dataesr/scientific-paragraphs-categorization huggingface dataset:
Proximity Ligation Assay Proximity ligation assay experiment was done with the Duolink ® In Situ Red Starter Kit according to the manufacturer instructions (DUO92101, Sigma Aldrich, Saint-Quentin-Fallavier, France) on MCF-7 cells and using mouse anti-HSD10 antibody (sc-136326, Santa Cruz Biotechnology, Dallas, TX, USA) and rabbit anti-SFXN1 antibody (HPA063745, Sigma Aldrich, Saint-Quentin-Fallavier, France). A negative control without primary antibodies was also carried out. Briefly, MCF-7 cells were cultured on coverslips in 6-well plates. Cells were washed 3 times with PBS, fixed 10 min in 3. 7% PFA and permeabilized 10 min with 0. 1% Triton X-100 at 37 • C. The cells were washed thrice for 5 min with PBS between each step. The coverslips were placed in a humidified chamber and blocked with 50 µL Duolink blocking solution for 60 min at 37 • C.
Primary antibodies at 2 µg/mL were incubated overnight at 4 • C. After 2 washes for 5 min with the provided wash buffer A, PLA probes were added and incubated for 1 h at 37 • C in a humidified chamber. After 2 washes of 5 min with wash buffer A, the ligase (diluted 1/40 in ligation buffer) was incubated 30 min at 37 • C. The coverslips were washed twice, each time for 5 min, with buffer A. Amplification step in the presence of the DNA polymerase was performed during 100 min at 37 • C in the dark. Coverslips were washed twice, each time 10 min with buffer B. Subsequently nuclei were stained with DAPI for 15 min and the cover-slips were mounted in ProLong Gold Antifade Mountant (Invitrogen). Slides were observed using a Leica TCS SPE confocal microscope with the DAPI and TRITC filters. The number of red dots was quantified using Fiji software and the Cell Counter plug-ins  [22].
Biological Resources The lists of antibodies (Table  S5 ), cell lines (Table  S6 ) and plasmids (Table  S7 ) used in this study are given as supplemental material. The RRID portal (https://scicrunch. org/resources (accessed on 10 April 2020)) was used to provide antibody identifiers  [30]. The anti-SFXN1 antibody HPA063745 was used for IP. For WB, either HPA063745 or HPA019543 anti-SFXN1 antibodies were mainly used in this study and gave similar results. Those two antibodies were validated for WB analysis on protein extracts from SFXN1-depleted human cells.
Consistent with these findings, we found that SFXN1 is present in mitochondrial-enriched fractions from human MCF7, A2780 and COV434 cells and rat PC12 cells, as well as in mitochondrial fractions obtained from Drosophila larvae (Figure  1d ,e and Figure  S2 ). In addition, both endogenous SFXN1 and exogenous FLAG-tagged SFXN1 colocalized with mitochondria in our immunofluorescence studies (Figure  1f , Figures  S2  and S3 ). All these results are in agreement with the data available in the Human Atlas Protein database  [31] , where a punctate pattern resembling that of mitochondria is seen using a specific antibody raised against the C-terminus of SFXN1 (Figure  S4 ). This extremity was first predicted to be located in the intermembrane space of mitochondria  [32] , but recently, biochemical studies suggest instead that the C-terminus of SFXN1 is located inside the mitochondrial matrix  [19].
The automated analysis of gene annotations using Metascape Express analysis permitted us to identify significantly enriched terms linked to our 96 candidates. The top 20 enriched clusters are shown in Figure  4. Interestingly, the top three enriched clusters are related to mitochondrial transmembrane transport, mitochondrion organization and mitochondrial respiration. Because we immunoprecipitated SFXN1 from mitochondria enriched fractions, it is not really surprising to retrieve processes related to mitochondrial functions. Thus, we next tried to identify the processes related to mitochondrial functions that are preferentially enriched in our experimental dataset. For this purpose, a Metascape meta-analysis was performed to compare enriched terms associated with SFXN1 partners and those related to proteins known to be mitochondrial.
As shown in Figure  5 , among the most significant clusters related to mitochondrial proteins, five were significantly enriched for SFXN1 partners: mitochondrial transmembrane transport, respiratory electron transport, mitochondrial complex IV assembly, mitochondrial protein import and mitochondrial transport. Thus, potential SFXN1 physical partners identified in this study appear to be mainly involved in respiration and mitochondrial transport. Because we immunoprecipitated SFXN1 from mitochondria enriched fractions, it is not really surprising to retrieve processes related to mitochondrial functions. Thus, we next tried to identify the processes related to mitochondrial functions that are preferentially enriched in our experimental dataset. For this purpose, a Metascape meta-analysis was performed to compare enriched terms associated with SFXN1 partners and those related to proteins known to be mitochondrial.
• Protein-protein interactome network analysis using Metascape Metascape was utilized to automatically construct an SFXN1 protein-protein network from our list of putative SFXN1 physical partners based only on physical interactions available in the BioGrid, OmniPath, InWeb_IM and STRING databases. The protein interaction network formed by the putative SFXN1 physical partners is shown in Figure  6. Four densely connected protein complexes were identified, and among them, three were functionally labeled. SFXN1 and SFXN3 were found in two separate MCODE complexes respectively related to protein folding and mitochondrial transport. SFXN1 and ATAD3A were found in the same densely connected complex, highlighting the existence of a physical interaction between SFXN1 and ATAD3A, in agreement with our mass spectrometry analysis.
's list of putative SFXN1 partners. SHMT2 is the main mitochondrial enzyme of the OCM pathway, catabolizing serine into glycine following its entry inside the mitochondrion. However, we did not find this protein in our list of candidates. To identify pathways that are enriched both in Liu et al. 's dataset and ours, we further analyzed the meta-analysis results obtained with Metascape. Ten enrichment clusters are shared between the two lists, the most significant ones being related to mitochondrial respiration, mitochondrion organization and mitochondrial transport (Figure  8a ). A protein-protein interaction network was automatically constructed from a merge list including SFXN1 putative partners identified in both studies, and MCODE components were identified from the merged network (Figure  8b ).
Five shared genes were seen in the densely connected networks given by Metascape. For example, SFXN1 was found in a dense network described by the GO term "inner mitochondrial membrane organization", together with SLC25A12 and TOMM22. respiration, mitochondrion organization and mitochondrial transport (Figure  8a ). A protein-protein interaction network was automatically constructed from a merge list including SFXN1 putative partners identified in both studies, and MCODE components were identified from the merged network (Figure  8b ). Five shared genes were seen in the densely connected networks given by Metascape. For example, SFXN1 was found in a dense network described by the GO term "inner mitochondrial membrane organization", together with SLC25A12 and TOMM22.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/biology11091298/s1, Figure  S1. Specificity of the anti-SFXN1 antibody used for immunoblotting; Figure  S2. SFXN1 is a mitochondrial protein; Figure  S3. FLAGtagged SFXN1, SFXN2 and SFXN5 proteins colocalize with mitochondria; Figure  S4. Immunostaining of MCF7 cells with the HPA063745 antibody reveals a mitochondrial pattern; Figure  S5. SFXN1 is successfully immunoprecipitated from mitochondria-enriched fractions from MCF7 cells using a specific antibody recognizing its C-terminus (Atlas Antibody HPA063745, Sigma-Aldrich); Figure  S6. Analysis of STRING v11. 0-generated SFXN1 interactome reveals pathways linked to ribosome and OXPHOS; Figure  S7. Analysis of STRING v11.
Conceptualization and methodology, N. T. , M. B. , S. B. and N. L. F. ; investigation and validation, N. T. , M. B. , A. G. and J. M. D. l. H. ; writing-original draft preparation, N. T. , M. B. and N. L. F. ; writing-review and editing, all authors; funding acquisition, I. G. , B. M. and N. L. F. All authors have read and agreed to the published version of the manuscript. Funding: This research was partly funded by Agence Nationale de la Recherche (ANR), grant number ANR-21-CE13-0009-01 (SiFeMi project) and NeurATRIS ANR-11-INBS-00110, of the French Investissements d'Avenir Program run by the Agence Nationale pour la Recherche. Institutional Review Board Statement: Not Applicable.
Author Contributions: Conceptualization and methodology, N. T. , M. B. , S. B. and N. L. F. ; investigation and validation, N. T. , M. B. , A. G. and J. M. D. l. H. ; writing-original draft preparation, N. T. , M. B. and N. L. F. ; writing-review and editing, all authors; funding acquisition, I. G. , B. M. and N. L. F. All authors have read and agreed to the published version of the manuscript. Funding: This research was partly funded by Agence Nationale de la Recherche (ANR), grant number ANR-21-CE13-0009-01 (SiFeMi project) and NeurATRIS ANR-11-INBS-00110, of the French Investissements d'Avenir Program run by the Agence Nationale pour la Recherche. Institutional Review Board Statement: Not Applicable.
5 and E14. 5 wholemount pancreata (Fig. 2 ). After image acquisition, the data were processed using ZEN and Imaris software (Fig. 2B-G ) to generate 3D representations of the acquired images. We generated a set of high-resolution images from E12. 5 and E14. 5 pancreata, in which epithelial and mesenchymal (Fig. 2B-E ) or epithelial and endothelial cells (Fig. 2F, G ) were simultaneously marked in the same embryo. This dataset provided us with suitable material to analyze further the interactions between the pancreatic epithelium and surrounding tissues. Using the 'Surface' and 'Spots' components in Imaris, we reconstructed the pancreatic epithelium, mesenchyme and its vascular network in three dimensions and performed different analyses on the obtained 3D surface renderings, such as quantification of blood vessel diameter and distribution of endothelial/mesenchymal cells around the pancreatic epithelium (Figs  3  and 4 ).
The Pancreas Embryonic Cell Atlas as an open access data repository All 3D LSFM images visualizing the three main cell types (epithelium, endothelium and mesenchyme) of the murine embryonic pancreas have been annotated and deposited on the open-source database openBIS (open Biology Information System) (https://openbis. ch/) to start building a Pancreas Embryonic Cell Atlas (Table  S1 ). We built a customized data-management platform (A) Representative LSFM 3D images of the endocrine pancreas from wild-type embryos at E14. 5, E16. 5 and newborns at P0. WMIF for VEcad (red) marks the endothelium, glucagon (blue) labels the endocrine α-cells, Pdx1 (green) labels the pancreatic epithelium at E14. 5 and becomes enriched in endocrine cells from E16. 5 onward. 3D images are shown as merged (leftmost panels) and individual channels.
, 2010). This angiogenic signal might promote the recruitment and expansion of endothelial cells around the pancreatic epithelium, resulting in more intimate epithelial-endothelial contacts. We have provided access to our acquired dataset through the Pancreas Embryonic Cell Atlas, an open source data repository (Fig. S4 ). So far, 3D LSFM-based analyses of the mouse embryonic pancreas together with its neighboring mesenchymal and endothelial tissues have not been reported. Thus, the Pancreas Embryonic Cell Atlas will provide the scientific community with a valuable source of data for future investigation. Similar to other open source data repositories, such as the human 'Pancreatlas' (https://www. pancreatlas. org/) (Saunders et al. , 2020) , we envision the Pancreas Embryonic Cell Atlas as a dynamic platform that will evolve with continued input from us and others.
Foremost, the collection of raw data deposited in the Pancreas Embryonic Cell Atlas will help the scientific community to develop and test novel hypotheses on tissue interactions that guide pancreatic differentiation and morphogenesis. Also, we provide datasets of already rendered surfaces (shown in Fig. 3 ) that can be viewed using the free Imaris Viewer software, ImageJ and QuPath (Fig. S5 ), and do not require access to commercial software or high-performance computing. Finally, we envision that Pancreas Embryonic Cell Atlas image collections will identify collaborations also outside the pancreas field and lay the foundation of interdisciplinary work that integrates cellular-resolution data within the context of whole-organ architecture.
Pancreas Embryonic Cell Atlas and browsing features The images are stored on the data management platform openBIS (https:// openbis. ch/). For repository access, first please contact the PAN3DP consortium (https://www. pan3dp-project. eu/ or pan3dp@kcl. ac. uk) to be granted guest access to the platform and then visit https://openbis-pancreasatlas. ethz. ch/. The collections are accessible within the 'PAN3DP' space in the project called 'PANCREAS_ATLAS'. Each collection corresponds to one developmental stage. Within each collection, the objects correspond to specific genotypes, i. e. transgenic mouse lines. Each object contains individual samples, i. e. the image datasets. For a schematic representation of data organization in the repository, see Fig. S4. Within each sample, images in all different formats can be retrieved using advanced filter commands and downloaded separately or collectively.
To access the filters, go to the 'Utilities' section of the navigation menu (left side of the webpage) and click 'Advanced Search'. Set the 'Search For' box to 'Dataset' and insert the chosen filtering criteria. The data will be displayed in the 'Results' section and can be quickly accessed for viewing and download. The information displayed in the 'Results' table can be adapted according to individual needs using the 'Columns' drop-down menu. For the most informative overview on the 3D architecture of the pancreas, it is recommended to view the surfaces combined with the multi-channel microscope images in the Imaris proprietary format (. ims) using the free Imaris Viewer (https://imaris. oxinst. com/imaris-viewer). To perform further modification on the data, the commercial version of Bitplane Imaris software is required.
Here, we compared amplification factors (AF) for two sites (Borgsweer and Loppersum) calculated with the model V S and the field V S (Chmiel et al. , Geophysical  Journal International, 218(3), 1781-1795, 2019  and new data). Our AF results over periods relevant for seismic risk (0. 01-1. 0 s) show that model and field V S profiles agree within the uncertainty range generally accepted in geo-engineering. In addition, we compared modelled spectral accelerations using either field V S or model V S in Loppersum to the recordings of an earthquake that occurred during the monitoring period (M L 3. 4 Zeerijp on 8 January 2018). The modelled spectral accelerations at the surface for both field V S and model V S are coherent with the earthquake data for the resonance periods representative of most buildings in Groningen (T = 0.
, 2017a)  and geological models  (Kruiver et al. , 2017b). During recent years, new V S data in Groningen have become available. Passive seismic surveys on various large 40-60 km 2 and ~1 km 2 small square arrays of flexible seismic monitoring networks were conducted in Groningen targeted at gathering V S information to a depth of 800 m and 100 m, respectively. These data provide an independent source of V S , although of limited spatial extent. These square arrays of so-called field V S data provide an opportunity to check whether the level of spatial variability in the GMM using model V S data is sufficient. Comparing site response amplification using either model V S or field V S for two selected blocks of flexible array data provides insights on the sensitivity of the results to variations in V S.
The general workflow (Fig. 1 ) is reflected in the structure of the paper. This paper first presents the V S datasets from field measurements from surface to 800 m depth and more detailed datasets from surface to 100 m depth. The V S dataset from the field to 800 m depth  (Chmiel et al. , 2019)  lacked detail in the top ~100 m (by design). The resolution in the top 100 m was improved with new field data. The acquisition and processing of the new V S data to 100 m depth are included in the paper. The next section describes the site response methodology, including the input datasets. Next, the comparison of amplification results between model V S and field V S shows that model AF and field AF are comparable or that the model AF is conservative. In the discussion, the spatial variation from the GMM is compared to the recordings of an earthquake that was recorded by one of the flexible array network blocks.
5 km by 10 km for Borgsweer (December 2016/January 2017) and 8 km by 8 km for Loppersum (October/November 2016). The small array with a target depth of 100 m measured 1 km × 1 km in both cases. Data were collected in April/May 2018 for Borgsweer and in December 2017/January 2018 for Loppersum. The ambient noise surface wave tomography uses ambient seismic noise from natural and anthropogenic sources for subsurface imaging and monitoring. Cross-correlation between receiver pairs is used to extract an estimate of the Green's function (e. g. Bensen et al. , 2007; Lecocq et al. , 2014)  and analysis of dispersion of surface wave from the cross-correlated data generates a near-surface velocity model  (Shapiro and Campillo, 2004; Mordret et al. , 2013; Boué et al. , 2016). The 100 m target depth dataset was processed using the same approach as the 800 m target depth dataset  (Chmiel et al.
, 2019). In the following, we summarise the processing workflow using the Borgsweer flexible array dataset. The Loppersum array was processed in the same way. First, we performed a quality check of the recorded signals for every station through the probabilistic power spectral density. Next, following the procedure in  Bensen et al. (2007)  and  Chmiel et al. (2019)  the records were correlated for the 97,020 possible station pairs to retrieve the surface waves (Fig. 4 , step a). Beamforming analysis  (Rost & Thomas, 2002; Fig. 4. Flow chart of the processing workflow used for computing the V S profiles from ambient seismic noise. Steps are identical for V S-to-800 m and V S-to-100 m. The illustrations show examples from V S-to-100 m from Borgsweer. Numbers on the axes are not shown for better readability.
Site response methodology Site response analyses were carried out using the 1D equivalent linear approach for vertically propagating shear waves, consistent with the GMM development for the region  (Bommer et al. , 2017a, b). The software program STRATA uses Random Vibration Theory in the frequency domain  (Rathje and Ozbey, 2006; Kottke and Rathje, 2008). Standard settings were used for effective strain ratio (0. 65) and damping for response spectra (5%). All motions and response spectra were defined as outcrop (2A). The reference baserock horizon in the GMM lies at ~800 m depth and is formed by the base of the North Sea Supergroup. The North Sea Supergroup is made up of unconsolidated sediments and overlies the limestones of the Cretaceous Chalk Group. This transition corresponds to a significant impedance contrast.
The intermediate depth range from 50 m to ~130 m depth consists of V S data resulting from the modern inversion of groundroll from the legacy seismic reflection surveys of the 1980s. The depth range from ~100 m to~800 m is formed by the conversion of the compressional wave (V P ) in the pre-stack depth migration velocity model to V S using V P /V s from well logs of two reservoir-deep wells in Groningen. The V P /V s ratio in the Upper North Sea Group varies with depth and corresponds to a Poisson's ratio varying between 0. 45 and 0. 47  (Kruiver et al. , 2017a). The V P /V s ratio of the Lower North Sea Group is constant 3. 2, corresponding to a Poisson's ratio of 0. 446  (Kruiver et al. , 2017a). The V S-to-800 m and V S-to-100 m datasets were spliced together for the 1 km × 1 km extent of the V S-to-100 m blocks on the corresponding grid.
The depth of the velocity increases in the V S-to-800 m field data generally occurs at a larger depth than in the model V S based on the imaging seismic data  (Kruiver et al. , 2017a). The model V S has a sharp jump in V S at the bedrock depth, whereas the V S-to-800 m field data have a more gradual increase. In the site response analyses, a common bedrock depth for model and field V S have been defined of 750 m (Borgsweer) and 800 m (Loppersum), with a bedrock V S of 1400 m/s. No new information for stratigraphy and soil type was available for the field V S data. Therefore, the field V S data were combined with the stratigraphy and soil type model of the model V S data in order to obtain all input parameters for the site response calculations. The model V S data were also resampled on the 100 m field V S data grid.
In this way, differences in calculated amplifications are only related to variations in V S , since the stratigraphy and soil types between the model V S data and field V S data are identical. This resulted in 529 soil profiles for Borgsweer and 541 soil profiles for Loppersum. The input motions comprise 3,600 Fourier Amplitude Spectra (FAS) from the Version 6 GMM  (Bommer et al. , 2019). The FAS motions represent ground motions at the reference baserock horizon defined as outcrop motions and were created using EXSIM  (Motazedian & Aktinson, 2005; Boore, 2009). They span a range of magnitudes from M 1. 5 to 7. 5 and of rupture distance from 3. 0 to 60 km in 20 log-spaced steps  (Bommer et al. , 2019). For all soil columns, 10 random input motions were selected covering the range from weak to strong Groningen motions.
11. This figure shows that the model underpredicts the AF at some riskrelevant periods and overpredicts at other risk-relevant periods. In the used risk model, however, only the overall AvgSa is important. The average relative difference in AF has been calculated over the risk-relevant periods for all combinations of field data and model data for Borgsweer and Loppersum. The results are summarised in Fig. 12. The results are different for Borgsweer and for Loppersum. Looking at the full field data AF (B) compared to the full model data AF (A), the model overpredicts the AF relative to the field data by 16% on average for Borgsweer. Using the field data for the top 90 m (C) results in a similar overprediction of 19%. For Loppersum, however, the average AF is very similar between the model and field data: the model underpredicts relative to the field data by only 2%.
This is well within the range of the commonly acceptable uncertainties in geo-engineering (10-20%). Moreover, the error bars in Fig. 12  always encapsulate the zero line, indicating that the overall AvgSa in the risk calculations is unbiased. Using only the deeper field data (>90 m, case D), the results between Borgsweer and Loppersum are similar: the model slightly underpredicts relative to the field data by 3-4%. The shallower layers have a more profound influence on the AF than the deeper layers. This is reflected in the larger standard deviation for cases B and C (with a difference in V S in the top 90 m relative to case A) than for case D (identical to A in the top 90 m).