dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteolysis of purified SARS-CoV-2 virus The inactivated purified virus sample (equivalent to 7. 25x10 5 PFU/mL) was quantified in terms of protein concentration (0. 614 mg/mL) by UV spectrophotometry. A volume of 60 µL was mixed with 20 µL of LDS3X to obtain a protein fraction of 0. 46 mg/mL. After denaturation at 99°C for 5 min, a volume of 25 µL (11. 5 µg of proteins) was deposited on a NuPAGE 4-12% gel (Invitrogen) and subjected to 5 min electrophoretic migration. The whole proteome was excised as a single polyacrylamide gel band and subjected to trypsin proteolysis as previously described  (24). An aliquot of 50 µL of peptides was extracted. MS/MS analysis was performed to confirm the high content of viral proteins in this sample (data not shown).
Peptide assignation and proteomics data analysis MS/MS spectra from the nasopharyngeal swabs were searched against the generalist NCBInr database (108,307,546 sequences totalling 41,817,980,956 amino acids) with the MASCOT Daemon 2. 3. 2 search engine (Matrix Science). The search parameters were as follows: fulltrypsin specificity, maximum of two missed cleavages, mass tolerances of 5 ppm on the parent ion and 0. 02 Da on the MS/MS, carbamidomethylated cysteine (+57. 0215) as a fixed modification, and oxidized methionine (+15. 9949) and deamidation of asparagine and glutamine (+0. 9848) as variable modifications. PSMs with an FDR<1% were selected for peptide inference. Peptides were assigned to taxa using the Unipept 4. 3 web interface  (26)  with default parameters (equate I/L, filter duplicate peptides).
MS/MS spectra from the simili SARS-CoV-2 contaminated swabs and from the COVID-19 nasopharyngeal swabs were assigned with the MASCOT Daemon 2. 3. 2 search engine (Matrix Science) as follows: the spectra were first queried against the cRAP_contaminants_2020-05-18. fasta file and then against the Swissprot_Human_ISL_410545_2020-05-18 database (20,139 sequences totalling 11,330,214 amino acids) in follow-up mode and with the decoy option activated. This last database is the merge of the SARS-CoV-2 viral proteins and the Swissprot Human proteome. The MASCOT search was performed with the same parameters as above. All peptide matches presenting a MASCOT peptide score with a FDR lower than 1% were assigned to protein sequences. MS1 peak areas were evaluated with Skyline  (27). Briefly, we created spectral libraries based on the DAT files from each MASCOT search (cut-of 0.
Metaproteomics analysis of the two nasopharyngeal swabs excludes the presence of abundant microorganisms. The MS/MS spectra acquired over 90 min on the two nasopharyngeal swabs were analyzed to infer the main microbial components present in these samples as such presence should be taken into account for creating an ad hoc database for MS/MS interpretation. Swabs R1 and R2 yielded 60,932 and 61,053 MS/MS spectra, respectively, from which 13,602 and 13,802 were attributed to 6,460 and 6,421 peptide sequences from organisms present in the NCBInr database (FDR <1%). These peptide sequences were analyzed with the Unipept tool  (26)  to assess the biodiversity present in each sample through their taxon-specificity characteristics based on the lowest common ancestor approach. Only a small proportion of the peptide sequences mapped by Unipept belonged to microorganisms (Table  S2 ).
Detection of SARS-CoV-2 viral peptides in the simili swabs. Simili swabs containing specific quantities of SARS-CoV-2 virus and the equivalent of 8. 4% of the nasal matrix protein material collected during sampling were analysed by MS/MS with a short gradient. We first confirmed on the most diluted fraction that the bacterial signal was negligible for both fractions, thus not to consider at the MS/MS attribution search stage. For this, the two datasets were searched against the generalist database NCBInr to check for the presence of non-human peptides in the swab peptidomes. The Unipept analysis of the detected peptide sequences showed that only 3 and 2 peptides, from replicate 1 and 2, respectively, were attributed to Bacteria and no bacterial species could be confidently identified (Table  S2 ).
The results from the short gradient MS analysis on the simili swabs against the specific human/virus database yielded 139,404 MS/MS spectra recorded in the fourteen samples. From these, 34,647 were attributed to 2,919 peptide sequences with a FDR below 1% (Table  S3 ). This data allowed for the identification of 1,094 protein groups (Table  S4 ). A small fraction of 173 peptide-to-spectrum matches (PSMs), corresponding to 0. 5% of the total PSMs, allowed identifying 18 different viral peptide sequences, including the five peptides of interest. The 18 peptides report for 3 structural proteins from the virus: 8 peptides from the nucleocapsid protein (N), 7 peptides from the spike protein (S), and 3 peptides from the membrane glycoprotein (M). At least one viral peptide was identified in all samples independently of the concentration of the viral material, from 460 ng (544 PFU) to 2 ng (2 PFU).
DISCUSSION To foster the development of alternative detection methods for SARS-CoV-2, we performed a proof-of-concept study to assess the potential of MS/MS for proteotyping SARS-CoV-2: i) in simulated nasal swabs containing different quantities of viral peptides; and ii) in nasopharyngeal swabs from COVID-19 diagnosed patients. The two nasal peptidomes collected from healthy donors for the first experiment were first analyzed with a gradient of 90 min to check for the presence of detectable microorganisms from the natural microbiota. A search against a generalist database such as NCBInr detected only trace levels of very low abundant bacteria commonly found in the nasal tract  (30) , thus confirming the absence of a measurable microbiome in the swab samples. Based on this metaproteomic analysis, we used a human-only database as representative of the nasopharyngeal matrices for the subsequent analysis.
Acknowledgements The authors are indebted to  Dr Silvia Meschi  ( National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, via Portuense 292, 00149 Rome, Italia ) for making the Human 2019-nCoV strain 2019-nCoV/Italy-INMI1 ( 008N-03893 ) available. This publication was supported by the  European Virus Archive goes Global (EVAg)  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  N°653316. The authors are also grateful to the  French Alternative Energies and Atomic Energy Commission (CEA) , and the  ANR program  " Phylopeptidomics " ( ANR-17-CE18-0023-01 ) that supported part of this study.
Scale bar: 10 μm. b PCR-based quantification of bacterial load in feces (upper) and adhered to the mucosa (lower). c-e Total RNA was extracted from colons, and the expression levels of Zg16 (c), Lyz1 (d) and Defb1 (e) were quantified by qPCR and normalized to those of 36B4. f Enzymatic activity of lysozyme against peptidoglycan in feces supernatants. Data are presented as the mean ± SEM. Significance was determined by unpaired two-tailed student's t-test (n = 5-6 mice/ group; *P < 0. 05, n. s. non-significant). Fig. 2  The microbiota composition of PepT1 -/-mice is altered. a Principal component analysis (PCA) of the unweighted UniFrac distance matrix of fecal WT and PepT1 -/-microbiota. b Microbiota richness and diversity in WT and PepT1 -/-mice, based on the number of OTUs per sample. c Bacterial-taxon-based analysis of feces at the phylum level.
Mice were euthanized at D84 post transplantation. f Scheme of the experimental design. g Principal component analysis (PCA) of the weighted UniFrac distance matrix of fecal microbiota from WT (red) and PepT1 -/-(blue) microbiota recipient mice at D14 and D28 post-transplant. h Body weights of conventionalized mice from transplant to euthanasia at D84. i Survival rate. Mice were euthanized after losing 20% of their initial body weight. j Colon weight/colon length ratio at D84. k MPO activity in the distal colon at D84. l Number of tumors per mouse. m Tumor size, determined using a dissecting microscope. Significance was determined by unpaired two-tailed Student's t-test (n = 5-7 mice/group; *P < 0. 05, **P < 0. 01, n. s. non-significant). Data are presented as the mean ± SEM. The protective effect of the PepT1 -/-microbiota is lost in separately housed littermates One logical explanation for the above-described results is that the PepT1-null genotype exerts a beneficial effect on the intestinal microbiota relative to a WT genotype.
16S rRNA gene sequence analysis and metagenome prediction The sequences were demultiplexed, quality filtered using the Quantitative Insights Into Microbial Ecology (QIIME, version 1. 8. 0) software package  41  , and forward and reverse Illumina reads were joined using the fastq-join method (https://expressionanalysis. github. io/ea-utils/)  42. We used QIIME default parameters for quality filtering (reads truncated at first low-quality base and excluded if: (1) there were more than three consecutive low quality base calls; (2) less than 75% of read length was consecutive high quality base calls; (3) at least one uncalled base was present; (4) more than 1. 5 errors were present in the barcode; (5) any Phred qualities were below 20; or (6) the length was less than 75 bases). Sequences were assigned to OTUs using the UCLUST algorithm  43  with a 97% threshold of pairwise identity and with the creation of new clusters with sequences that did not match the reference sequences.
OTUs were taxonomically classified using the Greengenes reference database  44. A single representative sequence for each OTU was aligned and a phylogenetic tree was built using FastTree  45. The phylogenetic tree was used for computing the unweighted UniFrac distances between samples  46, 47. Rarefied OTU table were used to compare abundances of OTUs across samples. Principal component analysis (PCA) plots were used to assess the variation between experimental group (beta diversity), alpha diversity curves were determined for all samples using the determination of the number of observed species, and OTU table was rarefied at various taxonomic levels using QIIME. Data were also represented as heatmap using Morpheus 48 and taxa summarization. LEfSE (LDA Effect Size) was used to investigate bacterial members that drive.
ACKNOWLEDGEMENTS This work was supported by the  National Institutes of Health of Diabetes and Digestive and Kidney  ( R01DK107739  to D. M. ;  R01DK116306  to D. M. and A. G. ), the  Department of Veterans Affairs  ( Merit Award   BX002526  to D. M. ), D. M. is a recipient of a  Senior Research Career Scientist Award  ( BX004476 ) from the  Department of Veterans Affairs. E. V is recipient of the  Career Development Award  from the  Crohn's and Colitis Foundation  and an  Individual Fellowship Marie Sklodowska-Curie grant  from the  European commission Research Executive Agency. B. C. is supported by a Starting Grant from the  European Research Council , an  Innovator Award  from the  Kenneth Rainin Foundation , and a Chaire d'Excellence from  Paris University. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Using this dataset, we explore how diversity effects on tree growth change during the early stages of stand development in regenerating boreal forests in Finland. We hypothesize that diversity effects should become increasingly positive with time and that this shift should coincide with the period of canopy closure-which occurs approximately 20-25 years after a stand-replacing disturbance in these forests  (Angelstam & Kuuluvainen, 2004; Shorohova, Kuuluvainen, Kangur, & Jõgiste, 2009). To complement this analysis, we then compare these growth responses with those observed in the Satakunta experiment in Finland-one of the world's longest running tree diversity experiments where the same combinations of species we sampled in closed-canopy forests were planted two decades ago. We expect that temporal trends in the strength of diversity effects on tree growth in these experimental plots should mirror those observed in closed-canopy forests.
, 2014). This is important, as growth trajectories and responses to competition of canopy dominant and suppressed trees can vary considerably  (Luo, McIntire, Boisvenue, Nikiema, & Chen, 2020). Wood cores were extracted using a 5. 15-mm diameter increment borer (Haglöf AB) and stored in polycarbonate sheeting to air dry. Cores were then mounted on wooden boards and sanded with progressively finer grit sizes before being digitally scanned using a high-resolution flatbed scanner (2,400 dpi optical resolution). From the scanned images we measured annual radial increments for all cored trees using the software CDendro (Cybis Elektronik & Data). Individual chronologies were crossdated against species-level reference curves generated by pooling all samples belonging to a given species to detect any misplaced or missing ring boundaries.
From these chronologies we calculated the annual diameter increment of each cored tree (D incr , in cm/year), as well as its age. For trees in which cores did not include the pith, we estimated the number of missing rings by first calculating the distance to pith from the innermost visible ring using the pith locator tool in CDendro and then dividing this distance by the mean increment of the five innermost rings  (Rozas, 2003). Finally, the true age of each tree was adjusted to account for the number of years needed for trees to reach a height of 1. 3 m at which cores were extracted. We did this by fitting species-specific height-age functions using data from the Satakunta experiment (see section below and Appendix S3 for details). We chose to use D incr to represent tree growth instead of basal area increments because the former showed a simpler relationship with tree age which we were able to capture using well-established nonlinear plant growth models (see below and Appendix S4 for details).
| Tree growth measurements Tree growth was monitored at four points in time during the experiment. In 2004 In , 2009 In and 2011, D and H , D and H  were measured for a random subset of 10 trees per species in each plot. Additionally, because only 53% of selected trees had reached a height of 1. 3 m by age 7, in 2004 we also recorded the basal stem diameter at 10 cm aboveground (D base , in cm) of each tree. While D base was, on average, 2. 3 cm greater than D, the two measures of tree size were closely correlated with one another (Pearson's correlation coefficient = 0. 77, p < 0. 0001). In 2016, an additional census was conducted, during which D was measured for a random subset of five trees per species in each plot. However, because half of the Satakunta plots were experimentally thinned in 2013, for the 2016 census we only included growth data from 18 unthinned plots.
For the purposes of model fitting we restricted the analysis to include only the first 30 years of growth, as beyond this threshold the number of trees with complete chronologies dropped off sharply (Appendix S1). In order to test whether growth differences between treatments were statistically significant, we used Monte Carlo simulations as implemented by the predictNLS function in the propagate r package to estimate 95% confidence intervals for each fitted model  (Spiess, 2018). (1).
Our analysis makes two important assumptions about the D incr = + Ae -A , (2) D = A + ( A + 1) e -A 2 + 2 , F I G U R E 1 Schematic diagram illustrating how tree ring data from the FunDivEUROPE plots were used to test how diversity effects on tree growth vary through time. (a) Radial increments measured from tree cores were used to generate diameter growth time series for each sampled tree. (b) Nonlinear growth models were used to fit diameter growth curves for trees growing in monoculture (dashed line) and in mixture with other species (solid line). (c) By calculating the difference between the two growth trajectories we can identify periods when growth was faster in monoculture (in red below the dashed line) or in mixture (in blue above the dashed line) The second assumption is that stand structural attributes known to influence tree growth-such as the number and size of trees in a plot  (Coomes et al.
establishing the permanent plots, and are grateful to D. Avăcăriței, I. Bărnoaiea, G. Duduman, I. Dănilă, L. Nichiforel and A. Benneter for assisting with field data collection. The research leading to these results received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 265171. T. Jucker was supported by a UK NERC Independent Research Fellowship (grant number: NE/S01537X/1). AUTH O R S ' CO NTR I B UTI O N S D. A. C. , L. F. and O. B. helped design the FunDivEUROPE research platform, while J. K. runs the Satakunta tree diversity experiment; T. J. designed the study, collected the tree ring data, performed the analyses and wrote the first draft of the manuscript; G. I. and O. B. collected and curated the data from the second census of the FunDivEUROPE plots.
3) into the model, as had been done in the past (e. g. ,  Lantos and Kundu, 1972) , in order to explain why the observed center-to-limb variation shows less brightening than the homogeneous models predict, or even shows darkening. Solar observations with the Atacama Large mm and sub-mm Array (ALMA) are providing new information on the structure of the low atmosphere. Alissandrakis et al. (2017 Alissandrakis et al. ( , 2020) )  used the center-to-limb variation of the brightness temperature from ALMA full-disk data at 1. 25 and 3 mm and data from  Bastian et al. (1993)  at 0. 85 mm to invert the transfer equation and obtained the electron temperature as a function of the optical depth (Figure  2 ). Their results were close (5% lower) to the predictions of the  Fontenla et al. (1993)  average QS model C.
The work of  Mercier and Chambe (2009)  confirmed that, at long dm wavelengths, coronal holes are the most prominent feature; in agreement with previous observations (e. g. ,  Lantos et al. , 1987) , their contrast decreases at longer wavelengths (see the hole near the center of the disk in Figure  3  of  Mercier and Chambe, 2009). At decameter waves coronal holes are sometimes seen in emission  (Dulk and Sheridan, 1974; Lantos et al. , 1987; Rahman et al. , 2019). A possible interpretation is in terms of refraction effects  (Alissandrakis, 1994, c. f. Figure  3  of  Lantos, 1999)  and/or scattering in inhomogeneities; a similar conclusion was reached by  Rahman et al. (2019). In addition, these authors computed metric solar images using parameters derived from the MAS code (see above) and found qualitative similarities with high frequency observations, but could not reproduce the darkto-bright transition at low frequencies.
ACKNOWLEDGMENTS This review is based on a presentation to a CESRA summer school on solar radio physics. The author would like to thank  Dr. L. Klein  of  Meudon  observatory for organizing the school and for comments on an early version of the manuscript. This work makes use of the following ALMA data: ADS/JAO. ALMA# 2011. 0. 00020. SV and  ADS/JAO. ALMA2016. 1. 00202. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC and ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO , and  NAOJ. In preparing this review the author used publicly available data from a number of instruments: the  Nobeyama Radioheliograph (NoRH) , the  Siberian Solar Radio Telescope (SSRT) , the  Mauna Loa Solar Observatory (MLSO) , the  Big Bear Solar Observatory (BBSO) , the  Yohkoh mission , the  Solar Terrestrial Environment Observatory (STEREO) , the  Helioseismic and Magnetic Imager (HMI)  and the  Atmospheric Imaging Assembly (AIA)  aboard the  Solar Dynamics Observatory (SDO) , the  Predictive Science Inc.
Spitzer Space Telescope We observed Don Quixote with the IRAC instrument  (Fazio et al. 2004 ) on board the Spitzer Space Telescope  (Werner et al. 2004 ) during its warm mission phase as part of the Cycle 13 DDT program "Spitzer identification of potentially active Near-Earth Asteroids" (PID 13164). The observations (Astronomical Observation Request, AOR 63156224) took place between 2017 October 1 18:04:51 UT and 2017 October 2 02:10:31 UT for a total of 8. 08hr clock time and a total integration time of 6. 1hr (220 individual frames with 100 s frames in High Dynamic Range mode) for both bands. At the time of the observations, Don Quixote was inbound in its orbit with a heliocentric distance of 2. 72au and 2. 60au from Spitzer. Don Quixote was observed at a solar phase angle of 21°. 9and true anomaly of 251°.
9. Don Quixote moved a total of 2 4 during our observations. The AOR used the "Moving Cluster" mode and alternately placed the target on the 3. 6μm (IRAC CH1) and 4. 5μm (IRAC CH2) array. A medium dither pattern was used to enable background subtraction. Mosaics in both bands were constructed using the IRACproc software  (Schuster et al. 2006)  in the moving frame of the target. The final mosaics are presented in the left column of Figure  1. To search for extended emission around the target, we perform a Point-Response-Function (PRF) subtraction in both IRAC channels. In this subtraction, we use the warm mission PRFs  (Hora et al. 2012 ) as a model, scale it to the target's PRF, and subsequently subtract the fitted models (see  Mommert et al. 2014, for details). The PRF-subtracted mosaics shown in the center column of Figure  1  clearly show extended emission at 4.
Despite increasing background brightness levels, passing clouds, and seeing around 3″ FWHM, extended emission around the target is apparent and has been reported by  Mommert et al. (2018). Follow-up observations were taken with the same instrument on 2018 April 25, between 09:14 and 10:04 UT, ending during astronomical twilight and with better seeing of around 2″ FWHM. We obtained 33 frames with 30s integration time each and using the VR broadband filter. The data show a distinct coma and a faint tail emerging from the nucleus. We obtained additional observations with the Magellan Baade Telescope using the Inamori-Magellan Areal Camera and Spectrograph (IMACS) in imaging mode. On 2018 June 22, 48 V-band frames with exposure times of 40s were taken between 09:38 and 10:40 UT. These observations suffer from extremely high background levels due to cirrus and a bright Moon at 70%illumination.
Facilities: Spitzer(IRAC), SOAR(GOODMAN), Magellan (IMACS). Software:astropy (Astropy Collaboration et al. 2013), GILDAS (http://www. iram. fr/IRAMFR/GILDAS), IRACproc  (Schuster et al. 2006) , matplotlib  (Hunter 2007) , photometrypipeline (Mommert 2017), sbpy  (Mommert et al. 2019 ).
M. M. acknowledges support through  NASA   NEOO grant  No. 80NSSC18K1687. J. A. and Y. K. were supported by the  European Research Council Starting  grant  757390   CAstRA. M. W. acknowledges that this material is based on work supported by the  National Science Foundation  under grant No. AST-1945950. Results are based on observations obtained at the  Southern Astrophysical Research (SOAR) telescope , which is a joint project of the  Ministério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC) do Brasil , the  U. S. National Optical Astronomy Observatory (NOAO) , the  University of North Carolina at Chapel Hill (UNC) , and  Michigan State University (MSU). This work is based on observations carried out under project number  001-18  with the IRAM 30 m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany) , and  IGN (Spain).