dataesr/scientific-paragraphs-categorization huggingface dataset:
The present study investigates the applicability of this concept to the Western Alps to identify geomorphologically active areas and to estimate mean annual sediment yield (SY) in mainly debris-flow-prone catchments. We use a statistical approach that shows a parameter optimisation and a linear regression of SY on SCA extent. We use a dataset of $25 years of assessed coarse sediment accumulation in 35 sediment retention basins. In the investigated catchments, sediment transport is governed by several factors, mainly by the extent of vegetation-free areas with a minimum slope of 23 that is coupled to the channel network with a very low gradient of the latter. With our improved framework, we can show that the SCA approach can be applied to catchments that are widely distributed, in a large spatial scale (hectare area) and very heterogeneous in their properties.
, 2011)  and (ii) the overland flow distance to the channel network is shorter than the 'maximum distance from channel network' threshold (100 m in  Haas et al. , 2011). Then, the upslope contributing area is derived for each (coupled) channel cell from the masked DEM, using the D8 flow routing algorithm; the result represents the SCA. In addition to these topographic parameters,  Haas (2008)  and  Haas et al. (2011)  considered that vegetation-covered areas are less prone to sediment mobilisation and transport than vegetation-free areas. Thus they included a vegetation-dependent weighting factor in their model: Where an orthophoto-based landcover map indicated bare soil, the weight was 1 (100%), while vegetation-covered SCA cells were counted with only 20% of their original size. Finally, the size of the (weighted) SCA is determined by applying a flow accumulation algorithm to the SCA raster dataset.
, 2012) For the inclusion of the vegetation cover, we used the Corine Land Cover dataset of 2012 (cell size 100 × 100 m; Copernicus Land Monitoring Service, 2012). The catchment areas of the 35 investigated sites reflect the lithological and topographical diversity of the study region which was outlined above. In addition, Table  3  shows the different properties of the catchments, their sediment retention basins and their properties relevant for this study  (Jousse, 2009; Peteuil et al. , 2012). The scatterplot of log. SY against log. catchment size (Figure  4 ) T A B L E 3 (Continued) shows a large scatter. The regression model explains 28% of the variance observed in log. mean annual SY, indicating that additional factors than catchment size governs the amount of SY.
, 2015)  in the Northern Calcareous Alps (50 km south of Munich/next to Bad Tölz, Germany) and used one year of data from 14 sediment traps (of the same type as those used by  Haas et al. , 2011). Data from two sediment retention basins with an SCA of up to 1 km 2 later increased the spatial extent of the study. Figure  11  combines our data with the previously cited SCA studies conducted in the Northern Alps  (Haas, 2008; Haas et al. , 2011; Huber et al. , 2015). While slightly different parameter settings for the delineation of SCA and different temporal extents (see discussion below) prevent a combination of the three datasets, Figure  11  clearly shows a linear dependence of log SY on log SCA in all datasets. Considering the logarithmic data, this relationship is, in fact, that of a power law.
Introduction The SARS-CoV-2 pandemic has put the global health care services to the test and many researchers are racing to face its swift and rapid spread. Since the outbreak of the virus in China and in other European countries, several studies are using sequencing technologies to track the geographical origin of SARS-Cov-2 and to analyze the evolution of sequence variants  [1] [2] [3]  or to understand the role played by human genes on viral databases such as GISAID  [11] , GenBank  [12] , and the COVID-19 Data Portal allows to build a complete catalog of variants of a viral population. Such a catalog can be used to reduce the complexity of comparative analysis of genetic variants of sequencing samples. For this goal, it is crucial that users are assisted by an efficient and easy-to-use method for building and updating the catalog and for calling and annotating variants that are in this catalog.
To achieve this twofold aim, we specifically designed a pipeline that extracts variants from the set of all the assemblies available on GenBank  [12]. Figure  1  depicts the main steps of the pipeline. The set of assemblies is initially preprocessed by filtering out sequences with more than τ N = 1% ambiguous nucleotides, then the resulting set is subsampled based on their Pango lineage  [3]. Subsampling is performed by assigning the lineage to each assembly using pangolin  [15] , then by keeping 1% of the sequences assigned to each Fig. 1  Precomputed catalog creation. Schematic representation of the pipelines used to create the precomputed SARS-CoV-2 catalogs from the set of public assemblies available on GenBank lineage with a minimum of min lin and a maximum of max lin sequences for each lineage.
If a lineage has less than min lin sequences, then all its sequences are kept. Parameters min lin and max lin are chosen in order to provide the best trade-off between completeness of the catalogs and computational efficiency of the tool. Currently they are set to 50 and 100, respectively, but their values are subject to change in future updates depending on the number (and the quality) of genome assemblies deposited to GenBank. Due to the importance of lineages classified as Variants Of Concern/Interest (VOC/ VOI) for epidemiology  [16]  and to ensure that they are sufficiently represented in the precomputed catalogs, the parameters min lin and max lin for those lineages are increased to 5 × min lin and 5 × max lin , respectively. Currently, we increased the two parameters for lineages B.
1. 1. 7, B. 1. 351, P. 1, A. 23. 1, and B. 1. 525 , that are the subjects of the cov-lineages. org global reports  [22]. Table  1  summarizes the frequency distribution of lineages in GenBank as of April 7, 2021 and provides the indication of the most represented lineages (left) and of VOC/VOIs (right) in our precomputed catalogs. As an example of how sequences were sampled, on April 7, 2021, there were 9390 sequences assigned to lineage B. 1 (over a total of 78,098 sequences that passed max ambiguous nucleotides filtering), hence only 93 of them were randomly selected to create the catalog. As another example, there were 16,844 sequences assigned to lineage B. 1. 2 and, since the 1% of 16,844 is greater than max lin = 100 , only 100 of them were selected to create the catalog. On the other hand, there were 1835 sequences assigned to lineage A.
Differently from snp-sites, our custom script is also able to extract indels, that, besides SNPs, apparently have a role in determining the characteristics of the virus. For example, the spike deletion 69-70del has been described in the context of evasion to the human immune response  [25]. On the other hand, the inclusion of indels might introduce some false variations due to technical artifacts in the input assemblies. Custom catalogs. If the user wants a finer control over the variant catalog or if she/ he wants to use a private set of assemblies (that cannot be freely shared), MALVIRUS interface allows to create a custom catalog from a set of assemblies or to directly upload a catalog in VCF format. For the automatic creation of the catalog starting from a set of assemblies, MALVI-RUS requires as input the reference genome of the viral species under investigation (for example, to study species different from SARS-CoV-2), the set of assemblies, and, if available, the annotation of the genes.
This experiment should reflect the more complex scenario in which a user wants to analyze a sample with respect to a given population of sequences of interest; for example a set of genomes that cannot be freely shared-such as GISAID itself-or a set of already available private samples for which a custom catalog needs to be generated. For both experimental evaluations we randomly selected a total of 41 samples with raw reads available on the Sequence Read Archive (SRA) and that were also cross-referenced on the GISAID database (see Table  2  for the list of samples). In particular, we specifically included 5 randomly-selected samples of the lineages B. 1. 1. 7, B. 1. 351, P. 1, A. 23. 1, and B. 1. 525 since these lineages are considered as Variants of Concern due to their peculiar epidemiological characteristics.
For lineage P. 1 we were able to include only a single sample since we were not able to find other read samples on SRA cross-referenced on GISAID and assigned to that lineage. To balance the chosen set of samples, we included 20 samples assigned to other lineages (without stratification). Furthermore, the selected set includes samples sequenced using either Illumina or Oxford Nanopore Technologies in order to assess the accuracy of MALVIRUS on both technologies. All the experiments can be reproduced (for the second experimental evaluation, after non-freely redistributable data are downloaded from GISAID) using the Snakemake workflows available at https:// github. com/ AlgoL ab/ MALVI RUS-repro/.
Genotyping accuracy In this part we assess the accuracy of MALVIRUS in genotyping a read sample on the precomputed catalogs that are readily available to the users and that have been computed as described in the Implementation section. We recall that the precomputed catalogs are built starting from all the SARS-CoV-2 genomic sequences deposited on GenBank, that are then filtered and subsampled according to their lineage and finally aligned to the reference. From the alignment, two catalogs of variants are computed using the pipelines A (on SNPs only) and B (on both SNPs and indels) as described before. To better evaluate how the choice of the subsampling parameters min lin and max lin affects the accuracy, we ran the two pipelines with three different combinations of the parameters value: the default setting min lin = 50 and max lin = 100 , a more stringent setting min lin = 20 and max lin = 50 , and a more relaxed setting min lin = max lin = 100.
As such, a total of 6 dif- ferent catalogs were generated and considered in this part. We compared the accuracy of MALVIRUS with that of two state-of-the-art callers: BCFtools and lofreq (as indicated in most of the workflows published on Galaxy). Differently from MALVIRUS, these tools rely on the alignments of the input read sample to the reference genome to call variations. We aligned Illumina samples using BWA  [28]  whereas we mapped ONT samples with minimap2  [8]. Accuracy has been evaluated in terms of precision and recall and we considered as ground truth the variations induced by the alignment to the SARS-CoV-2 reference of the full-length genomic assembly associated to each sample as deposited to GISAID. The "true" genotype of each sample has been extracted from the alignment with the script that we use in the pipeline B for the construction of the precomputed catalogs.
However, since these catalogs are precomputed and bundled with the application, this step is not executed on the user's computer. To complete the analysis of MALVIRUS results, we also analyzed how many samples have been assigned to the correct Pango lineage. MALVIRUS classified correctly 36-38 (over 41) of them (depending on the considered catalog, 36 for the smallest catalogs and 37-38 for the largest ones). MALVIRUS could not correctly classify all the samples since the set of assemblies currently freely available on NCBI does not contain a sufficient amount of information (i. e. , enough assemblies) for correct lineage inference. In the following section, we thus further explore MALVIRUS effectiveness in inferring a lineage from a sample by considering a more complete and exhaustive (but not redistributable) database, i.
Lineage assignment accuracy The second experiment has the goal to assess the quality of MALVIRUS in the assignment of SARS-CoV-2 lineages. In order to build a catalog as representative as possible of the different lineages, in this part we relied on not redistributable sequences from GISAID data (Additional file 1). We constructed a total of 8 catalogs from these sequences using the two previously described pipelines (A and B)-all reproducible with our published procedures after obtaining the data from the database, since those data cannot be shared. We used this platform because it currently (as of  April 7, 2021)  contains around 1 million of assembled sequences-in contrast to the approximately 70 thousands in NCBI/ENA-thus including a much high number of lineages. In more detail, we downloaded all complete sequences available on GISAID (accessed on April 7, 2021) and we filtered out all those sequences having more than 5% ( τ N parameter) ambiguous nucleotides.
From the remaining set of assemblies, we randomly selected either 5 to 20 or exactly 50 samples from each lineage and computed VCF catalogs according to the pipelines A and B. Then we pruned variants that had less than either 2 or 5 sequences supporting them to reduce the noise and errors contained in the large GISAID dataset. The different choice of the parameters τ N , min lin , max lin and the addition of a final pruning step, compared to the procedure used to build the precomputed catalogs from publicly available data, is motivated by the significant difference in the number of sequences involved. Indeed, we increased τ N from 1 to 5% in order to avoid discarding upfront lineages represented by a small number of sequences. At the same time, we have been more restrictive on subsampling (i.
Availability of data and materials All the programs, scripts, and workflows needed to reproduce the analyses are available in the GitHub MALVIRUS-repro repository: https:// github. com/ AlgoL ab/ MALVI RUS-repro/. The datasets analysed in first experimental part (Genotyping accuracy) are available from GenBank and SRA and the aforementioned repository provides detailed instraction to retrieve them. The datasets analysed in the second experimental part also include some data available from GISAID but restrictions apply to redistribution of these data, which were used under license for the current study, and are publicly available from the GISAID website subject to GISAID's Terms and Conditions (https:// www. gisaid. org/ regis trati on/ termsof-use/). The aforementioned repository provides detailed instructions to retrieve them.
Image acquisition and data processing The shadowgraphy system included homogeneous lightemitting diode panels (LED-panels) that were installed behind the transparent quasi-two-dimensional jet clarifier and the CCD cameras (Flow sense EO, Dantec Dynamic) placed at 90 • to the LED-panels. Acquisition windows width were fixed with a resolution of 1024 × 1024 pixels corresponding to 10 × 10 mm 2 (1 pixel ≈ 10 m). Every 15 min, sets of 1000 images were acquired at the frequencyof 5 Hz i. e. that 200 s were needed to acquire the 1000 images. The number of flocs acquired during each set of 1000 images varied in the range 7000-39,000. The overall duration of the experiment was conditioned by the size of the tank and thus depended on the flow rate. The same image processing technique than those of  Laupsien et al.
(2019)  was applied. The first step consisted in subtracting the background light that was measured before the injection of the suspension. A gray filter level is then used to further intensify object contrasts and determine the position of the interface floc-water. To that end the intensity of the gray level gradient is used by applying a threshold of 0. 5 corresponding to 1024 gray levels for a 10-bit image. The binarized images were then analyzed to extract the surface A of each floc. The floc diameter d CED was derived from Eq. 12. d CED = 4A 1/2 (12) The population of flocs has been first analyzed in terms of mean surface diameter (D mean ). The statistical convergence of the data has been checked by plotting the cumulative average of mean surface diameter (D mean ) versus the number of analyzed images, it was concluded that the statistic is reliable (data not shown).
Prior to analysis, a part of the sample was attacked with H2SO4 + HNO3 at 250-300 °C to detect the contents of P, Fe, Na, Al, Mg, K, Mo, Mn, Zn, whereas for Ca and Ba, samples were dissolved by HClO4 + HNO3 at 250-300 °C. Regarding the following atoms, Si, Ag, As, Cd, Co, Cr, Cu, Ni, Pb, Sb, Se, Ti, T1, V, Zr, Sr, Te, Bi, Ga, In, Ta and Hf, the samples were first melt etched with lithium tetraborate and then heated in a muffle furnace until 1100 °C with 20% HCl. The pH values of each solution were determined using a PHM 210 Standard pH meter (Radiometer) connected to a combined glass electrode. The zeta potentials were measured using a CAD ZetaCompact instrument for the detection of electrophoretic mobility of ACs particles in aqueous suspensions. The IR data were collected in the diffuse reflectance mode (DRIFTS) using a Spectra-Tech® reaction cell fitted with KBr windows.
Computational procedure Molecular geometries of PEME were optimized using the functional B3LYP and 6-31G (d, p) atomic basis considering isolated molecules. Gaussian v16-c01 program package was employed for geometry optimizations. Afterwards (on the previously optimized structures), continuous solvation single point calculations were performed by the COSMO methodology aiming to establish the polarized charge density distribution on the PEME surface. COSMO calculations were carried out with the functional BP86 and TZVPD atomic basis in Turbomole v7. 1. So as to improve the quality of the COSMO calculations, the algorithm FINE recently implemented in Turbomole was used to create the solvent cavity. The pKa calculations were done by COSMO-RS method performed with the COSMOthermX v19. 0 program package using the BP_TZVPD_FINE_19 parametrizations to elucidate the PEME structure under different pH conditions.
Acknowledgements Authors gratefully acknowledge the contribution of the  China Scholarship Council  in supporting a doctoral thesis study for Bomin FU. The authors are also very grateful to  Université Claude Bernard Lyon 1 ,  Centre National de la Recherche Scientifique, Institut de Recherches sur la Catalyse et l'Environnement de Lyon (IRCELYON ) for their financial support. In particular, the authors would like to thank the  IRCELYON  for its technical services such as Raman, physical and chemical analysis on this work. The authors also thank the  Centro de Computación Científica at the Universidad Autonoma de Madrid  for the computational support to this work.
ACKNOWLEDGMENTS We acknowledge the first reviewer for suggesting timedependent dispersion. The research leading to these results has received support from the  French Agency for Research (Agence Nationale de la Recherche, ANR)  through the  Equipex Planex   ANR-11-EQPX-36 , the  Labex Voltaire   ANR-10-LABX-100-01 , the grant  CATCH ANR-18-CE05-0035 , and through the  FraMatI project  under contract  ANR-19-CE05-0002. It has also received financial support from the  CNRS  through the  MITI interdisciplinary programs , and from the  UK EPSRC  funded project on  Direct Numerical Simulation for Additive Manufacturing in Porous Media  (grant reference  EP/P031307/1 ). Finally, SR and JM would like to thank  ALLIANCE (Partenariat Hubert Curien)  for the grant that enable the scholar exchange of scientists.
Materials and Methods A systematic review of the literature identified from the PubMed database was performed until June 2021 in accordance with the PRISMA guidelines. A comprehensive search using keywords [("dioxin" OR "2,3,7,8-Tetrachlordibenzo-p-dioxin") AND ("mouse model" OR "murine model" OR "mice" OR "rat") AND ("fertility" OR "reproductive toxicity" OR "testicular toxicity" OR "sperm" OR "ovarian toxicity" OR "preterm birth" OR "pregnancy") AND ("trangenerational" OR "multigenerational" OR "multiple generations" OR "three-generation" OR "epigenetic")] was conducted, including all studies from database inception until June 2021. Reviews, studies that did not publish a full manuscript, and publications that were not in English were excluded.
Articles Selection A total of 19 articles were retrieved from the literature search. After title/abstract screening for matching inclusion/exclusion criteria, nine of these papers were then assessed for eligibility. The references of all of the computer-identified publications were searched for additional studies. Ultimately, 15 studies were included in the data analysis  [10, [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67].
Moreover, they identified the genes associated with each disease-specific DMR dataset and that were linked to a specific disease  [10]. For instance, in samples from ancestrally exposed F3 rats with testis disease, DMR-associated genes included genes linked to testis physiology and male infertility  [10]. Similarly, Prokopec et al. analyzed the testes from ancestrally exposed F3 rats by targeted bisulfite sequencing of entire chromosomes or selected genes and detected many DMRs at specific genes, such as melanocortin 5 receptor (Mc5r), protein phosphatase 1, regulatory subunit 27 (Ppp1r27), family with sequence similarity 109, member a (Fam109a), heat shock protein 8 (Hspa8), peptidase inhibitor 16 (Pi16), and RAS protein activator like-3 (Rasal3). Conversely, they did not find any DMR in or around Ahr or any "AHR-core" genes  [67].
Acknowledgments: We would like to thank  Anna Richardson ,  La Maison de Science MSH-SUD  for supporting the Projet PAGOPI " Approche interdisciplinaire des conséquences de la pollution environnementale par l'Agent Orange et les pesticides utilisés par les agriculteurs dans la péninsule Indochinoise" (Pierre Journoud ) and  Région Occitanie  ( Carole Delga ) as part of the PhD Scholarship grant of  Laura Gaspari  (grant n • n •  20007389/ALDOCT-001014 ).
The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with a geometric diameter up to 20 µm (PM 20 ) is approximately 5000 Tg yr -1 , which is greater than most models account for. This larger PM 20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded datasets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this dataset is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.
, 2008; Martin and Kok, 2019) , mineralogy  (Perlwitz et al. , 2015a) , and soil moisture  (McKenna Neuman and Nickling, 1989; Fécan et al. , 1999). These and other soil properties control both the dust emission threshold and the intensity of dust emissions once wind exceeds the threshold  (Gillette, 1979; Shao, 2001; Kok et al. , 2014b). Models lack accurate high-resolution datasets of pertinent soil properties, which also limits the use of dust emission parameterizations that incorporate the effect of these soil properties (e. g. ,  Darmenova et al. , 2009). As a result of these fundamental challenges in accurately representing dust emission, most models use both a source function map  (Ginoux et al. , 2001)  and a global dust emission tuning constant to produce a global dust cycle that is in reasonable agreement with measurements  (Cakmur et al.