226 research outputs found
Applications of High-Throughput Sequencing Data Analysis in Transcriptional Studies
High-throughput sequencing has become one of the most powerful tools for studies in
genomics, transcriptomics, epigenomics, and metagenomics. In recent years, HTS protocols for enhancing the understanding of the diverse cellular roles of RNA have been designed, such as RNA-Seq, CLIP-Seq, and RIP-Seq. In this work, we explore the applications of HTS data analysis in transcriptional studies. First, the differential expression analysis of RNA-Seq data is discussed and applied to a sheep RNA-Seq dataset to examine the biological mechanisms of the sheep resistance to worm infection. We develop an automatic pipeline to analyze the RNA-Seq dataset, and use a negative binomial model for gene expression analysis. Functional analysis is conducted over the differentially expressed genes, and a broad range of mechanisms providing protection against the parasite are identified in the resistant sheep breed. This study provides insights into the underlying biology of sheep host resistance. Then, a deep learning method is proposed to predict the RNA binding protein binding preferences using CLIP-Seq data. The proposed method uses a deep convolutional autoencoder to effectively learn the robust sequence features, and a softmax classifier to predict the RBP binding sites. To demonstrate the efficacy of the proposed method, we evaluate its performance over a dataset containing 31 CLIP-Seq experiments. This benchmarking shows that the proposed method improves the prediction performance in terms of AUC, compared with the existing methods. The analysis also shows that the proposed method is able to provide insights to identify new RBP binding motifs. Therefore, the proposed method will be of great help in understanding the dynamic regulations of RBPs in various biological processes and diseases. Finally, a database is created to facilitate the reuse of the public available mouse RNA-Seq dataset. The metadata
of the publicly available mouse RNA-Seq datasets is manually curated and is served by a well-designed website. The database can be scaled up in the future to serve more types of HTS data
Bolt Detection Signal Analysis Method Based on ICEEMD
The construction quality of the bolt is directly related to the safety of the
project, and as such, it must be tested. In this paper, the improved complete
ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt
detection signal analysis. The ICEEMD is used in order to decompose the anchor
detection signal according to the approximate entropy of each intrinsic mode
function (IMF). The noise of the IMFs is eliminated by the wavelet soft
threshold de-noising technique. Based on the approximate entropy, and the
wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is
proposed. From the analysis of the vibration analog signal, as well as the bolt
detection signal, the result shows that the ICEEMD-De method is capable of
correctly separating the different IMFs under noisy conditions, and also that
the IMF can effectively identify the reflection signal of the end of the bolt
CM points, class numbers, and the Mahler measures of
We study the Mahler measures of the polynomial family using the method previously developed by the authors. An
algorithm is implemented to search for CM points with class numbers , we employ these points to derive interesting formulas that link the Mahler
measures of to -values of modular forms. As a by-product, three
conjectural identities of Samart are confirmed. For
, we also prove an equality that expresses a
determinant with entries the Mahler measures of as some
multiple of the -value of two isogenous elliptic curves over
.Comment: 17 pages, 2 tables. Comments are welcom
Numerical simulation of the optimal two-mode attacks for two-way continuous-variable quantum cryptography in reverse reconciliation
We analyze the security of the two-way continuous-variable quantum key
distribution protocol in reverse reconciliation against general two-mode
attacks, which represent all accessible attacks at fixed channel parameters.
Rather than against one specific attack model, the expression of secret key
rates of the two-way protocol are derived against all accessible attack models.
It is found that there is an optimal two-mode attack to minimize the
performance of the protocol in terms of both secret key rates and maximal
transmission distances. We identify the optimal two-mode attack, give the
specific attack model of the optimal two-mode attack and show the performance
of the two-way protocol against the optimal two-mode attack. Even under the
optimal two-mode attack, the performances of two-way protocol are still better
than the corresponding one-way protocol, which shows the advantage of making a
double use of the quantum channel and the potential of long-distance secure
communication using two-way protocol.Comment: 14 pages, 8 figure
Improvement of two-way continuous-variable quantum key distribution with virtual photon subtraction
We propose a method to improve the performance of two-way continuous-variable
quantum key distribution protocol by virtual photon subtraction. The Virtual
photon subtraction implemented via non-Gaussian post-selection not only
enhances the entanglement of two-mode squeezed vacuum state but also has
advantages in simplifying physical operation and promoting efficiency. In
two-way protocol, virtual photon subtraction could be applied on two sources
independently. Numerical simulations show that the optimal performance of
renovated two-way protocol is obtained with photon subtraction only used by
Alice. The transmission distance and tolerable excess noise are improved by
using the virtual photon subtraction with appropriate parameters. Moreover, the
tolerable excess noise maintains a high value with the increase of distance so
that the robustness of two-way continuous-variable quantum key distribution
system is significantly improved, especially at long transmission distance.Comment: 15 pages, 6 figure
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