dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sets Data used in Case Study 1 were pilot data obtained from Wearable Assessments in the Clinic and Home in PD (WATCH-PD), a 12-month multicentre, longitudinal, digital assessment study of PD progression in subjects with early untreated PD (clinicaltrials. gov#: NCT03681015). Its primary goal is to generate and optimize a set of candidate objective digital measures to complement standard clinical assessments in measuring the progression of disease and the response to treatment. A secondary goal is to understand the relationship between standard clinical assessments, research-grade digital tools used in a clinical setting, and more user-friendly consumer digital platforms to develop a scalable approach for objective, sensitive, and frequent collection of motor and nonmotor data in early PD.
ACKNOWLEDGEMENTS The authors thank  Michael Lawton  for support in preparation of the manuscript. Critical Path Institute is supported by the  Food and Drug Administration (FDA) of the U. S. Department of Health and Human Services (HHS)  and is 56. 5% funded by  FDA/ HHS , totaling $ 16 , 749 , 891 , and  43. 5%  funded by non-government source(s), totaling $12,895, 366. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement by,  FDA/HHS  or the  U. S. Government. For more information, please visit FDA. gov. The  Critical Path Institute's Critical Path for Parkinson's (CPP )  3DT  initiative consists of a subset of CPP member organizations and is funded by following members:  AbbVie ,  Biogen ,  GSK ,  Lundbeck ,  Merck Sharp and Dohme ,  Roche ,  Takeda ,  UCB , and the  Michael J Fox Foundation.
CODE AVAILABILITY Python source code of the custom software used to conduct the calculations presented in Case Studies 1 and 2 is available upon reasonable request from coauthors KPK and DH correspondingly. In Case Studies 1, the APDM Mobility Lab software (cf. https://apdm. com/mobility/) now part of Clario, was used with default parameters. Co-author TRH developed the custom software employed using python and incorporating the GaitPy module. GaitPy was implemented following the parameters suggested by ref. 31.
E. Simulation Results The algorithm has been tested to build trajectories between 100 points on the border of the 500-m zone and 20 destination points at 45 m from an offshore installation. Both sets are equally distributed along the perimeter of the corresponding geometries. The equivalent current ϕ env is directed at 50 •. A total of 2000 trajectories, built in each case, are denoted by a row in comparison Tables VII-IX. The time was measured with a prototype in Python; NetworkX library was used for the resultant graph handling. Increasing the grid resolution affects the computational time; however, 15-m value is deemed to be suitable for the dimensions of the vessel used as an example in Fig. 10. The length of the computed trajectory is divided by a straight distance, between its beginning and the end, to produce a normalized distance L/L 0.
V. CONCLUSION This article proposes a solution for safe maneuvering at marine assets as a combination of tasks. Static and dynamic safety regions are identified. An alert status generation algorithm is offered based on a fault tree, allowing automatic selection of the preferable emergency response strategy for a dynamically positioned vessel of class 2 and in compliance with the onboard ASOG document. A motion planning algorithm is suggested, taking into account the specific requirements of a 500-m safety zone. The generated trajectory is subject to speed and heading constraints that depend on the environmental disturbance. An overall structure of the algorithm is presented in the flowchart of Appendix B. A part of the above proposals has been implemented industrially in the DP SafeWatch software, which is a decision support tool.
ACKNOWLEDGEMENTS We would like to acknowledge support from the  Hellenic Foundation for Research and Innovation (H. F. R. I)  under the " First Call  for H. F. R. I Research Projects to support faculty members and researchers and the procurement of high-cost research equipment grant", Grant ID: 1855-BOLOGNA. GAP was also supported by the project 'The  Greek Research Infrastructure for Personalised Medicine (pMedGR) ' ( MIS 5002802 ), which is implemented under the Action ' Reinforcement of the Research and Innovation Infrastructure ', funded by the  Operational Program 'Competitiveness, Entrepreneurship and Innovation'  ( NSRF 2014-2020 ) and co-financed by Greece and the  European Union  ( European Regional Development Fund). Finally, this article was partially funded by the  Greek matching funds for the Marie Skłodowska-Curie Individual Fellowships Grant-MSCA-IF-EF-CAR  (Grant ID:  838018-H2020-MSCA-IF-2018 ).
Image acquisition and pre-processing Structural T1-weighted brain MRI scans of each participant were acquired at each site. We used standardized protocols for image analysis and feature extraction (N features = 153) across multiple cohorts (http://enigma. ini. usc. edu/ protocols/imaging-protocols/). FreeSurfer  [34]  was used to segment and extract volumes bilaterally for 14 subcortical gray matter regions (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus), 2 lateral ventricles, along with 68 regional cortical thickness and 68 regional cortical surface area measures, and total intracranial volume (ICV). Cortical parcellations were based on the Desikan/Kiliani atlas  [35]. Segmentations were visually inspected and statistically examined for outliers. Further details of image acquisition parameters, software descriptions, and quality control may be found in Supplementary Table  S4  and Supplementary Material.
Brain age prediction We used the publicly available ENIGMA brain age model (https://photonai. com/enigma_brainage). As described and discussed in Han et al. [24] , brain age models were developed separately for males and females. The training samples were based on structural brain measures from 952 males and 1236 female healthy individuals (18-75 years of age) from the ENIGMA Major Depressive Disorder (MDD) group. There is no known participant overlap between the training samples and the participant data used in this work. Briefly, FreeSurfer measures from the left and right hemispheres were combined by calculating the mean ((left + right)/2)) of volumes for subcortical regions and lateral ventricles, and thickness and surface area for cortical regions, resulting in 77 features. The 77 average structural brain measures were used as predictors in a multivariable ridge regression to model chronological age in the healthy training samples (separately for males and females), using the Python-based sklearn package  [36].
ACKNOWLEDGEMENTS The ENIGMA Schizophrenia Working Group acknowledges the  National Institutes of Health (NIH)  Big Data to Knowledge ( BD2K ) award for foundational support and consortium development (Grant No. U54 EB020403  to PMT). For a complete list of ENIGMA-related grant support please see here: http://enigma. ini. usc. edu/about-2/funding/. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of  NIH. CC was supported by grant  MR/N0137941/1  for the  GW4 BIOMED Doctoral Training Partnership awarded  to the  Universities of Bath, Bristol, Cardiff  and Exeter from the  Medical Research Council (MRC)/ UK Research & Innovation (UKRI). JHC was supported by a  UKRI Innovation Fellowship  ( MR/R024790/2 ). LE was supported by  Desert-Pacific Mental Illness Research Education and Clinical Center, US, NIH  grant  R01MH083968-05.
TH was funded by the  German Research Foundation  (DFG grants  HA7070/2-2 ,  HA7070/3 ,  HA7070/4  to TH). LH was supported by a  Rubicon fellowship  (grant number  452020227 ) provided by The  Dutch Research Council (NWO). NJ and LS were supported by  NIH  grant  R01 MH117601. NJ was also supported by  NIH  grants  R01AG059874 ,  R01MH11760. LS was also supported by a  National Health and Medical Research Council (NHMRC)   Career Development Fellowship  ( 1140764 ) and a  University of Melbourne Dame Kate Campbell  fellowship. PMT was supported in part by  NIH  grant  R01 MH116147. EW was supported by the  European Union   Horizon 2020 research and innovation programme (EarlyCause , grant number  848158 ). Acknowledgements for the various participating data contributors follow -ASRB: the  Australian Schizophrenia Research Bank  (ASRB) was supported by  NHMRC  ( Enabling Grant ,  ID 386500 ), the  Pratt Foundation ,  Ramsay Health Care , the  Viertel Charitable Foundation  and the  Schizophrenia Research Institute.
Chief Investigators for ASRB were Carr, V. , Schall, U. , Scott, R. , Jablensky, A. , Mowry, B. , Michie, P. , Catts, S. , Henskens, F. , Pantelis, C. We thank Loughland, C. , the  ASRB Manager , and acknowledge the help of  Jason Bridge  for ASRB database queries. CP was also supported by a  NHMRC Senior Principal Research Fellowship  ( 1105825 ), an  NHMRC   L3 Investigator Grant  ( 1196508 ). CSW was funded by the  NSW Ministry of Health, Office of Health and Medical Research  and a recipient of a  National Health and Medical Research Council (Australia)   Principal Research Fellowship (PRF)  (# 1117079 ). CAMH: the datasets were generated and shared with support from the  CAMH Foundation  and the  Canadian Institutes of Health Research. CASSI: this study was supported by  NHMRC  grant  568807  (awarded to CSW and TW).
CSW was funded by the  NSW Ministry of Health, Office of Health and Medical Research  and a recipient of a  NHMRC (Australia)   Principal Research Fellowship (PRF)  (# 1117079 ). COBRE: the study and investigators were supported by  NIH  grants  R01MH118695 ,  R01MH121101 , as well as  National Science Foundation (NSF)  grant  2112455  (awarded to VC). JT (senior author) was supported by  NIH  grant  R01MH121246. EONCKS: all processing of contributed neuroimaging data was conducted on the  Centre for High Performance Computing, Cape Town, South Africa. ESO: the study and investigators (AC, FS, DT) was funded by the  Ministry of Health, Czech Republic -Conceptual Development of Research Organization (Institute for Clinical and Experimental Medicine -IKEM ,  IN 00023001 ), and  Ministry of Health of the Czech Republic , grants nr.
NU20-04-00393  and  NU21-08-00432. FBIRN: The study was supported by  National Institutes of Health  grant numbers:  NIH  1  U24 RR021992  ( Function Biomedical Informatics Research Network ),  NIH  1  U24 RR025736  ( Biomedical Informatics Research Network Coordinating ). FBIRN data was processed by the  UCI High Performance Computing cluster  supported by  Joseph Farran ,  Harry Mangalam , and  Adam Brenner  and the  NCRR  and the  National Center for Advancing Translational Sciences. JT (senior author) was also supported by  NIH  grant  R01MH121246. FIDMAG: this study was supported by  Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)  and the  Catalonian Government, Generalitat de Catalunya  ( 2017-SGR-1271  to EP-C from AGAUR). PFC was supported by a  Sara Borrell  contract ( CD19/00149 ), funded by  Instituto de Salud Carlos III , co-funded by the  European Union  ( European Regional Development Fund/European Social Fund [ERDF/ESF ], " Investing in your future ").
FSLRome: the  IRCCS Santa Lucia Foundation of Rome study (FSLRome)  was partially supported by the  Italian Ministry of Health  ( RC12-13-14-15A  Grant) and by the  European Commission ERA-Net  NEURON joint transnational calI 2010 (European Research Projects on Mental Disorders: NEUCONNECT). GROUP: This work was supported by the  Geestkracht program  of the  Dutch Health Research Council (ZON-MW , grant number:  10-000-1002 ), and the  European Community 's  Seventh Framework Program  under grant agreement No. HEALTH-F2-2009-241909  (Project  EU-GEI ). Huilong: this study was funded by the  National Natural Science Foundation of China  ( 81761128021 ; 31671145 ; 81401115 ; 81401133 ),  Beijing Municipal Science & Technology Commission  grant ( Z141107002514016 ) and  Beijing Natural Science Foundation  ( 7162087 ,  Beijing Municipal Administration of Hospitals Clinical medicine Development of special funding  ( XMLX201609 ;  zylx201409 ).
IGP: The  Imaging Genetics in Psychosis (IGP) study  was supported by  NHMRC  Project Grant  APP630471 ,  APP1051673 ,  APP1081603  (awarded to MJG), and the  Macquarie University's ARC Centre of Excellence in Cognition and its Disorders  ( CE110001021 ). MJG was supported by an  Australian Research Council   Future Fellowship  ( FT0991511 ;  2009-13 ) and a R. D. Wright  Biomedical Career Development Award  from the  NHMRC  ( 1061875 ;  2014-17 ). IMH: this study was supported by the  National Healthcare Group, Singapore  ( SIG/05004 ;  SIG/05028 ) and the  Singapore Bioimaging Consortium  ( RP C009/06 ) research grants (awarded to KS). iRELATE: this study was supported by grants to GD from the  European Research Council  ( ERC-2015-STG-677467 ) and  Science Foundation Ireland  ( SFI-16/ERCS/3787 ).
JBNU: YC was supported by the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (grant no. HI18C2383 ). Madrid: this research was supported by the  Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III  (grants  SAM16PE07CP1 ,  PI16/02012 , and  PI19/01024  awarded to CAra;  PI17/01249  awarded JJ; and  PI17/00481 ,  JR19/00024  and  PI20/ 00721  awarded to CMD-C), co-financed by ERDF Funds from the  European Commission , "A way of making Europe",  CIBERSAM ;  Madrid Regional Government  ( B2017/BMD-3740 AGES-CM-2  awarded to CAra),  European Union   Structural Funds ;  European Union   Seventh Framework Program  under grant agreements  FP7-4-HEALTH-2009-2.
2. 1-2-241909  (Project  EU-GEI ),  FP7-HEALTH-2013-2. 2. 1-2-603196  (Project  PSYSCAN ) and  FP7-HEALTH-2013-2. 2. 1-2-602478  (Project  METSY ) awarded to Cara; and  European Union   H2020 Program  under the  Innovative Medicines Initiative 2 Joint Undertaking  (grant agreement No  115916 , Project  PRISM , and grant agreement No  777394 , Project  AIMS-2-TRIALS  awarded to CAra),  Fundación Familia Alonso and Fundación Alicia Koplowitz. MCIC: this work was primarily supported by the  Department of Energy  ( DE-FG02-99ER62764 ) and the  Mind Research Network. VC was also supported by  NSF  (grant no. 2112455 ) and  NIH  (grant no. R01MH118695 ,  R01MH121101 ). MPRC: support was received from  NIH  grants  R01MH123163 ,  R01EB015611 ,  R01NS114628  (awarded to PK). PAFIP: this study has been funded by  Instituto de Salud Carlos III  through the project  PI17/01056  (co-funded by  ERDF/ ESF  " A way to make Europe"/"Investing in your future ").
RSCZ: the study was supported by  National Institutes of Health  grant number:  NIMH R21MH097196  to TGMvE. SCORE: this study was supported in part by grant  3232BO_119382  from the  Swiss National Science Foundation. We thank the  FePsy (Frueherkennung von Psychosen ; early detection of psychosis) Study Group from the  University of Basel, Department of Psychiatry, Switzerland , for the recruitment of the study participants. The FePsy study was supported in part by grant No. SNF 3200-057216/1 , ext. /2, ext. /3. SNUH: this research was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  and by the  KBRI Basic Research Program  through the  Korea Brain Research Institute , funded by the  Ministry of Science & ICT , funded by the  Ministry of Science & ICT  (Grant no.
2020M3E5D9079910  and  21-BR-03-01  awarded to JSK;  2019R1C1C1002457  and  21-BR-03-01  awarded to MK). UNIBA: this study was supported by grant funding from the  Italian Ministry of Health  ( PE-2011-02347951 ). PS was supported by a PhD studentship jointly funded by the  NIHR-BRC at SLaM  and the  Department of Neuroimaging, King's College London. Zurich: this study was supported by the  Swiss National Science Foundation  (SNSF; grant no. 105314_140351  and  10001CL_169783  to SK). MK acknowledges funding from  SNSF  (grant no. P2SKP3_178175 ).
Gene expression analyses The RNA-seq raw reads were checked for quality using FastQC version 0. 72  [63]  and subjected to adapter trimming and quality filtering using Trim Galore! version 0. 4. 3. 1  [64]. Additional 12 bp and 3 bp were also removed at the 5' and 3' extremity, respectively, to avoid bias not directly related to adapter sequences or basecall quality according to FastQC outputs, and only reads with a minimum length of 50 bp were retained. We used the reference nuclear (Dunsal1 v. 2, GenBank accession: GCA_002284615. 2), chloroplastic (GenBank accession: GQ250046) and mitochondrial (GenBank accession: GQ250045) genomes of D. salina strain CCAP 19/18 (closely related to CCAP 19/15 used here) for trimmed reads alignment, using HISAT2 version 2. 1. 0 [65]  with default parameters for PE reads and spliced alignment option.
Statistical analyses Differential DNA methylation and gene expression We used Bioconductor's methylKit package  [67]  to identify differentially methylated regions (DMRs), that is, non-overlapping 100bp windows with methylation levels that varied significantly among evolutionary treatments, or between salinities for each treatments. The significance of calculated methylation differences was determined using Fisher's exact tests. We used the Benjamini-Hochberg (BH) adjustment of P-values (FDR < 0. 05) and methylation difference cutoffs of 20%. Similarly, the differential gene expression analyses were performed with the Bioconductor's package DESeq2 version 1. 30. 1  [68]. We identified differentially expressed transcripts among the evolutionary treatments, the salinity, and their interaction, by building a general linear model as implemented in DESeq2.
We used the Wald test when comparing two salinities, and transcripts with FDR < 0. 05 (P-values after BH adjustment) and |log2FC| > 1 were considered as differentially expressed. Significance of evolutionary treatments (predictability ρ 2 ) and ρ 2 × salinity interaction term was assessed using likelihood ratio tests (LRT) comparing models with and without the interaction term  [68]. We then used the Gene Ontology (GO) assignments from Leung, et al. [23]  to classify the functions of D. salina transcripts and to functionally annotate the identified DE transcripts. Enriched GO terms of the DE transcripts were identified using topGO R package  [69]  classic algorithm and based on p-value generated using Fisher's exact method, for the three GO categories, i. e. Molecular Function, Cellular Component and Biological Process.
Evolution of the degree plasticity at multiple levels Since phenotypic plasticity is an adaptation to fluctuating and predictable environment  [3] [4] [5] [6] [7] , we wished to quantify to what extent environmental predictability during populations evolution contributed to key mechanisms underlying phenotypic plasticity. We first applied a redundancy analyses [RDA, 70], computed with the function rda() from the vegan R package  [71] , to quantify the proportion of the total epigenetic and gene expression variation that are significantly explained by the evolutionary treatments (predictability ρ 2 ) and assay environment. We performed a RDA using the table of DNA methylation levels of 100bp windows regions, or the table of rlog transformed transcript count, as response variables, and the evolutionary treatments (predictability ρ 2 ), salinity, and the ρ 2 × salinity interaction, as explanatory variables, with population identity as covariates to account for paired samples between salinities.
We treated the target ρ 2 (low, intermediate or high) and salinity (low or high) as categorical variables. We quantified the contribution to the total genetic variation using the adjusted R 2 , and tested its significance by ANOVA-like permutation tests using 999 randomizations of the data  [72]. For both RNA-seq and WGB-seq data, we presented the results of the RDA ordinations as a biplot, to visualize the variation among samples along its major axes. We performed all multivariate statistical analyses with the vegan R package  [71]. To investigate how the overall degree of plasticity at multiple levels evolved in our experiment, we followed the protocol described in Leung, et al. [5] , which we here applied to DNA methylation level, gene expression and cells morphological variation. (Note that the morphological data was here replicated, rather than just reproducing values from Leung, et al.
[5] ). First, we assessed the degree of plasticity of each experimental population by computing the Euclidean distance between the multivariate means from the CpG-methylation level table, the rlog transformed transcript count table, or the raw cells morphological data measured, at low ([NaCl] = 0. 8 M) versus high ([NaCl] = 4. 0 M) salinity. The realized autocorrelation of a given time series can vary to some extent from its long-term stationary expectation, because of the randomness of the stochastic process in finite time. For a more quantitative relationship between plasticity and environmental predictability, we thus used the realized (rather than the target) ρ 2 as index of environmental predictability [as also done in 5]. For each time series, we thus calculated the realized environmental autocorrelation ρ as the correlation between salinities at two subsequent transfers.
Acknowledgments This work was supported by the  European Research Council  (Grant  678140-FluctEvol ) to LMC, a  Fonds de Recherche du Québec -Nature et Technologies (FRQNT) postdoctoral fellowship  to CL, and a travel grant for collaboration provided by the  GDR Plasticité Phénotypique  ( GDR 3715 ) from  CNRS.
37 M 31. 66 M 8. 53 M 4. 47 M 43. 83% 8. 18% 1. 22% SAMN19677486 47. 03 M 40. 31 46. 61 M (99. 10%) 17. 84 M 12. 88% 536. 34 M 29. 00 M 7. 46 M 4. 31 M 39. 63% 7. 42% 1. 19% SAMN19677488 50. 90 M 43. 63 50. 22 M (98. 65%) 19. 33 M 13. 97% 574. 38 M 32. 50 M 1. 39 M 4. 43 M 42. 41% 1. 27% 1. 14% SAMN19677489 51. 80 M 44. 40 50. 84 M (98. 15%) 19. 33 M 18. 78% 532. 67 M 29. 98 M 7. 48 M 4. 54 M 41. 85% 7. 41% 1. 26% SAMN19677490 48. 41 M 41. 50 47. 22 M (97. 53%) 16. 59 M 13. 55% 496. 96 M 28. 95 M 7. 39 M 4. 28 M 44. 14% 7. 67% 1. 28% SAMN19677492 55. 62 M 47. 67 55. 32 M (99. 46%) 19. 92 M 11. 10% 693. 64 M 45. 23 M 8. 25 M 5. 61 M 51. 33% 5. 90% 1. 20% SAMN19677493 48. 79 M 41. 82 48. 52 M (99. 46%) 17. 44 M 10. 26% 620. 39 M 40. 25 M 7. 17 M 5. 16 M 51. 86% 5. 69% 1. 24% SAMN19677494 51.
23 M 43. 91 50. 85 M (99. 26%) 18. 78 M 12. 09% 600. 03 M 36. 14 M 7. 62 M 4. 86 M 45. 86% 6. 53% 1. 20% SAMN19677499 52. 76 M 45. 23 52. 14 M (98. 82%) 19. 96 M 14. 77% 609. 52 M 32. 61 M 8. 19 M 4. 94 M 39. 55% 7. 14% 1. 20% SAMN19677500 57. 53 M 49. 31 56. 99 M (99. 07%) 21. 43 M 14. 48% 655. 66 M 36. 42 M 9. 30 M 5. 27 M 41. 52% 7. 43% 1. 19% SAMN19677501 54. 14 M 46. 40 53. 55 M (98. 91%) 20. 36 M 14. 09% 616. 86 M 33. 66 M 8. 05 M 4. 73 M 40. 50% 6. 87% 1. 13% SAMN19677503 43. 92 M 37. 65 43. 28 M (98. 55%) 16. 90 M 15. 30% 483. 33 M 25. 42 M 1. 12 M 3. 62 M 38. 71% 1. 24% 1. 10% SAMN19677504 53. 14 M 45. 54 52. 62 M (99. 02%) 19. 75 M 14. 53% 597. 58 M 32. 95 M 7. 50 M 4. 68 M 41. 33% 6. 51% 1. 16% SAMN19677505 54. 72 M 46. 91 54. 04 M (98. 76%) 17. 80 M 14. 67% 526. 53 M 28. 74 M 7.
74 M 4. 52 M 40. 29% 7. 70% 1. 27% SAMN19677507 50. 07 M 42. 92 48. 91 M (97. 68%) 17. 44 M 15. 49% 481. 82 M 25. 15 M 6. 62 M 4. 25 M 38. 24% 7. 33% 1. 31% SAMN19677508 46. 82 M 40. 13 44. 91 M (95. 92%) 15. 31 M 16. 26% 423. 57 M 23. 08 M 5. 86 M 3. 77 M 40. 31% 7. 20% 1. 32% SAMN19677509 47. 91 M 41. 06 46. 93 M (97. 97%) 16. 95 M 13. 91% 503. 94 M 27. 33 M 6. 53 M 4. 32 M 40. 54% 6. 80% 1. 27% Populations founded from a single cell: SAMN19677483 55. 29 M 47. 39 54. 46 M (98. 50%) 20. 10 M 13. 98% 596. 41 M 32. 82 M 9. 31 M 4. 74 M 40. 50% 8. 26% 1. 18% SAMN19677487 47. 81 M 40. 98 47. 24 M (98. 79%) 17. 97 M 13. 01% 551. 24 M 32. 13 M 1. 31 M 4. 32 M 44. 04% 1. 24% 1. 16% SAMN19677491 54. 56 M 46. 76 53. 85 M (98. 70%) 20. 75 M 13. 83% 626. 74 M 34. 69 M 8. 91 M 5. 59 M 40. 86% 7. 53% 1.