dataesr/scientific-paragraphs-categorization huggingface dataset:
We removed two genomes with haplotypes identical to other genomes from  Kim et al. (2013)  and then inferred a maximum likelihood tree with IQ-TREE 2 v. 2. 0. 7. using a generalized time reversible model with four gamma rates (GTR+G4). The tree was midpoint-rooted, and we then used TreeTime v0. 8. 1  (Sagulenko, Puller, and Neher 2018)  to perform ancestral sequence reconstruction. Trees were visualized using baltic 0. 2. 2 (https://github. com/evogytis/baltic). We inferred a maximum likelihood tree using the entire mitochondrial genomes (genomes where only the cytochrome B gene was available were padded with Ns) with IQ-TREE 2. The tree was midpoint-rooted, and then we used TreeTime to perform ancestral sequence reconstruction. The inferred sequence for the most recent common ancestor (MRCA) of the non-Japanese clade of raccoon dog genomes (Fig.
5b ) was used as a reference genome for subsequent analyses. Using pysam 0. 21. 0, we calculated the major allele frequency, minor allele frequency, allele frequency matching the reconstructed MRCA sequence, and allele frequency matching the inferred cytochrome B substitutions relative to the reconstructed MRCA sequence (Fig. 5b ). We created heatmaps with the latter mutation allele frequencies using Seaborn 0. 12. 2. Transcriptomics assembly and BLAST. The de novo transcriptomic assemblies for 180 adapter cleaned samples were generated using rnaSPADES (v3. 15. 5)  (Bushmanova et al. 2019). The resulting assembled transcripts were searched using blastn (v 2. 14. 0+)  (McGinnis and Madden 2004)  against the NCBI non-redundant nucleotide database (last update; 29 th May 2023) downloaded on 1 June 2023 https://ftp.
ncbi. nlm. nih. gov/blast/db/) using blastn (v 2. 14. 0+). The specific parameters used are: -outfmt '6 qseqid sseqid pident evalue score bitscore length qstart qend sstart send stitle' -max_target_seqs 2. The output files were filtered to exclude hits with alignment length of less than 100. BLAST (v2. 14. 0+. ) searches were also performed against the in-house database of genome sequences assemblies from 108 animal species (Excel sheet with accession numbers). The genome assemblies were downloaded from the NCBI genome database. The blastn search parameters used are: -outfmt '6 qseqid sseqid pident evalue score bitscore length qstart qend sstart send stitle' -max_target_seqs 2. The output files were filtered to exclude hits with alignment length of less than 300 bp and <99. 5% nucleotide identity to the reference.
Acknowledgements We gratefully acknowledge all data contributors for generating the data on which our analyses are based. The Huanan market sequence data were generated by Liu et al. (Liu et al. 2023) and shared via NCBI BioProject PRJNA948658. We have also analyzed data in this work from BioProject PRJNA793740 and  PRJNA795267. Figure 1 includes data shared on GISAID (Supplementary Table S19), and we gratefully acknowledge the authors from the originating laboratories and the submitting laboratories, who generated and shared through GISAID the viral genomic sequences and metadata on which this research is based (Khare et al. 2021). This project has been funded in part with federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH) ,  Department of Health and Human Services  (contract no.
| Soil spatial data Spatially explicit soil data are obtained from three vector conventional soil maps with different spatial scales (Figure  1d-f ). Each soil map presents complex Soil Mapping Units (SMU) composed of a set of several Soil Type Units (STU). The STUs are not spatially delineated but their relative abundances inside each SMU is known. In this study, the three soil maps are equally pre-processed and simplified: only the dominant STU is considered, as suggested by  INRA Infosol (2005). Morphological and analytical properties of the dominant soils are extracted from the soil database associated with the soil map at the scale of 1:50,000. Such properties, along with the SMU correspondences among each map scale, are listed in Tables  S1  and S2. The first conventional soil map (S1000), at a scale of 1:1,000,000 is the Soil Geographical Database of France (Figure  1d ).
This map is part of the European Soil Geographical Database of Europe. At such a coarse scale, the Saclay plateau is entirely covered by two different SMUs (Figure  1d ). According to the World Reference Base for soil resources (IUSS Working Group WRB, 2015), soils developed in the loess deposit on flat plateau positions were classified as Luvisols (SMU 330712) and those formed in sandy slope deposits as Arenosols (SMU 330126). At this scale, the areas classified as urbanised were reclassified as Luvisol where finer scale soil maps or field observations indicated the absence of artificialised areas. The second conventional soil map (S250) is at a scale of 1:250,000 (Figure  1e ) and is provided by the Référentiels Régionaux Pédologiques (RRP), which are the geographic soil databases at the regional level in France  (Roque, 2004).
| Quantifying and mapping the effects of changing the scales of soil input data on the levels and spatial distributions of soil-based ESs The effect of changing the scale of soil input data on the levels and spatial distributions of soil-based ESs were quantified following two successive steps. The maps of individual services and of their spatial co-occurrence at the three scales (1:1,000,000, 1:250,000, and 1:50,000) were first compared. Then, the pedodiversity represented in the different soil maps as well as the individual and joint supply of the four considered ESs obtained in the different ES maps were computed at the landform, the land use, and the municipal levels. 2. 4. 1 | Comparing the soil-based ESs maps at 1:1,000,000, 1:250,000, and at 1:50,000 In order to compare the maps of individual soil-based services and of their spatial co-occurrence, deviation maps showing the relative difference in ESs level were calculated according to  Huang (2017)  and  Zhang (2018).
This paper presents a new Remote Hyperspectral Imaging System (RHIS) embedded on an Unmanned Aquatic Drone (UAD) for plastic detection and identification in coastal and freshwater environments. This original system, namely the Remotely Operated Vehicle of the University of Littoral Côte d'Opale (ROV-ULCO), works in a near-field of view, where the distance between the hyperspectral camera and the water surface is about 45 cm. In this paper, the new ROV-ULCO system with all its components is firstly presented. Then, a hyperspectral image database of plastic litter acquired with this system is described. This database contains hyperspectral data cubes of different plastic types and polymers corresponding to the most-common plastic litter items found in aquatic environments. An in situ spectral analysis was conducted from this benchmark database to characterize the hyperspectral reflectance of these items in order to identify the absorption feature wavelengths for each type of plastic.
For this purpose, waste samples representative of the most-common marine litter items found in the coastal environment were collected. This collection contained different plastic types (HDPE, LDPE, PET, PP, PVC, PS, etc. ) and other materials such as wood, paper, rubber, and vegetation. Hyperspectral images of these waste items were then acquired by the RHIS system in laboratory-controlled conditions to build a benchmark database. RHIS provides high-spatial-resolution images and hyperspectral data cubes that cover the NIR (900 nm) to SWIR (1700 nm) range of the electromagnetic spectrum. From this database, an in situ spectral analysis was carried out to check the compliance of the spectra with the literature. Standard machine learning methods were then applied to evaluate the plastic waste recognition performance of our system.
The second section of the paper first presents the ROV-ULCO system with all its components, as well as the collected waste samples used in the experiments. This section describes how the waste samples are scanned by the ROV-ULCO to provide hyperspectral images of the proposed benchmark database. Two kinds of datasets were then derived from this database. The first dataset was constituted by the mean spectral reflectance computed over various parts of each marine litter sample observed under different conditions. This dataset was used to conduct an in situ spectral analysis in order to characterize each type of marine litter by a reference hyperspectral reflectance, as described in Section 3. This analysis aimed to compare the absorption features of each reference hyperspectral reflectance with the literature and thus confirmed which wavelengths were the most-efficient to discriminate the different plastic types.
The second dataset was a set of same-sized image patches manually selected from the whole hyperspectral images and labeled with the ground-truth of each available marine litter category. Section 4 presents the experiments conducted with this dataset in order to assess the ability of our original RHIS to recognize different categories of marine litter. In this section, several supervised machine learning models, such as K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), were trained on a set of training image patches. Then, the best-trained models were used to evaluate their performance on the testing image patches of the marine litter so that the testing patches were independent of the training ones to reproduce realistic conditions. Finally, the conclusion highlights that the ROV-ULCO is a promising approach to detect and identify plastic litter in aquatic environments.
Materials This section describes the materials used in this study. The new remote hyperspectral imaging ROV-ULCO system is first presented with all its components in Section 2. 1. The experimental setup to conduct the acquisitions with this system is then described in Section 2. 2. This setup aimed to create a benchmark hyperspectral image database of plastic litter in a controlled laboratory environment, which reproduces different real situations. This database is presented in Section 2. 3.
com/ Pika-IR, accessed on 28 March 2023). This line-scan imager collects data one line at a time, and a two-dimensional image is completed by assembling line-by-line the multiple line-images acquired successively as the object is translated. To obtain hyperspectral data, signals from each pixel of a line-image enter at the same time into a spectrometer, which provides the spectrum of incoming light intensity as a function of wavelength for every pixel of the image. The two-dimensional image thus-acquired can be interpreted as a stack of single-band grayscale images, called a data cube, where each image of the stack corresponds to a different wavelength. This hyperspectral camera is provided with the SpectrononPro software, version 3. 4. 4 (Spectronon software, Hyperspectral Software: https://resonon.
For the linear motion simulation, a linear translation stage (linear scanner) was used to move the black container (Figure  4 ). An embedded computer with the Spectronon Pro software was used to calibrate the camera, focus the objective lens, capture the hyperspectral data cubes, control the integration time and the frame rate of the camera, and drive the motor for the linear scanner. Although hyperspectral cameras are spectrally calibrated, they usually provide raw data, which need to be calibrated to obtain the absolute reflectance of the scanned objects. For this purpose, both the instrument sensor response and illumination functions were considered to correct the acquired images. This calibration, also named flat field correction, was, thus, performed by a dark correction followed by a response correction.
• Integration time: ti = 2. 049 ms; • Frame rate: F = 22 fps (frames per second). The speed of the linear translation stage was then adjusted to maintain a unity aspect ratio so that the observed objects were not distorted in the acquired images. We obtained hyperspectral images with the spatial resolution given by the ratio between the line of An embedded computer with the Spectronon Pro software was used to calibrate the camera, focus the objective lens, capture the hyperspectral data cubes, control the integration time and the frame rate of the camera, and drive the motor for the linear scanner. Although hyperspectral cameras are spectrally calibrated, they usually provide raw data, which need to be calibrated to obtain the absolute reflectance of the scanned objects. For this purpose, both the instrument sensor response and illumination functions were considered to correct the acquired images.
Benchmark Image Database In our study, plastic waste samples were collected from estuarine and coastal beaches along the Eastern English Channel French coast. In addition, some virgin plastics were used to expand the plastic library, which led to a set of plastic objects that contained and represented all plastic types. A categorized overview of these plastics objects and their types is shown in Table  1. They were divided into the following categories: (1) HDPE, (2) LDPE, (3) PET, (4) PP, (5) PVC, (6) PS, (  7 ) PolyURethane (PUR), (  8 ) PolyOxyMethylene (POM),  (9)  and ABS. A tenth category of non-plastic materials found in the aquatic environment (wood, vegetation, cardboard, clear seawater, etc. ) and named "Other" was also added. We can notice in Table  1  that the number of objects was different depending on the plastic type.
This variation was representative of the diversity of products made with each type of plastic. The polymer that constituted each type of plastic object presented in Table  1  was then identified by a Macro-Raman Spectrometer (MacroRAM, Horiba Scientific, France, Palaiseau) using a laser of a 785 nm wavelength with a power of 7-450 mW and a fixed grating of 685 gr. mm -1 with a spectral range from 100-3400 cm -1  [26, 27]. This spectrometer was equipped with a CCD detector for a spectral resolution of 8 cm -1 at 914 nm. The signal acquisition and processing were realized with Labspec software and its identification using the KnowItAll software (KnowItAll, BioRad ® ) and the free-access spectra libraries of Horiba (Raman-Forensic-Horiba) and SLoPP/SLoPP-E. These identifications served as a ground-truth to label the data of our benchmark waste hyperspectral image database.
To create a hyperspectral image database of real marine litter, we performed 39 acquisitions with all the plastic objects presented in Table  1  under several positions using the RHIS in a controlled environment. Similar to  [28] , two cases were studied: dry and wet objects. The same object was scanned three times under several positions (face up or face down, side up or side down, etc. ) and different views of its presence as an object floating (dry and wet) on seawater. We, thus, obtained a database of hyperspectral images which contained hyperspectral data cubes of the nine plastic types (HDPE, LDPE, PET, PP, PVC, PS, PUR, POM, ABS) and the "Other" category. Two different datasets were then derived from this benchmark image database in order to carry out the two experiments presented in Sections 3 and 4, respectively.
Spectral Reflectance Dataset Using the new RHIS and Spectronon Pro software, the reflectance spectra were defined as references to characterize and verify the spectral reflectance of each object category. For this purpose, each hyperspectral data cube image was visualized (Figure  5 ) and different Regions Of Interest (ROIs) were then selected with various sizes and numbers depending on the size of each object. Large-area objects allowed selecting a large size and/or a large number of ROIs, while small-area objects limited the size and/or the number of ROIs. Each ROI was labeled by its type of plastic or Other. For each waste sample presented in Table  1 , the following steps were applied: 1. Manual selection of a Region Of Interest (ROI) with a random size; 2. Computation and plotting of the hyperspectral reflectance spectrum of the selected ROI as the mean value over all pixels in the ROI; 3.
Finally, a dataset of 382 reference mean hyperspectral reflectance spectra was computed (104 for HDPE; 26 for LDPE, 34 for PET, 129 for PP, 18 for PVC, 22 for PS, 13 for PUR, 4 for POM, 9 for ABS, 23 for Other). An overview of this dataset can be found in the Supplementary Materials (Spreadsheet S1).
Plastic Litter Recognition Using Machine Learning This section aims to show that the new RHIS is able to automatically recognize the plastic type of the observed objects by hyperspectral image analysis. To evaluate the recognition performances, it was necessary to dispose of a ground-truth where the category of each analyzed data is known. From the benchmark database presented in Section 2. 3, a dataset of image patches was, thus, built, where each patch was labeled by a class of plastic or other (Section 4. 1). This dataset was then used to apply classical supervised machine learning methods in order to classify the images of waste samples (Section 4. 2). All calculations were performed with Matlab ® R2021b and a Windows 10TM computer with an Intel(R) Core(TM) i9-9880H CPU with 2. 30 GHz, 32 GB RAM, and an Nvidia ® Quadro RTX 3000 graphics card with 16 GB GDDR5X memory.
Waste Image Patch Dataset To build the waste image patch dataset, hyperspectral data cubes were extracted from the benchmark image database presented in Section 2. 3. Manually labelling all the pixels of these images was a laborious task, which was not easily feasible. Each hyperspectral data cube was, therefore, divided into patches of size 16 × 16 × 143. The small size of certain plastic objects (Figure  10 ) led us to choose this patch size. Representative patches of each type of waste were then manually selected from different objects present in the images. Each selected patch was finally manually labeled according to its class, namely its type of plastic or other. SWIR range centered on wavelengths of: 1148 nm, 1200 nm, 1212 nm, 1222 nm, 1400 nm, 1405 nm, 1420 nm, 1425 nm, 1445 nm, 1550 nm, and 1650 nm.
Plastic Litter Recognition using Machine Learning This section aims to show that the new RHIS is able to automatically recognize the plastic type of the observed objects by hyperspectral image analysis. To evaluate the recognition performances, it was necessary to dispose of a ground-truth where the category of each analyzed data is known. From the benchmark database presented in Section 2. 3, a dataset of image patches was, thus, built, where each patch was labeled by a class of plastic or other (Section 4. 1). This dataset was then used to apply classical supervised machine learning methods in order to classify the images of waste samples (Section 4. 2). All calculations were performed with Matlab ® R2021b and a Windows 10TM computer with an Intel(R) Core(TM) i9-9880H CPU with 2. 30 GHz, 32 GB RAM, and an Nvidia ® Quadro RTX 3000 graphics card with 16 GB GDDR5X memory.
Waste Image Patch Dataset To build the waste image patch dataset, hyperspectral data cubes were extracted from the benchmark image database presented in Section 2. 3. Manually labelling all the pixels of these images was a laborious task, which was not easily feasible. Each hyperspectral data cube was, therefore, divided into patches of size 16 × 16 × 143. The small size of certain plastic objects (Figure  10 ) led us to choose this patch size. Representative patches of each type of waste were then manually selected from different objects present in the images. Each selected patch was finally manually labeled according to its class, namely its type of plastic or other. In order to assess the machine learning model performances, two subsets of image patches representative of the ten classes were created: a training subset for the model learning and a testing subset for the model evaluation.
The training and testing image patches were extracted from different original images so that they were as independent as possible and represented a realistic situation in seawater with all its challenges. For the training and the testing subsets, a total of 788 and 312 image patches of size 16 × 16 × 143 were, respectively, selected from the images of the benchmark database. Table  2  shows, in the first column, the different considered classes (nine plastic types and one class "other"). The number of patches that were used for training and testing are shown in the second and third column, respectively. This number depended on the number and size of the available objects for each type of plastic. An overview of the representative patches counted per plastic object can be found in the Supplementary Materials (File S1).
Waste Image Patch Classification In this section, three well-known supervised machine learning methods are applied to classify the image patches of the dataset presented in the previous section. As can be observed in Table  2 , the number of examples for each class varied. Some classes were represented with a small number of samples for classification, and the difference between the number of patches for the plastic types PUR, POM, PS, and ABS, and the Other types was significant. A class imbalance usually makes it harder to identify (and, hence, classify) a minority class. In our case, the plastic types of PUR, POM, PS, and ABS were minority classes. Imbalanced classification is a challenge for predictive modeling because most machine learning algorithms used for classification are designed around the assumption of an equal number of examples for each class.
Remote Sens. 2023,15,  x FOR PEER REVIEW 13 of 26 similar with the absorption features located at the same wavelengths. The level of each spectrum along the reflectance axis can vary depending on the color and opacity of the plastic under consideration. Finally, a dataset of 382 reference mean hyperspectral reflectance spectra was computed (104 for HDPE; 26 for LDPE, 34 for PET, 129 for PP, 18 for PVC, 22 for PS, 13 for PUR, 4 for POM, 9 for ABS, 23 for Other). An overview of this dataset can be found in the Supplementary Materials (Spreadsheet S1).
Funding: This work was financially supported by the  European Union , the  European Regional Development Fund (ERDF) , the  French State , and the  French Region Hauts-de-France and Ifremer , in the framework of the project  CPER IDEAL 2021-2027. This work has been partially financially supported by " ANR-21-EXES-00 11 " as part of the IFSEA graduate school, which originates from  National Research Agency  under the  Investments for the Future program.
Positive selection drives genome dynamics in the first 250 generations of experimental evolution. To better characterize mutations occurring through the experiment, we deep-sequenced each ancestral strain as a reference and then each population at G250 and G1000. We speculated that mutations at G250 would correlate to the earlier increase in growth rate and the emergence of biofilm-formation capacity. Deep sequencing of the 12 populations at G250 revealed 128 mutations observed in more than 25% of the reads obtained from each culture (Data Set S1A). Among them, we found 45 different mutations among all populations. The number of fixed DNA changes varied from 2 to 21 between each individual population (Fig. 4A ). On average, each fixed mutation occurred in two or three populations, strongly indicating that they were under positive selection.