Sweet cherry(Prunus avium L.)is a stone fruit widely consumed and appreciated for its organoleptic properties,as well as its nutraceutical potential.We here investigated the characteristics of six non-commercial Tusca...Sweet cherry(Prunus avium L.)is a stone fruit widely consumed and appreciated for its organoleptic properties,as well as its nutraceutical potential.We here investigated the characteristics of six non-commercial Tuscan varieties of sweet cherry maintained at the Regional Germplasm Bank of the CNR-IBE in Follonica(Italy)and sampled ca.60 days post-anthesis over three consecutive years(2016-2017-2018).We adopted an approach merging genotyping and targeted gene expression profiling with metabolomics.To complement the data,a study of the soluble proteomes was also performed on two varieties showing the highest content of flavonoids.Metabolomics identified the presence of flavanols and proanthocyanidins in highest abundance in the varieties Morellona and Crognola,while gene expression revealed that some differences were present in genes involved in the phenylpropanoid pathway during the 3 years and among the varieties.Finally,proteomics on Morellona and Crognola showed variations in proteins involved in stress response,primary metabolism and cell wall expansion.To the best of our knowledge,this is the first multi-pronged study focused on Tuscan sweet cherry varieties providing insights into the differential abundance of genes,proteins and metabolites.展开更多
The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an ass...The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.展开更多
文摘Sweet cherry(Prunus avium L.)is a stone fruit widely consumed and appreciated for its organoleptic properties,as well as its nutraceutical potential.We here investigated the characteristics of six non-commercial Tuscan varieties of sweet cherry maintained at the Regional Germplasm Bank of the CNR-IBE in Follonica(Italy)and sampled ca.60 days post-anthesis over three consecutive years(2016-2017-2018).We adopted an approach merging genotyping and targeted gene expression profiling with metabolomics.To complement the data,a study of the soluble proteomes was also performed on two varieties showing the highest content of flavonoids.Metabolomics identified the presence of flavanols and proanthocyanidins in highest abundance in the varieties Morellona and Crognola,while gene expression revealed that some differences were present in genes involved in the phenylpropanoid pathway during the 3 years and among the varieties.Finally,proteomics on Morellona and Crognola showed variations in proteins involved in stress response,primary metabolism and cell wall expansion.To the best of our knowledge,this is the first multi-pronged study focused on Tuscan sweet cherry varieties providing insights into the differential abundance of genes,proteins and metabolites.
基金the French National Research Agency under the Investments for the Future Program,referred as ANR-16-CONV-0004 PIA#Digitag.Institut Convergences Agriculture Numérique,Hiphen supported the organization of the competition.Japan:Kubota supported the organization of the competi-tion.Australia:Grains Research and Development Corpora-tion(UOQ2002-008RTX machine learning applied to high-throughput feature extraction from imagery to map spatial variability and UOQ2003-011RTX INVITA-a technology and analytics platform for improving variety selection)sup-ported competition.
文摘The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.
