allows to obtain a binary classi cation between normal and abnormal motion, without the need of pre-processing and by exploiting all the images of the cardiac cycle. In each short- axis MRI slice level (basal, median, and apical), the spatio-temporal pro les are extracted from the selection of a subset of diametrical lines crossing opposites LV segments. Initialized at end-diastole phase, the pro les are concatenated with their corresponding projections into the succesive temporal phases of the cardiac cycle. These pro les are associated to di erent types of information that derive from the image (gray levels), Fourier, Wavelet or Curvelet domains. The approach has been tested on a set of 14 abnormal and 6 healthy patients by using a leave-one-out cross validation and two kernel functions for SVM classi er. The best classi cation performance is yielded by using four-level db4 wavelet transform and SVM with a linear kernel. At each slice level the results provided a classi cation rate of 87.14% in apical level, 95.48% in median level and 93.65% in basal level.

Pollen source effects on growth of kernel structures and embryo chemical compounds in maize.

PubMed

Tanaka, W; Mantese, A I; Maddonni, G A

2009-08-01

Previous studies have reported effects of pollen source on the oil concentration of maize (Zea mays) kernels through modifications to both the embryo/kernel ratio and embryo oil concentration. The present study expands upon previous analyses by addressing pollen source effects on the growth of kernel structures (i.e. pericarp, endosperm and embryo), allocation of embryo chemical constituents (i.e. oil, protein, starch and soluble sugars), and the anatomy and histology of the embryos. Maize kernels with different oil concentration were obtained from pollinations with two parental genotypes of contrasting oil concentration. The dynamics of the growth of kernel structures and allocation of embryo chemical constituents were analysed during the post-flowering period. Mature kernels were dissected to study the anatomy (embryonic axis and scutellum) and histology [cell number and cell size of the scutellums, presence of sub-cellular structures in scutellum tissue (starch granules, oil and protein bodies)] of the embryos. Plants of all crosses exhibited a similar kernel number and kernel weight. Pollen source modified neither the growth period of kernel structures, nor pericarp growth rate. By contrast, pollen source determined a trade-off between embryo and endosperm growth rates, which impacted on the embryo/kernel ratio of mature kernels. Modifications to the embryo size were mediated by scutellum cell number. Pollen source also affected (P < 0.01) allocation of embryo chemical compounds. Negative correlations among embryo oil concentration and those of starch (r = 0.98, P < 0.01) and soluble sugars (r = 0.95, P < 0.05) were found. Coincidently, embryos with low oil concentration had an increased (P < 0.05-0.10) scutellum cell area occupied by starch granules and fewer oil bodies. The effects of pollen source on both embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early established sink strength (i.e. sink size and sink activity) of the embryos.

7 CFR 868.254 - Broken kernels determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall be...

7 CFR 51.2090 - Serious damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

... defect which makes a kernel or piece of kernel unsuitable for human consumption, and includes decay...: Shriveling when the kernel is seriously withered, shrunken, leathery, tough or only partially developed: Provided, that partially developed kernels are not considered seriously damaged if more than one-fourth of...

Anisotropic hydrodynamics with a scalar collisional kernel

NASA Astrophysics Data System (ADS)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

Ideal regularization for learning kernels from labels.

PubMed

Pan, Binbin; Lai, Jianhuang; Shen, Lixin

2014-08-01

In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently. Copyright © 2014 Elsevier Ltd. All rights reserved.

Numerical study of the ignition behavior of a post-discharge kernel injected into a turbulent stratified cross-flow

NASA Astrophysics Data System (ADS)

Jaravel, Thomas; Labahn, Jeffrey; Ihme, Matthias

2017-11-01

The reliable initiation of flame ignition by high-energy spark kernels is critical for the operability of aviation gas turbines. The evolution of a spark kernel ejected by an igniter into a turbulent stratified environment is investigated using detailed numerical simulations with complex chemistry. At early times post ejection, comparisons of simulation results with high-speed Schlieren data show that the initial trajectory of the kernel is well reproduced, with a significant amount of air entrainment from the surrounding flow that is induced by the kernel ejection. After transiting in a non-flammable mixture, the kernel reaches a second stream of flammable methane-air mixture, where the successful of the kernel ignition was found to depend on the local flow state and operating conditions. By performing parametric studies, the probability of kernel ignition was identified, and compared with experimental observations. The ignition behavior is characterized by analyzing the local chemical structure, and its stochastic variability is also investigated.

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

Training the max-margin sequence model with the relaxed slack variables.

PubMed

Niu, Lingfeng; Wu, Jianmin; Shi, Yong

2012-09-01

Sequence models are widely used in many applications such as natural language processing, information extraction and optical character recognition, etc. We propose a new approach to train the max-margin based sequence model by relaxing the slack variables in this paper. With the canonical feature mapping definition, the relaxed problem is solved by training a multiclass Support Vector Machine (SVM). Compared with the state-of-the-art solutions for the sequence learning, the new method has the following advantages: firstly, the sequence training problem is transformed into a multiclassification problem, which is more widely studied and already has quite a few off-the-shelf training packages; secondly, this new approach reduces the complexity of training significantly and achieves comparable prediction performance compared with the existing sequence models; thirdly, when the size of training data is limited, by assigning different slack variables to different microlabel pairs, the new method can use the discriminative information more frugally and produces more reliable model; last but not least, by employing kernels in the intermediate multiclass SVM, nonlinear feature space can be easily explored. Experimental results on the task of named entity recognition, information extraction and handwritten letter recognition with the public datasets illustrate the efficiency and effectiveness of our method. Copyright © 2012 Elsevier Ltd. All rights reserved.

Hi-Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance

PubMed Central

Cruz-Bastida, Juan P.; Gomez-Cardona, Daniel; Li, Ke; Sun, Heyi; Hsieh, Jiang; Szczykutowicz, Timothy P.; Chen, Guang-Hong

2016-01-01

Purpose: The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. Methods: A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0–16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. Results: At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. Conclusions: The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions. PMID:27147351

Hi-Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance.

PubMed

Cruz-Bastida, Juan P; Gomez-Cardona, Daniel; Li, Ke; Sun, Heyi; Hsieh, Jiang; Szczykutowicz, Timothy P; Chen, Guang-Hong

2016-05-01

The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions.

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.

Development of a kernel function for clinical data.

PubMed

Daemen, Anneleen; De Moor, Bart

2009-01-01

For most diseases and examinations, clinical data such as age, gender and medical history guides clinical management, despite the rise of high-throughput technologies. To fully exploit such clinical information, appropriate modeling of relevant parameters is required. As the widely used linear kernel function has several disadvantages when applied to clinical data, we propose a new kernel function specifically developed for this data. This "clinical kernel function" more accurately represents similarities between patients. Evidently, three data sets were studied and significantly better performances were obtained with a Least Squares Support Vector Machine when based on the clinical kernel function compared to the linear kernel function.

Manycore Performance-Portability: Kokkos Multidimensional Array Library

DOE PAGES

Edwards, H. Carter; Sunderland, Daniel; Porter, Vicki; ...

2012-01-01

Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel executionmore » performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].« less

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.

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

NASA Astrophysics Data System (ADS)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a potential to improve the reliability of imaging biomarker, especially in evaluating the longitudinal changes of EI even when the patient CT scans were performed with different kernels.

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 ...

Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture is a key trait that governs milling performance, flour starch damage, flour particle size, flour hydration properties, and baking quality. Kernel texture is commonly measured using the Perten Single Kernel Characterization System (SKCS). The SKCS returns texture values (Hardness...

7 CFR 868.203 - Basis of determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... FOR CERTAIN AGRICULTURAL COMMODITIES United States Standards for Rough Rice Principles Governing..., heat-damaged kernels, red rice and damaged kernels, chalky kernels, other types, color, and the special grade Parboiled rough rice shall be on the basis of the whole and large broken kernels of milled rice...

7 CFR 868.203 - Basis of determination.

Code of Federal Regulations, 2011 CFR

2011-01-01

... FOR CERTAIN AGRICULTURAL COMMODITIES United States Standards for Rough Rice Principles Governing..., heat-damaged kernels, red rice and damaged kernels, chalky kernels, other types, color, and the special grade Parboiled rough rice shall be on the basis of the whole and large broken kernels of milled rice...

7 CFR 868.304 - Broken kernels determination.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the use...

7 CFR 868.304 - Broken kernels determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the use...

Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations

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

Carter, L.L.; Hendricks, J.S.

1983-01-01

The exponential transform is often used to improve the efficiency of deep-penetration Monte Carlo calculations. This technique is usually implemented by biasing the distance-to-collision kernel of the transport equation, but leaving the scattering kernel unchanged. Dwivedi obtained significant improvements in efficiency by biasing an isotropic scattering kernel as well as the distance-to-collision kernel. This idea is extended to anisotropic scattering, particularly the highly forward Klein-Nishina scattering of gamma rays.

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

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

PubMed

Makanza, R; Zaman-Allah, M; Cairns, J E; Eyre, J; Burgueño, J; Pacheco, Ángela; Diepenbrock, C; Magorokosho, C; Tarekegne, A; Olsen, M; Prasanna, B M

2018-01-01

Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure. A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

A Kernel-based Lagrangian method for imperfectly-mixed chemical reactions

NASA Astrophysics Data System (ADS)

Schmidt, Michael J.; Pankavich, Stephen; Benson, David A.

2017-05-01

Current Lagrangian (particle-tracking) algorithms used to simulate diffusion-reaction equations must employ a certain number of particles to properly emulate the system dynamics-particularly for imperfectly-mixed systems. The number of particles is tied to the statistics of the initial concentration fields of the system at hand. Systems with shorter-range correlation and/or smaller concentration variance require more particles, potentially limiting the computational feasibility of the method. For the well-known problem of bimolecular reaction, we show that using kernel-based, rather than Dirac delta, particles can significantly reduce the required number of particles. We derive the fixed width of a Gaussian kernel for a given reduced number of particles that analytically eliminates the error between kernel and Dirac solutions at any specified time. We also show how to solve for the fixed kernel size by minimizing the squared differences between solutions over any given time interval. Numerical results show that the width of the kernel should be kept below about 12% of the domain size, and that the analytic equations used to derive kernel width suffer significantly from the neglect of higher-order moments. The simulations with a kernel width given by least squares minimization perform better than those made to match at one specific time. A heuristic time-variable kernel size, based on the previous results, performs on par with the least squares fixed kernel size.

Optimized Kernel Entropy Components.

PubMed

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Ling, Fan

2013-01-01

Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called “structure kernel”, which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels. PMID:23666108

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants.

PubMed

Lippert, Christoph; Xiang, Jing; Horta, Danilo; Widmer, Christian; Kadie, Carl; Heckerman, David; Listgarten, Jennifer

2014-11-15

Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test-a score test-with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene-gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods. After reviewing theoretical differences in performance between the score and LR tests, we find empirically on real data that the LR test generally has more power. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test-up to 23 more associations-whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data, we find that the LR test yielded up to 12% more associations, consistent with our results on real data, but also observe a regime of extremely small signal where the score test yielded up to 25% more associations than the LR test, consistent with theory. Finally, our computational speedups now enable (i) efficient LR testing when the background kernel is full rank, and (ii) efficient score testing when the background kernel changes with each test, as for gene-gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500. Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/. heckerma@microsoft.com Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Comprehensive genotyping of the USA national maize inbred seed bank

PubMed Central

2013-01-01

Background Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world. Results The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits. Conclusions The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity. PMID:23759205

Comprehensive genotyping of the USA national maize inbred seed bank.

PubMed

Romay, Maria C; Millard, Mark J; Glaubitz, Jeffrey C; Peiffer, Jason A; Swarts, Kelly L; Casstevens, Terry M; Elshire, Robert J; Acharya, Charlotte B; Mitchell, Sharon E; Flint-Garcia, Sherry A; McMullen, Michael D; Holland, James B; Buckler, Edward S; Gardner, Candice A

2013-06-11

Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world. The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits. The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity.

Imaging and automated detection of Sitophilus oryzae (Coleoptera: Curculionidae) pupae in hard red winter wheat.

PubMed

Toews, Michael D; Pearson, Tom C; Campbell, James F

2006-04-01

Computed tomography, an imaging technique commonly used for diagnosing internal human health ailments, uses multiple x-rays and sophisticated software to recreate a cross-sectional representation of a subject. The use of this technique to image hard red winter wheat, Triticum aestivm L., samples infested with pupae of Sitophilus oryzae (L.) was investigated. A software program was developed to rapidly recognize and quantify the infested kernels. Samples were imaged in a 7.6-cm (o.d.) plastic tube containing 0, 50, or 100 infested kernels per kg of wheat. Interkernel spaces were filled with corn oil so as to increase the contrast between voids inside kernels and voids among kernels. Automated image processing, using a custom C language software program, was conducted separately on each 100 g portion of the prepared samples. The average detection accuracy in the five infested kernels per 100-g samples was 94.4 +/- 7.3% (mean +/- SD, n = 10), whereas the average detection accuracy in the 10 infested kernels per 100-g sample was 87.3 +/- 7.9% (n = 10). Detection accuracy in the 10 infested kernels per 100-g samples was slightly less than the five infested kernels per 100-g samples because of some infested kernels overlapping with each other or air bubbles in the oil. A mean of 1.2 +/- 0.9 (n = 10) bubbles (per tube) was incorrectly classed as infested kernels in replicates containing no infested kernels. In light of these positive results, future studies should be conducted using additional grains, insect species, and life stages.

Relationship of source and sink in determining kernel composition of maize

PubMed Central

Seebauer, Juliann R.; Singletary, George W.; Krumpelman, Paulette M.; Ruffo, Matías L.; Below, Frederick E.

2010-01-01

The relative role of the maternal source and the filial sink in controlling the composition of maize (Zea mays L.) kernels is unclear and may be influenced by the genotype and the N supply. The objective of this study was to determine the influence of assimilate supply from the vegetative source and utilization of assimilates by the grain sink on the final composition of maize kernels. Intermated B73×Mo17 recombinant inbred lines (IBM RILs) which displayed contrasting concentrations of endosperm starch were grown in the field with deficient or sufficient N, and the source supply altered by ear truncation (45% reduction) at 15 d after pollination (DAP). The assimilate supply into the kernels was determined at 19 DAP using the agar trap technique, and the final kernel composition was measured. The influence of N supply and kernel ear position on final kernel composition was also determined for a commercial hybrid. Concentrations of kernel protein and starch could be altered by genotype or the N supply, but remained fairly constant along the length of the ear. Ear truncation also produced a range of variation in endosperm starch and protein concentrations. The C/N ratio of the assimilate supply at 19 DAP was directly related to the final kernel composition, with an inverse relationship between the concentrations of starch and protein in the mature endosperm. The accumulation of kernel starch and protein in maize is uniform along the ear, yet adaptable within genotypic limits, suggesting that kernel composition is source limited in maize. PMID:19917600

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

PubMed

Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

2016-11-01

In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects. Copyright © 2016 Crop Science Society of America.

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.

A Nonparametric Approach For Representing Interannual Dependence In Monthly Streamflow Sequences

NASA Astrophysics Data System (ADS)

Sharma, A.; Oneill, R.

The estimation of risks associated with water management plans requires generation of synthetic streamflow sequences. The mathematical algorithms used to generate these sequences at monthly time scales are found lacking in two main respects: inability in preserving dependence attributes particularly at large (seasonal to interannual) time lags; and, a poor representation of observed distributional characteristics, in partic- ular, representation of strong assymetry or multimodality in the probability density function. Proposed here is an alternative that naturally incorporates both observed de- pendence and distributional attributes in the generated sequences. Use of a nonpara- metric framework provides an effective means for representing the observed proba- bility distribution, while the use of a Svariable kernelT ensures accurate modeling of & cedil;streamflow data sets that contain a substantial number of zero flow values. A careful selection of prior flows imparts the appropriate short-term memory, while use of an SaggregateT flow variable allows representation of interannual dependence. The non- & cedil;parametric simulation model is applied to monthly flows from the Beaver River near Beaver, Utah, USA, and the Burrendong dam inflows, New South Wales, Australia. Results indicate that while the use of traditional simulation approaches leads to an inaccurate representation of dependence at long (annual and interannual) time scales, the proposed model can simulate both short and long-term dependence. As a result, the proposed model ensures a significantly improved representation of reservoir storage statistics, particularly for systems influenced by long droughts. It is important to note that the proposed method offers a simpler and better alternative to conventional dis- aggregation models as: (a) a separate annual flow series is not required, (b) stringent assumptions relating annual and monthly flows are not needed, and (c) the method does not require the specification of a "water year", instead ensuring that the sum of any sequence of flows lasting twelve months will result in the type of dependence that is observed in the historical annual flow series.

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing, manufacturing, packing, processing, preparing, treating...

Local Observed-Score Kernel Equating

ERIC Educational Resources Information Center

Wiberg, Marie; van der Linden, Wim J.; von Davier, Alina A.

2014-01-01

Three local observed-score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias--as defined by Lord's criterion of equity--and percent relative error. The local kernel item response…

7 CFR 51.1241 - Damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

... which have been broken to the extent that the kernel within is plainly visible without minute... discoloration beneath, but the peanut shall be judged as it appears with the talc. (c) Kernels which are rancid or decayed. (d) Moldy kernels. (e) Kernels showing sprouts extending more than one-eighth inch from...

7 CFR 981.61 - Redetermination of kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Redetermination of kernel weight. 981.61 Section 981... GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.61 Redetermination of kernel weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds...

7 CFR 981.60 - Determination of kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Determination of kernel weight. 981.60 Section 981.60... Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which settlement...

7 CFR 999.400 - Regulation governing the importation of filberts.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Definitions. (1) Filberts means filberts or hazelnuts. (2) Inshell filberts means filberts, the kernels or edible portions of which are contained in the shell. (3) Shelled filberts means the kernels of filberts... Filbert kernels or portions of filbert kernels shall meet the following requirements: (1) Well dried and...

7 CFR 51.1404 - Tolerances.

Code of Federal Regulations, 2010 CFR

2010-01-01

.... (2) For kernel defects, by count. (i) 12 percent for pecans with kernels which fail to meet the... kernels which are seriously damaged: Provided, That not more than six-sevenths of this amount, or 6 percent, shall be allowed for kernels which are rancid, moldy, decayed or injured by insects: And provided...

Enhanced gluten properties in soft kernel durum wheat

USDA-ARS?s Scientific Manuscript database

Soft kernel durum wheat is a relatively recent development (Morris et al. 2011 Crop Sci. 51:114). The soft kernel trait exerts profound effects on kernel texture, flour milling including break flour yield, milling energy, and starch damage, and dough water absorption (DWA). With the caveat of reduce...

End-use quality of soft kernel durum wheat

USDA-ARS?s Scientific Manuscript database

Kernel texture is a major determinant of end-use quality of wheat. Durum wheat has very hard kernels. We developed soft kernel durum wheat via Ph1b-mediated homoeologous recombination. The Hardness locus was transferred from Chinese Spring to Svevo durum wheat via back-crossing. ‘Soft Svevo’ had SKC...

7 CFR 51.2560 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... are excessively thin kernels and can have black, brown or gray surface with a dark interior color and the immaturity has adversely affected the flavor of the kernel. (2) Kernel spotting refers to dark brown or dark gray spots aggregating more than one-eighth of the surface of the kernel. (g) Serious...

7 CFR 51.2560 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... are excessively thin kernels and can have black, brown or gray surface with a dark interior color and the immaturity has adversely affected the flavor of the kernel. (2) Kernel spotting refers to dark brown or dark gray spots aggregating more than one-eighth of the surface of the kernel. (g) Serious...

7 CFR 51.1416 - Optional determinations.

Code of Federal Regulations, 2010 CFR

2010-01-01

... throughout the lot. (a) Edible kernel content. A minimum sample of at least 500 grams of in-shell pecans shall be used for determination of edible kernel content. After the sample is weighed and shelled... determine edible kernel content for the lot. (b) Poorly developed kernel content. A minimum sample of at...

Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach.

PubMed

Koutroumpas, Konstantinos; Ballarini, Paolo; Votsi, Irene; Cournède, Paul-Henry

2016-09-01

Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC-SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels. In this article, we employ Dirichlet process mixtures (DPMs) to design optimal transition kernels and we present an ABC-SMC algorithm with DPM kernels. We illustrate the use of the proposed methodology using real data for the canonical Wnt signaling pathway. A multi-compartment model of the pathway is developed and it is compared to an existing model. The results indicate that DPMs are more efficient in the exploration of the parameter space and can significantly improve ABC-SMC performance. In comparison to alternative sampling schemes that are commonly used, the proposed approach can bring potential benefits in the estimation of complex multimodal distributions. The method is used to estimate the parameters and the initial state of two models of the Wnt pathway and it is shown that the multi-compartment model fits better the experimental data. Python scripts for the Dirichlet Process Gaussian Mixture model and the Gibbs sampler are available at https://sites.google.com/site/kkoutroumpas/software konstantinos.koutroumpas@ecp.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

3DRT-MPASS

NASA Technical Reports Server (NTRS)

Lickly, Ben

2005-01-01

Data from all current JPL missions are stored in files called SPICE kernels. At present, animators who want to use data from these kernels have to either read through the kernels looking for the desired data, or write programs themselves to retrieve information about all the needed objects for their animations. In this project, methods of automating the process of importing the data from the SPICE kernels were researched. In particular, tools were developed for creating basic scenes in Maya, a 3D computer graphics software package, from SPICE kernels.

The association between the availability of sugar-sweetened beverage in school vending machines and its consumption among adolescents in California: a propensity score matching approach.

PubMed

Shi, Lu

2010-01-01

There is controversy over to what degree banning sugar-sweetened beverage (SSB) sales at schools could decrease the SSB intake. This paper uses the adolescent sample of 2005 California Health Interview Survey to estimate the association between the availability of SSB from school vending machines and the amount of SSB consumption. Propensity score stratification and kernel-based propensity score matching are used to address the selection bias issue in cross-sectional data. Propensity score stratification shows that adolescents who had access to SSB through their school vending machines consumed 0.170 more drinks of SSB than those who did not (P < .05). Kernel-based propensity score matching shows the SSB consumption difference to be 0.158 on the prior day (P < .05). This paper strengthens the evidence for the association between SSB availability via school vending machines and the actual SSB consumption, while future studies are needed to explore changes in other beverages after SSB becomes less available.

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.

Generalization Performance of Regularized Ranking With Multiscale Kernels.

PubMed

Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

2016-05-01

The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

PubMed Central

Zhang, Shujun

2018-01-01

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study. PMID:29377896

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.

Oecophylla longinoda (Hymenoptera: Formicidae) Lead to Increased Cashew Kernel Size and Kernel Quality.

PubMed

Anato, F M; Sinzogan, A A C; Offenberg, J; Adandonon, A; Wargui, R B; Deguenon, J M; Ayelo, P M; Vayssières, J-F; Kossou, D K

2017-06-01

Weaver ants, Oecophylla spp., are known to positively affect cashew, Anacardium occidentale L., raw nut yield, but their effects on the kernels have not been reported. We compared nut size and the proportion of marketable kernels between raw nuts collected from trees with and without ants. Raw nuts collected from trees with weaver ants were 2.9% larger than nuts from control trees (i.e., without weaver ants), leading to 14% higher proportion of marketable kernels. On trees with ants, the kernel: raw nut ratio from nuts damaged by formic acid was 4.8% lower compared with nondamaged nuts from the same trees. Weaver ants provided three benefits to cashew production by increasing yields, yielding larger nuts, and by producing greater proportions of marketable kernel mass. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

Small convolution kernels for high-fidelity image restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1991-01-01

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

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

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.

Adaptive MCMC in Bayesian phylogenetics: an application to analyzing partitioned data in BEAST.

PubMed

Baele, Guy; Lemey, Philippe; Rambaut, Andrew; Suchard, Marc A

2017-06-15

Advances in sequencing technology continue to deliver increasingly large molecular sequence datasets that are often heavily partitioned in order to accurately model the underlying evolutionary processes. In phylogenetic analyses, partitioning strategies involve estimating conditionally independent models of molecular evolution for different genes and different positions within those genes, requiring a large number of evolutionary parameters that have to be estimated, leading to an increased computational burden for such analyses. The past two decades have also seen the rise of multi-core processors, both in the central processing unit (CPU) and Graphics processing unit processor markets, enabling massively parallel computations that are not yet fully exploited by many software packages for multipartite analyses. We here propose a Markov chain Monte Carlo (MCMC) approach using an adaptive multivariate transition kernel to estimate in parallel a large number of parameters, split across partitioned data, by exploiting multi-core processing. Across several real-world examples, we demonstrate that our approach enables the estimation of these multipartite parameters more efficiently than standard approaches that typically use a mixture of univariate transition kernels. In one case, when estimating the relative rate parameter of the non-coding partition in a heterochronous dataset, MCMC integration efficiency improves by > 14-fold. Our implementation is part of the BEAST code base, a widely used open source software package to perform Bayesian phylogenetic inference. guy.baele@kuleuven.be. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 3 2011-04-01 2011-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2013 CFR

2013-01-01

... generally conforms to the “light” or “light amber” classification, that color classification may be used to... 7 Agriculture 2 2013-01-01 2013-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be...

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2014 CFR

2014-01-01

... generally conforms to the “light” or “light amber” classification, that color classification may be used to... 7 Agriculture 2 2014-01-01 2014-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be...

Nutrition quality of extraction mannan residue from palm kernel cake on brolier chicken

NASA Astrophysics Data System (ADS)

Tafsin, M.; Hanafi, N. D.; Kejora, E.; Yusraini, E.

2018-02-01

This study aims to find out the nutrient residue of palm kernel cake from mannan extraction on broiler chicken by evaluating physical quality (specific gravity, bulk density and compacted bulk density), chemical quality (proximate analysis and Van Soest Test) and biological test (metabolizable energy). Treatment composed of T0 : palm kernel cake extracted aquadest (control), T1 : palm kernel cake extracted acetic acid (CH3COOH) 1%, T2 : palm kernel cake extracted aquadest + mannanase enzyme 100 u/l and T3 : palm kernel cake extracted acetic acid (CH3COOH) 1% + enzyme mannanase 100 u/l. The results showed that mannan extraction had significant effect (P<0.05) in improving the quality of physical and numerically increase the value of crude protein and decrease the value of NDF (Neutral Detergent Fiber). Treatments had highly significant influence (P<0.01) on the metabolizable energy value of palm kernel cake residue in broiler chickens. It can be concluded that extraction with aquadest + enzyme mannanase 100 u/l yields the best nutrient quality of palm kernel cake residue for broiler chicken.

Oil point and mechanical behaviour of oil palm kernels in linear compression

NASA Astrophysics Data System (ADS)

Kabutey, Abraham; Herak, David; Choteborsky, Rostislav; Mizera, Čestmír; Sigalingging, Riswanti; Akangbe, Olaosebikan Layi

2017-07-01

The study described the oil point and mechanical properties of roasted and unroasted bulk oil palm kernels under compression loading. The literature information available is very limited. A universal compression testing machine and vessel diameter of 60 mm with a plunger were used by applying maximum force of 100 kN and speed ranging from 5 to 25 mm min-1. The initial pressing height of the bulk kernels was measured at 40 mm. The oil point was determined by a litmus test for each deformation level of 5, 10, 15, 20, and 25 mm at a minimum speed of 5 mmmin-1. The measured parameters were the deformation, deformation energy, oil yield, oil point strain and oil point pressure. Clearly, the roasted bulk kernels required less deformation energy compared to the unroasted kernels for recovering the kernel oil. However, both kernels were not permanently deformed. The average oil point strain was determined at 0.57. The study is an essential contribution to pursuing innovative methods for processing palm kernel oil in rural areas of developing countries.

Dynamic Changes in Phenolics and Antioxidant Capacity during Pecan (Carya illinoinensis) Kernel Ripening and Its Phenolics Profiles.

PubMed

Jia, Xiaodong; Luo, Huiting; Xu, Mengyang; Zhai, Min; Guo, Zhongren; Qiao, Yushan; Wang, Liangju

2018-02-16

Pecan ( Carya illinoinensis ) kernels have a high phenolics content and a high antioxidant capacity compared to other nuts-traits that have attracted great interest of late. Changes in the total phenolic content (TPC), condensed tannins (CT), total flavonoid content (TFC), five individual phenolics, and antioxidant capacity of five pecan cultivars were investigated during the process of kernel ripening. Ultra-performance liquid chromatography coupled with quadruple time-of-flight mass (UPLC-Q/TOF-MS) was also used to analyze the phenolics profiles in mixed pecan kernels. TPC, CT, TFC, individual phenolics, and antioxidant capacity were changed in similar patterns, with values highest at the water or milk stages, lowest at milk or dough stages, and slightly varied at kernel stages. Forty phenolics were tentatively identified in pecan kernels, of which two were first reported in the genus Carya , six were first reported in Carya illinoinensis , and one was first reported in its kernel. The findings on these new phenolic compounds provide proof of the high antioxidant capacity of pecan kernels.

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.

Novel characterization method of impedance cardiography signals using time-frequency distributions.

PubMed

Escrivá Muñoz, Jesús; Pan, Y; Ge, S; Jensen, E W; Vallverdú, M

2018-03-16

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P = 0.780) and the extended modified beta distribution (P = 0.765) provided similar results, higher than the rest of analyzed kernels. Graphical abstract Flowchart for the optimization of time-frequency distribution kernels for impedance cardiography signals.

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.

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.

Antioxidant and antimicrobial activities of bitter and sweet apricot (Prunus armeniaca L.) kernels.

PubMed

Yiğit, D; Yiğit, N; Mavi, A

2009-04-01

The present study describes the in vitro antimicrobial and antioxidant activity of methanol and water extracts of sweet and bitter apricot (Prunus armeniaca L.) kernels. The antioxidant properties of apricot kernels were evaluated by determining radical scavenging power, lipid peroxidation inhibition activity and total phenol content measured with a DPPH test, the thiocyanate method and the Folin method, respectively. In contrast to extracts of the bitter kernels, both the water and methanol extracts of sweet kernels have antioxidant potential. The highest percent inhibition of lipid peroxidation (69%) and total phenolic content (7.9 +/- 0.2 microg/mL) were detected in the methanol extract of sweet kernels (Hasanbey) and in the water extract of the same cultivar, respectively. The antimicrobial activities of the above extracts were also tested against human pathogenic microorganisms using a disc-diffusion method, and the minimal inhibitory concentration (MIC) values of each active extract were determined. The most effective antibacterial activity was observed in the methanol and water extracts of bitter kernels and in the methanol extract of sweet kernels against the Gram-positive bacteria Staphylococcus aureus. Additionally, the methanol extracts of the bitter kernels were very potent against the Gram-negative bacteria Escherichia coli (0.312 mg/mL MIC value). Significant anti-candida activity was also observed with the methanol extract of bitter apricot kernels against Candida albicans, consisting of a 14 mm in diameter of inhibition zone and a 0.625 mg/mL MIC value.

The Influence of Reconstruction Kernel on Bone Mineral and Strength Estimates Using Quantitative Computed Tomography and Finite Element Analysis.

PubMed

Michalski, Andrew S; Edwards, W Brent; Boyd, Steven K

2017-10-17

Quantitative computed tomography has been posed as an alternative imaging modality to investigate osteoporosis. We examined the influence of computed tomography convolution back-projection reconstruction kernels on the analysis of bone quantity and estimated mechanical properties in the proximal femur. Eighteen computed tomography scans of the proximal femur were reconstructed using both a standard smoothing reconstruction kernel and a bone-sharpening reconstruction kernel. Following phantom-based density calibration, we calculated typical bone quantity outcomes of integral volumetric bone mineral density, bone volume, and bone mineral content. Additionally, we performed finite element analysis in a standard sideways fall on the hip loading configuration. Significant differences for all outcome measures, except integral bone volume, were observed between the 2 reconstruction kernels. Volumetric bone mineral density measured using images reconstructed by the standard kernel was significantly lower (6.7%, p < 0.001) when compared with images reconstructed using the bone-sharpening kernel. Furthermore, the whole-bone stiffness and the failure load measured in images reconstructed by the standard kernel were significantly lower (16.5%, p < 0.001, and 18.2%, p < 0.001, respectively) when compared with the image reconstructed by the bone-sharpening kernel. These data suggest that for future quantitative computed tomography studies, a standardized reconstruction kernel will maximize reproducibility, independent of the use of a quantitative calibration phantom. Copyright © 2017 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

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.

Fast metabolite identification with Input Output Kernel Regression.

PubMed

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-06-15

An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. celine.brouard@aalto.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

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

Fast metabolite identification with Input Output Kernel Regression

PubMed Central

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-01-01

Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. Availability and implementation: Contact: celine.brouard@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307628

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.

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.

Salt stress reduces kernel number of corn by inhibiting plasma membrane H+-ATPase activity.

PubMed

Jung, Stephan; Hütsch, Birgit W; Schubert, Sven

2017-04-01

Salt stress affects yield formation of corn (Zea mays L.) at various physiological levels resulting in an overall grain yield decrease. In this study we investigated how salt stress affects kernel development of two corn cultivars (cvs. Pioneer 3906 and Fabregas) at and shortly after pollination. In an earlier study, we found an accumulation of hexoses in the kernel tissue. Therefore, it was hypothesized that hexose uptake into developing endosperm and embryo might be inhibited. Hexoses are transported into the developing endosperm by carriers localized in the plasma membrane (PM). The transport is driven by the pH gradient which is built up by the PM H + -ATPase. It was investigated whether the PM H + -ATPase activity in developing corn kernels was inhibited by salt stress, which would cause a lower pH gradient resulting in impaired hexose import and finally in kernel abortion. Corn grown under control and salt stress conditions was harvested 0 and 2 days after pollination (DAP). Under salt stress sucrose and hexose concentrations in kernel tissue were higher 0 and 2 DAP. Kernel PM H + -ATPase activity was not affected at 0 DAP, but it was reduced at 2 DAP. This is in agreement with the finding, that kernel growth and thus kernel setting was not affected in the salt stress treatment at pollination, but it was reduced 2 days later. It is concluded that inhibition of PM H + -ATPase under salt stress impaired the energization of hexose transporters into the cells, resulting in lower kernel growth and finally in kernel abortion. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

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.

Effect of Fungal Colonization of Wheat Grains with Fusarium spp. on Food Choice, Weight Gain and Mortality of Meal Beetle Larvae (Tenebrio molitor)

PubMed Central

Guo, Zhiqing; Döll, Katharina; Dastjerdi, Raana; Karlovsky, Petr; Dehne, Heinz-Wilhelm; Altincicek, Boran

2014-01-01

Species of Fusarium have significant agro-economical and human health-related impact by infecting diverse crop plants and synthesizing diverse mycotoxins. Here, we investigated interactions of grain-feeding Tenebrio molitor larvae with four grain-colonizing Fusarium species on wheat kernels. Since numerous metabolites produced by Fusarium spp. are toxic to insects, we tested the hypothesis that the insect senses and avoids Fusarium-colonized grains. We found that only kernels colonized with F. avenaceum or Beauveria bassiana (an insect-pathogenic fungal control) were avoided by the larvae as expected. Kernels colonized with F. proliferatum, F. poae or F. culmorum attracted T. molitor larvae significantly more than control kernels. The avoidance/preference correlated with larval feeding behaviors and weight gain. Interestingly, larvae that had consumed F. proliferatum- or F. poae-colonized kernels had similar survival rates as control. Larvae fed on F. culmorum-, F. avenaceum- or B. bassiana-colonized kernels had elevated mortality rates. HPLC analyses confirmed the following mycotoxins produced by the fungal strains on the kernels: fumonisins, enniatins and beauvericin by F. proliferatum, enniatins and beauvericin by F. poae, enniatins by F. avenaceum, and deoxynivalenol and zearalenone by F. culmorum. Our results indicate that T. molitor larvae have the ability to sense potential survival threats of kernels colonized with F. avenaceum or B. bassiana, but not with F. culmorum. Volatiles potentially along with gustatory cues produced by these fungi may represent survival threat signals for the larvae resulting in their avoidance. Although F. proliferatum or F. poae produced fumonisins, enniatins and beauvericin during kernel colonization, the larvae were able to use those kernels as diet without exhibiting increased mortality. Consumption of F. avenaceum-colonized kernels, however, increased larval mortality; these kernels had higher enniatin levels than F. proliferatum or F. poae-colonized ones suggesting that T. molitor can tolerate or metabolize those toxins. PMID:24932485

Using the Intel Math Kernel Library on Peregrine | High-Performance

Science.gov Websites

Computing | NREL the Intel Math Kernel Library on Peregrine Using the Intel Math Kernel Library on Peregrine Learn how to use the Intel Math Kernel Library (MKL) with Peregrine system software. MKL architectures. Core math functions in MKL include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2014 CFR

2014-04-01

... the Act, are as follows: Common name Botanical name of plant source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed Cydonia oblonga Miller. [42 FR 14640, Mar...

7 CFR 51.2954 - Tolerances for grade defects.

Code of Federal Regulations, 2010 CFR

2010-01-01

... chart. Tolerances for Grade Defects Grade External (shell) defects Internal (kernel) defects Color of kernel U.S. No. 1. 10 pct, by count for splits. 5 pct. by count, for other shell defects, including not... tolerance to reduce the required 70 pct of “light amber” kernels or the required 40 pct of “light” kernels...

7 CFR 51.2284 - Size classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

...: “Halves”, “Pieces and Halves”, “Pieces” or “Small Pieces”. The size of portions of kernels in the lot... consists of 85 percent or more, by weight, half kernels, and the remainder three-fourths half kernels. (See § 51.2285.) (b) Pieces and halves. Lot consists of 20 percent or more, by weight, half kernels, and the...

Measurements of oleic acid among individual kernels harvested from test plots of purified runner and spanish high oleic seed

USDA-ARS?s Scientific Manuscript database

Normal oleic peanuts are often found within commercial lots of high oleic peanuts when sampling among individual kernels. Kernels not meeting high oleic threshold could be true contamination with normal oleic peanuts introduced via poor handling, or kernels not meeting threshold could be immature a...

THERMOS. 30-Group ENDF/B Scattered Kernels

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

McCrosson, F.J.; Finch, D.R.

1973-12-01

These data are 30-group THERMOS thermal scattering kernels for P0 to P5 Legendre orders for every temperature of every material from s(alpha,beta) data stored in the ENDF/B library. These scattering kernels were generated using the FLANGE2 computer code. To test the kernels, the integral properties of each set of kernels were determined by a precision integration of the diffusion length equation and compared to experimental measurements of these properties. In general, the agreement was very good. Details of the methods used and results obtained are contained in the reference. The scattering kernels are organized into a two volume magnetic tapemore » library from which they may be retrieved easily for use in any 30-group THERMOS library.« less

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

PubMed Central

Lippert, Christoph; Xiang, Jing; Horta, Danilo; Widmer, Christian; Kadie, Carl; Heckerman, David; Listgarten, Jennifer

2014-01-01

Motivation: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test—a score test—with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene–gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods. Results: After reviewing theoretical differences in performance between the score and LR tests, we find empirically on real data that the LR test generally has more power. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test—up to 23 more associations—whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data, we find that the LR test yielded up to 12% more associations, consistent with our results on real data, but also observe a regime of extremely small signal where the score test yielded up to 25% more associations than the LR test, consistent with theory. Finally, our computational speedups now enable (i) efficient LR testing when the background kernel is full rank, and (ii) efficient score testing when the background kernel changes with each test, as for gene–gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500. Availability: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/. Contact: heckerma@microsoft.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25075117

7 CFR 810.2003 - Basis of determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Basis of determination. Each determination of heat-damaged kernels, damaged kernels, material other than... shrunken and broken kernels. Other determinations not specifically provided for under the general...

Single kernel ionomic profiles are highly heritable indicators of genetic and environmental influences on elemental accumulation in maize grain (Zea mays)

USDA-ARS?s Scientific Manuscript database

The ionome, or elemental profile, of a maize kernel represents at least two distinct ideas. First, the collection of elements within the kernel are food, feed and feedstocks for people, animals and industrial processes. Second, the ionome of the kernel represents a developmental end point that can s...

Detection of aflatoxin B1 (AFB1) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

Short wave infrared hyperspectral imaging (SWIR) (1000-2500 nm) was used to detect aflatoxin B1 (AFB1) in individual maize kernels. A total of 120 kernels of four varieties (or 30 kernels per variety) that had been artificially inoculated with a toxigenic strain of Aspergillus flavus and harvested f...

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...

Chemical components of cold pressed kernel oils from different Torreya grandis cultivars.

PubMed

He, Zhiyong; Zhu, Haidong; Li, Wangling; Zeng, Maomao; Wu, Shengfang; Chen, Shangwei; Qin, Fang; Chen, Jie

2016-10-15

The chemical compositions of cold pressed kernel oils of seven Torreya grandis cultivars from China were analyzed in this study. The contents of the chemical components of T. grandis kernels and kernel oils varied to different extents with the cultivar. The T. grandis kernels contained relatively high oil and protein content (45.80-53.16% and 10.34-14.29%, respectively). The kernel oils were rich in unsaturated fatty acids including linoleic (39.39-47.77%), oleic (30.47-37.54%) and eicosatrienoic acid (6.78-8.37%). The kernel oils contained some abundant bioactive substances such as tocopherols (0.64-1.77mg/g) consisting of α-, β-, γ- and δ-isomers; sterols including β-sitosterol (0.90-1.29mg/g), campesterol (0.06-0.32mg/g) and stigmasterol (0.04-0.18mg/g) in addition to polyphenols (9.22-22.16μgGAE/g). The results revealed that the T. grandis kernel oils possessed the potentially important nutrition and health benefits and could be used as oils in the human diet or functional ingredients in the food industry. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modulation of antioxidant potential in liver of mice by kernel oil of cashew nut (Anacardium occidentale) and its lack of tumour promoting ability in DMBA induced skin papillomagenesis.

PubMed

Singh, Bimala; Kale, R K; Rao, A R

2004-04-01

Cashew nut shell oil has been reported to possess tumour promoting property. Therefore an attempt has been made to study the modulatory effect of cashew nut (Anlacardium occidentale) kernel oil on antioxidant potential in liver of Swiss albino mice and also to see whether it has tumour promoting ability like the shell oil. The animals were treated orally with two doses (50 and 100 microl/animal/day) of kernel oil of cashew nut for 10 days. The kernel oil was found to enhance the specific activities of SOD, catalase, GST, methylglyoxalase I and levels of GSH. These results suggested that cashew nut kernel oil had an ability to increase the antioxidant status of animals. The decreased level of lipid peroxidation supported this possibility. The tumour promoting property of the kernel oil was also examined and found that cashew nut kernel oil did not exhibit any solitary carcinogenic activity.

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.

Searching for efficient Markov chain Monte Carlo proposal kernels

PubMed Central

Yang, Ziheng; Rodríguez, Carlos E.

2013-01-01

Markov chain Monte Carlo (MCMC) or the Metropolis–Hastings algorithm is a simulation algorithm that has made modern Bayesian statistical inference possible. Nevertheless, the efficiency of different Metropolis–Hastings proposal kernels has rarely been studied except for the Gaussian proposal. Here we propose a unique class of Bactrian kernels, which avoid proposing values that are very close to the current value, and compare their efficiency with a number of proposals for simulating different target distributions, with efficiency measured by the asymptotic variance of a parameter estimate. The uniform kernel is found to be more efficient than the Gaussian kernel, whereas the Bactrian kernel is even better. When optimal scales are used for both, the Bactrian kernel is at least 50% more efficient than the Gaussian. Implementation in a Bayesian program for molecular clock dating confirms the general applicability of our results to generic MCMC algorithms. Our results refute a previous claim that all proposals had nearly identical performance and will prompt further research into efficient MCMC proposals. PMID:24218600

An iterative kernel based method for fourth order nonlinear equation with nonlinear boundary condition

NASA Astrophysics Data System (ADS)

Azarnavid, Babak; Parand, Kourosh; Abbasbandy, Saeid

2018-06-01

This article discusses an iterative reproducing kernel method with respect to its effectiveness and capability of solving a fourth-order boundary value problem with nonlinear boundary conditions modeling beams on elastic foundations. Since there is no method of obtaining reproducing kernel which satisfies nonlinear boundary conditions, the standard reproducing kernel methods cannot be used directly to solve boundary value problems with nonlinear boundary conditions as there is no knowledge about the existence and uniqueness of the solution. The aim of this paper is, therefore, to construct an iterative method by the use of a combination of reproducing kernel Hilbert space method and a shooting-like technique to solve the mentioned problems. Error estimation for reproducing kernel Hilbert space methods for nonlinear boundary value problems have yet to be discussed in the literature. In this paper, we present error estimation for the reproducing kernel method to solve nonlinear boundary value problems probably for the first time. Some numerical results are given out to demonstrate the applicability of the method.

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.

Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures.

PubMed

Li, Lishuang; Zhang, Panpan; Zheng, Tianfu; Zhang, Hongying; Jiang, Zhenchao; Huang, Degen

2014-01-01

Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information.

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

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

PubMed

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

A Robustness Testing Campaign for IMA-SP Partitioning Kernels

NASA Astrophysics Data System (ADS)

Grixti, Stephen; Lopez Trecastro, Jorge; Sammut, Nicholas; Zammit-Mangion, David

2015-09-01

With time and space partitioned architectures becoming increasingly appealing to the European space sector, the dependability of partitioning kernel technology is a key factor to its applicability in European Space Agency projects. This paper explores the potential of the data type fault model, which injects faults through the Application Program Interface, in partitioning kernel robustness testing. This fault injection methodology has been tailored to investigate its relevance in uncovering vulnerabilities within partitioning kernels and potentially contributing towards fault removal campaigns within this domain. This is demonstrated through a robustness testing case study of the XtratuM partitioning kernel for SPARC LEON3 processors. The robustness campaign exposed a number of vulnerabilities in XtratuM, exhibiting the potential benefits of using such a methodology for the robustness assessment of partitioning kernels.

Genome-Wide Association Mapping for Kernel and Malting Quality Traits Using Historical European Barley Records

PubMed Central

Röder, Marion S.; van Eeuwijk, Fred

2014-01-01

Malting quality is an important trait in breeding barley (Hordeum vulgare L.). It requires elaborate, expensive phenotyping, which involves micro-malting experiments. Although there is abundant historical information available for different cultivars in different years and trials, that historical information is not often used in genetic analyses. This study aimed to exploit historical records to assist in identifying genomic regions that affect malting and kernel quality traits in barley. This genome-wide association study utilized information on grain yield and 18 quality traits accumulated over 25 years on 174 European spring and winter barley cultivars combined with diversity array technology markers. Marker-trait associations were tested with a mixed linear model. This model took into account the genetic relatedness between cultivars based on principal components scores obtained from marker information. We detected 140 marker-trait associations. Some of these associations confirmed previously known quantitative trait loci for malting quality (on chromosomes 1H, 2H, and 5H). Other associations were reported for the first time in this study. The genetic correlations between traits are discussed in relation to the chromosomal regions associated with the different traits. This approach is expected to be particularly useful when designing strategies for multiple trait improvements. PMID:25372869

Hardware for dynamic quantum computing experiments: Part I

NASA Astrophysics Data System (ADS)

Johnson, Blake; Ryan, Colm; Riste, Diego; Donovan, Brian; Ohki, Thomas

Static, pre-defined control sequences routinely achieve high-fidelity operation on superconducting quantum processors. Efforts toward dynamic experiments depending on real-time information have mostly proceeded through hardware duplication and triggers, requiring a combinatorial explosion in the number of channels. We provide a hardware efficient solution to dynamic control with a complete platform of specialized FPGA-based control and readout electronics; these components enable arbitrary control flow, low-latency feedback and/or feedforward, and scale far beyond single-qubit control and measurement. We will introduce the BBN Arbitrary Pulse Sequencer 2 (APS2) control system and the X6 QDSP readout platform. The BBN APS2 features: a sequencer built around implementing short quantum gates, a sequence cache to allow long sequences with branching structures, subroutines for code re-use, and a trigger distribution module to capture and distribute steering information. The X6 QDSP features a single-stage DSP pipeline that combines demodulation with arbitrary integration kernels, and multiple taps to inspect data flow for debugging and calibration. We will show system performance when putting it all together, including a latency budget for feedforward operations. This research was funded by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), through the Army Research Office Contract No. W911NF-10-1-0324.

Oil palm genome sequence reveals divergence of interfertile species in old and new worlds

PubMed Central

Singh, Rajinder; Ong-Abdullah, Meilina; Low, Eng-Ti Leslie; Manaf, Mohamad Arif Abdul; Rosli, Rozana; Nookiah, Rajanaidu; Ooi, Leslie Cheng-Li; Ooi, Siew–Eng; Chan, Kuang-Lim; Halim, Mohd Amin; Azizi, Norazah; Nagappan, Jayanthi; Bacher, Blaire; Lakey, Nathan; Smith, Steven W; He, Dong; Hogan, Michael; Budiman, Muhammad A; Lee, Ernest K; DeSalle, Rob; Kudrna, David; Goicoechea, Jose Louis; Wing, Rod; Wilson, Richard K; Fulton, Robert S; Ordway, Jared M; Martienssen, Robert A; Sambanthamurthi, Ravigadevi

2013-01-01

Oil palm is the most productive oil-bearing crop. Planted on only 5% of the total vegetable oil acreage, palm oil accounts for 33% of vegetable oil, and 45% of edible oil worldwide, but increased cultivation competes with dwindling rainforest reserves. We report the 1.8 gigabase (Gb) genome sequence of the African oil palm Elaeis guineensis, the predominant source of worldwide oil production. 1.535 Gb of assembled sequence and transcriptome data from 30 tissue types were used to predict at least 34,802 genes, including oil biosynthesis genes and homologues of WRINKLED1 (WRI1), and other transcriptional regulators1, which are highly expressed in the kernel. We also report the draft sequence of the S. American oil palm Elaeis oleifera, which has the same number of chromosomes (2n=32) and produces fertile interspecific hybrids with E. guineensis2, but appears to have diverged in the new world. Segmental duplications of chromosome arms define the palaeotetraploid origin of palm trees. The oil palm sequence enables the discovery of genes for important traits as well as somaclonal epigenetic alterations which restrict the use of clones in commercial plantings3, and thus helps achieve sustainability for biofuels and edible oils, reducing the rainforest footprint of this tropical plantation crop. PMID:23883927

Fouling mechanism in ultrafiltration of vegetable oil

NASA Astrophysics Data System (ADS)

Ariono, D.; Wardani, A. K.; Widodo, S.; Aryanti, Putu T. P.; Wenten, I. G.

2018-03-01

Energy efficient and cost-effective separation of impurities from vegetable oil is a great challenge for vegetable oil processing. Several technologies have been developed, including pressurized membrane, chemical treatment, and chemical free separation methods. Among those technologies, ultrafiltration membrane is one of the most attractive processes with low operating pressure and temperature. In this work, hydrophobic polypropylene ultrafiltration membrane was used to remove impurities such as non-dissolved solids from palm kernel oil. Unfortunately, the hydrophobicity of polypropylene membrane leads to significant impact on the reduction of permeate flux due to membrane fouling. This fouling is associated with the accumulation of substances on the membrane surface or within the membrane pores. For better understanding, fouling mechanism that occurred during palm kernel oil ultrafiltration using hydrophobic polypropylene membrane was investigated. The effect of trans-membrane pressure and feed temperature on fouling mechanism was also studied. The result showed that cake formation became the dominant fouling mechanism up to 50 min operation of palm kernel oil ultrafiltration. Furthermore, the fouling mechanism was not affected by the increase of trans-membrane pressure and feed temperature.

Continuity properties of the semi-group and its integral kernel in non-relativistic QED

NASA Astrophysics Data System (ADS)

Matte, Oliver

2016-07-01

Employing recent results on stochastic differential equations associated with the standard model of non-relativistic quantum electrodynamics by B. Güneysu, J. S. Møller, and the present author, we study the continuity of the corresponding semi-group between weighted vector-valued Lp-spaces, continuity properties of elements in the range of the semi-group, and the pointwise continuity of an operator-valued semi-group kernel. We further discuss the continuous dependence of the semi-group and its integral kernel on model parameters. All these results are obtained for Kato decomposable electrostatic potentials and the actual assumptions on the model are general enough to cover the Nelson model as well. As a corollary, we obtain some new pointwise exponential decay and continuity results on elements of low-energetic spectral subspaces of atoms or molecules that also take spin into account. In a simpler situation where spin is neglected, we explain how to verify the joint continuity of positive ground state eigenvectors with respect to spatial coordinates and model parameters. There are no smallness assumptions imposed on any model parameter.

Three-dimensional Fréchet sensitivity kernels for electromagnetic wave propagation

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

Strickland, C. E.; Johnson, T. C.; Odom, R. I.

2015-08-28

Electromagnetic imaging methods are useful tools for monitoring subsurface changes in pore-fluid content and the associated changes in electrical permittivity and conductivity. The most common method for georadar tomography uses a high frequency ray-theoretic approximation that is valid when material variations are sufficiently small relative to the wavelength of the propagating wave. Georadar methods, however, often utilize electromagnetic waves that propagate within heterogeneous media at frequencies where ray theory may not be applicable. In this paper we describe the 3-D Fréchet sensitivity kernels for EM wave propagation. Various data functional types are formulated that consider all three components of themore » electric wavefield and incorporate near-, intermediate-, and far-field contributions. We show that EM waves exhibit substantial variations for different relative source-receiver component orientations. The 3-D sensitivities also illustrate out-of-plane effects that are not captured in 2-D sensitivity kernels and can influence results obtained using 2-D inversion methods to image structures that are in reality 3-D.« less

Kinetic study of nickel laterite reduction roasting by palm kernel shell charcoal

NASA Astrophysics Data System (ADS)

Sugiarto, E.; Putera, A. D. P.; Petrus, H. T. B. M.

2017-05-01

Demand to process nickel-bearing laterite ore increase as continuous depletion of high-grade nickel-bearing sulfide ore takes place. Due to its common nickel association with iron, processing nickel laterite ore into nickel pig iron (NPI) has been developed by some industries. However, to achieve satisfying nickel recoveries, the process needs massive high-grade metallurgical coke consumption. Concerning on the sustainability of coke supply and positive carbon emission, reduction of nickel laterite ore using biomass-based reductor was being studied.In this study, saprolitic nickel laterite ore was being reduced by palm kernel shell charcoal at several temperatures (800-1000 °C). Variation of biomass-laterite composition was also conducted to study the reduction mechanism. X-ray diffraction and gravimetry analysis were applied to justify the phenomenon and predict kinetic model of the reduction. Results of this study provide information that palm kernel shell charcoal has similar reducing result compared with the conventional method. Reduction, however, was carried out by carbon monoxide rather than solid carbon. Regarding kinetics, Ginstling-Brouhnstein kinetic model provides satisfying results to predict the reduction phenomenon.

KSOS Computer Program Development Specifications (Type B-5). (Kernelized Secure Operating System). I. Security Kernel (CDRL 0002AF). II. UNIX Emulator (CDRL 0002AG). III. Security-Related Software (CDRL 0002AH).

DTIC Science & Technology

1980-12-01

Commun- ications Corporation, Palo Alto, CA (March 1978). g. [Walter at al. 74] Walter, K.G. et al., " Primitive Models for Computer .. Security", ESD-TR...discussion is followed by a presenta- tion of the Kernel primitive operations upon these objects. All Kernel objects shall be referenced by a common...set of sizes. All process segments, regardless of domain, shall be manipulated by the same set of Kernel segment primitives . User domain segments

Short Message Service (SMS) Command and Control (C2) Awareness in Android-based Smartphones Using Kernel-Level Auditing

DTIC Science & Technology

2012-06-14

Display 480 x 800 pixels (3.7 inches) CPU Qualcomm QSD8250 1GHz Memory (internal) 512MB RAM / 512 MB ROM Kernel version 2.6.35.7-ge0fb012 Figure 3.5: HTC...development and writing). The 34 MSM kernel provided by the AOSP and compatible with the HTC Nexus One’s motherboard and Qualcomm chipset, is used for this...building the kernel is having the prebuilt toolchains and the right kernel for the hardware. Many HTC products use Qualcomm processors which uses the

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...

Vis- and NIR-based instruments for detection of black-tip damaged wheat kernels: A comparative study

USDA-ARS?s Scientific Manuscript database

Black-tip (BT) present in wheat kernels is a non-mycotoxic fungus that attacks the kernels wherein any of a number of molds forms a dark brown or black sooty mold at the tip of the wheat kernel. Three spectrometers covering the spectral ranges 950-1636nm (Spec1), 600-1045nm (Spec2), and 380-780nm (S...

Diamond High Assurance Security Program: Trusted Computing Exemplar

DTIC Science & Technology

2002-09-01

computing component, the Embedded MicroKernel Prototype. A third-party evaluation of the component will be initiated during development (e.g., once...target technologies and larger projects is a topic for future research. Trusted Computing Reference Component – The Embedded MicroKernel Prototype We...Kernel The primary security function of the Embedded MicroKernel will be to enforce process and data-domain separation, while providing primitive

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...

Quantitative comparison of noise texture across CT scanners from different manufacturers

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

Solomon, Justin B.; Christianson, Olav; Samei, Ehsan

2012-10-15

Purpose: To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). Methods: The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625/0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section ofmore » the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. Results: The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm{sup 2} (0.002 mm{sup -1}) to 0.29 mm{sup 2} (0.74 mm{sup -1}). The GE kernels 'Soft,''Standard,''Chest,' and 'Lung' closely matched the Siemens kernels 'B35f,''B43f,''B41f,' and 'B80f' (RMSD < 0.05 mm{sup 2}, |PFD| < 0.02 mm{sup -1}, respectively). The GE 'Bone,''Bone+,' and 'Edge' kernels all matched most closely with Siemens 'B75f' kernel but with sizeable RMSD and |PFD| values up to 0.18 mm{sup 2} and 0.41 mm{sup -1}, respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. Conclusions: It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner.« less

Quantitative comparison of noise texture across CT scanners from different manufacturers.

PubMed

Solomon, Justin B; Christianson, Olav; Samei, Ehsan

2012-10-01

To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625∕0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section of the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm(2) (0.002 mm(-1)) to 0.29 mm(2) (0.74 mm(-1)). The GE kernels "Soft," "Standard," "Chest," and "Lung" closely matched the Siemens kernels "B35f," "B43f," "B41f," and "B80f" (RMSD < 0.05 mm(2), |PFD| < 0.02 mm(-1), respectively). The GE "Bone," "Bone+," and "Edge" kernels all matched most closely with Siemens "B75f" kernel but with sizeable RMSD and |PFD| values up to 0.18 mm(2) and 0.41 mm(-1), respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner.

A Experimental Study of the Growth of Laser Spark and Electric Spark Ignited Flame Kernels.

NASA Astrophysics Data System (ADS)

Ho, Chi Ming

1995-01-01

Better ignition sources are constantly in demand for enhancing the spark ignition in practical applications such as automotive and liquid rocket engines. In response to this practical challenge, the present experimental study was conducted with the major objective to obtain a better understanding on how spark formation and hence spark characteristics affect the flame kernel growth. Two laser sparks and one electric spark were studied in air, propane-air, propane -air-nitrogen, methane-air, and methane-oxygen mixtures that were initially at ambient pressure and temperature. The growth of the kernels was monitored by imaging the kernels with shadowgraph systems, and by imaging the planar laser -induced fluorescence of the hydroxyl radicals inside the kernels. Characteristic dimensions and kernel structures were obtained from these images. Since different energy transfer mechanisms are involved in the formation of a laser spark as compared to that of an electric spark; a laser spark is insensitive to changes in mixture ratio and mixture type, while an electric spark is sensitive to changes in both. The detailed structures of the kernels in air and propane-air mixtures primarily depend on the spark characteristics. But the combustion heat released rapidly in methane-oxygen mixtures significantly modifies the kernel structure. Uneven spark energy distribution causes remarkably asymmetric kernel structure. The breakdown energy of a spark creates a blast wave that shows good agreement with the numerical point blast solution, and a succeeding complex spark-induced flow that agrees reasonably well with a simple puff model. The transient growth rates of the propane-air, propane-air -nitrogen, and methane-air flame kernels can be interpreted in terms of spark effects, flame stretch, and preferential diffusion. For a given mixture, a spark with higher breakdown energy produces a greater and longer-lasting enhancing effect on the kernel growth rate. By comparing the growth rates of the appropriate mixtures, the positive and negative effects of preferential diffusion and flame stretch on the developing flame are clearly demonstrated.

Improved modeling of clinical data with kernel methods.

PubMed

Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart

2012-02-01

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems. Copyright © 2011 Elsevier B.V. All rights reserved.

Improved Prediction of Non-methylated Islands in Vertebrates Highlights Different Characteristic Sequence Patterns

PubMed Central

Vingron, Martin

2016-01-01

Non-methylated islands (NMIs) of DNA are genomic regions that are important for gene regulation and development. A recent study of genome-wide non-methylation data in vertebrates by Long et al. (eLife 2013;2:e00348) has shown that many experimentally identified non-methylated regions do not overlap with classically defined CpG islands which are computationally predicted using simple DNA sequence features. This is especially true in cold-blooded vertebrates such as Danio rerio (zebrafish). In order to investigate how predictive DNA sequence is of a region’s methylation status, we applied a supervised learning approach using a spectrum kernel support vector machine, to see if a more complex model and supervised learning can be used to improve non-methylated island prediction and to understand the sequence properties of these regions. We demonstrate that DNA sequence is highly predictive of methylation status, and that in contrast to existing CpG island prediction methods our method is able to provide more useful predictions of NMIs genome-wide in all vertebrate organisms that were studied. Our results also show that in cold-blooded vertebrates (Anolis carolinensis, Xenopus tropicalis and Danio rerio) where genome-wide classical CpG island predictions consist primarily of false positives, longer primarily AT-rich DNA sequence features are able to identify these regions much more accurately. PMID:27984582

High-specificity quantification method for almond-by-products, based on differential proteomic analysis.

PubMed

Zhang, Shiwei; Wang, Shifeng; Huang, Jingmin; Lai, Xintian; Du, Yegang; Liu, Xiaoqing; Li, Bifang; Feng, Ronghu; Yang, Guowu

2016-03-01

A highly specific competitive enzyme-linked immunosorbent assay (ELISA) protocol has been developed to identify and classify almond products based on differential proteomic analysis. We applied two-dimensional electrophoresis to compare the differences between almond and apricot kernels to search for almond-specific proteins. The amino acid of apricot Pru-1 was sequenced and aligned to almond Pru-1. One peptide, RQGRQQGRQQQEEGR, which exists in almond but not in apricot, was used as hapten to prepare monoclonal antibody against almond Pru-1. An optimized ELISA method was established using this antibody. The assay did not exhibit cross-reactivity with the tested apricot kernels and other edible plant seeds. The limit of detection (LOD) was 2.5-100μg/g based on different food samples. The recoveries of fortified samples at levels of twofold and eightfold LOD ranged from 82% to 96%. The coefficients of variation were less than 13.0%. Using 7M urea as extracting solution, the heat-treated protein loss ratios were 2%, 5% and 15% under pasteurization (65°C for 30min), baking (150°C for 30min) and autoclaved sterilization (120°C for 15min), respectively. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting.

PubMed

Zivoli, Rosanna; Gambacorta, Lucia; Piemontese, Luca; Solfrizzo, Michele

2016-01-19

The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted) from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B₁ and B₂ were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB₁ + AFB₂, whereas AFG₁ and AFG₂ were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%-19.9% of total peeled kernels) removed 97.3%-99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%-99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB₁ + AFB₂ measured in rejected fractions (15%-18% of total kernels) ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01-0.05 µg/kg) was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB₁ and from 0.06 to 1.79 μg/kg for total aflatoxins.

QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize.

PubMed

Huo, Dongao; Ning, Qiang; Shen, Xiaomeng; Liu, Lei; Zhang, Zuxin

2016-01-01

The kernel number is a grain yield component and an important maize breeding goal. Ear length, kernel number per row and ear row number are highly correlated with the kernel number per ear, which eventually determines the ear weight and grain yield. In this study, two sets of F2:3 families developed from two bi-parental crosses sharing one inbred line were used to identify quantitative trait loci (QTL) for four kernel number-related traits: ear length, kernel number per row, ear row number and ear weight. A total of 39 QTLs for the four traits were identified in the two populations. The phenotypic variance explained by a single QTL ranged from 0.4% to 29.5%. Additionally, 14 overlapping QTLs formed 5 QTL clusters on chromosomes 1, 4, 5, 7, and 10. Intriguingly, six QTLs for ear length and kernel number per row overlapped in a region on chromosome 1. This region was designated qEL1.10 and was validated as being simultaneously responsible for ear length, kernel number per row and ear weight in a near isogenic line-derived population, suggesting that qEL1.10 was a pleiotropic QTL with large effects. Furthermore, the performance of hybrids generated by crossing 6 elite inbred lines with two near isogenic lines at qEL1.10 showed the breeding value of qEL1.10 for the improvement of the kernel number and grain yield of maize hybrids. This study provides a basis for further fine mapping, molecular marker-aided breeding and functional studies of kernel number-related traits in maize.

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.

PubMed

Jacquin, Laval; Cao, Tuong-Vi; Ahmadi, Nourollah

2016-01-01

One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel "trick" concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available.

High-speed mixture fraction and temperature imaging of pulsed, turbulent fuel jets auto-igniting in high-temperature, vitiated co-flows

NASA Astrophysics Data System (ADS)

Papageorge, Michael J.; Arndt, Christoph; Fuest, Frederik; Meier, Wolfgang; Sutton, Jeffrey A.

2014-07-01

In this manuscript, we describe an experimental approach to simultaneously measure high-speed image sequences of the mixture fraction and temperature fields during pulsed, turbulent fuel injection into a high-temperature, co-flowing, and vitiated oxidizer stream. The quantitative mixture fraction and temperature measurements are determined from 10-kHz-rate planar Rayleigh scattering and a robust data processing methodology which is accurate from fuel injection to the onset of auto-ignition. In addition, the data processing is shown to yield accurate temperature measurements following ignition to observe the initial evolution of the "burning" temperature field. High-speed OH* chemiluminescence (CL) was used to determine the spatial location of the initial auto-ignition kernel. In order to ensure that the ignition kernel formed inside of the Rayleigh scattering laser light sheet, OH* CL was observed in two viewing planes, one near-parallel to the laser sheet and one perpendicular to the laser sheet. The high-speed laser measurements are enabled through the use of the unique high-energy pulse burst laser system which generates long-duration bursts of ultra-high pulse energies at 532 nm (>1 J) suitable for planar Rayleigh scattering imaging. A particular focus of this study was to characterize the fidelity of the measurements both in the context of the precision and accuracy, which includes facility operating and boundary conditions and measurement of signal-to-noise ratio (SNR). The mixture fraction and temperature fields deduced from the high-speed planar Rayleigh scattering measurements exhibited SNR values greater than 100 at temperatures exceeding 1,300 K. The accuracy of the measurements was determined by comparing the current mixture fraction results to that of "cold", isothermal, non-reacting jets. All profiles, when properly normalized, exhibited self-similarity and collapsed upon one another. Finally, example mixture fraction, temperature, and OH* emission sequences are presented for a variety for fuel and vitiated oxidizer combinations. For all cases considered, auto-ignition occurred at the periphery of the fuel jet, under very "lean" conditions, where the local mixture fraction was less than the stoichiometric mixture fraction ( ξ < ξ s). Furthermore, the ignition kernel formed in regions of low scalar dissipation rate, which agrees with previous results from direct numerical simulations.

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2012 CFR

2012-01-01

... color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth... wrinkled; (g) Internal flesh discoloration of a medium shade of gray or brown extending more than one...

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2011 CFR

2011-01-01

... color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth... wrinkled; (g) Internal flesh discoloration of a medium shade of gray or brown extending more than one...

7 CFR 868.202 - Definition of other terms.

Code of Federal Regulations, 2011 CFR

2011-01-01

... (commonly known as barnyard grass, watergrass, and Japanese millet). (h) Other types. (1) Whole kernels of... in paragraph (h) of this section. (d) Damaged kernels. Whole or broken kernels of rice which are...

7 CFR 868.202 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

... (commonly known as barnyard grass, watergrass, and Japanese millet). (h) Other types. (1) Whole kernels of... in paragraph (h) of this section. (d) Damaged kernels. Whole or broken kernels of rice which are...