The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data anal...The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics.Plant Phenomics aims also to connect phenomics to other science domains,such as genomics,genetics,physiology,molecular biology,bioinformatics,statistics,mathematics,and computer sciences.The journal should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.展开更多
Maize is pivotal in supporting global agriculture and addressing food security challenges.Crop root systems are critical for water uptake and nutrient acquisition,which impacts yield.Quantitative trait phenotyping is ...Maize is pivotal in supporting global agriculture and addressing food security challenges.Crop root systems are critical for water uptake and nutrient acquisition,which impacts yield.Quantitative trait phenotyping is essential to understand better the genetic factors underpinning maize root growth and development.Root systems are challenging to phenotype given their below-ground,soil-bound nature.In addition,manual trait annotations of root images are tedious and can lead to inaccuracies and inconsistencies between individuals,resulting in data discrepancies.In this study,we explored juvenile root phenotyping in the presence and absence of auxin treat-ment,a key phytohormone in root development,using manual curation and gene expression analyses.In addition,we developed an automated phenotyping pipeline for field-grown maize crown roots by leveraging open-source software.By examining a test set of 11 diverse maize genotypes for juvenile-adult root trait correlations and gene expression patterns,an inconsistent correlation was observed,underscoring the developmental plasticity preva-lent during maize root morphogenesis.Transcripts involved in hormone signaling and stress responses were among differentially expressed genes in roots from 20 diverse maize genotypes,suggesting many molecular processes may underlie the observed phenotypic variance.In particular,co-expressed gene expression networks associated with module-trait relationships included 1,3-β-glucan,which plays a crucial role in cell wall dynamics.This study furthers our understanding of genotype-phenotype relationships,which is relevant for informing agricultural strategies to improve maize root physiology.展开更多
Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research.However,the lack of publicly available high-qua...Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research.However,the lack of publicly available high-quality image datasets with detailed annotations has severely hindered the development of these models.In this work,we present a comprehensive multicultivar and multiview rice plant image dataset(CVRP)created from 231 landraces and 50 modern cultivars grown under dense planting in paddy fields.The dataset includes images capturing rice plants in their natural environment,as well as indoor images focusing specifically on panicles,allowing for a detailed investigation of cultivar-specific differences.A semiautomatic annotation process using deep learning models was designed for annotations,followed by rigorous manual curation.We demonstrated the utility of the CVRP by evaluating the performance of four state-of-the-art(SOTA)semantic segmentation models.We also conducted 3D plant reconstruction with organ segmentation via images and annotations.The database not only facilitates general-purpose image-based panicle identification and segmentation but also provides valuable re-sources for challenging tasks such as automatic rice cultivar identification,panicle and grain counting,and 3D plant reconstruction.The database and the model for image annotation are available at.展开更多
Anthesis prediction is crucial for breeding wheat.While current tools provide estimates of average anthesis at the field scale,they fail to address the needs of breeders who require accurate predictions for individual...Anthesis prediction is crucial for breeding wheat.While current tools provide estimates of average anthesis at the field scale,they fail to address the needs of breeders who require accurate predictions for individual plants.Hybrid breeders have to finalize their plans for pollination at least 10 days before such flowering is due and biotechnology field trials in the United States and Australia must report to regulators 7-14 days before the first plant flowers.Currently,predicting anthesis of individual wheat plants is a labour-intensive,inefficient,and costly process.Individual wheat of the same cultivar within the same field may exhibit substantial variations in anthesis timing,due to significant variations in their immediate surroundings.In this study,we developed an efficient and cost-effective machine vision approach to predict anthesis of individual wheat plants.By integrating RGB imagery with in-situ meteorological data,our multimodal framework simplifies the anthesis prediction problem into binary or three-class classification tasks,aligning with breeders' requirements in individual wheat flowering prediction on the crucial days before anthesis.Furthermore,we incorporated a few-shot learning method to improve the model's adaptability across different growth environments and to address the challenge of limited training data.The model achieved an F1 score above 0.8 in all planting settings.展开更多
Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of p...Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.展开更多
Previous observational and genomic-wide association studies(GWAS)suggested the association between several phenotypic factors and keratinocyte carcinoma,including lifestyle and dietary,photodamage-related conditions a...Previous observational and genomic-wide association studies(GWAS)suggested the association between several phenotypic factors and keratinocyte carcinoma,including lifestyle and dietary,photodamage-related conditions and socioeconomic status.The causal effect of these phenotypic factors in keratinocytes carcinoma etiology remains unclear.In this study,we utilized two-sample mendelian randomization analysis from multiple large-scale GWAS datasets of keratinocytes carcinoma including more than 300,000 patients.Genetic instrumental variables(IVs)were constructed by identifying single nucleotide polymorphisms(SNPs)that associate with corresponding factors.The inverse variance weighted(IVW)method and four robust MR approaches,including weighted median estimator,MR-Egger regression,simple mode and weighted mode were implemented for causal inferences and assess the sensitivity across findings.In this analysis,ease of skin tanning was identified as casual protective factor of keratinocyte carcinoma(Basal cell carcinoma:IVW OR=0.718,95%CI 0.654-0.788,p<0.001;Cutaneous squamous cell carcinoma:IVW OR=0.601,95%CI 0.516-0.701,p<0.001).Other phenotypic factors,such as coffee intake,alcohol consumption,smoking and socioeconomic status,indicated insignificant effects on keratinocyte carcinoma risk in the analysis,and therefore,our study does not support their roles in modifying keratinocytes carcinoma risks.Our extensive analysis provides strong evidence of the causative protective effect of ease of skin tanning in keratinocyte carcinoma.The findings suggest that individuals who are less prone to tanning may need to pay greater attention to sun protection in their daily activities to reduce the potential risk of keratinocyte cancers.展开更多
Capturing crop physiological information by phenotyping is a key trend in smart agriculture.However,current studies underutilize spatial structural information in phenotypic imaging.To evaluate the feasibility of crop...Capturing crop physiological information by phenotyping is a key trend in smart agriculture.However,current studies underutilize spatial structural information in phenotypic imaging.To evaluate the feasibility of crop cold stress monitoring based on phenotypic spatial variability,we conducted controlled experiments on'Toyonoka'strawberry plants under four dynamic cooling gradients and three stress durations and analyzed the dependence of their photosynthetic physiology and phenotypic traits on temperature-time interactions.The results revealed that NPQ/1D-Parallel/TENT,Y(NO)/2D-Region/INEM,and qP/1D-Parallel/TENT presented the highest mutual information,with the maximum net photosynthetic rate(Pmax),relative electrolyte conductivity(REC),and total chlorophyll content(Chl_(a+b)),respectively.The difference between the Photosynthetic Physiological Potential Index(PPPI)and relative negative accumulated temperature(RNAT)/650 effectively was used to calculate the cold damage risk(CDRI).An XGBoost-based model integrating the PPPI and RNAT outperformed AdaBoost and RandomForest,achieving an R^(2) of 0.98,an RMSE of 0.337,a classification accuracy of 92.13%,and a Kappa coefficient of 0.904.qP/1D-Parallel/TENT contributed the most to the model.This study provides a scientific basis for phenotypic information mining and agro-meteorological disaster monitoring.展开更多
Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simula...Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.展开更多
Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog...Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or oversegmentation.Moreover,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation outcomes.We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds.First,CT images are acquired using a multislice CT scanner.To improve the efficiency of maize kernel CT imaging,a batch scanning method is used.Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm.Second,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize kernels.The conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named CSFTU-Net.The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm.Finally,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.展开更多
Plant height(PH)is a key agronomic trait influencing plant architecture.Suitable PH values for cotton are important for lodging resistance,high planting density,and mechanized harvesting,making it crucial to elucidate...Plant height(PH)is a key agronomic trait influencing plant architecture.Suitable PH values for cotton are important for lodging resistance,high planting density,and mechanized harvesting,making it crucial to elucidate the mechanisms of the genetic regulation of PH.However,traditional field PH phenotyping largely relies on manual measurements,limiting its large-scale application.In this study,a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging(LiDAR)was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field.Different strategies were used to extract PH values from two sets of sensor data,and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions.These predictions were consistent with manual measurements of the PH for the LiDAR(R^(2)=0.934)and RGB(R^(2)=0.914)data.The predicted PH values were used for GWAS analysis,and 34 PH-related genes,two of which have been demonstrated to regulate PH in cotton,namely,GhPH1 and GhUBP15,were identified.We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G.hirsutum cultivar ZM24 harvested on the 15th,35th,and 70th days after sowing compared with those from a dwarf mutant(pag1),which presented shortened stem and internode phenotypes.The overexpression of GhPH_UAV1 significantly promoted cotton stem development,whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth,suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH.This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations,which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.展开更多
High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses.PhenoGazer,a high-throughput phenotyping system,enhances crop moni-tori...High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses.PhenoGazer,a high-throughput phenotyping system,enhances crop moni-toring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics,four Raspberry Pi cameras,and blue LED lights.This system allows for comprehensive assessment of plant health and development.PhenoGazer features automated moveable upper and lower racks for continuous measure-ments.The lower rack,equipped with four blue LED lights and spectrometer fiber optics,captures blue light-induced chlorophyll fluorescence at night.The upper rack,carrying four spectrometer fiber optics and cam-eras,captures hyperspectral reflectance and RGB images during the day.This dual capability enables detailed evaluation of plant phenology,stress responses,and growth dynamics throughout the entire crop growth cycle.Fully automated and managed by a Raspberry Pi running Python scripts,PhenoGazer ensures precise control and data acquisition with minimal human intervention.Additionally,it includes continuous measurements through a datalogger to acquire photosynthetically active radiation(PAR),soil moisture and temperature,and features expansion capability for additional analog or digital sensors as desired by end users.To test the system,soybean plants representing three conditions,healthy well watered,healthy droughted,and diseased,were monitored to evaluate growth and stress responses.PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber.By combining nighttime blue light induced chlorophyll fluorescence,hyperspectral reflectance-based vegetation indices,and RGB imagery,PhenoGazer represented a significant advancement in plant phenotyping technology,enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.展开更多
Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-a...Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-and-effect relationship,are given priority for integration.However,few efficient integrating methods are available.Results:Here,we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency.To verify its feasibility,two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants.Centered on salt tolerance,data are combined through constructing metabolite-based spectral indices outlining tolerance-related metabolic changes in primary screening,and models converting hyperspectral data to metabolite content for detailed characterizing in secondary screening.Target phenotype could be discriminated after five-day salt-treatment,much earlier than phenotypic difference appearance.20 mutants with salt-tolerant phenotype were successfully identified from about 1000 mutants,almost tripled that of unintegrated analysis.Accuracy rate,confirmed with salt-tolerance analysis for experimental verification,reached 90%,which can be optimized to 100%theoretically utilizing results from hierarchical-clustering-assisted Principal Component Analysis.Conclusions:Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data.Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects,providing a powerful tool for phenotype selection in new-generation crop breeding.展开更多
Water stress is a crucial environmental factor that impacts the growth and yield of rice.Complex field micro-climates and fluctuating water conditions pose a considerable challenge in accurately evaluating water stres...Water stress is a crucial environmental factor that impacts the growth and yield of rice.Complex field micro-climates and fluctuating water conditions pose a considerable challenge in accurately evaluating water stress.Measurement of a particular crop trait is not sufficient for accurate evaluation of the effects of complex water stress.Four comprehensive indicators were introduced in this research,including canopy chlorophyll content(CCC)and canopy equivalent water(CEW).The response of the canopy-specific traits to different types of water stress was identified through individual plant experiments.A hybrid method integrating the PROSAIL radiative transfer model and multidimensional imaging data to retrieve these traits.The synthetic dataset generated by PROSAIL was utilized as prior knowledge for developing a pre-trained machine learning model.Subsequently,reflectance separated from hyperspectral images and phenotypic indicators extracted from front-view images were innovatively united to retrieve water stress-related traits.The results demonstrated that the hybrid method exhibited improved stability and accuracy of CCC(R=0.7920,RMSE=24.971μg cm^(-2))and CEW(R=0.8250,RMSE=0.0075 cm)compared to both data-driven and physical inversion modeling methods.Overall,a robust and accurate method is proposed for assessing water stress in rice using a combination of radiative transfer modeling and multidimensional image-based data.展开更多
The disease syndrome"basses richesses"(SBR)leads to a significant reduction in sugar beet biomass and sugar content,negatively affecting the sugar economy.The mechanistic understanding regarding growth and p...The disease syndrome"basses richesses"(SBR)leads to a significant reduction in sugar beet biomass and sugar content,negatively affecting the sugar economy.The mechanistic understanding regarding growth and photo-assimilates distribution within the sugar beet taproot diseased with SBR is currently incomplete.We combined two tomographic methods,magnetic resonance imaging(MRI)and positron emission tomography(PET)using 11C as tracer,to non-invasively determine SBR effects on structural growth and photoassimilates distribution within the developing taproot over six weeks.MRI analysis revealed a deformed cross-sectional anatomical structure from an early stage,as well as a reduction in taproot volume and width of inner cambium ring structures of up to 26 and 24%,respectively.These SBR disease effects were also confirmed by post-harvest analysis of the taproot.PET analysis revealed a heterogeneous distribution of labeled photoassimilates for diseased plants:sectors of the taproot with characteristic SBR symptoms showed little to very low ^(11)C tracer signal.The heterogeneity of SBR disease effects is most likely due to a partial inoculation of leaves leading to an uneven distribution of the SBR pathogen in the taproot through the strong vascular interconnection between shoot and root.Also,the pathogen needs to spread non-uniformly within the taproot to explain the observed marked increase of the SBR disease effects over time.Our results indicate that SBR affects photoassimilates sink capacity at an early stage of taproot development.Co-registration of MRI and PET may support an early judging of susceptibility and selection of promising genotype candidates for future breeding programs.展开更多
High-altitude polycythemia(HAPC)is a prevalent chronic condition affecting individuals at high altitudes,including both highland and plains populations.This study,involving 2248 participants,explored genetic susceptib...High-altitude polycythemia(HAPC)is a prevalent chronic condition affecting individuals at high altitudes,including both highland and plains populations.This study,involving 2248 participants,explored genetic susceptibility to HAPC among ethnic groups,with 898 HAPC patients(450 Han,448 Tibetan).The Genome-wide Association Study,encompassing 198 cases(100 Han,98 Tibetan),identified eight Tibetan HAPC-susceptibility single-nucleotide polymorphisms and four in Han individuals.The common polymorphism locus rs7618658(SNX4,pcombine<5×10^(-8))was validated in both popula-tions.The investigation of Tibetan EPAS1 revealed the rs1374749 locus,along with linked loci,as a potential prevalence factor for HAPC.The GGTAC haplotype containing this locus emerged as a Protect haplotype for HAPC(p=2.461×10^(-12),OR=0.344).Enrichment analysis revealed that Tibetans exhibited susceptibility in oxygen-sensing pathways,such as EPAS1,associated with phenotypes like hemoglobin and platelets.In contrast,Han Chinese showed significant sensitivity in cell differentiation and angiogenesis,closely linked to hemoglobin,hematocrit,and platelets.展开更多
Location-based methods for counting rice panicles have often been underestimated,primarily due to their perceived inferior performance when compared to detection-based techniques.However,we argue that the po-tential o...Location-based methods for counting rice panicles have often been underestimated,primarily due to their perceived inferior performance when compared to detection-based techniques.However,we argue that the po-tential of these location-based methods has not been fully realized,largely owing to the limitations of existing model architectures.In response to this challenge,we introduce LKNet,an innovative model developed on the foundation of the location-based framework P2Pnet.To enhance the performance of panicle counting across diverse types and growth stages,we implemented several key strategies.Firstly,we reconstructed the localization loss function as a predictive probability distribution to reduce the influence of manual labeling.Additionally,we dynamically adapted the receptive field to better accommodate different panicle types through the use of large kernel convolutional blocks.We evaluated LKNet on several publicly available counting task datasets and ach-ieved state-of-the-art performance on the Diverse Rice Panicle Detection dataset.Furthermore,we employed a rice panicle dataset collected at an altitude of 7 m,which includes various panicle types and growth stages for model training and evaluation.The results showed that LKNet effectively accommodates variations in panicle morphology,with R^(2) values ranging from 0.903 to 0.989.These findings highlight LKNet's potential to enhance precision in panicle counting in rice breeding programs.展开更多
Ensuring food security has become a global challenge owing to climate change and population growth.High-throughput phenotyping can effectively drive crop genetic enhancement,which can potentially solve food crisis.Phe...Ensuring food security has become a global challenge owing to climate change and population growth.High-throughput phenotyping can effectively drive crop genetic enhancement,which can potentially solve food crisis.Phenotyping robot is an essential part of crop ground phenotyping information monitoring,although there are challenges such as the inability to adjust the fixed track width,poor load capacity of the detection robotic arm,and inability to fuse information in real-time.This study reports a phenotyping robot with a gantry-style chassis featuring an adjustable wheeltrack(1400-1600 mm)to adapt to different row spacing arrangements and reduced damage,and function effectively in both dry field and paddy field environments.A six-degree-of-freedom sensor gimbal with high payload capacity is also developed to enable precise height(1016-2096 mm)and angle ad-justments.Additionally,this study introduces an enhanced method for data acquisition from multiple imaging sensors through registration and fusion using Zhang's calibration and feature point extraction algorithm,calcu-lating a homography matrix for high-throughput data collection at fixed positions and heights.The experimental validation results demonstrate that the RMSE of the registration algorithm does not exceed 3 pixels.The gimbal data strongly correlated with that of a handheld instrument data(r^(2)>0.90).The robot is practical,reliable,and fully functional,offering a solid theoretical foundation and equipment support for high-throughput phenotyping.展开更多
The treatment efficacy of anti-diabetic therapies is highly heterogeneous among patients with type 2 diabetes(T2D)(Ahmad et al.2022).Predictive biomarkers can be used to stratify patients into subgroups with varying e...The treatment efficacy of anti-diabetic therapies is highly heterogeneous among patients with type 2 diabetes(T2D)(Ahmad et al.2022).Predictive biomarkers can be used to stratify patients into subgroups with varying efficacy before receiving the treatment,and help advance the understanding of disease and treatment(Ahmad et al.2022).Thus,identifying predictive biomarkers is important for precision medicine of patients with T2D.Approved in China in October 2021 as an adjunct to diet and exercise for improving glycemic control in adult patients with T2D,chiglitazar is a non-thiazolidinedione agonist of theα,δandγsubtypes of the peroxisome proliferator-activated receptors(PPARs)(Deeks 2022).展开更多
Cardiovascular diseases(CVD)are the primary cause of death worldwide.About 17.9 million people died from CVD in 2019,accounting for 32%of deaths globally and threat-ening public health(WHO 2021).
An autoimmune disease such as rheumatoid arthritis(RA)is chronic synovial inflammation affecting skeletal muscle and bone.There is a need to track the onset,progression and drug response in RA.With the advancement in ...An autoimmune disease such as rheumatoid arthritis(RA)is chronic synovial inflammation affecting skeletal muscle and bone.There is a need to track the onset,progression and drug response in RA.With the advancement in techniques and technologies,metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression.We aim to provide a comprehensive insight into the biomarkers of RA that decipher the RA pathogenesis and drug.Certain amino acids and lipids may provide important information before the onset of the disease and predict disease severity and treatment response.展开更多
文摘The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics.Plant Phenomics aims also to connect phenomics to other science domains,such as genomics,genetics,physiology,molecular biology,bioinformatics,statistics,mathematics,and computer sciences.The journal should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
基金supported by the United States Department of Agriculture(USDA)the National Institute of Food and Agriculture(NIFA)+5 种基金the Agriculture and Food Research Initiative(AFRI)award number GRANT12907916 to DRK and JW Wthe Hatch Act State of Iow a funds IOW 03649 and IOW 05745 to DRKthe Hatch Act and State of Iow a funds IOW04108 to JWWthe Iowa State University Plant Science Institute(JWW)the ISU Crop Bioengineering Center(DRK and JWW)and a Department of Defense(DOD)Science,Mathematics,and Research for Transformation(SMART)scholarship to JBC.HS was supported by NSF BIORETS award number 2147083.
文摘Maize is pivotal in supporting global agriculture and addressing food security challenges.Crop root systems are critical for water uptake and nutrient acquisition,which impacts yield.Quantitative trait phenotyping is essential to understand better the genetic factors underpinning maize root growth and development.Root systems are challenging to phenotype given their below-ground,soil-bound nature.In addition,manual trait annotations of root images are tedious and can lead to inaccuracies and inconsistencies between individuals,resulting in data discrepancies.In this study,we explored juvenile root phenotyping in the presence and absence of auxin treat-ment,a key phytohormone in root development,using manual curation and gene expression analyses.In addition,we developed an automated phenotyping pipeline for field-grown maize crown roots by leveraging open-source software.By examining a test set of 11 diverse maize genotypes for juvenile-adult root trait correlations and gene expression patterns,an inconsistent correlation was observed,underscoring the developmental plasticity preva-lent during maize root morphogenesis.Transcripts involved in hormone signaling and stress responses were among differentially expressed genes in roots from 20 diverse maize genotypes,suggesting many molecular processes may underlie the observed phenotypic variance.In particular,co-expressed gene expression networks associated with module-trait relationships included 1,3-β-glucan,which plays a crucial role in cell wall dynamics.This study furthers our understanding of genotype-phenotype relationships,which is relevant for informing agricultural strategies to improve maize root physiology.
基金supported in part by grants from Biological Breeding-National Science and Technology Major Project(Grant No.2023ZD04076)the National Natural Science Foundation of China(Grant No.32170647)+2 种基金the National Science Foundation of Jiangsu Province in China(Grant Nos.BK20212010 and BE2022383)the Jiangsu Engineering Research Center for Plant Genome Editing,Southern Japonica Rice Research and Development Co.LTDthe Jiangsu Collaborative Innovation Center for Modern Crop Production.
文摘Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research.However,the lack of publicly available high-quality image datasets with detailed annotations has severely hindered the development of these models.In this work,we present a comprehensive multicultivar and multiview rice plant image dataset(CVRP)created from 231 landraces and 50 modern cultivars grown under dense planting in paddy fields.The dataset includes images capturing rice plants in their natural environment,as well as indoor images focusing specifically on panicles,allowing for a detailed investigation of cultivar-specific differences.A semiautomatic annotation process using deep learning models was designed for annotations,followed by rigorous manual curation.We demonstrated the utility of the CVRP by evaluating the performance of four state-of-the-art(SOTA)semantic segmentation models.We also conducted 3D plant reconstruction with organ segmentation via images and annotations.The database not only facilitates general-purpose image-based panicle identification and segmentation but also provides valuable re-sources for challenging tasks such as automatic rice cultivar identification,panicle and grain counting,and 3D plant reconstruction.The database and the model for image annotation are available at.
基金This work was supported by the ARC Training Centre for Accelerated Future Crops Development IC210100047)South Australian Research and Development Institute and the University of Adelaide Research Scholarships.
文摘Anthesis prediction is crucial for breeding wheat.While current tools provide estimates of average anthesis at the field scale,they fail to address the needs of breeders who require accurate predictions for individual plants.Hybrid breeders have to finalize their plans for pollination at least 10 days before such flowering is due and biotechnology field trials in the United States and Australia must report to regulators 7-14 days before the first plant flowers.Currently,predicting anthesis of individual wheat plants is a labour-intensive,inefficient,and costly process.Individual wheat of the same cultivar within the same field may exhibit substantial variations in anthesis timing,due to significant variations in their immediate surroundings.In this study,we developed an efficient and cost-effective machine vision approach to predict anthesis of individual wheat plants.By integrating RGB imagery with in-situ meteorological data,our multimodal framework simplifies the anthesis prediction problem into binary or three-class classification tasks,aligning with breeders' requirements in individual wheat flowering prediction on the crucial days before anthesis.Furthermore,we incorporated a few-shot learning method to improve the model's adaptability across different growth environments and to address the challenge of limited training data.The model achieved an F1 score above 0.8 in all planting settings.
基金Global wheat was directly supported by Analytics for the Australian Grains Industry(AAGI).
文摘Computer vision is increasingly used in farmers'fields and agricultural experiments to quantify important traits.Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features,including size,shape,and colour.Although today's AI-driven foundation models segment almost any object in an image,they still fail for complex plant canopies.To improve model performance,the global wheat dataset consortium assembled a diverse set of images from experiments around the globe.After the head detection dataset(GWHD),the new dataset targets a full semantic segmentation(GWFSS)of organs(leaves,stems and spikes)covering all developmental stages.Images were collected by 11 institutions using a wide range of imaging setups.Two datasets are provided:ⅰ)a set of 1096 diverse images in which all organs were labelled at the pixel level,and(ⅱ)a dataset of 52,078 images without annotations available for additional training.The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca.90%.However,the precision for stems with 54%was rather lower.The major advantages over published models are:ⅰ)the exclusion of weeds from the wheat canopy,ⅱ)the detection of all wheat features including necrotic and se-nescent tissues and its separation from crop residues.This facilitates further development in classifying healthy vs.unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
基金supported by the Clinical Research Fund of National Clinical Medical Research Center for Geriatric Diseases,Xiangya Hospital,Central South University(2021LNJJ07)the Huxiang Youth Talent Support Foundation of China(2020RC3059).
文摘Previous observational and genomic-wide association studies(GWAS)suggested the association between several phenotypic factors and keratinocyte carcinoma,including lifestyle and dietary,photodamage-related conditions and socioeconomic status.The causal effect of these phenotypic factors in keratinocytes carcinoma etiology remains unclear.In this study,we utilized two-sample mendelian randomization analysis from multiple large-scale GWAS datasets of keratinocytes carcinoma including more than 300,000 patients.Genetic instrumental variables(IVs)were constructed by identifying single nucleotide polymorphisms(SNPs)that associate with corresponding factors.The inverse variance weighted(IVW)method and four robust MR approaches,including weighted median estimator,MR-Egger regression,simple mode and weighted mode were implemented for causal inferences and assess the sensitivity across findings.In this analysis,ease of skin tanning was identified as casual protective factor of keratinocyte carcinoma(Basal cell carcinoma:IVW OR=0.718,95%CI 0.654-0.788,p<0.001;Cutaneous squamous cell carcinoma:IVW OR=0.601,95%CI 0.516-0.701,p<0.001).Other phenotypic factors,such as coffee intake,alcohol consumption,smoking and socioeconomic status,indicated insignificant effects on keratinocyte carcinoma risk in the analysis,and therefore,our study does not support their roles in modifying keratinocytes carcinoma risks.Our extensive analysis provides strong evidence of the causative protective effect of ease of skin tanning in keratinocyte carcinoma.The findings suggest that individuals who are less prone to tanning may need to pay greater attention to sun protection in their daily activities to reduce the potential risk of keratinocyte cancers.
基金This work was supported by the National Natural Science Foundation of China[grant number 42275200]the Postgraduate Research&Practice Innovation Program of Jiangsu Province[grant number KYCX24_1446]the National Natural Science Foundation of China[grant number 32360443].
文摘Capturing crop physiological information by phenotyping is a key trend in smart agriculture.However,current studies underutilize spatial structural information in phenotypic imaging.To evaluate the feasibility of crop cold stress monitoring based on phenotypic spatial variability,we conducted controlled experiments on'Toyonoka'strawberry plants under four dynamic cooling gradients and three stress durations and analyzed the dependence of their photosynthetic physiology and phenotypic traits on temperature-time interactions.The results revealed that NPQ/1D-Parallel/TENT,Y(NO)/2D-Region/INEM,and qP/1D-Parallel/TENT presented the highest mutual information,with the maximum net photosynthetic rate(Pmax),relative electrolyte conductivity(REC),and total chlorophyll content(Chl_(a+b)),respectively.The difference between the Photosynthetic Physiological Potential Index(PPPI)and relative negative accumulated temperature(RNAT)/650 effectively was used to calculate the cold damage risk(CDRI).An XGBoost-based model integrating the PPPI and RNAT outperformed AdaBoost and RandomForest,achieving an R^(2) of 0.98,an RMSE of 0.337,a classification accuracy of 92.13%,and a Kappa coefficient of 0.904.qP/1D-Parallel/TENT contributed the most to the model.This study provides a scientific basis for phenotypic information mining and agro-meteorological disaster monitoring.
基金supported by the National Key Research and Development Program of China(2022YFD2001001)the Jiangsu Independent Innovation Fund Project of Agricultural Science and Technology[CX(21)1006]+1 种基金the Jiangsu Collaborative Innovation Center for Modern Crop Production(JCICMCP)the 111 Project.
文摘Genotype-environment interaction(G×E)models have potential in digital breeding and crop phenotype pre-diction.Using genotype-specific parameters(GSPs)as a bridge,crop growth models can capture G×E and simulate plant growth and development processes.In this study,a dataset containing multi-environmental planting and flowering data for 169 genotypes,each with 700K single nucleotide polymorphism(SNP)markers was used.Three rice growth models(ORYZA,CERES-Rice,and RiceGrow),SNPs,and climatic indices were in-tegrated for flowering time prediction.Significant associations between GSPs and quantitative trait nucleotides(QTNs)were investigated using genome-wide association study(GWAS)methods.Several GSPs were associated with previously reported rice flowering genes,including DTH2,DTH3 and OsCOL15,demonstrating the genetic interpretability of the models.The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors(RMSE),compared to the traditional model calibration.The predictions of crop model were further modified using the machine learning(ML)methods and climate indicators.The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach.In addition,the Multi-model ensemble(MME)was comparable to that of the best individual model.Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.
基金supported by the National Key Research and Development Program(2021YFD1200705)the Collaborative Innovation Center of the Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Science and Technology Innovation Special Construction Funded Program of the Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401).
文摘Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or oversegmentation.Moreover,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation outcomes.We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds.First,CT images are acquired using a multislice CT scanner.To improve the efficiency of maize kernel CT imaging,a batch scanning method is used.Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm.Second,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize kernels.The conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named CSFTU-Net.The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm.Finally,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.
基金supported by the National Key R&D Program of China(2022YFF1001400)the National Natural Science Foundation of China(32360509)+6 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01A150)the Key Research and Development Program of Xinjiang Autonomous Region(2023B02014)the Corps of Agricultural Science and Technology Innovation Project Special(NCG202316)Key Research and Development Program of Xinjiang(2022B02052)the National Natural Science Foundation of China(32301888)the National Key Laboratory of Cotton Bio-breeding and Integrated Utilization(CBIU2024004)the Natural Science Foundation of Henan(232300421253).
文摘Plant height(PH)is a key agronomic trait influencing plant architecture.Suitable PH values for cotton are important for lodging resistance,high planting density,and mechanized harvesting,making it crucial to elucidate the mechanisms of the genetic regulation of PH.However,traditional field PH phenotyping largely relies on manual measurements,limiting its large-scale application.In this study,a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging(LiDAR)was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field.Different strategies were used to extract PH values from two sets of sensor data,and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions.These predictions were consistent with manual measurements of the PH for the LiDAR(R^(2)=0.934)and RGB(R^(2)=0.914)data.The predicted PH values were used for GWAS analysis,and 34 PH-related genes,two of which have been demonstrated to regulate PH in cotton,namely,GhPH1 and GhUBP15,were identified.We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G.hirsutum cultivar ZM24 harvested on the 15th,35th,and 70th days after sowing compared with those from a dwarf mutant(pag1),which presented shortened stem and internode phenotypes.The overexpression of GhPH_UAV1 significantly promoted cotton stem development,whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth,suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH.This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations,which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.
基金This work was supported by a Cooperative Research And Development Agreement with JB Hyperspectral Devices,GmbH(CRADA No.58-8042-2-029F).
文摘High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses.PhenoGazer,a high-throughput phenotyping system,enhances crop moni-toring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics,four Raspberry Pi cameras,and blue LED lights.This system allows for comprehensive assessment of plant health and development.PhenoGazer features automated moveable upper and lower racks for continuous measure-ments.The lower rack,equipped with four blue LED lights and spectrometer fiber optics,captures blue light-induced chlorophyll fluorescence at night.The upper rack,carrying four spectrometer fiber optics and cam-eras,captures hyperspectral reflectance and RGB images during the day.This dual capability enables detailed evaluation of plant phenology,stress responses,and growth dynamics throughout the entire crop growth cycle.Fully automated and managed by a Raspberry Pi running Python scripts,PhenoGazer ensures precise control and data acquisition with minimal human intervention.Additionally,it includes continuous measurements through a datalogger to acquire photosynthetically active radiation(PAR),soil moisture and temperature,and features expansion capability for additional analog or digital sensors as desired by end users.To test the system,soybean plants representing three conditions,healthy well watered,healthy droughted,and diseased,were monitored to evaluate growth and stress responses.PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber.By combining nighttime blue light induced chlorophyll fluorescence,hyperspectral reflectance-based vegetation indices,and RGB imagery,PhenoGazer represented a significant advancement in plant phenotyping technology,enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA26030102)the CAS-CSIRO Project(063GJHZ2022047MI)the CAS Special Research Assistant(SRA)Program(Y973RG1001).
文摘Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-and-effect relationship,are given priority for integration.However,few efficient integrating methods are available.Results:Here,we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency.To verify its feasibility,two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants.Centered on salt tolerance,data are combined through constructing metabolite-based spectral indices outlining tolerance-related metabolic changes in primary screening,and models converting hyperspectral data to metabolite content for detailed characterizing in secondary screening.Target phenotype could be discriminated after five-day salt-treatment,much earlier than phenotypic difference appearance.20 mutants with salt-tolerant phenotype were successfully identified from about 1000 mutants,almost tripled that of unintegrated analysis.Accuracy rate,confirmed with salt-tolerance analysis for experimental verification,reached 90%,which can be optimized to 100%theoretically utilizing results from hierarchical-clustering-assisted Principal Component Analysis.Conclusions:Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data.Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects,providing a powerful tool for phenotype selection in new-generation crop breeding.
基金funded by the National Natural Science Foundation of China,China(52279042)the National Key Research and Development program of China,China(2021YFC3201204)the Key Research and Development Program in Guangxi,China(AB23026021).
文摘Water stress is a crucial environmental factor that impacts the growth and yield of rice.Complex field micro-climates and fluctuating water conditions pose a considerable challenge in accurately evaluating water stress.Measurement of a particular crop trait is not sufficient for accurate evaluation of the effects of complex water stress.Four comprehensive indicators were introduced in this research,including canopy chlorophyll content(CCC)and canopy equivalent water(CEW).The response of the canopy-specific traits to different types of water stress was identified through individual plant experiments.A hybrid method integrating the PROSAIL radiative transfer model and multidimensional imaging data to retrieve these traits.The synthetic dataset generated by PROSAIL was utilized as prior knowledge for developing a pre-trained machine learning model.Subsequently,reflectance separated from hyperspectral images and phenotypic indicators extracted from front-view images were innovatively united to retrieve water stress-related traits.The results demonstrated that the hybrid method exhibited improved stability and accuracy of CCC(R=0.7920,RMSE=24.971μg cm^(-2))and CEW(R=0.8250,RMSE=0.0075 cm)compared to both data-driven and physical inversion modeling methods.Overall,a robust and accurate method is proposed for assessing water stress in rice using a combination of radiative transfer modeling and multidimensional image-based data.
基金This study was funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany's Excellence Strategy-EXC 2070-390732324.
文摘The disease syndrome"basses richesses"(SBR)leads to a significant reduction in sugar beet biomass and sugar content,negatively affecting the sugar economy.The mechanistic understanding regarding growth and photo-assimilates distribution within the sugar beet taproot diseased with SBR is currently incomplete.We combined two tomographic methods,magnetic resonance imaging(MRI)and positron emission tomography(PET)using 11C as tracer,to non-invasively determine SBR effects on structural growth and photoassimilates distribution within the developing taproot over six weeks.MRI analysis revealed a deformed cross-sectional anatomical structure from an early stage,as well as a reduction in taproot volume and width of inner cambium ring structures of up to 26 and 24%,respectively.These SBR disease effects were also confirmed by post-harvest analysis of the taproot.PET analysis revealed a heterogeneous distribution of labeled photoassimilates for diseased plants:sectors of the taproot with characteristic SBR symptoms showed little to very low ^(11)C tracer signal.The heterogeneity of SBR disease effects is most likely due to a partial inoculation of leaves leading to an uneven distribution of the SBR pathogen in the taproot through the strong vascular interconnection between shoot and root.Also,the pathogen needs to spread non-uniformly within the taproot to explain the observed marked increase of the SBR disease effects over time.Our results indicate that SBR affects photoassimilates sink capacity at an early stage of taproot development.Co-registration of MRI and PET may support an early judging of susceptibility and selection of promising genotype candidates for future breeding programs.
基金National Key R&D Program of China(2023YFA1801200)National Science Foundation of China(32288101,32200536,82241023)+9 种基金The Science and Technology Department of Tibet(08080002)2019 School-level Cultivation Project of Tibet University(ZDTSJH19-08)Special Funds from the Central Finance to Support the Development of Local Universities(2018)No.54(2019)No.1-19(2020)No.79,(2021])No.1,(00060695/003)Special of the Disposal of the Science and Technology Department of Tibet Autonomous Region in 2023(18080280)Tibet Autonomous Region Natural Science Foundation Group Medical Assistance Project Plan(XZ2023ZR-ZY34(Z))Municipal Natural Science Foundation(joint)project(RKZ2023ZR-014(Z))Shanghai Municipal Science and Technology Major Project(2023SHZDZX02,2017SHZDZX01)CAMS Innovation Fund for Medical Sciences(2019-I2M-5-066).
文摘High-altitude polycythemia(HAPC)is a prevalent chronic condition affecting individuals at high altitudes,including both highland and plains populations.This study,involving 2248 participants,explored genetic susceptibility to HAPC among ethnic groups,with 898 HAPC patients(450 Han,448 Tibetan).The Genome-wide Association Study,encompassing 198 cases(100 Han,98 Tibetan),identified eight Tibetan HAPC-susceptibility single-nucleotide polymorphisms and four in Han individuals.The common polymorphism locus rs7618658(SNX4,pcombine<5×10^(-8))was validated in both popula-tions.The investigation of Tibetan EPAS1 revealed the rs1374749 locus,along with linked loci,as a potential prevalence factor for HAPC.The GGTAC haplotype containing this locus emerged as a Protect haplotype for HAPC(p=2.461×10^(-12),OR=0.344).Enrichment analysis revealed that Tibetans exhibited susceptibility in oxygen-sensing pathways,such as EPAS1,associated with phenotypes like hemoglobin and platelets.In contrast,Han Chinese showed significant sensitivity in cell differentiation and angiogenesis,closely linked to hemoglobin,hematocrit,and platelets.
基金funded by the joint fund for the National Key Research and Development Program of China"Research and demonstration of integrative approaches to synergistically improve yield,quality and efficiency in rice production in Southern China"(2022YFD2300700)Zhejiang"Ten thousand talents"plan science and technology innovation leading talent project(2020R52035)+1 种基金the Agricultural Science and Technology Innovation Program(CAAS-ZDRW202001)the Industry-Academia-Research Cooperation Project of Zhuhai,China(ZH22017001210013PWC).
文摘Location-based methods for counting rice panicles have often been underestimated,primarily due to their perceived inferior performance when compared to detection-based techniques.However,we argue that the po-tential of these location-based methods has not been fully realized,largely owing to the limitations of existing model architectures.In response to this challenge,we introduce LKNet,an innovative model developed on the foundation of the location-based framework P2Pnet.To enhance the performance of panicle counting across diverse types and growth stages,we implemented several key strategies.Firstly,we reconstructed the localization loss function as a predictive probability distribution to reduce the influence of manual labeling.Additionally,we dynamically adapted the receptive field to better accommodate different panicle types through the use of large kernel convolutional blocks.We evaluated LKNet on several publicly available counting task datasets and ach-ieved state-of-the-art performance on the Diverse Rice Panicle Detection dataset.Furthermore,we employed a rice panicle dataset collected at an altitude of 7 m,which includes various panicle types and growth stages for model training and evaluation.The results showed that LKNet effectively accommodates variations in panicle morphology,with R^(2) values ranging from 0.903 to 0.989.These findings highlight LKNet's potential to enhance precision in panicle counting in rice breeding programs.
基金The work was supported by the National Key Research and Development Program of China(Grant No.2021YFD2000101).
文摘Ensuring food security has become a global challenge owing to climate change and population growth.High-throughput phenotyping can effectively drive crop genetic enhancement,which can potentially solve food crisis.Phenotyping robot is an essential part of crop ground phenotyping information monitoring,although there are challenges such as the inability to adjust the fixed track width,poor load capacity of the detection robotic arm,and inability to fuse information in real-time.This study reports a phenotyping robot with a gantry-style chassis featuring an adjustable wheeltrack(1400-1600 mm)to adapt to different row spacing arrangements and reduced damage,and function effectively in both dry field and paddy field environments.A six-degree-of-freedom sensor gimbal with high payload capacity is also developed to enable precise height(1016-2096 mm)and angle ad-justments.Additionally,this study introduces an enhanced method for data acquisition from multiple imaging sensors through registration and fusion using Zhang's calibration and feature point extraction algorithm,calcu-lating a homography matrix for high-throughput data collection at fixed positions and heights.The experimental validation results demonstrate that the RMSE of the registration algorithm does not exceed 3 pixels.The gimbal data strongly correlated with that of a handheld instrument data(r^(2)>0.90).The robot is practical,reliable,and fully functional,offering a solid theoretical foundation and equipment support for high-throughput phenotyping.
基金the National Natural Science Foundation of China(T2425013,32370701,32470692,32170657)the National Key R&D Project of China(2023YFC3402501)+1 种基金Shanghai Municipal Science and Technology Major Project,the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA12040104)the 111 Project(B13016).
文摘The treatment efficacy of anti-diabetic therapies is highly heterogeneous among patients with type 2 diabetes(T2D)(Ahmad et al.2022).Predictive biomarkers can be used to stratify patients into subgroups with varying efficacy before receiving the treatment,and help advance the understanding of disease and treatment(Ahmad et al.2022).Thus,identifying predictive biomarkers is important for precision medicine of patients with T2D.Approved in China in October 2021 as an adjunct to diet and exercise for improving glycemic control in adult patients with T2D,chiglitazar is a non-thiazolidinedione agonist of theα,δandγsubtypes of the peroxisome proliferator-activated receptors(PPARs)(Deeks 2022).
基金supported by grants from“Pioneer”and“Leading Goose”R&D Programs of Zhejiang Province(2023C03163,2025C02104)the FORCHN Holding Group-Zhejiang University Collaborative Project(2020-KYY-518051-0066)+4 种基金Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions(2023GH034)Shanghai Municipal Science and Technology Major Project(2017SHZDZX01)Zhejiang Key Laboratory of Intelligent Preventive Medicine(2020E10004)Zhejiang University Global Partnership FundZhejiang University School of Public Health Interdisciplinary Research Innovation Team Development Project.
文摘Cardiovascular diseases(CVD)are the primary cause of death worldwide.About 17.9 million people died from CVD in 2019,accounting for 32%of deaths globally and threat-ening public health(WHO 2021).
文摘An autoimmune disease such as rheumatoid arthritis(RA)is chronic synovial inflammation affecting skeletal muscle and bone.There is a need to track the onset,progression and drug response in RA.With the advancement in techniques and technologies,metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression.We aim to provide a comprehensive insight into the biomarkers of RA that decipher the RA pathogenesis and drug.Certain amino acids and lipids may provide important information before the onset of the disease and predict disease severity and treatment response.
