芝麻是八大类食物过敏原之一,快速准确识别芝麻过敏原对预防其过敏有重要意义。核酸适配体可以高效识别靶标过敏原,在过敏原检测中有良好的应用前景。为了获得芝麻主要过敏原Ses i 2的特异性核酸适体,本研究以Ses i 2为靶标,通过磁珠筛...芝麻是八大类食物过敏原之一,快速准确识别芝麻过敏原对预防其过敏有重要意义。核酸适配体可以高效识别靶标过敏原,在过敏原检测中有良好的应用前景。为了获得芝麻主要过敏原Ses i 2的特异性核酸适体,本研究以Ses i 2为靶标,通过磁珠筛选法(磁珠-SELEX)开展10轮筛选,经由高通量测序获得6条候补序列(S1~S6),并进行家族性、同源性分析及二级结构预测。结果表明,6条候选核酸适体的重复率可达46.38%,其自由能在-9.02到-2.47 kcal·moL^(-1)之间,根据自由能能量稳定原则,S1和S5吉布斯自由能最低最稳定,分别为-6.70和-9.02 kcal·moL^(-1)。利用ELISA试验进行亲和力测试,结果表明核酸适体S1和S2的亲和能力较强,S1:KD=67.02 nmol·L^(-1),R2=0.925 8,S2:KD=97.65 nmol·L^(-1),R2=0.795 1。核酸适体S1与过敏原Ses i 2的结合力和其他过敏原蛋白相比有显著差异,可视为具有特异性。本研究最终获得一条兼具良好亲和力和特异性的核酸适体S1,为芝麻过敏原快速检测提供了技术支撑。展开更多
The high Ba-Sr rocks can provide significant clues about the evolution of the continent lithosphere,but their petrogenesis remains controversial.Identifying the Late Cretaceous high Ba–Sr granodiorites in the SE Lhas...The high Ba-Sr rocks can provide significant clues about the evolution of the continent lithosphere,but their petrogenesis remains controversial.Identifying the Late Cretaceous high Ba–Sr granodiorites in the SE Lhasa Block could potentially provide valuable insights into the continent evolution of the Qinghai-Tibet Plateau.Zircon U–Pb ages suggest that the granodiorites were emplaced at 87.32±0.43 Ma.Geochemically,the high Ba–Sr granodiorites are characterized by elevated K_(2)O+Na_(2)O contents(8.18-8.73 wt%)and K_(2)O/Na_(2)O ratios(0.99-1.25,mostly>1),and belong to high-K calc-alkaline to shoshonitic series.The Yonglaga granodiorites show notably high Sr(653-783 ppm)and Ba(1346-1531 ppm)contents,plus high Sr/Y(30.92-38.18)and(La/Yb)_(N)(27.7-34.7)ratios,but low Y(20.0-22.8 ppm)and Yb(1.92-2.19 ppm)contents with absence of negative Eu anomalies(δEu=0.83-0.88),all similar to typical high Ba–Sr granitoids.The variable zirconεHf(t)values of-4.58 to+12.97,elevated initial^(87)Sr/^(86)Sr isotopic ratios of 0.707254 to 0.707322 and lowεNd(t)values of-2.8 to-3.6 with decoupling from the Hf system suggest that a metasomatized mantle source included significant recycled ancient materials.The occurrence of such high Ba–Sr intrusions indicates previous contributions of metasomatized mantle-derived juvenile material to the continents,which imply the growth of continental crust during the Late Cretaceous in the SE Lhasa.Together with regional data,we infer that the underplated mafic magma provides a significant amount of heat,which leads to partial melting of the juvenile crust.The melting of the metasomatized mantle could produce a juvenile mafic lower crust,from which the high Ba–Sr granitoids were derived from reworking of previous mafic crust during the Late Cretaceous(ca.100-80 Ma)in the SE Lhasa.展开更多
【目的】设计MobileNet with large convolution Unit(Mobile-LU)模型,解决由于辣椒病害种类复杂和类间差异不明显而造成的病害识别困难、准确率低等问题。【方法】重新构建MobileNetV3的特征提取层,在并行分支单元结构中采用不同尺度...【目的】设计MobileNet with large convolution Unit(Mobile-LU)模型,解决由于辣椒病害种类复杂和类间差异不明显而造成的病害识别困难、准确率低等问题。【方法】重新构建MobileNetV3的特征提取层,在并行分支单元结构中采用不同尺度的分离卷积,增强模型对辣椒病害尺寸差异特征的表达能力;引入Squeeze-and-Excitation(SE)注意力机制,加强模型对病害相关的特征的学习,提高病害识别准确率;同时使用Leaky ReLU激活函数,在负值区域引入小的斜率,避免网络神经元死亡问题;调整输出层节点个数,更好适应辣椒病害分类任务。【结果】Mobile-LU模型的识别准确率达到98.2%,相较于MobilenetV3-small、ResNet34、VGG16、Alexnet、Swin Transformer、MobileVIT等模型分别高出8.9、7.3、4.4、20.4、6.0、8.3个百分点,且Mobile-LU模型在精确率、召回率、特异度以及F1分数等关键性能指标上也均有优势。【结论】Mobile-LU模型对辣椒病害的识别性能更优,能更好满足辣椒病害识别任务。展开更多
文摘芝麻是八大类食物过敏原之一,快速准确识别芝麻过敏原对预防其过敏有重要意义。核酸适配体可以高效识别靶标过敏原,在过敏原检测中有良好的应用前景。为了获得芝麻主要过敏原Ses i 2的特异性核酸适体,本研究以Ses i 2为靶标,通过磁珠筛选法(磁珠-SELEX)开展10轮筛选,经由高通量测序获得6条候补序列(S1~S6),并进行家族性、同源性分析及二级结构预测。结果表明,6条候选核酸适体的重复率可达46.38%,其自由能在-9.02到-2.47 kcal·moL^(-1)之间,根据自由能能量稳定原则,S1和S5吉布斯自由能最低最稳定,分别为-6.70和-9.02 kcal·moL^(-1)。利用ELISA试验进行亲和力测试,结果表明核酸适体S1和S2的亲和能力较强,S1:KD=67.02 nmol·L^(-1),R2=0.925 8,S2:KD=97.65 nmol·L^(-1),R2=0.795 1。核酸适体S1与过敏原Ses i 2的结合力和其他过敏原蛋白相比有显著差异,可视为具有特异性。本研究最终获得一条兼具良好亲和力和特异性的核酸适体S1,为芝麻过敏原快速检测提供了技术支撑。
基金supported by the National Natural Science Foundation of China[Grants.41802054]supported by a Royal Society SinoBritish Fellowship Trust International Exchanges Award[Grant No.IESR3213093]。
文摘The high Ba-Sr rocks can provide significant clues about the evolution of the continent lithosphere,but their petrogenesis remains controversial.Identifying the Late Cretaceous high Ba–Sr granodiorites in the SE Lhasa Block could potentially provide valuable insights into the continent evolution of the Qinghai-Tibet Plateau.Zircon U–Pb ages suggest that the granodiorites were emplaced at 87.32±0.43 Ma.Geochemically,the high Ba–Sr granodiorites are characterized by elevated K_(2)O+Na_(2)O contents(8.18-8.73 wt%)and K_(2)O/Na_(2)O ratios(0.99-1.25,mostly>1),and belong to high-K calc-alkaline to shoshonitic series.The Yonglaga granodiorites show notably high Sr(653-783 ppm)and Ba(1346-1531 ppm)contents,plus high Sr/Y(30.92-38.18)and(La/Yb)_(N)(27.7-34.7)ratios,but low Y(20.0-22.8 ppm)and Yb(1.92-2.19 ppm)contents with absence of negative Eu anomalies(δEu=0.83-0.88),all similar to typical high Ba–Sr granitoids.The variable zirconεHf(t)values of-4.58 to+12.97,elevated initial^(87)Sr/^(86)Sr isotopic ratios of 0.707254 to 0.707322 and lowεNd(t)values of-2.8 to-3.6 with decoupling from the Hf system suggest that a metasomatized mantle source included significant recycled ancient materials.The occurrence of such high Ba–Sr intrusions indicates previous contributions of metasomatized mantle-derived juvenile material to the continents,which imply the growth of continental crust during the Late Cretaceous in the SE Lhasa.Together with regional data,we infer that the underplated mafic magma provides a significant amount of heat,which leads to partial melting of the juvenile crust.The melting of the metasomatized mantle could produce a juvenile mafic lower crust,from which the high Ba–Sr granitoids were derived from reworking of previous mafic crust during the Late Cretaceous(ca.100-80 Ma)in the SE Lhasa.
文摘【目的】设计MobileNet with large convolution Unit(Mobile-LU)模型,解决由于辣椒病害种类复杂和类间差异不明显而造成的病害识别困难、准确率低等问题。【方法】重新构建MobileNetV3的特征提取层,在并行分支单元结构中采用不同尺度的分离卷积,增强模型对辣椒病害尺寸差异特征的表达能力;引入Squeeze-and-Excitation(SE)注意力机制,加强模型对病害相关的特征的学习,提高病害识别准确率;同时使用Leaky ReLU激活函数,在负值区域引入小的斜率,避免网络神经元死亡问题;调整输出层节点个数,更好适应辣椒病害分类任务。【结果】Mobile-LU模型的识别准确率达到98.2%,相较于MobilenetV3-small、ResNet34、VGG16、Alexnet、Swin Transformer、MobileVIT等模型分别高出8.9、7.3、4.4、20.4、6.0、8.3个百分点,且Mobile-LU模型在精确率、召回率、特异度以及F1分数等关键性能指标上也均有优势。【结论】Mobile-LU模型对辣椒病害的识别性能更优,能更好满足辣椒病害识别任务。
