Phenotyping Tools Research Articles

AF‐FLOW Global Registry Confirms Validity of Electrographic Flow Mapping as a Phenotyping Tool for Atrial Fibrillation

ABSTRACTBackgroundElectrographic flow (EGF) mapping allows for the visualization of global atrial wavefront propagations. One mechanism of initiation and maintenance of atrial fibrillation (AF) is stimulation from EGF‐identified focal sources that serve as driver sites of fibrillatory conduction. Electrographic flow consistency (EGFC) further quantifies the concordance of observed wavefront patterns, indicating that a healthier substrate shows more organized wavefront propagation and higher EGFC. Freedom from AF (FFAF) recurrence has accordingly been shown to be higher in patients with ablated vs. unablated sources and with high vs. low EGFC.Objectives(1) Measure FFAF across EGF‐derived phenotypes in patients enrolled in the AF‐FLOW Global Registry; (2) determine if a relationship exists between EGFC and percentage of healthy voltage as measured from bipolar voltage maps.MethodsThe AF‐FLOW Global Registry is a multicenter, prospective study of 25 all‐comer AF patients who underwent concomitant high‐density bipolar voltage mapping with a 16‐electrode grid mapping catheter and EGF mapping with a 64‐pole basket catheter. The EGF algorithm detects extra‐pulmonary vein sources as origins of excitation from a singularity of divergent flow vectors and was used to localize RF ablation targets. Overall, EGFC per atrium was also computed as the average of the modulus of individual EGF vectors, where the vector length represents the consistency of flow patterns. Patients were then assigned phenotypes on the basis of source presence or absence and EGFC, and rates of FFAF at 1‐year were compared across the four resulting phenotypes. Atrial EGFC was also compared to the percentage of healthy tissue determined by bipolar voltage mapping.ResultsPatients with paroxysmal AF had higher FFAF than persistent AF (PeAF) and long‐standing PeAF patients; patients receiving de novo ablation had higher FFAF than those receiving redo ablation. Patient phenotyping revealed that those with high EGFC had higher FFAF than those with low EGFC (p = 0.015). Atrial EGFC was also correlated to the percent of high voltage tissue across all patients (r = 0.651, p < 0.0001).ConclusionsEGF mapping provides insights into the mechanistic nature of AF and the atrial health of the underlying substrate. Therefore, further studies are needed to develop phenotype‐specific treatments for the disease.Trial RegistrationClinicalTrials.gov identifier: NCT05481359.

Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm

In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R2) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production.

A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification

The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling forces, thus preventing pathogen attack such as downy mildew and anthracnose. Moreover, leaf hairs as a favorable habitat may considerably affect the abundance of biological control agents. The unavailability of accurate and efficient objective tools for quantifying leaf hair density makes the study intricate and challenging. Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). We trained modified ResNet CNNs with a minimalistic number of images to efficiently classify the area covered by leaf hairs. This approach achieved an overall model prediction accuracy of 95.41%. As final validation, 10,120 input images from a segregating F1 biparental population were used to evaluate the algorithm performance. ResNet CNN-based phenotypic results compared to ground truth data received by two experts revealed a strong correlation with R values of 0.98 and 0.92 and root-mean-square error values of 8.20% and 14.18%, indicating that the model performance is consistent with expert evaluations and outperforms the traditional manual rating. Additional validation between expert vs. non-expert on six varieties showed that non-experts contributed to over- and underestimation of the trait, with an absolute error of 0% to 30% and -5% to -60%, respectively. Furthermore, a panel of 16 novice evaluators produced significant bias on set of varieties. Our results provide clear evidence of the need for an objective and accurate tool to quantify leaf hairiness.

A Digital Phenotypic Assessment in Neuro-Oncology (DANO): A Pilot Study on Sociability Changes in Patients Undergoing Treatment for Brain Malignancies.

The digital phenotyping tool has great potential for the deep characterization of neurological and quality-of-life assessments in brain tumor patients. Phone communication activities (details on call and text use) can provide insight into the patients' sociability. We prospectively collected digital-phenotyping data from six brain tumor patients. The data were collected using the Beiwe application installed on their personal smartphones. We constructed several daily sociability features from phone communication logs, including the number of incoming and outgoing text messages and calls, the length of messages and duration of calls, message reciprocity, the number of communication partners, and number of missed calls. We compared variability in these sociability features against those obtained from a control group, matched for age and sex, selected among patients with a herniated disc. In brain tumor patients, phone-based communication appears to deteriorate with time, as evident in the trend for total outgoing minutes, total outgoing calls, and call out-degree. These measures indicate a possible decrease in sociability over time in brain tumor patients that may correlate with survival. This exploratory analysis suggests that a quantifiable digital sociability phenotype exists and is comparable for patients with different survival outcomes. Overall, assessing neurocognitive function using digital phenotyping appears promising.

Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms.

Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals. A highly efficient gradient-boosting decision tree (LightGBM), eXtreme gradient-boosting (XGBoost) and support vector machine (SVM) algorithms were evaluated and compared to the prediction of BW. The coefficient of determination, root mean square error and mean absolute error values were used as comparison criteria. The results showed that LightGBM, XGBoost and SVM algorithms were well fit for the BW using the biometric measures with over 95% accuracy for both train and test sets. The BL was determined as the most explanatory variable on body weight.

Advances in Sorghum Improvement for Climate Resilience in the Global Arid and Semi-Arid Tropics: A Review

Sorghum is a climate-resilient crop which has been cultivated as a staple food in the semi-arid areas of Africa and Asia for food and nutrition security. However, the current climate change is increasingly affecting sorghum performance, especially at the flowering stage when water availability is critical for grain filling, thus lowering the sorghum grain yield. The development of climate-resilient, biotic and abiotic stress-tolerant, market-preferred, and nutrient-dense sorghum varieties offers a potentially cost-effective and environmentally sustainable strategy for adapting to climate change. Some of the common technologies for sorghum improvement include mass selection, single seed descent, pure line selection, and marker-assisted selection, facilitated by backcrossing and genotyping using molecular markers. In addition, recent advancements including new machine learning algorithms, gene editing, genomic selection, rapid generation advancement, and recycling of elite material, along with high-throughput phenotyping tools such as drone- and satellite-based images and other speed-breeding techniques, have increased the precision, speed, and accuracy of new crop variety development. In addition to these modern breeding tools and technologies, enhancing genetic diversity to incorporate various climate resilience traits, including against heat and drought stress, into the current sorghum breeding pools is critical. This review covers the potential of sorghum as a staple food crop, explores the genetic diversity of sorghum, discusses the challenges facing sorghum breeding, highlights the recent advancements in technologies for sorghum breeding, and addresses the perceptions of farmers on sorghum production under the current climate change conditions.

Sunflower-like self-sustainable plant-wearable sensing probe.

Powering and communicating with wearable devices on bio-interfaces is challenging due to strict weight, size, and resource constraints. This study presents a sunflower-like plant-wearable sensing device that harnesses solar energy, achieving complete energy self-sustainability for long-term monitoring of plant sap flow, a crucial indicator of plant health. It features foldable solar panels along with all essential flexible electronic components, resulting in a compact system that is lightweight enough for small plants. To tackle the low-energy density of solar power, we developed an ultralow-energy light communication mechanism inspired by fireflies. Together with unmanned aerial vehicles and deep learning algorithms, this approach enables efficient data retrieval from multiple devices across large agricultural fields. With its simple deployment, it shows great potential as a low-cost plant phenotyping tool. We believe our energy and communication solution for wearable devices can be extended to similar resource-limited and challenging scenarios, leading to exciting applications.

BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8

BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines.

Spatial analysis with unoccupied aircraft systems data in wheat breeding yield trials

AbstractAn important aspect of reliable cultivar development is good field trial evaluations. Unoccupied aircraft systems (UAS also known as drones or UAVs) are a popular high‐throughput phenotyping tool that has been used to successfully evaluate plant stress and other canopy characteristics in a field. In precision agriculture applications, UAS imagery has been used to identify spatial variability in field settings. Here, we use UAS spectral imagery to improve field trial spatial analysis, better control spatial variability, and reduce errors for more reliable selections. UAS imagery data were collected across 47 breeding trials planted in an augmented complete block design (ACBD) or alpha‐lattice replicated designs from 2020 through 2023. Trials were evaluated using three spatial analysis strategies: linear models incorporating block effect, row‐column effect, or 2D splines. UAS‐derived spectral reflectance indices (SRI) were combined with each model as covariates. Modeling strategies were used across all trials and evaluated for autocorrelation, model fitness, and coefficient of variation (CV). Akaike information criterion (AIC) was used to assess model fitness. For spatial analysis trials, SRIs significantly lowered model AIC by an average of 38.4 for alpha‐lattice trials and 69.1 for ACBD trials. CV scores were also lowered when SRIs were utilized, with average CV values being 2.6 lower for alpha‐lattice and 2.1 for ACBD trials. This study highlights the potential for SRIs to improve the analyses of field breeding trials despite extreme environmental variables and climate conditions, improving experiment reliability and changing the way breeders plan and implement breeding experiments.

Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

Integrating Hyperspectral Reflectance-Based Phenotyping and SSR Marker-Based Genotyping for Assessing the Salt Tolerance of Wheat Genotypes under Real Field Conditions.

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F8 RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R2 = 0.56-0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes.

A portable multi-taxa phenotyping device to retrieve physiological performance traits

Organismal phenotyping to identify fitness traits is transforming our understanding of adaptive responses and ecological interactions of species within changing environments. Here we present a portable Multi-Taxa Phenotyping (MTP) system that can retrieve a suite of metabolic and photophysiological parameter across light, temperature, and/or chemical gradients, using real time bio-optical (oxygen and chlorophyll a fluorescence) measurements. The MTP system integrates three well-established technologies for the first time: an imaging Pulse Amplitude Modulated (PAM) chlorophyll a fluorometer, custom-designed well plates equipped with optical oxygen sensors, and a thermocycler. We demonstrate the ability of the MTP system to distinguish phenotypic performance characteristics of diverse aquatic taxa spanning corals, mangroves and algae based on metabolic parameters and Photosystem II dynamics, in a high-throughput capacity and accounting for interactions of different environmental gradients on performance. Extracted metrics from the MTP system can not only provide information on the performance of aquatic taxa exposed to differing environmental gradients, but also provide predicted phenotypic responses of key aquatic organisms to environmental change. Further work validating how rapid phenotyping tools such as the MTP system predict phenotypic responses to long term environmental changes in situ are urgently required to best inform how these tools can support management efforts.

Realizing the potential of plant genetic resources: the use of phenomics for genebanks

Societal Impact StatementGenebanks contribute to global food security, directly influencing societal well‐being, by providing access to seed or genetic material that is more resilient to pests, diseases, and environmental stresses. The ability to develop crops adapted to changing environmental conditions and with high nutritional value means societies can better cope with the adverse effects of climate change, safeguarding food production and rural livelihoods. Enhancing sustainable crop traits, such as improved water‐use efficiency and nutrient utilization, supports the transition toward more sustainable agricultural practices. New technologically advanced phenotyping tools are required to optimally realize the potential of these genetic resources, to identify plant genetic resources that will best enable us to address current global challenges.SummaryPlant genebanks have a crucial role as specialized repositories, preserving diverse plant genetic resources and providing essential access to researchers, breeders, and farmers for developing resilient crops. With over 7 million global accessions, these genebanks significantly contribute to global food security, climate change mitigation, and sustainable agriculture. This opinion paper, inspired by an international workshop in 2022 in Wageningen (Netherlands), focuses on the transformative impact of phenomics technology within genebanks, offering a view on its potential applications and implications for preserving and utilizing plant genetic resources. Discussions from the 2022 workshop organized by the International Plant Phenotyping Network are integrated, emphasizing the potential of phenomics for global collaboration without requiring major policy developments. The workshop prioritized traits like disease resistance and drought tolerance, highlighting roots as a critical organ for phenotyping. Participants expressed a keen interest in global collaboration, emphasizing the need for partnerships between genebanks and advanced phenotyping facilities. The workshop's outcomes underscore the transformative impact of phenomics on genebanks, promoting innovation, collaboration, and sustainable agricultural practices. These results will guide future pilot studies, marking a significant step toward integrating phenomics into genebank practices and ensuring the continued prosperity of plant genetic heritage.

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments.

A screen for cell envelope stress uncovers an inhibitor of prolipoprotein diacylglyceryl transferase, Lgt, in Escherichia coli

The increasing prevalence of antibiotic resistance demands the discovery of antibacterial chemical scaffolds with unique mechanisms of action. Phenotypic screening approaches, such as the use of reporters for bacterial cell stress, offer promise to identify compounds while providing strong hypotheses for follow-on mechanism of action studies. From a collection of ∼1,800 Escherichia coli GFP transcriptional reporter strains, we identified a reporter that is highly induced by cell envelope stress-pProm rcsA -GFP. After characterizing pProm rcsA -GFP induction, we assessed a collection of bioactive small molecules for reporter induction, identifying 24 compounds of interest. Spontaneous suppressors to one compound in particular, MAC-0452936, mapped to the gene encoding the essential prolipoprotein diacylglyceryl transferase, lgt. Lgt inhibition by MAC-0452936 inhibition was confirmed through genetic, phenotypic, and biochemical approaches. The oxime ester, MAC-0452936, represents a useful small molecule inhibitor of Lgt and highlights the potential of using pProm rcsA -GFP as a phenotypic screening tool.

Quantitative sensory testing and chronic pain syndromes: a cross-sectional study from TwinsUK

ObjectiveThe chronic pain syndromes (CPS) include syndromes such as chronic widespread pain (CWP), dry eye disease (DED) and irritable bowel syndrome (IBS). Highly prevalent and lacking pathognomonic biomarkers, the CPS...

Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Rheumatoid arthritis (RA) is a complex immune-mediated inflammatory disorder in which patients suffer from inflammatory-erosive arthritis. Recent advances on histopathology heterogeneity of RA synovial tissue revealed three distinct phenotypes based on cellular composition (pauci-immune, diffuse and lymphoid), suggesting that distinct etiologies warrant specific targeted therapy which motivates a need for cost effective phenotyping tools in preclinical and clinical settings. To this end, we developed an automated multi-scale computational pathotyping (AMSCP) pipeline for both human and mouse synovial tissue with two distinct components that can be leveraged together or independently: (1) segmentation of different tissue types to characterize tissue-level changes, and (2) cell type classification within each tissue compartment that assesses change across disease states. Here, we demonstrate the efficacy, efficiency, and robustness of the AMSCP pipeline as well as the ability to discover novel phenotypes. Taken together, we find AMSCP to be a valuable cost-effective method for both pre-clinical and clinical research.

CMR to characterize myocardial structure and function in heart failure with preserved left ventricular ejection fraction.

Despite remarkable progress in therapeutic drugs, morbidity, and mortality for heart failure (HF) remains high in developed countries. HF with preserved ejection fraction (HFpEF) now accounts for around half of all HF cases. It is a heterogeneous disease, with multiple aetiologies, and as such poses a significant diagnostic challenge. Cardiac magnetic resonance (CMR) has become a valuable non-invasive modality to assess cardiac morphology and function, but beyond that, the multi-parametric nature of CMR allows novel approaches to characterize haemodynamics and with magnetic resonance spectroscopy (MRS), the study of metabolism. Furthermore, exercise CMR, when combined with lung water imaging provides an in-depth understanding of the underlying pathophysiological and mechanistic processes in HFpEF. Thus, CMR provides a comprehensive phenotyping tool for HFpEF, which points towards a targeted and personalized therapy with improved diagnostics and prevention.

High‐throughput phenotyping platforms for pulse crop biofortification

Societal Impact StatementPulse crops, including dry pea, lentil, and chickpea, are rich sources of protein, low digestible carbohydrates, and micronutrients. With the increasing demand for plant‐based protein with gluten‐free and allergen‐free foods, pulse crops have become of global importance for meeting the nutritional demand of growing populations. Breeding for nutritional quality is becoming a bottleneck for most breeding programs globally due to the cost of these available tools. Therefore, low‐cost, high‐throughput phenotyping tools will be a focus of interest for the selection of elite germplasm for cultivar development and gene identification for pulse cultivar development. This publication explains the emerging and future trends of phenotyping tools that are feasible for pulse breeding and improving nutritional quality.SummaryPrecision agriculture tools based on spectroscopic and imaging techniques now contribute to high‐throughput phenotyping (HTP) pipelines for nutritional and agronomic traits to speed breeding and selection for cultivar development. Fourier transform mid‐infrared (FT‐MIR) spectroscopy has been a reliable HTP tool for macro nutritional traits in pulse crops. Hyperspectral, multispectral, and RGB (red‐green‐blue) imaging with unmanned aerial systems (UAVs) have been developed to measure agronomic traits for cereals, but these techniques have yet to be developed and validated for pulse crops. This review summarizes different phenotyping techniques applied to nutritional and agronomic traits for crop breeding and reviews applications of machine learning tools for optimizing HTP.