dataesr/scientific-paragraphs-categorization huggingface dataset:
Expression and purification of proteins Among the 16 GSTO genes found in the genome of Trametes versicolor, 8 have been selected in the most extended subclass III  [12] , all of them exhibiting a serinyl residue in their putative catalytic site (accession number in the JGI database: TvGSTO-1S: Tv75639; TvGSTO-2S: Tv56280; TvGSTO-3S: Tv48691; TvGSTO-4S: Tv65402; TvGSTO-5S: Tv54358 and TvGSTO-6S: Tv23671). Recombinant plasmids were obtained by synthetic gene synthesis, cloning into the NdeI and NotI restriction site of pet 26b plasmid. The sequences have been designed to add a C-term His-tag for purification (S1 Table ). Expression was performed at 37°C using the transformed Escherichia coli Rosetta2 (DE3) pLysS strain (Novagen) in LB medium containing kanamycin (50 μgÁml-1) and chloramphenicol (34 μgÁml-1).
Glutathionylation assays The glutathione transferase acitivity was measured following the glutathionylation of CDNB (1-chloro-2,4-dinitrobenzene) as described by Lallement et al (2014)  [16]. Concerning the competition experiments performed using the CMFDA test; data used in the principal component analysis were calculated as follow. The slope (S) (linear increase of fluorescence determined during the first 10 min after addition of the product) was calculated, the value obtained only in presence of DMSO being the reference (Sr). The principal component analysis was performed using the normalized value I with I = (S/Sr). Principal component analysis was implemented using the xlstat software (Pearson correlation). A matrix of the T and I values determined as described above for the different studied proteins were used as input (S2 Table).
The kinetic parameters are reported in the S3 Table. These data are in agreement with previous results obtained on Phanerochaete chrysosporium isoforms showing that GSTO harboring a serinyl residue in their catalytic site exhibit a classical glutathione transferase activity  [11, 12]. Concerning PcUre2ps, we used previously characterized Ure2ps from Phanerochaete chrysosporium, four isoforms belonging to the Ure2pA subclass (PcUre2pA4, 5, 6 and 8), and the unique member of the Ure2pB subclass in this fungus  [8, 9]. In both cases, GSTO and Ure2p, these proteins belong to the most extended GST class in Trametes versicolor and Phanerochaete chrysosporium respectively. Fungal GSTs as GSTs from other organisms are known to display both catalytic activities and ligandin activities (ability to bind/interact with ligands)  [17].
TvGSTOs and hardwood acetone extracts Whereas the ecology of P. chrysosporium remains unclear, T. versicolor is a widespread fungus known as a prime wood decomposer of hardwood in temperate forests. The interactions between the six studied TvGSTOs and acetone extracts from several hardwoods and larch have also been specifically analyzed. Larch extracts have been integrated in this study since this softwood exhibits an extract composition similar to that one found in hardwoods  [13]. The relative chemical composition of these extracts was determined using GC-MS analysis (Table  1 ). They contain different main classes of molecules as sugars, terpenes, lignans or flavonoids and also a more or less important uncharacterized fraction. PCA has been implemented on the data obtained after FTS and CE performed on TvGSTOs and chosen extracts (S2 and S4 Tables).
Acknowledgments The authors want to thank the forest managers  D. Collinet  and  Y. Ehrhardt , from the  National Forest Service ONF and AgroParisTech , private nursery Adam et Fils respectively,  M. Lopez  from  IRSTEA Aix-en-Provence  and  J. -P. Charpentier  from  INRA Orléans  for providing the wooden material. The authors would like to warmly thank  Tiphaine Lamant ,  Loic Dailly ,  Bruno Garnier  and  Nicolas Houssement  for their technical help.
This work was supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE). The PhD grant of AD was funded by  ANR-11-LABX-0002-01. The authors gratefully acknowledge the  Institut National de la Recherche Agronomique (INRA)  (CJS contract n°. 12-00006889 ).
ACKNOWLEDGMENTS This work has been supported by the  Academy of Finland  under the  Finnish Center of Excellence Programme  ( 2012-2017 ). The authors also thank the  GAMMAPOOL European Spectroscopy Resource  for the loan of the detectors for the JUROGAM II array. Support has also been provided by the  EU  7th framework programme, Project No. 262010  (ENSAR). U. J. acknowledges support from the  Finnish Academy of Science and Letters  and the  Vilho, Yrjö , and  Kalle Väisälä Foundation.
Material and Method 2. 1. Patients and Clinical Data. Tissue samples were obtained from patients who underwent surgery between 1993 and 2012 for either prophylactic salpingo-oophorectomy or ovarian cancer at the Division of Gynecologic Oncology (Centre hospitalier de l'Université de Montréal (CHUM), Hôpital Notre-Dame). All patients gave consent for the banking and use of their tissue samples and clinical data (SARDO database). The ethic review board at the CHUM approved the study.
, 760-500). Counterstaining was achieved with hematoxylin and bluing reagent (Ventana Medical System Inc. , number 760-2021 and number 760-2037). Substitution of the primary antibody with phosphate-buffered saline served as a negative control. All sections were scanned using a VS-110 microscope with a 20x 0. 75NA objective with a resolution of 0. 3225 m (Olympus). Images were analysed with the OlyVIA software (Olympus).
Scoring and Statistical Analysis. Protein expression was scored according to the extent (as a percentage of cells of interest) and intensity (value of 0 for absent, 1 for low, 2 for moderate, and 3 for high) of staining based on no-automated visualization and a previously described semiquantitative score was used  [11]. All slides were independently scored in a blind manner by 2 observers and interrating agreement was >80%. In case of differences between the two scorings, the core was reevaluated to reach a consensus. Statistical analyses were performed using SPSS Statistics 20 software (IBM). The nonparametric Mann-Whitney  test was used to compare protein expression between groups. A  value below 0. 05 was considered as statistically significant.
Acknowledgments This research was supported by funding through the  Institut du cancer de Montreal  and a grant by the  Institut du cancer de Montreal (Spyder Defi Initiative). The  Centre de Recherche du Centre Hospitalier de l'Université de Montréal  received support from the  Fonds de Recherche du Québec-Santé (FRQS). Tumor banking was supported by the  Banque de Tissus et Données of the Réseau de Recherche Sur le Cancer of the FRQS  associated with the  Canadian Tumor Repository Network (CTRNet).
Experimental designs and diets The FHP data originate from seven studies designed to determine the effect of diet characteristics on components of HP in growing male broilers and to evaluate the interest of a NE system for growing broilers and, more generally, for poultry (Table  1 ). In each study, groups of broilers were kept in a respiration chamber for 5 or 6 days for measuring the HP and the metabolizable energy (ME) content of the diet as well as calculating the protein, fat and energy balances in a fed state. Broilers then spent an additional day in the respiration chamber without receiving feed to estimate the FHP; water remained available. This methodology used in our laboratory has been described in detail by  Barea et al. (2010) , Labussière et al. (2009) and  Rivera-Torres et al.
The b value of that analysis was used to express the FHP values per unit of metabolic BW (BW b ). The latter values were tested for the effects of trial and diet (within trial) on one hand and the effect of breed (ISA v. Ross) and trial (within breed) on the other hand. Daily FHP values were also analyzed according to a covariance model with BW b as covariate, the coefficient of the covariate being dependent on trial and diet (within trial). All statistical analyses were carried out with the GLM procedures of SAS (SAS Institute Inc. , Cary, NC, USA).
The corresponding interobserver agreement was further calculated on a set of 18 sequences (8 inflammatory, 9 malignant, and 3 benign) that had not been reviewed previously by any of the endoscopists rating those videos for interobserver agreement. SAS 9. 2 was used to conduct the statistical analyses. To measure agreement between study participants, we calculated Cohen's kappa coefficient before and after the teaching session. The results were interpreted based on the standards for strength of agreement proposed by Landis and Koch: poor agreement ≤0; slight agreement: 0 to 0. 20; fair agreement: 0. 21 to 0. 40; moderate agreement: 0. 41 to 0. 60; substantial agreement: 0. 61 to 0. 80; almost perfect agreement: 0. 81 to 1. 0.
Understanding the role of predators in food webs can be challenging in highly diverse predator/prey systems composed of small cryptic species. DNA based dietary analysis can supplement predator removal experiments and provide high resolution for prey identification. Here we use a metabarcoding approach to provide initial insights into the diet and functional role of coral-dwelling predatory fish feeding on small invertebrates. Fish were collected in Moorea (French Polynesia) where the BIOCODE project has generated DNA barcodes for numerous coral associated invertebrate species. Pyrosequencing data revealed a total of 292 Operational Taxonomic Units (OTU) in the gut contents of the arc-eye hawkfish (Paracirrhites arcatus), the flame hawkfish (Neocirrhites armatus) and the coral croucher (Caracanthus maculatus).
Analysis of FLX sequencing data We followed a sequence data analysis pipeline optimized for analyzing large COI datasets. The pipeline detailed in  Leray et al. (2013a)  takes advantage of the coding properties of the barcoding region to discard all dubious sequences. The initial step denoised flowgrams using Pyronoise  (Quince et al. , 2011)  implemented in Mothur  (Schloss et al. , 2009). We then further quality filtered the dataset by removing any reads that met the following criteria: shorter than 200bp; more than two mismatches in the primers sequence; any ambiguous base calls (e. g. , "N"); or with any homopolymer regions longer than 8bp. Remaining sequences were subsequently aligned to a high quality reference dataset (Moorea BIOCODE barcode library) based on amino acid translations using the option "enrichAlignment" in MACSE  (Ranwez et al.
, 2011 ) and all sequences with any of the following were also discarded: stop codon; frame shift; insertion; or more than three deletions. Finally, potential chimeric sequences identified using UCHIME  (Edgar et al. , 2011)  were removed to obtain a high quality sequence dataset for downstream analysis. To evaluate prey richness and composition, sequences were clustered in Operational Taxonomic Units (OTUs) using a Bayesian algorithm implemented in CROP  (Hao, Jiang & Chen, 2011). This program delineates OTUs based on the natural distribution of sequence dissimilarity in the data and within a range of sequence similarity values defined by the user. This approach performs better for clustering sequences obtained from environmental samples than a fixed dissimilarity cutoff (e. g. , 5%) because they contain a diversity of phyla that differ in their rate of COI evolution.
The lower and upper bound variance were set to 3 and 4 respectively (which corresponds to 6% and 8%) as they were shown to provide the best results for marine invertebrates  (Leray et al. , 2013a; Leray et al. , 2013b). Following OTU delineation, a representative sequence per OTU was used for taxonomic identification using BLAST searches in the local BIOCODE database and in GENBANK. We considered that there was a species level match when sequence similarity was at least 98%  (Machida et al. , 2009; Plaisance et al. , 2009). Whenever sequence similarity was lower than 98%, we used a Bayesian approach implemented in the Statistical Assignment Package (SAP,  Munch et al. , 2008)  to assign OTUs to a higher taxonomic group. SAP conducts assignments by building 10,000 unrooted phylogenetic trees from a collection of homologue sequences retrieved from a sequence database.
It then calculates the probability that a query sequence belongs to a monophyletic group within that set of homologues. Here, we allowed SAP to retrieve 50 homologues from GENBANK with >70% sequence similarity to each query sequence (i. e. , each OTU representative sequence) and accepted taxonomic assignments at an 80% posterior probability cutoff. Importantly, SAP can only assign sequences to taxonomic groups that are represented in the database, as is also the case with other assignment methods. Therefore, to minimize misidentification at lower taxonomic levels, we only report assignments to the phylum, class and order levels (Appendix S1).
2 ) which indicates that further sequencing effort would be necessary for a more exhaustive dietary analysis of these predatory fish. Whenever OTU sequence similarity to a reference barcode was <98%, we used the Phylogenetic Bayesian assignment tool implemented in SAP to assign OTUs to a higher taxonomic group. The diversity of dietary items spanned 25 classes belonging to 17 phyla. Malacostraca was the dominant taxonomic prey group (36. 7%, 21. 5% and 43. 7% for the arc-eye hawkfish, the flame hawkfish and the coral croucher, respectively). The arc-eye hawkfish also consumed numerous species of Actinopterygii (17. 7% total OTUs) and Maxillopoda (10. 9% total OTUs). A significant proportion of the flame hawkfish and coral croucher's diet was represented by Maxillopoda (12. 1% and 6. 2% total OTUs, respectively) and Gastropoda (9.
Vermetid snails are particularly prevalent in Moorea where they can reduce coral growth by up to 81% and survival by up to 52%  (Shima, Osenberg & Stier, 2010). Predation on parasites may compensate for the negative effects of the reduction in density of decapod mutualists in corals facing environmental stressors. We also recovered a significant proportion of sequences belonging to Pocillopora from the flame hawkfish and the coral croucher gut contents, which suggest that these predatory fish also feed on mucus released by their biogenic habitat. The absence of Symbiodinium COI sequences from our dataset also supports the consumption of mucus rather than coral polyps. Alternatively, Pocillopora DNA may have been sufficiently abundant and well preserved in the gut contents of mucus feeding prey (e.
These results demonstrate that levels of trophic partitioning have likely been underestimated. We strongly encourage further empirical approaches to dietary studies prior to making assumptions of trophic equivalency in food web reconstruction  (Leibold & McPeek, 2006). The extent to which secondary prey co-amplification could lead to errors in food web analysis has not been quantified in marine systems (see  Sheppard et al. , 2005  for an example in a terrestrial system). In the present dataset, numerous prey species identified in fish gut contents are either grazers or detritivores (e. g. , isopods, amphipods, decapods, ophiuroids and gastropods) and are therefore unlikely to consume each other. Some fish species detected in the gut contents are higher-level predators (e. g. , Caranx melampygus) that could consume benthic grazers and detritivores as adults, but they were most likely fed upon at a younger developmental stage (egg, larva or juvenile) given the size of predators.
Demospongiae, Ascidiacea and Gymnolaemata represented by few or a single sequence in the dataset were, however, possibly ingested unintentionally as secondary prey or epiphytes on the carapace of spider crabs (e. g. , Menaethius monoceros and Perinia tumida). Parasites of prey (e. g. , parasitic isopods of coral crabs of the genus Trapezia, Appendix S1) and parasites of a predator's digestive track (e. g. , Trematoda and Cestoda) may also confound food web reconstructions and care should be taken to consider the targeted roles these fish predators have on various parasites. The recovery of secondary prey may artificially inflate dietary partitioning if those lower levels are also partitioned. However, we expect the amount of DNA that these secondary prey items represent in the guts of our target predators should be minor and highly digested in comparison to primary prey.
A recent metabarcoding analysis of benthic samples  (Leray & Knowlton, 2015)  showed evidence of a correlation between amount of DNA and number of reads. Thus if secondary prey is quickly degraded, those taxa should be represented by one or few reads only. The present dataset shows minor dietary overlap both with and without rare OTUs (<1% of total OTUs, Fig. 4 ), further supporting our conclusions regarding the extent of trophic partitioning among all three fish species. Importantly, our analysis shows that in-depth sequencing would enable a more comprehensive representation of trophic links in this multi-faceted ecosystem. Additional reads would provide more OTUs matching reference barcodes (in GENBANK, BOLD or BIOCODE) but also a higher proportion of unidentified OTUs represented by a single sequence ("singleton", Fig.
INTRODUCTION Chronic myeloid leukemia (CML) is a rare disease which went along with an almost always fatal outcome until a few years ago  (1). Though the excellent results of TKI treatment  (1) (2) (3) (4) (5)  are raising the legitimate expectation that a considerable number of patients will achieve a condition of treatment-free remission (TFR), the prevalence of patients with CML, treated with TKIs is expected to increase by about 10% per year so that CML is a challenge for health care systems worldwide  (4) (5) (6) (7). With average TKI treatment costs of between forty and seventy thousand Euros per patient per year in Europe the challenge is how to optimize treatment for patients' maximum benefit with an affordable allocation of the resources  (4) (5) (6) (7) (8). The information on the course of CML is based on studies reporting the results of treatment, being company-sponsored approval studies (9-13), investigator-sponsored therapy studies  (14) (15) (16) (17) , and single reference centre database reports  (18, 19).
In Sweden, the Czech Republic and Germany the registration was based on already existing registries. In Sweden, the registration of all newly diagnosed cases of CML was already required by law since 2002  (20). In all the other countries, the registration was based on the active participation of hematologic and oncologic centres and physicians. Pathologic and laboratory databases were used throughout, but all newly diagnosed patients were registered by clinical institutions, so as to provide not only the baseline characteristics but also to collect follow up information. Pseudonymised baseline and follow-up data were recorded, including demographic, clinical, hematologic, cytogenetic, and molecular variables using a web-based eCRF-System at ELN's Data Centre in Munich. The clinical and hematologic data used for the calculation of the prognostic scores had to be measured at diagnosis, prior to any treatment  (21, 23, 24).
Statistics To summarize patients' characteristics, medians and percentages were calculated. Raw and standardized incidences were computed and adjusted to the registration period so the incidences are given per 100 000 inhabitants per year per region or country. For standardization the European standard population was used  (25). The registry and the standard population were truncated so only patients from 20 years on were included for the calculation of incidences. Population data from the United Nations database was used for calculations in countries that were covered nationwide, while the study groups provided the population numbers of the specified regions for countries that were observed partially. All calculations were performed using SAS Version 9. 2 software (Cary, NC, USA) and R version 3.
Limitations This study has got two characteristics that may have led to underreporting, namely the registration through clinical institutions and the short registration period. Registering through clinical institutions, was intended as clinical and outcome data were collected additionally. But clinical institutions are not always trained in epidemiological surveys and at least some patients may have escaped their attention for various reasons. Luckily, in Sweden, the registration of all new cases of CML was already statutory  (20) , so that Swedish data provided a sort of completeness control for the data of all the other countries. Comparing the Swedish incidences to the incidences of the EUTOS registry distinct differences are only notable in the very senior male population, where the sample size was rather small (Figure  1 ).
The difference is particularly relevant for the main company-sponsored phase III-trials that evaluated the therapeutic effects of TKIs to obtain the marketing authorisation from major drug authorities like the FDA and EMA. (9-13) (Table  5 ). The main difference concerned age, with a median of 55 years in the EUTOS population and of 46 to 51 years for company-sponsored trials. The number of missing data in Table  4  underscores that many baseline characteristics important for prognostic calculation, including spleen size and differential (particularly blast cells, basophils and eosinophils) were not reported in some company-sponsored trials. The WBC count as reported in company-sponsored trials was so low that it may be explained only assuming that in those studies the WBC count was not recorded baseline, prior to any treatment, as in EUTOS, but only after some antileukemic treatment, most likely hydroxyurea.
ACKNOWLEDGEMENTS For the contribution to patients registration, data collection, and follow-up we kindly acknowledge:  Daniela Zackova, Brno ,  Hana Klamova ,  Prague , and  Jan Muzik, Brno ,  Czech Republic ;  Antonio de Vivo  and  Riccardo Ragionieri ,  Bologna , and  Alessandra Cupi  and  Antonio Spitalieri , Catania, Italy;  C. M. Kulikow  and  E. Yu Chelysheva ,  Moscow , and  Elza Lomaia ,  St. Petersburg , Russia;  M. Hoglund, Uppsala ,  Sweden; Luis-Felipe Casado ,  Toledo , and  Pilar Giraldo ,  Zaragoza, Spain ;  Marzena Watek  and  Marek Dudzinsky ,  Krakow , and  Witold Prejzner  and  Monika Szarejko , Gdansk, Poland;  Florence Tartarin , Poirtiers, France;  Barbara Braithwhite , Liverpool, UK;  Violeta Milosevic  and  Olivera Markovic ,  Belgrade, Serbia; Tadas Zvirblis, Vilnius, Lithuania; Michael Ploetscher, Wels, Austria; Ingrida Udre, Riga, Latvia ;  Saara Vaalas  and  Minna Pajuportti ,  Helsinki, Finland ;  E.
Data National Snow and Ice Data Center bootstrap sea ice concentration data retrieved from the Nimbus-7 Scanning Multichannel Microwave Radiometer and Defense Meteorological Satellite Program (-F8, -F11, and -F13) and Special Sensor Microwave Imager (-F17) passive microwave measurements are used to analyze changes in SIC (for brevity the data set will hereafter be referred to as SSMI, but comprises SMMR, SSM/I, and SSMIS data  [Comiso, 2000 [Comiso, , updated 2014]] ). The data have a spatial resolution of 25 km, and monthly data are employed in the analysis. (Analysis of a subset of daily data suggests that the results are not sensitive to this choice, and hence the monthly data set is employed to reduce the computational burden. ) The data used in this study span the period 1979-2013. For verification purposes, the analysis has been repeated using combined Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and AMSR2 sea ice concentration.