scholarly journals A semi-automated design for high-throughput Lepidoptera larvae feeding bioassays

2020 ◽  
Author(s):  
Inoussa Sanane ◽  
Judith Legrand ◽  
Christine Dillmann ◽  
Frederic Marion-Poll
Keyword(s):  
High Throughput ◽  
European Corn Borer ◽  
Large Scale ◽  
Time Course ◽  
3D Models ◽  
R Package ◽  
Leaf Disk ◽  
Source Image ◽  
Corn Borer ◽  
Feeding Activities

Lepidopteran pests cause considerable damage to all crops over the world. As larvae are directly responsible for these damages, many research efforts are devoted to find plant cultivars which are resistant against them. However, such studies take time, efforts and are costly, especially when one wants to not only find resistance traits but also evaluate their heritability. We present here a high throughput approach to screen plants for resistance or chemicals for their deterrence, using a leaf-disk consumption assay, which is both suitable for large scale tests and economically affordable. To monitor larvae feeding on leaf disks placed over a layer of agar, we designed 3D models of 50 cages arrays. One webcam can sample simultaneously 3 of such arrays at a rate of 1 image/min, and follow individual feeding activities in each cage as the movements of 150 larvae. The resulting image stacks are first processed with a custom program running under an open-source image analysis package (Icy) to measure the surface of each leaf disk over time. We further developed statistical procedures running under the R package, to analyze the time course of the feeding activities of the larvae and to compare them between treatments. As a test case, we compared how European corn borer larvae respond to quinine, considered as a bitter alkaloid for many organisms, and to Neemazal containing azadirachtin, which is a common antifeedant against pest insects. We found that increasing doses of azadirachtin reduce and delay feeding. However, contrary to our expectation, quinine was found poorly effective at the range of concentrations tested. The 3D printed model of the cage, of the camera holder, the plugins running under Icy, and the R procedures are freely available, and can be modified according to the particular needs of the users.

scholarly journals High-Throughput Feeding Bioassay for Lepidoptera Larvae

2021 ◽  
Author(s):  
Inoussa Sanané ◽  
Judith Legrand ◽  
Christine Dillmann ◽  
Frédéric Marion-Poll
Keyword(s):  
High Throughput ◽  
European Corn Borer ◽  
Time Course ◽  
Leaf Disk ◽  
Source Image ◽  
Test Case ◽  
Analysis Program ◽  
Corn Borer ◽  
Feeding Activities ◽  
Camera Holder

AbstractFinding plant cultivars that are resistant or tolerant against lepidopteran pests, takes time, effort and is costly. We present here a high throughput leaf-disk consumption assay system, to screen plants for resistance or chemicals for their deterrence. A webcam capturing images at regular intervals can follow the feeding activities of 150 larvae placed into individual cages. We developed a computer program running under an open source image analysis program to analyze and measure the surface of each leaf disk over time. We further developed new statistical procedures to analyze the time course of the feeding activities of the larvae and to compare them between treatments. As a test case, we compared how European corn borer larvae respond to a commercial antifeedant containing azadirachtin, and to quinine, which is a bitter alkaloid for many organisms. As expected, increasing doses of azadirachtin reduced and delayed feeding. However, quinine was poorly effective at the range of concentrations tested (10–5 M to 10–2 M). The model cage, the camera holder, the plugins, and the R scripts are freely available, and can be modified according to the users’ needs.

scholarly journals Trendy: Segmented regression analysis of expression dynamics for high-throughput ordered profiling experiments

2017 ◽  
Author(s):  
Rhonda Bacher ◽  
Ning Leng ◽  
Li-Fang Chu ◽  
James Thomson ◽  
Christina Kendziorski ◽  
...  
Keyword(s):  
High Throughput ◽  
Time Course ◽  
Expression Patterns ◽  
Critical Time ◽  
R Package ◽  
Global Dynamics ◽  
Segmented Regression ◽  
Dynamic Changes ◽  
Specific Expression ◽  
Dynamic Activity

AbstractHigh throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for profiling detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time-points. We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each gene’s expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-seq datasets. Trendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available as an R package with a full vignette at https://github.com/rhondabacher/Trendy.

Design and Testing of a Boom Sprayer Prototype to Release Trichogramma ostriniae (Hymenoptera: Trichogrammatidae) in Sweet Corn for Biocontrol of Ostrinia nubilalis (Hübner) (Lepidoptera: Crambidae)

2018 ◽  
Vol 61 (6) ◽  
pp. 1867-1879
Author(s):  
Ariane Dionne ◽  
Mohamed Khelifi ◽  
Silvia Todorova ◽  
Guy Boivin
Keyword(s):  
Ostrinia Nubilalis ◽  
European Corn Borer ◽  
Large Scale ◽  
Sweet Corn ◽  
Negative Impact ◽  
Real Field ◽  
Corn Production ◽  
Corn Borer ◽  
Egg Masses ◽  
Parasitism Rates

Abstract. Sweet corn requires many insecticide applications to control its main pest: the European corn borer () (Lepidoptera: Crambidae). The use of is an effective biological alternative to control the European corn borer in sweet corn. However, manual introduction at large scale of using Trichocards is time-consuming. Mechanized introduction of using a boom sprayer is an innovative and advantageous solution. The objective of this study was to design and test a boom sprayer to spray (Hymenoptera: Trichogrammatidae) in sweet corn canopy under real field conditions. parasitized eggs were sprayed at a rate of 800,000 individuals ha-1 using a boom sprayer designed at the Department of Soils and Agri-Food Engineering of Université Laval, Québec, Canada. parasitized eggs were also introduced at a rate of 500,000 individuals ha-1 using Trichocards. Overall, eight releases were made during the 2016 season. Field trial results showed a 17.22% emergence rate reduction of in the sprayed plots compared to Trichocards. Total fecundity and longevity of sprayed females were not negatively affected by spraying; indicating that spraying did not have any negative impact on their quality. The parasitism rates observed on natural egg masses of and on sentinel egg masses of were comparable for both methods. At harvest, sprayed and Trichocards treatments resulted in adequate control of the European corn borer. Obtained results also showed that spraying was 1.7 times faster than the manual introduction of Trichocards. Overall, the results indicate that spraying is a promising technique for an efficient and viable introduction of parasitized eggs. However, more research is recommended to further optimize the spraying parameters. The spraying system successfully used in sweet corn could also be used in corn production and adapted to other crops such as pepper, beans, and potatoes to control the European corn borer. Keywords: Biological control, European corn borer, Ostrinia nubilalis, Trichogramma, Trichogramma ostriniae, Sweet corn, Corn production, Spraying, Boom sprayer, Beneficial insects, Trichocards.

scholarly journals Soft windowing application to improve analysis of high-throughput phenotyping data

2019 ◽  
Vol 36 (5) ◽  
pp. 1492-1500 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C Mason ◽  
Violeta Munoz-Fuentes ◽  
Federico López-Gómez ◽  
Kolawole Babalola ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Methodological Approach ◽  
R Package ◽  
Supplementary Information ◽  
Complex Data ◽  
Control Data ◽  
International Mouse Phenotyping Consortium ◽  
High Throughput Phenotyping ◽  
The Uk

Abstract Motivation High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. Results Here we introduce ‘soft windowing’, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype–phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. Availability and implementation The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Soft Windowing Application to Improve Analysis of High-throughput Phenotyping Data

2019 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Mason C. Jeremy ◽  
Violeta Munoz-Fuentes ◽  
Federico López-Gómez ◽  
Kolawole Babalola ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Methodological Approach ◽  
R Package ◽  
Complex Data ◽  
Control Data ◽  
International Mouse Phenotyping Consortium ◽  
High Throughput Phenotyping ◽  
Improve Analysis ◽  
The Uk

AbstractMotivationHigh-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximises analytic power while minimising noise from unspecified environmental factors.ResultsHere we introduce “soft windowing”, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant p-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft windowed and non-windowed approaches, respectively, from a set of 2,082 mutant mouse lines. Our method is generalisable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.Availability and ImplementationThe method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.

scholarly journals Visualization and analysis of high-throughputin vitrodose–response datasets with Thunor

2019 ◽  
Author(s):  
Alexander L. R. Lubbock ◽  
Leonard A. Harris ◽  
Vito Quaranta ◽  
Darren R. Tyson ◽  
Carlos F. Lopez
Keyword(s):  
Cell Proliferation ◽  
High Throughput ◽  
Dose Response ◽  
Large Scale ◽  
Time Course ◽  
Proliferation Rate ◽  
Research Assessment ◽  
Response Curves ◽  
User Friendly ◽  
Dose Dependent

AbstractQuantifying the effects of drugs and other environmental factors on cell proliferation in vitro continues to be one of the most prevalent assays in biomedical research. Assessment of the dose-dependent nature of drug effects is typically performed with a variety of commercial software applications or using freely available, but more technically demanding, statistical programming environments such as Python or R. However, with the advent of large, publicly-available drug response databases and continued advancements in high-throughput experimentation, there is a growing need for user-friendly software platforms that can efficiently and reliably facilitate analysis within and across large datasets. Here we introduce Thunor, an open-source software platform for the management, analysis, and visualization of large-scale dose-dependent cell proliferation datasets. Thunor provides a simple, user-friendly interface to upload cell count data and a graphical plate map tool to annotate plate wells with cell lines and drugs. Best-fit dose–response curves are generated based on either cell viability or proliferation rate drug effect metrics. Derived dose–response parameters, such as IC50, Emax, and activity area, are automatically calculated by the software back-end. An arrayed plot interface supports multiple plot types, including time course, dose–response curve, box/bar/scatter plots of derived parameters, and quality control analyses, among others. We demonstrate the features of Thunor on large-scale, publicly-available viability data and an in-house, high-throughput proliferation rate dataset. Software, documentation, and an online demo are all available at thunor.net.

Four Species of Noctuid Moths Degrade Sex Pheromone by a Common Antennal Metabolic Pathway

1996 ◽  
Vol 31 (4) ◽  
pp. 404-413 ◽  
Author(s):  
Jerome A . Klun ◽  
William J. E. Potts ◽  
James E. Oliver
Keyword(s):  
Sex Pheromone ◽  
Trichoplusia Ni ◽  
European Corn Borer ◽  
Time Course ◽  
Fall Armyworm ◽  
Cabbage Looper ◽  
Beet Armyworm ◽  
Corn Borer ◽  
Black Cutworm

Z-9-tetradecenyl acetate (Z-9-14:OAc) is a component in the female sex pheromones of the cabbage looper, Trichoplusia ni (Hübner), beet armyworm, Spodoptera exigua (Hübner), fall armyworm, Spodoptera frugiperda (J. E. Smith), and black cutworm, Agrotis ipsilon (Hufnagel). We compared the in vivo catabolism of Z-9-14:OAc in time course fashion after the tritiated compound was applied topically to the antennae of males in the four species. Catabolism of tritiated European corn borer, Ostrinia nubilalis (Hübner), sex pheromone (Z-11-14:OAc) was monitored concomitantly so direct comparisons could be made between the male borer and the noctuid males. Results showed that catabolism of pheromone in all four noctuid moths proceeded along the same hydrolysis-alcohol oxidation pathway as has been observed in the European corn borer male. Catabolism was mathematically modeled with first-order differential equations as a four-compartment degradative system in which tritiated pheromonal acetate was sequentially converted to tetradecenol, tetradecenoic acid and water. The modeling revealed subtle differences in catabolism from one species to another and that most species exhibited a finite capacity to catabolize the pheromone.

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

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