dataesr/scientific-paragraphs-categorization huggingface dataset:
The daily rainfall (mm day -1 ) was manually measured using three, two and three bucket-rain gauges within a distance of 11 km from the tower location at the plantation, degraded and intact sites, respectively. Each rain gauge was installed 1. 5 m above the ground, in an open area so that rainfall was not affected by surrounding vegetation. The GWL was monitored as the water elevation relative to the ground surface, taking the base of the hollows as a datum  17. Data were recorded as negative distance below the surface, with positive values indicating ponding above the surface. The GWL loggers (four around the plantation tower, one in the degraded site and six in the intact site) to record the GWL every 30 min using a pressure transducer (Levelogger Model 3001, Solinst) were placed in perforated polyvinyl chloride pipes that were inserted vertically into the peat and anchored into the underlying clay (Fig.
1 ). Each GWL logger also recorded the water temperature in the pipe 1. 5 m below the peat surface. Further GWL data were manually recorded biweekly at seven and three locations at the plantation and degraded sites, respectively, and on a quarterly basis at eight more locations in the intact site (Fig. 1 ). Peat subsidence was measured at 11, four and 14 locations in the plantation, degraded and intact sites, respectively (Fig. 1 ), with hollow, perforated 5-cm-diameter polyvinyl chloride poles inserted vertically into the peat and anchored into the underlying mineral subsoil following the approach described in ref. 25. Annual average subsidence rates were derived from measurements during October 2016-May 2021 for the plantation site and during December 2017-May 2022 for the degraded and intact sites.
Eddy covariance data processing The eddy covariance fluxes of CO 2 , CH 4 and evapotranspiration were computed from the 10-Hz concentration and vertical wind velocity data using EddyPro software (version 6. 2. 0, LI-COR) at a standard half-hour averaging interval  54. A despiking procedure was applied to detect and eliminate individual out-of-range values for vertical wind velocity and concentration data  63. Detrending was carried out using the block-averaging method. A coordinate correction was applied to force the average vertical wind velocity to zero by the planar-fit method  64. Frequency response loss corrections were applied to compensate for the flux losses at low and high frequencies  65. The Webb-Pearman-Leuning correction  66  for air-density fluctuations induced by temperature (thermal expansion) and water vapour (dilution) was applied.
Nighttime CO 2 exchanges were considered equivalent to the ecosystem respiration (R eco ) value  78. The GWL is reported as the main controlling factor of R eco from tropical peatlands  17, 56. Therefore, we used the GWL, air temperature and soil temperature as environmental factors for the lookup table to derive the nighttime CO 2 exchanges using the MDS gap-filling algorithm. Following other regional eddy covariance studies in peat swamp forests  17, 56  , we performed MDS gap-filling using the REddyProc package (https://CRAN. R-project. org/package=REddyProc) on a half-hourly basis  77. ANN and RF procedures were iterated 20 times. For ANN and RF, PPFD, VPD, air temperature, GWL and friction velocity were used as predictive variables for the daytime and the PPFD and VPD data were excluded in the nighttime.
80. The standard deviation of three different flux values derived from friction velocity thresholds of the 5th, 50th and 95th percentiles were applied as an uncertainty because of the friction velocity threshold using the REddyProc package  77. The gap-filling flux uncertainty was calculated from the standard deviation of the MDS procedure  77. Averages of the 20 ANN and RF modelled values were used to fill gaps and the standard deviation was used to quantify the uncertainty owing to gap filling. The total uncertainty in eddy covariance measurements of CO 2 , CH 4 and evapotranspiration included gap-filling, random and friction velocity uncertainty  81. The annual estimate of CO 2 , CH 4 , evapotranspiration and GHG balance includes total uncertainty calculated using the propagation of errors law.
Extended Data Table 2 | Annual average with standard deviation of environmental variables at Acacia plantation and degraded and intact peat swamp forest in Sumatra, Indonesia GWLs were averaged from 11 locations around the Acacia plantation tower site, four locations around the degraded tower site and 15 locations in the intact site. The numbers marked with an asterisk (*) are cumulative estimates over eight months. Negative GWL values indicate the water level below the peat hollow surface. Article Extended Data Table 3 | Net ecosystem CO 2 and CH 4 exchanges measured using eddy covariance over Acacia plantation and degraded and intact peat swamp forest in Sumatra, Indonesia Annual estimates with total uncertainty includes random error, friction velocity criteria and gap-filling approach. Annual oxidative peat decomposition measured using the soil-chamber technique at the Acacia plantation is given with standard deviation from four soil flux chambers.
Data analysis EddyPro commercial software (version 6. 2. 0, LI-COR) for preliminary CO2, CH4 and H2O eddy covariance data processing; commercial software (version 4. 0, LI-COR) for preliminary oxidative peat decomposition and storage term data processing. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy All data that support the findings of this study are archived on https://doi. org/10. 5281/zenodo. 7500659.
Sampling small amounts of biofilm from harsh environments such as the biofilm present on the walls of a radioactive material storage pool offers few analytical options if taxonomic characterization and estimation of the different biomass contributions are the objectives. Although 16S/18S rRNA amplification on extracted DNA and sequencing is the most widely applied method, its reliability in terms of quantitation has been questioned as yields can be species-dependent. Here, we propose a tandem-mass spectrometry proteotyping approach consisting of acquiring peptide data and interpreting then against a generalist database without any a priori. The peptide sequence information is transformed into useful taxonomical information that allows to obtain the different biomass contributions at different taxonomical ranks.
, 1995). Indeed, microbial biofilms have been detected in very inhospitable environments such as spent nuclear fuel pools  (Gales et al. , 2004; Sarro et al. , 2005) , but their exact composition and functioning have not been established  (Chicote et al. , 2005; Rivasseau et al. , 2013). Assessing the microbial diversity of aquatic environments is usually done by extracting the DNA content from the sample, followed by amplifying 16 or 18S rRNA gene, sequencing the amplicons and comparing sequences with reference databases  (Esposito and Kirschberg, 2014). This approach has been challenged because the number of copies of the 16S rRNA gene per chromosome and the number of copies of the chromosomes in bacteria can vary depending on the strain and its physiological state  (Tourova, 2003; Vetrovsky and Baldrian, 2013).
The analysis of this specific microbial biofilm was further investigated by comparative proteogenomics. This revealed how genomic variation within closely related bacteria could contribute to their ecological divergence  (Denef et al. , 2010). Other studies of environmental biofilm communities have focused on marine biofilms sampled from destroyer hulls  (Leary et al. , 2014)  and wastewater treatment biofilms  (Abram et al. , 2009) , leading to a taxonomic classification of the major components of the microbial communities and the description of the metabolic activities of key microorganisms. The most common strategy for conducting a metaproteomic study is to first collect metagenomic data on the same sample in order to obtain a sample-specific protein sequence database. This can be done either by performing a systematic six-frame translation of the reads or contigs or by their tentative annotation, resulting in an inflated proteogenomics protein sequence database (Blakeley-Ruiz and  Kleiner, 2022).
Subsequently, MS/MS spectra recorded from peptides generated by proteolysis of the extracted proteins are assigned according to this metagenomics information. However, for precious samples obtained in small quantities, or for faster analysis, obtaining this metagenomic information can be far too complex and alternatives based on the use of generic database have proved valuable even for complex samples such as sediments  (Jouffret et al. , 2021). In this work, we collected minute amounts of biofilms from an industrial facility and applied such a new proteotyping approach consisting in the acquisition and interpretation of metaproteomic data against a generalist database. The assigned peptides were then analyzed to get insight into the taxonomy of the organisms present in the sample. This method is being used for the first time to decipher the composition of biofilms harvested from a pool used to store radioactive sources in a nuclear facility, and understand the metabolism of the main microbial components.
Proteotyping MS/MS data interpretation The proteotyping interpretation of MS/MS spectra followed the same procedure as described previously  (Grenga et al. , 2022). The version of the National Center for Biotechnology Information nonredundant (NCBInr) database used includes 76,068,736 protein sequence entries totaling 27,658,295,194 amino acids. For peptide inference, MS/MS spectra were searched using the MASCOT 2. 2. 04 search engine (Matrix Science, London, UK). Peptides were assigned to MS/MS spectra using Mascot with the following parameters: full trypsin specificity, maximum one missed cleavage, mass tolerances of 5 ppm on the parent ion and 0. 02 Da on the MS/MS, static modification of carboxyamidomethylated cysteine (+57. 0215) and oxidized methionine (+15. 9949) as dynamic modification.
Post-processing of Mascot dat files was done as follows using Python version 2. 6. 6 taxonomy identification procedures. Mascot DAT files were parsed using the Python version of Matrix Science msparser version 2. 5. 1 with the ms_peptidesummary function. 1 Peptide-Spectrum Matches (PSMs) were validated with a Mascot expectation value below 0. 05 using Mascot identity threshold (MIT), and by allowing multiple PSMs per MS/MS spectrum for ion scores higher than 98% of the top ion score. Peptides associated to spectra were assigned to taxa, resulting in Taxon-Spectrum Matches (TSMs) at each possible taxonomic level, species, genus, family, order, class, phylum, and superkingdom, as previously defined  (Pible et al. , 2020). For each taxon at each level, an identification of matching peptide sequences and TSMs was performed, as well as a count of specific peptide sequences and corresponding specific PSMs.
TSMs corresponding to validated taxa were added to a pool of TSMs at the taxonomical rank (RankValTSMs). This was reiterated for the next taxa in order, one per parent, until no more taxa with sufficient specific peptides could be found. The subtaxa were then reordered by the number of corresponding TSMs, excluding RankValTSMs, and compared to the threshold TSM, cycling through the parent taxa. The process was ended when the number of RankValTSMs (rank) was above 0. 992 × RankValTSMs (parents), or when no additional taxon could meet validation criteria. The conjunction of a Mascot MIT p-value of 0. 05, which is very stringent for large databases such as NCBInr, and of high thresholds (7 specific peptides to validate a taxon on a large database and reduced MS/MS data sets is very drastic) contributes to the selection of high-confidence taxa, at the expense of sensitivity.
Metaproteomics interpretation For the metaproteomic interpretation of the MS/MS spectra, the database used was the merge of all protein sequences of 1 http://www. matrixscience. com/msparser. html Sphingomonas, Caulobacter, and Acidovorax contained in the NCBInr fasta file, as well as the most common proteomics contaminants (MaxQuant contaminants. fasta database). The Sphingomonas-Caulobacter-Acidovorax pan-proteomic database comprises 498,167 protein sequence entries totaling 163,630,882 amino acids from 4,032 different annotated genomes. Peptide-to-MS/MS spectrum matches (PSMs) with a MASCOT expectation p-value below 0. 002612 were selected. This p-value threshold was obtained by searching the merged data set on the forward and reverse database as a decoy, and adjusting the p-value to correspond to a false discovery rate of 1%.
Proteins were grouped on the basis of their shared peptides. Protein abundances were assessed by their spectral counts. Gene Ontology (GO) annotations of proteins were obtained using DIAMOND  (Buchfink et al. , 2015)  to search for protein sequences against the Swiss-Prot database. Hits with more than 30% identity were retained, which indicates proteins of similar tertiary structures, and allows retrieval of most of the closest proteins with GO annotation as previously demonstrated in the first critical assessment of protein function annotation (CAFA) experiment  (Radivojac et al. , 2013). The Gene Ontology Annotation (GOA) database 2  was used to map Swiss-Prot entries with GO terms. However, given the poor fine-grained results of most approaches, including the reference BLAST approach in the CAFA experiment, we report GO ontologies at a higher level in the GO tree using GOslim terms.
Proteotyping the microbial composition of biofilms by phylopeptidomics Figure  2  shows the experimental workflow used to identify the taxa present in the different samples: protein extraction, peptide analysis by tandem mass spectrometry coupled to reverse phase chromatography, and metaproteomics analysis of the acquired data. Due to the scarcity of biological material recovered from the sponge, only one analytical run could be performed per sample. To proteotype the microbial composition of the biofilm, MS/MS spectra were interpreted against the NCBInr database, a generalist database comprising the annotated proteomes of several tens of thousands of reference organisms. The identified peptide sequences were then mapped to taxa whose proteomes contained these sequences. Taxa-specific peptides were those specifically associated with a given taxonomic unit, taking into account all different taxonomical ranks.
02 and 0. 1 mg of biological material as other experiments performed with a pure microbial culture under the same analytical conditions yielded ≈50,000 MS/MS spectra for 1. 9 mg of wet biological material when one tenth of the peptides produced were injected into the mass spectrometer. The TNW biofilm was the less abundant in terms of extracted proteins. However, this data set, acquired within 1 h of tandem mass spectrometry, is still relatively rich in information as a total of 594 peptide sequences could be assigned. The percentage of MS/MS spectra assigned per sample varies between 10% (BWW) and 16% (TNW) as shown in Table  1. This number illustrates the conjunction of two factors: an expected lack of completeness of the generalist database used for the interpretation on the one hand, and a possible decrease in the quality of some MS/MS spectra due to the co-elution of diverse peptide entities on the other hand.
, 2020). While the decrease in the number of specific peptides is rather sharp along the taxonomical ranks considered (from 724 at the Superkingdom taxonomic rank to 389 at the Genus taxonomical rank), the TSMs parameter is rather stable (from 1,168 to 1,111, respectively). Thus, no other main contributor to the biofilm, such as an unsequenced branch of life of bacteria, should be considered to explain the proteomic signal. The identification of organisms at a lower taxonomic rank than the genus rank would require a larger experimental dataset, thus more starting material, and a more comprehensive database for the three genera identified. Because the biofilm may comprise several closely related species whose genome have not yet been sequenced and reported in the NCBInr database, the identification at the species level in this study is only speculative.
The biofilm composition is relatively similar at three sampling points Table  2  presents also the phylopeptidomics data for the other three biofilms. For the TNW biofilm, a low quantity of material was noted from the number of MS/MS spectra recorded (4,657 compared to 14,677 for BNW). Although this decrease Experimental workflow for metaproteomic analysis. The eight main steps applied to the four biofilm samples are symbolized. is important (one third of BNW), the phylopeptidomics signal could be confidently assigned to the genus level as previously described for the BNW biofilm. Here, the presence of three orders was validated: Sphingomonadales, Caulobacterales, and Burkholderiales, with 264, 14 and 9 unique specific peptides, respectively. At the Family taxonomic rank, sufficient signal could certify the presence of Sphingomonadaceae (259 taxon-specific peptides) and Caulobacteraceae (14 taxon-specific peptides), but not of Comamonadaceae (only 3 taxon-specific peptides).
The BWW biofilm stands out from the others The latest biofilm sample, BWW, provided the largest MS/MS spectra data set, i. e. , corresponding to the most material-rich sample. In this case, only one class was validated (Alphaproteobacteria), with a large number of taxon-specific peptides (703). The Betaproteobacteria class counts only three specific peptides, which is below the validation threshold. Two orders, Sphingomonadales and Caulobacterales, contributed to this signal. The genera identified were: Sphingomonas and Caulobacter, with 497 and 7 taxon-specific peptides, respectively. Therefore, the BWW biofilm stands out from the others as no evidence of Acidovorax could be detected in this biofilm although a larger amount of material was available. At the species taxonomical rank, the most-closely related species identified by their specific peptides in all the sampled biofilms were the same as those identified for the BNW biofilm, with the exception of BWW, which does not comprise any detectable Acidovorax signal.
The BWW biofilm signal differed from the others due to the absence of Betaproteobacteria phylopeptidomics signal. Some slight discrepancies in terms of relative ratio of each genus were noted between the different taxonomic ranks (Figure  3 ). However, because the higher taxonomical ranks benefit from a greater number of genome-sequenced microorganisms in the database than the lower taxonomic ranks and thus from a greater number of protein sequences, their reliability in terms of quantitation should be better.
Sphingomonas-Caulobacter-Acidovorax consortium Although the signal was relatively low, metaproteomics interpretation could be achieved using a reduced pan-proteomics  Proportion of each genus in the four biofilms after phylopeptidomic analysis. Biofilms were recovered from four locations (TWW, BWW, TNW, and BNW). The ratios of microorganisms were established either by Class-specific TSMs, Order-specific TSMs, Family-specific TSMs or Genus-specific TSMs for the four samples. database encompassing the protein sequences of all genomewide sequenced strains belonging to the three genera detected in the samples and usual contaminants in proteomics experiments. This pan-proteomic database included a total of 498,167 protein sequences from 4,032 different annotated genomes. By merging the MS/MS datasets from the four biofilms, a total of 3,165 peptides were identified and 695 proteins were validated (Supplementary Tables  1, 2 ).
A functional analysis of the gene ontology was performed: Supplementary Table  2  shows the results at the protein information level and Supplementary Tables 3, 4 display the data from the enriched functional analysis. Supplementary Table  3  includes results from the irradiated zone BWW, while Supplementary Table  4  only considers the 3 sampling zones submitted to similar irradiation in the 3 weeks prior to sampling. Enrichment was performed on the basis of the generic GOslim ontologies for the three classes: molecular function (GOslim_MF), biological process (GOslim_BP), and cellular component (GOslim_CC) as defined by the Gene Ontology consortium. Each protein in each GO term was weighted by the number of spectral counts divided by the number of residues. Each GOslim term was then quantified by the sum of the corresponding proteins for each sample and for each of the three genera, normalized by the sum of the GOslim terms within each GOslim class and genus.
05 mg of biological starting material. With an average length of 10 amino acids per peptide, these peptides are totaling 7,240 amino acid positions. Because two amino acids (leucine and isoleucine) are indistinguishable by tandem mass spectrometry, the theoretical combinations of all amino acids lead to an impressive number of combinations (19 7240 ), which can theoretically be obtained by sequencing the equivalent of a nucleic acid sequence totaling 42 trillions of nucleotide positions. However, due to the history of Life starting from a common ancestor, the molecular relationships between organisms and the constraints of protein folding for stable structure and function, the sequences possibilities are more limited than this theoretical estimation. Anyway, the taxonomic identification based on the biofilm dataset collected in the present study is highly reliable because it is based on a large number of taxon-specific peptides.
By bypassing the protein inference problem, the peptide-centric approach provides a more direct and unbiased view of the taxonomical units present in the sample, and thus a better estimation of their contributions to the biomass  (Pible et al. , 2020). This said, the limits of our approach can be discussed hereafter. First, the current lack of the NCBInr database to comprehensively represent the biodiversity on Earth is trivial. The method may not be applicable to identify new microorganisms from branches of Life whose genome has not yet been sequenced, or to accurately provide the most exact taxonomic rank (species, subspecies, or strains ranks). Currently, the generous efforts of the scientific community to sequence the complete genome of pathogens, emergent pathogens and environmental samples are helping to fill this gap over time.
Consequently, it is expected that the peptide-centric method proposed in the present study become more powerful and efficient in the future with further database updates. A second caveat relates to the current detection limit of tandem mass spectrometers and the impossibility to amplify the peptide signal as in nucleic acid polymerase chain reaction. However, we have shown here that an amount of approximately 0. 02 mg of biological starting material was sufficient to identify the three main microbial components of the TNW biofilm, as well as their respective contributions to the biomass using a Q-Exactive HF tandem mass spectrometer. For sure, this current sensitivity limit may be lowered with newer tandem mass spectrometers as suppliers tend to regularly increase the sensitivity and acquisition speed of their instruments.