Background: Protein structure comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years...
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Background: Protein structure comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging. Results: Here, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6-100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database (About 360,000 protein chains in total), as well as additional features such as 3D structure alignment visualization. the MADOKA web server is freely available at: http://***/ Conclusions: MADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs.
Network packet flow classification is the fundamental method for managing the security of network packets and is extensively applied within network security management systems, including network security isolation, in...
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ISBN:
(数字)9798331505639
ISBN:
(纸本)9798331505646
Network packet flow classification is the fundamental method for managing the security of network packets and is extensively applied within network security management systems, including network security isolation, intrusion protection, and security encryption. Withthe exponential growth of digital information in recent years, the rate of new-generation network communications has progressively escalated from the traditional 10 / 100 / 1000 M to the ultra-high-speed 10G / 40G / 100G. the ultra-high-speed network communication rate also brings new challenges to the performance of network data stream classification. this paper investigates four traditional mainstream network flow classification methods: rule tree matching, Ternary Content Addressable Memory (TCAM) matching, Hash table matching and direct comparison matching. A thorough analysis of the strengths and weaknesses of each method is presented. Given the absence of a method capable of dynamically updating the rule base, while simultaneously achieving full fuzzy matching of a high- speed, extensive rule base, this paper introduces a novel highspeed network flow classification approach using FieldProgrammable Gate Array (FPGA) technology. this method uses BRAM to build a rule array, calls DSP48E1 hard core and LUT resources in FPGA in a resource-balanced way to build a comparator, rearranges and splices the network data flow, and then matches it in parallel. the method is verified on the XC7V690T chip of XILINX. the results show that when performing 1000 five-tuple rule matching of fully fuzzy network, this method can not only update the five-tuple rule set in real time, but also use only 55,745 LUTs 1600 DSPs and 400 BRAMs resources to achieve the processing performance of 153MPPS throughput and 125ns latency, exceeding the minimum packet linear matching rate of 100G network.
18thinternational Symposium of Cipa by D. M. Barber, p.505 48th Photogrammetric Week by A. T. Smart, p.506 5thconference on Optical 3D Measurement Techniques by G. Hunter, p.509
18thinternational Symposium of Cipa by D. M. Barber, p.505 48th Photogrammetric Week by A. T. Smart, p.506 5thconference on Optical 3D Measurement Techniques by G. Hunter, p.509
the Baikal-GVD is a gigaton-volume neutrino observatory under construction in Lake Baikal. It currently generates around 200 GB of data daily. To handle this, a software system has been developed for automatic pr...
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