High-throughput Phenotyping Methods Research Articles

Phenotyping grapevine resistance to downy mildew: deep learning as a promising tool to assess sporulation and necrosis

BackgroundDowny mildew is a plant disease that affects all cultivated European grapevine varieties. The disease is caused by the oomycete Plasmopara viticola. The current strategy to control this threat relies on repeated applications of fungicides. The most eco-friendly and sustainable alternative solution would be to use bred-resistant varieties. During breeding programs, some wild Vitis species have been used as resistance sources to introduce resistance loci in Vitis vinifera varieties. To ensure the durability of resistance, resistant varieties are built on combinations of these loci, some of which are unfortunately already overcome by virulent pathogen strains. The development of a high-throughput machine learning phenotyping method is now essential for identifying new resistance loci.ResultsImages of grapevine leaf discs infected with P. viticola were annotated with OIV 452–1 values, a standard scale, traditionally used by experts to assess resistance visually. This descriptor takes two variables into account the complete phenotype of the symptom: sporulation and necrosis. This annotated dataset was used to train neural networks. Various encoders were used to incorporate prior knowledge of the scale’s ordinality. The best results were obtained with the Swin transformer encoder which achieved an accuracy of 81.7%. Finally, from a biological point of view, the model described the studied trait and identified differences between genotypes in agreement with human observers, with an accuracy of 97% but at a high-throughput 650% faster than that of humans.ConclusionThis work provides a fast, full pipeline for image processing, including machine learning, to describe the symptoms of grapevine leaf discs infected with P. viticola using the OIV 452–1, a two-symptom standard scale that considers sporulation and necrosis. If symptoms are frequently assessed by visual observation, which is time-consuming, low-throughput, tedious, and expert dependent, the method developed sweeps away all these constraints. This method could be extended to other pathosystems studied on leaf discs where disease symptoms are scored with ordinal scales.

Advancing selective breeding in leopard coral grouper (P. leopardus) through development of a high-throughput image-based growth trait

Utilizing image-based computer vision techniques, many high-throughput phenotyping methods have been employed to capture intricate growth trait characteristics, offering reliable estimates of phenotypic traits crucial for breeding programs. In this study, we explored the application of partial differential equation (PDE)-based level set approaches to introduce image-based body area percentage (IBAP) as a novel growth trait in Plectropomus leopardus, as a substitution for the traditional growth trait body weight (BW). Assessing the genetic parameters essential for robust growth trait improvement in P. leopardus breeding programs, we estimated SNP-based heritability for IBAP and BW using a comprehensive set of SNPs (673,039 SNPs with MAF >2%). Results revealed heritability estimates of 0.515 (S.E. 0.06) for IBAP and 0.542 (S.E. 0.06) for BW. Moreover, strong phenotypic and genetic correlations of 0.812 (S.E. 0.001) and 0.903 (S.E 0.021) between IBAP and BW, respectively, underscored the potential for IBAP as a surrogate trait of BW for the genetic improvement of P. leopardus. We established a linear regression model of IBAP and BW (y ​= ​−730 ​+ ​1700×, (R2 ​= ​0.71)), after rigorous assessments of linearity, normality, and homoscedasticity, to confirm model fit. Evaluation of breeding value prediction accuracies using two linear models (rr-GBLUP and Bayes B) and a non-linear (RKHS) model demonstrated the superior performance of RKHS across IBAP and BW. Exploring the impact of varied marker densities for SNP selection on genomic prediction accuracy for IBAP and BW demonstrated a threshold of 10,000 SNPs for maximal model accuracy. These findings provide essential reference information and methodological groundwork for leveraging image-based traits in P. leopardus breeding endeavors, facilitating more efficient and precise genetic improvement programs.

YOLO-Based Phenotyping of Apple Blotch Disease (Diplocarpon coronariae) in Genetic Resources after Artificial Inoculation

YOLO-Based Phenotyping of Apple Blotch Disease (Diplocarpon coronariae) in Genetic Resources after Artificial Inoculation

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance.

The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson's correlation coefficient (r) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.

Biomechanical phenotyping pipeline for stalk lodging resistance in maize

Stalk lodging (structural failure crops prior to harvest) significantly reduces annual yields of vital grain crops. The lack of standardized, high throughput phenotyping methods capable of quantifying biomechanical plant traits prevents comprehensive understanding of the genetic architecture of stalk lodging resistance. A phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented. The methods were developed using principles from the fields of engineering mechanics and metrology and they enable retention of plant-specific data instead of averaging data across plots as is typical in most phenotyping studies. This pipeline was specifically designed to be implemented in large experimental studies and has been used to phenotype over 40,000 maize stalks. The pipeline includes both lab- and field-based phenotyping methodologies and enables the collection of metadata. Best practices learned by implementing this pipeline over the past three years are presented. The specific instruments (including model numbers and manufacturers) that work well for these methods are presented, however comparable instruments may be used in conjunction with these methods as seen fit.•Efficient methods to measure biomechanical traits and record metadata related to stalk lodging.•Can be used in studies with large sample sizes (i.e., > 1,000).

Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling.

The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs. The RGB (red, green, and blue) and infrared images are used as inputs to construct the multimodal image segmentation model-the RGB & Infrared Feature Fusion Segmentation Network (RIFSeg-Net). Subsequently, the segment anything model was employed to extract complete individual leaves from the segmentation results obtained from RIFSeg-Net. These leaf aspect ratios facilitated the accurate categorization of soybean populations into 2 distinct varieties: oval leaf type variety and lanceolate leaf type variety. Finally, dynamic modeling was conducted to identify 5 phenotypic traits associated with the canopy development rate that differed substantially among the classified soybean varieties. The results showed that the developed multimodal image segmentation model RIFSeg-Net for extracting soybean canopy cover from UAV images outperformed traditional deep learning image segmentation networks (precision = 0.94, recall = 0.93, F1-score = 0.93). The proposed method has high practical value in the field of germplasm resource identification. This approach could lead to the use of a practical tool for further genotypic differentiation analysis and the selection of target genes.

Chlorophyll fluorescence is a potential indicator to measure photochemical efficiency in early to late soybean maturity groups under changing day lengths and temperatures.

In this study, we employed chlorophyll a fluorescence technique, to indicate plant health and status in response to changing day lengths (photoperiods) and temperatures in soybean early and late maturity groups. Chlorophyll a fluorescence study indicates changes in light reactions in photosystem II. Experiments were performed for 3-day lengths (12.5, 13.5, and 14.5 h) and five temperatures (22/14°C, 26/18°C, 30/22°C, 34/26°C, and 40/32°C), respectively. The I-P phase declined for changing day lengths. Active reaction centers decreased at long day length for maturity group III. We observed that low temperatures impacted the acceptor side of photosystem II and partially impacted electron transport toward the photosystem I end electron acceptor. Results emphasized that higher temperatures (40/32°C) triggered damage at the oxygen-evolving complex and decreased electron transport and photosynthesis. We studied specific leaf areas and aboveground mass. Aboveground parameters were consistent with the fluorescence study. Chlorophyll a fluorescence can be used as a potential technique for high-throughput phenotyping methods. The traits selected in the study proved to be possible indicators to provide information on the health status of various maturity groups under changing temperatures and day lengths. These traits can also be deciding criteria for breeding programs to develop inbreed soybean lines for stress tolerance and sensitivity based on latitudinal variations.

A high-throughput phenotyping method for sugarcane rind penetrometer resistance and breaking force characterization by near-infrared spectroscopy

BackgroundSugarcane (Saccharum spp.) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Rind penetrometer resistance (RPR) and breaking force are two kinds of regular parameters for mechanical strength characterization. However, due to the lack of efficient methods for determining RPR and breaking force in sugarcane, genetic approaches for improving these traits are generally limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time.ResultsBased on well-established laboratory measurements of sugarcane stalk internodes collected in the years 2019 and 2020, considerable variations in RPR and breaking force were observed in the stalk internodes. Following a standard NIRS calibration process, two online models were obtained with a high coefficient of determination (R2) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R2 and RPD values as high as 0.997 and 17.70, as well as showing relatively low root mean square error values at 0.44 N mm−2 during global modeling, demonstrating excellent predictive performance.ConclusionsThis study delivered a successful attempt for rapid and precise prediction of rind penetrometer resistance and breaking force in sugarcane stalk by NIRS assay. These established models can be used to improve phenotyping jobs for sugarcane germplasm on a large scale.

High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.

Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.

Enhancing Crop Improvement through Synergistic Integration of Advanced Plant Breeding and Proximal Remote Sensing Techniques: A Review

Accelerating crop improvement with enhanced adaptability to changing climatic conditions and meeting the ever-increasing global food demand requires urgent action. To achieve this, we must employ advanced molecular breeding techniques, such as marker-assisted selection, marker-assisted backcrossing, genomic selection, genome editing, and targeted mutation. However, these approaches demand the screening of large populations to identify potential genes and genotypes. Unfortunately, a significant bottleneck lies in the absence of high-throughput plant phenotyping methods that can rapidly and cost-effectively facilitate data-driven genotype selection in plant breeding. Traditional phenotyping methods, reliant on trained experts, are slow, expensive, labour-intensive, subjective, and often require destructive sampling. Proximal remote sensing technologies, including RGB imaging, thermal imaging, hyperspectral imaging, multispectral imaging, and fluorescence imaging, offer a non-destructive and rapid collection of detailed phenotypic data, providing valuable insights into various plant traits at different growth stages. High-throughput phenotyping platforms, such as Conveyor-Type Indoor, Benchtop-Type Indoor, Unmanned Aerial Platform (UAP), and Manned Aerial Platform (MAP), utilize a combination of the aforementioned remote sensing technologies. This review article aims to explore the integration of proximal remote sensing and molecular breeding approaches, showcasing how this synergistic approach can expedite crop improvement efforts. By emphasizing the benefits, challenges, and future prospects of this integrative approach, we hope to pave the way for sustainable and productive agriculture, ensuring food security in the face of changing environmental conditions.

Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Image-Based High-Throughput Phenotyping in Horticultural Crops.

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

Monitoring Drought Stress in Common Bean Using Chlorophyll Fluorescence and Multispectral Imaging.

Drought is a significant constraint in bean production. In this study, we used high-throughput phenotyping methods (chlorophyll fluorescence imaging, multispectral imaging, 3D multispectral scanning) to monitor the development of drought-induced morphological and physiological symptoms at an early stage of development of the common bean. This study aimed to select the plant phenotypic traits which were most sensitive to drought. Plants were grown in an irrigated control (C) and under three drought treatments: D70, D50, and D30 (irrigated with 70, 50, and 30 mL distilled water, respectively). Measurements were performed on five consecutive days, starting on the first day after the onset of treatments (1 DAT-5 DAT), with an additional measurement taken on the eighth day (8 DAT) after the onset of treatments. Earliest detected changes were found at 3 DAT when compared to the control. D30 caused a decrease in leaf area index (of 40%), total leaf area (28%), reflectance in specific green (13%), saturation (9%), and green leaf index (9%), and an increase in the anthocyanin index (23%) and reflectance in blue (7%). The selected phenotypic traits could be used to monitor drought stress and to screen for tolerant genotypes in breeding programs.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes.

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings

BackgroundHigh-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge.ResultsWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy (R^2=0.996 and 0.923 for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations (R^2=0.938 for lateral root growth).ConclusionsWe designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.

UAV-BASED MULTISPECTRAL DATA FOR SUGARCANE RESISTANCE PHENOTYPING OF ORANGE AND BROWN RUST

The main bottleneck to accelerating the development of new sugarcane varieties with desirable traits to meet the demands of the sugar-energy sector and adaptation to climate change is the absence of high-throughput phenotyping methods for evaluating varieties in the field. Traditional methods of field phenotyping depend on trained specialists for visual evaluations that are slow, laborious, and subjective. In this study, we investigated UAV-based multispectral data and machine learning algorithms to improve efficiency in the evaluation of field phenotyping of sugarcane varieties regarding the resistance to infection by orange and brown rusts. Spectral data from five bands (Blue, Green, Red, Red-edge, and NIR) and 14 vegetation indices were tested in direct correlations with infection scores collected in the field for the two types of rust. Sugarcane varieties were classified according to their resistance to rusts using three machine learning algorithms (Random Forest, radial SVM, and KNN). Correlations between the Red band data and infection scores of the two types of rust were significant (r = 0.67) for evaluations made at 165 days after planting (DAP). Conversely, regarding the varietal classification into three resistance classes, a high level of overall (88.1%) and balanced (Resistant = 90.3, Moderately resistant = 88.6, and Susceptible = 82.1) accuracy was reached at 195 DAP with the radial SVM model. UAV-based multispectral data is able to assist in the phenotyping of new sugarcane varieties regarding the resistance to these diseases.

Fast X-ray fluorescence microscopy provides high-throughput phenotyping of element distribution in seeds.

The concentration, chemical speciation, and spatial distribution of essential and toxic mineral elements in cereal seeds have important implications for human health. To identify genes responsible for element uptake, translocation, and storage, high-throughput phenotyping methods are needed to visualize element distribution and concentration in seeds. Here, we used X-ray fluorescence microscopy (μ-XRF) as a method for rapid and high-throughput phenotyping of seed libraries and developed an ImageJ-based pipeline to analyze the spatial distribution of elements. Using this method, we nondestructively scanned 4,190 ethyl methanesulfonate (EMS)-mutagenized M1 rice (Oryza sativa) seeds and 533 diverse rice accessions in a genome-wide association study (GWAS) panel to simultaneously measure concentrations and spatial distribution of elements in the embryo, endosperm, and aleurone layer. A total of 692 putative mutants and 65 loci associated with the spatial distribution of elements in rice seed were identified. This powerful method provides a basis for investigating the genetics and molecular mechanisms controlling the accumulation and spatial variations of mineral elements in plant seeds.

Field evaluation of the effect of Aspergillus niger on lettuce growth using conventional measurements and a high-throughput phenotyping method based on aerial images.

Plant microbiome engineering is a promising tool to unlock crop productivity potential and exceed the yield obtained with conventional chemical inputs. We studied the effect of Aspergillus niger inoculation on in-field lettuce (Lactuca sativa) growth in soils with limiting and non-limiting P concentrations. Lettuce plants originating from inoculated seeds showed increased plant diameter (6.9%), number of leaves (8.1%), fresh weight (23.9%), and chlorophyll content (3.8%) as compared to non-inoculated ones. Inoculation of the seedling substrate just before transplanting was equally efficient to seed inoculation, while application of a granular formulation at transplanting did not perform well. Plant response to P addition was observed only up to 150 kg P2O5 ha-1, but A. niger inoculation allowed further increments in all vegetative parameters. We also employed a high-throughput phenotyping method based on aerial images, which allowed us to detect changes in plants due to A. niger inoculation. The visible atmospherically resistant index (VARI) produced an accurate prediction model for chlorophyll content, suggesting this method might be used to large-scale surveys of croplands inoculated with beneficial microorganisms. Our findings demonstrate that A. niger inoculation surpasses the yield obtained with conventional chemical inputs, allowing productivity gains not reached by just increasing P doses.

Scanning the medical phenome to identify new diagnoses after recovery from COVID-19 in a US cohort.

ObjectiveCOVID-19 survivors are at risk for long-term health effects, but assessing the sequelae of COVID-19 at large scales is challenging. High-throughput methods to efficiently identify new medical problems arising after acute medical events using the electronic health record (EHR) could improve surveillance for long-term consequences of acute medical problems like COVID-19.Materials and MethodsWe augmented an existing high-throughput phenotyping method (PheWAS) to identify new diagnoses occurring after an acute temporal event in the EHR. We then used the temporal-informed phenotypes to assess development of new medical problems among COVID-19 survivors enrolled in an EHR cohort of adults tested for COVID-19 at Vanderbilt University Medical Center.ResultsThe study cohort included 186 105 adults tested for COVID-19 from March 5, 2020 to November 1, 2021; of which 30 088 (16.2%) tested positive. Median follow-up after testing was 412 days (IQR 274–528). Our temporal-informed phenotyping was able to distinguish phenotype chapters based on chronicity of their constituent diagnoses. PheWAS with temporal-informed phenotypes identified increased risk for 43 diagnoses among COVID-19 survivors during outpatient follow-up, including multiple new respiratory, cardiovascular, neurological, and pregnancy-related conditions. Findings were robust to sensitivity analyses, and several phenotypic associations were supported by changes in outpatient vital signs or laboratory tests from the pretesting to postrecovery period.ConclusionTemporal-informed PheWAS identified new diagnoses affecting multiple organ systems among COVID-19 survivors. These findings can inform future efforts to enable longitudinal health surveillance for survivors of COVID-19 and other acute medical conditions using the EHR.