Biology subjects

Griffiths, S.

Publications and source records attributed to Griffiths, S..

24 records · Page 2Linked to original sources

bioRxiv2019

Natural selection towards wild-type in composite cross populations of winter wheat

Most of our crops are grown in monoculture with single genotypes grown over wide acreage. An alternative approach, where segregating populations are used as crops is an exciting possibility, but outcomes of natural selection upon this type of crop are not well understood. We tracked allelic frequency changes in evolving composite cross populations (CCPs) of wheat grown over ten generations under organic and conventional farming. At three generations, each population was genotyped with 19 SSR and 8 SNP markers. The latter were diagnostic for major functional genes. Gene diversity was constant at SSR markers but decreased over time for genic markers. Population differentiation between the four locations could not be detected, suggesting that organic vs. non-organic crop management did not drive allele frequency changes. However, we did see changes for genes controlling plant height and phenology in all populations independently and consistently. We interpret these changes as the result of a consistent natural selection towards wild-type. Independent selection for alleles that are associated with plant height suggests that competition for light was central, resulting in the predominance of stronger intraspecific competitors, and highlighting a potential trade-off between individual and population performance.

genetics↗

bioRxiv2019

Analysis of the recombination landscape of hexaploid bread wheat reveals genes controlling recombination and gene conversion frequency

Sequence exchange between homologous chromosomes through crossing over and gene conversion is highly conserved among eukaryotes, contributing to genome stability and genetic diversity. Lack of recombination limits breeding efforts in crops, therefore increasing recombination rates can reduce linkage-drag and generate new genetic combinations. We use computational analysis of 13 recombinant inbred mapping populations to assess crossover and gene conversion frequency in the hexaploid genome of wheat (Triticum aestivum). We observe that high frequency crossover sites are shared between populations and that closely related parental founders lead to populations with more similar crossover patterns. We demonstrate that gene conversion is more prevalent and covers more of the genome in wheat than in other plants, making it a critical process in the generation of new haplotypes, particularly in centromeric regions where crossovers are rare. We have identified QTL for altered gene conversion and crossover frequency and confirm functionality for a novel RecQ helicase gene that belongs to an ancient clade that is missing in some plant lineages including Arabidopsis. This is the first gene to be demonstrated to be involved in gene conversion in wheat. Harnessing the RecQ helicase has the potential to break linkage-drag utilizing widespread gene conversions.

genetics↗

bioRxiv2019

Feedback training can increase detection of happiness in ambiguous facial expressions in children and adults with autism spectrum disorders

Recognition of subtle emotional facial expressions is challenging for some individuals with autism spectrum disorders (ASD). Training that targets recognition of low intensity emotional expressions may therefore be effective as an intervention to improve social-emotional skills. This paper reports the results of two randomised controlled experiments looking at the effect of a training methodology designed to increase the recognition of happy emotion in low intensity happy facial expressions. The first study implements this training with children with autism spectrum disorder (ASD, N = 14) and the second study implements this training with adults with ASD (N = 27). The training paradigm used images from a morph sequence that mixed a happy expression with a mixed-emotion norm expression to create a sequence of varying intensity happy expressions. Participants were asked to say whether or not individual faces from the sequence were happy, to measure their happiness detection threshold. Participants that received active training were given biased feedback to shift their detection threshold, while participants that received control training were given feedback consistent with their baseline threshold. There was some statistical evidence that thresholds in the active training group shifted more than in the control group. This suggests training was successful in increasing the number of expressions that individuals identified as happy. However, there was no evidence that training increased facial expression recognition accuracy, as measured by the Reading the Mind in the Eyes task completed after training (Study 2).

clinical trials↗

bioRxiv2018

CropMonitor: a scalable open-source experiment management system for distributed plant phenotyping and IoT-based crop management

BackgroundHigh-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales.\n\nFindingsCropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted.\n\nConclusionsAs an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.

plant biology↗

bioRxiv2017

Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

BackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.\n\nResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.\n\nAs Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.\n\nConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

plant biology↗

bioRxiv2017

CropQuant: An automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture

Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to todays crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.

plant biology↗