Glaucoma is an eye disease characterized by pathologically elevated intraocular pressure,optic nerve atrophy,and visual field defects,which can lead to irreversible vision loss.In recent years,the rapid development of...Glaucoma is an eye disease characterized by pathologically elevated intraocular pressure,optic nerve atrophy,and visual field defects,which can lead to irreversible vision loss.In recent years,the rapid development of artificial intelligence(AI)technology has provided new approaches for the early diagnosis and management of glaucoma.By classifying and annotating glaucoma-related images,AI models can learn and recognize the specific pathological features of glaucoma,thereby achieving automated imaging analysis and classification.Research on glaucoma imaging classification and annotation mainly involves color fundus photography(CFP),optical coherence tomography(OCT),anterior segment optical coherence tomography(AS-OCT),and ultrasound biomicroscopy(UBM)images.CFP is primarily used for the annotation of the optic cup and disc,while OCT is used for measuring and annotating the thickness of the retinal nerve fiber layer,and AS-OCT and UBM focus on the annotation of the anterior chamber angle structure and the measurement of anterior segment structural parameters.To standardize the classification and annotation of glaucoma images,enhance the quality and consistency of annotated data,and promote the clinical application of intelligent ophthalmology,this guideline has been developed.This guideline systematically elaborates on the principles,methods,processes,and quality control requirements for the classification and annotation of glaucoma images,providing standardized guidance for the classification and annotation of glaucoma images.展开更多
Natural products(NPs)have long held a significant position in various fields such as medicine,food,agriculture,and materials.The chemical space covered by NPs is extensive but often underexplored.Therefore,high-throug...Natural products(NPs)have long held a significant position in various fields such as medicine,food,agriculture,and materials.The chemical space covered by NPs is extensive but often underexplored.Therefore,high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems.Mass spectrometry(MS)has emerged as a powerful platform for the annotation and discovery of NPs.MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information.Additionally,the released annotation methodologies,based on a variety of informatics tools,continuously improve the ability to annotate the structure and properties of compounds.This review examines the current mainstream databases and annotation methodologies,focusing on their advantages and limitations.Prospects for future technological advancements are then discussed in terms of novel applications and research objectives.Through a systematic overview,this review aims to provide valuable insights and a reference for MS-based NPs annotation,thereby promoting the discovery of novel natural entities.展开更多
The quality of traditional Chinese medicine(TCM)prescriptions(TCMPs)is critical to clinical efficacy;however,evaluating their consistency and identifying sources of variability remain challenging.This study proposes a...The quality of traditional Chinese medicine(TCM)prescriptions(TCMPs)is critical to clinical efficacy;however,evaluating their consistency and identifying sources of variability remain challenging.This study proposes an integrated strategy to assess the quality of 100 widely sold TCMPs.A"one-for-all"chromatographic method was employed to analyze 645 sample batches.This large-scale data collection enabled statistical evaluations,such as hierarchical cluster analysis(HCA)and similarity heatmap,to identify quality inconsistencies.The introduction of a TCM-specific mass spectrometry(MS)database allowed for rapid,automated annotation of chemicals across 100 prescriptions and facilitated the tracing of raw material sources.Results indicate that 19%of prescriptions exhibited chemical inconsistencies,which are associated with high market value,low pricing,and substantial price disparities.The MS database allowed rapid annotation of 761 and 673 compounds in positive and negative modes,respec-tively,in 100 TCMPs,with 73 prescriptions reported for the first time.The tracing efforts succeeded in identifying>40%of the raw material sources for 51 prescriptions.P93(Yinianjin(YNJ))is a case in which the chromatographic profiles from three manufacturers displayed inconsistencies.Analysis using the database traced divergent peaks to Rhei Radix et Rhizoma(RRER).Verification with self-prepared samples confirmed that manufacturers utilized three distinct botanical sources.This integrated strategy provides a scalable framework for quality control in TCMPs.展开更多
Understanding genetic variant functionality is essential for advancing animal genomics and precision breeding.However,the lack of comprehensive functional genomic annotations in animals limits the effectiveness of mos...Understanding genetic variant functionality is essential for advancing animal genomics and precision breeding.However,the lack of comprehensive functional genomic annotations in animals limits the effectiveness of most variant function assessment methods.In this study,we gather 1030 raw epigenomic datasets from 10 animal species and systematically annotate 7 types of key regulatory regions,creating a comprehensive functional annotation map of animal genomic variants.Our findings demonstrate that integrating variants with regulatory annotations can identify tissues and cell types underlying economic traits,underscoring the utility of these annotations in functional variant discovery.Using our functional annotations,we rank the functional potential of genetic variants and classify over 127 million candidate variants into 5 functional confidence categories,with high-confidence variants significantly enriched in eQTLs and trait-associated SNPs.Incorporating these variants into genomic prediction models can improve estimated breeding value accuracy,demonstrating their practical utility in breeding programs.To facilitate the use of our results,we develop the Integrated Functional Mutation(IFmut:http://www.ifmutants.com:8212)platform,enabling researchers to explore regulatory annotations and assess the functional potential of animal variants efficiently.Our study provides a robust framework for functional genomic annotations in farm animals,enhancing variant function assessment and breeding precision.展开更多
Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summariza...Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.展开更多
针对腹部部分注释数据集的嗜铬细胞瘤图像分割缺乏不同器官间的特征学习,导致分割中难以准确区分肿瘤及周边器官边缘的问题,提出一种基于多尺度与空间频率特征的嗜铬细胞瘤图像分割网络(MF-Net)。首先,构建多尺度空间频率通道注意力模块...针对腹部部分注释数据集的嗜铬细胞瘤图像分割缺乏不同器官间的特征学习,导致分割中难以准确区分肿瘤及周边器官边缘的问题,提出一种基于多尺度与空间频率特征的嗜铬细胞瘤图像分割网络(MF-Net)。首先,构建多尺度空间频率通道注意力模块(MSFCA)对图像频域信息和相邻编码器的多尺度特征图进行加权融合,以强化器官间纹理和边界特征的捕捉,从而突出肿瘤区域的特征表示能力;其次,引入上采样多尺度特征融合模块(UMFF)通过结合上采样得到的不同尺度特征图,增强模型对图像中不同大小对象的识别能力;最后,利用自适应目标损失函数(AOb)对有注释腹部器官标签进行损失计算,并根据注释器官类别调整损失权重大小,从而优化分割网络的学习过程。实验结果表明,在腹部器官和嗜铬细胞瘤数据集上,MF-Net的分割准确率相较于单独训练的nnU-Net(no new U-Net)分别提升了3.33和3.18个百分点,而Dice系数(Dice)和归一化表面距离(NSD)分别为89.07%和92.85%;在域外数据集上,MF-Net的Dice和NSD分别为84.66%和90.55%。此外,可视化结果表明,MF-Net能更好地处理嗜铬细胞瘤图像中的复杂背景和模糊边界,为嗜铬细胞瘤的精确诊断和治疗提供了更好的技术支持。展开更多
基金Supported by Guangdong Basic and Applied Basic Research Foundation(No.2025A1515011627)San Ming Project of Medicine in Shenzhen(No.SZSM202311012).
文摘Glaucoma is an eye disease characterized by pathologically elevated intraocular pressure,optic nerve atrophy,and visual field defects,which can lead to irreversible vision loss.In recent years,the rapid development of artificial intelligence(AI)technology has provided new approaches for the early diagnosis and management of glaucoma.By classifying and annotating glaucoma-related images,AI models can learn and recognize the specific pathological features of glaucoma,thereby achieving automated imaging analysis and classification.Research on glaucoma imaging classification and annotation mainly involves color fundus photography(CFP),optical coherence tomography(OCT),anterior segment optical coherence tomography(AS-OCT),and ultrasound biomicroscopy(UBM)images.CFP is primarily used for the annotation of the optic cup and disc,while OCT is used for measuring and annotating the thickness of the retinal nerve fiber layer,and AS-OCT and UBM focus on the annotation of the anterior chamber angle structure and the measurement of anterior segment structural parameters.To standardize the classification and annotation of glaucoma images,enhance the quality and consistency of annotated data,and promote the clinical application of intelligent ophthalmology,this guideline has been developed.This guideline systematically elaborates on the principles,methods,processes,and quality control requirements for the classification and annotation of glaucoma images,providing standardized guidance for the classification and annotation of glaucoma images.
基金supported by the National Natural Science Foundation of China(Nos.82274064,82374026,and 82204591)。
文摘Natural products(NPs)have long held a significant position in various fields such as medicine,food,agriculture,and materials.The chemical space covered by NPs is extensive but often underexplored.Therefore,high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems.Mass spectrometry(MS)has emerged as a powerful platform for the annotation and discovery of NPs.MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information.Additionally,the released annotation methodologies,based on a variety of informatics tools,continuously improve the ability to annotate the structure and properties of compounds.This review examines the current mainstream databases and annotation methodologies,focusing on their advantages and limitations.Prospects for future technological advancements are then discussed in terms of novel applications and research objectives.Through a systematic overview,this review aims to provide valuable insights and a reference for MS-based NPs annotation,thereby promoting the discovery of novel natural entities.
基金the Guangxi Science and Technology Major Project/Program,China(Grant Nos.:GUIKEAA22096029,GUIKEAA23023035)State Key Laboratory of Drug Regulatory Science Project,National Institutes for Food and Drug Control(NIFDC),China(Grant No.:2024SKLDRS0101)+1 种基金Shanghai Institute of Material Medical(SIMM)-National EngineeringResearch Center of TCM Standardization Technology Independent Research Program,Chinathe staff members of the Large-scale MS System at the National Facility for Protein Science in Shanghai(NFPs),Zhangjiang Lab,China for providing technical support.
文摘The quality of traditional Chinese medicine(TCM)prescriptions(TCMPs)is critical to clinical efficacy;however,evaluating their consistency and identifying sources of variability remain challenging.This study proposes an integrated strategy to assess the quality of 100 widely sold TCMPs.A"one-for-all"chromatographic method was employed to analyze 645 sample batches.This large-scale data collection enabled statistical evaluations,such as hierarchical cluster analysis(HCA)and similarity heatmap,to identify quality inconsistencies.The introduction of a TCM-specific mass spectrometry(MS)database allowed for rapid,automated annotation of chemicals across 100 prescriptions and facilitated the tracing of raw material sources.Results indicate that 19%of prescriptions exhibited chemical inconsistencies,which are associated with high market value,low pricing,and substantial price disparities.The MS database allowed rapid annotation of 761 and 673 compounds in positive and negative modes,respec-tively,in 100 TCMPs,with 73 prescriptions reported for the first time.The tracing efforts succeeded in identifying>40%of the raw material sources for 51 prescriptions.P93(Yinianjin(YNJ))is a case in which the chromatographic profiles from three manufacturers displayed inconsistencies.Analysis using the database traced divergent peaks to Rhei Radix et Rhizoma(RRER).Verification with self-prepared samples confirmed that manufacturers utilized three distinct botanical sources.This integrated strategy provides a scalable framework for quality control in TCMPs.
基金supported by the National Natural Science Foundation of China(32341051)the grant from Department of Agriculture and Rural Affairs of Hubei Province(HBZY2023B006-02)+2 种基金the National Funding(2023ZD04050)the National Natural Science Foundation of China Outstanding Youth(32125035)the National Key R&D Young Scientists Project(2022YFD1302000).
文摘Understanding genetic variant functionality is essential for advancing animal genomics and precision breeding.However,the lack of comprehensive functional genomic annotations in animals limits the effectiveness of most variant function assessment methods.In this study,we gather 1030 raw epigenomic datasets from 10 animal species and systematically annotate 7 types of key regulatory regions,creating a comprehensive functional annotation map of animal genomic variants.Our findings demonstrate that integrating variants with regulatory annotations can identify tissues and cell types underlying economic traits,underscoring the utility of these annotations in functional variant discovery.Using our functional annotations,we rank the functional potential of genetic variants and classify over 127 million candidate variants into 5 functional confidence categories,with high-confidence variants significantly enriched in eQTLs and trait-associated SNPs.Incorporating these variants into genomic prediction models can improve estimated breeding value accuracy,demonstrating their practical utility in breeding programs.To facilitate the use of our results,we develop the Integrated Functional Mutation(IFmut:http://www.ifmutants.com:8212)platform,enabling researchers to explore regulatory annotations and assess the functional potential of animal variants efficiently.Our study provides a robust framework for functional genomic annotations in farm animals,enhancing variant function assessment and breeding precision.
基金The National Natural Science Foundation of China(No.61133012)the Humanity and Social Science Foundation of the Ministry of Education(No.12YJCZH274)+1 种基金the Humanity and Social Science Foundation of Jiangxi Province(No.XW1502,TQ1503)the Science and Technology Project of Jiangxi Science and Technology Department(No.20121BBG70050,20142BBG70011)
文摘Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.
文摘针对腹部部分注释数据集的嗜铬细胞瘤图像分割缺乏不同器官间的特征学习,导致分割中难以准确区分肿瘤及周边器官边缘的问题,提出一种基于多尺度与空间频率特征的嗜铬细胞瘤图像分割网络(MF-Net)。首先,构建多尺度空间频率通道注意力模块(MSFCA)对图像频域信息和相邻编码器的多尺度特征图进行加权融合,以强化器官间纹理和边界特征的捕捉,从而突出肿瘤区域的特征表示能力;其次,引入上采样多尺度特征融合模块(UMFF)通过结合上采样得到的不同尺度特征图,增强模型对图像中不同大小对象的识别能力;最后,利用自适应目标损失函数(AOb)对有注释腹部器官标签进行损失计算,并根据注释器官类别调整损失权重大小,从而优化分割网络的学习过程。实验结果表明,在腹部器官和嗜铬细胞瘤数据集上,MF-Net的分割准确率相较于单独训练的nnU-Net(no new U-Net)分别提升了3.33和3.18个百分点,而Dice系数(Dice)和归一化表面距离(NSD)分别为89.07%和92.85%;在域外数据集上,MF-Net的Dice和NSD分别为84.66%和90.55%。此外,可视化结果表明,MF-Net能更好地处理嗜铬细胞瘤图像中的复杂背景和模糊边界,为嗜铬细胞瘤的精确诊断和治疗提供了更好的技术支持。
