dataesr/scientific-paragraphs-categorization huggingface dataset:
Taste and smell characterizations of every species were compiled but did not constituted an exhaustive experimental data collection for a complete correlation "organoleption to bioactivity". Our survey led to collect and identify 59 plants (and 1 insect nest) for their antiinfectious use and to assess their antimicrobial activity. The minimal inhibitory concentration (MIC) was determined in vitro against 36 microbial strains belonging to 24 different species.
Methodology and data analysis It must be considered that indigenous diagnosis is inherently relative, due to cultural reasons. Indeed, according to nosological folk theories, indigenous people more frequently identify and focus on symptoms (fever, cold, tiredness, diarrhea…) and cultural concept of illness rather than on specific biological aetiologies such as microbes or parasites  (Roumy et al. 2007 ). Consequently, any specific identified "bacteria" were ascribed to a so-called "traditional use". Therefore, plant extracts were tested on a large panel of 36 bacteria or yeast, in order to be more representative of the infectious pathogens enhanced in the described disease. Control antibiotics had been previously performed and were available for each strain (Table  2 ), but it is important to remind that antibiotics are pure active compounds that do not have a vegetal origin, therefore it cannot be expected that plant extracts could have the same efficiency as antibiotics  (Das and Tiwari, 2010).
3; v/v/v) in ascending mode (8 mL/min during 30 min), then extrusion with stationary phase was realised (30 mL/min during 10 min). CPC was performed using a 250 mL rotor (SCPC-250-L, Armen Instrument) and a Shimadzu Prominence LC-20AP binary pump. Fractions were collected every minute and 10 subfractions (1 to 10) were then pooled according to TLC analysis. Fraction number 9 (providing by extrusion) was purified on preparative silica TLC (eluted by Hex/MeOH; 45:55; V/V) to obtain 9 mg of compound 1. Erysotrine 1: yellow amorpheous solid (9 mg), identified by NMR and MS spectra (See supplementary data). HRESIMS: m/z [M+H] + 314. 1762 (calcd for C19H24NO3, 314. 1751). Spectral data were consistent with those of Sarragiotto  (Sarragiotto et al. , 1981). Myrciaria dubia methanolic extracts from leave, seed and peel 600 g of leaves were extracted according previous process (used for E.
Acknowledgements The authors gratefully acknowledge  S. Mahieux  ( laboratoire de bactériologie, Université de Lille ), the ARPIA Peru association, F. M. Lambert, the  French embassy in Peru , the  Nord Pas de Calais region (SISA) , the project  BioScreen-Smartbiocontrol (Interreg V and CPER Alibiotech ),  Gaël Ostyn , as well as  Clara Mori ,  Corina Güivinsinti , the indigenous federation Quechua from  Pastaza (FEDIQUEP) ,  Luis Chino Butuna  and other Peruvian people who were willing to share with us their knowledge about medicinal plants.
0. The pH of the sample was maintained at 6. 5 during lipolysis using 2 M NaOH solution controlled using Tiamo version 2. 5 software (Metrohm, Herisau, Switzerland). Raman Spectroscopy. The experimental configuration for low-frequency Raman spectroscopy has been described previously. 25 In brief, a 785 nm laser source (Ondax, Inc. Monrovia, CA, USA) was filtered by two BragGrate bandpass filters (OptiGrate Corp. Oviedo, FL, USA) and focused (∼500 μm sample spot) on the quartz capillary containing the digestion media at a 135°angle relative to the collecting lens. The backscattered light from the sample was collected and filtered through a set of volume Bragg gratings (Ondax, Inc. Monrovia, CA, USA) and focused into a LS 785 spectrograph (Princeton Instruments, Trenton, NJ, USA), which dispersed the scattered light onto a PIXIS 100 BR CCD (Princeton Instruments, Trenton, NJ, USA).
Spectra were collected using WinSpec/32 software (Princeton Instruments, Trenton, NJ, USA) over a spectral window of -360 to 2030 cm -1 with 5-7 cm -1 resolution. A spectrum was collected every half a minute (0. 5 s acquisition time, ×60 accumulations per frame) for a total of about 45 min, i. e. , 90 frames. Small-Angle X-ray Scattering. The digesting formulation was aspirated through a fixed quartz capillary mounted in the X-ray beam (13 keV, wavelength λ = 0. 954 Å), using a peristaltic pump operating at approximately 10 mL/min. The sample-to-detector distance was approximately 0. 6 m, covering 0. 04 < q < 2. 00 Å -1 ). The 2D SAXS patterns were recorded using a Pilatus 1 M detector with a 5 s acquisition time and a delay of 15 s between measurements, i. e. , one measurement every 20 s.
A reverse-phase C 18 column (4. 6 × 75 mm 2 , 3. 5 μm; Waters Symmetry, MA, USA) was used for the separation, and the drugs were detected at 255 nm. The mobile phase gradient consisted of 65-90% B for 10 min, 90% B for 1 min, 90-65% B for 0. 5 min, and 65% B for 3. 5 min. The retention time for lumefantrine was 8. 3 min and 7. 4 min for halofantrine. Multivariate Analysis. Principal component analysis (PCA) was applied to the Raman spectra to qualitatively monitor drug dissolution during in vitro digestion in milk and infant formula using Orange software version 3. 16. 26 This analysis was useful as a first-line approach to determine whether changes relating to drug signals with digestion time were detectable. Injection of lipase and the progress of digestion of milk and infant formula resulted in changes to the baseline of the spectra, and as such, the linear baseline correction of the low-frequency Raman data between -250 and 250 cm -1 was performed.
After baseline correction, the spectral region between 8 and 200 cm -1 was then subjected to vector normalization prior to PCA of the ferroquine and lumefantrine samples. PCA was also performed on the mid-frequency region (300-1800 cm -1 ) after rubber-band baseline correction and normalization. The emissive nature of clofazimine meant a modified preprocessing was required compared to nonemissive samples. In this instance, the spectral region was reduced to 1000-1800 cm -1 , and the data was preprocessed using a rubber-band baseline correction with vector normalization. PCA was carried out on sample combinations shown in Table  1 , where time-dependent spectral changes relating to the digestion of milk/infant formula blanks + drug could be distinguished from spectral changes associated with the digestion of milk/infant formula blanks.
Molecular Pharmaceutics spectral region from 300 to 1800 cm -1 was performed and no changes in PC2 values (Figure  6c ) were observed during digestion in milk when compared to digestion in tris buffer. Despite the similar trends between the analyzed data in the low-and mid-frequency regions, the spread of the PC2 values was much larger for the mid-frequency region, providing more confidence in the conclusion that lumefantrine is not significantly solubilized in digesting milk or infant formula. Clofazimine. In the final case study, solubilization of clofazimine in milk, infant formula, and tris buffer during digestion were characterized by analyzing the Raman spectra using PCA. The three principal components covered 99% of the total variance in the samples, with the third PC being positively correlated to crystallized clofazimine (see the loadings plot for PC3 in Figure  7b ).
■ CONCLUSIONS In this work, the solubilization of four basic model drugs (ferroquine, halofantrine, lumefantrine, and clofazimine) in milk-based formulations was studied in real-time using lowfrequency Raman spectroscopy, and the behaviors were compared to synchrotron SAXS and phase separation approaches. The disappearance of the distinct phonon modes in the low-frequency Raman region could be observed for ferroquine and halofantrine during digestion in milk and/or infant formula, and no changes were evident in tris buffer, where no fat was present, clearly linking the changes in the Raman spectrum to drug solubilization. Signals arising from the digestion of lipids in milk and infant formula could also be detected using low-frequency Raman spectroscopy, which may contribute to interference during data processing.
ACKNOWLEDGMENTSThis work was funded by the Bill and Melinda Gates Foundation [OPP1160404]. Funding is also acknowledged the Australian Research Council under the Discovery Projects scheme (DP160102906), and A. J. C. is the recipient of a Discovery Early Career Research Award (DE190100531). The SAXS experiments for this work were conducted on the SAXS/WAXS beamline of the Australian Synchrotron, part of ANSTO. K. B. and J. J. S. thank the University of Otago for Ph. D. scholarships. The support of the Dodd-Walls Centre for Photonic and Quantum Technologies is gratefully acknowledged.
■ ACKNOWLEDGMENTSThis work was funded by the Bill and Melinda Gates Foundation [OPP1160404]. Funding is also acknowledged the Australian Research Council under the Discovery Projects scheme (DP160102906), and A. J. C. is the recipient of a Discovery Early Career Research Award (DE190100531). The SAXS experiments for this work were conducted on the SAXS/WAXS beamline of the Australian Synchrotron, part of ANSTO. K. B. and J. J. S. thank the University of Otago for Ph. D. scholarships. The support of the Dodd-Walls Centre for Photonic and Quantum Technologies is gratefully acknowledged.
Acknowledgments: The authors acknowledge the  Association François Aupetit  for its unwavering support. Conflicts of Interest: P. S. declares consulting fees from  Abbvie ,  Takeda ,  Merck-MSD ,  Pfizer ,  Astellas ,  Janssen , and  Biocodex  and grants from  Biocodex  and  Janssen. COI are not related with the present work. A. P. , G. C. , L. B. , D. R. , J. -P. G. , and  J. -M. M. declare  no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Funding: This research was funded by  Fondation pour la Recherche Médicale , grant number  EQU202003010171  and by  Association François Aupetit  (projects research on AHLs 2013 and 2019). A. Peyrottes was the recipient of a fellowship from  Sorbonne University IPV  (Interface pour le vivant) program and G. Coquant was the recipient of a fellowship from  Fondation pour la Recherche Médical   FRM ECO201806006843.
Results: The genome of R. rhodochrous ATCC BAA-870 is the first Rhodococcus genome fully sequenced using Nanopore sequencing. The circular genome contains 5. 9 megabase pairs (Mbp) and includes a 0. 53 Mbp linear plasmid, that together encode 7548 predicted protein sequences according to BASys annotation, and 5535 predicted protein sequences according to RAST annotation. The genome contains numerous oxidoreductases, 15 identified antibiotic and secondary metabolite gene clusters, several terpene and nonribosomal peptide synthetase clusters, as well as 6 putative clusters of unknown type. The 0. 53 Mbp plasmid encodes 677 predicted genes and contains the nitrile converting gene cluster, including a nitrilase, a low molecular weight nitrile hydratase, and an enantioselective amidase. Although there are fewer biotechnologically relevant enzymes compared to those found in rhodococci with larger genomes, such as the well-known Rhodococcus jostii RHA1, the abundance of transporters in combination with the myriad of enzymes found in strain BAA-870 might make it more suitable for use in industrially relevant processes than other rhodococci.
Furthermore, upon contact with organic solvents, the cell wall composition changes, becoming more resistant to many solvents and more stable under industrially relevant conditions like high substrate concentration and relatively high concentrations of both watermiscible and -immiscible solvents. This results in a longer lifetime of the whole cell biocatalyst and subsequent higher productivity. Rhodococcal species isolated from soil are known to have diverse catabolic activities, and their genomes hold the key to survival in complex chemical environments  [10]. The first full Rhodococcus genome sequenced was that of Rhodococcus jostii RHA1 (NCBI database: NC_ 008268. 1) in 2006  [10]. R. jostii RHA1 was isolated in Japan from soil contaminated with the toxic insecticide lindane (γ-hexachlorocyclohexane)  [11]  and was found to degrade a range of polychlorinated biphenyls (PCBs)  [12].
Applying third generation sequencing (Oxford Nanopore Technology) enabled the full assembly of the genome, while the second generation (Illumina) reads provided the necessary proof-reading. This resulted in a total genome size of 5. 9 Mbp, consisting of a 5. 37 Mbp circular chromosome and a 0. 53 Mbp linear plasmid. The presence of the plasmid was confirmed by performing Pulse Field Gel Electrophoresis using non-digested DNA  [42]. The complete genome sequence of R. rhodochrous ATCC BAA-870 is deposited at NCBI GenBank, with Bioproject accession number PRJNA487734, and Biosample accession number SAMN09909133.
Taxonomy and lineage of R. rhodochrous ATCC BAA-870 The R. rhodochrous ATCC BAA-870 genome encodes four 16S rRNA genes, consistent with the average 16S gene count statistics of Rhodococcus genomes. From a search of The Ribosomal RNA Database, of the 28 Rhodococcus genome records deposited in the NCBI database, 16S rRNA gene counts range from 3 to 5 copies, with an average of 4  [43]. Of the four 16S rRNA genes found in R. rhodochrous ATCC BAA-870, two pairs are identical (i. e. there are two copies of two different 16S rRNA genes). One of each identical 16S rRNA gene was used in nucleotide-nucleotide BLAST for highly similar sequences  [44]. BLAST results (complete sequences with percentage identity greater than 95. 5%) were used for comparison of R. rhodochrous ATCC BAA-870 to other similar species using 16S rRNA multiple sequence alignment and phylogeny in ClustalO and ClustalW respectively  [45] [46] [47]  (Fig.
Genome annotation The assembled genome sequence of R. rhodochrous ATCC BAA-870 was submitted to the Bacterial Annotation System web server, BASys, for automated, in-depth annotation  [51]. The BASys annotation was performed using raw sequence data for both the chromosome and plasmid of R. rhodochrous ATCC BAA-870 with a total genome length of 5. 9 Mbp, in which 7548 genes were identified and annotated (Fig. 2 , Table  1 ). The plasmid and chromosome encode a predicted 677 and 6871 genes, respectively. 56. 9% of this encodes previously identified proteins of unknown function and includes 305 conserved hypothetical proteins. A large proportion of genes are labelled 'hypothetical' based on sequence similarity and/or the presence of known signature sequences of protein families (Fig. 3 ). Out of 7548 BASys annotated genes, 1481 are annotated enzymes that could be assigned an EC number (20%).
The RAST subsystem annotations are assigned from the manually curated SEED database, in which hypothetical proteins are annotated based only on related genomes. RAST annotations are grouped into two sets (genes that are either in a subsystem, or not in a subsystem) based on predicted roles of protein families with common functions. Genes belonging to recognised subsystems can be considered reliable and conservative gene predictions. Annotation of genes that do not belong to curated protein functional families however (i. e. those not in the subsystem), may be underpredicted by RAST, since annotations belonging to subsystems are based only on related neighbours. Based on counts of total genes annotated in RAST (5535), only 26% are classified as belonging to subsystems with known functional roles, while 74% of genes do not belong to known funtional roles.
Warren et al. 2010 , used high performance computational methods to show that current annotated prokaryotic genomes are missing 1153 candidate genes that have been excluded from annotations based on their size  [77]. These missing genes do not show strong similarities to gene sequences in public databases, indicating that they may belong to gene families which are not currently annotated in genomes. Furthermore, they uncovered ~38,895 intergenic ORFs, currently labelled as 'putative' genes only by similarity to annotated genes, meaning that the annotations are absent. Therefore, prokaryotic gene finding and annotation programs do not accurately predict small genes, and are limited to the accuracy of existing database annotations. Hypothetical genes (genes without any functional assignment), genes that are assigned too generally to be of use, misannotated genes and undetected real genes remain the biggest challenges in assigning annotations to new genome data  [78] [79] [80] [81].
As such, there is the possibility that we are under-estimating the number of genes present on this genome. Apart from possible misannotation, the algorithm or software used for the annotation plays a huge role in the outcome. In this research both BASys (Fig. 2 ) and RAST (Fig. 4 ) were used as annotation tools, resulting in 7548 and 5535 predicted genes respectively. BASys annotation may provide an overprediction of gene numbers, due to sensitive GLIMMER ab initio gene prediction methods that can give false positives for higher GC content sequences  [82]. This shows the importance of the bioinformatics tool used, which makes comparison to other genomes more difficult.
Size and content of the genome The genomic content of R. rhodochrous ATCC BAA-870 was outlined and compared to other rhodococcal genomes. Sequences of other Rhodococcus genomes were obtained from the Genome database at NCBI  [83]  and show a large variation in genome size between 4 and 10 Mbp (Additional file 1: Table  S1 ), with an average of 6. 1 ± 1. 6 Mbp. The apparent total genome size of R. rhodochrous ATCC BAA-870, 5. 9 Mbp (consisting of a 5. 37 Mbp genome and a 0. 53 Mbp plasmid), is close to the average. From the well-described rhodococci (Table  1 ), the genome of R. jostii RHA1 is the largest rhodococcal genome sequenced to date (9. 7 Mbp), but only 7. 8 Mbp is chromosomal, while the pathogenic R. hoagii genomes are the smallest at ~5 Mbp. All rhodococcal genomes have a high GC content, ranging from 62 to 71%.
Illumina sequencing Genomic DNA of R. rhodochrous BAA-870 was used to obtain two libraries with different insert sizes. One 300 cycle paired-end library with insert-size of 550 bp was sequenced in-house on a MiSeq sequencer (Illumina, San Diego, CA) using TruSeq PCR-free library preparation. The second, a 50 cycle mate pair library with 5 kb insert-size, was performed at BaseClear (Leiden, The Netherlands). Data is available at NCBI under Bioproject accession number PRJNA487734.
MinION sequencing For Nanopore sequencing a 1D sequencing library (SQK-LSK108) was loaded onto a FLO-MIN106 (R9. 4) flowcell, connected to the MinION Mk1B (Oxford Nanopore Technology, Oxford, United Kingdom). Min-KNOW software (version 1. 11. 5; Oxford Nanopore) was used for quality control of active pores and for sequencing. Raw files generated by MinKNOW were base called, on a local compute server (HP ProLiant DL360 G9, 2x XEON E5-2695v3 14 Cores and 256 RAM), using Albacore (version 1. 2. 5; Oxford Nanopore). Reads, in fastq format, with a minimum length of 1000 bps were extracted, yielding 5. 45 Gigabase sequence with an average read length of 9. 09 kb.
Annotation The assembled genome sequence of R. rhodochrous ATCC BAA-870 was submitted to the Bacterial Annotation System web server, BASys, for automated, in-depth annotation of the chromosomal and plasmid sequences  [51]. BASys annotates based on microbial ab initio gene prediction using GLIMMER  [82]. The genome sequence was also run on the RAST (Rapid Annotation using Subsystem Technology) server using the default RASTtk annotation pipeline for comparison  [155, 156]. RAST annotation uses the manually curated SEED database to infer gene annotations based on protein functional roles within families  [157]. The two annotation pipelines offered different but useful and complimentary input formats and results, and gene annotations of interest could be manually compared and confirmed.
Funding This research was partly funded by The Department of Science and Technology (DST) Biocatalysis Initiative (Grant 0175/2013), South Africa, the CSIR Thematic Programme, and the Welch Foundation (grant E-1264). Also the European Science Foundation COST Action CM0701 funded part of the project. The funding bodies had no role in the design of the study and no role in the collection, analysis, and interpretation of data or writing of the manuscript. Ethics approval and consent to participate Not applicable.
04 None 5. 04 69 4540 103S d equi Genome Consortium) [17] determined R. jostii RHA1 24-07-2006 Genome British Columbia, Vancouver NC_008268 7. 8 1,123,075; 9. 7 67 8690 [10] 442,536; 332,361 R. erythropolis 31-03-2005 Sequencing Center: National Institute NC_012490 6. 5 271,577; 6. 9 62 6321 PR4 of Technology and Evaluation, Japan [18] 104,014; 3637 a All sequences are completed and fully assembled, except GCF_000234335, which consists of 282 contigs b Date completed refers to genome sequence completion/submission to database; plasmids may have been completed at another time. Total genome size comprises the chromosome and the plasmid sequence. Genome information of strains other than BAA-870 is obtained from the NCBI database c Two separate references, therefore 2 entries d R. equi is renamed to R.
Enzymatic Activity Assay One hundred micrograms of liver microsomes were used to perform 4OH-tamoxifen glucuronidation assays. methanol. Samples were then diluted (1:5), and an IS (see below) was added to each sample. Samples were centrifuged for 15 min at 20,000 g, and 4 • C and the supernatants were dried under vacuum and then suspended in 45 µl of 50% methanol/0. 1% formic acid (v/v) prior to LC-MS/MS analysis. K m and V max were obtained with a Michaelis-Menten plot following a non-linear curve fit with the least-squares method (GraphPad Prism 6 software).
Ion transfer tube was heated at 297 • C. Q1 and Q2 resolutions were set at 0. 7 FWHM, whereas collision gas (CID, argon) was set to 2 mTorr. Identification of the compounds was based on precursor ion, selective fragment ions, and retention times. Selection of the monitored transitions and optimization of collision energy and RF Lens parameters were manually determined (see Supplementary Table  1  for details). Qualification and quantification were performed in MRM mode using Quan Browser software (Thermo Scientific).
FUNDING This work was funded by  Fondation Alsace Contre le Cancer (YG) ,  ITMO Cancer (YG) ,  Inserm (YG) ,  CNRS (YG) ,  University of Strasbourg (YG) , and  Ministère Délégué à la Recherche et à l'Enseignement Supérieur  (Ph. D. fellowship to IW and FG).
, 2005). A pooled database of three population studies conducted from 1964 to 1992, showed that total alcohol consumption itself was not associated with gastric cancer, but that type of alcohol appeared to influence risk. Compared with non-wine drinkers, participants who drank one to six glasses of wine/week had a relative risk ratio of 0. 76 (95 % confidence interval (CI: 0. 50-1. 16), whereas those who drank > 13 glasses of wine/week had a relative risk ratio of 0. 16 (95 % CI: 0. 02-1. 18). A Linear trend test showed a significant association with a relative risk ratio of 0. 60 (95 % CI: 0. 39-0. 93) per glass of wine consumed/day. These relationships persisted after adjustment for age, gender, educational level, body mass index, smoking habits, inhalation and physical activity. There was no association between beer or spirits drinking and gastric cancer, and it was suggested OENO One 2020, 4, 813-832 Pierre-Louis  Teissedre et al.
ACKNOWLEDGEMENTS This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Most authors are government delegates and/or experts to the  International Organization of Vine and Wine's (OIV) Commission IV Safety and Health. Although this work was initiated under the auspices of the  OIV  and its Consumption, Nutrition and Health expert group, the statements herein are the sole responsibility of the undersigned authors. Authors from  Université de Bordeaux  prepared this paper in the framework of  Oenoviti International network  projects (without any specific budget).