dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998 [1]  and new ones proposed by Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most N  N  N  N  N  N  N  N  N .
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998 [1]  and new ones proposed by Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most N  N  N  N  N  N  N  N  N  In the laboratory, the measured quantity is the bi-conical Reflectance Factor (BCRF) corresponding to the conical-conical case (ρ cc ) of Table  1. BCRF is, by definition, the ratio of the reflected flux to the flux that would be reflected by an ideal perfectly diffuse standard surface illuminated by a collimated source.
Materials As this work was motivated by contributing to PICS characterization most commonly used, the priority was given to sand samples from the six PICS endorsed by the Comity of Earth Observation Satellites (CEOS) (Algeria3, Algeria5, Libya1, Libya4, Mauritania1, and Mauritania2) and then to the other PICS defined by  Cosnefroy et al. in 1998 [1]  and new ones proposed by Bacour et al. in 2019 (Arabia_PICSAND1 and Namibia_PICSAND1, PICSAND being the name of the database presented in Section 5. 5)  [3]. Collecting sand samples from these sites has been a challenging task. Indeed, most of the sites are located in remote places with difficult access from a logistic point of view. Moreover, geopolitical contexts (as in Libya or Mauritania) enhance the accessibility issue. Therefore, samples were collected for only a few of these PICS.
Mineralogy The French geological survey named BRGM (Bureau de Recherches Géologiques et Minières) has established the mineralogy of the main samples. The fraction of the crystalline phase of the samples has been determined by X-ray diffractometry after reducing a small fraction of each sample into powder. The diffractometer used is a Bruker D8 Advance, and the diagrams were analyzed using the DIFFRAC software. The quantitative analysis was carried out with the SiroQuant V4. Software (using the Rietveld refinement method). For BCRF assessment, the set up enables us to measure the radiance for the sample for various illumination and observation angles. Just before and just after, the sample is replaced by a reference panel (Spectralon©), and the corresponding radiance is measured for one angular configuration.
Mineralogy The French geological survey named BRGM (Bureau de Recherches Géologiques et Minières) has established the mineralogy of the main samples. The fraction of the crystalline phase of the samples has been determined by X-ray diffractometry after reducing a small fraction of each sample into powder. The diffractometer used is a Bruker D8 Advance, and the diagrams were analyzed using the DIFFRAC software. The quantitative analysis was carried out with the SiroQuant V4. Software (using the Rietveld refinement method).
Grain Size Distribution The whole results can be found in the database (cf. Section 5. 5). For the various samples, Table  5  lists the diameters corresponding to the maximum of the grain size distribution. Note that due to grain diameters below 50 µm, the Rail Road Valley sample cannot be considered as pure sand. The distribution for the Arabia_PICSAND_SPL5 case is given in Figure  18  as an illustration.
Overview of the PICSAND Database The measurements for the samples presented in Table  2  have been complemented with other available optical measurements over sand (performed either in situ or in the laboratory) and available in the literature. They are described in Table  6  for spectro-directional datasets and in Table  7  for spectral datasets. The different datasets have been merged into the so-called PICSAND database. The PICSAND database is freely accessible at https://picsand. noveltis. fr. It is also referenced from the CEOS/IVOS portal: http://calvalportal. ceos. org. The database can be accessed to and explored thanks to a user-friendly interface enabling display (for some predefined conditions for the spectral/spectro-directional figures of the reflectance) and downloaded. A search tool is available in order to filter datasets depending on a few predefined criteria (i.
e. , spectro-directional vs. spectral data, data associated with the PICS sites or not, in situ measurements vs. laboratory measurements). An example of the polar plot is shown in Figure  19  for the Arabia_PICSAND_SPL5 sample at 600 nm. The different datasets were processed in order to generate homogenized data files. The processing was performed so that the output data are as close as possible to the original ones, keeping only three significant digits for the reflectance data and removing abnormal reflectance values. In situ measurements can be strongly contaminated by atmospheric absorption in specific absorption bands (water vapor and carbon dioxide mainly). We chose to keep the corresponding measurements, even if very noisy, and hence, likely non-usable. However, we added a 'quality' flag  The different datasets were processed in order to generate homogenized data files.
Given the fewer number of parameters to be optimized along with the performance, the RPV model seems to be a better choice than the Snyder one. Thus coming back to the method described in  [4] , future work could compare the directional behavior of the RPV surface model retrieved from MERIS measurements and the directional behavior of the RPV model fitted from the laboratory sand sample measurements. Considering spectral behavior, the spectral variation of the parameters of the RPV model fitted on the samples could be compared to the ones obtained with the fit on MERIS data for the spline spectral interpolation case. If the agreement is good enough, which means that the sample is representative of the mean behavior of the whole site, it could be used to constrain the spectral interpolation.
Conclusions The presented work results in a database giving access to an unprecedented set of optical and physical properties of various sand samples. This database can be used to check assumptions and enhance radiometric calibration methodology based on PICS. One of the key and practical results of the study concerns the modeling of the BRDF properties of sandy sites. Ignoring the peculiarities of the backscattering direction, the RPV and the Snyder BRDF models provide the highest fitting performances, whatever the site, the tested wavelength, and the observational case: spectro-directional properties derived from satellite measurements or measurements on sand sample carried out in the laboratory. Although the spatial scale of the sample measured in laboratory (or in-situ) measurement is different from the kilometric spatial resolution of medium resolution space-borne sensor, the sand spectral characteristics stored in the database should be useful to spectrally interpolate the PICS surface BRDF models retrieved from space for some narrow spectral bands.
It would allow a more accurate radiometric comparison of sensors as per  [4]  or  [5]. The wealth of the information collected and produced opens large future prospects not only for calibration of earth observation sensors but also for spectro-directional signature analysis aiming at characterizing and understanding the relationship between sample BCRF, grain size, and mineralogy. We aim in the future at maintaining PICSAND as a living database: anyone willing to contribute to the database is invited to send a first contact email to picsand@noveltis. fr.
Acknowledgments: We wish to acknowledge all the contributors to the database:  Jerzy Cierniewski  ( University in Pozna ń, Poland ) and  Arnon Karnieli  ( Blaustein International Center for Desert Studies, Israel ),  Craig Coburn  ( University of Lethbridge, Canada ),  Jouni Peltoniemi  ( Finnish Geodetic Institute, Masala, Finland ),  Peter Roosjen  ( Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, The Netherlands ),  Zhongqiu Sun  ( School of Geographical Science, Northeast Normal University, Changchun, Jilin, China ),  Hong Zhang , and  Zhengchao Chen  ( Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China ),  Kenneth Voss  ( University of Miami, Miami, USA ) and  Hao Zhang  ( China University of Geosciences, Wuhan, China ),  Claire Greenwell  ( National Physical Laboratory, UK ),  Andreas Hueni  ( University of Zurich, Switzerland ),  Kevin White  ( University of Reading, UK ) and  Joanna Bullard  ( Loughborough University, UK ), and the sand samples providers:  Charles Bristow  from the  Birkbeck University of London  ( United Kingdom ),  Ian Lau  from  CSIRO (Australia) ,  Laurent Poutier  from  ONERA (France) ,  Michael Schaepmann  from  University of Zurich (Switzerland) ,  Kevin White  from the  University of Reading  ( United Kingdom ),  Burton Jones  and  Wolfgang Heidrich  from  King Abdullah University of Sciences and Technology (Kingdom of Saudi Arabia)  and the  French Defense Forces.
A variety of techniques have therefore been developed to identify the effective RNA targets of such factors, ranging from affinity purification (such as RIP or CLIP) to transcriptome and polysome profiling after RBP silencing, providing the groundwork to address systematic questions about post-transcriptional regulation. In this study, we searched for broad determinants of mRNA storage and decay in unstressed human cell lines, using our transcriptome of purified PBs and several transcriptomic analyses performed after silencing of general translation and decay regulatory factors, including DDX6, PAT1B and XRN1. We also used datasets available from the literature, including a transcriptomic analysis after DDX6 silencing, a DDX6-CLIP experiment and various lists of RBP and miRNA targets. Their combined analysis revealed the central role of mRNA GC content which, by impacting codon usage, PB targeting and RBP binding, influences mRNA fate and contributes to the coordination between two opposite processes: decay and storage.
The online version of this article includes the following figure supplement(s) for figure  1 : Most remarkably, mRNA accumulation in PBs was dependent on their global nucleotide composition, with a strong correlation between GC content and PB localization (r s = À0. 64, p<0. 0001). When transcripts were subdivided into six classes ranging from <40% to >60% GC, PB enrichment was predominant for those <45% GC (Figure  1B , Figure  1 -figure supplement 1D,E). While reminiscent of the low GC content reported for stress granule mRNAs in HEK293 cells  (Khong et al. , 2017) , our reanalysis of the published dataset indicated that stress granule localization correlated weakly with the gene GC content (r s = À0. 12, p<0. 0001) and almost not at all with the mRNA GC content (r s = À0. 06, p<0. 0001).
45, p<0. 0001; Figure  3 -figure supplement 1F) and negative for changes in polysomal RNA (r s = À0. 32, p<0. 0001; Figure  3 -figure supplement 1G). Therefore, DDX6 depletion affected different mRNA subsets in total and polysomal RNA. The extent of mRNA stabilization steadily increased with the GC content and became predominant for transcripts with >50% GC (Figure  3A , left panel, Figure  3 -figure  supplement 2A ). This analysis was repeated on an independent dataset available from the ENCODE project (ENCODE Project Consortium, 2012), obtained in a human erythroid cell line, K562, following induction of a stably transfected DDX6 shRNA, and using an oligo(dT)-primed library. Despite the differences in cell type, depletion procedure and sequencing methods, again, mRNA stabilization preferentially concerned those with high GC content (r s = 0.
59, p<0. 0001; Figure  3A , right panel, Figure  3 -figure supplement 2A; Supplementary file 1, sheet2). In contrast, following DDX6 silencing in HEK293 cells, the translation rate predominantly increased for transcripts with less than 45% GC (r s = À0. 53, p<0. 0001; Figure  3B , Figure  3 -figure supplement 2A). As a result, mRNAs with the most upregulated translation rate were the least stabilized, and conversely (Figure  3 -fig- ure supplement 2B). To investigate how DDX6 activity was related to its binding to RNA, we used the CLIP dataset of K562 cells, also available from the ENCODE project. In both HEK293 and K562 cells, the mRNAs clipped to DDX6 were particularly stabilized after DDX6 knockdown, as compared to all mRNAs  (Figure  3 -figure supplement 2C; Supplementary file 1, sheet3), while they were not translationally derepressed in HEK293 cells (Figure  3 -figure supplement 2D).
Specific mRNA decay factors and translation regulators target mRNAs with distinct GC content Having shown that GC content is a distinctive feature of DDX6 and XRN1 versus PAT1B targets, we investigated the link between this global sequence determinant and a variety of sequence-specific post-transcriptional regulators for which relevant genome-wide datasets are available (Figure  5 -figure supplement 1A). On the mRNA decay side (group I lists), we considered the Nonsense Mediated Decay (NMD) pathway, taking as targets the mRNAs cleaved by SMG6  (Schmidt et al. , 2015) , and the m 6 A-associated decay pathways, using the targets of the YTHDF2 reader defined by CLIP  (Wang et al. , 2014; Yang et al. , 2015). We also analyzed mRNAs with a 5'UTR-located G4 motif, which have been shown to be preferential substrates of murine XRN1 in vitro  (Bashkirov et al.
Intriguingly, compared to all mRNAs, group I list mRNAs were GC-rich, as well as TOP mRNAs and ATXN2 targets, whereas all other group II lists were AU-rich (Figure  5A ). Furthermore, they shared common behavior in the various experiments. This is summarized in Figure  5B  in a heatmap representing their median value in each dataset, while Figure  5 -figure supplements 1 and 2 provide detailed analysis, as described below. Group I list mRNAs tended to be dependent on DDX6 and XRN1 but not on PAT1B for stability (Figure  5 -figure supplement 1B-D), with nevertheless some variation between cell lines, as only SMG6 targets were sensitive to XRN1 depletion in HeLa cells (Figure  5 -figure supplement 1C, upper panel). They did not accumulate in PBs and their translation rate was independent of DDX6 (Figure  5 -figure  supplement 2A, B ).
, 2017; Franks and Lykke-Andersen, 2007). (FC >0. 8, n = 199, solid lines)  and all mRNAs (n = 13760, dashed lines) in the siXRN1 dataset. The data were analyzed as in (C). (E) Clustering analysis of mRNAs depending on their GC content, their differential expression after silencing DDX6, XRN1 or PAT1B, and their enrichment in PBs. Raw GC content and log2 transformed ratio of the other datasets were used for the clustering of both transcripts (lines) and datasets (columns). The values were color-coded as indicated on the right scale, and the Spearman correlation matrix is presented below (all p<10 À48 ). The heatmap highlights the distinct fate of GC-rich and AU-rich mRNAs. The online version of this article includes the following figure supplement(s) for figure  4 : Among the three group II outsiders, ATXN2 targets and TOP mRNAs behaved like group I lists, except that they were not dependent on DDX6 for stability.
Overall, they also shared common behavior in the various silencing experiments and PB dataset, with nevertheless some differences. This is summarized in Figure  5D  in a heatmap representing their median value in each dataset, while Figure  5 -figure supplement 3 provides detailed analysis, as described below. The AGO targets tended to accumulate in PBs (Figure  5 -figure supplement 3C; note that the number of AGO4 targets was too small to reach statistical significance) and their translation rate was   DDX6-dependent (Figure  5 -figure supplement 3D). In terms of stability, only AGO2 targets were marginally DDX6-dependent (Figure  5 -figure  supplement 3E ), and the effects were not stronger when analyzing separately the mRNAs enriched or excluded from PBs (Figure  5 -figure supplement 3F).
The GC content of mRNAs shapes post-transcriptional regulation As the global GC content appeared closely linked to mRNA fate, but also to RBP and miRNA binding, as well as to translation activity, our analyses then aimed at ranking the importance of these various features. We first assessed the respective weight of the GC content and the binding capacity of particular RBPs. To this aim, we binned the whole transcriptome depending on its GC content (bin size of 500 transcripts). The median fold-changes of the bins in each RNAseq dataset were calculated and plotted as a function of their median GC content. Median values were similarly calculated for the various group I and II target lists and overlaid for comparison (Figure  5 -figure supplement 4). Surprisingly, the fold changes of the targets of particular RBPs generally fell very close to the tendency plot based on GC content only.
) were AU-rich and had a median behavior in all datasets similar to other mRNAs of same GC content. While the GC bias of the targets of the various RBPs and miRNAs are likely to reflect their sequence preference, some RBPs may not reach sufficient concentration to occupy a significant number of binding sites. Moreover binding does not mean activity, and these factors could require cofactors or post-translational modifications to become 'productive' in terms of mRNA regulation. Of note, while TOP mRNAs clearly constituted an exception in terms of GC content, they are particular too in terms of their translation repression mechanism, with a regulatory sequence located at the 5' end. PBs add another layer to translation regulation, by storing translationally repressed mRNAs. It was already known that ARE-containing mRNAs bound to ARE-BPs such as TTP and BRF were recruited to PBs  (Franks and Lykke-Andersen, 2007)  and that miRNA targets accumulate in PBs upon miRNA binding in a reversible manner  (Bhattacharyya et al.
7 ml) is diluted about 100 fold (200 mg in 100 ml). For imaging experiments, the reactions were centrifuged at 11000 xg for 7 min at 4˚C, resuspended in 5 mL of Mowiol (PolySciences) mounting medium, mixed by vortexing and mounted between glass slide and coverslip. Microscopy was performed on a Leica DMR microscope (Leica) using a 63 Â 1. 32 oil-immersion objective. Photographs were taken using a Micromax CCD camera (Princeton Scientific Instruments) driven by MetaMorph software (Molecular Devices). Images were processed with NIH ImageJ software.
Protein expression was evaluated by densitometry (NIH ImageJ). Primary antibodies were: rabbit polyclonal anti-DDX6 (1:15000; Novus Biological), rabbit polyclonal anti-ribosomal S6 (1:5000; Cell Signaling Technology), rabbit polyclonal anti-XRN1 (1:1000, Bethyl Laboratories, and 1:5000 Novus Bioscience), rabbit polyclonal anti-Pol II (1:100, Santa Cruz), mouse monoclonal anti-tubulin (1:30000, Sigma-Aldrich). q-RT-PCR analysis Total RNA (1 mg) was reverse transcribed for 1 hr at 50˚C using the SuperScript II First-Strand Synthesis System for RT-PCR (Invitrogen) with 1 mg random primers (Promega). Reverse primers for Firefly and Renilla luciferases were also added for the luciferase reporter assay. No amplification was detected in negative controls omitting the reverse transcriptase. qPCR amplifications (12 ml) were done in duplicates using 1 ml of cDNA and the GoTaq Probe 2X Master Mix (Promega) on a LightCycler 480 (Roche), programmed as follows: 5 min, 95˚C; 40 cycles [10 s, 95˚C; 15 s, 60˚C; 10 s, 72˚C].
A last step (5 min progressive 95˚C to 72˚C) calculating the qPCR product Tm allowed for reaction specificity check. Primers for ACTB, APP, BACE1, LSM14A, LSM14B, MFN2, PNRC1, TIMP2, TRIB1, HPRT1, REN-low-GC, RENhigh-GC and FIREfly were either gifts from R. Blaise or designed using the Primer three software  (Untergasser et al. , 2012). The results were normalized using either HPRT1 or FIREfly.
Library preparation and RNA-Seq data processing For polysome profiling after DDX6 silencing and transcriptome after PAT1B silencing in HEK293 cells, rRNA was depleted using the Ribo-Zero kit Human/Mouse/Rat (Epicentre), and libraries were prepared using random priming. Triplicate and duplicate libraries were generated from three and two independent experiments, respectively, and processed as detailed previously  (Hubstenberger et al. , 2017; Vindry et al. , 2017). For the transcriptome after XRN1 silencing in HeLa cells, libraries were prepared from 500 ng of total RNAs and oligo(dT) primed using TruSeq Stranded Total RNA kit (Illumina) with two technical replicates for each sample. Libraries were then quantified with KAPA Library Quantification kit (Kapa Biosystems) and pooled. 4 nM of this pool were loaded on a high output flowcell and sequenced on a NextSeq500 platform (Illumina) with 2 Â 75 nt paired-end chemistry.
, 2017). For the transcriptome after induction of a stably transfected DDX6 shRNA for 48 hr in K562 cells, the. fastq files from experiments ENCSR119QWQ (DDX6 shRNA) and ENCSR913CAE (control shRNA) were processed according to the same pipeline as DDX6 silencing, except that the control and DDX6 shRNA experiments were not paired to compute the corrected p-values of RNA differential expression in EdgeR_3. 6. 2. The ENCODE dataset of mRNAs clipped to DDX6 in K562 cells was generated using ENCODE. bam files aligned on the hg19 genome corresponding to (i) the DDX6 eClip experiment ENCS-R893EFU, and (ii) the total RNA-seq of K562 experiment ENCSR109IQO. The enrichment in the CLIP dataset compared to the total RNA sample was calculated as in the DDX6 shRNA experiment.
The translation rate was defined as the polysomal to total mRNA ratio, since polysome accumulation can result from both regulated translation and a change in total RNA without altered translation. For GC profiling of the transcripts in various organisms (Figure  7 -figure supplement 1), transcripts were downloaded from ENSEMBL (version 92) with their associated gene GC content. Boxplot representations and statistical tests were performed using the GraphPad Prism software (GraphPad software, Inc) and the R suite (https://www. R-project. org) (R Core Team 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria). We chose to systematically use the Spearman correlation in the interest of consistency, since the variables under consideration showed both nearly linear (e.
g. accumulation in PBs and GC content of the mRNA) and non-linear (e. g. accumulation in PBs and mRNA length) relationships. Statistical tests for differences between Spearman correlation coefficients were performed using the R package cocor and the 'meng1992' test. Partial correlations were computed using the ppcor package. Other graphical representations were generated using Excel and the Excel Analysis ToolPak (Microsoft). Hierarchical clustering of all transcripts in Figure  4E  was performed using the Cluster 3. 0/Treeview softwares (Kendall's tau distance, average linkage,  de Hoon et al. , 2004). Heatmap representation of the targets of the various regulators in Figure  5B  and D was performed online using Morpheus (https://software. broadinstitute. org/morpheus). Gene meiotic recombination rates were computed as crossover rates between gene start and gene end using the genetic map from the HapMap project  (Frazer et al.