dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectra were acquired in reflection mode in the spectral range 4000-0 cm -1 with a resolution of 6 cm -1 by recording 1000-2000 scans at 2. 2 kHz of slew rate in order to get good signal to noise ratios. Spectra were processed using Blackman-Harris apodization function and 2 levels of zerofilling. The laser power used for the measurements was 500-600 mW corresponding to approximately 200 mW at the sample. Samples were scanned at least at 2 different positions, thanks to an x, y, z motorized stage, to account for sample heterogeneity. Spectra acquisition was controlled with the OPUS 7. 5 software (Bruker, Ettlingen, Germany). The resolution of the instrument (∼10-20 µm) does not allow single cell analysis, therefore the spectra reported in this study refer to bulk analysis including both cells that were adhered to the SPs and the planktonic ones.
The same set of samples prepared for the FT-Raman measurements were also scanned by means of FTIR-spectroscopy to obtain complementary information about the metabolic profiles of P. putida populations. Diffuse reflectance spectra (DRIFTS) acquisition was carried out with a Bruker Vector 22 (Bruker, Ettlingen, Germany) equipped with a diffuse reflection collection system (Praying Mantis; Harrik, Pleasantville, NY, United States). Spectra were recorded with 64 scans co-added and averaged in the spectral range 4000-400 cm -1 with a resolution of 4 cm -1. Background spectra were recorded at the edges of the cell deposit with the same instrumental settings. The spectrometer was controlled with the OPUS software version 4. 5  (Bruker, Ettlingen, Germany). Band assignment for the Raman and IR spectra was based on literature references  (Naumann, 2001; Movasaghi et al.
Spectra Pre-processing and Multivariate Modeling All spectra were exported from the OPUS software for further pre-processing and multivariate modeling steps computed in the R environment  (version 3. 4. 3; R Development Core Team, 2010). A first visual screening, aimed at eliminating the spectra with a low signal to noise ratio, resulted in a total of 346 for the FT-Raman dataset and 465 for the FTIR one. Base line corrections of Raman spectra were performed with the package "baseline"  (Liland et al. , 2015)  using a 2nd derivative constrained weighted regression, cut in the spectral ranges 3019-2819 and 1780-400 cm -1 and normalized (in order to compare peak intensities and remove problems related to different sample thickness) using the standard normal variate function (SNV,  Barnes et al.
, 1989). FTIRspectra were converted to 2nd derivatives by the Savitzky-Golay algorithm  (Savitzky and Golay, 1964)  using a quadratic polynomial function with nine smoothing points. Prior to calculations, spectra were cut in the spectral ranges 3019-2819 and 1780-1200 cm -1 because of the strong clay bands overlapping to the cellular components in the lower frequency range of the IR spectrum. However, other two bands corresponding to the spectral ranges, 1091-1076 and 973-958 cm -1 were further selected because not overlapping with clay signals. Finally, the spectra were normalized using the SNV function. The Partial Least Square  (Wold et al. , 2001 ) Discriminant Analysis (PLS-DA;  Barker and Rayens, 2003)  was used to unravel consistent patterns in the spectral datasets and to extract information regarding the macromolecules involved in the classification.
The PLS-DA is a chemometric analysis particularly useful when it comes to handle metabolic datasets with highly collinear variables  (Fonville et al. , 2010). The PLS-DA was implemented in the R software using the 'pls' package developed by  Mevik et al. (2016). To test our hypothesis, two models for each spectral dataset were developed. The first discriminated each experimental treatment (control and the 4 SPs and the 2 respective C/energy source; 10 classes) and the second discriminated the C/energy sources (i. e. , glucose and phenanthrene; 2 classes). The models were calibrated by matching each set of predictors (X-variables; i. e. , FT-Raman and FTIR spectra) with the corresponding discriminating classes (Y-variables). The matrices (i. e. , Y variables) were created assigning integer values of 0 and +1 (dummy variables) to the experimental treatments (SPs + C/energy source) and C/energy source (glucose and phenanthrene).
In turn, to each class is assigned the values +1 and to all the others 0. In this way, a calibration can be built around each classification matrix. To quantify the accuracy of the classification models, the sensitivity (the ability of identifying true positives) and the specificity (the ability of identifying true negatives) were estimated as described in  Sackett et al. (2013) , using the "caret" package  (Kuhn, 2017)  present in the R software. Spectra with predicted Y-values ≤ 0. 5 were classified as 0, whereas predicted Y-values ≥ 0. 5 were classified as 1. The algorithm used for the PLS-DA was the canonical powered partial least square  (Indahl et al. , 2009). The prediction abilities of the models were inferred from the root mean squared error of calibration and prediction (RMSEC and RMSEP, respectively), the latter calculated from the leave-one-out cross-validation (LOOCV), and the coefficient of determination (R 2 ).
The first eight components explained 37% of the overall variance present in the spectral dataset (i. e. , the X matrix) (Supplementary Table  S1 ). The best discrimination, and therefore the greatest differences in the metabolic profiles of the cells, occurred for the cells grown in the presence of montmorillonite, nontronite and humic acids (Table  2  and Figure  2A ). The combination of these SPs with phenanthrene, as a C/energy source, resulted in the highest variance explained (77, 65, and 71%, respectively) (Table  2 ). The higher degree of macromolecular changes was also confirmed by the higher sensitivity (i. e. , the ability of the model to discriminate those classes of cells from the rest of the treatments) for montmorillonite, nontronite and humic acids in the presence of phenanthrene being 69, 43, and 69% respectively (Table  2 ).
Metabolic Profiling by FTIR Spectroscopy and PLS-DA The average FTIR spectra for each experimental condition are reported as Supplementary Figure  S3. Even in this case, the first model was calibrated to discriminate the different growth conditions (i. e. , SPs and C/energy source). Similarly to the respective FT-Raman model, the first eight components explained 38% of the total spectral variance present in the dataset (Supplementary Table  S1 ). The highest variance explained by the spectra was reached with glucose and in the absence of SPs, in the presence of montmorillonite combined with phenanthrene or glucose and nontronite with phenanthrene, explaining 51, 68, 60, and 66% of the variance respectively (Table  2 ). For these set of conditions, the model presented also the highest sensitivity, specificity and the best predictive abilities (Table  2 ), reflecting the greatest changes in the macromolecular composition of the cells.
The second model was calibrated to discriminate for the C-source. The first eight components captured 87% of the total spectral variance (Supplementary Table  S3 ). The REMSP was 0. 28, the R 2 C was 0. 71 and sensitivity and specificity were 92 and 94%, respectively. Although the PLS-PC1 and PLS-PC2 already explained 50% of the total variance in the spectral dataset, no clear distinction between the cells grown on glucose and on phenanthrene could be observed (Supplementary Figure  S4 ). The only separation, but rather weak, was evident along the PC2 (14% of variance) between the cells grown in the absence of SPs or in the presence of sand and the one grown on clays and humic acids. However, no separation occurred on the basis of the C-source, as shown by the overlapping scores (Supplementary Figure  S4 ).
1d ), further suggesting that these 2 plants do not possess anti-ZIKV activities. We next wondered whether the antiviral activity of A. theiformis extract was affect by the extraction process (Fig. S1a ). Three extractions were performed using plants harvested at different times in the year 2016 and their antiviral activity was evaluated. Our data showed that the antiviral activity of A. theiformis was not affected by the extraction itself, suggesting that the concentration of the active components is stable and does not vary from one extraction to the other. To ensure that A. theiformis extract did not exert non-lethal cytotoxic effects that would have been missed in the MTT assays (Fig. 1a ), the effect of 500 µg. mL -1 of A. theiformis extract on the growth of Vero cells was evaluated using flow cytometry analysis (Fig.
2f, g ). Together, these results showed that A. theiformis exerts a potent antiviral effect against an African and an Asian ZIKV strains. A. theiformis extract inhibits ZIKV binding to the cell surface. Our data suggest that A. theiformis acts by interfering with at least one of the early steps of ZIKV infection. To determine which of these step(s) was/ were affected, cell-binding assays were performed. These assays were performed with the MR766 strain and with HD78, another African ZIKV strain that replicated very efficiently in insect cells. MR766 and HD78 ZIKV particles were incubated 1 hour with A. theiformis extract and then added on cell monolayer at 4 °C to prevent viral internalisation. The cells were washed with ice-cold PBS to remove the unbound virus then shifted to 37 °C for 90 minutes.
1 or with ZIKV at an MOI of 0. 4 for 48 h. Recombinant IFN-α 2A (200 IU. mL -1 ) was added 2 h post infection and used as positive control. The percentage of E positive cells was evaluated using the Operetta High-Content Imaging System (PerkinElmer). Results are means ± SD of three independent experiments. (b) The residual infectivity of treated particles was titrated in C6/36 cells using a fociforming immunodetection assay. Data represent the means ± SD from three independent experiments. Oneway ANOVA and Dunnett's test (*p < 0. 05; **p < 0. 01; ***p < 0. 001).
ZIKV-MR766 and GFP-expressing strain of ZIKV-MR766 (ZIKV GFP ) are molecular clones of the historical strain MR766 of ZIKV  22. Zika strain HD78788 was obtained from the Biological Resource Centre of Pasteur Institute and was grown on C6/36 cells. The clinical isolate of Zika virus (ZK BR 2015/15261) was isolated from a patient with Zika fever from Northeast of Brazil in 2015 and was amplified on C6/36 cells. DENV-1/ FGA/89 was isolated in 1989 from a South American patient suffering from DF (GenBank: AF226687). DENV-2/ ICC-265 was isolated from a DF patient in Brazil in 2009. DENV3/5532 was isolated in 2007 from a fatal case of dengue with visceral manifestations in a patient in Paraguay (GenBank: HG235027). DENV-4/TVP360 is a laboratory strain that was kindly provided by Dr. Ricardo Galler (Fundação Oswaldo Cruz, Rio de Janeiro, Brazil; GenBank: KU513442).
Cell cycle analysis. Vero cells were seeded at 2 × 10 5 cells/well in glass bottom 6-well plates. Cell cycle distribution was determined by flow cytometry  43. Cells were trypsinised and fixed in 70% ethanol at -20 °C for at least 2 h. Cells were re-suspended in PBS containing 40 µg. mL -1 propidium iodide and 100 µg. mL -1 RNase A. After incubation for 1 h at 37 °C cells were characterised. Data were acquired using the CytExpert software for CytoFLEX (Beckman Coulter). For each experiment, 10 4 cells were analysed. Immunofluorescence assay. Cells grown on coverslips were fixed with 3. 7% of PFA at room temperature (RT) for 10 min. Fixed cells were permeabilised with Triton X-100 (0. 15%) in PBS for 4 min and stained using the mouse anti-pan flavivirus envelope E protein mAb 4G2 (1:1,000 dilution).
Alexa-Fluor 546-conjugated ZIKV probe set recognizes a region between nt position 2 and 1144 of the ZIKV genome. Nuclei were stained with NucBlue (Thermo Fisher Scientific) for 15 min at RT. Images were acquired with a Zeiss LSM 700 laser scanning confocal microscope equipped with a X63 objective. Images were processed with Zen2 (blue edition) software and analysed with the ICY software (icy. bioimageanalysis. org) using the Spot Detector plugin. Virus binding assay. Virus binding assay was performed as previously described  22. Briefly, Vero cells were seeded at 2 × 10 5 cells/well in 6-well plates. Cell monolayers were washed in cold PBS and cooled at 4 °C at least 30 min in presence of cold MEM supplemented with 5% FBS. Pre-chilled cells were incubated at 4 °C with MR766-ZIKV at MOI of 1 with or without of A.
RT-qPCR. Total RNA including genomic viral RNA was extracted from cells with RNeasy kit (Qiagen) and reverse transcribed using E reverse primer and M-MLV reverse transcriptase (Life Technologies) at 42 °C for 50 min. Quantitative PCR was performed on a CFX96 Real-Time PCR Detection System (Bio-Rad). Briefly, cDNA were amplified using 0. 2 μM of each primer and 1× GoTaq Master Mix (Promega). For each single-well amplification reaction, a threshold cycle (Ct) was calculated using the CFX96 program (Bio-Rad) in the exponential phase of amplification. Relative changes in gene expression were determined using the ΔΔCt method and reported relative to the control. The primers used in this study were previously reported in  22. A synthetic gene coding for nucleotides 954 to 1306 of the MR766 strain (GenBank: LC002520) cloned in the pUC57 plasmid was used as template to generate a standard curve, which then served to make absolute quantitation of bound viruses.
Acknowledgements We are grateful to  Claude Marrodon  for helpful discussion on ethnopharmacological uses of plants. We thank  Ségolène Gracias  for production of ZIKV-HD78 stocks as well as  Marie Watson ,  Laurent Janci  ( Extraits De Bourbon ) and  Claire Desvignes  for contribution in plant extracts preparation. This project was supported by  Research Federation BioST ,  University of La Reunion ,  Centre National de la Recherche Scientifique (CNRS) ,  Institut Pasteur ,  CAPES-COFECUB Me889/17   ZIKANET  project and  ZIKAlert project (European Union-Région Réunion programme  under grant agreement n° SYNERGY:  RE0001902 ). E. C. is supported by a grant DIRED from the  Conseil Régional de La Réunion , France and L. S. is supported by the  Pasteur Paris University (PPU)   International PhD program  through an ' Institut Carnot Pasteur Maladies Infectieuses' grant.
The force-frequency curves were determined by stepwise increasing stimulation frequency, with resting periods > 30 s between stimuli in order to avoid effects due to fatigue. Force signal was sampled at 1000 Hz using a Powerlab system and Labchart software (ADinstruments). Histology and immunofluorescence analyses Mouse Fascia of TA muscles was removed, then muscles were frozen in nitrogen-chilled isopentane and kept at À80 C until use. 8 mm-thick cryosections were prepared for hematoxylin-eosin (HE), masson trichrome (MT) and immunolabelings. For double immunolabelings, cryosections were labeled with antibodies (from Abcam unless indicated) against F4/80 (ab6640, ab74383) overnight at 4 C and labeling using the second antibody was performed for 2 h at 37 C. Antibodies were directed against Arginase 1 (sc-18355, Santa Cruz), CCL3 (ab32609), CCR2 (ab32144), CD206 (sc-58987, Santa Cruz), CD301 (ab59167), Collagen I (ab292, ab34710 or Biotech 131001), Cox2 (ab23672), Dectin 1 (ab53427), IL-10 (ab9969), iNOS (ab15323), Laminin (L9393, sigma aldrich, or ab78287), PDGFRa (AF1062, R&D System), TNFa (ab34839), LTBP4 (sc-33144, Santa Cruz), MMP14 (ab51074) and BMP1 (ab118520).
QUANTIFICATION AND STATISTICAL ANALYSIS Mouse histology HE-stained muscle sections were recorded with a Nikon E800 microscope at 20X magnification connected to a QIMAGING camera. Necrotic myofibers were defined as pink pale patchy fibers, phagocyted myofibers were defined as pink pale fibers invaded by basophilic single cells (macrophages). Fluorescent immunolabelings were recorded with a DMI 6000 Leica microscope connected to a Coolsnap camera at 20X magnification. For confocal analysis, pictures were taken on a TCS SP5 X microscope (Leica Microsystems). For each condition of each experiment, at least 8-10 fields chosen randomly were counted. The number of labeled macrophages was calculated using the cell tracker in ImageJ software and expressed as a percentage of total macrophages. Areas (PDGFR-a, Collagen 1) were calculated with ImageJ software.
A macro was designed in ImageJ software to identify clusters containing small (< 200 mm 2 ) and large (> 200 mm 2 ) myofibers. The number of macrophages per fiber was counted in these areas defined using this macro (Figure  S2 ). TGFb1 mean fluorescence intensity (MFI) was measured using ImageJ. First, a mask was generated using either TGFb1 or F4/80 labeling to select the areas of TGFb1 or F4/80 expression. TGFb1 MFI was then measured in both areas and the ratio of TGFb1 MFI in F4/80 areas/ total TGFb1 MFI was calculated.
ACKNOWLEDGMENTS This work was funded by grants from the  Framework Programme   FP7 Endostem  (under grant agreement  241440 ),  Association Francaise contre les Myopathies  (grant  16029  and  MyoNeurAlp Alliance) ,  Fondation pour la Recherche Medicale (Equipe FRM   DEQ20140329495 ), and the  Canadian Institutes of Health Research. We thank  Peter Mertens  ( Otto-von-Guericke-Universita ¨t, Magdeburg, Germany ) for the generous gift of the collagen I-Luc plasmid. We thank the  Genom'ic , the  Cytometry CyBIO , and the  Cellular Imaging facilities of Institut Cochin, Paris , and particularly  Thomas Guilbert  for building the macros in ImageJ, the  Anira-Cytometry facility of SFR Biosciences, Lyon.
Background: Study of meta-transcriptomic datasets involving non-model organisms represents bioinformatic challenges. The production of chimeric sequences and our inability to distinguish the taxonomic origins of the sequences produced are inherent and recurrent difficulties in de novo assembly analyses. As the study of holobiont meta-transcriptomes is affected by challenges invoked above, we propose an innovative bioinformatic approach to tackle such difficulties and tested it on marine models as a proof of concept. Results: We considered three holobiont models, of which two transcriptomes were previously published and a yet unpublished transcriptome, to analyze and sort their raw reads using Short Read Connector, a k-mer based similarity method. Before assembly, we thus defined four distinct categories for each holobiont meta-transcriptome: host reads, symbiont reads, shared reads, and unassigned reads.
However non-model holobiont RNA-seq datasets correspond to a mixture of data coming simultaneously from the host and from the symbiont(s) (Fig. 1 ). Such datasets are a priori low complexity meta-transcriptomic datasets (i. e. , that involve a reduce number of actors in comparison to soil, human gut, or marine microbial samples) and require de novo assembly of transcripts sequences, which implies large computational resources and introduces biases such as the creation of numerous chimeric sequences resulting from the misassembly of RNA fragments from the host and from the symbiont(s)  [18, 19]. A variety of analysis strategies has been developed to address meta-transcriptomic challenges. Some of them avoid the assembly step to focus on identifying abundant species and significant functional differences between meta-transcriptomes directly from raw data mapping  [20, 21].
Other strategies use statistical tools and machine learning algorithms to improve the quality of de novo meta-transcriptome assembly by learning from their abundance information  [22]. Here, we developed an original strategy aiming at improving the study of meta-transcriptomic datasets from holobionts. The concept relies on sorting the holobiont reads before the assembly step in order to distinguish the different actors (Fig. 1 ), and afterwards on processing independent de novo assemblies on each subset. To this end, we used a highly scalable tool, the Short Read Connector software in its Counter version (SRC_c)  [23]. SRC_c is a fast kmer-based method initially developed to estimate the similarity between numerous (meta-)genomic datasets by extracting their common sequences. We focused on holobiont meta-transcriptomes for which a priori no or little genomic knowledge has been previously produced for host and symbionts, and we used SRC_c to compare these holobiont sequences to publicly available databases.
In the second holobiont model (M2), the marine sponge Xestospongia muta (Porifera) harbors a dense (∼ 40% of its volume) and diverse microbial community including marine protists (e. g. , fungi), archaea, and mainly bacteria  [29] [30] [31]  (Fig. 2b ). The symbiotic associations between sponges and bacteria (suggested to be commensalism  [32] ) have become a major research focus to understand how sponges and their microbial communities can perform a variety of functional roles such as nutrition, cycling of metabolites, and host defense allowing them to proliferate in oligotrophic conditions  [33, 34]. We chose a third, yet unpublished, holobiont dataset (M3) involving two distinct lineages of protists (unicellular eukaryotes): the radiolarian Collozoum sp. as host and Dinophyta symbionts belonging to the Brandtodinium nutricula species  [6].
Restricting the symbiont library to the genus Symbiodinium spp. sequences allowed obtaining similar results with 64. 5% of the reads identified as specific to the host library and 7. 1% as specific to the symbiont library (analysis M1b, Table  2 ). On the contrary, when Symbiodinium spp. is removed from the library, only 0. 6% of the holobiont reads could be assigned to the symbionts, and the proportion of reads assigned to the host increases up to 67. 3% (analysis M1c, Table  2 ). Our tests on the symbionts library showed that the library content affected drastically the reads retrieval by SRC_c and demonstrated the sensitivity of the strategy. Considering these results, we focused on the M1a dataset for downstream analyses. We also noticed that shared reads (i. e. , found  in both host and symbiont libraries) always represent the lowest proportion of holobiont reads (M1a, M2, and M3).
De novo assembly, contigs evaluation, and downstream analyses for M1 and M2 For each holobiont meta-transcriptome, four subsets of reads were independently de novo assembled, producing contigs from which protein domains were then predicted and functionally annotated. For holobiont models M1a and M2, an overview of the analyses is available in the Additional file 1. The assembly metrics, statistics, and functional annotations from our contigs are summarized in Table  3. These metrics were directly compared to the one obtained in the original publications  [24, 29]  (Fig. 3 ). Compared to previous studies, it is worth noticing that we used a more up-to-date assembler  [37]  and a distinct annotation pipeline (cf. details in the "Methods" section). Reference databases for sequences annotation have also evolved since 2015, so the comparison of the quantitative values with previous studies are informative but have to be interpreted with caution.
Discussion The use of SRC_c to tackle meta-transcriptomic challenges The strategy proposed here is a practical and scalable solution for transcriptomic assembly of non-model holobiont organisms, from which no or limited genomic information is available. The present implementation of SRC_c  [23]  based on reference databases of putative partners involved in the holobiont consortium, and our analysis strategy, enabled the categorization of holobiont reads into four subsets. With respect to the reference libraries, as exemplified in M1, when the expected symbiotic partner (i. e. , Symbiodinium spp. ) is missing from the reference library, the number of reads assigned to the symbiont category decreases drastically from 50M reads to nearly 5M reads (Table  2 ). The M2 and M3 libraries do not contain reference data for the expected host partner, and consequently, only a low proportion of the holobiont reads are assigned to the host (19 and 3%, respectively).