dataesr/scientific-paragraphs-categorization huggingface dataset:
Capacity Analysis According to  Pastoors and Weniger (2011), architect Neufert (1951)  estimated that a person occupies an area of 0. 77 m 2 when standing and 1. 75 m 2 when lying down. Ruiz-Redondo (2014)  proposed an additional measurement of 0. 90 m 2 square meters for people sitting or squatting. The script takes the first and the last measurements as a reference (the second measurement can be also added in the script). Having done this, it readjusts the frequency maps, based on their area and the aforementioned measurements, to obtain the capacities (number of people accommodated). These capacities have been classified according to the level of visibility of the potential observers (OPs): low, for a visibility of between 0. 01 and 33. 33 percent of the GUs; medium, for values of between 33.
33 and 66. 66 percent; and high, for values of between 66. 66 and 100 percent of the decorated surfaces in a sector. The script will estimate the capacity of the areas from which the GUs selected for the analysis would be visible. At this point, we recommend following the same criteria as in the previous step. Instead of establishing the sector based on subjective appraisals (e. g. topographical features), we have defined groups as sets that contain figures interspersed at a maximum distance of 2. 99 m from one another, coinciding with the estimated radius of illumination of a prehistoric torch  (Medina-Alcaide et al. , 2021). In any case, we can perform different measurements and subsequently compare the results.
Prior to the analysis, geomorphologic studies must be used to determine the changes that have occurred in the cave since the Magdalenian. The workflow has been summarised in a Python script for ArcGIS® to make it more accessible to other researchers and can therefore be easily replicated in other caves or edited to include new variables and values (e. g. psychological perceptibility, reflectance). The results of our research show that in the three caves studied, some of the figures were executed in those parts of the natural supports that afford the greatest visibility. That is, these Palaeolithic societies arranged some of their graphic work in places where the possibility of being seen was greater, even if this involved risks for the artists due to difficulties of access. On the contrary, there are also other examples where there seems to have been an intention to conceal and physically limit their visibility, taking advantage of the particular morphology of some sites, such as niches or cornices.
Funding Open Access funding provided thanks to the  CRUE-CSIC agreement with Springer Nature. This research was made within the project  PID2019-107262GB-100  " Before art: social investment in symbolic expressions during the Upper Palaeolithic in the Iberian Peninsula" (PI Diego Garate ) funded by  MCIN/ AEI /10. 13039/501100011033  ( Spanish Ministry of Science, Innovation and Universities) ; the four-year multidisciplinary study project ( 2016-2020 ) " Study of rock art in Atxurra cave " led by  Dr Diego Garate  and funded by the  Cultural Heritage Service of the Diputación Foral de Bizkaia ; the two-year research project  021-KOI-2020  " Kobabes: kobazuloen dinamika eta labar artearen babesa" (PI Diego Garate ), funded by  Department of Culture and Linguist Politics of the Basque Government  and the project " Ilunpetako sekretuak argiztatzen" (PI Iñaki Intxaurbe ) funded with the grant " Barandiaran 2022 " by the  Jose Miguel de Barandiaran Foundation.
However, the cells shifted to the stressful media showed a markedly different gene expression behaviour (Fig. 1c  and Supplementary Fig. 3 ), suggesting that the induced changes in gene expression are largely due to the changes in media composition. Although our RNA-seq analyses were carried out at very early time-points following the shift from TSB, we already observed transcriptomic changes that were previously detected after hours of growth in similar media. For example, after shifting to RPMI, we observed upregulation in iron-related transcripts, including members of the isd, feu, fhu and sir operons (Supplementary Fig. 4a )  28, 29. Additionally, previous work studying transcriptome changes in S. aureus in response to blood and serum exposure identified upregulation of amino acid metabolism, immune evasion proteins, virulence factors and transcripts involved in iron acquisition  28  , some of which were recapitulated in our RPMI shifts (Supplementary Fig.
For USA300, we performed CLASH on cells grown in TSB and after shifting to RPMI, LPM pH 7. 6 and LPM pH 5. 4 medium. For JKD6009, we only performed CLASH in the TSB and RPMI growth conditions (Supplementary Fig. 5c ). Because the bulk of gene expression changes were already detected 15 min after the shift, CLASH experiments were performed on samples harvested at this timepoint. Sequencing data from 3 or 4 independent biological replicates were merged and significantly enriched interactions were identified using hyb  46  combined with a probabilistic pipeline  47  , as previously described  33, 34. Analysis of the USA300 data was challenging as the quality of the annotation of this genome (USA300 FPR3757) was not equal to that of JKD6009. To improve the annotation of USA300, we used the Rockhopper software  48  on our RNA-seq data to map untranslated regions (UTRs) and identify novel transcripts.
These were primarily between sRNAs and mRNAs, but a noteworthy number of sRNA-sRNA interactions were also identified (Fig. 3b ). As such, we conclude that RNase III recognises many bf sRNA-target duplexes. The interactome is visualised in Supplementary Fig. 7. We reasoned that bf sRNAs predicted to base-pair at or near the mRNA Shine-Dalgarno (SD) sequence and/or start codon would most likely have an impact on the mRNA or protein steady-state levels. Indeed, a large number of our bf sRNA-mRNA interactions (Fig. 3c ) included these ribosome binding sites, implying a canonical mode of sRNA-mediated regulation  62. We also found examples of bf sRNAs base-pairing with the coding sequences as well as the extreme 3ʹ end of transcripts involved in operons (Supplementary Fig. 6b ). This included the RNAIII-murQ interaction that was also detected by Mediati et al.
9c  and d ). However, we did not pursue this interaction further as its functional significance was unclear (see Supplementary Fig. 9e-g  and Supplementary Data for a description of the results). (((((( &  (((((((( &   (((((((((( & ) ))) )). ))))  ((((((((( & Fig. 2  Folding and structural analyses of hybrids identified through RNase III CLASH. a Boxplots showing the minimum folding energy (MFE) of identified RNA-RNA interactions in all independent RNase III CLASH datasets (n = 4). Plotted are the previously verified interactions ("Known") captured through CLASH, all the interactions identified by CLASH; only those containing a bona fide sRNA (bf sRNAs); and those containing a bona fide sRNA and filtered for MFE < -10 kcal/mol. The boxplot extends from first to the third quartile values of the data, with a line at the median.
"Total" indicates the total number of unique RNA-RNA interactions identified in each dataset. Colour of each stacked bar denotes the type of interaction represented. b As in a, but only for interactions containing a bona fide sRNA. c Top: distribution of the mRNA fragments in bf sRNA-mRNA interactions around the translational start codon (AUG). Orange line shows data from USA300 and the black line from JKD6009. Bottom: heatmaps showing the read distribution for the mRNA fragments for each individual interaction. Interactions highlighted in green are those that have previously been experimentally verified, and in red are those interactions involving toxins. Interactions coloured in black are other novel interactions identified in this study. The interaction between RsaI and the highly conserved RsaE (RoxS in Bacillus subtilis) has previously been proposed in Grampositive bacteria  17, 19, 64.
However, precisely how these sRNAs base-pair and the functionality of this interaction was unclear. RsaE is characterized by a sequence duplication comprising the characteristic UCCCC seed motif. Our CLASH data imply that the two G-rich regions of RsaI base-pair with the 5ʹ and the 3ʹ UCCCC motifs of RsaE when cells are transferred to RPMI and LPM medium (Supplementary Fig. 10b ). The reason we primarily detected the RsaI-RsaE interaction in RPMI and LPM media could be explained by the expression levels of the two sRNAs under these stress conditions; RsaI was rapidly upregulated in response to RPMI and LPM exposure but not in TSB (Supplementary Fig. 10c ). RsaE remained relatively stable in the TSB control and RPMI, while decreasing in the two LPM media. We performed electrophoretic mobility shift assays (EMSAs) with (mutant) RsaI and RsaE transcripts (Supplementary Fig.
Strikingly, we identified many sRNAs fused to fragments of toxin-encoding mRNAs, including phenol-soluble modulins (PSMs) (Fig. 6a , Supplementary Fig. 14 ). In JKD6009, we identified RsaE interacting with members of both the alpha and beta PSMs (α/βPSMs), psmα2, psmα3 and psmβ2, during growth in TSB (Supplementary Fig. 7 ). These cytolytic peptides  65  are crucial for S. aureus virulence  66  through inducing blood cell lysis  66, 67  , phagosomal escape  68, 69  and detachment from biofilms  70, 71. The stability of the αPSM operon RNA transcript is known to be regulated by the sRNA Teg41, which is predicted to bind within the coding sequence of psmα4 72. Our CLASH data indicate that RsaE uses its 3ʹ UCCCC motif to base-pair with the Shine-Dalgarno (SD) sequences of psmα2 and psmα3 mRNAs (Fig.
6b  and Supplementary Fig. 14a ). However, similar basepairing interactions can also be drawn with the SD sequences of the psmα1 and psmα4 transcripts and with the 5ʹ UCCCC motif of RsaE (Fig. 6c ). We also found an interaction between RNAIII and psmα4 in both JKD6009 and USA300, primarily found after the shift to both LPM media (Fig. 6a , Supplementary Fig. 14b ). In silico analyses predict that RNAIII binds to a sequence in psmα4 that normally sequesters the SD sequence. This suggests RNAIII could stimulate PSMα4 production by liberating the SD sequence from a stem structure, making it more accessible to ribosomes, in a similar way as it regulates α-toxin  73. Finally, we also identified sRNA-toxin interactions with the PSMs. We identified an interaction between helix 9 of RNAIII and esxA (Fig.
15d ). Taken together, these data imply that RsaE positively regulates the expression of αPSM-mediated haemolysis. We propose that RsaE acts to couple metabolic pathways to virulence through post-transcriptional regulation of its target mRNAs. RNAIII is required for optimal esxA toxin production. We applied numerous approaches to validate the interaction identified between helix 9 of RNAIII and esxA (Fig. 7a ). Firstly, we recapitulated the interaction in vitro using an EMSA. The CLASH data suggested that RNAIII's ninth helix makes extensive basepairing interactions with the esxA coding sequence just after the SD sequence (Fig. 7a ); our EMSAs demonstrated that the interaction was specific to this region of RNAIII (Fig. 7b, c ). We were also able to confirm the interaction in vivo using a GFP reporter assay  76.
7d ). As controls, we confirmed that the expression of RNAIII did not affect fluorescence of GFP alone nor GFP fused to the 5ʹ UTR of a transcript that is not regulated by RNAIII (gyrB; Supplementary Fig. 16 ). We conclude that RNAIII directly binds the esxA mRNA to enhance its translation. To investigate the significance of this interaction at the protein level, we created a Δrnaiii mutant and examined EsxA expression through Western blotting (Fig. 7e, f ). EsxA levels were almost undetectable in the RNAIII deletion mutant but were completely restored when RNAIII was expressed from a plasmid. Deletion of RNAIII also reduced the levels of esxA mRNA (Fig. 7e, f ), suggesting that RNAIII binding to esxA stabilises the mRNA. Deletion of RNase III, the bait protein used for the CLASH experiments, did not strongly impact esxA mRNA levels, suggesting that the endonuclease does not play a significant role in regulating the stability of the esxA mRNA (Fig.
Processing of RsaI was not previously observed in S. aureus, c Schematic representation of the αPSM operon (blue the location of the four αPSM genes in the operon (red arrow) and the location of the Teg41 sRNA in this genomic location. Below that, a multiple sequence alignment of psmα1-4 Shine-Dalgarno (red) and translational start codon (green) regions aligned to the RsaE seed sequence motifs. CLASH detected interactions between the 3ʹ UCCCC motif (light blue) of RsaE with the psmα2 and psmα3 transcripts but similar base-pairing interactions can also be drawn with all the PSM transcripts and with the 5ʹ UCCCC motif (light blue). d The 3ʹ UCCCC motif of RsaE is involved in base-pairing with psmα3. EMSA between radiolabelled RsaE and psmα3 transcript containing the binding sites predicted by CLASH.
It is possible that base-pairing of RsaE with the αPSM operon only directly impacts a subset of the PSMα toxins, such as PSMα1 and PSMα4 that were significantly reduced in the RsaE deletion mutant. The fact that we identified the interaction between RsaE and psmα2 and psmα3 when using RNase III as a bait protein indicates that RsaE base-pairing could trigger RNase III-dependent cleavage of the mRNAs. The four αPSMs are produced as a single polycistronic mRNA that is highly structured and the SD and/or translational start codon are predicted to be sequestered within stem or stem-loop structures for all the psmα transcripts (Supplementary Fig. 17 ). Interestingly, even though these toxins are encoded on a single operon, they are differentially expressed at the protein level; in USA300, PSMα1 and PSMα4 are expressed the highest.
The PCR was run on a LightCycler 480 (Roche). Analysis of the qPCR data was performed as previously described  34. Briefly, the IDEAS2. 0 software was used to calculate Ct values using the absolute quantification/fit points method with default parameters, and the fidelity of the PCR was examined through melt curve genotyping analyses. To calculate the relative fold-change of genes, the 2^(ΔΔCt) method was employed using 5 S rRNA as a control. Each qPCR experiment was performed in technical triplicate. For final data analyses, the mean and standard error of the mean of three biological triplicates was calculated and plotted. All oligonucleotides used for qPCR analyses are listed in Supplementary Data 2. RNase III activity assay. Over-expression and purification of RNase III from E. coli were performed as described with the following modifications  54.
For hybridisation, membranes were firstly prehybridised in 10 mL of UltraHyb (Am-bion). Membranes were then probed with a 32 P-labelled DNA oligonucleotide (Supplementary Data 2) at 42 °C for 20 h. Membranes were washed twice in 2×SSC with 0. 5% SDS for 10 min. Membranes were then imaged using a phosphorimager screen and FujiFilm FLA-5100 scanner using the IP-S filter. For 5 S rRNA, imaging was also performed through autoradiography. Electrophoretic mobility shift assays. In vitro transcription and radiolabelling of RNA was carried out using a MEGAscript T7 transcription kit (ThermoFisher) as per the manufacturer's instructions. The RNAs were refolded in structure buffer (25 mM Tris, 150 mM KCl, 1 mM MgCl 2 ) using a thermal cycler by heating to 95 °C for 1 min, slowly cooled to 25 °C for 5 min and finally incubated at 25 °C for 20 min.
Libraries were sequenced on an Illumina HiSeq 4000. The adaptor and oligonucleotide sequences are provided in Supplementary Data 2. Haemolytic activity assays. For comparative analysis of supernatants, culture growth was synchronized. Overnight cultures of each strain were diluted 1:100 in 10 mL of fresh TSB and grown for 3 h. These 3-h cultures were subsequently diluted into 25 mL of fresh TSB to a starting OD 600 of 0. 05. The cultures were then grown for 15 h. Culture supernatants were extracted by centrifugation of the cell suspension at 20,0000 × g. Supernatants were then diluted 1:2 in haemolysis assay buffer (40 mM CaCl 2 , 1. 7% NaCl). In total, 200 μL of diluted supernatant was incubated at 37 °C in a tube revolver with 25 μL of whole human blood for 10 min. The samples were centrifuged at 5500 × g and 100 μL of the supernatant was transferred to a 96-well plate.
Five μL of each tryptic peptide sample was loaded on an Aurora column (IonOptiks, Australia, 250 mm length), and separated by an increasing ACN gradient, using a reverse-phase 120 min gradient (from 3%-40% ACN) at a flow rate of 400 nL/min. Data was acquired with the mass spectrometer using the following settings: MS 70k resolution in the Orbitrap, 350 to 1500 precursor scan, 1. 4 m/z Quad isolation; MS/MS obtained by HCD fragmentation (26% HCD collision energy), read out in the orbitrap with a resolution of 17. 5k with a cycle-time of 2 seconds. Nanopore cDNA sequencing. Nanopore cDNA libraries were prepared using total RNA isolated from wild type USA300 grown in TSB or human serum. Total RNA was DNase I (TURBO DNase; Thermo Fisher) treated for 1 h at 37 °C in the presence of 2 U of SUPERasin.
Computational analyses Pre-processing of raw sequencing data. Raw sequencing data were first processed using the pyCRAC package  113. In particular, the CRAC_Pipeline_PE. py script was used that automates almost the entire processing pipeline. The pipeline firstly demultiplexed the raw sequencing data based on the in-read barcode sequences found in the L5 adaptors using pyBarcodeFilter. py. Flexbar (version 3. 5. 0) was used to then remove the 3ʹ adaptor sequences and any flanking nucleotides with a Phred score below 23. The reads were then collapsed to remove PCR duplicates and then aligned to either the S. aureus JKD6009 (https://www. ncbi. nlm. nih. gov/assembly/ GCF_900607245. 1/) or USA300 genome (https://bacteria. ensembl. org/ Staphylococcus_aureus_subsp_aureus_usa300_fpr3757_gca_000013465/Info/ Index/) using Novoalign (version 2.
07). To improve the annotations of the USA300 genome, a Gene Transfer Format (GTF) file was generated. This file describes the start and end positions of all annotated sequences and what RNA class they belong to. In order to generate this file, a minimal GTF was obtained from ENSEMBL and UTR annotations were added by analysing the RNAtag-seq data with the Rockhopper2 software 48 (version 2. 0. 3). The pyReadCounters. py script then used the output from Novoalign to quantify the number of reads for each transcriptional unit. Identification of hybrids. The hyb pipeline (version 0. 0)  46  was used to detect chimeric reads as previously described  34. Briefly, FLASH2  114  was used to merge overlapping paired reads into a single read. These merged reads were then analysed using hyb. The -anti option for the hyb pipeline was used to allow use of a genomic database.
Hybrid distribution plots. Only statistically significant interactions that contained a bona fide sRNA and had an MFE of less than -10 kcal/mol were considered. pyBinCollector. py was then used to plot the mRNA reads within each interaction relative to the start codon. Each interaction was counted only once to avoid biasing the output for abundant interactions. RNase binding to target transcripts. To calculate where the RNase bound its target transcripts, the pyBinCollector tool from the pyCRAC package (version 1. 5. 0) was used. Here, each target transcript was divided into 100 equally sized bins and the nucleotide read density for each bin was calculated and the total plotted. Interactome plots. Only statistically significant interactions that contained a bona fide sRNA and had an MFE of less than -10 kcal/mol were considered.
3. 13) was used  117. Data was log 2 normalised and clustered using the STEM clustering method with 50 model profiles to examine which genes showed changed expression following shift to RPMI or LPM. Mass spectrometry analysis. MaxQuant 118 (version 1. 6. 17. 0) was used for mass spectra analysis and peptide identification via Andromeda search engine  119. Match between runs and LFQ were chosen. Trypsin was chosen as protease with a minimum peptide length of 7 and a maximum of two missed cleavage sites. Carbamidomethyl of cysteine was set as a fixed modification and methionine oxidation and protein N-terminal acetylation as variable modifications. Proteome databases were made using 31 toxin sequences. The first search peptide tolerance was 20 ppm, and the main search peptide tolerance was set at 4.
5. Peptide spectrum match (PSM) was filtered to 1% FDR. Protein intensities were log transformed and missing values imputed. Moderate t-test was performed on log 2 transformed protein intensities using the limma package  120. Nanopore cDNA data analysis. Basecalling, demultiplexing and quality analysis were done with gruppy. Reads with mean quality above 7 were kept for further analysis. Next, the orientation of the reads was determined with cdna. claasifier. py (pychopper, https://github. com/nanoporetech/pychopper. git) and mapped to the USA300 genome with minimap2 121 (version2. 24; https://github. com/lh3/ minimap2). After mapping, sam files were converted into sorted bam files and then to bedgraphs with normalized coverage using Samtools 122 (version 1. 9; http://www. htslib. org) and bedtools 123 (version 2.
Acknowledgements We are grateful to  Wei Gao ,  Tim Stinear  and  Benjamin Howden  for providing the JKD6009 RNase III-HTF strain,  Philippe Bouloc  for the RsaE over-expression plasmid and  Tracy Palmer  for the polyclonal EsxA antibody. We would like to thank  Pascale Romby  and  Emma Denham  for fruitful discussions and helpful suggestions. We also thank  Julia Wong  for help with the FACS analyses,  Lucas Herrgott  for producing recombinant RNase III and  Jimi-Carlo Wills  and  Alexander von Kriegsheim  from the proteomics facility at the  Institute of Genetics and Molecular Medicine (IGMM) at the University of Edinburgh  for performing the mass-spectrometry analyses. Finally, we would like to thank the members of the  Granneman lab  for critically reading the manuscript. This work was supported by a  Medical Research Council   Non-Clinical Senior Research Fellowship  ( MR/R008205/1  to S.
G), a  Wellcome Trust  grant ( 109093/Z/15/A  to S. W. M), a  Wellcome Trust Senior Research Fellowship  ( 207507  to G. K), the  labEx NetRNA framework  ( ANR-10-LABX-0036 ) and the  French investment for the future framework  ( ANR-17-EURE-0023 ). J. J. T and D. G. M were supported by a grant from the  National Health and Medical Research Council Australia  ( GNT1139313 ). J. R. F was funded by institute strategic grant funding  ISP2: BBS/E/D/ 20002173  and  BBS/E/D/20002174  from the  Biotechnology and Biological Sciences Research Council  ( United Kingdom ), SHIELD grant  MR/N02995X/1  from the  Medical Research Council  ( United Kingdom ) and a  Wellcome Trust  collaborative award  201531/Z/16/Z. R. K. C and R. L. Z were supported in part by grant  AI128376  from the  US National Institute of Allergy and Infectious Diseases  (to R.
Code availability The python pyCRAC  113  , GenomeBrowser packages (version 1. 6. 3) and the CRAC  43  and CLASH  34  pipelines used for analysing the data are available from https://git. ecdf. ed. ac. uk/sgrannem/ and pypi (https://pypi. org/user/g_ronimo/). The hyb pipeline used for identifying chimeric reads is available from https://github. com/gkudla/hyb (version 0. 0). The structural motifs (version 0. 1) code used for identifying enriched structural motifs (Fig. 2d ) can be obtained from https://github. com/gkudla/structural_motifs. The scripts for statistical analysis of hyb data  33  is available from https://bitbucket. org/jaitree/hyb_ stats/. The FLASH2 algorithm (version 1. 2. 11) for merging paired reads is available from https://github. com/dstreett/FLASH2. The code used for has also been uploaded to Zenodo  125  and is provided as Supplementary Software.