dataesr/scientific-paragraphs-categorization huggingface dataset:
• Geographic boundaries were established with a Global Shapefile from the global administrative areas (GDAM v4) database  65. Layers for world regions (represented by the boundaries of 25 commonly recognised regions (Supplementary Methods, Table  S5 ), as well as latitudes and longitude grids at 30 degrees, were retrieved as Esri ArcGIS Data and Maps products. • Climate zones were classified as in FAO's Global Ecological Zones (GEZ)  41  , consisting of 19 classes based on essential biological boundaries, i. e. with relatively homogeneous vegetation physiognomy (Supplementary Methods, Table  S6 ). Once all individual (contiguous pixels) marginal lands were characterised, it was necessary to consolidate them into larger target areas, according with the desired scope and granularity. In our example, we consolidated all marginal lands within the same GEZ (e.
Identification and characterisation of plant species (step 3). A preselection of potential biopumps was performed and the plant data recorded into a database to evaluate their suitability to grow on identified and consolidated target areas based on their pedoclimatic requirements, and to inform the SOC model (e. g. plantbased organic C-inputs). Initially a list of 164 herbaceous and woody plants were considered, and data compiled from diverse data sources specific to agricultural perennial  67  and lignocellulosic bioenergy  68  crops, including all 68 industrial crops initially considered in the EU H2020 MAGIC project (www. magic-h2020. eu/). The preselection was performed via a semi-quantitative multi-criteria analysis by scoring and ranking SOC sequestration performance from above and below-ground C-inputs to the soil, feedstock productivity, and marginal land suitability (detailed in Supplementary Methods, Table  S7 ).
Data on the pedoclimatic tolerances provided in Ref. 43  for the matching were collected from the FAO's ECO-CROP database  42  , per biopump, for the following variables: temperature [°C], precipitation [mm], pH, soil texture (coarse, medium, fine), altitude [m], and global climate zones. Other essential soil properties (e. g. soil drainage, rooting deep) were previously considered as limiting factors for determining target areas suitable for biomass cultivation. The ECOCROP climate zone classification system based on Köppen  69  was harmonised with the FAO's GEZ  70  used for target areas consolidation, to enable the matching (harmonisation detailed in Supplementary Methods, Table  S6 ). Moreover, several plant species with the same common name were recorded, showing different tolerances, to ensure a broader initial pool for the matching exercise, and because species specifications were lacking for some of the previously used datasets.
The land use class of biopumps was classified into three types based on the life form described in retained datasets  42, 67, 71  : grass (e. g. bahiagrass, hemp, miscanthus, banana, bamboo, etc. , including small woody or herbaceous shrubs (e. g. blueberry, etc. ), crops (e. g. sugar cane, maize, loofah, etc. ), and trees (e. g. short rotation coppice, orchards, tree-nuts, etc. , as well as woody shrubs that are tree-like, e. g. due woody stem, lifetime, size and physiognomy of the plant). Uncertainties may have accumulated where the boundaries between grass and woody species are not clear. Data provided in Ref. 43  on plant organic C-inputs [Mg ha -1 year -1 ] were computed by the fractioning and C partitioning approach  72  , individually for above-(product, stem, and leaves) and below-(roots) ground compartments and C contents per fraction [%]  73.
Model estimations of SOC turnover, be it produced by Earth system models or by soil/agroecosystem models, and especially at large (e. g. global) scales, are often in disagreement. The causes are multiple, and relate in the case of Earth system models to differences in simulated state variables  79  , and in the case of soil models and agroecosystem models to structural model differences (e. g. humidity and temperature effects sub-models)  80. For illustration purposes, we retained the RothC model, previously used for global estimations, given its flexibility and suitability under varying pedoclimatic conditions  4, [81] [82] [83]. To run RothC for a combination of multiple sites and biopumps we used the SoilR package v1. 1  84  , which provides a library of functions and tools under the R environment.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the Grant Agreement ID: 869192 in the frame of the  NEGEM  project (https:// www. negem proje ct. eu). L. H. contributions were partially funded by the  French National Research Agency ,  Programme Investissement d' Avenir  ( ANR-17-MGPA-0006 ) and  Region Occitanie  under the  Cambioscop  project  ID 18015981. The authors thank  Shivesh Karan  for cross-referencing initial data on land cover and soil organic carbon classes.
More recently, five new species were described based on morphological and on multi loci phylogeny  (Brahmanage et al. , 2019; Marin-Felix et al. , 2019; Crous et al. , 2020). Stemphylium species associated with gray leaf spot symptoms of tomato have been reported in several countries of the Mediterranean basin  (Blancard et al. , 1986 (Blancard et al. , 2012)) , but distribution of many species has not been thoroughly investigated in Algeria. The main purpose of the present study was to evaluate the composition of Stemphylium spp. on leaves and fruits of tomato collected in the northwest regions of the country. Specific objectives were: (1) to identify Stemphylium species using sequence analyses of combined datasets of the ITS, gpd, and cmdA gene regions, because morphological criteria were not reliable enough for this purpose; and (2) to test and compare the pathogenicity of different isolates under experimental conditions.
Phylogenetic analyses The resulting sequences from each locus were concatenated and aligned through ClustalW algorithm and refined manually using MEGA 7  (Kumar et al. , 2016). Sequences of the type isolates were retrieved from Gen-Bank and included in the analysis. Alternaria alternata isolate GV14-634a1 was used as an outgroup. Phylogenetic analyses were carried out using the maximum likelihood (ML) under IQTree v. 1. 6. 11 (Nguyen et al. , 2015) , and Bayesian inference (BI) with MrBayes v. 3. 2. 1  (Ronquist and Huelsenbeck, 2003). The best-fit evolutionary models for each dataset calculated by ModelFinder  (Kalyaanamoorthy et al. , 2017)  under the Bayesian Information Criterion (BIC) selection procedure were K2P + G4 for ITS and gpd, and HKY + F + G4 for cmdA. The ML analysis was carried out with 1000 ultrafast bootstrap replicates, and only values above 70% were considered significant.
nih. gov/ij/plugins/). Symptoms expressed were studied and compared with those that occurred on the infected leaves collected from field-grown plants. Isolations were made from diseased inoculated plants for assessing fulfilment of Koch's postulates. Mean percentage of necrotic leaf area (n. l. a) and AUDPCs were determined for three replicates. Statistical analyses of obtained data were carried out using R software for ANOVA and Tukey post-hoc tests.
Species identification A multi-gene phylogeny approach was used to identify Stemphylium isolates at the species level, using currently accepted methods to differentiate Stemphylium species  (Woudenberg et al. , 2017). The combined dataset of ITS, gpd, and cmdA sequences from 29 tomato isolates and from 68 representative strains of 31 recognized species of Stemphylium were compared. The dataset had a total length of 1462 bp (ITS: 451 bp, gpd: 394 bp, cmdA: 617 bp) of which 265 bp were phylogenetically informative (ITS: 20 bp, gpd: 89 bp, cmdA: 156 bp). The topologies of the trees inferred by the two phylogenetic methods (ML and BI) were similar. The ML phylogenetic tree with high bootstrap support (99%) and posterior probability values (1. 0) indicated that the 29 tomato isolates grouped into five well-supported phylogenetic species (Figure  3 ).
This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy. Glossina (G. ) spp. (Diptera: Glossinidae), also called tsetse flies, are well-known cyclical vectors of Human African Trypanosomiasis (HAT) that cause sleeping, also known as sleeping sickness in humans, and African Animal Trypanosomiasis (AAT) or nagana in domestic livestock.
Hence, as a proof of concept, we investigate the reliability and specificity of WIPs for species diagnostic in Glossina spp. We first examined the feasibility of detecting WIPs on the wings of a relatively large (6 to more than 10 mm) tsetse. Then we validated the stability of the recorded pattern according to various parameters: light polarization, wing orientation (intrados, extrado left and right), the insect genera, and geographical origin. Since field-collected tsetse are stored in ethanol, another goal has been to investigate if such a conservative process significantly influences the color pattern display on wings. We then build up a reference database representative of 23 species or subspecies and combine WIPs and DL approaches to train a classifier to classify WIPs pictures taken from the wings of Glossina spp.
Material and methods Tsetse selection and storage. To establish the first tsetse reference collection of WIPs, we use well-established laboratory-reared tsetse flies listed in Table  1. In addition, we selected tsetse samples of ARIM collection belonging to IRD (Institut de Recherche pour le Développement) (https:// arim. ird. fr/). Finally, we included field-caught tsetse specimens as well. The description of the samples used in this study is given in Table  1. Image acquisition and database construction. Insect wings were dissected and deposited on a glass slide. For samples preserved in 70° ethanol, wings were layered overnight at room temperature on a glass slide before being photographed. For image acquisition, a cover slide is deposited on the sample. The picture was taken using a Keyence™ VHX 1000 microscope, the VH-Z20r camera, and a Keyence VH K20 adapter allowing an illumination incidence of 10°.
Image acquisition was performed using the High Dynamic Range (HDR) function. To exclude the size as a discriminating parameter of Glossina identification, magnification was adjusted to ensure constant-size pictures. The numerical parameter settled were as follow: • Camera White Balance: 3200 K • Shutter Speed: preset 1/15(sec) • Gain: 0 dB • Frame rate 15F/s HDR function: • Brightness: 15% • Texture: 15% • Contrast: 45% • Color: 100% Drawing pictorial identification key using WIPs pictures. The color patterns (blue, red, yellow) were drawn from enlarged image layers of the WIPs. The layers were scanned, and colors were added using the "top color filling" option of the free software GIMP version 2. 10. 30 (Available from: https:// www. gimp. org)  21. Image pre-processing, WIPs dataset splitting for training and testing.
The WIPS dataset, which consisted of 5516 pictures of dipterann insects WIPs, including 1766 pictures of Glossina, is available on request for glossina images. The link is provided in the Additionnal information section Specimens genus was not considered to build the training and test datasets. All non-Glossina images were discarded from this publicly available dataset. Under-sampled species or subspecies (less than 10 samples) are also discarded to prevent overfitting. Images were resized to 256 and 116 pixels for width and height, respectively. Pixel values were normalized within the (0, 1) range  22. The dataset was then prepared for k-fold cross-validation, with k = 5 (Fig. 2 ). The dataset was randomly shuffled and partitioned into k equal-size subsets with similar class distributions.
A separate model was evaluated for each subgroup using this subset for validation and the remaining k -1 as training data (Fig. 3 ). This strategy allowed measuring the mean accuracy of 5 distinct models and was the most accurate neural network performance estimation method. Training the neural network. To automatically classify tsetse species and subspecies with the aforementioned dataset, the original MobileNet 23 , ResNet  24  , and YOLOv2 25 architecture were considered. To consider the smaller size of the database as compared to classic DL databases like Imagenet, a smaller architecture was developed for image recognition and classification. The first architecture inspired from MobileNet takes advantage of depth-wise convolution  23. Our architecture uses one less scale (the scale with 1024 filters was not used) and one depth-wise convolution per scale, compared to MobileNet, which had 2 depth-wise convolutions.
We also tested a more standard approach based on extracting SURF descriptors, a Bag of Features (BoF) representation using a 4000 codewords dictionary, and an SVM with a polynomial kernel. This was a similar approach to Sereno et al. 22  : they used SIFT with VLAT representations and a linear SVM as a classifier. VLAT, which was a second-order statistic-based method, has much more information than the zero-order BoF one but has the advantage of being smaller and faster. From the implementation perspective, all images were resized to 256 × 256 pixels. During the training process, various image augmentation techniques were applied, such as horizontal and vertical flipping, random rotation of the image, zooming into the picture, etc. The main classic idea here was to strengthen the existing WIPs dataset through random variations of images.
Training and classification. We explored training classifiers on the dataset alone and on a dataset where negative samples were added containing various non-Glossina insects as negative samples. Training the CNN (Convolutional Neural Network) on a combination of Glossina and non-Glossina images can improve the model to make correct predictions. The database was constructed by a total of 5516 pictures of dipteran insects WIPs in  which 1766 pictures belonged to Glossina species. We deliberately crop and adjust all photos at the same dimension implying that (1) the size of wings cannot be used as a discriminative criterion for the classification process; (2) we cannot use landmarks to classify wings process. We primarily focused our analysis on Glossina species and subspecies documented as proven vectors for HAT and AAT, i.
e. , G. p. palpalis, G. p. gambiensis, G. f. fuscipes, G. f. quazensis, G. f. martinii, G. m. morsitans, G. m. submorsitans, G. m. centralis, G. tachninoides, G. caliginea, G. swynertoni, G. pallidipides, and G. longipalpis. Our database contains more than 80% of the Glossina species with medical or veterinary interest. Only G. f. martinii was absent in our database among Glossina species involved in Trypanosoma transmission. The dataset we constituted represents about 70% of species diversity as it contains WIPs pictures of 23 Glossina species and subspecies described. Unfortunately, some were represented by only a few images, and for 9, no more than 15 specimens were used (Table  1 ). We then ascertained the accuracy of the classification process at various taxonomic levels of genus, species, and subspecies.
Glossina palpalis palpalis and G. p. gambiensis are primary vectors of HAT in West Africa. They can hybridize in the laboratory, but offspring males are sterile  23. These two subspecies are challenging to identify, even if males show some morphological differences in the inferior clasper's terminal dilatation of their genitalia  24. The deep learning methodology was somewhat highly accurate, with an accuracy of up to 97% (see Table  3C ). Although the algorithm failed to identify 2 Glossina classes during the test, this can be explained, in part, by an extremely low number of WIPs pictures representative of the species in the test dataset (only 2 images for each class). Based on our dataset-splitting approach, we found these classes account for 8 images for training. This is a case of overfitting caused by insufficient training data, despite our self-imposed constraint of 10 total images per class.
In addition, raw images of the acquisition process were tested for identification (Table  4 ). The modification was performed on chosen images from the training dataset (Table  4A ) and the test dataset (Table  4B ). In both cases, alterations of the image impact the identification accuracy. First, tsetse specimens insufficiently represented in the dataset failed to be identified (Glossina fusca fusca Walker, 1849). For most specimens, blurring (gaussian or lens) did not drastically modify the capacity of the trained model to identify specimens at the species level accurately. The video degradation affected the Glossina identification of some specimens. For G. f. fuscipes, G. f. Table  4. Identification accuracy on manually and computed transformed WIPs pictures, modified pictures taken in the training dataset (A) and the test dataset (B) Legends: Green 100% match of identification, yellow identification below 100%, red no match of identification, white no samples submitted for identification.
*The technical cost includes sample preparation (DNA, protein extraction, slides, etc. ) and expenses linked to the need for a skilled professional. $ Samples destruction can be necessary for some species of medically important insects like Psychodidae, sp species, Ssp subspecies. species and subspecies delineation will be amenable for tsetse flies. Nevertheless, even if our dataset is rich in terms of species diversity, serious efforts must be undertaken to supplement it with properly qualified pictures of most, if not all, Glossina species and subspecies currently described. Another aspect of the identification process using image acquisition would be its potential to be used on smartphones. The advantages of our computer-aided methodology for species and subspecies recognition of Glossina flies are summarized compared with the other methodology available in Table  5  with respect to five criteria.
Methods The application of Deep learning leads to robust results in terms of classification performance. Therefore, it is worth evaluating, even qualitatively, if the proposed approach could be usable in real-life scenarios regarding several important criteria: cost, computational resources, analyzing time, damaged samples, and the taxonomic level of the classification. Considering the mentioned criteria, our proposed architecture ends up with a good compromise compared to other methodologies and the ability to classify up to the subspecies taxonomic level. This architecture was thought to be used on portable devices. Future development and technical implementation of this methodology include the following aspects: • To strengthen the database in terms of species diversity, the use of GANs (Generative adversarial network) will allow filling up the database with new species, even with a low number of representatives.
This DL approach makes it possible to generate original samples with characteristics learned on the initial database. • A key point of DL approaches is the loss function used during the training process for updating the archi- tecture weights. By default, the cross-entropy function was used here, but other types of loss with a better fit to the kind of data would improve the performance, and these must be investigated. Second, from an application point of view: • This work was carried out on tsetse. It can be extended to other insect families have to be carried to check to what extent such approaches can be used as a generic one for dipteran insect identification. • Implementing a Mobile app. and SaaS platform would offer a complete service for both in-the-field and remotely localized computers, with an internet connection.
Acknowledgements We want to address our thanks to  Bernadette Tchikaya  for providing us samples of colony reared  Glossina. Pr P Marty  ( CHU l' Archet, Nice, France ),  Pr M Tchuente  ( University Yahounde 1, Yahounde, Cameroun ), and  Dr D Fontenille  ( UMR MiVeGeC, Montpellier, France ) for their constant support. Dr S Ravel  ( UMR InterTryp, Monptellier, France ) for critical reading of the Manuscript.
ACKNOWLEDGMENTS We thank  Emmanuelle Na  ¨ser (Genotoul  TRI-IPBS platform, Toulouse, France ) for cell sorting and  Flavie Moreau ,  Celine Berrone , and  Aline Tridon  ( Genotoul Anexplo-IPBS platform, Toulouse ) for mouse care. We thank  Richard Locksley  ( UCSF ) and  Etienne Meunier  ( IPBS, Toulouse ) for the gift of Red5 mice and Stat1 À/À mice, respectively. We thank  Vanja Lazarevic  ( NCI, Bethesda, MD, USA ),  Sophie Laffont  ( CPTP, Toulouse ), and  Andrea Corral Pichler  ( NIAID, Bethesda, MD, USA ) for helpful discussions and critical reagents. This work was supported by  Centre de la Recherche Scientifique (CNRS) , the  University of Toulouse III Paul Sabatier , the  French Ministry for Higher Education, Research, and Innovation (fellowship  to D. C. ), the  Fondation pour la Recherche Medicale  (grant  DEQ20160334902 , to O.
N. ), the  Bettencourt Schueller Foundation  (grants  Coup d'E ´lan pour la recherche francaise and Explore-TB , to O. N. ),  MSDAVENIR  (grant Fight-TB, to O. N. ), the  Agence Nationale de la Recherche  (grants  ANR-18-CE15-0004-01 , to  D. H. , and  ANR-11-EQUIPEX-0003 , to O. N. ), and the  European Commission  (grant  TBVAC2020  no. 643381 , to O. N. ). M. R. H. is supported by a  Royal Society  and  Wellcome Trust   Sir Henry Dale fellowship  ( 105644/Z/14/Z ), a  Lister Institute of Preventative Medicine Prize , and a  BBSRC   Project grant  ( BB/T014482/1 ). We thank  ANR  and  Inserm Transfert  for the  ANR-20-CE-CE14-0028-01  and  CoPoC MAT-PI-17493-A-04  grants (to R. J. A. )This work was supported in part by the  intramural research program  of  NIH ,  NIAID  (K. D. M. -B. and Y.
Simulation code We use 10 7  -particle N -body realizations of NFW haloes, computed using the ZENO 7 software package developed by Joshua Barnes at the Univ ersity of Ha w aii. This softw are uses Monte Carlo sampling of a given distribution function to generate systems in virial equilibrium. We allow the halo to fully relax prior to introducing it into the MW potential, by running it first in isolation for 5 Gyr using the publicly available GADGET-2 simulation code  (Springel 2005 ). Once the halo has relaxed, the N -body model is evolved on each of the orbits described in Section 2. 3 and in the potential of Section 2. 2 for ∼10 Gyr. Forces between particles are smoothed with a Plummerequi v alent softening length of P = 7 pc, and we consider our results converged outside r conv = 84 pc.
We therefore use the core radius when comparing simulation results against observations, which is less affected by assumptions about the outer profile shape. (ii) The pericentric distance of Crater II on its orbit around the MW is not well constrained. We use the latest available data on the sky position, distance, radial velocity, and proper motions of Crater II, together with a Galactic potential model that matches the observed circular velocity at the solar radius, to find that the allowed pericentric distance lies roughly between ∼10 and ∼50 kpc. (iii) The ne gativ e Galactocentric radial v elocity of Crater II, together with its large distance, implies that Crater II is just past its latest apocentric passage. More importantly, this also implies that the latest pericentric passage occurred roughly ∼1 Gyr ago.