dataesr/scientific-paragraphs-categorization huggingface dataset:
Filtering and standardization Because time intervals between locations differed among device types and datasets, we used a single location per day (i. e. , the lowest common interval) from each individual track, nearest to local noon, for analysis. GLS, PTT, and GPS devices provide positions in different ways, resulting in different levels of spatial accuracy. Data from GLS devices are used to estimate location based on day length and the relative timing of local noon and have the highest spatial error, of around SD ±186 km (43). We applied a series of filters to GLS datasets in addition to those used by data contributors. We also performed a resampling procedure to test the sensitivity of our measures of the importance of political areas to spatial error and concluded that the effects on estimated richness and time spent were very small and had no substantial effects on the results (see Supplementary Materials and Methods for details and fig.
S3  for results). Nine species (all Diomedea and Phoebetria spp. and gray-headed albatross Thalassarche chrysostoma) breed biennially, and because tracking data coverage during the nonbreeding year is generally poor, we retained only data from the first 365 days following logger attachment for each individual track. Hence, all results pertain to the distribution of adult birds during a breeding year. After filtering, there were a total of 842,527 tracking days available for analysis from 5775 individual birds (Fig. 2A , fig. S1 , and data S4). These data represent movements sampled between 1989 and 2017, across 87 breeding populations from 39 of the 40 species of albatrosses and large petrels (Table  1  and data S3 ). These populations represent a total of 44 million of the estimated 58 million breeding albatrosses and large petrels worldwide and a mean of 85% (range, 3 to 100%) of the global breeding population per species (Table  1 ).
g. , "disputed -Russia/Japan"), unless they had breeding colonies with tracking data, which was only the case for the Falklands/Malvinas, South Georgia/Georgias del Sur, and the Chafarinas/Zafarin Islands. For these cases, the analysis was run twice with breeding populations alternately assigned to each disputing country (see alternate results in data S5, C and D, and S6, C and D). Species richness Species richness was calculated for each national jurisdiction and the high seas as a whole based on tracking data occurrence and reflects the number of (i) breeding species, (ii) visiting species, and (iii) species that both breed locally and visit from elsewhere. To visualize this pattern in space, maps were constructed by binning total richness on a global hexagonal grid of with cell-center spacing of 495 km (SD ±30) which is a grid resolution large enough to encompass the average error of GLS devices (43).
Annual time spent We then summarized the monthly time spent across all months to estimate the standalone importance of maritime zones e in terms of the total amount of time spent in a year (T e ; in bird-years) by the global community of albatrosses and large petrels T e = ∑ p=1 p max ∑ m=1 m max ( T spme ) ─ 12 (2) where the total time spent within each month (T spme ) was summed across the months for which tracking data were available (m max ) and divided by 12 to convert the unit from bird-months to birdyears. The number of bird-years per population was then summed across all populations in maritime zone e to give an estimate of the total annual time spent there by all species. Continuing our example of Cory's shearwaters from Madeira, the total monthly time spent in the high seas (T spme ) was calculated for the 11 months of the year where tracking data existed (m 1 to m max ) and then divided by the full 12 months of the year to convert the unit from bird-months to bird-years.
A virtual application is available at https://birdlifeseabirds. shinyapps. io/seabird-connections/ to facilitate viewing the specific connections albatrosses and large petrels create between different national jurisdictions and high seas areas, including RFMOs. All analyses were run using the R statistical computing environment; maps were made using the R packages "dggridR," "ggplot2," and "sf," and networks were constructed using "ggraph" (44-48). Analysis scripts are available at https://github. com/MartinBeal/political_connectivity.
Acknowledgments: We thank all the field workers and data managers involved in the collection and curation of this unique dataset. We would like to thank  J. Croxall  and  C. Small  for efforts in establishing the Seabird Tracking Database, without which this project would not have been possible. Special thanks to  R. A. Ronconi ,  R. Alderman ,  R. Gales ,  A. Baylis ,  H. Otley ,  S. Poncet ,  R. Snape , and  A. Wolfaardt  for data collection and provisioning and logistical and field support. Thanks to a host of field workers who have contributed over the years. Last, thanks, most of all, to the animals themselves. Funding: This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie  grant agreement no. 766417.
Eta is likely to have originated in Nigeria but was rst identi ed in the UK, and it contains immune escape mutations (del69-70 & E484K) and increased tness mutations (P681R)  12. Though largely unheralded, B. 1. 1. 318 had been responsible for the second wave in Mauritius  13. Given the spread of B. 1. 1. 318 in Ghana, Mauritius and over 30 countries, it looked poised to become a dominant SARS-CoV-2 variant. From our dataset, B. 1. 1. 318 appeared to replace Alpha as the predominant lineage in community infections in March 2021 and remained dominant until June 2021, when it was replaced by Delta and Delta plus. The combination of spike substitutions T95I, E484K, D614G, P681H, and D796H in B. 1. 1. 318 may have allowed it to escape Alpha-induced immunity in the Ghanaian population and replace the Alpha variant.
Generation of Genomes Base-calling and demultiplexing of MinION Fast5 les were performed using Guppy (version 3. 4. 3 -5. 0. 7) according to the ARTIC bioinformatic protocols  23. Sequencing QC was performed using pycoQC, and predemultiplexed reads were aggregated and length ltered using ARTIC guppyplex for a minimum of 400 reads and a maximum of 700 reads to remove chimeric reads. Read QC was performed using NanoPlot before reading alignment, variant calling and consensus generations using ARTIC MinION software (ARTIC version1. 2. 1). Alignment metrics, amplicon coverage, variant annotation, and consensus assessment were performed using samtools, mosdepth, BCFtools, SnpEFF, and Quast according to the nfcore/viralrecon pipeline (version 2. 2)  24, 25. Variant annotation, validation and quality assessment of the consensus sequence were performed using Viral Annotation De neR (VADR) (version 1.
Acknowledgements The study was facilitated by the  Government of Ghana  through the  Ghana Health Service. ARTIC  donated the primers used for the study. All data storage and analyses were performed on Zuputo®, the  University of Ghana 's high-performance computing cluster. We acknowledge the Community and Public engagement members;  Kyerewaa A. Boateng  and  Simon Donkor ,  Andrew M. Nantogmah  and the entire WACCBIP and UHAS COVID-19 Teams.
Among these elements, select a maximal linearly independent subset; it is a basis for A(g). For each pair of basis elements, one may calculate the product ⋄ using (4. 1), and express the result in terms of the chosen basis. This gives the "structure constants" for the algebra. Magma  [BCP90]  code implementing this recipe can be found at github. com/skipgaribaldi/chayet-garibaldi. Polynomial identities. Among the algebras A(g) for G in Deligne's exceptional series, the cases A(sl 2 ) and A(sl 3 ) are unusual for being Jordan algebras and in particular power-associative, whereas A(g) is not power-associative for other choices of g (Prop. 5. 3(2)). It is natural, then, to ask what identities A(g) does satisfy in those cases. It does not satisfy any polynomial identity of degree ≤ 4 that is not implied by commutativity (Prop.
1a. We collected sets of 3D coherent 111 Pt Bragg diffraction patterns at 450 and 500 °C for different gas compositions. The gas condition at near atmospheric pressure was changed in the following sequence at 450 °C: (1) pure Ar, (2) CO (2% in Ar), (3) CO (2% in Ar) + O 2 (0. 4% in Ar), (  4 ) CO (2% in Ar) + O 2 (1% in Ar), (  5 ) CO (2% in Ar) + O 2 (4% in Ar) (data not saved), (  6 ) O 2 (4% in Ar), (7) Ar (data not saved), and (  8 ) CO (2% in Ar) (see Table  S1  and Fig. 2 ). The temperature was then increased to 500 °C under (9) CO (2% in Ar). BCDI measurements were performed twice after each change of the gas composition. Diffraction patterns of the 111 Pt Bragg peak for different gas compositions at 450 °C are shown in Figure  S2. The intensity distribution shows interference arising from single-particle coherent diffraction.
Acknowledgements The authors are grateful to the  European Synchrotron Radiation Facility  for allocating beamtime. BCDI measurements were performed at beamline ID01. We thank  Hamid Djazouli  for his excellent support during the preparation of the experiment. Laue microdiffraction measurements were measured at beamline BM32. The authors would like to thank  Vincent Favre-Nicolin  for his continuous improvement of the PyNX package used here for phase retrieval. E. J. M. H. acknowledges financial support of  NWO TOP  and KNAW CEP grants. M. -I. R. acknowledges financial support from  ANR Charline  ( ANR-16-CE07-0028-01 ) and  ANR   Tremplin ERC  ( ANR-18-ERC1-0010-01 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
818823 ). N. G. , E. A. and E. R. wish to thank the support by a grant from the  Ministry of Science & Technology ,  Israel & France 's  "Centre National de la Recherche Scientifique (CNRS) ". The thin film processing was performed at the  Micro-Nano Fabrication and Printing Unit (MNF&PU) ,  Technion. The calculations presented in this paper were performed using the Froggy platform of the CIMENT infrastructure (https://ciment. ujfgrenoble. fr),which is supported by the  Rhône-Alpes region  ( GRANT CPER07_13 CIRA ) and the  Equip@ Meso  project ( ANR-10-EQPX-29-01 ) of the " Programme Investissements d'Avenir " supervised by the  "Agence Nationale pour la Recherche". This work was supported by the  Netherlands Center for Multiscale Catalytic Energy Conversion (MCEC) , an  NWO Gravitation programme  funded by the  Ministry of Education, Culture and Science of the government of the Netherlands.
Code availability The phasing algorithm PyNX 91 is available at http://ftp. esrf. fr/pub/scisoft/PyNX/. The scripts used for BCDI data post-processing, PRTF calculations and stereographic projections belong to the BCDI 92 package (https://doi. org/10. 5281/zenodo. 3257616), that can be downloaded from PyPI (https://pypi. org/project/bcdi/) or GitHub (https:// github. com/carnisj/bcdi).
7, 13 This is mainly related to the high variability of indications for LT,  12  the heterogeneity of surgical approaches adopted for LT,  17, 18  the variable degree of experience among surgeons,  7  and the lack of a uniform dataset in this regard. Thus, we examined a cohort of consecutive patients undergoing a PBLT for the first time by the same surgeon (PA) with the goal of determining (1) whether a learning curve effect can be identified and (2) the factors that impact the overall operative time and blood transfusion.
Data collection This retrospective cohort study was conducted at the Hepato-Pancreato-Biliary Surgery and Liver Transplantation Center of the University of Strasbourg, France. Data were obtained from all patients undergoing a PBLT for the first time from a single surgeon (PA) between June 1, 2014 and December 30, 2019. Previous surgeon experience (PA) included a 3-year fellowship in abdominal and hepato-pancreato-biliary surgery including liver procurement (70 cases), first assistance in liver transplantation (80 cases), and doing part of liver transplants (25 cases). The exclusion criteria were early or late liver retransplantation (n ¼ 14) and cases lacking radiological data (n ¼ 15). IRB approval was received. The data collected included demographics, preoperative laboratory data, and operative variables, such as operative time, presence and need for red blood cell (RBC) unit transfusion, reoperation rate, 90-day morbidity and mortality rates, and 1-year graft and patient survival.
Details about the indications, model for end-stage liver disease (MELD) score, and biological values were recorded. Operative time was defined as the time from skin incision to skin closure. The lengths of cold ischemia and the weights of liver grafts and recipient livers were systematically recorded. The clinical, operative, and survival data were collected prospectively in a dedicated database and then analyzed retrospectively. Radiological data were retrospectively gathered by analysis of preoperative computed tomography (CT)-scans on a dedicated Philips Intellispace portal console (Koninklijke Philips NV, Amsterdam, The Netherlands). All LTs were performed by the same surgeon from skin incision to skin closure. Assistants during LTs were usually surgical fellows and senior residents who varied through the entire experience.
The anthropometrics of the recipient included body weight, body height, body mass index (BMI), intra-abdominal fat (IAF), outer abdominal fat (OAF), and modified sagittal abdominal diameter (M-SAD)  22, 23  (Fig  1 ). IAF was defined as the greatest distance between the kidney and the abdominal wall measured at the level of the renal veins. OAF was defined as the thickest abdominal wall distance at the level of the umbilicus. M-SAD was defined as the deepest distance between the abdominal wall and the origin of the celiac trunk. These 3 measures were recorded from preoperative CT scans of the LT patients. The liver morphology data included the type of segment 1 (complete or partial encirclement of the IVC),  10  liver conformation (atrophic, normal, or hypertrophic livers), and the length of the IVC covered by segment 1.
Statistical analysis The results for continuous data are expressed as the mean ± standard deviation or median and range as appropriate, whereas categorical variables are presented as numbers and percentages. Differences between groups were assessed by the c 2 or Fisher exact test for categorical variables as appropriate. In cases of continuous variables, the Wilcoxon rank sum test or the Student's t test were used as appropriate. Estimated patient survivals were calculated according to the Kaplan-Meier method, and differences were assessed by the log-rank test. Two analyses were performed. In the first analysis, the cumulative sum (CUSUM) method was used for quantitative analysis of the learning curve. This method was used for operative time and blood loss (need for transfusion of !6 RBC units).
Univariable and multivariable analysis was performed for factors that were predictive of longer operative time and blood losses (!6 RBC) using logistic regression. The second adjusted CUSUM analysis was performed by adjusting for these factors. Survival estimates were calculated by the Kaplan-Meyer method. All analyses were conducted using the following python modules (Python version 3. 7. 5): statsmodel 0. 11. 0 (https://www. statsmodels. org/) and the SAS system version 9. 4 (SAS Inc, Cary, NC).
Artificial Butterfly Models We used artificial butterflies to assay predation of H. doriswarning colorations in three distinct geographic locations with known divergence in H. do ris wing colorations (Panama versus French Guiana red hindwing pattern). Artificial butterfly experiments in natural populations have proven to be efficient means to record predator attacks for several Heliconius species and warning colorations  (Arias et al. , 2016; Chouteau et al. , 2016; Finkbeiner et al. , 2018; Seymoure et al. , 2018). At each of the three locations in Panama and French Guiana, we used artificial butterflies of threeH. doris warning colorations and the cryptic Pierella hyceta , which we used as a palatable control following the method in  Chouteau et al. , (2016). This model allowed us to obtain additional data on the intensity of selection at each locality, however it also provided a comparative insight on the selective advantages of crypsis versus aposematism.
175-mm diameter sensor (QR600-7-UV125BX; Ocean Optics) inserted in a miniature black chamber. Reflectance spectra were taken at 90º for all colors except for the blue structural coloration which was taken at 45º incidence relative to a 99% reflectance standard (300-700 nm; Spectralon) and to a dark current. Spectra were recorded with SpectraSuite 1. 0 software (Ocean Optics). color spectra from real and printed wings were then compared using the method described by Osorio (1998) in Avicol v. 6 software  (Gomez, 2006). We contrasted blue, black, red and yellow, under two main avian vision systems: blue tit (Parus caeruleus ) for UV vision, with cone proportion and sensitivity as described by  Hart et al. , (2000) , and wedge-tailed shearwater (Puffinus pacificus ) as described by  Hart (2004)  for violet (V) vision.
Acknowledgements This work was supported by an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ) to MC and BAC, an  NSF  award  1736026  to BAC and RP and the  Smithsonian Tropical Research Institute. We thank  Neil Rosser  for assistance with information on the distributions of H. doris. We also thank  Melanie McClure  for fieldwork assistance in French Guiana and members of the  McMillan laboratory  for logistical support and discussions on early versions of the manuscript.
It allows for the evaluation of trade-offs among demographic parameters, the influence of past reproductive history on the caring parent's survival status, breeding probability, and litter size probability, while accounting for imperfect detection of family units. We assess the model performance using simulated data and illustrate its use with a long-term dataset collected on the Svalbard polar bears (Ursus maritimus). 3. Our model performed well in terms of bias and mean square error and in estimating demographic parameters in all simulated scenarios, both when offspring departure probability from the family unit occurred at a constant rate or varied during the field season depending on the date of capture. For the polar bear case study, we provide estimates of adult and dependent offspring survival rates, breeding probability, and litter size probability.
K E Y W O R D S apex predator, arctic ecosystem, Bayesian modeling, capture-recapture, dependency among individuals, family structure, parental care, state uncertainty, timing at independence et al. , 2010), social and mating system (e. g. , helping behavior in humans,  Kramer, 2005) , or other environmental determinants (e. g. , food supply,  Eldegard & Sonerud, 2010). To our knowledge, no model is available to tackle both the issues of multiple-year dependency among individuals and variable timing at offspring independence. Because of these methodological challenges, the population-level consequences of EPC remain to be understood, especially in free-ranging animal populations. Here, we develop a CR model specifically for species providing In what follows, we present the model, assess its performance using simulated data, and illustrate its use with a long-term dataset collected on the Svalbard polar bears.
| Simulation study The simulation study was aimed at evaluating the performance of our model to estimate demographic parameters under various assumptions about the timing at offspring independence (constant vs. seasonal departure rate) and various degrees of capture probability (low p =. 25, high p =. 7), for a medium-size dataset (T = 15 sampling occasions, with R = 80 newly marked at each occasion in equal proportion among the 11 alive states). We simulated data for a virtual long-lived mammal species mimicking the polar bear using the model described in section 1 above. We used = 0. 9 for independent female bear survival (aged 2 + y. o. ), s 1 = l 01 = 0. 6 for singleton cub survival, s 2 = 0. 55 for twin litter' s cub survival (corresponding to twin litter survival l 02 = 0. 7975), s 3 = 0.
We chose an intermediate value of α = 0. 5 (if independence occurred always after the field season, α = 0, vs. always before the field season, α = 1). In the seasonal scenario, we assumed that departure rate varied with date of capture (d) following a logistic relationship (regression coefficients were estimated from the polar bear data, see Appendix S2). Most of the two-year-old offspring were captured with their mother at the beginning of the field season, while departure probability increased logistically up to 80% at the end of the field season. We simulated 100 CR datasets for each of the four scenarios (S1: low detection with constant departure, S2: low detection with seasonal departure, S3: high detection with constant departure, S4: high detection with seasonal departure). We simulated the data using program R.
We fitted the model using program jags called from R  (Plummer et al. , 2016). For each parameter and each dataset, we calculated absolute bias as B = ̂ -, and root mean squared error as RMSE = √ ( ̂ -) 2 , with θ the parameter used to simulate the data and ̂ the mean value of the estimated parameter. Appendix S1 containing guidance, R code, and files to simulate data and fit the model is available on GitHub at https://github. com/SCuba ynes/Appen dix1_exten dedpa renta lcare.
We could not assess formally the fit of our model because no test is yet available for multievent models. However, the multistate version of our model (without uncertainty on timing at independence) fitted the data adequately. Adding a level of complexity should make the model even more adequate. Using the conditional probabilities estimated in the model, we calculated the net probability for a female to raise none, Pr(X = 0), one, Pr(X = 1), or two offspring, Pr(X = 2) to independence over a 3-year period (Figure  1 , further details are provided in Appendix S2). We considered adult females without dependent offspring at the beginning of the time period, so that we have: Appendix S2 containing guidance, R code, data files to fit the model to the polar bear data, and additional results is available on GitHub at https://github.
F I G U R E 2 Performance of the model on simulated data with low detection with seasonal departure (scenario S2). For each of the 100 simulated datasets, we displayed the mean (circle) and the 95% confidence interval (horizontal solid line) of the parameter. The actual value of the parameter is given by the vertical dashed red line. The estimated absolute bias and root-mean-square error are provided in the legend of the X-axis for each parameter. Regarding notations, stands for juvenile, subadult and adult survival, is the probability of first reproduction at age 5, s is dependent offspring survival conditioned upon mother survival (s 1 and s 2 for singleton cub resp. yearling; s 3 and s 4 for twin litter's cub resp. yearling), is breeding probability conditioned upon mother and offspring survival status ( 1 following loss of a cub litter, 2 loss of a yearling litter, 3 for successful breeder, 4 for female without dependent offspring), is the probability of producing a singleton litter conditioned upon mother's and offspring's survival status and upon breeding decision ( 1 following loss of a cub litter, 2 loss of a yearling litter, 3 for successful breeder, 4 for female without dependent offspring), and p is detection probability.