dataesr/scientific-paragraphs-categorization huggingface dataset:
The same procedure was performed for the 18 marine biophysical predictors resulting in the following least correlated subset: bathymetry, east/west aspect (biogeo1), north/south aspect (biogeo2), plane curviture (biogeo3), distance to shore (biogeo5), bathymetric slope (biogeo6), concavity (biogeo7), sea surface salinity of the saltiest month (bio-geo10), annual variance of sea surface salinity (bio-geo12), sea surface temperature of warmest month (biogeo15), and annual range of sea surface temperature (biogeo16). Biotic predictors included host identity (host class or host phylogeny), host trophic diet (for internal dataset), external surface type, and immune system complexity. Either host class or a numeric nMDS proxy of host phylogeny was used to represent host identity. Host class information was obtained from the Encyclopedia of Life database (https://eol.
org/). The host phylogeny proxy was created via the following steps. First, using timetree. org, we recovered a time-calibrated phylogenetic tree of host species represented in our study  [128]. Second, patristic pairwise distances, i. e. , branch lengths separating taxa, were then calculated with the Ape and Adephylo packages in R  [129, 130]. Third, we performed nonmetric multidimensional scaling (nMDS), constrained to one dimension, on the patristic distance matrix in SPSS v24 (IBM Corp, Armonk, NY, USA) to create a univariate host phylogeny variable for use in model-based analyses. The nMDS proxy phylogeny variable was chosen because it captured the phylogenetic relationships among hosts and is a numeric variable available for path analyses. Host trophic diet levels was estimated based on the diet category, including primary producers (level 0), herbivores (1), omnivores (2), carnivores (3), and scavengers/detritivores (4).
Analysis of alpha diversity Model selection procedures implementing generalized linear models (GLMs) and comprehensive path models were used to analyze alpha diversity for each of our datasets. We performed GLM model selection procedures to identify which of our main biotic and abiotic variables best predicted richness, measured as number of sOTUs and Faith's Phylogenetic Diversity for each of our three main datasets. More specifically, for internal, external, and marine microbiomes, we screened a total of 25 explanatory variables, including host phylogeny/ identity, trophic diet (internal only), external habitat types (marine only), absolute latitude, elevation, normalized index vegetation index (NDVI; external only), 8 least cross-correlated bioclimatic variables (internal and external only), and 11 least cross-correlated ocean biophysical variables (marine only; see above for least correlated sets).
Competing models were ranked based on the Akaike Information Criterion (AIC), and we reported the most parsimonious model for each of our three datasets for each response variable. Multicollinearity was evaluated in each of the final models using the variance inflation factor (VIF) calculated with car package  [131]  in R. We applied path analyses with maximum Wishart likelihood (500 iterations) to test for the relative strength of direct and indirect associations among abiotic/biotic predictors and microbiome diversity of our internal and external microbiome datasets. Path models were not built for the marine dataset due to low predictive power of included variables in the GLM model selection. We built models including the most parsimonious set of explanatory variables predicting microbiome diversity according to our GLM model selection procedures (see above).
Analysis of beta diversity We performed permutational multivariate analysis of variance (PERMANOVA) to analyze microbial beta diversity and identify abiotic and biotic parameters influencing internal, external, and marine microbiomes. Weighted and unweighted Unifrac distances were calculated in QIIME, and PERMANOVA models were implemented in R with the adonis2() function in the vegan package  [133]. Independent models were run for the internal, external, and marine datasets. Predictor variables included in beta diversity models were hypothesis-driven and included all variables from the best alpha diversity model for the given dataset as well as selected biotic factors. We visualized overarching patterns in microbial beta diversity through principal coordinates analysis using ggplot2 in R. Additionally, we visualized the microbial abundance of major bacterial phyla across host trophic diets.
Functional analysis with PICRUSt Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) predicts metagenomic function using marker gene (such as 16S rRNA) surveys  [21]. GreenGenes 13-5 OTU IDs are required for PICRUSt analyses; therefore, we clustered our internal microbiome dataset sOTUs to Greengenes OTU IDs at 97% similarity in QIIME. A custom R script was used to combine these GG IDs with abundance information to create a PICRUSt-formatted OTU table. We then performed the following steps: normalization by copy number, metagenome prediction, and consolidation of predictions into KEGG pathways. Results were filtered to retain data with an NSTI score of 0. 06 or lower; 247 internal samples were represented in the final analysis. Nearest Sequenced Taxon Index (NSTI) is a confidence measure for PICRUSt predictions.
Analyses on immune system complexity We assessed the effect of immune system complexity on microbial diversity by (a) comparing sOTU richness and phylogenetic diversity between samples from hosts with adaptive vs. innate only immune systems for both the internal and external dataset-pairwise comparisons were implemented with Wilcoxon tests in R; (b) performing a correlation test between microbial richness and immune complexity using Kendall Tau correlation statistics in R; and (c) running our comprehensive path model including the immune complexity scale variable derived from Flajnik  [68].
Results Socio-demographics are presented in Table  1. Among the 83 dyads included in the study, three were removed from analyses because of poor data quality. Also, one child dropped out of the experiment prior to the direct expression test. Therefore, observational fear learning was tested on 80 children, whereas direct expression, fear extinction learning, and direct retention (Day 2) were tested on 79 children. Fear conditioning analyses were conducted on 72 parents, since one parent's electrodermal activity data was unusable. Parents. Fear conditioning. During the direct fear conditioning phase, parents' SCRs were larger for the conditioned stimulus paired to a shock (CS +) than the one that was not (CS -) [F(1,71) = 45. 37, p < 0. 001, d = 0. 53], which confirmed successful fear conditioning.
They were explained why deception was used and why maintaining uncertainty about shock delivery was necessary to induce conditioned fear responses. Psychophysiological recordings and data processing. Skin conductance recordings. Skin conductance levels (SCL) were recorded continuously during all phases of the paradigm (MP160 recording system, Biopac Systems Inc. , Goleta, CA). Skin conductance responses (SCRs) were obtained by subtracting the mean SCL during the 2-s interval prior to CS onset from the maximum SCL (peak) obtained during the 6-s interval following CS onset. SCR data were then square-root transformed. Physiological synchrony. Physiological synchrony analyses were performed for the conditioning session (when parents and strangers underwent direct conditioning and when the children viewed both videos, i.
e. , observational learning stage). The raw electrodermal activity signal recorded during this session was filtered with a 0. 05-1 Hz passband and downsampled to 8 Hz. This signal was z-scored to ensure comparability between parents and children as well as between strangers and children. Here, we used cross-recurrence quantification analyses (CRQA) as a proxy for physiological synchrony. These analyses were performed in R with the crqa package  44. Basically, CRQA is used to quantify the time-dependent coupling of two signals  45. This analysis yields a cross-recurrence plot (see Fig. 3b  for an example), where blue dots represent states of recurrence between two-time series. To be considered as recurrent, points must be within a given radius. Using the optimizeParam function, parameters (radius, delay, embed dimension) were set individually for each pair to provide a mean recurrence rate between 2 and 4%  26.
Another parent-child dyad was excluded because of poor data quality for the parent. Physiological synchrony analyses were therefore performed on 76 parent-child dyads. Eight children were excluded from stranger-child synchrony analyses, because of data acquisition problems during the viewing of the stranger's video. Physiological synchrony between strangers' and children's electrodermal activity was therefore computed for 71 children. For both parent-child and stranger-child analyses, we performed a principal component analysis (PCA) using varimax-rotation on these four metrics to reduce the number of analyses  26, 46. We used the resulting first factors (PC1 Parent-child, PC1 Stranger-child) of this analysis to predict children's CS + and CS -responses during the direct expression test.
Data analysis. To assess fear conditioning in parents, SCRs were analyzed with a mixed ANOVA with the within-subjects factor Stimulus (CS + , CS -) and the between-subjects factor Parent's sex (male, female). In children, SCRs during the observational learning phase were analyzed with a mixed ANOVA, with the withinsubjects factors Stimulus (CS + , CS -) and Demonstrator (parent, stranger), and the between-subjects factors Demonstrator's sex and Child's sex (male, female). In order to test the direct expression of fear, fear extinction learning, and the direct retention of fear within the same statistical model, we only included the first four trials of the direct retention test, so that all phases have four trials. Data from these three phases were assessed with a mixed ANOVA, with the within-subjects factors Phase (direct expression test, fear extinction learning, direct retention test), Stimulus (CS + Parent, CS + Stranger, CS -) and Trials (4 trials), and the between-subjects factors Parent's sex and Child's sex (male, female).
Significant interactions were decomposed with subsequent ANO-VAs and post-hoc Bonferroni tests. Greenhouse-Geisser corrections were applied when sphericity assumption was not met. To assess the role of physiological synchrony in fear learning, we conducted Pearson correlations between both the Parents' and Strangers' PC1 and children's SCRs for the first trial of each stimulus type dur-ing the direct expression test. To compare correlations coefficients between conditions, we used the procedure of Hittner et al. 47  , which is a modification of Dunn and Clark 48 using a backtransformed average Fisher Z procedure  49. This test is implemented in the cocor (https ://compa ringc orrel ation s. org) online software  50.
Acknowledgements This work was supported by the  Fonds de recherche du Québec -Santé , through a career award to  Marie-France Marin  (# 251601 ;  265447 ) and a Master's scholarship to  Alexe Bilodeau-Houle  (# 260391 ), and by the  Canada Foundation for Innovation  (# 36210 ) and the  Fondation de l'Institut universitaire en santé mentale de Montréal. We would like to express our gratitude to every parent-child dyad who participated in this project. We want to thank  Nathan Sévigny ,  Maxime Mariscalchi ,  Mélanie Bouliane ,  Yasmine Zerroug ,  Maryse Arcand ,  Clémence Peyrot  and  Valérie Bouchard  for helping with data collection as well as  Nadia Durand et Pierre Orban  who both served as the stranger adults. We also thank  Philip Pärnamets  who help us with the R code and the parameters for physiological synchrony.
Chlorophyll Measurement and Microscopy Analysis of Chloroplasts Chlorophylls were extracted following the method described by  Lichtenthaler and Wellburn (1983). Chlorophyll content was calculated as chlorophyll micrograms per fresh weight grams. For chloroplast observation in living plant tissues, wild-type and mutant cotyledons at the same development stage were cut into tissue pieces of 1 cm length × 1 cm width, and they were examined under a DM2000 LED bright field microscope (Leica) equipped with a DFC420 digital camera (Leica). Confocal laser scanning microscopy images were obtained using an Eclipse Ti-E (Nikon) inverted microscope selecting the 650-to 720-nm filter. Images were captured with a C2plus (Nikon) camera using the Nikon NIS-Elements C software. For transmission electron microscopy (TEM) analysis, tissue samples of 2 mm length × 2 mm width were fixed in a solution of 2.
(2017). The cloning of genomic sequences flanking the wls-2372 T-DNA insertion was performed with an anchor-PCR assay as previously described  (Garcia-Alcazar et al. , 2017). Primers used for this purpose are detailed in Supplementary Table  1. PCR amplification products obtained with this assay were purified using a GenElute PCR Clean-up Kit (Sigma Aldrich) and sequencing reactions and electrophoreses were carried out with ABI PRISM BigDye Terminator Cycle Sequencing kits on an ABI PRISM 3500 Genetic Analyzer (Applied Biosystems), respectively. The cloned sequences were compared with the SGN Database (https://solgenomics. net) to map the T-DNA insertion site to the tomato genome. The co-segregation analysis was done by PCR amplification using the VIPP1-F1 and VIPP1-R1 primer pair (to amplify the wild-type allele), and with the VIPP1-F1 and T-DNA-LB T-DNA specific primer pair (to amplify the mutant allele).
Gene Constructions and Transgenic Lines To silence the VIPP1 gene, an interference RNA (RNAi) construct was generated as described in  Perez-Martin et al. (2018) , with some modifications. A 326-bp fragment of Solyc11g008990 cDNA was amplified using the VIPP1-RNAi_F/R primers and cloned in sense and antisense orientations into the pKannibal vector, as described in  Wesley et al. (2001). The plasmid obtained in this way was digested with NotI and the resulting restriction fragment was subcloned into the pART27 binary vector  (Gleave, 1992). CRISPR/Cas9 editing was performed as described in  Yuste-Lisbona et al. (2020). To design the VIPP1 gRNA target sequence, we used the Breaking-Cas web software (https:// bioinfogp. cnb. csic. es/tools/breakingcas/) (Oliveros et al. , 2016). To obtain the 35S pro : SlVIPP1 overexpression construct, the VIPP1 complete open reading frame was PCR amplified from tomato cv.
Bioinformatic Analysis Sequences of VIPP1 and PspA proteins were obtained from UniprotKB (https://www. uniprot. org/) and Phytozome v12. 1 (https://phytozome. jgi. doe. gov/pz/portal. html) databases, or using BLASTP (https://blast. ncbi. nlm. nih. gov/Blast. cgi; Altschul et al. , 1997) , with default settings. Multiple amino acid sequence alignments were obtained using the ClustalW algorithm (http:// www. ebi. ac. uk/Tools/msa/clustalo/; Larkin et al. , 2007) , and shaded with BOXSHADE3. 21 (http://www. ch. embnet. org/ software/BOX_form. html). A phylogenetic tree of VIPP1/PspA homologs was constructed using MEGA 7. 0 (http://www. megasoftware. net; Kumar et al. , 2016)  by the neighbor-joining method with 1,000 replications. Predictions of natively disordered regions in Arabidopsis and tomato VIPP1 proteins were obtained at the Protein DisOrder prediction System server (PrDOS; http://prdos.
The genomic region flanking the T-DNA insertion in the wls-2372 line was identified by Sanger sequencing of anchor-PCR amplified products (see Materials and Methods). The left and right borders (LB and RB) of the T-DNA were found in the 8 th and 9 th introns of the Solyc11g008990 gene, respectively. Therefore, the T-DNA insertion found in Solyc11g008990 causes a deletion of 688 bp, which includes the partial loss of its 8 th and 9 th introns and the complete loss of its 9 th exon (Figure  1D ). These results strongly suggest that wls-2372 mutant plants bear a null allele of the Solyc11g008990 gene. The genomic organization of Solyc11g008990 includes 11 exons, and it encodes a protein of 330 amino acids highly homologous to Arabidopsis VIPP1, which is described in the Solanaceae Genomics Network database (https://solgenomics.
net/) as "Phage shock protein A, PspA". In other databases, including The Arabidopsis Information Resource (TAIR; https:// www. arabidopsis. org/index. jsp), Solyc11g008990 is considered the tomato ortholog of Arabidopsis VIPP1. BLASTP analyses of the VIPP1 proteins of tomato, Arabidopsis and pea, concluded that tomato VIPP1 is 71% and 74% identical to Arabidopsis VIPP1 and pea IM30, respectively. Therefore, we will refer hereafter to Solyc11g008990 as the Solanum lycopersicum VIPP1 (SlVIPP1) gene. To establish a possible correlation between the T-DNA inserted in the SlVIPP1 gene and the albino phenotype of the wls-2372 mutant, a co-segregation PCR analysis was performed using genomic DNA extracted from 20 seedlings obtained from a sibling population segregating for the mutant phenotype. The specific genomic forward (VIPP1-F1) and reverse (VIPP1-R1) primers, whose sequences hybridize at the flanking regions of the T-DNA insertion in Solyc11g008990 (Figure  1D ), were used to amplify the wild-type allele; VIPP1-F1 and the specific T-DNA border primer (T-DNA-LB), which hybridizes at the LB of T-DNA (Figure  1D ), were used to amplify the wls-2372 mutant allele.
The predicted number of amino acids of the chloroplast transit peptide of tomato VIPP1 was 58, 64 for Arabidopsis, and 43 for pea. These values are in agreement with those already published for Arabidopsis and pea, in the UniprotKB database (https://www. uniprot. org/). Moreover, BLASTP analysis of the predicted mature proteins showed that identity reached 79. 4% between tomato and Arabidopsis, and 80. 2% for tomato and pea, respectively. These results revealed a high degree of conservation among the VIPP1 mature proteins of higher plants. The high conservation among plant VIPP1 mature proteins includes their C-tail region, which in Arabidopsis and tomato extends from residues 260 and 280, respectively. Strikingly, a short region comprissing residues 304-309 of Arabidopsis VIPP1 and 303-309 of SlVIPP1 was not conserved, although its biological significance is not known so far (Supplementary Figures  1  and 3 ).
The first region is part of the chloroplast signal peptide, as previously mentioned, while the second matches the disordered region of the C-terminal tail of Arabidopsis VIPP1 (Supplementary Figures  1  and 3 ). In our UniprotKB database analysis, the 286-330 amino acid region of SlVIPP1 was also found predicted to be disordered. We also obtained a phylogenetic tree of PspA and VIPP1 proteins from Bacteria, Chlorophytes, Bryophytes, and Embryophites. Some organisms harbor only one PspA (as Escherichia coli) or VIPP1 (as tomato) protein, others harbor both, PspA and VIPP1 proteins (as Synechocystis), and others have two (as Zea mays) or even three (as Glycine max) VIPP1 proteins. In the case of plants with two VIPP1 protein, both paralogs usually clustered, suggesting that are the result of a recent duplication and could be co-orthologs.
Furthermore, it has been described that the C-terminal extension contains an a-helical structure that is connected to the rest of the protein by a random coil spacer that begins with one leucine and one or two proline residues (LP or LPP)  (Zhang et al. , 2016) , which are the last three amino acids in the KGDLPP domain. BLASTP searches here performed with the tomato KGDLPP and the Arabidopsis KGELPP sequences in species-specific and generic protein databases revealed that this region is only present in plant VIPP1 proteins. The detailed characterization of these CRISPR/ Cas9 allelic series and the further directed mutagenesis of the KGDLPP domain will provide us a better understanding of the functional relevance of the SlVIPP1 protein domains. Exogenous Sucrose-Supply Partially Rescue the Loss of Function of the SlVIPP1 Gene Sugars are the most relevant products of photosynthesis.
In fact, the VIPP1 ortholog of Pisum sativum was identified, as previously mentioned, as a 37-kDa precursor, whose processing results into a mature protein of 30 kDa  (Li et al. , 1994). In the UniprotKB database, the amino acids 1-44 of pea IM30 and 1-64 of Arabidopsis VIPP1 are annotated as their respective transit peptides (https://www. uniprot. org/). On the other hand, transit peptides are highly heterogeneous in length (from 20 to more than 100 amino acids) and sequence, hindering its prediction in not few cases  (Jarvis and Robinson, 2004). As previously mentioned, Synechocystis sp. has two homologs of SlVIPP1: the PspA and Vipp1 paralogs. Escherichia coli, which is not a photosynthetic organism, only has a PspA protein. Both Escherichia coli and Synechocystis sp. PspA proteins lack the Cterminal extension that is present in VIPP1 proteins from photosynthetic organisms, including tomato SlVIPP1  (Westphal et al.
ACKNOWLEDGMENTS This work was supported by research grants from the  Spanish Ministry of Science and Innovation  and the  UE-European Regional Development Fund  (grants  PID2019-110833RB-C31  and  PID2019-110833RB-C32 ) and the  Research and Innovation Programme of the European Union Horizon 2020  ( BRESOV Project ,  ID 774244 ). A PhD fellowship to MG-A was funded by the  FPU Programme  of the  Spanish Ministry of Science and Innovation. The authors thank research facilities provided by the  Campus de Excelencia Internacional Agroalimentario  ( CeiA3 ).
co-expression of genes In an earlier Hi-C work by Dong and colleagues, tomato and maize genomes were shown to form a large number of long-range chromatin loops linking interstitial active chromatin regions  [51] , suggesting spatial clustering of expressed genes. Later on, the interaction networks of maize active chromatin were revealed by two research groups using the ChIA-PET method, and suggested a role for these physical interactions on gene expression  [33, 34]. Albeit the datasets from these two teams are difficult to compare due to the use of different growth conditions, tissues types, and antibodies (for ChIP)  [33, 34] , three consensus patterns can be extracted. Firstly, a substantial fraction of the identified chromatin loops connects gene loci; secondly, genes forming long-range chromatin interactions tend to show higher expression levels than those without; thirdly, gene pairs linked with chromatin loops tend to show co-expression.
Besides, multiple H3K9me-marked loci can form puncta in the nucleoplasm (v), which is likely driven by liquid-liquid phase separation mediated by plant-specific ADCP1 proteins  [72]. LAD, lamina-associate domains; NAD, nucleolus-associated domains. (b) Location of LADs and NADs loci across the Arabidopsis genome. This circos plot is generated based on domain coordinates described in  [39]  and  [41]. across LADs (c) and NADs (d). Note that the dataset describing nascent DNA strand is from a study by Sequeira-Mendes et al. [66] , in which a size cutoff (0. 3 to 2 kb) was used so that the recovered nascent strands were primarily leading strands in ORIs. This information, in turn, can be used to infer whether ORIs occur across a given genomic region evenly. For instance, the curves in (c) imply that around LAD boundary regions, ORIs fire more often outside LADs than inside.
Plant materials used for generating these datasets are partly comparable: Repli-seq, 7-day-old seedlings; nascent DNA strands, 4-day-old and 10-day-old seedlings; LADs, 10-day-old seedlings; and NADs, 3-week-old seedlings. Datasets and scripts for reproducing plots in panels (a-d) are available from figshare repository with DOI: 10. 6084/m9. figshare. 8953235. Before publication, these datasets and scripts are accessible with this private link: https://figshare. com/s/e9ec0a926a1840e2455b Protein sequences were downloaded from TAIR10 https://www. arabidopsis. org/. The identification of IDR-containing proteins was based on text mining with following criteria: R/G motifs were called if they exactly matched text string "GGRG"; Q/N motifs were called when at least 12 Q/N residues were found in a window of 30 amino acids.