dataesr/scientific-paragraphs-categorization huggingface dataset:
Literature data cover 1763 to 2020. INRAE data cover 1998 to 2020. Usage licenceUsage licence: Оpen Data Commons Open Database License (ODbL)Data resourcesData package title: Compilation of occurrence data for two psyllid species of the Cacopsylla pruni complex (Hemiptera: Psylloidea). Resource link: https://doi. org/10. 15454/VC9UR5 Number of data sets: 1 Data set name: Cacopsylla pruni_occurrences_v29. csv Character set: text/tab-separated-values Download URL: https://data. inrae. fr/dataset. xhtml?persistentId=doi: 10assigns a unique code to each of the 1975 records (NS0001 to NS1975). phylum The full scientific name of the phylum in which the taxon is classified. class The full scientific name of the class in which the taxon is classified. order The full scientific name of the order in which the taxon is classified.
The three different sources of information used to compile the dataset: "GBIF" (i. e. data from the Global Biodiversity Information Facility); "literature" (i. e. any data resulting from a text-mining from different sources -manuscript, book, article etc. accessible or not on the web); "INRAE" (i. e. data from collections by INRAE Montpellier, not published to date). ownerInstitutionCodeThe name (or acronym) in use by the institution having ownership of the object(s)or information referred to in the record. locationAccordingTo Information about the source of this Location information. Could be a publication (gazetteer), institution or team of individuals. Here, detailed title of the original reference associated with the locality; "no data" (i. e. no information found for a particular country, for example, Kyrgyzstan, Malta).
Acknowledgements We are very grateful to  Josiane Peyre  for her valuable technical assistance for the genotyping of thousands of psyllids and the following individuals for their contributions to the collection of psyllid or plant samples:  N. Courtieu & J. -M. Broquaire  ( SICA Centrex ),  N. Galabert & J. Delnatte  ( SICA L'Edelweiss ),  B. Rouillé  ( SRPV-PACA ),  E. Navarro  ( Terroir de Crau ),  P. Delon  ( CA-Gard ),  E. Falezan  ( GIE-Tain l'Hermitage ),  P. Exbrayat  ( CA-Drôme ),  M. Léon-Chapoux  and  V. Delaunay  (SEFRA) and  G. Devènes  (Agroscope). Many students also took part in the collection of the psyllids, for which they are warmly thanked:  Ghislaine Sagna ,  Léa Merlet ,  Piroska Czibulyás ,  Elise Découvreur ,  Clara Bouchet ,  Clara Sauvion ,  Zo-Norosoa Andrianjaka-Camps ,  Florent Décugis  and  Olivier Lachenaud.
Part of this work benefitted from a postdoctoral grant to NS funded by an  INRA-CIRAD   SDIPS grant  ( Speciation and Molecular Diagnosis of Insect Pest Species Complexes ). Field and molecular work for this study were supported by several projects during 15 years:  2005-07 :  ECOGER "Ecologie et adaptation des insectes phytophages en gestion de leurs populations" founded by le Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation-France ;  2007-08 :  SEE-ERA. NET, network 'Phytoplasma epidemiology' , funded by the 6th  EU Framework Programme for Research and Development  (contract number  ERA- CT-2004-515805 ).
: Literature data cover 1763 to 2020. INRAE data cover 1998 to 2020. Usage licenceUsage licence: Оpen Data Commons Open Database License (ODbL)Data resourcesData package title: Compilation of occurrence data for two psyllid species of the Cacopsylla pruni complex (Hemiptera: Psylloidea). Resource link: https://doi. org/10. 15454/VC9UR5 Number of data sets: 1 Data set name: Cacopsylla pruni_occurrences_v29. csv Character set: text/tab-separated-values Download URL: https://data. inrae. fr/dataset. xhtml?persistentId=doi: 10assigns a unique code to each of the 1975 records (NS0001 to NS1975). phylum The full scientific name of the phylum in which the taxon is classified. class The full scientific name of the class in which the taxon is classified. order The full scientific name of the order in which the taxon is classified.
sourceCategory The three different sources of information used to compile the dataset: "GBIF" (i. e. data from the Global Biodiversity Information Facility); "literature" (i. e. any data resulting from a text-mining from different sources -manuscript, book, article etc. accessible or not on the web); "INRAE" (i. e. data from collections by INRAE Montpellier, not published to date). ownerInstitutionCodeThe name (or acronym) in use by the institution having ownership of the object(s)or information referred to in the record. locationAccordingTo Information about the source of this Location information. Could be a publication (gazetteer), institution or team of individuals. Here, detailed title of the original reference associated with the locality; "no data" (i. e. no information found for a particular country, for example, Kyrgyzstan, Malta).
Placental DNA methylation assessment and quality control Whole-genome DNA methylation was measured at >485,000 CpGs using the Infinium HumanMethylation 450 BeadChips (Illumina, San Diego, CA, USA) following standard manufacturer's protocols. After quality control, normalization and filtering of outliers (for details see Supplement) 379,904 methylation sites remained for further analyses (Fig. 1 ). For global DNA methylation, pyrosequencing was used to measure each individual's methylation levels of four CpG sites of repetitive Alu elements (Alu) and long interspersed nucleotide elements 1 (LINE-1,  Yang, 2004). One participant missed data on Alu and LINE-1 methylation, so the final sample size was n = 201.
Associations between phenol urinary concentrations, CpG-specific, and global DNA methylation To investigate associations between phenol urinary concentrations and DNA methylation changes at the individual CpG sites level, we performed an adjusted EWAS with methylation levels of each CpG site as the response variable and standardized log 2 -transformed maternal phenol concentration as a predictor of the robust linear regression (MASS R package,  Venables and Ripley, 2002). Each chemical was tested in a separate model and p-values were calculated using the Wald test from the survey R package  (Lumley, 2004)  and corrected for false discovery rate (FDR) regarding the number of CpGs tested for each phenol  (Benjamini and Hochberg, 1995). FDR corrected p-values <0. 05 were considered as statistically significant.
Regional Manhattan plots were generated using the comMET R package  (Martin et al. , 2015). Genomic inflation factor (λ) and Q-Q plots were generated using the QCEWAS R package  (Van der Most et al. , 2017). Bayesian inflation factor (BIF) was calculated using the bacon R/Bioconductor package  (Iterson et al. , 2017). Gene annotations were based on information from Illumina's v1. 2 annotation available in the IlluminaHumanMethylation450kanno. ilmn12. hg19 R/Bioconductor package for the hg19 reference genome  (Hansen, 2016)  and from the University of California, Santa Cruz (UCSC, https://genome. ucsc. edu). To test the associations between phenol concentrations and global placental DNA methylation we fitted one adjusted robust linear regression per phenol with the median methylation level of each repetitive element (Alu or LINE-1) as the dependent variable.
Analysis of differentially methylated regions (DMRs) Biological functions are in general more robustly associated with genomic regions than single CpGs  (Svendsen et al. , 2016). Therefore, we also identified differentially methylated regions (DMRs) associated with phenol concentrations with the comb-p Python module  (Pedersen et al. , 2012). The method combines p-values for CpGs detected in the EWAS in sliding windows accounting for their spatial correlations across the genome using Stouffer-Liptak-Kechris correction  (Kechris et al. , 2010 ). Obtained regional p-values were adjusted for multiple testing by Šidák correction  ( Šidák, 1967). Significant DMRs ( Šidák-corrected p-value <0. 05) included at least two probes (p-value <0. 001 to start a region) at a maximum distance of 500 bp. Information on genes encompassed by the DMRs identified as associated with phenol exposure were retrieved from the GeneCards Human Gene Database  (Stelzer et al.
Identification of imprinted genes To test the enrichment of phenol-associated DMRs with imprinted genes we looked for overlap between the DMRs containing at least five probes and the list of imprinted genes compiled from MetaImprint (htt ps://openebench. bsc. es/tool/metaimprint, Wei et al. , 2014) , GeneImprint (http://www. geneimprint. com, Jirtle, 1999), and igc. otago (htt p://igc. otago. ac. nz, Morison et al. , 2005)  databases as well as the imprinted gene loci defined by  Yuen et al. , (2010)  and  Hamada et al. , (2016). From the initial list of 304 imprinted genes, 297 were covered by the Illumina assay used in the current study. The difference between the proportions of the imprinted genes in the identified DMRs and those present among the 20,283 genes available for this study was evaluated using Fisher's exact test.
Analysis of mediation via the placental cell composition To estimate placental tissue heterogeneity, we followed the pipeline recommended by  Decamps et al. , 2020 , as described in the Supplement. We identified six putative constituent cell types and estimated tissue heterogeneity using a reference-free based method from the RefFreeE-WAS R package  (Houseman et al. , 2016). To explore whether placental cell heterogeneity could mediate the associations between phenol concentrations and DNA methylation levels, we estimated the percentage difference between regression estimates obtained for the models unadjusted and adjusted for the estimated placental tissue heterogeneity. Then, mediation analysis was performed using the mediation R package  (Tingley et al. , 2014) , as described in the Supplement.
Research data and source code All analyses were conducted using R v. 4. 0. 3 (R Core Team and R Foundation for Statistical Computing 2020), RStudio v. 1. 3. 1093  (RStudio Team, 2020) and Python v. 3. 7. 4 (van Rossum and Drake, 2009). The data used in this study can only be provided upon reasonable request after approval by the EDEN steering committee. The source code is available upon request to the corresponding authors.
Acknowledgements We are indebted to the midwife research assistants for data collection and to  P. Lavoine  and  J. Sahuquillo  for checking, coding, and data entry and to  L. Giorgis-Allemand  for data management. The authors thank the EDEN mother-child cohort study group, whose members are I. Annesi-Maesano ,  J. Y. Bernard ,  M. A. Charles ,  P. Dargent-Molina ,  B. de Lauzon-Guillain ,  P. Ducimetière ,  M. de Agostini ,  B. Foliguet ,  A. Forhan ,  X. Fritel ,  A. Germa ,  V. Goua ,  R. Hankard ,  B. Heude ,  M. Kaminski ,  B. Larroquey ,  N. Lelong ,  J. Lepeule ,  G. Magnin ,  L. Marchand ,  C. Nabet ,  F Pierre ,  R. Slama ,  M. J. Saurel-Cubizolles ,  M. Schweitzer , and  O. Thiebaugeorges. We would like to acknowledge  T. Jia  and the  late X. Ye (Centers for Disease Control and Prevention, Atlanta, USA)  for their work on quantification of phenol biomarkers.
Paulina Jedynak  was supported by the  French National Agency for Research (ANR , grant n •  ANR-15-IDEX-02 ) and  Milan Jakobi  was supported by the  ANR  grant n •  ANR-18-CE36-0005. DNA methylation measurements were obtained through funding from the  Fondation de France  (n •  2012-00031593  and n •  2012-00031617 ) and the  ANR  (n •  ANR-13-CESA-0011 ). Phenols measurements were obtained thanks to funding from the  French Agency for Environmental Health Safety (ANSES , grant n •  EST-2010/2/126 ). The EDEN cohort has been funded by the  Foundation for Medical Research (FRM) ,  ANR ,  National Institute for Research in Public Health (IRESP: TGIR cohorte santé 2008 program ),  French Ministry of Health (DGS) ,  French Ministry of Research, Inserm Bone and Joint Diseases, National Research (PRO-A)  and  Human Nutrition National Research Programs, Paris-Sud University, Fondation Nestlé, French National Institute for Population Health Surveillance (InVS) ,  French National Institute for Health Education (INPES) , the  European Union   Seventh Framework Program  (FP7/2007-2013, HELIX, ESCAPE, ENRIECO, Medall projects),  Diabetes National Research Program  [through a collaboration with the  French Association of Diabetic Patients (AFD) ],  ANSES ,  Mutuelle Générale de l'Education Nationale (MGEN) ,  French National Agency for Food Security  and the  Frenchspeaking Association for the Study of Diabetes and Metabolism (ALFE- DIAM ).
Validation analyses. Similar main findings were shown when repeating the analyses in an independent, multisite dataset  24. Again, we extracted ten lesion atoms that captured typical stroke patterns in the left and right hemispheres in low dimensions (Supplementary Fig. 3 ). While there were subtle differences of lesion embeddings, likely expressing sample-specific lesion distributions, lesion atoms of the derivation and validation dataset were overall highly correlated. This high correlation indicated that our unsupervised approach facilitated the derivation of similar lesion topography embeddings in both independent datasets (Supplementary Table  2 ). In addition, back-projected region-wise relevances were highly correlated when computed based on the other cohort's lesion embedding (derivation cohort: r = 0.
68, HPDI = -4. 92 to -0. 733 (i. e. , no overlap with zero)). Sex differences relating to this specific left-sided lesion atom thus appeared to be the most pronounced and robust. Ancillary analyses. We aimed to gain further insights into the influences of (i) the exact time of imaging and stroke severity acquisition, (ii) cardioembolic vs. non-cardioembolic stroke subtypes, and (iii) potential hormonal effects. While data were acquired within the first 48 h after admission for the entire derivation cohort, we aimed to reduce potential confounds due to varying times of imaging and stroke severity acquisition, as well as any acute revascularization therapy effects by specifically investigating those patients with MRI imaging and NIHSS score available upon their admission. These criteria were fulfilled by 152 patients in total (men: 87, mean age(SD): 63.
Of note, female-specific effects relating to lesion atom 10, representing left-hemispheric presumably PCA-supplied regions, were the only ones that consistently emerged in all of the three ancillary analyses. Thus, these results confirmed the notion arising from the main derivation and validation analyses that sex differences concerning this lesion atom were the most reliable ones. The sex difference for lesion atom 10 can be considered the most pronounced and robust one, given it was reliably observable in all ancillary analyses and was replicated in an independent dataset of stroke patients. All analyses were corrected for overall sex effects, i. e. , lesion atom-independent effects (e. g. , due to potential female-specific delayed hospital arrival). Of note, this correction was independent of the explicit knowledge or measurement of the causes of these global sex differences.
This difference was consistently observable across datasets and did not appear to represent an artifact of a more frequent cardioembolic stroke genesis, more severe strokes or a higher age at stroke onset in women. It is important to note that the strongest observed sex differences were strictly lateralized to the left hemisphere. Previous research suggests that male or female sex and respective sex hormones contribute to induce functional cerebral asymmetries  27. Men appear to have a stronger hemispheric asymmetry; however, while robustly replicated, determined effect sizes have been small  28. Such an enhanced asymmetry in men was also found in some early lesion studies on intelligence  29. However, further early lesion studies suggested that lateralization differences between the sexes might be even more complex, i.
Based on existing knowledge on these regions' physiological functions, it may be suggested that lesions in these regions more likely underlie (higher) cognitive, than, for example, basic motor functions. This predilection toward specific anatomical regions within the left hemisphere could also explain differences seen in earlier lesion studies, as these studies stratified patients only based on left-or right-sided lesion locations without investigating any greater spatial detail of lesions  29, 30, 32. Altogether, a sex-specific lateralization effect, comparable to the considerable extent and spatial distribution of the one that we detected in two independent datasets, has not been described in previous lesion studies, nor in studies focused on sex discrepancies in functional cerebral asymmetries in healthy adults  28.
Such supervised approaches could reduce variability between lesion embeddings of different datasets, since we here saw subtle differences in automatically derived lesion atoms. Eventually, confidence in our results could then be increased in case of successful replication despite these methodological alterations. What is more, our Bayesian models could, for example, be modified to integrate an additional hierarchical level to differentiate between rich-club and non-rich-club regions  60, 61  or contrast agglomerated and separately modeled sets of brain regions to test brain modes, as introduced by Godefroy and colleagues  62, 63. Lastly, we did carefully account for interindividual differences in important sociodemographic characteristics (e. g. , age), comorbidities (e. g. , atrial fibrillation), markers of chronic brain health (e.
While bilateral subcortical and left-hemispheric inferior frontal, superior, and middle temporal regions, i. e. , presumed motor and language regions, explained more severe strokes in both men and women, effects in women were more widespread and similar lesions underlay more severe strokes in women compared to men. In particular, lesions in the posterior circulation of the left hemisphere were associated with a higher stroke severity exclusively in women. These differences were robustly validated in a second independent, international multisite lesion dataset, and could not be explained by sex differences in lesion volume or more severe strokes in women in general.
Methods Participant recruitment. AIS patients (n = 555), considered as derivation cohort in this study, were admitted to Massachusetts General Hospital and enrolled as of part the Genes Associated with Stroke Risk and Outcomes Study (GASROS; mean age(SD): 65. 0(14. 8) years, 38% females, c. f. , Table  1  for sex-specific numbers)  61, 67. Inclusion was generally considered for any AIS patient that met the following criteria; (i) adult patients ≥18 years of age, (ii) admitted to the emergency department with signs and symptoms of AIS, and (iii) neuroimaging confirmation of an acute infarct. Only patients with MRI data obtained within 48 h from admission, as well as complete phenotypic data, such as stroke severity and stroke risk factors were included in this study (i. e. , complete case analyses, c.
We masked lesioned tissue during this normalization step to mitigate the risk of image distortions  71. The same transformation was applied to the DWI image, as well as the corresponding DWI-derived binary lesion mask. This preprocessing pipeline, especially featuring the co-registration of DWI and FLAIR images, was optimized to generate as reliable and accurate spatial normalizations for as many patients as possible. The quality of normalized DWIlesion masks was carefully inspected by two experienced raters (A. K. B. and M. B). Insufficient quality, predominantly arising from moderate to severe motion artifacts and/or moderate to severe normalization errors led to the exclusion of patients (c. f. Supplementary Information for details). The WMH volume (WMHv) was computed based on manual WMH outlines onto FLAIR images performed using the MRIcro software (04/2010, 08/2014, and 2019 versions)  72.
We repeated analyses in an independent dataset of 503 ischemic stroke patients (mean age(SD): 65. 0(14. 6), sex: 40. 6% female, mean NIHSS(SD): 5. 48 (5. 35), c. f. , Supplementary Table  1  for further clinical characteristics), acquired within the framework of the multisite MRI-GENIE study  24  , to test the robustness of our findings (c. f. , Supplementary Information for details). To further ascertain the robustness of our unsupervised lesion embedding, we correlated NMF components as computed for the derivation and validation cohort. Moreover, we recomputed region-wise relevances in final nonhierarchical analyses based on the other cohort's lesion embedding (i. e. , we first estimated the NMF transformation based on one cohort's imaging data and then applied this transformation to the data of the other cohort).
Acknowledgements We are grateful to our colleagues at the  J. Philip Kistler Stroke Research Center  for valuable support and discussions and would like to specifically acknowledge valuable comments by  Parashkev Nachev, M. D. , Ph. D. on previous versions on this manuscript. Furthermore, we are grateful to our research participants without whom this work would not have been possible. M. B. acknowledges support from the  Société Française de Neuroradiologie ,  Société Française de Radiologie , and  Fondation ISITE-ULNE. A. V. is in part supported by  NIH-NINDS  ( R01 NS103824 ,  RF1 NS117643 ,  R01 NS100417 ,  U01NS100699 , and  U01NS110772 ). C. J. acknowledges support from the  Swedish Research Council  ( 2018-02543 ), the  Swedish state  under the agreement between the Swedish government and the county councils, the  ALF  agreement ( ALFGBG-720081 ); the  Swedish Heart and Lung Foundation  (20190203).
A. G. L. acknowledges support from the  Swedish Research Council  ( 2019-01757 ), The  Swedish Government  (under the " Avtal om Läkarutbildning och Medicinsk Forskning, ALF "), The  Swedish Heart and Lung Foundation ,  Region Skåne, Lund University, Skåne University Hospital, Sparbanksstiftelsen Färs och Frosta, Fremasons Lodge of Instruction Eos in Lund  and  NIH  ( 1R01NS114045-01 ). D. B. has been funded by the  Brain Canada Foundation , through the  Canada Brain Research Fund , with the financial support of  Health Canada, National Institutes of Health  ( NIH R01 AG068563A ), the  Canadian Institute of Health Research  ( CIHR 438531 ), the  Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund) ,  Google  ( Research Award, Teaching Award ), and by the  CIFAR Artificial Intelligence Chairs program  ( Canada Institute for Advanced Research).
Code availability Preprocessing of MRI images was conducted in a Matlab 2019b framework (The Mathworks, Natick, MA, USA), the packages Statistical Parametric Mapping (SPM12; http://www. fil. ion. ucl. ac. uk/spm/), and the ancillary package SPM_Superres (https://github. com/brudfors/ spm_superres). Further analyses were implemented in Python 3. 7 (primarily packages: nilearn  84  and pymc3; ref. 85  ). Full code for reproducibility and reuse is available here: https:// github. com/AnnaBonkhoff/BMH_stroke_severity_sex_differences 86.
Data availability The authors agree to make the data of development and validation cohorts available to any researcher for the express purposes of reproducing the here presented results, and with the explicit permission for data sharing by Massachusetts General Hospital's institutional review board. The Harvard-Oxford and JHU DTI-based white matter atlases are accessible online (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/Atlases). Source data are provided with this paper.