dataesr/scientific-paragraphs-categorization huggingface dataset:
Cells of the pallial and subpallial lineages coexist within the dorsal V-SVZ and show different dynamics Expression of markers for either pallial  (Emx1, Neurod1, Neurod6, Neurog2, Tbr1, i. e. 3862 cells)  and subpallial lineages (Gsx2, Dlx6, Dlx5, Sp9, Dlx2, Gad1, i. e. 2452 cells), confirmed the coexistence of two spatially segregated lineages within aNSCs (Fig. 2F ), but also more broadly throughout the entire datasets (Fig. 2G ). A smaller population of cells expressing both pallial and subpallial markers ("Dual cells", Fig. 2F-G ), which were not doublets as revealed by their averaged RNA content (Fig. S4A ), as well as by "scDblFinder" and "doubletfinder" analyses, suggesting lineage transition for a small number of cells. Quantification of the proportion of cells expressing pallial (Emx1) and subpallial (Gsx2) markers revealed a differential contribution of both lineages to defined cell types (Fig.
2J-L ). Immunostaining for Gsx2 at P12 confirmed the emergence of a population of pallial cells expressing subpallial markers (Fig. 2M ). In addition, colocalization of Dlx2 and/or GAD67::GFP with Tbr2 confirmed that co-expression was not restricted to NSCs, but also includes TAPs/NBs (Fig. 2N ) in which the proportion of dual cells culminates (i. e. 8% of the cells; Fig. S4B-C ). To confirm these results and explore the dynamic of pallial and subpallial lineages within the dorsal SVZ, we produced two additional datasets early after birth (P2) as well as at weaning (P22) (Fig. S1B ). Results confirmed the presence of cells of the two lineages (Fig. 2O ). Interestingly, pallial cells peaked at P12, because of a massive increase in the number of pallial qNSCs, but declined by P22 at the expense of cells of the subpallial lineage.
Pallial NSCs enter quiescence at postnatal stages within the dorsal V-SVZ and are characterized by Hopx expression Both the velocity analysis (Fig. 1G ), see also velocity analysis of pallial and subpallial lineages in Fig. S4D-F ), as well as the absence of pallial qNSC in our P2 dataset (Fig. 2O ), suggest their postnatal generation within the dorsal V-SVZ. To confirm this finding, we first integrated our datasets with those of E14. 5 and P0 pallial cells  (Loo et al. , 2019)  (Fig. 3A ). Differentiated mature cells present in both datasets (i. e. microglial cells, endothelial cells and astrocytes) overlapped extensively, validating the robustness of the integration (Fig. 3B ). Further, significant overlaps were observed for aNSCs/TAPs with progenitor/cycling cell populations, a fraction of TAPs/NBs, and cortical neurons revealing strong transcriptional similarities among these datasets.
TAPs/NBs separated in two trajectories corresponding to cortical inhibitory and excitatory neurons, with these cells being rather rare in our datasets due to our microdissection procedure restricted to the dorsal V-SVZ (Fig. S4G-H ). Remarkably, in sharp contrast to extensive overlaps in all other cell clusters, qNSCs were only observed within our datasets supporting their postnatal emergence (Fig. 3B ). This was further supported by a ClusterMap analysis highlighting significant similarities in gene expression between all aforementioned cell types, while on the other hand, qNSCs from our datasets were entirely segregated from cell types present within the pallium at E14 or P0 (Fig. 3C ). To confirm the postnatal generation of qNSCs within the dorsal V-SVZ, we used a label-retaining protocol.
Pallial and subpallial NSCs are defined by distinct levels of quiescence and TGFβ signalling We next used our transcriptomic datasets to characterize the transcriptional profile of Hopx expressing cells. Identification of differentially expressed genes among Hopxhigh (mostly pallial/qNSC1 cells) and Hopxlow (mostly subpallial/qNSC2 cells, Fig. S5D ) expressing cells (731 cells Hopxhigh = Hopx>2. 5, 651 cells Hopxlow = Hopx<0. 4) resulted in the resolving of key ontological categories. Interestingly, Hopxhigh expressed genes involved in "negative regulation of DNA binding transcription factor activity" (GO:0043433), "negative regulation of cell development" (GO:0010721) and "TGFβ signalling pathway" (GO:0007179). In sharp contrast, Hopxlow expressing cells expressed genes involved in "stem cell differentiation" (GO:0048863), "cell maturation" (GO:0048469), and various metabolic pathways, suggesting primed activation (Fig.
Postnatal induction of deep pallial quiescence is paralleled by a rapid blockade of glutamatergic progenitors/nascent neurons production and differentiation To investigate the neuronal output of dorsal NSCs, we isolated cells of aNSC3 neuronal trajectory (i. e. aNSC3 subclusters 4 and 5 from Fig. 2E  and TAPs and nascent neurons). Following removal of Olig1/2 positive cells, we obtained 1029 cells with a majority (66%) expressing Pax6 and Rlbp1, markers of dorsally born NBs (Cebrian  Silla et al. , 2021) , while almost none (3/1029) expressed markers of ventrally born NBs (Runx1t1 and Vax1), confirming the exclusion of NBs from ventral regions in our datasets. New clustering of these cells followed by cluster identification using neural cell subtype markers (see methods for details) resulted in the generation of a UMAP distinguishing two main lineages corresponding to GLU and GABA cells (Fig.
5E  and Data S6). In contrast, several GO categories related to "negative regulation of cell development", "negative regulation of neurogenesis" and "negative regulation of nervous system development" were enriched in GLU cells (Fig. 5F  and Data S6). These results are in line with the sustained expression of transcriptional repressors (i. e. Id1, Id2, Zhx2;), as well as with the higher expression of anti-proliferation and negative regulator of cells growth genes (i. e. Btg2 and Ppp2r2b, respectively) in GLU cells (Fig. 5G ). Finally, we subclustered GLU cells from the P2, P12 and P22 datasets to generate a new Umap. While postmitotic cells were prevalent at P2, their proportion gradually decreased at later timepoints (Fig. 5H ), illustrating the rapid blockade of GLU cells differentiation.
A large population of aNSCs is observed within our dataset, illustrating the sustained germinal activity observed within this region at early postnatal time points  (Azim et al. , 2012). We could observe an early fate priming of these cells, as illustrated by their clear segregation in cell clusters expressing TFs known to instruct neurogenesis, astrogenesis and oligodendrogenesis. Further, our results support the co-existence of both unipotent and bipotent NSCs, as reflected by the coexpression of neurogenic and oligodendrogenic TFs (e. g. Eomes and Olig2, not shown) in some aNSCs and TAPs. These observations are in line with the early priming of adult NSCs  (Llorens-Bobadilla et al. , 2015) , as well as with recent clonal fate-mapping studies within the postnatal V-SVZ  (Figueres-Oñate et al.
, 2020) , which correlates with the demonstration of a subpopulation of olfactory bulb gabaergic neurons deriving from Emx1 progenitors at postnatal ages  (Kohwi et al. , 2007)  under the influence of Shh signalling which activity rises in the dorsal V-SVZ at postnatal stages  (Tong et al. , 2015). These findings are in line with our observation of a significant number of dual cells, co-expressing markers of both the pallial and subpallial lineages, within our dataset, as well as by our fate mapping of E14. 5 pallial NSCs. Our results however show that this "reprogramming" of pallial NSCs is however largely incomplete, with a subpopulation of TAPs and nascent neurons presenting a clear glutamatergic identity observable by scRNA-Seq until at least P22, in line with our previous histological and fate mapping studies  (Azim et al.
Analysis of single-cell RNA-sequencing data Quality control and filtering. We filtered 11 298 cells (P12 dataset) 4037 cells (P2 dataset) and 4254 cells (P22 dataset), based on two-quality control criteria: the number of genes per cell and the fraction of counts from mitochondrial genes per cell. Cells with <2500 genes or >7500 and with >10% mitochondrial genes fraction were removed. Clustering analysis. We first focused our analysis on the P12 replicates. Filtering and Data analysis were performed using the R package Seurat (version 3. 1)  (Stuart et al. , 2019). First, genes expressed in less than 3 cells were removed in each dataset. Gene expression was normalized using the standard Seurat workflow and the 2000 most variable genes were identified. Then, the 2 batches were integrated using the integration function of Seurat and anchor genes were scaled without any type of regression and used for PCA at 50 dimensions.
We then performed preliminary clustering with permissive parameters to identify and remove low-quality clusters. We only removed one cluster of 19 cells that expressed fewer genes compared to average. The remaining cells were subjected to second-level clustering (20 PCs; resolution = 0. 5) yielding 15 final clusters (full_identity), merged into 9 clusters (simplified_identity). The clusters were visualized in two dimensions using the RunUMAP() function (minimum distance = 0. 3; n_neighbors = 30 L; UMAP. method = 'UMAP-learn'; metric = 'correlation'). We then performed differential expression analysis and categorized each cluster as one of the following major cell types. Identification of cell clusters within the P2 and P22 datasets was achieved based on P12 cell types using MapQuery() function.
Transcriptional criteria for We first annotated spliced, unspliced and ambiguous reads using the run10x command (velocyto. py). RNA velocities were then predicted using the R package scVelo (La  Manno et al. , 2018)  based on the 2000 most variable genes and 30 PCs and 30 neighbours. Estimated RNA velocities are represented on the UMAP with streamlines (scv. pl. velocity_embedding_stream). The direction of the arrows indicates the estimated future state of the current cells. Long arrows correspond to large gene expression changes. We determine RNA velocity using stochastic and dynamical models.
Data visualization. To display the gene expression, the preprocessed UMI matrix was normalized with the function library. size. normalization of the R package  Magic (van Dijk et al. , 2018). The dropout corrected data were displayed on the Seurat FeaturePlots with the viridis scale colors only. Otherwise, the grey-red scaled FeaturePlots illustrate the original non-corrected UMI matrix. Pseudotime. Both pallial and subpallial quiescent cell lineages have been isolated and the R package slingshot used to calculate the pseudotime values using the cluster aNSC3 as root of the trajectory (Fig. 3E ).
Differential expression analysis. To identify enriched genes, we performed a non-parametric Wilcoxon rank-sum test using the FindMarkers() function from Seurat. p-value adjustment is performed using bonferroni correction based on the total number of genes in the datasets. Genes with a p-value <0. 05, at least 0. 25 average fold change (log scale) and at least 25% clusterspecific detection (percentage of cells expressing a particular gene in a cluster) were defined as enriched genes for each cluster.
Cell cycle assignment. For each cell, we computed a score based on its expression of G2/M and S phase markers; cells that express neither of these markers are likely not cycling or are in G0/G1 phase. The scoring strategy is described in  (Kowalczyk et al. , 2015). We used Seurat's implementation of this strategy in the CellCycleScoring() function. Identification of differentially expressed genes. Identification of differentially expressed genes was based on the following criteria min. pct = 0. 1, logfc. threshold=0. 25. Identification of transcription factors and transcriptional regulators. Differentially expressed genes were compared to gene sets "DNA Binding" (GO:0003677) et "Transcriptional activity" (GO:0140110). Gene set enrichment. Over-representation analyses were performed using the R package "Cluster Profiler" (https://guangchuangyu.
github. io/2016/01/go-analysis-using-clusterprofiler/) (Yu et al. , 2012). DEG were extracted by using the following criteria  (Logfc. threshold=0. 25, min. pct=0. 1). GSEA analyses  (Subramanian et al. , 2005)  were performed by using the GSEA software v4. 1. 0 [Built 27] from the board institute (GSEA (gsea-msigdb. org)), using the following curated gene sets databases:  Hallmarks "hall. v7. 3. symbols. gmt", Kegg "c2. cp. kegg. v7. 3. symbols. gmt", Reactome "c2. cp. reactome. v7. 3. symbols. gmt", Gene Ontology c5. go. bp. v7. 3. symbols. gmt". Obtained results were exported in cytoscape 3. 8. 1 for visualization and analysis. (www. cytoscape. org).
ClusterMap analysis A correlative analysis was performed using the R package "ClusterMap". It provides a way for automatically and unbiasedly matching sub-groups based on a list of marker genes detected in the analysis. ClusterMap can generate circular graphs, called circos plots, between several datasets. It identifies the similarity of clusters, based on transcriptional similarities, and represents them as an arc linking one or more clusters together. First, the gene list for each sub-cluster in each sample is used to match the sub-cluster between samples. The file containing the gene list can be the direct output of the Seurat package's "FindAllMarkers" function. The arcs in the circos plot indicate the subgroups' relationships. The width of the gray or pink sectors represents the proportion of cells in each sample.
Integration of multiple datasets Integration was done by identification of cells that are in the same biological state (anchors) using the function FindIntegrationAnchors() of the Seurat R package. The function IntegrateData() was then applied with the previous anchor set to perform dataset integration using the rpca (Reciprocal PCA) approach. The following datasets were used for integration: GSE123335 dataset corresponding to embryonic (E14. 5) and neonatal (P0) pallium and overlying cortex  (Loo et al. , 2019) ; GSE134918 dataset corresponding to P56 and P70 V-SVZ and olfactory bulb  (Mizrak et al. , 2020).
Acknowledgments We are grateful to  Kasum Azim  for his critical comments onto the manuscript,  Timothy Capeliez  for help with the histology, as well as  Julien Courchet  and  Geraldine Meyer-Dilhet  for their help with the electroporation experiments. We thank the  Single-Cell Bioinformatics Platform of the Labex Cortex , the  Centre de Recherche en Cancérologie de Lyon (CRCL) , as well as the  Centre Leon Berard (CLB) in Lyon, France , for their help in producing the transcriptional datasets presented here, as well as for their guidance in performing the analysis.
Funding: This research was supported by  ANR   ProgenID  ( ANR-18-CE16-0014 ),  ANR NeoRepair  ( ANR-17-CE16-0009 ) as well as by the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir" (decision n°   2019-ANR-LABX-02 ) operated by the  French National Research Agency (ANR).
Implications of all the available evidence Mondo can be leveraged by researchers and clinicians for disease annotations and data integration to aid in clinical diagnosis, treatment and advancement of human health care. Mondo is a freely available, open terminology that contains over 20,000 disease classes. Mondo is iteratively developed with contributions from the intended community and is under continuous revision, with future plans to further revise the toplevel classes. Recently, efforts to classify rare diseases have centered on retrieving terms from various sources to provide a unified resource. Mondo can be explored using any of a variety of ontology browsers such as the Ontology Lookup Service (OLS) (ebi. ac. uk/ols/ontologies/mondo), and the ontology files and current releases are available on GitHub (github.
Introduction In the past decade, there have been major advances in computational approaches to disease diagnosis and care management. However, the reference data on which these tools depend are not only heterogeneous and disaggregated, but also growing and ever changing. Standard terminologies and data sources such as the Human Phenotype Ontology 1 , the Online Mendelian Inheritance in Man 2 , and Orphanet 3 have helped standardize medical terminology for rare disease. However, reconciling the many terminologies used to name diseases and represent their inherent meaning has continued to be challenging, making knowledge and data integration difficult. It is critical to develop an unambiguous resource for disease name reconciliation such that evidence can be accurately gathered on individuals with these diseases and leveraged to inform their diagnosis, care, and treatment.
Furthermore, standard clinical enterprise terminologies such as SNOMED-CT or ICD-11 are not released with sufficient frequency to be able to keep up to date with constantly-changing disease knowledge; also, neither of these includes rarer disease codes. Combined with slow code adoption and miscoding, it continues to be very challenging to identify patients with a given disease in Electronic Health Record systems. Furthermore, numerous clinical and research systems, such as laboratory variant pipelines and repositories such as ClinVar, require up-to-date disease information. At the time the report is written or the data submitted, the disease entities should have identifiers that can be reconciled across sources and over time as knowledge changes. Fundamentally, a mechanism is needed to computationally harmonize disease classifications in order to best take advantage of our collective disease knowledge and heterogeneous data assets.
Mondo Disease Ontology Terminology systems often take the form of taxonomies (simple classifications) or ontologies (conceptual domain models). The utilization of an ontology for biomedical knowledge representation enables data integration and navigation of large amounts of heterogeneous data. Additionally, an ontology encodes hierarchical and other relationships and definitions, which supports modern computational methods. Mondo includes multiple parentage, enabling concepts to be classified in multiple ways, which allows for more sophisticated querying and analytics (Figure  2 ). For example, adult Refsum disease is a type of 'neurometabolic disease' and 'phytanoyl-CoA hydroxylase deficiency'. Mondo currently harmonizes knowledge from 17 disease resources, collectively representing approximately 90,000 source concepts, and merges them into 22,157 distinct disease concepts (Table  1 ).
These resources were selected based on their scope, strengths, and usage (https://mondo. monarchinitiative. org/pages/sources/). Mondo covers several disease categories, including rare diseases, infectious diseases, cancers, and Mendelian diseases (Table  2 ). Terms in Mondo have a permanent, unique identifier using the MONDO namespace, and integrate synonyms from sources that are scoped as exact, narrow, broad or related. In addition, database cross references (dbxrefs) to external sources are included, with precise semantics noting if the dbxref is equivalent or related (Figure  3 ). The Human Genome Nomenclature Committee (HGNC) standardizes human gene naming, but there is no comparable global standard for reconciling the heterogeneity of disease naming systems and making them semantically interoperable.
Disease names vary -not only by language and region -but also over time due to changing social norms and improved understanding of underlying pathogenic mechanisms; moreover, different stakeholders that speak the same language may still prefer different names for different reasons. As a consequence, it is vital to have reliable disease identifiers which durably refer to the same concept over time -accommodating both changes and preferences. Mondo functions as a broker for disease nomenclature; the disease names and respective identifiers are a handle (i. e. stable reference), whereby synonyms, related knowledge and definitions can evolve over time with full provenance. Mondo supports multiple synonyms and synonym types as well as annotating which labels are preferred by which groups. In Mondo, synonyms are classified as exact, broad, narrow, and related  18.
21, 22  Mondo leverages a wealth of expert knowledge and authoritative terminologies to create a resource that is optimized for computational use in diagnostic, clinical, and research applications. Released on a monthly cycle, Mondo is iteratively developed to meet the evolving needs of a diverse, global community of contributors. There are currently more than 100 clinical and domain expert contributors from over 25 institutions that help evolve the resource, including ClinGen 23 , OMIM  24  , GARD  25  , Orphanet 26 , and others. Mondo also has a rich community of users that have implemented Mondo in a variety of settings, including incorporation into standards, such as in the Global Alliance for Genomics and Health (GA4GH)  27  and ISO standards such as Phenopackets  28  , in the HL7 Terminology Authority  29  , use in tools and data management programs such as PhenoTips  30  , as well as in databases such as ClinGen  23  , MedGen  31  , Gabriella Miller Kids First 32 , Pharos  33  , and many others.
The provenance for the synonyms is captured as a database cross-reference in the Mondo ontology file. C1. Representation of the identifiers (IDs) of synonym sources, which are also database crossreferences for this Mondo term. C2. Representation of the mappings between source terms and other sources. For example, UMLS:C0034960 maps to OMIM:266500. D. A solid line represents an unscoped mapping (database cross-reference, i. e. the semantics of the mapping is not defined). A dotted line represents a broad (more general) to narrow (more specific) mapping. For example, Orphanet:773 is broader than OMIM:614879. E. Represents mappings between the source term to another external term that we reviewed and determined that they are not equivalent but there is no way in the source ontologies to determine that based on the information given in the source.
Acknowledgements Mondo is generously supported by the  NIH National Human Genome Research Institute Phenomics First Resource ,  NIH-NHGRI  #  1 RM1 HG010860-01 , a  Center of Excellence in Genomic Science ; and a  NIH Office of the   Director Grant  # 5R24OD011883  for the  Monarch Initiative. Additional support for this research/work was supported in part by the  National Center for Biotechnology Information of the National Library of Medicine (NLM) ,  National Institutes of Health. Thank you to  Damien Goutte-Gattat  for assistance in mining GitHub for our list of contributors.
Sequential transmission, however, led to roughly twice as many reconstitutions of the complete genome than parallel transmissions. Over all test plants, the 'grand mean' of those containing all eight FBNSV segments after parallel transmission is 13. 6% while it is of the order of 25% after sequential transmission. This observation holds whether we calculate the mean over both experiments or only the "parallel vs sequential" experiment, over both pairs of segments or only over C/U4 (which are the only segments involved in parallel transmissions). It is worth noting, however, that the differences in reconstitution rate between parallel and sequential transmission modes are due only to the U4-||C-treatment (with the data of only the first experiment z=2. 011, p=0. 044; with the data of both experiments in Fig.