dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison to UProC and a combination of UProC and MetaCLADE UProC  [38]  (version 1. 2. 0, downloaded at https://github. com/gobics/UProC) is a tool designed for large-scale sequence analysis. Given a read dataset, UProC decides whether a domain is present or not in a sequence, but it does not point out precisely where the domain is localised in the sequence by defining starting and ending positions, nor it associates an E value to the matching. The complementary performance of UProC compared to Meta-CLADE suggests to combine the two tools in order to achieve best performance in terms of number of correctly predicted domains. For this reason, we define "Meta-CLADE+UProC" by adding to MetaCLADE's annotations all those obtained by UProC on reads left unannotated by MetaCLADE. In a similar way, we define the symmetric "UProC+MetaCLADE" annotation.
The use of clans and InterPro families In order to evaluate MetaCLADE performance on datasets of simulated sequences, we identify domains at the Pfam clan  [55]  or InterPro family  [56]  level. This is done because sequence similarity within domains in the same Pfam clan is usually high and genome annotation is often mislead by domains belonging to the same clan. This is even more true in MG datasets, where one often needs to annotate fragments of a domain displaying a weaker signal due to the reduced length. Pfam clans are groups of proteins for which common ancestry can be inferred by similarity of sequence, structure or profile HMM  [55]. The list of Pfam clans was retrieved at http://ftp://ftp. ebi. ac. uk/pub/databases/ Pfam/releases/Pfam27. 0/Pfam-A. clans. tsv. gz. InterPro families represent groups of evolutionarily related proteins that share common functions.
Functional analysis of annotated real datasets In order to validate MetaCLADE on all real MT and MG datasets, we associated a function based on GO classification to both the domains identified with MetaCLADE and the domains identified with HMMer (hmmscan). We used Pfam2GO  [75]  and annotated biological process terms of GO-Slim  [56, 76]. Pfam2GO was retrieved from http://geneontology. org/external2go/ pfam2go, and GO-Slim classification for MG was retrieved from http://geneontology. org/page/downloadontology. To highlight the differences between Meta-CLADE and HMMer, we compared domain abundance in all GO-term classes. For this, we normalised domain count in each MT dataset with respect to the size of the sample as described above.
A dataset of simulated reads generated from bacterial and archaeal genomes We generated a set of fragmented sequences from a set of 11 archaeal and 44 bacterial fully sequenced genomes. The list of species, NCBI accession numbers and genome lengths is reported in Additional file 1: Table  S2. We assumed all species be equally abundant. In order to generate a set of 500,000 clones, we first used MetaSim  [53] , according to a normal distribution with a mean of 800 bp and a standard deviation of 100 bp. Then, we applied FlowSim  [77]  to the set of clones to obtain actual reads simulated with realistic insertion and deletion errors expected during DNA sequencing. More precisely, the simulation was performed according to the FlowSim platform 454 GS-FLX Titanium (error rate ∼ 1%). The read dataset, which contained sequences of ∼ 523 bp on average, was finally processed with FragGeneScan  [78]  in order to predict the ORFs.
This resulted in about 500,000 reads that were given as input to MetaCLADE. In parallel, we used information available in the XML file associated to each genome in the EBI site (http://www. ebi. ac. uk/genomes/) to identify coding sequences included in the SwissProt database. For these coding sequences, we retrieved annotated domains and their positions from the InterPro site through the UniProtKB proteins file (http://ftp. ebi. ac. uk/pub/ databases/interpro/protein2ipr. dat. gz). Domain annotation of coding sequences was used to evaluate Meta-CLADE performance. For this simulated dataset, we selected positive domain hits with a probability threshold of 0. 85 estimated by the naive Bayes classifier. The dataset is available at http://www. lcqb. upmc. fr/ metaclade/.
Three datasets of simulated reads to compare with UProC and HMM-GRASPx MetaCLADE was compared to HMM-GRASPx and UProC on the Guerrero Negro Hypersaline Microbial Mats (GNHM) MG dataset  [79]  used in  [38]  to demonstrate UProC performance versus profile-based methods on short reads. GNHM was downloaded from http:// uproc. gobics. de. GNHM is a dataset presenting a large variety of species of low abundance, with archaeal and eukaryotic species much less abundant than bacterial ones. From GNHM data, following the protocol in  [38] , we generated two sets of short reads simulating different read lengths of 100 bp and 200 bp, respectively. The only difference with the described protocol was in the latest HMMer, version 3. 1b2 was used instead of the previous one (3. 0) that was used in  [38].
For the two sets of reads, we considered two annotations as gold standard, one realised with HMMer (hmmscan with GA cutoff ) and the other with CLADE  [43]. Both annotations are based on domains known in Pfam 27. The comparison between MetaCLADE and HMM-GRASPx was realised on two simulated datasets of reads of length 100 bp and 200 bp, generated from a marine MG dataset containing 23 marine microbial genomes from the Alteromonas, Candidatus, Erythrobacter, Flavobacteriales, Nitrosococcus, Photobacterium, Prochlorococcus, Roseobacter, Shewanella, Synechococcus and Vibrio groups. Relative abundances of these bacteria were simulated according to their environmental composition  [37]  with an average coverage of ∼ 27. 5X (Additional file 1: Table  S11 ). We considered these 23 bacterial genomes along with a selected number of important metabolic pathways (see Additional file 1: Table  S12 ).
Only those Pfam (version 27) domain families involved in each one of these pathways were finally considered (family-pathway association was performed in  [37]  using KEGG's release 73. 0 of January 2015). The same domain families were taken into account in HMM-GRASPx and MetaCLADE. Illuminalike paired-end nucleotide reads were generated with wgsim (version 0. 3. 1-r13 obtained from http://github. com/lh3/wgsim, with error rate parameter -e 0. 01) and translated into short peptide reads using FragGeneScan  [78]  (version 1. 30, with parameters -complete=0 -train=illumina_10). For each pathway, the gold standard was defined by searching the corresponding Pfam models against the complete proteomes and transferring the annotation to those reads that mapped to a domain hit for at least the 60% of their length.
Nine simulated datasets to test MetaCLADE's sensitivity to sequence origin In order to test how MetaCLADE is sensitive to the distribution of species in a dataset, we created nine simulated datasets with increasing percentages of eukaryotic sequences, and we annotated them in order to examine the origin of the CCMs used by MetaCLADE. Specifically, each dataset is composed by 75,000 randomly chosen UniProt coding sequences that had not been picked to build any model of CLADE's library. The percentage of eukaryotic CDS among the datasets varies from 10 to 90% with steps of 10%. From such CDS, a set of 50 aa fragments has been uniformly extracted in order to reach a 1. 5X coverage. Additional file 1: Table  S9  describes the simulated datasets (e. g. average CDS length, number of generated fragments).
Overall, we employed 3128 bacterial, 9403 eukariotic, 218 archaeal and 2127 viral species and we annotated with 1,397,356 bacterial, The study was funded by LabEx CALSIMLAB (public grant ANR-11-LABX-0037-01 constituting a part of the "Investissements d'Avenir" program-reference ANR-11-IDEX-0004-02) (AU, RV), the French Ministry of Research and Higher Education for a teaching assistantship (AU), the Institut Universitaire de France (AC), access to the HPC resources of the Institute for Scientific Computing and.
MetaCLADE is also suitable for technologies producing very short reads like Illumina HiSeq 2000 sequencing system. We analysed the predicted ORFs left without annotation by InterPro from one run of the O'Connor lake dataset. The dataset contains 1,315,435 input reads and 1,211,131 predicted ORFs, with an upper bound on the missed ORFs of 104,304. InterProScan annotated 273,903 ORFs, leaving unannotated 937,228 ORFs. The two sets of predicted and unannotated ORFs have a mean ORF length of 123 bp, with a minimum of 100 bp and a maximum of 135 bp. MetaCLADE analysed the 937,228 unannotated ORFs and succeeded to annotate 57,356 of them. The distribution of E values for MetaCLADE annotations is shown in Additional file 1: Figure.
Availability of data and materials All datasets generated and analysed during the current study are publicly available, and their address is provided in our Additional file 1. All generated datasets are available at http://www. lcqb. upmc. fr/metaclade. Data from the study in  [57]  are available from the corresponding author on reasonable request. MetaCLADE package is available at the addresses http://www. lcqb. upmc. fr/metaclade and http://sourcesup. renater. fr/projects/metaclade/.
Largest annotations are reported in bold. See Fig. 4 and Additional file 1: Figures S7-S10, S12-S15 for additional information a b c d Fig. 4 Read annotations of the Puerto Rico Rainforest MG dataset obtained with InterProScan and MetaCLADE. aDomainannotation of the five tools: Pfam (yellow), Gene3D (blue), TIGRFAM (purple), PRINTS&ProSite (orange) and MetaCLADE (green). The Venn diagram representing the number of reads annotated by one or several tools is reported. bDistribution of species originating CCMs used to annotate the dataset with MetaCLADE. cDistributions of E values associated to the sets of domains identified in an exclusive manner by each tool. For instance, for MetaCLADE, we considered 37,811 domains (see a). E values are plotted on the x-axis using alog 10 scale. dDistribution of E values associated to all domains identified by MetaCLADE.
As in c, E values are plotted on the x-axis using alog 10 scale and Gene3D annotate the largest number of domains, together with MetaCLADE that largely agrees with them. In Fig. 4a, for instance, over 260,000 domains annotated by MetaCLADE, only 38,811 are identified exclusively by MetaCLADE, while all others are found by at least another tool. TIGRFAM and PRINTS&ProSite (the union was considered) annotate the least, and their annotation is largely covered by other libraries. In particular, notice that MetaCLADE annotates a large number of reads that are missed by InterProScan for all five datasets. Table 1 reports the number of ORFs annotated by each library (top) and those exclusively annotated by a single library (bottom). MetaCLADE shows a high number of uniquely annotated ORFs, and, in this respect, it clearly demonstrates to go far beyond InterProScan based on different domain libraries.
Moreover, on all datasets, the distribution of E values associated to MetaCLADE annotationshows a higher statistical confidence compared to Inter-ProScan with all its libraries. This is illustrated by the density curves in Fig. 4cand Additional file 1: FiguresS7C-S10Cwhere the peak of the MetaCLADE curve lies at the rightmost side compared to all other domain annotation tools. In particular, this is true for the Gene3D library, whose peak corresponds to the acceptance threshold for this tool. This allows to explain why MetaCLADE seems to perform poorly on the domains exclusively.
Identification of motifs in short reads: the example of ABC transporters MetaCLADE is also suitable for technologies producing very short reads like Illumina HiSeq 2000 sequencing system. We analysed the predicted ORFs left without annotation by InterPro from one run of the O'Connor lake dataset. The dataset contains 1,315,435 input reads and 1,211,131 predicted ORFs, with an upper bound on the missed ORFs of 104,304. InterProScan annotated 273,903 ORFs, leaving unannotated 937,228 ORFs. The two sets of predicted and unannotated ORFs have a mean ORF length of 123 bp, with a minimum of 100 bp and a maximum of 135 bp. MetaCLADE analysed the 937,228 unannotated ORFs and succeeded to annotate 57,356 of them. The distribution of E values for MetaCLADE annotations is shown in Additional file 1: Figure.
Growth factor concentrations The contents of bFGF (#DFB50), EGF (#DEG00), insulin-like growth factor (IGF)-1 (#DG100), and hepatocyte growth factor (HGF, #DHG00) were measured in triplicate in two batches of hPL and two batches of HS by ELISA (Bio-techne, Minneapolis, MN, USA), following the manufacturer's instructions. The absorbance was measured using an Infinite ® M200 PRO spectrometer (Tecan, Männedorf, Switzerland), and the results were analyzed using Magellan™ data analysis software (Tecan).
Flow cytometry analysis Immunophenotype analyses were performed on expanded hMuStem cells HS and hMuStem cells hPL (P4). Cells were incubated (30 min at 4 °C in darkness) with antibody (Ab) against the appropriate cell-surface marker (Additional file 1: Table  S1 ) and washed three times with PBS/2% HS. Fluorescence minus one control samples using isotype-matched Ab were used as negative controls for gating and analyses for multicolor labeling. Labeled cells were acquired using a FACS Aria flow cytometer (BD Biosciences). Data were analyzed using FlowJo software (FlowJo, Ashland, OR, USA). At least 10 4 cells and 3 × 10 4 cells were analyzed for single and multicolor labeling protocols, respectively.
Reverse-transcription and real-time semiquantitative PCR Total RNA was extracted using the RNeasy mini or micro kit following the manufacturer's instructions (Qiagen, Santa Clara, CA, USA), quantified using a NanoDrop spectrophotometer (Labtech, Wilmington, DE, USA) after DNase treatment (Ambion, Austin, TX, USA), and converted to cDNA by reverse transcription as described previously  [57]. Oligonucleotide primers used for semiquantitative RT-PCR analysis of gene expression were designed using Oligo Primer Analysis Software v. 7 (Molecular Biology Insights Inc. , Colorado Springs, CO, USA) and are listed in Additional file 3: Table  S3. Data were normalized to mRNA levels of the housekeeping gene RPS18 and were calculated using the 2 -ΔCt method.
A library of 350-800 base pairs (bp) was run on an Illumina HiSeq 2500 using a TruSeq Rapid SBS kit (Illumina, San Diego, CA, USA). Sequencing-derived data were deposited in the Gene Expression Omnibus  [61]  and can be accessed using GEO accession number GSE99085. Read pairs used for analysis fulfilled the following criteria: all 16 bases of the first read must have quality scores of at least 10 and the first six bases must correspond exactly to a designed well-specific barcode. The second reads were aligned to RefSeq human mRNA sequences (hg19, obtained from the UCSC Genome Browser) using bwa version 0. 7. 4 4 with the nondefault parameter "-l 24". Reads mapping to several positions in the genome were filtered out from the analysis. Digital gene expression (DGE) profiles were generated by counting the number of unique UMIs associated with each RefSeq gene for each sample.
Differential gene expression Differential expression analysis was performed using the DESeq2 Bioconductor package version 1. 14. 1  [62]. The data were analyzed using a multifactor design to take into account the differences between patients while estimating the effect attributable to culture conditions. Genes showing significant differences in expression between hMuStem cells HS and hMuStem cells hPL were identified using a fold-change threshold ≥ 2 and adjusted p < 0. 05.
We found that the proportions of Myf5 + , MyoD + , and desmin + cells were consistently smaller in hMuStem cells hPL versus hMuStem cells HS , which exhibited decreases of 2. 6-3. 0fold, 4. 0-15. 4-fold, and 1. 3-1. 4-fold, respectively (Fig. 3e ). These data suggest that commitment to the myogenic lineage is reduced in hMuStem cells isolated and expanded in hPL-GM versus HS-GM. To further evaluate the effects of the nutritive substitutes HS and hPL on global gene expression in hMuStem cells, a DGE-seq protocol was performed. Of the 14,936 genes detected in at least one out of the six cell batches (hMuStem cells HS , n = 3; hMuStem cells hPL , n = 3), few differentially expressed genes were identified (Additional file 5: Table  S5 ). The volcano plot in Fig. 4  illustrates the general gene expression pattern in the two hMuStem cell preparations.
Results of one representative donor out of three independent donors presented. Myogenic (CD56) and perivascular (CD140b and CD146) cell lineage markers analyzed either in (c) entire viable cell population or (d) CD56 + cell subsets. Results presented for three independent donors: when significant but below 100, percentage of positive cells indicated in upper right corner of corresponding histograms. e Immunocytochemistry analysis of myogenic regulatory factors (MRFs) Myf5, MyoD, and desmin in hMuStem cells HS and hMuStem cells hPL at P4. Representative pictures presented. Nuclei counterstained with DAPI (blue). Proportions of positive cells recorded for three independent donors presented. Donors #4, #5, and #6 represented by squares, triangles, and circles, respectively. Data presented as mean ± SD for each donor.
Acknowledgements The authors would like to thank  Sophie Derenne  and  Alix Duquesne  from  EFS Pays de La Loire (Nantes, France ) for helpful advice and discussions to improve the experimental design. They are grateful to  Christopher Desjardins  ( Broad Institute, Cambridge, MA, USA ) for his help with the DGE RNA-sequencing data analysis and to  Olivier Collin at GenOuest's bioinformatics core facility  ( IRISA/ INRIA, Rennes, France ). Sequencing was performed by the  Genomics Core facility ,  Nantes (France). The authors also thank  Chantal Thorin  ( Laboratoire de Physiopathologie Animale et Pharmacologie fonctionnelle, Oniris, Nantes, France ) for assisting us with our statistical analysis.
Funding This study was supported by a grant from the  Fond Européen de Développement Régional (FEDER ;  N°38436  and  N°PL0003686 ). It was conducted under the framework of the  IHU-Cesti  project, which received French government financial support via the  National Research Agency's Investment  for the  Future program  ( ANR-10-IBHU-005 ). The  IHU-Cesti  project is also supported by  Nantes Metropole  and the  Pays de la Loire region  ( N°2012-13464 ).
Hence, when necessary in the wave section, we will only use the former ones and consider that the trajectory dynamics are mainly due to deep circulation processes. The discussion of such consideration is thoroughly made by  Ollitrault and Rannou (2013). In the case of the 656 float, we also consider the DOXY measurements. These data were calibrated (Alain Fumenia, personal communication, March 2017) based on the CTD profiles made just before the float was deployed during the OUTPACE cruise. The calibration for the 656 float is DOXY clb = 0. 97125 DOXY raw + 14. 3755, where DOXY raw is the dissolved oxygen concentration data from the float and DOXY clb is the calibrated dissolved oxygen concentration data, both expressed in micromoles per kilogram (µmol kg -1 ).
The 3D printing is a time-consuming procedure, and it is also true that radiological characteristics of the printing materials have not been studied in details. To print an anthropomorphic phantom, a digital (software) model is required, which may be obtained either from medical imaging techniques such as MRI, CT, PET or created by using a dedicated (CAD) software. Once the software models are created, they are routinely converted to stereolithography files and printed with 3D printers. Such an approach has been followed by  Winslow et al (2009)  for the production of three different adult anthropomorphic phantoms from original segmented CT patient datasets. In the field of x-ray breast imaging, few works have been focused on measurements of the attenuation coefficients of materials, which may be used in manufacturing of breast phantoms, by exploiting 3D printing.
Data for PMMA, polyethylene, paraffin wax, polycarbonate and liquid water were taken from NIST database (Berger et al 2010), while these for BR12 from  White et al (1978). Data for Nylon, PLA, ABS, PET-G were taken from Alssabbagh et al  (2017). The materials, evaluated in the current study are listed in table 2. Seventeen of them are used by the 3D printing technology. The selection of those materials was based on the fact that they are common, inexpensive and largely available on the market. The first seven materials (ABS, Brick, Hybrid, Nylon, PET-G, PLA and PVA) are thermoplastic polymers used by Fused Deposition Modelling (FDM) printing technology. They are available in the form of a wire spool. Materials 8-17 (Black, Clear, Flex, Gray, NDBase, NDC + B, NDCast, NDSG, Tough, White) are polymer resins in a liquid form and are used with stereolithography (SLA) technology.
They have different properties and are used in diverse applications, such as dental medicine, aerospace, optical prototyping, etc. In order to study the x-ray properties of the selected materials, three sizes of step-wedge phantoms (scale S, M and L) (figure 1(a)) were manufactured with the help of three 3D printers. Designspark Mechanical software (www. rs-online. com/designspark/mechanical-software) was used to design the models. The dimensions of the step wedge phantoms are specified in the insert of figure  1(a). The size of each step area was made large enough in order to have high number of pixels on the x-ray projection images, necessary for averaging the calculations of the linear attenuation coefficients over the sample surface area. The unknown material density was determined experimentally-the weights of the samples were measured with the precise digital scale equipment (0.
The slope of each such line corresponds to the linear attenuation coefficient of the studied material and energy. The reference data for the monochromatic linear attenuation coefficients were derived from the online version of the XCOM database (Berger et al 2010) using the elemental composition from table 1. In order to validate the linear attenuation coefficients from measurements at ESRF and to calculate the refractive index decrement δ, elemental composition analysis of selected (in regards to their absorption properties to the reference breast tissues) materials was performed using the elemental analyzer EURO EA3000, CHNSO. The complex refractive index of the material is given by  Born and Wolf (1980) : n (E) = [1 -δ (E)] + iβ (E) (2) where the absorption coefficient β is: β(E) = hc 4πE µ (E), (3) h is the Planck constant, and c is the speed of the light.
Validation of the experimental procedure for determining µ The linear attenuation coefficient for the PMMA derived from NIST database (noted as PMMA_NIST), the one also from NIST database but based on the chemical analysis of the used sample (noted as PMMA_LAB), and the experimentally determined one (PMMA), as outlined in figure  4 (a), are plotted in figure  5. An overall excellent coincidence between measured and calculated linear attenuation coefficients for the PMMA is observed for the three incident energies. The calculated maximum relative difference between measured (PMMA) and calculated from the elemental analysis coefficients (PMMA_LAB) was 1. 9% for 45 keV, which confidently confirms the consistency of the approach for estimating the linear attenuation coefficients of the investigated materials.
Acknowledgments This work is supported by  MaXIMA  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  692097. This research is also supported by the  Bulgarian National Science Fund  under grant agreement  DN17/2. The authors thank  Prof Tsanka Dikova  for the provided NDC+B, NDBase, NDCast and NDSG samples.