dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We present in this article a detailed study of artefacts in BCDI reconstructions induced by both dataset collection and phase retrieval algorithms (i. e. , for instance, aliasing due to detector size and detector gaps, detector dynamic range, etc. ). The methodology used in the paper helps to isolate statistical errors from systematic errors that are inherent in experimental setups. The resolution in the retrieved strain is limited by artefacts which can reach 10 Table  1. RMSE strain values obtained for the surface voxel layer and the bulk of the nanocrystal, depending on the data processing scheme. Apodization was realized using a Blackman window. Values in parentheses correspond to the isosurface used for the statistical analysis. Difference between the simulated strain (red) and the retrieved strain (black), with a too low isosurface (32.
5%) corresponding to volume conservation. (h) Difference between the simulated strain and the retrieved strain, with an isosurface determined by our criterion (70%). Tick spacing corresponds to 50 nm. The background has been artificially set to grey outside the support. when large masked areas are present in the dataset. The best approach is, when possible, to avoid using a detector with gaps. Regarding the size of the FFT window used for phasing, the best practice is to select the smallest window containing signal, while not truncating the diffraction pattern in order to avoid any unnecessary loss in resolution. Finally, adding a post-processing filtering step can help further reducing high-frequency noise. BCDI is a technique applied increasingly to in situ or operando systems, in which the initial qualitative approach tends to be replaced by a quantitative analysis of displacement and strain for probing subtle changes under real application conditions.
A typical counting time was 1 s per angle, to get good resolution while preserving the stability of the particle. The sample was mounted on a Physik Instrumente Mars xyz piezoelectric stage with a lateral stroke of 100 μm and a resolution of 2 nm, sitting on a hexapod that was mounted on a (3 + 2 circles) goniometer. Simulation details. 3D simulations were realized using the same pipeline as for experimental data. Using a support retrieved from a real dataset, a complex object was created using a particular phase pattern (flat or not). Then, the data was interpolated back into the detector plane using the same geometric parameters as the experiment. The diffraction pattern was calculated on a 10 3 × 10 3 × 10 3 grid to avoid as much as possible introducing aliasing and converted into photons to a defined total diffracted intensity.
No noise was introduced into the simulated diffraction pattern. Then, phase retrieval was performed as described in the next section. For 2D simulations, we used an asymmetric 2D core-shell model with a particular phase. The diffraction pattern was then calculated using FFT or a kinematic sum. Finally, phase retrieval was performed as described in the next section. Phase retrieval. Phase retrieval was carried out on the raw diffracted intensity data using PyNX package  36  , imposing at each iteration that the calculated Fourier intensity of the guessed object agrees with the measured data. The metric used to estimate the goodness of fit during phasing was the free log-likelihood  42  , available in PyNX. Defective pixels for experimental data and gaps in the detector were not used for imposing the reciprocal space constraint mentioned above and thus were evolving freely during phasing.
Acknowledgements The authors are grateful to the  European Synchrotron Radiation Facility  for allocating beamtime. The measurement was performed at beamline ID01. We thank  Hamid Djazouli  for his excellent support during the preparation of the experiment. The authors would like to thank  Vincent Favre-Nicolin  for his continuous improvement of the PyNX package used here for phase retrieval. E. J. M. H. acknowledges financial support of  NWO TOP  and KNAW CEP grants. M. -I. R. acknowledges financial support from  ANR Charline  ( ANR-16-CE07-0028-01 ),  ANR   Tremplin ERC  ( ANR-18-ERC1-0010-01 ) and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 818823 ).
Data availability The data reported in this paper is available upon request. The simulation scripts belong to the BCDI package (https://doi. org/10. 5281/zenodo. 3257617), that can be downloaded from PyPI (https://pypi. org/project/bcdi/) or GitHub (https://github. com/carnisj/bcdi). The phasing algorithm PyNX is available at http://ftp. esrf. fr/pub/ scisoft/PyNX/.
They particularly investigated the cloud effects on the differences between observations and simulations and thus proposed an appropriate quality check and dedicated observation errors. In this study, we are interested in IASI observations, where the radiances are considered with collocated cluster statistical properties of the Advanced Very High Resolution Radiometer (AVHRR) also on board the Metop platform. AVHRR has a horizontal resolution of 1 km at nadir  (Cayla, 2001). Collocated AVHRR data provide information on surface properties and the presence of clouds in the IASI field of view (FOV). They can therefore be used for cloud detection. The AVHRR cluster information associated with IASI has already proven to be useful for selection purposes in the context of cloudy data assimilation, with an explicit treatment of microphysical variables in the AROME model by  Martinet et al.
The ARPEGE model and its 4D-Var system The ARPEGE model is the global NWP model at Météo-France, used operationally since the early 1990s  (Courtier et al. , 1991). This system is fully integrated within the ARPEGE-IFS software conceived, developed and maintained in collaboration with ECMWF. This spectral global model has a stretched grid with a horizontal resolution of around 7. 5 km over France and 37 km over the antipodes. It has 105 vertical levels with a terrainfollowing pressure hybrid coordinate, with the first level located at 10 m above the surface and an upper level at around 70 km. Clouds and precipitation are described by using three different schemes in the ARPEGE model. The stratiform clouds in terms of cloud profile and precipitation are explicitly modelled from the microphysical condensation scheme by  Lopez (2002).
If the four cloud types were "sea", the IASI pixel is considered to be clear, if the same cloud type is found in SEVIRI pixels, the IASI observation is set to homogeneous cloudy observation, otherwise it is considered to be a heterogeneous observation. Table  2  presents the results of this comparison. A total of 67 599 IASI observations were collocated with SEVIRI data during the day on 30 January 2017. In the global dataset, we found that 12 % are clear homogeneous observations, 51 % are cloudy homogeneous observations and 37 % of IASI observations are made of different SEVIRI cloud types. This corresponds well to the results obtained with the results found with the percentage of cloudy AVHRR pixels in the IASI field of view (Table  3 ). When applying the M2013 criteria, 11 % of clear observations are retained.
This number does not vary a lot when applying the other criteria. The number of homogeneous cloudy observations is more sensitive to the criteria because it varies between 9 % for E2014 and 35 % for the www. atmos-meas-tech. net/12/3001/2019/ Atmos. Meas. Tech. , 12, 3001-3017, 2019  Obs_HOM one. The COMPR criteria provide a compromise between M2013 ad E2014 in terms of selected observations. The percentage of cloudy AVHRR pixels in the IASI field was also used to assess the choice of homogeneity criteria (Table  3 ). Our global dataset is made of 50 % of the observations entirely covered by clouds and 12 % of clear observations according to the AVHRR cloud cover. These results obtained over the global set agree well with the ones obtained with SEVIRI data over the Atlantic Ocean. The percentage of selected observations for each selection method is larger (+2/4 %) with the SEVIRI data evaluation than with the AVHRR cloud cover.
The bias and standard deviation of observations minus simulations (O -G), are shown in Fig. 3a  for the 314 IASI channels used at Météo-France. As expected, the best statistics are obtained for channels less affected by clouds (e. g. CO 2 and water vapour high peaking channels). For the whole dataset, window channels present a bias of around -0. 6 K. The standard deviations are larger (around 12 K) for window channels sensitive to the surface. With the M2013 selection method (Fig. 3b ), the standard deviation of window channels is reduced to around 4 K and the bias reduced close to zero. The standard deviation of the other channels (680-780 cm -1 ) is also decreased. The E2014 selection method (Fig. 3c ) improves the bias and the standard deviation (2. 0 K for window channels) for all the channels.
As expected, the impact is larger for surface-sensitive (and thus cloud-sensitive) channels than for the tropospheric channels (680-780 cm -1 ). Conversely, with the Obs_HOM method (Fig. 3d ), small improvement of the statistics is obtained for the standard deviation and the bias. The statistics obtained with the COMPR method (Fig. 3e ) are reduced compared to the whole dataset and slightly worse than with the initial E2014 method (for window channels the standard deviation is around 2. 2 K instead of 2 K for E2014). To complete the comparison, the probability density function (PDF) of the O -G differences was studied (Fig. 4 ). Three channels were assessed: the window channel 1271 (962. 5 cm -1 , whose weighting function peaks at around 1000 hPa), the mid-tropospheric water vapour channel 2701 (1320 cm -1 , weighting function maximum at around 400 hPa) and the low-tropospheric water vapour channel 5403 (1955 cm -1 , weighting function peaking at around 900 hPa).
Using the M2013 criteria, differences in O -G for the window channel are significantly reduced, from -18 to 20 K, and from -7 to 9 K using the E2014 criteria (Fig. 4g, h, i ). With Obs_HOM criteria (Fig. 4j , k, l), the O -G distribution is not much improved for all channels. When the homogeneity criterion in the model space is added using the COMPR selection, the O -G distributions become symmetrical, get closer to the Gaussian distribution and centre around zero for the three previously selected channels (Fig. 4m, n, o ), which indicates the data are correctly diagnosed as homogeneous. Table  4  summarizes statistics in terms of bias and standard deviation of background departure for the different datasets. The bias and standard deviation obtained by the M2013 method have some reasonable statistics before the assimilation (-0.
2 K and standard deviation of 10. 5 K). When the test on observations minus simulations of the infrared channels AVHRR is added by the COMPR method, results are improved. For window channels the bias is reduced to -0. 09 K and the standard deviation to 2. 1 K compared to -0. 6 and 11. 7 K for all observations, which presents a good score compared to the M2013 and Obs_HOM methods. In addition, 36 % of the observations are retained, compared to the whole dataset, with 10 % clear observations and 11 % cloudy observations of the total, which represents twice the number of cloudy observations selected by E2014, which removes many more observations, and shows that the proposed methodology is effective. The cloud cover distribution corresponding to the number of observations that is kept (36 %) is made of 28 % clear observations and 29 % observations totally covered by clouds.
Conclusion and perspectives A new method using collocated AVHRR cluster information to improve the selection of homogeneous IASI observation Atmos. Meas. Tech. , 12, 3001-3017, 2019  www. atmos-meas-tech. net/12/3001/2019/ scenes within the numerical weather prediction ARPEGE model has been developed at Météo-France for data assimilation purposes and has been presented in this study. The first step consisted in adapting the IASI observation operator based on the RTTOV radiative transfer model by using the RTTOV-CLD module with cloudy microphysical parameters (liquid water content (ql), ice content (qi) and cloud fraction) for the simulation of cloudy radiances. A qualitative evaluation of such a module showed realistic simulated cloud structures at various locations around the globe with a quite good agreement against IASI observations.
Acknowledgements. CNES and  Météo-France  funded this work through a  PhD grant for Imane Farouk. Cloud classification data from MSG are provided by  Météo-France/CMS. We thank the  ICARE Data and Services Center  for providing access to the data used in this study. Jean-Francois Mahfouf  and  Jean Maziejewski  are warmly thanked for their help in revising a previous version of the paper. Review statement. This paper was edited by  Brian Kahn  and reviewed by two anonymous referees.
Data availability. IASI and AVHRR data are available from the EUMETSAT website (http://www. eumetsat. int, last access: 14 May 2019). MSG cloud classifications are available from ICARE Data and Services Center embedded in the AERIS facility (https: //en. aeris-data. fr/direct-access-icare/, last access: 14 May 2019). The source code of ARPEGE cannot be obtained but the data in the figures are available from the corresponding author upon request.
2 ). Human DUX4 comprises two homeodomains (HD1: residues 19-81; and HD2: 94-153; UniProt numbering), an intrinsically disordered region (IDR: 154-370) and a C-terminal domain (residues 371-424; Fig. 2a ). A stretch of hydrophobic residues (308-323) within IDR is conserved in primates and could have a regulatory role by interacting with other transcription co-regulators or masking the C-terminal domain from solvent when no interacting partners are present. The C-terminal domain is predicted to have two structurally ordered and evolutionarily conserved regions: residues 371-387 and residues 414-423 (Fig. 2a ). As a secondary structure the Cterminal domain is predicted to contain three alpha helices and may form a stable fold similar to that revealed by NMR for the Pax8 paired box domain (PDB: 2K27;  15  ).
We found a nine amino acid transactivation domain (9aaTAD) located at the C-terminal domain (371-379), also recently reported by Mitsuhashi et al. 16. This motif is also present in the PRD-like homeoprotein LEUTX  17  and might be recognized by other proteins involved in the regulation of transcription, similar to the 9aaTAD motif of the MLL that interacts with the cAMP-response element binding protein, a transcriptional co-activator  18. In the crystal structure of DUX4 (PDB: 6E8C;  19  ) HD1 and HD2 bind DNA in a symmetric manner (Fig. 2b ). Residue R21, located at the N-terminal loop of HD1, interacts with the residues I121, E135 and Q139 of HD2 and, equivalently, R96 located at the N-terminal arm of the HD2 interacts with the residues I46, E60 and Q64 of HD1 (Fig. 2b, c, d ). Moreover, the main-chain carbonyl groups of G19 of HD1 and G94 of HD2 respectively form a hydrogen bond with the main-chain nitrogen atom of I121 of HD2 and I46 of HD1.
DUX4 protein interacts with transcriptional mediators and chromatin modifiers Abundant nuclear and cytoplasmic DUX4 in the human embryos and the modelled stable structure of the DUX4 when not bound to DNA suggested that DUX4 might have functions beyond DNA binding. To study this, we applied a recently published MAC-tag method to identify DUX4 protein-to-protein interactions  20, 21. We identified altogether 158 BioID and 43 AP-MS high-confidence DUX4 interactions, out of which 19 appeared in both datasets (FDR < 0. 05, corresponding to > 0. 73 SAINT Score). Single BioID interactions and AP-MS interactions together with the interactions that appeared in both data sets, based on the scored frequency of interaction with DUX4, are shown in Fig 3a. We concentrated on the DUX4 interacting proteins that scored above the median value (Fig  3b ).
Overrepresentation Enrichment Analysis (ORA) of protein pathway markers (Reactome, KEGG) showed significant enrichment (p < 0. 05, FDR < 0. 01) of markers linked to generic transcription and 'RNA Polymerase II Transcription', 'Chromatin organization' and 'Chromatin modifying enzymes'. Comparing our list of genes to protein complex databases such as ComplexPortal and Corum using Fisher's Exact Test yielded significant overrepresentation of several variants of the SWI/SNF ATP-dependent chromatin remodelling complex, Core mediator complex, NSL histone acetyltransferase complex, SRCAP histone exchanging complex and the NuA4 histone acetyltransferase complex (p < 0. 05, FDR 0. 01) (Fig. 3b ). There were 28 DUX4 interacting proteins classified as RNA binding (GO:0003723) and 19 out of these were linked to spliceosome and pre-mRNA-splicing.
In the protein complex database Corum, DUX4 interactors were significantly overrepresented in the Spliceosome, with 10 interactors comprising 7% of the whole complex. In addition, we found six DUX4 interacting proteins (ZSCAN4, ZSCAN5C, ZSCAN5D, RFPLA, 10 RFPLB, RNF8, PTOV1, and MB3LB) that have not appeared in the analyses of other transcription factors. As the protein interaction assay was run in a non-embryonic cell line (HEK), we next studied which of the identified DUX4 interacting proteins are expressed by human oocytes or embryos  3, 11. Importantly, the vast majority of the genes coding for the DUX4 interacting proteins were expressed in oocytes (maternal genes), embryos, or both. These results suggested that DUX4 could potentially regulate maternal and embryonic proteins in the cytoplasm and nucleus during the OET.
siDUX4 embryos did not arrest during the experiment by live imaging follow-up, but their transcriptome was dysregulated in comparison to that of the siCTRL embryos. A number of transcripts downregulated in the siCTRL appeared enriched in the siDUX4 embryos (Fig. 4c ). In order to study how the siDUX4 enriched transcripts typically behave during the first three days of development, we next compared siDUX4 and siCTRL embryos to our published gene expression data set  3, 10   TopAnat  24  for the altogether 91 genes enriched in siDUX4 embryos resulted in terms such as 'female germ cell' and 'oocyte', in agreement with non-degraded maternal transcripts. As shown for the siDUX4 enriched gene set, a number of well-known maternal genes such as GDF9, ZP1, ZP2, ZP3, KHDC3L, WEE2, NPM3, TUBB8, and RERE failed to downregulate (Fig.
Preprocess of raw STRT RNAseq reads The sequenced STRT raw reads were processed by STRTprep  23  , v3dev branch 91a62d2 commit at https://github. com/shka/STRTprep/tree/v3dev. 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 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. 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 Töhönen et al 2015  3.
Downstream STRT RNA-sequencing data analysis Differentially expressed genes and TFEs required significantly different tendency on the expression levels between two groups (q-value < 0. 05), and significantly larger variation than the technical variation (adjusted p-value < 0. 05 by BH correction). The former tendency was tested by the R package SAMstrt v0. 99. 0  45  , and the latter variation (fluctuation) was estimated based on gene-to-spikein (or TFE-to-spikein) ratios in the squared coefficient of variation, described in Supplementary Text 1 of Krjutskov et al. 2016  23. The minimum value but non-zero was added to all the 20 normalized read counts and then the counts were divided by the minimum value so that the logarithm of zero counts become zero. Enrichment analysis of anatomical terms for the list of upregulated genes by siDUX4 was performed using the TopAnat  24  (https://bgee.
org/?page=top_anat). All human genes in the Bgee database were used as background. STRT data of human early embryo were obtained from Töhönen et al. 2015 and 2017  3, 10  and were overlapped with TFEs using the intersectBed function from BEDTools  46  (v2. 27. 1). DUX4 ChIP-seq data was obtained from GSE33838  26  and scores around the FEs were calculated with computeMatrix and visualized with plotProfile from deepTools  47 (v3. 1. 3). Motif enrichment was analyzed using the command findMotifsGenome. pl from HOMER  48  (v4. 10. 3) with the option "-size -300,100". Enrichment analysis with publicly available ChIP-seq datasets was conducted with ChIP-Atlas  49  (http://chip-atlas. org). A total of 7,216 human transcription factor ChIP-seq datasets which had more than 500 peaks were analyzed.
Bioinformatics analysis and molecular dynamics simulations of the DUX4 protein The sequence for the human DUX4 (Q9UBX2) protein was obtained from the UniProt database (The UniProt Consortium  30  ), whereas other sequences were retrieved from the non-redundant database of NCBI using blastp  51  and with human DUX4 as the query sequence. Multiple sequence alignment was carried out using MAFFT  52. Secondary structures, solvent accessibility and disordered regions were predicted using POLYVIEW-2D  53  , SABLE  54  , SCRATCH  55  and RaptorX-Property  56. The 9aaTAD web server  57  was used to predict 9aaTAD motifs. The 2. 12 Å resolution crystal structure of the DUX4 HD1-linker-HD2 fragment bound to DNA  19  (PDB: 6E8C) was obtained from the Protein Data Bank  58. PyMOL (version 1. 6; Schrödinger LLC) and Bodil  59  were used to analyze inter-HD interactions.
Based on the DUX4 structure, molecular dynamics (MD) simulations, over all atoms, were used to explore the dynamic states of the (1) double HD complex with (HD1-HD2 + DNA) and (  2 ) without (HD1-HD2) bound DNA and the individual HDs with bound DNA: (3) HD1 + DNA and (4) HD2 + DNA. MD simulations of these four structures were carried out with the AMBER package  60  (version 18) using the ff14SB 61 (for protein) and OL15  62  (for DNA) force fields. The structures were solvated with explicit TIP3P water molecules  63  within an octahedral box ensuring a 12 Å distance between the boundaries of the simulation box and solute atoms. Sodium counter ions were added to neutralize the system and additional Na + /Cl -ions were added to bring the salt concentration to 150 mM. Periodic boundary conditions were employed and the particle-mesh Ewald algorithm  64  was applied to electrostatic interactions with a distance cutoff of 9 Å.
Dynamic exclusion was set for 30 s. Database search was performed with Proteome Discoverer 1. 4 (Thermo Scientific) using the SEQUEST search engine on the Reviewed human proteome in UniProtKB/SwissProt databases (http://www. uniprot. org, downloaded Nov. 2018). Trypsin was selected as the cleavage enzyme and maximum of 2 missed cleavages were permitted, precursor mass tolerance at ±15 ppm and fragment mass tolerance at 0. 05 Da. Carbamidomethylation of cysteine was defined as a static modification. Oxidation of methionine and biotinylation of lysine and N-termini were set as variable modifications. All reported data were based on high-confidence peptides assigned in Proteome Discoverer (FDR < 0. 01).
Contaminant Repository for Affinity Purification (CRAPome, http://www. crapome. org) were used to discover statistically significant interactions from the AP-MS data  69. The DUX4 LC-MS data was ran alongside a large dataset of other transcription factors, as well as a large GFP control set. Final results represent proteins with a SAINT score higher than 0. 73, and present in all four replicates.