dataesr/scientific-paragraphs-categorization huggingface dataset:
Source counts The associated catalogue allows us to construct the standard Euclidean-normalized differential source counts plot for the LO-FAR sample, and this is shown in Fig. 13. For comparison at the bright end, we plot the 6C 151-MHz source counts of  Hales et al. (1988). There is excellent agreement between the normalization and slope of the 6C and LOFAR data where they overlap, given the Poisson uncertainties on numbers of sources at the bright end in the LOFAR data. Our source counts are corrected for completeness (Section 3. 4) and of course take account of physical associations between objects in the original catalogue, but are not corrected for any other effects. W16, in their similar but higher-resolution study, suggest that resolution bias, i. e. the fact that resolved sources are less likely to be detected, affects the counts significantly below a few mJy, where the SNR is low, and this can be seen affecting the sub-mJy flux counts in the comparison of their results with ours in Fig.
(in prep. ) respectively. The currently available H-ATLAS catalogue of the NGP field contains 539,757 sources detected at approximately 2σ significance, of which 443,500 overlap with the LOFAR images. For the purposes of flux comparisons we restrict ourselves to sources with 250-µm signal-to-noise (taking account of confusion noise) > 4. 0, of which there are 94,008 in the LOFAR field; this is a similar significance level to the cut that will be applied in the forthcoming NGP data release, and implies a typical 250-µm flux density limit of around 30 mJy. Clearly only a small fraction of these Herschel sources are detected with LOFAR. We cross-matched on both LO-FAR positions and the positions of optical identifications, using the same maximum-likelihood crossmatch as described above for radio catalogue matches, with a maximum permitted offset of 8 arcsec.
These are, of course, only the optically identified giants, and we would expect to be biased against optical IDs of luminous sources, which will tend to be at high redshift, as well as of large sources, which are inherently difficult to identify. Our smallest (in angular size) giant is 140 arcsec in length, and there are a further 10 composite sources with sizes > 2 arcmin in the associated catalogue, many of which may be high-z giants. Although the numbers are small, these are substantially higher sky densities than were found by  Lara et al. (2001) , and suggest that the Tier 1 surveys will be a fruitful hunting ground for giant sources. The surface brightness limitations in these full-resolution observations suggest that it would be useful to re-image the facetcalibrated data at low resolution (20-30 arcsec) to allow a search for low-surface brightness sources: Saripalli et al.
ACKNOWLEDGMENTS We thank  Shea Brown  for providing the WSRT image of the Coma cluster,  V. N. Pandey  for providing the model of  3C 196 , and  Michal Michalowski  and  Eduardo Ibar  for helpful comments on the paper. We thank the anonymous referee and scientific editor, whose comments improved the paper's presentation. MJH and WLW acknowledge support from the  UK Science and Technology Facilities Council  [ ST/M001008/1 ]. GG thanks the  University of Hertfordshire  for a research studentship. PNB is grateful for support from the  UK STFC  via grant  ST/M001229/1. RM gratefully acknowledges support from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)  ERC Advanced Grant   RADIOLIFE-320745. TWS acknowledges support from the  ERC   Advanced Investigator programme NewClusters 321271.
LD and SJM acknowledge support from  ERC Advanced  and Consolidator grants Cosmic ISM and Cosmic Dust. GJW gratefully acknowledges support from the  Leverhulme Trust. This research has made use of the  University of Hertfordshire  high-performance computing facility (http://stri-cluster. herts. ac. uk/) and the  LOFAR-UK computing facility  located at the  University of Hertfordshire  and supported by  STFC  [ ST/P000096/1 ]. This research made use of ASTROPY, a community-developed core Python package for astronomy (Astropy Collaboration et al. 2013) hosted at http://www. astropy. org/, of  APLPY , an open-source astronomical plotting package for Python hosted at http://aplpy. github. com/,and of  TOP-CAT  and  STILTS (Taylor 2005 ). LOFAR, the  Low Frequency Array designed  and constructed by  ASTRON , has facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the  International LOFAR Telescope (ILT)  foundation under a joint scientific policy.
The AMS predicts graft function longitudinally across three independent cohorts In the models presented so far, we have considered the prediction of graft function separately at different time points. An alternative analysis would consider time since transplantation, as well as other established predictors of graft function as covariates in the model. This is particularly useful when studying cohorts where graft function was assessed at several distinct time points (e. g. , in the French cohort, clinical data describes graft function from 1 to 96 months post transplantation, but few time points have observations for all recipients). To implement this alternative analysis, we fit a mixed linear model of the form: eGFR ~donor age at time of transplant + AMS + T + (1|P) (Equation  3 ), where T is the time post-transplantation, measured in months, and (1|P) a random effect which models separate model intercepts for each donor/recipient pairs.
Cornell validation cohort: Transplant recipients, donors and DNA samples We studied 24 kidney transplant recipients who had a living donor transplant at the NewYork-Presbyterian Weill Cornell Medical Center. This was an independent cohort and none of the recipients had participated in the CTOT-04 trial. Recipients were selected randomly based on the availability of archived paired recipient-donor DNA specimens obtained at the time of transplantation at our Immunogenetics and Transplantation Laboratory. DNA extraction from peripheral blood was done using the EZ1 DNA blood kit (Qiagen) based on the manufacturer's recommendation. constructed with metaR analysis scripts and edited with Illustrator CS6 to increase some font sizes or adjust the text of some axis labels. The model that includes the time post-transplantation as a covariate was constructed in metaR and JMP.
The R implementation of train linear model uses the lm R function. This model was executed using the R language 3. 1. 3 (2015-03-09) packaged in the docker image fac2003/rocker-metar:1. 4. 0 (https://hub. docker. com/r/fac2003/rocker-metar/). Models with random effects were estimated with metaR 1. 5. 1 and R (train mixed model and compare mixed models statements, which use the lme4 R package  [34] ). Comparison of fit for models with random effects was obtained by training each model alternative with REML = FALSE an performing an anova test, as described in  [35]. We distribute the code necessary to reproduce most of the analysis presented in this manuscript at https://bitbucket. org/campagnelaboratory/ allogenomicsanalyses.
Materials and Methods Participants We studied participants with subjective memory complaint followed in the Memory Clinic of Angers University Hospital, France, and recruited in the GAIT study between November 2009 and January 2013. The GAIT study is an observational cross-sectional study designed to examine gait in older community-dwellers reporting subjective memory complaint. The sampling and data collection procedures have been described elsewhere  [14]. The main exclusion criteria were age below 60 years, MMSE score <10, inability to walk independently, history of stroke, any acute medical illness in the preceding 3 months, current delirium, severe depression, and inability to understand or answer the study questionnaires. All participants included in the present analysis received a full medical examination, a neuropsychological assessment, and a 1.
5 Tesla MRI scanner (Magnetom Avanto, Siemens Medical Solutions, Erlangen, Germany) at the University Hospital of Angers, France, using a standard MRI protocol  [15]. A high-resolution 3D T1-weighted volume was obtained covering the whole brain (acquisition matrix = 256x256x144, FOV = 240mm, TE/TR/TI = 4. 07ms/2170ms/1100ms, flip angle = 15°, voxel size 1mm×1mm×1. 3mm). Voxel-based morphometry with DARTEL analysis. Basic voxel-based morphometry (VBM) with DARTEL analysis (http://www. neuro. uni-jena. de/vbm8/VBM8-Manual. pdf) was conducted using standard functionalities (default options) available in the VBM8 toolbox (http://dbm. neuro. uni-jena. de/VBM8/) implemented in the SPM8 software (http:// www. fil. ion. ucl. ac. uk/spm). VBM analysis was performed following standard procedures (http://www.
fil. ion. ucl. ac. uk/~john/misc/VBMclass10. pdf). The default options of the VBM procedure provided in VBM8 were used. Native MR images were segmented into distinct tissue classes: GM, WM and cerebrospinal fluid (CSF), using a new segmentation approach available in SPM8. The extended option "thorough cleanup", which is particularly useful for atrophic brain, was used during the first module "estimate and write". Customized DARTEL-templates were created using affine registered tissue segments  [16]. These customized DARTEL templates replaced the default DARTEL templates. Hence, GM and WM volumes were normalized using high dimensional spatial normalization to a customized DARTEL template. A modulation of the segmented and normalized GM (modulated GM) and WM (modulated WM) volumes were undertaken  [17].
Data analysis The smoothed, modulated, normalized imaging datasets were used for voxelwise statistical analysis using SPM8. We correlated GM and WM volumes with the MMSE score and the SMMSE score (quantitative variables) using separate multiple regressions models. All statistical parametric maps were interpreted after applying a family-wise error (FWE) correction for multiple comparisons at the whole-brain level with a significance level p-value (corrected) < 0. 05. Minimum cluster size was set at 10 contiguous voxels. Anatomy toolbox 2. 1 was used for anatomical localizations  [21]. To facilitate interpretation and for the sake of visual clarity, correlation scatter plots were built for the voxel that showed within the cluster the highest correlation with the MMSE and SMMSE scores in the VBM analyses (i.
Results Two hundred and seven participants (mean±standard deviation 70. 88±5. 88 years; 57% female; 22% graduate studies; 33. 8% diagnosed as cognitively healthy; 44. 9% with MCI; 21. 2% with AD) met the selection criteria and were included in the present analysis. The mean MMSE score was 26. 2±3. 9 [95% confidence interval (CI): 25. 7-26. 7] (range, 10-30) among studied participants, and the mean SMMSE score was 5. 1±1. 1 [95%CI: 4. 9-5. 3] (range, 0-6). The mean TIV was 1397. 0±127. 1 ). Of note, using a more permissive threshold in VBM, the patterns of brain structure where the GM volume positively correlated with the MMSE and SMMSE scores were the same but appeared more symmetrical (data not shown). However, the plot correlations between bilateral homologous regions showed a persistent right and left predominance for amygdala and hippocampus respectively (Fig  3.
Acknowledgments We are grateful to the participants for their cooperation. We also thank  Melinda Beaudenon, MSc ,  Jennifer Gautier ,  MSc , and  Romain Simon , MSc, from the  Research Center on Autonomy and Longevity (CeRAL), France , for daily assistance. Patient level data are freely available from the corresponding author at CeAnnweiler@chu-angers. fr. There is no personal identification risk within this anonymized raw data, which is available after notification and authorization of the competent authorities.
X-ray observations Being close to η Carinae, Tr16-22 is frequently observed at high energies. Since the observations presented in  Nazé et al. (2014a) , three new XMM-Newton exposures are available in the archives and an additional one was kindly provided by Dr Kenji Hamaguchi (Table  1 ). In these observations, the target only appears on the MOS2 camera: it falls outside the field of view of the other two cameras (because of dead CCDs in MOS1 or the use of the small window mode in MOS1 or pn). These new data were reduced with SAS v14. 0. 0 using calibration files available in December 2015 and following the recommendations of the XMM-Newton team 1. Only the best quality data (P AT T ERN of 0-12) were kept and background flares were discarded. A source detection was performed on each EPIC dataset using the task edetect chain on the 0.
4-10. 0 keV energy band and for a likelihood of 10 to find the best-fit position of the target in each dataset. We note that the source is not bright enough to present pile-up. EPIC spectra were extracted using the task especget for circular regions of 35" radius centred on these best-fit positions and, for the backgrounds, at positions as close as possible to the target considering crowding and CCD edges. The spectra were finally grouped, using specgroup, to obtain an oversampling factor of five and to ensure that a minimum signal-to-noise ratio of three (i. e. a minimum of 10 counts) was reached in each spectral bin of the background-corrected spectra. The X-ray spectra were then fitted within Xspec v12. 7. 0 using the same models as in  Nazé et al. (2014a) , i. e. absorbed optically thin thermal plasma models, i.
g. ±1. 6 × 10 -6 , does not significantly change the reported values) after discarding edges and deviant points, after rectifying the Stokes profiles, and after selecting spectral windows centred on lines (see  Nazé et al. , 2012, for further discussion). Table  3  yields the resulting field values, along with the values for the old datasets (recalculated for the same spectral windows). Table  2. Results from the X-ray spectral fitting. A. Model wabs × phabs × (apec + apec) ID norm1 norm2 χ 2 (dof) F obs X L ISM cor X HR (cm -5 ) erg cm -2 s -1 erg s -1 0691970101 1. 45±0. 18e-3 3. 43±0. apec ID norm1 norm3 norm4 χ 2 (dof) F obs X L ISM cor X HR (cm -5 ) erg cm -2 s -1 erg s -1 0691970101 2. 44±0. 97e-3 3. 63±0. 47e-4 1. 04±0. 25e-4 0. 87 (45) 2. 71±0. 21e-13 1. 71e32 0. 58±0. 07 0742850301 1.
Results and discussion In the X-ray range, the new data are in line with previous observations. First, the new data appear amongst the old ones in the flux-hardness relation (see Fig. 1 ). Second, the periodogram does not change much if the new data are included: ∼54 d is still the favoured period and folding with this timescale yields the same, coherent behaviour as shown before (see right panel of Fig. 1  and  Nazé et al. , 2014a). However, ambiguities on the period remain because aliases are still numerous (typically spaced by 4 × 10 -4 d -1 ). As mentioned in  Nazé et al. (2014a) , the datasets recorded in 2003 provide a hint that the variation timescale may not be very long as the observed flux increased that year by about 30% over two weeks at the end of July and beginning of August, then went back to its level in mid-August.
Nevertheless, no full cycle was observed in a single observing run, and only observations with a higher cadence might be able to remove the ambiguity on the periodicity. Concerning magnetic field measurements, the 2015 FORS2 data formally provide only three secure detections (i. e. detections at 5 -6σ; see  Bagnulo et al. 2012 ). Nevertheless, these new data sample very different phases of the favoured X-ray timescale, allowing it to be tested (see phases in the second column of Table  2 ). Unfortunately, the longitudinal field values do not show coherent variations when folded with this timescale (see Fig. 2 ): secure detections and non-detections are both notably found around phase 0. 3. The problem remains even when using only the 2015 data, where the precision of the ephemeris has less impact on the folding; for example, only the first of two consecutive days (May 19 and 20) provide a secure detection.
These results appear somewhat at odds with the X-ray variations. In the context of an oblique ro- 5 We applied several period search algorithms: (1) the Fourier algorithm adapted to sparse/uneven datasets  (Heck et al. , 1985; Gosset et al. , 2001 , a method rediscovered recently by Zechmeister & Kürster 2009 -these papers also note that the method of Scargle 1982, while popular, is not fully correct, statistically), (2) two different string length methods  (Lafler & Kinman, 1965; Renson, 1978) , (3) three binned analyses of variances  (Whittaker & Robinson 1944; Jurkevich 1971 , which is identical but with no bin overlap, to the "pdm" method of  Stellingwerf 1978; and Cuypers 1987 , which is identical to the "AOV" method of Schwarzenberg-Czerny 1989), and (4) conditional entropy  (Cincotta et al.
Conclusion Spectropolarimetric, X-ray, and high-resolution spectroscopic data of Tr16-22 have been obtained. They show a good agreement with previous results: the X-ray properties are in line with those already reported and the magnetic field values are similar (within errors) to previous measurements. However, there are also surprises. The optical spectroscopy indicates the presence of RV changes in Tr16-22, suggesting that it is a long-term SB1, but the sparse sampling of the optical data prohibits us from deriving a full orbital solution. The probable binary nature of Tr16-22 cannot explain its exceptional X-ray emission, however. First, colliding-wind emission in the system could be seen as one possible source of X-ray variability, but (1) the long timescale of the RV variations may be difficult to reconcile with the shorter timescale detected in X-ray data and (2) such late-type O stars do not exhibit bright colliding-wind X-ray emission.
Second, a low-mass (PMS) companion could be envisaged as the source of the X-ray variations because such objects display X-ray flares, but (1) the observed variations are large (about a factor of 2 corresponding to an increase in flux of > 10 32 erg s -1 ), an extreme value for typical PMS flares, (2) PMS flares occur on relatively short timescales while the 2003 data indicate that a month was needed to return to "normal" flux levels, and (3) the combination of a long period and a large RV amplitude (at least 20 km s -1 ) of the O-type Table  4. Radial velocities (in km s -1 ) and equivalent widths (in Å) measured for some important lines. The third column indicates the instrument (F=FEROS@2. 2mESO, L=échelle@LCO, C=REOSC@Casleo), while the fifth provides the signal-to-noise ratios (S/N) of the spectra near 5000 Å.
9 0. 53 -5. 5 0. 87 11. 1 -3. 47 The systematic differences in CASLEO EW measurements of the He i 5876 line are probably produced by the scattered light in the background (not perfectly subtracted). For this detector, we also note that this line and the neighbouring interstellar lines appear at the edge of the detector, hence they are less reliable. There are some values missing for CASLEO data: the Hα line is outside the range of the spectrograph and, in a few other cases, lines were too noisy to be reliable hence their measurements are not quoted here. Typical errors on RVs are about 1 km s -1 for LCO and FEROS data and 3-5 km s -1 for CASLEO data, while those on EWs are below 5% in the LCO and FEROS spectra and between 5 and 10% for the CASLEO spectra. star rule out a companion with very low mass.
Acknowledgements. The authors acknowledge  Dr Nolan Walborn  for fruitful discussions,  Dr Kenji Hamaguchi  for sharing his private Xray data, as well as  Francesco Di Mille  and  Claudio Germana  who were observing on the du Pont telescope and kindly obtained the Feb. 2016 observation under NM's request. YN acknowledges support from the  Fonds National de la Recherche Scientifique (Belgium) , the  Communauté Française de Belgique , the  PRODEX XMM contract (Belspo) , and an  ARC  grant for concerted research actions financed by the  French community of Belgium (Wallonia-Brussels federation). RHB is grateful for financial support from  FONDECYT Regular  Project No. 1140076. RG was supported by grant  PIP 112-201201-00298  (CONICET). RG and NM were visiting Astronomer,  Complejo Astronómico El Leoncito  operated under agreement between the  Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina  and the  National Universities of La Plata, Córdoba , and  San Juan.
RelaƟve expression LIP2 RedStar2 POX2 PGK1 Fig. 4  Transcription of mRNA from LIP2 (encodes an extracellular lipase), RedStar2 (encodes the DsRed fluorescent protein), POX2 (encodes acyl-CoA oxidase 2), and PGK1 (encodes 3-phoshoglycerate kinase) in strain JMY3742 after 24 h of growth in CMD, CMOA, and CMDOA. The data were processed using the Livak method, and expression levels were normalized based on the results obtained in CMOA. The values are the means calculated from three independent replicates, and the standard deviations were less than 10 % of the means. Media abbreviations are as in Fig. 1  In a related experiment, the intracellular accumulation of lipids in lipid bodies was monitored using Bodipy. In Fig. 5 , the area delimited by gate R2 corresponds to the low-lipid-accumulation phenotype (LLA), while gate R4 corresponds to the high-lipid-accumulation phenotype (HLA).
The reference gene (ACT-encoding actin) was amplified using the ACT-F/ACT-R primer pair  [36]. Average amplicon size was 200 bp. Quantitative amplification was carried out using a StepOne ™ Real-Time PCR System (Applied Biosystems). The following reaction protocol was employed: 95 °C for 10 min, 40 cycles at 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 15 s. Reaction mixtures were prepared using the Mesa GRreen qPCR MasterMix Plus Kit (Eurogentec) as follows: 12. 5 μL of the Mesa Green qPCR Master Mix Plus solution was combined with 1 μL of each primer (0. 2 μM), 5 μL of cDNA (5 ng), and 5. 5 μL of DNase/RNase free water. Primer specificity was validated using a melting-curve analysis. The amplification efficiency of each oligonucleotide was similar (84-107 %). The Livak method was used to analyze the raw data, and the results were standardized using the data from the complex medium supplemented with oleic acid (CMOA) as a reference.
Flow cytometry and data analysis Lipid bodies stained with Bodipy and cells containing 2-NBDG were detected using a BD Accuri ™ C6 Flow Cytometer. For each sample, 40,000 cells were analyzed using the FL1 channel to identify fluorescence associated with Bodipy and 2-NBDG presence (excitation was performed with a 20-mW, 488-nm solid-state blue laser; the emission wavelength was 533/30 nm). Flow rate was set to medium and threshold of 80,000 AU was applied on the forward scatter (FSC) channel for reducing background noise. For both the FL1 and the SSC, the area of the global signal was recorded (FL1-A, SSC-A). The flow cytometry dotplots (FL1/SSC) were analyzed using CFlowPlus software (Accuri, BD Bioscience). For further processing, the raw data were exported as. fcs files and loaded in MatLab using the fca_readfsc function (downloaded from the MatLab File Exchange file server; http://www.
Major benefits of cancer cell lines are their continuous availability, their fast growth, and relatively easy handling, making them suitable also for high-throughput screenings  (15)  and a large range of experimental possibilities  (16). Of note, advantages and limitations of cell lines have been recently reviewed  (15). In order to select suitable in vitro models, the characterization of molecular features and their comparison to tumor tissues are needed. A detailed Cancer Cell Line Encyclopedia was recently established containing a genomic dataset for 947 human cancer cell lines, from which 58 are colorectal cancer lineages  (17). The Cancer Cell Line Encyclopedia includes data collections on genomic characterization, point mutation frequencies, DNA copy number, and mRNA expression levels.