dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Re-public of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Volcaniclastic and igneous geochemistry Sampling, data acquisition, and data treatment Whole-rock geochemistry data presented here primarily cover the magmatic addition to the shallow (<1. 8 km) crust of the Izu rear arc. Analyses were carried out by pXRF and ICP-AES, and samples included ash and tuff (pXRF n = 88; ICP-AES n = 26), lapilli-tuff and lapillistone (pXRF n = 7; ICP-AES n = 14), igneous clasts (pXRF n = 39; ICP-AES n = 9), and a rhyolite sheet (pXRF n = 1; ICP-AES n = 1). Reconnaissance pXRF and ICP-AES analyses generally agree within <20% (relative) for elements that can be analyzed by both techniques (see Geochemistry in the Expedition 350 methods chapter  [Tamura et al. , 2015a]  for additional information on analytical techniques, instrumentation, and data quality). ICP-AES data reported here include only samples with analytical totals of 100% ± 5% (after ignition).
Unfortunately, this meant that a significant number of altered volcaniclastic sediment and rock analyses had to be discarded because of low totals that likely resulted from problems with ignition at 700°C, which may have only incompletely devolatilized the samples. This comparatively low ignition temperature was required because samples fused and stuck to the crucible at higher Figure  F129. Site U1437 tuffaceous mud/mudstone compositions plotted as K 2 O vs. CaO and Rb vs. Sr. Data were determined by ICP-AES and pXRF. Averages for Izu arc-front basalt-dominant island volcanoes, Izu arc-front rhyolite-dominant submarine calderas, Izu rear-arc lava, Ryukyu dacite, Chinese loess, and marine carbonate are also plotted together with a schematic mixing triangle, suggesting three mixing components.
Data plotting outside the triangle can be mostly explained by simplifications in the end-member compositions. Tuffaceous mud data for fore-arc Site U1436 are plotted for comparison. For data sources see Volcaniclastic and igneous geochemistry,  Scudder et al. (2009) , and  Morse and Mackenzie (1990). Identifying sources for volcaniclastics at Site U1437 requires the detrimental effects of pervasive alteration to be accounted for, which causes element mobility (see also Pore fluid analysis). These effects are most severe for alkali elements and Ba  (Gill et al. , 1994). Elements that are insoluble in seawater (Ti, Al, and Fe; e. g. ,  Stroncik and Schmincke, 2002)  are therefore more reliable indicators for magmatic compositions. This also holds for high-field-strength element ratios such as Zr/Y that remain largely unaffected by alteration  (Gill et al.
, 2009)  ash derived from arc-front volcanoes, but this cannot be confirmed without onshore in situ analyses of glass. The coarse-grained deposits from Units II and IV indicate proximal provenance. Active Manji Seamount chain volcanoes around the time of deposition of Unit II (4. 3-4. 4 Ma) were the Meireki Seamount (3. 76 Ma;  Ishizuka et al. , 1998) , ~20 km to the north, and the Daigo-Nishi-Aogashima Knoll (5. 05 Ma,  Ishizuka et al. , 2003b) , ~40 km to the northeast of Site U1437. Both are rhyolite volcanoes with similar SiO 2 contents (72-76 wt%), whereas Meireki Seamount volcanic rocks have higher K 2 O (~3 wt%) but lower Zr/Y (~2. 8) compared to Daigo-Nishi-Aogashima Knoll (K 2 O ~1. 5 wt%, Zr/Y ~4. 4)  (Hochstaeder et al. , 2001). Although the limited data (five analyses in total) available for both seamounts preclude reliable geochemical matching with Unit II volcaniclastics, they are potential sources for coarse-grained lapilli from Unit II considering their geographic locations, ages, and chemical compositions.
Similarly, single clast compositions of Unit IV can be tentatively matched to available data for Manji Seamount volcanic rocks (~6. 5-6. 9 Ma;  Ishizuka et al. , 2002). Two high-K 2 O volcaniclastic samples resemble the high-K Manji rocks with potassic alteration, whereas most volcaniclastics from Unit IV follow the trend for altered Manji rocks, leading to depletions in CaO with increasing SiO 2 (Figure  F135 )  (Ishizuka et al. , 2002). Unit V comprises primarily tuffaceous mud, and given the pervasive alteration throughout this interval it is difficult to provide an accurate provenance for the volcaniclastic samples. Based on ICP-AES Zr/Y data and the only moderately elevated median Zr/Y from pXRF data  (2. 7 ; n = 39) (Figure  F133 ), we tentatively interpret Unit V to be geochemically transitional, with volcaniclastic intervals and clast compositions that include both rear-arc and arc-front sources.
The BS3 reagent bridges free-amine moieties at surface lysines and N-termini with Cα spacing within approximately 25 Å. Cross-links were assigned with the Protein Prospector platform and a refined scoring function  (Trnka et al. , 2014). The 320,767 initial product ion spectra were searched against a database containing the sequences of the yeast transcriptional machinery as well as 520 randomized decoy sequences, identifying 20,388 potential cross-linked spectra (Figure  1A ). Classification of these matches and the application of quality filters resulted in the identification of 3196 holoenzyme cross-linked spectra at a false discovery rate (FDR, fraction of decoy hits above score cutoff) of 0. 7% (Figure  1A ). These spectra defined 402 unique pairs of linked amino acid residues ('cross-links') with a FDR of 4.
Validation of holoenzyme cross-link dataset The pol II component of the holoenzyme provided an internal control, enabling validation of the crosslink dataset by two approaches. First, the X-ray crystal structure of pol II and also that of the Mediator Head module could be used to assess the validity of cross-links, as 130 of the 402 high confidence cross-links were between residues in these structures. Only five of these 130 cross-links occurred between residues further apart than the 35 Å threshold (Figure  1B ). This number of violations was therefore consistent with the FDR of 4. 1%, estimated from the number of decoy database hits. Second, we constructed an integrative model of pol II based on the cross-link dataset, a cryo-EM electron density map (EMD-1883, 20. 9 Å resolution using FSC 0.
The N-Termini of Med14 and Med17 are components of the Mediator Middle module We augmented our Mediator cross-link dataset with 38 Mediator Middle module cross-links from a dataset collected on a recombinant preparation of S. cerevisiae Middle module containing 6 subunits  (Lariviere et al. , 2013). The 38 cross-links were selected according to our minimal length requirement of four amino acid residues per cross-linked peptide; three cross-links were common to both datasets. This modest overlap is likely due to the use of a different chemical cross-linker, as well as to the presence of additional subunits in our full native Middle module. The complete dataset of 294 intersubunit cross-links defined an interaction map of 19 of the 21 Mediator proteins (Figure  1D ). The two subunits for which there were no inter-subunit cross-links have been crystallized in complexes with other Mediator subunits: Med6 as part of the Head module  (Robinson et al.
Integrative modeling of Mediator complex Although cross-linking was performed on the holoenzyme, about 90% of the cross-links in our dataset were within Mediator modules (intra-modular) or within pol II. Due to the paucity of cross-links between Mediator and pol II, and because a cryo-EM map at sufficient resolution for molecular Mediator subunits are colored according to their location within the Head, Middle and Tail modules. All Inter-subunit cross-links and selected intra-subunit cross-links from the current study as well from  (Lariviere et al. , 2013)   modeling was only available for Mediator without pol II, modeling calculations were performed for Mediator alone. The four-step modeling ('integrative structure determination') procedure (Figure  2 -figure supplement 1) entailed: (1) gathering data; (2) representing and translating the data into spatial restraints; (3) sampling the conformational space and identifying good scoring solutions; and (4) analyzing and assessing the ensemble of solutions.
All IMP input files, scripts, and output models for this study are available at http://salilab. org/mediator/. In step 1, the data comprised our holoenzyme cross-link dataset, X-ray crystal structures of some subunits and subunit regions, homology models for some subunits, and the highest resolution cryo-EM map of free Mediator available  (Tsai et al. , 2014). In step 2, we constructed a set of 21 Mediator subunit model representations (Figure  2 -figure supplement 2). Atomic models from X-ray crystallography or homology models were represented by rigid bodies (Figure  2 -figure supplement 2, blue subunit shading), and unmodeled subunit regions were represented as flexible strings of beads (Figure  2 -figure supplement 2, yellow subunit shading). The entire Head module was represented as a rigid body (based on the X-ray structure PDB 4GWP), with the exception of the unmodeled N-terminus of Med17 (1-181), the last 103 residues of Med6, and other missing regions, all of which were represented as flexible strings of beads.
EM spatial restraints were based on a Gaussian decomposition of the electron density (Materials and methods). The cross-linking data was encoded in a Bayesian scoring function  (Erzberger et al. , 2014; Shi et al. , 2014). The scoring function allowed the independent optimization of the relative weights of arbitrary subsets of the cross-linking data. We split the cross-link dataset into two subsets composed of inter-module (8% of the total) and intra-module (92% of the total) cross-links (Figure  1 -source data 1). In step 3, we conducted a large number of independent modeling runs. The positions and orientations of rigid bodies and flexible strings of beads were repeatedly perturbed in an effort to satisfy the scoring function consisting of the excluded volume, sequence connectivity, EM, and crosslinking restraints.
In fact, the Bayesian scoring function automatically assigned a low scoring weight to the inter-modular cross-links, pointing to an inconsistency of the inter-modular cross-links in the holoenzyme dataset with the EM density map of the free Mediator. The satisfaction of intra-modular cross-links suggests that the arrangement of Mediator subunits within modules of the holoenzyme is similar to that in the free Mediator, whereas the inconsistency of the inter-modular cross-links with the EM map suggests that motions of the modules relative to one another occur upon association with pol II in the holoenzyme. Indeed, EM studies have indicated large motions about hinges between the modules, opening the compact structure of free Mediator for wrapping around pol II in the holoenzyme  (Asturias et al.
(E) Localization by labeling (blue triangle) and domain deletion (areas encircled by light red closed lines) mapped on the EM density map  (Tsai et al. , 2014; Wang et al. , 2014). Straight light-red lines represent domain deletion assays that split the complex. DOI: 10. 7554/eLife. 08719. 007 The following figure supplements are available for figure  2 : Med5, also comes from two-hybrid data  (Guglielmi et al. , 2004). Although it also relates proteins located in different modules (Middle and Tail), the two proteins are less than 30 Å apart in our model. The holoenzyme cross-link dataset includes one cross-link of Med5 (Tail) to Med1 (Middle) and two cross-links of Med5 (Tail) to Med15 (Tail). Because the Med1-Med5 cross-links are inter-modular, they were assigned lower weights by our scoring function and were not satisfied in the final model.
Discussion Through integration of data from diverse sources, we have arrived at a 3-D model of Mediator, which explains virtually all previous findings, and which provides a complete picture, including internal details, of the complex. One source of data was cross-linking, performed on a native holoenzyme, comprising pol II and Mediator, for several reasons: Mediator structure was thereby analyzed in a more functionally relevant state; the native holoenzyme, isolated from yeast, was much more soluble and stable than a reconstituted holoenzyme formed by mixing pol II and Mediator in vitro; and the X-ray crystal structure of the pol II component of the holoenzyme provided an internal control for the modeling process. We restricted our analysis to the internal organization of the three Mediator modules, because their relationship to one another is believed to differ between free Mediator and the holoenzyme, and free Mediator was the source of our EM information.
(2015)  places the Tail module on the opposite side of pol II. We have noted the Mediator conformational change required to satisfy our Tail module-pol II cross-links (Figure  7D ). Tail module interactions with pol II in the vicinity of the active center cleft may be relevant to the role of Tail module subunits Med14, 15 and 16 in transcriptional repression at multiple loci in yeast  (Covitz et al. , 1994; Sussel et al. , 1995). The success of the integrative modeling approach in the derivation of Middle and Tail module structures warrants application to the issues raised here and to other challenging problems. It is of immediate interest to apply IMP to the holoenzyme with an expanded cross-link dataset and a high resolution holoenzyme EM map. Further studies may be directed towards a complete RNA polymerase II pre-initiation complex including Mediator, and to additional biological particles of great size and complexity.
Interpretation of cross-linking-MS data MS Peaklists were generated such that all monoisotopic ions detected within the selection window of the linear ion trap (4 m/z units) were annotated as possible precursors to a given product ion spectrum, as described previously  (Robinson et al. , 2012; Trnka et al. , 2014). Peaklists were first searched against the full SwissProt database to check the composition of the sample. Cross-link searches were then carried out against a target database consisting of the 52 components of the S. cerevisiae Preinitiation Complex. For each polypeptide sequence, 10 randomized versions were generated and concatenated to the target database for a total of 572 sequences (52 target and 520 decoy). Decoy protein sequences were the same length as their corresponding target sequences and sampled from the natural distribution of S.
cerevisiae amino acid frequency. Peaklists were then searched against this database using Protein Prospector (version 5. 13. 1) to assign putative cross-linked peptides  (Trnka et al. , 2014). Carbamidomethylation of cysteine was considered as a fixed modification. N-terminal methionine loss with and without acetylation, peptide N-terminal glutamine conversion to pyroglutamate, oxidation of methionine, and 'dead-end' modification of lysine and the protein N-terminus by semi-hydrolyzed BS3 were considered as variable modifications in addition to cross-linking by BS3. Up to 3 variable modifications per peptide were considered. Mass tolerances of 13 ppm and 20 ppm were used for precursor and product ions respectively. Trypsin specificity with 3 missed cleavages was used to generate theoretical peptides.
85 product ion signals from each MS2 spectrum were used for assessment. For experiments involving isotopic mixtures of cross-linking reagents, the searches were performed twice, once specifying heavy BS3 and once with light BS3. The search results were later merged. Both light and heavy dead-end modifications were considered in each of these database searches. For all experiments, cross-link spectral matches (CSM) were discarded if the following Protein Prospector parameters fell outside the threshold values: peptide score below 20, peptide/protein/ worse_peptide expectation values above 50, and score_difference below 0. A linear support vector machine (SVM) model was constructed to classify CSMs between decoy and target classes  (Trnka et al. , 2014). Briefly, the CSMs were split evenly into two sets.
SVM models were trained on half of the data and evaluated for their ability to correctly classify true negatives (hits to the decoy database) on the test set. CSMs were considered decoy matches if either of the two constituent peptides came from the decoy database. The final SVM model included a metric reflecting the number of product ions matched to the less fragmented of the two peptides (score_difference) and a metric for the overall match of the spectrum (percentage of the total ion current annotated). The inclusion of additional metrics did not increase the predictive power of the model. An SVM decision value of 0 was taken as the acceptance threshold for reporting cross-linked peptides. FDRs were calculated by dividing the number of decoy hits at this threshold by 10 (to account for the larger decoy database size) and dividing by the number of hits to the target database.
Cross-linking results and annotated HCD spectra may be viewed online using Protein Prospector's MS-Viewer program: http://prospector2. ucsf. edu/prospector/cgi-bin/msform. cgi?=msviewer. Search key: qzpxihtngx All raw mass spectrometry files and peaklists used in the study have been deposited to the MassIVE proteomics repository and are available through the ProteomeXchange consortium. MassIVE accession: MSV000079237 ProteomeXchange accession: PXD002723.
Integrative structure modeling Our integrative structure modeling of RNA Pol II and Mediator complexes proceeds through four stages  (Lasker et al. , 2010; Fernandez-Martinez et al. , 2012; Lasker et al. , 2012)  (Figure  2 ): (1) gathering of data, (2) representation of subunits and translation of the data into spatial restraints, (3) configurational sampling to produce an ensemble of models that satisfies the restraints, and (4) analysis and assessment of the ensemble. The modelling protocol (i. e. , stages 2, 3, and 4) was scripted using the Python Modelling Interface (https://github. com/salilab/pmi), version be72c15, a library to model macromolecular complexes based on our open source IMP package (http://salilab. org/imp/), version 829c3f0  (Russel et al. , 2012). Files containing the input data, scripts, and output models are available at http://salilab.
Molecular architecture of the Mediator complex revealed by integrative modeling Stage 1: gathering of data The current study identified 260 BS3 unique cross-linked positional-pairs ('cross-links') corresponding to the Mediator complex (Figure  1D , Figure  1 -source data 1). This dataset was supplemented by 38 unique cross-links (filtered by our acceptance criteria where applicable) from a previous independent study of reconstituted Middle module  (Lariviere et al. , 2013). The experiment of Larivi ère et al. utilized the CBDPS reagent, which is a few Å longer than BS3 and also lysine directed. There were 4 cross-links in common between the datasets producing 294 cross-links in the combined data. These cross-links were identified by a total of 1900 spectral matches, which consist of multiple peptide sequences, charge states, or replicates.
Exhaustive docking of head module within EM density Finally, an 18 Å resolution cryo-EM density map of the Mediator complex was recently determined (EMD-2634, 25 Å resolution using FSC 0. 5)  (Tsai et al. , 2014) , which is currently the highest resolution map available. We used an unbiased, exhaustive computational docking procedure to determine whether the Head module can be docked unambiguously within this map. We employed the Colores search module within Situs 2. 7  (Chacon and Wriggers, 2002)  that performs a 3-stage 6D crosscorrelation search of a single rigid body PDB structure within a map density at a user-defined resolution and angular sampling. Because the EM map resolution is worse than 10 Å, a Laplacian filter is applied by default to maximize the fitting contrast. We ran Colores using the map resolution value (25 Å) and threshold (0.
35) reported in the EM database (using FSC 0. 5 criterion) with the default angular sampling of 30˚. The unnormalized correlation coefficient of the top-scoring docking solution was within the range 0. 3-0. 5 expected for appropriately docked Laplacian-filtered densities. The top scoring solution (unnormalized CC 0. 302) was 24% higher than the next best unormalized docking solution (unnormalized CC 0. 243) and was found to be in close agreement with previous manual docking and localization studies. This agreement gave us high confidence that the Head module could be appropriate docked into the EM map at a single fixed position for the modelling study. The EM density map was segmented into regions corresponding to the head, middle, and tail modules of Mediator by first subtracting the density associated with the docked head module and then apportioning the remaining density between middle and tail modules such that the volume of each was proportional to their molecular weight.
Likelihood function The BS3 cross-link dataset was used to construct a likelihood function that restrained the distances spanned by the cross-linked residues. The cross-link restraints were applied to the fine scale representation for the X-ray structures and comparative models as well as to the coarse scale of the strings. The likelihood function for a cross-link d n is pðd n jX; IÞ = ψ • ð1f n ðXÞÞ + f n ðXÞ • ð1-ψÞ, where ψ is the uncertainty of observing a cross-link, and is approximately equal to the fraction of inconsistent crosslinks. The joint likelihood function pðDjM; IÞ for a dataset D = {d n } of N XL independently observed cross-links is the product of likelihood functions for each data point. We promoted cross-links with high spectral redundancy, and we assigned different weight parameters to the inter-and intra-module cross-links.
The goal was to lessen the impact of inter-module cross-links while benefitting from intramodule ones. Because the cross-links were collected on a sample of Mediator Holoenzyme complex, we expected the inter-module cross-links to be less accurate than the intra-module cross-links in describing the apo-mediator state. This finding is consistent with EM studies in which the presence of RNA pol II induces large motions about the hinge regions between the Mediator modules, while the intra-module topology appears unchanged  (Asturias et al. , 1999; Davis et al. , 2002). We grouped the cross-links into two classes, including a class of inter-module cross-links (23 cross-links defined by 44 spectral matches), and a class with intra-module cross-links (271 cross-links defined by 1856 spectral matches) (Figure  1 -source data 1).