dataesr/scientific-paragraphs-categorization huggingface dataset:
Habitat and oxygen tolerance. We applied the predictive analysis also to other environmental variables, namely, to habitat and oxygen tolerance. We explore the habitat, a simplified variable consisting of a "Symbiont" class with 554 bacteria living in a host, an "Environment" class, with 395 free-living bacteria, and a "Mixed" class, with 235 bacteria that can live in a host or freely in the environment (see "Methods"). These nodes are ones found in more than 90% of cross validated models (b). X. X. X. X codes (X a number) are Enzyme Commission (EC) numbers that are associated with enzymes. We appended "_0", "_1" to EC numbers to designate different reactions catalysed by the same enzyme. CXXXXX are KEGG database C numbers, associated with metabolites. (d) KEGG pathways of the most predictive nodes, and the associated average number of pathway nodes per cross-validation fold, among 50 most predictive nodes.
However, it is important to keep in mind that although the number of prokaryotic species studied is high, as they were taken from the complete list of species of the KEGG database, they have the same biases found in the database: a very low number of extremophiles when compared to mesophiles, a bigger number of bacteria studied than archaea, as well as some very well studied species, where multiple strains are considered, compared to less studied species. We handled this problem by subsampling mesophiles in the growth temperature analysis. Another important remark is that the scope is a property completely based on topology, which allows its ease of use on a wide range of species, but it ignores stoichiometry and biomass dynamics. It is also strongly dependent on the input metabolites and the completeness of our networks, which greatly impacts the size of the scope.
However, the results found still give sufficient information and, as we have just mentioned, are corroborated by what is found in literature. For the sake of completeness, we tested whether the presence or absence of enzymes could also accurately predict the growth temperature using similar methods. This approach is akin to a classical differential genomics analysis without going through the network derivation and scope evaluation (albeit using only enzymes and not the whole genome, where some genes may not be annotated or code for an enzyme). In Supplementary Figure  S2  of the supplementary material, we show that it displays similar prediction accuracies compared to our approach despite being of higher complexity. Moreover, our scope analysis adds comparable functional metabolic information thanks to the connectivity information, possible redundancy of pathways, and also as it is observed on the same medium.
This explains why the enzymes and pathways of interest found by the random forest models for these two cases are different (see supplementary information). In this work, we have mainly focused on a generic medium for our scope, built on different species examples from each temperature class. As it happens, it also encompasses the various oxygen tolerance classes. As a potential research avenue, it would be interesting to also test a medium based on the different habitat classes, even though it is not a trivial task, as even a single habitat class may display a large variability. Overall, our results suggest that large scale datasets of prokaryotic species can be compared using interpretable embedding that also reduces dimensionality. Our use of the scope of metabolic networks provided key metabolites and pathways that are characteristic to environmental pressure that have somewhat been validated (or at least hinted) by wet lab experiments.
Methods The Python code reproducing our results, as well as the networks and metadata is publicly available on GitHub. Data set. All information on the species was extracted from the databases in November-December 2019. Our data set contains 5610 prokaryotic species from KEGG database  1. This represents almost all prokaryotic species in the database as of November 2019. Among these, 301 are archaea and 5309 are bacteria. We obtained further taxonomic information from the National Center for Biotechnology Information (NCBI) Taxonomy database  24. A synthesis of the data set and the associated metadata can be found on Fig. 1a. Growth temperature. We obtained the growth temperature values for 3392 species (190 archaea and 3202 bacteria). The temperature information originates from BacDive database  25.
Habitat information. We extracted the habitat information from the FusionDB database  26  , which stores functional data and metadata for 1374 bacteria. This database divides habitats into the following categories: 'Fresh water' , 'Marine' , 'Soil' , 'Other' , 'Human' , 'Host' , and 'Multi' (a combination of the others). These categories may be further detailed with the specific habitat where the species has been found. In order to simplify the variable so as to focus on the hypothesis that free-living and non free-living species are metabolically different, we created an 'Environment' class, consisting of the free-living categories: 'Fresh water' , 'Marine' , 'Soil' categories and 'Multi' category combining the other three. The second class created was 'Symbiont' , consisting of 'Human' and 'Host' , representing the species living in other organisms.
The last class was 'Mixed' , consisting of the 'Other' category, and the 'Multi' categories that combine a 'Symbiont' category and an 'Environment' one. We obtained 554 bacteria in the 'Symbiont' class, 395 in the 'Environment' class, and 235 in the 'Mixed' class. Oxygen tolerance information. We acquired the species' oxygen tolerance information from the Genomes OnLine Database (GOLD)  27  , completed with FusionDB database  26. There are 6 different categories, from mandatory oxygen to mandatory lack of oxygen: 'Obligate aerobe' , ' Aerobe' , 'Microaerophilic' , 'Facultative' , ' Anaerobe' , 'Obligate anaerobe'. However, as classes 'Obligate aerobe' , 'Microaerophilic' , 'Obligate anaerobe' amount to only 8% of oxygen tolerance information, we removed them. We, therefore, have data for 2103 bacteria and 128 archaea, consisting of 917 ' Aerobe' , 782 'Facultative' , and 532 ' Anaerobe'.
Metabolic networks. There exist a number of ways to produce metabolic networks from chemical reactions. The nodes of such a metabolic reconstruction can be metabolites (small molecules, substrates and products of the enzymes), or enzymes. The analysis in this paper is based on a representation using both metabolites and enzymes as nodes. There is a directed edge between substrates and the catalysing enzyme, and the catalysing enzyme is linked to each product. In order to have a very extensive dataset and maintain database consistency, our networks were directly derived from the KEGG database  1. We started with the first KEGG brite entry for each species. The brite entry lists gene names and the annotated enzyme commission (EC) codes for a given organism. From each entry we extracted all EC codes if they were found in complete form, i.
The expansion stops if there isn't any enzyme left with available substrates or if the whole network has been traversed. The metabolites and enzymes of the expanded network form the scope of the network (Fig. 1b  in orange and yellow). Hence, it represents the metabolic capacity of the species from the input medium. The seed metabolites we used are metabolites from the union of four chemically-defined growth mediums used to grow a mesophilic species  14  , a psychrophilic species  15  and a hyperthermophilic species  16  respectively, so as to cover the diversity of our species. The thermophilic medium  17  is already included in the union of the three other mediums. The mesophilic and hyperthermophilic species mediums were found in MediaDB  30  , a database of chemically-defined growth conditions, as it enables easy deduction of metabolites in medium.
We evaluated which nodes are in the scope for each species, and built a binary matrix providing information whether the nodes are in/out of the scope for each species. The rows of this matrix are all species of the data set, and the columns are the nodes. Species were sorted per taxonomy. The nodes were sorted according to the number of species having nodes present in scope. The nodes found in or out of the scope for all species were removed. The algorithm implemented is based on a Breadth First Search approach, starting at each metabolite from the growth mediums. Reactions are considered successively. A reaction will be in the scope if all its substrates are, resulting in all products of the reaction also in the scope. See the algorithm in the supplementary material.
CXXXXX are KEGG database C numbers, associated with metabolites. (d) KEGG pathways of the most predictive nodes, and the associated average number of pathway nodes per cross-validation fold, among 50 most predictive nodes. We only show pathways with more than 3 nodes. (e) Neural network growth temperature prediction. R 2 = 77. 09%. In orange, y = x. HT hyperthermophiles, T thermophiles, M mesophiles, P psychrophiles.
, 2017). They have been used by  Protopapadaki et al. (2017)  to reconstruct UT cloud systems. Recently these datasets have been extended, so that they now cover  Sep 2002 to Aug 2019. 70  On the other hand, the space-borne active lidar and radar measurements of the CALIPSO and CloudSat missions  (Stephens et al. , 2002)  supply the cloud vertical structure, in particular the radiative heating rates  (Henderson et al. , 2013). As this information is only available along successive narrow nadir tracks, separated by about 2500 km, we employed machine learning techniques on cloud, atmospheric and surface properties to build a 3D description of these cloud systems. These techniques were already successfully applied to extend IR brightness temperature  (Kleynhans et al. , 2017)  and snow water 75  (Snauffer et al.
(2005)  using ISCCP data. In addition, we provide the clear sky fraction estimated from AIRS within these grid cells. The reconstruction of UT cloud systems is based on two independent variables, pcld and ecld  (Protopapadaki et al. 2017 ): The AIRS cloud data were first merged to grid cells of 0. 5° latitude x 0. 5° longitude, and only grid cells with more than 70% UT 110 clouds were used to reconstruct UT cloud systems. After having filled the data gaps between adjacent orbits, UT cloud systems were built from adjacent elements of similar cloud height, given by pcld; in a second step convective cores, thick cirrus and thin cirrus were identified within the anvils by using ecld. This cloud system approach was used in section 4 to identify MCSs and to relate the radiative heating / cooling profiles of their convective cores and their anvils to different surface conditions.
Since the AIRS cloud height is retrieved as pressure and the input parameters are probably not precise enough to predict such a fine vertical structure, we transformed the FLXHR-LIDAR heating rates to 22 pressure layers between 70 hPa and the surface. For each of the AIRS footprints this collocated dataset also includes the number of detected cloud layers, as well as cloud top and cloud base of each of these cloud layers, detected by lidar or radar, at the spatial resolution of the radar footprints from the 2B-GEOPROF-LIDAR data. In section 3 we use the 195 number of detected cloud layers to evaluate the clear sky identification by AIRS alone. As version R05 of the FLXHR-LIDAR data was only released when we were finishing our analyses of section 4, we present the results which used 2B-FLXHR-LIDAR (R04) data for the training of the artificial neural networks (ANN), keeping in mind that the cirrus HRs above a height corresponding to 200 hPa are more reliable than those from ground-based measurements by may be still slightly underestimated compared to newer versions with different ice microphysics  (Protat et al.
, 2014; Ham et 200 al. , 2017; Hang et al. , 2019). Within the overall uncertainties described in this section and in section 3, the results in Section 4 are still valid. In the future we will train the ANN models again with the improved version of the FLXHR-LIDAR data and a new version of the CIRS data (using ERA5 ancillary data, as ERA-Interim data production ceased in August 2019).
Artificial Neural Network Construction The challenge in creating a complete 3D description of the UT cloud systems and their environment lies in the lateral expansion 205 of the information on the vertical structure, only available at the locations sampled along the lidar-radar nadir tracks, illustrated in Figure  1. In order to achieve this goal, we developed nonlinear regression models based on ANNs on the combined AIRS and ERA-Interim data as described in sections 2. 1 and 2. 2. ANNs have seen spectacular progress during the last few years, especially in the automation of finding the most appropriate weights used in the ANN layers. We used the TensorFlow framework (https://www. tensorflow. org) to train machine learning models available in the Keras program library 210 (https://keras. io) for Python, with training and testing along the nadir tracks, using four years of collocated data.
Hsieh (2009)  states that often a single hidden layer is already adequate to model any nonlinear continuous function. To improve the performance, we used the rectified linear unit (ReLU) activation function, corresponding to max(0,x). For a better efficiency we use the Adaptive Moment Estimation 220 (Adam) optimizer, using adaptive learning rates  (Kingma and Ba, 2014). The training dataset is randomly separated into three portions: 80% are used for training, 10% for validation and 10% for testing. In order to have similar cloud type, day-night and ocean-land statistics in these portions, we stratified the data by cloud type, ocean-land and day-night for LW and by cloud type and ocean-land for SW (only available during daytime). We used the mean absolute error (MAE) between the prediction and the target value from the lidar-radar observations as metrics for the 225 quality of the regression model.
The input variables have first been normalized by subtracting the mean and dividing by the standard deviation over the fouryear dataset. However, this metrics is difficult to use in phase 2, when we apply the model to all data, because in this case we would need to re-compute the mean and standard deviation of each variable over the whole data period, as they may change with time. In addition, not all input variable distributions are Gaussian. Therefore, once we had established sensitivity tests to 230 estimate the most appropriate variables, we trained final scene-dependent models by standardizing the input variables, which means subtraction of an acceptable minimum and division by the difference between acceptable maximum and minimum. These acceptable minimum and maximum values have been established for each variable and adapted to the scenes for which the models were trained: ocean or land, all cloud types, clear sky, high clouds or mid-and low-level clouds.
Sensitivity to input variables The input variables describing the cloud, atmosphere and surface properties used for the prediction of the radiative HRs are 245 summarized in Table  1. The training for the SW HRs is based only on data recorded at 1:30PM LT, while the training for the LW HRs exploits data for both 1:30AM and 1:30PM. Since the CALIPSO data are slightly more sensitive during night-time, we used for the LW training a day-night flag as additional input variable. The choice of input variables slightly differs for the prediction of LW and SW HRs: For the training of LW HRs, we used surface spectral IR emissivities while for the training of SW HRs we used 250 surface albedo and solar zenith angle. The MLP regression models compute from about 40 input variables radiative HRs for 22 pressure layers from 70 hPa to 1000 hPa.
Conclusions and Outlook The active lidar and radar measurements from CALIPSO and CloudSat make it possible to derive radiative HR profiles, but only on narrow nadir tracks. On the other hand, AIRS, also part of the A-Train satellite constellation, provides cloud properties with a large instantaneous horizontal coverage. By using these radiative HRs for the training and applying the resulting ANN 575 models on cloud properties from AIRS and atmospheric and surface properties from ECMWF meteorological reanalyses, we constructed 3D HR fields within 30N to 30S, for the period 2003 to 2018. We demonstrated that non-linear ANN regression models, trained on four years of collocated data along the nadir tracks, are appropriate methods to estimate tropical radiative HRs from about 40 cloud, atmospheric and surface properties.
This value is larger than earlier results of about 20%, using ISCCP cloud data. Our result 595 may still be slightly underestimated, because of the not comprised diurnal variation of UT clouds, the cloud contamination of the clear sky scenes identified by AIRS and the slightly underestimated LW warming above 12 km in the original FLXHR-LIDAR (R04) data linked to cirrus microphysical assumptions. However, the shape of the heating effects compared to those of  Li et al. (2013)  are significantly different, with our estimation indicating a much more vertically extended heating. This suggests an underestimation of the heating in the middle troposphere of the earlier result, which can be explained by the 600 shading effect of underlying low-level clouds on ground-based measurements and by a mixture of cirrus and surrounding single-layer low-level clouds linked to the coarse spatial resolution.
This difference in 'horizontal gradient' can be solely understood by the larger anvil size. With respect to global Tsurf anomalies, the MCS coverage is relatively stable (or very slightly decreasing) with warming, with 640 -1. 3 ± 0. 6 %/K, while the coverage of cold MCSs relative to all MCSs significantly increases by +18 ± 5 %/K. Furthermore, the surrounding thin cirrus area relative to the anvil area increases slightly by +0. 041 ± 0. 008 / K. In the future, we will add the latent heating profiles derived from the Tropical Rainfall Measuring Mission (TRMM) to this synergistic data set, which provides for the first time a 3D view of the radiative heating profiles over a long time period,. As the coincidences in time with AIRS are small, we will use again machine learning techniques, similar to the ones described in 645 this article.
This data base of UT cloud systems is being constructed within the framework of the GEWEX (Global Energy and Water Exchanges) Process Evaluation Study on Upper Tropospheric Clouds and Convection (GEWEX UTCC PROES, https://gewex-utcc-proes. aeris-data. fr/) to advance our knowledge on the climate feedbacks of UT clouds. In general, climate feedback studies are undertaken by climate model simulations, which rely upon their representation of convection and detrainment. The cloud system approach has already proved its usefulness in the evaluation of a new bulk ice cloud scheme in 650 the LMD GCM  (Stubenrauch et al. , 2019) , and the HRs may be used to distinguish between parameterizations of ice cloud radiative properties. Furthermore, this data base, in particular when including the total 3D diabatic heating, will be used to quantify the dynamical response of the climate system to the atmospheric heating induced by the anvil cirrus, refining and extending the studies of  Schumacher et al.
Acknowledgements and Data 655 This work is supported by the Centre National de la Recherche Scientifique (CNRS) and the Centre National d'Etudes Spatiales (CNES). The authors thank the members of the AIRS, CALIPSO and CloudSat science teams for their efforts and cooperation in providing the data, as well as the engineers and space agencies who control the data quality. AIRS CIRS data have been produced by the French Data Centre AERIS and will be distributed at https://cirs. aeris-data. fr. In section 4. 3, we used global Tsurf anomalies derived by the GISTEMP.
Team, The dataset was accessed in 2019 at https:// 660 data. giss. nasa. gov/gistemp/. Monthly indices of ENSO strength (ONI) and of PDO were obtained from NOAA (https://origin. cpc. ncep. noaa. gov/products/analysis_monitoring/ensostuff/detrend. nino34. ascii. txt and https://www. ncdc. noaa. gov/teleconnections/pdo/, respectively).
2014 ; Min et al. 2018 ). In addition, genome mining efforts have also allowed to understand the silencing or activation of biosynthetic pathways in microbes with the development of bioinformatics software, such as antiSMASH, SMURF and PRISM  (Baral et al. 2018 ). Traditionally, BGCs include non-ribosomal peptide synthase (NRPS), polyketide synthase (PKS), and ribosomally synthesized and post-translationally modi ed peptide (RiPP) family clusters. For instance, a total of 104 antiSMASHpredicted putative BGCs for secondary metabolites with high novelty and diversity were identi ed in nine Ktedonobacteria genomes  (Zheng et al. 2019 ). In the study of  Hu et al. (2018) , seven of the PKS and NRPS gene clusters related to antibiotics compounds, including friulimicin, lobophorin, laspartomycin, colabomycin, borrelidin, pristinamycin and kanamycin, have ben discovered in the genome of an actinomycetale streptomyces strain isolated from plant samples collected from high salt environment.
5. 0. 4 assembler (CLC bio, Waltham, MA). The genomic DNA base content (mol % GC) was directly calculated from the draft genome data. The draft genome sequence of P. halophilum reported in this study has been deposited at DDBJ/ENA/GenBank under the accession NOWF00000000. 1 (https://www. ncbi. nlm. nih. gov/nuccore/NOWF00000000. 1); BioProject: PRJNA395604 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA395604); BioSample: SAMN07411558 (https://www. ncbi. nlm. nih. gov/biosample/7411558). This WGS version of the project consists of sequences NOWF01000001-NOWF01000213. The assembled genome sequence was annotated using the National Center for Biotechnology Information Prokaryotic Genome Annotation Pipeline version 4. 2 (PGAAP, released 2013, https://www. ncbi. nlm. nih. gov/genome/annotation_prok/), Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Kanehisa et al.
2012 ) (http://www. genome. jp/kegg/), and Clusters of Orthologous Genes (COG) database (http://www. ncbi. nlm. nih. gov/COG) (Galperin et al. 2015). Antibiotics and Secondary Metabolite Analysis Shell (Anti-SMASH) analysis of the whole genome sequence was used to identify known and novel gene clusters that may have potential for the production of secondary metabolites  (Blin et al. 2019 ). To ensure the exact phylogenetic position of P. halophilum within the Thermoactinomycetaceae family, multiple genome alignment was generated by Mauve software (http://asap. ahabs. wisc. edu/mauve/) (Darling et al. 2010) , and a phylogenomic tree was visualized by FigTree and EvolView tools. The online OrthoVenn, available at website http://www. bioinfogenome. net/OrthoVenn/ was used for developing Venn diagram.
halophilum with other thermoactinomycete strains, the whole genome sequence of the strain was aligned and compared with those of the different strains selected from among the genomes submitted to the NCBI genome sequence database using Mauve software (http://asap. ahabs. wisc. edu/mauve/). It was found that P. halophilum clustered under a separate node with the three Thermoactinomycete, Kroppenstedtia eburnean, Plani lum fulgidum and Melghirimyces thermohalophilus as shown by the phylogenomic tree construction (Fig. 2 ). These results con rm a previous study based on the comparison of rRNA16S gene sequences of the strain with similar species deposed in Gene Bank (Frikha-Dammak et al. 2016). The genome-scale phylogeny constructed in this work clearly places P. halophilum among taxons in Firmicute Phylum under the family of Thermoactinomycetaceae and con rms that P.