dataesr/scientific-paragraphs-categorization huggingface dataset:
This assumption is supported by the FTFP_BERT physics list, for which the value of m for pions agrees within uncertainties with the value of m for protons, extracted from the dependence on available energy. The value of the parameter m for pions from data is higher than the estimate from FTFP_BERT. Both parameters for protons, extracted from data and FTFP_BERT, agree within uncertainties. The ratio of the calorimeter response of protons to pions of the same initial energy is called the p/π ratio (p/π = E p E π ). Figure  4 (a) shows the p/π ratio extracted in this study for the Fe-AHCAL together with the results obtained for two other iron-scintillator calorimeters: the CDF End Plug hadron calorimeter  [24]  and the ATLAS Tile hadron calorimeter  [11]. The results obtained from the measurements performed with different calorimeters are in good agreement.
The difference between pion and proton response, which remains after taking into account the available energy effect, amounts to 2-5% as follows from Fig. 4(b). This remaining difference is related to the lower probability of π 0 production in the interaction of a proton with a nucleus  [23]. Both physics lists tend to underestimate the p/π ratio above 20 GeV. The FTFP_BERT physics list underestimates the p/π ratio due to an overestimate of the pion response while the proton response is reproduced within uncertainties. The predictions of QGSP_BERT are closer to the data because both pion and proton response is overestimated by this physics list above 20 GeV. At the same time, abnormal behaviour is visible around the model transition region in the QGSP_BERT physics list.
Longitudinal shower depth The observable Z0 does not depend on the shower start position and is convenient for the comparison of showers induced by different types of hadrons with different nuclear interaction lengths. Typical distributions of Z0 are shown in Fig. 6  for pions and protons. The longitudinal shower depth of a pion shower tends to be closer to the shower start than that of a proton shower. The mean longitudinal shower depth Z0 is extracted from the distributions shown in Fig. 6. The energy dependence of Z0 in Fig. 7  increases logarithmically with energy from ∼1λ eff p at 10 GeV to ∼1. 5λ eff p at 80 GeV. Figure  8  shows the simulation to data ratios. The QGSP_BERT physics list underestimates Z0 by ∼5-7% for both pions and protons above 20 GeV. The FTFP_BERT physics list gives a very good prediction of Z0 for pions and slightly overestimates the rate of growth for protons.
The spatial shower development for both types of hadrons is much better predicted by the FTFP_BERT physics list, especially above 20 GeV. The event-by-event fluctuations of the spatial characteristics are also quite well reproduced. The simulated showers are still narrower than those observed in data but the FTFP_BERT physics list predicts the mean shower radius with an accuracy of ∼5-7%. The most significant discrepancy between test beam data and simulations is seen in the absolute energy resolution for pions. The simulated width of the energy distribution of pion showers increases faster with pion initial energy than observed in data. The relative energy resolutions for pions and protons are in good agreement and are well reproduced by simulations with both physics lists studied.
Acknowledgments We would like to thank the technicians and the engineers who contributed to the design and construction of the prototypes. We also gratefully acknowledge the DESY and CERN managements for their support and hospitality, and their accelerator staff for the reliable and efficient beam operation. The authors would like to thank the  RIMST (Zelenograd) group  for their help and sensors manufacturing. This work was supported by the  Bundesministerium für Bildung und Forschung, Germany ; by the the  DFG cluster of excellence 'Origin and Structure of the Universe' of Germany ; by the  Helmholtz-Nachwuchsgruppen  grant  VH-NG-206 ; by the  BMBF , grant no. 05HS6VH1 ; by the  Alexander von Humboldt Foundation  (including Research Award IV, RUS1066839 GSA); by the  Russian Ministry of Education and Science contracts 4465.
We identified 1,629 MOTUs, among which the dominant groups were Metazoa (with representatives of 19 phyla), Alveolata, Stramenopiles, and Rhizaria. There was a marked small-scale heterogeneity as shown by differences in replicates within corers and within localities. The spatial variability between canyons was significant, as was the depth component in one of the canyons where it was tested. Likewise, the composition of the first layer (1 cm) of sediment was significantly different from deeper layers. We found that qualitative (presence-absence) and quantitative (relative number of reads) data showed consistent trends of differentiation between samples and geographic areas. The subset of exclusively benthic MOTUs showed similar patterns of β-diversity and community structure as the whole dataset.
A hypervariable fragment of the 18S rRNA gene in the v7 region (usually 100-110 bp) was amplified with a new universal primer pair for eukaryotes designed by PT and EC using the eco-Primers program  [32]. This program optimizes the primers by taking into account both the conservation of the primers' targets and the variability of the amplified region. The primer pair was labelled 18S_allshorts (Forward 5'-TTTGTCTGSTTAATTSCG-3' and Reverse 5'-GCAATAACAGGTCTGTG-3'). Primer logos  [56]  and other parameters reflecting the specificity and suitability of these primers for eukaryotes are presented in S2 Fig. Amplification  was performed in a total volume of 30 μl with 0. 24 μl of AmpliTaq 1 Gold DNA polymerase (Applied Biosystems) 5U/μl, 1. 2 μl of 5 μM of forward and reverse primers mix, 3 μl of buffer 10x, 3 μl of MgCl2, 2.
Read filtering and taxon assignment The sequence reads were analysed using the OBITools software  [57]. First, the paired ends of each sequence were assembled. Exact matches of sample tags were then used to assign reads to samples, and low-quality sequences (fastq average quality score<35) and sequences of nonsuitable length (<80 bp) were removed. Strictly identical sequences were dereplicated and assigned a count number per sample. Rare sequences (i. e. , sum of counts<10) were eliminated. The obiclean program was then run to detect amplification/sequencing errors and chimeric sequences. This denoising procedure gives each sequence within a PCR product the status of ''head" (most common sequence among all sequences that can be linked with a single indel or substitution), ''singleton" (no other variant with a single difference in the relevant PCR product), or ''internal" (all other sequences not being ''head" or ''singleton", i.
e. most likely corresponding to amplification/sequencing errors)  [57]. Only sequences that were more often 'head' or 'singleton' than 'internal' in the global dataset were kept for the following steps. The resulting dataset was further checked with UCHIME  [58] , using both de novo and reference-based searches, and no remaining chimeric sequences were detected. The sequences were then clustered (heuristic clustering in sumaclust program) using a cutoff value of 96% sequence similarity. The rationale for the choice of the threshold value is presented in S1 File. For each cluster, the sequence with the higher number of reads was taken as the cluster representative for further analyses, and the counts of the remaining sequences were added to it. The taxonomic assignment was performed using the ecotag program  [59]  which finds similar sequences in a reference database and assigns the query sequence to their most recent ancestor using the NCBI taxonomy database  [60].
The reference database was constructed with the program ecoPCR  [61]  for the 18S fragment based on the release 117 of the EMBL database. Only the sequences with a best identity match of 90% or more were kept at this step. This filtering would remove any remaining PCR artefacts, but at the cost of losing organisms from deep branches or those not adequately represented in reference databases. The trade-off between excluding artefacts and genuine, albeit rare, sequences is not easily solved  [62]. Our 90% threshold represents a conservative approach, following other studies in benthic communities (e. g. ,  [2, 8, 9, 15] ). In the interval 90-100%, the frequency of MOTUs showing a given similarity was quite evenly distributed, with an increase at similarities 98%, which made up for 25% of all MOTUs (S3 Fig).
Once the taxa list was acquired, further filtering processes were carried out to refine the dataset. We set to 0 the counts of sequences per sample that might correspond to samples cross-contamination (due to the tagging system used to identify the samples, and to the library preparation step before sequencing): for each sequence the counts per sample were ordered from lowest to highest and those corresponding to a cumulative frequency inferior to 0. 01 were set to 0. Second, sequences present in the negative controls after the previous step were removed. Finally, we manually reviewed retained sequences and eliminated clearly non-marine organisms (these could be contaminations or DNA of continental origin present in the sediment). The final retained sequences were considered as molecular operational taxonomic units (MOTUs) on which we performed the analyses.
These sequences, as well as the OBITools commands used, have been uploaded to the DRYAD repository (doi: 10. 5061/dryad. 520gq) For the analysis of distribution patterns, we grouped our sequences following the major Super-Groups of eukaryotes suggested by  [63] , with one exception: we split the Super-Group Opisthokonta into Metazoa, Fungi, and other Opisthokonta. As we were particularly interested in the Metazoa, we also analysed their abundance data by phylum.
Data analysis A table was constructed with the number of reads of each MOTU per sample. This dataset was used both qualitatively and quantitatively. A similarity index based on presence-absence data (Jaccard index) was obtained with the Primer v6 statistical package  [64]. Quantitative inference using number of reads of a given MOTU is controversial  [5, 65, 66]  and it has been suggested that relative abundances, rather than absolute values, are more reliably obtained from metabarcoding data  [6, 13]. We therefore calculated the relative number of reads of each MOTU per sample, and averaged these values over samples to obtain a measure of global relative abundance for each MOTU or group of MOTUs. The relative number of reads was square-root transformed and used to calculate a quantitative similarity matrix using the Bray-Curtis index in Primer v6.
To assess the small-scale heterogeneity of the samples, we compared the similarities found with both qualitative and quantitative data using different groupings of samples. Thus, we compared similarities within the same box-corer in the INDEMARES project (three replicates from the same haul) with the ones found in the DOSMARES samples (two independent hauls from the same locality), and with the similitudes obtained when comparing samples from different localities in the same zone (CC, BC, OS, MC, ST), different zones within the same area (Iberian Peninsula and Balearic Islands), and different areas. Rarefaction curves were obtained with the vegan 2. 0-7 package for R  [67] , using function rarecurve, to assess the gain in MOTU richness as we increase the number of reads for each sample. We also used the specaccum function, using random addition of samples and 1000 permutations, to investigate the relationship of MOTU richness with increasing numbers of samples.
We also analysed the geographic span of the different MOTUs in terms of the number of localities and zones where a given MOTU is present. MOTU richness (rarefied to the number of reads corresponding to the sample with less reads to allow for statistical comparisons) was also calculated using the function DIVERSE of Primer v6. These values were compared among zones (all samples), across layers of sediment (DOSMARES samples), and among depths (DOSMARES samples). Permutational analyses of variance were performed with the Windows PERMANOVA module  [68]  incorporated in Primer v6. We first combined the DOSMARES and INDEMARES samples to test the effect of zone (fixed factor with five levels: BC, OS, CC, MC, ST) and locality (nested within zone). PERMANOVA analyses were also performed for the DOSMARES samples alone to test the effect of layer (fixed, with three levels: first cm, second cm, and rest of the sample) and depth (fixed, 7 levels).
We performed the nmMDS ordinations on distance matrices based on the Jaccard coefficient (in distance form) using presence-absence data, and on matrices based on the Bray Curtis index (in distance form) taking into account relative abundances. MDS configurations were obtained for the different localities studied (both projects pooled) and, for the DOSMARES project alone, for the different layers of sediment and the different depths sampled. The latter analysis was performed separately for the first layer of sediment and for the other two layers pooled (following PERMANOVA results, see below). The different configurations obtained with presence-absence and relative abundance data were compared using Procrustes analyses  [70]  as implemented in vegan (function Protest). Function envfit of vegan was used to correlate depth with the ordinations obtained for the two sediment layers considered, and to obtain and plot the corresponding gradient vectors in nmMDS ordinations.
Additionally, a Mantel test was conducted to check for correlations between geographic distances among localities (in kilometres) and MOTU dissimilarity using the Jaccard index (transformed to distance). The Mantel test was performed using the ade4 package for R (function mantel. rtest) and its significance was tested by permutation  [71]. To gain insights into the ecological function of the main groups of organisms, a separate analysis was performed using only MOTUs that could be assigned to benthic groups. We considered as benthic MOTUs those with a known holobenthic cycle or, if pelago-benthic, with restricted dispersal phase (lecitotrophic larvae). This classification was based on an exhaustive perusal of available literature on the closest GenBank matches of each MOTU and on general characteristics of the group (Class, Family, Genus) where the MOTU belongs.
Results The MiSeq partial run produced a total of 3,840,493 reads. After sample assignment, quality and sequence-length filtering, and elimination of rare sequences, we were left with 2,720,318 reads corresponding to 12,751 different sequences. Further sequence error pruning and chimera removal (obiclean) reduced the number of sequences finally retained to 8,215. These sequences were clustered (96% similarity cut-off) and, after final checking, 1,629 MOTUs with >90% sequence identity to their best hit were retained. They belonged to 10 eukaryote Super-Groups (modified from  [63] , see Methods). The mean number of reads and MOTUs per sample were 11,412±90. 51 and 221. 44±11. 69 (mean±SE). The controls featured less than 750 reads per sample on average. The list of the MOTUs considered, their taxonomic assignment, and the number of reads in the samples is available in the Dryad repository (doi:10.
5061/dryad. 520gq). The ranking of the different Super-Groups, as per total number of MOTUs, is plotted in Fig 1. The relative number of reads per sample is also represented. Metazoa was the most abundant group for both variables. However, they had a disproportionally higher abundance when considering relative read numbers, likely due to their bigger sizes which translate into higher ribosomal DNA presence in the sediment environment. Alveolata (mostly dinoflagellates and ciliates), Rhizaria and Stramenopiles were the next most abundant groups (again both in terms of total MOTUs and relative abundance, Fig  1 ). The other Super-Groups had a marginal representation in our samples. The proportion of MOTUs of the different Super-groups as per sampling locality is shown in S4 Fig. Within metazoans, the most MOTU-rich phylum was Arthropoda (Crustacea), followed by Annelida (Polychaeta) and Nematoda.
Analyses of main groups The benthic MOTUs and the most MOTU-rich groups of metazoans (phyla Annelida, Arthropoda, and Nematoda) were analysed separately. We identified 742 MOTUs as benthic organisms. Their pattern of similarity among samples followed the same decreasing trend seen in the global dataset (S8 Fig). The same pattern was found for the major metazoan phyla, although in the case of annelids there was no decrease in the similarity when comparing between zones and between areas. In addition, in this group the similarity of mini-corers within the same boxcorer was higher (48. 02%) than in the global dataset (31. 67%) or in the other phyla considered (less than 30%). The spatial distribution of the MOTUs in these groups (  Fig 7)  showed that in all cases the highest frequency of occurrence was at two localities (as in the global dataset) and the mean span was ca.
5. 5 localities for benthic MOTUs and 5. 3 for annelids and arthropods. Nematodes were the group less widespread, with each MOTU present in an average of 3. 9 localities and only one MOTU present in more than 11 localities. Per zones, benthic MOTUs and nematodes had the highest frequency of occurrence at one or two zones, while annelids and arthropods tended to be more widespread (with a higher frequency of MOTUs common to two and three zones,  Fig 7). The nmMDS ordinations of the main groups (Fig  8 ) recovered a similar configuration as in the global dataset for benthic MOTUs, while the differentiation between zones appeared less distinct with the other groups, particularly the separation between the two zones in the Balearic Islands (Fig  8 ). Nevertheless, PERMANOVA analyses showed a significant effect of the factor zone in all cases (S2 Table ), and an heterogeneous level of dispersion in benthic taxa, annelids and arthropods.
We tried to avoid these shortcomings using a stringent filtering procedure. For instance, we eliminated not only singletons  [79] , but all sequences with less than 10 reads. We also discarded all reads with less than 90% similarity with their best hit in GenBank. The associated risk it that they may correspond to deeply divergent lineages  [8, 39, 46 ] not adequately represented in the current databases. The ecotag procedure assigns sequences to a taxon that is the most recent common ancestor of the sequence in the database with the highest similarity (best hit) to the query sequence, and all other sequences in the database within this level of similarity to the best hit. This procedure therefore places robustly a sequence in a given taxonomic rank. When considering sequences whose best hit was <90%, however, ecotag could not assign a rank (other than Eukarya) to most (92%) of them; another 4% could be reliably placed within Metazoa, but of these the majority could not be assigned to a phylum.
Lacking a robust taxonomic placement, we preferred not to use these sequences. The compromise between reliability and efficiency is not easy to reach  [62] , and the need for comprehensive reference databases should be stressed, in particular for deep-sea communities. Any metabarcoding dataset is as good as the reference database is. In general, we have taken conservative options at the several steps involved in the analysis. We consider, then, than the final dataset is reliable. Another aspect that needs to be taken into consideration is the potential of metabarcoding for quantitative analyses  [5, 66]. Assessing abundance of organisms using read number is risky and needs previous calibration, as the number of reads will depend on the size of an organism, on differences in rDNA copy number among groups  [3, 80] , and on the potential primer bias (leading to unequal amplification success in different taxa).
g. [7, 83] ), nematodes are the most abundant group in meiofaunal assemblages in general  [38, 41, 84]. In our samples, however, Nematoda ranked third in total MOTU richness, and seventh among Metazoa in relative read abundance. The latter is expectable given than the number of reads may be down weighted by their small size. In other metabarcoding studies nematodes dominated the meiofauna  [8, 9] , although not necessarily at all sites or times  [10, 16]. It should be noted that in all these works the meiofaunal fraction was physically separated prior to extraction of DNA directly from organisms, while we used unsieved samples and extracellular DNA. We had, therefore, DNA from organisms of all size fractions, not just meiofauna. Our primers were adequate for nematodes, as checked with the available databases, so theoretically there should be no amplification bias against this group.
Surprisingly, when we analysed MOTUs with supposedly restricted dispersal (benthic), the pattern of differentiation was very similar to the one obtained with the whole dataset (including DNA from planktonic and widely dispersing organisms). This is likely because our study area is split by two permanent hydrographic density fronts: the Catalan front over the Iberian Peninsula slope, and the Balearic front over the Balearic Islands slope  [85]. This double barrier is probably unpassable by small pelagic organisms, and as a result both benthic and pelagic organisms in our samples tended to be distributed over a relatively restricted number of study zones. When we analysed separately the three main metazoan groups, the overall picture obtained is similar to the one found with the general dataset.
Deep-sea environments cover more than 65% of the earth's surface and support key ecosystem services, yet they are amongst the least known habitats on earth. It has been estimated that less than 1% of deep-sea species are presently described  [8, 37, 97]. Although there remain a number of biases and pitfalls to be addressed (e. g. [3, 79, 98, 99] ), metabarcoding can produce comprehensive biodiversity datasets in a short time frame. Morphological approaches are invaluable, but they are more time-consuming and require taxonomic expertise often difficult to obtain. Recent works have shown that molecular results compare well with high quality datasets obtained by conventional methods  [11, 24, 100, 101]. We have shown how a metabarcoding approach can provide community characterization and αand β-diversity values for deep-sea environments, which are the raw materials on which ecology and management build.
0, 20 mM EDTA pH 8. 0, 2 % SDS) and incubated at 65 C for 13-15 h. The sample was diluted with same volume of TE (10 mM Tris pH 8. 0, 1 mM EDTA pH 8. 0) and digested by RNase A (0. 2 mg/ml, 2 h, 37 C) and Proteinase K (0. 2 mg/ ml, 2 h, 55 C). DNA was purified by 2 phenol:chloroform: isoamylalcohol (25:24:1, pH 8. 0) extraction steps and a subsequent PCR column purification (Qiagen). DNA fragmentation of input and ChIP samples was controlled on a bioanalyser DNA chip to shear between 200-300 bp. Processing of the RNAPII ChIP-seq data was performed as described previously. 22 Data reported for RNAPII, Ser2P, Ser5P, Ser7P, and Tyr1P can be found at Gene Expression Omnibus database under the accession number GSE52914 and under GSEXXX for K7me1/2.
Liquid chromatography coupled to tandem TiO2 mass spectrometry (LC-MS/MS) Phospho-enriched peptides were automatically injected into in Ultimate 3000 HPLC system (Dionex), desalted online in a C18 micro column (300 mm i. d. £ 5mm, Pep-Map100 C18 5 mm, 100 A , Dionex) and separated on an analytical column C18 micro column (75 mm i. d. £ 10 cm, packed in-house with Reprosil Pur C18 AQ 2. 4 mm, Doctor Maisch) using a 60 min gradient from 5 to 30 % acetonitrile in 0. 1% formic acid. The effluent from the HPLC was subsequently electrosprayed into a LTQ Orbitrap XL mass spectrometer (Thermo). The MS instrument was operated in a data dependent mode to automatically switch between full scan MS and MS/MS acquisition. Survey full scan MS spectra (from m/z 300 -2000) were acquired in the Orbitrap with a resolution of R = 60,000 at m/z 400 (after accumulation to a 'target value' of 500,000 in the linear ion trap).