dataesr/scientific-paragraphs-categorization huggingface dataset:
Usage Notes The Tara Oceans data policy follows the open science principle of open access and early release of raw and validated data sets. All data presented here (Data Citations 6-8) are published under the Creative Commons Attribution 3. 0 Unported (CC-by 3. 0) and must therefore be cited when used in scientific papers, posters or presentations. As with most scholarly publications, data citations have authors, a title, a year of publication and a digital object identifier (see data citations in the Reference Section). Furthermore, we kindly ask to include the Tara Oceans Consortium in the acknowledgements. When referring to the Tara Oceans Data or to the sampling strategy and methodology of the Tara Oceans Expedition, please cite the present paper.
Acknowledgements We thank the commitment of the following people and sponsors who made this singular expedition possible: CNRS (in particular the Groupement de Recherche  GDR3280 ),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA, Fund for Scientific Research-Flanders ,  VIB ,  Stazione Zoologica Anton Dohrn, UNIMIB ,  ANR  ( projects POSEIDON/ANR-09-BLAN-0348 ,  BIOMARKS/ANR-08 -.
Both methods identified differentially expressed (DE) genes at all time points (Table  1and S1 Table). All DE genes identified by EBarrays were also DE by LIMMA and, consequently, to be conservative, subsequent analyses herein include genes that were classified as DE by EBarrays (S1 Table ). The majority of DE genes in SREBΔ exhibited a 1. 5-fold or greater change in transcript abundance when controlling the false discovery rate at 5% (Table  1 ). In the yeast phase (37°C), the ratio of DE genes with increased to decreased transcription was 1. 56:1. At 22°C, this ratio was nearly equal (1. 16:1 at 6-hrs, 0. 87:1 at 24-hrs; 1. 02:1 at 48-hrs). At 22°C, the majority of DE genes (570 / 1109 for 6-hrs, 1196 / 1772 for 24-hrs, 1345 / 1910 for 48-hrs) did not overlap with DE genes found at baseline (37°C).
Deletion of SREB affected the transcription of genes involved with diverse processes To facilitate functional classification of DE genes, gene ontology (GO) terms were assigned to proteins encoded by the B. dermatitidis genome using InterProScan. GO analysis revealed DE     ). Integrative analysis suggested several biological themes including iron ion binding, lipid biosynthesis, and amino acid metabolism. Differentially expressed genes predicted to encode iron-binding proteins included those with electron-carrier activity and stress response. The majority of DE genes encoding proteins with electron carrier functions contained cytochrome (19 / 48 genes, 39. 6%), acyl-CoA dehydrogenase/oxidase (11 / 48, 22. 3%), and ferric reductase (4 / 48 genes, 8. 3%) domains. Cytochrome and acyl-CoA dehydrogenase/oxidase encoding genes exhibited reduced transcription (22 / 30 genes, 73.
Weighted gene co-expression network analysis (WGCNA) WGCNA was performed to identify groups of co-expressed genes. All genes across all time points were included in the analysis. We identified 30 color-coded modules including 11 modules that were enriched for GO terms (Table  3 ). In 4 of these 11 modules, the majority of genes were differentially expressed (53. 5%-85. 0%; Table  3 ). Moreover, 3 modules were enriched for GO terms previously identified including metabolic process (midnight blue module), fatty acid biosynthesis / catalytic activity (green-yellow module), and transmembrane transport (blue module) (Tables  2  and 3 and Fig 2A  -2C ). Although these modules were enriched for specific GO terms, the predicted functions for DE genes within each module was diverse. Moreover, a substantial number of DE genes in each module had unknown function (Fig 2A  -2D ).
Collectively, these findings indicate that the acquisition of iron is important for the growth of B. dermatitidis. Gene expression microarray analysis indicated SREB functioned as a major regulator of iron assimilation. SREBΔ failed to repress genes involved with siderophore biosynthesis and transport under iron-replete conditions at 37°C and 22°C (Fig 4A  -4C ). Moreover, a siderophore biosynthetic gene cluster (SBGC) containing 10 genes across 31. 295 kb was identified (Fig  4B ). With the exception of a gene predicted to encode a conserved hypothetical protein with unknown function (BDBG_00049), all genes in the predicted SBGC were differentially expressed in SREBΔ when compared to a WT isolate (Fig 4A  -4C ). DELTA-BLAST analysis against the NCBI (National Center for Biotechnology Information) database predicted that genes in the SBGC were involved with the biosynthesis of siderophores (dimerum acid and coprogen) secreted into the extracellular environment.
ChIP-qPCR was performed using SREB that was engineered to contain a C-terminal 3x-hemagglutinin tag. Integration of gene expression microarray data with genome-wide GATA motif analysis was used to identify putative GATA transcription factor binding sites. All genes analyzed were differentially expressed and contained a GATA motif (ATC-w-gAta-a or A/T-GATA-A/G) in the upstream promoter. For MIRB, the upstream motif in strain 26199 was ATC-A-GATT-A, whereas in other strains including SLH14081, ER-3, and ATCC 18188, the motif was ATC-A-GATA-A. BDBG is the locus number for genes in the B. dermatitidis genome database at the Broad Institute (www. broadinstitute. org). ). Differentially expressed genes involved with conversion of DAG to TAG in the glycerolipid biosynthetic pathway (BDBG_02489, BDBG_07403, BDBG_08114) were analyzed by ChIP-qPCR at 48-hrs 22°C.
In contrast, the majority of DE genes in A. fumigatus SREAΔ were increased  [52]. In C. albicans, conflicting results have been obtained from different SFU1Δ strains  [50, 53]. To further illuminate how deletion of SREB affected transcription during the phase transition, WGCNA was performed to identify groups of co-expressed genes. This demonstrated that deletion of SREB affected transcription differently among co-expressed gene sets. This included subsets of DE genes in SREBΔ that failed to properly increase or decrease following a drop in temperature as well as genes with reduced transcript abundance at 37°C and 22°C. GO analysis offered additional support that deletion of SREB affected the transcription of genes with diverse functions. Analysis of DE genes identified 17 GO terms enriched in SREBΔ ranging from iron ion binding, catalytic activity, fatty acid biosynthetic process to carbohydrate metabolic process.
Statistical analysis of microarray data To identify differentially genes in SREBΔ versus WT at each point, two different statistical programs were used, LIMMA (Linear Models for Microarray data) and EBarrays (Empirical Bayes Methods for Microarrays)  [30, 31]. Both programs were executed using Rbased software. LIMMA used a Bayesian approach to fit fluorescent intensity data for all genes across the different arrays into a linear model, which was followed by analysis for differential gene expression between SREBΔ and WT using a moderated t-statistic  [30]. We used q-values to adjust Limma results in order to remain at a 5% false discovery rate  [77]. EBarrays used fluorescent intensity for all genes across all microarrays to model the posterior probability of a single gene to determine whether it had equivalent (µ 1 = µ 2 ) or different expression (µ 1 µ 2 ) in SREBΔ versus WT  [31].
A lognormal-normal model with moderated variance was used in the EBarrays analysis because there were 3 biological replicates for SREBΔ and WT at each time point. An individual gene was considered differentially expressed if statistical significance was met for both LIMMA (q-value < 0. 05) and EBarrays (posterior probability > 0. 95) analyses. Heat maps were generated using CIMminer from the Genomics and Bioinformatics Group (Laboratory of Molecular Pharmacology, Center for Cancer Research, NCI, NIH, DHHS) (http://discover. nci. nih. gov). Gene expression microarray data have been deposited to the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) (accession number: GSE67724).
We further filtered peaks with less than 20 reads per one million reads at the summit. We annotated the resulting peaks by overlapping them with the 500 bps upstream of the transcription start sites using the 9180 TSS groups defined in blastomyces_dermatitidis_atcc_26199_1_transcripts. gtf (www. broadinstitute. org). ChIP-seq data have been deposited to the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) (accession number: GSE68680).
Acknowledgments The generosity of  Bruce S. Klein  and members of his laboratory is greatly appreciated regarding use of space and equipment. We thank  Christina Cuomo  for MAST analysis,  Garret Suen  for InterProScan analysis,  Isabelle Cooperstein  for help with lipid droplet analysis,  Natacha Bourdon  for technical help in sterols analysis, and  David Andes  for use of equipment.
0) with data assimilation. Assimilated data include temperature and salinity profiles over 1958-2009 from the v2a version of the EN3 database constructed by the Met Office Hadley Centre  (Good et al. , 2013) , alongtrack altimetry-based sea level anomalies and global sea level trend from AVISO, sea surface temperature and sea ice from the ERA-40 archive (prior to November 1981), from NCEP (National Centers for Environmental Prediction) OI version 2 (1981 until December 2009) and from OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis; January 2010 onwards). The ORAS4 temperature and salinity data are available at monthly intervals over 42 depth levels ranging from the ocean surface down to 5350 m depth, on a global 1 • × 1 • grid from January 1958 to December 2014 (see  Balmaseda, 2013b , for more details).
Detrended residuals Figure  2  shows that the residual time series also display important high-frequency (sub-seasonal to interannual) anomalies of up to 4 mm amplitude. These anomalies are highly correlated for all GMSL products, in particular for AVISO, NOAA, CSIRO and CCI data sets. In the following, we analyze the detrended residual time series. Only three GMSL data sets are considered: the AVISO, CU and CCI GMSL data (AVISO and CU being representative of group 1 and group 2, respectively). In order to understand whether a given variable (GMSL, ocean mass or steric sea level) is responsible for all -or part -of the observed short-term (from sub-seasonal to interannual) residuals, we correlate this variable (trend removed) with its associated (detrended) residual. What we would expect, if all data sets were error free, is to see no correlation between the detrended variable and its  associated (detrended) residual.
This period corresponds to a La Niña event. While the 2011 La Niña is well explained by the mass plus steric components (see  Boening et al. , 2012, and Cazenave et al. , 2014) , it is surprising that the same data sets do not explain the negative GMSL anomaly related to the 2007-2008 La Niña. During the period February 2007 to June 2008, the correlation computed between the CCI, AVISO and CU residual curves and associated detrended GMSL amounts to 0. 79, 0. 89 and 0. 92, respectively. This high correlation and amplitude comparison suggests that the residual anomaly during this particular time span at least partly comes from the GMSL data. We cannot rule out however that the steric or ocean mass components also contribute. We will indeed see below that the observed discrepancy at this particular date also partly arises from errors in the GRACE and Argo data.
Over the whole time span  (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) , the correlations are 0. 02, 0. 26 and 0. 55 for the CCI, AVISO and CU GMSL, respectively (see Table  2 ). The lowest correlation is obtained for the CCI data, indicating that the CCI residuals contain fewer GMSL short-term errors than the other two data sets.
Short-term (from sub-seasonal to interannual) errors in the global ocean mass We perform a similar comparison with the GRACE-based ocean mass products. For that purpose we only consider a single GMSL data set (i. e. , CCI) and superimpose the detrended CCI residual time series computed separately for each ocean mass product with the corresponding detrended GRACE data set. These are shown in Fig. 6a-c. In Table  2  are given the correlation between the detrended residual curve and its associated detrended ocean mass component. The rms of the residual time series are also given. The correlation is relatively high in all three cases, 0. 46, 0. 55 and 0. 57 for the CSR, GFZ and JPL data, respectively. The detrended global ocean mass and residual time series coincide almost perfectly between  mid-2006 mid- and mid-2007 mid- and between mid-2009 mid- and early 2012 (Fig.
Short-term (from sub-seasonal to interannual) Argo-based steric sea level errors The rms of the residual time series based on the IPRC, JAM-STEC, SCRIPPS and KVS Argo data (linear trend removed from each time series) are in the range 1. 3-1. 6 mm (see Fig. 7  and Table  2 ). The lowest rms are obtained with SCRIPPS data when using the CCI and CU GMSL. For AVISO, the lowest rms are obtained with the KVS steric sea level. Overall, no best Argo product emerges, rms differences being small. As mentioned previously, in the early part of the time series  (2005) (2006) , we note larger dispersion between all Argo products compared to the subsequent years. These differences can be explained by a still incomplete global coverage of Argo data during this period  (Lyman and Johnson, 2014; Roemmich et al.
Sea level budget using the ORAS4 ocean reanalysis Errors in Argo-based steric sea level estimates arise from different sources (gaps in some regions, data editing, mapping techniques, etc. ;  Abraham et al. , 2013; Lyman and Johnson, 2014; von Schuckmann et al. , 2014). To investigate further the effect of Argo sampling, as well as other Argo data processing errors, on the residual time series, we recomputed the residuals using steric data from the ORAS4 ocean reanalysis  (Balmaseda et al. , 2013b). The integration is performed over the whole ocean depth range (0-5350 m) and between 66 • S and 66 • N. Figure  9  shows the residual time series computed with the CCI GMSL and the mean of the four Argo products (black curve) and ORAS4 data (dotted curve). The detrended CCI GMSL is superimposed.
Contribution of the Indonesian region and other areas not covered by Argo; uncertainty in the absolute residual trend The ORAS4 minus mean Argo time series (integration down to 2000 m; trend not removed) is shown in Fig. 11. It displays significant short-term fluctuations, up to 4 mm, and a trend of 0. 28 mm yr -1 (the ORAS4 steric trend being larger than the mean Argo trend). The ORAS4 reanalysis provides gridded steric data with no gaps, unlike the Argo products. In effect, the coverage of Argo data is not fully global, some regions (e. g. , the Indonesian region and the Gulf of Mexico) not being covered. In Fig. 11 , the ORAS4 contribution for the 2000-5350 m depth layer is also shown. It only explains the 0. 06 mm yr -1 sea level trend and (as expected) shows no short-term anomalies, as seen in the residual curve when using Argo.
It is likely that both trend difference and shortterm anomalies seen in ORAS4 minus Argo time series result from gaps in the Argo geographical coverage (von  Schuckmann et al. , 2014). This is illustrated also in Fig. 11 , which shows the steric sea level contribution from the Indonesian region (0-2000 m layer) computed with ORAS4. Part of the short-term anomalies of the difference curve is due to the lack of Argo data in this region (e. g. , in 2011, coinciding with the La Niña event). Moreover, in terms of trend, the Indonesian region explains about the whole trend difference between Argo-based and ORAS4-based steric sea level. This suggests that the steric sea level trend estimated when using Argo is underestimated by ∼ 0. 25 mm yr -1. Hence, the residual (GMSL minus steric sea level minus ocean mass) trend may be in error (i.
g. , choice of the averaging method and geophysical corrections), as previously discussed by  Masters et al. (2012)  and  Henry et al. (2014). While trying to identify the outliers and select the best corrections to be used is beyond the scope of the present study, we stress that this is definitely an important goal to pursue in the future. In terms of absolute residual trends, we identified the contribution of the Indonesian region, not covered by Argo, as contributing about 0. 25 mm yr -1 (the computed residual trends being overestimated by about this amount). Contributions from other regional gaps in the Argo coverage (e. g. , the Gulf of Mexico) estimated using ORAS4 data are found to be negligible as far as absolute residual trends are concerned. Thus, if we account for the residual trend overestimate due to lack of Argo data in the Indonesian region, the residuals computed with the CCI, AVISO and NOAA GMSL data (using Argo) become close to zero (i.
e. , 0. 00 mm yr -1 , 0. 04 mm yr -1 and 0. 16 mm yr -1 , respectively), while residual trends computed with the CU and GSFC data become negative (-0. 29 mm yr -1 and -0. 33 mm yr -1 , respectively). This suggests that the sea level budget can be closed when using the CCI, AVISO and NOAA data. Hence, in these cases, the deep ocean (below 2000 m) contribution appears negligible. It is worth mentioning that the residual trend (with the CCI GMSL) amounts to about zero (exactly 0. 00 mm yr -1 ) when using ORAS4 (0-2000 m; Indonesian region accounted for), in agreement with the above statements. Moreover, as mentioned above, the ORAS4 steric sea level trend for the 2000-5350 m depth range amounts to 0. 06 mm yr -1. However, further investigation is needed on that issue before drawing any definitive conclusion.
Another result from our study is the attribution of the short-term (from sub-seasonal to interannual) anomalies of the residual time series to errors in both Argo-based steric sea level and GRACE-based ocean mass. Short-term errors in these two components sometimes act in concert (thus amplifying the residual errors, e. g. , during the 2007-2008 La Niña) or affect the residuals at different periods (e. g. , over 2011-2014 for Argo, or in 2006 for GRACE). To summarize the findings of this study, the main source of differences reported in the residual trends appears to be related to altimetry-based sea level data processing. In terms of absolute residual trends, missing Argo data in the Indonesian region contribute as much as 0. 25 mm yr -1. Accounting for this value leads to closure of the sea level budget, at least with the CCI, AVISO and NOAA GMSLs.
At subseasonal to interannual timescales, the main source of uncertainty arises from short-term errors in GRACE and Argo data. More work is required by the different communities involved in either satellite altimetry or GRACE and Argo data processing, to clearly identify the causes of these errors and reduce/eliminate them. This is a challenge of primary importance if we want to precisely address a number of key issues, like the deep ocean heat uptake and its role in the current "hiatus".
Acknowledgements. We thank  M. Balmaseda  for providing us with the ORAS4 reanalysis data set. We also thank  J. François Legeais  and  L. Zawadski  for helpful discussions about errors in altimetry data processing. H. B. Dieng is supported by a PhD grant from the  European Space Agency  in the context of the  Climate Change Initiative Programme. Edited by:  J. M. Huthnance.
74 ± 0. 13 0. 33 IPRC 0. 76 0. 31 JAMSTEC 0. 94 ± 0. 16 0. 14 SCRIPPS 0. 83 0. 24 Mean 0. 82 ± 0. 08 ORAS4 (0-5350 m) 1. 14 -0. 06 • N domain, except for the CSIRO data averaged over 65 • S to Ocean Sci. , 11, 789-802, 2015 www. ocean-sci. net/11/789/2015/ H. B.
Culture of ZHBTc6 mouse ESCs Mouse ESCs were cultured and OCT4 was induced as previously described  15. To avoid any leakage of the Tc-regulated transgene, and to ensure a precise increase in OCT4 when tetracycline is removed, the ZHBTc6 parental cell line kindly given by Dr Niwa was subcloned and three different selected subclones were used in the study without significant differences in data obtained with these cell lines. Removal of tetracycline led to a twofold increase in OCT4 protein. 3D FISH-Amplicons (1. 2 Kb) including Sox-2C and Sox-17 enhancers were amplified by PCR from bacterial artificial chromosoma (BACS) and were labelled with ChromaTide Alexa Fluor 488-5 dUTP and ChromaTide Alexa Fluor 568-5 dUTP (Life Technologies), respectively, by Nick translation. The FISH protocol was adapted from ref.
The temperature transition rate was 20 °Cs -1. Fluorescence was measured at the end of each extension step. After amplification, a melting curve was acquired by heating the product at 20 °Cs -1 to 95 °C, cooling it at 20 °Cs -1 to 70 °C, keeping it at 70 °C for 20 s and then slowly heating it at 20 °Cs -1 to 95 °C. Fluorescence was measured through the slow heating phase. Melting curves were used to determine the specificity of PCR products, which were confirmed using conventional gel electrophoresis and sequencing. Data were analysed according to Pfaffl  59. Primers specific for human genes are described in ref. 60 and include MIXL1, forward: 5′-TCTTCCGACAGACCATGTACC and reverse 5′-TTCTGGAACCACACCTGGAT-3′; SPARC, forward 5′-AAGATCCATG AGAATGACAAG-3′ and reverse: 5′-AAAAGCGGGTGGTGCAATG-3′; NOGGIN, forward 5′-CCATCATTTCCGAGTGCAAGTGCT-3′ and reverse 5′-AAGCTAGGTCTCTGTAGCCCAGAA-3′; and PAX6, forward 5′-AGTGAATCAGCTCGGTGGTGTCTT-3′ and reverse 5′-TGCAGAATTCGGGAAATGTCGCAC-3′.
ChIP on chip ChIP was performed as described above. The pulled down DNA was amplified and hybridized to Affymetrix-GeneChip Human Promoter 1. 0R tiling Array according to the manufacturer. The array includes 4. 6 million probes tiled through over 25,500 human promoter/enhancer regions. Experiments were performed in duplicate of biological samples. To identify OCT4 target sites, we compared the ChIP-chip intensities of the HUESCs nucleofected with OCT4-iA cDNA to those of GFP-nuclected HUESCs. Before the binding site detection, raw intensity data were normalized between the controls and the corresponding Oct4iAOE cells using the cross-correlation method  63. Enriched binding sites were identified based on peak height intensity, peak weight and peak neighbouring intensities. To ensure that the identified binding sites is enriched, statistical distributions of log2 fold changes of the enriched binding sites is compared with the distribution of background noise and 5% false discovery rate is used to select enriched binding sites.
Protein immunoprecipitation and western blots Cells were nucleofected with OCT4-iA cDNA or backbone pcDNA using the kit V (Amaxa). Chromatin was then extracted 2 days later according to the ChIP protocol described above. Co-Immunoprecipitation was performed in radio-immunoprecipitation assay (RIPA) buffer and western blot analysis was conducted as previously reported  16. The anti-OCT4 antibodies used were from Santa Cruz (N19 and H-134). Normalization of blots was performed using an anti-actin antibody (Sigma-Aldrich A2066 used at 1/ 1,000). Quantification of bands was carried out using Quanti one software (BioRad). SALL4 and OCT4 western blots were performed using the anti-SALL4 from Abcam (ab29112) and anti-OCT4 (H-134) antibody recognizing both OCT4-iA and iB and anti-RAD21 antibody (05-908 Millipore) all used at 1/1,000 dilution.