dataesr/scientific-paragraphs-categorization huggingface dataset:
(C) Solved structure (X-ray; PDB accession code 5H3J) and the AlphaFold2 (AF2) model of the mouse Golgin-45/GRASP complex. AF2 predicted structure almost overlaps the experimentally solved one (RMSD 3. 040 Å for the Cα of the last 16 amino acids of the Golgin-45 C-terminal peptide). (D) AF2 models of holozoan GRASPs in complex with their conspecific Golgin-45 C-termini. Echinoderms, Strongylocentrotus purpuratus; annelids, Platynereis dumerilii; arthropods, Drosophila melanogaster; nematodes, Caenorhabditis elegans; mollusks, Crassostrea gigas; cnidarians, Nematostella vectensis; ctenophores, Mnemiopsis leidyi; placozoans, Trichoplax adherens; filastereans, Capsaspora owczarzaki. Altered conformations, with respect to the mouse complex, are indicated by the arrows (blue for the PDZ-binding motif, red for the cysteine pair and green for the GRASP groove-interacting residues).
0. 11. 3, Trimmomatic v. 0. 33 and SPades v. 3. 15. 3 on the Galaxy platform ( https://africa. usegalaxy. eu/ ). Resistance genes were screened on Staramr, version 0. 9. 1 ( https://africa. usegalaxy. eu/ ) and ResFinder ( https://cge. cbs. dtu. dk/ services ) tools. Multilocus sequence typing (MLST) and plasmid detection were done through Pathogenwatch ( https://cgps. gitbook. io/pathogenwatch/ ) and Abricate, version 1. 0. 1 ( https:// africa. usegalaxy. eu/ ). All genomes were aligned with the reference genome, available on the NCBI website, and complete genome sequences were deposited in NCBI's GenBank database. All three isolates were resistant to carbapenem and to all the other tested antimicrobials (100%) with the exception of amikacin and nitrofurantoine ( Table  1  ); the acquired resistance genes conferring resistance to the most antimicrobial families were beta-lactams ( bla CMY-2 , bla CTX-M-15 , bla NDM-5 , bla OXA-1 , bla TEM-1B ), aminoglycosides [ aac(3)-Iid, aac(  6 )-Ib-cr,aadA2, aadA5, aph(3 ')-Ib, aph(6)-Id ], tetracycline [ tet (B)], macrolides [ mph (A)], trimethoprim ( dfrA12, dfrA17 ), and sulfonamides ( sul1, sul2 ).
Nucleotide sequence accession number: This  Whole Genome Shotgun  project has been deposited at  DDBJ/ENA /GenBank under the accession  JAOEGJ0 0 0 0 0 0 0 0 0 ,  JAOPKR0 0 0 0 0 0 0 0 0  and  JAOPKS0 0 0 0 0 0 0 0 0. The version described in this paper is the first version:  JAOEGJ0 0 0 0 0 0 0 0 0 ,  JAOPKR0 0 0 0 0 0 0 0 0  and  JAOPKS0 0 0 0 0 0 0 0 0  (NCBI link: https://submit. ncbi. nlm. nih. gov/ subs/sra/ ).
, 2021). Fossil woods might also have recorded this biotic turnover and the differential diversification between high and low latitudes  (Wheeler and Baas, 2019). Few localities in the world have yielded Paleocene wood specimens  (Poole, 2000) , and about 43% of all Paleocene fossil woods in the InsideWood database  (InsideWood, 2004 on; Wheeler, 2011)  are from India, mainly from the Deccan intertrappean beds. The Deccan fossil wood specimens are dated to the latest Maastrichtian-earliest Paleocene (67-65 Ma according to recent redating of Deccan basalts;  Schoene et al. , 2019)  and located near the equator at that time. Seemingly, their taxonomic diversity (many Malpighiales, Malvales, and Sapindales) is already quite close to modern or Miocene India  (Wheeler et al. , 2017). The anatomical features of these wood specimens are also surprisingly "modern" in a Baileyan way  (Bailey and Tupper, 1918)  compared to other paleotropical fossil woods from the same age around the world  (Wheeler et al.
Pit diameters were measured horizontally. The minimum stem diameter was roughly estimated on the slides using the average geometrical intersection of the rays to locate a virtual center  (De Franceschi et al. , 2008; Dufraisse et al. , 2020)  and ranges from 3 to 16. 5 cm and is frequently under 10 cm, which means some of the fragments may represent juvenile wood. Botanical affinities were determined using the InsideWood database  (InsideWood, 2004 onward; Wheeler, 2011; Wheeler et al. , 2020)  and literature on fossil  (Gregory et al. , 2009)  and references therein. For most of the specimens, we first searched the InsideWood database for affinities using a small list of obviously present features. Using an Excel spreadsheet, we then extracted the resulting potential matches and used combinations of features (e.
g. , scanty OR vasicentric paratracheal parenchyma) to manually filter out any with ambiguous characters. This approach yielded a wider range of potential botanical affinities, that are discussed and compared with the fossils. Additionally, we described each specimen in IAWA Feature codes as is done on InsideWood (the numbers correspond to the IAWA features, with "v" for the variable presence of the feature and "?" for the uncertain presence of the feature). Features that are numbered in the 300's are unique to the fossil wood database. All microscopic slides were deposited in the MNHN (Appendix 1). The remains of the original specimens are to be sent back to the collection of the Department of Geology at the University of Yangon (Myanmar) and are temporarily stored at the MNHN until they can be safely returned to their country of origin.
One small branch trace present in tangential section (Figure  5M ). F I G U R E 4 Wood- Estimated minimal diameter-between 7 and 16 cm Description in InsideWood codes (fossil code in parentheses)-5  13 22 26 27v 31 41 42? 47 52 56v 65? 66  69 76? 79 80? 83? 92 93 97 98 104? 106 114 115 136 142 168  (300 302 308 313 315)  Affinities and remarks This wood is characterized by (1) simple perforation plates, (2) medium to large, alternate intervessel pits, (3) vessel-ray parenchyma pits with reduced borders or simple, (4) parenchyma mostly paratracheal, (5) rays commonly >3seriate, (6) rays heterocellular with procumbent body cells and a few marginal rows of square/upright cells, (7) vessel element length short to medium, (8) tyloses present. These features are all present and commonly occur in Moraceae  (Metcalfe and Chalk, 1950; Koek-Noonnan et al.
Kribs's ray type heterogeneous IIB. Sheath cells absent. Fibers apparently non-septate, arranged in radial rows (Figure  6A ), 7-23 in tangential diameter (mean 15 μm). No mineral inclusion seen. Estimated minimal diameter-Imprecise due to poor preservation, between 5-16. 5 cm (more likely 12-16. 5 cm) Description in InsideWood codes (fossil code in parentheses)-2 5 13 22 25 42 46? 47? 52 75? 76? 79? 97 98v 106 107 115 168 (302 303v 313 315) Affinities and remarks This specimen does not show any distinctive diagnostic features. We searched the InsideWood database (2004 onward) using the following features: diffuse-porous wood with simple perforation plates (5p, 13p, 14a), alternate non-vestured intervessel pits (IVP) (20a, 22p, 29a), absence of large IVP (27a), vessels between 100-200 μm in tangential diameter and fewer than 20/mm² (40a, 42p, 48a, 49a, 50a), short vessel elements (52p), 1-3-seriate rays not of two sizes (96a, 97p, 99a, 103a), rays commonly with 1-4 marginal cells (105a, 106p, 107p, 115p), absence of storied rays (118a) and radial canals (130a).
Kribs's ray type heterogeneous IIB. Sheath cells absent. Fibers arranged in radial rows, 16-31 μm in tangential diameter (mean 23 μm). Lacunae, possibly idioblasts (oil cells) or evidence of decay, visible in tangential section (Figure  7G ). Some cells in transverse section (Figure  7B , arrowhead) could represent these possible oil cells or axial parenchyma cells. Estimated minimal diameter-between 5 and 8 cm Description in InsideWood codes (fossil code in parentheses)-5 9 13 22 26 27v 30? 31? 32? 41? 42? 47 52 65? 66 78 79 80? 92 93? 97 98 102v 106 107v 115 136 142 168 (300? 302 308 313 315) Affinities and remarks-In this wood, it is difficult to ascertain some important diagnostic features, such as the axial parenchyma arrangement, type of vessel-ray parenchyma pits, presence of crystals and septate fibers.
The combined features of the specimen are not found in any extant species in the InsideWood (2004 onward) database. We searched the InsideWood database (following the spreadsheet method mentioned in Materials and Methods) using the following features: diffuse-porous wood (5p), exclusively simple perforation plates (13p, 14a), alternate, non-vestured pitting that is not minute, at least medium (22p, 24a, 26p, 29a), vessel diameter, neither narrow or wide (40a, 43a) between 5 and 40 vessels/mm² (46a, 49a, 50a), paratracheal parenchyma (78p and/or 79p and/or 80p), 1-3-and 4-10-seriate rays (96a, 97p, 98p, 99a, 100a), heterocellular rays (104a, 105a), 2 or more cells per axial parenchyma strand (90a), and excluding rare or absent parenchyma (75a), sheath cells (110a or 110v), storied rays (118a) and radial canals (130a or 130v).
The database yielded seven families with these features: Anacardiaceae, Cannabaceae, Lauraceae, Moraceae, Phyllanthaceae, Rhamnaceae, Meliaceae. The enlarged marginal ray cells (Figure  7L ) could be oil cells or crystalliferous idioblasts. In tangential sections, large lacunae are sometimes observed (Figure  7G ) and could be a result of wood decay or represent large oil cells (yellowish droplets in these shapes could be oil). The presence of oil cells would indicate that this wood is a Lauraceae, close to the genus Beilschmiedia Nees, a genus that has vasicentric parenchyma, few oil cells among fibers, and few septate fibers. Actinodaphne Nees, Aspidostemon Rohwer & H. G. Richt. and some Ocotea Aubl. share some similarities, especially regarding the parenchyma arrangement, the few oil cells sometimes among fibers, and the size of rays or the diameter of vessels for some species.
Kribs's ray type heterogeneous III to IIA. Sheath cells absent. Fibers probably non-septate, poorly preserved, arranged in radial rows, 12-28 μm in tangential diameter (mean 20 μm). No mineral inclusions. Two smalls branch traces present (Figure  8J ). Helical thickenings present in the elements of the branch traces, likely primary xylem (Figure  8C ). Estimated minimal diameter-Difficult to estimate due to poor preservation, between 3 and 7 cm, (more likely 4 cm) Description in InsideWood codes (fossil code in parentheses)-  2 5 13 22 25 26 27 31 42 47 52 56 66 78?  79? 92 97 106v 107v 108v 109v 115 168 (300 302 313 315)  Affinities and remarks Diffuse-porous wood (5p), with simple perforation plates (13p), intervessel pits large, non-vestured and alternate (22p, 24a, 27p, 29a), vessel-ray pits with reduced borders to apparently simple (31p), tyloses (56p), vasicentric parenchyma (79p), short rays uni-and biseriate (97p, 98a, 99a, 102a), heterocellular (104a, 105a) strongly recall Anacardiaceae and Burseraceae  (InsideWood, 2004 onward).
Kribs's ray type heterogeneous III. Fibers apparently non-septate, cell walls poorly defined, radially aligned, 8-24 μm in tangential diameter (mean 15. 5 μm). Crystals maybe present in upright and procumbent ray cells (Figure  9B, D, E ). Estimated minimal diameter-Difficult to estimate due to bad preservation and compression, between 4 and 7 cm Description in InsideWood codes (fossil code in parentheses)-2 5 9? 13 22 26  27 30 31v 41? 42? 46? 47? 66 78 96 106 115 136 137 138 168 (300v 302 307 313 315)  Affinities and remarks This specimen is characterized by (1) diffuse-porous wood, (2) simple perforation plates, (3) large, non-vestured, alternate intervessel pits, (4) vessel-ray parenchyma pits with reduced borders to apparently simple, (5) scanty paratracheal or vasicentric parenchyma, (6) <30 cells high uniseriate rays, (7) heterocellular rays with mostly 1 row of upright marginal cells, (8) crystals in ray cells.
Fibers seemingly non-septate (Figure  10F ), radially and often tangentially aligned, 13-31 in tangential diameter (mean 21 μm). Estimated minimal diameter-between 5 and 7 cm Description in InsideWood codes (fossil code in parentheses)-2 5 13 22 26 27 31? 41? 42? 47 52 66 78 79 97 106 107 114v 115v 168 (300? 302 313 315).
Kribs's ray type heterogeneous IIB (to IIA). Fibers non-septate, arranged in radial rows, 9-22 μm in tangential diameter (mean 16 μm). Crystals in (sometimes enlarged) ray cells, mostly the upright ones, 23-27 μm maximum diameter (Figure  11B, E, F ). Estimated minimal diameter-not possible to determine Description in InsideWood codes (fossil code in parentheses)-2 5 13 22 26 27v 30? 31? 41? 42? 47? 52 66 78? 79 97 106 107v 115 136 137 156? 168 (300? 302 307 313 315) Affinities and remarks This specimen is very similar to the other Group 2 specimens: (1) diffuse-porous wood with simple perforation plates, (  2 ) alternate, non-vestured intervessel pits, (3) thin rays mainly 1-2-seriate, less than 30 cells high, (4) heterocellular rays with 1-3 marginal rows of upright cells, (5) scanty paratracheal or vasicentric parenchyma.
), Sabiaceae (Meliosma Blume, although parenchyma is rare or scanty) and Styracaceae (Styrax L. ). The combination of some of these features also occurs in Violaceae, Pandaceae, Putranjivaceae (Putranjiva Wall. ), Symplocaceae (Symplocos Jacq. ), Siparunaceae (Siparuna Aubl. ), Celastraceae, and Stemonuraceae. Exclusively scalariform perforation plates with few bars, alternate intervessel pits, parenchyma diffuse-in-aggregates, and tall rays with mostly square and upright cells do not occur in combination in any of the families cited above. As a consequence, the present fossil could either be placed at the base of the asterids, close to Malpighiales, close to Proteales at the base of Eudicots, or even in Laurales. In the fossil wood database of InsideWood, there are only a few descriptions for woods with alternate intervessel pits and scalariform perforation plates with relatively few bars.
ACKNOWLEDGMENTS The authors thank  V. Rommevaux  ( CNRS ,  UMR  7207) for preparing wood sections;  Myat Kay Thi ,  Hnin Hnin Swe ,  Amy Gough , and  Daniel Perez-Pinedo  for participating in the fossil sample collection;  Megan Mueller  and  Tamas Ugrai  for U-Pb dating the tuff;  Elizabeth Wheeler  and an anonymous reviewer for helpful comments that enhanced this manuscript;  Adeline Kerner  for help with the InDoRES Data Repository. This work was funded with  French Research Agency ANR  grant  ANR-19-ERC7-0007.
DATA AVAILABILITY STATEMENT All microscopic slides are deposited and available at the MNHN and the remains of the original specimens until further restitution to the collection of the Department of Geology at the University of Yangon (Myanmar). All voucher numbers with links to the MNHN collection database are listed in the Appendix 1. The raw and fullresolution versions of the individual photos used in the figures and additional photos are available in the InDoRES Data Repository: https://doi. org/10. 48579/PRO/YTWOSL  (Gentis, 2023).
, 2010). For each neuron, we deconvolved single-event responses by using the optimal number of iterations that had a cross-validation error lower than the PSTHs (5. 2+/-0. 2 iterations for the entire dataset, Fig. 4, 5 ). The analysis of raw PSTH aligned to the stimulus revealed that excitatory responses occurred at a stereotypical latency (58+/-4ms, Fig. 4A, C, D ) with variable durations (1. 57+/-0. 17 s, Fig. 4A, C, E ). The removal of the contribution of neighboring events by deconvolution had no effect on the onset latency distribution of stimulus-evoked excitations (KS=0. 147, P=0. 09, Fig. 4D ) but strongly decreased their durations (KS=0. 278, P=3. 4x10 -5 , Fig. 4E ). With raw PSTH, most lever-press-evoked excitations emerged before the occurrence of the event (-0. 830+/-0. 05 s) and lasted up to 4 s (1.
MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPc computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPc returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location.
However, pairwise correlation approaches applied to omics profiles produce very dense networks containing direct and indirect associations that are very difficult to distinguish. Here, we propose a novel in silico method, called MoPc (Multi-omics Partial Correlation analysis), to analyze post-transcriptional regulation by miRNAs by integrating information at both mRNA and protein level. The MoPc method predicts miRNA-target potential interactions using high-throughput mRNA, protein, and miRNA expression datasets from matched samples. It computes the conditional dependence (partial correlation) of mRNA and protein levels, taking into account the effect of a third variable, the expression of a given regulatory miRNA. the underlying hypothesis assumes that if a miRNA regulates a given gene, then the partial correlation between the mRNA and the protein expression of that gene conditioned by the expression of the miRNA will be greater than the bivariate correlation between the mRNA and protein levels of the gene.
Therefore, to identify candidate miRNA targets, MoCP selects those genes whose partial correlation between the level of RNA and protein levels conditioned to the expression of a given miRNA is more significant than the bivariate correlation between the RNA and the protein. MoPc has been applied to TCGA/CPTAC omics datasets of breast, glioblastoma, and lung cancer, predicting key miRNAs involved in these diseases. To assess the reliability of the interactions predicted by the MoPc, they have been tested for the enrichment in predicted targets by three independent databases, namely miRDB  [15, 16] , TargetScan  [17] , and miRTarBase  [18]. The targets predicted by MoPC in breast and lung cancer show significant overlap with those predicted by the three independent databases. In contrast, the targets predicted in glioblastoma significantly overlap with targets predicted in TargetScan and miRTarBase.
z = r yx -(r yz )(r xz ) 1 -r 2 yz 1 -r 2 xz (1) The bivariate Pearson correlation r yx between mRNA expression x and protein expression y is also computed. Relevant miRNA-target interactions are selected as those for which a lower p-value is observed with partial correlation than that obtained with bivariate correlation. To ensure a large increase in the significance of the p-value, only interactions with an improvement in the p-value in the upper quartile of the distribution are selected. The MoPc returns two outputs: 1. miRNA-target significant interactions. This list reports all miRNA-target candidate interactions based on the partial correlation measure. Additional columns contain the validation information (e. g. , if the miRNA-target interaction was previously known in the literature according to three databases: miRDB, TargetScan, and miRTarBase).
September 19, 2022 3/11 2. summary heatmap. The heatmap reports genes on the rows and miRNAs on the column ordered according to their chromosomal location. Significant miRNA-targets are reported in red (the more intense the color, the higher the partial correlation estimate). MiRNA-targets both significant and validated in at least one of the three databases (miRDB, TargetScan, and miRTarBase) are reported in dark green. The user can identify miRNAs targeting multiple genes by visually inspecting horizontal or vertical bands in the heatmap. The method is freely available as an R package at the following link: https://github. com/martalovino/MoPc Data pre-processing The MoPc method was tested on three TCGA/CPTAC datasets from breast, glioblastoma, and lung cancer studies  [19] [20] [21]. The breast cancer dataset was obtained from the work of Meryins et al.
, published in 2016  [19]. The published matrices contain the quantifications of 17814 genes, 7665 proteins, and 332 miRNAs for 77 samples, respectively. The glioblastoma dataset was obtained from the publication of Wang et al. of 2021  [20] , including mRNA, protein, and miRNA expression values for 106 tumor samples. The input matrices contain, respectively, 45914 genes, 10998 proteins, and 2883 miRNAs. The lung squamous cell tumor dataset comes from the 2021 publication of Satpathy et al. , in which mRNA, proteins, and miRNA expression are quantified  [21]. More than 200 samples have been analyzed, of which 201 have all three omics quantifications of our interest. Overall, 21792 genes, 11111 proteins, and 2585 miRNAs are reported. The three datasets were processed according to the following procedure.
These datasets contain missing values, particularly in protein and miRNA expression matrices. For our purposes, proteins and miRNAs with less than 10% of missing values across samples were kept. The remaining missing values were imputed by imposing the median value. Finally, the mRNA e miRNA matrices were log2 transformed. In all datasets, only the top 50% most highly variable proteins were included, since MoPc requires a certain variance in the expression profiles. Table  1  shows the datasets size supplied to MoPc.
MoPC detects biologically relevant regulatory miRNAs in cancer datasets MoPc was applied to three omic datasets from TCGA/CPTAC projects, namely breast, glioblastoma, and lung cancer. MoPc output returns 1) a table of miRNAs and predicted target genes and 2) a heatmap to visualize the chromosomal position of September 19, 2022 4/11 miRNAs and target genes. In particular, the table listing miRNAs and target genes contains both the estimate value and p-value returned from the partial correlation (the higher the estimate and the lower the FDR adjusted p-value, the more significant the interaction between that gene and that miRNA). In addition, it returns if the gene-miRNA target interaction is already annotated in the three external databases (miRDB, TargetScan, and miRTarBase).
Computational validation In order to test the reliability of MoPC predictions, the enrichment in miRDB, TargetScan, and miRTarBase predicted targets was assessed for each of the three cancer datasets, using hypergeometric test. In addition, the same enrichment test was performed for miRNA-target predictions based on the bivariate correlation between mRNA and miRNA expression and between protein and miRNA expression. Pearson correlation with FDR corrected p-values was computed and the miRNA-target pairs with a p-value lower than alpha 0. 05 were selected. The same conditions of the partial correlation were used to compute the bivariate correlations to perform a fair comparison. Tables  2, 3 , and 4 report for each of the three cancer datasets the percentage of miRNA-targets selected as significant, and the p-value of the hypergeometric test in each of the three databases (miRDB, TargetScan, and miRTarBase) both for the MoPc method and the mRNA-miRNA and protein-miRNA bivariate correlation.
MiRNA-targets visualization map MoPc returns a heatmap where miRNAs and predicted targets are arranged according to their chromosomal position. The heatmap represents in red the miRNA-targets significantly predicted according to the MoPc partial correlation analysis and in dark green, the miRNA-targets statistically predicted and validated in at least one of the three databases (miRDB, TargetScan, and miRTarBase). The user can visually explore the presence of miRNAs that regulate multiple genes and vice-versa (the same gene regulated by multiple miRNAs.