dataesr/scientific-paragraphs-categorization huggingface dataset:
77 Pg C in 2010) to a weak sink (-0. 12 Pg C in 2013), a 85% reduction in the strength of net CO 2 uptake (Fig. 4a, b ; Supplementary Fig. S5 ). Our multiple observation-based datasets thus demonstrated the fact that the large semi-arid net CO 2 uptake originated from extreme La Niña wet period to be very short-lived and a transitory event, in which drought occurrence can rapidly reduce the magnitude of carbon uptake (Fig. 4a, b ; Supplementary Fig. S5 ). Australia is situated in a region of extreme climate variability, creating several-fold larger fluctuations in precipitation and terrestrial primary productivity than encountered in all other continents (Supplementary Fig. S2 ). Exceptionally large vegetation productivity was recorded over Australia during both of the recent two La Niña-induced very wet periods (2000-01 and 2010-11) (Fig.
Methods We use multiple observation-based data sources to diagnose the geographic distribution and temporal evolution of Australia's land carbon sinks in extremely wet years. Continental-wide biospheric CO 2 fluxes were inverted using retrievals of atmospheric CO 2 from Japanese Greenhouse gases Observing SATellite (GOSAT), with the Bayesian uncertainty statistics of the estimated fluxes were defined by a posterior error covariance matrix (see Supplementary information: Atmospheric inversion of biospheric carbon fluxes). Vegetation photosynthetic activity was indicated by two satellite measures: (1) Enhanced Vegetation Index (EVI), which is a composite measure of leaf area, chlorophyll content, and canopy architecture, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's Terra satellite (MOD13C1, Collection 5); (2) Solar-Induced chlorophyll-Fluorescence (SIF), which is emitted by the photosystem II of the chlorophyll molecules of assimilating leaves, derived from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument on-board EUMETSAT's MetOp-A/B satellites (version 26, level-3) (see Supplementary information: Satellite measured vegetation photosynthetic capacity).
To determine drought severity, we use the Standardised Precipitation-Evapotranspiration Index (SPEI), which is a multi-scalar drought index that takes into account both precipitation and temperature (see Supplementary information: Standardised Precipitation-Evapotranspiration drought Index). Total water storage change (TWSC) derived from NASA's Gravity Recovery and Climate Experiment (GRACE) observations was used as an integrative measure of water storage change over continental scale (see Supplementary information: GRACE Terrestrial Total Water Storage Change). We use gridded rain gauge and temperature datasets provided by the National Climate Centre, Australian Bureau of Meteorology (see Supplementary information: Gridded meteorological datasets). Micrometeorological flux tower measured net ecosystem production (NEP) data (Level 3) is provided by OzFLUX network (see Supplementary information: In-situ measurements of NEP).
To infer the contribution of fire emissions to the sink-source dynamics and further refine our understanding of carbon loss pathway over semi-arid ecosystems, we used fire emission data from the fourth-generation global fire emissions database (GFED4. 1s) (see Supplementary information: Global Fire Emissions Database). Major vegetation group map provided by Australian National Vegetation Information System (NVIS, v4. 1) was used to derive the contribution of each biome type to continental carbon fluxes (see Supplementary information: Vegetation map). The 26 NVIS vegetation groups were grouped into five major biomes: forest, savanna, shrubland, grassland, and agriculture. The savanna biome, a tree-shrub-grass multi-strata system, includes all open forests, woodlands, as well as parts of shrublands, with both Eucalyptus, Acacia or other trees as the dominant canopy species.
Acknowledgements This work was funded by an  Australian Research Council -Discovery Project  " Impact of extreme hydrometeorological conditions on ecosystem functioning and productivity patterns across Australia " ( ARC-DP140102698 ,  CI Huete ). The first author also acknowledges the support from an  Early Career Research Grant (ECRG)  from  University of Technology Sydney  " Fingerprinting Australian ecosystem threats from climate change and biodiversity loss " ( PRO16-1358 , CI Ma). We would like to thank the opening of flux tower data for public access by the  Australian Terrestrial Ecosystem Research Network  (TERN, www. tern. org. au)-OzFlux facility (www. ozflux. org. au), as well as the availability of multiple processed satellite products by TERN's AusCover remote sensing facility (www.
Genome Assembly Genomic paired and mate-pair reads were quality trimmed and filtered as described in Ro ¨delsperger et al. 2014. Genomic paired-end libraries were used to generate a first draft assembly with the Velvet assembler (Version 1. 2. 10) with a k-mer size of 41  (Zerbino and Birney 2008). In a second step, we used SSPACE (version 2. 0) with default settings for scaffolding using the mate-pair data  (Boetzer et al. 2011). Intrascaffold gaps were closed using the Gapclosing module (version 1. 10) of the SOAP package. For removal of contamination, we ran a BLASTn search against the NCBI nt database, searching for hits with sequence identity >95% over a length of 100 bp. After taxonomic inspection of significant hits, the contaminating contigs (mostly of fungal origin) were removed from the assembly.
The final assembly comprised 150,243 scaffolds spanning 518 Mb (494 Mb excluding gaps) with an N50 of 105 kb. The largest scaffold spans 1. 1 Mb. The genome-wide GC content was 34. 4%. To obtain independent estimates of genome size and repeat content we used the software jellyfish (version 1. 1. 4) to generate kmer spectra of original raw sequencing data (Marcais and Kingsford 2011).
Gene and Repeat Annotation Repeats were identified using the RepeatModeler and RepeatMasker pipeline, which identified 154 Mb (29. 2%) as repetitive (~4. 5% LINE elements, ~10% DNA elements, and ~14. 4% unclassified). Transcriptomic reads were aligned to the Oryctes borbonicus assembly using TopHat (v2. 0. 3), and reference-guided transcriptome assemblies were generated using the software Cufflinks (v2. 0. 1). Transcriptome assemblies were used to train the gene finder AUGUSTUS (v2. 6. 1) (Stanke et al. 2006) , which predicted 23,278 protein coding genes in the repeat masked assembly. To compare gene predictions with a purely evidence-based set of gene annotations, we made use of the MAKER2 pipeline  (version 2. 31. 8; Holt and Yandell 2011). MAKER2 was run once on the repeat masked genome using only de novo assembled transcripts (Trinity version: trinityrnaseq_r20140413p1;  Grabherr et al.
2011)  and protein homology data from D. ponderosae and T. castaneum. Thus, no implicit call of ab initio gene finders was done for this set of gene annotations. Protein domains were annotated using the hmmsearch program of the HMMER3 package and the included PFAM profiles. For analysis of core eukaryotic genes, we downloaded a set of 458 profile HMMs, which were searched against the database of predicted proteins using hmmsearch (evalue < 0. 001) (CEGMA database,  Parra et al. 2007).
Comparative Genomic Data We downloaded protein sequences of two more beetles and of seven representative species from seven different insect orders from Ensembl Metazoa release 25. This data set comprised 13,457 protein sequences from Dendroctonus ponderosae (Coleoptera), 16,526 sequences from Tribolium castaneum (Coleoptera), 30,362 protein sequences representing 13,918 genes from Drosophila melanogaster (Diptera), 15,314 sequences from Apis mellifera (Hymenoptera), 15,441 sequences from Rhodnius prolixus (Hemiptera), 12,829 sequences from Heliconius melpomene (Lepidoptera), 10,788 sequences from Pediculus humanus (Phthiraptera), 14,610 sequences from Zootermopsis nevadensis (Isoptera). In the case of D. melanogaster, we used the longest isoform per gene for further analysis. Conserved synteny between beetle genomes was detected by means of CYNTENATOR software (Ro ¨delsperger and Dieterich 2010), which computes gene order alignments using a phylogenetic scoring function.
This approach identified conserved syntenic blocks between O. borbonicus and the T. castaneum genome spanning the 42 Mb of the T. castaneum genome and 39 Mb of conserved syntenic blocks between O. borbonicus and D. ponderosae. Conserved synteny was used to identify contigs that are likely of X-chromososomal origin (supplementary fig. S3 , Supplementary Material online). For the CYP family, manually curated sequences, including those from the moth Bombyx mori, were retrieved from the website of David Nelson (http://drnelson. uthsc. edu/biblioC. html, last accessed June 6, 2016), and experimentally characterized sequences of other beetles (Ips paraconfusus, Phyllopertha diversa, and Leptinotarsa decemlineata) were retrieved from Genbank. Artifactual fusions and fissions in protein predictions were manually corrected as described previously  (Markov et al.
Ortholog Clustering and Phylogenetic Analysis In order to identify orthologous clusters we used orthomcl  (Li et al. 2003)  software with default settings. We identified 2,355 1:1 orthologs from 14,748 orthomcl clusters using a custom perl script. We generated multiple sequence alignments for each 1:1 ortholog cluster using Clustal Omega  (Sievers et al. 2011). After model testing with ProtTest 3  (Darriba et al. 2011 ) 2,355 individual gene trees and one tree based on all concatenated alignments were constructed using RAxML  (version 8. 1. 20, Stamatakis 2014). Concatenation as well as looking at the most frequently reconstructed gene trees, resulted in the same species tree topology (fig. 1A ). The analysis of the GST and CYP gene families was done by aligning sequences using MUSCLE  (Edgar 2004 ).
About 23,278 protein-coding genes were predicted using the AUGUSTUS gene finder after training with RNAseq data. Again, this number of protein-coding genes is substantially larger than the values reported for the two previously sequenced beetle genomes (N = 13,457 for D. ponderosae and N = 16,526 for T. castaneum). We used the MAKER2 pipeline (version 2. 31. 8; Holt and Yandell 2011) to generate a set of 20,504 evidence-based gene annotations based on de novo assembled transcripts and protein sequences from other insects  (Grabherr et al. 2011; Holt and Yandell 2011). These annotations were used to further evaluate the AUGUSTUS predictions showing that ~81% of evidence-based exons overlap predicted exons. Second, we tested for the representation of core eukaryotic genes in the predicted gene set.
We used the definition of core eukaryotic genes as provided by the CEGMA database  (Parra et al. 2007 ) and found that 445 (97%) of the 458 core eukaryotic gene profiles are represented in the predicted proteome of O. borbonicus and the best hit covered in median 92% (interquartile range: 73-98%) of the query profile. Third, we generated our "own" core insect gene data set by assembling homologous sequences into orthologous clusters  (Li et al. 2003)  in order to identify genes that are presented as 1:1 orthologs in all three beetle-, as well as in the six nonbeetle insect genomes (see Materials and Methods). This approach identified 2,355 genes that have a 1:1 relationship in all nine insect genomes. We used these orthologous clusters to test  -Assembly features such as size and number of sequences were collected for the raw Contig assembly, scaffolded genome, and three types of gene annotations.
To further characterize lineage-specific patterns in the evolution of beetle genomes, we first screened for drastic changes in the size of gene families. In the ideal case, detailed phylogenetic analyses (see below) would be necessary to reconstruct the exact evolutionary history of duplication and gene loss events. However, comparisons of approximate gene family sizes provide a proxy to prioritize candidate gene families for more detailed investigation. Here, we defined gene families based on the presence of a certain protein PFAM domain and performed all three pair-wise comparisons of the distributions of gene family sizes (fig. 1C-E ). In the comparison between T. castaneum and D. ponderosae, reverse transcriptases (PF00078) and seven transmembrane proteins (PF02949 and PF08395) are present in much higher number in T.
Nonetheless, detailed phylogenetic analyses are ultimately needed to confirm this second expansion. While expansions of reverse transcriptases and retroviral integrases (PF00665) in the O. borbonicus and T. castaneum lineages could point to recent activity of retroviruses and retrotransposons, the expansion of seven transmembrane proteins could be due to a variety of different events. Proteins of the seven transmembrane families are involved in many essential signaling pathways, for example as receptors for neuropeptides. These short proteins have been shown to be crucial not only for developmental processes, such as molting and diapause  (Verlinden et al. 2015) , but also play important roles in the maintenance of homeostasis of metabolites and proteins, in the regulation of water balance and in several behaviors  (Scherkenbeck and Zdobinsky 2009; van Hiel et al.
Acknowledgments This work was funded by the  Max Planck Society. We would like to thank the  Office National des Forets  and the  Parc National de La Reunion , for supplying collection permits. Jacque Rochat  and  Sophie Gasnier  ( Insectarium de La Reunion ) for their help with beetle collection. Finally, we thank  Metta Riebesell  for language corrections and  David Nelson  for naming and further polishing of Oryctes CYP sequences.
This project has been deposited at the NCBI sequence read archive under the accession SRP066828. The genome assembly has been deposited at NCBI Genbank under the accession LJIG00000000. 1. The complete annotations are available at http://www. pristionchus. org/ download/. Raw reads have been submitted to the NCBI sequence read archive under the study accession PRJNA293509. The raw RNA-seq reads are available in the NCBI sequence read archive (SRX1458313).
congolense protein database (33144 entries comprising forward and reversed sequences) obtained from the Welcome Trust Sanger Institute (http://www. sanger. ac. uk/ resources/downloads/protozoa/trypanosoma-congolense. html). The following parameters were used: trypsin was selected with proteolytic cleavage only after arginine and lysine, number of internal cleavage sites was set to 1, mass tolerance for precursors and fragment ions was 1. 0 Da, considered dynamic modifications were +15. 99 Da for oxidized methionine. Peptide matches were filtered using the q-value and Posterior Error Probability calculated by the Percolator algorithm ensuring an estimated false positive rate below 5%. The filtered Sequest HT output files for each peptide were grouped according to the protein from which they were derived and their individual number of peptide spectral matches was taken as an indicator of protein abundance.
Cloning glycosomal aldolase genes, expression and Nb474 binding analysis Nucleotide sequences coding for aldolase of T. congolense (EMBL-EBI accession no. CCC93713. 1), T. b. brucei (EMBL-EBI accession no. M19994. 1) and Leishmania mexicana (EMBL-EBI accession no. CAB55315. 1) were retrieved from the GenBank and codon-optimized for expression in E. coli. The codon-optimized sequences were further processed by incorporating nucleotides coding for His 6 -tag and a proximal tomato etch virus cleavage site. Finally, NdeI and XhoI cutting sites were added to the 5 0 and 3 0 , respectively, followed by in silico construction in pET-21b (+) expression vector. Assembled sequences were submitted to a commercial company (GenScript) for synthesis and ligation in the expression vector. The constructs were electro-transformed into E.
Acknowledgments The authors would like to acknowledge  Prof. Fred Opperdoes ,  Dr. Guy Caljon  and  Dr. Didier Vertommen  for the assistance rendered during identification of the glycosomal aldolase (antigen);  Dr. Vincent Delespaux  for T. congolense strains and  Dr. Philippe Holzmuller  for T. congolense secretome;  Prof. Luis Neves  for providing cattle sera;  Susan Ndyanabo  for providing the clinical field samples;  Dr. Emmanuel Obishakin ,  Julie Goossens ,  Joar Pinto ,  Lea Brys , and  Ella Omasta  for their technical assistance.
Fluorescence microscopic analysis Images were acquired using a Zeiss Axio Imager M2 microscope (Zeiss, Le Pecq, France) with a 100 Â objective and Zeiss filters 45 and 46 for fluorescent microscopy. Axiovision 4. 8 software (Zeiss, Le Pecq, France) was used for image acquisition. Lipid bodies and extracellular lipids visualisation was performed by addition of BodiPy s Lipid Probe (2. 5 mg/ml in ethanol; Invitrogen) to the sample and after incubation for 10 min at room temperature.
Acknowledgments This work has benefited of aid managed by the  Agence Nationale de la Recherche  under the " Investissements d'avenir" program  with the reference  ANR-11-BTBR-0003. R. Ledesma-Amaro  received financial support from the  European Union  in the framework of the  Marie-Curie   FP7 COFUND People Program  ( FP7-267196 ) in the form of an AgreenSkills' Fellowship. X. Niehus is recipient of a Ph. D. scholarship from the  Consejo Nacional de Ciencia y Tecnología  ( 336376/234603 ) in Mexico ( CONACYT ).
Results Symbiont complexity varies greatly across the Porifera. Richness of microbial symbiont communities varies widely across different host species within the phylum Porifera (Fig. 1 ; Supplementary Data 1). Complexity (as assessed by number of OTUs) ranges from 50 to 3,820 genetically distinct symbionts per host. Seawater operational taxonomic units (OTUs) were removed from sponge samples as they were considered likely to represent 'environmental contaminants' obtained during filter feeding and sampling (see Methods for details). The large richness estimates are unlikely to be inflated by sequencing errors as approximately one-third of samples reached complete saturation (Fig. 1 ). Variation of richness across the sponge samples contrasted with the more consistent richness estimates found within seawater and sediment samples (Fig.
Environmental and host factors are known to influence the composition of host-associated communities  12, 36, 37  ; however, the impact of host evolutionary history on the structure and composition of symbiont comunities has only recently been explored  37. Considering the phylogenetic breadth of sponge species sampled here, we were able to evaluate the relationship between host phylogeny and microbial diversity. Diversity was assessed using the inverse Simpson's index (D), while Blomberg's K was calculated using the phylosignal function in the R package picante  38  (Fig. 10 ; see Methods for details). K values of 1 correspond to a random process, values closer to zero correspond to patterns of convergent or random evolution and values 41 indicate phylogenetic conservatism  38. We observed a significant value of K for the inverse Simpson's index (K ¼ 0.
1. 31. 2 (ref. 45). First, quality-filtered, demultiplexed fastq sequences were trimmed according to quality (using the trim. seqs command: parameters qwindowaverage ¼ 30, qwindowsize ¼ 5, maxambig ¼ 0, maxhomop ¼ 8, minlength ¼ 100). Files were reduced to non-identical sequences (unique. seqs and count. seqs) to minimize computational effort. Non-redundant sequences were aligned (align. seqs: flip ¼ t) to a trimmed reference SILVA 102 (ref. 46) bacteria database (pcr. seqs: start ¼ 11894, end ¼ 25319, keepdots ¼ F), which was provided by mothur  47. Only sequences that were aligned to the expected position were kept (screen. seqs: start ¼ 1968, end ¼ 4411; filter. seqs: vertical ¼ T, trump ¼. ). Aligned reads were reduced to non-redundant sequences (unique. seqs). Chimeric sequences were detected using Uchime (chimera.
uchime: dereplicate ¼ t)  48  , and filtered out (remove. seqs). Pairwise distances between aligned sequences were calculated (dist. seqs: cutoff ¼ 0. 05) and used for clustering. Prior to clustering, aligned sequences were phylogenetically classified based on the trimmed SILVA database (classify. seqs)  49. Sequences were clustered (cluster. split: fasta ¼ , count ¼ , taxonomy ¼ , splitmethod ¼ classify, taxlevel ¼ 4, cutoff ¼ 0. 03, hard ¼ t, method ¼ furthest) and converted to. shared file format (make. shared:list ¼ , count ¼ , label ¼ 0. 03). Finally, OTU representative sequences were retrieved based on the distance among the cluster sequences (get. oturep: list ¼ , label ¼ 0. 03, fasta ¼ , count ¼ ) and were further classified based on SILVA, Greengenes (version gg_13_5_99 from May 2013), and RDP taxonomies (classify.
seqs: fasta ¼ , template ¼ , taxonomy ¼ , cutoff ¼ 60)  46, 50, 51. Furthermore, Fastq sequences from additional samples (n ¼ 340) that were generated at a later time point (using the same sequencing procedure as described above) were processed with the same pipeline. These sequences were integrated into the shared file using QIIME 1. 8 (ref. 52), based on their similarity to the OTU representative sequences (parallel_pick_otus_uclust_ref. py: --similarity 0. 985 --optimal_uclust). Sequences that were not similar to the OTU representative sequences were separately clustered with mothur and integrated into the previous files (. shared and taxonomy files). The integrated OTU table (. shared file) was filtered to remove low-abundance sequences (sequences o0. 001% across the whole data set) and chloroplasts (according to SILVA or Greengenes).
In addition, counts from seawater-like OTUs (40. 01% across all seawater samples) were removed from sponge samples. File manipulation and processing was carried out with python scripts (http://www. python. org). OTU tables are available in Supplementary Data 4. Calculation of community metrics. Rarefaction curves were generated using the R package vegan 2. 2-1 (ref. 53). Inter-sample rarefaction curves were generated by mothur (rarefaction. shared). Distances of the samples in a group (sponge species, seawater or sediment) and their respective group centroids were calculated based on Bray-Curtis distances by the function betadisper from the vegan package in R (ref. 53). Richness indicators (Chao, Ace, Sobs) were also calculated with vegan. Nonmetric multidimensional scaling was calculated with vegan package based on Hellinger transformed OTU counts  54.
Taxonomic profiles were obtained based on Greengenes, which provided more phylum-level assignments than the SILVA or RDP databases. Briefly, percentage OTU counts were averaged by species/ environment with the R package analogue 0. 16-0 (ref. 55). Phylum percentages were calculated by summing averaged OTU percentages. Bray-Curtis dissimilarities were calculated and heatmap was obtained using the package pheatmap 1. 0. 7 (ref. 56). Sponge-bacteria bipartite network analysis. A bipartite interaction network was constructed using the presence of specific OTUs within each of the sponge species in the data set. OTUs were considered part of the network only if they were found in at least 25 distinct samples from the whole data set. In this bipartite host-microbe interaction network, nodes represent sponge species (on one side) and OTUs (on the other); and links among them represent the presence of an OTU in the microbial community of the sponges to which it is linked.