168 research outputs found
Quantum Synchronizable Codes From Quadratic Residue Codes and Their Supercodes
Quantum synchronizable codes are quantum error-correcting codes designed to
correct the effects of both quantum noise and block synchronization errors.
While it is known that quantum synchronizable codes can be constructed from
cyclic codes that satisfy special properties, only a few classes of cyclic
codes have been proved to give promising quantum synchronizable codes. In this
paper, using quadratic residue codes and their supercodes, we give a simple
construction for quantum synchronizable codes whose synchronization
capabilities attain the upper bound. The method is applicable to cyclic codes
of prime length
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Characterization of Cell Glycocalyx with Mass Spectrometry Methods.
The cell membrane plays an important role in protecting the cell from its extracellular environment. As such, extensive work has been devoted to studying its structure and function. Crucial intercellular processes, such as signal transduction and immune protection, are mediated by cell surface glycosylation, which is comprised of large biomolecules, including glycoproteins and glycosphingolipids. Because perturbations in glycosylation could result in dysfunction of cells and are related to diseases, the analysis of surface glycosylation is critical for understanding pathogenic mechanisms and can further lead to biomarker discovery. Different mass spectrometry-based techniques have been developed for glycan analysis, ranging from highly specific, targeted approaches to more comprehensive profiling studies. In this review, we summarized the work conducted for extensive analysis of cell membrane glycosylation, particularly those employing liquid chromatography with mass spectrometry (LC-MS) in combination with various sample preparation techniques
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Identification of potential sialic acid binding proteins on cell membranes by proximity chemical labeling.
The cell membrane contains a highly interactive glycan surface on a scaffold of proteins and lipids. Sialic acids are negatively charged monosaccharides, and the proteins that bind to sialic acids play an important role in maintaining the integrity and collective functions of this interactive space. Sialic acid binding proteins are not readily identified and have nearly all been discovered empirically. In this research, we developed a proximity labeling method to characterize proteins with oxidation by localized radicals produced in situ. The sites of oxidation were identified and quantified using a standard proteomic workflow. In this method, a clickable probe was synthesized and attached to modified sialic acids on the cell membrane, which functioned as a catalyst for the localized formation of radicals from hydrogen peroxide. The proteins in the sialic acid environment were labeled through amino acid oxidation, and were categorized into three groups including sialylated proteins, non-sialylated proteins with transmembrane domains, and proteins that are associated with the membrane with neither sialylated nor transmembrane domains. The analysis of the last group of proteins showed that they were associated with binding functions including carbohydrate binding, anion binding, and cation binding, thereby revealing the nature of the sialic acid-protein interaction. This new tool identified potential sialic acid-binding proteins in the extracellular space and proteins that were organized around sialylated glycans in cells
Data Asset Management and Visualization Based on Intelligent Algorithm: Taking Power Equipment Data as An Example
Data asset management is adequate in solving the problem of data silence and data idleness for enterprises. Through intelligent algorithms such as neural network, in-depth learning and block chain, and guided by business needs, it extracts, analyzes and visualizes the existing business precipitation data, and forms scattered and disordered data into valuable information to support the development of the company, so as to activate data assets. Taking the management data of electric power equipment as an example, this paper proposes a method of fusion of multiple intelligent control algorithms. The specific modules include the fusion of heterogeneous data; feature extraction of equipment asset management data based on machine learning; intelligent control of multi-objective optimization environment based on energy consumption data; BIM data visualization based on data classification-energy extraction-neural network (SVM-CART-SAE-DNN) algorithm fusion. The algorithm can effectively improve the efficiency of equipment management and enhance the security and economy of power infrastructure through intelligent control of equipment management
Information-Coupled Turbo Codes for LTE Systems
We propose a new class of information-coupled (IC) Turbo codes to improve the
transport block (TB) error rate performance for long-term evolution (LTE)
systems, while keeping the hybrid automatic repeat request protocol and the
Turbo decoder for each code block (CB) unchanged. In the proposed codes, every
two consecutive CBs in a TB are coupled together by sharing a few common
information bits. We propose a feed-forward and feed-back decoding scheme and a
windowed (WD) decoding scheme for decoding the whole TB by exploiting the
coupled information between CBs. Both decoding schemes achieve a considerable
signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct
the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and
our proposed IC Turbo codes from the EXIT functions of underlying convolutional
codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes
is derived and calculated by the constructed EXIT charts. Numerical results
show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for
various code parameters at a TB error rate level of , which complies
with the derived SNR gain upper bound.Comment: 13 pages, 12 figure
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