dataesr/scientific-paragraphs-categorization huggingface dataset:
This historical hydrographic station located 7 km off St. John's Harbor in the Avalon channel provides more than 50 years of in situ measurements of coastal ocean parameters in monthly sampling resolution. Station 27 data are available from June 1946 onward. However, because of data gaps in the years 1946-1949 (more than 4 measurements/year missing), only data from 1950 onward were used in this study. The distance between Station 27 and SPM archipelago via ocean currents is about 400 km. To test the effect of regional and local environmental conditions on shell growth, the master chronology was also compared to regional environmental datasets (Figure  1 ), namely the LC volume transport along the Tail of Grand Banks, the position of the Shelf Slope Front (SSF), the St Lawrence River fresh water runoff and the sea ice coverage off Newfoundland.
The dataset used in this study extends from 1870 to 2010 and is available at: http://www. cgd. ucar. edu/cas/catalog/ climind/AMO. html. The NAO is the difference in atmospheric pressure between Azores high pressures and Icelandic low pressures, which drive the westerlies. In this region, the negative (positive) phase of the NAO is related to lower (stronger) north-westerly winds, reduces (enhances) heat losses by the ocean, and deep convection in the Labrador Sea and sea ice cover over the Newfoundland shelf  (Wanner et al. , 2001). Several datasets on NAO are available and can differ significantly (even in the last decades) depending on the atmospheric data used (mainly their location) and their treatments. In this study, we used three datasets: -The DFO  (Canada)   The SPG time series used in this study come from  Berx and Payne (2017)  and cover the period 1992-2016 on a monthly basis.
This index is based on maps of sea surface height. Its variability seems to be weakly dependent of the spatial domain and compares favorably with previous studies  (Häkkinen and Rhines, 2009). Positive values of this index are associated with a strong sub-polar gyre circulation with a wide spread and a strong LC over the Labrador and northeastern Newfoundland Slope. In comparison, negative values of SPG index are associated with a weak sub-polar gyre and westward retraction and weak LC transport over the Labrador and northeastern Newfoundland Slope. The data set is available at: https://data. marine. gov. scot/ dataset/sub-polar-gyre-index. To assess the spatial correlation between SGI and North Atlantic hydrology, we used the gridded objective analysis of world ocean temperature (averaged from the surface to 100 m) of the UK Met Office Hadley centre EN4.
2. 1  (Good et al. , 2013 , with bias corrections from  Levitus et al. , 2009). This dataset has a spatial resolution of 1 • , 42 vertical levels, and a monthly time step, and extends from 1900 to present. We decided to use only the last 66 years  because of the larger uncertainties of environmental measurements during the first half of the 20th century  (Wolfe et al. , 2019). These data are available at: https://www. metoffice. gov. uk/hadobs/en4/download-en4-2-1. html#l09_analyses.
Statistics For comparisons between the master chronology and environmental datasets, the Pearson correlation was used. The commonly used alpha value of 0. 05 was chosen as the threshold value for significance of the correlations. In some cases, correlations were performed using monthly averaged environmental variables given that hydrographic conditions during specific months can strongly influence annual bivalve growth. In addition, to consider water mass transit time between the SSF and SPM archipelago the correlations between A. islandica annual growth rate and SSF position at different longitudes were performed between August (Y-1) and June (Y).
Shell Growth Variability and Environmental Data Correlation With Regional Environmental Datasets Shell growth was positively correlated (N = 18, r = 0. 43, p < 0. 05) with mean local chlorophyll a concentration from April to October over the period 1998-2015. The SPG western flank consists of the LC, which includes two branches, a main offshore branch and a smaller inshore one  (Matthews, 1914; Smith et al. , 1937). Station 27 located in the Avalon channel within the LC inshore branch provides the longest time series of hydrologic properties in this area. Correlations between SGI chronology and Station 27 temperature have been sought at each depth level monthly from 1950 to 2015 (Figure  4 ). Strong positive correlations were observed between shell growth and Station 27 temperature during January and February at all bathymetric levels and almost all year long below 100 m.
Correlation With Spatial Datasets The spatial correlations between A. islandica SGI chronology and monthly near surface (average between surface and 100 m) temperature, from the UK Met Office Hadley centre EN4. 2. 1, are represented in Figure  7  for mid-winter (February), mid-spring (May), mid-summer (August), and mid-fall (November). Given the spatial heterogeneities of the environmental observations in the first half of the 20th century, the correlations were calculated only for the last 66 years. The most striking features were the strong positive correlations with temperature, which cover a large area in the NE of Newfoundland and south of Greenland, encompassing the Labrador and Irminger seas (Figure  7 ). This area broadly delineates the SPG.
Correlation With Global Climatic Indices (AMO, NAO, SPG) The correlation between SGI (10-year running mean) and AMO dataset from the UCAR series (1870-2011) was significant (N = 142, r = 0. 34, p < 0. 05). Even, if we do not find any correlations between our master chronology and the full CRU NAO time series, a weak negative correlation was found between our master chronology and the full DFO NAO series (N = 120, r = -0. 16, p < 0. 1), this correlation being slightly higher (N = 120, r = -0. 23, p < 0. 05) if we smooth the series over 3 years, as suggested by  Petrie (2007)  results which showed that the persistence of the same NAO phase for at least 2 years is likely to have a stronger influence on environmental conditions on the Newfoundland-Labrador shelf. Weak negative correlations were found between SGI master chronology and short NAO time series  from the NCEP (N = 66, r = -0.
36, p < 0. 05), United Kingdom (N = 66, r = -0. 28, p < 0. 05), and DFO (N = 66, r = -0. 36, p < 0. 05) datasets. A. islandica SGI chronology was negatively correlated (N = 24, r = -0. 56, p < 0. 05) with the annual SPG index from Berx and Payne (2017) over the period 1992-2015.
However, the short time period covered by these satellite-based chlorophyll a time series led us to investigate other environmental variables spanning longer time periods at a broader geographical scale which are an indirect reflection of phytoplankton production in SPM surface water. Therefore, our goal was to identify the environmental drivers that can explain the inter-annual to inter-decadal variations in A. islandica growth over the last 160 years in SPM by inferring potential links between different oceanographic parameters variability and local nutrient availability and primary production. Our approach uses environmental datasets sourced from observations as much as possible because climatic indices arising from coupled climate models are rarely successful in accurate reproduction of large spatial scale climatic indices  (Pyrina et al.
ACKNOWLEDGMENTS We thank the  LEMAR (UMR 6539) Secretariat team (Anne-Sophie Podeur ,  Geneviève Cohat , and  Yves Larsonneur ) for their invaluable assistance during the administrative preparation of the field trip associated with this publication. We also thank the DTAM divers' crew ( Yoann Busnot ,  Luc Thillais , and  Jean-Marc Derouet ) for their help during A. islandica sampling off Miquelon Island. We are sincerely grateful to the  Club Nautique Saint-Pierrais  for renting their boat and especially to its president,  Stephane Salvat , for his incredible availability and kindness. In addition, we express our sincere gratitude to  Herlé Goraguer ,  IFREMER  delegate in SPM, for his help with local authorisations and logistics. We also thank  Eric Dabas  for his technical assistance during sclerochronological sample preparation and  Sébastien Hervé  for conceiving Figures 1,8.
RyhB represses nuo and sdh expression RyhB inhibition of Sdh and Nuo activities may be due to the repression of the synthesis and / or of the maturation of the complexes. Expression of sdh has already been shown to be repressed by RyhB  [20, 30]. In contrast, although pointed out in global approaches, RyhB regulation of nuo genes expression still awaited investigation  [17, [31] [32] [33]. Using the RNA-fold software (http://unafold. rna. albany. edu), we could predict a base-pairing in between RyhB and the 5' un-translated region of the first gene of operon, nuoA  [34]. This base-pairing involves 21 nucleotides (nt) of RyhB and includes the ribosome-binding site (RBS) and the start codon of nuoA (Fig  4A ). Overexpression of ryhB on a plasmid decreased the activity of a P BAD -nuoA-lacZ fusion of about 4-fold, as compared to cells transformed with an empty vector (Fig  4B ).
We then measured Nuo activities of the different mutants grown until mid-exponential phase (OD 600 = 0,6). In agreement with the literature  [7] , Nuo activity was decreased more than 5 fold in an isc mutant wherein the Suf machinery alone is responsible for Fe-S biogenesis (Fig  5A ). Nuo activities of the isc ryhB mutant remained low in iron-deprived conditions. Nuo activity of the Δsuf strain was comparable to that of the WT and DIP treatment inflicted the same drop inactivity in both strains. Strikingly however, deleting ryhB in the Δsuf mutant almost completely restored Nuo activity of cells grown in low iron condition. These data strongly suggest that the contribution of Isc to maturation of Nuo complex is of paramount importance, even in growth conditions limited in iron availability.
Targeting these pathways has been shown to be an effective strategy for inhibiting growth in GBM animal models  (An & Weiss, 2016). Cancer cells have clearly developed mechanisms for an accumulation of excessive intracellular cholesterol in order to support their growth. Cholesterol metabolism might therefore represent a potential therapeutic target. However, the key factors contributing to the accumulation of cholesterol in GBMs remain unclear. Here, we used large-scale in silico analyses of whole-transcriptome databases to identify dysregulated genes in GBMs involved in cholesterol homeostasis. One of the most dramatically down-regulated genes was cholesterol 24-hydroxylase (CYP46A1), a brain-specific enzyme responsible for the elimination of cholesterol through conversion of cholesterol into 24(S)-hydroxycholesterol (24OHC)  (Moutinho et al, 2016).
Cholesterol level promotes GBM cell growth First, we determined the difference in cholesterol regulation between GBM and normal brain. Gene set enrichment analysis (GSEA) based on the Rembrandt data revealed that GBM (n = 217) and normal brain tissues (n = 28) displayed a distinct enrichment of cholesterol metabolic processes compared with normal brain  (Appendix Fig S1A). Cholesterol synthesis, however, was positively associated with normal brain (adjusted P < 0. 05, respectively; Appendix Fig S1A ), which is consistent with previous reports indicating that GBM cells mainly depend on exogenous cholesterol synthesized by normal human astrocytes for growth  (Villa et al, 2016). To confirm that GBM cells depend on exogenous cholesterol, two GBM cell lines (LN229 and U251) and GSCs from a primary GBM (GBM#P3) were cultured in media with FBS or in lipoprotein-deficient serum (LPDS).
CYP46A1 is a tumour suppressor candidate in GBM To identify the most dysregulated cholesterol-related genes in GBM, we performed bioinformatic analysis on publicly available genomic datasets. First, we derived a gene signature of 176 genes involved in cholesterol biology based on Gene Ontologies  (Alfaqih et al, 2017). Through differential analysis using the Rembrandt dataset, we uncovered a total of 13 differentially expressed genes between GBM (n = 217) and normal brain tissue (n = 28)  (Fig 1A and B). The brain-specific cholesterol regulator, CYP46A1, emerged as the most differentially expressed gene with decreased expression in GBM (log 2 fold change = 2. 335, adjusted P = 5. 85E-25). Differential analysis based on the Chinese Glioma Genome Atlas (CGGA) dataset also revealed CYP46A1 as one of the most dysregulated transcripts (log 2 fold change = 1.
966, adjusted P = 4. 63E-09) between GBM (n = 128) and normal brain (n = 5; Fig EV1A  -C ). Univariate Cox regression analysis in GBM patients was also used to screen the cholesterol-related genes based on prognostic values. After ranking by P-value, CYP46A1 emerged among the top 3 genes (APOBR, CELA3A and CYP46A1) associated with GBM prognosis  (Fig EV1D). By analysing the Cancer Genome Atlas (TCGA) pan-cancer data including 31 different cancer types, the expression of CYP46A1 was found to be significantly increased in normal brain compared with  GBM and LGG (Appendix Fig S2A). Loss of CYP46A1 in GBM was further confirmed by analysing several public glioma datasets (over 1,500 samples were enrolled; P < 0. 01, respectively; Appendix Fig S2B ). The intra-tumoral expression pattern of CYP46A1 in GBMs was further determined using the IVY GBM RNA-seq data (http://gliobla stoma.
CYP46A1 expression was found to be cytoplasmic. Expression was also consistently high in normal brain tissue (5/6; 88. 3%) compared with LGG (14/24; 58. 3%) and GBM (6/34; 17. 6%), and decreased with increasing glioma grade  (Fig 1C and D). IHC staining in one specific GBM sample demonstrated that CYP46A1 was positively expressed in adjacent brain tissue while absent in GBM  (Fig 1E). IHC data from the Human Protein Atlas (n = 17) corroborated these results, confirming high expression of CYP46A1 in normal brain  (Fig EV2A) , which decreased with increasing tumour grade (Fig EV2B  and C ). We subsequently assessed CYP46A1 levels based on the 2016 WHO classification of gliomas, using the TCGA data. CYP46A1 was higher in three LGG subtypes (LGG-Oligo, LGG-Astro and LGG-IDHwt) and lower in GBM-IDHmt and GBM-IDHwt (Fig  1F ).
Although there is a general down-regulation of CYP46A1 in GBM compared with normal brain, higher expression of CYP46A1 was also observed in the Neural GBM molecular subtype  (Fig 1G) , which is associated with a more favourable prognosis, relative to the other subtypes based on the TCGA Verhaak-2010 molecular classification of GBM  (Noushmehr et al, 2010). To address why CYP46A1 is decreased in GBMs, we examined its promoter/enhancer regions using the combined chromatin immunoprecipitation sequencing (ChIP-seq) data from GEO and ENCODE databases. Here, H3K27ac peaks (marker of active promoters) within the promoter region of CYP46A1 were lower in GBMs compared with normal brain tissue  (Appendix Fig S3A). We also examined the active enhancer landscape of CYP46A1 across three matched pairs of GSCs and differentiated glioma cells (DGCs).
Decreased CYP46A1 levels correlate with worse survival in glioma patients To determine the clinical significance of CYP46A1, Kaplan-Meier analysis was performed using the CGGA dataset. GBM patients with high CYP46A1 mRNA levels (based on the median value) exhibited significantly better overall survival (OS) as well as progression-free survival (PFS)  (Fig 1H and I). CYP46A1 was also a prognostic indicator in LGG patients  (Fig 1H and I). The prognostic value of CYP46A1 was further validated in TCGA, Rembrandt and Phillips datasets (Appendix Fig  S4A-C ). CYP46A1 was also validated as an independent prognostic indicator using univariate and multivariate Cox regression analysis of OS (HR = 0. 390, 95% CI = 0. 262 to 0. 581, P < 0. 001; Appendix Table  S1 ) in CGGA patients and showed a prognostic trend in multivariate analysis of TCGA patients.
We found that 24OHC triggered GBM cell death in the presence of LXR-623 (P < 0. 05; Fig  5G ), which is an effective synthetic LXR agonist  (Villa et al, 2016). These results indicate that 24OHC might kill GBM cells not only through activation of LXR, but also through other mechanisms. Based on the RNA-seq data, 24OHC was also found to suppress SREBP signalling (Fig  5D  and E ). Analysis of the genomic datasets consistently showed that SREBP1 was the predominant SREBP isotype expressed in GBMs predicting poor overall survival  (Appendix Fig S7B). This is consistent with prior reports showing that SREBP1, but not SREBP2, contributes to GBM progression and tumorigenesis  (Geng et al, 2016). We thus reasoned that 24OHC influenced SREBP1 transcriptional activity in GBM. On Western blots, proteins levels were correspondingly altered; 24OHC caused a decrease in protein levels of nuclear SREBP1 (N-SREBP1), precursor SREBP1 (P-SREBP1) and LDLR as well as an induction of The penetration of the blood-brain barrier (BBB) by EFV has been well-documented in several studies.
Corresponding reagents such as 24OHC (HY-N2370, MedChemExpress) and efavirenz (EFV; HY-10572, MedChemExpress) were added to cells in DMEM containing 1% FBS and 1% penicillin/streptomycin for established cell lines or neurobasal medium for patient-derived GSCs. Cell viability assays were performed using the trypan blue assay (15250061, Gibco/ Thermo Fisher Scientific) or the Cell Counting Kit-8 (CK04, Dojindo; Rockville, MD) according to the manufacturer's protocol. Raw OD values at 450 nm were analysed using PRISM software for CCK-8. For IC 50 analysis, EFV or 24OHC concentrations were log10-transformed, and values were normalized to per cent viability relative to vehicle-treated cells. For the IncuCyte proliferation assays, cells were treated with DMSO, 24OHC or EFV at various concentrations.
Results We conducted a large-scale in silico analysis of publicly available genomic datasets of gliomas. Using this approach, we identified the gene CYP46A1 as one of the most dramatically down-regulated genes involved in GBM cholesterol homeostasis. CYP46A1 is a brain-specific enzyme responsible for the elimination of cholesterol through the conversion of cholesterol into 24(S)-hydroxycholesterol (24OHC). Mechanistically, ectopic expression of CYP46A1 suppressed glioma stem cell proliferation and in vivo tumour growth by increasing 24OHC, which led to a decrease in GBM cholesterol levels. These results led us to examine the blood-brain barrier permeable anti-HIV drug, efavirenz (EFV), which is known to activate CYP46A1, for the treatment of GBM. EFV increased 24OHC levels in GBM cells and suppressed GBM cell growth.
Acknowledgements We thank  Dr. Justin Vareecal Joseph  for establishing and providing primary GBM cells. This work was supported by the  National Natural Science Foundation of China  ( 81972351 ,  81702474  and  81702475 ), the  Department of Science & Technology of Shandong Province  ( 2017CXGC1502 ,  2018CXGC1503  and  2018GSF118082 ), the  Special Foundation for Taishan Scholars  (ts20110814, tshw201502056 and ts201511093), the  Shandong Provincial Natural Science Foundation  ( ZR2017MH116 ), the  China Postdoctoral Science Foundation  ( 2018M642666 ), the  Jinan Science and Technology Bureau of Shandong Province  ( 201704096 ), the  Norwegian Cancer Society , the  Norwegian Research Council  ( ES563961 ),  Haukeland University Hospital, Helse-Vest  and the  University of Bergen.
Data availability The datasets in this study are available in the following databases: The RNA-seq data: Gene Expression Omnibus (GEO) with the record numbers GSE54047 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE54047), GSE46016 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE46016) and GSE46014 (https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE46014). Expanded View for this article is available online.
Raw data were aligned against the human genome (hg19) using BWA 0. 7. 5. Variant calling and annotation were processed using GATK and ANNOVAR. (ii) Variant prioritization Pedigree-based variant prioritization and co-segregation were performed with the Variant Annotation and Filtering Tool (VarAFT), version 2. 12 (https://varaft. eu/). To pinpoint putatively pathogenic and causal variants we adopted the following filtering strategy: we first excluded variants with a minor allele frequency (MAF) >1% in the gnomAD database (http://gnomad. broadinstitute. org/). The remaining variants were filtered based on their type and genomic localization; thus, synonymous, intronic, variants in intergenic, 3´and 5´UTR regions were discarded. The obtained variants list was then filtered according to the in silico pathogenicity prediction.
On the last evaluation, at 8 months, she had a severe stunted growth (Table  1 ), more evident dysmorphic face, cardiac murmur, mild hepatomegaly, normal psychomotor development and a normal pulmonary examination. However, she died at home of a respiratory infection a few days later. (ii) Whole exome sequencing Considering the overlapping features between Noonan and CFC syndrome in our patient, her severe clinical profile and the genetic heterogeneity of RASopathies, we performed exome sequencing of the patient, her unaffected brother and both parents to identify the disease-causing mutation. WES data from the family were simultaneously analysed and segregated using VarAFT software  (Desvignes et al. , 2018). The filtering strategy is detailed in the 'Materials and Methods' section. By focusing on 20 genes of the RAS/MAPK pathway, only one exonic variant in exon 7 of the RAF1 gene was found.
Ser259Tyr has only been reported once in a fatal case of Noonan syndrome  (Hakami et al. , 2016). To assess the functional impact of the p. Ser259Tyr variant, several in silico prediction tools were used (Table  3 ). Moreover, the RAF1 c. 776C > A; p. Ser259Tyr is absent from the following databases: gnomAD (http://gnomad. broadinstitute. org), 1000 Genomes (http://www. internationalgenome. org), and GME Variome (http://igm. ucsd. edu/gme/). Therefore, no frequency data were available. The variant was also absent from our in-house database, gathering WES data from 70 Tunisian individuals (140 chromosomes). This result as well as the mode of transmission were validated by Sanger sequencing. The electropherograms shown in Figure  3  confirmed the p. Ser259Tyr mutation and the mode of inheritance.
marine. copernicus. eu/). Bottom environmental data for each population was obtained using trilinear interpolation  (Assis et al. , 2017)  weighting location (longitude and latitude) and depth. Long-term minimum (productivity and currents) and maximum (temperature) extremes were averaged for the years 2000-2014. Slope was computed using the ''terrain'' function of the R package ''raster'' (3. 5. 2 version; R Core Team, 2018) in bathymetry. Given the sampling design (no precise coincidence of depths between sites) we adopted the Generalized Linear Mixed Models (GLMM;  Bolker et al. , 2009)  framework. This is suitable for modelling unbalanced designs  (Zhang & Chen, 2013 ) and complex spatial and temporal correlation structures since it accounts for dependencies within hierarchical groups by introducing random effects  (Bolker et al.
We used the suggested and more appropriate (realistic) parameters for microsatellites: the Two-Phase Model (TPM)  (Luikart & Cornuet, 1998; Piry, Luikart & Cornuet, 1999)  with a step in mutations of 0. 9 (ps) and a variance in mutations of 12  (Piry, Luikart & Cornuet, 1999; Busch, Waser & DeWoody, 2007). Because the genetic dataset has less than 20 loci, we used the Wilcoxon test to address the null hypothesis of no heterozygosity excess (on average) across loci  (Cornuet & Luikart, 1996; Luikart & Cornuet, 1998).