dataesr/scientific-paragraphs-categorization huggingface dataset:
2013 (Vika et al. , 2014;; Vulcani et al. 2014; Kennedy et al. 2016). Using such a multiband approach, one can obtain a more reliable model based on several observations of the same galaxy at different wavelengths. For the purpose of this work, we make use of the galfit wrapper deca written in Python (we refer the reader to Mosenkov 2014 for a complete description of the initial version of the package). We constructed 2D photometric models of the light profiles for each galaxy, selected in Sect. 2, using a general Sérsic model (eq. 1). In addition, the galfitm code is also incorporated in deca for a simultaneous fitting of galaxy images in different bands. deca is especially suitable for a large sample of galaxies, when a modelling of each individual galaxy by hand is problematic. It can work in a fully automated regime if we use a unified Sérsic model for all galaxies selected.
We applied it ten times and took the scatter in the fitted parameters as their uncertainties. Unfortunately, this operation is very time consuming, therefore we only did it for one galaxy. We found the Sérsic index n 250 for this galaxy to be 0. 79 ± 0. 22, which means that the estimated uncertainty is approximately 25% of the retrieved parameter. For the effective radius we found r 250 e = 18. 3 ′′ ± 1. 9 ′′ , i. e its uncertainty is within 10% of the parameter value. The tables with the results can be accessed from the Dust-Pedia database and from the VizieR catalogue service 5. The final pipeline product includes separate tables with the model results for the WISE W1 band and for the Herschel bands. For the WISE W1 band, we also provide some supplementary information described in the Appendix D, e.
2010). A closer look at their figs. 2 and 3, indeed, does not reveal a deficiency in the central regions of their SB profiles. On the contrary, we can see a little excess in the centre (within r/r 25 < 0. 1, where r 25 is defined as the radius of the isophotal level 25 mag arcsec -2 in the B band) at 250 µm and 350 µm. Using their subsample of 45 large galaxies (r 25 > 1. 5 ′ ), we found 27 common galaxies with our sample. = 2, the border value to split galaxies into bulgeless (n 3. 4 < 2) and bulgedominated galaxies (n 3. 4 > 2). This number is almost two times smaller than the number of the selected HRS galaxies because in our subsample only galaxies with all five PACS 100-SPIRE 500 observations were selected. Some galaxies (for example, NGC 4298 and NGC 5363) have the 'C', 'R' or 'N' flags in the DustPedia database, and, therefore, we did not include them in our subsample.
SPIRE 250 profiles for the 10 common galaxies with  Pohlen & Trujillo (2006). No correction for the galaxy inclination has been done. The orange cross is related to the characteristic radius found in this paper, the blue and red circles represent the break radii from  Pohlen & Trujillo (2006)  in the g and r bands, respectively. Each profile is shifted down relative to the previous one by 1. 6 mJy arcsec -2. file in the inner part of M 100 is related to the more prominent bulge, bar, or inner-disc component, whereas in M 81 a small AGN boosts the emission from transiently heated dust grains. Using the NED database, we selected galaxies which are classified as 'Sy' or 'LINER', meaning that with these galaxies some activity in the centre is associated. The total number of such galaxies in the DustPedia sample is 90, from which 34 galaxies have determined Sérsic models.
On the contrary, classical bulges exhibit remarkably regular star formation profiles in the inner region with a sharp change (decline) in star formation rate toward the galaxy centre. In addition, it was shown that galaxies with bars or ovals, on average, have brighter central PAH emission than galaxies without strong drivers of secular evolution (see also  Sakamoto et al. 1999; Sheth et al. 2005). To verify that the bulge type and the bar strength have impact on dust emission, we selected galaxies with available multicomponent decomposition in the S 4 G database. Fisher et al. (2009)  showed that more massive pseudobulges have higher SFR density. In Fig. 14 , left plot, we show the correlation between the bulge fraction to the total galaxy luminosity (mass) in the 3. 6 µm band and the Sérsic index in SPIRE 250 (which is related to the central SFR density), for classical bulges and pseudobulges (we identify them by the Sérsic index n 3.
Conclusions We have studied the two-dimensional distribution of stellar and dust tracers in galaxies selected from the DustPedia sample. For this purpose we used WISE W1 and Herschel (from PACS 100 to SPIRE 500) observations gathered in the DustPedia database. All galaxy images have been homogeneously treated and prepared for fitting. Using a new version of the Python wrapper deca, we fitted the 2D SB distribution of the galaxies with a single Sérsic profile and determined their best-fit structural parameters. For the galaxy images in the WISE W1 band, we used the galfit code, whereas for the Herschel observations, both galfit and galfitm were applied. Using deca, we were able to properly fit even those galaxies which reside in a close pair or interact, and most of the galaxies where a bright foreground star contaminates the light from the target galaxy.
), we observe a SB profile of the cold dust emission with a steeper decline in this region (no depression in the centre). We confirm the result by  Pohlen et al. (2010)  that the dust mostly shows the same breaks of Type II in the radial profiles as seen in the optical and NIR at r br ≈ 0. 6 r 25. The Type III breaks are seen in the FIR and optical as well. 4. The effective radius of the dust SB profile steadily increases with wavelength, from 0. 90±0. 27 in PACS 100 to 1. 41±0. 43 in SPIRE 500, most likely because of the cold-dust temperature gradient with galactocentric distance. The fit results for the WISE W1 and Herschel data are available on the DustPedia database. Our future follow-up studies will be focused on a detailed decomposition of well-resolved DustPedia galaxies, by taking into account several dust components which are associated with different galaxy substructures.
Administration. This research has made use of the HyperLEDA database (http://leda. univ-lyon1. fr/; Makarov et al. 2014 ). This research makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, and NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the National Aeronautics and Space Administration. This work is based in part on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA.
The histograms for these parameters are plotted in Figs. 4 5 6 7 8. Table  B. 2. The results of the fitting for the mock galaxy images with a double-exponential and Sérsic dust disc. Only the average values for the whole sample of 300 galaxies are listed. The effective radius for an exponential disc is related to its scalelength by r λ e ≈ 1. 678 h λ R. Band n λ n λ /n 3. 4 r λ e r λ e /r 3. 4 A&A proofs: manuscript no. decomp. 1f, we show the comparison between the Hyper-Leda inclinations and those found in the Appendix D. One can see a large scatter in this correlation which can be explained by a large scatter in the correlation between the apparent flattening logr25 from HyperLeda (this is the axis ratio of the galaxy isophote of 25 mag arcsec -2 in the B band) and the apparent flattening fitted in this work.
For the obtained residual frames and for the frames of galaxies with only one fitted object (the galaxy), we filled the masked pixels with the model pixels and then measured the total flux within the master aperture from C18. By so doing, we were able to reliably reconstruct the direct flux coming from the target galaxy taking into account the light from the detected contaminants. The extracted fluxes were then converted to Vega magnitudes 11  and then to AB magnitudes 12. Extended-source correction needs to be applied when performing aperture photometry upon WISE maps 13  , to account for the fact that the calibration of WISE data is based upon profile fitting of point sources. To convert the calculated AB-magnitudes into stellar masses, we computed the total galaxy luminosities using the 'best' distances tabulated in C18 and applied the mass-tolight luminosity ratio from  Kettlety et al.
Acknowledgements. A. V. M. expresses gratitude for the grant of the Russian Foundation for Basic Researches number mol_a 18-32-00194. IDL gratefully acknowledges the support of the Flemish Fund for Scientific Research (FWO-Vlaanderen). The DustPedia project is funded by the European Union, as a European Research Council (ERC) 7 th Framework Program (FP7) project (PI Jon Davies, proposal 606824). JF acknowledges the financial support from UNAM-DGAPA-PAPIIT IA104015 grant, México. This research made use of montage (http://montage. ipac. caltech. edu/), which is funded by the National Science Foundation under Grant Number ACI-1440620, and was previously funded by the National Aeronautics and Space Administration's Earth Science Technology Office, Computation Technologies Project, under Cooperative Agreement Number NCC5-626 between NASA and the California Institute of Technology.
Finally, both datasets were integrated to generate a unique statistical dataset at regional level NUTS 3. Yield and production statistics, expressed with a given moisture content, were transformed to dry matter, based on the moisture content reported by Eurostat. When this information was not reported in the statistics, the values of cropspecific reference moisture content in the Eurostat Handbook  (Eurostat, 2015)  were used instead. For potatoes and sugar beet, Eurostat has no reference value; therefore, the moisture content measured in field experiments from  Deblonde, Haverkort, and Ledent (1999)  and  Draycott (2006) , respectively, were applied. 2. 2 | Deriving empirical models to predict crop residues production  Kluts, Wicke, Leemans, and Faaij (2017)  review of existing studies assessing Europe's bioenergy potential provides an accurate overview of the existing methodologies used to assess the different potentials of residual biomass.
To calculate consistently the HI or RPR, both Y and R must refer always to dry-matter weight  (Donald & Hamblin, 1976). Other studies, as  Bentsen et al. (2014) ,  Monforti et al. (2013)  or  Scarlat et al. (2010) , use empirical regression models (e. g. exponential, logarithmic, etc. ) between economic yield and the RPR or the HI for different crops, assuming that a positive correlation between crop yield and the proportion of biomass allocated to plant storage organs (grains, fruits, tubers, roots…) exists for all crops. According to that, any increment in the total crop biomass production would be mostly located in the plant storage organs (grains or fruits, tubers, etc. ). To verify the hypothesis of previous works, understand the relationship between these variables, analyse the influence of factors investigated -direct or indirectly-by the scientific community, and identify the most appropriate way to infer empirically residues from economic yield, a dataset of 1,580 experimental observations of crop economic yield (Y), residue yield (R) and the HI was collected from a selection of 84 scientific papers published in English (see Table  1  for references, crops covered and geographical distributions).
This dataset resulted from filtering and transforming the samples originally collected (around 2,500 observations from more than 130 studies) to make them comparable among experiments in terms of weight per area and moisture content for yields, and especially for what regards HI definition. The experimental data on Y, R and HI collected from scientific literature were statistically processed to generate a predictive regression model and confidence intervals at 95%. Heteroscedasticity appears mainly in regressions between Y and R: in some crops, the variance of R increases progressively with Y, while in other crops exactly the opposite behaviour is observed (see Section 3). The presence of heteroscedasticity violates the assumption of uncorrelated variances, leading to unsatisfactory results in regression analysis.
After gap-filling the algorithm calculates regional weights on each dataset as the relative A/P of a given region r at NUTS level n to the A/P of the region R at NUTS level n -1 to which it belongs to. Once the full set of regional weights is established for a given crop and year, the area or production value for any region r at NUTS level n can be retrieved by multiplying the national level Eurostat value of the country (NUTS 0) region r belongs to by the regional weight of r and the weights of all the regions R from level 1 to l + 1 containing r. The downscaled production into the 25 km grid over the EU is also provided as Supplemental Material in a separate GIS file. (3) P t = log P -x i λ-P + x i δ+γ (4) P t = log ⎡ ⎢ ⎢ ⎣ � � P -x i λ � 2 + 1 + P -x i λ ⎤ ⎥ ⎥ ⎦ δ+γ (5) Pt = aY + b ± CI (6) P = λ 2 1 -e -Pt -γ δ 1 + e -Pt -γ δ + 1 + x i (7) P = λ 2 e Pt -γ δ - 1 e Pt -γ δ + x i (8) R = Y HI -Y (9) B c,g = n ∑ i B c,i,N=3 * L g∩i L i,N=3 Comment citer ce document : García-Condado, S.
2. 1 | Generation of a complete dataset on crop area, yield and production in the EU Economic production (P) and harvested crop area (A) from 1998 to 2015 were collected from Eurostat -the statistical office of the European Union-, whose database provides statistics in the European NUTS system (Nomenclature d'Unités Territoriales Statistiques). Concretely, P and A at national (NUTS 0) and subnational (NUTS 2) level were extracted from the table called apro_cpnh1 (Eurostat database, 2017) for the following crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugar beet.
Discussion These and previous results  (Schilthuizen et al. 2017 and unpublished)   Konstantinov and Duckett (2005) , it is likely that these minute litter-dwelling beetles, many of which are flightless, have small ranges and high degrees of endemism. We therefore suspect that the taxonomy of Clavicornaltica, which currently stands at 26 species, has barely scratched the surface of the true diversity. Given the morphological inaccessibility of this diversity, we expect DNA barcoding to be of some use. We hope the sequence we present here for C. belalongensis will be a starting point for further building up a database of DNA barcodes for this genus. Despite the present single-species description based on limited material from a single locality, we believe that taxonomic work is best carried out in the context of larger revisions.
Acknowledgements We thank the following Taxon Expeditions participants and instructors for assistance in the field and discussions:  Simon Berenyi ,  Brock Thomas Boslem ,  Massimo Delledonne ,  Hendrik Freitag ,  Werner de Gier , and  Clister Pangantihon. We gratefully acknowledge  Mr. Rodzay Wahab  and his staff  Mohammad Salleh  and  Teddy Chua  at KBFSC for support and staff at IBER for assistance in organising the field course. This field course was carried.
Culturomics is also a culture-based omics approach that uses multiple culture conditions, MALDI-TOF mass spectrometry and 16S rRNA sequencing for the rapid identification of bacterial species  [15]. Proteomics, initially defined by microbiologists as the study of all proteins expressed by a single organism, is in full emergence thanks to its application to complex bacterial communities. As a result, the analysis of the protein content of the microbial communities, such as gut microbiota is now named "metaproteomics"  [16]. A metaproteomic analysis typically comprises 4 steps: 1) extraction and purification of proteins, 2) enzymatic digestion of proteins into peptides, 3) separation of peptides, usually by chromatography, followed by mass spectrometric analysis and 4) protein identification by database sequence comparison  [17] [18] [19].
Metaproteomics is a rising technique but has some disadvantages related to the complexity of the sample, including both the complexity of the matrix as well as the microbial community itself. First, metaproteomes includes up to more than one thousand different species, each containing several hundred proteins, generating a myriad of peptides after digestion  [20]. In addition, many peptides are common to many bacterial species or similar protein sequences, making data processing even more complex with a resultant high false-positive rate. Second, mass spectrometry generates hundreds of thousands of spectra, but the data analysis requires considerable bioinformatic effort to develop algorithms that will allow a reduction in the computational time needed. Third, one of the main elements of a successful metaproteomic study is the availability of a relevant database in order to match sequences with mass spectra.
Moreover, a drawback of metaproteomics is its potential to generate numerous false positives from the use of large databases. In addition, data interpretation is recognized as a major limitation for metaproteomic analysis because huge amounts of data often result in high False Discovery Rates (FDR). Solutions are required to validate protein identifications across different MS and database search algorithms. Furthermore, metagenomics, metatranscriptomics, metabolomics and culturomics data can be integrated with metaproteomic to provide insight into the functioning of bacterial communities in the gut. In light of these considerations, this review presents the current status of shotgun metaproteomic (bottom-up) studies applied to the human gut microbiota and highlights experimental and bioinformatics approaches, providing several examples.
Nevertheless, it is a mode that often presents a loss of information especially in the case of weak peptide signals. Unlike the DDA mode, fragmentation of peptides is performed without pre-selection of the precursor during DIA  [55]. DIA has recently been selected for a metaproteomic study on host-microbial interactions  [56] , but has not yet been used for the study of the human gut microbiota. Using this approach, all peptide ions are fragmented in the collision cell and all the resulting fragment ions are then recorded with alternate scans. This acquisition mode allows the recording of MS/MS data of all peptide signals, which greatly reduces information loss  [57]. However, with this acquisition mode, many fragments are non-informative. Generally, the high number of fragment ions generated complicates the analysis in a classical database search strategy.
Metaproteomics data computation 1. 3. 1 Conventional sequence database search The human gut microbiota is a complex environment and can be associated with a high number of protein sequences. On the other hand, mass spectrometry generates hundreds of thousands of peptide spectra that need to be compared with protein sequences. The number of identified proteins, as well as the identified taxonomies and functional annotations result from protein database selection. Large databases searches (>10 6 sequences), such as NCBI or Uniprot/Trembl are a challenge for metaproteomics studies in terms of computation times and the large number of peptides sequences matches (PSM)  [58, 59]. Another important limitation with large databases concerns the evaluation of FDR, which may lead to the rejection of true protein identifications  [60].
The assessment of FDR is performed by "Target-Decoy" methods  [61, 62, 63]. FDR can then be calculated based on the matching scores. The sequence identifications are filtered according to the matching score to get an FDR lower than the defined threshold (generally <1% on the peptides). However, strict filtering based on the FDR to avoid false-positive matches and the use of a restricted database would compromise the identification of microbial proteins by an increase of false negatives, thus limiting the number of peptide matches. Protein databanks based on metagenomic data tend to get closer to the real protein content of samples. However, this approach may not provide a complete coverage of the protein content in the sample because there can be many different species in a single sample, for most of which a full genome is not available.
Indeed, sequencing, assembly and annotation of the genome still generates incomplete or false metagenomic data  [64]. Over the last few years, software has been developed in order to facilitate the automated analysis of high-throughput mass spectrometry-based proteomic data. Specialized algorithms have been implemented in research software to meet the requirements of mass spectrometry data. Generally, search engines assign a score to the peptide identifications  [65]. The computation of this score differs between search algorithm. The most commonly used software programs in the field of proteomics are: Mascot [66], OMSSA  [67] , Sequest, X!Tandem  [68] , and ProteinLynx Global Server  [69]. Table  1  describes the software used in the case of metaproteomics of the human gut. Despite the availability of software dedicated to metaproteomics, and the advances in DNA and RNA sequencing, metagenomes of the human gut usually contains hundreds of organisms with more than 10 6 proteins sequences.
Customized iterative database approach To meet computational challenges, customized databases are increasingly being used for protein identification. Taxonomic assignment is essential before any sequence search. Indeed, when searching for sequences, peptide sequences were often not proteotypic  [70, 71]. It may, therefore, be difficult to distinguish certain organisms from a single peptide. In fact, shared peptides can be identified (potentially between homologous proteins between species). Thereby, bioinformatic pipelines were developed to assign peptides to their lowest unambiguous taxonomic rank, using an implementation of the Lowest Common Ancestor (LCA) algorithm generating taxonomic profiles at different levels. These pipelines are generally divided into two distinct steps: a first step where the peptides are matched to a reference database containing complete bacterial genome sequences assembled from NCBI or a reference protein database such as Uniprot or from metagenomes; a second step where peptides that passed the script of the first step are assigned to taxonomic ranks in an interactive tree in which a given tryptic peptide occurs  [72].
These pipelines allow the generation of complete taxonomic profiles and lists of species-unique peptides (i. e. , discriminative peptides). Metagenomic taxonomy-guided research strategies are increasingly being used in metaproteomics to improve the construction of protein databases. These strategies interactively explore the taxonomical content of the data using an algorithm based on the LCA peptides in order to assign each peptide to a taxon  [73]. Unipept is an open source web application using the LCA algorithm to determine the taxonomic specificity of peptides  [72]. For example, Tanca et al used the Unipept taxonomic assignment to generate a customized "host-microbiome" database containing sequences from specific microbial taxa and the host  [25]. Thereby, iterative workflows can be used to build specific databases of biological samples.