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
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
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
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
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
Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serio...Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.展开更多
Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key t...Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.展开更多
High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine ...High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.展开更多
Genomic selection （GS） and high-throughput phenotyping have recently been captivating the interest of the crop breeding com- munity from both the public and private sectors world-wide. Both approaches promise to rev...Genomic selection （GS） and high-throughput phenotyping have recently been captivating the interest of the crop breeding com- munity from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa- rable to genomic selection. Despite the fact that the two method- ological approaches represent the extremes of what is understood as the breeding process （phenotype versus genome）, they both consider the targeted traits （e.g. grain yield, growth, phenology, plant adaptation to stress） as a black box instead of dissectingthem as a set of secondary traits （i.e. physiological） putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy （NIRS） and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.展开更多
Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years.These technologies provide precise measurements of desired traits among thousands of field-grown plants under di...Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years.These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments;this is a critical step towards selection of better performing lines as to yield,disease resistance,and stress tolerance to accelerate crop improvement programs.High-throughput phenotyping techniques and platforms help unrave-ling the genetic basis of complex traits associated with plant growth and development and targeted traits.This review focuses on the advancements in technologies involved in high-throughput,field-based,aerial,and unmanned platforms.Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques,which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.展开更多
Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one o...Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies.Nevertheless,recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years.In this article,we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades.We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies.Finally,we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap.It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.展开更多
Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms ha...Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms have been developed to study complex traits related to the growth, yield, and adaptation to biotic or abiotic stress, but the ability to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics. In recent years, researchers around the world have conducted extensive experiments and research on high-throughput, image-based phenotyping techniques,including visible light imaging, fluorescence imaging,thermal imaging, spectral imaging, stereo imaging, and tomographic imaging. This paper considers imaging technologies developed in recent years for high-throughput phenotyping, reviews applications of these technologies in detecting and measuring plant morphological, physiological, and pathological traits, and compares their advantages and limitations.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
The mussel is one of the main cultivated species in the world.A significant challenge faced by suspension-cultured mussels is the high incidence of mussel fall-off from cultivation ropes,adversely impacting harvest yi...The mussel is one of the main cultivated species in the world.A significant challenge faced by suspension-cultured mussels is the high incidence of mussel fall-off from cultivation ropes,adversely impacting harvest yields,which have been documented at commercial mussel farms in the United Kingdom,the United States of America,Canada,Spain,New Zealand and China.Byssus is an important attachment structure for marine mussels,and weakness in byssal thread attachment is a major factor leading to mussel detachment from ropes.To investigate the relationship between genetic variability and byssal thread phenotypic characteristics in the hard-shelled mussel(Mytilus coruscus),we collected three wild populations of M.coruscus from different latitudes in the East China Sea,including the Shengsi(SS),Jiaojiang(JJ),and Fuding(FD)populations.The genetic diversity and structure of these populations were investigated using 10 microsatellite loci.The mean observed heterozygosity(Ho)in the SS population was 0.44,higher than the mean Ho values of the JJ(0.40)and FD(0.39)populations.The mean inbreeding coefficients(F_(is))in the SS population was 0.20,lower than the mean F_(is)values of the JJ(0.33)and FD populations(0.40).These results revealed that the SS population exhibited higher genetic diversity compared to the other two populations.The different numbers of private alleles(P_(a))in the three populations,ranging from 10 to 17,suggest that these populations have experienced selective pressures from various environments.Moreover,genetic differentiation was observed in the genetic distance between the SS population and the other two populations.We also examined the phenotypic characteristics of their byssal threads.There were significant differences in byssus attachment strength among the three populations,with the SS population located at the highest latitude secreting more byssal threads and exhibiting greater byssal breaking force and plaque adhesion strength,while the Fuding(FD)population located at the lowest latitude had the weakest byssal attachment.The observed differentiation in private alleles and byssus phenotypes might suggest that the three wild populations have experienced different environmental selective pressures.This study provides insight for future genetic enhancement programs aimed at improving byssus attachment in M.coruscus.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
Pygmy lorises are arboreal primates primarily found in forest environments across Southeast Asia(Nekaris 2014).Theyhave a diverse diet,including plant secretions,nectar,fruits,invertebrates,tree bark,and bird eggs.All...Pygmy lorises are arboreal primates primarily found in forest environments across Southeast Asia(Nekaris 2014).Theyhave a diverse diet,including plant secretions,nectar,fruits,invertebrates,tree bark,and bird eggs.All 9 known speciesof pygmy lorises are listed as globally endangered species(Nekaris 2014).Pygmy lorises exhibit a range of unique phenotypic characteristics rarely seen among primates.展开更多
The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrat...The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.展开更多
In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial co...In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.展开更多
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.
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.
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 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.
文摘Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04025 to Xiu’e Wang)the Seed Industry Revitalization Project of Jiangsu Province(JBGS(2021)006 to Xiu’e Wang)+3 种基金the National Natural Science Foundation of China(32070400 to Ji Zhou)Ji Zhou,Robert Jackson,and Greg Deakin were partially supported by the Allan&Gill Gray Foundation’Sustainable Productivity for Crop Improvement(G118688 to the University of Cambridge and Q-20-0370 to NIAB)Ji Zhou was supported by the United Kingdom Research and Innovation’s(UKRI)Biotechnology and Bio logical Sciences Research Council(BBSRC)AI in Bioscience Grant(BB/Y513969/1 to Ji Zhou)The UK-China research activities were supported by the BBSRC’s International Partnership Grant(BB/Y514081/1 to NIAB)
文摘Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.
基金supported by the JST CREST[grant number:JPMJCR16O2]and MEXT KAKENHI[grant number:JP22H02306].The funders had no role in the study design,data collection and analysis,decision to publish,or manuscript preparation.
文摘High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.
基金Participation of Jos Luis Araus and María Dolors Serret was supported by the Spanish Project AGL2010-20180 (subprogram AGR)the FP7 European Project OPTICHINA (266045)
文摘Genomic selection （GS） and high-throughput phenotyping have recently been captivating the interest of the crop breeding com- munity from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa- rable to genomic selection. Despite the fact that the two method- ological approaches represent the extremes of what is understood as the breeding process （phenotype versus genome）, they both consider the targeted traits （e.g. grain yield, growth, phenology, plant adaptation to stress） as a black box instead of dissectingthem as a set of secondary traits （i.e. physiological） putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy （NIRS） and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.
文摘Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years.These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments;this is a critical step towards selection of better performing lines as to yield,disease resistance,and stress tolerance to accelerate crop improvement programs.High-throughput phenotyping techniques and platforms help unrave-ling the genetic basis of complex traits associated with plant growth and development and targeted traits.This review focuses on the advancements in technologies involved in high-throughput,field-based,aerial,and unmanned platforms.Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques,which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.
基金the National Key Research and Development Program of China(2016YFD0100101-18,2016YFD0100103)the National Natural Science Foundation of China(31770397,21800305)+2 种基金the Fundamental Research Funds for the Central Universities(2662017PY058,2662017QD044)UK-China grant BBSRC(grant no.BB/R02118X/1)the National Institute of Food and Agriculture,U.S.Department of Agriculture,Hatch project(ALA014-1-16016).
文摘Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies.Nevertheless,recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years.In this article,we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades.We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies.Finally,we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap.It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
基金supported by China Scholarships for Study Abroad
文摘Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms have been developed to study complex traits related to the growth, yield, and adaptation to biotic or abiotic stress, but the ability to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics. In recent years, researchers around the world have conducted extensive experiments and research on high-throughput, image-based phenotyping techniques,including visible light imaging, fluorescence imaging,thermal imaging, spectral imaging, stereo imaging, and tomographic imaging. This paper considers imaging technologies developed in recent years for high-throughput phenotyping, reviews applications of these technologies in detecting and measuring plant morphological, physiological, and pathological traits, and compares their advantages and limitations.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
文摘The mussel is one of the main cultivated species in the world.A significant challenge faced by suspension-cultured mussels is the high incidence of mussel fall-off from cultivation ropes,adversely impacting harvest yields,which have been documented at commercial mussel farms in the United Kingdom,the United States of America,Canada,Spain,New Zealand and China.Byssus is an important attachment structure for marine mussels,and weakness in byssal thread attachment is a major factor leading to mussel detachment from ropes.To investigate the relationship between genetic variability and byssal thread phenotypic characteristics in the hard-shelled mussel(Mytilus coruscus),we collected three wild populations of M.coruscus from different latitudes in the East China Sea,including the Shengsi(SS),Jiaojiang(JJ),and Fuding(FD)populations.The genetic diversity and structure of these populations were investigated using 10 microsatellite loci.The mean observed heterozygosity(Ho)in the SS population was 0.44,higher than the mean Ho values of the JJ(0.40)and FD(0.39)populations.The mean inbreeding coefficients(F_(is))in the SS population was 0.20,lower than the mean F_(is)values of the JJ(0.33)and FD populations(0.40).These results revealed that the SS population exhibited higher genetic diversity compared to the other two populations.The different numbers of private alleles(P_(a))in the three populations,ranging from 10 to 17,suggest that these populations have experienced selective pressures from various environments.Moreover,genetic differentiation was observed in the genetic distance between the SS population and the other two populations.We also examined the phenotypic characteristics of their byssal threads.There were significant differences in byssus attachment strength among the three populations,with the SS population located at the highest latitude secreting more byssal threads and exhibiting greater byssal breaking force and plaque adhesion strength,while the Fuding(FD)population located at the lowest latitude had the weakest byssal attachment.The observed differentiation in private alleles and byssus phenotypes might suggest that the three wild populations have experienced different environmental selective pressures.This study provides insight for future genetic enhancement programs aimed at improving byssus attachment in M.coruscus.
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 Shaanxi FundamentalScience Research Project for Chemistry&Biology(grant no.22JHQ049)Basic Research Program of Natural Sciencesof Shaanxi Province(2019JM-339).
文摘Pygmy lorises are arboreal primates primarily found in forest environments across Southeast Asia(Nekaris 2014).Theyhave a diverse diet,including plant secretions,nectar,fruits,invertebrates,tree bark,and bird eggs.All 9 known speciesof pygmy lorises are listed as globally endangered species(Nekaris 2014).Pygmy lorises exhibit a range of unique phenotypic characteristics rarely seen among primates.
基金supported by the National Key Research and Development Plan of China(2023YFB3210400)the Natural Science Innovation Group Foundation of China(T2321004)+3 种基金the National Natural Science Foundation of China(62174101)Shandong University Integrated Research and Cultivation Project(2022JC001)Key Research and Development Plan of Shandong Province(Major Science and Technology Innovation Project2022CXGC020501).
文摘The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.
基金supported by the National Ecological Environment Survey and Assessment(2024-vertical-0107)the Fundamental Research Funds for the Central Public-interest Scientific Institution(2023YSKY-26)the Hulun Buir Grassland Ecological Restoration Comprehensive Survey Project(DD20230474).
文摘In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.
