dataesr/scientific-paragraphs-categorization huggingface dataset:
This process was repeated by changing the training/validation subsamples. Classification accuracy was assessed using OA and the Kappa index, which expresses the proportional decrease in error generated by the classification compared to the error of a completely random classification  [65]. Finally, the vegetation cover used to map land use was classified using the algorithm with the highest OA. The classification process was tested for the two approaches: object-based and pixel-based. Sentinel-1 and Sentinel-2 parameters were classified separately and by combining optical and SAR data in the same dataset, that is, we realized a fusion at the lowest processing level (pixels level) referring to the merging of measured physical parameters  [66]. The combination of Sentinel-1 and -2 parameters were then classified.
Winter Land Use Classification Accuracy of the winter land use classification obtained from optical and SAR parameters varied significantly depending on (i) the classification approach (i. e. , pixel-based or object-based), (ii) the classification algorithm (i. e. , RF or SVM) and (iii) the time-series dataset (i. e. , Sentinel-1, Sentinel-2 or a combination of both). Results of the pixel-based and object-based approaches to identifying winter land use had similar accuracy for the Sentinel-2 dataset and the combined Sentinel-1 and -2 dataset. The objectbased approach had a slightly higher OA (68-83%, Kappa = 0. 64-0. 77) than the pixel-based approach (55-81%, Kappa = 0. 51-0. 77) (Figure  7 ). Although this is consistent with other studies of LULC  [71] , few studies have presented advantages of using the object-based approach instead of the pixel-based approach to identify and characterize LULC  [72, 73].
Winter Land Use Classification Accuracy of the winter land use classification obtained from optical and SAR parameters varied significantly depending on (i) the classification approach (i. e. , pixel-based or object-based), (ii) the classification algorithm (i. e. , RF or SVM) and (iii) the time-series dataset (i. e. , Sentinel-1, Sentinel-2 or a combination of both). Results of the pixel-based and object-based approaches to identifying winter land use had similar accuracy for the Sentinel-2 dataset and the combined Sentinel-1 and -2 dataset. The object-based approach had a slightly higher OA (68-83%, Kappa = 0. 64-0. 77) than the pixel-based approach (55-81%, Kappa = 0. 51-0. 77) (Figure  7 ). Although this is consistent with other studies of LULC  [71] , few studies have presented advantages of using the object-based approach instead of the pixel-based approach to identify and characterize LULC  [72, 73].
In contrast, results obtained with the Sentinel-1 data showed the superiority of the object-based approach, due to the heterogeneity of SAR values within fields in winter. This study evaluated the respective advantages of Sentinel optical and SAR time-series to identify winter land use. Classification was better using a combination of Sentinel-1 and -2 parameters (median OA = 81%, Kappa = 0. 77) (Table  4 ), with OA ranging from 75-82% (Kappa = 0. 68-0. 77). Conversely, classifications based on either Sentinel-1 or Sentinel-2 parameters alone had OAs of 68-78% and 74-80%, respectively (Figure  7 ). While the results highlight the utility of Sentinel-1 and Sentinel-2 individually, they also emphasize that classification using the Sentinel-2 dataset always outperformed that using the Sentinel-1 dataset.
The classification results highlight the advantages of using the combined Sentinel-1 and -2 datasets, with OA ranging from 68-83% (Kappa = 0. 64-0. 77). Therefore, our study confirms the effectiveness of Sentinel-1 and -2 time-series for identifying land use, as previous studies have demonstrated  [26, 27]  and also shows the potential of using the combined Sentinel-1 and -2 datasets for this purpose. Additionally, the originality of this study is the identification of land use in winter. Concerning the classification techniques, the RF algorithm had higher OA than the SVM algorithm (median OA = 81% and 79%, respectively). The RF algorithm also had less variation in OA than SVM (72-83% (Kappa = 0. 67-0. 77) and 68-80% (Kappa = 0. 64-0. 76), respectively). The potential of SVM and RF algorithms for remote sensing studies has been widely demonstrated  [59] [60] [61].
Our results show that the RF algorithm is slightly more accurate effective than the SVM algorithm, which is consistent with results of other studies  [59, 61, 74]. This study evaluated the respective advantages of Sentinel optical and SAR time-series to identify winter land use. Classification was better using a combination of Sentinel-1 and -2 parameters (median OA = 81%, Kappa = 0. 77) (Table  4 ), with OA ranging from 75-82% (Kappa = 0. 68-0. 77). Conversely, classifications based on either Sentinel-1 or Sentinel-2 parameters alone had OAs of 68-78% and 74-80%, respectively (Figure  7 ). While the results highlight the utility of Sentinel-1 and Sentinel-2 individually, they also emphasize that classification using the Sentinel-2 dataset always outperformed that using the Sentinel-1 dataset.
The classification results highlight the advantages of using the combined Sentinel-1 and -2 datasets, with OA ranging from 68-83% (Kappa = 0. 64-0. 77). Therefore, our study confirms the effectiveness of Sentinel-1 and -2 time-series for identifying land use, as previous studies have demonstrated  [26, 27]  and also shows the potential of using the combined Sentinel-1 and -2 datasets for this purpose. Additionally, the originality of this study is the identification of land use in winter. The best classification, with OA of 81% and Kappa of 0. 77, used an object-based approach and 30 parameters derived from a combination of Sentinel-1 and -2 parameters. Misclassification errors were observed between bare soils (under-and over-estimation rates of 64% and 94%, respectively) and the other classes (Table  5 ).
Conclusions This study evaluated the respective advantages of using Sentinel optical and SAR time-series to identify winter land use, using SVM and RF algorithms with pixel-based and object-based approaches. Our study used high spatial and temporal resolutions of Sentinel data to identify land use types in winter and to our knowledge, this is the first time such a study has been undertaken. Results show that winter land use can be identified accurately using combined Sentinel-1 and -2 time-series with a pixel-based approach using an RF algorithm. Analysis of using Sentinel-1 and -2 parameters to identify winter land use led to recommendations for extracting features when mapping winter land use. Results reveal the advantage of using backscattering coefficients alone or combined with the NDVI.
Genomic and transcriptomic data The GTEx preliminary cleaned genome-wide genotype data and RNA-seq transcriptome data across 53 unique tissues (released on 2015-01-12) were downloaded from dbGaP (accession phs000424. GTEx. v6. p1). It included 183 GTEx donors genotyped on Illumina's Omni 5M and 267 GTEx donors genotyped on Omni 2. 5M. Genomic and transcriptomic data were processed according to the GTEx protocol (http://www. gtexportal. org/home/documentationPage). The Omni 2. 5M portion of hard-called genotypes from the Omni 2. 5M or Omni 5M across all 450 donors were extracted and merged for analysis. We excluded variants with a genotyping call rate < 98%, with differential missingness between Omni 2. 5M and Omni 5M arrays, with Hardy-Weinberg equilibrium P-value<10 -6 (for subjects of European ancestry), or with batch effects.
The expression of a gene for individual i in tissue t, Y i,t , is modeled as Y i,t = Y i CT + Zʹ i β + ϵ i,t The mixed effect model parameters were estimated using the lme4 package in R. Posterior modes of the subject level random intercepts were used as estimates of the cross-tissue components  (18). Cross-tissue model included gene expression from 6,124 GTEx tissue samples from 369 unique European individuals who had genome-wide genotype data available.
Ovarian-specific and cross-tissue genetically regulated expression model building We built an expression prediction model for each gene using the elastic net method as implemented in the glmnet R package, with a ridge-lasso mixing parameter of α = 0. 5 and a penalty parameter lambda chosen through 10-fold cross-validation  (18, 21, 22). The elastic net method with α = 0. 5 is a compromise between the ridge-regression penalty (α = 0) for solutions with many parameters (each of small effects) and the lasso penalty (α = 1) for solutions with fewer parameters (each of large effects)  (18). The genetically regulated expression for each gene was estimated by including SNPs within 1 Mb of the gene start or end, as defined by GENCODE V19 gene annotations. Expression prediction models were built for protein-coding genes, long non-coding RNAs (lncRNAs), microRNAs (miRNAs), processed transcripts, immunoglobulin genes, and T cell receptor genes, according to categories described in the GENCODE V19 gene annotation file.
The performance of MetaXcan has been shown to be similar to PrediXcan that uses individual level genetic data for the identification of genes with expression that is associated with disease risk  (21, 22). Details of individual contributing studies were previously reported  (12). Briefly, the OCAC summary statistics were based on analysis of 40,941 controls and 25,509 population-based EOC cases (22,406 invasive cases and 3,103 borderline cases). OCAC cases included 1,954 serous borderline ovarian cancers, 1,149 mucinous borderline ovarian cancers, 1,417 mucinous invasive ovarian cancer, 1,012 low-grade serous ovarian cancers, 13,037 high-grade serous ovarian cancers, 2,810 endometrioid ovarian cancers, 1,366 clear-cell ovarian cancer and 2,764 other EOC cases. The CIMBA summary statistics were based on the analysis of 19,036 BRCA1 and 12,412 BRCA2 mutation carriers, of whom 2,933 and 954, respectively, were diagnosed with EOC.
Acknowledgments The Ovarian Cancer Association Consortium is supported by a grant from the  Ovarian Cancer Research Fund  thanks to donations by the family and friends of  Kathryn Sladek Smith  ( PPD/RPCI. 07 ). The scientific development and funding for this project were in part supported by the  US National Cancer Institute GAME-ON Post-GWAS Initiative  ( U19-CA148112 ). This study made use of data generated by the  Wellcome Trust  Case Control consortium that was funded by the  Wellcome Trust  under award  076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the  National Cancer Institute and National Human Genome Research Institute  (dbGap accession number phs000178. v8. p7). The OCAC OncoArray genotyping project was funded through grants from the  U.
S. National Institutes of Health  ( U19-CA148112  (T. A. Sellers),  R01-CA149429  (C. M. Phelan) and  R01-CA058598  (M. T. Goodman);  Canadian Institutes of Health Research  ( MOP-86727 (L. E. Kelemen) ) and the  Ovarian Cancer Research Fund  ( A. Berchuck ). The  COGS  project was funded through a  European Commission's Seventh Framework Programme  grant (agreement number  223175 -HEALTH-F2-2009-223175 ). ( C1312/A15589 ); STA:  NIH  grants  U01 CA71966  and  U01 CA69417 ; SWE:  Swedish Cancer foundation , WeCanCureCancer and årKampMotCancer foundation; SWH:  NIH (NCI)  grant  R37-CA070867 ; TBO:  National Institutes of Health  ( R01-CA106414-A2 ),  American Cancer Society  ( CRTG-00-196-01-CCE ),  Department of Defense  ( DAMD17-98-1-8659 ),  Celma Mastery Ovarian Cancer Foundation ; TOR:  NIH  grants  R01 CA063678  and  R01 CA063682 ; UCI:  NIH   R01-CA058860  and the  Lon V Smith Foundation  grant  LVS-39420 ; UHN:  Princess Margaret Cancer Centre Foundation-Bridge  for the Cure; UKO: The UKOPS study was funded by  The Eve Appeal (The Oak Foundation ) and supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre ; UKR:  Cancer Research UK  ( C490/A6187 ),  UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge ; USC:  P01CA17054 ,  P30CA14089 ,  R01CA61132 ,  N01PC67010 ,  R03CA113148 ,  R03CA115195 ,  N01CN025403 , and  California Cancer Research Program  ( 00-01389V-20170 ,  2II0200 ); VAN:  BC Cancer Foundation ,  VGH & UBC Hospital Foundation ; VTL:  NIH   K05-CA154337 ; WMH:  National Health and Medical Research Council of Australia , Enabling Grants  ID 310670 & ID 628903.
Acknowledgements for individual studies: AOV: We thank  Jennifer Koziak ,  Mie Konno ,  Michelle Darago ,  Faye Chambers  and the  Tom Baker Cancer Centre Translational Laboratories ; AUS: The AOCS also acknowledges the cooperation of the participating institutions in Australia and acknowledges the contribution of the study nurses, research assistants and all clinical and scientific collaborators to the study. The complete AOCS Study Group can be found at www. aocstudy. org. We would like to thank all of the women who participated in these research programs; BEL: We would like to thank  Gilian Peuteman ,  Thomas Van Brussel ,  Annick Van den Broeck  and  Joke De Roover  for technical assistance; BGS: The BGS is funded by  Breast Cancer Now  and the  Institute of Cancer Research (ICR). ICR acknowledges NHS funding to the  NIHR Biomedical Research Centre.
We thank the  Study  staff, study participants, doctors, nurses, health care providers and health information sources who have contributed to the study; BVU: The dataset(s) used for the analyses described were obtained from  Vanderbilt University Medical Center's BioVU  which is supported by institutional funding, the  1S10RR025141-01  instrumentation award, and by the  Vanderbilt CTSA  grant  UL1TR000445  from  NCATS/NIH ; CAM: This work was supported by  Cancer Research UK ; the  University of Cambridge; National Institute for Health Research Cambridge Biomedical Research Centre ; CHA:  Innovative Research Team in University (PCSIRT) in China  ( IRT1076 ); CHN: To thank all members of  Department of Obstetrics and Gynaecology, Hebei Medical University, Fourth Hospital and Department of Molecular Biology, Hebei Medical University, Fourth Hospital ;  COE :  Gynecologic Cancer Center of Excellence  ( W81XWH-11-2-0131 ); CON: The cooperation of the 32 Connecticut hospitals, including  Stamford Hospital , in allowing patient access, is gratefully acknowledged.
The  Malaysian Ovarian Cancer Genetic Study  is funded by research grants from the  Malaysian Ministry of Higher Education  ( UM. C/HIR/MOHE/06 ) and charitable funding from  Cancer Research Initiatives Foundation ; MCC: MCCS cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 , 251553 and 504711 and by infrastructure provided by  Cancer Council Victoria. Cases and their vital status were ascertained through the  Victorian Cancer Registry (VCR)  and the  Australian Institute of Health and Welfare (AIHW) , including the  National Death Index  and the Australian Cancer Database; MOF: the  Total Cancer Care™ Protocol  and the Collaborative Data Services and  Tissue Core Facilities at the H. Lee Moffitt Cancer Center & Research Institute , an  NCI designated Comprehensive Cancer Center  ( P30-CA076292 ),  Merck Pharmaceuticals  and the state of Florida; NHS: The  NHS/NHSII  studies thank the following state cancer registries for their help: AL, AZ,  AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND , OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY; NJO:  L.
and Katherine B. Andersen Foundation ; Fraternal Order of Eagles; MAL: Funding for this study was provided by research grant  R01-CA61107  from the  National Cancer Institute ,  Bethesda, MD , research grant  94 222 52  from the  Danish Cancer Society, Copenhagen, Denmark ; and the Mermaid I project; MAS:  Malaysian Ministry of Higher Education  ( UM. C/HlR/MOHE/06 ) and  Cancer Research Initiatives Foundation ; MAY:  National Institutes of Health  ( R01-CA122443 ,  P30-CA15083 ,  P50-CA136393 );  Mayo Foundation ;  Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation ; MCC:  Cancer Council Victoria, National Health and Medical Research Council of Australia (NHMRC)  grants number  209057 ,  251533 ,  396414 , and 504715; MDA: DOD  Ovarian Cancer Research Program  ( W81XWH-07-0449 ); MEC:  NIH  ( CA54281 ,  CA164973 ,  CA63464 ); MOF:  Moffitt Cancer Center ,  Merck Pharmaceuticals , the state of Florida,  Hillsborough County , and the city of Tampa; NCO:  National Institutes of Health  ( R01-CA76016 ) and the  Department of Defense  ( DAMD17-02-1-0666 ); NEC:  National Institutes of Health   R01-CA54419  and  P50-CA105009  and  Department of Defense   W81XWH-10-1-02802 ; NHS:  UM1 CA186107 ,  P01 CA87969 ,  R01 CA49449 ,  R01-CA67262 ,  UM1 CA176726 ; NJO:  National Cancer Institute  ( NIH-K07 CA095666 ,  R01-CA83918 ,  NIH-K22-CA138563 ,  P30-CA072720 , and  P30-CA008748 ) and the  Cancer Institute of New Jersey ; NOR: Helse Vest, The  Norwegian Cancer Society, The Research Council of Norway ; NTH:  Radboud University Medical Centre ; OPL:  National Health and Medical Research Council (NHMRC) of Australia  ( APP1025142 ) and  Brisbane Women 's Club; ORE:  OHSU Foundation ; OVA: This work was supported by  Canadian Institutes of Health   Research grant  ( MOP-86727 ) and by  NIH/NCI  1  R01CA160669-01A1 ; PLC:  Intramural Research Program  of the  National Cancer Institute ; POC:  Pomeranian Medical University ; POL:  Intramural Research Program  of the  National Cancer Institute ; PVD:  Canadian Cancer Society and Cancer Research Society GRePEC Program ; RBH:  National Health and Medical Research Council of Australia ; RMH:  Cancer Research UK ,  Royal Marsden Hospital ; RPC:  National Institute of Health  ( P50 CA159981 ,  R01CA126841 ); SEA:  Cancer Research UK  ( C490/A10119 C490/A10124 );  UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge ; SIS:  NIH, National Institute of Environmental Health Sciences ,  Z01 ES044005  and  Z01-ES049033 ; SMC: The  Swedish Research Council ; SON:  National Health Research and Development Program, Health Canada , grant  6613-1415-53 ; SRO:  Cancer Research UK  ( C536/A13086 ,  C536/A6689 ) and  Imperial Experimental Cancer Research Centre.
Abstract-The toxicokinetic and toxicodynamic models (TK-TD) are very well-known for their ability, at both the individual and the population level, to accurately describe life cycles such as the growth, reproduction and survival of sentinel organisms under the influence of an ecological biomarker. Being dynamics, the consistent inference of life history and environmental traits parameters that engender them is sometimes very complex numerically, especially as these parameters vary from one individual to another. In this paper, we estimate the parameters of a survival model TK-TD already applied and validated by the implementation of the R package GUTS (the General Unified Threshold Model of Survival) by another coding applied to another very recent implementation of Bayesian inference with the R package deBInfer in order to evaluate the survival effects of our ecotoxicological biomarker called Deltamethrin on our Daphnia sample.
This approach is designed with a MCMC diagnosis of inference, the visualization of posterior distributions of the parameters and trajectories of the model used. This manuscript assesses the long-term survival effects of a toxic substance (a pesticide) called Deltamethrin via the use of the highly reputable GUTS model for assessing the survival of living organisms under stressors such as toxic or pharmaceuticals. The plan adopted for the organization of this article is as follows: in the second section (II), we explain the experimental protocol established in the laboratory and present the model TK-TD GUTS used to translate our experimental protocol. In the third section (III), we discuss the results of the Bayesian analysis. We end in section (IV) with a conclusion and discussion.
Only the Bayesian approach allows to integrate the knowledge that one has of a system by taking advantage of the experimental information  [22]. It is a conjunction of the information provided by the probabilistic model by a prior distribution and experimental data. The R package used for our model parameters inferring is deBInfer  [30]. We use the R package deSolve  [20] ,  [39]  as underlined in  [30]  for the resolution of the implemented TK-TD model. To extrapolate likelihood on our experimental data, we use the Poisson log-likelihood function as defined in the equation (  4 ). The log-likelihood of the data given the parameters, underlying model, and initial conditions is then a sum over the n observations at 1 The Gamma distribution 2 The Beta distribution 3 The Log-normal distribution each time point in t : L (Y |θ ) = n ∑ t N t log λ -nλ (4) Here we use small corrections (ec i ) i=1,••• ,6 that are needed because of the differential equations solutions can equal zero, whereas the parameter lambda of the poison likelihood must be strictly positive.
III. RESULTS AND DISCUSSION The inference results are presented in tables III and IV. They were obtained using the major functions ode() of the R package deSolve  [20]  and de_mcmc() of the R package deBInfer  [30]. Tables III and IV respectively give the empirical mean and standard deviation for each variable, plus standard error of the mean and the quantiles for each variable. The threshold concentration above which there are effects on the survival of our test species (Daphnia magna) commonly called NEC is estimated cap 6. 042 ± 2. 418 ng. L -1. It is similar to that estimated in one of our studies on the risk assessment of Deltamethrin on growth and reproduction treated separately  [10]. This result is Biomath 7 (2018), 1812177, http://dx. doi. org/10. 11145/j. biomath. 2018. 12. 177 and NEC, the two most important parameters in GUTS  [17].
IV. CONCLUSION This paper is very instructive in that it adapts the GUTS model to our survival data collected at LIEC through a more recent implementation of Bayesian inference (the R library deBInfer). Thus we ignored the use of GUTS (R package GUTS) implementation. With this new R library, it is easy to encode any toxicico-kinetic and toxicodynamic dynamics (TK-TD) then infer the parameters that compose it. Once differential en-coding is complete, the R package deBInfer has a function named de_mcmc() where is integrated that of ode() function of the R package deSolve specially designed for system differential solving such that ordinary, partial or delay differential equations. These last facilitate access to a lot of users types whether they are specialists in the field or not. Most of the life phenomena are modeled using Ordinary Differential Equations (EDO), Partial Differential Equations (PDE), or the Delay-Differential Equations (DDE).
As a result, this R package deBInfer facilitates the transition from determinism to stochastic. As part of our study, it allowed us to consistently address our survival analysis with the GUTS TK-TD model use. It really facilitated the manipulation and inference of the parameters of a mechanistic model to describe the bioaccumulation kinetics and dynamics of survival effects in a contaminated environment of the pesticide Deltamethrin. MITIC). We are very grateful for their financial supports.