dataesr/scientific-paragraphs-categorization huggingface dataset:
RAD data cleaning and phylogenetic inference The quality of the raw data was assessed using FastQC (Babraham Institute, Bioinformatics Group http://www. bioinformatics. bbsrc. ac. uk/projects/fastqc). Raw data were processed using the Perl wrapper RADIS  [49]  that relies on Stacks  [50, 51]  for demultiplexing of data, removing PCR duplicates and building individual and catalog loci. For data cleaning (RADIS_step1_da-ta_cleaning. pl), parameters in the RADIS. cfg files were set as follows (radis_readlen = 125; radis_nttrim_read1_5p = 5 to remove the complementary overhang of the restriction site; radis_nttrim_read1_3p = 0; radis_nttrim_read2_5p = 0, radis_nttrim_read2_3p = 0). Individual RAD loci were built using m (minimum depth of coverage to create a stack) = 3 and, M (maximum distance allowed between stacks) = 2; with removal (r) and deleveraging (d) algorithms enabled.
Six data sets were then built to test a possible impact of analytical parameters on our phylogenetic analyses. Catalogs of loci were built with n (number of mismatches allowed between sample tags when generating the catalog) set to 6, 8 or 10. Loci / samples selection was performed as follows: only samples with more than 100 RAD loci in the catalog were kept (radis_nloci_min), only locus for which at least either 50% or 75% of the samples had sequences were kept (radis_nsample_min) and loci for which at least one sample had 3 sequences or more were removed to discard possible paralogs (radis_npbloci_cutoff). Phylogenetic inferences were performed using RAxML 8. 2. 9 (multi-threaded version with AVX extensions)  [52]  and a rapid bootstrap search with 100 replicates, followed by a thorough ML search (GTR GAMMA), without partitioning of the data set.
[55]  (i. e 3. 54 ± 0. 38% divergence per Myr) to obtain minimum and maximum ages for the split between the two species of each pair following Godefroid et al. [56]. A timetree was built with the RelTime method  [57]  as implemented in MEGA X  [58]  using the RelTime-ML option, a GTR+G evolutionary model (with 4 discrete Gamma categories as for the RAxML analysis) and all nucleotide sites (including gaps and missing data). The RAxML RAD tree obtained with the cstacks n = 10, radis_nsample_min = 50% of the samples (Table  2 ; ie the tree obtained with the largest data set) was used as input and Ph. (Adlerius) simici and Ph. (Larroussius) neglectus were considered as outgroups. Uniform distributions were used as calibration densities. We inferred the biogeographic history of the subgenus Paraphlebotomus using the R package BioGeoBEARS 1.
1. 1 [59]. The chronogram built with RelTime was used as input but only one specimen per species was included in the analysis and outgroups (including Ph. alexandri) were removed to avoid artefacts (taxa were pruned from the chronogram with the R package ape  [60]. We compared the fit of the models DEC, BAYAREALIKE, and DIVALIKE with AICc but did not consider the jump parameter for founder events (+J) following recent criticisms  [61]. Geographic ranges of the species were coded using six geographical subregions of the West Palearctic: A: West Mediterranean; B: Saharo-Arabian; C: Sudanian; D: Somalian; E: East Mediterranean; F: Irano-Turanian. The maximum number of areas species may occupy was set to 6. To consider the main geologic events that occurred during the last 15 Myr in the peri-mediterranean area, we defined two time periods with different dispersal rate scalers: 1) from the Mid Miocene climatic optimum (ca 15 Ma) to the end of the Messinian crisis (5.
3 Ma); 2) from the Zanclean flood (5. 3 Ma) to present. The matrix of geographical ranges for the Table  2. Properties of the RAD data sets obtained with different parameters combinations. n = number of mismatches allowed when merging individual loci in cstacks, radis_nsample_min = minimum number of samples that should have sequences for a RAD locus to be included in the analysis. In all data sets, loci for which at least one sample had three or more sequences (i. e. possible paralogs) were removed (radis_npbloci_cutoff = 3). different species as well as the dispersal multipliers for each time period are available as S1 Text.
DNA barcoding and calibrations COI sequences were successfully obtained from all samples but one (Ph. saevus for which all DNA was included in the RAD library). The maximum likelihood tree confirmed that COI is not informative enough to solve relationships among species of phlebotomine sand flies (S1 Fig). To avoid issues due to mitochondrial introgression and putative paralogs (heteroplasms, coding pseudogenes) that could be contained in the COI data set, only splits between highly supported species pairs that were recovered both in the RAD (S2.
RAD data sets and phylogenetic inferences In average, 97,054 RAD loci were obtained per specimen (ustacks, S1 Table ). Depending on the value of the parameters n (number of mismatches allowed when merging individual loci) and radis_nsample_min (minimum number of samples that should have sequences for a RAD locus to be included in the analysis) data sets included 1,423 to 8,906 RAD loci of 115 bp each and contained only 16. 6-34. 6% missing data (Table  2 ). All RAxML analyses yielded the same fully resolved tree topology (Figs  2  and S2 ). The subgenus Paraphlebotomus was paraphyletic. Ph. alexandri was recovered sister to the rest of Paraphlebotomus species and a clade consisting of members of the Phlebotomus (Ph. papatasi and Ph. duboscqi) and Synphlebotomus (Ph. ansarii) subgenera. The remaining Paraphlebotomus species clustered into a strongly supported clade in which relationships between species were fully resolved.
Acknowledgments The authors thank  Carlos Alves-Pires ,  Azzedine Bounamous ,  Vit Dvořak ,  Nabil Haddad ,  Hanafi Hanafi ,  Najoua Haouas ,  Vladimir Ivovic ,  Kaouther Jaouadi ,  Mireille and Bob ( †) Killick-Kendrick ,  Andreas Kru ¨ger ,  Nicole Leger ,  Michele Maroli ,  Laor Oshan ,  Yusuf O ¨zbel ,  Jean-Antoine Rioux  ( †) , and  Petr Volf  for providing specimens and/or for fruitful discussions about the distribution of Ph. similis and Ph. sergenti in Turkey and in the Balkans. We thank  Zoe Adams  ( NHM, London ) and  Zoubir Harrat  ( Pasteur Institute of Algeria ) who arranged the loans of type-specimens of Ph. alexandri and Ph. sergenti, respectively. We also are grateful to  Morad Belbagra  for the technical assistance in data processing, to  Laure Saune  ´for advices during library preparation, to  Montpellier GenomiX  ( MGX, France ) for sequencing and to the genotoul bioinformatics platform  Toulouse Occitanie  ( Bioinfo Genotoul , doi:  10.
2014). In this study, 4°C was used as the T b  (Errecart et al. 2012). In this report, we relate the patterns of herbage accumulation to bite mass (BM) and STIR by sheep grazing various SSH levels in the same experimental pasture area, which previously reported in detail by  Szymczak et al. (2020b). Brie y, STIR was determined by calculating intake rate of sheep at various SSH levels using pre-and post-grazing animal weights corrected for insensible weight losses, insensible time loss, and effective eating time. Institute of Grassland and Environmental Research Behaviour Recorders (Ultra Sound Advice, London, UK)  (Rutter et al. 1997)  were used to record grazing jaw movements and the effective eating time, and the data were analyzed with the Graze software  (Rutter et al. 2000)  and used to calculate BM.
Statistical analysis The HM, LLM, PSM and SM (means of the four experimental units sampled per initiation date) were tted to thermal-time accumulation using the Gompertz (Eq. 2, Fig. 2 ) with software R (R Development Core Team, 2016). Four parameters of the model were estimated through software R, according to  Barker et al. (2010) : where H is the herbage mass, H ∆ is the maximum (asymptotic) herbage mass, H min is the lower asymptote for herbage mass, t is the time in thermal time accumulated and a and b were parameters that determined the shape and curvature of the Gompertz curve, respectively. The calculation of the instantaneous herbage accumulation rate (HAR i ) was obtained by Eq. 3: The logistic model of Gompertz is characterized by an asymmetric curve, which increases the biological value to the forage accumulation model, because it is sensitive to changes in initial growth rates and suppression of growth  (Barker et al.
Declarations Author contributions LSS conceived and designed the study, performed all experiment, analyzed the data and wrote the manuscript, AM conceived and designed the study and revised manuscript, RMS and DB analyzed the data and revised manuscript, ALGM supplied the experimental animals and revised manuscript, RFM helped on samples collection and analysis, CRL and GL revised manuscript and PCFC supplied experimental equipment and revised manuscript. All authors read and approved the nal manuscript. 95:1131-1143. doi:10. 1093/aob/mci136 Tables  Table 1  Gompertz equations of herbage mass (H) curves and their coe cients of determination (R 2 ), equations of the instantaneous herbage accumulation rate (HAR i ), values of the maximum instantaneous herbage accumulation rate (HAR i-max ), optimal total herbage mass at HAR i-max , and sward surface height (SSH) at HAR i-max , for seven growth periods evaluated.
Acknowledgements This work was part of the PhD thesis of the rst author developed at the  Post-Graduate Program   in Agronomy of the Federal University of Paraná (UFPR). We thank the employees of the  Department of Crop Production and Protection (UFPR) , the employees of  Canguiri experimental farm (UFPR)  and the undergraduate student of  Technological Center of Innovation in Agriculture (UFPR)  for the contributions in the development and conduct of the eld experiment. Special thanks for the help of.
Funding This was partly sponsored by a postgraduate scholarship of  Coordination for the Improvement of Higher Education Personnel (CAPES)  of the  Brazilian Ministry of Education. We thank the  National Council for Scienti c and Technological Development (CNPq)  for the research grants. This work was supported by the  International Cooperation Program CAPES /COFECUB [88887. 140961/2017-1 ]. Financed by  CAPES -Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Ministry of Education of Brazil. Declaration of Competing Interest The authors report no declarations of interest.
Paclitaxel is a cytotoxic drug exerting its effect at the M phase of the cell cycle by inhibiting microtubular spindle disassembly, driving cells to apoptosis  (14). The Lutonix AV Global Registry was a prospective, multicenter, single-arm real-world registry with the purpose to investigate the clinical use and safety of the Lutonix DCB for the treatment of dysfunctional dialysis VA and to provide data on different lesion characteristics.
Patients were excluded if they were currently participating in an investigational drug or device study that had not yet reached its primary endpoint, had an uncontrollable allergy to contrast medium; or had another medical condition with a life expectancy insufficient to allow for completion of the procedure and follow-up examinations. A detailed table of inclusion and exclusion criteria of the study is provided in Table  1 , and a flowchart of the study is in Figure  1  (Lutonix AV Global Registry, NCT02746159).
DISCUSSION There is increasing evidence on the benefit of using DCBs for the treatment of dysfunctional VA, whereas there are, at the same time, studies with conflicting results  (5, 8, 13, (16) (17) (18). A study by Kitrou et al (  19 ) also described the benefit of using the Lutonix DCB in consecutive treatments. The Lutonix Global AV registry recruited 320 subjects from Europe and Asia, who underwent DCB angioplasty in 392 treatment areas. Access and lesion characteristics reflected real-world scenarios encountered by physicians in their everyday practice. The study included both AVFs and AVGs in a 3:1 proportion. The TLPP at 6 months in AVFs (78. 1%) was superior to the available previously published data on the Lutonix use in AVFs by Trerotola et al (  5 ) (71% ± 4%) and Kitrou et al (6) (72.
2%). Moreno-Sanchez et al  (21)  presented a mortality rate of 5. 7% at 1 year, while Lucev et al  (22)  and Trerotola et al (17) a 12. 9% and a 23% mortality rate at 2 years, respectively. With death rates of dialysis patients reaching 34. 1% according to a meta-analysis by Siga et al  (23)  and 33% based on the United States Renal Data System (24) at 2 years, one can understand that the ability of a procedure or device, as in the case of DCBs, to provide uninterrupted hemodialysis to these patients has a high impact in their quality of life. The study suffers the inherent limitations of a single-arm registry lacking a comparator. Additionally, the primary endpoint of TLPP was based on the clinical evaluation of the VA, and no imaging was performed to verify the presence of stenosis in the cases that the VA was considered functional and patent.
As the study was powered only for the primary endpoints, findings of any subgroup analysis were observational only. The lack of a comparator and the 6month timeframe also are limiting factors for drawing conclusions on mortality. However, given the large number of subjects included, this study provides an important dataset of information. In conclusion, the 6-month results in this prospective, multicenter, single-arm real-world registry confirmed the Lutonix DCB to be safe and effective. Procedural characteristics of balloon inflation time and vessel preparation were proved to improve outcomes significantly.
Statistical Plan Descriptive summaries were performed for study accountability, follow-up, demographics, medical history, and procedural information. Standard descriptive statistics included the number and percent of subjects for categorical data and the number observed, mean, standard deviation, minimum value and maximum value for numeric variables. The primary analysis of success rates for TLPP, ACPP, and abandonment of permanent access in the index extremity were based on time-to-event survival analysis methods performed on the dataset of all subjects enrolled and treated with the Lutonix catheter where appropriate. For the primary safety, device success, procedural success, and clinical success, exact binomial number and percent of successes along with the exact binomial confidence intervals were provided.
ACKNOWLEDGMENTS The investigators of the Lutonix AV Global Registry are as follows:  Dimitrios Karnabatidis  (Global Principal Investigator),  Pedro Ponce ,  Tze Tec Chong ,  Radoslaw Pietura ,  Jean-Dominique Pegis ,  Po-Jen Ko ,  Chia Hsun Lin ,  Matteo Tozzi  ( University of Insubria/Ospedale Circolo, Varese Italy ),  Carlos Lucas  ( Hospital da Cruz Vermelha, Lisbon Portugal ),  John Chien-Hwa Chang  ( Dalin Tzu Chi General Hospital, Dalin Taiwan ),  Hannes Deutschmann  ( Universitatsklinik fur Radiologie Medizinische Universitat Graz, Graz Austria ),  Ounali Jaffer  ( St. Bartholomews Hospital  and Barts and the  Royal London Hospital, London United Kingdom ),  Angel Ferrario  ( Cliniques Les Fontaines Melun, Melun France ),  Guido Saracino  ( Casa di Cura Maria Rosaria, Pompei Italy ),  Kate Steiner  ( Lister Hospital, Stevenage United Kingdom ),  Jackie Pei Ho  ( National University Hospital, Singapore ),  Jos van den Berg (EOC Lugano ,  Lugano Switzerland ),  Benoit Boura  ( Institut Montsouris Paris, France ),  Dmytro Mishunin  ( UKGM Gieben, Institut fur Radiologie, Gieben Germany ),  Farhan Ahmad  ( Royal Berkshine NHS Foundation Trust, Reading United Kingdom ),  Ulf Teichgraeber  ( Inst.
Tagging We deployed two types of tags on breeding Laysan albatrosses December to June from 2002 through 2006 at Tern and Guadalupe Islands: 6 g archival global location sensing (GLS; LTD 2400, Lotek Wireless, St Johns, NF, Canada, GLS tags here after) and 35 g satellite platform terminal transmitters (PTTs; Pico-100, Microwave Telemetry, Columbia, MD, United States or Cricket, Wildlife Computers, Redmond, WA, United States). Deployments consisted of PTTs deployed in tandem with GLS tags (Tern n = 69, Guadalupe n = 99), PTT tags only (Tern n = 7), or GLS tags only (Tern n = 50, Guadalupe n = 65). The combined mass of PTT and GLS tags was less than 2. 5% of the ∼3. 2 kg adult body mass, as required by our permits. A summary of tags recovered with viable data by colony and period is shown in Table  1.
Location Data Post Processing Tracking data were analyzed with purpose-built routines created in MATLAB R2007b (The Mathworks, Natick, MA, United States). Location data from GLS tags were postprocessed following methods described in  Shaffer et al. (2005)  to yield two locations per day (local noon and midnight). PTT data were processed and analyzed following methods in   Kappes et al. (2010). In order to use both types of location data comparably, the PTT data were interpolated using a Hermite spline following methods in  Tremblay et al. (2006)  and then subsampled to provide two locations per day (like the GLS data) sampled at local noon and midnight.
Distribution and At-Sea Behavior Utilization distribution (UD) kernel estimates were used to determine spatial habitat distribution  (Worton, 1989)  for each combination of location/species/breeding period (e. g. , incubation, chick-rearing). Laysan albatrosses are known to adjust their range according to ocean conditions and breeding period  (Kappes et al. , 2010 (Kappes et al. , , 2015)) , we treated each bird × breeding period × breeding year as the sample unit for kernel distribution and bootstrap analyses (described below). Utilization distributions (95, 50, and 25%) were calculated in MATLAB using methods in  Kappes et al. (2010). For each trip, we estimated the maximum distance from the breeding colony, total distance traveled (km), duration at sea (days), mean percent time on water (the ratio of sea surface to non-sea surface temperatures recorded by GLS tags), and mean distance to shore.
Reproductive Metrics For each population, we determined annual breeding success (proportion of chicks fledged per eggs laid) and population counts (number of breeding pairs). Data for Tern Island was sourced from the US Fish and Wildlife Service  (2003) (2004) (2005) (2006) (2007) (2008) (2009)  and published data  (Arata et al. , 2009; BirdLife International, 2021). Guadalupe Island data were compiled from the literature for years prior 2003  (Dunlap, 1988; Pitman, 1988; Gallo-Reynoso and Figuero-Carranza, 1996; Pitman and Ballance, 2002)  and by the authors for 2003-2010. Chicks were considered fledged if observed in a healthy condition at the colony within a month of the average estimated fledging date. We compared the fledging success between colonies (2002-2010) and slopes of the natural log of the breeding population size  (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009).
Statistical Analyses Statistical tests were performed in MATLAB and R (R Development Core Team, 2010). To compare ocean habitats surrounding each colony, we used the oceanographic climatology data bounded by the respective albatross 50% utilization distributions for each colony. Analysis of variance was used to test the dependent environmental variable (contained within the 50% albatross utilization distribution) and covariate distance to colony for each 1 • × 1 • grid cell with an interaction of breeding location (Central Pacific or Eastern Pacific). Albatross habitats were modeled across wind speed, SST, CHL, EKE, WEKM, and SSH using the maximum entropy (MaxEnt version 3. 3. 3e) 2 GUI-based approach  (Phillips et al. , 2006; Elith et al. , 2010 Elith et al. , , 2011)). We used resampling (10,000 iterations with replacement) to test the null hypothesis of no difference between Central Pacific and Eastern Pacific locations  (Henry, 2011).
ACKNOWLEDGMENTS We thank  Alfonso Aquirre Muñoz ,  Ikerne Aguirre Muñoz ,  Dan Barton ,  José M. Barredo Barberena ,  Allison B. Birkhead ,  Melinda Conners ,  Travis Cooper ,  Anna Cummings ,  Elizabeth Flint ,  Irene Espinosa ,  Ricardo González Gómez ,  Mario Alberto Guerrero Madriles, Savannah  and  Esmé Henry ,  M. Ángel Hermosillo Bueno ,  Julio C. Hernández Montoya ,  Brad Keitt ,  Caitlin Lee-Rodney ,  Kristen Lindquist ,  Alejandro Manríquez Ayub ,  Virginia McDermott ,  Luciana Luna Mendoza ,  Diane R. Menuz ,  Charles Moore  ( Alguita Foundation ),  Gaby Calli Ramírez de Arellano ,  Marlenne Rodríguez Malagón ,  Araceli Samiago Herrera ,  Jose Angel Sanchez-Pacheco ,  Scott Seganti ,  Jonathon Steinberg ,  Noé Silva Estudillo ,  Flor M. Torres García ,  Shaye Wolf ,  Grupo de Ecología y Conservación de Islas, Island Conservation, Secretaría de Marina -Armada de México, Sociedad Cooperativa de Producción Pesquera de Participación Estatal Abuloneros y Langosteros, and U.
FUNDING This research was part of the  Tagging of Pacific Predators (TOPP) program , funded in part by the  National Ocean Partnership Program  ( N00014-02-1-1012 ), the  Office of Naval Research  ( N00014-00-1-0880  and  N00014-03-1-0651 ), the  Gordon and Betty Moore ,  David  and  Lucille Packard , and Alfred P. Sloan Foundations. RH was supported by a  US Environmental Protection Agency Greater Research Opportunity Fellowship  ( MA-916588 ). Additional funding provided by the:  Frank M. Chapman Memorial Fund ,  American Museum of Natural History, Friends of Long Marine Lab, Will J. Reid Foundation, Dr. Earl H. Myers and Ethel M. Myers Oceanographic and Marine Biology Trust ,  National Marine Fisheries Service Office of International Affairs, Island Conservation , and  Grupo de Ecología y Conservación de Islas.
Movement reconstruction Otolith radius was used as a proxy for sh size at natal and freshwater exit. The otolith radius for each 87 Sr/ 86 Sr measurement was estimated by measuring the distance from the otolith core to the center of each laser pit along a standardized 90˚ axis  48. Strontium isotope pro les representing changes in 87 Sr/ 86 Sr values as a function of otolith distance from the core were created for each otolith. Speci c location 87 Sr/ 86 Sr threshold values were used to identify the movement of Central Valley spring-run Chinook juveniles from one rearing region to the other. These values come from a Central Valley isoscape database  50, 53, 54. We considered four distinct regions in this study: Natal tributary (i. e. , Mill and Deer Creeks), Sacramento River, Sacramento-San Joaquin Delta (hereon "Delta"), and San Francisco-San Pablo Bay (hereon "Bay") & Ocean.
We used changes in 87 Sr/ 86 Sr along the otolith transect to identify two key habitat shifts to reconstruct the size at which individuals exited (1) the natal tributary, and (  2 ) freshwater (exit location is Chipps Island, river kilometer 73). Otolith radius at natal exit was calculated by linearly interpolating between otolith distances at the 87 Sr/ 86 Sr measurements on either side of the upper Sacramento River (point of Mill or Deer Creek exit and Sacramento River entry) strontium threshold value. We used the lowest 87 Sr/ 86 Sr value found for the upper Sacramento River region in the Central Valley isoscape database. If for a given sh this threshold was never crossed, we determined it by visually identifying the closest point to the Mill/Deer Creek habitat 87 Sr/ 86 Sr threshold value in the strontium pro les.