dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, the topological vicinities demonstrated in Fig. 5  show that there is some significant degree of agreement between the methods, and a Shimodaira-Hasegawa test  (Shimodaira & Hasegawa, 1999)  of all the trees obtained from the anterior lobe dataset shows that none of the trees yielded by the other methods has a significantly worse likelihood than the ML tree (all p-values > 0. 26, test performed with Phylip's CONTML). In contrast, the molecular reference tree has a significantly lower likelihood with the anterior lobe data (p < 0. 001). We take this to be indicative of all the methods being misled and converging into the same optimality plateau, away from the more plausible molecular tree. Previous studies have found evidence of significant convergence of shell shapes in testudinids  (Claude et al.
They found that the inclusion of landmark characters had a small topological effect compared to univariate continuous characters, and was accompanied by reduction in nodal support. The authors favoured the trees obtained from discrete characters alone and discrete combined with continuous characters. Squared-change parsimony has long been recommended for the treatment of morphometric shape variables in a phylogenetic context  (Rohlf, 2001 (Rohlf, , 2002 ; also criticised by  Catalano, Goloboff & Giannini, 2010) , but its use for phylogenetic inference has remained limited to small datasets that do not require heuristic searches  (Klingenberg & Gidaszewski, 2010). Here, we presented the first implementation, to our knowledge, of heuristic searches with the minimisation of the sum of squared changes as the optimality criterion.
In our study, SCP often performed worse than other methods with our data (except with the carapace dataset), but the performance of all the methods was poor, with TA dQ values typically lower to 0. 4. Further studies will be needed to arrive at more general conclusions about the performance and utility of SCP in phylogenetic analysis using continuous characters. As expected with any kind of data analysed with methods that perform similarly, the greatest impact on topology was determined by the particular properties of the samples chosen. The datasets yielded recognisably different sets of topologies, although none consistently better than the others in terms of accuracy, and in the fossil placement analyses the carapace dataset outperformed the shell and plastron datasets. At the same time, the number of landmarks in the configurations was not the major factor driving the topology when multiple configurations were included in the same analysis.
The Brownian motion model that we used here under the criterion of ML is also naturally applicable to Bayesian inference  (Parins-Fukuchi, 2017 , 2018) , and the field of Bayesian analysis of morphological characters is developing rapidly (see  Wright, 2019  for an overview of the state of the art). Much work remains to be done, but the software RevBayes  (Höhna et al. , 2016)  has emerged as a flexible and performant platform for Bayesian phylogenetic analysis, and seems suitable for future experimentation with continuous characters and their integration with more traditional discrete morphological characters and molecular data. On the parsimony side, similar tools are available on TNT and already mature, despite the recency of LAUP. However, the computational burden of LAUP might discourage potential users, especially since in the studies published to date landmark data has not decisively altered the conclusions reached from discrete data.
The set of structures represented by our morphometric data does not completely overlap with the set of structures represented in the discrete morphological matrices. The morphometric representation fails to consider classic diagnostic characters, such as the number and position of longitudinal carapacial keels and musk duct foramina. Conversely, a recent revision of discrete shell characters  (Garbin, Ascarrunz & Joyce, 2018)  does not include characters for the overall shape of the carapace, and very few characters pertaining the angles formed by the bone sutures and the scute sulci. Furthermore, from the results of the present study, it is apparent that the morphometric data can capture synapomorphies not previously found using discrete character coding. These potential morphometric synapomorphies allowed the methods to frequently recover most or all the Mauremys species as a clade in the carapace trees and almost all the Rhinoclemmys clustering together as paraphyletic in the plastron trees.
Those two clades are difficult to recover with discrete morphological characters. Given the limitations of the landmark data of the present study and the discrete matrix from  Garbin, Ascarrunz & Joyce (2018) , it becomes clear that inferences about the phylogenetic position of fossil geoemydids will need to rely on molecular data to resolve the global relationships within the clade. A way to achieve the integration of morphological and molecular data is by means of fossil placement analyses on a molecular scaffold. We evaluated the performance of those analyses with landmark data in this study, and Garbin, Ascarrunz & Joyce (2018) did the same with discrete characters. Globally, the accuracy of the discrete characters from the entire shell is very similar to the accuracy obtained by carapace landmarks alone (Figs.
6  and 7 ), with the landmark data also having the advantage of giving fully resolved relationships whereas the discrete data yielded multiple optimal parsimony placements for 56% of the species (number of most parsimonious placements: median 3, maximum 15). The performance of landmarks from the entire shell dataset was slightly worse. The fact that the mere accumulation of the configurations (i. e. , increasing the number of shape variables) does not improve phylogenetic inference from landmarks in our data highlights the importance of misleading signal from the plastron data, which is particularly unfortunate, as this is the structure whose geometry is best preserved in fossils, and the necessity to boost the signal of the most reliable characters. Parins-Fukuchi (2018; based on previous work by  Berger & Stamatakis, 2010)  presented a method for continuous data that calibrates characters (e.
g. landmarks) according to their fit to a reliable phylogeny obtained by external means (e. g. molecular analyses). We will explore this approach in an upcoming contribution. From a practical perspective, geoemydid shells are almost never found with their intact original geometry, which hampers the utilisation the carapace landmarks. However, a strength of landmarks as a means of data acquisition and encoding is that a wealth of linear characters can be derived from them, and therefore it should be possible to identify linear measurements corresponding to localised shell features that would not be strongly affected by global deformation. Among such measurements, valuable phylogenetic characters may be discovered. Our future work will present a simple method to achieve this.
CONCLUSIONS Our results show that geoemydid shell landmark data behave similarly to traditional discrete shell characters: they do not suffice for the reliable estimation of phylogenetic relationships, regardless of the method of analysis used, but their combination with molecular trees in phylogenetic placement analyses yields more promising results. More work will be needed to make practical the application of landmark data in palaeontological studies of geoemydids, particularly because the structures that bear the strongest phylogenetic signal are not the ones best preserved in fossils. We believe that such further effort is warranted, nevertheless, as our landmark datasets and the currently available discrete character matrices do not cover completely overlapping sets of morphological features, and as much observed variation is better represented by continuous measurements anyway.
ACKNOWLEDGEMENTS We would like to thank  Alan Resetar  (FMNH),  Peter Pritchard  (PCHP),  José Rosado  (MCZ), and  Gregory Watkins-Colwell  ( YPM ) for providing us with access to geoemydid specimens in their care. Santiago Catalano kindly helped to set up the LAUP analyses on TNT. We thank the  Willi Hennig Society  for subsidising the development of TNT and making it publicly available for free. Caroline Parins-Fukuchi  and an anonymous reviewer are thanked for providing thoughtful comments that helped improve the quality of this manuscript.
Data Availability The following information was supplied regarding data availability: The raw data and code are available in the Supplemental Files. The raw landmark coordinates are available in Dataset S5, and the collection data of the corresponding specimens is given in Dataset S1. The specimen accession numbers for all 175 specimens studied are available in Dataset S2. The biological definitions of the landmarks are provided in Datasets S3 and S4. Dataset S6 contains the scripts used for producing cleaning up the data and producing the landmark datasets for the phylogenetic analyses. Dataset S7 provides the custom R code for the squared-change parsimony analyses.
Anthropogenic forcing has led to an increased extent of hypoxic bottom areas in the Baltic Sea during recent decades. The Baltic Sea ecosystem is naturally prone to the development of hypoxic conditions due to its geographical, hydrographical, geological, and climate features. Besides the current spreading of hypoxia, the Baltic Sea has experienced two extensive periods of hypoxic conditions during the Holocene, caused by changing climate conditions during the Holocene Thermal Maximum (HTM; 8-4. 8 cal ka BP) and the Medieval Climate Anomaly (MCA; 1-0. 7 cal ka BP). We studied the variations in surface and bottom water salinity and primary productivity and their relative importance for the development and termination of hypoxia by using microfossil and geochemical data from a sediment core retrieved from the Landsort Deep during IODP Expedition 347 (Site M0063).
During summer, the excess phosphate available leads to potentially harmful blooms of nitrogen-fixing bacteria, which fertilize   Andrén et al. , 2015). The black dots represent the sites 435-2  (Warden et al. , 2017)  and 9303  (Bianchi et al. , 2000) , used for comparison of carbon data and corresponding ages to data of core M0063D. White lines indicate 100 m depth contours. Black arrows show the directions of inflowing deep-water. Geospatial data are provided by HELCOMs Map and Database Service (http://maps. helcom. fi). the sea with nitrogen  (Kononen, 2001; Vahtera et al. , 2007; Funkey et al. , 2014).
Hypoxia Between c. 2-0. 8 ka BP The second period of hypoxia is recorded in the Baltic proper between 2 and 0. 8 ka BP  (Zillén et al. , 2008) , partly coinciding with the Roman Warm Period and the Medieval Climate Anomaly (MCA). Pollen-inferred temperature reconstructions from Northern Europe  (Seppä et al. , 2009)  show a warm climate anomaly between c. 3 and 1 ka BP. Temperature peaks around c. 2 ka BP, likely reflecting the Roman Warm Period (c. 2. 5-1. 6 ka BP;  Mann, 2007). The  MCA (c. 1-0. 7 ka BP; Mann et al. , 2009)  is observed as a pronounced warm period in multiproxy databases, albeit not as warm as present-day  (Mann et al. , 2008 (Mann et al. , , 2009)). Salinity reconstructions for the Baltic Sea hitherto show no agreement during this hypoxic period. Emeis et al. (2003)  show a steady increase in salinity that commenced c.
835 ambsf and 5. 22 mcd, respectively, and was therefore included. The presented age-depth model for site M0063 (Figure  2 ) is based on 5 calibrated radiocarbon dates ( 14 C dates published in  Obrochta et al. , 2017)  from the upper ∼27 ambsf (Table  1 ). It is worth noting that the age-depth model assumes that the top of the core has an age of -63 year BP as the year of coring was 2013. Considering the coring method, however, it is not likely that the very soft and water saturated very topmost sediments were recovered  (Andrén et al. , 2015). The age-depth modeling was performed using the agemodeling software CLAM version 2. 2  (Blaauw, 2010). Linear interpolation was carried out between dated levels with 10000 iterations using a specially generated calibration dataset based on the IntCal 13 calibration curve  (Reimer et al.
CONCLUSION Based on microfossil, carbon, and silica data we reconstructed the variability in surface and bottom water salinity and primary productivity in the Landsort Deep over the past c. 7600 years. We assessed the relative importance of such changes for the establishment and termination of hypoxic conditions at the sea floor and conclude the following: 1. increased bottom water salinity set the stage for the development of hypoxic conditions during the Holocene Thermal Maximum 2. the return to oxic conditions after this first period of hypoxia is mainly due to decreased surface water temperatures 3. no considerable changes in salinity are recorded before, during and, after the hypoxic period attributed to the Medieval Climate Anomaly Our results emphasize the impact of natural climate forcing on the development of hypoxic conditions in the Baltic Sea.
ACKNOWLEDGMENTS This study used samples from IODP Expedition 347, and we are very grateful to IODP and ESSAC for funding and organizing the expedition. We thank the crew and science party of IODP Exp. 347, as well as the technical staff at MARUM, Bremen, for their support during the expeditions off-and onshore phases. We are also grateful for technical support from the different laboratories involved in the study. Karen Luise Knudsen is thanked for discussion on foraminiferal species taxonomy and ecology. We also thank the reviewers and editor for giving constructive input which has helped to improve the manuscript.
The black dots represent the sites 435-2 (Warden et al. , 2017)  and 9303 (Bianchi et al. , 2000) , used for comparison of carbon data and corresponding ages to data of core M0063D. White lines indicate 100 m depth contours. Black arrows show the directions of inflowing deep-water. Geospatial data are provided by HELCOMs Map and Database Service (http://maps. helcom. fi).
Details on the pilot study can be found elsewhere  [36, 42, 61]. Values reported are numbers (%), unless stated otherwise. BMI: Body Mass Index. 1  Pregnant woman interviewed by a SEPAGES fieldworker in an ultrasound medical center who met the SEPAGES inclusion criteria and did not want to participate to the study. 2 Database of birth certificates provided 8 days after birth and covering Isère département, where Grenoble is located. The population was restricted to women (1) who gave birth in one of the 4 maternity wards of Grenoble area, (2) who were older than 18 years old when they gave birth and (3) whose date of last menstrual period was between March 2014 (to match with the SEPAGES population) and February 2017 (no data were available after this date). 3 Source: 2016 French Perinatal Survey  [62].
Outdoor Exposome Air Pollutants The data collected will in particular allow to estimate (1) the average (outdoor) air pollution (PM 2. 5 , PM 10 , nitrogen dioxide) exposure of the participant at the home address; (2) the outdoor exposure taking into account time-space activity  [42] , both during pregnancy and for the child; (3) PM 2. 5 indoor levels at the home address in a subgroup of 80 participants; (4) personal exposure to PM 2. 5 , PM 2. 5 oxidative potential, nitrogen dioxide (for both mother and child), specific volatile organic compounds (benzene, toluene, ethylbenzene, xylenes, for mothers only) and soot (in a subgroup of mothers) from personal dosimeters (Figure  3a ). Residential addresses before, during and after pregnancy as well as addresses of maternal work place, kindergarten, school and any place where the child spends a significant amount of time were collected prospectively and geocoded.
A subgroup of 49 participants also carried an accelerometer (ActiGraph, Actigraph Corp, Pensacola, FL, USA) on the waist  [66]. Indoor concentrations of PM 2. 5 at the participants' homes were assessed in a subgroup. Participants were instructed to leave devices on during all the measurement week and to keep the devices close to them, including at nighttime. Any deviation from the protocol had to be recorded in a diary. Data registered by the different devices were uploaded to a secured server, except for the passive samplers, which were sent to Passam AG laboratory for analysis. Active air samplers were then calibrated and reusable devices were used again for another measurement week. The participants had the possibility to switch off the GPS application at any time, and relevant ethical agreements for the use of GPS were obtained.
Other Components of the Outdoor Exposome Along with the devices used to measure air pollutant exposure during the repeated measurement weeks, noise was assessed using NoiseTube application (Software Languages Lab, Vrije University, Brussel, Belgium) installed on the smartphone carried by participants. Temperature was measured every 15 minutes during the measurement week using a thermometer (Table  4  and Figure  3a ). Data on outdoor levels of meteorological parameters (temperature, humidity, pressure) were recorded through Meteofrance monitoring network. Activity and sleep assessments were performed using an accelerometer (ActiSleep, from ActiGraph Corp. ) at 2 months, one year (for a subsample of children, worn at the ankle) and three years (worn at the waist).
The oculometric data will help describing idiosyncratic aspects of infant development. The examinations took place at BabyLab center (Laboratoire de Psychologie et Neurocognition, University Grenoble-Alpes, CNRS). 30-34 weeks of amenorrhea in the framework of the normal pregnancy follow-up. Measurements included biparietal diameter, head circumference, abdominal circumference, transverse abdominal diameter and femoral length. Weight, height and head circumference of the child along with placental weight were measured at birth. Weight, height and head circumference of infants, skinfold thickness and body impedance (only at 36 months, using BodyStat 1500 MDD, Bodystat, Isle of Man, UK) were measured in a standardized way during the 6-week, 12-and 36-month study examinations. Additionally, all weight, height and head circumference measurements performed by health care practitioners were copied from the child health booklet.
Acknowledgments: We acknowledge the role of SEPAGES cohort study group:  E. Eyriey ,  A. Licinia ,  A. Vellement  ( Groupe Hospitalier Mutualiste, Grenoble ),  I. Pin ,  P. Hoffmann ,  E. Hullo ,  C. Llerena  ( Grenoble University Hospital, La Tronche ),  X. Morin  ( Clinique des Cèdres, Echirolles ),  A. Morlot  ( Clinique Belledonne ,  Saint-Martin  d'Hères),  J. Lepeule ,  S. Lyon-Caen ,  C. Philippat ,  I. Pin ,  J. Quentin ,  V. Siroux ,  R. Slama  ( Inserm, CNRS, University Grenoble  Alpes IAB research center). We thank  A. Benlakhryfa ,  L. Borges ,  Y. Gioria , clinical research assistants;  J. Giraud ,  M. Marceau ,  M. -P. Martin , nurses;  E. Charvet ,  A. Putod , midwives;  M. Graca ,  K. Gridel ,  C. Pelini , fieldworkers;  K. Guichardet ,  A. Levanic ,  C. Martel ,  E. Quinteiro  neuropsychologists; the staff from  Grenoble Center for Clinical Investigation (CIC) :  J.
S. Gauchez ,  D. Guergour ,  P. Faure ,  J. Arnaud  for thyroid hormones assessment. We thank the team of  L. Chaperod (EFS)  for its implication on the immunological aspects of the project. We thank  G. Uzu  ( IRD ) and  J. -L. Jaffrezo  ( CNRS ) for their implication on PM oxidative potential assessment. We thank  F. -X. Leupert ,  O. Bonnet  and  L. Goirand  for the access to the birth certificate database from the  Conseil Général de l'Isère. Sépages biospecimens are stored at  Grenoble University Hospital (CHU-GA ) biobank (bb- 0033-00069 ); we would like to thank the whole CRB team, led by  P. Mossuz  and  P. Lorimier , and in particular the technicians for the huge work of biospecimens processing and pooling:  W. Jayar  and  L. Than , as well as G. Schummer. The Internet platform for secured data collection was developed by  Epiconcept Paris  ( E.
Slama), by the European Community's Seventh Framework Programme (FP7/2007-206, grant N • 308333-HELIX, PI, M. Vrijheid), by ANR, the French Research Agency (PAPER project ANR-12-PDOC-0029-01, PI, J. Lepeule; SHALCOH project, 14-CE21-0007-01, PI, R. Slama; GUMME project, PI, R. Slama; ETAPE ANR 18-CE36-005, PI, J. Lepeule; SYMER project, ANR-15-IDEX-02, PI, U. Schlattner, Mobil'Air project, ANR-15-IDEX, PI, S. Mathy, supported by University Grenoble-3Alpes), by ANSES (CNAP project, PI C. Philippat, PENDORE project, PI, V. Siroux), by Plan Cancer (Canc'Air project, PI, P. Guénel), by Association de Recherche sur le Cancer (ARC, PI, P. Guénel), by AGIR pour les maladies chroniques (PI, R. Slama and PRENAPAR project, V. Siroux), and Fonds de Recherche pour la Santé Respiratoire (FRSR, PI, I. Pin) and by Fondation de France (CLIMATHES-00081169, J.
To evaluate if there was a score difference in the impact of achieving a specific SDG on animal welfare vs. the impact of improving animal welfare on the achievement of an SDG, a Wilcoxon-signed-rank test was done among the pair of scores given by the 12 participants. All analyses were done using SAS R software (version 9. 4). After the meeting, participants were asked to consider in more detail the targets under each SDG and their links to animal welfare. Pre-workshop the participants had done this only for the three allocated SDGs. Given that there are 169 targets, a full scoring was considered impractical at this stage. Rather participants were asked to decide whether each target was associated with animal welfare or not. The number of targets associated with animal welfare was then counted.
Results We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond.
Both programs were engineered to handle long reads and it is surprising that such DBG-based approach was never applied till now to correct highly accurate SGS short reads such as those produced by Illumina sequencers. Finally, Rcorrector  [22]  relies on an abundance-annotated de Bruijn graph to correct Illumina RNA-seq reads. Despite its interest, this approach hardly scales to large genomic datasets and yields lower result quality compared to the tools tailored for genomic datasets. Here, we implement a DBG-based corrector geared towards Illumina reads, which are characterized by errors that are solely substitutions and affect less than one percent of the output bases. We then show that this corrector, dubbed Bcool, vastly outperforms state-of-the-art k-merspectrum correctors while being both scalable and resource-efficient.
With highly covered data, our approach also allows the use of large k-mer size, improving correction of complex regions. Bcool's workflow is depicted in Figure  3. Each of its components is either an independent tool already published or an independent module that could be reused in other frameworks. We describe below the different key steps of the workflow. tools that process or generate them. Ntcard  [24]  is used to select the best-suited k-mer size. A compacted DBG is then constructed using Bcalm2  [25]. The Btrim  [26]  module cleans the graph, and the reads are finally mapped back on the de Bruijn graph using Bgreat2  [27].