为评价国内外二倍体芋种质资源的群体结构和遗传多样性,本研究选取来自国内和东南亚的69份二倍体芋种质资源,利用扩增片段长度测序(specific-locus amplified fragment sequencing, SLAF-seq)技术开发SNP标记;基于这些高质量的SNP标记,...为评价国内外二倍体芋种质资源的群体结构和遗传多样性,本研究选取来自国内和东南亚的69份二倍体芋种质资源,利用扩增片段长度测序(specific-locus amplified fragment sequencing, SLAF-seq)技术开发SNP标记;基于这些高质量的SNP标记,进行了群体结构、系统进化树、主成分和遗传多样性分析。结果显示,经筛选共获得了27 121个高质量SNP标记;将69份芋种质分为5个群,与形态分类的结果一致。其中,野芋和匍匐茎魁芋的遗传关系较近,槟榔芋与花用芋遗传关系较近;野芋遗传多样性最高(Nei氏遗传多样性指数H=0.246,核苷酸多样性指数π=0.260),而槟榔芋的遗传多样性最低(H=0.127,π=0.118);5种形态类型的二倍体芋遗传分化明显(Fst=0.109~0.422),能够解释67.1%的遗传变异;而地理分布对芋种质资源的遗传变异贡献仅为18.5%。本研究为芋种质资源鉴定、保存和遗传育种提供理论基础。展开更多
针对命名数据网络(named data networking,NDN)路径选择过程中传输速度慢的问题,采用蝙蝠算法优化NDN网络的路径选择。为了证明蝙蝠算法在路径选择上的优势,将蝙蝠算法、遗传算法和粒子群算法进行对比,得出蝙蝠算法求得最优解的概率更...针对命名数据网络(named data networking,NDN)路径选择过程中传输速度慢的问题,采用蝙蝠算法优化NDN网络的路径选择。为了证明蝙蝠算法在路径选择上的优势,将蝙蝠算法、遗传算法和粒子群算法进行对比,得出蝙蝠算法求得最优解的概率更高、收敛速度更快,为此提出了一种基于蝙蝠算法的NDN网络路径选择(bat algorithm-path selection in NDN,BA-PSNDN)方法。对传输过程中的节点进行实时更新并计算,通过迭代选出最优路径进行数据传输。使用ndnSIM2.7软件进行仿真,通过在兴趣包和数据包中加入自定义段,保存数据包传输过程中的信息并进行路径选择,仿真出最优路径后输出时延信息。结果表明,BA-PSNDN方法在减少网络传输时延方面更优。展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
紫色的芋肉纤维因其潜在的保健功能而深受市场欢迎,被认为是重要的品质性状。本试验以芋肉纤维紫色的荔浦芋为母本,以芋肉纤维黄色的乐平野芋为父本构建F2分离群体,利用特异长度扩增片段测序(specific length amplified fragment sequen...紫色的芋肉纤维因其潜在的保健功能而深受市场欢迎,被认为是重要的品质性状。本试验以芋肉纤维紫色的荔浦芋为母本,以芋肉纤维黄色的乐平野芋为父本构建F2分离群体,利用特异长度扩增片段测序(specific length amplified fragment sequencing,SLAF-seq)技术结合集群分离分析法(bulked segregant analysis,BSA),开发与芋肉纤维颜色连锁的分子标记。结果表明:通过SLAF-seq技术共获得有效reads为37.65 Mb,开发出293363个高质量的SLAF标签,获得SNP位点57710个。利用SNP-index进行关联区域内2个亲本之间的SNP分析,获得了27个与芋肉纤维颜色性状紧密关联的SNP位点。设计特异引物进行等位特异性PCR(allele specific PCR,AS-PCR)验证SNP位点的多态性,其中Marker44764能对芋肉纤维颜色进行有效的基因分型。本试验建立了一套快捷简便的芋的基因分型方法,可用来对材料进行苗期的分子标记辅助选择,进而提高芋育种效率。展开更多
文摘针对命名数据网络(named data networking,NDN)路径选择过程中传输速度慢的问题,采用蝙蝠算法优化NDN网络的路径选择。为了证明蝙蝠算法在路径选择上的优势,将蝙蝠算法、遗传算法和粒子群算法进行对比,得出蝙蝠算法求得最优解的概率更高、收敛速度更快,为此提出了一种基于蝙蝠算法的NDN网络路径选择(bat algorithm-path selection in NDN,BA-PSNDN)方法。对传输过程中的节点进行实时更新并计算,通过迭代选出最优路径进行数据传输。使用ndnSIM2.7软件进行仿真,通过在兴趣包和数据包中加入自定义段,保存数据包传输过程中的信息并进行路径选择,仿真出最优路径后输出时延信息。结果表明,BA-PSNDN方法在减少网络传输时延方面更优。
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
文摘紫色的芋肉纤维因其潜在的保健功能而深受市场欢迎,被认为是重要的品质性状。本试验以芋肉纤维紫色的荔浦芋为母本,以芋肉纤维黄色的乐平野芋为父本构建F2分离群体,利用特异长度扩增片段测序(specific length amplified fragment sequencing,SLAF-seq)技术结合集群分离分析法(bulked segregant analysis,BSA),开发与芋肉纤维颜色连锁的分子标记。结果表明:通过SLAF-seq技术共获得有效reads为37.65 Mb,开发出293363个高质量的SLAF标签,获得SNP位点57710个。利用SNP-index进行关联区域内2个亲本之间的SNP分析,获得了27个与芋肉纤维颜色性状紧密关联的SNP位点。设计特异引物进行等位特异性PCR(allele specific PCR,AS-PCR)验证SNP位点的多态性,其中Marker44764能对芋肉纤维颜色进行有效的基因分型。本试验建立了一套快捷简便的芋的基因分型方法,可用来对材料进行苗期的分子标记辅助选择,进而提高芋育种效率。
