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Background Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS). Results We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set. Conclusions PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap.

BackgroundImprovements in high-throughput phenotyping technologies are rapidly expanding the scope and capacity of plant biology studies to measure growth traits. Nevertheless, the costs of commercial phenotyping equipment and infrastructure remain prohibitively expensive for wide-scale uptake, while academic solutions can require significant local expertise. Here we present a low-cost methodology for plant biologists to build their own phenotyping system for quantifying growth rates and phenotypic characteristics of Arabidopsis thaliana rosettes throughout the diel cycle.ResultsWe constructed an image capture system consisting of a near infra-red (NIR, 940 nm) LED panel with a mounted Raspberry Pi NoIR camera and developed a MatLab-based software module (iDIEL Plant) to characterise rosette expansion. Our software was able to accurately segment and characterise multiple rosettes within an image, regardless of plant arrangement or genotype, and batch process image sets. To further validate our system, wild-type Arabidopsis plants (Col-0) and two mutant lines with reduced Rubisco contents, pale leaves and slow growth phenotypes (1a3b and 1a2b) were grown on a single plant tray. Plants were imaged from 9 to 24 days after germination every 20 min throughout the 24 h light–dark growth cycle (i.e. the diel cycle). The resulting dataset provided a dynamic and uninterrupted characterisation of differences in rosette growth and expansion rates over time for the three lines tested.ConclusionOur methodology offers a straightforward solution for setting up automated, scalable and low-cost phenotyping facilities in a wide range of lab environments that could greatly increase the processing power and scalability of Arabidopsis soil growth experiments.Electronic supplementary materialThe online version of this article (10.1186/s13007-017-0247-6) contains supplementary material, which is available to authorized users.

Background Passiflora (passionflowers) makes an excellent model for studying plant evolutionary development. They are mostly perennial climbers that display axillary tendrils, which are believed to be modifications of the inflorescence. Passionflowers are also recognized by their unique flower features, such as the extra whorls of floral organs composed of corona filaments and membranes enclosing the nectary. Although some work on Passiflora organ ontogeny has been done, the developmental identity of both Passiflora tendrils and the corona is still controversial. Here, we combined ultrastructural analysis and expression patterns of the flower meristem and floral organ identity genes of the MADS-box AP1/FUL clade to reveal a possible role for these genes in the generation of evolutionary novelties in Passiflora.ResultsWe followed the development of structures arising from the axillary meristem from juvenile to adult phase in P. edulis. We further assessed the expression pattern of P. edulis AP1/FUL homologues (PeAP1 and PeFUL), by RT-qPCR and in situ hybridization in several tissues, correlating it with the developmental stages of P. edulis. PeAP1 is expressed only in the reproductive stage, and it is highly expressed in tendrils and in flower meristems from the onset of their development. PeAP1 is also expressed in sepals, petals and in corona filaments, suggesting a novel role for PeAP1 in floral organ diversification. PeFUL presented a broad expression pattern in both vegetative and reproductive tissues, and it is also expressed in fruits.ConclusionsOur results provide new molecular insights into the morphological diversity in the genus Passiflora. Here, we bring new evidence that tendrils are part of the Passiflora inflorescence. This points to the convergence of similar developmental processes involving the recruitment of genes related to flower identity in the origin of tendrils in different plant families. The data obtained also support the hypothesis that the corona filaments are likely sui generis floral organs. Additionally, we provide an indication that PeFUL acts as a coordinator of passionfruit development.Electronic supplementary materialThe online version of this article (doi:10.1186/s13227-017-0066-x) contains supplementary material, which is available to authorized users.

Plant touch-sensitive organs have been described since Darwin's observations and are related to a quick response to environment stimuli. Sensitive flower organs have been associated to an increase in the chances of cross pollination but there are few studies regarding this topic. Here we describe for the first time the kinetic of the androgynophore movement of 4 Passiflora species (P. sanguinolenta, P. citrina, P. capsularis, and P. rubra). For that, we collected flowers and recorded the movement after mechano-stimulating the androgynophore. From the recordings, we described the movement regarding its response and sensibility to mechanical stimulus and calculated the duration, speed, and the angle formed by the androgynophore before and after the movement. From our data we were able to propose a link to the pollination habit of these species. The movement of the androgynophore in these Passiflora is a noteworthy floral feature that might lead us to another astonishing example of a mechanism that evolved among angiosperms to assure sexual reproduction.

Three-dimensional printing applications in separation science are currently limited by the lack of materials compatible with chromatographic operations and three-dimensional printing technologies. In this work, we propose a new material for Digital Light Processing printing to fabricate functional ion exchange monoliths in a single step. Through copolymerization of the bifunctional monomer [2-(acryloyloxy)ethyl] trimethylammonium chloride, monolithic structures with quaternary amine ligands were fabricated. The novel formulation was optimized in terms of protein binding and recovery, microporous structure, and its swelling susceptibility by increasing its cross-link density and employing cyclohexanol and dodecanol as pore forming agents. In static conditions, the material demonstrated a maximum binding capacity of 104.2 ± 10.6 mg/mL for bovine serum albumin, in line with commercially available materials. Its anion exchange behavior was validated by separating bovine serum albumin and myoglobin on a monolithic bed with Schoen gyroid morphology. The same column geometry was tested for the purification of C-phycocyanin from clarified as well as cell-laden Arthrospira platensis feedstocks. This represents the first demonstration of one-step printed stationary phases to capture proteins directly from solid-laden feedstocks. We believe that the material presented here represents a significant improvement towards implementation of three-dimensional printed chromatography media in the field of separation science.

After obtaining the raw measurements as gene copies per litre using RT-qPCR, a normalisation process is required prior to reporting the data as RNA copies per person. This is because the concentration of viral RNA in wastewater is affected by both the population of the catchment area at each waterworks, as well as the amount of flow into the works. For example, an area with heavy rainfall will have high volumes of fluid flow, which will dilute RNA values. Therefore, parameters such as the flow volume of wastewater and population size are used to overcome this bias. Unfortunately, the flow data are not always accessible for all sites. Three methods were developed by Biomathematics and Statistics Scotland (BioSS) to estimate the flow for the normalization process, depending on data availability: 1. Normalisation using ammonia concentration to estimate flow; 2. Normalisation using flow historical average (when data for method 1 are not available); 3. Normalisation using predicted ammonia to estimate flow (when data for methods 1 and 2 are not available). For all methods, the normalised outputs were reported as a daily value of RNA copies per person. These methods are described separately in this protocol. For other methodologies involving SARS-CoV-2 quantification in wastewater, such as viral RNA isolation and RT-qPCR, please refer to: dx.doi.org/10.17504/protocols.io.bzv5p686 (RNA extraction from wastewater for detection of SARS-CoV-2) dx.doi.org/10.17504/protocols.io.bzwap7ae (RT-qPCR for detection of SARS-CoV-2 in wastewater)

As part of the global response to the 2019 novel Coronavirus (SARS-CoV-2) pandemic, it was determined that SARS-CoV-2 RNA was detectable in the faeces of both symptomatic and asymptomatic patients (1). Further analysis demonstrated that a wastewater epidemiological (WWE) approach, similar to that used to track other viruses (i.e. Poliovirus), could be employed to monitor the spread of SARS-CoV-2. The presence of, or changes in concentration of viral RNA within the wastewater network can assist in monitoring the emergence of further viral peaks (2). Thus, monitoring the spread of Covid-19 using the WWE approach has been extensively explored in several countries (3). This procedure, developed by the Scottish Environment Protection Agency (SEPA) based upon work of the Corbishley group at the Roslin Institute, University of Edinburgh, outlines the method for the concentration of viral RNA in wastewater, as well as in other types of environmental and potable water samples. In this method, a known volume of sample is concentrated using a centrifugal filter to allow further extraction and detection of SARS-CoV-2 RNA. For further analysis using RT-qPCR please search for "RT-qPCR for detection of SARS-CoV-2 in wastewater" by the same authors of this protocol on protocols.io References: 1. Jones, D. L., Baluja, M. Q., Graham, D. W., Corbishley, A., McDonald, J. E., Malham, S. K., Hillary, L. S., Connor, T. R., Gaze , W. H., Moura , I. B., Wilcox, M. H., & Farkas , K. (2020).Shedding of SARS-CoV-2 in feces and urine and its potential role in person-to-person transmission and the environment-based spread of COVID-19.Science of the Total Environment.https://doi.org/10.1016/j.scitotenv.2020.1413644. 2. Fitzgerald, S., Rossi, G., Low, A., McAteer, S., O’Keefe, B., Findlay, D., Cameron, G. J., Pollard, P., Singleton, P. T. R., Ponton, G., Singer, A. C., Farkas, K., Jones, D., Graham, D. W., Quintela-Baluja, M., Tait-Burkard, C., Gally, D., Kao, R., & Corbishley, A.(2021).Site specific relationships between COVID-19 cases and SARS-CoV-2 viral load in wastewater treatment plant influent. Environmental Science and Technology. https://doi.org/10.1021/acs.est.1c05029 3. Wastewater SARS Public Health Environmental Response (W-SPHERE). https://sphere.waterpathogens.org/about