dataesr/scientific-paragraphs-categorization huggingface dataset:
C. numbers), KEGG Orthology, Gene Ontology and MetaPhOrs orthology that were integrated in the databases. In the note section for each gene/ protein page, the information relative to the annotation results are recorded to allow the researchers to evaluate the confidence for each putative function assigned to a protein  (20). Useful external cross-links, such as to NCBI's RefSeq or genomic databases of arthropod communities (i. e. AphidBase, VectorBase and Hymenoptera Genome Database), are also integrated thanks to the CycADS pipeline. Moreover, as InterProScan (70) analysis identifies functional domains, we included links to the InterPro external database identifiers when appropriate (see Figure  2  example page). It is important to underline that, as data formats in genomics can be very disparate depending on the source file format used (i.
e. GFF, Gbk), data on each arthropod genome were manually checked to ensure that a unique identifier for genes and products was present and that clear relations could be established among the different features. The flexible CycADS parsers were parameterized using its detailed configuration file  (20). Finally, all automatic tasks of Pathway Tools consistency checker were run before databases saving and publishing. With the perspective to make available an up-to-date database representative of the fast evolving field of arthropod genomics, ArthropodaCyc will be updated annually.
Discussion on features and usage The ArthropodaCyc collection of enriched BioCyc databases for arthropods whose genome has been fully sequenced and assembled is a key resource for all members of the Arthropod Genomics Consortium (http://arthropodg enomes. org/wiki/Main_Page). Our collection takes advantage of CycADS (20): a powerful annotation management system allowing to manage multiple genomes and to generate a set of BioCyc database where each organism has been annotated using identical tools and automatized procedures. Furthermore, our collection is enriched by phylogeny-based orthology predictions available in PhylomeDB/MetaPhOrs  (71, 72)  and customized hyperlinks to organism specific genome browsers. The ArthropodaCyc collection of databases takes also full advantage of the rich BioCyc interface and tools for metabolism data analysis  (73).
Several analyses can be performed using the BioCyc online interface that includes advanced query tools  (74) , and powerful web-based genomic data viewers  (75). Moreover, ArthropodaCyc offers the possibility to download the data in formats suitable for data analysis: either using other tools, such as for example Cytoscape  (76)  and MetExplore  (77) , or for use in personally developed analysis software and pipelines. We are already using ArthropodaCyc to contribute to the analysis of the metabolism in genome annotation projects currently ongoing on different insect species: the green peach aphid Myzus persicae [manuscript in preparation], the milkweed bug Oncopeltus fasciatus [manuscript in preparation](78-80) and the rice weevil Sitophilus oryzae. Beyond the single organism analysis, the BioCyc interface provides the user with tools for comparative analyses (51) to identify interesting features of a given organism metabolism that could shed light on its biology.
The interest in comparative analyses will greatly grow as more arthropods are sequenced. Even if a full comparative analysis in arthropods is beyond the purpose of this article, we provide here a few examples of its usage. ArthropodaCyc can be used for the identification of enzymes and/or pathways unique to a given organism or group of organisms. As an example, we used the present version of the ArthropodaCyc database to verify the lack of the tyrosine degradation pathway that we had originally described in the pea aphid genome by using the AcypiCyc database  (20, 21)  and comparing this genome to the few insect genomes available at that time. We could confirm that A. pisum is the only insect lacking this pathway among the 25 available in the database at present (Figure  3A ). We interpreted this loss as an explanation of the pea aphid lifestyle and we linked it to the high demand for tyrosine by pea aphids in connection with their unbalanced plant phloem sap diet.
Even though the nutrition of these insects is complemented by their primary symbiont, Buchnera aphidicola, this bacterium provides only precursors for tyrosine biosynthesis. The enzymes catalysing the last two steps of tyrosine synthesis are in fact encoded in  the aphid genome. In particular, multiple genes coding the aspartate transaminase (E. C. 2. 6. 1. 1), the enzyme involved in the synthesis of phenylalanine from phenylpyruvate are present in the pea aphid genome with one of them (ACYPI004243) specifically regulated during embryo development in aphids reproducing by parthenogenesis  (81). We thus checked whether this aspartate transaminase gene expansion was also present in the genomes of the other 27 arthropods present in the ArthropodaCyc database. Remarkably, only six out of the other 27 arthropods show a gene expansion comparable to the pea aphid (5 or 4 genes), and all the other 21 arthropods present only 2 or 3 genes encoding for this enzyme (Supplementary Table  S3 ).
Even if further phylogenetic analyses would be needed to better understand the origin of the differences in this enzyme-coding gene, this is an example of another possible use of ArthropodaCyc to easily explore the number of genes and their structures for specific enzymes (an interesting genome variation beyond the relatively rare presence/absence case of enzymatic reactions). As another example application, we also decided to explore the database to search for pathways that would be characteristic of a specific group of insects and we identified the 2-aminoadipate transaminase (E. C. 2. 6. 1. 39) in the lysine degradation pathway as uniquely missing in the genomes of the five dipteran species available in ArthropodaCyc (A. aegypti, A. gambiae, C. quinquefasciatus, D. melanogaster and G. morsitans) (Figure  3B ).
Conclusions We present here ArthropodaCyc, the most comprehensive collection of BioCyc databases for arthropods, which we expect to be of great interest for a broad community of scientists. Several genomes of arthropods are being sequenced and many more will be sequenced in the future as part of the i5K initiative. The CycADS pipeline empowers both the development and the update of the PGDB in ArthropodaCyc. Our databases are an arthropod research resource that is also linked, whenever possible, to single organism community based genomic databases, thus offering to the researchers an integrated access to different sources of annotations.
Figure1. ArthropodaCyc databases list and summary. This table shows the distribution of reactions in the Cyc databases across the six top-level categories identified by the Enzyme Commission (E. C. ). Included in this table are all reactions in each database which have been assigned either full or partial E. C. numbers, and for which an enzyme has been identified (these statistics do not include pathway holes). Phylogenetic relationships between species are displayed using a cladogram based on available data (46) (47) (48) (49) (50).
Acknowledgements The authors would like to thanks:  Daniel Lawson  ( European Bioinformatics Institute, Cambridge, UK ) for the hyperlinks to/from VectorBase (https://www. vectorbase. org/); Fabrice Legeai  and  Anthony Bretaudeau  at the  INRA BioInformatics Platform for Agro-ecosystems Arthropods (BIPAA)  (http://www6. inra. fr/bipaa)for the hyperlinks to/from AphidBase. The authors would also like to thank the following resources for genome sequence data: AphidBase (http://www. aphidbase. com/)(52), Hymenoptera Genome Database (http://hymenopteragenome. org/)( 55 ), VectorBase (https://www. vectorbase. org/)(60),NCBI genome (http://www. ncbi. nlm. nih. gov/genome/)(61),FlyBase (http://flybase. org/)(56), BeetleBase (http://beetlebase. org/)(54),OrcAE (http://bio informatics. psb. ugent.
Funding The  ANR   Blanc Program IMetSym  ( ANR-13-BSV7-0016-03 ), the  Spanish Ministry of Economy and Competitiveness grants, 'Centro de Excelencia Severo Ochoa   2013-2017' SEV-2012-0208  and  BIO2012-37161  cofounded by  European Regional Development Fund (ERDF ; to T. G. group). Funding for open access charge:  ANR   Blanc Program IMetSym  ( ANR-13-BSV7-0016-03 ).
Haplotype analyses have revealed that the association signal can best be explained by two partially correlated SNPs (rs10811661 and rs10757282, CEU r 2 = 0. 360) in the recombination interval, acting together, in cis, to impact disease risk  (25) (26) (27). European ancestry GWAS have also previously hinted at a distinct association signal at this locus, mapping to the non-coding CDKN2B-AS1 (ANRIL) transcript (  4 ). Through exact conditioning, our transancestral meta-analysis has demonstrated that the association at this locus can be delineated by two distinct signals (Supplementary Material, Fig. S3 ), both of which map to the 12 kb intergenic recombination interval described above (rs10965246, MANTRA log 10 BF = 37. 45, P = 8. 4 × 10 -40 ; rs10757282, MANTRA log 10 BF = 10. 31, P = 2. 0 × 10 -12 ).
Acknowledgements L. P. is a recipient of a Ramon y Cajal contract from the  Spanish Ministry of Economy and Competitiveness  ( RYC 2014-0069 ) and a  Rising Star Award  from the  European Foundation for the Study of Diabetes (EFSD). J. C. F. is a  Massachusetts General Hospital Research Scholar. N. K. is a recipient of the Okinga Scholarship, and thanks  Dr Shoichi Okinaga ,  Dr Hiroko Okinaga  and other staff at  Teikyo University, Japan. M. I. M. is a  Wellcome Trust Senior Investigator  and  NIHR Senior Investigator. A. P. M. is a  Wellcome Trust Senior Research Fellow in Basic Biomedical Science. The BBJ study thanks all the participants and the staff of the  BioBank Japan  project. The CAGE study thanks  Dr Eitaro Nakashima ,  Dr Jiro Nakamura ,  Dr Mitsuhiro Yokota  and  Dr Masato Isono  for their assistance in collecting the DNA samples and the accompanying clinical information, and genotyping.
Funding This work was supported by  Action on Hearing Loss  ( G51 ); American Federation for Aging; Einstein Glenn Center; Association Francaise des Diabetiques;  British Heart Foundation  ( SP/04/002 );  CARDIOMED BSC0122(6 )-CSIR, India ;  Centre Hospitalier Universitaire Poitiers ; CNAMTS;  Endocrinology-Diabetology Department of the Corbeil-Essonnes Hospital ;  European Union  ( FP7.
Preliminaries -Image Registration In this work, "elastix"  [33, 34] , an open-source software initially designed for medical image registration, is adapted for DIC application, i. e. , the assessment of displacement and strain fields. There is actually no genuine difference between these two terms: DIC and image registration. The former is usually specific to the field of solid mechanics, and the later represents a more general concept widely employed in the fields of computer vision  [35] , medical imaging  [36] , etc. In the present section the employed framework of image registration method will be briefly outlined, partly as a means to introduce the chosen notation. Two images are involved in the image registration, which characterize the original and deformed surface of a material subjected to a known loading.
Approximation Approach and Validation The projection basis represents the heterogeneous deformation pattern appearing on single crystal surface: the active  slip bands demonstrate a different deformation mechanism than the interslip bands, and the interface between each pair of them acts actually as a kinematic discontinuity. If this discontinuity is not taken into account in the strain calculation from displacement fields, using a general differential approach, e. g. , finite difference method, may produce important errors due to the displacement jumps at the slip interfaces. Concerning the projection method, the displacement data in each band of projection basis are expected to be fitted into an analytical function, then the strain in each band can be differentiated analytically. In this way, the strains are evaluated by taking into consideration of the deformation discontinuities appearing on single crystal surface.
Acknowledgements. We acknowledge  Y. Trémolet  and  M. Fisher  for providing the OOPS DA library and the QG model sources. We thank  Philippe Moinat  for the ASIS model sources and technical help. We finally thank  Gérald Desroziers  for the useful discussions on the 4DEnVar algorithm and the anonymous referees for their contribution in improving the manuscript. This work was supported by the  HERMES  project, funded by the  French LEFE INSU program.
This will provide us with valuable flexibility for interpreting data in a wide kinematic regime. The following sections highlight the physics opportunities in measuring PDFs, TMDs and GPDs to map out the quarkgluon structure of the proton at the EIC. An essential feature throughout will be the broad reach of the EIC in the kinematic plane of the Bjorken variable x (see the Sidebar on page 18) and the invariant momentum transfer Q 2 to the electron. While x determines the momentum fraction of the partons probed, Q 2 specifies the scale at which the partons are resolved. Wide coverage in x is hence essential for going from the valence quark regime deep into the region of gluons and sea quarks, whereas a large lever arm in Q 2 is the key for unraveling the information contained in the scale evolution of parton distributions.
The figure also shows the vast expansion in x, Q 2 reach that an EIC would provide, as will be discussed below. Over the past 15 years, an additional powerful line of experimental study of nucleon spin structure has emerged: semi-inclusive deep-inelastic scattering. In these measurements, a charged or identified hadron h is observed in addition to the scattered lepton. The relevant spin-dependent structure function, g h 1 (x, Q 2 , z) = 1 2 q e 2 q ∆q(x, Q 2 )D h q (z, Q 2 ) + ∆q(x, Q 2 )D h q (z, Q 2 ) , (2. 11) depends on fragmentation functions D h q,q (z, Q 2 ), where z is the momentum fraction that is transferred from the outgoing quark or anti-quark to the observed hadron h. The non-perturbative fragmentation functions are at present determined primarily from precision data on hadron production in e + e -annihilation through perturbative QCD analysis  [37, 38, 39, 40, 41].
Data from the B-factories and the LHC are helping to further improve their determination  [41]. Also measurements of hadron multiplicities at an EIC would contribute to a better knowledge of fragmentation functions. Insights from the semi-inclusive measurements are complementary to those from the inclusive measurements. Specifically, they make it possible to delineate the quark and anti-quark spin contributions by flavor, since ∆q and ∆q appear with different weights in Eq. (2. 11). A large body of semi-inclusive data sensitive to nucleon helicity structure has been collected by the experiments at CERN  [42, 43, 44]  and DESY  [45]. , where p T is the observed jet (pion) transverse momentum, and an x value that is representative for the measurement at that scale. The x-ranges probed at different scales are wide and have considerable overlap.
Their reach in x and Q 2 is also indicated in Fig. 2. 5. Unlike DIS, the processes used at RHIC do not probe the partons locally in x, but rather sample over a region in x. RHIC also provides complementary information on ∆u, ∆ū, ∆d, ∆ d for 0. 05 < x < 0. 5  [56, 57, 58, 59] , with a beautiful technique that exploits the violation of parity (mirror symmetry) in nature and does not rely on knowledge of fragmentation. The carriers of the charged-current weak interactions, the W bosons, naturally select left-handed quarks and right-handed anti-quarks, and their production in p+p collisions at RHIC and calculable leptonic decay hence provide an elegant probe of nucleon helicity structure. Combined next-to-leading order QCD analyses  [60, 61, 62, 63]  of the published data from inclusive and semi-inclusive deepinelastic scattering and from p+p scattering at RHIC have been performed, which provide the best presently available information of the nucleon's helicity structure.
This requires a new suite of measurements, using exclusive processes such as deeply-virtual Compton scattering and transverse-spin asymmetries. The associated physics and the prospects of measurements at an EIC will be described in Sections 2. 3 and 2. 4. The envisioned polarized electron ion collider brings unique capabilities to the study of nucleon spin. Its high center-of-mass energy of up to √ s = 173 GeV affords access to a vast region in x and Q 2 that will probe 1 ÷ 2 orders of magnitude smaller values in x than the body of existing and forthcoming data and comparably harder scales Q 2 , as is clearly visible from Fig. 2. 5. The high luminosity and polarization will allow one to do so with precision using a suite of probes (see Table  2. 1). In this way, the EIC aims to provide answers to the questions raised above.
The EIC will also vastly expand our knowledge of the quark flavor structure, a key element in mapping out the proton "landscape". A powerful measurement available to achieve this is semi-inclusive deepinleastic scattering which at the EIC would extend to much higher Q 2 than in fixedtarget scattering, where the interpretation becomes significantly cleaner, less afflicted by corrections suppressed by 1/Q 2 , and better tractable theoretically. The kinematic coverage in x and Q 2 will be similar to what can be achieved in inclusive scattering. 1. 3×10 -4 (+36) 2. 1×10 -4 (+31) 3. 3×10 -4 (+27) The data set for each x is offset by a constant c(x) for better visibility. The bands indicate the current uncertainty as estimated in the "DSSV+" analysis (see text). 5. 2×10 -4 (+24) 8. 2×10 -4 (+21) 1.
A minimum Q 2 of 1 GeV 2 was imposed, as well as W 2 > 10 GeV 2 , a depolarization factor of the virtual photon of D(y) > 0. 1, and 0. 01 ≤ y ≤ 0. 95. Figure  2. 6 shows the pseudo-data for the inclusive structure function g 1 (x, Q 2 ) of the proton versus Q 2 at fixed x. Collisions at √ s 70 GeV at an EIC are seen to provide access to x values down to about 2 × 10 -4. The anticipated size of the asymmetry A 1 (x, Q 2 ) g 1 (x, Q 2 )/F 1 (x, Q 2 ) at these x values is O(10 -3 ), which sets the scale for the required data samples and control of experiment systematics. These and other aspects are discussed further in Sec-tion 6. Data from a higher-energy EIC, shown for electron beam energies up to 20 GeV, is seen to provide access to significantly smaller x and larger Q 2. As demonstrated in Fig.
For comparison, the present uncertainty bands are also displayed. As one can see, an impressive reduction in the width of the bands would be expected from EIC data, in particular, towards lower values of x. Ev-idently, extractions of ∆g from scaling violations, and of the light-flavor helicity distributions ∆u, ∆d and their anti-quark distributions from semi-inclusive scattering will be possible with exquisite precision. With dedicated studies of kaon production, the strange and anti-strange distributions will also be accessible. All this is anticipated to yield new insights into the question of why it is that the combined quark and anti-quark spin contribution to the proton spin turns out to be so small. [60, 61]  (light bands) and with EIC data (darker bands), using projected inclusive and semi-inclusive EIC data sets (see text).
Note that for this analysis only data with x ≥ 10 -3 were used, for which Q 2 ≥ 2. 5 GeV 2. Right: χ 2 profiles for the truncated x integral of ∆g over the region 10 -4 ≤ x ≤ 10 -2 with and without including the generated EIC pseudo-data in the fit. Results are shown for three different EIC center-of-mass energies. The right part of the figure shows the χ 2 profile of the truncated first moment of the gluon helicity distribution, 0. 01 0. 0001 dx∆g(x, Q 2 ), at Q 2 = 10 GeV 2 , again compared to the "DSSV+" estimate. Also here, the impact of EIC data is evident. One also observes the importance of high energies. For instance, running at the highest energy clearly constrains the small-x region much better. Overall, the EIC data greatly improves the χ 2 profile, even more so when all data in Fig.
2. 6 are included. The light shaded area in Fig. 2. 9 displays the present accuracies of the integrals of ∆Σ and ∆g over 0. 001 ≤ x ≤ 1, along with their correlations. The inner areas represent the improvement to be obtained from the EIC, based on the global analysis studies with pseudo-data described above. We stress that similar relative improvements would occur for any other benchmark set of polarized parton distribution functions, such as the latest DSSV  [62]  set. The results shown in the figure clearly highlight the power of an EIC in mapping out nucleon helicity structure. The anticipated kinematic range and precision of EIC data will give unprecedented insight into the spin contributions S q and S g. Their measurements, by subtracting from the total proton spin 1/2, will provide stringent and independent constraints on the total contribution of quark and gluon orbital momenta, L q +L g.
Besides polarized proton beams, the EIC design envisions beams of polarized deuterons or helium-3. The neutron's g 1 (x, Q 2 ) can thus be determined, potentially with a precision that is comparable to the data on g 1 (x, Q 2 ) of the proton. The difference of the moments of proton and neutron g 1 (x, Q 2 ) allows a test of the fundamental sum rule by Bjorken  [75]. The data from polarized fixed target experiments have verified the sum rule to a precision of about 10% of its value. The extended kinematic range and improved precision of EIC data allow for more stringent tests of this sum rule, as well as its corrections, to an accuracy that is currently anticipated to be driven mostly by advances in hadron beam polarimetry (cf. Section 6. 2. 5). 9: Accuracies for the correlated truncated integrals of ∆Σ and ∆g over 0.
To illustrate the achievable statistical precision, the event rate for the production of π ± in semi-inclusive DIS was used, see, for example, Fig. 2. 15. This new set of pseudo-data was then analysed like the real data in Ref. [77]. Fig. 2   showed the total up quark Sivers function, while Fig. 2. 16  shows the valence and the sea quarks separately. Here, we emphasize the importance of the high Q 2 reach of the EIC for SIDIS measurements. Most of the existing experiments focus on the Q 2 range of a few GeV 2. The EIC will, for the first time, reach Q 2 values up to hundreds and more GeV 2. This will provide an unique opportunity to investigate the scale evolution of the Sivers asymmetries, which has attracted strong theoretical interests in the last few years  [85, 86, 87, 88, 89, 90]. As a leading power contribution in the spin asymmetries, the associated energy evolution unveils the underlying strong interaction dynamics in the hard scattering processes.
Acknowledgments This white paper is a result of a community wide effort through the many EIC workshops organized by the physics communities associated with both BNL and  JLab , which culminated in the INT Report [2]. We thank the following colleagues who made valuable comments to the draft of this document:  Christine Aidala  ( Univ. of Michigan ),  Yasuyuki Akiba  ( RIKEN, Japan )  Kieran Boyle  ( RIKEN BNL Research Center at BNL ),  Jian-Ping Chen  ( Jefferson Laboratory )  Leonard Gamberg  ( Penn State University )  Yuji Goto  ( RIKEN, Japan ),  John Harris  ( Yale University ),  Thomas Hemmick  ( Stony Brook University ),  Barbara Jacak  ( Stony Brook University ),  Peter Jacobs  ( Lawrence Berkeley Laboratory ),  Zhongbo Kang  ( Los Alamos National Laboratory ),  David Kaplan  ( Institute of Nuclear Theory & U.