dataesr/scientific-paragraphs-categorization huggingface dataset:
We exclude stars near the edge of the detector or in pixel regions which do not behave like the bulk of the detector. Stars for which any raw counts are out of the linear range for PolyCam (<12,500 DN) are also rejected. Despite the significant defocus, stars are still well resolved and can be automatically detected, identified and measured. Fig. 4  Lightcurve data and models for Bennu on 2 November 2018. The blue crosses are the OSIRIS-REx observations with their associated 1-sigma photometric uncertainties. The black curve (best fit to peaks and minima) shows the YORP acceleration determined from fitting the Approach phase observations with 1-sigma uncertainties. The orange lightcurve using the previously reported acceleration value  34  does not fit as well as the black curve. The green curve assumes a constant rotation rate since 2009.
The inset plot shows the quadratic increase of rotation phase with time, consistent with rotational acceleration due to YORP. Source data are provided as a Source Data file unresolved companions, leaving 187 stars in our sample. In addition to being an open cluster, NGC 3532 is also a diffuse nebula, so we estimate and remove the local background of the nebula at each star. Given the panchromatic filter's 650 nm center wavelength, the integrated star flux is then compared to the R magnitude reported by the American Association of Variable Star Observers (AAVSO) NGC 3532 Standard Field catalog. The fit between the logarithm of measured DN/s and the AAVSO Catalog m R magnitudes is very good (R = 0. 998). When corrected to the OSIRIS-REx reference temperature that fit is: m R ¼ À2:5Log10ðDN=sðPolyCam T ref ÞÞ þ 18:2180 ð1Þ By design all OCAMS panchromatic filters have identical bandpasses.
Resulting parameters for the Lommel-Seeliger, as well as the IAU H,G, Muinonen H,G 1 ,G 2 and revised H,G 12 models are given in Supplementary Table  2. We fitted the v-band phase function data of Bennu with both the original and the revised H, G 12 models  29, 30  We used the implementation of both H, G 12 models in the photometry module of the Python package for small-body planetary astronomy sbpy that is currently under development  46. The non-linear fitting was performed with the Levenberg-Marquardt algorithm  47  as implemented in the fitting module in astropy, which is a community-developed core Python package for astronomy  48. Rotation rate of Bennu. We obtained photometric measurements over two full asteroid rotations (around 8. 6 h). We used the integrated flux from MapCam images by adding up the radiance from all of the pixels on Bennu to compute a lightcurve.
Acknowledgements This material is based upon work supported by  NASA  under Contract  NNM10AA11C  issued through the  New Frontiers Program. This work made use of sbpy (http://sbpy. org), a community-driven Python package for small-body planetary astronomy supported by  NASA PDART  Grant No. 80NSSC18K0987. A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. M. A. B. and S. F. acknowledge financial support from  CNES. We thank  Vishnu Reddy  for useful suggestions to improve the manuscript.
Data availability Raw through calibrated OCAMS images will be available via the Planetary Data System (PDS) (https://sbn. psi. edu/pds/resource/orex/). Data are delivered to the PDS according to the OSIRIS-REx Data Management Plan available in the OSIRIS-REx PDS archive. Higher-level products, such as co-added dust images, Bennu photometry, and phase function model solutions, will be available in the Planetary Data System one year after departure from the asteroid. The source data underlying Figs. 3  and 4  are provided as a Source Data file. Source data for Fig. 2  is also contained in Supplementary Table  1  and for Fig. 3  in Supplementary Table  2. The AAVSO Standard Field photometry for star cluster NGC 3532 can be found at https://www. aavso. org/apps/vsp/photometry/?east = right&fov = 30.
Geobacillus genus bacteria are Gram-positive, rod-shaped, aerobic or facultatively anaerobic, spore-forming thermophiles with an optimum growth temperature range of 55 to 65 °C1-3. By the end of September 2018, 20 species have been classified to Geobacillus genus with validly published names  4  , and in the NCBI database 85 Geobacillus genomes have been sequenced to unveil the genes with different potential biotechnological applications. Over the past century, Geobacillus and other thermophilic bacteria have extended our understanding of biochemistry under inhospitable environmental conditions. The Geobacillus spp. strains are irreplaceable microbial contributors to the biotechnological industry due to their valuable thermostable lignocellulolytic enzymes and other thermoactive bioproducts  5.
Fifteen genes were identified that encoded ABC transporters for secretion of EPS using Transporter Classification Database (TCDB) (Supplementary Table  S7 ). The presence of these ABC transporters suggests that the EPS export of WSUCF1 follows the ABC transporter-dependent pathway. Besides, the absence of the genes encoding Wzx flippase, Wzy polymerase, and polysaccharide synthase also indicates that WSUCF1 secretes EPS through ABC transporter-dependent mechanism. Using the genome information of WSUCF1, a preliminary model for the export process of EPS is depicted in Fig. 3.
Methods Bacterium strain and sequence. The draft genome of thermophilic Geobacillus sp. strain WSUCF1 which was sequenced and assembled in our previous work  41  was downloaded from the NCBI database. Sequencing data for strain WSUCF1 are available online as BioProject PRJNA192273, NCBI taxonomy ID 886559. The Whole Genome Shotgun project of strain WSUCF1 was deposited at DDBJ/EMBL/GenBank under the accession number ATCO00000000. Gene prediction and annotation. The genome of strain WSUCF1 was uploaded to the web annotation service Pathosystems Resource Integration Center (PATRIC; https://www. patricbrc. org/) 66 as well as Rapid Annotations using Subsystems Technology (RAST; http://rast. nmpdr. org/rast. cgi)  67 for automated annotation followed by manual scan. The PATRIC gene features were chosen as a basis for annotation.
The output from PATRIC was assessed and validated by comparing to that from RAST Server, and the analysis of genomic and metabolic pathways was performed using both PATRIC and RAST. These data sources were combined to assert product description for predicted proteins. The gene features of essential biosystems were also manually verified using BLASTp (https://blast. ncbi. nlm. nih. gov/Blast. cgi) against National Center for Biotechnology Information (NCBI) non-redundant database and re-annotated as necessary. The amino acid sequences encoded by annotated genes were compared with all proteins from complete microbial genomes, and the alignment length over 90% of its own length and over 60% match identity were selected as candidates. The best BLAST hit with highest alignment length percentage and match identity was assigned as the annotation of predicted gene  68.
The functions of essential enzymes were obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www. genome. jp/kegg/). Then the metabolic pathways related to EPS biosynthesis were reconstructed based on the functional annotation. The sequence alignment of the glycosyltransferases of strain WSUCF1 with priming GTs was performed using BLASTp (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The CRISPR/Cas systems in strain WSUCF1 were identified with CRISPR finder (http://crispr. i2bc. paris-saclay. fr/Server/). The glycosyltransferases for EPS biosynthesis were further identified using dbCAN2 meta server (http://cys. bios. niu. edu/dbCAN2/index. php)  69. The genes coding for ABC transporter involved in EPS export were verified using the similarity searches against the Transporter Classification Database (TCDB) (www.
Acknowledgements This research was supported by the  National Science Foundation in the form of BuG ReMeDEE initiative  (Award # 1736255 ) and the  Department of Chemical and Biological Engineering at the South Dakota School of Mines and Technology. Authors also gratefully acknowledge financial support from the  CNAM-Bio Center and "Proof of Concept " grant provided by the  South Dakota Governor's Office of Economic Development. Dr. K. M. Goh  was thankful to  Universiti Teknologi Malaysia GUP  grant  16H89.
1  (6, 9) ], as well as wild avocados of the West Indian variety (P. americana var. costaricensis). Persea shiedeana (the edible coyo), a species relatively closely related to P. americana  (10) , was also included (SI Appendix, Tables  S1  and S2 ). De novo and evidence-directed annotation revealed a similar number of protein coding genes in each genome: 22,441 from the Mexican race and 24,616 from Hass (Table  1 , SI Appendix, section 2, and Datasets S2 and S3). We next used the Benchmarking Universal Single-Copy Orthologs (BUSCO) software to estimate the presence of 1,440 conserved embryophyte single-copy genes  (11)  in the annotations, leading to estimated completeness percentages of 85% and 86. 3% for Hass and Mexican avocado, respectively (Table  1 ). The Mexican race was sequenced using the short-read, high-coverage Illumina sequencing platform, while the Hass genome was sequenced using the long-read Pacific Biosciences sequencing technology.
5). SNPs, Population Structure, and the Parentage of Hass. To study avocado from a population genomic perspective, we resequenced accessions of different races and cultivars and mapped the reads against the Hass reference genome assembly (SI Appendix, Table  S2 ). The estimated depth of coverage ranged from 3. 3 to 39×, with breadth of coverage between 70 and 92% (SI Appendix, section 3. 2). Given the uneven sequencing coverage, we used ANGSD to call SNPs across the entire (unanchored) genome assembly, followed by a stringent pruning based on per-site depth, minor allele frequencies, and linkage disequilibrium, that resulted in 179,029 high-quality SNP variants. Phylogenetic, principal component, and identity-by-state (IBS) analyses derived from this dataset (Fig. 1A  and SI Appendix, section 4) cluster the samples belonging to the Hass cultivar and Guatemalan variety into 2 groups as expected according to their genetic background.
S14  and Dataset S6). Furthermore, IBS clustering placed Hass intermediate between the Guatemalan and Mexican subpopulations, agreeing with the hybrid nature of this variety (SI Appendix, Fig. S16 ). To account for further evidence of admixture in the Hass reference genome, we used NGSAdmix (  14 ) modeling different possible numbers of source populations (K = 1 to 6) (SI Appendix, section 5 and Fig. S17 ). The Akaike information criterion (AIC) indicated K = 1 as the preferred model, reflecting poor population structuring within avocado as a whole. However, since we know Hass is admixed a priori, we chose the smallest (most parsimonious) K for which Hass admixture appears (K = 3). This criterion predicts the following 3 populations: 1) P. schiedeana, 2) the West Indian plus the Guatemalan varieties, and the 3) Mexican accessions (Fig.
1B ). Combining the IBS and NGSAdmix observations, we specifically calculated the contribution of Guatemalan and Mexican backgrounds into the Hass subpopulation. EIGMIX  (15)  revealed that the greatest admixture proportion, 61%, stemmed from the Mexican race (SI Appendix, Fig. S18  and Table  S8 ). Although based on ∼46% anchoring of scaffolds to chromosomes, we investigated chromosomewise signatures of admixture in the Hass genome (SI Appendix, section 5. 2). We calculated the f d , f dM , and d XY estimators of introgression and divergence (SI Appendix, Fig. S19  and Dataset S7) according to Martin et al. (16)  in nonoverlapping 100-kb windows, controlling the directionality of gene flow from the Guatemalan race into Hass versus the Mexican race into Hass, setting P. schiedeana as the outgroup and leaving Tiny Charly out of the Mexican subpopulation to avoid the bias this divergent accession could introduce into calculations (Fig.
2 , SI Appendix, section 5. 2 and Figs. S21 and S23-S34). Chromosome 4 illustrates these analyses well, demonstrating that a huge Guatemalan block, which could encompass an entire chromosome arm, is present in the Hass genome (Fig. 2A ). The length of this Guatemalan-derived block, uninterrupted by recombination, reflects the extremely recent hybrid origin of the cultivar. We also calculated the level of nucleotide diversity [π  (17, 18) ] in each population (Mexican, Guatemalan, and Hass), the F ST index  (19)  to determine regions of high differentiation between varieties, and Tajima's D  (20)  in order to evaluate any deviations from neutral evolution (SI Appendix, section 5. 3 and Dataset S7). We observed that Hass has the lowest nucleotide diversity (π = 0. 06) and very high Tajima's D in all chromosomes (genomic average of 1.
1 and Dataset S4). Phylogenetic trees based on 176 stringently filtered single-copy gene alignments (SI Appendix, section 7. 1 and Datasets S8 and S9) gave different results for amino acid versus inferred codon data. Based on protein sequences, avocado was resolved as sister to monocots plus eudicots (i. e. , branching before their divergence from each other; cf. refs. 28 and 29), whereas from coding sequences avocado was placed as sister to monocots only (cf. ref. 30) (SI Appendix, Figs. S40 and S41, respectively). In a different analysis we included Gnetum (a gymnosperm) and Selaginella (a nonseed plant) in orthogroup classification to generate a rooted species tree from all gene trees (4,694) that contained one or more (i. e. , paralogous) gene copies from all species (SI Appendix, section 7.
34  and also below). Rapid species divergences can lead to real genetree/species-tree discordances through enhanced occurrence of incomplete lineage sorting (ILS), wherein polymorphic allele states in ancestral populations do not have enough time to fix according to the species tree  (35) (36) (37). In an experimental approach to the problem, we further investigated the possible role of ILS using gene family turnover analysis as incorporated in BadiRate (38) (SI Appendix, section 7. 4). Trees with the 3 alternative placements of avocado were converted into time-calibrated ultrametric trees, and the likelihoods of duplicate gains versus losses were evaluated under 4 different branch models (SI Appendix, section 7. 4). The AIC clearly favored free-rates (FR) models, supporting heterogeneous rates of multigene family evolution across lineages (Dataset S10).
Interestingly, such uneven rates of gene turnover cannot be entirely explained by lineage-specific WGD/whole-genome triplication (WGT) events, given that FR models fit multigene family data better than WGD/WGT models alone. Additionally, allowing turnover rates to vary in each short branch (<10 My) also improved likelihood and AIC values, although the fit was still worse than under the FR model (Dataset S10). That FR models fit gene count data significantly better could be explained by their flexibility to accommodate variation that is not explicitly accounted for by current turnover models, such as gene copy variation within species. Intraspecific variation, segregating in an ancestral population, can be inherited differently by 2 splitting lineages, which will thus start diverging with a significant fraction of differentiation.
g. , refs. 46 and 47). The avocado genome provides no exception to this rule; we identified precisely these overrepresentation patterns among Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) categories for these different classes of gene duplicates, separated using the CoGe platform (SI Appendix, section 8). Among 2,433 total polyploid duplicates, "regulation of transcription, DNA-templated" was significantly overrepresented by 352 genes (Dataset S12). Enriched functions among tandem duplicates was highly illustrative of the secondary metabolic landscape particular to avocado (Dataset S12). We show that "phenylpropanoid biosynthesis" and closely related KEGG pathways (Dataset S13) are significantly enriched among tandem duplicates (P = 2. 08e-08; Fisher's exact test, Bonferroni-corrected).
274e-07), whereas polyploid duplicates did not show enrichment (SI Appendix, section 9). We interpret these results to indicate that tandem duplicates are the most dynamic component of the avocado duplicate gene space under pathogen treatment. We also examined functional enrichments for up-versus downregulated tandem duplicates (Dataset S16). The only significantly enriched category was xyloglucan:xyloglucosyl transferase activity (P = 0. 038984; Fisher's exact test, Bonferroni-corrected). Among genes with this annotation were 4 homologs of XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE 22 [XTH22; also known as TOUCH 4 (TCH4) (66)]. XTH22 and similar genes encoding cell-wall-modifying proteins have been shown to up-or down-regulate after Citrus Huanglongbing infection  (67) , whitefly infestation  (65) , and herbivore  (68)  or mechanical stimulation (69), the latter provoking a Botrytis-protective response.
Quantification tools Morphogenesis data result from the quantification of local tissue deformation rate and underlying cellular processes as described in  [Guirao et al. , 2015]. The similarity between two tensors is quantified by the standard Euclidian metric. The homogeneity of a quantity within a given region, i. e. the similarity between measurements of this quantity within a region, is measured by silhouette, a standard tool of cluster analysis. For a measurement of similarity between tissue segmentations, we use the Rand index, which indicates how well two data clusterings agree.
Region growing algorithm Algorithm 1 shows a pseudo code of the region growing image segmentation in Matlablike syntax. It divides a bitmap image stored in a data object dataMap. In the algorithm, a number of regions, a limit to update the seeds, and a metric are given as parameters. With the parameters, supporting objects seedList, meanList, regionsList, meter, and seeder are allocated and initialized. The seedList, meanList, and region-sList are instances of data object with a property var representing seeds and means of regions and regions, shared among the supporting objects. The meter is an object measuring distance between the mean of region and a point adjacent to the region. A method measure returns the distance measured by the given metric. The seeder is an object choosing seeds of regions.
Methods initalSeeds and initialMeans return indices of randomly chosen points and their values. Once the dataMap was divided into regions, methods newSeeds and newMeans return indices of points at center of the regions and mean values of the regions. A method initalQueue returns an array where its element represents a point adjacent to one of the seeds and holds the region and distance to the region's mean value. Inside a loop, a point in the queue with the smallest distance to the region's mean value is added to the region, and points adjacent to the point, returned by a method neighborsOfPoint of dataMap, are added to the queue. In our tissue segmentation, a Matlab custom function run region growing() iterates this algorithm for given time, returning a stack of resultant partitions.
Label propagation on a consensus matrix Algorithm 2 shows a pseudo code of the label propagation. It divides N objects into clusters based on an N × N consensus matrix M whose rows and columns correspond to the objects, and an element m ij represents frequency the i-th and j-th objects were included in a cluster among given clustering results. A parameter t M indicates a threshold value, where elements in M smaller than t M is ignored in the label propagation. In the tissue segmentation, 50 results of region growing were converted to the consensus matrix and given to a Matlab custom function run label propagation() implementing the label propagation. Number of resultant regions is influenced by t M , and thus a Matlab custom function run cm thresholding lp() screens t M value so that run label propagation() returns the same number of regions with the given partitions.
Cellular Potts model Algorithm 3 shows a pseudo code of the cellular Potts model. It simulates a deformation of regions (partition of dataMap) by giving small fluctuations. In the algorithm, an array of function handles, coefficients to combine the functions results, the system temperature, and number of label updates are given as parameters. With the regions and parameters, supporting objects analyser and dict are allocated and initialized. The functions in the array calculate system energy with the analyser and dict. For each fluctuation, one of points at regions rim returned by analyser rim points is selected randomly, and a label of neighboring points is also selected randomly and copied. Connectedness of a region is checked locally, with a coordinate of neighboring points returned by dataMap coordinates.
Materials and methods A total of thirty-seven strains of species, subspecies and phylogroups related to K. pneumoniae were included (Table  1 , Fig. 1 ). Strain 1266 T (internal strain bank identifier, SB5531) was chosen as reference strain for phylogroup Kp5, whereas strain 200023 T (internal strain bank identifier, SB5857) was included as reference strain of Kp7 phylogroup. The genome (Accession Number ERS214332) of the strain 38679  [13]  was included for phylogenetic comparisons even though the strain was not available at the time of this study. Strains were grown in tryptocasein soy agar (TSA) (BioRad, Marnes-La-Coquette, France). The biochemical characterization of the isolates was performed using API20E strips (BioMérieux) and Biolog phenotype microarrays plates PM1 and PM2 (Hayward, CA) following the protocol described by Blin et al.
[3]. To extract the DNA, bacteria were plated on TSA and one colony was grown overnight with shaking at 37°C in 10 ml tryptocasein soy broth. DNA was extracted using the Wizard Genomic DNA purification kit (Promega, Charbonnières-les-Bains, France) and stored at -20°C. Genomic sequencing was performed using NextSeq-500 instrument (Illumina, San Diego, USA) with a 2 x 150 nt paired-end protocol. Contig sequences were assembled using SPAdes v3. 10. 0  [14]  and annotated with Prokka v1. 12  [15]. JSpeciesWS  [16]  was used to calculate the BLAST-based average nucleotide identity (ANI). The in silico DNA-DNA hybridization (isDDH) was estimated using the GGDC tool (http://ggdc. dsmz. de; formula 2)  [17]. Barrnap (https://github. com/tseemann/barrnap) was used to detect and extract 16S rRNA sequences from genome assemblies, whereas internal portions of genetic markers (fusA, gapA, gyrA, leuS, rpoB) were detected and extracted using BLAST and compared with previously reported sequences  [4].
Chromosomal beta-lactamases were also extracted, and the new LEN and OKP amino-acid sequence variants were submitted to the Institut Pasteur MLST nomenclature database (https://bigsdb. pasteur. fr/klebsiella) for variant number attribution, and to NCBI for accession number attribution. Phylogenetic analyses based on the 16S rRNA gene sequence, concatenation of five housekeeping genes (2,609 aligned bp in total) and chromosomal betalactamases were performed using MEGA v7. 0 with the neighbor-joining method. Genetic distances were estimated using the Jukes-Cantor substitution model in the case of nucleotide sequences and using the Jones-Taylor-Thornton (JTT) model for beta-lactamase protein sequences. For genome-based phylogenetic analysis, a phylogenetic tree was reconstructed from the concatenation of 1,703 genes defined as core genes using Roary v3.