dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection of selection signatures We detected five significant regions along the caprine genome in our dataset. The selection signatures detected in this study thus could have resulted from natural selection or from an individual selection of breeders over the centuries. The latter concerns mainly production traits for economic reasons and morphologic traits for other reasons such as coat color, morphology, hardiness, etc. In each significant region, therefore, we looked at genes that could be related to one of these reasons more specifically. In our detection of selection signatures, we were looking for either 'hard sweeps', which means a rapid fixation of an initially rare variant in a population, or 'soft sweeps', which means a selection on standing variation. Indeed, the hapFLK differentiation-based approach can detect these two selective signals.
Among the 74 protein-coding genes in the significant region on chromosome 5, the ADAMTS20 gene (coding for a disintegrin-like and metalloprotease with thrombospondin type-1 motifs) was considered a good functional candidate, as it has been shown to modify coat color patterns in mice  (Rao et al. 2003; Baxter et al. 2004; Silver et al. 2008) , which match some breeds in our dataset. In addition, this gene was closest to the most significant SNP in the haplotype analysis and quite close to the two significant SNPs identified with the SNP approach, making it also a good positional candidate. In mice, mutations in the ADAMTS20 gene cause the emergence of a belted phenotype (white spotting of the dorsal and ventral torso), which results from a defect in melanocyte development. Local examination of the allele and haplotype differentiation patterns in the signature (Fig.
58 as an evolutionary hub. This representation provides information on positions that covary with the P2. 58 pattern. However, this arrangement is dependent on the number of top pairs selected. To obtain a representation of the covariation data independent of a user selected parameter, we carried out the principal component analysis (PCA)  [20]  of the double-centered covariation matrix (specifically, an eigen-decomposition of this matrix), obtained from the MSA of human class A GPCRs (S1 File). The next step was to apply the same approach to the MSA of the human CHEM sub-family (S2 File). This analysis (Fig  1C ) highlights residues associated with the divergence of chemokine receptors on the first component and the split between homeostatic and inflammatory receptors on the second component.
History of the sodium binding site In the two crystal structures of sodium bound P2. 58 receptors, the δ-opioid receptor (OPRD, PDB entry 4N6H)  [21]  and the proteinase activated receptor 1 (PAR1, PDB entry 3VW7)  [22] , positions 3. 35 and 7. 45 participate in sodium binding, either directly (N3. 35 in OPRD) or through a water molecule (N7. 45 in OPRD and N3. 35/S7. 45 in PAR1). This ion acts as a negative modulator of GPCRs and stabilizes the inactive structure  [18]. Mutations of N3. 35 to smaller residues (Ser or Ala) in CXCR4  [23]  and CXCR3  [24]  yield constitutively active mutants. In the closely related angiotensin II receptor AT1, the N3. 35G mutant has also high constitutive activity  [25]. The presence of a glycine residue at position 3. 35 in inflammatory chemokine receptors is thus surprising, and this prompted us to investigate the history of position 3.
45H mutation. Our molecular dynamics simulations of CXCR4 and CCR5 indicate that H7. 45 participates in sodium binding. Indeed, in the neutral state, one nitrogen atom of the imidazole ring (usually NE2) has a lone-pair of electrons that allow His to act as a Lewis Base to form coordination complexes. Coordination to divalent ions (e. g. , Fe ++ , Zn ++ , Ni ++ , Cu ++ ) is frequently found in proteins  [26]. Coordination to the sodium ion is less frequent but is also observed either in model systems  [34]  or in proteins  [26]. The distance of about 2. 5 Å (Table  2 ) that we observed is in agreement with statistical analysis of the Cambridge Structural Database  [35]. In chemokine receptors (Fig  2B ), H7. 45 can only be substituted by Arg, which allows direct salt bridge interaction with D2.
The stability of the sodium binding site is reinforced by the face-to-edge interactions between H7. 45 and W6. 48 (Fig 4A). This geometry should strongly stabilize the inactive state of CXCR4 in the absence of agonists. We verified that the four residues involved in sodium coordination are conserved from P. Marinus (Sea Lamprey, Uniprot access number: Q802H1) to humans. A very different pattern is observed in CCR5. The absence of side chains at positions 3. 35 and 3. 39 creates a large water-filled pocket (Fig  6D ). Sodium is highly mobile within this pocket and experiences different binding modes that depend on the rotameric states of W6. 48 and H7. 45 and involve two to five water molecules (Fig  6 ). Different binding modes of the sodium ion within the allosteric binding pocket have also been reported in MD simulations of A2A adenosine receptor  [39] , opioid receptors  [30, 40]  and dopaminergic D2 receptor  [41].
Sequence analysis The analyzed sequences included non-redundant sets of class A GPCRs from different genomes: H. sapiens, D. rerio, B. floridae, C. elegans, N. vectensis, and T. adhaerens. All the sets were prepared, aligned, and assigned as described previously  [16]. In this analysis, B. Floridae was chosen as the representative chordate because it corresponds to a reference proteome (www. uniprot. org). The DARC/Duffy/ACKR1 atypical chemokine receptor was not included in the human set because of its low sequence identity with other chemokine receptors (< 16%) and loss of typical class A sequences patterns in TM1, TM2, TM3, and TM5, making it a remote outlier. Neighbor joining trees were obtained with the MEGA5 software  [57] , using the Dayhoff matrix option and 500 bootstrap replicates.
Sequence logos were obtained from the WebLogo site (web. logo. berkeley. edu) [58]. Covariation analysis was carried out on two sequence sets from humans: the non-redundant set of class A GPCRs (282 sequences) and the CHEM subfamily subset (46 sequences)  [15]. The sequence analyses were performed using positions with less than 2% gap in the MSA (extended transmembrane helices). The MSAs that were used for sequence analysis are available as Supporting Information (S1, S2 and S3 Files for, respectively, the human class A set, the human CHEM set, and the SO receptors from the six species investigated). The GPCR positions were numbered by reference to the most conserved position i. 50 in each helix i, according to Ballesteros' numbering  [59]. In CXCR4, the anchor residues are: N56, D84, R134, W131, P211, P254, and P299.
Covariation analysis The covariation score between any two positions i and j in the alignment of length N was measured by the OMES method  [60]  which is suited to find sequence covariation related to family divergence  [17]. The covariation matrix, denoted COV, was subsequently double-centered to give the matrix S according to the formula: S ¼ I À 1 N 1 ! Â COV Â I À 1 N 1 ! T ð1Þ where I denotes the N by N identity matrix, and 1 an N by N matrix of ones. The matrix S was subsequently analyzed by principal component analysis (specifically, by an eigen-decomposition)  [20] : S ¼ UΛU T ð2Þ where U and Λ are, respectively, the matrix of the eigenvectors and the diagonal matrix of the eigenvalues of S. The coordinate matrix (a. k. a. , factor scores matrix  [20, 72] ), denoted F, gives the coordinates of each position of the alignment on the principal component axes and is computed as: F ¼ UΛ 1 2 : ð3Þ The analysis was performed with functions written in the R programing language.
Molecular modeling The models of CXCR4 (residues 28 to 319) and CCR5 (residues 19 to 313) with a bound sodium ion were built with MODELLER version 9. 8  [61] , using as templates the crystal structure of CXCR4 (PDB entry 3ODU)  [11]  and CCR5 (PDB entry 4MBS)  [10] , respectively, and the high resolution (1. 8 Å) crystal structure of the δ-opioid receptor (PDB entry 4N6H)  [21]  that includes a bound sodium ion interacting with D2. 50 and N3. 35. The water molecules contributing to the coordination of the sodium ion were included in the models. The three templates correspond to receptors in inactive states. The stabilizing mutations present in the crystallized CXCR4 and CCR5 receptors were reversed to the amino acids present in the human wild type receptor. The ICL3 loops that were replaced by the lysozyme and rubredoxin in the crystallized CXCR4 and CCR5, respectively, were reversed to the human sequences of the corresponding receptors.
Molecular dynamics simulations Molecular dynamics simulations of the CXCR4 and CCR5 models embedded in an hydrated POPC bilayer were carried out using NAMD v2. 9 MD software  [66]  and the CHARMM36 parameter set  [67, 68]. They were performed using the HPC resources of IDRIS, granted by GENCI (www. genci. fr). The entire assembly was subjected to energy minimization for 5000 steps to remove close contacts between atoms. Equilibration of the system was carried out with a modified version of a protocol developed elsewhere  [69]. The protocol included six interlinked equilibration steps in which harmonic restraints were gradually taken off to achieve a smooth relaxation, for a total of 1 ns. Then a 20 ns equilibration step was carried out under the same conditions as the production run to achieve stable conditions.
In the first two equilibration steps, the NVT ensemble at 310 K and time step of 1 fs were used. The following equilibration and production steps were carried out at constant temperature (310 K) and pressure (1 atmosphere), using a 2 fs time-step for integration. The Particle Mesh Ewald method (PME) was used to calculate the electrostatic contribution to non-bonded interactions with a cutoff of 12 Å. The cutoff distance of the van der Waals interaction was 12. 0 Å. The SHAKE algorithm was applied to the system. Each trajectory lasted 420 ns (20 ns for equilibration and 400 ns for production). Three and five replicates were carried out for CXCR4 and CCR5, respectively. The initial assembled models for the different runs and the scripts used for the equilibration and production steps are available as Supporting Information (S4 File) A CCR5 trajectory displaying transient contacts to a putative alternative site was extended to 1 microsecond (Fig  7A ).
We used the 20-120 ns range of classical MD simulations to calculate the acceleration parameters. The snapshot obtained after 120 ns of classical MD simulations was used to initiate the accelerated simulation (S5 File). This technique allowed obtaining snapshots with the sodium ion interacting with the carbonyl oxygen of G3. 35 and the hydroxyl oxygen of S2. 49. A typical snapshot with the sodium ion in this position (S6 File) was used to restart the classical MD protocol to study the stability of this putative sodium site. Five replicates of 200 ns were performed and a representative run was extended to 400 ns. Trajectories were analyzed with the VMD software  [72]  and the Bio3D R package  [73] , using in house-developed scripts. PYMOL (DeLano Scientific LLC, San Francisco, USA) was used for figure preparation.
). (D-F) Logo plots of positions that are highlighted by PCA analysis in different GPCR sets. In (D), human class A GPCRs are split into P2. 58 receptors (top) and the complementary sequences (bottom). In (E), the human CHEM sub-family is split into chemokine receptors (top) and the complementary sequences (bottom). In (F), the human chemokine receptors (chemR) are split into homeostatic (top) and inflammatory chemokine receptors (bottom). (G, H) Ribbon representation of CXCR4 (G) and CCR5 (H), with highlighted positions shown as sticks. The color code is the same as in plots (B) and (C). The CXCR4 and CCR5 models are based on the PDB entries 3ODU and 4MBS, respectively, as described in Methods. https://doi. org/10. 1371/journal. pcbi. 1006209. g001 We also analyzed position 7. 45 in the same set of SO receptors (Fig 2A).
This work was supported by the grant  ANR-11-BSV2-026  from  Agence Nationale de la Recherche  to MC, by the grants  2016-100567  and  2017-100567  from  GENCI (Grand equipement national de calcul intensif)  to MC, by the grant MIR from the  University of Angers  to MC, and by institutional grants from  CNRS ,  INSERM , and the  University of Angers. The funders had no role in.
3. 35 35 ). (D-F) Logo plots of positions that are highlighted by PCA analysis in different GPCR sets. In (D), human class A GPCRs are split into P2. 58 receptors (top) and the complementary sequences (bottom). In (E), the human CHEM sub-family is split into chemokine receptors (top) and the complementary sequences (bottom). In (F), the human chemokine receptors (chemR) are split into homeostatic (top) and inflammatory chemokine receptors (bottom). (G, H) Ribbon representation of CXCR4 (G) and CCR5 (H), with highlighted positions shown as sticks. The color code is the same as in plots (B) and (C). The CXCR4 and CCR5 models are based on the PDB entries 3ODU and 4MBS, respectively, as described in Methods. https://doi. org/10. 1371/journal. pcbi. 1006209. g001 We also analyzed position 7. 45 in the same set of SO receptors (Fig 2A).
Nanoparticle average hydrodynamic diameters (D H ) and polydispersity indexes (PdI) were determined by Dynamic Light Scattering (DLS) (Malvern Zetasizer Nano-ZS, Malvern Instruments, Worcestershire, UK). Each nanosuspension was analysed in triplicate at 20 • C at a scattering angle of 173 • , for each sample, after 1/20 dilution in water. Pure water was used as a reference dispersing medium. ζ-(zeta) potential data were collected through electrophoretic light scattering at 20 • C, 150 V, in triplicate for each sample, after 1/20 dilution in water. The instrument was calibrated with a Malvern-68 mV standard before each analysis cycle. The shape and the surface morphology of the nanogels were investigated by atomic force microscopy (AFM) (Catalyst, BrukerNano) in tapping mode. Samples were prepared by placing a drop of nanoparticle suspension on a freshly-cleaved mica sheet and the experiments were performed in fluid tapping mode to keep the integrity of the NPs.
Intracellular ROS Measurement Intracellular ROS generation was measured using 2 ,7 -dichlorofluorescein diacetate (DCFH-DA) (Sigma-Aldrich, St. Louis, MO, USA). Briefly, a 10 mM DCFH-DA stock solution was diluted 1000-fold in free of serum cell culture medium to yield a 10 µM working solution. After exposure of SVEC4-10 cells to 2. 5 µM and 10 µM of GdCA⊂CS-TPP/HA nanogels (GdCA = GdDOTA or GdDOTP) for 6 and 24 h, these were washed with PBS and incubated in a 2 mL working solution of 10 µM DCFH-DA for 45 min at 37 • C in the dark. Then, the cells were washed with PBS and resuspended in PBS for analysis. The intensity of DCF fluorescence was measured at an excitation and emission wavelengths of 485 and 530 nm, respectively, using a FlexStation 3 Multi-Mode Microplate Reader from Molecular Devices LLC and the SoftMax Pro software.
The slides were washed twice for 5 min with pre-chilled deionized water and then, these were electrophoresed in a horizontal chamber at low voltage (300 mA, 25 V) for 30 min. Following electrophoresis, the slides were immersed in 70% ethanol for 5 min. The samples were allowed to air dry, stained with a Vista Green DNA dye for 15 min and visualized by epifluorescence microscopy (Olympus IX 71, Tokyo, Japan) using a fluorescein isothiocyanate (FITC) filter. Data were analysed using the Open Comet program. The percentage of DNA in tail was selected as indicator of DNA damage.
Acknowledgments: The work was supported by  ANR  ( Gadolymph  project n •  ANR-13-ENM2-0001-01 , through the EuroNanoMed 2013 framework) and by the  Romanian National Authority for Scientific Research CCCDI-UEFISCDI  (project number  4-006/2014 , EuroNanoMed II), by the  Region Champagne Ardenne , the  DRRT Champagne Ardenne (through MESR)  and the  EU-program FEDER  ( Project NanoBio2 ,  Nano'Mat Platform). Amandine Destrebecq  is thanked for her help with ICP-OES titrations.
and they thus present a useful framework within which the presence or absence of Fv1 can be established. To initially investigate the presence of Fv1 immediately beyond the genus Mus, we analyzed three sequences from the genus Apodemus: Apodemus sylvaticus from an archived genome assembly and Apodemus uralensis and Apodemus semosus from targeted assemblies of the region. Fv1 was present in all instances, with complete ORFs visible in A. sylvaticus and A. uralensis. Both ORFs exhibited activity directed against MLVs when assayed for restriction capacity (Table  1 ). The divergence of the Apodemini and Murini tribes predates that of the Murini and Praomyini  (14) , represented by Mastomys, and these data presented likely confirmation of the previously published Southern blotting data  (12). Encouraged, we thus sought to extend this analysis to reexamine the point of insertion of Fv1's progenitor virus.
ERV-L-like provirus within Spalax galili that surrounded Fv1, including remnants of the 5′ LTR (Fig. 1 ). While only this species was found to contain such regions, it nevertheless remained feasible to use consensus data to better represent the ancestral gag gene from which Fv1 derives. We thus conducted a more detailed search of basal genera within the Muroidae. Fv1 was amplified and sequenced for the murines Phloeomys pallidus and Chiropodomys gliroides and the nesomyine Nesomys audeberti. Separately, the region was assembled from published whole genome sequencing (WGS) data for the cricetoyine Cricetomys gambianus. Again, no intact ORFs were determined, but together, these complete and fragmentary sequences were aligned with those previously identified and used to build a consensus model with a 1,320-bp ORF (Dataset S1).
Central coloring represents residue involvement (SI Appendix, Fig. S1B ) [above: low (blue) to high (red)] and structural predictions from Jpred4 [below: no prediction/unstructured (black), alpha helix (green), and beta sheet (gold)]. The previously identified variable regions, V A and V B  (11) , are boxed. combination (SI Appendix, Fig. S1B ). Again, however, this did not support the continued annotation of V C. Analyses of likely secondary structures with this larger dataset confirm previous α-helix predictions within the N-terminal region, thought to form a coiled coil required for dimerization, as well as the presence and length of the unstructured linker region (19) (Fig. 4 ). Overall, 10 of 14 and 6 of 7 sites under pervasive and episodic positive selection, respectively, were within the Cterminal region, which has been shown to confer restriction specificity  (19).
83  (43) ]: λ sub = μ sub × 3 × P stop × ℓ λ indel = μ indel × P shift × ℓ. The half-life, t 1=2 , and mean lifetime, τ, are thus calculated with standard formulas: t 1=2 = lnð2Þ = λ sub + λ indel τ = 1 = λ sub + λ indel. Mutation Simulation. Two Python programs, mutator (commit c9ae773) and orf_scanner (commit 7d70b14), were written to simulate mutational processes and to assess their impact on ORF length. ORF retention rates for 1,000 replicates were used to fit a standard model for exponential decay within R and to derive mean lifetime, τ, and half-life, t 1=2. Software. The programs developed for this study are available under permissive license at https://github. com/A-N-Other/pedestal. We encourage code reuse and comment. Other Methods. Full materials and methods are included in SI Appendix.
ACKNOWLEDGMENTS. We acknowledge the museums that provided samples for this study-without these excellent collections, this work would not have been possible. We thank  Milos Macholán  ( Czech Academy of Sciences ),  Ond rej Mikula  ( Czech Academy of Sciences ),  François Catzeflis  ( University of Montpellier ),  Jean-Pierre Quéré  ( French National Institute for Agricultural Research ), and  Serge Morand  ( CIRAD/University of Liège ) for samples. We also thank  Aris Katzourakis  ( Oxford University ) for his thoughtful comments on the manuscript. G. R. Y. , M. W. Y. , and J. P. S. were supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001162 ), the  UK Medical Research Council  ( FC001162 ), and the  Wellcome Trust  ( FC001162 ). S.
Consequently, the use of national registries to generate real-world data is of great value, particularly in complex diseases such as inflammatory bowel disease (IBD), namely Crohn's disease and ulcerative colitis. Iron deficiency anemia (IDA) is ranked as one of the world's ten leading causes of years lived with disability  [6]  and can manifest in a broad range of symptoms, including chronic fatigue, increased susceptibility to infection, tachycardia, sleep disturbance, impaired cognitive function, hair loss, and changes to skin and nails  [7]. Iron deficiency without anemia (ID) and IDA are two of the most common systemic complications of IBD; estimates of prevalence indicate that ID occurs in 60%-80% of people with IBD, with the prevalence of anemia ranging from 16% to 74%  [8]. For patients with IBD, IDA can be caused by a variety of factors [8] and affect a range of physical, personal, and social parameters, impairing work productivity and ultimately compounding the diminished quality of life (QoL) associated with IBD alone  [9].
The economic burden of IBD is elevated by the presence of IDA; a retrospective review of healthcare claims data in the US estimated the direct treatment costs for each IBD patient to be substantially higher for those with anemia versus those without  [10]. The natural history of IDA in IBD is not fully understood and its occurrence remains underestimated  [11]  , so patients are potentially undertreated. The gaps in our understanding require comprehensive longitudinal data collection-an important source of which may be the collection of iron parameter data from patient registries. The objectives of this review article are to describe the status of current European IBD registries and the data they generate, to highlight the potential role of IBD registries in collecting IDA data and contributing to our understanding of IDA, and to provide practical information in regard to the incorporation of collated iron parameter data into IBD registries.
Registry searches A literature search was conducted to identify IBD registries that are currently collecting data in Europe, using the PubMed online database and internet searches. The search term "inflammatory bowel disease registry" was used and results were reviewed to identify articles meeting the inclusion criteria. The reference lists of any article meeting the inclusion criteria were reviewed manually to identify additional relevant publications. Where articles were identified, the name of the registry was used as a search term in the Google internet search engine in an attempt to find further relevant information. Where a web page did not provide information in English, translation software was used to determine if the information was relevant. For inclusion in this review, studies were required to have met the following criteria, in regard to the registry:  (1)  Being disease-specific with information on IBD; (2) being located in a European country; and (3) currently collecting data.