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 image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.ResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented 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 a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation 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, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way.ConclusionsLeaf-GP is a sophisticated software application that provides three approaches to quantify 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 HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementatio...

Summary Dengue virus (DENV) is an emerging threat causing an estimated 390 million infections per year. Dengvaxia, the only licensed vaccine, may not be adequately safe in young and seronegative patients; hence, development of a safer, more effective vaccine is of great public health interest. Virus‐like particles (VLPs) are a safe and very efficient vaccine strategy, and DENV VLPs have been produced in various expression systems. Here, we describe the production of DENV VLPs in Nicotiana benthamiana using transient expression. The co‐expression of DENV structural proteins (SP) and a truncated version of the non‐structural proteins (NSPs), lacking NS5 that contains the RNA‐dependent RNA polymerase, led to the assembly of DENV VLPs in plants. These VLPs were comparable in appearance and size to VLPs produced in mammalian cells. Contrary to data from other expression systems, expression of the protein complex prM‐E was not successful, and strategies used in other expression systems to improve the VLP yield did not result in increased yields in plants but, rather, increased purification difficulties. Immunogenicity assays in BALB/c mice revealed that plant‐made DENV1‐SP + NSP VLPs led to a higher antibody response in mice compared with DENV‐E domain III displayed inside bluetongue virus core‐like particles and a DENV‐E domain III subunit. These results are consistent with the idea that VLPs could be the optimal approach to creating candidate vaccines against enveloped viruses.

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