dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). The development of a new Australia-Pacific plate boundary is recorded across more than 3,000 km, from the eastern margin of Norfolk Ridge to the Southern Ocean. We interpret the dramatic changes in sedimentation pattern in New Caledonia and New Zealand at 46-44 Ma to be a consequence of regional plate boundary inception and the rigid relative rotation of two tectonic plates. Our new magnetic-polarity-based chronology from New Caledonia, integrated with similar data recently acquired from New Zealand and data from the literature, gives a precise time constraint on this large-scale tectonic event.
Acknowledgments This research was supported by  Deutsch Forschungsgemeinschaft (DFG)  Project  DA1757/1-1  to  E. Dallanave. Field  sampling and analyses by  C. Hollis ,  D. Strogen ,  H. Morgans , and G. Dickens were supported by  New Zealand Marsden Fund  Grant  GNS1201. We thank  Andrew Roberts ,  Suzanna van de Lagemaat , and two anonymous reviewers for the comments that greatly improved this work. Foraminiferal samples were prepared by  Hima Hassenruck-Gudipati  and  Sonja Bermudez. We warmly thank  Giovanni Muttoni  for the access at the  Alpine Laboratory of Paleomagnetism.
Methods: We conducted a secondary data use analysis of a prospective multicenter database (14 ICUs) between 1997 and 2016 on consecutive ICU stroke patients requiring mechanical ventilation at admission. We excluded patients with stroke of traumatic origin, subdural hematoma or cerebral venous thrombosis. The primary outcome was survival 1 year after ICU admission. Factors associated with the primary outcome were identified using a multivariable Cox model stratified on inclusion center.
Patient data source This study was conducted using data from the French prospective multicenter (n = 30 ICUs) OUTCOMEREA database, from patients included between 1996 and 2016. The OUTCOMEREA database has been described in previous publications and has been approved by the French Advisory Committee for Data Processing in Health Research (CCTIRS) and the French Informatics and Liberty Commission (CNIL, registration no. 8999262)  [21, 22]. The database protocol was submitted to the Institutional Review Board of the Clermont-Ferrand University hospital (Clermont-Ferrand, France), who waived the need for informed consent  (IRB no. 5891).
Study population and definitions We included adult patients with acute stroke, admitted to the intensive care unit (ICU) and requiring invasive mechanical ventilation within 24 h of ICU admission. All ICU stays in the database were screened for a diagnosis of stroke, using the International Classification of Diseases 10th Revision (ICD-10) codes I60 ("Subarachnoid hemorrhage"), I61 ("Intracerebral hemorrhage"), I62 ("Other nontraumatic intracranial hemorrhage"), I63 ("Cerebral infarction") and I64 ("Stroke, not specified as hemorrhage or infarction"). ICU stays were considered as related to acute stroke in cases of (1) direct ICU admission following stroke onset, or (2) ICU admission during the initial acute care hospital stay following stroke onset. We excluded patients without hospitalization reports, and if the stroke was related to traumatic brain injury.
Data collection Data were prospectively collected at admission (demographics, chronic disease, admission features, baseline severity indexes, admission diagnosis, and admission type), and daily throughout the ICU stay (clinical and biological parameters, assessment of organ functions, requirement for MV, length of stay (LOS), WLST decision, and vital status at ICU and hospital discharge), through an anonymized electronic case report form using the Vigirea, Rhea, and e-Rhea softwares (OutcomeRea, Aulnay-sous-Bois, France). Long-term survival after hospital discharge was collected by each local investigator. For each stay, we collected the following retrospective data in the medical charts: (1) stroke history, including date of stroke, location, acute-phase stroke therapy (i. e. , thrombolysis or endovascular thrombectomy for AIS and neurosurgery or embolization for ICH and SAH); (2) chronic diseases potentially related to stroke, including arterial hypertension, atrial fibrillation, history of ischemic or hemorrhagic stroke, diabetes, chronic alcohol consumption and the mRS at ICU discharge  [7].
For stratification, centers with less than 10% of the cohort were regrouped in one stratum. Missing data were all completely at random with less than 10% missing value per variable, and were handled by simple imputation with median/most frequent method  [28]. All statistical analyses were carried out with SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). A p value of 0. 05 and lower was considered statistically significant.
Discussion In this reanalysis of a prospective database of 419 critically ill stroke patients requiring invasive mechanical ventilation, we show that survival 1 year after ICU admission is poor (23%) with no improvement over the 21 years of the study period. After adjustment for stroke subtype, neurological presentation and the extent of non-neurological organ failure, the reason for intubation remained independently associated with survival. Intubation for acute respiratory failure or coma was associated with comparable survival hazard ratios, whereas intubation for seizure was not associated with a worse prognosis than for elective procedure. By contrast, receiving an acute-phase therapy was associated with improved survival. Although 1-year survival did not improve over the study period, stroke patients included in the most recent period had more comorbidities and presented higher ICU admission SOFA scores.
The strengths of our study include a multicenter population from a high-quality prospective database with a focus on a well-defined population of acute stroke patients requiring invasive mechanical ventilation. However, our study has also limitations. First, the OUTCOMEREA database has not been built specifically for stroke studies, and all data regarding stroke are retrospective, collected from hospitalization records. Hence, data on potentially useful scores for prognostication in this setting, such as the NIHSS scores, are lacking  [6]. Furthermore, only long-term vital status was available, and evaluation of long-term functional outcomes with an appropriate tool (i. e. , the modified Rankin scale) would have added valuable information. Second, the results of the study may lack generalizability as this is an exclusively French cohort including only medical and polyvalent ICUs and no specialized neuro-ICU.
Population This study was approved by the local ethics committee and all participants gave written informed consent before inclusion for both the antidepressant study and the MRI study. The study was not registered in ClinicalTrials. All participants were recruited from an outpatient clinic at the Douglas Mental Health University Institute in Montréal, Canada, between February 2012 and October 2016. They were assessed by an experienced psychiatrist (SRD). Patients were all suffering from a current major depressive episode as part of a major depressive disorder as evaluated by the SCID-I  (First et al. , 2012)  following DSM-IV criteria. Exclusion criteria included comorbidity with other major psychiatric disorders, bipolar disorder, alcohol or substance abuse over the last 6 months, and a severe medical condition.
These registration steps were performed in ANTs software (https://stnava. github. io/ANTs/, "N4BiasFieldCorrection" and "DenoiseImage"). EPI dataset was evaluated based on our preceding study  (Pereira et al. , 2010). Briefly, slicetiming correction was employed using Fourier function. Head motion was corrected using mutual-information metric and were performed using AFNI software (http://afni. nimh. nih. gov/, "3dTshift" and "3dvolreg" command-line tools respectively). The first image was used as a target in which remaining ones were registered to. Basic Spline regularization was also employed to increase accuracy on motion correction  (Tustison andAvants, 2013)  and an average image was computed from all time-point dataset. Together with the normalized and the native T1 images, the average image was used to estimate non-linear spatial transformation from native EPI space to the MNI standard space using ANTs software ("antsRegistration" command-line tool).
Image analyses Brain networks were obtained via Probabilistic Independent Component Analysis computed with MELODIC v3. 14, a toolbox of FSL software (https://fsl. fmrib. ox. ac. uk/fsl). Independent Components (IC) were extracted from as many spatial maps as necessary to achieve 99% of the variability of the pre-processed EPI dataset. These ICs were masked with four functional atlases  (Shirer et al. , 2012) : BGN, DMN, CEN, and SN. To determine what IC best matches each one of the four brain networks, z-score values were extracted within and outside each mask and a 'Goodness-of-Fit' (GoF) approach was performed according to our preceding study  (Lopez et al. , 2017). Mental pain indices were independently correlated with each brain network using spearman rank correlations. In order to convert these outcomes into a z-score map, Fischer z-transformation were employed.
A t-test was computed using this "new" random/permuted dataset. The second step was iteratively performed 10,000 times, and it generated a single "null" 3D t-statistic map in each iteration. Third, the "null" t-statistic maps were thresholded using p-values 0. 001, and the biggest cluster was preserved for accumulate statistics across the 10,000 t-maps. Finally, the cluster-size threshold was determined to achieve less than 5% of the false-positive. All steps were performed using the "3dttest++", a command-line tool of AFNI.
Reads that lost their mates through the cleaning process were aligned independently from the reads that still had pairs. Quantification on each gene's expression was conducted using HTSeq-count and a reference transcript annotation from ENSEMBL. Counts for the paired and orphaned reads for each sample were added to each-other. Normalization was conducted on the resulting gene matrix using DESeq2. Ninety-nine genes were a priori chosen from selected networks based on hypotheses mentioned above. This included the following systems: -cortisol (CRH, CRHR1, CRHR2, MC2R, POMC, AVPR1B), -immunity/inflammation (CRP, IL6, IL6R, IL6ST, IFNA1, IFNA10, IFNA11P, IFNA12P, IFNA13, IFNA14, IFNA16, IFNA17, IFNA2, IFNA20P, IFNA21, IFNA22P, IFNA4, IFNA5, IFNA6, IFNA7, IFNA8, IFNAR1, IFNAR2, IFNB1, IFNE, IFNG, IFNGAS1, IFNGR1, IFNGR2, IFNK, IFNL1, IFNL2, IFNL3, IFNL3P1, IFNL4, IFNL4P1, IFNLR1, IFNNP1, IFNW1, IFNWP15, IFNWP18, IFNWP19, IFNWP2, IFNWP4, IFNWP5, IFNWP9, TNF, TNFRSF1A, TNFRSF1B, TLR1, TLR2, TLR3, TLR4, VDR), -serotonergic (MAOA, MAOB, TPH1, TPH2, SLC6A4, HTR1A, HTR1B, HTR1D, HTR1DP1, HTR1E, HTR1F, HTR2A, HTR2AAS1, HTR2B, HTR2C, HTR3A, HTR3B, HTR3C, HTR3D, HTR3E, HTR3EAS1, HTR4, HTR5A, HTR5AOS, HTR5BP, HTR6, HTR7, HTR7P1), -kynurenine (HAAO, TDO2, IDO1, IDO2), -opioid (OPRD1, OPRK1, OPRL1, OPRM1, PDYN, PNOC, POMC, GPRASP1).
Individuals who dropped out between T0 and T8 were retained for all correlations conducted at T0. An α threshold of 0. 05 was set a priori for all analyses. Following the conservative Bonferroni's correction, this value was decreased to 0. 001 for correlations between gene expression and mean psychological pain (0. 05/41 genes) and to 0. 0001 for correlations between gene expression and brain clusters (0. 05/41 genes*11 clusters). SPSS 24. 0 (SPSS, Chicago, IL) was used for these analyses.
OPRL1 (also named NOP) gene encodes for the receptor of nociceptin/orphanin FQ peptide, a molecule with widespread effects  (Lambert, 2008). OPRL1 and its endogenous ligand NOP are considered non-opioid members of the opioid receptor family implicated in a variety of functions including pain and reward  (Lambert, 2008) , and associated with suicide  (Lutz et al. , 2015). Interestingly, one exogenous ligand of this receptor is the painkiller buprenorphine  (Lutfy andCowan, 2004) , a drug that was recently found to reduce suicidal ideation possibly through a reduction in mental pain  (Yovell et al. , 2016). In the present study, OPRL1 was only correlated with clusters of the DMN for an unclear reason. Finally, we analyzed the relationships between suicidal ideas and mental pain. First, our data confirmed a significant while weak positive relationship between both variables.
Funding information the  French Agricultural Research Centre for International Development (CIRAD) ; the  French General Directorate for Food (DGAL) ;  French National Research Agency  under the Investments for the  Future Program , Grant/Award Number:  ANR-16-CONV-0004 ;  H2020  " Monitoring outbreak events for disease surveillance in a data science context" (MOOD ), Grant/Award Number:  874850 ;  SONGES  Project  FEDER and Occitanie.
Consequently, sphingid caterpillars digest more nutrients per bite and need less time to reach a given full size than do saturniids  (Bernays and Janzen 1988). A massive amount of data is available on the larval food plants in the wild of the two families, both in literature and in institutional and personal databases. For the Lepidoptera as a whole, the HOSTS database  (Robinson et al. 2010 ) comprises the most comprehensive collation of information about what caterpillars overall are believed to eat. It contains some 180,000 records for about 22,000 Lepidoptera species extracted from 1600 documents  (Robinson et al. 2010). Although HOSTS has not been updated for almost a decade, the subset of records for the superfamily Bombycoidea has been independently maintained and added to by IJK and this updated version is used here.
Another spectacular effort towards gathering food plant data for Lepidoptera is the inventory of caterpillars in the Area de Conservacion Guanascate (ACG) in north-western Costa Rica  (Janzen and Hallwachs 2016, Janzen and Hallwachs 2020). It comprises ~ 70,000 records of reared wild-caught larvae of Saturniidae and Sphingidae linked to their DNA barcodes. Besides these two main public data repositories, one of the authors (JH) has built his own personal database for Sphingidae over 20 years, compiling records from literature, web resources, personal field observations and communications from collaborators. In addition, food plant information is also scattered across the published literature, including a few more recent food plant catalogues, such as in  Stone (1994) ,  Santin (2004) ,  Meister (2011) , but also webpages and personal databases, all of which makes the process of collating and resolving the information very difficult and time consuming.
All three databases cited above are and remain independently maintained and updated. Here we publish a single dataset resulting from their combination. Our aim is to make this massive amount of information available as a single dataset that allows its use for ecological and evolutionary analyses. In particular, we want to investigate the role of food plant use in the evolution of the two families  (Arnal et al. , in prep. ) , especially with respect to the degree of polyphagy, defined as the plasticity in the use of different food plants for caterpillars to complete their development. We provide further details about the contents of this dataset in the following sections, as well as a number of caveats to avoid incorrect interpretation and use of these data. In addition to variables summarising the level of polyphagy of the caterpillars of sphingid and saturniid moths, we also provide a polyphagy score, based on a calculation of Phylogenetic Diversity (Faith 1992) of the food plant families used by the species included in the database.
Purpose: The food plant dataset This dataset (Suppl. material 1) is a synthesis of current knowledge regarding the food plants eaten by the caterpillars of two families of Lepidoptera (Saturniidae and Sphingidae). It aims to capture the state of knowledge at the time of assembly of the dataset so that it can be used to investigate the role of food plants use breadth in the spatial and temporal evolution of both families  (Arnal et al. , in prep. ). This dataset of larval food plant records for sphingids and saturniids worldwide is the result of the integration, with significant data reconciliation and standardisation, of these three largely independent data sources: 1) Information for Sphingidae and Saturniidae embedded in the HOSTS database  (Robinson et al. 2010) ; as further added to and refined by IJK, downloaded on 2 March 2018) (hereafter HOSTS); 2) An inventory of the caterpillars, their food plants and parasitoids of Area de Conservacion Guanacaste (ACG, Janzen DH, downloaded on 16 July 2018 for Saturniidae and 18 July 2018 for Sphingidae) (hereafter DHJ); 3) The personal database of Jean Haxaire (Associate Researcher to MNHN, imported on 17 July 2018) (hereafter JH).
A "record" refers to a unique combination of caterpillar species, plant species and source. Records in the dataset resulting from rearing experiments in captivity or from introduced plant species are listed separately as they often do not represent natural insect-plant associations. Redundancy (duplication) of records amongst the three databases following their combination was not a concern for our research objectives; the dataset should be treated as qualitative and the frequency of records ignored (see list of points in next section). A total of 25,937 records was compiled from the three databases in a single dataset given as Suppl. material 1. Table  1  below provides details of the number of records contributed by each of the independent databases. We followed the plant taxonomy of the International Plant Names Index (IPNI; https://www.
ipni. org) and the latest moth taxonomy  (Kitching et al. 2018) , though both do not coincide with some of the names used by all three sources (see 'call for caution' below). This compilation provides information for 137 genera and 757 species of Saturniidae and 166 genera and 725 species of Sphingidae. As an example of the uses of this dataset, we report basic polyphagy variables as well as a polyphagy score, based on the Phylogenetic Diversity (PD, Faith 1992) of the food plants used by the caterpillars of saturniid and sphingid moths. Using a recent dated angiosperm phylogeny  (Magallón et al. 2015) , we measured the PD score, i. e. the total length of all phylogenetic tree branches connecting the different families of plants eaten by a given moth species in natura, using the pd function of the picante R package  (Kembel et al.
2010). The species scores were then averaged within each genus to obtain genus scores in Suppl. material 2. Note that gymnosperm records were excluded from our calculations of PD scores to avoid bias caused by the considerable phylogenetic distance between angiosperms and gymnosperms. The genus-level polyphagy variables and the polyphagy scores of Saturniidae and Sphingidae genera are provided as Suppl. material 2. Additional information: Calls for caution: 1. The correctness of food plant identifications in databases and in literature should be treated with considerable caution, as they were largely made by non-botanists; food plant names used are also subject to taxonomic and nomenclatural uncertainty and their correctness and validity may be considered equivocal in some cases.
3. The food plant dataset is derived from known food plant records at the time of its compilation; as such, it represents a snapshot of the knowledge at that time and it may differ from the data compiled in the original sources and then updated independently (e. g. new records and/or corrections (e. g. identification errors or synonymies of the moth/caterpillar or the plant or both)). 4. All records are meant to represent actual instances in which caterpillars were found feeding and developing on the food plant. Records in the DHJ database all result from rearing trials of caterpillars found in the field on the food plant in question and, in many cases, identification of the caterpillar was confirmed through DNA barcoding of the resultant adult moths. A few records, recognised as questionable (e.
g. inconsistent locality/identification data) in the HOSTS and JH databases, were filtered out and are not included in the present combined dataset. 5. The food plant dataset does not account for the frequency of use of a given food plant amongst other plants also listed for the same species of moth. The DHJ database, because it is based on individual specimen records, does include quantitative data; however, this information is not incorporated into the combined dataset, although it could bring additional information on local food plant preferences of species and populations. We note that this information would nevertheless be very difficult to analyse and interpret as it is conditional upon the local availability of food plants, as well as possibly seasonal conditions, local variations through time and difficulty of collecting.
This can only be approximated by considering sibling individuals (as is sometimes the case in the DHJ database), individuals from the same population or, ultimately, from the same species or higher taxonomic categories. We thus acknowledge that the scores of polyphagy at species and genus level should be recognised as human abstractions. 8. Polyphagy is constrained in situ by the local availability of food plants -an individual caterpillar cannot be polyphagous on species of plants that are not present. 9. Here we approximated polyphagy scores at species level for saturniid and sphingid moths and we assume that they represent valuable information about the level of plasticity of individuals of the populations of a species to use different food plants. These scores were then used to calculate polyphagy scores at the genus level.
Acknowledgements This work is a product of the ACTIAS group, funded by the synthesis center CESAB of the  French Foundation for Research on Biodiversity  (FRB; www. fondationbiodiversité. fr). PA and RR are supported by  ANR  grant  SPHINX ANR-16-CE02-0011-01. We thank the entire  ACG  parataxonomist team (see  Janzen and Hallwachs 2020 ) for finding, rearing and processing the specimens described and discussed here in database DHJ and we thank the  Centre for Biodiversity Genomics at the University of Guelph  for DNA barcoding them and otherwise managing their molecular data;  Area de Conservacion Guanacaste/MINAE  and donors to the  Guanacaste Dry Forest Conservation Fund  for preserving the ACG rain forest in which they live; the  Systematic Entomology Laboratory  of the  USDA  and  US National Museum/Smithsonian Institution  for receiving and permanently housing the specimens and providing the human resources to carry out this taxonomic work and, finally, many dozens of members of the taxasphere for applying names to both the moths and their food plants.
IJK would like to thank the  Trustees of the Loke Wan Tho Memorial Foundation  for their generous support of the  HOSTS  project and his colleagues at the  NHM  who undertook the original  HOSTS  project:  Phillip Ackery ,  George Beccaloni ,  Luis Hernández ,  Adrian Hine ,  Sven Loburg ,  Mike Lowndes  and, most of all, the late  Gaden Robinson , whose dedication saw the project to completion. He is also extremely grateful to the many people who contributed their own rearing records of Lepidoptera or personal accumulations of data for inclusion in the HOSTS database, particularly  Mike Bigger (UK) ,  John W. Brown (USA) ,  Chris Conlan (USA) ,  Rob Ferber (USA) ,  Konrad Fiedler  (Germany),  Jeremy Holloway (UK) ,  Frank Hsu (USA) ,  Jurie Intachat (Malaysia), Alec McClay (Canada), Bill Palmer (Australia) ,  Pierre Plauzoles (USA)  and the generous individuals who contributed rearing records through the  WorldWideWeb  and who are known to us only as an email address.
IJK is particularly grateful to  Julian Donahue  and the  Los Angeles County Museum of Natural History  for allowing us to include data into HOSTS on Microlepidoptera from the card catalogue prepared by the late  J. A. Comstock  and  C. Henne  and for access to manuscript records by  Noel McFarland. Full acknowledgements for the HOSTS database can be found at https://www. nhm. ac. uk/our-science/data/ hostplants/#9.