dataesr/scientific-paragraphs-categorization huggingface dataset:
If the back trajectories intersect a region where the GEOS-Chem simulation indicates an intensive CO source and the travel duration is within ± 2 hr of the observed enhancement, then this specific CO source could contribute to the observed enhancement over the HTP. The transport trajectories for this CO source are finally determined. Meanwhile, global fire atlas data archived by the Fire Information for Resource Management System (FIRMS) is used to determine if intensive fire events occur around the location of this CO source https://doi. org/10. 5194/acp-2020-810 Preprint. Discussion started: 9 September 2020 c Author(s) 2020. CC BY 4. 0 License. (https://firms. modaps. eosdis. nasa. gov/download/, last access on 23 April 2020). This CO source is regarded as BB source if concurrent intensive fire numbers are observed.
The diurnal cycle is characterized by a bimodal pattern with two maximums occurring around 9:00 to 11:00 local time (LT) in the daytime and 21:00 to 23:00 LT in the nighttime. The trend in surface CO VMR from 2015 to 2020 over the HTP spanned a large range of (-21. 6 ± 4. 5) % to (11. 9 ± 1. 38) % per yr, indicating a regional representative of each dataset. However, surface CO VMR from 2015 to 2020 in most cities over the HTP showed negative trends. The in situ measurements are for the first time used to assess the performance of GEOS-Chem full-chemistry model for the specifics of topography and meteorology over the HTP. Generally, GEOS-Chem can capture the measured variability of low CO levels but shows large discrepancies in high CO levels. Distinct dependencies on a short life time species of NO2 almost in all cities over the HTP were observed, implying local emissions to be predominant.
Using convolutional neural network (CNN), Huang et al. [12]  were able to differentiate rice from unspecified weeds, with 97% accuracy. Bah et al. [26]  developed a new automatic learning method also based on CNN for weed detection in UAV images, with an unsupervised training dataset. Support vector machine (SVM) are powerful supervised learning classifiers to discriminate vegetation in a complex case, e. g. , overlapping leaves. Pérez-Ortiz et al. [20]  used SVM to classify crop/weed from RGB images in sunflower and maize fields. Suh et al. [27]  compared different machine learning classifiers to differentiate sugar beet from volunteer potato plants, concluding that SVM was more efficient than random forest or neural networks. Pflanz et al. [8]  could discriminate between wheat and weeds with 98% accuracy and differentiate various weed species with 87% accuracy.
Study Site and Data Acquisition The study site is located in Dijon, Burgundy, France (47 •   The destructive measurements served as a reference. The aerial parts of the wheat and the weeds were collected separately and were packed into paper bags. We measure the leaf area index (LAI expressed in m 2. m -  ) using a planimeter. Then, for each sampling date, the above-ground dry matter biomass (BM, g. m -2 ) was determined by weighing plants, after being oven dried at 80 • C for 48 h. In parallel, we built a movable sensing platform made of PVC pipes, on which a Canon EOS 450D (Canon Inc. , Tokyo, Japan) commercial digital camera was mounted at a height of 1 m (Figure  1b ). Capturing vertical images allows comparing the results with LAI at early growth stages. The main parameters of the camera are described in Table  A1  (Appendix A).
On the first date only, the whole surface of the plot was imaged. As for a UAV platform, we built an orthomosaic photo of the entire plot with a ~60% overlap between successive images and a ~40% overlap between passes. We used Image Composite Editor (Version 2. 0. 3. 0, 2015, Microsoft Corporation, Redmond, WA, USA), an image stitcher software, to create a panoramic image.
All the image pre-processing and processing algorithms were implemented in Matlab (Version 2016b, The Mathworks, Natick, MA, USA). Greenness identification from MetaIndex (step B-1). We propose the new vegetation index called MetaIndex, which combines the advantages of six vegetation indices (Table  2 ) commonly used in the literature and recently reported in Baniaich et al. [45] ,  Meyer et al. [16] , Guo et al. [46] , and Yang et al. [47]. It consists in assigning a pixel to a class (vegetation or other) by a majority vote performed on four indices over the six listed in Table  2. This method is completed by a geodesic segmentation to refine the results and obtain a B&W vegetation image also called B&W vegetation mask (white pixel for the vegetation, black for the background). Feature extraction (step B-2).
The input data of the two-class SVM-RBF classifier is not the RGB image but a vector of main features specific to each class (crop vs. weed). Extraction of these features is quite complex and requires several image-processing steps. To reduce computation time, superpixels (128 × 128 px) are then created with the SLIC (Simple Linear Iterative Clustering) algorithm commonly used in the literature  [26]. Combining a RGB image with its vegetation mask, only superpixels of vegetation are kept in the thumbnail images. We used the labelled ones to create the crop and weed training dataset (Figure  3 ). The SURF (speeded-up robust features) descriptor algorithm  [51]  allows extracting features (~1000) from these images. Thousands of variables are then extracted in each stand to construct a 500-dimensional BoVW vector, containing the most influential features (~500) owing the Bag of Visual World method  [27, 52, 53].
This approach is very popular in agriculture to discriminate between crop and weeds  [8, 27, 51]. With the labelled image database, the BoVW vector of each thumbnail is associated to a class, wheat or weeds. From this technique, we increase easily the number of labelled images of the training dataset. Supervised classification and evaluation (step B-3). The classifier learns how to distinguish the crop from the weed flora (training phase). It is based on a support vector machine (SVM) algorithm  [54]  with a radial basis function (Gaussian type). Among all supervised techniques used for crop/weed discrimination, SVM-RBF is one of the most powerful classification algorithms based on machine learning  [8, 11, 21, 23, 25, 55]. The learning dataset is made of 7860 thumbnails labelled into two classes: crop and weed.
Crop/Weed Map from SVM Classifier and Classification Performance The input data of this supervised classification are the BoVW vectors that contain 500 features for each class. These vectors are built during the training phase with labelled thumbnail images automatically assigned as crop or weeds (Figure  3 ). The classification accuracy is quantified using a classical metric deduced from the confusion matrix. At the end, more than 7682-labelled thumbnails have been obtained, 3841 for the 'Wheat' class and 3841 for the 'Weeds' class (Table  3 ). Then the labelled dataset was randomly split into 85% training dataset and 15% testing dataset. The SVM-RBF classifier is assessed from the confusion matrix (Table  4 ). The overall classification accuracy was 93%, the Recall (also known as sensitivity) 94%, and the Precision (also known as selectivity) 92%.
To this end, a spectral database is built with laboratory hyperspectral imagery from 51 dry pure mineral samples and intimate mineral mixtures of controlled abundances. Six spectral preprocessings, standard normal variate (SNV), continuum removal (CR), continuous wavelet transform (CWT), Hapke model, first derivative (1st SGD) and pseudo-absorbance (Log(1/R)), are applied and compared with reflectance spectra. Two linear unmixing methods, fully constrained least square method (FCLS) and multiple endmember spectral mixture analysis (MESMA), and two non-linear unmixing methods, generalized bilinear method (GBM) and multi-linear model (MLM), are compared. Global results showed that the benefit of spectral preprocessings occurs when spectral absorption features of minerals overlap for SNV, CR, CWT and 1st SGD, whereas the use of reflectance spectra performs the best when no overlap is present.
[29, 30]  obtained a RMSE of 3% for illite, kaolinite and smectite in clay-quartz-organic mixtures). Neural networks have been tested for mineral sub-pixel classification  [31] , on a Hyperion dataset. Compared to ground reference, fraction prediction provides a correlation coefficient r² value of 0. 62 for illite/montmorillonite class and 0. 6 for kaolinite class. The second class of methods using unmixing relies on the knowledge of pure mineral spectra or endmembers  [32] [33] [34] [35]. Linear unmixing methods only consider materials at the surface that are contiguously distributed (named in the following "aeral mixture",  [36]  Figure  1a ) while non linear unmixing methods consider mixed pixels several soil elements of volumetric distribution (also called "intimate mixture",  [32, 36]  Figure  1b ).
For example,  [34]  showed with laboratory spectroscopy datasets of quartz-alunite intimate mixtures that the Hapke model delivers the best result (bias of 1%-2% between estimated and measured abundances), then MLM (bias of 10%-20%) and at last both GBM and FCLS have a similar performance (bias 30%-40%). Robertson et al. [47]  compared a version of the Shkuratov model  [48]  with the Hapke model to quantify laboratory mineral mixtures of montmorillonite-gypsum. In these samples, minerals fractions are estimated with an error less than 10% for both models. Unmixing methods can also be embedded in expert systems such as Tetracorder in order to increase the accuracy of mineral discrimination  [27]. The advantage of using unmixing methods is to be not site-dependent and they do not require a learning stage with a representative training dataset, which is the case with the first class of methods.
The main objective of this study is to compare the performance of both linear and non-linear unmixing methods combined with spectral preprocessings to estimate montmorillonite abundance in mineral mixtures. For this purpose, this pioneer work is based on spectroscopic measurements of intimate mixtures composed of different mineral types (clays, calcite and quartz) and controlled abundances, manually generated in the laboratory with dry conditions and further used as proxies for soil samples. Performances of the spectral preprocessings and unmixing methods will be assessed from the spectral database deriving from them. Another scenario considering synthetic mineral aeral mixtures computed from spectral measurements of pure minerals will be also analyzed in order to compare the performances of spectral preprocessings and unmixing methods between aeral and intimate mixtures.
Spectral Database The spectral database was built from five pure clay mineral samples (illite, kaolinite, montmorillonite, calcite and quartz) and two mixture types were considered, aeral and intimate, for a total of 46 mixtures. In one hand, the spectral reflectance of aeral mixtures was computed from the linearly mixed combination of pure mineral samples pondered by their respective abundance. On the other hand, the spectral reflectance of intimate mixtures was measured from the facilities presented in Section 2. 1. This last scenario is a proxy to consider soil mixtures, without any organic matter and soil water content affecting spectral signatures  [54]. Investigating unmixing performance with aeral mixtures enables to (i) highlight the benefit or not of spectral preprocessings over reflectance spectra, and (ii) compare the performance of linear unmixing methods independently of non-linear effects occurring in intimate mixtures.
Synthetic Mineral Aeral Mixtures Aeral mixtures are synthetically generated using spectral data of pure minerals (Figure  3 ). In order to reproduce variability of pure mineral spectra, 1000 pixel spectra have been randomly selected in each hyperspectral image of pure minerals. Then, using these spectra, a linear mixing was applied to these spectra for each mineral following the equation below: = ∑ ( ) ℎ 0 ≤ ≤ 1 and ∑ ( ) = 1, (2) With the abundance of mineral i and the spectral reflectance. As the focus of the study is on the discrimination of the montmorillonite, only binary aeral mixtures combined with montmorillonite are considered (Figure  4 ): illite-montmorillonite, kaolinite-montmorillonite, calcite-montmorillonite and quartz-montmorillonite. Each mineral abundance of a given mixture was measured by weight percentage (wt %).
Unmixing Methods Four unmixing methods were chosen: two linear methods, FCLS (the most commonly used) and MESMA (accounts for intraclass variability), and two non-linear methods, GBM (able to considered first and second order interactions) and MLM (multiple interactions; Table  3 ). In MESMA, each endmember was modeled by six spectra (mean spectrum, mean spectrum ± standard deviation, mean spectrum ± 3 × standard deviation and median spectrum). The non-linear contribution was estimated by GBM with its γij parameter and by MLM with the P parameter. The implementation of the FCLS algorithm comes from the pysptools python's package. We coded our own version of MESMA based on  [39]. GBM and MLM implementation from  [44]  were used.
Acknowledgements We thank  Mrs. A. Benlakhryfa ,  Mrs. L. Borges , clinical research assistants, and the staff from  Grenoble Center for Clinical Investigation (CIC) :  Prof. J. -L. Cracowski ,  Dr. C. Cracowski ,  Dr. E. Hodaj ,  Mrs. D. Abry ,  Mr. N. Gonnet  and  Mrs. A. Tournier. We are grateful for the support of  Dr. M. Althuser ,  Mr. S. Althuser ,  Dr. F. Camus-Chauvet ,  Mr. P. Dusonchet ,  Mrs. S. Dusonchet ,  Dr. L. Emery ,  Mrs. P. Fabbrizio ,  Prof. P. Hoffmann ,  Dr. D. Marchal André ,  Dr. X. Morin ,  Dr. E. Opoix ,  Dr. L. Pacteau ,  Dr. P. Rivoire ,  Mrs. A. Royannais ,  Dr. C. Tomasella ,  Dr. T. Tomasella ,  Dr. D. Tournadre ,  Mr. P. Viossat ,  Mrs. E. Volpi ,  Mrs. S. Rey ,  Dr. E. Warembourg , and clinicians from the  Grenoble University Hospital  in the recruitment of the study volunteers.
Funding This project was supported by  ANSES  ( EST-2010-2-130 ),  INSERM ,  ANR  ( Shalcoh ,  ANR-14-CE21-0007 ; EDeN,  19-CE36-0003-01 ); Fonds Agir Pour les Maladies Chroniques 2011 (APMC,  CDMR R13076CC ); AGIRàdom. Dorothy Nakiwala  was supported by a doctoral grant from  Université Grenoble Alpes.
Perspectives Although the AOP framework was proposed more than a decade ago, there are still not many 'encyclopedic AOPs' in the AOP-wiki. Notably, there is only one AOP (AOP7) that addresses female reproductive disorders in mammals (AOPwiki. org). Clearly, there is an urgent need to substantially update the AOP database and include more AOPs focusing on female reproductive toxicity. In this review, we have proposed ten pAOPs that we believe will be of use to researchers and regulators alike going forward. Neither of the pAOPs are yet included in the AOPwiki and much work remains before they become encyclopedic. From our synthesis of available literature, however, it is clear that alternative test assays are available to assess effects of chemicals on some early KEs. However, to use these alternative assays to predict AOs, plausible cause-effect relationships must be established to underpin any reliable data interpretation.
In this study, we examined whether long-term air pollution exposure is associated with the prevalence of current eczema, rhinoconjunctivitis and asthma in a large population of young children from seven areas participating in five recent population-based birth cohorts. These cohorts were selected as (1) they have outcomes defined according to MeDALL-guidelines (although deviations exist at younger ages), (2) they have available air pollution exposure models (created using ESCAPE protocols or comparable methods) and importantly, (3) these air pollution exposure models were developed using air pollution monitoring data collected at the time of birth or during the first years of life of the participants. Furthermore, although air quality limits continue to be regularly exceeded, the children participating in these cohorts were born during a time when average levels of certain pollutants were decreasing in Europe (e.
Exposure assignment For nearly all cohorts, long-term (annual average) concentrations of nitrogen dioxide (NO 2 ), nitrogen oxides (NO x ), mass of particulate matter with an aerodynamic diameter < 10 μm (PM 10 ), between 10 μm and 2. 5 μm (PM coarse) and < 2. 5 μm (PM 2. 5 ), as well as PM 2. 5 absorbance were estimated to each participant's home address at birth and at the time of outcome assessment (four and eight years) using land-use regression (LUR) models developed following ESCAPE protocols  (Beelen et al. , 2013; Cyrys et al. , 2012; Eeftens et al. , 2012a Eeftens et al. , , 2012b)). Briefly, as part of the ESCAPE study, three two-week air pollution monitoring campaigns were performed at chosen sites in the study areas. Site-specific annual averages were calculated using the average of these three measurement periods and were adjusted for temporal variation using data from a centrally located background reference site which operated continuously throughout the measurement year.
5 is road traffic. The study area of INMA-Gipuzkoa is characterized by low road traffic intensity but a high metalindustry pressure  (Lertxundi et al, 2010) , and thus, PM 2. 5 levels were derived as the average measured PM 2. 5 concentration from the closest monitoring site to each participant's address during the entire pregnancy period  (Lertxundi et al. , 2010 (Lertxundi et al. , , 2015)). For INMA-Valencia, PM 2. 5 levels were assigned using inverse distance weighting based on data collected from five monitoring stations during the pregnancy period of the participants. For these latter two cohorts (INMA-Gipuzkoa and INMA-Valencia), PM 2. 5 levels were also available from the Europeanwide LUR model described above  (Wang et al. , 2014)  and these estimates were used in a sensitivity analysis.
For BIB, this information was only collected for a subset of the cohort and is thus not included. e Information not collected in RHEA. f For EDEN, only includes information on mould as there was no data on dampness. g For all INMA centers (Guipuzcoa, Sabadell and Valencia), pet information was collected at month 8. For BIB, this information was only collected for a subset of the cohort and is thus not included. maternal and paternal education, breastfeeding, older siblings, daycare attendance during first year of life, maternal smoking during pregnancy, parental smoking at home during early-life, mould or dampness at home during early-life, furry pets in the home during early-life and use of gas for cooking during early-life. Use of gas for cooking was not included in the models in RHEA (data not collected) and GASPII (99.
a No meta-analysis was conducted as data were available for only one cohort. Association reported is calculated within one cohort. associations towards the null. A similar observation was made in the Swedish BAMSE birth cohort, in which associations between NO x and PM 10 during the first year of life were only apparent for prevalent asthma at 12 years (1. 66 (1. 01, 2. 72) per 46. 8 µg/m 3 increase and 1. 96 (1. 08, 3. 53) per 7. 2 µg/m 3 increase, respectively, N = 4089) but not at younger ages  (Gruzieva et al. , 2013). It is indeed intriguing that in the current study, the ORs were most consistently greater than one for associations with asthma at age eight years using both the MeDALL-based outcome definition (Table  4 ) and the single question on ever doctor diagnosis (Supplemental Material, Table  S6 ).
However, given the number of tests conducted and the more limited number of cohorts contributing data to these models, these results should be interpreted with caution. It should be noted that although a high percentage of European urban populations continue to be exposed to levels that exceed European Union and World Health Organization reference levels, air pollution levels in general have decreased in Europe during the last decades  (Colette et al. , 2017; Guerreiro et al. , 2014). Hence the children included in the current analysis, who are participating in more recently established birth cohorts, would likely have been exposed to lower average levels of air pollution than in earlier decades. Ultimately, we can only speculate regarding the reasons behind the lack of consistent associations with asthma, but it is possible that a future re-analysis of the participating cohorts, in which repeated and longitudinal data are available on asthma at older ages, could yield different results.
Strengths and limitations This analysis included all available data from several large prospective well characterized birth cohorts, which allowed adjustments for many early-life and current known risk factors for asthma and allergic diseases. We chose to adjust the models for the same covariates as used in a previous similar analysis  (Gehring et al. , 2015)  to increase the compatibility of our results. Unfortunately, because of the lack of existing data in most cohorts, we were unable to adjust for area-level socioeconomic variables (although maternal and paternal education were included as markers of individual-level socioeconomic status), consider gene-environment interactions (for which the epidemiological evidence is strongest for asthma (e. g. (MacIntyre et al. , 2014) ), or stratify the associations by atopic status (for which there is growing evidence that air pollution may be more strongly associated with nonallergic asthma compared to allergic asthma  (Khreis et al.
5 mass, using the European-wide LUR model PM 2. 5 mass estimates  (Wang et al. , 2014)  for INMA-Gipuzkoa and INMA-Valencia, or excluding EDEN from the analyses with NO 2 , which was the only area which did not use the ESCAPE LUR models to assign NO 2 exposure levels. Despite these differences in exposure assessment, this study is notable in that the monitoring data used to inform the development of the air pollution exposure models were collected at (four areas) or near (within four to seven years, three areas) the time of birth of the participants. As such, the years in which monitoring data were collected differs by cohort. This likely reduced some exposure misclassification for the air pollutants estimated to the home address at birth, as there is no need to assume that the spatial distribution of the air pollution sources (large roads, land use patterns, industrial areas) and their associated pollutants has not changed between the birth of a participant and the time the monitoring data were collected.
Funding The research leading to these results was by the European Community's Seventh Framework Program under grant agreement numbers 261357 (MeDALL) and 211250 (ESCAPE). Information about cohort-specific funding is available in the cohorts' publications, and also, for INMA, in the Supplemental Material, Additional cohort funding sources section. The funding sources were not involved in the study design, in the collection, analysis and interpretation of data, the writing of the report and in the decision to submit the article for publication. ISGlobal is a member of CERCA Programme/Generalitat de Catalunya.
Acknowledgements. Andrew H. MacDougall ,  Micheal Eby ,  H. Damon Matthews , and  Kirsten Zickfeld  are all grateful for support from the  National Science and Engineering Research Council of Canada Discovery Grants program. Thomas L. Frölicher ,  Friedrich A. Burger , and  Aurich Jeltsch-Thömmes  acknowledge support from the  Swiss National Science Foundation. Thomas L. Frölicher ,  Friedrich A. Burger ,  Joeri Rogelj ,  Roland Séférian , and  Martine Michou  acknowledge support from the  EU   Horizon 2020 Research and Innovation Programme. Chris D. Jones ,  Andrew Wiltshire ,  Victor Brovkin ,  Roland Séférian  and  Martine Michou  acknowledge the  EU Horizon 2020   CRESCENDO  project. Chris D. Jones  and  Andrew Wiltshire acknowledge the Joint UK BEIS-Defra Met Office Hadley Centre Climate Programme.