scholarly journals A method for obtaining field wheat freezing injury phenotype based on RGB camera and software control

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Xiuqing Fu ◽  
Yang Bai ◽  
Jing Zhou ◽  
Hongwen Zhang ◽  
Jieyu Xian
Keyword(s):  
High Throughput ◽  
Wheat Seedling ◽  
Image Features ◽  
Freezing Stress ◽  
Freezing Injury ◽  
Data Management System ◽  
Freezing Damage ◽  
Phenotypic Analysis ◽  
Phenotypic Data ◽  
Wheat Varieties

Abstract Background Low temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, a more efficient and accurate method for freezing damage identification is urgently needed. Results A high-throughput phenotyping system was developed in this paper, namely, RGB freezing injury system, to effectively and efficiently quantify the wheat freezing injury in the field environments. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. In this experiment, a total of 128 wheat varieties were planted, three nitrogen concentrations were applied and two biological and technical replicates were performed. And wheat canopy images were collected at the seedling pulling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. We compared different test parameters and found that the coverage had a greater impact on freezing injury. Therefore, we preliminarily divided four grades of freezing injury according to the test results to evaluate the freezing injury of different varieties of wheat at the seedling stage. Conclusions The automatic phenotypic analysis method of freezing injury provides an alternative solution for high-throughput freezing damage analysis of field crops and it can be used to quantify freezing stress and has guiding significance for accelerating the selection of wheat excellent frost resistance genotypes.

scholarly journals A Method for Obtaining Field Wheat Freezing Injury Phenotype Based on RGB Camera and Software Control

2021 ◽  
Author(s):  
xiuqing fu ◽  
Yang Bai ◽  
Jing Zhou ◽  
Hongwen Zhang ◽  
Jieyu Xian
Keyword(s):  
High Throughput ◽  
Wheat Breeding ◽  
Image Features ◽  
Seedling Stage ◽  
Freezing Stress ◽  
Freezing Injury ◽  
Data Management System ◽  
Phenotypic Analysis ◽  
Phenotypic Data ◽  
Wheat Varieties

Abstract Low temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, to effectively and efficiently quantify the wheat freezing injury in the field environments, a high-throughput phenotyping system was developed in this paper , namely, RGB FREEZING INJURY SYSTEM. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. A group of 128 wheat varieties were planted with replicates under a freezing environment. Canopy images of the wheat were collected at the seedling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. The results show that the developed methods can clearly distinguish wheat samples with different wheat freezing injury scores. The automatic phenotypic analysis method of freezing injury provides a solution for high-throughput phenotypic analysis of field wheat and can quantify the stress caused by freezing injury at the seedling stage. The method has a certain guiding significance for wheat breeding.

scholarly journals Benchmarking of Multivariate Similarity Measures for High-Content Screening Fingerprints in Phenotypic Drug Discovery

2013 ◽  
Vol 18 (10) ◽  
pp. 1284-1297 ◽  
Author(s):  
Felix Reisen ◽  
Xian Zhang ◽  
Daniela Gabriel ◽  
Paul Selzer
Keyword(s):  
Feature Selection ◽  
Drug Discovery ◽  
High Throughput ◽  
Similarity Measures ◽  
Cellular Responses ◽  
Image Features ◽  
High Content Screening ◽  
Selection Methods ◽  
Phenotypic Analysis ◽  
Kendall’S Τ

High-content screening (HCS) is a powerful tool for drug discovery being capable of measuring cellular responses to chemical disturbance in a high-throughput manner. HCS provides an image-based readout of cellular phenotypes, including features such as shape, intensity, or texture in a highly multiplexed and quantitative manner. The corresponding feature vectors can be used to characterize phenotypes and are thus defined as HCS fingerprints. Systematic analyses of HCS fingerprints allow for objective computational comparisons of cellular responses. Such comparisons therefore facilitate the detection of different compounds with different phenotypic outcomes from high-throughput HCS campaigns. Feature selection methods and similarity measures, as a basis for phenotype identification and clustering, are critical for the quality of such computational analyses. We systematically evaluated 16 different similarity measures in combination with linear and nonlinear feature selection methods for their potential to capture biologically relevant image features. Nonlinear correlation-based similarity measures such as Kendall’s τ and Spearman’s ρ perform well in most evaluation scenarios, outperforming other frequently used metrics (such as the Euclidian distance). We also present four novel modifications of the connectivity map similarity that surpass the original version, in our experiments. This study provides a basis for generic phenotypic analysis in future HCS campaigns.

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals OpenStats: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput Phenotypic Data

2020 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C Mason ◽  
Ann-Marie Mallon ◽  
Damian Smedley ◽  
Terrence F Meehan ◽  
...  
Keyword(s):  
High Throughput ◽  
Software Package ◽  
Computational Time ◽  
Phenotypic Data ◽  
Creative Commons ◽  
International Mouse Phenotyping Consortium ◽  
Software Packages ◽  
Current Production ◽  
High Throughput Phenotyping ◽  
Reproducible Analysis

AbstractReproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

scholarly journals Automated Counting of Cancer Cells by Ensembling Deep Features

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1019 ◽  
Author(s):  
Liu ◽  
Junker ◽  
Murakami ◽  
Hu
Keyword(s):  
High Throughput ◽  
Pearson Correlation ◽  
Image Features ◽  
Cell Counting ◽  
Single Type ◽  
Imaging Features ◽  
Neural Network Models ◽  
Regression Problem ◽  
Microscopic Images ◽  
Cell Counts

High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image analysis techniques, such as cell counting, are in high demand. Here, cell counting was treated as a regression problem using image features (phenotypes) extracted by deep learning models. Three deep convolutional neural network models were developed to regress image features to their cell counts in an end-to-end way. Theoretically, ensembling imaging phenotypes should have better representative ability than a single type of imaging phenotype. We implemented this idea by integrating two types of imaging phenotypes (dot density map and foreground mask) extracted by two autoencoders and regressing the ensembled imaging phenotypes to cell counts afterwards. Two publicly available datasets with synthetic microscopic images were used to train and test the proposed models. Root mean square error, mean absolute error, mean absolute percent error, and Pearson correlation were applied to evaluate the models’ performance. The well-trained models were also applied to predict the cancer cell counts of real microscopic images acquired in a biological experiment to evaluate the roles of two colorectal-cancer-related genes. The proposed model by ensembling deep imaging features showed better performance in terms of smaller errors and larger correlations than those based on a single type of imaging feature. Overall, all models’ predictions showed a high correlation with the true cell counts. The ensembling-based model integrated high-level imaging phenotypes to improve the estimation of cell counts from high-content and high-throughput microscopic images.

scholarly journals Large-scale, high-throughput screening for coagulation and hematologic phenotypes in mice*

2002 ◽  
Vol 11 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Luanne L. Peters ◽  
Eleanor M. Cheever ◽  
Heather R. Ellis ◽  
Phyllis A. Magnani ◽  
Karen L. Svenson ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Inbred Mouse ◽  
Inbred Strains ◽  
Sample Volume ◽  
Small Sample ◽  
Mouse Strains ◽  
Phenotypic Data ◽  
Multiple Tests

The Mouse Phenome Project is an international effort to systematically gather phenotypic data for a defined set of inbred mouse strains. For such large-scale projects the development of high-throughput screening protocols that allow multiple tests to be performed on a single mouse is essential. Here we report hematologic and coagulation data for more than 30 inbred strains. Complete blood counts were performed using an Advia 120 analyzer. For coagulation testing, we successfully adapted the Dade Behring BCS automated coagulation analyzer for use in mice by lowering sample and reagent volume requirements. Seven automated assay procedures were developed. Small sample volume requirements make it possible to perform multiple tests on a single animal without euthanasia, while reductions in reagent volume requirements reduce costs. The data show that considerable variation in many basic hematological and coagulation parameters exists among the inbred strains. These data, freely available on the World Wide Web, allow investigators to knowledgeably select the most appropriate strain(s) to meet their individual study designs and goals.

scholarly journals Auxological and endocrine phenotype in a population-based cohort of patients with PROP1 gene defects

2005 ◽  
Vol 153 (3) ◽  
pp. 389-396 ◽  
Author(s):  
Jan Lebl ◽  
Jan Vosáhlo ◽  
Roland W Pfaeffle ◽  
Heike Stobbe ◽  
Jana Černá ◽  
...  
Keyword(s):  
Genomic Analysis ◽  
Population Based ◽  
Hormone Deficiency ◽  
Heterozygous Mutation ◽  
Pituitary Hormone ◽  
Phenotypic Analysis ◽  
Phenotypic Data ◽  
Sibling Pairs ◽  
Pituitary Development ◽  
Gene Defects

Objective: Multiple pituitary hormone deficiency (MPHD) may result from defects of transcription factors that govern early pituitary development. We aimed to establish the prevalence of HESX1, PROP1, and POU1F1 gene defects in a population-based cohort of patients with MPHD and to analyse the phenotype of affected individuals. Design and methods: Genomic analysis was carried out on 74 children and adults with MPHD from the Czech Republic (including four sibling pairs). Phenotypic data were collected from medical records and referring physicians. Results: One patient carried a heterozygous mutation of POU1F1 (71C > T), and 18 patients (including three sibling pairs) had a PROP1 mutation (genotypes 150delA/301delGA/9/, 301delGA/301-delGA/8/, or 301delGA/349T > A/1/). A detailed longitudinal phenotypic analysis was performed for patients with PROP1 mutations (n = 17). The mean ( ±s.d.) birth length SDS of these patients (0.12 ± 0.76) was lower than expected based on their mean ( ±s.d.) birth weight SDS (0.63 ± 1.27; P = 0.01). Parental heights were normal. The patients’ mean ( ±s.d.) height SDS declined to −1.5 ± 0.9, −3.6 ± 1.3 and −4.1 ± 1.2 at 1.5, 3 and 5 years of age, respectively. GH therapy, initiated at 6.8 ± 3.2 years of age (mean dose: 0.022 mg/kg per day), led to substantial growth acceleration in all patients. Mean adult height (n = 7) was normal when adjusted for mid-parental height. ACTH deficiency developed in two out of seven young adult patients. Conclusions: PROP1 defects are a prevalent cause of MPHD. We suggest that testing for PROP1 mutations in patients with MPHD might become standard practice in order to predict risk of additional pituitary hormone deficiencies.

scholarly journals High Throughput Phenotypic Analysis of Mycobacterium tuberculosis and Mycobacterium bovis Strains' Metabolism Using Biolog Phenotype Microarrays

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e52673 ◽  
Author(s):  
Bhagwati Khatri ◽  
Mark Fielder ◽  
Gareth Jones ◽  
William Newell ◽  
Manal Abu-Oun ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Mycobacterium Bovis ◽  
High Throughput ◽  
Phenotypic Analysis ◽  
Phenotype Microarrays

scholarly journals Cellular Phenotypic Analysis of Macrophage Activation Unveils Kinetic Responses of Agents Targeting Phosphorylation

2016 ◽  
Vol 22 (1) ◽  
pp. 51-57
Author(s):  
Qian Cao ◽  
Junlin Yao ◽  
Heyuan Li ◽  
Bo Tao ◽  
Yibo Cai ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Macrophage Activation ◽  
Protein Tyrosine Kinase ◽  
Interferon Γ ◽  
Host Immune System ◽  
Phenotypic Analysis ◽  
Dependent Cell ◽  
Pathogen Clearance ◽  
Dose Dependent

Macrophages are highly plastic cells, which serve as sentinels of the host immune system due to their ability to recognize and respond to microbial products rapidly and dynamically. Appropriate regulation of macrophage activation is essential for pathogen clearance or preventing autoimmune diseases. However, regularly used endpoint assays for analyzing macrophage functions have the limitations of being static and non–high throughput. In this study, we introduced a real-time and convenient method based on changes in cellular impedance that are detected by microelectronic biosensors. This new method can record the time/dose-dependent cell response profiles (TCRPs) of macrophages in real time and generates physiologically relevant data. The TCRPs generated from classically interferon-γ/lipopolysaccharide-activated macrophages showed considerable consistency with the data generated from standard endpoint assays. We further explored this approach by using it for global screening of a library of protein tyrosine kinase/phosphatase (PTK/PTP) inhibitors to investigate their impact on macrophage activation. Collectively, our findings suggest that the cellular impedance-based assay provides a promising approach for dynamically monitoring macrophage functions in a convenient and high-throughput manner.

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