dataesr/scientific-paragraphs-categorization huggingface dataset:
1 to 10 mgL -1. For coutaric acid and grape reaction product, concentrations were expressed in mg L -1 caftaric acid equivalent. The area of eluted compounds were measured by the Waters Empower software. The chromatographic methodology was improved from a previous study  (Coelho et al. , 2015). Typical chromatograms are represented in Figure  S1. Chemically pure standards of gallic acid, protocatechuic acid, hydroxybenzoic acid, hydroxytyrosol, tyrosol, catechin, (2)-epicatechin, dimer B1 and B2, caftaric acid, gentisic acid, caffeic acid, coumaric acid, chlorogenic acid and ferulic acid were prepared by dilution with water. Gallic acid, protocatechuic acid, hydroxybenzoic acid, hydroxytyrosol, tyrosol, catechin, (2)-epicatechin, dimer B1 and B2, GRP (Grape reaction product), coutaric acid, caftaric acid, gentisic acid, caffeic acid, coumaric acid, chlorogenic acid, ferulic acid and salicylic acid were detected and grouped together into families of phenolic compounds.
Fluorescence detection was realized at an emission of 320 nm with an excitation of 280 nm. Injection volume was 20 µL. The system was calibrated with protein standard markers (Sigma Aldrich) to analyse wine colloids with molecular weight ranging from 5 to 300 kDa, and adapted from a recent comparative study  (Coelho et al. , 2017)  using the following standards: Bovine Thyroglobulin (670 kDa), Ferritin (440 kDa), IgG (150 kDa), Ovalbumin (44 kDa), carbonic anhydrase (29 kDa) Ribonucléase (13. 7 kDa) and uridin (244 Da). Wine colloids content were determined by integrating the calibrated area below the size exclusion chromatogram, using EZChrom Elite R software. The calibration curve and wine colloids content are shown in Figure  S2.
9%, acetonitrile 2%, formic acid 0. 1%) and solvent B (ultrapure Milli-Q water 19. 9%, acetonitrile 80%, formic acid 0. 1%). Peptides were eluted in a 120 min gradient mode: 0 ′ : 10% B, 100 ′ : 35% B, 105 ′ : 98% B, 109 ′ : 98% B, 110 ′ : 10% B and 120 ′ : 10% B. Mass data acquisition was made by one scan in the window 400-1,700 m/z, with a resolution of 120,000; followed by 20 scans in MS/MS on the 20 more intense ions from the previous MS scan. Peptides were matched with wine proteins by using the MASCOT software (www. matrixscience. com) within the NCBI, Tandem and Comet databases. Validation of proteins was carried via Protein and PeptideProphet (tools. proteomecenter. org/TPP. php). Peptides were filtered in order to keep peptides that were found in two of the three technical replicates for one white wine sample.
Peptides retention times were aligned following the alignment method from MassChroQ software  (Valot et al. , 2011). Peptides were quantified with the same software by integrating peak areas on extracted ion chromatograms. A quality control of the nano-LC separation and of the ESI-Trap mass detection was realized on a 0. 1 pM BSA standard solution (Table  S2 ). A reproducibility control enabled to isolate 170 peptides and 123 proteins over the eight white wines.
Statistical Analysis Differences in REDOX sensory scores were statistically tested using analysis of variance (ANOVA) to (α = 5%). These data were subjected to three-way ANOVAs with wine closures (synthetic stopper or screw cap) and turbidity (300, 600, and 800 NTU) as within subject factors. The turbidity x closure interaction was also tested. When a significant effect was reached, pairwise comparisons were carried using Newman-Keuls' test (α = 5%). All statistical analyses concerning sensory analysis were carried out using XLStat 2015 (Addinsoft, Paris, France). The effect of must turbidity on wine polyphenolic and colloidal content and on sensory ratings was analyzed by a oneway ANOVA and a Tukey multiple comparison test taking must turbidity and vintage as factors with a confidence interval of 95%.
turbidities (low and high) used for white wine elaboration for 2010 vintage; and on the z-axis: the number of matched peptides for individual wine proteins. The number of matched peptides in the samples were represented by the intensity of the red color. For example, the wine protein number 14 had been identified as a beta-fructofuranosidase from Vitis vinifera grapes (accession number 225466093) and presented two matched peptides SSLAVDDVDQR and TFFCTDLSR, with molecular weight of 60,279,412 and 57,376,632 Da, respectively. These two matched peptides were found for the seven analyzed wines and seemed to be still present after 5 years of bottling, regardless of the level of must clarification. In contrast, some proteins were only present in wines elaborated from a low must turbidity, like for instance the wine protein number 63, identified as pyruvate decarboxylase isozyme (accession number P06169) and presenting two matched peptides NATFPGVQMK and NPVILADACCSR, with molecular weight of 54,677,923 and 68,832,675 Da, respectively.
FUNDING The research was funded by the IABECA research federation working on Agriculture, Biodiversity, Environment, Behavior and Food. We thank the Bureau Interprofessionnel des Vins de Bourgogne and Domaine Vincent Girardin for the white wine samples. We also thank Geraldine Lucchi and Caroline Truntzer for helping us in the white wine proteomic analysis at Clipp proteomic platform (Dijon, Burgundy). We finally thank the first year DNO students (2015/2016) who kindly participated to our sensory sessions for this experiment. Finally we thank both reviewers for their positive comments that improve the manuscript.
Metabolomic profiling of CSF identified novel biomarkers, including lactate, and implicated indoleamine-2,3 dioxygenase activity with CNS lymphoma progression on lenalidomide. We conclude that lenalidomide penetrates ventricular CSF and is active as monotherapy in relapsed CNS lymphomas. We provide evidence that maintenance lenalidomide potentiates response duration after salvage in relapsed PCNSL and delays whole brain radiotherapy (WBRT). This trial was registered at www. clinicaltrials. gov as #NCT01542918.
Notably, based on the lenalidomide maintenance data presented in this study, 2 randomized phase 2 studies are in development to evaluate low-dose lenalidomide maintenance in older PCNSL  Five patients received salvage treatment with focal irradiation only, before lenalidomide (supplemental Figure  3 ), and 5 were retreated at last salvage with high-dose methotrexate at the same doses used during induction, before lenalidomide (Table  3 ). (B) Median overall survival is 45 months. With a median follow-up of 12. 5 months, thus far, only 1 patient has progressed on lenalidomide to receive whole brain irradiation and 4 have succumbed to DLBCL. Patients on maintenance lenalidomide were closely monitored and examined in the clinic on an at least monthly basis for the first year, with restaging studies performed as indicated and with similar frequency as after previous therapies.
Acknowledgments The authors are grateful for the clinical research support of  Mallory Kock ,  Wesley Cheung , and  Eseosa Igbinedion. This research was supported by the  Leukemia & Lymphoma Society ;  National Institutes of Health, National Cancer Institute  ( R01CA139-83-01A1 );  Sandler Program   for Breakthrough Biomedical Research  (J. L. R. ); and  Cancer Center Support  Grant  P30CA082103  from the  National Institutes of Health, National Cancer Institute.
The top 5 candidates, Ebf3, Mycn, Etv5, Zfp939, and Hmx1, are presented in Table  1  (bottom). A correlation analysis between the different transcription factors and RET expression levels in our dataset of human primary neuroblastoma (GSE12460) revealed a positive and significant correlation only for ETV5 (r = 0. 4, P-value = 0. 001). Moreover, within the group of MYCN/Alk mut tumors we observed a strong correlation between Ret and Etv5 expression (Supplementary Fig. 3 ). Interestingly, in MYCN/Ret M919T tumors the Etv5 expression level is similar to the one observed in MYCN tumors (Supplementary Fig. 4 ), consistently with Etv5 being downstream of ALK and upstream of Ret. Altogether, Etv5 appeared as a relevant candidate transcription factor The ETV5 transcription factor is regulated both at the protein and mRNA levels by the ALK receptor The ETV5 protein is modulated at short-term (30 min) upon ALK activation or inhibition We then explored how ETV5 expression was modified upon ALK activation or inhibition in our panel of neuroblastoma cell lines.
In contrast to MYCN/Alk mut tumors that exhibit an increased Alk expression compared with MYCN tumors due to a positive feedback loop  [16, 30] , the Alk expression level in MYCN/ Ret M919T tumors is similar to the one observed in MYCN tumors (Supplementary Fig. 4 ). The murine Mycn gene is upregulated in MYCN/Ret M919T although in a more modest extend compared to what is observed in MYCN/Alk mut tumors  [16, 31]  and the Ret transcript is slightly increased in MYCN/Ret M919T compared to MYCN tumors (Supplementary Fig. 4 ). Altogether, the closely features of MYCN/ Ret M919T and MYCN/Alk mut tumors clearly document the crucial role of Ret in ALK-mutated and MYCN-driven neuroblastoma oncogenesis. We previously reported an upregulation of RET in primary neuroblastoma tumors with activated ALK  [16]  on the dataset of Schulte and colleagues  [32].
ChIP-Seq analysis ChIP-seq reads were mapped to the human reference genome hg19/GRCh37 using Bowtie2  [48]. Low mapping quality reads (Q < 20) and duplicate reads were discarded. Enriched regions (peaks) were called using HMCan v1. 19  [49]  as previously described  [47]. Motif discovery in ChIPseq peaks was performed using the Position Analysis tool of the RSAT package  [50]  on the 2500 highest peaks detected by HMcan.
Acknowledgements The  U830 Inserm laboratory  is supported by grants from the  Institut National du Cancer  ( PRT-K 14061 ), the  Ligue Nationale contre le Cancer (Equipe labellisée) , the  Association Hubert Gouin «Enfance et Cancer», Les Bagouz à Manon ,  les amis de Claire, la Fédération Enfants et Santé et la Société Française de Lutte contre les Cancers et les Leucémies de l'Enfant  et l'Adolescent. LL-D was the recipient of a fellowship of the  Fondation pour la Recherche Médicale  ( FDT20140930966 ). DS is supported by the  Institut Curie-SIRIC  ( Site de Recherche Intégrée en Cancérologie) program. VB is supported by the  ATIP-Avenir  and the "Who am I?" programs. High-throughput sequencing has been performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  ( «Investissements d'Avenir» program ), by the  Canceropole Ile-de-France  and by the  SiRIC-Curie program -SiRIC Grant «INCa-DGOS-4654».
Patients A total of 540 patients with asthma receiving step 3 or 4 treatment as defined by BTS guidelines were screened, 493 were enrolled and randomized, and 481 were included in the cross-sectional phase FAS (Spiromax followed by Turbuhaler, n = 240; Turbuhaler followed by Spiromax, n = 241) (Fig. 2 ). Eleven patients did not have device assessment because empty Spiromax and Turbuhaler devices were not available at the site, and one additional patient did not complete device assessment for reasons not recorded. Prior to enrollment in this study, screened patients were using breath-actuated inhalers (Easi-breathe [n = 2], Autohaler [n = 3]), regular metered dose inhalers (n = 436), and dry powder inhalers (DPIs) (Diskus (n = 56), Novolizer (n = 1); prior inhaler use data were unavailable for 42 patients.
c Only patients who achieved device mastery on both devices could proceed to stage 2. d Data were captured retrospectively. e Other reasons for study withdrawal during the longitudinal phase included the following: Visit 2 was not scheduled and patient inclusion/exclusion criteria were not met. Note: Numbers in parentheses are numbers of patients. Percentages are based on the number of patients randomly assigned device order in the cross-sectional phase (n = 493). BF: budesonide formoterol; FAS: full analysis set; ITT: intent-to-treat rhinitis (25% among patients randomized to use Spiromax followed by Turbuhaler; 24% in those randomized to Turbuhaler followed by Spiromax).
Acknowledgments The authors would like to thank  Sandra Kreuzaler ,  Francis Appiagyei ,  Tanith Hjelmbjerg ,  Latife Hardaker ,  Miriam Cottle ,  Andrew McLoughlin ,  Christina Callan ,  Elwira Fidziukiewcz ,  Katy Savage ,  Jo Steele ,  Marjan Bojarnejad , and  Tahreem Sarwar  of the Observational and  Pragmatic Research Institute Pte Ltd. , Singapore  for their contributions to the management, co-ordination and conduct of this study. Medical writing support was provided by  Nadia Korfali  and  Ian Grieve  of  Ashfield Healthcare Communications , part of  UDG Healthcare plc , and was funded by  Teva Branded Pharmaceutical Products R & D, Inc. (Frazer, PA). Teva provided a full review of the article.
Additional file 1: Dataset used in this Research. SPORT: sport group; H: stature; SH: sitting height; LL: leg length; BM: body mass; SKT: skinfold triceps; SKSB: skinfold subscapular; SKSP: skinfold suprailiac; SKAB: skinfold abdominal; SKTA: skinfolds thigh anterior; SKCM: skinfolds calf medial; WBBMC: whole body bone mineral content; SHBMC: subhead bone mineral content; TKBMC: trunk bone mineral content; ULBMC: upper limbs bone mineral content; LLBMC: lower limbs bone mineral content; WBBMD: whole body bone mineral density; SHBMD: subhead bone mineral density; TKBMD: trunk bone mineral density; ULBMD: upper limbs bone mineral density; LLBMD: lower limbs bone mineral density; WBLST: whole body lean soft tissue; TKLST: trunk lean soft tissue; ULLST: upper limbs lean soft tissue; LLLST: lower limbs lean soft tissue; WBFT: whole body fat tissue; TKFT: trunk fat tissue; ULFT: upper limbs fat tissue; LLFT: lower limbs fat tissue.
Acknowledgments The early contribution of  Filipe Simões  (  †2013 ) in the current project is remembered. Our thanks also go to  Alexandra Silva ,  Nuno Amado  and  Shirley Souza  for their participation in the data collection. The authors gratefully acknowledge the effort of the participants, their parents and coaches. Funding CIDAF is supported by the  Portuguese Foundation for Science and Technology  ( uid/dtp/04213/2016 ). JVdS ( SFRH/BPD/100470/2014 ) and JPD ( SFRH/BD/101083/2014 ) were partially supported by the  Portuguese Foundation for Science and Technology. No other current funding sources for this study. The  Portuguese Foundation for Science and Technology  played no role in the design, collection, analysis or interpretation of the data, nor in the preparation of the manuscript or decision to submit the manuscript for publication.
Histogram of the transverse relaxation time T 2 Fixed tissues generally suffer from a significant reduction of their transverse relaxation time drastically reducing the time window available to acquire the signal with a good signal-tonoise ratio (SNR)  (Pfefferbaum et al. 2004 ). To map the transverse relaxation time at each voxel of our sample, a standard multi-spin-multi-echo (MSME) pulse sequence  (Meiboom and Gill 1958)  was used. In total, 12 echoes were collected corresponding to 12 echo times linearly spaced between 6. 4 and 76. 8 ms. Imaging parameters for this sequence were: isotropic spatial resolution of 300 μm , 12 averages, TR = 16, 000 ms , and a total scan duration of 10 h 14 min. Collected MSME data were then used to fit the log-linear model corresponding to the T 2 -decay with a Levenberg-Marquardt algorithm care- fully initialized to get robust estimates.
Diffusion scan Diffusion data were acquired using a conventional PGSE sequence. The protocol included a first single-shell high angular resolution diffusion imaging (HARDI) data set used to infer the structural connectivity of the human hippocampal formation, to which a multiple-shell hybrid diffusion imaging (HYDI) data set was added to infer quantitative microstructural features using diffusion MR microscopy. The structural connectivity was established from the HARDI data set collected at b = 4500 s∕mm 2 along 500 directions uniformly distributed over a sphere. It was split into 15 blocks of 32 directions and one of 16 because of the memory limitation of the system. For each block, 6 b = 0 images were acquired. Scanning parameters were: isotropic spatial resolution of 300 μm ; TR∕TE = 9000∕24.
2 ms ; ∕ = 14. 4∕4. 3 ms ; matrix size: 192 × 192 ; 176 slices; and a total scan time of 8 days and 18 h. The b value was calibrated taking into account the reduction of the average diffusivity from D = 0. 7 × 10 -3 mm 2 ∕s in vivo to D = 0. 16 × 10 -3 mm 2 ∕s in our case, to compensate for the loss of contrast due to the observed reduction factor. As mentioned earlier, the data set was used first to perform tractography, but also to map microstructural features when merged with the next HYDI data set. In addition to the HARDI data set, a further multipleshell HYDI data set was acquired for 3 different shells corresponding to 3 different diffusion sensitization at b = 4500 s∕mm 2 , b = 7500 s∕mm 2 and b = 10,000 s∕mm 2. The choice of the b-values was carried out based on the usual sensitizations taken for NODDI or ActiveAx models (2) TE max ≤ 69 e -b max D ≤ 9361 s∕mm 2.
Delineation of the inner and surrounding structure of the hippocampus In-house developed software (by FP), PtkVoi, was used to trace the hippocampal subfields and surrounding structures. It was performed manually by two independent experts from the T 2 -weighted anatomical scan, using ana- tomical landmarks and following the different strategies prescribed and detailed in the literature  (Insausti 1998; Duvernoy 2005; Wisse et al. 2012; Insausti and Amaral 2012; Boutet 2014). Segmentations were traced in the coronal plane and the three-dimensional consistency was ensured by checking the axial and sagittal planes, as well as the 3D shapes of the delineated structures with respect to their known morphology. Considering the main intra-hippocampal circuits, we endeavored to identify: the entorhinal cortex, the dentate gyrus (including the hilus), CA2/CA3, CA1, the alveus, the fimbria, and the subicular complex.
Inference of the structural connectivity using diffusion MRI The diffusion analyses were performed using the Connectomist toolbox. The HARDI data set was used to compute fields of ODFs stemming from the analytical Q-ball model and from the DTI model, as well as conventional quantitative maps stemming from the tensor model, including the FA map, the MD maps, and the color-encoded direction (CED) map. The analytical Q-ball model reconstruction was applied using a spherical harmonics order 8 and a regularization factor = 0. 006 , as defined in  Descoteaux et al. (2007). A streamline regularized deterministic (SRD) and a streamline regularized probabilistic (SRP) tractography algorithms  (Perrin et al. 2005)  were applied to the whole sample using the maps of aQBI and DTI ODFs previously computed, with the following parameters: 8 seeds per voxel yielding dense tractograms containing 11.
2006). The use of a single shell at 4500 s∕mm 2 was motivated by the literature, that typically mention the use of b values of at least 4000 s∕mm 2 to scan post-mor- tem pieces. In particular,  Dyrby et al. (2011)  demonstrated that any HARDI acquisition with a b value between 2000 and 8000 s∕mm 2 allows for the inference of multiple fiber populations from ex vivo fixed specimens scanned at room temperature, with an optimal value around 4000 s∕mm 2. Furthermore, the application of a probabilistic fiber tracking method contributed to more robustly manage fiber crossings than deterministic approaches. It would also be of great value to investigate alternative reconstructions using the acquired HYDI data set, to go beyond HARDI models. Advanced models could be considered, like the mean average propagator (MAP-MRI) reconstruction  (Özarslan et al.
Nowadays, the NODDI model has become very popular due to its easy implementation from the acquisition protocol to the analysis pipeline. Alternative models should be investigated in the future, in particular those probing further features like the mean axon diameter or the myelin water fraction. Because we took a special care to establish an acquisition protocol densely sampling the q and diffusion time spaces, the ActiveAx model could be investigated in the future using our HYDI diffusion data set. Its investigation is ongoing, but beyond the scope of this preliminary study. The latest improvements of the model now integrate a time dependence for the extra-axonal space that should allow to finely probe maps of the mean axon diameter within the hippocampus with fewer bias.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement no. 720270  ( Human Brain  Project  SGA1 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Methods The EQA was designed and coordinated by Medical Entomology Work Package leaders of the MediLabSecure Network, none of whom belonged to any of laboratories that took part in the EQA. A full list of the 19 participating laboratories is given in Additional file 1: Table  S1. A set of blind samples consisting of seven or eight adult specimens plus four stage IV larvae in alcohol was given to each participant laboratory. The adults were double-mounted on micro-pins while fresh, then preserved in insect boxes for a period not exceeding 2 years. A senior entomologist double-checked the previously identified samples before they were taken to the participating laboratories. An identification code was attributed to each specimen. Overall, 138 adult mosquitoes and 76 larvae (total = 214) were distributed to the laboratories.
Mock-infected blood samples preparation G. obscuriglobus (DSM 5831 T ) and G. massiliana (DSM 26013 T ) purchased from the Deutsche Sammlung von Mikrorganismen and Zellkuturen (Braunshwing, Germany) were plated on Caulobacter solid medium containing 2 g/L Bacto peptone (Sigma-Aldrich, Saint-Quentin Fallavier, France), with 1 g/L yeast extract (Sigma-Aldrich) and 0. 2 g/L MgSO 4 heptahydrate (Sigma-Aldrich) at 30 °C for 21 days  [12]. Growing colonies were identified by matrix-assisted laser desorption ionisation-time of flight mass spectrometry (MALDI-TOF-MS) (Bruker Daltonics, Weissemburg, France) and a local database able to identify Planctomycetes thanks to a reproducible, unique protein profile comprising 23-39 peaks ranging in size from 2403 to 12,091 Da and differentiating the different species  [13, 14]  and specific PCR-sequencing of the Planctomycetes 16S rRNA  [5, 15].
Development of a Gemmata specific qPCR system A real-time PCR specific to this work was designed for detecting Gemmata spp. rpoB gene. We chose rpoB gene as one of the few universal bacterial genes whose sequence has been shown to accurately identify closely related bacterial species  [17]. Primers and probes were selected using primer3 software  [18, 19] , primerBlast  [20]  and The Sequence Manipulation Suite  [21]. Primers and probe specificity was tested in silico by a BLAST analysis against all sequences deposited in GenBank database (May, 2016). The specificity was then experimentally assessed using the DNA extracted from G. massiliana, G. obscuriglobus and 90 other bacterial species (Additional file 1). Three negative controls (containing the PCR mix without DNA) and two positive controls (containing the mix and G.