dataesr/scientific-paragraphs-categorization huggingface dataset:
Bootstrap analysis A total of 2500 sets of 457 proteins chosen randomly from those included in the BIOSTR database were used to create mock data sets comparable with Cov-MulNet19. Degrees were calculated for all nodes as the number of edges to human proteins (either putative SARS-CoV-2 interactors in CovMulNet19 or random proteins in the mock networks). We define the structural degree as the number of connections of each node to human proteins and the structural strength as the ratio of the structural degree to the total number of connections to proteins in the considered network (node-type dependent). Z-scores were calculated according to the standard formula Z = x À l r , with x being the structural strength (or structural degree) of a node measured in CovMulNet19, and m and s being the mean structural strength (or mean structural degree) and the standard deviation structural strength (or standard deviation structural degree) of the same node across the random networks where it was found, respectively.
Data Availability The CovMulNet19 data set consists of two text files, named COVID19-GDDS457-nodes and COVID19-GDDS457-edges, respectively, both in csv format, deposited on the public repository FigShare and publicly available at the web addresses https://figshare. com/ articles/CovMulNet19_zip/12563192/2. The first file includes the 17,111 biological entities representing the nodes of the CovMulNet19 network. Each row has three columns, detailing the node name, an integer code for the node type, and the node type description, with the following notation: 0 Viral Gene; 1 Human PPI (target); 3 Disease; 4 Symptom; 5 Drug; 6 GO. The second file includes the 57,526 interactions between pairs of nodes: each row consists of three comma-separated columns, with the names of the two nodes being linked and their type of association (disease-symptom, human PPI (target)-drug, human PPI (target)-GO, etc.
). We decided to share CovMulNet19 in a flat text file format to maximize its usability within different analytical frameworks and to allow its easy visualization on multiple platforms. Apart from the data set, we provide access to an interactive dashboard at the https://covmulnet19. fbk. eu/ allowing to visually explore the CovMulNet19 network and its metadata. part of the Chair of Bioinformatics in Oncology of the CRCT (to N. V. and V. P. ); BioInfo4Women programme at the Barcelona Supercomputing Center (to V. P. ).
Bioinformatics analysis The quality of all the sequences was systematically veri ed (additional le 1). Brie y, sequences were processed using Mothur pipeline version 1. 39. 5  [19]. Paired-end sequencing reads of 16S rRNA were rst merged and the resulting concatenates were then subsequently ltered and only those that met the following criteria were analyzed further : (a) sequence were at least 170 bp in length; (b) sequence were shorter than 265 bp; (c) had no ambiguous base; (d) had a maximum homopolymer length of 5. Chimeric sequences were ltered out using the chimera. vsearch function of mothur. Sequence alignment to the 16S database: silva. nr_v132 and then de novo operational taxonomic unit (OTU) clustering was performed on the basis of sequence similarity with a threshold of 3% using Mothur's opticlust algorithm.
Taxonomic assignment was also performed with mothur, up to the rank of family genus or species according a threshold identity of ≥ 80%. The resulting OTU table count was then ltered further by removing all OTU's represented by less than three couples of paired-end reads overall. Rarefaction was done by random subsampling with 5000 reads repeated 1000 times. All the script of the analysis is deposited on https://github. com/RespiratoryMicrobiomeMethods. All sequences are deposited in the European nucleotid archive under the study submission number XXXX (it will be determined soon).
Obviously, 16SrRNA gene sequencing is not the most suitable technique for identi cation at the species level and our results are concordant to previous results. This limit depends of the database used and as SILVA database is updated we can hope to identi ed more OTUs at the species level in the close future. Third, we focus only on PCR primer choice and our study exclude the potential effects of others aspects of sample processing. As there is no previous methodological study concerning lung microbiota it would have been interesting to evaluate all the potential bias to found the optimal methodology. However, as our global methodology leads us to process all type of samples (BAL, ETA and OS) with concordant results with conventional microbiology, we conclude that it was adapted to conduct larger cohort study.
Data compilation The study followed PRISMA (preferred reporting items for systematic reviews and meta-analyses) protocols to assemble a dataset suitable for metaanalytic evaluation  (Liberati et al. 2009 ) (Supplemental Material Section S1). Between 1 Oct. 2019 and 1 April 2020, we conducted a two-tiered literature search to compile relevant peer-reviewed and grey literature. A systematic literature search was conducted using Google Scholar, Web of Science, the Western and Central Pacific Fisheries Commission's Bycatch Management Information System online database of references (https://www. bmis-bycatch. org/references), and the Consortium for Wildlife Bycatch Reduction's online database of references (https://www. bycatch. org/) (Supplemental Material Section S1). We then conducted an unstructured literature search by reviewing reference lists and contacting authors of relevant publications and reports from the systematic search to identify additional studies, and posting a query on ResearchGate.
We refer to all species and higher taxonomic groupings simply as 'species' hereafter for convenience. Due to small sample sizes, records were not included in the study where artificial bait or pieces of large marine species were used for bait (Supplemental Material Section S6). The systematic literature search identified 28,684 unique publications, of which 22 met the criteria for inclusion in the meta-analysis (Supplemental Material Fig. S1 ). An additional 12 publications and databases that met the inclusion criteria were compiled through the unstructured search. If data reported in a publication could be split into subsets so that only a single hook shape, hook size and/or leader material was employed in each subset, then these subsets of data were included as separate records in the meta-analysis.
(2016b)  in a comprehensive meta-regression analysis of shark catch rates on various hook types. We calculated the study-specific log risk ratios for each species using the escalc() function in the metafor package for R  (Viechtbauer 2010 ). The commonly used continuity correction for zero counts of adding 0. 5  (Higgins and Green 2011)  is the default in the escalc() function, which we used to calculate the effects size metric. This is, however, not necessary in a fully Bayesian approach (Gu ¨nhan et al. 2020) since a suitable prior is used in that inference framework  (Tuyl et al. 2008). Prior to further modelling, we explored potential publication bias  (Murtaugh 2002; Nakagawa and Santos 2012 ) using those calculated effect sizes and a contour-enhanced funnel plot  (Peters et al. 2008 ) of the model predicted estimates for each species using the metafor package for R  (Viechtbauer 2010).
The HDIs were summarized from the posterior samples for each species meta-analytic model fit using the tidybayes package for R  (Kay 2019). We used the ggplot2 package for R for all data visualization  (Wickham 2016). For some species (tunas, sharks) we also included the observed studyspecific effect size for comparison to illustrate why the model-based estimates are used to account for studies of varying precision-study-specific estimates with low precision are shrunk towards the pooled estimate while the estimates with high precision show little if any shrinkage. We also fitted a binomial-normal hierarchical model to the blue shark and tuna effects sizes using the MetaStan interface to Stan (Gu ¨nhan et al. 2020) that does not require any zero count continuity correction, but this gave very similar results to the same species-specific normal-normal hierarchical model and so is not considered further.
The meta-analyses used a simple random effects structure that addressed each study as independent within each species. However, some studies used in our species-specific models were undertaken by the same author(s), possibly resulting in correlated effects between studies by the same authors or research group  (Konstantopoulos 2011). Gilman et al. (2016b)  addressed this issue using a three-level hierarchical mixed-effects model in their multi-species metaregression modelling of pelagic longline catch rates for sharks. But there were too few species-specific studies in this bait-specific catch dataset to support a more complex multilevel or random effects structure.
For example, there is higher hook retention of squid than forage fish species  (Shomura 1955; Broadhurst and Hazin 2001; Ward and Myers 2007). The longer the release of chemical attractants and higher the hook retention rate of a bait type, the higher the catch risk. If rates of bait depredation vary by bait type, then this could reduce the accuracy of estimates of the Findings reported by both  Foster et al. (2012)  and  Watson et al. (2005)  are included as a single record: a subset of the database analyzed by  Foster et al. (2012)  had been analyzed by  Watson et al. (2005)  b Pieces of blue shark were used as a small component of a mix of primarily forage fish species being used for bait in  Baez et al. (2010) , who compared marine turtle catch in sets using a mix of squid and fish bait versus in sets using only fish bait c  Stokes et al.
Acknowledgements We are grateful to the fisheries scientists who conducted the research and authored the publications compiled and pooled for this meta-analysis. Support for this project was provided by  Vulcan Inc. , the  Pew Fellows Program in Marine Conservation at The Pew Charitable Trusts and The Safina Center Fellowship Program. The views expressed are those of the authors and do not necessarily reflect the views of  Vulcan Inc. , The Pew Charitable Trusts or The Safina Center. We thank  Jose Carlos Ba  ´ez and  Lucia Rueda Ramirez  of the  Instituto Espan ˜ol de Oceanografia, Andres Gonzalez Pizarro of the Chile Instituto de Fomento Pesquero ,  Rowan Trebilco  and  Emma Lawrence  of  CSIRO Australia , and  Barry Baker  of  Latitude 42 Environmental Consultants  for contributing information to supplement their publications.
e. , without any synthesis residues, such as Cl compounds)  (30). However, the formation of cadmium carboxylates was revealed by FTIR (data not shown; see table S1 for details). In a second step, the effects of thermal aging at RH ≥ 95% (without exposure to light) were studied. This resulted in a highly relevant phenomenon: the formation of S VI -rich aggregates within the S -II -based matrix, as evidenced by the S speciation distributions (Fig. 6, A  and D ). The LCF result of the S K-edge -XANES spectra (Fig. 6E ) shows that the aggregate (pt. 02 S ) is mainly composed of CdSO 4 /CdSO 4 •nH 2 O and that lower amounts of the same compounds (pts. 01 S and 03 S ) are sporadically present in the CdS-based matrix. In the S VI -rich aggregate (pt. 02 S ), an additional peak at 2. 4783 keV is ascribable to SO 3 2-species (~15%) that are likely formed as intermediates of the oxidation process of the pigment  (29).
MA-XRF mapping MA-XRF scanning was carried out using the CRONO instrument by XGLab (Bruker Nano Analytics)  (34). The system has a measurement head equipped with a large area silicon drift detector and a Rh anode tube operating at 50 kV and 200 A. The measurement head is mounted on a motorized XYZ stage (scanning area, 450 mm by 600 mm and focusing axis, 75 mm). The painting was scanned using a 1-mm pinhole collimator with a linear speed of 20 mm/s and acquisition time of 40 ms per spectrum. XRF elemental maps were produced by the PyMca  (35)  and Datamuncher (36) software packages.
Fluorescence hyperspectral imaging Two Honle LEDLINE 500 light-emitting diode systems with emission at 405 nm were used as excitation sources for the luminescence measurements. Investigations were performed by means of a Surface Optics Corporation SOC710 hyperspectral camera. The system uses a whiskbroom line scanner, producing a 696 × 520 pixels hypercube in the spectral range of 400 to 1000 nm, with 128 bands and 4. 5 nm spectral resolution, radiometrically calibrated in the whole spectral range. The lateral resolution was continuously modulated by an adjustable focal length of the mounted objective. The fluorescence hypercube of the whole painting has been reconstructed by assembling 15 single frames collected at a resolution of about 70 dots per inch, using the pixel-based mosaicking routine present in the ENVI Classic software.
FTIR measurements A portable ALPHA spectrometer (Bruker) was used for performing reflection investigations at selected spots of the painting and attenuated total reflection (ATR) measurements at the surface of all CdS-based oil paint mock-ups before and after aging. Pseudo-absorbance spectra [log(1/R); R is reflectance] were obtained using an external reflection module from areas of about 7 mm 2. Data were acquired in the range of 7400 to 345 cm -1 , at a resolution of 4 cm -1 and for 3 min. ATR spectra were collected by means of a Platinum QuickSnap ATR sampling module (A220/D-01) equipped with a diamond crystal plate. Data were recorded in the range of 4000 to 345 cm -1 , with 4 cm -1 spectral resolution and 168 scans.
Structural properties of Sic An extracted grain with a dark blue color and largest dimensions smaller than 10 ×10 ×10 μm 3 was selected for SCXRD experiments (Fig. 2 ). A full data collection has been performed on a Rigaku Synergy S single crystal diffractometer equipped with a Cu micro focus X-ray source and a Dectris Eiger 1M photon counting detector. A first analysis of the full diffraction pattern has been done using the 6H-SiC polytype with the cell parameters a ≈ 3. The new integration leads to a Rint value of about 12% for the mmm Laue class. This value far to be perfect suggests an imperfect data quality certainly related to the tiny dimension of the crystal. Nevertheless, the structure has been determined by charge -flipping method  16  with Superflip 17 in the Cmc2 1 space group and then refined using Jana2006  18.
, USA) equipped with a 900 lines/mm diffracting grating. The highest quality spectra (highest signal-to-noise) were obtained using green excitation (532 nm, Nd: YAG laser). Raman measurements were carried out at low laser power (approximately 1 mW at the sample surface) to avoid unintentionally heating alteration, structural modifications or phase transitions. A 100x magnification long working distance objective was used to focus the laser onto the sample and collect the scattered light in a backscattering geometry. Raw data was collected over a range of 80-2200 cm -1. The spectral region 3200-4000 cm investigated for some minerals species when OH stretching vibration modes were potentially expected. The spot diameter of the laser was estimated at 0. 6 μm. Raman spectra were systematically recorded twice with an integration time of 60 s.
SCXRD measurements. Single crystal X-ray diffraction. A Synergy S Rigaku 4-circles diffractometer equiped with Cu micro focus source and an Eiger 1M Dectric detector has been used to analyze the SiC grains. A full data collection was performed on the highest quality sample. The analysis of the reciprocal space is done using Crysalis Pro software  32. The structure has been solved using Superflip software  17  and refined with Jana2006  18. Computational details. The Raman-active vibrational modes of 60-SiC were calculated through Density Functional Theory (DFT) as implemented in the Crystal17 package  33. The B3LYP approximation was used and the wave functions were expanded as a linear combination of Bloch functions using Gaussian-type atomic orbitals (6-31 G*)  34. The Coulomb and exchange series were truncated with overlap thresholds of 10 -8 , 10 -8 , 10 -8 , 10 -8 and 10 -16 , A Monkhorst-Pack sampling scheme of 16 × 16 × 16 for cubic SiC, 16 × 16 × 10 for hexagonal 6H-SiC and orthorhombic SiC were chosen after some tests.
Creating 3D patient model For the internal organs, the 3D shape of the pig's body, before and after insufflation, was created from the MRI images using the ITK and VTK software. 17, 18 The VTK pipeline used to generate the 3D mesh of the pig's body is illustrated in Fig. 1. First, the boundaries of the pigs' body were extracted from the 2D slices using the thresholding method. Second, using the voxel spacing, the resulted binary images (segmented parts in 2D slices) were converted to the 3D points. Finally, the generated 3D points were given to the surface reconstruction algorithm (Alpha Shape Method)  19  to create a uniform mesh, as shown in Fig. 2. The orientation of the images for all the pigs was anterior to posterior, superior to inferior, and left to right. For the rest of this paper, the X axis is along the anterior to posterior direction, the Y axis represents the superior to inferior direction, and the Z axis is from the left to the right of the pig's body (see Fig.
Volume and target region measurements The 3D shape (mesh) of the pigs' liver, spleen, stomach, and kidneys (right and left) was reconstructed from the MRI slices before and after insufflation (except for Pig 2, as parts of the liver and stomach images were damaged by artifacts). The boundaries of the organs (liver, stomach, spleen, and kidneys) were extracted manually from T2 slices as shown in Fig. 3 , and the segmented parts (a set of contours that represent the shape of the organ in each slice) were combined to form the 3D shapes (meshes) of the organs in the abdominal cavity, as shown for the spleen in Fig. 4. One of the authors, an expert animal radiologist used the ITK-SNAP software to mark the boundaries of the target organ in each slice manually. 20 The validity of ITK-SNAP for medical image segmentation has been established in several studies.
0 column with a Metrosep C 4 guard / 4. 0 protecting the separation column. All columns were produced and distributed by MetrOhm, Switzerland. Data analysis was conducted with MagicIC Net software. For the determination of total dissolved nitrogen (TDN) and total dissolved phosphorus (TDP) we used the Kjeldahl method where samples were destructed using a mixture of potassium sulfate, sulfuric acid and selenium, heated to 400 °C for 90 minutes  (ISO, 1984). This converts all N and P containing compounds into NH4 + and HPO4 2-, which are subsequently measured on a SAN++ Continuous Flow Analyser (Skalar, The Netherlands). Following this, TDN is calculated by adding the measured NH4 + from the SAN++ CFA to the NO3 -measured by the ion chromatography, DON is calculated by subtracting the NH4 + measured by the ion chromatography from the NH4 + measured by the SAN++ CFA and DOP is calculated by subtracting the HPO4 2-measured by ion chromatography from HPO4 2-measured by the SAN++ CFA.
The data of four of the 13 measured ions was zero inflated and the occurrence of zeroes ranged between 12. 5 and 58%. In these cases, we split the analysis into two parts. In the first part we constructed a new binomial vector containing information on the presence or absence of the ion of interest. We then used a generalized linear mixed effects (GLMER) model with setup (atmospheric deposition or throughfall) as fixed effect and sampling date as random effect to test if the occurrence of zeroes differed between the bulk precipitation and throughfall. For the second part we removed the zeroes from the dataset and applied LMER models to see if, in the cases there is measurable ion flux, there were differences between bulk precipitation and throughfall. Again we entered setup (bulk precipitation or throughfall) as fixed effect and sampling date as random effect.
Acknowledgements This research was supported by the  European Research Council Synergy  grant  ERC-2013 -SyG. 610028-IMBALANCE-Pand the Paracou station is supported by an " Investissement d'Avenir " grant from the  Agence Nationale de la Recherche  ( CEBA , ref  ANR-10-LABX-25-01 ). We are grateful to  Benoit Burban  for providing us with the rainfall data collected at the Paracou flux tower, to  Valentine Alt  for assistance in the field and for  Tom Van der Spiet  for laboratory analyses.
S2 , Supplementary Material online) were mapped on the genome of L. dentata. A detailed description of the results is given in supplementary Data S1, Supplementary Material online. This allowed us to identify genes belonging to the gene sets defined above in the two Lucifuga species using the program EXONERATE and the coding sequences of the zebrafish. Using the same method, homologs were retrieved from the published genomes of three surface fishes (Brotula barbata, Carapus acus and Lamprogrammus exutus) belonging to the same order, Ophidiiformes; from the published genomes of an A. mexicanus cavefish from Pachón cave and two surface fishes (a conspecific A. mexicanus surface fish and the piranha Pygocentrus nattereri), all belonging to the order Characiformes; and from the published genomes of two Sinocyclocheilus cavefishes (S.
These data suggest that the circadian clock has most likely been impaired in S. rhinocerous but less so in S. anshuiensis (fig. 1   Pigmentation genes: among the list of 257 zebrafish pigmentation genes, 237 genes were retrieved from Lucifuga genomes, 8 being pseudogenized in L. dentata and 7 in L. gibarensis While smtla and myo7ab have been lost independently in the two lineages, a premature STOP codon and an insertion are shared in adamts20. The number of pseudogenes in these cavefishes does not greatly differ from those found in some surface relatives, as 6 pseudogenes were identified among 230 pigmentation genes in Lamprogrammus exutus (fig. 1 and fig. 2 ). Among Sinocyclocheilus species, only 3% (15/484) of pseudogenes were found in S. grahami while 6% (28/490) were found in S. rhinocerous and 7% (35/487) in S.
S3 , Supplementary Material online). Therefore, was computed for three sets of concatenated gene sequences (vision, circadian clock and pigmentation genes), to obtain more reliable estimates and to compare the shift of selective pressure on these gene sets in different fish lineages. In order to obtain a reliable distribution of  in surface fishes, gene sequences were retrieved from other fish genomes: tetraodon (Dichotomyctere nigroviridis), cod (Gadus morhua), stickleback (Gasterosteus aculeatus), spotted gar (Lepisosteus oculatus), tilapia (Oreochromis niloticus), medaka (Oryzias latipes), platyfish (Xiphophorus maculatus). Taking into account the known fish phylogenetic relationships (supplementary fig. S4 , Supplementary Material online) and using the PAML package  (Yang 2007) , we compared three nested branch models assuming: 1) only one ; 2) one CF for blind cavefishes (A.
mexicanus and L. dentata) and one SF for the other fishes (two-ratio model); 3) one  for each branch (free-ratio model). For each dataset, likelihood ratio tests suggested that the two-ratio model was better than the one-ratio model (CF > SF) and the best model was the free-ratio model (supplementary table S2, Supplementary Material online). With the free-ratio model, Lucifuga dentata had the highest  (0. 409) for vision genes. For circadian clock genes, both A. mexicanus blind cavefish and L. dentata had ScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901 Support: (434) 964-4100 the highest  (0. 29). For pigmentation genes,  was similar in cave and surface fishes (fig. 4 , supplementary fig. S4 , Supplementary Material online). Independently, we analyzed the same sets of genes in Sinocyclocheilus species.