Upland cotton （Gossypium hirsutum L.） is an allotetraploid species originated from interspecific hybridization between AA-genome diploid （G. arboretum） and DD-genome diploid （G. raimondii） （Wendel et al., 1992...Upland cotton （Gossypium hirsutum L.） is an allotetraploid species originated from interspecific hybridization between AA-genome diploid （G. arboretum） and DD-genome diploid （G. raimondii） （Wendel et al., 1992）. Cotton fibers are single-celled trichomes that emerge from the ovule epidermal cells. Indexed by the number of days post-anthesis （dpa）, fiber morphogenesis includes four distinct but overlapping steps： initiation （0-3 dpa）, elongation （3-20 dpa）, secondary cell wall thickening （15-45 dpa） and maturation （40-60 dpa） （Yang et al., 2008, Du et al., 2013）. The efficiency and duration of each morphogenesis stage is important to the quality attributes of the mature fiber. Cell elongation is critical for fiber length, whereas secondary cell wall thickening is important for fiber fineness and strength （Meinert and Delmer, 1977）.展开更多
Aims and objectives: The frequent and unprescribed use of antibiotics has led to the development of resistance by microorganisms responsible for urinary tract infection (UTI). In order to facilitate the follow-up of t...Aims and objectives: The frequent and unprescribed use of antibiotics has led to the development of resistance by microorganisms responsible for urinary tract infection (UTI). In order to facilitate the follow-up of this microbial resistance, the aim of this study was to determine the bacteria resistant phenotypes. Method: To achieve the following objectives, this study was conducted from June to August 2023. The isolation and identification were performed by standard methods why susceptibility testing was done by Kirby-Bauer disk diffusion technique according to CLSI guidelines. Double-disk synergy test was applied to determine the presence of extended-spectrum β-lactamase (ESBL) produced by bacteria. The Imipenem EDTA Combined Disc Test (CDT) for Metallo beta lactamase (MBL) screening, the D-zone test to detect macrolides, lincosamides and streptogramins type B (MLSB) and Meticillin resistant Staphylococcus aureus (MRS A) which was assessed using a Cefoxitin (30 µg) disc. Results: A total of 40 bacteria were identified with a prevalence of 37.03%. Overall, E. coli was the predominant isolate 14 (35%), followed by Staphylococcus aureus 10 (25%) and Klesbsiella pneumonia 4 (10%). Pseudomonas aeruginosa, Salmonella arinosa and Enterobacter were the most sensible (100%) bacteria against ciprofloxin, ceftriaxone and cefixime. Almost all bacteria were resistant to Amoxicillin/clavulanic acid (>50%). The isolates were in the majority resistant to imipenem. ESBL-producing Enterobacteriaceae represented 25.92%, with a higher rate among E. coli. No MBL production was found among the isolates while 38.46% represented cMLSB, 15.38% represented iMLSB, 23.07% represented MSB and 23.07% represented MRSA. Conclusion: Clinical strains of UTI from Protestant Hospital of Ngaoundere exhibiting ESBL, cMLSB, iMLSB, MSB and MRSA. The regular updating of antibiotic resistance statistics of isolated strains allows for a better adaptation of probabilistic antibiotic therapy.展开更多
With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the...With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the accurate,high-throughput acquisition and analysis of multi-dimensional phenotypes during crop growth at organism-wide levels,ranging from cells to organs,individual plants,plots,and fields.Here we offer an overview of crop phenomics research from technological and platform viewpoints at various scales,including microscopic,ground-based,and aerial phenotyping and phenotypic data analysis.We describe recent applications of high-throughput phenotyping platforms for abiotic/biotic stress and yield assessment.Finally,we discuss current challenges and offer perspectives on future phenomics research.展开更多
Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult hom...Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type （WT） cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass： 23 previously reported genes （Calcr, Cebpb, Crtap, Dcstamp, Dkkl, Duoxa2, Enppl, Fgf23, Kissl/Kisslr, Kl （Klotho）, Lrp5, Mstn, Neol, Npr2, Ostml, Postn, Sfrp4, S1c30a5, Sic39a13, Sost, Sumf1, Src, Wnt10b）, five novel genes extensively characterized （Cldn18, Fam20c, Lrrkl, Sgpll, Wnt16）, five novel genes with preliminary characterization （Agpat2, RassfS, Slc10a7, Stc26a7, Slc30a10） and three novel undisclosed genes coding for potential osteoporosis drug targets.展开更多
The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping techniques.In this study,two wheat access...The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping techniques.In this study,two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to post-anthesis.Red-green-blue(RGB)imaging was performed on 17 of the 22 d following the start of drought imposition.Destructive measurements from all plants were performed at the conclusion of the experiment.The effect of line was signifcant for shoot dry matter,spike dry matter,root dry matter,and tller number,while the water treatment was significant on shoot dry matter and root dry matter.The temporal,non-destructive nature of HTP allowed the drought treatment to be significantly differentiated from the well-watered treatment after 6 d in a line from Argentina and 9 d in a line from Chile.This difference of 3 d indicated an increased degree of drought tolerance in the line from Chile.Furthermore,HTP from the final day of imaging accurately predicted reference plant height(r=1),shoot dry matter(r=0.95)and tller number(r=0.91).This experiment ilustrates the potential of HTP and its use in modeling plant growth and development.展开更多
Recent technological advances in cotton(Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis.High-throughput phenotyping(HTP) is a non-destructive and rapid a...Recent technological advances in cotton(Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis.High-throughput phenotyping(HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth,yield,and adaptation to biotic or abiotic stress.Researchers have conducted extensive experiments on HTP and developed techniques including spectral,fluorescence,thermal,and three-dimensional imaging to measure the morphological,physiological,and pathological resistance traits of cotton.In addition,ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems.This review paper highlights the techniques and recent developments for HTP in cotton,reviews the potential applications according to morphological and physiological traits of cotton,and compares the advantages and limitations of these HTP systems when used in cotton cropping systems.Overall,the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton.However,because of its relative novelty,HTP has some limitations that constrains the ability to take full advantage of what it can offer.These challenges need to be addressed to increase the accuracy and utility of HTP,which can be done by integrating analytical techniques for big data and continuous advances in imaging.展开更多
Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have rev...Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.展开更多
In genetic association studies of complex diseases, endo-phenotypes such as expression profiles, epigenetic data, or clinical intermediate-phenotypes provide insight to understand the underlying biological path of the...In genetic association studies of complex diseases, endo-phenotypes such as expression profiles, epigenetic data, or clinical intermediate-phenotypes provide insight to understand the underlying biological path of the disease. In such situations, in order to establish the path from the gene to the disease, we have to decide whether the gene acts on the disease phenotype primarily through a specific endo-phenotype or whether the gene influences the disease through an unidentified path which is characterized by different intermediate phenotypes. Here, we address the question that a genetic locus, given its effect on an endo-phenotype, influences the trait of interest primarily through the path of the endo-phenotype. We propose a Bayesian approach that can evaluate the genetic association between the genetic locus and the phenotype of interest in the presence of the genetic effect on the endo-phenotype. Using simulation studies, we verify that our approach has the desired properties and compare this approach with a mediation approach. The proposed Bayesian approach is illustrated by an application to genome-wide association study for childhood asthma (CAMP) that contains expression profiles.展开更多
High-throughput phenotyping of growth kinetics and organ size in the model plant Arabidopsis thaliana requires rapid and precise methods for trait estimation.To address this need,we developed the Arabidopsis Phenotypi...High-throughput phenotyping of growth kinetics and organ size in the model plant Arabidopsis thaliana requires rapid and precise methods for trait estimation.To address this need,we developed the Arabidopsis Phenotypic Trait Estimation System,APTES,an open-access,high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs.The enhanced segmentation model Cascade Mask Region-based Convolutional Neural Network(Mask R-CNN)achieved precision(measure of positive prediction accuracy),recall(sensitivity in detection),and F1 score values(harmonic mean of precision and recall)of 0.965,0.958,and 0.961,respectively,for individual leaf segmentation.These metrics demonstrated a consistent improvement of approximately 1 percentage point over the baseline model.For silique segmentation,our enhanced DetectoRS model for silique segmentation attained precision,recall,and F1 scores of 0.954,0.930,and 0.942,respectively.Notably,precision increased by 1%,while the F1 score improved by 2 percentage points.Trait parameters were automatically calculated with coefficient of determination values for leaf and silique traits ranging from 0.776 to 0.976 and mean absolute percentage error values from 1.89%to 7.90%.We phenotyped 166 Arabidopsis accessions,using APTES,and subjected the resulting values to a genome-wide association study(GWAS),revealing 1,042 single-nucleotide polymorphisms(SNPs)as being significantly associated with 18 leaf and silique traits,and one significant SNP on chromosome 3 linked to silique number.Furthermore,we validated APTES across other public Arabidopsis databases and other plant species,with segmentation results demonstrating its applicability across diverse datasets.In conclusion,APTES is a valuable automated tool for leaf and silique segmentation and trait estimation,which should offer benefits to the broader plant science community.展开更多
Sweet potato leaf tips have high nutritional value,and exploring the differences in the metabolic profiles of leaf tips among different sweet potato varieties can provide information to improve their qualities.In this...Sweet potato leaf tips have high nutritional value,and exploring the differences in the metabolic profiles of leaf tips among different sweet potato varieties can provide information to improve their qualities.In this study,a UPLC-Q-Exactive Orbitrap/MS-based untargeted metabolomics method was used to evaluate the metabolites in leaf tips of 32 sweet potato varieties.Three varieties with distinct overall metabolic profiles(A01,A02,and A03),two varieties with distinct profiles of phenolic acids(A20 and A18),and three varieties with distinct profiles of flavonoids(A05,A12,and A16)were identified.In addition,a total of 163 and 29 differentially expressed metabolites correlated with the color and leaf shape of sweet potato leaf tips,respectively,were identified through morphological characterization.Group comparison analysis of the phenotypic traits and a metabolite-phenotypic trait correlation analysis indicated that the color differences of sweet potato leaf tips were markedly associated with flavonoids.Also,the level of polyphenols was correlated with the leaf shape of sweet potato leaf tips,with lobed leaf types having higher levels of polyphenols than the entire leaf types.The findings on the metabolic profiles and differentially expressed metabolites associated with the morphology of sweet potato leaf tips can provide useful information for breeding sweet potato varieties with higher nutritional value.展开更多
Root architecture,which determines the water and nutrient uptake ability of crops,is highly plastic in response to soil environmental changes and different cultivation patterns.Root phenotyping for field-grown crops,e...Root architecture,which determines the water and nutrient uptake ability of crops,is highly plastic in response to soil environmental changes and different cultivation patterns.Root phenotyping for field-grown crops,especially topological trait extraction,is rarely performed.In this study,an image-based semi-automatic root phenotyping method for field-grown crops was developed.The method consisted of image acquisition,image denoising and segmentation,trait extraction and data analysis.Five global traits and 40 local traits were extracted with this method.A good consistency in 1st-order lateral root branching was observed between the visually counted values and the values extracted using the developed method,with R^(2)=0.97.Using the method,we found that the interspecific advantages for maize mainly occurred within 5 cm from the root base in the nodal roots of the 5th-7th nodes,and that the obvious inhibition of soybean was mostly reflected within 20 cm from the root base.Our study provides a novel approach with high-throughput and high-accuracy for field research on root morphology and branching features.It could be applied to the 3D reconstruction of field-grown root system architecture to improve the inputs to data-driven models(e.g.,OpenSimRoot)that simulate root growth,solute transport and water uptake.展开更多
Biolog MicroPlates TM. are employed to characterize Trichoderma isolates based on differential assimilation of test substrates and redox reactions in a 96-well test plate. The Biolog method is potentially advantageous...Biolog MicroPlates TM. are employed to characterize Trichoderma isolates based on differential assimilation of test substrates and redox reactions in a 96-well test plate. The Biolog method is potentially advantageous in being relatively simple, fast and economical, and data acquisition can be automated using a microplate reader and applicable software. Several research applications of the Biolog system are presented: i) “monophenetic groups” from cluster analyses of phenotype array data are investigated for previously undetected new species in Trichoderma, ii) metabolic characters differentiating species are identified, and multivariate analyses performed to complement molecular data in validating new species and significant variants, and iii) phenotype array data for more than 1200 Trichoderma strains are analysed to select strains that might be exploited for bioconversions and commercial production of enzymes. Phenotype arrays are much more sensitive to strain level variation than molecular techniques, however, phenotype array data do not consistently reflect phylogenies constructed from molecular data. Nevertheless, the Biolog phenotype array is an economical alternative method for surveying biological diversity, and provides data that complements molecular data in phylogenetic studies.展开更多
Systemic lupus erythematosus （SLE） is a complex autoimmune disease characterized by the production of autoantibodies to a broad range of self-antigens. Profiling the autoantibody repertoire using array-based technol...Systemic lupus erythematosus （SLE） is a complex autoimmune disease characterized by the production of autoantibodies to a broad range of self-antigens. Profiling the autoantibody repertoire using array-based technology has emerged as a powerful tool for the identification of biomarkers in SLE and other autoimmune diseases. Proteomic microarray has the capacity to hold large number of self-antigens on a solid surface and serve as a high-throughput screening method for the determination of autoantibody specificities. The autoantigen arrays carrying a wide variety of self-antigens, such as cell nuclear components （nucleic acids and associated proteins）, cytoplas- mic proteins, phospholipid proteins, cell matrix proteins, mucosal/secreted proteins, glomeruli, and other tissue-specific proteins, have been used for screening of autoantibody specificities associated with different manifestations of SLE. Arrays containing synthetic peptides and molecular modified proteins are also being utilized for identification of autoantibodies targeting to special antigenic epi- topes. Different isotypes of autoantibodies, including IgG, IgM, IgA, and IgE, as well as other Ig subtypes, can be detected simultaneously with multi-color labeled secondary antibodies. Serum and plasma are the most common biologic materials for autoantibody detection, but other body fluids such as cerebrospinal fluid, synovial fluid, and saliva can also be a source of autoantibody detection.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
High-throughput phenotyping(HTP)technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources.To better understand the status of forage phenotyping research an...High-throughput phenotyping(HTP)technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources.To better understand the status of forage phenotyping research and identify key directions for development,this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years.This paper reviews the unique aspects and research priorities in forage phenotypic monitoring,highlights key remote sensing platforms,examines the applications of advanced sensing technology for quantifying phenotypic traits,explores artificial intelligence(AI)algorithms in phenotypic data integration and analysis,and assesses recent progress in phenotypic genomics.The practical applications of HTP technology in forage remain constrained by several challenges.These include establishing uniform data collection standards,designing effective algorithms to handle complex genetic and environmental interactions,deepening the cross-exploration of phenomics-genomics,solving the problem of pathological inversion of forage phenotypic growth monitoring models,and developing low-cost forage phenotypic equipment.Resolving these challenges will unlock the full potential of HTP,enabling precise identification of superior forage traits,accelerating the breeding of superior varieties,and ultimately improving forage yield.展开更多
Understanding the genetic basis of quantitative traits related to crop growth,yield,and stress response requires the acquisition of large-scale,high-quality phenotypic datasets.High-throughput phenotyping platforms ha...Understanding the genetic basis of quantitative traits related to crop growth,yield,and stress response requires the acquisition of large-scale,high-quality phenotypic datasets.High-throughput phenotyping platforms have become effective tools for meeting this requirement.Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads,their operational flexibility,and their proximity to crops,which allows for higher imaging resolution.In this study,we introduce PhenoRob-F(a phenotyping robot for the field),a cross-row,wheeled robot designed for efficient and automated phenotyping under field conditions.The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field pheno-typing,with integrated visual and satellite navigation systems enabling autonomous operation.We validated the performance of the robot through a series of experiments involving various crop canopies.By capturing RGB images of rice and wheat,we independently performed wheat ear detection and rice panicle segmentation.For wheat ear detection,we achieve a precision of 0.783,a recall of 0.822,and a mean average precision(mAP)of 0.853 when the YOLOv8m model is used.For rice panicle segmentation,the SegFormer_BO model yielded a mean intersection over union(mIoU)of 0.949 and an accuracy of 0.987.Additionally,by capturing RGB-D data of maize canopies,we performed 3D reconstructions to calculate plant height,achieving an R^(2) of 0.99 compared with manual measurements.Similar experiments with rapeseed yielded an R^(2) of 0.97.Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories,with classification accuracies ranging from 0.977 to 0.996.Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.展开更多
Norway spruce(Picea abies Karst L.)is one of the most ecologically and economically significant tree species in Europe,accounting for nearly half of the continent's forest economic value.However,drought is a signi...Norway spruce(Picea abies Karst L.)is one of the most ecologically and economically significant tree species in Europe,accounting for nearly half of the continent's forest economic value.However,drought is a significant stress factor associated with increasing Norway spruce mortality across Europe.Provenance trials,a traditional approach to assess adaptive variation,face limitations stemming from the finite number of sites,seed sources involved,and their required labor-intensive nature.In response,we developed a comprehensive multisensor high-throughput phenotyping method and integrated it with metabolomics,transcriptomics,and anatomical analyses to study the drought stress responses in two climatically contrasting but geographically proximal provenances at the seedling stage by exposing them to drought stress for a period of 21 days.Based on more than 50 physiological and growth-related traits assessed by the phenotyping platform,it was possible to characterize early and late drought stress responses.Consistent with phenotypic data,mRNA-seq,and metabolic profiles revealed apparent differences between treatments.While during the drought stress the metabolic data indicated an increased pro-duction of ABA,α-tocopherol,zeaxanthin,lutein,and phenolics,mRNA-seq showed modulation of related pathways and downregulation of photosystem transcripts.Although drought responses were largely conserved between the two provenances,they differed phenotypically in traits related to the activation of re-oxidation of the plastoquinone pool,and molecularly in transcriptional and phenolic profiles.In conclusion,our study demon-strates the potential of the high-throughput phenotyping approach for evaluating drought stress adaptation in Norway spruce thus accelerating the screening and selection of best adapted provenances.展开更多
Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lot...Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lotaxy.Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants.As a result,in simulations of the leaf canopies of grain crops such as maize and sorghum,this trait is frequently approximated as alternating 180°angles between sequential leaves.We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alter-nating phyllotaxy across a diverse set of sorghum genotypes.Using a voxel-carving-based approach,we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum geno-types from the sorghum association panel.The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals.Automated phyllotaxy measurements exhibited a repeatability of R^(2)=0.41 across imaging timepoints separated by a period of two days.A resampling based genome wide association study(GWAS)identified several putative genetic associations with lower-canopy phyllotaxy in sorghum.This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.展开更多
Emerging single-cell technologies create new opportunities for unraveling tumor heterogeneity.However,the development of high-content phenotyping platform is still at its infancy.Here,we develop a microfluidic chip fo...Emerging single-cell technologies create new opportunities for unraveling tumor heterogeneity.However,the development of high-content phenotyping platform is still at its infancy.Here,we develop a microfluidic chip for two-dimensional(2D)profiling of tumor chemotactic and molecular features at single cell resolution.Individual cells were captured by the triangular micropillar arrays in the cell-loading channel,facilitating downstream single-cell analysis.For 2D phenotyping,the chemotactic properties of tumor cells were visualized through cellular migratory behavior in microchannels,while their protein expression was profiled with multiplex surface enhanced Raman scattering(SERS)nanovectors,in which Raman reporter-embedded gold@silver core-shell nanoparticles(Au@Ag REPs)were modified with DNA aptamers targeting cellular surface proteins.As a proof of concept,breast cancer cells with diverse phenotypes were tested on the chip,demonstrating the capability of this platform for simultaneous chemotactic and molecular analysis.The chip is expected to provide a powerful tool for investigating tumor heterogeneity and promoting clinical precision medicine.展开更多
基金supported by the grants from the State Key Basic Research and Development Plan (No. 2010CB126003)the National Transgenic Animals and Plants Research Project (Nos. 2011ZX08005-003 and 2011ZX08009-003)
文摘Upland cotton （Gossypium hirsutum L.） is an allotetraploid species originated from interspecific hybridization between AA-genome diploid （G. arboretum） and DD-genome diploid （G. raimondii） （Wendel et al., 1992）. Cotton fibers are single-celled trichomes that emerge from the ovule epidermal cells. Indexed by the number of days post-anthesis （dpa）, fiber morphogenesis includes four distinct but overlapping steps： initiation （0-3 dpa）, elongation （3-20 dpa）, secondary cell wall thickening （15-45 dpa） and maturation （40-60 dpa） （Yang et al., 2008, Du et al., 2013）. The efficiency and duration of each morphogenesis stage is important to the quality attributes of the mature fiber. Cell elongation is critical for fiber length, whereas secondary cell wall thickening is important for fiber fineness and strength （Meinert and Delmer, 1977）.
文摘Aims and objectives: The frequent and unprescribed use of antibiotics has led to the development of resistance by microorganisms responsible for urinary tract infection (UTI). In order to facilitate the follow-up of this microbial resistance, the aim of this study was to determine the bacteria resistant phenotypes. Method: To achieve the following objectives, this study was conducted from June to August 2023. The isolation and identification were performed by standard methods why susceptibility testing was done by Kirby-Bauer disk diffusion technique according to CLSI guidelines. Double-disk synergy test was applied to determine the presence of extended-spectrum β-lactamase (ESBL) produced by bacteria. The Imipenem EDTA Combined Disc Test (CDT) for Metallo beta lactamase (MBL) screening, the D-zone test to detect macrolides, lincosamides and streptogramins type B (MLSB) and Meticillin resistant Staphylococcus aureus (MRS A) which was assessed using a Cefoxitin (30 µg) disc. Results: A total of 40 bacteria were identified with a prevalence of 37.03%. Overall, E. coli was the predominant isolate 14 (35%), followed by Staphylococcus aureus 10 (25%) and Klesbsiella pneumonia 4 (10%). Pseudomonas aeruginosa, Salmonella arinosa and Enterobacter were the most sensible (100%) bacteria against ciprofloxin, ceftriaxone and cefixime. Almost all bacteria were resistant to Amoxicillin/clavulanic acid (>50%). The isolates were in the majority resistant to imipenem. ESBL-producing Enterobacteriaceae represented 25.92%, with a higher rate among E. coli. No MBL production was found among the isolates while 38.46% represented cMLSB, 15.38% represented iMLSB, 23.07% represented MSB and 23.07% represented MRSA. Conclusion: Clinical strains of UTI from Protestant Hospital of Ngaoundere exhibiting ESBL, cMLSB, iMLSB, MSB and MRSA. The regular updating of antibiotic resistance statistics of isolated strains allows for a better adaptation of probabilistic antibiotic therapy.
基金supported by the National Key Research and Development Program of China(2016YFD0100101-18,2020YFD1000904-1-3)the National Natural Science Foundation of China(31601216,31770397)Fundamental Research Funds for the Central Universities(2662019QD053,2662020ZKPY017)。
文摘With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the accurate,high-throughput acquisition and analysis of multi-dimensional phenotypes during crop growth at organism-wide levels,ranging from cells to organs,individual plants,plots,and fields.Here we offer an overview of crop phenomics research from technological and platform viewpoints at various scales,including microscopic,ground-based,and aerial phenotyping and phenotypic data analysis.We describe recent applications of high-throughput phenotyping platforms for abiotic/biotic stress and yield assessment.Finally,we discuss current challenges and offer perspectives on future phenomics research.
文摘Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type （WT） cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass： 23 previously reported genes （Calcr, Cebpb, Crtap, Dcstamp, Dkkl, Duoxa2, Enppl, Fgf23, Kissl/Kisslr, Kl （Klotho）, Lrp5, Mstn, Neol, Npr2, Ostml, Postn, Sfrp4, S1c30a5, Sic39a13, Sost, Sumf1, Src, Wnt10b）, five novel genes extensively characterized （Cldn18, Fam20c, Lrrkl, Sgpll, Wnt16）, five novel genes with preliminary characterization （Agpat2, RassfS, Slc10a7, Stc26a7, Slc30a10） and three novel undisclosed genes coding for potential osteoporosis drug targets.
基金Support was from the College of Agriculture of Purdue University to Mohsen Mohammadi,USDA(1013073).
文摘The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping techniques.In this study,two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to post-anthesis.Red-green-blue(RGB)imaging was performed on 17 of the 22 d following the start of drought imposition.Destructive measurements from all plants were performed at the conclusion of the experiment.The effect of line was signifcant for shoot dry matter,spike dry matter,root dry matter,and tller number,while the water treatment was significant on shoot dry matter and root dry matter.The temporal,non-destructive nature of HTP allowed the drought treatment to be significantly differentiated from the well-watered treatment after 6 d in a line from Argentina and 9 d in a line from Chile.This difference of 3 d indicated an increased degree of drought tolerance in the line from Chile.Furthermore,HTP from the final day of imaging accurately predicted reference plant height(r=1),shoot dry matter(r=0.95)and tller number(r=0.91).This experiment ilustrates the potential of HTP and its use in modeling plant growth and development.
文摘Recent technological advances in cotton(Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis.High-throughput phenotyping(HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth,yield,and adaptation to biotic or abiotic stress.Researchers have conducted extensive experiments on HTP and developed techniques including spectral,fluorescence,thermal,and three-dimensional imaging to measure the morphological,physiological,and pathological resistance traits of cotton.In addition,ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems.This review paper highlights the techniques and recent developments for HTP in cotton,reviews the potential applications according to morphological and physiological traits of cotton,and compares the advantages and limitations of these HTP systems when used in cotton cropping systems.Overall,the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton.However,because of its relative novelty,HTP has some limitations that constrains the ability to take full advantage of what it can offer.These challenges need to be addressed to increase the accuracy and utility of HTP,which can be done by integrating analytical techniques for big data and continuous advances in imaging.
基金supported by a grant from the Standardization and Integration of Resources Information for Seed-cluster in Hub-Spoke Material Bank Program,Rural Development Administration,Republic of Korea(PJ01587004).
文摘Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.
文摘In genetic association studies of complex diseases, endo-phenotypes such as expression profiles, epigenetic data, or clinical intermediate-phenotypes provide insight to understand the underlying biological path of the disease. In such situations, in order to establish the path from the gene to the disease, we have to decide whether the gene acts on the disease phenotype primarily through a specific endo-phenotype or whether the gene influences the disease through an unidentified path which is characterized by different intermediate phenotypes. Here, we address the question that a genetic locus, given its effect on an endo-phenotype, influences the trait of interest primarily through the path of the endo-phenotype. We propose a Bayesian approach that can evaluate the genetic association between the genetic locus and the phenotype of interest in the presence of the genetic effect on the endo-phenotype. Using simulation studies, we verify that our approach has the desired properties and compare this approach with a mediation approach. The proposed Bayesian approach is illustrated by an application to genome-wide association study for childhood asthma (CAMP) that contains expression profiles.
基金supported by grants from the National Key Research and Development Program of China(2023YFF1000100,2022YFD2002304)the National Natural Science Foundation of China(U21A20205,32270281,32300281)+6 种基金the Key Agricultural Core Technology Research Project in Hubei Province(HBNYHXGG2023-9)the Natural Science Foundation of Henan Province(232300421028,222300420107)Fundamental Research Funds for the Central Universities(2662024ZKPY003)the Henan Provincial Science and Technology R&D Program Joint Fund(242301420130)the China-UK Joint Phenomics Consortium(BB/R02118X/1)a National Capability Grant in Plant Phenotyping(BBS/E/W/0012844A)the European Plant Phenotyping Network(Grant Agreement No.731013).
文摘High-throughput phenotyping of growth kinetics and organ size in the model plant Arabidopsis thaliana requires rapid and precise methods for trait estimation.To address this need,we developed the Arabidopsis Phenotypic Trait Estimation System,APTES,an open-access,high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs.The enhanced segmentation model Cascade Mask Region-based Convolutional Neural Network(Mask R-CNN)achieved precision(measure of positive prediction accuracy),recall(sensitivity in detection),and F1 score values(harmonic mean of precision and recall)of 0.965,0.958,and 0.961,respectively,for individual leaf segmentation.These metrics demonstrated a consistent improvement of approximately 1 percentage point over the baseline model.For silique segmentation,our enhanced DetectoRS model for silique segmentation attained precision,recall,and F1 scores of 0.954,0.930,and 0.942,respectively.Notably,precision increased by 1%,while the F1 score improved by 2 percentage points.Trait parameters were automatically calculated with coefficient of determination values for leaf and silique traits ranging from 0.776 to 0.976 and mean absolute percentage error values from 1.89%to 7.90%.We phenotyped 166 Arabidopsis accessions,using APTES,and subjected the resulting values to a genome-wide association study(GWAS),revealing 1,042 single-nucleotide polymorphisms(SNPs)as being significantly associated with 18 leaf and silique traits,and one significant SNP on chromosome 3 linked to silique number.Furthermore,we validated APTES across other public Arabidopsis databases and other plant species,with segmentation results demonstrating its applicability across diverse datasets.In conclusion,APTES is a valuable automated tool for leaf and silique segmentation and trait estimation,which should offer benefits to the broader plant science community.
基金This work was supported by grants from the construction and operation of the Food Nutrition and Health Research Center of Guangdong Academy of Agricultural Sciences,China(XTXM 202205)the earmarked fund for CARS-10Sweetpotato,and the Guangdong Modern Agro-industry Technology Research System,China(2022KJ111).
文摘Sweet potato leaf tips have high nutritional value,and exploring the differences in the metabolic profiles of leaf tips among different sweet potato varieties can provide information to improve their qualities.In this study,a UPLC-Q-Exactive Orbitrap/MS-based untargeted metabolomics method was used to evaluate the metabolites in leaf tips of 32 sweet potato varieties.Three varieties with distinct overall metabolic profiles(A01,A02,and A03),two varieties with distinct profiles of phenolic acids(A20 and A18),and three varieties with distinct profiles of flavonoids(A05,A12,and A16)were identified.In addition,a total of 163 and 29 differentially expressed metabolites correlated with the color and leaf shape of sweet potato leaf tips,respectively,were identified through morphological characterization.Group comparison analysis of the phenotypic traits and a metabolite-phenotypic trait correlation analysis indicated that the color differences of sweet potato leaf tips were markedly associated with flavonoids.Also,the level of polyphenols was correlated with the leaf shape of sweet potato leaf tips,with lobed leaf types having higher levels of polyphenols than the entire leaf types.The findings on the metabolic profiles and differentially expressed metabolites associated with the morphology of sweet potato leaf tips can provide useful information for breeding sweet potato varieties with higher nutritional value.
基金supported by the National Key Research and Development Program of China (2016YFD0300202)the Science and Technology Project of Yunna, China (2017YN07)the Science and Technology Major Project of Inner Mongolia, China (2019ZD024 and 2020GG0038)
文摘Root architecture,which determines the water and nutrient uptake ability of crops,is highly plastic in response to soil environmental changes and different cultivation patterns.Root phenotyping for field-grown crops,especially topological trait extraction,is rarely performed.In this study,an image-based semi-automatic root phenotyping method for field-grown crops was developed.The method consisted of image acquisition,image denoising and segmentation,trait extraction and data analysis.Five global traits and 40 local traits were extracted with this method.A good consistency in 1st-order lateral root branching was observed between the visually counted values and the values extracted using the developed method,with R^(2)=0.97.Using the method,we found that the interspecific advantages for maize mainly occurred within 5 cm from the root base in the nodal roots of the 5th-7th nodes,and that the obvious inhibition of soybean was mostly reflected within 20 cm from the root base.Our study provides a novel approach with high-throughput and high-accuracy for field research on root morphology and branching features.It could be applied to the 3D reconstruction of field-grown root system architecture to improve the inputs to data-driven models(e.g.,OpenSimRoot)that simulate root growth,solute transport and water uptake.
文摘Biolog MicroPlates TM. are employed to characterize Trichoderma isolates based on differential assimilation of test substrates and redox reactions in a 96-well test plate. The Biolog method is potentially advantageous in being relatively simple, fast and economical, and data acquisition can be automated using a microplate reader and applicable software. Several research applications of the Biolog system are presented: i) “monophenetic groups” from cluster analyses of phenotype array data are investigated for previously undetected new species in Trichoderma, ii) metabolic characters differentiating species are identified, and multivariate analyses performed to complement molecular data in validating new species and significant variants, and iii) phenotype array data for more than 1200 Trichoderma strains are analysed to select strains that might be exploited for bioconversions and commercial production of enzymes. Phenotype arrays are much more sensitive to strain level variation than molecular techniques, however, phenotype array data do not consistently reflect phylogenies constructed from molecular data. Nevertheless, the Biolog phenotype array is an economical alternative method for surveying biological diversity, and provides data that complements molecular data in phylogenetic studies.
基金supported by the National Natural Science Foundation of China(Grant No.81270852)
文摘Systemic lupus erythematosus （SLE） is a complex autoimmune disease characterized by the production of autoantibodies to a broad range of self-antigens. Profiling the autoantibody repertoire using array-based technology has emerged as a powerful tool for the identification of biomarkers in SLE and other autoimmune diseases. Proteomic microarray has the capacity to hold large number of self-antigens on a solid surface and serve as a high-throughput screening method for the determination of autoantibody specificities. The autoantigen arrays carrying a wide variety of self-antigens, such as cell nuclear components （nucleic acids and associated proteins）, cytoplas- mic proteins, phospholipid proteins, cell matrix proteins, mucosal/secreted proteins, glomeruli, and other tissue-specific proteins, have been used for screening of autoantibody specificities associated with different manifestations of SLE. Arrays containing synthetic peptides and molecular modified proteins are also being utilized for identification of autoantibodies targeting to special antigenic epi- topes. Different isotypes of autoantibodies, including IgG, IgM, IgA, and IgE, as well as other Ig subtypes, can be detected simultaneously with multi-color labeled secondary antibodies. Serum and plasma are the most common biologic materials for autoantibody detection, but other body fluids such as cerebrospinal fluid, synovial fluid, and saliva can also be a source of autoantibody detection.
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 2023 Inner Mongolia Autonomous Region“Unveiling and Hanging”Project[grant number 2023JBGS0008]the 2023 Hohhot to introduce high-level innovative and entrepreneurial talents(team)[grant number 2023RC-High Level7].
文摘High-throughput phenotyping(HTP)technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources.To better understand the status of forage phenotyping research and identify key directions for development,this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years.This paper reviews the unique aspects and research priorities in forage phenotypic monitoring,highlights key remote sensing platforms,examines the applications of advanced sensing technology for quantifying phenotypic traits,explores artificial intelligence(AI)algorithms in phenotypic data integration and analysis,and assesses recent progress in phenotypic genomics.The practical applications of HTP technology in forage remain constrained by several challenges.These include establishing uniform data collection standards,designing effective algorithms to handle complex genetic and environmental interactions,deepening the cross-exploration of phenomics-genomics,solving the problem of pathological inversion of forage phenotypic growth monitoring models,and developing low-cost forage phenotypic equipment.Resolving these challenges will unlock the full potential of HTP,enabling precise identification of superior forage traits,accelerating the breeding of superior varieties,and ultimately improving forage yield.
基金This work was supported by the National Key Research and Development Program of China(2021YFD1200504,2022YFD2002304)the National Natural Science Foundation of China(32471992)+1 种基金the Key Core Technology Project in Agriculture of Hubei Province(HBNYHXGG2023-9)the Supporting Project for High-Quality Development of the Seed Industry of Hubei Province(HBZY2023B001-06).
文摘Understanding the genetic basis of quantitative traits related to crop growth,yield,and stress response requires the acquisition of large-scale,high-quality phenotypic datasets.High-throughput phenotyping platforms have become effective tools for meeting this requirement.Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads,their operational flexibility,and their proximity to crops,which allows for higher imaging resolution.In this study,we introduce PhenoRob-F(a phenotyping robot for the field),a cross-row,wheeled robot designed for efficient and automated phenotyping under field conditions.The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field pheno-typing,with integrated visual and satellite navigation systems enabling autonomous operation.We validated the performance of the robot through a series of experiments involving various crop canopies.By capturing RGB images of rice and wheat,we independently performed wheat ear detection and rice panicle segmentation.For wheat ear detection,we achieve a precision of 0.783,a recall of 0.822,and a mean average precision(mAP)of 0.853 when the YOLOv8m model is used.For rice panicle segmentation,the SegFormer_BO model yielded a mean intersection over union(mIoU)of 0.949 and an accuracy of 0.987.Additionally,by capturing RGB-D data of maize canopies,we performed 3D reconstructions to calculate plant height,achieving an R^(2) of 0.99 compared with manual measurements.Similar experiments with rapeseed yielded an R^(2) of 0.97.Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories,with classification accuracies ranging from 0.977 to 0.996.Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.
基金Financial support was provided by the Austrian Research Promotion Agency(FFG)and grant Nu.2021–0.382.292(project:climate smart forests:provenance selection and planting method_AP2)with funds from the departmental research program through dafne.at with resources from the Federal Ministry of Agriculture,Regions,and Water Management.
文摘Norway spruce(Picea abies Karst L.)is one of the most ecologically and economically significant tree species in Europe,accounting for nearly half of the continent's forest economic value.However,drought is a significant stress factor associated with increasing Norway spruce mortality across Europe.Provenance trials,a traditional approach to assess adaptive variation,face limitations stemming from the finite number of sites,seed sources involved,and their required labor-intensive nature.In response,we developed a comprehensive multisensor high-throughput phenotyping method and integrated it with metabolomics,transcriptomics,and anatomical analyses to study the drought stress responses in two climatically contrasting but geographically proximal provenances at the seedling stage by exposing them to drought stress for a period of 21 days.Based on more than 50 physiological and growth-related traits assessed by the phenotyping platform,it was possible to characterize early and late drought stress responses.Consistent with phenotypic data,mRNA-seq,and metabolic profiles revealed apparent differences between treatments.While during the drought stress the metabolic data indicated an increased pro-duction of ABA,α-tocopherol,zeaxanthin,lutein,and phenolics,mRNA-seq showed modulation of related pathways and downregulation of photosystem transcripts.Although drought responses were largely conserved between the two provenances,they differed phenotypically in traits related to the activation of re-oxidation of the plastoquinone pool,and molecularly in transcriptional and phenolic profiles.In conclusion,our study demon-strates the potential of the high-throughput phenotyping approach for evaluating drought stress adaptation in Norway spruce thus accelerating the screening and selection of best adapted provenances.
基金supported by the Foundation for Food and Agriculture Research(602757)USDA-NIFA(2020-68013-32371 and 2024-67013-42449)+3 种基金Department of Energy the Office of Science(BER),U.S.DOE(DESC0020355)the National Science Foundation(IOS-2412930,2417510,and 2412928)the University of Nebraska-Lincoln's Complex Biosystems Graduate Programsupported by the National Science Foundation Graduate Research Fellowship Program under Grant No.2034837.
文摘Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lotaxy.Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants.As a result,in simulations of the leaf canopies of grain crops such as maize and sorghum,this trait is frequently approximated as alternating 180°angles between sequential leaves.We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alter-nating phyllotaxy across a diverse set of sorghum genotypes.Using a voxel-carving-based approach,we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum geno-types from the sorghum association panel.The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals.Automated phyllotaxy measurements exhibited a repeatability of R^(2)=0.41 across imaging timepoints separated by a period of two days.A resampling based genome wide association study(GWAS)identified several putative genetic associations with lower-canopy phyllotaxy in sorghum.This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
基金This study was supported by the National Science Fund for Excellent Young Scholars(No.61822503)the National Natural Science Foundation of China(Nos.62175030 and 62175027)+3 种基金China Postdoctoral Science Foundation(No.2021TQ0147)Jiangsu Innovation and Entrepreneurship Program(No.JSSCBS20210126)Nanjing Science and Technology Innovation Project for Returned Overseas Chinese Scholars(No.1106000308)the Fundamental Research Funds for the Central Universities(Nos.3206002104D and 3206002108A1).
文摘Emerging single-cell technologies create new opportunities for unraveling tumor heterogeneity.However,the development of high-content phenotyping platform is still at its infancy.Here,we develop a microfluidic chip for two-dimensional(2D)profiling of tumor chemotactic and molecular features at single cell resolution.Individual cells were captured by the triangular micropillar arrays in the cell-loading channel,facilitating downstream single-cell analysis.For 2D phenotyping,the chemotactic properties of tumor cells were visualized through cellular migratory behavior in microchannels,while their protein expression was profiled with multiplex surface enhanced Raman scattering(SERS)nanovectors,in which Raman reporter-embedded gold@silver core-shell nanoparticles(Au@Ag REPs)were modified with DNA aptamers targeting cellular surface proteins.As a proof of concept,breast cancer cells with diverse phenotypes were tested on the chip,demonstrating the capability of this platform for simultaneous chemotactic and molecular analysis.The chip is expected to provide a powerful tool for investigating tumor heterogeneity and promoting clinical precision medicine.
