dataesr/scientific-paragraphs-categorization huggingface dataset:
Because we compiled data from different countries that are therefore not all equally independent, the main analyses on the evolution of modeled perceived facial trustworthiness were conducted using mixed effects linear models taking country as random effect variable. As preregistered, we computed the proportion of sovereigns having more than one portrait in our dataset. As this proportion exceeds 30% (it is 36. 0%), we also included sovereigns' Wikidata item as random effect variable. In all our models we also included several controls that are well-known potential confounding variables: age, gender, and modeled perceived facial dominance. Indeed, these three variables are known to correlate with modeled perceived facial trustworthiness  [24, 26]  and might vary with time. Crucially, because modeled facial dominance was computed in a similar manner to modeled facial trustworthiness, including this variable in our model also allows us to control for the specificity of changes in our dependent variable relative to other facial trait-driven perceptions modeled using our method.
Introduction The goal of our second study was to test if we could replicate Safra et al. 's findings obtained on the National Portrait Gallery  [22]  on a high-quality digitalized portraits database from another country. To do so, we tried to find a database of good quality portraits. Indeed, previous studies (see https://osf. io/3bf54/) showed that the quality of the portraits is important to derive statistical analyses from them. Thanks to a collaboration with the curator of the Chateau de Versailles, we were able to access 2,291 portraits, from 1483 to 1938, all digitized in very high resolution.
Data collection and pre-processing. Thanks to the generosity of its curator, we were able to access the entire digitized collection of the portraits of the Chateau de Versailles (N = 2,247 portraits painted between the 15 th century and the 20 th century, Fig  1B ). In addition, a dataset containing information on these portraits (date, subject, painter, technique, etc. ) was provided. When possible, we also calculated the age of the subject of the portrait and identify her/his gender. Gender was manually coded based on the names of the sitter and could have been retrieved for 1,399 portraits. However, it should be noted that, contrary to the National Portrait Gallery database, because the portraits do not necessarily represent wellknown personalities, several birth and death dates were not available online.
In the analyzed dataset, the age could be retrieved only for 836 portraits. Age and gender could be retrieved for 764, which constituted the final database on which the analyses were conducted (Table  4 ). Concerning the economic data, we collected GDP per capita estimates from the Maddison project  [25]. A GDP per capita value was retrieved for all the portraits of the final database. This study was pre-registered (see https://osf. io/cz72y).
Computation of perceived trustworthiness and perceived dominance levels and statistical analyses. The exact same analyses were conducted as in the previous study. Contrary to the previous study, 5 non-White individuals were represented in the portraits of this database, with one of them being the main character of the portraits and the other four being secondary characters. None of these faces were detected which is certainly mainly be due to the profile position of most of these faces and some inherent issues of OpenFace 3. 0 to detect faces of non-White individuals. Although this element is an important limit of the used method, in the specific context of our studies it has a very limited impact on the robustness of our results given the very small percentage of non-White faces in our database of historical European portraits.
Discussion This study fails to replicate the results from Safra et al. [22]. One explanation could be that the number of portraits having values for gender and age is too low. However, this is quite unlikely. Indeed, as shown in Study 1, samples with sizes bigger than 750 are very likely to yield p-values for the effect of time lower than 0. 05. Moreover, the excellent quality of the digitalization of these portraits might have reduced the noise and therefore allow for the detection of small effects with lower sample sizes. A second possibility is that the absence of results is due to the inhomogeneous distribution of the portraits over time (see  Fig 1). The dataset of the Chateau de Versailles is less homogeneously distributed over time than the three other datasets where an effect of time has been found (Fig  1B ).
General discussion These two studies aimed at testing the robustness the results observed in Safra et al. 's paper (2020). Study 1 did replicate the results on the association between time and modeled perceived facial trustworthiness, using a database controlled for the social status of the sitters. However, Study 2 failed to replicate these results and in both databases, the effect of economic development was not significant when time was included as a covariate. This result highlights the limit of the initial findings and of this new methodology. The studied phenomenon (variations in the importance of appearing trustworthy) is probably small and thus requires large enough and controlled samples evenly distributed in time. Such samples are difficult to create in pre-modern societies where paintings were expansive and photograph did not exist.
Cognitive sciences and computational social sciences may have a lot to offer to cultural and economic history. They can use traditional material (i. e. portraits, but also texts, costumes, music) in a new way to uncover cultural and potentially psychological traits that are crucial to understand the dynamics of past societies. Using insights from cognitive science studies in artworks are thus one more tool in the emerging field of quantitative psychological history  [10, 19, 21, 32, 33]. Obviously, as with any scientific study, these findings must be taken with caution and require replication on different databases and, ideally, using different artifacts to assess their robustness. In the present study, we successfully replicated only one of the results reported by Safra et al. [22]  using an independent database in only one of our studies.
Map generation A grid-structured urban area of 6 km × 6 km, divided into 100 blocks of 0. 36 square kilometers is created. stations are created at the intersections of the 11 horizontal and vertical streets, so there can be a maximum of 121 stations, as shown in figure  6. In order to represent the actual urban traffic situation as realistically as possible, we extract the travel speed from Uber Movement (https://movement. uber. com), which provides historical data on the average hourly speed of road segments, for several world metropolises. Given the available data and the configuration of the city, data based on the city of San Francisco in January 2020 was chosen.
Algorithm quality of initial stage In this paper, we deal with instances too large to be solved with exact methods, even for the initial solution. Instead, we compare our heuristic algorithm with LocalSolver, which is a global optimisation solver, combining exact and heuristic techniques. We limited its execution time to 24 hours (86400 seconds). We compared four different instances' size, with 200, 300, 400 and 600 passengers. The number of buses is fixed to 50, each with capacity equal to 8. All code was written in C++ and all tests were performed on a computer with an Intel ® Core T M i7-8850H 2. 60Ghz processor and 16GB RAM running Windows 10. The results are summarised in table 1. This table shows the average CPU time per instance size to obtain the first feasible solution and the best solution found for each method tested, as well as the URT associated with these solutions.
In this wind turbine control system, three faults are considered, which influence the three measured variables from the wind turbine: β d , T d , ω g. The software LMI control toolbox in MATLAB environment is used to obtain the solution to the LMI optimization problem. To demonstrate the effectiveness of the proposed approach, we consider the results in  [33]  without faults and with different faults occurring in the wind turbine system. A =       0 1 -1 0 -K s J r -B s +B r J r B s J r - k r,β J r K s J g B s J g - B s +B g J g 0 0 0 0 -1 σ       , B =      0 0 0 0 0 B s J g 1 σ 0      D =      0 k r,V J r 0 0      , C = 0 0 1 0 0 0 B g J g 0 The parameters of the wind turbine are given in the following Table  1 : In this case, the proposed MPC has been used for the wind turbine system as the nominal controller without any faults.
A fundamental step in the influenza A virus (IAV) replication cycle is the coordinated packaging of eight distinct genomic RNA segments (i. e. vRNAs) into a viral particle. Although this process is thought to be controlled by specific vRNA-vRNA interactions between the genome segments, few functional interactions have been validated. Recently, a large number of potentially functional vRNA-vRNA interactions have been detected in purified virions using the RNA interactome capture method SPLASH. However, their functional significance in coordinated genome packaging remains largely unclear. Here, we show by systematic mutational analysis that mutant A/SC35M (H7N7) viruses lacking several prominent SPLASH-identified vRNA-vRNA interactions involving the HA segment package the eight genome segments as efficiently as the wild-type virus.
Bioinformatical analyses of SPLASH datasets using RNAswarm The workflow described below is a mixture of automated steps and manual curation. The script package used is called RNAswarm and can be found in our GitHub repository (https://github. com/gabriellovate/ RNAswarm/releases/tag/v0. 1. 0). We first pre-processed the raw Illumina SPLASH data by trimming adapters and filtering for quality using fastp (50) (version 0. 23. 2, default parameters). The corresponding fastqc files (51) (version 0. 11. 8, default parameters) before and after trimming are available in our OSF repository (https://osf. io/berj7/). To detect chimeric reads resulting from cross-linking of intraand intersegmental RNA-RNA interactions, we mapped the pre-processed sequencing data to all eight segments of the corresponding viral reference genomes (Supplementary Table  S1 ) using segemehl  (52, 53 ) (version 0.
3. 4) with the '-S flag' to enable split-read alignment mode. The trns. txt output files were used as input to our in-house script plot heatmaps. py, which selects and counts the intersegmental chimeric reads and generates heatmaps of pairwise segment interactions. Multiple trns. txt files from biological replicates were used as input to generate 'merged' interaction heatmaps. We then extracted interaction clusters from these 'merged' heatmaps by manual inspection. 'Merged' heatmaps and extracted interaction clusters are accessible from the OSF repository. For each interaction cluster, we determined the peak position and selected interaction regions by extending the peak position by 19 nucleotides in each direction. The number of chimeric reads mapping to the resulting 39-by-39 nucleotide window was used to generate a count table of the interaction regions using the in-house script make counttable.
py. We normalized these raw chimeric read counts to the total number of mapped chimeric reads using the in-house script get library size. py, resulting in reads per million (RPM) values. For comparative analysis of two different viruses, the raw chimeric read count tables were used as input to a modified DEseq2 (54) script called run DESeq2. r. Of note, this analysis requires at least two biological replicates and was therefore not performed for samples i-iii shown in Figure  6. To visualize our vRNA interactome data, we used circos (55) (v0. RPM values are provided in Supplementary Table  S2. DEseq2 data are provided in Supplementary Table  S3.
RNA structure prediction and calculation of MFE values To predict RNA structures and minimum free energy (MFE) values of the 39-by-39 nucleotide interaction regions (described in the previous section), we used RNAcofold from the Vienna RNA package (57) (version 2. 5. 0) with '-noLP -C' options and otherwise default parameters. The structure prediction script is available at https://github. com/ desiro/Jakob2023 structures. MFE differences ( MFE) between wild-type and mutant interactions were calculated as follows: MFE mut -MFE WT. MFE and MFE values are provided in Supplementary Table  S3.
The rPB1 TPS virus carrying only the PB1 TPS vRNA showed reduced infectivity compared to rWT (Figure  3A ) and an increased production of noninfectious virions (Figure  3B ), associated with reduced packaging of the PB1 segment (Figure  3C ), as shown previously  (34). However, contrary to our hypothesis, the combinatorial mutants rHA 4x + PB1 TPS and rHA 4x + 4x + PB1 TPS replicated as efficiently as rPB1 TPS (Figure  3A ), did not produce more noninfectious viral particles (Figure  3B    not inefficiently package any genome segment other than the PB1 vRNA (Figure  3D  and E ). These results show that an HA vRNA lacking multiple high-and low-frequency vRNA-vRNA interactions detected by SPLASH is efficiently packaged, even in the presence of a vRNA that is known to reveal 'hidden' packaging defects.
We confirmed that viral replication (Figure  4A ) and the number of non-infectious virus particles (Figure  4B ) of rHA TPS1 were comparable to those of the wild-type virus. This mutant also packaged all eight genome segments efficiently (Figure  4C ). In contrast, the combinatorial mutant rHA TPS1 + PB1 TPS was severely attenuated compared to rPB1 TPS (Figure  4A ) and produced more non-infectious virus particles (Figure  4B ). Importantly, rHA TPS1 + PB1 TPS showed a different packaging defect than rPB1 TPS, as it largely failed to package the PA, HA and NA segments in addition to the PB1 segment (Figure  4D ). Thus, the HA TPS1 region in the 5 -end of the HA segment is a functional packaging signal. SPLASH analysis revealed that the rHA TPS1 virus had an overall similar vRNA-vRNA interaction network compared to the wild-type virus (Figure  4E ); however, RNAswarm detected the loss of two low-frequency interactions of the mutated HA TPS1 locus with the M and PB1 segments (Figure  4F ), possibly due to reduced thermodynamic stability of these interactions in the mutant (Figure  4G ).
ACKNOWLEDGEMENTS The authors thank  Dr Steffen Wolter ,  Dr Tamara Werner , and  Prof. Silke Laßmann  at the  Institute of Surgical Pathology  for cooperation with NextSeq 550 (Illumina) sequencing capacities. The authors also thank  Dr Brad Gilbertson  at the  University of Melbourne, Australia , for providing the pHW2000 plasmids for the generation of rPR8:Wy-PB1/NA and rPR8:Wy-PB1/NA Ud-sub. The authors also thank  Dr Jonas Fuchs  at the  Institute of Virology, Freiburg, Germany , for technical assistance with the sequencing of virus stocks. The authors further thank  Dr Michael Knight  ( currently at Evotec in Hamburg, Germany ) and  Dr David LV Bauer  (currently at the  Francis Crick Institute in London, UK ) from the  University of Oxford, UK , for technical support and training in the SPLASH workflow.
FUNDING FluCode Project  [ ANR-19-CE11-0027 ] funded by the  French National Research Agency (ANR)  and the  German Research Foundation (DFG)  [to C. J. , H. B. , M. S. , R. M. ];  DFG  [ NFDI 28/1] (NFDI4Microbiota ) and  EXC 2051 (Balance of the Microverse)  (to M. M. ); European Union's  Horizon 2020 research and innovation program , under the  Marie Skłodowska-Curie Actions Innovative Training Networks [955974 (VIROINF)  to G. L. L. , M. M. ];  Helmholtz Association Young Investigator Group  [ VH-NG-1347  to R. S. ]. Funding for open access charge:  Flu-Code Project  [Projet  ANR-19-CE11-0027 ].
The Target gene regulation database of p53 and cell-cycle genes  [23]  was reported to include putative DREAM binding sites for many human genes, based on separate genomewide searches for 7 bp-long E2F or 5 bp-long CHR motifs, but the predicted sites were not tested experimentally. By contrast, positional frequency matrices designed to find bipartite DREAM binding sites were used to analyze only a few promoters, but their predictions were confirmed experimentally  [24, 25]. Our interest in the p53-DREAM pathway stems from the analysis of a mouse model with increased p53 activity. We observed that mutant mice expressing p53 Δ31 , a truncated protein lacking 31 residues of the C-terminal domain, exhibited all the phenotypic traits associated with dyskeratosis congenita and its severe variant the Hoyeraal-Hreidarsson syndrome, two bone marrow failure syndromes caused by defective telomere maintenance  [26].
We observed that p53 activation correlated with cell differentiation in this system, because genes known to be transactivated by p53 (e. g. Cdkn1a, Mdm2) were induced, whereas genes repressed by p53 (e. g. Rtel1, Fancd2) were downregulated after tamoxifen withdrawal (Figure  1a ; see also Figure  S1  for additional examples of p53-regulated genes). Thus, to investigate the impact of p53 on telomere biology we performed a gene ontology (GO) analysis of the expression data obtained with this system (Gene Expression Omnibus #GSE21299), which relied on 45,101 microarray probes corresponding to 20,627 genes, of which 17,461 are associated with a GO term according to the Gene Ontology enRIchment anaLysis and visuaLizAtion tool (GOrilla)  [35]. We focused on genes downregulated at least 1. 5-fold upon tamoxifen withdrawal.
In sum, BMC differentiation correlated with the decreased expression of 76 genes that may impact on telomere biology (Figure  1b , Table  S1 ). Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative p53 expression score(s) in the Target gene regulation (TGR) database  [23] , which indicates that they were downregulated upon p53 activation in most experiments carried out in mouse and/or human cells (Figure  1b , Table  S1 ). We previously showed that p53 activation leads to an increased binding of the E2F4 repressor at the promoter of 4 of these 72 telomere-related genes (Brca2, Fancd2, Rad51, Rtel1)  [25] , which contributed to provide evidence that p53-mediated gene repression often occurs indirectly, through the recruitment of the E2F4-containing complex DREAM, often close to transcription start sites.
Accordingly, GOrilla revealed a significant enrichment for genes of the "interstrand cross-link repair" GO term among the genes downregulated in murine differentiated BMCs (Table  S4a ). We found 55 genes down-regulated upon BMC differentiation, encompassing genes mutated in Fanconi anemia, regulating the Fanconi DNA repair pathway and/or belonging to the GOrilla interstrand cross-link repair GO term, or to a recently proposed list of FA-related genes  [40] , including 52 genes downregulated by p53 according to the TGR database (Figure  S2a , Table  S5 ). Out of these 52 genes, twelve are also known to impact on telomere biology (Atm, Atr, Brca2, Dclre1b, Fancd2, Fen1, Hus1, Rad51, Rad51ap1, Rpa2, Telo2, Xrcc3). ChIP-seq experiments revealed E2F4 binding at 51, and LIN9 binding at 41 of the 52 genes (Figure  S2b ), within regions frequently overlapping transcription start sites (Table  S6 ).
These 28 GO terms are partially overlapping and correspond to 168 different genes, of which 10 (Dkc1, Exosc10, Gar1, Gnl3l, Naf1, Nat10, Nhp2, Nop10, Prkdc, Shq1) are also known to impact on telomere biology. Furthermore, we noticed 3 additional genes encoding subunits of RNA Polymerase I (Polr1d, Taf1c, Taf1d) that were downregulated upon BMC differentiation, raising the total of candidates to 171 genes, of which 162 are downregulated by p53 according to the TGR database (Figure  S3 , Table  S8 ). E2F4 binding was found at 152, and LIN9 binding at 50 of the 162 murine genes, at sequences frequently overlapping transcription start sites (Figure  S3 , Table  S9 ). ChIP-seq data from human cells indicated binding by E2F4 for 153/162, and by LIN9 for 115/162 homologous genes, often within regions overlapping transcription start sites (Figure  S3 , Table  S10 ).
Out of these 17 genes, 15 were reported to be downregulated upon p53 activation (Figure  S4 , Table  S11 ). Out of the 15 genes, 14 were bound by E2F4 and 6 were bound by LIN9 in murine cells (Figure  S4 , Table  S12 ) and 13 were bound by E2F4 and 12 by LIN9 in human cells (Figure  S4 , Table  S13 ). We also used the Human Phenotype Ontology website from the Jackson Laboratory (https://hpo. jax. org)  [43]  to search for genes associated with abnormalities of blood and bloodforming tissues (ontology term #HP:0001871), and found that, out of a list of 1322 genes, 336 candidates were downregulated at least 1. 5 times upon murine BMC differentiation, including 277 reported to be downregulated by p53 according to the Target gene database (Figure  S5 , Table  S14 ). Out of these 277 genes, 243 were bound by E2F4 and 102 by LIN9, close to the transcription start site in most cases (Figure  S5 , Table  S15 ).
Out of the 277 human homologous genes, 245 were bound by E2F4 and 198 by LIN9 (Figure  S5 , Table  S16 ). Together, the differentiation of bone marrow cells correlated with the decreased expression of a total of 571 genes implicated in hematopoiesis, including 499 genes downregulated by p53 according to the TGR database (Table  S17a -b, see also Figure  3c  for a summary of our approach). For 374 of these genes, E2F4 and LIN9 were found to bind at identical regions in at least one species (Table  S17c , Figure  3c ). Furthermore, to focus on the best candidate p53-DREAM targets, we also considered the ChIP scores for E2F4 and LIN9 binding for each of the 374 genes. For each gene, we added the ChIP scores of E2F4 and LIN9 in both species, for a maximal value of 4000 (Table  S18 ). Total ChIP scores ranged from 313 to 4000, and we noticed total ChIP scores of 656 and 720, respectively for Fbl and Dkc1, two genes reported to be directly repressed by p53 binding  [26, 44]  and a score of 979 for Exosc5, a gene previously proposed to be regulated by DREAM  [14].
We thus considered the 269 genes with a total ChIP score ≥ 979 as the most likely candidate p53-DREAM targets (Tables S17d, S18 and Figure  3c ). To estimate the relevance of this list of 269 candidates, we analyzed the dataset GSE171697, which includes RNAseq data from hematopoietic stem cells of unirradiated p53 KO mice, unirradiated WT mice, or irradiated WT mice  [45]. We also analyzed GSE204924, with RNAseq data from splenic cells of irradiated p53 Δ24/-or p53 +/-mice  [46]. The public data from this dataset, although incomplete, appeared interesting because p53 Δ24 is a mouse model prone to bone marrow failure  [47]  and the spleen is an hematopoietic organ in mice  [48]. As expected, increased p53 activity correlated with an average increase in expression for 15 genes known to be transactivated by p53, with 13/15 genes upregulated at least 1.