dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  Université de Rennes 1, CNRS ,  Rennes Métropole and Région Bretagne  for financial support. The  CRMPO of the Institut des Sciences Chimiques de Rennes  for mass measurement, the  Centre de Diffractométrie  X for X-ray analyses and  FEDER  funds for acquisition of single-crystal equipment are gratefully acknowledged. Part of this work has been performed using the  PRISM core facility  ( Biogenouest©, UMS Biosit, Université de Rennes.
Processing of sequences was performed using the DADA2 pipeline (version 1. 10)  (Callahan et al. , 2016)  in R (version 3. 4. 2) with following parameters: trimLeft=c(19, 20), truncLen=c(240, 200), maxN=0, maxEE=c(2, 2), truncQ=2. Briefly, the pipeline combines the following steps: filtering and trimming, dereplication, sample inference, chimera identification, and merging of paired-end reads. It provides exact amplicon sequence variants (ASVs) from sequencing data with one nucleotide difference instead of building operational taxonomic units (OTUs) based on sequence similarity. ASVs were assigned against SILVA release 132 database  (Quast et al. , 2013). Singletons and sequences assigned to chloroplast and mitochondria were removed prior to subsequent analyses. Sequences were submitted to NCBI under the accession number PRJNA630555.
Thus, we decided to consider this initial DOC excess (DOC(t0)-C-glucose(t0)) as a nonreactive pool and subtract it from the concentrations at the beginning and end of each incubation. This is the most conservative way to calculate the %DOC released as DOM by the strains, since, alternatively, the strains could have taken up some of this extra DOC and then released a fraction of it back as bacterial DOM. Prokaryotic growth efficiency (PGE) in the biodegradation experiments was calculated as the increase in cell biomass between t0 and tf divided by the decrease in DOC between t0 and tf in the incubations using this formula: PGE (%) = ∆*+,-. // (0-,1 2 -4 ) ∆567 (0-,1 2 -4 ) 1 x100 (2) using a general cell to biomass conversion factor of 20 fg cell -1  (Watson et al. , 1977 ). Differences in HP abundance between treatments in the biodegradation experiments were tested using repeated measures ANOVA and post-hoc pairwise t-tests (package "rstatix" in R).
Differences in prokaryotic community structure were tested by ANOSIM tests (package "vegan" in R). 1. Dissolved organic carbon (DOC, µmol L -1 , dark grey columns) and glucose-C (µmol C L -1 , light grey columns) at the beginning (t0) and end (tf) of the growth experiments of each strain. Whiskers: Standard deviation; p1, p2: P. angustum s1, s2: S. alaskensis as described on Table  2. Table  3. Initial and final values of dissolved organic carbon (DOC) concentration (µmol L -1 ), ∆DOC per time (µmol L -1 h -1 ), ∆DOC/DOCt0 (%), ∆Biomass/DOC (%) and DOM fluorescence (protein-like peak T, humic-like peaks A, C and M, Raman Units) in all biodegradation experiments; tf: mean ± SD of biological triplicates   Supplementary Table  1. Inorganic nutrients (phosphate, nitrate+nitrite, ammonium µmol L -1 ) at the onset (t0) and end (tf) of all strain incubations and biodegradation experiments.
Acknowledgements This work was supported by the  Caramba  project (European commission,  H2020-MSCA-IF-2015-703991 ). Flow cytometric analyses were performed at the BioPic cytometry and imaging platform ( Sorbonne University/CNRS ). DNA extractions and PCR amplifications were conducted at the  Bio2Mar platform (Sorbonne University/CNRS ). Dr. Yan Liu  kindly provided assistance in PCR and DNA cleaning steps as well as in sequence processing. The 16s samples were sequenced in the INRAE platform. We thank the crew of  R/V 'Nereis II'  and the technicians of the Banyuls observation service for their assistance in getting Mediterranean Sea samples for the biodegradation experiments.
9 µm 2 ± 2. 4). While at high magnification, we found menopausal tissue to be composed of smaller pore numbers occupying greater areas than at reproductive age (1054 ± 470); a similar observation at lower magnification revealed tighter network organization at menopause (0. 32 µm 2 ± 0. 15). These pore-spacing differences between age groups may play a role in permeability change with age. Boxplots display 25th and 75th percentile, median (line), and the whiskers extending to the last data point not considered outlier. Fiber and pore analyses were conducted using DiameterJ. One-way ANOVA (post hoc: Tukey) was used to compare pore area. Differences in fiber morphology and pore number were analyzed by Kruskal-Wallis test (post hoc: Dunn) (*p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001).
Patients enrolled in this study were carefully selected to avoid any possible bias or heterogeneity related to non-synchronous cycle phases between reproductiveage patients or use of hormone replacement therapy after menopause. Therefore, only fertile patients under ovarian contraceptive treatment and menopausal patients not taking hormonal replacement therapy were included in this study. Fiber and pore characterization. SEM images were analyzed using the Diameter J plugin  62  of ImageJ software V1. 48 (Wayne Rasband, NIH, USA) to measure fiber thickness as well as pore space and number at ×5000 and ×20,000 magnification. Briefly, all images were converted to binary images (black and white pixels only) and segmented into background and foreground, according to 1 of the 24 segmentation methods generated by Diameter J  62.
Segmented pictures contain only black and white pixels, with black pixels representing the background and white pixels representing fibers. After visual inspection of all generated binary pictures, only those that closely resembled to the original SEM image were manually selected for further analyses in batch mode. Fiber thickness (diameter) was calculated using super pixel determination  62  , and mean fiber diameter was calculated by fitting a Gaussian Curve to the radius data and finding the curve's mean value. Pores were considered as black pixel clusters. The algorithm counts the number of pixels in each cluster not touching the picture frame edges and then reports their area, which equates to pore area. Mean pore area was calculated by averaging all cluster areas. Scanning electron microscopy.
Sirius Red staining. Five-µm-thick paraffin sections were deparaffinized, rehydrated, and treated with a saturated picric acid solution containing 1. 3% Sirius Red for 1 h at room temperature for collagen staining. Mounted slides were digitized at ×20 magnification by automated whole-slide image capture using a Mirax digital slide scanner (3DHISTECH Ltd, Budapest, Hungary). Images were acquired at the following settings: excitation length, 450 nm; emission length, 550 nm; and exposure time, 100 ms. Orientation analysis. Three to five regions per slide were selected from Sirius Red scans for fiber orientation analysis, using CaseViewer V2. 4 (3DHISTECH Ltd, Budapest, Hungary). CT-FIRE V2. 0 Beta  56  Matlab-based open source software was used for fiber tracking, segmentation, and orientation analysis.
The software combines curvelet transform (CT), a fast discrete preprocessing mechanism that de-noises the image and enhances fiber edges, and fiber tracking (FIRE), an algorithm to extract single fibers from fiber networks and measure individual fiber metrics. The fiber angle in CT-FIRE is defined as the angle between the line linking two endpoints of a fiber and the positive horizontal direction or positive X axis (Supplementary Fig. S1  and Table  1 ). To calculate average fiber orientation and its confidence interval, and visualize data on semi-polar plots, we used the Matlab toolbox CircHist  63. To investigate fiber organization and remodeling around primordial, primary, and secondary follicles at prepuberty and reproductive age, follicles were selected from ×40 magnified images and their borders were delineated manually and selected as regions of interest (ROIs).
CurvAlign V4. 0 Beta 56 was applied to study fiber orientation relative to ROI borders (Table  1 ). To visualize data on fiber orientation against follicle borders, we used the Polarplot function on Matlab 2019a (MathWork, Eindhoven, the Netherlands). Fiber straightness analysis. Fiber straightness is defined as the ratio of fiber Feret length divided by its geodesic length, and its value ranges from 0 to 1 (Table  1 ). A perfectly straight fiber would have a straightness value of one and increasing complexity (non-straightness) as the value approaches zero. This parameter was quantified using the CT-FIRE software on Sirius Red-stained slides. Sample preparation for AFM. After thawing, cryopreserved ovarian biopsies were immediately embedded in optimal cutting temperature compound (OCT Tissue-Tek, VWR International, Edmonton, Canada) and snap-frozen in liquid nitrogen, without prior labeling, fixation, or dehydration.
Stiffness data were projected onto topographical maps using Matlab. To measure the viscoelasticity of ovarian tissue, we conducted repeated and successive long indentation cycles, followed by partial force release. For indentation portion, force was kept constant, while for the release portion, deformation was constant, allowing us to monitor the evolution of both deformation and force. The viscoelastic measurement (force spectroscopy) immediately followed elastic measurement (force mapping scan) and was performed in two points of the same tested region according to a force ramp design loop of ~240 s duration: cantilever extension with force increase (4-µm indention, 100 ms); constant height maintenance at the contact point (20 s); cantilever retraction and force decrease (0. 8 µm, 20 ms); and finally constant force maintenance (20 s).
S3 ). We observed that our experimental data are best fitted by two different relaxation times, which could be described by the generalized Kelvin-Voigt model. The double exponential fit modeled our data better than a single exponential  64  , suggesting the presence of at least two processes behind the viscoelastic response. For more details on the model fitting, please consult the Matlab scripts at https://github. com/inatamara/AFManalysisMatlab 70. Topography measurement. Stereo pairs of SEM images of the same tissue region at ×12,000 magnification were created by tilting samples by +5°along the X axis (Fig. 7D ). By rotating the stage between two image takes, we ensured that a significant feature of the sample was always positioned at the same point and that contrast and intensity between images were similar in order to facilitate point matching and guarantee eucentricity.
Mountains SEM software 8 (Digital Surf, Besançon, France) was used to enable 3D stereoscopic reconstruction and characterize surface roughness by calculating the arithmetic average of 3D roughness (Sa) according to calculation conditions defined in ISO25178. In case of a highly uneven surface, Mountains SEM filters were used to level and normalize surface data prior to analysis. Statistical analysis. Statistical analysis was conducted using the GraphPad Prism software package, version 8. 0 (San Diego, USA). Descriptive analyses of values (D'Agostino-Pearson or Shapiro-Wilk normality tests) were performed before each evaluation to select the appropriate statistical test. Kruskal-Wallis test followed by Dunn's multiple comparison correction were used to compare ovarian tissue differences between age groups, including fiber thickness, pore number, elasticity, and viscoelasticity.
One-way analysis of variance followed by Tukey's post hoc multiple comparisons were applied to compare fiber straightness, pore area, surface roughness (Sa), and angle frequency of fiber orientation around different follicle stages within the same age group. Differences in fiber remodeling around follicles at the same follicle stage between prepubertal and reproductive-age tissue were determined by comparing their angle frequencies using the paired t test. Circular statistics  71  were used to compare global collagen fiber orientation at prepuberty, reproductive age, and menopause. The Rayleigh test was applied using the Matlab CircHist toolbox  63  to assess whether fibers were uniformly oriented from 0°to 180°w ithin each age group or had a common mean direction  72. Watson's parametric multiple sample test and Kuiper's test were carried out using the circular statistics toolbox from Matlab  73  to successively compare mean directions and directional distributions between study groups.
Acknowledgements The authors thank  Corry Charlier  ( Electron Microscopy Department, University of Namur ) for his technical assistance during SEM image acquisition and  François Petiteau  ( Digital Surf ) for his help with the Mountains SEM software. We also thank  Vincent Fleury  for moderating the established collaboration to conduct AFM analyses and  Didier Vertommen  for his critical review of the manuscript from a biochemical perspective. We greatly thank  Yuming Liu  for sharing his expertise on using CT-FIRE. We are also very grateful to  Mira Hryniuk , BA, for reviewing the English language of the manuscript. This study was supported by grants from the  Fonds National de la Recherche Scientifique de Belgique (FNRS)  (C. A. A. is an FRS-FNRS Research Associate; grant MIS # F4535 16  awarded to C.
Study subjects Newly diagnosed women with TNBC were identified at the department of Obstetrics and Gynecology of Mohammed V Military Teaching Hospital in Rabat. TNBC patients were chosen according to the following criteria: age at diagnosis ≤ 41 years without family history or belonging to a family history. This family history of cancer was defined as diagnosed BC or OC in first-and second-degree relatives. Clinico-pathological data of TNBC, including tumor site, histological type and grade and TNM classification were collected at BC diagnosis. A total of 30 early onset TNBC patients were enrolled between January 2020 and June 2020, including 18 (60%) without a family history and 12 (40%) with a family history. Informed consent was obtained from all participants at the time of peripheral blood draw.
Bioinformatics Analysis De-multiplexing was performed using bcl2fastq2 Conversion Software (Illumina). Sequencing reads were aligned to the University of California Santa Cruz hg19 reference genome assembly by Burrows-Wheeler Aligner  [19]. Recalibration of base quality scores (BaseRecalibrator) and realignment (Realigner-TargetCreator, IndelRealigner) were carried out using Genome Analysis Toolkit (GATK) and PICARD tools, as recommended by Eurogentest guidelines  [20]. A variant calling was made using GATK Haplotype-Caller and annotated using Ensemble Variant Effect Predictor  [21]. Variants were filtered for quality score ≥ 30, depth ≥ 30x, and presence in ≥ 20% of reads.
Interpretation of variants An Interpretation of variants was conducted by following the classification system recommended by the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) Standards and Guideline for the Interpretation of Sequence Variants  [22]. The process can result in 1 of 5 classifications: benign, likely benign, unknown/uncertain significance (VUS), likely pathogenic, and pathogenic. Likely benign and benign variants were not clinically reported. All classifications were ultimately evaluated by AL, MC and referred to NU. The clinical significance of each sequence variant was also based on a set of criteria such as allele frequency as well as the information from clinical genome databases including ClinVar (https://www. ncbi. nlm. nih.
In silico prediction A potential clinical effect of VUS was evaluated by the analysis of the severity of the amino acid changes and their conservation across species. These analyses were performed using the Mutation Taster through Alamut® Visual v. 2. 11. 0 including Alignment-Grantham variation Grantham deviation (Align GVGD; http://agvgd. iarc. fr/agvgd_input. php), Polymorphism Phenotyping-2 (Poly-Phen-2; http://genetics. bwh. harvard. edu/pph2/), and Sorting Intolerant from Tolerant (SIFT; http://blocks. fhcrc. org/sift/SIFT. html) scores. The Alamut interactive software provides results and/or links to the following databases used for variant annotation: Exome Aggregation Consortium (ExAC), Genome Aggregation Database (gnomAD: https://gnomad. broadinstitute. org/), Database of Short Genetic Variation (dbSNP) and ClinVar.
Our panel sequencing allowed identification of several sequence variations in most investigated genes, among which we found novel variation in PALB2 and BARD1 genes. The PALB2 c. 3290dup (p. Lys1098) variant has not been reported before in the BIC database. The PALB2 c. 3290dup variant was detected in a young patient diagnosed with TNBC at an age ≤ 36 years. Family history was negative in the PV carrier. Thus, we concluded that this PV is not linked to family history of BC and/or OC. The PALB2 c. 3290dup variant is a frame-shift variation due to the insertion of C nucleotide at acid 3290 of codon 1097 in exon 12, which is predicted to lead to a premature stop codon 1098 and a truncated protein. The BARD1 c. 1333G>T (p. Glu445), that has not been reported previously, is classified as pathogenic (Class 5).
Transcriptome Assembly MGX provided raw reads cleaned by removing 3 adaptors and poly-A/T tails. Data were further processed using Galaxy Version 2. 8. 3. FastQC was run on each sample and lowquality reads (<30) and sequences shorter than 20 bp were trimmed using Trimmomatic  (Bolger et al. , 2014). Ribosomal RNAs were removed using Ribopicker  (Schmieder et al. , 2011). A reference transcriptome was assembled with all the data generated in this study using Trinity  (Grabherr et al. , 2011)  with default parameters. After reconstruction, the transcriptome was filtered using two criteria: (1) transcripts with no read support (expected count < 1) were identified using Bowtie (v1. 0. 0)  (Langmead et al. , 2009)  and discarded, (2) CD-HIT-EST was used to remove transcript redundancy with a similarity threshold of 0.
90 and a word size of 8. Open reading frames (ORFs) encoding proteins of more than 50 amino acids were predicted by TransDecoder  (Haas et al. , 2013). We used the Benchmarking Universal Single-Copy Orthologs tool (BUSCO v3. 0. 2)  (Simão et al. , 2015)  against the Insecta odb10 dataset (1,367 reference genes) to assess the completeness of the assembled transcriptome.
Identification of Chemosensory Genes To identify putative chemosensory genes (ORs, GRs, IRs, OBPs, CSPs, and SNMPs), we used amino acid sequences of proteins previously annotated in S. littoralis and the related species S. frugiperda and S. litura  (Legeai et al. , 2011; Jacquin-Joly et al. , 2012; Poivet et al. , 2013; Cheng et al. , 2017; Gouin et al. , 2017; Walker et al. , 2019)  as queries in a blastp search on the translated ORFs predicted from the reference transcriptome. Annotations of all retrieved sequences were confirmed by blastp on the NCBI non-redundant database (NR). Sequences described for the first time in S. littoralis were named according to their ortholog in S. frugiperda  (Gouin et al. , 2017) , except IRs that were named following orthology with S. litura  (Zhu et al.
Transcriptome Description The de novo transcriptome presented in this paper was generated from four libraries: proboscis from each sex, labial palps and forelegs from males, and labial palps, forelegs and ovipositors from females. A total of 418 million raw reads were obtained and deposited at the NCBI Sequence Read Archive (BioProject ID PRJNA693435, Biosamples SAMN17386877, SAMN17386878, SAMN17386879, SAMN17386880). After cleaning, a total of 325 million reads were pooled and assembled to generate a de novo transcriptome of 234,643 transcripts. Removing transcripts with no read support led to an assembly of 203,642 transcripts. Last, CD-HIT-EST was applied to remove transcript redundancy, leading to a final assembly of 154,302 transcripts. Out of the transcripts, 47,853 ORFs could be identified.
ACKNOWLEDGMENTS We thank the  BioInformatics Platform for Agroecosystems Anthropods (BIPAA) ,  Rennes, France , for assistance on Galaxy transcriptomic data analysis. We also thank  Pascal Roskam  (iEES) for insect rearing. We acknowledge the  French National Research Agency  ( ANR-16-CE02-0003-01  and  ANR-16-CE21-0002-01  grants) and the  SPE department at INRAE  for funding.
Fabrication and Characterization of the Basic PCL Scaffold Our first objective was to produce a flat mat of partially aligned PCL fibers. A mediumrange drum rotation speed of approximately 1000 rpm was thus applied during the electrospinning process. SEM images (Figure  1a ) of the resulting PCL matrices showed smooth fibers with an average diameter of 776 ± 250 nm (Figure  1b ), and a preferred orientation between -20°and 20°for 70% of them (Figure  1c, d ). The wettability of the materials was assessed by the contact angle at each stage in the preparation for cell culture. First, the dry PCL electrospun sheet showed a contact angle (θ) of 130 ± 2. 5°, corresponding to hydrophobic properties. After 45 min of ethanol treatment, the angle was about 115 ± 2. 72°, the drop on the dry PCL scaffold retaining its shape with no change over time (data not shown).
PEG-diacrylate (MW 575) was purchased from Sigma-Aldrich. For the UV photo-crosslinking process, the liquid PEG was mixed with 1% w/v of photo-initiator (2-hydroxy-2-methylpropiophenone, Darocur 1173, Sigma Aldrich). Then the mixture was dropped on electrospun scaffolds by spin coating (SPINCOATER model P6700) and exposed to a UV light source for 20 s (Kloé UV-KUB 2, 365 nm, 40 mW/cm 2 ) through a photomask. The patterned scaffold was washed carefully in the dark with distilled water to remove the PEG precursor solution. The topography of the electrospun scaffolds was observed using environmental scanning electron microscopy (Philips XL30 ESEM-FEG). Fiber diameter was measured after setting up the scale bar. Average fiber diameters (n = 100 fibers) were analyzed with ImageJ software (NIH, Bethesda, Maryland).
The isotropy value of the scaffold (n = 6) was analyzed using Mountain™ version 8. 0 software (with smoothing and maintaining the default frequency thresholds at 5% and 80% Str ISO 25178) and the main orientations of the fibers were analyzed using the Fourier Transformation method (n = 6). Gold deposits over the electrospun scaffold were investigated using Energy Dispersive X-ray Spectroscopy (EDS) analysis with the detector present in the microscope. The measurement is based on the energy and intensity distribution of X-ray signals produced by the electron beam striking the surface of the target scaffold. Samples for transmission electron microscope (TEM) analysis were prepared by dropping a dilute suspension of gold nanoparticles on to copper grids covered by a carbon film. Grids were observed with a Philips Tecnai 12 transmission electron microscope.
Myotube Measurement The influence of the biomaterials on myoblast differentiation, myotube length, orientation and total area occupied was measured using ImageJ (NIH, Bethesda, MD, USA) and Cell ProfilerTM (Broad Institute, Cambridge, MA, USA) software  [65]. Myotube length was defined as the line distance from one extremity of the myotube to the other. The total area occupied by differentiated cells was counted in 3-5 pictures, selecting an area of 2 mm × 2 mm for each sample. The percentage of cell alignment was defined based on the measurement of myotubes (n = 100) aligning with ± 20°from the pattern.
Statistical Analysis Statistical analysis and graph drawing were carried out using GraphPad Instat 3. 10 and Prism v 6. 0 (GraphPad Software, San Diego, CA, USA). All data are represented as mean ± standard deviation from at least three independent cultures (n ≥ 3). Group comparisons were performed using the Mann-Whitney nonparametric two-tailed test and the Kruskal-Wallis nonparametric test with Dunn's multiple comparisons post-test. Significance is indicated on the graph by * p < 0. 05; ** p < 0. 01; and *** p < 0. 001.