dataesr/scientific-paragraphs-categorization huggingface dataset:
2a ): 1. Parsing of Illumina data: To retrieve accurate cellBC and UMI information, BAM files with the cellBC and UMI assigned Illumina data generated by the 10x Genomics Cell Ranger software were parsed. For each gene, we extracted the cellBC from the Illumina data and identified the UMIs found for each gene/cellBC combination. We also associated genomic regions (window size 500 nt. ) with cellBCs and UMIs to account for reads that match outside of annotated genes. The parsed Illumina data were stored in nested Hash tables as serialized Java objects. 2. Search for poly(A) tail: Our software searches for a poly(T) and a poly(A) sequence (default 85% A or T, ≥20 nt. ) within 100 nucleotides from the 5′ or 3′ end of the read, respectively. Reads without poly (A or T) and reads with a poly(A or T) on both ends are not further analyzed.
Search for internal adapter and poly(A): To flag reads corresponding to chimeric cDNA generated during library preparation, we searched for internal adapter sequences in proximity of a A-or T-rich sequence and flagged those reads as chimeric reads in the output file. In our dataset we found internal adapters in 3. 5% of the reads. 5. Search for cellBCs: Cell barcodes in high accuracy Illumina reads are typically assigned by grouping reads that differ by not more than one position (edit distance: ED = 1). Indels are typically not considered. In consequence just 48 possible permutations of the 16 nt. cellBC reads need to be analyzed and assignment of reads to a barcode is highly reliable. Nanopore reads still have a mean error rate of about 5-10% (substitutions and indels). In consequence higher edit distances need to be examined and indels must be considered.
We also extracted the barcode sequences for the preceding and following position to account for eventual terminal indels in the adapter read. We then compared the extracted Nanopore barcode sequences and all possible permutation up to a defined edit distance with: (i) The cellBCs identified by Illumina sequencing for the same gene if the Nanopore read matches a known gene in the Illumina dataset; (ii) Illumina cellBCs sequences for the genomic region (500 nt. window) if the Nanopore read matches an unannotated genomic region or if the gene name was not found in the Illumina data. Since the Nanopore barcodes are only compared with Illumina barcodes found for the same gene or genomic region, the complexity of the search set is reduced. Barcode matches, eventual second-best barcode matches and their quality (edit distance) were recorded in the output BAM file.
The maximal ED is dynamically selected (ED limit 1-4) and depends on the number of cellBCs found for the same gene or genomic region in the Illumina data (the complexity of the search set). The software allows to define the maximal false assignment percentage as a parameter. We used computer simulations of collision frequencies to define the complexity of the search set (number of Illumina barcodes) allowed for different edit distances and maximal false assignment percentages. Simulation data are supplied as an XML file and can be easily adapted. Optionally the software also allows the definition of fixed edit distance limits. 6. Search for UMIs: To identify the ten nucleotide UMI, 14 nucleotides (to allow for insertions in the read) following the end of the cellBC sequence (position corrected for barcode indels) were extracted.
For UMI assignment, we used the same strategy as for cellBC assignment. We searched for matching UMIs in Illumina data for the same cellBC and either genomic region or gene identified in the previous step. This means the complexity of the search set corresponds to the copy number of a given gene in just one cell. The maximal allowed edit distance which is dynamically adjusted depending on the complexity of the search set (detailed above). 7. Determination of cellBC and UMI assignment accuracy: To evaluate the accuracy of cellBC and UMI assignment, we first scanned each Nanopore SAM record for a matching Illumina cellBC or UMI. We then repeated the scan where we replaced each cellBC or UMI sequence extracted from the Nanopore read by a random sequence and mutated this random sequence allowing the same number of mutations (edit distance) that was used for the same SAM record in the previous scan and searched for matching cellBC or UMIs in the Illumina dataset.
In a second approach to assess the accuracy of cellBC and UMI assignment we guided the barcode and UMI assignment to the Nanopore reads with Illumina short-read sequencing data from an independent mouse brain single-cell sequencing experiment. The unrelated Illumina dataset was from an 885 cell P18 mouse brain sample (GEO accession number GSM4224249, 219k reads/cell, 88. 1% sequencing saturation, 6550 UMIs/cell, 2739 genes/cell). While in the first approach (replacement against random sequences), the maximal edit distance tested for each read was limited to the edit distance for which a match was found (if any) with the non-random sequences, in the second approach, the cellBC and UMI search was allowed to proceed up to the maximal allowed edit distance. E. g. when for a given Nanopore read a cellBC match was found with the correct Illumina dataset at ED = 0, a cellBC match at a higher edit distance in the unrelated Illumina dataset was considered a match.
8. Maximal possible Barcode and UMI assignment efficiency with 10xGenomics data: Since our software scans for matches of the Nanopore cell barcodes with barcodes associated with cells (the relevant barcodes), barcodes associated with empty drops are ignored. The maximal possible barcode discovery rate in Nanopore reads corresponds to the percentage of reads associated with cells in the Illumina dataset: 83. 7% and 85. 1% for the 190 cell and the 951 cell sample, respectively. The maximal UMI discovery rate for Nanopore reads depends on the Illumina sequencing depth of the same sample and corresponds to the sequencing saturation computed by the 10xGenomics Cell Ranger software after Illumina sequencing. The sequencing saturation is the probability that a matching UMI is found in the Illumina dataset for a given read.
Potentially chimeric reads (terminal Soft/Hard-clipping of >150 nt; 1. 93% and 3. 95% for the 190 and 951 cell dataset respectively) and reads with low-quality genome alignments (minimap2 mapping quality values = 0) were filtered out. SAM records for each cell and gene were grouped by UMI. The cDNA sequence, between TSO end (TE SAM tag) and poly(A) start (PE SAM tag), was extracted for consensus sequence computation using the Sicelore ComputeConsensus method. Depending on the number of available reads for the UMI the following sequence was assigned as the consensus sequence for the UMI: (i) just one read, the cDNA sequence of the read was assigned to the UMI; (ii) two reads, the cDNA sequence of the best mapping read (lowest "de" minimap2 SAM record tag value) was defined as the consensus sequence; (iii) More than 2 reads, a consensus sequence of all cDNA sequences for the UMI was generated after poa  13  multiple alignment and polished with racon 14 using all cDNA sequences for the UMI.
2a, b ) and for the transcripts isoforms t-SNE (Fig. 2e ). Single-cell gene expression quantification and determination of major cell types. Raw gene expression matrices generated by Cell Ranger were processed using R/Bioconductor (version 3. 5. 2) and the Seurat R package (version 3. 1. 4). A total of 190 cells and 951 cells were detected with default Cell Ranger cutoffs for the two replicates. Cells with over 95% dropouts were removed. From the 186 and 935 remaining cells (hereafter called 1121 cells dataset), gene expression matrices were cell level scaled to 10. 000 and log-normalized. The top 2000 highly variable genes were selected based on the variance-stabilizing transformation method and used for Principal Component Analysis. Due to differences in sequencing depth of both replicates, data were integrated using the Seurat CCA method.
The first 11 aligned canonical correlations were used to define the integrated sub space for clustering and t-SNE visualization of the 1121 remaining cells. Clusters in the t-SNE plot were assigned to known cell types using canonical marker genes (Supplementary Fig. 8a , Supplementary Data 1). Using Seurat multi-modal capabilities, we integrated Illumina and Nanopore gene-level and isoform-level datasets allowing direct comparison of gene and isoform expression in individual cells. Identification of novel transcript isoforms. UMIs of the 1121 cells dataset were used for the identification of novel transcripts isoforms using the Sicelore Col-lapseModel method. cDNA consensus sequences for UMIs with an exon structure not supported by Gencode (at least one splice junction different when compared with annotated Gencode transcripts, see Supplementary Fig.
6 , Supplementary Note) were first grouped by gene and sequences with identical exon structure were used to define potential novel transcript isoforms. Novel isoforms supported by less than five UMIs were discarded. Isoforms with identical exon layout that differ in SNVs or 5′ or 3′ ends were considered as identical isoforms. Novel isoforms were classified as suggested by Tardaguila et al. 15  : (i) combination of known splice junctions, only composed of exon-exon junctions found in Gencode transcripts; (ii) combination of known splice sites, individual donor and acceptor sites are known, but the resulting splice junction is novel; (iii) at least one donor or acceptor site is not found in Gencode transcripts. We next filtered novel transcripts isoforms requiring: (i) all exon-exon junctions either described in Gencode or confirmed in an E18 cortex/ midhindbrain Illumina short-read dataset (GEO accession GSE69711); (ii) a 5′ end located within 50 nucleotides of a known transcription start site identified by CAGE (FANTOM5 mm9 reference UCSC liftover to mm10, https://fantom.
gsc. riken. jp/5/datafiles/ latest/extra/CAGE_peaks/); (iii) a 3′ end within 50 nucleotides of a polyadenylation site (GENCODE vM24 PolyA feature annotation, https://www. gencodegenes. org/ mouse/). Correlation of gene or isoform expression between replicates. To analyze expression correlations between clusters, Illumina gene-level and Nanopore gene-and isoform-level data were downsampled (R package DropletUtils  16  downsampleMatrix method) to the median UMIs/cell of the 1121 cell Nanopore transcript isoform-level dataset (3795 UMIs/cell). We then grouped for each replicate cells for each cluster (see Fig. 2d, e ), and used the mean expression of each gene or isoform in the clusters to produce a Pearson correlation matrix (R cor function). Heatmaps in Supplementary Fig. 7  were generated after cluster agglomeration with the Ward method (pheatmap package).
Gria2 data analysis. 9593 reads (2105 UMIs) corresponding to Gria2 (mm10: chr3:80,682,936-80,804,791) were extracted from the 951 and the 190 cell dataset. A consensus sequence for each molecule was computed and re-mapped to the mm10 genome for SNP calling using the Sicelore SNPMatrix method. Gria2 mRNAs are huge (> 6 kB) and inefficiently converted into full-length cDNA in the 10x Genomics workflow. This is likely due to: (i) some RNA degradation within the droplet between cell lysis and reverse transcription. (ii) internal reverse transcription priming at A-rich sites within the cDNA leading to cDNAs that cover only part of the transcripts. In consequence, we noticed 3′ bias and fragmented coverage for certain long transcripts such as Gria2 where a total of 456 cDNA molecules (UMIs) had the R/G-editing-and 233 had both the R/G and the Q/Rediting-site.
Acknowledgements This project was funded by grants from the  Institut National contre le Cancer  ( PLBIO2018-156 ), the  Conseil Départemental des Alpes Maritimes  ( 2016-294DGADSH-CV ),  FRM  ( DEQ20180339158 ), the  Inserm Cross-cutting Scientific Program HuDeCA 2018 , the  National Infrastructure France Génomique  ( Commissariat aux Grands Investissements ,  ANR-10-INBS-09-03 ,  ANR-10-INBS-09-02 ). This publication is part of the Human Cell Atlas -https://www. humancellatlas. org/publications.
Quantitative analysis of BiFC: Image acquisition was performed on a Leica SP5 using the same settings and number of z slices for the different genetic contexts. BiFC fluorescence was quantified using ImageJ software. Region of interest (ROI) was hand-drawn following the contours of VC tagged protein expression as visualized by antiVC staining. The pixel value sum in the ROI of the BiFC channel was used for comparative quantification. The mean value was calculated from at least 20 wing discs from wandering L3 larvae of each genotype. In each disc, the same area located in the adjacent wing disc tissue was used to estimate tissue auto-fluorescence and remove this noise from the quantification.
Sequence conservation analysis C-ZF domains from the Drosophila and human GATA family members were extracted from the NCBI web site (https://www. ncbi. nlm. nih. gov/protein/), aligned with the MAFFT software (https://mafft. cbrc. jp/alignment/server/), using default parameters, and amino acid conservation were visualized with Jalview 2. 10. 5 version (http://www. jalview. org/) using Clustal coloring.
Fundings and additional information This work was supported by grants from the  French "Ministère de l'Enseignement et de la Recherche" (C. I. ) , the  Ligue Nationale contre le Cancer  (C. I. and A. P. ), the  Association pour la Recherche sur le Cancer  ( ARC PJA 20141201932 ), the  Agence Nationale de Recherche  ( ANR-16 CE12-0021-01 ), and we benefitted from the support of the  Centre National de Recherche Scientifique (CNRS)  and  Toulouse III University.
Methods Our aim was to estimate trends in mean total, HDL and non-HDL cholesterol for 200 countries and territories (Supplementary Table  2 ). We used non-HDL cholesterol rather than low-density lipoprotein (LDL) cholesterol because most studies in our analysis had measured total cholesterol and HDL cholesterol, from which non-HDL cholesterol can be calculated through subtraction. By contrast, LDL cholesterol was directly measured in only around 14% of studies. When LDL cholesterol is not directly measured, its calculation requires data on triglycerides, which were available in approximately 64% of the studies. Furthermore, the most-commonly used estimation method-that is, the Friedewald equation-can be inaccurate, particularly at high levels of triglycerides  34. Non-HDL and LDL cholesterol were highly correlated (Pearson correlation coefficient = 0.
Data sources We used a database of population-based data on cardiometabolic risk factors collated by the NCD Risk Factor Collaboration (NCD-RisC), a worldwide network of health researchers and practitioners that systematically monitors the worldwide trends and variations in non-communicable disease (NCD) risk factors. The database was collated through multiple routes for identifying and accessing data. We accessed publicly available population-based multi-country and national measurement surveys (for example, Demographic and Health Surveys and surveys identified through the Inter-University Consortium for Political and Social Research and European Health Interview & Health Examination Surveys Database). We requested, via the World Health Organization (WHO) and its regional and country offices, from ministries of health and other national health and statistical agencies to identify and access population-based surveys.
Requests were also sent via the World Heart Federation to its national partners. We made a similar request to the co-authors of an earlier pooled analysis of cardiometabolic risk factors  7, [41] [42] [43]  , and invited the co-authors of the analysis to reanalyse data from their studies and join NCD-RisC. Finally, to identify major sources that were not accessed through the above routes, we searched and reviewed published studies as described in the Supplementary Information and invited all eligible studies to join NCD-RisC. For each data source, we recorded the available information about the study population, start year and duration of measurement, sampling approach and measurement methods. The information about study population was used to establish that each data source was population-based, and to assess whether it covered the whole country, multiple subnational regions or one or a small number of communities, and whether it was rural, urban or combined.
We carefully checked all data sources in terms of how they met our inclusion and exclusion criteria listed below. We identified duplicate data sources by comparing studies from the same country and year. Additionally, all NCD-RisC members are asked periodically to review the list of sources from their country, to suggest additional sources not in the database, and to verify that the included data meet the inclusion criteria listed below and are not duplicates. The NCD-RisC database is continuously updated through the above routes and through regular contact with NCD-RisC members. Anonymized individual record data from sources included in NCD-RisC were reanalysed according to a common protocol. Within each survey, we included participants aged 18 years and older who were not pregnant. We removed participants with implausible total cholesterol levels (defined as total cholesterol levels of <1.
06% of all participants with total cholesterol and HDL cholesterol measurements)  [44] [45] [46]. We calculated mean total cholesterol, mean HDL cholesterol and mean non-HDL cholesterol, and associated standard errors and sample sizes, by sex and age group (18-19 years, 20-29 years, followed by 10-year age groups and 80+ years). All analyses incorporated appropriate sample weights and complex survey design in calculating age-sex-specific means when applicable. To ensure summaries were prepared according to the study protocol, computer code was provided to NCD-RisC members who requested assistance. All submitted data were checked independently by at least two researchers. Questions and clarifications were discussed with NCD-RisC members and resolved before the data were incorporated in the database.
Finally, we obtained data not accessed through the above routes by extracting data from published reports of all additional national health surveys identified through the above-described strategies, as well as eight sites of the WHO Multinational MONItoring of trends and determinants in CArdiovascular disease (MONICA) project that were not deposited in the MONICA Data Centre. Data were extracted from published reports only when reported by sex and in age groups no wider than 20 years. We also used data from a previous pooling study  7  when such data did not overlap with those accessed through the above routes.
Data inclusion and exclusion Data sources were included in NCD-RisC database if: (1) measured data on total, LDL, HDL cholesterol and/or triglycerides were available; (2) study participants were 10 years of age or older; (3) data were collected using a probabilistic sampling method with a defined sampling frame; (4) data were from population samples at the national, subnational (covering one or more subnational regions, more than three urban communities or more than five rural communities) or community (one or a small number of communities) level; (5) data were collected in or after 1950; and (6) data were from the countries and territories listed in Supplementary Table  2. We excluded all data sources that included only hypercholesterolaemia or dyslipidaemia diagnosis history or medication status without measurement of cholesterol levels.
We also excluded data sources on population subgroups for which the lipid profile may differ systematically from the general population, including: (1) studies that had included or excluded people on the basis of their health status or cardiovascular risk; (2) studies for which the participants were only from ethnic minorities; (3) studies that had recruited only specific educational, occupational or socioeconomic subgroups, with the exception noted below; and (4) studies that had recruited participants through health facilities, with the exception noted below. We used school-based data in countries and for age-sex groups, for which secondary school enrolment was 70% or higher. We used data for which the sampling frame was health insurance schemes in countries in which at least 80% of the population was insured.
Finally, we used data collected through general practice and primary-care systems in high-income and central European countries with universal insurance, because contact with the primary-care systems tends to be as good as or better than response rates for population-based surveys. We used data sources regardless of fasting status, because the differences between fasting and non-fasting measurements are negligible for total, non-HDL and HDL cholesterol  39  , and therefore non-fasting lipid profiles are now widely endorsed for the estimation of cardiovascular risk  36, 37.
Data used in the analysis For this paper, we used data from the NCD-RisC database for years 1980 to 2018 and individuals aged 18 years and older. A list of the data sources that we used in this analysis and their characteristics is provided in Supplementary Table  1. The data comprised 1,127 population-based measurement surveys and studies that included measurements of blood lipids on 102. 6 million participants aged 18 years and older. We had at least one data source for 161 of the 200 countries that we made estimates for, covering 92. 4% of the world's population in 2018 (Extended Data Fig. 1 ); and at least two data sources for 104 countries (87. 5% of the world population). Of these 1,127 sources, 409 (36. 3%) sampled from national populations, 250 (22. 2%) covered one or more subnational regions, and the remaining 468 (41.
Adjusting for the differences in mean cholesterol between portable device and laboratory measurements In 112 (10%) of the 1,127 data sources used in our analysis (11. 5% and 5. 8% of age-sex-specific data points for total and HDL cholesterol, respectively) lipids were measured using a portable device. Some portable devices have narrower analytical ranges than laboratory methods, which results in truncations of blood cholesterol data that are outside their range (Supplementary Table  3 ). This may in turn affect the population mean. Although cholesterol concentrations that fall outside the analytical range are displayed as 'high' (above the measurement range) or 'low' (below the measurement range) by these devices, different surveys record and code cholesterol concentrations outside the analytical range in different ways, for example using 'too low', 'too high' and 'error' codes; assigning the minimum or maximum value to individuals whose cholesterol was below or above the analytical range, respectively; setting values outside the analytical range to missing; and so on.
The regression coefficients were estimated from data sources for which lipids were measured in a laboratory, and thus had the full range of measurement and could be used to calculate both dependent and independent variables. When estimating the regression coefficients, we constructed the dependent variable using the full data, and the independent variable by dropping the values outside the above-mentioned restricted cholesterol range of each device, mimicking those that would be expected if a portable device had been used. Separate models were developed according to the specific range of the different portable devices. All regressions included terms for age and sex, as well as interactions between predictors and age and sex, based on the Bayesian information criterion  47. The regressions for mean non-HDL cholesterol also included mean total cholesterol and mean HDL cholesterol because non-HDL cholesterol is calculated from total cholesterol and HDL cholesterol.