dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data are expressed as mean ± standard deviation with individual fish as the experimental unit. Normality and homogeneity of variances were tested using Shapiro-Wilk and Levene test, and data were either log-transformed or arcsin square-root transformed (for data expressed in percentage) when needed. When data respected the assumptions of normality, a two-way ANOVA with two independent factors (group and time) was performed followed by LSD post hoc tests to identify significant differences. The level of significance used in all tests was P < 0. 05. When data were not normally distributed, a Kruskal-Wallis test was performed followed by the nonparametric Mann-Whitney U-test. All statistical analyses were performed using the STATISTICA software (StatSoft, Tulsa, OK, USA).
Acknowledgments The authors are grateful to the  INRA institute of Champenoux, France  for access to histological equipment and their help. The authors would like to thank all the members of team  DAC  ( University of Lorraine, France ) who helped during fish sampling. This study was supported by the FranceAgrimer establishment (French establishment of national agricultural and marine products). As well as by the  Ministry of Education, Youth and Sports of the Czech Republic , projects: CENAKVA (No. CZ. 1. 05/2. 1. 00/01. 0024 )and  CENAKVA II  (No. LO1205  under the  NPU I program ).
The Hitomi SXS spectrum of the Perseus cluster, with ∼5 eV resolution in the 2-9 keV band, offers an unprecedented benchmark of the atomic modeling and database for hot collisional plasmas. It reveals both successes and challenges of the current atomic codes. The latest versions of AtomDB/APEC (3. 0. 8), SPEX (3. 03. 00), and CHIANTI (8. 0) all provide reasonable fits to the broad-band spectrum, and are in close agreement on best-fit temperature, emission measure, and abundances of a few elements such as Ni. For the Fe abundance, the APEC and SPEX measurements differ by 16%, which is 17 times higher than the statistical uncertainty. This is mostly attributed to the differences in adopted collisional excitation and dielectronic recombination rates of the strongest emission lines. We further investigate and compare the sensitivity of the derived physical parameters to the astrophysical source modeling and instrumental effects.
2001; Foster et al. 2012) , SPEX  (Kaastra et al. 1996) , and CHIANTI  (Dere et al. 1997; Del Zanna et al. 2015). The AtomDB code descends from the original work of  Raymond & Smith (1977) , SPEX started with  Mewe (1972) , and CHIANTI started with  Landini & Monsignori Fossi (1970). All these codes have evolved significantly since their initial beginnings, often stimulated by the challenges imposed by new generations of instruments. It is clear that the code comparison is strongly needed to verify the scientific output and to understand systematic uncertainties in the results originating from the codes and atomic databases. However, few code comparisons have been done (e. g. ,  Audard et al. 2003) , and in particular, so far there is no comparison based on high-resolution X-ray spectra of galaxy clusters.
Data reduction In this paper, the cleaned event data in the pipeline products version 03. 01. 005. 005 are analyzed with the Hitomi software version 005a and the calibration database (CALDB) version 005  (Angelini et al. 2016)   1. There are four Hitomi observations of the Perseus cluster (name: sequence number = "Obs 1": 100040010, "Obs 2": 100040020, "Obs 3": 100040030-100040050, and "Obs 4": 100040060). The instrument had nearly reached thermal equilibrium by Obs 4  (Fujimoto et al. 2016) , and the calibrations of Obs 2 and Obs 3 can be checked against Obs 4 because of their overlapping fields of view (FOVs), but the FOV of Obs 1 does not overlap the others and the instrument was the most out of equilibrium during that pointing. Hence only the Obs 2, 3, and 4 are used in this work. Events registered during low-Earth elevation angles below two degrees and passages of the South Atlantic Anomaly were already excluded by the pipeline processing which created the cleaned events file.
The best-fit parameters of our model are given in table 1. It is beyond the scope of this paper to discuss the astrophysical interpretation of the temperature, abundances, and resonance scattering; these are discussed in much greater detail by T, Z, and RS papers, respectively. In the following sections, that form the core of our paper, we investigate in more detail the systematic effects that affect the best-fit parameters of this baseline model. We do so by showing in table 1 the difference in best-fit C-statistic and the best-fit model parameters, for different assumptions in our modeling. In several cases we also show the relative difference in the predicted model spectra. We consider the following systematic effects: the plasma code that is used (section 4), the atomic database in the background (section 5), different choices for details of the plasma modeling (section 6), astrophysical modeling effects (section 7), the role of other spectral components apart from the main hot plasma (section 8), and spectral fitting techniques (section 9).
Systematic factors affecting the derived source parameters: plasma code We consider in this paper apart from SPEX version 3. 03 (the baseline plasma model) also the old SPEX version 2/Mekal plasma model, the latest SPEX version before the launch of Hitomi (hereafter, the pre-launch version: SPEX version 3. 00), as well as the pre-launch and the latest APEC/AtomDB versions 3. 0. 2 and 3. 0. 8 (Smith et al. 2001; Foster et al. 2012) , respectively, CHIANTI version 8. 0  (Dere et al. 1997; Del Zanna et al. 2015) , and Cloudy version 13. 04  (Ferland et al. 2013 ) plasma models. The best-fit models with these codes highlighting the Fe and Ni Heα bands are compared in figure  1. The full-band results as well as the relevant atomic data are compared between these codes in appendices 4 and 5 (see also section 4.
APEC APEC runs were conducted for both the pre-launch version, AtomDB version 3. 0. 2, and the latest version, AtomDB version 3. 0. 8. Since the launch of Hitomi, several updates have been made to the database to reflect the needs of the Hitomi data. These updates were not made to "fit" the Hitomi SXS data, but instead to reflect the priorities that analysis revealed. These changes were: 1. The ionization and recombination rate calculation was switched from an interpolatable grid to a fit function, which has a few percent effect on several ion populations depending on the temperatures/ion involved. 2. Wavelengths for higher n transitions of the H-and He-like ions were changed to match Ritz values from the NIST Atomic Spectra Database. 3. Wavelengths for valence shell transitions of Li-like ions were changed to match Ritz values from NIST.
The line energy data in APEC version 3. 0. 8 are in general good agreement with SPEX version 3. 03 (see table 10 in appendix 5 for details). As listed in table 1, the APEC code gives a similar bestfit temperature as the SPEX baseline model. The metal abundances obtained with APEC are lower by 5-10% for Si, S, Ar, Ca, and Ni than the best-fit baseline values, while the Cr abundances obtained with the two codes agree within error bars. The largest difference is with the Fe abundance, which is 16% lower in the latest APEC/AtomDB (version 3. 0. 8) than SPEX. The best-fit turbulent velocity in σv (LOS dispersion) derived with the latest APEC code is 16 km s -1 lower than the SPEX result.
CHIANTI Another atomic code/database widely used in the UV and X-ray spectroscopy for optically thin, collisionally dominated plasma is the CHIANTI code. Compared to the APEC and SPEX codes, CHIANTI is more focused on modeling the spectra from relatively cooler plasma in the solar and stellar atmosphere, while in this work, we are testing it in the conditions of hot ICM emission. The latest version 8. 0  (Dere et al. 1997; Del Zanna et al. 2015)  is used. The current CHIANTI database includes all the relevant H-like and He-like ions except for Cr and Mn, which means that these abundances cannot be estimated. We calculate the collisional ionization equilibrium spectrum using an IDL-version isothermal model, setting the ionization balance to  Bryans et al. (2009) , and change the solar abundance table to  Lodders & Palme (2009)  proto-solar values.
To perform the fit to the data, the IDL calculation is implemented as an input to the user model in SPEX, and the fitting engine of SPEX repeatedly triggers the IDL run until a best-fit is reached. Since the CHIANTI code does not provide line broadening information, we apply a multiplicative SPEX Gaussian broadening model vgau to the CHIANTI model. This is only a firstorder approximation, since the thermal broadening should vary with the atomic number. A detailed comparison on the best-fit spectra shown in appendix 4 (figures 23-25) reveals several differences in emission features from the baseline model, at levels ranging from a few % up to about 20%. Most of these differences are traced back to the different input atomic data, which can be found in appendix 5 (tables 10-11). The C-statistic value increases by 327 when fitting with the CHIANTI code.
In addition, inner-shell ionization of Fe XXIV drives 8%, and radiative transitions from the 3p ( 3 P1) and 4p ( 3 P2) levels both provide 4% of the population. The metastable level can decay to the ground only via radiative transitions. We compare the atomic data extracted from the SPEX, AtomDB, and CHIANTI databases, as well as the collision data from the Open-ADAS database 3 , and the radiative transition data from the FAC calculation. The effective collision strengths used in SPEX, AtomDB version 3. 0. 2 and CHIANTI version 8. 0, and AtomDB version 3. 0. 8 are taken from the published data in  Zhang & Sampson (1987, distorted wave) ,  Whiteford (2005, R-matrix) , and  Whiteford et al. (2001, R-matrix) , respectively. The data from Open-ADAS is calculated with the distorted-wave approximation.
Of course, the accuracy of the physical parameters derived from global fits also relies upon complete and accurate atomic databases. In the case of completeness, global models contain potentially millions of atomic transitions, most of which have not been experimentally verified. While "spot" checks do exist, in most cases the accuracy of the data is not well known, i. e. , estimates of uncertainties are determined by comparing results from different theoretical calculations, or by using uncertainties from portions where experimental results do exist. In our case, we have shown that the calibration and completeness of the spectral models is not perfect but good enough to yield a very good description of the Perseus spectrum. Ultimately, local and global fits must be used in a complementary way.
Important factors We have shown in this paper the dependencies of several astrophysically interesting parameters, mainly focusing on the plasma modeling, i. e. , plasma codes and atomic databases. We have also investigated the dependencies on astrophysical modeling as well as spectral fitting techniques. For a proper astrophysical modeling of the present Perseus cluster spectra, as presented here but in greater detail discussed in a set of other papers  (Hitomi Collaboration et al. 2016 , 2017a , Z, T, RS, V, and AGN papers), it is crucial to understand the possible systematic biases on the derived parameters. Table  1  provides a comprehensive list of the estimated biases, which enables us to intercompare various aspects of systematic uncertainty. The effects of some of the plasma modeling factors are comparable to or even larger than the statistical or instrumental uncertainty (appendix 3).
Implications to other observations So far we have reviewed the state-of-the-art knowledge, mostly on the K-shell transitions, for modeling the hot (several keV) tenuous plasma in collisional ionization equilibrium. We caution that the atomic uncertainties derived from the Perseus data cannot simply be copied to observations of other sources, as the accuracy of atomic data depends strongly on the types of transitions (tables 2 and 3), as well as on the plasma conditions, such as electron temperature (figures 2 and 4) and ion charge states (figures 6 and 7). For instance, X-ray emission from a stellar corona (or an elliptical galaxy) is dominated by transitions in the Fe-L complex, which are known to be computationally more intricate than those in the Fe-K (e. g. ,  Bernitt et al. 2012) , and hence less accurate (e.
Atomic data needs As shown throughout this paper, the reliability of a spectral modeling package lies not only in the accuracy and completeness of its atomic data, but also in its ability to properly synthesize the atomic data as a function of physical parameters, i. e. , the plasma conditions, such as temperature and density. Synthesizing the data is tedious and computationally taxing owing to the fact that the databases employed are large, containing millions of data points, including transition energies, excitation and ionization cross sections, resonant (multi-electronic) and non-resonant (radiative) recombination cross sections, and nonthermal processes, such as CX recombination. Different models use atomic databases of varying levels of completeness and accuracy as well as different synthesis methods in their calculations.
Estimates of a model's accuracy is often given by comparison to other models. However, a true measure of a model's accuracy can only be determined by comparing to laboratory benchmark measurements. Benchmark measurements, generally, come in two forms: as isolated experiments where a single ionic species or atomic process is studied, or as integrated experiments, where emission or absorption is measured from several simultaneous ions and atomic processes as a function of temperature or density. Isolated experiments include those conducted at electron beam ion traps, advanced light sources, or storage rings. Integrated experiments include experiments using, for example, tokamaks or laser-produced plasmas. Isolated experiments generally test portions of atomic databases, and integrated experiments test synthesis models.
Examples of isolated experiments include measurements of absolute electron-impact excitation cross sections as a function of electron energy, transition energies, natural line widths, and oscillator strengths  (Beiersdorfer et al. 1992; Brown et al. 2006; Rudolph et al. 2013). Examples of integrated experiments include the spectral signature of the Heα complex as a function of electron temperature  (Bitter et al. 2008; Gu et al. 2012; Rosen et al. 2014; Rice et al. 2015) , or full Fe-K and Fe-L shell spectral signatures as a function of temperature and density. Providing laboratory benchmarks for the atomic database in all physical regimes for all astrophysically relevant ions is not tractable. Hence, models are tested by comparing to measurements where available. Typically, models agree with measurements at the 10-20% level in the cases of excitation and ionization processes.
Prospects for XARM, Athena and other missions The Hitomi SXS observation of Perseus, with its highresolution spectrum in the 1. 9-9. 5 keV band, showed both the strengths and weaknesses of existing plasma codes. Pre-launch versions of both the SPEX and AtomDB codes provided generally plausible fits to the observation, matching the continuum and many of the strong lines well. While neither fit was formally statistically acceptable (see table  1 ), the two codes agreed (to within ±0. 3 keV) on the best-fit temperature, and to within ±0. 2 on elemental abundances. At CCD resolution, these discrepancies could easily be understood as calibration issues or inadequacies of the collisional isothermal model; only at the resolution of the SXS were the clear problems with both codes apparent. As described above, many of these disagreements could be addressed by updating wavelengths and cross sections for a few weaker lines and by fixing minor code bugs.
A. 1. 3 Binning of the data For the binning of our X-ray data, we have followed the approach of  Kaastra & Bleeker (2016)  for optimal binning. The optimal bin size depends on the spectral resolution, number of resolution bins and local intensity of the spectrum and is different for each energy. It is achieved by issuing the obin command in SPEX. Since we started with spectrum with 0. 5-eV resolu-the baseline run without the NXB component gives a larger Cstatistic value (δCstat =9) than the original run, and the impact on the fitted parameters is minor, ≤1% on temperature and abundances.
Acknowledgments We acknowledge reviewer  Paul Bryans  for the constructive comments and advices. We thank the support from the  JSPS Core-to-Core Program. We acknowledge all the JAXA members who have contributed to the  ASTRO-H (Hitomi)  project. All U. S. members gratefully acknowledge support through the  NASA Science Mission Directorate. Stanford and SLAC members acknowledge support via DoE contract to  SLAC National Accelerator Laboratory   DE-AC3-76SF00515. Part of this work was performed under the auspices of the  U. S. DoE by LLNL  under Contract  DE-AC52-07NA27344. Support from the  European Space Agency  is gratefully acknowledged. French members acknowledge support from  CNES , the  Centre National d' Études Spatiales. SRON is supported by  NWO , the  Netherlands Organization for Scientific Research.
The following  NASA  grants are acknowledged:  NNX15AC76G ,  NNX15AE16G ,  NNX15AK71G ,  NNX15AU54G ,  NNX15AW94G , and  NNG15PP48P  to  Eureka Scientific. H. Akamatsu  acknowledges support of  NWO  via  Veni grant. C. Done  acknowledges  STFC  funding under grant  ST/L00075X/1. A. Fabian  and  C. Pinto  acknowledge  ERC Advanced Grant   340442. P. Gandhi  acknowledges  JAXA International Top Young Fellowship and UK Science and Technology Funding Council (STFC)  grant  ST/J003697/2. Y. Ichinohe ,  K. Nobukawa , and  H. Seta  are supported by the  Research Fellow of JSPS for Young Scientists. N. Kawai  is supported by the  Grant-in-Aid for Scientific Research on Innovative Areas  " New Developments in Astrophysics Through Multi-Messenger Observations of Gravitational Wave Sources ". S. Kitamoto  is partially supported by the  MEXT   Supported Program for the Strategic Research Foundation at Private Universities ,  2014-2018.
ChIP-PCR, ChIP-seq, and data analysis Fully expanded leaf samples from 5-week-old NS and HS NP:HSFA1b and Col-0 plants were used for ChIP experiments. A detailed step-wise protocol can be found as a Supplementary Methodology at JXB online. PCR analysis to detect HSFB2a in ChIP DNA samples was carried out as previously described  (Bechtold et al. , 2013). Library preparation and massively parallel DNA sequencing of ChIP DNA samples (ChIP-seq) were carried out at The Genome Analysis Centre (now the Earlham Institute; http://www. earlham. ac. uk/) using Illumina TruSeq ChIP Library Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Libraries were sequenced on an Illumina HiSeq2000 platform using 100 bp paired ends and a sequencing depth of ≥10 million reads per library.
2e8 -f BAMPE -q 0. 05 -B -trackline -FE 2. Peaks within pericentromeric regions and broad peaks on gene bodies of highly transcribed genes were considered as false positives regardless of their q and FE values  (Nix et al. , 2008; Chen et al. , 2012). K-means clustering of ChIP-seq signals on the regions occupied by HSFA1b and generation of density heat maps were carried out using seqMINER  (Ye et al. , 2011). Annotation of the closest genomic features to the regions bound by HSFA1b was carried out with ChIPpeakAnno  (Zhu et al. , 2010)  using the batch annotation function and a dedicated function for peaks on bidirectional promoters. Overlap between annotated target genomic features was determined and Venn diagrams were generated using Jvenn  (Bardou et al. , 2014). Gene Ontology (GO) analysis on the target genomic features was carried out using the Singular Enrichment Analysis (SEA) tool in the AgriGO database  (Du et al.
, 2010). Sequences of the regions bound by HSFA1b were used for de novo motif discovery using MEME  (Bailey et al. , 2009)  with a cut-off P<0. 0001 and using default options. The published ChIP-seq data analysed in this study were downloaded from the NCBI Gene Expression Omnibus (GEO) database and subjected to the same analysis using MACS2 and criteria as described above. The GEO accession codes for each TF are as follows: HBI1, GSE53078; LFY, GSE24568; PIF5/PIF4, GSE68193; PRR5, GSE36361; PRR7, GSE49282; and SEP3, GSE46987.
RNA-seq data analysis RNA was extracted from NS WT, HS WT, and NS 35S:HSFA1b plants (three biological replicates of each) as described previously  (Bechtold et al. , 2013)  and analysed using massively parallel sequencing (RNA-seq) as follows: synthesis of cDNA, library preparation, and sequencing were carried out at Earlham Institute on the Illumina HiSeq2000 platform using 50 bp paired-end sequencing with minimum read depth ≥25 million reads per library. Quality control of raw fastq files was carried out as for the ChIPseq data with the following modifications: filtered RNA-seq reads were mapped against the Arabidopsis transcriptome using GSNAP with the known splices option for RNA-seq (five mismatches allowed). Transcript assembly and differential expression analysis were carried out using Cufflinks and Cuffdiff  (Trapnell et al.
Identification of lincRNAs and cisNAT RNAs The sequenced reads were aligned using TopHat 2  (Kim et al. , 2013)  on the TAIR10 DNA sequence. The GFFProf script included with  RNAprof (Tran et al. , 2016)  was used to predict all new transcriptional units compared with Araport11 gene annotations  (Cheng et al. , 2017). Only the transcriptional units >200 nucleotides were kept. The coding potential was estimated using existing annotation (repTAS;  Liu et al. , 2012)  and CANTATAdb  (Szcześniak et al. , 2016)  and, if absent, it was predicted using COME  (Hu et al. , 2017). Using R (http://www. R-project. org/), all annotated target genomic features of HSFA1b were intersected with transcribed genomic features to generate normalized FPKM (fragments per kilobase of transcript per million mapped reads) values.
Detection of long non-coding RNAs In WT plants under NS and HS, 7137 differentially expressed genes (DEGs; q-value ≤0. 05) were identified in response to HS (Supplementary Data S3). Of these, 721 were HSFA1bbound genes (Fig. 3A ; Supplementary Data S4). Enriched GO terms revealed that down-regulated HS-responsive genes were enriched for a number of growth functions (Fig. 3B ; Supplementary Data S5). The prevalence of HSFA1b binding internal to and downstream of protein-coding genes suggested that it binds to genomic regions in or near cis natural antisense long non-coding (cisNAT) RNA genes  (Ariel et al. , 2015). Using the RNAseq data on WT (NS and HS) plants, we predicted all transcripts in addition to those present in the Araport11 database  (Cheng et al. , 2017). The transcripts were then classified as coding transcripts, cisNAT or long intergenic non-coding (linc) RNAs (see the Materials and methods).
, 2014) , PHYTOCHROME INTERACTING FACTOR4 (PIF4) and PIF5 (BHLH TFs;  Pedmale et al. , 2016) , PRR5, and PRR7. Furthermore, there was considerable overlap in target genes not only between HSFA1b and the seven TFs but also between the seven TFs (Fig. 7C ; Supplementary Data S9), examples of which are shown in Fig. 7E. Since the published ChIP-seq data sets were from NS plants, we scored for co-occurrence of the seven TF motifs only in Groups I and II (i. e. under NS conditions; Supplementary Data S9). In Group I genomic features, shared sites were between 3% and 6. 4% and in Group II between 15. 5% and 27. 8%. The target genes shared between HSFA1b and each of the seven TFs (Supplementary Data S9) showed significant enrichment of GO terms for response to stress and endogenous stimuli (Fig. 7D ; Supplementary Data S10).