dataesr/scientific-paragraphs-categorization huggingface dataset:
A MATLAB script was thus implemented to execute the Bayesian combination with several changes: 1) Instead of the Gaussian variogram model, the exponential variogram model was found to provide a better fit to the experimental variogram for the kriging interpolation. 2) Instead of using the block kriging method, the OK method was used to interpolate grid-averaged rain gauge data. The main difference between the ordinary and block kriging methods lies in the way variograms are calculated from data. The former calculates the variance between two points, while the latter calculates the variance between point and block (which is the average of the point-to-point variances). To distinguish the two different implementations, the RAINMUSIC Bayesian combination will be subscripted as BC R , whereas the replicate (MATLAB) model is annotated as BC.
e. Hydrological evaluation A first-level hydrological assessment of the data quality was conducted using the catchment water balance with the runoff ratio RR defined as the ratio of the precipitation that contributes to runoff: RR 5 R P. ( 19 ) The RR values calculated using the different outputs from the merging were compared to known values from the literature  (Campling et al. 2002; Buytaert et al. 2006b; Rollenbeck and Anhuf 2007). The merged precipitation products were subsequently used to drive a hydrological model. The Joint UK Land Environment Simulator (JULES;  Best et al. 2011) , which is a physics-based community model derived from the Met Office land surface scheme, was parameterized over 2040 grid boxes of 0. 1258 3 0. 1258 (approximately 14 km 3 14 km) in the catchment using best-available land cover and soil data to generate runoff.
The runoff was then routed offline along the river network using a delay function to simulate streamflows at various points in the river basin. The river network was developed using hydrographic data (90-m resolution) from the Hydrological Data and Maps Based on Shuttle Elevation Derivatives at Multiple Scales (HydroSHEDS;  Lehner et al. 2008). Model optimization was only used to obtain the routing parameters (flood wave celerities); any inconsistent model compensation of the precipitation errors across the merging products is not expected. For further description of the model, the reader is referred to  Zulkafli et al. (2013). The impact of merged precipitation forcing data on streamflow simulations is assessed by comparison with observed daily discharges. The evaluation is done at four streamflow gauging stations, identified in  Fig.
However, the resulting BC R field suggests that a significantly high weight was given to the block kriged estimates (i. e. , measurements), which could be attributed to the (estimation) error covariance of the block kriged rain gauge estimates being much smaller than that of the TMPA rainfall estimates. Through the comparison of the experimental variograms and the associated theoretical variogram models fitted by the RAINMUSIC software (not shown here), it was found that the theoretical variograms consistently underestimated the sill and/or overestimated the range, and as such, underestimated the error covariance of block kriging estimates. This relation between theoretical variogram fitting and (estimation) error covariance can be demonstrated through a sensitivity analysis (see Fig. 3 ).
High-throughput sequencing Total genomic DNA and ribosomal RNA-depleted retrotranscribed cDNA from both samples were sequenced on an Illumina Genome Analyzer IIx (Supplementary Materials and methods). Raw metagenomic and metatranscriptomic sequence data files are accessible in nucleic acid databases under BioProject PRJNA230567, sample LAO-A01 (SRX612782 and SRX612783) and LAO-A02 (SRX389533 and SRX389534).
Metagenomic and metatranscriptomic sequence assembly, gene annotation and determination of gene abundances Raw 100 nt paired-end sequencing reads from the metagenome and metatranscriptome libraries from each of the two sampling dates were first trimmed and quality filtered using the trim-fastq. pl script from the PoPoolation package  32  and overlapping read pairs were assembled using the PAired-eND Assembler 33 (PANDAseq). Non-redundant assembled PANDAseq read pairs and non-assembled reads from metagenomic and metatranscriptomic data sets of both sampling dates were then used as a single input for the MOCAT assembly pipeline. 34 The resulting nonredundant contigs and PANDAseq-assembled read pairs that had not been used were then combined and filtered with a minimum length threshold of 150 bp.
Protein-coding genes were predicted using the Prodigal gene finder 35 (v2. 60, contigs above 500 bp) or FragGeneScan 36 (contigs between 150 and 500 bp). The resulting amino acid sequences from both contig sets were merged and made non-redundant using CD-HIT. 37 All predicted gene sequences are accessible through MG-RAST 38 as ID MGM4550606. 3. The Kyoto Encyclopedia of Genes and Genome 39 database version 64. 0 was used to functionally annotate genes with Kyoto Encyclopedia of Genes and Genome orthologous groups (KOs) for ensuing metabolic network reconstruction (Supplementary Materials and methods, Supplementary Figure  1 ). To allow meaningful comparisons between gene copy and transcript numbers from the two seasons, identical numbers of reads were sampled from the metagenomic and the metatranscriptomic libraries of both seasons (Supplementary Materials and methods) using an in-house developed Perl-script.
The resulting reads were then mapped to the annotated gene sets. Cross-mapping reads were equally weighted according to the number of genes they mapped to and mapped reads were counted per gene. Finally, metagenomic and metatranscriptomic counts were normalised by the effective length of the gene sequence,  40  yielding normalised gene copy abundances and normalised transcript abundances, respectively. KO abundances were inferred from the sums of normalised gene copy or transcript abundances of all genes belonging to a given KO (Supplementary Materials and methods). Relative gene expression values were determined per KO by calculating the ratio of normalised transcript abundances to normalised gene copy abundances (Supplementary Materials and methods, Supplementary Dataset 3).
Metaproteome processing and analysis Isolated and purified protein fractions were separated using onedimensional SDS polyacrylamide gel electrophoresis. The proteins were reduced, alkylated, and digested with trypsin. The resulting peptides were then analysed by liquid chromatography coupled to tandem mass spectrometry. Peptide identification was carried out by database searching using the X!Tandem software  41  with the amino acid sequence database generated from the genes predicted from the combined metagenomic and metatranscriptomic assembly. Protein identification was carried out using peptide-spectrum matches using the Trans-Proteomic Pipeline,  42  with a probability of being correctly assigned to each protein determined by PeptideProphet. 43 The protein inferences from each fraction were determined using ProteinProphet and then combined with iProphet 44 to obtain a master set of identified proteins at a 1% false discovery rate.
All proteomic data have been deposited in the PeptideAtlas mass spectrometry raw file repository at http://www. peptideatlas. org/PASS/PASS00512. Identified proteins were assigned KO numbers using BLAT-based  45  alignment against the Kyoto Encyclopedia of Genes and Genome database v64. 0 (Supplementary Materials and methods). Relative protein abundances were obtained using the normalised spectral index, as described previously  24  (Supplementary Materials and methods, Supplementary Figure  4 ).
Community-wide metabolic network reconstructions Community-wide metabolic networks were reconstructed from the KOs with metabolic functions identified in the predicted gene sets from the combined metagenomic and metatranscriptomic assembly. The network reconstructions were rendered season-specific by using only KOs with mapped metatranscriptomic reads from each of the two sampling dates. The reconstructed networks reflect a connectivity-centred view of metabolism whereby enzymes grouped by KOs are represented by nodes and metabolites are represented by undirected edges, which represent either substrate or products of reactions catalysed by the respective KOs. 12  Each KO was assigned a pair-set of substrate and product metabolites according to the RPAIR  46  annotation in Kyoto Encyclopedia of Genes and Genome database version 67.
RESULTS AND DISCUSSION Identification of functions encoded and expressed in OMMCs in autumn and winter High-resolution coupled metagenomic, metatranscriptomic and metaproteomic data were generated from the OMMCs sampled in autumn and winter. A total of 16. 2 gigabases (Gb) of shotgun metagenomic paired-end 100 nt read sequence data as well as 38. 6 Gb of metatranscriptomic sequence data were obtained. 6. 5 million genes were predicted from a 6. 7 million contigs of a combined assembly (1. 6 Gb total length) of all metagenomic and metatranscriptomic reads (Supplementary Table  3 ). Based on reconstructed 16S ribosomal RNA gene sequences from the metagenomic data (Supplementary Materials and methods), the autumn and winter communities are dominated by Perlucidibaca spp. and Microthrix spp. , respectively (Figure  2c, d , Supplementary Dataset 1).
A total 830,679 predicted genes were annotated with KOs and regrouped (Materials and methods), yielding a total of 7,270 unique KOs. In the autumn sample, 10,074 protein groups (identified proteins grouped together because they share detected peptides) were identified using 19,248 non-redundant peptides out of a total of 727,155 mass spectra. In the winter sample, 7,106 protein groups were identified from 15,966 nonredundant peptides out of a total of 620,488 tandem mass spectra. A total 4,906 and 5,007 proteins were unambiguously identified in the autumn and winter samples, respectively. The congruency between the metagenomic and metatranscriptomic data was high, as 92% of KOs represented in the metagenomic data are also present in the metatranscriptomic data for both autumn and winter data sets (Supplementary Dataset 2).
Similar results were found in the comparison of KOs from metabolic pathways (Figure  3c ). This analysis highlights a relatively limited difference in terms of genetic potential and gene expression between the two seasonally distinct OMMCs despite stark differences in community structure (Figure  2c, d ). For each identified KO, we calculated relative gene expression, which is considered to be more informative than simple transcript abundance because expression levels are normalised to metagenomic gene copy numbers. 53 Furthermore, it allows quantitative insights into the contribution of low abundance members (such populations may be potential keystone species) to overall community activity to be obtained. 54 KOs with high relative expression in both seasons (Figure  3d ,e, Supplementary Dataset 3) were further analysed, as these are good candidates for genes which likely affect the overall community phenotype.
Among these, enrichments were found in KOs linked to nitrogen metabolism, as well as oxidative phosphorylation and nonribosomal peptide synthesis in both seasons (Supplementary Dataset 3). The highly expressed KOs involved in nitrogen metabolism represent enzymes for ammonium assimilation and oxidation, denitrification and nitrification. In particular, they include genes encoding likely subunits of ammonia monooxygenase (AMO; K10944, K10945 and K10946). AMO has a key role in the first step of nitrification carried out by aerobic ammonia-oxidising bacteria, mainly belonging to Nitrosomonas spp. and Nitrosospira spp. 54 AMO was previously found to be highly expressed in BWWT biomass. 55 In addition to the nitrogen metabolism enzymes expressed at a high level in both seasons, a nitrite reductase gene (K00363) was highly expressed in the autumn sample.
In the winter sample, the glycerolipid metabolism was enriched within highly expressed KOs. In particular, triacylglycerol lipase (K01046) exhibited pronounced transcript levels and its expression was also confirmed at the protein level (Supplementary Dataset 2). The most highly expressed genes of the 6,222 genes belonging to this KO could be matched to Acinetobacter spp. , which are known to occur in BWWT plants and accumulate triacylglycerols. 56 urthermore, out of the genes with detectable expression, the two gene sequences with the highest gene copy numbers (i. e. , abundance in the metagenomic data) were matched to the genome sequence of Microthrix parvicella BIO17-1 (ref. 57), which is enriched in KOs involved in lipid metabolism  57  (11. 3% of its annotated genes). The presence of these enzymes was recently suggested to be essential for lipid accumulation in a metabolic model reconstruction of Microthrix parvicella,  58  but not until now were they found to be expressed in biological wastewater treatment communities.
The pronounced expression of the aforementioned KOs involved in ammonium oxidation and the hydrolysis of triacylglycerols during both seasons emphasises the capability of the OMMCs to remove two of the main compounds present in wastewater, i. e. , ammonia  59  and lipids. 60 n the winter sample, KOs from the TCA cycle were also strongly expressed and the majority could be detected at the proteome level. Rather surprisingly, in the autumn sample, photosynthesis KOs were enriched. Expression of photosystem I in autumn was also confirmed by proteomics suggesting that phototrophic organisms are part of the floating OMMC during this season. Reconstruction of a generalised and season-specific OMMC-wide metabolic networks A community-wide metabolic network was reconstructed using the KOs expressed in the autumn and winter samples (Materials and methods, Supplementary Figure  5 , Supplementary Dataset 4).
The reconstructed network comprised 1,432 KO nodes with 29,988 edges representing non-unique metabolites. Season-specific networks were reconstructed analogous to the generalised OMMC-wide network, but by only using the 1,885 KOs or 1,775 KOs expressed in autumn or winter, respectively (Figure  3f ,g, Supplementary Datasets  5  and 6 ). This yielded networks comprising 1,298 nodes with 25,842 edges and 1,375 nodes with 27,370 edges forming a connected network for winter and autumn, respectively. Among the KOs specific to the autumn network, functions in the metabolic pathways for porphyrin and chlorophyll metabolism, sesquiterpenoid, triterpenoid and carotenoid biosynthesis pathways (ko00860, ko00909 and ko00906) were found to be enriched. This reinforces the notion that photosynthesis occurs in the OMMC sampled in autumn, while photosynthetic gene appear to be below the detection limit in the winter sample.
Identification of season-specific metabolic traits The autumn-and winter-specific community-wide metabolic network reconstructions exhibit similar structures (Figure  3f, g ) and represent 1,605 common KOs (i. e. , 88 or 94% of the KOs included in the autumn or winter network reconstructions, respectively). Based on the reconstructed networks, a detailed network topological analysis was carried out (Supplementary Dataset 7). Load scores (Equation  1 ) were determined in the reconstructed season-specific community-wide metabolic networks (Materials and methods). Most of the nodes in both the autumn-and winterspecific networks, which feature a high degree, represent KOs involved in amino acid synthesis. The relative small average shortest path lengths of 3. 21 and 3. 29 in the autumn and winter network reconstructions demonstrate that these represent 'small world' networks.
61 Among the nodes with the highest betweenness centrality, i. e. , the highest number of shortest paths passing through a node,  62  in both metabolic reconstructions, KOs with functions in pyruvate metabolism, glycolysis or gluconeogenesis and glycerolipid metabolism were enriched (false discovery rateadjusted P value o0. 05). In contrast, relatively higher betweenness centrality of the nodes representing KOs in fatty acid metabolism pathway (ko01212) was observed in the network reconstruction from the winter data set (median fold change of 4; Wilcoxon signed rank test P value o0. 001; enriched with false discovery rate-adjusted P value o 0. 00001; Supplementary Figure  6 , Supplementary Dataset 7) suggesting distinct substrate usage in both seasons. Other functions, in which this subset of KOs was enriched, included porphyrin and chlorophyll metabolism, biotin metabolism, polyketide sugar unit biosynthesis, lipoic acid metabolism and fluorobenzoate degradation (ko00860, ko00780, ko00523, ko00785 and ko00364), while only phosphoinositol metabolism (ko00562) was significantly enriched among the functions of the nodes with a higher betweenness centrality in the autumn network.
Selected genes are reported and potential 'choke points' are indicated in Supplementary Dataset 7. According to Rahman and Schomburg, choke points are special cases of load points, which consume and/or produce unique metabolites. Given that uniqueness of a metabolite is a strong claim in the context of the reconstructed community-wide metabolic networks as much of community metabolism remains unknown (only 13% of the predicted genes could be confidently annotated with a function), the identification of key functionalities by using load points was chosen as a more robust and appropriate measure in the present case. The positions of the key functionalities within the networks as per our criteria (Figure  1b ) are indicated in Figure  4  and Supplementary Figure  7. KOs involved in porphyrin and chlorophyll metabolic pathways are enriched among the selected genes in the autumn community, as are KOs with a function in degradation of aromatic compounds.
Among the genes encoding key functionalities in the winter OMMCs, no significant enrichment among KOs from a particular pathway could be observed. However, one of these genes is K03921, coding for an acyl-[acyl-carrier-protein] desaturase, which is part of the biosynthesis pathway for polyunsaturated fatty acids. In both the autumn and winter sets of season-specific key genes, the subunits of ammonia or methane monooxygenase (AMO or MMO) stand out. As discussed above and given the sampling from a nitrifying-denitrifying wastewater treatment plant, this is likely an AMO which catalyses the first essential step of nitrification by converting ammonia to hydroxylamine. 64 In contrast, MMO is involved in methane oxidation, which is less likely to be expressed in the sampled environment. Linking genes encoding key functionalities to community members Having selected genes encoding key functionalities within the sampled OMMCs using the reconstructed community-wide metabolic networks (Supplementary Dataset 7), we were interested in revealing which organisms expressed these genes within the community.
As these genes contribute essential functionalities to the community and are characterized by relatively high expression, they are likely to be encoded by keystone species. Contigs containing genes annotated with one of the genes encoding key functionalities were selected from the combined metagenomic and metatranscriptomic data sets. These contigs were aligned to the NCBInr nucleotide database (Supplementary Dataset 7) to identify organisms encoding genes with similarity to the expressed genes of interest. For five such genes (K03921, K01186, K01576, K01709 and K03335), no significant matches could be identified. On the other hand, three of these key genes from the winter-specific network (K01251, K00789 and K03527) were expressed from a multitude of contigs, which could be aligned well to over 50 different species.
Half of the matched contigs encoding the five autumn key genes from the chlorophyll-and porphyrin-synthesis pathway (K03403, K03404, K03405, K04034, K04035) were most similar to sequences encoded by the genome of the cyanobacterium Oscillatoria nigro-viridis PCC 712. The relative expression of these genes accounted for 85% of the expression of these genes in autumn (Supplementary Dataset 7). Some Oscillatoria spp. are found in wastewater, where they have been found to participate in nitrate removal. 65 rom the list of genes encoding key functionalities, we further selected the acyl-[acyl-carrier protein] desaturase (K03921) and the three subunits of AMO or MMO (K10944, K10945 and K10946) for further analysis. In all, 922 out of 1,067 contigs belonging to the AMO or MMO complex matched best to sequences of Nitrosomonas spp.
(Figure  5a , Supplementary Table  4 ). To estimate the abundance of Nitrosomonas spp. in the sampled OMMCs, metagenomic and metatranscriptomic reads were mapped against the genome sequence of Nitrosomonas sp. Is79 (ref. 67), yielding approximately twice as many metagenomic reads in winter compared with autumn (Supplementary Table  5 ). The ratio of metatranscriptomic to metagenomic coverage was four times higher in winter than in autumn, indicating a higher general level of activity of Nitrosomonas spp. in the winter OMMC, although AMO activity was high in both seasons. In contrast to the compelling link between the putative AMO genes and Nitrosomonas spp. , linking the acyl-[acyl-carrier protein] desaturase unambiguously to an organismal group could not be achieved by simple alignment to reference genomes in public databases.
67 Mbp genome with a GC-content of 62. 4% (Figure  5b , Supplementary Dataset 8). Based on the use of 31 bacterial protein coding marker genes, this isolate was identified as a Rhodococcus sp. 68 Supplementary Dataset 8). A detailed genomic analysis revealed a high number of genes involved in lipid metabolism encoded by this organism (Supplementary Results and Discussion) and non-polar storage granules were also observed microscopically (Supplementary Figure  8 ). As Rhodococcus spp. are known to exhibit lipid accumulation phenotypes,  69  it is likely that this organism is a keystone species within the OMMC. Recruitment of metagenomic and metatranscriptomic reads to the isolate's genome (Supplementary Dataset 8) revealed a low abundance of this organismal group in both autumn and winter, with a relative high transcriptional activity only in winter (Figure  5b , Supplementary Table  5 ) potentially directly linking its activity to the high community-wide lipid accumulation phenotype observed in winter.
ACKNOWLEDGEMENTS The authors thank  Mr Bissen  and  Mr Di Pentima  from the  Syndicat Intercommunal à Vocation Ecologique (SIVEC)  for their permission to collect samples and gain access to the monitoring platform of the Schifflange wastewater treatment plant;  Dr Nicolás Pinel  for his refinement of genome assembly and visualisation of Rhodococcus sp. LCSB065 and helpful discussions; and  Dr Aidos Baumuratov  for his support for microscopic analyses of the OMMC isolates. This work was supported by an  ATTRACT programme grant  ( ATTRACT/A09/03 ) and a  European Union Joint Programming in Neurodegenerative Diseases grant  ( INTER/JPND/12/01 ) both to PW, and  Aide à la Formation Recherche (AFR)  grants to HR ( PHD-MARP-04 ), EELM ( PRD-2011-1/SR ),  CCL  ( PHD/4964712 ) and  SN  ( PHD-2014-1/7934898 ), all funded by the  Luxembourg National Research Fund (FNR).
The project received financial support from the  Integrated Biobank of Luxembourg  with funds from the  Luxembourg Ministry of Higher Education and Research. The work of PM was funded by the  'Plan Technologies de la Santé du Gouvernement du Grand-Duché de Luxembourg'  through the  Luxembourg Centre for Systems Biomedicine (LCSB) ,  University of Luxembourg. This work was also supported in part with US federal funds from the  National Science Foundation   MRI grant  No. 0923536 , by funds through  National Institutes of Health ; from the  National Institute of General Medical Sciences  under grant No. 2P50 GM076547/Centre  for  Systems Biology  and  S10RR027584.