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BACKGROUND Glecaprevir and pibrentasvir are direct-acting antiviral agents with pangenotypic activity and a high barrier to resistance. We evaluated the efficacy and safety of 8-week and 12-week courses of treatment with 300 mg of glecaprevir plus 120 mg of pibrentasvir in patients without cirrhosis who had hepatitis C virus (HCV) genotype 1 or 3 infection. METHODS We conducted two phase 3, randomized, open-label, multicenter trials. Patients with genotype 1 infection were randomly assigned in a 1:1 ratio to receive oncedaily glecaprevir-pibrentasvir for either 8 or 12 weeks. Patients with genotype 3 infection were randomly assigned in a 2:1 ratio to receive 12 weeks of treatment with either glecaprevir-pibrentasvir or sofosbuvir-daclatasvir. Additional patients with genotype 3 infection were subsequently enrolled and nonrandomly assigned to receive 8 weeks of treatment with glecaprevir-pibrentasvir. The primary end point was the rate of sustained virologic response 12 weeks after the end of treatment. RESULTS In total, 1208 patients were treated. The rate of sustained virologic response at 12 weeks among genotype 1-infected patients was 99.1% (95% confidence interval [CI], 98 to 100) in the 8-week group and 99.7% (95% CI, 99 to 100) in the 12-week group. Genotype 3-infected patients who were treated for 12 weeks had a rate of sustained virologic response at 12 weeks of 95% (95% CI, 93 to 98; 222 of 233 patients) with glecaprevir-pibrentasvir and 97% (95% CI, 93 to 99.9; 111 of 115) with sofosbuvir-daclatasvir; 8 weeks of treatment with glecaprevir-pibrentasvir yielded a rate of 95% (95% CI, 91 to 98; 149 of 157 patients). Adverse events led to discontinuation of treatment in no more than 1% of patients in any treatment group. CONCLUSIONS Once-daily treatment with glecaprevir-pibrentasvir for either 8 weeks or 12 weeks achieved high rates of sustained virologic response among patients with HCV genotype 1 or 3 infection who did not have cirrhosis. (Funded by AbbVie

While carbapenem use is prohibited in the poultry production chain and carbapenem-resistant Enterobacteriaceae (CRE) are absent from hatchery farms, New Delhi metallo-β-lactamase-producing CRE contamination of commercial broiler chicken farms (grow-out farms) can occur via living hosts such as flies. However, it is not known whether the inanimate factors from in-house environment play a role in the persistence of CRE on commercial farms. Herein, we monitored one typical broiler house in Hebei Province, China, from January 2017 to April 2018. We collected 350 cloacal samples from four broiler batches along with 582 environmental samples (194 in the raising period and 388 in the vacancy period) from sites including the surfaces of drooping boards, feeding troughs, nipple drinkers, corridor floors, sewage trenches, and air. All samples were screened for blaNDM and cultured for NDM-producing isolates. The resistance profiles, genotypes, and genetic context of blaNDM in CRE isolates were further characterized. Results showed that 1-day-old broilers, which were transferred from a hatchery farm and negative for CRE, acquired blaNDM within 24 h of transfer (2 days of age), with a detection rate of up to 18.6%. High blaNDM detection rates (26.8%–31.4%) were obtained among all environmental samples except air after standard cleaning and disinfection during the vacancy period. blaNDM carriage rates (52.9%–72.9%) within the flocks remain stable and high across the next three broiler batches. Overall, 279 NDM-producing bacteria, including 259 Enterobacteriaceae (8 species), 14 Morganellaceae (3 species), three Alcaligenes faecalis and three Pseudomonas putida isolates, were recovered from 85 (24.3%) cloacal and 101 (17.4%) environmental samples. Three NDM variants, including NDM-5 (n = 181), NDM-1 (n = 92), and NDM-9 (n = 3), and a novel NDM-like-metallo-β-lactamase (NLM, n = 3) were identified among the samples. The predominant NDM-producing CRE species among the samples were K

We adapt existing approaches for privacy-preserving publishing of linked data to a setting where the data are given as Description Logic (DL) ABoxes with possibly anonymised (formally: existentially quantified) individuals and the privacy policies are expressed using sets of concepts of the DL. We provide a chacterization of compliance of such ABoxes w.r.t. policies, and show how optimal compliant anonymisations of ABoxes that are non-compliant can be computed. This work extends previous work on privacy-preserving ontology publishing, in which a very restricted form of ABoxes, called instance stores, had been considered, but restricts the attention to compliance. The approach developed here can easily be adapted to the problem of computing optimal repairs of quantified ABoxes.

Plastic pollution is a global concern given its prevalence in aquatic and terrestrial ecosystems. Studies have been conducted on the distribution and impact of plastic pollution in marine ecosystems, but little is known on terrestrial ecosystems. Plastic mulch has been widely used to increase crop yields worldwide, yet the impact of plastic residues in cropland soils to soil health and crop production in the long term remained unclear. In this paper, using a global meta-analysis, we found that the use of plastic mulch can indeed increase crop yields on average by 25%-42% in the immediate season due to the increase of soil temperature (+8%) and moisture (+17%). However, the unabated accumulation of film residues in the field negatively impacts its physicochemical properties linked to healthy soil and threatens food production in the long term. It has multiple negative impacts on plant growth including crop yield (at the mean rate of -3% for every additional 100 kg/ha of film residue), plant height (-2%) and root weight (-5%), and soil properties including soil water evaporation capacity (-2%), soil water infiltration rate (-8%), soil organic matter (-0.8%) and soil available phosphorus (-5%) based on meta-regression. Using a nationwide field survey of China, the largest user of plastic mulch worldwide, we found that plastic residue accumulation in cropland soils has reached 550,800 tonnes, with an estimated 6%-10% reduction in cotton yield in some polluted sites based on current level of plastic residue content. Immediate actions should be taken to ensure the recovery of plastic film mulch and limit further increase in film residue loading to maintain the sustainability of these croplands.

The emission and deposition of global atmospheric phosphorus (P) have long been considered unbalanced, and primary biogenic aerosol particles (PBAP) and phosphine (PH3) are considered to be the only atmospheric P sources from the ecosystem. In this work, we found and quantified volatile organic phosphorus (VOP) emissions from plants unaccounted for in previous studies. In a greenhouse in which lemons were cultivated, the atmospheric total phosphorus (TP) concentration of particulate matter (PM) was 41.8% higher than that in a greenhouse containing only soil, and the proportion of organic phosphorus (OP) in TP was doubled. 31P nuclear magnetic resonance tests (31P-NMR) of PM showed that phosphate monoesters were the main components contributed by plants in both the greenhouse and at an outside observation site. Atmospheric gaseous P was directly measured to be 1–2 orders of magnitude lower than P in PM but appeared to double during plant growing seasons relative to other months. Bag-sampling and gas chromatography mass spectrometry (GCMS) tests showed that the gaseous P emitted by plants in the greenhouse was triethyl phosphate. VOP might be an important component of atmospheric P that has been underestimated in previous studies.

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings, and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize, and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high-quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard, yielding competitive results. AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.

Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O emissions were 17.0 (minimum-maximum estimates: 12.2-23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9-17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2-11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N2O emissions in emerging economies-particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O-climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions.

Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables). Last year, the SemTab challenge was introduced to unify different efforts to evaluate table annotation algorithms by providing a common interface and several general-purpose datasets as a ground truth. The SemTab dataset is useful to have a general understanding of how these algorithms work, and the organizers of the challenge included some artificial noise to the data to make the annotation trickier. However, it is hard to analyze specific aspects in an automatic way. For example, the ambiguity of names at the entity-level can largely affect the quality of the annotation. In this paper, we propose a novel dataset to complement the datasets proposed by SemTab. The dataset consists of a set of high-quality manually-curated tables with non-obviously linkable cells, i.e., where values are ambiguous names, typos, and misspelled entity names not appearing in the current version of the SemTab dataset. These challenges are particularly relevant for the ingestion of structured legacy sources into existing knowledge graphs. Evaluations run on this dataset show that ambiguity is a key problem for entity linking algorithms and encourage a promising direction for future work in the field.

Short-lived climate forcers (SLCFs) like methane, ozone and aerosols have a shorter atmospheric lifetime than CO2 and are often assumed to have a short-term effect on the climate system: should their emissions cease, so would their radiative forcing (RF). However, via their climate impact, SLCFs can affect carbon sinks and atmospheric CO2, causing additional climate change. Here, we use a compact Earth system model to attribute CO2 RF to direct CO2 emissions and to climate–carbon feedbacks since the pre-industrial era. We estimate the climate–carbon feedback contributed 93 ± 50 mW m−2 (~5%) to total RF of CO2 in 2010. Of this, SLCF impacts were −13 ± 50 mW m−2, made up of cooling (−115 ± 43 mW m−2) and warming (102 ± 26 mW m−2) terms that largely cancel. This study illustrates the long-term impact that short-lived species have on climate and indicates that past (and future) change in atmospheric CO2 cannot be attributed only to CO2 emissions.