dataesr/scientific-paragraphs-categorization huggingface dataset:
MD simulations The initial structure of a DNA duplex (PDB ID: 2NQJ  [11] ) was manually truncated to a 12mer and edited according to a nucleotide sequence being studied containing 2-aPu and/or DHU residues. Zn 2 þ ions were placed in the PDB file according to refs. [12, 13]  and the data obtained on the CheckMyMetal server and RaptorX-Binding server  [14]. Parameterization of Zn 2 þ ions in a protein for MD simulations remains a challenge with classical mechanics. In this work, we tested different approaches to  Zn 2 þ parameterization: the cationic dummy atom (CaDA) approach  [15]  that involves virtual atoms to impose an orientational requirement for zinc ligands; the polarizable atomic multipole-based electrostatic model  [16] ; and the classic nonbonded atom method  [17]. Finally, we found that the nonbonded atom method is more suitable for our purposes; accordingly, in this work, we chose this approach.
Parameterization of Zn 2 þ ions was carried out as in ref. [17]. Structure refinement and molecular dynamic simulation were performed as in  [7]  using AMBER 14 molecular modeling suite  [18, 19]. The force field parameters for the 2-aminopurine-5 0 -phosphate residue were retrieved from ref. [20]. The partial atom charges and force fields for the DHU residue were custom-parameterized calculated by the RESP method  [21]  based on the quantum mechanical calculation in the HF/6-31 G* using Gaussian'09 software  [22]. A 45 ns MD simulation was conducted using the AMBER 14 GPUaccelerated code  [18, 23]  by means of the ff99SB force field  [24, 25]. Molecular graphics, MD movie generation, and trajectory analysis were carried out in the UCSF Chimera software  [26].
Acknowledgements This research was supported by the  Federal Agency of Scientific Organizations  ( VI. 57. 1. 2 , 0309-2016-0001 ) to O. S. F. , grants from  Russian Foundation for Basic Research  ( 16-04-00037 ) to O. S. F. and  Russian Foundation for Basic Research  ( 18-04-00596 ) to V. V. K. , the  Russian Ministry of Education and Science (NSU-SB RAS Collaborative Lab)  under 5- 100 Excellence Programme  to V. V. K. , Equipe LNCC 2016 and PRC CNRS/RFBR n1074 REDOBER to A. A. I. Transparancy document. Supporting information Transparancy data associated with this article can be found in the online version at http://dx. doi. org/10. 1016/j. dib. 2018. 09. 007.
(1990) , namely: IC 10, NGC 628,  UGC 12914, NGC 3646, NGC 4217, NGC 4449, and NGC 5457. In analysing galaxy spectra to study the role of thermal absorption, we were primarily interested in galaxies with Hubble types corresponding to spiral and irregular objects, galaxies that are known to have recent star formation. In order to have a uniform sample of such galaxies, we chose the following selection criteria: included in  Yun et al. (2001) , supplemented with the above seven additional galaxies; radio flux density > 50 mJy at 1. 4 GHz; located in the northern hemisphere (Dec>0 degrees); morphological type T ≥ 0 (according to the HyperLeda database) to avoid elliptical galaxies; not dominated by an active galactic nucleus (AGN), for example, excluding NGC 1275 and NGC 4258, but taking into consideration M 51 and M 81 (with a low-luminosity LINER/Seyfert nucleus.
) Our initial sample fulfilling these criteria consisted of 200 galaxies. Fig. 1. Radio-FIR relation for the low-frequency MSSS (left) and high-frequency NVSS data (middle). The red, blue and green symbols represent faint, medium bright, and bright NVSS galaxies, respectively. The MSSS-NVSS luminosity diagram is shown on the right. The solid line is a bisector fit, while the dashed lines represent simple X vs. Y and Y vs. X linear regressions in the logarithmic space of the parameters. We used the preliminary MSSS mosaics from the first internal version of the MSSS source catalogue (e. g. see details in  Kokotanekov et al. 2017). The typical resolution of the mosaics is 3 and the root mean square (r. m. s. ) noise level varies between about 15 and 25 mJy beam -1. Differences in noise levels may result from fluctuations of the Milky Way foreground emission, distributions of strong sources in mosaics, imperfect calibration and ionospheric weather conditions during observations.
g. Chyży et al. 2007). Table  5  presents the galaxies from our original sample that were detected in the MSSS survey and have reliable flux densities at 150 MHz. All galaxies have redshifts less than 0. 04 and constitute our sample of 129 sources. Some of these objects have been identified as galaxy pairs, unresolved in the MSSS mosaics. In these cases, the measured flux density refers to the system as a whole. Table  5  also provides the galaxy morphology (Hubble T-type), inclination angle of the galaxy disk (90 • corresponds to an edge-on object), the distance, and supplemental information concerning the flux density measurements and the constructed global spectra characterized by four different kinds of flags: interaction flag (according to the NED database): 1 -single source; 2 -luminous infrared galaxy (LIRG); 3 -strongly interacting galaxy but not LIRG; flux density flag: 1 -flux density for a single source with an undisturbed disk; 2 -flux density for a single source but with unclear or amorphous disk plane, morphologically irregular dwarf, or starburst galaxy; 3 -flux density for a double or a triple source; spectral flag (see Sect.
Deriving galaxy spectra Our analysis of the spectral characteristics of radio emission of galaxies is based on their global spectra. We carried out an extensive search of the literature for galaxy flux densities at different frequencies, using mainly information from the NED and SIMBAD databases. We avoided old measurements, for example early interferometric arrays before 1980. For 31 galaxies we found data in the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM;  Hurley-Walker et al. 2017)  survey, which provides a catalogue of integrated fluxes from 20 narrowband images as well as a fitted 200 MHz integrated flux density. For 19 galaxies from our sample, we did not find literature data of sufficient quality to derive spectra. Such objects, marked in Table  5  by spectral flag 3, were excluded from further analysis.
We also dropped double and multiple galaxy systems marked by the flux density flag 3 (Sect. 2. 3). The resulting sample therefore consists of 106 galaxies. Some examples of spectral trends, integrated flux density measurements from the MSSS survey, and published data are presented in Fig. 2. The MSSS and GLEAM measurements are consistent with each other and follow the general spectral trends found in the published data. Our main interpretation of these diagrams is that the spectra tend to slightly flatten at low frequencies (e. g. NGC 1569 and NGC 4102). However, there are also spectra that appear to be quite straight (e. g. NGC 3646 and NGC 5936).
Spectral flattening The spectral curvatures seen in Fig. 2  are small and usually not limited to just the lowest frequencies (e. g. NGC 972 and NGC 4102). Therefore, in order to evaluate spectral slopes, we constructed both low-and high-frequency spectral indices. The low-frequency index (α low ) was obtained by fitting a power-law to the interpolated MSSS flux density at 150 MHz and to literature data from 50 MHz up to 1. 5 GHz. When the GLEAM data were available we used the fitted flux density at 200 MHz, and not the correlated data from individual sub-bands, which is similar to the approach with the MSSS data. The high-frequency index (α high ) between 1. 3 GHz and 5 GHz was calculated in a similar way. We did not consider frequencies higher than this so as to avoid the effects of spectral flattening due to the increasing thermal component: for example for a sample of galaxies, the average thermal fraction given by  Niklas et al.
Summary and conclusions We compiled a sample of 129 nearby star-forming galaxies from the LOFAR MSSS survey and measured their integrated flux densities at 150 MHz (Table  5 ). Combining them with flux densities at various frequencies from the literature, we constructed global radio spectra for 106 objects. A statistical analysis of the data led us to the following conclusions. -The low-frequency radio-FIR relation resembles the highfrequency one, which confirms (i) the good quality of the MSSS data, (ii) the proper removal of AGN-dominated galaxies from the sample, and (iii) the tight relation between dust emission and the almost pure synchrotron radiation at low radio frequencies. It may also indicate that the effect of thermal absorption in the sample is relatively weak at 150 MHz. -In general, the global spectra of galaxies in our sample are statistically flatter at lower frequencies, or equivalently, steeper at higher frequencies (Fig.
Acknowledgements. WJ acknowledges support by the  Polish National Science Centre  grant No. 2013/09/N/ST9/02511. MJH acknowledges support from the  UK Science and Technology Facilities Council  [ ST/M001008/1 ]. GG acknowledges the  CSIRO OCE  Postdoctoral Fellowship. AOC and AMS gratefully acknowledge support from the  European Research Council (ERC)  under grant  ERC-2012-StG-307215  LODESTONE. RJvW acknowledges support from the  ERC  Advanced Investigator programme NewClusters 321271. RPB acknowledges funding from the  ERC  under the European Union's  Horizon 2020 research and innovation programme  ( ERC Starting Grant  agreement  715051  "Spiders"). We acknowledge the help of the  Academic Computer Centre CYFRONET AGH  in Krakow, Poland, where our numerical analyses were partly performed with the use of the computing cluster Prometheus.
We acknowledge the use of the Hy-perLeda (http://leda. univ-lyon1. fr), NED  (http://nedwww. ipac. caltech. edu),and SINBAD (http://simbad. u-strasbg. fr)databases. We thank  Fatemeh Tabatabaei  and an anonymous referee for helpful comments on the manuscript. This paper is based (in part) on data obtained with the  International LOFAR Telescope (ILT). LOFAR (van Haarlem et al. 2013) is the  Low Frequency Array designed  and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefited from the following recent major funding sources:  CNRS-INSU, Observatoire de Paris and Université d'Orléans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The Netherlands; The  Science and Technology Facilities Council, UK, Ministry of Science and Higher Education, Poland.
Observations and data reduction The spectra presented in this paper have been acquired in the course of two programs approved by the ESO and GEMINI observatories. The programs have been designed to be "filler" programs, meaning that they can acquire useful data in weather conditions when most programs cannot operate and have no constraint on the Moon phase. Our spectra have been acquired with FORS  (Appenzeller et al. 1998 ) at the ESO VLT 8. 2 m telescope and with GMOS  (Hook et al. 2004 ) at the GEMINI 8. 2m telescope. The FORS spectra have been observed in service mode during the ESO Programme 099. D-0791, between 01/04/2017 and 16/08/2017. We used GRIS 1200B with a central wavelength of 436 nm and a slit width of 0. ′′ 29, which provides a resolving power R ∼ 5000 at the central wavelength and a continuous spectral coverage in the range 366 nm to 511 nm.
2016). The three detectors are arranged in a line along the dispersion direction, which implies that there are two gaps in the wavelength range. The gap ranges are 438. 7 nm -440. 8 nm and 493. 0 nm -495. 1 nm. We used a 2 × 2 binning, which is the default. For each star we observed three exposures of 441 s. The data were processed with GEMINI IRAF 5  package 6  , to perform bias subtraction, flat-fielding, and mosaicking of the different sub-images. We then used the ESO MIDAS 7  LONG context to perform the wavelength calibration using the CuAr lamp spectra and the optimal extraction (including sky-subtraction). The three exposures for each star where then added and analysed with MyGIsFOS.
Results The high-throughput sequencing generated more than 135 megabases (Mb) for the enriched 16 rRNA gene V4 regions from all 72 samples, representing a total of 4. 784. 028 reads. Over 2. 775. 137 of the total reads from each sample passed quality control (Supplementary Table  1 ). The rarefaction curves approximated to a stable asymptote for all groups, meaning that the number of reads obtained was sufficient to represent the whole diversity in each group (Supplementary Figure  1 ). A total of fifteen phyla was obtained (Fig. 1 ) in all groups, and the most abundant were Bacteroidetes (70. 8% ± 2. 9%), Firmicutes (20. 6% ± 4%), and Proteobacteria (4. 3% ± 1. 4%). From the 161 bacterial OTUs that were mapped to the database 1 belonged to Synergistetes phylum, 1 to Thermus, 1 to Planctomycetes, 1 to Fusobacteria, 1 to Spirochaetes, 2 to Verrucomicrobia, 2 to Deferribacteres, 2 to Cyanobacteria, 4 to Tenericutes, 8 to Actinobacteria, 28 to Bacteriodetes, 41 to Proteobacteria and 68 to Firmicutes.
Briefly, the raw fastq file was processed to strip barcodes using Usearch package  77. Then, quality filtering was performed including removal of truncated and low-quality sequences (Phred score smaller than 20). Next, sequences were submitted to dereplication, abundance sorting, singleton removal, OTU clustering (97% similarity), and chimera filtering using Vsearch  78. Finally, pre-processed sequences were assigned taxonomically using QIIME requiring 97% of sequence similarity threshold against the Greengenes 13. 8 database  79. The two barcodes with mock communities were processed using the same steps.
Acknowledgements The authors would like to thank  CNPq ,  Fapemig  and  CAPES  for their financial support and fellowships. The funding agencies  Coordination for the Improvement of Higher Education Personnel (CAPES) ,  National Council of Technological and Scientific Development (CNPq)  and  Foundation  for the Support to the Researches in Minas Gerais (Fapemig) were responsible for giving financial support to this study.
05; fold-change ≥1. 5). A total of 249 genes were significantly upregulated and 272 were downregulated in CR mice compared with ad libitum mice (Supplementary Tables  S1A, B ). This gene expression response to CR in the duodenum mucosa was assessed by gene ontology (GO) functional enrichment analysis  30  , which defined 19 enriched terms associated with metabolic processes for the upregulated genes (Figs. 1A, B , Supplementary Table  S2A ) and 31 terms associated with immune and inflammatory responses for the downregulated genes (Fig. 1A ,B, Supplementary Table  S2B ). The lack of any overlapping GO term between the up-and downregulated genes suggested a strong dichotomisation of biological processes for metabolism and immunity. Furthermore, enrichment analysis of tissue-associated proteins carried out using the Uni-Prot datasets ('UP_Tissue') (Supplementary Table  S2C ) showed that the 249 metabolic associated gene subset was represented by proteins referring to small intestine, colon, kidney and liver, while the 272 immune system associated gene subset was represented by the proteins connected to jejunal and colic lymph nodes (at 87.
Sequencing the 16 S rDNA genes and metataxonomic analysis. Bacterial DNA was extracted and sequenced; the results were analysed as described previously  83. In brief, DNA was extracted using a FastDNA Spin Kit for Soil (MP Biomedicals, UK). Following PCR amplification of the V4 and V5 regions of the 16 S rDNA genes, DNA sequencing was performed using the high-throughput GS FLX Titanium 454 pyrosequencing platform (the GS FLX Titanium platform)  84. Analyses of the sequencing reads were performed using the Quantitative Insights Into Microbial Ecology (QIIME) pipeline. The resulting taxonomic data were analysed by principal component analysis (PCA). 1 H NMR metabolomics. Faecal and duodenum extract preparations, NMR spectroscopy, and data pre-treatment were conducted as previously described  83.
Acknowledgements The authors would like to acknowledge the help of the staff at the  Metabolic Evaluation Facility and Genomic Technologies Facility , as well as of  Leonore Wigger from Vital-IT (all at the Center for Integrative Genomics, University of Lausanne ), and  Arlaine Amoyo-Brion  from the  National Cancer Center in Singapore. The authors also thank  Cécile Canlet  from the  French National Infrastructure of Metabolomics and Fluxomics (MetaboHUB- ANR-11-INBS-0010 ) in Toulouse for her help with the NMR analyses and  Olivier Cloarec  from  Korrigan Sciences Limited (Maidenhead, Berkshire, UK)  for providing the MATLAB functions for analysis of the NMR data. This study was funded by the  Swiss National Science Foundation (WW) ; the 7th  EU program  TORNADO (WW, AN); the  Bonizzi-Theler-Stiftung (WW) ; the  Région Midi-Pyrénées  ( HG, Mathusalem Program ;  WW, Chaire d'Excellence Pierre de Fermat) ; the  Etat de Vaud (WW) ; and the  Start-Up  Grant (WW) from  Lee Kong Chian School of Medicine, Nanyang Technological University Singapore.
The numbers quoted in table 1 cannot be used directly for the combination, as a breakdown into correlated and uncorrelated components of the total uncertainty is needed. For the 7 TeV analysis, the result before marginalisation is used; A ATLAS7 C = 0. 006 ± 0. 010 (stat) ± 0. 005 (syst) = 0. 006 ± 0. 011 (stat ⊕ syst). For the 8 TeV combination, the expected uncertainties after marginalisation are used: the systematic uncertainty from each source is evaluated by building pseudo-data with variations of the predictions and for each one repeating the unfolding procedure, yielding A ATLAS8 C = 0. 0090 ± 0. 0044 (stat) ± 0. 0025 (syst). The sum in quadrature of these individually estimated uncertainties is equal to the total uncertainty quoted in table  1. The charge asymmetry is also measured as a function of several kinematic tt variables, including the mass of the tt system.
Classification of uncertainties and correlation of measurements Dedicated studies are performed to estimate the influence of various systematic uncertainties on the results for all analyses considered for the combinations. In the ATLAS analyses, the expected impact of systematic uncertainties is studied with alternative pseudo-data distributions built from the expected signal and background contributions. These alternative pseudo-data sets are generated by varying each source of systematic uncertainty by one standard deviation (±1σ). The unfolding procedure is then repeated using the baseline background templates and response matrices. At 7 TeV, the impact of the uncertainty on the measured A C value is reported as the difference between the mean value of the nominal posterior and the mean value of the varied posterior, and the maximum between the positive and negative variation is chosen as symmetric uncertainty.
Uncertainties at 8 TeV Statistical uncertainty in data (Correlation: 0) For the ATLAS analysis and the CMS template analysis, this category includes the statistical uncertainty in the resulting asymmetries. In the ATLAS measurement, the statistical uncertainty is obtained by running the unfolding procedure without taking any systematic effects into account. For the CMS unfolding analysis, this category includes, in addition, the uncertainties in the normalisation of the background processes and the uncertainty due to the number of simulated events in the simulated signal samples. The uncertainties in the normalisation of the background processes and the uncertainty due to the number of simulated events are treated as systematic uncertainties in the ATLAS analysis and the CMS template analysis.
The statistical uncertainties are considered to be uncorrelated between the ATLAS and CMS analyses. Statistical uncertainty in simulation (Correlation: 0 / -) In the ATLAS analysis, the impact of the size of the simulated signal event sample is investigated by repeating the unfolding several times on one single pseudo-data set with randomly drawn response matrices. A similar approach is followed in the CMS unfolding analysis, with the only differences being that the unfolding is carried out on the measured data instead of one pseudo-data set, and that the resulting uncertainty is included in the statistical uncertainty of the result. The CMS template analysis uses ensembles of alternative templates, generated by varying the original templates according to Poisson statistics -11 -.
For the combination of the ATLAS analysis and CMS template analysis, these uncertainties are considered fully correlated, while they do not apply to the CMS unfolding analysis. • Modelling of multijet production (Correlation: 0) In the ATLAS analysis, the uncertainties in the multijet background come from the size of the data sample used in the matrix method, as well as from the uncertainties in the estimation of the misidentified lepton probability in different control regions. In the CMS template analysis, the uncertainty comes mainly from the size of the data sideband sample used for the modelling of the multijet process. In the CMS unfolding analysis, variations in the shapes of the multijet templates derived from sideband regions in data, are used to estimate the uncertainty in the modelling of this background component.
JHEP04(2018)033 jets. The CMS template analysis constructs an alternative W+jets template by increasing or decreasing the number of events with heavy-flavour jets. In addition, the uncertainty in the estimated W+jets normalisation is propagated into this uncertainty. The W+jets component of the data set is estimated in situ in the ATLAS analysis using separate templates for the different flavours, and therefore no additional uncertainty is taken into account. Method (Correlation: -/0) The uncertainty from the unfolding procedure is determined using pseudo-data sets in which the tt signal events are reweighted to simulate different values of the charge asymmetry by applying the default response matrix and comparing the true charge asymmetry values with the values obtained after unfolding. In the ATLAS analysis, a linear fit of the generator-level asymmetry versus the unfolded asymmetry is performed and the uncertainty in the unfolding is calculated from the slope and the offset of this fit.
In the CMS unfolding analysis, different scenarios for reweighted tt events are examined and the differences between the unfolded asymmetries and the reweighted true asymmetries are taken to be a measure of the model dependence of the unfolding procedure. The uncertainty is assumed to be uncorrelated between ATLAS measurement and the CMS unfolding measurement. In the CMS template analysis, pseudo-data sets are constructed for different values of A C by changing the mixture of symmetric and antisymmetric components in the sample of simulated signal events. The difference between the mean charge asymmetry measured for each alternative model and the actual charge asymmetry is found to be negligible. Even if the systematic uncertainties for the 7 and 8 TeV results are not fully evaluated in the same way, most of the assigned correlations between the ATLAS and CMS measurements are identical between 7 and 8 TeV.