scholarly journals Robust colony recognition for high-throughput growth analysis from suboptimal low-magnification brightfield micrographs

2018 ◽  
Author(s):  
Yevgeniy Plavskin ◽  
Shuang Li ◽  
Naomi Ziv ◽  
Sasha F. Levy ◽  
Mark L. Siegal
Keyword(s):  
High Throughput ◽  
Time Lapse ◽  
Manual Curation ◽  
Technological Advances ◽  
Wide Range ◽  
Colony Recognition ◽  
Gradient Based ◽  
High Throughput Phenotyping ◽  
High Resolution Images ◽  
Object Center

AbstractNew technological advances have enabled high-throughput phenotyping at the single-cell level. However, analyzing the large amount of data automatically and accurately is a great challenge. Currently available software achieves cell and colony tracking through the use of either manual curation of images, which is time consuming, or high-resolution images requiring specialized microscopy setups or fluorescence, which limits applicability and results in greatly decreased experimental throughput. Here we introduce a new algorithm, Processing Images Easily (PIE), that automatically tracks colonies of the yeast Saccharomyces cerevisiae in low-magnification brightfield images by combining adaptive object-center detection with gradient-based object-outline detection. We tested the performance of PIE on low-magnification brightfield time-lapse images. PIE recognizes colony outlines very robustly and accurately across a wide range of image brightnesses and focal depths. We show that PIE allows for unbiased and precise measurement of growth rates in a large number (>90,000) of microcolonies in a single time-lapse experiment.

scholarly journals High-throughput phenotyping in cotton: a review

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Irish Lorraine B. PABUAYON ◽  
Yazhou SUN ◽  
Wenxuan GUO ◽  
Glen L. RITCHIE
Keyword(s):  
High Throughput ◽  
Cropping Systems ◽  
Three Dimensional ◽  
Growth Yield ◽  
Analytical Techniques ◽  
Phenotypic Traits ◽  
Technological Advances ◽  
Recent Developments ◽  
Potential Applications ◽  
High Throughput Phenotyping

Abstract Recent technological advances in cotton (Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis. High-throughput phenotyping (HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth, yield, and adaptation to biotic or abiotic stress. Researchers have conducted extensive experiments on HTP and developed techniques including spectral, fluorescence, thermal, and three-dimensional imaging to measure the morphological, physiological, and pathological resistance traits of cotton. In addition, ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems. This review paper highlights the techniques and recent developments for HTP in cotton, reviews the potential applications according to morphological and physiological traits of cotton, and compares the advantages and limitations of these HTP systems when used in cotton cropping systems. Overall, the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton. However, because of its relative novelty, HTP has some limitations that constrains the ability to take full advantage of what it can offer. These challenges need to be addressed to increase the accuracy and utility of HTP, which can be done by integrating analytical techniques for big data and continuous advances in imaging.

scholarly journals High-throughput live-imaging of embryos in microwell arrays using a modular, inexpensive specimen mounting system

2017 ◽  
Author(s):  
Seth Donoughe ◽  
Chiyoung Kim ◽  
Cassandra G. Extavour
Keyword(s):  
High Throughput ◽  
Fruit Fly ◽  
Time Lapse ◽  
Live Imaging ◽  
Model Systems ◽  
Wide Range ◽  
Amphipod Crustacean ◽  
Live Image ◽  
Time Lapse Imaging ◽  
Annelid Worm

AbstractLive-imaging embryos in a high-throughput manner is essential for shedding light on a wide range of questions in developmental biology, but it is difficult and costly to mount and image embryos in consistent conditions. Here, we present OMMAwell, a simple, reusable device that makes it easy to mount up to hundreds of embryos in arrays of agarose microwells with customizable dimensions and spacing. OMMAwell can be configured to mount specimens for upright or inverted microscopes, and includes a reservoir to hold live-imaging medium to maintain constant moisture and osmolarity of specimens during time-lapse imaging. All device components can be cut from a sheet of acrylic using a laser cutter. Even a novice user will be able to cut the pieces and assemble the device in less than an hour. At the time of writing, the total materials cost is less than five US dollars. We include all device design files in a commonly used format, as well as complete instructions for its fabrication and use. We demonstrate a detailed workflow for designing a custom mold and employing it to simultaneously live-image dozens of embryos at a time for more than five days, using embryos of the cricket Gryllus bimaculatus as an example. Further, we include descriptions, schematics, and design files for molds that can be used with 14 additional vertebrate and invertebrate species, including most major traditional laboratory models and a number of emerging model systems. Molds have been user-tested for embryos including zebrafish (Danio rerio), fruit fly (Drosophila melanogaster), coqui frog (Eleutherodactylus coqui), annelid worm (Capitella teleta), amphipod crustacean (Parhyale hawaiensis), red flour beetle (Tribolium castaneum), and three-banded panther worm (Hofstenia miamia), as well mouse organoids (Mus musculus). Finally, we provide instructions for researchers to customize OMMAwell inserts for embryos or tissues not described herein.Summary StatementThis Techniques and Resources article describes an inexpensive, customizable device for mounting and live-imaging a wide range of tissues and species; complete design files and instructions for assembly are included.

scholarly journals Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

2017 ◽  
Author(s):  
Zhikai Liang ◽  
Piyush Pandey ◽  
Vincent Stoerger ◽  
Yuhang Xu ◽  
Yumou Qiu ◽  
...  
Keyword(s):  
High Throughput ◽  
Measurement Errors ◽  
Hyperspectral Image ◽  
Image Data ◽  
Field Trials ◽  
Plant Phenotyping ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
University Of Nebraska Lincoln ◽  
The Cost

ABSTRACTMaize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds – primarily recently off patent lines – were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.

scholarly journals Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3319
Author(s):  
Stuart A. Bagley ◽  
Jonathan A. Atkinson ◽  
Henry Hunt ◽  
Michael H. Wilson ◽  
Tony P. Pridmore ◽  
...  
Keyword(s):  
High Throughput ◽  
Low Cost ◽  
Time Lapse ◽  
Plant Phenotyping ◽  
Environmental Sensors ◽  
Controlled Conditions ◽  
Sensor Platform ◽  
High Throughput Phenotyping ◽  
Controlled Environments ◽  
Time Lapse Imaging

High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.

Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS

2021 ◽  
Vol 4 (1) ◽  
pp. 1-19
Author(s):  
Lisa Bastarache
Keyword(s):  
High Throughput ◽  
Association Studies ◽  
International Classification Of Diseases ◽  
Complex Data ◽  
Common Genetic Variants ◽  
Wide Range ◽  
Classification Of Diseases ◽  
High Throughput Phenotyping ◽  
Data Source ◽  
Electronic Health

Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.

scholarly journals Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

2020 ◽  
Vol 11 ◽  
Author(s):  
Tiago Bresolin ◽  
João R. R. Dórea
Keyword(s):  
Electromagnetic Radiation ◽  
High Throughput ◽  
Complex Traits ◽  
Infrared Spectrometry ◽  
Electromagnetic Spectrum ◽  
Wide Range ◽  
Level Information ◽  
High Throughput Phenotyping ◽  
The Impact ◽  
Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

scholarly journals SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Soumyashree Kar ◽  
Vincent Garin ◽  
Jana Kholová ◽  
Vincent Vadez ◽  
Surya S. Durbha ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area ◽  
High Throughput ◽  
Missing Values ◽  
Time Series Data ◽  
Series Data ◽  
Genotypic Differences ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
Over Time

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

scholarly journals Unsupervised automated high throughput phenotyping of RNAi time-lapse movies

2013 ◽  
Vol 14 (1) ◽  
pp. 292 ◽  
Author(s):  
Henrik Failmezger ◽  
Holger Fröhlich ◽  
Achim Tresch
Keyword(s):  
High Throughput ◽  
Time Lapse ◽  
High Throughput Phenotyping

Three-fold astigmatism: An important TEM aberration

1993 ◽  
Vol 51 ◽  
pp. 972-973 ◽  
Author(s):  
O.L. Krivanek ◽  
M.L. Leber
Keyword(s):  
High Resolution ◽  
Spatial Frequency ◽  
Field Emission ◽  
Azimuthal Angle ◽  
Hollow Cone ◽  
Small Probe ◽  
Wide Range ◽  
High Resolution Images ◽  
Image Position

Three-fold astigmatism resembles regular astigmatism, but it has 3-fold rather than 2-fold symmetry. Its contribution to the aberration function χ(q) can be written as:where A3 is the coefficient of 3-fold astigmatism, λ is the electron wavelength, q is the spatial frequency, ϕ the azimuthal angle (ϕ = tan-1 (qy/qx)), and ϕ3 the direction of the astigmatism.Three-fold astigmatism is responsible for the “star of Mercedes” aberration figure that one obtains from intermediate lenses once their two-fold astigmatism has been corrected. Its effects have been observed when the beam is tilted in a hollow cone over a wide range of angles, and there is evidence for it in high resolution images of a small probe obtained in a field emission gun TEM/STEM instrument. It was also expected to be a major aberration in sextupole-based Cs correctors, and ways were being developed for dealing with it on Cs-corrected STEMs.

Towards high-throughput phenotyping of complex patterned behaviors: Lessons from mouse self-grooming

2011 ◽  
Author(s):  
E. Kyzar ◽  
S. Gaikwad ◽  
M. Pham ◽  
J. Green ◽  
A. Roth ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Phenotyping
Sign in / Sign up

Export Citation Format

Share Document