dataesr/scientific-paragraphs-categorization huggingface dataset:
H. Mackay et al. :  The 852/3 CE Mount Churchill eruption 2. 4 Northern Hemisphere (NH) tree-ring summer temperature and drought reconstructions NH summer (JJA) temperatures in the 850s were reconstructed using 13 NH tree-ring width and 12 maximum latewood density chronologies from the NVOLC v2 dataset  (Guillet et al. , 2017 (Guillet et al. , , 2020)). Full details of the nested principal component regression (PCR) used to reconstruct NH JJA temperature anomalies (with respect to  and the model calibration are provided in Appendix C. To place the summer temperature anomalies within the context of climate variability at the time of major volcanic eruptions, we removed longer timescale variations by filtering the final reconstruction, which involved calculating the difference between the raw time series and the 31-year running mean.
Pollen vegetation reconstructions The 9th century in Ireland was a time of significant socioeconomic reorganisation and possibly population decline  (Kerr et al. , 2009; McLaughlin et al. , 2018; McLaughlin, 2020). To investigate the extent to which these events may have been driven by the effects of either the 852/3 CE eruption or the transition to the MCA, we compiled land-use proxy data from five pollen records (Fig. 2e ) that included the Churchill (AD860B) tephra as a chronological tie-point  (Hall, 2005; Coyle McClung, 2012; Plunkett, unpublished data). Raw data were re-categorised by biotope, with a specific focus on the ratio of arboreal pollen (AP) to nonarboreal pollen (NAP) and the representation (percentage of total dryland pollen) of taxa associated with pastoral or arable farming.
During this 14-year period, no other volcanic eruptions were included except the 852/853 CE Churchill eruption. An eruption occurring in the Southern Hemisphere in 853 CE is excluded in the simulations. Mann-Whitney U tests were used to test the statistical significance of changes in temperature and precipitation after the Churchill eruption. The null hypothesis of a Mann-Whitney U test states that the two datasets share the same statistical distribution derived from the same population. In this study, the distributions of the temperature and precipitation anomalies after the Churchill eruption (853, 854 and 855 CE individually) derived from all ensemble members were compared to those of the 845-852 CE pre-eruption period ensemble. We assume that changes in temperature and precipitation after the eruption are statistically significant if the null hypothesis of the Mann-Whitney U test is rejected at a 5 % confidence level.
PCs with eigenvalues > 1 were included as predictors in multiple linear regression models calibrated on JJA temperature  (1805 ( -1972 CE) CE)  extracted from the Berkeley Earth Surface (BEST) dataset (http://berkeleyearth. org/data/, last access: 5 June 2021). We assessed the robustness of each model using a split calibration-verification procedure using a bootstrap approach repeated 1000 times. We computed the final reconstruction of each nest as the median of the 1000 realisations. The final 500-2000 CE reconstruction combines the 23 nests with their mean and variance adjusted to be identical to the 1230-1972 CE most replicated one. To place the summer temperature anomalies within the context of climate variability at the time of major volcanic eruptions, we removed longer timescale variations by filtering the final reconstruction, which involved calculating the difference between the raw time series and the 31-year running mean.
The target field (predictand) used for the reconstruction is the BEST JJA gridded temperature dataset (1 • × 1 • latitudelongitude grid). We divided the NH into 11 subregions defined according to the spatial distribution of the 25 tree-ring records and their correlation. Chronologies were grouped in the same subregion when their correlation coefficients over their overlapping period exceeded 0. 3. Only one chronology was included in the Quebec, western and central Europe, Siberia -Taymir, Siberia -Yakutia, and China -Qilian Mountains subregions. In these clusters, we used an ordinary least square regression to reconstruct JJA temperatures. In the other subregions such as western and central Europewhich includes five tree-ring width and maximum late-wood density records -we used the nested PCR approach (see above) to reconstruct gridded summer temperature anomalies.
Based on this approach, we reconstructed robust temperature anomalies back to 500 CE for 3486 NH grid points. Appendix G: Eruption information for tree-ring-inferred coldest years between 500 and 2000 CE Table  G1. Top 30 coldest years during the period of 500-2000 CE based on tree-ring temperature anomalies filtered using a 3-year mean and corresponding eruption information for proximal calendar years. Eruption dates and volcanic stratospheric sulfate injection (VSSI) estimates taken from the eVolv2k reconstruction  (Toohey and Sigl, 2017) , representing the most immediate preceding eruption in the dataset. Roman font: eruption occurred within 2 years of the coldest reported year; italic font: eruption occurred more than 2 years before the coldest reported year. Unidentified eruption: UE.
Acknowledgements. This paper benefitted from discussion at events of the  Past Global Changes (PAGES)  working group " Volcanic Impacts on Climate and Society " (VICS) as well as with  Angus M. Duncan  and  Richard J. Payne. PAGES is supported by the  Chinese Academy of Sciences (CAS)  and the  Swiss Academy of Sciences (SCNAT). Helen Mackay  and  Matthew Amesbury  were supported by the  UK Natural Environment Research Council  ( PRECIP  project grants  NE/G019851/1 ,  NE/G020272/1 ,  NE/G019673/1  and  NE/G02006X/1  and  MILLI-PEAT  project grant  NE/1012915/1 ). A Quaternary Research Association New Research Workers award was granted to  Helen Mackay  and the  NERC Radiocarbon Facility   NRCF010001  (allocation numbers  1744. 1013  and  1789. 0414 ). Christophe Corona and  Markus Stoffel  were supported by the  Swiss National Science Foundation Sinergia project CALDERA  (grant agreement no.
Thomas J. Aubry acknowledges support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 835939  and from  Sidney Sussex College  through a  Junior Research Fellowship. We are grateful to all reviewers for their constructive comments and valuable suggestions. Financial support. This research has been supported by the  Natural Environment Research Council  (grant nos. NE/G019851/1 ,  NE/G020272/1 ,  NE/G019673/1 ,  NE/G02006X/1  and  NE/1012915/1 ), the  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung  (grant nos. CR-SII5_183571 ,  200020_172745  and  200020_200492 ), the  National Science Foundation  (grant no. 1824770 ), the  Irish Research Council  (grant no. IRCLA/2017/303 ) and the  H2020 European Research Council  (grant nos.
Data availability. The volcanic forcing data are accessible online: https://doi. org/10. 5281/zenodo. 5725495 (Kim et al. , 2021b). The Community Earth System Model Ensemble Simulation data are accessible online: https://doi. org/10. 5281/zenodo. 6545625 (Kim and Raible, 2022)  Testate amoeba data are available on NEOTOMA: https://apps. neotomadb. org/explorer/ (last access: 12 May 2022; https://doi. org/10. 21233/B7SC-4515, Mackay et al. , 2022a; https://doi. org/10. 21233/VBAE-XM49, Mackay et al. , 2022b; https://doi. org/10. 21233/0FNY-EP27, Mackay et al. , 2022c; https://doi. org/10. 21233/P27F-GH47, Mackay et al. , 2022d; https://doi. org/10. 21233/RCPG-WB72, Mackay et al. , 2022e; https://doi. org/10. 21233/12S2-FD03, Mackay et al. , 2022f). The pollen data will be available when the extended records are published.
Material Availability All unique/stable reagents generated in this study are available from the lead contact without restriction. Data and code availability d All data reported in this paper will be shared by the lead contact upon request. This paper analyzes existing, publicly available data whose accession numbers are listed in the key resources EXPERIMENTAL MODEL AND SUBJECT DETAILS Mouse G4 ESCs (XY, 129S6/SvEvTac x C57BL/6Ncr F1 hybrid) were grown as described previously on a mitomycin-inactivated CD1 mouse embryonic fibroblast feeder monolayer on gelatinised dishes at 37 o C, 7. 5% CO 2  (Andrey and Spielmann, 2017; George et al. , 2007). CTCF-AID-GFP and Rad21-AID-GFP E14 ESCs were cultured feeder-free on gelatinised dishes at 37 o C, 7. 5% CO 2. All ESCs were cultured in ESC medium containing knockout DMEM with 4,5 mg/ml glucose and sodium pyruvate supplemented with 15% FCS, 10 mM Glutamine, 1x penicillin/streptomycin, 1x non-essential amino acids, 1x nucleosides, 0.
Bisulfite-cloning sequencing Genomic DNA from E11. 5 forelimbs was obtained using Quick-DNA/RNA Microprep Plus Kit. Bisulfite conversion was performed on 1mg of DNA using the EpiTect Bisulfite Kit. Bisulfite-treated DNA was PCR amplified by nested PCR at the Zfp42 promoter and subsequently cloned into a pbluescript vector and sequenced. 10-20 clones from 2 replicates per samples were Sanger sequenced and a total of 12 CpG were analysed with BiQ Analyzer software  (Bock et al. , 2005).
QUANTIFICATION AND STATISTICAL ANALYSIS RNA-seq differential expression analysis Single-end, 100 bp reads from Illumina sequencing were mapped to the reference genome (mm10) using the STAR mapper (splice junctions based on RefSeq; options: --alignIntronMin20 --alignIntronMax500000 --outFilterMismatchNmax 10). Differential gene expression was ascertained using the DESeq2 package  (Love et al. , 2014). The cut-off for significantly altered gene expression was an adjusted P value of 0. 05.
Single cell RNA-seq The expression of Triml2, Zfp42, and Fat1 genes was investigated in three sc-RNAseq datasets of early mammal development, whole placenta  (Marsh and Blelloch, 2020) , whole embryo gastrulation  (Pijuan-Sala et al. , 2019) , and whole embryo organogenesis  (Cao et al. , 2019). For visualization, we used the originally reported Uniform Manifold Approximation and Projection (UMAP) embeddings for the whole placenta and the gastrulation datasets and the t-Distributed Stochastic Neighbor Embedding (tSNE) for the organogenesis dataset. Likewise, we used the reported cell type definitions for visualization. For the whole placenta dataset, we used the ''integrated_snn_res. 0. 6'' cell variable to color cell types. UMI counts for Triml2, Zfp42, and Fat1 were plotted for all datasets in the range 0 to >2.
DamID-seq analysis Raw reads from DamID-seq experiments were mapped to the mouse mm10 reference genome using the alignment tool BWA-MEM (v. 0. 7. 12)  (Li and Durbin, 2009). The counts of mapped reads overlapping a DpnII (GATC) restriction fragment side were normalized by reads per kilobase, divided by the length of the fragment, per million mapped reads (RPKM). Based on these normalized counts the log 2 fold change between the Dam-Lamin B1 transduced samples and the respective Dam-only-encoding samples was calculated. Finally, LADs were called within 20 kb bins using HMMt which quantifies DamID signal using a modified Baum-Welch algorithm with t emissions (https://github. com/gui11aume/HMMt).
Enhancer prediction Enhancers were predicted using a series of established tools for ATAC-seq peak prediction and enhancer / promoter prediction. First, Genrich (not published, https://github. com/jsh58/Genrich/) was used to predict ATAC-seq peaks. We filtered for those that overlap a enhancer predicted by CRUP  (Ramisch et al. , 2019)  and do not overlap an annotated TSS (UCSC) or a promoter predicted by eHMM  (Zehnder et al. , 2019). Enhancer conservation analysis ATAC-seq peaks and predicted enhancers were projected between mouse, opossum and chicken using a published stepped pairwise sequence alignment approach across multiple bridging species  (Baranasic et al. , 2021). For a genomic region with conserved synteny, any non-alignable coordinate can be approximately projected from one genome to another by interpolating its relative position between two alignable anchor points.
For compartment analysis, hic-files were converted at 100kb bin size to the cool format using hic2cool (v0. 8. 2) (https://github. com/ 4dn-dcic/hic2cool) and balanced using cooler (v0. 8. 5)  (Abdennur and Mirny, 2020). Afterwards, compartment analysis was performed using cooltools (v0. 3. 0) (https://github. com/open2c/cooltools) and using the GC content as reference track. TADs were identified by applying TopDom v. 0. 0. 228 on 50-kb binned and KR-normalized maps using a window size of 10  (Shin et al. , 2016). Insulation scores were calculated using Cooltools (https://github. com/open2c/cooltools/tree/v0. 4. 1).
, 2020 )normalized counts-per-million for each sample. As many of the sample in the FANTOM CAGE data were highly correlated (due e. g. to being replicates or adjacent time points), we performed hierarchical clustering on the 829 remaining datasets, and then merged libraries with a pearson correlation of 0. 95 or greater, resulting in a final 349 metasamples. Co-expression between two genes was then defined as pearson correlation across these 349 metasamples. To identify housekeeping genes (Figure  S6B ), we replicated the procedure used by FANTOM previously  (Consortium et al. , 2014). Here, the 2D density of median and maximum normalized expression over all samples is first plotted, and then setting a cutoff on median expression that separated ubiquitous from non-ubiquitous genes. To assess the relationship between co-expression and linear gene distance separation or TAD co-occupancy and co-expression we next identified TADs in ESCs, E11.
WGBS processing Raw reads were subjected to adapter and quality trimming using cutadapt (version 2. 4; parameters: -quality-cutoff 20 -overlap 5 -minimum-length 25; Illumina TruSeq adapter clipped from both reads), followed by trimming of 10 nucleotides from the 5' end of the first read, 15 nucleotides from the 5' end of the second read and 5 nucleotides from the 3' end of both reads  (Kechin et al. , 2017). The trimmed reads were aligned to the mouse genome (mm10) using BSMAP (version 2. 90; parameters: -v 0. 1 -s 16 -q 20 -w 100 -S 1 -u -R)  (Xi and Li, 2009). Duplicates were removed using the 'MarkDuplicates' command from GATK (version 4. 1. 4. 1; -VALIDATION_STRINGENCY=LENIENT -REMOVE_DUPLICATES=true)  (McKenna et al. , 2010). Methylation rates were called using mcall from the MOABS package (version 1.
Three body overlaps shown in Figures  S4E  and S4G  involving Zfp42R or Fat1 with D1+D2, are defined as: overlap 123 = ðA 12 + A 13 Þ=ðA 1 + A 2 + A 3 À A 12 À A 13 À A 23 Þ, where object 1 can be Zfp42R or Fat1. As for 3D distances, overlap values are averaged over the last 20 frames of each simulation. Analogously, a geometric mapping factor of 1. 2 is found when comparing with pairwise experimental medians. Sphericity is defined using the standard formula: sphericity = ðp 1=3 ð6VÞ 2=3 Þ =A, where A and V are area and volume of the object respectively. Area and volume are estimated from the coordinates of the polymer beads belonging to the region under consideration by means of a 3D convex hull approximation, computed with the Python package scipy. spatial. Sphericity measurements can be viewed in Figures  S4E  and S4H.
Polymer graphics Polymer 3D snapshots shown in Figures  3  and S4  are representative single molecule structures taken from real MD simulations. Regions corresponding to Fl1, D1, Zfp42R, D2, Fat1, Fl2 are differently colored. A slice of the simulated NE is rendered as a thick spherical wall colored as in FISH imaging. To clarify the relationship between the polymer and NE, each image is presented from the same point-of-view through a geometrically calibrated 3D rotation matrix. For visual purposes, polymers are shown in a coarse-grained version of a smooth third-order polynomial spline passing through bead coordinates. See Table  S5  for a summary of statistical measurements from polymer modelling. Oligopaint FISH image analyses Image analysis was performed using Fiji and MATLAB (R2018-2019 and image processing toolbox).
ACKNOWLEDGMENTS We thank  Guillaume Andrey  and the entire  Mundlos lab  for input and discussions,  Elphege Nora  for kindly providing Rad21-and  CTCF-AID-GFP ESCs ,  Robin van der Weide  for aiding LAD calling;  Katja Zill  for mouse line maintenance,  Ute Fischer  and  Asita Stiege  for genotyping and cloning assistance,  Carola Dietrich  for Hi-C sample processing,  Norbert Brieske  for WISH assistance,  Vanessa Suckow  for western blot assistance,  Karsten Beyer  for manufacturing a cover slip carrier, and  J. Fiedler  and  K. Macura  ( MPIMG transgenic unit ) for aggregations, and  Myriam Hochradel  for cHi-C processing (MPIMG sequencing core facility). We thank the  Montpellier Ressources Imagerie facility  ( BioCampus Montpellier ,  Centre National de la Recherche Scientifique [CNRS], INSERM, University of Montpellier ) and for computer resources from CINECA (ISCRA grant thanks to computer resources from  INFN  and  CINECA [ISCRA  Grant  HP10C8JWU7 ]).
G. C. , Q. S. , and F. B. were supported by a grant from the  European Research Council  (Advanced Grant  3DEpi ,  788972 ) and by the  CNRS. This work was funded by  EMBO  and the  Wellcome Trust  ( ALTF1554-2016  and  206475/Z/17/Z ; to M. I. R. ) as well as the  Deutsche Forschungsgemeinschaft  ( KR3985/7-3  and  MU 880/16-1  to S. M. ).
5 limbs and ESCs Anti-DIG-AP, 150 U rabbit anti-DNMT3A Ribonuclease A from bovine pancreas, ChIP-seq in wildtype mouse ESCs Type 1-A, RNase A Rnase Inhibitor (2000 U) ATAC-seq in wildtype mouse E11. 5 limbs Diagenode Diagenode Merck Millipore Thermo Fisher This study Fisher Bioreagents This study Roche Diagnostics This study Merck Millipore SIGMA-ALDRICH This study Abcam Sigma Aldrich R&D Systems R&D Systems This study Roche Diagnostics Abcam Sigma Aldrich This study Roche Diagnostics This study C15410174 C15410037 51985026 GEO: GSE185775 GEO: GSE185765 10003927 GEO: GSE185766 1000144 07-449 P8833 GEO: GSE185768 07-473 ab160486 #423-F8-025/CF GEO: GSE185771 #1324-WN-010/CF 40839 11093274910 ab188470 lot GR224165-2 R4875 GEO: GSE185772 3335399001 GEO: GSE185774 rabbit antiDNMT3B Roti-Phenol/ Chloroform/ Isoamylalcohol Cell Signaling Carl Roth cs48488, lot 1 A156.
Next, we will build upon the work of Fink and Hannam  47  on Boolean compositions and augment their approach for counting the number of possible BFs under Boolean compositions by accounting for the fact that the different input variables are distinguishable and so are non-equivalent under permutation. We then compare the restriction in the logic rules in gene regulatory networks due to Boolean compositions with the restriction due to different types of biologically meaningful BFs, and thereafter analyze how often Boolean compositions display biologically meaningful properties. Finally, we evaluate the enrichment (depletion) and relative enrichment (depletion) of composed BFs in a compiled empirical dataset of 2687 BFs from published reconstructed Boolean models of biological systems.
For instance the family of TFs called auxin response factors (ARFs) includes over 20 members in numerous plants and it has been shown that they form heterodimers that activate gene transcription  54, 56. However, a quantitative assessment of the frequency at which heteromeric complexes contribute to gene regulation has not been carried out. The prevalence of such complexes in real-world gene regulatory networks can provide empirical support for the (frequent or not) occurrence of non-trivial composition structures. We obtained a list of 1325 macromolecular complexes in H. sapiens from the EBI Complex Portal database  57  , and the list of 1639 human TFs from http:// human tfs. ccbr. utoro nto. ca/ provided by Lambert et al. 58. Among the 1639 human TFs, we selected only those TFs that were reviewed in the SWISS-PROT  59  protein database, resulting in a list of 1617 human TFs that was used for further analysis.
Thus for the 1617 TFs in H. sapiens we used the JASPAR database  64  to obtain the associated TF families; JASPAR provides a manually curated list of DNA transcription factor binding motifs, the corresponding TFs, family information etc. Focusing on the TFs of the bZIP and bHLH families, we found 36 TFs in the first family and 38 in the second family (see Supplementary Table  S2 ). Although our current data suggests that TF complexes are not so prevalent, we cannot rule out that this conclusion is an artifact of insufficient experimental evidence on complexes regulating genes. A similar count of complexes involved in transcriptional regulation in Saccharomyces cerevisiae is presented in the Supplementary Information and Supplementary Tables  S3  and S4. That system leads us to a similar conclusion as the one made from complexes in humans.
Enrichments of composed BFs in reconstructed biological networks. In this section, we present the results of our analyses of the abundances of composed BFs in a compiled reference biological dataset of 2687 BFs from 88 published Boolean network models of biological systems  38. More explicitly, we do not reconstruct Table  1. Comparison of the number and fraction of BFs allowed by different composition structures, with and without including all possible permutations of input variables. The composition structures in the bipartite Boolean network framework of transcriptional gene regulation are categorized based on the number of inputs k to a gene in the corresponding unipartite Boolean network framework. The column "Number of composed BFs" gives the number of distinct BFs in a composition structure, and the subcolumns provide a comparison of the number of such BFs both without and with the accounting for all possible permutations of the input variables.
The column "Fraction of composed BFs" gives the fraction of distinct BFs in a composition structure among all possible BFs for a given number k of inputs, and the subcolumns provide a comparison of the fraction of such BFs both without and with the accounting for all possible permutations of the input variables. the composition structures associated with each of the 2687 BFs in our database, but rather determine which composition structure each of the 2687 BFs belong to, by comparing the real BFs with the composed BFs of various composition structures. In the "Methods" section, we provide details about the compilation and curation of this empirical dataset. To begin, we computed two proportions for each possible composition structure. The first is the proportion of BFs with k inputs in the reconstructed biological networks that belong to the given composition structure (bar plots in Fig.
4a ). The second is the corresponding proportion in the "random ensemble" with k inputs; that proportion is thus given by the number of BFs with k inputs that are compatible with the given composition structure, divided by the total number of BFs (black dots in Fig. 4a ). The results show that the proportions of composed BFs in the reference biological dataset are larger than in the ensemble of random BFs, for each composition structure, indicating that non-trivial composed BFs are enriched in real biological networks. Note that the sets of BFs allowed by different composition structures overlap with each other (see Fig. 3 ), allowing for the sum of the height of the bars in Fig. 4a  to be larger than 1. To consider this question in greater depth, we define the "enrichment factor" as the ratio of the first and the second proportions.