dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, PepPSy has been revealed as an efficient companion tool for neXtProt biocuration and quality management workflows, and for the C-HPP project which aims to get an accurate picture of the validation status of all human protein coding genes. In the near future we will extend the scope of PepPSy to stay abreast of rapid technological advances. We will gather other relevant datasets in PepPSy to cover other biological topics by including other tissues, specific cell types from single-cell RNA-seq data, and chemical-induced/diseaseassociated experimental samples. Finally, we are also currently planning to develop a community tool embedded in PepPSy that will stimulate the annotation of other missing proteins by facilitating collaborative work.
Acknowledgements The authors thank  Antoine D. Rolland ,  Ramona Britto ,  Emmanuelle Becker ,  La€ etitia Guillot  and the  GenOuest bioinformatics facility  for stimulating discussion as well as for beta-testing this web server. They also thank  Lional Breuza  and  Sylvain Poux  from the  UniProt group at SIB  for their input and their work in updating proteins in UniProtKB/ Swiss-Prot. More generally, they thank all the curators from  UniProt  and the CCDS consortia for their dedication in providing up-to-date high-quality annotations for human genes and proteins, thus providing neXtProt with a solid foundation.
We run XID+ on the COSMOS SPIRE maps from Her-MES, using a 24 µm catalogue as a prior and show the marginalised SPIRE colourcolour plot and marginalised contribution to the cosmic infrared background at the SPIRE wavelengths. XID+ is a core tool arising from the Herschel Extragalactic Legacy Project (HELP) and we discuss how additional work within HELP providing prior information on fluxes can and will be utilised. The software is available at https://github. com/H-E-L-P/XID_plus. We also provide the data product for COSMOS. We believe this is the first time that the full posterior probability of galaxy photometry has been provided as a data product.
As part of the Herschel Extragalactic Legacy Project (HELP; Vaccari 2016; Oliver in prep. ), we have developed an alternative prior based approach for source extraction in confusion-dominated maps. Our new method, XID+, is built upon a Bayesian probabilistic framework which provides a natural way in which to introduce additional prior information. By using the Bayesian inference tool,  (Stan, Stan Development Team 2015a,b)  to sample the full posterior distribution, we are also able to provide more accurate flux density error estimates, whilst avoiding some of the issues associated with the maximum likelihood and LASSO fitting approach used by DESPHOT. In this paper, we show that XID+ outperforms DESPHOT when using just positional information. In Section 2 we discuss the algorithm, and show how the software performs on simulated SPIRE maps in Section 3.
XID+ ALGORITHM The basic goal of XID+ is to use the SPIRE maps to infer the likely SPIRE flux of sources we already know about. Bayesian inference is well suited to these requirements. It allows the use of prior information and provides a posterior distribution of the parameter(s) after taking into account the observed data. We also want to provide a framework to do science directly with the maps rather than adding the additional step of first creating catalogues, which in essence is a form of lossy data compression.
To give an example of the dimensionality, our fit to a mock simulation (described in the following section), uses a HEALPix tile at order 9, fitting over 600 sources on average to ≈10,500 pixels at 250 microns, ≈5500 pixels at 350 microns and ≈2500 pixels at 500 microns. That means for each tile we are fitting over 1800 parameters simultaneously. The choice of HEALPix pixel size affects the computational time of XID+. The required CPU time is found to scale linearly with the amount of data (i. e. number of image pixels).
SIMULATIONS In order to test and quantify the performance of XID+, we use simulated SPIRE maps of the COSMOS field, a good example of a deep map i. e. where confusion noise (σ conf. ) is much larger than instrumental noise (σinst. ). In order to get realistic clustering, we use the mock catalogues from the latest version of the Durham semi-analytic model, GALFORM  (Lacey et al. 2015; Cowley et al. 2015). The model is designed to populate Millennium-class, dark matter only, Nbody simulations with a WMAP7 cosmology and minimum halo mass of 1. 9 × 10 10 h -1 M. The dust model is motivated by the radiative transfer code GRASIL  (Silva et al. 1998 ) and can accurately reproduce the predictions for restframe wavelengths λrest > 70µm. We pass this mock catalogue through the HerMES mapmaker pipeline (e.
Performance with a shallower prior list So far, we have shown the performance of XID+ with the type of prior lists that are available for the HerMES fields (i. e. both relatively deep and selected at a similar wavelength to the SPIRE bands). We now demonstrate the expected performance of XID+ for HATLAS fields, where the prior list is likely to be derived from shallow, optical ancillary data. We use the same simulated maps as before, but now our prior list only use sources with an r-band magnitude < 19. 8 or a 250 µm flux > 15. 48 mJy. With this cut, our prior list contains 5536 sources. Not only does this cut mimic the type of ancillary data available in the HATLAS fields (i. e. shallow optical data and additional bright SPIRE sources detected via blind source detection), but is the same cut used to test the Lambda Adaptive Multi-band Deblending Algorithm in R (LAMBDAR  Wright et al.
COSMOS FIELD Having satisfactorily demonstrated the performance on simulations, we have run XID+ on the HerMES COSMOS SPIRE maps from the 2nd Data release. As a prior, we take the MIPS 24 µm catalogue (Le  Floc'h et al. 2009) , which covers an area of 2. 265 square degrees and includes 52,092 sources with a 24 µm flux density above 60 µJy, which corresponds to a signal to noise cut of 3. Figure  9  shows the marginalised probability density of our MIPS 24 µm prior catalogue in SPIRE colour-colour space and is constructed by combining the 1500 samples from the posterior, for all 52,092 sources. The redshift tracks for a red and blue star-forming galaxy spectral energy distribution template, empirically derived from Herschel sources  (Berta et al. 2013) , are over-plotted and run through the highest density region at redshifts of around 2-3.
As a comparison, we show the true colour distribution for our mock simulation used in section 3 and the corresponding distribution from the XID+ fit. The probability distribution from the fit to the simulation is far wider than the truth. This is not surprising as, at present, the three SPIRE bands are being fit independently, and so any correlation in colour is being dispersed by confusion noise and non-detections. Interestingly, the fit to the real COSMOS data shows a more constrained probability distribution. Whether this is a consequence of slightly different selection, or less variation in SED shape will require further investigation. Table  1  shows the total contribution to the cosmic infrared background (CIRB) at 250, 350 and 500 µm from our MIPS 24 µm prior catalogue, alongside the FIRAS absolute measurements from Lagache &  Puget (2000) , the contribution to the CIRB from SPIRE resolved sources  (Oliver et al.
CONCLUSIONS In this paper we have introduced the prior based source detection software, XID+. By using the Bayesian inference tool Stan, we are able to fully sample the posterior probability distribution, which in turn gives a better understanding of the uncertainty associated with the source flux. Having run XID+ on simulated maps, we have shown this is extremely advantageous for maps that are confusion limited, such as the Herschel observations that are part of HerMES. In comparison to the current maximum likelihood based software DESPHOT, XID+ performs far better in all three main metrics; flux accuracy, precision and uncertainty accuracy. We have run XID+ on the HerMES COSMOS SPIRE maps from the 2nd Data release, using the MIPS 24 µm catalogue (Le  Floc'h et al. 2009 ) as our prior. Using the full posterior, we have created a marginalised SPIRE colourcolour plot, illustrating the probability distribution of our MIPS 24 µm catalogue in SPIRE colour-colour space.
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7/2007-2013/  under grant agreement no. 607254. This publication reflects only the author's view and the  European Union  is not responsible for any use that may be made of the information contained therein. Seb Oliver  acknowledges support from the  Science and Technology Facilities Council  (grant numberST /L000652/1 ) and  Michael Betancourt  is supported under  EPSRC  grant  EP/J016934/1. HCSS/HSpot/HIPE  are joint developments by the  Herschel Science Ground Segment Consortium , consisting of  ESA , the  NASA Herschel Science Center , and the  HIFI,PACS  and SPIRE consortia. SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including  Univ.
In the present work, the energetic performances of Si-FER-type zeolite are studied by means of MgCl 2 21H 2 O (2. 65M) aqueous solution intrusion-extrusion experiments. MgCl 2 was selected as electrolyte -instead of the previously used LiCl -because of the higher diffusion factor of Mg cation, which makes the Rietveld refinement of the XRD data relative to the intruded system easier. The samples, before and after such experiments, were fully characterized by conventional XRPD, thermogravimetric analyses and N 2 adsorption-desorption experiments. In situ HP synchrotron XRPD experiments were carried out to obtain structural information during the intrusion-extrusion process. To our knowledge, this is the first case of a detailed structural description of the nature and location of the species intruded under pressure in zeolite porosities using an electrolyte solution as penetrating medium.
In situ HP XRPD experiments. In situ HP synchrotron XRPD experiments were performed on the SNBL1 (BM01a) beamline at ESRF (Grenoble), with fixed wavelengths of 0. 7023 Å using modified Merril-Bassett DACs  [32]  and a MgCl 2 21H 2 O aqueous solution as Pressure Transmitting Medium (PTM). Pressure was calibrated using the ruby fluorescence method  [33]  on the non-linear hydrostatic pressure scale  [34]. The estimated precision in the P values is 0. 05 GPa. The experiment was performed in the pressure range P amb -2. 6 GPa. Some patterns were also collected upon decompression from the highest pressure to P amb. The diffraction data were collected in the Debye-Scherrer geometry on Pilatus IP detectors (with pixel dimensions of 172 m x 172 m, respectively) at a fixed distance of 230 mm. One-dimensional diffraction patterns were obtained by integrating the two dimensional images with the program FIT2D  [35].
Unit cell parameters were determined by Rietveld profile fitting up to 1. 47 GPa. The data quality at higher pressure and the crystallization of a MgCl-bearing phase from the solution prevented further refinements. The Si-FER sample underwent a phase transition from Pmnn to P12 1 /n1 s. g. above 0. 28 GPa (280 MPa). The quality of the collected powder data allowed the structural refinement to converge successfully for the experiment at 0. 28 GPa. On the pattern collected upon pressure release only a Lebail profile fitting was possible, due to the low quality of the data and the cell parameters were successfully refined in the P 12 1 1 s. g. Rietveld profile fitting was performed using the GSAS package  [36]  with the EXPGUI  [37]  interface. The starting atomic coordinates were taken from the structural model  [29]  and the intruded extraframework species were located after the inspection of the Fourier Difference Map of the electronic density.
The textural properties of the different Si-FER samples were determined from N 2 adsorption-desorption measurements. In order to avoid a dehydroxylation process, the samples were outgassed at 90 °C under vacuum. The isotherms are reported in Figure  5. They are of type I, characteristic of microporous materials. No significant changes are observed between the non-intruded and intruded-extruded samples. In both cases, the micropore volume is quite similar and close to 0. 12-0. 13 mL/g which is in quite good agreement with the published data  [41]. At high relative pressures (p/p° = 0. 95) and particularly for the intruded-extruded sample, an increase of the adsorbed volume corresponding to an interparticular porosity is observed. Such a porosity might be explained by the breaking of some ferrierite crystals under high pressure.
ACKNOWLEDGMENTS The BM01 beamline at the  European Synchrotron Radiation Facility  is acknowledged for allocation of experimental beamtime. This work was supported by the  Italian MIUR , within the framework of the following projects:  PRIN2010-11  '' Dalle materie prime del sistema Terra alle applicazioni tecnologiche: studi cristallochimici e strutturali '';  FIRB, Futuro in Ricerca ''Impose Pressure and Change Technology''  ( RBFR12CLQD ).
The 53 class I COPIA families and the 40 class II Mutator-like (MuDR) families are the most mobile, as they account for 1408 and 729 of the 2835 non-reference TE insertions with TSDs identified in total, respectively (Figure  1H  and Figure  1 -source data 2 and 3). However, the number of non-reference insertions per accession is always small (<16) for any given family (Figure  1 -source data 2), thus suggesting a lack of recent transposition bursts. The ability to detect non-reference TE insertions with TSDs using split-reads is strongly dependent on read depth as well as sequence composition at the insertion site (Figure  1 -figure supplement 2A;  Henaff et al. , 2015). To assess the extent of this limitation, we used the assembled Ler-1 genome sequence recently obtained using PacBio long reads (see 'Materials and methods').
Data sources Whole genome sequencing data (Illumina short paired-end reads) for A. thaliana accessions were obtained from the NCBI SRA archive for 210 accessions collected worldwide  (Schmitz et al. , 2013)  SRA012474 as well as for the 180 Swedish accessions  (Long et al. , 2013; SRA052536). Illumina short paired-end reads from Ler-1  (Schneeberger et al. , 2011)  and the assembled Ler-1genome obtained using PacBio long reads (unpublished) were retrieved from http://1001genomes. org/data/MPI/ MPISchneeberger2011/releases/current/Ler-1/Reads/ and https:// github. com/ PacificBioscienc es/ DevNet/ wiki/ Arabidopsis-P5C3, respectively. High quality SNP imputations for these accessions were obtained from the 1001 Genomes Project (http://1001genomes. org/). Processed MethylC-seq and RNA-seq data from accessions collected worldwide  (Schmitz et al.
, 2013)  were obtained from NCBI GEO (GSE43857 and GSE43858, respectively). Processed BS-seq data for the met2a mutant  (Stroud et al. , 2013)  were obtained from NCBI GEO (GSE39901). Flowering time data were retrieved from the phenotypic information deposited at https://www. arabidopsis. org/servlets/Tair-Object?type=germplasm&id=6530472136 as well as  (Li et al. , 2010; Lempe et al. , 2005). Local climate data and coordinates for the 211 accessions analyzed using CNV were retrieved from  (Hancock et al. , 2011)  (http://bergelson. uchicago. edu/wp-content/uploads/2015/04/all-vars948_notnormd_011311. txt. zip). TE-capture sequencing data have been deposited in the ENA short read archive under project number PRJEB11706 and accessions codes ERR1121179 to ERR1121190.
Estimation of sensitivity The rate of false negatives was estimated indirectly by first searching for TE insertions with TSDs that are present in the Col-0 reference genome but absent from the Ler-1 assembled genome sequence recently obtained using PacBio long-reads (unpublished). Specifically, annotated TE sequences together with 1 kb of upstream and downstream sequences were aligned using BLAT to the Ler-1 genome sequence. Heavily truncated TE sequences (covering less than 50% of full-length copies) were not considered, as most of these are unlikely to have been mobilized recently. Out of the 7931 TE sequences analyzed, 200 are not present in the Ler-1 genome sequence and among these, 142 have TSDs (Figure  1 -source data 4). This value was then compared to that obtained by mapping Col-0 Illumina short split-reads to the Ler-1 genome sequence.
GWAS of CNVs Unlike for the initial CNV analysis, we considered this time only the annotated TE sequences that are at least half the size of the corresponding reference TE sequence (obtained from the A. thaliana RepeatMasker repeat library, http://repeatmasker. org) as this improved significantly the correlation between CN and the number of non-reference TE insertions. For each of the 113 families identified as mobile by both the split-reads approach and TE-capture, aggregated CN was obtained for each accession by summing the CNs estimated for each annotated copy. GWAS on these CN values was carried out with imputed SNPs (MAF > 5%) from the 211 accessions collected worldwide  (Schneeberger et al. , 2011; Schmitz et al. , 2013) , the 180 Swedish accessions  (Long et al. , 2013)  and the joined dataset using a linear mixed model (LMM)  (Kang et al.
Reconstruction of the historical recombination landscape Historical recombination was estimated using LDhat  (McVean et al. , 2004)  as described before  (Choi et al. , 2013). Briefly, biallelic SNPs (MAF ! 0. 1) from the 210 accessions collected worldwide were selected and split into blocks of 5000 SNPs with overlap of 500 SNPs. SNPs located in inner pericentromeres were excluded from the analysis. Blocks of SNPs were formatted using the 'convert' program. A likelihood lookup table was generated for 210 individuals with program 'complete' using the following parameters: -n 210 -rhomax 100 -n_pts 100 -theta 0. 001. Population-scaled recombination rate (r/kb, r = 4Ner, where Ne is the effective population size and r is the per-generation recombination rate) was calculated using the 'interval' program with the following parameters: -its 60000000 -bpen 5 -sam 40000.
Identification of DNA sequence motifs overrepresented at nonreference TE insertion sites with TSDs Sequence spanning non-reference insertion sites were analyzed using Bioprospector Release 2  (Liu et al. , 2001)  using the following parameters: -r 1 -n 200 -a 1 -W 'TSD size'. Background sequence distribution for the reference genome was obtained using the 'genomebg' program. Sequence logo was produced using the seqLogo package version 1. 36. 0.
Gene expression profile of genes affected by long-distance DNA methylation Expression data in the Col-0 accession were obtained from http://www. weigelworld. org/resources/ microarray/AtGenExpress/AtGE_dev_gcRMA. txt. zip/at_download/file for the 224 and 162 genes affected respectively by short-and long-distance DNA methylation in accessions with non-reference TE insertions with TSDs (by definition, these TE insertions are absent in Col-0). Triplicate data for each developmental time point was averaged and then normalized across the developmental timepoint series. Average expression level was then calculated for each time point for all genes affected by short-distance DNA methylation and compared to the average calculated for all genes affected by long-distance DNA methylation. Statistical significance of differences between these two averages was calculated using the non-parametric Mann-Whitney U test.
Determination of FLC haplotypes Haplotype analysis was performed as described previously  (Li et al. , 2014). Briefly, SNPs within 100 kb of FLC were retrieved for the 211 worldwide accessions and used as input into fastPHASE version 1. 4. 0  (Scheet and Stephens, 2006). Default parameters were kept, except for the -Pzp option. For each SNP, haplotype membership with the highest likelihood was assigned. The following previously published datasets were used:.
2-Reads aligning partially over TE extremities are extracted and clipped. 3-The unmapped portion of these split-reads are re-mapped on the Arabidopsis reference genome. 4-Nonreference TE insertions with TSDs are identified by searching for overlapping clusters of 5' and 3' split-reads. (D) Genome browser tracks showing splitreads for two non-reference ATCOPIA31 insertions and TSD reconstruction. Figure 1-source data 2 contains the coordinates of all non-reference TE insertions with TSDs. (E) Distribution frequency of allele counts for non-reference TE insertions with TSDs. (F) Number of mobile TE families per accession identified using split-read and TE-sequence capture. (G) Cumulative plot of the number of mobile TE families detected with increasing numbers of accessions. (H) The total number of non-reference TE insertions with TSDs is indicated in relation to the number of accessions with such insertions, for each of the 131 mobile TE families.
Acknowledgements We thank members of the  Colot lab and especially Mathilde Etcheverry  for discussions. We thank  Edith Heard  and  Pierre Capy  for critical reading of an earlier version of the manuscript. This work was supported by the  European Union Seventh Framework Programme Network of Excellence Epi-GeneSys  ( Award 257082 , to VC), the  Investissements d'Avenir   ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02 PSL* Research University  and  ANR-12-ADAP-0020-01  (to VC) and the  Chaire Blaise Pascal  (to RAM). LQ was the recipient of postdoctoral fellowships from the  ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02   PSL* Research University. ABS was the recipient of postdoctoral fellowships from the  ANR-12-ADAP-0020-01  and from the  Brazilian National Council for Scientific and Technological Development (CNPq).
Tikhonov regularization analysis The Tikhonov program used in our analysis closely followed the one found in DeerAnalysis. 36 This implementation was faster and more flexible with regard to the use of non-conventional kernels. Our implementation was validated by comparing the results against those obtained using DeerAnalysis for a set of PELDOR measurements. Our Fortran-90 program was an adaption of the program FTIKREG  39  which minimizes a functional: G(P) = JKP À FJ 2 +lJLPJ, (6) where F(T) = K(T,R)P(R) (7)   and for PELDOR measurements, as described previously, 36 F(T) is the PELDOR form factor, P(R) is the distance distribution and K(T,R) is the kernel function. The conventional kernel function that was used was identical to that used in DeerAnalysis. 36, 40  was the second derivative operator.
Acknowledgements This work was supported by a joint  ANR/DFG  project ( MnHFPEL-DOR ,  ANR-DFG Chemistry 2011-INTB-1010-01 ) and  DFG  ( PR 294/ 14-1 ), the  French Infrastructure for Integrated Structural Biology (FRISBI ,  ANR-10-INSB-05-01 ), the  CNRS ''Interface PCB'' program , the  DFG   Priority Program 1601  ( New Frontiers in Sensitivity for EPR Spectroscopy ). The W-band EPR spectrometer was funded by the  Region Ile-de- France ''Sesame'' program , the  CEA , and  CNRS  and the J-band EPR spectrometer by the  Cluster of Excellence Frankfurt (CEF) Macromolecular Complexes.
Ambient noise data and preprocessing Most of the ambient noise analyzed in this study was recorded by 18 broad-band stations operated by the Integrated Plate Boundary Observatory Chile (IPOC), CSN and ONEMI over a period of 3 yr (2012 January 1-2014 December 31). This primary dataset is supplemented by recordings from 29 broad-band stations deployed by the DGF, CSN and ONEMI near Pisagua for 4 months  (2014 March 29-2014 July 31)  and 12 broad-band stations of the Iquique network operated by GFZ Potsdam for 7 months (2012 January 10-2013 September 5). The 59 total stations in these combined networks (Fig. 3 ) provide 1422 contemporaneous station pairs from which Green's functions may be estimated. For the most part we followed the pre-processing steps described in  Bensen et al. (2007)  to generate estimated Green's functions (EGFs) from the vertical component of cross-correlated noise, using a version of the CU-Boulder ANCC software, 1  modified to permit sample rates and frequency bands appropriate for our data.