dataesr/scientific-paragraphs-categorization huggingface dataset:
We then fit the CFA once for each language using lavaan  (Rosseel, 2012). The structures specified for the models came from the results of the EFA, represented by loading each of the colours that were paired together onto a common factor (only considering loadings greater than. 3). Model fit was assessed both by robust CFI and RMSEA estimates, two common measures of fit. As can be seen in Table  2 , the CFA was at least a moderate fit for all languages (see the guidelines in  Hooper et al. , 2008) , with values that are moderate to good for German and English. These results tell us that the semantic structure imposed by the natural language data is consistent with the semantic structure in the artificial language, suggesting sufficient similarity between the two such that the confirmatory model is not rejected.
63; standard deviation SD = 0. 49; English: M = 2. 25; SD = 0. 76). In French, this depended on whether the 7-factor or the 8-factor structure was used (7-factor: M = 2. 41; SD = 0. 67; 8-factor: M = 2. 44; SD = 0. 62), but was overall still more varied than for German. The analyses were performed on subsets of the data limited to pairs of the same native language that had played at least 50 trials together. We used separate logistic mixed-effects models (lme4 package in R;  Bates et al. , 2015)  for each language to test the effect of the number of boundaries on accuracy on a trial-by-trial level, controlling for the fixed effects of the number of trials a pair had played together and the number of colours in the senders' array. Random intercepts were added for participant pairs, colour arrays, and the game mode.
Data collection Participants were recruited through the platform Prolific Academic  (Peer, Brandimarte, Samat, & Acquisti, 2017)  in April-May 2020. Participants were eligible to participate if they were UK residents, aged of 18 or above, fluent in English, had access to a computer with an internet connection, and had no dietary restrictions. Participants were asked to complete the study using their laptop or computer only (i. e. not on a mobile phone or tablet device). We intended to recruit a sample stratified by gender (approx. 50% male, 50% female) and highest educational qualification (approx. 50% A-level or below, 50% above A-level). Eligible participants who completed the study received monetary compensation in return for their participation (£2. 09). The study was approved by the Health and Life Sciences Research Ethics Committee at the University of Liverpool (reference: 4612).
As secondary analyses, the primary analysis was replicated using two alternative measures of SEP (equivalised income and subjective social status). As exploratory analyses, we examined whether the measures of inhibition, working memory, healthiness and weight control motives moderated the effect of the labelling intervention on selfserved energy. 2 All statistical analyses were performed using SAS version 9. 3 (SAS Institute, Inc. , 2012 SAS® 9. 3. Cary, NC). Statistical tests level of significance was set at p < 0. 05 for main and sensitivity analyses, p < 0. 01 in secondary analyses, and p < 0. 001 in exploratory analyses to account for multiple testing, unless otherwise specified. Sample size calculation. A meta-analysis of six studies investigating the impact of kcal labelling on energy consumed from a single food did not demonstrate a statistically significant effect  (Crockett et al.
Results Small RNA sequencing of rice leaves infected with Xoo BAI3 identifies sRNA loci that are upregulated in a T3SS-dependant manner. To gain genome-wide insight on host sRNA species that may be involved in the disease process, we generated sRNA Illumina sequencing data from RNA collected from the Japonica O. sativa rice cultivar Nipponbare leaves confronted by pathogenic Xoo. Briefly, sRNAs were analyzed from leaf samples taken 24 h post-infiltration (hpi) with the wild-type African Xoo reference strain BAI3, a non-virulent T3SS mutant, which is unable to inject virulence effectors (herein referred to as BAI3H) or a water control (H2O). We inferred potential sRNA generating loci based on sRNA reads coverage profile along the Nipponbare genomic sequences, and tested for differential accumulation of sRNA reads in treatment comparisons as summarized in Supplementary Fig.
Even if overall lower, their normalized expression is not at odd with pri-miRNA loci levels (Fig. 1D ). The most highly expressed loci, namely, xisRNA023, 01, 02, 05, 07, 09 and 10 reach several thousand rpm in leaves inoculated with virulent BAI3 bacteria (Fig. 1C ). Regarding read sizes distributions, no major global bias in MIRNA genes-derived reads sizes and coverage patterns were detected (Supplementary Fig. S2 ). While this suggests that read populations in our dataset are not anomalously biased, read length distribution for xisRNA loci was broader than for MIRNA genes (Fig. 1C ). Furthermore, with a few exceptions (e. g. xiRNA059, 62, 12, 53 and 51), xisRNA read sizes ranged overall from 20 to 22 nt, which is comparable to rice siRNAs. In order to relate xisRNA loci with existing rice miRNA annotation, we asked whether they overlap with O.
sativa MIRNA gene loci annotated in MirBase but found no match. Consistent with this observation, none of the xisRNA loci was classified by ShortStack  23  as a possible MIRNA locus (category 'N14' , 'N15' or 'Y'), suggesting that their features are incompatible with their being previously undocumented MIRNA genes. We also examined the overlap of experimental sRNA loci with annotated genes and transcripts in the Rice Genome Annotation Project (MSU7 annotation)  24. As illustrated in Fig. 1E , the distribution of the overlap type with MSU loci is different for xisRNA loci as compared to other experimental sRNA loci. Notably, 58 out of 64 xisRNA loci (91%) overlap with an annotated MSU exon which is significantly greater than for other experimental sRNA loci (One-sided Fisher's Exact test adjusted p-value = 1.
BAI3 xisRNAs loci generate single sRNA duplexes. A majority of xisRNA loci overlap with exons of annotated protein-coding genes. We therefore examined the coverage profile of mapped xisRNAs signatures along the Nipponbare genome sequence in regions encompassing annotated MSU loci (see individual coverage plots in our dataverse). For example, highly expressed and typical xisRNA002 and xisRNA023 loci display a T3SS-dependant ~ 25 bp-wide coverage peak comprised of reads mapping on an annotated exon in either sense or antisense orientation (Fig. 1F, G ). To extent this observation, we inspected read mappings at these genomic locations (Supplementary Fig. S3 ) and created multiple alignments of the most abundant unique read signatures for each strand of the xisRNA loci (Fig. 1F, G ). On these alignments, the pattern of complementarity between sense and anti-sense xisRNA major reads, with ~ 2 nt 3′-overhangs, is evocative of guide-passenger strands of sRNAs duplexes resulting from DCL processing.
In the case of PXO99A, these signals were also T3SS-dependent. While xisRNA023 was not tested for the US Xo strain X11-5A, xisRNA023 signals appeared to be specific to the Xoo pathovar as these sRNAs were readily detected in response to both Asian and African Xoo strains (Supplementary Fig. S6 ). As a complementary way to establish that xisRNA induction is not limited to African Xoo infection, we exploited the dataset generated by Zhao et al. 22  where sRNAs expression temporal dynamics in Xoo PXO99 versus mock-inoculated rice leaves was profiled at early time points following leaf clipping inoculation. A second dataset consisting of sRNA sequencing data 24 hpi with the Azacytidine-resistant derivative of PXO99 strain, PXO99A from the Plant MPSS databases  25  was also included. The DE statistics of experimental sRNA loci whose expression changed in relevant treatment comparisons and that overlapped with BAI3 xisRNA loci are summarized in Fig.
2B. Coverage profiles of sRNA reads in xisRNA loci further indicate that PXO99-induced reads map exactly to the same location as BAI3-induced reads (see coverage plots in our dataverse). In addition to confirming that PXO99 also induces xisRNA accumulation, the higher sensitivity of sRNA-seq applied to leaf clipping assays samples indicates that the most abundant xisRNAs accumulates as early as six hours following inoculation in an assay that more closely recapitulates a natural infection of xylem vessels as opposed to massive infiltration of the leaf mesophyll. Those overlapping a gene annotation element only are classified as 'Gene'. Others are classified as 'Intergenic'. (F) Genome browser snapshot of the MSU7 LOC_Os03g15770 locus annotation overlapping with the xisRNA002 ShortStack segment and read coverage tracks (reads per million) for the various experimental treatments.
To address this, we performed a second set of rice sRNA-seq experiments, designated as the 'Diversity' dataset. African Xoo MAI1 strain was selected because in northern blot experiments it strongly induced xisRNAs. As representatives of the Xoc pathovar, the African BAI11 strain and the Asian BLS256, together with a T3SS mutant derivative (BLS256H), were also selected to investigate T3SS-dependency and Xoc pathovar specific xisRNAs. The setup of these experiments as well as the outcome of the primary genome segmentation and DE analysis are summarized in Supplementary Fig. S7  and Supplementary Table  S2. Across both datasets, a total of 163 distinct loci met the criteria defined for xisRNA loci, 99 of which were not identified in the first 'BAI3' dataset. Supplementary Table  S3  records expression measures, features of mapping sRNA reads and annotated cis-genes information for these loci while Fig.
3  depicts a graphical representation of this data for the 60 most highly expressed xisRNA loci. The clustering of DE patterns across comparisons (Fig. 3  and Supplementary Fig. S8 ) is complex but pointed to few main classes of xisRNA loci: 16 are broadly induced by all tested Xo strains (e. g. xisRNA01, 02), 22 are induced by African Xoo and Xoc strains (e. g. xisRNA009, 22) and 57 are specifically induced by the Asian Xoc BLS256. In order to ascertain again that the sRNA reads mapping at dataset-specific xisRNA loci in this extended inventory were congruent with the hypothesis that xisRNAs derive from a duplex of sRNAs molecules, we inspected the coverage of all reads from both datasets. With the exception of xisRNA094 for which the shape of read coverage is not consistent with a sRNA duplex model, all other loci displayed a single major ~ 25 bp wide coverage peak.
We therefore defined these shorter genomic intervals as 'xisRNA duplex loci' (see xisRNA loci-specific genome browser views in our dataverse). As shown in Supplementary Fig. S8  and Fig. 3 , duplex loci reads complementary to the cis-gene strand have a proportion of 5′-T that is well above the proportion of Ts in the rice genome. A hallmark of sRNA loaded into rice AGO1s complexes is a strong sequence bias for 5′ Uracil  26. This implies that the composition of cis-gene antisense xisRNA 5′-nucleotide is in line with their possible loading into an AGO1 complex. The joint analysis of our sRNA-seq datasets generated a larger collection of xisRNA loci detected independently with diverse Xo strains and confirmed that the main features of xisRNAs described in the BAI3 dataset were also conserved in the Diversity dataset.
A summary of xisRNAs features in the analyzed sRNA-seq datasets. In the complex heatmap layout, the top 60 xisRNA loci with the highest maximum treatment mean expression across datasets are described row-wise. The first component, on the left, reports on expression (reads per million) in the various inoculation treatments of the different datasets. In the type of treatment legend, the "Xoo_Af " and "Xoo_As" labels stand for African Xoo and Asian Xoo strains, respectively. The DE patterns component shows whether xisRNA loci were detected as DE in various comparisons. The "nDE" dot plot depicts the number of comparisons where the xisRNA locus was deferentially expressed. Cells in gray indicate that the xisRNA locus was not considered in the dataset because the loci was not detected by ShortStack or because reads in this dataset did not pass our xisRNA loci features criteria.
The subsequent plots on the right depicts the size distribution of reads ("Read_Length_Rel_Freq"), the fraction of antisense reads ("AS_Fraction"), the proportion of antisense reads with distinct 5′-end first nucleotide ("AS_Reads_N1_Rel_Freq") and the specific nucleotide mismatch ratios for relative positions -22 to -1 of antisense reads using xisRNA loci mapping reads from the "BAI3" and "Diversity" datasets. The MSU7 identifier of the gene overlapping with a xisRNA duplex locus is indicated together with the kinase subfamily into which the encoded protein is classified ("Kin_Fam. " column) as well as the RLCK protein name, when relevant and available. Finally, the "In_Exon" column indicates whether the xisRNA duplex loci is fully included in an exon of an annotated mRNA of the gene. All numerical values reported in the graphic were computed using reads mapping to the xisRNA duplex loci.
OsDCL4 is the preponderant rice DICER-LIKE responsible for the cleavage of dsRNA precursors that releases ~ 21 nt siRNAs associated with inverted repeat transgenes, hp-siRNAs and TAS3 ta-siRNAs  27. As noted above and shown in Fig. 5D , the African X. oryzae-specific xisRNA022 duplex is located on the last exon of LOC_Os04g43050 which precisely corresponds to OsDCL4/SHO1  16. To test if OsDCL4 is also involved in xisRNA synthesis, we examined the effect of the null dcl4-1 allele on xisRNA accumulation in northern blot experiments. The dcl4-1 mutation, identified in the L16S indica variety, carries a ~ 1. 5 kb deletion (Fig. 5D )  27. The AK120922 transcript can form a hairpin-like structure and has been shown to be processed by OsDCL4 to produce 21 nt hp-siRNAs  27. A northern blot oligonucleotide was designed to detect one of the most abundant reads on the genomic region of AK120922 in our BAI3 dataset and was used as a probe to monitor AK120922-derived hp-siRNAs.
Leaves were infiltrated with the indicated strains and collected 24 hpi. These results are representative of two (OsDCL1) and three (OsHEN1 and Tal1c blots) replicate experiments. In (C), the plot on top of the gel images represent OsHEN1 Q-RT-PCR expression data from four experiments (light gray point) with mean (dark gray points) and standard error (line ranges). The BLS256H strains correspond to a T3SS minus mutant derivative of BLS256. The "+" sign indicate that the strains were transformed with a plasmid expressing Tal1c or the corresponding empty vector (EV). Full-length blots are presented in Supplementary Fig. S15. (D) Diagram depicting the general exon-intron structure of OsDCL4 and a close-up genome browser view of the last exon of the gene with the 3′ UTR. The dcl4-1 deletion is not drawn to scale but span ~ 1.
5 kb on the 5′-side of the gene which includes upstream promoter sequences up to the first 72 bp of exon 1  27. The genome view represents reads coverage in various treatments, the MSU7 annotation, and the xisRNA022 locus defined in the "BAI3" and "Diversity" sRNA-seq datasets as well as its duplex locus. For details see the legend of Fig. 1F. (E) Northern blot analysis conducted on total RNA extracted at 48 hpi from leaves of homozygous wild type (WT) or homozygous dcl4-1 individuals. This experiment was repeated three times with similar results. In (A), (B) and (E), lanes with the same labels correspond to biological replicates of total RNA samples extracted from a single infiltrated leaf area from individual independent plants. The hyphens on the right of individual blots mark a size of 21 nt.
For each panel, individual blot images derive from successive stripping, hybridization and detection rounds of the same membrane. ◂ cis-regulatory activity of xisRNAs, we applied a standard transcriptomics data analysis approach based on the postulate that candidate target genes are expected to be downregulated in a treatment comparison where the corresponding sRNA is upregulated. We generated paired-end mRNA-seq data using samples from the set of experiments that were also used to obtain the BAI3 sRNA-seq dataset. In addition, we included sequences from Nipponbare samples collected 24 h after infiltration with the Xoo MAI1 strain and a water control from a previously published dataset  44. Finally, sequences from Nipponbare samples collected 48 h after infiltration with the Xoc strains BLS256, BAI11 (a.
k. a. CFBP7342), or the mock control were also retrieved from another published  45  dataset. Differential expression metrics were computed for comparisons involving samples inoculated with a virulent strain versus samples inoculated with the corresponding T3SS mutant strain or mock treatment. First, we examined the distribution of the log2 transform of the fold change ratio of genes in the various comparisons depending on whether an annotated transcript of the gene overlapped with a xisRNA duplex loci that is upregulated in the corresponding comparison (Fig. 6A ). If upregulation of a cognate xisRNA generally suppressed cis-gene expression, we would expect the median log2(FC) of this set of DE records to be inferior to zero. The computed median log2(FC) is 0. 111 and, on the contrary, is significantly superior to zero (one-sided Wilcoxon rank sum test: W = 25,008, p = 8e-06).
S11 ). To visualize these similarities, the multiple alignment of all major reads in the clusters was used to generate the consensus logo reproduced in Fig. 7A. In line with our earlier observation regarding 5′ nucleotides distribution of antisense reads, major reads frequently start with a "TC" (or "UC" for an RNA sequence) dinucleotide on their 5′-end. The consensus pattern further points to conserved positions that seem to be phased with a 3 nt offset, reminiscent of codon code degeneracy. This pattern of conserved nucleotides in xisRNA sequences hints to the possibility that xisRNA duplex loci correspond to cis-transcripts regions encoding related protein motifs. To determine if xisRNA duplexes correspond to the coding sequences of documented domains, we searched InterProScan hits on cis-transcripts translation products and analyzed their overlap with xisRNA-associated protein subsequences (97 out of 122 that overlap an annotated transcript are also strictly within a CDS).
Domains that repeatedly overlapped with a xisRNA-associated protein subsequences were connected with kinase activity. For example, 50 out of 97 overlap with a ProSite "Protein kinase" domain (PS50011). As illustrated in Fig. 7B ,D, 39/97 xisRNA-associated protein regions overlapped with at least one residue matching with an "active site" features in the NCBI CDD database "STKc_IRAK" domain (cd14066). The corresponding STKc_IRAK conserved sites consensus motif 'YEYM' is involved in ATP binding according to its the CDD domain description. As a broader complementary approach, we measured the degree of similarity between translation products of xisRNA duplex loci sequences following a procedure similar to the one used above for xisRNA major reads sequences (Supplementary Fig. S12 ). While many clusters displayed no particular similarity with one another, a subset of six clusters with a total of 47 sequences appeared to be related as judged by dissimilarity score patterns off the matrix diagonal.
Even if akin to siRNAs, xisRNA loci reads coverage supports the existence of a single xisRNA duplex at each locus (Figs. 1 and 6A and our dataverse). In contrast, endogenous siRNAs loci such as hp-siRNAs, phasiRNA, transgene-derived siRNAs, or 24 nt heterochromatic siRNAs generally give rise to several distinct siRNA duplexes that may be phased and that cover more or less uniformly the entire precursor sequence  5. While xisRNAs do not fit well into canonical classes of plant sRNAs (Table  1 ), they resemble a subset of DCL1-dependent 21 nt cis-nat-siRNAs from rice and Arabidopsis deriving from one specific site in the region of antisense transcripts overlap  49. If extrapolated to all xisRNA loci, our interpretation that xisRNA022 accumulation requires a sense OsDCL4-encoding mRNA (Fig. 5 ) is consistent with this view.
However, neither the reference rice Nipponbare annotation nor our BAI3-infected leaves mRNA-seq data (see coverage plots in our dataverse) argue for the widespread existence of an antisense messenger transcript complementary to the annotated protein coding mRNA. This antisense pol II RNA transcript could still be a very short-lived intermediate. Alternatively, the antisense strand of the xisRNA dsRNA precursor could be the product of RDR activity (Supplementary Fig. S14 ). Interestingly, polarized signatures of untemplated 3′ terminal nucleotides were detected only in xisRNA reads with a sense orientation relative to the orientation of the overlapping annotated gene (Fig. 1  and Supplementary Figs. S3  and S4 ). The RDR2 strand of P4R2 RNAs and their DCL3-processing heterochromatic siRNA products carry a single 3′ end untemplated random nucleotide as a result of RDR2 nucleotidyl transferase activity  50.
RNA next-generation sequencing. The sRNA-seq data for the BAI3 dataset was generated by the Fasteris company (Geneva, Switzerland). Briefly, sRNA libraries were prepared with total RNA extracted from duplicate samples collected at 24 hpi using the Illumina TruSeq small RNA kit with polyacrylamide gel purified 18-25 nt sRNA fractions and sequenced on a portion of a Illumina HiSeq 2000 lane, 1 × 50 single-reads. The sRNA-seq data for the Diversity dataset was generated by the Novogene company (Hong Kong, China) from total RNA extracted from triplicate samples collected at 24 hpi using a NEBNext Small RNA Library Prep Set for Illumina kit optimized by Novogene on the supplied total RNAs. Ultimately, size selected 18-47 bp inserts were sequenced on a Illumina HiSeq (1 × 50 bp). The 'BAI3' mRNA stranded sequencing was performed on a HiSeq 2000 lane (2 × 100 bp) by the Fasteris company (Geneva, Switzerland) with libraries prepared following a dir-mRNA-dUTP protocol that includes poly-adenylated transcripts selection followed by cDNA library construction using Illumina TruSeq Stranded mRNA Library Prep kit.
Transcript levels in a sample were calculated using the ∆∆Ct method from two technical replicate reactions from the same cDNA preparation using the OsEF-1α gene for normalization. Deep sequencing data primary analysis. All primary analysis steps were conducted separately for each individual dataset. The first stage of our sRNA sequencing data processing pipeline included a 3′ adaptor trimming step performed with AdapterRemoval (v. 2. 2. 2-https:// github. com/ Mikke lSchu bert/ adapt errem oval) using these key parameters : "--mm 5 --minadapteroverlap 3 --minlength 18 --maxlength 50". Illumina reads were mapped onto the nuclear rice genome with bwa aln 65 version 0. 7. 17 with default parameters. sRNA loci inference was performed with ShortStack (v. 3. 8. 5)  23 using the following key parameters: "--dicermin 20 --dicermax 24 --mincov 2 rpm --pad 75 --foldsize 400 --strand_cutoff 0.
8". Read counts were obtained using the sum-marizeOverlaps function from the GenomicAlignments Bioconductor package. Experimental sRNA loci that accumulated less than 30 total reads across all libraries in the dataset were excluded. Likewise, sRNA loci that overlapped with rice loci with feature types "tRNA", "rRNA" or "pseudogenic_tRNA" in the gff annotation file for NCBI RefSeq assembly accession GCF_001433935. 1 were excluded from differential expression (DE) analysis. For replicated datasets, standard DE analysis was performed with the DESeq2 R Bioconductor package  66. For normalization, size factors were estimated using the "iterate" method and the "shorth" function to compute a location for a sample. Dispersions were fitted using the "parametric", by default, or the "local" method, in case estimates did not converge.