dataesr/scientific-paragraphs-categorization huggingface dataset:
Phyre2  [33] , a protein structure prediction software, utilized PDB code 4WL3 as the sole template, and 321 out of 325 residues (99% of the sequence) were modeled with 100% confidence (Figure  3B ). Overall, the model suggests that Cm33407BSH presents two antiparallel β-sheets sandwiched between α-helices and adopting the core catalytic four-layered αββα fold of the N-terminal nucleophile (Ntn)-hydrolases superfamily. The superimposed structure of Cm33407BSH with Ef BSH revealed similarities among their structures and their catalytic active sites contain the nucleophile residue (Cys2) (Figure  3C ). Notably, four loops constituting the substrate binding pocket were modeled, as well as an assembly loop that holds the protein multimer. All key residues for binding and catalysis are conserved between the two proteins, preserving the binding pocket conformation, with the striking difference of Leu 18 and Leu 20.
Indeed, the two aromatic and hydrophobic residues, F18 and Y20, in Ef BSH are replaced by Leu residues, a more hydrophobic amino acid without an aromatic ring. S1  for accession numbers and references. Conserved residues are highlighted in red. Black stars indicate the amino acid residues that are conserved in the choloylglycine hydrolase superfamily. Black triangles indicate the amino acid residues that were shown to be important in catalysis and substrate binding in previous studies  [43]. The dashed black rectangles indicate the four substrate binding pocket loops (also represented in panel (B)). (B) Tertiary structure homology model of Cm33407BSH. Superposition of a BSH Phyre2 homology model  [33]  (cyan) with the BSH from Enterococcus faecalis str. T2 (Ef BSH; PDB ID 4WL3  [19] , light pink).
(C) Superposed image of the four substrate binding pocket loops and comparison of Cm33407BSH and Ef BSH. The image shows the conservation of the key residues within the four loops, including Cysteine ("C"), Tyrosine ("Y") and Glutamic acid ("E") in positions 2, 65 and 269, respectively, with the notable exception of the Phenylalanine ("F") and Tyrosine ("Y") (light pink) for Ef BSH and Leucines ("L") (cyan) for Cm33407BSH residues located at position 18 and 20 respectively. Key: Efaeca: Enterococcus faecalis T2; Cm33407: C. minuta DSM33407; Cperfr: Clostridium perfringens; Blongu: Bifidobacterium longum; Lsaliv: Lactobacillus salivarius; Btheta: Bacteroides thetaiotaomicron; Lsphae: Lysinibacillus sphaericus. S1  for accession numbers and references. Conserved residues are highlighted in red.
Black stars indicate the amino acid residues that are conserved in the choloylglycine hydrolase superfamily. Black triangles indicate the amino acid residues that were shown to be important in catalysis and substrate binding in previous studies  [43]. The dashed black rectangles indicate the four substrate binding pocket loops (also represented in panel (B)). (B) Tertiary structure homology model of Cm33407BSH. Superposition of a BSH Phyre2 homology model  [33]  (cyan) with the BSH from Enterococcus faecalis str. T2 (EfBSH; PDB ID 4WL3  [19] , light pink). (C) Superposed image of the four substrate binding pocket loops and comparison of Cm33407BSH and EfBSH. The image shows the conservation of the key residues within the four loops, including Cysteine ("C"), Tyrosine ("Y") and Glutamic acid ("E") in positions 2, 65 and 269, respectively, with the notable exception of the Phenylalanine ("F") and Tyrosine ("Y") (light pink) for EfBSH and Leucines ("L") (cyan) for Cm33407BSH residues located at position 18 and 20 respectively.
The BSH Protein Carried by C. minuta sp. Clusters away from all Identified Putative BSH Sequences in the Human Gut Microbiome We then decided to interrogate how specific Cm33407BSH is within known human metagenomes and therefore examined the distribution of BSH amongst the constituent members of the human gut microbiota. To investigate the sequence diversity within the BSH family, we first collected a non-redundant set of all available reference BSH sequences that could be associated with this family from public databases. As described in the flowchart in Figure  S1 , we first extracted all protein sequences annotated as choloylglycine hydrolase, bile salt hydrolase, linear amide C-N hydrolase and choloylglycine hydrolase family protein from the NCBI protein database. This step allowed us to retrieve a total of 14,319 sequences putatively annotated as BSH enzymes (Table  S2 ).
To refine this dataset by only selecting the sequences that can be found in the human gut, we blasted all 14,319 sequences against the gastrointestinal tract Human Microbiome Project (HMP) protein database and against the Unified Human Gastrointestinal Protein (UHGP) database  [36, 38, 39]. Here, we obtained drastically different results since the HMP database only recovered 1658 sequences at 100% identity with a BSH annotation while the UHGP blast returned 5179 sequences at this threshold (Figure  S3 , Table  S3 ). We thus decided to continue the analysis using the UHGP annotated sequences as a representative catalog of the proteins carried by the human microbiota. Using a minimum threshold of identity of 85%, 6701 sequences out of 14,319 were retrieved as putative BSH proteins from the human gut (see Figure  S1  and Table  S4  for a list of raw data).
These sequences were all associated with an E-value of 0 and the lowest coverage was 86%, which gives high confidence in the overall quality of the dataset. In order to construct a phylogenetic tree of the putative BSH sequences from the human gut microbiome, we clustered these 6701 sequences into 413 clusters of sequences using a 70% identity threshold (see Table  S5  for raw data). The reference sequences of the 413 clusters were used to perform a phylogenetic analysis: the sequences were aligned, and the phylogenetic tree obtained is presented in Figure  5. Based on the topology of the tree, 16 major clusters were identified (see Table  S6  for raw data). The christensenella subcluster was thus identified within the BSH-C1 cluster (Figure  5 ) largely dominated by Enterococcus spp. sequences.
Similarly, we observed a modification of Y65 in L. salivarius (PDB ID 5HKE), which has been described as involved in the specific activity of the enzyme towards glycine-conjugated bile acids  [55, 56]. Yet, more studies must be performed to determine this accurately. Nevertheless, these observations strongly support the distinctive nature of C. minuta's BSH. We finally explored the distribution of putative BSH sequences in the gut microbiome in order to evaluate the sequence of Cm33407BSH with regard to the diversity of the gut ecosystem. To perform this analysis, we needed to collect a sequence catalog of all publicly available putative BSH sequences. A similar type of collection was recently published by Song et al. [22]  but because C. minuta's BSH gene is publicly annotated as a linear amide C-N hydrolase, these sequences were missed.
Thus, we adjusted our methods accordingly and included over 9000 additional sequences in our collection from the NCBI refseq protein database (Figure  S1 ). By doing so, we performed the most comprehensive analysis of putative BSH sequences in gut microbiomes to date. In addition, the study by Song et al. only used the HMP catalog, while our analysis showed that using the UHGP database allowed a more accurate identification of the putative BSH proteins from the human gut microbiome. As mentioned by Almeida et al. [36] , the UHGP database was built using reference genomes from cultured gut microbiota and Metagenome Assembled Genomes (MAGs) from several studies. Thus, the UHGP database was built from more than 200,000 non-redundant genomes from around 4650 prokaryotes whereas the HMP gastrointestinal tract protein database only contains 457 unique genomes.
This additional diversity in the UHGP database allowed an increased specificity in the identification of putative BSH sequences from the human gut microbiome. Interestingly, the number of sequences as well as the number of genera represented in the 16 major BSH clusters identified by the phylogenetic analysis, are uneven between the clusters without any obvious correlation between the two parameters (Figure  S6 ). We hypothesize that this is the result of a bias caused by an overabundance of studies on a few genera such as Enterococcus spp. , Staphylococcus spp. , Bacillus spp. , Salmonella spp. , Bacteroides spp. and Lactobacillus spp. [18, 19, 21]. Nevertheless, although C. minuta's BSH is included in one of the most diverse clusters, both in number of sequences and in number of genera (Figure  S6 ), the phylogenetic analysis revealed that the amino acid sequence of this BSH was unique.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/10. 3390/microorganisms9061252/s1, Figure  S1 : Flowchart applied to select BSH sequences, Figure  S2 : Alignment of the BSH protein sequences from C. minuta strainsDSM22607 and DSM33407, Figure  S3 : pH-rate of recombinant Xla1_01266 BSH enzyme using glycocholic acid as the substrate, Figure  S4 : Comparison of the HMP and UHGP database, Figure  S5 : Phylogenetic subtree of the BSH-C1 cluster shown in Figure  6 , Figure  S6 : Diversity of genus and number of sequences in the 16 major BSH clusters, Table  S1 : Characterized structures identified in the phylogenetic tree, Table  S2 : Sequences selected from the NCBI protein database, Table  S3 : Distribution of the identities between the selected BSH sequences and HMP or UHGP database, Table  S4 : BSH that shared more than 85% identity with at least one of the sequences in the UHGP database, Table  S5 : Results of clustering (70% identity) on the selected BSH sequences, Table  S6 : Genus composition of the clusters identified in the phylogenetic tree, Table  S7 : Sequences from the clusters close to the BSH from Christensenella minuta strains.
T2 (EfBSH; PDB ID 4WL3 [19] , light pink). (C) Superposed image of the four substrate binding pocket loops and comparison of Cm33407BSH and EfBSH. The image shows the conservation of the key residues within the four loops, including Cysteine ("C"), Tyrosine ("Y") and Glutamic acid ("E") in positions 2, 65 and 269, respectively, with the notable exception of the Phenylalanine ("F") and Tyrosine ("Y") (light pink) for EfBSH and Leucines ("L") (cyan) for Cm33407BSH residues located at position 18 and 20 respectively. Key: Efaeca: Enterococcus faecalis T2; Cm33407: C. minuta DSM33407; Cperfr: Clostridium perfringens; Blongu: Bifidobacterium longum; Lsaliv: Lactobacillus salivarius; Btheta: Bacteroides thetaiotaomicron; Lsphae: Lysinibacillus sphaericus.
Introduction Malaria caused by Plasmodium vivax presents control challenges for several reasons, among them the possibility of increased failure of P. vivax treatment. Chloroquine is an antimalarial used as a first-line drug in the treatment of the blood phase of the parasite, but chloroquine resistance (CQR) remains a hurdle. Treatment failure appears as a problem in Brazil and various countries of South America  [1, 2] , Central America  [3] , and other continents  [4, 5]. Even with these existing reports of CQR, further studies on the real magnitude of resistance and its space-time distribution are still necessary  [6]. Brazil maintains a surveillance system that keeps records of malaria cases (SIVEP-malaria). This system provides individual information on all malaria cases in the Brazilian Amazon basin, allowing data analysis of recurrences as well as incidence of P.
vivax, clinical trials must be performed  [17]. However, we can use information from large datasets such as SIVEP-malaria, which are adjusted using knowledge from clinical trials to construct models and thus make inferences using the observed recurrence time. Recurrence occurs for several reasons  [18] , including drug resistance, and short-time recurrences are highly linked to recrudescence  [3]. In the P. vivax malaria literature, there are many reports of CQR, but further large-scale studies on the extent of this resistance are still needed  [6]. The analysis of recurrence data up to 28 days, their estimates of trends, and spatial-temporal distribution in epidemiological scenarios in the Brazilian Amazon may reveal important information on the therapeutic failure of CQ. Here, we aim to estimate likely CQR burden, risk factors, trends, and spatialtemporal distribution in distinct epidemiological scenarios in the Brazilian Amazon.
Study Design and Data Collection Datasets containing individual records were used to analyze the risk factors of recurrences for up to 28 days. An observational study design was applied to analyze recurrence risk factors by variables such as age. Spatial analysis as an ecological study also considers data aggregated by municipalities on likely infection in different years, selected according to malaria incidence. Our study focused on three different scenarios, depending on the incidence of P. vivax malaria in the Amazon from 2003 to 2016, classified as High (2005), Intermediate (2010), and Low (2015) (Figure  1 ). In these years, individual-level data provide the number of positive patients for P. vivax and thus accounts for the number and time of recurrences. Next, data aggregation of likely infection by municipalities demonstrates the spatial and temporal variation of recurrences for up to 28 days in the Amazon.
Data Processing and Recurrence Identification Although SIVEP-malaria is a very robust and comprehensive information system, no information on patient follow-up is available. Thus, linking multiple notifications for the same potential patients has some hurdles: the absence of a unique identifier, typos when writing patient names, duplicated entries, and homonymous patients. In the absence of a unique identifier, we use the names of patients along with other personal variables, via linkage tools  [9, 11]. The linkage to obtain recurrences in the SIVEP-malaria database involved several processes: gathering and organizing the database; standardization of names; searching for similar names; construction of a unique identifier. A probabilistic function using patient's name, date of birth, mother's name, federation unit, and municipality of likely infection was applied to identify the same individual in the database.
For better computational performance, processing excludes words for joining names and surnames in the patient's and mother's names, e. g. , accentuation, excess of blanks, numbers, and the prepositions used between the names ("DA", "DE", "DAS" e "DOS"). A combination of three components represents the patient by first name, surname, and phonetic name. A soundexBR function returns the phonetic name as an alphanumeric code (soundex code). We obtained a selection of notification pairs identified as likely to be from the same patients by automatic verification, applying a probability threshold (probability > 0. 7). The next step of notification inspection includes visual confirmation of homonyms, possible siblings (twins), and duplicity. A new identification (ID) for notifications from patients deemed as recurrent were provided.
Statistical Analysis A binomial regression model with random effects in the municipality of likely infection provides the effects of risk factors for recurrence up to 28 days, as odds ratios or predicted probabilities. A multilevel model was applied considering the first level as the individual, and the second level as the municipality. We used a spatial cluster detection tool with counting data. Because of the study design, some municipalities presented zero recurrences and, in order to deal with that, scan statistics for zero-inflated Poisson (ZIP) models were applied accordingly, which uses a priori information of the expected value of ZIP  [20]. The decision to use spatial scanning is due to discontinuous data over time for municipalities.
Software The SIVEP-malaria databases were imported, and data curated and analyzed in the R software (version 3. 5. 0), Rstudio Server (version 1. 2. 1) installed on a server with Linux CentOS operating system version 7, 64 bits, with 90 GB of RAM and 12 physical processing cores. R software packages for larger databases (data. table, RecordLinkage and tidyverse) were used, in addition to GAMLSS, lme4, hnp, soundexBR, scanstatistic, sf, spedp, SpatialEpi and tmap. The significance level for the tests was 0. 05.
Since 2003, the MoH of Brazil defined SIVEP-malaria as the main information system for malaria surveillance in the Amazon and a huge database has been constructed since  [9, 19]. However, missing data for some variables hinder risk factor analyzes limited to a subset of variables  [19]. The findings presented here demonstrate that the probability of recurrence up to 28 days is associated with the age group. In addition, high incidences of vivax malaria in the municipalities increase the variations in recurrence. Recurrences at low API are mostly composed of resistance. The probability of recurrences in municipalities with a high incidence of vivax malaria possibly increases; however, great uncertainty still remains, since municipalities with extreme incidence make inference harder  [26]. The analysis here considered autochthonous cases since information on municipalities of likely infection was used in the Amazon region.
Acknowledgments:  Wuelton Monteiro ,  Marcus Lacerda , and  Daniel Villela  are  CNPq Research fellows. Daniel Villela  had support from  Conselho Nacional de Desenvolvimento Científico e Tecnológico (Ref. 424141/2018-3 ,  309569/2019-2 ). Authors are grateful for support from  PRINT-FIOCRUZ-CAPES program. This study was partially supported by the  Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES)-Finance Code 001.
The ENCA methodological frame There are four core accounts that evaluate 1. Land cover and use, 2. Water quantity and quality accounts, 3. Bio-carbon accounting and 4. Ecosystem landscape and rivers infrastructure. The latter three represent thematic accounts framed in the land accounts and articulated by socio-ecological landscape units (SELUs) (Figure  1 ). For each thematic account, the data fall into four sets of accounting tables (available at http://www. ecosystemaccounting. net/? page_id=173). A sample of a workflow is shown in Supplementary Material S2a and of an accounting table in Supplementary Material S2b. The detailed methods can be found at https://osf. io/j93xu/ (ENCA. Proof-of-concept folder). Table  2  presents the definitions and calculation formulas of main indicators. The results are based on spatial-temporal geographic information and socio-ecological units.
A political resource space where decisions are made on territorial resources in the form of governed projects that reflect the potential and the specifics of the corresponding resource space. That context facilitates coordinating disjoint public policies (water, land, agriculture, health, environment) and private sector activities with a long view on the common purpose (public interest). Abbreviations: ECU, ecological capability units; ENCA, ecosystem natural capital accounting. Meteorological data come from a dataset of annual data 2000-2012, assimilated for the purpose of the swbEWA modelling exercise. It was kindly provided to the project by  Kurnik et al. (2014). The coarse spatial resolution is compensated by the consistency of the variables on rainfall, actual evapotranspiration, soil humidity and runoff, important for deriving accounting results.
Accessibility The accessible resource is the surplus, not all water stock can be exploited as a resource. For lakes and reservoirs, estimated by default as 10% of stock. For rivers, the Total inflows-Reserved runoff (runoff exceeding the 3rd quartile of the period-2. 5% of mean annual discharge). Accessible resource is the amount of a resource that is accessible for uses in a sustainable way. It is not the stock itself nor the total stock plus inflow. Accessible resource is calculated by adjustment of the 'available resource' from all the elements which limit its use: respect of sustainable yields to avoid depletion, timeliness, distance, affordable economic costs of operation, respect of environmental norms and other legal constraints. Use Estimated from RMC water agency database on water abstraction.
Bio-carbon Stocks Biomass is the sum of: 1. Bio-carbon tonnes of trees by hectare were calculated using the volume of trees and area (m3 ha-1) from the forest inventory at department level  (IGN, 1992 (IGN, -2004)) , the vector database of forested polygons from the  IGN (2006 IGN ( -2017)) , the tree relative cover in the watershed  (Hansen et al. , 2013) , and conversion coefficients to biomass dry weight and to carbon content (Intergovernmental Panel on Climate  Change, 2003; 2006) , 2. Bio-carbon in litter and deadwood The estimation of this stock corresponds only to deadstanding trees and wind-throw in forested areas. The carbon tonnes by hectare were calculated from the volume and area (m3 ha-1) of the forest inventory at regional level  (IGN, 2011 (IGN, -2015)) , the spatial data of forested area from the  IGN (2006 IGN ( -2017)) , and coefficients of the Intergovernmental Panel on Climate  Change (2003; 2006) , and 3.
The inventory also helped to delimit the Rhône basin and hydrological sub-basins. The combination of DLCTs and river basin boundaries defines the SELUs, the basic statistical units of ENCA (Table  2  and Figure  1c ). SELUs allow for the integration of landscapes and the rivers that connect them though their run-off. The SELU has been designed to assimilate and compile input data of different sources and types (spatial resolution, geographic data, statistical data, etc. ). Thus, the production of the accounts is done at the object level. We used landcover map datasets from CORINE (2017) to analyse land cover and change. Once the land cover and river extent frame has been defined, quantitative and qualitative data are organised for each core account. Quantitative tables record (Table  2)  1.
General sequence of account production The preparatory work consisted in: 1. Spatio-temporal data collection (e. g. , global, national, regional, municipal). 2. Data assimilation and integration preprocess (e. g. , different tools according to the type of data, OpenOffice, Excel, QGIS, SAGA-GIS). 3. Spreadsheet programmes (e. g. , OpenOffice or Excel). 4. Analysis (e. g. , Postgis/PostgreSQL spatial data server and calculator Excel). For example, the ENCA protocol includes data derived from satellite images of Earth and other maps, meteorological and hydrological data, soil maps, biodiversity monitoring data, agriculture and forestry statistics, population censuses and administrative registries. Software and geomatic treatment details are shown in Supplementary Material S3. We then applied the following sequence.
Collecting data relating to the geographical information infrastructures necessary for the implementation of the method: delimitation of (sub)watersheds, reliefs, rivers, roads, administrative boundaries. Collecting and organizing various sets of data (i. e. , the detailed database) for accounting, namely 1. Socio-economic and environmental statistics, 2. Land cover, as land use defined by the CLC nomenclature and indexed by DLCTs (Figure  2a ), 3. Bio-carbon (measured for total vegetation cover and soil carbon), 4. Water resources and 5. Ecological infrastructures. Creation of geospatial data for each item of the thematic accounts. Data are converted to grids (rasters) of the same pixel size (1-ha pixel-size) to facilitate the calculations needed for the accounting. The processing makes it possible to aggregate the results at the level of predefined natural or artificial entities (e.