dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , socio-economic and cultural) of these resources to farmers, rural communities and society as a whole. The overall conclusion is that despite the fact that the livestock sector has been relatively well-organized in the application of genetic methodologies to date, there is still a large gap between the current state-of-the-art in the use of tools to characterize genomic resources and its application to many non-commercial and local breeds, hampering the consistent utilization of genetic and genomic data as indicators of genetic erosion and diversity. The livestock genomic sector therefore needs to make a concerted effort in the coming decade to enable to the democratization of the powerful tools that are now at its disposal, and to ensure that they are applied in the context of breed conservation as well as development.
Next Generation Phenotyping The need for high-resolution phenotypic data to be collected for in-depth characterization of FAnGR was identified, especially in light of the rapid advances that have been made in molecular breed characterization. Developing methods for phenotypic characterization was also identified by  Cardellino and Boyazoglu (2009)  following from FAO recommendations  (FAO, 2007a)  and has clearly remained an under-explored research area. However, with the richness of molecular data increasing dramatically since 2009, the mismatch between molecular and phenotypic data is widening for all except highly commercial transboundary breeds and lines with genomic breeding values. Inherent in high-resolution breed characterization is a need to define key phenotypic traits and characteristics (particularly those potentially involved in local adaptation) based on guidelines that can be used as common measures for such studies with stringent field protocols for their collection.
Microsatellites (STRs) vs. SNPs How to integrate data from the STRs and SNPs, and how to manage the transition from STR-to SNP-based characterization of FAnGR? 11. GW diversity statistics Which combination of parameters will be required to adequately summarize genome diversity? 12. Data management How can links between major FAnGR databases be promoted to be able to federate resources and act as an educational central point? 13. Data availability Which format should be used to make NGS, phenotyping and GIS data publicly available, and how can industry contribute toward population and maintenance of such database? 14. Participatory projects How can participatory projects, including citizen science, for example, the use of smart-phone technologies be encouraged to enable data collection on FAnGR at a large scale? 15.
Prioritization for conservation Why are prioritization methods not being applied by policy makers and managers and is there a lack of dissemination or penetrance? 16. Genomic prioritization How to implement genomic approaches systematically in conservation prioritization to include genes important in functionally valuable traits? 17. Utilization in practice How to reconcile the cost of genomic analysis vs. the economic returns on genotyped stock to allow for a wider use of genomic data to assist conservation, production and management of FAnGR? What is the demand and willingness to pay within the sector? 18. Systematic collection How to ensure that genetic and genomic data are collected sufficiently systematically to be applied to new indicators? 19. Defining goals Which indicators can be applied to most efficiently monitor genetic trends in domestic populations? 20.
Managing the Transition from Microsatellite to SNP Data The transition from microsatellite markers to SNPs has happened rapidly in FAnGR for commercial/transboundary breeds due to the availability of relatively inexpensive 50K SNP genotyping arrays for most common livestock species  (Matukumalli et al. , 2009). However, SNP arrays are not yet affordable tools for much of the world's FAnGR and are not yet available for all species (see above). This therefore raises the immediate problem of how to integrate data from the two marker types and how to manage the transition from microsatellite-based FAnGR characterization (much of which has been carried out using markers recommended by ISAG, FAO, 2011) to SNPbased characterization. One option is to re-genotype many of the breeds that already have microsatellite genotypes with SNPs (Ajmone-  Marsan et al.
, 2014) , but this would be expensive and if implemented would raise the question as to whether the new data would again be replaced by a newer technology (e. g. , wholegenome resequencing). Pragmatically, it seems that microsatellite data are perfectly adequate for estimating genetic diversity and describing demographic relationships (e. g. ,  Ferrando et al. , 2014). However, for cost reasons the full set of microsatellite markers was frequently not applied, especially in developing countries. Also, microsatellite data will not be as efficient for enabling the identification and targeted conservation of genomic regions under selection since data are usually produced with a few tens of quasi-neutral markers (e. g. ,  Herrero-Medrano et al. , 2013). Nevertheless, it is becoming clear that data produced using SNP arrays are more repeatable and do not suffer from scoring differences that have made the combination of microsatellite datasets sometimes problematic and requiring statistical evaluation  (Lenstra et al.
Data Accessibility As also identified by  Cardellino and Boyazoglu (2009)  there remains a major need to provide much better links between the major FAnGR databases, which have largely been set up independently and are breed-focused  (Groeneveld et al. , 2010). The livestock genomics community needs either to build on an existing platform (such as the ARKDB, http://www. thearkdb. org/arkdb/ and the European Nucleotide Archive, http://www. ebi. ac. uk/ena), that have some level of connectivity, e. g. , with Ensembl (http://www. ensembl. org/index. html) or to establish an independent community-based initiative(s) under the form of a user-friendly global web portal and would include web services able to federate resources and act as an educational central point. Such resources are already being developed, including the Adaptmap project for goats (http://www.
goatadaptmap. org/). Information on livestock related data should be made available and useful recommendations are required to inform stakeholders on how to record data, and where to store what type of information. In particular, it is important to promote within the community of users that raw and meta-data are key components and that they should be made available in public datasets together with elaborated datasets. When there are existing public resources for a given datatype such as those listed above, they should be used for their ability to set standards and centralize data access. For other data types, open digital repositories such as Dryad (http://datadryad. org/), Zenodo (https://zenodo. org/), or figshare (http://figshare. com/) comprise invaluable tools acting as incentives for people to maintain and upgrade their datasets as data can be submitted and authors are provided with a reference which can be cited.
This data ecosystem becomes especially important with the myriad of SNP array datasets that are now available and the incompatibility among different versions of these arrays within the same species  (Nicolazzi et al. , 2015). Moreover, to add value to genetic resources, federating gene bank resources is one step that needs to be completed by explicit connection-through geographical coordinates-with phenotypic data, but also with socio-economic, socio-demographic, climatic, environmental, and policy information. This requires links to existing online digital resources  (Joost et al. , 2010)  that are currently rarely used by the FAnGR community and need to be listed on such a global portal.
Data Availability While many genotyping projects on commercial livestock breeds are funded by industry, rendering all except summary data unavailable in many cases, in principle raw data from publicly funded projects should be made publicly available. Indeed, when data are open, it first makes the information more credible, makes data re-usable, and also enables reproducibility an important scientific principle  (Ertz et al. , 2014). Increasingly, international consortia, such as FAANG on animal functional genomics follow the Toronto protocol and immediately place data in the public domain (http://www. faang. org; The Toronto International Data Release Workshop Authors, 2009;  Andersson et al. , 2015). A next generation phenotyping database should also be established, including GIS and anonymized farm level data, animal photographs and meta-data-this could partly follow the format of the EU FP5 project Econogene (http://www.
econogene. eu) and would be most efficiently linked with FAO's DAD-IS and EFABIS (http://efabis. tzv. fal. de). The ownership and hosting of such a resource would be logistically and financially challenging, and could provide an opportunity for the agri-industry to contribute toward conservation of the genetic resources it has utilized in the past and may need again in the future. This could also be part of the community-based action mentioned above, with many advantages (logistic and funding), but requiring a strong leadership. An approach to data resourcing such has been exemplified with human data by the 1000 Genomes project (http://www. 1000genomes. org) and the 1001 Arabidopsis genomes resource (http://1001genomes. org with data being publicly available either immediately or after an agreed embargo period, could be very applicable to livestock studies.
Defining Goals The Convention on Biological Diversity's Aichi Target 13, which recommends that: "strategies have been developed and implemented for minimizing genetic erosion and safeguarding genetic diversity" is reflected in the Target for Strategic Priority Area 4 of the Global Plan of Action for Animal Genetic Resources  (FAO, 2007b). These resource indicators contribute to the measurement of progress toward Aichi Target 13  (FAO, 2012b)  and are calculated at national, regional and global levels, based on data entered by National Coordinators for the Management of Animal Genetic Resources 1  (172 countries had nominated a National Coordinator as of July, 2014) into the Domestic Animal Diversity Information System (DAD-IS). The following indicators have been agreed by the Commission on Genetic Resources for Food and Agriculture: • the number of locally adapted breeds; • the proportion of the total population accounted for by locally adapted and exotic breeds; and • the number of breeds classified as at risk, not at risk and unknown.
The Global Databank for Animal Genetic Resources, the backbone of DAD-IS, enables National Coordinators to enter breed-specific data, including data on the size and structure of breed populations, required to calculate their risk status. FAO produces biannual  Status and Trends Reports (FAO, 2015a). For the first report on The State of the Worlds Animal Genetic Resources, a risk status classification based on population size data was used. The (lack of) availability of global data currently makes a more elaborate system involving, for example, molecular diversity indices, population structure/fragmentation, pedigree data, number and size of herds, and geographic distribution inoperable. While genomic methods might help to overcome these data deficiencies, if they are to be applied to livestock conservation, it is important to define the goals of such approaches and how the data could be used to improve or augment the current set of indicators using data that could be collected on trends in effective population size, admixture, inbreeding and genome-wide diversity.
ACKNOWLEDGMENTS The  European Science Foundation (ESF)   GENOMIC-RESOURCES Research Networking Programme  (RNP) was supported by:  Fonds zur Förderung der wissenschaftlichen Forschung (FWF) ,  FWF Austrian Science Fund, Austria-Fonds National de la Recherche Scientifique (FNRS) ,  Belgium-Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO) ,  The Research Foundation-Flanders ,  Belgium-Nacionalna zaklada za znanost, visoko školstvo i tehnologijski razvoj Republike Hrvatske ,  Croatian Science Foundation ,  Republic of Croatia-Suomen Akatemia, Biotieteiden ja ympäristön tutkimuksen toimikunta ,  Academy of Finland ,  Research Council for Biosciences and Environment, Finland-Deutsche Forschungsgemeinschaft (DFG) ,  German Research Foundation ,  Germany-Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) , The  Netherlands Organisation for Scientific Research, The Netherlands-Norges Forskningsråd, The Research Council of Norway, Norway-Forskningsrådet  för miljö, areella näringar och samhällsbyggande,  Swedish Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS), Sweden-Schweizerischer Nationalfonds (SNF) ,  Swiss National Science Foundation, Switzerland-Biotechnology and Biological Sciences Research Council (BBSRC) ,  United Kingdom.
With reasonable experimental cuts, the contributions of lepton PDFs is usually extremely suppressed. On the contrary, the photon PDF contribution is not negligible and has to be carefully studied for precise predictions at the LHC and FCC-hh. We also presented representative results for BSM processes (sleptonantislepton production in the MSSM) and rare SM processes such as same-sign and/or different-flavour dilepton production. More detailed studies, including higher-order effects as well as realistic cuts and background simulations, are envisaged, since the role of the lepton PDFs could be relevant in this context. The combined QCD+QED evolution with lepton PDFs has been implemented in APFEL version 2. 4. 0 and later, publicly available at: https://github. com/scarrazza/apfel The sets of PDFs used in this work, produced with APFEL, are also publicly available in the LHAPDF6 library format from the APFEL webpage 4 : apfel nn23nlo0118 lep0, apfel nn23nnlo0118 lep0, apfel nn23qedlo0118 lept0, apfel nn23qednlo0118 lept0, apfel nn23qednnlo0118 lept0, apfel nn23qedlo0118 lept, apfel nn23qednlo0118 lept, apfel nn23qednnlo0118 lept, apfel mrst04qed lept0, apfel mrst04qed lept and: Σ u = u + + c + + t + , V u = u -+ c -+ t -, Σ d = d + + s + + b + , V d = d -+ s -+ b -, Σ = l + e + l + µ + l + τ , V = l - e + l - µ + l - τ.
Acknowledgments We are grateful to  Roberto Salerno ,  Lorenzo Calibbi ,  Stefano Frixione ,  Fabio Maltoni ,  Michelangelo Mangano ,  Juan Rojo ,  Stefano Forte  and the MG5 aMC@NLO Collaboration members for fruitful discussions and suggestions during the development of this project. This work is partially supported by an  Italian   PRIN2010  grant (S. C. ), by a  European Investment Bank  EIBURS grant (S. C. ) and by the  ERC  grant  291377 , LHCtheory: Theoretical predictions and analyses of LHC physics: advancing the precision frontier (V. B. , S. C. and D. P. ). The work of M. Z. is supported by the  ERC  grant  Higgs@LHC  and partially by the  ILP LABEX  ( ANR-10-LABX-63 ), in turn supported by  French  state funds managed by the  ANR  within the  "Investissements d'Avenir" programme  under reference  ANR-11-IDEX-0004-02.
Acknowledgements. We thank  Thorsten Mauritsen  and  Erich Roeckner  for input on the protocol for G7cirrus. We also thank  Bjorn Stevens  and the CMIP6 organizing committee for more general comments on the experimental protocol and two anonymous reviewers for their insightful comments. Ben Kravitz  and  Hailong Wang  are supported by the  Fund for Innovative Climate and Energy Research (FICER). The  Pacific Northwest National Laboratory  is operated for the  US Department of Energy by Battelle Memorial Institute  under contract  DE-AC05-76RL01830. Simulations performed by  Ben Kravitz  were supported by the  NASA   High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at  Goddard Space Flight Center. Alan Robock  is supported by  NSF  grants  AGS-1157525  and  GEO-1240507.
The  National Center for Atmospheric Research  is funded by the  National Science Foundation. Andy Jones  was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). Helene Muri  is supported by the  Norwegian Research Council project EXPECT  (grant no. 229760/E10 ) and computing time was provided by  NOTUR. U. Niemeier  is supported by the priority program 1689 of the  German Research Foundation  within project  CEIBRAL. Jana Sillmann  is supported by the  Norwegian Research Council  project  NAPEX (229778 ). Shingo Watanabe  is supported by the  SOUSEI program ,  MEXT, Japan. Edited by:  D. Roche.
Three-dimensional structure modeling of N ␣ -acetyltransferases from Arabidopsis Local sequence pairwise alignments of putative Arabidopsis Nat encoded by loci AT2G39000 (Q9ZV08) against human HsNAA50 (2PSW, Q9GZZ1) were performed with BLASTP 2. 2. 30  [25]. A tertiary structure was defined for the regions at residues 63 -112 and 195 -254 of AT2G39000 by comparative modeling against the resolved quaternary structure of 2PSW with MODELLER9. 1 (applied settings: "automodel. very_fast", [26] ). Conservation analysis and visualization was performed with the UCSF Chimera package  [27]. Functional protein domains were named according to the Conserved Domains Database (CDD, http://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtml).
LC-MS/MS analysis Analysis of the SCX-LC fractions was performed as previously described  [32]. Rapidly, selected samples are loaded on the pre-column (NS-MP-10, Nano-separation,) of the Easy nLC II system (Thermo Scientific) followed by the separation on a C18 reverse phase column (NikkyoTechnos capillary column,) at a flow of 300 nL/min on 40 min gradient. The nano-LC was coupled to an Orbitrap TM Velos (Thermo Scientific). The survey scan was acquired by Fourier-Transform MS scanning 400-2000 Da at 30 000 resolution using internal calibration (lock mass) using the Top-20 acquisition method with 20 s exclusion time. Raw data files were extracted and exported with Proteome Discoverer (Thermo Scientific, Ver. 1. 4) for ion signal higher than 1 counts and S/N higher than 1. 5.
Data processing for protein identification and quantification MASCOT 2. 4 software was used for protein identification and co-/post-translational modification characterization using the E. coli K12 strain reference proteome subset extracted from UniProtKB (version 112) which also included the sequence of the recombinant proteins AtNAA10, AtNAA15 and AtNAA70. Trypsin/P rules were used with parent and fragment mass tolerance defined as 10 ppm and 0. 7, respectively. Carbamidomethylcysteine and d3-acetyl on Lys were considered as fix modifications whereas Met-oxidation, protein NTA and d3-NTA were considered as variable modifications. All data were filtered at 1% protein false discovery rate and only peptides with score higher than 25 are retained for the final data treatment. To extract specifically N-terminal peptides, MASCOT searches were exported in xml format and submitted to an in-house script written in Python.
The parsing function searched for modifications and collected peptides with defined modifications such as d0/d3-NTA. SILProNAQ quantification required the aid of MASCOT Distiller (ver. 2. 5. 1, Matrix Science) to combine the processing of the raw data and MASCOT identification results. MASCOT distiller extraction parameters were optimized for the OrbiTrap "Top-20" acquisition files with a minimum S/N of 1, precursor charge from 1-5, Corr. Thr. > 0. 7 and no grouping assignments. MASCOT distiller submissions for protein identification were performed using the MASCOT 2. 4 identification tool against the reference proteome for E. coli K12 strain extracted from UniProtKB (version 112) using Trypsin/P cleavage rules. The parent and fragment mass tolerance were defined to 10 ppm and 0. 7 Da, respectively, with carbamidomethylcysteine and d3-acetyl on Lys as fix modifications.
Few variable modifications were used including oxidized Met (Met-Ox) and d0/d3-NTA quantitation method. Based on characterized proteins, MASCOT distiller determined NTA quantitation from the raw data. Since this tool was designed to deliver quantification values at the protein level (including all characterized peptides and not the protein N-terminus only), we developed an in-house Python script able to parse MASCOT distiller xml export files and to recalculate the NTA yield based only on the data related to the N-terminus peptides sharing a common starting position. This tool extracts a few parameters including H/L ratio, signal quality coefficient (MASCOT distiller defined parameters: fraction, E value, correlation) and peptide related data (peptide start position, MASCOT score, peptide mass error).
For each distinct N-terminus position, EnCOUTer aggregates the data passing the filtering criteria defined by the user and especially the signal quality coefficient (Corr > 0. 5, E value < 0. 1, score > 30, std. err. < 0. 1, fraction > 0. 5). Then, the geometric mean and geometric deviation was re-calculated including all data associated with the same N-terminus position that pass the filtering criteria defined by the user. H/D value is finally converted to NTA yield. Filtering parameters defined and provided by MASCOT distiller were also used in the extraction script, i. e. E val. < 0. 1, std. err. < 0. 1, MASCOT score > 30, corr. > 0. 5, fraction > 0. 5, Sc. P > 0. 3. NTA yield was determined from the d0/d3 ratio and expressed in % of NTA for the different N-termini.
Identification of potential chloroplastic N ␣ -acetyltransferases in Arabidopsis thaliana Several studies demonstrated the presence of acetylated N-termini from plastid localized proteins  [18] [19] [20]. However, the few characterized plant Nat proteins are all localized in the cytosol  [19, 23] , and thus could not be responsible for the N-terminal acetylation of these proteins. In a search of plastidic Nats, sequences of known Nats from human, yeast and Arabidopsis were used as templates in a blast search on TAIR to find unknown Nat candidates  [13, 14, 23, 33]. This sequence homology based search revealed 25 putative Nats in the Arabidopsis genome (Supporting Information Fig. 1 ). According to the SubCellular Proteomic Database (http://suba. plantenergy. uwa. edu. au/) seven of the 25 candidates are predicted to localize in chloroplast (listed in Supporting Information Table  2 ).
The protein encoded by AT2G39000 displayed the highest conservation of the acetyl-CoA binding motif RxxGxG/A, which is conserved in all major Nats of eukaryotes  [34]. Transcription of the AT2G39000 gene has been verified by qRT-PCR in roots, stem, flower, rosette and cauline leaves as according to  [35]  and was found to be approximately five-fold higher in rosette and cauline leaves when compared to non-green tissues (flower and root). However, AT2G39000 is ubiquitously transcribed during development of Arabidopsis thaliana according to public available transcriptome profiling repository databases (Supporting Information Fig. 2 ).
As a result of its broad substrate specificity AtNAA70 might be able to acetylate many of N-termini which were experimentally characterized to be acetylated in the chloroplast stroma (Fig. 4B  and C ). However, a minor number of Ile, Glu and Gln starting N-termini might not be acetylated by AtNAA70 according to the here determined substrate specificity (Fig. 4A ), suggesting the existence of other chloroplastic Nats. Prime candidates for Nats targeting N-termini of proteins starting with Ile, Glu and Gln are the six putatively chloroplast localized Nat-like proteins identified in our homology based bioinformatics screen. The MS proteomics data in this paper have been deposited in the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  [46] : dataset identifier PXD001947.