dataesr/scientific-paragraphs-categorization huggingface dataset:
If Γ = 2, the mean magnetic field is simply expressed as F sync F IC 10B 2 -5. (C. 5) We can thus estimate the mean magnetic field using the X-ray flux derived with ROSAT/PSPC data and the TeV flux and spectral index reported in the HGPS. Below the method is applied on the SNR RX J1713. 7-3946 for illustration. The spectral extraction radius of the SNR RX J1713. 7-3946 is R spec = 0. 6 • and the spectral parameters reported in the HGPS are F 1 TeV = (2. 13 ± 0. 94) × 10 -11 cm -2 s -1 TeV -1 and Γ TeV = 2. 32 ± 0. 02. We masked in the ROSAT/PSPC images all the 1RXS and 2RXS cataloged sources whose centroid are located outside R spec. Figure C. 1 (left)  shows the significance map using the reflected background method, which is similar to that obtained using the ring background method. As expected, the significance is well above the 5σ threshold.
Acknowledgements. We thank the anonymous referee for his/her comments that improved the manuscript. J. D. and M. L. G. acknowledge support from  Agence Nationale de la Recherche  (grant  ANR-17-CE31-0014 ). GMRT is run by the  National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. This research has made use of data from the  Karl G. Jansky Very Large Array  and from the  MOST , which is operated by  The University of Sydney  with support from the  Australian Research Council  and the  Science Foundation for Physics within The University of Sydney. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under 13 https://www. hs. uni-hamburg. de/hserosita/cooperative agreement by  Associated Universities, Inc. The research presented in this paper has used data from the  Canadian Galactic Plane Survey , a  Canadian  project with international partners, supported by the  Natural Sciences and Engineering Research Council.
Acknowledgments: We acknowledge  Nuttee Suree  ( Faculty of Science-Chiang Mai University ) for his inspiration about BET family proteins and BETis. We appreciate the valuable coordination from  Léo Meyer  and  Mathieu Rivalain  ( IUT Louis Pasteur, University of Strasbourg ). Also, we are grateful to  Andrea Janossy  ( Faculté des Sciences de la Vie-University of Strasbourg ) for carefully and critically revising the language in the manuscript.
9" (Program-ID 2019. 1. 01702. S, PI Valentino). The ALMA CO(5-4) and CO(2-1) observations and the data reduction procedure are described in a previous paper  25. For a detailed description of the full data-set we refer the reader to Reference  57. The rest-frame optical spectrum of ID2299 covering the [OII] 3726,3729 doublet was obtained as part of the DEIMOS 10K Spectroscopic survey  16. We retrieved this spectrum from the COSMOS repository (http://cosmos. astro. caltech. edu/). The source has SFR= 550±10 M yr -1. This is measured from the bolometric far-IR luminosity of the dust L FIR integrated over λ ∈ [8-1000] µm cleaned by the AGN contribution. We measure L FIR by fitting the SuperDeblended far-IR/(sub)-millimetre catalog in COSMOS  58  using the procedure described in a previous paper  58.
The ALMA CO(2-1), CO(5-4) and [CI](2-1)+CO(7-6) spectra are extracted fitting the data in the UV space accounting for source extension modeled as a Gaussian of radius R eff,ALMA = 1. 54 kpc corresponding to the effective radius of the galaxy in the sub-millimeter  25. We fit and subtract the continuum using a power law in the form S ν = S 0 × ( ν ν 0 ) 3. spectra leaving all the fit parameters free to vary. The resulting emission line centroid and velocity dispersion measured in each spectrum are remarkably consistent. However, the single Gaussian fits yield significant residuals suggesting that an additional component is required (light blue curve in the bottom panels of Fig. 1 , see also the next section). Therefore, we fit two Gaussian functions to the CO(2-1) spectrum leaving all the fit parameters free to vary.
We obtain M ,ALMA = 2. 1 × 10 10 M and this includes the contribution from the main galaxy and the putative merging companion. Conservatively assuming a 50% splitting between the two putative galaxies and adding to the gas mass, the total falls short by a factor of 0. 85 dex (∼ 7×) with respect to M dyn,tot,b. We thus conclude that the discrepancy between the dynamical and baryonic mass is significant, also when accounting for the reported observational uncertainties as well as the systematic uncertainties associated to the dynamical status of the system  39. We note that the stellar mass is robust against dust attenuation effects. This is because we include the K s and IRAC photometric bands in the SED fitting (see Extended Data Figure  1 ), corresponding to 1-3µm in the rest-frame. The emission at these restframe wavelengths is known to get aligned with CO and radio even for the most obscured starbursts 39, 64, 103, 104 and is thus representative of the bulk of the stellar mass.
discussion. AP acknowledges funding from  Region Île-de-France  and  Incoming CEA  fellowship from the  CEA-Enhanced Eurotalents program , co-funded by  FP7 Marie-Skłodowska-Curie COFUND program  (Grant Agreement  600382 ). A. P. also gratefully acknowledges financial support from  STFC  through grants  ST/T000244/1  and  ST/P000541/1. M. P. acknowledges support from the  Comunidad de Madrid  through the  Atracción de Talento Investigador Grant   2018-T1/TIC-11035  S. J. acknowledges financial support from the  Spanish Ministry of Science, Innovation and Universities (MICIU)  under grant  AYA2017-84061-P , co-financed by  FEDER (European Regional Development Funds). Author contributions: A. P. and E. D. reduced the data, interpreted the results and wrote the paper. All other authors contributed to the observing proposals, to the scientific discussion and elaboration of the results, and provided comments to the manuscript.
Data Availability: The ALMA data analysed in this study are publicly available from the ALMA archive (http://almascience. nrao. edu/aq/, Program IDs: 2015. 1. 00260. S, 2016. 1. 00171. S, 2019. 1. 01702. S). The DEIMOS spectrum of the source is also publicly available and can be retrieved through the COSMOS archive (http://cosmos. astro. caltech. edu/). Code Availability: The ALMA data are processed using a series of GILDAS-based scripts available at https: //github. com/1054/Crab. Toolkit. PdBI. The GILDAS software is publicly available at http: //www. iram. fr/IRAMFR/GILDAS. The CO/[CI] emission of the source have been modelled with the MICHI2 software which is publicly available at https://ascl. net/code/v/2533.
Characteristics of COVID-19 individuals. We screened two series of nasopharyngeal swabs from RT-qPCR diagnosed COVID-19 We titrated viral infectivity in the first series of 200 patients. 107 patients (53%) harboured detectable viable virus in their nasopharyngeal swabs (Fig. 2a ). The infectious titers were highly variable, ranging from 0 to >10 6 IU/ml. To further characterize these variations, we plotted Ct values, days POS and levels of anti-SARS-CoV-2 IgG or IgA for each individual (Fig. 2a ). Data were ranked from high to low infectious titers in RDT + and RDT-categories. The RDT-category was then ranked from low to high levels of SARS-CoV-2 IgG. There was a strong inverse correlation between infectious titers and viral RNA levels, defined by the Ct value (Fig. 2b ). The median titer within positive individuals was 10 3 IU/ml (Fig.
Images were acquired with an Opera Phenix high content confocal microscope (PerkinElmer). The GFP area and the number of nuclei were quantified using the Harmony software (PerkinElmer). The viral titer (Infectious units /mL) was calculated from the last positive dilution with 1 infectious unit (IU) being 3 times the background (GFP area in non-infected controls). S-Flow Assay. The S-Flow assay was performed as described  17, 39. Briefly, HEK-293T (referred as 293T) cells were acquired from ATCC (ATCC® CRL-3216TM) and tested negative for mycoplasma. 293T Cells stably expressing Spike or control cells were transferred into U-bottom 96-well plates (10 5 cells/well). Cell were incubated at 4°C for 30 min with nasopharyngeal swabs (1:5 dilution) in PBS containing 0. 5% BSA and 2 mM EDTA, washed with PBS, and stained using anti-IgG AF647 or Anti-IgA AF647 (Jackson ImmunoResearch).
Cells were washed with PBS and fixed 10 min using 4% PFA. Data were acquired on an Attune Nxt instrument (Life Technologies). Stainings were also performed on control (293T Empty) cells. Results were analyzed with FlowJo 10. 7. 1 (Becton Dickinson). The positivity of a sample was defined as a specific binding above 20%. The specific binding was calculated as follow: 100 x (% binding 293T Spike -% binding 293T Empty)/ (100 -% binding 293T Empty).
Statistical analysis. Flow cytometry data were analyzed with FlowJo v10 software (TriStar). Calculations were performed using Excel 365 (Microsoft). Figures were drawn on Prism 9 (GraphPad Software). Statistical analysis was conducted using GraphPad Prism 9. Statistical significance between different groups was calculated using the tests indicated in each figure legend. Partial least square regression with "leave-one-out" strategy was performed with R version 4. 0. 2 on RStudio Desktop 1. 3. 959 (R Studio, PBC) using the Package pls version 2. 7-3. Briefly, numerical data were centered and scaled before performing the regression and the number of components was selected with the selectNcomp function. Significance of the variation coefficients was assessed with Jackknife approximate t tests (jack.
test). We performed logistic regressions on different parameters depending on time since onset of symptoms with the glm function of the stats package. The goodness of fit was assessed by calculating the area under the ROC curve between observed and predicted results (AUCROC) (package pROC: http://expasy. org/tools/pROC). We compared curves using a Chisquare test of the regression with time and variant parameters. For other R analysis we used the following packages: corrplot (https://github. com/taiyun/corrplot) and readxl (https://CRAN. Rproject. org/package=readxl) Provide access to biological samples and clinical information: NDG, JR, JP, VE, LB, GO, LN, VM, JF, MW, SI, HP, DV Manuscript writing: DP, LG, TB, OS Manuscript editing: BM, NS, JPDS, HP, DV b. Titration of infectious SARS-CoV-2 in the swabs.
Acknowledgments. We thank  Michael Maaran Rajah ,  Nicoletta Casartelli ,  Michael White  and  Jeremy Dufloo  for discussion and critical reading of the manuscript. We thank  AGEPS, Assistance Publique -Hôpitaux de Paris , for providing the RDTs. We thank the patients who participated in the study, the clinical staff involved in their management and  Théo Hirsch  for the stimulating discussions. We thank the members of the  Virus and Immunity Unit  for discussion and help,  Nathalie Aulner  and the  UtechS Photonic BioImaging (UPBI) core facility (Institut Pasteur) , a member of the  France BioImaging network , for image acquisition and analysis, and  Cyril Planchais & Hugo Mouquet  for the kind gift of mAb48.
Funding. Work in OS lab is funded by  Institut Pasteur ,  Urgence COVID-19 Fundraising Campaign of Institut Pasteur ,  ANRS , the  Vaccine Research Institute  ( ANR-10-LABX-77 ),  Labex IBEID  ( ANR-10-LABX-62-IBEID ),  ANR/FRM   Flash Covid PROTEO-SARS-CoV-2 and IDISCOVR ,  Fondation pour la Recherche Médicale. DP is supported by the  Vaccine Research Institute. LG is supported by the  French Ministry of Higher Education, Research and Innovation. DD and JDS are funded by the  ANR-20-COVI-0053  grant. The funders of this study had no role in study design, data collection, analysis and interpretation, or writing of the article. Competing interests: L. G. , I. S. , T. B. , J. B. , F. G. -B. , and O. S. are coinventors on provisional patent no. US 63/020 ,063 entitled "S-Flow: a FACS-based assay for serological analysis of SARS-CoV2 infection" submitted by  Institut Pasteur.
MAGs can be taxonomically annotated using multi-marker gene approaches, and organism-level functional profiles can be drawn from their genomic content  [19] [20] [21]. Reads from environmental meta-omics datasets can also be mapped to their reconstructed sequences to obtain abundance measurements both at MAG and single protein level  [21] [22] [23]. MAGs can be considered as representative of the genetic potential of natural populations, hence allowing to retrieval of genomes of cultivable, uncultivable, or even unknown species present in the environment. They constitute a promising tool for investigating as a whole the functional potential of known and unknown planktonic life forms. Recently, a genomics-based model revealed that the gene content of planktonic communities is more relatable to biogeochemical gradients than taxonomic content  10.
In another study, omics data were used to quantitatively estimate global nitrogen fixers abundance through machine learning algorithms  24. It illustrates how quantitative, data-driven biogeochemical models can be built from global omics datasets. However, these studies focused only on a relatively small number of well-described genes (e. g. , nif or amtB genes, involved in dinitrogen and ammonium fixation, respectively)  10, 24  , far from exploiting the rich functional diversity observed in omic samples. In this way, the large proportion of unknown sequences detected in environmental metaomics datasets, that is to say, the open reading frames (ORFs) which can not be linked to any biological functions (usually around 40% for bacteria and archaea, and about 50% for eukaryotes), is as yet untapped  4, 5, 23, [25] [26] [27].
These results highlight the potential of function-clustering-based approaches for deciphering global ocean biogeochemistry but could be further extended by skipping any sequence pre-selection step requiring database-dependent metabolic pathways annotations. In this study, we followed a similar approach while avoiding any a priori choices of particular genes or metabolic pathways. We used 51 quantitative and qualitative environmental variables to detect both known and unknown protein clusters that are sensitive to environmental gradients. We re-analyzed 885 highquality MAGs from marine planktonic Bacteria (n = 820) and Archaea (n = 65), assembled by Delmont et al. 21. using 93 Tara Oceans picoplanktonic metagenomes from the surface of the global ocean. With these almost 2 million sequences, we built functional clusters of proteins using a sequence similarity network (SSN), i.
Results From SSN to PFCs. We analyzed the 1,914,171 proteins from 885 MAGs from marine plankton, recovered from 12 geographically bound assemblies of metagenomic sets corresponding to a total of 93 Tara Oceans samples from the 0. 2 to 3 µm and 0. 2 to 1. 6 µm size fractions  21. A flowchart of our bioinformatic pipeline is available in Supplementary Fig. 1. 39. 6% of the MAGs' proteins (757,457) were involved in our SSN, i. e. , they had at least one similarity relationship with another protein that satisfied the chosen threshold of 80% similarity and 80% coverage (see "Methods"). In total, 51. 1% of the network proteins could be annotated to 4922 unique molecular function IDs in the KEGG database  37  , associated with 327 distinct metabolic pathways (a full list of these pathways is available in Supplementary Data 1).
In total, 85. 2% of the network proteins were annotated to 17,009 eggNOG functional descriptions  38, 39. The SSN involved 233,756 connected components (CCs), i. e. , groups of nodes (here proteins) connected together by at least one path and disconnected from the rest of the network. According to KEGG and eggNOG databases, 15. 3% and 48. 5% of the CCs remained without any functional annotation (i. e. , all sequences from the CC were unmatched in the databases, or had a match but were not yet linked to any biological function, Table  1 ), and 14. 8% were functionally unannotated for both databases. We ranked the functional homogeneity of CCs involving at least one functional annotation from 0 (all annotations in the CC are different) to 1 (all annotations in the CC are the same) and found mean homogeneity scores of 0.
99 over 1 for KEGG annotations and 0. 94 over 1 for eggNOG ones (see "Methods" for score calculation details). Only 88 (0. 04%) CCs had a homogeneity score below 0. 5 in both annotation databases, all with sizes below five proteins. 177 CCs (0. 07%) had a score below 0. 8 in both databases, all under 12 proteins in size. These CCs were kept in the analysis while tagged as poorly homogenous. We thereafter considered each CC as a PFC, numbered from #1 to #233,756. To check for the influence of taxonomic relationships between the MAGs on our PFCs, we computed different metrics based on MAGs taxonomic annotations provided by Delmont et al. 21. (Table  1 ). This taxonomic annotation based on 43 single-copy core genes allowed to annotate 100% of the MAGs at the domain level, and 95% of the MAGs at the phylum level, the remaining 5% corresponding to Bacteria MAGs of unidentified phyla  21.
Only 1330 PFCs (0. 6%) mixed proteins from the Archaea and Bacteria domains. PFCs were very homogeneous at the phylum level, then the homogeneity decreased at lower taxonomic rank, meaning that PFCs studied here were generally not specific from a single class, order, family, genus, or MAG (Table  1 ). In total, 7834 PFCs (3. 4%) were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and no taxonomic annotation under the phylum level. Their sizes ranged from 2 to 30 proteins (mean of 2. 62). Their 20,552 proteins came from Euryarchaeota MAGs (12,458; 60. 6%), Bacteria MAGs of unidentified phylum (2742; 13. 3%), Candidatus Marinimicrobia MAGs (2451; 11. 9%), Proteobacteria MAGs (1528; 7. 4%), Acidobacteria MAGs (1031; 5%), Verrucomicrobia MAGs (103; 0. 5%), Planctomycetes MAGs (89; 0.
246 KEGG pathways were associated with the 14,585 hlePFCs, i. e. , 75% of the pathways identified on the full network were detected in hlePFCs. Supplementary Data 1 gives a detailed list of all pathways detected in our network, along with their total number of occurrences in PFCs, and their number of occurrences in hlePFCs. The functional homogeneity of the 14,585 hlePFCs was similar to the one of the total 233,756 PFCs (Tables  1, 2 ). In parallel, 9. 2% of the hlePFCs were functionally unannotated (e. g. , only composed of unannotated proteins in the KEGG and the eggNOG functional databases), while 14. 8% of the PFCs were functionally unannotated. Proportions of taxonomically homogeneous PFCs were similar between hlePFCs (Table  2 ) and total PFCs (Table  1 ), all above 90% at the phylum, class, order, and family level when considering only PFCs including at least one protein taxonomically annotated.
g. , biosynthesis of antibiotics, Fig. 2  Canonical correspondence analysis (CCA) on abundances of the 14,585 protein functional clusters highly linked to environmental variables (hlePFCs). hlePFCs are represented as gray dots, quantitative environmental variables as arrows, and samples as points colored and shaped according to their biogeographical province (correspondence between four letters codes used here and full biogeographical provinces names, as well as descriptions of all other environmental variables are available in Supplementary Data 2). A map of Longhurst biogeographical provinces  42  colored using the same color scale is shown in the upper right panel. For simplification issues, other qualitative variables (season moment, depth, and ocean region) were not represented. The closest sample from the Mediterranean ones in the CCA space, which comes from the closest Atlantic station to the strait of Gibraltar, was highlighted by a plain black arrow.
5  Detecting functionally unannotated protein functional clusters highly linked to environmental conditions. A Relationships between normalized sequence abundance and temperature for six selected protein functional clusters highly linked to the environment (hlePFCs) that were only composed of functionally unannotated sequences. The three graphs on the left, in purple, correspond to the three hlePFCs functionally unannotated in both KEGG and eggNOG databases that had the lowest positions along the second axis of the canonical correspondence analysis (CCA2) (cold and nutrient-rich waters). The three graphs in the middle, in green, correspond to the three hlePFCs functionally unannotated in both KEGG and eggNOG databases that had the highest positions along CCA1 (oligotrophic and warm waters).
We then propose that the strait of Gibraltar and the Suez canal could shape the genetic and functional structure of some planktonic bacterial/archaeal populations, as it is observed in some eukaryotic species  52, 53. Finally, our analysis of PFCs showing R 2 values above 0. 25 (Section II of supplementary notes) highlighted station 93 as a strong outlier in PFC composition. This station is the closest to the Chilean coast in our dataset and corresponds to an upwelling zone, which makes it an original station in terms of environmental conditions. It was characterized by the over-abundance of PFCs associated with the Pseudoalteromonas genus. Pseudoalteromonas is a cosmopolitan genus of Gammaproteobacteria that is known for producing biologically active molecules notably including toxins, anti-bacterial and anti-fungal agents, sometimes used as weapons against other organisms and which can be pharmaceutically relevant  54, 55.
Pseudoalteromonas bacteria are known for being able to survive in extreme environments  56  and are not commonly associated with upwelling areas, although they are known to interact with macroalgae in such nutrient-rich coastal environments  57. Here, we give the public access to a set of 1928 PFCs associated with the Pseudoalteromonas genus that was particularly abundant in a strong upwelling area, among which 127 were unannotated in both KEGG and eggNOG databases (see "Data availability"). These clusters could be related to ecological interactions between Pseudoalteromonas bacteria and algae, but the originality of station 93 in our dataset as a coastal and upwelling area prevents us from drawing any strong conclusions on their ecological role here. In section II of the Supplementary notes, we discuss why these PFCs were not found among hlePFCs, and highlight how the inclusion of more samples from similar coastal and/or upwelling areas would help to strengthen our results concerning these Pseudoalteromonas related PFCs.
They included 166 dark hlePFCs, mainly related to Proteobacteria and Candidatus Marinimicrobia MAGs, the latter being associated with Mediterranean samples. In addition, more than half (52. 9%) of the models associated with dark PFCs showed R 2 values above 0. 25, and the mean R 2 values over the 7834 models associated with dark PFCs were similar to the one over all models. We then show that the response of dark PFCs to environmental gradients is comparable to the one of taxonomically and functionally annotated PFCs. Our method being applicable to any set of sequences, we predict that an accumulation of similar results on multiple datasets will help identify recurrent unannotated protein clusters linked to specific environmental niches  62. It could further help to target wet-lab studies towards the description of unknown proteins particularly adapted to specific conditions, like subtropical nutrient-poor waters or oxygen minimum zones  51.