Search

Abstract Safe and Sustainable by Design (SSbD) is a new regulatory concept guiding chemical and material innovation. The European Commission has recommended a two-stage SSbD framework and plan to revise it based on stakeholder feedback. The framework involves establishing key (re)design SSbD principles and assessment of the final innovation, however the applicability of the framework to advanced materials remains to be addressed. Here, we applied the SSbD framework on Ti3C2Tx MXenes, an emerging advanced material, by (1) reviewing health, environmental and safety information; (2) characterizing MXenes transformation/dissolution in different media; (3) conducting (eco)toxicological experiments, and (4) completing a prospective life-cycle assessment. Our analysis suggests that Ti3C2Tx is safe and sustainable when applying the SSbD framework. Further research is needed regarding long-term hazardous effects of Ti3C2Tx and the sustainable production of the titanium precursor. Guidance is also needed on how much weight one should assign to the different lines of evidence in the overall SSbD assessment.

Abstract The paper describes the production and evaluation of global grassland extent mapped annually for 2000–2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery). Custom probability thresholds (based on five-fold spatial cross-validation) were used to derive dominant class maps with balanced user’s and producer’s accuracy, resulting in f1 score of 0.64 and 0.75 for cultivated and natural/semi-natural grassland, respectively. The produced maps (about 4 TB in size) are available under an open data license as Cloud-Optimized GeoTIFFs and as Google Earth Engine assets. The suggested uses of data include (1) integration with other compatible land cover products and (2) tracking the intensity and drivers of conversion of land to cultivated grasslands and from natural / semi-natural grasslands into other land use systems.

Citizen science has become a proven method across a range of scientific disciplines, able to collect new and complementary data which enhances and adds context to existing scientific methods. By upscaling to a transnational level, citizen science could collect, analyse and exploit a vast amount of data across Europe and beyond, achieving a higher impact through creating a multinational community of citizen scientists. However, many citizen science initiatives start at a small-scale, facing technical and practical challenges when attempting to upscale to a wider level, with current EU mechanisms not providing the support or resources required to assist their effort. The CROPS project (crops-cs.eu) will evolve the EU Research & Innovation system so that it can support the transition of citizen science from small-scale to a Europe-wide level, changing it towards a modern, open-science approach. CROPS consists of four activities: (i) appraisal of existing citizen science, their activities and their suitability for upscaling; (ii) creation of protocols and guidance for the upscaling of citizen science, replicating and building on best practice that exists; (iii) providing guidance regarding practical considerations such as open data sharing, sustainability, RRI and diverse funding opportunities; and (iv) development of transnational citizen science communities, including establishing societal coalitions and prospective citizen science champions to raise awareness of the potential of citizen science when addressing Horizon Europe EU Mission goals.

Abstract Marine fisheries are increasingly impacted by climate change, affecting species distribution and productivity, and necessitating urgent adaptation efforts. Climate vulnerability assessments (CVA), integrating expert knowledge, are vital for identifying species that could thrive or suffer under changing environmental conditions. This study presents a first CVA for the Western Baltic Sea's fish community, a crucial fishing area for Denmark and Germany. Characterized by a unique mix of marine, brackish, and freshwater species, this coastal ecosystem faces significant changes due to the combined effects of overfishing, eutrophication and climate change. Our CVA involved a qualitative expert scoring of 22 fish species, assessing their sensitivity and exposure to climate change. Our study revealed a dichotomy in climate change vulnerability within the fish community of the Western Baltic Sea because traditional fishing targets cod and herring as well as other species with complex life histories are considered to face increased risks, whereas invasive or better adaptable species might thrive under changing conditions. Our findings hence demonstrate the complex interplay between life-history traits and climate change vulnerability in marine fish communities. Eventually, our study provides critical knowledge for the urgent development of tailored adaptation efforts addressing existing but especially future effects of climate change on fish and fisheries in the Western Baltic Sea, to navigate this endangered fisheries systems into a sustainable future.

Abstract Fisheries worldwide face uncertain futures as climate change manifests in environmental effects of hitherto unseen strengths. Developing climate-ready management strategies traditionally requires a good mechanistic understanding of stock response to climate change in order to build projection models for testing different exploitation levels. Unfortunately, model-based projections of fish stocks are severely limited by large uncertainties in the recruitment process, as the required stock-recruitment relationship is usually not well represented by data. An alternative is to shift focus to improving the decision-making process, as postulated by the decision-making under deep uncertainty (DMDU) framework. Robust Decision Making (RDM), a key DMDU concept, aims at identifying management decisions that are robust to a vast range of uncertain scenarios. Here we employ RDM to investigate the capability of North Sea cod to support a sustainable and economically viable fishery under future climate change. We projected the stock under 40,000 combinations of exploitation levels, emission scenarios and stock-recruitment parameterizations and found that model uncertainties and exploitation have similar importance for model outcomes. Our study revealed that no management strategy exists that is fully robust to the uncertainty in relation to model parameterization and future climate change. We instead propose a risk assessment that accounts for the trade-offs between stock conservation and profitability under deep uncertainty.

Abstract Gas sensors based on micro‐electromechanical systems (MEMS) offer advantages such as a broad spectrum of potentially sensitive materials and analytes, easy miniaturization and integration, high sensitivity, and low costs. This paper introduces a novel MEMS sensor platform utilizing a suspended gate field effect transistor (SGFET) transducer. In this approach, the flexible gate membrane of the SGFET is coated with a sensitive material exhibiting responsive swelling behavior. For the proof of concept, kraft lignin hydrogel is chosen as a biorenewable material for humidity sensing. A precision dispensing technique is used to deposit kraft lignin hydrogel on the SGFETs. The sensor measurements yield reversible shifts in the sensor's output current of up to 9% in response to 5000 ppm water vapor. The results successfully demonstrate the feasibility of this new sensing platform.

Abstract Neuronal ceroid lipofuscinosis 6 (CLN6) is a rare and fatal autosomal recessive disease primarily affecting the nervous system in children. It is caused by a pathogenic mutation in the CLN6 gene for which no therapy is available. Employing an untargeted metabolomics approach, we analyzed the metabolic changes in CLN6 subjects to see if this system could potentially yield biomarkers for diagnosis and monitoring disease progression. Neuronal-like cells were derived from human fibroblast lines from CLN6-affected subjects (n = 3) and controls (wild type, n = 3). These were used to assess the potential of a neuronal-like cell-based metabolomics approach to identify CLN6 distinctive and specific biomarkers. The most impacted metabolic profile is associated with sphingolipids, glycerophospholipids metabolism, and calcium signaling. Over 2700 spectral features were screened, and fifteen metabolites were identified that differed significantly between both groups, including the sphingolipids C16 GlcCer, C24 GlcCer, C24:1 GlcCer and glycerophospholipids PG 40:6 and PG 40:7. Of note, these fifteen metabolites were downregulated in the CLN6 disease group. This study is the first to analyze the metabolome of neuronal-like cells with a pathogenic mutation in the CLN6 gene and to provide insights into their metabolomic alterations. This could allow for the development of novel biomarkers for monitoring CLN6 disease.

Abstract Circular RNAs (circRNAs) are a regulatory RNA class. While cancer-driving functions have been identified for single circRNAs, how they modulate gene expression in cancer is not well understood. We investigate circRNA expression in the pediatric malignancy, neuroblastoma, through deep whole-transcriptome sequencing in 104 primary neuroblastomas covering all risk groups. We demonstrate that MYCN amplification, which defines a subset of high-risk cases, causes globally suppressed circRNA biogenesis directly dependent on the DHX9 RNA helicase. We detect similar mechanisms in shaping circRNA expression in the pediatric cancer medulloblastoma implying a general MYCN effect. Comparisons to other cancers identify 25 circRNAs that are specifically upregulated in neuroblastoma, including circARID1A. Transcribed from the ARID1A tumor suppressor gene, circARID1A promotes cell growth and survival, mediated by direct interaction with the KHSRP RNA-binding protein. Our study highlights the importance of MYCN regulating circRNAs in cancer and identifies molecular mechanisms, which explain their contribution to neuroblastoma pathogenesis.

In this study, the authors analyse contact tracing records for ~650,000 suspected or confirmed COVID-19 cases in New York City during the second epidemic wave. They reconstruct transmission networks and find that vaccination and zone-based control policies likely contributed to control of the epidemic.

Measurement(s) land cover Technology Type(s) visual interpretation of satellite imagery Sample Characteristic - Location Indonesia

Nighttime lights from satellite are combined with a map of human settlements, showing that 19% of these areas, mainly in Africa, the Middle East and Asia, have no detectable artificial light. These data were then used in models to predict well-being.

Measurement(s) forest management type Technology Type(s) Geo-Wiki toolbox

The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency’s WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

Improving the sustainability of the European cattle sector requires improved knowledge not only of the density of cattle, but also of the grazing patterns. Only in this way can the potential negative impacts of cattle related to local ecosystem degradation, as well as positive ones such as preserving cultural landscapes through grazing, be analyzed. While data on livestock distribution often used in scientific analyses can provide estimates on density, the separation between the livestock that has access to outdoor grazing and those that remain indoors is not available. This is problematic because it prevents the identification of the intensity and type of grassland management, as well as the consequential environmental impacts of grazing livestock. Here we present an approach where we combined agricultural and veterinary statistics, in-situ data, expert surveys and machine learning to develop a map of grazing cattle distribution for the wider European Union region. Our approach and the resulting data allow for the differentiation between cattle that are actually grazing versus those that do not. We also compare our method to traditional approaches that do not have a clear separation between grazing and non-grazing cattle, illustrating the implications that this can have for agricultural, land use and environmental assessments.

The Amazon forests act as a global reserve for carbon, have very high biodiversity, and provide a variety of additional ecosystem services. These forests are, however, under increasing pressure, coming mainly from deforestation, despite the fact that accurate satellite monitoring is in place that produces annual deforestation maps and timely alerts. Here, we present a proof of concept for rapid deforestation monitoring that engages the global community directly in the monitoring process via crowdsourcing while subsequently leveraging the power of deep learning. Offering no tangible incentives, we were able to sustain participation from more than 5500 active contributors from 96 different nations over a 6-month period, resulting in the crowd classification of 43,108 satellite images (representing around 390,000 km2). Training a suite of AI models with results from the crowd, we achieved an accuracy greater than 90% in detecting new and existing deforestation. These findings demonstrate the potential of a crowd–AI approach to rapidly detect and validate deforestation events. Our method directly engages a large, enthusiastic, and increasingly digital global community who wish to participate in the stewardship of the global environment. Coupled with existing monitoring systems, this approach could offer an additional means of verification, increasing confidence in global deforestation monitoring.

Measurement(s) drivers of tree loss Technology Type(s) visual inspection of very high resolution satellite imagery Sample Characteristic - Organism trees Sample Characteristic - Environment Forests Sample Characteristic - Location Tropics and subtropics

Measurement(s) built-up areas Technology Type(s) visual interpretation of satellite imagery Factor Type(s) geographic location Sample Characteristic - Environment land • area of developed space Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17068160

Background Tele-emergency physicians (TEPs) take an increasingly important role in the need-oriented provision of emergency patient care. To improve emergency medicine in rural areas, we set up the project ‘Rural|Rescue’, which uses TEPs to restructure professional rescue services using information and communication technologies (ICTs) in order to reduce the therapy-free interval. Successful implementation of ICTs relies on user acceptance and knowledge sharing behavior. Method We conducted a factorial design with active knowledge transfer and technology acceptance as a function of work satisfaction (high vs. low), workload (high vs. low) and point in time (prior to vs. after digitalization). Data were collected via machine readable questionnaires issued to 755 persons (411 pre, 344 post), of which 304 or 40.3% of these persons responded (194 pre, 115 post). Results Technology acceptance was higher after the implementation of TEP for nurses but not for other professions, and it was higher when the workload was high. Regarding active communication and knowledge sharing, employees with low work satisfaction are more likely to share their digital knowledge as compared to employees with high work satisfaction. This is an effect of previous knowledge concerning digitalization: After implementing the new technology, work satisfaction increased for the more experienced employees, but not for the less experienced ones. Conclusion Our research illustrates that employees’ workload has an impact on the intention of using digital applications. The higher the workload, the more people are willing to use TEPs. Regarding active knowledge sharing, we see that employees with low work satisfaction are more likely to share their digital knowledge compared to employees with high work satisfaction. This might be attributed to the Dunning-Kruger effect. Highly knowledgeable employees initially feel uncertain about the change, which translates into temporarily lower work satisfaction. They feel the urge to fill even small knowledge gaps, which in return leads to higher work satisfaction. Those responsible need to acknowledge that digital change affects their employees’ workflow and work satisfaction. During such times, employees need time and support to gather information and knowledge in order to cope with digitally changed tasks.

Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles.

Abstract Understanding tipping point dynamics in harvested ecosystems is of crucial importance for sustainable resource management because ignoring their existence imperils social-ecological systems that depend on them. Fisheries collapses provide the best known examples for realizing tipping points with catastrophic ecological, economic and social consequences. However, present-day fisheries management systems still largely ignore the potential of their resources to exhibit such abrupt changes towards irreversible low productive states. Using a combination of statistical changepoint analysis and stochastic cusp modelling, here we show that Western Baltic cod is beyond such a tipping point caused by unsustainable exploitation levels that failed to account for changing environmental conditions. Furthermore, climate change stabilizes a novel and likely irreversible low productivity state of this fish stock that is not adapted to a fast warming environment. We hence argue that ignorance of non-linear resource dynamics has caused the demise of an economically and culturally important social-ecological system which calls for better adaptation of fisheries systems to climate change.

Abstract Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%). Yet remote sensing products indicate increased vegetation productivity, tree cover and above-ground biomass. Here, we challenge these statistics with a combination of recent National Forest Inventory and remote sensing data to provide an alternative estimate of the growing stock of Russian forests and to assess the relative changes in post-Soviet Russia. Our estimate for the year 2014 is 111 ± 1.3 × 109 m3, or 39% higher than the value in the State Forest Register. Using the last Soviet Union report as a reference, Russian forests have accumulated 1163 × 106 m3 yr-1 of growing stock between 1988–2014, which balances the net forest stock losses in tropical countries. Our estimate of the growing stock of managed forests is 94.2 × 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory.

Measurement(s) 2-D extent • age Technology Type(s) Classification Factor Type(s) input dataset Sample Characteristic - Organism Elaeis guineensis Sample Characteristic - Environment oil palm plantation Sample Characteristic - Location Southeast Asia Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13899713

Abstract A metadata schema, named Plasma-MDS, is introduced to support research data management in plasma science. Plasma-MDS is suitable to facilitate the publication of research data following the FAIR principles in domain-specific repositories and with this the reuse of research data for data driven plasma science. In accordance with common features in plasma science and technology, the metadata schema bases on the concept to separately describe the source generating the plasma, the medium in which the plasma is operated in, the target the plasma is acting on, and the diagnostics used for investigation of the process under consideration. These four basic schema elements are supplemented by a schema element with various attributes for description of the resources, i.e. the digital data obtained by the applied diagnostic procedures. The metadata schema is first applied for the annotation of datasets published in INPTDAT—the interdisciplinary data platform for plasma technology.

IntroductionHIV prevalence and sexual risk have been estimated very high for transgender people. However, the limited sampling and data collection methods used in current research on transgender people potentially led to overrepresentation and generalisation of people at risk for HIV. Current HIV prevalence estimates in transgender populations are generalised from studies mainly focusing on transgender women engaging in sex work. Moreover, studies focusing on non-binary people, who identify with a broad range of identities beyond the traditional male and female gender identities, are scarce.ObjectivesTo estimate the HIV prevalence rate in the Flemish and Brussels (Belgium) transgender population, including transgender women, transgender men and non-binary people, and to identify the associated risk factors.MethodsIn this community-based cross-sectional study, self-identified transgender and non-binary (TGNB) people will be recruited through a two-stage time-location sampling approach. First, community settings in which TGNB people gather will be mapped to develop an accurate sampling frame. Secondly, a multistage sampling design is applied involving a stratification based on setting type (healthcare facilities vs outreach events), a selection of clusters by systematic sampling and a simple random selection of TGNB people within each cluster. Participants will complete an electronic self-reported survey to measure sociological, sexual and drug-using behaviors (risk factors) and oral fluid aliquots will be collected and tested for HIV antibodies. Logistic regression models will be used to evaluate risk factors independently associated with HIV infection. The presented study is registered at ClinicalTrials.gov (NCT04930614).DiscussionThis study will be the first to investigate the HIV prevalence rates and associated risk behaviors in an accurate representation of the TGNB population in a Western European country. The findings will globally serve as a knowledge base for identifying subgroups at risk for becoming infected with HIV within TGNB people and to set up targeted prevention programs.

[This corrects the article DOI: 10.1371/journal.pone.0251194.].

Involving members of the public in image classification tasks that can be tricky to automate is increasingly recognized as a way to complete large amounts of these tasks and promote citizen involvement in science. While this labor is usually provided for free, it is still limited, making it important for researchers to use volunteer contributions as efficiently as possible. Using volunteer labor efficiently becomes complicated when individual tasks are assigned to multiple volunteers to increase confidence that the correct classification has been reached. In this paper, we develop a system to decide when enough information has been accumulated to confidently declare an image to be classified and remove it from circulation. We use a Bayesian approach to estimate the posterior distribution of the mean rating in a binary image classification task. Tasks are removed from circulation when user-defined certainty thresholds are reached. We demonstrate this process using a set of over 4.5 million unique classifications by 2783 volunteers of over 190,000 images assessed for the presence/absence of cropland. If the system outlined here had been implemented in the original data collection campaign, it would have eliminated the need for 59.4% of volunteer ratings. Had this effort been applied to new tasks, it would have allowed an estimated 2.46 times as many images to have been classified with the same amount of labor, demonstrating the power of this method to make more efficient use of limited volunteer contributions. To simplify implementation of this method by other investigators, we provide cutoff value combinations for one set of confidence levels.

Circular RNA (circRNA) is a noncoding RNA class with important implications for gene expression regulation, mostly by interaction with other RNA species or RNA-binding proteins. While the commonly applied short-read Illumina RNA-sequencing techniques can be used to detect circRNAs, their full sequence is not revealed. However, the complete sequence information is needed to analyze potential interactions and thus the mechanism of action of circRNAs. Here, we present an improved protocol to enrich and sequence full-length circRNAs by using the Oxford Nanopore long-read sequencing platform. The protocol involves an enrichment of lowly abundant circRNAs by exonuclease treatment and negative selection of linear RNAs. Then, a cDNA library is created and amplified by PCR. This protocol provides enough material for several sequencing runs. The library is used as input for ligation-based sequencing together with native barcoding. Stringent quality control of the libraries is ensured by a combination of Qubit, Fragment Analyzer and qRT-PCR. Multiplexing of up to 4 libraries yields in total more than 1-2 Million reads per library, of which 1-2% are circRNA-specific reads with >99% of them full-length. The protocol works well with human cancer cell lines. We further provide suggestions for the bioinformatic analysis of the created data, as well as the limitations of our approach together with recommendations for troubleshooting and interpretation. Taken together, this protocol enables reliable full-length analysis of circRNAs, a noncoding RNA type involved in a growing number of physiologic and pathologic conditions. Metadata Associated content. https://dx.doi.org/10.17504/protocols.io.rm7vzy8r4lx1/v2.

Background Surgical site infections (SSI) present a substantial burden to patients and healthcare systems. This study aimed to elucidate the prevalence of SSIs in German hospitals and to quantify their clinical and economic burden based on German hospital reimbursement data (G-DRG). Methods This retrospective, cross-sectional study used a 2010–2016 G-DRG dataset to determine the prevalence of SSIs in hospital, using ICD-10-GM codes, after surgical procedures. The captured economic and clinical outcomes were used to quantify and compare resource use, reimbursement and clinical parameters for patients who had or did not have an SSI. Findings Of the 4,830,083 patients from 79 hospitals, 221,113 were eligible. The overall SSI prevalence for the study period was 4.9%. After propensity-score matching, procedure type, immunosuppression and BMI ≥30 were found to significantly affect the risk of SSI (pInterpretation The dataset offers robust information about the “real-world” clinical and economic burden of SSI in hospitals in Germany. The significantly increased mortality of patients with SSI, and their underfunding, calls for a maximization of efforts to prevent SSI through the use of evidence-based SSI-reduction care bundles.

Epitope tags are widely used in various applications, but often lack versatility. Here, the authors introduce a small, alpha helical tag, which is recognized by a high affinity nanobody and can be used in a range of different applications, from protein purification to super-resolution imaging and in vivo detection of proteins.

BackgroundSepsis is a potentially life-threatening condition characterized by a deregulated body's response to infection causing injury to its own tissues and organs. Sepsis is the primary cause of death from infection. If not recognized and treated timely, it can evolve within minutes/hours to septic shock. Sepsis is associated with an acute deficiency of Vitamin C. Despite the proof-of-concept of the benefit of administering Vitamin C in patients with sepsis or septic shock, Vitamin C administration is not yet current practice.ObjectiveTo investigate the potential benefit of early administration of high doses of Vitamin C in addition to standard of care in patients with sepsis or septic shock.MethodsThis phase 3b multi-center trial is conducted in 8 hospitals throughout Belgium. In total 300 patients will be randomly assigned to one of two groups in a 1:1 allocation ratio. The intervention group will receive 1.5 g Vitamin C 4 times a day during 4 days, started within 6 hours after admission. The primary outcome is the average post-baseline patient SOFA score.ConclusionThis trial will determine whether the early administration of Vitamin C in patients with sepsis or septic shock can lead to a more rapid solution of shock and less deterioration from sepsis to septic shock, hereby reducing morbidity and mortality as well as the length of hospital stay in this patient population.Trial registrationThe C-EASIE trial has been registered on the ClinicalTrials.gov website on 10 February 2021 with registration number NCT04747795.Trial sponsorUZ Leuven (sponsor's reference S63213).

Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of longitudinal data of 11 articles published in PLOS ONE. Inclusion criteria were the availability of data and author consent. We investigated the types of methods and software used and whether we were able to reproduce the data analysis using open source software. Most articles provided overview tables and simple visualisations. Generalised Estimating Equations (GEEs) were the most popular statistical models among the selected articles. Only one article used open source software and only one published part of the analysis code. Replication was difficult in most cases and required reverse engineering of results or contacting the authors. For three articles we were not able to reproduce the results, for another two only parts of them. For all but two articles we had to contact the authors to be able to reproduce the results. Our main learning is that reproducing papers is difficult if no code is supplied and leads to a high burden for those conducting the reproductions. Open data policies in journals are good, but to truly boost reproducibility we suggest adding open code policies.

The development of remotely sensed products such as land cover requires large amounts of high-quality reference data, needed to train remote sensing classification algorithms and for validation. However, due to the lack of sharing and the high costs associated with data collection, particularly ground-based information, the amount of reference data available has not kept up with the vast increase in the availability of satellite imagery, e.g. from Landsat, Sentinel and Planet satellites. To fill this gap, the Geo-Wiki platform for the crowdsourcing of reference data was developed, involving visual interpretation of satellite and aerial imagery. Here we provide an overview of the crowdsourcing campaigns that have been run using Geo-Wiki over the last decade, including the amount of data collected, the research questions driving the campaigns and the outputs produced such as new data layers (e.g. a global map of forest management), new global estimates of areas or percentages of land cover/land use (e.g. the amount of extra land available for biofuels) and reference data sets, all openly shared. We demonstrate that the amount of data collected and the scientific advances in the field of land cover and land use would not have been possible without the participation of citizens. A relatively conservative estimate reveals that citizens have contributed more than 5.3 years of the data collection efforts of one person over short, intensive campaigns run over the last decade. We also provide key observations and lessons learned from these campaigns including the need for quality assurance mechanisms linked to incentives to participate, good communication, training and feedback, and appreciating the ingenuity of the participants.

Stakeholder involvement is pivotal EU governance. In this paper, we complete a stakeholder analysis of the European Chemicals Agency's recent Annex XV restriction proposal process on intentionally added microplastics. The aim of this study is to map the interests, influence and importance of active stakeholders in order to understand the arguments being put forward by different stakeholders and provide recommendations to policy-makers on how to ensure a balanced consideration of all stakeholder perspectives. Stakeholders were identified through niche media analysis and by scrutinising comments from the public consultation on the restriction proposal. Their importance and influence were mapped using three approaches: "scale from low to high", "psychometric scale" and "qualitative ranking". We identified 205 different stakeholders out of which 77 were industry and trade associations, 25 were large companies and only four were small and medium-sized enterprises. National authorities and researchers did not comment on the restriction proposal, whilst large companies were very active providing comments. Industry trade associations and sports-related non-governmental organizations articulated anxiety about the costs associated with the implementation of the restriction proposal. Among environmental non-governmental organizations, there was consensus that plastics should be handled like other substances under EU's chemical regulation. Primary stakeholders identified exhibited high importance, but varying degrees of influence, while the opposite applied to the major European institutions. Based on our analysis, we recommend that: The European Chemicals Agency implement measures to include "silent" stakeholders and invite guest experts to participate in their committees on Risk Assessment and Socio-Economic Analysis; Researchers should be more active in the public consultation; and that special emphasis should be put on helping small and medium-sized enterprises. With regards to stakeholder consultation, we find that media analysis is a good supplement to stakeholder analysis and that a more objective top-down measure of stakeholder importance and influence is needed.

Nuclear pore complexes (NPCs) lined with intrinsically disordered FG-domains act as selective gatekeepers for molecular transport between the nucleus and the cytoplasm in eukaryotic cells. The underlying physical mechanism of the intriguing selectivity is still under debate. Here, we probe the transport of ions and transport receptors through biomimetic NPCs consisting of Nsp1 domains attached to the inner surface of solid-state nanopores. We examine both wildtype FG-domains and hydrophilic SG-mutants. FG-nanopores showed a clear selectivity as transport receptors can translocate across the pore whereas other proteins cannot. SG mutant pores lack such selectivity. To unravel this striking difference, we present coarse-grained molecular dynamics simulations that reveal that FG-pores exhibit a high-density, nonuniform protein distribution, in contrast to a uniform and significantly less-dense protein distribution in the SG-mutant. We conclude that the sequence-dependent density distribution of disordered proteins inside the NPC plays a key role for its conductivity and selective permeability.

Along with the creation of new maps, current efforts for improving global land cover (GLC) maps focus on integrating maps by accounting for their relative merits, e.g., agreement amongst maps or map accuracy. Such integration efforts may benefit from the use of multiple GLC reference datasets. Using available reference datasets, this study assesses spatial accuracy of recent GLC maps and compares methods for creating an improved land cover (LC) map. Spatial correspondence with reference dataset was modeled for Globcover-2009, Land Cover-CCI-2010, MODIS-2010 and Globeland30 maps for Africa. Using different scenarios concerning the used input data, five integration methods for an improved LC map were tested and cross-validated. Comparison of the spatial correspondences showed that the preferences for GLC maps varied spatially. Integration methods using both the GLC maps and reference data at their locations resulted in 4.5%–13% higher correspondence with the reference LC than any of the input GLC maps. An integrated LC map and LC class probability maps were computed using regression kriging, which produced the highest correspondence (76%). Our results demonstrate the added value of using reference datasets and geostatistics for improving GLC maps. This approach is useful as more GLC reference datasets are becoming publicly available and their reuse is being encouraged.

Timely and accurate information on the global cropland extent is critical for applications in the fields of food security, agricultural monitoring, water management, land-use change modeling and Earth system modeling. On the one hand, it gives detailed location information on where to analyze satellite image time series to assess crop condition. On the other hand, it isolates the agriculture component to focus food security monitoring on agriculture and to assess the potential impacts of climate change on agricultural lands. The cropland class is often poorly captured in global land cover products due to its dynamic nature and the large variety of agro-systems. The overall objective was to evaluate the current availability of cropland datasets in order to propose a strategic planning and effort distribution for future cropland mapping activities and, therefore, to maximize their impact. Following a very comprehensive identification and collection of national to global land cover maps, a multi-criteria analysis was designed at the country level to identify the priority areas for cropland mapping. As a result, the analysis highlighted priority regions, such as Western Africa, Ethiopia, Madagascar and Southeast Asia, for the remote sensing community to focus its efforts. A Unified Cropland Layer at 250 m for the year 2014 was produced combining the fittest products. It was assessed using global validation datasets and yields an overall accuracy ranging from 82%–94%. Masking cropland areas with a global forest map reduced the commission errors from 46% down to 26%. Compared to the GLC-Share and the International Institute for Applied Systems Analysis-International Food Policy Research Institute (IIASA-IFPRI) cropland maps, significant spatial disagreements were found, which might be attributed to discrepancies in the cropland definition. This advocates for a shared definition of cropland, as well as global validation datasets relevant for the agriculture class in order to systematically assess existing and future cropland maps.

Herein, we describe the syntheses of a complex biosynthesis-intermediate analogue of the potent antitumor polyketide borrelidin and of reference molecules to determine the stereoselectivity of the dehydratase of borrelidin polyketide synthase module 3. The target molecules were obtained from a common precursor aldehyde in the form of N-acetylcysteamine (SNAc) thioesters and methyl esters in 13 to 15 steps. Key steps for the assembly of the polyketide backbone of the dehydratase substrate analogue were a Yamamoto asymmetric carbocyclisation and a Sakurai allylation as well as an anti-selective aldol reaction. Reference compounds representing the E- and Z-configured double bond isomers as potential products of the dehydratase reaction were obtained from a common precursor aldehyde by Wittig olefination and Still–Gennari olefination. The final deprotection of TBS ethers and methyl esters was performed under mildly acidic conditions followed by pig liver esterase-mediated chemoselective hydrolysis. These conditions are compatible with the presence of a coenzyme A or a SNAc thioester, suggesting that they are generally applicable to the synthesis of complex polyketide-derived thioesters suited for biosynthesis studies.

Mapping the extent and location of field boundaries is critical to food security analysis but remains problematic in the Global South where such information is needed the most. The difficulty is due primarily to fragmentation in the landscape, small farm sizes, and irregular farm boundaries. Very high-resolution satellite imagery affords an opportunity to delineate such fields, but the challenge remains of determining such boundaries in a systematic and accurate way. In this paper, we compare a new crowd-driven manual digitization tool (Crop Land Extent) with two semi-automated methods (contour detection and multi-resolution segmentation) to determine farm boundaries from WorldView imagery in highly fragmented agricultural landscapes of Ethiopia. More than 7000 one square-kilometer image tiles were used for the analysis. The three methods were assessed using quantitative completeness and spatial correctness. Contour detection tended to under-segment when compared to manual digitization, resulting in better performance for larger (approaching 1 ha) sized fields. Multi-resolution segmentation on the other hand, tended to over-segment, resulting in better performance for small fields. Neither semi-automated method in their current realizations however are suitable for field boundary mapping in highly fragmented landscapes. Crowd-driven manual digitization is promising, but requires more oversight, quality control, and training than the current workflow could allow.

3-[18F]Fluoropropanesulfonyl chloride, a recently proposed prosthetic agent for fluorine-18 labelling, was prepared in a two-step radiosynthesis via 3-[18F]fluoropropyl thiocyanate as an intermediate. Two benzenesulfonate-based radiolabelling precursors were prepared by various routes. Comparing the reactivities of 3-thiocyanatopropyl nosylate and the corresponding tosylate towards [18F]fluoride the former proved to be superior accounting for labelling yields of up to 85%. Conditions for a reliable transformation of 3-[18F]fluoropropyl thiocyanate to the corresponding sulfonyl chloride with the potential for automation have been identified. The reaction of 3-[18F]fluoropropanesulfonyl chloride with eight different aliphatic and aromatic amines was investigated and the identity of the resulting 18F-labelled sulfonamides was confirmed chromatographically by comparison with their nonradioactive counterparts. Even for weakly nucleophilic amines such as 4-nitroaniline the desired radiolabelled sulfonamides were accessible in satisfactory yields owing to systematic variation of the reaction conditions. With respect to the application of the 18F-fluoropropansulfonyl group to the labelling of compounds relevant as imaging agents for positron emission tomography (PET), the stability of N-(4-fluorophenyl)-3-fluoropropanesulfonamide against degradation catalysed by carboxylesterase was investigated and compared to that of the analogous fluoroacetamide.

The most common intermediate and long-term complications of total knee arthroplasty (TKA) include aseptic and septic failure of prosthetic joints. These complications cause suffering, and their management is expensive. In the future the number of revision TKA will increase, which involves a greater financial burden. Little concrete data about direct costs for aseptic and two-stage septic knee revisions with an in depth-analysis of septic explantation and implantation is available.A retrospective consecutive analysis of the major partial costs involved in revision TKA for aseptic and septic failure was undertaken to compare 1) demographic and clinical characteristics, and 2) variable direct costs (from a hospital department's perspective) between patients who underwent single-stage aseptic and two-stage septic revision of TKA in a hospital providing maximum care. We separately analyze the explantation and implantation procedures in septic revision cases and identify the major cost drivers of knee revision operations.A total of 106 consecutive patients (71 aseptic and 35 septic) was included. All direct costs of diagnosis, surgery, and treatment from the hospital department's perspective were calculated as real purchase prices. Personnel involvement was calculated in units of minutes.Aseptic versus septic revisions differed significantly in terms of length of hospital stay (15.2 vs. 39.9 days), number of reported secondary diagnoses (6.3 vs. 9.8) and incision-suture time (108.3 min vs. 193.2 min). The management of septic revision TKA was significantly more expensive than that of aseptic failure ($12,223.79 vs. $6,749.43) (p

The permeability barrier of nuclear pore complexes (NPCs) controls bulk nucleocytoplasmic exchange. It consists of nucleoporin domains rich in phenylalanine-glycine motifs (FG domains). As a bottom-up nanoscale model for the permeability barrier, we have used planar films produced with three different end-grafted FG domains, and quantitatively analyzed the binding of two different nuclear transport receptors (NTRs), NTF2 and Importin β, together with the concomitant film thickness changes. NTR binding caused only moderate changes in film thickness; the binding isotherms showed negative cooperativity and could all be mapped onto a single master curve. This universal NTR binding behavior – a key element for the transport selectivity of the NPC – was quantitatively reproduced by a physical model that treats FG domains as regular, flexible polymers, and NTRs as spherical colloids with a homogeneous surface, ignoring the detailed arrangement of interaction sites along FG domains and on the NTR surface.

Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adapt the Land Cover Classification System (LCCS) for harmonization, (iii) assess the final product, and (iv) compare the final product with two existing datasets. Ten datasets were compared and combined through an expert-based approach in order to create the derived map of cropland areas at 250 m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1 km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3,591 pixels of 1km regularly distributed over Africa and interpreted using high resolution images, which were collected using the Geo-Wiki tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for places where the cropland represents more than 30% of the area of the validation pixel.

A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics from different maps or as a map’s legend is changed. In this paper, we introduce a new website and a collection of R functions to facilitate map assessment. We apply these tools to illustrate some pitfalls of error metrics and point out existing and newly developed solutions to them. Some of these problems have been previously noted, but all of them are under-appreciated and persist in published literature. We show that binary and categorical metrics, including information about true-negative classifications, are inflated for rare categories, and more robust alternatives should be chosen. Most metrics are useful to compare maps only if their legends are identical. We also demonstrate that combining land-cover classes has the often-neglected consequence of apparent improvement, particularly if the combined classes are easily confused (e.g., different forest types). However, we show that the average mutual information (AMI) of a map is relatively robust to combining classes, and reflects the information that is lost in this process; we also introduce a modified AMI metric that credits only correct classifications. Finally, we introduce a method of evaluating statistical differences in the information content of competing maps, and show that this method is an improvement over other methods in more common use. We end with a series of recommendations for the meaningful use of accuracy metrics by map users and producers.

Global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images (up to 50 cm × 50 cm) available on Google Earth, it is becoming possible for every Internet user (including non remote sensing experts) to distinguish land cover features with a high degree of reliability. Such an approach is inexpensive and allows Internet users from any region of the world to get involved in this global validation exercise. The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g., to determine the potential of additional agricultural land available to grow crops in Africa), volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved hybrid global land cover map.

BACKGROUND:Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. RESULTS:This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. CONCLUSIONS:The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.

During autophagy, members of the ubiquitin-like Atg8 protein family get conjugated to phosphatidylethanolamine and act as protein-recruiting scaffolds on the autophagosomal membrane. The Atg4 protease produces mature Atg8 from C-terminally extended precursors and deconjugates lipid-bound Atg8. We now found that Xenopus laevis Atg4B (xAtg4B) is ideally suited for proteolytic removal of N-terminal tags from recombinant proteins. To implement this strategy, an Atg8 cleavage module is inserted in between tag and target protein. An optimized xAtg4B protease fragment includes the so far uncharacterized C-terminus, which crucially contributes to recognition of the Xenopus Atg8 homologs xLC3B and xGATE16. xAtg4B-mediated tag cleavage is very robust in solution or on-column, efficient at 4°C and orthogonal to TEV protease and the recently introduced proteases bdSENP1, bdNEDP1 and xUsp2. Importantly, xLC3B fusions are stable in wheat germ extract or when expressed in Saccharomyces cerevisiae, but cleavable by xAtg4B during or following purification. We also found that fusions to the bdNEDP1 substrate bdNEDD8 are stable in S. cerevisiae. In combination, or findings now provide a system, where proteins and complexes fused to xLC3B or bdNEDD8 can be expressed in a eukaryotic host and purified by successive affinity capture and proteolytic release steps.

Volunteered geographic data are being used increasingly to support land cover mapping and validation, yet the reliability of the volunteered data still requires further research. This study proposes data-based guidelines to help design the data collection by assessing the reliability of volunteered data collected using the Geo-Wiki tool. We summarized the interpretation difficulties of the volunteers at a global scale, including those areas and land cover types that generate the most confusion. We also examined the factors affecting the reliability of majority opinion and individual classification. The results showed that the highest interpretation inconsistency of the volunteers occurred in the ecoregions of tropical and boreal forests (areas with relatively poor coverage of very high resolution images), the tundra (a unique region that the volunteers are unacquainted with), and savannas (transitional zones). The volunteers are good at identifying forests, snow/ice and croplands, but not grasslands and wetlands. The most confusing pairs of land cover types are also captured in this study and they vary greatly with different biomes. The reliability can be improved by providing more high resolution ancillary data, more interpretation keys in tutorials, and tools that assist in coverage estimation for those areas and land cover types that are most prone to confusion. We found that the reliability of the majority opinion was positively correlated with the percentage of volunteers selecting this choice and negatively related to their self-evaluated uncertainty when very high resolution images were available. Factors influencing the reliability of individual classifications were also compared and the results indicated that the interpretation difficulty of the target sample played a more important role than the knowledge base of the volunteers. The professional background and local knowledge had an influence on the interpretation performance, especially in identifying vegetation land cover types other than croplands. These findings can help in building a better filtering system to improve the reliability of volunteered data used in land cover validation and other applications.

There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme II) using a validation data set that was collected by students through the Geo-Wiki tool. The ultimate aim was to understand the usefulness of these products for agricultural monitoring. Data were collected wall-to-wall for Kilosa district and for a sample across Tanzania. The results show that the amount of and spatial extent of cropland in the different products differs considerably from 8% to 42% for Tanzania, with similar values for Kilosa district. The agreement of the validation data with the four different products varied between 36% and 54% and highlighted that cropland is overestimated by the ESA-CCI and underestimated by FROM-GC. The validation data were also analyzed for consistency between the student interpreters and also compared with a sample interpreted by five experts for quality assurance. Regarding consistency between the students, there was more than 80% agreement if one difference in cropland category was considered (e.g., between low and medium cropland) while most of the confusion with the experts was also within one category difference. In addition to the validation of current cropland products, the data set collected by the students also has potential value as a training set for improving future cropland products.

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement.