1,636 research outputs found
RScan: fast searching structural similarities for structured RNAs in large databases
<p>Abstract</p> <p>Background</p> <p>Many RNAs have evolutionarily conserved secondary structures instead of primary sequences. Recently, there are an increasing number of methods being developed with focus on the structural alignments for finding conserved secondary structures as well as common structural motifs in pair-wise or multiple sequences. A challenging task is to search similar structures quickly for structured RNA sequences in large genomic databases since existing methods are too slow to be used in large databases.</p> <p>Results</p> <p>An implementation of a fast structural alignment algorithm, RScan, is proposed to fulfill the task. RScan is developed by levering the advantages of both hashing algorithms and local alignment algorithms. In our experiment, on the average, the times for searching a tRNA and an rRNA in the randomized <it>A. pernix </it>genome are only 256 seconds and 832 seconds respectively by using RScan, but need 3,178 seconds and 8,951 seconds respectively by using an existing method RSEARCH. Remarkably, RScan can handle large database queries, taking less than 4 minutes for searching similar structures for a microRNA precursor in human chromosome 21.</p> <p>Conclusion</p> <p>These results indicate that RScan is a preferable choice for real-life application of searching structural similarities for structured RNAs in large databases. RScan software is freely available at <url>http://bioinfo.au.tsinghua.edu.cn/member/cxue/rscan/RScan.htm</url>.</p
Quantum Algorithm for Solving Quadratic Nonlinear System of Equations
High-dimensional nonlinear system of equations that appears in all kinds of
fields is difficult to be solved on a classical computer, we present an
efficient quantum algorithm for solving -dimensional quadratic nonlinear
system of equations. Our algorithm embeds the equations into a
finite-dimensional system of linear equations with homotopy perturbation method
and a linearization technique, then we solve the linear equations with quantum
linear system solver and obtain a state which is -close to the
normalized exact solution of the original nonlinear equations with success
probability . The complexity of our algorithm is
, which provides an exponential improvement
over the optimal classical algorithm in dimension .Comment: 9 pages; Modify the format error of tex source fil
Can Variational Quantum Algorithms Demonstrate Quantum Advantages? Time Really Matters
Applying low-depth quantum neural networks (QNNs), variational quantum
algorithms (VQAs) are both promising and challenging in the noisy
intermediate-scale quantum (NISQ) era: Despite its remarkable progress,
criticisms on the efficiency and feasibility issues never stopped. However,
whether VQAs can demonstrate quantum advantages is still undetermined till now,
which will be investigated in this paper. First, we will prove that there
exists a dependency between the parameter number and the gradient-evaluation
cost when training QNNs. Noticing there is no such direct dependency when
training classical neural networks with the backpropagation algorithm, we argue
that such a dependency limits the scalability of VQAs. Second, we estimate the
time for running VQAs in ideal cases, i.e., without considering realistic
limitations like noise and reachability. We will show that the ideal time cost
easily reaches the order of a 1-year wall time. Third, by comparing with the
time cost using classical simulation of quantum circuits, we will show that
VQAs can only outperform the classical simulation case when the time cost
reaches the scaling of - years. Finally, based on the above
results, we argue that it would be difficult for VQAs to outperform classical
cases in view of time scaling, and therefore, demonstrate quantum advantages,
with the current workflow. Since VQAs as well as quantum computing are
developing rapidly, this work does not aim to deny the potential of VQAs. The
analysis in this paper provides directions for optimizing VQAs, and in the long
run, seeking more natural hybrid quantum-classical algorithms would be
meaningful.Comment: 18 pages, 7 figure
Radiative thermal switch via metamaterials made of vanadium dioxide-coated nanoparticles
In this work, a thermal switch is proposed based on the phase-change material
vanadium dioxide (VO2) within the framework of near-field radiative heat
transfer (NFRHT). The radiative thermal switch consists of two metamaterials
filled with core-shell nanoparticles, with the shell made of VO2. Compared to
traditional VO2 slabs, the proposed switch exhibits a more than 2-times
increase in the switching ratio, reaching as high as 90.29% with a 100 nm
vacuum gap. The improved switching effect is attributed to the capability of
the VO2 shell to couple with the core, greatly enhancing heat transfer with the
insulating VO2, while blocking the motivation of the core in the metallic state
of VO2. As a result, this efficiently enlarges the difference in photonic
characteristics between the insulating and metallic states of the structure,
thereby improving the ability to rectify the NFRHT. The proposed switch opens
pathways for active control of NFRHT and holds practical significance for
developing thermal photon-based logic circuits
Metformin improves the angiogenic functions of endothelial progenitor cells via activating AMPK/eNOS pathway in diabetic mice
Additional file 3: Figure S3. BM-EPC functions under the osmotic pressure equal to that of high glucose (HG). Compared with the normal glucose (NG), BM-EPCs treated by mannitol to make equal osmotic pressure with HG showed no significant changes in tube formation and migration.**P < 0.01, vs NG; # P < 0.05 vs HG. Values are mean ± SEM (n = 5 per group)
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