240 research outputs found
New methods for measuring CSA tensors : applications to nucleotides and nucleosides
A novel version of the CSA (Chemical Shift Anisotropy) amplification experiment which results in large amplification factors is introduced. Large xa (up to 48) are achieved by sequences which are efficient in terms of the number of π pulses and total duration compared to a modification due to Orr et al. (2005), and greater flexibility in terms of the choice of amplification factor is possible than in our most recent version. Furthermore, the incorporation of XiX decoupling ensures the overall sensitivity of the experiment is optimal. This advantage has been proved by extracting the CSA tensors for a novel vinylphosphonate-linked nucleotide.
The application of CSA amplification experiment to six nucleosides is also discussed. The measured principal tensor values are compared with those calculated using the recently developed first-principles methods. Throughout this work, the NMR parameters of all nucleosides are presented.
Finally, high-resolution multi-nuclear solid-state NMR experiments are used to study some novel vinyl phosphonate-linked oligo-nucleotides
Genetic Characterization of Cynodon Accessions by Morphology, Flow Cytometry and Dna Profiling
Crop Scienc
The dynamical second-order transport coefficients of smeared Dp-brane
The smeared Dp-brane is constructed by having the black Dp-brane uniformly
smeared over several transverse directions. After integrating the spherical
directions and the smeared directions, the smeared Dp-brane turns out to be a
Chamblin-Reall model with one background scalar field. Within the framework of
the fluid/gravity correspondence, we not only prove the equivalence between the
smeared Dp-brane and the compactified Dp-brane by explicitly calculating the 7
dynamical second-order transport coefficients of their dual relativistic
fluids, but also revisit the Correlated Stability Conjecture for the smeared
Dp-brane via the fluid/gravity correspondence.Comment: 25pages, 2 table
New methods for measuring CSA tensors : applications to nucleotides and nucleosides
A novel version of the CSA (Chemical Shift Anisotropy) amplification experiment which results in large amplification factors is introduced. Large xa (up to 48) are achieved by sequences which are efficient in terms of the number of π pulses and total duration compared to a modification due to Orr et al. (2005), and greater flexibility in terms of the choice of amplification factor is possible than in our most recent version. Furthermore, the incorporation of XiX decoupling ensures the overall sensitivity of the experiment is optimal. This advantage has been proved by extracting the CSA tensors for a novel vinylphosphonate-linked nucleotide.
The application of CSA amplification experiment to six nucleosides is also discussed. The measured principal tensor values are compared with those calculated using the recently developed first-principles methods. Throughout this work, the NMR parameters of all nucleosides are presented.
Finally, high-resolution multi-nuclear solid-state NMR experiments are used to study some novel vinyl phosphonate-linked oligo-nucleotides
Comparative analysis reveals chromosome number reductions in the evolution of African bermudagrass (Cynodon transvaalensis Burtt-Davy)
African bermudagrass (Cynodon transvaalensis Burtt-Davy) (2n = 2x = 18) belongs to the genus Cynodon, tribe Cynodonteae, subfamily Chloridoideae in the grass family Poaceae. The species is frequently crossed with common bermudagrass (Cynodon dactylon Pers.) in developing high-quality hybrid turf cultivars. Molecular resources for C. transvaalensis are scarce; thus, its genomic evolution is unknown. Recently, a linkage map consisting of 1278 markers provided a powerful tool for African bermudagrass genomic research. The objective of this study was to investigate chromosome number reduction events that resulted in the nine haploid chromosomes in this species. Tag sequences of mapped single nucleotide polymorphism markers in C. transvaalensis were compared against genome sequences of Oropetium thomaeum (L.f.) Trin. (2n = 2x = 20), a genomic model in the Cynodonteae tribe. The comparative genomic analyses revealed broad collinearity between the genomes of these two species. The analyses further revealed that two major interchromosomal rearrangements of the paleochromosome ρ12 (ρ1– ρ12–ρ1 and ρ6–ρ12–ρ6) resulted in nine chromosomes in the genome of C. transvaalensis. The findings provide novel information regarding the formation of the initial diploid species in the Cynodon genus.Horticulture and Landscape ArchitecturePlant and Soil Science
Development of a genome-wide multiple duplex-SSR protocol and its applications for the identification of selfed progeny in switchgrass
Background: Switchgrass (Panicum virgatum) is a herbaceous crop for the cellulosic biofuel feedstock development in the USA and Europe. As switchgrass is a naturally outcrossing species, accurate identification of selfed progeny is important to producing inbreds, which can be used in the production of heterotic hybrids. Development of a technically reliable, time-saving and easily used marker system is needed to quantify and characterize breeding origin of progeny plants of targeted parents.Results: Genome-wide screening of 915 mapped microsatellite (simple sequence repeat, SSR) markers was conducted, and 842 (92.0%) produced clear and scorable bands on a pooled DNA sample of eight switchgrass varieties. A total of 166 primer pairs were selected on the basis of their relatively even distribution in switchgrass genome and PCR amplification quality on 16 tetraploid genotypes. Mean polymorphic information content value for the 166 markers was 0.810 ranging from 0.116 to 0.959. From them, a core set of 48 loci, which had been mapped on 17 linkage groups, was further tested and optimized to develop 24 sets of duplex markers. Most of (up to 87.5%) targeted, but non-allelic amplicons within each duplex were separated by more than 10-bp. Using the established duplex PCR protocol, selfing ratio (i.e., selfed/all progeny x100%) was identified as 0% for a randomly selected open-pollinated 'Kanlow' genotype grown in the field, 15.4% for 22 field-grown plants of bagged inflorescences, and 77.3% for a selected plant grown in a growth chamber.Conclusions: The study developed a duplex SSR-based PCR protocol consisting of 48 markers, providing ample choices of non-tightly-linked loci in switchgrass whole genome, and representing a powerful, time-saving and easily used method for the identification of selfed progeny in switchgrass. The protocol should be a valuable tool in switchgrass breeding efforts.Peer reviewedPlant and Soil Science
Complexity analysis of weakly noisy quantum states via quantum machine learning
Quantum computers capable of fault-tolerant operation are expected to provide
provable advantages over classical computational models. However, the question
of whether quantum advantages exist in the noisy intermediate-scale quantum era
remains a fundamental and challenging problem. The root of this challenge lies
in the difficulty of exploring and quantifying the power of noisy quantum
states. In this work, we focus on the complexity of weakly noisy states, which
we define as the size of the shortest quantum circuit required to prepare the
noisy state. To analyze the complexity, we propose a quantum machine learning
(QML) algorithm that exploits the intrinsic-connection property of structured
quantum neural networks. The proposed QML algorithm enables efficiently
predicting the complexity of weakly noisy states from measurement results,
representing a paradigm shift in our ability to characterize the power of noisy
quantum computation
A Quantum Federated Learning Framework for Classical Clients
Quantum Federated Learning (QFL) enables collaborative training of a Quantum
Machine Learning (QML) model among multiple clients possessing quantum
computing capabilities, without the need to share their respective local data.
However, the limited availability of quantum computing resources poses a
challenge for each client to acquire quantum computing capabilities. This
raises a natural question: Can quantum computing capabilities be deployed on
the server instead? In this paper, we propose a QFL framework specifically
designed for classical clients, referred to as CC-QFL, in response to this
question. In each iteration, the collaborative training of the QML model is
assisted by the shadow tomography technique, eliminating the need for quantum
computing capabilities of clients. Specifically, the server constructs a
classical representation of the QML model and transmits it to the clients. The
clients encode their local data onto observables and use this classical
representation to calculate local gradients. These local gradients are then
utilized to update the parameters of the QML model. We evaluate the
effectiveness of our framework through extensive numerical simulations using
handwritten digit images from the MNIST dataset. Our framework provides
valuable insights into QFL, particularly in scenarios where quantum computing
resources are scarce
Winter Survival of Experimental Bermudagrasses in the Upper Transition Zone
A winter with very cold temperatures in 2017–2018 allowed for good separation of standard and experimental bermudagrasses for freezing tolerance. When evaluated in May 2018, survival of commonly used cultivars was: Tifway, 0%; Latitude 36, 20%; Northbridge, 25%. Some experimental progeny had up to 98% winter survival on the same rating date
Trainability Analysis of Quantum Optimization Algorithms from a Bayesian Lens
The Quantum Approximate Optimization Algorithm (QAOA) is an extensively
studied variational quantum algorithm utilized for solving optimization
problems on near-term quantum devices. A significant focus is placed on
determining the effectiveness of training the -qubit QAOA circuit, i.e.,
whether the optimization error can converge to a constant level as the number
of optimization iterations scales polynomially with the number of qubits. In
realistic scenarios, the landscape of the corresponding QAOA objective function
is generally non-convex and contains numerous local optima. In this work,
motivated by the favorable performance of Bayesian optimization in handling
non-convex functions, we theoretically investigate the trainability of the QAOA
circuit through the lens of the Bayesian approach. This lens considers the
corresponding QAOA objective function as a sample drawn from a specific
Gaussian process. Specifically, we focus on two scenarios: the noiseless QAOA
circuit and the noisy QAOA circuit subjected to local Pauli channels. Our first
result demonstrates that the noiseless QAOA circuit with a depth of
can be trained efficiently,
based on the widely accepted assumption that either the left or right slice of
each block in the circuit forms a local 1-design. Furthermore, we show that if
each quantum gate is affected by a -strength local Pauli channel with the
noise strength range of to 0.1, the noisy QAOA circuit with
a depth of can also be trained
efficiently. Our results offer valuable insights into the theoretical
performance of quantum optimization algorithms in the noisy intermediate-scale
quantum era
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