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2020年05月06日

【期刊论文】Transcriptome data of control and Ascosphaera apis infected Apis mellifera ligustica larval guts

Huazhi Chen, Yu Du, Zhiwei Zhu, Cuiling Xiong, Yanzhen Zheng, Dafu Chen, Rui Guo

Data in Brief,2020,29(1):1-4

2020年02月08日

摘要

Ascosphaera apis is an obligate fungal pathogen of honeybee larvae that leads to chalkbrood, which causes heavy losses for the apiculture in China and many other countries. In this article, guts of 4-, 5-, 6-day-old Apis mellifera ligustica larvae challenged by A. apis (AmT1, AmT2, AmT3) and normal 4-day-old larval guts (AmCK) were sequenced using next-generation sequencing technology. On average, 29196197, 28690943, 29779715 and 30496725 raw reads were yielded from these four groups; an average of 29540895 clean reads were obtained after quality control. In addition, the mapping ratio of clean reads in treatment and control groups to the Apis mellifera genome were over 97.16%. For more insight please see “Uncovering the immune responses of Apis mellifera ligustica larval gut to Ascosphaera apis infection utilizing transcriptome sequencing” [1]. The raw data were submitted to the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under accession numbers: SRR4084091, SRR4084092, SRR4084095, SRR4084096, SRR4084097, SRR4084098, SRR4084099, SRR4084100, SRR4084101, SRR4084102, SRR4084093, SRR4084094.

关键词: western honeybee, Apis mellifera ligustica, Ascosphaera apis, larvae, gut, transcriptome

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2020年11月25日

【期刊论文】Group Competition-Cooperation Optimization Algorithm

冯翔, 陈海娟

Applied Intelligence,2020,待定(待定):1-16

2020年10月15日

摘要

In order to solve complex practical problems, the model of deep learning can not be limited to models such as deep neural networks. To deepen the learning model, we must actively explore various depth models. Based on this, we propose a deep evolutionary algorithm, that is group competition cooperation optimization (GCCO) algorithm. Unlike the deep learning, in the GCCO algorithm, depth is mainly reflected in multi-step iterations, feature transformation, and models are complex enough. Firstly, the bio-group model is introduced to simulate the behavior that the animals hunt for the food. Secondly, according to the rules of mutual benefit and survival of the fittest in nature, the competition model and cooperation model are introduced. Furthermore, in the individual mobility strategy, the wanderers adopt stochastic movement strategy based on feature transformation to avoid local optimization. The followers adopt the variable step size region replication method to balance the convergence speed and optimization precision. Finally, the GCCO algorithm and the other three comparison algorithms are used to test the performance of the algorithm on ten optimization functions. At the same time, in the actual problem of setting up the Shanghai gas station the to improve the timely rate, GCCO algorithm achieves better performance than the other three algorithms. Moreover, Compared to the Global Search, the GCCO algorithm takes less time to achieve similar effects to the Global Search.

关键词: Deep evolution, Competition model, Cooperation model, Feature transformation

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