dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Edward Topp  ( AAFC, Canada ) for his assistance with language issues,  Marion Vittecoq  ( La Tour du Valat, France ) for the suggestion of search terms relating to the wildlife,  Céline Roose-Amsaleg  ( CNRS, France ) for her helpful reading and  Christine Sireyjol  ( INRA, France ) for her help in the building of search strings and for the use of the Digimind ® tool.
• Bielefeld Academic Search Engine (www. base-search. net). • BIVI database of AFNOR (https://bivi. afnor. org/). • European Committee on Antimicrobial Susceptibility Testing (https://www. eucast. org). • European Food Safety Authority (https://www. efsa. europa. eu). • Food and Agriculture Organization (https://www. fao. org). • French Ministry for the Ecological and Inclusive Transition (https://www. ecologique-solidaire. gouv. fr). • French Ministry for Solidarity and Health (https:// www. social-sante. gouv. fr). • Grey Literature Network Service (https://www. grey- net. org). • Hyper Articles in Line (https://www. hal. archives- ouvertes. fr). • Open grey literature in Europe (https://www. open- grey. eu). • National Public Health Agency (https://www. santepubliquefrance. fr). • National Agency of Drug and Health Products Safety (https://www.
Discussion This work is a longitudinal study starting in 2007 which used a combination of microarray analysis and direct Sanger sequencing to identify ABCA4 mutations in a large French cohort with a clinical diagnosis of STGD1 disease. At that time targeted NGS and whole exome sequencing (WES) was not available in our laboratory. The rate of bi-allelic variants detected in the population using this approach was 75. 6%, which is in accordance with previously reported data (about 75%)  [15] [16] [17] [18] [19]. The authors believe that the same results would have been obtained using only direct Sanger sequencing on the entire cohort. Initially, patients were analyzed with microarray to evaluate the prevalence of known mutations, which was at this time commonly used to reduce screening costs.
Materials and Methods Patients with a presumed diagnosis of STGD1 disease were recruited at the Reference Center for rare diseases, Referet of the Quinze-Vingts hospital, Paris. Informed consent was obtained from each patient after explanation of the study and its potential outcome. The study protocol adhered to the tenets of the Declaration of Helsinki and was approved by a national ethics committee (CPP Ile de France V, Project number 06693, N•EUDRACT 2006-A00347-  44, 11 December 2006). All the patients and available family members were asked to donate a blood sample for genetic screening for ABCA4 mutations. Subjects with at least one novel variant were called back to undergo a complete phenotype analysis as described below.
5 mM MgCl 2 at an annealing temperature of 58 • C for 1 min. The PCR products were enzymatically purified (ExoSAP-IT, USB Corporation, Cleveland, OH, USA, purchased from GE Healthcare, Orsay, France) sequenced and investigated as previously reported  [62]. Nucleotide numbering reflects cDNA numbering with +1 corresponding to the A of the ATG translation initiation codon in the reference sequence, according to journal guidelines (www. hgvs. org/ mutnomen). All variants were classified following the American College of Medical Genetics and Genomics (ACMG) standards and guidelines and the Association of Molecular Pathology (AMP) Clinical Practice Guidelines and Reports based on previous publications (as compiled in the Human Gene Mutation Database (HGMD)  [63]  and in the Leiden Open (source) Variation Database (LOVD)  [64] ), population data, computational data and functional data.
The Exome Aggregation Consortium (ExAC) database was used to check variant frequency. Novel variants with unknown frequency or minor allele frequency (MAF) ≤0. 05 were further tested using Alamut Visual software v. 2. 7. 1 where the in silico predictive programs PolyPhen2 (Polymorphism Phenotyping, http://genetics. bwh. harvard. edu/pph2/) [65] , SIFT (Sorting Intolerant from Tolerant; http://sift. bii. a-star. edu. sg/) [66] , Mutation Taster (http://www. mutationtaster. org/)  [67]  are implemented. For splice site variants three different algorithms of prediction were used: MaxEntScan (http: //genes. mit. edu/burgelab/maxent/Xmaxentscan_scoreseq. html) [68] , NNSplice  [69] , and human splicing finder v 3. 0 (http://www. umd. be/HSF3/) [70]. Evolutionary conservation was investigated using the 46-way Vertebrate Multiz Alignment and Conservation of the University of California Santa Cruz (UCSC) genome browser  [71, 72].
When RPE atrophy was present the measurement of the area was performed using RegionFinder software (Heidelberg Engineering, Dossenheim, Germany; software version 2. 5. 8. 0) on SW-AF images  [76]. The 50 • images were used to establish the extension of fundus abnormalities and for evaluating the peripapillary area  [56]. A single horizontal high-resolution OCT B-Scan (9 mm) was obtained together with a simultaneously acquired infra-red (IR) fundus image in a transverse plane through the fovea. This image was used to evaluate the preservation of the ellipsoid zone (EZ) through the fovea  [57]  and the possible foveal sparing from the disease  [38]. A cube of OCT scans was then obtained in high-speed mode with the automated real-time mode activated and set to 10 (creating a mean image of ten repeated identical B-scans to improve signal-to-noise ratio).
Statistical Analysis All data analysis was conducted using IBM SPSS Statistics software v. 21. 0 (Chicago, IL, USA). For BCVA, MV, and CRT we used the Wilcoxon signed-rank test to prove the agreement between eyes. Differences between the two genotype groups were analyzed using the Wilcoxon-Mann-Whitney test for age of onset, age at visit, duration of the disease, BCVA, MV, and CRT. The Goodman-Kruskal Gamma test was used to analyze the difference between NM and MM groups for fundus stage, NIR-FAF, and IR-FAF groups, the presence of RPE atrophy, foveal sparing, peripapillary sparing, EZ band integrity, and ERG group distribution. For these descriptive variants, when there was discordance between the right and left eye, we considered the eye with the worse phenotype. A p value inferior to 0.
Discovery of the AA14 family among fungal wood decayers The white-rot basidiomycete Pycnoporus coccineus is an efficient degrader of both hardwood and softwood  16. While studying the effect of different types of biomass on P. coccineus growth using transcriptomics and secretomics, we identified a gene encoding a protein of unknown function that was highly up-regulated on pine and poplar as compared to control  16. The corresponding protein (JGI ID 1372210; GenBank ID #KY769370) was secreted only during growth on pine and poplar suggesting a role in wood decay. A BLAST search against public sequence databases identified more than 300 proteins with significant similarity to #KY769370 from P. coccineus, many of which from well-known saprotrophic fungi. Sequence alignment revealed a conserved N-terminal histidine (Supplementary Fig.
1 ), commensurate with a copper-binding histidine brace active site  10  , which is a hallmark of known LPMOs. A phylogenetic analysis shows that the newly identified sequences strongly cluster together with high bootstrap values and are very distant from AA9, AA10, AA11 and AA13 sequences (Supplementary Fig. 2 ), thereby defining a new LPMO family designated AA14 in the CAZy database. AA14 members are found in all well-known white-rot (Pleurotus ostreatus, Phanerochaete chrysosporium, Trametes versicolor) and brown-rot (Serpula lacrymans, Coniophora puteana, Postia placenta) basidiomycetes and in some wood-inhabitants ascomycetes within the Xylariaceae and Hypocreaceae families. A slight gene family expansion is observed in wood-decaying basidiomycetes (average number per species 3. 35 in basidiomycetes and 1.
Crystal structure of PcAA14 The structure of PcAA14B was solved by multiple-wavelength anomalous dispersion data recorded at the gadolinium edge, and refined at 3. 0 Å resolution. The core of the protein folds into a largely antiparallel immunoglobulin-like β-sandwich (Fig. 2a ), a fold globally similar to that seen in LPMOs from other families. The active site of PcAA14B constituted by His1, His99 and Tyr176 forming the canonical histidine brace is exposed at the surface (Fig. 2b ). In contrast to the flat substrate-binding surfaces observed in AA9 LPMOs  17  , the PcAA14B surface has a rippled shape with a clamp formed by two prominent surface loops (Supplementary Fig. 5 ). Both loops are located in the N-terminal half of PcAA14B, and are equivalent to the L2 and L3 loop regions in AA9 LPMOs.
Bioinformatic analysis of AA14 LPMOs P. coccineus AA14 sequences (Genbank ID KY769369 and KY769370) were compared to the NCBI non redundant sequence database using BlastP  29  i n F e b r u a r y 2 0 1 6. B l a s t s e a r c h e s conducted with AA14 did not retrieve AA9s, AA10s, AA11s or AA13s with significant scores, and vice-versa. MUSCLE  30  was used to perform multiple alignments. To avoid interference from the presence or absence of additional residues, the signal peptides and C-terminal extensions were removed. Bioinformatic analyses were performed on 286 fungal genomes sequenced and shared by JGI collaborators. Protein clusters are available thanks to the JGI (https://goo. gl/ZAa2NX) for each of these fungi. A phylogenetic tree has been inferred using 100 cleaned and merged alignments of proteins from selected clusters of proteins.
Production of P. coccineus AA14 LPMOs The sequences corresponding to PcAA14A (Genbank ID KY769369) and PcAA14B (Genbank ID KY769370) genes from P. coccineus BRFM310 were synthesized after codon optimization for expression in P. pastoris (GenScript, Piscataway, USA). The region corresponding to the native signal sequence was kept while the C-terminal extension region was removed. Synthesized genes were further inserted with into a modified pPICZαA vector (Invitrogen, Cergy-Pontoise, France) using BstBI and XbaI restriction sites in frame with the (His) 6 -tag located at the C-terminus of recombinant proteins. Fusion of PcAA14A with CBM1 was carried using the CBM1 domain of PaLPMO9E, which was added to PcAA14A at the end of the catalytic module using the linker sequence of PaLPMO9E 20. Constructs without (His) 6 -tag sequence were also designed by adding a stop codon at the end of the AA14 catalytic module.
Matrix-assisted laser desorption ionization/mass spectrometry Matrix-assisted laser desorption ionization mass spectra analyses were performed on a Microflex II mass spectrometer (Bruker Daltonics). One μL of matrix [10 mg of 2,5-dihydroxybenzoic acid in 1 mL of CH 3 CN/H 2 O 50/50 (v/v), 0. 1% formic acid (v/v)] was added to 1 μL of intact PcAA14A or PcAA14B protein sample (100 pmoles) in the same solution. Then, mixtures were allowed to dry at room temperature. Data acquisition was operated using the Flex control software. External mass calibration was carried out on Peptide calibration standard (Bruker Daltonics).
ICP/MS Analysis To obtain apo enzymes, 100 mM EDTA treatment was performed overnight. Prior to the analysis, samples were mineralized in a mixture containing 2/3 of nitric acid (Sigma-Aldrich, 65% Purissime) and 1/3 of hydrochloric acid (Fluka, 37%, Trace Select) at 120°C. The residues were diluted in ultra-pure water (2 mL) before ICP/MS analysis. The ICP-MS instrument was an ICAP Q (ThermoElectron, Les Ullis, France), equipped with a collision cell. The calibration curve was obtained by dilution of a certified multi-element solution (Sigma-Aldrich). Copper concentrations were determined using Plasmalab software (Thermo-Electron), at a mass of interest m/z=63.
4 at a volume ratio of 3:1. PcAA14B crystals grew to dimensions of 0. 15×0. 15×0. 05 mm in one week. Crystals belong to space group P4 1 2 1 2 with cell axes 204×204×110 Å and two molecules per asymmetric unit. Crystals of PcAA14B were soaked for 5 min in a solution where 2. 4 M (NH 4 ) 2 SO 4 of the mother liquor was replaced by 2. 4 M Li 2 SO 4 for the sake of cryoprotection prior to flash-cooling in liquid nitrogen. As X-ray fluorescence scans on native crystals did not reveal a significant presence of copper within the crystals, a heavy atom derivative was prepared by soaking the crystals in reservoir solution supplemented with 55 mM of the gadolinium complex gadoteridol prior to cryo-cooling. Native diffraction data were collected on beamline ID23-1, while a MAD dataset at wavelengths of 1.
711 and 1. 698 Å for peak/inflection and remote energies, was collected on beamline ID30B at the European Synchrotron Radiation Facility (ESRF), Grenoble, France. Data were indexed and integrated in space group P4 1 2 1 2 using XDS  41  and subsequent processing steps were performed with the CCP4 software suite  42. Determination of the Gd 3+ substructure and subsequent phasing combined with solvent flattening were carried out with SHELXC/D/E 42 , leading to a pseudo-free correlation coefficient of 71. 8%. Starting from experimental phases, an initial model comprising 526 residues (out of 584), was automatically built with Buccaneer  43  and manually completed with Coot  44. This initial model was used for rigid body refinement followed by restrained refinement against native data with the program Refmac  45.
A random set of 5% of reflections was set aside for cross-validation purposes. Model quality was assessed with internal modules of Coot  44  and using the Molprobity server  46. Figures representing structural renderings where generated with the PyMOL Molecular Graphics System For all statistics, n = 3 values were used to calculate the standard error of the mean. Values resulted from independent experiments. For all representative results, experiments were repeated at least two times and at least 20 images were collected for microscopy analyses.
Accession codes PcAA14A and PcAA14B sequences were deposited in GenBank under accession numbers KY769369 and KY769370, respectively. The X-ray structure of PcAA14B was deposited in the Protein Data Bank with accession 5NO7. Raw EPR data are available on request through the Research Data York (DOI: 10. 15124/8758d712-1e67-467e-b0f0-f0dd99f0232a).
Acknowledgements. We thank the  European Synchrotron Radiation Facility (Grenoble) , and the synchrotron Soleil ( Gif-sur-Yvette ) for beam time allocation and assistance. We thank  S. Tapin  for providing cellulose fibers,  E. Bonnin  and  J. Vigouroux  for compositional analyses,  G. Toriz  and  P. Gatenholm  for providing purified wood xylan,  L. Foucat  and  X. Falourd  for their valued assistance with treatments of the NMR data,  E. Perrin  for the excellent technical support for TEM images,  B. Seantier  for the access and assistance to AFM facilities,  D. Hartmann  and  E. Bertrand  for their help with enzyme production in bioreactor, and  D. Navarro  and  G. Anasontzis  for insightful discussions. MC was funded by a  Marie Curie International Outgoing Fellowship  within the 7 th European Community Framework Program (328162).
SL, MNR and JGB were funded by the  Microbio-E A* MIDEX  project ( ANR-11-IDEX-0001-02 ). This work was supported in part by the  CNRS  and the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01. NL and BH were supported by  Agence Française de l'Environnement et de la Maîtrise de l'Energie  ( 1201C102 ). PHW, GJD and LC thank the  UK Biotechnology and Biological Sciences Research Council  ( BB/L001926/1  and  BB/L021633/1 ) for funding. GJD is the  Royal Society Ken Murray Research Professor.
GPAT2 modulates the expression of snoRNA derived piRNAs and piRNAs related to breast cancer cell proliferation Although total piRNAs abundance did not change after GPAT2 silencing, an upper shift in read length distribution was observed (Figure  2A ). In SC cells, length distribution was bimodal, with peaks at 27 and 30 nt, whereas in SH cells, only one peak at 29 nt was obtained. Differential expression analysis revealed that of the 137 piRNAs identified in SC cells, 77 (56%) were differentially expressed after GPAT2 silencing (p≤0. 05, FC≥|1. 5|), with 38 upregulated (28%) and 39 downregulated (28%) (Figure  2B , Supplementary Table  2 ). Genomic features of deregulated piRNAs were annotated according to their sequences based on curated databases (piRNA bank, NCBI_Nucleotide and UCSC genome Browser).
05 when compared with the upregulated piRNAs. Moreover, piR-36011, a multiple copy downregulated piRNA, maps to the loci of the SNar genes (small NF90-associated RNAs). By constrast, none of the upregulated piRNAs is hosted in a SNOR or SNAR gene. Considering that it has been proposed that certain piRNAs are derived from snoRNAs precursors  [15] , and that piRNAs are tissue restricted, we evaluated whether there is a correlation in tissue distribution among the downregulated piRNAs and their hosted snoRNAs. Using DASHR database we analyzed the tissue profile of the piRNAs and the host snoRNAs that were available in the database. Unsupervised clustering based on Pearson correlation was assayed on the nine pairs of piRNA-snoRNA obtained from the search. In all cases an almost prefect correlation (~1) was observed, coincident with a co-expression pattern (Figure  2D ).
We considered the percentage of complementarity, the strand sense, the mRNA region mapped (5'UTR or 3'UTR), the total score provided by the NCBI Nucleotide Blast, the conservation among species, and not only mRNAs but also pseudogene transcripts and long ncRNAs (Supplementary Table  3 ). The number of targets varied considerably for each piRNA, ranging from no-hits to hundreds of mRNAs. After filtering, based on the mentioned criteria, we obtained a reduced list of targets (Supplementary Table  3 ). The functional enrichment of piRNA targets yielded terms mainly linked to lipid metabolism that included sphingolipid de novo biosynthesis, peroxisomal lipid metabolism and synthesis and interconversion of nucleotide di-and triphosphates, among others (Supplementary Table  3 ). The expression of the putative piRNA target ACSS3, a gene coding for acyl-CoA synthetase short-chain family member 3, was assessed by qPCR; as expected, ACSS3 gene expression decreased 90% in SH cells (Supplementary Figure  3 ).
0001) to the proliferation tRNAs subset (Figure  4A , Supplementary Table  6 ). Because tRNAs are the supply of amino acids to build proteins, we asked whether from the frequency of upregulated and downregulated tRF we could establish a protein profile with the aim of having a different approximation about which biological processes are being affected by GPAT2 silencing. We used the abundance of each species of tRF and defined its frequency based on the amino acid they carry (Supplementary Table  7 ). We then used the CompSite expasy database and obtained a list of scored putative proteins (Supplementary Table  7 ). Interestingly, functional enrichment of these proteins enabled us to identify the biological processes previously associated to GPAT2, such as phosphatidic acid biosynthesis, phospholipid acyl chain remodeling and regulation of cell death, among others (Figure  4B ).
GPAT2 silencing affects the expression of miRNAs targeting to genes related to cell growth and lipid metabolism In contrast to piRNAs and tRF, miRNAs abundance significantly decreased after GPAT2 silencing (Figure  1 , Supplementary Table  8 ). Unsupervised hierarchical clustering analysis of differentially expressed miRNAs demonstrated a clear segregation of SC and SH cells (Figure  5A ). Statistical analysis revealed 213 transcripts differentially expressed (109 upregulated & 104 downregulated) between the two cell line conditions (Supplementary Table  8 ). We choose miR-5100 and miR-34 to validate small RNAseq data. Semiquantitative RT-PCR experiment demonstrate that, as expected, pre-miR-5100 was upregulated whereas pre-miR-34 was downregulated in SH cells (Supplementary Figure  4 ). To predict putative targets, miRDB database was used and the 50 best ranked putative targets for each deregulated miRNA were selected (Supplementary Table  9 ).
By pivot tables (cross tabulations), the more relevant targets present in at least 5 miRNAs (>5%) were extracted (Supplementary Table  9 ). This means to select the genes that constitute targets for more than five miRNAs. Following this criterion, two lists of putative gene targets were obtained, one of 51 genes for the upregulated miRNAs, and the other of 109 genes for the downregulated miRNAs (Supplementary Table  9 ). To identify biological processes associated to miRNA targets, functional enrichment analysis using ENRICHR database was performed. Pathways analysis revealed specific terms associated to mitochondrial biogenesis and IGF1R signaling for genes associated to upregulated miRNAs, and oxidative metabolism of lipids and lipoproteins for genes associated to downregulated miRNAs (Figure  5B ).
GPAT2 modulates miRNAs associated with poor prognosis in breast cancer To determine whether deregulated miRNAs could have an impact on the survival of patients with breast cancer (BC), we performed an analysis using the YM500 database. According to YM500 there are 226 miRNAs differentially expressed between BC tumors (n=994) and normal breast (n=103) (Supplementary Table  10 ). We compared this group with the 213 deregulated miRNAs identified in our study. We used the normal approximation to the binomial distribution as previously described  [21]   to calculate whether the number of deregulated miRNAs derived from each cross-platform comparison was of statistical significance. We found sixty-five miRNAs common to both groups (pvalue≤0. 05, Figure  6A ). Of the 65 miRNAs, 45 are upregulated and 20 downregulated in YM500 BC tumors, while 36 and 29 are upregulated and downregulated, respectively, in the SH cells from our analysis.
miRNAs are the most studied ncRNAs in the context of cancer biology. In their processing, they markedly differ from piRNAs and tRNAs. Of the three classes of sncRNAs analyzed in this study, only miRNAs showed a significant variation in the total abundance of aligned reads, with a decrease in the SH cells, suggesting an impact in the overall production of miRNAs. Using bioinformatics analysis, we identify a set of potential targets for the upregulated and downregulated miRNAs. Target genes of miRNAs would be associated with processes linked to lipid biosynthesis, cell growth and proliferation. To evaluate if the deregulated miRNAs in the MDA-MB-231 cells might have a role in breast cancer, we used the YM500 database, which contains >8000 small RNA sequencing data sets, and integrated analysis results for various cancers miRNome studies.
We found a significant overlap between the miRNAs differentially expressed in the comparison normal breast vs breast cancer obtained from YM500 database, with those found affected by GPAT2 silencing in the present study. We also identified 9 miRNAs downregulated by GPAT2 silencing that are usually upregulated in breast cancer and are associated with a worse survival prognosis. Therefore, differentially expressed miRNAs identified here in SC vs SH cells show a similar pattern in normal vs breast cancer. On the other hand, within a cohort of PR-and ER-tumors miRNA expression was not correlated with overall survival. We speculated that there might be different reasons. First, the reduced number of cases with hormone receptor negative status and follow up data. Second, although there have been defined different molecular subtypes of hormone receptor negative tumors, particularly triple negative breast cancer tumors, they usually constitute a discrete breast cancer subgroup with a homogenous behavior in respect of the prognosis and overall survival.