Reliable Phenotyping Research Articles

Modeling mTORopathy-related epilepsy in cultured murine hippocampal neurons using the multi-electrode array

The mechanistic target of rapamycin complex 1 (mTORC1) signaling pathway is a ubiquitous cellular pathway. mTORopathies, a group of disorders characterized by hyperactivity of the mTORC1 pathway, illustrate the prominent role of the mTOR pathway in disease pathology, often profoundly affecting the central nervous system. One of the most debilitating symptoms of mTORopathies is drug-resistant epilepsy, emphasizing the urgent need for a deeper understanding of disease mechanisms to develop novel anti-epileptic drugs. In this study, we explored the multiwell Multi-electrode array (MEA) system as a tool to identify robust network activity parameters in an approach to model mTORopathy-related epilepsy in vitro. To this extent, we cultured mouse primary hippocampal neurons on the multiwell MEA to identify robust network activity phenotypes in mTORC1-hyperactive neuronal networks. mTOR-hyperactivity was induced either through deletion of Tsc1 or overexpression of a constitutively active RHEB variant identified in patients, RHEBp.P37L. mTORC1 dependency of the phenotypes was assessed using rapamycin, and vigabatrin was applied to treat epilepsy-like phenotypes. We show that hyperactivity of the mTORC1 pathway leads to aberrant network activity. In both the Tsc1-KO and RHEB-p.P37L models, we identified changes in network synchronicity, rhythmicity, and burst characteristics. The presence of these phenotypes is prevented upon early treatment with the mTORC1-inhibitor rapamycin. Application of rapamycin in mature neuronal cultures could only partially rescue the network activity phenotypes. Additionally, treatment with the anti-epileptic drug vigabatrin reduced network activity and restored burst characteristics. Taken together, we showed that mTORC1-hyperactive neuronal cultures on the multiwell MEA system present reliable network activity phenotypes that can be used as an assay to explore the potency of new drug treatments targeting epilepsy in mTORopathy patients and may give more insights into the pathophysiological mechanisms underlying epilepsy in these patients.

Low-cost and reliable substrate-based phenotyping platform for screening salt tolerance of cutting propagation-dependent grass, paspalum vaginatum

BackgroundSalt tolerance in plants is defined as their ability to grow and complete their life cycle under saline conditions. Staple crops have limited salt tolerance, but forage grass can survive in large unexploited saline areas of costal or desert land. However, due to the restriction of self-incompatible fertilization in many grass species, vegetative propagation via stem cuttings is the dominant practice; this is incompatible with current methodologies of salt-tolerance phenotyping, which have been developed for germination-based seedling growth. Therefore, the performance of seedlings from cuttings under salt stress is still fuzzy. Moreover, the morphological traits involved in salt tolerance are still mostly unknown, especially under experimental conditions with varying levels of stress.ResultsTo estimate the salt tolerance of cutting propagation-dependent grasses, a reliable and low-cost workflow was established with multiple saline treatments, using Paspalum vaginatum as the material and substrate as medium, where cold stratification and selection of stem segments were the two variables used to control for experimental errors. Average leaf number (ALN) was designated as the best criterion for evaluating ion-accumulated salt tolerance. The reliability of ALN was revealed by the consistent results among four P. vaginatum genotypes, and three warm-season (pearl millet, sweet sorghum, and wild maize) and four cold-season (barley, oat, rye, and ryegrass) forage cultivars. Dynamic curves simulated by sigmoidal mathematical models were well-depicted for the calculation of the key parameter, Salt50. The reliability of the integrated platform was further validated by screening 48 additional recombinants, which were previously generated from a self-fertile mutant of P. vaginatum. The genotypes displaying extreme ALN-based Salt50 also exhibited variations in biomass and ion content, which not only confirmed the reliability of our phenotyping platform but also the representativeness of the aerial ALN trait for salt tolerance.ConclusionsOur phenotyping platform is proved to be compatible with estimations in both germination-based and cutting propagation-dependent seedling tolerance under salt stresses. ALN and its derived parameters are prone to overcome the species barriers when comparing salt tolerance of different species together. The accuracy and reliability of the developed phenotyping platform is expected to benefit breeding programs in saline agriculture.

Comparison of two different experimental environments for resistance screenings for the leafhopper-transmitted wheat dwarf virus in wheat

Wheat dwarf virus (WDV) causes high yield losses in wheat and other cereals and is therefore an important pathogen transmitted by the leafhopper Psammotettix alienus. Climate change will increase infections by insect-transmitted viruses due to the increasing spread of vectors. In the context of integrated pest management, the cultivation of WDV-resistant/tolerant varieties is an effective way of controlling WDV. Evaluation of tolerant/resistant genotypes is based on inoculation with viruliferous leafhoppers and subsequent phenotyping in gauze houses under semi-field conditions. For successful screening, it is important to ensure the uniform and reproducible inoculation of plants. Abiotic conditions, particularly temperature, have a critical influence on inoculation success, and thus, variations in infection rates were observed within and between previous replicates in the field. Furthermore, the leafhopper population reared in the greenhouse has to be reestablished after each infection, which delays the screening process. We addressed these issues by developing an improved inoculation assay in which plants are inoculated in small infection hoods in the greenhouse before being planted out in gauze houses. This procedure allows optimal environmental conditions for WDV infection of test plants and allows the plants with WDV infection to develop under natural environmental conditions for symptom scoring. In addition, the viruliferous leafhoppers were recollected from the test plants after infection, allowing a sustainable use of the insects. The method thus enables more reliable phenotyping by increasing infection success and testing a greater number of genotypes in a shorter time.

Potential and Uncertainties of RejectClass in Acute Kidney Graft Dysfunction: An Independent Validation Study.

Kidney graft rejections are classified based on the Banff classification. The RejectClass algorithm, initially derived from a cohort comprising mostly protocol biopsies, identifies data-driven phenotypes of acute rejection and chronic pathology using Banff lesion scores. It also provides composite scores for inflammation activity and chronicity. This study independently evaluates the performance of RejectClass in a cohort consisting entirely of indication biopsies. We retrospectively applied RejectClass to 441 patients from the German TRABIO (TRAnsplant BIOpsies) cohort who had received indication biopsies. The primary endpoint was death-censored graft failure during 2 y of follow-up. The application of RejectClass to our cohort demonstrated moderately comparable phenotypic features with the derivation cohort, and most clusters indicated an elevated risk of graft loss. However, the reproduction of all phenotypes and the associated risks of graft failure, as depicted in the original studies, was not fully accomplished. In contrast, adjusted Cox proportional hazards analyses substantiated that both the inflammation score and the chronicity score are independently associated with graft loss, exhibiting hazard ratios of 1.7 (95% confidence interval, 1.2-2.3; P = 0.002) and 2.2 (95% confidence interval, 1.8-2.6; P < 0.001), respectively, per 0.25-point increment (scale: 0.0-1.0). The composite inflammation and chronicity scores may already have direct utility in quantitatively assessing the disease stage. Further refinement and validation of RejectClass clusters are necessary to achieve more reliable and accurate phenotyping of rejection.

Abstract 5187: VistaPlex immunofluorescence assay panels provide reliable deep spatial phenotyping for diverse immuno-oncology applications

Abstract Multiplex immunofluorescence (mIF) is a powerful tool that enables deep phenotyping of immune and cancer cells across a wide variety of tissue types. The initial effort and cost required to develop high-plex mIF panels is significant and poses a considerable hurdle for adoption of spatial biology. To streamline high-plex assay development, we developed a process for creating and validating multiplex antibody panels for use in immuno-oncology applications that leverage CellScape™, an automated end-to-end platform for Precise Spatial Multiplexing. Our multiplex assay development was a rigorous, multi-stage, iterative process to evaluate antibodies for suitability, specificity, and reproducibility. These assay panels are formulated as ready-to-use VistaPlex™ assay kits and are intended to enable rapid, reliable spatial phenotyping of key immune populations. To confirm the utility of VistaPlex, we deployed the Human FFPE Spatial Immune Profiling Panel to stain and image various FFPE tissue samples on the CellScape platform. With CellScape, tissues are mounted in enclosed fluidic chambers while on-instrument staining, imaging and signal erasure are conducted in iterative rounds. The panel presented here contains antibodies against 16 common biomarkers used in immune-oncology applications. Among these are CD3, CD4, CD8, CD20, and CD45 to detect cell lineages, PD-1 and PD-L1, to interrogate immune checkpoints, and markers that inform about cell state and activation. Spatial immune profiling data were obtained from human tonsil, colon, breast, and lung tissue samples. Deep immune phenotyping was performed using unsupervised clustering analysis and AI-assisted cluster labelling, and biomarker distributions were studied in the context of cellular neighborhoods and other proximity analyses. Overall, we identified more than 19 cell phenotypes with varying frequencies and spatial arrangements across tissue samples. Our data are robust and demonstrate that VistaPlex Assay kits for spatial profiling present an efficient and cost-effective solution for users that want to break into spatial biology. Citation Format: Arne Christians, Charles E. Jackson, Jannik Boog, Matthew H. Ingalls, Adam Northcutt, Kayla E. Cashion, Jonah Rauer, Karen Kwarta, J Spencer Schwarz, Oliver Braubach. VistaPlex immunofluorescence assay panels provide reliable deep spatial phenotyping for diverse immuno-oncology applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5187.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient's eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.

Multi-growth stage plant recognition: A case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)

Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ‘Palmer amaranth growth stages – 8 (PAGS8)’ is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4.

A 3D printed plant model for accurate and reliable 3D plant phenotyping.

This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool. Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from -10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community. Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.

Single-cell phenotypes revealed as a key biomarker in bacterial-fungal interactions: a case study of Staphylococcus and Malassezia.

Microbial interactions are determined by competition, cooperation, and inaction, depending on the transcriptional response to the metabolites secreted by each other. The consequence of these interactions is reflected in the cell phenotype by the composition of cell materials, including lipids, nucleotides, and proteins. We studied the phenotypic plasticity of single cells involved in microbial interactions using Raman spectroscopy and evaluated the types of interactions by linking phenotypes to transcriptional profiles. We used a membrane-based co-culture system to induce chemical interactions between skin-dominant bacteria and fungi, Staphylococcus and Malassezia. Staphylococcus species exhibited an amensalism interaction with Malassezia based on growth and cell viability. Malassezia co-cultured with S. epidermidis and S. aureus resulted in significant changes in the Raman spectra of nucleic acids and proteins in the Raman phenotypes, respectively. The observed differences in phenotypes during co-culture with Malassezia were significantly associated with changes in transcriptomic profiles, which demonstrated variations in the defense/resistance and biofilm-formation mechanisms of S. epidermidis, whereas no meaningful changes were observed for the transcriptome of S. aureus. These approaches provide a robust, simple, and reproducible method for comprehending bacterial-fungal interactions.IMPORTANCEEvaluating bacterial-fungal interactions is important for understanding ecological functions in a natural habitat. Many studies have defined bacterial-fungal interactions according to changes in growth rates when co-cultivated. However, the current literature lacks detailed studies on phenotypic changes in single cells associated with transcriptomic profiles to understand the bacterial-fungal interactions. In our study, we measured the single-cell phenotypes of bacteria co-cultivated with fungi using Raman spectroscopy with its transcriptomic profiles and determined the consequence of these interactions in detail. This rapid and reliable phenotyping approach has the potential to provide new insights regarding bacterial-fungal interactions.

Phenotype predictions of two-person mixture using single cell analysis

Over a decade after the publication of the first forensic DNA phenotyping (FDP) studies, DNA-based appearance predictions are now becoming a reality in routine crime scene investigations. The significant number of publications dedicated to the subject of FDP clearly demonstrates a sustained interest and a strong need for further method development. However, the implementation of FDP in routine work still encounters obstacles, and one of these challenges is making phenotype predictions from DNA mixtures. In this study, we examined single-cell sequencing as a potential tool to enable reliable phenotyping of contributors within mixtures. Two mock mixtures, each containing two contributors with similar and different physical appearances, were analyzed using two different workflows. In the first workflow, the mixtures were sequenced using the Ion AmpliSeq™ PhenoTrivium Panel, which includes 41 HIrisPlex-S (HPS) markers. Subsequently, the genotypes were analyzed using the HPS Deconvolution Tool to predict the phenotypes of both contributors. The second workflow involved the introduction of single-cell separation and collection using the DEPArray™ PLUS System. Two different PhenoTrivium amplification protocols were tested, and the phenotype predictions from single cells were compared with the results obtained using the HPS Tool. Our results suggest that the approach presented here allows for the obtainment of nearly complete HIrisPlex-S profiles with accurate genotypes and reliable phenotype predictions from single cells. This method proves successful in deconvoluting mixtures submitted to forensic DNA phenotyping.

RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection

The RaspberrySet dataset is a valuable resource for those working in the field of agriculture, particularly in the selection and breeding of ecologically adaptable berry cultivars. This is because long-term changes in temperature and weather patterns have made it increasingly important for crops to be able to adapt to their environment. To assess the suitability of different cultivars or to make yield predictions, it is necessary to describe and evaluate berries’ characteristics at various growth stages. This process is typically carried out visually, but it can be time-consuming and labor-intensive, requiring significant expert knowledge. The RaspberrySet dataset was created to assist with this process, and it includes images of raspberry berries at five different stages of development. These stages are flower buds, flowers, unripe berries, and ripe berries. All these stages of raspberry images classified buds, damaged buds, flowers, unripe berries, and ripe berries and were annotated using ground truth ROI and presented in YOLO format. The dataset includes 2039 high-resolution RGB images, with a total of 46,659 annotations provided by experts using Label Studio software (1.7.1). The images were taken in various weather conditions, at different times of the day, and from different angles, and they include fully visible buds, flowers, berries, and partially obscured buds. This dataset is intended to improve the efficiency of berry breeding and yield estimation and to identify the raspberry phenotype more accurately. It may also be useful for breeding other fruit crops, as it allows for the reliable detection and phenotyping of yield components at different stages of development. By providing a homogenized dataset of images taken on-site at the Institute of Horticulture in Dobele, Latvia, the RaspberrySet dataset offers a valuable resource for those working in horticulture.

Volatilomics of raspberry fruit germplasm by combining chromatographic and direct-injection mass spectrometric techniques.

The application of direct-injection mass spectrometric (DI-MS) techniques, like Proton Transfer Reaction Time of Flight Mass Spectrometry (PTR-ToF-MS) has been suggested as a reliable phenotyping tool for fruit volatilome assessment in both genetic and quality-related studies. In this study the complexity of raspberry aroma was investigated by a comprehensive untargeted VOC analysis, done by combining SPME-GC-MS and PTR-ToF-MS assessments with multi-block discriminant analysis using the DIABLO mixOmics framework. The aim was to acquire an exhaustive characterization of the raspberry volatilome according to different fruit ripening stages (pink, ripe, and overripe) and genetic variances (50 accessions), as well as to investigate the potential of PTR-ToF-MS as a rapid and high throughput VOC phenotyping tool to address issues related to raspberry fruit quality. Results of this study demonstrated the complementarity between SPME-GC-MS and PTR-ToF-MS techniques to evaluate the raspberry aroma composition. PTR-ToF-MS generates reliable raspberry VOC fingerprints mainly due to a reduced compound fragmentation and precise content estimation. In addition, the high collinearity between isomers of monoterpenes and norisoprenoids, discovered by GC analysis, reduces the main analytic limitation of PTR-ToF-MS of not being able to separate isomeric molecules. The high similarity between the VOC matrices obtained by applying PTR-ToF-MS and SPME-GC-MS confirmed the possibility of using PTR-ToF-MS as a reliable high throughput phenotyping tool for raspberry volatiolome assessment. In addition, results provided by the germplasm collection investigation enabled to distinguish the best performing accessions, based on VOCs composition, to be used as superior parental lines for future breeding programs.

Application of quantitative proteomics to discover biomarkers for tick resistance in cattle.

Breeding for tick resistance is a sustainable alternative to control cattle ticks due to widespread resistance to acaricidal drugs and the lack of a protective vaccine. The most accurate method used to characterise the phenotype for tick resistance in field studies is the standard tick count, but this is labour-intensive and can be hazardous to the operator. Efficient genetic selection requires reliable phenotyping or biomarker(s) for accurately identifying tick-resistant cattle. Although breed-specific genes associated with tick resistance have been identified, the mechanisms behind tick resistance have not yet been fully characterised. This study applied quantitative proteomics to examine the differential abundance of serum and skin proteins using samples from naïve tick-resistant and -susceptible Brangus cattle at two-time points following tick exposure. The proteins were digested into peptides, followed by identification and quantification using sequential window acquisition of all theoretical fragment ion mass spectrometry. Resistant naïve cattle had a suite of proteins associated with immune response, blood coagulation and wound healing that were significantly (adjusted P < 10- 5) more abundant compared with susceptible naïve cattle. These proteins included complement factors (C3, C4, C4a), alpha-1-acid glycoprotein (AGP), beta-2-glycoprotein-1, keratins (KRT1 & KRT3) and fibrinogens (alpha & beta). The mass spectrometry findings were validated by identifying differences in the relative abundance of selected serum proteins with ELISA. The proteins showing a significantly different abundance in resistant cattle following early and prolonged tick exposures (compared to resistant naïve) were associated with immune response, blood coagulation, homeostasis, and wound healing. In contrast, susceptible cattle developed some of these responses only after prolonged tick exposure. Resistant cattle were able to transmigrate immune-response related proteins towards the tick bite sites, which may prevent tick feeding. Significantly differentially abundant proteins identified in this research in resistant naïve cattle may provide a rapid and efficient protective response to tick infestation. Physical barrier (skin integrity and wound healing) mechanisms and systemic immune responses were key contributors to resistance. Immune response-related proteins such as C4, C4a, AGP and CGN1 (naïve samples), CD14, GC and AGP (post-infestation) should be further investigated as potential biomarkers for tick resistance.

Mapping QTLs for Reproductive Stage Salinity Tolerance in Rice Using a Cross between Hasawi and BRRI dhan28.

Salinity stress is a major constraint to rice production in many coastal regions due to saline groundwater and river sources, especially during the dry season in coastal areas when seawater intrudes further inland due to reduced river flows. Since salinity tolerance is a complex trait, breeding efforts can be assisted by mapping quantitative trait loci (QTLs) for complementary salt tolerance mechanisms, which can then be combined to provide higher levels of tolerance. While an abundance of seedling stage salinity tolerance QTLs have been mapped, few studies have investigated reproductive stage tolerance in rice due to the difficulty of achieving reliable stage-specific phenotyping techniques. In the current study, a BC1F2 mapping population consisting of 435 individuals derived from a cross between a salt-tolerant Saudi Arabian variety, Hasawi, and a salt-sensitive Bangladeshi variety, BRRI dhan28, was evaluated for yield components after exposure to EC 10 dS/m salinity stress during the reproductive stage. After selecting tolerant and sensitive progeny, 190 individuals were genotyped by skim sequencing, resulting in 6209 high quality single nucleotide polymorphic (SNP) markers. Subsequently, a total of 40 QTLs were identified, of which 24 were for key traits, including productive tillers, number and percent filled spikelets, and grain yield under stress. Importantly, three yield-related QTLs, one each for productive tillers (qPT3.1), number of filled spikelets (qNFS3.1) and grain yield (qGY3.1) under salinity stress, were mapped at the same position (6.7 Mb or 26.1 cM) on chromosome 3, which had not previously been associated with grain yield under salinity stress. These QTLs can be investigated further to dissect the molecular mechanisms underlying reproductive stage salinity tolerance in rice.

Mycotoxin DON Accumulation in Wheat Grains Caused by Fusarium Head Blight Are Significantly Subjected to Inoculation Methods.

The disease severity and mycotoxin DON content in grains caused by fusarium head blight (FHB) have been two prioritized economical traits in wheat. Reliable phenotyping is a prerequisite for genetically improving wheat resistances to these two traits. In this study, three inoculation methods: upper bilateral floret injection (UBFI), basal bilateral floret injection (BBFI), and basal rachis internode injection (BRII), were applied in a panel of 22 near-isogenic lines (NILs) contrasting in Fhb1 alleles. The results showed that inoculation methods had significant influence on both disease severity and mycotoxin accumulation in grains, and the relationship between them. UBFI method caused chronic FHB symptom characterized as slow progress of the pathogen downward from the inoculation site, which minimized the difference in disease severity of the NILs, but, unexpectedly, maximized the difference in DON content between them. The BBFI method usually caused an acute FHB symptom in susceptible lines characterized as premature spike death (PSD), which maximized the difference in disease severity, but minimized the difference in DON content in grains between resistant and susceptible lines. The BRII method occasionally caused acute FHB symptoms for susceptible lines and had relatively balanced characteristics of disease severity and DON content in grains. Therefore, two or more inoculation methods are recommended for precise and reliable evaluation of the overall resistance to FHB, including resistances to both disease spread within a spike and DON accumulation in grains.

The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping

A breeder can select a visually appealing phenotype, whether for ornamentation or landscaping. However, the organic vision is not accurate and objective, making it challenging to bring a reliable phenotyping intervention into implementation. Therefore, the objective of this study was to develop an innovative solution to predict the intensity of the flower’s color upon the external shape of the crop. We merged the single linear iterative clustering (SLIC) algorithm and box-counting method (BCM) into a framework to extract useful imagery data for biophysical modeling. Then, we validated our approach by fitting Gompertz function to data on intensity of flower’s color and fractal dimension (SD) of the architecture of white-flower, yellow-flower, and red-flower varieties of Portulaca umbraticola. The SLIC algorithm segmented the images into uniform superpixels, enabling the BCM to precisely capture the SD of the architecture. The SD ranged from 1.938315 to 1.941630, which corresponded to pixel-wise intensities of 220.85 and 47.15. Thus, the more compact the architecture the more intensive the color of the flower. The sigmoid Gompertz function predicted such a relationship at radj2 &gt; 0.80. This study can provide further knowledge to progress the field’s prominence in developing breakthrough strategies toward improving the control of visual quality and breeding of ornamentals.

Potential for genomic selection on feed efficiency in gilthead sea bream (Sparus aurata), based on individual feed conversion ratio, carcass and lipid traits

Genetic improvement of feed efficiency is key to improve the economic and environmental sustainability of fish farming. However, it requires individual phenotypes of feed efficiency, which are difficult if not impossible to obtain when fish are reared in tanks or cages. Here, we applied and validated on gilthead sea bream a method to evaluate individual feed efficiency based on individual rearing of fish in aquariums under restricted feeding. We collected individual phenotypes of feed efficiency in aquariums on 538 sea bream (average weight = 54.50 g). Based on these individual phenotypes, fish (average weight = 174.6 g) were reared in groups of divergent phenotypes (high or low feed efficiency), validating that individual feed efficiency had an impact on group feed efficiency at a later stage. All 538 fish, their parents as well as 794 sibs reared in cages in a production environment, were genotyped on a 57k SNP array to estimate genomic heritability and correlations between traits. We showed that feed efficiency was heritable but did not find significant associated QTLs. We also showed that feed efficiency was negatively genetically correlated to viscera yield, indicating that the most efficient fish had less viscera than the least efficient ones. Altogether, these results support that measuring individual feed efficiency in aquariums under restricted feeding may be used as a reliable phenotyping method to genetically improve feed efficiency, despite the bias intrinsically linked to individual rearing.

COMPARATIVE RISK-ORIENTED INTEGRATED ASSESSMENT OF LUNG HEALTH STATUS IN PREVENTIVE PLANNING OF REHABILITATION TREATMENT PROGRAMS IN A SANATORIUM

Introduction. COPD is a heterogeneous disease, the variability and diversity of which depends on a combination of genetics and risk factors. At present, during the period of the deciphered human genome, an earlier assessment of the prognostic risk of the development of cochlea in young people is needed to understand the possibilities of establishing reversible lung lesions. Aim as a part of preventive phenotyping in young people; substantiate the importance of a comparative risk-oriented complex epigenetic and genetic assessment of the lung health status for predictive assessment of the risk of developing COPD. Materials and methods. In this cohort study, 293 young people (11-23 years old) took part, including 142 male and 151 female, who were divided into four groups. 1st group - 132 conditionally healthy non-smokers (51 male and 81 female), 2nd - 54 conditionally healthy smokers (39 male and 15 female), 3rd - 86 non-smokers, with chronic lung diseases (52 persons – chronical tracheobronchitis; 43 persons – bronchial asthma), 4th - 21 smokers with chronic lung diseases (HRTB - 12 persons, BA - 9 persons). To assess the differences in epigenetic and genetic criteria, methods such as spirometry, gas analysis, electronic auscultation, digital acoustic analysis of respiratory work, analysis of gene polymorphisms for the alpha1-antitrypsin-SERPINA-1 complex in venous blood serum were used. We have developed a computer program, taking into account the risk factors for the prognostic development of COPD in a cohort of smoking and non-smoking young people aged 11-23 years with or without a diagnosis of chronic lung disease. Results. Among the most reliable, informative, sensitive and specific phenotyping indicators in assessing the prognostic degree of COPD risk in adolescents: cough, sputum production, ARI frequency per year, smoking index, electronic auscultation data, FEV1%, FVC%, FEV1 / FVC%, CO, O2, AAT, SERPINA. These criteria made up the characteristics of risk phenotypes, which allows the subjects to be graded according to groups (phenotypes) of the prognostic risk of COPD - low, moderate, high or extremely high, depending on the scale of criterion values. In young people in the context of predictive preventive personalized medicine. This made it possible to draw up an algorithm for assessing risk groups and a computer program based on this algorithm, which makes it possible to determine the phenotypes of COPD risk in young people. Conclusion: Starting from adolescence, it is possible to record the initial functional disorders of the respiratory status in combination with genetic polymorphisms, which confirms the need for preventive screening for the primary identification of risk groups and the formation of preventive programs in young people. The developed algorithm and computer program make it possible to determine the prognostic risk of COPD in young people - from low to moderate, high and extremely high, in terms of the implementation of the concept of 3P (personalized, preventive, and predictive) medicine.

A genome-wide association study of mare fertility in the Pura Raza Español horse

Despite the economic importance of fertility for the horse industry, few efforts have been made to achieve a better understanding of the genetic mechanisms underlying its control. This is probably due to the difficulty of obtaining reliable phenotypes and the complexity of modelling the environmental and management factors. This work is novel in that we propose to use reproductive efficiency (RE) as an indicator of mare fertility. To achieve this, we performed a genome-wide association study in the Pura Raza Español horse aimed at identifying genomic variants, regions, and candidate genes associated with fertility in mares. The dataset included 819 animals genotyped with the Affymetrix Axiom™ Equine 670 K single-nucleotide polymorphisms (SNPs) Genotyping Array and the deregressed breeding values for RE trait, obtained using a ssBLUP model, employed as pseudo-phenotypic data. Our results showed 28 SNPs potentially associated with RE, which explained 87.19% of the genetic variance and 6.61% of the phenotypic variance. Those results were further validated in BayesB, showing a correlation between observed and predicted RE of 0.57. In addition, 15 candidate genes (HTRA3, SPIRE1, APOE, ERCC1, FOXA3, NECTIN-2, KLC3, RSPH6A, PDPK1, MEIOB, PAQR4, NM3, PKD1, PRSS21, IFT140) previously related to fertility in mammals were associated with the markers and genomic regions significantly associated with RE. To our knowledge, this is the first genome-wide association study performed on mare fertility.

Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

BackgroundRice is a major staple food crop for more than half the world’s population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed.ResultsWe use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area.ConclusionsWe have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.