dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, we allow for a time delay of 2 years when calculating our period fertility measures using EU-SILC; i. e. , we observe births during the calendar year 2010 by using the survey wave of 2012. Following the same logic, we use the survey wave of 2011 to observe births during the year 2009, and the 2013 wave for births during the year 2011. Using survey waves 2011, 2012, and 2013 of the cross-sectional EU-SILC sample allows us to cover 28 European countries. 8 observations for the cross-sectional database. In the cross-sectional database a quarter of individuals are observed for the first time, a quarter for the second time, a quarter for the third time, and a quarter for the fourth time. This integrated design reduces measurement bias due to accumulated respondent burden and sample attrition.
Post-stratification To be consistent with national fertility estimates, we post-stratify our birth probabilities in order to get estimates leading to the published values. This allows international comparisons for specific groups (e. g. , highly educated women) or for particular dimensions of fertility behavior (e. g. , childlessness). Due to our post-stratification procedure, these comparisons are not biased by overall biases in fertility differences between countries. We therefore identify, as a first step, the relative difference between total fertility rates coming from official statistics (the World Bank World Development Indicators -WB WDI, World Bank Group 2018) and total fertility rates calculated with our EU-SILC database. In a second step, we post-stratify fertility rates by a multiplicative factor for each country to suppress this difference.
We furthermore illustrate, in online Appendix II, total birth intensities by education for 2005, 2010, and 2015 covering all countries available in EU-SILC, and use the example of Germany and Sweden for an annotated comparison of 2010 and 2015. Finally, the supplementary material contains two STATA do files (one for the data compilation, one for the data output) as well as an Excel file and a readme file documenting in detail our data compilation and calculation procedure ("datacompil. do", "dataoutputDE. do"; Excel: example for Germany 2010 "Germany2010. xls", "readme. txt"). 14  Figure  1  presents the birth intensities by age and education for France. For first childbirth, Figure  1  illustrates that low-educated women tend to have their first child before age 30 and that 94% of low-educated women have at least one child by the age of 49.
In most countries the share of highly educated women has increased over recent decades, and we have shown that higher shares of highly educated women come hand in hand with higher period fertility levels. Further analyses based on our rich dataset will reveal whether, how far, and under which institutional circumstances the educational expansion among women also comes hand in hand with an eventual reversal of the educational gradient of period fertility. At this stage the presented research results are purely descriptive, as this paper focuses mainly on methodological advances for measuring the educational gradient of fertility for women of childbearing age. However, we would like to highlight at this point that existing studies on determinants of educational differences in fertility outcomes have shown that the causes of fertility changes in high-income countries are diverse and differ between individuals and countries  (Kohler, Billari, and Ortega 2006; Balbo, Billari, and Mills 2013).
Building on the analyses provided by  Greulich and Dasré (2017) , we have reinforced the quality of period fertility measures based on EU-SILC by applying Bayesian statistics and poststratification. Providing access to these measures and to the detailed compilation protocol allows the research community to now retrace our compilation of measures and to pursue and improve data quality checks. In this article we presented data for the year 2010 to allow for comparison with census data. Our priority is now to provide a systematic data quality check. We therefore will compare EU-SILC-based measures of the educational gradients of period fertility to those that will be calculated with alternative, more demographic datasets, for as many countries and years as possible. The focus will be on the identification of education-specific biases.
Acknowledgements We would like to thank Matthieu Sarnin and Yanick Savina for their valuable research assistance. This study is based on data from Eurostat, EU Statistics on Income and Living Conditions  [cross-sectional waves 2011, 2012, 2013]. The responsibility for all conclusions drawn from the data lies entirely with the authors. According to these results, it could appear convenient to adjust only first birth probabilities, based on the assumption that non-response is larger among childless women -all the more that this adjustment leads to a rather proportional change in births intensities at all birth orders. But the consequence in terms of distribution by final parity is very different. When all probabilities are modified the decline in first births is lower, and it is compensated by the decline is second-birth rates, leading to a small increasing proportion of women with one child (Figure E-1).
Proteomics Wildtype and homozygous (n = 6 per group) ventricular samples, previously collected from embryos at E15. 5, were prepared and labelled using solutions provided in EasyPepTM mini MS sample prep kit (Thermo Fisher Scientific, Waltham, MA, USA) following manufacturer's instructions. For data analysis, the MS and MS/MS scans were searched against the Uniprot database using Proteome Discoverer 2. 2 (Thermo Fisher Scientific, Waltham, MA, USA) with 5% false discovery rate (FDR) criteria. Multiple test correction was performed using the Benjamini and Hochberg test (Perseus software  [21] ). Data were further analysed through the use of Ingenuity pathway analysis (IPA, QIAGEN Inc, Germantown, MD, USA), https://digitalinsights. qiagen. com/IPA, (accessed on 22 February 2023)  [22] ). Detailed information is provided in the Supplementary Materials.
Image Analysis ImageJ version 1. 53a was used for the colocalisation analysis and densitometry of Western blots. The 'JACoP' plugin (https://imagej. net/plugins/jacop, accessed on 22 February 2023) was used to calculate a correlation coefficient. The Image J plugin 'Colocalisation finder' (http://punias. free. fr/ImageJ/colocalization-finder. html, accessed on 22 February 2023) was used to generate cytofluorograms to visualise colocalisation. Cell Profiler 4. 2. 1. was used for nuclear assessment  [23]. Sarcomere length was analysed as described  [24] , using MatLab (version 2021a) and the script 'ZLineDetection' (https://github. com/Cardiovascular-Modeling-Laboratory/zlineDetection, accessed on 22 February 2023).
Proteomics Analysis Gives Insights into Disturbances in the Hom Hearts In order to gain insight into the disturbances leading to embryonic death, WT and Hom E15. 5 heart protein samples were subjected to unbiased proteomics. Overall, 244 proteins were found to be upregulated and 133 proteins were downregulated in the Hom hearts when compared to WT (Figure  6A , Tables  S6  and S7 ). Further, ingenuity pathway analysis (IPA) revealed dysregulation in a number of key canonical pathways, the most prominent being oxidative phosphorylation, mitochondrial dysfunction, sirtuin signalling and the citric acid (TCA) cycle (Figure  6B ). Moreover, proteins belonging to the ubiquitination pathway and unfolded protein response were also enriched in the dataset (Figure  6B , Table  S8 ). A deeper analysis of the data highlighted key mitochondrial protein complexes to be downregulated (Figures  6C  and S18 ), while proteins associated with protein processing and translation, including proteasomal activity, were upregulated (Figure  6B, C ).
Destabilisation of Actn2 Protein in the Hom Hearts A targeted interrogation of the proteomics dataset indicated that Actn2 protein levels were reduced by approximately 25% in the Hom hearts (p < 0. 01, Table  S6 ). To validate this, we probed for alpha-actinins 1-4 at the mRNA level. The transcripts for Actn2 and Actn3 were upregulated in Hom ventricles (Figures  7A  and S19A ), while transcripts for other alpha-actinins, Actn1 and Actn4, were not affected. Equally, the expression of cardiac actin, Actc1, was normal, while skeletal muscle actin, Acta1, was downregulated. In contrast to the Actn2 upregulation observed at the transcript level, the protein levels for Actn2 were significantly reduced (Figure  7B ). At the protein level, Actn3 expression was unchanged (Figure  S19B, C ). The reduced Actn2 levels in the Hom hearts suggested that the p.
Met228Thr Actn2 is subject to protein degradation. Ubiquitin-proteasomal system (UPS) and autophagy are the main protein degrading machineries in cells  [25]. We probed for autophagy makers (p62 and LC3, Figure  S21 ), but failed to observe any changes in the Hom hearts. Furthermore, proteolytic activities and total ubiquitination showed no signs of changes in the Hom hearts (Figure  S20 ). However, the fact that the UPS was enriched in the proteomics dataset (Figure  6C ) argues for the UPS being responsible for the destabilisation of the Actn2 protein. In summary, the p. Met228Thr genetic variant renders the protein less stable. In homozygous animals, cell-cycle defects and mitochondrial dysfunction are observed; these impaired functions result in a range of morphological abnormalities of the embryonic hearts and are incompatible with life.
Destabilisation of Actn2 Protein in the Hom Hearts A targeted interrogation of the proteomics dataset indicated that Actn2 protein levels were reduced by approximately 25% in the Hom hearts (p < 0. 01, Table  S6 ). To validate this, we probed for alpha-actinins 1-4 at the mRNA level. The transcripts for Actn2 and Actn3 were upregulated in Hom ventricles ( Figures  7A  and S19A ), while transcripts for other alpha-actinins, Actn1 and Actn4, were not affected. Equally, the expression of cardiac actin, Actc1, was normal, while skeletal muscle actin, Acta1, was downregulated. In contrast to the Actn2 upregulation observed at the transcript level, the protein levels for Actn2 were significantly reduced (Figure  7B ). At the protein level, Actn3 expression was unchanged (Figure  S19B, C ). The reduced Actn2 levels in the Hom hearts suggested that the p.
Nevertheless, this cannot make up for the decreased stability at the protein level and subsequent degradation. The mutation may lead to the partial mis-folding of the actin binding domain, which is supported by the enrichment of the 'unfolded protein response' pathway in the proteomics dataset (Figure  6B ). Such mis-folded proteins are recognised and targeted for protein degradation. There are two major protein degrading pathways: the ubiquitin-proteasomal system (UPS) and autophagy  [42]. We have no evidence to suggest that autophagy is involved. In contrast, unbiased proteomics revealed that UPS activity is more active in Hom hearts (Figure  6B ,C and Table  S8 ), so it is likely responsible for the increased turnover of the mutant protein. While the measurements of proteolytic activities (Figure  S20A ) do not support the increased activity of the UPS, future experiments using proteasomal or autophagy inhibitors on cultured cells, e.
Acknowledgments: We would like to thank  Roman Fischer  and  Raphael Heilig  ( University of Oxford ) for mass spectrometry services;  Caitlin McCaugherty  for technical assistance with the image analysis,  Lisa Leinhos  for technical assistance with skeletal muscle preparations, and  Nigel Drury  for helpful discussions about embryonic blood circulation. We also thank the  Centre for Ultrastructural Imaging, King's College London , and  University of Birmingham Advanced Mass Spectrometry Facility  for assistance.
Funding: This work was supported by the  British Heart Foundation (BHF)  [ FS/12/40/29712  and  PG/15/113/31944  to KG,  FS/17/55/33100  to DBS]; the  Oxford BHF Centre of Research Excellence  [ RE/13/1/30181  to KG, DBS and JIK-S]; the  Wellcome Trust  [ 201543/B/16/Z  to KG, HJ, CH, VS, HW, MG and KDjC; Core Award  090532/Z09/Z  to BD]; the  Medical Research Council  [ MR/V009540/1  to SBS and KG] and the  John Fell Oxford University Press Research Fund  [ 0006108  to DBS]. Work in EE's laboratory is funded by the  BHF  and the  UKRI-Medical Research Council. The  Institute of Cardiovascular Sciences, University of Birmingham , has received an Accelerator Award by the  British Heart Foundation  [ AA/18/2/34218 ].
Methods and data We use satellite data of Chl a and SST and build on the approach of  Liu and Levine (2016)  to distinguish between persistent and ephemeral fronts. We evaluate the impact of both types of fronts on Chl a on the basis of three indicators: the excess (or deficit) Chl a over fronts at the local scale of the front, the surplus Chl a attributable to fronts at the scale of regional biomes, and the change in the timing of the Chl a spring bloom over fronts.
Data Our approach combines daily satellite SST data, which are used to detect fronts and sort them by their strength, with daily satellite surface Chl a, from which we derive anoma-lies over fronts. For Chl a, we used the L3 product distributed by ACRI-ST over the period 2000-2020, generated by Copernicus-GlobColour 1  , constructed with data from different sensors (SeaWIFS, MODIS Aqua and Terra, MERIS, VIIRS-SNPP and JPSS1, and OLCI-S3A and S3B), merged and reprocessed, and available daily at 4 km resolution  (Garnesson et al. , 2019 (Garnesson et al. , , 2021)). For SST, we used the European Space Agency Sea Surface Temperature Climate Change Initiative analysis product version 2. 1  (Merchant et al. , 2019; Good et al. , 2020a, b) , also available daily at 4 km resolution over the period 2000-2020.
We first filtered the Chl a median time series with a low-pass Butterworth filter of order 2 and cutting frequency 1/20 d -1. The filtered time series displayed strong variations in their phenology from year to year, but a bloom was always discernible. We considered data from February to July, which allowed us to isolate the spring bloom and exclude the fall bloom. We detected the maximum value of Chl a in this time window and defined the bloom onset as the time of maximum Chl a derivative prior to the time of maximum Chl a. We defined the uncertainty in bloom onset date as the standard deviation of all days for which the Chl a derivative is above 90 % of its maximum value. We defined the uncertainty in lag L as the square root of the sum of squared uncertainties in fronts and background. Finally, we estimated the mean lag (over the 20 years of data and for each latitudinal band) as the weighted averaged lag, with the weights equal to the inverse of the uncertainties; and we estimated the mean lag uncertainty as the weighted standard deviation of all lag values around the mean lag, with the weights equal to the inverse of the uncertainties.
Acknowledgements. Clément Haëck benefited from a PhD scholarship by the ENS CHANEL chair. We thank  Daniele Iudicone ,  Francesco d'Ovidio ,  Sakina-Dorothée Ayata , and  Amala Mahadevan  for the useful discussions which helped to refine our methodology. We thank  Xiao Liu  for helping us reproduce their work. Financial support. This research has been supported by the  Centre National d'Etudes Spatiales (TOSCA). Review statement. This paper was edited by  Peter Landschützer  and reviewed by two anonymous referees.
Code and data availability. Chl a GlobColour data were last downloaded in June 2022 from the E. U. Copernicus Marine Service (https://doi. org/10. 48670/moi-00280, Garnesson et al. , 2021). These data have been previously developed, validated, and distributed by ACRI ST, France. SST data were also last downloaded in June 2022 from the E. U. Copernicus Marine Service (https://doi. org/10. 48670/moi-00169, Good et al. , 2020b). All the scripts needed to reproduce our results, as well as the intermediate data necessary to generate the figures, are available on a Zenodo repository (https://doi. org/10. 5281/zenodo. 7470199, Haëck et al. , 2022).
| Air temperature, GST, and thaw depth measurements The air temperature was measured at the Bylocamp station during 2016-2018 at 1-h intervals, and the data were retrieved from the Nordicana website. 39 GST was measured continuously from July 1, 2016 to July 1, 2018 (730 days) at a network of 100 temperature micro data loggers (Figure  1 ). Loggers were small (<2 cm) iButtons (DS1922L model) with a 0. 5 C accuracy and set at a 3-h sampling interval. The loggers were installed at 3 cm below the ground surface (bare soil or moss) to avoid direct insolation during the snow-free season. The moss cover was considered part of the ground rather than part of the vegetation canopy so some of the loggers were recording the temperature inside mosses. The objective of the sampling design was to efficiently distribute the available loggers across the range of elevation and slope aspects and over contrasting morphological features (hummocks, polygons) (Figure  1 ).
Eighteen hummocks and seven low-centered ice-wedge polygons were chosen randomly. More loggers were allocated to hummocks as these landforms are predominant at the site. Loggers were deployed at the top and bottom of the hummocks (18 pairs) and on the rims and centers of the polygons (seven pairs). The remaining loggers were distributed over mostly flat terrain (Table  S2 ). The position of each logger was recorded with a differential global navigation satellite system (FOIF A30 GNSS) with ±1-cm accuracy. The loggers were collected in early July 2018, and GST data were retrieved. The second year of data was excluded from the analysis because the loggers were removed in early July 2018, shortening the representation of summer GSTs. Of the 100 loggers deployed, seven were lost or broken, leaving 93 available for analysis.
| Environmental variables, topoclimatic variables, and meteorological data Environmental variables including vegetation type and cover, soil moisture, and texture were measured sporadically next to all temperature loggers during summers of 2016 and 2017, while snow depth was measured during late winter 2017 (Table  1 ). The portion of ground covered either by bare soil or by vegetation was estimated visually within a 2 Â 2-m plot centered around each logger in summer 2017 using cover classes (0-1%, 1-5%, 5-10%, 10-25%, 25-50%, 50-75%, >75%). Vegetation cover was measured separately for deciduous shrubs, evergreen shrubs, graminoids, forbs, mosses, lichens, and cryptogamic crust. To simplify the statistical analyses, the measured vegetation types were merged into three groups: vascular plants (including deciduous shrubs, evergreen shrubs, graminoids, and forbs), mosses, and lichen-cryptogamic crust.
ACKNOWLEDGEMENTS This research was funded by the  Natural Sciences and Engineering Research Council of Canada , Grants  RGPIN-2015-03844  ( C. Kinnard ) and  RGPIN-2015-05319  ( E. Lévesque ); the  Canada Research Chair program , Grant  231380  ( C. Kinnard ); and the  Centre de Recherche sur les Interactions Bassins Versants-Ecosystèmes Aquatiques (RIVE)  ( H. Mohammadzadeh Khani ). Logistical support was provided by the  Polar Continental Shelf Program  ( Natural Resources Canada ). Support from the  Community of Pond Inlet, Parks Canada , and the  Centre d' Etudes Nordiques (CEN)  is acknowledged. We thank  Prof. Gilles Gauthier  for his logistical management of the research camp and  Maria Peter ,  Matthieu Loyer ,  Denis Sarrazin , and  Audrey Roy  for their participation in fieldwork.
Our ANME-1c MAGs represent two different genera, 'Candidatus Methanoxibalbensis' and 'Candidatus Methanospirare' within the same family with an average nucleotide identity of 76%, represented by species 'Candidatus Methanoxibalbensis ujae' (species 1) and 'Candidatus Methanospirare jalkutatii' (species 2, Methods). Based on genome coverage, these two ANME-1 species were the most abundant organisms in rock samples 12,019 and NA091. 008, whereas they were hardly detected in rocks 11,868 and 11,719 and in hydrothermal sediments (Fig. 2a ). So far, all ANME-1c MAGs and 16S rRNA gene sequences from the National Center for Biotechnology Information (NCBI; https://www. ncbi. nlm. nih. gov/) and SILVA (https://www. arb-silva. de/) databases have originated from hydrothermal environments, specifically the sediments of Guaymas  11, 22  and Southern Pescadero basins  23.
Article https://doi. org/10. 1038/s41564-022-01297-4 12,019 -original rock https://doi. org/10. 1038/s41564-022-01297-4 NA091. 008 -original rock ANME-1 S7_bin35 ANME-1 S6_bin16 ANME-1 THS ANME-1 Co_bin222 ANME-1 SpSt-1198 ANME-1 UWMA-0191 ANME-1 SA_02444 ANME-1_WYZ-LMO12 ANME-1_WYZ-LMO13 ANME1_FW4382_bin035 ANME1_NA091. 008_bin2 ANME1_FW4382_bin044 ANME-1_B22_G9 ANME1_FW4382_bin126 whereas Chaacviridae and Ixchelviridae were unified within the order that we name Coyopavirales and do not encode MCPs with sequence homology to other known viruses. However, structural modelling of the candidate MCPs conserved in 'Chaacviridae' and 'Ixchelviridae' using AlphaFold2  38  and RoseTTAFold 39 revealed unambiguous similarity to the MCPs with a DJR fold (Fig. 4g ). Phylogenetic analysis revealed that these DJR MCPs form three highly divergent groups, MCP-1-3 (Fig.
008 (ref. 34). Additional sampling information can be found in Supplementary Table  1. Mineralogical analysis by X-ray Powder Diffraction (XRD) identified barite in several of these samples, collected from two locations in the Auka vent https://doi. org/10. 1038/s41564-022-01297-4 field, including on the western side of the Matterhorn vent (11,719, NA091. 008), and one oil-saturated sample (12,019) recovered from the sedimented flanks from the southern side of Z vent. Our analysis also includes metagenomic data from two sediment cores from the Auka vent field (DR750-PC67 and DR750-PC80) collected in April 2015 with the ROV Doc Ricketts and R/V Western Flyer (MBARI2015), previously published (ref. 23).
The density-separated cells were filtered on 25 mm polycarbonate filters with a pore size of 0. 22 µm (Millipore GTTP2500), and rinsed using 1× PBS. Fluorescence in situ hybridizations were carried out as described previously 7 using a 1:1 mixture of an ANME-1 targeted probe (ANME-1-350 9 labelled with Cy3) and the general bacterial probe mix EUB338 I-III (https://probebase. csb. univie. ac. at/), labelled with Alexa-488 in a 35% formamide solution (VWR EM-FX0420-8). Hybridized samples were imaged using a ×100 objective using a Zeiss Elyra structured illumination microscope with the Zen Black software.
Genomic relative abundance analysis We used the software coverM v. 0. 5 (https://github. com/wwood/Cov-erM) to calculate the genomic relative abundance of the different organisms of our samples, using all the MAGs we have extracted from our metagenomic analysis. We ran the software with the following additional parameters for dereplication ('-dereplication-ani 95dereplication-prethreshold-ani 90-dereplication-precluster-method finch'). Results were visualized in R v. 4. 2. 1.
The 16S rRNA gene phylogenetic tree was calculated for the 16S rRNA genes predicted from our genome dataset that were full length. We included these full-length 16S rRNA genes in the SILVA_132_SSURef_ NR99 database  67  and with ARB v. 6. 1 (ref. 68) we calculated a 16S phylogenetic tree using the maximum-likelihood algorithm RAxML with GTRGAMMA as the model and a 50% similarity filter. In total, 1,000 bootstrap analyses were performed to calculate branch support values. The tree with the best likelihood score was selected. For the construction of the hydrogenase phylogenetic tree (Supplementary Table  6 ), we used the predicted protein sequence for the large subunit of the NiFe hydrogenase present in the genomes of our dataset (Supplementary Table  2 ), a subset of the large subunit hydrogenases present in the HydDB database  30  and the predicted hydrogenases present in an archaeal database using the COG motif for the large NiFe hydrogenase (COG0374) with the Anvio v.