#朴志晟[超话]# ✨#朴志晟超绝可爱# 【预览】210208 下班路
CR :mamelia _for _8
2021生日应援part 1:https://t.cn/A65zXQzf
2021生日应援part 2:https://t.cn/A65UL8di
2021生日应援part 3:https://t.cn/A655qv5k
2021生日应援part 4:https://t.cn/A65Mv227
2021生日应援part 5:https://t.cn/A65C7rdX
2021生日应援part 6:https://t.cn/A650YpwV
2021生日应援part 7:https://t.cn/A65Hc8Lt
2021生日应援part 8:https://t.cn/A65R453G
2021生日应援part 9:https://t.cn/A65RFt1v
2021生日应援part 10:https://t.cn/A65n5zMc
志晟专属存钱罐:https://t.cn/A6hbHUth
吧主招新:https://t.cn/ExUYdqd
♀️工作组招新: https://t.cn/A6Z2zy36
CR :mamelia _for _8
2021生日应援part 1:https://t.cn/A65zXQzf
2021生日应援part 2:https://t.cn/A65UL8di
2021生日应援part 3:https://t.cn/A655qv5k
2021生日应援part 4:https://t.cn/A65Mv227
2021生日应援part 5:https://t.cn/A65C7rdX
2021生日应援part 6:https://t.cn/A650YpwV
2021生日应援part 7:https://t.cn/A65Hc8Lt
2021生日应援part 8:https://t.cn/A65R453G
2021生日应援part 9:https://t.cn/A65RFt1v
2021生日应援part 10:https://t.cn/A65n5zMc
志晟专属存钱罐:https://t.cn/A6hbHUth
吧主招新:https://t.cn/ExUYdqd
♀️工作组招新: https://t.cn/A6Z2zy36
“德香4103”和“德优4727”是院水稻高粱所高效安全水稻种质创新与应用团队近年来育成的优质高产杂交水稻品种,分别于2012年和2016年被农业部认定为“超级稻”,“德香074A”是这两个品种共同的母本。为了挖掘“德香074A”的优异基因资源,该团队利用简化基因组测序技术对“德香074B”和“蒲江6号”的重组自交系F8群体构建了高密度遗传图谱,共有98个与经济性状相关的QTL在多年多点试验中均被检测到,其中39个QTL位点与“基因-环境互作”相关,16个与高产相关的QTL位点在本研究中被首次发现。该研究结果深入解析了“德香074A”作为母本培育出优质高产水稻品种的分子机制。
近日,该团队以“Genome‐wide SNP discovery and QTL mapping for economic traits in a recombinant inbred line of Oryza sativa”为题将现有研究成果发表在国际著名期刊《Food and Energy Security》上,该团队郑家奎研究员、蒋开锋研究员和中国水稻研究所郭龙彪研究员为该文的共同通讯作者,李赓觅博士、杨乾华副研究员和四川农业大学李德强博士为该文的共同第一作者。经中国科学院文献情报中心和Web of Science查询,《Food and Energy Security》为中科院1区Top级期刊及JCR Q1区期刊,影响因子5.24分,符合科技部对“三高”论文的要求。该研究得到国家自然科学基金(31901520)、四川省青年科技创新研究团队项目(2020JDTD0031)和省农科院前沿科学基金(2019QYXK011)的资助。
近日,该团队以“Genome‐wide SNP discovery and QTL mapping for economic traits in a recombinant inbred line of Oryza sativa”为题将现有研究成果发表在国际著名期刊《Food and Energy Security》上,该团队郑家奎研究员、蒋开锋研究员和中国水稻研究所郭龙彪研究员为该文的共同通讯作者,李赓觅博士、杨乾华副研究员和四川农业大学李德强博士为该文的共同第一作者。经中国科学院文献情报中心和Web of Science查询,《Food and Energy Security》为中科院1区Top级期刊及JCR Q1区期刊,影响因子5.24分,符合科技部对“三高”论文的要求。该研究得到国家自然科学基金(31901520)、四川省青年科技创新研究团队项目(2020JDTD0031)和省农科院前沿科学基金(2019QYXK011)的资助。
几篇论文实现代码:
《Spectral Leakage and Rethinking the Kernel Size in CNNs》(2021) GitHub:https://t.cn/A6532v5i [fig4]
《Few-Shot Unsupervised Continual Learning through Meta-Examples》(NeurIPS 2020) GitHub:https://t.cn/A6532v5c [fig2]
《Partially-Aligned Data-to-Text Generation with Distant Supervision》(EMNLP 2020) GitHub:https://t.cn/A6532v5b [fig3]
《FcaNet: Frequency Channel Attention Networks》(2020) GitHub:https://t.cn/A6532v5f [fig1]
《Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception》(2020) GitHub:https://t.cn/A6532v5I
《CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks》(2020) GitHub:https://t.cn/A6532v5N
《Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data》(2020) GitHub:https://t.cn/A6532v5G
《GSum: A General Framework for Guided Neural Abstractive Summarization》(2020) GitHub:https://t.cn/A6532v5t
《Improving Post Training Neural Quantization:Layer-wise Calibration and Integer Programming》(2020) GitHub:https://t.cn/A6532v5q
《Spectral Leakage and Rethinking the Kernel Size in CNNs》(2021) GitHub:https://t.cn/A6532v5i [fig4]
《Few-Shot Unsupervised Continual Learning through Meta-Examples》(NeurIPS 2020) GitHub:https://t.cn/A6532v5c [fig2]
《Partially-Aligned Data-to-Text Generation with Distant Supervision》(EMNLP 2020) GitHub:https://t.cn/A6532v5b [fig3]
《FcaNet: Frequency Channel Attention Networks》(2020) GitHub:https://t.cn/A6532v5f [fig1]
《Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception》(2020) GitHub:https://t.cn/A6532v5I
《CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks》(2020) GitHub:https://t.cn/A6532v5N
《Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data》(2020) GitHub:https://t.cn/A6532v5G
《GSum: A General Framework for Guided Neural Abstractive Summarization》(2020) GitHub:https://t.cn/A6532v5t
《Improving Post Training Neural Quantization:Layer-wise Calibration and Integer Programming》(2020) GitHub:https://t.cn/A6532v5q
✋热门推荐