dataesr/scientific-paragraphs-categorization huggingface dataset:
The failure of this strategy in our patient could be due to the profound level of immunosuppression and/or the mode of Ig administration (rhythm, period) although we administrated the same total dose as in reported successful experiences. Step PCR Master Mix reagent (Life Technologies) and previously published primers and probes for GI  [18]  and GII  [10]. For quantitative assessment of fecal viral load, the number of NoV RNA copies was estimated by comparing the sample C T value with standard curves. Among the five available samples, four were positive for GII. 7 in the polymerase gene and for GII. 6 in the capsid gene. Then, as described previously  [10] , the ORF1/ORF2 junction was sequenced using the same primers. The nucleotide sequences were aligned and compared to corresponding sequences of NoV strains available in the GenBank database using Fasta program.
Among the five available samples, four were positive for GII. 7 in the polymerase gene and for GII. 6 in the capsid gene. Then, as described previously [10] , the ORF1/ORF2 junction was sequenced using the same primers. The nucleotide sequences were aligned and compared to corresponding sequences of NoV strains available in the GenBank database using Fasta program. These sequences showed a homology of 92% and 98% for GII. 7 and GII. 6, respectively, and a 100% homology between them. Abbreviations: Ig, immunoglobulins; IV, intravenous; NoV, norovirus; W, week.
Statistical and microarray analysis Quantitative data are expressed as mean ± SEM values for each treatment group and group numbers are indicated within the text or legends. Comparisons of results were conducted by using nonparametric Mann-Whitney test (Prism 4. 01; Graphpad Software, San Diego, CA). The Agilent feature extraction software was used to process microarray image files. Only signal intensities above background were included. Signal intensity values were background subtracted and uploaded following instructions by Miltenyi Biotec GmbH (Stefan Tomiuk) and Perkin Elmer (Matt Hudson) into GeneSifter Analysis Edition v4. 0 (http://login. genesifter. net/) for further analysis as previously described  (Gustavsson et al. , 2007). The pre-processed signal intensity values were median normalized and the gene expression in IL-1b and PBS controls were compared at P5 and P10 using t-test (p < 0.
Acknowledgements The authors would like to thank  Dr Claire Thornton  and  Dr Michelle Porritt  for their critical appraisal of the manuscript. The authors' research is funded by the  Wellcome Trust  ( WT094823 ),  Inserm ,  Université Paris 7, Fondation Leducq  ( DSRR_P34404 ),  Fondation Grace de Monaco ,  Fondation Roger de Spoelberch, PremUP ,  VR 2012-3500 ,  ALFGBG 137601 ,  Fondation des Gueules Cassées and Seventh Framework Program of the European Union  (Grant agreement No. HEALTH-F2-2009-241778/Neurobid ). The authors acknowledge financial support from the  Department of Health  via the  National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's & St Thomas '  NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust.
ACKNOWLEDGMENTS. We thank  Peter Huijser  ( Max Planck Institute for Plant Breeding Research ) and  Peter Hedden  ( Rothamsted Research Centre ) for generously providing materials and  Amaury de Montaigu ,  René Richter ,  Maarten Koornneef , and  Luis Barboza  for comments on the manuscript. This work was financially supported by the  Deutsche Forschung Gemeinschaft  through the  European Research Area-Net Plant Genomics Programme and Cluster of Excellence in Plant Science , the  European Union  via the  SYSFLO Training Network  (project code  237909 ), a  Marie Curie Intra-European Fellowship for Career Development (Project Intra-European Grant  Agreement-2009-251839) (to F. A. ), a von Humboldt postdoctoral fellowship (to J. M. ), and the  Max Planck Society  through a  core grant  (to G.
The cloud impact is obviously characterized by their top altitude. Therefore, the portion of FoV where high-altitude clouds are present may reduce the instantaneous aperture for EAS observation, while it is still possible to detect the EAS  [8]. Moreover, EAS are very fast and localized events that take place over a relatively small area and, therefore, accurate information about the atmospheric conditions, with high enough resolution to show small changes in the atmospheric profile that might have blurred the UHECR signal, is crucial to ensure an accurate UHECR reconstruction. Therefore, JEM-EUSO will implement its own Atmospheric Monitoring System (AMS), while data from other atmospheric satellites will be used to complement and enhance our understanding of the atmospheric conditions where the UHECR has been detected, as these satellite measurements can come with lower resolution and have a few hours delay from the time the UHECR has been observed in the JEM-EUSO FoV.
The FEE communicates with the ICU and provides it with the uncompressed raw images. The core of the FEE shall be a FPGA, VIRTEX family, in charge of implementing the main FEE functions. This includes the control of the UL04171, the generation of all the synchronism (including the generation of the clocks), and the interface with the sequencer. The polarization of the detector (bias, gain, offset generation and control) will also be controlled by the FPGA. The data acquisition will be implemented with an Analog Digital Converter (ADC) in each detector output channel before the FPGA input. The ADC number of bits will be chosen according to the pixel data resolution required by the IR-Camera. To reduce the NETD (Noise Equivalent Temperature Difference) of the target under investigation, the technique of the "Frame Averaging" will be used.
An End to End (E2E) simulation of the infrared camera will give us simulated images of those we expect to obtain with the instrument. It provides us with the ability to study the impact of several scenarios found in the atmosphere, in terms of the accuracy of the cloud top height reconstruction, analyzing the detection capabilities, calibration procedures, and the correction factor that will be taken into account for the final data released by the AMS of the JEM-EUSO Space Mission  [22]. The first step is to simulate the IR scenario using atmospheric simulation software, like the Satellite Data Simulator Unit (SDSU) software  [23]  or real satellite IR images, taken by missions like MODIS  [24]  or CALIPSO  [25]. After the input scene is read by the simulator, an optic element simulation takes place, starting from the simulation of the diffraction, distortion, and efficiency of the optics module.
This simulation is performed, based on an evaluation of the design, using optics design software Code-V  [17]  by the Instituto Nacional de Tecnica Aeroespacial (INTA, Spain). In order to create a model of a detector, we have relied on the test described in Section 3. Therefore, we can translate the input values to analog voltage values that should be similar to the detector response. Moreover, we can apply the ADC (Analog to Digital Conversion) of 12 bits, and its corresponding change to 10 bits. As a last step, in the instrument simulation, we have compressed the image, using HP (Hewlet Packard) code LOCO-I/JPEG-LS algorithm  [26]  with near loss-less code. Figure  11  shows a sample of a simulated IR image. In the IR scenario, cloud and atmosphere have been generated by the SDSU code. The effect of the optics and the detector has been included in the E2E simulation software to obtain the product that the IR-Camera would provide.
Since the baseline of the IR-Camera is bi-spectral with two 1 μm-width bands centered at 10. 8 and 12 μm (hereafter referred as B 1 and B 2 respectively), we can develop SWA to retrieve the Cloud Top Temperature (CTT) of water clouds from brightness temperatures measured by the IR-Camera in these bands. The retrieval algorithms are based on radiometric simulations of the physical problems: the emitters (Earth surface, cloud and atmosphere) and the interfering atmosphere (atmospheric absorbing gases, mainly water vapor). The simulations were performed by means of the radiative transfer equation and MODTRAN atmospheric simulation code which is used to calculate the transmittance of atmospheric gases in the spectral bands of the IR-Camera by using average vertical profiles of temperature and gases concentrations corresponding to standard atmospheric models  [41].
Moreover, meteorological cameras use infrarred bands close to the JEM-EUSO camera, allowing new algorithms designed for JEM-EUSO to be tested. Since these cameras have higher resolution and accuracy, the performance of the obtained algorithm represents an upper limit with respect to the JEM-EUSO infrared camera. To assess cloud top height uncertainties, an IR scene, obtained from SEVIRI (on-board Meteosat-9 satellite), has been analyzed and the CTH has been compared with those derived from MODIS (on-board NASAs Aqua satellite) and CALIOP (main instrument of CALIPSO satellites constellation). The two first sensors are infrared radiometers  [47, 48]  and the third is a three-channel LIDAR that uses a Nd:YAG laser emitting linearly polarized pulses of light at 1064 nm and 532 nm. The data from atmospheric radiosoundings are used to convert the measured cloud temperature into the related cloud height.
The analyzed scene has been collected by SEVIRI on July 10, 2011, over the Gulf of Guinea, close to sub-satellite point. Cloud top height estimated by MODIS has been obtained by MY D06 L2 cloud product relative to Aqua acquisition on 12:05 UTC. CTHs derived from SEVIRI been obtained with two different estimations of cloud top temperature: -T 1 cloud = BT @ 10. 8 μm without any correction -T 2 cloud = 1. 0178 BT @ 10. 8 4. 149 The T 1 cloud correction has been derived from radiative simulations of different atmospheres and thick clouds at different levels. As for the SWA, the radiative transfer equation and ModTran atmospheric simulation code have been used in the simulations. The scene is characterized by sea surface underlying a compact deck of low and middle clouds. Warm sea surface improves cloud detection due to high differences with cloud temperature.
Conclusions In the UHECR regime observed above 10 19 eV by JEM-EUSO, the existence of clouds is an important characteristic of the atmosphere. Therefore, the monitoring of the cloud coverage by the JEM-EUSO Atmospheric Monitor System (AMS) is crucial for estimating the effective exposure with high accuracy, and the atmosphere calibration for the analysis of the UHECRs events just above the threshold energy of the telescope. From the prototype test, we can conclude that this detector appears to be a good choice for our mission. The response of the ULIS μbolometer to the incident radiation is similar to those data reported by the manufacturer, and predicted by theory. Therefore, the design of the camera should continue using this prototype as the baseline, if the requirements do not change and the advances in the electronics will not bring a better detector in the upcoming years.
Acknowledgements This work is supported by the  Spanish Government MICINN & MINECO  under the  Space Program : projects  AYA2009-06037-E/AYA ,  AYA-ESP 2010-19082 ,  AYA-ESP 2011-29489-C03 ,  AYA-ESP 2012-39115-C03 ,  CSD2009-00064  ( Consolider MULTIDARK ) and by  Comunidad de Madrid  under project  S2009/ESP-1496. M. D. Rodriguez Frias  deeply acknowledge  Instituto de Astrofisica de Canarias (IAC)  the grant under the " Excelence Severo Ochoa Program " to perform a mid-term visit at this institute ( Tenerife, Canary Islands).
Histological Evaluation Myofiber Analysis Cross sections (8 mm) of isopentane-frozen quadriceps, gastrocnemius, or triceps muscles were taken midway down the length of the muscle and stained with H&E for evaluation with the use of an Olympus BX53 microscope with an Olympus DP72 camera and cellSens Standard software version 1. 5 (Olympus, Center Valley, PA). Fiber size was determined through measurements of MinFeret diameter, as we have previously used,  12  because this measurement offers a myofiber size measurement that is relatively independent of fiber orientation. 19 Overall fiber size and the number of internally nucleated fibers were manually measured with micrographs of two Â100 magnification fields (using a 10Â objective lens) of H&E-stained slides (range, 259 to 1113 fibers; n Z 4 to 5 animals quantified per treatment group).
Other antibodies used for Western blot studies recognize myostatin (MAB788; R&D Systems, Minneapolis, MN), ActRIIB (ab76940; Abcam), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH; G8795; Sigma-Aldrich) and were visualized with chemiluminescent horseradish peroxidase antibody detection reagent (Denville Scientific, Metuchen, NJ). Adequacy of transfer was determined by Ponceau S staining. Quantification of protein levels normalized to GAPDH was performed with ImageJ software version 1. 45s (NIH, Bethesda, MD). Samples from four animals per treatment group were used for analysis. Two additional Mtm1 p. R69C/vehicle and Mtm1 p. R69C/ ActRIIB-mFc animals were added to the quantitative evaluation of total and p-rpS6 (based on results from replicate gels) to further establish the degree of interanimal variability.
Among them, NADP-isocitrate dehydrogenase (NADP-IDH; EC 1. 1. 1. 42), citrate synthase (EC 2. 3. 3. 1), and Ala-AT (EC 2. 6. 1. 2) increased quite dramatically during ripening. Finally, phosphoenolpyruvate carboxylase (PEPC; EC 4. 1. 1. 31), enolase  (EC 4. 2. 1. 11) , and NAD + malate dehydrogenase (MDH; EC 1. 1. 1. 37) showed a different pattern, because their activities decreased in a quasilinear fashion throughout fruit development. These data are in agreement with previously published data  (Steinhauser et al. , 2010). For example, the maintenance of high Suc phosphate synthase (SPS) activity at ripening is confirmed. However, this dataset covers earlier developmental stages (8, 15, and 21 DPA) corresponding to cell division and early cell expansion, at which time most of the activities were much higher than later stages.
In contrast, the second principal component (PC2), which explains 24. 4% of total variance, clearly separates the cell expansion phase (from 21 DPA to Turning) from the two other phases. Thus, PC2 can be interpreted as expressing transitions between the three phases of fruit development. The loading plot (Fig. 5B ) indicates that all enzymes participate in a balanced way in the separation of the developmental stages, because the loadings are relatively high for all of them. Positions of enzymes, thus, correspond to the stages at which they are highest. PCA analysis was next used to compare developmental changes in enzyme activities of fruits grown on different trusses and under different conditions. To facilitate interpretation, loadings and scores of controls were maintained, and data obtained under other growth conditions were added as supplementary individuals  (Lê et al.
B, Corresponding loadings plot. C, Scores plots obtained by adding data obtained for trusses 5 (numbers in green), 6 (numbers in blue), and 7 (numbers in pink). D, Scores plots obtained by adding data obtained under water shortage (numbers in red) and shading (numbers in gray). E, Scores plots obtained by adding data obtained from fruits grown in Avignon, France (numbers in green) and Oxford, United Kingdom (numbers in orange). Abbreviations for enzymes are the same as in Figure  2. were found for aconitase (P = 0. 05, CV = 20%) and NAD-GAPDH (P = 2E-3, CV = 22%). Finally, during ripening, no significant difference was found for any of 36 enzymes. The fact that differences observed between trusses were very limited might be explained by growth conditions being optimal. Assuming that light intensity  (Guan and Janes, 1991)  and watering  (Mitchell et al.
Comparison of Developmental Changes in Enzyme Activities of Fruits Grown at Three Locations To increase heterogeneity in growth conditions, fruits obtained from two other batches of plants grown in glasshouses in Oxford (United Kingdom) during early spring and Avignon (France) during late spring were analyzed, and data were compared with the previous data (controls of Sainte-Livrade culture). It is worth mentioning that, in addition to different light and temperature regimes, Oxford and Avignon used pots and soil, whereas plants were grown in Sainte-Livrade hydroponically. Figure  5E  presents the PCA score plot obtained for Oxford and Avignon samples (Supplemental Table  S5 ). Contrary to the Sainte-Livrade data, these fruit were staged according to DPA ("Materials and Methods") and not standards of the Organisation for Economic Co-operation and Development (http://www.
7, C, E,  and G ). Although Glc-1-P Figure  6. Changes in major metabolites throughout tomato fruit development. Fruit age is given in DPA. Total amino acids, Glc, Fru, malate, and citrate are expressed in micromoles per gram fresh weight 2 6 SD, and Suc and starch are expressed in Glc equivalents (micromoles glucose per gram fresh weight2 6 SD). A, Data obtained from fruits grown in Sainte-Livrade under optimal growth conditions on trusses 5-7 (n = 5). B, Data from fruits grown under control (means for trusses 5-7), water-limited (n = 5), and shaded (n = 5) growth conditions. C, Data obtained from fruits grown in Sainte-Livrade (same as in B), Avignon (n = 5), and Oxford (n = 5). was negatively correlated with starch, no relationship was apparent for Glc-6-P and Fru-6-P. When their amounts were calculated on a per-fruit basis (Fig.
Integration of Changes in Enzyme Activities and Metabolite Levels The integration of multilevel metabolic data strongly depends on how the data are expressed. We assume that enzymes can control both metabolite concentrations and fluxes. Among all the possible ways of expressing enzyme activities and metabolite contents or net accumulation rates (per gram fresh weight, per protein, and per fruit), results with the largest scattering of metabolites were obtained for enzyme activities per protein and net accumulation rates of metabolites per fruit (Fig. 8A ; Supplemental Figs. S2, S3, S4, and S5 show the complete set). PCA was used to integrate enzyme and metabolite data. Variables have been normalized over the three treatments (control, water limitation, and shading) to enable comparison. Loadings have first been computed with the control dataset (Fig.
Three growing scenarios have been performed: (1) control with commercial production practices (276 plants), (2) water shortage by providing only one-half of the volume of nutritive solution compared with the control plant (138 plants), and (3) low light treatment by hanging a shadow net in a part of the greenhouse (the shadow net stopped 60% of the photosynthetically active radiation; 138 plants). Stresses were applied at the flowering of the fourth truss. Environmental variables (temperature, relative humidity, and photosynthetically active radiation and light intensity) were recorded on data loggers (Delta-T Devices). Lateral stems were systematically removed. Each flower anthesis was recorded, and trusses were pruned at six developed fruits to limit fruit size heterogeneity. In the Avignon experiment (south of France, 43°579 0099N and 4°499 0199E), on January 31, 2011, seeds were sown in petri dishes containing Murashige and Skoog medium  (Murashige and Skoog, 1962).
Plants were grown in a temperature-controlled and light-controlled greenhouse (16 h of light from 6 AM to 10 PM and 8 h of dark) under high-pressure sodium and metal halide lamps at a density of 4 plants m 22. Environmental conditions were recorded using a Hobo U12 data logger (onsetcomp. com) set to one recording per hour. On average, the light intensity was 200 mmol m 22 s 21 during the illumination period, with an average temperature of 25. 7°C and 32% relative humidity. During darkness, the average temperature was 19°C, with 42% relative humidity. Plants were watered daily from the top, with the addition of liquid fertilizer (Levington Tomorite) one time per week when the plants started flowering. Fruit samples were collected between March 2 and May 4, 2011 from plants sown on November 26, 2010 and January 4, 2011.
Statistical Analysis All ANOVA, PCA, and Tukey's test (P , 0. 05) analyses were performed using R Software (http://www. r-project. org/) and the package FactomineR  (Lê et al. , 2008). Hierarchical clustering and heat maps were performed on mean-centered data scaled to unit variance using MEV software v4. 8. 1. (Saeed et al. , 2003)  with Pearson's correlations and complete linkage.
ACKNOWLEDGMENTS We thank  Dr. Alisdair Fernie  and  Dr. Sonia Osorio  for providing the seeds used in the study,  Pierre Gaillard ,  Jacques Longuesserre ,  Emilie Labadie  and  Benoît Collu  for technical help, and our colleagues for their friendly contribution during harvests and sample preparation. Received October 29, 2013; accepted January 28, 2014; published January 28, 2014. Plant Physiol. Vol. 164, 2014.
The best crystals were obtained in 30% PEG 6000, 1. 0 M LiCl, 0. 1 M Tris-HCl, pH 8. 0, with 28 mg/ml of protein and in all three molar ratios of the peptide. Crystals of the 1:4 sample were flash-cooled in liquid nitrogen before data collection at the BM14 beamline in the European Synchrotron Radiation Facility in Grenoble, France. Data were processed and scaled using XDS  (31). The structure was solved by molecular replacement using Phaser  (32)  and the apo structure of PcrH (PDB code 2XCC (  8 )) as a search model. Cycles of refinement and model building were performed with Refmac 5. 6  (33)  and Coot  (34). The quality and stereochemistry of the final model was checked with SFCHECK  (35)  and Procheck  (36). Figures were generated using PyMOL.
Acknowledgments-We thank J. Marquez and the HTX Laboratory team for access to and help with high throughput crystallization, Eric Faudry and  François Cretin  for help with ␤-Lac measurements,  J. -P. Andrieu  ( Institut de Biologie Structurale-Grenoble Partnership for Structural Biology Platform ) for N-terminal sequencing, and the  European Synchrotron Radiation Facility  for access to beamlines. This work used the platforms of the Grenoble Instruct center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology.
Recently, we analyzed ERPs recorded during the behavioural test that immediately followed the exposure phase. We found a late fronto-central negative component that was larger for unfamiliar than for familiar pseudo-words. We interpreted this familiarity effect as a greater difficulty in accessing unfamiliar pseudo-word representations  [36]. In a further experiment  [37]  we compared a group of adult musicians to a group of nonmusicians. While musicians barely outperformed nonmusicians at the behavioural level, electrophysiological measures revealed a larger familiarity effect over fronto-central regions in musicians than in nonmusicians. These findings have been recently replicated in a longitudinal study with children who followed a music-training program during two school years  [38]. However, data collected during the behavioural test are smeared by decisional, memory and rehearsal processes and thus, compared to data collected during the exposure phase behavioural data reflect more the result of the learning than the learning process ''per se''.