dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, by March 2019, entering the keywords ''chronic heart failure'' and ''mesenchymal stromal cells'' in the ClinicalTrials. gov website retrieved 24 trials, among which the event-driven DREAM-HF study generates a strong interest because of its large sample size (566 patients randomized to receive allogeneic MSCs or placebo). The last patient has recently been dosed, and trial outcomes might become available within the next 12 months. Despite the interest in strategies employing noncardiac cell therapy, an important output of research in the area of cardiac repair has been the recognition that better outcomes might be achieved with cells phenotypically close to those of the target organ. This gave rise to the second generation of clinical trials entailing the use of cardiac-committed cells.
Review In parallel, other groups, including ours, have taken a different approach consisting of starting from PSCs, in practice hESCs, and coaxing them to generate cardiac cells closer to the native ones, either at a progenitor state  (Bellamy et al. , 2015)  or more fully differentiated into cardiomyocytes  (Liu et al. , 2018). Thus, in our clinical study (ESCORT, NCT02057900), we have used early-committed Isl-1 + progenitors embedded in a fibrin gel, which was then delivered onto the epicardium of the infarct area during a coronary artery bypass grafting in 6 patients with severe left ventricular dysfunction  (Menascheet al. , 2018). Aside from demonstrating that generation of such a composite construct was doable under Good Manufacturing Practice (GMP) standards, our trial successfully met its primary safety endpoint in that, with a maximal follow-up of 4 years and 5 months in March 2019, none of the patients has presented complications that could be specifically ascribed to the cells (arrhythmias, tumor, clinically relevant allo-immunization).
These advantages include a standardized pharma-like manufacturing process, a likely reduced immunogenicity (at least when the secreting cells are hypoimmunogenic themselves), and a functional stability under cryo-storage compatible with off-the-shelf availability. Of note, from a regulatory viewpoint, cell-derived secretomes, or more specifically EVs, are not considered ''Advanced Therapy Medicinal Products'' but ''Biological Medicines,'' which corresponds to a different (albeit not necessarily easier) regulatory path. Along a similar a-cellular line of reasoning, others investigate bioinspired materials such as a decellularized cardiac extracellular matrix also credited for activating endogenous repair processes (the Ventrigel trial, NCT02305602). Regardless of the mechanism of action, a major issue is to ensure an effective engraftment.
ACKNOWLEDGMENTS We thank the  LabEx REVIVE  ( ANR-10-LABX-73 ), the  Fondation de France  ( FDF/2014 00047970 ), the  Fondation pour la Recherche Medicale  ( Prix Victor  et  Erminia Mescle ,  PME20180639496 ), and the  Fondation de l'Avenir  ( AP-RM-18-017 ) for their funding support. MD is supported by a grant from the  Fondation Marion Elisabeth Brancher.
U-series dating method. A first set of seven U-series dates was measured by thermal ionization mass spectrometry (TIMS) at the CEREGE (Aix-en Provence, France) and GEOTOP (Montreal, Canada), using two different 236 U -233 U -229 Th mixed spikes. These spikes were calibrated following the procedure described by Deschamps  24  (Supplementary Methods). A second set of 14 U-series dates was performed at Nanjing Normal University (China). The procedures used for U-Th chemical separation and isotopic measurements are detailed in Shao  25  (Supplementary Methods). Age data were processed using the StalAge algorithm, specifically designed for speleothem age models  26. Scanning electron microscopy method. Fresh rock fragments were observed and scanned under a ZEISS ZIGMA SEM microscope coupled to an Oxford Instrument X-MAX EDS detector at the Ecole Normale Supérieure in Paris (France).
Rock magnetic methods. Hysteresis. Hysteresis curves and first-order reversal curves (FORC) were carried out using a vibrating sample magnetometer (VSM) from Princeton Measurements Corporation. Since the magnetization of speleothems is very weak, each sample was crushed into powder and tightly packed into a medicinal capsule, in order to maximize the amount of material for the measurement, instead of using a millimetric chip fixed by grease, as is usual with the VSM. FORC diagrams were processed using the FORCinel software  27  and the VARIFORC option  28. High and low temperature. Thermal variation of magnetic susceptibility was measured using AGICO KLY3S (sensitivity ∼10 -7 SI), in an argon-controlled atmosphere and using steps of 9 °C. The low field magnetic susceptibility was too weak to be measured on the core.
5 mT direct magnetic field, both conducted along the vertical axis of the core. The magnetic measurements were made over the whole length of the 229 cm core but data were only processed up to 221 cm. Deconvolution was performed using graphical UDECON software  29. The demagnetization curves were analyzed using DAEI software  30  , where all the standard demagnetization diagrams (orthogonal projection diagrams, stereographic projection of unit vectors, variation of remanence intensity during stepwise demagnetization) are featured. The ChRM and the Maximal Angular Deviation (MAD) are computed by principal component analysis (PCA) at selected intervals of demagnetization steps, following the Kirschvink method  31.
Acknowledgements We are grateful to  Soprintendenza Archeologica della Liguria  and  Glauco Rosa  for logistic support. We thank  Damien Deldicque  for the microscopic views and analysis of magnetic minerals. We acknowledge contributions of colleagues from  CEREGE  and  J. Beauchamp  ( MNHN ) for fruitful discussions. The U-Th analysis in NNU was supported by the  China NSFC  grant  41877430. This work was supported by  ECLIPSE-INSU  project and the  PPF "Structure et Evolution des Ecosystemes " project of the  Museum National d'Histoire Naturelle in Paris.
ACKNOWLEDGEMENTS. CC-BY-ND 4. 0 International license available under a not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made We would like to acknowledge  Patrick Brunson  for helping with P. tricornutum promoter and terminator sequences,  Diego Bustos  and  Javiera López  for advice and support on yeast transformation methods. We would like to thank  Andrea Calixto  for kindly proofreading the manuscript and providing useful comments.
FUNDING This work was funded by the  Gordon and Betty Moore Foundation  [ GBMF5007. 01  to CLD and VB,  GBMF4981. 01  to FF and PvD]. BP was supported by the  Programa de Apoyo a Centros con Financiamiento Basal  [grant number  AFB 170004 ]. AK was supported by the  National Institutes of Health  [grant number  1R01DK11077001A1 ]. FF, BP, TM and IN were supported by  Instituto Milenio  iBio and PvD by the  Instituto Milenio de Oceanografía  [grant number  IC120019 ] of the  Iniciativa Científica Milenio MINECON. IN was also supported by a  PUC VRI  PhD studentship and AC was supported by  CONICYT scholarship for doctoral degrees in Chile  [grant number  21150778 ].
We performed network enrichment analysis using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING database)  (Jensen et al, 2009)  to identify enrichment in protein-protein interaction partners. Consistent with the well-characterized PBmediated hepatic xenobiotic response, STRING-db sub-networks enriched in shared DHS-TSS highlighted phase I metabolism genes (e. g. , Cyp2b10, Cyp2c55, etc. ) as well as signal transduction genes (e. g. , Stat3, Pten, etc. ) (Fig  2A  and Table S3 ). The most connected genes in the STRING-db sub-networks enriched in B6C3F1 Δ-DHSassociated genes pointed out important regulatory factors, including Klf4, a key regulator of stem cell pluripotency  (Takahashi et al, 2007; McConnell & Yang, 2010) , Wnt/β-catenin signaling genes such as Wnt16 and Tcf7l2 (alias Tcf4) (network 4), and Src (network 6), which was previously reported to contribute PB-mediated mode-of-action, including through β-catenin signaling regulation  (Groll et al, 2016).
Exploring transcriptional regulators of the noncoding, strainselective Δ-DHS landscape The vast majority of strain-selective changes in DHSs after PB treatment do not account for proximal changes in gene expression but may still represent important gene regulatory elements underlying mouse strain differences in tumor promotion sensitivity (Fig  5A ). The cis-acting gene regulatory element landscape is in particular strongly enriched for transcription factor-binding sites (TFBSs) and represents critical genome regulatory information  (Consortium, 2012; Shen et al, 2012; Thurman et al, 2012; Johanson et al, 2018). We, therefore, performed TF motif enrichment analysis individually on the up-or down-regulated PBresponsive strain-selective Δ-DHS landscape for each mouse strain. Because TF-binding motifs frequently overlap between different TFs, we used the root motifs from the motif clustering provided in the JASPAR database in our analysis.
To evaluate the biological function of the different TF motifs clusters, we first predicted gene targets of Δ-strain DHS landscape for each of the enriched TF clusters and ran GO term enrichment analysis  (Fig 5C and Table S5). Overall, this analysis highlighted a number of biological functions of potential relevance to early tumor development that were predominantly observed in the B6C3F1 strain after treatment with PB (e. g. , change in nuclear functions, metabolic changes, epithelial to mesenchymal transition, cellmatrix adhesion/organization, etc. ). To help filter and assess functional relevance of the identified TF motifs, we next examined individual TFs using different knowledgebased sources such as STRING-db, GeneCards database, and literature assessments. We systematically interrogated the prior knowledge towards relevance of the TFs in PB response, CAR activation, Wnt signaling, and/or hepatocarcinoma.
DHS calling and differential analysis on DNase-seq data DHSs were identified and bedGraphs were created applying a q-value cutoff (-q) of 0. 01 using MACS2 v2. 1. 1. 20160309 (Zhang et al, 2008). Differential peak analysis was performed in R 3. 2. 3 using the Bioconductor package DiffBind v1. 16. 3 (Ross-Innes et al, 2012). Consensus peaks for each condition were created using dba. counts requiring that at least three samples show a significant peak in the same genomic window (minOverlap = 3). The DBA_EDGER method was used to call differential peaks including treatment and strain in the contrast. Regions showing an absolute log2 fold-change |log2 FC| ≥ 0. 58 between selected contrasts and a FDR ≤ 0. 01 (Benjamini-Hochberg) are called differential.
Overlapping DHSs with gene annotation To assign DHSs to promoters, intra-, or intergenic elements, the function assignChromosomeRegion in the ChIPpeakAnno v3. 16. 1 R package  (Zhu et al, 2010; Zhu, 2013)  was used on the UCSC known gene annotation (https://genome. ucsc. edu/). DHSs assigned to the categories fiveUTRs, threeUTRs, Exons, or Introns were called intragenic, whereas DHSs assigned to the categories Intergenic. Region and Immediate Downstream were called Intergenic (categories as per R package).
Browser track visualization of DNase-seq data For visualization of individual loci, edgeR-normalized DNase-seq tracks were produced converting BAM files into BedGraphs using BEDtools 2. 24. 0 genomecov with -bg and -scale parameters  (Quinlan, 2014). Sizes of the libraries were obtained from the dba. analyse object (bFullLibrarySize = TRUE). Scaling factors were calculated by dividing the minimum library count by the size of each individual library. Bigwig files were compiled from normalized BedGraphs using bedGraphToBigWig v4. Coverage of individual loci were plotted in R3. 5. 0 using the wiggleplotr 1. 6. 1 R package.
STRING-db sub-network enrichment analysis The STRINGdb v1. 22. 0 R package  (Franceschini et al, 2013)  was used to identify enriched protein-protein interaction sub-networks among predefined gene sets. The complete network (version 10) for the Mus Musculus (10,090) organism was selected and only gene symbol that mapped to STRINGdb IDs were retained. Sub-networks were identified using the Walktrap community-finding algorithm implemented in igraph v. 1. 2. 4. Total RNA extraction and sequencing 25-50 mg of frozen liver material was used for total RNA extraction using 1 ml TRIzol reagent (cat. no. 15596026; Thermo Fisher Scientific). Samples were homogenized using a MagNA Lyser homogenizer (Roche) using two runs of 30 s at 6,500 rpm. Aliquots of ~600 μl of homogenized material were transferred into pre-spun 2-ml Phase Lock Gel-Heavy tubes (5Prime) and incubated for 5 min at room temperature.
TFBS enrichment We searched the different groups of DHSs for enrichment of TF binding sites using the HOMER software  (Heinz et al, 2010). We performed both, de novo motif analysis and enrichment analysis of known TF motifs and overlapped the results to strengthen the robustness of the identified motifs. For the enrichment analysis, we obtained known TF binding motifs from the JASPAR database  (Khan et al, 2018). Because motifs of different TFs can be very similar, we used the root motifs from the TF motif clustering provided in the JASPAR database as input for HOMER. The score cutoffs to call binding sites were optimized for each root motif by running HOMER's findMotifs on all DHSs and taking the score that defined the 88% quantile based on the resulting score distribution. Finally, we ran HOMER's findMotif on the individual DHS groups providing the fasta files of all DHS regions for background parameter calculation and obtained enrichment and de novo analysis results.
Acknowledgements Technical sequencing help was given by  S Dessus-Babus  and  E Oakeley. We also thank  M Stadler ,  F Hahne ,  P Marc ,  P Couttet ,  B Peterson ,  S Hoersch ,  O Grenet ,  A Vicart , and  S-D Chibout  for scientific discussions. This work was supported by  Innovative Medicine Initiative Joint Undertaking  ( 115001 ) (MARCAR project: http://www. imi. marcar. eu/)and  Novartis. C R Wolf  was a recipient of a  Cancer Research UK program  grant  C4639/A10822. J Perner ,  J Beil ,  J Zhu ,  A Del Río-Espínola ,  L Morawiec ,  M Westphal ,  V Dubost ,  M Altorfer ,  U Naumann ,  A Mueller ,  K Kapur ,  M Borowsky ,  J Moggs , and  R Terranova  are full time employees of  Novartis. A Vitobello  was a recipient of a  Novartis Institutes for Biomedical Research Postdoctoral Fellowships.
Non-small cell lung cancer (NSCLC) has been the leading cause of cancer-related death worldwide, over the last few decades. Survival remains extremely poor in the metastatic setting and, consequently, innovative therapeutic strategies are urgently needed. Inhibitor of Growth Gene 2 (ING2) is a core component of the mSin3A/Histone deacetylases complex (HDAC), which controls the chromatin acetylation status and modulates gene transcription. This gene has been characterized as a tumor suppressor gene and its status in cancer has been scarcely explored. In this review, we focused on ING2 and other mSin3A/HDAC member statuses in NSCLC. Taking advantage of existing public databases and known pharmacological properties of HDAC inhibitors, finally, we proposed a therapeutic model based on an ING2 biomarker-guided strategy.
Histone deacetylases enzymes could promote adjacent histones deacetylation that would in turn result in chromatin remodeling and regulation of gene transcription, mainly inducing transcription repression  [5]. In addition, as part of the p53 pathway, ING2 is involved in diverse cellular processes that are recognized as hallmarks of cancer  [6] [7] [8]  and its deletion in mice led to spontaneous soft tissue sarcomas formation  [9]. ING2 is now characterized as a tumor suppressor gene and as such, its expression is frequently altered in human tumors. In this review, we assess literature and public databases to evaluate ING2 and other mSin3A/HDAC member statuses in NSCLC, in an effort to uncover new therapeutic opportunities.
To explore further ING2 status in cancer, we used The Cancer Genome Atlas database  [60, 61]  to analyze alterations of the ING2 gene and the members of the mSin3A/HDAC complex. This analysis revealed that 221 out of 9,892 (2. 23%) human cancer samples included in the TCGA Pancancer study presented an alteration of ING2 gene and 161 of these 221 (73%) alterations were deletions. Moreover, 2,384 out of 9,892 (24. 1%) tumor samples presented an alteration in in at least one member of the Sin3a HDAC complex. Interestingly, one of the tumor types with the most frequent alteration in ING2 was the lung squamous cell carcinoma, comprising 5. 54% of the tumors involved (Figure  2 , panel A). Sin3a HDAC complex. Interestingly, one of the tumor types with the most frequent alteration in ING2 was the lung squamous cell carcinoma, comprising 5.
ING2 loss was correlated with lymph node metastasis status and TNM stage in squamous cell carcinoma, but not in adenocarcinome  [63]. Whether these discrepancies between these two studies are due to antibody specificity or ethnic disparities (notably, oncogenic driver mutational status was unknown in both studies), remains to be elucidated. Moreover, some study supports a transcriptional control of ING2 expression in NSCLC showing a correlation with ING2 mRNA decrease and low protein expression  [62]. Consistently, studies also reported that ING2 was downregulated at the mRNA level in lung cancer cell lines  [49, 50]. Nevertheless, contradictory results have been found when analyzing TCGA and The Human Protein Atlas (THPA) databases, as the authors did not find any correlation between INGs mRNA and protein expression  [52].
Therefore, it is still unclear if the deletion of ING2 alone or the whole chromosomic region is implicated in promoting tumorigenesis. Concerning ING2 mutations, only silent ones have been found in NSCLC. As an example, a study reported a substitution (C to T) in exon 1 at codon 13 in 6 out of 31 lung cancer (without any change of the encoded amino acid (Alanine)  [49] ) was likely to be a polymorphism (Table  1 ). Interestingly, the analysis of the mSin3A/HDAC complex member alterations in NSCLC via the TCGA database revealed that ING2 was altered in 20 out of 408 (4. 9%) NSCLC and was found to be higher in squamous cell carcinoma than in adenocarcinoma (6. 7% and 2. 6%, respectively) (Figure  2 , panel A). Moreover, the ING2 gene deep deletion was the most frequent alteration with 17 out of 408 (3.
54%) NSCLC and was found to be higher in squamous cell carcinoma than in adenocarcinoma (6. 18% and 2. 61%, respectively). Only one missense mutation (E204Q) of ING2 of unknown significance was reported out of the 408 NSCLC samples. Focusing on all members of the mSin3A/HDAC complex, data revealed that 152 of the 408 (37. 3%) NSCLC presented an alteration in at least one member of the mSin3A/HDAC complex, occurring in 38. 8% of squamous cell carcinoma specimens and 36. 1% of adenocarcinoma specimens (Figure  2 , panel B). Notably, we found that patients presenting an alteration in at least one member of the mSin3A/HDAC complex had a higher probability of prolonged overall survival, in comparison with patients without any alteration of the complex in lung squamous cell carcinoma (p = 0. 0451) (data not shown).
Exploring ING2 Dependencies in Cancer Cells Recently, lot of great efforts have been made to uncover genetic vulnerabilities in cancer, notably taking advantage of the emergent CrispR-screening methods. The Cancer Dependency Map project, led by the Broad Institute, is a systematic high-throughput screening of genotype-specific cancer vulnerabilities in tumor cell lines  [69]. According to the latest dataset (CrispR AVANA public 19Q2), many mSin3A/HDAC members are found among the top ING2 gene co-dependencies in cancer cell lines (Figure  3, panel A ). These results suggest that many tumor cells lines are dependent on a functionally active mSin3A/HDAC complex for proliferation. Consequently, we speculate that it could be utilized as an "Achilles' heel", potentially leading to a biomarker-guided therapeutic model (Figure  3 , panel B).
6% of adenocarcinoma and 17. 53% of squamous cell carcinoma) (Figure  2 , Panel B), clearly pledges for a "gain-of-function" phenotype predisposing tumors cells to a selective advantage. Additionally, small molecule inhibitors targeting Paired Amphipathic Helix 2 (PAH2) domain of mSin3A was shown to induce transcriptional reprogramming, impairment of clonogenic abilities, inhibition of proliferation, and metastasis in triple negative breast cancer models  [75]. Coherently, the Cancer Dependency Map project database characterizes SIN3A gene as a "common essential" gene in a vast majority of cancer cell lines. It is of note that SIN3A-knockout mice embryos stop developing after 6. 5 weeks  [76]. Consequently, targeting mSin3A itself, or specific partners of the complex, appears to be an attractive therapeutic strategy in cancer.
B, model of the crystal structure of the E. coli Hsp90 dimer in the apo form (PDB code 2IOQ)  (32)  with the C-terminal domains aligned to the crystal structure of the isolated C-terminal domain (PDB code 1SF8)  (19). C, dimer structure of E. coli Hsp90 in the ADP-bound conformation (PDB code 2IOP)  (32). D, dimer structure of Saccharomyces cerevisiae Hsp82 in the AMP-PNP-bound conformation (PDB code 2CG9)  (41). E, dimer structure of Canis lupus familiaris endoplasmic reticulum Hsp90, GRP94, in the AMP-PNP-bound conformation (PDB code 5ULS)  (30). F, dimer structure of Danio rerio (zebrafish) mitochondrial Hsp90, TRAP1, in the AMP-PNP-bound conformation (PDB code 4IPE)  (31). B-F, all images were prepared using PyMOL, and the domains are colored as in A. C-F, bound nucleotide is shown in black as CPK models.
In the ATPbound state, the ␤-sheet subdomain of the SBD is displaced from the ␣-helical lid, and both subdomains bind to different regions on the NBD leaving the substrate-binding site accessible for rapid, low-affinity substrate interactions (Fig. 2B )  (67, 68). Additionally, in the ATP-bound conformation the interdomain linker is docked into a crease exposed in the NBD when the ␣-helical lid of the SBD rotates to interact with the NBD (Fig. 2B ) (67-69). (61)  and in the open (B) ATP-bound conformation (PDB code 4B9Q), which is stabilized in an oxidized triple mutant (E47C, T199A, and F529C) in the presence of ATP  (67). The DnaK protomer contains an NBD (gray) connected by a flexible linker (blue) to an SBD that is made up of two subdomains, an ␣-helical subdomain, the "lid" (teal), and a ␤-sheet subdomain that contains the substrate-binding site (purple).
D, surface rendering of DnaK in the ATP-bound conformation (PDB code 4B9Q) (67) showing the change in position of the SBD ␣-helical and ␤-sheet subdomains (light orange) relative to the NBD (gray) and the Hsp90 Ec -interacting residues (blue). E, docked surface-rendered model of the apo structure of Hsp90 Ec (shown in A) and a cartoon model of ADP-bound DnaK (shown in B) (12). The Hsp90 Ec dimer is shown with one protomer in gray, one in light blue, and the DnaK-interacting region in the MD shown in red. DnaK in shown in light orange with Hsp90 Ec -interacting residues in the NBD shown in blue as CPK models. F, docked homology models of apo yeast Hsp82 and ADP-bound Ssa1 (18). The Hsp82 dimer is shown as a surface-rendered model in gray and light green with the Ssa1-binding region on the M-domain shown in red.