dataesr/scientific-paragraphs-categorization huggingface dataset:
(2018 Michel et al. ( ) (period:1979 Michel et al. ( -2014) )  and  Stoll et al. (2018 Stoll et al. ( ) (period: 1979 Stoll et al. ( -2016)) , found no significant trends in PL numbers in either HadGEM3-GA3 present-day simulations or in the NCEP-CFS reanalysis. Finally, PLs duration has been analyzed and showed that HadGEM3-GA3 PLs have a typical lifetime of 25 hr (standard deviation: 14 hr), with 50% of the PLs lasting less than 24 hr and 91% of PLs lasting less than 48 hr. The NCEP-CFS PLs have a mean lifetime of PLs of 30 hr (standard deviation: 18 hr). These results are relatively similar to the literature using observational and reanalysis datasets, which span from a mean lifetime of around 15 hr  (Laffineur et al. , 2014; Smirnova et al. , 2016)  with more than 70% of PLs lasting less than 30 hr  (Rojo et al.
, 2015). In summary, the results presented here suggest that, (a) the detection of PLs in NCEP-CFS is found to be within the uncertainty range of previous studies using observational data and reanalyzes, and (b) the representation of present-day PLs in the HadGEM3-GA3 model is found to be reasonably close to those in the NCEP-CFS reanalysis. Hence these results suggest it is reasonable to use the projections from the HadGEM3-GA3 model to understand how PLs number and spatial distribution may change with climate change.
) and the large-scale environment (SST and surface energy fluxes feedback, thermodynamical properties of the atmosphere, etc. ), could arise from using an atmospheric-only global climate model. Future research would hence benefit from using fully coupled climate models, such as the ones from the Coupled Model Intercomparison Project Phase 6 (CMIP6) project. Finally, it would be important to improve PL climatologies, with both observational and in-situ datasets as well as high-resolution reanalyzes (such as the ERA5 and Copernicus Arctic Regional Reanalysis). This would not only help us improve our current understanding of PL characteristics and lifecycle in the present-day climate, but also enhance the accuracy of future projections of PLs under climate change.
Data Availability Statement The study is based on the UPSCALE data set licensed from the University of Reading, hosted on the JASMIN platform and available at http://proj. badc. rl. ac. uk/upscale (met-upscale@lists. reading. ac. uk). The UPSCALE data was produced by a MetOffice-NCAS-CMS collaboration using the HadGEM3 model with support from NERC and the MetOffice and the PRACE Research Infrastructure resource HERMIT. The NCEP-CFS Reanalysis and NCEP-CFSv2 Operational Analysis, developed by NOAA NCEP and from the NOAA Operational Model Archive and Distribution System (NOMADS), are available at https://www. ncdc. noaa. gov/data-access/model-.
Instead, our results show a large variability in gene transcription patterns (Fig. S1 ), with many cell-cycle genes showing intense, non-synchronous early bursts of transcription that may, or may not, be subsequently maintained (Fig. S1  and Fig. 1C ). Therefore, without the required temporal resolution, the differential reactivation kinetics characterising individual genes remains unnoticed (Fig. 1C ). As a final control of the quality of our dataset, we monitored transcription of histone genes, which is known to occur specifically during the S phase 21. Accordingly, and in contrast to most other genes (Fig. S1 ), histones displayed much higher transcriptional levels in asynchronous cells than after division, reflecting a prevalence of cells in S phase in cycling ES cells (60 to 70%). Nevertheless, all histone genes showed signs of substantial transcriptional reactivation as soon as 50' after release (Fig.
Nevertheless, careful examination of the PCA (Fig. 4B ) suggests that small differences exist along PC1 between + and -IAA treatments, 20 and 40min after mitosis. At later time-points, small differences appear instead in PC2. In contrast, both +/-IAA datasets in asynchronous and 2h post-mitosis showed identical PC1 and PC2 scores, providing an internal control to the small but measurable effects mediated by CTCF as cells exit mitosis. Hence, while the global effect of CTCF following mitosis is undeniably small, these observations prompted us to identify gene-specific responses that could be more directly associated with CTCF binding, especially in mitotic cells. Mitotic bookmarking of CTCF is required for proper reactivation of selected promoter-proximal target genes. Using standard RNA-seq differential expression analysis (DEseq2 24 ), we identified 611 genes displaying differential pre-mRNA expression in +/-IAA in either asynchronous or in at least two post-mitosis samples (Fig.
For each timepoint/treatment, 1-2 x 10 6 cells were seeded in separate dishes in a volume of 1 ml, so that upon collection only the cells to be harvested were taken out of the incubator. Cells were detached from the dish by pipetting, spun down at 4°C (1 min, 150 g in a benchtop centrifuge), and lysed in 500 µl of cold TRIzol (ThermoFisher, 15596026). The procedure was also monitored by FACS analysis of CCNA-GFP, cultured in the presence of 20 µM Hoechst-33342 for 20 min, trypsinised, filtered through a 40 µm cell strainer and kept on ice. Hoechst and GFP levels were analysed using a LSR Fortessa instrument (Becton-Dickinson) and the FlowJo software (Tree Star). CTCF depletion in CTCF-AID cells was achieved with 0. 5 mM auxin (IAA Sigma, I5148) for 2h, which leads to an acute depletion  11  , starting after 2h of nocodazole treatment.
Raw data is available at Gene Expression Omnibus (GSE196889). Bioinformatic analyses. Analyses were performed in R (3. 6. 3). Genes with mean TPM>0. 5 in asynchronous or in >2 samples post mitosis and present in the Refseq curated repository (UCSC) were considered. Post-mitotic fold-changes were calculated per replicate and then averaged to generate gene expression plots and heatmaps (Complex Heatmap package 33 ). Smoothed profiles used for visualisation purposes only were obtained with a loess regression. PCA was run with prcomp function with centred data corresponding to post-mitotic log2(FC) for CCNA-GFP and log2(TPM) for CTCF-AID to capture direct differences between IAA treatments. GO analyses were performed with enrichR 34. To correlate genes and TF binding and to compute CTCF/Smc1 enrichments, we first selected a single TSS for each gene, which corresponded to the TSS displaying highest levels of RNAPII, H3K4me3 and DNaseI accessibility using available ES cell data from the Encode consortium.
Acknowledgements. The authors acknowledge  E Nora  and  B Bruneau  for  CTCF-AID ES cells ;  N Owens  for discussions on experimental design and bioinformatic analyses and  J Dekker  and  M Oomen  for discussions;  M Cohen-Tannoudji ,  T Gregor ,  G Cecere  and  G Blobel  for critical reading of the manuscript. PN acknowledges the  Institut Pasteur , the  CNRS, and Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ) for recurrent funding and the  Ligue contre le Cancer  ( LNCC EL2018 NAVARRO ) and the  European Research Council (ERC-CoG-2017 BIND)  for financial support.
Study Sites Coral reef fish species were recorded in 2009-2011 at 76 dive stations aligned to 45 sites which ranged from 1-33 m in depth (in the supplementary Table  S1 ), spread across locations in four countries in the WIO: Madagascar, Comoros, Mozambique, and Tanzania (Figure  1 ) spanning latitude -5. 84° (Zanzibar) to -14. 47° (Nacala) and longitude 39. 17 (Chumbe) to 50. 01 (Vohemar). Sites surveyed in Comoros were in Ngazidja (Grand Comore) and Mwali (Moheli), two of the three islands in the Union of Comoros, which are referred to hereafter as Comoros. The fourth southeastern island in the Comoros Archipelago, Mayotte, an overseas department of France, is a larger island with considerably more reef habitat, but was not surveyed here. Each country's dataset is a sample and cannot claim to be representative of the country as a whole.
Survey Method Coral reef fish diversity was measured by recording presence/absence of species on a SCUBA based underwater visual census (UVC) survey, which involved a timed swim by one observer throughout (MS), recording all species within visibility (mean 14. 1 m), supplemented with a few snorkel dives in shallow waters. The method was designed to provide sufficient breadth of species sampling, while remaining practically feasible for relatively rapid dive surveys across a large number of locations. The 19 families were selected based on those that are most speciose, are amenable to UVC (diurnal and not cryptic), have fishery importance, and included four Tetraodontiform families to extend the taxonomic diversity of the dataset (Table  1 ). A complete species inventory of 19 families (Table  1 ) was recorded at each dive.
Dive time and species richness were significantly correlated (R 2 = 0. 19, slope = 0. 27x + 91, p < 0. 01) though taxonomic diversity was not (R 2 < 0. 01, slope = -0. 001x + 63, p < 0. 86). The species richness curve showed a species plateau at >75 min dive duration. Consequently, data from the two replicate dive stations at each site were combined to ensure that each reef site was represented by 75-85 min of underwater observations. This conforms to recommended dive times of 60-90 min  [27]. Species identifications were checked using photographs, taxonomic references, and photographic guides (see Table  S2  for species list and references). Species names were verified from the online Catalog of Fishes  [42].
Data Analyses To assess overall patterns of species diversity, the species presence/absence data per reef site was used to calculate the total number of species and the average taxonomic distinctness (D+). The average taxonomic distinctness is a measurement of the average taxonomic path length between two randomly chosen species in the assemblage  [43]  and was increasingly applied because it is considered a good proxy of biodiversity and it is relatively independent of sampling effort  [44]. Species richness and taxonomic distinctness were tested for differences between areas within countries and dive time. Species accumulation curves derived from the Michaelis-Menten index using 9999 permutations  [43]  were utilized to predict maximum species richness for each country. The Michaelis-Menten equation was chosen because it is independent of the rarity of a species, being based solely on presence.
Acknowledgments: Grateful thanks to the following for comments and suggestions on the survey method:  Howard Choat ,  Phil Heemstra ,  Ali Green , and  Dave Bellwood. Gerry Allen's rapid reef fish diversity survey method for Conservation International , his comprehensive species lists for Madagascar, and help with identification of species from photographs were all invaluable. Thanks to  Bemafaly Randriamanantsoa ,  Saleh Yahya , and  Isabel da Silva  as dive partners and for sharing their local knowledge of fish species. Thanks and appreciation to  David Obura  for stimulating discussions, fund raising, and help in several field trips.
All patients (34 families included, according to the phenotype complexity) were screened for variants in genes known to cause DSD by analysis of exome datasets and confirmed by Sanger sequencing of candidate genes. All patients met the revised criteria of the Pediatric Endocrine Society (LWPES)/European Society for Paediatric Endocrinology (ESPE)  (Hughes et al. , 2006). This study was approved by the local French ethical committee (2014/ 18NICB-registration number IRB00003835) and consent to genetic testing was obtained from adult probands or from the parents when the patient was under 18 years.
Exome Sequencing WES was performed in 125 patients (including 34 families) who presented with anomalies of gonad/genital formation and differentiation. Exonic and adjacent intronic sequences were enriched from genomic DNA using Agilent SureSelect Human All Exon V4, and paired-end sequencing on the Illumina HiSeq2000 platform with TruSeq v3 chemistry. Data analysis was generated from the sequencing platform using manufacturer's proprietary software. Reads were mapped against the human reference genome (NCBI, GRCh37/hg19 or GRCh38/hg38) via Burrows-Wheeler aligner. Single-nucleotide variants and small insertions and deletions (InDel) were carried out with GATK version 1. 6. For each patient, duplicate reads and BAM files manipulations were selected by Picard version 1. 62 (http://broadinstitute.
github. io/picard/), and SAMtools version 0. 1. 18, respectively. Single-nucleotide polymorphism (SNP) and InDel variants were annotated to dbSNP 138 identifiers using the Genome Analysis Toolkit (GATK) Unified Genotyper. The SNP Effect Predictor bioinformatics tools on the Ensembl website (http://www. ensembl. org/homosapiens/userdata/ uploadvariations), gnomAD (https://gnomad. broadinstitute. org/ ) and ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) were used to annotated the novel variants, followed by manual screening of all variants by using the Human Gene Mutation Database Professional Biobase (http://www. biobaseinternational. com/ product/hgmd/). To determine if rare/novel variants were associated with DSD or any of the somatic feature of the phenotype in the syndromic forms of DSD, we screened all variants against currently available data in OMIM, Pubmed, ClinVar, Orphanet etc, using the online tool VarElect (https:// ve.
V1033fs) in CCDC141 gene. Biallelic but not monoallelic variants in CCDC141 are associated with hypogonadotropic hypogonadism  (Turan et al. , 2017)  and hence this heterozygous variant was classified as a VUS. Although the twin boys are monozygotic, they did carry subtle differences in their exome datasets, which were confirmed by Sanger sequencing. The affected twin was homozygous for a novel missense variant (p. P3L) in the VEGFB gene, whereas this variant was heterozygous in the unaffected brother, as well as both parents. The affected brother also carried a novel and de novo heterozygous missense variant in the SCLY gene (p. P2S) that was not present in the unaffected brother (Figure  5 ). The familial occurrence of DSD is considered to be exceptionally rare  (Sarafoglou and Ostrer, 2000).
In each of these 14 cases the variants explained partially or totally the phenotype. A further two cases (DSD cases 87 and 102) harbored pathogenic variants in DSD-related genes (NR5A1 and PROKR2) as well as gene variant likely to cause the syndromic form of the phenotype (BBS7, FGFR3, FLNA and GLI3 genes). One subject (DSD 93) harbored a P homozygous variant in HSD17B3 as well as gene variant likely to cause the syndromic form of the phenotype (COL1A1 gene; MIM 259420). Two subjects (DSD cases 101 and 104) carried pathogenic variants which explain only the somatic features (CACNA1F and NGLY1, respectively) of the phenotype but not the DSD. Within this 46, XY DSD subgroup, 20. 5% (8/ 39) carried variants that are either B or VUS. An analysis of the exome datasets did not identify gene variants that may be responsible for the phenotype in 10.
The p. P2S amino acid change is absent from all public SNP databases. The SCLY gene encodes selenocysteine lyase, a pyridoxal 5′-phosphatedependent enzyme that specifically catalyzes the decomposition of L-selenocysteine to L-alanine and elemental selenium  (Mihara et al. , 2000). In the mouse, the Sclyprotein is known to be expressed in Leydig cells and selenium acts directly on testosterone production  (Seale et al. , 2018). An independent DSD case (DSD 60) carried a heterozygous missense variant p. D154N in SCLY which is considered likely disease causing (REVEL score 0. 579). This child presented with micropenis and bilateral cryptorchidism. The parents of this child were not available for study and the inheritance of the variant is unknown. These variants may contribute to the phenotype but without further supporting genetic or experimental evidence they are classified as VUS.
Clinical phenotype, hormonal profile and details of variants identified in 1 individual with 46, XY persistent Müllerian duct syndrome (PMDS). ACMG American College of Medical Genetics, AD autosomal dominant, AMH anti-Müllerian hormone, AR autosomal recessive, B benign, BC bilateral cryptorchidism, FSH follicle stimulating hormone, gnomAD genome aggregation database, GV gene variants previously associated with the disease, Het heterozygous, HCG human chorionic gonadotropins, Hom homozygous, L left, LB likely benign, LH luteinizing hormone, LOF loss-of-function, M male, MAF minor allele frequency, Mo month, NA not available, NR normal range, P pathogenic, PMDS persistent Müllerian duct syndrome, PoGI Pattern of disease inheritance usually associated with the gene, R right, LB likely benign, Ref reference, REVEL rare exome variant ensemble learner.
FUNDING This work is funded in part by a research grant from the  European Society of Pediatric Endocrinology , and by the  Agence Nationale de la Recherche  ( ANR ),  ANR-10-LABX-73   REVIVE ,  ANR-17-CE14-0038-01 ,  ANR-19-CE14-0022 ,  ANR-20-CE14-0007 , and  ANR-19-CE14-0012. The presented work resulted from collaboration made possible through the  ESPE sponsored programme "ESPE Visiting Professorship ".
DATA AVAILABILITY STATEMENT The data presented in the study are deposited in the ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) repository, accession numbers (VCV001202599. 1, VCV000974911, VCV001202584, VCV001202585, VCV001202591. 1, VCV001202603. 1, VCV001202602. 1, VCV000194727. 30, VCV001199404. 1, VCV001199394. 3, VCV001202593. 1, VCV001202605, VCV000800795. 2, VCV001202594. 1, VCV001202595. 1, VCV001199404. 1, VCV001199398. 2, VCV001202601. 1, VCV001202589, VCV001202586, VCV001199396. 2, VCV001202600. 1, VCV001202592. 1, VCV001199405. 1, VCV001199399. 1, VCV001202590).
G325A:p. V109I GLI3:NM_000168: c. G1527C: p. E509D ACMG, american college of medical genetics; AD, autosomal dominant; AMH, anti-Müllerian hormone; AR, autosomal recessive, B benign, BC, bilateral cryptorchidism, D day, DSD, disorders/differences of sex development, F female, FSH, tolerated (1), PolyPhen2: B (0. 007), REVEL: 0. 052 (B) Novel/SIFT: deleterious (0. 02), PolyPhen2: B (0. 038), REVEL: 0. 122 (B) Het AD VUS/NA/NA follicle stimulating hormone; GD, gonadal dysgenesis, gnomAD genome aggregation database; GV, gene variants previously associated with the disease; HCG, human chorionic gonadotropins, Hem hemizygous, Het heterozygous, Hom homozygous; I. D. , initial diagnosis, L left, LB, likely benign; LDC, likely disease causing; LH, luteinizing hormone; LOF, loss-of-function, LP, likely pathogenic; LPG, left palpable gonad, M male, MAF, minor allele frequency, Mo month, NA not available; NPG, non palpable gonads; NPLG, non palpable left gonad; NPRG, non palpable right gonad; NR, normal range, P pathogenic, PG, palpable gonads; PGD, partial gonadal dysgenesis, PoGI, pattern of disease inheritance usually associated with the gene, R right, Ref reference, REVEL, rare exome variant ensemble learner; RPG, right palpable gonad; TRS, testicular regression syndrome; UC, unilateral cryptorchidism; VUS, variant of uncertain significance, Y year.
In neuroblastoma, MYCN amplification is associated with sparse immune infiltrate and poor prognosis. Dendritic cells (DC) are crucial immune sentinels but their involvement in neuroblastoma pathogenesis is poorly understood. We observed that the migration of monocytes, myeloid and plasmacytoid DC induced by MYCN-nonamplified neuroblastoma supernatants was abrogated by the addition of anti-CCL2 antibodies, demonstrating the involvement of the CCR2/CCL2 axis in their recruitment by these tumors. Using public RNA sequencing and microarray datasets, we describe lower level of expression of CCL in MYCN-amplified neuroblastoma tumors, and we propose a working model for T-cell recruitment in neuroblastoma tumors in which CCL2 produced by neuroblastoma cells initiates the recruitment of monocytes, myeloid and plasmacytoid DCs.
Such development will benefit from an improved understanding of interactions between tumor cells and the immune system to increase anti-neuroblastoma cell immune responses while minimizing or blocking immunosuppressive immune responses. Although DC are the pillars of efficient immune responses, little is known about their recruitment and function in neuroblastoma. In the current study, by using in vitro models, we characterized human DC subsets migration and function in the context of neuroblastoma, with a special emphasis on MYCNamplified tumor. We also analyzed public genomic datasets, to study the role of the CCL2/CCR2 axis in DC recruitment in primary tumors, and its deficiency in MYCN-amplified tumors.
Cell Lines and Peripheral Blood Mononuclear Cells Adherent neuroblastoma cell lines derived from bone marrow metastases SK-N-DZ (DZ, passage 71 to 82, RRID:CVCL_1701), SK-N-SH (SH, passage 26 to 37, RRID:CVCL_0531), and SK-N-AS (AS, passage 2 to 7, RRID:CVCL_1700) were obtained from the ATCC and IGR-N-N91 (N91, passage 32 to 42, RRID:CVCL_8883) was from Institut Gustave Roussy Paris (Villejuif, France). Their identity was verified by HLA-A and B typing (PCR sequence-specific oligonucleotide method, EFS-AURA Grenoble, HLA laboratory). The cells were tested negative for Mycoplasma (MycoAlert Mycoplasma detection kit, Lonza). The neuroblastoma cell lines were cultured in DMEM high glucose supplemented with 10% FCS, 1× MEM Non-Essential amino acids, 20 μg/mL gentamicin (Thermo Fisher Scientific), referred to as complete medium at 37°C and 5% CO 2.
After staining 15 minutes with antibodies, cells were washed, resuspended at 10 7 cells/mL in HBSS before acquisition on the FACs. Fluorescence of Fura-Red was recorded at 510 and 760 nm upon excitation with the violet and blue laser, respectively, and the ratio 510/760 fluorescence over time was plotted with the FlowJo software. CCL2 (65 ng/mL; R&D Systems) was added after baseline recording for 25 seconds. Vehicle (PBS) and ionomycin (1 μg/mL; Sigma) were used as negative and positive controls, respectively. Flow cytometry experiments and analyses were performed using an 8-color FACSCanto II flow cytometer with the Diva 8 or the FlowJo software (BD Biosciences).
S4  for gating strategy). The cells were then fixed with BD FACS Lysing Solution (BD Biosciences) and analyzed with a FACSCanto II using the BD FACS Diva software, and further analyzed with FlowJo or Diva, and treated using the GraphPad Prism software (GraphPad). The quantification of the absolute numbers of each immune cell subset for the migration assay was performed using this formula:.
Microarray and RNA Sequencing Analysis Gene expression data from the Gene Expression Omnibus (GEO) dataset GSE62564 was downloaded from GEO (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE62564) as log 2 RPM. The same set of samples is submitted under GEO accession GSE49711. The GSE62564 dataset is a reanalysis of the data. It contains transcriptional data from 498 neuroblastoma primary tumors. MYCN amplification status was extracted from the GSE49711 series matrix file. Because the MYCN amplification status of some tumors was unknown, we adopted the convention of naming MYCN-high or -low if log 2 RPM were above or below 8, as most MYCN-high neuroblastoma had MYCN amplification (Supplementary Fig. S5 ). The GEO datasets GSE3960 and GSE85047 were downloaded from the GEO at NCBI (https://www.
ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE3960). In GSE 3960, 101 primary neuroblastoma tumors were selected for quantification by microarray  (24) , while in GSE85047, 283 primary untreated neuroblastoma tumors were analyzed. MYCN amplification status was extracted from the series matrix files. For differential gene analysis or representation, raw data were Robust Multi-array Average normalized. Spearman correlation analyses were performed in R. Because sample sizes and sequencing technologies were different between datasets, threshold for high correlation were defined individually for each dataset. For each gene g, a "high correlation" was defined as a correlation coefficient equal to or higher than 95% of correlations between the gene g and all other genes in the dataset. We used strong correlations to build a mechanistic model.