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101.
Sucrose transporters of higher plants 总被引:7,自引:0,他引:7
102.
Abstract: Four new genera and species of annelid, Hunsrueckochaeta hohensteini , Ewaldips feyi , Crocancistrius lutzi and Scopyrites magnus , are described from the Hunsrück Slate (Lower Emsian) of Germany, as well as new material of Bundenbachochaeta eschenbachensis Bartels and Blind, 1995 . The specimens preserve details of the appendages and other aspects of the morphology as a result of pyritization. A phylogenetic analysis using the morphological data matrix of Rouse and Fauchald places four of the five genera basal to the Aciculata; the fifth Ewaldips falls within the Scolecida. The diversity of annelids represented in the Hunsrück Slate is similar to that in the other major Palaeozoic Konservat-Lagerstätten that yield them, including the Cambrian Burgess Shale and the Late Carboniferous Mazon Creek biota. 相似文献
103.
Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data. 相似文献
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Mario PL Calus 《遗传、选种与进化》2014,46(1):24
Background
Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.Methods
In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed “improved residual updating”. The second alternative algorithm, here termed “right-hand-side updating” (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model.Results
Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm.Conclusions
The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models. 相似文献107.
Christos Dadousis Roel F Veerkamp Bj?rg Heringstad Marcin Pszczola Mario PL Calus 《遗传、选种与进化》2014,46(1)
Background
Genomic prediction faces two main statistical problems: multicollinearity and n ≪ p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction.Methods
The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK.Results
In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC.Conclusions
On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.Electronic supplementary material
The online version of this article (doi:10.1186/s12711-014-0060-x) contains supplementary material, which is available to authorized users. 相似文献108.
Yvonne CJ Wientjes Roel F Veerkamp Piter Bijma Henk Bovenhuis Chris Schrooten Mario PL Calus 《遗传、选种与进化》2015,47(1)
Background
Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which reference individuals and selection candidates are from different populations, and to investigate the impact of differences in allele substitution effects across populations and of the number of QTL underlying a trait on the accuracy.Methods
A deterministic formula to estimate the accuracy of across-population genomic prediction was derived based on selection index theory. Moreover, accuracies were deterministically predicted using a formula based on population parameters and empirically calculated using simulated phenotypes and a GBLUP (genomic best linear unbiased prediction) model. Phenotypes of 1033 Holstein-Friesian, 105 Groninger White Headed and 147 Meuse-Rhine-Yssel cows were simulated by sampling 3000, 300, 30 or 3 QTL from the available high-density SNP (single nucleotide polymorphism) information of three chromosomes, assuming a correlation of 1.0, 0.8, 0.6, 0.4, or 0.2 between allele substitution effects across breeds. The simulated heritability was set to 0.95 to resemble the heritability of deregressed proofs of bulls.Results
Accuracies estimated with the deterministic formula based on selection index theory were similar to empirical accuracies for all scenarios, while accuracies predicted with the formula based on population parameters overestimated empirical accuracies by ~25 to 30%. When the between-breed genetic correlation differed from 1, i.e. allele substitution effects differed across breeds, empirical and deterministic accuracies decreased in proportion to the genetic correlation. Using a multi-trait model, it was possible to accurately estimate the genetic correlation between the breeds based on phenotypes and high-density genotypes. The number of QTL underlying the simulated trait did not affect the accuracy.Conclusions
The deterministic formula based on selection index theory estimated the accuracy of across-population genomic predictions well. The deterministic formula using population parameters overestimated the across-population genomic accuracy, but may still be useful because of its simplicity. Both formulas could accommodate for genetic correlations between populations lower than 1. The number of QTL underlying a trait did not affect the accuracy of across-population genomic prediction using a GBLUP method. 相似文献109.
SUSANNE WORISCHKA CHRISTOPH KOEBSCH CLAUDIA HELLMANN CAROLA WINKELMANN 《Freshwater Biology》2012,57(11):2247-2261
1. The spatial heterogeneity of ecosystems as well as temporal activity patterns of organisms can have far‐reaching effects on predator–prey relationships. We hypothesised that spatiotemporal constraints in mesohabitat use by benthic fish predators would reduce habitat overlap with benthic invertebrates and lead to mesohabitat‐specific predation risks. 2. We analysed the spatiotemporal activity patterns of two small‐bodied benthivorous fishes, gudgeon (Gobio gobio) and stone loach (Barbatula barbatula), and of benthic invertebrates in a small temperate stream during three 24‐h field experiments. By applying a novel method of field video observation, we monitored the spatiotemporal foraging behaviour of the fish in their natural environment. A parallel analysis of invertebrate mesohabitat use by means of small area Hess sampling allowed a direct estimation of habitat overlap at a pool–riffle scale. 3. Gudgeon showed a dominant spatial activity pattern preferring pools at all times of day, whereas stone loach used both mesohabitats but with a distinct temporal (nocturnal) activity pattern. The patterns of residence were not identical with those of active foraging. Invertebrate community composition differed significantly between mesohabitats but not between times of day. More than half of the total dissimilarity between pools and riffles was accounted for by six invertebrate taxa. Five of these were subject to higher fish predation in pools than in riffles. The total prey consumption of the two fish species together in pools was about three times as high as in riffles. Trophic niche breadth of stone loach and thus its predation range was broader than that of gudgeon. 4. These results indicate that the potential predation risk for stream invertebrates depends on the combination of spatial and temporal patterns of both predator and prey. Given the distinct differences in predation risk found between pools and riffles, we conclude that spatial heterogeneity at the mesohabitat scale can influence mechanisms and consequences of selective predation. We also suggest that the analysis of spatiotemporal predator–prey relationships should not be based on the premise that the main residence habitat and active foraging habitat of a predator are identical. 相似文献
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