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
[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
目的探究基于MRI的瘤内及瘤周影像组学特征在术前无创预测子宫内膜癌(endometrial carcinoma,EC)患者淋巴脉管间隙浸润(lymphovascular space invasion,LVSI)的价值。材料与方法回顾性分析222例经术后病理证实的EC患者的临床及常规影像...目的探究基于MRI的瘤内及瘤周影像组学特征在术前无创预测子宫内膜癌(endometrial carcinoma,EC)患者淋巴脉管间隙浸润(lymphovascular space invasion,LVSI)的价值。材料与方法回顾性分析222例经术后病理证实的EC患者的临床及常规影像学特征。基于患者MRI的T2加权成像(T2-weighted imaging,T2WI)序列、T1加权成像(T1-weighted imaging,T1WI)增强延迟期及表观弥散系数(apparent diffusion coefficient,ADC)图像中肿瘤及瘤周区域提取影像组学特征,行单因素及多因素logistic分析,筛选LVSI的独立危险因素,构建临床、瘤内、瘤周、瘤内结合瘤周及临床-影像组学联合模型。采用受试者工作特征(receiver operating characteristic,ROC)曲线筛选评估模型的预测能力,以校准曲线评估模型校准度,以决策曲线分析评价模型的临床价值。结果基于T2WI序列瘤周3 mm建立的影像组学模型效能最优,训练组及验证组曲线下面积(area under the curve,AUC)分别为0.902,0.803,联合肿瘤最大径、ADC值及影像组学模型构成的临床-影像组学联合模型对LVSI具有良好的预测价值,在训练组的AUC值为0.903,验证组为0.801。联合模型在校准曲线结果显示校准度良好,在决策曲线分析中净收益最高。结论基于MRI的EC瘤内及瘤周影像组学模型具有良好临床表现能力,可应用于术前无创性预测EC的脉管侵犯状态。展开更多
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。
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.
文摘目的探究基于MRI的瘤内及瘤周影像组学特征在术前无创预测子宫内膜癌(endometrial carcinoma,EC)患者淋巴脉管间隙浸润(lymphovascular space invasion,LVSI)的价值。材料与方法回顾性分析222例经术后病理证实的EC患者的临床及常规影像学特征。基于患者MRI的T2加权成像(T2-weighted imaging,T2WI)序列、T1加权成像(T1-weighted imaging,T1WI)增强延迟期及表观弥散系数(apparent diffusion coefficient,ADC)图像中肿瘤及瘤周区域提取影像组学特征,行单因素及多因素logistic分析,筛选LVSI的独立危险因素,构建临床、瘤内、瘤周、瘤内结合瘤周及临床-影像组学联合模型。采用受试者工作特征(receiver operating characteristic,ROC)曲线筛选评估模型的预测能力,以校准曲线评估模型校准度,以决策曲线分析评价模型的临床价值。结果基于T2WI序列瘤周3 mm建立的影像组学模型效能最优,训练组及验证组曲线下面积(area under the curve,AUC)分别为0.902,0.803,联合肿瘤最大径、ADC值及影像组学模型构成的临床-影像组学联合模型对LVSI具有良好的预测价值,在训练组的AUC值为0.903,验证组为0.801。联合模型在校准曲线结果显示校准度良好,在决策曲线分析中净收益最高。结论基于MRI的EC瘤内及瘤周影像组学模型具有良好临床表现能力,可应用于术前无创性预测EC的脉管侵犯状态。
