dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar studies only provide the global percent of reads mapping back to the reads  [6, [46] [47] [48] [49] [50] [51] -which, in our case, indicate that the assemblies are well supported by read data. To assess the recovery of genes, we chose to perform a BLASTX alignment analysis against the SwissProt database and finally selected sequences that represent nearly full-length proteins to make our reference Genespace, that is contigs that cover more than 70% proteinlength of a homologous protein from SwissProt database. With these supplementary selective conditions, we restricted ourselves to a narrow annotatability of the reference Unigene and we may have missed out some well assembled sequences or biased our reference Unigene towards well-conserved sequences between species. However, according to our goal that was to develop molecular resources with a high level of reliability, this choice was appropriate to prevent from keeping sequences with assembly errors  [52, 24].
Despite filtering steps and due to the database selected for BLASTX alignments, well-assembled contaminant sequences were identified in the 10,060 sequences firstly selected in the Unigene and were subsequently removed to obtain a cleaned reference set of 8, 640 coding sequences. The BUSCO results indicated that our cleaned reference Unigene is partly complete. Indeed, the level of completeness of our Unigene reached 55% of the BUSCO Embryophyta data set. This result is lower than that obtained from the whole genome sequencing of lavender for which 92% of completeness was achieved  [23]. However, our reference is of good quality because most of genes found are complete and in single copy. Only 8% of genes are fragmented or duplicated  [28]. Moreover, the number of complete genes in single copy is higher in our Unigene (729 genes) compared to the complete genome (592 gene).
Table) from leaves, roots and flower buds of 'Maillette'. After trimming on quality, length, ambiguous nucleotide and adapters, a total of 144,322,837 reads totaling 19. 7 Gb (77% of initial dataset) remained for de novo transcriptome assembly with a mean PHRED score per read around 40. Depending on the genotype, the number of sequenced paired-end DNA reads ranged from 29,985,684 (4,527,838,284 bp) to 145,227,504 (21,929,353,104 bp) (S2 Table.
Acknowledgments We would like to thank  Vegepolys Innovation , more specifically  Fabienne Mathis , for their contribution in raising the  GenoParfum  project. We thank all the EPGV group, with special thanks to  Remi Bounon  and  Isabelle Le Clainch  for libraries construction and to  Elodie Marquand  for data management. For supporting INRA-EPGV group for QC of DNA and Illumina high throughput sequencing, thanks to  Anne Boland ,  Marie-Therèse Bihoreau  and their staff from the  CEA-IG/CNG. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing and storage resources. We would like to thank all our colleagues from  INRA (Angers, Orleans, Orsay, Rennes, Versailles ) for the enriching and constructive exchanges around this project, especially  Patricia Faivre-Rampant  for reviewing the article.
37 3. 18 https://doi. org/10. 1371/journal. pone. 0243853. t001duplicatedand 23 (0. 2%) were fragmented (S2 Fig). The reference Unigene was annotated with various pipelines and softwares to retrieve gene ontology (Trinotate, TRAPID), KEGG pathways (Trinotate, KAAS), gene families (TRAPID) and additional functional annotation (Trinotate pipeline). Detailed results from these softwares are summarized in S3 Table. Of the 10,060 sequence of the reference Unigene, 9,816 (97. 6%) had a BLASTP hit against Swissprot database, 8,928 (88. 7%) sequences had at least one GO annotation, 4,931(49%) sequences had at least one KEGG pathway annotation and 4,647 (46%) sequences had a KEGG and a GO.
Live imaging Embryos were prepared for live imaging as previously described  57  and movies were acquired at room temperature (22°C) at stage 7-8 for ectodermal cells during GBE and at stage 13-14 for amnioserosa cells during DC. All live imaging has been performed using a dual camera (QImaging, Rolera EM-C 2 , EMCCD) spinning disc (CSU-X1, Yokogawa) on a Nikon Eclipse Ti inverted microscope (distributed by Roper) managed by the MetaMorph software. Dual color imaging of eGFP and mCherry/mKate2 FPs was obtained by simultaneously exciting fluorophores with a 491 nm and a 561 nm laser and using a dichroic mirror to collect emission signals on two cameras. We used the following Nikon objectives : 10X/N. A. 0. 25 dry, 40X/N. A. 1. 25 water and a 100X/N. A. 1. 4 oil. For the 100X movies, we focused on the most apical part of epithelial cells, performing Z-series of 1 to 6 planes separated by 0.
Automatic cell segmentation To measure fluorescence intensities in the medio-apical cortex, we first designed an automated cell segmentation procedure based on watershed algorithms (Supplementary Fig. 1b ). To achieve this, we used the eGFP::UtrCH signal from the lower planes of our Z-series (AJs planes) and reduce the images size (x 0. 33) to speed up the procedure. We then run the DIPimage watershed algorithm (retrieved from http://www. diplib. org/dipimage) and made a custom Matlab/ImageJ code to select and track segmented cells over time. If required (especially for the 40x movies), we manually corrected the segmentation results using the Tissue Analyzer ImageJ plugin (B. Aigouy, retrieved from https://grr. gredclermont. fr/labmirouse/software/WebPA)  58. The Tissue Analyzer plugin was also used in Fig.
Measuring the level of pulsed contractility During this study we repeatedly assessed the level of pulsed contractility by comparing the medial actomyosin pulsatility and the apical cell area fluctuations (Fig. 2f  ; 3d,g ; 5c,d,g ; 6e). To do so, we measured the standard deviations (SDs) of individual cell profiles after normalizing data to the mean and removing the low frequency components using an IIR Butterworth high-pass filter (Matlab signal processing toolbox). We choose a cutoff frequency (1/300s for ectodermal cells during GBE and 1/600s for amnioserosa cells during DC) that we multiply by 0. 75 or 1. 25 to respectively define the stop and the pass band frequency (Supplementary Fig. 1d ). The high-pass filter allowed us to isolate the effect of pulsed contractility by discarding the contribution of slower variations of the apical surface.
Quantifying the pulsatile Rho1 activity To quantify the levels of Rho1 activity during pulses we measured the amplitude of AniRBD::eGFP accumulations during contractile events (Rho1GTP sensor) (Fig. 5h-j ). However, since we were unable to segment cells using the AniRBD::eGFP signal, we designed a live pulse tracking procedure working directly on unsegmented images (Supplementary Fig. ??). To do so, we used a Matlab implementation of the DBSCAN clustering algorithm (S. Mostapha Kalami Heris, retrieved from http://yarpiz. com/255/ypml110-dbscan-clustering) integrated to a custom code for automatization. This method allowed us to isolate clusters of AniRBD::eGFP signal based on two main parameters : the distance ε and the minimum number of points that must be within an ε radius for these points to be considered as a cluster.
KLT / pulse analysis To quantify the propagation of MyoII pulsatile stresses within the cortex, we measured the speed at which tracked apical F-Actin structures (eGFP::UtrCH signal) displace toward the pulse center during contractile events. We first tracked these apical F-Actin structures using a Kanade-Lucas-Tomasi features tracking algorithm (KLT)  59, 60  implemented in C (S. Birchfield, retrieved from https://cecas. clemson. edu/~stb/klt) whose tracking results have been exported to Matlab for further processing. Briefly, the KLT algorithm first detects discrete features within the image by examining the minimum eigenvalue of each 2 by 2 gradient matrix. These features are then tracked over time following a Newton-Raphson method of minimizing the difference between the two windows. Finally, to minimize tracking mistakes, each feature is checked by an affine consistency test, comparing the spatial distribution of the feature and its neighbors at given time-point to the time-point at which the feature was first detected  61.
In our case, since the medio-apical F-Actin is in a constant reorganization, we were not able to follow features over long periods and therefore had to replace them during the movie in order to produce enough data. Considering only the tracks whose duration exceed three time-points, we obtained in average ̴ 50,000 tracks per embryo, each of these containing the x y coordinates of the tracked feature. We next sought to detect pulses, in time and space, in order to define the reference points from which our propagation measurements were made. To do so, we first designed an automated pulse detection procedure, working on the smoothed derivatives of high-pass filtered total medial MyoII levels (see Auto. pulse detection (time) in Supplementary Fig. 2b ). This procedure, based on the "findpeaks" function of the Matlab Signal Processing Toolbox, allowed us to detect and define temporal landmarks within pulses (ti : initial time ; tdmax : max derivative ; tmax : max amplitude ; tdmin : min derivative ; tf : final time) and to adjust the detection sensitivity using parameters such as the peak prominence, the peak width and the peak prominence over width ratio.
In a second time, we implemented another Matlab routine to monitor the medial MyoII center of mass within contracting cells (see Auto. pulse detection (space) in Supplementary Fig. 2c ). To perform this analysis, we first removed the junctional MyoII (post-processing) and used a combination of gaussian blurs and thresholding steps to isolate the MyoII signal actually belonging to a pulse. Once defined, we averaged the positions of the MyoII center of mass between the tdmax and tdmin time-points and used this reference point (pulse center) for our propagation measurements (Fig. 7d ). Finally, to produce the propagation curves in Fig. 7e  we averaged the speed, between tdmax and tdmin, at which the KLT tracked apical F-Actin structures converge toward the pulse center (define as describe above).
Acknowledgements We are grateful to  József Mihály  ( Biological Research Centre, HAS, Szeged, Hungary ) and  Andreas Jenny  ( Albert Einstein College of Medicine, The Bronx, NY, USA ) for providing fly stocks. We thank members of the Lecuit and Lenne groups for stimulating discussions and comments during the course of this project. We also thank  FlyBase  for maintaining databases and the  Bloomington Drosophila Stock Center  for providing fly stocks. The experiments were performed using the  PiCSL-FBI core facility  ( IBDM, Marseille, France ), a member of the France-BioImaging national research infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04-01 , " Investissements d'Avenir "). B. D. was supported by the  ERC  (grant Biomecamorph # 323027 ) and  Fondation Bettencourt Schueller.
Testing of mifepristone. We decided to further investigate the potential role for GR in AR-resistant prostate cell lines, since it had previously been implicated in castrate resistant disease, it had higher levels of expression than ESR1 in the prostate cancer cell lines, showed an inverse correlation in expression with AR in both the cell lines and patient samples in the TCGA data set, and treatment with dexamethasone altered enzalutamide response in 2 of our 3 AR-null cell lines. We treated selected prostate cancer cell lines with the PGR/GR antago- nist mifepristone to determine the ability of this drugs to inhibit the growth of prostate cancer cell lines. We used the cell lines LNCaP (as a control) and HH870, PC3, MDAPCa1, and DU145 (all of which expressed moderate to high levels of GR; see Fig.
They were then probed with 1:10,000 donkey anti-rabbit IgG-HRP conjugate (Jackson Laboratories #711-035-152) in 5% BSA in TBS-T for 45 min at room temperature on a rocking platform and rinsed as previously described. Membranes were placed on clear plastic and a chemiluminescent substrate (ThermoFisher Scientific #34080) added. Membranes were visualized on a Syngene PXi imaging system. Membranes were then rinsed and re-probed for the loading control at 1:1000 with GAPDH (Cell Signaling Technology #2118) in 5% BSA in TBS-T for 2 h room temperature, rinsed, probed with secondary and imaged as previously described. RNAseq. RNA was isolated from subconfluent cell lines using an RNeasy minikit (Qiagen) and submitted to the OHSU massively parallel sequencing core for RNAseq analysis. We used the Kallisto software to determine RNA expression as Fragments per Kilobase Million (FKPM).
The scripts used to process the data are available at: https ://githu b. com/danie lderr ick/prost _RNAse q_repro cessi ng. Data was visualized using standard clustering approaches  42. Gene set enrichment analysis was performed using the online GenePattern tool from the Broad Institute and the Hallmarks gene set  43, 44. The log transformed FKPM values for the cell lines are available in supplementary Table  S3. SNP6. 0 copy number analysis. DNA was isolated from the cell lines as previously described  21. Samples were submitted to either the Lawrence Berkeley National Laboratory HTA microarray facility or at the OHSU genome core facility for analysis on the SNP6. 0 chip platform. The resulting data was process using a Python3 wrapper (https ://gitla b. com/biotr ansis tor/myasc at) that ran the R based ASCAT (allele-specific copy number analysis of tumors) SNP analysis pipeline (version 2.
4. 4) to generate copy number data  45. Briefly, we used 1258 SNP6 cell files, available from the HapMap project (phase3 2009-04-02), as the normal reference samples. The 13 prostate cell line samples from 2009 and 8 prostate cell line samples run in 2015 were processed in separate runs then combined. SNP6. 0 cel files were transformed into the BAF and LogR illummina file format, utilizing apt-probeset-genotype and apt-probeset-summarize commands from affymetrix command line tools (version 1. 18. 0) and the normalize_affy_geno_cluster. pl script form PennCSV (version 1. 0. 3), as described in the ASCAT documentation. ASCAT was run on the BAF and LogR files to generate major minor allele track files (segemnet. txt) and PCF (piecewise constant fitting) segmented BAF and LogR track files. These track files were then transformed into seg files to be able to study the minor and major allele, the B allele frequency and the total copy number of each genomic locus tracks in the IGV genome browser (http://igv.
Acknowledgements The work reported in this manuscript was supported by a  DOD synergistic IDEA  award to JEK ( W81XWH-12-PCRP-SIDA ) and JA ( W81XWH-13-1-0420 ). The authors wish to thank  Joe W. Gray  for valuable discussions and support, and  Silje Nordgard  and  Kerstin Haase  for ASCAT related assistance. Finally, we thank all the researchers listed in Table 1 who provided their prostate cancer cell lines to us.
Most algorithms use a single satellite band that is switched to other bands for different ranges of turbidity. The necessity to switch bands is due to the saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach for SPM retrievals that also provides an estimate of uncertainty, where the latter is based on both uncertainties in reflectance and in the assumed optical properties of SPM. The approach proposed is general and can be applied to any ocean color sensor or in-situ radiometer system with red and near-infra-red bands. We apply it to six globally distributed in-situ datasets of spectral water reflectance and SPM measurements over a wide range of SPM concentrations collected in estuaries and coastal environments (the focus regions of our study). Results show good performance for SPM retrieval at all ranges of concentration.
We caution the readers that if, in their applications, phytoplankton and CDOM dominate the remote sensing reflectance signal at the bands we have retained, our algorithm may not work well as it was not tested in such waters. The approach used here could be generalized to other environments but appropriate datasets would be necessary for testing and possibly modification of the algorithm (e. g. , explicitly solving for phytoplankton and CDOM), though this is out of the scope of this paper. Assigning an uncertainty to SPM enhances user confidence and defines the range of possible applications of data products  [19]. It is thus important that uncertainties in ocean color remote sensing data and in the products derived from it (e. g. , SPM) are documented. Several techniques have been proposed  [19] [20] [21] , mostly for Chlorophyll or IOPs which are derived from Remote Sensing Reflectance (R rs ).
Data Sources Data used to evaluate our algorithm performance were collected worldwide (Figure  1 ) representing a variety of water and SPM types. The datasets include in-situ measurements from different coastal and estuarine environments. The field data measurements are characterized by a wide range of SPM concentrations (four orders of magnitude of SPM concentration from approximately 0. 4-3980 g. m -3 -Table  1 ) as well as spanning a range of particle composition and characteristics (or sources). Water absorption is of significant influence to R rs (λ) at the range of wavelengths (red, NIR, and SWIR) used to retrieve SPM and temperature contributes to it significantly in that range. For some of the datasets (e. g. , Yangtze19, MCR13) we had concurrent temperature data. When in-situ temperature measurements were not available, climatological monthly sea surface temperature was used instead (https://seatemperature.
Field Data We have assembled a total of 420 in-situ data points with both R rs (λ) and SPM, spanning nine sites (Table  1 ). About 39% of these data were provided from Reference  [22]  (hereon Nechad15); 32% from Reference  [23] , (hereon Knaeps18); 12% from the RIVERCOLOR project (hereon Rivercolor14); 8% from the Mouth of the Columbia River dataset (hereon MCR13); 4% from Reference  [24]  (Yangtze19); and 4% from the RIVET project (NewRiver12). While SPM measurement protocol and data processing are similar across the datasets, the spectral radiance and irradiance measurements follow different approaches depending on the equipment available to the operator (Table  2 ). Measurements were also made in different sky conditions for which we do not have complete information. Under such variable circumstances the assessment of quality for acquired data becomes necessary.
Ruddick et al. [25]  suggested the application of a quality control of above-water reflectance measurements named by the authors as 'similarity NIR reflectance spectrum'. This proposed approach is based on the theory that the shape of the reflectance spectra rarely changes in the range of 710-900 nm given the strong influence light absorption by water molecules , a w (λ), has on the water-leaving reflectance signal, ρ w (λ). Because the spectral shape is almost invariant it could be used to assess data quality. The method was applied to our six datasets resulting in the total removal of sixteen spectra not meeting the similarity spectrum criteria. This resulted in the removal of one spectra for Nechad15 for Indonesian waters, fifteen spectra for MCR13, and the removal of parts of the spectrum unusual shapes (Knapes18 for the Scheldt Estuary, Nechad15 for the North Sea region, and MCR13 for the Mouth of Columbia River).
Dataset Instrument Spectral Range [nm] Method Yangtze19 ASD spectrometer 400-1075 L + u , L sky , L spec , θ v = 45 • , ∆ ϕ ∼ 135 • Knaeps18 ASD spectrometer 355-1300 L + u , L sky , L spec , θ v = 40 • , ∆ ϕ = 90 • -135 • Nechad15 Trios RAMSES 318. 2-950. 9 L + u , L sky , E + d , θ v = 45 • , ∆ ϕ ∼ 135 • Trios RAMSES 350-850 L + u , L sky , E + d , θ v = 40 • , ∆ ϕ = 135 • Rivercolor14 Trios RAMSES 350-950 L + u , L sky , E + d , θ v = 45 • , ∆ ϕ ∼ 135 • MCR13 WISP 3 400-800 L + u , L sky , E + d , θ v = 40 • , ∆ ϕ =∼ 135 • ; HyperSAS based calibration NewRiver12 HyperPro in buoy mode 349-801. 4 L - u , E + d 2.
The MW algorithm A schematic of the MW algorithm is provided in Figure  2. 3  for symbols, definitions, and units. The MATLAB script used for this approach is available at https://github. com/OceanOptics/MW_ algorithm. git. δ u (λ) Estimated u(λ) uncertainty - σ 84,16 SPM uncertainty range g. m -3 σ 84,  16  Weighted SPM uncertainty range g. m -3 σ SPM Weighted SPM uncertainty g. m -3 W(λ) Spectral weights m 3. g -1 SPM SPM weighted-median g. m -3 S ap Exponent of exponential spectral shapes nm -1 T, T re f Temperature, reference temperature water-leaving reflectance -.
In order to treat all of our datasets similarly, we use the results from recent studies (e. g. , References  [34, 36] ) covering the wavelength range of interest. We account for the temperature dependence using the Taylor series of Reference  [37] , applying the temperature coefficients by Reference  [38]. Temperature changes by one degree Kelvin result in relative differences in a w ranging from -0. 4 to 0. 7% depending on wavelength. a w (λ, T) = a w (λ, T re f ) + Ψ T (T -T re f ). (6) Non-algal particles (NAP) can also contribute significantly to the IOPs and reflectance in coastal regions. NAP absorption (a N AP ) is modeled as SPM multiplied by mass-specific absorption (Equation (  7 )). Mass-specific absorption varies significantly less than SPM. The spectrum of particulate absorption has been found to be well approximated by an exponential shape (Equation (  8 )) in the visible wavelengths and a range of values for the exponent have been reported (see Table  2 ).
2. 4). The same procedure is applied to the 16th and 84 th percentiles to provide us with the uncertainty estimate (Equations (  17 ) and (  18 )). σ 84,16 = ∑ N i=1 P 16,84 [SPM(λ)] i W(λ) i ∑ N i=1 W(λ) i 1 √ M , (18) ± σ SPM = σ 84 -σ 16 2 , ( 19 ) where P 16,84 are respectively the 16th and the 84th percentiles of SPM solutions. The ( √ M) -1 factor in Equation (  18 ) account for the fact that we are looking for an uncertainty in SPM and not the spread around it (similar to the computation of the standard error of the mean), with M the number of degrees of freedom. While we have datasets with hundreds of wavelengths of reflectance these measurements are not truly independent requiring us to investigate the information content they exhibit. Principal Component Analysis (PCA) is one method one could use to compute it (e.
g. , Reference  [47] ). we performed a PCA to establish the likely number of degrees of freedom in the input reflectances using the 'broken stick' method  [48]. We applied the PCA to each reflectance dataset (normalized by the respective area under the curve) to estimate the degrees of freedom based on the number of PCA modes needed to explain the spectral variance in the data. For each dataset we computed the number of spectral modes describing >98% of the variance of the r rs spectra, and used in M in Equation  (18)  (varied from two to four).
Novoa et al. , 2017 Algorithm The Reference  [10]  algorithm was developed based on two regional field datasets: Gironde Estuary and Bourgneuf Bay. The algorithm developed based on Gironde Estuary was chosen for our study. The dataset used to develop this algorithm ranged from of 2. 6 g. m -3 to 1579 g. m -3. Novoa17 is a multi-conditional algorithm applicable to a broad range of low to high SPM concentrations using band-switch weights to ensure a smooth transition between the different SPM algorithms. The wavelength switching algorithm as well as the switching criteria for SPM are described in Table  5.