dataesr/scientific-paragraphs-categorization huggingface dataset:
e. projections onto the y-axis and x-axis, are shown in Additional file 1: Figure  S8 ). The quantitative measure of goodness-of-fit defined above is coherent with the curves' visual aspects: for the Size Model the distance D between the model and the data ranges from 17% to 20% for f i data (16% to 26% for s i data) whereas for the Age Model it ranges from 51% to 93% for f i data (45% to 125% for s i ). The experimental data has a limited precision. In particular, the division time is difficult to determine precisely by image analysis and the resolution is limited by the time step of image acquisition (for s i and f i data, the time step represents respectively 5% and 8% of the average division time). By performing stochastic simulations of the Size Model (detailed in Additional file 1: Section 6), we evaluated the effect of measurement noise on the goodness of fit of the Size Model.
Variability in individual growth rate has a negligible effect on the size distribution For each single cell, a growth rate can be defined as the rate of the exponential increase of cell length with time  [25, 26]. By doing so, we obtained the distribution of the growth rate for the bacterial population (Additional file 1: Figure  S4A ). In our dataset this distribution is statistically compatible with a Gaussian distribution with a coefficient of variation of approximately 8% (standard deviation/mean = 0. 08). We recently extended the Size Model to describe the growth of a population with single-cell growth rate variability (the equation is given in Additional file 1: Section 5)  [28]. We simulated this extended Size Model using the growth rate distribution of f i data. The resulting size distribution is virtually identical to the one obtained without growth rate variability (Figure  4A , red and blue lines).
Conclusions In the present study, we present statistical evidence to support the hypothesis that a size-dependent division rate can be used to reconstruct the experimental age-size distribution of E. coli. In contrast, this distribution cannot be generated by a timer model where the division rate depends solely on age. Even though the timer model can maintain cell size homeostasis, it is quantitatively incompatible with the observed size distribution. Our analysis of two different datasets shows the robustness of our conclusions to changes in experimental setup and image analysis methods. Our results therefore confirm the hypothesis of size control of division in E. coli. In addition, our analysis of the timer model shows that it is very sensitive to mathematical assumptions for the growth law of very rare cells of abnormal size, suggesting that this model is unrealistic for any exponentially growing organisms.
Data analysis The data of Stewart et al. contain the results of several experiments performed on different days, each of them recording the simultaneous growth of several microcolonies of the MG1655 E. coli strain on LB-agar pads at 30°C, with a generation time of approximately 26 min  [25]. The first 150 min of growth were discarded to limit the effects of non-steady-state growth (cells undergo a slight plating stress when put on microscopy slides and it takes several generations to recover a stable growth rate). For the dataset obtained by Wang et al. , the MG1655 E. coli strain was grown in LB at 37°C in a microfluidic device with a doubling time of approximately 20 min. To avoid any effect of replicative aging such as described in  [26] , we only kept the first 50 generations of growth.
In addition the first ten generations were discarded to ensure steadystate growth. Both datasets were generated by analyzing fluorescent images (the bacteria express the Yellow Fluorescent Protein) using two different software systems. For s i data, cell segmentation was based on the localization of brightness minima along the channel direction (see  [26] ). In the same spirit, for f i data, local minima of fluorescence intensity were used to outline the cells, following by an erosion and dilation step to separate adjacent cells (see  [25] ). To measure its length, a cell was approximated by a rectangle with the same second moments of pixel intensity and location distribution (for curved cells the measurement was done manually). For both datasets we extracted data from three experiments done on different days.
Numerical simulations and estimation procedures All the estimation procedures and simulations were performed using MATLAB. Experimental age-size distributions, such as those shown in Figure  3A, B , were estimated from the size and age measurements of every cell at every time step using the MATLAB kde2D function, which estimates the bivariate kernel density. This estimation was performed on a regular grid composed of 2 7   equally spaced points on [0, A max ] and 2 7 equally spaced points on [0, X max ], where A max is the maximal cell age in the data and X max the maximal cell size (for instance A max = 60 min and X max = 10 µm for the experiment f 1 , as shown in Figure  3A ). To estimate the size-dependent division rate B s for each experiment, the distribution of size at division was first estimated for the cell size grid [0, X max ] using the ksdensity function.
3 µm and x > 5. 3 µm, v(x) = max(p(x), 0), with p(x) = -0. 0033x 3 + 0. 036x 2 -0. 094x + 0. 13. Likewise, for the fit of the experiment s 1 shown in Figure  3D , for x < 3. 5 µm and x > 7. 2 µm, v(x) = max(p(x), 0), with p(x) = -0. 0036x 3 + 0. 063x 2 -0. 33x + 0. 67. For each dataset the polynomial p(x) was chosen as an interpolation of the function giving the length increase as a function of length (shown in Figure  2B  for f 1 data). Simulations of the extended size models with variability in growth rates or septum positioning (Equations (  23 ) and  (24)  in Additional file 1) were performed as for the Age & Size Model, with an upwind finite volume scheme. To simulate Equation (  23 ), we used a grid composed of 2 7  equally spaced points on [0, X max ] and 100 equally spaced points on [0.
Experimental design NEMO-PISCES was first spun up during 3000 years using constant pre-industrial dynamical forcings fields from IPSL-CM5A-LR  (Dufresne et al. , 2013)  without activating the N 2 O parameterizations. This spin-up phase was followed by a 150 yr long simulation, forced by the same dynamical fields now with N 2 O production and N 2 O seato-air flux embedded. The N 2 O concentration at all grid points was prescribed initially to 20 nmol L -1 , which is consistent with the MEMENTO database average value of 18 nmol L -1 below 1500 m  (Bange et al. , 2009). During the 150 yr spin-up, we diagnosed the total N 2 O production and N 2 O sea-to-air flux and adjusted the α, β, γ and θ parameters in order to achieve a total N 2 O sea-to-air flux in the two parameterizations at equilibrium close to 3.
Contemporary N 2 O concentrations and the relationship to O 2 The model results at present day were evaluated against the MEMENTO database  (Bange et al. , 2009) , which contains about 25 000 measurements of co-located N 2 O and dissolved O 2 concentrations. Table  1  summarizes the SD and correlation coefficients for P. and Gruber, 2003) , without being subject to the strong potential biases introduced by the model's deficiencies in simulating the distribution of O 2. This is particularly critical in the OMZs, where all models exhibit strong biases  (Cocco et al. , 2012; Bopp et al. , 2013)  (see also Fig. 3c ). P. TEMP (Fig. 3a  our choice of a too low N 2 O consumption rate under essentially anoxic conditions. The O 2 distribution in the model (Fig. 3c ) shows a deficient representation of the OMZs, with higher concentrations than those from observations in the oxygen-corrected World Ocean Atlas  (Bianchi et al.
Nevison et al. (1995)efficients between P. TEMP and P. OMZ parameterizations with respect to MEMENTO database observations(Bange et al. , 2009). sametimeperiod,(c)data product ofNevison et al. (1995)and (d) latitudinal N 2 O sea-to-air 807 flux (in TgN deg -1 yr -1 ) fromNevison et al. (1995)(black), P. TEMP (blue) and P. OMZ (red).
Acknowledgements. We thank  Cynthia Nevison  for providing us the N 2 O sea-to-air flux dataset. We thank  Annette Kock  and  Herman Bange  for the availability of the MEMENTO database (https://memento. geomar. de). Comments by  Parvadha Suntharalingam  improved significantly this manuscript. Nicolas Gruber  acknowledges the support of  ETH Zürich. This work has been supported by the  European Union  via the Greencycles  II FP7-PEOPLE-ITN-2008 , number  238366. We thank  Christian Ethé  for help analyzing PISCES model drift.
Acknowledgments The authors would like to thank the four anonymous reviewers for their constructive and thoughtful comments and suggestions, and they also thank the  USGS  for downloading freely Landsat 8 images from their data archive. This work was supported by the program of the  National Natural Science Foundation of China  (Grant Nos. 41401375 ,  41230747 ,  41331171 ,  41231170  and  41471430 ), the  China Post-doctoral Fund  (Grant No. 2014M550551 ), the  National Basic Research Program of China  (Grant No. 2013CB733402 ) and the  High Resolution Earth Observation Systems of National Science and Technology Major Projects.
77% produce significant hits in the available databases. The investigation of the reads mapping toxin class revealed a high diversification with the major part consistent with the classical hymenopteran venom protein signature represented by venom allergen (33. 3%), followed by a diverse toxin-expression profile including several distinct isoforms of phospholipase A 1 and A 2 , venom serine protease, hyaluronidase, protease inhibitor and secapin. Moreover, our results revealed for the first time the presence of toxin-like peptides that have been previously identified from unrelated venomous animals such as waprin-like (snakes) and agatoxins (spiders and conus). The non-toxin transcripts were mainly represented by contigs involved in protein folding and translation, consistent with the protein-secretory function of the venom gland tissue.
Finally, about 40% of the generated contigs have no hits in the databases with 25% of the predicted peptides bearing signal peptide emphasizing the potential of the investigation of these sequences as source of new molecules. Among these contigs, six putative novel peptides that show homologies with previously identified antimicrobial peptides were identified. Conclusions: To the best of our knowledge, this work reports the first large-scale analysis of genes transcribed by the venomous gland of the ant species T. bicarinatum and helps with the identification of Hymenoptera toxin arsenal. In addition, results from this study demonstrate that de novo transcriptome assembly allows useful venom gene expression analysis in a species lacking a genome sequence database.
The contigs recorded from the ant whole body sample were used to assess differentially expressed genes. Among these detected transcripts (contigs), 27,873 have at least one predicted ORF with 59. 77% producing significant hits in the available databases. Gene ontology (GO) functional categorization of the annotated contigs from T. bicarinatum venom glands is provided on Figure  2. The GO analysis in relation to molecular functions revealed that the largest number of transcripts was related to protein binding, followed by catalytic activities and at a lower rate to structural protein and transporter activities (Figure  2A ). Analysis of the biological processes revealed two dominant major gene categories of cellular and metabolic processes. The following categories are related to biological regulation, localization, response to stimulus, biogenesis and signaling (Figure  2B ).
In order to assess differentially expressed genes in the venom glands, we used the previously described DESeq approach implemented in R package which allows investigation of differential expressed transcripts from high-throughput data without replicates  [15]. Regarding a likely important expression level variation between the two tested samples, the analysis failed to give workable results. This bias could be linked to the necessary preamplification of the venom gland sample prior to library construction compared to ant whole body sample from which extracted RNA quantity was sufficient to be directly processed to cDNA library construction (see Methods section). To bypass this bias probably originating from the amplification step, we set an arbitrary 10 3 fold expressionthreshold to the 37,818 contigs obtained from both sequenced samples.
Due to a lower quality of the sequencing of the read2, we decided to perform our analysis using only the read1 for both samples. In total, we generated a list of 502 contigs that are 1000 fold more expressed in the venom gland, that we called the 'venom-gland overexpressed' transcripts. Analysis of the functional diversification among this group revealed a significant abundance (77%) of contigs that have no hits in the databases (Figure  3 ). On the other hand, contigs that matched predicted toxins and non-toxin proteins, accounted for 11% and 3%, respectively. The remaining group of 9% consisted of uncharacterized compounds (Figure  3 ). Moreover, about 235 contigs have significant hit with available bacterial sequences. These sequences have not been eliminated given that their low number does not affect the overall assemblage quality.
Cluster relevant to cellular functions Among the 502 'over-expressed' transcripts, 3% clusters presented significant hits in the databases involved in various cellular functions (Figure  3 ). The most relatively high expressed transcripts of this category are those matching methyl-transferases (61%) having transcription co-activator activities. This finding is in agreement with previous work showing that 'gene transcription protein' are the most abundant cellular transcripts reflecting the functional feature of the specialized tissues such as venom gland cells  [16]. In addition, significant transcripts (4%) matched the glucose dehydrogenase involved in carbohydrate metabolism. This enzyme, that belongs to a family of oxido-reductases, has been identified in a recent work based on genome mining and proteomic analysis and was described as a novelty in insect venoms  [6].
The status and features of these peptides have been discussed in our previous work where we showed that different isoforms and families of pilosulinlike sequences occur in T. bicarinatum  [13]. Recently, two short peptides (named bicarinalin and peptide2) from T. bicarinatum venom have been characterized by de novo sequencing using mass spectrometry and Edman degradation  [21, 22]. The authors show that these mature peptides, identified among the most abundant in the venom of this ant species have no homolog in the public database. In our study, we have recovered these peptides and identified their cDNA sequences (Additional file 2: Table  S1 ). However, when we blast the whole peptide sequences predicted from the cDNA, we found that their leader sequence and propeptide have homologies with those of pilosulin sequences (Figure  4 ).
Moreover, the bicarinalin has been shown to have a potent and broad antibacterial activity as much as pilosulins  [21]. Hence, it seems that the bicarinalin and peptide2 or the pilosulin-like forms from T. bicarinatum have the same features as some bioactive peptides in that they have a conserved secretory  ]. JZ168521-JZ168537 refer to GenBank accession numbers of transcripts from T. bicarinatum contig recovered from our previous study  [13]. Amino acids that are identical in all or the majority of the sequences are highlighted with different shades of blue that represent their degree of conservation. The active peptide sequence of bicarinalin identified in a previous study  [21]  is indicated by a red box. Putative signal peptide is underlined. leader and propeptide sequence but differ greatly in their mature peptide  [23, 24].
T. bicarinatum PLA2-like Phospholipases (PLAs) are relatively common in social Hymenoptera venoms occurring in different forms and are able to disrupt the phospholipid layers of several types of biological membranes responsible of many hemolytic and neurotoxic effects (eg:  [30] ). In this work, five different contigs from T. bicarinatum matched with databases proteins of phospholipases A2 (PLA2) from Hymenoptera organisms but also from lizard and scorpion species (Figure  7A ). One contig (Tb34851) was found to be closely related (91. 29% of similarities) with PLA2 previously described in the fire ant S. invicta. The remaining four contigs (Tb14525, Tb23564, Tb5926 and Tb21409) showed a mean range similarities of 31% for S. invicta and 33% for Apis mellifera. According to the amino-acid alignment, T.
Given that the venom gland library was amplified prior to sequencing, only a biochemical characterization (See figure on previous page. ) Figure  7  The predicted phospholipase A2-like (PLA2) from T. bicarinatum. (A) Amino acid alignment of the sequences of PLA2-like from T. bicarinatum (Tb21409, Tb14525, Tb34851, Tb5926 and Tb23564) with PLA2 from Apis mellifera [GenBank: P00630], Bombus terrestriris [GenBank: P82971], S. invicta [GenBank: EFZ20207], Heloderma suspectum [GenBank: P80003], Hadrurus gertschi [GenBank: P0C8L9], Pandinus imperator [GenBank: P59888] and Anuroctonus phaiodactylus [GenBank: Q6PXP0]. Strictly conserved and less conserved cysteines are indicated by black and grey shading, respectively and the predicted signal peptide cleavage sites for T. bicarinatum PLA2-like is indicated by a solid green triangle.
The underlined regions indicate the Ca 2+ binding loop and the active site: solid red triangle, Ca 2+ binding residues; *, active site residues. The numbering of the amino acid (Gly32, His48, Asp49 and Asp99) follows that of the PLA2 from bovine pancreas  [31]. (B) Phylogenetic relationships of PLA2 from venomous animals based on amino acid sequence alignment. Phylogeny has been performed using the maximum likelihood method implemented in the PhyML program at www. phylogeny. fr [72]. Numbers at nodes indicate bootstrap supports based on 100 replicates. Protein codes are as described in Figure  7A. and/or a quantitative PCR should elucidate its actual expression level in T. bicarinatum.
Non hymenopteran toxins The blast search against nr database of T. bicarinatum contigs Tb16400 and Tb34742 of 1303 bp and 1169 bp, respectively showed similarities with waprin-like (WAPs) proteins from Hymenoptera species that were submitted to GenBank following whole genome sequencing or genome mining. In order to check the possible homologies with known WAP from other venomous species, a blast search against an in-house toxin database (see Methods section) has been achieved and has revealed homologies with snake-venom WAP. An average similarity of 33% with the matched snake species was recorded. Domain search blast of the identified contigs against the PRO-SITE database  [50]  revealed that the predicted sequences (94 amino acid long each) contain the WAP-type 'fourdisulfide core' domain profile or whey acidic protein motif.
In addition to WAPs-like discussed above, agatoxinlike sequences have also been detected in our analysis (Additional file 2: Table  S1 ). Blast search against specific toxin in-house library shows a similarity of 28% and 29% of the contigs Tb37135 and Tb25047, respectively, to agatoxin sequence from the spider Agelena orientalis [GenBank accession: Q5Y4U4. 1], with a completely cysteine pattern conservation (Figure  12 ). The amino acid sequences deduced from both contigs have an agatoxin signature of 16 residues N-terminal signal peptide and 46 predicted mature residues. NCBI Blast of these two contigs shows consistent similarities (ranging from 68 to 91%) with predicted agatoxin-like isoforms from the honey bee A. mellifera [GenBank accession: XP_003249808] and the bumble bee Bombus impatiens [GenBank accession: XP_003485909].
Other toxins Other putative toxins/enzymes have been detected to be expressed by T. bicarinatum venom glands, namely secapin, venom serine carboxylesterase, venom serine carboxypeptidase, lectizyme, disintegrin and metalloproteinase-like, chymotrypsin-like inhibitors. Details on their respective contigs, expression level are provided in (Additional file 2: Table  S1 ). All these predicted toxin-like showed consistent similarities and homology with hymenopteran venom sequences mainly from ant species that have been submitted to public databases in the frame of whole genome sequencing. Their description in this study strengthens their existence/expression in the venom glands. No hits and putative 'new' venom toxins from T. bicarinatum In addition to the venom peptides and proteins described above, the venom gland library contained about 40% of contigs that were not identified in the public or toxin specific databases (Additional file 3: Table  S2 ).
About 3% of these contigs with no match have been found among the arbitrary 'overexpressed' contigs where they form the most abundant group (77%). That 'no-hits' level reflects and emphasizes the limited amount of information available for Tetramorium species and more broadly from other ants in databases. Definitely, this set of "no hit sequences" constitutes a potentially rich reservoir for the identification of novel toxins. In order to investigate the potential of these peptides as 'new' toxins, we searched for clues, such as the prediction of signal peptide and cysteine pattern. Details on statistic features of the 'no-hit' contigs are provided in (Additional file 3: Table  S2 ). Thus, about 17% of these contigs belonging to the 'over-expressed' group have a predicted signal peptide and at least two cysteins.
About 8% have signal sequences with no cysteine residues. As peptides from ant venom are more and more suggested to exhibit a defensive role against microbial pathogens, associated with prey introductions and/or ingestion  [57] , special investigation of T. bicarinatum 'no-hits' sequences was carried out using the blast search against the antimicrobial peptide database (APD). In addition, their signal peptide sequences were blasted against an in-house toxin-specific signal peptide database. Six original toxin candidates were recorded based on their relative high expression level, presence of signal peptide and on homologies (ranged between 27 to 53%) with established antimicrobial peptides. The length of the predicted mature peptide from these sequences ranged from 23 to 72 amino acids. The features of these putative novel toxins and the amino-acid alignment with their homologs from the APD are provided in (Additional file 4: Table  S3 ).
Sequence accession numbers The original, unmerged sequencing reads of the venom gland library were submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive under accession number SRR1106145. The processed and assembled data (toxin and nontoxin sequences) investigated in this study were submitted to the GenBank Transcriptome Shotgun Assembly (TSA) database. The TSA project has been deposited at DDBJ/ EMBL/GenBank under the accession GASM00000000. The version described in this paper is the first version, GASM01000000.
Transcriptome assembly and analysis Quality of reads generated by deep sequencing was checked within the ng6 environment using fastQC program (available at http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and Burrows-Wheeler Aligner tool (BWA) to search for contamination  [59]. The transcriptome de novo assembly was performed using Velvet/Oases  [60]. The first step consisted of nine independent assemblies using different k-mers (k-mers for velveth:  25, 31, 37, 43, 49, 55, 61, 65, 69 ; parameters for velvetg: -read_trkg yes -min_contig_lgth 100 -cov_cutoff 4 and parameters for oases: -cov_cutoff 4). The raw transcripts files were filtered to retain only 1% of transcripts per locus with a modified version of a Perl script developed at Brown University (https://sites. google. com/ a/brown.