dataesr/scientific-paragraphs-categorization huggingface dataset:
Only samples with at least 20 objects are represented here. Cerulo et al. (2019)  with a sample of 74275 BCGs is not represented here for better lisibility. The samples represented in black use ground-based telescope data, space-based data excluding HST, or a mix of ground-based and space-based data, while those in red only use HST data. Our initial sample is represented by the red and green dotted line, and our final BCG sample in green (see  Section 4). Section 3, and  the modelization of their luminosity profiles in Section 4. The results obtained as well as a short study of the link between the BCG masses, distance between the BCG and the X-ray center of the cluster, and physical properties are given in Section 5. A final discussion and conclusions are presented in Section 6. Throughout this paper, we assume H 0 = 70 km s - 1  Mpc -1 , Ω M = 0.
Retrieving data and cluster information We retrieve all the FITS images from the Hubble Legacy Archive (HLA 3  ). We look for combined or mosaic images according to what is available and download stacked images directly from the HLA. To avoid handling such heavy files, we first crop these images, define the new center on the cluster coordinates found in NED, and create a new image 1. 2 Mpc wide. Linear scales (arcsec/Mpc) are determined from the cluster redshifts in the literature (from  Jee et al. 2011; Postman et al. 2012b; Bai et al. 2014; Donahue et al. 2015; West et al. 2017; DeMaio et al. 2019; Durret et al. 2019; Sazonova et al. 2020 , or found in NED for the five other clusters we added). Cluster information can be found in Table  1. It is necessary to add the keywords 'GAIN' and 'RD-NOISE' in the header of the FITS images, which will be used later by SExtractor or GALFIT.
Luminosity profiles We fit 2D analytical models on sources with GALFIT  (Peng et al. 2002). Once the BCG is defined, we run SExtractor one last time to return model fit parameters in the available filter closest to the F606W rest frame at redshift z (see Figure  5 ), which is at a wavelength above the 4000 Å break and thus is in the spectral region where we will get the highest flux. The chosen filters can be found in Table  1. We note that there are 37 BCGs out of the 149 for which HST data are not available in the F606W rest frame or redder. The reddest filter is either bluer than the 4000 Å break or contains it, which means that we are not only looking at the oldest, reddest star population, but at the youngest bluest stars as well. These BCGs are marked by blue squares in plots. The redshift distribution of all our BCGs is plotted in Figure  2 , the blue histogram represents the clusters with filters which are bluer than the 4000 Å break.
n do f is then: n do f = n res -n f ree -1 (3) The P-value is then calculated with the routine f. cdf from scipy. stats in python. We set P 0 = 0. 32, which represents a 1σ threshold value (Margalef private communication). We show the distribution of P-values as a function of redshift in Figure  9. A P-value ≤ P 0 means that we need a second component to correctly model the BCG light distribution. On this plot are not represented BCGs which could not be fitted by either model: 9 BCGs could only be fitted with a single component, 22 could only be fitted with two components, 2 could only be fitted by fixing the Sérsic index n = 4 (de Vaucouleurs profile), and 2 BCGs could not be fitted or returned a poor fit for either of the models. On Figure  10 , it appears that BCGs that need a second component to obtain a good fit tend to be at lower redshifts (peak at z = 0.
Acknowledgements. We thank the referee,  M. West , for his constructive comments and suggestions. F. D. acknowledges continuous support from CNES since 2002. IM acknowledges financial support from the  State Agency for Research of the Spanish MCIU  through the  "Center of Excellence Severo Ochoa" award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ), and through the programs  AYA2016-76682C3-1-P  and  PID2019-106027GB-C41. Based on observations made with the  NASA/ESA Hubble Space Telescope , and obtained from the  Hubble Legacy Archive , which is a collaboration between the  Space Telescope Science Institute (STScI/NASA) , the  Space Telescope European Coordinating Facility  ( ST-ECF/ESA ) and the  Canadian Astronomy Data Centre  ( CADC/NRC/CSA ).
Our optimized protocol secured ~7-fold more viable embryonic MNs compared to a traditional trypsin-based primary MN collection protocol (Extended Data Fig. 1a , see Methods)  29. We found that gentle papain-based spinal cord tissue dissociation, followed by FACS using a wide-diameter nozzle (85 μm) and low sheath pressure (45 psi), and then collection of the cell population displaying only the highest GFP intensity achieved an optimal balance between quantity and viability of collected spinal MNs. Before performing single-cell isolation with a 10x Genomics platform, we performed bulk qPCR and primary culture to verify GFP high cell identity and viability. The GFP high cells displayed bona fide MN identity and high expression of hallmark genes for MNs (Mnx1, Isl1, Chat), together with minimal expression of an interneuron marker gene (En1) (Extended Data Fig.
Among these neurons, a tiny group of cells (rostral: ~300 cells; caudal: ~435 cells) expressed spinal interneuron (IN) markers such as En1, Pou4f1 or Pax2, whereas the remaining groups of cells (rostral: 5,162 cells (~95%); caudal: 4699 cells (~90%)) expressed cholinergic markers (Slc18a3 and Chat) and spinal MN markers (Mnx1 or Isl1), in agreement with bona fide MN identity (Extended Data Fig. 1d, 2b, c, e, f ). Unsupervised clustering of MNs using Seurat R package  30  identified seven and six major clusters among the rostral and caudal sets, respectively (Fig. 1e  and 1f , marker genes in Supplementary Tables  1  and 2 ). We were able to attribute four clusters to known motor columns 3 by inspecting the expression of established markers: limb-innervating MNs (LMC: Foxp1 + and Aldh1a2 + ), epaxial muscle-innervating MNs (MMC: Mecom + and Lhx3 + )  31  , sympathetic ganglion-innervating preganglionic column MNs (PGC: Nos1 + and Zeb2 + ) and hypaxial intercostal muscle-innervating hypaxial motor column MNs (HMC: Lhx3 -, Foxp1 -, Isl1 + ) (Fig.
5 spinal cord sections (Fig. 2a, b ). The rostral Calb1 + cluster was so named according to its expression of Calb1, which encodes a calcium-binding protein and is reported to be a distinguishing feature of Renshaw cells, an early-born subtype of V1 interneurons  35. Cells co-expressing both Mnx1 and Calb1 were found at the most ventromedial region of the spinal cord, distinct from Calb1 + -only Renshaw cells positioned ventrolateral to MNs (Fig. 2b ). This cluster of Mnx1 + Calb1 + cells displayed cholinergic neurotransmitter identity, as revealed by their coexpression of the Chat, Slc5a7 and Slc18a3 marker genes in our scRNA-seq dataset. Immunostaining at E15. 5 demonstrated that Calb1 + MNs co-expressed Chat (Fig. 2c ). The Mnx1 + Chat + Calb1 + cluster was only observed in the brachial and upper thoracic segments, implying potential muscle group innervation specifically in the upper body (Fig.
2d ). To examine if, like other MN subtypes, the Mnx1 + Chat + Calb1 + neuron population extends its axons outside the spinal cord, we monitored axon trajectories from Calb1 Cre ;Tau-mGFP together with Mnx1-RFP reporter embryos. Strikingly, we observed that the axons of Mnx1 + Chat + Calb1 + neurons extended beyond the spinal cord (Fig. 2e ). Therefore, we have identified a novel brachial MN column manifesting Mnx1 + Chat + Calb1 + expression that extends its axons out from the spinal cord. In contrast, the Nfib + Grm5 + cluster was identified exclusively in the caudal sample (Fig. 1f  and Extended Data Fig. 3c ). Nfib is dynamically expressed in many MN subtypes, so we used an additional marker, Grm5, to verify their sacral distribution in embryonic spinal cord (Extended Data Fig. 3b ). Immunostaining indeed validated that Nfib + Grm5 + co-expressing neurons were only observed in sacral spinal segments (Foxp1 -Hoxd10 -) (Fig.
5a ; see Methods). An analysis of DEGs demonstrated that each subcluster was represented by a distinct combination of marker genes (Extended Data Fig. 5b, c , Supplementary Table  7 ). Consistent with previous studies  [48] [49] [50]  , TFs, cell adhesion molecules, axon guidance molecules, receptors and ion channels topped the list of the most variable genes in brachial LMC MNs for functions relating to cell fate specification, motor pool sorting and positioning, axon pathfinding and synaptogenesis (Fig. 3g , 3h and Extended Data Fig. 5d , Supplementary Table  8 ). Strikingly, neuropeptides also stood out as a prominent gene term, indicative of plausible cluster-specific expression. Thereafter, we sought to identify each subcluster from our scRNA-seq dataset based on their respective marker genes.
Notably, one neuropeptide-related GO term also emerged from the segmentally-enriched GO dataset (Extended Data Fig. 8b ) We performed in situ hybridization and detected expression of the neuropeptides Grp and Npy in subsets of brachial LMC MNs, but less so in lumbar segments, validating our comparative analysis between brachial and lumbar LMC subclusters (Fig. 5i, j ). Conversely, Igf1 was enriched in lumbar segments, and more specifically in Hoxd10 + medial LMC MNs. Thus, we have demonstrated that like TFs and cell adhesion molecules, neuropeptides not only exhibit strong heterogeneous expression among LMC subclusters but they also differ between the brachial and lumbar segments. Neuropeptides are widely known as neuromodulators affecting behavior and sensory processing in postnatal mice  63, 64  , and our data indicate they play unappreciated roles in embryonic development that warrant further investigation.
7d ). Next, we examined expression of MMC marker genes in chicken spinal cord using immunostaining and ISH of chick spinal cord sections. We detected Mecom expression in a majority of Lhx3 + MMC MNs. Moreover, chicken MMC MNs also expressed Nr2f2, Satb2 and Bcl11b, with Satb2 and Nr2f2 expression being mutually exclusive and displaying a preferential mediolateral distribution similar to the distribution of MMC subtypes observed for mouse embryos (Fig. 7e ). Finally, to determine if MMC MN diversity is conserved in human, we investigated the distributions of SATB2, NR2F2 and BCL11B in human embryo spinal cord sections at Carnegie stage 18, from Gestational Week (GW) 6. 3 (equivalent to mouse E13. 5 embryos when most MN columnar identities have been established)  79, 80  (Fig. 7f  and Extended Data Fig.
Additionally, we noticed that although the rostrocaudal identities of spinal cord cell types are determined and maintained by cross-regulation among Hox proteins  10  , our single-cell analysis revealed that Hox transcripts, such as Hoxa5 and Hoxc8, can still be co-expressing within the same cell, despite at protein level, they are known to be segregated. This outcome is consistent with our previous studies showing that post-transcriptional mechanisms such as those exerted by microRNAs are indispensable for governing unambiguously spatiotemporal Hox protein expression and for defining rostrocaudal MN subtype identities in the spinal cord  84, 85. Thus, although single-cell gene expression analysis facilitates identification of transcriptionally distinct known and novel cell types and states, integration of other modalities such as proteomics with our dataset would reveal more information about gene regulation during the cell type determination process.
From our scRNA-seq dataset, we discovered two previously uncharacterized columnar MNs, i. e. , Calb1 + MNs in the brachial region and Nfib + Grm5 + MNs in the sacral segment. We have defined these two new types of neurons as MNs based on the facts that: (1) they co-express MN hallmark genes Mnx1, Chat, and Slc18a3 (VAChT); and (2) both types extend their axons from the spinal cord to peripherals. Although Calb1 is a salient marker for V1 subtype Renshaw cells  86  , the presence of a small number of Calb1 + MNs among adult spinal MNs has been reported previously  87, 88. In those studies, it was hypothesized that MNs are protected from glutamate-induced and calcium-mediated excitotoxicity via increased Calb1 expression. Given that we observed persistent presence of Calb1 + MNs to the postnatal stage, it is tempting to examine if they are resistant to neurodegenerative disease.
Moreover, neuronal expression of the metabotropic glutamate receptor Grm5 was shown previously to play pivotal roles in development of the mammalian CNS  89. For example, Grm5 expression prevented programmed cell death in in vitro cultures of rat cerebellar granule cells during the early maturation stage. Intriguingly, our novel sacral Nfib + Grm5 + MNs express Grm5, which is believed to exert neuroprotective effects on spinal MNs  90. Given that sacral MNs are disease-resistant  91  , our newly identified Nfib + Grm5 + MNs may have implications for tackling MN disease. Thus, our identification of two previously uncharacterized Calb1 + and Nfib + Grm5 + neuronal types from our scRNA-seq dataset may shed light on "disease-resistant" MN subtypes. Future detailed functional characterizations of these two new columnar types and their peripheral innervating targets are clearly warranted.
While we made substantial progress in deciphering and characterizing LMC diversity compared to the observations in previous studies, our scRNA-seq approach did not characterize all ~60 known LMC motor pools. There are two possible reasons for this outcome. First, the molecularly distinct identities of motor pools at the embryonic stage might not be reflected by the transcriptome alone. Increasing number of studies have demonstated that single-cell multi-omics analysis such as joint single-cell transcriptomic and epigenomic profiling likely increases the capability of dissecting cellular heterogeneity  99, 100. We envisage that multimodal analysis of spinal MNs would allow systematic positional allocations for all MNs, including our identified LMC subclusters and other vulnerable LMC neurons.
Clustering analysis of scRNA-seq data We performed normalization, dimensionality reduction and cell clustering using Seurat package version 2. 3. 4  30. The digital data matrices were normalized by a global method, whereby the expression value of each gene was divided by the total expression in each cell and multiplied by a scale factor (10,000 by default). These values were then log-transformed with a pseudocount of 1. Highly variable genes across cells were selected using the and Slc5a7. Only clusters expressing MN markers were considered and further analyzed using the same pre-processing procedures as described above, but with different numbers of PCs and cluster resolutions. To cluster MNs, cell clusters were obtained using the top 31 PCs with a resoultion of 0. 08 for the rostral sample and the top 24 PCs with a resolution of 0.
Similarity analysis between two groups of cells Similarity between two groups of cells representing rostral or caudal MNs was determined using the calculateDistMat function (method = "trend") in the CIDER package  114. In brief, differentially expressed signatures (DES) were first identified using limma-trend data for each group of cells against all other cells within the dataset, whereby DES were computed by fitting a linear regression model. The similarity was then measured according to the Pearson's correlation coefficient of DES between two groups. To visually examine relateness among LMC subclusters of brachial and lumbar segments, we merged all LMC neurons from the brachial and lumbar segments and then performed joint dimensional reduction via UMAP by using the corrected embedding learned from the Harmony integration method 59.
Finally, to increase the robustness of the estimated similarity, we computed an average overlap ratio by using different numbers of neighbors (k = 10, 15, 20) to identify MNNs. The similarity matrix was visualized using the ComplexHeatmap R package 115. To intuitively visualize the relationships of LMC subclusters between brachial and lumbar segments, we created a alluvial diagram using the ggalluvial R package (https://www. rdocumentation. org/packages/ggalluvial/versions/0. 12. 3). Lines of low similarity values (i. e. , <0. 09) were omitted to accentuate relatively strong similarity and enhance readability.
Differential expression analysis and gene set enrichment analysis Differentially expressed genes (DEGs) between segments or subclusters were identified using the FindMarkers function by performing likelihood-ratio tests. Genes with an adjusted pvalue <0. 05 and log fold-change >0. 25 were considered differentially expressed. Gene Ontology (GO) enrichment analysis of DEGs was performed using the clusterProfiler R package (v3. 14. 3) 116 , focusing on Biological Processes (BP) and Molecular Functions (MF). Redundant GO terms were removed using the simplify function with default parameters.
Analysis of the spatial distribution of MMC MN subpopulations The positioning of MMC MN subpopulations in E13. 5 brachial spinal segments was analyzed similarly to previous descriptions  103, 122. Coordinates (x,y) were assigned based on the position of each MMC MN using the "spots" function in the imaging software Imaris 9. 5. 1 (Bitplane). Four additional coordinates were exported, i. e. midpoints for the dorsoventral and mediolateral spinal cord boundary. To account for differences in spinal cord size and shape, sections were normalized to a standardized hemi-section spinal cord (midline to lateral = 400 μm; dorsal to ventral edge = 800 μm). Density distributions for cells were plotted using the ggplot2 'geom_density' function.
Data and code availability The E13. 5 mouse spinal MNs scRNA-seq raw data generated for this study is available in the GEO database, which will be released for download upon publication. The codes used for scRNA-seq data analysis were an adaptation of standard R packages, as described in the Methods section. The codes used for estimating the number of clusters and performing similarity analyses between cell groups are available on GitHub at https://github. com/sqjin/MotorNeuron. More detailed information is available upon request.
Acknowledgements Nr2f2 floxed mice were generously provided by  Ming-Jer Tsai  ( Baylor College of Medicine ) and  Li-Ru You  ( National Yang Ming Chiao Tung University ). We also thank  Hynek Wichterle ,  Tom Jessell , and  Susan Morton  ( Columbia University ) for many gifts of antibodies used in this study. We acknowledge the FACS, Transgenics, Genomics, Bioinformatic and Imaging cores of  IMB Academia Sinica  for their strong technical assistance, and the  IMB Scientific English Editing Core  for further editing the manuscript. We thank  Shen-Ju Chou  and  Suewei Lin  ( Academia Sinica ) for valuable inputs and members of the  JAC lab  for discussion and proofreading. This work was supported by  Academia Sinica  ( CDA-107-L05  and  AS-GC-109-03 ),  MOST  ( 110-2326-B-001-009 ,  109-2314-B-001-010-MY3 ,  108-2311-B-001-011 ) and  NHRI  ( NHRI-EX110-10831NI ) (J.
Primary antibodies used were: rabbit anti-Lhx3 (1:2000, Abcam Cat# ab14555, RRID:AB_301332); rabbit anti-Foxp1(1:20000, Abcam Cat# ab16645, RRID:AB_732428); goat anti-Foxp1(1:100 R&D systems Cat# AF4534, RRID:AB_2107102); rabbit anti-Hoxc8 (1:5000 Sigma-Aldrich Cat# HPA028911, RRID:AB_10602236); goat anti-Hoxd10 (1:1000 Santa-Cruz Cat# sc-33005, RRID:AB_648462); sheep anti-GFP (1:1000 AbD Serotec/Bio-Rad Cat# 4745-1051, RRID:AB_619712); rabbit anti-RFP (1:500 Abcam Cat# ab62341, RRID: AB_945213); mouse anti-MNR2/MNX1/HB9 (1:50 DSHB Cat# 81. 5C10, ); mouse anti-COUP-TF2/NR2F2 (1:200 R&D systems Cat# PP-H7147-00, RRID:AB_2155627); rabbit anti-Evi1/Mecom (1:500 Cell Signaling Technology Cat# 2593, RRID:AB_2184098); goat anti-Isl1 (1:1000 Neuromics Cat# GT15051, RRID:AB_2126323); mouse anti-SMI-32 (1:1000 BioLegend Cat# 801701, RRID:AB_2564642); rabbit anti-Satb2 (1:1000 Abcam Cat# ab92446, RRID:AB_10563678); guinea pig anti-Satb2 (1:1000 Synaptic Systems Cat# 327004, RRID:AB_2620070); rat anti-Bcl11b/Ctip2 (1:1000 Abcam Cat# ab18465, RRID:AB_2064130); rabbit anti-Zfhx4 (1:200 Novus Cat# NBP1-82156, RRID:AB_11020060); rabbit anti-nNos (1:10000 Immunostar Cat# 24287, RRID:AB_572256); sheep anti-Onecut2 (1:500 Novus Cat# AF6294, RRID:AB_10640365); rabbit anti-Calbindin D-28K/Calb1 (1:1000 Millipore/Sigma Cat# AB1778, RRID:AB_2068336); rabbit anti-Nfib (1:1000 Novus Cat# NBP1-81000, RRID:AB_11027763); rabbit anti-Grm5 (1:500 Millipore/Sigma Cat# AB5675, RRID:AB_2295173); sheep anti-Ebf2 (1:500 Novus Cat# AF7006, RRID:AB_10972102); rabbit anti-cleaved caspase-3 (1:500 Cell Signaling Cat# 9661, RRID:AB_2341188).
2 mM PTU (1-phenyl-2-thiourea) at 28 o C to block pigmentation and to permit visualization, before being fixed overnight with 4% PFA in PBST (0. 1% Tween-20 in 1xPBS) at 4 o C. The fixed embryos were washed with 1x PBST, dehydrated with methanol, and then stored at -20 o C until subjected to in situ hybridization (ISH). The PCR primers used to amplify cDNAs for probe synthesis were designed based on Zfin database sequences 120 and are listed in Supplementary Table11. ISH was performed following standard procedures 121. For dual-fluorescence in situ hybridization (FISH), the embryos were hybridized with DIG-labeled RNA probe (Roche) and DNP-labeled RNA probe (Mirus Bio), and signals were detected using a Tyramide Signal Amplification (TSA).
We present an approach for discourse segmentation and discourse connective identification, both at the sentence and document level, within the DISRPT 2021 shared task, a multi-lingual and multi-formalism evaluation campaign. 1 Building on the most successful architecture from the 2019 similar shared task, we leverage datasets in the same or similar languages to augment training data and improve on the best systems from the previous campaign on 3 out of 4 subtasks, with a mean improvement on all 16 datasets of 0. 85%. Within the Disrpt 21 campaign the system ranks 3rd overall, very close to the 2nd system, but with a significant gap with respect to the best system, which uses a rich set of additional features. The system is nonetheless the best on languages that benefited from crosslingual training on sentence internal segmentation (German and Spanish).
, 2019). This campaign made available several existing corpora in different language in a common format, expressing the task as a sequence tagging problem, where tokens are to be classified as beginning a segment or not, or, in the case of PDTB corpora, being part of a discourse connective signalling a relation between textual arguments. Segmentation in itself has also shown a lot of potential as an auxiliary task in machine translation  (Chen et al. , 2020)  and summarization  (Xu et al. , 2020) , independently of discourse parsing. DISRPT 2021 shared task reproduces the same setting as DISRPT 2019, with some additional data and minor modifications of the original datasets, segmentation as task 1, connective identification as task 2, and adds the prediction of relations between segments, assuming those are known, as task 3.
The following shows examples from the English datasets, illustrating respectively task 1 and task 2, with intended units to recover marked between brackets: • [Three seats currently are vacant] [and three others are likely to be filled within a few years] (. ) •  [But]  [in the end] his resignation as Chancellor of the Exchequer may be a good thing (. ) In the first case, tokens "Three" and "and" would be marked as segment beginnings. In the second case, where the target connectives are "but" and "in the end", beginning tokens "but" and "in" would be marked "B" (begin) and "the", "end", would be marked as "I" (inside). In both examples, other tokens would be marked "out". Since sentences are almost always discourse units in existing frameworks, segmentation can be seen as two sub-problems: detecting sentences, and detecting intra-sentence segment boundaries.