Vine tea is documented in ancient Chinese books as having the function of promoting blood circulation.However,its effects and mechanisms remain unclear.The aim of this study was to comprehensively investigate the prot...Vine tea is documented in ancient Chinese books as having the function of promoting blood circulation.However,its effects and mechanisms remain unclear.The aim of this study was to comprehensively investigate the protective potential and mechanisms of vine tea in high-fat diet rat through a combination of in vivo and in vitro experiments.The efficacy of vine tea was evaluated using a high-fat diet rat model and an oxidized low-density lipoprotein-treated cell model,with physiological and biochemical indicators measured in rat serum and cell supernatants.Transcriptomics was utilized to investigate alterations mRNA expression following the administration of dihydromyricetin in cell model.Metabolomics and 16S rRNA sequencing was employed to examine changes in metabolites in the serum and changes in gut microbiota of high-fat diet rats after administering vine tea extract.Vine tea extract and dihydromyricetin can reduce elevated levels of lipids,including total cholesterol and triglycerides,following modeling.Transcriptomic data indicate that dihydromyricetin exerts its effects by regulating ferroptosis signaling pathways.Metabolomic analysis demonstrates that the administration of vine tea extract influences the vitamin K cycle and glutathione,thereby alleviating the progression of ferroptosis.Additionally,16S rRNA sequencing reveals that vine tea extract increases Lactobacillaceae in the gut microbiota,which subsequently affects the levels of lysophosphatidylcholine,a major phospholipid component of oxidized low-density lipoprotein in serum.Our results indicate that vine tea can regulate ferroptosis signaling pathways and increase Lactobacillaceae in the gut microbiota,thereby exhibiting cardiovascular protective effects in high-fat diet rats.展开更多
Global warming increases the vulnerability of plants, especially alpine herbaceous species, to local extinction. In this study, we collected species distribution information from herbarium specimens for ten selected C...Global warming increases the vulnerability of plants, especially alpine herbaceous species, to local extinction. In this study, we collected species distribution information from herbarium specimens for ten selected Cyananthus and Primula alpine species endemic to the Himalaya-Hengduan Mountains(HHM).Combined with climate data from WorldClim, we used Maximum Entropy Modeling(MaxEnt) to project distributional changes from the current time period to 2070. Our predictions indicate that, under a wide range of climate change scenarios, the distributions of all species will shift upward in elevation and northward in latitude; furthermore, under these scenarios, species will expand the size of their range. For the majority of the species in this study, habitats are available to mitigate upward and northward shifts that are projected to be induced by changing climate. If current climate projections, however, increase in magnitude or continue to increase past our projection dates, suitable habitat for future occupation by alpine species will be limited as we predict range contraction or less range expansion for some of the species under more intensified climate scenarios. Our study not only underscores the value of herbarium source information for future climate model projections but also suggests that future studies on the effects of climate change on alpine species should include additional biotic and abiotic factors to provide greater resolution of the local dynamics associated with species persistence under a warming climate.展开更多
The formation of root system architecture(RSA)plays a crucial role in plant growth.OsDRO1 is known to have a function in controlling RSA in rice,however,the role of potato StDRO2,a homolog of rice OsDRO1,in root growt...The formation of root system architecture(RSA)plays a crucial role in plant growth.OsDRO1 is known to have a function in controlling RSA in rice,however,the role of potato StDRO2,a homolog of rice OsDRO1,in root growth remains unclear.In this study,we obtained potato dro2 mutant lines by Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR-Associated 9(CRISPR/Cas9)-mediated genome editing system.The mutant lines were generated from a splicing defect of the StDRO2 intron 1,which causes a nonsense mutation in StDRO2.Furthermore,the secondary structure of StDRO2 mRNA analyzed with RNAfold Web Server was altered in the dro2 mutant.Mutation of StDRO2 conveys potato adaptation through changing the RSA via alteration of auxin transport under drought stress.The potato dro2 lines showed higher plant height,longer root length,smaller root growth angle and increased tuber weight than the wild-type.The alteration of RSA was associated with a disturbance of IAA distribution in the dro2 mutant,and the levels of StPIN7 and StPIN10 detected by using real-time PCR were up-regulated in the roots of potato dro2 lines grown under drought stress.Moreover,the microRNAs(miRNAs)PmiREN024536 and PmiREN024486 targeted the StDRO2 gene,and auxin positively and negatively regulated the expression of StDRO2 and the miRNAs PmiREN024536 and PmiREN024486,respectively,in the potato roots.Our data shows that a regulatory network involving auxin,StDRO2,PmiREN024536 and PmiREN024486 can control RSA to convey potato fitness under drought stress.展开更多
Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,...Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.展开更多
Recent advancements in artificial intelligence(AI)have shown promising potential for the automated screening and grading of cataracts.However,the different types of visual impairment caused by cataracts exhibit simila...Recent advancements in artificial intelligence(AI)have shown promising potential for the automated screening and grading of cataracts.However,the different types of visual impairment caused by cataracts exhibit similar phenotypes,posing significant challenges for accurately assessing the severity of visual impairment.To address this issue,we propose a dense convolution combined with attention mechanism and multi-level classifier(DAMC_Net)for visual impairment grading.First,the double-attention mechanism is utilized to enable the DAMC_Net to focus on lesions-related regions.Then,a hierarchical multi-level classifier is constructed to enhance the recognition ability in distinguishing the severities of visual impairment,while maintaining a better screening rate for normal samples.In addition,a cost-sensitive method is applied to address the problem of higher false-negative rate caused by the imbalanced dataset.Experimental results demonstrated that the DAMC_Net outperformed ResNet50 and dense convolutional network 121(DenseNet121)models,with sensitivity improvements of 6.0%and 3.4%on the category of mild visual impairment caused by cataracts(MVICC),and 2.1%and 4.3%on the category of moderate to severe visual impairment caused by cataracts(MSVICC),respectively.The comparable performance on two external test datasets was achieved,further verifying the effectiveness and generalizability of the DAMC_Net.展开更多
基金supported by Project of Zhangjiajie City’s Industry-Academia-Research Collaborative Innovation‘Open Project Leader’,Zhangjiajie City’s Project of Taking on Challenging Tasks by Responding to Calls for Solutions(JB20230530)Basic Research Funds Project for Central Universities of China(2023XCZX001,2020kfyXJJS120)Academician and Expert Workstations in Yunnan Province(202205AF150090).
摘要Vine tea is documented in ancient Chinese books as having the function of promoting blood circulation.However,its effects and mechanisms remain unclear.The aim of this study was to comprehensively investigate the protective potential and mechanisms of vine tea in high-fat diet rat through a combination of in vivo and in vitro experiments.The efficacy of vine tea was evaluated using a high-fat diet rat model and an oxidized low-density lipoprotein-treated cell model,with physiological and biochemical indicators measured in rat serum and cell supernatants.Transcriptomics was utilized to investigate alterations mRNA expression following the administration of dihydromyricetin in cell model.Metabolomics and 16S rRNA sequencing was employed to examine changes in metabolites in the serum and changes in gut microbiota of high-fat diet rats after administering vine tea extract.Vine tea extract and dihydromyricetin can reduce elevated levels of lipids,including total cholesterol and triglycerides,following modeling.Transcriptomic data indicate that dihydromyricetin exerts its effects by regulating ferroptosis signaling pathways.Metabolomic analysis demonstrates that the administration of vine tea extract influences the vitamin K cycle and glutathione,thereby alleviating the progression of ferroptosis.Additionally,16S rRNA sequencing reveals that vine tea extract increases Lactobacillaceae in the gut microbiota,which subsequently affects the levels of lysophosphatidylcholine,a major phospholipid component of oxidized low-density lipoprotein in serum.Our results indicate that vine tea can regulate ferroptosis signaling pathways and increase Lactobacillaceae in the gut microbiota,thereby exhibiting cardiovascular protective effects in high-fat diet rats.
基金funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31010000)the National Key Basic Research Program of China (2014CB954100)the Program of Science and Technology Talents Training of Yunnan Province (2017HA014)
摘要Global warming increases the vulnerability of plants, especially alpine herbaceous species, to local extinction. In this study, we collected species distribution information from herbarium specimens for ten selected Cyananthus and Primula alpine species endemic to the Himalaya-Hengduan Mountains(HHM).Combined with climate data from WorldClim, we used Maximum Entropy Modeling(MaxEnt) to project distributional changes from the current time period to 2070. Our predictions indicate that, under a wide range of climate change scenarios, the distributions of all species will shift upward in elevation and northward in latitude; furthermore, under these scenarios, species will expand the size of their range. For the majority of the species in this study, habitats are available to mitigate upward and northward shifts that are projected to be induced by changing climate. If current climate projections, however, increase in magnitude or continue to increase past our projection dates, suitable habitat for future occupation by alpine species will be limited as we predict range contraction or less range expansion for some of the species under more intensified climate scenarios. Our study not only underscores the value of herbarium source information for future climate model projections but also suggests that future studies on the effects of climate change on alpine species should include additional biotic and abiotic factors to provide greater resolution of the local dynamics associated with species persistence under a warming climate.
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.32260085,31860064,31660501,31970609,32260718 and 31901870)the Key Projects of the Applied Basic Research Plan of Yunnan Province(Grant No.202301AS070082)+3 种基金the Start-up fund from Xishuangbanna Tropical Botanical Garden,the‘Top Talents Program in Science and Technology’from Yunnan Province,the Major Science and Technology Project in Yunnan Province(Grant Nos.202102AE090042 and 202202AE090036)the Young and Middle-Aged Academic and Technical Leaders Reserve Talent Program in Yunnan Province(Grant No.202205AC160076)China Postdoctoral Science Foundation(Grant No.2019M653849XB)the High-level Talents Introduction Plan of Yunnan Province-Young Talents Special Project。
摘要The formation of root system architecture(RSA)plays a crucial role in plant growth.OsDRO1 is known to have a function in controlling RSA in rice,however,the role of potato StDRO2,a homolog of rice OsDRO1,in root growth remains unclear.In this study,we obtained potato dro2 mutant lines by Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR-Associated 9(CRISPR/Cas9)-mediated genome editing system.The mutant lines were generated from a splicing defect of the StDRO2 intron 1,which causes a nonsense mutation in StDRO2.Furthermore,the secondary structure of StDRO2 mRNA analyzed with RNAfold Web Server was altered in the dro2 mutant.Mutation of StDRO2 conveys potato adaptation through changing the RSA via alteration of auxin transport under drought stress.The potato dro2 lines showed higher plant height,longer root length,smaller root growth angle and increased tuber weight than the wild-type.The alteration of RSA was associated with a disturbance of IAA distribution in the dro2 mutant,and the levels of StPIN7 and StPIN10 detected by using real-time PCR were up-regulated in the roots of potato dro2 lines grown under drought stress.Moreover,the microRNAs(miRNAs)PmiREN024536 and PmiREN024486 targeted the StDRO2 gene,and auxin positively and negatively regulated the expression of StDRO2 and the miRNAs PmiREN024536 and PmiREN024486,respectively,in the potato roots.Our data shows that a regulatory network involving auxin,StDRO2,PmiREN024536 and PmiREN024486 can control RSA to convey potato fitness under drought stress.
基金the National Natural Science Foundation of China(No.62276210,82201148,61775180)the Natural Science Basic Research Program of Shaanxi Province(No.2022JM-380)+3 种基金the Shaanxi Province College Students'Innovation and Entrepreneurship Training Program(No.S202311664128X)the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(No.2022RC069,2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)。
摘要Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 61775180)the Natural Science Basic Research Program of Shaanxi Province(No.2022JM-380)。
摘要Recent advancements in artificial intelligence(AI)have shown promising potential for the automated screening and grading of cataracts.However,the different types of visual impairment caused by cataracts exhibit similar phenotypes,posing significant challenges for accurately assessing the severity of visual impairment.To address this issue,we propose a dense convolution combined with attention mechanism and multi-level classifier(DAMC_Net)for visual impairment grading.First,the double-attention mechanism is utilized to enable the DAMC_Net to focus on lesions-related regions.Then,a hierarchical multi-level classifier is constructed to enhance the recognition ability in distinguishing the severities of visual impairment,while maintaining a better screening rate for normal samples.In addition,a cost-sensitive method is applied to address the problem of higher false-negative rate caused by the imbalanced dataset.Experimental results demonstrated that the DAMC_Net outperformed ResNet50 and dense convolutional network 121(DenseNet121)models,with sensitivity improvements of 6.0%and 3.4%on the category of mild visual impairment caused by cataracts(MVICC),and 2.1%and 4.3%on the category of moderate to severe visual impairment caused by cataracts(MSVICC),respectively.The comparable performance on two external test datasets was achieved,further verifying the effectiveness and generalizability of the DAMC_Net.