dataesr/scientific-paragraphs-categorization huggingface dataset:
The expressed carbohydrate-active enzymes represented 73 protein families from the CAZy database: 50 glycoside hydrolases, 12 glycosyltransferases, eight carbohydrate-binding modules and three polysaccharide lyases families (Table  S5 ). Interestingly, at 40 g fibre, three genes involved in mucin degradation were downregulated: CAZy GH101 family, encoding endoa-N acetylgalactosaminidases involved in mucin degradation, α-L-arabinofuranosidases (GH43) and the recently described GH115 family of α-glucuronidases (Table  S5 ). In contrast, amylase (GH13) and its dockerin domain (GH13-DOC) were significantly upregulated. Consistent with a higher fermentation, archaeal genes involved in methanogenesis, such as methyl-CoM reductase (COG4058) and formylmethanofuran dehydrogenase (K00205) were overexpressed at 40 g of fibre.
Subjects and study design The study was registered as a clinical trial (http:// clinicaltrials. gov/, Calibrated Diets and Human Intestinal Microflora NCT00639561) and carried out at the Center for Clinical Investigation (CCI) of Grenoble University Hospital (France). The protocol was explained to the volunteers and their written informed consent was obtained. Inclusion criteria were: age from 18 to 30, BMI [body weight (kg)/height (m2)] between 18. 5 and 25, no intake of antibiotics, laxatives or other gastrointestinal medications 3 months prior to the beginning of the study. Nineteen healthy young adults (nine males, 10 females, aged from 19 to 25 years) with no history of gastrointestinal problems completed the study. Subjects were randomly assigned to receive each meal plan during the 5 days with a 2-week washout period: 10 subjects followed a first round of 10 g dietary fibre per day for 5 days, followed by 2-week washout, then a second round of 40 g per day for 5 days, followed by a 7 days washout sequence.
qPCR assays Quantitative PCR procedures were applied as previously described [  (Furet et al. , 2009)  and Supplementary Notes section 1. 1 and 1. 2]. The qPCR assays targeted total bacteria, Clostridium coccoides group (cluster XIVa), C. leptum group (cluster IV), Bacteroides/Prevotella group, Bifidobacterium genus and E. coli species. These data were normalized to account for the differences in water content between faecal samples, as described previously  (Furet et al. , 2009). The all-bacteria results were presented as the mean of the log10 value ± SD/g of stool (i. e. bacteria equivalents per gram) after corrections for the concentrations of each targeted bacterial species or group mentioned above.
16S rRNA genes molecular inventories Prior to more extensive sample analyses, eight identical DNA samples were submitted to two companies for pyrosequencing, and were found to produce a highly significant correlation in terms of genus distributions (P < 0. 05, Spearman methods). Second, to compare 454 GS FLX Ti and Sanger sequencing technologies, one sample was analysed by both methods and the datasets were found to share the same trends (Fig. S7 ). Based on these findings, the DNA samples from all individuals, before and after each diet (time point 2, 3, 4, 5 shown in Fig. S1 ) were analysed by targeted amplification and pyrosequencing of the V3-V4 region of the 16S rRNA genes using V3F (TACGGRAGGCAGCAG 343-357 E. coli position) and V4R (GGACTACCAGGGTATCTAAT 787 -806 E. coli position) primers (For more details, see Text S1 section 1.
3). Sequences were clustered into OTU at 97% similarity with LOTUS software  (Hildebrand et al. , 2014)  using 150 nucleotides as minimal sequence length (see Fig. S8  for individual rarefaction curves at OTUs levels). The 76 16S rRNA genes sequence files from this study were deposited to European Nucleotide Archive under the reference ERA008162 (http://www. ebi. ac. uk/ena/data/view/ ERA008162).
Sequencing was performed using 454 GS FLX titanium technology (Beckman genomics, Grenoble, France) with quality control performed as previously described  (Huse et al. , 2007). The cDNA datasets were cleaned of any rRNA sequence reads by using a Hidden Markov Model-based algorithm  (Huang et al. , 2009). From 603 463 reads, 23% (118,301 reads) passed quality control and rRNA removal (Table  S4 ). Assembly bins were made with CAP3 software (Huang and Madan, 1999) using low stringency parameter (option -p 66), (Text S1 section 3). The BLASTN program  (Altschul et al. , 1997)  was used to compare the sequences to the MetaHIT project database (http:// gutmeta. genomics. org. cn/). The KEGG Orthology, NOG and COG distributions were then retrieved for each sample. To profile the glycan digestion enzymes in the various individuals, cDNA contigs and singletons were assigned to CAZy families  (Cantarel et al.
Statistical analysis All statistical analyses were computed with the GNU R software (www. r-project. org). Variations analyses were made using multi-table functions from ADE-4 library  (Chessel et al. , 2004). Notably, between (BCA) and within (WCA) class analyses were computed to assess the impact of the diet and the host, used as instrumental variables, on the whole inertia for different datasets. Impacts were then measured on how gut microbiota inertia was attributed to different factors. Co-inertia using RV coefficient was analysed to detect significant co-structure between datasets  (Robert and Escoufier, 1976) , meaning that different sets of variables (e. g. microbial genera abundance and SCFA profiles) were not independent and shared a fraction of inertia. Based on genera distribution, the JSD between samples were computed, as previously described  (Arumugam et al.
, 2011)  in order to measures microbiota stability between sampling point. Non-parametric paired Wilcoxon, Spearman correlation and Monte-Carlo tests were used to confirm observed relations between different datasets, assuming a P value < 0. 05. Differential expression of metatranscriptomics data was analysed with the SHOTGUNFUNCTIONALIZER library using paired comparisons in a Poisson model  (Kristiansson et al. , 2009). In case of multiple comparisons, P values were adjusted by Benjamini Hochberg False Discovery Rate. GNU R source code and dataset used for this study are available at http://github. com/ tapj/AlimIntest. Study design metadata, SCFA profile, qPCR and comet assays, OTUs count table and taxonomic assignation are available in the supporting information as Table  S7.
Acknowledgements Julien Tap  and this project were supported by the  French National Agency for Research ,  ANR/DEDD/PNRA/PROJ/ 200206-01-01 , within the  AlimIntest program. The clinical study was sponsored by  University Hospital of Grenoble  and carried out in its Clinical Research Center  CIC1406. We are very grateful to  Marie-France Nguyen Vaillant  for designing all the menus. We thank  Hervé Blottière  for providing us with HT29 cells,  Aurelia Bruneau  for contributing to faecal sample collection and  Deborah Jardet  for her technical support. We are grateful to  Timothy Swartz  for reading the manuscript.
Dependencies The code provided is a stand-alone C++ code, available at https://doi. org/10. 5201/ipol. 2015. 102. It uses the C++ standard template library extensively. The user can choose between the single-threaded implementation and its parallel version. The single-threaded version does not rely on any external libraries. The parallelization is done through OpenMP 2 , a standard API for shared memory multiprocessing programming. The code was tested successfully on Ubuntu 14. 04 with g++ 4. 8, and on MacOS 10. 8 using g++4. 8. 0. The compilation is done through the CMake build system to be cross-platform. The code compiles with g++ and with clang, but there is no support yet for OpenMP with the clang compiler, so that the parallelism is deactivated in that case.
For the pyramid point set (Figure  6 ) for example, the Scale-Space Meshing Algorithm builds a mesh with 99. 24% of the input point set as vertices compared to only 79. 36% for the Ball Pivoting Algorithm. Figures  3  and 5  provide comparisons of the Scale-Space Meshing and the Ball Pivoting Algorithms with the exact same meshing radius. In particular, Figure  5  shows that the details are much better preserved with the Scale-Space meshing Algorithm. Figure  6  shows the shape evolution of a pyramid point set  [4]  with respect to the scale. The shape loses its details and becomes smoother: as a consequence, computations done at the low scale will be more robust than those done at the highest scales. The result of the processing thus strongly depends on the choice of the scale. Finally, we show the performance of the Scale-Space Meshing using various input shapes from the Farman dataset  [4].
For each RSC, we used principal component analysis (PCA) to capture the characteristic gene expression pattern for each tumor type and to reduce dimensionality, generating a different PCA space for each RSC. We decreased the number of dimensions to the first eight components for each space. We converted the PCA space into a correlation space by projecting the 240 tumors, correlating the expression profile of each tumor with that of each of the eight components. We then reduced the data again by computing the Euclidean centroid for each tumor type from the coordinates of its 30 tumors. The candidate cell line then was projected similarly into the correlation space, and we calculated the Euclidean distances between the cell line and the centroid for each tumor type (Figure  S1D ). We evaluated the representation of melanoma specificity by selecting spaces for which the cell line position in the eight-dimensional correlation space was twice as close to the melanoma centroid as to any other centroid.
The contribution of genes contribution to melanoma specificity was evaluated by calculating their frequency of occurrence in the RSCs corresponding to the selected spaces, with respect to all the RSCs generated. Fisher's exact test was used to evaluate the significance of enrichment. Finally, genes were ordered by decreasing contribution to melanoma specificity (increasing p value). An SD median (SDM) score was generated to take inter-cell line variability into account and to identify the top 100 genes (Figure  1A ). The top 100 genes (Table  S1 ) of the mRNA signature defined by SCA (Figure  S1E ; SCA-MEL-mRNA signature or SCA-MEL-mRNA-100) showed expression to be more similar in the 30 melanomas and 63 melanoma cell lines than between melanoma cell lines and other tumor types. The numbers of contributing samples and tumor types were not exhaustive, but SCA can manage larger amounts of data if required.
The SCA-Melanoma-mRNA-55UP Signature Is Correlated with Phenotypic Characteristics In Vitro We investigated the correlation between SCA-melanoma-mRNA-55UP signature expression and in vitro behavior. We obtained expression profiles for 23 independent melanoma cell lines not used in the SCA analysis, in house, with a microarray platform different from that used to generate the previous tumor dataset. Two characteristic groups (groups 1 and 2) were obtained for clustering with the SCA-MEL-mRNA-55UP signature (Figure  2A ). Two wellseparated groups (groups 1b and 2b) also were obtained (Figure  S2 ) when clustering with the SCA-MEL-mRNA-100 signature. In this second analysis, SK29, Mull, and Gerlach melanoma cell lines shifted from group 1 to group 2, suggesting that these cell lines were less representative of the two groups.
The Melanoma-Specific Regulatory Network Is Associated with Melanoma Survival The SCA-MEL-mRNA-55UP signature includes eight transcription factor genes as follows: ALX1, DLX2, GAS7, LEF1, MITF, PAX3, SOX10, and TFAP2A. Ingenuity Pathway Analysis, based on published findings, and available chromatin immunoprecipitation sequencing (ChIP-seq) datasets for these transcription factors and their surrogates (Table  S1 ) suggested probable mutual control for these factors (Figure  5A ). Moreover, 90% of the SCA-MEL-mRNA-55UP signature was connected to at least one of these transcription factors (Figure  5B ); the myelin basic protein gene (MBP) was potentially regulated by five of the eight transcription factors (MITF, SOX10, LEF1, PAX3, and TFAP2A). Significant enrichment was observed for TFAP2A, as 40 of the SCA-MEL-mRNA-55UP genes had TFAP2A ChIP-seq peaks in their promoters (p < 3.
, 2014). miR-221-3p targets MITF, thereby favoring invasion over proliferation. Epithelial-to-mesenchymal transition (EMT) inducers such as FOSL1 (FRA-1) and TGFB1 or TWIST1 (Figure  S5C ) have been shown to control the expression of miR-10a-5p and miR-221-3p, respectively  (Li et al. , 2013; Stinson et al. , 2011). Finally, we assessed the clinical relevance of melanoma SCA signatures by survival analyses of independent external data-sets. We downloaded RNA sequencing (RNA-seq) data for 292 melanoma metastases (the Cancer Genome Atlas [TCGA] portal, skin cutaneous melanoma cohort) and clustered them with the SCA-MEL-mRNA-28SDE signature (Figure  5D ). Cluster 2 patients (n = 68), generally displaying underexpression of the 28 genes, survived significantly longer overall (p = 0. 0191). Studies of two other published datasets  (Bogunovic et al.
, 2009; Jönsson et al. , 2010)  confirmed that patients overexpressing the 28 SDE core genes had significantly lower overall (p = 0. 0362) and distant metastasis-free (p = 0. 0205) survival, respectively (Figures  S5D-S5G ). By contrast, the melanoma phenotype-specific expression (MPSE) gene (n = 97) signature, derived from melanoma cell lines only, was not informative for survival analysis  (Figures S5H and S5I; Widmer et al. , 2012). However, a 46-gene signature, consisting mostly of immune-related genes and shown to be informative for patient survival  (Mann et al. , 2013) , also was discriminant for patients' survival in the TCGA dataset (Figures  S5J  and S5K ). Thus, overexpression of the 28 SDE signature, associated with a less aggressive phenotype in vitro, was associated with a worse patient outcome in vivo.
, 2011). By contrast, high MITF levels activate differentiation genes driving melanin production, such as tyrosinase (TYR) and melanosome biogenesis enzyme genes  (Cheli et al. , 2010). Cell lines consist of largely homogeneous dividing cells. We considered cell lines to be aggressive if they invaded, migrated, and formed tumors in nude mice, even if they divided slowly. Conversely, even rapidly dividing non-aggressive cell lines did not invade, migrate, or form tumors in mice. We evaluated the prediction by the SCA signature of MEKi and BRAFi drug sensitivity in melanoma cell lines. Strong SCA-MEL-mRNA-28SDE signature expression was associated with sensitivity to MEK and BRAF inhibition (Figures  S3B-S3E ). Also, we evaluated the SCA-MEL-mRNA-28SDE signature using publicly available primary melanoma samples (n = 297, GEO: GSE57715) and melanoma cell lines (n = 63, GEO: GSE7127) (Figure  6 ).
There is increasing evidence for this phenotype switching phenomenon  (Hoek and Goding, 2010). We considered the average expression pro-files of melanomas to be the predominant phenotypic state of all melanoma cells within the lesion. A previously defined invasive signature  (Widmer et al. , 2012)  did not overlap with our SCA signature because the invasive program is common to different cancers and, thus, not specific to melanoma (Figure  S3F ). SCA may become a standard tool for extracting lineage-specific molecular dependencies for any tumor type. Increasing computational power will make it possible to analyze larger cohorts. SCA can highlight cancer regulatory networks operating in vitro and is particularly valuable for drug development. S1 ) were retrieved from GEO. We aggregated 30 randomly selected samples from each set into a single dataset, which was then quantile-normalized for interdisease scaling.
SCA Data Transformation and Context Generation-Expression data for eight tumor types (Table PCA Space Computation and Tumor Projection-For each RSC, PCA was performed on the tumor dataset in the same manner as singular value decomposition (SVD) was used by  Sandberg and Ernberg (2005) , but limited here to the RSC features, whereas Sandberg and Ernberg used a single signature of 303 genes. We limited the dimensions of the resulting PCA space to its first eight, assuming the different types of cancer to be the major source of variance in the tumoral dataset. Tumors used for PCA space construction were then projected into the space by Pearson correlation of their expression to yield a new eight-dimensional correlation space. Euclidean centroids were calculated for each tumor type in this correlation space.
Features were then ordered by increasing p values, after false discovery rate correction by the Benjamini-Hochberg method. For core generation, we pooled the feature-ranking results for all independently analyzed cell lines and assigned a score (SDM score) to each feature, by calculating the SD of its rank across cell lines divided by its median rank. After ordering features in descending order of scores, cores were built by taking the 100 best features for mRNA and all features with a score above 1. 0 for miRNA. The SCA code is provided as an R script (https://github. com/ aoumess/SCA). (Ma, melanoma; Co, colon; Ov, ovary; Br, breast; Lu, lung; Li, liver; Ew, Ewing sarcoma; Ne, neuroblastoma)  were acquired and preprocessed to generate randomly selected contexts (RSCs) or packs of 20 arbitrarily chosen genes.
Three miRNAs  (221-3p, 10a-5p, and 211-5p)  were found to be differentially expressed; miR-221-3p and miR-10a-5p were more strongly expressed in group 1 than in group 2 melanoma cell lines, whereas miR-211-5p was less strongly expressed in group 1 than in group 2 melanoma cell lines. Interactions between genes/proteins of the melanoma-specific signature (55UP) were inferred from Ingenuity Pathway Analysis (IPA, literature-based) and publicly available ChIP-seq data (Table  S1 ). The ALX1-and DLX2-related ChIP-seq data were not available at the time of publication. (A) A core of eight transcription factors (TFs) characteristically overexpressed in melanoma and identified by SCA. Directed arrows of a specific color indicate that a specific TF regulates or potentially regulates other TFs. Potential regulation is evaluated according to the integration of ChIP-seq data, expression, and known interactions by IPA (see Table  S1  and GEO: GSE67638).
The patients of the two principal clusters differed significantly in terms of overall survival (p = 0. 0191, Mantel-Cox test). (E) Melanoma metastases (TCGA portal) were clustered on the basis of the expression of the three core miRNAs generally overexpressed in melanoma and SDE between aggressive and non-aggressive melanoma cell lines. The patients of the two principal clusters differed significantly in terms of overall survival (p = 0. 0031, Mantel-Cox test). (F) Summary of the model linking molecular in vitro and in vivo features for melanoma based on SCA is shown. (A) Primary melanomas (n = 297, GEO: GSE57715) and melanoma cell lines (n = 63, GEO: GSE7127) were clustered using the SCA-MEL-mRNA-28SDE signature (20 of 28 genes were exploitable in this dataset). The dendrogram shows two distinct clusters containing each primary melanomas and melanoma cell lines.
Acknowledgments We thank  Audrey Bechadergue  for excellent technical assistance and  Richard Marais  for helpful comments. We thank  Thomas Robert  from the  Agilent micro-array platform at Gustave-Roussy  and the team caring for the animals at  Institut Curie , especially  J. D. Mam  and  H. Harmange. This work was supported by the  Ligue Nationale Contre le Cancer (Equipe labellisée) ,  Cancéropole Ile-de-France ,  Institut National du Cancer (INCa) ,  Labex CelTisPhyBio  ( ANR-11-LBX-0038 ), and  Pair Mélanome.
, Pleasanton, USA). Images were recorded using a magnification of 34,000 × on the CCD and 41,000 x on the K2 summit, corresponding to a pixel size of 0. 6 or 0. 54 in the final image, respectively. Zero loss filtered tomographic tilt series were collected in a range of -60 • to +60 • , at increments of 2 • -2. 5 • and a defocus of 6-10 µm using the Gatan Digital Micrograph Latitude software (Gatan Inc. , Pleasanton, USA) The maximum cumulative dose was 150 e -/A 2. For electron tomography at room temperature, samples were high-pressure frozen and embedded in epon resin as described in  Perras et al. (2014). Tilt series were recorded at 120 kV on a JEOL JEM-2100, equipped with a LaB 6 cathode and a 2 × 2 k F214 fast scan CCD camera (TVIPS, Gauting, Germany) in a range of -60 • to +60 • in steps of 2 • , a defocus of 3-6 µm and a magnification of 14,000 ×, which equals a pixel size of 1 nm in the final micrograph.
Ultrastructural Analysis Using Transmission Electron Microscopy (TEM) And Scanning Electron Microscopy (SEM) For analysing the cell surface appendages, the unfixed, purified hami were deposited on a carbon-coated copper grid and negatively stained with uranylacetate [2% (w/v), pH 4. 5]. The samples were examined with a CM12 transmission electron microscope (FEI Co. , Eindhoven, The Netherlands) operated at 120 kV. All images were digitally recorded using a slow-scan CCD camera that was connected to a computer with TVIPS software (TVIPS GmbH, Gauting, Germany). Scanning electron microscopy was performed as described in  Probst et al. (2014).
Particle Characterization within the Cells For element analysis of the particles within coccoid cells, Ca. A. hamiconexum biofilm flocks were embedded in TAAB embedding resin (TAAB, Aldermaston, UK) and thin sectioned as described in  Milić et al. (2015)  followed by staining with platinum blue and lead citrate. Energy Filtered TEM (EFTEM) was performed with a Gatan GIF Quantum 963 energy filter using an FEI Tecnai 20 microscope at 120 kV acceleration voltage. To visualize the elemental distributions, elemental maps were made using the three window method at the standard losses provided by Gatan Digital Micrograph software (see also  Teubl et al. , 2014). Energy Dispersive X-Ray spectroscopy was performed using an Edax silicum type ultrathin unit (SUTW) detector, as described in  Milić et al.
64 mg/ml, Sigma Aldrich, Germany; dilution 1:500) was added and incubated for 1 h at 37 • C. After washing two times with PBST [+0. 1% (v/v) SDS], the slide was rinsed with H 2 O again, DAPI counterstained and analyzed by fluorescence microscopy (Olympus BX53F, Hamburg, Germany) with epifluorescence equipment and the respective imaging software (cellSens, Olympus).
Transcriptomic Analysis of Hamus Gene(S) The presence or absence of specific hamus subunit transcripts was tested via specific mRNA detection in archaeal biofilm samples (see Supplementary Table  S1 , containing list of genes and primers). Three hamus subunits (i. e. , MSIBFv1_A2980002, MSIBFv1_A2020020, MSIBFv1_A3210004; deposited in the European Nucleotide Archive; accession code: PRJEB6121;  Probst et al. , 2014)  were examined in detail. RNA was isolated using the PowerBiofilm ™ RNA Isolation Kit (Mobio Laboratories Inc. , Carlsbad, USA), following the manufacturers' instructions (DNA digestion was performed for 30 min). DNAse treatment was repeated after precipitation of nucleic acids. Contamination of the RNA extraction by residual DNA was excluded by PCR amplification, using a universal archaeal forward primer combined with a reverse primer binding in a non-transcribed region (e.