Stability of immunohaematological reagents used for blood typing of recipients in the tube technique.

BACKGROUND AND OBJECTIVES: The storage temperature of immunohaematological reagents generally ranges from 2 to 8°C, and they should be utilised at room temperature. This study aimed to analyse the stability of immunohaematological reagents used in ABO and RhD typing. METHODS: The evaluation encompassed the potency, specificity, and integrity of anti-A, anti-B, anti-D, RhD control sera, and A1 and B red blood cells (RBC) reagents after long (8 h) and short (4 h) daily periods of exposure to room temperature (20-24°C), 5 days a week for 4 weeks. Additionally, the A1 and B RBC reagents were exposed daily for 11 h and 30 min at room temperature, including 30 more minutes at room temperature with simultaneous homogenisation through equipment. For the control, an aliquot of each reagent was constantly stored at refrigeration temperature, while another was exposed to room temperature for 12 h daily. Tests conducted included reaction intensity, titration, and avidity for antisera, reaction intensity, free haemoglobin determination, and electrical conductivity for the RBC reagents. RESULTS: The antisera maintained the reaction intensity. The titre and avidity of the antisera satisfied the minimum Brazilian requirements after different exposure periods. A higher free haemoglobin concentration was noted in the RBC reagents subjected to room temperature and simultaneous homogenisation, although this did not affect the potency and specificity. The electrical conductivity average of the RBC reagent remained consistent. CONCLUSION: The findings indicate that the immunohaematological reagents from a specific manufacturer are stable under the tested temperature, ensuring the quality of the results under these conditions.

Speech and language patterns in autism: Towards natural language processing as a research and clinical tool.

Speech and language differences have long been described as important characteristics of autism spectrum disorder (ASD). Linguistic abnormalities range from prosodic differences in pitch, intensity, and rate of speech, to language idiosyncrasies and difficulties with pragmatics and reciprocal conversation. Heterogeneity of findings and a reliance on qualitative, subjective ratings, however, limit a full understanding of linguistic phenotypes in autism. This review summarizes evidence of both speech and language differences in ASD. We also describe recent advances in linguistic research, aided by automated methods and software like natural language processing (NLP) and speech analytic software. Such approaches allow for objective, quantitative measurement of speech and language patterns that may be more tractable and unbiased. Future research integrating both speech and language features and capturing "natural language" samples may yield a more comprehensive understanding of language differences in autism, offering potential implications for diagnosis, intervention, and research.

The 4T and 7T introgressions from Amblyopyrum muticum and the 5Au introgression from Triticum urartu increases grain zinc and iron concentrations in Malawian wheat backgrounds.

Micronutrient deficiencies (MNDs) particularly zinc (Zn) and iron (Fe) remain widespread in sub-Saharan Africa (SSA) due to low dietary intake. Wheat is an important source of energy globally, although cultivated wheat is inherently low in grain micronutrient concentrations. Malawian wheat/Am. muticum and Malawian wheat/T. urartu BC1F3 introgression lines, developed by crossing three Malawian wheat varieties (Kenya nyati, Nduna and Kadzibonga) with DH-348 (wheat/Am. muticum) and DH-254 (wheat/T. urartu), were phenotyped for grain Zn and Fe, and associated agronomic traits in Zn-deficient soils, in Malawi. 98% (47) of the BC1F3 introgression lines showed higher Zn above the checks Paragon, Chinese Spring, Kadzibonga, Kenya Nyati and Nduna. 23% (11) of the introgression lines showed a combination of high yields and an increase in grain Zn by 16-30 mg kg -1 above Nduna and Kadzibonga, and 11-25 mg kg -1 above Kenya nyati, Paragon and Chinese Spring. Among the 23%, 64% (7) also showed 8-12 mg kg -1 improvement in grain Fe compared to Nduna and Kenya nyati. Grain Zn concentrations showed a significant positive correlation with grain Fe, whilst grain Zn and Fe negatively and significantly correlated with TKW and grain yield. This work will contribute to the efforts of increasing mineral nutrient density in wheat, specifically targeting countries in the SSA.

Flow Cytometry and Platelets.

Clinical assessment of platelet activation by flow cytometry is useful in the characterization and diagnosis of platelet-specific disorders and as a measure of risk for thrombosis or bleeding. Platelets circulate in a resting, "unactivated" state, but when activated they undergo alterations in surface glycoprotein function and/or expression level, exposure of granule membrane proteins, and exposure of procoagulant phospholipids. Flow cytometry provides the means to detect these changes and, unlike other platelet tests, is appropriate for measuring platelet function in samples from patients with low platelet counts. The present review will focus on flow cytometric tests for platelet activation markers.

Validation of a Major and Clinically Relevant Nonmajor Bleeding Phenotyping Algorithm on Electronic Health Records.

PURPOSE: Bleeding is an important health outcome of interest in epidemiological studies. We aimed to develop and validate rule-based algorithms to identify (1) major bleeding and (2) all clinically relevant bleeding (CRB) (composite of major and all clinically relevant nonmajor bleeding) within real-world electronic healthcare data. METHODS: We took a random sample (n = 1630) of inpatient admissions to Singapore public healthcare institutions in 2019 and 2020, stratifying by hospital and year. We included patients of all age groups, sex, and ethnicities. Presence of major bleeding and CRB were ascertained by two annotators through chart review. A total of 630 and 1000 records were used for algorithm development and validation, respectively. We formulated two algorithms: sensitivity- and positive predictive value (PPV)-optimized algorithms. A combination of hemoglobin test patterns and diagnosis codes were used in the final algorithms. RESULTS: During validation, diagnosis codes alone yielded low sensitivities for major bleeding (0.16) and CRB (0.24), although specificities and PPV were high (>0.97). For major bleeding, the sensitivity-optimized algorithm had much higher sensitivity and negative predictive values (NPVs) (sensitivity = 0.94, NPV = 1.00), however false positive rates were also relatively high (specificity = 0.90, PPV = 0.34). PPV-optimized algorithm had improved specificity and PPV (specificity = 0.96, PPV = 0.52), with little reduction in sensitivity and NPV (sensitivity = 0.88, NPV = 0.99). For CRB events, our algorithms had lower sensitivities (0.50-0.56). CONCLUSIONS: The use of diagnosis codes alone misses many genuine major bleeding events. We have developed major bleeding algorithms with high sensitivities, which can ascertain events within populations of interest.

Balancing the efforts of chart review and gains in PRS prediction accuracy: An empirical study.

OBJECTIVE: Phenotypic misclassification in genetic association analyses can impact the accuracy of PRS-based prediction models. The bias reduction method proposed by Tong et al. (2019) has demonstrated its efficacy in reducing the effects of bias on the estimation of association parameters between genotype and phenotype while minimizing variance by employing chart reviews on a subset of the data for validating phenotypes, however its improvement of subsequent PRS prediction accuracy remains unclear. Our study aims to fill this gap by assessing the performance of simulated PRS models and estimating the optimal number of chart reviews needed for validation. METHODS: To comprehensively assess the efficacy of the bias reduction method proposed by Tong et al. in enhancing the accuracy of PRS-based prediction models, we simulated each phenotype under different correlation structures (an independent model, a weakly correlated model, a strongly correlated model) and introduced error-prone phenotypes using two distinct error mechanisms (differential and non-differential phenotyping errors). To facilitate this, we used genotype and phenotype data from 12 case-control datasets in the Alzheimer's Disease Genetics Consortium (ADGC) to produce simulated phenotypes. The evaluation included analyses across various misclassification rates of original phenotypes as well as quantities of validation set. Additionally, we determined the median threshold, identifying the minimal validation size required for a meaningful improvement in the accuracy of PRS-based predictions across a broad spectrum. RESULTS: This simulation study demonstrated that incorporating chart review does not universally guarantee enhanced performance of PRS-based prediction models. Specifically, in scenarios with minimal misclassification rates and limited validation sizes, PRS models utilizing debiased regression coefficients demonstrated inferior predictive capabilities compared to models using error-prone phenotypes. Put differently, the effectiveness of the bias reduction method is contingent upon the misclassification rates of phenotypes and the size of the validation set employed during chart reviews. Notably, when dealing with datasets featuring higher misclassification rates, the advantages of utilizing this bias reduction method become more evident, requiring a smaller validation set to achieve better performance. CONCLUSION: This study highlights the importance of choosing an appropriate validation set size to balance between the efforts of chart review and the gain in PRS prediction accuracy. Consequently, our study establishes a valuable guidance for validation planning, across a diverse array of sensitivity and specificity combinations.

Genomic Prediction of The Performance of Tropical Doubled Haploid Maize Lines under Artificial Striga hermonthica (Del.) Benth. Infestation.

Striga hermonthica (Del.) Benth., a parasitic weed, causes substantial yield losses in maize production in sub-Saharan Africa (SSA). Breeding for Striga resistance in maize is constrained by limited genetic diversity for Striga resistance within the elite germplasm and phenotyping capacity under artificial Striga infestation. Genomics-enabled approaches have the potential to accelerate identification of Striga resistant lines for hybrid development. The objectives of this study were to evaluate the accuracy of genomic selection for traits associated with Striga resistance and grain yield (GY) and to predict genetic values of tested and untested doubled haploid (DH) maize lines. We genotyped 606 DH lines with 8,439 rAmpSeq markers. A training set of 116 DH lines crossed to two testers was phenotyped under artificial Striga infestation at three locations in Kenya. Heritability for Striga resistance parameters ranged from 0.38‒0.65 while that for GY was 0.54. The prediction accuracies for Striga resistance-associated traits across locations, as determined by cross validation (CV) were 0.24 to 0.53 for CV0 and from 0.20 to 0.37 for CV2. For GY, the prediction accuracies were 0.59 and 0.56 for CV0 and CV2, respectively. The results revealed 300 DH lines with desirable genomic estimated breeding values (GEBVs) for reduced number of emerged Striga plants (STR) at 8, 10, and 12 weeks after planting. The GEBVs of DH lines for Striga resistance associated traits in the training and testing sets were similar in magnitude. These results highlight the potential application of genomic selection in breeding for Striga resistance in maize. The integration of genomic-assisted strategies and DH technology for line development coupled with forward breeding for major adaptive traits will enhance genetic gains in breeding for Striga resistance in maize.

Validation of a phenotyping method to identify PRRSV-resilient sows and its impact on sow stayability.

Endemic and epidemic outbreaks of porcine reproductive and respiratory syndrome virus (PRRSV) are causing large economic losses in commercial pig production worldwide. Given the complexity of controlling this disease with vaccines or other biosecurity measures, the selection for pigs with a natural resilience to this infection has been proposed as an alternative approach. In this context, we previously reported a vaccine-based protocol to classify 6-week-old female piglets from one farm into resilient and susceptible phenotypes. Subsequent analysis showed that resilient sows had fewer lost piglets during a PRRSV epidemic. In the present study, we validated the results in four additional farms by showing a robust effect on the percentage of piglets lost (P<0.05). We were able to associate the resilient phenotype with a 2-4% reduction in piglet losses on sow farms in both endemic and endemic/epidemic situations. Also consistent with previous results, susceptible sows delivered on average, almost 0.5 more piglets born per parity (P<0.05). However, we show here that resilient sows have a longer stayability in the farm (+57 d; P<0.05) and +0.3 more successful parities (P<0.05), which balances the total number of piglets born and born alive in the full productive life of the sow between the two groups. Resilient sows thus contribute towards to a more sustainable production system, reducing sow replacement and piglet mortality. The validation of this protocol on four independent production farms paves the way for the study of the genetic variation underlying the resilient/susceptible classification, with a view to incorporating this information into selection programs in the future.

Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study.

BACKGROUND: Perinatal depression affects a significant number of women during pregnancy and after birth, and early identification is imperative for timely interventions and improved prognosis. Mobile apps offer the potential to overcome barriers to health care provision and facilitate clinical research. However, little is known about users' perceptions and acceptability of these apps, particularly digital phenotyping and ecological momentary assessment apps, a relatively novel category of apps and approach to data collection. Understanding user's concerns and the challenges they experience using the app will facilitate adoption and continued engagement. OBJECTIVE: This qualitative study explores the experiences and attitudes of users of the Mom2B mobile health (mHealth) research app (Uppsala University) during the perinatal period. In particular, we aimed to determine the acceptability of the app and any concerns about providing data through a mobile app. METHODS: Semistructured focus group interviews were conducted digitally in Swedish with 13 groups and a total of 41 participants. Participants had been active users of the Mom2B app for at least 6 weeks and included pregnant and postpartum women, both with and without depression symptomatology apparent in their last screening test. Interviews were recorded, transcribed verbatim, translated to English, and evaluated using inductive thematic analysis. RESULTS: Four themes were elicited: acceptability of sharing data, motivators and incentives, barriers to task completion, and user experience. Participants also gave suggestions for the improvement of features and user experience. CONCLUSIONS: The study findings suggest that app-based digital phenotyping is a feasible and acceptable method of conducting research and health care delivery among perinatal women. The Mom2B app was perceived as an efficient and practical tool that facilitates engagement in research as well as allows users to monitor their well-being and receive general and personalized information related to the perinatal period. However, this study also highlights the importance of trustworthiness, accessibility, and prompt technical issue resolution in the development of future research apps in cooperation with end users. The study contributes to the growing body of literature on the usability and acceptability of mobile apps for research and ecological momentary assessment and underscores the need for continued research in this area.

Profiling Dynamic Patterns of Single-Cell Motility.

Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single-cell behaviors, at baseline and in response to perturbations. In this study, CaMI is introduced, a computational framework designed to leverage the single-cell nature of motility data. CaMI identifies and classifies distinct spatio-temporal behaviors of individual cells, enabling robust classification of single-cell motility patterns in a large dataset (n = 74 253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single-cell motility behaviors across various biological conditions and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, a multivariate framework is presented to classify emergent patterns of single-cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations.

Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State & Future Promise.

Artificial intelligence (AI) and machine learning (ML) research within medicine has been exponentially increasing over the last decade, with studies showcasing the potential of AI/ML algorithms to improve clinical practice and outcomes. Ongoing research and efforts to develop AI-based models have expanded to aid in the identification of inborn errors of immunity (IEI). The utilization of larger electronic health record (EHR) datasets, coupled with advances in phenotyping precision and enhancements in ML techniques, has the potential to significantly improve the early recognition of IEI, thereby increasing access to equitable care. In this review, we provide a comprehensive examination of AI/ML for IEI, covering the spectrum from data preprocessing for AI/ML analysis to current applications within immunology, and address the challenges associated with implementing clinical decision support systems (CDSS) to refine the diagnosis and management of IEI.

A template and tutorial for preregistering studies using passive smartphone measures.

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers must make multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. The implementation of open science practices is only beginning to emerge in digital phenotyping studies and varies widely across studies. Well-intentioned researchers may fail to report on some decisions due to the variety of choices that must be made. To address this issue and enhance reproducibility in digital phenotyping studies, we propose the adoption of preregistration as a way forward. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Therefore, our objective was to develop a preregistration template that is easy to use and understandable for researchers. Additionally, we explain this template and provide resources to assist researchers in making informed decisions regarding data collection, cleaning, and analysis. Overall, we aim to make researchers' choices explicit, enhance transparency, and elevate the standards for studies utilizing passive smartphone measures.

From phenotyping to genetic mapping: identifying water-stress adaptations in legume root traits.

BACKGROUND: Climate change induces perturbation in the global water cycle, profoundly impacting water availability for agriculture and therefore global food security. Water stress encompasses both drought (i.e. water scarcity) that causes the drying of soil and subsequent plant desiccation, and flooding, which results in excess soil water and hypoxia for plant roots. Terrestrial plants have evolved diverse mechanisms to cope with soil water stress, with the root system serving as the first line of defense. The responses of roots to water stress can involve both structural and physiological changes, and their plasticity is a vital feature of these adaptations. Genetic methodologies have been extensively employed to identify numerous genetic loci linked to water stress-responsive root traits. This knowledge is immensely important for developing crops with optimal root systems that enhance yield and guarantee food security under water stress conditions. RESULTS: This review focused on the latest insights into modifications in the root system architecture and anatomical features of legume roots in response to drought and flooding stresses. Special attention was given to recent breakthroughs in understanding the genetic underpinnings of legume root development under water stress. The review also described various root phenotyping techniques and examples of their applications in different legume species. Finally, the prevailing challenges and prospective research avenues in this dynamic field as well as the potential for using root system architecture as a breeding target are discussed. CONCLUSIONS: This review integrated the latest knowledge of the genetic components governing the adaptability of legume roots to water stress, providing a reference for using root traits as the new crop breeding targets.

An advanced three-dimensional phenotypic measurement approach for extracting Ginkgo root structural parameters based on terrestrial laser scanning.

The phenotyping of plant roots is essential for improving plant productivity and adaptation. However, traditional techniques for assembling root phenotyping information are limited and often labor-intensive, especially for woody plants. In this study, an advanced approach called accurate and detailed quantitative structure model-based (AdQSM-based) root phenotypic measurement (ARPM) was developed to automatically extract phenotypes from Ginkgo tree root systems. The approach involves three-dimensional (3D) reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter (RD), length, surface area, and volume. To evaluate the proposed method, two approaches [minimum spanning tree (MST)-based and triangulated irregular network (TIN)-based] were used to reconstruct the Ginkgo root systems from point clouds, and the number of lateral roots along with RD were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds [coefficient of determination (R 2) = 0.99, root-mean-square error (RMSE) = 0.41 cm] outperformed the results of 3D models (MST-based: R 2 = 0.71, RMSE = 2.20 cm; TIN-based: R 2 = 0.54, RMSE = 2.80 cm). Additionally, the MST-based model (F1 = 0.81) outperformed the TIN-based model (F1 = 0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with a different cloud parameter (CP), and the CP value of 0.002 (r = 0.94, p < 0.01) was found to be advantageous for better extraction of structural phenotypes. This study has helped with the extraction and quantitative analysis of root phenotypes and enhanced our understanding of the relationship between architectural parameters and corresponding physiological functions of tree roots.

Genome-wide association studies from spoken phenotypic descriptions: a proof of concept from maize field studies.

We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of 'tallness' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment.

Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry?

Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the "Decade of the Brain" was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise development and implementation approach, similar to the one that has been successful in the prediction and prevention of cardiovascular disease, to achieve clinically actionable predictions in psychiatry.

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design.

BACKGROUND: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. RESULTS: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass ( R 2 = 0.82 ) and root surface area ( R 2 = 0.8 ). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. CONCLUSION: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping.

Milk thistle, Silybum marianum (L.), is a well-known medicinal plant used for the treatment of liver diseases due to its high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds, which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosomes using only image analysis is difficult, and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using the Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets, and the precise predictions were manually selected and used as new labeled images for retraining the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for retraining the model, allowed the production of sufficient labeled data for retraining. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image-based high-throughput phenotyping in ImageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits.

Case report: Persistent hypogammaglobulinemia and mixed chimerism after HLA class-II disparate-hematopoietic stem cell transplant.

Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative treatment for various hematological, immunological and metabolic diseases, replacing the patient's hematopoietic system with donor-derived healthy hematopoietic stem cells. HSCT can be complicated by early and late events related to impaired immunological recovery such as prolonged hypogammaglobulinemia post-HSCT. We present a 16-year-old female patient with sickle-cell disease who underwent HSCT with stem cells from a human leukocyte antigen (HLA) class-II mismatched family donor. While cellular recovery was good post-HSCT, the patient developed mixed chimerism and suffered from cervical lymphadenopathy, recurrent airway infections and cutaneous SLE. She presented with hypogammaglobulinemia and was started on immunoglobulin substitution therapy and antibiotic prophylaxis. B-cell phenotyping showed that she had increased transitional and naïve mature B cells, reduced memory B cells, and diminished marginal zone/natural effector cells. In-depth immunophenotyping and B-cell receptor repertoire sequencing ruled out an intrinsic B-cell defect by expression of activation-induced cytidine deaminase (AID), presence of somatic hypermutations and differentiation into IgG- and IgA-producing plasma cells in vitro. Immunohistochemistry and flow cytometry of lymph node tissue showed a clear block in terminal B-cell differentiation. Chimerism analysis of sorted lymph node populations showed that exclusively patient-derived B cells populated germinal centers, while only a minor fraction of follicular helper T cells was patient-derived. Given this discrepancy, we deduced that the HLA class-II disparity between patient and donor likely hinders terminal B-cell differentiation in the lymph node. This case highlights that studying disturbed cognate T-B interactions in the secondary lymphoid organs can provide unique insights when deciphering prolonged hypogammaglobulinemia post-HSCT.