dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolomic analysis Leaves, stems, flowers, and roots of V. minor were frozen in liquid nitrogen, then freeze-dried, ground into powder, and 2 replicates for each organ were sonicated with methanol and 0. 1% of formic acid to extract metabolites. After centrifugations, extracts have been injected at 0. 01 mg/ml on a UPLC system coupled to a quadrupole time-of-flight mass spectrometer, and the acquisition was made using full MS survey scans to acquire high-resolution MS1 data for metabolomics analysis and using a fast data-dependent acquisition to acquire MS/MS scans to help compounds identifications. Metabolomics data have been processed with MzMine2  (Pluskal et al. 2010 ). Peak areas have been corrected by dividing the peak area by the ratio: dry weight of plant/extract weight obtained in order to compare metabolite abundance between organs.
0 Mb, contig number to 296 and increased N50 length to 6. 0 Mb. Finally, the collapsed assembly was polished twice using Illumina short-reads (Supplementary Table  2 ) with pilon  (Walker et al. 2014) , resulting in a final assembly length of 679 Mb (1. 3 Â the estimated assembly length), consisting of 296 scaffolds with an N50 scaffold length of 6. 0 Mb (Table  2 ). 95. 4% of this final assembly could be covered by Illumina short reads, indicating a good level of continuity. The base level QV of 28. 6878 (Table  2 ), corresponding to more than 99. 8% accuracy, highlights the good quality of this final assembly. Genome completeness was determined by identifying conserved orthologs from the eudicot lineage dataset in the genome using BUSCO (Benchmarking Universal Single-Copy Orthologs). This analysis revealed that 96.
6% of 2,326 eudicot lineage genes could be identified in the genome, indicating a very high level of completion (Fig. 2a ). RNA-seq-based gene model prediction using RNA-seq reads from the young leaves, old leaves, and adventitious roots of V. minor  (Stander et al. 2020) , were used to annotate a total of 29,624 protein-coding genes and 38,299 transcripts in the V. minor genome (Table  1 ), in comparison to the 34,363 and 22,617 transcripts reported in C. roseus and G. sempervirens  (Franke et al. 2019). Depending on the RNAseq sample, 91. 95-94. 8% of reads uniquely mapped on this final assemble; 3. 63-5. 54% highlighted multiple matches and 1. 19-2. 51% did not match (Supplementary Table  3 ), indicating a good annotation quality. Among these 38,299 transcripts, 87. 1% (33,357) presented a complete CDS based on TransDecoder ORF prediction.
BUSCO analysis of the predicted transcripts revealed a complete BUSCO score of 88. 6% and a low fragmentation score of 1. 6%, indicating that a large number of genes with high-quality sequences have been annotated (Fig. 2a ). These sequences were functionally annotated against Pfam, SwissProt, GO, and KEGG databases resulting in the annotation of 81. 9% genes (24,272 predicted genes, Fig. 2b ), and 82. 4% transcripts (31,572 predicted transcripts, Supplementary Table  4 ; Fig. 2c ). A quarter of the total genes were annotated by all 4 databases, whereas 53% of the genes were only annotated with GO and KEGG databases. Next, we explored evolutionary WGD artifacts by identifying paralogous gene pairs across different plant species, and calculating the synonymous substitutions per synonymous site (Ks) for each gene pair (Fig.
Selected ion monitoring method (vincadifformine: m/z 339; 16hydroxyvincadifformine: m/z 355) along with the standards were employed to detect vincadifformine or the hydroxylated product of vincadifformine. The representative replicate results are displayed. RT, retention time. methoxylation cluster as already described in C. roseus and G. sempervirens for tabersonine and rankinidine, respectively. This reinforces the potential interest of this genomic data set for gene discovery and contributes to the construction of an MIAorientated pan-genome. Therefore, beyond widening our knowledge on MIA biosynthesis, the future elucidations of MIA biosynthetic routes resulting from this study will also allow the development of new MIA supply approaches based on the gene transfer in heterologous hosts such as yeast or bacterium  (Courdavault et al.
Camacho et al. 2009ng et al. 20041), and Papaver somniferum(Guo et al. 2018). For each dataset, duplicated gene pairs were identified using a discontiguous MegaBLAST(Ma et al. 2002;Zhang et al. 2004) which selected sequences that shared at least 40% sequence similarity over 300 bp. The open reading frame for each gene pair was established by comparison to the NCBI's plant RefSeqs protein database (2021 May 21) using BLASTX (v. 2. 6. 0-1,Camachoet al. 2009) and retaining only the best hits with a sequence similarity threshold of 30% over 150 sites. GeneWise.
Data availability Raw DNA-seq and the genome assembly have been deposited in the NCBI database under the BioProject accession number: PRJNA873287 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA873287). The genome annotation, transcript expression abundances, and script used for searching for MIA gene clusters are available on the figshare: https:// doi. org/10. 6084/m9. figshare. 20749096. v1. Supplemental material available at G3 online.
Camacho et al. 2009ng et al. 20041), and Papaver somniferum(Guo et al. 2018). For each dataset, duplicated gene pairs were identified using a discontiguous MegaBLAST(Ma et al. 2002;Zhang et al. 2004) which selected sequences that shared at least 40% sequence similarity over 300 bp. The open reading frame for each gene pair was established by comparison to the NCBI's plant RefSeqs protein database (2021 May 21) using BLASTX (v. 2. 6. 0-1,Camachoet al. 2009) and retaining only the best hits with a sequence similarity threshold of 30% over 150 sites. GeneWise (Lamesch et al. 2012) events in V. minor, Arabidopsis thaliana(Lamesch et al. 2012), C. roseus (Franke et al. 2019), Mitragyna speciosa (Brose et al. 2021), Solanum lycopersicum (Hosmani et al. 2019), Camptotheca acuminata (Kang et al. 2021), Calotropis gigantea (Hoopes et al.
Identification of co-localized MIA gene regions on the Vinca minor genome and synteny analysis Regions of co-localized biosynthetic genes were annotated on the V. minor genome using a personalized script that is available from https://doi. org/10. 6084/m9. figshare. 20749096. v1. Briefly, the script uses the genome. gff file, MIA blastn results in output format 6, and uniprot search results to anchor the putative MIA orthologs from the blastn analysis onto the genome. The script then searches 100,000 bp regions around each side of the annotation for additional genes annotated by pfam accessions: PF03171 (2OG-Fe(II) oxygenase superfamily), PF14226 (non-haem dioxygenase in morphine synthe- sis N-terminal), PF00891 (O-methyltransferase domain), PF08240 (Alcohol dehydrogenase GroES-like domain), PF00067 (Cytochrome P450), PF08031 (Berberine and berberine-like), and PF00201 (UDP- glucosyl transferase).
Slides were then counterstained with hematoxylin. All paraffin-embedded liver sections were scanned with a digital slide scanner (Nanozoomer 2. 0-RS, Hamamatsu Photonics, Massy, France) and files were analyzed with the NDP viewer 2. 5 software (Hamamatsu, Hamamatsu City, Japan). Signal quantifications were performed with an image analysis software (NIS-Element AR analysis software, Nikon, Tokyo, Japan).
Statistical Analysis Data were expressed as means ± SEM for all mice treated similarly. Mean differences between experimental groups were assessed using the non-parametric Mann-Whitney Utest. All statistical analysis was achieved with the GraphPad Prism5 software. Significance is shown as follows: * p < 0. 05, ** p < 0. 01, *** p < 0. 001, and **** p < 0. 0001.
Acknowledgments: We would also like to thank  Mathieu JM. Bertrand  and  Peter Vandenabeele , both from the  Inflammation Research Center, VIB (Belgium)  for the provision of Ripk1 LPC-KO mice and the team of  Peter J. Gough  ( GlaxoSmithKline, Collegeville, PA, USA ) for the provision of Ripk1 K45A mice. We also extend our thanks to  Gevorg Ghukasyan ,  Ghania H. Kara-Ali ,  Mélanie Simoes-Eugénio ,  Ghiles Imerzoukene  and  Sarah Dion  for their technical assistance. We acknowledge the " Laboratoire de Biochimie-Toxicologie " from the  "CHU de Rennes " for transaminase measurements. For immunohistochemistry, histology analysis and animal house facilities, we would like to thank dedicated platforms (i. e. , H2P2 and ARCHE animal house platforms) of  SFR BIOSIT-UMS 3480 ,  US_S 018 , France.
Structural commentary The shape of the cobalt complex in the title compound is pseudooctahedral (Fig. 1 ). The cobalt(III) atom is coordinated by six nitrogen atoms from three dip ligands (dip = 4,7diphenyl-1,10-phenanthroline). The Co-N bond lengths are in the range 1. 934 (3)-1. 954 (3) A ˚(Table  1 ) and correlate well with literature values observed for Co III species. Indeed, the average Co-N bond length is 2. 128 A ˚in Co I cations (three hits in the Cambridge Structural Database (CSD;  Groom et al. , 2016) , 2. 115 A ˚in Co II cations (106 hits), and 1. 952 A ˚in Co III cations (28 hits) in reported Co(phen) 3 n+ (phen = phenanthroline) species. The bond angles between the axially bound ligand atoms are in the range 175. 62 (13)-176. 52 (13) while the equatorial bond angles fall in the range 83.
Database survey A search of the CSD (version 5. 43, last updated November 2021;  Groom et al. , 2016)  for similar M(dip) 3 n+ compounds gave three hits: two compounds with Ru II as the central metal cation (n = 2; CSD refcodes LAKCIN: Alatrash & Macdonnell, 2020; DOWREM:  Goldstein et al. , 1986)  and one compound with Ni II (n = 2; refcode EYAHUI:  Hadadzadeh et al. , 2011).
Acknowledgements The synthetic, catalytic, and crystallographic parts of this research were supported by the  U. S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division  at the  Lawrence Berkeley National Laboratory  under Contract  DE-AC02-05CH11231. The  Catalysis Laboratory  in the DOE Catalysis Program also provided resource for instrumentation used in this work. We also thank the  University of Edinburgh. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 740311 , P. L. A. ) and the  EPSRC  are acknowledged for research funding through the  UK Catalysis Hub  ( EP/K014714/1 , P. L. A.
). We acknowledge the  National Institutes of Health (NIH)  for funding the  UC Berkeley College of Chemistry NMR  facility under grant no. SRR023679A ,  S10OD024998  and  1S10RR016634-01 , and Beamline 12. 2. 1 of the  Advanced Light Source , which is a  DOE Office of Science User Facility  under contract no. DE-AC02-05CH11231. L. M. is a senior member of the  Institut Universitaire de France. The  Chinese Academy of Science and the Chinese Scholarship Council  are acknowledged for nancial support and CALMIP is thanked for a generous grant of computing time. The authors thank  Rex C. Handford ,  Francis Y. T. Lam ,  Nicholas J. Katzer ,  Gabe Herrera ,  Dr Amy N. Price  and  Dr Anthony R. Wong  for crystallographic and/or experimental help, as well as  Prof. Rebecca J. Abergel ,  Dr Leticia Arnedo Sanchez  and  Dr Jennifer Wacker  for help with photophysical measurements and  Mr Gabriel Herrera  for the LCMS and HRMS data.
10 ), indicating that the main bottleneck was still within the tabersonine/ catharanthine module and further enhancing the vindoline module expression was detrimental to catharanthine production because of a stronger pull of dihydroprecondylocarpine acetate, which is also a precursor to catharanthine (Fig. 1 ). Subsequently, strain MIA-EM-2 was grown in fed-batch cultivation in ambr 250 bioreactors, with continuous feeding of 3× SC containing 2% glucose and 3 mM tryptophan (between 20 and 72 h), and daily dosing of 3 g l -1 galactose during days 4-10. Samples were collected every 24 h to analyse the MIA metabolites. At the end of the fermentation (day 11), the catharanthine and vindoline titres reached 91. 4 μg l -1 (271 nM) and 13. 2 μg l -1 (29. 0 nM), respectively (Extended Data Fig. 11  and Supplementary Fig.
For all editing were transformed with a plasmid expressing a single or multiple-targeting guide-RNA fragments, as well as DNA parts as repair templates Transformants were plated on SC agar lacking the appropriate amino acid(s) (uracil, leucine, histidine or tryptophan) for selection of the guide-RNA plasmid, and grown for 3-4 days at 30 °C. All strains with genomic integration were confirmed by genotyping PCR and Sanger sequencing of the integrated gene cassettes. For all strictosidine strains except MIA-AU and MIA-AW-2, a unique guide-RNA sequence was included at the beginning of all gene cassettes (beginning of the promoter sequence) for easy swapping of the promoter or deletion of whole gene cassettes (Extended Data Fig. 3a ). To swap a promoter/gene in an expression cassette, yeast cells were transformed with a plasmid expressing the guide-RNA, together with a new promoter/gene and parts overlapping with sequences up-and downstream of this promoter/gene (Extended Data Fig.
3b ). Similarly, to delete a whole cassette, yeast cells were transformed with a plasmid expressing the specific guide-RNA and two parts (with a 50-60 bp overlapping sequence) that are homologous to the up-and downstream of the locus to be deleted (Extended Data Fig. 3c ). Transformants were plated on SC agar lacking either leucine or uracil for selection of the guide-RNA plasmid and confirmed by colony PCR and sequencing.
9 ). All cultures were grown in 300 μl of SC medium in 96-well square-shaped microtitre plates for at least 3 days. Cultures were grown at 30 °C with shaking speed of 250 r. p. m. and the cell density was measured every 30 min. Data from the exponential growth phase were used to determine the maximum specific growth rate by linear regression. Small-scale production assays were performed using either 96-well (with 0. 5 ml of medium) or 24-well (with 2 ml of medium) deep-well plates. After 1 day (typically reaching the stationary phase), 10 μl of precultures were transferred to either SC lacking appropriate amino acid(s) or YPD medium supplemented with one or more precursors. For testing strictosidine production, yeast strains were fed with 0. 1-1 mM tryptamine and one of the precursors including geraniol, 8-hydroygeraniol, 8-oxogeranial, cis-trans-nepetalactol, loganic acid, loganin and secologanin.
The data were acquired with Agilent MassHunter Workstation Software v. B. 08. 02. LC-MS Data Acquisition operating in dynamicMRM mode. Multiple reaction monitoring (MRM) transitions for the targeted proteins were generated by Skyline software v. 20. 2 (MacCoss Laboratory Software) and selection criteria excluded peptides with Met/Cys residues, tryptic peptides followed by further cut sites (KK/RR) and peptides with proline adjacent to K/R cut sites. The data and Skyline methods are available on Panoramaweb (https://panoramaweb. org/microbial-synthesis-of-vinblastine. url). Proteins from the MIA-DJ (tabersonine-vinblastine double module) strain were analysed using the Cap-LC system equipped with a C18 easy spray column (Thermo Fisher Scientific), coupled to Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific)  50.
The resulting data were analysed using Proteome Discover v. 2. 3 (Thermo Fisher Scientific) by searching against the S. cerevisiae proteome data (Uniprot ID UP000002311) combined with all heterologous protein sequences. The abundance of each protein is reported as the relative intensity to the total intensity of all identified peptides. 1 ). Asterisk indicates the data were from a different experiment but under the same condition (SC medium + 100 μM secologanin + 1 mM tryptamine, cultivated for six days and analyzed on LC-MS). Data presented are mean ± s. d. (n = 3). Green color indicates significantly higher production compared to CroSGD (p-value < 0. 05, Student's two-tailed t-test). More statistical analysis is available in the source data file.
Acknowledgements This study was supported by grants from the  Novo Nordisk Foundation  (grant no. NNF10CC1016517 ),  Horizon 2020 (MIAMi , grant no. 814645 ) and  BII Foundation  (grant nos. NNF19OC0055591  and  NNF20SA0067054 ). We thank  S. Rose  ( Axyntis Group, France ) for providing the tabersonine standard. We thank  P. Charusanti  and  L. Ding  for technical assistance. Confocal microscopy analysis was carried out at  Center for Advanced Bioimaging, University of Copenhagen.
Data availability All metabolite data shown in figures and extended data figures are available in the Source data provided with this paper. Accession of all heterologous genes used in this study are listed in Supplementary Table  1. Targeted proteomics data for strain MIA-AU are available at ProteomeXchange with identifier PXD024976 (https://panoramaweb. org/microbial-synthesis-of-vinblastine. url). Untargeted proteomics data for strain MIA-DJ are available at ProteomeXchange with identifier PXD025067.
MATERIALS AND METHODS A retrospective, single-center study was conducted in a 1,673bed teaching hospital (Amiens-Picardie teaching hospital; France). The study was approved and registered by Amiens-Picardie teaching hospital's local research department under the number PI2022_843_0008. Postgraduate pharmacists and pharmacy residents analyzed computerized medical prescriptions on the hospital prescription assistance software DxCare ™ (Medasys, Le  Plessis-Robinson, France, version 7. 7. 2) or Clinisoft ™ (for intensive care units) (GE Healtcare, Barrington, Illinois, United States, version 7. 0). In France, pharmacy or medical residents are students who have already completed at least 5 (pharmacy residency) or 6 (medical residency) years of study and who perform many of the tasks assigned to postgraduate pharmacists and postgraduate physicians.
All PIs transcribed on Act-IP© between November 2018 and April 2019 were extracted and analyzed. The following variables were extracted from the Act-IP© database: • Patient age. • Patient gender. • Care unit. • Status of the pharmacist (postgraduate or resident) performing the PI. • Location in which the PI was performed (pharmacy or care unit). • Status of the prescriber (postgraduate or resident). • Contact method of the PI. • Anatomical Therapeutic Chemical (ATC) drug class concerned by the PI. • Problem encountered according to the SFPC coding system  (Allenet et al. , 2006 ). • Type of PI according to the SFPC coding system  (Allenet et al. , 2006 ). • CLinical, economic, and organizational (CLEO) impact of the PIs according to the recently published CLEO scale  (Vo et al. , 2019). Briefly the clinical dimension reflects the risk of complication avoided by the PI and is coded on a Likert scale (from -1C: negative PIs to 4C: vital PIs).
A PI is considered to have a major clinical impact on patient health when it avoids a risk of prolonged hospitalization, a permanent disability or handicap. The economic and organizational impact are coded on a 3-point Likert scale (from -1 to +1). • Use, or not, of the medical decision support system (MDSS) Pharmaclass ®. • Pharmacist intervention carried out or not following the reconciliation of drug treatment. • Outcome of PI (accepted or not by the physician). An independent complementary documentation was performed for all PIs whose acceptance was recorded as unknown to complete missing data on that variable, based on the actual modification of the prescription.
Acceptance Rate of Pharmacist Interventions A total of 2,415 PIs (77. 9%) were traced as accepted and 515 (16. 6%) as refused, while the acceptance status of 170 (5. 5%) pIs could not be found despite retrospective analysis of patient records. Under the maximum bias assumption, the acceptance rate ranged from 77. 9%, considering missing data as refusals, to 83. 4%, considering missing data as acceptances. There was a significant difference (p < 0. 0001 by Chi 2 test) in the rate of missing data on PI acceptance according to the method of contacting the prescriber, with 2. 1% (35/1,658) missing data in cases of verbal exchange, 3. 0% (14/469) by telephone, 12. 4% (120/966) by the prescriber assistance software, 0% (0/6) by paper, and 100% (1/1) by e-mail. Subsequent statistical analyses excluded observations with missing data on PI acceptance status, and thus 2,930 PIs were retained.
DISCUSSION The percentage of accepted PIs in this study (82. 4%) is higher than the values published in the literature which were, for example, 56% in a French psychiatric hospital  (Tambon et al. , 2019) , 67. 2% in a French rheumatology department  (Yailian et al. , 2019) , 74. 1% in a French general hospital  (Loustalot et al. , 2019) , but notably 67. 8 and 67. 6% in studies of the entire Act-IP© database between 2006 and 2009  (Bedouch et al. , 2015)  and between 2014 and 2018  (Videau et al. , 2021). This acceptance rate is similar to that recently published by a Spanish team who reported an accepted PI rate of 74%, but included 15% of PIs whose status was not indicated  (Garin et al. , 2021). The percentage of acceptance in the present study may be due to the maturation of the activity over time and the implementation of several specialized clinical pharmacy projects, but biases related to the open nature of the study should not be overlooked.
Despite the prioritization of pharmaceutical care in intensive care, geriatrics, orthopedic surgery, hematology, and oncology units, the profile of the type of PIs in the study is comparable to previously published data, regarding the leading problems encountered, i. e. , non-adherence to guidelines and dosing errors  (Bedouch et al. , 2015; Loustalot et al. , 2019). The main drug classes targeted by the PIs in this study were anti-infectives (30. 3% of all PIs), nervous system drugs (18. 7% of PIs), and blood and hematopoietic organ drugs (17. 3% of PIs). These data are similar to those found in a French teaching hospital  (Loustalot et al. , 2019) , in the Act-IP © database  (Bedouch et al. , 2015) , and in a Spanish study  (Garin et al. , 2021). In a raw analysis, PIs were more often accepted when they were performed in the care units, which is in line with a French multicenter study conducted in 2010  (Bedouch et al.
, 2009; Philippe et al. , 2017; Gougeard et al. , 2021). Mobile clinical pharmacy activity using MDSS tools could be a good compromise between proximity and prioritization of targeting. Pharmacist interventions were more often transmitted and accepted by medical residents than by postgraduate physicians, which is attributed to a higher prescribing rate, greater availability, and openness to discussions about the appropriateness of prescriptions. Nevertheless, this difference disappeared for PIs rated major or vital, suggesting the recovery of the prescriber's attention. The parameters statistically correlated with an accepted PI are qualitatively and quantitatively different from those previously published from the extraction of the entire national PI registration database  (Bedouch et al.
5 vs. 1. 7%)]. The variability in acceptance grouped by pharmacist is very high, represented by an inter-pharmacist standard deviation estimated at 12. 6% after adjustment. In future works, it seems essential to us to collect new factors characterizing the pharmacist-physician relationship (respective experience, relationship of trust, existence of situational protocols) as well as to better characterize the moment of the exchange (time, respective arguments, modification of the PI during the exchange). The study has several limitations. It is a retrospective, single center study and the documentation of the outcome was performed without independent reevaluation for all PIs. Pharmacists were instructed not to record PIs involving generic substitutions. As this is a daily activity among others, a lack of time to manually archive some PIs may also contribute to the non-exhaustiveness of the database  (Bedouch et al.