dataesr/scientific-paragraphs-categorization huggingface dataset:
LC was performed on a Accucore Phenyl Hexyl (100 × 2. 1 mm, 2. 6 μm) (Thermo Scientific) using the following gradient elution: initial conditions of 99:1 (A:B) maintained for 1 min, increasing to 1:99 (A:B) for 9 min, followed by a 1. 5 min plateau with 1:99 (A:B) and return to initial conditions 99:1 (A:B) for equilibration. This corresponded to a total chromatographic run of 15 min. The flow rate was 500 μL/min, the column temperature was maintained at 40 °C, and the injection volume was 10 μL. For mass spectrometry, the instrument operated in an ESI positive mode and the range for acquisition was 150-450 m/z. Ion precursor selection was performed in the data dependent mode of operation where the most intense ion from the previous scan was selected for fragmentation. Full scan (MS1) data were acquired for each ionization mode at a resolution of 35,000 FWHM, with an AGC target of 1e6 and a maximum injection time of 120 ms.
Source parameters were as follows: source voltage + 3. 0 kV, sheath gas flow 60 units, auxiliary gas flow 10 units, capillary temperature 320 °C, S-Lens RF level 60 units. MS/MS (MS2) data were acquired at a resolution of 17,500 FWHM with an AGC target of 1e5, maximum injection time was 50 ms, a TopN of 5, an isolation window of 2. 0 m/z. The normalized collision energy (NCE) was stepped at 17. 5, 35, and 52. 5, and the dynamic exclusion time set at 3 s.
Molecular networking generation Spectral data allowed us to generate MN using semi-quantitative bioinformatic approach. Data processing, visualization, and network analysis, using open source softwares, have been described in details elsewhere  (16). Briefly, raw data were converted to an open MS format (. mzXML) with ProteoWizard's MSConvert module  (23). The mzXML files were http://mc. manuscriptcentral. com/dta Drug Testing and Analysis preprocessed (deconvolution, deisotoping, alignment, gap-filling, filtering) with MZmine 2 software  (24). The single. mgf output file was then loaded on the Global Natural Products Social networking (GNPS) web-based platform in order to generate the multi-matrix molecular network  (17). Parameters were used at m/z 0. 02 for the mass tolerance of precursor and fragment ions used for MS/MS spectral library searching, and m/z 0.
02 for the mass tolerance of fragment ions used for MN. Links between nodes were created when the cosine score was greater than 0. 7, and the minimum number of common fragment ions shared by two MS/MS spectra was 6. Full data processed through the GNPS platform are accessible through the following links: https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=d9878185cfc642308b46d413c56fb689 The molecular network was visualized using Cytoscape 3. 8. 0 software  (25). The nodes were annotated by spectral matching using SIRIUS 4. 0 software (26) and information propagation (identified node can serve as a starting point to identify another node in the same cluster) as already reported  (21).
032 m/z). For example, M14 and M18 are respective glucuronides of M12 and M17. Even though HepaRG cells are deemed capable of producing phase II metabolites  (14, 30) , no 2F-DCK glucuronides were observed with this in vitro model in the present study. Glucuronidation of 2F-DCK phase I metabolites have been already described  (27). Here, we identified three 2F-DCK-derived glucuronides in bile and urine (M14, M18, and M20) from a forensic case. The present study provides new data about the in vitro and in vivo metabolism of 2F-DCK. The main 2F-DCK metabolites identified are identical between i) an acellular biological model (HLM), ii) a cellular model (HepaRG), and iii) post-mortem biological samples from a real case. The observed metabolic ratios suggest that nor-2F-DCK (M09) and dihydro-nor-2F-DCK (M12) could be proposed as reliable metabolites to be recorded in MS/MS or HRMS libraries in order to improve detection of 2F-DCK use by toxicologists.
Protein Expression Analysis via Western Blotting and Cell Immunofluorescence Primary human hepatocytes were lysed at 4°C for 30 minutes in 30 mM Tris (pH 7. 4), 150 mM NaCl, 0. 02% NaN 3 , protease inhibitors (PMSF, INH) and 1% Triton X-100. CD81 and AQP9 expression was analysed via western blotting using a mouse anti-hCD81 mAb (1:1000) and a mouse anti-m/hAQP9 mAb (G3, Santa Cruz) (1:100), respectively, followed by a goat anti-mouse Alexa Fluor 680 conjugate (Molecular Probes, Life Technologies) (1:15000). Monoclonal mouse anti-tubulin (Abcam) (1:5000) was used as a standard. Data were acquired and quantified with an Odyssey Infrared Imaging System (LI-COR Biosciences, Lincoln, NE). Quantification of hCD81 and AQP9 expression at the surface of human hepatocytes was performed as previously described for murine hepatocytes  (Yalaoui et al.
ACKNOWLEDGMENTS The authors are indebted to  Dr. Olivier Silvie  for fruitful discussions and critical reading of the manuscript and to  Dr. Georges Snounou  for fruitful discussions. We are grateful to  Mr. Thierry Houpert  for his expertise and precious technical assistance with the maintenance of the Anopheles stephensi colony. Microarray hybridization and analysis were performed at the " Plateforme post-geńomique de la Pitie-Salpetrière ". We would like to thank  Perrine Frere  and  Aureĺien Dauphin  for confocal microscopy assistance in the imaging facilities " Plateforme d'Imagerie Cellulaire Pitie-Salpetrière  ( Shared Resource Facilities of CRICM ,  IFR14 ,  IFR113 ,  INSERM, UPMC). We also thank  Catherine Blanc  and  Beńedicte Hoareau  for flow cytometry assistance ( Flow Cytometry Core CyPS, UniversitéPierre et Marie Curie & Groupe hospitalier Pitie-Salpetrière ).
Pedigree information is of fundamental importance in breeding programs and related genetics efforts. However, many individuals have unknown pedigrees. While methods to identify and confirm direct parent-offspring relationships are routine, those for other types of close relationships have yet to be effectively and widely implemented with plants, due to complications such as asexual propagation and extensive inbreeding. The objective of this study was to develop and demonstrate methods that support complex pedigree reconstruction via the total length of identical by state haplotypes (referred to in this study as "summed potential lengths of shared haplotypes", SPLoSH). A custom Python script, HapShared, was developed to generate SPLoSH data in apple and sweet cherry. HapShared was used to establish empirical distributions of SPLoSH data for known relationships in these crops.
These distributions were then used to estimate previously unknown relationships. Case studies in each crop demonstrated various pedigree reconstruction scenarios using SPLoSH data. For cherry, a full-sib relationship was deduced for 'Emperor Francis, and 'Schmidt', a half-sib relationship for 'Van' and 'Windsor', and the paternal grandparents of 'Stella' were confirmed. For apple, 29 cultivars were found to share an unknown parent, the pedigree of the unknown parent of 'Cox's Pomona' was reconstructed, and 'Fameuse' was deduced to be a likely grandparent of 'McIntosh'. Key genetic resources that enabled this empirical study were large genome-wide SNP array datasets, integrated genetic maps, and previously identified pedigree relationships. Crops with similar resources are also expected to benefit from using HapShared for empowering pedigree reconstruction.
For example, plants can give rise to far more offspring, many can tolerate a far higher level of inbreeding, and many are hermaphroditic, allow self-fertilization, and/or are clonally propagated allowing an individual to occur in multiple generations of a pedigree. Extreme endogamy (continuous interbreeding within a small group) and clonal propagation can result in pedigrees that are more complex than, and sometimes not possible, in humans. The development and implementation of methods using shared haplotype length information for pedigree reconstruction in plants could provide similar depth of insights in their pedigrees as has been done in humans. The ever-growing SNP array datasets for apple  3, 21, 40  and cherry  21, 41, 42  , as well as high-quality integrated linkage maps available  21, 43, 44  , make these two clonally propagated, outcrossing crops suitable subjects to test shared haplotype length information as a method to reconstruct plant pedigrees.
Discussion This study successfully developed and demonstrated method for efficiently exploiting genome-wide SNP data for identifying then reconstructing the nature of close relationships among cultivars of the outbreeding, perennial, clonally propagated, long-lived crops of apple and sweet cherry. The newly developed Python script Hap-Shared produced a practical measure of genome-sharing among pairs of individuals, SPLoSH, using either unphased or phased genotypic data. Empirically determined associations between this haplotype sharing and known pedigree relationships in apple and cherry successfully established a baseline for estimating close pedigree relationships among any pair of individuals. Subsequent steps of deducing close pedigree relationships using haplotype sharing were demonstrated, which led to numerous discoveries and increased the known relatedness among ancestors of these crops.
Technical limitations of the SNP array used Some regions of the genome might be insufficiently informative due to low SNP density and/or a lack of informativeness of the SNPs there. Both could lead to the detection of long shared haplotypes that are IBS but not truly IBD. The issue of SNP density was likely a more frequent issue in the cherry dataset (2. 4 SNPs/cM) than apple (8. 3 SNPs/ cM). However, even in the apple dataset there were 30 gaps greater than 2 cM, with the largest being 4. 2 cM  44  , indicating that these issues would be present in apple to some degree as well. Limitations to pedigree reconstruction methods used: Crop-specific limitations Some limitations in pedigree reconstruction might be crop-specific. In our study, SPLoSH value distributions for cherry were wider and less well defined than those of apple, which could be because of the lower SNP density, less pedigree information, the less diversity among the tested cultivars, and the lower number of chromosomes in cherry compared to apple.
In cherry, the distribution of the NKCR group was particularly wide (Fig. 1  and Fig. S2 ), as insufficient pedigree information prevented limiting the included individuals to only those with known grandparents as was strictly done for apple. Requirements for effective pedigree reconstruction are not readily available for many crops. These requirements are a high-quality genetic map, many pedigree-connected individuals, and highly curated SNP datasets for phasing and for building of reference relationship distributions. Additionally, crops with a recent history of hybridization between sexually compatible but otherwise isolated and differentiated species may greatly complicate this type of pedigree reconstruction. However, the use of shared haplotype length data might still be the most efficient way to reveal genetic relationships even if reference relationships are unavailable.
All parent-offspring relationships included (Table  S1 ) that were previously known through pedigree records, literature, unpublished results from the FruitBreedomics project  50  , or from the ongoing pedigree reconstruction project described in Howard et al. 40  were validated via methods described in Vanderzande et al. 21. A set of 164 unique diploid sweet cherry accessions was used in this study (Table  S2 ), for which curated, high quality SNP data were available from Vanderzande et al. 21 or a set of 1617 SNPs from the Infinium cherry 6 K SNP array  51. Known pedigree relationships and the genetic map used in the present study were those reported in Vanderzande et al. 21. An additional relationship included for sweet cherry was that 'Early Burlat' and 'Moreau' being full-sibs, deduced from their high degree of SSR allele sharing  52  and close affinity revealed by SNP genotypic data in the present dataset.
Generation of shared haplotype length information A custom Python script was created and used to generate lists of shared haplotype lengths between every pair of accessions within each crop given the genetic map used. The script, named HapShared, can handle both phased and unphased genotypic data and also so-called null-alleles. A shared haplotype in HapShared was defined as a region that starts and ends with SNPs where both individuals had at least one allele in common and where each SNP in between also shared at least one allele or had missing data for one or both individuals. The provision for missing data was included to avoid truncation of true shared haplotypes by any occasional missing data. Hap-Shared identifies the start and stop positions of shared haplotype, calculates the length of each shared haplotype, and sums those that pass a user-defined length threshold into the parameter "summed potential lengths of shared haplotypes" (SPLoSH).
The apple NKCR group comprised 1199 pairs of individuals from a group of 123 accessions. The pedigree of each accession in this group was known up to at least the grandparent level and each pair had no known relationship up to the 4x great-grandparent level. Frequency distributions of SPLoSH information were built for each relationship group. Separate distributions were built for each relationship group using shared haplotypes greater than thresholds of 20, 25, 30, 35, and 40 cM. A separate dataset was constructed for a comparison of SPLoSH information with COR values. These were calculated for all 6670 pairs among a set of 116 accessions with pedigrees known to at least the grandparent level for all four grandparents (Table  S4 ). COR values were calculated using known pedigree relationships up to the great-grandparent level, where known.
For apple, this meant comparing the SPLoSH value to the regression between SPLoSH values and COR. A minimum length threshold of 25 cM was used for the apple pedigree reconstruction case studies because it had the most balanced R 2 and residual standard errors across the phasing and length threshold levels evaluated. For cherry, the Close Relationship Estimator was used (Table  S6 ). The next steps of the process for deducing the relationships depended on the specific scenarios encountered and the level of phased genotypic data available. These steps were applied to larger apple and cherry datasets as part of ongoing pedigree reconstruction studies, with only results relevant to the included case studies described here. For cases where a full-sib relationship was likely or possible, the genome-wide degree and patterns of double vs.
Some HSIB relationships were confirmed by identifying a group of individuals with SPLoSH values between all pairs of the group being consistent with a likely HSIB or GPGC relationship, with confirmation of the HSIB relationship being conducted via imputation of the genomewide genotypes of the hypothetical common parent (case study 4). For this imputation, when the SNP data for the other parent of an individual was available, data for SNPs heterozygous in the prospective half-sib were also used if the known parent was homozygous for that SNP (e. g. , half-sib individual = AB and known parent = AA, deduced allele of the other parent inherited by half-sib = B). When the other parent was unavailable in the dataset, only data for SNPs that were homozygous could be used. During imputation, the genome-wide SNP profile for the hypothetical common parent was compared to each individual in the entire putative HSIB group to determine whether one homolog of each chromosome in each putative half-sib was composed of haplotypes from the imputed SNP profile of the hypothetical common parent, allowing for occasional recombination consistent with Mendelian inheritance.
This determination was considered confirmation of each individual belonging to the HSIB group. Alternative and more complex pedigree strategies were necessary for individuals with one or two unknown parents when the unknown parents were unable to be imputed due to a lack of siblings and when both grandparents of an unknown parent were not available in the dataset. In these cases, candidate ancestors, sometimes more distant than GPGC, were identified using SPLoSH information. Candidate ancestors were those that shared relatively high SPLoSH values and were plausible in view of available provenance information. Use of phased genotypic data for an individual was necessary in these cases because phased data was more discerning for identifying close ancestors and could be used to identify recombination evidence confirm the present (as demonstrated in case studies 5 and 6).
Acknowledgements The authors thank the following groups for providing accessions used in this study:  Joanie Cooper , who provided samples from the  Temperate Orchard Conservancy in Molalla, Oregon, USA ;  Dan Bussey , who provided samples from  Seed Savers Exchange in Decorah, Iowa, USA ;  Dankwart Seipp , who provided samples from  Esteburg Obstbauzentrum Jork, Germany ;  Detlef Strang , who provided samples from  Ökowerk in Emden, Germany ; and  Hans-Joachim Bannier , who provided samples from his private orchard in Bielefeld,  Germany. Funding  for this research was in part provided by the  Niedersächsisches Ministerium für Wissenschaft und Kultur  through the  EGON  project: " Research for a sustainable agricultural production: Development of organically bred fruit cultivars in creative commons initiatives ", the  USDA NIFA Specialty Crop Research Initiative  projects, " RosBREED: Enabling marker-assisted breeding in Rosaceae " ( 2009-51181-05808 ) and " RosBREED 2: Combining disease resistance with horticultural quality in new rosaceous cultivars " ( 2014-51181-22378 ),  USDA NIFA Hatch  project  1014919 , and  State Agricultural Experiment Station -University of Minnesota  Project  MIN-21-040.
Funding: This work was funded by the  French National Research Agency  (NADHeart  ANR-17-CE17-0015-02  and  CORRECT_LMNA ANR-19-CE17-0013-02 ), the  Fondation pour la Recherche Médicale  ( EQU201903007852 ), and the  Leducq Foundation  ( 18CVD05 ) and was supported by the  University Research Federation  against heart failure ( FHU2019 ,  PREVENT_Heart Failure ).
e -(z+(a-b)r) t → ∞ then R → ∞, (7. 3) yields lim t→∞ E e -E R t ,ϕ = E exp -αC(b)W ∞ (θ) 2(β -1) θe -θz 1 -e -Ψ b [ϕ](z). As a result, using [BBCM20, Lemma 4. 4], the proof of Theorem 1. 3 is now complete, with c (III) = αC(b) 2(β-1) and D (III) the law of D b defined in (3. 19). Acknowledgements. The authors are partially funded by ANR-16-CE93-0003 (ANR MALIN). Additionally, M. A. B. is partially supported by Cofund MathInParis project from FSMP. This program has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 754362.
It should also be noted that studies relating antioxidant activity of plant extracts to their metabolome, published today, are almost always biased by the targeted metabolite profiling methods on which they are based. A plethora of structures is present in the specialized plant metabolome, of which the majority has never been described, is not included in databases and is hard to detect by targeted approaches. Detection and characterization of biochemically related compounds can be aided by untargeted approaches via the construction of molecular networks. In this approach, pairs of features (ionized metabolites detected by LC-MS) are connected based on mass spectral similarity or based on mass differences that potentially represent well-known decorations or biotransformations  [3, 143]. Only a wider coverage of the specialized metabolome, including the modifications and decorations that were not recorded in databases, will allow more reliable studies of its functioning.
A prerequisite for a real study of how the plant specialized metabolism functions as a whole, is that we can relatively easily identify, and to some extent also quantify, a large percentage of the metabolome, present under different environmental or developmental conditions, in different tissues of different species. A high-throughput technique similar to what RNAseq or microarrays represent for transcriptomics, is far from the reality today in the field of metabolomics. However, with the recent concerted efforts of analytical chemists (GC-MS, LC-MS, NMR), computational biologists, computer programmers and database developers, automated annotation and structural characterization of unknown metabolites is steadily making progress. As the Buddhist poet Issa (1772-1858) probably would say: "Also the snail climbs mount Fuji, but slowly, slowly".
The recent revival of the study of organic natural products as renewable sources of medicinal drugs, cosmetics, dyes, and materials motivated the creation of general purpose structural databases. Dereplication, the efficient identification of already reported compounds, relies on the grouping of structural, taxonomic and spectroscopic databases that focus on a particular taxon (species, genus, family, order, etc. ). A set of freely available python scripts, CNMR_Predict, is proposed for the quick supplementation of taxon oriented search results from the naturaL prOducTs occUrrences database (LOTUS, lotus. naturalproducts. net) with predicted carbon-13 nuclear magnetic resonance data from the ACD/Labs CNMR predictor and DB software (acdlabs. com) to provide easily searchable databases. The database construction process is illustrated using Brassica rapa as a taxon example.
Introduction The motivation behind the renewed interest in natural product (NP) studies arises from their ability to propose highly diverse and renewable sources of medicinal drugs, cosmetics, dyes, and materials in the broader sense. Dereplication in the context of NP chemistry may be defined as the identification of known chemotypes, so that structure re-elucidation and possibly compound re-isolation can be avoided  [1, 2]. Establishing whether an organic compound is known requires the availability of a collection of identity cards of known compounds, possibly organized as a computer database (DB). The existence, availability, scope, and limitations of the numerous NP DBs has been thoroughly reviewed recently, resulting in the creation of a new DB named COCONUT in which the content of numerous DBs was collected  [3] [4] [5].
An even more recent work led to LOTUS, a comprehensive fully open source open data DB that connects NP molecular structures with the taxonomic classification of the organisms they originate from in an unprecedented way and constitutes a significantly useful source of data for NP chemists  [6, 7]. Moreover, the LOTUS database provides bibliographic links to compound descriptions. NP chemical studies start from taxonomically well-defined biological resources from which the products of the metabolism, primary and specialized, are extracted. Extraction has considerably evolved during the last decades, involving a wide range of physical and chemical processes adapted to the nature of the starting material and to the desired extraction selectivity  [8]. Crude NP extracts are generally substances made of highly complex compound mixtures.
The dereplication of NPs relies on NP DBs containing structural, taxonomic and spectroscopic data  [19]. Merging spectroscopic and biological taxonomy data offers a way to reduce, possibly to one, the number of annotations for a given compound. Restricting the set of candidate structures for dereplication to the chemical entities produced by the organisms that are taxonomically related to the one under study finds its justification in the coevolution of species and of the compounds they produce to establish relationships with their environment. This communication reports a way to create a database related to a given taxon with included data for dereplication through 13 C NMR spectroscopy. A similar approach, KnapsackSearch  [19] , was reported, resorting to the internet to access the KNApSAcK DB  [20].