dataesr/scientific-paragraphs-categorization huggingface dataset:
In consequence, an approximate epicentral distance of fifteen kilometers is considered for the definition of the database. This choice leads to an average intensity value of V at this distance according to the model of  Arroucau et al. [2006]  (i. e. , for an epicentral intensity of VII). Table  3  provides the number of buildings by typology according to in situ observations in the area of interest (the data are extracted from  Poursoulis [2017] ). A qualitative distribution of the buildings in different damage states (DS i ) has been done according to the description of the observed degradation (see Figure  5  for examples of ob-served degradation). In order to illustrate the qualitative classification performed, an illustration for the case of a masonry house built in 1747 is given. From the archeoseismological campaign performed by  Poursoulis [2017] , several damages and repairs linked to an earthquake have been identified (see Figure  6 ).
Due to the large distribution of the damages (several masonry reprisal) and the severity (crushing of the bottom corner of the masonry, modification of the openings, introduction of a tie rod), the house has been classified in damage state 3 (i. e. , DS3). The parameters for deriving the damage probability curves for the different typologies encountered in the region of interest are defined in Table  1. The probability P (D i |IM, m, r, s) obtained for each typology i considered independently, and including every damage state (3), is given in Table  1 , as well as the evaluation of the probability for the whole database P (D|IM, m, r, s) (4). It should be mentioned that a large part of the houses in the region are made with adobe brick. As it is complex to evaluate the effect of an old earthquake on this type of structure, only stone masonry houses are used in this study.
Updating of the magnitude GMM can be more or less complex according to the number and type of parameters used (site proxies, magnitude,. ). In order to limit the choice of arbitrary values, a GMM is chosen based on a limited number of data (V S 30 ) and with a representativeness of the region of interest  [Kotha et al. , 2020]. This GMM has been derived from the European Strong Motion dataset (ESM). The choice of the GMM may influence the results obtained but a comparative study is out of the scope of the present work and might be addressed in a later study. An uncertainty is considered for the soil characteristics and the focal depth. Regarding the uncertainty related to the soil, a log-normal distribution for V S 30 is used with a median equal to 300 m•s -1 and a log-standard deviation of 0.
2 (Figure  7(b) ). The value of V S 30 is linked to the geological context of the region of interest. For the focal depth H , a log-normal distribution with values provided in  Manchuel et al. [2018]  is considered, with a median value equal to 6 km and a log-standard deviation equal to 0. 54 (Figure  7 (a)). A Gaussian distribution is chosen for the prior magnitude distribution assigned to the earthquake of La Tour-du-Pin, with the average magnitude M w = 4. 4 and uncertainty err M w = 0. 42, according to  Manchuel et al. [2018]. The choice of a log-normal distribution for the variables H , V S 30 , and M w is motivated by physical considerations. Indeed, this distribution ensures that the values are always positives. In Figure  8 , the update of the magnitude with the proposed methodology is performed for each building typology separately, and for the whole database.
[2020]  using r JB = 7 km and the distribution used previously for the hypocentral depth. A fictitious site amplification due to alluvial deposits is considered with a constant amplification factor for the PGA taken equal to 5. The σ value is reduced to 0. 3 in order to simulate a better knowledge of the site. This configuration could correspond to a case where a nonergodic site-specific GMM and more detailed site response analyses are available. The same database for buildings and observed damages is considered for this example. Figure  9  provides the posterior distribution obtained from this virtual case study. One can observe an evolution of the mean value (M post w = 4. 34) as well as a reduction of the standard deviation (err post M w = 0. 40) with actualization process. From this reference case study, the influence of different parameters in the actualization are analyzed.
Data and resources The city boundaries that have been used to define the area of interest were collected at: https://www. data. gouv. fr/fr/datasets/ (last access: February 2020). The report related to the survey on the area of interest to observe cracks, traces of repair work was provided by EDF and is proprietary. The data are collected in an internal report (G. Poursoulis, Rapport de mission réponse à la demande concernant: Etude architecturale, amélioration de la connaissance du séisme de 1889, localization supposée à la Tour-du-Pin, Archéosismicité-La mémoire des lieux (2017)). They cannot be released to the public. The data in SisFrance can be obtained at http://www. sisfrance. net (last access: December 2020).
X-ray diffraction of C1 and C2 complexes Single-crystal X-ray measurements were performed at 100 K using an Oxford Cryostream 700 (Oxford Cryosystem). Data for both C1 and C2 complexes were collected on an Apex II CCD 4K Bruker diffractometer equipped with a MoKα Incoatec microsource (λ = 0. 71073 Å). The structures were solved using SHELXT  [24]  and refined by least-squares procedures on F 2 using SHELXL  [25]. The most important details concerning crystal data, experiment and refinement are summarized in Supporting Information.
Data Overview We utilize data from the Cluster  (Escoubet et al. , 2001)  and MMS  (Burch et al. , 2016)  missions. Cluster made observations at high latitudes (|Z GSE | > 5 R E ), while MMS made observations at low latitudes (-5 R E < Z GSE < 5 R E ). We take data from Cluster 1 and Cluster 3. For MMS, we take data only from MMS 1 since all the MMS spacecraft observe identical features across FTE scale size. For Cluster, we use the FTE list from  Fear et al. (2012). The observations were made between November 2002 and June 2003 during the Cluster dayside season. We performed a visual inspection to determine the FTE time interval for each event. The criteria for selection are: (a) clear symmetric and bipolar variation of B N (the magnetic field component perpendicular to the unperturbed magnetopause), and (b) a clear enhancement in the magnetic field strength.
, 1997)  instrument at about 4s resolution on-board Cluster, and the Fast Plasma Investigation (FPI;  Pollock et al. , 2016)  measurements in both burst and survey modes with resolutions of 0. 03 s/0. 15 s (electrons/ions) and 4. 5 s, respectively. Finally, we use solar wind data from the OMNI database  (King & Papitashvili, 2005) , where the measurements were taken by the Advanced Composition Explorer (ACE) and Wind spacecraft and time-shifted to the bow-shock nose, at 5-min resolution.
Solar Wind Conditions To revisit the correlation between the IMF B y and the FTE helcity sign, we analyse the IMF conditions preceding the detection of the FTEs, which would affect the local conditions in which magnetic reconnection takes place on the dayside magnetopause. As OMNI data provide solar wind conditions at the nose of the bowshock, we estimate the propagation time of the solar wind flow to be approximately 15 min to cross the magnetosheath and reach the magnetopause. The results are not sensitive with intervals between 15 and 30 min. Figure  4  shows the distribution of the 15-min averaged IMF clock angles (θ CA = arctan(B y /B z )) preceding the events in polar histograms. Panel (a) shows the distribution for RH events and panel (b) shows the distribution for LH events. Positive IMF clock angles (0° < θ CA < 180°) correspond to duskward IMF B y , while the negative IMF clock angles (-180° < θ CA < 0°) correspond to dawnward IMF B y.
Discussion We have investigated the helicity sign of 166 quasi force-free FTEs, with 82 from Cluster and 84 from MMS observations. We found that the helicity sign of most events is ordered by the IMF By polarity, and so that positive IMF clock angles correspond to duskward IMF B y , while negative IMF clock angles (-180° < θ CA < 0°) correspond to dawnward IMF B y. We also found that 21% of the events have a helicity sign that does not correspond to the expected IMF By polarity. Our findings are consistent with the main results of  Kieokaew et al. (2021) , where right-handed FTEs are associated with positive IMF B y and left-handed FTEs are associated with negative IMF B y. To investigate the local conditions associated with the FTE formation, we analysed the magnetic shear angle using both modeling and in-situ data at the FTE locations.
For moderate and low shear angles, the guide field expected at the reconnection sites, where FTEs were formed, would control the core field of FTEs, and thus the helicity sign. This situation is consistent with the fact that the IMF B y controls the helicity sign of the regular group as the IMF B y represents the main component that provides the reconnection guide field  (Kieokaew et al. , 2021). For the outlier group, in addition to the model, we have investigated the shear angle using in-situ data surrounding each outlier FTE. We found that they are observed at higher magnetic shear locations meaning lower guide field closer to the reconnection sites. In this case, it is less clear what controls the core field of the outlier FTEs. In particular, there are higher uncertainties on the IMF mapping and therefore a higher randomness may be expected in the determination of helicity and core field under low guide field at the reconnection site.
4A-D ) and we see the opposite shift for genes involved in proliferation (Fig. 4E-H ). In all cases, the 48H reverting cell expression patterns for those genes shifted back to patterns closer to the 0H cells. At the time of observation and especially for genes up in differentiation, the 48H reverting cell expression patterns are not completely similar to those of 0H cells. This was further confirmed by using a dedicated statistical tool, sparse PLS (see below). To go further on gene distribution comparisons, we computed Wasserstein distances, a geometric distance well suited for comparing multimodal distributions, for each 2000 genes of the scRNAseq dataset between each condition two by two. We then obtain 6 distributions of Wasserstein distance values. Finally, we computed the Gini index as a measure of statistical dispersion in each distribution (the higher the Gini index is, the higher inequality among the values).
48H reverting cells retain molecular traces of a commitment into differentiation To further characterize the molecular changes that persisted after reversion, we sought to identify predictive genes that discriminate the most the 48H reverting cells and the undifferentiated cells. We performed logistic regression combined with dimension reduction (partial least square  [20] ) between 48H reverting cells and 0H cells and retained common most discriminating genes between scRT-qPCR and scRNAseq datasets. Interestingly, our results showed that only 3 common genes discriminate between the two cell groups: HBBA, TBC1D7, and HSP90AA1, the expression of which is shown in Fig. 6. HBBA is a subunit of the hemoglobin complex which carries oxygen, TBC1D7 is presumed to have a role in regulating cell growth and differentiation  [27] , and HSP90AA1 codes for an isoform of the HSP90 protein chaperone, which its specific transcription is known to be induced in response to insulin  [28].
Bio-informatic pipeline Fastq files were pre-processed through a bio-informatic pipeline developed in the team on the Nextflow platform  [54]. Briefly, the first step removed Illumina adaptors. The second step de-multiplexed the sequences according to their plate barcodes. Then, all sequences containing at least 4T following cell barcode and UMI were kept. Using UMItools whitelist, the cell barcodes and UMI were extracted from the reads. The sequences were then mapped on the reference transcriptome (Gallus GallusGRCG6A. 95 from Ensembl) and UMI were counted. Finally, a count matrix was generated for each plate.
Data filtering, normalization, and analysis All analyses were carried out using R software (version 4. 0. 5; [55] ). Matrixes from the eight plates were pooled together. Cells were filtered based on several criteria: reads number, gene number, count number, and ERCC content. For each criterion, the cutoff values were determined based on SCONE  [56]  pipeline and were calculated as follows: We selected genes present in at least two cells. The filtered matrix was then normalized using SCTransform from the Seurat package  [57]  and we corrected for batch effect, time effect, and sequencing depth effect. The expression matrix was finally log1p transformed. Variable genes were identified using FindVariableFeatures from Seurat, vst method  [58]. Based on visualization of gene variance, we retained the 2000 most variable features.
Statistical analysis All statistical analyses were performed using the R software (version 4. 0. 5; [55] ). Dimensionality reduction and visualization were performed using UMAP  [59]. UMAP was performed directly on the 2000 most variable genes (from the scRNAseq dataset) or 83 genes (from the scRT-qPCR dataset) using default parameters. PCA was performed using prcomp function from the stats R package (version 3. 6. 2). Adaptive sparse PLS for logistic regression was performed using the plsgenomics package  [20]. For this analysis, scRT-qPCR data were scaled. Sparse PLS is a supervised statistical analysis that allows to predict the most discriminant variables between two groups. Wasserstein distance computation was done using the Transport R package  [60]  and was accomplished for each gene of the scRNAseq dataset.
Acknowledgements We gratefully thank all members of the SBDM team and particularly  Gerard Benoit  for very fruitful discussions, suggestions, and commentaries on our project. We also thank  Ghislain Durif  for his great technical support during PLS computation and  G. Yvert  for helpful comments about the manuscript. We thank the computational center of  IN2P3 (Villeurbanne/France ) and  Pôle Scientifique de Modélisation Numérique (PSMN, Ecole Normale Supérieure de Lyon)  where computations were performed. We acknowledge the contribution of the  AniRA-Cytométrie core facility of SFR BioSciences  ( UAR3444/US8 ). We thank the  BioSyL Federation  and the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the  University of Lyon  for inspiring scientific events.
Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information file and publicly available repositories. Pipelines and analysis scripts are available at https:// gitbio. ens-lyon. fr/ LBMC/ sbdm/ mars_ seq. Previous scRT-qPCR data are available in the SRA repository http://www. ncbi. nlm. nih. gov/ sra/ SRP07 6011  [19]. Previous data mRNA half-life are available in the OSF repository https:// osf. io/ gkedt/  [29]. The datasets supporting the conclusions of this article are available in the NIH repository, accession number PRJNA802343  [68] , and in the OSF repository https:// osf. io/ upw8d/.
A reduction in synaptic activity is observed in neurons derived from more sPD patients using a different protocol and selecting a subset of the neurons We next analyzed data that were acquired using a different differentiation protocol (see Methods, second DA differentiation); in addition, only neurons that were defined as type 5 neurons (see definition in Methods) were used for the analysis. Strikingly, despite the different methods used both in differentiating the neurons and in analyzing the data, the main phenotype of a reduction in synaptic activity was present in the sPD neurons as well. We recorded from one control individual (line 40102) and two sPD patients (UKERfRJO-X-001, UKERfR66-X-001). It should be noted that one of the patients had a heterozygous missense mutation in the EIF4G1 gene (UKERfRJO-X-001).
Acknowledgments The authors would like to thank  K. E. Diffenderfer  for technical assistance and  M. L. Gage  for editorial comments. They would also like to acknowledge the  Salk Institute Stem Cell Core, Waitt Biophotonics Core , and  Next Generation Sequencing Core  for technical support. Funding to the cores provided in part by  NIH-NCI CCSG :  P30 014195. Funding: This work was supported by the  JPB Foundation ,  American Heart Association/Paul G. Allen Frontiers Group Brain Health & Cognitive Impairment Initiative  ( 19PABHI34610000 ),  Dolby Charitable Trust ,  Leona M. and Harry B. Helmsley Charitable Trust  # 2017-PG-.
We reconstructed surface mass balance (SMB) around Dome Fuji, Antarctica, over the last 5000 years using the data from 15 shallow ice cores and seven snow pits. The depth-age relationships for the ice cores were determined by synchronizing them with a layer-counted ice core from West Antarctica (WAIS Divide ice core) using volcanic signals. The reconstructed SMB records for the last 4000 years show spatial patterns that may be affected by their locations relative to the ice divides around Dome Fuji, proximity to the ocean, and wind direction. The SMB records from the individual ice cores and snow pits were stacked to reconstruct the SMB history in the Dome Fuji area. The stacked record exhibits a long-term decreasing trend at -0. 037±0. 005 kg m -2 per century over the last 5000 years in the preindustrial period.
For the averaging periods, we chose (1) 1461-1816 CE (all cores), (2) 1-1850 CE (DF1, DF2, DFS2010, DFS2011, NDFN and NDF2018 cores), and (3) 2000 BCE-1 CE (DF1, DF2, NDFN and NDF2018 cores) (Fig. 7 ) for the following climatological reasons. 1. We chose 1461-1816 CE because it is the most accurately constrained period by the volcanic years common for all the studied ice cores and also a good representation of the preindustrial late Holocene for averaging SMB without decadal noise. 2. We chose 1-1850 CE because 1 CE is the start year of the "PAGES 2k network" datasets, which is connected to the Paleoclimate Modelling Intercomparison Project (PMIP) for providing the baseline information about natural climate variability (e. g. ,  Martrat et al. , 2019) , while 1850 CE is chosen for investigating the average preindustrial SMB within the PAGES 2k framework.
1 ). The uncertainties of Stack 1 and Stack 2 were estimated by a Monte Carlo approach, in which the SMB value of each core in each age segment (between the age control points) was randomly varied 1000 times according to the error estimate and stacked. The uncertainties in the long-term trends of Stack 1 and Stack 2 were also estimated as the standard deviation of the slope of regression lines to the same 1000 stacked records by the Monte Carlo method. The mean SMB of Stack 2 is 25. 2 ± 0. 8 kg m -2 yr -1 over the entire period. The centennial-scale variabilities in both stacks are very similar to each other (Fig. 8a  and b ), although the amounts of stacked data are different between the two methods for the last 1500 years. There are general longterm declining trends in both stacked records over the entire period (∼ 5000 years), with a minimum accumulation rate around 1700-1800 CE and the greatest increase during the last 150 years.
The slopes from the binned reconstructions are smaller than that of Stack 1, probably because the binning method reconstructs a lower accumulation rate from ∼ 1000 BCE to ∼ 1000 CE than Stack 1 by taking the median values rather than the average values. The long-term trends for the periods of 1 to 1850 CE (Table 3) for Stack 1 and Stack 2, as well as all the binned records, also show negative trends (Figs. 8  and 9 ). We also made three local stacks using the subsets of data from sites close to each other as follows: "DF stack" with DF1, DF2, and DF1997 cores; "DFS stack" with DFS2010 and DFS2011 cores; and "NDF-NDFN stack" with NDFN, NDF2013, and NDF2018 cores (Fig. 10 ). For the last 2700 years, the local SMB stacks show systematic differences (DF > DFS > NDF-NDFN) with similar centennialscale variations.
The Dome Fuji and DML records show minima in more recent periods (around 1850 CE in the Dome Fuji record and around 1900 CE in the DML record), which are not seen in the other records. For the most recent part, all records show increases with different durations (over 100-300 years). We further compare our Dome Fuji record with other published reconstructions. For Princess Elizabeth Land, the SMB anomaly is relatively high for ∼ 1207-1450 CE, low for ∼ 1450-1850 CE and significantly increased afterward (until 1996 CE)  (Li et al. , 2009). In the South Pole record  (Ferris et al. , 2011) , the SMB anomaly is relatively large for ∼ 1200-1400 CE, reduced during the period of 1500-1900 CE, and then increased afterward. On the continental scale, a stacked SMB record by  Frezzotti et al. (2013)  for the last 800 years, which includes more data from the Antarctic Plateau than the composite of  Thomas et al.
13 ). Note that the eruption in 1458 was previously estimated as being from Kuwae, but it is now interpreted as an unknown event (perhaps in the Southern Hemisphere)  (Hartman et al. , 2019). On the other hand, the Oort and Wolf grand solar minima do not correspond to low SMB anomaly periods at Dome Fuji, but they coincide with the low SMB anomaly periods in the EA composite. Thus, our data are partly consistent with the previous suggestions for the last 1000 years. As discussed above, the Dome Fuji stacked record shows a negative SMB anomaly for the 15th to 19th centuries. During this period, in addition to strong volcanic forcing and weak solar activity, the radiative forcing of GHGs becomes the smallest in the last 2 kyr, which may have contributed to the lower SMB. For the older period, the detrended SMB for 1500 BCE-1000 CE shows variability similar to the last 1000 years, and it shows reduced variability before 1500 BCE (Fig.
Immunohistochemistry. Primary and secondary antibodies used in this study are summarized in Tables  2  and 3 , respectively. Immunochemistry labeling was performed as follows: slices were rinsed, permeabilized and blocked in 10% Normal Goat Serum and PBS containing 0. 25% Triton-X100 (PBST) for 2 h. Primary antibodies were then incubated for up to 48 h at 4 °C in PBST containing 1% serum and 0. 01% azide, washed three times and secondary antibodies were finally added for 2 h in PBST containing 2% serum. Slices were then rinsed and counterstained with DAPI, mounted and imaged with a confocal microscope (LSM 700, Zeiss Zen Black software) or epifluorescence microscope (Axiovert 200, Zeiss) equipped with an Apotome system (Zeiss, Zeiss Zen 2. 6 Blue software). A secondary antibody control was included by omitting the primary antibodies in the staining protocol.
Slices were finally rinced, mounted (Fluoromount) and imaged with epifluorescence microscope (Axiovert 200, Zeiss) equipped with an Apotome system (Zeiss) and the Zeiss Zen 2. 6 Blue software. eTCs: large (~20 μm diameter) somata in the inner part of the GL, a single dendrite and tuft ramifying within one glomerulus, an axon extending into the EPL and a relatively low input resistance (~200 MΩ). PG cells: smaller somata (~8-10 μm diameter) residing in the GL, with high input resistance (~500-1000 MΩ). Highly ramified dendrite arbor in only one glomerulus. sSACs: larger soma in the GL (>10 μm diameter), low input resistance (~200-300 MΩ), unique dendritic arbors that are exclusively periglomerular, span multiple glomeruli, lack tufts, and are poorly branched. TCs: large soma (10-20 µm diameter) in the inner part of the EPL (20-150 µm above the MCL).