dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Samir Alliouane  and the divers and boat crew of the  Institut de la Mer de Villefranche  for oceanographic sensor maintenance and mussel collection from the coastal site. We thank  Remy Valdes ,  Behzad Mostajir , and  Sébastien Mas  from the  Mediterranean Platform for Marine Ecosystem Experimental Research  for their assistance with sensor deployment and mussel collection at the lagoon. We thank  Angelica Miglioli  for extensive experimental assistance and the  University of Chicago Genomics Facility  for sequencing assistance. We thank  Nicholas VanKuren  for insightful feedback on the manuscript. Last, we thank three anonymous reviewers for numerous insightful comments and suggestions on the manuscript. M. C. B. was supported by a  National Science Foundation   Graduate Research Fellowship Program grant  ( 1746045 ) and a  Department of Education  grant ( P200A150101 ).
Data and Code Availability All raw data and referenced supplemental files in this article have been deposited in the Dryad Digital Repository (https://doi. org/10. 5061/dryad. tqjq2bvxc; Bitter et al. 2020). All code associated with statistical analyses and figure generation for this article are publicly available at GitHub (https://github. com/MarkCBitter/pHFluctuation_Plasticity) and Zenodo (https://doi. org/10. 5281/zenodo. 4306829; Bitter 2020).
7%) being present in all three extracted fractions. The E fraction contained 413 unique analytes (16. 9% of the total analytes detected), while fraction IW had 191 (7. 8%) and fraction IM had 45 unique ions representing 1. 8% of the total. It should be noted that a greater number of common analytes were observed in the intracellular compartment between IW and IM (609, 24. 9%), compared to the IM and E fractions, which shared only 194 analytes (7. 9%), and the IW and E fractions, which shared only 141 ions (5. 8%). Further fragmentation experiments were performed on these three cellular fractions using LC-MS/MS positive and negative ionization modes. Out of a total of 2441 analytes detected by simple LC-MS experiments, 1530 could have been analyzed by LC-MS/MS. These MS/MS fragmentation data were used for metabolite annotation (Table  S2 ) and molecular network construction (Figure  2d ).
To date, more than 90 variants of microginins have been referenced in databases [25], isolated mainly from cyanobacteria of the genus Microcystis and Planktothrix but also some cyanobacteria belonging to the genus Nostoc  [26]. Measuring the metabolome variations over a culture period of 28 days under different experimental conditions revealed that time of culture was the main driver controlling the relative composition of both intra-and extra-cellular contents of Aliinostoc sp. PMC 882. 14. The samples corresponding to the different points of the time series were discriminated along with Component 1 for samples ranging from D0 to D14, then along with Component 2 for later samples (Figure  4 ). This progressive temporal change of the intracellular metabolite contents across two distinguishable phases (D0-D14 and D15-D28) was in perfect correspondence with the growth phases (exponential phase from D0 to D14, then stationary phase from D15 to D28) observed from monitoring growth of the cultures (Figure  3  and Figure  S2 ).
Biomass Dynamics Cyanobacterial biomass was followed using absorbance at 750 nm by in vivo spectrophotometric (Agilent Cary 500; Santa Clara, CA, USA), chlorophyll-a concentration  [35]  and cell abundance by Malassez' cell counts  [36]. Data have been transformed into a Neperian logarithm, and statistical analysis between treatments and the control (ANOVA with Bonferonni's correction) was conducted with GraphPad Prism 5. 01 software.
Simple MS Mode Analyses Compounds were initially analyzed in simple MS positive and negative modes, without quadrupole fragmentation. This mode of analysis notably allows observing a signal intensity of each ion (measured in number of detected counts over the time range necessary for its elution) that is directly proportional to the quantity of ions present in the different samples. This analysis was carried out in order to compare metabolites composition in the three extraction fractions on the pre-culture (representing a total 2441 intracellular analytes extracted with acidified water or methanol and extracellular analytes, when data were merged) and in the samples collected from the experimentation (for either extraor pooled intra-cellular fractions, analyzed on positive mode only). MS data were processed using MetaboScape 4.
MS/MS Mode Analyses For qualitative investigation of metabolites, additional ion selection with the quadrupole and fragmentation was carried out in tandem by collision ion dissociation (CID) according to the autoMS/MS analysis, performed in positive mode. The ions with the highest intensities in single MS (MS1) were then selected by the quadrupole and fragmented in a collision cell. The resulting ions of the fragmentation of their respective parent were transferred and detected. This series of MS/MS datasets containing a fragment list of all selected ions allow qualitatively the characterization of the diversity of the corresponding metabolites produced by Aliinostoc sp. PMC 882. 14. through the comparison of the fragmentation profiles. The files containing the fragmentation information for each ion analyzed were exported in mgf format using MetaboScape 4.
0 (Bruker, Bremen, Germany) software before being used for the generation of the molecular network of structural similarity. Metabolites annotations were made from MS/MS data (mgf file) by generating a molecular network for the comparison of fragmentation profiles using the MetGem software (version 1. 1. 2) and the GNPS algorithm  [37]. Public and generalist spectral databases GNPS library, NIH Clinical collections, and EMBL metabolomics were used. Annotations were supplemented by a match with the CyanoMetDB database  [25, 38] , which contains all the raw formulae of the 2100 cyanobacterial metabolites already described [25].
Acknowledgments: We would like to thank the  UMR 7245 MCAM ,  Muséum National d'Histoire Naturelle, Paris, France  for laboratories facilities and the  Thermes de Balaruc-les-Bains  for founds. This work was supported by the  "Société Publique Locale d'Exploitation des Thermes de Balaruc-les-Bains (SPLETH) , the town of Balaruc-les-Bains and the  National Muséum of Natural History. The authors also would like to thank  Gary Bentley  for English language editing.
Metabolites 2021 , 16 Figure 2. 2021162 Figure 2. Metabolic composition of the three extraction fractions (triplicates) illustrated by (a) a heatmap with hierarchical classification, (b) a principal component analysis, (c) a Venn diagram, and (d) a molecular network representation of the molecules detected in each one of the three extraction fractions. E, extracellular; IW, intracellular-water; IM, intracellularmethanol. On the molecular network, the dots represented the different ions analyzed in autoMS/MS (merged positive and negative mode dataset). The colors of the dots corresponded to the fraction in which the analytes have been detected. The annotations were made with the MetGem 1. 2. 1 software (red and green dotted circles) or with the GNPS web tool (blue dotted circle) using public spectral databases.
In this paper, we consider threechannel images combined of visible range components of Sentinel-2 images  [25]. The main reason we consider separate compressions of component images with central wavelengths 492 nm (Blue), 560 nm (Green), and 665 nm (Red) of Sentinel-2 data (https: //www. esa. int/Applications/Observing_the_Earth/Copernicus/Sentinel-2, accessed on 27 October 2021) is that they have the equal resolution that differs from the resolution of most other (except NIR-band) component images (in other words, we assume that Sentinel-2 images can be compressed in several groups, taking into account different resolutions in different component images, namely, 10 × 10, 20 × 20, and 60 × 60 m 2 ). Besides, earlier, discrete atomic compression (DAC) was designed for color images (i. e. , for three-channel image compression).
Since component images in the visible range of multispectral Sentinel-2 data have a high signal-to-noise ratio (that corresponds to noise invisibility in case of image visual inspection), we further consider these images noise-free. We assume that available computational resources, preference of mathematical tools to be used for compression are not of prime importance. Meanwhile, we can state that the DAT-based compression analyzed below possesses high computational efficiency  [57]. Aggregating all these, we concentrate on the following: • DAT is used as the basis of compression and we are interested in the analysis of its performance since it is rather fast, allows providing privacy of data, and has some other advantages  [40, 41, 47, 57 ];.
Discrete Atomic Compression of Test Data In the current research, we used 100 digital images (see Figure  7 ) of the European Space Agency (ESA). They were downloaded from ESA official site: https://www. esa. int/ESA_ Multimedia/Images, accessed on 27 October 2021. In addition, these test data are available at the link to Google-drive folder (here, RGB-images in BMP-format, short information about them, and tables with results of their processing can be found): https://drive. google. com/drive/folders/1PSld3GqFQJYfrNs_b4uaxieY4m3AlwW2?usp=sharing, accessed on 27 October 2021. One of the principal features of the test data applied is the presence of a great number of small details and sharp changes of color (Figure  8a ). In other words, mostly images with complex content are used. However, some o them contain domains of relatively constant color in combination with smooth colo changes (Figure  8b ).
For testing purposes, it can be used in situ datasets too. These data can be collecte during different land surveys, for example along the roads (Figure  9 ), and can be espe cially useful for crop classification. During data preprocessing, it could be prepared sma polygons, which can be some representation of different homogeneous land cover classe (a) (b)  In other words, mostly images with complex content are used. However, some of them contain domains of relatively constant color in combination with smooth color changes (Figure  8b ). For testing purposes, it can be used in situ datasets too. These data can be collected during different land surveys, for example along the roads (Figure  9 ), and can be especially useful for crop classification. During data preprocessing, it could be prepared small polygons, which can be some representation of different homogeneous land cover classes.
In other words, mostly images with complex content are used. However, some of them contain domains of relatively constant color in combination with smooth color changes (Figure  8b ). For testing purposes, it can be used in situ datasets too. These data can be collected during different land surveys, for example along the roads (Figure  9 ), and can be especially useful for crop classification. During data preprocessing, it could be prepared small polygons, which can be some representation of different homogeneous land cover classes. (1) fix the structure of DAT and its depth in the case of DAT2; (2) fix parameters { } [Y] [Cr] [Cb] i, j i, j i, j , , δ δ δ and compute UBMAD; (3) for each test image perform the following: compress the current image; In this paper, Tables with the results obtained are not given due to their huge size (these data are presented in files Efficiency_indicators.
In this paper, Tables with the results obtained are not given due to their huge size (these data are presented in files Efficiency_indicators. pdf, ESA_data_DAT_1. xlsx, and ESA_data_DAT_2. xlsx, which are available at the link to Google-drive folder https://drive. google. com/drive/folders/1PSld3GqFQJYfrNs_b4uaxieY4m3AlwW2?usp=sharing, accessed on 27 October 2021). Further, we present their analysis. First, we study the correlation of quality loss metrics RMSE, PSNR, and MAD. Since PSNR is a function of RMSE, it is sufficient to investigate the dependence of RMSE on MAD. In Figure  10 , scatter plots of RMSE vs. MAD are shown. In addition, we have computed Pearson's correlation coefficient R and Spearman's rank-order correlation coefficient ρ  [63]. Moreover, using the least-square method  [63] , linear regression equations have been constructed.
Analysis of Classification Characteristics Classification accuracy depends on many factors including a used classifier and its parameters, methodology of its training, image properties, and compression parameters. The classifier type, its parameters, and methodology of its training are fixed. Two images of different complexity will be analyzed in this Section. The main emphasis here is on the impact of compression parameters. Let us start by considering the simpler structure image (Figure  12a ). Let us analyze more in detail confusion matrices for compression with DAT of depth = 1 for different MAD values. The obtained results are presented in Table  6. Analysis of data in Table  6  shows the following. First, classes are recognized with sufficiently different probabilities. The class Water is usually recognized in the best way although this is not the case for MAD = 16.
03. Certainly, these two examples are not enough to obtain full imagination of classification accuracy of compressed images and give some final recommendations. Besides, classification results usually depend on a classifier applied. To partly move around these shortcomings of previous analysis, we have carried out additional experiments for other Sentinel-2 images of different complexity, the Landsat image, and a neural network classifier. The obtained data are briefly presented below (a more detailed data can be found at the following link to the Google drive folder: https://drive. google. com/drive/folders/14 m7TLLM7o836yGzJo9NKlaUc9sLsL5VK?usp=sharing, accessed on 27 October 2021). The experiments have been performed for 512 × 512 pixel fragments of Sentinel-2 and Landsat images (Figure  20 ).
-Lossy compression based on DAT is controlled by UBMAD but we have got approximations that allow recalculating UPMAD to MAD, RMSE, and PSNR and, thus, providing the desired quality of compressed images quite accurately and without iterations; this is a useful property especially if compression should be performed quickly, e. g. , onboard of satellite or airborne carrier with compressing large volumes of RS data; -classification results (obtained for ML classifier) for lossy compressed data depend on image complexity; for the image of low complexity, lossy compression has a low negative impact on classification accuracy if MAD is less than 35 (PSNR is larger than about 34 dB); for the image of high complexity, lossy compression for the same conditions might lead to a reduction of total probability of correct classification by about 3% that seems to be acceptable for practice.
Acknowledgments: The authors gratefully acknowledge the funding received by the  National Research Foundation of Ukraine  from the state budget  2020/01. 0273 " Intelligent models and methods for determining land degradation indicators based on satellite data " and  2020. 01/0268 " Information technology for fire danger assessment and fire monitoring in natural ecosystems based on satellite data" (NRFU Competition  " Science for human security and society ") and  2020. 02/0284 " Geospatial models and information technologies of satellite monitoring of smart city problems" (NRFU Competition "Leading and Young Scientists Research Support "). The research performed in this manuscript was also partially supported by the  French Ministries of Europe and Foreign Affairs (MEAE)  and  Higher Education, Research and Innovation (MESRI)  through the  PHC Dnipro 2021 project  n  • 46844Z.
Background: Streptococcus intermedius, a member of the S. anginosus group, is a commensal bacterium present in the normal microbiota of human mucosal surfaces of the oral, gastrointestinal, and urogenital tracts. However, it has been associated with various infections such as liver and brain abscesses, bacteremia, osteo-articular infections, and endocarditis. Since 2005, high throughput genome sequencing methods enabled understanding the genetic landscape and diversity of bacteria as well as their pathogenic role. Here, in order to determine whether specific virulence genes could be related to specific clinical manifestations, we compared the genomes from 27 S. intermedius strains isolated from patients with various types of infections, including 13 that were sequenced in our institute and 14 available in GenBank.
The development of high throughput nucleic acid sequencing technologies has enabled observing variations of the genetic repertoire among strains of a given bacterial species. Our present study analysis aimed at describing the genetic diversity and pathogenesis substratum of S. intermedius. Twenty-seven genomic sequences from S. intermedius strains, including 13 newly sequenced from our laboratory and 14 from public databases, were used for pan-genomic analysis. Predicted genes were compared among strains to determine the size of the core and dispensable gene pools, the pangenome, the gain/loss of putative virulence determinants, and to identify genomic islands.
Genome assembly, annotation and comparison After sequencing, the obtained reads were assembled using the A5 software  [12]  with default parameters and then contigs were compared to NCBI using BLASTn to remove contaminations. Then, the online tool Fasta dataset joiner (http://users-birc. au. dk/biopv/php/fabox/ fasta_joiner. php) was used to merge sequences into a single molecule. The Mauve software was used for multiple genomic sequence alignment  [13]. Genes were annotated using the Prokka software with default parameters  [14]  in which the similarity e-value cut-off is 0. 000001 and the minimum contig size is 200 bp. This pipeline also includes several other tools like Aragorn for tmRNA detection, Barnap to count rRNAs and Prodigal to identify coding sequences. To estimate the mean level of sequence similarity at the genome level among studied strains, we used the OrthoANI  [15]  and Genome-to-Genome Distance Calculator (GGDC)  [16]  softwares, with the following respective threshold values of 95-96 and 70 %.
Phylogenetic analysis A 16 S rRNA-based phylogenetic analysis of the 27 studied S. intermedius strains was performed using the   [17]. For constructing the phylogenetic tree, the following options were used: Maximum Likelihood method; Kimura 2-parameter model for substitution model; uniform rates among sites; partial deletion option for gaps/missing data; 1000 bootstrap replicates. Using genomic sequences and the Roary program  [18] , a clustered heatmap of core genes was drawn on the basis of the presence/absence approach  [18]. We also detected SNPs with the snp-sites program  [19]  from the core genome alignment and drew a phylogenetic tree with CGEwebface  [20].
Virulence factor analysis Virulence-associated genes were detected by comparing studied genomic sequences with the virulence factor database (VFDB)  [21]  and sequences described in recent publications  [22]. The BLASTp search was performed using the threshold scores reported by Olson et al. : 35 % identity and highest scoring pair length of 50 %  [22]. Additionally, we reviewed the literature to identify the proteins involved in interactions with the host  [10, 23, 24]. A principal component analysis was performed using the XLSTAT program (Data Analysis and Statistical Solution for Microsoft Excel, Addinsoft, Paris, France 2017) in which the Fisher's least significant difference (LSD, α = 0. 005) and Pearson's correlation coefficients were used, to detect any association of virulence-associated genes with specific clinical conditions.
Functional classification of orthologous cluster analysis The Clusters of Orthologous Groups (COGs) database was used to identify gene functions  [28]  using BLASTP (E-value 1e -03 , coverage 0. 7 and identity percent 30 %). A circular comparison of genomes was obtained using the online GView Server (https://server. gview. ca/) with S. intermedius strain ATCC 27,335 as reference genome  [29]. ResFinder and the ARG-ANNOT database were used to search antibiotic resistance-related markers  [30, 31]. The presence of CRISPR repeats and prophages was predicted using the CRISPRFinder  [32]  and PHAS TER softwares, respectively  [33].
Strain characterization The 27 studied S. intermedius strains originated from China, Canada, South Korea, US, Japan and France. The patients' data was not available for some strains. The 13 French strains (G1552-G1557 and G1562-G1568, Tables  1  and 2 ) were isolated in our laboratory from patients with various infections (Table  2 ), from August 2014 to November 2016, on 5 % sheep blood-enriched Columbia agar (BioMérieux) at 37 °C in anaerobic atmosphere. Their identification was confirmed by the high scores (> 2) obtained using MALDI-TOF MS. In addition, 14 S. intermedius genome sequences were retrieved from GenBank. The 27 strains were divided into 8 groups according to their isolation source (Table  2 ). The genome sizes and gene numbers among S. intermedius strains were relatively similar, consisting for each strain in a single chromosome but no plasmid was identified in any strains and ranging in size from 1.
Functional classification of orthologous cluster The overall distribution of S. intermedius proteins in COG categories was quite similar in all 27 studied strains (Fig. 5 ). Previous studies of other Streptococcus species also suggested that, within a given species, the majority of strains had a similar COG profile  [36] [37] [38]. Approximately 79. 72 % of all proteins predicted in all strains were identified in COG superfamilies. The Less than half of strain-specific genes, but more than 90 % of core genes, had a match in the COGs database. The most abundant functions in core genes were associated with metabolism (Fig. 5a ). The overall proportion of metabolic functions in core genes was 32. 47 %, whereas that in strain-specific genes was 9. 58 %. More specifically, energy production and conversion (C), amino acid transport and metabolism (E), nucleotide transport and metabolism (F), carbohydrate transport and metabolism (G) and coenzyme transport and metabolism (H) were noticeably more abundant in core genes (p-value < 0.
intermedius by playing a key role in attachment to host cells  [40] ; the hyaluronidase (hysA) acts in the liquification of tissues and is also involved in biofilm formation, which protects bacteria from host defenses and antibiotics, and plays a role in infection  [9] ; the ily-coded intermedilysin can directly damage host tissues and immune defense cells, causing human cell death by membrane bleb formation  [23]. It has been also reported that intermedilysin helps in invasion and adhesion of bacteria to human liver cells, and in cytotoxicity  [41] ; the galE gene codes galactose which plays a role in biofilm formation and its key residues are essential for epimerase activity  [42] ; the   Amino acid identity values were obtained by comparing each gene from each strain to the VFDB database  [21]  laminin-binding surface protein, homologous to that in Streptococcus agalactiae is coded by the Pac gene and is essential in binding and invasion of different host surfaces, and is present in almost all group B Streptococcus strains causing pneumonia, septicemia and meningitis  [43, 44] ; psaA codes a surface lipoprotein that plays a role in Streptococcus pneumoniae systemic infections by interacting with monocytes  [45] ; we also identified the heat shock protein-coding gene htbB that is known in Legionella pneumophila to act in adhesion to host fibronectin  [46] ; the clpC gene codes a heat shock protein involved in the invasion of hepatocytes in Listeria monocytogenes and has an ATPase activity  [47] ; ATPase proteins were shown to play a role in the survival and virulence in Salmonella typhimurium and S.