dataesr/scientific-paragraphs-categorization huggingface dataset:
The carnoglycocin B629 BGC also encompasses a CDS (CMALT430_v1_10021) encoding a peptidase domain-containing ABC transporter that may be involved in the export and signal peptide removal of the bacteriocin (Figure  6 , Table  2 ). The carnolysin A1-A2 BGC was identified in the genome of five strains, namely C maltaromaticum 9. 4, IFIP 710, CIP100481, DSM20590, and JIP 05/93 (Figure  5 ). The primary structure of the bacteriocins shares 100% identity with the carnolysin A1-A2 described in the databases (query coverage of 100%). Carnolysins A1 and A2 combine to form a two peptide lantibiotic synthesized by C. maltaromaticum C2. It is an extensively modified bac teriocin containing D-alanine and D-aminobutyrate derived from serine and threonine respectively. This bacteriocin shows antimicrobial activity against other LAB, such as Lac tilactobacillus sakei, as well as Listeria innocua and L.
mal taromaticum 10040100629 and exhibits an alanine residue instead of glycine at position 6 (Figure  7 ). Carnobacteriocin B2. 2 was found in the genome of C. maltaromaticum F7 (Figur 5) and presents a tyrosine residue instead of asparagine at position 20 (Figure  7 ). The var The carnolysin A1-A2 BGC was identified in the genome of five strains, namely C. maltaromaticum 9. 4, IFIP 710, CIP100481, DSM20590, and JIP 05/93 (Figure  5 ). The primary structure of the bacteriocins shares 100% identity with the carnolysin A1-A2 described in the databases (query coverage of 100%). Carnolysins A1 and A2 combine to form a two-peptide lantibiotic synthesized by C. maltaromaticum C2. It is an extensively modified bacteriocin containing D-alanine and D-aminobutyrate derived from serine and threonine, respectively.
Discussion In this study, the antimicrobial properties of C. maltaromaticum were investigated by combining interference competition network analysis and genome mining. From the inhibition data obtained with 76 C. maltaromaticum strains, the relationship between the potency of inhibition, the sender degree (i. e. , inhibitory spectrum), and the anti-L. monocytogenes EGDe properties were studied. The genome of a selection of 29 strains, representative of the diversity of the inhibition profiles, was analyzed in order to predict the genes with functions associated with antimicrobial activity. The results have shown that the potency of inhibition tended to increase when the inhibition spectrum broadened. In addition, the strains that were able to effectively inhibit L. monocytogenes EGDe also exhibited a broad inhibition spectrum of C.
maltaromaticum. Similarly, LAB are mainly reported to produce specialized metabolites of the type bacteriocin  [67]. By contrast, few BGCs potentially encoding NRPS/PKS are described in the literature for LAB  [5] [6] [7]. The tremendous amount of available bacterial genomic and metagenomic data provided opportunities to investigate the distribution of BGCs in bacterial taxa. Genome mining of the human microbiome project's reference genome database revealed that bacteriocin BGCs are mainly found in the phyla Firmicutes and Proteobacteria and less frequently encountered in other phyla including Actinobacteria  [68]. Conversely, NRPS/PKS BGCs are widely encountered in Actinobacteria, Proteobacteria, Verrumicrobia, and Bacteroidetes  [69]. In the phylum Firmicutes, PKS BGC is preferably encountered in the order Bacillales of the phylum Firmicutes  [70].
maltaromaticum activity (Figure  2 ). This suggests that genome mining would not be able to identify BGCs allowing the production of antimicrobial substances acting on conspecific strains. The basis of functional prediction in genome mining is the availability of experimental data providing information on the function of genes. In the field of antimicrobials produced by LAB, the literature describing data obtained from wet experiments is tremendously dedicated to the characterization of compounds inhibiting unwanted microorganisms, such as L. monocytogenes. This documentary bias may have led to missing genes encoding the synthesis of antimicrobial substances whose sequence properties are unrelated to those already described in the databases. However, these substances could play an important role in ecosystems by contributing to the structuring of microbial food communities.
MVP The design of the MVP has been previously described  (4). Briefly, participants were recruited from approximately 60 Veteran Health Administration (VHA) facilities across the United States since 2011 with the current enrollment at >800,000. Informed consent is obtained for all participants to provide a blood sample for genetic analysis and to access their full clinical and health data. A total of 485,856 samples from participants enrolled between 2011 and 2017 were genotyped on a custom Axiom array designed specifically for MVP (MVP 1. 0). The genotyping array design and data quality controls were extensively described elsewhere  (12). After excluding variants with high genotype missingness (>5%) and those deviated from the expected allele frequency observed in the reference populations, genotype data was imputed to the 1000 Genomes Project Phase 3 reference panel  (13).
MADCaP The MADCaP Network dataset included 405 prostate cancer cases and 396 controls from sub-Saharan Africa, as previously described  (5, 15) , with a substantial proportion of cases diagnosed at late stages. The MADCaP samples were genotyped on a customized array designed to capture common genetic variation in diverse African populations, and genotyping and quality control have been described in detail elsewhere  (5). GWAS data were imputed using the 1000 Genomes Project Phase 3 reference panel  (13).
Data Availability Statement Data availability: This investigation included published results from the following studies under DOI numbers 10. 1038/s41588-020-00748-0 and 10. 1093/jnci/djab058. The MVP individual level data is available to approved VA researchers through standard mechanisms. Full GWAS summary statistics can be found in dbGaP (https://www. ncbi. nlm. nih. gov/gap/) under the MVP accession (phs001672). Publicly available data described in this manuscript can be found from the following websites: 1000 Genomes Project (https://www. internationalgenome. org/); SEER (https://seer. cancer. gov/); National Center for Health Statistics, and CDC (https://www. cdc. gov/nchs/index. htm). Code availability: All analyses were performed using R statistical packages freely available at https://cran.
Molecular Docking The 3D structure of MOPV NP-exo (PDB: 5LRP) was energy minimized by the steepest gradient method of energy minimization followed by conjugate gradient minimization, using the MMTK and Amber packages  (Cornell et al. , 1995; Hinsen, 2000; Lindorff-Larsen et al. , 2010). Mol2 and PDB file formats of the ligands and receptor were converted to PDBQT format using UCSF Chimera  (Pettersen et al. , 2004)  prior to docking. All the water and solvent atoms of the protein were removed and the polar hydrogens and polar charges were added onto the ions and ligand prior to docking. The protein was kept rigid while the ligand was allowed to rotate and explore more flexible binding modes. Docking of the ligands onto the NP-exo MOPV was performed iteratively using AutoDock vina -version 1.
1. 2 (Trott and Olson, 2010). The best poses from the first round of docking were used as seeds for the second round. The grid box size was further optimized to dimensions a = 23. 2 Å, b = 25. 6 Å, c = 21. 2 Å thus covering the binding pocket. The default scoring function was used for docking. Binding modes of the docked complexes were obtained and sorted based on their binding energy. Ions and amino acid residues present at a distance of less than 3 Å were considered as the binding partners of the ligands. Geosmin synthase from Streptomyces coelicolor (ScGS) in complex with three Mg 2+ ions and ALD (PDB:5DZ2) was used as a control experiment. The same protocol was applied to the 3D structure of ScGS depleted of its solvent and ALD. The ScGS -ALD binding pose resulting from our docking protocol was superimposed over the experimentally derived structure.
The interaction figures representing the docked complexes have been generated using UCSF chimera. Exploration of various possible dynamic poses of ALD in the MOPV NP-exo structure with Mn 2+ obtained in this study (PDB:6SX8) was performed with Autodock Vina with a grid (dimensions: a = 29. 34 Å, b = 28. 71 Å, c = 28. 00 Å) with the ion placed in the center of the grid to allow an extensive exploration of possible poses.
The products were heated at 343. 15 K for 10 minutes and rapidly cooled on ice before being analyzed by denaturing PAGE using sequencing gels (20% acrylamide:bisacrylamide (19:1), 8 M urea with TTE buffer (89 mM Tris pH 8. 0, 28 mM taurine (2-aminoethanesulfonic acid), 0. 5 mM EDTA). RNA product bands were visualized using photo-stimulated plates and an Amersham TM Typhoon TM biomolecular imager (Cytiva). RNA degradation was quantified using the ImageQuant TL program (Cytiva). IC 50 values were determined using GraphPad PRISM version 7. 0. Experiments were performed in triplicate. Human TREX2 assays were carried out with 0. 25 µM hTREX2 in reaction buffer 20 mM Tris, pH 7. 0 containing 120 mM NaCl, pH 7. 0, 5 mM MnCl 2 (or MgCl 2 ) and 0. 5 µM of radio-labeled DNA substrates. The negative controls were carried out in absence of enzyme.
Data collection and structure Diffraction data were collected on the Proxima 1 beamline at the Synchrotron Soleil (Saint-Aubin, France) with the large in-vacuum PILATUS 6M detector (Dectris, Switzerland) at a wavelength of 0. 97857 Å with 0. 1° oscillations, 0. 097 s exposure time and also on the ID23-1 beamline at the European Synchrotron Radiation Facility (ESRF), Grenoble, France using the large in-vacuum PILATUS 6M-F detector (Dectris, Switzerland) at a wavelength of 0. 9787 Å with 0. 1 o oscillation, 0. 037 s exposure time and a temperature of 100 K. Collected data were subsequently processed using autoProc toolbox  (Vonrhein et al. , 2011). The data were integrated using the program XDS  (Kabsch, 2010) , then analyzed with POINTLESS  (Evans, 2006)  and scaled with AIMLESS  (Evans and Murshudov, 2013).
The structure was solved using the MOPV NP-exo as a search model (PDB:5LS4 ;  (Yekwa et al. , 2017) ). The search model was prepared by removing all ligands, metallic ions and water molecules from the original structure. Phases were obtained by molecular replacement using Phaser  (McCoy et al. , 2007). Refinement was performed by successive and alternate rounds of using BUSTER  (Bricogne et al. , 2018)  or PHENIX and model improvement using COOT  (Emsley and Cowtan, 2004). The structures were validated using MolProbity  (Williams et al. , 2018)  and PROCHECK  (Laskowski et al. , 1993). Structural analysis was performed using UCSF Chimera  (Pettersen et al. , 2004). The PDB X-ray structure was validated and deposited in Protein Data Bank. Data collection, refinement statistics and accession numbers are shown in Table  1.
Structure-based virtual screening and molecular docking The high resolution crystal structure of MOPV NP-exo (1. 9 Å) complexed with Mg 2+ (PDB: 5LRP) was used for this study. The active site comprises five catalytic residues D390, E392, D534, D467 and H529 which form an acidic cavity. Three of these residues directly coordinate one of the catalytic ions, in this case a Mg 2+ as shown in Fig. 1 (a). In order to identify ligands with high affinity for MOPV NP-exo with the aim of inhibiting its 3'-5' exonuclease activity, we performed a structure-based virtual screen of a total of 10,000 ligands from the MTi-Openscreen Diverse-lib (see Material and methods). The diverse library is composed of the sampling of 12 chemical libraries from PubChem BioAssay Database containing drug-like compound collection.
Since there is no crystal structure of an arenavirus NP-exo domain in complex with ALD to the best of our knowledge, in order to access the efficiency of prediction of our model, we validated the docking protocol on the structure of geosmin The best ALD binding energy obtained after Autodock Vina is -7. 62 kcal/mol, which is consistent with the result from the screening -7. 6 kcal/mol, Table  S2 ). The binding mode of ALD with MOPV NP-exo and its interactions with the Mg 2+ ion in site A (present in PDB:5LRP) as well as the active site residues is shown in Fig. 1  positions itself in such a way that the two phosphonate groups establish polar interactions with both metal ions. In addition to the residues cited earlier it also makes polar interactions with the main chain of G463 and G464. The binding energy of the best ALD pose in the MOPV NP-exo model with two metal ions (-16.
Crystal Structures of the MOPV NP-Exo complexed with Mn and soaked with ALD. In order to structurally investigate the mechanism of action of ALD to inhibit the 3'-5' exonuclease activity in presence of Mn 2+ ions, and see if ALD could fix the second catalytic ion, we crystallized MOPV NP-Exo complexed with Mn 2+ and soaked the crystals with ALD applying different soaking times (6 min to over night). We obtained four structures, which are listed in Table  1. The first structure is complexed with one Mn 2+ and represent our reference structure (PDB:6SX8, Fig  3(a) ). Its general structure and ion location are similar to the ones reported with Mg 2+ and Ca 2+  (Yekwa et al. , 2017). MOPV NP-exo in complex with Mn 2+ consists of 6 mixed strands forming a central β-sheet sandwiched by 3 α-helices on one side and 5 α-helices on the opposite side.
Two flexible regions are present within the structure. Two anti-parallel strands form a 'basic loop' (residues 514-526) near the catalytic pocket; and the C-terminal arm (residues 549-570) extends away from the protein core. The MOPV NPexo structure presents a structural conserved Zinc-binding domain. The Zn 2+ metal ion is coordinated by E400, C507, H510 and C530. The Mn 2+ ion is coordinated by three catalytic residues D390, E392, D534 and three H 2 O molecules (Fig. 3(b) ). The presence of Mn 2+ and Zn 2+ ions in the crystal were confirmed by an X-ray fluorescent scan (Fig. S2 ). The other three structures were obtained after soaking crystals with ALD for 6 min (PDB:6T6L), 12 min (PDB:6T2A) and overnight (PDB:6SY8). The overall structures are not changed by the soaking except for the fact that the basic loop is clearly defined in all three structures issued from soaking with ALD in contrast to the one complexed with Mn 2+ indicating that ALD contributes to lower the energy of this structural element.
(c). The structure of ALD contains an alkyl chain of 5 C, substituted by an hydroxyl group and terminated by a primary amine and two geminal phosphonate groups PO(O -) 2 negatively-charged that are able to establish electrostatic interaction. The phosphonate groups establish hydrogen bond interactions with D534 (2. 4 Å), D390 (2. 7 Å),I391 (3. 0 Å) and direct polar interactions with E392 (2. 5 Å and 3. 0 Å) and the Mg 2+ ion (1. 7 Å and 1. 9 Å). As the active site of Arenaviridae NP-exo can harbour two catalytic metal ions, a model of MOPV NP-exo with two Mg 2+ ions was generated based on the homologous structure of a dsRNA bound to LASV NP-exo containing two Mn 2+ ions (PDB: 4GV9). The dsRNA from the original crystal structure was removed while conserving the second Mg 2+ ion within the MOPV model that was then used for docking.
m. s. deviations -Bonds (Å) 0. 007 0. 009 0. 007 0. 014 -Angles (°) 1. 18 1. 38 0. 81 1. 61 Average B-factors (Å 2 ) 43. 00 29. 27 25. 97 53. 84 -Protein 43. 76 28. 03 25. 39 53. 96 -Ligand 38. 10 20. 22 41. 34 40. 17 -Water 47. 04 37. 11 39. 87 50. 75 Ramachandran Plot -Favoured (%) 97. 43 96. 89 97. 93 97. 43 -Allowed (%) 2. 57 3. 11 2. 07 2. 57 -Outliers (%) 0. 00 0. 00 0. 00 0. 00 Clashcore 6. 77 6. 72 6. 83 4. 10 PDB code 6SX8 6T6L 6T2A 6SY8 Statistics for the highest-resolution shell are shown in parentheses.
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows our network to only be supervised with vegetation occupancy values aggregated over cylindrical plots containing thousands of points. Such ground truth is easier to produce than pixelwise or point-wise annotations. Our method outperforms handcrafted and deep learning baselines in terms of precision by up to 30%, while simultaneously providing visual and interpretable predictions. We provide an open-source implementation along with a dataset of 199 agricultural plots to train and evaluate weakly supervised occupancy regression algorithms.
Furthermore, these methods require databases with precisely segmented trees, which makes them less applicable to the operational setting of vegetation structure prediction. This is particularly problematic for natural forests for which tree' canopies often intersect, making segmentation a difficult and sometimes dubious process. 3D Deep Learning. The main difficulty in analysing 3D point clouds with deep learning is their irregular structure and varying sampling density. This has been alleviated by relying on images  [45, 46, 47] , 3D regular grids  [48, 49, 50, 51] , graphs  [52, 53] , or continuous-space convolutions  [54, 55]. A simpler class of algorithms considers point clouds as unordered sets of points  [56, 57, 58] , and does not need any of the pre-processing steps required by the aforementioned methods.
Since we consider the strata occupancy prediction problem for plots individually, and with the objective of scalability and computational efficiency, we choose the straightforward PointNet network  [56]. Weakly Supervised Learning. Training deep networks typically requires a large training database. However, manually producing dense annotations of LiDAR 3D point clouds of vegetation is a laborious task, often made even more complicated by visual ambiguities  [59]. Furthermore, since most monitoring tasks are done at the plot level, such a degree of detail is unnecessary in practice. Similarly to what Long et al. developed for images  [60] , our approach only requires sparse annotations to be trained. Our network can be entirely supervised from a single aggregated occupancy per cylindrical plot and per stratum.
Such values can be obtained by an operator estimating visually the stratum occupancy of their immediate surrounding area. While this requires an in-situ intervention, the annotation task is less tedious than annotating individual points. As commonly encountered in weakly supervised schemes  [61, 62] , our method requires regularization terms for more realistic outputs. The key contributions of this paper are as follows: • We show that a simple deep network can produce two-dimensional stratum maps using only plot-aggregated weak annotation. • We propose regularization terms improving the realism and generality of the predicted occupancy maps without requiring expert knowledge. • We introduce an open-access dataset of multi-spectral 3D point clouds corresponding to plots of agricultural parcels along with aggregated stratum occupancy annotations.
Dataset Description In this subsection, we present the new proposed dataset and give further precision on the task of automated stratum occupancy prediction. They are colored using the aerial images in (b) (the plots are represented by the circles). A human annotator visually assesses their surroundings (c) and estimates the occupancy ratio of the lower, medium, and higher vegetation stratum. In both represented plots, the occupancy ratio of the lower stratum is 50%, while the occupancy higher strata differs. Dataset Composition. We gathered a total of 199 aerial LiDAR scans of cylindrical plots with a 10m radius with an average 10-pulse per square meter density. The plots have been selected by forestry experts, and correspond to typical pasture land parcels in South-Eastern France (see Fig- Each plot comprises between 3 000 to 17 000 3D points, and each point is attributed with a total of 9 features: (i) absolute 3D coordinates in Lambert-93 system, (ii) RGB and Near-InfraRed reflectance values acquired by the aerial camera, (iii) uncalibrated laser intensity and return number as provided by the LiDAR device.
The occupancy value ôL characterizes the proportion of the ground surface occupied by grass or low vegetation, as opposed to stone, soil, or sand. ôM characterizes the proportion of the surface of the plot occupied by the footprint of medium vegetation, ie, with a height between 0. 5 and 1. 5m. This type of vegetation, typically bush-like, is the most accessible by pasture animals and represents an important indicator for land-use monitoring agencies. Note that the trunks of trees exceeding 1. 5m do not not contribute to this coverage. Finally, the canopy occupancy is defined as the ratio of the plot surface occupied by the footprint of the canopy of trees over 1. 5m. Our dataset  [64]  is publicly available at https://doi. org/10. 5281/zenodo. 5555758.
To simplify the problem, we group the three strata into two groups: ground G and non-ground NG (medium and high vegetation). We can estimate the parameters {α i , β i } i∈G,NG ∈ R 2 of both Gamma distributions as well as the mixture parameter {ρ G , ρ NG } ∈ [0, 1] 2 with the expectation-conditional-maximization (ECM) algorithm  [65]  detailed in Algorithm 1. This procedure is entirely unsupervised but requires a meaningful initialization of mixture parameters, which can be achieved by trial-and-error guided by the resulting likelihood value. The ECM algorithm and its inner Newton-Rachson optimization [66, Chapter 3. 2] converges in only a few iterations, and this step is only required to be performed once per dataset. In Figure  6 , we represent the mixture of Gamma distributions obtained with the ECM algorithm.
Implementations Details Our entire pipeline is implemented in PyTorch 1. 7 and CUDA 10. 2. Our network is trained with a batch size of 20 plots for 100 epochs, and we use the ADAM optimizer  [68]  with a learning rate of 0. 001 divided by 10 after 50 epochs, and all other default parameters. We add a dropout layer  [69]  with probability 0. 4 before the last layer to increase the model's robustness. Our network can be trained in under 24 minutes on an NVIDIA GeForce RTX 3060 GPU and a Xeon W-2123 CPU with 64GB of RAM. Our python impementation of the ECM algorithm converges in under 5 seconds on a standard workstation for over 500 000 points. During both training and inference, we sample a fixed number of M = 4096 points for each plot, and duplicate points for plots with fewer points. This allows us to use efficient batch-parallel computing, as well as adding sampling stochasticity to decrease overfitting.
Experimental Setting We perform 5-fold cross-validation on the dataset presented in Section 2. 1, which is composed of T = 199 cylindrical plots. We report the mean absolute occupancy error e L , e M , e H between the predicted and true occupancy for each stratum, as well as the inter-stratum macro-average e: e K = 1 T T n=1 ôK n -o K n for K ∈ {L, M, H} (8) e = 1 3 (e L + e M + e H ). ( 9 ) Our code is available at https://github. com/ekalinicheva/plot_vegetation_coverage.
Conclusion In this paper, we presented a 3D deep learning method for predicting occupancy across three vegetation strata: lower, medium, and higher. Using only three aggregated values per example plot, our model is able to perform a point-wise classification and produce vegetation occupancy rasters with a high precision and at a small computational cost. Moreover, our model was 30% more accurate and 6 times faster than a handcrafted approach. Our code is released in open access, along with the first forestry dataset with occupancy annotations. f 1 m = MLP 1 (x m ), ∀m ∈ M, (10) f 2 m = MLP 2 (f 1 m ), ∀m ∈ M, (11) G = MAX(F 2 ), F 2 = {f 2 1 ,. , f 2 M }, (12) p m = MLP 3 ( f 1 m || G ), ∀m ∈ M, (13) N = upsample(N, M ), (14) where [. ||. ] is the concatenation operator.