dataesr/scientific-paragraphs-categorization huggingface dataset:
This product is available for the time period January 1993 to the present. It is provided by the Copernicus Marine Environment Monitoring Service (CMEMS). Associated absolute geostrophic velocities derived from this product were used to compare with the trajectories of the seabirds to establish possible relations between the seabird drift data and the surface geostrophic currents. An eddy identification and tracking tool (py-eddy-tracker  32  ) was applied to the gridded ADT data to identify mesoscale eddies in the region of study during the seabird campaigns. The algorithm searches for closed ADT contours and then applies several tests to decide if a feature is an eddy. Version 3. 0 of the eddy tracker is used. We explored the ADT contours of the identified eddies to assess the proportions of seabirds that settle inside or outside of the eddy boundaries.
Wind product. We used the Cross-Calibrated Multi Platform version 2 (CCMPv2) gridded surface L3 ocean vector wind analysis product. This dataset  44  is produced by combining Version-7 RSS radiometer wind speeds, QuikSCAT and ASCAT scatterometer wind vectors, moored buoy wind data, and ERA-Interim model wind fields using a Variational Analysis Method (VAM). It has a temporal resolution of 6 hours and a spatial resolution of ¼° × ¼°. Wind fields provided by this product in the western Mediterranean Sea were compared with the positions of tracked seabirds to establish a possible relationship between the drift data and the wind patterns. Surface buoy data. Data provided by three surface buoys from the Spanish Deep-Water Buoy Network deployed by Puertos del Estado along the eastern Spanish mainland coasts and the Balearic Islands over the 200 m isobath were utilized for validation of both surface geostrophic currents derived from altimetry and wind patterns obtained from CCMP.
Acknowledgements The seabird fieldwork was conducted by  SEO/BirdLife  in the frame of  Projects Important Bird Areas for seabirds in Spain  ( LIFE04NAT/ES/00049 ) and  LIFE + INDEMARES. We would like to thank all people helping in the fieldwork campaigns used in this study, particularly  Beneharo Rodríguez ,  Juan Bécares ,  Asunción Ruiz ,  Santiago Bateman ,  Javier Blasco ,  Albert Cama ,  Raül Escandell ,  David García ,  Meritxell Genovart ,  Marcel Gil ,  José Manuel Igual ,  Eli Miralles  and  Blanca Sarzo. The study was conducted in the framework of the  CMEMS Sea Level Thematic Assembly Centre (SL-TAC ) and partially supported by  CNES  (grant  OSTST-MANATEE ).
Data Availability Altimetry data analysed during this study are generated, processed and freely distributed by CMEMS (http:// marine. copernicus. eu). CCMP Version-2. 0 vector wind analyses are produced by Remote Sensing Systems. Data used in this study are available at www. remss. com. The in situ surface buoy data used to validate both the wind product and altimetry data was provided by Puertos del Estado (www. puertos. es). Version 3. 0 of the py-eddy-tracker software used in this study is available at https://bitbucket. org/emason/py-eddy-tracker. SRTM bathymetry data were downloaded from http://topex. ucsd. edu/WWW_html/srtm30_plus. html. Data from GPS loggers attached to seabirds used to estimate the trajectories are available from the corresponding author on reasonable request.
Bioinformatic Analysis Sequences were collected in genomic and cDNA databases by similarity searching using the BLAST tools  (15) , all available on the NCBI website (http://www. ncbi. nlm. nih. gov/). Sequences for endogenous retrovirus were extracted from REPBASE using CENSOR tool  (16)  available on GIRI web site (http://www. girinst. org/) and from RepeatMasker (http://www. repeatmasker. org/). Multiple alignments for the genomic analysis were done using CLUSTALW  (17)  followed by visual inspection and manual editing when necessary. The Neighbor-Joining trees were obtained using Kalign algorithm  (18).
This alignment is interrupted by a ∼5 kb segment characterized by long terminal repeats (LTR) suggesting a retroviral origin (Figure  1A ). Annotation from Repeatmasker showed that these LTR present high homology with LTR from MER57F, a class I human endogenous retrovirus. No homology with the classical retrovirus genes gag, pol and env was found in the sequence. Analysis of the internal part of the sequence revealed a high homology with MER34B, a non-autonomous retroelement (Supplementary   1B ). A nucleotide sequence potentially encoding DX 2 YX 2 LX 11 YX 2 L in another composite endogenous retrovirus is located in chromosome 11p14. 3, between the NELL1 and TMEM16E genes. This sequence is not associated to any known transcript. The vestigial IC3 exon downstream the endogenous retrovirus, which was homologous to FCGR2B IC3, has never been found in FCGR2A (or FCGR2C) mRNA, even in the EST database.
ACKNOWLEDGMENTS We thank  Dr. Baptiste Mulot  from  ZooParc de Beauval (Saint Aignan sur Cher)  and  Pascal Guery from La vallée des singes (Romagne)  for buccal/blood Gorilla and Orangutan samples. We also thank  Marc Ohresser  for his pioneering bioinformatic studies, and  Marie-Noëlle Marson  for performing the PCR analyses.
Implementation All methods are available in the Structural Bioinformatics Library (http://sbl. inria. fr, [23] ). Three executables implementing the methods (sbl-rmsd-flexible-proteins. exe, sbl-rmsd-flexible-conformations. exe, sbl-rmsd-flexible-motifs. exe) are provided in the package Molecular_distances_flexible (https://sbl. inria. fr/doc/Molecular_distances_ flexible-user-manual. html). Additional details are provided in SI Section 7. 2. Also of particular interest is the Structural_motifs package (see https://sbl. inria. fr/doc/Structural_ motifs-user-manual. html), which makes available various methods to compute structural motifs  [22].
The ectodomain of this protein was crystallized both in soluble form (PDB: 1SVB,  [24] , 395 residues) and in postfusion conformation (PDB: 1URZ,  [25] , 400 residues) (Fig. 2 ). We use structural motifs computed using a method reported in a companion paper  [22]  to demonstrate the RMSD Comb. in such a setting (RMSD_MODE_MOTIF, SI Sec. 7. 2. 3). Results. At first glance, a global lRMSD calculation yields lRMSD = 11. 32. To further assess the presence of rigid motifs moving relatively to one another, we computed motifs (Def. 2) using the method presented in the companion paper  [22]. In a nutshell, this methods identifies structurally conserved motifs, each such region being a connected domain whose connectivity is ensured by pairs of atoms whose distance is conserved between the structures studied.
Building a phylogeny from structural data: the example of class II fusion proteins Biological context. Following on the previous example, we now illustrate the interest of combined RMSD to build phylogenies of class II fusion proteins. As a dataset, we use 6 monomers of class II fusion proteins in their post-fusion conformation (Fig. 3 ). In order to assess the merits of the usual lRMSD and that of the combined RMSD to build phylogenies via dendograms, we carry out the analysis at two levels, namely using whole domains and the 22 motifs defined by SSE (Fig. 3(b) ). (This corresponds to the setting RMSD_MODE_SEQ for homologous proteins, see SI Section 7. 2. 2. ) Results. Out of the six structures and for each pairwise comparison-15 of them, we used the executables provided in the SBL to compute: (1) a structural alignment using the Apurva algorithm  [20].
Based on these reference structures, a combination of various biophysical experiments  [31]  and macroscopic analysis (using angles and distances to qualify the quaternary structures)  [32]  had provided insights on gas binding by hemoglobin. Recently, these models were questioned by crystal structures which revealed that each tetramer captured hemoglobin in three quaternary conformations  [33]. (NB: the three crystal structures are: half-liganded with phosphate (HL+ , PDB: 4N7P), half-liganded without phosphate (HL-, PDB: 4N7O), and fully water-liganded met-hemoglobin with phosphate (FL+ , PDB: 4N7N). ) Importantly, each new crystal was found to contain 3 new quaternary conformations denoted A, B, C. An assignment procedure based on difference distance matrices of the α 1 β 2 subunits using R as a reference, (SI Sect.
Indeed, the hierarchical clustering isolates conformation C from the FL+ crystal (PDB: 4N7N). It also singles out HL-[A], which is less coherent with the other two [A] but also with R and R2 (SI Tables  4  and 5 ). This somewhat mitigates the analysis from  [33] , where HL-[A] is reported as a relaxed state between R and R2. Third, the RMSD Comb. values required to form the clusters are tighter than those corresponding to RMSD values of clusters formed using the aforementioned angles and distances  [32]. While a much larger dataset was used in this latter paper, the mean values reported for lRMSD between R2, R and T states are 0. 40, 0. 43 and 0. 36, against worst case values ∼ 0. 27, 0. 27 and 0. 37 in our case. Summarizing, the combined RMSD at the SSE level provides insights on the assignment of quaternary structures of hemoglobin, without using any reference state.
Participants Forty-five participants (15 males, 30 females) between the ages of 16 and 37 years took part in the study (Mage = 22. 8 years, SD = 3. 4 years). This number of participants resulted in a statistical power of 0. 95, using a Cohen's d effect size of 0. 8 derived from a pilot study  (Aguinis & Harden, 2009). All of the participants were either students or staff from the Auckland University of Technology. The participants were recruited online by means of an advertisement posted on social media networks (i. e. , Facebook and Instagram) and informally by word-of-mouth. Participants were excluded if they were smokers, or reported any hearing loss, eating disorders, or other health problems that may have interfered with their hearing, taste, or sense of smell. All of the data was collected between 11:00 am and 1:00 pm, and participants were directed not to eat for the two hours prior to the study.
Emotion The emotional responses of participants were determined while listening to the different soundscapes using an unstructured 150 mm line scale anchored with the labels "none" and "extreme". These emotional responses were divided into two groups; positive (amusement, enjoyment, happiness, love, and satisfaction), and negative (anger, contempt, disappointment, and disgust; see  Kantono et al. , 2016b). Prior to the experiment, the participants were provided with definitions of these emotions in order to ensure that they understood them and used them consistently. Additionally, these definitions were also made available throughout the experiment. All emotion data acquisition was carried out using the FIZZ Acquisition software (v. 2. 46b: Biosystemes, France).
The sensors were then placed carefully on the participant's body and the equipment functions were then verified using the BioTrace software. As soon as the verification procedure had been completed, the recording of the participants' heart rate (HR), skin conductance (SC), respiration (RESP), and blood volume pulsation (BVP) commenced. Participants were not allowed to move their non-dominant hand throughout the duration electrophysiological measurements was obtained. Initially, a ten-minute baseline electrophysiological measurement was obtained in which participants were instructed to sit in an upright comfortable position. In the second phase of the study, the participants listened to the cafe (control) soundscape overlaid with bird (café-bird), forest (café-forest), and machine (café-machine) soundscapes (randomised and counterbalanced across participants using a Latin square design) while electrophysiological measurements were obtained.
In the third and final phase of the study, electrophysiological measurements were obtained when participants consumed a spoonful chocolate ice-cream (10. 0 ± 1. 0 g), either while listening the control soundscape or else the overlaid soundscapes, with each measurement period lasting for 45 s. This was chosen as phasic electrodermal measures typically peak around 2-5 s after stimulus delivery with recovery occurring after 30 s. Note that the recovery rate is dependent upon the participant's emotional state, and so an average of 45 s was calculated. All electrophysiological indices, including HR, were analysed at a rate of 32 data samples/s.
TDS curves TDS dominance curves were obtained to display the dominance ratings of all attributes over time using an in-built spline-based smoothing algorithm proprietary to the FIZZ software  (Pineau et al. , 2009). Temporal dominance curves represent the percentage of participants who identified the prescribed attribute as being dominant at a given point in time after the start of the trial  (Pineau et al. , 2009). TDS time was presented as standardized time (ST), and the data was converted to percentages (0-100%). The significant (Ps) and the chance (P0) levels were determined from the panel curves, and calculated according to  Pineau et al. (2009) : 𝑃𝑃 0 = 1 𝑝𝑝 � 𝑃𝑃 𝑠𝑠 = 𝑃𝑃 0 + 1. 645 � 𝑃𝑃 0 (1 -𝑃𝑃 0 ) 𝑛𝑛 Where P0 is chance level, p is number of attributes, Ps is significance level, and n is the number of TDS observations (number of participant x replication).
Analysis of electrophysiological measures Average values of electrophysiological indices (i. e. , BVP, RESP, SC, and HR) were calculated with reference to a ten-minute baseline measurement. The percentage change from the baseline was calculated according to Zhang et al. (  2009 ):  ℎ (%) = (raw value -mean baseline value) mean baseline value × 100 Repeated measures ANOVA was applied to the modified data in order to test the effect of soundscape conditions. Tukey post-hoc comparisons with Bonferroni correction were applied when significance was found.
Canonical Variate Analysis Canonical Variate Analysis (CVA) was performed on the perceptual, emotion, and electrophysiological data. It is important to note that only three soundscape mixes were projected in the CVA as the control condition was calculated to measure the percentage change for each soundscape condition in terms of the electrophysiological measures. CVA is a multivariate analysis method that explores the relationship between groups of variables in a dataset. The CVA biplots in this study are plotted based on correlational basis to explore the relationship between temporal flavour, emotion, and ANS responses. Additionally, the Hotelling-Lawley Multivariate Analysis of Variance (MANOVA) test was performed in order to determine whether significant differences existed between the three soundscape mixes in terms of standardized durations of flavour (α =.
Psychoacoustical analysis According to  Zwicker and Fastl (2013) , psychoacoustics parameters other than sound pressure levels can be used to describe a sound. These parameters include fluctuation strength, roughness, and sharpness. Fluctuation strength provides a measure of cyclic variations in amplitude modulation, roughness is a measure of lower frequency modulation (15-300 Hz), and sharpness is a measure of the relative content of high frequencies in a signal. All psychoacoustical analysis were carried out using the National Instruments LabVIEW 2013 software (Austin, TX, U. S. A).
General Note on the Statistical Analyses All statistics were performed with R v. 3. 5. 0  [38]. The significance level was set at 0. 05. Mixed generalized linear models were constructed using the glmer function in the lme4 package  [39] , and statistical tests were performed using the Anova function in the car package which calculates analysis-of-variance tables based on Type-II Wald tests, even for fixed effects in generalized linear mixed-effects models  [40]. Post-hocs tests with a Tukey correction were performed using the emmeans function in the emmeans package  [41]. Considering that only two subjects were involved in the study, we decided to fit some models with Subject as the fixed effect rather than random factor and to aggregate the data across sequences and sessions to avoid any risk of pseudo-replication.
Acknowledgments: The Authors thank the caregivers of  La Menagerie  ( Christelle Hano ,  Flavien Dias Pereira ,  Valérie Martinez ,  Maud Bongibault ,  Thibault Pers , and  Tommy Tanchoux ) and its director ( Michel Saint Jalme ) for administrative and technical support and for approving the study. We are grateful to three anonymous reviewers for helpful comments.
Funding: The research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement N • 324115-FRONTSEM (PI: Schlenker). Research was partly conducted at  Institut d'Etudes Cognitives, Ecole Normale Supérieure-PSL Research University. Institut d'Etudes Cognitives  is supported by grants  ANR-10-LABX-0087 IEC et ANR-10-IDEX-0001-02 PSL*.
| Sequence analysis of the ETH_00005190 locus The ToxoDB database was queried in order to gain more insight into the E. tenella locus of interest. The locus ETH_00005190 is annotated as a rhoptry protein kinase (Figures  1a  and S1 ). The predicted protein primary sequence encoded by this locus, sharing 27% identity with TgROP17, contains 665 amino acids resulting in an apparent molecular weight of 73 kDa. It comprises an NTE also observed in  TgROP2, 5, 8, 17, and 18 (Figure 1a). The protein contains a N-terminal predicted signal peptide with a cleavage site between residues 20 and 21 (p =. 853) and a SɸXE motif at positions 79-82, which is a subtilisin cleavage site conserved in most TgROP sequences (Figure  S1 ). Prosite database predicted a serine/threonine protein kinase domain at residues 257-549, but important residues involved in ATP positioning or in conformational changes necessary for kinase activity were missing: the glutamate and the phenylalanine in subdomain III and VII, respectively, supporting a noncanonical mechanism or the impairment of kinase activity.
Results showed that the wild-type EtROP1 is active and can phosphorylate both casein and MBP substrates in vitro. However, the catalytic domain alone and the dead mutant failed to phosphorylate casein or MBP. Unlike the C-terminal domain, the NTE domain of EtROP1 was essential for kinase activity (Figure  2c ). Autophosphorylation was observed for active forms of EtROP1, and mass spectrometry analysis indicated that the phosphorylation sites were serine residues located at C-terminal part of the kinase (data not shown). EtROP1 is therefore the first ROPK characterised in Eimeria. In order to identify parasite or host cell EtROP1 partners, purified recombinant EtROP1 was incubated with a host cell lysate (HEK293T and LMH, a primary avian hepatocellular carcinoma epithelial cell line) in a kinase activity assay (Figure  2d ).
| Sequence analyses Homology analyses were performed using BLAST (http://www. ncbi. nlm. nih. gov/blast/) and the Conserved Domain Database CD-Search (http://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi) with default settings. Proteins were aligned using MultAlin  (Corpet, 1988). Potential signal peptide cleavage sites were identified with SignalP 3. 0 (http://www. cbs. dtu. dk/services/SignalP/; Bendtsen, Nielsen, von Heijne, & Brunak, 2004). Potential alpha helices were identified using PSIPRED v3. 0 (http://bioinf. cs. ucl. ac. uk/psipred/; Buchan, Minneci, Nugent, Bryson, & Jones, 2013)  and PSSpred (http://zhanglab. ccmb. med. umich. edu/PSSpred/; Yan, Xu, Yang, Walker, & Zhang, 2013). Functional domains were predicted using Prosite  (Sigrist et al. , 2010).
| Mass spectrometry analysis Proteins were subjected to proteolysis using trypsin, and peptides were analysed by an LTQ Orbitrap Velos mass spectrometer (Thermo Electron) coupled with a nano-UHPLC Ultimate 3000 RSLC (Thermo Electron). Data were processed using Mascot Distiller v2. 2 and Mascot Daemon 2. 3 (Matrix Science). Mascot results obtained from the target database searches, with human and the alveolata and the NCBI nonredundant database (December 2015), were subjected to Scaffold software (Proteome Software Inc. , Portland, USA). Peptide identifications were accepted, if they could be established at greater than 95% probability by the Peptide Prophet algorithm  (Keller, Nesvizhskii, Kolker, & Aebersold, 2002). Protein identifications were accepted if they could be established at greater than 95% probability and contained at least two unique peptides.
Protein probabilities were assigned by the Protein Prophet algorithm  (Nesvizhskii, Keller, Kolker, & Aebersold, 2003). Proteins that contained similar peptides and could not be differentiated on the basis of MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. Functional annotations and quantifications of identified proteins were explored using ScaffoldMS R package (https://forgemia. inra. fr/aurelien. brionne/ScaffoldMS).
| Cell cycle assay Transient transfected cells were grown for 2 days, fixed in ethanol, and frozen for 2 hr at -20°C. Then, cells were permeabilised with 0. 25% Triton-X-100 and treated with RNases (0. 2 mg/ml, Sigma), stained with propidium iodide (Molecular Probes, 5 μg/ml) and analysed by flow cytometry (MoFlo Astrios EQ Beckman Coulter). The percentage of cells in each cell cycle phase was assessed by using the MultiCycle software for Windows (Phoenix flow system, Inc. ). At least 10,000 cells were collected for each condition, in three independent experiments.
ACKNOWLEDGEMENTS We gratefully acknowledge  I. Dimier-Poisson ,  N. Moiré , and J. F. Dubremetz for providing anti-TgROP2 and anti-SAG1 antibodies. We thank  Cassandra Mazaud  for the help with in vivo experiment and apoptosis measures in epithelial cell from the caecum from infected chicken. We thank  Valérie Labas ,  Lucie Combes-Soia , and  Grégoire Harichaux  of the  Plate-Forme Chirurgie et Imagerie pour la Recherche et l'Enseignement (Nouzilly, France ) for the help with mass spectrometry identification. We thank  Luc Reininger  for providing us the pGEX-PfTLK (for kinase assay control) and fruitful discussions. We thank  Laetitia Fragnet-Trapp  ( INRA Centre Val de Loire-UMR1282 ) for helpful discussions on the cell cycle phases. We thank  Dr. Mohamed-Ali Hakimi  for fruitful discussion on the manuscript.
The detector to sample distance (551. 61 mm), beam center position and detector tilt were determined using a CeO 2 standard. Sample heating was performed with off-axis Yb-Fiber laser heating system, as provided by the beamline (10-30 μm heating spot on sample). Data processing. The collected X-ray diffraction data were integrated and processed using the Rietveld software MAUD  41  in order to extract the cell parameters of Pv, pPv, and the Ar pressure medium along with the weight proportions of all phases (Supplementary Fig. 1 ). The background was corrected for by using polynomial functions. The fitting did not include the effect of stress nor texture, which is not directly observable in such experiment. Pressure and temperature. Sample temperatures were estimated on both sample sides by pyrometry of the emitted light with a Czerny-Turner spectrograph equipped with a CCD.
The expression in Eq. (  3 ) is simplified from this model and was found to fit the data reasonably well. In the expression of Eq. (  3 ), exp Q 0 =ðRTÞ ½ accounts for the thermal activation and sinh V Ã ΔP=ðRTÞ ½ models the effect of distance from the phase boundary. Fitting of kinetics models. For both the nucleation and growth and shear transformation mechanisms, k 2 , Q 0 , and V Ã were adjusted to the data of Table  1. Due to the lack of a proper thermodynamic database for Pv and pPv, and as in previous studies  49  , ΔG r is estimated from ΔPΔV, where ΔP is the overpressure and ΔV is the molar volume change of the transformation. ΔV was determined directly from the X-ray diffraction data. ΔP was calculated by comparing the pressure difference between the measured P/T point and the Pv/Pv+pPv equilibrium boundary.