dataesr/scientific-paragraphs-categorization huggingface dataset:
Another data matrix was created for all selection frequency outputs (20 in total, one per scenario). Another analogy between Marxan output data and community data is that the matrix contains many zero entries for "species" (or planning units). The data matrices were therefore Hellinger-transformed, which allowed meaningful use of parametric ordination methods (which are Euclidean-based), while circumventing the problems associated with Euclidean distance to analyse matrices with many zeros (see  [55]  for details). We used parametric ordination methods because of the higher level of statistical power possible with such tests.
Prioritisation-factor level Once the data matrices were transformed, a Euclidean dissimilarity matrix was calculated ('vegan' R package;  [56] ). This involved measuring, pair-wise, the dissimilarities (or distance) between all 2000 solutions (presence-absence) or all 20 solutions (selection frequencies). From these dissimilarity matrices, we used an average-linkage hierarchical cluster analysis to determine, through visual interpretation, the spatial similarity between solutions and whether any of the tested factors (i. e. , planning-unit size, thematic resolution, and variability of costs) appeared to influence similarity ('sparcl' R package for plotting;  [57] ). The cluster analysis on the selection frequency outputs allowed us to check whether the clusters derived from individual solutions were affected by spatially idiosyncratic solutions.
Since the research objective was to ascertain the ability of coarse-resolution priorities produced with large planning units to spatially capture fine-resolution priorities produced with small planning units, we examined the two prioritisation factors of potential influence: thematic resolution and cost variability. Our scenarios with large planning units were intended to reflect, to varying degrees, the coarse prioritisations usually necessary over large extents. Our two "test" scenarios with small planning units were intended to reflect the higher-quality data usually available only after closer study of relatively localised parts of large planning regions (except that our highquality data covered whole planning regions in this instance). To assess the extent of spatial nestedness, we examined the amount of spatial overlap between high-priority areas defined at two levels: planning units with selection frequencies of ! 50 and ! 75 (Fig  6 ).
(2016)   [73] , however, found a significant negative relationship between planning-unit cost and incidental representation of environmental classes in the Great Barrier Reef marine reserve network, Australia. The less spatially-biased selection of planning units that occurs when no (or uniform) costs are considered allows a greater chance for incidental representation of finer-resolution objectives, with this effect being almost indistinguishable between the different levels of thematic resolution tested in this study. With our increasing understanding of the ability of cost data to influence conservation priorities  [61, 74, 75] , we must recognise the imperative to use accurate and representative cost layers in conservation prioritisations. Such data rarely exist across extensive regions, due to the expense and complicated logistics involved in obtaining them.
Hormone Assay After thawing, samples were centrifuged at 3000g for 10 minutes and were assayed using commercially available ELISA kits for high-sensitivity cortisol and DHEAS (DRG Diagnostics, Marburg, Germany). The assays were analysed using a Biotek ELX800 (Bio-Tek, Vermont, USA) plate reader and Gen5 software (Bio-Tek, Vermont, USA). Each sample was assayed in duplicate, with the mean value between wells being recorded as the sample value. Two days of saliva collection were obtained, and values for both hormones were meaned across the two days to control for differences in daily activity. There were no significant differences for either hormone across the two days of collection (p>. 05). Intra-assay % coefficient of variation (%CV) for cortisol was 9. 21%, and mean inter-assay %CV was 5.
Data Analyses Statistical analyses were undertaken using IBM SPSS version 22. Paired samples t-tests were used to assess differences between the hormone assessments across the two days of collection, with no significant differences observable. The mean values across both days for cortisol and DHEAS were positively skewed, and were therefore subject to log10 transformation prior to analysis. Area under the curve (AUC with respect to ground (AUCG) was calculated using the trapezoid method  (Pruessner et al. 2003)  were carried out with non-transformed values. Only cortisol was examined for awakening response (CAR), as DHEAS does not increase after awakening  (Hucklebridge et al. 2005 ). As we were interested in establishing a healthy CAR, analyses of difference (ANOVA) were conducted on T2 cortisol, controlling for T1 values as per recommended guidelines  (Stadler et al.
Sample characteristics Summaries of the demographic, psychological, and endocrine assessments of the two samples are presented in tables 1. As can be seen from table 1, the present sample is majority female, white, and were of comparable age across the employed and non-employed groups. Initially we set out to examine differences between more subgroups; using permanent employed, temporary or selfemployed, short-term unemployed (<12 months) and long-term unemployed (>12 months), however there were insufficient differences between all four groups with regard to their endocrine data to warrant this. Those who were unemployed had a mean duration of unemployment of 27. 1 months (SD=33. 34, range: <0-129 months) and this was not associated with endocrine functioning; implying that duration of unemployment, in this particular sample, would not need to be considered as an explanatory confound in later analyses.
It consists of two double layers of scintillation counters placed far from the interaction region, on both sides: one in the ALICE cavern at z = 17. 0 m and one in the LHC tunnel at z = -19. 5 m. The pseudorapidity coverage of the two AD arrays is 4. 8 < η < 6. 3 and -7. 0 < η < -4. 9, respectively. The data were collected after the startup of LHC Run 2 in June 2015. Beams consisting of 39 bunches η and p T distributions of charged particles in pp at √ s = 13 TeV ALICE Collaboration were circulating in the machine, with about 8 × 10 9 protons per bunch. In the ALICE interaction region, 15 pairs of bunches were colliding, leading to a luminosity of about 5 × 10 27 cm -2 s -1. This value corresponds to a rate of about 350 Hz for inelastic proton-proton collisions. The probability that a recorded event contains more than one collision was estimated to be around 10 -3 , which is consistent with the fraction of events containing more than one distinct vertex and tagged as pileup.
The luminous region had an RMS width of about 5 cm in the z direction and about 85 µm in the transverse direction. The data were collected using a minimum-bias trigger requiring a hit in either the V0 scintillators or in the AD arrays. The events were recorded in coincidence with signals from two beam pick-up counters each positioned on either side of the interaction region to tag the arrival of proton bunches from both directions. Control triggers taken for various combinations of beam and empty buckets were used to measure beaminduced and accidental backgrounds. The contamination from background events is removed offline by using the timing information from the V0 and the AD detectors, which have a time resolution better than 1 ns. Background events are also rejected by exploiting the correlation between the number of clusters of pixel hits and the number of tracklets (short track segments pointing to the primary vertex) in the SPD.
5 and 0. 01, respectively. The vertex position requirement results in an effective |η| < 1. 8 coverage. Differences in strange-particle content between data and simulations, observed at lower beam energies  [21, 22] , are taken into account by scaling the strangeness production in the Monte Carlo event sample by a factor 1. 85 (strangeness correction), resulting in a further contamination correction of about 1%. The transverse-momentum distribution is measured from tracks reconstructed using the information from the ITS and TPC detectors. Candidate tracks are selected with cuts on the number of space points used for tracking and on the quality of the track fit, as well as on the distance of closest approach to the reconstructed vertex. Details on the track-reconstruction algorithm and quality cuts can be found in  [10, 11, 14].
Collective (flow-like) effects are incorporated in the EPOS3 version  [35]  and treated via parametrisations in the EPOS LHC version  [28]. These event generators, benefitting from the tuning performed on the LHC data in Run 1, describe the p T spectrum reasonably well, although not in detail. It is interesting to note that both PYTHIA 8 and EPOS LHC models show a similar pattern in the ratio to data with discrepancies up to 20% and that PYTHIA 6 overestimates particle production at high p T. Figure  4  shows the ratio of transverse-momentum spectra of charged particles at √ s = 13 TeV and 7 TeV. The published data at √ s = 7 TeV  [10]  were for INEL events. We have recalculated the normalisation of the spectrum to correspond to INEL>0 events in a similar manner as done for √ s = 13 TeV. The trigger and event-selection efficiency for INEL>0 events at √ s = 7 TeV was estimated using the same Monte Carlo simulations used for the publication  [10].
In first order, this correlation arises naturally from jets, giving the leading high-p T hadron and a significant contribution to multiplicity. The general features seen in the data, which are similar to those first seen at √ s = 0. 9 TeV  [9] , are reproduced by PYTHIA 8 and EPOS LHC fairly well, but some disagreements are noticeable too, in particular in the p T region of a few GeV/c. This is more prominent for EPOS LHC. It was shown earlier  [44]  that both EPOS LHC and PYTHIA 8 reproduce well, although slightly overpredicting, the correlation of p T with N ch. The present data on spectral shape highlight some deficiencies in both models concerning the description of spectral shapes as a function of multiplicity.
Bisulfite treatment and pyrosequencing. Based on our previously published methylation 450 K array data on psoriasis (accession number: GSE63315)  24  , 13 probes for OAS2 were found. Among these CpG sites, three were differentially methylated in psoriasis compared to matched controls (|delta-beta| > 0. 1 and adjusted P-value < 0. 01) (Supplementary Table  S3 ). Of these, two were located 391 and 213 bp upstream of the transcription start site (TSS) respectively and one in the first exon. In order to validate the array data and evaluate its representativity of the surrounding area, four commercially available PyroMark CpG assays from Qiagen were used to determine methylation levels in two areas in which these three CpG sites were located (Supplementary Fig. S1 ). Information of the four PyroMark CpG assays detecting a total of 11 CpG sites is summarized in Supplementary Table  S4.
Real time RT-PCR. Real-time RT-PCR was performed to detect OAS2 mRNA levels in tumour and adjacent tumour-free controls. Complementary DNA (cDNA) was synthesized from 500 ng of total RNA with oligo (dT) primer, according to the instructions for the RevertAid H minus first strand cDNA synthesis kit (Fermentas, Thermo Scientific, Wilmington, DE, USA). Real time RT-PCR was performed using an IQ5 multicolor real-time PCR detection system with IQ SYBR Green Supermix (Bio-Rad Laboratories, Hercules, CA, USA). Primers for OAS2 were ordered from Bio-rad (Bio-Rad, assay ID qHsaCED0037726). NormFinder software was used to analyze a set of reference gene candidates based on our own whole-genome expression data, and a total of four genes were confirmed to be good reference genes in our study, including LAD1 (Forward: CCTCCCACCCGTCACACT, Reverse: CTGCTGTAGGTTCGCTGTGT), RPS12 (Forward: TGCTGCTGGAGGTGTAATGG, Reverse: GCACACAAAGATGGGCTTGG), GAPDH and USB).
Pyrosequencing confirmed hypomethylation of OAS2 in psoriasis. Methylation status of 11 OAS2 CpG sites in psoriasis and control skin samples were successfully quantified using pyrosequencing. As both 450K array  24  and pyrosequencing data were available for CpG1, 2, 6 and 9, we first evaluated the correlation between data determined by pyrosequencing and methylation array. Spearman correlation analysis indicated good correlation between the two platforms, with the strongest correlation seen for CpG1 (rho = 0. 963, P = 0. 000) (Supplementary Table  S5 ). Individual methylation levels at the 11 CpG sites are presented in Fig. 1b  showing a high degree of inter-individual and inter-locus variation. In general, high methylation levels were seen at CpG1 to 4 and low levels at CpG5 to 11. When comparing psoriasis to control skin, an overall demethylation was observed and a total of 8 CpG sites were identified as hypomethylated (mean methylation difference > 10%, P < 0.
Correlation between DNA methylation and gene expression. In order to evaluate whether OAS2 DNA methylation could be involved in regulating gene expression, correlation between DNA methylation and gene expression was investigated using our previous gene expression profiling data for psoriasis (accession number GSE53431). OAS2 expression in SCCHN was studied using real time RT-PCR. Expression data are from the same subjects as the methylation data. In Fig. 2 , we can see that similar to overexpression of OAS2 in psoriasis (P = 0. 000, mean fold change = 8. 1), OAS2 was significantly overexpressed in tongue SCC compared to tumour-free tissues (P = 0. 003, mean fold change = 11. 8). Overexpression of OAS2 was also seen in tonsillar SCC (P = 0. 013, mean fold change = 2. 4) but levels were much lower than in psoriasis and tongue SCC.
Acknowledgements This study was supported by grants from  Hudfonden ,  Lion 's  Cancer Research Foundation ,  Umeå University, The Swedish Cancer Society  contract number  15 0637 ,  Umeå University  and project  MEYS-NPSI-LO1413  from the  Ministry of Education, Youth and Sports  and  GACR P206/12/G151  (PC) in the Czech Republic.
To reduce the complexity of this dataset, only coral proteins that matched to at least five of the virome fragments were considered for further analyses (2 503 proteins total; figure  3 ; electronic supplementary material, table  S3 )  [17]. The potential functions of this group of proteins was predicted by comparing to the human proteome using BLASTp (electronic supplementary material, file S3), as well as a functional domain analysis using the conserved domain database (CDD) (electronic supplementary material, file S4)  [18]. This showed that 83 proteins related to PAMP sensing and apoptosis (figure  4 ; electronic supplementary material, figure S1 and table  S4a )  [18]. Electronic supplementary material, table  S5 , provides descriptions of protein domains classified as PAMP sensing  [19] [20] [21] [22] [23]  and apoptosis  [24] [25] [26] [27] [28] [29] [30].
rspb. royalsocietypublishing. org Proc. R. Soc. B 283: 20161200 tissue and compared with the host transcriptome to identify protein candidates that are predicted targets of viral proteins and therefore predicted to be involved with host immunity. This candidate group of immune-associated proteins is then compared with a protein domain database and the proteome of an established model organism to remove proteins whose function can already be predicted. Proteins that lack similarity to either database represent the final group of candidate immune genes with completely unknown function that can be further explored using in vitro and in vivo experimentation (figure  5 ).
(c) Beyond conserved domains-novel predictions from viral communities Domain-based annotation allows for the rapid prediction of host immune proteins. However, the databases used to generate those annotations are largely based on experimental evidence from only three animal phyla (i. e. Chordata, Nematoda and Arthropoda)  [2]. While many protein domains are expected to be conserved across phyla, relying exclusively on domainbased annotations will fail to identify immune proteins that lack previously characterized domains. The pipeline described here identified 159 predicted coral immune proteins that could not be annotated using the CDD and lack homology to any human protein. In addition to missing completely novel immune domains, comparing the host proteome to the CDD will also fail to identify proteins involved with immunity that lack canonical immune domains (electronic supplementary material, table  S5 ).
In total, 275 coral proteins could be annotated with the CDD but did not contain any PAMP sensing or apoptotic domains or possess any similarity to human proteins (electronic supplementary material, file S5). This group of proteins, as well as the 159 proteins that do not match any domain in the CDD, may be involved in coral-specific immunological processes. The pipeline described here provides a more comprehensive prediction of the host immune repertoire by combining the power of existing databases with new predictions generated from resident viral communities.
For example, the established model organism Nematostella vectensis (sea anemone), a fellow anthozoan related to corals, has well-developed molecular methods that could be used to test the function of predicted coral immune proteins  [16, 49]. While this study has focused on characterizing metazoan immunity, the pipeline proposed here could also elucidate interactions between bacteria and their resident bacteriophage communities to identify novel bacterial immune proteins. Combining these data with metazoanvirus predictions would provide a broad understanding of immune processes occurring within the entire holobiont. The pipeline presented here predicts immune system structure by combining model organism-based databases with a domainindependent approach based on resident viral communities. This method can be applied to any holobiont with the potential to discover novel immunological processes.
To determine whether any of the remaining 585 proteins function could be predicted based on the presence of conserved domains they were analysed using the conserved domain database (CDD) resulting in 426 coral proteins with positive matches to a known domain (see electronic supplementary material, file S4 for full results of CDD analysis). Within this group of proteins 138 were considered 'viral-like' based on the presence of domains associated with viral proteins. In total 159 coral proteins possessed no similarity to either database and therefore represent predicted immune proteins with unknown function.
g. human or mouse) and domain prediction database (e. g. conserved domain CDD, Pfam). Proteins that lack hits to either database represent predicted immune genes with unknown function and are selected for further biochemical investigations using in vitro and in vivo experimentation. rspb. royalsocietypublishing. org Proc. R. Soc. B 283: 20161200 the Covaris M220 Focused Ultrasonicator (40 s at 9 W). Sheared DNA was then end-repaired and ligated to LASL Linker A (Linker A FWD: 5 0 -P-GTATGCTTCGTGATCTGTGTGGGTGT-3 0 , Linker A REV: 5 0 -CCACACAGATCACGAAGCATAC-3 0 ) followed by size selection with a target size of 500 base pairs using Pippin Prep (Sage Science)  [51]. For each biological sample, four PCR reactions were prepared using a barcoded primer and 17 cycles of PCR were performed (PfuTurbo Cx Hotstart DNA Polymerase, Agilent).
Replicates of the same samples were combined, purified and separated into four aliquots for further amplification with three cycles of reconditioning PCR. Finally, all barcoded samples were quantified and pooled for library preparation and sequencing on MisSeq platform using the MiSeq Reagent Kit v3 600 cycles chemistry (Illumina). (b) Quality control of sequence data Preceding downstream analysis quality control of sequence data was performed using a variety of bioinformatic tools. Paired end reads were first joined using COPE  [52] , low-quality sequences (less than 100 base pairs in length, mean quality score less than 25, or containing more than 10% N's) were removed using PRIN-SEQ and any remaining bacterial or human sequences were removed with DeconSeq  [14, 15]  (see electronic supplementary material, table  S1  for the number of sequences removed at each pre-processing step).
Figure5. Proposed pipeline to predict immune proteins in uncharacterized systems. Viruses are extracted from the non-model organism of interest using a virus-like particle extraction protocol of choice and nucleic acid is sequenced. If a gene model is not available total RNA is also extracted from host tissue and an assembled transcriptome is prepared. Viral gene segments are compared with the translated transcriptome through tBLASTn to identify matches to host proteins. These proteins are further analysed through comparison with a well-characterized immune system using BLASTp (e. g. human or mouse) and domain prediction database (e. g. conserved domain CDD, Pfam). Proteins that lack hits to either database represent predicted immune genes with unknown function and are selected for further biochemical investigations using in vitro and in vivo experimentation.
III. NUMERICAL SIMULATIONS The realization of the beam splitter is done by means of a circular cluster of hole-gold-shell inclusions embedded in a triangular lattice of lattice constant a. The circular cluster has a radius R c =10. 5a and a background thickness h b =0. 5a. Inside the lens the position-dependent thickness and inner-outer radius are designed so that the refractive indexes of the three modes meet the profiles of the selected deflection angle shown in Fig. 2. Therefore we follow the design procedure explained in section II. The performance of the beam splitter is studied numerically by the commercial FEM software COMSOL Multiphysics. The plane wave for each polarization is excited as a boundary force in the x, y, z directions for the S 0 , SH 0 , and A 0 modes, respectively, while the propagating direction is set along the x-axis.
ACKNOWLEDGMENTS This work was supported by the  "Agence Nationale de la Recherche (ANR) " and the  "Délégation Générale de l'Armement (DGA) " under the project  Metactif , Grant No. 443 ANR-11-ASTR-015  and by the  LabEx AMADEus  ( ANR-10-444LABX-42 ) in the framework of  IdEx Bordeaux  ( ANR-10-445IDEX-03-02 ),  France. Yabin Jin  acknowledges a scholarship provided by  China Scholarship Council  No. 201406260170.
Analysis setup The model described in Eqs. (2. 1) and (2. 2) has been implemented in the UFO format  [69]  through the Feynrules package  [70]  and event samples have been generated through MadGraph5_aMC@NLO  [71]. In particular, the 13 TeV pp → γγ and pp → jj cross sections were computed at parton level and convoluted with the CTEQ6L1  [72]  parton distribution functions 2. Furthermore, we have also calculated the width of the resonance within the same set up. DM observables have been computed with the micrOMEGAs4. 1 package  [73] , with the exception of the spin-independent WIMPnucleon scattering cross section, which has been calculated analytically as described in App. B. In order to exploit the constraints arising from 8 TeV data on monojet signatures, we have used a recast version of the ATLAS monojet search ATLAS-SUSY-2013-21  [74]  3 , implemented in the MadAnalysis5  [76]  package and described on the Public Analysis Database (PAD)  [77].