Objectives: Lectin-like adhesins of enteric bacterial pathogens such as Escherichia coli are an attractive target for vaccine or drug development. Here, we have developed e-Membranome as a database of genome-wide putative adhesins in Escherichia coli (E. coli). Methods: The outer membrane adhesins were predicted from the annotated genes of Escherichia coli strains using the PSORTb program. Further analysis was performed using Interproscan and the String database. The candidate proteins can be investigated for homology modeling of the three-dimensional (3D) structure (I-TASSER version 5.1), epitope region (ABCpred), and the glycan array. Results: e-Membranome is implemented using the Django (version 2.2.5) framework. The Web Application Server Apache Tomcat 6.0 is integrated in the platform on Ubuntu Linux (version 16.04). MySQL database (version 5.7) is used as a database engine. The information of homology model of the 3D structure, epitope region, and affinity information from the glycan array will be stored in the e-Membranome database. As a case study, we performed a genome-wide screening of outer membrane-embedded proteins from the annotated genes of E. coli using the e-Membranome pipeline. Conclusion: This platform is expected to be a valuable resource for advancing research of outer membrane proteins for the construction of lectin-glycan interaction network of E. coli. In addition, the e-Membranome pipeline can be extended to other similar biological systems that need to address host-pathogen interactions.
During a sustained muscle contraction, it is observed that the power spectrum of the myoelectric (ME) signal shifts toward lower frequencies. The mean frequency can be a parameter to monitor the local muscle fatigue. We found that the 2nd order Maximum Entropy Method (MEM) approach provides more accurate and noise-immune mean frequency estimation than the FFT method. The advantage of the 2nd order MEM is simple and fast, and it yields unbiased and consistent estimation of mean frequency of ME signal.
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