dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants As reported in  Gao et al. (2021) , a group of 32 young healthy participants aged 19-35 (mean age = 21. 97 ± 3. 47 years; 19 females) was recruited from the University of York. They were all right-handed, native English speakers, with normal or corrected-to-normal vision and no history of psychiatric or neurological illness. The study was approved by the Research Ethics Committee of the York Neuroimaging Centre. All volunteers provided informed written consent and received monetary compensation or course credit for their participation. The data from one task was excluded for four participants due to head motion, and one additional WM dataset was excluded due to errors in recording the responses. The final sample included 28 participants for the semantic task and 27 participants for the WM task, with 26 participants completing both tasks.
Semantic task In the on-line semantic task in fMRI, participants had to judge whether pairs of words were semantically related or unrelated. The stimuli were 192 English concrete noun word-pairs (abstract words and words drawn from the same taxonomic category were excluded, so that only thematic links were evaluated). The stimuli had parametrically varying degrees of thematic relatedness depending on their frequency of co-occurrence. The degree of relatedness was quantified using distributed representations of word meanings obtained from the word2vec neural network, trained on the 100 billion-word Google News dataset  (Mikolov et al. 2013) , defining the strength of the semantic relationship between pairs of words using the cosine similarity method. The stimulus set was manipulated so there was a parametric continuum of relatedness, from 'not related at all' to 'strongly related'.
Availability of data and material Gradient maps one to ten from the group-averaged dimension reduction analysis described in Sect. Neuroimaging below are publicly available on NeuroVault in a collection (https:// neuro vault. org/ colle ctions/ 6746/). Raw fMRI and questionnaire data are restricted in accordance with ERC and EU regulations. Code availability All code used in the production of this manuscript is available from the corresponding authors upon reasonable request.
Acknowledgements This work was supported by  FONDECYT :  Fondo Nacional de Desarrollo Científico y Tecnológico de Chile. Research Project  FONDECYT Nº11170140 : " EL LUGAR DEL ARTE CONTEMPORÁNEO EN SANTIAGO DE CHILE. Análisis de obras Emblemáticas de  arquitectura publica y su vinculación con los habitantes desde la dimensión histórica, arquitectónica, urbana y sociocultural".
Vascular pericytes are responsible for regulating brain perfusion and contribute to the regulation of endothelial permeability and immune activation  7, 10. Multiple in vivo imaging and post mortem neuropathological studies, as well as studies of preclinical models, provide evidence for impaired regulation of cerebral blood flow and maintenance of the integrity of the blood brain barrier (BBB) in early AD  [11] [12] [13] [14]. Recent work has begun to elucidate the transcriptional mechanisms  [15] [16] [17]. We have performed an integrated analysis of our own single-nuclei RNA sequencing (snRNAseq) data with that from a previously published dataset  18  to quantitatively define the enrichment of brain microvascular cells for the expression of AD risk genes as a test of their potential causal contributions to disease genesis  5.
Endothelial cells are enriched in genes associated with genetic risk for AD Our analyses were based on data from 57 different brain samples from donors with AD (n=31) or non-diseased controls (NDC, n=26). Fluorescence-activated sorting (FACS) of nuclei isolated before snRNAseq removed neuronal and oligodendrocyte nuclei to achieve a better representation of the less abundant brain cell types of interest. Data was integrated using LIGER  19  and clustered with UMAP  20  (Figure  1A ). AD and NDC donor nuclei and nuclei from different datasets, brain regions and sexes were well-mixed after integration (Figure  S1 ). Nuclei numbers did not differ significantly between AD and the NDC samples. Feature plots of canonical cell markers identified major brain cell types in the integrated dataset (Figure  S2 ).
Endothelial cells (EC) expressed marker genes FLT1, VWF, NOSTRIN (Figure  S3A ), CLDN5 and IFI27  17, 21  (Figure  1C ). Specific expression of COL1A1, COL12A1, COL6A1 (Figure  S3B ) and COL5A1 was used to identify fibroblasts (FB) (Figure  1C ). A separate, heterogeneous cluster of vascular mural cell nuclei expressed PDGFRB, RGS5 and GRM8 (characteristic of PC  17  ) (Figure  S3C ) and ACTA2 and MYH11 (highly expressed in smooth muscle cells (SMC)  17  ) (Figure  1C ). To distinguish PC from SMC nuclei, we re-clustered the EC, FB and vascular mural cell (PC and SMC) nuclei from the total dataset (Figure  1B ) to separate those expressing high levels of ACTA2 and MYH11 with very low levels of RGS5 and GRM8 (corresponding to SMC) from those expressing high levels of RGS5 and GRM8 with very low levels of ACTA2 and MYH11 (corresponding to PC) (Figure  1C  and S4 ).
14/61 of these genes were expressed in at least 5% of the nuclei across all four cell types (ADAM10, APOE, CD2AP, CELF1, CLU, CNTNAP2, FERMT2, IQCK, MEF2C, PICALM, SORL1, SPPL2A, USP6NL, WWOX). We employed MAGMA. Celltyping to test for the significance of the enrichment of vascular nuclei across the larger set of genomic loci associated with AD  5. First, we analysed a dataset that included all the canonical cell types of the brain (Figure  S6-S8 ). This showed that the AD risk gene expression enrichment is greatest in microglia, as has been reported previously  5  (Figure  1D ). Vascular cells also were relatively enriched, albeit less than microglia. To partition enrichment amongst the individual vascular cell types, the analysis was repeated with vascular cell data alone. Only EC were significantly enriched for expression of AD risk genes (Figure  1E ).
Co-expression modules for angiogenesis, lysosomal processing and interferon activation are differentially regulated with AD To identify gene co-expression modules differentially expressed with AD, we first performed gene co-expression network analyses separately for EC, FB and PC pooled across AD and NDC (MEGENA 30 ). We then determined which gene co-expression modules were differentially associated with AD (limma 31 ). SMC were not included in this analysis because of the relatively low number of nuclei available for analysis and consequent sparse co-expression representation. Our results defined cell-specific gene regulation pathways associated with AD.
Brain cortical tissue sequencing Local tissue access and data generation was carried out in accordance with the Regional Ethics Committee and Imperial College Use of Human Tissue guidelines. Tissues and processing were described previously  63. Cases were selected from the London Neurodegeneration (King's College London) and Parkinson's UK (Imperial College London) Brain Banks. Entorhinal and somatosensory cortex from 6 non-diseased control (NDC) cases (Braak stage 0-II) and 6 AD cases (Braak stage III-VI) were used (total of 24 brain samples). Brains used for this study excluded cases with clinical or pathological evidence for small vessel disease, stroke, cerebral amyloid angiopathy, diabetes, Lewy body pathology (TDP-43), or other neurological diseases. Where the information was available, cases were selected for a brain pH greater than 6 and all but one had a post mortem delay of less than 24 hr.
The nuclei pellet was washed and filtered twice in PBS buffer (PBS + 1% BSA + 0. 2 U/ml RNAseIn). Isolated nuclei were labelled in suspension in 1 ml PBS buffer with 1:500 anti-NeuN antibody (Millipore, MAB377, mouse) and 1:250 anti-Sox10 antibody (R&D, AF2864, goat) for 1 hr on ice. Nuclei were washed twice with PBS buffer and centrifuged at 500 g for 5 min. Nuclei were incubated with Alexa-fluor secondary antibodies at 1:1000 (goat-anti-mouse-647 and donkey-anti-goat-488) and Dapi (1:1000) for 30 min on ice, and washed twice. Nuclei were FACS-sorted on a BD Aria II, using BD FACSDiva software, gating first for Dapi +ve nuclei, then singlets and then Sox10-and NeuN-negative nuclei. A minimum of 150,000 double-negative nuclei were collected. We also isolated nuclei, from adjacent localizations of the same tissue samples as described above, which were not subjected to the FACS-enrichment step but were directly processed for single nucleus capture and snRNA sequencing as described in the following section.
In this way, we obtained an unbiased representation of all the brain cell types. The resulting dataset was used for the analysis described in the "Enrichment of brain cell types in AD and WMH GWAS signal" section. Sorted nuclei were centrifuged at 500 g, resuspended in 50 μl PBS buffer and counted on a LUNA-FL Dual Fluorescence Cell Counter (Logos Biosystems, L20001) using Acridine Orange dye to stain nuclei. Sufficient nuclei were added for a target of 7,000 nuclei for each library prepared. Barcoding, cDNA synthesis and library preparation were performed using 10X Genomics Single Cell 3' Gene Expression kit v3 with 8 cycles of cDNA amplification, after which up to 25 ng of cDNA was taken through to the fragmentation reaction and a final indexing PCR was carried out to 14 cycles. cDNA concentrations were measured using Qubit dsDNA HS Assay Kit (ThermoFisher, Q32851), and cDNA and library preparations were assessed using the Bioanalyzer High-Sensitivity DNA Kit (Agilent, 5067-4627).
Quality control, dataset integration, dimension reduction and clustering Feature-barcode matrices from CellRanger produced corresponding to the local dataset produced as described above were jointly processed with the feature-barcode matrices from a previously published dataset  18. that were downloaded from the Gene Expression Omnibus (accession number GSE148822). Together, the two datasets were generated from 57 brain samples. Quality control (QC), dataset integration, dimension reduction and clustering were performed using the Nextflow pipeline nf-core/scflow  65. QC was performed separately on each sample. Nuclei that had less than 200 features were excluded, whereas for the higher feature filtering criterion, an adaptive threshold was estimated in each sample, which was four median absolute deviations above the median feature number in the sample.
Nuclei with more than 5% of mitochondrial gene counts were also excluded. Only genes that had at least one count in 5 nuclei per sample were retained. The QC also included an ambient RNA profiling using the EmptyDrops package 66 using default parameters. Finally, multiple identification was performed using DoubletFinder 67 using 10 principal components based on the 2000 most variable features and a pK value of 0. 005. Sample integration was performed using the Liger package (v1. 0. 0)  19  incorporated in the nf-core/scflow pipeline (v0. 7. 1)  65. The k value was optimized at 20 and the lambda value at 5. 3000 genes were employed in the integration process. The integration threshold was 0. 0001 and the maximum number of performed iterations was set to 100. The normalized cell factors from the integrated dataset were then used as an input for dimension reduction and clustering, which was performed using UMAP  20.
Clustering was performed with the Leiden method using a resolution parameter of 0. 00001 and a k value of 50. Cell-type identification of clusters was performed by plotting canonical cell markers using the FeaturePlot function in Seurat (v3. 2. 3) 68. To efficiently separate the vascular mural cells, we isolated the ECs and the vascular mural cell clusters and re-ran the steps of the integration, dimension reduction and clustering. Cluster specific genes were identified using the FindMarkers function in Seurat (using the MAST method  25  with the function arguments set to default). To validate the cell-type specificity of the clusters and their identity, we compared the top 100 cluster markers of our dataset with the top 100 cluster markers of the same cell types from previously published human and mouse datasets  17, 21  using an overrepresentation analysis.
Overrepresentation analysis Overrepresentation analysis was performed to determine if the overlap between two gene sets is significantly higher that if it occurred by chance. This was done using with the "enrichment" function of the R package bc3net (v1. 0. 4) (https://github. com/cran/bc3net), which performs a Fisher's exact test (FET). The p values associated with the Fisher's exact test correspond to the probability that the overlap between the two gene sets and has occurred by chance.
We first calculated the enrichment in AD and WMH GWAS signal across all the brain cell types on the dataset that had not been subjected to the FACS enrichment step to remove neurons and oligodendrocytes (see "Nuclei isolation and enrichment for lower abundancy cell populations" section) (Figure  1D  and S9A, respectively). This was performed using the calculate_celltype_associations function with default parameters and the "linear" enrichment mode. This analysis was repeated after controlling for the microglial enrichment of the GWAS signal. Next, we calculated the enrichment in AD and WMH GWAS signal on each of the vasculature-associated cell types (EC, FB, PC and SMC) (Figure  1E  and S9B, respectively). Finally, we assessed if the enrichment of the vasculature-associated cell types in our dataset in AD GWAS signal changed after controlling for the enrichment in WMH GWAS signal.
Models were fit with and without the dependent variable and compared using a likelihood ratio test. Units for differential expression are defined as log2 fold difference in AD vs NDC nuclei. The inclusion of a "dataset" term in the model was not necessary because the inclusion of the brain region term completely accounted for it. In the subset of samples that corresponded to the dataset produced in our laboratory, we also performed an exploratory regression analysis of gene expression against the two histopathological features (using pTau or Ab as markers) using MAST. The model specification was zlm(~histopath_marker + (1|sample) + cngeneson + pc_mito + sex + brain_region, sca, method = "glmer", ebayes = F). In this case, units for differential expression are defined as log2 fold difference/% pTau positive cells (or log2 fold difference/% Ab plaque area), i.
Gene ontology and pathway enrichment analysis The gene ontology (GO) enrichment and the pathway enrichments analysis were carried out using the R package enrichR (v 3. 0), which uses Fisher's exact test (Benjamini-Hochberg FDR < 0. 1)  72. Genesets with minimum and maximum genes of 10 and 500 respectively were considered. To improve biological interpretation of functionally related gene ontology and pathway terms and to reduce the number of redundant gene sets, we first calculated a pairwise distance matrix using Cohen's kappa statistics based on the overlapping genes between the enriched terms and then performed hierarchical clustering of the enriched terms 73.
Gene co-expression analysis Gene co-expression modules and hub-genes were identified separately for each cell type using the MEGENA (v1. 3. 7) package  30. MEGENA constructs a hierarchy of co-expression modules with larger ("parent") that are further divided into subset ("children") modules. "Children" modules are subsets of the "parent" ones and have higher numbers as names than their "parents". To reduce the effect of noise, due to the sparsity of the expression matrix in a snRNAseq experiment, a sample-level pseudo-bulking was performed by summing the raw counts of all the nuclei in a sample. Genes expressed in at least 50% of the samples were used as input. The MEGENA pipeline then was applied using default parameters, using Pearson's correlations and a minimum module size of 10 genes.
Gene Set Enrichment Analyses (GSEA) AUCell 75 (R package v1. 6. 1) was used to quantify the enrichment of the co-expression modules in our nuclei. Normalised data was processed in AUCell using the AUCell_buildRankings function. The resulting rankings, along with the gene lists of interest, were then put into the function AUCell_calcAUC (aucMaxRank set to 5% of the number of input genes). The statistical comparison of the enrichment of the co-expression modules in our AD nuclei vs the NDC nuclei was performed using the limma package in R 31. The module enrichment matrix was log2-normalized. The default configuration of the limma package was employed with the following linear model (which corresponds to the model employed in the DGE analysis with MAST package): ~diagnosis+nFeature+pc_mito+brain_region+sex, where nFeatures is the total number of distinct features expressed in each nucleus (to account for the fact that nuclei that express a higher number of features may have higher AUCell scores).
Cell-cell communication analysis Cell-cell communication analysis was performed using CellChat v0. 5. 5  47. CellChat employs a curated database of potential signalling ligand-receptor pairs from the literature. Among all these potential ligand-receptor pairs, cell-cell interactions are identified based on mass action models, along with differential expression analysis and statistical tests on cell groups. The CellChat algorithm with default parameters (unless otherwise specified) was applied to the subset of the dataset that corresponded to the ECs, FBs and PCs, separately on the AD and NDC samples. The human CellChat database was used for the ligand-receptor pairs. Communications that involved less that 100 nuclei were filtered out (using the min. cells argument in the filterCommunication function).
Mass spectrometric data were recorded on a Finnigan MAT 95 (EI, 20 eV) and are reported as m/z (rel. intensity in %). High-resolution TEM (HRTEM) and SAED measurements were carried out on a JEOL JEM-2200FS microscope (Peabody, London, UK). Data collection for X-ray structure elucidation was performed on a STOE IPDS 2T diffractometer (Darmstadt, Germany) using graphite-monochromated Mo-Kα radiation (0. 71071 Å). The data were corrected for Lorentz and polarization effects. A numerical absorption correction based on crystal-shape optimization was applied for all data. The.
Mass spectrometric data were recorded on a Finnigan MAT 95 (EI, 20 eV) and are reported as m/z (rel. intensity in %). High-resolution TEM (HRTEM) and SAED measurements were carried out on a JEOL JEM-2200FS microscope (Peabody, London, UK). Data collection for X-ray structure elucidation was performed on a STOE IPDS 2T diffractometer (Darmstadt, Germany) using graphite-monochromated Mo-K α radiation (0. 71071 Å). The data were corrected for Lorentz and polarization effects. A numerical absorption correction based on crystal-shape optimization was applied for all data. The software used in this work are STOE's X-Area, SHELXL  [60, 61] , and ShelXle  [62]  (Version 93, Darmstadt, Germany). CCDC 2102248 contains the supplementary crystallographic data for this article. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre, via www.
After washing, the membranes were incubated for 1 hour at room temperature with secondary HRP conjugated antibody (#7076, #7074) and visualized using ECL detection kit (Thermo Scientific). The semi-quantification densitometric analysis density of the specific bands was carried out using the ImageJ software to relatively quantify protein bands from western blot films. The quantification reflects the ratio of protein band relative to the housekeeping protein ß-actin band. Statistical analyses showed a significantly higher AGRIN immunoreactivity in daytime pineal glands in comparison to nighttime. In contrast, ß2microglobulin showed a significantly higher immunoreactivity in nighttime in comparison to the daytime.
Mass-spectrometry acquisition was done using XCalibur (v 2. 0) in a data-dependent acquisition mode. Survey scan MS were acquired in the Orbitrap on the 300-2000 m/z range with the resolution set to a value of 70 000 at m/z = 400 in profile mode (AGC target at 1E6). The 15 most intense ions per survey scan were selected for HCD fragmentation (NCE 27), and the resulting fragments were analyzed in the Orbitrap at 17 500 of resolution (m/z 400). Isolation of the parent ion was fixed at 2. 5 m/z. Dynamic exclusion was employed within 20s. Data were acquired in profile mode.
| Data analysis Data were searched using MaxQuant (1. 4. 1. 2 version) (with the Andromeda search engine) against the UniProt human database (2014. 05. 15, 88 473 entries). 51 Proteins were identified by searching MS and MS/MS data of peptides against. Carbamidomethylation of cysteines was set as a fixed modification. Oxidation of methionine and protein N-terminal acetylation were set as variable modifications. The minimum peptide length was specified to be 7 amino acids. The initial maximal mass tolerance in MS mode was set to 7 ppm, whereas fragment mass tolerance was set to 10 ppm for MS and MS/MS. Two peptides were required for protein identification and quantitation. The maximum false-positive rate (FDR) for peptides and for proteins was specified as 0. 01. Label-free analysis was done by using the "match between run" feature of MaxQuant (2 minutes time window).
| Detection of rhythmic protein abundance In a complementary approach to the comparison between day and night, we also investigated the rhythmicity in protein abundance. We first used a standard chronobiology tool: the non-parametric JTK-cycle algorithm,  52  which combines the Jonckheere-Terpstra test for monotonic ordering and Kendall's τ test for association of measured quantities. We took advantage of the version 3. 1 which considers missing values and replicates. Transcripts were considered circadian if JTK P <. 01. For this significance, a FDR was calculated by permutations of the data and corresponded to 5%. To assess the stability of the detected cycling proteins, we used an alternative method from astrophysics, Generalized Lomb-Scargle periodogram (GLS), which allows to detect periodic signals in unevenly sampled data  53  To take into account the replicate and missing value, we used a Bayesian implementation of GLS (code available online here: https://github.
| Protein-protein interaction visualization We concatenated five human interactome datasets provided online by the Dana Farber Cancer Institute: four high-quality binary protein-protein interactions (PPI) using a systematic primary yeast two-hybrid assay (Y2H): HI-I-05,  58  Venkatesan-09,  59  Yu-11,  60  and HI-II-14,  61  plus one highquality binary literature dataset Lit-BM-13 comprising all PPI that are binary and supported by at least two traceable pieces of evidence (publications and/or methods). 61 Proteins associated with, either statistical differences in intensity between day and night, or marked as only present during day or during the night, were then projected in the PPI network.
While most of the associated biological pathways were downregulated in autism, interestingly some proteins with a higher expression in the autism group were also known to be involved in those processes (eg, inhibition of apoptosis). Among the proteins detected with a higher abundance in the non-diagnosed controls, 15 were listed in the SFARI database that lists genes/proteins associated with autism. Among them, CNTNAP2, a cell adhesion molecule associated with autism is observed in 7 of the 18 non-diagnosed control pineal glands and absent of the 7 pineal glands from the individuals with autism. A complete list of the proteins displaying different levels in individuals with autism and non-diagnosed controls is available in Table  S5. The pineal samples studied were previously used to measure ASMT enzyme activities.