dataesr/scientific-paragraphs-categorization huggingface dataset:
NOTE: Only computed from records of plant species eaten in natural environments; records on Gymnosperms were dicarded prior calculation (see text). Number_PlantSpecies Subfamily Total number of plant species known to be eaten in natural environments by Taxonomic subfamily of the moth genus Tribe caterpillars of this species of moth Taxonomic tribe of the moth genus Plant_SpeciesNames Moth_Genus_Name Names of plant species known to be eaten in natural environments by caterpillars of this species of moth Genus name Number_PlantFamily Total number of plant families known to be eaten in natural environments by caterpillars of this species of moth Plant_FamilyNames Names of plant families known to be eaten in natural environments by caterpillars of this species of moth Rank Number_PlantOrders Scientific Name Total number of plant orders known to be eaten in natural environments by Common Name family Saturniidae caterpillars of this species of moth Wild silkmoths family Plant_OrderNames Sphingidae Names of plant orders known to be eaten in natural environments by caterpillars of Hawkmoths this species of moth Number_PlantGenus_Capt Total number of plant genera known to be eaten in captivity by caterpillars of this Usage licence species of moth Usage licence: Open Data Commons Attribution License Plant_GenusNames_Capt Names of plant genera known to be eaten in captivity by caterpillars of this species of moth Number_PlantSpecies_Capt Total number of plant species known to be eaten in captivity by caterpillars of this Data resources species of moth Data package title: Global food plant dataset and polyphagy scores for Sphingidae and Plant_SpeciesNames_Capt Names of plant species known to be eaten in captivity by caterpillars of this species Saturniidae of moth Number of data sets: 2 Number_PlantFamily_Capt Total number of plant families known to be eaten in captivity by caterpillars of this Data set name: Global food plant dataset for Saturniidae and Sphingidae species species of moth Plant_FamilyNames_Capt Names of plant families known to be eaten in captivity by caterpillars of this species Data format: Excel data spreadsheet of moth Description: Suppl.
Bulk RNA sequencing has indeed identified differences in gene expression between nose and bronchi  (5). The recent advent of single-cell RNA sequencing offers an excellent opportunity to carefully analyze and compare cellular composition and gene expression from nasal to the successive generations of the lung airways. In the framework of the Human Cell Atlas (HCA) consortium, we have now established a precise airway epithelium cell atlas in a population of 10 healthy living volunteers. Minimally invasive methods were set up to collect biopsies and brushings using bronchoscopy. A high-quality dataset of 77,969 single cells comprising a large panel of epithelial cell subtypes was generated from 35 distinct samples taken at precise positions in the nose, trachea and bronchi. Data integration and analysis provide a unique view of the cell type proportions and gene signatures from the first to approximately the 12 th division of the airways.
Methods The atlas of the airway epithelium (nose to approximately 12 th division of bronchi) was obtained from biopsies and brushings from 10 healthy non-smoking volunteers. Each donor was sampled 4-5 times in different regions of upper (nose) and lower airways (tracheal, intermediate, distal bronchi), located in different lobes (Figure  E1 , Tables  E1-2 ). Single-cell capture was carried out using the 10X Genomics Chromium device  (3' V2). Large integrative analysis of the 35 samples composing the atlas was done using the fastMNN R package  (6)  and the following analysis was done using the Scanpy framework (Python)  (7)  to provide robust cell type annotation. Additional differential gene expression analysis was done using the edgeR R package  (8)  to investigate both cell distributions and gene expression heterogeneity along the airways.
5, logCPM >1. 5 and p-value <0. 05, whereas 499 genes were found up-regulated in the trachea/bronchi with identical thresholds (Table  E3 ). We noticed an enriched expression of SCGB1A1, SCGB3A1, KLK11 and SERPINF1 in the bronchi, and of LYNX1, S100A4, CEACAM5, LYPD2, PI3 and MUC4 in the nose (Figure  2D , Figure  E9 ). Immunostainings confirmed the nasal expression of PI3 and MUC4 on independent brushing and biopsies (Figure  2F ). Thirty seven additional transcripts were confirmed, based on a comparison with the Protein Atlas database (24) (Table  E4 ). First insights about distinctive functional properties were obtained after a gene set enrichment analysis. In bronchi, functional terms related to defense and innate immune response to aggression were found. In nasal epithelium, enrichment in terms related to differentiation and motility supports the existence of a higher cellular turnover (Figure  2E , Table  E5 ).
In our dataset, around 60 cells were found positive for both FOXJ1 and MUC5AC. They were equally distributed between the secretory and the multiciliated cell clusters. We have used SoupX to remove gene counts that may emerge from cell-free mRNA contamination, thus avoiding interference with the quantification of multiciliating-goblet cells (Figure  3E ). We confirmed the presence of these cells using RNAscope in situ RNA hybridization (Figure  3F ). When these cells were superimposed in a PAGA representation of tracheobronchial cell lineages, we noticed that they were located close to multiciliated cells, while in a similar representation of nasal cell lineages, they were located between secretory and deuterosomal cells, nearer to these latter (Figure  E10B ). This result supports our previous description of goblet cells as precursors of multiciliated cells in homeostatic and healthy epithelium and additionally suggests that transition through this stage may have slightly distinct dynamics between nasal and tracheobronchial epithelia  (12).
Discussion We have established a reference single-cell atlas of normal human airways after analyzing 35 fresh tissue samples collected by bronchoscopy in 10 healthy volunteers, resulting in a largescale gene expression profiling that also integrated spatial information of each sample. This approach was well adapted to collect samples from the nose to the mid airways but excluded the bronchiolar compartment and the parenchyma, for which alternative experimental approaches have already been proposed  (30, 31). Establishing a comprehensive airway atlas will result from the combination of our atlas with these other datasets. That saying, our approach provides a unique opportunity to build a single-cell gene expression resource based on well-characterized healthy volunteers which are rarely accessible in most large scale studies.
Ethics statement The study was approved by the Comité de Protection des Personnes Sud Est IV (approval number: 17/081) and informed written consent was obtained from all participants involved. All experiments were performed during 8 months, in accordance with relevant guidelines and French and European regulations. No deviations were made from our approved protocol named 3Asc (An Atlas of Airways at a single cell level -ClinicalTrials. gov identifier: NCT03437122).
Chromium 10X Genomics library and sequencing We followed the manufacturer's protocol (Chromium™ Single Cell 3' Reagent Kit, v2 Chemistry) to obtain single cell 3' libraries for Illumina sequencing. Libraries were sequenced with a NextSeq 500/550 High Output v2 kit (75 cycles) that allows up to 91 cycles of pairedend sequencing: Read 1 had a length of 26 bases that included the cell barcode and the UMI; Read 2 had a length of 57 bases that contained the cDNA insert; Index reads for sample index of 8 bases. Cell Ranger Single-Cell Software Suite v2. 3. 0 was used to perform sample demultiplexing, barcode processing and single-cell 3′ gene counting using standards default parameters and human build hg19. All single-cell datasets that we generated, and the corresponding quality metrics are displayed in Table  E1.
Primary data analysis Initially, each dataset was roughly analysed using Seurat (v3) [1] to determine the best analysis workflow needed for the merged dataset. Permissive filtering was done on low-quality cells followed by median normalization, identification of highly variable genes and Louvain clustering. Marker genes of cell-clusters were identified using Wilcoxon's rank test and shared top genes across datasets resulted in a common and robust cell type annotation. This initial annotation was later used to create a reference for the precise annotation of the merged dataset (Figure  E8 ).
Ambient mRNA correction Dissociation of complex tissue, such as brushing and biopsies, results in a certain proportion of cell lysis. It results in the presence of ambient mRNA that spreads across all droplets of a single experiment. This gene expression background is highly dependent of the cell-type composition which might lead to misleading analysis. We used soupX (https://github. com/constantAmateur/SoupX) for background correction and compared analysis results between corrected and uncorrected dataset (corrected dataset being very sparse). Background corrected data were mainly used for visualization purposes.
Normalization Size factors were calculated for the complete (merged) dataset using 'ComputeSumFactor' from the scran R package [2]. Cells were pre-clustered with the 'quickCluster' function, method 'igraph' and minimum and maximum cluster size of 100 and 3,000, respectively. Raw counts were then normalized and log-transformed with cell-specific size factors. A count of 1 was added to each value prior to log transformation.
Batch Correction and Data Integration Batch effects were removed using the 'fastMNN' function in the scran R package on 50 principal components computed from the HVGs only [3]. Correction was performed incrementally from the most homogeneous samples to the most heterogeneous (in QC and cell composition). Datasets were first corrected between sampling position and method (nasal biopsies, nasal brushings, tracheal biopsies, intermediate biopsies and distal brushings), merging sequentially from the sample containing the most cells to the samples containing the least. Positions were merged as follow: intermediate, tracheal, distal, nasal. The resulting batch-corrected principal component analysis was then used for further analysis steps. The compared analysis was performed between batch-corrected and uncorrected datasets.
Gene expression differential analysis Differential analysis between specific clusters (secretory vs. secretory N for instance) was designed differently from the rank_gene_group function from scanpy to overcome the sample-specific gene expression background still present even after correction and to amplify the statistical power of the differential analysis. Pseudo-bulk samples were created from each cell-clusters by summing the raw counts of each gene in multiple single cells. Each bulk was designed to be composed of an equal number of cells (to get similar library size between bulks), and to contain randomly picked cells from a homogeneous mix of all the donor samples (to have a similar gene expression background between all bulks from the same cell type). Differential analysis was performed using glmFit function from the R package edgeR [6].
Cycling cell identification and cell cycle analysis Cycling cells were identified in the batch-uncorrected analysis of the dataset as a single cluster, and this specific cell type annotation was reported in the batch-corrected dataset. Cell cycle scoring (S phase and G2M phase) was performed using the function score_genes_cell_cycle from scanpy tools and the associated cell cycle genes [1]. The G1 phase score was estimated as the opposite of both the S and G2M phase.
Inference of transcriptional regulatory units We inferred transcriptional regulatory units using the GRNboost2 algorithm implemented in the arboreto package (https://arboreto. readthedocs. io/en/latest/). Expression correlations between transcription factors and potential target genes were computed from a raw count data matrix where we set a maximum threshold of 5000 cells by cell types. We obtained 1222 modules composed of the 50 first top correlated genes with a confirmed transcription factor. We scored the activity of those modules in each cell of the complete dataset using the score_genes function from scanpy tools. Cell type-specific activity of each module was determined with a Wilcoxon's rank test. [5] Levine Figure Legends.
Acknowledgements We are grateful to the  UCAGenomiX platform  for fruitful discussions and technical help on single-cell RNA sequencing, to  Frederic Brau ,  Sophie Abelanet  and  Julie Cazareth  from the IPMC imaging platform, for fruitful discussions and technical help on imaging and to  Jennifer Griffonnet  for her invaluable help in building an experimental protocol that respects ethical rules, in collaboration with the  French health authorities (Agence Nationale de Sécurité du Médicament et des produits de santé and Comité de Protection des Personnes).
With the inclusion of motion we also excluded individuals for which no resting-state fMRI was available. The characteristics of the final autism sample are listed in Table  1. Case-control differences versus age-normative modelling. Our first analysis examined conventional case-control differences using linear mixed effect modelling including site, sex, age, inscanner head motion  37  and Euler index  38  as covariates. As expected from prior papers utilizing large-scale datasets for casecontrol analysis  14, 27  , a small subset of regions (8. 7%, 27/308 regions) pass FDR correction. Of these regions, most are of small effect size, with 26 of the detected 27 regions showing an effect <0. 2 standard deviations of difference (Fig. 2a ). We suspected that such small effects could be largely driven by a few ASD patients  39  with highly age-atypical CT.
6  and 7 ). The median age of outliers across brain regions ranged from [10. 6-20. 2] years old, with an overall skewed distribution towards the younger end of the spectrum (Supplementary Fig. 7 ), showing that CT atypicality potentially normalizes with increasing age in ASD, though it should be noted that this may partially be explained by the overall skewed age distribution in the overall dataset. Patients with CT atypicality were also largely those that expressed such atypicality within specific brain regions and were not primarily subjects with globally atypical CT. To show this we computed a w-score ratio across brain regions (Supplementary  Figs. 6  and 7 ) that helps us isolate patients that show globally atypical CT across most brain regions. The small number of patients with a ratio indicating a global difference (ratio > 0.
Here we used w-scores to compute Spearman correlations for the most commonly shared phenotypic features in the ABIDE dataset: ADOS, SRS, SCQ, AQ, FIQ and Age. After correcting for multiple comparisons across phenotype and region (6 phenotypic measures*308 regions = 1848 tests) we identified a number of brain regions that survive multiple comparison corrections for the SRS and ADOS scores (Fig. 4 , Supplementary Fig. 8 ). SRS is associated with w-scores primarily in areas of lateral frontal and parietal cortex, while ADOS is associated with w-scores primarily in lateral and inferior temporal cortex. Notably, these regions are largely different from regions that appear to show on-average differentiation in case-control and w-score analyses. Sensitivity analysis. Sensitivity analyses on the effects of reconstruction quality using Euler index as well as residual effects of inscanner head motion from the resting-state acquisition did not reveal a significant impact on thresholded case-control differences or w-score.
Discussion In the present study, we find that with a highly powered dataset, conventional case-control analyses reveal small differences in CT in autism and are restricted to a small subset of regions. In general, this idea about subtle effect sizes for case-control comparisons is compatible with other recent papers utilizing partially overlapping data-Haar and colleagues utilized only ABIDE I data  14  , while van Rooji and colleagues  27  utilized both ABIDE I and II dataset combined with further data from the ENIGMA consortium. While these statements about small effect sizes are not novel, our findings suggest that even these small effect sizes may be misleading and over-optimistic. Utilizing normative modelling as a way of identifying and removing CT-atypical outlier patients, we find here that most small case-control differences are driven by a small subgroup of patients with high CTatypicality for their age, which indeed begs the question of the existence of on-average atypical cortical morphology in autism  14.
Again, it should be noted that this may partially be explained by the overall skewed age distribution in the overall dataset. Future studies with either more balanced developmental samples or samples that cover the entire lifespan will be better positioned to confirm this age-related skewed profile in 'atypical' brain regions. In addition, we also identify a very small group of individuals that have atypical patterns in over 50% of brain regions. Unfortunately, not much behavioural or phenotypic information was available for this sub-group. We hope that future studies will obtain more detailed phenotypic information in order to delineate more precisely what the clinical and or more broad behavioural implications might be of this atypicality. Furthermore, it is clear from the present work that this subgroup only covers a very small subset in the autism population and thus future studies will require large sample sizes to be able to identify this subgroup.
We also conducted exploratory analyses to relate the w-scores back to phenotypic information more broadly, insofar as this was available in ABIDE. Here, we find collections of areas that are largely different from regions normally detected with on-average case-control or on-average non-zero w-score differences. Interestingly, the associations with ADOS and SRS show somewhat differential spatial topography which may suggest that the overall scores are related to different underlying neurobiological mechanisms. Overall, these results could suggest that the normative model is sensitive to signal related to behavioural variability. However, it should be emphasized that ADOS and SRS scores are not available for the full dataset and the reported effects were small and should thus be considered exploratory.
In addition, ADOS and SRS are often only collected on individuals with a diagnosis already, which makes the general inference of this brain-behaviour relationship potentially biased. Based on present results however we expect future studies, with more comprehensive phenotypic information such as EU-AIMS2-trials (https://www. aims-2-trials. eu), to be able to confirm this brain-behaviour relationship. It will be interesting to see whether in a larger more comprehensive sample the same topological dissociation becomes apparent as well. The current results can be contrasted with a recent study on the EU-AIMS LEAP cohort  44. This study differs from the current work in being based on a completely independent dataset (EU-AIMS LEAP vs. ABIDE). The studies also differ in how normative models are estimated-LOESS and centiles vs.
Thus, despite the methodological differences, the overall consistency suggests that many of the inferences from these works generalize to the autism population. There are a number of caveats to consider in the present study. First and foremost, the present data are cross-sectional and the normative age modelling approach cannot make claims about trajectories at an individual level. With longitudinal data, this normative modelling approach could be extended. However, at the moment the classifications of highly age-atypical CT individuals are limited to static normative statistics within discrete agebins rather than based on statistics from robust normative trajectories. The dataset also represents ASD within an age range that misses very early developmental and also very late adulthood periods.
Second, the dataset also presents a post-hoc collection of sites accumulated through the ABIDE initiative, whereby scanners, imaging acquisition sequences and parameters, sample ascertainment, etc. , are highly heterogeneous. As a result, we observed that site had a large effect on explaining variance in CT and this is compatible with observations made by other studies  14. Furthermore, it is likely that there may be systematic interactions between scanner site and some variables of interest such as age (e. g. different scanning sites will likely have recruited specific age cohorts). Third, there are a number of different approaches to normative modelling that all have pros and cons (see ref. 45  for an excellent review). We chose to use LOESS estimation as it is computationally efficient and the resulting w-scores are easily interpretable.
We show that results from casecontrol analyses, even within large datasets, can be highly susceptible to the influence of 'outlier' subjects. Removing these outlier subjects from analyses can considerably clean up the inferences being made about on-average differences that apply to a majority of the ASD population. Rather than only being nuisances for standard group-level analyses, these outlier patients are meaningful in their own light, and can be identified with our normative age modelling approach. Normative models may provide an alternative to case-control models that test hypotheses at a group-level, by allowing additional insight to be made at more individualized levels, and thus help further progress towards personalized medicine for ASD. Furthermore, the current approach is in line with the original normative modelling approach advocated by Marquand and colleagues  21  which suggested the development of methods to move away from the traditional case-vs.
Methods Participants. In this study, we first sought to leverage large neuroimaging datasets to yield greater statistical power for identifying subtle effects. To achieve this, we utilized the ABIDE datasets (ABIDE I and II; 15) (see Supplementary Fig. 1 ). Informed consent was given at each site included in the ABIDE studies, see the website for more details: http://fcon_1000. projects. nitrc. org/indi/abide/. Given that the normalized modelling approach gives us individual level measures we chose to also include sites with limited numbers of subjects. Groups were subsequently matched on age using the non-parametric nearest neighbour matching procedure implemented in the Matchit package in R (https://cran. r-project. org/web/packages/ MatchIt/index. html)  52. After matching case and control groups and excluding scans of poorer quality (see supplementary materials) we were left with a sample size N = 870 per group (Tables  2  and 3 ).
In the full sample we found a small but significant difference in both hemispheres (Supplementary Fig. 2 ) with the autism group having overall slightly worse scan quality (d = 0. 176 and d = 0. 187 for left and right hemispheres, respectively). Therefore, we chose to exclude the top 10% of subjects with an extreme Euler index (corresponding to a Euler index of ~300) and reran the Matchit genetic matching algorithm to check for matched samples. To further ensure adequate control for scan quality we included the index itself as a confound variable in all models. Across both ABIDE I and ABIDE II CT was extracted for each subject using two different parcellations schemes: an approximately equally sized parcellation of 308 regions (~500 mm 2 each parcel)  33, 56, 57  and a parcellation of 360 regions derived from multi-modal features extracted from the Human Connectome Project (HCP) dataset  58.