dataesr/scientific-paragraphs-categorization huggingface dataset:
Accession numbers All the new Smc6 sequences are available at GenBank under accession numbers MF624755 to MF624761. A, Amino acid differences at the sites of "genetic conflict" in Smc6 between the host species tested in panel B. Note that all statistically significant marks of potential evolutionary "armsrace" identified in Figure  2  are represented. Asterisks denote the sites that were found under positive selection by at least two methods. B, The human (HBx) and woodchuck (WHx) HBV X proteins promote degradation of the Smc5/6 complex in primate, rodent, and carnivore cells (n=6 species). Cells were either mock transduced or transduced with a lentivector encoding GFP, GFP-HBx or GFP-WHx. After 4-5 days, cell lysates were prepared and run on an SDS gel and probed for Smc6 and Nsmce4A, two essential subunits of the Smc5/6 complex, using primary antibodies against the human protein.
Acknowledgements We would like to very sincerely thank  Léa Picard  and  Laurent Guéguen  for the Bio++ analyses and input on evolutionary analyses,  Véronique Barateau  for technical support with the flow cytometry analyses,  Andrea Cimarelli ,  Olivier Hantz  and  Isabelle Chemin  for scientific discussions, and  Loic Peyrot ,  Anaelle Dubois , and  Francoise Berby  for technical assistance. We thank  Maud Michelet ,  Jennifer Molle ,  Loic Peyrot ,  Anaelle Dubois ,  Océane Floriot ,  Laura Dimer ,  Marie-Laure Plissonnier  and  Julie Lucifora  ( CRCL ) for the isolation of primary human hepatocytes, and  Pr Michel Rivoire 's,  Pr. Jean-Yves Mabrut 's and  Dr. Guillaume Passot 's staff for providing liver resections. We thank  Michael Emerman ,  Andrea Cimarelli  and  Oliver Fregoso  for their comments on the manuscript.
We thank all the contributors of publically available genome sequences. This work was supported by an amfAR  Mathilde Krim Phase II Fellowship  (# 109140-58-RKHF  to LE), an  FRM "Projet Innovant" grant  ( #ING20160435028  to LE), a  FINOVI "recently settled scientist" grant  (to LE), an  ANR LabEx ECOFECT  grant (GrASP to LE), an  ANRS  grant (# ECTZ19143  to LE), a fellowship from  La Ligue Contre le Cancer  (to LG), a grant from the  Swiss National Science Foundation  ( 310030-149626  to MS and FA) and by the  Canton of Geneva.
Temperature and pH were measured immediately at the end of the fertilization trials from control vials that were handled experimentally without sperm or egg additions. pH T25°C was determined spectrophotometrically  (Dickson et al. , 2007)  using unpurified m-cresol purple (Sigma-Aldrich ® ). This method potentially underestimates pH T by 0. 032 ± 0. 006  (Kapsenberg, Kelley, Shaw, Martz, & Hofmann, 2015). Carbonate system parameters were calculated at in situ temperatures from pH T25°C , A T , and salinity using the program CO2Calc [Version 1. 0. 1, 2010, U. S. Geological Survey] with dissociation constants from  Mehrbach, Culberso, Hawley, and Pytkowic (1973)  refit by  Dickson and Millero (1987).
We estimated models using GLMMs with a binomial likelihood estimated with the lme4 R package  (Bates, Maechler, Bolker, & Walker, 2015). Because model residuals exhibited overdispersion (larger error variance than accounted for under a binomial distribution), we added an additional Gaussian noise term on the logit-transformed probabilities as in  Okamoto (2016). For each model, we tested whether each term was statistically significant using likelihood ratio tests. Main effects were tested in the absence of all interactions containing the focal effect, while two-way interactions were tested in the absence of the three-way interaction. These contrasts were chosen because (1) any nonsignificant interactions may reduce statistical power, (2) one or more significant interactions already suggest a significant effect, and (3) we examine interaction effects in detail using effect size metrics (described below).
We restricted data used in analyses to sperm concentrations where either TF or AbnF was not uniformly 0 or 100% (1 < sperm/μl < 2 × 10 5 for TF and 1 × 10 2 < sperm/μl < 2 × 10 7 for   (Johnson, 2014)  to the method of Nakagawa and Schielzeth (2013) implemented with the MuMIn R package  (Bartoń, 2016). We used the resulting TF and AbnF models with full three-way interactions to generate effect size metrics. Specifically, we calculated the sperm concentration required to achieve (1) 50% NF (S NF50 ), (2) optimal NF (S OptNF ), and (3) 25% AbnF (S AbnF25 ). We generated expected NF function using TF and AbnF (Equation  3 ). For AbnF, 25% was chosen as it is the approximate observed extent for most treatments so as not to extrapolate beyond observation boundaries. For each site × pH treatment combination within each metric, we used the model fixed effects estimates and covariance matrices to numerically solve for the sperm concentrations required to achieve those metrics.
| Effect of pH on fertilization Fertilization response to pH was assessed in urchins from three sites with radically different pH variability regimes: wide with frequent low pH exposure (FC), intermediate (BMR), and narrow with rare low pH exposures (SB). Where pH effects were significant, lower pH increased concentrations of sperm required to achieve a given fertilization rate (i. e. , right shifting the curve). The data were analyzed via two approaches. First, the more flexible statistical approach of using GLMMs identified site-specific differences in fertilization metrics wherein urchin fertilization sensitivity to low pH was detected for FC and SB when sperm concentrations were limiting or near optimal (i. e. , not so abundant as to produce substantial abnormal fertilization or abnormal development).
ACKNOWLEDGMENTS We thank members of the  Ocean Margins Ecosystem Group for Acidification Studies consortium (OMEGAS , a multi-institution collaboration of scientists along the  U. S. West Coast ) for the oceanographic data collection supporting this study, particularly  Dr. Francis Chan  who conducted the time series analyses for FC and BMR, and for urchin collections ( Menge Lab at Oregon State University  and  Dr. Eric Sanford  at  UC Davis Bodega Marine Laboratory ). Mohawk Reef pH data are courtesy of the  National Science Foundation  ( NSF) Long Term Ecological Research program  ( Santa Barbara Coastal LTER ,  OCE-0620276 ,  OCE-1232779 ). We thank  Christoph Pierre  for urchin collections at SB,  Evan Hunter ,  Tullio Rossi , and  Ella Bendrick-Chartier  for their assistance with the fertilization experiments and sperm concentration determination, and  Dr.
Kathy Foltz ,  Dr. Mary Sewell , and two anonymous reviewers for comments on the manuscript. This work was funded by  NSF  award  OCE-1040960  to GEH in support of OMEGAS research consortium; LK was supported by an  NSF   Graduate Research Fellowship. During the writing of this manuscript, LK was supported by an  NSF   Postdoctoral Research Fellowship  ( OCE-1521597 ), and DKO was supported by an  NSERC   Strategic grant  ( STPGP 447247-13 ) and the  Santa Barbara Coastal LTER  ( OCE-1232779 ). Data will be made available on the Pangaea database for ocean acidification studies upon publication.
Our knowledge for the distribution of Atlantic mackerel (Scomber scombrus) in the Mediterranean Sea is limited and fragmented. In the current work habitat suitability modeling was applied to summer acoustic surveys data of Atlantic mackerel juveniles derived from the north part of the Mediterranean (i. e. , acoustic data from the Gulf of Lions, pelagic trawls held during acoustic surveys in Spanish Mediterranean waters, south Adriatic Sea, Strait of Sicily, and North Aegean Sea) using generalized additive models (GAMs) along with satellite environmental and bathymetry data. Bathymetry along with sea surface temperature and circulation patterns, expressed through sea level anomaly and the zonal component of the absolute geostrophic velocity, were the environmental variables best to describe nursery grounds.
g. ,  Guisan and Zimmermann, 2000; Planque et al. , 2007; Bellido et al. , 2008; Weber and McClatchie, 2010; Giannoulaki et al. , 2013)  can provide habitat maps presenting geographic areas where environmental variables, in the absence of explicit biotic interactions (such as competition or predation) are considered suitable for the presence of a particular species. This approach has been successfully applied in the Mediterranean to define the potential spawning and nursery grounds of anchovy  (Giannoulaki et al. , 2013)  and sardine  (Giannoulaki et al. , 2011; Tugores et al. , 2011). In the present study, we used data from standardized acoustic surveys that have been carried out routinely in the European Mediterranean areas and applied habitat modeling techniques to identify areas throughout the basin where juvenile mackerels present a high likelihood of long-term persistence, thus defining areas of particular importance for the maintenance of the stocks.
Mackerel Samplings As no suitable data were available from the specific summer surveys to estimate length at maturity (L 50 ), this was based on the available literature review from the Atlantic (L50 = 28. 6 cm,  Froese and Pauly, 2016)  and the Adriatic Sea (smallest mature female was 20 cm of fork Length and the smallest male was 20. 5 cm,  Bottari et al. , 2004). However, according to  Collette and Nauen, 1983 ) mackerel may have to wait for ∼30 cm to reach maturity in the eastern part of the Mediterranean. Thus within a precautionary approach, individuals with total length <20 cm were selected as juveniles for our analyses. Acoustic data from standard monitoring stock assessment surveys held in the Gulf of Lions (Western Mediterranean) were used to model the presence of Atlantic mackerel juveniles during summer.
Acoustic Sampling Acoustic sampling was performed in the Gulf of Lions during summer for the period  (July 2004 (July -2010) )  by means of scientific split-beam echosounders (Simrad EK500, Simrad EK60) and frequencies used at 38, 70, 120, and 200 kHz, calibrated following standard techniques  (Foote et al. , 1987). Data were collected on board the R/V "L'EUROPE" along predetermined parallel transects, perpendicular to the bathymetry 12 nautical miles (nmi, 1. 852 km) apart and were recorded at a constant speed of 8 nmi h -1. Minimum sampling depth varied between 10 and 20 m depending on the area. The size of the Elementary Distance Sampling Unit (EDSU) was one nmi. Acoustic data analysis was made using the Movies+ software. Midwater pelagic trawl sampling was carried out in order to identify and verify mackerel echotraces as well as to assess the size distribution and distinguish between juveniles and adults.
emodnetbathymetry. eu). All monthly-averaged satellite images from daily measurements were processed as regular grids under a GIS (Geographic Information Systems) environment using ArcInfo GRID software. Satellite variables were mostly used at their best available resolution provided by the online satellite data distribution archives (Table  2 ) in order to obtain environmental characteristics for each sampling point at an average spatial resolution of 1. 5 km  (Valavanis et al. , 2008) , adequately defining environmental spatial heterogeneity in relation to both the applied EDSU (1. 852 km) of acoustic data and the best available resolution of the explanatory environmental variables.
Habitat Modeling Generalized additive models (GAMs;  Hastie and Tibshirani, 1990)  were applied in a presence/absence approach to define the set of environmental factors that describe mackerel juveniles' distribution. The presence/absence approach allowed us to standardize Atlantic mackerel presence from different sources i. e. , acoustic sampling and pelagic haul sampling. Furthermore, in order to assess the quality of our models, we had to separate the dataset between a training dataset (data that were used to estimate parameters) and a validation dataset (data that were not used in the model parameterization but used afterwards to assess the quality of the fit). The training dataset was defined in order to ensure (a) merging data from different areas that cover a wider range of environmental conditions (b) merging presence/absence data from acoustic surveys and pelagic hauls we succeeded eliminating any autocorrelation in our training dataset thus avoiding any Type I error in our resulted model and (c) selecting years of data that balanced absence and presence records thus avoiding any prevalence of positive or negative areas that are likely to cause bias to model performance  (Fielding and Bell, 1997).
As such, GAMs were applied to pooled data from the Gulf of  Lions (2006 Lions ( -2010)) , the North Aegean Sea  (2006, 2008)  and the Strait of Sicily (  2006 ) to define a "mean" model that we can subsequently use for mapping. The selection of the GAM smoothing predictors was done using the MGCV library in the R statistical software (R Development Core Team, 2012). In each model fit, a double penalty was applied to the penalized regression solved by MGCV, which allows variables to be solved out of the model entirely  (Marra and Wood, 2011) , being more robust to identify important features. The degree of smoothing was chosen based on the observed data and the restricted maximum likelihood (REML) estimation that outperforms the generalized cross validation (GCV) smoothing parameter selection, as suggested by  Marra and Wood (2011).
Model Validation In a subsequent step, the final model was tested and evaluated for its predictive performance, in other words the possible error in the predictions. For this purpose we used the validation dataset including data not used for modeling, i. The advantage of the specific validation dataset is that it covered to the highest possible extent the entire basin and the prevalence of the positive values was at 0. 75 thus reducing the possibility to result into a misleading high overall accuracy using trivial rules when for example the dataset is characterized by low prevalence  (Fielding and Bell, 1997). Generally, using the specific pooled dataset we aimed to (a) balance the number of presences and absences as much as possible (b) increase the extent of occurrence/absence data and cover a wide extent of the territory under study and (c) add absences from more environmentally distant locations compared to presences in order to increase the rate of well-predicted absences.
ACKNOWLEDGMENTS This study was partly supported by the  Commission of the European Union  through the  MEDISEH  project ( MAREA PROJECT MEDISEH:Mediterranean Sensitive Habitats ,  Specific  Contract No 2,  SI2. 600741 ) and the MEDIAS workshops of the DCF. We thank the captains and crews of the  RV Philia, RV L'Europe, IEO RV Cornide de Saavedra , and  RV Dallaporta  as well as all the scientists for their assistance during the on board surveys.
Significance A dataset from one of the last protected tropical peat swamps in Southeast Asia reveals how fluctuations in rainfall on yearly and shorter timescales affect the growth and subsidence of tropical peatlands over thousands of years. The pattern of rainfall and the permeability of the peat together determine a particular curvature of the peat surface that defines the amount of naturally sequestered carbon stored in the peatland over time. This principle can be used to calculate the long-term carbon dioxide emissions driven by changes in climate and tropical peatland drainage. The results suggest that greater seasonality projected by climate models could lead to carbon dioxide emissions, instead of sequestration, from otherwise undisturbed peat swamps. Fig. 1. Ecosystem feedback leading to peat accumulation.
Methods Field Measurements. We established a field site in a pristine peat forest in Brunei Darussalam (Borneo) to study a peat dome where current processes affecting peat accumulation are essentially similar to those during its long-term development (Fig. 2 ). At the site, we installed 5 piezometers along a 2. 5-km trail, 12 piezometers along a 180-m transect, and 3 throughfall gauges. We completed a total station survey of peat surface elevation along the transect to characterize peat surface microtopography. To characterize large-scale peatland morphology, we also obtained LiDAR data for the entire study area. To study peat dome development, we collected nine peat cores from which we obtained 35 radiocarbon dates. To test whether our undisturbed site behaved similarly to sites studied by other groups, we installed four soil respiration chambers and a piezometer at a nearby logged but undrained site.
We also used the transect survey and piezometer data to find the land surface p along the microtopography survey transect (SI Methods). Groundwater Flow. We model the dynamics of the water table H subject to net precipitation qn (rainfall intensity R minus evapotranspiration, ET), using Boussinesq's equation for essentially horizontal groundwater flow Sy ∂H ∂t = qn + ∇ • (T∇H), [1] E5188 | www. pnas. org/cgi/doi/10. 1073/pnas. 1701090114 Cobb et al. (D) Piezometers (triangles) at the Mendaram site (colors are explained in Fig. 6 ). (E) Survey points in microtopography transect (Fig. 3B ). where the specific yield Sy is the amount of water required for a differential increment in water table elevation, and transmissivity T is the volumetric flow per perimeter driven by a particular head gradient ∇H.
ACKNOWLEDGMENTS. We thank  Mahmud Yussof  of  Brunei Darussalam Heart of Borneo Center  and the  Brunei Darussalam Ministry of Industry and Primary Resources  for their support of this project;  Hajah Jamilah Jalil  and  Joffre Ali Ahmad  of the  Brunei Darussalam Forestry Department  for facilitation of field work and release of staff;  Amy Chua  for logistical support; and  Bernard Jun Long Ng ,  Rahayu Sukmaria  binti  Haji Sukri ,  Watu bin Awok ,  Azlan Pandai ,  Rosaidi Mureh ,  Muhammad Wafiuddin Zainal Ariffin , and  Sylvain Ferrant  for field assistance. We also thank  Paul Glaser  and two anonymous reviewers for their detailed comments on the manuscript. This research was supported by the  National Research Foundation Singapore  through the  Singapore-MIT Alliance for Research and Technology's Center for Environmental Sensing and Modeling interdisciplinary research program , by the  US National Science Foundation  under Grants  1114155  and  1114161  (to C.
Materials and methods Instrumentation Fieldwork was conducted at the Pointe Suzanne Kerguelen shag colony (49˚26'S, 70˚26'E), Kerguelen Island, southern Indian Ocean, during the 2014/15 breeding season. This study was approved by the ethics committee of the French Polar Institute (Program IPEV 394, resp. C. A. Bost) and therefore meets ethics guidelines. All animals in this study were cared for in accordance with its guidelines. Sampling occurred during two sessions. During the first session, a total of 20 individuals (both partners from 10 nests) were instrumented with GPS data loggers (I-gotU GT120, Mobile Action, Taiwan; 44. 5 x 28. 5 x 13 mm, 22 g in air corresponding to ca 1% of mean body mass) for 3-6 d at the end of incubation/early chick rearing (26-Nov-2014 to 10-Dec-2014; hereafter incubation), when chicks were no older than a week.
During the second session, a total of 22 birds (both partners from 11 nests) were instrumented for 3-12 d during late chick rearing (6-Jan-2015 to 18-Jan-2015; hereafter chick-rearing), of which 10 birds had previously been sampled and were deployed only with GPS data loggers while the remaining 12 individuals were equipped both with GPS data loggers and time-depth recorders (TDR, LAT1800S, Lotek Wireless Inc. ; 36 x 11 x 7. 2 mm, 4. 8 g in air corresponding to ca 0. 2% of mean body mass). All chick-rearing instrumented birds had a single chick, except for one nest, which had two chicks at the beginning of deployments but lost one of them within a few days. GPS loggers were programmed to sample positions every one min at incubation and every 2 min in chick-rearing. The TDR units were set to record depth and temperature at 1 s intervals.
Blood isotopic values were obtained from 32 individuals and feather values from 31 individuals (including 9 shags that were sampled twice, at incubation and then in chick-rearing; 12 of those individuals had GPS and TDR data over consecutive trips and 15 of those had GPS data only over consecutive trips). Regurgitate samples were stored frozen until processing in the laboratory. They were first defrosted and the fresh and accumulated fractions were weighed separately. The fresh fraction was sorted into different prey categories (annelids, cephalopods and fish) that were weighed separately. Items were identified to species level when possible, according to  [41]  for fish, and to a reference collection, i. e. bones and otoliths for fish, chitinized beaks for cephalopods and jaws for annelids.
Data processing and statistical analyses All data analyses were conducted in the R Statistical Environment version 3. 2. 0 [44]. GPS records for each individual were visually inspected and individual trips were determined. On average, GPS points during foraging trips were obtained every 3. 1 minutes for GPS loggers set to record every minute, and every 3. 8 minutes for the ones set to record every 2 minutes. The diveMove package was used to apply a speed filter to the GPS data to remove erroneous locations with a threshold of 18 mÁs -1 , and obtain summaries of diving metrics from TDR records (only dives deeper > 1 m were considered). Means of diving behaviour metrics and coefficients of variation (CV) per individual were calculated for dive duration, depth, and sum of vertical distance. GPS records were linearly interpolated to the 1 s intervals in the adehabitatLT package  [45]  to provide spatial information for the dive records.
To assess whether spatial information alone could reflect consistency in foraging locations, this index was compared for the GPS derived tracks and the dive locations alone. There was a significant positive correlation between the consistency index calculated from GPS data alone and diving locations obtained using GPS and TDR (R 2 = 0. 74, P < 0. 001). Therefore, it was considered that consistency in spatial space use was representative of consistency in foraging space use. Different grid cell sizes were used to calculate the index of consistency in space use (from 0. 5x0. 5 km to 5x5 km) to check the influence of grid cell size on our estimate of spatial consistency. Indices obtained, regardless of cell grid sizes, were highly correlated. In order to investigate the factors influencing dive behaviour, spatial use and consistency in foraging behaviour, linear mixed effects models were fitted, using the nlme package  [48].
The model included sex as an explanatory variable, and individual as a random effect. In order to quantify how specialised shags were in diving behaviour, we used a variance components analysis to calculate the variance, standard deviation and proportion of total variance occurring at the levels of individual, and trip within individual using the R package ape  [49]  following  [20]. An estimate of individual specialisation is given by the proportion of variance explained by the individual variance component  [6, 20, 50]. To understand the influence of sex and stage on spatial metrics, total distance travelled was used as an explanatory variable as it was correlated with maximum distance and trip duration; sex, stage and their interaction were used as explanatory variables, individual was used as a random effect and a sex and stage identity variance structure was applied.