dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSIONS Individual specialisation can be calculated by using repeated measurements for each individual, then calculating the within-and between-individual variation. Although there are several alternatives (see Table  1 ), the most common and flexible approach is to calculate repeatability, using the variances extracted from GLMMs with individual as a random effect (either random intercepts or random slopes). To obtain estimates of individual specialisation, care needs to be taken to exclude effects that can lead to pseudo-repeatability. In addition, analysing a variety of data types simultaneously can provide better insights. Analysis and interpretation can be improved if the study involves a representative number of individuals having access to the same habitat and resources (preferably over the same periods), and over short and long timescales.
Acknowledgements. This study represents a contribution to the Ecosystems component of the  British Antarctic Survey 's  Polar Science for Planet Earth Programme , funded by The  Natural Environment Research Council. We thank the anonymous referees for their many helpful suggestions for improvements to the paper. LITERATURE CITED Anderson ORJ ,  Phillips RA ,  Shore RF ,  McGill RAR ,  McDonald RA , Bearhop S (2009) Diet, individual specialisation and breeding of brown skuas (Catharacta antarctica lonnbergi): an investigation using stable isotopes. Polar Biol 32: 27-33 Annett CA, Pierotti R (1999) Long-term reproductive output in western gulls: consequences of alternate tactics in diet choice. Ecology 80: 288-297  Araújo MS, Bolnick DI , Layman CA (2011) The ecological causes of individual specialisation.
The output signal is analyzed using a high-speed photodetector (25 GHz bandwidth) and a 23 GHz real-time oscilloscope with 512 MPts/channel capacity allowing to record time series up to 10 s with a 500 ps/point resolution in order to have sufficient statistics. Contrary to previous studies involving ultrafast lasers, the timescales considered in our system do not require any real-time characterization techniques  3, 34  as rogue pulses can be detected using standard fast electronics. Such measurements however come with the drawback of heavy data files and long data processing times. Here, SBS in self-pulsing laser operation can be evidenced by the presence of a Stokes wave shifted by ν ν λ ∆ = n 2 / B a from the pump, where n, v a and λ are respectively the refractive index, the acoustic velocity and the pump wavelength.
Acknowledgements This work was supported by the  French Agence Nationale de la Recherche  through the  LabEx EMC3 program ,  UBRIS2  ( ANR-13-BS09-0018 ) and  RIFT  ( ANR-15-CE08-0018-01 ) projects, the  European Union  with the  European Regional Development Fund (ERDF)  and the  Regional Council of Normandie.
A Qiagen multiplex PCR Kit was used and reaction mixtures contained 1 µl DNA, 6. 25 µl Master Mix (Qiagen, Hilden, Germany) with 1. 25 µl primer mix (100 pM/ µl) and 3. 5 µl RNAse-free water to a final volume of 12 µl. The reaction comprised an activation step at 95 °C for 5 min, followed by 35 cycles of initial denaturation at 95 °C for 30 s, T a for 30 s (Table  1 ) and 72 °C for 30 s, ending with a final extension step at 60 °C for 30 min, and followed by a holding step at 20 °C. Fluorescently-labeled PCR products were run on an ABI PRISM 3130 DNA Sequencer (Applied Biosystems) with the GeneScan-500 (LIZ) internal size standard and analyzed with the Gen-eMapper software (Applied Biosystems). Fifteen loci (Multiplex 1 : 9 loci; Multiplex 2 : 5 loci; Singleplex: 1 locus; Table  1 ) were successfully amplified and genotyped in 210 specimens.
We used R package adegenet_2. 0. 1  [16]  to estimate the number of alleles (NA), gene diversity He (expected heterozygosity), and observed heterozygosity (Ho) at each locus (Table  1 ). We used GENEPOP 4. 1. 3 [17]  with a Markov chain method (10,000 dememorization steps, 100 batches and 5000 iterations per batch) to calculate inbreeding coefficients (F IS ) and test for Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium. Significance levels were adjusted for multiple tests using the sequential Bonferroni technique  [18]. We used ML-NULLFREQ to estimate null allele frequencies based on maximum likelihood methods  [19]. We investigated the fine-scale genetic structure using a global spatial autocorrelation technique (Mantel correlograms)  [20, 21]  implemented in the software GenAlEx 6.
5  [22]. We used data of genotyped individuals that were resident in the study area (seven individuals dispersed, therefore n = 203). Spatial coordinates corresponded to occupied dens or capture sites. We used even distance classes of 4 km since this distance represents the mean radius of an arctic fox's home range  [23]. Statistical testing was based on 999 random permutations of individual genotypes. The complete dataset was first used, and then divided by sex (n = 99 males and 104 females) to test for sex-biased kin clustering. In addition, we used a spatially explicit multivariate method, the spatial principal component analysis (sPCA) implemented in adegenet_2. 0. 1. The sPCA takes into account both the genetic diversity based on allele frequencies (variance) and the spatial autocorrelation between individuals (Moran's I).
Results and discussion The number of alleles per locus varied from five to 17. Expected heterozygosity ranged from 0. 667 to 0. 890, while observed heterozygosity ranged from 0. 576 to 0. 886. Null alleles occurred at low frequency (0. 000-0. 020) for 12 loci and at medium frequency for three loci (0. 038-0. 090). Significant departures from Hardy-Weinberg equilibrium after Bonferroni correction were detected at four loci (Table  1 ). Eight pairs of loci (7. 6%) deviated significantly from linkage equilibrium after Bonferroni correction, a number only slightly higher than what may be expected by chance alone  (5. 25). As these deviations were very likely the result of the significant spatial genetic structure detected in the subsequent analyses, we included all loci in the analyses. No significant spatial genetic structure was detected by the Mantel correlogram using the complete dataset (Mantel r = -0.
023 to 0. 003; all p > 0. 066) or sexspecific datasets (males: Mantel r = -0. 032 to 0. 016, all p > 0. 097; females: Mantel r = -0. 026 to 0. 005; all p > 0. 112). These results contrast with those of Ehrich et al. [6] , who reported an isolation-by-distance pattern and female kin clustering in Svalbard. The observed trend was however very weak in their study (R 2 = 0. 0007, p = 0. 027) and the median distance reported between first-order relatives (35. 7 km) was relatively high  [6]. The Monte-Carlo tests from the sPCA using the complete dataset revealed the existence of at least one global pattern (observed value = 0. 009, p = 0. 003) but no local structure (observed value = 0. 010, p = 0. 445). The first two eigenvalues were retained after examination of the eigenvalues barplot and the sPCA screeplot (Additional file 1: Figure  S1 ).
We assume that AT2017gfo is an unusual, supernova-like explosion in NGC4993 that exploded within 16 days of GW170817. The number of supernovae expected within the four-dimensional space (volume and time) defined by the LIGO distance range for GW170817, (73 Mpc) and within the refined 90% sky area of 28 square degrees (reduced in the final released map  6  ), and within 16 days is n SN = 0. 005, assuming a supernova rate  17  of R SN = 1. 0 × 10 -4 Mpc -3 yr -1. It is unlike any known nearby, or distant, supernova (see Extended Data Figures  1  and 3 ). If we assume that the rate of events similar to AT2017gfo is ∼1% of the volumetric supernova rate (see Methods Section) then the probability of a chance coincidence in space and time is p = 5 × 10 -5 (equivalent to 4σ significance). We calculated a bolometric lightcurve and total luminosity emitted assuming a distance to NGC4993 of d = 40 ± 4 Mpc and appropriate Galactic foreground extinction (see Methods section for details of the calculation).
There are two broad and blended structures at 7400 Å and 8300 Å, respectively, which become stronger in the subsequent spectra. We have extended the TARDIS atomic database to include lines of elements with atomic number 31 < Z < 60 (or 60 < A < 140) from the Kurucz atomic line list  28  , although the available atomic data for these heavy elements is of limited quality and quantity. We propose the broad feature at 7000-7500 Å is from neutral Caesium (A = 133), and is the 6s 2 S → 6p 2 P resonance doublet (λλ8521, 8943) at a photospheric velocity of ∼ 0. 15 -0. 20c (see Figure  4 ). Our model predicts no other strong features of Cs I in the observed region, which could be used to confirm (or refute) this identification. For the redder absorption, we can identify an intriguing potential match with the Tellurium (A = 128) 5p 3 ( 4 S)6s 5 S → 5p 3 ( 4 S)6p with that used in our light-curve model at this phase.
11 mag. These reddening corrections were applied to the photometry to calculate absolute magnitudes and bolometric luminosities. Hubble Space Telescope pre-discovery data NGC4993 was observed by the Hubble Space Telescope using the Advanced Camera for Surveys (ACS) Wide Field Channel on 2017 April 28, less than four months prior to the discovery of AT2017gfo. 2×348 s exposures were taken with the F606W filter (comparable to Sloan r ). As this is the deepest image of the site of AT2017gfo taken prior to discovery, we examined it for any possible pre-discovery counterpart. We localised the position of AT2017gfo on the ACS image by aligning this to the GROND i images taken on each of the nights from 2017 Aug 18 to 21. Nine point sources common to both the GROND and ACS images were matched, and the final position on the ACS image has an uncertainty of 28, 50 mas in x and y respectively, determined from the scatter among the positions as measured on different GROND images.
No sources were detected by the DOLPHOT  37  photometry package at a significance of 3σ or higher, within a radius > 3× the positional uncertainty. We determined the limiting magnitude at the position of AT2017gfo to be F606W> 27. 5 (VEGAMAG), based on the average magnitude of sources detected at 3σ within a 100×100 pixel region centred on the position of AT2017gfo. For our adopted distance modulus and foreground reddening, this implies that any source at the position of AT2017gfo must have an absolute magnitude F606W > -5. 8. ATLAS system and observational data and upper limit to the rate of kilonova events The Asteroid Terrestrial-impact Last Alert System (ATLAS)  38  , is a full-time near Earth asteroid survey. ATLAS observes in two wide-band filters, called "cyan" or "c", which roughly covers the SDSS/Pan-STARRS g and r filters, and "orange" or "o", which roughly covers the SDSS/Pan-STARRS r and i.
The observing cadence for identifying moving asteroids is typically to observe each footprint 4-5 times (30 s exposures, slightly dithered) within about an hour of the first observation of each field. All data immediately go through an automatic data processing pipeline. This produces de-trended, sky-flattened images which are astrometrically corrected to the Gaia stellar reference frame and photometrically corrected using Pan-STARRS1 reference stars  16. Difference images are produced using a static-sky template and source extraction is carried out on both the target and difference images using DOPHOT on the target frames  39  and a custom written package for PSF-fitting photometry, which we call TPHOT (on the difference frames). Sources found on the difference images are then cataloged in a MySQL database and merged into astrophysical objects if there are at least three detections from the five (or more) images.
These objects are subject to a set of quality filters, a machine-learning algorithm and human scanning  16, 40. Our database did not contain any astrophysical object at the position of AT2017gfo between MJD 57380. 64463 and 57966. 26370. The position was observed 414 times and on each of these we forced flux measurements at the astrometric position of the transient on the difference image. We measured 5σ flux limits and any epochs with greater than 5σ detections. The 5σ flux limits were in the range o > 18. 6 ± 0. 5 (AB mag, median and standard deviation) and c > 19. 3 ± 0. 4 (see Extended Data Figure  2 ). We found 44 images which formally had flux detections greater than 5σ, but on visual inspection we rule out these being real flux variability at the transient position. They all appear to be residuals from the host galaxy subtraction.
With ATLAS, we rule out any variability down to 18. 6 to 19. 3 (filter dependent) during a period 601 to 16 days before discovery of AT2017gfo. We can estimate an approximate upper limit to the rates of these kilonovae, without a GW trigger from the ATLAS survey. Extended Data Figure  2  implies that we would be sensitive to objects like AT2017gfo to 60 Mpc. ATLAS typically surveys 5000 sq deg per night, 4-5 times, which provides a sampled volume of 10 -4 Gpc 3 within 60 Mpc. If we assume that a kilonova lightcurve is visible for 4 days and we have observations every 2 -4 days, and observe 60% of clear time then the control time is 0. 9 yr. We have no candidates, therefore the simple Poisson probabilities of obtaining a null result are 50%, 16% and 5% when the expected values are 0. 7, 1. 8 and 3.
88 mm in diameter or 3. 0 degrees. This corresponds to field-of-view area of 7. 06 square degrees, and an active region of about 5 square degrees. The filter system (which we denote grizy P1 ) is similar to the SDSS  44  and is described in detail in two papers  43, 45. Images from Pan-STARRS1 are processed immediately with the Image Processing Pipeline  46. The existence of the Pan-STARRS1 3π Survey data  43  provides a ready made template image of the whole sky north of δ = -30 • , and we furthermore have proprietary i P1 data in a band between -40 • < δ < -30 • , giving a reference sky in the i P1 band down to this lower declination limit. Images in i P1 z P1 y P1 were taken on 7 nights, at high airmass due to the position of AT2017gfo. A series of dithered exposures were taken in the three filters during the first available night (starting 2017 Aug 18 05:33:01 UT), and we placed the target on a clean detector cell.
7 arcmin × 2. 5 arcmin. Observations were carried out during twilight and the early hours of the night at low altitude using 30 sec exposures. Observations were reduced and analysed using a custom pipeline for this telescope. All photometric observations were taken using a clear filter and then converted to SDSS r using four Pan-STARRS1 reference stars. GROND system and observational data Observations with GROND 54 at the 2. 2 m Max-Planck telescope at La Silla ESO started on 2017 Aug 18 23:15 UT  55. Simultaneous imaging in g r i z JHK s continued daily, weather allowing until 2017 Sept 4 (see Tables  2  and 3 ). GROND data were reduced in the standard manner using pyraf/IRAF  56. PSF photometry of field stars was calibrated against catalogued magnitudes from Pan-STARRS1  43, 48  for g r i z images and 2MASS for JHK s images.
The images were template subtracted using the ISIS2. 2 package  53. GROND g r i z images from 2017 Aug 26. 97 UT and JHK s images from 2017 Aug 29. 99 UT were used as reference images. These were the best quality images we had with no detection of the source. The photometry results in typical absolute accuracies of ± 0. 03 mag in g r i z and ± 0. 05 mag in JHK s. SNe within 30-60 Mpc) have never uncovered a similar transient  57, 58. In the ATLAS survey, during the period up to Aug 2017, we have found 75 transients (all supernovae) in galaxies within 60 Mpc and no objects like AT2017gfo. This implies that objects like AT2017gfo have a rate of around 1% or less of the local supernova rate, justifying our probability calculation in the main text.
Data Availability Statement The reduced, calibrated spectral data presented in this paper are openly ety, MPIA Heidelberg and MPE Garching, Johns Hopkins University, Durham University, University of Edinburgh, Queen's University Belfast, Harvard-Smithsonian Center for Astrophysics, Las Cumbres Observatory Global Telescope Network Incorporated, National Central University of Taiwan, Space Telescope Science Institute, National Science Foundation under Grant No. AST-1238877, University of Maryland, and Eotvos Lorand University (ELTE) and the Los Alamos National Laboratory. We acknowledge EU/FP7-ERC Grants [291222], [615929] and STFC funding through grant ST/P000312/1 and ERF ST/M005348/1. AJ acknowledges Marie Sklodowska-Curie grant No 702538. MG, AH, KAR and ŁW thank Polish NCN grant OPUS 2015/17/B/ST9/03167, JS is funded by Knut and Alice Wallenberg Foundation.
CB, MDV. , NE-R. , AP and GT are supported by the PRIN-INAF 2014. MC is supported by the David and Ellen Lee Prize Postdoctoral Fellowship at the California Institute of Technology. MF is supported by a Royal Society -Science Foundation Ireland University Research Fellowship. MS and CI acknowledge support from EU/FP7-ERC grant no [615929]. PGJ acknowledges the ERC consolidator grant number [647208]. GREAT is funded by VR. JDL gratefully acknowledges STFC grant ST/P000495/1. TWC, PS and PW acknowledge the support through the Alexander von Humboldt Sofja Kovalevskaja Award. JH acknowledges financial support from the Vilho, Yrjö and Kalle Väisälä Foundation. JV acknowledges FONDECYT grant number 3160504. LG was supported in part by the US National Science Foundation under Grant AST-1311862. MB acknowledges support from the Swedish Research Council and the Swedish Space Board.
Code Availability The lightcurve fitting code described here is publicly available at the following website: https://star. pst. qub. ac. uk/wiki/doku. php/users/ajerkstrand/start. A code to produce the posteriors in this paper is available at: https://github. com/mcoughlin/gwemlightcurves. TARDIS is an open-source Monte Carlo radiative-transfer spectral synthesis code for 1D models of supernova ejecta and is publicly available here https://tardis. readthedocs. io/en/latest/. Standard software within the IRAF environment was used to carry out the spectral, and imaging reductions and photometry.
AG-Y is supported by the EU via ERC grant No. 725161, the Quantum Universe I-Core program, the ISF, BSF and by a Kimmel award. LS acknowledges IRC grant GOIPG/2017/1525. AJR is supported by the Australian Research Council Cen- tre of Excellence for All-sky Astrophysics (CAASTRO) through project number CE110001020. IRS was supported by the Australian Research Council Grant FT160100028. We acknowledge Millennium Science Initiative grant IC120009. This paper uses observations obtained at the Boyden Observatory, University of the Free State, South Africa.
Method Seven users of the HiRes 90K CI, manufactured by Advanced Bionics, took part: AB1, and AB102 to AB107. Throughout this article, subject numbers greater than 100 refer to patients implanted and tested in Belgium, whereas those lower than 100 were implanted and tested in Cambridge, England. Their details are given in Table  1. Stimuli were generated and controlled using the BEDCS software provided by Advanced Bionics, and by a customised version of the APEX software package that acted as a wrapper around the low-level BEDCS routines. All stimuli were checked using a test implant and digital storage oscilloscope. PTCs were measured using a symmetric cathodic-1st pTP or TP probe; the proportion of current returned by the flanking electrodes depended on the amount of current needed for the probe to reach at least a Bsoft^level.
We therefore measured on-site masking for equally loud TP_PSA, TP_PSC, and MP_SYM maskers for the five subject/masker electrode combinations of experiment 2, plus a total of five other electrodes. The aim was to use this fast method of comparing excitation pattern widths to test a larger number of subject/ masker electrode combinations and to determine whether there was a consistent overall difference between the three types of stimulation. The subject/masker electrode combinations tested are shown in the first column of Table  5. The rows shown in bold italics are for the combinations tested in experiment 2. Two methods of equating masker loudness were used. In the majority of cases, we simply adjusted the level of each masker to its MCL, as in the preliminary stages of experiment 2. In three cases (AB3,e8, AB5,e4, and AB5,e8), we had already obtained on-site masked thresholds for the three maskers set to a slightly softer level (point 5 on the loudness chart) and those data were used here.
Results The masker levels used are shown in Table  5. We obtained masked thresholds for subject AB4, electrode 4, using both methods for matching the loudness of the different maskers, and in this case, we averaged the masked thresholds from the two methods. The results are shown in Figure  6. A repeated-measures ANOVA found no main effect of masker type (F(2,18) = 2. 61, p = 0. 134), but Bonferonni-corrected pairwise comparisons showed that the TP_PSA masker produced significantly more masking than TP_PSC (p = 0. 004). No other pairwise comparisons were statistically significant (TP_PSA vs MP_SYM, p = 0. 29; TP_PSC vs MP_SYM, p = 1. 0). We also analysed the individual subjects' data by performing t tests between the TP_PSA and each of the TP_PSA and MP_SYM maskers. Instances where the TP_PSA produced significantly higher masked thresholds are shown by the asterisks in Figure  6.
Then we tested the effects of urban green, forest, water and wasteland, assessed in terms of coverage within a circle with 1 km radius around the home address of the participants, onto structural integrity in the a priori defined brain regions. Our results reveal a significant positive association between the coverage of forest and amygdala integrity. Since the present data set is cross-sectional in nature and we cannot infer a causal directionality of the effects. However within the model we assumed that an effect from environmental variables onto brain structure is more likely than the reverse directionality where one would have to suggest that participants chose their geographical surrounding based on their brain structural integrity. In line with this assumed directionality, positive effects from enriched environments on morphological changes in the brain have been demonstrated in several animal studies  5, 30, 31.
Moreover a British longitudinal panel study has shown that over a period of five years those participants who relocated to a new residential area between the second and third year that was greener, mental health was significantly better in the post-move years  17. Greater beneficial effects of local green space have been found in individuals that spend more time in their local neighbourhood (e. g. people not in work, older people), most likely due to the greater exposure to this green space  32. This makes older adults a very good population to study relationships between environmental factors surrounding their home and health variables. Remarkably our effect links the amount of forest in a 1 km radius to brain integrity, not urban green. The European Urban Atlas contains data of the year 2006, whereas the brain data has been acquired in 2014/2015 resulting in a relatively large period of time for potential land use changes.