dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation with microwave satellite-derived melt extent As in  Fettweis et al. (2011b) , we use the brightness temperatures collected at K-band horizontal polarization (T19H) to retrieve the daily melt extent from the scanning multichannel microwave radiometer  (SMMR; 1979 -1987)  and the special sensor microwave/imager (SSM/I; 1988-2010) data distributed by the National Snow and Ice Data Center (NSIDC, Boulder, Colorado;  Armstrong et al. , 1994; Knowles et al. , 2002). A grid cell is considered as melting in MAR (in satellite-based datasets) if the daily meltwater production (T19H) is higher than 8 mm. w. e. yr -1 (227. 5 K). We refer to  Fettweis et al. (2011b)  for more details about the meltretrieving methodology. As already presented in  Fettweis et al. (2011b) , the comparison of the melt extent simulated by MAR and retrieved from the passive microwave satellites is encouraging (see   Bales et al.
MAR forced by ERA-20C suggests a continuous run-off increase from the 1900s to 1930s, while MAR forced by 20CRv2(c) and, to a lesser extent, BOX13 suggests a run-off decrease from the 1900s until the 1910s, followed by a melt increase reaching a first maximum at the beginning of the 1930s. Similar discrepancies can be seen in the MAR-simulated near-surface temperatures. MAR also simulates a significant snowfall increase from the 1910s to the 1940s. Reconstructions from  Box et al. (2013)  and ice cores  (Mernild et al. , 2015)  also suggest an accumulation increase over this period but smaller than MAR's increase, while  Hanna et al. (2011)  suggested a decrease of the accumulation. Long-term ice core data facilitate validation of an overall ice sheet snowfall increase in the first half of the last century, and the comparison with MAR is good where a few ice cores are available.
This increase is, however, bracketed in several ice cores in the dry north as well simulated by MAR, but not for the only core (see D1 in Fig. 9 ) in the south-east showing decreasing snowfall. Thus, the ice sheetaveraged core trend is almost insignificant, while MAR suggests a significant increase along the south-east coastal ridge where ice cores are missing. This suggests that new ice core drillings are needed in this area to confirm the MAR accumulation increase. Moreover, this accumulation increase in MAR coincides with an increase of the daily sea level pressure variability in forcing reanalyses, which impacts the amount of humidity advected into the MAR integration domain. The 20CRv2(c) reanalysis is an ensemble mean of 56 members, suggesting that the lower the amount of assimilated data is, the smoother the pressure fields are.
Therefore, the increase of the daily sea level pressure variability could just be an artefact coming from forcing reanalysis. While ERA-20C is not an ensemble mean, MAR forced by this reanalysis shows the same magnitude of precipitation increase as MAR forced by 20CRv2(c), but not at the same locations. On the other hand, the amount of data assimilated into ERA-20C is lower during this period. Therefore, without gauge observations in the areas where the changes are the highest, it is hard to conclude whether this MAR-based significant accumulation increase along the south-east coastal ridge over the first half of the last century is robust or whether it is just an artefact coming from the forcing reanalyses (which need to be more constrained to be in agreement before the 1930s). Belleflamme et al.
starting in mid-2007 is presented over the common period covered by the forcing datasets used here: 2008-2010. The raw PROMICE data are used here without any filtering or withdrawing of aberrant values. The MAR values at each station are based on an interpolation of the four nearest MAR grid cells weighted by the inverse distance to the station. As the elevation difference between MAR and AWS is not corrected, the comparison is only carried out on the 12 AWSs listed in TableS1of the Supplement that have an elevation difference within 100 m of the interpolated MAR 20 km topography. Scatter plots are shown in Fig. 5and statistics are listed in Table2.
Acknowledgements. The  PROMICE (Programme for Monitoring of the Greenland Ice Sheet ) network is funded by the  Danish Energy Agency (DANCEA)  programme. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under grant no. 2. 5020. 11and the Tier-1 supercomputer (Zenobe) of the  Fédération Wallonie-Bruxelles  infrastructure funded by the  Walloon Region  under the grant agreement no. 1117545. Finally, X. Fettweis is a Research Associate from the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS) , and  J. Box  was supported by the  Danish Research Council  grant  FNU 4002-00234. Edited by:  E. Hanna Reviewed  by:  E. Hanna  and one anonymous referee.
Data availability. All MARv3. 5. 2 outputs presented here are available at ftp://ftp. climato. be/fettweis/MARv3. 5/Greenland/, and the source code of MARv3. 5. 2 is available at ftp://ftp. climato. be/ fettweis/MARv3. 5/. src/. The ECMWF reanalyses (ERA-Interim, ERA-40 and ERA-20C) were downloaded from http://apps. ecmwf. int/datasets/. The NCEP-NCARv1, NCEP-NCARv2 and the 20CRv2(c) reanalyses come from http://www. esrl. noaa. gov/ psd/data/, while the JRA-55 reanalysis comes from https:// climatedataguide. ucar. edu/climate-data. The brightness temperatures used to retrieve the melt extent from the satellite were downloaded from http://nsidc. org/. Finally, the PROMICE and MACHGUTH16 data used to validate MAR are available at http: //www. promice. dk/. The Supplement related to this article is available online at doi:10.
71 26. 92 0. 96 -17. 98 29. 41 0. 83 MARv2 ERA-Interim -1. 8 27. 64 0. 95 -19. 52 31. 42 0. 81 promice. dk) containing a total of ∼ 3000 measurements 3. The revised version (fully described in Kjeldsen et al. , from 46 sites from 1892 to 2015 and mostly cover- 2015) of the 5 km reconstruction of the near-surface air ing the ablation area of the GrIS and local glaciers temperature and the land ice SMB from Box (2013), (Machguth et al. , 2016). For each site, the MAR SMB hereafter BOX13, spanning 1840-2012 and calibrated value is corrected as a function of the elevation differ- to outputs from RACMO2. 1/GR forced by ERA-40 and ence between the MACHGUTH16 database and the in- ERA-Interim (van Angelen et al. , 2011). In contrast to terpolated MAR 20 km topography using a local and the MAR-based reconstructions, this reconstruction is time varying SMB vs.
elevation gradient as explained not forced with reanalyses, except for the calibration in Franco et al. (2012). Moreover, the MAR values with RACMO2, but is based on in situ observations (here in m. w. e. ) are an integration of daily MAR out- (Box, 2013). Absolute uncertainty for the revised SMB puts over the exact period given for each record in the estimates from Box (2013) is estimated by comparison MACHGUTH16 database. The data are not converted against field data. A total of 208 in situ annual abla- to m. w. e. yr -1 , as some MACHGUTH16 records some- tion rates over 1985-1992 yield an ablation root mean times cover only several months in the melt season. square error of 35 %, similar to the one found with Only the records included in the 1958-2010 period with RACMO2. 1/GR. The comparison with ice-core-derived an elevation difference with the MAR topography of net accumulation time series from 86 sites shows a 30 % less than 500 m and inside the MAR ice sheet mask accumulation RMSE.
A fundamental assumption is that are considered here. The comparison is therefore lim- the calibration regression factors, derived over 1960- ited to 1616 records from the MACHGUTH16 database 2012 vs. (Machguth et al. , 2016). Similarly, the same dataset has also been used in Noël et al. (2016) for the validation of RACMO2. 3. ice cores, from meteorological station temperatures and with RACMO2. 1/GR, are stationary in time. Figure 5c illustrates MAR ERA-Interim SMB validation results. Statistics are listed inTable 3. Correlation exceeds 0. 9 www. the-cryosphere. net/11/1015/2017/ The Cryosphere, 11, 1015-1033, 2017.
NF-κB, which depends on IκBα degradation, was also activated after 48 and 9 h, respectively. Importantly, as shown in Figure  1 , s-RNYexpression was also induced after 48 and 9 h of oxLDL or PA treatment, respectively. Small RNA derived from the RNY5 was not measured by stem-loop RT-qPCR because only the 3' fragment is generated from this RNA in lipid-laden macrophages. 1 These data indicate a timing correlation between s-RNY induction and cell signaling activation, inferring a possible role of s-RNYs in regulating both NF-κBdependent inflammation and caspase-dependent cell death. s-RNYs regulate apoptosis and inflammatory response in lipid-laden monocytes/macrophages. We evaluated the effects of s-RNYs on macrophage apoptosis using flow cytometry. As siRNA against the terminal loop of RNY generates s-RNYs (Supplementary Figures  S1a  and c ) associated with Ro60 (ref.
Acknowledgements. This work was supported by  FRM  (grant # DEQ201-40329551 ) and  ANR  through the ' Investments for the Future ' #  ANR-11-LABX-0028-01  (LABEX SIGNALIFE) to MT. ZH is a recipient of a fellowship from the  French Ministry of Education and Research (MRT). ER was supported by the  Fondation Lefoulon Delalande  and  FRM  ( ING20140129224 ).
The D0 experiment measured the fiducial prompt J/ψ pair cross-section using data collected at √ s = 1. 96 TeV  [29]. The LHCb experiment measured the pair production of prompt J/ψ mesons in the forward rapidity region using data collected at √ s = 7 TeV  [7]. The present measurement of PP di-J/ψ production uses a different kinematic range and a larger data set. The rest of this document is organised as follows. In Section 2, a brief description of the ATLAS detector and the data samples used in this study is provided. In Section 3, the methods used in the event reconstruction as well as the selection criteria used in this analysis are reported. Section 4 focuses on the removal of non-J/ψ, non-prompt J/ψ, and pile-up backgrounds as well as the calculation of the detector and selection efficiencies used in the extrapolated signal yields.
186  [52]  with the AU2 set of tuned parameters  [53]  and CTEQ6L1 parton distribution functions  [54]. The MC samples are passed through ATLAS detector simulation  [55]  based on GEANT4  [56] , and are reconstructed with the same software as the data. Using the single-muon-trigger efficiencies, the correction term for correlations and the MC correction, the total efficiency for the J/ψ dimuon trigger, , is then calculated using a modified form of the "tag and probe" method presented in Ref. [57]. A correction to the muon reconstruction efficiency is applied using the efficiency scale factors described in Ref. [49]. Efficiency scale factors have been determined in bins of q × η and φ separately for muons with and without an ID track that matches an MS track, where q is the charge of the muon.
The DPS contribution is normalised to the data in the region ∆y ≥ 1. 8 and ∆φ ≤ π/2, where DPS is assumed to dominate and SPS is assumed to be negligible. The DPS-dominated region is determined after a careful study of the data. The ∆y requirement is determined as before this region the data drops off quickly with ∆y and after it flattens out which is indicative of a dominant DPS contribution. After examining the data, it is observed that the peak at ∆φ ≈ π has a large tail in ∆y and therefore an additional requirement is placed to avoid this tail. Additionally, theoretical predictions  [10, 11]  show that SPS is negligible in this region. The assumption of and sensitivity to the definition of the DPS-dominated region is tested by increasing the ∆y and varying the ∆φ requirements. By increasing the ∆y requirement to a smaller region in which SPS is known to be negligible, the possibility of a SPS tail making it into the normalisation region is determined.
Also in this cultivar, higher expression of flagellar genes (FlaI), which are recognized as PAMP (pathogen-associated molecular pattern) by the innate immune systems of plants, was noted. Additionally, several genes that have not yet been proven to play a role in the pathogenic abilities of E. amylovora were found to be differentially expressed in the two apple cultivars. Conclusions: This RNA-seq analysis generated a novel dataset describing the transcriptional response of the lowly virulent strain of E. amylovora in susceptible and resistant apple cultivar. Most genes were regulated in the same way in both apple cultivars, but there were also some cultivar-specific responses suggesting that the environment in Free Redstar is more stressful for bacteria what can be the reason of their inability to infect of this cultivar.
2 ). The accuracy of the RNA-seq data was verified using reverse transcription quantitative real-time polymerase chain reaction, RT-qPCR. Fold changes in expression values under different experimental conditions obtained with these two techniques were plotted on a scatter graph, with fold change values obtained from RT-qPCR on the X-axis and those obtained from RNA-seq on the Y-axis (Fig. 3 ). A high value for the Pearson correlation coefficient (r = 0. 954; p < 0. 001; R 2 = 0. 909) indicated a positive correlation between the two variables.
This fact suggests that although their role is unknown, their function could be important during interactions with a host plant. The importance of genes coding for hypothetical proteins was observed even during a study with a minimal cell concept, where an experimental design of a minimal synthetic genome revealed a surprising number of genes of unknown functionca. 30% of the genome essential for bacterial life  [67]. This is the general problem in genomics. At present, numerous genome projects are adding thousands of nucleotide sequences to public databases each day. The challenge is in translating sequence into function. The most common approach is to search databases for well-characterized proteins that have similar amino acid sequences to the protein encoded by a new gene and employ a method to explore the gene's function from there.
Using this approach, only a fraction of predicted genes will have annotated products and functions. In the genome of E. amylovora CFBP1430, over 850 predicted genes are annotated to be putative proteins, proteins of unknown function or hypothetical proteins. Approximately 40% of genes cannot be classified to any COG category or are classified to category S: function unknown. This is related to the fact that although studies on genes and their function have been conducted for many years by many teams, even using new challenging techniques, there is still much work ahead for scientists to fully understand all the processes in bacterial and eukaryotic cells. Additionally, some mistakes can be generated in RNA-seq data analyses because of weak points of the algorithms applied for data normalization and gene expression fold change calculation  [68].
Conclusions This RNA-seq analysis generated a novel dataset describing the transcriptional response of the lowly virulent strain of E. amylovora in susceptible and resistant apple cultivar. The genes known as important for the E. amylovora pathogenicity were only slightly differentially expressed between apple cultivars. However, the higher expression of E. amylovora flagellar genes (recognized as PAMP) in Free Redstar can explain why strain 650 cannot effectively attack Free Redstar trees. Also higher expression of stress related genes and genes of multidrug efflux pumps and permeases can suggest that the environment in Free Redstar is more stressful for bacteria what can be the barrier for the efficient infection of this cultivar. Among genes with the highest fold change in expression between experimental combinations or with the highest transcript abundance, there are many genes without ascribed functions, which have never been tested for their role in pathogenicity.
Library preparation and sequencing The rRNA depleted sample concentration was measured using the 2100 Bioanalyzer (Agilent) and an RNA 6000 Pico Kit (Agilent, 5067-1513). Since the RNA concentration was low, the maximum allowable volume (6 μl) was used for the library construction using NEBNext Ultra Directional RNA Library Preparation Kit for Illumina (New England Biolabs, E7420S). The libraries have been sequenced on the MiSeq (Illumina) using the MiSeq Reagent Kit v2 (500-cycles) (Illumina, MS-102-2003) in the PE250 read mode. The resulting reads were additionally trimmed with Cutadapt  [77]. These sequence data have been submitted to the ArrayExpress (EMBL) databases under accession number E-MTAB-5630.
Bioinformatic analysis Low quality sequence ends (ambiguous base limit: 2, quality limit: 0. 05) were trimmed using CLC Genomics Workbench (v. 8. 1) (Qiagen) Trim Sequences tool. For mapping, E. amylovora strain CFBP 1430 genome (FN434113, FN434114), which consists of chromosome (3,805,573 bp) and pEA29 plasmid (28,259 bp) and carries 3706 and 28 CDS on chromosome and plasmid, respectively  [35]  was used. High quality sequences were aligned to this genome using the CLC RNA-seq reference mapping algorithm with settings appropriate for Prokaryotic genomes (mapping to gene regions only). A quality control to check whether the overall variability of the samples reflected their grouping and the reproducibility between repetitions was performed with Principal Component Analysis (PCA). For the differentially expressed gene (DEG) analysis, expression values were normalized using the Trimmed Mean of M values (TMM)  [78] , and DEGs were analysed using the Empirical Analysis of DGE tool based on Exact Test incorporated in the EdgeR Bioconductor package and implemented in CLC Genomics Workbench.
A gene was considered to be differentially regulated between two conditions when the gene showed a total read number larger than five, a > 1. 5-fold absolute fold-change ratio and an FDR-adjusted p value <0. 05. For the newly annotated genes and non-coding RNAs, the expression values were normalized as a percent of total reads mapped to the E. amylovora CFBP 1430 genome. The results were subjected to ANOVA, and significance of differences between means were tested using the Newman-Keuls test at p < 0. 05. Gene annotation based on Gene Ontology (GO) terms in biological process, molecular function and cellular component categories for the E. amylovora CFBP 1430 coding sequences were downloaded from the UniProt database (http://www. uniprot. org). To summarize the pathway information protein sequence, fasta files were submitted to KAAS (KEGG automatic annotation server)  [79] , and KEGG orthology assignments were obtained (Additional file 15: Table  S12 ).
The eggNOG Fig. 6  Diagram showing the study design. One-year-old, potted apple trees cultivars Idared and Free Redstar grafted on M. 26 were inoculated with E. amylovora strain 650 in greenhouse conditions. Inoculation was made on actively growing shoots, with bacterial suspension grown overnight in TY medium. After 24 h and 6 days from the inoculation time total RNA was isolated from inoculated shoots. At each time point, RNA was isolated separately from at least 6 shoots of each apple cultivar. Additionally, RNA was isolated from the pure culture of E. amylovora 650 grown overnight in TY medium. In each time point, RNA was isolated from 3 biological replicates (in case of pure bacterial culture -two biological replicates). The replicates constituted the sample (marked by red circle) 4. 5 database  [80]  was used to annotate genes with common denominators or functional categories (i.
Acknowledgements This work was supported by the  EC  through the the  Marie Sklodowska-Curie ITN  project  iSwitch  ( GA-642196 ), the  ERC  project  SUPRAFUNCTION  ( GA-257305 ), the  FET  project  UPGRADE  ( GA-309056 ) and  Marie Curie IEF GALACTIC  ( PIEF-GA-2013-628563 ), the  Agence Nationale de la Recherche  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ) and the  International Center for Frontier Research in Chemistry (icFRC). Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Methods Participants. Twenty-two observers (15 female; age range 19-47 years) volunteered as subjects. Based on previous work  14  , we aimed to collect data from 20 subjects. We allowed 22 people to sign up in case of dropouts and stopped data collection after testing all those who did show up. All subjects had normal or corrected-to-normal vision and provided informed written consent prior to the experiment. The study was approved by the Comité d'Evaluation Ethique de l'INSERM in Paris, France and conducted in accordance with relevant guidelines and regulations.
, San Rafael, CA, USA). All stimuli are freely available at https://osf. io/f7snh. Stimuli were presented and responses recorded using PsychToolbox 3 for Matlab (http://www. psychtoolbox. org)  40, 41. Observers were seated approximately 60 cm from a HP ZR2440w monitor (24 inch screen diagonal size, 1920 × 1200 pixel resolution; 60 Hz refresh rate). Face stimuli were scaled to a size of 9° × 12°. Procedure. The experimental procedure consisted of three phases: familiarization, training, and testing (for details see Supplementary Information). In the familiarization phase, subjects were familiarized with two facial identities "Laura" and "Susan" and their basic facial forms and facial motions (Fig. 1b , upper row "old off ") while performing a same-different task on each identity separated in two blocks for about 10 min.
+ - >. J x c J x c J c J b (1 ) ( 0 6 ) (1 ) (1) m m f f m 2 f In the best-cue model, the observer only relies on the cue with the highest J. Thus, the decision rule for when to report "Susan", equation (  1 ), gets replaced by σ σ σ σ > < > >. x b x b if if (2) m m f f m f In the simple-average model, the observer responds "Susan" when + >. x x b 2 (3) m f Experimental predictions. Finally, we derive experimental predictions for each of the models, based on their respective decision rules (equations (1), (  2 ), (  3 )). To this end, we need the probability that the decision rule returns "Susan" for a given experimental condition (which is characterized by s m , s f , and c). In the optimal model, the probability of responding "Susan" is: λ λ λ | = + -Φ        -+ - - + -         s s c J s b c J s b J c J Pr(respond "Susan" , , , ) , m f m m 2 f f m 2 f where Φ is the conventional notation for the cumulative standard normal distribution (in Matlab: normcdf(…, 0, 1)), and λ the probability that the subject guesses randomly.
In the best-cue model, the left-hand side of equation (1) has mean s m and variance σ m 2 if σ m < σ f , and mean 0. 6c + (1c)s f and variance σ f 2 if σ m > σ f. The response probabilities are given by In the simple-average model, the probability of responding "Susan" is Model fitting and model comparison. We fitted all parameters using maximum-likelihood estimation for each model and each subject. Parameter fitting was implemented using the Matlab function fmincon with 10 different random initializations per optimization. To verify that our fitting procedure could recover the model parameters, we performed parameter recovery. To validate our model comparison process, we performed model recovery on synthetic data sets. In addition to the joint fitting based on all data, we performed a predictive analysis using only the data from the single-cue conditions to fit all parameters and tested those on the combined conditions.
Acknowledgements We are grateful to the  Max Planck Institute for Biological Cybernetics  for their support with the experimental stimuli. We thank  Mathilde Chevalier  for her assistance with data collection. This work was supported by a  DFG research fellowship  ( DO 1933/1-1 ) to K. D. and by a grant from the  French Research Agency  ( Vis-Ex   ANR-12-JSH2-0004 ) to L. R.