dataesr/scientific-paragraphs-categorization huggingface dataset:
DataBase structure, features and content MechanoProDB is publicly available and freely-accessible at https://mechanoprodb. ibdm. univ-amu. fr. At the time of writing this paper, a total of 82 manually annotated entries are saved within the main datatable of MechanoProDB. Each entry stores mechanical parameters about a specific protein extracted from the literature and obtained under specific experimental or simulated conditions such as pulling velocity and/or loading rate, temperature, pulling geometry or even potential hydrogen (pH) condition. Each protein entry is displayed in one row that has 30 columns storing a variety of relevant information ranging from structural and functional to mechanical (Figure  1 ). One important aspect of MechanoProDB is that it includes SCOP  16  annotations for the stored proteins.
SCOP is a well-known resource that provides evolutionary information and structural classification of proteins which is useful for clustering and analysis of structural data and is manually annotated. Importantly, within MechanoProDB, these structural annotations are linked to the SCOPe database  17  which is the extended version of SCOP that contains new protein structures. This link allows users to access a dedicated web-page in the SCOPe database, which provides detailed information about the structural fold of the protein as well as UniProt information. This link is valuable for users who want to further investigate the structural, functional and even evolutionary characteristics of the proteins of MechanoProDB. Furthermore, in our database, we provide mechanical clamps described previously  [20] [21] [22].
These indicate which parts of the protein are bearing the force. A link to the web-based topology diagram  23  is also provided when clicking at each mechanical clamp. Detailed information about the original publication reporting the mechanical information is also provided in MechanoProDB. For example, internal links show relevant figures of force-distance curves and dynamic force spectra (force versus pulling speeds or loading rates). Moreover, if the PDB accession code of the protein or a close ortholog is available, MechanoProDB provides external links for each protein to its Protein DataBank (PDB) page in addition to the bibliographic reference from which mechanical data have been extracted and annotated. All the information of the database can be exported in a csv tabulated file to enable users to store and analyze data locally.
Descriptive statistics of database content: In MechanoProDB, we incorporate interactive plots of relevant mechanical parameters stored in the database. On the main page, the first plot, shown in Figure  6A , represents unfolding forces versus pulling velocities of all entries. In addition to the force-velocity plot, a visualization of the percentage of each protein structural class, according to the SCOP annotation, is provided in a pie chart (Figure  6B ). This percentage-based plot offers an immediate insight into the distribution of structural folds within the database. The third visualization (Figure  6C ) shows boxplots that represent the distribution of unfolding forces across different protein structural folds as classified by SCOP. We can notice here that all beta proteins unfold at higher forces than all alpha proteins.
User contributions In the spirit of encouraging collaborative engagement and expanding the utility of MechanoProDB, we enable user contributions and feedback (Supp. Figure  2 ). This allows users to actively participate in augmenting the database's comprehensiveness. Users desiring to insert new proteins with a solved 3D structure only need to provide the PDB-identifier as input. The PDB ID is supplied to an automated Python script that extracts complementary information. In scenarios where a given protein lacks a solved structure, users have to provide this information manually. Up to four unfolding forces can be submitted, each together with its corresponding pulling speed and/or loading rate. In case users possess a dataset surpassing four entries, MechanoProDB permits to submit data in various formats ranging from Comma-Separated Values (CSV) files to other tabulated formats (eg: dat, xls, txt,doc).
DISCUSSION & CONCLUSIONS: The principal aim of MechanoProDB is to provide a comprehensive compilation of mechanical properties of proteins. This compilation is meticulously extracted from published articles. The information stored within the database includes a variety of experimental and computational techniques. Experimental data includes atomic force microscopy, optical and magnetic tweezers data obtained in the force spectroscopy mode. Complementary to this, our database integrates computational data derived from steered molecular dynamics simulations data both from all-atom and coarse-grained modes. The development of MechanoProDB represents a significant contribution to the growing field of molecular mechanobiology and single molecule biophysics. It addresses a critical need for a centralized and freely accessible resource that stores and makes available the mechanical properties of proteins, otherwise dispersed and difficult to localize by non-experts.
MechanoProDB provides an expansive dataset of mechanical properties with high-quality manual annotations of the unfolding pathways. The inclusion of SCOP annotations and functional classifications enhances and advances our understanding of the relationship between protein sequence, structure and mechanics which contextualizes these mechanical profiles within the broader structural landscape. The interactive visualization offered by MechanoProDB empowers users to analyze unfolding forces with experimental conditions, functional classes, and protein folds. The boxplots showing the unfolding forces with respect to SCOP annotations consolidate prior findings  6  regarding the correlation between unfolding forces of proteins and structural classes (all beta proteins versus all alpha proteins). Indeed, proteins composed of β-sheets unfold at higher forces than those composed of α-helixes, a previously suggested generalization now quantitatively confirmed.
The integration of the molecular visualization package Mol* further improves analytical capabilities of sequence and structure, adding an extra layer of insight by offering a simultaneous sequence and structural visualization, which may help understanding structural mechanics. A notable advantage of MechanoProDB is its provision of detailed and accurate information about the mechanical unfolding of proteins from existing published works using experimental and computational approaches. Compared to the only other existing database, BSDB  13  , MechanoProDB collects a more complete scope of information, including mechanics, structure and function of experimental and computational data from the literature. Additionally, compared to BSDB, MechanoProDB goes beyond unfolding forces, velocities, and CATH references by providing a broader range of data and comprehensive unfolding annotations, including unfolding pathways, experimental conditions, and energy landscape parameters such as free energy, distance to transition state and intrinsic unfolding rate.
Moreover, MechanoProDB is a dynamic platform that allows users to plot and visualize the stored data and gives them the possibility to contribute their own data and feedback. These functionalities facilitate rigorous and insightful research, positioning our database as an important, community-shared platform for scientists interested in protein mechanics. We hope that MechanoProDB will enable a deeper exploration of the relationships between sequence, structure, function and mechanics. By stimulating user contributions and feedback, MechanoProDB provides a collaborative platform in which researchers collectively contribute to advancing the understanding of protein (un)folding mechanics. This participative model emphasizes the fundamental principles of open science, enabling dynamic exchange of knowledge and insights within the protein research community.
DATA AVAILABILITY MechanoProDB is freely available at https://mechanoprodb. ibdm. univ-amu. fr. To ensure optimal data utilization, MechanoProDB, offers a dedicated tutorial page within the website. This page serves as a guide, elucidating the functionalities and features of the database. Thereby, MechanoPrDB empowers users to effectively navigate and explore its potential. Within the tutorial page, we also provide an informative video tutorial. This visual resource illustrates the database's interface, features and interactions. All code is freely accessible on GitLab via the following link: https://gitlab. com/fm4b_lab/mechanoprotein-db.
ACKNOWLEDGEMENTS We acknowledge  Dr. Rafael Tapia-Rojo  for contributing with data. We thank  Dr. Mar Benavides  and  Dr. Jean-Luc Pellequer  for inspiring comments. FUNDING The project leading to this publication has received funding from  France 2030 , the  French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University  -A* MIDEX. European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  772257  to FR).
Acknowledgements We are grateful to  Danijela Matic Vignjevic  for critical review of the manuscript, to  Denisa Belisova  for mouse husbandry, and to  Maria Luisa Martin Faraldo  for the LAMA5 antibody. We acknowledge the core facility CELLIM of CEITEC, supported by the  Czech-BioImaging large RI  project ( LM2018129  funded by  MEYS CR ), for their support with obtaining scientific data presented in this paper. We gratefully acknowledge the Cell and Tissue Imaging Platform ( PICT-IBiSA)  at  Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR-10-INBS-04 ). We are thankful to Enantis for providing FGF2-wt. J. S. is supported by  Barrande Fellowship (Ministry of Education, Youth and Sports)  and  FRM FDM202106013570 , and by  Brno PhD. Talent Scholarship , funded by the  Brno City Municipality.
G:Profiler functional profiling of these transcriptome signatures with KEGG and reactome databases uncovered significant stage-dependent changes (Fig. S2, Supp_data_1 ). S-aNSC overexpressed genes involved in metabolism of glyoxylate and dicarboxylate metabolism. S-qNSC rather overexpressed genes related to metabolism of lipids and Rho GTPase cycle. S-TAP significantly displayed genes involved in Mitotic, G1 phase and G1/S transition, Activation of the pre-replicative complex. S-mNB specifically overexpressed genes involved in axon guidance and nervous system development. Conversely, s-iNB signatures contained many genes linked to cell cycle, prometaphase, M phase, and cell cycle checkpoints, clearly distinguishing them from the other cell populations including s-mNB. Alternative splicing (AS) plays various functions in brain development including neural cell differentiation and neuronal migration  [26] [27] [28] [29] [30].
s-iNB correspond to specific SVZ cell clusters identified by single cell RNA sequencing (scRNAseq) We compared our microarray datasets to published scRNAseq datasets from adult mouse SVZ  14, 17, 32. Violin plots show that s-qNSC and s-mNB perfectly matched with the corresponding populations identified in these studies, which was less obvious for s-aNSC and s-TAP (Fig. S3 ). s-iNB matched with the clusters termed "Mitosis" (Class VI) in the study of Llorens et al. 32  and "Mitotic TAP" in that of Zywitza et al. 17  , and with clusters 8, 10, 17, 16 linked to "Mitosis", but also with clusters 6 and 15 described as DCX + Ki67 + neuroblasts in the study by Cebrian_Silla et al. 14. These comparisons thus confirm that s-iNB comprise cycling SVZ cells exhibiting both TAP and NB features, which illustrates the difficulties to determine the exact nature of cell clusters identified by scRNAseq.
Single cell library preparation for single cell RNA sequencing SVZ lateral ventricle walls were dissected from two non-irradiated mice brains (2 month old) and two 5 days post-irradiation 4Gy-irradiated mice brains (2-month old). Protocol of SVZ digestion with Papain (Worthington, LK003150) to obtain single cell suspensions were strictly identical to those performed in the whole genome microarray experiment described above, including the Percoll gradient step to remove myelin. Cells were resuspended in 0. 04% BSA in PBS passed through a 70µm cell strainer (ThermoFisher Scientific). applying the RMA algorithm normalization. Functional profiling were generated using REACTOME and KEGG datasets obtained from the g:Profiler interface (https://biit. cs. ut. ee/gprofiler/gost). Signatures of cell sorted populations were delineated using Venn diagrams comparing the five populations of neural progenitors (s-qNSC, s-aNSC, s-TAP, s-iNB and s-mNB) (https://bioinformatics.
Acknowledgments We thank  C. Devanand  and  C. Duwat  for their contribution for animal experiments and  G. Piton  for 10x single cell library preparations. We are grateful to  Marie-Justine Guerquin  and  Pierre Fouchet , and the  ARTBIO platform (Université Paris  6) for helpful discussions and assistance in bioinformatics analyses. B. C has a fellowship from  ARSEP (fondation pour l'Aide à la   Recherche sur la Sclérose En Plaques ). This work was supported by grants of  IRBIO (Commissariat à l'Energie Atomique et aux Energies Alternatives, CEA)  and  Electricité de France (EDF). The funders have no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To this end, EP was computed from P by EP = -P + C where C is a constant, defined in such a way that the probabilities of the two states determined by fitting a Gaussian Mixture Model intersect at 0 (SI Fig. S4e-f ). Example of pieces belonging to each state (randomly selected from the dataset) are shown in SI Fig. S4g. To distinguish engaged and exploration phases of single trajectories, EP was computed on a one hour rolling window over the trajectories. Engaged phases (EP > 0) and exploration phases (EP < 0) can be visualized on single cell trajectories (Fig. 4f  and SI Fig. S4h-i ).
ACKNOWLEDGMENTS The authors acknowledge insightful discussions with  Sébastien Sart ,  Andrey Aristov  and all members of the  Baroud  and Alcover teams. The authors acknowledge the support of the Biomaterials and Microfluidics platform as well as  FabLab  at  Institut Pasteur. Erik Maikranz  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement  N°899987. M. Madec  was funded by an  Institut Pasteur Cancer   Initiative Masters scholarship ,  M. Mastrogiovanni  has received a scholar of the  Pasteur Paris University International Doctoral Program  and was supported by the  Institut Pasteur , the  European Union   Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie grant  agreement  665807  ( COFUND -PASTEURDOC ) and  La Ligue Contre Le Cancer-Allocation doctorale 4ème annee de  thèse.
, 2018; Müller et al. , 2001). Our data suggested that Ackr4 might be a gene transactivated by p53-a and/or p53-AS isoforms. Consistent with this, by extracting data from a transcriptome-wide study in MEFs  (Younger et al. , 2015)  we found evidence for a p53-dependent transactivation of Ackr4 in response to doxorubicin (Figure  3F ). Furthermore, ChIP-Atlas, the database of chromatin immunoprecipitation experiments  (Oki et al. , 2018) , indicated p53 binding to sequences within the intron 1 of Ackr4 in doxorubicintreated MEFs, and we identified a candidate p53 responsive element in this intron (Figure  3G ). We next used luciferase assays to show that this p53 responsive element can be bound and regulated by both p53-a and p53-AS (Figures  3G-H  and S3C ). Together, these data show that Ackr4 is indeed a p53 target gene, although RNAseq data indicated that it is expressed at much lower levels than classical p53 targets like Cdkn1a or Mdm2 in the splenocytes of Eμ-Myc mice (Table  S1 ).
Relevance of ACKR4 expression in Burkitt lymphomas Murine and human alternatively spliced p53 isoforms exhibit structural differences  (Marcel et al. , 2011)  and the ChIP-Atlas database  (Oki et al. , 2018)  does not report p53 binding to the human ACKR4 intron 1. Nevertheless, we found that p53 activation in human cells also led to an increased ACKR4 expression abrogated by 17-b estradiol, whereas 17-b estradiol had no significant effect on the p53-mediated transactivation of CDKN1A (Figure  4A ). This led us to investigate the potential relevance of ACKR4 expression in human B-cell lymphomas. We analyzed public databases of B-cell lymphoma patients with clinical and gene expression information. We first analyzed #GSE4475  (Hummel et al. , 2006) , a dataset of mature aggressive B-cell lymphomas previously used to define Burkitt lymphoma-like specific transcriptomes, comprising 159 patients (91 men, 68 women) with clinical follow-up.
Overall, ACKR4 gene expression was not significantly different between male and female patients (Figure  4B , left). However, average mRNA levels appeared higher in males when we considered the 30% patients of each sex with the highest ACKR4 expression (Figure  4B , right). Strikingly, when we compared the survival of 30% patients with the highest ACKR4 mRNA levels to the survival of the 30% patients with the lowest ACKR4 mRNA levels, high ACKR4 expression correlated with a better prognosis in men, but not in women (Figure  4C ). By contrast, for MT2A, the human homolog of Mt2, differences in mRNA levels did not correlate with significant differences in survival for either sex (Figure  S4A ). We also analyzed dataset #GSE181063  (Lacy et al. , 2020) , comprising mostly diffuse large B-cell lymphomas (DLBCL; 613 men, 536 women) and a few Burkitt lymphomas (65 men, 18 women).
We found no difference in survival curves of DLBCL patients with low versus high ACKR4 levels, neither in men nor in women. However, there was again an increased survival for Burkitt lymphoma male patients with high ACKR4 expression, but not for women (Figure  S4B ). Finally, we analyzed #phs000235  (Morin et al. , 2011) , a Burkitt lymphoma-specific dataset (65 men, 37 women) comprising mostly patients diagnosed at 0-17 years of age, hence providing cohorts homogeneous for both tumor type and age of onset. Again, ACKR4 was expressed at higher levels in a subset of male patients (Figure  4D ) and high ACKR4 expression correlated with a better prognosis only in males (Figure  4E ). We thus conclude that, as in Eμ-Myc mice, ACKR4 is a male-specific positive prognostic factor in Burkitt lymphoma, the archetype of MYC-driven B-cell lymphomas.
Luciferase assays The candidate p53 responsive element (p53 RE) in the Ackr4 promoter was identified by using the JASPAR database of binding profiles  (Fornes et al. , 2020)  with the position weight matrix scanner PWMscan  (Ambrosini et al. , 2018). A 1. 5 kb fragment from Ackr4 intron 1, containing a WT or mutant p53 RE at its center, was cloned upstream a SV40 minimal promoter and a luciferase reporter gene in the backbone of a PGL3 plasmid (Promega). We used lipofectamine 2000 to transfect p53 -/-MEFs with 2 μg of either luciferase expression vector, 2 μg of an expression vector for p53 WT , p53 AS or the DNA-binding mutant p53 R270H , and 30 ng of a renilla luciferase expression plasmid (pGL4. 73, Promega) for normalization. Transfected cells were incubated for 24 h then trypsinized, resuspended in 75 μl culture medium with 7.
Acknowledgments This project was supported by grants from the  Comité Tumeurs of Fondation de France  (to F. T. ), the  Comité Ile-de-France and Comité national  ( Labellisation 2014-18 ) of the  Ligue Nationale Contre le Cancer  (to F. T. ), and the  Fondation ARC pour la recherche sur le Cancer  (to F. T. ). I. S. , E. E. and J. R. were PhD fellows of the  Ministère de la Recherche ; J. L. and A. M. were post-doctoral fellows of  Cancéropôle Ile-de-France  and  Institut National du  Cancer. M. G. was paid by  European Research Council   875532-Prostator-ERC-2019-PoC  attributed to A. M. ; J. C. B. was supported by  Cancer Research-UK. We thank  K. Fernandes  for his participation in making the ΔAS mutation, and members of the  Institut Curie  platforms:  I. Grandjean ,  H. Gautier ,  C. Daviaud ,  M.
Exon tree analysis The Aphid program was applied to three primate data sets downloaded from the OrthoMaM v10 database  (Scornavacca et al. , 2019). These data sets, depicted in Supplementary Table  2 , correspond to triplets of species from genus Macaca (macaques), tribe Papionini (baboons and allied), and subfamily Homininae (African apes). Speciation times for species in these triplet range from 3. 3 to 8. 6 Mya according to the TimeTree of Life (http://www. timetree. org/). Gene trees reconstructed from alignments of exons of length 400 bp or above were selected -exons were previously identified as appropriate gene tree reconstruction units in mammals  (Scornavacca and Galtier, 2017). There were 2687 (Macaca), 2677 (Papionini) and 3398 (Homininae) eligible exon trees per data set after filtering for taxon sampling and clock-likeness (see above 2.
Acknowledgements The author thanks  Iago Bonicci  and  Khalid Belkhir  from the  Montpellier Bioinformatics & Biodiversity platform ,  Richard Durbin ,  Julien Dutheil ,  Julien Joseph ,  Alan Rogers ,  Marjolaine Rousselle ,  Céline Scornavacca ,  Carole Smadja ,  Ziheng Yang  and two anonymous reviewers for comments, advice and help. Preprint version 3 of this article has been peer-reviewed and recommended by Peer Community In Mathematical and Computational Biology (https://doi. org/ 10. 24072/pci. mcb. 100199 , Rogers, 2024).
Plant Genotyping μ i h σ) U C 1 = μ + ihσ U C 2 = μ + ihσ a σ a U C 2 The BC1S2 individuals and the parental lines, with the exception of D3 and D7, were genotyped using a customized Maize Illuminia In nium 25k SNP XT array. D3 and D7 were genotyped with the Maize Illumina In nium 50k SNP array  (Ganal et al. 2011). Thanks to the overlap between both arrays (16587 markers) and the 25K genotyping of their progenies, we imputed the D3 and D7 25K genotyping. The percentage of incoherent markers between parental and progeny genotyping, called illegitimate rate, was computed. Sixteen BC1S2 individuals were discarded from the dataset because of a high illegitimate rate (> 5%). For A1xD2, A1xD7 and A2xD1 crosses, the average illegitimate rate was initially 8. 78%, 7. 65% and 8. 87%, respectively, which was higher than in other families (0.
e. a three-way cross). This cross was removed from the dataset for variance decomposition analysis (see below). Considering the updated pedigree, genotypic missing data were imputed using AlphaPlant Impute software  (Gonen et al. 2018). Plant Phenotyping BC1S2:3 individuals were crossed to the dent tester line associated to the recipient line to produce hybrid progenies. In the following, hybrids derived from a same RxD cross are considered as a family. Hybrids were evaluated in 3 different locations in France (Blois, Loir-et-Cher; Saint-Martin de Hinx, Landes; Villers, Pas-de-Calais) in 2019 (further referred to as Blo19, Smh19 and Vil19). Each trial was composed of 1512 elementary two-row plots with an area of 9. 6 m², 9. 3 m² or 10. 9 m², for Blo19, Smh19 and Vil19, respectively. Plant density was 9.
Acknowledgements We are grateful to all colleagues involved in BC1S2 production and eld experimentation:  Jean René Loustalot  at  INREA ,  So ane Mezmouk  at  KWS ,  Pierre Carolo ,  Pierre Garraud  and  Emmanuel Voinot  at  LIDEA ,  Cédric Dufour  and  Simon Genin  at  Limagrain Europe ,  Laurent Bossay  at  MASSEED  and  Hélène Pascal  and  Marine Pouquet  at RAGT2n. We are grateful to  Milagros Garcia  ( LIDEA ) and  Carole Derue  ( LIDEA ) for helpful discussions on the results.