dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis P value significance were analysed by running specific statistical tests on the GraphPad Prism software. Single-cell data from quantitative microscopy analysis were extracted from the MicrobeJ interface and transferred to GraphPad. P value significance of single-cell quantitative data was performed using unpaired non-parametric Mann-Whitney two-sided statistical test, which allows to compare differences between independent data groups without normal distribution assumption. P value significance for the frequency of transconjugants obtained by plating assays were evaluated using One-way analysis of variance (ANOVA) with Dunnett's multiple comparisons test, which allows to determine the statistical significance of differences observed between the means of three or more independent experimental groups against a control group mean (corresponding to the Fwt).
To test the hypothesis that additional genes, beyond CRISPR-Cas, are involved in defence against phage DMS3, we next generated a transposon mutant library of the ∆CRISPR strain. Using the same method, we identified multiple Tn insertions in a restricted genomic region containing a predicted operon of 8 genes  31, 32  (Extended Data Fig. 2c ). This putative operon did not contain a known defence system based on PADLOC and DefenseFinder analyses  3, 33   1 ). However, PADLOC identified 3 genes in this operon as putative components of DNA-modification systems: a kinase, a specificity subunit and a methylase (Supplementary Table  1 ). Therefore, we hypothesised that this operon might encode a defence system that has not been previously described, which we coined MADS (Methylation Associated Defence System) (Fig.
Distant bacterial classes carry MADS To determine how widespread and conserved MADS is, we built a MacSyFinder model  34  and searched through all bacterial and archaeal genomes in the RefSeq database. We identified 422 MADS in 100 different species belonging to Alpha, Beta, Gamma and Deltaproteobacteria, Actinobacteria and Nostocales (Extended Data Fig. 3a ). The mad1-8 operon was most commonly found in the genera Escherichia (n=113, 27% of systems detected), Pseudomonas (n=71, 17%), Ralstonia (n=51, 12%), Streptomyces (n=33, 8%), Klebsiella (n=31, 7%) and Vibrio (n=15, 4%) (Extended Data Fig. 3b ). This analysis also revealed varying levels of completeness of MADS operon , with some genes missing or being detected less frequently in some bacterial genera, suggesting the existence of multiple subtypes of MADS (Extended Data Fig.
The combination of phenotypic and sequencing data therefore supports the hypothesis that MADS uses a methylation-based self/non-self discrimination mechanism. To further corroborate the hypothesis that MADS provides resistance against MGE with unmethylated 5'-TCAGNNNNTCC-3' sequence, but not against MGE with a methylated sequence, we measured the transformation efficiency of methylated and unmethylated plasmids with the target sequence relative to a plasmid without this sequence. Consistent with our hypothesis, a methylated plasmid carrying a 5'-TCAGCGCGTCC -3' sequence had a 100-fold increased relative transformation efficiency (RTE) compared to the non-methylated plasmid when recipient cells carried MADS (SMC4386-WT or SMC4386∆CRISPR, Fig. 3e ), while no differences could be observed when strains lacked MADS (∆MADS or ∆CRISPR∆MADS).
This demonstrates that failed infections cause bacteria to become immunosuppressed when the phage carries the acrIE3 gene (Extended Data Fig. 5b ). Moreover, a similar increase in the relative transformation efficiency of the targeted plasmid was observed when bacteria encode only one (the ∆MADS strain) or both defence systems (SMC4386-WT) (Extended Data Fig. 5b ). Hence, these data validate the model assumption that phages carrying acrIE3 are imperfect in their ability to by-pass CRISPR-Cas, and that failed infections cause cells to enter into an immunosuppressed state. Next, we performed infection assays over a broad range of initial phage inoculi to test that (i) phage amplification requires higher initial phage densities when both defences are present and (ii) that, in the presence of CRISPR-Cas, escape phage emergence is suppressed compared to the situation where bacteria only carry MADS.
Analysis of protein domains and HMM profiles MADS locus protein sequences were determined from genome annotations made using Prokka v1. 14. 6, which uses Prodigal to predict protein regions. These proteins were searched against the pfam (protein family) database, as well as Phyre2 and HHpred, to perform HMM-HMM matching-based remote homology detection and obtain structural predictions  54, 55. Curated alignments for each gene were constructed by identifying homologues for each protein sequence using HHpred. A probability cut-off score of 50% was set, with default parameters for all other options, searching against the PDB database. Alignments extracted from HHpred outputs (max 250 sequences) were then used to generate profile-HMMs using the hmmbuild function from HMMER v3. 0. For further investigations of remote homology, HHpred was run against the PDB, COG, NCBI conserved domains and Pfam-A databases.
Macromolecular models for MADS MacSyFinder v2, a tool for macromolecular systems detection  56  , was used to develop models for MADS. This tool requires the specification of mandatory or accessory components within the system, which are not biological definitions but rather describe whether a protein is easy to detect or more divergent and thus harder to detect with a single HMM profile, as well as whether components are frequently missing from systems. After making sure that no known anti-phage systems could be identified by PADLOC  57  and DefenseFinder  58  within the MADS operon (Supplementary Table  1 ), genomes were retrieved in genbank (. gbff) format and ordered protein fasta files were created by extracting the CDS features using SeqIO from Biopython. Initial models with various levels of system completeness and genomic distance between components were tested.
Initially, all P. aeruginosa RefSeq genomes (n=6,103) were searched. Next, preliminary tests, and manual inspection of hits from a subset of the entire Bacterial and Archaeal RefSeq dataset (n=15,000), the following model parameters were set. Proteins MAD6, 7 and 8 were defined as mandatory and their presence was required for the system to be detected. Proteins MAD1, 2 and 5 were defined as accessory due to their homology to Restriction-Modification system components and widespread regulators, which could lead to false positive hits if made mandatory. Finally, proteins MAD3 and 4 were also classified as accessory, due to them not being reliably detected in all systems. In addition, the maximum inter gene distance was set to 10.
Reproducibility and computational resources The University of Exeter's Advanced Research Computing Facilities were used to carry out for this bioinformatics analysis. A Snakemake pipeline was used to run MacSyFinder searches on all genomes, whilst the remaining analyses were carried out using R v4. 0. 4 and Python version 3. 9. 7 with Biopython v1. 79. All scripts used for this analysis are available at https://github. com/elliekpursey/Maestri.
The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992-2020) of annual land cover maps at 300-m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need 2D parameters rather than 2D land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992-2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method.
The new PFT product is generated by fusing the annual CCI MRLC map series with existing high-resolution auxiliary data products that individually characterize one surface type with high accuracy. The resulting 300 m PFT product is a companion time series of continuous field PFT fractions that is consistent with the existing CCI MRLC map series. The global PFT product has an annual resolution, covering 1992-2020, and indicates the specific percentage cover of 14 PFTs for each pixel at 300 m resolution. The set of 14 PFTs represented in the product includes the full set of 13 PFTs initially developed by  Poulter et al. (2015)  complemented with a new built-up surface type. The full set of PFTs includes bare soil, built, water, snow and ice, natural grasses, managed grasses (i. e. , herbaceous cropland), broadleaved deciduous trees, broadleaved evergreen trees, needleleaved deciduous trees, needleleaved evergreen trees, broadleaved deciduous shrubs, broadleaved evergreen shrubs, needleleaved deciduous shrubs, and needleleaved evergreen shrubs.
In cases of inconsistency between the land cover product and the auxiliary datasets for example, if the tree cover percentage derived from the auxiliary products falls outside of the range suggested by the class legend for a 300 m pixelthe characteristics from the land cover classification are maintained. This achieves a strong coupling between the CCI MRLC maps dataset and this new CCI PFT dataset. Deference to the class legend provides guardrails for the temporal extrapolation of the PFT fractional covers across the entire time series  given the lack of available auxiliary inputs extending across the full era. The approaches used to estimate the PFT fractions at 300 m resolution differ for (1) pixels that did not experience a change in land cover classification over the period 1992-2020 (termed "static pixels", described in Sect.
The 15 sub-classes, also called "regional classes," are defined only in geographic regions where appropriate training data is available and are those with a numeric classification code that has a final digit of 1, 2, or 3 (Table  1 ). The 22 primary classes and 15 sub-classes are collectively referred to here as simply "classes. " For each year of the time series, each 300 m pixel in the dataset is assigned as a single land cover class. The change detection algorithm monitors thirteen possible land cover transitions through time. For a pixel to register a change in its assigned land cover class, the algorithm must identify the change for two consecutive years in the workflow. A lack of change in a pixel's assigned class does not necessarily indicate an absence of change in the land surface over the time series; rather, it indicates that any change that has occurred in the pixel was limited enough in scale or duration that the assigned class did not change.
The full time series and an associated set of quality flags are freely available at https://maps. elie. ucl. ac. be/CCI/viewer/ (last access August 2022) in GeoTiff and https://cds. climate. copernicus. eu/cdsapp#!/dataset/satellite-land-cover?tab=overview (last access August 2022) in netCDF. This CCI PFT product is based on v2. 0. 8 of the CCI MRLC time series, which includes corrections for the known overestimation of cropland relative to grassland in South America  (Defourny et al. , submitted).
Tree canopy cover product (30 m) A Landsat-based tree canopy cover product  (Hansen et al. , 2013)  is used to derive the tree cover fractions for 300 m pixels belonging to vegetated classes (except where otherwise noted in Sect. 2. 2). The product (obtained at https://glad. umd. edu/Potapov/TCC_2010/) is based on the application of a regression tree model to growing-season Landsat 7 ETM+ data (https://glad. umd. edu/dataset/global-2010-tree-cover-30-m). The dataset indicates the maximum tree canopy cover percentage (integer values of 1-100 %) at 30 m resolution (80°N-60°S) and is approximately representative of 2010.
Tree canopy height product (30 m) The global forest canopy height product from  Potapov et al. (2021)  is used to derive the fractional covers of trees and shrubs in 300 m pixels classified as shrubland. The 30 m product (obtained at https://glad. umd. edu/dataset/gedi/) was created by combining the footprint-level lidar forest height measurements (using the 95 th percentile relative height metric) for April-October 2019 from the Global Ecosystem Dynamics Investigation with wall-to-wall Landsat optical data to perform spatiotemporal extrapolation. The resulting dataset indicates the canopy height (0-60 m) at 30 m resolution (52°N-52°S), where canopy heights < 3 m were set to 0 m under the assumption that the pixel lacks woody vegetation.
Built-up product (38 m) The Landsat-based Global Human Settlement Layer (GHSL) dataset produced by the Joint Research Centre  (Pesaresi et al. , 2013)  is used to derive the built-up fraction for 300 m pixels classified as urban land cover by the Global Urban Footprint (GUF) dataset  (Esch et al. , 2017). The built-up fraction of the PFT dataset is defined as buildings, roads, and man-made structures. The GHSL (alpha version dated November 2014) consists of globally consistent built-up maps for four consecutive years  (1975, 1992, 2000, and 2014 ) at 38 m resolution. Built-up areas include both permanent and temporary above-ground buildings.
Zonation products In addition, three zonation products are used complementarily to consolidate the assignment of the phenology type (deciduous or evergreen) and leaf type (broadleaved or needleleaved) to shrubs and, in a very small number of pixels, to trees belonging to a class legend of mixed trees. The Köppen-Geiger climate zone product from  Beck et al. (2018)  divides the Earth's land surface into 30 distinct climate zones at 0. 0083° resolution (about 1 km) based on present-day  temperature and precipitation records. Data were obtained at https://figshare. com/articles/dataset/Present_and_future_K_ppen-Geiger_climate_classification_maps_at_1-km_resolution/6396959/2. The landform dataset from  Sayre et al. (2014)  identifies landformssurface water, plains, hills, or mountainsat 250 m resolution for 83.
6°N-56°S and is derived from a digital elevation model (USGS GMTED2010:  Danielson and Gesch, 2011). The data product was obtained at https://rmgsc. cr. usgs. gov/outgoing/ecosystems/Global/. Finally, world regions follow the definitions used in the Integrated Model to Assess the Global Environment 3. 0 (IMAGE03)  (Stehfest et al. , 2014). The IMAGE03 regional classification framework has been harmonized with the CCI MRLC grid by reconstructing the original dataset using the IMAGE-based list of countries per region (available at https://models. pbl. nl/image/index. php/Region_classification_map) along with country boundaries from the FAO Global Administrative Unit Layers (available at https://data. apps. fao. org/), expanding the list to include Antarctica, Greenland, and additional small islands.
CCI medium-resolution water body product The CCI MRLC water body product  (Lamarche et al. , 2017)  is used to delineate between inland water and ocean. The dataset (available at http://maps. elie. ucl. ac. be/CCI/viewer/download. php) designates all pixels at 150 m resolution as either ocean or https://doi. org/10. 5194/essd-2022-296 Preprint. Discussion started: 16 September 2022 c Author(s) 2022. CC BY 4. 0 License. non-ocean, the latter of which includes both land and inland water. The dataset is consistent with the water body class (code 210) of the land cover maps of the CCI MRLC. An updated version 4. 1 of the product was used here, in which the North American Great Lakes are now considered to be inland water rather than ocean. It is available at http://maps. elie. ucl. ac. be/CCI/viewer/download.
PFT dataset development The overall approach assumes that the definition of the MRLC class is the basis for harmonizing the four existing highresolution land cover data sets. It proceeds through a systematic sequence of estimating water fraction and tree cover fraction, using tree height to assign life form, and finally deriving phenology. This step-by-step approach is first applied to static pixels before extending it to pixels that undergo changes over time, as identified in the CCI MRLC map series.
Nonetheless, inherent differences between the classes necessitate the use of different input datasets and methods in some cases. For each class, only a subset of the 14 PFTs is permitted nonzero fractions (Table  1 ). Because the PFT fractional composition is estimated independently for each 300 m pixel of a class, in some cases, an individual pixel of the class can have zero fractional cover even for a PFT that is allowed non-zero cover for that class. For all pixels, the sum of PFT fractions is 100 %. The 30 m water frequency occurrence dataset of  Pekel et al. (2016)  is used to estimate the permanent inland water fraction of the 300 m pixels for all but the permanent snow and ice class, which has no liquid surface water cover. A threshold of 90 % frequency occurrence is applied to assign 30 m pixels as either water (frequency occurrence ≥ 90 %) or non-water (frequency occurrence < 90 %).
The resulting binary representation of water/non-water is aggregated to 300 m to estimate the percentage of the 300 m pixel that is permanent inland water PFT. The percentage of the 300 m pixel that is vegetated is calculated as 100 % minus the inland water percentage; that is, for all vegetation-containing classes except for the sparse vegetation classes, which have bare soil PFT cover, all non-inland-water area in the 300 m pixel is entirely vegetated (0 % bare soil PFT) in the PFT product. Pixels belonging to the shrubland classes (codes 120-122 and 180) can have a mixture of trees, shrubs, and herbaceous cover. For pixels of non-shrubland vegetationcontaining classes, the vegetated portion of the pixel is composed of trees and herbaceous cover (i. e. , cropland and/or natural grass). The percentage of the 300 m pixel that is tree cover is estimated using the 30 m tree cover dataset for 2010 from  Hansen et al.
(2013). This Landsat-based dataset provides the percentage of tree canopy cover (integers 1-100 %) based on growing season observations. The tree cover percentage of the vegetated (i. e. , non-water) portion of the 300 m pixel is obtained from the median of the tree canopy cover fractions of the non-water 30 m pixels, where the 30 m non-water pixels are identified using the binary water/non-water representation derived using the surface water occurrence dataset. The tree cover percentage of the entire 300 m pixel is calculated as the product of this value (the tree cover fraction of the non-water part of the grid cell) https://doi. org/10. 5194/essd-2022-296 Preprint. Discussion started: 16 September 2022 c Author(s) 2022. CC BY 4. 0 License. and the non-water fraction of the grid cell. This approach harmonizes the Landsat-based surface water occurrence and tree canopy cover datasets such that the combined tree and water percentages never exceed 100 %.
For the tree cover classes 50-82, the class legend specifies an expected range for the tree cover percentage (Table  1 , class description column). For the tree cover classes 90, 160, and 170, a tree cover fraction of >15 % is implicit from the UN LCCS. Based on the spatial and temporal consistency of the map series, deference is made to the class legend for pixels in which the estimated tree cover fraction derived from the auxiliary datasets disagrees with the class legend. This allows the PFT product to retain the advantages of the CCI MRLC map series while improving the translation of the land cover dataset into PFT maps. For tree cover class pixels in which the estimated tree cover fraction derived from the auxiliary datasets disagrees with the class legend, the mean tree cover among all static 300 m pixels of its class is calculated over the 0.
25° longitude × 0. 25° latitude window overlapping the pixelthat is, a window with width and height of 0. 25° with the pixel of interest at the centre. The mean is based on the initially calculated tree cover fractions derived from the auxiliary data products (i. e. , the tree cover fraction harmonized with the surface water occurrence dataset). The window is expanded to 0. 5° longitude × 0. 5° latitude if no static pixels of the class exist in the smaller window. (Because class 82 has so few pixels globally, class 72 pixels are additionally applied in the window mean calculation for class 82 pixels. ) One of five cases is possible: (1) If the mean tree fraction for the window falls within the expected range based on the class legend, then the tree cover fraction of the pixel of interest is assigned as the mean tree fraction for the window.
A mixture of tree and shrub woody vegetation types is assigned to pixels of the shrubland classes  (codes 120, 121, 122, and 180). The 30 m resolution tree canopy height dataset from  Potapov et al. (2021)  is applied to discriminate between shrubs and trees in pixels that are covered by this data product (52°N-52°S). The percentage cover of the 3-5 m height class is taken to be the percentage shrub cover in the 300 m pixel and the percentage cover of the > 5 m height class is taken to be the percentage tree cover in the 300 m pixel, recognizing that there may be some bias introduced by 30 m pixels in the input dataset that contain both shrubs and trees. In deference to the class legend, 300 m pixels with shrub cover < 16 % are assigned as having 16 % shrub cover and those with tree cover > 15 % are assigned as having 16 % tree cover.
Pixels classified as urban (code 190) can have non-zero fractions of inland water, trees, natural grass, and urban impervious (built-up) PFTs. In the land cover classification, pixels are assigned as an urban class when a minimum threshold of 50 % built was exceeded based on the GUF dataset  (Esch et al. , 2017). In the PFT product, the tree and surface water fractions are derived using the same protocol as the one applied to the vegetated classes. The urban impervious fraction is derived from the GHSL dataset  (Pesaresi et al. , 2013)  by aggregating the built-up pixels from the four epochs into a binary built-up / non-built-up distribution at 38 m. Re-sampling to 300 m provides the percentage of the 300 m pixel that is built PFT, introducing local variability which at the global scale ranges from 0-100 % built.
Only pixels classed as urban by GUF are assigned a non-zero urban impervious fraction in the PFT dataset. Non-urban pixels (i. e. , those with less than 50 % urban land cover according to GUF) are not refined with GHSL data or assigned a percentage built-up. The GHSL appears to capture urban impervious areas more consistently whereas GUF misses road fractions in the built fractions. This is most notable in rural areas and a few selected locations in city centres. If the sum of the urban impervious, tree, and water fractions exceeds 100 %, then the urban impervious percentage is retained and the water and tree percentages are scaled down proportionally to a total sum of 100 %; otherwise, the residual of the urban impervious, tree, and water percentages is assigned as the natural grass percentage.