dataesr/scientific-paragraphs-categorization huggingface dataset:
Dust evolution The code implements the two population model of dust growth developed by  Birnstiel et al. ( 2012 )  because it has the advantage of closely reproducing the results of full coagulation simulations  (Brauer, Dullemond & Henning 2008 ; Birnstiel et al. 2010  ) at a much lower computational cost. In summary, two populations of solids are considered: one is made of small, monomer-sized grains ( a 0 = 10 -5 cm , constant in time and space), the other is composed by large grains dominating the mass. Their size, the maximum grain size, a max , changes with time as the disc evolves and is set at any radius by the combination of growth (with maximally efficient sticking, f grow = 1; Booth & Owen 2020 ), fragmentation (assuming u f = 10 m s -1 as velocity threshold for shattering, typical of ice-coated grains; e.
We refer to  Birnstiel et al. ( 2012 )  for further details and to  Booth et al. ( 2017 )  for the implementation. Once the grain sizes are known, they are used to compute the dust velocities of both particle species as in  Tanaka, Himeno & Ida ( 2005 )  and advect dust along the gas flow. To do so, the dust fraction is updated according to the one-fluid approach of  Laibe & Price ( 2014 ) , assuming that dust is initially distributed as the gas and 100 times less abundant (as in the ISM; e. g. Bohlin, Savage & Drake 1978 ). The code allows to take into account the back reaction of the dust on the gas (e. g. Dipierro et al. 2018 ; G árate et al. 2020  ). Ho we ver, this is likely unimportant given that the dust fraction decreases fast due to the efficient radial drift of solids  (Rosotti et al.
2019b ). In this paper, dust entrainment in the wind is neglected based on pre vious works sho wing that only small grains can be efficiently remo v ed in MHD winds (e. g. Giacalone et al. 2019 ; Rodenkirch & Dullemond 2022  ). We discuss this assumption more e xtensiv ely in Section 6. Dust diffusion is also considered. The original code implements a diffusion coefficient D = ν/Sc, where Sc is the dust Schmidt number, computed as in  Youdin & Lithwick ( 2007 ). To a v oid nonsmooth (numerical) features in the purely magnetic case, where viscosity is negligible, we prescribe D = ( ψ + 1) ν/Sc, so that there is a fixed relationship between angular momentum transport ef ficiency and dif fusi vity, regardless of the mechanism for angular momentum transport. Although we do not expect angular momentum transport due to magnetic winds to contribute to turbulent diffusion of solids, their mixing efficiency remains uncertain.
2008 ) , Upper Sco ( ≈ 5 -10 Myr ;  Preibisch et al. 2002 ). This is ideal to test our model predictions o v er a long-time interval. In Table  2  , we summarize the main properties of the data sets taken into account. We also compare the behaviour of our models with Lupus and Upper Sco data in the disc size versus luminosity plane. We use the sizes and fluxes computed by  Hendler et al. ( 2020 )  from ALMA Band 7 data and those derived by  Tazzari et al. ( 2021a )  from ALMA Band 3  (Tazzari et al. 2021b  ) data in sub-sample of Lupus discs.
Then, the median R 68,flux is computed o v er the time corresponding to the av erage age of each star-forming region, for different values of the initial disc radius and ψ, neglecting dust emission below S b , 0. 85mm ≈ 6 × 10 7 Jy sr -1. Fig. 7  shows the results of our comparison. Observed discs with resolved sizes are displayed as black and grey dots for Lupus, Chamaeleon I, and Upper Sco. To a v oid all, the discs in each starforming region to fall on the same position along the x axis, we use the swarmplot function in the python data visualization library seaboarn , which prevents them from overlapping. In addition, to give a fla v our of the tentative underlying disc size distribution in each star-forming re gion, we dra w a violin plot within the range of the observed data. Instead of showing discrete bins (e.
Deeper surv e ys in star-forming regions with a large age difference could be fruitful to observe the predicted differences between dust disc size evolution in viscous and magnetic wind models. None the less, such observations could be challenging even for ALMA; (iii) The model predictions are in agreement with the currently av ailable observ ationally inferred dust disc sizes from Lupus, Chamaeleon I, and Upper Sco Band 7 (0. 89 mm ) data within 1 σ about the median, regardless of ψ. Some purely magnetic wind models predict smaller sizes than in the data. This is not necessarily a problem given the limited angular resolution of the current surveys. None of the models can explain very large discs. A possible explanation is that several of these sources are sub-structured; (iv) Our models predict smaller flux sizes for longer wavelengths because radial drift se gre gates the largest grains in the inner disc.
This is in contrast with disc sizes inferred from Band 3 (3. 10 mm ) Lupus observations, which are in line with Band 7 ones; (v) In Lupus, our models predict a similar size-luminosity correlation as the one detected at 0. 89 mm and (marginally) at 3. 10 mm. In Upper Sco, ho we ver, the slope of the correlation is steeper in the models than in the data. A more fruitful comparison with the data would be possible by exploring the model dependence on other key parameters, such as the initial disc mass, stellar mass, temperature, and ultimately running disc population synthesis models where the initial conditions are randomly chosen from observ ationally moti v ated parameter distributions. Predictions on the size evolution in the presence of sub-structures are also required to interpret the data.
DATA AVA I L A B I L I T Y The code used in this paper is publicly available on GitHub at gith ub. com/rbooth200/DiscEvolution. The data underlying this paper are available in the ALMA archive as explained in  Hendler et al. ( 2020 )  and Tazzari et al. ( 2021a , b ). The PPVII chapter summary table of  Manara et al. ( 2022 )  can be found at ppvii. org/chap ter/15/.
, 2019). In the future, the installation of a seismic array dedicated to flood could help further detect and constrain flood dynamics and triggered earthquakes (e. g. ,  Meng and Ben-Zion, 2017; Eibl et al. , 2020; Chmiel et al. , 2021). Finally, the results from this study pave the way to further analysis based on the presented unique dataset to study surface and deep earth processes associated with storm Alex. for each studied catchment, the distances between each Digital Elevation Model (DEM) grid cell and the considered outlet and use the distance to root the runoff at the studied catchment outlets. A distinction is made between the distance travelled on the slopes and the distance travelled in the river (i. e. , within the hydrographic network): the flow velocity on the slopes (fixed here at 0.
We use only vertical components of the seismograms and we automatically scan the seismic data between September 27 and December 10, 2020. A new earthquake is detected if the cross-correlation coefficient exceeds a threshold of 0. 6. This value allows the detection of earthquake waveforms that might slightly differ from the templates (if, for example, the origin location is not the same) while minimizing the number of false detections. Finally, the magnitude of detected earthquakes is estimated from the ratio between its maximum amplitude and the maximum amplitude of the best-correlated template (local magnitude ML). An example of templates and detected events by template matching are presented in Figure  B9 -B11. The peak arrival times are manually picked by taking the beginning of the maximum above fixed seismic power (PSD) thresholds (Figure  B3 ,B8).
Acknowledgements. We thank  Observatoire de la Côte  d'Azur that partially founded this work. We also thank the  ANR  project  ANR SEIS-MORIV   ANR-17-CE01-0008  granted to FG. We thank  Didier Brunel ,  Christophe Maron ,  Jerome Cheze  and all the persons in charge of the permanent seismic network and data. We thank  Florent Adamo  and  Cerema  for kindly allowing us to use their photos. 27 https://doi. org/10. 5194/nhess-2021-339Preprint. Discussion started: 12 November 2021 c Author(s)  2021. CC BY 4. 0 License.
In our study, the simulated VBP locations obtained from a forward use of GBF are compared with the observed data from La Soufrière (Section 4. 1). The procedure implemented to assess quantitatively the probabilistic hazard differs from the one proposed by  Biass et al. (2016)  as ours it is based on the independent evaluation of the probability of exceeding a given energy threshold and the probability of having clast fallout of a given size per cell (Section 4. 2). This results in an alternative version of the post-processing routine which has been specifically coded in MATLAB for the purposes of this paper (see Supplementary Material). In  Biass et al. (2016) , the probability P to exceed a given energy threshold E t in the cell A ij , conditional to clast ejection, is quantified as: Table  1  Main events occurred at La Soufrière de Guadeloupe classified as: non-magmatic, non-explosive edifice collapses (E); magmatic explosive (M); phreatic events (P), or failed magmatic (F), as in 1976.
Exposed elements and analysis of the potential impact The built environment in Basse-Terre island has been described in  Spence et al. (2008)  where an integrated multi-risk impact analysis for the 1530 CE sub-Plinian eruption scenario is discussed  (Boudon et al. , 2008; Komorowski et al. , 2008). Twenty building classes were identified (from BDTOPO digital database, National Institute of Geography IGN;  Spence et al. , 2008)  for different impact zones in order to describe the differences between buildings, age and stories. Data by  Spence et al. (2005)  and  Pomonis (2006)  show that the buildings surrounding the volcano (not inclusive of all the area that could be potentially impacted) are made by reinforced concrete type for 57%, by masonry MW (medium weak) type for 30% and by timber MW to WE (weak) type for 13%.
Integration with a publicly available single-cell RNA-seq dataset  (Yan et al. , 2013)  indicated that upregulated genes are usually expressed in oocytes, zygotes, 2-cell and 4-cell stages while downregulated genes are expressed in 8-cell, morula and late-blastocyst stages (Figure  3E ). These data suggest that the knockdown of DUX4 in human blastomeres leads to minor changes in embryonic gene expression program.
(D) Boxplots showing the expression levels in individual siControl and siDUX4 cells for selected oocyte-specific genes (left) and 8-cell stage genes seen during embryonic genome activation (right). All genes are statistically significant by Wilcoxon test, FDR < 0. 05. In each box, the median line is indicated, the edges are the 25 th and 75 th percentiles, the whiskers extend to the data points not considered outliers. (E) Heatmaps showing Z score normalized RNA-seq expression levels  (Yan et al. , 2013)  (GEO: GSE36552) for significantly upregulated (left) and downregulated (right) genes from (C). Upregulated genes (left) are mainly expressed in oocyte, zygote and 2-cell stages while downregulated genes (right) are expressed from 8-cell stage onwards. See also Figure  S6. DUX4 protein-protein interactome.
As a negative control we used GFP with nuclear localization signal in the same plasmid backbone as DUX4. MAC-tag allows identification of both stable (AP-MS) and dynamic (BioID-MS) protein-protein interactions gathered over the course of 20 h  (Liu et al. , 2018; Varjosalo et al. , 2013). We identified 43 stable AP-MS and 158 transient BioID-MS high-confidence (BFDR < 0. 05) DUX4 interactions, including the previously shown DUX4 interaction partners EP300 and cAMP-response element-binding protein (CREB)-binding protein (CBP)  (Choi et al. , 2016)  (Figure  S7  and Table  S4 , including the protein interactions of DUX4 and the negative control). Comparison of our list of DUX4-interacting proteins to the protein complex database (CORUM) yielded significant overrepresentation of the SWI/SNF chromatin remodeling complex, NSL and NuA4 histone acetyltransferase complex, SRCAP histone exchanging complex, and the Core Mediator complex, (FDR < 0.
, 2016). This suggested that DUX4 C-terminus competes with the full-length DUX4 for EP300/CBP. We modeled the 9aaTAD peptide 371GLLLDELLA379 and the KIX binding motif (KBM) 416EYRALL421 into the MLL and pKID/c-Myb site of the ternary complex NMR structure of human KIX from CBP  (Bruschweiler et al. , 2013)  (PDB: 2LXT) (Figure  4C  and Data S1). The hydrophobic residues of 9aaTAD and KBM complement well what is seen in the KIX:MLL:pKID complex. Indeed, experimental tight binding (Figures  S9A-S9C ) was detected for peptides overlapping the 9aaTAD (K d z 0. 2 mM) and KBM (K d z 0. 6 mM) sequences of DUX4 to KIX domain, and for KBM binding in the presence of 9aaTAD (K d z 1. 1 mM). MED15 contains a KIX domain  (Thakur et al. , 2014)  and thus, we speculated that the DUX4 C-terminal KIX binding motif 416EYRALL421 recruits the KIX domain of MED15.
To test this, we cloned DUX4 without the C-terminal KBM motif (DUX4-KBM mutant) and performed a co-immunoprecipitation. While the V5-tagged MED15 was precipitated with the HAtagged wildtype DUX4, no interactions were found in the presence of the DUX4-KBM mutant (Figure  4D ). In summary, our analyses suggest that the 6 amino acid KBM at the end of the DUX4 C-terminus mediates interaction with MED15, alluding to DUX4 having all the attributes needed for rapid target activation. We observed prominent DUX4 immunofluorescence signal in the cytoplasm of the human early embryos (Figures  2B  and 2C ). We thus asked whether the homeodomain1-linker-homeodomain2 structure would be stabile as a unit without bound DNA and subjected the crystal structure of DUX4 (PDB: 6E8C  (Lee et al. , 2018) , Data S2) to molecular dynamics simulations.
ATAC-sequencing data analysis Bcl files were converted and demultiplexed to fastq using the bcl2fastq program. STAR  (Dobin et al. , 2013)  was used to index the human reference genome (hg19), obtained from UCSC  (Kent et al. , 2002) , and align the resulting fastq files. The resulting bam files with the mapped reads were then converted to tag directories with subsequent peaks calling using the HOMER suit of programs  (Heinz et al. , 2010). HOMER was also employed for counting the reads in the identified peak regions. Peak calling was performed twice, the first time with the default setting of HOMER and the second time with flag -style histone to identify broad peaks. Peaks were merged using the mergePeaks function in HOMER. Reads mapping to the merged peaks were counted using the annotatePeaks.
pl function the -noadj flag. The raw tag counts from the peaks were then imported to R/Bioconductor and differential peak analysis between EmGFP (À) and EmGFP (+) DUX4TetOn-hESCs with four biological replicates each was performed using the edgeR package and its general linear models pipeline. Peaks with an FDR-adjusted p value < 0. 05 were termed as control or DUX4-activated.
Library preparation, sequencing and read-alignment for CAGE-based data Nascent RNA from flash-frozen cells was isolated as described by  Hirabayashi et al. , (2019)   (Hirabayashi et al. , 2019)  with the following modifications: (i) DNase I enzyme (50 Units, Thermo Fisher Scientific) was used to prepare the DNase I solution (50 mL), (ii) the samples were incubated for up to 1 h at 37 C while being pipetted up and down several times every 10 min, and (iii) RNA quality was measured using a TapeStation4200 (Agilent). CAGE-based libraries were generated according to the no-amplification non-tagging CAGE libraries for Illumina next-generation sequencers (nAnT-iCAGE) protocol  (Murata et al. , 2014). All CAGE-based libraries were sequenced in single-read mode on an Illumina NextSeq500 platform.
Reads were split by barcode using the MOIRAI  (Hasegawa et al. , 2014)  package. Cutadapt v 1. 1. 8 (Martin, 2011)  (http://code. google. com/p/cutadapt/) was used to trim reads to 73 bp and remove reads below base quality 33 and 'N' bases. Reads aligning to ribosomal RNA sequences (GenBank U13369. 1) were removed using the rRNAdust script within the MOIRAI package. The resulting reads were aligned to the human genome (hg19) using STAR v 2. 5. 0a (Dobin et al. , 2013)  with Gencode v27lift37 (''comprehensive'')  (Harrow et al. , 2012)  as the reference gene model. Mapping was performed with the following parameters: -runThreadN 12 -outSAMtype BAM SortedByCoordinate -out FilterMultimapNmax 1. Following alignment, the re-sequenced replicates were merged using the Picard Toolkit v 2. 0. 1 with the MergeSamFiles program (Broad Institute, Picard Toolkit, 2018.
http:// broadinstitute. github. io/picard) resulting in two control (dox -) and two DUX4-expressing samples each for CAGE and NET-CAGE. BAM files were indexed using Samtools v 0. 1. 19 (Li et al. , 2009) converted to bed files using BEDTools v 2. 27. 2  (Quinlan and Hall, 2010). Transcription start sites (TSSs) were identified for all CAGE and NET-CAGE samples according to http://fantom. gsc. riken. jp/5/sstar/Protocols: HeliScopeCAGE_read_alignment. The TSSs bed files were converted to strand specific bedGraph files and subsequently to bigWig files using the UCSC software bedGraphtobigWig.
Identification of transcribed promoters and enhancers To identify promoter and enhancer regions, TSSs that mapped close to each other on the same strand were grouped into clusters. This was performed using decomposition peak identification  (Forrest et al. , 2014)  (https://github. com/hkawaji/dpi1/blob/master/identify_tss_peaks. sh) with default parameters but without the decomposition composition parameter. TSS clusters with at least three supporting CAGE tags were retained and used as input to identify bidirectionally transcribed enhancers. (https://github. com/anderssonrobin/enhancers/blob/master/scripts/bidir_enhancers). Promoter TSS clusters that were defined as those that did not overlap enhancers and mapped to +/À 300bp of the 5 0 -end of GENCODE v 27 transcripts. For differential expression (DE) analysis between control and DUX4 expressing hESC, we first counted the TSSs mapping to promoters and enhancers.
Next, coverage at single-base-pair resolution was calculated with BEDTools v 2. 27. 2 (http://bedtools. readthedocs. io/en/latest/) using only the 5 0 ends of the reads. The resulting forward and reverse bedGraph files were then converted into bigWig files using the UCSC software bedGraphtobigWig. Counting was performed using in a strand-specific manner using UCSC software bigWigAverageOverBed. Normalization and DE was performed using egdeR v3. 26. 8 (McCarthy et al. , 2012; Robinson et al. , 2010). (https://bioconductor. org/packages/release/bioc/html/edgeR. html). Promoter counts were normalized using calcNormFactors function with relative log expression, and counts were converted to log2 counts per million (CPM). A prior count of 0. 25 was added to the raw counts. For enhancers forward and reverse counts were summed up.
Repeat element analysis RepeatMasker table was downloaded from UCSC table browser (http://hgdownload. soe. ucsc. edu/ goldenPath/hg19/database/rmsk. txt. gz) and converted to BED format. The length of ATAC-seq peaks and NET-CAGE promoters and enhancers was extended from the center such that all regions are 600 bp long. Random background regions were generated using the bedtools random function such that the number and length of the background regions were the same as the region of interest. Repeat elements overlapping the regions of interest or background were identified using intersectBed with parameter -wo from BEDTools v2. 27. 2  (Quinlan and Hall, 2010). If more than one repeat element overlapped the regions, then the element with the longest overlap (base pairs) was chosen. The frequency of repeat elements overlapping the regions of interest and background was calculated.
20 ng of total RNA from each sample was used for library preparations. The libraries were prepared using the STRT method as above, with minor modifications. Briefly, RNA samples were placed in a 48-well plate in which a universal primer, template-switching oligos, and a well-specific 8 bp barcode sequence were added to each well  (Krjutskov et al. , 2016a). The synthesized cDNAs from the samples were then pooled into one library and amplified by single-primer PCR with the universal primer sequence. The resulting amplified library was then sequenced using an Illumina NextSeq500 instrument. Alignment of raw reads to the hg19 reference genome, normalization and DE was performed as per the STRTprep pipeline  (Krjutskov et al. , 2016a).
KHDC1P1 and ZSCAN4 enhancer validation Putative KHDC1P1 and ZSCAN4 enhancer regions were predicted from DUX4TetOn hESC NET-CAGE dataset. The guide RNAs targeting the each of the putative enhancers were designed using the Benchling CRISPR tool (https://benchling. com), targeting them +/À200 base pairs of the putative enhancer midpoint. Guide sequences were selected according to their on-and off-target score and position. Guide RNA oligos are shown in key resources table. Guide RNA transcriptional units (gRNA-PCR) were prepared by PCR amplification with Phusion polymerase (Thermo Fisher), using as template U6 promoter and terminator PCR products amplified from pX335 together with a guide RNA sequence-containing oligo to bridge the gap. The oligos for guide RNA transcriptional units are as in  (Balboa et al.
The DDCT method was followed to quantify the relative gene expression where CYCLOPHILIN G (PPIG) was used as endogenous control. Relative expression of each gene was normalized to the expression without doxicycline treatment. The primer sequences are listed in the key resources table. Cells transfected with either dCas9-DUX4-C or dCas9-VP192 transactivator together with TdTomato targeting guide plasmid and were used as controls. Data analyses on published single-cell tagged reverse transcription (STRT) data from human oocytes and embryos We analysed single cell RNA-sequencing data from  Tohonen et al. , (2015 ) (Tohonen et al. , 2015)  for MII oocytes (n = 20), zygotes (n = 59), 2-cell (n = 4), 4-cell (n = 15) and 8-cell (n = 14) embryos. The expression of DUX4 is elusive due to a high number of identical or nearly identical copies present in the human genome.
redundant reads were excluded, and the processed nonredundant reads were aligned to hg19 human reference genome sequences, ERCC spike-in sequences and human ribosomal DNA unit (GenBank: U13369) with RefSeq transcript alignments as a guide of exon junctions. For TFE-based statistics, the mapped reads were assembled according to the alignments, and uniquely mapped reads within the first exons of the assembled transcripts were counted, as described in  Tohonen et al. , (2015 ) (Tohonen et al. , 2015). For gene-based statistics, uniquely mapped reads within (i) the 5'-UTR or the proximal upstream (up to 500 bp) of the RefSeq protein coding genes, and (ii) within the first 50 bp of spike-in sequences, were counted. Prior to downstream analysis, low quality samples (or cells) were filtered out based on (i) ERCC spike-in read counts, (ii) human genome mapped / ERCC spike-in read counts (this represents the relative abundance of poly (A) transcripts), (iii) percentage of ERCC spike-in 5'-end reads, and (iv) percentage of reads mapping to the 5'-end of coding genes were excluded.
Details about these parameters have been provided here: (https://github. com/shka/STRTprep/blob/master/doc/result. md). Additionally, for the DUX4 knockdown experiment, the distribution of cells based on the number of coding genes was estimated. Cells below the first quartile Q1 where the number of genes expressed was <1100 were also excluded. After filtering, 12 siControl and 15 siDUX4 cells were used for downstream analysis. Normalization and DE was performed using SAMstrt v 0. 99. 0  (Katayama et al. , 2013; Krjutskov et al. , 2016a). Gene counts were normalised to ERCC spike-in counts using the SAMstrt. normalization function with parameter nresamp = 1000. DE between siControl and siDUX4 cells was performed using the SAMseq function with parameters resp. type = 'Two class unpaired', nperms = 1000 and fdr.