Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing

Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.

Phenotypes, Etiotypes, and Endotypes of Exacerbations of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease is a major health problem with a high prevalence, a rising incidence, and substantial morbidity and mortality. Its course is punctuated by acute episodes of increased respiratory symptoms, termed exacerbations of chronic obstructive pulmonary disease (ECOPD). ECOPD are important events in the natural history of the disease, as they are associated with lung function decline and prolonged negative effects on quality of life. The present-day therapy for ECOPD with short courses of antibiotics and steroids and escalation of bronchodilators has resulted in only modest improvements in outcomes. Recent data indicate that ECOPD are heterogeneous, raising the need to identify distinct etioendophenotypes, incorporating traits of the acute event and of patients who experience recurrent events, to develop novel and targeted therapies. These characterizations can provide a complete clinical picture, the severity of which will dictate acute pharmacological treatment, and may also indicate whether a change in maintenance therapy is needed to reduce the risk of future exacerbations. In this review we discuss the latest knowledge of ECOPD types on the basis of clinical presentation, etiology, natural history, frequency, severity, and biomarkers in an attempt to characterize these events.

Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato

Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red-green-blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop's status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring.

Optimization Strategies to Adapt Sheep Breeding Programs to Pasture-Based Production Environments: A Simulation Study

Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain.

Engineering a multicompartment in vitro model for dorsal root ganglia phenotypic assessment

Despite the significant global prevalence of chronic pain, current methods to identify pain therapeutics often fail translation to the clinic. Phenotypic screening platforms rely on modeling and assessing key pathologies relevant to chronic pain, improving predictive capability. Patients with chronic pain often present with sensitization of primary sensory neurons (that extend from dorsal root ganglia [DRG]). During neuronal sensitization, painful nociceptors display lowered stimulation thresholds. To model neuronal excitability, it is necessary to maintain three key anatomical features of DRGs to have a physiologically relevant platform: (1) isolation between DRG cell bodies and neurons, (2) 3D platform to preserve cell-cell and cell-matrix interactions, and (3) presence of native non-neuronal support cells, including Schwann cells and satellite glial cells. Currently, no culture platforms maintain the three anatomical features of DRGs. Herein, we demonstrate an engineered 3D multicompartment device that isolates DRG cell bodies and neurites and maintains native support cells. We observed neurite growth into isolated compartments from the DRG using two formulations of collagen, hyaluronic acid, and laminin-based hydrogels. Further, we characterized the rheological, gelation and diffusivity properties of the two hydrogel formulations and found the mechanical properties mimic native neuronal tissue. Importantly, we successfully limited fluidic diffusion between the DRG and neurite compartment for up to 72 h, suggesting physiological relevance. Lastly, we developed a platform with the capability of phenotypic assessment of neuronal excitability using calcium imaging. Ultimately, our culture platform can screen neuronal excitability, providing a more translational and predictive system to identify novel pain therapeutics to treat chronic pain.

Phenotyping mice with skin, hair, or nail abnormalities: A systematic approach and methodologies from simple to complex

The skin and adnexa can be difficult to interpret because they change dramatically with the hair cycle throughout life. However, a variety of methods are commonly available to collect skin and perform assays that can be useful for figuring out morphological and molecular changes. This overview provides information on basic approaches to evaluate skin and its molecular phenotype, with references for more detail, and interpretation of results on the skin and adnexa in the mouse. These approaches range from mouse genetic nomenclature, setting up a cutaneous phenotyping study, skin grafts, hair follicle reconstitution, wax stripping, electron microscopy, and Köbner reaction to very specific approaches such as lipid and protein analyses on a large scale.

Natural variation of warm temperature-induced raffinose accumulation identifies TREHALOSE-6-PHOSPHATE SYNTHASE 1 as a modulator of thermotolerance

High-temperature stress limits plant growth and reproduction. Exposure to high temperature, however, also elicits a physiological response, which protects plants from the damage evoked by heat. This response involves a partial reconfiguration of the metabolome including the accumulation of the trisaccharide raffinose. In this study, we explored the intraspecific variation of warm temperature-induced raffinose accumulation as a metabolic marker for temperature responsiveness with the aim to identify genes that contribute to thermotolerance. By combining raffinose measurements in 250 Arabidopsis thaliana accessions following a mild heat treatment with genome-wide association studies, we identified five genomic regions that were associated with the observed trait variation. Subsequent functional analyses confirmed a causal relationship between TREHALOSE-6-PHOSPHATE SYNTHASE 1 (TPS1) and warm temperature-dependent raffinose synthesis. Moreover, complementation of the tps1-1 null mutant with functionally distinct TPS1 isoforms differentially affected carbohydrate metabolism under more severe heat stress. While higher TPS1 activity was associated with reduced endogenous sucrose levels and thermotolerance, disruption of trehalose 6-phosphate signalling resulted in higher accumulation of transitory starch and sucrose and was associated with enhanced heat resistance. Taken together, our findings suggest a role of trehalose 6-phosphate in thermotolerance, most likely through its regulatory function in carbon partitioning and sucrose homoeostasis.

RootBot: High-throughput root stress phenotyping robot

Premise

Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil.

Methods and Results

RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data.

Conclusions

The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.

Genetic Dissection of Seasonal Changes in a Greening Plant Based on Time-Series Multispectral Imaging

Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.

Population Structure and Genetic Diversity of the 175 Soybean Breeding Lines and Varieties Cultivated in West Siberia and Other Regions of Russia

Soybean is a leguminous plant cultivated in many countries and is considered important in the food industry due to the high levels of oil and protein content in the beans. The high demand for soybeans and its products in the industry requires the expansion of cultivation areas. Despite climatic restrictions, West Siberia is gradually expanding its area of soybean cultivation. In this study, we present the first analysis of the population structure and genetic diversity of the 175 soybean Glycine max breeding lines and varieties cultivated in West Siberia (103 accessions) and other regions of Russia (72 accessions), and we compare them with the cultivated soybean varieties from other geographical locations. Principal component analysis revealed several genetic clusters with different levels of genetic heterogeneity. Studied accessions are genetically similar to varieties from China, Japan, and the USA and are genetically distant to varieties from South Korea. Admixture analysis revealed four ancestry groups based on genetic ancestry and geographical origin, which are consistent with the regions of cultivation and origin of accessions and correspond to the principal component analysis result. Population statistics, including nucleotide diversity, Tajima's D, and linkage disequilibrium, are comparatively similar to those observed for studied accessions of a different origin. This study provides essential population and genetic information about the unique collection of breeding lines and varieties cultivated in West Siberia and other Russian regions to foster further evolutionary, genome-wide associations and functional breeding studies.

A Simplified Microscopy Technique to Rapidly Characterize Individual Fiber Traits in Cotton

Recent advances in phenotyping techniques have substantially improved the ability to mitigate type-II errors typically associated with high variance in phenotyping data sets. In particular, the implementation of automated techniques such as the High-Volume Instrument (HVI) and the Advanced Fiber Information System (AFIS) have significantly enhanced the reproducibility and standardization of various fiber quality measurements in cotton. However, micronaire is not a direct measure of either maturity or fineness, lending to limitations. AFIS only provides a calculated form of fiber diameter, not a direct measure, justifying the need for a visual-based reference method. Obtaining direct measurements of individual fibers through cross-sectional analysis and electron microscopy is a widely accepted standard but is time-consuming and requires the use of hazardous chemicals and specialized equipment. In this study, we present a simplified fiber histology and image acquisition technique that is both rapid and reproducible. We also introduce an automated image analysis program that utilizes machine learning to differentiate good fibers from bad and to subsequently collect critical phenotypic measurements. These methods have the potential to improve the efficiency of cotton fiber phenotyping, allowing for greater precision in unravelling the genetic architecture of critical traits such as fiber diameter, shape, areas of the secondary cell wall/lumen, and others, ultimately leading to larger genetic gains in fiber quality and improvements in cotton.

Computational phenotyping with the All of Us Research Program: identifying underrepresented people with HIV or at risk of HIV

Objective

This study aims to identify the people living with HIV (PWH) and pre-exposure prophylaxis (PrEP) users in the All of Us (AoU) database by integrating information from both electronic health record (EHR)- and self-reported survey data.

Methods

We identified PWH and PrEP users if they met the inclusion criterion by conditions, lab measurements, or medications related to HIV in EHR data or confirmed questions in the Survey data.

Results

We evaluated the latest data release through July 1, 2022 in AoU. Through computational phenotyping, we identified 4575 confirmed and 3092 probable adult PWH and 564 PrEP users. PWH was most identified by a combination of medications and conditions (3324, 43.4%) and drug exposure alone (2191, 28.6%), then less commonly by survey data alone (608, 7.9%) and lab alone (81, 1.1%).

Discussion and conclusion

Our methods serve as an overall framework for other researchers using AoU data for conducting HIV-related research.

Natural History and Phenotypic Spectrum of GAA-FGF14 Sporadic Late-Onset Cerebellar Ataxia (SCA27B)

BACKGROUND

Heterozygous GAA expansions in the FGF14 gene have been related to autosomal dominant cerebellar ataxia (SCA27B-MIM:620174). Whether they represent a common cause of sporadic late-onset cerebellar ataxia (SLOCA) remains to be established.

OBJECTIVES

To estimate the prevalence, characterize the phenotypic spectrum, identify discriminative features, and model longitudinal progression of SCA27B in a prospective cohort of SLOCA patients.

METHODS

FGF14 expansions screening combined with longitudinal deep-phenotyping in a prospective cohort of 118 SLOCA patients (onset >40 years of age, no family history of cerebellar ataxia) without a definite diagnosis.

RESULTS

Prevalence of SCA27B was 12.7% (15/118). Higher age of onset, higher Spinocerebellar Degeneration Functional Score, presence of vertigo, diplopia, nystagmus, orthostatic hypotension absence, and sensorimotor neuropathy were significantly associated with SCA27B. Ataxia progression was ≈0.4 points per year on the Scale for Assessment and Rating of Ataxia.

CONCLUSIONS

FGF14 expansion is a major cause of SLOCA. Our natural history data will inform future FGF14 clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Phenotypic and Genotypic Characteristics of Non-Hemolytic L. monocytogenes Isolated from Food and Processing Environments

Increasingly, Listeria monocytogenes (LM) with atypical phenotypic and genotypic characteristics are being isolated from food, causing problems with their classification and testing. From 2495 soil, food, and swab samples from the food industry, 262 LM isolates were found. A total of 30 isolates were isolated, mainly from soil and plant food, and were classified as atypical LM (aLM) because they lacked the ability to move (30/11.4%) and perform hemolysis (25/9.5%). The isolation environment affected aLM incidence, cell size, sugar fermentation capacity, antibiotic sensitivity, and the number of virulence genes. Therefore, despite several characteristics differentiating all aLMs/non-hemolytic isolates from reference LMs, the remaining phenotypic characteristics were specific to each aLM isolate (like a fingerprint). The aLM/non-hemolytic isolates, particularly those from the soil and meat industries, showed more variability in their sugar fermentation capacity and were less sensitive to antibiotics than LMs. As many as 11 (36.7%) aLM isolates had resistance to four different antibiotics or simultaneously to two antibiotics. The aLM isolates possessed 3-7 of the 12 virulence genes: prfA and hly in all aLMs, while iap was not present. Only five (16.7%) isolates were classified into serogroups 1/2c-3c or 4a-4c. The aLM/non-hemolytic isolates differed by many traits from L. immobilis and atypical L. innocua. The reference method of reviving and isolating LM required optimization of aLM. Statistical analyses of clustering, correlation, and PCA showed similarities and differences between LM and aLM/non-hemolytic isolates due to individual phenotypic traits and genes. Correlations were found between biochemical traits, antibiotic resistance, and virulence genes. The increase in the incidence of atypical non-hemolytic LM may pose a risk to humans, as they may not be detected by ISO methods and have greater antibiotic resistance than LM. aLM from LM can be distinguished based on lack of hemolysis, motility, growth at 4 °C, ability to ferment D-arabitol, and lack of six specific genes.

Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review

Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.

Effects of measurement methods and growing conditions on phenotypic expression of photosynthesis in seven diverse rice genotypes

Introduction

Light response curves are widely used to quantify phenotypic expression of photosynthesis by measuring a single sample and sequentially altering light intensity within a chamber (sequential method) or by measuring different samples that are each acclimated to a different light level (non-sequential method). Both methods are often conducted in controlled environments to achieve steady-state results, and neither method involves equilibrating the entire plant to the specific light level.

Methods

Here, we compare sequential and non-sequential methods in controlled (greenhouse), semi-controlled (plant grown in growth chamber and acclimated to field conditions 2-3 days before measurements), and field environments. We selected seven diverse rice genotypes (five genotypes from the USDA rice minicore collection: 310588, 310723, 311644, 311677, 311795; and 2 additional genotypes: Nagina 22 and Zhe 733) to understand (1) the limitations of different methods, and (2) phenotypic plasticity of photosynthesis in rice grown under different environments.

Results

Our results show that the non-sequential method was time-efficient and captured more variability of field conditions than the sequential method, but the model parameters were generally similar between two methods except the maximum photosynthesis rate (Amax). Amax was significantly lower across all genotypes under greenhouse conditions compared to the growth chamber and field conditions consistent with prior work, but surprisingly the apparent quantum yield (α) and the mitochondrial respiration (Rd) were generally not different among growing environments or measurement methods.

Discussion

Our results suggest that field conditions are best suited to quantify phenotypic differences across different genotypes and nonsequential method was better at capturing the variability in photosynthesis.

The history and evolution of HLA typing external proficiency testing schemes in UK NEQAS for H&I

The UK National External Quality Assessment Service (NEQAS) provide an external proficiency testing (EPT) service for clinical laboratories. UK NEQAS for Histocompatibility and Immunogenetics (H&I) has been providing EPT schemes for over 45 years and has grown during this time to provide 19 EPT schemes. Accurate human leucocyte antigen (HLA) typing is critical to support safe clinical services, including transplantation, therefore high quality, relevant EPT schemes are required as part of a laboratory's quality assurance. This article reviews the development of the HLA typing EPT schemes, from the first HLA phenotyping scheme in 1975, via the first HLA genotyping scheme in 1992, through to the introduction in 2017 of HLA third field assessment results from next-generation sequencing technology. In addition, the introduction of EPT schemes to cover HLA associated diseases and pharmacogenetic reactions, including HLA-B27, HLA*B*57:01 and HLA-DQ for coeliac disease are discussed. The accuracy of laboratory EPT results for HLA phenotyping are >96% (2018-2022), HLA genotyping >99% (2020-2022), HLA-B27 testing >99% (2018-2022) and B*57:01 testing >99% (2017-2022). However, for HLA genotyping for coeliac disease 22%-46% of laboratories made errors in 2020-2022. On investigation, the high rate of unsatisfactory performance was attributed to laboratories lacking specific knowledge to interpret HLA genotyping results and accurately report HLA types for coeliac disease. A misleading commercial kit insert was also identified. The assessment of scheme results has uncovered several issues which have been addressed with the intention of educating participants and improving clinical services. The UK NEQAS for H&I EPT schemes have evolved over the past four decades to reflect changes in HLA typing technology, laboratory clinical practice and to cover post-analytical interpretative elements of HLA typing.

Analysis of Clinical Phenotypes through Machine Learning of First-Line H. pylori Treatment in Europe during the Period 2013-2022: Data from the European Registry on H. pylori Management (Hp-EuReg)

The segmentation of patients into homogeneous groups could help to improve eradication therapy effectiveness. Our aim was to determine the most important treatment strategies used in Europe, to evaluate first-line treatment effectiveness according to year and country. Data collection: All first-line empirical treatments registered at AEGREDCap in the European Registry on Helicobacter pylori management (Hp-EuReg) from June 2013 to November 2022. A Boruta method determined the "most important" variables related to treatment effectiveness. Data clustering was performed through multi-correspondence analysis of the resulting six most important variables for every year in the 2013-2022 period. Based on 35,852 patients, the average overall treatment effectiveness increased from 87% in 2013 to 93% in 2022. The lowest effectiveness (80%) was obtained in 2016 in cluster #3 encompassing Slovenia, Lithuania, Latvia, and Russia, treated with 7-day triple therapy with amoxicillin-clarithromycin (92% of cases). The highest effectiveness (95%) was achieved in 2022, mostly in Spain (81%), with the bismuth-quadruple therapy, including the single-capsule (64%) and the concomitant treatment with clarithromycin-amoxicillin-metronidazole/tinidazole (34%) with 10 (69%) and 14 (32%) days. Cluster analysis allowed for the identification of patients in homogeneous treatment groups assessing the effectiveness of different first-line treatments depending on therapy scheme, adherence, country, and prescription year.

The phenotype-genotype reference map: Improving biobank data science through replication

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

Opportunities and challenges for biomarker discovery using electronic health record data

Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.

Severe asthma: follow-up after one year from the Italian Registry on Severe Asthma (IRSA)

Summary

Background. Asthma affects millions of people worldwide, with a subgroup suffering from severe asthma (SA). Biologics have revolutionized SA treatment, but challenges remain in managing different patient traits. This study analyzed data from the Italian Registry on Severe Asthma (IRSA) to investigate changes in SA characteristics and effectiveness of treatments after one year of follow-up, and to identify factors associated with response to treatments in a real-world setting. Methods. Data on SA patients with one year of follow-up were extracted from IRSA. Asthma control, exacerbations, lung function, and treatments, were assessed at follow-up and analyzed against baseline characteristics. Results. After one year of follow-up, notable improvements were observed in all the outcomes of SA of the included patients (n = 570). The effectiveness of biologic therapies was particularly evident, as they contributed significantly to these positive outcomes. Additionally, certain factors were found to be associated with improvement, namely T2 phenotype, baseline eosinophil count (BEC), and area of residence. On the other hand, comorbidities (obesity, gastro-esophageal reflux disease) and poor lung function were risk factors. Notably, poor-responders to biologics exhibited lower level of education, BEC, and exacerbations, and higher frequency of atopy and ACT score ≥ 20. Conclusions. The findings demonstrate the effectiveness of biologics in asthma management, when implemented as part of a planned follow-up strategy aimed at optimizing and fine-tuning the therapy. Moreover, the study highlights the importance of considering key traits such as the T2 phenotype, BEC, education, and comorbidities when tailoring SA treatment. Overall, this study contributes to enhancing our understanding of SA management and guiding the development of personalized treatment approaches for patients with SA.

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca)

Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2  = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2  = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.

Variability in drought stress response in a panel of 100 faba bean genotypes

Faba bean is an important protein crop for food and feed worldwide and provides a range of advantages in crop rotations. Its limited use in modern agriculture is mainly due to the high fluctuations in yield. A well known limiting factor in most legumes, and particularly in faba bean, is the high sensitivity to water shortage, which is further aggravated by climate change. The present study was undertaken to exploit the genetic variation in drought stress response in a faba bean collection of 100 accessions with diverse origins and to assess selection criteria for identifying drought tolerant genotypes. Physiological, phenological and yield related traits evaluated under drought or water-sufficient conditions responded significantly to the end-terminated drought stress. Comparison of yield relations showed the advantage of using a stress tolerance index (STI) to identify genotypes combining high yield potential with high stress yield. With regard to physiological traits, SPAD (chlorophyll content) values were significantly related to yield as well as to STI, while the other traits also contributed to different extents to variation in yield formation. Among the yield related traits, seeds per plant proved to be the most important trait followed by pods per plant. Interestingly, the eight genotypes with the best STI performance use different strategies to cope with drought stress.

Evaluation of Pupillometry for CYP2D6 Phenotyping in Children Treated with Tramadol

Following the contraindication of codeine use in children, increasing use of tramadol has been observed in pain management protocols. However, tramadol's pharmacokinetics (PK) and pharmacodynamics are influenced by cytochrome P450 (CYP)2D6 activity, similarly to codeine. Previous studies in adults have demonstrated a correlation between pupillary response and tramadol PK. Our objective was to evaluate pupillometry as a phenotyping method to assess CYP2D6 activity in children treated with tramadol. We included 41 children (mean age 11 years) receiving a first dose of tramadol (2 mg/kg) in the emergency room (ER) as part of their routine care. CYP2D6 phenotyping and genotyping were performed. The concentrations of tramadol and its active metabolite, M1, were measured, and static and dynamic pupillometry was conducted using a handheld pupillometer at the time of tramadol administration and during the ER stay. Pupillometric measurements were obtained for 37 children. Tramadol affected pupillary parameters, with a decrease in pupil diameter in 83.8% of children (p = 0.002) (mean decrease 14.1 ± 16.7%) and a decrease in reflex amplitude constriction in 78.4% (p = 0.011) (mean decrease 17.7 ± 34.5%) at T150 compared to T0. We were unable to identify a correlation between pupillometry measurements and CYP2D6 activity. Likely confounding factors include light intensity, pain, and stress, making the procedure less feasible in paediatric emergency settings.

Application of Near Infrared Spectroscopy for the Rapid Assessment of Nutritional Quality of Different Strawberry Cultivars

Strawberry is the most cultivated berry fruit globally and it is really appreciated by consumers because of its characteristics, mainly bioactive compounds with antioxidant properties. During the breeding process, it is important to assess the quality characteristics of the fruits for a better selection of the material, but the conventional approaches involve long and destructive lab techniques. Near infrared spectroscopy (NIR) could be considered a valid alternative for speeding up the breeding process and is not destructive. In this study, a total of 216 strawberry fruits belonging to four different cultivars have been collected and analyzed with conventional lab analysis and NIR spectroscopy. In detail, soluble solid content, acidity, vitamin C, anthocyanin, and phenolic acid have been determined. Partial least squares discriminant analysis (PLS-DA) models have been developed to classify strawberry fruits belonging to the four genotypes according to their quality and nutritional properties. NIR spectroscopy could be considered a valid non-destructive phenotyping method for monitoring the nutritional parameters of the fruit and ensuring the fruit quality, speeding up the breeding program.

Genetic analysis of heat tolerance in hot pepper: insights from comprehensive phenotyping and QTL mapping

High temperatures present a formidable challenge to the cultivation of hot pepper, profoundly impacting not only vegetative growth but also leading to flower and fruit abscission, thereby causing a significant reduction in yield. To unravel the intricate genetic mechanisms governing heat tolerance in hot pepper, an F2 population was developed through the crossing of two distinct genotypes exhibiting contrasting heat tolerance characteristics: DLS-161-1 (heat tolerant) and DChBL-240 (heat susceptible). The F2 population, along with the parental lines, was subjected to comprehensive phenotyping encompassing diverse morphological, physiological, and biochemical heat-related traits under high temperature conditions (with maximum temperature ranging from 31 to 46.5°C and minimum temperature from 15.4 to 30.5°C). Leveraging the Illumina Nova Seq-6000 platform, Double digest restriction-site associated DNA sequencing (ddRAD-seq) was employed to generate 67.215 Gb data, with subsequent alignment of 218.93 million processed reads against the reference genome of Capsicum annuum. Subsequent variant calling and ordering resulted in 5806 polymorphic SNP markers grouped into 12 LGs. Further QTL analysis identified 64 QTLs with LOD values ranging from 2.517 to 11.170 and explained phenotypic variance ranging from 4.05 to 19.39%. Among them, 21 QTLs, explaining more than 10% phenotypic variance, were identified as major QTLs controlling 9 morphological, 3 physiological, and 2 biochemical traits. Interestingly, several QTLs governing distinct parameters were found to be colocalized, suggesting either a profound correlation between the QTLs regulating these traits or their significant genomic proximity. In addition to the QTLs, we also identified 368380 SSR loci within the identified QTL regions, dinucleotides being the most abundant type (211,381). These findings provide valuable insights into the genetics of heat tolerance in hot peppers. The identified QTLs and SSR markers offer opportunities to develop heat-tolerant varieties, ensuring better crop performance under high-temperature conditions.

Development and application of Single Primer Enrichment Technology (SPET) SNP assay for population genomics analysis and candidate gene discovery in lettuce

Single primer enrichment technology (SPET) is a novel high-throughput genotyping method based on short-read sequencing of specific genomic regions harboring polymorphisms. SPET provides an efficient and reproducible method for genotyping target loci, overcoming the limits associated with other reduced representation library sequencing methods that are based on a random sampling of genomic loci. The possibility to sequence regions surrounding a target SNP allows the discovery of thousands of closely linked, novel SNPs. In this work, we report the design and application of the first SPET panel in lettuce, consisting of 41,547 probes spanning the whole genome and designed to target both coding (~96%) and intergenic (~4%) regions. A total of 81,531 SNPs were surveyed in 160 lettuce accessions originating from a total of 10 countries in Europe, America, and Asia and representing 10 horticultural types. Model ancestry population structure clearly separated the cultivated accessions (Lactuca sativa) from accessions of its presumed wild progenitor (L. serriola), revealing a total of six genetic subgroups that reflected a differentiation based on cultivar typology. Phylogenetic relationships and principal component analysis revealed a clustering of butterhead types and a general differentiation between germplasm originating from Western and Eastern Europe. To determine the potentiality of SPET for gene discovery, we performed genome-wide association analysis for main agricultural traits in L. sativa using six models (GLM naive, MLM, MLMM, CMLM, FarmCPU, and BLINK) to compare their strength and power for association detection. Robust associations were detected for seed color on chromosome 7 at 50 Mbp. Colocalization of association signals was found for outer leaf color and leaf anthocyanin content on chromosome 9 at 152 Mbp and on chromosome 5 at 86 Mbp. The association for bolting time was detected with the GLM, BLINK, and FarmCPU models on chromosome 7 at 164 Mbp. Associations were detected in chromosomal regions previously reported to harbor candidate genes for these traits, thus confirming the effectiveness of SPET for GWAS. Our findings illustrated the strength of SPET for discovering thousands of variable sites toward the dissection of the genomic diversity of germplasm collections, thus allowing a better characterization of lettuce collections.

Morpho-physiological and biochemical characterization of African spider plant (Gynandropsis gynandra (L.) Briq.) genotypes under drought and non-drought conditions

The African spider plant (Gynandropsis gynandra (L.) Briq.) is a nutrient-dense, climate-resilient indigenous vegetable with a C4 carbon fixation pathway. Understanding African spider plant drought tolerance mechanisms is essential for improving its performance in water-stressed areas. The objective of this study was to evaluate the stress tolerance potential of African spider plant accessions based on thirteen morphological, physiological, and biochemical traits under three different water treatment regimes. Eighteen accessions were evaluated over two growing seasons in the greenhouse using a split-split plot design with four replications and three water treatment-regimes namely optimum (100% field capacity), intermediate drought (50% field capacity) and, severe drought (30% field capacity). The results revealed that water regime had a significant effect (P< 0.01) on the accessions for the traits studied. A significant reduction across most of the studied traits was observed under drought conditions. However, proline content in all the accessions significantly rose under drought conditions. The principal component analysis revealed a considerable difference in the performance of the 18 African spider plant accessions under optimum and drought stress conditions. Several morphological and physiological parameters, including days to 50% flowering (r = 0.80), leaf length (r = 0.72), net photosynthesis (r = 0.76) and number of leaves per plant (r = 0.79), were positively associated with leaf yield under drought conditions. Cluster analysis categorized the 18 accessions and 13 measured parameters into 4 clusters, with cluster-1 exhibiting greater drought tolerance for most of the studied traits, and cluster-4 having the most drought-sensitive accessions. Among the accessions tested, accessions L3 and L5 demonstrated excellent drought tolerance and yield performance under both conditions. As a result, these accessions were selected as candidates for African spider plant drought tolerance breeding programs. These findings will serve as the foundation for future studies and will aid in improving food and nutrition security in the face of drought.

Simultaneous Assessment of mTORC1, JAK/STAT, and NLRP3 Inflammasome Activation Pathways in Patients with Sarcoidosis

The unknown etiology of sarcoidosis, along with the variability in organ involvement and disease course, complicates the effective treatment of this disease. Based on recent studies, the cellular inflammatory pathways involved in granuloma formation are of interest regarding possible new treatment options, such as the mechanistic (formerly mammalian) target of rapamycin complex 1 (mTORC1) pathway, the Janus kinase/signal transducers and activators of transcription (JAK/STAT) pathway, and the nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP3) inflammasome pathway. The aim of this study was to explore the potential coexpression of these three inflammatory pathways in patients with sarcoidosis and see whether possible differences were related to disease outcome. The tissue of 60 patients with sarcoidosis was used to determine the activity of these three signaling pathways using immunohistochemistry. The activation of NLRP3 was present in 85% of all patients, and the activation of mTORC1 and JAK/STAT was present in 49% and 50% of patients, respectively. Furthermore, the presence of NLRP3 activation at diagnosis was associated with a chronic disease course of sarcoidosis. Our finding of different new conceptual inflammatory tissue phenotypes in sarcoidosis could possibly guide future treatment studies using the available inhibitors of either NLRP3, JAK-STAT, and mTORC1 inhibitors in a more personalized medicine approach.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Clinical Research Informatics: Contributions from 2022

OBJECTIVES

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.

METHOD

A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.

RESULTS

Among the 1,324 papers returned by the search, published in 2022, that were in the scope of the various areas of CRI, the full review process selected four best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define a process and to gain multi-decision-maker acceptance of broad consent for the reuse of health data for research whilst remaining compliant with the European General Data Protection Regulation. The authors of the second-best paper present a federated architecture for the conduct of clinical trial feasibility queries that utilizes HL7 Fast Healthcare Interoperability Resources and an HL7 standard query representation. The third best paper aligns with the overall theme of this Yearbook, the inclusivity of potential participants in clinical trials, with recommendations to ensure greater equity. The fourth proposes a multi-modal modelling approach for large scale phenotyping from electronic health record information. This year's survey paper has also examined equity, along with data bias, and found that the relevant publications in 2022 have focused almost exclusively on the issue of bias in Artificial Intelligence (AI).

CONCLUSIONS

The literature relevant to CRI in 2022 has largely been dominated by publications that seek to maximise the reusability of wide scale and representative electronic health record information for research, either as big data for distributed analysis or as a source of information from which to identify suitable patients accurately and equitably for invitation to participate in clinical trials.

A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce

Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.

Levilactobacillus brevis, autochthonous to cucumber fermentation, is unable to utilize citric acid and encodes for a putative 1,2-propanediol utilization microcompartment

The metabolic versatility of Levilactobacillus brevis, a heterofermentative lactic acid bacterium, could benefit environmentally compatible and low salt cucumber fermentation. The biodiversity of Lvb. brevis autochthonous to cucumber fermentation was studied using genotypic and phenotypic analyses to identify unique adjunct cultures. A group of 131 isolates autochthonous to industrial fermentations was screened using rep-PCR-(GTG)5 and a fermentation ability assay under varied combinations of salt (0 or 6%), initial pH (4.0 or 5.2), and temperature (15 or 30°C). No apparent similarities were observed among the seven and nine clusters in the genotypic and phenotypic dendrograms, respectively. A total of 14 isolates representing the observed biodiversity were subjected to comparative genome analysis. The autochthonous Lvb. brevis clustered apart from allochthonous isolates, as their genomes lack templates for citrate lyase, several putative hypothetical proteins, and some plasmid- and phage-associated proteins. Four and two representative autochthonous and allochthonous Lvb. brevis, respectively, were subjected to phenotype microarray analysis using an Omnilog. Growth of all Lvb. brevis strains was supported to various levels by glucose, fructose, gentiobiose, 1,2-propanediol, and propionic acid, whereas the allochthonous isolate ATCC14890 was unique in utilizing citric acid. All the Lvb. brevis genomes encode for 1,2-propanediol utilization microcompartments. This study identified a unique Lvb. brevis strain, autochthonous to cucumber, as a potential functional adjunct culture for commercial fermentation that is distinct in metabolic activities from allochthonous isolates of the same species.

Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches

BACKGROUND

Although suicide is a leading cause of death among children, the optimal approach for using health care data sets to detect suicide-related emergencies among children is not known.

OBJECTIVE

This study aimed to assess the performance of suicide-related International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and suicide-related chief complaint in detecting self-injurious thoughts and behaviors (SITB) among children compared with clinician chart review. The study also aimed to examine variations in performance by child sociodemographics and type of self-injury, as well as develop machine learning models trained on codified health record data (features) and clinician chart review (gold standard) and test model detection performance.

METHODS

A gold standard classification of suicide-related emergencies was determined through clinician manual review of clinical notes from 600 emergency department visits between 2015 and 2019 by children aged 10 to 17 years. Visits classified with nonfatal suicide attempt or intentional self-harm using the Centers for Disease Control and Prevention surveillance case definition list of ICD-10-CM codes and suicide-related chief complaint were compared with the gold standard classification. Machine learning classifiers (least absolute shrinkage and selection operator-penalized logistic regression and random forest) were then trained and tested using codified health record data (eg, child sociodemographics, medications, disposition, and laboratory testing) and the gold standard classification. The accuracy, sensitivity, and specificity of each detection approach and relative importance of features were examined.

RESULTS

SITB accounted for 47.3% (284/600) of the visits. Suicide-related diagnostic codes missed nearly one-third (82/284, 28.9%) and suicide-related chief complaints missed more than half (153/284, 53.9%) of the children presenting to emergency departments with SITB. Sensitivity was significantly lower for male children than for female children (0.69, 95% CI 0.61-0.77 vs 0.84, 95% CI 0.78-0.90, respectively) and for preteens compared with adolescents (0.66, 95% CI 0.54-0.78 vs 0.86, 95% CI 0.80-0.92, respectively). Specificity was significantly lower for detecting preparatory acts (0.68, 95% CI 0.64-0.72) and attempts (0.67, 95% CI 0.63-0.71) than for detecting ideation (0.79, 95% CI 0.75-0.82). Machine learning-based models significantly improved the sensitivity of detection compared with suicide-related codes and chief complaint alone. Models considering all 84 features performed similarly to models considering only mental health-related ICD-10-CM codes and chief complaints (34 features) and models considering non-ICD-10-CM code indicators and mental health-related chief complaints (53 features).

CONCLUSIONS

The capacity to detect children with SITB may be strengthened by applying a machine learning-based approach to codified health record data. To improve integration between clinical research informatics and child mental health care, future research is needed to evaluate the potential benefits of implementing detection approaches at the point of care and identifying precise targets for suicide prevention interventions in children.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

Selection of oat (Avena sativa L.) drought-tolerant genotypes based on multiple yield-associated traits

BACKGROUND

Most plant breeding and agricultural practices are based on selecting genotypes for yield. However, this is inadequate to screen crop varieties for specific attributes, such as drought tolerance. In this study, we quantified the response of oat (Avena sativa L.) plant physiological and morphological traits to drought stress and selected some key traits to establish a genotype by yield*trait (GYT)-based method for ranking 30 oat genotypes. The effectiveness of this method was also evaluated under drought conditions.

RESULTS

Water-deficit treatment significantly reduced leaf chlorophyll, root morphological traits, groat yield and associated components, such as mean grain weight. We observed that the genotypes 'JUSTICE' and 'BOLINA' had the smallest and largest yield loss, respectively, after exposure to drought stress, but showed opposite trends in the biomass allocation of roots and grains. This indicated that drought tolerance was highly dependent on the distribution of photoassimilates. Our results also illustrated that the GYT method is a trade-off approach and more effective in selecting oat ideotypes under drought conditions than the yield-related index method because it combines yield, yield stability, and related agronomic traits in the calculation process.

CONCLUSION

Drought-tolerant genotypes had more biomass allocated to roots and grains with higher chlorophyll content and better root structure, e.g. longer root lengths than drought-sensitive lines. By integrating yield and yield-related traits, the GYT approach is more practical than traditional single-trait selection methods when assessing drought tolerance. © 2023 His Majesty the King in Right of Canada. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada.

Early-Stage Phenotyping of Sweet Potato Virus Disease Caused by Sweet Potato Chlorotic Stunt Virus and Sweet Potato Virus C to Support Breeding

Sweet potato virus disease (SPVD) is a global constraint to sweetpotato (Ipomoea batatas) production, especially under intensive cultivation in the humid tropics such as East Africa. The objectives of this study were to develop a precision SPVD phenotyping protocol, to find new SPVD-resistant genotypes, and to standardize the first stages of screening for SPVD resistance. The first part of the protocol was based on enzyme-linked immunosorbent assay results for sweet potato chlorotic stunt virus (SPCSV) and sweet potato virus C (SPVC) with adjustments to a negative control (uninfected clone Tanzania) and was performed on a prebreeding population (VZ08) comprising 455 clones and 27 check clones graft inoculated under screenhouse conditions. The second part included field studies with 52 selected clones for SPCSV resistance from VZ08 and 8 checks. In screenhouse conditions, the resistant and susceptible check clones performed as expected; 63 clones from VZ08 exhibited lower relative absorbance values for SPCSV and SPVC than inoculated check Tanzania. Field experiments confirmed SPVD resistance of several clones selected by relative absorbance values (nine resistant clones in two locations; that is, 17.3% of the screenhouse selection), supporting the reliability of our method for SPVD-resistance selection. Two clones were promising, exhibiting high storage root yields of 28.7 to 34.9 t ha-1 and SPVD resistance, based on the proposed selection procedure. This modified serological analysis for SPVD-resistance phenotyping might lead to more efficient development of resistant varieties by reducing costs and time at early stages, and provide solid data for marker-assisted selection with a quantitative tool for classifying resistance.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer's and Parkinson's diseases

Objective

Biases within probabilistic electronic phenotyping algorithms are largely unexplored. In this work, we characterize differences in subgroup performance of phenotyping algorithms for Alzheimer's disease and related dementias (ADRD) in older adults.

Materials and methods

We created an experimental framework to characterize the performance of probabilistic phenotyping algorithms under different racial distributions allowing us to identify which algorithms may have differential performance, by how much, and under what conditions. We relied on rule-based phenotype definitions as reference to evaluate probabilistic phenotype algorithms created using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation framework.

Results

We demonstrate that some algorithms have performance variations anywhere from 3% to 30% for different populations, even when not using race as an input variable. We show that while performance differences in subgroups are not present for all phenotypes, they do affect some phenotypes and groups more disproportionately than others.

Discussion

Our analysis establishes the need for a robust evaluation framework for subgroup differences. The underlying patient populations for the algorithms showing subgroup performance differences have great variance between model features when compared with the phenotypes with little to no differences.

Conclusion

We have created a framework to identify systematic differences in the performance of probabilistic phenotyping algorithms specifically in the context of ADRD as a use case. Differences in subgroup performance of probabilistic phenotyping algorithms are not widespread nor do they occur consistently. This highlights the great need for careful ongoing monitoring to evaluate, measure, and try to mitigate such differences.

A fast phenotype approach of 3D point clouds of Pinus massoniana seedlings

The phenotyping of Pinus massoniana seedlings is essential for breeding, vegetation protection, resource investigation, and so on. Few reports regarding estimating phenotypic parameters accurately in the seeding stage of Pinus massoniana plants using 3D point clouds exist. In this study, seedlings with heights of approximately 15-30 cm were taken as the research object, and an improved approach was proposed to automatically calculate five key parameters. The key procedure of our proposed method includes point cloud preprocessing, stem and leaf segmentation, and morphological trait extraction steps. In the skeletonization step, the cloud points were sliced in vertical and horizontal directions, gray value clustering was performed, the centroid of the slice was regarded as the skeleton point, and the alternative skeleton point of the main stem was determined by the DAG single source shortest path algorithm. Then, the skeleton points of the canopy in the alternative skeleton point were removed, and the skeleton point of the main stem was obtained. Last, the main stem skeleton point after linear interpolation was restored, while stem and leaf segmentation was achieved. Because of the leaf morphological characteristics of Pinus massoniana, its leaves are large and dense. Even using a high-precision industrial digital readout, it is impossible to obtain a 3D model of Pinus massoniana leaves. In this study, an improved algorithm based on density and projection is proposed to estimate the relevant parameters of Pinus massoniana leaves. Finally, five important phenotypic parameters, namely plant height, stem diameter, main stem length, regional leaf length, and total leaf number, are obtained from the skeleton and the point cloud after separation and reconstruction. The experimental results showed that there was a high correlation between the actual value from manual measurement and the predicted value from the algorithm output. The accuracies of the main stem diameter, main stem length, and leaf length were 93.5%, 95.7%, and 83.8%, respectively, which meet the requirements of real applications.

A temporal analysis and response to nitrate availability of 3D root system architecture in diverse pennycress (Thlaspi arvense L.) accessions

Introduction

Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand plastic responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity.

Methods

Using a root imaging and analysis pipeline, the 4D architecture of the pennycress root system was characterized under four nitrate regimes, ranging from zero to high nitrate concentrations. These measurements were taken at four time points (days 5, 9, 13, and 17 after sowing).

Results

Significant nitrate condition response and genotype interactions were identified for many root traits, with the greatest impact observed on lateral root traits. In trace nitrate conditions, a greater lateral root count, length, density, and a steeper lateral root angle was observed compared to high nitrate conditions. Additionally, genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density.

Discussion

These findings illustrate root trait variance among pennycress accessions. These traits could serve as targets for breeding programs aimed at developing improved cover crops that are responsive to nitrate, leading to enhanced productivity, resilience, and ecosystem service.

Computer-Based Intelligent Solutions for the Diagnosis of Gastroesophageal Reflux Disease Phenotypes and Chicago Classification 3.0

Gastroesophageal reflux disease (GERD) is a multidisciplinary disease; therefore, when treating GERD, a large amount of data needs to be monitored and managed.The aim of our study was to develop a novel automation and decision support system for GERD, primarily to automatically determine GERD and its Chicago Classification 3.0 (CC 3.0) phenotypes. However, phenotyping is prone to errors and is not a strategy widely known by physicians, yet it is very important in patient treatment. In our study, the GERD phenotype algorithm was tested on a dataset with 2052 patients and the CC 3.0 algorithm was tested on a dataset with 133 patients. Based on these two algorithms, a system was developed with an artificial intelligence model for distinguishing four phenotypes per patient. When a physician makes a wrong phenotyping decision, the system warns them and provides the correct phenotype. An accuracy of 100% was obtained for both GERD phenotyping and CC 3.0 in these tests. Finally, since the transition to using this developed system in 2017, the annual number of cured patients, around 400 before, has increased to 800. Automatic phenotyping provides convenience in patient care, diagnosis, and treatment management. Thus, the developed system can substantially improve the performance of physicians.

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

Introduction

Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies.

Methods

Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values.

Results

We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation.

Discussion

A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

The plant response to high CO2 levels is heritable and orchestrated by DNA methylation

Plant responses to abiotic environmental challenges are known to have lasting effects on the plant beyond the initial stress exposure. Some of these lasting effects are transgenerational, affecting the next generation. The plant response to elevated carbon dioxide (CO₂ ) levels has been well studied. However, these investigations are typically limited to plants grown for a single generation in a high CO₂ environment while transgenerational studies are rare. We aimed to determine transgenerational growth responses in plants after exposure to high CO₂ by investigating the direct progeny when returned to baseline CO₂ levels. We found that both the flowering plant Arabidopsis thaliana and seedless nonvascular plant Physcomitrium patens continue to display accelerated growth rates in the progeny of plants exposed to high CO₂ . We used the model species Arabidopsis to dissect the molecular mechanism and found that DNA methylation pathways are necessary for heritability of this growth response. More specifically, the pathway of RNA-directed DNA methylation is required to initiate methylation and the proteins CMT2 and CMT3 are needed for the transgenerational propagation of this DNA methylation to the progeny plants. Together, these two DNA methylation pathways establish and then maintain a cellular memory to high CO₂ exposure.

Corrigendum: The effects of sampling and instrument orientation on LiDAR data from crop plots

[This corrects the article DOI: 10.3389/fpls.2023.1087239.].

X-Linked Retinoschisis Masquerading Uveitis

X-linked retinoschisis (XLRS) shows features also seen in patients with uveitis and is recognized as an uveitis masquerade syndrome. This retrospective study aimed to describe characteristics of XLRS patients with an initial uveitis diagnosis and to contrast these to patients with an initial XLRS diagnosis. Patients referred to a uveitis clinic, which turned out to have XLRS (n = 4), and patients referred to a clinic for inherited retinal diseases (n = 18) were included. All patients underwent comprehensive ophthalmic examinations, including retinal imaging with fundus photography, ultra-widefield fundus imaging, and optical coherence tomography (OCT). In patients with an initial diagnosis of uveitis, a macular cystoid schisis was always interpreted as an inflammatory macular edema; vitreous hemorrhages were commonly interpreted as intraocular inflammation. Patients with an initial diagnosis of XLRS rarely (2/18; p = 0.02) showed vitreous hemorrhages. No additional demographic, anamnestic, and anatomical differences were found. An increased awareness of XLRS as a uveitis masquerade syndrome may facilitate early diagnosis and may prevent unnecessary therapies.

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.

Impacts of heat, drought, and combined heat-drought stress on yield, phenotypic traits, and gluten protein traits: capturing stability of spring wheat in excessive environments

Wheat production and end-use quality are severely threatened by drought and heat stresses. This study evaluated stress impacts on phenotypic and gluten protein characteristics of eight spring wheat genotypes (Diskett, Happy, Bumble, SW1, SW2, SW3, SW4, and SW5) grown to maturity under controlled conditions (Biotron) using RGB imaging and size-exclusion high-performance liquid chromatography (SE-HPLC). Among the stress treatments compared, combined heat-drought stress had the most severe negative impacts on biomass (real and digital), grain yield, and thousand kernel weight. Conversely, it had a positive effect on most gluten parameters evaluated by SE-HPLC and resulted in a positive correlation between spike traits and gluten strength, expressed as unextractable gluten polymer (%UPP) and large monomeric protein (%LUMP). The best performing genotypes in terms of stability were Happy, Diskett, SW1, and SW2, which should be further explored as attractive breeding material for developing climate-resistant genotypes with improved bread-making quality. RGB imaging in combination with gluten protein screening by SE-HPLC could thus be a valuable approach for identifying climate stress-tolerant wheat genotypes.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.