dataesr/scientific-paragraphs-categorization huggingface dataset:
225, P = 0. 047; Supplementary Fig. SM1 ). Both methods detected some of the most characteristic giant kelp forest species in great numbers (i. e. , reads or biomass), including señorita (Oxyjulis californica), black surfperch (Embiotoca jacksoni), sheephead (Semicossyphus pulcher), blacksmith (Chromis punctipinnis), pile perch (Rhacochilus vacca), halfmoon (Medialuna californiensis) and garibaldi (Hypsypops rubicundus) (Fig. 2  and Supplementary Fig. SM1 , Supplementary Table  SM1 ). Eleven taxa that were not detected using eDNA included one that was discarded due to a small number of reads (Porichthys notatus), five taxa that had no sequence available in the reference database (Phanerodon furcatus, Anisotremus davidsonii, Alloclinus holder, Rhacochilus toxotes and Bothidae), and five taxa that eDNA failed to detect despite being visually detected and represented in the library (Supplementary Table  SM1 ).
Further investigation of sampling and methodological approaches to improve this relationship would be extremely valuable. The Santa Barbara Coastal Long Term Ecological Research program (SBC LTER) has a nearly 20-year time series of fish community data in the Santa Barbara Channel to compare to our snapshot of eDNA metabarcoding results. Interestingly, eDNA metabarcoding detected taxa never recorded since the time series began in 2000. For instance, we detected nine reads of great white shark at a single site (9: Carpinteria) where juvenile great white sharks are known to occur, are often recorded via acoustic tags and visual sightings, and were also detected in another study using a species-specific eDNA assay  55  , but are highly mobile, making their detection by divers unlikely. Barred surfperch and California lizardfish, species characteristic of adjacent sandy bottom habitats, were also not previously recorded by SBC LTER, and some species previously recorded by SBC LTER were only detected in this sampling event by eDNA metabarcoding, including the less common and highly mobile elasmobranchs leopard sharks and bat rays.
Organisms that were difficult to visually identify to species (e. g. Gibbonsia spp. ) were lumped into higher taxonomic categories and treated as a single taxon in our analyses. A complete description of the sampling methods, geographic and taxonomic coverage and links to the data and metadata are found in Reed  63. eDNA sampling, extraction and sequencing. Triplicate water samples were collected within an hour, simultaneously with UVC, from July 17th to July 25th at each transect survey in 2017 using a 1 L Nalgene polycarbonate bottle 1 m above bottom at the center of each transect. Within each site, we randomly selected all three replicates from one transect and one replicate at the remaining transects for eDNA analysis. In total, we analyzed 49 water samples, allowing us to hierarchically assess variability within transects, among transects within sites, and among sites.
A 10% PhiX spike was added. Primers complementary to the Fluidigm CS1 & CS2 oligomers were added to appropriate wells of the reagent cartridge to server as sequencing and index read primers. Base calling was done by Illumina Real Time Analysis (RTA) v1. 18. 54 and output of RTA was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v2. 18. 0. We also amplified two negative extraction controls and three PCR controls and sequenced them in parallel with the 49 samples. Also amplified on each sequencing plate was a set of three PCR no-template controls (NTCs) as well as extraction negative controls which were extracted alongside the environmental samples on the plate (1 × per plate corresponding to these samples). Bioinformatics. Illumina MiSeq sequences were processed using a bioinformatic pipeline adapted from the banzai pipeline  64  (https:// github.
com/ jimmy odonn ell/ banzai/ blob/ master/ banzai. sh), which links together bioinformatic programs through a shell script. Reads were merged through PEAR  65  , quality filtered through VSEARCH  66  , demultiplexed using awk, primers removed through cutadapt  67  , dereplicated, and clustered using swarm with d = 1  68. Taxonomy was assigned through blastn searches to NCBI GenBank's non-redundant nucleotide database (nt). Blast results were filtered using MEGAN6's lowest common ancestor (LCA) algorithm  69. Only hits with ≥ 85% sequence identity, ≥ 100 bitscore and whose bitscores were within the top 2% of the highest bitscore value for each Operational Taxonomic Unit (OTU) were considered by MEGAN6. The MEGAN6 parameter LCA percent was set to 85, allowing for up to 15% of top hits to be off target and still have the majority taxonomy assigned.
This parameter value was chosen to allow for minor numbers of incorrectly annotated GenBank entries-effectively allowing for OTUs which had many high-quality hits to a taxa to still be assigned to that taxa even if there existed a high-bitscore hit to another GenBank sequence annotated to an unrelated taxa. We decided this was more advantageous than the disadvantage caused by ignoring small numbers of true closely related sequences. Furthermore, post-MEGAN6 filtering was performed to ensure only contigs with a hit of ≥ 97% sequence identity and ≥ 300 bitscore were annotated to the species level. Only contigs with a hit of ≥ 95% sequence identity and ≥ 200 bitscore were annotated to the genus level. Annotations were elevated to the next highest taxonomic level for contigs that failed those conditions.
Our assumption is that the unassigned OTU is likely the result of sequence error or minor sequence variation within a species that makes it a worse match to the reference sequence. Here we are conservatively measuring diversity by merging these two OTUs and assigning their taxonomy to the most abundant one. This reduces our ability to detect a closely related species not found within our reference database but for the purpose of this study we considered annotation errors caused by sequence variation from a dominating organism were more detrimental in our dataset than losing sequence variation caused from detecting a mixture of species absent and present within our reference database. This leads us to conservatively estimate total diversity detected by our sequence data. We hierarchically merged at both the genus and family levels in the presence of unassigned OTUs.
All Bray-Curtis dissimilarities were based on the square root number of reads for eDNA and observed biomass for UVC of fish taxa. All analyses were performed using R version 3. 6. 0  76. Additive diversity partitioning was performed using the adipart function of the vegan package  77  and randomizations of the original data were carried out using the oecosimu function from vegan. Piecewise-linear relationships were build using the R package segmented  72. Power analysis of Mantel's test was performed using the function mantelPower from R package biotools  74. (1) γ eDNA = α BOT + β BOT + β TR + β SITE. (2) γ UVC = α TR + β TR + β SITE.
Acknowledgements We thank  J. Clint Nelson ,  Shannon H. Harrer  and numerous others that helped collect field data, and  Kristine Waltz  for performing the DNA extraction, PCR, and library preparation. We also thank  Paul Barber  and one anonymous reviewer for providing constructive recommendations. This work was supported by the  US. National Science Foundation 's  Long-Term Ecological Research program  under  Division of Ocean Sciences  Grant  9982105  and  0620276 , by  NSF Bio-Ocean  award  0962306 , and by the  National Aeronautics and Space Administration, Biodiversity and Ecological Forecasting Program  (Grant  NNX14AR62A  and  NNX14AP62A ), the  Bureau of Ocean Energy Management ,  Environmental Studies Program (BOEM Agreement   MC15AC00006 ), the  National Oceanic and Atmospheric Administration  in support of the  Santa Barbara Channel Marine Biodiversity Observation Network , and the  David and Lucile Packard Foundation (FPC).
Results DNA methylation at ITGA1, ITGA2, ITGA3, ITGA4, ITGA6, ITGA7, ITGA9, ITGB1, NID1, NID2, and DAG1 gene promoters in normal and malignant breast tissues and cell lines. Data for the integrin, nidogen, and dystroglycan genes 5′-cytosine-phosphate-guanine-3′ (CpG) islands assessed by methylation-sensitive restriction enzyme digestion PCR (MSRE-PCR) are summarized in Table  1. Based on the results of MSRE-PCR, the genes were classified into two categories: genes with CpG islands nonmethylated in normal breast tissues but prone to abnormal hypermethylation in BC (ITGA1, ITGA4, ITGA7, ITGA9, NID1, and NID2), and genes with CpG islands found to be nonmethylated in all breast tissues, no matter normal or cancerous (ITGA2, ITGA3, ITGA6, ITGB1, and DAG1). Breast cancer cell lines were analyzed alongside with the clinical samples in order to validate the results of our MSRE-PCR assays, and to provide reference information that can be reevaluated by other researchers.
By now, the ENCODE project 33 collection of data on DNA methylation obtained by reduced representation bisulfite sequencing (RRBS) contains information regarding two of the cell lines assessed in our study, MCF7 and T47D. For these, our MSRE-PCR results demonstrated in Table  1  recapitulate the ENCODE RRBS data for the ITGA1, ITGA4, ITGA9, NID1 and NID2 promoters approving the validity of our approach. ITGA7 gene promoter is not covered by the ENCODE RRBS. Nonmethylated status of the promoter CpG islands of the ITGA1, ITGA2, ITGA3, ITGA4, ITGA6, ITGA9, ITGB1, NID1, NID2 and DAG1 genes determined by MSRE-PCR in normal breast tissues in our study is in line with the ENCODE RRBS results for the normal breast tissue sample (BC_Breast_02-03015; breast, donor 02-03015, age 21, Caucasian, DNA extract).
ITGA7 and NID2 promoter regions were bisulfite sequenced in one normal sample and four tumor samples and the results confirmed MSRE-PCR findings in all cases. DNA methylation within a DAG1 gene intron. Alongside with the gene promoters' methylation information, our XmaI-RRBS results  34  provide information on the methylation status of CpG-rich fragments of an intron separating the two coding exons of DAG1, one of the genes of interest in this study. The results presented on Fig. 2  demonstrate overwhelming prevalence of methylated alleles in BC samples and BC cell lines, as well as in normal breast tissues, indicating no or negligible evolutionary advantage of this area hypomethylation for breast cancer cells. Of note, this region is not covered in the available ENCODE project RRBS dataset  33.
ITGA7 promoter CpG island was abnormally hypermethylated in 4% (6/156) samples in our study. Although The Cancer Genome Atlas data assessed by TCGA Wanderer 38 indicate that ITGA7 methylation at the region that we assessed tend to downregulate its expression, the number of hypermethylated samples is too small in our collection to draw any conclusions on the associations with clinical and molecular characteristics of the tumors. In the present study, the ITGA9 gene showed a high (39%) frequency of abnormal methylation in our collection of BC samples. This supports a previous report where in breast tumors, ITGA9 expression was downregulated or totally absent in 44% of cases and intact or upregulated in 45% of cases, and the frequency of ITGA9 promoter methylation was 90% in the former group; while treatment with the demethylating agent 5-aza-dC restored integrin α9 expression in ITGA9-negative cells of the MCF7 line  18.
Validation of MSRE-PCR results by RRBS. For the validation of MSRE-PCR results by RRBS, two RRBS datasets were used, one from the ENCODE project  33  , and another from our previous XmaI-RRBS study  34  performed on a subset of 64 BC samples, five BC cell lines and six normal breast samples matching the samples from the collection described here. XmaI-RRBS was performed as described by us earlier  40.
Statistical analysis. Statistical significance of associations between the methylated gene status and high HER2 expression was assessed by the χ 2 and Fisher's exact tests. Associations between the methylated gene status and genome-wide tumor methylotype were assessed by the Fisher's exact test. Multiple correspondence analysis (Fig. 1 ) was carried out with Multiple Correspondence Analysis (MCA) software STATISTICA Ver-sion10. Distribution of methylation beta values in respect to HER2 expression in tumor samples was illustrated (Fig. 3 ) using ggplot2 package for R language. Influence of the methylation status of exact CpG dinucleotides on gene expression in BC was assessed using The Cancer Genome Atlas (TCGA) datasets using TCGA Wanderer 38 at http://mapla b. imppc. org/wande rer.
Methods Cohorts of HGSOC patients. Institut Curie's ovarian cancer microarray data set used for transcriptomic analyses is freely accessible in the Gene Expression Omnibus under the accession number GSE26193. Ovarian tumors were obtained from a cohort of 107 patients treated at the Institut Curie between 1989 and 2005. Ovarian tumor samples have been analyzed on Human Genome U133 Plus 2. 0 microarrays (Affymetrix). Clinical features of the cohort have already been described in  29, 34, 41, 44. The project developed here is based on surgical tumor tissues, from the Institut Curie Hospital Group, that are available after histo-pathological analyses and not needed for diagnosis. There is no interference with the clinical practice. Analysis of tumor samples was performed according to the relevant national law providing protection to people taking part in the biomedical research.
Individual tumor volumes were calculated as (V) = a × b 2 /2 with a being the major and b the minor diameter. This was followed by tumor imaging twice a week, using Aixplorer and evaluation of the tumor area with SonicSoftware tool (see below). All protocols involving mice and animal housing were in accordance with institutional guidelines as proposed by the French Ethics Committee (CEEA-IC 118). Establishment and maintenance of PDX models at Institut Curie have been approved by the French ministry of Higher Education, Research and Innovation (agreement number #2163. 02). Moreover, projects using mouse models of breast and ovarian tumors from the "Stress and Cancer" lab headed by Dr. F. Mechta-Grigoriou have received authorization from the French ministry of Higher Education, Research and Innovation (agreement number #02300.
Stiffness maps measurement. The built in SonicSoftware tool (Supersonic Imagine, Aix en Provence, France) was used to perform stiffness measurements: from each cine loop recorded, we chose one representative B-mode/SWE image, based on limited signal from the gel, as it corresponds to noise. We drew the tumor contour using B-mode image. The software provided the minimum, maximum, mean and standard deviation of stiffness value inside this Region Of Interest (ROI). We thus determined the global stiffness of each tumor by averaging the mean stiffness values obtained with the software of over 3 SWE acquisitions performed for each tumor at each time point. Soft and stiff Mesenchymal tumors were imaged and removed either at a global mean stiffness ranging from 8. 5-22. 6 kPa (soft OV26) and 11. 8-20 kPa (soft OV21) or 54.
Stiffness maps analysis. To further analyze the stiffness distribution within the tumor area, we took advantage of the Quantitative DICOM software option (provided by the manufacturer) to retrieve the stiffness value in each pixel from each selected image. Indeed, this option allowed for each single acquisition frame exported in DICOM format the addition of the original stiffness map in a proprietary DICOM field. A Matlab dedicated software was programmed to retrieve from DICOM files both tumor contour (from B-mode image) and stiffness values of each pixel inside the corresponding area. Histograms of stiffness from pixels inside the ROI can be calculated then. This map also allowed calculating the distance of each pixel inside the ROI from the tumor barycenter and relating it to its stiffness value.
Collagen density was analyzed using Image J software  110. Pictures from Masson's trichrome stained tumor sections were taken at low magnification (1×), using the Image Management Software from Philips Digital Pathology in order to get the whole tumor section in one image. After color deconvolution and identical threshold set up for all pictures analyzed (one per tumor), we used the freehand tool to select the area of the tumor that we calibrated in square millimeter, and we measured Integrated Density (product of area and mean grey value i. e. average of grey values for all pixels within the area of selection) considered as collagen density. For Ki67, α-SMA and YAP IHC, we used a streptavidin-peroxidase protocol (Vectastain ABC kit; Vecto Labs, #PK-6101) and the Autostainer 480 Labvision (Thermoscientific), as previously described  34, 41, 44.
Leica HC FLUOTAR VISIR 25×/0. 95 W objective was used to excite and collect SHG. Signal was detected in epi-collection through a 405/15-nm bandpass filter, by NDD PMT detector (Leica Microsystems) with a constant voltage supply, at constant laser excitation power, allowing direct comparison of SHG intensity values. LAS software (Leica, Germany) was used for laser scanning control and image acquisition. Z-stack of 400 µm thickness and x-y mosaic reconstructions were captured and reconstructed by overlap stitching in a representative tumor. The collagen fibers structure was analyzed using homemade routines Image J software  110  on maximum intensity projected stacks. To assess collagen fiber length, freehand lines were drawn along at least 100 fibrils per condition. Fiber thicknesses were obtained by applying a threshold on projected z-stack ROI and drawing straight lines across at least 100 fibrils per condition.
Image J software  110  was used for densitometry analysis of immunoblots. RT-qPCR from PDX tumors. The soft (n = 6) and stiff (n = 6) OV26 PDX tumors removed from mice were snap-frozen in liquid nitrogen and store at -80 °C. As previously published by the "Stress and Cancer" lab headed by Dr. F. Mechta-Grigoriou  44  , miRNeasy kit (Qiagen, #217004) was used to extract RNA according to the Manufacturer's instructions. RNA concentrations were determined using a Nanodrop (Nanodrop Technologies). After reverse transcription of 1 μg of total RNA using the iScript Reverse Transcription Kit (Bio-Rad, #1708840), qPCR was performed using Power SYBR Green PCR Master Mix (Applied Biosystems, #4367659) on a Chromo4 Real-Time PCR detection System (Bio-rad) with primer concentration at 300 nM. All the primers were designed using the primerQuest tool (IDT).
Limiting dilutions of primer were performed to assess their efficiency and we only used those with 95 to 100% efficacy. Data were analyzed using an Opticon Monitor (Bio-Rad) and normalized to Cyclophilin A mRNA. The primer sequences used were as follow (forward and reverse): CYR61: 5′-GAG TGG GTC TGT GAC GAG GAT-3′; 5′-GGT TGT ATA GGA TGC GAG GCT-3′. CTGF: 5′-AGG AGT GGG TGT GTG ACG A-3′; 5′-CCA GGC AGT TGG CTC TAA TC-3′. ANXA3 5′-GAA CCG AAT AAT GGT GTC CAG-3′; 5′-GTA TGA GAA GAA GTA AGG TGGA-3′. ANKRD1 5′-AGT AGA GGA ACT GGT CAC TGG-3′; 5′-TGG GCT AGA AGT GTC TTC AGAT-3′.
Microarrays. In order to classify HGSOC PDX models, including the models OV26, OV21 and OV33 used in our study, as Mesenchymal or Non-Mesenchymal, transcriptomic data from 13 PDX were combined with transcriptomic data from Curie cohort. As the two datasets were coming from two different experiments and two different platforms (Affymetrix Human Genome U133 Plus 2. 0 array for patient tumors and Affymetrix Human Exon 1. 0 ST array for PDX tumors), standardization per array was applied (for each gene, mean equal to zero and standard deviation equal to 1) in order to avoid technical artifacts. A hierarchical clustering using Euclidean distance and Complete agglomeration method was performed on 36 genes, correlated and anti-correlated with the miR-200a, defining Mesenchymal and Non-Mesenchymal tumors.
Acknowledgements We thank all members of F. M-G's laboratory for fruitful discussions and  A. Peltier  for critical reading of the manuscript. We are grateful to  R. Leclere ,  E. Martel  and  A. Nicolas  from the experimental pathology platform (IC),  David Gentien  from  Genomic platform (IC) ,  C. Marchiol  and  I. Lagoutte  from the  Life Imaging Facility (Institut Cochin ), and  Fabienne Regnier  from  Institut Cochin  for their help and advice. C. G. was supported by funding from the  French Research Ministry  and from the  Foundation ARC (Association pour la Recherche sur le Cancer ). F. M-G and V. M. are supported by  Institut National de la Santé et de la Recherche Médicale (Inserm). Y. K. was funded by the  Foundation pour la Recherche Médicale (FRM) ,  SIRIC (INCa-DGOS-4654 ) and  Institut National du Cancer INCa  ( INCa-DGOS-9963 ).
The experimental work was supported by grants from  Ligue Nationale Contre le Cancer (Labelisation) ,  Inserm ,  Institut Curie  ( PIC TME/T-MEGA  and  PIC3i CAFi), ICGex  ( ANR-10-EQPX-03 ),  SIRIC (INCa-DGOS-4654) ,  INCa  ( STROMAE INCa-DGOS-9963 ,  INCa - 2016-166 ,  CaLYS INCa-11692, INCa-DGOS-Inserm-12554 ) and the  Fondation "Chercher et Trouver ". SHG images acquisition and analysis were performed at the  IMAG'IC Facility , supported by the  National Infrastructure France BioImaging  (grant  ANR-10-INBS-04 ) and IBISA consortium. In vivo imaging was performed at the  Life Imaging Facility of Paris Descartes University (Plateforme Imageries du Vivant-PIV ), supported by  France Life Imaging  (grant  ANR-11-INBS-0006 )  Région Ile-de-France , and IBISA consortium. F. M-G acknowledge both the  French Pink Ribbon Association «Le cancer du sein , Parlons-en» and the  Simone and Cino del Duca Foundation  for attribution of their "Grand Prix".
The NMR spectrum was then recorded directly; however, no reaction occurred (data not shown), also in line with Wong's observations. [24] he reaction of 1 with AgPhCC was further challenged in different solvents. In the case of DMSO, the reaction was carried out directly in the NMR tube at lower scale under similar conditions, that is, 3 equiv. of AgPhCC, 50 °C, 24 h. In this case, the reaction yield was estimated by 1 H NMR spectroscopy using an internal standard (see Experimental Section for details). The obtained yields of product P formation after purification are reported in Table  1 , and show that DMSO is the most favourable solvent for the cross-coupling reaction. Acetonitrile was also found to be suitable, with the 2 nd highest yield of product P formation (63 %), whilst only traces of the product could be observed when using DMF or water.
Full geometry optimizations were performed in the acetone solvent, implicitly reproduced by the polarisable continuum model (PCM). [33] Transition state structures were found by the synchronous transit guided quasi-Newton method. [34] Vibration frequency calculations, within the harmonic approximation, were performed to check that each optimized geometry corresponded to a minimum or to a first-order saddle point (for transition state structures) in the potential energy surface, and to evaluate their standard Gibbs free energy values at 298. 15 K. All calculations were performed by the Gaussian 09 program package. [35] Ray diffraction data: R1, P3a and P3b co-crystallized by layering n-pentane on a saturated solution of R1 in DCM. Data were collected on a Bruker D8 Venture single-crystal X-ray diffractometer equipped with a CMOS detector (Bruker Photon-100), a TXS rotating anode with Mo Kα radiation (λ = 0.