dataesr/scientific-paragraphs-categorization huggingface dataset:
ACK N OWLED G M ENTS We would like to thank  L. Crépin ,  D. Evrard , and  A. Pinczewski  for their kind help during sampling as well as  S. Vancraenenbroeck  and  G. Carreaux  for their contribution to the chemical analyses. This research was funded by the  "Fonds de la Recherche Fondamentale et Collective " ( FRFC  project  2. 4613. 10 , FRS-FNRS, Belgium ). P. -L. Zerck was Ph. D. student funded by the  Belgian Fund for Scientific Research (FRS-FNRS ). MV received financial support from the  Belgian FRS-FNRS  through a postdoctoral grant.
Since the inception of the International Agency for Research on Cancer (IARC) in the early 1970s, the IARC Monographs Programme has evaluated more than 1000 agents with respect to carcinogenic hazard; of these, up to and including Volume 119 of the IARC Monographs, 120 agents met the criteria for classification as carcinogenic to humans (Group 1). Volume 100 of the IARC Monographs provided a review and update of Group 1 carcinogens. These agents were divided into six broad categories: (I) pharmaceuticals; (II) biological agents; (III) arsenic, metals, fibers, and dusts; (IV) radiation; (V) personal habits and indoor combustions; and (VI) chemical agents and related occupations. Data on biological mechanisms of action (MOA) were extracted from the Monographs to assemble a database on the basis of ten key characteristics attributed to human carcinogens.
After some grouping of similar agents, the characteristic profiles were examined for 86 Group 1 agents for which mechanistic information was available in the IARC Monographs up to and including Volume 106, based upon data derived from human in vivo, human in vitro, animal in vivo, and animal in vitro studies. The most prevalent key characteristic was "is genotoxic", followed by "alters cell proliferation, cell death, or nutrient supply" and "induces oxidative stress". Most agents exhibited several of the ten key characteristics, with an average of four characteristics per agent, a finding consistent with the notion that cancer development in humans involves multiple pathways. Information on the key characteristics was often available from multiple sources, with many agents demonstrating concordance between human and animal sources, particularly with respect to genotoxicity.
The present analysis was based upon a review of human cancer mechanisms, conducted by participants in the two-part Workshop on Tumour Site Concordance and Mechanisms of Carcinogenesis, which was convened by IARC in April and November 2012 in Lyon. This approach initially involved retrieval of information from the IARC Monographs on 24 toxicological end-points identified as likely indicators of biological processes at the cellular and molecular level and postulated to be relevant to carcinogenesis occurrence. Data on these 24 end-points was derived from human in vivo, human in vitro, animal in vivo, and animal in vitro studies  (Al-Zoughool et al. 2019)  the November 2012 meeting, the Workshop participants identified ten broader key characteristics of carcinogens  (Smith et al. 2016). Information on these characteristics was extracted from the IARC Monographs and employed to develop a database of key characteristics for Group 1 agents  (Al-Zoughool et al.
Database of mechanistic characteristics Al-Zoughool et al. (  2019 ) assembled a database of toxicological end-points for the 86 Group 1 agents identified up to and including Volume 106 of the IARC Monographs. The database includes information from in vivo and in vitro studies in humans and animals. Data on the 24 toxicological endpoints was retrieved from Section 4 ("Other relevant data") of the IARC Monographs. The database of key characteristics was then created by mapping the 24 toxicological end-points to the ten characteristics as indicated in Because the database includes observations derived from human in vivo, human in vitro, animal in vivo, and animal in vitro sources, it is possible to aggregate this information according to human and animal sources (by combining across in vivo and in vitro sources) or according to in vivo and in vitro sources (by combining across human and animal sources).
Of primary interest here is aggregation across all four sources combined, to obtain an overall indicator of whether any of the key characteristics is associated with each of the 86 Group 1 agents of interest. A detailed analysis of the database developed by Al-Zoughool et al. (  2019 ) was conducted in collaboration with participants in collaboration with other participants in the Workshop on Tumour Site Concordance and Mechanisms of Carcinogenesis sponsored by the IARC in 2012  (Baan, Stewart, and Straif 2019). Highlights of this analysis are provided below.
(2018)  also noted that the vast majority (17/18) of Group 2B or Group 3 agents considered displayed at most one key characteristic. (This initial observation of fewer key characteristics identified for agents in lower classifications would be worth reexamining more broadly as additional data on key characteristics of agents evaluated within the Monographs Programme accumulate). Genotoxicity was the most prevalent mechanistic characteristic, demonstrated by 85 of the 86 agents considered, possibly reflecting the fact that the process of carcinogenesis necessarily involves genomic changes. This finding is consistent with an earlier assessment of 180 Group 1, Group 2A, and Group 2B agents conducted by  Bartsch and Malaveille (1989) , who reported that 80-90% of the agents in these three categories presented genotoxic characteristics.
(2018)  recently reported that in vivo animal data have been most often used as evidence of this key characteristic in assessments of agents in Volume 112 and in subsequent Monographs. Some caution needs to be employed in interpreting the distribution of key characteristics across Group 1 agents considered here. It is possible that the near universality of genotoxicity as a carcinogenic mechanism may be related to the manner in which the IARC Monographs were compiled, with emphasis on reporting of genotoxicity data. This would have been partially mitigated by inclusion of mechanistic information from outside the IARC Monographs in the preparation of the mechanistic database as evaluated separately by  Birkett et al. (2019). It should also be noted that the IARC Monographs have been published over a long time span, extending from the early 1970s to the present  (Saracci and Wild 2015).
Investigations of substances in earlier Monographs focused on changes such as DNA damage that were detected using techniques available at that time. These compounds may not have been assessed exhaustively for pathways that were identified more recently, such as those involving the multifactorial nature of carcinogenesis, or multiplicity of pathways operating during the process of agent-induced carcinogenesis. A related limitation of the present analysis is that it did not distinguish direct genotoxicity of the agent or its metabolites from genotoxicity that occurs as a result of other responses, because this knowledge was not generally provided in the IARC Monographs from which the mechanistic data on the Group 1 agents were obtained. It is recommended that the distinction between primary and secondary genotoxic effects be noted in future Monographs, whenever possible.
Another limitation of the present results is that they are based upon information on mechanisms in Section 4 ("Other relevant data") of the IARC Monographs, which focused primarily on "established" and "likely" mechanisms. A full systematic review of the entire literature on biological mechanisms for all Group 1 agents was not undertaken, such that the database may not reflect all mechanistic characteristics of different agents. As a sensitivity analysis to examine the extent to which the Monographs captured most of the relevant knowledge in this regard,  Birkett et al. (2019)  conducted a supplementary PubMed search to identify additional data on key characteristics not cited in the Monographs, or published since 2009. Although this sensitivity analysis was not based upon an exhaustive search, it did identify additional information sources, of which the most notable was the identification of evidence for six additional agents that demonstrated modulation of receptor-mediated effects, beyond the seven agents noted in Figure  1.
2019b). Despite the inherent reliance on human epidemiological data in identifying agents that may enhance human cancer risk occurrence, the IARC Monographs increasingly provides detailed descriptions of the MOA by which agents under review may act, including agents not assigned to Group 1. The epigenetic characteristics of the 74 Group 1 agents considered in Volumes 100A-E were assessed by  Herceg et al. (2013). As in the present analysis,  Herceg et al. (2013)  used DNA methylation, histone modification, and altered expression of microRNAs as indicators of epigenetic alterations. Information was considered from both IARC Monographs and the general scientific literature, and identified 22 of the 74 Group 1 agents (30%) as demonstrating epigenetic effects. The present analysis, which examined Group 1 agents in Monographs 100A-F as well as Volumes 105 and 106, identified 33 of the 86 Group 1 agents (38%) as mediating epigenetic alterations.
However, to conduct a series of comprehensive systematic reviews of key characteristics of all 86 agents considered in the present analysis would require a considerable effort, and was not attempted as part of the present project. The expert opinion of future IARC Working Groups charged with evaluating mechanistic data on new agents selected for examination by IARC Monographs would be of considerable value in this regard, but would ideally be supported by a concomitant systematic review of the relevant scientific literature on key characteristics to ensure that the analysis would be as complete as possible. Another issue that arises when discussing key characteristics of human carcinogens is whether indirect effects need to be considered. Many agents have a direct carcinogenic effect, but in other cases the carcinogenic characteristic is the result of a secondary event along the mechanistic pathway.
For example, cell proliferation might arise either as a result of a direct action of the agent on the cell or indirectly, (1) as a result of cytotoxicity that stimulates cell proliferation to replace cells, through alterations in cell signalling without cytotoxicity, or (2) via inhibition of cell proliferation that subsequently leads to selection of an altered clone of cells with a high proliferation rate. Although the downstream effect is similar as evidenced by enhanced cell proliferation, the pathway leading to that response may vary. A similar issue arises with genotoxicity where many agents are not directly genotoxic but produce DNA damage by stimulating a chain of molecular changes such as chronic inflammation. The current database does not contain the knowledge required to address these issues and cannot be used to draw conclusions regarding the detailed MOA of an agent.
The complexity of the pathways involved in carcinogenesis and the fact that cellular response to carcinogen exposure is modulated by host cell physiology, genetics, and other variables prompted development and application of sensitive assays that measure toxicity pathways and perturbations in the molecular functioning of the cell. The newly proposed toxicological testing paradigm  (Andersen et al. 2010; Krewski et al. 2010 Krewski et al. , 2014) )  focused on high-throughput screening to detect changes in the molecular pathways of the cell in response to chemical exposure. This new paradigm might be useful in comprehensive cancer risk assessment and may be able to detect distinct key mechanistic pathways operating after carcinogen exposure. In a similar initiative, the Kyoto Encyclopedia of Genes and Genomes (KEGG) website has compiled a comprehensive list of pathways associated with specific diseases (see the KEGG pathway database at http://www.
Conclusions In considering the results presented in this review, it is important to emphasize that these mechanistic analyses are a first step in understanding the biological mechanisms underlying cancer occurrence in humans. These findings need to be viewed as preliminary and require refinement through more exhaustive systematic reviews of the relevant scientific literature and/or through discussion with a broad panel of experts on the mechanisms underlying carcinogenesis development. Equally important is to consider the nature of the evidence needed to establish that specific mechanistic characteristics are associated with human carcinogens. The current database has relied on the demonstration of certain toxicological end-points as evidence of these mechanistic characteristics; further consideration of these and other possible markers of the key characteristics of human carcinogens is warranted.
Acknowledgments Parts of this article appear in Krewski, D. ,  Al-Zoughool, M. , Bird, M. , Birkett, N. , Billard, M. , Milton, B. , Rice , J. M. , Grosse, Y. , Cogliano, V. J. , Hill, M. A. , Little,  J. , and Zielinski, J. M, in collaboration with other participants in the Workshop on  Tumour Site Concordance and Mechanisms of Carcinogenesis. Chapter 22 -Analysis of key characteristics of human carcinogens. In:  Robert A. Baan ,  Bernard W. Stewart ,  Kurt Straif , editors. Tumour site concordance and mechanisms of carcinogenesis. Lyon, IARC Scientific Publication n°165; 2019 [reproduced here with permission of the IARC]. We are grateful to  Abdallah Alami  and  Nawal Farhat  for assistance in the final formatting of this article. Julian Little  is the  Canada Research Chair in Human Genome Epidemiology at the University of Ottawa.
2010 ) using the COI gene fragment proposed by  Hebert et al. (2003a Hebert et al. ( , 2003b) )  as a standard DNA barcode  (Ratnasingham and Hebert 2007)  with the goal to initiate a library of DNA barcode sequences for Zygaenidae species as a new tool for species identification in this family. Moreover, it was expected to confirm known species-complexes and possibly find new ones, as well as so far overlooked cryptic diversity. Each Zygaenidae barcode record in the Barcode of Life Data Systems (BOLD, http://www. boldsystems. org) (Ratnasingham and Hebert 2007 ) is accompanied by specimen images, detailed and geo-referenced collection data, complete taxonomic information and voucher repository data. These activities were undertaken in close cooperation between the Crimean Federal University (Crimea), the Tiroler Landesmuseen, Ferdinandeum (Austria) and the Biodiversity Institute of Ontario at the University of Guelph (Canada) under the framework of the "International Barcode of Life" (iBOL) project.
Data Analysis All obtained data were processed by the analytical tools available in BOLD 3. 0. Sequence divergences were calculated using the Kimura 2 Parameter (K2P) model; the distances to the nearest neighbour were retrieved using the Barcode Gap analysis. Specimens of 171 species of Procridinae, 24 Chalcosiinae, 1 Callizygaeninae, and 32 Zygaeninae with COI sequence length of more than 550 bp were used for tree construction. The Neighbour Joining ID-Tree was constructed in BOLD under the K2P-model and submitted alignment (see Suppl. material 2).
Results We obtained 1031 COI gene sequences for more than 240 described and undescribed species of Zygaenidae. The library comprises 975 public records from 60 countries, 247 BIN clusters. Complete specimen records, including images, specimen data and voucher information, GPS coordinates, applied primers, sequence and trace files, can be accessed in BOLD public dataset for ZYGMO (http://www. boldsystems. org/index. php/Public_SearchTerms). Critical analysis of DNA barcode results in the light of traditionally applied species names demonstrates the specificity of sequences of this fragment of the COI gene at species level in the majority of the studied taxa (sequences longer than 550 bp). The mean intraspecific K2P divergence (within species) is 1. 36%, interspecific (within genus) 7. 44% and intergeneric (within family) 13.
Acknowledgements We are indebted to  Prof Dr F. Can  (Turkey),  Mr J. -M. Desse  (France),  Mr E. Drouet  ( France ),  Dr O. G. Gorbunov  (Russia),  Mrs E. Hayashi  (Japan),  Prof Dr P. Jakšić  (Serbia),  Mr Th. Keil  (Germany),  Mr J. Klír  ( Czech Republic ),  Dr E. Kokanova  (Turkmenistan),  Mr A. M. Kosenko  ( Ukraine ),  Dr C. Koshio  (Japan),  Mr B. Mollet  (France),  Ms A. Nahirnić  (Serbia),  Dr I. G. Pljushtch  (Ukraine),  Mr P. V. Ruchko  (Russia),  Dr E. V. Rutjan  (Ukraine), and  Mr A. N. Zamesov  ( Russia ) for fruitful cooperation. This research work was partly supported by the  Project of Development Program   of V. I. Vernadsky Crimean Federal University Network 'Academic mobility of Young Scientists of Russia -AMMUR ' in 2017.
Currently control measures based on restrictions of oyster movements by authorities, modification of cultural practices and resistance breeding constitute serious ways to limit virus propagation and to decrease mortality rates. For several years, OsHV-1 (GenBank accession no. AY509253, "reference" type) and variants had been detected during mortality outbreaks or without oyster mortality around the world, such as in France  (Nicolas et al. , 1992; Renault et al. , 1994; Renault and Lipart, 1998; Le Deuff and Renault, 1999; Garcia et al. , 2011; Martenot et al. , 2011 Martenot et al. , , 2015)) , Ireland  (Lynch et al. , 2012; Peeler et al. , 2012; Clegg et al. , 2014) , Spain  (Da Silva et al. , 2008; Aranguren et al. , 2012; Roque et al. , 2012) , Italy  (Dundon et al. , 2011; Domeneghetti et al.
Statistical Analysis Data obtained from in vivo trials were analyzed with Generalized Linear Mixed Model (GLMM) to compare spat mortality between the different experimental conditions  (Bolker et al. , 2009). Data obtained from in vitro trials were analyzed with the Shapiro-Wilk test and then a t-test (if the sample distribution was normal) or Mann-Whitney test (if normal distribution could not be demonstrated) by using XLSTAT-Pro R 2014. 5. 03 software (Addinsoft, Paris, France). This statistical analysis was performed to compare viral transcript numbers and viral DNA amounts obtained from one in vitro condition test (virus incubated with antiviral antibodies or dextran sulfate) to those obtained from the control condition (virus alone) and for each targeting ORF. This approach was also used to compare viral transcript numbers and viral DNA amounts between adult oysters presenting a high susceptibility to OsHV-1 infection and adult oysters which are less susceptible to OsHV-1 infection.
a b s t r a c t This paper describes data collected on 2 sets of 8 French red wines from two grape varieties: Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Headspace Solid Phase Micro-Extraction e Gas Chromatography e Mass Spectrometry (HS-SPME-GC-MS) and (iii) odor-active compounds identification by Headspace Solid Phase Micro-Extraction e Gas Chromatography e Mass Spectrometry e Olfactometry (HS-SPME-GC-MS-O). The raw data are hosted on an open-access research data repository  [1]. © 2019 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/).
Data The dataset gathered, for the 16 wines from two grape varieties, 4 blocks of data:  (1)  the experimental factors (the grape variety, the vintage and the Protected Designation of Origin; Table  1 ),  (2)  the sensory descriptive data obtained with a trained panel using 33 sensory descriptors (Table  2 ), (3) the volatile organic compounds (VOC) quantification data obtained for 45 target odorants by Headspace Solid Phase Micro-Extraction e Gas Chromatography e Mass Spectrometry (Table  3 ) and  (4).
Value of the data The data can help researchers to link sensory qualities of wines to their chemical composition  [2]. The data can be used along with other datasets as a benchmark to develop methods and tools to predict the odor of wines  [3]. The data can be compared to other wines varying in grape variety and vintage. compounds, identified by Headspace Solid Phase Micro-Extraction e Gas Chromatography e Mass Spectrometry e Olfactometry (Table  4 ).
The list was elaborated by compiling terms from other lists employed in the description of wines from different varieties and geographical origins. Descriptors were arranged in the list by odor families: animal, burnt, floral, fruity, herbaceous, mineral, nut, spicy, undergrowth and others. Panelists modified the initial list of terms by removing those terms they considered irrelevant, ambiguous or redundant and by adding new attributes they considered pertinent while describing 15 wines of similar characteristics (grape variety and origin) as those considered in the present dataset. Finally, the terms cited by less than 15% of the panel were eliminated from the list. At the end of the training, the list included 33 descriptors (Table  2 ). During the sensory descriptive experiment, the judges had to evaluate monadically the 16 wines (orthonasal and retronasal olfaction) and to rate the intensity of 33 sensory descriptors on linear scales (14 cm); ratings were transformed into scores from 0 to 10.
The protocol consisted in 3 repetitions by panelist for the orthonasal olfaction and 2 repetitions by panelist for the retronasal olfaction. Panelists Table  4  Linear Retention Index (apex) of odorant zones detected in GC-MS-O analysis of the wines, the name of the corresponding identified compounds and their CAS numbers. Compounds that appear in italics were tentatively identified owing to MS spectra, odor quality and LRI but available data could not allow discriminating between isomers. thus performed 5 evaluation sessions (one per week) and started with the 3 orthonasal sessions followed by the 2 retronasal sessions. The presentation order of the wines was counterbalanced according to a Williams Latin square.
LRI Odorant In order to depict the data collected through orthonasal olfaction, the rating scores were averaged over panelists and repetitions and then submitted to a standardized Principal Components Analysis (PCA) using the R software (version 3. 4. 0) and the FactoMineR package (version 1. 34). The configuration of the 16 wines, as well as the correlations of the sensory descriptors with the two first principal components are shown in Fig. 1.
The fiber was thermally desorbed in the 250 C splitless/split inlet of a GC (Shimadzu 2010) coupled with a mass spectrometer (Shimadzu QP2010þ). Volatile compounds were separated on a PEG modified column (DB-FFAP 30 m Â 0. 32 mm Â 0. 25 mm). Mass spectra were recorded in electron impact mode (70 eV) with a scan/SIM scanning method. The identification of acetaldehyde, dimethyl sulfide, ethyl acetate, acetic acid, 2-ethylhexan-1-ol, propionic acid and phenol were carried out by comparison with reference mass spectra (WILEY257, NIST, in-house databases). Their quantification was based on an internal calibration by isotopic dilution with ethanal-13 C2, dimethyl sulfide-d 6 , ethyl acetate-13 C2, acetic acid-d 4 , 2-ethylhexan-1-ol-d 17 , propionic acid-d 5 and phenol-d 6. The identification and quantification of all other compounds was based on a calibration method with these compounds as reference.
One randomly chosen wine sample was analyzed in five replicates in order to estimate the coefficient of variation on each compound, which ranged from 1. 8% for phenol to 58. 3% for 2-isobutyl-3-methoxypyrazine. PCA plots, based on the two first dimensions, illustrating the configuration of the 16 wines evaluated using 33 sensory descriptors of orthonasal olfaction. For each sensory descriptor, the rating data were averaged over panelists and repetitions, and standardized (unit scaling).