dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This work was supported by grants from the  FFG Austrian Research Promotion Agency  ( BRIDGE-842388-CBL-AIM ) and  FWF Austrian Science Fund  ( W1101-B18 ,  P23537-B13 ,  P25044-B21 , '' Hertha-Firnberg ''  T264-B13 , and '' Lise-Meitner ''  M1636-B23 ), the  Austrian Cancer Society, Tyrol as well as the Deutsche Forschungsgemeinschaft  ( DFG WO1877/1-1 ). We are grateful to  Michaela Kind  and  Nina Posch  (both from our institute in Innsbruck) for technical assistance.
Discussion Myometrial macrophage infiltration at labour has been implicated in both the initiation/propagation of inflammation during parturition as well as the resolution of inflammation and tissue repair post-partum Figure  2  Secretion of IL-6 and IL-8 from neutrophil/PHM1-41 and monocyte/PHM1-41 co-cultures. Supernatants from PHM1-41 cells cultured alone or in a 10:1 ratio with either primary monocytes (Mono) or neutrophils (Neut) from female, non-pregnant donors, in the presence or absence of LPS (100 ng/ml) for 24 h were assayed by ELISA for (A) IL-6 and (B) IL-8. Data shown are the mean + SD, n ¼ 3 independent experiments conducted in triplicate. (Hutchinson et al. , 2011). Recent studies suggest that macrophage infiltration is an early event occurring at or prior to parturition and that it is regulated by increased chemokine production  (Hamilton et al.
(D) PHM1-41 gel viable cell numbers was compared between treatment groups using a colourimetric assay, with no differences between any of the treatments. Data are shown as mean + SEM, n ¼ 5 independent experiments conducted with six replicates per treatment. Data were analysed by two-way ANOVA and post hoc Sidak or Bonferroni multiple comparisons, ns, non-significant, *P , 0. 05, **P , 0. 01. One possible mechanism of enhanced myocyte contraction could involve cytokine-mediated prostaglandin secretion  (Rauk and Chiao, 2000; Astle et al. , 2007). Additionally, IL-1b and TNF are two likely candidates for the observed myocyte contraction, since they can modulate myocyte intracellular calcium thus enhancing excitability, and directly cause contraction  (Tribe et al. , 2003; Fitzgibbon et al. , 2009; Hutchinson et al.
Acknowledgements The authors thank  Professor B. Sanborn  ( Colorado State University, Fort Collins, CO, USA ) for her kind gift of PHM1-41 cells,  Dr A. Henke  ( University Clinic Cologne, Germany ) for his gift of rat tail collagen,  I. Crawford  and  N. Hibbert  for their assistance in patient recruitment,  Simon Riley  and colleagues at the  Edinburgh Reproductive Tissue Biobank  and all the participants who generously donated their time and samples to this study.
Satellite tags were used for short-term deployments during incubation and brooding, whereas geolocation tags were used to obtain foraging positions during chick-rearing, and to supplement data from the incubation period. While geolocation tag deployments also spanned the brooding period, the single position derived per day by light-level based geolocation did not have sufficient resolution to accurately capture movements during brooding when average trip durations were 2-3 days (Table  1 ; satellite tags are accurate to <10 km  [53] ; geolocation tags are accurate to ~200 km  [54] ). Tracking activities were conducted during five consecutive breeding seasons, from 2002-03 through 2006-07; satellite tracks were obtained during the 2002-03 to 2005-06 seasons and geolocation data were obtained during the 2003-04 to 2006-07 seasons.
Satellite tracking One hundred and forty-seven adult albatrosses (76 Laysan and 71 black-footed) were equipped with satellite platform terminal transmitters (30 g Pico-100,   [56]. The combined mass of devices deployed on individuals was less than 2 % of total bird body mass in all cases, below the recommended limit for studies involving albatrosses  [57]. Before calculating trip characteristics, satellite locations were first delimited by observations of departure and arrival times at the breeding colony or by visual inspection of the tracks. To remove unlikely locations from the data set, we applied an iterative forward/ backward averaging speed filter  [58]  implemented in Matlab (The MathWorks, Natick, MA) with a maximum speed limit of 80 km h -1 (following  [29, 59] ) to remove unrealistic flight speeds  [60].
Due to the nature of the SSTprocessing algorithm  [54, 64] , it was necessary to first delimit individual foraging trips so that on-colony locations were removed from the dataset. We calculated minimum daily temperatures recorded by geolocation tags to determine days when albatrosses were likely at sea; evaporative cooling due to immersion in water would lead to cooler minimum temperatures on days when albatrosses were off-colony. In order to detect shifts in minimum daily temperatures indicative of onor off-colony periods, we used an algorithm developed for detecting climate regime shifts  [65] , which implements a sequential version of the partial CUSUM method combined with the t-test, and is available for download at: http://www. beringclimate. noaa. gov/regimes/. For those individuals for which exact colony attendance patterns were known (25 individuals during incubation), this detection algorithm correctly classified 99.
Foraging movements Descriptive characteristics of all Argos-and geolocationderived tracks were calculated to compare foraging behavior during each reproductive stage. Maximum distance traveled from the colony was calculated using great-circle distances to account for the earth's curvature. Destination bearings, as defined by the azimuth to the most distant point, were calculated to describe overall direction of foraging trips. Trip duration was defined as either the time elapsed between the observed departure and arrival of the bird, or the time elapsed between the estimated departure and arrival times. We used linear mixed-effects models  [66] , with individual as a random effect, to compare maximum ranges and trip durations between species and reproductive stages. Mixed-effects models were followed by contrast analysis with the multcomp package  [67]  in the program R  [68].
Foraging distribution To determine patterns of interspecific habitat segregation during each reproductive stage, we used kernel estimation  [70]  to determine utilization distributions (UD) for each species. Because individuals contributed varying numbers of foraging trips to the overall data set, a single trip was randomly selected for each individual during each reproductive stage so that the influence of individual behaviors on estimation of kernel densities was reduced. Geographic coordinates of interpolated albatross locations were transformed using a Lambert Cylindrical Equal Area projection  [71] , and UDs were computed on a 50-km grid using the R package adehabitat  [72]. To allow comparisons between species, the smoothing factor (h) was set to the mean of the h values calculated from each species, as determined using least-squares crossvalidation  [73].
Tremblay, unpublished) designed to identify landings based on rapid changes in temperature, and stable periods associated with sitting on the sea surface  [56]. We used civil twilight (sun no more than 6°below the horizon) to define daylight hours, based on temporallymatched tracking locations and NOAA's solar calculator, as implemented in the maptools package in R  [77]. To test whether percent time in flight and landing rates differed between high-resolution temperature records (12-40 s) and low-resolution records (480 s and 540 s), we subsampled high resolution records to the lower resolution and compared these metrics. While percent time in flight did not differ between high resolution and subsampled records (paired t-test: t 140 = 1. 11, P = 0. 27), landing rates were significantly lower in the subsampled group (paired t-test: t 140 = 20.
As albatrosses use near-surface winds to engage in gliding flight  [98] , we also characterized wind speed and direction for all locations used in the habitat analysis. Because Hawaiian albatrosses periodically visit the continental margins of the western coast of North America and the Aleutian Islands during breeding  [28, 29, 32] , and bathymetric features can stimulate local production and aggregate prey  [99, 100] , we also used sea floor depth to characterize marine habitats; bathymetry data was extracted from NOAA's ETOPO2 data set (http:// www. ngdc. noaa. gov/mgg/global/etopo2. html). Environmental data were obtained by querying the NOAA OceanWatch Live Access Server using Matlab and ERDDAP (http://coastwatch. pfel. noaa. gov/erddap/). Where possible, we used satellite products with the finest temporal resolution available (daily or weekly); we used monthly composites when necessary to compensate for data gaps due to cloud cover.
We used wind stress curl as a metric of surface convergence/divergence  [50] , as a proxy for local aggregation of neustonic or buoyant prey (e. g. , fish eggs, fish and squid larvae, zooplankton, fishing discards). For each location of interest, we calculated a mean wind stress curl from the previous 30 days to get a representation of past physical forcing in the area of interest. EKE was calculated from surface ocean currents derived from four satellite altimeters (Jason-1, ENVISAT, ERS-1 and 2, and TOPEX/Poseidon) provided by the AVISO program at a resolution of 0. 25 degrees. For each location of interest, a 10 x 10-degree spatial mean was removed from each 7-day composite of zonal and meridional surface ocean currents to generate zonal and meridional velocity anomalies (u' and v'). The EKE (per unit mass) was then calculated as EKE ¼ 1 2 u 02 þ v 02 À Á ð1Þ SSHa was derived from AVISO sea level data compared to the mean geoid as measured from 1993-1995.
Oceanographic data were extracted so that composite data matched closest in time to tag-derived locations (and matching control locations). The median value of each oceanographic variable was calculated for grid cells falling within the approximate error of Argos or geolocation positions. To estimate error of Argos locations, we conducted trials on seven satellite tags affixed to permanent structures at the University of California Santa Cruz, Long Marine Lab, for a period of three weeks. Argos locations were then compared to GPS-derived locations; mean error for all location qualities was 0. 06°( 6. 7 km) from a total of 2,585 location fixes. To be conservative, satellite tag-derived locations (and matching control locations) were extracted within a 0. 15°( 16. 7 km) longitude by 0. 15°(16.
1, the approximate guide suggested by Quinn and Keough  [105]. GAMMs were implemented within the gamm4 package in R  [106]. All candidate covariates were fitted using cubic regression splines; a cyclic spline was used to model wind direction. We first investigated the potential effects of transmitter duty-cycle and tag type (geolocation versus satellite tracking), as well as year and sex effects, by comparing intercept-only GAMMs for each species and reproductive stage to models including each of the above terms. In all cases, Akaike's information criterion (AIC) values were not improved by the addition of these terms, therefore they were not subsequently included in models of habitat preference. To arrive at an inferential model for each species and breeding stage, we used AIC as a guide, and relied on cluster-level cross-validation for final model selection  [80].
This approach was taken due to the fact that model selection based on AIC alone can lead to overparameterized models when data are spatially or temporally autocorrelated, as is expected with both tracking and oceanographic data  [80]. Therefore, we randomly selected two-thirds of the individuals tracked during each breeding stage for each species to use in the first stage of model selection. We started with intercept-only models and used forward model selection with AIC to arrive at a suite of candidate models for each speciesstage. We then fit data from the remaining individuals to the sequence of models obtained from forward selection. The model with the lowest AIC from the crossvalidation step was retained as the inferential model. To reduce the chance of over-fitting, we then replaced each spline in turn by a linear term and selected a final inferential model for each species-stage based on AIC values.
Greater dietary partitioning during brooding could provide a means of reducing competitive interactions due to shared feeding areas. There is limited information, however, on changes in diet throughout the reproductive period in these species; in other albatross species, diet can change considerably over the course of the breeding season  [121]. The most comprehensive dietary dataset for Hawaiian albatrosses are mainly representative of the chick-rearing period and are not separated for each stage  [86]. Other diet studies have focused on by-caught birds in fisheries  [87]  or analysis of regurgitated pellets from chicks during the rearing period only  [122]. Comparison of a limited number of chick diets from the brooding period suggests some dietary segregation during this stage; squid and fish were found more frequently in the diet of Laysan albatross chicks, and flying fish eggs were found more frequently in the diet of black-footed albatross chicks (M.
However, low accuracy of geolocation data may have limited our ability to detect responses to mesoscale activity. The environmental covariate with the greatest influence on habitat preference of Laysan and black-footed albatrosses was SST. Sea floor depth, SSHa, PP, d TZCF , and EKE were also retained as covariates in final habitat models, however, these terms represented a smaller proportion of explained deviance. This suggests that after accounting for SST preference, the location of largescale fronts (d TZCF ), mesoscale activity (EKE and SSHa), and bathymetric domain do not have a strong influence on habitat selection of Hawaiian albatrosses. Other environmental covariates describing frontal characteristics (dySST) and wind-driven oceanic upwelling or downwelling (wind stress curl) were not retained in final models, despite characterizing physical features or processes that could drive prey availability for Hawaiian albatrosses  [88] [89] [90] [91] 93].
Acknowledgements The authors dedicate this manuscript to the memory of our colleague and friend,  David Foley. We thank  Angela Anders ,  Jill Awkerman ,  Daniel Barton ,  Sarah Chisholm ,  Melinda Conners ,  Kirsten Lindquist ,  Frank Mayer ,  Scott Seganti , and many  U. S. Fish and Wildlife Service  volunteers for invaluable assistance in the field. We also thank the  Hawaiian Islands National Wildlife Refuge, U. S. Fish and Wildlife Service ,  Department of the Interior , for permission to conduct research on  Tern Island  and logistical support. Lindsay Young  conducted the gender analysis. Alan Swithenbank  and  Steve Teo  provided the SST-correction algorithm for light-based geolocation data. Jason Matthiopoulos  and  Ewan Wakefield  provided helpful suggestions for defining accessibility and selecting control locations.
, 2012) , which thus provide a unique snapshot of the summer distribution of this species prior to its severe depletion. These two species of right whales have a similar feeding behaviour, being both slow cruisers and specialist copepods filter feeders  (Kenney, 2009) , and share the same body morphology in adaptation to this particular diet  (Woodward et al. , 2006). In fact, for lack of evidence of significant differences, they were long considered a single species  (Rice, 1998)  and only recently distinguished through genetic analyses  (Rosenbaum et al. , 2000a; Sasaki et al. , 2005). Phylogenetically related species often share the same ecological traits that affect how their distributions relate to environmental conditions  (Wiens et al. , 2010). Historical occurrence data for the NARW are too deficient to generate a basin-wide SDM.
But if these two species share similar environmental preferences, data from the well-documented historical summer distribution of the NPRW can inform the little-known historical feeding grounds of the North Atlantic species. Here, we predict the potential summer distribution of NARWs from a SDM incorporating environmental data and whaling records of NPRWs, assuming that the two species select the same type of environments. We then investigate how informative such predictions are by discussing whether they are supported by historical records in the North Atlantic.
Historical records of NPRWs Distribution records of NPRWs were obtained from 19thand early 20th-century logbooks kept aboard American offshore whaling ships  (Maury, 1852; Townsend, 1935; Smith et al. , 2012). Logbooks provide information on the date, location and species of whales seen or caught, as well as information on the days when none were reported. We examined records of NPRW presence and absence in summer (June-September) north of 20°N, obtained from a sample of these logbooks  (Smith et al. , 2012). The dataset included c. 46,000 point records, including c. 4400 presences, between 1819 and 1904 (91% of presence records from 1840 to 1850). Point records were gridded at a 1°9 1°resolution, excluding cells with fewer than three observations to minimize the effect of false absences (Appendix S4 in Supporting Information).
Environmental data During the summer months, right whales are actively engaged in feeding, and their distribution is believed to be mostly driven by the distribution of their prey, mainly copepods  (Nemoto, 1970; Murison & Gaskin, 1989; Mayo & Marx, 1990). No dataset is currently available on the distribution of copepods at a global scale, but other oceanographic and topographic variables correlated to their distribution can be used  (Rutherford et al. , 1999; Friedlaender et al. , 2006; Gregr & Coyle, 2009). We considered three climate variablessea surface temperature (SST)  (Norberg & DeAngelis, 1997; Rutherford et al. , 1999; Gregr & Coyle, 2009) , mixed layer depth (MLD)  (Baumgartner & Mate, 2003)  and net primary productivity (NPP)  (Norberg & DeAngelis, 1997)  and three bathymetric variablesdepth (DEPTH), slope (SLOPE) and distance to land (LANDDIST)  (Hamazaki, 2002; Kaschner et al.
, 2006; Gregr, 2011; Torres et al. , 2013). They were selected for being known to be correlated with the presence of right whales and/or copepods in summer (see Appendix S2). SST, MLD and NPP were averaged over the summer months (June-September) to capture the environmental conditions encountered during the main feeding period of NPRWs  (Clapham et al. , 2004). A long-term climatology was obtained by averaging SST and MLD over almost the entire 20th century (1900-92) and for NPP, which relies on more recent satellite data, over the period 1998-2007. Environmental data were aggregated at a 1°9 1°resolution, using the 'raster' package  (Hijmans, 2014)  in R 3. 0. 2 (R Development Core Team, 2013) to match the occurrence data.
, 2008) , are presented in Appendix S3. Predictions of environmental suitability from the BRT were mapped on a 1°9 1°grid in the North Pacific and the North Atlantic, by assigning a probability value to each cell based on its environmental values and the model's fitted functions. Before predicting into new areas, we eliminated cells with environmental values outside the range used to calibrate the models. The envelope of predicted presence was defined by applying a threshold which maximizes the sum of sensitivity and specificity (defined, respectively, as the proportion of accurately predicted absences/presences)  (Jim enez-Valverde & Lobo, 2007). Statistics on classification rates were then calculated to check on the predictive ability of the model within the calibration area by performing a fivefold cross-validation with a training and test dataset (a random selection of, respectively, 75% and 25% of the whaling data).
and absence records (blue, cells visited by whalers, but where no right whales were reported in the examined logbooks); white cells were not visited. (c) Predicted environmental suitability from a species distribution model fitted to the historical records. Shades of red indicate progressively higher suitability as predicted by the BRT models (above the P = 0. 32 threshold); blue cells correspond to areas where the species is predicted as absent; and white cells are areas for which no reliable predictions can be made. All data are presented on a 1°9 1°grid in Bonne projection (standard parallel: 30°N; central meridian: 170°W). BRT, boosted regression trees.
As previously discussed, the species' similarities in ecology and morphology justify that we explore this hypothesis. The main drivers of our prediction (SST, NPP and MLD) are consistent with those found in previous studies on the distribution of right whales  (Baumgartner & Mate, 2003; Gregr, 2011; Torres et al. , 2013) , reinforcing our assumption that right whales' distributions are generally driven by the same mechanisms. The North Pacific whaling records are an exceptional dataset for developing a SDM. They can nonetheless include a number of false absences and false presences, as a result of biases (in sampling effort across space and time) and errors (in location and in species' identifications). We attempted to reduce these by analysing only cells with a reasonable sampling effort and by reducing sources of confusion with bowheads (Appendix S1).
Nonetheless, we were unable to correct for the poor representation of shallow-depth data in the NPRW records (given that they correspond to offshore whaling operations) and its potential effects on the model's dispower in shallow-depth areas. Our analysis is based on a geographic extrapolation from the North Pacific to the North Atlantic. We restricted predictions to the environmental envelope sampled by whalers in the Pacific Ocean to ensure that areas where we predict high environmental suitability are represented in the dataset used to calibrate the model, reducing one of the main sources of uncertainty under predictions of new scenarios  (Zurell et al. , 2009). We were, however, unable to control for other potential differences between basins (e. g. in prey availability, in the interactions between oceanographic conditions) which might limit the transferability of the statistical correlation found in one ocean basin into the other one  (Randin et al.
All data are presented on a 1°9 1°grid in Bonne projection (standard parallel: 30°N, central meridian: 20°W). BRT, boosted regression trees. The NARW historical records we compiled constitute the most comprehensive dataset currently available on the historical summer occurrence of this species. Yet, it is biased spatially (towards coastal areas, where whales were more accessible) and temporally (towards a recent period when records became more detailed, despite the species being rarer) and plagued by geographic imprecision (location is only approximate for 23% of records) and by taxonomic uncertainty (for 13% of records). These limitations preclude the use of these data in a quantitative validation of the model's predictions. Instead, we discuss qualitatively, region by region, the extent to which the historical (Table  S2 ) and recent (Table  S3 ) records either support or contradict the model's predictions  (Figs 2c, S3 and S4) , keeping in mind the strengths and limitations of both the data and the model.
5 anti-human CD14; Pacific Blue anti-human CD19 and pacific-blue anti-human HLA-DR. All samples were acquired on a Gallios (Beckman Coulter, Pasadena, CA, USA) and the results analysed with Kaluza software (Beckman Coulter). A minimum of 1 × 10 4 events were acquired per surface marker. One replicate was analysed per independent experiment (n = 4).