dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by  CEA -I 2 BM intramural programs, as well as the European Union's  Seventh Framework Programme  [FP7/2007-2013 ] INMiND  (Grant agreement n°  HEALTH-F2-2011-278850 ). The authors wish to thank  LGCR Analytical Sciences (Sanofi, Chilly Mazarin, France)  for logD 7. 4 measurements and  DMPK (Sanofi, Montpellier, France)  for preliminary metabolism studies. HRMS determination has benefited from the facilities and expertise of the  Small Molecule Mass Spectrometry platform of IMAGIF  (www. imagif. cnrs. fr)(Gif-sur-Yvette, France). H and 13 C NMR spectra of compound 1…………………………………………………. S3 H and 13 C NMR spectra of compound 11…………………………………………………S4 H and 13 C NMR spectra of compound 12…………………………………………………S5 H and 13 C NMR spectra of compound 13…………………………………………………S6 H and 13 C NMR spectra of compound 14…………………………………………………S7 H and 13 C NMR spectra of compound 15…………………………………………………S8 H and 13 C NMR spectra of compound 16…………………………………………………S9 H and 13 C NMR spectra of compound 17…………………………………………………S10 H and 13 C NMR spectra of compound 18…………………………………………………S11 H and 13 C NMR spectra of compound 19…………………………………………………S12 H and 13 C NMR spectra of compound 26…………………………………………………S13 H and 13 C NMR spectra of compound 27…………………………………………………S14 H and 13 C NMR spectra of compound 28…………………………………………………S15 H and 13 C NMR spectra of compound 29…………………………………………………S16 H and 13 C NMR spectra of compound 30…………………………………………………S17 H and 13 C NMR spectra of compound 31…………………………………………………S18 H and 13 C NMR spectra of compound 32…………………………………………………S19 H and 13 C NMR spectra of compound 33…………………………………………………S20.
To assess whether the HA R149K substitution had an impact on viral growth, we measured the replication efficiency of NC/02 and NC/02 HA149 in MDCK, MDCK-SIAT1, and differentiated normal human bronchial epithelial cells (dNHBE). Both viruses grew to similar titers in MDCK cells (Fig. 3a ). In contrast, NC/02 HA149 replicated to significantly higher titers than did NC/02 in MDCK-SIAT1 cells  13  , which overexpress α 2,6-SA-linked receptors (Fig. 3b ). Similarly, NC/02 HA149 grew to higher titers in dNHBE cells than did NC/02 (Fig. 3c ). Consistent with the observed increased affinity to α 2,6-SA-linked receptors, these findings suggest that the HA R149K substitution enhances virus replication in cells expressing high levels of α 2,6-SA-linked receptors. Of note, and similar to its impact on transmission in ferrets, the reverse K149R mutation in TN/09 did not affect growth in dNHBE cells consistent with our previous data showing the mutation did not impact TN/09 binding to erythrocytes  7.
Gaussian Network Model (GNM) analysis. We applied GNM  16, 17  to one of the HA1 chains of the HA protein from the H1N1pdm09 virus A/California/04/2009 (PDB ID: 3UBE)  19, 20. In GNM, the slowest modes describe the major fluctuations; the most cooperative motions taking place in the largest time scales. It can be seen that the stretch of positions 136-152, 173-182, and 231-240 of the A/California/04/2009 RBD fluctuate to a lesser extent than their surroundings, and serve as local hinges for the first slow mode and as major hinges in the second and especially the third slow modes (Supplementary Fig. S8 ). These regions are a part of the Ca antigenic and sialic acid binding sites  21. The average correlation of the motion of position 149 with the motion of the rest of the HA1 chain is displayed on the structure of 3UBE (Fig.
5 ). Position 149 shows cooperative fluctuations with most of the receptor-binding and vestigial-esterase domains, especially with positions 63-88 (including the Cb antigenic region), 92-99 (a segment containing the Tyr98 binding residue), 133-153 (a part of the Ca antigenic region, containing binding site residues Lys133 A , Gly134, Val135, Thr136, Ala137, and Trp153) and 252-256. In attempt to understand the impact of the R149K mutation on the cooperativity between residue motion in the RBD we applied a GNM cross correlation analysis to six different H1N1 variants: A/Darwin/2001/2009 (PDB ID: 3M6S)  22  , A/California/04/2009 (PDB ID: 3UBE), A/Solomon Islands/03/2006 (PDB ID: 3SM5, A/Berlin/6/2006 for HA2)  23  , A/Swine/Iowa/15/30 (PDB ID: 1RVT)  24  , A/South Carolina/1/18 (PDB ID: 1RUZ)  24  , and A/Puerto Rico/8/34 (PDB ID: 1RU7)  24  ; the former three have a lysine in position 149 while the latter three contain an arginine.
The analysis showed that position 149 is a part of a major dynamic network that includes the sialic acid binding and antigenic sites. As such, the R149K mutation could affect the RBD dynamics and allosterically modify characteristics of the sialic acid binding site, providing a possible explanation for why a relatively mild substitution from arginine to lysine in this position causes dramatic phenotypic changes including enhanced transmission. Position 149 is in an evolutionarily conserved cavity, perhaps a new binding pocket, which is allosterically coupled to key functional regions in the protein, including the sialic acid binding and antigenic sites. It is noteworthy that HA's of other subtypes share similar dynamic behavior and allosteric coupling. In particular, the equivalents of position 149 in HA proteins from H5N1 (PDB ID: 2FK0) and H7N7 (4DJ6) are major hinges which are dynamically coupled to the same sialic acid binding and antigenic sites (data not shown).
Of the 1254 North American swine H1 HA sequences (from 1930 to 2008) in the influenza sequence database (www. fludb. org), only 12 contained lysine at position 149, the large majority of sequences harboring arginine and only 5 sequences a non-R and non-K residue at position 149. Consistent with the finding of Jayaraman et al. 31  , who have shown correlations between receptor-binding affinities and experimentally enhanced H1N1pdm09 transmission, our study demonstrates that subtle changes in a residue distal to the RBD were critical for the emergence of the 2009 pandemic virus from its swine virus progenitors.
Washed arrays were dried by centrifugation and then scanned for Alexa488 signal on a confocal microarray scanner (ScanArray Express, Perkin Elmer). Resultant images were analyzed in Imagene (Biodiscovery) and mean signal minus background values for each spotted compound were calculated and plotted using MS Excel. Statistical Analysis. Statistically significant differences in virus titers among the groups were determined by one-way and two-way ANOVA followed by Tukey's posttest using Prism software; values were considered significantly different when the p value was less than 0. 05.
Acknowledgements We thank the following colleagues from  St. Jude Children's Research Hospital :  J. C. Crumpton ,  B. Marathe ,  J. Trushar , and  A. Webb  for excellent technical assistance,  C. Guess  for editing the manuscript and  S. S. Wang ,  M. Zanin , and  H. Zarket  for critically reviewing the manuscript. We thank  Y. K. Choi  ( Chungbuk National University, South Korea ) and  J. K. Kim  ( Korea University, South Korea ) for informative discussions. This work was supported by the  National Institute of Allergy and Infectious Diseases (NIAID) ,  Centers of Excellence for Influenza Research and Surveillance (CEIRS)  under Contract No. HHSN266200700005C ,  Cancer Center  core grant  CA21765 , and by  ALSAC. N. B. -T. acknowledges the support of the  Ministry of Science and Technology, Israel.
Spaceborne observation, including passive and active remote sensing systems, is a good approach to get such information of canopy structures. Passive multispectral and hyper-spectral sensors produce two-dimensional atmosphere/ground information, whereas their ability to represent 3D spatial patterns is limited. For instance, their use for tropical dense forests is not adequate because the remote sensor has difficulty to penetrate the upper canopy layer  [17]. On the other hand, active sensors, including lidar and radar, have been shown to be valuable tools for 3D vegetation mapping and characterization. Recent developments in P-band radar technology and data processing techniques allow accurate estimates with a large spectrum of forest biomass  [18] [19] [20]. For instance, the BIOMASS Earth Explorer mission selected by ESA in the framework of its Living Planet program is envisaged as a P-band spaceborne Synthetic Aperture Radar (SAR) satellite.
Overview The modeling of the lidar signal can be achieved based on a semi-empirical approach using the airborne lidar measurements and the radiative transfer equation applied to the laser beam propagation into a scattering medium (leaves, branches, trunks). The lidar instrument ULICE (Ultraviolet LIdar for Canopy Experiment) was embedded on an Ultra-Light Aircraft (ULA) (Figure  1 ) as for previous atmospheric  [41]  and canopy  [25]  studies. It performed airborne measurements over different forest biomes between 2008 and 2014, from temperate to tropical forests, to obtain a representative database of lidar vertical profiles. This database is used to initialize analytical and statistical modeling of the lidar signal, so as to simulate spaceborne observations. The canopy lidar or topographic lidar typically use wavelengths in the near infrared (NIR, i.
Atmospheric Distributions The sampling frequency of the onboard lidar is chosen to be 10 Hz by taking into account the spatial horizontal resolutions of the considered satellite data (no significant statistical differences are observed for higher PRF). For each lidar shot, the corresponding AOT and COT were derived from the nearest (in space and time) MODIS data. For one revisit cycle, we calculated the distribution (histogram) and then the cumulative distribution function F(τ) of the total atmospheric optical thickness τ for the lidar shots inside the two selected areas (Figure  14 ). As mentioned before, for a given lidar system and a chosen orbit, there is a TOTmax below which the detections are always good (SNRGE ≥ 10). The atmospheric and the forest optical thicknesses are then complementary.
Discussion and Conclusions Airborne lidar measurements were performed over several temperate and tropical forests sites, which allowed for building a representative database of lidar vertical profiles. From these lidar measurements, a semi-empirical approach was applied using the radiative transfer equation applied to the laser beam propagation into a scattering medium. An end-to-end simulator was developed to simulate and analyze the performance of both air-and space-borne lidar systems. The uncertainties on structural and optical parameters (tree top height, quadratic mean canopy height, and extinction coefficient) for spaceborne observations were estimated. The surface slope and the multiple scattering effects on the lidar signal were discussed, and proved to be not negligible for spaceborne observations leading to a relative error ~10%-30% on the retrieved parameters.
Obviously, the pixel size of POLDER is much bigger than the spaceborne lidar footprint (e. g. , 20 m in diameter) and the POLDER information is much more integrated. Nevertheless, this is the only spaceborne instrument making multidirectional measurements at the global scale. The sampling sites of the GLC2000 reference database are not located in France because the POLDER pixel size (~6 km) is not compatible with the homogeneity of French forests. They are not exactly the same from one month to another since certain scenes could be cloudy  [85]. An example of the cumulative probability density functions (CPDF) derived from the assessments of P is given in Figure  B1  for the "Mixed Forest" at 490 nm and 865 nm. The RMS at 865 nm is smaller than the one at 490 nm, because of the less absorption of trees.
Acknowledgment The experiments have been funded by the  Centre National d'Etudes Spatiales  ( CNES, English: National Centre for Space Studies ) and the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA, English: French Alternative Energies and Atomic Energy Commission ). The authors also thank the support offered by the  Direction Générale de l'Armement  ( DGA, English: General Directorate for Armament ). The POLDER/PARASOL BRDFs databases, offered by  CNES , have been elaborated by the  LSCE , and provided by the  POSTEL Service Center. The authors are grateful to  Fabien Marnas  and  Julien Totems  for their help during the lidar experiments. The authors also thank  Nicolas Baghdadi ,  Patrick Rairoux ,  Shushi Peng  and  Julien Totems  for reviewing this paper.
nature. com/nature. Correspondence and requests for materials should be addressed to O. P. (pourquie@genetics. med. harvard. edu). Microarray data were deposited in GEO database under the accession number GSE39615 and pending. (e-g) hiPS cells culture after 40 days of myogenic differentiation, stained for Fast MyHC (e, scale bar is 500 µm), or double-stained for MF20 / Pax7 (f) and Ki67 / Pax7 (g) (Scale bar is 100 µm). Figure Legends Targeted ES cells were recovered with a 0. 5% frequency, and efficient germline transmission was used to assay cells' pluripotency. In order to generate Pax3GFP ES cells, targeted ES cells were electroporated with a construct containing the FLP recombinase. Myog-repV line. The same strategy as for Msgn1-repV was used with the Myog-repV transgene using a 1. 6kb fragment of the mouse Myogenin promoter  55.
0 arrays. Each microarray was then washed and stained on a GeneChip fluidics station 450 and further scanned with a GeneChip Scanner 3000 7G. Finally, raw data (. CEL Intensity files) were extracted from the scanned images using the Affymetrix GeneChip Command Console (AGCC) version 3. 1. for Mesp2 64 , Raldh2 65 and Fgf8 66 were already described. Images were acquired on a Leica M125 stereomicroscope. For Immunohistochemistry, cell cultures were fixed for 2 hours in 4% formaldehyde. Cultures were rinsed three times in PBS, followed by Blocking buffer composed of Trisbuffered saline (TBS) supplemented with 1% FBS and 0. 1% Triton-X100. Primary antibodies were then diluted in blocking buffer and incubated overnight at 4°C. Cultures were then washed 3 times with TBST (TBS supplemented with 0.
Myofibers density count A fiber density estimate was calculated by counting the number of fibers in 3 to 4 weeks old control cultures. Quantifications were manually done on an average of 5-10 pictures (at 40-100x magnification) per well, acquired from MyHC-stained myofibers fields. Alternatively, staining was quantified by performing automated cell count using the Array scan XTI (Thermofisher) with a 10x objective (144 fields/well). Image analysis was performed with HCS Studio software. Using the Tube Formation Bioapplication we identified fibers according to Myosin staining and measured their area, length and width. Three independent differentiation experiments were quantified. Results are expressed as averages of number of fibers per square millimeter.
Image acquisition and Processing Live or fixed brightfield and fluorescent images were acquired either on a Zeiss Axiovert, Evos FL, Zeiss LSM780, or Leica SP5 systems. Images were processed with Adobe Photoshop, and quantification and measurements were done with Fiji 69 software. For muscle contractions, movies were taken with a Leica DMRB microscope using a photometric FX camera and a 20x objective. Images were taken at 100ms intervals. For movie processing, image sequences were imported and processed in Fiji. Movies were saved in AVI format at a frequency matching the acquisition rate of 33 frames per second.
mouse embryos (Fig 1a). This microarray series allowed us to generate a database representing expression of all transcripts expressed in the differentiating PSM (Fig 1b and Extended data Fig 1). These histograms provide an accurate representation of gene expression along the PSM when compared with in situ hybridization patterns as exemplified for well characterized genes such as the Fibroblast Growth Factor (FGF) ligand Fgf8 or the retinoic acid biosynthetic enzyme Raldh2, which show anterior or posterior graded expression in the PSM or for Mesp2 which shows a striped expression (Fig 1c). Therefore this microarray series provides a faithful representation of the transcriptome regulation during early stages of paraxial mesoderm differentiation.
Acknowledgments: We thank  Christopher Henderson ,  Karim Hnia ,  Marie Knockaert  and members of the  Pourquié lab  for comments. We are grateful to  Jennifer Pace ,  Tania Knauer-Meyer ,  Gonçalo Vilhais-Neto , and  Merry McLaird  for help. We thank  Claudine Ebel ,  Christelle Thibault-Carpentier ,  Anne Magloth-Roth , and the  Imaging Facility. We thank the Microinjection and Phenotyping teams of the  Mouse Clinical Institute. We thank  Michael Durnin  from the  Stowers Institute Cell Culture and Animal Facilities. This work was supported by  Stowers Institute for Medical Research , by the  Howard Hughes Medical Institute , by the  FP7 EU grant Plurimes  (agreement no. 602423 ) and by a  Strategic grant  from the  French Muscular Dystrophy Association (AFM)  to OP. (h-j) Muscle fibers differentiated in vitro in long-term serum-free cultures and stained for Titin (h) and the fast MyHC (j) (scale bar is 200 µm).
To study the influence of DNA configuration on the direct damage yield. No indirect effect has been accounted for. Methods. The GEANT4-DNA code was used to simulate the interactions of protons and alpha particles with geometrical models of the A-, B-, and Z-DNA configurations. The direct total, single, and double strand break yields and site-hit probabilities were determined. Certain features of the energy deposition process were also studied. Results. A slight increase of the site-hit probability as a function of the incident particle linear energy transfer was found for each DNA configuration. Each DNA form presents a well-defined sitehit probability, independently of the particle linear energy transfer. Approximately 70% of the inelastic collisions and ∼60% of the absorbed dose are due to secondary electrons.
Acknowledgments M. A. Bernal wishes to thank the FAEPEX fund of the  Universidade Estadual de Campinas  and the  FAPESP foundation ,  Sao Paulo , for financing his research activities through the Project  2011/51594-2. He also thanks  Mr. F. S. Cavalcanti  for his help during figure preparations. The  GEANT4-DNA  project receives funding from the  French Agence Nationale de la Recherche  Contract no. ANR-09-BLAN-0135-01  and from the  European Space Agency  under Contract no. AO6041-22712/09/NL/AT.
Water filters (Pall Acro® 50, 0. 45 μm) were used to prevent humidity penetrating the detectors. Detector accuracy was first checked in the laboratory using gas mixtures with H 2 concentrations of 100 and 1000 ppm in air-like mixtures (80 % N 2 , 20 % Data for all field H 2 measurements were obtained with a GA2000 Plus detector, except in fields marked with *, which were measured with the MDS detector. Total gas measured for each sample is equal to 100 %. Discrepancies between field measurements and laboratory measurements are attributable to the sampling method (see explanation in "Field methods" subsection). Field measurements were usually about twice the laboratory measurements, except where sampling failed. For all field measurements, see Additional file 1:.
Background: Breast cancer biological characteristics change as age advances. Today, there is a lack of knowledge regarding age-specific molecular alterations that characterize breast tumours, notably in elderly patients. The vast majority of studies that aimed at exploring breast cancer in function of age are based on clinico-pathological data. Gene-expression signatures (GES), which in some ways capture biological information in a non-reductionist manner, represent powerful tools able to explore tumour heterogeneity. Methods: Twenty-five GES were used for functional annotation of breast tumours in function of age: five for molecular subtyping, seven for immune response, three for metabolism, seven for critical pathways in cancer and three for prognosis. Affymetrix® genomics datasets were exclusively used to avoid cross-platform normalization issues.
Available corresponding clinico-pathological data were also retrieved and analysed. Results: Fifteen publicly available datasets were pooled for a total of 2378 breast cancer patients (whole cohort), out of whom 1413 were of Caucasian origin. Three age groups were defined: ≤ 40 years (AG1), > 40 to < 70 years (AG2) and ≥ 70 years (AG3). We confirmed that age influenced the incidence of molecular subtypes. We found a significant growing incidence of luminal B and a decreasing kinetics for basal-like in function of age. We showed that AG3 luminal B tumours were less aggressive than AG1 luminal B tumours based on different GES (iron metabolism, mitochondrial oxidative phosphorylation and reactive stroma), recurrence score prognostic GES and histological grade (SBR). Contrary to tumours of young patients, tumours of elderly patients concentrated favourable GES scores: high oestrogen receptor and mitochondrial oxidative phosphorylation, low proliferation, basal-like, glycolysis, chromosomal instability and iron metabolism, and low GES prognostic scores (van't Veer 70-GES, genomic grade index and recurrence score).
Available clinico-pathological data (oestrogen receptor [ER] status, HER2 status, nodal status [N], Scarff-Bloom-Richardson [SBR] histological grade, tumour size and evolution data) were analysed in parallel. Because of a possible ethnic bias, analyses were done twice, on a whole cohort composed of patients of mixed geographic origins, and on a Caucasian subcohort; numbers of patients were 2378 and 1413, respectively. Functional annotation by means of 25 GES tested in this study demonstrated a decreasing aggressiveness of breast tumours in function of age based on continuous GES scoring. Furthermore, we showed that luminal B tumour of elderly patients were less aggressive than luminal B tumour of young patients.
Data selection We exclusively looked for publicly available breast cancer Affymetrix® genomic datasets associated with clinico-pathological information, including age at diagnosis, prognosis and geographic origin of the cohorts in repositories such as Gene Expression Omnibus (GEO), ArrayExpress, author's individual web pages, and in articles, selecting those with a medium to large sample size  [11, 12]. Among other things, our study aimed at exploring molecular subtype distribution, which is known to vary in function of population origin  [10, [13] [14] [15] [16]. For this reason, we selected a Caucasian cohort from the whole population. However, precise origin of the patients was exceptionally indicated in clinico-pathological characteristics associated with genomic data. So, we selected European cohorts and three non-European ones (E_TABM_158, GSE7849 and GSE17907), for whom ethnicity was reported; non-Caucasian patients were excluded from these three studies.
GES subtyping and scoring were done on patients for whom at least 75 % of GES genes were available in their expression data (Additional file 1). Twenty four continuous GES (all GES except PAM50) score correlations were illustrated with a correlation plot along with the dendrogram corresponding to average-linkage hierarchical clustering algorithm of GES with Pearson correlation distance measure. We considered a two-sided p-value of less than 0. 05 to be statistically significant (for multiple comparisons, Bonferroni correction was applied); the same level was used for significance analysis of microarrays (SAM) qvalue. Mantel-Haenszel chi-square trend test, UMLR, hierarchical clustering algorithm and SAM method were done with R software (version 3. 0. 2) and packages; coin, VGAM, amap and samr, respectively.
Included patients We exclusively focused on Affymetrix® genomic datasets to avoid cross-platform normalization issues. Fifteen publicly available datasets were pooled for a total of 2378 patients, out of whom 1413 were of Caucasian origin (Table  1 ). Three patients' age groups were defined: ≤ 40 years (AG1), > 40 to < 70 years (AG2) and ≥ 70 years (AG3). Numbers of patients in each age group for both populations are displayed in Tables  2  and 3.