dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, a correlation between an upward tilting of the LSC and semi-aquatic lifestyle is not supported by current data. Noteworthily, the most aquatic species of the dataset, Hippopotamus amphibius, stands out on the scatter-plots as an outlier (Fig. 4 ). This is due to the large difference between its strongly anteriorly tilted neutral head posture and its almost horizontal reconstructed cranial orientation (difference = 39°). Hippopotamus amphibius normally spends very little time on land  49  , and it can be hypothesized that its bony labyrinth morphology would be more adapted to life in water than on land  96. This hypothesis is supported by the orientation of the head while swimming in H. amphibius, which is more consistent with its reconstructed cranial orientation (Fig. 12 ), and the fact that the difference between the neutral and reconstructed head postures in the more terrestrial Hexaprotodon liberiensis (23°) falls more within the range of variation of "ungulates" (Fig.
Another hypothesis would be that the re-organization of the braincase serves to accommodate the development of the large cranial apparatuses found in most head-butting species (e. g. horns and antlers)  29. In our dataset, 56% of species without horns do not head-butt, and 83% of species with horns do head-butt (Supplementary Table  S1 ), which would partly support this hypothesis. In contrast, intraspecific variations in the presence or absence of horns appear to have no impact on neutral head posture on average (Fig. 14 ; Supplementary Table  S1 ). These two hypotheses will need further observations and biomechanical modeling to be addressed adequately.
Acknowledgements The authors are grateful to the zoos that supported this study:  SANBI National Zoological Garden, Pretoria (Tracy Rehse ),  Johannesburg City Parks and Zoo  ( Dr. Arnold Kanengoni ),  Montecasino Bird Garden  ( Elaine Bratt ),  Lory Park Animal and Owl Sanctuary, Midrand  ( Kara Heynis ),  Ménagerie du Jardin des Plantes et Parc Zoologique de Paris ,  CNRS  ( Dr. Michel Saint-Jalme  and  Dr. Alexis Lécu ) with the participation of the  National Museum of Natural History-France ,  Prague Zoo  ( Schneiderová Irena ),  Conservation and Science, North of England Zoological Society  ( Chester Zoo ),  Dr. Tobias Rahde  ( Zoologischer Garten Berlin ),  Tierpark Berlin  ( Dr. Florian Sicks ),  Zooparc de Beauval ,  Kölner Zoo  ( K. H. Vogel  for the pictures of the saïga and Alexander Sliwa).
Rodolphe Tabuce ),  Zoological Museum of the University of Zurich  ( Martina Schenkel ),  American Museum of Natural History  ( Eileen Westwig ),  Yale Peabody Museum of Natural History (Kristof Zyskowski ). This study was made possible by the support of the  DST-NRF Centre of Excellence in Palaeosciences (CoE in Palaeosciences). Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the  CoE in Palaeosciences. MB and LC acknowledge  Georg Schulz  and  Bert Müller  for scanning the specimens and the financial support from the  Swiss National Science Foundation  through projects  SNF  Project  200021_159854/1  and  200021_178853/1. AF acknowledges financial support from a  National Science Foundation Graduate Research Fellowship and Society of Integrative and Comparative Biology.
Data availability The datasets analysed during the current study are available in the Supplementary Table  S1  and online at this URL: https ://osf. io/4vpnj /?view_only=3dc98 7012f cd44a 6a64a d7d89 49ec0 1f (https ://doi. org/10. 17605 /Osf. Io/4vpnj ). Data replicated in the Wits Data Archive and can be guaranteed to be in the holding of the University of Witwatersrand.
Cellulose Content Measurement Homozygous pas2-1 seeds cannot be maintained because pas2-1 mutants are seedling lethal. Therefore, F2 seeds generated from the pas2-1/+ heterozygous plants were plated on MS plates and grown in the dark for 4 days or under light for 15 days (light grown seedlings were kept in dark for 24 h for de-starching before collecting roots). pas2-1 homozygous seedlings with severe mutant phenotype were selected for cellulose content measurement. For all other genetic backgrounds, etiolated 4-d-old homozygous seedlings or those under light for 15 days were collected from MS plates (light grown seedlings were kept in dark for 24 h for de-starching before collecting roots). Crystallized cellulose was measured using the Updegraff method  [41]. Data were collected from five technical replicates for each tissue sample.
Live Cell Imaging Seedlings were grown vertically on MS plates in dark for 2. 5 days. Images were taken from the epidermal cells in the regions about 3-7 cells below the apical hook. Imaging was performed on a Yokogawa CSUX1 spinning-disk system featuring a DMI6000 Leica motorized microscope, a Photometrics QuantEM: 512SC CCD camera and a Leica 100 × 1. 4 numerical aperture oil objective (W. NUHSBAUM, McHenry, IL, USA). An ATOF laser with three laser lines (440/491/561/nm) was used to enable fast switching between different excitations. Band-pass filters (520/50 nm for GFP, 620/60 nm for RFP) were used for emission filtering. Image analysis was performed using Metamorph (Molecular Devices, San Jose, CA, USA), ImageJ (version 1. 36b; http://rsbweb. nih. gov/ij/) and Imaris (Bitplane, Zurich, Switzerland) software.
Acknowledgments: We thank  Kian Hematy  ( Institut Jean-Pierre Bourgin, INRA ) for his kind help and critical reading of manuscript. We thank  Jean-Denis Faure  ( Institut Jean-Pierre Bourgin, INRA ) for his support of  Frédérique Tellier  and comments in the preparation of this manuscript. We thank  Kian Hematy  for providing GFP-CESA3 transgenic seeds and pas2-1. We thank  Jean-Denis Faure  for sharing the GFP-PAS2* transgenic line. We thank  Niko Geldner  for providing mCherry-SYP32, -Got1p, -VTI12, -RabA1g and -RabG3f transgenic seeds.
Results Structure, expression, and predicted rRNA target of the human snoRNA-jouvence Based on the tertiary (3D) structure of the Drosophila snoRNA-jouvence determined using the Infernal software (http://eddylab. org/infernal/), we identified the orthologue of jouvence in mice and humans, which has not yet been annotated  [13]. Human jouvence (hereafter named h-jou) is located on chromosome 11 (GRCh38. p12) in a long intron of the TEA domain family member 1 gene (named TEAD1) (for the genomic map of the human snoRNA-jouvence, see Suppl. Figure  11a  in  Soulé et al. , 2020)   [13]. The human genome contains a single copy of jouvence, while two copies have been identified in the mouse genome (for the human sequence of the snoRNA-jouvence, see Fig. 10  in  Soulé et al. , 2020)   [13]. Because jouvence has a primary sequence corresponding to an H/ACA box, its predicted secondary structure was deduced from the 'RNAfold web server', and as expected, it is folded to form hairpins (Fig.
RT-qPCR (real time quantitative polymerase chain reaction) on selected genes The differential expression of the snoRNA and selected genes were analysed by real-time PCR (QuantStudio 3, Applied Biosystems, France). The expression of the snoRNA was detected with TaqMan customized probes (Applied Biosciences, Life Technologies) and the Taq-Man Universal Master Mix II, no UNG (Applied Biosystems). All conditions were normalised to the GAPDH control gene. The Sybr Green RT-qPCR was performed for all the other genes with the Power UP SYBR-Green PCR Master Mix, according to the manufacturer's instructions (Applied Biosystems). Primers were designed using the "Primer 3 Plus" software (the primer sequences will be provided upon request). All conditions were normalised to the RPLP0 control gene. The results were analysed using the 2-ΔΔCt method, and displayed as the fold change compared to the control gene.
Transcriptomic analysis (RNA-seq) The transcriptomic analysis (RNA-seq) was performed by Novogene (China). Briefly, total RNA was extracted and after sample quality control, libraries enriched for polyA RNAs, were generated and checked for quality. Then the libraries were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. The resulting data were controlled, and analyzed with bioinformatic tools. For all the computational tools analysis, common defaults parameter values were used. The reference genome and gene model annotation files were downloaded from genome website directly (see: Availability of Data and Materials). Bowtie v2. 2. 3 was used to build the index of the reference genome. TopHat v2. 0. 12. was used to align paired-end clean reads to the reference genome, because it can generate a database of splice junctions based on the gene model annotation file, yielding to a better mapping result than other non-splice mapping tools.
For the quantification of gene expression level, HTSeq v0. 6. 1 was used to count the read numbers mapped to each gene. Then FPKM of each gene was calculated based on the length of the gene and read counts mapped to this gene. The expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced (FPKM) is the commonly used method for estimating gene expression levels. Indeed, it takes in account the effect of sequencing depth and gene length for the read counts at the same time. Differential expression analysis of two conditions (three biological replicates per condition) was performed using the DESeq R package (1. 18. 0). DESeq uses a model based on the negative binomial distribution to determine differential expression in digital gene expression data. Then, the P values were adjusted using the Benjamini & Hochberg method.
Corrected P-value of 0,05 and log2 (Fold-change) of 1 were set as the threshold for significant differential expression. The biological variation was eliminated (case with biological replicates), and the threshold was therefore normally set as p adjusted < 0,05. Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the GOseq R package, in which gene length bias was corrected. GO terms with corrected p-value less than 0,05 were considered significantly enriched by differential expressed genes. For the KEGG database  [15, 16] , KOBAS software was used to test the statistical enrichment of differential expression genes in KEGG pathways.
The paper is organized as follows. Next, there is an illustration of the theoretical approach adopted to explain the dynamics underpinning new industrial path development and institutional change in regions located on the knowledge periphery. A methodological section then briefly outlines how the original empirical data were collected and analysed. The following section illustrates the research strategy adopted for the empirical case study of Ruta N in Medellín. The main results are then reported and discussed, before concluding.
ACKNOWLEDGEMENTS The authors thank  Pierre-Alexandre Balland ,  Ron Boschma ,  Joaquim Oliveira Martins ,  Carlota Perez ,  Elvira Uyarra , two anonymous referees and the journal editor, as well as participants of the OECD-ERSA winter school 2019 in Trento and the 2018 RSA winter conference in London for their helpful comments on earlier versions of this paper.
Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug-drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration-time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated.
Based on an evaluation of the predictive performance of the drug model, these can subsequently be used to support alternative dosing regimens, clinical study design, or labeling. Guidelines require the PBPK platform to be qualified for its intended use, e. g. , the prediction of a DDI for a CYP1A2 substrate  [8]. For this purpose, a prespecified qualification dataset using reference substrates and inhibitors is needed to evaluate the PBPK platform before DDI prediction and verification in the specified scenario. Even though many perpetrator and victim drug models are available to explain certain study designs and conditions, further models and more comprehensive DDI networks suitable for general use are needed. The aim of this study was to develop a qualified PBPK DDI network for CYP2C19 and CYP1A2 interactions, thereby expanding the library of publicly available models for DDI predictions with qualified whole-body PBPK models, by building template perpetrator/victim PBPK models for fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol.
Data Sources and Software The published literature was searched to select CYP1A2 and CYP2C19 substrates and inhibitors for the DDI network and to identify and evaluate the available concentration-time data needed for model development and qualification. Recommendations for reference index substrates and inhibitors by the FDA  [10]  were also considered to ensure the inclusion of a range of strong and moderate CYP inhibition. Each substrate or inhibitor was validated by at least two independent clinical drug inhibition combination studies if data were available. A summary of the CYP2C19/CYP1A2 DDI network is shown in Figure  1. Qualification of the network was accomplished by prediction of the observed clinical interactions for the pairs listed in Table  1. Concentration-time data from relevant publications were digitized using DigitizeIt version 2.
Moclobemide-omeprazole Moderate CYP1A2 inhibition • Mexiletine-caffeine • Mexiletine-tizanidine • Oral contraceptives (ethinylestradiol)-caffeine • Oral contraceptives (ethinylestradiol)-tizanidine Concentration-time data from relevant publications were digitized using DigitizeIt version 2. 0. 5 (Digitizeit, Braunschweig, Germany) (www. digitizeit. de), supplemented with Boehringer Ingelheim study data, and collated by compound, dose, formulation, route of administration, and key patient characteristics. Additionally, studies sponsored by Boehringer Ingelheim were added to those data (see Supplementary Material S1). PBPK analyses were performed using the open source software Open Systems Pharmacology (OSP) suite version 7. 2. 2 or higher (http://www. open-systems-pharmacology. org), and a requalification work was conducted in version 9.
Strong CYP2C19 inhibition • Fluvoxamine-omeprazole • Fluvoxamine-S-mephenytoin Strong CYP1A2 inhibition • Fluvoxamine-caffeine • Fluvoxamine-tizanidine • Fluvoxamine-mexiletine Moderate CYP2C19 inhibition • Omeprazole-moclobemide • Moclobemide-omeprazole Moderate CYP1A2 inhibition • Mexiletine-caffeine • Mexiletine-tizanidine • Oral contraceptives (ethinylestradiol)-caffeine • Oral contraceptives (ethinylestradiol)-tizanidine PBPK analyses were performed using the open source software Open Systems Pharmacology (OSP) suite version 7. 2. 2 or higher (http://www. open-systems-pharmacology. org), and a re-qualification work was conducted in version 9. 1 in order to upload the models as templates on OSP. In vitro/in vivo extrapolation (using data sources summarized in Supplementary Material S1) was attempted for relevant processes or, if available, in vivo information was directly derived from literature/unpublished internal sources.
In this analysis, the standard set of PK-Sim model parameters visible in simple view (including e. g. , solubility, metabolizing enzymes, organ volumes, and blood flows and all parameters included in the sensitivity analysis) were investigated, and a list of the input parameters with the most impact on either AUC or C max ranked by their respective sensitivity were generated. Parameters that contributed to 90% of the cumulated sensitivity were further investigated. The supplementary documents to this paper are compiled as a transparent and comprehensive documentation and reference manual, providing detailed information on all PBPK models and modeled DDI studies. Model files will be freely available on GitHub.
Fluvoxamine A PBPK model for fluvoxamine was developed with two parallel metabolic pathways via CYP2D6 as a saturable and CYP1A2 as a linear process. For model development and qualification, data from 21 clinical studies after intravenous and oral administration using different formulations were used, with seven studies assigned to the training set (see Supplementary Material S1. 1). For DDI prediction with fluvoxamine as a perpetrator, the in vivo competitive inhibition constant (K i ) corrected for protein binding was used. The model predictions described the observed concentration-time profiles well after single and multiple dosing. DDI simulations with fluvoxamine as a CYP1A2 inhibition perpetrator, and caffeine, tizanidine, or mexiletine as victim substrates, demonstrated good to excellent predictions of the inhibitory potential of fluvoxamine on CYP1A2, with mean predicted/observed concentration ratios of around 1 (range 1.
Omeprazole Similar to other approaches from the literature  [11] , a PBPK model for omeprazole as racemate and for the single enantiomers esomeprazole and R-omeprazole was developed with two parallel metabolic pathways via CYP2C19 and CYP3A4 as linear processes. Furthermore, a CYP2C19 autoinhibitory process via time-dependent inhibition was included. The omeprazole model was intended to be used as a substrate or perpetrator. Data from 34 clinical studies or study subgroups after intravenous and oral administration using different formulations were used for model development and qualification, with nine studies or study subgroups assigned to the training set.
Omeprazole Similar to other approaches from the literature  [11] , a PBPK model for omeprazole as racemate and for the single enantiomers esomeprazole and R-omeprazole was developed with two parallel metabolic pathways via CYP2C19 and CYP3A4 as linear processes. Furthermore, a CYP2C19 autoinhibitory process via time-dependent inhibition was included. The omeprazole model was intended to be used as a substrate or perpetrator. Data from 34 clinical studies or study subgroups after intravenous and oral administration using different formulations were used for model development and qualification, with nine studies or study subgroups assigned to the training set (Supplementary Material S1. 2). CYP2C19 expression in gut was lowered to account for the higher-than-expected oral bioavailability of the R-omeprazole with expression levels from PK-Sim databases  [12].
S-mephenytoin The classical path of PBPK modeling was not possible, as no PK data were available in humans after intravenous dosing, and there were only limited data following oral administration. Therefore, a PBPK model for S-mephenytoin as a victim substrate was developed with an unspecific liver clearance scaled from an in vivo measured apparent clearance  [13, 14]  as the main clearance process and without further fitting of any other model parameter. For qualification, data from five clinical studies after oral administration using different studies were used (Supplementary Material S1. 3). DDIs with fluvoxamine as perpetrator were simulated, and simulations were used to investigate several values found in the literature for K i. Using a K i value reflecting the in vivo unbound situation for fluvoxamine with S-mephenytoin as a victim led to excellent DDI predictions (predicted/observed ratios ranged from 1.
Moclobemide A PBPK model for moclobemide was developed with two metabolic pathways: saturable metabolism via CYP2C19 and an unspecified metabolic route (most likely flavin-containing monooxygenase 3 metabolism)  [15] [16] [17]. In addition, a time-dependent autoinhibition (TDI) pathway was included to improve the description of observed concentrations after multiple dosing. The moclobemide model was intended to be used as both substrate and perpetrator. For model development and qualification, data from nine clinical studies or study subgroups after intravenous and oral administration using different formulations were used, with eight studies assigned to the training set (Supplementary Material S1. 4). DDI simulations with moclobemide as perpetrator and omeprazole as victim substrate demonstrated a good prediction of omeprazole levels for different CYP2C19 phenotypes.
Tizanidine A PBPK model for tizanidine as a substrate with linear CYP1A2 metabolism as the only metabolic pathway was developed that accurately predicted the concentration-time profiles following single and multiple dosing of tizanidine. Data from nine clinical studies/study subgroups after oral administration using different formulations were used for model development and qualification, with seven studies assigned to the training set (Supplementary Material S1. 5). DDI simulations with tizanidine as the victim substrate and fluvoxamine as the perpetrator demonstrated a good prediction of tizanidine concentrations (predicted/observed ratios: 1. 18 for AUC, and 1. 36 for C max ) when using an optimized value of fluvoxamine K i (Figure  2b ). However, DDI simulations with tizanidine as the victim substrate and mexiletine as the perpetrator underpredicted tizanidine concentrations (predicted/observed ratios: 0.
Mexiletine The mexiletine model included three parallel metabolic pathways via CYP2D6, CYP1A2, and an unspecific clearance as linear processes. Data from 17 clinical studies or study subgroups after intravenous and oral administration using different formulations were used for model development and qualification, with 13 studies assigned to the training set (Supplementary Material S1. 6). The model was able to describe concentration-time profiles of CYP2D6 EM and PM, and it was able to predict drug accumulation after multiple doses in both phenotypes, despite a slight underprediction of clearance for EM subjects (predicted/observed AUC ratio: 0. 76 in EM, vs. 1. 01 in PM); however, C max was well predicted by the model (predicted/observed C max ratio: 1. 13 in EMs and 1. 10 in PMs). DDI simulations with mexiletine as victim substrate and fluvoxamine as perpetrator demonstrated an excellent prediction of mexiletine levels (predicted/observed ratios: 1.
Ethinylestradiol The model comprised five parallel metabolic pathways via CYP3A4, 1A2, 2C9, and 2C19 as linear processes and uridine 5 -diphosphoglururonosyl-transferase 1A1 as a saturable process. For model development and qualification, data from 20 clinical studies or study subgroups after intravenous and oral administration using different formulations were used with 13 studies assigned to the training set (Supplementary Material S1. 7). The ethinylestradiol model was able to accurately predict the concentration-time profiles following single and multiple dosing. In addition, during the model evaluation, a TDI mechanism on CYP1A2 was introduced to describe tizanidine DDI study data. DDI simulations with ethinylestradiol as perpetrator and caffeine or tizanidine as victims demonstrated a good prediction of both substrates (predicted/observed ratios for caffeine: 1.