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.

Individual detection of genetically modified maize varieties in non-identity-preserved maize samples.

PubMed

Akiyama, Hiroshi; Sakata, Kozue; Kondo, Kazunari; Tanaka, Asako; Liu, Ming S; Oguchi, Taichi; Furui, Satoshi; Kitta, Kazumi; Hino, Akihiro; Teshima, Reiko

2008-03-26

In many countries, the labeling of grains and feed- and foodstuffs is mandatory if the genetically modified organism (GMO) content exceeds a certain level of approved GM varieties. The GMO content in a maize sample containing the combined-trait (stacked) GM maize as determined by the currently available methodology is likely to be overestimated. However, there has been little information in the literature on the mixing level and varieties of stacked GM maize in real sample grains. For the first time, the GMO content of non-identity-preserved (non-IP) maize samples imported from the United States has been successfully determined by using a previously developed individual kernel detection system coupled to a multiplex qualitative PCR method followed by multichannel capillary gel electrophoresis system analysis. To clarify the GMO content in the maize samples imported from the United States, determine how many stacked GM traits are contained therein, and which GM trait varieties frequently appeared in 2005, the GMO content (percent) on a kernel basis and the varieties of the GM kernels in the non-IP maize samples imported from the United States were investigated using the individual kernel analysis system. The average (+/-standard deviation) of the GMO contents on a kernel basis in five non-IP sample lots was determined to be 51.0+/-21.6%, the percentage of a single GM trait grains was 39%, and the percentage of the stacked GM trait grains was 12%. The MON810 grains and NK603 grains were the most frequent varieties in the single GM traits. The most frequent stacked GM traits were the MON810xNK603 grains. In addition, the present study would provide the answer and impact for the quantification of GM maize content in the GM maize kernels on labeling regulation.

Characterization of Mesocarp and Kernel Lipids from Elaeis guineensis Jacq., Elaeis oleifera [Kunth] Cortés, and Their Interspecific Hybrids.

PubMed

Lieb, Veronika M; Kerfers, Margarete R; Kronmüller, Amrei; Esquivel, Patricia; Alvarado, Amancio; Jiménez, Víctor M; Schmarr, Hans-Georg; Carle, Reinhold; Schweiggert, Ralf M; Steingass, Christof B

2017-05-10

Morphological traits, total lipid contents, and fatty acid profiles were assessed in fruits of several accessions of Elaeis oleifera [Kunth] Cortés, Elaeis guineensis Jacq., and their interspecific hybrids. The latter featured the highest mesocarp-to-fruit ratios (77.9-78.2%). The total lipid contents of both E. guineensis mesocarp and kernel were significantly higher than for E. oleifera accessions. Main fatty acids comprised C16:0, C18:1n9, and C18:2n6 in mesocarp and C12:0, C14:0, and C18:1n9 in kernels. E. oleifera samples were characterized by higher proportions of unsaturated long-chain fatty acids. Saturated medium-chain fatty acids supported the clustering of E. guineensis kernels in multivariate statistics. Hybrid mesocarp lipids had an intermediate fatty acid composition, whereas their kernel lipids resembled those of E. oleifera genotypes. Principal component analysis based on lipid contents and proportions of individual fatty acids permitted clear-cut distinction of E. oleifera, E. guineensis, and their hybrids.

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.

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.

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.

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.

Identification of quantitative trait loci for popping traits and kernel characteristics in sorghum grain

USDA-ARS?s Scientific Manuscript database

Popped grain sorghum has developed a niche among specialty snack-food consumers. In contrast to popcorn, sorghum has not benefited from persistent selective breeding for popping efficiency and kernel expansion ratio. While recent studies have already demonstrated that popping characteristics are h...

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.

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...

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...

Nut traits and nutritional composition of hazelnut (Corylus avellana L.) as influenced by zinc fertilization.

PubMed

Özenç, Nedim; Özenç, Damla Bender

2015-07-01

Zinc is an essential element for plants and its deficiency is a widespread problem throughout the world, causing decreased yields and nutritional quality. In this study the effect of zinc fertilization on some nut traits and the nutritional composition of 'Tombul' hazelnut (Corylus avellana L.) variety cultivated in the Black Sea region of Turkey was investigated and the contribution of this nut to human nutrition determined. Trials were carried out at 'Tombul' hazelnut orchards, and zinc fertilizers were applied at 0, 0.2, 0.4, 0.8 and 1.6 kg Zn ha(-1) in three consecutive years. Significant differences in some nut traits and mineral composition (protein, total oil, ash, kernel percentage, empty and wrinkled nuts, copper, boron, manganese and molybdenum) were observed with zinc fertilizer applications. In terms of daily nutritional element requirements, 100 g of hazelnut provided about 44.74% phosphorus, 13.39% potassium, 19.32% calcium, 37.49% magnesium, 0.19% sodium, 51.63% iron, 25.73% zinc and 14.05% boron of the recommended daily amounts (RDAs), while copper, manganese and molybdenum contents exceeded their RDAs. In order to improve some nut traits and the mineral composition of hazelnut, 0.8 and 1.6 kg Zn ha(-1) fertilizations could be recommended in practice. © 2014 Society of Chemical Industry.

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

PubMed

Masojć, Piotr; Milczarski, P; Kruszona, P

2017-08-01

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

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

PubMed

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

2016-09-13

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

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.

Genetic analysis of grain attributes, milling performance, and end-use quality traits in hard red spring wheat (Triticum aestivum L.)

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture dictates U.S. wheat market class and culinary end-uses. Of interest to wheat breeders is to identify quantitative trait loci (QTL) for wheat kernel texture, milling performance, or end-use quality because it is imperative for wheat breeders to ascertain the genetic architecture ...

Genetic architecture of kernel composition in global sorghum germplasm.

PubMed

Rhodes, Davina H; Hoffmann, Leo; Rooney, William L; Herald, Thomas J; Bean, Scott; Boyles, Richard; Brenton, Zachary W; Kresovich, Stephen

2017-01-05

Sorghum [Sorghum bicolor (L.) Moench] is an important cereal crop for dryland areas in the United States and for small-holder farmers in Africa. Natural variation of sorghum grain composition (protein, fat, and starch) between accessions can be used for crop improvement, but the genetic controls are still unresolved. The goals of this study were to quantify natural variation of sorghum grain composition and to identify single-nucleotide polymorphisms (SNPs) associated with variation in grain composition concentrations. In this study, we quantified protein, fat, and starch in a global sorghum diversity panel using near-infrared spectroscopy (NIRS). Protein content ranged from 8.1 to 18.8%, fat content ranged from 1.0 to 4.3%, and starch content ranged from 61.7 to 71.1%. Durra and bicolor-durra sorghum from Ethiopia and India had the highest protein and fat and the lowest starch content, while kafir sorghum from USA, India, and South Africa had the lowest protein and the highest starch content. Genome-wide association studies (GWAS) identified quantitative trait loci (QTL) for sorghum protein, fat, and starch. Previously published RNAseq data was used to identify candidate genes within a GWAS QTL region. A putative alpha-amylase 3 gene, which has previously been shown to be associated with grain composition traits, was identified as a strong candidate for protein and fat variation. We identified promising sources of genetic material for manipulation of grain composition traits, and several loci and candidate genes that may control sorghum grain composition. This survey of grain composition in sorghum germplasm and identification of protein, fat, and starch QTL contributes to our understanding of the genetic basis of natural variation in sorghum grain nutritional traits.

Genetic analysis of kernel texture (grain hardness) in a hard red spring wheat (Triticum aestivum L.) bi-parental population

USDA-ARS?s Scientific Manuscript database

Grain hardness is a very important trait in determining wheat market class and also influences milling and baking traits. At the grain Hardness (Ha) locus on chromosome 5DS, there are two primary mutations responsible for conveying a harder kernel texture among U.S. hard red spring wheats: (1) the P...

Relationship between QTL for grain shape, grain weight, test weight, milling yield, and plant height in the spring wheat cross RL4452/'AC Domain'.

PubMed

Cabral, Adrian L; Jordan, Mark C; Larson, Gary; Somers, Daryl J; Humphreys, D Gavin; McCartney, Curt A

2018-01-01

Kernel morphology characteristics of wheat are complex and quantitatively inherited. A doubled haploid (DH) population of the cross RL4452/'AC Domain' was used to study the genetic basis of seed shape. Quantitative trait loci (QTL) analyses were conducted on a total of 18 traits: 14 grain shape traits, flour yield (Fyd), and three agronomic traits (Plant height [Plht], 1000 Grain weight [Gwt], Test weight [Twt]), using data from trial locations at Glenlea, Brandon, and Morden in Manitoba, Canada, between 1999 and 2004. Kernel shape was studied through digital image analysis with an Acurum® grain analyzer. Plht, Gwt, Twt, Fyd, and grain shape QTL were correlated with each other and QTL analysis revealed that QTL for these traits often mapped to the same genetic locations. The most significant QTL for the grain shape traits were located on chromosomes 4B and 4D, each accounting for up to 24.4% and 53.3% of the total phenotypic variation, respectively. In addition, the most significant QTL for Plht, Gwt, and Twt were all detected on chromosome 4D at the Rht-D1 locus. Rht-D1b decreased Plht, Gwt, Twt, and kernel width relative to the Rht-D1a allele. A narrow genetic interval on chromosome 4B contained significant QTL for grain shape, Gwt, and Plht. The 'AC Domain' allele reduced Plht, Gwt, kernel length and width traits, but had no detectable effect on Twt. The data indicated that this variation was inconsistent with segregation at Rht-B1. Numerous QTL were identified that control these traits in this population.

Relationship between QTL for grain shape, grain weight, test weight, milling yield, and plant height in the spring wheat cross RL4452/‘AC Domain’

PubMed Central

Cabral, Adrian L.; Jordan, Mark C.; Larson, Gary; Somers, Daryl J.; Humphreys, D. Gavin

2018-01-01

Kernel morphology characteristics of wheat are complex and quantitatively inherited. A doubled haploid (DH) population of the cross RL4452/‘AC Domain’ was used to study the genetic basis of seed shape. Quantitative trait loci (QTL) analyses were conducted on a total of 18 traits: 14 grain shape traits, flour yield (Fyd), and three agronomic traits (Plant height [Plht], 1000 Grain weight [Gwt], Test weight [Twt]), using data from trial locations at Glenlea, Brandon, and Morden in Manitoba, Canada, between 1999 and 2004. Kernel shape was studied through digital image analysis with an Acurum® grain analyzer. Plht, Gwt, Twt, Fyd, and grain shape QTL were correlated with each other and QTL analysis revealed that QTL for these traits often mapped to the same genetic locations. The most significant QTL for the grain shape traits were located on chromosomes 4B and 4D, each accounting for up to 24.4% and 53.3% of the total phenotypic variation, respectively. In addition, the most significant QTL for Plht, Gwt, and Twt were all detected on chromosome 4D at the Rht-D1 locus. Rht-D1b decreased Plht, Gwt, Twt, and kernel width relative to the Rht-D1a allele. A narrow genetic interval on chromosome 4B contained significant QTL for grain shape, Gwt, and Plht. The ‘AC Domain’ allele reduced Plht, Gwt, kernel length and width traits, but had no detectable effect on Twt. The data indicated that this variation was inconsistent with segregation at Rht-B1. Numerous QTL were identified that control these traits in this population. PMID:29357369

Evolution of phenotypic clusters through competition and local adaptation along an environmental gradient.

PubMed

Leimar, Olof; Doebeli, Michael; Dieckmann, Ulf

2008-04-01

We have analyzed the evolution of a quantitative trait in populations that are spatially extended along an environmental gradient, with gene flow between nearby locations. In the absence of competition, there is stabilizing selection toward a locally best-adapted trait that changes gradually along the gradient. According to traditional ideas, gradual spatial variation in environmental conditions is expected to lead to gradual variation in the evolved trait. A contrasting possibility is that the trait distribution instead breaks up into discrete clusters. Doebeli and Dieckmann (2003) argued that competition acting locally in trait space and geographical space can promote such clustering. We have investigated this possibility using deterministic population dynamics for asexual populations, analyzing our model numerically and through an analytical approximation. We examined how the evolution of clusters is affected by the shape of competition kernels, by the presence of Allee effects, and by the strength of gene flow along the gradient. For certain parameter ranges clustering was a robust outcome, and for other ranges there was no clustering. Our analysis shows that the shape of competition kernels is important for clustering: the sign structure of the Fourier transform of a competition kernel determines whether the kernel promotes clustering. Also, we found that Allee effects promote clustering, whereas gene flow can have a counteracting influence. In line with earlier findings, we could demonstrate that phenotypic clustering was favored by gradients of intermediate slope.

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

PubMed

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

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

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.

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.

The genetic architecture of maize (Zea mays L.) kernel weight determination.

PubMed

Alvarez Prado, Santiago; López, César G; Senior, M Lynn; Borrás, Lucas

2014-09-18

Individual kernel weight is an important trait for maize yield determination. We have identified genomic regions controlling this trait by using the B73xMo17 population; however, the effect of genetic background on control of this complex trait and its physiological components is not yet known. The objective of this study was to understand how genetic background affected our previous results. Two nested stable recombinant inbred line populations (N209xMo17 and R18xMo17) were designed for this purpose. A total of 408 recombinant inbred lines were genotyped and phenotyped at two environments for kernel weight and five other traits related to kernel growth and development. All traits showed very high and significant (P < 0.001) phenotypic variability and medium-to-high heritability (0.60-0.90). When N209xMo17 and R18xMo17 were analyzed separately, a total of 23 environmentally stable quantitative trait loci (QTL) and five epistatic interactions were detected for N209xMo17. For R18xMo17, 59 environmentally stable QTL and 17 epistatic interactions were detected. A joint analysis detected 14 stable QTL regardless of the genetic background. Between 57 and 83% of detected QTL were population specific, denoting medium-to-high genetic background effects. This percentage was dependent on the trait. A meta-analysis including our previous B73xMo17 results identified five relevant genomic regions deserving further characterization. In summary, our grain filling traits were dominated by small additive QTL with several epistatic and few environmental interactions and medium-to-high genetic background effects. This study demonstrates that the number of detected QTL and additive effects for different physiologically related grain filling traits need to be understood relative to the specific germplasm. Copyright © 2014 Alvarez Prado et al.

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

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

PubMed Central

Liu, Guozheng; Zhao, Yusheng; Gowda, Manje; Longin, C. Friedrich H.; Reif, Jochen C.; Mette, Michael F.

2016-01-01

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population. PMID:27383841

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

PubMed

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

2018-06-01

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

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

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...

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.

Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals.

PubMed

Fox, Glen; Manley, Marena

2014-01-30

Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK-NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK-NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost-effective technologies for high-speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK-NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK-NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain. © 2013 Society of Chemical Industry.

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.

Polymorphism, population structure, and multivariate relationships among secondary traits in open-pollinated corn heterotic groups

USDA-ARS?s Scientific Manuscript database

Plant, ear and kernel traits directly or indirectly associated with grain yield in corn (Zea mays) were suggested as "secondary" traits to select for larger grain yield, especially in open-pollinated corn varieties (OPVs) and their hybrids (OPVhs). Thirty-four secondary traits, besides grain yield, ...

Soft durum wheat - a paradigm shift

USDA-ARS?s Scientific Manuscript database

Two traits define most aspects of wheat quality and utilization: kernel texture (hardness) and gluten. The former is far simpler genetically and is controlled by two genes, Puroindoline a and Puroindoline b. Durum wheat lacks puroindolines and has very hard kernels. As such, durum wheat when milled ...

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

PubMed

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

2017-01-01

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

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

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.

Genetic analysis of kernel traits in maize-teosinte introgression populations

USDA-ARS?s Scientific Manuscript database

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is mos...

Locally Dependent Latent Trait Model and the Dutch Identity Revisited.

ERIC Educational Resources Information Center

Ip, Edward H.

2002-01-01

Proposes a class of locally dependent latent trait models for responses to psychological and educational tests. Focuses on models based on a family of conditional distributions, or kernel, that describes joint multiple item responses as a function of student latent trait, not assuming conditional independence. Also proposes an EM algorithm for…

QTLs associated with agronomic traits in the Attila × CDC Go spring wheat population evaluated under conventional management

PubMed Central

Zou, Jun; Iqbal, Muhammad; Chen, Hua; Asif, Mohammad; N’Diaye, Amidou; Navabi, Alireza; Perez-Lara, Enid; Pozniak, Curtis; Yang, Rong-Cai; Randhawa, Harpinder; Spaner, Dean

2017-01-01

Recently, we investigated the effect of the wheat 90K single nucleotide polymorphic (SNP) array and three gene-specific (Ppd-D1, Vrn-A1 and Rht-B1) markers on quantitative trait loci (QTL) detection in a recombinant inbred lines (RILs) population derived from a cross between two spring wheat (Triticum aestivum L.) cultivars, ‘Attila’ and ‘CDC Go’, and evaluated for eight agronomic traits at three environments under organic management. The objectives of the present study were to investigate the effect of conventional management on QTL detection in the same mapping population using the same set of markers as the organic management and compare the results with organic management. Here, we evaluated 167 RILs for number of tillers (tillering), flowering time, maturity, plant height, test weight (grain volume weight), 1000 kernel weight, grain yield, and grain protein content at seven conventionally managed environments from 2008 to 2014. Using inclusive composite interval mapping (ICIM) on phenotypic data averaged across seven environments and a subset of 1203 informative markers (1200 SNPs and 3 gene specific markers), we identified a total of 14 QTLs associated with flowering time (1), maturity (2), plant height (1), grain yield (1), test weight (2), kernel weight (4), tillering (1) and grain protein content (2). Each QTL individually explained from 6.1 to 18.4% of the phenotypic variance. Overall, the QTLs associated with each trait explained from 9.7 to 35.4% of the phenotypic and from 22.1 to 90.8% of the genetic variance. Three chromosomal regions on chromosomes 2D (61–66 cM), 4B (80–82 cM) and 5A (296–297 cM) harbored clusters of QTLs associated with two to three traits. The coincidental region on chromosome 5A harbored QTL clusters for both flowering and maturity time, and mapped about 2 cM proximal to the Vrn-A1 gene, which was in high linkage disequilibrium (0.70 ≤ r2 ≤ 0.75) with SNP markers that mapped within the QTL confidence interval. Six of the 14 QTLs (one for flowering time and plant height each, and two for maturity and kernel weight each) were common between the conventional and organic management systems, which suggests issues in directly utilizing gene discovery results based on conventional management to make in detail selection (decision) for organic management. PMID:28158253

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...

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.

Fine mapping and introgressing qFIS1-2, a major QTL for kernel fissure resistance in rice (Oryza sativa L.)

USDA-ARS?s Scientific Manuscript database

Rice (Oryza sativa L.) kernel fissuring increases breakage during milling and decreases the value of processed rice. This study employed molecular gene tagging methods to fine-map a fissure resistance (FR) locus in ‘Cybonnet’, a semidwarf tropical japonica cultivar, as well as transfer this trait to...

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.

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

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

PubMed

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

2016-12-01

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

Kernel Composition, Starch Structure, and Enzyme Digestibility of Opaque-2 Maize and Quality Protein Maize

USDA-ARS?s Scientific Manuscript database

Objectives of this study were to understand how opaque-2 (o2) mutation and quality protein maize (QPM) affect maize kernel composition and starch structure, property, and enzyme digestibility. Kernels of o2 maize contained less protein (9.6−12.5%) than those of the wild-type (WT) counterparts (12...

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.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Exploiting induced variation to dissect quantitative traits in barley.

PubMed

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

2010-04-01

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

Chemical and Nutritional Composition of Terminalia ferdinandiana (Kakadu Plum) Kernels: A Novel Nutrition Source

PubMed Central

Netzel, Michael E.; Tinggi, Ujang

2018-01-01

Terminalia ferdinandiana (Kakadu plum) is a native Australian fruit. Industrial processing of T. ferdinandiana fruits into puree generates seeds as a by-product, which are generally discarded. The aim of our present study was to process the seed to separate the kernel and determine its nutritional composition. The proximate, mineral and fatty acid compositions were analysed in this study. Kernels are composed of 35% fat, while proteins account for 32% dry weight (DW). The energy content and fiber were 2065 kJ/100 g and 21.2% DW, respectively. Furthermore, the study showed that kernels were a very rich source of minerals and trace elements, such as potassium (6693 mg/kg), calcium (5385 mg/kg), iron (61 mg/kg) and zinc (60 mg/kg) DW, and had low levels of heavy metals. The fatty acid composition of the kernels consisted of omega-6 fatty acid, linoleic acid (50.2%), monounsaturated oleic acid (29.3%) and two saturated fatty acids namely palmitic acid (12.0%) and stearic acid (7.2%). The results indicate that T. ferdinandiana kernels have the potential to be utilized as a novel protein source for dietary purposes and non-conventional supply of linoleic, palmitic and oleic acids. PMID:29649154

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.

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.

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

PubMed

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

2017-11-01

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

Fruit position within the canopy affects kernel lipid composition of hazelnuts.

PubMed

Pannico, Antonio; Cirillo, Chiara; Giaccone, Matteo; Scognamiglio, Pasquale; Romano, Raffaele; Caporaso, Nicola; Sacchi, Raffaele; Basile, Boris

2017-11-01

The aim of this research was to study the variability in kernel composition within the canopy of hazelnut trees. Kernel fresh and dry weight increased linearly with fruit height above the ground. Fat content decreased, while protein and ash content increased, from the bottom to the top layers of the canopy. The level of unsaturation of fatty acids decreased from the bottom to the top of the canopy. Thus, the kernels located in the bottom layers of the canopy appear to be more interesting from a nutritional point of view, but their lipids may be more exposed to oxidation. The content of different phytosterols increased progressively from bottom to top canopy layers. Most of these effects correlated with the pattern in light distribution inside the canopy. The results of this study indicate that fruit position within the canopy is an important factor in determining hazelnut kernel growth and composition. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

PubMed Central

Garriga, Miguel; Romero-Bravo, Sebastián; Estrada, Félix; Escobar, Alejandro; Matus, Iván A.; del Pozo, Alejandro; Astudillo, Cesar A.; Lobos, Gustavo A.

2017-01-01

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection. PMID:28337210

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

PubMed

Garriga, Miguel; Romero-Bravo, Sebastián; Estrada, Félix; Escobar, Alejandro; Matus, Iván A; Del Pozo, Alejandro; Astudillo, Cesar A; Lobos, Gustavo A

2017-01-01

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat ( Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ 13 C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and k NN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ 13 C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.

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

Nature and composition of fat bloom from palm kernel stearin and hydrogenated palm kernel stearin compound chocolates.

PubMed

Smith, Kevin W; Cain, Fred W; Talbot, Geoff

2004-08-25

Palm kernel stearin and hydrogenated palm kernel stearin can be used to prepare compound chocolate bars or coatings. The objective of this study was to characterize the chemical composition, polymorphism, and melting behavior of the bloom that develops on bars of compound chocolate prepared using these fats. Bars were stored for 1 year at 15, 20, or 25 degrees C. At 15 and 20 degrees C the bloom was enriched in cocoa butter triacylglycerols, with respect to the main fat phase, whereas at 25 degrees C the enrichment was with palm kernel triacylglycerols. The bloom consisted principally of solid fat and was sharper melting than was the fat in the chocolate. Polymorphic transitions from the initial beta' phase to the beta phase accompanied the formation of bloom at all temperatures.

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.

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.

Tocochromanols composition in kernels recovered from different apricot varieties: RP-HPLC/FLD and RP-UPLC-ESI/MS(n) study.

PubMed

Górnaś, Paweł; Mišina, Inga; Grāvīte, Ilze; Soliven, Arianne; Kaufmane, Edīte; Segliņa, Dalija

2015-01-01

Composition of tocochromanols in kernels recovered from 16 different apricot varieties (Prunus armeniaca L.) was studied. Three tocopherol (T) homologues, namely α, γ and δ, were quantified in all tested samples by an RP-HPLC/FLD method. The γ-T was the main tocopherol homologue identified in apricot kernels and constituted approximately 93% of total detected tocopherols. The RP-UPLC-ESI/MS(n) method detected trace amounts of two tocotrienol homologues α and γ in the apricot kernels. The concentration of individual tocopherol homologues in kernels of different apricots varieties, expressed in mg/100 g dwb, was in the following range: 1.38-4.41 (α-T), 42.48-73.27 (γ-T) and 0.77-2.09 (δ-T). Moreover, the ratio between individual tocopherol homologues α:γ:δ was nearly constant in all varieties and amounted to approximately 2:39:1.

Molecular mapping of QTLs for resistance to Gibberella ear rot, in corn, caused by Fusarium graminearum.

PubMed

Ali, M Liakat; Taylor, Jeff H; Jie, Liu; Sun, Genlou; William, Manilal; Kasha, Ken J; Reid, Lana M; Pauls, K Peter

2005-06-01

Gibberella ear rot, caused by the fungus Fusarium graminearum Schwabe, is a serious disease of corn (Zea mays) grown in northern climates. Infected corn is lower yielding and contains toxins that are dangerous to livestock and humans. Resistance to ear rot in corn is quantitative, specific to the mode of fungal entry (silk channels or kernel wounds), and highly influenced by the environment. Evaluations of ear rot resistance are complex and subjective; and they need to be repeated over several years. All of these factors have hampered attempts to develop F. graminearum resistant corn varieties. The aim of this study was to identify molecular markers linked to the genes for resistance to Gibberella ear rot. A recombinant inbred (RI) population, produced from a cross between a Gibberella ear rot resistant line (CO387) and a susceptible line (CG62), was field-inoculated and scored for Gibberella ear rot symptoms in the F4, F6, and F7 generations. The distributions of disease scores were continuous, indicating that resistance is probably conditioned by multiple loci. A molecular linkage map, based on segregation in the F5 RI population, contained 162 markers distributed over 10 linkage groups and had a total length of 2237 cM with an average distance between markers of 13.8 cM. Composite interval mapping identified 11 quantitative trait loci (QTLs) for Gibberella ear rot resistance following silk inoculation and 18 QTLs following kernel inoculation in 4 environments that accounted for 6.7%-35% of the total phenotypic variation. Only 2 QTLs (on linkage group 7) were detected in more than 1 test for silk resistance, and only 1 QTL (on linkage group 5) was detected in more than 1 test for kernel resistance, confirming the strong influence of the environment on these traits. The majority of the favorable alleles were derived from the resistant parent (CO387). The germplasm and markers for QTLs with significant phenotypic effects may be useful for marker-assisted selection to incorporate Gibberella ear rot resistance into commercial corn cultivars.

Bran data of total flavonoid and total phenolic contents, oxygen radical absorbance capacity, and profiles of proanthocyanidins and whole grain physical traits of 32 red and purple rice varieties

USDA-ARS?s Scientific Manuscript database

Phytochemicals in red and purple bran rice have potential health benefit to humans. We determined the phytochemicals in brans of 32 red and purple global rice varieties. The description of the origin and physical traits of the whole grain (color, length, width, thickness and 100-kernel weight) of th...

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.

Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs.

PubMed

Assanga, Silvano O; Fuentealba, Maria; Zhang, Guorong; Tan, ChorTee; Dhakal, Smit; Rudd, Jackie C; Ibrahim, Amir M H; Xue, Qingwu; Haley, Scott; Chen, Jianli; Chao, Shiaoman; Baker, Jason; Jessup, Kirk; Liu, Shuyu

2017-01-01

Stable quantitative trait loci (QTL) are important for deployment in marker assisted selection in wheat (Triticum aestivum L.) and other crops. We reported QTL discovery in wheat using a population of 217 recombinant inbred lines and multiple statistical approach including multi-environment, multi-trait and epistatic interactions analysis. We detected nine consistent QTL linked to different traits on chromosomes 1A, 2A, 2B, 5A, 5B, 6A, 6B and 7A. Grain yield QTL were detected on chromosomes 2B.1 and 5B across three or four models of GenStat, MapQTL, and QTLNetwork while the QTL on chromosomes 5A.1, 6A.2, and 7A.1 were only significant with yield from one or two models. The phenotypic variation explained (PVE) by the QTL on 2B.1 ranged from 3.3-25.1% based on single and multi-environment models in GenStat and was pleiotropic or co-located with maturity (days to heading) and yield related traits (test weight, thousand kernel weight, harvest index). The QTL on 5B at 211 cM had PVE range of 1.8-9.3% and had no significant pleiotropic effects. Other consistent QTL detected in this study were linked to yield related traits and agronomic traits. The QTL on 1A was consistent for the number of spikes m-2 across environments and all the four analysis models with a PVE range of 5.8-8.6%. QTL for kernels spike-1 were found in chromosomes 1A, 2A.1, 2B.1, 6A.2, and 7A.1 with PVE ranged from 5.6-12.8% while QTL for thousand kernel weight were located on chromosomes 1A, 2B.1, 5A.1, 6A.2, 6B.1 and 7A.1 with PVEranged from 2.7-19.5%. Among the consistent QTL, five QTL had significant epistatic interactions (additive × additive) at least for one trait and none revealed significant additive × additive × environment interactions. Comparative analysis revealed that the region within the confidence interval of the QTL on 5B from 211.4-244.2 cM is also linked to genes for aspartate-semialdehyde dehydrogenase, splicing regulatory glutamine/lysine-rich protein 1 isoform X1, and UDP-glucose 6-dehydrogenase 1-like isoform X1. The stable QTL could be important for further validation, high throughput SNP development, and marker-assisted selection (MAS) in wheat.

Impact of Corn Earworm (Lepidoptera: Noctuidae) on Field Corn (Poales: Poaceae) Yield and Grain Quality.

PubMed

Bibb, Jenny L; Cook, Donald; Catchot, Angus; Musser, Fred; Stewart, Scott D; Leonard, Billy Rogers; Buntin, G David; Kerns, David; Allen, Tom W; Gore, Jeffrey

2018-05-28

Corn earworm, Helicoverpa zea (Boddie), commonly infests field corn, Zea mays (L.). The combination of corn plant biology, corn earworm behavior in corn ecosystems, and field corn value renders corn earworm management with foliar insecticides noneconomical. Corn technologies containing Bacillus thuringiensis (Bt) Berliner (Bacillales: Bacillaceae) were introduced that exhibit substantial efficacy against corn earworm and may reduce mycotoxin contamination in grain. The first generation Bt traits in field corn demonstrated limited activity on corn earworm feeding on grain. The pyramided corn technologies have greater cumulative protein concentrations and higher expression throughout the plant, so these corn traits should provide effective management of this pest. Additionally, reduced kernel injury may affect physical grain quality. Experiments were conducted during 2011-2012 to investigate corn earworm impact on field corn yield and grain quality. Treatments included field corn hybrids expressing the Herculex, YieldGard, and Genuity VT Triple Pro technologies. Supplemental insecticide treatments were applied every 1-2 d from silk emergence until silk senescence to create a range of injured kernels for each technology. No significant relationship between the number of corn earworm damaged kernels and yield was observed for any technology/hybrid. In these studies, corn earworm larvae did not cause enough damage to impact yield. Additionally, no consistent relationship between corn earworm damage and aflatoxin contamination was observed. Based on these data, the economic value of pyramided Bt corn traits to corn producers, in the southern United States, appears to be from management of other lepidopteran insect pests including European and southwestern corn borer.

High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology

PubMed Central

Su, Chengfu; Wang, Wei; Gong, Shunliang; Zuo, Jinghui; Li, Shujiang; Xu, Shizhong

2017-01-01

Increasing grain yield is the ultimate goal for maize breeding. High resolution quantitative trait loci (QTL) mapping can help us understand the molecular basis of phenotypic variation of yield and thus facilitate marker assisted breeding. The aim of this study is to use genotyping-by-sequencing (GBS) for large-scale SNP discovery and simultaneous genotyping of all F2 individuals from a cross between two varieties of maize that are in clear contrast in yield and related traits. A set of 199 F2 progeny derived from the cross of varieties SG-5 and SG-7 were generated and genotyped by GBS. A total of 1,046,524,604 reads with an average of 5,258,918 reads per F2 individual were generated. This number of reads represents an approximately 0.36-fold coverage of the maize reference genome Zea_mays.AGPv3.29 for each F2 individual. A total of 68,882 raw SNPs were discovered in the F2 population, which, after stringent filtering, led to a total of 29,927 high quality SNPs. Comparative analysis using these physically mapped marker loci revealed a higher degree of synteny with the reference genome. The SNP genotype data were utilized to construct an intra-specific genetic linkage map of maize consisting of 3,305 bins on 10 linkage groups spanning 2,236.66 cM at an average distance of 0.68 cM between consecutive markers. From this map, we identified 28 QTLs associated with yield traits (100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, corn grains per row, ear weight, and grain weight per plant) using the composite interval mapping (CIM) method and 29 QTLs using the least absolute shrinkage selection operator (LASSO) method. QTLs identified by the CIM method account for 6.4% to 19.7% of the phenotypic variation. Small intervals of three QTLs (qCGR-1, qKW-2, and qGWP-4) contain several genes, including one gene (GRMZM2G139872) encoding the F-box protein, three genes (GRMZM2G180811, GRMZM5G828139, and GRMZM5G873194) encoding the WD40-repeat protein, and one gene (GRMZM2G019183) encoding the UDP-Glycosyltransferase. The work will not only help to understand the mechanisms that control yield traits of maize, but also provide a basis for marker-assisted selection and map-based cloning in further studies. PMID:28533786

Effect of kernel size and mill type on protein, milling yield, and baking quality of hard red spring wheat

USDA-ARS?s Scientific Manuscript database

Optimization of flour yield and quality is important in the milling industry. The objective of this study was to determine the effect of kernel size and mill type on flour yield and end-use quality. A hard red spring wheat composite sample was segregated, based on kernel size, into large, medium, ...

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

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.

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.

The Genetic Architecture of Response to Long-Term Artificial Selection for Oil Concentration in the Maize Kernel

PubMed Central

Laurie, Cathy C.; Chasalow, Scott D.; LeDeaux, John R.; McCarroll, Robert; Bush, David; Hauge, Brian; Lai, Chaoqiang; Clark, Darryl; Rocheford, Torbert R.; Dudley, John W.

2004-01-01

In one of the longest-running experiments in biology, researchers at the University of Illinois have selected for altered composition of the maize kernel since 1896. Here we use an association study to infer the genetic basis of dramatic changes that occurred in response to selection for changes in oil concentration. The study population was produced by a cross between the high- and low-selection lines at generation 70, followed by 10 generations of random mating and the derivation of 500 lines by selfing. These lines were genotyped for 488 genetic markers and the oil concentration was evaluated in replicated field trials. Three methods of analysis were tested in simulations for ability to detect quantitative trait loci (QTL). The most effective method was model selection in multiple regression. This method detected ∼50 QTL accounting for ∼50% of the genetic variance, suggesting that >50 QTL are involved. The QTL effect estimates are small and largely additive. About 20% of the QTL have negative effects (i.e., not predicted by the parental difference), which is consistent with hitchhiking and small population size during selection. The large number of QTL detected accounts for the smooth and sustained response to selection throughout the twentieth century. PMID:15611182

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-04

Cassava ( Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant. Copyright © 2018 Elias et al.

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.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

PubMed

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.

Composition and Free Radical Scavenging Activity of Kernel Oil from Torreya grandis, Carya Cathayensis, and Myrica Rubra

PubMed Central

Ni, Liang; Shi, Wei-Yong

2014-01-01

In this study, we measured the composition and free radical scavenging activity of several species of nuts, namely, Torreya grandis, Carya cathayensis, and Myrica rubra. The nut kernels of the aforementioned species are rich in fatty acids, particularly in unsaturated fatty acids, and have 51% oil content. T. grandis and C. cathayensis are mostly produced in ZheJiang province. The trace elements in the kernels of T. grandis and C. cathayensis were generally higher than those in M. rubra, except for Fe with a value of 64.41 mg/Kg. T. grandis is rich in selenium (52.91−68.71 mg/Kg). All three kernel oils have a certain free radical scavenging capacity, with the highest value in M. rubra. In the DPPH assay, the IC50 of M. rubra kernel oil was 60 μg/mL, and OH was 100 μg/mL. The results of this study provide basic data for the future development of the edible nut resources in ZheJiang province. PMID:24734074

Evaluation of corn germplasm lines for multiple ear-colonizing insect and disease resistance.

PubMed

Ni, Xinzhi; Xu, Wenwei; Blanco, Michael H; Wilson, Jeffrey P

2012-08-01

Ear-colonizing insects and diseases that reduce yield and impose health threats by mycotoxin contaminations in the grain, are critical impediments for corn (Zea mays L.) production in the southern United States. Ten germplasm lines from the Germplasm Enhancement of Maize (GEM) Program in Ames, IA, and Raleigh, NC, and 10 lines (derived from GEM germplasm) from the Texas Agricultural Experiment Station in Lubbock, TX, were examined in 2007 and 2008 with local resistant and susceptible controls. Four types of insect damage and smut disease (Ustilago maydis) infection, as well as gene X environment (G X E) interaction, was assessed on corn ears under field conditions. Insect damage on corn ears was further separated as cob and kernel damage. Cob penetration rating was used to assess corn earworm [Helicoverpa zea (Boddie)] and fall armyworm [Spodoptera frugiperda (J.E. Smith)] feeding on corn cobs, whereas kernel damage was assessed using three parameters: 1) percentage of kernels discolored by stink bugs (i.e., brown stink bug [Euschistus serous (Say)], southern green stink bug [Nezara viridula (L.)], and green stink bug [Chinavia (Acrosternum) hilare (Say)]; 2) percentage of maize weevil (Sitophilus zeamais Motschulsky)-damaged kernels; and 3) percentage of kernels damaged by sap beetle (Carpophilus spp.), "chocolate milkworm" (Moodna spp.), and pink scavenger caterpillar [Pyroderces (Anatrachyntis) rileyi (Walsingham)]. The smut infection rates on ears, tassels, and nodes also were assessed. Ear protection traits (i.e., husk tightness and extension) in relation to insect damage and smut infection also were examined. Significant differences in insect damage, smut infection, and husk protection traits were detected among the germplasm lines. Three of the 20 germplasm lines were identified as being multiple insect and smut resistant. Of the three lines, entries 5 and 7 were derived from DKXL370, which was developed using corn germplasm from Brazil, whereas entry 14 was derived from CUBA117.

How yield relates to ash content, Δ13C and Δ18O in maize grown under different water regimes

PubMed Central

Cabrera-Bosquet, Llorenç; Sánchez, Ciro; Araus, José Luis

2009-01-01

Background and Aims Stable isotopes have proved a valuable phenotyping tool when breeding for yield potential and drought adaptation; however, the cost and technical skills involved in isotope analysis limit its large-scale application in breeding programmes. This is particularly so for Δ18O despite the potential relevance of this trait in C4 crops. The accumulation of minerals (measured as ash content) has been proposed as an inexpensive way to evaluate drought adaptation and yield in C3 cereals, but little is known of the usefulness of this measure in C4 cereals such as maize (Zea mays). The present study investigates how yield relates to ash content, Δ13C and Δ18O, and evaluates the use of ash content as an alternative or complementary criterion to stable isotopes in assessing yield potential and drought resistance in maize. Methods A set of tropical maize hybrids developed by CIMMYT were subjected to different water availabilities, in order to induce water stress during the reproductive stages under field conditions. Ash content and Δ13C were determined in leaves and kernels. In addition, Δ18O was measured in kernels. Key Results Water regime significantly affected yield, ash content and stable isotopes. The results revealed a close relationship between ash content in leaves and the traits informing about plant water status. Ash content in kernels appeared to reflect differences in sink–source balance. Genotypic variation in grain yield was mainly explained by the combination of ash content and Δ18O, whilst Δ13C did not explain a significant percentage of such variation. Conclusions Ash content in leaves and kernels proved a useful alternative or complementary criterion to Δ18O in kernels for assessing yield performance in maize grown under drought conditions. PMID:19773272

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

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.

KMgene: a unified R package for gene-based association analysis for complex traits.

PubMed

Yan, Qi; Fang, Zhou; Chen, Wei; Stegle, Oliver

2018-02-09

In this report, we introduce an R package KMgene for performing gene-based association tests for familial, multivariate or longitudinal traits using kernel machine (KM) regression under a generalized linear mixed model (GLMM) framework. Extensive simulations were performed to evaluate the validity of the approaches implemented in KMgene. http://cran.r-project.org/web/packages/KMgene. qi.yan@chp.edu or wei.chen@chp.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2018. Published by Oxford University Press.

The effect of microwave roasting on bioactive compounds, antioxidant activity and fatty acid composition of apricot kernel and oils.

PubMed

Al Juhaimi, Fahad; Musa Özcan, Mehmet; Ghafoor, Kashif; Babiker, Elfadıl E

2018-03-15

In this study, the effect of microwave (360W, 540W and 720W) oven roasting on oil yields, phenolic compounds, antioxidant activity, and fatty acid composition of some apricot kernel and oils was investigated. While total phenol contents of control group of apricot kernels change between 54.41mgGAE/100g (Soğancıoğlu) and 59.61mgGAE/100g (Hasanbey), total phenol contents of kernel samples roasted in 720W were determined between 27.41mgGAE/100g (Çataloğlu) and 34.52mgGAE/100g (Soğancıoğlu). Roasting process in microwave at 720W caused the reduction of some phenolic compounds of apricot kernels. The gallic acid contents of control apricot kernels ranged between 7.23mg/100g (Kabaaşı) and 11.23mg/100g (Çataloğlu) whereas the gallic acid contents of kernels roasted in 540W changed between 15.35mg/100g (Soğancıoğlu) and 21.17mg/100g (Çataloğlu). In addition, oleic acid contents of control group oils vary between 65.98% (Soğancıoğlu) and 71.86% (Hasanbey), the same fatty acid ranged from 63.48% (Soğancıoğlu) to 70.36% (Hasanbey). Copyright © 2017 Elsevier Ltd. All rights reserved.

Physiological Traits Associated with Wheat Yield Potential and Performance under Water-Stress in a Mediterranean Environment

PubMed Central

del Pozo, Alejandro; Yáñez, Alejandra; Matus, Iván A.; Tapia, Gerardo; Castillo, Dalma; Sanchez-Jardón, Laura; Araus, José L.

2016-01-01

Different physiological traits have been proposed as key traits associated with yield potential as well as performance under water stress. The aim of this paper is to examine the genotypic variability of leaf chlorophyll, stem water-soluble carbohydrate content and carbon isotope discrimination (Δ13C), and their relationship with grain yield (GY) and other agronomical traits, under contrasting water conditions in a Mediterranean environment. The study was performed on a large collection of 384 wheat genotypes grown under water stress (WS, rainfed), mild water stress (MWS, deficit irrigation), and full irrigation (FI). The average GY of two growing seasons was 2.4, 4.8, and 8.9 Mg ha−1 under WS, MWS, and FI, respectively. Chlorophyll content at anthesis was positively correlated with GY (except under FI in 2011) and the agronomical components kernels per spike (KS) and thousand kernel weight (TKW). The WSC content at anthesis (WSCCa) was negatively correlated with spikes per square meter (SM2), but positively correlated with KS and TKW under WS and FI conditions. As a consequence, the relationships between WSCCa with GY were low or not significant. Therefore, selecting for high stem WSC would not necessary lead to genotypes of GY potential. The relationship between Δ13C and GY was positive under FI and MWS but negative under severe WS (in 2011), indicating higher water use under yield potential and MWS conditions. PMID:27458470

Physiological Traits Associated with Wheat Yield Potential and Performance under Water-Stress in a Mediterranean Environment.

PubMed

Del Pozo, Alejandro; Yáñez, Alejandra; Matus, Iván A; Tapia, Gerardo; Castillo, Dalma; Sanchez-Jardón, Laura; Araus, José L

2016-01-01

Different physiological traits have been proposed as key traits associated with yield potential as well as performance under water stress. The aim of this paper is to examine the genotypic variability of leaf chlorophyll, stem water-soluble carbohydrate content and carbon isotope discrimination (Δ(13)C), and their relationship with grain yield (GY) and other agronomical traits, under contrasting water conditions in a Mediterranean environment. The study was performed on a large collection of 384 wheat genotypes grown under water stress (WS, rainfed), mild water stress (MWS, deficit irrigation), and full irrigation (FI). The average GY of two growing seasons was 2.4, 4.8, and 8.9 Mg ha(-1) under WS, MWS, and FI, respectively. Chlorophyll content at anthesis was positively correlated with GY (except under FI in 2011) and the agronomical components kernels per spike (KS) and thousand kernel weight (TKW). The WSC content at anthesis (WSCCa) was negatively correlated with spikes per square meter (SM2), but positively correlated with KS and TKW under WS and FI conditions. As a consequence, the relationships between WSCCa with GY were low or not significant. Therefore, selecting for high stem WSC would not necessary lead to genotypes of GY potential. The relationship between Δ(13)C and GY was positive under FI and MWS but negative under severe WS (in 2011), indicating higher water use under yield potential and MWS conditions.

Fatty acids and bioactive compounds of the pulps and kernels of Brazilian palm species, guariroba (Syagrus oleraces), jerivá (Syagrus romanzoffiana) and macaúba (Acrocomia aculeata).

PubMed

Coimbra, Michelle C; Jorge, Neuza

2012-02-01

Bioactive compounds are capable of providing health benefits, reducing disease incidence or favoring body functioning. There is a growing search for vegetable oils containing such compounds. This study aimed to characterize the pulp and kernel oils of the Brazilian palm species guariroba (Syagrus oleracea), jerivá (Syagrus romanzoffiana) and macaúba (Acrocomia aculeata), aiming at possible uses in several industries. Fatty acid composition, phenolic and carotenoid contents, tocopherol composition were evaluated. The majority of the fatty acids in pulps were oleic and linoleic; macaúba pulp contained 526 g kg⁻¹ of oleic acid. Lauric acid was detected in the kernels of all three species as the major saturated fatty acid, in amounts ranging from 325.8 to 424.3 g kg⁻¹. The jerivá pulp contained carotenoids and tocopherols on average of 1219 µg g⁻¹ and 323.50 mg kg⁻¹, respectively. The pulps contained more unsaturated fatty acids than the kernels, mainly oleic and linoleic. Moreover, the pulps showed higher carotenoid and tocopherol contents. The kernels showed a predominance of saturated fatty acids, especially lauric acid. The fatty acid profiles of the kernels suggest that these oils may be better suited for the cosmetic and pharmaceutical industries than for use in foods. Copyright © 2011 Society of Chemical Industry.

Extreme-phenotype genome-wide association study (XP-GWAS): a method for identifying trait-associated variants by sequencing pools of individuals selected from a diversity panel.

PubMed

Yang, Jinliang; Jiang, Haiying; Yeh, Cheng-Ting; Yu, Jianming; Jeddeloh, Jeffrey A; Nettleton, Dan; Schnable, Patrick S

2015-11-01

Although approaches for performing genome-wide association studies (GWAS) are well developed, conventional GWAS requires high-density genotyping of large numbers of individuals from a diversity panel. Here we report a method for performing GWAS that does not require genotyping of large numbers of individuals. Instead XP-GWAS (extreme-phenotype GWAS) relies on genotyping pools of individuals from a diversity panel that have extreme phenotypes. This analysis measures allele frequencies in the extreme pools, enabling discovery of associations between genetic variants and traits of interest. This method was evaluated in maize (Zea mays) using the well-characterized kernel row number trait, which was selected to enable comparisons between the results of XP-GWAS and conventional GWAS. An exome-sequencing strategy was used to focus sequencing resources on genes and their flanking regions. A total of 0.94 million variants were identified and served as evaluation markers; comparisons among pools showed that 145 of these variants were statistically associated with the kernel row number phenotype. These trait-associated variants were significantly enriched in regions identified by conventional GWAS. XP-GWAS was able to resolve several linked QTL and detect trait-associated variants within a single gene under a QTL peak. XP-GWAS is expected to be particularly valuable for detecting genes or alleles responsible for quantitative variation in species for which extensive genotyping resources are not available, such as wild progenitors of crops, orphan crops, and other poorly characterized species such as those of ecological interest. © 2015 The Authors The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Phenolic compounds and fatty acid composition of organic and conventional grown pecan kernels

USDA-ARS?s Scientific Manuscript database

In this study, differences in contents of phenolic compounds and fatty acids in pecan kernels of organically versus conventionally grown pecan cultivars (‘Desirable’, ‘Cheyenne’, and ‘Wichita’) were evaluated. Although we were able to identify nine phenolic compounds (gallic acid, catechol, catechin...

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.

Common variation at PPARGC1A/B and change in body composition and metabolic traits following preventive interventions: the Diabetes Prevention Program.

PubMed

Franks, Paul W; Christophi, Costas A; Jablonski, Kathleen A; Billings, Liana K; Delahanty, Linda M; Horton, Edward S; Knowler, William C; Florez, Jose C

2014-03-01

PPARGC1A and PPARGCB encode transcriptional coactivators that regulate numerous metabolic processes. We tested associations and treatment (i.e. metformin or lifestyle modification) interactions with metabolic traits in the Diabetes Prevention Program, a randomised controlled trial in persons at high risk of type 2 diabetes. We used Tagger software to select 75 PPARGCA1 and 94 PPARGC1B tag single-nucleotide polymorphisms (SNPs) for analysis. These SNPs were tested for associations with relevant cardiometabolic quantitative traits using generalised linear models. Aggregate genetic effects were tested using the sequence kernel association test. In aggregate, PPARGC1A variation was strongly associated with baseline triacylglycerol concentrations (p = 2.9 × 10(-30)), BMI (p = 2.0 × 10(-5)) and visceral adiposity (p = 1.9 × 10(-4)), as well as with changes in triacylglycerol concentrations (p = 1.7 × 10(-5)) and BMI (p = 9.9 × 10(-5)) from baseline to 1 year. PPARGC1B variation was only associated with baseline subcutaneous adiposity (p = 0.01). In individual SNP analyses, Gly482Ser (rs8192678, PPARGC1A) was associated with accumulation of subcutaneous adiposity and worsening insulin resistance at 1 year (both p < 0.05), while rs2970852 (PPARGC1A) modified the effects of metformin on triacylglycerol levels (p(interaction) = 0.04). These findings provide several novel and other confirmatory insights into the role of PPARGC1A variation with respect to diabetes-related metabolic traits. ClinicalTrials.gov NCT00004992.

Variation in trait trade-offs allows differentiation among predefined plant functional types: implications for predictive ecology.

PubMed

Verheijen, Lieneke M; Aerts, Rien; Bönisch, Gerhard; Kattge, Jens; Van Bodegom, Peter M

2016-01-01

Plant functional types (PFTs) aggregate the variety of plant species into a small number of functionally different classes. We examined to what extent plant traits, which reflect species' functional adaptations, can capture functional differences between predefined PFTs and which traits optimally describe these differences. We applied Gaussian kernel density estimation to determine probability density functions for individual PFTs in an n-dimensional trait space and compared predicted PFTs with observed PFTs. All possible combinations of 1-6 traits from a database with 18 different traits (total of 18 287 species) were tested. A variety of trait sets had approximately similar performance, and 4-5 traits were sufficient to classify up to 85% of the species into PFTs correctly, whereas this was 80% for a bioclimatically defined tree PFT classification. Well-performing trait sets included combinations of correlated traits that are considered functionally redundant within a single plant strategy. This analysis quantitatively demonstrates how structural differences between PFTs are reflected in functional differences described by particular traits. Differentiation between PFTs is possible despite large overlap in plant strategies and traits, showing that PFTs are differently positioned in multidimensional trait space. This study therefore provides the foundation for important applications for predictive ecology. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Wide Variability in Seed Characteristics, Kernel Quality, and Zein Profiles Among Diverse Maize Inbreds, Landraces, and Teosinte

USDA-ARS?s Scientific Manuscript database

All crop species have been domesticated from their wild relatives, and geneticists are just now beginning to understand the genetic consequences of artificial (human) selection on agronomic traits that are relevant today. The major consequence is severe reduction in genetic diversity for genes unde...

Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

NASA Astrophysics Data System (ADS)

Niazmardi, S.; Safari, A.; Homayouni, S.

2017-09-01

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

Anthocyanin composition and oxygen radical scavenging capacity (ORAC) of milled and pearled purple, black, and common barley.

PubMed

Bellido, Guillermo G; Beta, Trust

2009-02-11

The importance of anthocyanins to the total antioxidant capacity of various fruits and vegetables has been well established, but less attention has been focused on cereal grains. This study investigated the antioxidant capacity and anthocyanin composition of a bran-rich pearling fraction (10% outer kernel layers) and whole kernel flour of purple (CI-1248), black (PERU-35), and yellow (EX-83) barley genotypes. HPLC analysis showed that as much as 6 times more anthocyanin per unit weight (microg/g) was present in the bran-rich fractions of yellow and purple barley (1587 and 3534, respectively) than in their corresponding whole kernel flours (210 and 573, respectively). Delphinidin 3-glucoside, delphinidin 3-rutinoside, cyanidin 3-glucoside, petunidin 3-glucoside, and cyanidin chloride were positively identified in barley, with as many as 9 and 15 anthocyanins being detected in yellow and purple barley, respectively. Antioxidant activity analysis showed that the ORAC values for the bran-rich fractions were significantly (p < 0.05) higher than for the whole kernel flour.

The partial replacement of palm kernel shell by carbon black and halloysite nanotubes as fillers in natural rubber composites

NASA Astrophysics Data System (ADS)

Daud, Shuhairiah; Ismail, Hanafi; Bakar, Azhar Abu

2017-07-01

The effect of partial replacement of palm kernel shell powder by carbon black (CB) and halloysite nanotube (HNT) on the tensile properties, rubber-filler interaction, thermal properties and morphological studies of natural rubber (NR) composites were investigated. Four different compositions of NR/PKS/CB and NR/PKS/HNT composites i.e 20/0, 15/5, 10/10,5/15 and 0/20 parts per hundred rubber (phr) were prepared on a two roll mill. The results showed that the tensile strength and modulus at 100% elongation (M100) and 300% elongation (M300) were higher for NR/PKS/CB compared to NR/PKS/HNT composites. NR/PKS/CB composites had the lowest elongation at break (Eb). The effect of commercial fillers in NR/PKS composites on tensile properties was confirmed by the rubber-filler interaction and scanning electron microscopy (SEM) study. The thermal stability of PKS filled NR composites with partially replaced by commercial fillers also determined by Thermo gravimetric Analysis (TGA).

Characterizing invertebrate traits in wadeable streams of the contiguous US: differences among ecoregions and land uses

USGS Publications Warehouse

Zuellig, Robert E.; Schmidt, Travis S.

2012-01-01

Much is known about invertebrate community traits in basins across Europe, but no comprehensive description of traits exists for the continental US. Little is known about the trait composition of invertebrates in reference or least-disturbed basins of the US, how trait composition varies among ecoregions, or how consistently traits respond to land use. These elements are essential to development of trait-based tools for conservation and assessment of biological integrity. We compared invertebrate traits of least-disturbed basins among ecoregions of the US. Benthic invertebrate data (presence/absence) from 1987 basins were translated into 56 binary traits (e.g., bivoltine, clinger). Basins were classified as least-disturbed, agricultural, or urban, and grouped into 9 ecoregions. Landuse, climatic, physiographic, and hydrologic data were used to describe ecoregions and to evaluate least-disturbed basin quality. The unique habitat template of each ecoregion selected for trait compositions in least-disturbed basins that differed among ecoregions. Among the traits examined, life-history (e.g., voltinism, development) and ecological traits (e.g., rheophily, thermal preference) differed most among ecoregions. Agricultural and urban land uses selected for trait compositions that differed from least-disturbed, but the extent of the differences depended on ecoregion and quality of the least-disturbed basins. No trait compositions unique to specific land uses were found. However, a disturbance syndrome was observed in that the magnitude and direction of trait responses to urban and agricultural land uses were consistent among ecoregions. Each ecoregion had a unique trait composition, but trait compositions could be used to aggregate ecoregions into 3 broad regions: Western Mountains, Plains and Lowlands, and Eastern Highlands. Our results indicate that large-scale trait-based assessment tools for the US will require calibration to account for regional differences in the trait composition of basins and in the quality of least-disturbed basins.

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.

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

NASA Astrophysics Data System (ADS)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

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

Effects of roasting temperature and duration on fatty acid composition, phenolic composition, Maillard reaction degree and antioxidant attribute of almond (Prunus dulcis) kernel.

PubMed

Lin, Jau-Tien; Liu, Shih-Chun; Hu, Chao-Chin; Shyu, Yung-Shin; Hsu, Chia-Ying; Yang, Deng-Jye

2016-01-01

Roasting treatment increased levels of unsaturated fatty acids (linoleic, oleic and elaidic acids) as well as saturated fatty acids (palmitic and stearic acids) in almond (Prunus dulcis) kernel oils with temperature (150 or 180 °C) and duration (5, 10 or 20 min). Nonetheless, higher temperature (200 °C) and longer duration (10 or 20 min) roasting might result in breakdown of fatty acids especially for unsaturated fatty acids. Phenolic components (total phenols, flavonoids, condensed tannins and phenolic acids) of almond kernels substantially lost in the initial phase; afterward these components gradually increased with roasting temperature and duration. Similar results also observed for their antioxidant activities (scavenging DPPH and ABTS(+) radicals and ferric reducing power). The changes of phenolic acid and flavonoid compositions were also determined by HPLC. Maillard reaction products (estimated with non-enzymatic browning index) also increased with roasting temperature and duration; they might also contribute to enhancing the antioxidant attributes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Macrofaunal production and biological traits: Spatial relationships along the UK continental shelf

NASA Astrophysics Data System (ADS)

Bolam, S. G.; Eggleton, J. D.

2014-04-01

Biological trait analysis (BTA) is increasingly being employed to improve our understanding of the ecological functioning of marine benthic invertebrate communities. However, changes in trait composition are seldomly compared with concomitant changes in metrics of ecological function. Consequently, inferences regarding the functional implications of any changes are often anecdotal; we currently have a limited understanding of the functional significance of the traits commonly used. In this study, we quantify the relationship between benthic invertebrate trait composition and secondary production estimates using data spanning almost the breadth of the UK continental shelf. Communities described by their composition of 10 traits representing life history, morphology and behaviour showed strong relationships with variations in total secondary production. A much weaker relationship was observed for community productivity (or P:B), a measure of rate of energy turnover. Furthermore, the relationship between total production and multivariate taxonomic community composition was far weaker than that for trait composition. Indeed, the similarities between communities as defined by taxonomy were very different from those depicted by their trait composition. That is, as many studies have demonstrated, taxonomically different communities may display similar trait compositions, and vice versa. Finally, we found that descriptions of community trait composition vary greatly depending on whether abundance or biomass is used as the enumeration weighting method during BTA, and trait assessments based on biomass produced better relations with secondary production than those based on abundance. We discuss the significance of these findings with respect to BTA using marine benthic invertebrates.

Comparative study of growth traits and haematological parameters of Anak and Nigerian heavy ecotype chickens fed with graded levels of mango seed kernel (Mangifera indica) meal.

PubMed

Mbunwen, Ndofor-Foleng Harriet; Ngongeh, Lucas Atehmengo; Okolie, Peter Nzeribe; Okoli, Emeka Linus

2015-08-01

One hundred fifty Anak and 120 Nigerian heavy local ecotype (NHLE) chickens were used to study the effects of feeding graded levels of mango seed kernel meal (MKM) replacing maize diet on growth traits and haematological parameters. A 2 × 5 factorial arrangement was employed: two breeds and five diets. The birds were randomly allocated to five finisher diets formulated such that MKM replaced maize at 0, 10, 20, 30 and 40% (T1, T2, T3, T4 and T5) inclusion levels, respectively. The effect of breed and dietary treatments on growth performance and blood characteristics were determined. The results showed a significant (P < 0.05) breed effect on body weight and gain, shank length, thigh length, body width and body length. The growth traits of Anak breed were found to be superior to NHLE chickens. Within treatments, chicks on T1, T2 and T3, grew heavier than those on T4 and T5. However, feed intake, feed conversion ratio (FCR) and haematological indices (RBC, Hb, MCV, MCH and MCHC count) were not significant (P > 0.05) when the breeds and treatments were compared. It was concluded that inclusion of dietary MKM below 30% could replace maize in the diets of Anak and NHLE growing chickens without adverse effect on growth performance and blood constituents. This work suggests that genetic differences exist in growth traits of these breeds of chickens. This advantage could be useful in breed improvement programmes and better feeding managements of the NHLE and Anak chickens.

Factor regression for interpreting genotype-environment interaction in bread-wheat trials.

PubMed

Baril, C P

1992-05-01

The French INRA wheat (Triticum aestivum L. em Thell.) breeding program is based on multilocation trials to produce high-yielding, adapted lines for a wide range of environments. Differential genotypic responses to variable environment conditions limit the accuracy of yield estimations. Factor regression was used to partition the genotype-environment (GE) interaction into four biologically interpretable terms. Yield data were analyzed from 34 wheat genotypes grown in four environments using 12 auxiliary agronomic traits as genotypic and environmental covariates. Most of the GE interaction (91%) was explained by the combination of only three traits: 1,000-kernel weight, lodging susceptibility and spike length. These traits are easily measured in breeding programs, therefore factor regression model can provide a convenient and useful prediction method of yield.

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.

Willingness to Communicate in the Second Language: Understanding the Decision to Speak as a Volitional Process

ERIC Educational Resources Information Center

MacIntyre, Peter D.

2007-01-01

Previous research has devoted a great deal of attention to describing the long-term patterns and relationships among trait-level or situation-specific variables. The present discussion extracts kernels of wisdom, based on the literatures on language anxiety and language learning motivation, that are used to frame the argument that choosing to…

Thermal Properties of Starch From New Corn Lines as Impacted by Environment and During Line Development

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

Lenihan, Elizabeth M

The objectives of this research were to further characterize exotic by adapted corn inbreds by studying the impact of environment on their starch thermal properties, and investigating the development of starch thermal properties during kernel maturation by using differential scanning calorimetry (DSC). A method to expedite identification of unusual starch thermal traits was investigated by examining five corn kernels at a time, instead of one kernel, which the previous screening methods used. Corn lines with known thermal functions were blended with background starch (control) in ratios of unique starch to control starch, and analyzed by using DSC. Control starch wasmore » representative of typical corn starch. The values for each ratio within a mutant type were unique (α < 0.01) for most DSC measurements. These results supported the five-kernel method for rapidly screening large amounts of corn germplasm to identify unusual starch traits. The effects of 5 growing locations on starch thermal properties from exotic by adapted corn and Corn Belt lines were studied using DSC. The warmest location, Missouri, generally produced starch with greater gelatinization onset temperature (T oG), narrower range of gelatinization (R G), and greater enthalpy of gelatinization (ΔH G). The coolest location, Illinois, generally resulted in starch with lower T oG, wider R G, and lower ΔH G. Starch from the Ames 1 farm had thermal properties similar to those of Illinois, whereas starch from the Ames 2 farm had thermal properties similar to those of Missouri. The temperature at Ames 2 may have been warmer since it was located near a river; however, soil type and quality also were different. Final corn starch structure and function change during development and maturity. Thus, the changes in starch thermal properties during 5 stages of endosperm development from exotic by adapted corn and Corn Belt lines at two locations were studied by using DSC. The T oG tended to decrease during maturation of the kernel, whereas theΔH G tended not to change. Retrogradation parameters did not vary greatly among days after pollination (DAP) and between locations. Genotypes were affected differently by environments and significant interactions were found between genotype, environment,and DAP.« less

National Character Does Not Reflect Mean Personality Trait Levels in 49 Cultures

PubMed Central

Abdel-Khalek, A. M.; Ádám, N.; Adamovová, L.; Ahn, C.-k.; Ahn, H.-n.; Alansari, B. M.; Alcalay, L.; Allik, J.; Angleitner, A.; Avia, A.; Ayearst, L. E.; Barbaranelli, C.; Beer, A.; Borg-Cunen, M. A.; Bratko, D.; Brunner-Sciarra, M.; Budzinski, L.; Camart, N.; Dahourou, D.; De Fruyt, F.; de Lima, M. P.; del Pilar, G. E. H.; Diener, E.; Falzon, R.; Fernando, K.; Ficková, E.; Fischer, R.; Flores-Mendoza, C.; Ghayur, M. A.; Gülgöz, S.; Hagberg, B.; Halberstadt, J.; Halim, M. S.; Hřebíčková, M.; Humrichouse, J.; Jensen, H. H.; Jocic, D. D.; Jónsson, F. H.; Khoury, B.; Klinkosz, W.; Knežević, G.; Lauri, M. A.; Leibovich, N.; Martin, T. A.; Marušić, I.; Mastor, K. A.; Matsumoto, D.; McRorie, M.; Meshcheriakov, B.; Mortensen, E. L.; Munyae, M.; Nagy, J.; Nakazato, K.; Nansubuga, F.; Oishi, S.; Ojedokun, A. O.; Ostendorf, F.; Paulhus, D. L.; Pelevin, S.; Petot, J.-M.; Podobnik, N.; Porrata, J. L.; Pramila, V. S.; Prentice, G.; Realo, A.; Reátegui, N.; Rolland, J.-P.; Rossier, J.; Ruch, W.; Rus, V. S.; Sánchez-Bernardos, M. L.; Schmidt, V.; Sciculna-Calleja, S.; Sekowski, A.; Shakespeare-Finch, J.; Shimonaka, Y.; Simonetti, F.; Sineshaw, T.; Siuta, J.; Smith, P. B.; Trapnell, P. D.; Trobst, K. K.; Wang, L.; Yik, M.; Zupančič, A.

2009-01-01

Most people hold beliefs about personality characteristics typical of members of their own and others' cultures. These perceptions of national character may be generalizations from personal experience, stereotypes with a “kernel of truth,” or inaccurate stereotypes. We obtained national character ratings (N = 3,989) from 49 cultures and compared them to the average personality scores of culture members assessed by observer ratings and self-reports. National character ratings were reliable, but did not converge with assessed traits (Mdn r = .04). Perceptions of national character thus appear to be unfounded stereotypes that may serve the function of maintaining a national identity. PMID:16210536

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.

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

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

PubMed Central

Bandeira e Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-01-01

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. PMID:28455415

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

PubMed

Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-06-07

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.

Change of digestive physiology in sea cucumber Apostichopus japonicus (Selenka) induced by corn kernels meal and soybean meal in diets

NASA Astrophysics Data System (ADS)

Yu, Haibo; Gao, Qinfeng; Dong, Shuanglin; Hou, Yiran; Wen, Bin

2016-08-01

The present study was conducted to determine the change of digestive physiology in sea cucumber Apostichopus japonicus (Selenka) induced by corn kernels meal and soybean meal in diets. Four experimental diets were tested, in which Sargassum thunbergii was proportionally replaced by the mixture of corn kernels meal and soybean meal. The growth performance, body composition and intestinal digestive enzyme activities in A. japonicus fed these 4 diets were examined. Results showed that the sea cucumber exhibited the maximum growth rate when 20% of S. thunbergii in the diet was replaced by corn kernels meal and soybean meal, while 40% of S. thunbergii in the diet can be replaced by the mixture of corn kernels meal and soybean meal without adversely affecting growth performance of A. japonicus. The activities of intestinal trypsin and amylase in A. japonicus can be significantly altered by corn kernels meal and soybean meal in diets. Trypsin activity in the intestine of A. japonicus significantly increased in the treatment groups compared to the control, suggesting that the supplement of corn kernels meal and soybean meal in the diets might increase the intestinal trypsin activity of A. japonicus. However, amylase activity in the intestine of A. japonicus remarkably decreased with the increasing replacement level of S. thunbergii by the mixture of corn kernels meal and soybean meal, suggesting that supplement of corn kernels meal and soybean meal in the diets might decrease the intestinal amylase activity of A. japonicus.

Phenolic compounds and antioxidant activity of kernels and shells of Mexican pecan (Carya illinoinensis).

PubMed

de la Rosa, Laura A; Alvarez-Parrilla, Emilio; Shahidi, Fereidoon

2011-01-12

The phenolic composition and antioxidant activity of pecan kernels and shells cultivated in three regions of the state of Chihuahua, Mexico, were analyzed. High concentrations of total extractable phenolics, flavonoids, and proanthocyanidins were found in kernels, and 5-20-fold higher concentrations were found in shells. Their concentrations were significantly affected by the growing region. Antioxidant activity was evaluated by ORAC, DPPH•, HO•, and ABTS•-- scavenging (TAC) methods. Antioxidant activity was strongly correlated with the concentrations of phenolic compounds. A strong correlation existed among the results obtained using these four methods. Five individual phenolic compounds were positively identified and quantified in kernels: ellagic, gallic, protocatechuic, and p-hydroxybenzoic acids and catechin. Only ellagic and gallic acids could be identified in shells. Seven phenolic compounds were tentatively identified in kernels by means of MS and UV spectral comparison, namely, protocatechuic aldehyde, (epi)gallocatechin, one gallic acid-glucose conjugate, three ellagic acid derivatives, and valoneic acid dilactone.

Setting Up Decision-Making Tools toward a Quality-Oriented Participatory Maize Breeding Program

PubMed Central

Alves, Mara L.; Brites, Cláudia; Paulo, Manuel; Carbas, Bruna; Belo, Maria; Mendes-Moreira, Pedro M. R.; Brites, Carla; Bronze, Maria do Rosário; Gunjača, Jerko; Šatović, Zlatko; Vaz Patto, Maria C.

2017-01-01

Previous studies have reported promising differences in the quality of kernels from farmers' maize populations collected in a Portuguese region known to produce maize-based bread. However, several limitations have been identified in the previous characterizations of those populations, such as a limited set of quality traits accessed and a missing accurate agronomic performance evaluation. The objectives of this study were to perform a more detailed quality characterization of Portuguese farmers' maize populations; to estimate their agronomic performance in a broader range of environments; and to integrate quality, agronomic, and molecular data in the setting up of decision-making tools for the establishment of a quality-oriented participatory maize breeding program. Sixteen farmers' maize populations, together with 10 other maize populations chosen for comparison purposes, were multiplied in a common-garden experiment for quality evaluation. Flour obtained from each population was used to study kernel composition (protein, fat, fiber), flour's pasting behavior, and bioactive compound levels (carotenoids, tocopherols, phenolic compounds). These maize populations were evaluated for grain yield and ear weight in nine locations across Portugal; the populations' adaptability and stability were evaluated using additive main effects and multiplication interaction (AMMI) model analysis. The phenotypic characterization of each population was complemented with a molecular characterization, in which 30 individuals per population were genotyped with 20 microsatellites. Almost all farmers' populations were clustered into the same quality-group characterized by high levels of protein and fiber, low levels of carotenoids, volatile aldehydes, α- and δ-tocopherols, and breakdown viscosity. Within this quality-group, variability on particular quality traits (color and some bioactive compounds) could still be found. Regarding the agronomic performance, farmers' maize populations had low, but considerably stable, grain yields across the tested environments. As for their genetic diversity, each farmers' population was genetically heterogeneous; nonetheless, all farmers' populations were distinct from each other's. In conclusion, and taking into consideration different quality improvement objectives, the integration of the data generated within this study allowed the outline and exploration of alternative directions for future breeding activities. As a consequence, more informed choices will optimize the use of the resources available and improve the efficiency of participatory breeding activities. PMID:29312428

Nutritional composition of shea products and chemical properties of shea butter: a review.

PubMed

Honfo, Fernande G; Akissoe, Noel; Linnemann, Anita R; Soumanou, Mohamed; Van Boekel, Martinus A J S

2014-01-01

Increasing demand of shea products (kernels and butter) has led to the assessment of the state-of-the-art of these products. In this review, attention has been focused on macronutrients and micronutrients of pulp, kernels, and butter of shea tree and also the physicochemical properties of shea butter. Surveying the literature revealed that the pulp is rich in vitamin C (196.1 mg/100 g); consumption of 50 g covers 332% and 98% of the recommended daily intake (RDI) of children (4-8 years old) and pregnant women, respectively. The kernels contain a high level of fat (17.4-59.1 g/100 g dry weight). Fat extraction is mainly done by traditional methods that involve roasting and pressing of the kernels, churning the obtained liquid with water, boiling, sieving, and cooling. The fat (butter) is used in food preparation and medicinal and cosmetics industries. Its biochemical properties indicate some antioxidant and anti-inflammatory activities. Large variations are observed in the reported values for the composition of shea products. Recommendations for future research are presented to improve the quality and the shelf-life of the butter. In addition, more attention should be given to the accuracy and precision in experimental analyses to obtain more reliable information about biological variation.

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

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

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

PubMed Central

2014-01-01

Background Protein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods. Results We propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes. Conclusions We propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes. PMID:24564744

Studies of fatty acid composition, physicochemical and thermal properties, and crystallization behavior of mango kernel fats from various Thai varieties.

PubMed

Sonwai, Sopark; Ponprachanuvut, Punnee

2014-01-01

Mango kernel fat (MKF) has received attention in recent years due to the resemblance between its characteristics and those of cocoa butter (CB). In this work, fatty acid (FA) composition, physicochemical and thermal properties and crystallization behavior of MKFs obtained from four varieties of Thai mangoes: Keaw-Morakot (KM), Keaw-Sawoey (KS), Nam-Dokmai (ND) and Aok-Rong (AR), were characterized. The fat content of the mango kernels was 6.40, 5.78, 5.73 and 7.74% (dry basis) for KM, KS, ND and AR, respectively. The analysis of FA composition revealed that all four cultivars had oleic and stearic acids as the main FA components with ND and AR exhibiting highest and lowest stearic acid content, respectively. ND had the highest slip melting point and solid fat content (SFC) followed by KS, KM and AR. All fat samples exhibited high SFC at 20℃ and below. They melted slowly as the temperature increased and became complete liquids as the temperature approached 35°C. During static isothermal crystallization at 20°C, ND displayed the highest Avrami rate constant k followed by KS, KM and AR, indicating that the crystallization was fastest for ND and slowest for AR. The Avrami exponent n of all samples ranged from 0.89 to 1.73. The x-ray diffraction analysis showed that all MKFs crystallized into a mixture of pseudo-β', β', sub-β and β structures with β' being the predominant polymorph. Finally, the crystals of the kernel fats from all mango varieties exhibited spherulitic morphology.

Optimization of the acceptance of prebiotic beverage made from cashew nut kernels and passion fruit juice.

PubMed

Rebouças, Marina Cabral; Rodrigues, Maria do Carmo Passos; Afonso, Marcos Rodrigues Amorim

2014-07-01

The aim of this research was to develop a prebiotic beverage from a hydrosoluble extract of broken cashew nut kernels and passion fruit juice using response surface methodology in order to optimize acceptance of its sensory attributes. A 2(2) central composite rotatable design was used, which produced 9 formulations, which were then evaluated using different concentrations of hydrosoluble cashew nut kernel, passion fruit juice, oligofructose, and 3% sugar. The use of response surface methodology to interpret the sensory data made it possible to obtain a formulation with satisfactory acceptance which met the criteria of bifidogenic action and use of hydrosoluble cashew nut kernels by using 14% oligofructose and 33% passion fruit juice. As a result of this study, it was possible to obtain a new functional prebiotic product, which combined the nutritional and functional properties of cashew nut kernels and oligofructose with the sensory properties of passion fruit juice in a beverage with satisfactory sensory acceptance. This new product emerges as a new alternative for the industrial processing of broken cashew nut kernels, which have very low market value, enabling this sector to increase its profits. © 2014 Institute of Food Technologists®

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

PubMed

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

2015-03-01

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

Hydrological and environmental variables outperform spatial factors in structuring species, trait composition, and beta diversity of pelagic algae.

PubMed

Wu, Naicheng; Qu, Yueming; Guse, Björn; Makarevičiūtė, Kristė; To, Szewing; Riis, Tenna; Fohrer, Nicola

2018-03-01

There has been increasing interest in algae-based bioassessment, particularly, trait-based approaches are increasingly suggested. However, the main drivers, especially the contribution of hydrological variables, of species composition, trait composition, and beta diversity of algae communities are less studied. To link species and trait composition to multiple factors (i.e., hydrological variables, local environmental variables, and spatial factors) that potentially control species occurrence/abundance and to determine their relative roles in shaping species composition, trait composition, and beta diversities of pelagic algae communities, samples were collected from a German lowland catchment, where a well-proven ecohydrological modeling enabled to predict long-term discharges at each sampling site. Both trait and species composition showed significant correlations with hydrological, environmental, and spatial variables, and variation partitioning revealed that the hydrological and local environmental variables outperformed spatial variables. A higher variation of trait composition (57.0%) than species composition (37.5%) could be explained by abiotic factors. Mantel tests showed that both species and trait-based beta diversities were mostly related to hydrological and environmental heterogeneity with hydrological contributing more than environmental variables, while purely spatial impact was less important. Our findings revealed the relative importance of hydrological variables in shaping pelagic algae community and their spatial patterns of beta diversities, emphasizing the need to include hydrological variables in long-term biomonitoring campaigns and biodiversity conservation or restoration. A key implication for biodiversity conservation was that maintaining the instream flow regime and keeping various habitats among rivers are of vital importance. However, further investigations at multispatial and temporal scales are greatly needed.

Effects of Cerium and Titanium Oxide Nanoparticles in Soil on the Nutrient Composition of Barley (Hordeum vulgare L.) Kernels

PubMed Central

Pošćić, Filip; Mattiello, Alessandro; Fellet, Guido; Miceli, Fabiano; Marchiol, Luca

2016-01-01

The implications of metal nanoparticles (MeNPs) are still unknown for many food crops. The purpose of this study was to evaluate the effects of cerium oxide (nCeO2) and titanium oxide (nTiO2) nanoparticles in soil at 0, 500 and 1000 mg·kg−1 on the nutritional parameters of barley (Hordeum vulgare L.) kernels. Mineral nutrients, amylose, β-glucans, amino acid and crude protein (CP) concentrations were measured in kernels. Whole flour samples were analyzed by ICP-AES/MS, HPLC and Elemental CHNS Analyzer. Results showed that Ce and Ti accumulation under MeNPs treatments did not differ from the control treatment. However, nCeO2 and nTiO2 had an impact on composition and nutritional quality of barley kernels in contrasting ways. Both MeNPs left β-glucans unaffected but reduced amylose content by approximately 21%. Most amino acids and CP increased. Among amino acids, lysine followed by proline saw the largest increase (51% and 37%, respectively). Potassium and S were both negatively impacted by MeNPs, while B was only affected by 500 mg nCeO2·kg−1. On the contrary Zn and Mn concentrations were improved by 500 mg nTiO2·kg−1, and Ca by both nTiO2 treatments. Generally, our findings demonstrated that kernels are negatively affected by nCeO2 while nTiO2 can potentially have beneficial effects. However, both MeNPs have the potential to negatively impact malt and feed production. PMID:27294945

Switchgrass biomass composition traits and their effects on its digestion by ruminants and bioconversion to ethanol

USDA-ARS?s Scientific Manuscript database

Six generations of divergent breeding in switchgrass (Panicum virgatum L.) for forage in vitro digestibility (IVDMD) resulted in significant changes in 20 biomass composition traits. Stepwise multi-regression was used to determine which of the 20 composition traits had largest significant effects on...

Ultrasound use for body composition and carcass quality assessment in cattle and lambs

USDA-ARS?s Scientific Manuscript database

Genetic evaluation for carcass quality traits has evolved over time, in large part due to introduction of new technology such as ultrasound measures of body composition. Ultrasound measured body composition traits emulate important carcass traits, are very informative for selection purposes, are ac...

Defense Responses to Mycotoxin-Producing Fungi Fusarium proliferatum, F. subglutinans, and Aspergillus flavus in Kernels of Susceptible and Resistant Maize Genotypes.

PubMed

Lanubile, Alessandra; Maschietto, Valentina; De Leonardis, Silvana; Battilani, Paola; Paciolla, Costantino; Marocco, Adriano

2015-05-01

Developing kernels of resistant and susceptible maize genotypes were inoculated with Fusarium proliferatum, F. subglutinans, and Aspergillus flavus. Selected defense systems were investigated using real-time reverse transcription-polymerase chain reaction to monitor the expression of pathogenesis-related (PR) genes (PR1, PR5, PRm3, PRm6) and genes protective from oxidative stress (peroxidase, catalase, superoxide dismutase and ascorbate peroxidase) at 72 h postinoculation. The study was also extended to the analysis of the ascorbate-glutathione cycle and catalase, superoxide dismutase, and cytosolic and wall peroxidases enzymes. Furthermore, the hydrogen peroxide and malondialdehyde contents were studied to evaluate the oxidation level. Higher gene expression and enzymatic activities were observed in uninoculated kernels of resistant line, conferring a major readiness to the pathogen attack. Moreover expression values of PR genes remained higher in the resistant line after inoculation, demonstrating a potentiated response to the pathogen invasions. In contrast, reactive oxygen species-scavenging genes were strongly induced in the susceptible line only after pathogen inoculation, although their enzymatic activity was higher in the resistant line. Our data provide an important basis for further investigation of defense gene functions in developing kernels in order to improve resistance to fungal pathogens. Maize genotypes with overexpressed resistance traits could be profitably utilized in breeding programs focused on resistance to pathogens and grain safety.

Successional changes in functional composition contrast for dry and wet tropical forest.

PubMed

Lohbeck, Madelon; Poorter, Lourens; Lebrija-Trejos, Edwin; Martínez-Ramos, Miguel; Meave, Jorge A; Paz, Horacio; Pérez-García, Eduardo A; Romero-Pérez, I Eunice; Tauro, Alejandra; Bongers, Frans

2013-06-01

We tested whether and how functional composition changes with succession in dry deciduous and wet evergreen forests of Mexico. We hypothesized that compositional changes during succession in dry forest were mainly determined by increasing water availability leading to community functional changes from conservative to acquisitive strategies, and in wet forest by decreasing light availability leading to changes from acquisitive to conservative strategies. Research was carried out in 15 dry secondary forest plots (5-63 years after abandonment) and 17 wet secondary forest plots (< 1-25 years after abandonment). Community-level functional traits were represented by community-weighted means based on 11 functional traits measured on 132 species. Successional changes in functional composition are more marked in dry forest than in wet forest and largely characterized by different traits. During dry forest succession, conservative traits related to drought tolerance and drought avoidance decreased, as predicted. Unexpectedly acquisitive leaf traits also decreased, whereas seed size and dependence on biotic dispersal increased. In wet forest succession, functional composition changed from acquisitive to conservative leaf traits, suggesting light availability as the main driver of changes. Distinct suites of traits shape functional composition changes in dry and wet forest succession, responding to different environmental filters.

Assessing Goodness of Fit in Item Response Theory with Nonparametric Models: A Comparison of Posterior Probabilities and Kernel-Smoothing Approaches

ERIC Educational Resources Information Center

Sueiro, Manuel J.; Abad, Francisco J.

2011-01-01

The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…

Quantitative Trait Loci for Mercury Accumulation in Maize (Zea mays L.) Identified Using a RIL Population

PubMed Central

Zhang, Qinbin; Wang, Long; Zhang, Xiaoxiang; Song, Guiliang; Fu, Zhiyuan; Ding, Dong; Liu, Zonghua; Tang, Jihua

2014-01-01

To investigate the genetic mechanism of mercury accumulation in maize (Zea mays L.), a population of 194 recombinant inbred lines derived from an elite hybrid Yuyu 22, was used to identify quantitative trait loci (QTLs) for mercury accumulation at two locations. The results showed that the average Hg concentration in the different tissues of maize followed the order: leaves > bracts > stems > axis > kernels. Twenty-three QTLs for mercury accumulation in five tissues were detected on chromosomes 1, 4, 7, 8, 9 and 10, which explained 6.44% to 26.60% of the phenotype variance. The QTLs included five QTLs for Hg concentration in kernels, three QTLs for Hg concentration in the axis, six QTLs for Hg concentration in stems, four QTLs for Hg concentration in bracts and five QTLs for Hg concentration in leaves. Interestingly, three QTLs, qKHC9a, qKHC9b, and qBHC9 were in linkage with two QTLs for drought tolerance. In addition, qLHC1 was in linkage with two QTLs for arsenic accumulation. The study demonstrated the concentration of Hg in Hg-contaminated paddy soil could be reduced, and maize production maintained simultaneously by selecting and breeding maize Hg pollution-safe cultivars (PSCs). PMID:25210737

Identification of Fourier transform infrared photoacoustic spectral features for detection of Aspergillus flavus infection in corn.

PubMed

Gordon, S H; Schudy, R B; Wheeler, B C; Wicklow, D T; Greene, R V

1997-04-01

Aspergillus flavus and other pathogenic fungi display typical infrared spectra which differ significantly from spectra of substrate materials such as corn. On this basis, specific spectral features have been identified which permit detection of fungal infection on the surface of corn kernels by photoacoustic infrared spectroscopy. In a blind study, ten corn kernels showing bright greenish yellow fluorescence (BGYF) in the germ or endosperm and ten BGYF-negative kernels were correctly classified as infected or not infected by Fourier transform infrared photoacoustic spectroscopy. Earlier studies have shown that BGYF-positive kernels contain the bulk of the aflatoxin contaminating grain at harvest. Ten major spectral features, identified by visual inspection of the photoacoustic spectra of A. flavus mycelium grown in culture versus uninfected corn, were interpreted and assigned by theoretical comparisons of the relative chemical compositions of fungi and corn. The spectral features can be built into either empirical or knowledge-based computer models (expert systems) for automatic infrared detection and segregation of grains or kernels containing aflatoxin from the food and feed supply.

Molecular genetic basis of pod corn (Tunicate maize)

PubMed Central

Wingen, Luzie U.; Münster, Thomas; Faigl, Wolfram; Deleu, Wim; Sommer, Hans; Saedler, Heinz; Theißen, Günter

2012-01-01

Pod corn is a classic morphological mutant of maize in which the mature kernels of the cob are covered by glumes, in contrast to generally grown maize varieties in which kernels are naked. Pod corn, known since pre-Columbian times, is the result of a dominant gain-of-function mutation at the Tunicate (Tu) locus. Some classic articles of 20th century maize genetics reported that the mutant Tu locus is complex, but molecular details remained elusive. Here, we show that pod corn is caused by a cis-regulatory mutation and duplication of the ZMM19 MADS-box gene. Although the WT locus contains a single-copy gene that is expressed in vegetative organs only, mutation and duplication of ZMM19 in Tu lead to ectopic expression of the gene in the inflorescences, thus conferring vegetative traits to reproductive organs. PMID:22517751

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.

The effect of smelting time and composition of palm kernel shell charcoal reductant toward extractive Pomalaa nickel laterite ore in mini electric arc furnace

NASA Astrophysics Data System (ADS)

Sihotang, Iqbal Huda; Supriyatna, Yayat Iman; Ismail, Ika; Sulistijono

2018-04-01

Indonesia is a country that is rich in natural resources. Being a third country which has a nickel laterite ore in the world after New Caledonia and Philippines. However, the processing of nickel laterite ore to increase its levels in Indonesia is still lacking. In the processing of nickel laterite ore into metal, it can be processed by pyrometallurgy method that typically use coal as a reductant. However, coal is a non-renewable energy and have high enough levels of pollution. One potentially replace is the biomass, that is a renewable energy. Palm kernel shell are biomass that can be used as a reductant because it has a fairly high fix carbon content. This research aims to make nickel laterite ores become metal using palm kernel shell charcoal as reductant in mini electric arc furnace. The result show that the best smelting time of this research is 60 minutes with the best composition of the reductant is 2,000 gram.

Genomic prediction based on data from three layer lines using non-linear regression models.

PubMed

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional occurrence of large negative accuracies when the evaluated line was not included in the training dataset. Furthermore, when using a multi-line training dataset, non-linear models provided information on the genotype data that was complementary to the linear models, which indicates that the underlying data distributions of the three studied lines were indeed heterogeneous.

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

PubMed

Ledbetter, C A

2008-09-01

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

Modelling and estimating pollen movement in oilseed rape (Brassica napus) at the landscape scale using genetic markers.

PubMed

Devaux, C; Lavigne, C; Austerlitz, F; Klein, E K

2007-02-01

Understanding patterns of pollen movement at the landscape scale is important for establishing management rules following the release of genetically modified (GM) crops. We use here a mating model adapted to cultivated species to estimate dispersal kernels from the genotypes of the progenies of male-sterile plants positioned at different sampling sites within a 10 x 10-km oilseed rape production area. Half of the pollen clouds sampled by the male-sterile plants originated from uncharacterized pollen sources that could consist of both large volunteer and feral populations, and fields within and outside the study area. The geometric dispersal kernel was the most appropriate to predict pollen movement in the study area. It predicted a much larger proportion of long-distance pollination than previously fitted dispersal kernels. This best-fitting mating model underestimated the level of differentiation among pollen clouds but could predict its spatial structure. The estimation method was validated on simulated genotypic data, and proved to provide good estimates of both the shape of the dispersal kernel and the rate and composition of pollen issued from uncharacterized pollen sources. The best dispersal kernel fitted here, the geometric kernel, should now be integrated into models that aim at predicting gene flow at the landscape level, in particular between GM and non-GM crops.

Genomic Selection in Commercial Perennial Crops: Applicability and Improvement in Oil Palm (Elaeis guineensis Jacq.).

PubMed

Kwong, Qi Bin; Ong, Ai Ling; Teh, Chee Keng; Chew, Fook Tim; Tammi, Martti; Mayes, Sean; Kulaveerasingam, Harikrishna; Yeoh, Suat Hui; Harikrishna, Jennifer Ann; Appleton, David Ross

2017-06-06

Genomic selection (GS) uses genome-wide markers to select individuals with the desired overall combination of breeding traits. A total of 1,218 individuals from a commercial population of Ulu Remis x AVROS (UR x AVROS) were genotyped using the OP200K array. The traits of interest included: shell-to-fruit ratio (S/F, %), mesocarp-to-fruit ratio (M/F, %), kernel-to-fruit ratio (K/F, %), fruit per bunch (F/B, %), oil per bunch (O/B, %) and oil per palm (O/P, kg/palm/year). Genomic heritabilities of these traits were estimated to be in the range of 0.40 to 0.80. GS methods assessed were RR-BLUP, Bayes A (BA), Cπ (BC), Lasso (BL) and Ridge Regression (BRR). All methods resulted in almost equal prediction accuracy. The accuracy achieved ranged from 0.40 to 0.70, correlating with the heritability of traits. By selecting the most important markers, RR-BLUP B has the potential to outperform other methods. The marker density for certain traits can be further reduced based on the linkage disequilibrium (LD). Together with in silico breeding, GS is now being used in oil palm breeding programs to hasten parental palm selection.

Quantitative trait loci for live animal and carcass composition traits in Jersey and Limousin back-cross cattle finished on pasture or feedlot.

PubMed

Morris, C A; Pitchford, W S; Cullen, N G; Esmailizadeh, A K; Hickey, S M; Hyndman, D; Dodds, K G; Afolayan, R A; Crawford, A M; Bottema, C D K

2009-10-01

A quantitative trait locus (QTL) study was carried out in two countries, recording live animal and carcass composition traits. Back-cross calves (385 heifers and 398 steers) were generated, with Jersey and Limousin breed backgrounds. The New Zealand cattle were reared on pasture to carcass weights averaging 229 kg, whilst the Australian cattle were reared on grass and finished on grain (for at least 180 days) to carcass weights averaging 335 kg. From 11 live animal traits and 31 carcass composition traits respectively, 5 and 22 QTL were detected in combined-sire analyses, which were significant (P < 0.05) on a genome-wise basis. Fourteen significant traits for carcass composition QTL were on chromosome 2 and these were traits associated with muscling and fatness. This chromosome carried a variant myostatin allele (F94L), segregating from the Limousin ancestry. Despite very different cattle management systems between the two countries, the two populations had a large number of QTL in common. Of the 18 traits which were common to both countries, and which had significant QTL at the genome-wise level, eight were significant in both countries.

Genetic architecture of kernel composition in global sorghum germplasm

USDA-ARS?s Scientific Manuscript database

Sorghum [Sorghum bicolor (L.) Moench] is an important cereal crop for dryland areas in the United States and for small-holder farmers in Africa. Natural variation of sorghum grain composition (protein, fat, and starch) between accessions can be used for crop improvement, but the genetic controls are...

The effects of food irradiation on quality of pine nut kernels

NASA Astrophysics Data System (ADS)

Gölge, Evren; Ova, Gülden

2008-03-01

Pine nuts ( Pinus pinae) undergo gamma irradiation process with the doses 0.5, 1.0, 3.0, and 5.0 kGy. The changes in chemical, physical and sensory attributes were observed in the following 3 months of storage period. The data obtained from the experiments showed the peroxide values of the pine nut kernels increased proportionally to the dose. On contrary, irradiation process has no effect on the physical quality such as texture and color, fatty acid composition and sensory attributes.

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software

PubMed Central

Fabregat-Traver, Diego; Sharapov, Sodbo Zh.; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL. PMID:25717363

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software.

PubMed

Fabregat-Traver, Diego; Sharapov, Sodbo Zh; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the 'omics' context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

Biochemical and molecular characterization of Avena indolines and their role in kernel texture.

PubMed

Gazza, Laura; Taddei, Federica; Conti, Salvatore; Gazzelloni, Gloria; Muccilli, Vera; Janni, Michela; D'Ovidio, Renato; Alfieri, Michela; Redaelli, Rita; Pogna, Norberto E

2015-02-01

Among cereals, Avena sativa is characterized by an extremely soft endosperm texture, which leads to some negative agronomic and technological traits. On the basis of the well-known softening effect of puroindolines in wheat kernel texture, in this study, indolines and their encoding genes are investigated in Avena species at different ploidy levels. Three novel 14 kDa proteins, showing a central hydrophobic domain with four tryptophan residues and here named vromindoline (VIN)-1,2 and 3, were identified. Each VIN protein in diploid oat species was found to be synthesized by a single Vin gene whereas, in hexaploid A. sativa, three Vin-1, three Vin-2 and two Vin-3 genes coding for VIN-1, VIN-2 and VIN-3, respectively, were described and assigned to the A, C or D genomes based on similarity to their counterparts in diploid species. Expression of oat vromindoline transgenes in the extra-hard durum wheat led to accumulation of vromindolines in the endosperm and caused an approximate 50 % reduction of grain hardness, suggesting a central role for vromindolines in causing the extra-soft texture of oat grain. Further, hexaploid oats showed three orthologous genes coding for avenoindolines A and B, with five or three tryptophan residues, respectively, but very low amounts of avenoindolines were found in mature kernels. The present results identify a novel protein family affecting cereal kernel texture and would further elucidate the phylogenetic evolution of Avena genus.

Determining the minimum required uranium carbide content for HTGR UCO fuel kernels

DOE PAGES

McMurray, Jacob W.; Lindemer, Terrence B.; Brown, Nicholas R.; ...

2017-03-10

There are three important failure mechanisms that must be controlled in high-temperature gas-cooled reactor (HTGR) fuel for certain higher burnup applications are SiC layer rupture, SiC corrosion by CO, and coating compromise from kernel migration. All are related to high CO pressures stemming from free O generated when uranium present as UO 2 fissions and the O is not subsequently bound by other elements. Furthermore, in the HTGR UCO kernel design, CO buildup from excess O is controlled by the inclusion of additional uranium in the form of a carbide, UC x. An approach for determining the minimum UC xmore » content to ensure negligible CO formation was developed and demonstrated using CALPHAD models and the Serpent 2 reactor physics and depletion analysis tool. Our results are intended to be more accurate than previous estimates by including more nuclear and chemical factors, in particular the effect of transmutation products on the oxygen distribution as the fuel kernel composition evolves with burnup.« less

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.

Composition and toxigenic potential of the Fusarium graminearum species complex from maize ears, stalks and stubble in Brazil

USDA-ARS?s Scientific Manuscript database

Detailed knowledge of the composition and toxigenic potential of the Fusarium graminearum species complex affecting maize crops in Brazil is lacking. A multilocus genotype approach was used to identify 539 isolates from three sub-collections: 1) maize kernels (n= 110) from five states spanning sout...

Protein fold recognition using geometric kernel data fusion.

PubMed

Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves

2014-07-01

Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.

Multi-task Gaussian process for imputing missing data in multi-trait and multi-environment trials.

PubMed

Hori, Tomoaki; Montcho, David; Agbangla, Clement; Ebana, Kaworu; Futakuchi, Koichi; Iwata, Hiroyoshi

2016-11-01

A method based on a multi-task Gaussian process using self-measuring similarity gave increased accuracy for imputing missing phenotypic data in multi-trait and multi-environment trials. Multi-environmental trial (MET) data often encounter the problem of missing data. Accurate imputation of missing data makes subsequent analysis more effective and the results easier to understand. Moreover, accurate imputation may help to reduce the cost of phenotyping for thinned-out lines tested in METs. METs are generally performed for multiple traits that are correlated to each other. Correlation among traits can be useful information for imputation, but single-trait-based methods cannot utilize information shared by traits that are correlated. In this paper, we propose imputation methods based on a multi-task Gaussian process (MTGP) using self-measuring similarity kernels reflecting relationships among traits, genotypes, and environments. This framework allows us to use genetic correlation among multi-trait multi-environment data and also to combine MET data and marker genotype data. We compared the accuracy of three MTGP methods and iterative regularized PCA using rice MET data. Two scenarios for the generation of missing data at various missing rates were considered. The MTGP performed a better imputation accuracy than regularized PCA, especially at high missing rates. Under the 'uniform' scenario, in which missing data arise randomly, inclusion of marker genotype data in the imputation increased the imputation accuracy at high missing rates. Under the 'fiber' scenario, in which missing data arise in all traits for some combinations between genotypes and environments, the inclusion of marker genotype data decreased the imputation accuracy for most traits while increasing the accuracy in a few traits remarkably. The proposed methods will be useful for solving the missing data problem in MET data.

The Interaction of Genotype and Environment Determines Variation in the Maize Kernel Ionome

PubMed Central

Asaro, Alexandra; Ziegler, Gregory; Ziyomo, Cathrine; Hoekenga, Owen A.; Dilkes, Brian P.; Baxter, Ivan

2016-01-01

Plants obtain soil-resident elements that support growth and metabolism from the water-flow facilitated by transpiration and active transport processes. The availability of elements in the environment interacts with the genetic capacity of organisms to modulate element uptake through plastic adaptive responses, such as homeostasis. These interactions should cause the elemental contents of plants to vary such that the effects of genetic polymorphisms will be dramatically dependent on the environment in which the plant is grown. To investigate genotype by environment interactions underlying elemental accumulation, we analyzed levels of elements in maize kernels of the Intermated B73 × Mo17 (IBM) recombinant inbred population grown in 10 different environments, spanning a total of six locations and five different years. In analyses conducted separately for each environment, we identified a total of 79 quantitative trait loci (QTL) controlling seed elemental accumulation. While a set of these QTL was found in multiple environments, the majority were specific to a single environment, suggesting the presence of genetic by environment interactions. To specifically identify and quantify QTL by environment interactions (QEIs), we implemented two methods: linear modeling with environmental covariates, and QTL analysis on trait differences between growouts. With these approaches, we found several instances of QEI, indicating that elemental profiles are highly heritable, interrelated, and responsive to the environment. PMID:27770027

The Interaction of Genotype and Environment Determines Variation in the Maize Kernel Ionome.

PubMed

Asaro, Alexandra; Ziegler, Gregory; Ziyomo, Cathrine; Hoekenga, Owen A; Dilkes, Brian P; Baxter, Ivan

2016-12-07

Plants obtain soil-resident elements that support growth and metabolism from the water-flow facilitated by transpiration and active transport processes. The availability of elements in the environment interacts with the genetic capacity of organisms to modulate element uptake through plastic adaptive responses, such as homeostasis. These interactions should cause the elemental contents of plants to vary such that the effects of genetic polymorphisms will be dramatically dependent on the environment in which the plant is grown. To investigate genotype by environment interactions underlying elemental accumulation, we analyzed levels of elements in maize kernels of the Intermated B73 × Mo17 (IBM) recombinant inbred population grown in 10 different environments, spanning a total of six locations and five different years. In analyses conducted separately for each environment, we identified a total of 79 quantitative trait loci (QTL) controlling seed elemental accumulation. While a set of these QTL was found in multiple environments, the majority were specific to a single environment, suggesting the presence of genetic by environment interactions. To specifically identify and quantify QTL by environment interactions (QEIs), we implemented two methods: linear modeling with environmental covariates, and QTL analysis on trait differences between growouts. With these approaches, we found several instances of QEI, indicating that elemental profiles are highly heritable, interrelated, and responsive to the environment. Copyright © 2016 Asaro et al.

Development and analysis of composite flour bread.

PubMed

Menon, Lakshmi; Majumdar, Swarnali Dutta; Ravi, Usha

2015-07-01

The study elucidates the effect of utilizing cereal-pulse-fruit seed composite flour in the development and quality analysis of leavened bread. The composite flour was prepared using refined wheat flour (WF), high protein soy flour (SF), sprouted mung bean flour (MF) and mango kernel flour (MKF). Three variations were formulated such as V-I (WF: SF: MF: MKF = 85:5:5:5), V-II (WF: SF: MF: MKF = 70:10:10:10), and V-III (WF: SF: MF: MKF = 60:14:13:13). Pertinent functional, physico-chemical and organoleptic attributes were studied in composite flour variations and their bread preparations. Physical characteristics of the bread variations revealed a percentage decrease in loaf height (14 %) and volume (25 %) and 20 % increase in loaf weight with increased substitution of composite flour. The sensory evaluation of experimental breads on a nine-point hedonic scale revealed that V-I score was 5 % higher than the standard bread. Hence, the present study highlighted the nutrient enrichment of bread on incorporation of a potential waste material mango kernel, soy and sprouted legume. Relevant statistical tests were done to analyze the significance of means for all tested parameters.

Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites

PubMed Central

Meinicke, Peter; Tech, Maike; Morgenstern, Burkhard; Merkl, Rainer

2004-01-01

Background Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals. Results We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon. Conclusions We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems. PMID:15511290

ZEAXANTHIN EPOXIDASE Activity Potentiates Carotenoid Degradation in Maturing Seed1[OPEN

PubMed Central

Magallanes-Lundback, Maria; Lipka, Alexander E.; Angelovici, Ruthie; DellaPenna, Dean

2016-01-01

Elucidation of the carotenoid biosynthetic pathway has enabled altering the composition and content of carotenoids in various plants, but to achieve desired nutritional impacts, the genetic components regulating carotenoid homeostasis in seed, the plant organ consumed in greatest abundance, must be elucidated. We used a combination of linkage mapping, genome-wide association studies (GWAS), and pathway-level analysis to identify nine loci that impact the natural variation of seed carotenoids in Arabidopsis (Arabidopsis thaliana). ZEAXANTHIN EPOXIDASE (ZEP) was the major contributor to carotenoid composition, with mutants lacking ZEP activity showing a remarkable 6-fold increase in total seed carotenoids relative to the wild type. Natural variation in ZEP gene expression during seed development was identified as the underlying mechanism for fine-tuning carotenoid composition, stability, and ultimately content in Arabidopsis seed. We previously showed that two CAROTENOID CLEAVAGE DIOXYGENASE enzymes, CCD1 and CCD4, are the primary mediators of seed carotenoid degradation, and here we demonstrate that ZEP acts as an upstream control point of carotenoid homeostasis, with ZEP-mediated epoxidation targeting carotenoids for degradation by CCD enzymes. Finally, four of the nine loci/enzymatic activities identified as underlying natural variation in Arabidopsis seed carotenoids also were identified in a recent GWAS of maize (Zea mays) kernel carotenoid variation. This first comparison of the natural variation in seed carotenoids in monocots and dicots suggests a surprising overlap in the genetic architecture of these traits between the two lineages and provides a list of likely candidates to target for selecting seed carotenoid variation in other species. PMID:27208224

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

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

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

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

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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

DOE PAGES

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

2017-10-12

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

Identification and genetic characterization of maize cell wall variation for improved biorefinery feedstock characteristics

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

Pauly, Markus; Hake, Sarah

2013-10-31

The objectives of this program are to 1) characterize novel maize mutants with altered cell walls for enhanced biorefinery characteristics and 2) find quantitative trait loci (QTLs) related to biorefinery characteristics by taking advantage of the genetic diversity of maize. As a result a novel non-transgenic maize plant (cal1) has been identified, whose stover (leaves and stalk) contain more glucan in their walls leading to a higher saccharification yield, when subjected to a standard enzymatic digestion cocktail. Stacking this trait with altered lignin mutants yielded evene higher saccharification yields. Cal-1 mutants do not show a loss of kernel and ormore » biomass yield when grown in the field . Hence, cal1 biomass provides an excellent feedstock for the biofuel industry.« less

Population structure and strong divergent selection shape phenotypic diversification in maize landraces.

PubMed

Pressoir, G; Berthaud, J

2004-02-01

To conserve the long-term selection potential of maize, it is necessary to investigate past and present evolutionary processes that have shaped quantitative trait variation. Understanding the dynamics of quantitative trait evolution is crucial to future crop breeding. We characterized population differentiation of maize landraces from the State of Oaxaca, Mexico for quantitative traits and molecular markers. Qst values were much higher than Fst values obtained for molecular markers. While low values of Fst (0.011 within-village and 0.003 among-villages) suggest that considerable gene flow occurred among the studied populations, high levels of population differentiation for quantitative traits were observed (ie an among-village Qst value of 0.535 for kernel weight). Our results suggest that although quantitative traits appear to be under strong divergent selection, a considerable amount of gene flow occurs among populations. Furthermore, we characterized nonproportional changes in the G matrix structure both within and among villages that are consequences of farmer selection. As a consequence of these differences in the G matrix structure, the response to multivariate selection will be different from one population to another. Large changes in the G matrix structure could indicate that farmers select for genes of major and pleiotropic effect. Farmers' decision and selection strategies have a great impact on phenotypic diversification in maize landraces.

Candidate Loci for Yield-Related Traits in Maize Revealed by a Combination of MetaQTL Analysis and Regional Association Mapping

PubMed Central

Chen, Lin; An, Yixin; Li, Yong-xiang; Li, Chunhui; Shi, Yunsu; Song, Yanchun; Zhang, Dengfeng; Wang, Tianyu; Li, Yu

2017-01-01

Maize grain yield and related traits are complex and are controlled by a large number of genes of small effect or quantitative trait loci (QTL). Over the years, a large number of yield-related QTLs have been identified in maize and deposited in public databases. However, integrating and re-analyzing these data and mining candidate loci for yield-related traits has become a major issue in maize. In this study, we collected information on QTLs conferring maize yield-related traits from 33 published studies. Then, 999 of these QTLs were iteratively projected and subjected to meta-analysis to obtain metaQTLs (MQTLs). A total of 76 MQTLs were found across the maize genome. Based on a comparative genomics strategy, several maize orthologs of rice yield-related genes were identified in these MQTL regions. Furthermore, three potential candidate genes (Gene ID: GRMZM2G359974, GRMZM2G301884, and GRMZM2G083894) associated with kernel size and weight within three MQTL regions were identified using regional association mapping, based on the results of the meta-analysis. This strategy, combining MQTL analysis and regional association mapping, is helpful for functional marker development and rapid identification of candidate genes or loci. PMID:29312420

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.

Using a trait-based approach to link microbial community composition and functioning to soil salinity

NASA Astrophysics Data System (ADS)

Rath, Kristin; Fierer, Noah; Rousk, Johannes

2017-04-01

Our knowledge of the dynamics structuring microbial communities and the consequences this has for soil functions is rudimentary. In particular, predictions of the response of microbial communities to environmental change and the implications for associated ecosystem processes remain elusive. Understanding how environmental factors structure microbial communities and regulate the functions they perform is key to a mechanistic understanding of how biogeochemical cycles respond to environmental change. Soil salinization is an agricultural problem in many parts of the world. The activity of soil microorganisms is reduced in saline soils compared to non-saline soil. However, soil salinity often co-varies with other factors, making it difficult to assign responses of microbial communities to direct effects of salinity. A trait-based approach allows us to connect the environmental factor salinity with the responses of microbial community composition and functioning. Salinity along a salinity gradient serves as a filter for the community trait distribution of salt tolerance, selecting for higher salt tolerance at more saline sites. This trait-environment relationship can be used to predict responses of microbial communities to environmental change. Our aims were to (i) use salinity along natural salinity gradients as an environmental filter, and (ii) link the resulting filtered trait-distributions of the communities (the trait being salt tolerance) to the community composition. Soil samples were obtained from two replicated salinity gradients along an Australian salt lake, spanning a wide range of soil salinities (0.1 dS m-1 to >50 dS m-1). In one of the two gradients salinity was correlated with pH. Community trait distributions for salt tolerance were assessed by establishing dose-dependences for extracted bacterial communities using growth rate assays. In addition, functional parameters were measured along the salt gradients. Community composition of sites was compared through 16S rRNA gene amplicon sequencing. Microbial community composition changed greatly along the salinity gradients. Using the salt-tolerance assessments to estimate bacterial trait-distributions we could determine substantial differences in tolerance to salt revealing a strong causal connection between environment and trait distributions. By constraining the community composition with salinity tolerance in ordinations, we could assign which community differences were directly due to a shift in community trait distributions. These analyses revealed that a substantial part (up to 30%) of the community composition differences were directly driven by environmental salt concentrations.. Even though communities in saline soils had trait-distributions aligned to their environment, their performance (respiration, growth rates) was lower than those in non-saline soils and remained low even after input of organic material. Using a trait-based approach we could connect filtered trait distributions along environmental gradients, to the composition of the microbial community. We show that soil salinity played an important role in shaping microbial community composition by selecting for communities with higher salt tolerance. The shift toward bacterial communities with trait distributions matched to salt environments probably compensated for much of the potential loss of function induced by salinity, resulting in a degree of apparent functional redundancy for decomposition. However, more tolerant communities still showed reduced functioning, suggesting a trade-off between salt tolerance and performance.

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.

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.

QTL detection for rice grain quality traits using an interspecific backcross population derived from cultivated Asian (O. sativa L.) and African (O. glaberrima S.) rice.

PubMed

Li, Jiming; Xiao, Jinhua; Grandillo, Silvana; Jiang, Longying; Wan, Yizhen; Deng, Qiyun; Yuan, Longping; McCouch, Susan R

2004-08-01

An interspecific advanced backcross population derived from a cross between Oryza sativa "V20A" (a popular male-sterile line used in Chinese rice hybrids) and Oryza glaberrima (accession IRGC No. 103544 from Mali) was used to identify quantitative trait loci (QTL) associated with grain quality and grain morphology. A total of 308 BC3F1 hybrid families were evaluated for 16 grain-related traits under field conditions in Changsha, China, and the same families were evaluated for RFLP and SSR marker segregation at Cornell University (Ithaca, N.Y.). Eleven QTL associated with seven traits were detected in six chromosomal regions, with the favorable allele coming from O. glaberrima at eight loci. Favorable O. glaberrima alleles were associated with improvements in grain shape and appearance, resulting in an increase in kernel length, transgressive variation for thinner grains, and increased length to width ratio. Oryza glaberrima alleles at other loci were associated with potential improvements in crude protein content and brown rice yield. These results suggested that genes from O. glaberrima may be useful in improving specific grain quality characteristics in high-yielding O. sativa hybrid cultivars.

Antinutritional factors and hypocholesterolemic effect of wild apricot kernel (Prunus armeniaca L.) as affected by detoxification.

PubMed

Tanwar, Beenu; Modgil, Rajni; Goyal, Ankit

2018-04-25

The present investigation was aimed to study the effect of detoxification on the nutrients and antinutrients of wild apricot kernel followed by its hypocholesterolemic effect in male Wistar albino rats. The results revealed a non-significant (p > 0.05) effect of detoxification on the proximate composition except total carbohydrates and protein content. However, detoxification led to a significant (p < 0.05) decrease in l-ascorbic acid (76.82%), β-carotene (25.90%), dietary fiber constituents (10.51-28.92%), minerals (4.76-31.08%) and antinutritional factors (23.92-77.05%) (phenolics, tannins, trypsin inhibitor activity, saponins, phytic acid, alkaloids, flavonoids, oxalates) along with the complete removal (100%) of bitter and potentially toxic hydrocyanic acid (HCN). The quality parameters of kernel oil indicated no adverse effects of detoxification on free fatty acids, lipase activity, acid value and peroxide value, which remained well below the maximum permissible limit. Blood lipid profile demonstrated that the detoxified apricot kernel group exhibited significantly (p < 0.05) increased levels of HDL-cholesterol (48.79%) and triglycerides (15.09%), and decreased levels of total blood cholesterol (6.99%), LDL-C (22.95%) and VLDL-C (7.90%) compared to that of the raw (untreated) kernel group. Overall, it can be concluded that wild apricot kernel flour could be detoxified efficiently by employing a simple, safe, domestic and cost-effective method, which further has the potential for formulating protein supplements and value-added food products.

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

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

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

Characteristics and composition of watermelon, pumpkin, and paprika seed oils and flours.

PubMed

El-Adawy, T A; Taha, K M

2001-03-01

The nutritional quality and functional properties of paprika seed flour and seed kernel flours of pumpkin and watermelon were studied, as were the characteristics and structure of their seed oils. Paprika seed and seed kernels of pumpkin and watermelon were rich in oil and protein. All flour samples contained considerable amounts of P, K, Mg, Mn, and Ca. Paprika seed flour was superior to watermelon and pumpkin seed kernel flours in content of lysine and total essential amino acids. Oil samples had high amounts of unsaturated fatty acids with linoleic and oleic acids as the major acids. All oil samples fractionated into seven classes including triglycerides as a major lipid class. Data obtained for the oils' characteristics compare well with those of other edible oils. Antinutritional compounds such as stachyose, raffinose, verbascose, trypsin inhibitor, phytic acid, and tannins were detected in all flours. Pumpkin seed kernel flour had higher values of chemical score, essential amino acid index, and in vitro protein digestibility than the other flours examined. The first limiting amino acid was lysine for both watermelon and pumpkin seed kernel flours, but it was leucine in paprika seed flour. Protein solubility index, water and fat absorption capacities, emulsification properties, and foam stability were excellent in watermelon and pumpkin seed kernel flours and fairly good in paprika seed flour. Flour samples could be potentially added to food systems such as bakery products and ground meat formulations not only as a nutrient supplement but also as a functional agent in these formulations.

Genetic Architecture of Ear Fasciation in Maize (Zea mays) under QTL Scrutiny

PubMed Central

Mendes-Moreira, Pedro; Alves, Mara L.; Satovic, Zlatko; dos Santos, João Pacheco; Santos, João Nina; Souza, João Cândido; Pêgo, Silas E.; Hallauer, Arnel R.; Vaz Patto, Maria Carlota

2015-01-01

Maize ear fasciation Knowledge of the genes affecting maize ear inflorescence may lead to better grain yield modeling. Maize ear fasciation, defined as abnormal flattened ears with high kernel row number, is a quantitative trait widely present in Portuguese maize landraces. Material and Methods Using a segregating population derived from an ear fasciation contrasting cross (consisting of 149 F2:3 families) we established a two location field trial using a complete randomized block design. Correlations and heritabilities for several ear fasciation-related traits and yield were determined. Quantitative Trait Loci (QTL) involved in the inheritance of those traits were identified and candidate genes for these QTL proposed. Results and Discussion Ear fasciation broad-sense heritability was 0.73. Highly significant correlations were found between ear fasciation and some ear and cob diameters and row number traits. For the 23 yield and ear fasciation-related traits, 65 QTL were identified, out of which 11 were detected in both environments, while for the three principal components, five to six QTL were detected per environment. Detected QTL were distributed across 17 genomic regions and explained individually, 8.7% to 22.4% of the individual traits or principal components phenotypic variance. Several candidate genes for these QTL regions were proposed, such as bearded-ear1, branched silkless1, compact plant1, ramosa2, ramosa3, tasselseed4 and terminal ear1. However, many QTL mapped to regions without known candidate genes, indicating potential chromosomal regions not yet targeted for maize ear traits selection. Conclusions Portuguese maize germplasm represents a valuable source of genes or allelic variants for yield improvement and elucidation of the genetic basis of ear fasciation traits. Future studies should focus on fine mapping of the identified genomic regions with the aim of map-based cloning. PMID:25923975

Genetic Architecture of Ear Fasciation in Maize (Zea mays) under QTL Scrutiny.

PubMed

Mendes-Moreira, Pedro; Alves, Mara L; Satovic, Zlatko; Dos Santos, João Pacheco; Santos, João Nina; Souza, João Cândido; Pêgo, Silas E; Hallauer, Arnel R; Vaz Patto, Maria Carlota

2015-01-01

Knowledge of the genes affecting maize ear inflorescence may lead to better grain yield modeling. Maize ear fasciation, defined as abnormal flattened ears with high kernel row number, is a quantitative trait widely present in Portuguese maize landraces. Using a segregating population derived from an ear fasciation contrasting cross (consisting of 149 F2:3 families) we established a two location field trial using a complete randomized block design. Correlations and heritabilities for several ear fasciation-related traits and yield were determined. Quantitative Trait Loci (QTL) involved in the inheritance of those traits were identified and candidate genes for these QTL proposed. Ear fasciation broad-sense heritability was 0.73. Highly significant correlations were found between ear fasciation and some ear and cob diameters and row number traits. For the 23 yield and ear fasciation-related traits, 65 QTL were identified, out of which 11 were detected in both environments, while for the three principal components, five to six QTL were detected per environment. Detected QTL were distributed across 17 genomic regions and explained individually, 8.7% to 22.4% of the individual traits or principal components phenotypic variance. Several candidate genes for these QTL regions were proposed, such as bearded-ear1, branched silkless1, compact plant1, ramosa2, ramosa3, tasselseed4 and terminal ear1. However, many QTL mapped to regions without known candidate genes, indicating potential chromosomal regions not yet targeted for maize ear traits selection. Portuguese maize germplasm represents a valuable source of genes or allelic variants for yield improvement and elucidation of the genetic basis of ear fasciation traits. Future studies should focus on fine mapping of the identified genomic regions with the aim of map-based cloning.

Mango kernel starch-gum composite films: Physical, mechanical and barrier properties.

PubMed

Nawab, Anjum; Alam, Feroz; Haq, Muhammad Abdul; Lutfi, Zubala; Hasnain, Abid

2017-05-01

Composite films were developed by the casting method using mango kernel starch (MKS) and guar and xanthan gums. The concentration of both gums ranged from 0% to 30% (w/w of starch; db). Mechanical properties, oxygen permeability (OP), water vapor permeability (WVP), solubility in water and color parameters of composite films were evaluated. The crystallinity and homogeneity between the starch and gums were also evaluated by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The scanning electron micrographs showed homogeneous matrix, with no signs of phase separation between the components. XRD analysis demonstrated diminished crystalline peak. Regardless of gum type the tensile strength (TS) of composite films increased with increasing gum concentration while reverse trend was noted for elongation at break (EAB) which found to be decreased with increasing gum concentration. The addition of both guar and xanthan gums increased solubility and WVP of the composite films. However, the OP was found to be lower than that of the control with both gums. Furthermore, addition of both gums led to changes in transparency and opacity of MKS films. Films containing 10% (w/w) xanthan gum showed lower values for solubility, WVP and OP, while film containing 20% guar gum showed good mechanical properties. Copyright © 2017 Elsevier B.V. All rights reserved.

Phorbol esters seed content and distribution in Latin American provenances of Jatropha curcas L.: potential for biopesticide, food and feed.

PubMed

Bueso, Francisco; Sosa, Italo; Chun, Roldan; Pineda, Renan

2016-01-01

Jatropha curcas L. (Jatropha) is believed to have originated from Mexico and Central America. So far, characterization efforts have focused on Asia, Africa and Mexico. Non-toxic, low phorbol ester (PE) varieties have been found only in Mexico. Differences in PE content in seeds and its structural components, crude oil and cake from Jatropha provenances cultivated in Central and South America were evaluated. Seeds were dehulled, and kernels were separated into tegmen, cotyledons and embryo for PE quantitation by RP-HPLC. Crude oil and cake PE content was also measured. No phenotypic departures in seed size and structure were observed among Jatropha cultivated in Central and South America compared to provenances from Mexico, Asia and Africa. Cotyledons comprised 96.2-97.5 %, tegmen 1.6-2.4 % and embryo represented 0.9-1.4 % of dehulled kernel. Total PE content of all nine provenances categorized them as toxic. Significant differences in kernel PE content were observed among provenances from Mexico, Central and South America (P < 0.01), being Mexican the highest (7.6 mg/g) and Cabo Verde the lowest (2.57 mg/g). All accessions had >95 % of PEs concentrated in cotyledons, 0.5-3 % in the tegmen and 0.5-1 % in the embryo. Over 60 % of total PE in dehulled kernels accumulated in the crude oil, while 35-40 % remained in the cake after extraction. Low phenotypic variability in seed physical, structural traits and PE content was observed among provenances from Latin America. Very high-PE provenances with potential as biopesticide were found in Central America. No PE-free, edible Jatropha was found among provenances currently cultivated in Central America and Brazil that could be used for human consumption and feedstock. Furthermore, dehulled kernel structural parts as well as its crude oil and cake contained toxic PE levels.

Epistatic effects between pairs of the growth hormone secretagogue receptor 1a, growth hormone, growth hormone receptor, non-SMC condensin I complex, subunit G and stearoyl-CoA desaturase genes on carcass, price-related and fatty acid composition traits in Japanese Black cattle.

PubMed

Komatsu, Masanori; Nishino, Kagetomo; Fujimori, Yuki; Haga, Yasutoshi; Iwama, Nagako; Arakawa, Aisaku; Aihara, Yoshito; Takeda, Hisato; Takahashi, Hideaki

2018-02-01

Growth hormone secretagogue receptor 1a (GHSR1a), growth hormone (GH), growth hormone receptor (GHR), non-SMC condensin I complex, subunit G (NCAPG) and stearoyl-CoA desaturase (SCD), are known to play important roles in growth and lipid metabolisms. Single and epistatic effects of the five genes on carcass, price-related and fatty acid (FA) composition traits were analyzed in a commercial Japanese Black cattle population of Ibaraki Prefecture. A total of 650 steers and 116 heifers for carcass and price-related traits, and 158 steers for FA composition traits were used in this study. Epistatic effects between pairs of the five genes were found in several traits. Alleles showing strain-specific differences in the five genes had significant single and epistatic effects in some traits. The data suggest that a TG-repeat polymorphism of the GHSR1a.5'UTR-(TG) n locus plays a central role in gene-gene epistatic interaction of FA composition traits in the adipose tissue of Japanese Black cattle. © 2017 Japanese Society of Animal Science.

How gut transcriptional function of Drosophila melanogaster varies with the presence and composition of the gut microbiota.

PubMed

Bost, Alyssa; Franzenburg, Soeren; Adair, Karen L; Martinson, Vincent G; Loeb, Greg; Douglas, Angela E

2018-04-01

Despite evidence from laboratory experiments that perturbation of the gut microbiota affects many traits of the animal host, our understanding of the effect of variation in microbiota composition on animals in natural populations is very limited. The core purpose of this study on the fruit fly Drosophila melanogaster was to identify the impact of natural variation in the taxonomic composition of gut bacterial communities on host traits, with the gut transcriptome as a molecular index of microbiota-responsive host traits. Use of the gut transcriptome was validated by demonstrating significant transcriptional differences between the guts of laboratory flies colonized with bacteria and maintained under axenic conditions. Wild Drosophila from six field collections made over two years had gut bacterial communities of diverse composition, dominated to varying extents by Acetobacteraceae and Enterobacteriaceae. The gut transcriptomes also varied among collections and differed markedly from those of laboratory flies. However, no overall relationship between variation in the wild fly transcriptome and taxonomic composition of the gut microbiota was evident at all taxonomic scales of bacteria tested for both individual fly genes and functional categories in Gene Ontology. We conclude that the interaction between microbiota composition and host functional traits may be confounded by uncontrolled variation in both ecological circumstance and host traits (e.g., genotype, age physiological condition) under natural conditions, and that microbiota effects on host traits identified in the laboratory should, therefore, be extrapolated to field population with great caution. © 2017 John Wiley & Sons Ltd.

Production of Low Enriched Uranium Nitride Kernels for TRISO Particle Irradiation Testing

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

McMurray, J. W.; Silva, C. M.; Helmreich, G. W.

2016-06-01

A large batch of UN microspheres to be used as kernels for TRISO particle fuel was produced using carbothermic reduction and nitriding of a sol-gel feedstock bearing tailored amounts of low-enriched uranium (LEU) oxide and carbon. The process parameters, established in a previous study, produced phasepure NaCl structure UN with dissolved C on the N sublattice. The composition, calculated by refinement of the lattice parameter from X-ray diffraction, was determined to be UC 0.27N 0.73. The final accepted product weighed 197.4 g. The microspheres had an average diameter of 797±1.35 μm and a composite mean theoretical density of 89.9±0.5% formore » a solid solution of UC and UN with the same atomic ratio; both values are reported with their corresponding calculated standard error.« less

Quantitative trait loci affecting oil content, oil composition, and other agronomically important traits in Oat (Avena sativa L.)

USDA-ARS?s Scientific Manuscript database

Groat oil content and composition are important determinants of oat quality. We investigated these traits in a population of 146 recombinant inbred lines from a cross between 'Dal' (high oil) and 'Exeter' (low oil). A linkage map consisting of 475 DArT markers spanning 1271.8 cM across 40 linkage gr...

Implications of Bt traits on mycotoxin contamination in maize: Overview and recent experimental results in southern United States.

PubMed

Abbas, Hamed K; Zablotowicz, Robert M; Weaver, Mark A; Shier, W Thomas; Bruns, H Arnold; Bellaloui, Nacer; Accinelli, Cesare; Abel, Craig A

2013-12-04

Mycotoxin contamination levels in maize kernels are controlled by a complex set of factors including insect pressure, fungal inoculum potential, and environmental conditions that are difficult to predict. Methods are becoming available to control mycotoxin-producing fungi in preharvest crops, including Bt expression, biocontrol, and host plant resistance. Initial reports in the United States and other countries have associated Bt expression with reduced fumonisin, deoxynivalenol, and zearalenone contamination and, to a lesser extent, reduced aflatoxin contamination in harvested maize kernels. However, subsequent field results have been inconsistent, confirming that fumonisin contamination can be reduced by Bt expression, but the effect on aflatoxin is, at present, inconclusive. New maize hybrids have been introduced with increased spectra of insect control and higher levels of Bt expression that may provide important tools for mycotoxin reduction and increased yield due to reduced insect feeding, particularly if used together with biocontrol and host plant resistance.

Pecan walnut (Carya illinoinensis (Wangenh.) K. Koch) oil quality and phenolic compounds as affected by microwave and conventional roasting.

PubMed

Juhaimi, Fahad Al; Özcan, Mehmet Musa; Uslu, Nurhan; Doğu, Süleyman

2017-12-01

In this study, the effects of conventional and microwave roasting on phenolic compounds, free acidity, peroxide value, fatty acid composition and tocopherol content of pecan walnut kernel and oil was investigated. The oil content of pecan kernels was 73.78% for microwave oven roasted at 720 W and 73.56% for conventional oven roasted at 110 °C. The highest free fatty acid content (0.50%) and the lowest peroxide value (2.48 meq O 2 /kg) were observed during microwave roasting at 720 W. The fatty acid profiles and tocopherol contents of pecan kernel oils did not show significant differences compared to raw samples. Roasting process in microwave oven at 720 W caused the reduction of some phenolic compounds, while the content of gallic acid exhibited a significant increase.

Environmental stressors as a driver of the trait composition of benthic macroinvertebrate assemblages in polluted Iberian rivers.

PubMed

Kuzmanovic, Maja; Dolédec, Sylvain; de Castro-Catala, Nuria; Ginebreda, Antoni; Sabater, Sergi; Muñoz, Isabel; Barceló, Damià

2017-07-01

We used the trait composition of macroinvertebrate communities to identify the effects of pesticides and multiple stressors associated with urban land use at different sites of four rivers in Spain. Several physical and chemical stressors (high metal pollution, nutrients, elevated temperature and flow alterations) affected the urban sites. The occurrence of multiple stressors influenced aquatic assemblages at 50% of the sites. We hypothesized that the trait composition of macroinvertebrate assemblages would reflect the strategies that the assemblages used to cope with the respective environmental stressors. We used RLQ and fourth corner analysis to address the relationship between stressors and the trait composition of benthic macroinvertebrates. We found a statistically significant relationship between the trait composition and the exposure of assemblages to environmental stressors. The first RLQ dimension, which explained most of the variability, clearly separated sites according to the stressors. Urban-related stressors selected taxa that were mainly plurivoltine and fed on deposits. In contrast, pesticide impacted sites selected taxa with high levels of egg protection (better egg survival), indicating a potentially higher risk for egg mortality. Moreover, the trait diversity of assemblages at urban sites was low compared to that observed in pesticide impacted sites, suggesting the homogenization of assemblages in urban areas. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Segregating the Effects of Seed Traits and Common Ancestry of Hardwood Trees on Eastern Gray Squirrel Foraging Decisions.

PubMed

Sundaram, Mekala; Willoughby, Janna R; Lichti, Nathanael I; Steele, Michael A; Swihart, Robert K

2015-01-01

The evolution of specific seed traits in scatter-hoarded tree species often has been attributed to granivore foraging behavior. However, the degree to which foraging investments and seed traits correlate with phylogenetic relationships among trees remains unexplored. We presented seeds of 23 different hardwood tree species (families Betulaceae, Fagaceae, Juglandaceae) to eastern gray squirrels (Sciurus carolinensis), and measured the time and distance travelled by squirrels that consumed or cached each seed. We estimated 11 physical and chemical seed traits for each species, and the phylogenetic relationships between the 23 hardwood trees. Variance partitioning revealed that considerable variation in foraging investment was attributable to seed traits alone (27-73%), and combined effects of seed traits and phylogeny of hardwood trees (5-55%). A phylogenetic PCA (pPCA) on seed traits and tree phylogeny resulted in 2 "global" axes of traits that were phylogenetically autocorrelated at the family and genus level and a third "local" axis in which traits were not phylogenetically autocorrelated. Collectively, these axes explained 30-76% of the variation in squirrel foraging investments. The first global pPCA axis, which produced large scores for seed species with thin shells, low lipid and high carbohydrate content, was negatively related to time to consume and cache seeds and travel distance to cache. The second global pPCA axis, which produced large scores for seeds with high protein, low tannin and low dormancy levels, was an important predictor of consumption time only. The local pPCA axis primarily reflected kernel mass. Although it explained only 12% of the variation in trait space and was not autocorrelated among phylogenetic clades, the local axis was related to all four squirrel foraging investments. Squirrel foraging behaviors are influenced by a combination of phylogenetically conserved and more evolutionarily labile seed traits that is consistent with a weak or more diffuse coevolutionary relationship between rodents and hardwood trees rather than a direct coevolutionary relationship.

Accuracies of univariate and multivariate genomic prediction models in African cassava.

PubMed

Okeke, Uche Godfrey; Akdemir, Deniz; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2017-12-04

Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

A locally adaptive kernel regression method for facies delineation

NASA Astrophysics Data System (ADS)

Fernàndez-Garcia, D.; Barahona-Palomo, M.; Henri, C. V.; Sanchez-Vila, X.

2015-12-01

Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.

Assessing the Utility of Compound Trait Estimates of Narrow Personality Traits.

PubMed

Credé, Marcus; Harms, Peter D; Blacksmith, Nikki; Wood, Dustin

2016-01-01

It has been argued that approximations of narrow traits can be made through linear combinations of broad traits such as the Big Five personality traits. Indeed, Hough and Ones ( 2001 ) used a qualitative analysis of scale content to arrive at a taxonomy of how Big Five traits might be combined to approximate various narrow traits. However, the utility of such compound trait approximations has yet to be established beyond specific cases such as integrity and customer service orientation. Using data from the Eugene-Springfield Community Sample (Goldberg, 2008 ), we explore the ability of linear composites of scores on Big Five traits to approximate scores on 127 narrow trait measures from 5 well-known non-Big-Five omnibus measures of personality. Our findings indicate that individuals' standing on more than 30 narrow traits can be well estimated from 3 different types of linear composites of scores on Big Five traits without a substantial sacrifice in criterion validity. We discuss theoretical accounts for why such relationships exist as well as the theoretical and practical implications of these findings for researchers and practitioners.

Selection enhanced estimates of µ-calpain, calpastatin, and dacylglycerol O-acyltransferase 1 genetic effects on pre-weaning performance, carcass quality traits, and residual variance of tenderness in composite ... cattle

USDA-ARS?s Scientific Manuscript database

Selection of the composite MARC III population for markers allowed better estimates of effects and inheritance of markers for targeted carcass quality traits (n=254) and nontargeted traits and an evaluation of SNP specific residual variance models for tenderness. Genotypic effects of CAPN1 haplotyp...

Fumonisin B(1)-nonproducing strains of Fusarium verticillioides cause maize (Zea mays) ear infection and ear rot.

PubMed

Desjardins, A E; Plattner, R D

2000-11-01

Fumonisins are polyketide mycotoxins produced by Fusarium verticillioides (synonym F. moniliforme), a major pathogen of maize (Zea mays) worldwide. Most field strains produce high levels of fumonisin B(1) (FB(1)) and low levels of the less-oxygenated homologues FB(2) and FB(3), but fumonisin B(1)-nonproducing field strains have been obtained by natural variation. To test the role of various fumonisins in pathogenesis on maize under field conditions, one strain producing FB(1), FB(2), and FB(3), one strain producing only FB(2), one strain producing only FB(3), and one fumonisin-nonproducing strain were applied to ears via the silk channel and on seeds at planting. Disease severity on the harvested ears was evaluated by visible symptoms and by weight percent symptomatic kernels. Fumonisin levels in kernels were determined by high-performance liquid chromatography. The presence of the applied FB(1)-nonproducing strains in kernels was determined by analysis of recovered strains for fumonisin production and other traits. All three FB(1)-nonproducing strains were able to infect ears following either silk-channel application or seed application at planting and were as effective as the FB(1)-producing strain in causing ear rot following silk-channel application. These results indicate that production of FB(1), FB(2), or FB(3) is not required for F. verticillioides to cause maize ear infection and ear rot.

Risk Classification with an Adaptive Naive Bayes Kernel Machine Model.

PubMed

Minnier, Jessica; Yuan, Ming; Liu, Jun S; Cai, Tianxi

2015-04-22

Genetic studies of complex traits have uncovered only a small number of risk markers explaining a small fraction of heritability and adding little improvement to disease risk prediction. Standard single marker methods may lack power in selecting informative markers or estimating effects. Most existing methods also typically do not account for non-linearity. Identifying markers with weak signals and estimating their joint effects among many non-informative markers remains challenging. One potential approach is to group markers based on biological knowledge such as gene structure. If markers in a group tend to have similar effects, proper usage of the group structure could improve power and efficiency in estimation. We propose a two-stage method relating markers to disease risk by taking advantage of known gene-set structures. Imposing a naive bayes kernel machine (KM) model, we estimate gene-set specific risk models that relate each gene-set to the outcome in stage I. The KM framework efficiently models potentially non-linear effects of predictors without requiring explicit specification of functional forms. In stage II, we aggregate information across gene-sets via a regularization procedure. Estimation and computational efficiency is further improved with kernel principle component analysis. Asymptotic results for model estimation and gene set selection are derived and numerical studies suggest that the proposed procedure could outperform existing procedures for constructing genetic risk models.

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

PubMed Central

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

2013-01-01

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

Electron-beam irradiation effects on phytochemical constituents and antioxidant capacity of pecan kernels [ Carya illinoinensis (Wangenh.) K. Koch] during storage.

PubMed

Villarreal-Lozoya, Jose E; Lombardini, Leonardo; Cisneros-Zevallos, Luis

2009-11-25

Pecans kernels (Kanza and Desirable cultivars) were irradiated with 0, 1.5, and 3.0 kGy using electron-beam (E-beam) irradiation and stored under accelerated conditions [40 degrees C and 55-60% relative humidity (RH)] for 134 days. Antioxidant capacity (AC) using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and oxygen radical absorbance capacity (ORAC) assays, phenolic (TP) and condensed tannin (CT) content, high-performance liquid chromatography (HPLC) phenolic profile, tocopherol content, peroxide value (PV), and fatty acid profiles were determined during storage. Irradiation decreased TP and CT with no major detrimental effects in AC. Phenolic profiles after hydrolysis were similar among treatments (e.g., gallic and ellagic acid, catechin, and epicatechin). Tocopherol content decreased with irradiation (>21 days), and PV increased at later stages (>55 days), with no change in fatty acid composition among treatments. Color lightness decreased, and a reddish brown hue developed during storage. A proposed mechanism of kernel oxidation is presented, describing the events taking place. In general, E-beam irradiation had slight effects on phytochemical constituents and could be considered a potential tool for pecan kernel decontamination.

Predictability of bee community composition after floral removals differs by floral trait group.

PubMed

Urban-Mead, Katherine R

2017-11-01

Plant-bee visitor communities are complex networks. While studies show that deleting nodes alters network topology, predicting these changes in the field remains difficult. Here, a simple trait-based approach is tested for predicting bee community composition following disturbance. I selected six fields with mixed cover of flower species with shallow (open) and deep (tube) nectar access, and removed all flowers or flower heads of species of each trait in different plots paired with controls, then observed bee foraging and composition. I compared the bee community in each manipulated plot with bees on the same flower species in control plots. The bee morphospecies composition in manipulations with only tube flowers remaining was the same as that in the control plots, while the bee morphospecies on only open flowers were dissimilar from those in control plots. However, the proportion of short- and long-tongued bees on focal flowers did not differ between control and manipulated plots for either manipulation. So, bees within some functional groups are more strongly linked to their floral trait partners than others. And, it may be more fruitful to describe expected bee community compositions in terms of relative proportions of relevant ecological traits than species, particularly in species-diverse communities. © 2017 The Author(s).

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.

Mapping Quantitative Trait Loci (QTL) in sheep. III. QTL for carcass composition traits derived from CT scans and aligned with a meta-assembly for sheep and cattle carcass QTL.

PubMed

Cavanagh, Colin R; Jonas, Elisabeth; Hobbs, Matthew; Thomson, Peter C; Tammen, Imke; Raadsma, Herman W

2010-09-16

An (Awassi × Merino) × Merino single-sire backcross family with 165 male offspring was used to map quantitative trait loci (QTL) for body composition traits on a framework map of 189 microsatellite loci across all autosomes. Two cohorts were created from the experimental progeny to represent alternative maturity classes for body composition assessment. Animals were raised under paddock conditions prior to entering the feedlot for a 90-day fattening phase. Body composition traits were derived in vivo at the end of the experiment prior to slaughter at 2 (cohort 1) and 3.5 (cohort 2) years of age, using computed tomography. Image analysis was used to gain accurate predictions for 13 traits describing major fat depots, lean muscle, bone, body proportions and body weight which were used for single- and two-QTL mapping analysis. Using a maximum-likelihood approach, three highly significant (LOD ≥ 3), 15 significant (LOD ≥ 2), and 11 suggestive QTL (1.7 ≤ LOD < 2) were detected on eleven chromosomes. Regression analysis confirmed 28 of these QTL and an additional 17 suggestive (P < 0.1) and two significant (P < 0.05) QTL were identified using this method. QTL with pleiotropic effects for two or more tissues were identified on chromosomes 1, 6, 10, 14, 16 and 23. No tissue-specific QTL were identified.A meta-assembly of ovine QTL for carcass traits from this study and public domain sources was performed and compared with a corresponding bovine meta-assembly. The assembly demonstrated QTL with effects on carcass composition in homologous regions on OAR1, 2, 6 and 21.

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.

Fatty acid, triacylglycerol, phytosterol, and tocopherol variations in kernel oil of Malatya apricots from Turkey.

PubMed

Turan, Semra; Topcu, Ali; Karabulut, Ihsan; Vural, Halil; Hayaloglu, Ali Adnan

2007-12-26

The fatty acid, sn-2 fatty acid, triacyglycerol (TAG), tocopherol, and phytosterol compositions of kernel oils obtained from nine apricot varieties grown in the Malatya region of Turkey were determined ( P<0.05). The names of the apricot varieties were Alyanak (ALY), Cataloglu (CAT), Cöloglu (COL), Hacihaliloglu (HAC), Hacikiz (HKI), Hasanbey (HSB), Kabaasi (KAB), Soganci (SOG), and Tokaloglu (TOK). The total oil contents of apricot kernels ranged from 40.23 to 53.19%. Oleic acid contributed 70.83% to the total fatty acids, followed by linoleic (21.96%), palmitic (4.92%), and stearic (1.21%) acids. The s n-2 position is mainly occupied with oleic acid (63.54%), linoleic acid (35.0%), and palmitic acid (0.96%). Eight TAG species were identified: LLL, OLL, PLL, OOL+POL, OOO+POO, and SOO (where P, palmitoyl; S, stearoyl; O, oleoyl; and L, linoleoyl), among which mainly OOO+POO contributed to 48.64% of the total, followed by OOL+POL at 32.63% and OLL at 14.33%. Four tocopherol and six phytosterol isomers were identified and quantified; among these, gamma-tocopherol (475.11 mg/kg of oil) and beta-sitosterol (273.67 mg/100 g of oil) were predominant. Principal component analysis (PCA) was applied to the data from lipid components of apricot kernel oil in order to explore the distribution of the apricot variety according to their kernel's lipid components. PCA separated some varieties including ALY, COL, KAB, CAT, SOG, and HSB in one group and varieties TOK, HAC, and HKI in another group based on their lipid components of apricot kernel oil. So, in the present study, PCA was found to be a powerful tool for classification of the samples.

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

DOE PAGES

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

2017-07-04

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

Plant Trait Variation along an Altitudinal Gradient in Mediterranean High Mountain Grasslands: Controlling the Species Turnover Effect

PubMed Central

Pescador, David S.; de Bello, Francesco; Valladares, Fernando; Escudero, Adrián

2015-01-01

Assessing changes in plant functional traits along gradients is useful for understanding the assembly of communities and their response to global and local environmental drivers. However, these changes may reflect the effects of species composition (i.e. composition turnover), species abundance (i.e. species interaction), and intra-specific trait variability (i.e. species plasticity). In order to determine the relevance of the latter, trait variation can be assessed under minimal effects of composition turnover. Nine sampling sites were established along an altitudinal gradient in a Mediterranean high mountain grassland community with low composition turnover (Madrid, Spain; 1940 m–2419 m). Nine functional traits were also measured for ten individuals of around ten plant species at each site, for a total of eleven species across all sites. The relative importance of different sources of variability (within/between site and intra-/inter-specific functional diversity) and trait variation at species and community level along the considered gradients were explored. We found a weak individual species response to altitude and other environmental variables although in some cases, individuals were smaller and leaves were thicker at higher elevations. This lack of species response was most likely due to greater within- than between-site species variation. At the community level, inter-specific functional diversity was generally greater than the intra-specific component except for traits linked to leaf element content (leaf carbon content, leaf nitrogen content, δ13C and δ15N). Inter-specific functional diversity decreased with lower altitude for four leaf traits (specific leaf area, leaf dry matter content, δ13C and δ15N), suggesting trait convergence between species at lower elevations, where water shortage may have a stronger environmental filtering effect than colder temperatures at higher altitudes. Our results suggest that, within a vegetation type encompassing various environmental gradients, both, changes in species abundance and intra-specific trait variability adjust for the community functional response to environmental changes. PMID:25774532

Plant trait variation along an altitudinal gradient in mediterranean high mountain grasslands: controlling the species turnover effect.

PubMed

Pescador, David S; de Bello, Francesco; Valladares, Fernando; Escudero, Adrián

2015-01-01

Assessing changes in plant functional traits along gradients is useful for understanding the assembly of communities and their response to global and local environmental drivers. However, these changes may reflect the effects of species composition (i.e. composition turnover), species abundance (i.e. species interaction), and intra-specific trait variability (i.e. species plasticity). In order to determine the relevance of the latter, trait variation can be assessed under minimal effects of composition turnover. Nine sampling sites were established along an altitudinal gradient in a Mediterranean high mountain grassland community with low composition turnover (Madrid, Spain; 1940 m-2419 m). Nine functional traits were also measured for ten individuals of around ten plant species at each site, for a total of eleven species across all sites. The relative importance of different sources of variability (within/between site and intra-/inter-specific functional diversity) and trait variation at species and community level along the considered gradients were explored. We found a weak individual species response to altitude and other environmental variables although in some cases, individuals were smaller and leaves were thicker at higher elevations. This lack of species response was most likely due to greater within- than between-site species variation. At the community level, inter-specific functional diversity was generally greater than the intra-specific component except for traits linked to leaf element content (leaf carbon content, leaf nitrogen content, δ13C and δ15N). Inter-specific functional diversity decreased with lower altitude for four leaf traits (specific leaf area, leaf dry matter content, δ13C and δ15N), suggesting trait convergence between species at lower elevations, where water shortage may have a stronger environmental filtering effect than colder temperatures at higher altitudes. Our results suggest that, within a vegetation type encompassing various environmental gradients, both, changes in species abundance and intra-specific trait variability adjust for the community functional response to environmental changes.

Genetic diversity of bread wheat genotypes in Iran for some nutritional value and baking quality traits.

PubMed

Amiri, Reza; Sasani, Shahryar; Jalali-Honarmand, Saeid; Rasaei, Ali; Seifolahpour, Behnaz; Bahraminejad, Sohbat

2018-02-01

Genetic variation among 78 irrigated bread wheat genotypes was studied for their nutritional value and baking quality traits as well as some agronomic traits. The experiment was conducted in a randomized complete block design with three replicates under normal and terminal drought stress conditions in Kermanshah, Iran during 2012-2013 cropping season. The results of combined ANOVA indicated highly significant genotypic differences for all traits. All studied traits except grain yield, hectoliter weight and grain fiber content were significantly affected by genotype × environment interaction. Drought stress reduced grain yield, thousand kernel weight, gluten index, grain starch content and hectoliter weight and slightly promoted grain protein and fiber contents, falling number, total gluten and ratio of wet gluten to grain protein content. Grain yield by 31.66% and falling number by 9.20% attained the highest decrease and increase due to drought stress. There were negative and significant correlations among grain yield with grain protein and fiber contents under both conditions. Results of cluster analysis showed that newer genotypes had more grain yield and gluten index than older ones, but instead, they had the lower grain protein and fiber contents. It is thought that wheat breeders have bred cultivars with high grain yield, low protein content, and improved bread-making attributes during last seven decades. While older genotypes indicated significantly higher protein contents, and some of them had higher gluten index. We concluded from this study that it is imperative for breeders to pay more attention to improve qualitative traits coordinated to grain yield.

Investigating light curve modulation via kernel smoothing. II. New additional modes in single-mode OGLE classical Cepheids

NASA Astrophysics Data System (ADS)

Süveges, Maria; Anderson, Richard I.

2018-04-01

Detailed knowledge of the variability of classical Cepheids, in particular their modulations and mode composition, provides crucial insight into stellar structure and pulsation. However, tiny modulations of the dominant radial-mode pulsation were recently found to be very frequent, possibly ubiquitous in Cepheids, which makes secondary modes difficult to detect and analyse, since these modulations can easily mask the potentially weak secondary modes. The aim of this study is to re-investigate the secondary mode content in the sample of OGLE-III and -IV single-mode classical Cepheids using kernel regression with adaptive kernel width for pre-whitening, instead of using a constant-parameter model. This leads to a more precise removal of the modulated dominant pulsation, and enables a more complete survey of secondary modes with frequencies outside a narrow range around the primary. Our analysis reveals that significant secondary modes occur more frequently among first overtone Cepheids than previously thought. The mode composition appears significantly different in the Large and Small Magellanic Clouds, suggesting a possible dependence on chemical composition. In addition to the formerly identified non-radial mode at P2 ≈ 0.6…0.65P1 (0.62-mode), and a cluster of modes with near-primary frequency, we find two more candidate non-radial modes. One is a numerous group of secondary modes with P2 ≈ 1.25P1, which may represent the fundamental of the 0.62-mode, supposed to be the first harmonic of an l ∈ {7, 8, 9} non-radial mode. The other new mode is at P2 ≈ 1.46P1, possibly analogous to a similar, rare mode recently discovered among first overtone RR Lyrae stars.

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

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

Knight, Travis W.

2010-01-31

The focus of this effort was on the development of an advanced fuel for gas-cooled fast reactor (GFR) applications. This composite design is based on carbide fuel kernels dispersed in a ZrC matrix. The choice of ZrC is based on its high temperature properties and good thermal conductivity and improved retention of fission products to temperatures beyond that of traditional SiC based coated particle fuels. A key component of this study was the development and understanding of advanced fabrication techniques for GFR fuels that have potential to reduce minor actinide (MA) losses during fabrication owing to their higher vapor pressuresmore » and greater volatility. The major accomplishments of this work were the study of combustion synthesis methods for fabrication of the ZrC matrix, fabrication of high density UC electrodes for use in the rotating electrode process, production of UC particles by rotating electrode method, integration of UC kernels in the ZrC matrix, and the full characterization of each component. Major accomplishments in the near-term have been the greater characterization of the UC kernels produced by the rotating electrode method and their condition following the integration in the composite (ZrC matrix) following the short time but high temperature combustion synthesis process. This work has generated four journal publications, one conference proceeding paper, and one additional journal paper submitted for publication (under review). The greater significance of the work can be understood in that it achieved an objective of the DOE Generation IV (GenIV) roadmap for GFR Fuel—namely the demonstration of a composite carbide fuel with 30% volume fuel. This near-term accomplishment is even more significant given the expected or possible time frame for implementation of the GFR in the years 2030 -2050 or beyond.« less

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

NASA Astrophysics Data System (ADS)

Bigaouette, Nicolas

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

Estimates of genetic parameters for chemical traits of meat quality in Japanese black cattle

PubMed Central

Sakuma, Hironori; Saito, Kaoru; Kohira, Kimiko; Ohhashi, Fumie; Shoji, Noriaki

2016-01-01

Abstract Genetic parameters for 54 carcass and chemical traits, such as general composition (moisture, crude fat and crude protein), fatty acid composition and water‐soluble compounds (free amino acids, peptides, nucleotides and sugars) of 587 commercial Japanese Black cattle were assessed. Heritability estimates for carcass traits and general composition ranged between 0.19–0.28, whereas those for fatty acid composition ranged between 0.11–0.85. Most heritability estimates for water‐soluble compounds were lower than 0.30; these traits were affected by aging period. Moderate heritability was observed for glutamine, alanine, taurine, anserine, inosine 5′‐monophosphate (IMP), inosine and myo‐inositol. In particular, heritability estimates were the highest (0.66) for taurine. Traits with moderate heritability were unaffected by aging period, with the exception of IMP, which was affected by aging period but exhibited moderate heritability (0.47). Although phenotypic correlations of water‐soluble compounds with carcass weight (CW), beef marbling standard (BMS) and monounsaturated fatty acid were generally low, genetic correlations between these traits were low to high. At the genetic level, most of the water‐soluble compounds were positively correlated with monounsaturated fatty acid but negatively correlated with CW and BMS. Thus, our results indicate that genetic variance and correlations could exist and be captured for some of the water‐soluble compounds. PMID:27146072

Triacylglycerol and triterpene ester composition of shea nuts from seven African countries.

PubMed

Akihisa, Toshihiro; Kojima, Nobuo; Katoh, Naoko; Kikuchi, Takashi; Fukatsu, Makoto; Shimizu, Naoto; Masters, Eliot T

2011-01-01

The compositions of the triacylglycerol (TAG) and triterpene ester (TE) fractions of the kernel fats (n-hexane extracts; shea butter) of the shea tree (Vitellaria paradoxa; Sapotaceae) were determined for 36 samples from seven sub-Saharan countries, i.e., Cote d' Ivoire, Ghana, Nigeria, Cameroun, Chad, Sudan, and Uganda. The principal TAGs are stearic-oleic-stearic (SOS; mean 31.2%), SOO (27.7%), and OOO (10.8%). The TE fractions contents are in the range of 0.5-6.5%, and contain α-amyrin cinnamate (1c; mean 29.3%) as the predominant TE followed by butyrospermol cinnamate (4c; 14.8%), α-amyrin acetate (1a; 14.1%), lupeol cinnamate (3c; 9.0%), β-amyrin cinnamate (2c; 7.6%), lupeol acetate (3a; 7.2%), butyrospermol acetate (4a; 5.8%), and β-amyrin acetate (2a; 4.9%). Shea kernel fats from West African provenances contained, in general, higher levels of high-melting TAGs such as SOS, and higher amount of TEs than those from East African provenances. No striking regional difference in the composition of the TE fractions was observed. Copyright © 2011 by Japan Oil Chemists' Society

Factors associated with herd bulk milk composition and technological traits in the Italian dairy industry.

PubMed

Benedet, A; Manuelian, C L; Penasa, M; Cassandro, M; Righi, F; Sternieri, M; Galimberti, P; Zambrini, A V; De Marchi, M

2018-02-01

The aim of the present study was to investigate sources of variation of milk composition and technological characteristics routinely collected in field conditions in the Italian dairy industry. A total of 40,896 bulk milk records from 620 herds and 10 regions across Italy were analyzed. Composition traits were fat, protein, and casein percentages, urea content, and somatic cell score; and technological characteristics were rennet coagulation time, curd firming time, curd firmness 30 min after rennet addition to milk, and titratable acidity. Data of herd bulk milks were analyzed using a model that included fixed effects of region, herd nested within region, and season of milk analysis. An average good milk quality was reported in the dairy industry (especially concerning fat, protein, and casein percentages), and moderate to high correlations between composition and technological traits were observed. All factors included in the statistical model were significant in explaining the variation of the studied traits except for region effect in the analysis of casein and somatic cell score. Northeast and central-southern Italian regions showed the best performance for composition and technological features, respectively. Traits varied greatly across regions, which could reflect differences in herd management and strategies. Overall, less suitable milk for dairy processing was observed in summer. Results of the present study suggested that a constant monitoring of technological traits in the dairy industry is necessary to improve production quality at herd level and it may be a way to segregate milk according to its processing characteristics. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data

NASA Astrophysics Data System (ADS)

Pinar, Anthony; Masarik, Matthew; Havens, Timothy C.; Burns, Joseph; Thelen, Brian; Becker, John

2015-05-01

This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

Effect of Temperature and Moisture on the Development of Concealed Damage in Raw Almonds (Prunus dulcis).

PubMed

Rogel-Castillo, Cristian; Zuskov, David; Chan, Bronte Lee; Lee, Jihyun; Huang, Guangwei; Mitchell, Alyson E

2015-09-23

Concealed damage (CD) is a brown discoloration of nutmeat that appears only after kernels are treated with moderate heat (e.g., roasting). Identifying factors that promote CD in almonds is of significant interest to the nut industry. Herein, the effect of temperature (35 and 45 °C) and moisture (<5, 8, and 11%) on the composition of volatiles in raw almonds (Prunus dulcis var. Nonpareil) was studied using HS-SPME-GC/MS. A CIE LCh colorimetric method was developed to identify raw almonds with CD. A significant increase in CD was demonstrated in almonds exposed to moisture (8% kernel moisture content) at 45 °C as compared to 35 °C. Elevated levels of volatiles related to lipid peroxidation and amino acid degradation were observed in almonds with CD. These results suggest that postharvest moisture exposure resulting in an internal kernel moisture ≥ 8% is a key factor in the development of CD in raw almonds and that CD is accelerated by temperature.

Chemical and functional properties of fibre concentrates obtained from by-products of coconut kernel.

PubMed

Yalegama, L L W C; Nedra Karunaratne, D; Sivakanesan, Ramiah; Jayasekara, Chitrangani

2013-11-01

The coconut kernel residues obtained after extraction of coconut milk (MR) and virgin coconut oil (VOR) were analysed for their potential as dietary fibres. VOR was defatted and treated chemically using three solvent systems to isolate coconut cell wall polysaccharides (CCWP). Nutritional composition of VOR, MR and CCWPs indicated that crude fibre, neutral detergent fibre, acid detergent fibre and hemicelluloses contents were higher in CCWPs than in VOR and MR. MR contained a notably higher content of fat than VOR and CCWPs. The oil holding capacity, water holding capacity and swelling capacity were also higher in CCWPs than in VOR and MR. All the isolates and MR and VOR had high metal binding capacities. The CCWPs when compared with commercially available fibre isolates, indicated improved dietary fibre properties. These results show that chemical treatment of coconut kernel by-products can enhance the performance of dietary fibre to yield a better product. Copyright © 2013 Elsevier Ltd. All rights reserved.

Mineral contents and proximate composition of Pistacia vera kernels.

PubMed

Harmankaya, Mustafa; Ozcan, Mehmet Musa; Al Juhaimi, Fahad

2014-07-01

The mineral contents of Pistacia vera kernels were determined by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). The minimum and maximum values of K, P, Ca, Mg, and S elements ranged from 6,333 to 8,064 mg/kg, 3,630 to 5,228 mg/kg, 1,614 to 3,226 mg/kg, 1,716 to 2,402 mg/kg, and 1,417 to 1,825 mg/kg, respectively. In addition, the mean values of Fe, Zn, Cu, Mn, B, Mo, Cr and Ni elements were determined as 42.48, 20.52, 12.81, 7.48, 11.31, 0.106, 0.511 and 1.67 mg/kg, respectively. Ash levels of kernels were found between 2.28 % (Urfa) and 2.79 % (Halebi). In addition, crude oil and protein contents were determined between 48.8 % (Halebi) to 55.3 % (Siirt) and 23.33 % (Uzun) to 27.16 % (Halebi), respectively.

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

McMurray, Jacob W.; Lindemer, Terrence B.; Brown, Nicholas R.

There are three important failure mechanisms that must be controlled in high-temperature gas-cooled reactor (HTGR) fuel for certain higher burnup applications are SiC layer rupture, SiC corrosion by CO, and coating compromise from kernel migration. All are related to high CO pressures stemming from free O generated when uranium present as UO 2 fissions and the O is not subsequently bound by other elements. Furthermore, in the HTGR UCO kernel design, CO buildup from excess O is controlled by the inclusion of additional uranium in the form of a carbide, UC x. An approach for determining the minimum UC xmore » content to ensure negligible CO formation was developed and demonstrated using CALPHAD models and the Serpent 2 reactor physics and depletion analysis tool. Our results are intended to be more accurate than previous estimates by including more nuclear and chemical factors, in particular the effect of transmutation products on the oxygen distribution as the fuel kernel composition evolves with burnup.« less

Influence of indigenous minor components on fat crystal network of fully hydrogenated palm kernel oil and fully hydrogenated coconut oil.

PubMed

Chai, Xiu-Hang; Meng, Zong; Cao, Pei-Rang; Liang, Xin-Yu; Piatko, Michael; Campbell, Shawn; Koon Lo, Seong; Liu, Yuan-Fa

2018-07-30

Purification of triglycerides from fully hydrogenated palm kernel oil (FHPKO) and fully hydrogenated coconut oil (FHCNO) was performed by a chromatographic method. Lipid composition, thermal properties, polymorphism, isothermal crystallization behaviour, nanostructure and microstructure of FHPKO, FHPKO-triacylglycerol (TAG), FHCNO and FHCNO-TAG were evaluated. Removal of minor components had no effect on triglycerides composition. However, the presence of the minor components did increase the slip melting point and promote onset of crystallization. Furthermore, the thickness of the nanoscale crystals increased, and polymorphic transformation from β' to β occurred in FHPKO after the removal of minor components, and from α to β' in FHCNO. Sharp changes in the values of the Avrami constant K and exponent n suggested that the presence of minor components changed the crystal growth mechanism. The PLM results indicated that a coarser crystal structure with lower fractal dimension appeared after the removal of minor components from both FHPKO and FHCNO. Copyright © 2018 Elsevier Ltd. All rights reserved.

Infrared microspectroscopic imaging of plant tissues: spectral visualization of Triticum aestivum kernel and Arabidopsis leaf microstructure

PubMed Central

Warren, Frederick J; Perston, Benjamin B; Galindez-Najera, Silvia P; Edwards, Cathrina H; Powell, Prudence O; Mandalari, Giusy; Campbell, Grant M; Butterworth, Peter J; Ellis, Peter R

2015-01-01

Infrared microspectroscopy is a tool with potential for studies of the microstructure, chemical composition and functionality of plants at a subcellular level. Here we present the use of high-resolution bench top-based infrared microspectroscopy to investigate the microstructure of Triticum aestivum L. (wheat) kernels and Arabidopsis leaves. Images of isolated wheat kernel tissues and whole wheat kernels following hydrothermal processing and simulated gastric and duodenal digestion were generated, as well as images of Arabidopsis leaves at different points during a diurnal cycle. Individual cells and cell walls were resolved, and large structures within cells, such as starch granules and protein bodies, were clearly identified. Contrast was provided by converting the hyperspectral image cubes into false-colour images using either principal component analysis (PCA) overlays or by correlation analysis. The unsupervised PCA approach provided a clear view of the sample microstructure, whereas the correlation analysis was used to confirm the identity of different anatomical structures using the spectra from isolated components. It was then demonstrated that gelatinized and native starch within cells could be distinguished, and that the loss of starch during wheat digestion could be observed, as well as the accumulation of starch in leaves during a diurnal period. PMID:26400058

Carbon partitioning between oil and carbohydrates in developing oat (Avena sativa L.) seeds.

PubMed

Ekman, Asa; Hayden, Daniel M; Dehesh, Katayoon; Bülow, Leif; Stymne, Sten

2008-01-01

Cereals accumulate starch in the endosperm as their major energy reserve in the grain. In most cereals the embryo, scutellum, and aleurone layer are high in oil, but these tissues constitute a very small part of the total seed weight. However, in oat (Avena sativa L.) most of the oil in kernels is deposited in the same endosperm cells that accumulate starch. Thus oat endosperm is a desirable model system to study the metabolic switches responsible for carbon partitioning between oil and starch synthesis. A prerequisite for such investigations is the development of an experimental system for oat that allows for metabolic flux analysis using stable and radioactive isotope labelling. An in vitro liquid culture system, developed for detached oat panicles and optimized to mimic kernel composition during different developmental stages in planta, is presented here. This system was subsequently used in analyses of carbon partitioning between lipids and carbohydrates by the administration of 14C-labelled sucrose to two cultivars having different amounts of kernel oil. The data presented in this study clearly show that a higher amount of oil in the high-oil cultivar compared with the medium-oil cultivar was due to a higher proportion of carbon partitioning into oil during seed filling, predominantly at the earlier stages of kernel development.

Retrobiosynthetic nuclear magnetic resonance analysis of amino acid biosynthesis and intermediary metabolism. Metabolic flux in developing maize kernels.

PubMed

Glawischnig, E; Gierl, A; Tomas, A; Bacher, A; Eisenreich, W

2001-03-01

Information on metabolic networks could provide the basis for the design of targets for metabolic engineering. To study metabolic flux in cereals, developing maize (Zea mays) kernels were grown in sterile culture on medium containing [U-(13)C(6)]glucose or [1,2-(13)C(2)]acetate. After growth, amino acids, lipids, and sitosterol were isolated from kernels as well as from the cobs, and their (13)C isotopomer compositions were determined by quantitative nuclear magnetic resonance spectroscopy. The highly specific labeling patterns were used to analyze the metabolic pathways leading to amino acids and the triterpene on a quantitative basis. The data show that serine is generated from phosphoglycerate, as well as from glycine. Lysine is formed entirely via the diaminopimelate pathway and sitosterol is synthesized entirely via the mevalonate route. The labeling data of amino acids and sitosterol were used to reconstruct the labeling patterns of key metabolic intermediates (e.g. acetyl-coenzyme A, pyruvate, phosphoenolpyruvate, erythrose 4-phosphate, and Rib 5-phosphate) that revealed quantitative information about carbon flux in the intermediary metabolism of developing maize kernels. Exogenous acetate served as an efficient precursor of sitosterol, as well as of amino acids of the aspartate and glutamate family; in comparison, metabolites formed in the plastidic compartments showed low acetate incorporation.

Selection on male sex pheromone composition contributes to butterfly reproductive isolation

PubMed Central

Bacquet, P. M. B.; Brattström, O.; Wang, H.-L.; Allen, C. E.; Löfstedt, C.; Brakefield, P. M.; Nieberding, C. M.

2015-01-01

Selection can facilitate diversification by inducing character displacement in mate choice traits that reduce the probability of maladaptive mating between lineages. Although reproductive character displacement (RCD) has been demonstrated in two-taxa case studies, the frequency of this process in nature is still debated. Moreover, studies have focused primarily on visual and acoustic traits, despite the fact that chemical communication is probably the most common means of species recognition. Here, we showed in a large, mostly sympatric, butterfly genus, a strong pattern of recurrent RCD for predicted male sex pheromone composition, but not for visual mate choice traits. Our results suggest that RCD is not anecdotal, and that selection for divergence in male sex pheromone composition contributed to reproductive isolation within the Bicyclus genus. We propose that selection may target olfactory mate choice traits as a more common sensory modality to ensure reproductive isolation among diverging lineages than previously envisaged. PMID:25740889

Measures of methane production and their phenotypic relationships with dry matter intake, growth, and body composition traits in beef cattle.

PubMed

Herd, R M; Arthur, P F; Donoghue, K A; Bird, S H; Bird-Gardiner, T; Hegarty, R S

2014-11-01

Ruminants contribute up to 80% of greenhouse gas (GHG) emissions from livestock, and enteric methane production by ruminants is the main source of these GHG emissions. Hence, reducing enteric methane production is essential in any GHG emissions reduction strategy in livestock. Data from 2 performance-recording research herds of Angus cattle were used to evaluate a number of methane measures that target methane production (MPR) independent of feed intake and to examine their phenotypic relationships with growth and body composition. The data comprised 777 young bulls and heifers that were fed a roughage diet (ME of 9 MJ/kg DM) at 1.2 times their maintenance energy requirements and measured for MP in open circuit respiration chambers for 48 h. Methane traits evaluated included DMI during the methane measurement period, MPR, and methane yield (MY; MPR/DMI), with means (± SD) of 6.2 ± 1.4 kg/d, 187 ± 38 L/d, and 30.4 ± 3.5 L/kg, respectively. Four forms of residual MPR (RMP), which is a measure of actual minus predicted MPR, were evaluated. For the first 3 forms, predicted MPR was calculated using published equations. For the fourth (RMPR), predicted MPR was obtained by regression of MPR on DMI. Growth traits evaluated were BW at birth, weaning (200 d of age), yearling age (400 d of age), and 600 d of age, with means (± SD) of 34 ± 4.6, 238 ± 37, 357 ± 45, and 471 ± 53 kg, respectively. Body composition traits included ultrasound measures (600 d of age) of rib fat, rump fat, and eye muscle area, with means (± SD) of 3.8 ± 2.6 mm, 5.4 ± 3.8 mm, and 61 ± 7.7 cm(2), respectively. Methane production was positively correlated (r ± SE) with DMI (0.65 ± 0.02), MY (0.72 ± 0.02), the RMP traits (r from 0.65 to 0.79), the growth traits (r from 0.19 to 0.57), and the body composition traits (r from 0.13 to 0.29). Methane yield was, however, not correlated (r ± SE) with DMI (-0.02 ± 0.04) as well as the growth (r from -0.03 to 0.11) and body composition (r from 0.01 to 0.06) traits. All the RMP traits were strongly correlated to MY (r from 0.82 to 0.95). These results indicate that reducing MPR per se can have a negative impact on growth and body composition of cattle. Reducing MY, however, will likely have the effect of reducing MPR without impacting productivity. Where a ratio trait is undesirable, as in animal breeding, any of the RMP traits can be used instead of MY. However, where independence from DMI is desired, RMPR should be a trait worth considering.

Community variability and ecological functioning: 40 years of change in the North Sea benthos.

PubMed

Clare, D S; Robinson, L A; Frid, C L J

2015-06-01

Using established associations between species traits (life history, morphological and behavioural characteristics) and key ecological functions, we applied biological traits analysis (BTA) to investigate the consequences of 40 years of change in two North Sea benthic communities. Ecological functioning (trait composition) was found to be statistically indistinguishable across periods that differed significantly in taxonomic composition. A temporary alteration to functioning was, however, inferred at both sampling stations; coinciding with the North Sea regime shift of the 1980s. Trait composition recovered after 1 year at the station located inside the grounds of a trawl fishery, whereas the station located outside the main area of fishing activity underwent a six-year period of significantly altered, and temporally unstable, trait composition. A further alteration to functioning was inferred at the fished station, when the population of a newly established species rapidly increased in numbers. The results suggest that density compensation by characteristically similar (redundant) taxa acts to buffer changes to ecological functioning over time, but that functional stability is subject to aperiodic disruption due to substitutions of dissimilar taxa or uncompensated population fluctuations. The rate at which ecological functioning stabilises and recovers appears to be dependent on environmental context; e.g. disturbance regime. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ecological Trait Composition of Freshwater Fish Across Gradients of Environmental Variability in North-Eastern Australia

NASA Astrophysics Data System (ADS)

Kennard, M. J.; Pusey, B. J.; Arthington, A. H.

2005-05-01

North-eastern Australia encompasses 18o of latitude, monsoonal/tropical to sub-tropical/temperate climates, geomorphologically diverse rivers, and flow regimes with markedly varied seasonality, constancy and predictability. Fish assemblages in the region vary in relation to the predictability of aquatic habitat availability and other topographic, climatic and/or biogeographic factors. This paper examines how environmental, biogeographic and phylogenetic factors may constrain ecological trait composition at local and regional scales. We derived 17 categories of ecological traits to describe the morphology, behaviour, habitat, life history and trophic characteristics of 114 fish species from 64 river basins. Trait composition varied substantially across the region. The number of riffle dwelling species, maximum size and longevity of fishes was greater in the hydrologically predictable and constant rivers of the Wet Tropics region than in more unpredictable or seasonal environments. The importance of herbivory was also greater in the tropics. Historical biogeographic and phylogenetic factors may confound our ability to understand the role of environmental factors in determining spatial variation in ecological trait composition. Understanding the functional linkages between environmental drivers of fish species distributions via their ecological characteristics should provide a foundation for predicting future impacts of environmental change in a region of Australia subject to increasing human pressures.

Evaluation of temperament and transportation stress on body composition traits and meat quality in beef cattle

USDA-ARS?s Scientific Manuscript database

The objective of the first study was to evaluate the combined effects of transportation stress and animal temperament on real-time ultrasound body composition traits (primarily percentage of intramuscular fat) in Angus Crossbred (n = 68) and Brahman (n = 60) steers. Temperament scores (1 to 5 scale)...

Application of a new IBD-based QTL mapping method to common wheat breeding population: analysis of kernel hardness and dough strength.

PubMed

Crepieux, Sebastien; Lebreton, Claude; Flament, Pascal; Charmet, Gilles

2005-11-01

Mapping quantitative trait loci (QTL) in plants is usually conducted using a population derived from a cross between two inbred lines. The power of such QTL detection and the estimation of the effects highly depend on the choice of the two parental lines. Thus, the QTL found represent only a small part of the genetic architecture and can be of limited economical interest in marker-assisted selection. On the other hand, applied breeding programmes evaluate large numbers of progeny derived from multiple-related crosses for a wide range of agronomic traits. It is assumed that the development of statistical techniques to deal with pedigrees in existing plant populations would increase the relevance and cost effectiveness of QTL mapping in a breeding context. In this study, we applied a two-step IBD-based-variance component method to a real wheat breeding population, composed of 374 F6 lines derived from 80 different parents. Two bread wheat quality related traits were analysed by the method. Results obtained show very close agreement with major genes and QTL already known for those two traits. With this new QTL mapping strategy, inferences about QTL can be drawn across the breeding programme rather than being limited to the sample of progeny from a single cross and thus the use of the detected QTL in assisting breeding would be facilitated.

Effects of Plant Traits on Ecosystem and Regional Processes: a Conceptual Framework for Predicting the Consequences of Global Change

PubMed Central

CHAPIN, F. STUART

2003-01-01

Human activities are causing widespread changes in the species composition of natural and managed ecosystems, but the consequences of these changes are poorly understood. This paper presents a conceptual framework for predicting the ecosystem and regional consequences of changes in plant species composition. Changes in species composition have greatest ecological effects when they modify the ecological factors that directly control (and respond to) ecosystem processes. These interactive controls include: functional types of organisms present in the ecosystem; soil resources used by organisms to grow and reproduce; modulators such as microclimate that influence the activity of organisms; disturbance regime; and human activities. Plant traits related to size and growth rate are particularly important because they determine the productive capacity of vegetation and the rates of decomposition and nitrogen mineralization. Because the same plant traits affect most key processes in the cycling of carbon and nutrients, changes in plant traits tend to affect most biogeochemical cycling processes in parallel. Plant traits also have landscape and regional effects through their effects on water and energy exchange and disturbance regime. PMID:12588725

Trichothecene-Genotypes Play a Role in Fusarium Head Blight Disease Spread and Trichothecene Accumulation in Wheat

USDA-ARS?s Scientific Manuscript database

In the current study, we evaluated the impact of the observed North American evolutionary shift in the Fusarium graminearum complex on disease spread, kernel damage, and trichothecene accumulation in resistant and susceptible wheat genotypes. Four inocula were prepared using composites of F. gramin...

Functional and nutritional characteristics of soft wheat grown in no-till and conventional cropping systems

USDA-ARS?s Scientific Manuscript database

The effects of no-till vs. conventional farming practices were evaluated on soft wheat functional and nutritional characteristics, including kernel physical properties, whole wheat composition, antioxidant activity and end-product quality. Soft white winter wheat cv. ORCF 102 was evaluated over a tw...

A Semiparametric Approach for Composite Functional Mapping of Dynamic Quantitative Traits

PubMed Central

Yang, Runqing; Gao, Huijiang; Wang, Xin; Zhang, Ji; Zeng, Zhao-Bang; Wu, Rongling

2007-01-01

Functional mapping has emerged as a powerful tool for mapping quantitative trait loci (QTL) that control developmental patterns of complex dynamic traits. Original functional mapping has been constructed within the context of simple interval mapping, without consideration of separate multiple linked QTL for a dynamic trait. In this article, we present a statistical framework for mapping QTL that affect dynamic traits by capitalizing on the strengths of functional mapping and composite interval mapping. Within this so-called composite functional-mapping framework, functional mapping models the time-dependent genetic effects of a QTL tested within a marker interval using a biologically meaningful parametric function, whereas composite interval mapping models the time-dependent genetic effects of the markers outside the test interval to control the genome background using a flexible nonparametric approach based on Legendre polynomials. Such a semiparametric framework was formulated by a maximum-likelihood model and implemented with the EM algorithm, allowing for the estimation and the test of the mathematical parameters that define the QTL effects and the regression coefficients of the Legendre polynomials that describe the marker effects. Simulation studies were performed to investigate the statistical behavior of composite functional mapping and compare its advantage in separating multiple linked QTL as compared to functional mapping. We used the new mapping approach to analyze a genetic mapping example in rice, leading to the identification of multiple QTL, some of which are linked on the same chromosome, that control the developmental trajectory of leaf age. PMID:17947431

Onion Hybrid Seed Production: Relation with Nectar Composition and Flower Traits.

PubMed

Soto, Veronica C; Caselles, Cristian A; Silva, Maria F; Galmarini, Claudio R

2018-05-28

Onion (Allium cepa L.) is one of the main vegetable crops. Pollinators are required for onion seed production, being honeybees the most used. Around the world, two types of onion varieties are grown: open pollinated (OP) and hybrids. Hybrids offer numerous advantages to growers, but usually have lower seed yields than OP cultivars, which in many cases compromise the success of new hybrids. As pollination is critical for seed set, understanding the role of floral rewards and attractants to pollinator species is the key to improve crop seed yield. In this study, the correlation of nectar-analyzed compounds, floral traits, and seed yield under open field conditions in two experimental sites was determined. Nectar composition was described through the analysis of sugars, phenol, and alkaloid compounds. Length and width of the style and tepals of the flowers were measured to describe floral traits. Floral and nectar traits showed differences among the studied lines. For nectar traits, we found a significant influence of the environment where plants were cultivated. Nonetheless, flower traits were not influenced by the experimental sites. The OP and the male-sterile lines (MSLs) showed differences in nectar chemical composition and floral traits. In addition, there were differences between and within MSLs, some of which were correlated with seed yield, bringing the opportunity to select the most productive MSL, using simple determinations of morphological characters like the length of the style or tepals size.

Leaf-trait plasticity and species vulnerability to climate change in a Mongolian steppe.

PubMed

Liancourt, Pierre; Boldgiv, Bazartseren; Song, Daniel S; Spence, Laura A; Helliker, Brent R; Petraitis, Peter S; Casper, Brenda B

2015-09-01

Climate change is expected to modify plant assemblages in ways that will have major consequences for ecosystem functions. How climate change will affect community composition will depend on how individual species respond, which is likely related to interspecific differences in functional traits. The extraordinary plasticity of some plant traits is typically neglected in assessing how climate change will affect different species. In the Mongolian steppe, we examined whether leaf functional traits under ambient conditions and whether plasticity in these traits under altered climate could explain climate-induced biomass responses in 12 co-occurring plant species. We experimentally created three probable climate change scenarios and used a model selection procedure to determine the set of baseline traits or plasticity values that best explained biomass response. Under all climate change scenarios, plasticity for at least one leaf trait correlated with change in species performance, while functional leaf-trait values in ambient conditions did not. We demonstrate that trait plasticity could play a critical role in vulnerability of species to a rapidly changing environment. Plasticity should be considered when examining how climate change will affect plant performance, species' niche spaces, and ecological processes that depend on plant community composition. © 2015 John Wiley & Sons Ltd.

Effects of whole-plant corn silage hybrid type on intake, digestion, ruminal fermentation, and lactation performance by dairy cows through a meta-analysis.

PubMed

Ferraretto, L F; Shaver, R D

2015-04-01

Understanding the effect of whole-plant corn silage (WPCS) hybrids in dairy cattle diets may allow for better decisions on hybrid selection by dairy producers, as well as indicate potential strategies for the seed corn industry with regard to WPCS hybrids. Therefore, the objective of this study was to perform a meta-analysis using literature data on the effects of WPCS hybrid type on intake, digestibility, rumen fermentation, and lactation performance by dairy cows. The meta-analysis was performed using a data set of 162 treatment means from 48 peer-reviewed articles published between 1995 and 2014. Hybrids were divided into 3 categories before analysis. Comparative analysis of WPCS hybrid types differing in stalk characteristics were in 4 categories: conventional, dual-purpose, isogenic, or low-normal fiber digestibility (CONS), brown midrib (BMR), hybrids with greater NDF but lower lignin (%NDF) contents or high in vitro NDF digestibility (HFD), and leafy (LFY). Hybrid types differing in kernel characteristics were in 4 categories: conventional or yellow dent (CONG), NutriDense (ND), high oil (HO), and waxy. Genetically modified (GM) hybrids were compared with their genetically similar non-biotech counterpart (ISO). Except for lower lignin content for BMR and lower starch content for HFD than CONS and LFY, silage nutrient composition was similar among hybrids of different stalk types. A 1.1 kg/d greater intake of DM and 1.5 and 0.05 kg/d greater milk and protein yields, respectively, were observed for BMR compared with CONS and LFY. Likewise, DMI and milk yield were greater for HFD than CONS, but the magnitude of the difference was smaller. Total-tract NDF digestibility was greater, but starch digestibility was reduced, for BMR and HFD compared with CONS or LFY. Silage nutrient composition was similar for hybrids of varied kernel characteristics, except for lower CP and EE content for CONG than ND and HO. Feeding HO WPCS to dairy cows decreased milk fat content and yield and protein content compared with the other kernel-type hybrids. Hybrids varying in kernel characteristics did not affect intake, milk production, or total-tract nutrient digestibilities by lactating dairy cows. Nutrient composition and lactation performance were similar between GM and ISO. Positive effects of BMR and HFD on intake and milk yield were observed for lactating dairy cows, but the reduced total-tract starch digestibility for these hybrids merits further study. Except for negative effects of HO on milk components, differences were minimal among corn silage hybrids differing in kernel type. Feeding GM WPCS did not affect lactation performance by dairy cows. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Effects of anthropogenic salinization on biological traits and community composition of stream macroinvertebrates.

PubMed

Szöcs, Eduard; Coring, Eckhard; Bäthe, Jürgen; Schäfer, Ralf B

2014-01-15

Salinization of rivers resulting from industrial discharge or road-deicing can adversely affect macroinvertebrates. Trait-based approaches are a promising tool in ecological monitoring and may perform better than taxonomy-based approaches. However only little is known how and which biological traits are affected by salinization. We investigated the effects of anthropogenic salinization on macroinvertebrate communities and biological traits in the Werra River, Germany and compared the taxonomic and trait response. We found a change in macroinvertebrate community and trait composition. Communities at saline sites were characterized by the three exotic species Gammarus tigrinus, Apocorophium lacustre and Potamopyrgus antipodarum. The frequencies of trait modalities long life cycle duration, respiration by gill, ovoviviparity, shredder and multivoltinism were statistically significantly increased at saline sites. The trait-based ordination resulted in a higher explained variance than the taxonomy-based ordination, indicating a better performance of the trait-based approach, resulting in a better discrimination between saline and non-saline sites. Our results are in general agreement with other studies from Europe, indicating a trait convergence for saline streams, being dominated by the traits ovoviviparity and multivoltinism. Three further traits (respiration by gill, life cycle duration and shredders) responded strongly to salinization, but this may primarily be attributed to the dominance of a single invasive species, G. tigrinus, at the saline sites in the Werra River. © 2013 Elsevier B.V. All rights reserved.

KRN4 Controls Quantitative Variation in Maize Kernel Row Number

PubMed Central

Liu, Lei; Du, Yanfang; Shen, Xiaomeng; Li, Manfei; Sun, Wei; Huang, Juan; Liu, Zhijie; Tao, Yongsheng; Zheng, Yonglian; Yan, Jianbing; Zhang, Zuxin

2015-01-01

Kernel row number (KRN) is an important component of yield during the domestication and improvement of maize and controlled by quantitative trait loci (QTL). Here, we fine-mapped a major KRN QTL, KRN4, which can enhance grain productivity by increasing KRN per ear. We found that a ~3-Kb intergenic region about 60 Kb downstream from the SBP-box gene Unbranched3 (UB3) was responsible for quantitative variation in KRN by regulating the level of UB3 expression. Within the 3-Kb region, the 1.2-Kb Presence-Absence variant was found to be strongly associated with quantitative variation in KRN in diverse maize inbred lines, and our results suggest that this 1.2-Kb transposon-containing insertion is likely responsible for increased KRN. A previously identified A/G SNP (S35, also known as Ser220Asn) in UB3 was also found to be significantly associated with KRN in our association-mapping panel. Although no visible genetic effect of S35 alone could be detected in our linkage mapping population, it was found to genetically interact with the 1.2-Kb PAV to modulate KRN. The KRN4 was under strong selection during maize domestication and the favorable allele for the 1.2-Kb PAV and S35 has been significantly enriched in modern maize improvement process. The favorable haplotype (Hap1) of 1.2-Kb-PAV-S35 was selected during temperate maize improvement, but is still rare in tropical and subtropical maize germplasm. The dissection of the KRN4 locus improves our understanding of the genetic basis of quantitative variation in complex traits in maize. PMID:26575831

Identification and validation of QTL for grain yield and plant water status under contrasting water treatments in fall-sown spring wheats.

PubMed

Zhang, Junli; Gizaw, Shiferaw Abate; Bossolini, Eligio; Hegarty, Joshua; Howell, Tyson; Carter, Arron H; Akhunov, Eduard; Dubcovsky, Jorge

2018-05-16

Chromosome regions affecting grain yield, grain yield components and plant water status were identified and validated in fall-sown spring wheats grown under full and limited irrigation. Increases in wheat production are required to feed a growing human population. To understand the genetic basis of grain yield in fall-sown spring wheats, we performed a genome-wide association study (GWAS) including 262 photoperiod-insensitive spring wheat accessions grown under full and limited irrigation treatments. Analysis of molecular variance showed that 4.1% of the total variation in the panel was partitioned among accessions originally developed under fall-sowing or spring-sowing conditions, 11.7% among breeding programs within sowing times and 84.2% among accessions within breeding programs. We first identified QTL for grain yield, yield components and plant water status that were significant in at least three environments in the GWAS, and then selected those that were also significant in at least two environments in a panel of eight biparental mapping populations. We identified and validated 14 QTL for grain yield, 15 for number of spikelets per spike, one for kernel number per spike, 11 for kernel weight and 9 for water status, which were not associated with differences in plant height or heading date. We detected significant correlations among traits and colocated QTL that were consistent with those correlations. Among those, grain yield and plant water status were negatively correlated in all environments, and six QTL for these traits were colocated or tightly linked (< 1 cM). QTL identified and validated in this study provide useful information for the improvement of fall-sown spring wheats under full and limited irrigation.

Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits.

PubMed

Gebreyesus, Grum; Lund, Mogens S; Buitenhuis, Bart; Bovenhuis, Henk; Poulsen, Nina A; Janss, Luc G

2017-12-05

Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls. Single-nucleotide polymorphisms (SNPs), from 50K SNP arrays, were grouped into non-overlapping genome segments. A segment was defined as one SNP, or a group of 50, 100, or 200 adjacent SNPs, or one chromosome, or the whole genome. Traditional univariate and bivariate genomic best linear unbiased prediction (GBLUP) models were also run for comparison. Reliabilities were calculated through a resampling strategy and using deterministic formula. BayesAS models improved prediction reliability for most of the traits compared to GBLUP models and this gain depended on segment size and genetic architecture of the traits. The gain in prediction reliability was especially marked for the protein composition traits β-CN, κ-CN and β-LG, for which prediction reliabilities were improved by 49 percentage points on average using the MT-BayesAS model with a 100-SNP segment size compared to the bivariate GBLUP. Prediction reliabilities were highest with the BayesAS model that uses a 100-SNP segment size. The bivariate versions of our BayesAS models resulted in extra gains of up to 6% in prediction reliability compared to the univariate versions. Substantial improvement in prediction reliability was possible for most of the traits related to milk protein composition using our novel BayesAS models. Grouping adjacent SNPs into segments provided enhanced information to estimate parameters and allowing the segments to have different (co)variances helped disentangle heterogeneous (co)variances across the genome.

Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.

PubMed

Bolormaa, S; Pryce, J E; Kemper, K; Savin, K; Hayes, B J; Barendse, W; Zhang, Y; Reich, C M; Mason, B A; Bunch, R J; Harrison, B E; Reverter, A; Herd, R M; Tier, B; Graser, H-U; Goddard, M E

2013-07-01

The aim of this study was to assess the accuracy of genomic predictions for 19 traits including feed efficiency, growth, and carcass and meat quality traits in beef cattle. The 10,181 cattle in our study had real or imputed genotypes for 729,068 SNP although not all cattle were measured for all traits. Animals included Bos taurus, Brahman, composite, and crossbred animals. Genomic EBV (GEBV) were calculated using 2 methods of genomic prediction [BayesR and genomic BLUP (GBLUP)] either using a common training dataset for all breeds or using a training dataset comprising only animals of the same breed. Accuracies of GEBV were assessed using 5-fold cross-validation. The accuracy of genomic prediction varied by trait and by method. Traits with a large number of recorded and genotyped animals and with high heritability gave the greatest accuracy of GEBV. Using GBLUP, the average accuracy was 0.27 across traits and breeds, but the accuracies between breeds and between traits varied widely. When the training population was restricted to animals from the same breed as the validation population, GBLUP accuracies declined by an average of 0.04. The greatest decline in accuracy was found for the 4 composite breeds. The BayesR accuracies were greater by an average of 0.03 than GBLUP accuracies, particularly for traits with known genes of moderate to large effect mutations segregating. The accuracies of 0.43 to 0.48 for IGF-I traits were among the greatest in the study. Although accuracies are low compared with those observed in dairy cattle, genomic selection would still be beneficial for traits that are hard to improve by conventional selection, such as tenderness and residual feed intake. BayesR identified many of the same quantitative trait loci as a genomewide association study but appeared to map them more precisely. All traits appear to be highly polygenic with thousands of SNP independently associated with each trait.

Relating belowground microbial composition to the taxonomic, phylogenetic, and functional trait distributions of trees in a tropical forest.

PubMed

Barberán, Albert; McGuire, Krista L; Wolf, Jeffrey A; Jones, F Andrew; Wright, Stuart Joseph; Turner, Benjamin L; Essene, Adam; Hubbell, Stephen P; Faircloth, Brant C; Fierer, Noah

2015-12-01

The complexities of the relationships between plant and soil microbial communities remain unresolved. We determined the associations between plant aboveground and belowground (root) distributions and the communities of soil fungi and bacteria found across a diverse tropical forest plot. Soil microbial community composition was correlated with the taxonomic and phylogenetic structure of the aboveground plant assemblages even after controlling for differences in soil characteristics, but these relationships were stronger for fungi than for bacteria. In contrast to expectations, the species composition of roots in our soil core samples was a poor predictor of microbial community composition perhaps due to the patchy, ephemeral, and highly overlapping nature of fine root distributions. Our ability to predict soil microbial composition was not improved by incorporating information on plant functional traits suggesting that the most commonly measured plant traits are not particularly useful for predicting the plot-level variability in belowground microbial communities. © 2015 John Wiley & Sons Ltd/CNRS.

Evaluating the dimensionality of first grade written composition

PubMed Central

Kim, Young-Suk; Al Otaiba, Stephanie; Folsom, Jessica S.; Greulich, Luana; Puranik, Cynthia

2013-01-01

Purpose We examined dimensions of written composition using multiple evaluative approaches such as an adapted 6+1 trait scoring, syntactic complexity measures, and productivity measures. We further examined unique relations of oral language and literacy skills to the identified dimensions of written composition. Method A large sample of first grade students (N = 527) was assessed on their language, reading, spelling, letter writing automaticity, and writing in the spring. Data were analyzed using a latent variable approach including confirmatory factor analysis and structural equation modeling. Results The seven traits in the 6+1 trait system were best described as two constructs: substantive quality, and spelling and writing conventions. When the other evaluation procedures such as productivity and syntactic complexity indicators were included, four dimensions emerged: substantive quality, productivity, syntactic complexity, and spelling and writing conventions. Language and literacy predictors were differentially related to each dimension in written composition. Conclusions These four dimensions may be a useful guideline for evaluating developing beginning writer’s compositions. PMID:24687472

Contrasting outcomes of species- and community-level analyses of the temporal consistency of functional composition.

PubMed

Katabuchi, Masatoshi; Wright, S Joseph; Swenson, Nathan G; Feeley, Kenneth J; Condit, Richard; Hubbell, Stephen P; Davies, Stuart J

2017-09-01

Multiple anthropogenic drivers affect every natural community, and there is broad interest in using functional traits to understand and predict the consequences for future biodiversity. There is, however, no consensus regarding the choice of analytical methods. We contrast species- and community-level analyses of change in the functional composition for four traits related to drought tolerance using three decades of repeat censuses of trees in the 50-ha Forest Dynamics Plot on Barro Colorado Island, Panama. Community trait distributions shifted significantly through time, which may indicate a shift toward more drought tolerant species. However, at the species level, changes in abundance were unrelated to trait values. To reconcile these seemingly contrasting results, we evaluated species-specific contributions to the directional shifts observed at the community level. Abundance changes of just one to six of 312 species were responsible for the community-level shifts observed for each trait. Our results demonstrate that directional changes in community-level functional composition can result from idiosyncratic change in a few species rather than widespread community-wide changes associated with functional traits. Future analyses of directional change in natural communities should combine community-, species-, and possibly individual-level analyses to uncover relationships with function that can improve understanding and enable prediction. © 2017 by the Ecological Society of America.

Hypoxia affects performance traits and body composition of juvenile hybrid striped bass (Morone chrysops x M. saxatilis)

USDA-ARS?s Scientific Manuscript database

Performance traits and body composition of juvenile hybrid striped bass (Morone chrysops x M. saxatilis) in response to hypoxia were evaluated in replicate tanks maintained at constant dissolved oxygen concentrations that averaged 23.0 +/- 2.3%, 39.7 +/- 3.0%, and 105.5 +/- 9.5% dissolved oxygen sat...

Genotype x prenatal and post-weaning nutritional environment interaction in a composite beef cattle breed using reaction norms and multi-trait model

USDA-ARS?s Scientific Manuscript database

Environmental effects have been shown to influence several economically important traits in beef cattle. In this study, genetic x nutritional environment interaction has been evaluated in a composite beef cattle breed (50% Red Angus, 25% Charolais, 25% Tarentaise). Four nutritional environments (MAR...

Does Virtual Team Composition Matter? Trait and Problem-Solving Configuration Effects on Team Performance

ERIC Educational Resources Information Center

Turel, Ofir; Zhang, Yi

2010-01-01

Due to the increased importance and usage of self-managed virtual teams, many recent studies have examined factors that affect their success. One such factor that merits examination is the configuration or composition of virtual teams. This article tackles this point by (1) empirically testing trait-configuration effects on virtual team…

Association between breed composition, phenotypic residual feed intake, temperament, ELISA scores for paratuberculosis, and ultrasound carcass traits in an Angus-Brahman multibreed herd.

USDA-ARS?s Scientific Manuscript database

Ultrasound carcass measurements are an important tool for preliminary assessment of carcass worth in beef cattle. Breed composition, phenotypic residual feed intake (RFI), temperament, and subclinical paratuberculosis in dams may affect calf ultrasound traits. The objective was to evaluate the assoc...

Ultrasound body composition traits response to an endotoxin challenge in Brahman heifers supplemented with Omnigen-AF

USDA-ARS?s Scientific Manuscript database

This study examined the effect of feeding OmniGen-AF (OG; Prince Agri Products) on the body composition traits response of newly-weaned heifers to an endotoxin (lipopolysaccharide; LPS) challenge. Brahman heifers (n=24; 183 ± 5 kg) from the Texas AgriLife Research Center in Overton, TX, were separat...

Genomic prediction and genome-wide association analysis of female longevity in a composite beef cattle breed

USDA-ARS?s Scientific Manuscript database

Longevity is a highly important trait to the efficiency of beef cattle production. The objective of this study was to evaluate the genomic prediction of longevity and identify genomic regions associated with this trait. The data used in this study consisted of 547 Composite Gene Combination (CGC) c...

Improved conversion of herbaceous biomass to biofuels: Potential for modification of key plant characteristics

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

Sladden, S.E.; Bransby, D.I.

1989-10-01

Biomass crops are converted to fuels via biochemical and thermochemical processes. The process preferred depends on properties and cost of available feedstocks, and on the specific products desired. Since most mature biomass crops are composed of up to 80% cell wall fibers, the properties of these fibers determine, to a large degree, the conversion potential of the crop. However, biomass crops also contain small amounts of proteins, soluble carbohydrates and interfering materials (e.g., tannins and silica) which also influence the desirability of the feedstock in specific conversion processes. Fortunately, wide variation exists in the chemical composition of potential biomass crops.more » Although the chemical composition of feedstocks can be influenced significantly with judicious management has species selection, some traits are sufficiently heritable to permit breeding for improved feedstock composition. In addition to breeding for specific compositional traits directly, selection for in vitro digestibility or for easily-measured canopy or physiological traits may lead to more rapid and efficient progress in feedstock improvement, provided those measurements are highly-correlated with desirable feedstock composition. At the same time breeders must improve, or at least avoid damaging, stand longevity, tendency of plants to lodge, and establishment traits (e.g., disease resistance and seedling vigor). 46 refs., 8 tabs.« less

Root morphology and seed and leaf ionomic traits in a Brassica napus L. diversity panel show wide phenotypic variation and are characteristic of crop habit.

PubMed

Thomas, C L; Alcock, T D; Graham, N S; Hayden, R; Matterson, S; Wilson, L; Young, S D; Dupuy, L X; White, P J; Hammond, J P; Danku, J M C; Salt, D E; Sweeney, A; Bancroft, I; Broadley, M R

2016-10-04

Mineral nutrient uptake and utilisation by plants are controlled by many traits relating to root morphology, ion transport, sequestration and translocation. The aims of this study were to determine the phenotypic diversity in root morphology and leaf and seed mineral composition of a polyploid crop species, Brassica napus L., and how these traits relate to crop habit. Traits were quantified in a diversity panel of up to 387 genotypes: 163 winter, 127 spring, and seven semiwinter oilseed rape (OSR) habits, 35 swede, 15 winter fodder, and 40 exotic/unspecified habits. Root traits of 14 d old seedlings were measured in a 'pouch and wick' system (n = ~24 replicates per genotype). The mineral composition of 3-6 rosette-stage leaves, and mature seeds, was determined on compost-grown plants from a designed experiment (n = 5) by inductively coupled plasma-mass spectrometry (ICP-MS). Seed size explained a large proportion of the variation in root length. Winter OSR and fodder habits had longer primary and lateral roots than spring OSR habits, with generally lower mineral concentrations. A comparison of the ratios of elements in leaf and seed parts revealed differences in translocation processes between crop habits, including those likely to be associated with crop-selection for OSR seeds with lower sulphur-containing glucosinolates. Combining root, leaf and seed traits in a discriminant analysis provided the most accurate characterisation of crop habit, illustrating the interdependence of plant tissues. High-throughput morphological and composition phenotyping reveals complex interrelationships between mineral acquisition and accumulation linked to genetic control within and between crop types (habits) in B. napus. Despite its recent genetic ancestry (<10 ky), root morphology, and leaf and seed composition traits could potentially be used in crop improvement, if suitable markers can be identified and if these correspond with suitable agronomy and quality traits.

Genome-wide association study identifies Loci and candidate genes for body composition and meat quality traits in Beijing-You chickens.

PubMed

Liu, Ranran; Sun, Yanfa; Zhao, Guiping; Wang, Fangjie; Wu, Dan; Zheng, Maiqing; Chen, Jilan; Zhang, Lei; Hu, Yaodong; Wen, Jie

2013-01-01

Body composition and meat quality traits are important economic traits of chickens. The development of high-throughput genotyping platforms and relevant statistical methods have enabled genome-wide association studies in chickens. In order to identify molecular markers and candidate genes associated with body composition and meat quality traits, genome-wide association studies were conducted using the Illumina 60 K SNP Beadchip to genotype 724 Beijing-You chickens. For each bird, a total of 16 traits were measured, including carcass weight (CW), eviscerated weight (EW), dressing percentage, breast muscle weight (BrW) and percentage (BrP), thigh muscle weight and percentage, abdominal fat weight and percentage, dry matter and intramuscular fat contents of breast and thigh muscle, ultimate pH, and shear force of the pectoralis major muscle at 100 d of age. The SNPs that were significantly associated with the phenotypic traits were identified using both simple (GLM) and compressed mixed linear (MLM) models. For nine of ten body composition traits studied, SNPs showing genome wide significance (P<2.59E-6) have been identified. A consistent region on chicken (Gallus gallus) chromosome 4 (GGA4), including seven significant SNPs and four candidate genes (LCORL, LAP3, LDB2, TAPT1), were found to be associated with CW and EW. Another 0.65 Mb region on GGA3 for BrW and BrP was identified. After measuring the mRNA content in beast muscle for five genes located in this region, the changes in GJA1 expression were found to be consistent with that of breast muscle weight across development. It is highly possible that GJA1 is a functional gene for breast muscle development in chickens. For meat quality traits, several SNPs reaching suggestive association were identified and possible candidate genes with their functions were discussed.

A global analysis of CNVs in swine using whole genome sequence data and association analysis with fatty acid composition and growth traits.

PubMed

Revilla, Manuel; Puig-Oliveras, Anna; Castelló, Anna; Crespo-Piazuelo, Daniel; Paludo, Ediane; Fernández, Ana I; Ballester, Maria; Folch, Josep M

2017-01-01

Copy number variations (CNVs) are important genetic variants complementary to SNPs, and can be considered as biomarkers for some economically important traits in domestic animals. In the present study, a genomic analysis of porcine CNVs based on next-generation sequencing data was carried out to identify CNVs segregating in an Iberian x Landrace backcross population and study their association with fatty acid composition and growth-related traits. A total of 1,279 CNVs, including duplications and deletions, were detected, ranging from 106 to 235 CNVs across samples, with an average of 183 CNVs per sample. Moreover, we detected 540 CNV regions (CNVRs) containing 245 genes. Functional annotation suggested that these genes possess a great variety of molecular functions and may play a role in production traits in commercial breeds. Some of the identified CNVRs contained relevant functional genes (e.g., CLCA4, CYP4X1, GPAT2, MOGAT2, PLA2G2A and PRKG1, among others). The variation in copy number of four of them (CLCA4, GPAT2, MOGAT2 and PRKG1) was validated in 150 BC1_LD (25% Iberian and 75% Landrace) animals by qPCR. Additionally, their contribution regarding backfat and intramuscular fatty acid composition and growth-related traits was analyzed. Statistically significant associations were obtained for CNVR112 (GPAT2) for the C18:2(n-6)/C18:3(n-3) ratio in backfat and carcass length, among others. Notably, GPATs are enzymes that catalyze the first step in the biosynthesis of both triglycerides and glycerophospholipids, suggesting that this CNVR may contribute to genetic variation in fatty acid composition and growth traits. These findings provide useful genomic information to facilitate the further identification of trait-related CNVRs affecting economically important traits in pigs.

Colonisation of winter wheat grain by Fusarium spp. and mycotoxin content as dependent on a wheat variety, crop rotation, a crop management system and weather conditions.

PubMed

Czaban, Janusz; Wróblewska, Barbara; Sułek, Alicja; Mikos, Marzena; Boguszewska, Edyta; Podolska, Grażyna; Nieróbca, Anna

2015-01-01

Field experiments were conducted during three consecutive growing seasons (2007/08, 2008/09 and 2009/10) with four winter wheat (Triticum aestivum L.) cultivars - 'Bogatka', 'Kris', 'Satyna' and 'Tonacja' - grown on fields with a three-field crop rotation (winter triticale, spring barley, winter wheat) and in a four-field crop rotation experiment (spring wheat, spring cereals, winter rapeseed, winter wheat). After the harvest, kernels were surface disinfected with 2% NaOCl and then analysed for the internal infection by different species of Fusarium. Fusaria were isolated on Czapek-Dox iprodione dichloran agar medium and identified on the basis of macro- and micro-morphology on potato dextrose agar and synthetic nutrient agar media. The total wheat grain infection by Fusarium depended mainly on relative humidity (RH) and a rainfall during the flowering stage. Intensive rainfall and high RH in 2009 and 2010 in the period meant the proportions of infected kernels by the fungi were much higher than those in 2008 (lack of precipitation during anthesis). Weather conditions during the post-anthesis period changed the species composition of Fusarium communities internally colonising winter wheat grain. The cultivars significantly varied in the proportion of infected kernels by Fusarium spp. The growing season and type of crop rotation had a distinct effect on species composition of Fusarium communities colonising the grain inside. A trend of a higher percentage of the colonised kernels by the fungi in the grain from the systems using more fertilisers and pesticides as well as the buried straw could be perceived. The most frequent species in the grain were F. avenaceum, F. tricinctum and F. poae in 2008, and F. avenaceum, F. graminearum, F. tricinctum and F. poae in 2009 and 2010. The contents of deoxynivalenol and zearalenon in the grain were correlated with the percentage of kernels colonised by F. graminearum and were the highest in 2009 in the grain from the four-field crop rotation. The content of T-2/HT-2 toxins was the highest in 2010 in grain from the three-field crop rotation and it was correlated with the isolation frequency of F. langsethiae.

Classification of Astrocytomas and Oligodendrogliomas from Mass Spectrometry Data Using Sparse Kernel Machines

PubMed Central

Huang, Jacob; Gholami, Behnood; Agar, Nathalie Y. R.; Norton, Isaiah; Haddad, Wassim M.; Tannenbaum, Allen R.

2013-01-01

Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample’s histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry. PMID:22256188

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

PubMed Central

Xavier, Alencar; Muir, William M.; Rainey, Katy Martin

2016-01-01

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. PMID:27317786

Mango kernel fat fractions as potential healthy food ingredients: A review.

PubMed

Jin, Jun; Jin, Qingzhe; Akoh, Casimir C; Wang, Xingguo

2018-01-16

Mango kernel fat (MKF) has been reported to have high functional and nutritional potential. However, its application in food industry has not been fully explored or developed. In this review, the chemical compositions, physical properties and potential health benefits of MKF are described. MKF is a unique fat consisting of 28.9-65.0% of 1,3-distearoyl-2-oleoyl-glycerol with excellent oxidative stability index (58.8-85.2 h at 110 °C), making the fat and its fractions suitable for use as high-value added food ingredients such as cocoa butter alternatives, trans-free shortenings, and a source of natural antioxidants (e.g., sterol, tocopherol and squalene). Unfortunately, the long period of dehydration of mango kernels at hot temperature results in the hydrolysis of triacylglycerols. The high levels of hydrolysates (mainly free fatty acids and diacylglycerols) limit the application of MKF in manufacturing these food ingredients. It is suggested that the physico-chemical and functional properties of MKF could be further improved through moderated refining (e.g., degumming and physical deacidification), fractionation, and interesterification.

Transition of phenolics and cyanogenic glycosides from apricot and cherry fruit kernels into liqueur.

PubMed

Senica, Mateja; Stampar, Franci; Veberic, Robert; Mikulic-Petkovsek, Maja

2016-07-15

Popular liqueurs made from apricot/cherry pits were evaluated in terms of their phenolic composition and occurrence of cyanogenic glycosides (CGG). Analyses consisted of detailed phenolic and cyanogenic profiles of cherry and apricot seeds as well as beverages prepared from crushed kernels. Phenolic groups and cyanogenic glycosides were analyzed with the aid of high-performance liquid chromatography (HPLC) and mass spectrophotometry (MS). Lower levels of cyanogenic glycosides and phenolics have been quantified in liqueurs compared to fruit kernels. During fruit pits steeping in the alcohol, the phenolics/cyanogenic glycosides ratio increased and at the end of beverage manufacturing process higher levels of total analyzed phenolics were detected compared to cyanogenic glycosides (apricot liqueur: 38.79 μg CGG per ml and 50.57 μg phenolics per ml; cherry liqueur 16.08 μg CGG per ml and 27.73 μg phenolics per ml). Although higher levels of phenolics are characteristic for liqueurs made from apricot and cherry pits these beverages nevertheless contain considerable amounts of cyanogenic glycosides. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

PubMed Central

Ried, Janina S.; Jeff M., Janina; Chu, Audrey Y.; Bragg-Gresham, Jennifer L.; van Dongen, Jenny; Huffman, Jennifer E.; Ahluwalia, Tarunveer S.; Cadby, Gemma; Eklund, Niina; Eriksson, Joel; Esko, Tõnu; Feitosa, Mary F.; Goel, Anuj; Gorski, Mathias; Hayward, Caroline; Heard-Costa, Nancy L.; Jackson, Anne U.; Jokinen, Eero; Kanoni, Stavroula; Kristiansson, Kati; Kutalik, Zoltán; Lahti, Jari; Luan, Jian'an; Mägi, Reedik; Mahajan, Anubha; Mangino, Massimo; Medina-Gomez, Carolina; Monda, Keri L.; Nolte, Ilja M.; Pérusse, Louis; Prokopenko, Inga; Qi, Lu; Rose, Lynda M.; Salvi, Erika; Smith, Megan T.; Snieder, Harold; Stančáková, Alena; Ju Sung, Yun; Tachmazidou, Ioanna; Teumer, Alexander; Thorleifsson, Gudmar; van der Harst, Pim; Walker, Ryan W.; Wang, Sophie R.; Wild, Sarah H.; Willems, Sara M.; Wong, Andrew; Zhang, Weihua; Albrecht, Eva; Couto Alves, Alexessander; Bakker, Stephan J. L.; Barlassina, Cristina; Bartz, Traci M.; Beilby, John; Bellis, Claire; Bergman, Richard N.; Bergmann, Sven; Blangero, John; Blüher, Matthias; Boerwinkle, Eric; Bonnycastle, Lori L.; Bornstein, Stefan R.; Bruinenberg, Marcel; Campbell, Harry; Chen, Yii-Der Ida; Chiang, Charleston W. K.; Chines, Peter S.; Collins, Francis S; Cucca, Fracensco; Cupples, L Adrienne; D'Avila, Francesca; de Geus, Eco J .C.; Dedoussis, George; Dimitriou, Maria; Döring, Angela; Eriksson, Johan G.; Farmaki, Aliki-Eleni; Farrall, Martin; Ferreira, Teresa; Fischer, Krista; Forouhi, Nita G.; Friedrich, Nele; Gjesing, Anette Prior; Glorioso, Nicola; Graff, Mariaelisa; Grallert, Harald; Grarup, Niels; Gräßler, Jürgen; Grewal, Jagvir; Hamsten, Anders; Harder, Marie Neergaard; Hartman, Catharina A.; Hassinen, Maija; Hastie, Nicholas; Hattersley, Andrew Tym; Havulinna, Aki S.; Heliövaara, Markku; Hillege, Hans; Hofman, Albert; Holmen, Oddgeir; Homuth, Georg; Hottenga, Jouke-Jan; Hui, Jennie; Husemoen, Lise Lotte; Hysi, Pirro G.; Isaacs, Aaron; Ittermann, Till; Jalilzadeh, Shapour; James, Alan L.; Jørgensen, Torben; Jousilahti, Pekka; Jula, Antti; Marie Justesen, Johanne; Justice, Anne E.; Kähönen, Mika; Karaleftheri, Maria; Tee Khaw, Kay; Keinanen-Kiukaanniemi, Sirkka M.; Kinnunen, Leena; Knekt, Paul B.; Koistinen, Heikki A.; Kolcic, Ivana; Kooner, Ishminder K.; Koskinen, Seppo; Kovacs, Peter; Kyriakou, Theodosios; Laitinen, Tomi; Langenberg, Claudia; Lewin, Alexandra M.; Lichtner, Peter; Lindgren, Cecilia M.; Lindström, Jaana; Linneberg, Allan; Lorbeer, Roberto; Lorentzon, Mattias; Luben, Robert; Lyssenko, Valeriya; Männistö, Satu; Manunta, Paolo; Leach, Irene Mateo; McArdle, Wendy L.; Mcknight, Barbara; Mohlke, Karen L.; Mihailov, Evelin; Milani, Lili; Mills, Rebecca; Montasser, May E.; Morris, Andrew P.; Müller, Gabriele; Musk, Arthur W.; Narisu, Narisu; Ong, Ken K.; Oostra, Ben A.; Osmond, Clive; Palotie, Aarno; Pankow, James S.; Paternoster, Lavinia; Penninx, Brenda W.; Pichler, Irene; Pilia, Maria G.; Polašek, Ozren; Pramstaller, Peter P.; Raitakari, Olli T; Rankinen, Tuomo; Rao, D. C.; Rayner, Nigel W.; Ribel-Madsen, Rasmus; Rice, Treva K.; Richards, Marcus; Ridker, Paul M.; Rivadeneira, Fernando; Ryan, Kathy A.; Sanna, Serena; Sarzynski, Mark A.; Scholtens, Salome; Scott, Robert A.; Sebert, Sylvain; Southam, Lorraine; Sparsø, Thomas Hempel; Steinthorsdottir, Valgerdur; Stirrups, Kathleen; Stolk, Ronald P.; Strauch, Konstantin; Stringham, Heather M.; Swertz, Morris A.; Swift, Amy J.; Tönjes, Anke; Tsafantakis, Emmanouil; van der Most, Peter J.; Van Vliet-Ostaptchouk, Jana V.; Vandenput, Liesbeth; Vartiainen, Erkki; Venturini, Cristina; Verweij, Niek; Viikari, Jorma S.; Vitart, Veronique; Vohl, Marie-Claude; Vonk, Judith M.; Waeber, Gérard; Widén, Elisabeth; Willemsen, Gonneke; Wilsgaard, Tom; Winkler, Thomas W.; Wright, Alan F.; Yerges-Armstrong, Laura M.; Hua Zhao, Jing; Carola Zillikens, M.; Boomsma, Dorret I.; Bouchard, Claude; Chambers, John C.; Chasman, Daniel I.; Cusi, Daniele; Gansevoort, Ron T.; Gieger, Christian; Hansen, Torben; Hicks, Andrew A.; Hu, Frank; Hveem, Kristian; Jarvelin, Marjo-Riitta; Kajantie, Eero; Kooner, Jaspal S.; Kuh, Diana; Kuusisto, Johanna; Laakso, Markku; Lakka, Timo A.; Lehtimäki, Terho; Metspalu, Andres; Njølstad, Inger; Ohlsson, Claes; Oldehinkel, Albertine J.; Palmer, Lyle J.; Pedersen, Oluf; Perola, Markus; Peters, Annette; Psaty, Bruce M.; Puolijoki, Hannu; Rauramaa, Rainer; Rudan, Igor; Salomaa, Veikko; Schwarz, Peter E. H.; Shudiner, Alan R.; Smit, Jan H.; Sørensen, Thorkild I. A.; Spector, Timothy D.; Stefansson, Kari; Stumvoll, Michael; Tremblay, Angelo; Tuomilehto, Jaakko; Uitterlinden, André G.; Uusitupa, Matti; Völker, Uwe; Vollenweider, Peter; Wareham, Nicholas J.; Watkins, Hugh; Wilson, James F.; Zeggini, Eleftheria; Abecasis, Goncalo R.; Boehnke, Michael; Borecki, Ingrid B.; Deloukas, Panos; van Duijn, Cornelia M.; Fox, Caroline; Groop, Leif C.; Heid, Iris M.; Hunter, David J.; Kaplan, Robert C.; McCarthy, Mark I.; North, Kari E.; O'Connell, Jeffrey R.; Schlessinger, David; Thorsteinsdottir, Unnur; Strachan, David P.; Frayling, Timothy; Hirschhorn, Joel N.; Müller-Nurasyid, Martina; Loos, Ruth J. F.

2016-01-01

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways. PMID:27876822

Infrared microspectroscopic imaging of plant tissues: spectral visualization of Triticum aestivum kernel and Arabidopsis leaf microstructure.

PubMed

Warren, Frederick J; Perston, Benjamin B; Galindez-Najera, Silvia P; Edwards, Cathrina H; Powell, Prudence O; Mandalari, Giusy; Campbell, Grant M; Butterworth, Peter J; Ellis, Peter R

2015-11-01

Infrared microspectroscopy is a tool with potential for studies of the microstructure, chemical composition and functionality of plants at a subcellular level. Here we present the use of high-resolution bench top-based infrared microspectroscopy to investigate the microstructure of Triticum aestivum L. (wheat) kernels and Arabidopsis leaves. Images of isolated wheat kernel tissues and whole wheat kernels following hydrothermal processing and simulated gastric and duodenal digestion were generated, as well as images of Arabidopsis leaves at different points during a diurnal cycle. Individual cells and cell walls were resolved, and large structures within cells, such as starch granules and protein bodies, were clearly identified. Contrast was provided by converting the hyperspectral image cubes into false-colour images using either principal component analysis (PCA) overlays or by correlation analysis. The unsupervised PCA approach provided a clear view of the sample microstructure, whereas the correlation analysis was used to confirm the identity of different anatomical structures using the spectra from isolated components. It was then demonstrated that gelatinized and native starch within cells could be distinguished, and that the loss of starch during wheat digestion could be observed, as well as the accumulation of starch in leaves during a diurnal period. © 2015 The Authors The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Functional composition of epifauna in the south-eastern North Sea in relation to habitat characteristics and fishing effort

NASA Astrophysics Data System (ADS)

Neumann, Hermann; Diekmann, Rabea; Kröncke, Ingrid

2016-02-01

Analysis of ecosystem functioning is essential to describe the ecological status of ecosystems and is therefore directly requested in international directives. There is a lack of knowledge regarding functional aspects of benthic communities and their environmental and anthropogenic driving forces in the southern North Sea. This study linked functional composition of epibenthic communities to environmental conditions and fishing effort and investigated spatial correlations between habitat characteristics to gain insight into potential synergistic and/or cumulative effects. Functional composition of epifauna was assessed by using biological trait analysis (BTA), which considered 15 ecological traits of 54 species. Functional composition was related to ten predictor variables derived from sediment composition, bottom temperature and salinity, hydrodynamics, annual primary production and fishing effort. Our results revealed significantly different functional composition between the Dogger Bank, the Oyster Ground, the West and North Frisian coast. Mobility, feeding type, size and adult longevity were the most important traits differentiating the communities. A high proportion of trait modalities related to an opportunistic life mode were obvious in coastal areas especially at the West Frisian coast and in the area of the Frisian Front indicating disturbed communities. In contrast, functional composition in the Dogger Bank area indicated undisturbed communities with prevalence of large, long-lived and permanently attached species being sensitive towards disturbance such as fishing. Tidal stress, mud content of sediments, salinity, stratification and fishing effort were found to be the most important habitat characteristics shaping functional composition. Strong correlations were found between variables, especially between those which changed gradually from the coast to offshore areas including fishing effort. Unfavourable extremes of these factors in coastal areas resulted in disturbed epibenthic communities, while the relative influence of a single factor on functional composition cannot be quantified. Coastal communities seemed to be well adapted to disturbance and the prevalence of opportunistic trait modalities not necessarily revealed a poor ecological status according to the Marine Strategy Framework Directive (MSFD). The integration of functional aspects into the assessment of ecosystem health is recommended, since widely used structural measures failed in naturally disturbed habitats.

Sentence Combining: A Literature Review.

ERIC Educational Resources Information Center

Phillips, Sylvia E.

Sentence combining--a technique of putting strings of sentence kernels together in a variety of ways so that completed sentences possess greater syntactic maturity--is a method offering much promise in the teaching of writing and composition. The purpose of this document is to provide a literature review of this procedure. After defining the term…

Development of SNP Genotyping Assays for Seed Composition Traits in Soybean

PubMed Central

Patil, Gunvant; Chaudhary, Juhi; Vuong, Tri D.; Jenkins, Brian; Qiu, Dan; Kadam, Suhas; Shannon, Grover J.

2017-01-01

Seed composition is one of the most important determinants of the economic values in soybean. The quality and quantity of different seed components, such as oil, protein, and carbohydrates, are crucial ingredients in food, feed, and numerous industrial products. Soybean researchers have successfully developed and utilized a diverse set of molecular markers for seed trait improvement in soybean breeding programs. It is imperative to design and develop molecular assays that are accurate, robust, high-throughput, cost-effective, and available on a common genotyping platform. In the present study, we developed and validated KASP (Kompetitive allele-specific polymerase chain reaction) genotyping assays based on previously known functional mutant alleles for the seed composition traits, including fatty acids, oligosaccharides, trypsin inhibitor, and lipoxygenase. These assays were validated on mutant sources as well as mapping populations and precisely distinguish the homozygotes and heterozygotes of the mutant genes. With the obvious advantages, newly developed KASP assays in this study can substitute the genotyping assays that were previously developed for marker-assisted selection (MAS). The functional gene-based assay resource developed using common genotyping platform will be helpful to accelerate efforts to improve soybean seed composition traits. PMID:28630621

Multilevel image recognition using discriminative patches and kernel covariance descriptor

NASA Astrophysics Data System (ADS)

Lu, Le; Yao, Jianhua; Turkbey, Evrim; Summers, Ronald M.

2014-03-01

Computer-aided diagnosis of medical images has emerged as an important tool to objectively improve the performance, accuracy and consistency for clinical workflow. To computerize the medical image diagnostic recognition problem, there are three fundamental problems: where to look (i.e., where is the region of interest from the whole image/volume), image feature description/encoding, and similarity metrics for classification or matching. In this paper, we exploit the motivation, implementation and performance evaluation of task-driven iterative, discriminative image patch mining; covariance matrix based descriptor via intensity, gradient and spatial layout; and log-Euclidean distance kernel for support vector machine, to address these three aspects respectively. To cope with often visually ambiguous image patterns for the region of interest in medical diagnosis, discovery of multilabel selective discriminative patches is desired. Covariance of several image statistics summarizes their second order interactions within an image patch and is proved as an effective image descriptor, with low dimensionality compared with joint statistics and fast computation regardless of the patch size. We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of degenerative disc disease (DDD) detection on cortical shell unwrapped CT maps and colitis detection on CT key images. The proposed approach is validated with promising quantitative results on these challenging tasks. Our experimental findings and discussion also unveil some interesting insights on the covariance feature composition with or without spatial layout for classification and retrieval, and different kernel constructions for SVM. This will also shed some light on future work using covariance feature and kernel classification for medical image analysis.

Macroinvertebrate Taxonomic and Functional Trait Compositions within Lotic Habitats Affected By River Restoration Practices

NASA Astrophysics Data System (ADS)

White, J. C.; Hill, M. J.; Bickerton, M. A.; Wood, P. J.

2017-09-01

The widespread degradation of lotic ecosystems has prompted extensive river restoration efforts globally, but many studies have reported modest ecological responses to rehabilitation practices. The functional properties of biotic communities are rarely examined within post-project appraisals, which would provide more ecological information underpinning ecosystem responses to restoration practices and potentially pinpoint project limitations. This study examines macroinvertebrate community responses to three projects which aimed to physically restore channel morphologies. Taxonomic and functional trait compositions supported by widely occurring lotic habitats (biotopes) were examined across paired restored and non-restored (control) reaches. The multivariate location (average community composition) of taxonomic and functional trait compositions differed marginally between control and restored reaches. However, changes in the amount of multivariate dispersion were more robust and indicated greater ecological heterogeneity within restored reaches, particularly when considering functional trait compositions. Organic biotopes (macrophyte stands and macroalgae) occurred widely across all study sites and supported a high alpha (within-habitat) taxonomic diversity compared to mineralogical biotopes (sand and gravel patches), which were characteristic of restored reaches. However, mineralogical biotopes possessed a higher beta (between-habitat) functional diversity, although this was less pronounced for taxonomic compositions. This study demonstrates that examining the functional and structural properties of taxa across distinct biotopes can provide a greater understanding of biotic responses to river restoration works. Such information could be used to better understand the ecological implications of rehabilitation practices and guide more effective management strategies.

Using genomics to enhance selection of novel traits in North American dairy cattle

USDA-ARS?s Scientific Manuscript database

Genomics offers new opportunities for the effective selection of novel traits. For traits such as mastitis resistance, hoof health, or the prediction of milk composition from mid-infrared (MIR) data, for example, enough records are usually available to carry out genomic evaluations using sire genoty...

Genome-wide association study for carcass traits in a composite beef cattle breed

USDA-ARS?s Scientific Manuscript database

Improvement of carcass traits is highly emphasized in beef cattle production in order to meet consumer demands. Discovering and understanding genes and genetic variants that control these traits is of paramount importance. In this study, different genome wide association approaches (ssGWAS, Bayes A...

Do temperate tree species diversity and identity influence soil microbial community function and composition?

PubMed

Khlifa, Rim; Paquette, Alain; Messier, Christian; Reich, Peter B; Munson, Alison D

2017-10-01

Studies of biodiversity-ecosystem function in treed ecosystems have generally focused on aboveground functions. This study investigates intertrophic links between tree diversity and soil microbial community function and composition. We examined how microbial communities in surface mineral soil responded to experimental gradients of tree species richness (SR), functional diversity (FD), community-weighted mean trait value (CWM), and tree identity. The site was a 4-year-old common garden experiment near Montreal, Canada, consisting of deciduous and evergreen tree species mixtures. Microbial community composition, community-level physiological profiles, and respiration were evaluated using phospholipid fatty acid (PLFA) analysis and the MicroResp ™ system, respectively. The relationship between tree species richness and glucose-induced respiration (GIR), basal respiration (BR), metabolic quotient (qCO 2 ) followed a positive but saturating shape. Microbial communities associated with species mixtures were more active (basal respiration [BR]), with higher biomass (glucose-induced respiration [GIR]), and used a greater number of carbon sources than monocultures. Communities associated with deciduous tree species used a greater number of carbon sources than those associated with evergreen species, suggesting a greater soil carbon storage capacity. There were no differences in microbial composition (PLFA) between monocultures and SR mixtures. The FD and the CWM of several functional traits affected both BR and GIR. In general, the CWM of traits had stronger effects than did FD, suggesting that certain traits of dominant species have more effect on ecosystem processes than does FD. Both the functions of GIR and BR were positively related to aboveground tree community productivity. Both tree diversity (SR) and identity (species and functional identity-leaf habit) affected soil microbial community respiration, biomass, and composition. For the first time, we identified functional traits related to life-history strategy, as well as root traits that influence another trophic level, soil microbial community function, via effects on BR and GIR.

Certain composition formulae for the fractional integral operators

NASA Astrophysics Data System (ADS)

Agarwal, Praveen; Harjule, Priyanka

2017-09-01

In this paper we establish some (presumably new) interesting expressions for the composition of some well known fractional integral operators Ia+ μ,Da+ μ,Ia+ γ ,μ and also derive an integral operator ℋa+;p ,q ;β w ;m ,n ;α whose kernel involves the Fox's H- function. By suitably specializing the coefficients and the parameters in these functions we can get a large number of (new and known) interesting expressions for the composition formulae which occur rather frequently in many problems of engineering and mathematical analysis but here we can mention only those which follow as particular cases of the Srivastava et al.

An experimental validation of genomic selection in octoploid strawberry

PubMed Central

Gezan, Salvador A; Osorio, Luis F; Verma, Sujeet; Whitaker, Vance M

2017-01-01

The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013–2014 and 2014–2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values. PMID:28090334

Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

PubMed

Sun, Xiaochun; Ma, Ping; Mumm, Rita H

2012-01-01

Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

Nonparametric Method for Genomics-Based Prediction of Performance of Quantitative Traits Involving Epistasis in Plant Breeding

PubMed Central

Sun, Xiaochun; Ma, Ping; Mumm, Rita H.

2012-01-01

Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression. PMID:23226325

Trait modality distribution of aquatic macrofauna communities as explained by pesticides and water chemistry.

PubMed

Ieromina, O; Musters, C J M; Bodegom, P M; Peijnenburg, W J G M; Vijver, M G

2016-08-01

Analyzing functional species' characteristics (species traits) that represent physiological, life history and morphological characteristics of species help understanding the impacts of various stressors on aquatic communities at field conditions. This research aimed to study the combined effects of pesticides and other environmental factors (temperature, dissolved oxygen, dissolved organic carbon, floating macrophytes cover, phosphate, nitrite, and nitrate) on the trait modality distribution of aquatic macrofauna communities. To this purpose, a field inventory was performed in a flower bulb growing area of the Netherlands with significant variation in pesticides pressures. Macrofauna community composition, water chemistry parameters and pesticide concentrations in ditches next to flower bulb fields were determined. Trait modalities of nine traits (feeding mode, respiration mode, locomotion type, resistance form, reproduction mode, life stage, voltinism, saprobity, maximum body size) likely to indicate pesticides impacts were analyzed. According to a redundancy analysis, phosphate -and not pesticides- constituted the main factor structuring the trait modality distribution of aquatic macrofauna. The functional composition could be ascribed for 2-4 % to pesticides, and for 3-11 % to phosphate. The lack of trait responses to pesticides may indicate that species may have used alternative strategies to adapt to ambient pesticides stress. Biomass of animals exhibiting trait modalities related to feeding by predation and grazing, presence of diapause form or dormancy, reproduction by free clutches and ovoviviparity, life stage of larvae and pupa, was negatively correlated to the concentration of phosphate. Hence, despite the high pesticide pollution in the area, variation in nutrient-related stressors seems to be the dominant driver of the functional composition of aquatic macrofauna assembly in agricultural ditches.

The meaning of functional trait composition of food webs for ecosystem functioning.

PubMed

Gravel, Dominique; Albouy, Camille; Thuiller, Wilfried

2016-05-19

There is a growing interest in using trait-based approaches to characterize the functional structure of animal communities. Quantitative methods have been derived mostly for plant ecology, but it is now common to characterize the functional composition of various systems such as soils, coral reefs, pelagic food webs or terrestrial vertebrate communities. With the ever-increasing availability of distribution and trait data, a quantitative method to represent the different roles of animals in a community promise to find generalities that will facilitate cross-system comparisons. There is, however, currently no theory relating the functional composition of food webs to their dynamics and properties. The intuitive interpretation that more functional diversity leads to higher resource exploitation and better ecosystem functioning was brought from plant ecology and does not apply readily to food webs. Here we appraise whether there are interpretable metrics to describe the functional composition of food webs that could foster a better understanding of their structure and functioning. We first distinguish the various roles that traits have on food web topology, resource extraction (bottom-up effects), trophic regulation (top-down effects), and the ability to keep energy and materials within the community. We then discuss positive effects of functional trait diversity on food webs, such as niche construction and bottom-up effects. We follow with a discussion on the negative effects of functional diversity, such as enhanced competition (both exploitation and apparent) and top-down control. Our review reveals that most of our current understanding of the impact of functional trait diversity on food web properties and functioning comes from an over-simplistic representation of network structure with well-defined levels. We, therefore, conclude with propositions for new research avenues for both theoreticians and empiricists. © 2016 The Author(s).

The meaning of functional trait composition of food webs for ecosystem functioning

PubMed Central

Albouy, Camille

2016-01-01

There is a growing interest in using trait-based approaches to characterize the functional structure of animal communities. Quantitative methods have been derived mostly for plant ecology, but it is now common to characterize the functional composition of various systems such as soils, coral reefs, pelagic food webs or terrestrial vertebrate communities. With the ever-increasing availability of distribution and trait data, a quantitative method to represent the different roles of animals in a community promise to find generalities that will facilitate cross-system comparisons. There is, however, currently no theory relating the functional composition of food webs to their dynamics and properties. The intuitive interpretation that more functional diversity leads to higher resource exploitation and better ecosystem functioning was brought from plant ecology and does not apply readily to food webs. Here we appraise whether there are interpretable metrics to describe the functional composition of food webs that could foster a better understanding of their structure and functioning. We first distinguish the various roles that traits have on food web topology, resource extraction (bottom-up effects), trophic regulation (top-down effects), and the ability to keep energy and materials within the community. We then discuss positive effects of functional trait diversity on food webs, such as niche construction and bottom-up effects. We follow with a discussion on the negative effects of functional diversity, such as enhanced competition (both exploitation and apparent) and top-down control. Our review reveals that most of our current understanding of the impact of functional trait diversity on food web properties and functioning comes from an over-simplistic representation of network structure with well-defined levels. We, therefore, conclude with propositions for new research avenues for both theoreticians and empiricists. PMID:27114571

Functional Trait Changes, Productivity Shifts and Vegetation Stability in Mountain Grasslands during a Short-Term Warming.

PubMed

Debouk, Haifa; de Bello, Francesco; Sebastià, Maria-Teresa

2015-01-01

Plant functional traits underlie vegetation responses to environmental changes such as global warming, and consequently influence ecosystem processes. While most of the existing studies focus on the effect of warming only on species diversity and productivity, we further investigated (i) how the structure of community plant functional traits in temperate grasslands respond to experimental warming, and (ii) whether species and functional diversity contribute to a greater stability of grasslands, in terms of vegetation composition and productivity. Intact vegetation turves were extracted from temperate subalpine grassland (highland) in the Eastern Pyrenees and transplanted into a warm continental, experimental site in Lleida, in Western Catalonia (lowland). The impacts of simulated warming on plant production and diversity, functional trait structure, and vegetation compositional stability were assessed. We observed an increase in biomass and a reduction in species and functional diversity under short-term warming. The functional structure of the grassland communities changed significantly, in terms of functional diversity and community-weighted means (CWM) for several traits. Acquisitive and fast-growing species with higher SLA, early flowering, erect growth habit, and rhizomatous strategy became dominant in the lowland. Productivity was significantly positively related to species, and to a lower extent, functional diversity, but productivity and stability after warming were more dependent on trait composition (CWM) than on diversity. The turves with more acquisitive species before warming changed less in composition after warming. Results suggest that (i) the short-term warming can lead to the dominance of acquisitive fast growing species over conservative species, thus reducing species richness, and (ii) the functional traits structure in grassland communities had a greater influence on the productivity and stability of the community under short-term warming, compared to diversity effects. In summary, short-term climate warming can greatly alter vegetation functional structure and its relation to productivity.

Functional Trait Changes, Productivity Shifts and Vegetation Stability in Mountain Grasslands during a Short-Term Warming

PubMed Central

Debouk, Haifa; de Bello, Francesco; Sebastià, Maria-Teresa

2015-01-01

Plant functional traits underlie vegetation responses to environmental changes such as global warming, and consequently influence ecosystem processes. While most of the existing studies focus on the effect of warming only on species diversity and productivity, we further investigated (i) how the structure of community plant functional traits in temperate grasslands respond to experimental warming, and (ii) whether species and functional diversity contribute to a greater stability of grasslands, in terms of vegetation composition and productivity. Intact vegetation turves were extracted from temperate subalpine grassland (highland) in the Eastern Pyrenees and transplanted into a warm continental, experimental site in Lleida, in Western Catalonia (lowland). The impacts of simulated warming on plant production and diversity, functional trait structure, and vegetation compositional stability were assessed. We observed an increase in biomass and a reduction in species and functional diversity under short-term warming. The functional structure of the grassland communities changed significantly, in terms of functional diversity and community-weighted means (CWM) for several traits. Acquisitive and fast-growing species with higher SLA, early flowering, erect growth habit, and rhizomatous strategy became dominant in the lowland. Productivity was significantly positively related to species, and to a lower extent, functional diversity, but productivity and stability after warming were more dependent on trait composition (CWM) than on diversity. The turves with more acquisitive species before warming changed less in composition after warming. Results suggest that (i) the short-term warming can lead to the dominance of acquisitive fast growing species over conservative species, thus reducing species richness, and (ii) the functional traits structure in grassland communities had a greater influence on the productivity and stability of the community under short-term warming, compared to diversity effects. In summary, short-term climate warming can greatly alter vegetation functional structure and its relation to productivity. PMID:26513148

Different responses of functional traits and diversity of stream macroinvertebrates to environmental and spatial factors in the Xishuangbanna watershed of the upper Mekong River Basin, China.

PubMed

Ding, Ning; Yang, Weifang; Zhou, Yunlei; González-Bergonzoni, Ivan; Zhang, Jie; Chen, Kai; Vidal, Nicolas; Jeppesen, Erik; Liu, Zhengwen; Wang, Beixin

2017-01-01

Functional traits and diversity indices have provided new insights into community responses to stressors. Most traits of aquatic organisms have frequently been tested for predictability and geographical stability in response to environmental variables, but such tests of functional diversity indices are rare. We sampled macroinvertebrates at 18 reference sites (RS) and 35 disturbed sites (DS) from headwater streams in the upper Mekong River Basin, Xishuangbanna (XSBN), China. We selected 29 qualitative categories of eight traits and then calculated five functional diversity indices, namely functional richness (FRic), functional evenness (FEve), functional dispersion (FDis), functional divergence (FDiv) and Rao's Quadratic Entropy (RaoQ), and two trait diversity indices, namely trait richness (TR) and trait diversity (TD). We used combination of RLQ and fourth-corner to examine the response of traits and functional diversity to the disturbance and environmental variables. We used variance partitioning to explore the relative role of environmental variables and spatial factors in constraining trait composition and functional diversity. We found that the relative frequency of ten trait categories, and the values of TD, TR, FRic and FDis in RS were significantly different (p<0.05) from DS. In addition, the seven traits (except for "habit") demonstrated a predictable response of trait patterns along the integrative environmental gradients. Environmental variables significantly contributed to most of the traits, functional diversity and trait diversity. However, spatial variables were mainly significant in shaping ecological traits, FRic and FEve. Our results confirm the dominant role of environmental variables in the determination of community trait composition and functional diversity, and substantiate the contribution of spatial vectors in explaining the variance of functional traits and diversity. We conclude that the traits "Refuge", "External protection", "Respiration" and "Body shape", and diversity indices FDis, TD, and TR are promising indicators of stream conditions at XSBN. Copyright © 2016 Elsevier B.V. All rights reserved.

Leaf Trait-Environment Relationships in a Subtropical Broadleaved Forest in South-East China

PubMed Central

Kröber, Wenzel; Böhnke, Martin; Welk, Erik; Wirth, Christian; Bruelheide, Helge

2012-01-01

Although trait analyses have become more important in community ecology, trait-environment correlations have rarely been studied along successional gradients. We asked which environmental variables had the strongest impact on intraspecific and interspecific trait variation in the community and which traits were most responsive to the environment. We established a series of plots in a secondary forest in the Chinese subtropics, stratified by successional stages that were defined by the time elapsed since the last logging activities. On a total of 27 plots all woody plants were recorded and a set of individuals of every species was analysed for leaf traits, resulting in a trait matrix of 26 leaf traits for 122 species. A Fourth Corner Analysis revealed that the mean values of many leaf traits were tightly related to the successional gradient. Most shifts in traits followed the leaf economics spectrum with decreasing specific leaf area and leaf nutrient contents with successional time. Beside succession, few additional environmental variables resulted in significant trait relationships, such as soil moisture and soil C and N content as well as topographical variables. Not all traits were related to the leaf economics spectrum, and thus, to the successional gradient, such as stomata size and density. By comparing different permutation models in the Fourth Corner Analysis, we found that the trait-environment link was based more on the association of species with the environment than of the communities with species traits. The strong species-environment association was brought about by a clear gradient in species composition along the succession series, while communities were not well differentiated in mean trait composition. In contrast, intraspecific trait variation did not show close environmental relationships. The study confirmed the role of environmental trait filtering in subtropical forests, with traits associated with the leaf economics spectrum being the most responsive ones. PMID:22539999

Leaf trait-environment relationships in a subtropical broadleaved forest in South-East China.

PubMed

Kröber, Wenzel; Böhnke, Martin; Welk, Erik; Wirth, Christian; Bruelheide, Helge

2012-01-01

Although trait analyses have become more important in community ecology, trait-environment correlations have rarely been studied along successional gradients. We asked which environmental variables had the strongest impact on intraspecific and interspecific trait variation in the community and which traits were most responsive to the environment. We established a series of plots in a secondary forest in the Chinese subtropics, stratified by successional stages that were defined by the time elapsed since the last logging activities. On a total of 27 plots all woody plants were recorded and a set of individuals of every species was analysed for leaf traits, resulting in a trait matrix of 26 leaf traits for 122 species. A Fourth Corner Analysis revealed that the mean values of many leaf traits were tightly related to the successional gradient. Most shifts in traits followed the leaf economics spectrum with decreasing specific leaf area and leaf nutrient contents with successional time. Beside succession, few additional environmental variables resulted in significant trait relationships, such as soil moisture and soil C and N content as well as topographical variables. Not all traits were related to the leaf economics spectrum, and thus, to the successional gradient, such as stomata size and density. By comparing different permutation models in the Fourth Corner Analysis, we found that the trait-environment link was based more on the association of species with the environment than of the communities with species traits. The strong species-environment association was brought about by a clear gradient in species composition along the succession series, while communities were not well differentiated in mean trait composition. In contrast, intraspecific trait variation did not show close environmental relationships. The study confirmed the role of environmental trait filtering in subtropical forests, with traits associated with the leaf economics spectrum being the most responsive ones.

Influence of Stenocarpella maydis infected corn on the composition of corn kernel and its conversion into ethanol

USDA-ARS?s Scientific Manuscript database

Stenocarpella ear rot (formerly Diplodia ear rot) is resurfacing as a concern in the central United States Corn Belt. There are reports of some fields containing more than 50% mummified ears. Ears infected within two weeks of silking may be completely mummified with white to grayish brown mycelium c...

Influence of Stenocarpella maydis infected corn on the composition of corn kernel and its conversion into ethanol

USDA-ARS?s Scientific Manuscript database

Widespread epidemics of Stenocarpella ear rot (formerly Diplodia ear rot) have occurred throughout the central U.S. Corn Belt in recent years, but the influence of S. maydis infected grain on corn ethanol production is unknown. In this study, S. maydis infected ears of variety 'Heritage 4646' were h...

Expanding Omics Resources for Improvement of Soybean Seed Composition Traits

PubMed Central

Chaudhary, Juhi; Patil, Gunvant B.; Sonah, Humira; Deshmukh, Rupesh K.; Vuong, Tri D.; Valliyodan, Babu; Nguyen, Henry T.

2015-01-01

Food resources of the modern world are strained due to the increasing population. There is an urgent need for innovative methods and approaches to augment food production. Legume seeds are major resources of human food and animal feed with their unique nutrient compositions including oil, protein, carbohydrates, and other beneficial nutrients. Recent advances in next-generation sequencing (NGS) together with “omics” technologies have considerably strengthened soybean research. The availability of well annotated soybean genome sequence along with hundreds of identified quantitative trait loci (QTL) associated with different seed traits can be used for gene discovery and molecular marker development for breeding applications. Despite the remarkable progress in these technologies, the analysis and mining of existing seed genomics data are still challenging due to the complexity of genetic inheritance, metabolic partitioning, and developmental regulations. Integration of “omics tools” is an effective strategy to discover key regulators of various seed traits. In this review, recent advances in “omics” approaches and their use in soybean seed trait investigations are presented along with the available databases and technological platforms and their applicability in the improvement of soybean. This article also highlights the use of modern breeding approaches, such as genome-wide association studies (GWAS), genomic selection (GS), and marker-assisted recurrent selection (MARS) for developing superior cultivars. A catalog of available important resources for major seed composition traits, such as seed oil, protein, carbohydrates, and yield traits are provided to improve the knowledge base and future utilization of this information in the soybean crop improvement programs. PMID:26635846

Plant traits determine the phylogenetic structure of arbuscular mycorrhizal fungal communities.

PubMed

López-García, Álvaro; Varela-Cervero, Sara; Vasar, Martti; Öpik, Maarja; Barea, José M; Azcón-Aguilar, Concepción

2017-12-01

Functional diversity in ecosystems has traditionally been studied using aboveground plant traits. Despite the known effect of plant traits on the microbial community composition, their effects on the microbial functional diversity are only starting to be assessed. In this study, the phylogenetic structure of arbuscular mycorrhizal (AM) fungal communities associated with plant species differing in life cycle and growth form, that is, plant life forms, was determined to unravel the effect of plant traits on the functional diversity of this fungal group. The results of the 454 pyrosequencing showed that the AM fungal community composition differed across plant life forms and this effect was dependent on the soil collection date. Plants with ruderal characteristics tended to associate with phylogenetically clustered AM fungal communities. By contrast, plants with resource-conservative traits associated with phylogenetically overdispersed AM fungal communities. Additionally, the soil collected in different seasons yielded AM fungal communities with different phylogenetic dispersion. In summary, we found that the phylogenetic structure, and hence the functional diversity, of AM fungal communities is dependent on plant traits. This finding adds value to the use of plant traits for the evaluation of belowground ecosystem diversity, functions and processes. © 2017 John Wiley & Sons Ltd.

A genome-wide association study of seed composition traits in wild soybean (Glycine soja).

PubMed

Leamy, Larry J; Zhang, Hengyou; Li, Changbao; Chen, Charles Y; Song, Bao-Hua

2017-01-05

Cultivated soybean (Glycine max) is a major agricultural crop that provides a crucial source of edible protein and oil. Decreased amounts of saturated palmitic acid and increased amounts of unsaturated oleic acid in soybean oil are considered optimal for human cardiovascular health and therefore there has considerable interest by breeders in discovering genes affecting the relative concentrations of these fatty acids. Using a genome-wide association (GWA) approach with nearly 30,000 single nucleotide polymorphisms (SNPs), we investigated the genetic basis of protein, oil and all five fatty acid levels in seeds from a sample of 570 wild soybeans (Glycine soja), the progenitor of domesticated soybean, to identify quantitative trait loci (QTLs) affecting these seed composition traits. We discovered 29 SNPs located on ten different chromosomes that are significantly associated with the seven seed composition traits in our wild soybean sample. Eight SNPs co-localized with QTLs previously uncovered in linkage or association mapping studies conducted with cultivated soybean samples, while the remaining SNPs appeared to be in novel locations. Twenty-four of the SNPs significantly associated with fatty acid variation, with the majority located on chromosomes 14 (6 SNPs) and seven (8 SNPs). Two SNPs were common for two or more fatty acids, suggesting loci with pleiotropic effects. We also identified some candidate genes that are involved in fatty acid metabolism and regulation. For each of the seven traits, most of the SNPs produced differences between the average phenotypic values of the two homozygotes of about one-half standard deviation and contributed over 3% of their total variability. This is the first GWA study conducted on seed composition traits solely in wild soybean populations, and a number of QTLs were found that have not been previously discovered. Some of these may be useful to breeders who select for increased protein/oil content or altered fatty acid ratios in the seeds. The results also provide additional insight into the genetic architecture of these traits in a large sample of wild soybean, and suggest some new candidate genes whose molecular effects on these traits need to be further studied.

Joint analysis of quantitative trait loci and major-effect causative mutations affecting meat quality and carcass composition traits in pigs.

PubMed

Cherel, Pierre; Pires, José; Glénisson, Jérôme; Milan, Denis; Iannuccelli, Nathalie; Hérault, Frédéric; Damon, Marie; Le Roy, Pascale

2011-08-29

Detection of quantitative trait loci (QTLs) affecting meat quality traits in pigs is crucial for the design of efficient marker-assisted selection programs and to initiate efforts toward the identification of underlying polymorphisms. The RYR1 and PRKAG3 causative mutations, originally identified from major effects on meat characteristics, can be used both as controls for an overall QTL detection strategy for diversely affected traits and as a scale for detected QTL effects. We report on a microsatellite-based QTL detection scan including all autosomes for pig meat quality and carcass composition traits in an F2 population of 1,000 females and barrows resulting from an intercross between a Pietrain and a Large White-Hampshire-Duroc synthetic sire line. Our QTL detection design allowed side-by-side comparison of the RYR1 and PRKAG3 mutation effects seen as QTLs when segregating at low frequencies (0.03-0.08), with independent QTL effects detected from most of the same population, excluding any carrier of these mutations. Large QTL effects were detected in the absence of the RYR1 and PRKGA3 mutations, accounting for 12.7% of phenotypic variation in loin colour redness CIE-a* on SSC6 and 15% of phenotypic variation in glycolytic potential on SSC1. We detected 8 significant QTLs with effects on meat quality traits and 20 significant QTLs for carcass composition and growth traits under these conditions. In control analyses including mutation carriers, RYR1 and PRKAG3 mutations were detected as QTLs, from highly significant to suggestive, and explained 53% to 5% of the phenotypic variance according to the trait. Our results suggest that part of muscle development and backfat thickness effects commonly attributed to the RYR1 mutation may be a consequence of linkage with independent QTLs affecting those traits. The proportion of variation explained by the most significant QTLs detected in this work is close to the influence of major-effect mutations on the least affected traits, but is one order of magnitude lower than effect on variance of traits primarily affected by these causative mutations. This suggests that uncovering physiological traits directly affected by genetic polymorphisms would be an appropriate approach for further characterization of QTLs.

Joint analysis of quantitative trait loci and major-effect causative mutations affecting meat quality and carcass composition traits in pigs

PubMed Central

2011-01-01

Background Detection of quantitative trait loci (QTLs) affecting meat quality traits in pigs is crucial for the design of efficient marker-assisted selection programs and to initiate efforts toward the identification of underlying polymorphisms. The RYR1 and PRKAG3 causative mutations, originally identified from major effects on meat characteristics, can be used both as controls for an overall QTL detection strategy for diversely affected traits and as a scale for detected QTL effects. We report on a microsatellite-based QTL detection scan including all autosomes for pig meat quality and carcass composition traits in an F2 population of 1,000 females and barrows resulting from an intercross between a Pietrain and a Large White-Hampshire-Duroc synthetic sire line. Our QTL detection design allowed side-by-side comparison of the RYR1 and PRKAG3 mutation effects seen as QTLs when segregating at low frequencies (0.03-0.08), with independent QTL effects detected from most of the same population, excluding any carrier of these mutations. Results Large QTL effects were detected in the absence of the RYR1 and PRKGA3 mutations, accounting for 12.7% of phenotypic variation in loin colour redness CIE-a* on SSC6 and 15% of phenotypic variation in glycolytic potential on SSC1. We detected 8 significant QTLs with effects on meat quality traits and 20 significant QTLs for carcass composition and growth traits under these conditions. In control analyses including mutation carriers, RYR1 and PRKAG3 mutations were detected as QTLs, from highly significant to suggestive, and explained 53% to 5% of the phenotypic variance according to the trait. Conclusions Our results suggest that part of muscle development and backfat thickness effects commonly attributed to the RYR1 mutation may be a consequence of linkage with independent QTLs affecting those traits. The proportion of variation explained by the most significant QTLs detected in this work is close to the influence of major-effect mutations on the least affected traits, but is one order of magnitude lower than effect on variance of traits primarily affected by these causative mutations. This suggests that uncovering physiological traits directly affected by genetic polymorphisms would be an appropriate approach for further characterization of QTLs. PMID:21875434

Mechanical response of common millet (Panicum miliaceum) seeds under quasi-static compression: Experiments and modeling.

PubMed

Hasseldine, Benjamin P J; Gao, Chao; Collins, Joseph M; Jung, Hyun-Do; Jang, Tae-Sik; Song, Juha; Li, Yaning

2017-09-01

The common millet (Panicum miliaceum) seedcoat has a fascinating complex microstructure, with jigsaw puzzle-like epidermis cells articulated via wavy intercellular sutures to form a compact layer to protect the kernel inside. However, little research has been conducted on linking the microstructure details with the overall mechanical response of this interesting biological composite. To this end, an integrated experimental-numerical-analytical investigation was conducted to both characterize the microstructure and ascertain the microscale mechanical properties and to test the overall response of kernels and full seeds under macroscale quasi-static compression. Scanning electron microscopy (SEM) was utilized to examine the microstructure of the outer seedcoat and nanoindentation was performed to obtain the material properties of the seedcoat hard phase material. A multiscale computational strategy was applied to link the microstructure to the macroscale response of the seed. First, the effective anisotropic mechanical properties of the seedcoat were obtained from finite element (FE) simulations of a microscale representative volume element (RVE), which were further verified from sophisticated analytical models. Then, macroscale FE models of the individual kernel and full seed were developed. Good agreement between the compression experiments and FE simulations were obtained for both the kernel and the full seed. The results revealed the anisotropic property and the protective function of the seedcoat, and showed that the sutures of the seedcoat play an important role in transmitting and distributing loads in responding to external compression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Marine ecosystem resilience during extreme deoxygenation: the Early Jurassic oceanic anoxic event.

PubMed

Caswell, Bryony A; Frid, Christopher L J

2017-01-01

Global warming during the Early Jurassic, and associated widespread ocean deoxygenation, was comparable in scale with the changes projected for the next century. This study quantifies the impact of severe global environmental change on the biological traits of marine communities that define the ecological roles and functions they deliver. We document centennial-millennial variability in the biological trait composition of Early Jurassic (Toarcian) seafloor communities and examine how this changed during the event using biological traits analysis. Environmental changes preceding the global oceanic anoxic event (OAE) produced an ecological shift leading to stressed benthic palaeocommunities with reduced resilience to the subsequent OAE. Changes in traits and ecological succession coincided with major environmental changes; and were of similar nature and magnitude to those in severely deoxygenated benthic communities today despite the very different timescales. Changes in community composition were linked to local redox conditions whereas changes in populations of opportunists were driven by primary productivity. Throughout most of the OAE substitutions by tolerant taxa conserved the trait composition and hence functioning, but periods of severe deoxygenation caused benthic defaunation that would have resulted in functional collapse. Following the OAE recovery was slow probably because the global nature of the event restricted opportunities for recruitment from outside the basin. Our findings suggest that future systems undergoing deoxygenation may initially show functional resilience, but severe global deoxygenation will impact traits and ecosystem functioning and, by limiting the species pool, will slow recovery rates.

Chemical properties and oxidative stability of Arjan (Amygdalus reuteri) kernel oil as emerging edible oil.

PubMed

Tavakoli, Javad; Emadi, Teymour; Hashemi, Seyed Mohammad Bagher; Mousavi Khaneghah, Amin; Munekata, Paulo Eduardo Sichetti; Lorenzo, Jose Manuel; Brnčić, Mladen; Barba, Francisco J

2018-05-01

The oxidative stability, as well as the chemical composition of Amygdalus reuteri kernel oil (ARKO), were evaluated and compared to those of Amygdalus scoparia kernel oil (ASKO) and extra virgin olive oil (EVOO) during and after holding in the oven (170 °C for 8 h). The oxidative stability analysis was carried out by measuring the changes in conjugated dienes, carbonyl and acid values as well as oil/oxidative stability index and their correlation with the antioxidant compounds (tocopherol, polyphenols, and sterol compounds). The oleic acid was determined as the predominant fatty acid of ARKO (65.5%). Calculated oxidizability value and an iodine value of ARKO, ASKO and EVOO were reported as 3.29 and 3.24, 2.00 and 100.0, 101.4 and 81.9, respectively. Due to the high wax content (4.5% and 3.3%, respectively), the saponification number of ARKO and ASKO (96.4 and 99.8, respectively) was lower than that of EVOO (169.7). ARKO had the highest oxidative stability, followed by ASKO and EVOO. Therefore, ARKO can be introduced as a new source of edible oil with high oxidative stability. Copyright © 2018. Published by Elsevier Ltd.

A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity.

PubMed

Rastgarpour, Maryam; Shanbehzadeh, Jamshid

2014-01-01

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Genomic prediction of continuous and binary fertility traits of females in a composite beef cattle breed

USDA-ARS?s Scientific Manuscript database

Reproduction efficiency is a major factor in the profitability of the beef cattle industry. Genomic selection (GS) is a promising tool that may improve the predictive accuracy and genetic gain of fertility traits. There is a wide range of traits used to measure fertility in dairy and beef cattle inc...

Proteomes of hard and soft near-isogenic wheat lines reveal that kernel hardness is related to the amplification of a stress response during endosperm development.

PubMed

Lesage, Véronique S; Merlino, Marielle; Chambon, Christophe; Bouchet, Brigitte; Marion, Didier; Branlard, Gérard

2012-01-01

Wheat kernel texture, a major trait determining the end-use quality of wheat flour, is mainly influenced by puroindolines. These small basic proteins display in vitro lipid binding and antimicrobial properties, but their cellular functions during grain development remain unknown. To gain an insight into their biological function, a comparative proteome analysis of two near-isogenic lines (NILs) of bread wheat Triticum aestivum L. cv. Falcon differing in the presence or absence of the puroindoline-a gene (Pina) and kernel hardness, was performed. Proteomes of the two NILs were compared at four developmental stages of the grain for the metabolic albumin/globulin fraction and the Triton-extracted amphiphilic fraction. Proteome variations showed that, during grain development, folding proteins and stress-related proteins were more abundant in the hard line compared with the soft one. These results, taken together with ultrastructural observations showing that the formation of the protein matrix occurred earlier in the hard line, suggested that a stress response, possibly the unfolded protein response, is induced earlier in the hard NIL than in the soft one leading to earlier endosperm cell death. Quantification of the albumin/globulin fraction and amphiphilic proteins at each developmental stage strengthened this hypothesis as a plateau was revealed from the 500 °Cd stage in the hard NIL whereas synthesis continued in the soft one. These results open new avenues concerning the function of puroindolines which could be involved in the storage protein folding machinery, consequently affecting the development of wheat endosperm and the formation of the protein matrix.

Rediscovering the Kernels of Truth in the Urban Legends of the Freshman Composition Classroom

ERIC Educational Resources Information Center

Lovoy, Thomas

2004-01-01

English teachers, as well as teachers within other disciplines, often boil down abstract principles to easily explainable bullet points. Students often pick up and retain these points but fail to grasp the broader contexts that make them relevant. It is therefore sometimes helpful to revisit some of the more common of these "rules of thumb" to…

Quantitative trait loci for energy balance traits in an advanced intercross line derived from mice divergently selected for heat loss

PubMed Central

Nielsen, Merlyn K.; Thorn, Stephanie R.; Valdar, William; Pomp, Daniel

2014-01-01

Obesity in human populations, currently a serious health concern, is considered to be the consequence of an energy imbalance in which more energy in calories is consumed than is expended. We used interval mapping techniques to investigate the genetic basis of a number of energy balance traits in an F11 advanced intercross population of mice created from an original intercross of lines selected for increased and decreased heat loss. We uncovered a total of 137 quantitative trait loci (QTLs) for these traits at 41 unique sites on 18 of the 20 chromosomes in the mouse genome, with X-linked QTLs being most prevalent. Two QTLs were found for the selection target of heat loss, one on distal chromosome 1 and another on proximal chromosome 2. The number of QTLs affecting the various traits generally was consistent with previous estimates of heritabilities in the same population, with the most found for two bone mineral traits and the least for feed intake and several body composition traits. QTLs were generally additive in their effects, and some, especially those affecting the body weight traits, were sex-specific. Pleiotropy was extensive within trait groups (body weights, adiposity and organ weight traits, bone traits) and especially between body composition traits adjusted and not adjusted for body weight at sacrifice. Nine QTLs were found for one or more of the adiposity traits, five of which appeared to be unique. The confidence intervals among all QTLs averaged 13.3 Mb, much smaller than usually observed in an F2 cross, and in some cases this allowed us to make reasonable inferences about candidate genes underlying these QTLs. This study combined QTL mapping with genetic parameter analysis in a large segregating population, and has advanced our understanding of the genetic architecture of complex traits related to obesity. PMID:24918027

Assessing trait-based scaling theory in tropical and temperate forests spanning a broad temperature gradients

NASA Astrophysics Data System (ADS)

Enquist, B. J.

2017-12-01

Tropical and temperate elevation gradients are natural laboratories to assess how changing climate can influence tropical forests. However, there is a need for theory and integrated data collection to scale from traits to ecosystems. We assess predictions of a novel trait-based metabolic scaling theory including whether observed shifts in forest traits across a broad tropical temperature gradient is consistent with local phenotypic optima and adaptive compensation for temperature. We tested a new anaytical theory - Trait Driver Theory - that is capable of scaling from traits to entire stands and ecosystems across several elevation gradients spanning 3300m. Each gradient consists of thousands of tropical and temperate tree trait measures taken from forest plots. In several of these plots, in particular in southern Perú, gross and net primary productivity (GPP and NPP) were measured. We measured multiple traits linked to variation in tree growth and assessed their frequency distributions within and across the elevation gradient. We paired these trait measures across individuals within forests with simultaneous measures of ecosystem net and gross primary productivity. Consistent with theory, variation in forest NPP and GPP primarily scaled with forest biomass but the secondary effect of temperature on productivity was much less than expected. This weak temperature dependency appears to reflect directional shifts in several mean community traits that underlie tree growth with decreases in site temperature. The observed shift in traits of trees that dominant more cold environments appear to reflect `adaptive/acclimatory' compensation for the kinetic effects of temperature on leaf photosynthesis and tree growth. Forest trait distributions across the gradient showed peaked and skewed distributions, consistent with the importance of local filtering of optimal growth traits and recent shifts in species composition and dominance due to warming from climate change. Trait-based metabolic scaling theory provides a basis to predict how shifts in climate have and will influence the trait composition and ecosystem functioning of temperate and tropical forests.

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.

Strategies to predict and improve eating quality of cooked beef using carcass and meat composition traits in Angus cattle.

PubMed

Mateescu, R G; Oltenacu, P A; Garmyn, A J; Mafi, G G; VanOverbeke, D L

2016-05-01

Product quality is a high priority for the beef industry because of its importance as a major driver of consumer demand for beef and the ability of the industry to improve it. A 2-prong approach based on implementation of a genetic program to improve eating quality and a system to communicate eating quality and increase the probability that consumers' eating quality expectations are met is outlined. The objectives of this study were 1) to identify the best carcass and meat composition traits to be used in a selection program to improve eating quality and 2) to develop a relatively small number of classes that reflect real and perceptible differences in eating quality that can be communicated to consumers and identify a subset of carcass and meat composition traits with the highest predictive accuracy across all eating quality classes. Carcass traits, meat composition, including Warner-Bratzler shear force (WBSF), intramuscular fat content (IMFC), trained sensory panel scores, and mineral composition traits of 1,666 Angus cattle were used in this study. Three eating quality indexes, EATQ1, EATQ2, and EATQ3, were generated by using different weights for the sensory traits (emphasis on tenderness, flavor, and juiciness, respectively). The best model for predicting eating quality explained 37%, 9%, and 19% of the variability of EATQ1, EATQ2, and EATQ3, and 2 traits, WBSF and IMFC, accounted for most of the variability explained by the best models. EATQ1 combines tenderness, juiciness, and flavor assessed by trained panels with 0.60, 0.15, and 0.25 weights, best describes North American consumers, and has a moderate heritability (0.18 ± 0.06). A selection index (I= -0.5[WBSF] + 0.3[IMFC]) based on phenotypic and genetic variances and covariances can be used to improve eating quality as a correlated trait. The 3 indexes (EATQ1, EATQ2, and EATQ3) were used to generate 3 equal (33.3%) low, medium, and high eating quality classes, and linear combinations of traits that best predict class membership were estimated using a predictive discriminant analysis. The best predictive model to classify new observations into low, medium, and high eating quality classes defined by the EATQ1 index included WBSF, IMFC, HCW, and marbling score and resulted in a total error rate of 47.06%, much lower than the 60.74% error rate when the prediction of class membership was based on the USDA grading system. The 2 best predictors were WBSF and IMFC, and they accounted for 97.2% of the variability explained by the best model.

Risk assessment of salinity and turbidity in Victoria (Australia) to stream insects' community structure does not always protect functional traits.

PubMed

Kefford, Ben J; Schäfer, Ralf B; Metzeling, Leon

2012-01-15

Ecological risk assessments mostly consider measures of community composition (structure) across large spatial scales. These assessments, using species sensitivity distributions (SSDs) or the relative species retention (RSR), may not be protective of ecosystem functions and services at smaller spatial scales. Here we examine how changes in biological traits, as proxy for ecosystem functions/services, at a fine spatial scale relate to larger scale assessment of structure. We use functional traits of stream insect species in south-east Australia in two habitats (riffle and edge/pool). We find that the protection of community structure in terms of 95% of species over multiple sites against adverse effects of salinity (as electrical conductivity) and turbidity will mostly, but not always, protect traits at smaller scales. Considering different combinations of trait modalities, contaminants and habitat, a mean of 17.5% (range 0%-36.8) of cases would result in under-protection of trait modalities despite protecting species composition (in terms of Jaccard's Index). This under-protection of trait modalities is only because of the different spatial scales that community structure and the traits were considered. We recommend that where the protection of biological traits, ecosystem functions or ecosystem services from stressors is a management goal, protective targets should not be solely set using measures of community structure such as SSDs or RSR. To protect both structural and functional attributes separate risk assessments should be done. Copyright © 2011 Elsevier B.V. All rights reserved.

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

On processed splitting methods and high-order actions in path-integral Monte Carlo simulations.

PubMed

Casas, Fernando

2010-10-21

Processed splitting methods are particularly well adapted to carry out path-integral Monte Carlo (PIMC) simulations: since one is mainly interested in estimating traces of operators, only the kernel of the method is necessary to approximate the thermal density matrix. Unfortunately, they suffer the same drawback as standard, nonprocessed integrators: kernels of effective order greater than two necessarily involve some negative coefficients. This problem can be circumvented, however, by incorporating modified potentials into the composition, thus rendering schemes of higher effective order. In this work we analyze a family of fourth-order schemes recently proposed in the PIMC setting, paying special attention to their linear stability properties, and justify their observed behavior in practice. We also propose a new fourth-order scheme requiring the same computational cost but with an enlarged stability interval.

Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief networks

NASA Astrophysics Data System (ADS)

Rachmatia, H.; Kusuma, W. A.; Hasibuan, L. S.

2017-05-01

Selection in plant breeding could be more effective and more efficient if it is based on genomic data. Genomic selection (GS) is a new approach for plant-breeding selection that exploits genomic data through a mechanism called genomic prediction (GP). Most of GP models used linear methods that ignore effects of interaction among genes and effects of higher order nonlinearities. Deep belief network (DBN), one of the architectural in deep learning methods, is able to model data in high level of abstraction that involves nonlinearities effects of the data. This study implemented DBN for developing a GP model utilizing whole-genome Single Nucleotide Polymorphisms (SNPs) as data for training and testing. The case study was a set of traits in maize. The maize dataset was acquisitioned from CIMMYT’s (International Maize and Wheat Improvement Center) Global Maize program. Based on Pearson correlation, DBN is outperformed than other methods, kernel Hilbert space (RKHS) regression, Bayesian LASSO (BL), best linear unbiased predictor (BLUP), in case allegedly non-additive traits. DBN achieves correlation of 0.579 within -1 to 1 range.

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...

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

The spectral details of observed and simulated short-term water vapor feedbacks of El Niño-Southern Oscillation

NASA Astrophysics Data System (ADS)

Pan, F.; Huang, X.; Chen, X.

2015-12-01

Radiative kernel method has been validated and widely used in the study of climate feedbacks. This study uses spectrally resolved longwave radiative kernels to examine the short-term water vapor feedbacks associated with the ENSO cycles. Using a 500-year GFDL CM3 and a 100-year NCAR CCSM4 pre-industry control simulation, we have constructed two sets of longwave spectral radiative kernels. We then composite El Niño, La Niña and ENSO-neutral states and estimate the water vapor feedbacks associated with the El Niño and La Niña phases of ENSO cycles in both simulations. Similar analysis is also applied to 35-year (1979-2014) ECMWF ERA-interim reanalysis data, which is deemed as observational results here. When modeled and observed broadband feedbacks are compared to each other, they show similar geographic patterns but with noticeable discrepancies in the contrast between the tropics and extra-tropics. Especially, in El Niño phase, the feedback estimated from reanalysis is much greater than those from the model simulations. Considering the observational data span, we carry out a sensitivity test to explore the variability of feedback-deriving using 35-year data. To do so, we calculate the water vapor feedback within every 35-year segment of the GFDL CM3 control run by two methods: one is to composite El Nino or La Nina phases as mentioned above and the other is to regressing the TOA flux perturbation caused by water vapor change (δR_H­2O) against the global-mean surface temperature a­­­­nomaly. We find that the short-term feedback strengths derived from composite method can change considerably from one segment to another segment, while the feedbacks by regression method are less sensitive to the choice of segment and their strengths are also much smaller than those from composite analysis. This study suggests that caution is warranted in order to infer long-term feedbacks from a few decades of observations. When spectral details of the global-mean feedbacks are examined, more inconsistencies can be revealed in many spectral bands, especially H2O continuum absorption bands and window regions. These discrepancies can be attributed back to differences in observed and modeled water vapor profiles in responses to tropical SST.

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...

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

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.

Genotype distribution and allele frequencies of the genes associated with body composition and locomotion traits in Myanmar native horses.

PubMed

Okuda, Yu; Moe, Hla Hla; Moe, Kyaw Kyaw; Shimizu, Yuki; Nishioka, Kenji; Shimogiri, Takeshi; Mannen, Hideyuki; Kanemaki, Misao; Kunieda, Tetsuo

2017-08-01

Myanmar native horses are small horses used mainly for drafting carts or carriages in rural areas and packing loads in mountainy areas. In the present study, we investigated genotype distributions and allele frequencies of the LCORL/NCAPG, MSTN and DMRT3 genes, which are associated with body composition and locomotion traits of horses, in seven local populations of Myanmar native horses. The genotyping result of LCORL/NCAPG showed that allele frequencies of C allele associated with higher withers height ranged from 0.08 to 0.27, and 0.13 in average. For MSTN, allele frequencies of C allele associated with higher proportion of Type 2B muscular fiber ranged from 0.05 to 0.23, and 0.09 in average. For DMRT3, allele frequencies of A allele associated with ambling gait ranged from 0 to 0.04, and 0.01 in average. The presences of the minor alleles of these genes at low frequencies suggest a possibility that these horse populations have not been under strong selection pressure for particular locomotion traits and body composition. Our findings of the presence of these minor alleles in Southeast Asian native horses are also informative for considering the origins of these minor alleles associated with body composition and locomotion traits in horse populations. © 2016 Japanese Society of Animal Science.

Litter sex composition affects life-history traits in yellow-bellied marmots.

PubMed

Monclús, Raquel; Blumstein, Daniel T

2012-01-01

1. The presence of siblings might have long-lasting fitness consequences because they influence the early environment in which an animal develops. Several studies under laboratory conditions have shown long-lasting consequences from the presence of male siblings in utero on morphology and life-history traits. However, in wild animals, such effects of litter sex composition are unexplored. 2. We capitalized on a long-term study of individually marked yellow-bellied marmots (Marmota flaviventris) and documented the effects of weaned litter sex composition and anogenital distance on several life-history and fitness traits. 3. First, we demonstrated that the number of males in a litter influenced anogenital distance. Then, we found that masculinized females, those with larger anogenital distances, were less likely to survive their first hibernation, were more likely to disperse and were less likely to become pregnant and wean young. Males from male-biased litters had lower growth rates, but we failed to detect longer-term consequences. 4. Taken together, our results show profound sex-dependent effects of litter sex composition, probably due to differential prenatal exposure to androgens, in free-living animals. We conclude that masculinization might constitute an alternative mechanism explaining variation in different demographic traits. This finding highlights the importance of studying these maternal effects, and they enhance our concern over the widespread use of endocrine disrupting compounds. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

A trait-based approach reveals the feeding selectivity of a small endangered Mediterranean fish.

PubMed

Rodríguez-Lozano, Pablo; Verkaik, Iraima; Maceda-Veiga, Alberto; Monroy, Mario; de Sostoa, Adolf; Rieradevall, Maria; Prat, Narcís

2016-05-01

Functional traits are growing in popularity in modern ecology, but feeding studies remain primarily rooted in a taxonomic-based perspective. However, consumers do not have any reason to select their prey using a taxonomic criterion, and prey assemblages are variable in space and time, which makes taxon-based studies assemblage-specific. To illustrate the benefits of the trait-based approach to assessing food choice, we studied the feeding ecology of the endangered freshwater fish Barbus meridionalis. We hypothesized that B. meridionalis is a selective predator which food choice depends on several prey morphological and behavioral traits, and thus, its top-down pressure may lead to changes in the functional composition of in-stream macroinvertebrate communities. Feeding selectivity was inferred by comparing taxonomic and functional composition (13 traits) between ingested and free-living potential prey using the Jacob's electivity index. Our results showed that the fish diet was influenced by 10 of the 13 traits tested. Barbus meridionalis preferred prey with a potential size of 5-10 mm, with a medium-high drift tendency, and that drift during daylight. Potential prey with no body flexibility, conical shape, concealment traits (presence of nets and/or cases, or patterned coloration), and high aggregation tendency had a low predation risk. Similarly, surface swimmers and interstitial taxa were low vulnerable to predation. Feeding selectivity altered the functional composition of the macroinvertebrate communities. Fish absence favored taxa with weak aggregation tendency, weak flexibility, and a relatively large size (10-20 mm of potential size). Besides, predatory invertebrates may increase in fish absence. In conclusion, our study shows that the incorporation of the trait-based approach in diet studies is a promising avenue to improve our mechanistic understanding of predator-prey interactions and to help predict the ecological outcomes of predator invasions and extinctions.

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...

Secular rise in economically valuable personality traits.

PubMed

Jokela, Markus; Pekkarinen, Tuomas; Sarvimäki, Matti; Terviö, Marko; Uusitalo, Roope

2017-06-20

Although trends in many physical characteristics and cognitive capabilities of modern humans are well-documented, less is known about how personality traits have evolved over time. We analyze data from a standardized personality test administered to 79% of Finnish men born between 1962 and 1976 ( n = 419,523) and find steady increases in personality traits that predict higher income in later life. The magnitudes of these trends are similar to the simultaneous increase in cognitive abilities, at 0.2-0.6 SD during the 15-y window. When anchored to earnings, the change in personality traits amounts to a 12% increase. Both personality and cognitive ability have consistent associations with family background, but the trends are similar across groups defined by parental income, parental education, number of siblings, and rural/urban status. Nevertheless, much of the trends in test scores can be attributed to changes in the family background composition, namely 33% for personality and 64% for cognitive ability. These composition effects are mostly due to improvements in parents' education. We conclude that there is a "Flynn effect" for personality that mirrors the original Flynn effect for cognitive ability in magnitude and practical significance but is less driven by compositional changes in family background.

Secular rise in economically valuable personality traits

PubMed Central

Jokela, Markus; Pekkarinen, Tuomas; Sarvimäki, Matti; Terviö, Marko; Uusitalo, Roope

2017-01-01

Although trends in many physical characteristics and cognitive capabilities of modern humans are well-documented, less is known about how personality traits have evolved over time. We analyze data from a standardized personality test administered to 79% of Finnish men born between 1962 and 1976 (n = 419,523) and find steady increases in personality traits that predict higher income in later life. The magnitudes of these trends are similar to the simultaneous increase in cognitive abilities, at 0.2–0.6 SD during the 15-y window. When anchored to earnings, the change in personality traits amounts to a 12% increase. Both personality and cognitive ability have consistent associations with family background, but the trends are similar across groups defined by parental income, parental education, number of siblings, and rural/urban status. Nevertheless, much of the trends in test scores can be attributed to changes in the family background composition, namely 33% for personality and 64% for cognitive ability. These composition effects are mostly due to improvements in parents’ education. We conclude that there is a “Flynn effect” for personality that mirrors the original Flynn effect for cognitive ability in magnitude and practical significance but is less driven by compositional changes in family background. PMID:28584092

Floral traits influence pollen vectors' choices in higher elevation communities in the Himalaya-Hengduan Mountains.

PubMed

Zhao, Yan-Hui; Ren, Zong-Xin; Lázaro, Amparo; Wang, Hong; Bernhardt, Peter; Li, Hai-Dong; Li, De-Zhu

2016-05-24

How floral traits and community composition influence plant specialization is poorly understood and the existing evidence is restricted to regions where plant diversity is low. Here, we assessed whether plant specialization varied among four species-rich subalpine/alpine communities on the Yulong Mountain, SW China (elevation from 2725 to 3910 m). We analyzed two factors (floral traits and pollen vector community composition: richness and density) to determine the degree of plant specialization across 101 plant species in all four communities. Floral visitors were collected and pollen load analyses were conducted to identify and define pollen vectors. Plant specialization of each species was described by using both pollen vector diversity (Shannon's diversity index) and plant selectiveness (d' index), which reflected how selective a given species was relative to available pollen vectors. Pollen vector diversity tended to be higher in communities at lower elevations, while plant selectiveness was significantly lower in a community with the highest proportion of unspecialized flowers (open flowers and clusters of flowers in open inflorescences). In particular, we found that plant species with large and unspecialized flowers attracted a greater diversity of pollen vectors and showed higher selectiveness in their use of pollen vectors. Plant species with large floral displays and high flower abundance were more selective in their exploitation of pollen vectors. Moreover, there was a negative relationship between plant selectiveness and pollen vector density. These findings suggest that flower shape and flower size can increase pollen vector diversity but they also increased plant selectiveness. This indicated that those floral traits that were more attractive to insects increased the diversity of pollen vectors to plants while decreasing overlap among co-blooming plant species for the same pollen vectors. Furthermore, floral traits had a more important impact on the diversity of pollen vectors than the composition of anthophilous insect communities. Plant selectiveness of pollen vectors was strongly influenced by both floral traits and insect community composition. These findings provide a basis for a better understanding of how floral traits and community context shape interactions between flowers and their pollen vectors in species-rich communities.

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...

Community- Weighted Mean Plant Traits Predict Small Scale Distribution of Insect Root Herbivore Abundance

PubMed Central

Jeltsch, Florian; Wurst, Susanne

2015-01-01

Small scale distribution of insect root herbivores may promote plant species diversity by creating patches of different herbivore pressure. However, determinants of small scale distribution of insect root herbivores, and impact of land use intensity on their small scale distribution are largely unknown. We sampled insect root herbivores and measured vegetation parameters and soil water content along transects in grasslands of different management intensity in three regions in Germany. We calculated community-weighted mean plant traits to test whether the functional plant community composition determines the small scale distribution of insect root herbivores. To analyze spatial patterns in plant species and trait composition and insect root herbivore abundance we computed Mantel correlograms. Insect root herbivores mainly comprised click beetle (Coleoptera, Elateridae) larvae (43%) in the investigated grasslands. Total insect root herbivore numbers were positively related to community-weighted mean traits indicating high plant growth rates and biomass (specific leaf area, reproductive- and vegetative plant height), and negatively related to plant traits indicating poor tissue quality (leaf C/N ratio). Generalist Elaterid larvae, when analyzed independently, were also positively related to high plant growth rates and furthermore to root dry mass, but were not related to tissue quality. Insect root herbivore numbers were not related to plant cover, plant species richness and soil water content. Plant species composition and to a lesser extent plant trait composition displayed spatial autocorrelation, which was not influenced by land use intensity. Insect root herbivore abundance was not spatially autocorrelated. We conclude that in semi-natural grasslands with a high share of generalist insect root herbivores, insect root herbivores affiliate with large, fast growing plants, presumably because of availability of high quantities of food. Affiliation of insect root herbivores with large, fast growing plants may counteract dominance of those species, thus promoting plant diversity. PMID:26517119

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

Culture and the Behavioral Manifestations of Traits: An Application of the Act Frequency Approach

PubMed Central

Church, A. Timothy; Katigbak, Marcia S.; Miramontes, Lilia G.; del Prado, Alicia M.

2009-01-01

The behavioral manifestations of Big Five traits were compared across cultures using the Act Frequency Approach. American (n = 176) and Filipino (n = 195) students completed a Big Five measure and act frequency ratings for behaviors performed during the past month. Acts for specific traits cohered to an equivalent degree across cultures. In both cultures, the structure of act composites resembled the Big Five and the strength of trait-behavior relationships was very similar. Many acts were multidimensional and analyses revealed cultural commonalities and differences in the relevance and prevalence of acts for the Big Five traits. The results were more consistent with trait than cultural psychology perspectives, because traits predicted behavior equally well, on average, in the two cultures. PMID:19865595

A kernel regression approach to gene-gene interaction detection for case-control studies.

PubMed

Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

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.

Short communication: influence of composite casein genotypes on additive genetic variation of milk production traits and coagulation properties in Holstein-Friesian cows.

PubMed

Penasa, M; Cassandro, M; Pretto, D; De Marchi, M; Comin, A; Chessa, S; Dal Zotto, R; Bittante, G

2010-07-01

The aim of the study was to quantify the effects of composite beta- and kappa-casein (CN) genotypes on genetic variation of milk coagulation properties (MCP); milk yield; fat, protein, and CN contents; somatic cell score; pH; and titratable acidity (TA) in 1,042 Italian Holstein-Friesian cows. Milk coagulation properties were defined as rennet coagulation time (RCT) and curd firmness (a(30)). Variance components were estimated using 2 animal models: model 1 included herd, days in milk, and parity as fixed effects and animal and residual as random effects, and model 2 was model 1 with the addition of composite beta- and kappa-CN genotype as a fixed effect. Genetic correlations between RCT and a(30) and between these traits and milk production traits were obtained with bivariate analyses, based on the same models. The inclusion of casein genotypes led to a decrease of 47, 68, 18, and 23% in the genetic variance for RCT, a(30), pH, and TA, respectively, and less than 6% for other traits. Heritability of RCT and a(30) decreased from 0.248 to 0.143 and from 0.123 to 0.043, respectively. A moderate reduction was found for pH and TA, whereas negligible changes were detected for other milk traits. Estimates of genetic correlations were comparable between the 2 models. Results show that composite beta- and kappa-CN genotypes are important for RCT and a(30) but cannot replace the recording of MCP themselves. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Urbanization reduces and homogenizes trait diversity in stream macroinvertebrate communities.

PubMed

Barnum, Thomas R; Weller, Donald E; Williams, Meghan

2017-12-01

More than one-half of the world's population lives in urban areas, so quantifying the effects of urbanization on ecological communities is important for understanding whether anthropogenic stressors homogenize communities across environmental and climatic gradients. We examined the relationship of impervious surface coverage (a marker of urbanization) and the structure of stream macroinvertebrate communities across the state of Maryland and within each of Maryland's three ecoregions: Coastal Plain, Piedmont, and Appalachian, which differ in stream geomorphology and community composition. We considered three levels of trait organization: individual traits, unique combinations of traits, and community metrics (functional richness, functional evenness, and functional divergence) and three levels of impervious surface coverage (low [<2.5%], medium [2.5% to 10%], and high [>10%]). The prevalence of an individual trait differed very little between low impervious surface and high impervious surface sites. The arrangement of trait combinations in community trait space for each ecoregion differed when impervious surface coverage was low, but the arrangement became more similar among ecoregions as impervious surface coverage increased. Furthermore, trait combinations that occurred only at low or medium impervious surface coverage were clustered in a subset of the community trait space, indicating that impervious surface affected the presence of only a subset of trait combinations. Functional richness declined with increasing impervious surface, providing evidence for environmental filtering. Community metrics that include abundance were also sensitive to increasing impervious surface coverage: functional divergence decreased while functional evenness increased. These changes demonstrate that increasing impervious surface coverage homogenizes the trait diversity of macroinvertebrate communities in streams, despite differences in initial community composition and stream geomorphology among ecoregions. Community metrics were also more sensitive to changes in the abundance rather than the gain or loss of trait combinations, showing the potential for trait-based approaches to serve as early warning indicators of environmental stress for monitoring and biological assessment programs. © 2017 by the Ecological Society of America.

Multilevel assessment of fish species traits to evaluate habitat degradation in streams of the upper midwest

USGS Publications Warehouse

Goldstein, R.M.; Meador, M.R.

2005-01-01

We used species traits to examine the variation in fish assemblages for 21 streams in the Northern Lakes and Forests Ecoregion along a gradient of habitat disturbance. Fish species were classified based on five species trait-classes (trophic ecology, substrate preference, geomorphic preference, locomotion morphology, and reproductive strategy) and 29 categories within those classes. We used a habitat quality index to define a reference stream and then calculated Euclidean distances between the reference and each of the other sites for the five traits. Three levels of species trait analyses were conducted: (1) a composite measure (the sum of Euclidean distances across all five species traits), (2) Euclidean distances for the five individual species trait-classes, and (3) frequencies of occurrence of individual trait categories. The composite Euclidean distance was significantly correlated to the habitat index (r = -0.81; P = 0.001), as were the Euclidean distances for four of the five individual species traits (substrate preference: r = -0.70, P = 0.001; geomorphic preference: r = -0.69, P = 0.001; trophic ecology: r = -0.73, P = 0.001; and reproductive strategy: r = -0.64, P = 0.002). Although Euclidean distances for locomotion morphology were not significantly correlated to habitat index scores (r = -0.21; P = 0.368), analysis of variance and principal components analysis indicated that Euclidean distances for locomotion morphology contributed to significant variation in the fish assemblages among sites. Examination of trait categories indicated that low habitat index scores (degraded streams) were associated with changes in frequency of occurrence within the categories of all five of the species traits. Though the objectives and spatial scale of a study will dictate the level of species trait information required, our results suggest that species traits can provide critical information at multiple levels of data analysis. ?? Copyright by the American Fisheries Society 2005.

Phenotypic Microdiversity and Phylogenetic Signal Analysis of Traits Related to Social Interaction in Bacillus spp. from Sediment Communities.

PubMed

Rodríguez-Torres, María Dolores; Islas-Robles, África; Gómez-Lunar, Zulema; Delaye, Luis; Hernández-González, Ismael; Souza, Valeria; Travisano, Michael; Olmedo-Álvarez, Gabriela

2017-01-01

Understanding the relationship between phylogeny and predicted traits is important to uncover the dimension of the predictive power of a microbial composition approach. Numerous works have addressed the taxonomic composition of bacteria in communities, but little is known about trait heterogeneity in closely related bacteria that co-occur in communities. We evaluated a sample of 467 isolates from the Churince water system of the Cuatro Cienegas Basin (CCB), enriched for Bacillus spp. The 16S rRNA gene revealed a random distribution of taxonomic groups within this genus among 11 sampling sites. A subsample of 141 Bacillus spp. isolates from sediment, with seven well-represented species was chosen to evaluate the heterogeneity and the phylogenetic signal of phenotypic traits that are known to diverge within small clades, such as substrate utilization, and traits that are conserved deep in the lineage, such as prototrophy, swarming and biofilm formation. We were especially interested in evaluating social traits, such as swarming and biofilm formation, for which cooperation is needed to accomplish a multicellular behavior and for which there is little information from natural communities. The phylogenetic distribution of traits, evaluated by the Purvis and Fritz's D statistics approached a Brownian model of evolution. Analysis of the phylogenetic relatedness of the clusters of members sharing the trait using consenTRAIT algorithm, revealed more clustering and deeper phylogenetic signal for prototrophy, biofilm and swimming compared to the data obtained for substrate utilization. The explanation to the observed Brownian evolution of social traits could be either loss due to complete dispensability or to compensated trait loss due to the availability of public goods. Since many of the evaluated traits can be considered to be collective action traits, such as swarming, motility and biofilm formation, the observed microdiversity within taxonomic groups might be explained by distributed functions in structured communities.

Effects of breed of sire on carcass composition and sensory traits of lamb

USDA-ARS?s Scientific Manuscript database

This experiment was conducted to compare the meat quality and carcass composition of a diverse sampling of sheep breeds. Finnsheep, Romanov, Dorper, White Dorper, Katahdin, Rambouillet, Suffolk, Texel, Dorset, and Composite rams were mated to mature Composite ewes. Lambs (n = 804) were reared inte...

Plant traits and decomposition: are the relationships for roots comparable to those for leaves?

PubMed Central

Birouste, Marine; Kazakou, Elena; Blanchard, Alain; Roumet, Catherine

2012-01-01

Background and Aims Fine root decomposition is an important determinant of nutrient and carbon cycling in grasslands; however, little is known about the factors controlling root decomposition among species. Our aim was to investigate whether interspecific variation in the potential decomposition rate of fine roots could be accounted for by root chemical and morphological traits, life history and taxonomic affiliation. We also investigated the co-ordinated variation in root and leaf traits and potential decomposition rates. Methods We analysed potential decomposition rates and the chemical and morphological traits of fine roots on 18 Mediterranean herbaceous species grown in controlled conditions. The results were compared with those obtained for leaves in a previous study conducted on similar species. Key Results Differences in the potential decomposition rates of fine roots between species were accounted for by root chemical composition, but not by morphological traits. The root potential decomposition rate varied with taxonomy, but not with life history. Poaceae, with high cellulose concentration and low concentrations of soluble compounds and phosphorus, decomposed more slowly than Asteraceae and Fabaceae. Patterns of root traits, including decomposition rate, mirrored those of leaf traits, resulting in a similar species clustering. Conclusions The highly co-ordinated variation of roots and leaves in terms of traits and potential decomposition rate suggests that changes in the functional composition of communities in response to anthropogenic changes will strongly affect biogeochemical cycles at the ecosystem level. PMID:22143881

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.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

7 CFR 981.7 - Edible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

Kernel K-Means Sampling for Nyström Approximation.

PubMed

He, Li; Zhang, Hong

2018-05-01

A fundamental problem in Nyström-based kernel matrix approximation is the sampling method by which training set is built. In this paper, we suggest to use kernel -means sampling, which is shown in our works to minimize the upper bound of a matrix approximation error. We first propose a unified kernel matrix approximation framework, which is able to describe most existing Nyström approximations under many popular kernels, including Gaussian kernel and polynomial kernel. We then show that, the matrix approximation error upper bound, in terms of the Frobenius norm, is equal to the -means error of data points in kernel space plus a constant. Thus, the -means centers of data in kernel space, or the kernel -means centers, are the optimal representative points with respect to the Frobenius norm error upper bound. Experimental results, with both Gaussian kernel and polynomial kernel, on real-world data sets and image segmentation tasks show the superiority of the proposed method over the state-of-the-art methods.

The crack problem in a reinforced cylindrical shell

NASA Technical Reports Server (NTRS)

Yahsi, O. S.; Erdogan, F.

1986-01-01

In this paper a partially reinforced cylinder containing an axial through crack is considered. The reinforcement is assumed to be fully bonded to the main cylinder. The composite cylinder is thus modelled by a nonhomogeneous shell having a step change in the elastic properties at the z=0 plane, z being the axial coordinate. Using a Reissner type transverse shear theory the problem is reduced to a pair of singular integral equations. In the special case of a crack tip touching the bimaterial interface it is shown that the dominant parts of the kernels of the integral equations associated with both membrane loading and bending of the shell reduce to the generalized Cauchy kernel obtained for the corresponding plane stress case. The integral equations are solved and the stress intensity factors are given for various crack and shell dimensions. A bonded fiberglass reinforcement which may serve as a crack arrestor is used as an example.

The crack problem in a reinforced cylindrical shell

NASA Technical Reports Server (NTRS)

Yahsi, O. S.; Erdogan, F.

1986-01-01

A partially reinforced cylinder containing an axial through crack is considered. The reinforcement is assumed to be fully bonded to the main cylinder. The composite cylinder is thus modelled by a nonhomogeneous shell having a step change in the elastic properties at the z = 0 plane, z being the axial coordinate. Using a Reissner type transverse shear theory the problem is reduced to a pair of singular integral equations. In the special case of a crack tip touching the bimaterial interface it is shown that the dominant parts of the kernels of the integral equations associated with both membrane loading and bending of the shell reduce to the generalized Cauchy kernel obtained for the corresponding plane stress case. The integral equations are solved and the stress intensity factors are given for various crack and shell dimensions. A bonded fiberglass reinforcement which may serve as a crack arrestor is used as an example.

Accumulation of primary and secondary metabolites in edible jackfruit seed tissues and scavenging of reactive nitrogen species.

PubMed

Fernandes, Fátima; Ferreres, Federico; Gil-Izquierdo, Angel; Oliveira, Andreia P; Valentão, Patrícia; Andrade, Paula B

2017-10-15

Studies involving jackfruit tree (Artocarpus heterophyllus Lam.) focus on its fruit. Nevertheless a considerable part of jackfruit weight is represented by its seeds. Despite being consumed in several countries, knowledge about the chemical composition of these seeds is scarce. In this work, the accumulation of primary and secondary metabolites in jackfruit seed kernel and seed coating membrane was studied. Sixty-seven compounds were identified, sixty of them being reported for the first time in jackfruit seed. Both tissues had a similar qualitative profile, but significant quantitative differences were found. The capacity of aqueous extracts from jackfruit seed kernel and seed coating membranes to scavenge nitric oxide radical was also evaluated for the first time, the extract prepared from the seed coating membrane being the most potent. This work increases the potential revenue from a food that is still largely wasted. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

The Role of Individual Traits and Environmental Factors for Diet Composition of Sheep

PubMed Central

Mysterud, Atle; Austrheim, Gunnar

2016-01-01

Large herbivore consumption of forage is known to affect vegetation composition and thereby ecosystem functions. It is thus important to understand how diet composition arises as a mixture of individual variation in preferences and environmental drivers of availability, but few studies have quantified both. Based on 10 years of data on diet composition by aid of microhistological analysis for sheep kept at high and low population density, we analysed how both individual traits (sex, age, body mass, litter size) linked to preference and environmental variation (density, climate proxies) linked to forage availability affected proportional intake of herbs (high quality/low availability) and Avenella flexuosa (lower quality/high availability). Environmental factors affecting current forage availability such as population density and seasonal and annual variation in diet had the most marked impact on diet composition. Previous environment of sheep (switch between high and low population density) had no impact on diet, suggesting a comparably minor role of learning for density dependent diet selection. For individual traits, only the difference between lambs and ewes affected proportion of A. flexuosa, while body mass better predicted proportion of herbs in diet. Neither sex, body mass, litter size, ewe age nor mass of ewe affected diet composition of lambs, and there was no effect of age, body mass or litter size on diet composition of ewes. Our study highlights that diet composition arises from a combination of preferences being predicted by lamb and ewes’ age and/or body mass differences, and the immediate environment in terms of population density and proxies for vegetation development. PMID:26731411

Phenotypic integration among trabecular and cortical bone traits establishes mechanical functionality of inbred mouse vertebrae.

PubMed

Tommasini, Steven M; Hu, Bin; Nadeau, Joseph H; Jepsen, Karl J

2009-04-01

Conventional approaches to identifying quantitative trait loci (QTLs) regulating bone mass and fragility are limited because they examine cortical and trabecular traits independently. Prior work examining long bones from young adult mice and humans indicated that skeletal traits are functionally related and that compensatory interactions among morphological and compositional traits are critical for establishing mechanical function. However, it is not known whether trait covariation (i.e., phenotypic integration) also is important for establishing mechanical function in more complex, corticocancellous structures. Covariation among trabecular, cortical, and compositional bone traits was examined in the context of mechanical functionality for L(4) vertebral bodies across a panel of 16-wk-old female AXB/BXA recombinant inbred (RI) mouse strains. The unique pattern of randomization of the A/J and C57BL/6J (B6) genome among the RI panel provides a powerful tool that can be used to measure the tendency for different traits to covary and to study the biology of complex traits. We tested the hypothesis that genetic variants affecting vertebral size and mass are buffered by changes in the relative amounts of cortical and trabecular bone and overall mineralization. Despite inheriting random sets of A/J and B6 genomes, the RI strains inherited nonrandom sets of cortical and trabecular bone traits. Path analysis, which is a multivariate analysis that shows how multiple traits covary simultaneously when confounding variables like body size are taken into consideration, showed that RI strains that tended to have smaller vertebrae relative to body size achieved mechanical functionality by increasing mineralization and the relative amounts of cortical and trabecular bone. The interdependence among corticocancellous traits in the vertebral body indicated that variation in trabecular bone traits among inbred mouse strains, which is often thought to arise from genetic factors, is also determined in part by the adaptive response to variation in traits describing the cortical shell. The covariation among corticocancellous traits has important implications for genetic analyses and for interpreting the response of bone to genetic and environmental perturbations.

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.

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.

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.

A phylogenetic Kalman filter for ancestral trait reconstruction using molecular data.

PubMed

Lartillot, Nicolas

2014-02-15

Correlation between life history or ecological traits and genomic features such as nucleotide or amino acid composition can be used for reconstructing the evolutionary history of the traits of interest along phylogenies. Thus far, however, such ancestral reconstructions have been done using simple linear regression approaches that do not account for phylogenetic inertia. These reconstructions could instead be seen as a genuine comparative regression problem, such as formalized by classical generalized least-square comparative methods, in which the trait of interest and the molecular predictor are represented as correlated Brownian characters coevolving along the phylogeny. Here, a Bayesian sampler is introduced, representing an alternative and more efficient algorithmic solution to this comparative regression problem, compared with currently existing generalized least-square approaches. Technically, ancestral trait reconstruction based on a molecular predictor is shown to be formally equivalent to a phylogenetic Kalman filter problem, for which backward and forward recursions are developed and implemented in the context of a Markov chain Monte Carlo sampler. The comparative regression method results in more accurate reconstructions and a more faithful representation of uncertainty, compared with simple linear regression. Application to the reconstruction of the evolution of optimal growth temperature in Archaea, using GC composition in ribosomal RNA stems and amino acid composition of a sample of protein-coding genes, confirms previous findings, in particular, pointing to a hyperthermophilic ancestor for the kingdom. The program is freely available at www.phylobayes.org.

Within- and Trans-Generational Effects of Variation in Dietary Macronutrient Content on Life-History Traits in the Moth Plodia interpunctella.

PubMed

Littlefair, Joanne E; Knell, Robert J

2016-01-01

It is increasingly clear that parental environment can play an important role in determining offspring phenotype. These "transgenerational effects" have been linked to many different components of the environment, including toxin exposure, infection with pathogens and parasites, temperature and food quality. In this study, we focus on the latter, asking how variation in the quantity and quality of nutrition affects future generations. Previous studies have shown that artificial diets are a useful tool to examine the within-generation effects of variation in macronutrient content on life history traits, and could therefore be applied to investigations of the transgenerational effects of parental diet. Synthetic diets varying in total macronutrient content and protein: carbohydrate ratios were used to examine both within- and trans-generational effects on life history traits in a generalist stored product pest, the Indian meal moth Plodia interpunctella. The macronutrient composition of the diet was important for shaping within-generation life history traits, including pupal weight, adult weight, and phenoloxidase activity, and had indirect effects via maternal weight on fecundity. Despite these clear within-generation effects on the biology of P. interpunctella, diet composition had no transgenerational effects on the life history traits of offspring. P. interpunctella mothers were able to maintain their offspring quality, possibly at the expense of their own somatic condition, despite high variation in dietary macronutrient composition. This has important implications for the plastic biology of this successful generalist pest.

Within- and Trans-Generational Effects of Variation in Dietary Macronutrient Content on Life-History Traits in the Moth Plodia interpunctella

PubMed Central

Knell, Robert J.

2016-01-01

It is increasingly clear that parental environment can play an important role in determining offspring phenotype. These “transgenerational effects” have been linked to many different components of the environment, including toxin exposure, infection with pathogens and parasites, temperature and food quality. In this study, we focus on the latter, asking how variation in the quantity and quality of nutrition affects future generations. Previous studies have shown that artificial diets are a useful tool to examine the within-generation effects of variation in macronutrient content on life history traits, and could therefore be applied to investigations of the transgenerational effects of parental diet. Synthetic diets varying in total macronutrient content and protein: carbohydrate ratios were used to examine both within- and trans-generational effects on life history traits in a generalist stored product pest, the Indian meal moth Plodia interpunctella. The macronutrient composition of the diet was important for shaping within-generation life history traits, including pupal weight, adult weight, and phenoloxidase activity, and had indirect effects via maternal weight on fecundity. Despite these clear within-generation effects on the biology of P. interpunctella, diet composition had no transgenerational effects on the life history traits of offspring. P. interpunctella mothers were able to maintain their offspring quality, possibly at the expense of their own somatic condition, despite high variation in dietary macronutrient composition. This has important implications for the plastic biology of this successful generalist pest. PMID:28033396

Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein.

PubMed

Gebreyesus, G; Lund, M S; Janss, L; Poulsen, N A; Larsen, L B; Bovenhuis, H; Buitenhuis, A J

2016-04-01

Genetic parameters were estimated for the major milk proteins using bivariate and multi-trait models based on genomic relationships between animals. The analyses included, apart from total protein percentage, αS1-casein (CN), αS2-CN, β-CN, κ-CN, α-lactalbumin, and β-lactoglobulin, as well as the posttranslational sub-forms of glycosylated κ-CN and αS1-CN-8P (phosphorylated). Standard errors of the estimates were used to compare the models. In total, 650 Danish Holstein cows across 4 parities and days in milk ranging from 9 to 481d were selected from 21 herds. The multi-trait model generally resulted in lower standard errors of heritability estimates, suggesting that genetic parameters can be estimated with high accuracy using multi-trait analyses with genomic relationships for scarcely recorded traits. The heritability estimates from the multi-trait model ranged from low (0.05 for β-CN) to high (0.78 for κ-CN). Genetic correlations between the milk proteins and the total milk protein percentage were generally low, suggesting the possibility to alter protein composition through selective breeding with little effect on total milk protein percentage. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem.

PubMed

Dahlin, Kyla M; Asner, Gregory P; Field, Christopher B

2013-04-23

Understanding how and why plant communities vary across space has long been a goal of ecology, yet parsing the relative importance of different influences has remained a challenge. Species-specific models are not generalizable, whereas broad plant functional type models lack important detail. Here we consider plant trait patterns at the local scale and ask whether plant chemical traits are more closely linked to environmental gradients or to changes in species composition. We used the visible-to-shortwave infrared (VSWIR) spectrometer of the Carnegie Airborne Observatory to develop maps of four plant chemical traits--leaf nitrogen per mass, leaf carbon per mass, leaf water concentration, and canopy water content--across a diverse Mediterranean-type ecosystem (Jasper Ridge Biological Preserve, CA). For all four traits, plant community alone was the strongest predictor of trait variation (explaining 46-61% of the heterogeneity), whereas environmental gradients accounted for just one fourth of the variation in the traits. This result emphasizes the critical role that species composition plays in mediating nutrient and carbon cycling within and among different communities. Environmental filtering and limits to similarity can act strongly, simultaneously, in a spatially heterogeneous environment, but the local-scale environmental gradients alone cannot account for the variation across this landscape.

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.

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.

Adaptive evolution of seed oil content in angiosperms: accounting for the global patterns of seed oils.

PubMed

Sanyal, Anushree; Decocq, Guillaume

2016-09-09

Studies of the biogeographic distribution of seed oil content in plants are fundamental to understanding the mechanisms of adaptive evolution in plants as seed oil is the primary energy source needed for germination and establishment of plants. However, seed oil content as an adaptive trait in plants is poorly understood. Here, we examine the adaptive nature of seed oil content in 168 angiosperm families occurring in different biomes across the world. We also explore the role of multiple seed traits like seed oil content and composition in plant adaptation in a phylogenetic and nonphylogenetic context. It was observed that the seed oil content in tropical plants (28.4 %) was significantly higher than the temperate plants (24.6 %). A significant relationship between oil content and latitude was observed in three families Papaveraceae, Sapindaceae and Sapotaceae indicating that selective forces correlated with latitude influence seed oil content. Evaluation of the response of seed oil content and composition to latitude and the correlation between seed oil content and composition showed that multiple seed traits, seed oil content and composition contribute towards plant adaptation. Investigation of the presence or absence of phylogenetic signals across 168 angiosperm families in 62 clades revealed that members of seven clades evolved to have high or low seed oil content independently as they did not share a common evolutionary path. The study provides us an insight into the biogeographical distribution and the adaptive role of seed oil content in plants. The study indicates that multiple seed traits like seed oil content and the fatty acid composition of the seed oils determine the fitness of the plants and validate the adaptive hypothesis that seed oil quantity and quality are crucial to plant adaptation.

The Human Microbiome and the Missing Heritability Problem

PubMed Central

Sandoval-Motta, Santiago; Aldana, Maximino; Martínez-Romero, Esperanza; Frank, Alejandro

2017-01-01

The “missing heritability” problem states that genetic variants in Genome-Wide Association Studies (GWAS) cannot completely explain the heritability of complex traits. Traditionally, the heritability of a phenotype is measured through familial studies using twins, siblings and other close relatives, making assumptions on the genetic similarities between them. When this heritability is compared to the one obtained through GWAS for the same traits, a substantial gap between both measurements arise with genome wide studies reporting significantly smaller values. Several mechanisms for this “missing heritability” have been proposed, such as epigenetics, epistasis, and sequencing depth. However, none of them are able to fully account for this gap in heritability. In this paper we provide evidence that suggests that in order for the phenotypic heritability of human traits to be broadly understood and accounted for, the compositional and functional diversity of the human microbiome must be taken into account. This hypothesis is based on several observations: (A) The composition of the human microbiome is associated with many important traits, including obesity, cancer, and neurological disorders. (B) Our microbiome encodes a second genome with nearly a 100 times more genes than the human genome, and this second genome may act as a rich source of genetic variation and phenotypic plasticity. (C) Human genotypes interact with the composition and structure of our microbiome, but cannot by themselves explain microbial variation. (D) Microbial genetic composition can be strongly influenced by the host's behavior, its environment or by vertical and horizontal transmissions from other hosts. Therefore, genetic similarities assumed in familial studies may cause overestimations of heritability values. We also propose a method that allows the compositional and functional diversity of our microbiome to be incorporated to genome wide association studies. PMID:28659968

Trait-specific responses of wild bee communities to landscape composition, configuration and local factors.

PubMed

Hopfenmüller, Sebastian; Steffan-Dewenter, Ingolf; Holzschuh, Andrea

2014-01-01

Land-use intensification and loss of semi-natural habitats have induced a severe decline of bee diversity in agricultural landscapes. Semi-natural habitats like calcareous grasslands are among the most important bee habitats in central Europe, but they are threatened by decreasing habitat area and quality, and by homogenization of the surrounding landscape affecting both landscape composition and configuration. In this study we tested the importance of habitat area, quality and connectivity as well as landscape composition and configuration on wild bees in calcareous grasslands. We made detailed trait-specific analyses as bees with different traits might differ in their response to the tested factors. Species richness and abundance of wild bees were surveyed on 23 calcareous grassland patches in Southern Germany with independent gradients in local and landscape factors. Total wild bee richness was positively affected by complex landscape configuration, large habitat area and high habitat quality (i.e. steep slopes). Cuckoo bee richness was positively affected by complex landscape configuration and large habitat area whereas habitat specialists were only affected by the local factors habitat area and habitat quality. Small social generalists were positively influenced by habitat area whereas large social generalists (bumblebees) were positively affected by landscape composition (high percentage of semi-natural habitats). Our results emphasize a strong dependence of habitat specialists on local habitat characteristics, whereas cuckoo bees and bumblebees are more likely affected by the surrounding landscape. We conclude that a combination of large high-quality patches and heterogeneous landscapes maintains high bee species richness and communities with diverse trait composition. Such diverse communities might stabilize pollination services provided to crops and wild plants on local and landscape scales.

Trait-Specific Responses of Wild Bee Communities to Landscape Composition, Configuration and Local Factors

PubMed Central

Hopfenmüller, Sebastian; Steffan-Dewenter, Ingolf; Holzschuh, Andrea

2014-01-01

Land-use intensification and loss of semi-natural habitats have induced a severe decline of bee diversity in agricultural landscapes. Semi-natural habitats like calcareous grasslands are among the most important bee habitats in central Europe, but they are threatened by decreasing habitat area and quality, and by homogenization of the surrounding landscape affecting both landscape composition and configuration. In this study we tested the importance of habitat area, quality and connectivity as well as landscape composition and configuration on wild bees in calcareous grasslands. We made detailed trait-specific analyses as bees with different traits might differ in their response to the tested factors. Species richness and abundance of wild bees were surveyed on 23 calcareous grassland patches in Southern Germany with independent gradients in local and landscape factors. Total wild bee richness was positively affected by complex landscape configuration, large habitat area and high habitat quality (i.e. steep slopes). Cuckoo bee richness was positively affected by complex landscape configuration and large habitat area whereas habitat specialists were only affected by the local factors habitat area and habitat quality. Small social generalists were positively influenced by habitat area whereas large social generalists (bumblebees) were positively affected by landscape composition (high percentage of semi-natural habitats). Our results emphasize a strong dependence of habitat specialists on local habitat characteristics, whereas cuckoo bees and bumblebees are more likely affected by the surrounding landscape. We conclude that a combination of large high-quality patches and heterogeneous landscapes maintains high bee species richness and communities with diverse trait composition. Such diverse communities might stabilize pollination services provided to crops and wild plants on local and landscape scales. PMID:25137311

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.

Phenomic approaches and tools for phytopathologists

USDA-ARS?s Scientific Manuscript database

Plant phenomics approaches aim to evaluate traits such as growth, performance, and composition of plants using a suite of non-invasive technologies. The ultimate goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and ge...

A random walk description of individual animal movement accounting for periods of rest

NASA Astrophysics Data System (ADS)

Tilles, Paulo F. C.; Petrovskii, Sergei V.; Natti, Paulo L.

2016-11-01

Animals do not move all the time but alternate the period of actual movement (foraging) with periods of rest (e.g. eating or sleeping). Although the existence of rest times is widely acknowledged in the literature and has even become a focus of increased attention recently, the theoretical approaches to describe animal movement by calculating the dispersal kernel and/or the mean squared displacement (MSD) rarely take rests into account. In this study, we aim to bridge this gap. We consider a composite stochastic process where the periods of active dispersal or `bouts' (described by a certain baseline probability density function (pdf) of animal dispersal) alternate with periods of immobility. For this process, we derive a general equation that determines the pdf of this composite movement. The equation is analysed in detail in two special but important cases such as the standard Brownian motion described by a Gaussian kernel and the Levy flight described by a Cauchy distribution. For the Brownian motion, we show that in the large-time asymptotics the effect of rests results in a rescaling of the diffusion coefficient. The movement occurs as a subdiffusive transition between the two diffusive asymptotics. Interestingly, the Levy flight case shows similar properties, which indicates a certain universality of our findings.

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

PubMed

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

2016-01-01

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

A random walk description of individual animal movement accounting for periods of rest.

PubMed

Tilles, Paulo F C; Petrovskii, Sergei V; Natti, Paulo L

2016-11-01

Animals do not move all the time but alternate the period of actual movement (foraging) with periods of rest (e.g. eating or sleeping). Although the existence of rest times is widely acknowledged in the literature and has even become a focus of increased attention recently, the theoretical approaches to describe animal movement by calculating the dispersal kernel and/or the mean squared displacement (MSD) rarely take rests into account. In this study, we aim to bridge this gap. We consider a composite stochastic process where the periods of active dispersal or 'bouts' (described by a certain baseline probability density function (pdf) of animal dispersal) alternate with periods of immobility. For this process, we derive a general equation that determines the pdf of this composite movement. The equation is analysed in detail in two special but important cases such as the standard Brownian motion described by a Gaussian kernel and the Levy flight described by a Cauchy distribution. For the Brownian motion, we show that in the large-time asymptotics the effect of rests results in a rescaling of the diffusion coefficient. The movement occurs as a subdiffusive transition between the two diffusive asymptotics. Interestingly, the Levy flight case shows similar properties, which indicates a certain universality of our findings.

A random walk description of individual animal movement accounting for periods of rest

PubMed Central

Tilles, Paulo F. C.

2016-01-01

Animals do not move all the time but alternate the period of actual movement (foraging) with periods of rest (e.g. eating or sleeping). Although the existence of rest times is widely acknowledged in the literature and has even become a focus of increased attention recently, the theoretical approaches to describe animal movement by calculating the dispersal kernel and/or the mean squared displacement (MSD) rarely take rests into account. In this study, we aim to bridge this gap. We consider a composite stochastic process where the periods of active dispersal or ‘bouts’ (described by a certain baseline probability density function (pdf) of animal dispersal) alternate with periods of immobility. For this process, we derive a general equation that determines the pdf of this composite movement. The equation is analysed in detail in two special but important cases such as the standard Brownian motion described by a Gaussian kernel and the Levy flight described by a Cauchy distribution. For the Brownian motion, we show that in the large-time asymptotics the effect of rests results in a rescaling of the diffusion coefficient. The movement occurs as a subdiffusive transition between the two diffusive asymptotics. Interestingly, the Levy flight case shows similar properties, which indicates a certain universality of our findings. PMID:28018645

Physical modification of palm kernel meal improved available carbohydrate, physicochemical properties and in vitro digestibility in economic freshwater fish.

PubMed

Thongprajukaew, Karun; Yawang, Pinya; Dudae, Lateepah; Bilanglod, Husna; Dumrongrittamatt, Terdtoon; Tantikitti, Chutima; Kovitvadhi, Uthaiwan

2013-12-01

Unavailable carbohydrates are an important limiting factor for utilization of palm kernel meal (PKM) as aquafeed ingredients. The aim of this study was to improve available carbohydrate from PKM. Different physical modifications including water soaking, microwave irradiation, gamma irradiation and electron beam, were investigated in relation to chemical composition, physicochemical properties and in vitro carbohydrate digestibility using digestive enzymes from economic freshwater fish. Modified methods had significant (P < 0.05) effects on chemical composition by decreasing crude fiber and increasing available carbohydrates. Improvements in physicochemical properties of PKM, such as water solubility, microstructure, relative crystallinity and lignocellulosic spectra, were mainly achieved by soaking and microwave irradiation. Carbohydrate digestibility varied among the physical modifications tested (P < 0.05) and three fish species had different abilities to digest PKM. Soaking was the appropriate modification for increasing carbohydrate digestion specifically in Nile tilapia (Oreochromis niloticus), whereas either soaking or microwave irradiation was effective for striped snakehead (Channa striata). For walking catfish (Clarias batrachus), carbohydrate digestibility was similar among raw, soaked and microwave-irradiated PKM. These findings suggest that soaking and microwave irradiation could be practical methods for altering appropriate physicochemical properties of PKM as well as increasing carbohydrate digestibility in select economic freshwater fish. © 2013 Society of Chemical Industry.

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.

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.

A genetic linkage map of the Durum x Triticum dicoccoides backcross population based on SSRs and AFLP markers, and QTL analysis for milling traits.

PubMed

Elouafi, I; Nachit, M M

2004-02-01

Durum wheat ( Triticum turgidum L. var durum) is mainly produced and consumed in the Mediterranean region; it is used to produce several specific end-products; such as local pasta, couscous and burghul. To study the genetics of grain-milling quality traits, chromosomal locations, and interaction with the environment, a genetic linkage map of durum was constructed and the quantitative trait loci QTLs for the milling-related traits, test weight (TW) and thousand-kernel weight (TKW), were identified. The population constituted 114 recombinant inbred lines derived from the cross: Omrabi 5 /Triticum dicoccoides 600545// Omrabi 5. TW and TKW were analyzed over 18 environments (sites x years). Single-sequence-repeat markers (SSRs), Amplified-fragment-length-polymorphism markers (AFLPs), and seed storage proteins (SSPs) showed a high level of polymorphism (>60%). The map was constructed with 124 SSRs, 149 AFLPs and 6 SSPs; its length covered 2,288.8 cM (8.2 cM/marker). The map showed high synteny with previous wheat maps, and both SSRs and AFLPs mapped evenly across the genome, with more markers in the B genome. However, some rearrangements were observed. For TW, a high genotypic effect was detected and two QTLs with epistasic effect were identified on 7AS and 6BS, explaining 30% of the total variation. The TKW showed a significant transgressive inheritance and five QTLs were identified, explaining 32% of the total variation, out of which 25% was of a genetic nature, and showing QTLxE interaction. The major TKW-QTLs were around the centromere region of 6B. For both traits, Omrabi 5 alleles had a significant positive effect. This population will be used to determine other QTLs of interest, as its parents are likely to harbor different genes for diseases and drought tolerance.

Effects of incorporating agro-industrial by-products into diet of New Zealand rabbits: Case of rebus of date and apricot kernel meal.

PubMed

Mennani, Achour; Arbouche, Rafik; Arbouche, Yasmine; Montaigne, Etienne; Arbouche, Fodil; Arbouche, Halima Saâdia

2017-12-01

The aim of this study was to determine the effects of incorporating the by-products complex of date and apricot on the fattening performance of the New Zealand breed of rabbits, to reduce the economic costs of the food formula. A total of 288 young New Zealand rabbits aged 35 days were divided into four equal groups each containing 72 animals and into sub-groups of 6 rabbits per cage, depending on the rate of substitution of corn by date rebus and of soybean meal by apricot kernel meal (0%, 10%, 20%, and 30%). The change in weight from day 35 to 77 and the average daily gain are not significantly different, regardless of the diet. The pH and water content are proportional to the substitution rates (6.4-6.6% and 66.5-68.8%). Meat protein levels increased significantly, in particular for the 10% and 30% groups (+8.1% and 6%) while the fat and mineral content levels decreased significantly, in particular for the 30% group displaying -16% and -17%, respectively. Incorporation of dates and apricot kernel meal into the ration of rabbits reduces the cost of the kilogram of food produced of -9%, with an opportunity cost of 165 Algerian dinars (DZD). The date rebus/apricot kernel meal complex can be used as an alternative to the corn/soybean meal complex at substitution rates of up to 30% without adverse effects on growth rates, feed contribution, or slaughter yield. It improves the chemical composition of the meat and reduces the cost price of the quintal of feed produced.

Effects of incorporating agro-industrial by-products into diet of New Zealand rabbits: Case of rebus of date and apricot kernel meal

PubMed Central

Mennani, Achour; Arbouche, Rafik; Arbouche, Yasmine; Montaigne, Etienne; Arbouche, Fodil; Arbouche, Halima Saâdia

2017-01-01

Aim: The aim of this study was to determine the effects of incorporating the by-products complex of date and apricot on the fattening performance of the New Zealand breed of rabbits, to reduce the economic costs of the food formula. Materials and Methods: A total of 288 young New Zealand rabbits aged 35 days were divided into four equal groups each containing 72 animals and into sub-groups of 6 rabbits per cage, depending on the rate of substitution of corn by date rebus and of soybean meal by apricot kernel meal (0%, 10%, 20%, and 30%). Results: The change in weight from day 35 to 77 and the average daily gain are not significantly different, regardless of the diet. The pH and water content are proportional to the substitution rates (6.4-6.6% and 66.5-68.8%). Meat protein levels increased significantly, in particular for the 10% and 30% groups (+8.1% and 6%) while the fat and mineral content levels decreased significantly, in particular for the 30% group displaying −16% and −17%, respectively. Incorporation of dates and apricot kernel meal into the ration of rabbits reduces the cost of the kilogram of food produced of −9%, with an opportunity cost of 165 Algerian dinars (DZD). Conclusion: The date rebus/apricot kernel meal complex can be used as an alternative to the corn/soybean meal complex at substitution rates of up to 30% without adverse effects on growth rates, feed contribution, or slaughter yield. It improves the chemical composition of the meat and reduces the cost price of the quintal of feed produced. PMID:29391686

A point kernel algorithm for microbeam radiation therapy

NASA Astrophysics Data System (ADS)

Debus, Charlotte; Oelfke, Uwe; Bartzsch, Stefan

2017-11-01

Microbeam radiation therapy (MRT) is a treatment approach in radiation therapy where the treatment field is spatially fractionated into arrays of a few tens of micrometre wide planar beams of unusually high peak doses separated by low dose regions of several hundred micrometre width. In preclinical studies, this treatment approach has proven to spare normal tissue more effectively than conventional radiation therapy, while being equally efficient in tumour control. So far dose calculations in MRT, a prerequisite for future clinical applications are based on Monte Carlo simulations. However, they are computationally expensive, since scoring volumes have to be small. In this article a kernel based dose calculation algorithm is presented that splits the calculation into photon and electron mediated energy transport, and performs the calculation of peak and valley doses in typical MRT treatment fields within a few minutes. Kernels are analytically calculated depending on the energy spectrum and material composition. In various homogeneous materials peak, valley doses and microbeam profiles are calculated and compared to Monte Carlo simulations. For a microbeam exposure of an anthropomorphic head phantom calculated dose values are compared to measurements and Monte Carlo calculations. Except for regions close to material interfaces calculated peak dose values match Monte Carlo results within 4% and valley dose values within 8% deviation. No significant differences are observed between profiles calculated by the kernel algorithm and Monte Carlo simulations. Measurements in the head phantom agree within 4% in the peak and within 10% in the valley region. The presented algorithm is attached to the treatment planning platform VIRTUOS. It was and is used for dose calculations in preclinical and pet-clinical trials at the biomedical beamline ID17 of the European synchrotron radiation facility in Grenoble, France.

Extensions of Island Biogeography Theory predict the scaling of functional trait composition with habitat area and isolation.

PubMed

Jacquet, Claire; Mouillot, David; Kulbicki, Michel; Gravel, Dominique

2017-02-01

The Theory of Island Biogeography (TIB) predicts how area and isolation influence species richness equilibrium on insular habitats. However, the TIB remains silent about functional trait composition and provides no information on the scaling of functional diversity with area, an observation that is now documented in many systems. To fill this gap, we develop a probabilistic approach to predict the distribution of a trait as a function of habitat area and isolation, extending the TIB beyond the traditional species-area relationship. We compare model predictions to the body-size distribution of piscivorous and herbivorous fishes found on tropical reefs worldwide. We find that small and isolated reefs have a higher proportion of large-sized species than large and connected reefs. We also find that knowledge of species body-size and trophic position improves the predictions of fish occupancy on tropical reefs, supporting both the allometric and trophic theory of island biogeography. The integration of functional ecology to island biogeography is broadly applicable to any functional traits and provides a general probabilistic approach to study the scaling of trait distribution with habitat area and isolation. © 2016 John Wiley & Sons Ltd/CNRS.

Functional biodiversity of marine soft-sediment polychaetes from two Mediterranean coastal areas in relation to environmental stress.

PubMed

Nasi, F; Nordström, M C; Bonsdorff, E; Auriemma, R; Cibic, T; Del Negro, P

2018-06-01

Biological Traits Analysis (BTA) was used to identify functional features of infaunal polychaete assemblages associated with contamination in two Italian coastal areas: the harbour of Trieste (Adriatic Sea) and the Mar Piccolo of Taranto (Ionian Sea). The analysis was performed on 103 taxa, collected at four stations in each area. The two areas differed in species composition. The low diversity and the presence of stress-tolerant species in more polluted sites were not reflected in functional diversity, due to species contributing little to community functions or being functionally redundant. Sand and clay fractions were significant drivers of trait category expressions, however other environmental parameters (depth, total organic carbon and nitrogen, and Hg in sediments) influenced traits composition. Motile was the prevalent trait in environments with coarse sediments, and tube-builder were related to fine-grained ones. Motile, endobenthic and burrower were essential traits for living in contaminated sediments. Epibenthic and sessile polychaetes dominated at stations subjected to high organic loads. BTA offers an integrative approach to detect functional adaptations to contaminated sediments and multiple anthropogenic stressors. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Role of Species Traits in Mediating Functional Recovery during Matrix Restoration

PubMed Central

Barnes, Andrew D.; Emberson, Rowan M.; Krell, Frank-Thorsten; Didham, Raphael K.

2014-01-01

Reversing anthropogenic impacts on habitat structure is frequently successful through restoration, but the mechanisms linking habitat change, community reassembly and recovery of ecosystem functioning remain unknown. We test for the influence of edge effects and matrix habitat restoration on the reassembly of dung beetle communities and consequent recovery of dung removal rates across tropical forest edges. Using path modelling, we disentangle the relative importance of community-weighted trait means and functional trait dispersion from total biomass effects on rates of dung removal. Community trait composition and biomass of dung beetle communities responded divergently to edge effects and matrix habitat restoration, yielding opposing effects on dung removal. However, functional dispersion—used in this study as a measure of niche complementarity—did not explain a significant amount of variation in dung removal rates across habitat edges. Instead, we demonstrate that the path to functional recovery of these altered ecosystems depends on the trait-mean composition of reassembling communities, over and above purely biomass-dependent processes that would be expected under neutral theory. These results suggest that any ability to manage functional recovery of ecosystems during habitat restoration will demand knowledge of species' roles in ecosystem processes. PMID:25502448

The role of species traits in mediating functional recovery during matrix restoration.

PubMed

Barnes, Andrew D; Emberson, Rowan M; Krell, Frank-Thorsten; Didham, Raphael K

2014-01-01

Reversing anthropogenic impacts on habitat structure is frequently successful through restoration, but the mechanisms linking habitat change, community reassembly and recovery of ecosystem functioning remain unknown. We test for the influence of edge effects and matrix habitat restoration on the reassembly of dung beetle communities and consequent recovery of dung removal rates across tropical forest edges. Using path modelling, we disentangle the relative importance of community-weighted trait means and functional trait dispersion from total biomass effects on rates of dung removal. Community trait composition and biomass of dung beetle communities responded divergently to edge effects and matrix habitat restoration, yielding opposing effects on dung removal. However, functional dispersion--used in this study as a measure of niche complementarity--did not explain a significant amount of variation in dung removal rates across habitat edges. Instead, we demonstrate that the path to functional recovery of these altered ecosystems depends on the trait-mean composition of reassembling communities, over and above purely biomass-dependent processes that would be expected under neutral theory. These results suggest that any ability to manage functional recovery of ecosystems during habitat restoration will demand knowledge of species' roles in ecosystem processes.

Genomic Studies in Soybean: Toward Understanding Seed Oil and Protein Production

USDA-ARS?s Scientific Manuscript database

The molecular mechanisms that influence soybean seed composition are not well understood. Insight into the genetic controls involved in these traits is important for future soybean improvement. In this study, we identified candidate genes at the major soybean protein quantitative trait locus at Link...

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.

Climate- and successional-related changes in functional composition of European forests are strongly driven by tree mortality.

PubMed

Ruiz-Benito, Paloma; Ratcliffe, Sophia; Zavala, Miguel A; Martínez-Vilalta, Jordi; Vilà-Cabrera, Albert; Lloret, Francisco; Madrigal-González, Jaime; Wirth, Christian; Greenwood, Sarah; Kändler, Gerald; Lehtonen, Aleksi; Kattge, Jens; Dahlgren, Jonas; Jump, Alistair S

2017-10-01

Intense droughts combined with increased temperatures are one of the major threats to forest persistence in the 21st century. Despite the direct impact of climate change on forest growth and shifts in species abundance, the effect of altered demography on changes in the composition of functional traits is not well known. We sought to (1) quantify the recent changes in functional composition of European forests; (2) identify the relative importance of climate change, mean climate and forest development for changes in functional composition; and (3) analyse the roles of tree mortality and growth underlying any functional changes in different forest types. We quantified changes in functional composition from the 1980s to the 2000s across Europe by two dimensions of functional trait variation: the first dimension was mainly related to changes in leaf mass per area and wood density (partially related to the trait differences between angiosperms and gymnosperms), and the second dimension was related to changes in maximum tree height. Our results indicate that climate change and mean climatic effects strongly interacted with forest development and it was not possible to completely disentangle their effects. Where recent climate change was not too extreme, the patterns of functional change generally followed the expected patterns under secondary succession (e.g. towards late-successional short-statured hardwoods in Mediterranean forests and taller gymnosperms in boreal forests) and latitudinal gradients (e.g. larger proportion of gymnosperm-like strategies at low water availability in forests formerly dominated by broad-leaved deciduous species). Recent climate change generally favoured the dominance of angiosperm-like related traits under increased temperature and intense droughts. Our results show functional composition changes over relatively short time scales in European forests. These changes are largely determined by tree mortality, which should be further investigated and modelled to adequately predict the impacts of climate change on forest function. © 2017 John Wiley & Sons Ltd.

Main sugar composition of floral nectar in three species groups of Scrophularia (Scrophulariaceae) with different principal pollinators.

PubMed

Rodríguez-Riaño, T; Ortega-Olivencia, A; López, J; Pérez-Bote, J L; Navarro-Pérez, M L

2014-11-01

In some angiosperm groups, a parallelism between nectar traits and pollination syndromes has been demonstrated, whereas in others there is not such relationship and it has been explained as due to phylogenetic constraints. However, nectar trait information remains scarce for many plant groups. This paper focuses on three groups of Scrophularia species, with different flower sizes and principal pollinators, to find out whether nectar sugar composition is determined by pollinator type or reflects taxonomic affinities. Since the species we examined have protogynous flowers, and gender bias in nectar sugar composition has been noted in few plant groups, we also investigated whether sexual phase influenced Scrophularia nectar composition. The sugar composition was found to be similar in all species, having high-sucrose nectar, except for the Macaronesian Scrophularia calliantha, which was the only species with balanced nectar; this last kind of nectar could be associated with the high interaction rates observed between S. calliantha and passerine birds. The nectar sugar composition (high in sucrose) was unrelated to the principal pollinator group, and could instead be considered a conservative taxonomic trait. No gender bias was observed between functionally female and male flowers for nectar volume or concentration. However, sexual phase significantly affected sucrose percentage in the largest-flowered species, where the female phase flowers had higher sucrose percentages than the male phase flowers. © 2014 German Botanical Society and The Royal Botanical Society of the Netherlands.

Composition Studies/English Education Connections

ERIC Educational Resources Information Center

Baker, W. Douglas; Brockman, Elizabeth; Bush, Jonathan; Richmond, Kia Jane

2007-01-01

This roundtable explores several different composition-related questions and topics. It raises two questions: (1) What theory from composition studies do you believe is important to include in classes for future elementary and/or secondary writing teachers? (2) What are the knowledge, background, traits, and abilities of a successful writing…

Predicting Risky Sexual Behavior: the Unique and Interactive Roles of Childhood Conduct Disorder Symptoms and Callous-Unemotional Traits.

PubMed

Anderson, Sarah L; Zheng, Yao; McMahon, Robert J

2017-08-01

Conduct disorder (CD) symptoms and callous-unemotional (CU) traits have been shown to be uniquely associated with risky sexual behavior (RSB) in adolescence and early adulthood, yet their interactive role in predicting RSB remains largely unknown. This study aimed to investigate the predictive value of CD symptoms and CU traits, as well as their interaction, on several RSB outcomes in adolescence and early adulthood. A total of 683 participants (41.7 % female, 47.4 % African American) were followed annually and self-reported age of first sexual intercourse, frequency of condom use, pregnancy, contraction of sexually transmitted infections, and engagement in sexual solicitation from grade 7 to 2-years post-high school. CD symptoms predicted age of first sexual intercourse, condom use, and sexual solicitation. CU traits predicted age of first sexual intercourse and pregnancy. Their interaction predicted a composite score of these RSBs such that CD symptoms positively predicted the composite score among those with high levels of CU traits but not among those with low levels of CU traits. The current findings provide information regarding the importance of both CD symptoms and CU traits in understanding adolescent and early adulthood RSB, as well as the benefits of examining multiple RSB outcomes during this developmental period. These findings have implications for the development and implementation of preventive efforts to target these risky behaviors among adolescents and young adults.

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.

A predictive assessment of genetic correlations between traits in chickens using markers.

PubMed

Momen, Mehdi; Mehrgardi, Ahmad Ayatollahi; Sheikhy, Ayoub; Esmailizadeh, Ali; Fozi, Masood Asadi; Kranis, Andreas; Valente, Bruno D; Rosa, Guilherme J M; Gianola, Daniel

2017-02-01

Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations. A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 - λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the "optimum" λ was determined using cross-validation. Estimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW-HHP and BM-HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together. Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens.

PubMed

Abdollahi-Arpanahi, Rostam; Morota, Gota; Valente, Bruno D; Kranis, Andreas; Rosa, Guilherme J M; Gianola, Daniel

2016-02-03

Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5' and 3' untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernel-based ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. All genic and non-genic regions contributed to phenotypic variation for the three traits studied. Overall, the contribution of additive genetic variance to the total genetic variance was much greater than that of dominance variance. Our results show that all genomic regions are important for the prediction of the targeted traits, and the whole-genome approach was reaffirmed as the best tool for genome-enabled prediction of quantitative traits.

Bias correction for estimated QTL effects using the penalized maximum likelihood method.

PubMed

Zhang, J; Yue, C; Zhang, Y-M

2012-04-01

A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.

Gene x dietary pattern interactions in obesity: analysis of up to 68,317 adults of European ancestry

USDA-ARS?s Scientific Manuscript database

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 bod...

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

PubMed

Fan, Ruzong; Wang, Yifan; Boehnke, Michael; Chen, Wei; Li, Yun; Ren, Haobo; Lobach, Iryna; Xiong, Momiao

2015-08-01

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies. Copyright © 2015 by the Genetics Society of America.

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.

Quantitative trait loci for organ weights and adipose fat composition in Jersey and Limousin back-cross cattle finished on pasture or feedlot.

PubMed

Morris, C A; Bottema, C D K; Cullen, N G; Hickey, S M; Esmailizadeh, A K; Siebert, B D; Pitchford, W S

2010-12-01

A QTL study of live animal and carcass traits in beef cattle was carried out in New Zealand and Australia. Back-cross calves (385 heifers and 398 steers) were generated, with Jersey and Limousin backgrounds. This paper reports on weights of eight organs (heart, liver, lungs, kidneys, spleen, gastro-intestinal tract, fat, and rumen contents) and 12 fat composition traits (fatty acid (FA) percentages, saturated and monounsaturated FA subtotals, and fat melting point). The New Zealand cattle were reared and finished on pasture, whilst Australian cattle were reared on grass and finished on grain for at least 180 days. For organ weights and fat composition traits, 10 and 12 significant QTL locations (P<0.05), respectively, were detected on a genome-wide basis, in combined-sire or within-sire analyses. Seven QTL significant for organ weights were found at the proximal end of chromosome 2. This chromosome carries a variant myostatin allele (F94L), segregating from the Limousin ancestry, and this is a positional candidate for the QTL. Ten significant QTL for fat composition were found on chromosomes 19 and 26. Fatty acid synthase and stearoyl-CoA desaturase (SCD1), respectively, are positional candidate genes for these QTL. Two FA QTL found to be common to sire groups in both populations were for percentages of C14:0 and C14:1 (relative to all FAs) on chromosome 26, near the SCD1 candidate gene. © 2010 AgResearch Ltd, Animal Genetics © 2010 Stichting International Foundation for Animal Genetics.

Predator-Prey Dynamics Driven by Feedback between Functionally Diverse Trophic Levels

PubMed Central

Wirtz, Kai; Gaedke, Ursula

2011-01-01

Neglecting the naturally existing functional diversity of communities and the resulting potential to respond to altered conditions may strongly reduce the realism and predictive power of ecological models. We therefore propose and study a predator-prey model that describes mutual feedback via species shifts in both predator and prey, using a dynamic trait approach. Species compositions of the two trophic levels were described by mean functional traits—prey edibility and predator food-selectivity—and functional diversities by the variances. Altered edibility triggered shifts in food-selectivity so that consumers continuously respond to the present prey composition, and vice versa. This trait-mediated feedback mechanism resulted in a complex dynamic behavior with ongoing oscillations in the mean trait values, reflecting continuous reorganization of the trophic levels. The feedback was only possible if sufficient functional diversity was present in both trophic levels. Functional diversity was internally maintained on the prey level as no niche existed in our system, which was ideal under any composition of the predator level due to the trade-offs between edibility, growth and carrying capacity. The predators were only subject to one trade-off between food-selectivity and grazing ability and in the absence of immigration, one predator type became abundant, i.e., functional diversity declined to zero. In the lack of functional diversity the system showed the same dynamics as conventional models of predator-prey interactions ignoring the potential for shifts in species composition. This way, our study identified the crucial role of trade-offs and their shape in physiological and ecological traits for preserving diversity. PMID:22096560

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.

Effect of polymorphisms in the CSN3 (κ-casein) gene on milk production traits in Chinese Holstein Cattle.

PubMed

Alim, M A; Dong, T; Xie, Y; Wu, X P; Zhang, Yi; Zhang, Shengli; Sun, D X

2014-11-01

This study was designed to evaluate significant associations between single nucleotide polymorphisms (SNPs) and milk composition and milk production traits in Chinese Holstein cows. Six SNPs were identified in the κ-casein gene using pooled DNA sequencing. The identified SNPs were genotyped by Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) methods from 507 individuals. Out of six, we identified three non-synonymous SNPs (g.10888T>C, g.10924C>A and g.10944A>G) that changed in the protein product. SIFT (Sorting_Intolerant_From_Tolerant) prediction score (0.01) demonstrated that protein changed Isoleucine > Threonine (g.10888T>C) will affect the phenotypes. Significant associations between identified SNPs and three yield traits (milk, protein and fat) and two composition traits (fat and protein percentages) were found whereas it did not reach significance for fat percentage in haplotypes association. Importantly, the significant SNPs in our results showed a large proportion of the phenotypic variation of milk protein yield and concentration. Our results suggest that CSN3 is an important candidate gene that influences milk production traits, and identified polymorphisms and haplotypes could be used as a genetic marker in programs of marker-assisted selection for the genetic improvement of milk production traits in dairy cattle.

Short communication: Effects of pregnancy on milk yield, composition traits, and coagulation properties of Holstein cows.

PubMed

Penasa, M; De Marchi, M; Cassandro, M

2016-06-01

The aim of this study was to investigate the effect of pregnancy stage on milk yield, composition traits, and milk coagulation properties in Italian Holstein cattle. The data set included 25,729 records from 3,995 first-parity cows calving between August 2010 and August 2013 in 167 herds. The traits analyzed were milk yield (kg/d), fat (%), protein (%), casein (%), and lactose (%) contents, pH, somatic cell score, rennet coagulation time (min), and curd firmness (mm). To better understand the effect of gestation on the aforementioned traits, each record was assigned to one of the following classes of pregnancy stage: (1) nonpregnant, (2) pregnant from 1 to 120d, (3) pregnant from 121 to 210d, and (4) pregnant from 211 to 310d. Gestation stage significantly influenced all studied traits with the exception of somatic cell score. Milk production decreased and milk quality improved from the fourth month of pregnancy onward. For all traits, nonpregnant cows performed very similarly to cows in the first period of gestation. Rennet coagulation time and curd firmness were influenced by pregnancy stage, especially in the last weeks of gestation when milk had better coagulation characteristics; this information should be accounted for to adjust test-day records in genetic evaluation of milk coagulation properties. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. 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...

Using Wild Olives in Breeding Programs: Implications on Oil Quality Composition.

PubMed

León, Lorenzo; de la Rosa, Raúl; Velasco, Leonardo; Belaj, Angjelina

2018-01-01

A wide genetic diversity has been reported for wild olives, which could be particularly interesting for the introgression of some agronomic traits and resistance to biotic and abiotic stresses in breeding programs. However, the introgression of some beneficial wild traits may be paralleled by negative effects on some other important agronomic and quality traits. From the quality point of view, virgin olive oil (VOO) from olive cultivars is highly appreciated for its fatty acid composition (high monounsaturated oleic acid content) and the presence of several minor components. However, the composition of VOO from wild origin and its comparison with VOO from olive cultivars has been scarcely studied. In this work, the variability for fruit characters (fruit weight and oil content, OC), fatty acid composition, and minor quality components (squalene, sterols and tocopherols content and composition) was studied in a set of plant materials involving three different origins: wild genotypes ( n = 32), cultivars ( n = 62) and genotypes belonging to cultivar × wild progenies ( n = 62). As expected, values for fruit size and OC in wild olives were lower than those obtained in cultivated materials, with intermediate values for cultivar × wild progenies. Wild olives showed a remarkably higher C16:0 percentage and tocopherol content in comparison to the cultivars. Contrarily, lower C18:1 percentage, squalene and sterol content were found in the wild genotypes, while no clear differences were found among the different plant materials regarding composition of the tocopherol and phytosterol fractions. Some common highly significant correlations among components of the same chemical family were found in all groups of plant materials. However, some other correlations were specific for one of the groups. The results of the study suggested that the use of wild germplasm in olive breeding programs will not have a negative impact on fatty acid composition, tocopherol content, and tocopherol and phytosterol profiles provided that selection for these compounds is conducted from early generations. Important traits such as tocopherol content could be even improved by using wild parents.

Whole-Genome Resequencing of Holstein Bulls for Indel Discovery and Identification of Genes Associated with Milk Composition Traits in Dairy Cattle.

PubMed

Jiang, Jianping; Gao, Yahui; Hou, Yali; Li, Wenhui; Zhang, Shengli; Zhang, Qin; Sun, Dongxiao

2016-01-01

The use of whole-genome resequencing to obtain more information on genetic variation could produce a range of benefits for the dairy cattle industry, especially with regard to increasing milk production and improving milk composition. In this study, we sequenced the genomes of eight Holstein bulls from four half- or full-sib families, with high and low estimated breeding values (EBVs) of milk protein percentage and fat percentage at an average effective depth of 10×, using Illumina sequencing. Over 0.9 million nonredundant short insertions and deletions (indels) [1-49 base pairs (bp)] were obtained. Among them, 3,625 indels that were polymorphic between the high and low groups of bulls were revealed and subjected to further analysis. The vast majority (76.67%) of these indels were novel. Follow-up validation assays confirmed that most (70%) of the randomly selected indels represented true variations. The indels that were polymorphic between the two groups were annotated based on the cattle genome sequence assembly (UMD3.1.69); as a result, nearly 1,137 of them were found to be located within 767 annotated genes, only 5 (0.138%) of which were located in exons. Then, by integrated analysis of the 767 genes with known quantitative trait loci (QTL); significant single-nucleotide polymorphisms (SNPs) previously identified by genome-wide association studies (GWASs) to be associated with bovine milk protein and fat traits; and the well-known pathways involved in protein, fat synthesis, and metabolism, we identified a total of 11 promising candidate genes potentially affecting milk composition traits. These were FCGR2B, CENPE, RETSAT, ACSBG2, NFKB2, TBC1D1, NLK, MAP3K1, SLC30A2, ANGPT1 and UGDH. Our findings provide a basis for further study and reveal key genes for milk composition traits in dairy cattle.

A challenge for the seed mixture refuge strategy in Bt maize: impact of cross-pollination on an ear-feeding pest, corn earworm.

PubMed

Yang, Fei; Kerns, David L; Head, Graham P; Leonard, B Rogers; Levy, Ronnie; Niu, Ying; Huang, Fangneng

2014-01-01

To counter the threat of insect resistance, Bacillus thuringiensis (Bt) maize growers in the U.S. are required to plant structured non-Bt maize refuges. Concerns with refuge compliance led to the introduction of seed mixtures, also called RIB (refuge-in-the-bag), as an alternative approach for implementing refuge for Bt maize products in the U.S. Maize Belt. A major concern in RIB is cross-pollination of maize hybrids that can cause Bt proteins to be present in refuge maize kernels and negatively affect refuge insects. Here we show that a mixed planting of 5% nonBt and 95% Bt maize containing the SmartStax traits expressing Cry1A.105, Cry2Ab2 and Cry1F did not provide an effective refuge for an important above-ground ear-feeding pest, the corn earworm, Helicoverpa zea (Boddie). Cross-pollination in RIB caused a majority (>90%) of refuge kernels to express ≥ one Bt protein. The contamination of Bt proteins in the refuge ears reduced neonate-to-adult survivorship of H. zea to only 4.6%, a reduction of 88.1% relative to larvae feeding on ears of pure non-Bt maize plantings. In addition, the limited survivors on refuge ears had lower pupal mass and took longer to develop to adults.

Evaluation of the lime-cooking and tortilla making properties of quality protein maize hybrids grown in Mexico.

PubMed

Serna-Saldivar, Sergio O; Amaya Guerra, Carlos A; Herrera Macias, Pedro; Melesio Cuellar, Jose L; Preciado Ortiz, Ricardo E; Terron Ibarra, Arturo D; Vazquez Carrillo, Gricelda

2008-09-01

Eleven experimental and three commercial white quality protein maize (QPM) hybrids and two regular endosperm controls were planted at Celaya, Guanajuato, Mexico with the aim of comparing grain physical characteristics, protein quality, lime-cooking and tortilla making properties. All genotypes were planted under irrigation using a density of 80,000 plants/ha and fertilized with 250 kg N-60 P-60 K per hectare. When compared with the controls these QPM genotypes had lower test (77.4 vs. 76.5 kg/hL) and 1,000 kernel weights (327 vs. 307 g), softer endosperm texture (2.5 vs. 1.8 where 1 = soft, 2 intermediate and 3 hard endosperm), lower protein (10.0 vs. 8.0%), higher nixtamal water uptake after 30 min lime-cooking (50.0 vs. 53.1% moisture) and lower pericarp removal scores. The lower thousand-kernel weight and softer endosperm texture observed in the QPM genotypes lowered the optimum lime-cooking time as estimated with regression equations. Most QPM genotypes had higher amounts of lysine, tryptophan and albumins/globulins when compared with the controls. QPMs HEC 424973, HEC 774986 and HEC 734286 had the best grain traits for nixtamalization and therefore the best potential for industrial utilization. The commercial use of these QPM hybrids should benefit Mexicans who depend on tortillas as the main staple.

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.

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.

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.

Distribution of Phenolic Compounds and Antioxidative Activities of Rice Kernel and Their Relationships with Agronomic Practice

PubMed Central

Kesarwani, Amit; Chiang, Po-Yuan; Chen, Shih-Shiung

2014-01-01

The phenolic and antioxidant activity of ethanolic extract of two Japonica rice cultivars, Taikeng no. 16 (medium and slender grain) and Kaohsiung no. 139 (short and round grain), grown under organic and conventional farming were examined. Analyses shows that Kaohsiung no. 139 contains the highest amount of secondary metabolites and continuous farming can increase its production. Results also suggest that phenolic content under different agronomic practices, has not shown significant differences but organically grown rice has proven to be better in higher accumulation of other secondary metabolites (2,2-diphenyl-1-picrylhydrazyl (DPPH), flavonoid content, and ferrous chelating capacity). In nutshell, genetic traits and environment have significant effect on phenolic compounds and the least variation reported under agronomic practices. PMID:25506072

Identification of subsurface structures using electromagnetic data and shape priors

NASA Astrophysics Data System (ADS)

Tveit, Svenn; Bakr, Shaaban A.; Lien, Martha; Mannseth, Trond

2015-03-01

We consider the inverse problem of identifying large-scale subsurface structures using the controlled source electromagnetic method. To identify structures in the subsurface where the contrast in electric conductivity can be small, regularization is needed to bias the solution towards preserving structural information. We propose to combine two approaches for regularization of the inverse problem. In the first approach we utilize a model-based, reduced, composite representation of the electric conductivity that is highly flexible, even for a moderate number of degrees of freedom. With a low number of parameters, the inverse problem is efficiently solved using a standard, second-order gradient-based optimization algorithm. Further regularization is obtained using structural prior information, available, e.g., from interpreted seismic data. The reduced conductivity representation is suitable for incorporation of structural prior information. Such prior information cannot, however, be accurately modeled with a gaussian distribution. To alleviate this, we incorporate the structural information using shape priors. The shape prior technique requires the choice of kernel function, which is application dependent. We argue for using the conditionally positive definite kernel which is shown to have computational advantages over the commonly applied gaussian kernel for our problem. Numerical experiments on various test cases show that the methodology is able to identify fairly complex subsurface electric conductivity distributions while preserving structural prior information during the inversion.

Documentation of the appearance of a caviar-type deposit in Oven 1 following a large scale experiment for heating oil with Upper Silesian coal (in German)

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

Rank

1942-03-26

When the oven was disassembled after the test, small kernels of porous material were found in both the upper and lower portion of the oven to a depth of about 2 m. The kernels were of various sizes up to 4 mm. From 1,300 metric ..cap alpha..ons of dry coal, there were 330 kg or the residue of 0.025% of the coal input. These kernels brought to mind deposits of spheroidal material termed ''caviar'', since they had rounded tops. However, they were irregularly long. After multiaxis micrography, no growth rings were found as in Leuna's lignite caviar. So, it wasmore » a question of small particles consisting almost totally of ash. The majority of the composition was Al, Fe, Na, silicic acid, S and Cl. The sulfur was found to be in sulfide form and Cl in a volatile form. The remains did not turn to caviar form since the CaO content was slight. The Al, Fe, Na, silicic acid, S and Cl were concentrated in comparison to coal ash and originate apparently from the catalysts (FeSO/sub 4/, Bayermasse, and Na/sub 2/S). It was notable that the Cl content was so high. 2 graphs, 1 table« less

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2012 CFR

2012-01-01

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

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2011 CFR

2011-01-01

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

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2013 CFR

2013-01-01

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

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

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

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2014 CFR

2014-01-01

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

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...

[The age-related changes in hemolymph cellular composition and in the spectrum of cytomorphological traits of hemocyte genetic damages in snail Lymnaea stagnalis].

PubMed

Koneva, O Iu; Afonin, V Iu; Dromashko, S E

2006-01-01

The age-related changes in hemolymph cellular composition of snail Lymnaea stagnalis (Gastropoda, Pulmonata) obtained from individuals of a natural population (the river Pripayt, Gomel region, Belarus) as well as in the spectrum of cytomorphological traits of hemocyte genetic damages have been studied. The percentage of the distinguished hemolymph cell types during the chosen age period was not revealed to change. The percentage of cells with different morphological attributes of cell death varied during ageing. The tendency to increase in the total level of dying cells was observed.

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.

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.

Responses of leaf traits to climatic gradients: adaptive variation versus compositional shifts

NASA Astrophysics Data System (ADS)

Meng, T.-T.; Wang, H.; Harrison, S. P.; Prentice, I. C.; Ni, J.; Wang, G.

2015-09-01

Dynamic global vegetation models (DGVMs) typically rely on plant functional types (PFTs), which are assigned distinct environmental tolerances and replace one another progressively along environmental gradients. Fixed values of traits are assigned to each PFT; modelled trait variation along gradients is thus driven by PFT replacement. But empirical studies have revealed "universal" scaling relationships (quantitative trait variations with climate that are similar within and between species, PFTs and communities); and continuous, adaptive trait variation has been proposed to replace PFTs as the basis for next-generation DGVMs. Here we analyse quantitative leaf-trait variation on long temperature and moisture gradients in China with a view to understanding the relative importance of PFT replacement vs. continuous adaptive variation within PFTs. Leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and nitrogen content of dry matter were measured on all species at 80 sites ranging from temperate to tropical climates and from dense forests to deserts. Chlorophyll fluorescence traits and carbon, phosphorus and potassium contents were measured at 47 sites. Generalized linear models were used to relate log-transformed trait values to growing-season temperature and moisture indices, with or without PFT identity as a predictor, and to test for differences in trait responses among PFTs. Continuous trait variation was found to be ubiquitous. Responses to moisture availability were generally similar within and between PFTs, but biophysical traits (LA, SLA and LDMC) of forbs and grasses responded differently from woody plants. SLA and LDMC responses to temperature were dominated by the prevalence of evergreen PFTs with thick, dense leaves at the warm end of the gradient. Nutrient (N, P and K) responses to climate gradients were generally similar within all PFTs. Area-based nutrients generally declined with moisture; Narea and Karea declined with temperature, but Parea increased with temperature. Although the adaptive nature of many of these trait-climate relationships is understood qualitatively, a key challenge for modelling is to predict them quantitatively. Models must take into account that community-level responses to climatic gradients can be influenced by shifts in PFT composition, such as the replacement of deciduous by evergreen trees, which may run either parallel or counter to trait variation within PFTs. The importance of PFT shifts varies among traits, being important for biophysical traits but less so for physiological and chemical traits. Finally, models should take account of the diversity of trait values that is found in all sites and PFTs, representing the "pool" of variation that is locally available for the natural adaptation of ecosystem function to environmental change.

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

PubMed

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

2018-05-07

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

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.

Experimental fertilization increases amino acid content in floral nectar, fruit set and degree of selfing in the orchid Gymnadenia conopsea.

PubMed

Gijbels, Pieter; Ceulemans, Tobias; Van den Ende, Wim; Honnay, Olivier

2015-11-01

Floral traits have evolved to maximize reproductive success by attracting pollinators and facilitating pollination. Highly attractive floral traits may, however, also increase the degree of self-pollination, which could become detrimental for plant fitness through inbreeding depression. Floral nectar is a trait that is known to strongly mediate pollinator attraction and plant reproductive success, but the particular role of the nectar amino acid (AA) composition is poorly understood. Therefore, we experimentally manipulated the nectar AA composition and abundance of the Lepidoptera-pollinated orchid Gymnadenia conopsea through soil fertilization, and we quantified AA content and AA composition through high performance anion exchange chromatography with pulsed amperometric detection. Mixed models were then used to evaluate differences in pollinia removal, fruit set, seed set and degree of selfing between fertilized and control individuals. Selfing rates were estimated using microsatellite markers. We found that fertilized individuals had a significantly higher nectar AA content and an altered AA composition, whereas plant height, number of flowers, nectar volume and sugar concentration remained unchanged. Fertilized individuals also had significantly more pollinia removed and a higher fruit set, whereas control plants that did not receive the fertilization treatment had significantly fewer selfed seeds, and more viable seeds. Although we cannot exclude a role of changes in floral scent following the fertilization treatment, our results strongly suggest a relation among nectar AA composition, fruiting success and selfing rates. Our results also indicate potential consequences of nutrient pollution for plant reproductive success, through the induced changes in nectar AA composition.

Exercise and diet affect quantitative trait loci for body weight and composition traits in an advanced intercross population of mice

PubMed Central

Kelly, Scott A.; Hua, Kunjie; Pomp, Daniel

2012-01-01

Driven by the recent obesity epidemic, interest in understanding the complex genetic and environmental basis of body weight and composition is great. We investigated this by searching for quantitative trait loci (QTLs) affecting a number of weight and adiposity traits in a G10 advanced intercross population produced from crosses of mice in inbred strain C57BL/6J with those in a strain selected for high voluntary wheel running. The mice in this population were fed either a high-fat or a control diet throughout the study and also measured for four exercise traits prior to death, allowing us to test for pre- and postexercise QTLs as well as QTL-by-diet and QTL-by-exercise interactions. Our genome scan uncovered a number of QTLs, of which 40% replicated QTLs previously found for similar traits in an earlier (G4) generation. For those replicated QTLs, the confidence intervals were reduced from an average of 19 Mb in the G4 to 8 Mb in the G10. Four QTLs on chromosomes 3, 8, 13, and 18 were especially prominent in affecting the percentage of fat in the mice. About of all QTLs showed interactions with diet, exercise, or both, their genotypic effects on the traits showing a variety of patterns depending on the diet or level of exercise. It was concluded that the indirect effects of these QTLs provide an underlying genetic basis for the considerable variability in weight or fat loss typically found among individuals on the same diet and/or exercise regimen. PMID:23048196

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.

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.

Analysis of intraspecific seed diversity in Astragalus aquilanus (Fabaceae), an endemic species of Central Apennine.

PubMed

Di Cecco, V; Di Musciano, M; D'Archivio, A A; Frattaroli, A R; Di Martino, L

2018-05-20

This work aims to study seeds of the endemic species Astragalus aquilanus from four different populations of central Italy. We investigated seed morpho-colorimetric features (shape and size) and chemical differences (through infrared spectroscopy) among populations and between dark and light seeds. Seed morpho-colorimetric quantitative variables, describing shape, size and colour traits, were measured using image analysis techniques. Fourier transform infrared (FT-IR) spectroscopy was used to attempt seed chemical characterisation. The measured data were analysed by step-wise linear discriminant analysis (LDA). Moreover, we analysed the correlation between the four most important traits and six climatic variables extracted from WorldClim 2.0. The LDA on seeds traits shows clear differentiation of the four populations, which can be attributed to different chemical composition, as confirmed by Wilk's lambda test (P < 0.001). A strong correlation between morphometric traits and temperature (annual mean temperature, mean temperature of the warmest and coolest quarter), colorimetric traits and precipitation (annual precipitation, precipitation of wettest and driest quarter) was observed. The characterisation of A. aquilanus seeds shows large intraspecific plasticity both in morpho-colorimetric and chemical composition. These results confirm the strong relationship between the type of seed produced and the climatic variables. © 2018 German Society for Plant Sciences and The Royal Botanical Society of the Netherlands.

Demographic drivers of functional composition dynamics.

PubMed

Muscarella, Robert; Lohbeck, Madelon; Martínez-Ramos, Miguel; Poorter, Lourens; Rodríguez-Velázquez, Jorge Enrique; van Breugel, Michiel; Bongers, Frans

2017-11-01

Mechanisms of community assembly and ecosystem function are often analyzed using community-weighted mean trait values (CWMs). We present a novel conceptual framework to quantify the contribution of demographic processes (i.e., growth, recruitment, and mortality) to temporal changes in CWMs. We used this framework to analyze mechanisms of secondary succession in wet tropical forests in Mexico. Seed size increased over time, reflecting a trade-off between colonization by small seeds early in succession, to establishment by large seeds later in succession. Specific leaf area (SLA) and leaf phosphorus content decreased over time, reflecting a trade-off between fast growth early in succession vs. high survival late in succession. On average, CWM shifts were driven mainly (70%) by growth of surviving trees that comprise the bulk of standing biomass, then mortality (25%), and weakly by recruitment (5%). Trait shifts of growing and recruiting trees mirrored the CWM trait shifts, and traits of dying trees did not change during succession, indicating that these traits are important for recruitment and growth, but not for mortality, during the first 30 yr of succession. Identifying the demographic drivers of functional composition change links population dynamics to community change, and enhances insights into mechanisms of succession. © 2017 by the Ecological Society of America.

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.

Variation in species diversity and functional traits of sponge communities near human populations in Bocas del Toro, Panama

PubMed Central

Matterson, Kenan O.; Freeman, Christopher J.; Archer, Stephanie K.; Thacker, Robert W.

2015-01-01

Recent studies have renewed interest in sponge ecology by emphasizing the functional importance of sponges in a broad array of ecosystem services. Many critically important habitats occupied by sponges face chronic stressors that might lead to alterations in their diversity, relatedness, and functional attributes. We addressed whether proximity to human activity might be a significant factor in structuring sponge community composition, as well as potential functional roles, by monitoring sponge diversity and abundance at two structurally similar sites that vary in distance to areas of high coastal development in Bocas Del Toro, Panama. We surveyed sponge communities at each site using belt transects and differences between two sites were compared using the following variables: (1) sponge species richness, Shannon diversity, and inverse Simpson’s diversity; (2) phylogenetic diversity; (3) taxonomic and phylogenetic beta diversity; (4) trait diversity and dissimilarity; and (5) phylogenetic and trait patterns in community structure. We observed significantly higher sponge diversity at Punta Caracol, the site most distant from human development (∼5 km). Although phylogenetic diversity was lower at Saigon Bay, the site adjacent to a large village including many houses, businesses, and an airport, the sites did not exhibit significantly different patterns of phylogenetic relatedness in species composition. However, each site had a distinct taxonomic and phylogenetic composition (beta diversity). In addition, the sponge community at Saigon included a higher relative abundance of sponges with high microbial abundance and high chlorophyll a concentration, whereas the community at Punta Caracol had a more even distribution of these traits, yielding a significant difference in functional trait diversity between sites. These results suggest that lower diversity and potentially altered community function might be associated with proximity to human populations. This study highlights the importance of evaluating functional traits and phylogenetic diversity in addition to common diversity metrics when assessing potential environmental impacts on benthic communities. PMID:26587347

Chronic human disturbance affects plant trait distribution in a seasonally dry tropical forest

NASA Astrophysics Data System (ADS)

Sfair, Julia C.; de Bello, Francesco; de França, Thaysa Q.; Baldauf, Cristina; Tabarelli, Marcelo

2018-02-01

The effects of human disturbance on biodiversity can be mediated by environmental conditions, such as water availability, climate and nutrients. In general, disturbed, dry or nutrient-depleted soils areas tend to have lower taxonomic diversity. However, little is known about how these environmental conditions affect functional composition and intraspecific variability in tropical dry forests. We studied a seasonally dry tropical forest (SDTF) under chronic anthropogenic disturbance (CAD) along rainfall and soil nutrient gradients to understand how these factors influence the taxonomic and functional composition. Specifically we evaluated two aspects of CAD, wood extraction and livestock pressure (goat and cattle grazing), along soil fertility and rainfall gradients on shrub and tree traits, considering species turnover and intraspecific variability. In addition, we also tested how the traits of eight populations of the most frequent species are affected by wood extraction, livestock pressure, rainfall and soil fertility. In general, although CAD and environmental gradients affected each trait of the most widespread species differently, the most abundant species also had a greater variation of traits. Considering species turnover, wood extraction is associated with species with a smaller leaf area and lower investment in leaf mass, probably due to the indirect effects of this disturbance type on the vegetation, i.e. the removal of branches and woody debris clears the vegetation, favouring species that minimize water loss. Livestock pressure, on the other hand, affected intraspecific variation: the herbivory caused by goats and cattle promoted individuals which invest more in wood density and leaf mass. In this case, the change of functional composition observed is a direct effect of the disturbance, such as the decrease of palatable plant abundance by goat and cattle herbivory. In synthesis, CAD, rainfall and soil fertility can affect trait distribution at community and species levels, which can have significant implications for the ecosystem functioning of SDTF under increasing levels of disturbance, climate change and soil nutrient depletion.

Winter severity determines functional trait composition of phytoplankton in seasonally ice-covered lakes.

PubMed

Özkundakci, Deniz; Gsell, Alena S; Hintze, Thomas; Täuscher, Helgard; Adrian, Rita

2016-01-01

How climate change will affect the community dynamics and functionality of lake ecosystems during winter is still little understood. This is also true for phytoplankton in seasonally ice-covered temperate lakes which are particularly vulnerable to the presence or absence of ice. We examined changes in pelagic phytoplankton winter community structure in a north temperate lake (Müggelsee, Germany), covering 18 winters between 1995 and 2013. We tested how phytoplankton taxa composition varied along a winter-severity gradient and to what extent winter severity shaped the functional trait composition of overwintering phytoplankton communities using multivariate statistical analyses and a functional trait-based approach. We hypothesized that overwintering phytoplankton communities are dominated by taxa with trait combinations corresponding to the prevailing winter water column conditions, using ice thickness measurements as a winter-severity indicator. Winter severity had little effect on univariate diversity indicators (taxon richness and evenness), but a strong relationship was found between the phytoplankton community structure and winter severity when taxon trait identity was taken into account. Species responses to winter severity were mediated by the key functional traits: motility, nutritional mode, and the ability to form resting stages. Accordingly, one or the other of two functional groups dominated the phytoplankton biomass during mild winters (i.e., thin or no ice cover; phototrophic taxa) or severe winters (i.e., thick ice cover; exclusively motile taxa). Based on predicted milder winters for temperate regions and a reduction in ice-cover durations, phytoplankton communities during winter can be expected to comprise taxa that have a relative advantage when the water column is well mixed (i.e., need not be motile) and light is less limiting (i.e., need not be mixotrophic). A potential implication of this result is that winter severity promotes different communities at the vernal equinox, which may have different nutritional quality for the next trophic level and ecosystem-scale effects. © 2015 John Wiley & Sons Ltd.

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

Characteristics of uranium carbonitride microparticles synthesized using different reaction conditions

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

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

2014-11-01

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

The primary case is not enough: Variation among individuals, groups and social networks modify bacterial transmission dynamics.

PubMed

Keiser, Carl N; Pinter-Wollman, Noa; Ziemba, Michael J; Kothamasu, Krishna S; Pruitt, Jonathan N

2018-03-01

The traits of the primary case of an infectious disease outbreak, and the circumstances for their aetiology, potentially influence the trajectory of transmission dynamics. However, these dynamics likely also depend on the traits of the individuals with whom the primary case interacts. We used the social spider Stegodyphus dumicola to test how the traits of the primary case, group phenotypic composition and group size interact to facilitate the transmission of a GFP-labelled cuticular bacterium. We also compared bacterial transmission across experimentally generated "daisy-chain" vs. "star" networks of social interactions. Finally, we compared social network structure across groups of different sizes. Groups of 10 spiders experienced more bacterial transmission events compared to groups of 30 spiders, regardless of groups' behavioural composition. Groups containing only one bold spider experienced the lowest levels of bacterial transmission regardless of group size. We found no evidence for the traits of the primary case influencing any transmission dynamics. In a second experiment, bacteria were transmitted to more individuals in experimentally induced star networks than in daisy-chains, on which transmission never exceeded three steps. In both experimental network types, transmission success depended jointly on the behavioural traits of the interacting individuals; however, the behavioural traits of the primary case were only important for transmission on star networks. Larger social groups exhibited lower interaction density (i.e. had a low ratio of observed to possible connections) and were more modular, i.e. they had more connections between nodes within a subgroup and fewer connections across subgroups. Thus, larger groups may restrict transmission by forming fewer interactions and by isolating subgroups that interacted with the primary case. These findings suggest that accounting for the traits of single exposed hosts has less power in predicting transmission dynamics compared to the larger scale factors of the social groups in which they reside. Factors like group size and phenotypic composition appear to alter social interaction patterns, which leads to differential transmission of microbes. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

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.

Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

DOE PAGES

Milano, Elizabeth R.; Payne, Courtney E.; Wolfrum, Edward J.; ...

2018-02-03

Biofuels derived from lignocellulosic plant material are an important component of current renewable energy strategies. Improvement efforts in biofuel feedstock crops have been primarily focused on increasing biomass yield with less consideration for tissue quality or composition. Four primary components found in the plant cell wall contribute to the overall quality of plant tissue and conversion characteristics, cellulose and hemicellulose polysaccharides are the primary targets for fuel conversion, while lignin and ash provide structure and defense. We explore the genetic architecture of tissue characteristics using a quantitative trait loci (QTL) mapping approach in Panicum hallii, a model lignocellulosic grass system.more » Diversity in the mapping population was generated by crossing xeric and mesic varietals, comparative to northern upland and southern lowland ecotypes in switchgrass. We use near-infrared spectroscopy with a primary analytical method to create a P. hallii specific calibration model to quickly quantify cell wall components. Ash, lignin, glucan, and xylan comprise 68% of total dry biomass in P. hallii: comparable to other feedstocks. We identified 14 QTL and one epistatic interaction across these four cell wall traits and found almost half of the QTL to localize to a single linkage group. Panicum hallii serves as the genomic model for its close relative and emerging biofuel crop, switchgrass (P. virgatum). We used high throughput phenotyping to map genomic regions that impact natural variation in leaf tissue composition. Understanding the genetic architecture of tissue traits in a tractable model grass system will lead to a better understanding of cell wall structure as well as provide genomic resources for bioenergy crop breeding programs.« less

Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

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

Milano, Elizabeth R.; Payne, Courtney E.; Wolfrum, Edward J.

Biofuels derived from lignocellulosic plant material are an important component of current renewable energy strategies. Improvement efforts in biofuel feedstock crops have been primarily focused on increasing biomass yield with less consideration for tissue quality or composition. Four primary components found in the plant cell wall contribute to the overall quality of plant tissue and conversion characteristics, cellulose and hemicellulose polysaccharides are the primary targets for fuel conversion, while lignin and ash provide structure and defense. We explore the genetic architecture of tissue characteristics using a quantitative trait loci (QTL) mapping approach in Panicum hallii, a model lignocellulosic grass system.more » Diversity in the mapping population was generated by crossing xeric and mesic varietals, comparative to northern upland and southern lowland ecotypes in switchgrass. We use near-infrared spectroscopy with a primary analytical method to create a P. hallii specific calibration model to quickly quantify cell wall components. Ash, lignin, glucan, and xylan comprise 68% of total dry biomass in P. hallii: comparable to other feedstocks. We identified 14 QTL and one epistatic interaction across these four cell wall traits and found almost half of the QTL to localize to a single linkage group. Panicum hallii serves as the genomic model for its close relative and emerging biofuel crop, switchgrass (P. virgatum). We used high throughput phenotyping to map genomic regions that impact natural variation in leaf tissue composition. Understanding the genetic architecture of tissue traits in a tractable model grass system will lead to a better understanding of cell wall structure as well as provide genomic resources for bioenergy crop breeding programs.« less

Chironomidae traits and life history strategies as indicators of anthropogenic disturbance.

PubMed

Serra, Sónia R Q; Graça, Manuel A S; Dolédec, Sylvain; Feio, Maria João

2017-07-01

In freshwater ecosystems, Chironomidae are currently considered indicators of poor water quality because the family is often abundant in degraded sites. However, it incorporates taxa with a large ecological and physiological diversity and different sensitivity to impairment. Yet, the usual identification of Chironomidae at coarse taxonomic levels (family or subfamily) masks genus and species sensitivities. In this study, we investigate the potential of taxonomic and functional (traits) composition of Chironomidae to detect anthropogenic disturbance. In this context, we tested some a priori hypotheses regarding the ability of Chironomidae taxonomic and trait compositions to discriminate Mediterranean streams affected by multiple stressors from least-disturbed streams. Both taxonomic and Eltonian trait composition discriminated sites according to their disturbance level. Disturbance resulted in the predicted increase of Chironomidae with higher number of stages with hibernation/diapause and of taxa with resistance forms and unpredicted increase of the proportion of taxa with longer life cycles and few generations per year. Life history strategies (LHS), corresponding to multivoltine Chironomidae that do not invest in hemoglobin and lack strong spring synchronization, were well adapted to all our Mediterranean sites with highly changeable environmental conditions. Medium-size animals favored in disturbed sites where the Mediterranean hydrological regime is altered, but the reduced number of larger-size/carnivore Chironomids suggests a limitation to secondary production. Results indicate that Chironomidae genus and respective traits could be a useful tool in the structural and functional assessment of Mediterranean streams. The ubiquitous nature of Chironomidae should be also especially relevant in the assessment of water bodies naturally poor in other groups such as the Ephemeroptera, Plecoptera, and Trichoptera, such as the lowland rivers with sandy substrates, lakes, or reservoirs.

Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs.

PubMed

Camarinha-Silva, Amelia; Maushammer, Maria; Wellmann, Robin; Vital, Marius; Preuss, Siegfried; Bennewitz, Jörn

2017-07-01

The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that were housed and slaughtered under standardized conditions, and phenotyped for daily gain, feed intake, and feed conversion rate. The pigs were genotyped with a standard 60 K SNP chip. The GIT microbiota composition was analyzed by 16S rRNA gene amplicon sequencing technology. Eight from 49 investigated bacteria genera showed a significant narrow sense host heritability, ranging from 0.32 to 0.57. Microbial mixed linear models were applied to estimate the microbiota variance for each complex trait. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota composition were used to predict the complex traits using genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods, respectively. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, in addition to SNP data, microbiota abundances are an informative source of complex trait predictions. Since the pig is a well-suited animal for modeling the human digestive tract, M-BLUP, in addition to G-BLUP, might be beneficial for predicting human predispositions to some diseases, and, consequently, for preventative and personalized medicine. Copyright © 2017 by the Genetics Society of America.

sPlot - the new global vegetation-plot database for addressing trait-environment relationships across the world's biomes

NASA Astrophysics Data System (ADS)

Purschke, Oliver; Dengler, Jürgen; Bruelheide, Helge; Chytrý, Milan; Jansen, Florian; Hennekens, Stephan; Jandt, Ute; Jiménez-Alfaro, Borja; Kattge, Jens; De Patta Pillar, Valério; Sandel, Brody; Winter, Marten

2015-04-01

The trait composition of plant communities is determined by abiotic, biotic and historical factors, but the importance of macro-climatic factors in explaining trait-environment relationships at the local scale remains unclear. Such knowledge is crucial for biogeographical and ecological theory but also relevant to devise management measures to mitigate the negative effects of climate change. To address these questions, an iDiv Working Group has established the first global vegetation-plot database (sPlot). sPlot currently contains ~700,000 plots from over 50 countries and all biomes, and is steadily growing. Approx. 70% of the most frequent species are represented by at least one trait in the global trait database TRY and gap-filled data will become available for the most common traits. We will give an overview about the structure and present content of sPlot in terms of spatial distribution, data properties and trait coverage. We will explain next steps and perspectives, present first cross-biome analyses of community-weighted mean traits and trait variability, and highlight some ecological questions that can be addressed with sPlot.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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...

Spatial and temporal functional changes in alpine summit vegetation are driven by increases in shrubs and graminoids.

PubMed

Venn, Susanna; Pickering, Catherine; Green, Ken

2014-01-01

Classical approaches to investigating temporal and spatial changes in community composition offer only partial insight into the ecology that drives species distribution, community patterns and processes, whereas a functional approach can help to determine many of the underlying mechanisms that drive such patterns. Here, we aim to bring these two approaches together to understand such drivers, using an elevation gradient of sites, a repeat species survey and species functional traits. We used data from a repeat vegetation survey on five alpine summits and measured plant height, leaf area, leaf dry matter content and specific leaf area (SLA) for every species recorded in the surveys. We combined species abundances with trait values to produce a community trait-weighted mean (CTWM) for each trait, and then combined survey results with the CTWMs. Across the gradient of summits, more favourable conditions for plant growth (warmer, longer growing season) occurred at the lower elevations. Vegetation composition changes between 2004 and 2011 (according to non-metric multi-dimensional scaling ordination) were strongly affected by the high and increasing abundance of species with high SLA at high elevations. Species life-form categories strongly affected compositional changes and functional composition, with increasing dominance of tall shrubs and graminoids at the lower-elevation summits, and an overall increase in graminoids across the gradient. The CTWM for plant height and leaf dry matter content significantly decreased with elevation, whereas for leaf area and SLA it significantly increased. The significant relationships between CTWM and elevation may suggest specific ecological processes, namely plant competition and local productivity, influencing vegetation preferentially across the elevation gradient, with the dominance of shrubs and graminoids driving the patterns in the CTWMs.

Spatial and temporal functional changes in alpine summit vegetation are driven by increases in shrubs and graminoids

PubMed Central

Venn, Susanna; Pickering, Catherine; Green, Ken

2014-01-01

Classical approaches to investigating temporal and spatial changes in community composition offer only partial insight into the ecology that drives species distribution, community patterns and processes, whereas a functional approach can help to determine many of the underlying mechanisms that drive such patterns. Here, we aim to bring these two approaches together to understand such drivers, using an elevation gradient of sites, a repeat species survey and species functional traits. We used data from a repeat vegetation survey on five alpine summits and measured plant height, leaf area, leaf dry matter content and specific leaf area (SLA) for every species recorded in the surveys. We combined species abundances with trait values to produce a community trait-weighted mean (CTWM) for each trait, and then combined survey results with the CTWMs. Across the gradient of summits, more favourable conditions for plant growth (warmer, longer growing season) occurred at the lower elevations. Vegetation composition changes between 2004 and 2011 (according to non-metric multi-dimensional scaling ordination) were strongly affected by the high and increasing abundance of species with high SLA at high elevations. Species life-form categories strongly affected compositional changes and functional composition, with increasing dominance of tall shrubs and graminoids at the lower-elevation summits, and an overall increase in graminoids across the gradient. The CTWM for plant height and leaf dry matter content significantly decreased with elevation, whereas for leaf area and SLA it significantly increased. The significant relationships between CTWM and elevation may suggest specific ecological processes, namely plant competition and local productivity, influencing vegetation preferentially across the elevation gradient, with the dominance of shrubs and graminoids driving the patterns in the CTWMs. PMID:24790129

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...

Phenotypic and genetic relationships between indicators of the mammary gland health status and milk composition, coagulation, and curd firming in dairy sheep.

PubMed

Pazzola, Michele; Cipolat-Gotet, Claudio; Bittante, Giovanni; Cecchinato, Alessio; Dettori, Maria L; Vacca, Giuseppe M

2018-04-01

The present study investigated the effect of somatic cell count, lactose, and pH on sheep milk composition, coagulation properties (MCP), and curd firming (CF) parameters. Individual milk samples were collected from 1,114 Sarda ewes reared in 23 farms. Milk composition, somatic cell count, single point MCP (rennet coagulation time, RCT; curd firming time, k 20 ; and curd firmness, a 30 , a 45 , and a 60 ), and CF model parameters were achieved. Phenotypic traits were statistically analyzed using a mixed model to estimate the effects of the different levels of milk somatic cell score (SCS), lactose, and pH, respectively. Additive genetic, herd, and residual correlations among these 3 traits, and with milk composition, MCP and CF parameters, were inferred using a Bayesian approach. From a phenotypic point of view, higher SCS levels caused a delayed gelification of milk. Lactose concentration and pH were significant for many milk quality traits, with a very intense effect on both coagulation times and curd firming. These traits (RCT, RCT estimated using the curd firming over time equation, and k 20 ) showed an unfavorable increase of about 20% from the highest to the lowest level of lactose. Milk samples with pH values lower than 6.56 versus higher than 6.78 were characterized by an increase of RCT (from 6.00 to 14.3 min) and k 20 (from 1.65 to 2.65 min) and a decrease of all the 3 curd firmness traits. From a genetic point of view, the marginal posterior distribution of heritability estimates evidenced a large and exploitable variability for all 3 phenotypes. The mean intra-farm heritability estimates were 0.173 for SCS, 0.418 for lactose content, and 0.206 for pH. Lactose (favorably), and SCS and pH (unfavorably), at phenotypic and genetic levels, were correlated mainly with RCT and RCT estimated using the curd firming over time equation and scarcely with the other curd firming traits. The SCS, lactose, and pH were significantly correlated with each other's. In conclusion, results reported in the present study suggest that SCS, pH, and lactose affect, contemporarily and independently, milk quality and MCP. These phenotypes, easily available during milk recording schemes measured by infrared spectra prediction, could be used as potential indicators traits for improving cheese-making ability of ovine milk. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Adaptation, acclimation, and assembly: How optimality principles govern the scaling of form, function, and diversity of ecosystem function in the light of climate change.

NASA Astrophysics Data System (ADS)

Enquist, B. J.

2016-12-01

The link between variation in species-specific traits - due to acclimation, adaptation, and how ecological communities assemble in time and space - and larger scale ecosystem processes is an important focus for global change research. Understanding such linkages requires synthesis of evolutionary, biogeograpahic, and biogeochemical approaches. Recent observations reveal several paradoxical patterns across ecosystems. Optimality principles provide a novel framework for generating numerous predictions for how ecosystems have and will reorganize and respond to climate change. Tropical elevation gradients are natural laboratories to assess how changing climate can ramify to influence tropical forest diversity and ecosystem functioning. We tested several new predictions from trait- and metabolic scaling theories by assessing the covariation between climate, traits, biomass and gross and net primary productivity (GPP and NPP) across tropical forest plots spanning elevation gradients. We measured multiple leaf physiological, morphological, and stoichiometric traits linked to variation in tree growth. Consistent with theory, observed decreases in NPP and GPP with temperature were best predicted by forest biomass, and scaled allometrically as predicted by theory but the effect of temperature was much less, characterized by a kinetic response much lower ( 0.1eV) than predicted ( 0.65eV). This is likely due to an observed exponential increase in the mean community leaf P:N ratio and photosynthetic nutrient use efficiency with decreases in temperature. Our results are consistent with predictions from Trait Driver Theory, where adaptive/acclamatory shifts in plant traits compensate for the kinetic effects of temperature on tree growth. Further, most of the traits measured showed significantly skewed trait distributions consistent with recent observations that observed shifts in species composition. The development of trait-based scaling theory provides a robust basis to predict how shifts in climate have and will influence functional composition and ecosystem functioning. Together, these results highlight the potential critical importance optimality principles for understanding the role of the biosphere within the integrated earth system.

A multi-scale comparison of trait linkages to environmental and spatial variables in fish communities across a large freshwater lake.

PubMed

Strecker, Angela L; Casselman, John M; Fortin, Marie-Josée; Jackson, Donald A; Ridgway, Mark S; Abrams, Peter A; Shuter, Brian J

2011-07-01

Species present in communities are affected by the prevailing environmental conditions, and the traits that these species display may be sensitive indicators of community responses to environmental change. However, interpretation of community responses may be confounded by environmental variation at different spatial scales. Using a hierarchical approach, we assessed the spatial and temporal variation of traits in coastal fish communities in Lake Huron over a 5-year time period (2001-2005) in response to biotic and abiotic environmental factors. The association of environmental and spatial variables with trophic, life-history, and thermal traits at two spatial scales (regional basin-scale, local site-scale) was quantified using multivariate statistics and variation partitioning. We defined these two scales (regional, local) on which to measure variation and then applied this measurement framework identically in all 5 study years. With this framework, we found that there was no change in the spatial scales of fish community traits over the course of the study, although there were small inter-annual shifts in the importance of regional basin- and local site-scale variables in determining community trait composition (e.g., life-history, trophic, and thermal). The overriding effects of regional-scale variables may be related to inter-annual variation in average summer temperature. Additionally, drivers of fish community traits were highly variable among study years, with some years dominated by environmental variation and others dominated by spatially structured variation. The influence of spatial factors on trait composition was dynamic, which suggests that spatial patterns in fish communities over large landscapes are transient. Air temperature and vegetation were significant variables in most years, underscoring the importance of future climate change and shoreline development as drivers of fish community structure. Overall, a trait-based hierarchical framework may be a useful conservation tool, as it highlights the multi-scaled interactive effect of variables over a large landscape.

The impact of nectar chemical features on phenotypic variation in two related nectar yeasts.

PubMed

Pozo, María I; Herrera, Carlos M; Van den Ende, Wim; Verstrepen, Kevin; Lievens, Bart; Jacquemyn, Hans

2015-06-01

Floral nectars become easily colonized by microbes, most often species of the ascomycetous yeast genus Metschnikowia. Although it is known that nectar composition can vary tremendously among plant species, most probably corresponding to the nutritional requirements of their main pollinators, far less is known about how variation in nectar chemistry affects intraspecific variation in nectarivorous yeasts. Because variation in nectar traits probably affects growth and abundance of nectar yeasts, nectar yeasts can be expected to display large phenotypic variation in order to cope with varying nectar conditions. To test this hypothesis, we related variation in the phenotypic landscape of a vast collection of nectar-living yeast isolates from two Metschnikowia species (M. reukaufii and M. gruessii) to nectar chemical traits using non-linear redundancy analyses. Nectar yeasts were collected from 19 plant species from different plant families to include as much variation in nectar chemical traits as possible. As expected, nectar yeasts displayed large variation in phenotypic traits, particularly in traits related to growth performance in carbon sources and inhibitors, which was significantly related to the host plant from which they were isolated. Total sugar concentration and relative fructose content significantly explained the observed variation in the phenotypic profile of the investigated yeast species, indicating that sugar concentration and composition are the key traits that affect phenotypic variation in nectarivorous yeasts. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

Agreeableness and Conscientiousness as Predictors of University Students' Self/Peer-Assessment Rating Error

ERIC Educational Resources Information Center

Birjandi, Parviz; Siyyari, Masood

2016-01-01

This paper presents the results of an investigation into the role of two personality traits (i.e. Agreeableness and Conscientiousness from the Big Five personality traits) in predicting rating error in the self-assessment and peer-assessment of composition writing. The average self/peer-rating errors of 136 Iranian English major undergraduates…

Fire severity filters regeneration traits to shape community assembly in Alaska's boreal forest

Treesearch

Teresa N. Hollingsworth; Jill F. Johnstone; Emily L. Bernhardt; F. Stuart Chapin

2013-01-01

Disturbance can both initiate and shape patterns of secondary succession by affecting processes of community assembly. Thus, understanding assembly rules is a key element of predicting ecological responses to changing disturbance regimes. We measured the composition and trait characteristics of plant communities early after widespread wildfires in Alaska to assess how...

Profeminist Group Experience: Effects of Group Composition on Males' Attitudinal Affective Response.

ERIC Educational Resources Information Center

Auerbach, Stephen M.; And Others

1980-01-01

Investigated the effects of an intensive group experience with a "profeminist" format on sex-role related attitudes and personality trait and state measures. No overall changes were obtained across testing periods on self-report measures of sex-role attitude, sex-role identity, or authoritarianism. Only self-reports of trait anxiety showed a…

Identification of quantitative trait loci (QTL) controlling protein, oil, and five major fatty acids’ contents in soybean

USDA-ARS?s Scientific Manuscript database

Improved seed composition in soybean (Glycine max L. Merr.) for protein and oil quality is one of the major goals of soybean breeders. A group of genes that act as quantitative traits with their effects can alter protein, oil, palmitic, stearic, oleic, linoleic, and linolenic acids percentage in soy...

Yield, fruit quality traits and leaf nutrient concentration of sapodilla cv ‘Prolific’ grafted onto 16 rootstocks in Puerto Rico

USDA-ARS?s Scientific Manuscript database

Research on sapodilla has been very limited. A field study was conducted to determine the yield potential, fruit quality traits, leaf nutrient composition and scion/rootstock compatibility of cultivar ‘Prolific’ grafted onto 16 sapodilla rootstocks. For this purpose cultivars ‘Adelaide’, ‘Arcilago’...