dataesr/scientific-paragraphs-categorization huggingface dataset:
5a ) and have no similarities with transposable elements. While RG motifs are often found in eukaryotic RNA-binding proteins  28  , P. macacae RGs do not possess an RNA-binding domain (Pfam domains PF00076, PF08675, PF05670, PF00035), suggesting a different role. In addition, P. macacae RGs lack functional annotation except for two proteins that encode a Dolichol-phosphate mannosyltransferase domain (PF08285) and a leucine zipper domain (PF10259), respectively. Of the 190 RGs, 134 have RNA-Seq based gene expression support, including five among the top highly expressed genes (Supplementary Fig. 5b ). Nearly half of RGs are located at subtelomeric regions, often found in close proximity to msg genes (Supplementary Data 1). RG proteins can be grouped in three main clusters (based on OrthoFinder clustering; Methods), have a reticulate phylogeny (Supplementary Fig.
High molecular weight genomic DNA fragments were isolated using the BluePippin (Sage Science) with the high-pass filtering protocol. A DNA library was prepared using the rapid Sequencing kit (SQK-RAD0004) from Oxford Nanopore Technologies (Oxford, UK) and loaded in the MinION sequencing device. Host reads were removed by mapping to the Rhesus macaque genome (NCBI accession number GCF_000772875. 2_Mmul_8. 0. 1) using Minimap2 v2. 10 43. Unmapped reads were aligned to the draft version of P. macacae assembly built previously using Illumina data (Supplementary Methods) with ngmlr v0. 2. 7  43. A total of 1,633,376 Nanopore reads were obtained, of which ~5% were attributed to Pneumocystis (27-fold coverage), which is much less than the 68% based on Illumina data (Supplementary Table  2 ). This suggests that many P.
macacae genomic DNA fragments were too short to pass the size selection filter. Pneumocystis Nanopore reads were assembled using Canu v. 1. 8. 0  44  , overlapped with the assembly using Racon v. 1. 3. 3  45  and polished with Pilon v1. 22  46  using the Illumina reads aligned with BWA MEM v0. 7. 17  47. Illumina RNA-Seq of the P. macacae sample P2C yielded 22 million reads, of which ~92% were attributed to Pneumocystis (Supplementary Table  2 ). Filtered reads were mapped to the P. macacae assembly using hisat2 v2. 2. 0  48  , sorted with SAMtools v1. 10 49 and filtered with PICARD v2. 1. 1 (http://broadinstitute. github. io/ picard). De novo transcriptome assembly of filtered reads was performed with Trinity  50. Quantification of transcript abundance was performed using Kallisto v0. 46.
1  51. P. wakefieldiae (2A) and P. murina RNA-Seq data were processed similarly (Supplementary Table  2 ). DNA transposons, retrotransposons and low complexity repeats were identified using RepeatMasker  52  , RepBase  53  and TransposonPSI (http://transposonpsi. sourceforge. net). Pneumocystis telomere motif "TTAGGG" 16 was searched using "FindTelomere" (available at https:// github. com/JanaSperschneider/FindTelomeres). The genomes of P. carinii strain Ccin  14  and strain SE6  12  were scaffolded with Satsuma 54 using the P. carinii strain B80 as reference genome  13. P. macacae, P. oryctolagi, P. canis Ck1, P. canis A, P. wakefieldiae, and P. carinii (strains Ccin and SE6) genome assemblies were annotated using Funannotate v1. 5. 3 (https://doi. org/10. 5281/zenodo. 1134477). The homology evidence consists of fungal proteins from UniProt  55  and BUSCO v9 fungal proteins  40.
For P. macacae and P. wakefieldiae, RNA-Seq mapping files (BAM) and de novo transcriptome assemblies were used as hints for AUGUSTUS  56. Ab initio predictions were performed using GeneMark-ES  57. All evidences were merged using EvidenceModeler  58. Taphrina genomes (T. deformans, T. wiesneri, T. flavoruba, and T. populina  32, 59  ) and P. canis Ck2 genome were annotated using MAKER2  60  because predicted gene models showed a better quality than those obtained from Funannotate. MAKER2 integrates ab initio prediction from SNAP  61  , AUGUSTUS built-in Pneumocystis gene models  62  and GeneMark-ES as well as BLAST-based homology evidences from a custom fungal proteins database. GPI prediction was performed using PredGPI  63  , big-PI  64  and KohGPI  65. Signal peptide leader sequences and transmembrane helices were predicted using Signal-P version 5  66  and TMpred  67  , respectively.
Protein domains were inferred using Pfam database version 3. 1  68  with PfamScan (https://bio. tools/ pfamscan_api). PRIAM  69  release JAN2018 was used to predict ECs using the following options: minimum probability > 0. 5, profile coverage > 70%, check catalytic-TRUE and e-value < 10 -3. Pneumocystis mitochondrial genome assembly and annotations are presented in Supplementary Methods. Three dimensional (3D) protein structure prediction of Msg proteins was performed using DESTINI  70  and visualized with PyMol (www. pymol. org). Comparative genomics. All genomes were pairwise aligned to the P. jirovecii strain RU7 genome NCBI accession GCF_001477535. 1  13  using LAST version 921  22  with the MAM4 seeding scheme  71. One-to-one pairwise alignments were created using maf-swap utility of LAST package and merged into a single multispecies whole-genome alignment using LAST's maf-join utility.
Phylogenetic network for Msg protein families was inferred using SplitTree  94. The detection of putative mosaic genes was performed using TOPALi v2. 5  95. Phylodating. Single-copy orthogroup nucleotide sequences were aligned using MACS v0. 9b1  96. Highly polymorphic msg sequences were excluded using BLASTn  79  with an e-value of 10 -5 as cutoff against 479 published msg sequences  13. We inferred the divergence timing using two datasets: (1) 24 single-copy nuclear gene orthologs shared by all Pneumocystis and S. pombe; and (2) 568 nuclear genes found in all Pneumocystis species. BEAST inputs were prepared using BEAUTi v2. 5. 1  97. Unlinked relaxed lognormal molecular clock models  98, 99  and calibrated birth-death tree priors  100  were used to estimate the divergence times and the credibility intervals.
The substitution site model HKY was applied  101. Three secondary calibration priors were used: (i) P. jirovecii/P. macacae divergence with a median time of 65 mya as 95% highest posterior density (HPD)  5  ), (ii) the emergence of the Pneumocystis genus at a minimum age of 100 mya  4  , and (iii) the Schizosaccharomyces-Pneumocystis split at ~467 mya  102. We sampled from various priors and found a minor difference between the marginal posterior distribution on rate and the marginal prior distribution. This indicates that the posterior simply reflects the prior. For the dataset 2, the 568 gene alignments were concatenated in a super alignment with 568 partitions, with each partition defined by one gene. Gene partitions were collapsed using Parti-tionFinder v2. 1. 1  103  with the "greedy" search to find optimal partitioning scheme.
The alignment was split in three partitions in BEAST. Three independent runs for each dataset were performed separately for 60 million generations using random seeds. Run convergence was assessed with Tracer v1. 7. 1 (minimum effective sampling size of 200 with a burn-in of 10%). Trees were summarized using TreeAnnotator v. 2. 5. 1 (http://beast. bio. ed. ac. uk/treeannotator) and visualized using FigTree v. 1. 4. 4 (http://tree. bio. ed. ac. uk/software/figtree) to obtain the means and 95% HPD. Host divergences were obtained from the most recent mammal tree of life 6 , available at http://vertlife. org/data/mammals. The dating of fungal gene families was performed similarly. Phylogenetic trees with geological time scale were visualized using strap version 1. 4  104. Population genomics. Sequence data sources and primary statistics are presented in Supplementary Table  2.
Acknowledgements This work has been funded in whole or in part with federal funds from the  Intramural Research Program  of the  US National Institutes of Health (NIH) Clinical Center  and the  National Institute of Allergy and Infectious Diseases (NIAID). This study used the  Office of Cyber Infrastructure and Computational Biology (OCICB) High Performance Computing (HPC ) cluster at the  National Institute of Allergy and Infectious Diseases (NIAID), Bethesda, MD. This study also utilized the high-performance computational capabilities of the Biowulf Linux cluster at the  NIH, Bethesda, MD  (http://biowulf. nih. gov). The content of this publication neither necessarily reflect the views or policies of the  Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  U.
S. Government. M. T. C. is a  VA Senior Research Career Scientist  supported by  5IK6BX005232. Her lab is funded by support from the  NIH   R01HL146266  and VA Grant  I01BX004441. J. E. S. and C. A. C. are CIFAR Fellows in the program Fungal Kingdom: Threats and Opportunities. Animal icons used in Figs. 3 and6 were obtained from http://phylopic. orgunder creative commons licenses https:// creativecommons. org/licenses/by/3. 0/.
Data availability The datasets generated during and/or analyzed during the current study are available at NCBI BioProject portal (https://www. ncbi. nlm. nih. gov/bioproject/): Pneumocystis macacae strain P2C (no. PRJNA632025); P. oryctolagi strain CS1 (PRJNA632560); P. canis strain Ck1 (PRJNA632556); P. canis strain Ck2 (PRJNA632878); P. canis strain A (PRJNA636786);P. wakefieldiae strain 2A (PRJNA632570); P. jirovecii strain 55 (PRJNA647920), P. jirovecii strain 54c (PRJNA648092), P. jirovecii strain 46 (PRJNA648096), P. macacae strain CJ36 (PRJNA648103), P. macacae strain ER17 (PRJNA648108), P. macacae strain UC86 (PRJNA648112), and P. macacae strain GL92 (PRJNA648115).
Code availability All custom bioinformatic analyses were conducted using Perl v5. 26. 0 (http://www. perl. org/) or Python v. 3. 6 (http://www. python. org) scripts. Pipelines were written with Snakemake v5. 11. 2  115. Custom codes generated for this project are available at GitHub: https://github. com/ocisse/pneumocystis_evolution and a stable released version is available at https://doi. org/10. 5281/zenodo. 4450766.
Dynamic Retrieval of Olive Tree Properties Using Bayesian Model and Sentinel-2 Images Hana Abdelmoula , Abdelaziz Kallel , Jean-Louis Roujean , and Jean-Philippe Gastellu-Etchegorry Abstract-The goal of this study is to provide a fine detection and monitoring of olive orchard trees over large areas to anticipate any damage. We developed an original method to assess the spatiotemporal dynamics of biophysical parameters in the olive orchards using satellite observations and radiative transfer models. Sentinel-2 time-series data collected over a four-year period were fused with Planet images from the same time period to enhance the temporal trends in olive orchards in the Sfax region located in southern Tunisia. These images also served to extract soil spectrum variations required by the 3-D discrete anisotropic radiative transfer model to account for canopy background effect.
The RTM inversion techniques to retrieve the biophysical parameters from Sentinel-2 simulated data and also the investigation of the inversion methods have been outlined in  [14]  and  [20]. The lookup table (LUT), the quasi-Newton (QNT)  [21] , and the neural network (NNT)  [22]  are the most popular techniques used to solve the inverse problem. The LUT method has been widely used to invert the RTM for the estimation of canopies biophysical parameters  [23]  since it is simple to implement and is low demanding in terms of computation time compared to other methods. Indeed, it seeks the closest simulated observation to the real measurement from a database of simulated observations, rather than requiring a priori information as in the QNT approach. Furthermore, unlike the NNT, it does not require a huge training database.
However, there are some limitations inherent to the LUT method, such as it cannot model the a priori information about the parameter distributions. Moreover, the LUT method cannot predict errors caused by observation noise and reflectance model inaccuracy, which degrades the performance of the retrieval. On the other hand, the inverse problem do not have a unique solution due to the presence of uncertainties in the datasets and also the possible inadequacy of RTM simulations. To overcome such issues, applying Bayesian models to RTM inversion processes provides a straightforward way to quantify the covariance and uncertainty of parameter estimates while integrating different sources of information. RTM inversion has relied on the use of independent prior information to address the otherwise underdetermined challenge of estimating a high number of parameters from a limited number of observations  [21] ,  [24] ,  [25].
A. Data Collection 1) Study Area: The study site is located in Châal (Sfax, southern Tunisia) (34 • 37 27. 77 N, 10 • 17 33. 78 E) and consisted of a 17 500-ha commercial olive area. The main olive cultivar is Chemlali grown in southern Tunisia. Within this area, two olive orchards, field 1 and field 2, with an area of 1704 and 419 ha, respectively, were selected as they differ in soil and olive tree properties (see Fig. 1 ). The soil of field 1 is a marginal soil (clay and gypsum), whereas the soil of field 2 is a deep sandy soil. Note that distance between two successive olive trees is always 24 m. The Sentinel-2 pixel resampled to 80-m resolution covers an area that contains around 11 olive trees. Hence, each measured group contains four homogeneous trees, either well developed or senescent, rather than a unique tree.
The Konica Minolta SPAD-502 leaf chlorophyll meter, which is a hand-held device, was used for the measurement of Cab. For each tree, we measured 20 individual leaves randomly selected with about 70% exposed to the sun, and we calculated the mean value. To measure Cab accurately, a calibration relationship is required between the chlorophyll meter readings and Cab content measured in the laboratory according to a suggested procedure  [29]. We used the calibration relationship proposed by  [30]  for olive trees in nonirrigated fields Cab = 0. 8271 × SPAD -12. 8; (R 2 = 0. 84). ( ) 2 Equation (  2 ) converts the SPAD readings into leaf Cab contents. 4) Satellite Data Acquisition: In order to test our algorithm, Planet and Sentinel-2 data time series during four consecutive years from January 2017 to January 2021 over Châal region were processed and analyzed.
Sentinel-2 images were fused with Planet images to reach a 3-m resolution. The Planet image acquisition date was as close as possible in time to the Sentinel-2 image acquisition, on the same day or at worst day before or after. Due to frequent high cloud coverage throughout the rainy season in the study areas, our time series include only one image per month, which appears to be sufficient for monitoring olive trees. The Sentinel-2 multispectral imager instrument (MSI) Level 2-A image covering Sfax (particularly Châal) is an atmospheric corrected product issued from the French Land Data Center (THEIA: theia. cnes. fr). The product is radiometrically calibrated into bottom-of-atmosphere (BOA) reflectance data. The Planet images are distributed as a four-band product (P1 = blue, P2 = green, P3 = red, and P4 = near infrared) and downloaded as a Planet Surface Reflectance image product.
A. Forward Radiative Transfer Modeling To create as realistic simulations as possible, an accurate forward RTM is required. The latter entails creating a LUT database with free parameters and simulated reflectance spectra. These RTMs take as input the biophysical parameters and the scene architecture and geometry to simulate reflectance. Inversion of these models provides an estimate of biophysical parameters given reflectance values as input. In this study, two RTMs were coupled to estimate the biophysical parameters of the vegetation: the DART model at canopy level was linked with the PROperties SPECTra (PROSPECT) leaf model, thereby combining canopy and leaf attributes. The DART model is able to simulate the radiation propagation in complex 3-D scenes for the entire optical domain from mock-ups created with 3-D imported objects composed of a set of triangle groups.
Some preprocessing was applied on the Sentinel-2 and Planet images to estimate the soil background reflectance (step 2). Considering that a Sentinel-2 image contains mixed pixels, the different components of the reflectance are evidenced from the sharpened Sentinel-2 image at the Planet resolution using the TsHARP method (see Section IV-A2). Particularly, the fused Sentinel-2 and Planet images were split into olive tree and surface units. Besides, a ground classification was performed to distinguish the various soils that affect differently the spectral signature of the satellite pixels. The LUT generation was performed by simulating many Sentinel-2 observations of olive tree fields for a set of predefined biophysical parameters: LAI, N , Cab, and Cw as well as soil reflectance (step 3). The resulting simulated database serves as input for the LUT inversion (used as a comparison method), which consists of retrieving the optimal set of biophysical properties in the LUT database from the best similarity criterion with the actual observation.
The LUT database is also found useful for the developed methods: MCMC for single date inversion and MCMCtimeseries for multidate inversion. These latter allow us to take into account statistical constraints such as the spatiotemporal regularization. Finally, the MCMC inversion approach is performed as well as the MCMCtimeseries inversion approach (step 4). For the LAI measurement protocol, smartphone images are used to calculate the gap fraction that serves to derive LAI olive tree ground value. In the following, these steps are described in separate sections.
B. DART Simulations 1) LUT Generator: The DART model is used to generate the LUT database to be compared with Sentinel-2 images. Simulations are performed for multispectral Sentinel-2 channels. In the present case, a realistic 3-D mock-up will draw an olive tree every 24 m, which is the constant row distance between two stands. We have retained the mock-up 2  of Fig. 4 (a) because it well depicts an olive tree of the Châal region at the peak season. In order to view and modify the chosen 3-D model, we use the Blender  3  3-D software that permits to adjust the olive tree size (tree height and crown diameter) in removing leafy material. This allows us to produce olive tree mock-ups similarly to the actual ones as well as to get correct LAI values within its physical domain of variations. The seven created olive tree mock-ups are illustrated in Fig.
5. DART simulates our scene using these mock-ups. DART decomposes the optical properties in two groups: one for the leaves and one for the woody material, merely trunk and branches. The optical properties of woody elements are simulated using the predefined spectral response of a bark-spruce-stem of the DART database. The leaf spectral response is simulated using the PROSPECT model. The LUT is then generated by simulating many observations (14000 simulations) in varying the structural and biophysical properties of interest of the olive tree such as the number of leaves in the tree (proportional to LAI), Cw, Cab, mesophyll structure (N ), and soil reflectance (ρ s ). A sensitivity analysis similar to  [35]  was conducted to evaluate the impact of each parameter (LAI, Cab, Cw, N , Cm, and Car) on Sentinel-2-simulated canopy reflectance.
N also influenced mainly the visible channels, but its domain of influence covers the whole spectral range, thereby enhancing the level of reflectance simulation. Car produces little variations and only in the vicinity of 530 nm. Cw and Cm barely contribute to variations in the visible. Cw intensely affects the NIR, and mostly, the SWIR and Cm infers small variations in the NIR domain. Finally, it was decided to take fixed values for Car = 15 μg • cm -2 and Cm = 0. 025 g • cm -2 in the LUT generation. The next step consists of optimizing the retrieval of freely variable biophysical parameters. Seven olive trees are created using the Blender software in varying the number of leaves. Table III illustrates the corresponding LAI value calculated by DART according to the number of leaves to match with the field measurements.
The acronyms and the imposed range of the 12 input parameters of DART for LUT creation are indicated in Table  IV. The illumination and viewing angles are input parameters of each image. Note that for the variable parameters (i. e. , LAI, Cab, Cw, N , Car, Cm, and ρ s ), their values were varied based on the range obtained from the field measurements and from other studies focused on the same crop  [36] -  [38]. C. Inversion Procedure 1) LUT Database: The inversion procedure using the built LUT database is performed in two steps. The first one consists of generating the LUT inherent to the Sentinel-2 image using the DART model in sampling the solutions space of the retrieved parameters. The second one is activated when a Sentinel-2 image is available. It consists of searching in the precomputed solutions space the one that fits the best image pixel reflectance.
Such an inversion procedure is based on computing a cost function that minimizes the differences between measured and simulated reflectance  [39]. In the LUT approach, the mean values of n best parameter combinations give the solution  [40] ,  [41]. In our case, the root-mean-square error (RMSE) between the measured and simulated reflectance is selected as the cost function. 2) MCMC Method: The inversion is carried on by searching the closest simulation to the actual observation using the LUT technique. However, this technique presents some uncertainty in case of noisy datasets. For such, we improved the inversion technique by taking into account atmospheric effects and sensor noise, also by considering the error on the simulation due to the possible inappropriateness of the forward model.
6. MGEs such as IS26, ISVsa3 and Tn2 were associated with several AMR genes where IS26 was associated with the most MGEs (Fig. 6 ). MGEs tended to be located at a fixed distance from a gene. This could be an indication that they are transferred as a unit. One indication that the AMR genes was mobilized by the MGEs are their tendency to be located arrays of repeated genes with variable length and content. These arrays have MGE located up to 4151 bp downstream of the first AMR (Table  S6 ). However due to the low number of observations these results are rather speculative. Abundance of AMR from metagenomic and whole genome sequencing. Fecal samples used for E. coli isolation were sequenced using shotgun metagenomics  8  and AMR genes were characterized in the metagenomics data using the ResFinder database.
coli carried approximately four plasmids on average but a minority of the MGEs were located on them. However, the number of mobilized MGEs are likely underestimated due to a combination of the limitations in the detection method of PlasmidFinder 20 and the difficulty assembling repetitive DNA, and with that plasmids, from short read sequence data. Moreover, a significant number of plasmid sequences in INSDC databases (DDBJ, EMBL-EBI, NCBI) may be misassembled, as evidenced by the presence of fragments of MGEs that are not explained by truncation by other MGEs  21. To further study the mobility and distribution of AMRs one would need to construct near complete the assemblies, preferably via hybrid assembly using long read sequencing as a template, and combine it with additional plasmid prediction tools that do bases it predictions on circular features in the data.