15,598 research outputs found

    Global transcriptome analysis of subterranean pod and seed in peanut (Arachis hypogaea L.) unravels the complexity of fruit development under dark condition

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    Peanut pods develop underground, which is the most salient characteristic in peanut. However, its developmental transcriptome remains largely unknown. In the present study, we sequenced over one billion transcripts to explore the developmental transcriptome of peanut pod using Illumina sequencing. Moreover, we identified and quantified the abundances of 165,689 transcripts in seed and shell tissues along with a pod developmental gradient. The dynamic changes of differentially expressed transcripts (DETs) were described in seed and shell. Additionally, we found that photosynthetic genes were not only pronouncedly enriched in aerial pod, but also played roles in developing pod under dark condition. Genes functioning in photomorphogenesis showed distinct expression profiles along subterranean pod development. Clustering analysis unraveled a dynamic transcriptome, in which transcripts for DNA synthesis and cell division during pod expansion were transitioning to transcripts for cell expansion and storage activity during seed filling. Collectively, our study formed a transcriptional baseline for peanut fruit development under dark condition

    Genetic diversity and distinctness based on morphological and SSR markers in peanut

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    The morphological and molecular diversity of 101 peanut varieties from South China were analyzed to identify distinctness among these varieties. No significant difference was observed for six morphological characteristics whereas a range of 0.25–0.51 of diversity index was observed for 11 morphological characteristics, with an average value of 0.39. Molecular characterization with 40 highly polymorphic simple sequence repeats (SSRs) generated a total of 167 alleles ranging from two to six alleles per marker with average 4.18 alleles per marker. The polymorphism information content (PIC) of these markers varied from 0.79 to 0.26 with an average value of 0.55 per marker. The diversity analysis using morphological and genotyping data grouped all the varieties into seven and six clusters, respectively, and varieties released by the same province tended to be grouped in the same cluster. Mantel testing revealed that the correlations between the similarity coefficient matrixes of the morphological characteristics and SSR markers of different varieties were weak (r = .347), implying that deployment of more SSR markers is needed for achieving distinctness among these peanut varieties. Nevertheless, the combination of morphological characteristics and SSR markers will effectively increase the accuracy of distinctiveness identification

    Genome-wide identification of microsatellite markers from cultivated peanut (Arachis hypogaea L.)

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    Background: Microsatellites, or simple sequence repeats (SSRs), represent important DNA variations that are widely distributed across the entire plant genome and can be used to develop SSR markers, which can then be used to conduct genetic analyses and molecular breeding. Cultivated peanut (A. hypogaea L.), an important oil crop worldwide, is an allotetraploid (AABB, 2n = 4× = 40) plant species. Because of its complex genome, genomic marker development has been very challenging. However, sequencing of cultivated peanut genome allowed us to develop genomic markers and construct a high-density physical map. Results: A total of 8,329,496 SSRs were identified, including 3,772,653, 4,414,961, and 141,882 SSRs that were distributed in subgenome A, B, and nine scaffolds, respectively. Based on the flanking sequences of the identified SSRs, a total of 973,984 newly developed SSR markers were developed in subgenome A (462,267), B (489,394), and nine scaffolds (22,323), with an average density of 392.45 markers per Mb. In silico PCR evaluation showed that an average of 88.32% of the SSR markers generated only one in silico-specific product in two tetraploid A. hypogaea varieties, Tifrunner and Shitouqi. A total of 39,599 common SSR markers were identified among the two A. hypogaea varieties and two progenitors, A. duranensis and A. ipaensis. Additionally, an amplification effectiveness of 44.15% was observed by real PCR validation. Moreover, a total of 1276 public SSR loci were integrated with the newly developed SSR markers. Finally, a previously known leaf spot quantitative trait locus (QTL), qLLS_T13_A05_7, was determined to be in a 1.448-Mb region on chromosome A05. In this region, a total of 819 newly developed SSR markers were located and 108 candidate genes were detected. Conclusions: The availability of these newly developed and public SSR markers both provide a large number of molecular markers that could potentially be used to enhance the process of trait genetic analyses and improve molecular breeding strategies for cultivated peanut

    A Unified Approach to the Classical Statistical Analysis of Small Signals

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    We give a classical confidence belt construction which unifies the treatment of upper confidence limits for null results and two-sided confidence intervals for non-null results. The unified treatment solves a problem (apparently not previously recognized) that the choice of upper limit or two-sided intervals leads to intervals which are not confidence intervals if the choice is based on the data. We apply the construction to two related problems which have recently been a battle-ground between classical and Bayesian statistics: Poisson processes with background, and Gaussian errors with a bounded physical region. In contrast with the usual classical construction for upper limits, our construction avoids unphysical confidence intervals. In contrast with some popular Bayesian intervals, our intervals eliminate conservatism (frequentist coverage greater than the stated confidence) in the Gaussian case and reduce it to a level dictated by discreteness in the Poisson case. We generalize the method in order to apply it to analysis of experiments searching for neutrino oscillations. We show that this technique both gives correct coverage and is powerful, while other classical techniques that have been used by neutrino oscillation search experiments fail one or both of these criteria.Comment: 40 pages, 15 figures. Changes 15-Dec-99 to agree more closely with published version. A few small changes, plus the two substantive changes we made in proof back in 1998: 1) The definition of "sensitivity" in Sec. V(C). It was inconsistent with our actual definition in Sec. VI. 2) "Note added in proof" at end of the Conclusio

    Measurement of \psip Radiative Decays

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    Using 14 million psi(2S) events accumulated at the BESII detector, we report first measurements of branching fractions or upper limits for psi(2S) decays into gamma ppbar, gamma 2(pi^+pi^-), gamma K_s K^-pi^++c.c., gamma K^+ K^- pi^+pi^-, gamma K^{*0} K^- pi^+ +c.c., gamma K^{*0}\bar K^{*0}, gamma pi^+pi^- p pbar, gamma 2(K^+K^-), gamma 3(pi^+pi^-), and gamma 2(pi^+pi^-)K^+K^- with the invariant mass of hadrons below 2.9GeV/c^2. We also report branching fractions of psi(2S) decays into 2(pi^+pi^-) pi^0, omega pi^+pi^-, omega f_2(1270), b_1^\pm pi^\mp, and pi^0 2(pi^+pi^-) K^+K^-.Comment: 5 pages, 4 figure

    Partial wave analysis of J/psi to p pbar pi0

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    Using a sample of 58 million J/ψJ/\psi events collected with the BESII detector at the BEPC, more than 100,000 J/ψ→ppˉπ0J/\psi \to p\bar p \pi^0 events are selected, and a detailed partial wave analysis is performed. The branching fraction is determined to be Br(J/ψ→ppˉπ0)=(1.33±0.02±0.11)×10−3Br(J/\psi \to p \bar p \pi^0)=(1.33 \pm 0.02 \pm 0.11) \times 10^{-3}. A long-sought `missing' N∗N^*, first observed in J/ψ→pnˉπ−J/\psi \to p \bar n \pi^-, is observed in this decay too, with mass and width of 2040−4+3±252040_{-4}^{+3}\pm 25 MeV/c2^2 and 230−8+8±52230_{-8}^{+8}\pm 52 MeV/c2^2, respectively. Its spin-parity favors 3/2+{3/2}^+. The masses, widths, and spin-parities of other N∗N^* states are obtained as well.Comment: Add one author nam

    Observation of Y(2175) in J/ψ→ηϕf0(980)J/\psi\to \eta\phi f_0(980)

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    The decays of J/ψ→ηϕf0(980)(η→γγ,ϕ→K+K−,f0(980)→π+π−)J/\psi\to \eta\phi f_0(980) (\eta\to \gamma\gamma, \phi \to K^+K^-, f_0(980)\to\pi^+\pi^-) are analyzed using a sample of 5.8×1075.8 \times 10^{7} J/ψJ/\psi events collected with the BESII detector at the Beijing Electron-Positron Collider (BEPC). A structure at around 2.182.18 GeV/c2c^2 with about 5σ5\sigma significance is observed in the ϕf0(980)\phi f_0(980) invariant mass spectrum. A fit with a Breit-Wigner function gives the peak mass and width of m=2.186±0.010(stat)±0.006(syst)m=2.186\pm 0.010 (stat)\pm 0.006 (syst) GeV/c2c^2 and Γ=0.065±0.023(stat)±0.017(syst)\Gamma=0.065\pm 0.023 (stat)\pm 0.017 (syst) GeV/c2c^2, respectively, that are consistent with those of Y(2175), observed by the BABAR collaboration in the initial-state radiation (ISR) process e+e−→γISRϕf0(980)e^+e^-\to\gamma_{ISR}\phi f_0(980). The production branching ratio is determined to be Br(J/ψ→ηY(2175))⋅Br(Y(2175)→ϕf0(980))⋅Br(f0(980)→π+π−)=(3.23±0.75(stat)±0.73(syst))×10−4Br(J/\psi\to\eta Y(2175))\cdot Br(Y(2175)\to\phi f_0(980))\cdot Br(f_0(980)\to\pi^+\pi^-)=(3.23\pm 0.75 (stat)\pm0.73 (syst))\times 10^{-4}, assuming that the Y(2175) is a 1−−1^{--} state.Comment: 5 pages, 4 figures, accepted by Phys. Rev. Let

    Measurements of J/ψJ/\psi and ψ(2S)\psi(2S) decays into ΛΛˉπ0\Lambda \bar{\Lambda}\pi^0 and ΛΛˉη\Lambda \bar{\Lambda}\eta

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    Using 58 million J/ψJ/\psi and 14 million ψ(2S)\psi(2S) events collected by the BESII detector at the BEPC, branching fractions or upper limits for the decays J/ψJ/\psi and ψ(2S)→ΛΛˉπ0\psi(2S) \to \Lambda \bar{\Lambda}\pi^0 and ΛΛˉη\Lambda \bar{\Lambda}\eta are measured. For the isospin violating decays, the upper limits are determined to be B(J/ψ→ΛΛˉπ0)<6.4×10−5{\cal B}(J/\psi \to \Lambda \bar{\Lambda}\pi^0)<6.4\times 10^{-5} and B(ψ(2S)→ΛΛˉπ0)<4.9×10−5{\cal B}(\psi(2S) \to \Lambda \bar{\Lambda}\pi^0)<4.9\times 10^{-5} at the 90% confidence level. The isospin conserving process J/ψ→ΛΛˉηJ/\psi \to \Lambda \bar{\Lambda}\eta is observed for the first time, and its branching fraction is measured to be B(J/ψ→ΛΛˉη)=(2.62±0.60±0.44)×10−4{\cal B}(J/\psi \to \Lambda \bar{\Lambda}\eta)=(2.62\pm 0.60\pm 0.44)\times 10^{-4}, where the first error is statistical and the second one is systematic. No ΛΛˉη\Lambda \bar{\Lambda}\eta signal is observed in ψ(2S)\psi(2S) decays, and B(ψ(2S)→ΛΛˉη)<1.2×10−4{\cal B}(\psi(2S) \to \Lambda \bar{\Lambda}\eta)<1.2\times 10^{-4} is set at the 90% confidence level. Branching fractions of J/ψJ/\psi decays into Σ+π−barΛ\Sigma^+ \pi^- bar{\Lambda} and Σˉ−π+Λ\bar{\Sigma}^- \pi^+ \Lambda are also reported, and the sum of these branching fractions is determined to be B(J/ψ→Σ+π−Λˉ+c.c.)=(1.52±0.08±0.16)×10−3{\cal B}(J/\psi \to \Sigma^+\pi^- \bar{\Lambda} + c.c.)=(1.52\pm 0.08\pm 0.16)\times 10^{-3}.Comment: 7 pages, 10 figures. Phys.Rev.D comments considere

    Sequencing of Cultivated Peanut, Arachis hypogaea, Yields Insights into Genome Evolution and Oil Improvement

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    Cultivated peanut (Arachis hypogaea) is an allotetraploid crop planted in Asia, Africa, and America for edible oil and protein. To explore the origins and consequences of tetraploidy, we sequenced the allotetraploid A. hypogaea genome and compared it with the related diploid Arachis duranensis and Arachis ipaensis genomes. We annotated 39 888 A-subgenome genes and 41 526 B-subgenome genes in allotetraploid peanut. The A. hypogaea subgenomes have evolved asymmetrically, with the B subgenome resembling the ancestral state and the A subgenome undergoing more gene disruption, loss, conversion, and transposable element proliferation, and having reduced gene expression during seed development despite lacking genome-wide expression dominance. Genomic and transcriptomic analyses identified more than 2 500 oil metabolism-related genes and revealed that most of them show altered expression early in seed development while their expression ceases during desiccation, presenting a comprehensive map of peanut lipid biosynthesis. The availability of these genomic resources will facilitate a better understanding of the complex genome architecture, agronomically and economically important genes, and genetic improvement of peanut
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