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This study presents results from the European Centre for Medium-RangeWeather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of columnaveraged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively) and an improved estimated precision of 1 ppm compared to the GOSAT BESD data (3.3 ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00 UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000 km) even up to day 5 compared to its own analysis.

Over the last decade, viral metagenomic studies have resulted in the discovery of thousands of previously unknown viruses. These studies are likely to play a pivotal role in obtaining an accurate and robust understanding of how viruses affect the stability and productivity of ecosystems. Among the metagenomics-based approaches that have been developed since the beginning of the 21st century, shotgun metagenomics applied specifically to virion-associated nucleic acids (VANA) has been used to disentangle the diversity of the viral world. We summarize herein the results of 24 VANA-based studies, focusing on plant and insect samples conducted over the last decade (2010 to 2020). Collectively, viruses from 85 different families were reliably detected in these studies, including capsidless RNA viruses that replicate in fungi, oomycetes, and plants. Finally, strengths and weaknesses of the VANA approach are summarized and perspectives of applications in detection, epidemiological surveillance, environmental monitoring, and ecology of plant viruses are provided.

Plant pest diagnosis is performed by official laboratories upon request of National Plant Protection Organizations (NPPOs), growers or traders, in samples that inspectors have collected in situ (a consignment, a place of production, an outbreak area, a buffer zone, etc.). Resources allocated to official laboratories have decreased over time, while trade in plants and plant products, and consequently the material to be tested, have increased steadily. As indicated in the Euphresco Strategic Research Agenda (priority R-6), on-site detection and identification tests, that are both high throughput and scalable at contained costs should be developed and validated to accelerate diagnosis (especially in the case of perishable goods) and to relieve pressure on laboratories. In particular, the application of on-site detection and surveillance methods for plant viruses and viroids is needed for the quick assessment of the health status of plant material. New protocols and technologies based on sequencing are under development (such as Oxford nanopore direct RNA sequencing - cDNA sequencing), but need to be tested, optimised and validated in order to be applicable in routine on-site testing. Furthermore, current barriers for the use of novel on-site technologies need to be addressed. The VIRFAST project explored the possibility of on-site virus diagnosis with the MinION technology (Oxford Nanopore sequencing). Early results indicated that the traditional RNA extraction protocol could be bypassed resulting in a quicker and low-tech solution for practical diagnostics on-site. However, skipping the extraction needs additional development for diagnostic purposes, as the sequencing threshold and, consequently, the sensitivity of the test is greatly reduced. However, it should be noted that validation was performed using dehydrated samples and moreover some participating laboratories used the Oxford nanopore sequencer on crude extract material for the first time. Better analytical sensitivity is expected when working with fresh tissue and if the users are familiar with the use of crude extract material. Two sample preparation kits were also compared starting from purified RNA extracts. In summary, better results for virus/viroid detection when using cDNA-PCR library preparation approach, compared to direct RNA sequencing approach, due to the higher relative amount of sequencing reads of viral origin. The increased analytical sensitivity when using the cDNA-PCR library preparation opens the possibility of using this method to monitor an outbreak in the field, in a middle-tech laboratory or in remote locations with limited access to plant diagnostics facilities. A survey of 26 NPPO laboratories showed that while only 15% were using high-throughput sequencing (HTS) in their diagnostic workflows in 2019, almost all wished to adopt it and that improved staff training and reduced costs would be essential for this uptake. The results of a survey on on-site testing allowed also to observe interesting trends. Twelve laboratories were already using on-site testing with an estimated volume ranging from 25 to 2,500 tests per year. The main context for using on-site testing kits was field inspection (63%), followed by screening test in the laboratory (19%). Time saving was the most important advantage for use. The laboratories also highlighted the ease of use. The three main limitations for a wider use of HTS were the lack of validation data, the limited sensitivity and the cost., Report of the Euphresco project 2017-A-243 'Faster, cheaper identification of emerging virus problems (VIRFAST)'

These guidelines describe a new and improved approach to analyse and report data of validation studies and inter-laboratory comparisons studies (i.e. test performance studies) for integration in the appropriate EPPO standards (i.e. PM 7/98 (2019) and PM 7/122 (2014)). The proposed methods of analysis including statistical methods, have been tested on the 10 data sets obtained from the test performance studies conducted in the framework of VALITEST (as part of Work package 1). This new approach uses, wherever possible and whenever applicable, statistical methods recommended by international standards with examples of their application to plant health diagnostic tests. This approach is a consensus agreement between VALITEST members. The analysis, presentation and interpretation of data are presented using data based on a real data set of a test performance study conducted by VALITEST (as part of Work package 1). It has been written in a simple manner for diagnostician with limited knowledge in statistics. More advanced statistical explanation are provided in Annexes.

The current plastic pollution throughout the world implies a crucial optimization of its (bio)degradation processes. In order to identify plastic degrading bacteria and associated enzymes, the gut microbiota of insects has raised interest. Some entomological models such as Tenebrio molitor (L. 1758), Plodia interpunctella (Hübner 1813) or Galleria mellonella (L. 1758) have the ability to ingest and degrade polyethylene. Then, it is promising to identify the composition and the role of the gut microbiota in this process. This study takes part in this issue by investigating G. mellonella as a biological model feeding with a polyethylene diet. Gut microbiome samples were processed by high throughput 16S rRNA sequencing, and Enterococcaceae and Oxalobacteraceae were found to be the major bacterial families. At low polyethylene dose, we detect no bacterial community change and no amplicon sequence variant associated with the polyethylene diet suggesting microbiome resilience. The functional analysis of insects gut content was promising for the identification of plastic degrading enzymes such as the phenylacetaldehyde dehydrogenase which participate in styrene degradation. This study allows a better characterization of the gut microbiota of G. mellonella and provides a basis for the further biodegradation study of polyethylene based on the microorganism valorization from insect guts.

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratifcation vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/ or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

High throughput sequencing of total RNA isolated from symptomatic leaves of a sweet cherry tree (Prunus avium cv. 0900 Ziraat) from Turkey identified a new member of the genus Robigovirus designated cherry virus Turkey (CVTR). The presence of the virus was confirmed by electron microscopy and overlapping RT-PCR for sequencing its whole-genome. The virus has a ssRNA genome of 8464 nucleotides which encodes five open reading frames (ORFs) and comprises two non-coding regions, 5' UTR and 3' UTR of 97 and 296 nt, respectively. Compared to the five most closely related robigoviruses, RdRp, TGB1, TGB2, TGB3 and CP share amino acid identities ranging from 43–53%, 44–60%, 39–43%, 38–44% and 45–50%, respectively. Unlike the four cherry robigoviruses, CVTR lacks ORFs 2a and 5a. Its genome organization is therefore more similar to African oil palm ringspot virus (AOPRV). Using specific primers, the presence of CVTR was confirmed in 15 sweet cherries and two sour cherries out of 156 tested samples collected from three regions in Turkey. Among them, five samples were showing slight chlorotic symptoms on the leaves. It seems that CVTR infects cherry trees with or without eliciting obvious symptoms, but these data should be confirmed by bioassays in woody and possible herbaceous hosts in future studies. © 2019 by the authors. Licensee MDPI, Basel, Switzerland., Javna Agencija za Raziskovalno Dejavnost RS, Funding: This work was supported by TUBITAK-TOVAG grant number 213O042, the Slovenian Research Agency grant P4-0072 and by the COST Action grant number FA1407 (DIVAS).

TheVALITEST technical periodic report M1-M18 details the work carried out by the partners and overviews the progress of the tasks as described in the Description of Actions (DoA) during the first reporting period M1-M18 (May 2018-October 2019).

ispartof: CABI Agriculture and Bioscience vol:1 issue:1 pages:1-22

Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 and 34 ppb, respectively). In comparison, the a priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

Recent developments in high-throughput sequencing (HTS), also called next-generation sequencing (NGS), technologies and bioinformatics have drastically changed research on viral pathogens and spurred growing interest in the field of virus diagnostics. However, the reliability of HTS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at detecting viruses in HTS data have been reported but little attention has been paid thus far to their sensitivity and reliability for diagnostic purposes. Therefore, we compared the ability of 21 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 12 plant viruses through a double-blind large-scale performance test using 10 datasets of 21- to 24-nucleotide small RNA (sRNA) sequences from three different infected plants. The sensitivity of virus detection ranged between 35 and 100% among participants, with a marked negative effect when sequence depth decreased. The false-positive detection rate was very low and mainly related to the identification of host genome-integrated viral sequences or misinterpretation of the results. Reproducibility was high(91.6%). This work revealed the key influence of bioinformatics strategies for the sensitive detection of viruses in HTS sRNA datasets and, more specifically (i) the difficulty in detecting viral agents when they are novel or their sRNA abundance is low, (ii) the influence of key parameters at both assembly and annotation steps, (iii) the importance of completeness of reference sequence databases, and (iv) the significant level of scientific expertise needed when interpreting pipeline results. Overall, this work underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.