dataesr/scientific-paragraphs-categorization huggingface dataset:
" Each block consisted of 36 trials. On each trial, we calculated online the likelihood of the set of previous participant responses for each of the 2,500 hypothetical psychometric curves. The curve with the maximum likelihood was chosen as the current estimate. The magnification factor for that given trial was determined by the 64%-response point of this current estimate psychometric curve. In this way, the algorithm is shown to converge rapidly to the participant's threshold  (Green, 1993). We furthermore inserted two catch trials (with factor level 0 regardless of the current psychometric curve estimate) the first 12 trials at random locations, as well as four more over the remaining 24 trials. Stimuli were written as MIDI files and then played through Timidity++ on a Windows computer, called by our Python (Pygame) graphical interface that registered the responses.
ACKNOWLEDGMENTS This work was supported by the  EBRAMUS ,  European Brain  and  Music Ph. D. Grant  to  Floris Tijmen Van Vugt  ( ITN MC FP7, GA 238157 ). The authors are indebted to Laura Lübbe data collection in Experiment II, as well as  Melinda Lee  for running Experiment III. We also thank  Meaghan Fowlie  for proofreading the manuscript.
chrysolophus, the eastern rockhopper E. filholi and the northern rockhopper E. moseleyi penguins, of which the two former are often found breeding in sympatry  [16]. Based on our extensive tracking dataset, we made comparisons between sympatric species and between parapatric populations, examining: (1) the animals' migration bearing towards their wintering area, with respect to the main currents governing the region, and (2) the inter-breeding marine habitat used. We assumed that birds had a strong evolutive inertia in both migration patterns and optimal habitats, based on previously published literature  [1, 22]. Site-specific adaptations for each seabird population would facilitate partitioning of food resources while also leading to coherent at-sea distribution patterns among individuals from the same locality (e.
These devices were leg-mounted using specially designed flexible leg bands, following  [13]. GLS loggers record ambient light level and time, allowing the estimation of latitude and longitude twice a day  [46, 47]. GLS tags also recorded ambient sea temperature, once during every 20 min period of continuous immersion, with a resolution of 0. 0625uC and an accuracy of 60. 5uC. After the GLSs were recovered, logged data were analyzed following previously published methods  [48] , using the package 'tripEstimation' in R 2. 9. 0  [49]  and assuming a mean daily travelling speed of 2 kmNh 21  [50]  in order to estimate the most probable track. Location estimates in this case are not as accurate as for PTTs (tens to hundreds kms versus ,1 km in the best cases, respectively  [51, 52] ), and GLSs need to be recovered in order to collect the data, unlike in the case of satellite linked PTTs.
Analytic Tools Used For all analyses we used R 2. 9. 0  [49]. Unreliable Argos locations were removed using the algorithm from the 'argosfilter' R package  [54] , with an upper-threshold speed of 2. 1 m s 21 according to previous measurements  [55]. In order to standardize the frequency of locations available along the tracks, we resampled the tracks obtained and made linear interpolations to conform to the 12 h frequency of GLS-derived estimates, using R packages 'sp' and 'trip'. Locations received from the PTTs were thereafter analysed in the same way as GLSs to standardise interpretation of all the tracks. Bearing was calculated between the point of origin at the colony and the farthest point reached for each animal studied, using  'circstat' package. This was expressed as a circular measurement in degrees, with 0u equivalent to a northwards direction.
We carried out Student's t-tests to compare these monthlyaveraged temperature records between species. For the three sites where two species of Eudyptes penguin breed sympatrically, all locations available for the inter-breeding period of each species were also binned by degree of latitude. From this dataset, Student's t-test was again used to examine for statistical differences in the latitudinal distributions of species. In all tests the threshold for significant differences was set at p = 0. 05. Habitat suitability for the penguins during the wintering period (as defined below) was modelled using Mahalanobis Distances Factor Analysis (MADIFA,  [56] ) in R package 'adehabitat'. This method is appropriate for building habitat suitability maps from presence-only data, such as tracking data (for a comparison of methods see  [57] ).
In the MADIFA, two principal components analyses (PCAs) successively summarize available information comprising: (a) the environment described by spatial variables; and (b) the relationship between the locations of animals and the environment. Environmental variables used were bathymetry (BATHY) and its gradient (BATHYG), sea-surface temperature (SST) and its gradient (SSTG), SST anomalies (SSTA), sea-surface chlorophyll a concentration (CHLA), mixed-layer depth (MLD) and eddy kinetic energy (EKE). MLD was a mean of annual data obtained since 1941. Previous studies have shown that these variables can be used to model at-sea movements of penguins (see  [58] [59] [60] ). The temporal resolution selected for dynamic variables was one month, and the spatial grid 1u in accordance with the geolocation technique accuracy.
The spatial data were obtained from the NOAA's ETOPO (http://www. ngdc. noaa. gov/mgg/ gdas/gd_designagrid. html?dbase = GRDET2), the Bloomwatch 180 (http://coastwatch. pfel. noaa. gov/coastwatch/ CWBrowserWW180. jsp), the LOCEAN (http://www. locean-ipsl. upmc. fr/ ˜cdblod/mld. html) and the AVISO (http://las. aviso. oceanobs. com/las/servlets/dataset) websites. We modelled winter at-sea distribution of the two species that were studied at more than one site (that is, E. chrysolophus and E. filholi). We focused on the year 2007 when most of the tracking data were collected and all sites were sampled. The habitat model was based on the at-sea distribution of the birds from Crozet, and the model predictions were projected on the whole study area in order to compare predictions with the actual locations of the birds from all sites.
Acknowledgments The present work was supported logistically by the  Institut Polaire Francais Paul-Emile Victor (IPEV , programmes no. 394 resp. C. -A. Bost and 109 resp. H. Weimeskirch), the  Terres Australes et Antarctiques Francaises and South Africa's Department of Environmental Affairs. The authors thank all the volunteers involved especially  H. Maheo ,  M. Berlincourt ,  Q. Delorme ,  A. Knochel ,  R. Perdriat ,  J. Nezan ,  S. Mortreux ,  Y. Charbonnier  and  N. Mignot  for their help in the field on the  French Southern Territories  and  B. M. Dyer  and  L. Visagie  for help at  Marion Island. C. Peron ,  A. Goarant ,  M. Louzao ,  C. Cotte  ´and  M. Authier  are thanked for their help and advice with analyses. We are grateful to  Grant Ballard  and an anonymous reviewer for their most helpful comments.
X-ray data collection Diffraction data were collected at the European Synchrotron Radiation Facility (ESRF), Grenoble, France. Data were collected from single crystals at 100 K using the following beamlines and wavelengths: MBP-SUFU-FL, ID14-1  (Wakatsuki et al. , 1998) , 0. 9334 A ˚; MBP-SUFU-Á crystal form I, ID23-1  (Nurizzo et al. , 2006) , 1. 0723 A ˚; MBP-SUFU-Á crystal form II, ID23-2  (Flot et al. , 2010) , 0. 8726 A ˚; MBP A216H_K220H -SUFU-Á W61D_L62S_G63F_P453A_Á454-456_K457A -GLI1 p and MBP A216H_K220H -SUFU-Á W61D_L62S_G63F_P453A_ Á454-456_K457A -GLI3 p , ID29 (de Sanctis et al. , 2012), 0. 9762 A (Table  1 ).
X-ray structure determination The MBP-SUFU-FL and MBP-SUFU-Á data sets were processed with iMosflm  (Battye et al. , 2011)  and integrated with SCALA  (Evans, 2006)  and TRUNCATE  (French & Wilson, 1978) ; the MBP A216H_K220H -SUFU-Á W61D_L62S_G63F_ P453A_Á454-456_K457A -GLI1 p and MBP A216H_K220H -SUFU-Á W61D_L62S_G63F_P453A_Á454-456_K4-7A -GLI3 p data sets were processed using XDS  (Kabsch, 2010)  in the xia2 pipeline  (Winter, 2010). The structure of MBP-SUFU-FL was solved by molecular replacement (MR) with  Phaser (McCoy et al. , 2007)  using an initial data set collected at 4. 1 A ˚resolution {overall R p. i. m. = 13. 4%; outer shell [4. 10-4. 32 A ˚, hI/(I)i = 2. 1] R p. i. m. = 37. 9%}; the search models were the MBP molecule from chain A of PDB entry 3d4g (residues Thr3-Ala371;  Monneet al.
, 2008)  and the N-terminal domain of SUFU from chain A of PDB entry 1m1l (residues Pro32-Asp262;  Merchant et al. , 2004). As in the case of MBP-ZP3  (Monneet al. , 2008) , correctness of the solution was confirmed by clear difference electron density for a molecule of d-(+)-maltose (coordinates for which were not included in the search ensemble) within the ligand-binding pocket of the two MBP molecules in the asymmetric unit. The structure of the SUFU C-terminal domain was manually built into A -weighted difference Fourier maps  (Read, 1986)  using Coot  (Emsley et al. , 2010). Refinement against a maximumlikelihood (ML) target was performed with phenix. refine (Adams et al. , 2010). Simulated annealing was initially used with a starting temperature of 5000 K, and translation/ libration/screw (TLS) refinement of B factors was performed during the final rebuilding cycles on the basis of TLSMD  (Painter & Merritt, 2006)  analysis of individually refined atomic displacement parameters; noncrystallographic symmetry (NCS) restraints were kept during all refinement steps based on manually determined local differences between molecules.
Riding H atoms were added with phenix. reduce and used throughout; validation was performed using MolProbity  (Chen et al. , 2010). The Ramachandran statistics were 98. 0% favoured, 2. 0% allowed and 0. 0% outliers. The structure of MBP-SUFU-Á crystal form I was solved by MR using the refined coordinates of MBP-SUFU-FL (MBP, Glu5-Ala371; SUFU, Pro32-Leu278, Ile361-Asp449, Glu455-Val478) as a search model. The MBP-SUFU-Á crystal form II data were also phased by MR using the SUFU moiety of MBP-SUFU-FL and the unliganded form of MBP (PDB entry 1omp, residues Lys1-Thr366;  Sharff et al. , 1992)  as search models; however, significant manual rebuilding was required to model three of the four MBP moieties within the asymmetric unit of this crystal (chains B-D), which are highly disordered (average B factor = 272 A ˚2) compared with the rest of the structure (average B factor = 122 A ˚2).
3% formic acid. An Orbitrap XL mass spectrometer (Thermo Scientific, USA) operated at 60 k resolution was used for the analysis. Several LC MS/MS runs were carried out to identify MBP-SUFU peptic peptides. The Mascot software (Matrix Science) was used to search an MBP-SUFU sequence database. Peptides with scores higher than 20 were selected for HDX kinetic studies. In addition, each selected peptide was further validated by manually inspecting the MS/MS spectrum. The HDExaminer software (Sierra Analytics, USA) was used to process all HDX MS data.
Small-angle X-ray scattering (SAXS) SAXS data were collected on a Rigaku BioSAXS-1000 using Cu radiation ( = 1. 5418 A ˚) from a Rigaku FR-E+ SuperBright rotating-anode X-ray generator. The BioSAXS-1000 consists of an optic, a vacuum chamber and a detector. The optic is a double-bounce Rigaku Confocal MaxFlux multilayer optic specifically designed for the BioSAXS-1000 and it focuses the X-rays to a point at the detector. The vacuum chamber contains a Kratky block, a sample holder and a beamstop with an integrated PIN diode. The Kratky block collimates the X-ray beam into the shape of a line that is approximately 0. 5 mm tall at the sample position; however, the beam is focused to a point at the detector. SAXS data were recorded using a DECTRIS PILATUS 100K detector and the camera length was 0.
5 m. The q range for all SAXS data was from 0. 01 to 0. 68 A ˚À1. All samples and buffers were loaded into 1. 0 mm quartz capillaries, placed under vacuum and measured at 20 C. Glucose isomerase was used as a secondary standard to assess I(0) data. MBP-SUFU proteins used for SAXS were purified from E. coli as described above, except that SEC was performed in 50 mM Na HEPES pH 7. 0, 50 mM NaCl, 5 mM -mercaptoethanol, 1. 5 mM maltose and only two fractions from the peak were pooled for analysis (Supplementary Fig. S1 ). The proteins were 99% pure as judged by Coomassie Blue staining of an SDS-PAGE gel. SEC buffer passed through the gel-filtration column immediately prior to protein purification was used as the blank. Scattering data were measured from frozen aliquots of MBP-SUFU that were thawed on ice, centrifuged at 20 000g for 10 min, diluted in SEC buffer and loaded into 1.
0 mm diameter quartz capillaries. For experiments with GLI peptide, MBP-SUFU-Á was mixed in a 1:10 molar ratio with GLI1 p in the same buffer. Three concentrations of each protein (between 1. 1 and 7. 3 mg ml À1 ) were measured to ensure that concentration effects did not influence data analysis. The protein concentration was determined by measuring the absorbance at 280 nm with extinction coefficients of 1. 4073 for MBP-SUFU-FL and 1. 5484 for MBP-SUFU-Á. Each SAXS profile was the result of a 60 min exposure in image-refreshing mode. In this mode, a 60 min exposure was the sum of six consecutive 10 min exposures that were individually inspected to ensure that radiation damage was not present in the sample. Initial data analysis, reduction of scattering images to onedimensional plots of intensity versus momentum transfer (q) followed by buffer subtraction, was performed by the Rigaku SAXSLab software package.
Buffer-subtracted data were then analyzed using the ATSAS program suite  (Petoukhov et al. , 2012). Radius of gyration (R g ) values were determined from Guinier plots in PRIMUS  (Konarev et al. , 2003)  and pair distance distribution functions, P(r), were computed with GNOM  (Svergun, 1992). V c , Q R and 2 free were calculated according to  Rambo & Tainer (2013). Ab initio molecular envelopes of MBP-SUFU were generated using DAMMIF  (Franke & Svergun, 2009). For MBP-SUFU-FL and MBP-SUFU-Á, 15 independent DAMMIF models were aligned, averaged and filtered using DAMAVER  (Volkov & Svergun, 2003)  and potential clusters were assessed with DAMCLUST  (Petoukhov et al. , 2012). For MBP-SUFU-FL there were clearly two clusters, each composed of seven models; the mean normalized spatial discrepancy for the averaging of cluster 1 was 0.
To verify the expression levels of transfected SUFU and GLI constructs, aliquots of cell lysates were separated by SDS-PAGE and subjected to immunoblot analysis as above. The densities of the visualized protein bands were quantified using the ImageJ 1. 47v image-analysis and image-processing software  (Schneider et al. , 2012). Sufu À/À mouse embryonic fibroblast (MEF) cells  (Sva ¨rd et al. , 2006)  were transfected with a mixture of p8x3 0 Gli-BS LucII or p8x3 0 Gli-mBS LucII reporter constructs  (Sasaki et al. , 1997;  generous gifts from Hiroshi Sasaki) together with pRL-SV40 (Promega) internal control using Lipofectamine LTX with Plus reagent (Invitrogen) according to the manufacturer's instructions. Following transfection, Sufu À/À MEF cells were grown until the culture reached confluency (24-48 h) and the cells were then switched to low-serum medium (0.
0 A ˚resolution (R = 20. 0%, R free = 24. 6%; Fig. 1a  and Table  2 ). The N-terminal domain exhibits the same structure as previously determined  (Merchant et al. , 2004) , despite the presence of the N-terminal MBP fusion partner. The C-terminal region folds into a domain that comprises a four-stranded and a six-stranded -sheet, both with mixed topologies, which are connected by two antiparallel -helices (Figs. 1a  and 1b ). This is a novel protein fold, with only weak structural similarity (DALI Z-scores of 2. 7-2. 8; Holm & Rosenstro ¨m, 2010) to proteosomal Jab1/MPN domain proteins (PDB entries 1oi0 and 1r5x;  Tran et al. , 2003; Ambroggio et al. , 2004). The first helix in the C-terminal domain of SUFU (helix 6) is bent and interacts with helix 5 via residues Arg386, Arg388, His391 and Arg393 (Fig.
Monne, M. Bokhove and H. Sadat Al Hosseini for their contribution to X-ray data collection, L. Zerrad and A. Round for preliminary SAXS experiments and M. Jerabek for help with microscale thermophoresis studies. Author contributions: CF, ALC and QJ generated constructs and performed protein expression and purification. ALC, LJ and MK crystallized proteins and determined structures. DdS assisted in data collection at ESRF beamline ID29. TS, JA-W and RAZ performed mass spectrometric analyses. MDC and ARC collected, processed and interpreted the SAXS data. ALC and CF carried out molecular and cell biology experiments. CF, ALC, LJ and RT designed the work and wrote the manuscript, with contributions from all other authors. The authors declare no competing financial interests.
Sequencing We first attempted to sequence the COI barcoding region with the primers developed by  Folmer et al. (1994)  on a subset of samples. Despite PCR optimization trials, amplification of this fragment largely failed. Instead, a 800 bp fragment of the 3' end of the COI gene was amplified using the primer set C1-J-2183 (5' CAACATT-TATTTTGATTTTTTGG 3') and TL2-N-3014 (5' TCCAATGCACTAATCTGC-CATATTA 3')  (Simon et al. 1994 ). This fragment does not overlap with the standard barcoding region. For samples O9 and V44 (Table  1 ) we used species-specific primers (F -5' GGGGCATCAGTAGACCTAGC 3' and R -5' TTCAGCAGGTGGT-ATTAGTTGG 3'), designed from sequences on GenBank and used to PCR amplify a 1089 bp stretch of the COI gene. Reactions were performed in total volumes of 40 µl containing 5. 2 µl of 10 × Taq buffer with 500 mM KCl (Fermentas, Thermo Scientific), 3.
Species / subspecies Code Primers Haplotype GenBank acc. no. primer (10 µM), 0. 8 U Taq DNA polymerase (Fermentas, Thermo Scientific), 26. 42 µl sterile distilled water. 12 µl of diluted DNA (3. 5-5 ng/ µl) was added. The temperature cycle was 94 °C for 1 min, then 5 cycles of 94 °C for 1 min, 45 °C for 1 min 30 s and 72 °C for 1 min and 30 s. This was followed by 40 cycles of 94 °C for 1 min, 50 °C for 1 min 30 s and 72 °C for 1 min, and finally a single cycle at 72 °C for 5 min. PCR products were cleaned enzymatically with DNA Clean & Concentra-tor™-5 (Zymo Research). When samples failed to amplify, mostly dried or bad quality samples, internal primers were used to allow amplification of four overlapping fragments of about 250 bp within the same 3' end of the COI gene: LCint1 (F -5' CTTCGGCCACCCAGAAGT 3' and R -5' TCCAGTAGGAACAGCAATRAT 3'), LCint2 (F -5' CGAGCCTACTTCACATCAGC 3' and R -5' GCAAAAACT-GCACCTATTGAAA 3'), LCint3 (F -5' GCTCACTTCCATTATGTACTTTCAA 3' and R -5' GAGAGCCAAATGATGAAATAATGTT 3') and LCint4 (F -5' CC-CTGATGCCTACACCACAT 3' and R -5' CCAATGCACTAATCTGCCATA 3').
Both forward and reverse primers were used except when internal primers were used for PCR, in which case sequencing was performed using the respective forward primers (except for five samples of L. (P. ) barbarossa, where both forward and reverse primers were used). COI sequences available on GenBank were added. The COI sequence of L. c. cervus obtained by  Lin et al. (2011; GenBank acc. no. FJ606555 ) was used as a reference for the subspecies with the highest number of specimens in this study. We selected two Asian stag beetle species, L. formosanus Planet, 1899 and L. hermani DeLisle, 1973, and Dorcus parallelipipedus (Linnaeus, 1758) (lesser stag beetle; Lucanidae) as outgroup species. Except for one available sample of the latter, the COI gene sequences of the taxa were obtained from GenBank (D.
parallelipipedus:  Hunt et al. 2007 ; GenBank acc. no. DQ156023; L. formosanus: Huang and Lin 2010; GenBank acc. no. FJ606632, FJ606630, FJ606628, FJ606626, FJ606624, FJ606622, FJ606583; L. hermani:  Lin et al. 2011; GenBank acc. no. : FJ606552). In the study of  Hunt et al. (2007)  the Dorcinae formed a sisterclade of the Lucaninae. Finally, part of the COI sequence of L. tetraodon obtained by  Hunt et al. (2007; GenBank acc. no. EF487727)  was used in addition to the sequence of L. t. provincialis.
Alignment and sequence quality control Overall quality of the sequences was evaluated manually. Only samples with high quality chromatograms for at least 300 bp were retained for further analyses. Sequences were aligned by hand and using CLUSTALW v1. 4  (Thompson et al. 1994 ) in BI-OEDIT v7. 0. 0  (Hall 1999). Sequences were trimmed to 670 bases. Duplicate haplotypes were removed using DUPLICATESFINDER v1. 1 (http://bioinfotutlets. blogspot. be/2009/09/duplicates-finder-java-standalone. html). We searched for potential NUMTs (nuclear mitochondrial pseudogene sequences) or heteroplasmy by manually checking for the presence of double peaks and indels, and by looking for stop codons  (Song et al. 2008 , Calvignac et al. 2011 ) using MEGA v5. 01 with the implemented invertebrate mtDNA genetic code to translate the sequences  (Tamura et al.
Alignment and sequence quality Of a total of 76 samples, thirteen samples with low quality sequences were removed: five L. c. cervus, one L. c. pentaphyllus, three L. c. turcicus and four L. (P. ) barbarossa. Three sequences showed a few double peaks: one L. (P. ) barbarossa (SB6: 5 ambiguous sites), one L. (P. ) macrophyllus (UB1: 7 ambiguous sites) and one unidentified species of Lucanus (J2: 2 ambiguous sites) (Table  1 ). None exhibited indels or stop codons which are indicative of the presence of NUMTs  (Buhay 2009). The remaining 63 samples and 11 sequences obtained from GenBank are listed in Table  1. The final alignment entailed 74 sequences, representing 60 haplotypes. Incomplete sequences were obtained for the following taxa: taxon H4 with 500 bp of which the reverse sequence failed and taxon J2 of which forward sequences of only the first and third smaller fragments could be produced, resulting in a total of 383 bp.
Both taxa were specimens of the unidentified Lucanus specimens (Table  1 ). Likewise, the sequence of L. tetraodon found in GenBank (named X2), was 122 bp short at the 3' end. One other taxon, H3 (Lucanus sp. ) missed a mere 5 bp at the 5' end. Both the NJ tree and the BI tree showed the same overall configuration (Figure  1  and Appendix 1, respectively) except for the position of the unidentified Lucanus specimens. In the NJ tree these specimens fall into two clusters with unresolved affinities (Figure  1 ). In the BI tree they form a single well-supported clade together with specimens identified as L. c. judaicus and L. c. laticornis (Appendix 1). The unidentified specimens fail to form a single monophyletic cluster as one subclade also includes L. c. judaicus. The BI tree showed L. c. laticornis to be monophyletic with probability 1, instead of paraphyletic as was shown in the NJ tree with bootstrap support below 70%.
Genetic distances The nucleotide composition of all the sequences was AT-rich, with 29. 5% A, 35. 2% T, 15. 5% G and 19. 7% C. There were 36. 4% nucleotide sites variable and 12. 1% variable amino acid sites, of which 94. 3% and 77. 8% were parsimony informative, respectively. When D. parallelipipedus was excluded from the dataset, variable sites decreased to 33. 3% for nucleotides and 7. 2% for amino acids (94. 2% and 56. 2% parsimony informative, respectively). Nucleotide composition and K2P-distances calculated for each codon position are shown in Table  3. Although specimen J2 of the unidentified specimens of Lucanus clustered with the other specimens of the same taxon in the NJ and BI trees, the pairwise interspecific K2P-distances with J2 differed substantially from those with H1 to H5 (comparisons with L.