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The response to abiotic and biotic stresses in plants and crops is considered a multifaceted process. Due to their sessile nature, plants have evolved unique mechanisms to ensure that developmental plasticity remains during their life cycle. Among these mechanisms, post-translational modifications (PTMs) are crucial components of adaptive responses in plants and transduce environmental stimuli into cellular signalling through the modulation of proteins. SUMOylation is an emerging PTM that has received recent attention due to its dynamic role in protein modification and has quickly been considered a significant component of adaptive mechanisms in plants during stress with great potential for agricultural improvement programs. In the present review, we outline the concept that small ubiquitin-like modifier (SUMO)-mediated response in plants and crops to abiotic and biotic stresses is a multifaceted process with each component of the SUMO cycle facilitating tolerance to several different environmental stresses. We also highlight the clear increase in SUMO genes in crops when compared with Arabidopsis thaliana. The SUMO system is understudied in crops, given the importance of SUMO for stress responses, and for some SUMO genes, the apparent expansion provides new avenues to discover SUMO-conjugated targets that could regulate beneficial agronomical traits.

Phosphorylation is a post-translational modification of great interest to researchers due to its relevance in many biological processes. LC-MS/MS techniques have enabled high-throughput data acquisition with studies claiming identification and localisation of thousands of phosphosites. The identification and localisation of phosphosites emerge from different analytical pipelines and scoring algorithms, with uncertainty embedded throughout the pipeline. For many pipelines and algorithms, arbitrary thresholding is used, but little is known about the actual global false localisation rate in these studies. Recently, it has been suggested using decoy amino acids to estimate global false localisation rates of phosphosites, amongst the peptide-spectrum matches reported. We here describe a simple pipeline aiming to maximize the information extracted from these studies by objectively collapsing from peptide-spectrum match to peptidoform-site level, as well as combining findings from multiple studies while maintaining track of false localisation rates. We show that the approach is more effective than current processes that use a simpler mechanism for handling phosphosite identification redundancy within and across studies. In our case study using 8 rice phophoproteomics data sets, 6,368 unique sites were identified confidently identified using our decoy approach compared to 4,687 using traditional thresholding in which false localisation rates are unknown.

Consistent gene annotation in crops is becoming harder as genomes for new cultivars are frequently published. Gene sets from recently sequenced accessions have different gene identifiers to those on the reference accession, and might be of higher quality due to technical advances. For these reasons there is a need to define pangenes, which represent all known syntenic orthologues for a gene model and can be linked back to the original annotation sources. A pangene set effectively summarizes our current understanding of the coding potential of a crop and can be used to inform gene model annotation in new cultivars. Here we present an approach (get_pangenes) to identify and analyze pangenes that is not biased towards the reference annotation. The method involves computing Whole Genome Alignments (WGA), which are used to estimate gene model overlaps. After a benchmark onArabidopsis, rice, wheat and barley datasets, we find that minimap2 performs better than the GSAlign WGA algorithm. Our results show that pangenes recapitulate known phylogeny-based orthologies while adding extra core gene models in rice. More importantly, get_pangenes can also produce clusters of genome segments (gDNA) that overlap with gene models annotated in other cultivars. By lifting-over CDS sequences, gDNA clusters can help refine gene models across individuals and confirm or reject observed gene Presence-Absence Variation. A collection of flowering-related genes from the barley pangenome are discussed in detail. Documentation and source code are available athttps://github.com/Ensembl/plant-scripts.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Flow cytometry is a commonly used diagnostic technique for haematological malignancies. The gold standard method for analysis of flow cytometry data is manual gating, which is time consuming and requires a highly skilled operator, generating a bottleneck in the workflow and potentially increasing time to diagnose malignancy. For nearly 20 years attempts have been made at replacing manual analysis with automated algorithms, however these are not deemed accurate enough for clinical practice. Clustering methods have been the focus of previous automated attempts, though supervised methods have been shown to be more accurate and require less manual intervention. Tree-based classification algorithms make decisions using an analogous process to manual gating. One hundred and fifty-two flow cytometry files were generated from peripheral blood samples of patients with suspected haematological malignancies. A trained operator labelled events in these files as one of nine cell types. CART, Random Forest and XGBoost were trained on the labelled dataset and the performance was evaluated against previously published clustering methods. Classification algorithms showed higher mean F1 scores than clustering methods. There was no significant difference between CART, Random Forest and XGBoost mean F1 scores, and all three algorithms showed mean prediction times per sample of less than 25 seconds. Tree-based methods struggled to differentiate B cell subtypes, which show similar phenotypic signatures and present an area for future improvement. This work demonstrates the effectiveness of tree-based classification algorithms for flow cytometry analysis. Overall, CART may offer a solution to automated flow cytometry analysis for the purpose of haematological malignancies due to showing high agreement with manual analysis, and short prediction and training times.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception twenty years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Motivation MHC-peptide binding prediction has been widely used for understanding the immune response of individuals or populations, each carrying different MHC molecules as well as for the development of immunotherapeutics. The results from MHC-peptide binding prediction tools are mostly reported as a predicted binding affinity (IC50) and the percentile rank score, and global thresholds e.g. IC50 value < 500 nM or percentile rank < 2% are generally recommended for distinguishing binding peptides from non-binding peptides. However, it is difficult to evaluate statistically the probability of an individual peptide binding prediction to be true or false solely considering predicted scores. Therefore, statistics describing the overall global false discovery rate (FDR) and local FDR, also called posterior error probability (PEP) are required to give statistical context to the natively produced scores. Result We have developed an algorithm and code implementation, called MHCVision, for estimation of FDR and PEP values for the predicted results of MHC-peptide binding prediction from the NetMHCpan tool. MHCVision performs parameter estimation using a modified expectation maximization framework for a two-component beta mixture model, representing the distribution of true and false scores of the predicted dataset. We can then estimate the PEP of an individual peptide’s predicted score, and conversely the probability that it is true. We demonstrate that the use of global FDR and PEP estimation can provide a better trade-off between sensitivity and precision over using currently recommended thresholds from tools. Availability and implementation https://github.com/PGB-LIV/MHCVision. Supplementary information Supplementary data are available at Bioinformatics online.

Muscle stem cells (MuSCs) are indispensable for muscle regeneration. A multitude of extracellular stimuli direct MuSC fate decisions from quiescent progenitors to differentiated myocytes. The activity of these signals is modulated by coreceptors such as syndecan-3 (SDC3). We investigated the global landscape of SDC3-mediated regulation of myogenesis using a phosphoproteomics approach which revealed, with the precision level of individual phosphosites, the large-scale extent of SDC3-mediated regulation of signal transduction in MuSCs. We then focused on INSR/AKT/mTOR as a key pathway regulated by SDC3 during myogenesis and mechanistically dissected SDC3-mediated inhibition of insulin receptor signaling in MuSCs. SDC3 interacts with INSR ultimately limiting signal transduction via AKT/mTOR. Both knockdown of INSR and inhibition of AKT restore Sdc3sup-/-/supMuSC differentiation to wild type levels. Since SDC3 is rapidly downregulated at the onset of differentiation, our study suggests that SDC3 acts a timekeeper to restrain proliferating MuSC response and prevent premature differentiation.

Phosphoproteomic methods are commonly employed to identify and quantify phosphorylation sites on proteins. In recent years, various tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified or to estimate the global false localization rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic datasets, and their statistical reliability on real datasets is largely unknown, potentially leading to studies reporting incorrectly localized phosphosites, due to inadequate statistical control. In this work, we develop the concept of scoring modifications on a decoy amino acid, that is, one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of amino acids, on both synthetic and real data sets, demonstrating that the selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys. We propose the use of a decoy amino acid to control false reporting in the literature and in public databases that re-distribute the data. Data are available via ProteomeXchange with identifier PXD028840.

Complex biological samples, in particular, in proteomics and metabolomics research, are often analyzed using mass spectrometry paired with liquid chromatography or gas chromatography. The chromatography stage adds a third dimension (retention time) to the usual 2D mass spectrometry output (mass/charge, detected ion counts). Experimental results are often discovered by complex computational analysis, but it is not always possible to know if the data has been correctly interpreted. To perform quality-control checks, it can often be helpful to verify the results by manually examining the raw data, and it is typically easier to understand the data in a graphical, rather than numerical, form. 3D graphics hardware is present in most modern computers but is rarely utilized by bioinformatics software, even when the data to be viewed are naturally 3D. lcmsWorld is new software that uses graphics hardware to quickly and smoothly examine and compare LC-MS data. A preprocessing step allows the software to subsequently access any area of the data instantly at multiple levels of detail. The data can then be freely navigated while the software automatically selects, loads, and displays the most appropriate detail. lcmsWorld is open source. Releases, source code, and example data files are available via https://github.com/PGB-LIV/lcmsWorld.

Motivation A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning. Results We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse-engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes and Inflammatory/Innate Response. Availability Data and code are available on the Gene Expression Omnibus and github. Supplementary information Supplementary data are available at Bioinformatics online.

To minimise unexpected toxicities in early phase clinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical test species and humans. We have used physiologically-based pharmacokinetic modelling to identify doses of the model hepatotoxin acetaminophen yielding similar hepatic burdens of the reactive metabolite N-acetyl-p-benzoquinoneimine in mice and rats, to enable comparison of tissue adaptive responses under conditions of equivalent chemical insult. Mice exhibited a greater degree of liver injury than rats, despite the equivalent hepatic NAPQI burden. Transcriptomic and proteomic analyses highlighted the stronger activation of stress response pathways (including the Nrf2 oxidative stress response and autophagy) in the livers of rats. Components of these pathways were also found to be expressed at a higher basal level in the livers of rats compared with both mice and humans. Our findings exemplify a systems approach to understanding differential species sensitivity to hepatotoxicity, and have important implications for species selection and human translation in the safety testing of new drug candidates.

The EXI instrument is a camera onboard the EMM spacecraft, with a field a view capable capturing the full disk of Mars throughout its nominal science orbit. Though the use of its multiple band passes (220, 260, 320, 437, 546, 635 nm) and the effective spatial resolution (2–4 km per native pixel), EXI’s primary goal is to provide both regional and global imaging of the Martian atmosphere with diurnal sampling over much of the planet on a time scale of approximately 10 days. This presentation will provide an overview of EXI’s on-orbit instrument performance, a brief description of the observation strategy employed with the start of Science Operations (23-May-2021, Ls=49°), and the retrieval results of the ice optical depth and their diurnal behavior for the period of mid-spring through late-summer in the northern hemisphere. More specifically, the presentation will cover: Status of the instrument calibration and plans for on-going on-orbit monitoring of instrument performance, including radiometric errors. Plus, some guidance on interpreting the metadata of the EXI publicly released raw and calibrated images; Illustration of the various disk geometries sampled during an EMM orbit of Mars, and how such observations are combined to provide diurnal coverage of the illuminated portion of the disk/atmosphere; Overview of the ice optical depth retrieval algorithm, and its application to the data obtained since the start Science Operations with an emphasis on the behavior of the aphelion cloud belt; including the formation and decay phases.

With ever-increasing amounts of data produced by mass spectrometry (MS) proteomics and metabolomics, and the sheer volume of samples now analyzed, the need for a common open format possessing both file size efficiency and faster read/write speeds has become paramount to drive the next generation of data analysis pipelines. The Proteomics Standards Initiative (PSI) has established a clear and precise XML representation for data interchange, mzML, receiving substantial uptake; nevertheless, storage and file access efficiency has not been the main focus. We propose an HDF5 file format ‘mzMLb’ that is optimised for both read/write speed and storage of the raw mass spectrometry data. We provide extensive validation of write speed, random read speed and storage size, demonstrating a flexible format that with or without compression is faster than all existing approaches in virtually all cases, while with compression, is comparable in size to proprietary vendor file formats. Since our approach uniquely preserves the XML encoding of the metadata, the format implicitly supports future versions of mzML and is straightforward to implement: mzMLb’s design adheres to both HDF5 and NetCDF4 standard implementations, which allows it to be easily utilised by third parties due to their widespread programming language support. A reference implementation within the established ProteoWizard toolkit is provided. Data files used in the paper.

Oxide dispersion strengthened FeCrAl alloys are considered for various high-temperature applications due to their excellent mechanical properties and oxidation resistance, but void formation is a c...

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Parasitic diseases caused by kinetoplastid parasites are a burden to public health throughout tropical and subtropical regions of the world. TriTrypDB (https://tritrypdb.org) is a free online resource for data mining of genomic and functional data from these kinetoplastid parasites and is part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org). As of release 59, TriTrypDB hosts 83 kinetoplastid genomes, nine of which, includingTrypanosoma brucei bruceiTREU927,Trypanosoma cruziCL Brener andLeishmania majorFriedlin, undergo manual curation by integrating information from scientific publications, high-throughput assays and user submitted comments. TriTrypDB also integrates transcriptomic, proteomic, epigenomic, population-level and isolate data, functional information from genome-wide RNAi knock-down and fluorescent tagging, and results from automated bioinformatics analysis pipelines. TriTrypDB offers a user-friendly web interface embedded with a genome browser, search strategy system and bioinformatics tools to support customin silicoexperiments that leverage integrated data. A Galaxy workspace enables users to analyze their private data (e.g., RNA-sequencing, variant calling, etc.) and explore their results privately in the context of publicly available information in the database. The recent addition of an annotation platform based on Apollo enables users to provide both functional and structural changes that will appear as ‘community annotations’ immediately and, pending curatorial review, will be integrated into the official genome annotation.

The Emirates Mars Mission (EMM) – Hope Probe – was developed to understand Mars atmospheric circulation, dynamics, and processes through characterization of the Mars atmosphere layers and its interconnections enabled by a unique high-altitude (19,970 km periapse and 42,650 km apoapse) low inclination orbit that will offer an unprecedented local and seasonal time coverage over most of the planet. EMM has three scientific objectives to (A) characterize the state of the Martian lower atmosphere on global scales and its geographic, diurnal and seasonal variability, (B) correlate rates of thermal and photochemical atmospheric escape with conditions in the collisional Martian atmosphere, and (C) characterize the spatial structure and variability of key constituents in the Martian exosphere. The EMM data products include a variety of spectral and imaging data from three scientific instruments measuring Mars at visible, ultraviolet, and infrared wavelengths and contemporaneously and globally sampled on both diurnal and seasonal timescale. Here, we describe our strategies for addressing each objective with these data in addition to the complementary science data, tools, and physical models that will facilitate our understanding. The results will also fill a unique role by providing diagnostics of the physical processes driving atmospheric structure and dynamics, the connections between the lower and upper atmospheres, and the influences of these on atmospheric escape.

The Emirates Exploration Imager (EXI) on-board the Emirates Mars Mission (EMM) offers both regional and global imaging capabilities for studies of the Martian atmosphere. EXI is a framing camera with a field-of-view (FOV) that will easily capture the martian disk at the EMM science orbit periapsis. EXI provides 6 bandpasses nominally centered on 220, 260, 320, 437, 546, 635 nm using two telescopes (ultraviolet (UV) and visible(VIS)) with separate optics and detectors. Images of the full-disk are acquired with a resolution of 2–4 km per pixel, where the variation is driven by periapsis and apoapsis points of the orbit, respectively. By combining multiple observations within an orbit with planetary rotation, EXI is able to provide diurnal sampling over most of the planet on the scale of 10 days. As a result, the EXI dataset allows for the delineation of diurnal and seasonal timescales in the behavior of atmospheric constituents such as water ice clouds and ozone.This combination of temporal and spatial distinguishes EXI from somewhat similar imaging systems, including the Mars Color Imager (MARCI) onboard the Mars Reconnaissance Orbiter (MRO) (Malin et al. in Icarus 194(2):501–512, 2008) and the various cameras on-board the Hubble Space Telescope (HST; e.g., James et al. in J. Geophys. Res. 101(E8):18,883–18,890, 1996; Wolff et al. in J. Geophys. Res. 104(E4):9027–9042, 1999). The former, which has comparable spatial and spectral coverage, possesses a limited local time view (e.g., mid-afternoon). The latter, which provides full-disk imaging, has limited spatial resolution through most of the Martian year and is only able to provide (at most) a few observations per year given its role as a dedicated, queue-based astrophysical observatory. In addition to these unique attributes of the EXI observations, the similarities with other missions allows for the leveraging of both past and concurrent observations. For example, with MARCI, one can build on the ∼6 Mars years of daily global UV images as well as those taken concurrently with EXI.

Phosphoproteomics methods are commonly employed in labs to identify and quantify the sites of phosphorylation on proteins. In recent years, various software tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified, or to estimate the global false localisation rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic data sets, and their statistical reliability on real datasets is largely unknown. As a result, there is considerable problem in the field of reporting incorrectly localised phosphosites, due to inadequate statistical control.In this work, we develop the concept of using scoring and ranking modifications on a decoy amino acid, i.e. one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of different amino acids to act as the decoy, on both synthetic and real data sets, demonstrating that the amino acid selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys, although we have a preference for alanine due to the risk of potential confusion between leucine and isoleucine amino acids. We propose that the phosphoproteomics field should adopt the use of a decoy amino acid, so that there is better control of false reporting in the literature, and in public databases that re-distribute the data. Data are available via ProteomeXchange with identifier PXD028840.

SARS-CoV-2 is the causative agent behind the COVID-19 pandemic, responsible for over 170 million infections, and over 3.7 million deaths worldwide. Efforts to test, treat and vaccinate against this pathogen all benefit from an improved understanding of the basic biology of SARS-CoV-2. Both viral and cellular proteases play a crucial role in SARS-CoV-2 replication. Here, we study proteolytic cleavage of viral and cellular proteins in two cell line models of SARS-CoV-2 replication using mass spectrometry to identify protein neo-N-termini generated through protease activity. We identify previously unknown cleavage sites in multiple viral proteins, including major antigens S and N: the main targets for vaccine and antibody testing efforts. We discover significant increases in cellular cleavage events consistent with cleavage by SARS-CoV-2 main protease, and identify 14 potential high-confidence substrates of the main and papain-like proteases. We show that siRNA depletion of these cellular proteins inhibits SARS-CoV-2 replication, and that drugs targeting two of these proteins: the tyrosine kinase SRC and Ser/Thr kinase MYLK, show a dose-dependent reduction in SARS-CoV-2 titres. Overall, our study provides a powerful resource to understand proteolysis in the context of viral infection, and to inform the development of targeted strategies to inhibit SARS-CoV-2 and treat COVID-19., During SARS-CoV-2 replication, viral and cellular proteases play crucial roles and have been shown to be promising anti-viral targets. Here, Meyer et al. apply mass spectrometry to characterize the proteolytic cleavage profile of viral and cellular proteins in vitro.

The availability of proteomics datasets in the public domain, and in the PRIDE database in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalysed 24 public proteomics datasets from healthy human individuals, to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples, coming from 489 individuals.We compared protein abundances between the different organs and studied the distribution of proteins across organs. We also compared the results with data generated in analogous studies. We also performed gene ontology and pathway enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where it can be accessed and visualised either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Soils play a critical role in the production of food and feed for a growing global population. Here, we review global patterns in soil characteristics, agricultural production and the fate of embedded soil nutrients. Nitrogen- and organic-rich soils supported the highest crop yields, yet the efficiency of nutrient utilization was concentrated in regions with lower crop productivity and lower rates of chemical fertilizer inputs. Globally, soil resources were concentrated in animal feed, resulting in large inefficiencies in nutrient utilization and losses from the food system. Intercontinental transport of soil-derived nutrients displaced millions of tonnes of nitrogen and phosphorus annually, much of which was ultimately concentrated in urban waste streams. Approximately 40% of the global agricultural land area was in small farms providing over 50% of the world's food and feed needs but yield gaps and economic constraints limit the ability to intensify production on these lands. To better use and protect soil resources in the global food system, policies and actions should encourage shifts to more nutrient-efficient diets, strategic intensification and technological improvement, restoration and maintenance of soil fertility and stability, and enhanced resilience in the face of global change. This article is part of the theme issue ‘The role of soils in delivering Nature's Contributions to People’.