dataesr/scientific-paragraphs-categorization huggingface dataset:
The observed regular pattern of small dots is indicative of viral factories with each of these dots representing a single viral particle. (G) A close up of the boxed area in (F), with a line drawn to generate (H), a line profile of pixel intensities. Full width half maxima of this plot give an average diameter of 60nm for these particles which appear to be an array of immature particles in the viral factory having yet to assemble an outer capsid before cell exit. (legend continued on next page) cryoSXT). In (A) are orthogonal views of the 3D registered SIM dataset (green channel: Au 150 nm beads coated with Alexa488; red channel: mitochondria stained with Mitotracker red). The scale bars shown on XY views, apply in ZX and YZ views also (the generated data are isotropic with voxel size of 10x10x10 nm).
Note that tomogram tilting shown in (B), was taken into account during 3D registration. In (B) are orthogonal views of the associated 3D X-ray tomogram used as target volume for 3D registration. The source volume was the pre-aligned 3D SIM dataset relocated in 2D as described in Figure  5. In (C) is the distribution of error when using only 5 beads (fewer points, localized within the SIM resolution) for the registration of the 9 beads visible in total within the fluorescence signal of this volume. This distribution is obtained by taking the histogram of the predicted average error of eC-CLEM error map which has been validated in  Paul-Gilloteaux et al. (2017) , but was checked against the beads here. X axis is the registration error, y axis is the number of voxels in this volume with this error.
Carbon-rich virus-induced domed structures are also seen in single vesicles. In (K), fluorescence is shown in smaller vesicles alongside large vesicle superstructures shown in Figure  6M. (A and B) In (A) is an XY slice of the 3D X-ray tomogram (extracted from 4 h post-infection datasets) and in (B) is the corresponding slice of the 3D error map generated using the point-set used for registration. The color codes for predicted average error is in nm, ranging from 89 to 813 nm in the full volume (two sub regions are selected for best and possible worst accuracy in this view (green shaded and red shaded respectively). Best accuracy areas overall lie near the gravity center of the point-set used for registration, dark blue). The scale bar is 100 nm. (C and D) In (C) are orthoviews of a crop of the merged dataset in the green sub region and in (D) is the corresponding crop in the error map.
This area given the registration used, corresponds to the best accuracy we can obtain in this particular volume (89 to 150 nm). Scale bar is 100 nm. (E and F) In (E) is an orthoview of a crop of the merged dataset in the red sub region of (A), and in (F) is the corresponding crop in the error map. This orthoview is centered in an area with a predicted accuracy of 300 nm, which can be seen in the discrepancy in Z on the focused endosome. Scale bar is 100 nm. (G-N) Example of accuracy measured on finer areas within the error map for different endosomal arrangements. The circled areas indicate local 2D areas selected for which average predicted errors was measured, and their matching position on the merged dataset. The predicted error is displayed above these in the error map in nm. (G)-(J) and (K)-(N) are taken the green and red sub regions respectively.
In order to assess the accuracy of our 3D rigid registration procedure, a sample with two fluorescent features (gold beads and mitochondria) was used. It has been demonstrated that real alignment error can be predicted under the assumption of a correct model (rigid model used to match rigidly linked dataset) (Paul-Gilloteaux et al. , 2017). We used this error prediction facility in eC-CLEM to assess correlated nanoparticle localization in both imaging modalities used (cryoSIM and.
ACKNOWLEDGMENTS We thank  Jeff Irwin  for his dedicated and enthusiastic support with all technical issues and applications relating to the TXM,  Damian Reynolds  for technical support,  Andrei Tkachuk  for SXT support,  Michael Schwertner  for cryo-microscopy expertise, support, and advice, and  Eva Pereiro  for guidance, encouragement, and troubleshooting with issues on soft X-ray tomography. We also thank staff at both Micron, DLS as well as B24 team members and support personnel (past and present) for their help and perseverance. Special thanks to  Adam Taylor  and  Adam Prescott  for technical support on a day-to-day basis,  Martin Harrow  for IT support,  Stephen Graham  for contributing to our user protocols, and  Liz Duke  for initiating and supporting the X-ray tomography project.
We thank  John Sedat  for sharing and discussing his prototype design of the light path, use of SLMs, timing device, and open source Python control software that formed the basis of the cryoSIM development. We would like to acknowledge the support of  Vibor Laketa  and the  Infectious Diseases Imaging Platform (IDIP) at the Center for Integrative Infectious Disease Research, Heidelberg, Germany. M. A. P. was funded via the  Wellcome Centre for Human Genetics  by the  Wellcome Trust ISSF  ( 105605/Z/14/Z ). The  Wellcome Trust  ( 203141/Z/16/Z ) funds the  Wellcome Centre for Human Genetics. I. M. D. and M. A. P. were funded in part by  Wellcome Strategic Awards  ( 091911  and  107457 ) (PI Ilan Davis). P. P. -G. acknowledges  CROCOVAL  ( ANR-18-CE45-0015 ) and is part of the  national infrastructure ''France BioImaging '' supported by the  ANR PIA1  ( ANR-10-INBS-04 ).
We computed the statistical significance of these gene set overlaps using the GeneOverlap R package  (Shen et al. , 2013). The GFP high :S1pr1 ECKO-up overlap was significant (151 transcripts, p-value=3. 80E-126), as was the GFP low :S1pr1 ECKO-down overlap  (38 transcripts, p-value=1. 3E-22) , and the other two tested overlaps were not significant (p-value>0. 3) (Figure  1 -figure  supplement 2F ). These data suggest that the transcriptome of MAECs exhibiting high S1PR1/ß-arrestin coupling (GFP high ) is more similar to that of S1pr1 ECKO. We used Ingenuity Pathway Analysis (IPA, Qiagen) to examine biological processes regulated by S1PR1 signaling and loss of function in MAECs. Transcripts involved in inflammatory processes were prominently up-regulated in both GFP high and S1pr1 ECKO MAECs (Figure  1D ; see also Supplementary file 2).
00001) of 73,492 for GFP low MAECs and 65,694 for GFP high MAECs (Figure  2A ). MAECs isolated from WT and S1pr1 ECKO mice harbored 93,859 and 76,082 peaks, respectively (Figure  2A ). Peaks were enriched in promoter and intragenic regions (Figure  2 -figure supplement 1A). We noted that the Cdh5 gene exhibited numerous open chromatin peaks, while Gata1 was inaccessible (Figure  2 -figure supplement 1B). Furthermore, we observed a global correlation between chromatin accessibility and mRNA expression for all 20,626 annotated coding sequences (CDS's) in the NCBI RefSeq database (Figure  2 -figure supplement 2). These data suggest that our ATAC-seq data is of sufficient quality for detailed interrogation. Differential chromatin accessibility analysis of GFP high versus GFP low MAECs identified 501 peaks with reduced accessibility (GFP low peaks) and 3612 peaks with greater accessibility (GFP high peaks) in GFP high MAECs (FDR < 0.
eGFP mRNA was primarily restricted to GFP high MAECs, particularly in the cluster designated aEC1 (Figure  3 -figure  supplement 1A ). Analysis of GFP high and GFP low MAECs using the velocyto/pagoda2 pipeline (R code in Source Code File 3)  (Fan et al. , 2016; La Manno et al. , 2018)  revealed nine clusters upon T-distributed stochastic neighborhood embedding (t-SNE) projection (Figure  3A ). 6 of the nine clusters grouped together in a 'cloud', whereas three clusters formed distinct populations. We used hierarchical differential expression analysis to identify signature marker genes of each cluster (Figure  3B ). Genes uniquely detected in one of the distinct clusters included vascular smooth muscle cell (VSMC)-specific transcripts such as Myh11, Myom1, and Myocd (Figure  3B  and C; Figure  3 -figure  supplement 1B ).
Notably, these depleted transcripts were highly enriched in aEC4, which is 30% GFP high MAECs. In contrast, LEC, vEC, and aEC1 are each >95% GFP high. We note that the abovementioned transcripts (Figure  3 -figure supplement 4B) were among those most differentially expressed between GFP high and GFP low MAECs by bulk RNA-seq (Figure 1-figure supplement 1C). This is demonstrative of consistency between our bulk and singlecell datasets. For functional insights into arterial EC clusters, we analyzed aEC1-aEC4 enriched transcripts with the Gene Set Enrichment Analysis (GSEA) tool (Figure  4A -D and Supplementary file 5). aEC1 cells were enriched with transcripts associated with GPCR/MAPK signaling (Rasgrp3, Rapgef4, Rgs10, Mapk4k3, S1pr1) as well as VEGF, integrin, and tight-junction pathways (Flt1, Vegfc, Pgf, Igf2, Vcan, Sema3g, S100a4, Jam2, Cldn5).
, 2018). Markers of these cells were also identified as top aEC3 markers (e. g. Fosb, Jun, Jund, Junb, Dusp1) suggesting that aEC3 cells are similar to the regenerative Atf3-positive cluster described by  McDonald et al. (2018). aEC4 cells were enriched with transcripts related to cell-ECM interations, glycosaminoglycan metabolism, and collagen formation (Pcolce2, Frzb, Spon1, Col4a4, Mfap5, Hyal2). The other two arterial clusters (aEC5 and aEC6) appeared less distinctive (i. e. harbored relatively few enriched transcripts with high Z-scores and fold change values) and therefore were not analyzed. Collectively, these data suggest that scRNA-seq identified more than four distinct MAEC subtypes with unique transcriptomic signatures. Next, we compared our dataset with information from two recent scRNA-seq studies that also sub-categorized ECs of the normal mouse aorta  (Kalluri et al.
Taken together, these data suggest that TSP1-expressing (aEC2-like) cells increase in frequency while aEC3-like cells disappear over time in the aorta intima, which was described previously  (McDonald et al. , 2018). Next, we explored the expression of Itga6 and Thbs1 (TSP1) in mouse embryos. First, we examined the 'mouse organogenesis cell atlas' database (https://oncoscape. v3. sttrcancer. org/atlas. gs. washington. edu. mouse. rna/landing), which includes scRNA-seq data from E9. 5 to E13. 5 mouse embryos  (Cao et al. , 2019). Itga6 transcripts were in many cell types, but were most abundant in endothelial cells (Figure  7 -figure supplement 3A). In contrast, high Thbs1 expression was restricted to megakaryocytes and endothelial expression was ~40 fold lower than endothelial Itga6 expression.
We sought to determine if aEC1-enriched transcripts other than Itga6 and Thbs1 show evidence of temporally-regulated expression in aortic endothelium. Examination of the embryonic gene expression database  (Cao et al. , 2019)  revealed that other aEC1-enriched transcripts, such as Igf2, Flt1, Slc6a6, and Lef1 were expressed at greater levels in embryonic ECs relative to aEC1-depleted transcripts (e. g. Vcam1, Frzb, Pcolce2, Cyp1b1, Sod3) (Figure  7 -figure supplement 3D) that are enriched in aEC2 and/or aEC4. Thus, aortic endothelium exhibits spatio-temporal regulation of genes such as Itga6 and Thbs1, with expression of aEC1 genes such as Itga6 becoming restricted to branch point orifices over time while expression of aEC2-enriched genes (e. g. Thbs1) increases throughout the aorta intima after P6.
These mRNA and chromatin signatures were shared between S1pr1 ECKO and GFP high MAECs, suggesting that persistent ß-arrestin recruitment to S1PR1 can result in downregulation of membrane-localized S1PR1 and a subsequent loss-of-function phenotype. There were many (2,145) DEGs between GFP high and GFP low MAECs, but relatively few (365) between ECKO and WT MAECs, which suggested that the GFP high and/or GFP low populations reported aortic EC subtypes in addition to S1PR1-regulated transcripts. Indeed, chromatin regions uniquely open in GFP high MAECs were enriched with binding sites for TFs that have well-defined but divergent roles in endothelial cells, such as SOX17 (arterial)  (Corada et al. , 2013; Zhou et al. , 2015)  and COUP-TFII (venous/lymphatic)  (Lindskog et al. , 2014). Nonetheless, we present the first collection of putative regulatory regions from freshly isolated mouse aortic ECs, which is a critical dataset for future studies of individual enhancer functionalities.
Gene-level counts over UCSC annotated exons were calculated using the Rsubread package and 'feature-Counts' script  (Liao et al. , 2013)  with options: -M -O -p -d 30 -D 50000. The resultant count table was input to edgeR  (Robinson et al. , 2010)  for differential gene expression analysis. The. bam files from STAR were input to the RSEM  (Li and Dewey, 2011)  script 'rsem-calculate-expression' with default parameters to generate FPKMs for each replicate.
, 2013). Transposed fragments were purified using the MinElute PCR Purification Kit (Qiagen, 28004) and Agilent DNA Tapestation D1000 High Sensitivity chips (Agilent) were used to quantify libraries. ~20-60 million paired-end reads (2 Â 75 bp) were sequenced for each sample on a NextSeq instrument (Illumina). Read alignment to the MGSCv37 (mm9) genome assembly was performed with bowtie2  (Langmead and Salzberg, 2012)  and the options: -very-sensitive -X 2000 -no-mixed -no-discordant. Duplicated fragments were removed using the Picard 'MarkDuplicates' script with the options: Remove_Duplicates = true Validation_stringency = lenient (http://broadinstitute. github. io/ picard/). Paired-end reads were separated, centered on Tn5 cut sites, and trimmed to 10 bp using a custom in-house script (Source Code File 1).
Peaks were called using the MACS2 'callpeak' script  (Zhang et al. , 2008)  with options: -B -keep-dup all -nomodel -nolambda -shift À75 -extsize 150. Reads mapping to murine blacklisted regions and mitochondrial DNA were masked out of peak lists using the Bedtools 'intersect -v' script  (Quinlan and Hall, 2010). Replicates from each biological group were merged using the bedops 'merge' script to generate one high-confidence peak set for each of the four biological groups (GFP high , GFP low , S1pr1 ECKO, S1pr1 WT)  (Neph et al. , 2012). These four peak sets were then merged to generate a merged, consensus peak set of 123,473 peaks. For each replicate, reads covering consensus peak intervals were counted using the Bedtools 'coverage' script with the '-counts' option  (Quinlan and Hall, 2010).
The resultant count table was input to edgeR  (Robinson et al. , 2010)  to determine differentially accessible peaks (DAPs). DAPs were used as input for the HOMER 'findMotifsGenome. pl' script with the option '-size given' to identify motifs enriched in peaks with enhanced accessibility in either GFP high , GFP low , S1pr1 ECKO, or S1pr1 WT MAECs  (Heinz et al. , 2010). Nucleotide-resolution coverage (bigWig) tracks were generated by first combining trimmed reads from each replicate, then inputting the resultant. bam files to the DeepTools (Ramı ´rez et al. , 2016) 'bamCoverage' script with options '-normalizeUsing RPGC -binSize 1'. Heatmaps of ATAC-seq reads within 600 bp of p65, NUR77, COUP-TFII, ATF1, GATA2, and GRE motifs were generated by centering coverage tracks on each motif identified in DAPs.
scRNA-seq analysis 1152 Fastq files (one per cell) were aligned to the GRCm38 (mm10) genome assembly using STAR with options -runThreadN 4 -outSAMstrandField intronMotif -twopassmode Basic. Bam files were input to the velocyto (http://velocyto. org/velocyto. py/) command-line script 'run-smartseq2' (La  Manno et al. , 2018). Expressed repetitive elments were downloaded from the UCSC genome browser and masked from analysis using the '-m' option of the 'run-smartseq2' script. The resultant table of read counts per transcript ('loom' file) was input to the PAGODA2 (https://github. com/hmsdbmi/pagoda2) R package for further analysis  (Fan et al. , 2016; La Manno et al. , 2018). The details of our R code are provided in Source Code File 3. After variance normalization, the top 3000 overdispersed genes were used for principal component analysis (PCA).
An approximate k-nearest neighbor graph (k = 30) based on a cosine distance of the top 100 principal components was used for clustering. Clusters were determined using the multilevel community detection algorithm. PCA results were plotted using the 'tSNE' embedding option of the PAGODA2 'r$getEmbedding' function. Heatmaps of gene expression embedded on hierarchical clustering, differential expression analyses, and expression of individual transcripts on the tSNE embedding were generated using the graphical user interface at http://pklab. med. harvard. edu/nikolas/pagoda2/frontend/current/pagodaLocal/. We generated the binary (. bin) file according to the Pagoda2 walkthrough: https://github. com/hms-dbmi/pagoda2/blob/master/vignettes/ pagoda2. walkthrough. oct2018. md. This binary file (Supplementary file 7) can be uploaded to the graphical user interface for exploration of our dataset.
Confocal microscopy and image analysis Images were acquired using a Zeiss LSM810 confocal microscope equipped with an Plan-Apochromat 20x/0. 8 or a Plan-Apochromat 40x/1. 4 oil DIC objective. Images were captured using Zen2. 1 (Zeiss) software and processed with Fiji (NIH). Zen2. 1 software was used to threshold GFP signal and manually count GFP+ nuclei per field (Figure  6 ). Fiji was used to quantify GFP signal over PROX1 +ASMA+ and PROX1+ASMA-areas (Figure  8 ), TSP1 signal normalized to total VEC pixels per field (Figure  7F ), as well aso for quantification of branch point and non-branch point GFP+ nuclei (Figure  7 ) using the watershed segmentation tool. Figures were assembled in Adobe Illustrator.
LC-MS/MS S1P measurement and data analysis The samples were analyzed with Q Exactive mass spectrometer coupled to a Vanquish UHPLC System (Thermo Fisher Scientific). Analytes were separated using a reverse phase column maintained at 60˚C (XSelect CSH C18 XP column 2. 5 mm, 2. 1 mm X 50 mm, Waters). The gradient solvents were as follows: Solvent A (water/methanol/formic acid 97/2/1 (v/v/v)) and Solvent B (methanol/acetone/ water/formic acid 68/29/2/1 (v/v/v/v)). The analytical gradient was run at 0. 4 mL/min from 50-100% Solvent B for 5. 4 min, 100% for 5. 5 min, followed by one minute of 50% Solvent B. A targeted MS2 strategy (also known as parallel reaction monitoring, PRM) was performed to isolate S1P (380. 26 m/ z) and D7-S1P (387. 30 m/z) using a 1. 6 m/z window, and the HCD-activated (stepped CE 25, 30, 50%) MS2 ions were scanned in the Orbitrap at 70 K.
The area under the curve (AUC) of MS2 ions (S1P, 264. 2686 m/z; D7-S1P, 271. 3125 m/z) was calculated using Skyline  (MacLean et al. , 2010). Quantitative linearity was determined by plotting the AUC of the standard samples (C18-S1P) normalized by the AUC of internal standard (D7-S1P); (y) versus the spiked concentration of S1P (x). Correlation coefficient (R2) was calculated as the value of the joint variation between x and y. Linear regression equation was used to determined analyte concentrations. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements We thank  Boston Children's Hospital  and  Harvard Stem Cell Institute Flow Cytometry Research Facility and Harvard Medical School Biopolymers Facility  for technical assistance. We also thank members of the  Hla laboratory  for critical comments on the project and  Dr. Sylvain Galvani  for technical advice regarding mouse aorta dissection. This work is supported by  NIH  grants ( R35-HL135821  to TH),  Fondation Leducq Transatlantic Network grant (SphingoNet)  to TH, CB, EC and RLP, and the  intramural research program  of  NIDDK  intramural program grants (RLP). MVL was the recipient of a postdoctoral fellowship from the  Fonds de Recherche en Santedu Quebec , AN is the recipient of a fellowship from the  Marie Curie Prestige program , HN is the recipient of a fellowship from the  Higher Education Commission, Pakistan , and AK was the recipient of a postdoctoral fellowship from the  American Heart Association.
Daily drone flight activity Daily drone flight activity was determined for three colonies on three different days, i. e. three measurements for each colony, during the months of October and November. On the experimental days, the number of drones leaving the hive entrance was counted every half an hour for 10 min from morning (07:00 h) to evening (19:00 h) (LD 12:12; sunrise time 06:10 h, sunset time 18:00 h). It should be noted that drones and foragers of A. mellifera do not fly during the dark. On these days, we also recorded temperature and humidity changes every minute using a data logger (EQ-172, Equinox, Valli Aqua And Process Instruments, Chennai, India).
qPCR To quantify the expression levels of clock genes using qPCR, around 800 ng of total RNA from each sample was converted into cDNA using Reverse Transcriptase Superscript III enzyme and oligo d(T)16 primers (Invitrogen). Then KAPA SYBR FAST qPCR Master Mix (Kapa Biosystems, Wilmington, MA, USA) in 7900HT Fast Real-Time PCR system (Applied Biosystems, Carlsbad, CA, USA) was used to perform qPCR. An internal control gene for normalization was selected after comparing the expression profiles of six different housekeeping genes: tubulin (XM_396338), RpS18 (XM_625101), actin (NM_001185145), ef-1a-f1 (NM_001011628), gapdh (XM_393605) and ribosomal protein49 (rp49) (NM_001011587). rp49 showed the highest stability (NormFinder;  Andersen et al. , 2004)  in our experimental conditions (Fig. S3 ) and was used for normalization of clock gene expression data.
Data analysis qPCR We used the cosinor package (https://github. com/sachsmc/cosinor) in R (http://www. R-project. org/) to fit a 24 h cosine model {y=intercept+amplitude×cos[2×π(x-acrophase)/24]}  (Nagari et al. , 2017)  in the circadian gene expression data. We also performed a non-parametric Jonckheere-Terpstra-Kendall (JTK) cycle analysis  (Hughes et al. , 2010; Patton et al. , 2014)  to detect the phase and amplitude of clock gene expression profiles.
RNA-seq Approximately 7-10 million pairs of 75 bp reads per sample were mapped to the A. mellifera genome (NCBI Assembly Amel_HAv3. 1;  Wallberg et al. , 2019)  using STAR RNA-seq aligner  (Dobin et al. , 2013). The alignment rate was more than 75% (75. 15-86. 82%) for all the samples. The number of reads aligning to each gene was counted using featureCounts, a program specifically designed for read summarization on genomic features  (Liao et al. , 2014). The read counts for each gene were normalized with the respected library size and differences in gene expression between the samples were calculated using DESeq2  (Love et al. , 2014). Genes showing expression differences with adjusted P-values (P adj ) less than 0. 05 (Wald test) were called differentially expressed genes (DEGs). The Pathview package  (Luo and Brouwer, 2013)  in R was used to map the above differential gene expression data to relevant pathway graphs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and to visualize.
In addition, the GAGE package  (Luo et al. , 2009)  in R was used for gene-set analysis (GSA) using normalized count data from featureCounts and the KEGG pathway database  (Kanehisa and Goto, 2000). Gene ontology (GO) enrichment analysis was done using g:Profiler  (Raudvere et al. , 2019)  with an alpha of 0. 05 as the cut off for significance.