Accurate Phenotyping Research Articles

Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images.

Accurate and high-throughput plant phenotyping is important for accelerating crop breeding. Spectral imaging that can acquire both spectral and spatial information of plants related to structural, biochemical, and physiological traits becomes one of the popular phenotyping techniques. However, close-range spectral imaging of plants could be highly affected by the complex plant structure and illumination conditions, which becomes one of the main challenges for close-range plant phenotyping. In this study, we proposed a new method for generating high-quality plant 3-dimensional multispectral point clouds. Speeded-Up Robust Features and Demons was used for fusing depth and snapshot spectral images acquired at close range. A reflectance correction method for plant spectral images based on hemisphere references combined with artificial neural network was developed for eliminating the illumination effects. The proposed Speeded-Up Robust Features and Demons achieved an average structural similarity index measure of 0.931, outperforming the classic approaches with an average structural similarity index measure of 0.889 in RGB and snapshot spectral image registration. The distribution of digital number values of the references at different positions and orientations was simulated using artificial neural network with the determination coefficient (R 2) of 0.962 and root mean squared error of 0.036. Compared with the ground truth measured by ASD spectrometer, the average root mean squared error of the reflectance spectra before and after reflectance correction at different leaf positions decreased by 78.0%. For the same leaf position, the average Euclidean distances between the multiview reflectance spectra decreased by 60.7%. Our results indicate that the proposed method achieves a good performance in generating plant 3-dimensional multispectral point clouds, which is promising for close-range plant phenotyping.

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery.

Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE (R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC (R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC (R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.

PhenoExam: gene set analyses through integration of different phenotype databases

BackgroundGene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases.ResultsPhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy.ConclusionsWe developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G′ and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes.Github R package URL is https://github.com/alexcis95/PhenoExam.Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/.

A Dynamic Detection Method for Phenotyping Pods in a Soybean Population Based on an Improved YOLO-v5 Network

Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management.

Genotype-by-environment interaction and stability of root mealiness and other organoleptic properties of boiled cassava roots

Genetic enhancement of cassava aimed at improving cooking and eating quality traits is a major goal for cassava breeders to address the demand for varieties that are desirable for the fresh consumption market segment. Adoption of such cassava genotypes by consumers will largely rely not only on their agronomic performance, but also on end-user culinary qualities such as root mealiness. The study aimed to examine genotype × environment interaction (GEI) effects for root mealiness and other culinary qualities in 150 cassava genotypes and detect genotypes combining stable performance with desirable mealiness values across environments using GGE biplot analysis. Experiments were conducted using an alpha-lattice design with three replications for two years in three locations in Nigeria. The analysis of variance revealed a significant influence of genotype, environment, and GEI on the performance of genotypes. Mealiness scores showed no significant relationship with firmness values of boiled roots assessed by a penetration test, implying that large-scale rapid and accurate phenotyping of mealiness of boiled cassava roots remains a major limitation for the effective development of varieties with adequate mealiness, a good quality trait for direct consumption (boil-and-eat) as well as for pounding into ‘fufu’. The moderate broad-sense heritability estimate and relatively high genetic advance observed for root mealiness suggest that significant genetic gains can be achieved in a future hybridization program. The genotype main effects plus genotype × environment interaction (GGE) biplot analysis showed that the different test environments discriminated among the genotypes. Genotypes G80 (NR100265) and G120 (NR110512) emerged as the best performers for root mealiness in Umudike, whereas G13 (B1-50) and the check, G128 (TMEB693) performed best in Igbariam and Otobi. Based on the results of this study, five genotypes, G13 (B1-50), G34 (COB6-4), G46 (NR010161), the check, G128 (TMEB693), and G112 (NR110376), which were found to combine stability with desirable mealiness values, were the most suitable candidates to recommend for use as parents to improve existing cassava germplasm for root mealiness.

Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping.

A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP. We compared how each resource produced top-ranked concept unique identifiers (CUIs) by term frequency-inverse document frequency with Jaccard matrices comparing single resources and the original PheMAP. We correlated top-ranked concepts from each resource to features used in established Phenotype KnowledgeBase (PheKB) algorithms for hypothyroidism, type II diabetes mellitus (T2DM), and dementias. Using resources separately, we calculated multiple phenotype risk scores for individuals from Vanderbilt University Medical Center's BioVU DNA Biobank and compared phenotyping performance against rule-based eMERGE algorithms. Lastly, we implemented an ensemble strategy which classified patient case/control status based upon PheMAP resource agreement. Jaccard similarity matrices indicate that the similarity of CUIs comprising single resource-based PheMAPs varies. Single resource-based PheMAPs generated from MedlinePlus and MedicineNet outperformed others but only encompass 81.6% of overall disease phenotypes. We propose the PheMAP-Ensemble which provides higher average accuracy and precision than the combined average accuracy and precision of single resource-based PheMAPs. While offering complete phenotype coverage, PheMAP-Ensemble significantly increases phenotyping recall compared to the original iteration. Resources comprising the PheMAP produce different phenotyping performance when implemented individually. The ensemble method significantly improves the quality of PheMAP by fully utilizing dissimilar resources to capture accurate phenotyping data from EHRs.

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

Machine learning approaches for electronic health records phenotyping: a methodical review.

Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies. Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions. Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released. Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.

Similar Patient-Reported Quality of Life in the US and UK in Patients with Sickle Cell: A Comparative Grndad/Cousin Study

Background: The Globin Research Network for Data and Discovery, GRNDaD (2016), registry and its recently initiated partner, Collaborating to Understand Sickle Cell Disease Internationally, CoUSIn (2021), were developed by providers at sickle cell centers in the US and UK, respectively. The overall aim is a robust registry that will lead to an increased understanding of the natural history of sickle cell disease (SCD) and to use quality improvement tools to measure and improve guideline-based care for all individuals with this disease. In addition to collecting clinical and laboratory patient-level data for an accurate and detailed phenotype, the registries also collect patient-reported outcomes including data on quality of life (QOL) through the Adult Sickle Cell Quality of Life Measurement Information System (ASCQ-Me) survey tool1. The purpose of this abstract is to compare QOL results from adult patients from the UK and US, and to describe clinical characteristics associated with QOL domains in available US data. Methods: GRNDaD is an ongoing prospective registry that is recruiting adult and pediatric patients at 19 sites across the US. CoUSIn is the UK equivalent, using the same variables which opened for enrollment for adults in September 2021. The full registry has over 300 unique variables extracted from medical records. In addition, survey instruments including ASCQ-Me and the brief pain inventory are available for patient direct data-entry into the REDCap registry. This analysis includes the first 106 participants from the UK and 486 participants in the US who have to date completed at least one ASCQ-Me questionnaire. All the ASCQ-Me metrics are created from standardized T-scores using an earlier SCD cohort, in which, for each measure, 50 is the average score, scores above which suggest better health or QOL. Different ASCQ-Me domains were collected in each country based on preference (Table). T-tests were used to compare differences in patient-reported outcomes from the UK and US. Wilcoxon rank sum tests were used to identify patient characteristics associated with the outcomes. Results: 486 of 1240 adult patients in the US and 106 of 236 UK patients have completed at least one ASCQ-Me survey. 58% of US and 62% of the UK respondents had hemoglobin SS/Sβ0thal. 55% of US and 60% of UK respondents were female. The median age of the US cohort was 35 (IQR-27-45) and the UK cohort was 39 (IQR-26-50). Results of ASCQ-Me by domain and country can be found in the Table and Figure. There were no statistically significant differences between patient-reported outcomes between UK and US respondents across the four common ASCQ-Me domains in which data was collected, when controlling for age and sex. For the US cohort, when comparing patients with SS/Sβ0thal on medication versus on chronic transfusion for their disease modifying therapy there was no statistically significant difference in any of the five ASCQ-Me domains. Discussion: While not yet fully mature, both the GRNDaD and CoUSIn registries demonstrate the feasibility of collecting patient reported outcomes from real world settings. Interestingly, despite vast differences in health care delivery, scores on ASCQ-Me are not different between countries and average scores are notable for being above the average score of the standardized subjects in whom the tool was developed, suggesting better QOL. In the US cohort, the lack of association of those on DMT requires further study to determine if these tools will be sensitive to changes in therapy in longitudinal follow-up and whether adherence to therapy might impact scores. In addition, planned registry improvements, to more concretely capture opioid and anti-inflammatory agent use may improve our patient-centered understanding of how best to decrease symptom burden in SCD. 1. Keller S, Yang M, Treadwell MJ, Hassell KL. Sensitivity of alternative measures of functioning and wellbeing for adults with sickle cell disease: comparison of PROMIS® to ASCQ-M CoUSIn is supported by a grant from bluebird bio Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Predicting starch content in cassava fresh roots using near-infrared spectroscopy.

The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740-1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350-2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400-2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2 P): 0.84-0.90; ratio of performance deviation (RPD): 2.49-3.11, ratio of performance to interquartile distance (RPIQ): 3.24-4.08, concordance correlation coefficient (CCC): 0.91-0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871-1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.

Novel method for the quantification of rosette area from images of Arabidopsis seedlings grown on agar plates.

The agar-based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping. We developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images. The rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad-sense heritability of leaf area measured using this method was 0.28. These results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.

A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record

While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP’s feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50–100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research.

Hemodynamic Aberrancies in Left Ventricular Assist Device–Associated Heart Failure Syndromes

Hemodynamic Aberrancies in Left Ventricular Assist Device–Associated Heart Failure Syndromes

Phenotyping strategies for Elsinöe ampelina symptoms in grapevine (Vitis spp.)

AbstractGrapevine anthracnose, caused by Elsinöe ampelina, is a fungal disease with no mapped resistance loci. Thus, this study aimed to evaluate different traits used to phenotype grapevine anthracnose looking for correlated traits and reproducibility during evaluations. Phenotyping was conducted with F1 siblings segregating for resistance to grapevine anthracnose. The progenies were inoculated with Elsinoë ampelina. Leaves, canes and shoots were phenotyped three times. Real severity and number of anthracnose spots were determined from digital photographs. Complementary, the number of spots was estimated using a scale from 0 to 9 and the severity using standard area diagram (SAD) developed for leaves and shoots. The results revealed positive correlation between real and estimated number of anthracnose spots, and the real and estimated severity. However, the number of anthracnose spots did not correlate with severity. The real data from severity and number of spots presented higher variation than the estimated data based on the SAD and scale, respectively. The evaluation strategy based on number of anthracnose spots showed reproducibility in the three independent bioassays, being the most accurate phenotyping strategy for grapevine anthracnose symptoms.

RGB-image method enables indirect selection for leaf spot resistance and yield estimation in a groundnut breeding program in Western Africa.

Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut (Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73–33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.

Recent Advances in Molecular Diagnosis of Pulmonary Fibrosis for Precision Medicine.

Pulmonary fibrosis is a serious, progressive lung disease characterized by scarring and stiffening lung tissues, affecting the respiratory system and leading to organ failure. It is a complex disease consisting of alveolar damage, chronic inflammation, and a varying degree of lung fibrosis. Significant challenges with pulmonary fibrosis include the lack of effective means to diagnose the disease at early stages, identify patients at higher risks of progress, and assess disease progression and treatment response. Precision medicine powered by accurate molecular profiling and phenotyping could significantly improve our understanding of the disease's heterogeneity, potential biomarkers for diagnosis and prognosis, and molecular targets for treatment development. This Review discusses various translational model systems, including organoids and lung-on-a-chip systems, biomarkers in single cells and extracellular vesicles, and functional pharmacodynamic markers. We also highlight emerging sensing technologies for molecular characterization of pulmonary fibrosis and biomarker detection.

A bibliometrics analysis and visualization of autism spectrum disorder.

BackgroundThe prevalence of autism spectrum disorder (ASD) increased rapidly in the last 20 years. Although related research has developed rapidly, little is known about its etiology, diagnostic marker, or drug treatment, which forces researchers to review and summarize its development process and look for the future development direction.MethodsWe used bibliometrics to analyze papers of ASD in the Web of Science from 1998 to 2021, to draw the network of authors, institutions, countries, and keywords in the ASD field, and visualize the results.ResultsA total of 40,597 papers were included with a continually increasing trend. It turns out that the research on ASD is mainly concentrated in universities. The United States has the largest number of ASD studies, followed by England and Canada. The quality of papers related to ASD is generally high, which shows that ASD research has become a hot spot of scientific research. The keywords of ASD etiology and diagnostic markers can be classified into at least 7 aspects. The detection of keywords shows that ASD research is mostly based on its subtypes, takes children as the study population, focuses on neurodevelopmental imaging or genetics, and pays attention to individual differences. And ASD research has changed greatly under the impact of Corona Virus Disease 2019 in the past 2 years.ConclusionWe consider the future development direction should be based on the improvement of case identification, accurate clinical phenotype, large-scale cohort study, the discovery of ASD etiology and diagnostic markers, drug randomized controlled trials, and telehealth.

2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification

An integrated custom cross-response sensing array has been developed combining the algorithm module’s visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 105 ~ 108 CFU/mL for Escherichia coli, 102 ~ 107 CFU/mL for E. coli-β, 103 ~ 108 CFU/mL for Staphylococcus aureus, 103 ~ 107 CFU/mL for MRSA, 102 ~ 108 CFU/mL for Pseudomonas aeruginosa, 103 ~ 108 CFU/mL for Enterococcus faecalis, 102 ~ 108 CFU/mL for Klebsiella pneumoniae, and 103 ~ 108 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification.Graphical abstract• A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism.• Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements’ performance has competitive reaction with the microorganism.• MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms.

Detection of Weak “B” Phenotype While Resolving an ABO Discrepancy

Quantitative and/or qualitative differences in ABO phenotypes give rise to ABO discrepancies causing difficulties in establishing the accurate phenotype. These discrepant serological reactions result in significant delays in blood bank. The correct typing of the blood group is imperative to prevent ABO incompatibility issues. However, the discrepant results encountered in routine settings are a challenge to immunohematologists. The variants of the ABO occur very rarely particularly true for B subgroups. Here, we present a case study of a healthy blood donor, discrepant results in forward and reverse blood grouping led us to perform adsorption-elution test. The presence of anti-B in the eluate confirmed the presence of weaker variant of B antigen on the surface of red blood cells. Correct identification of blood group is mutually important for donor and recipient to prevent the occurrence of any transfusion reaction. The serologically determined weak B phenotypes, however, require further confirmation through genomic analysis.

Abstract 3868: High throughput tissue phenotyping and imaging of the tumor immune microenvironment using novel FlexVUE™ multiplexed immunofluorescence assays

Abstract Immunotherapy has transformed the treatment of metastatic and recurrent solid tumors. Advances in technology in the past few years have created unprecedented opportunities to identify biomarkers of disease processes, especially by using multi-omics technologies and datasets to derive valid and useful signatures of disease. Advances in multiplexed immunofluorescence (mIF) staining and imaging technologies that allow for accurate phenotyping of individual cells while also preserving architectural features of the sample are critical to our understanding of the interplay of tumor and immune cells in the tumor immune microenvironment (TiME).Herein we describe the utility of our new flexible mIF assays (FlexVUE™ panels) to provide the necessary relevant distribution of infiltrating immune cells in tumors coupled to the use of our new UltiStacker™ software. This hardware and software package allows a detailed spatial characterization of specific cell phenotypes defined by co- or lack of expression that may help in predicting clinical responses and mechanisms of resistance to therapy. The FlexVUE™ mIF assay was used to conduct 8-plex immunophenotyping of FFPE tumor sections. Slides were stained with a cocktail of eight primary antibodies using Leica Biosystems BOND RX autostainer and imaged on the Zeiss Axio Scan.Z1. Following the first round of imaging, the slides were decoverslipped and the signal was removed. New targets were probed on the same tissue section in a reagent incubation step on the BOND RX, termed ‘exchange.’ Slides were re-imaged on the Zeiss Axio Scan Z1 using the same dye channels. The image pairs were automatically aligned using UltiStacker™ software and exported for downstream analysis with Indica Labs HALOv3.1 software. Our approach demonstrates a streamlined off the shelf workflow requiring no assay development time that supports whole slide imaging of a flexible high plex panel. In addition, our assays enable traditional same slide H&E fusion on a single tissue slide for comprehensive tissue immunophenotyping analysis of proliferating cells, tumor cells, tumor-infiltrating lymphocytes, macrophages or tertiary lymphoid structures. Citation Format: Yvette Cajigas, Peter Hamer, Alina Ainbinder, Angela Vasaturo, Douglas Wood, Kirsteen H. Maclean, Gourab Chatterjee. High throughput tissue phenotyping and imaging of the tumor immune microenvironment using novel FlexVUE™ multiplexed immunofluorescence assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3868.