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 $n$-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 $\epsilon$-close to the normalized exact solution of the original nonlinear equations with success probability $\Omega(1)$. The complexity of our algorithm is $O(\rm{poly}(\rm{log}(n/\epsilon)))$, which provides an exponential improvement over the optimal classical algorithm in dimension $n$.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 $10^0$-$10^2$ 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)