dataesr/scientific-paragraphs-categorization huggingface dataset:
The SCA dataset was used to compute a winter baseline for NDVI with a method similar to  [33]. First, the resampled SCA dataset was used to mask snow-free pixels. For each pixel, the 5th percentile of NDVI was computed on the whole time series to determine a per-pixel climatological minimum snow-free NDVI. This minimum snow-free map was used to mask each 8-day NDVI map, with values lower than the climatological minumum substituted with it on a pixel basis. Corrected 8-day maps were averaged to a monthly product which was used in all subsequent analyses.
Data Analysis The BFAST (Breaks For Additive Seasonal and Trend) method implemented in the bfast R package was used to calculate NDVI trends over the 2000-2018 period (TREND BFAST ). bfast integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components  [23, 24]. As such, this method is particularly suitable for the detection of disturbed time series and concomitant trend detection. Pixels showing clear breaks in either long-term trend or seasonal components were removed from the analysis of trends. Because the wide range in mean NDVI across different PFTs can complicate the interpretation of absolute NDVI trends, TREND BFAST values were also expressed as percentage of the long term pixel-based mean and further multiplied by 20 to represent the cumulated NDVI increase in the ∼two MODIS decades 2000-2018 (see Figure  2b ).
The relationship between TREND BFAST and environmental/climatic variables was investigated with Random Forests (RF) using the ranger package  [37]. Explanatory variables included in the model are summarized in Table  1. Several important climatic variables were excluded because of high autocorrelation, identified by inspecting variable importance table: these include average temperatures (e. g. , annual T, spring T both correlated to summer T) and phenological variables (growing season length, snow melt date, both correlated to summer T and maximum SWE). All models were run with the caret package  [38]  in R  [39]. Model performance metrics are calculated from 20-fold cross-validation. As for RF parameters, we let vary the number of variables to possibly split at in each node (mtry) and the splitting rule (variance vs.
extratrees, see  [40] ), whereas minimal node size and tree number were kept as default (5 and 500, respectively). The dynamics of colonization of new surfaces was showcased by extracting NDVI time series in the proglacial area of the Rutor Glacier (45 • 40 12 N, 6 • 59 11 E, 2560 m a. s. l. ). We focused on three points corresponding to markedly different ages of deglaciation, which was defined by visual inspection of historical aerial photographs retrieved from the web map service (WMS) of the Geoportale Nazionale (http://www. pcn. minambiente. it/mattm/en/view-service-wms/). All analyses were conducted with the R software for statistical computing, version 3. 6. 1  [39].
Conclusions This study provides evidence for widespread greening in the Alps and explores the climatic cues controlling its magnitude and spatial patterns. We found that warmer temperatures promote an increase in plant productivity, but that the seasonal timing of greening is regulated by other elevationand ecosystem-dependent factors. Among these, snow prevents spring plant development above 1800 m and photoperiod likely limits forested ecosystems in autumn, whereas at low elevation water and photoperiod both interact as limiting factors. While more work is needed to disentangle the effects of the various climatic and environmental cues on greening trends, this study serves as a starting point for future research. For example, the dataset of disturbed pixels identified by the BFAST method, discarded here because beyond the scope of the paper, deserves a dedicated study addressing questions, such as the recovery speed after different types of disturbances, including fires, land-use changes, forest pest attacks, avalanches, landslides, etc.
After being blocked with 5% milk in 0. 2% Tween 20 and PBS for 1 h, membranes were incubated overnight with a sheep polyclonal anti-NKCC1 antibody (2 g/ml, 1-260, University of Dundee). After washes in Tween 20 and PBS, membranes were incubated with an anti-goat antibody coupled to horseradish peroxidase (1:10,000, sc-2020, Santa Cruz Biotechnology). Stained proteins were revealed using Clarity ECL substrate, and images were acquired on an iBright CL1000 (Invitrogen). Quantification was done using iBright Analysis software, and data were normalized as a function of Ponceau staining. HKA2 hydrolytic activity. In the absence of Na ϩ in the medium, mouse CCDs display two types of K ϩ -stimulated ATPase activities: Sch28080-sensitive, ouabain-insensitive activity and Sch28080-sensitive, ouabain-sensitive activity, which reflect the presence of gastric and nongastric (HKA2) H ϩ -K ϩ -ATPase, respectively  (2).
This antibody was validated by Western blot analysis and immunofluorescence labeling on colon samples from WT or HKA2 Ϫ/Ϫ mice. The construction that was used to generate the HKA2 Ϫ/Ϫ mice leads to the generation of a truncated form of HKA2 that is neither stable nor functional. As shown in Fig. 1 , this antibody recognized only one band from colon protein extract of WT mice at a size of 100 kDa and a band at a lower size in HKA2 Ϫ/Ϫ mice. The immunofluorescence labeling of colon slices showed a clear signal in WT mice but not in HKA2 Ϫ/Ϫ mice. Statistics. Comparison between groups was performed using GraphPad Prism 6 software either by an unpaired Student's t-test or by variance analysis, as appropriate.
Study Selection Studies meeting the following criteria were included: (a) randomized controlled trials, observational cohort or case-control studies in (b) adult patients (c) with a diagnosis of RA, AS, Pso, psoriatic arthritis, Crohn's disease or ulcerative colitis, (d) reporting IFX-related acute infusion reactions; (e) patients treated with corticosteroid and/or antihistamine premedication included matched controls; (f) reporting relative risks (RR), hazard ratios (HR), odds ratios (OR), or standardized incidence rates with 95% confidence intervals (CI) or providing data allowing calculation of these parameters. Inclusion was not otherwise restricted by study size, language, or publication type. In the case of multiple publications based on the same cohort, data from the most recent comprehensive report were included.
Search strategy We conducted a comprehensive search of multiple electronic databases from inception to June 31, 2017 concerning adult patients, with no language restrictions using the following search terms: ("IBD" OR "IMIDs" OR "AS" OR "RA" OR "RA" OR "Pso") AND ("IFX") AND ("infusion reaction") OR ("premedication" OR " steroids" OR "antihistamines")). The databases included MEDLINE, and EMBASE. Conference abstracts (Digestive Disease Week, United European Gastroenterology Week, European Crohn and Colitis Organization congress, American College of Rheumatology, European League Against Rheumatism, American Academy of Dermatology and European Academy of Dermatology and Venereology annual meetings) from 2015 to 2017, and the references of the selected articles and review articles on the topic were also manually searched for additional studies, with no language restrictions.
Atom probe technique and data analysis methods For the characterization of the as-received (AR) materials, APT tips were electropolished using two different electrolytes: 75% acetic acid À25% perchloric acid and 98% ethylen glycol monobutyl ether e 2% perchloric acid, the latter being used as a final solution. After the irradiation, the APT tips were prepared by lift-out of a chunk and subsequent annular milling using a Scanning Electron Microscope e Focus Ion Beam (SEM-FIB ZEISS Crossbeam X540). At  least three APT tips have been analysed for each condition. The APT volumes has been extracted from at least two or three different places guarantying different analysis of different grains. An APT volume corresponds to one analysed tip. Total volume analysed ranges from 0. 3 to 1. 6$10 6 nm 3 per conditions.
1% and 0. 35%. Subsequently the APT volumes were reconstructed using IVAS 3. 6. 8 (CAMECA software). The reconstruction factors have been adjusted for every sample. The compression factor x has been systematically derived from the crystallographic angles between poles observed on the desorption maps and the field factor (k) has been derived from the expected interplanar distance at the poles. The values used for x and k have ranged between 1. 3-1. 8 and 2. 8e6. 4 respectively. The tip support (i. e. electropolished tip or tip on micro-coupon) has been noticed to influence the values of the reconstruction parameters (mainly k). Finally the APT data treatment has been made using the 3D Software developed by the Groupe de Physique de Mat eriaux (GPM) in the University of Rouen Normandie, France.
0. 5 dpa e IPM(NiSiP) As it has been mentioned earlier in this section, the IPM(NiSiP) would better describe the actual characteristics of the SRCs formed; indeed, according to a visual inspection of the 3D APT datasets at 0. 5 dpa, only considering P in the IPM is not correct since many clusters are missed in the identification. This indicates that some clusters are more diluted in P but richer in Si or Ni. These clusters are therefore only detected using IPM(NiSiP). Thus IPM(NiSiP) was also applied using the set of analysis parameters given in Table  2. As a consequence the detected f V is three times bigger and N v is almost double. The average size is larger, 1. 1 nm, resulting from a displacement of the size distribution towards larger sizes (Fig. 8(d) ). The larger average size of the clusters and the shift of the size distribution are likely to be due to the presence of Si and Ni atoms nearby the P atoms.
Acknowledgements This research was partly funded by the  Euratom 's  Seventh Framework Programme   FP7/2007e2013  under grant agreement No. 604862  ( MatISSE  project) and contributes to the  EERA (European Energy Research Alliance) Joint Programme on Nuclear Materials  (JPNM). Experiments were perfomed on the GENESIS platform. GENESIS is supported by the  R egion Haute-Normandie , the  M etropole Rouen Normandie , the  CNRS  via  LABEX EMC3  and the  French National Research Agency  as a part of the program " Investissements d'avenir " with the reference  ANR-11-EQPX-0020. The use of the  HZDR Ion Beam Center facilities  and the support by its staff is gratefully acknowledged.
We present 0. 3 (band 6) and 1. 5 (band 3) ALMA observations of the (sub)millimeter dust continuum emission for 25 radio galaxies at 1 < z < 5. 2. Our survey reaches a rms flux density of ∼50 µJy in band 6 (200-250 GHz) and ∼20 µJy in band 3 (100-130 GHz). This is an order of magnitude deeper than single-dish 850 µm observations, and reaches fluxes where synchrotron and thermal dust emission are expected to be of the same order of magnitude. Combining our sensitive ALMA observations with low-resolution radio data from ATCA, higher resolution VLA data, and infrared photometry from Herschel and Spitzer, we have disentangled the synchrotron and thermal dust emission. We determine the star-formation rates and AGN infrared luminosities using our newly developed Multiresolution and multi-object/origin spectral energy distribution fitting code (Mr-Moose).
ALMA Observations ALMA Cycle 2 band 6 (and band 4 for source TN J2007-1316) observations were carried out from June 2014 to September 2015 (Table  1 ). We used four 1. 875 GHz spectral windows, tuned to cover molecular lines at the specific redshift of each source  (Mc-Mullin et al. 2007). The data was calibrated in CASA (Common Astronomy Software Application) with the supplied calibration script (with exception of MRC 2224-273, for which the provided script was changed to correctly compensate for different averaging factor in one of the spectral windows). Since a significant fraction of our sources have a low SNR, we decided to optimize the sensitivity by using natural weighting to construct images. For all sources, except TN J2007-1316, atmospheric absorption noise was present in observations, therefore we excluded the affected channels from the final images.
ATCA 7 mm and 3 mm data The ATCA observations of 7 mm continuum in some of the sources were conducted over 2009 -2013. These continuum data were part of a survey to search for cold molecular CO(1-0) gas in high redshift radio galaxies  (Emonts et al. 2014). The data were obtained using the compact hybrid H75, H168 and H214 array configurations, with maximum baselines of 89, 192 and 247 m, respectively. This resulted in synthesized beams ranging from roughly 6-13 arcsec. We used the Compact Array Broadband Backend (CABB;  Wilson et al. 2011 ) with an effective 2 GHz bandpass and 1 MHz channels, centered on the redshifted CO(1-0) line  (32-48 GHz; Emonts et al. 2014). The data were calibrated using the software package, MIRIAD  (Sault et al. 1995). The observing and data reduction strategy, as well as the basic data products, were previously described in  Emonts et al.
(2011b)  and  Emonts et al. (2014). For MRC 0943-242, we also used the ATCA 3 mm ATCA/CABB system on March 21 2012 in the H168 array configurations and on September 30 and Oct 1 2012 in the H214 array configuration to obtain an upper limit of the radio continuum at 88. 2 GHz. This frequency corresponds to the redshifted CO(3-2) line, which was not detected in our observations. We used PKS 1253-055 (March) and PKS 0537-441 (Sept/Oct) for bandpass calibration, PKS 0919-260 for frequency gain calibration, and Mars (March) and Uranus (Sept/Oct) to set the absolute flux levels. The total on-source integration time was 5. 1 hrs. We used a standard observing and data reduction strategy to calibrate these 3 mm data, matching the strategy of the 7 mm data. The resulting synthesized beam of these 3 mm data is 2.
Mr-Moose Mr-Moose is a new SED fitting code, developed with the goal of being able to handle multi-resolution photometric data that have multiple spatially-resolved detections in the same photometric band. The specific motivation for developing this new code is to be able to make full use of the information contained in deep and high resolution ALMA data. When combining ALMA and radio interferometric (such as JVLA and ATCA) data with previous low resolution data (such as Herschel or Spitzer) where the beam is too large to resolve individual components, one needs a SED fitting tool able to handle multiple components in order to make full use of the interferometric data which often contain multiple resolved components. 1 : Details about the ALMA observations. Several sources were observed more than once because the first observation did not meet the requested sensitivity and/or resolution.
Fitting procedure Mr-Moose fits a pre-selected number of models to the rest-frame photometry of a galaxy. To do this, it uses Bayesian parameter estimation to find posterior distributions of the free parameters which are determined based on the prior distribution (uniform) and the likelihood function. Mr-Moose uses the Monte Carlo Markov Chain (MCMC) core provided in the Python package, Emcee (Foreman-  Mackey et al. 2013). The best fit model is determined by minimizing the likelihood (χ 2 ); a parameter of the goodness of the fit, calculated by comparing the observed data with the model values for the combination of all models at the same time for each photometric band. The parameter space is explored by "walkers" taking random steps. Each walker makes a chain of random steps with each new step being only dependent on the previous step in the sequence.
The code is not made to be applied blindly to a large sample of galaxies. Even though the process of assigning analytic models to each photometric data point may sound subjective, we actually let the code decide between multiple models as illustrated in Table  3. In case of doubt, we provide the code with many flexible options. For the sources with spatially-separated detections in the same band, it is easy to connect them to corresponding detections at other frequencies. To show how the set up is done, we take MRC 0114-211 as an example. This source has unresolved Spitzer IRS through to LABOCA data, resolved ALMA band 6 data with two spatially resolved continuum components, unresolved ALMA band 3, ATCA and VLA L data and resolved VLA C-and X-band data with two resolved components. The two radio components coincide with the two detections in ALMA.
The FIR data are unresolved and it is unclear if this is the combined flux from the two individual sources detected in ALMA or just the flux from the host galaxy with no contribution from the companion. For the two detections in ALMA it is unclear if these are two thermal dust emitting objects or the high frequency end of the synchrotron emission. The unresolved radio data is the total flux from both synchrotron components. In this situation, since we cannot decide what is the nature of the two ALMA components and therefore allow contributions to their flux from both synchrotron emission and the modified blackbody model. We refer the reader to the full list of components and how they are assigned to various models which is shown in Table  3. The code then determines what contributes to each component, not the astrophysicist.
The best fit is determined by minimizing χ 2 , which is calculated by fitting models to each spatial and photometric data point. The MCMC attempts to find the most likely solution considering all the data at the same time. For example, as we already outlined, in the case of MRC 0114-211, two black bodies and two synchrotron models were assigned to the spatially resolved ALMA band 6 data points and what came out of the fitting procedure is that the eastern component is consistent with being dominated by synchrotron emission and the western is dominated by thermal dust emission from the host galaxy (Sect. A. 2 and Table  3   and thus the SFR (see also  Archibald et al. 2001). The focus of this paper is to disentangle the emission of cold (assumed to be heated by young stars) and warm (assumed to be heated by the AGN) dust emission from individual components and to separate the emission from nearby objects and radio hot spots/lobes by identifying independent emission components.
For MRC 0152-209, the flux in the ALMA continuum data is divided between a northern and southern component (see  Emonts et al. 2015b, for details). upper limits  (Figs. 4, 5, 6, and 8). In two sources, MRC 0114-211 and PKS 0529-549, one out of two ALMA components are dominated by synchrotron emission and the other detection appears dominated by thermal dust emission. For three sources, MRC  0943-242, 4C 23. 56 and 4C 19. 71 , both the synchrotron lobes and host galaxies are detected in ALMA band 3 or 4. The best fit of these sources is consistent with the lobes being domi-nated by synchrotron emission. The detections of the host galaxies for MRC 0943-242 and 4C 19. 71 are consistent with thermal dust emission, while for 4C 23. 56, the modified blackbody component is completely unconstrained.
4 of  Drouart et al. (2014). The factor of four is estimated in the case one cannot discriminate between the most extreme starburst templates. Even in the less extreme case, there is still a factor of ∼2 between the sets of templates that are consistent with the same data points. The SED parameters that influence this difference include the assumed dust tempera- We identify four general categories where the SED fits lead to significant differences in L IR SF between the two studies: (1) only upper limits in the FIR; (2) one detected ALMA component and upper limits in the rest of the FIR bands; (3) two spatially resolved detections in the ALMA bands; and (4) when the FIR consists mainly of detections. It is clear that there are cases where the differences are not significant (e. g. , MRC 0156-252).
However, in some cases, isolating a sub-component in high resolution, 0. 3, sensitive ALMA imaging leads to a significantly lower L IR SF which simply is not possible using only the lowresolution Herschel data (e. g. , MRC 0251-273). There are also ). The ratio is given for three cases: the L IR estimated for sources with detections in both ALMA and Herschel and thus the infrared luminosities are constrained in both studies (Constrained L IR ); when the infrared luminosity is given as an upper limit in this paper but is given as a detection in Drouart et al. (L IR SF upper limit); and where L IR for the source is constrained but L IR SB is an upper limit in Drouart et al. (L IR SB upper limit). cases where significantly deeper ALMA data ( > ∼ 10× deeper than any previous submm/mm observations) still does not detect any emission (e.
A significant synchrotron contribution in submm Our high resolution ALMA data allowed us to do component separation such that the contribution of synchrotron to the mm emission is now clear, originating from radio lobes and the nuclei. For nine out of 25 sources, the best-fit SEDs of specific spatial components are consistent with a power-law extrapola-    2012 ) to the mean relation for AGNs over the redshift range 1. 5< z <2. 5 scaled up by a factor of two (see  Netzer et al. 2016, for details). We indicate when the luminosity due to star formation equals that of the AGN with a black solid line. tion from radio frequencies. These spatial components are likely dominated by synchrotron emission. The total flux of five galaxies, not just the lobes, are associated with pure synchrotron emission and are consistent with no contribution from thermal dust emission.
For two host galaxies, which have two spatiallyresolved emission components in the ALMA band, the majority of the mm-flux is due synchrotron with only a minor contribution from thermal dust emission. It is surprising to find our high frequency data is consistent with an extrapolation extrapolation of the synchrotron intensities in the radio  (Jaffe & Perola 1973; Carilli et al. 1991; Blundell et al. 2006 ). The energy loss though aging of the electrons and inverse Compton scattering should steepen the observed spectrum at higher frequencies. But what we see in a few sources is that the synchrotron spectrum continues straight out to high frequencies (e. g. ,  Gopal-Krishna et al. 2001 ). One possible explanation is that the electrons are continuously accelerated within the lobes, perhaps through strong oblique shocks (e.
Acknowledgements. M. D. L. wishes to thank the  ESO visitors program  for its continued support and  Marta Volonteri  and  Gary Mamon  for fascinating discussions. T. F. acknowledges financial support from  ESO  and  INSU/CNRS  for this work. T. F. , C. De B. and M. D. L. thank  Joe Silk  for interesting suggestions and analysis and an anonymous referee for their constructive criticisms and suggestions. BG acknowledge support from the  ERC   Advanced Programme DUSTYGAL  ( 321334 ) and  STFC  ( ST/P0000541/1 ). This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2012. 1. 00039. S, ADS/JAO. ALMA# 2013. 1. 00521. S, ADS/JAO. ALMA# 2015. 1. 00530. S, and  ADS/JAO. ALMA#2012. 1. 00242. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.