dataesr/scientific-paragraphs-categorization huggingface dataset:
Data reduction When observing with IRDIS in dual imaging at H2H3 (resp. K1K2), IRDIS produces two simultaneous images, the left one at H2 (resp. K1) and the right one at H3 (resp. K2). In classical imaging broadband H, two identical images are taken on the left and on the right detector quadrants. The IRDIS data were corrected for cosmetics and sky background using the SPHERE Data Reduction and Handling (DRH) pipeline  (Pavlov et al. 2008). The outputs include cubes of left and right images recentered onto a common origin using the satellite spots, and corrected from distorsion. After these first steps, the data were sorted out according to their quality, and the following algorithms were applied: Classical Angular Differential Imaging (cADI,  Marois et al. 2006) , Template Locally Optimized Combination of Images (TLOCI,  Marois et al.
A disk around HD 106906AB All IRDIS data sets (even the March, 30th, of poor quality) and reductions reveal a close to edge-on disk. A set of resulting median images obtained using different algorithms is shown in Fig. 1. The IFS image resulting from the May, 12 data is shown in Fig. 2. The later image is a median over wavelength of the image obtained with PCA (with 50 components). A low-pass median filter (size of 31 pixels) and a software mask leaving the ring between 12 (0. 09 ′′ ) and 108 pixels (0. 81 ′′ ) from field center have been used.
Search for other planets Our IRDIS images reveal 3 additional point sources in the H2H3, H or K1K2 FoV (see Table  4 ). Two of them (Star 1 and Star 2) were already present in ancillary HST ACS data (taken in 2004), and in the NICI planet b discovery images (taken in 2011). Given the long time span between the HST data (taken in 2004) and the present SPHERE data, and given HD 106906AB ppm, we could easily check that these sources do not share a common proper motion with HD 106906AB (see Fig. 5 ). Star 3 3  , located closer to the star was not seen in NICI nor ACS data. Finally, another very faint target is seen in the H images south of Star 3, that was not present in the HST or NICI data. No point source was identified in the IFS FoV. Using the SPHERE IRDIS and IFS data, we computed the contrasts achieved in these observations.
Acknowledgements. The project is supported by  CNRS , by the  Agence Nationale de la Recherche  ( ANR-14-CE33-0018 ), and the  Programme National de Planétologie (PNP, INSU)  and  Programme National de Physique Stellaire (PNPS, INSU). A. L. M, D. M. , and R. G. acknowledge support by  Italian MIUR  through " Premiale WOW 2013 ". JO acknowledges support from the  Millennium Nucleus RC130007 (Chilean Ministry of Economy ). We thank  P. Delorme  and  E. Lagadec  ( SPHERE Data Center ) for their efficient help during the data reduction process. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF -Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands)  in collaboration with  ESO.
SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for  FP6  ( 2004-2008 ), grant number  226604  for FP7 (2009)(2010)(2011)(2012) and grant number  312430  for FP7 (2013)(2014)(2015)(2016).
5 GeV/c. The collision time, used for the calculation of the time-of-flight to the TOF detector, is measured by an array of Cherenkov counters, the T0 detector, located at +350 cm and -70 cm from the interaction point along the beam direction  [54]. The Electromagnetic Calorimeter (EMCal), situated behind the TOF, is a sampling calorimeter based on Shashlik technology  [55]. Its geometrical acceptance is 107 • in azimuth and |η lab | < 0. 7. In this analysis, the azimuthal angle and η coverage were limited to 100 • and 0. 6, respectively, to ensure uniform detector performance. The minimum-bias (MB) p-Pb data sample used in this analysis was collected in 2013. The trigger condition required a coincidence of signals between the two V0 scintillator hodoscopes, placed on either side of the interaction point at 2.
General fMRI Methods fMRI Data Acquisition and Analysis MRI scanning was carried out on a Phillips Achieva 3T scanner in the F. M. Kirby Research Center for Functional Brain Imaging at the Kennedy Krieger Institute (Baltimore, MD, USA). Blood oxygenation level-dependent (BOLD) changes in the MRI signal were collected using a 32-channel SENSE head coil (MRI Devices, Inc. , Waukesha, WI, USA). Stimuli were presented on a laptop running MATLAB (The MathWorks; Natick, MA, USA) and Psychtoolbox  (Brainard, 1997; Pelli, 1997)  software. A liquid crystal display projector located outside of the scanning room back-projected the stimuli onto a screen located inside of the bore of the scanner. Participants viewed the stimuli via a mirror mounted to the top of the head coil. Responses were made with left-or right-thumb presses on hand-held button boxes connected via fiber-optic cables to a Cedrus RB610 response pad (Cedrus, San Pedro, CA, USA).
Anatomical images were acquired using a magnetization prepared rapid gradient echo (MPRAGE) T 1 -weighted sequence that yielded images with a 1mm resolution (150 coronal slices, TR = 7. 9 ms, TE = 3. 65 ms, flip angle = 8 • ). The functional T 2 *weighted MR scans were sequential gradient echo, echo planar images (EPI; 35 axial slices, TR = 2000 ms, TE = 30 ms, flip angle = 70 • , image matrix = 80 × 80, field of view = 240 mm, slice thickness = 2. 5 mm with a 0. 5 mm gap). Using each participant's MPRAGE scan, individual segmented and inflated cortical surface models were created in Freesurfer  (Dale et al. , 1999; Fischl et al. , 1999)  and then fMRI data were projected onto those surface models for analysis and visualization. Data were analyzed using the AFNI software package  (Cox, 1996)  and SUMA  (Saad and Reynolds, 2012).
Whole-Brain Analysis We also examined a whole brain analysis to isolate regions that were more sensitive to maintaining spatial relations vs. maintaining spatial locations in WM. Focusing on the delay period, we contrasted Relation trials vs. Location trials. We also used whole brain analysis in Experiment 2 to examine the effects of load within each trial type. Tests of node-wise significance were held to an uncorrected p < 0. 01 and corrected for multiple comparisons via spatial extent of activation. Holding each cluster of nodes to an experiment-wise p < 0. 05 required a minimum cluster size of 92 mm 2 , based on a Monte Carlo simulation with 1,000 iterations run via the SUMA software package, using the imposed smoothness of 4 mm FWHM. This smoothness value was greater than the measured smoothness of the residuals in the individual participant surface maps.
Thus, our estimate of the minimum cluster size required to reach the statistical threshold is more conservative than it would have been using this alternative method of using the smoothness of residuals  (Worsley et al. , 1996; Kiebel et al. , 1999). Of note, recent concerns about cluster correction have been raised  (Eklund et al. , 2016)  that include AFNI's 3dttest++ program used here. There are three pieces of evidence here that suggest our false positive rate is not inflated: (1) we used a smaller imposed smoothing (4 mm) than that used by  Eklund et al. (2016) , which makes our cluster threshold more conservative, (2) AFNI has made changes to address these problems  (Cox et al. , 2016) , and (3) our main results of interest were conducted and replicated across two experiments.
Experiment 1 Results Of the entire sample of 32 participants, there was one participant in which we could not identify any early visual areas from the quadrant localizer data. We additionally excluded four participants whose MRI data contained significant spiking artifacts and one participant who had excessive head motion. Finally, participants whose behavioral accuracy on the WM task in the scanner was >2SD below the group mean were excluded (N = 1). Therefore, our final sample used in the analyses reported below was N = 25.
Data collection and definitions Variables collected within the first 24 h of ICU admission included: age, gender, comorbidities according to the Charlson score  [14] , performance status  [15] , type of malignancy, disease status, bone marrow transplant status (type, conditioning regimen), presence of graft versus host disease (GVHD), neutropenia, reason for admission, and severity of illness (according to both SOFA score  [16]  and SAPS II  [17] ). Type of organ support (IMV, NIMV, renal replacement therapy, RRT and vasopressors) was collected within the first 48 hours and during the entire ICU stay. Mortality was evaluated at day-28, day-90, and 1 year. As previously described, high-grade malignancies included acute leukemia and aggressive lymphoma, whereas low-grade malignancies included chronic leukemia, indolent lymphoma, myeloma, Waldenström macroglobulinemia, and aplastic anemia  [9].
Statistical analysis Values are expressed as mean ± standard deviation (SD) and number (percentage), as appropriate. Clinical data were first compared in two-year periods over the timeframe of the study. Data were also compared according to the type of hematological malignancy. Comparisons were carried out using a oneway analysis of variance (ANOVA) for continuous data and the chi-squared test for categorical variables. Prognostic variables in determining day-28 and 1-year mortality were assessed by univariate analyses using an unpaired Student's t-test for continuous data and the chi-squared test or fisher's exact test for categorical data, as appropriate. All variables with p<0. 15 were included in multivariate analysis using a logistic regression model. As we chose to evaluate the impact of organ support, the reason for admission, SOFA, and SAPS 2 were excluded from the model because of redundancy.
Odds ratios (OR) were estimated with 95% confidence intervals (95% CI). Initial analyses included the whole cohort. Depending on day-28 outcome, patients were also analyzed according to treatment/condition details (allogeneic HSCT, neutropenia, IMV). Each of these three subgroups underwent univariate and multivariate analysis similar to that described above. These analyses were performed independently but the groups were not mutually exclusive. Time to death for the whole cohort and for the 3 predefined subgroups of patients were modeled by means of Kaplan-Meier estimates, and differences were compared by use of the log-rank test. Statistical calculations were performed using Medcalc Software version 7. 4. 3. 0 (Mariarke, Belgium). Differences were considered significant when p<0. 05.
Gene expression levels were calculated using FPKM method (fragments per kilobase of transcript per million mapped reads). An absolute value of the log2 ratio ≥ 1 and P ≤ 0. 05 were applied as thresholds to characterize the significance of gene expression level. The DEGs were annotated by aligning against the NCBI non-redundant nucleotide database. For further identification of pathways related to the SlRBZ gene, DEGs were compared with the Kyoto Encyclopedia of Genes and Genomes database (KEGG).
Isolation and Molecular Characterization of SlRBZ We cloned and identified a Ran-binding protein zinc finger gene (named as SlRBZ) in tomato. Based on the nucleotide sequence of this gene (http://solgenomics. net/), the primers for the full length were designed. We isolated the full-length ORF from the tomato cultivar Ailsa Craig (AC) using RT-PCR. The predicted coding sequence of SlRBZ was 996 bp and encoded a protein of 331 amino acid residues. Through genomic and cDNA sequence alignment, we determined that SlRBZ contains seven exons and six introns. SlRBZ harbors three dispersed RanBP-type zinc fingers that conform to the RanBP2-type consensus sequence pattern (W-XC-X(2)-C-X(3)-N-X(6)-C-X(2)-C). Carrying out the pBLAST with SlRBZ amino acid sequence, the homologous proteins of other species were retrieved from the NCBI database.
SlRBZ Inhibits the Expression of Chl and Photosynthesis-Related Genes To further characterize the molecular mechanism that triggers the phenotype of chlorosis and dwarfism mediated by SlRBZ, we further compared the gene expression profiles of SlRBZ transgenic and AC plants by RNA-Seq analysis (The transcriptome data about the transgenic tomato plants SlRBZ-OE and wild-type AC are available at http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE77340). Clean reads were obtained by discarding low-quality reads, resulting in 22,824,878 clean reads for AC and 22,958,774 clean reads for SlRBZ-OE. Among them, 13,836,447 (60. 62%) clean reads for AC, and 12836008 (55. 91%) clean reads for SlRBZ-OE were mapped to the reference genome ITAG2. 4, suggesting that the RNA-seq transcriptomes were sufficient for subsequent gene expression analyses.
Pretreatment of image mean spectra In order to optimize the prediction accuracy of the models by decreasing the scattering effects, three pre-processing methods were investigated, standard normal variate (SNV), multiple scattering correction (MSC), and Normalization (AREA, corresponding to the area under curve = 1). Data preprocessing were performed by using the PLS-Toolbox v. 7. 5 (Eigenvector Research) for MATLAB 2013b (The Mathworks Inc. , Natic, MA, USA).
Image texture features extraction Second-order statistics features were investigated in the present study to evaluate the potential of image texture features to discriminate muscles. [16]  To extract image texture features from muscle images, two methods were used. The first one consisted on the analysis of the gray level histogram difference matrix (GLHD) and the second one on the analysis of the Gray Level Co-occurrence Matrix (GLCM). The texture features were calculated in the same ROI (700 × 575 pixels) used to extract the SPECT data that enabled to decrease the time calculation and to prevent the sample side effects. For the GLCM method, texture features were calculated on four symmetric GLCM using a dipole length (d) of 1 and successively an orientation angle (θ) of 0°, 45°, 90°, and 135°.
By this method it Cluster prominence). [1] as possible to extract a total of 29 textural features (Fig. 2 ) as previously reported. [3, 7] As a result, a GLCM textural matrix of 240 image cubes × 29 variables were calculated. [3, 7] or the GLHD method, the same d and θ values were used enabling to extract up to five texture features (mean, variance, entropy, contrast, and energy; Fig. 3 ). As a result, a GLHD textural matrix of 240 image cubes × 4 variables were calculated. The different GLCM and GLHD textural features were calculated using a home program in MATLAB R2013b (The Mathworks Inc. , Natic, MA, USA), based on ximage toolbox.
Partial least square discriminant analysis (PLSDA) PLSDA was performed on auto-scaled data for GLCM and GLHD features and on mean centered image mean spectra after preprocessing in MATLAB R2013b (The Mathworks Inc. , Natic, MA, USA) using the PLS-Toolbox v. 7. 5 (Eigenvector Research). The total number of samples was divided into two sets, 70% (i. e. , 84) of the samples were used for model calibration and 30% (i. e. , 36) to verify the model robustness (validation), by using the nearest neighbor thinning method proposed in the PLS-Toolbox v. 7. 5 (Eigenvector Research). This method enabled to select validation samples which best fill out all covariance space. PLSDA is a well-spread technique and its aim is to predict the membership of an individual to a qualitative group defined as a preliminary.
Sample discrimination by MSI data PLSDA has been used in order to evaluate the discriminant ability, as a function of muscle types and types of animals, of texture features (GLCM and GLHD) and image mean spectra (i. e. , SPECT) extracted from MSIs. Preliminary, mean spectrum of each muscle was investigated and enabled a good discrimination of samples with respect to the types of animals and muscle types, with CC accuracy ranging from 77 to 82% after external validation. The results depicted that dissimilarity between characteristics of muscle types (BF, LT, and SM) is higher than the difference observed between types of animals as noted on mean spectrum. The present conclusion was consistent with a previous study conducted on biochemical and structural parameters on the same samples. [13] he discrimination between muscle types seems to arise from difference in reflectance intensities of meat water, fat, and protein contents and from their interactions.
Mechanical aspects TileCal is a non-compensating sampling calorimeter made of steel plates that act as absorber and provide mechanical structure into which the active scintillating tiles are inserted. Charged particles going through the tiles produce scintillation light that is collected by two wavelength-shifting (WLS) fibres, on each side of a tile. These fibres are then grouped in bundles to form cells, organised in three radial layers, as depicted in figure  2 , thus achieving a granularity of about 0. 1 in η 3  for the layers A and BC (the closest to the collision point) and around 0. 2 in the outermost layer. Azimuthally, the detector is segmented in 64 wedge-shaped modules (see figure  3 ), thus achieving a granularity of ∆φ = 0. 1. The E cells are non-standard calorimeter cells made of single large scintillators.
Detector readout Each fibre bundle, usually corresponding to one side of a cell, is read out by a photomultiplier tube: each standard cell is thus read out by two PMTs, the E cells being read out by a single PMT. Therefore, there are 9852 PMTs in total. The electric pulses generated by the PMTs are shaped  [4]  and digitised  [5]  at 40 MHz with two different gains, with a ratio of 64, in order to achieve a good precision in a wide energy range. These samples are then stored in a pipeline memory until the level-1 trigger decision is taken (ATLAS has a three-level trigger system, the first level giving a decision in 2. 5 µs during which the data are kept in the front-end electronics). If the decision is positive, seven samples, in time with the signal and with appropriate gain giving the best precision on the pulse amplitude, are sent to the off-detector electronics (Read Out Drivers or RODs  [6] ).
The Laser calibration system The first prototype of the Laser system has been built in 1993 and the first tests with a prototype module of the calorimeter were performed in October 1994  [11]. A second prototype was then built and tested in 1997  [12]. After several improvements, a first version of the system has been used during the qualification of the TileCal modules using particle beams  [10]. Finally, the system described in the following paragraphs was installed in its definitive location in 2008, in time for the calibration of TileCal before the first LHC collisions. After several years of running, the current Laser system has been upgraded in October 2014 for the LHC run 2, starting in 2015. This new system includes a new Laser source, new optical components and new electronic boards, and is not described in this article.
In order to further improve the splitting uniformity, the amount of light transmitted to the 400 secondary fibres can be varied with adjustable distance optical connectors  [15]. From these 400 outputs, 384 of them are connected to 80 m long clear fibres of similar model, that bring the light from the USA15 cavern to the TileCal modules. Inside a module, each clear fibre is connected to 17 (EB) or 48 (LB) fibres of the same type but with lengths varying between 0. 5 and 6 m; each of these fibres reaching one PMT. The light splitting is performed by the mean of an empty anodized aluminium tube: the incoming beam expands and reflects on the inner wall of the tube before reaching the bundle of output fibres. These tubes are 26 mm (respectively 40 mm) long with an internal diameter of 6 mm (10 mm) in the EB (LB).
The raw relative gain variation of a channel i is defined as follows: ∆ i = R i -R ref i R ref i (4. 2) where R ref i is the normalised response of the TileCal channel i during the reference Laser run. However, due to inhomogeneities in the light mixing of the light splitter or radiation damage to the long clear optical fibres, the light intensity may vary with time and from fibre to fibre (one fibre being linked to half of the PMTs of the same TileCal module). Therefore, ∆ i is corrected by  the term ∆ fibre f (i) ( f (i) is the number of the fibre coming from the Laser and connected to the given channel i) taking into account the gain variation due to a light variation of this specific fibre, as explained in the next paragraph. Gain variation [%] 4 - 3 - 2 - 1 - 0 1 2 3 To compute ∆ fibre f (i) , an iterative method is used.
First, the distribution of ∆ i of channels fed by the same fibre is considered (see figure  15 ). In this distribution, only PMTs connected to the D cells for the long barrel modules and the B13, B14, B15, D5 and D6 cells for the extended barrel modules are used, assuming that these selected channels are stable between two Cesium scans  9. The mean and the RMS of the ∆ i distribution are then calculated from these selected channels. To deal with single drifting channels that would bias the measurement of the shift due to the fibre itself, an iterative procedure is applied: all the channels that have a gain variation larger than twice the RMS are excluded from the distribution for the next iteration. The mean gain variation after five such iterations is the correction ∆ fibre f (i). The aim of this method is to correct the drifting channels of TileCal, not the variations of the fibres or any global variation.
Determination of the calibration constants In equation 1. 1, f Las represents the correction of the PMT gain variation computed with the Laser system. However, only channels that have undergone a significant deviation are corrected, i. e. the value of f Las is set to 1 if it is smaller than a predetermined threshold of about three times the overall precision of the Laser system, thus a threshold of 1. 5 % for the long barrel and 2 % for the extended barrels. The low gain (LG) and the high gain (HG) Laser runs are used to determine the Laser calibration constants. However, the LG data are more precise than the HG data because the HG signal amplitude is much smaller than the LG one (a factor 100 between them for the same channel). Therefore, the LG Laser calibration constants are used for both gains, while the HG Laser calibration constants are used for cross-checks.
These latter channels are not calibrated. The channels linked to E3 and E4 cells are not calibrated by the Cesium system, while the Laser system monitors all the TileCal channels, including those connected to these special cells. The fact that there is no Cesium calibration implies that there are no references for these channels. The chosen solution is to monitor these channels and provide constants with respect to a reference date set to March 18 th 2012 which is the date of the first Laser reference run in 2012. Even though the precision of the Laser system over a period of one year has been estimated to be less than 2 %, it is sufficient to calibrate these highly drifting cells. Some reduced-HV modules can not be calibrated by the Cesium system. As for the E3 and E4 cells, these channels are monitored and Laser constants are computed starting from the Laser reference run taken just before the beginning of the reduced-HV mode period.