dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was carried out as part of the IDEAS-QA4EO and Forest-Scan contracts funded by  ESA-ESRIN. Fieldwork at the Speulderbos site was funded by the  IDEAS+  contract funded by  ESA-ESRIN. The Australian fieldwork was funded by  BELSPO (Belgian Science Policy Office)  in the frame of the  STEREO III  programme -project  3D-FOREST (SR/02/355 ). Kim Calders was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  835398. Sruthi M. Krishna Moorthy  was funded by  BELSPO (Belgian Science Policy Office)  in the frame of the  STEREO III  programmeproject  3D-FOREST (SR/02/355 ). Di Wang  was supported by the  National Key R&D Program of China  ( 2021YFF0704600 ) and the  National Natural Science Foundation of China  under Grant No.
These traits were extracted from the genomic material of sequenced prokaryotic genomes available via the JGI/IMG platform  (Chen et al. , 2021; Mukherjee et al. , 2021). 1 For downstream analyzes we considered those genomes that are integrated into the default reference database in the PICRUSt2 software  (Douglas et al. , 2020)  and which infers the genomic content of uncultured prokaryotes 1 https://img. jgi. doe. gov/ from closely related genomes via taxonomic marker genes. In total 17,856 of the 20,000 genomes that are integrated in the PICRUSt2 default reference phylogenetic tree could be downloaded via the JGI/IMG database (date of download: 24. 08. 2021). These genomes cover a broad phylogeny (73 phyla; 172 classes; 382 orders; 762 families; 2,669 genera; 8,847 species) and include genomes from yet uncultured candidate phyla (Supplementary Table  S1 ; Supplementary Figure  S1 ).
On the other hand may multi copy genes that are under control of the same operon have an adaptive effect to stressful environments due to an enhanced dosage effect  (Kondrashov, 2012). Some genomic traits are provided directly by the JGI genome statistics, while others were computed from the genome sequences. An inspection of the JGI/IMG provided RRN data indicated inaccuracies for this specific genomic trait (Supplementary Figures  S1, S2 ). We therefore extrapolated RRNs for the JGI/IMG reference genomes affiliating with the same genus of genomes stored in the Ribosomal RNA Operon Copy Number Database (rrnDB) 2  which provides curated RRN data  (Stoddard et al. , 2015). In order to remove phylogenetic redundancy from the dataset we aggregated mean trait values for all genomes at the species level.
The affiliation of genomes at the species level was determined via the GTDB taxonomy  (Chaumeil et al. , 2020). In the case of genomes that could not be annotated at the species level to the GTDB taxonomy, we considered genomes sharing >94% of their average nucleotide identity as affiliating to the same species  (Konstantinidis and Tiedje, 2005; Jain et al. , 2018). Scripts that were used for determining the here presented genomic traits and the assignment of genomes to species are available via GitHub 3 and a detailed method description as well as a trait table are provided in the Supplementary material (Supplementary File 1; Supplementary Table  S1 ). Covariation patterns among the above introduced genomic traits in the multivariate trait space were illustrated via a principal component analysis (PCA, Figure  3 ).
The above suggested tradeoff should therefore be confirmed in other datasets, possibly in combination with other approaches to detected HGT events. The GC content did not exhibit a pronounced covariation with any other genomic trait. However, a certain level of covariation could be detected with genome size and the gene duplication level or with CUB and the fraction of horizontally transferred genes (Figure  3 ). The limited covariation of the GC content with any other trait could be due to the complex selection forces, including on the one hand nutrient availability and on the other hand exposure to heat or desiccation stress that in combination drive the GC content evolution  (Hellweger et al. , 2018; Chen et al. , 2020).
The roughly orthogonal position of several resistance and resilience traits in prokaryotes across whole range genome sizes reflects the results of some previous studies, which suggested that genome size and growth rate are not related  (Vieira-Silva and Rocha, 2010; Westoby et al. , 2021b). However, the analyzes presented here were based on a database that exceeded those used in these former studies by one to two orders of magnitude. An inspection of pairwise trait correlations indicated the presence of non-monotone relationships between several resistance and resilience related genomic traits that to our knowledge were not yet reported in earlier studies: for instance, genomes with a size up to roughly four million base pairs featured according to a local fitting approach a positive relationship with CUB, while after approximately five million base pairs it turned to a negative relationship (Figures  4, 5 ).
Furthermore, genomes up to this threshold continued to increase due to a combination of newly acquired genes and gene duplication events, while larger genomes increased rather due to gene duplication events (Figure  4 ). Last but not least aquatic environments are typically characterized by genomes smaller than four million base pairs, while soil environments usually harbor genomes larger than five million base pairs  (Giovannoni et al. , 2014). Correlation analyzes of partial datasets exhibited in agreement with the trendlines obtained by local fitting significant positive correlations between several resistance versus resilience related traits, if considering genomes up to a size of four million base pairs. The opposite was true in most cases if considering genomes larger than five million base pairs (Table  2 ).
, 2020; Piton et al. , 2021). This is in agreement with the negative correlation between several resistance and resilience related traits particularly among larger genomes. Habitat specific PCA patterns with genomes originating from soils, aquatic habitats or from the digestive tract furthermore supported our findings: depending on whether mean genome size of genomes from the habitats was >5 or <4 mbp resilience trait rotated either counterclockwise or clockwise relative to the global PCA (Figure  6 ). As a consequence and similar to the genome size dependent partial correlation analyzes, several resistance versus resilience related traits shifted with increasing mean genome size from positive towards negative correlations (Table  2 ). We though want to emphasize that only a minority of all genomes in our dataset could be assigned to one of the above mentioned habitats (n ≤ 565 species per habitat, Figure  6 ), and that the respective species may not be typically abundant representatives of these  habitats.
Particularly aquatic bacteria in our database seemed to be biased towards species with larger genome size: most aquatic habitats feature mean genome sizes between 1 and 3 Mbp  (Giovannoni et al. , 2014)  compared to the value of 3. 9 Mbp detected in this study (Figure  6C ). Natural aquatic habitats may consequently be characterized by more pronounced positive covariation of resistance and resilience traits than observed in our study (Figure  6C ). Beyond genome size driven differences in trait-trait variations also further habitat specific factors may exist that impact trait co-variation patterns. For instance, %TF and gene richness were tightly correlated in all habitats except the digestive tract. However, due to the limited number of species included in the habitat specific analyzes, we believe that a possible existence of habitat specific drivers for trait-trait variations other than genome size is at this point speculative and should be addressed in future studies.
, 2021; Romillac and Santorufo, 2021). This procedure is possible for all traits featuring a sufficiently strong phylogenetic signal (Box 3) and has been applied to determine genome sizes  (Barberan et al. , 2014)  or the CUB  (Weissman et al. , 2021)  of microbial communities based on 16s rRNA gene sequence data. Beside avoiding the possible biases outlined above, this strategy would allow to not only determine CWM, but also the other moments of trait distributions and thereby enable a more thorough evaluation of trait distributions in microbial communities. The PICRUSt2 software  (Douglas et al. , 2020)  that had been designed to extrapolate the genomic content of uncultured prokaryotes from closely related genomes via taxonomic marker genes can be analogously used to extrapolate the genomic traits outlined here.
Our trait table (Supplementary Table  S1 ) can be applied to predict genomic traits from 16s rRNA gene sequences via the hidden state prediction tool  (Louca and Doebeli, 2018)  integrated into the PICRUSt2 software and using the default PICRUSt2 species reference database. All above evaluated genomic traits featured overall significant phylogenetic signals (Table  3 ). In agreement with the phylogenetic signals, Mantel correlograms illustrated significant positive correlations between phylogenetic and trait value distances at least until a phylogenetic distance of 0. 5 (Table  3 ; Figure  7 ). Phylogenetic distance class specific phylogenetic signals from the Mantel correlograms (Figure  7 ; Table  3 ) can be used to evaluate if reference genomes used to predict genomic traits from 16s rRNA gene data are sufficiently close related to result in a robust prediction.
Due to possible biases of RRN values given in the JGI database (Supplementary Figures  S4, S5 ) we estimated the phylogenetic signal for RRN based on original entries of the rrnDB in combination with the corresponding rrnDB phylogeny. We assume that the comparably low phylogenetic signals detected for RRN (Table  3 ; Figure  7 ) may not be a purely biological signal, but could be due to inaccuracies of this parameter that, although not as pronounced as in the JGI database, seemed to be also inherent in the rrnDB database (Supplementary Figure  S5 ). One may wonder why genomic traits, such as prophages or %HGT at all exhibit phylogenetic signals, although individual events contributing to these traits are not inherited vertically. However, while the presence of a specific prophage or HGT gene in a genome should indeed not have a phylogenetic signal, it has been argued as outlined above that the characteristic of a genome to host multiple prophages or HGT derived genes is linked to the life-history of the corresponding organism.
001) transformed and Generation time was log(x) transformed). We inferred the RRN rrnDB phylogenetic signal from a phylogenetic tree calculated using rrndbDB 16s rRNA gene sequences (FastTree 2,  Price et al. , 2010)  and using the trait values available via the rrnDB (https:// rrndb. umms. med. umich. edu/static/download/, rrnDB-5. 7). The phylogeny for all JGI/ IMG values was inferred from the pro_ref. tre phylogenetic tree, which is used as backbone phylogeny of the PICRUSt2 software. All analyzes to compute phylogenetic signals were performed in R (R Core Team, 2021) and a script is available on GitHub (https://github. com/sarabeier/genomic. traits). resilience tradeoff (i. e. , soil communities) because these communities are either resistant or resilient  (Nimmo et al. , 2015). Even though specific CSR or YAS trait attributions may not generally be applicable for all prokaryotes, they should be applicable with adapted trait associations for communities harboring species within certain ranges of genome sizes, such as aquatic or soil communities.
001) transformed and Generation time was log(x) transformed). We inferred the RRN rrnDB phylogenetic signal from a phylogenetic tree calculated using rrndbDB 16s rRNA gene sequences (FastTree 2,  Price et al. , 2010)  and using the trait values available via the rrnDB (https://rrndb. umms. med. umich. edu/static/download/, rrnDB-5. 7). The phylogeny for all JGI/IMG values was inferred using the pro_ref. tre phylogenetic tree, which is used as backbone phylogeny of the PICRUSt2 software. All analyzes to compute Mantel correlograms were performed in R (R Core Team, 2021). Scripts with the code for all analyzes described in this figure legend are available on GitHub (https://github. com/sarabeier/genomic. traits). different trait dimensions, concerning their response to disturbances and nutrient availability.
(A) All genomes. (B) Genomes originating from soil habitats. (C) Genomes originating from aquatic habitats. (D) Genomes originating from the digestive tract. The three habitat types were determined via text search from the habitat information available via the JGI/IMG database: all genomes containing the strings 'soil' or 'rhizosphere' in the habitat description where classified as originating from soil habitats; all genomes containing the strings 'aquatic' or 'marine' or 'water' where classified as originating from aquatic habitats; all genomes containing the strings 'oral' or 'stomach' or 'gut' or 'intestinal' or 'feces' were classified as originating from the intestinal tract. The remaining genomes were not further classified.
SEM analyzes In order to identify the Pb-rich phases, the secondary electron microscope (SEM) Hitachi S3500N (LUTECE platform, IRSN, Fontenayaux-Roses) was used to localize the phases that host heavy elements using the SEM-Back Scattered Electron (BSE) detector under a voltage of 25 kV accelerating voltage. Then, the SEM-Energy Dispersive X-ray Spectroscopy (EDS) analysis was performed with two EDS Brücker XFlash SDD detectors working under a voltage of 25 keV at a working distance of 16. 8 mm in order to characterize the elemental composition of the phases, especially with respect to Pb and U. The SEM-EDS data were acquired at 8 µs/pixels at 100 kcps/s using the Esprit software 1. 9 for a pixel size of 0. 3 µm.
Acknowledgements We would like to warmly thank  Gilles Alcade ,  Olivier Diez  and  Cyrielle Jardin  for their helpful contributions on elemental analysis. This work was funded by the  IRSN's exploratory research program  ( ITUM  project:  Isotope Tracers  for the detection of  U Mining  activities in the environment). This is PATERSON, the  IRSN  mass spectrometry platform, contribution n • 12.
, 2004; Rojo, 2010; Chen et al. , 2020). In this review, we address the central role of CCR in plantmicrobe interactions in the rhizosphere. First, we provide a condensed overview of the different CCR mechanisms that have been characterized to date in various bacterial models. Then we continue to focus on microbial functions that are involved in interactions with plants and regulated by CCR. In addition, we perform a meta-analysis using available genomic and metatranscriptomic datasets to investigate the distribution of genes involved in bacterial catabolic repression within the plant rhizosphere. The results support our initial hypothesis that this mechanism is widespread among bacteria, specifically rhizobacteria. We use these new insights to discuss the ecological and environmental importance of CCR mechanisms in the rhizosphere.
CATABOLITE REPRESSION: EXPLORATION OF PREVALENT MECHANISMS IN BACTERIAL GENOMES The CCR system has been described in different model strains, but information on its distribution among microbial genomes is still limited  (Warner and Lolkema, 2003; Filiatrault et al. , 2013). Here, we investigated the presence of CCR systems in bacteria through BLAST analysis in two genomic databases (NCBI and MAGE) using different CCR models that are genetically well characterized (see the supplemental information). We focused on bacteria in which CCR master genes are well known. The presence and conservation of those master genes allowed us to assess their distribution among various available bacterial genomes. It is worth noting that such analyses are dependent on the availability as well as the quality of the sequenced genomes.
They more commonly include PGPR bacteria, such as Bacillus amyloliquefaciens and Paenibacillus polymyxa. Some of them are commonly used as biological control agents against phytopathogens in agriculture (e. g. , B. subtilis). In addition, several phylogenetic trees were constructed for each CCR system by including global master proteins (Crc, Crp, or CcpA; Supplemental Figures  1 2 3 ) in order to safely assign relationships, as well as reciprocal best hits. Globally, the trees confirmed the phylogenetic link between master proteins found   52 ) 100 (63) NCBI Table  3. Continued PSI-BlastP analyses were conducted with the sequences Crp (ECK3345), CyaA (ECK3800), and PTS (ECK2412) proteins from E. coli (strain K12) or CcpA (BSU29740), HprK (BSU35000), and HpR (BSU13900) proteins from Bacillus subtilis (strain 168) using MicroScope and NCBI genome databases.
The E. coli CCR system was found in different Proteobacteria, and the Bacillus CCR system was present in members of Firmicutes. This list was extracted for Supplemental Tables  3  and 4  by selecting only telluric bacteria and/or bacteria associated with plants (endophytic, epiphytic, or rhizobacteria). Habitat, taxonomy, and other phenotype information, including pathogenicity, were obtained from the NCBI microbial genomes database (http:// www. ncbi. nlm. nih. gov/genomes/lproks. cgi). Information concerning strains used is provided according to the genome browser. a All E-values are <10 E À10 and are included in Supplemental Tables  3  and 4. b Species have already been described as harboring functional CCR systems  (Tsuyumu, 1979; Reverchon et al. , 1991; Chen et al. , 2020). Plant Communications 3, 100272, March 14 2022 ª 2021 The Author(s).
CATABOLITE REPRESSION: IDENTIFICATION OF MECHANISMS EXPRESSED IN PLANT RHIZOSPHERES In a targeted approach, we explored whether genes encoding catabolite repression are expressed in the plant rhizosphere. We used different available rhizosphere metatranscriptomic datasets for BLAST analyses. Three protein sequences from three model strains were selected as queries: Crc from P. fluorescens F113, CcpA from B. subtilis (strain 168), and Crp from E. coli (strain K12). In total, 10 rhizosphere metatranscriptomic bioprojects available at NCBI were included (Supplemental Table  5 ). The datasets were obtained from rhizosphere soil and/or roots of Arabidopsis, corn, switchgrass, maize, canola, sorghum, Miscanthus, Populus, and Vellozia epidendroides. The reads were assembled, filtered, de-replicated, and screened for chimeras in order to obtain high-quality reads.
There have been several studies deriving A-Z and A-K dp relationships at the X-band using different approaches, including model calculations and also the direct use of observational data (e. g. Bringi and Chandrasakar, 2001; Gorgucci and Chadrasakar, 2005; Park et al. , 2005; Schneebeli and Berne, 2012; Matrosov et al. 2014; Yu et al. , 2018). Estimations of these coefficients and their ranges in variation were obtained in our study by processing the drop size distribution (DSD) data collected with a Parsivel 2 disdrometer located at the IGE site. The dataset includes 337 rainy days during the period April 2017-March 2020. The raw DSD measurements have a time resolution of 1 min. They are binned into 32 diameter classes with increasing sizes from 0. 125 mm up to 6 mm. Various filters have been applied to discard anomalous data and, in particular, to detect non-liquid precipitation, thanks to the falling speed spectra.
The volumetric concentration spectra have been computed at a 5 min resolution. DSD spectra with 5 min rain rate less than 0. 1 mm h -1 were discarded from the analysis. A dataset of about 14 600 DSD spectra was thus obtained, corresponding to all types of precipitation occurring in liquid phase in the Grenoble valley. As for the scattering model, we used the CANTMAT version 1. 2 software programme that was developed at Colorado State University by C. Tang and V. N. Bringi. The CANT-MAT software uses the T-matrix formulation to compute radar observables such as horizontal reflectivity, vertical reflectivity, differential reflectivity, co-polar cross-correlation, specific attenuation, specific phase shift, etc. , as a function of the DSD, the radar frequency, air temperature, oblateness models and canting models for the raindrops, as well as the incidence angle of the electromagnetic waves.
Acknowledgements. We thank the three anonymous reviewers, for their valuable comments, which helped us improving the article. We are grateful to  P. N. Gatlin  ( NASA Marshall Space Flight Center, Huntsville, AL ), for providing the CANTMAT version 1. 2 software developed at  Colorado State University  by  C. Tang  and  V. N. Bringi , who we also thank. Financial support. The RadAlp experiment is co-funded by the  Labex osug@2020 of the Observatoire des Sciences de l'Univers de Grenoble , the  Service Central Hydrométéorologique et d'Appui à la Prévision des Inondations (SCHAPI)  and  Electricité de France/-Division Technique Générale (EDF/DTG). Review statement. This paper was edited by  Gianfranco Vulpiani  and reviewed by three anonymous referees.
| Improving the use of currently available observations Despite the exciting progress described in section 2. 2, work is still needed to improve the use of currently available observations via improvements in satellite observation operators (see section 3. 5), and increasing understanding of polarimetric radar observations such as nonprecipitation echoes (e. g. ,  Augros et al. , 2018; Rennie et al. , 2011) , radar refractivity  (Dance et al. , 2019)  and differential phase  (Augros et al. , 2018). Waller et al. (2021)  showed that representation error biases and correlations may be critical for convectionpermitting NWP. However, computationally feasible methods for treating large datasets with long spatial error correlation lengths still need to be developed for operational purposes (see sections 2.
Long-term datasets can be obtained in a non-invasive manner, even if the mesocosms are not easily accessible. By reducing measurement intervals down to a minute, sensors offer higher resolutions and larger amounts of data than manual sampling  22  , which increases statistical robustness when testing for the effects of perturbations. Employing recent developments in the application of high-frequency automated sensors, we conducted two in situ mesocosm experiments during two productive seasons (spring and fall of 2018). The responses of natural coastal Mediterranean plankton communities to warming were compared in the Thau Lagoon, located in the northwestern Mediterranean. In this study, during both in situ mesocosm experiments, triplicate in situ mesocosms were heated at + 3 °C above the ambient lagoon temperature, and compared to other mesocosms, in triplicate, kept at lagoon temperature.
8. Some pigments can be used as taxonomic biomarkers because they are representative of particular phytoplankton groups  [23] [24] [25]. Accordingly, chlorophyll-b (chl-b) was attributed to green algae, prasinoxanthin to prasinophytes, fucoxanthin to diatoms, zeaxanthin to cyanobacteria, 19′-HF to prymnesiophytes, and peridinin to dinoflagellates. Ubiquitous pigments, being present in multiple groups, were not assigned a specific phytoplankton group. High-frequency sensor data acquisition, calibration, and correction. Raw fluorescence data were systematically transformed into µg chl-a L -1 (hereafter referred to as µg•L -1 ) following the manufacturer's recommendations. Each chl-a fluorometer was calibrated using an algae monoculture for which the chl-a concentrations were measured using high-performance liquid chromatography (HPLC).