dataesr/scientific-paragraphs-categorization huggingface dataset:
While the residuals are broadly similar for all epochs, there are visible differences at the highest energies probed by NuSTAR. For both panels, the data have been further rebinned for visual purposes, and only the FPMA data are shown for clarity. (NUSTARDAS; part of the HEASOFT distribution), adopting instrumental responses from NuSTAR CALDB version 20150316 throughout. The unfiltered event files were cleaned with the standard depth correction, which significantly reduces the instrumental background at high energies. Periods of Earth-occultation and passages through the South Atlantic Anomaly were excluded. Source products were obtained from large circular regions (radius ∼150 ′′ ) for each of the two focal plane modules (FPMs A and B). Owing to its brightness, there were no regions of the detector on which Cygnus X-1 was located that were free of source counts, so the background was estimated from a blank region on the opposite detector (each FPM carries four detectors in a square formation, see  Harrison et al.
In addition to the standard "science" data (hereafter mode 1), we also reduce the available "spacecraft science" data (referred to as mode 6) in order to utilize the maximum possible exposure from each observation. In brief, this is the data recorded during the periods of the observation for which the X-ray source is still visible, but the star tracker on the optics bench cannot return a good aspect solution, so the star trackers on board the spacecraft bus are utilized in-stead (see Appendix A). This provides an additional 10-40% livetime, depending on the observation (see Table  1 ). For epochs with multiple OBSIDs, the data from each observation were reduced separately, and the individual spectra from each FPM were then combined into a single average spectrum from that epoch using ADDASCASPEC, but we do not combine the data from FPMA and FPMB, as recommended  (Madsen et al.
SPECTRAL ANALYSIS In order to investigate the spectral variability exhibited by Cygnus X-1 in its soft state, we perform a detailed comparison of the NuSTAR data from each of the four epochs considered here. Throughout this work, our spectral analysis is performed with XSPEC v12. 6. 0f (Arnaud 1996). All our models include a neutral Galactic absorption component, modeled with TBABS  (Wilms et al. 2000). However, as NuSTAR is not particularly sensitive to the typical column (N H ∼ 6 × 10 21 cm -2 ; e. g. Tomsick et al. 2014 ) owing to its bandpass (3-79 keV), we fix the neutral absorption to 6 × 10 21 cm -2 for simplicity. 1  We adopt the abundances in  Wilms et al. (2000)  as our 'solar' abundance set, as appropriate for absorption by the Galactic interstellar medium, and as recommended we adopt the cross-sections of  Verner et al.
The constraint on the iron abundance is considerably weaker, but we still find a lower limit of Fe/solar > 4. 4 from the data above 10 keV alone, consistent with the results for the full NuSTAR bandpass. Interestingly, these data also provide a weak constraint on the spin of a * > 0. 3, also consistent with the full band fits. A high iron abundance is in qualitative agreement with other studies in which it was allowed to vary, even if there is some quantitative tension. Here, the quantitative results are reasonably well split by the reflection model utilized, and when the same model is compared good agreement is seen. Results obtained using the REFLIONX code  (Ross & Fabian 2005)  generally tend to return Fe/solar ∼ 2 (e. g. Duro et al. 2011; Fabian et al. 2012; Tomsick et al. 2014)  3 , while results obtained with XILLVER-based models  (García & Kallman 2010; García et al.
ACKNOWLEDGEMENTS The authors would like to thank the referee for their prompt and useful feedback. DB acknowledges financial support from the  French Space Agency (CNES). VG acknowledges financial support provided by  NASA  through the  Smithsonian Astrophysical Observatory (SAO)  contract  SV3-73016  to  MIT for Support of the Chandra X-Ray Center (CXC)  and  Science Instruments ; CXC is operated by  SAO  for and on behalf of  NASA  under contract  NAS8-03060. This research has made use of data obtained with NuSTAR, a project led by  Caltech , funded by  NASA  and managed by  NASA/JPL , and has utilized the NUSTARDAS software package, jointly developed by the  ASI Science Data Center (ASDC, Italy)  and  Caltech (USA). Swift BAT transient monitor results are provided by the  Swift BAT team.
Peripheral blood was collected in EDTA tubes at the time of interview and processed as rapidly as possible (generally within 2 h). For cases, blood draw was performed before surgery and any adjuvant treatment. Plasma samples were isolated by centrifugation of whole blood at 2000 ×g for 10 min at room temperature. Samples were stored at -80 °C. All specimens were obtained in accordance with the declaration of Helsinki guidelines and were approved by the local Institutional Review Board and the IARC Ethics Committee. A total of 52 SCLC cases and 165 controls were initially included but only 51 SCLC and 123 controls passed the sequencing QC criteria (see Sequencing Data Analyses, Annotation, and Filters), and were therefore included in down-stream analyses. In order to further evaluate the prevalence of circulating-mutated fragments in non-cancer controls, 114 additional controls were retrieved from two large multicenter case-control studies coordinated by IARC.
One was a study on alcohol-related cancers and genetic susceptibility in Europe (the ARCAGE study) that was conducted from 2002 to 2005 and from which we included hospital-based controls recruited in Prague (Czech Republic), Athens (Greece), Aviano, Padova and Turin (Italy). The second was the Latin American study of head and neck cancer conducted from 1998 to 2002 and from which we selected hospitalbased controls from two institutions in Buenos Aires (Argentina). Additional details of the 2 large multicenter case-control studies are included elsewhere  (Lagiou et al. , 2009; Ribeiro et al. , 2011). Out of the 114 controls initially included, 102 passed the sequencing QC criteria (see Sequencing Data Analyses, Annotation, and Filters), and were therefore included in down-stream analyses.
Sequencing Data Analyses, Annotation, and Filters Short reads were aligned to the hg19 human reference genome and BAM files were generated using the Torrent Suite software (v4. 4. 2) with default parameters. Reads with a mapping quality below 20 were excluded from subsequent analysis. We also excluded those libraries for which the on-target median coverage was significantly lower in comparison to the other libraries sequenced in the same batch. On-target median coverage for both libraries is shown in Table  S2. For the calling of variants we used Needlestack, a recently developed ultra-sensitive variant caller, which estimates the distribution of sequencing errors across multiple samples to reliably identify variants present in very low proportion (https://github. com/IARCbioinfo/ needlestack) (unpublished data; Delhomme et al.
For each variant, we also calculated the relative variant strand bias defined by: RVSB ¼ max AO p DP m ; AO m DP p À Á AO p DP m þ AO m DP p: where DP and AO denote respectively the total number of reads and the number of reads matching the candidate variant, with the subscripts p and m referring to the forward and reverse strands respectively. In the complete absence of strand bias, RVSB = 0. 5 and AO p /AO m = DP p /DP m , whereas for a completely biased variant, RVSB = 1. We filtered out variants with RVSB N 0. 85. Variant calls were annotated using ANNOVAR  (Yang and Wang, 2015). We only considered meaningful TP53 mutations those that matched the following criteria: the mutation has already been reported in COSMIC and the mutation is a nonsense, indel, splicing or a missense variant that is classified as deleterious by SIFT or Polyphen.
7% in the replication controls; Fig. 1d ). We used the IARC TP53 database to classify the missense mutations in functional, partially functional, or non-functional based on the in vitro transcriptional activity of the resulting protein. Most missense mutations found in SCLC tumors  (George et al. , 2015)  (92. 6%) and cfDNA from cases (100%) were classified as resulting in a non-functional protein. However, controls had a higher proportion of missense mutations that retained some transcriptional activity (~30%; Fig. 1d ). We also compared the allelic fractions (AFs) of the TP53 mutations found in the cfDNA of cases and controls. The AFs for a given mutation were similar in the two independent libraries, demonstrating the reproducibility of the assay (Table  S3 , S4, and S5). The AFs for the cases ranged from 0.
Diffantom is a whole-brain diffusion MRI (dMRI) phantom publicly available through the Dryad Digital Repository (doi:10. 5061/dryad. 4p080). The dataset contains two single-shell dMRI images, along with the corresponding gradient information, packed following the BIDS standard (Brain Imaging Data Structure, Gorgolewski et al. , 2015). The released dataset is designed for the evaluation of the impact of susceptibility distortions and benchmarking existing correction methods. In this Data Report we also release the software instruments involved in generating diffantoms, so that researchers are able to generate new phantoms derived from different subjects, and apply these data in other applications like investigating diffusion sampling schemes, the assessment of dMRI processing methods, the simulation of pathologies and imaging artifacts, etc.
Early digital phantoms started with simulation of simple geometries  (Basser et al. , 2000; Gössl et al. , 2002; Tournier et al. , 2002; Leemans et al. , 2005)  to evaluate the angular resolution as well. These tools generally implemented the multi-tensor model  (Alexander et al. , 2001; Tuch et al. , 2002)  to simulate fiber crossing, fanning, kissing, etc. Close et al. (2009)  presented the Numerical Fiber Generator, a software to simulate spherical shapes filled with digital fiber tracts. Caruyer et al. (2014)  proposed Phantomas to simulate any kind of analytic geometry inside a sphere. Phantomas models diffusion by a restricted and a hindered compartment, similar to  Assaf and Basser (2005). Wilkins et al. (2015)  proposed a whole-brain simulated phantom derived from voxel-wise orientation of fibers averaged from real dMRI scans and the multi-tensor model with a compartment of isotropic diffusion.
Neher et al. (2014)  proposed FiberFox, a visualization software to develop complex geometries and their analytical description. Once the geometries are obtained, the software generates the corresponding dMRI signal with a methodology very close to that implemented in Phantomas. An interesting outcome of FiberFox is the phantom dataset 1 created for the Tractography Challenge held in ISMRM 2015. This dataset was derived from the tractography extracted in one Human Connectome Project (HCP,  Van Essen et al. , 2012)  dataset. In the tractogram, 25 fiber bundles of interest were manually segmented by experts. Using FiberFox, the segmentation of each bundle was mapped to an analytical description, and finally simulated the signal. In this data report we present Diffantom, an in silico dataset to assess tractography and connectivity pipelines using dMRI real data as source microstructural information.
Diffantom is inspired by the work of  Wilkins et al. (2015) , with two principal novelties. First, since we use a dataset from the HCP as input, data are already corrected for the most relevant distortions. The second improvement is a more advanced signal model to generate the phantom using the hindered and restricted diffusion model of Phantomas  (Caruyer et al. , 2014). As a result, we provide a whole-brain digital phantom of dMRI data with structural information derived from an HCP dataset. We also openly release the diffantomizer workflow, the software package necessary to generate custom diffantoms. Diffantom is originally designed for the investigation of susceptibility-derived distortions, a typical artifact that produces geometrical warping in certain regions of dMRI datasets. In  Esteban et al.
DATA DESCRIPTION Microstructural Model The simulation process relies on a microstructural model derived from real data. On one hand, the diffantomizer workflow requires up to five fraction maps {T j | j ∈ {1,. , 5}} of freeand hindered-diffusion (see Figure  1A ). These compartments will be derived from the macroscopic structure of tissues within the brain, specified in the following order 2  : cortical gray matter (cGM), deep gray matter (dGM), WM, CSF, and abnormal tissue 3. On the other hand, the restricted-diffusion compartments are specified by up to three volume fractions {F i | i ∈ {1, 2, 3}} of three single fiber populations per voxel along with their corresponding direction maps {V i | i ∈ {1, 2, 3}}. The process to obtain the microstructural model from one dataset of the HCP can be described as follows (see also Figure  1B):  (1) The fiber orientation maps {V i } and their corresponding estimations of volume fraction {F ′ i } are obtained using the ball-and-stick model for multi-shell data of BEDPOSTX (Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques modeling crossing -X-fibres,  Jbabdi et al.
, 2012)  on the dMRI data. The HCP recommends BEDPOSTX to reconstruct their data  (Glasser et al. , 2013). A further advantage is that BEDPOSTX exploits the multishell acquisitions of the HCP while operating at whole-brain level. (2) A fractional anisotropy (FA) map is obtained after fitting a tensor model with MRTrix. As we shall see in the Appendix, the FA is used to infer F 1 (the fraction map of the most prevalent fiber), avoiding the extremely noisy estimation of F ′ 1 performed by BEDPOSTX in the previous step. (3) The original fiber fractions {F ′ i } and the FA map are denoised with a non-local means filter included in dipy  (Garyfallidis et al. , 2014). This step produces an important smoothing of the maps, while preserving the edges. Smoothing is also beneficial in simplifying the voxel-wise diffusion model.
(4) The macrostructural fractions {T ′ j } are extracted from the T1-weighted image of the dataset, using standard FSL segmentation tools  (Jenkinson et al. , 2012). (5) The images obtained previously (FA map, {V i }, {F ′ i }, and {T ′ j }) are combined as described in the Appendix to generate the final microstructural model ({V i }, {F i }, and {T j }), presented in Figure  1A.
Interpretation and Recommended Uses To illustrate the features of Diffantom, the example dataset underwent a simplified connectivity pipeline including constrained spherical deconvolution (CSD) and probabilistic tractography from MRTrix  (Tournier et al. , 2012). CSD was reconstructed using 8th-order spherical harmonics, and tractography with 1. 6 • 10 6 seed points evenly distributed across a dilated mask of the WM tissue. Figures  2A1, A3 , show the result of the tractography obtained with such pipeline for the original Diffantom and a distorted version. Finally, we applied tract querier  (Wassermann et al. , 2013)  to segment some fiber bundles such as the CST and the forceps minor (see Figures  2A2, A4 ). Particularly, due to its location nearby the orbitofrontal lobe, the forceps minor is generally affected by susceptibility distortions.
We recommend Diffantom as ground-truth in verification and validation frameworks (Figure  2B ) for testing pipelines. Diffantom is applicable in the unit testing of algorithms, the integration testing of modules in workflows, and the overall system testing. Some potential applications follow: • Investigating the impact of different diffusion sampling schemes on the local microstructure model of choice and on the subsequent global tractography outcome. Since the gradient scheme can be set by the user, Diffantom can be seen as a mean to translate the so-called b-matrix of the source dataset to any target scheme. • Assessment of sensitivity and robustness to imaging artifacts (noise, partial volume effect and CSF contamination, susceptibility-derived warping, Eddy-currents-derived distortions, etc.
) at unit, integration and systems testing levels. • Using Diffantom as in Figure  2B , it is possible to apply binary classification measures to evaluate the resulting connectivity matrix. Considering the connectivity matrix of the reference Diffantom and the resulting matrix of the test Diffantom, the receiver operating characteristic (ROC) of the pipeline can be characterized. • Simulation of pathological brains by altering the microstructural model accordingly (e. g. , as tumors were simulated in  Kaus et al. , 2000). In order to exemplify one of these intended uses, we also release a Diffantom including the susceptibility-derived distortion in simulation. These two images belong to a broader dataset, automatically generated, used in a study to quantify the impact of susceptibility distortions and correction methods on the connectome extraction  (Esteban, 2015, Chapter 5).
DISCUSSION Whole-brain, realistic dMRI phantoms are necessary in the developing field of structural connectomics. Diffantom is a derivative of  Wilkins et al. (2015)  in terms of methodology for simulation with two major advances. First, the correctness of the minimally preprocessed data  (Glasser et al. , 2013)  released within the HCP. Wilkins et al. (2015)  explicitly state that their original data were not corrected for certain artifacts, and thus, generated data are affected correspondingly. Second, Diffantom implements the hindered and restricted compartments model  (Assaf and Basser, 2005) , which is a more complete model than the multi-tensor diffusion model. A possible competitor to Diffantom is the phantom generated for the Tractography Challenge in ISMRM 2015. Similarly to Diffantom, the organizers used an HCP subject as source of structural information.
While this phantom is designed for the bundle-wise evaluation of tractography (with the scores defined in the Tractometer  (Côté et al. , 2013) , such as geometrical coverage, valid connections, invalid connections, missed connections, etc. ), Diffantom is intended for the connectome-wise evaluation of results, yielding a tractography with a large number of bundles. Therefore, Diffantom and FiberFox are complementary as the hypotheses that can be investigated are different. Moreover, Diffantom does not require costly manual segmentation of bundles, highly demanding in terms of physiology expertise and operation time. The software workflow released with this data report (the diffantomizer) ensures the reproducibility of Diffantom and enables the generation of custom diffantoms. The diffantomizer is designed for, but not limited to, use HCP datasets as source of structural information.
CONCLUSION Diffantom is a whole-brain digital phantom generated from a dataset from the Human Connectome Project. Diffantom is presented here to be openly and freely distributed along with the diffantomizer workflow to generate new diffantoms. We The phantom is designed to be used as ground-truth information in evaluation frameworks, to implement unit test of algorithms, to check integration of processing units within pipelines or to validate complete workflows. For instance, in order to evaluate artifacts, a perturbation can be induced in the microstructural model or after simulation to provide reference and test datasets.
Cognitive performance Participants completed two measures of cognitive performance on each study day. Inhibitory control was assessed using a modified Eriksen flanker task  [21]  in the afternoon of each study day. The task was presented on a computer, using E-Prime 2. 0 software (Psychology Software Tools, Inc. , Sharpsburg, PA). In each trial, a series of five white arrows were presented in the center of a black background. Some trials were "congruent" trials, in which the middle arrow (the "target") was pointed in the same direction (left or right) as the other four arrows (e. g. , > > > > >). In "incongruent trials", the target arrow was pointed in the opposite direction from the other four arrows (e. g. , > > < > >). Participants were asked to identify, via key press, whether the target arrow was pointing to the left or to the right, as quickly and accurately as possible.
Statistical analysis Data are expressed as mean ± SD, unless otherwise stated. Statistical analyses were performed with SPSS software (version 22. 0, IBM Corp, Armonk, NY). Time course of perceived energy, mood, hunger and appetite were analyzed using linear mixed models with condition, time and condition-by-time as fixed effects, time as a repeated measure, and subjects as a random effect. A post-hoc Bonferroni test was then used to examine the differences at each time point within each condition. Self-reported energy, hunger, appetite and mood data points were used to calculate areas under the curve (AUC) over the time period of measurement. AUCs and urine hormones were analyzed using linear mixed models with condition as a fixed effect and repeated measure, and subjects as a random effect, followed by post-hoc Bonferroni test to account for multiple comparisons.
I. INTRODUCTION In magnetic fusion devices, fast ions as the main source of heat and momentum can determine the efficiency of heating or current drive, and also possibly drive instabilities that can expel fast ions causing damage. The fast-ion distribution function is often essential to understand energetic ion transport and related instabilities. ] [4] [5] [6] [7] [8]  To investigate the fast ion behavior on EAST (Experimental Advanced Superconducting Tokamak), FIDA diagnostic system has been planned and built since 2014  9  with the diagnostic performance evaluated by an analytical code called simulation of spectra (SOS). 10 This paper will focus on the validation of FIDA measurements performed using MHD-quiescent discharges in 2015 campaign. To validate EAST FIDA spectrum measurements, two codes have been applied to calculate the D α spectrum: one is SOS, and the other is fortran90 version FIDASIM Monte Carlo code  6, 11  on the basis of the fast-ion distribution provided by the TRANSP/NUBEAM simulations.
With same beam characteristics (power, energy, fractions, beam size, etc. ), SOS calculation shows a good agreement with FIDASIM prediction with the matched shape. The observation geometry setting in FIDASIM code is corresponding to r/a ∼ -0. 1 in SOS modelling, which gives an exactly vertical view with no doppler shift of beam emission spectrum. Figure  4(c)  shows the comparison of "Thermal-ion CX" part. FIDASIM thermal ion CX width and amplitude matches SOS predictions with the SOS doppler width corresponding to T i = 1. 2 eV. Both SOS "Beam emission" and "Thermal-ion CX" parts appear to agree with the corresponding FIDASIM predictions, indicating that local CX emission rate as well as halo effect agrees in SOS and FIDASIM modelling. However, in Figure  4 (d) there is a discrepancy for "Fast-ion CX" part between these two codes; FIDASIM calculation is lower than SOS evaluation.
ACKNOWLEDGMENTS This is supported by  National Natural Science Foundation of China  Grant No. 11575249 ,  National Magnetic Confinement Fusion Energy Research Program of China  under Contract Nos. 2015GB110005 ,  2014GB109004 ,  2012GB101001 , and  JSPS-NRF-NSFC   A3 Foresight Program  in the field of  Plasma Physics  ( NSFC No. 11261140328 ).