dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA was resuspended in 15 µl H2O, incubated at 55ºC for 8 min and evaluated on a Nanodrop. NGS libraries were prepared and sequenced at EMBL GeneCore using a QuantSeq 3′ mRNA-Seq Library Prep Kit for Illumina (Lexogen) and a NextSeq 500 (Illumina). Bioinformatics analysis was performed as outlined in  [47]  using SLAM-DUNK  [48]  (http://t-neumann. github. io/slamdunk/) with default parameters. The tcount output file was then loaded into R and Spearman correlation coefficients between genotypes and replicas were calculated for the cumulative coverage on all Ts in the 3'-UTR (coverageOnTs; an indicator for transcript abundance in the sample). The DESeq2 package  [49]  was used to test if coverageOnTs were different between BN1158 (-FLP) and BN1190 (+ FLP) samples. Next, the countdata package  [50]  was used to perform a beta-binomial test on conversionsOnTs (number of T>C conversions) and coverageOnTs values to calculate p values for conversion rates (conversionsOnTs/coverageOnTs) of individual transcripts comparing BN1158 (-FLP) and BN1190 (+ FLP) samples.
Data availability See Table  S1 , Table  S2 , Table  S3  and Table  S4  for complete lists of primers, promoters, plasmids and strains, respectively. Representative strains will be made immediately available through the Caenorhabditis Genetics Center; plasmids and other strains can be requested from the authors. Table  S5  contains list of hypodermal genes identified by SLAM-ITseq. Raw and processed SLAM-ITseq files are deposited at Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/).
Comparing the T>C conversion rate per gene revealed a significant increase in nematodes expressing UPRT in the hypodermis (Figure  7B ). Next, we performed a beta-binominal test to identify genes that were significantly labelled across all replicas  [30] , which retrieved 197 genes (p<0. 003; FDR<0. 1 after adjustment for multiple comparisons; Figure  7C ; Supplementary Table  S5 ). To illustrate the reproducibility of the method, T>C fractions of 4 genes reported to be expressed in the hypodermis are shown (pars-1, mig-15, dpy-3 and nhr-97; FDR<0. 03; Figure  7D ). Finally, to evaluate the specificity of SLAM-ITseq, we compared the list of 197 nascent transcripts to three datasets of genes reported to be expressed in the hypodermis  [70, 67, 68]  (Figure  7E ). Our list overlapped significantly with all three datasets (168/197 = 85%; p<1e-10 Fisher's exact test), which represents the second highest overlap.
Moreover, of the 29 genes not overlapping with any of the three other datasets, at least 20 (69%) are reliably detected in hypodermal cells in single cell RNAseq performed on embryos  [73]  or L2 larvae  [74]. We conclude that SLAM-ITseq is an attractive and easily applicable method to identify nascent transcripts with high confidence. We expect that the method can be further optimized to increase the T>C fractions and thereby increase the sensitivity.
Acknowledgments We are grateful to  Andrew Chisholm ,  Carmen Ortiz González ,  Fernando Monje-Casas ,  Michael Krieg ,  Montserrat Porta ,  Ngang Heok Tang ,  Patricia Regina Rider García  and  Wayo Matsushima  for reagents and advice and  Raquel Romero-Bueno  for critical reading of the manuscript. We also wish to acknowledge  Ana Miñan Fernández  from the  CABD Functional Genomics Facility  for microinjections,  Laura Tomás  from the  CABD Proteomics and Biochemistry Facility  for recombinant proteins,  Katherina García  from the  CABD Imaging Facility  for technical assistance; the  Caenorhabditis Genetics Center (CGC) , which is funded by the  National Institutes of Health (NIH) Office of Research Infrastructure Programs  ( P40 OD010440 ) and WormBase. This project was funded by the  Spanish State Research Agency  and the  European Regional Development Fund  ( BFU2016-79313-P, MDM-2016-0687  and  PID2019-105069GB-I00 ).
Blood vessels supplying tumors are often dysfunctional and generally heterogeneous. The mechanisms underlying this heterogeneity remain poorly understood. Here, using multicolor lineage tracing, in vivo time-lapse imaging and single cell RNA sequencing in a mouse glioma model, we identify tumour-specific blood endothelial cells that originate from cells expressing the receptor for colony stimulating factor 1, Csf1r, a cytokine which controls macrophage biology. These Csf1r lineage endothelial cells (CLECs) form up to 10% of the tumour vasculature and express, besides classical blood endothelial cell markers, a gene signature that is distinct from brain endothelium but shares similarities with lymphatic endothelial cell populations. in silico analysis of pan-cancer single cell RNAseq datasets highlights the presence of a comparable subpopulation in the endothelium of a wide spectrum of human tumours.
The T_CLEC state is enriched for "Tip-like" and a lymphatic gene signature Gene set enrichment analysis (GSEA) identified that the T_CLEC state shows significant enrichment for a Tip-cell-like associated gene expression program (Fig. 5e ), thus supporting the morphological observations and their functional importance for branching and tumour growth. Intriguingly, GSEA identified that T_CLECs were also enriched for lymphatic gene signatures (Fig. 5e ). AUCell analysis  28  of scRNA-seq data from multiple human primary tumours identified subpopulations of endothelial cells that express the T_CLECs marker gene set (Fig. 5f-k , 442 out of 7314 ECs are candidates: 6. 0%; Supplementary Table  2 ). These bona-fide T_CLECs were highly enriched in tip cell, neophalanx cell, lymphatic endothelial cell subpopulations and particularly abundant in human pancreatic cancer (Fig.
Here the floor cells, specialized lymphatic endothelial cells that we identify as GFP and Csf1r positive line the capsule in lymph nodes in close apposition to the Csf1 expressing ceiling cells. Second, after acute tamoxifen injection the cells in the floor of the subcapsular sinus are uniquely double positive for Tomato and GFP, demonstrating acute and likely local recombination. Third, single cell RNAseq of T-CLECs identified lymphatic markers including the lymphatic master regulator Prox1 co-expressed with blood endothelial markers, and SCDE placed T_CLECs close to lymphatic endothelial cells from various mouse tumour models in recently published datasets. And finally, CLECs can also be found in lymphatics in subcutaneous tumour models and in skin. These independent observations clearly make the SCS floor cells prime candidates for the origin of T_CLECs, but future work will be necessary for a possible direct demonstration of this link.
Flow cytometric analysis Mice were anesthetized with ketamine/ xylazine, then tumour tissues or organs were harvested and incubated with PBS containing 1mg/ml collagenase I (Gibco), 2 mg/ ml Dispase I (Gibco), 100ug/ml DNase I (Roche) and 2 mM CaCl2 for 30 min at 37 °C. After incubation, the digested tissue was passed through a cell strainer and then washed by PBS including 2% FBS. For red cell exclusion, we incubated samples with red cell lysis buffer (Sigma) for 5 min at 37 °C and then washed by PBS including 2% FBS and 2 mM EDTA. Cells were stained with the following monoclonal antibodies: PE/ Cy7 anti-CD45 (552848, BD), APC anti-CD31 (561814, BD). Data acquisition was performed with BD FACSVerse and analysis was performed with BD FACSuite software, and FlowJo software.
8. 1). The "best fit" (numbers of clusters) was chosen based on the highest cophenetic correlation coefficient. Next, Single-cell Differential Expression analysis (SCDE) was performed between the different NMF-clusters using the global gene expression matrix  26. Expression values were library-size-factor-normalized (DESeq) and log2 transformed (log2+1). T_MAC, T_EC, T_CLEC and LN_CLECS, LN_EC, T_CLEC, T_EC cells were projected into a two-dimensional space using Uniform Manifold Approximation and Projection (dims=15, resolution = 0. 4) based on 5k most variable features (Seurat pipeline)  52. Monocle (2. 14) based trajectory analysis was performed on T_CLEC, T_EC, LN_CELC and LN_EC (total=454 cells) taking into account n=790 m3Drop genes. The same data set was subjected to CytoTRACE analysis using the online tool (https://cytotrace.
AUCell analysis of human scRNA-seq data in multiple tumours The droplet-based scRNA-seq data of 4 human tumour types were downloaded from publicly available sources including lung cancer (ArrayExpress: E-MTAB-6149 and E-MTAB-6653)  53  , PDAC (GSA: CRA001160)  54  , liver cancer (GEO: GSE125449)  55  , and BCC (GEO: GSE123814)  56. These datasets were processed and clustered per tumour type using Seurat (v3. 0. 4) package, and the endothelial cell clusters were identified using marker genes (CLDN5, VWF, PECAM1). The resulting endothelial cell data were pooled together and aligned using anchor-based canonical correlation analysis (CCA) to mitigate the difference related to distinct tumor types and scRNA-seq technologies. Initial graphbased clustering identified 10 clusters including 3 potential doublet clusters as predicted by DoubleFinder (v2), which were subsequently removed before second round of clustering.
Vessel morphology analysis Confocal images of the tumour vasculature were analyzed for two-dimensional vessel area, network length and number of network bifurcation points. The tdTomato and GFP channels were merged and a Gaussian blur filter as well as Otsu thresholding was applied to obtain binary vessel mask for sectional area estimation. To obtain the vessel network, a semi-automatic multicut workflow from ilastik  58  was applied to all images. The segmented vessels were skeletonized and converted into a network graph with nodes and edges by using the Python package ImagePy  59. The lengths of network edges were summed up to obtain the total network length for each image, all nodes with a degree of three or more were counted as bifurcations.
Population structure, network analysis and relationships between genetic and morphological data In addition to farmer's selection pressure for speci c types of landraces, natural selection was observed morphologically within a single landrace, Mahmoudi. Mahmoudi accessions collected from southern Tunisia showed signi cantly looser spikes than Mahmoudi accessions collected from central Tunisia, characterized by compact spikes. We might suggest that the relaxed spike characterized by an open glume in the Mahmoudi accessions originated from the south could provide tolerance to high temperature by maintaining fertility as it has been observed in rice germplasms  [36]. This differentiation between Mahmoudi accessions offers potential tools i) to use relaxed spike trait in breeding programs for heat stress tolerance, and ii) to identify genes and mechanisms involved in ower development useful for improving wheat adaptation to arid and marginal environments.
Local durum wheat collection and multiplication A collection of 304 old durum wheat accessions provided by the National Gene Bank of Tunisia (BNG) was used for this study. Accessions were collected from ve regions that belong to three distinct climatic zones -central Tunisia, which is characterized by a low semi-arid climate at Sousse and Mahdia regions and by a high-arid climate at Kairouan region, and southern Tunisia, which is characterized by a mid-arid climate at Gabes and Mednine regions. Global Positioning System (GPS) coordinates of 163 out of the 304 accessions were registered with their respective accession number (Table  S1 ). Each accession was sown and puri ed from a single spike-derived lineage by the BNG team and a BNG code has been assigned to each accession. All accessions were further multiplied for spike characterization.
The forward primers were labeled with uorescent dyes and SSR markers were multiplexed as described by Gautier et al. [37]. For each multiplex, PCRs and electrophoresis were accomplished according to a protocol established by Euro n (https://www. euro ns. fr). DNA ampli cation was performed by preheating the DNA at 95°C for 5 mn, followed by 35 cycles of 95°C for 30 s, 60°C for 90 s and 72°C for 30 s, with a nal extension step of 60°C for 30 mn. PCR products were checked for ampli cation on a 2% agarose gel and fragments were separated according to their size on an ABI Prism Genetic Analyzer (Applied Biosystems). Data was checked again using Peak scanner software version 1. 0. Two accessions had missing data for all used SSR and were discarded from our study.
Funding This was funded by 'RESIDUR', a national project awarded to primary institutions INAT and CRRGC from the  Institution of Agricultural Research and Higher Education (IRESA) of Tunisia. All funds are in local currency (Tunisian Dinar). Our collaborator  Dr. Thierry Marcel  hosted the rst author (PhD student)  Maroua Ouaja  in his laboratory at  BIOGER-Paris , and had technically assisted her to perform her experiments. During her internship at BIOGER,  Maroua Ouaja  was supported by the scholarship " Bourse d'alternance " awarded by the ministry of higher Education in Tunisia to cover the airfare tickets and the living cost in the hosting country.
Design and data source This retrospective cohort study was a nationwide population-based analysis using data from the national CAS registry in England, which is under the control of the National Cancer Registration and Analysis System (NCRAS) in Public Health England (PHE). CAS captures data on all cases of cancer that occur in people living in England who receive medical care in the National Health Service (NHS). The CAS registry contains the SACT dataset as well as the Cancer Outcomes and Services Dataset (COSD), and includes detailed information on patient demographics, staging, pathology, and mortality  [21]. Data on deaths were extracted from the National Death Registry.
Ethics This study was conducted in accordance with International Society for Pharmacoepidemiology (ISPE) Guidelines for Good Pharmacoepidemiology Practice (GPP) and applicable regulatory requirements. The study protocol was approved by the PHE NCRAS. As this was a retrospective study, informed consent was not required from patients. Nevertheless, in the UK, NHS patients may opt out of sharing their data within the CAS database (this is usually less than 1% of all patients).
Study population The study included all patients aged ≥18 years in the CAS registry who were newly diagnosed with MPM (International Classification of Disease, 10th revision [ICD-10] code [C45. 0]) within the inclusion period (January 1, 2013-December 31, 2017). All patients were followed from their initial diagnosis until the end of the study (  March 31, 2018)  or death, whichever occurred first. Patients with missing data on age, sex, or NHS number recorded in the CAS registry; any concomitant primary malignancy (except non-metastatic non-melanoma skin cancer; ICD-10 codes C44, C4A); or any SACT treatment received up to 5 years prior to MPM diagnosis were excluded.
Measures and analyses Patient characteristics, including age, sex, body mass index (BMI), and ECOG PS, were collected at diagnosis. ECOG PS was also collected at the start of each line of therapy. MPM histology was described based on the following International Classification of Diseases for Oncology (ICD-O)-2 codes: 90503 for mesothelioma not otherwise specified (NOS), 90513 for sarcomatoid mesothelioma, 90523 for epithelioid mesothelioma, and 90533 for biphasic mesothelioma. TNM staging was the best stage reported in the CAS data source at the time of MPM diagnosis. Therefore, strict mapping to a specific TNM staging system is not possible with this variable. The date of initial diagnosis was extracted from the CAS database. Initial treatment was defined as the first treatment received after diagnosis and any associated treatment within the following 60-120 days (the algorithm used to derive initial treatments is included in the supplementary information).
Discussion The results of this nationwide study, using data from the CAS national registry in England from 2013 to 2017, show that the OS of patients diagnosed with MPM remains poor, highlighting the significant unmet needs and difficulty in managing the disease. The demographics of patients with MPM in this study were consistent with the known profile of the disease, with a substantially higher proportion being male and a median age at diagnosis of 75 years  [6, 8, [22] [23] [24] [25]. This reflects the higher exposure to occupational asbestos among males, and the long latency period between exposure and disease onset  [2, 3, 26]. The diagnosis of MPM is challenging, and involves radiological, biomarker and pathological investigations  [11]. In our study, more than half of all patients had missing TNM data, which may reflect the difficulty in staging MPM in routine clinical practice, and a possible indifference to this factor by treating clinicians.
Patients with NOS MPM were older and had a higher or missing ECOG PS compared with those with confirmed histology. Most of these patients (70%) received BSC or palliative RT only after diagnosis. Thus, invasive diagnostic procedures, such as biopsy, might have been considered unsuitable for some of these patients, with diagnosis based on clinicalradiological findings without histological or cytological confirmation, as previously reported  [22]. In some cases, this may be due to data recording issues in the database rather than pathology not being obtained. The OS among patients with MPM was poor, at around 8 months in the overall population despite any treatment. This was lower than the median OS of 9. 5 months observed over 2007-2011 in England based on an analysis of the National Lung Cancer Audit (NLCA) database  [23] ; however, that study only included patients with pathologically confirmed MPM.
The OS reported for patients with a clinical diagnosis (nonpathological confirmation) in the NLCA database was 6. 8 months  [23]. Over 2007-2010, the OS for patients with pathologically confirmed MPM was 9. 2 months in the Netherlands and 10. 7 months in Belgium; correspondingly, the proportion of treated patients was higher in Belgium  [23]. Similarly, over 2000-2012, the OS for patients with pathologically confirmed MPM in Finland based on national registry data was 9. 7 months  [24]. Consistent with previous reports  [3, 25] , sarcomatoid histology was associated with shorter survival compared with biphasic and epithelioid histologies. In the UK, there have been no improvements in OS among patients with confirmed histology since 2008-2012  [22]  and similar results were observed over 2004-2012 based on national registry data in Belgium  [27].
This study has several strengths. First, it included a large, unselected real-world population of patients with MPM from clinical practice across England, and thus provides insight into treatment patterns and OS on a national level. Second, this analysis provides detailed clinical data covering all types of treatment. Finally, there is high confidence in the death data present in the dataset; these are obtained by linkage to the Office for National Statistics, which collect information on the date and cause of death from statutory death registration records. However, this study also had limitations. First, ECOG PS, a key variable for treatment decision-making, was poorly reported in the CAS database and was missing for one-third of the patients, even when restricting to treated patients only.
Considering other key variables, TNM staging was missing in 60. 4% of patients over the study period, and histology was not otherwise specified in 43. 3% of the patients. Thus, only limited conclusions can be reached for these subgroups. Second, data on smoking status, asbestos exposure, other malignancies, and comorbidities are not available in the CAS database. Third, despite the selection of surgical codes that are most likely related to radical surgery, patients may have received surgery with palliative intent. Therefore, the survival in this group may not reflect that in patients who are suitable for radical surgery. Fourth, patients diagnosed in 2017 had a maximum of 15 months of follow-up; therefore, any improvement in treatment outcomes occurring in these patients would not be properly captured in this study.
MEIS1 expression quantitative trait loci (eQTLs) Harnessing large-scale genotype-and tissue-specific gene expression data from GTEx (Consortium 2020), we inspected eQTLs of MEIS1 expression. eQTLs for different tissues and cell types occurred at vast linear chromosomal distances from the MEIS1 gene body (extending to ~550 kb 5' and ~830 kb 3'). The eQTLs showed a distinctive tissue-specific distribution, with for example amygdala eQTLs clustering distally 5' to the gene, cerebellum, tibial nerve, and fibroblast eQTLs distally 3' to the gene, and lung eQTLs proximally 5' to and within the gene (Fig. 7A ). Of note, caudate and cortex eQTLs clustered in intron 8 and 9, including within elements 602 and 617 (Fig. 7B ). As the ganglionic eminences give rise to both caudate (part of the striatum) and cortex, these eQTLs likely relate to effects of intron 8/9 variants that persist through development and into adulthood in cells of the ganglionic eminence lineage.
DNA methylation analysis Genome-wide DNA methylation from induced pluripotent stem cells (iPSC), neural stem cells (NSC), and iPSC-derived neural progenitor cells (NPC-FGF) was assessed using the Infinium Human Methylation EPIC BeadChip (Illumina), according to the manufacturer's protocol. The EPIC array covers over 850,000 methylation sites. Genome-wide raw signal intensity data were subjected to a standard quality control (QC) and quantile normalization pipeline using the minfi  (Aryee et al. 2014 ) and limma  (Ritchie et al. 2015)  packages in R v 4. 0. 2. Probes with poor quality (<95% call rate or detection P value > 0. 01), SNPs at the CpG site, and cross-reactivity were filtered out prior to differential methylation analysis. Methylation level for each CpG site was calculated as a beta value β=M/(M+U+100), where M>0 and U>0 denote the methylated and unmethylated signal intensities measured by the array, and the offset of 100 is added to M+U to stabilize beta values when both M and U are small.
Beta values range between 0 and 1, with 0 indicating no methylation and 1 full methylation. CpG sites were classified as hypo-or hypermethylated according to ENCODE guidelines. Probe-wise and region-wise differential methylation between the different cell types were estimated using M values in limma, obtaining moderated t-statistics, P values and adjusted P values using false discovery rate (FDR) for each CpG site.
Quantitative PCR Total RNA was extracted with the RNeasy Mini Kit (Qiagen). RNA was reverse transcribed into complementary DNA (cDNA) using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher). Quantitative PCR was performed in duplicate using TaqMan Universal PCR Master Mix (Thermo Fisher) with TaqMan gene expression assays: MEIS1 (Hs00180020_m1), PAX6 (Hs00240871_m1), FOXG1 (Hs01850784_s1), GSX2 (Hs00370195_m1), NKX2-1 (Hs00968940_m1), and GAPDH (Hs02758991_g). qPCR reactions were performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems) as follows: 2 min at 50°C, 10 min at 95°C, and 40 cycles of 15 sec at 95°C and 1 min at 60°C. For quantification, the 2 -ΔΔCT method was used with GAPDH as the endogenous reference. Data in top row are from  (Ziller et al. 2015) ; data in second row are from  (Choi et al.