dataesr/scientific-paragraphs-categorization huggingface dataset:
Details of the calculation In this paper we consider the processes pp → e + ν e µ -ν µ , pp → µ + ν µ e -e + , pp → µ + µ -e -e +. ( 1 ) We stress that the full matrix elements for four fermion production are used and no on-shell or double pole approximation is employed. In the following the three processes will be dubbed as WW , W Z, and ZZ production, and, collectively, as "diboson production". Although we will show results only for W + Z production, our code is fully general and W -Z production can be generated as well. The calculation of the NLO QCD + NLO EW corrections to diboson production matched to QCD and QED parton shower presented in this paper is performed in the POWHEG-BOX-RES framework  [68] , which is a framework designed to simulate processes involving intermediate decaying resonances with NLO+PS accuracy.
As discussed in Ref. [72] , the latter point is crucial when computing the NLO QCD + NLO EW +PS corrections to observables that are very sensitive to final-state QED radiation (FSR QED) but rather insensitive to initial-state QCD corrections (ISR QCD). This is the case, for instance, of the dilepton invariant-mass distributions in the region close to the nominal vector-boson masses, where the predictions, obtained with and without the allrad option, can differ at the percent level. In order to implement the NLO QCD and the NLO EW corrections to diboson production in POWHEG-BOX-RES, we had to define the list of all the contributing LO and real partonic subprocesses together with the corresponding resonance histories, and to provide the required Born, virtual, and real matrix elements. We decided to code all the three classes of diboson-production processes (namely, four charged leptons, three charged leptons plus one neutrino, and two charged leptons plus two neutrinos) in the same POWHEG package and let the user select the desired one from the input card.
We use the SM-2. 2. 2 Recola model file to compute the NLO QCD + NLO EW corrections in the SM, but in principle our code can be easily generalized to use any BSM Recola model file as far as the considered extension of the SM does not involve any modification of the interactions between photons and fermions or among partons (as such modifications might have an impact on the IR subtraction performed by POWHEG-BOX and on the event generation). We developed a completely general interface between POWHEG-BOX-RES and RECOLA2 that can be used for other processes of interest. 6 n order to deal with the presence of unstable particles, RECOLA2 implements the complex-mass scheme (CMS)  [89] [90] [91]. In this scheme, the W and Z boson masses are promoted to complex numbers with the replacement M 2 V → µ 2 V = M 2 V -i Γ V M V , and all the parameters derived from the gauge-boson masses (like, for instance, the sine of the weak mixing angle) get an imaginary part.
Cross-checks and validation In order to validate the implementation of the NLO QCD + NLO EW corrections to diboson-production processes in POWHEG-BOX-RES, we compare the predictions of our code with the ones of the Monte Carlo integrator used in Ref. [93]  (MC in the following). Both codes use RECOLA2 for the calculation of the matrix elements, however, the integration and the subtraction of the IR singularities is performed in a completely independent way in the two programs. In particular, POWHEG uses the FKS subtraction (modified to take into account the presence of resonances), while in MC the Catani-Seymour procedure  [105, 106]  is used. Tables 1-5 collect the results at the integrated cross-section level under the event selection of Eqs. (  10 ) and (  11 ) for the processes pp → e + ν e µ -ν µ , pp → µ + ν µ e -e + , and pp → µ + µ -e -e + (dubbed "WW", "WZ", and "ZZ" in the tables).
For the fixed-order part of the calculation, POWHEG computes the NLO QCD + NLO EW corrections additively. We checked that, when running our code with both QCD and EW corrections, δ QCD+EW from POWHEG is equal to the sum of δ QCD and δ EW computed with MC. We do not show here the plots, since the corresponding information can be read from the combination of Figs. 2  and 3.
The kinematics of the hard partonic event generated by POWHEG, together with the values of the transverse momenta (with respect to their emitters) of the generated partonic and/or photonic radiation, is then saved in the Les Houches event file. The transverse momentum of the initial-state radiation, if present, is used by the parton shower algorithm as upper bound for the generation of QED/QCD radiation from the hard production process. The transverse momentum of the photons from the final-state leptons (i. e. from the resonances) is used by the parton shower program as upper bound for further QED radiation. The results presented in this paper have been showered by PYTHIA8. This code allows to veto emissions harder than the ones generated by POWHEG by using dedicated UserHooks. We have also verified that we obtain fully compatible results if we let the PS generate unconstrained emissions and we subsequently check if the transverse momentum of the radiations with respect to the emitting particles is smaller than the POWHEG hardest ones.
The scale hdamp is used to separate the two terms. In the present simulation, no such separation of the real contribution has been performed, so that in Eqs. (  2 )-(  4 ) the full real matrix elements are used. The impact of the allrad option for the invariant mass of the dimuon pair and for the transverse momentum of the hardest Z in pp → e + e -µ + µ -is displayed in Fig. 7. We find an effect of about 1-2% below the Z resonance for the M(µ + µ -) distribution, where the QCD corrections are a flat normalization factor, while the QED corrections lead to large shape distortions. This is due to the fact that, without the allrad option, the upper scale for the generation of radiation used by the PS program is set by the transverse momentum of the hardest radiation generated by POWHEG, that, in most cases, is ISR QCD.
Although the reliability of the mixed effect provided by POWHEG can only be assessed through comparison with full calculations at O(αα S ), when such calculations will be available, an estimate of the related uncertainties can be obtained by comparing the results at NLO QCD + NLO EW + PS accuracy for diboson and diboson + jet production (possibly merged with the results for pp → VV ). Another class of uncertainties is related to the matching procedure, such as the scale choice for the parameter hdamp, or the use of the allrad option. In addition, different PS algorithms use different ordering variables, and such choices have a non negligible impact on the kinematic distributions. The present release of the code presented in this paper only includes an interface to PYTHIA8. 2, however, in order to study this class of uncertainties, dedicated interfaces to other shower Monte Carlo programs (like, for instance, PHOTOS  [116] [117] [118]  for the QED FSR) could be developed.
Conclusions We computed the NLO QCD + NLO EW corrections to diboson production at hadron colliders matched to a complete parton shower, where QCD and QED radiation is simulated. For diboson production this is the first calculation where the NLO EW corrections have been consistently matched to QED PS. As the considered processes involve the production and the decay of unstable particles, whose decay products can radiate photons, the calculation is based on the POWHEG-BOX-RES framework. The corresponding code is public and all the information for downloading it can be found in the POWHEG-BOX web page. 10 hough we did not perform an extensive phenomenological study that might be the subject of a future publication, we showed the potential of our code, and we pointed out that EW effects, consistently matched to QED parton shower, are relevant for several observables of interest.
The code relies on the RECOLA2 library for the calculation of the matrix elements for four leptons and four leptons plus photon/parton production. In particular, we developed a fully general interface between POWHEG-BOX and RECOLA2 that could be used for other processes. We performed our calculation in the Standard Model. However, given the possibility of RECOLA2 to compute tree-level and one-loop matrix elements in general extensions of the SM, in the future the code could be easily generalized to compute the NLO QCD + NLO EW corrections to diboson production matched to QCD and QED parton shower in the context of models beyond the SM, provided that the one-loop corrections are available in RECOLA2, and that the considered model does not alter the structure of QED and QCD interactions in a non-trivial way.
If this were the case, the subtraction of infrared singularities and the Sudakov form factors in the POWHEG-BOX-RES should also be generalized. As a final remark, the effect of NLO QCD corrections to diboson production is very large and it is dominated by real parton radiation, especially in the regions where one of the two vector bosons is soft with respect to the jet. It is thus important to include QCD corrections beyond NLO accuracy, for instance using a consistent merging of the predictions for VV and VV + jet production (V,V = W, Z) based on the MINLO  [63]  or MINNLO PS  [119]  procedures (at NLO and NNLO accuracy, respectively). The code presented in this paper can be taken as the starting point for the inclusion of the EW effects matched to QED PS in the treatment of diboson (+ jet) production in the MINLO or MINNLO PS framework.
Despite tumor heterogeneity, it is disputed and hypothesized that some backbone mutations might represent key events in determining cancer-specific phenotypes. Moreover, mutational concordance represents an indirect evidence that those mutations may have an important role in an evolution trajectory and/or in the establishment of metastases. Therefore, in order to identify genetic common patterns in both PT and MT, Venn diagrams of shared mutations have been depicted for both PTs (Fig. 4a, pattern A ) and MTs (Fig. 4b , pattern B). The identified genes are described with ClinVar dataset ID and their role in cancer. Interestingly, some mutations of unknown or potential clinical significance recurred in all PTs (AURKA, EPCAM, ERCC5, FAT1, LATS2, PTPRS, TP53) and MTs (ANKRD26, ERBB2, LATS2, TP53).
Tumor microenvironment characteristics Since the adaptive immune context of CRC has been associated with prognosis, CD3 + , CD8 + , FoxP3+ and GrzB+ cells were quantified in matched PT and MT tissues by IHC. The density of each T-cell subset is shown in Table  3. Notably, the density means of CD8 + and GrzB + cells (the principal anti-tumor effectors) recorded in PTs (68 and 36 cells/mm 2 , respectively) were below the upper limit of the lowest quartile reported in a previous referral study  10  (235 and 100 cells/mm 2 , respectively) as well as in our internal validation datasets. Results of total CD3 + and FoxP3 + cells are also reported in Table  3  and compatible with the previous report. Example of CD8 + T-cell subsets IHC staining (high-power field ×40) in PTs and matched MTs is reported in Fig.
We have used those data as genomic referral patterns of pluri-metastatic CRC and to compare them with our results (Supplementary Table  S1 ). Comparing our genetic patterns to the pluri-metastatic disease we focused on KRAS and SMAD4. Interestingly, in all studies of pluri-metastatic disease the RAS mutation status of PT is maintained in the matched MT; by converse, in some cases KRAS mutations are exclusive events of metastases. In two cases of the present report, this condition is not satisfied (MT of CL lost KRAS mutation, MT of FA lost a KRAS pathogenic mutation); the scarce immune cell infiltrate would suggest that this phenomenon is more likely related to "back mutations" rather than to immune-mediated sub-clonal selection. These data were repeated and confirmed with PEPP re-sampling and KRAS mutation PCR-based testing (data not shown).
Bioinformatics analysis and data presentation Illumina TruSigth Oncology 500 bioinformatics pipeline was applied to analyze sequencing results. A median of 118 million reads were generated for each sample and the coverage in the target region was above manufacturer's suggested threshold of 150X. Sequence data were aligned to the human reference genome GRCh37 (http://www. ncbi. nlm. nih. gov/projects/genome/assembly/grc/human/ index. shtml) using the Burrows-Wheeler Aligner with default parameters  35. Both population-and cancerspecific variants were intersected with GENCODE, dbNSFP, ICGC-PCAWG, COSMIC, 1000Genomes, ClinVar, CancerMine, OncoScore, CIViC, CBMDB databases to assess the clinical significance of the found mutations. Variants were filtered with unmatched normal datasets and removed if the global minor allele frequency was <1%.
The prioritization of variants was done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG  36. Variants of strong clinical significance in cancer were defined considering items with strongest evidence levels in the database for (i) clinical interpretations of variants in cancer (CIViC, civicdb. org) and (ii) Cancer Biomarkers (cancergenomeinterpreter. org/biomarkers). Results about variants shared between PTs and metastatic tumors (MTs) are shown with ID according to ClinVar; however, variants were also manually curated to exclude residual false positives. The scenario in which interpretation for the clinical consequence of a variant was "benign", but the review status quality was scored <2/4 or reporting conflicting results, was defined as "probably benign".
Phenolyzer is a computational tool that prioritize genes on the basis on updated existing knowledge (protein-protein interactions, sharing of biological pathways or gene family, gene-gene transcriptional regulation, etc. ). It integrates OMIM, Orphanet, ClinVar, Gene Reviews and GWAS Catalog as gene-disease databases. However, for a complete methodology description see Yang et al. 42  Results are expressed through a score system and a network visualization tool that integrates gene-gene and gene-cancer relationships providing readers with a panoramic view of the interactional context. The disease-free interval (DFI) was measured in months and it represented the time elapsed from the surgical removal of PTs to the occurrence of lung metastases. Densities of T-cell subsets were expressed as cells/mm 2 and results represented with the arithmetic averages ± 2 standard deviations (SD).
a global database for metacommunity ecology, integrating species, traits, environment and space alienor Jeliazkov et al. # the use of functional information in the form of species traits plays an important role in explaining biodiversity patterns and responses to environmental changes. although relationships between species composition, their traits, and the environment have been extensively studied on a case-by-case basis, results are variable, and it remains unclear how generalizable these relationships are across ecosystems, taxa and spatial scales. to address this gap, we collated 80 datasets from trait-based studies into a global database for metaCommunity Ecology: Species, Traits, Environment and Space; "CEStES". Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the sampling sites.
The topic of Trait-Environment Relationships (TER) has been extensively studied across the globe and across the tree of life. However, each study deals with a specific system, taxonomic group, and geographic region and uses different methods to assess the relationship between species traits and the environment. As a consequence, we do not know how generalizable apparent relationships are, nor how they vary across ecosystems, realms, and taxonomic groups. In addition, while there is an emerging synthesis about the role of traits for terrestrial plant communities  13, 14  , we know much less about other groups and ecosystem types. To address these gaps, we introduce the CESTES database -a global database for metaCommunity Ecology: Species, Traits, Environment and Space. This database assembles 80 datasets from studies that analysed empirical multivariate trait-environment relationships between 1996 (the first multivariate study of TER  15  ) and 2018.
All considered datasets include four data matrices (Fig. 1 ): (i) community data (species abundances or presences/ absences across multiple sites), (ii) species traits (sensu lato), (iii) environmental variables across sites, and (iv) spatial coordinates. The database is global in extent and covers different taxonomic groups, ecosystem types, levels of human disturbance, and spatial scales (Fig. 2 ). Several global trait databases already exist or are emerging, such as the Open Traits working group  16  , the Freshwater Information Platform and its Taxa and Autecology Database for Freshwater Organisms  17  , the PREDICTS database for Projecting Responses of Ecological Diversity In Changing Terrestrial Systems  18, 19  , and the TRY  20  plant trait database for Quantifying and scaling global plant trait diversity.
In comparison to these initiatives, the CESTES database has several unique features. Specifically, it maintains the original matching between the community, environmental, and spatial data that go along with the trait information. Keeping this original matching of the data ensures homogeneity in the data structure and allows for targeted analyses of TER. We include all taxonomic groups for which the appropriate matrices are available including groups poorly represented in most trait compilations (e. g. , invertebrates and bats). The trait information is particularly diverse, ranging from life-history and morphological to trophic traits, dispersal abilities and tolerances, and covering various ecological mechanisms. CESTES only includes data where georeferenced coordinates, or relative coordinates of the sampling sites (hereafter: spatial coordinates) and environmental variables are available to enable spatial and scaling community analyses.
We prioritized studies with abundance or biomass data (as opposed to presence/ absence) to facilitate the calculation of a broad range of biodiversity metrics and the study of different facets of biodiversity. The data available in CESTES are open access without restriction, except via citation of this paper (and any original paper that plays a particularly important role in the analyses). Importantly, the CESTES database is meant to be a live database  21  : it will be maintained in the future and new datasets will be added as they become available. The CESTES database aims to significantly contribute to research in biogeography, macroecology (including in complement with phylogenies), community and metacommunity ecology, and biodiversity-ecosystem functioning. On the one hand, the quality of its content and structure will allow meta-analyses and syntheses (e.
Methods Data compilation. Database scoping. The rationale for developing the CESTES database is generally for the study of TER in relation to metacommunity ecology and/or macroecological questions. As such, we focussed on datasets that were appropriate within the metacommunity or macroecology context (i. e. species assemblages distributed across space) and that focussed on traits to understand biodiversity patterns and responses. This prerequisite led us to identify multivariate trait-based studies as the most relevant and rich source of datasets that could fulfil these two requirements. Given the complexity that still pertains to trait typology  13  , we did not restrict ourselves to any specific definition of traits and integrated all possible species characteristics if they were used as "traits" in the original study.
We thus included ecophysiological, functional, life-history and biological traits, as well as response and effects traits. CESTES users can select traits according to their study needs. Each dataset also includes a DataKey (description of the entries of the Data tables), a Notes sheet (contact information for the dataset, and, when relevant, processing information), a Species list, and a Site list. The grey components can be the original data matrices, and additional information and do not appear in all the datasets, depending on specific needs (see Methods -Data processing section). We identified eligible datasets based on two strategies: 1. Literature search, aiming to initiate the database construction along a structured workflow, 2. Networking, aiming to extend the database and open the sharing possibilities, if the datasets fulfilled the CESTES requirements.
The main condition for dataset eligibility was that the TER was the focus of the study and data use. This ensured that: 1. the trait and the taxonomic information were collected from similar biogeographic areas (minimizing mismatches between the geographic origins of trait and taxonomic data), 2. the sampled sites were associated with contextual environmental information that was relevant to the community and traits under study. Literature search. We searched for multivariate trait-based studies published between 1996 and 2018 via a systematic literature search on the Clarivate Analytics Web of Science Core database. Following Leibold & Chase 2 , we focussed on studies that included (in any of their contents) the following terms (including spelling variations): "RLQ"  15  and "fourth-corner"  22, 23  because both of them are the predominant methods of multivariate trait-based analyses in ecology  24.
The "RLQ" refers to a co-inertia analysis that summarizes the overall link between the three matrices of species abundances/presences-absences (L), species traits (Q) and environment (R). The "fourth-corner" refers to a permutation analysis of these three matrices that tests individual trait-environment relationships. The use of RLQ and fourth-corner analyses on the datasets ensures that all of them: 1. are multivariate and include both several species, several traits, and several sites (potentially including spatial information) to align with a metacommunity-like structure, 2. have a comparable structure and can be used in comparative analyses and syntheses. The search query was: ALL = ("fourth-corner" OR "fourth corner" OR "fourthcorner" OR "RLQ") This search resulted in 368 papers. Note that the "fourth corner" term more generally and commonly refers to the widely studied question of the links between trait and environment variations  22.
Most studies that look at TER, regardless of the method of analysis they use, would often acknowledge the historical background of their question by referring in their paper to the "fourth corner problem". Consequently, by including the "fourth corner" search term, we identified eligible multivariate datasets that were not necessarily analysed by fourth corner analysis/RLQ, but also by e. g. trait-based generalized linear/additive models  25, 26. However, although this literature search strategy was well suited for identifying sources of multivariate datasets, it could appear as too specific. In order to relax the constraints due to this specificity, we complemented the data search by a networking strategy (see Networking section). Scanning strategy. Among the 368 studies resulting from the literature search, we scanned through the Introduction and Methods sections.