dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 1). These four simulations will be named Hist, SSP119, SSP370 and SSP585 respectively. All these different simulations use the CMIP6 dataset for the emission of the different aerosols or aerosol precursors. The historical dataset is based on two main sources, namely the anthropogenic emissions  (Hoesly et al. , 2018)  and the biomass burning emissions  (Van Marle et al. , 2017). The anthropogenic emissions of the different aerosols and their precursors are provided from the main sectors of activity (residential, agriculture, energy, industrial, air, land and sea transport, waste and solvents), for each country and for each grid point at 0. 5 degree resolution, while the emissions from biomass fires (natural and anthropogenic) are provided over the period 1750-2014. Future CMIP6 emissions are provided at the same format, derived from the different SSPs  (Gidden et al.
Anthropogenic CMIP6 emissions As mentioned above, the CMIP6 emissions of aerosols or aerosol precursors are provided by  Hoesly et al. (2018)  and  Van Marle et al. (2017)  for the historical period (1750-2014), and by  Gidden et al. (2019)  for the future period (2015-2100) following the various SSPs. The anthropogenic emissions, ranging from 1971 to 2100 over Europe, are presented in Figure  2  for sulphur dioxide, ammonia, black carbon and organic carbon according to the different existing scenarios. Compared to the previous CMIP5 inventory, which has been improved by taking better account of the different emission sectors as well as re-evaluated emission factors, this CMIP6 dataset presents some differences as showed in Figure  2 , in particular for ammonia emissions. Indeed, CMIP6 anthropogenic ammonia emissions show a decrease from the 1990s, whereas the previous CMIP5 inventory showed the opposite trend with an increase in ammonia emissions.
Despite this, for other aerosols or aerosol precursors, trends are similar between the two datasets, such as the sulphur dioxide which shows a peak in the early 1980s followed by a significant decrease in both emission inventories. Figure  2  shows a decrease in emissions over the historical period for all aerosols, particularly from the 1990s. The most significant decrease, by about 80%, concerns sulphur dioxide, a precursor of sulfate aerosols, which had the highest emissions. Indeed, sulphur dioxide emissions were approximately divided by five between 1980 and 2010. Concerning the different scenarios, this figure shows that, with the exception of ammonia, the majority of them predict a decrease in the different emissions in the future. However, in the ammonia case, several scenarios such as the SSP 5-3.
Acknowledgements. We would like to thank  Meteo-France  and the Occitania region for the financial support of the first author. This work is part of the  Med-CORDEX initiative  (www. medcordex. eu,last access: 9 October 2020) and a contribution to the  CORDEX Flagship Pilot Study  (FPS) on aerosols. It also provides a contribution to the ChArMEx programme, part of the  French multidisciplinary programme MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ).
Code and data availability. The code of the regional climate model CNRM-ALADIN63 is available as follows: the SURFEX code is accessible using a CECILL-C Licence (http://www. cecill. info/licences/Licence_CeCILL-C_V1-en. txt) at http://www. umr-cnrm. fr/surfex; OASIS3-MCT is available at https://verc. enes. org/oasis/download; XIOS at https://forge. ipsl. jussieu. fr/ioserver and the rest of the CNRM-ALADIN63 code is available upon request to the authors. The output of the different simulations presented here are available upon request from the authors (thomas. druge@meteo. fr).
Clear image Simplicity of putting parts together EI1 Device: order of assembly and weight of battery pack Use of program: change recording and saving buttons to be accessible to thumb. Switch controls for ISO (International Organization for Standardization) and focus, so the focus is easier to access, irrespective of which side is holding the phone, whichever hand. Much better picture quality, as the point of vision is next to the ear drum Can look around corners so can see full ear drum. "Vastly superior image. " Can show the patient the image. Relatives can view image in real time. Otoscope relies on a description or a picture drawn-the endoscope-i can store actual image of ear. Data storage is straightforward, once one is used to where images are stored. Images are anonymous, so can share.
Data Analysis The volatile data obtained from GC-MS analysis were imported as CDF files, and deconvoluted with Paradise (ver. 3, Copenhagen)  (Johnsen et al. , 2017). The resultant extracted compounds were integrated and calculated for their retention index. Compound identification was performed by comparing the electron ionisation mass spectra of the measured compounds either with the NIST 2017 GC-MS database (NIST, MD, USA), pure standards, or to literature for mass spec fragmentation patterns where required. Retention indices (RI) for the obtained features were calculated based on an alkane series (C5-C8 and C8-C20)  (van Den Dool & Kratz, 1963). The peaks of chromatograms from individual fruiting bodies were normalised by dividing individual peaks by the total ion count and analysed with principle component analysis (PCA) using  Unscrambler (ver.
10, Camo, Oslo). The peaks of chromatograms from the pooled truffle fruiting bodies (measured as with the GC-O samples) were processed in a similar manner as above, only that the odour active compounds identified (based on GC-O) were quantified through calibration curves. The quantified odour active compounds were subsequently analysed using one-way ANOVA using SPSS statistics Ver. 25 (SPSS, Inc. , Chicago) and significantly different means were analysed with Fisher's least significant difference (LSD) posthoc test. GC-O data were manually aligned across all assessors for both replicates per sample. Where compounds were not detected by certain panellists, data were replaced with 0 scores. The entire data matrix was analysed using univariate ANOVA taking samples and panellist as fixed and random effects, respectively.
Data from RATA (sensory evaluation) were first preprocessed by replacing missing values with 0 scores. The preprocessed data was analysed using univariate ANOVA, taking samples and consumers as fixed and random effects, respectively. Post hoc testing was further performed on significantly different attribute means using Tukey's Honest Significant Difference (HSD) test. Details of bioinformatic processing of sequencing data are given in Supporting Information Methods S3. In brief, obtained sequences from amplicon sequencing were analysed using FROGS (Find Rapidly OTU with Galaxy Solution)  (Escudié et al. , 2017)  following Standard Operation Procedures. Rare OTUs (≤ 0. 005 % of all sequences in all samples) were excluded for further analyses. Clusters were affiliated to one taxonomy by blasting OTUs against SILVA database  (Quast et al.
, 2012). OTUs were rarefied (adjusting sequences randomly to the total abundance in the smallest sample) to 45,846 using Phyloseq package in R  (McMurdie & Holmes, 2013). The raw data are deposited in the NCBI Sequence Read Archive website (http://www. ncbi. nlm. nih. gov/ sra) under the BioProject study accession number PRJNA663751. The volatile profiles from pooled truffle samples were correlated with the RATA data using multiple factor analysis (MFA)  (XLSTAT ver. 2020, Addinsoft, New York). The volatile profiles were taken as the mean concentration of compounds identified in the pooled truffle samples and the sensory data was taken as the significantly different (P < 0. 05) attribute means. The correlation of the data matrices was further reported with RV coefficients. The volatile profiles from individual truffle samples were correlated with the microbiome data using regularised canonical correlation analysis (rCCA) through the mixOmics R package  (Rohart et al.
Relative abundance of this OTU varied between 8% and 79% in these fruiting bodies, while it was found at an average of 0. 9% and rarely exceeded 2% in other fruiting bodies collected across the 6 other sites. To determine any potential correlations between VOCs and microbial community composition of fruiting bodies, data was analysed with rCCA and loadings projected with covariance values greater than 0. 5 are reported. Many of the fruiting bodies did not discriminate from each other, nor were there any differentiation by provenance (Fig. 7a  and b ). Five fruiting bodies were clearly separated from others in this analysis on the consensus plot; HUN2 (three), ITA1 and SER2 (one each). It is noteworthy that microbiota of none of the five samples were dominated by Bradyrhizobium. Instead, microbiota had on average a higher richness and had more balanced diversity patterns than other samples (P < 0.
magnatum might lack key genes for their synthesis  (Murat et al. , 2018). At a global level, the drivers of sensory characteristics as measured by RATA were determined through the quantified volatile data using the multiblock data analysis method MFA. This revealed that sensory characteristics specific to geographical origin were driven by concentration differences in volatile compounds, rather than presence/absence thereof. Thus, the concentration balance of volatile compound concentrations was what drove the aroma differences of truffles that originate from different sites at a global level. The comparisons of the data sets in the current study are unique in that the samples were from different matrices (fruiting bodies vs. silicon oil extracts). Ideally, the RATA data would be collected using fresh truffle fruiting bodies for a direct comparison with the volatile data.
Acknowledgements The authors would like to acknowledge all the participants who tirelessly smelt the samples during the olfactometry trial. JN acknowledges the consumers who voluntarily participated in the RATA study. The authors would like to thank the truffle hunters in complying with the scientific logistics of the study. JN's research was supported by the  Alexander von Humboldt Foundation. The study was approved by the  ethics committee of Goethe University. The  Deutsche Forschungsgemeinschaft (DFG)  is acknowledged for financing the installation of GC-MS peripherals. RS and JN thank  Žaklina S. Marjanović  for kindly providing truffle fruiting body samples. AD was supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-11-LABX 0002 01 ).
The crystals are then picked from the water surface with a Cu 200-mesh TEM grid coated with amorphous carbon. The orientation of the sample has been indexed to be (2 1 0). Theory. TD-DFT as implemented in the Gaussian 09 package (Gaussian 09, 2009)  53  was used for obtaining the natural transition orbitals (NTO) of the LS and HS state of [Fe II (PM-AzA) 2 (NCS) 2 ], [Fe II (bpy) 3 ](PF 6 ) 2 , and [Fe II (phen) 2 (NCS) 2 ] starting from geometry-optimized molecules with the B3LPY/LanL2DZ hybrid functional-basis set. Figure  1b  shows the electron density difference during the SCO processes. Supplementary Fig. 2  shows the calculated low frequency modes of three SCO systems.
Acknowledgements We thank  A. Hauser ,  E. Collet , and  D. Zhang  for helpful discussions. This research was supported by the  Max Planck Society  and the excellence cluster "The  Hamburg Centre for Ultrafast Imaging-Structure, Dynamics  and  Control of Matter at the Atomic Scale" of the Deutsche Forschungsgemeinschaft  (R. J. D. M. ), the  Natural Sciences and Engineering Research Council of Canada  (NSERC; L. L. , R. J. D. M. ), and the  People Programme  ( Marie Curie Actions ) of the European's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement No. 623994  (H. M. M. -W. ). W. G. acknowledges the financial support from  European XFEL GmbH  and partial support from the  National Science Centre (NCN)  in Poland under SONATA BIS 6 grant No. 2016/22/E/ST4/00543. S.
Data availability All raw images and source data are available from the authors upon reasonable request. The crystallographic coordinates for structures reported in this study have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under deposition numbers 1972572 (excited high spin state structure calculated from X-ray data and structural model) and 1972373 (excited high spin state structure refined from ultrafast electron diffraction data). These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif.
Data Meteorological data were collected from the Israel Meteorological database (https://ims. data. gov. il/), including daily temperature and relative humidity between 1 January 2016 and 30 November 2018, which were available for 97% of the dates. The human WNV case data were obtained from the Israeli Ministry of Health (https://www. health. gov. il/Subjects/disease/WNF/Pages/default. aspx) and curated to include longitude and latitude coordinates of patient residency. All WNV cases were laboratoryconfirmed, either by serum IgM or whole blood RNA tests. Incidence per 100,000 inhabitants was calculated using the population size of each district in 2017 as the denominator (Israel Central Bureau of Statistics; www. cbs. gov. il). Index P estimates, meteorological and case data are available through a (permanent) figshare repository at https://doi.
org/10. 6084/ m9. figshare. c. 4584086. v1. The main results can be reproduced by estimating index P with the freely available Mosquito-borne Viral Suitability Estimator (MVSE) R package (code as provided in Supplementary Text S2 section 3. 13 of  [33] ), using the Israeli meteorological data as input and the priors listed in the Table. The MVSE is freely available at https://sourceforge. net/projects/mvse/. In the Supplement, we include minimal code that estimates the index P from the Israeli meteorological stations included in this study.
Analyses and transformations All case data were transformed using a log 10 (cases + 1) transformation. Area under the curve (AUC) of index P was log 10 -transformed for each district, measured as the average AUC of all stations in the district (December 2016 and 2017 data were excluded for a correct comparison, as December 2018 was missing from the dataset). We note that December 2016 and 2017 had no recorded WNV cases, so the month's exclusion should not interfere with results.
Determinants of West Nile virus transmission potential and seasonality in Israel We obtained daily mean temperature and relative humidity data from 81 geo-located meteorological stations covering six Israeli districts -North, South, Central, Haifa, Jerusalem, Tel Aviv -and surrounding regions (Figure  1D , see Data section). Temperature measurements from all stations presented fluctuations that followed well-defined winter-summer seasonal patterns (Figure  2A ). Humidity, on the other hand, presented far more variable fluctuations with no immediately discernible patterns (Figure  2A  and Supplementary Figures  S2-3 ). These daily meteorological series were combined with biological priors for WNV, Culex spp. mosquitoes and bird hosts to estimate the index P with a high spatio-temporal resolution between 2016 and 2018.
Statistical analysis Statistical analyses were performed using StatSoft Statistica software v 7. 1. Correlations (e. g. between total lipid content and organic contaminant concentrations) were tested using simple linear regression coefficients. In view of the small sample size per group, data comparisons (biological and trophic parameters, organic contaminant concentrations and ratios) across fish groups were preferably performed using non-parametric tests (Mann-Whitney test for comparison of two independent samples, or one-way ANOVA Kruskal-Wallis's test), with a significance level (α) of 0. 05. Results were considered to be significant only when both tests gave significant results. Statistical analyses were conducted on summed or individual compounds detected in at least 50% of the samples.
, 2016). DDT levels reported in various species from the Northern Hemisphere (bearing in mind that some data were collected 20 years ago) were generally higher than those determined in our study, but the differences between hemispheres were lower than for PCBs. Less data are available for PFASs and liver is the most widely-studied fish tissue. Hart et al. (2008)  reported PFAS concentrations in the liver of SKJ collected from various locations in the Pacific and Indian Oceans. The lowest PFAS levels were determined in samples from openocean sites, with Mauritius in the Indian Ocean demonstrating the lowest concentrations (1. 04 ng g -1 ww), while samples collected off Japan were 10 to 80 times higher. PFOS and PFUnDA were the most-detected compounds (100%) and exhibited the highest concentrations, while PFNA was the least-detected compound (57%), mostly below LOQs.
Acknowledgements The authors would like to thank the fishermen from tropical tuna purse seiners operating in the western  Indian Ocean  and landing in the Seychelles and from the semi-industrial longliner MV Albacore operating in Seychelles waters who assisted with fish sampling. Special thanks also to the SFA research technicians for their invaluable help in processing the samples. This work is part of the  CONSWO  research project led by the  Seychelles Fishing Authority (SFA)  in collaboration with the  French Research Institute for Sustainable Development (IRD) , with the financial support of the  European Fisheries Partnership Agreement (EU-FPA ). We acknowledge  Laura Valentine  from  "English Assistance for Industry"  for the English corrections made to the manuscript.
Results Diversity of RNA viruses in the Yangshan harbour virome. RNA virome analysis performed using complementary DNA derived from approximately 10 l of samples from Yangshan Deep-Water Harbour yielded 4,593 nearly full-length RNA virus RdRPs that formed 2,192 clusters at 75% amino acid identity which represents virus diversity at a level between species and genus. Among the RdRP sequences from GenBank (October 2018), 2,021 comparable clusters were detected. Thus, the 10 l water sample analysed here more than doubles the known diversity of RNA viruses. Phylogenetic analysis assigned 85% of the RdRPs from the Yangshan RNA virome to 9 clades and one complex assemblage, each comprising more than 100 RdRps from several clusters (Fig. 2  and Supplementary Dataset 1). Seven of these clades blended into those defined previously, whereas two previously unknown clades and the assemblage were dominated by viruses from the Yangshan virome (Fig.
2 ). All these clades represented +RNA viruses of the phyla Lenarviricota, Pisuviricota and Kitrinoviricota, whereas no members of Negarnaviricota were found. Only six dsRNA viruses (Duplornaviricota) were identified, but were not further analysed. No enveloped +RNA viruses of the families Flaviviridae and Togaviridae were detected. Common +RNA viruses of terrestrial vertebrates and plants (for example, members of Picornaviridae, Caliciviridae, Virgaviridae or Potyviridae) were also absent from the Yangshan virome. The largest RdRP group in the Yangshan virome (854 members; Supplementary Datasets 1 and 2) belongs to the ' Aquatic picorna-like' clade (order Picornavirales)  30, 42  in the phylum Pisuviricota (Fig. 2 ). This clade contains the Marnaviridae and other protist-infecting viruses as well as viruses identified in holobionts of molluscs, annelids and other marine invertebrates whose diets include protists.
The largest of the 323 broad RdRP clusters in the Yangshan virome-OV. 1 (where OV indicates Ocean Viruses, an operational term for RdRP clusters), with 653 members (Supplementary Datasets 1 and 2)-fell entirely into the Marnaviridae, vastly expanding the diversity of this family and highlighting the need for a taxonomic upgrade  28. Given that isolated Marnaviridae infect diatoms and other aquatic Stramenopile protists  21, 22, 26, 39, 45  , most OV. 1 members are likely to infect related unicellular eukaryotes. The genome organizations of the previously recognized marnaviruses and those from a b c 32° 0′ N 31° 30′ N 31° 0′ N Latitude 30° 30′ N 30° 0′ N 124° 0′ E 123° 30′ E 123° 0′ E 122° 30′ E Longitude 122° 0′ E 121° 30′ E 121° 0′ E 120° 30′ E.
Nature Microbiology the Yangshan virome are nearly uniform: they encode either one or two polyproteins encompassing the same set of protein domains (Supplementary Dataset 2). Pisuviricota accommodated another large clade with 343 RdRPs related to those of Dicistroviridae (order Picornavirales; Fig. 2 ), which infect marine and terrestrial arthropods  30, 46. Although these and previously recognized dicistroviruses share the same genome organization, they form sister groups in the RdRP phylogeny (for example, OV. 9 and OV. 13 in Supplementary Datasets 1 and 2), suggestive of distinct host ranges. The third largest clade within Pisuriviricota (239 RdRPs, including OV. 12 and OV. 27) joined the lineage that includes plant Solemoviridae, fungal Barnaviridae and protist Alvernaviridae. The fourth clade (101 members) of the Yangshan virome RdRPs within Pisuviricota (including OV.
16 and OV. 23; Supplementary Datasets 1 and 2) is a sister group to Potyviridae, the largest family of plant viruses  47, 48. Because the marine virome appears to be ancestral to the terrestrial plant virome  49  , these aquatic relatives of the potyviruses probably resemble the common ancestor, and were accordingly dubbed Protopotyviruses (Fig. 2 ). Protopotyviruses share with potyviruses the conserved tandem of a chymotrypsin-like protease and the RdRP, but lack the SF2 helicase and the papain-like protease characteristic of potyviruses (Supplementary Dataset 2). Given the evolutionary affinity between the potyvirus SF2 helicase and the homologous helicase of flavi-like viruses  50  , this is likely to be a late acquisition in potyviruses. Most of the protopotyvirus genomes encode a single-jelly-roll capsid protein (SJR-CP), likely inherited from the common ancestor of all eukaryotic RNA viruses  42.
3 in Supplementary Dataset 2). Oomycetes, particularly those that parasitize diatoms  58  , are the plausible hosts for the noda-like viruses in the Yangshan virome, although free-living marine nematodes could not be ruled out as hosts  59. Unlike the known members of Nodaviridae, most of the noda-like viruses identified in the Yangshan virome have monopartite genomes, which appears compatible with an ancestral state. None of the major Yangshan virome clades among Kitrinoviricota joined the ' Alphavirus supergroup' (class Alsuviricetes) comprising viruses that infect mostly plants, as well as animals and fungi. A previously unknown, highly diverse assortment of RdRPs (719 members; hereafter, the Yangshan assemblage) consists of several clades positioned between the noda-like viruses and Alsuviricetes within Kitrinoviricota (Figs.
Here we developed a procedure for swapping domains in the permuted RdRPs to restore the original domain order and included these reconstructed RdRP sequences in the phylogenetic analysis (Fig. 2 ). In the resulting trees, permutotetraviruses and birnaviruses formed a well-supported clade within Pisuviricota that was far removed from the Yangshan assemblage (Extended Data Fig. 1 ), again pointing to convergent evolution of this trait in diverse viruses. Of the 387 long Yan-like contigs, 220 encode a distinct SJR-CP and 100 encode a capping enzyme. A HHpred comparison of the profile created from the sequence alignment of Yan-like virus capping enzymes against the profile database exclusively retrieved the capping enzymes of Alsuviricetes (PF01660. 17; Vmethyltransf; P = 99. 8; Extended Data Fig.