dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS As part of the experiment, the participants had to respond by performing overt movements with their arm along the table plane while holding the computer mouse. Their movements were recorded with a Mouse (Logitech, Inc), sampled at 1 kHz, which we used to track hand position. Given that the monitor was placed upright on the table and movements were performed on the table plane (horizontally, approximately from the center of the table to the left or right target side), the plane of movement was perpendicular to that of the screen, where the stimuli and finger trajectories were presented. Data analyses were performed with custom-built MATLAB scripts (The Mathworks, Natick, MA), licensed to the Universitat de Barcelona.
Using fluorescence-activated cell sorting (FACS), we collected cell populations with the highest GFP intensities (GFP high ) and subjected the samples to scRNA-seq (Fig. 1b  and Supplementary Fig. 1a-c ). As expected, by aligning our scRNA-seq samples using a customized GFP reference, we detected GFP transcripts in most cells in both samples (Supplementary Fig. 1d ). Although Mnx1 labels MNs and a population of spinal glutamatergic interneurons (IN) specifically in the spinal cord, we detected sparse Mnx1 expression in our single-cell dataset, likely due to a limitation of scRNA-seq sensitivity. As spinal MNs are all cholinergic, we thus defined MNs according to their expression of both cholinergic (Slc18a3, Chat) and spinal MN marker (Mnx1), with spinal MNs accounting for ~91% of the total cells upon quality filtering (rostral: 5162 MNs from a total of 5460 cells; caudal: 4699 MNs from a total of 5242 cells) (Supplementary Fig.
Identification of spinal MN subtypes and gene signatures To uncover MN heterogeneity, we clustered single-cell transcriptomes from the rostral and caudal datasets using Seurat  18  and assigned cluster identities based on the averaged expression of canonical marker genes (see "Methods"). Our analysis resulted in a total of 13 clusters and we were able to assign cell identities to 10 of them (Fig. 1c-f ): LMC lateral motor column (Foxp1 + and Aldh1a2 + )  19  , MMC medial motor column (Mecom + and Lhx3 + )  20  , PGC preganglionic motor column (Nos1 + and Zeb2 + ), HMC hypaxial motor column (Lhx3 -, Foxp1 -, Isl1 + ), Mnx1 + excitatory interneurons with a glutamatergic (Slc17a6 + ) property  21  , and nascent MNs (Neurod2 high and Ebf2 high )  22, 23. One cluster was denoted as "unknown" as we could not uncover a salient and characteristic gene set for it (Supplementary Fig.
We also compared LMC and MMC neurons (Supplementary Fig. 4a, b ). Apart from known genes (LMC: Foxp1; MMC: Mecom/Lhx4/Lifr/Il6st)  19, 29  , we uncovered selective expression of IgLON neural adhesion molecules (i. e. , encoded by Ntm and Lsamp) within these neurons (Fig. 3b , left, Fig. 3c , Supplementary Fig. 4b , and Supplementary Data 2). Notably, Ntm expression discriminates LMC from Lsamp high MMC neurons in the E13. 5 brachial spinal cord (Fig. 3b , left, Fig. 3c ). IgLON neural adhesion molecules are known to regulate axon pathfinding, dendritic arborization and synaptogenesis  30, 31  , and selective expression of the respective genes might contribute to the differences in neuronal pathfinding and circuit wiring between LMC and MMC MNs. Overall, our scRNA-seq data not only have identified most columnar MNs and revealed undescribed MN subtypes, but have also unveiled a list of markers for different subtypes that could potentially explain their physiological characteristics.
Subtypes within LMC MNs The identification of major columnar MNs within our MN dataset prompted us to uncover subtypes within these motor columns. Characterization of MN subtypes in the adult stage has revealed previously unreported heterogeneity in PGC MNs  13, 14  and the embryonic origin of these subtypes remains unclear. Although we sampled spinal cord tissue mainly at the limb segments, we had included small populations of PGC and HMC MNs within our collected samples. In agreement with observations from a previous study reporting that differential Isl1 expression could differentiate PGC MNs into PGCa and PGCb subtypes  32  , we observed a similar pattern in our single-cell data (Fig. 1c  and Supplementary Fig. 4c ). However, in this study, our objective was to analyze LMC and MMC MN subtypes, so we did not endeavor to characterize the PGC subclusters further.
5b, d  and Supplementary Fig. 6d-g ), whereas Pnoc and Grp coexpression delineated the cl3 subcluster from the Pnoc-only cl4 subcluster (Fig. 5b ). Remarkably, we also detected that the Etv4 + subcluster (cm1) expressed Sst and Grp, whereas the Pou3f1 + subcluster (cm3) displayed Penk expression (Fig. 5e, f ). Furthermore, we observed a significantly stronger correlation between the subcluster-specific TFs and neuropeptides compared to the correlation between randomly selected TFs and neuropeptides (Fig. 5g ), implying that motor pool TFs in combination with neuropeptides may exert subtype-specific physiological roles that warrant future experimental validation. Strikingly, upon comparing our embryonic MN dataset to previously published adult MN scRNAseq results  13, 14  , we found that though the embryonic LMC subtype TF/neuropeptide code is largely distinct from that of adult MN subtypes, adult skeletal MN subtypes still manifest a different set of neuropeptide combinations, implying that the neuropeptides might function differentially in both embryonic and adult stages (Supplementary Figs.
Convergence and divergence between brachial and lumbar LMC MNs LMC MNs are present in both the forelimbs and hindlimbs 1 , and some motor pools, such as Etv4 subtype  48  , are known to be present in both brachial and lumbar segments, indicating that there might be more convergent or divergent subclusters between the two segments. Accordingly, we performed an unbiased cross-segmental molecular comparison of these LMC MN subclusters with Harmony 49 to examine this hypothesis. We first compared gene expression in LMC MNs between brachial and lumbar segments and uncovered 112 DEGs, in which Hox genes were most prevalent (Fig. 6a  and Supplementary Data 4). This outcome is consistent with previous studies showing that Hox genes are the crucial TFs defining segmental MN identities  3   pathway and translation regulation-were more prominent in lumbar than brachial LMC MNs (Fig.
6d-g    Supplementary Fig. 9a, b ), corroborating their aforementioned cluster-specific expression (Fig. 5b ). The differential neuropeptide expression in either brachial or lumbar LMCs was not a reflection of a developmental timing delay, as when we compared E12. 5 brachial and E13. 5 lumbar LMC MN datasets, we found that the neuropeptide signaling pathway (GO:0007218) is still more enriched in the E12. 5 brachial 23 compared to E13. 5 lumbar segment, representing a similar outcome to our findings for segmental gene expression at the same stage (E13. 5) (Supplementary Fig. 9c, d ). Furthermore, expressions of brachial-enriched Npy and Grp were consistent from E13. 5 to E14. 5 (Supplementary Fig. 9e, f ). Therefore, our results indicate that, like TFs and cell adhesion molecules, neuropeptides not only exhibit heterogeneous expression among LMC subclusters but also differ between the brachial and lumbar segments.
Moreover, we present two previously uncharacterized segmentallyrestricted cell clusters, which display some shared features with MMC MNs but remain molecularly distinct. Furthermore, we leveraged the large number of MNs in our dataset to permit a more detailed and unbiased analysis of heterogeneity within LMC and MMC MNs, resulting in 16 and 10 LMC subclusters from brachial and lumbar segments, respectively, and 3 MMC subclusters. The unique molecular codes expressed by each subcluster may not only serve as markers, but also provide insights into the intrinsic characteristics of individual cell populations and how robust axon targeting and neural connectivity are achieved. In addition to known MN subtypes, we discovered two segmentally-restricted cell clusters in MNs, i. e. , Calb1 + MNs in the brachial and thoracic region and Nfib + Grm5 + MNs in the sacral segment.
Fourth, although we determined the number of subclusters using a robust ensemble approach based on mathematical spectral graph theory, single-cell transcriptomic data is inherently noisy and sparse in nature, which engenders additional challenges for estimating optimal numbers of clusters from such datasets. Further computational subclustering analysis with in vivo functional validation would likely enhance our understanding of LMC diversity. Axial MNs that control core muscles represent an evolutionarily ancient neuronal type and they play pivotal functions across most vertebrate species. Diversity within the relatively small population of MMC neurons in the spinal cord is less appreciated due to their largely unknown molecular profiles. In this study, we describe transcriptomic heterogeneity within MMC MNs.
Satb2 Bcl11b Nr2f2 Clusters Ligand/Receptor Genes % of cells Article identities in the spinal cord  23. Thus, although our single-cell gene expression analysis has facilitated the identification of transcriptionally distinct cell types and states, integrating other modalities such as proteomics with our dataset would reveal more information about gene regulation during the cell-type determination process. Furthermore, while we have also attempted to cluster the collected MNs after integrating rostral and caudal datasets (Supplementary Fig. 13 ), we noticed several integration complications that disagreed with in vivo observations reported by previous and our studies. For example, a small subset of Tac1-expressing cells was not differentiated as a single cluster (Supplementary Fig. 13 ). Some cells expressing the LMC markers (Foxp1 and Aldh1a2) were incorrectly assigned to another cluster which does not express these marker genes (Supplementary Fig.
Preprocessing and quality control of scRNA-seq data The FASTQ files were processed using the standard Cell Ranger pipeline (version 2. 1. 1, 10× Genomics) for demultiplexing, mapping to the mm10 reference, filtering, barcoding, and counting unique molecular identifiers (UMI). From the Cell Ranger pipeline, the total number of cells were 5528 and 5288, median numbers of genes detected per cell were 2843 and 2722, and the median numbers of unique transcripts were 11,906 and 11,090, respectively, for rostral and caudal samples. For both rostral and caudal samples, cells were retained for subsequent analysis if they displayed a number of genes between 1000 and 5300, UMI counts <30,500, and <10% mitochondrial counts. Quality filtering led to 5460 and 5242 cells for rostral and caudal samples, respectively, which were used for downstream analysis.
Clustering analysis of scRNA-seq data We performed normalization, dimensionality reduction, and cell clustering using Seurat package version 2. 3. 4  18. 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 FindVariableGenes function (parameters: x. low. cutoff = 0. 0125, x. high. cutoff = 3, y. cutoff = 0. 5). To identify cell clusters, principal component (PC) analysis was first performed using the top 34 PCs for the rostral sample and the top 31 PCs for the caudal sample, where the number of significant PCs was determined based on an elbow plot and jackstraw test.
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  79. 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 relatedness 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 using the corrected embedding learned from the Harmony integration method 49.
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  80. To intuitively visualize the relationships of LMC subclusters between brachial and lumbar segments, we created an 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 substantial similarity and enhance readability. This threshold was heuristically searched within a range supported by prior knowledge, such as on Etv4  48  , which should be shared among non-segment-specific clusters, as well as the relatedness of a given pair of clusters with other clusters in the UMAP space.
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 p value <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, RRID: SCR_016884)  81  , focusing on Biological Processes (BP) and Molecular Functions (MF). Redundant GO terms were removed using the simplify function with default parameters. Immunohistochemistry and in situ hybridization of mouse embryos All E13. 5 and E15. 5 mouse embryos were fixed with 4% PFA for 2 h (for immunostaining) or 4 h (for in situ hybridization).
Statistics and reproducibility Statistical analysis was performed by GraphPad Prism 6. 0 (GraphPad Software). All statistical results are presented as mean ± SD (standard deviation) of three or more independent biological replicates or the median ± interquartile range (IQR), as indicated. Appropriate statistical tests are performed for each analysis and specified in the respective figure legend. All staining experiment were repeated on at least three independent biological replicates unless indicated otherwise and representative images were presented in the manuscript. No statistical method was used to predetermine sample size. The experiments were not randomized nor blinded because the quantification analysis and the assessment was performed in an objective and semi-automated manner.
(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); 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, RRID:AB_2145209); goat anti-Chat (1:100 Millipore/Sigma Cat# ab144P, RRID:AB_2079751); mouse anti-COUP-TF2/NR2F2 (1:200 R&D systems Cat# PP-H7147-00, RRID:AB_2155627); mouse anti-COUP-TF1/NR2F1 (1:500 R&D systems Cat# PP-H8132-00, RRID:AB_2155494); 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); 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-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, RRI-D:AB_2295173); sheep anti-Ebf2 (1:500 Novus Cat# AF7006, RRI-D:AB_10972102); rabbit anti-Nrp2 (1:200 Cell Signaling Technology Cat# 3366, RRID:AB_2155250).
Acknowledgements We thank  Hynek Wichterle ,  Tom Jessell , and  Susan Morton  ( Columbia University ) for many gifts of antibodies used in this study. We thank  Virginie Rouiller-Fabre  and  Nour Nicolas  ( CEA, Fontenay aux Roses, France ) for providing human tissues. We acknowledge the  FACS, Transgenics, Genomics, Bioinformatics and Imaging cores of IMB Academia Sinica  for their substantial technical assistance, and  Dr. John O'Brien  for further editing the manuscript. We thank  Dr. Jr-Kai Yu ,  Dr. Yi-Hsien Su ,  Dr. Shen-Ju Chou  and  Dr. Suewei Lin  ( Academia Sinica ) for valuable inputs and members of the  JAC lab  for discussion and proofreading. J. -A. C. is 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 ) grants.
Data availability The E13. 5 mouse spinal MNs scRNA-seq raw data generated in this study have been deposited in the Gene Expression Omnibus repository and are accessible through accession code GSE183759. The public datasets of adult spinal MNs used in this study are available at http:// spinalcordatlas. org. All data supporting the findings of this study are available within the article and Supplementary Information. Source data are provided with this paper.
Data Extraction and Quality Assessment for Studies Reporting on Oculomotor Findings in Ataxia A quality assessment of included studies was performed by two independent reviewers (PG, AAT) based on eight predefined quality criteria covering items related to (i) the studycohort, (ii) data acquisition, and (iii) data analysis in studies reporting on oculomotor findings in ataxia, and included an evaluation for risk-of-bias for assessing test results. An overall study quality rating (high, moderate, or low) was derived from this quality assessment (see Appendix 2 for details). In brief, studies were considered "high quality" if they (i) included patients with genetically confirmed hereditary ataxia, (ii) included age-matched healthy control groups (confirmed on clinical examination, except for longitudinal or treatment-response trials), (iii) implemented a prespecified recording protocol, (iv) provided sufficient detail of recorded parameters to allow reproduction, (v) used appropriate (i.
For example, some default calibration procedures jump quickly from one calibration point to the next, and participants with severely reduced saccade velocities (e. g. , due to severely abolished saccade generation in NPC) or patients who require corrective saccades to reach the calibration points may not be able to perform a correct calibration with the default timing. Such patients may require a calibration sequence based on pursuit rather than saccades. There is no optimal one-fits-all calibration procedure given the variable pattern of oculomotor deficits across hereditary ataxias. Thus, we recommend that characteristics of the population of interest are reviewed before initiating a study. Calibration may potentially also require acquisition of some pilot data prior to the study. Additionally, we recommend that the calibration performance parameters such as accuracy and precision are recorded.
Safety is our first priority. Over the past 30 years data has been collected by our field to carefully assess the behavioral, neurocognitive, physiological, and biophysical effects of NIBS on the central nervous system. These studies have not only informed us about clinical efficacy, but have also yielded new evidence-based nonpharmacological NIBS treatments for neurological and psychiatric disorders that are used in a growing number of countries worldwide. Importantly, this long research period has also informed us about the risks, adverse and side effects that can occur when applying NIBS. Based on this vast amount of safety data collected, several publications, meta-analyses, reviews, guidelines and consensus papers have provided peer-reviewed evidence-based assessments of the safety of rTMS (e.
Photomicrographs represent maximal intensity projections derived from ApoTome images collected using the z-stack module of the Zen imaging software. Images to be used for figures were pseudocolored, adjusted for brightness and contrast, and merged using Adobe Photoshop 22. 1. 0 (Adobe Systems, San Jose, CA; RRID:SCR_014199). Anatomical landmarks were determined using the mouse brain atlas from Paxinos and Franklin. 32 Analyses were done in the tuberal region, where tanycytes are found in adult mice. The region was subdivided into four zones along the anteroposterior axis, according to the shape of the 3 V and the neuroanatomical characteristics of adjacent hypothalamic nuclei (Figure  1 ) as previously done. 12 In adult male mice, zone 1 extends from Bregma À1. 3 to À1. 6 mm, zone two from Bregma À1.
ACKNOWLEDGMENTS This work was supported by a  doctoral fellowship  from the  University of Lille  to A. C. -R. ,  Inserm  grant  U1172 , the  Association pour l'Etude des Anomalies Congénitales  (to A. S. ), the  European Research Council ERC-Synergy -Grant-2019-WATCH no. 810331  (to V. P. ). We thank the  Animal Core Facility of the University of Lille  ( PLBS UAR 2014-US41 ) for expert technical support.
TNF-α Measurement Tumor necrosis factor alpha (TNF-α) was quantified using an ELISA Kit (BMS607-3TEN, Thermo Fisher Scientific). Briefly, the culture medium used to maintain glial cells was recovered 24 h after the initiation of the test treatments and then frozen at -20 • C for subsequent analysis. TNF-α was measured according to the manufacturer's instructions using aliquots (50 µL) of undiluted samples. The sample absorbance was read at 450 nm using a SpectraMax M4 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA). ELISA standard curves were generated using a four-parameter logistic curve model (GraphPad Prism 8, GraphPad Software; San Diego, CA, USA).
Immunodetection Protocols for Cell Culture Cultures were fixed for 12 min at room temperature with 3. 7% formaldehyde diluted in Dulbecco s phosphate buffered saline (PBS) and then washed twice with PBS before incubation with primary antibodies for 24-48 h at 4 • C. The primary antibodies used for the cell culture protocols are listed in Table  1. Antibodies were pre-diluted in 0. 2% Triton X-100, except for CD45, which was diluted in PBS only. We used anti-mouse/rabbit IgG (H+L) or anti-chicken IgY (H+L) conjugated to Alexa Fluor fluorescent dyes as secondary antibodies (Thermo Fisher Scientific). For cell counting operations in culture, we used a Nikon TE 2000 inverted microscope (Champigny-sur-Marne, France) equipped with an ORCA-ER digital camera and the HCimage software (Hamamatsu Photonics, Massy, France).
H 2 O 2 and FCCP were added 2 h and 10 min before the addition of fluorogenic probes, respectively. Note that FCCP remained present during all steps of the imaging protocol. For each culture condition, fluorescent images from at least 5 randomly chosen fields were acquired with a 40x fluorescence objective using a Nikon TE 2000 inverted microscope equipped as described before. The excitation and emission wavelengths for DHR-123 were 490 nm and 525 nm, respectively. For TMRM, the corresponding values were 548 nm and 575 nm, respectively. The results are expressed as the percentage of change in fluorescence intensity relative to the baseline in control cultures. The open-source software FIJI  [37]  was used to quantify the fluorescent signals.