dataesr/scientific-paragraphs-categorization huggingface dataset:
The position of MCP-GFP clusters along the major axis of the cell was obtained with a workflow described in Figure  S9  and automated with a Python script in FIJI. As the signal to noise ratio varied among different strains, it was necessary to adapt the analysis for each MCP-GFP in order to obtain a realistic distribution of clusters. The fluorescence intensity was calculated with FIJI by subtracting from the measured fluorescence intensity of each cell the background measured in the same frame. Colocalizations were calculated with the plugin JACoP from FIJI.
Statistical analysis Relative net swarming in Figure  2  was calculated as the average ratio between the surfaces in pixels of the swarming mutant versus wild-type colonies (100%) after 48 h. Surfaces were normalized with the surface of a DpilA mutant (0%). The averages were obtained from at least three independent experiments performed in duplicates. Student's T-tests were used to determine the statistical significance. The relative cell length and the total distance covered by MCP were determined on 20 clusters by calculating, with the software R, data medians and Interquartile Ranges (IQRs: defined as the difference between the third and the first quartiles of the data) respectively. Statistical significance was calculated by using Wilcoxon tests. Figure  S5  S motility and fruiting body formation phenotypes of a difA-gfp strain.
Acknowledgments We would like to thank  Celine Brochier-Armanet  for her help on the phylogenetic analysis;  Lisa Lamasse  for helping us with the image analysis;  Susanne Mueller ,  Aurelie Barneoud  and all the present and former members of the  Zusman and Mignot Lab  for their insightful comments and suggestions;  Mireille Ansaldi ,  Thierry Doan ,  Dorothee Murat ,  Morgane Wartell  and  Salim Islam  for their critical reading of the manuscript.
This redundancy implies that both eIF4E1 and eIFiso4E are expressed similarly during the male gametophyte development. We looked at transcriptomes from different stages of male gametophyte development from data collected by microarray analysis. 21 The level of expression of eIF4E1 and eIFiso4E are very similar at the different stages of pollen development (Table  2 ). However, we noted that two other genes encoding 4E initiation factors, eIF4E3 (At1g29550) and nCBP (At5g18110), are expressed in male gametophytes, albeit at a lower level. The genetic results indicate that neither eIF4E3 nor nCBP can replace eIF4E1 and eIFiso4E during male gametophyte development. This might be due to their low expression level or to differential functions during translation initiation. The latter explanation is very likely for nCBP, which encodes a non-canonical eIF4E protein.
Visualization of the masticatory system A wild rabbit head was scanned using a Discovery MR750 MRI scanner (GE Medical Systems, USA) with a resolution of 127 Â 127 Â 1000 mm. This individual was not from the same rabbit group that was used for the bite force experiments. The head was subsequently dissected to identify the origin and insertion sites of the jaw closer muscles. This dissection aided segmentation of the MRI scan data within AVIZO image visualization software (AVIZO v. 6. 3. , Visualization Sciences Group, Inc. USA) to produce volumetric models of each jaw closer muscle (figure  1 ). rsif. royalsocietypublishing. org J. R. Soc. Interface 11: 20140564  Post-dissection, the cranium and mandible were scanned using an X-Tek HMX 160 mCT scanner (X-Tek Systems Ltd, UK) with a resolution of 48 mm in each direction, and volumetric models of each bone were constructed in AVIZO.
Multi-body dynamics modelling 2. 3. 1. Model construction An MDA model was created through importing volumetric models of the cranium and mandible into ADAMS 2013 (MSC Software Corp. USA). The mandible was modelled as a movable part, whereas the cranium was fixed. To allow a realistic range of motion at the TMJ, contact surfaces between the mandible and cranium were defined. The mass and inertial properties of the mandible were calculated within ADAMS based on volume and a standard tissue density of 1. 05 g cm 23  [2]. Each muscle was modelled as a series of strands in order to capture the differing fibre directions present within a single muscle (figure  3 ), based on observations from both manual dissection and segmentation of the MRI data (figure  1 ). The temporalis was modelled in superficial and deep parts (figure  3d ) using the descriptions of Weijs & Dantuma  [32].
At the onset of the slow closing phase, a second group of muscles (group 2) consisting of the working side superficial masseter, medial pterygoid and lateral pterygoid, and balancing side posterior deep masseter, anterior zygomaticomandibularis, posterior zygomaticomandibularis, superficial temporalis and deep temporalis, were activated. Muscle group 2 causes the mandibular condyle to protract, and produce rotation of the jaw back to the midline. These muscles reached peak activation half way through the slow closing phase. Comparison with EMG recordings demonstrates that the jaw closers were activated in a physiological sequence (i. e. in the correct muscle groupings; figures 6 and 7). However, it is important to note that the magnitude of the activation levels are not comparable as the two datasets are normalized to differing measures (MDA profiles are expressed as a percentage of maximum force, whereas EMG data are expressed as an excitation level (EX.
The balancing side lateral pterygoid was observed to assist in jaw opening, thus its EMG profile had a plateau of low activation during the fast and slow closing phases, whereas the MDA simulation recruited this muscle to assist in producing molar occlusion. Possible explanations for these differences could lie in the fact that the two datasets represent slightly different power strokes, and in the methodology of the DGO algorithm (for further explanation, see the Discussion). This coordinated activation of muscle groups 1 and 2 generated a bite force that is initially directed vertically. However, as the muscles in the group 1 reached their peak activations and the muscles in the group 2 increased in activation, the shearing components of the bite force became more prominent. Consequently, a resultant bite force of 35.
However, Gro ¨ning et al. [9]  recently demonstrated that altering pennation angles had only a minimal effect on bite force predictions, although its inclusion here would lower the maximum force of the jaw closer muscles. Furthermore, the use here of fibre length values from the literature also adds errors into the estimation of maximum muscle forces. It is also noted that the model uses a muscle stress which is towards the lower end of the range of values reported within the literature. Comparison between the predicted bite force and the experimental dataset must also consider the possibility the rabbits did not bite maximally during the force measurements. When measured EMG profiles and predicted muscle activations are compared, it is clear that peak EMG activity usually occurs earlier in the bite cycle (figures 6 and 7).
Calculation of each muscle's PCSA value would ideally consist of data measured from the individual dissected, however, fibre lengths were taken from literature in this study. Similarly, confidence in the bite force measurements and EMG recordings would be improved if they were obtained from the dissected individual. It was also assumed that incisor biting uses the same reduction bite cycle as molar mastication, with the slow closing phase during incisor biting being modelled with the same duration as molar biting. Because the biting simulated compression of the food bolus to half of its original height, the timings of the closing phases will only influence the duration of the muscle force curves, not the maximum activation levels reported in table  2. In conclusion, this paper has successfully combined MRI and mCT data, together with manual dissection, to construct an MDA of the rabbit skull.
Acknowledgement. We thank  Sue Taft  ( University of Hull ) for the mCT- scanning of the rabbit specimen used in this study. We also thank  Raphae ¨l Cornette ,  Jacques Bonnin ,  Laurent Dufresne , and l 'Amicale des Chasseurs Trappistes (ACT)  for providing permission and helping us capture the rabbits used for the in vivo bite force measurements at la Reserve  Naturelle Nationale de St Quentin en Yvelines, France.
Under the umbrella of systems biology, workflows of data-driven modeling and model-driven experimentation have led to the development of computational models that describe processes at all levels, including gene regulatory networks, signal transduction pathways and metabolic networks, cell populations, structured tissues, pharmacokinetic and pharmacodynamic models that analyze drug or vaccine action at the whole organism level, and pharmacogenomic models of disease risk and drug and vaccine exposure at the patient-population level. Major challenges exist for the development of quantitative and predictive models that span the gap between cell-level biochemical models and organism-level pharmacokinetic and pharmacodynamic models. One hurdle is the technical difficulty associated with generating sufficiently comprehensive quantitative datasets for large numbers of system variables, across different levels of organization.
Statistical analysis All data were analysed using the Statistical Analysis Systems Institute package (SAS version 8; SAS Institute Inc. ), and the level of statistical significance set at P < 0•05, for two-sided analyses. Data were analysed by two-way non-parametric or parametric (as appropriate) ANOVA using the mixed procedure of SAS. Diet, week and their interaction were included as fixed effects and the dog was included as a random term. Results were expressed as median (range) or means with their standard errors as appropriate. Results.
Fluorescence Microscopy Live synchronized parasite populations expressing GFP-tagged proteins were examined by epifluorescence microscopy on an Axioplan 2 microscope (Zeiss), equipped with a Plan Apochromat 1006/1. 4 oil immersion objective and an AxioCam HRc camera  [31]. Parasites were labelled with DAPI DNA stain, then a cell suspension was placed on a slide and overlaid with a Vaseline rimmed cover slip. Parasitized erythrocytes were viewed live, and the dual colour fluorescence images were captured using Axiovision 4. 6. 3 software and edited using Adobe Photoshop CS4. For indirect immunofluorescence smears of red blood cells infected with P. falciparum 3D7 strain were fixed for 5 min using 100% cold methanol. In the case of haemoglobin labelling, cells were pre-treated with saponin prior to fixation.
Acknowledgments We thank  Dominique Dorin-Semblat  and  Christian Doerig  for the gift of anti-PfCK1 specific antibodies. We also thank  Kimberley Zichichi  from the  Microscopy Facility at Yale University  for her technical electron microscopy competence. We gratefully acknowledge the  Institute Cochin's Imaging Facility and PlasmoDB  (http://plasmodb. org/plasmo/) and thank these community resources.
FBR was supported by a fellowship from the  Fondation de France  and CNE by an  AUF  fellowship. GL acknowledges INSERM, the  CNRS , the  Labex ParaFrap   ANR-11-LABX-0024  and  CEFIPRA  (grant No. 3303-3 ) for support. The study was funded in part by the  UK Medical Research Council  (file reference number  U117532067 ) and the  European Union  through the  EviMalaR Network of Excellence Health -2009-2. 3. 2-1-242095. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
General features of the genome Using a combination of Roche 454 and Illumina systems (in the Additional file 1: Table  S1 ), the genome of R. miehei was shot-gun sequenced. The final assembly genome size is 27. 6 Mb, N50 contig length is approximately 188. 9 kb and N90 contig is in 61. 4 kb. Gene prediction was performed using different models, which yielded 10,345 protein-coding genes with an average length of 1,621 bp (Table  1 ). Total size of the genome is consistent with the estimation of approximately 27 to 29 Mb from 10 separated chromosomes between 0. 55 to 5. 8 Mb by pulsed field gel electrophoresis (PFGE) (Figure  1 ). The R. miehei genome (27. 6 Mb) falls within the size of most available fungal genomes that has a range of 10-60 Mb with a median of 28 Mb (Table  2 ). This whole genome information has been deposited at DDBJ/EMBL/GenBank under the accession No.
2 bp (Table  1 ). Introns are of typical length in fungi, averaging between 80 bp and 150 bp  [24]. R. miehei genome contains 0. 877 Mb of repetitive sequences representing 3. 17% of the assembled genome (in the Additional file 1: Table  S2 ), which is much lower than that of R. oryzae (34. 77%)  [26]. The repetitive sequences in the R. miehei   genome consist of recognizable transposable elements (TEs) including short interspersed nuclear elements (SINEs), long interspersed nuclear elements (LINEs), long terminal repeat (LTR) elements and unclassified interspersed repeats. Of all predicted genes, 71. 6% (7,407 genes) of the translated proteins show sequence similarity to proteins in the known databases, and 57. 6% (5,961 genes) in R. miehei CAU432 can be annotated and attributed into different functional categories in Gene Ontology (GO) (Figure  2a ).
miehei CAU432 grown at two different temperatures (30°C and 50°C) were investigated by RNA-seq analysis (in the Additional file 2: Figure  S2a ). Approximately 35 million high-quality reads were generated from each library, and 62% reads were uniquely mapped to the genome (in the Additional file 2: Figure  S2a ). A high percentage of reads (67. 1%) were mapped to predicted protein-coding genes, while 3. 96% reads were distributed in splice junctions, 3. 49% reads in antisense transcripts, 2. 27% in introns, and 21. 9% in other genomic regions (in the Additional file 2: Figure  S2b ). The frequency of reads which mapped to different ranged from one to over 300,000 (in the Additional file 2: Figure  S2a  and S2c ). The expression profiles of R. miehei at 30°C and 50°C were compared using the DEGseq program (in the Additional file 2: Figure  S2d ).
Genome annotation and analysis The tRNAs were identified using the software tRNAscan-SE-1. 23. rRNAs were detected by using the software RNAmmer-1. 2. Repetitive sequences in the R. miehei genome were identified by using RepeatModeler-1. 0. 4 (http://www. repeatmasker. org/RepeatModeler. html) and were classified according to the classification criteria in the RepBase database (http://www. girinst. org/repbase/ index. html). The resulting 38 families of repeats were added into the search library in RepeatMasker to find the repetitive sequences in the R. miehei genome  [55]. ORFs were firstly predicted on all contigs by MAKER-2. 10 software which combines the information of the gene structure based on the results produced from the TopHat software that maps RNA-seq reads against genomic sequences of R.
miehei CAU432, with the gene structure information predicted from AUGUSTUS  [56]  (trained on sequences of Rhizopus oryzae) and GeneMark-ES version 1. 0. Secondly, genes were selected only if the predicted protein lengths were more than 50 amino acids or 30 amino acids with BLAST evidence. Thirdly, for the regions without any MAKER prediction, non-overlapping gene models (supported with blast hits of ≥ 60% average identity and ≥80% query coverage) were picked; otherwise, the GeneMark-ES prediction was preferred. The final predictions of protein-coding genes were checked individually according to the BLAST evidence. Predicted genes were named as the abbreviated organism (RhzM) followed by a five-digit number. All the protein-coding genes were functionally assigned by homology to annotated genes from the NCBI nonredundant database, and Uniprot database and classified according to Gene Ontology (GO) and KEGG metabolic pathways.
Construction of phylogenetic tree and detection of synteny blocks Ortholog conservation in fungi was characterized with by OrthoMCL  [57]. In total, 409 orthologous proteins were acquired and aligned with MUSCLE v3. 8. 31  [58]. A maximum likelihood phylogenomic tree was created using the concatenated amino acid sequences with the program TREE-PUZZLE using the Dayhoff model  [59]. The divergence time between species was estimated with the Langley-Fitch method with r8s  [60]  by calibrating against the reassessed origins of the Ascomycota and Eurotiomycetes (at 500-650 and 270-350 million years (MY) ago, respectively)  [61]. The protein families of the 12 fungi were identified by Pfam analysis with script pfam_scan. pl (ftp://ftp. sanger. ac. uk/pub/databases/Pfam/ Tools) against the Pfam database (ftp://ftp.
sanger. ac. uk/ pub/databases/Pfam/current_release)  [62]. The gene expansion and extraction of R. miehei relative to the most recent common ancestor of R. oryzae and M. circinelloides was analyzed with CAFE software using a stochastic birth and death model  [27]. Single copy of orthologous groups of R. miehei were used to detect syntenic blocks among R. miehei, R. oryzae and M. circinelloides using the software OrthoCluster  [63].
Identification of carbohydrate-active enzymes in fungal proteomes Annotation of carbohydrate-active genes was performed using the CAZy annotation pipeline  [64]. The identification of genes that code for carbohydrate-active enzymes (GH, Glycoside Hydrolase; GT, glycosyltransferase; PL, polysaccharide lyase and CE, carbohydrate esterase) and their associated carbohydrate-binding module (CBM) in fungi was performed using CAZy database (http://www. cazy. org). Briefly, sequences of the proteins in CAZy were first divided by their constitutive modules (catalytic modules, carbohydrate binding module and other noncatalytic modules). Each R. miehei protein sequence was compared by the BLAST analysis against the library of around 50,000 individual modules. Protein sequences with an e-value < 0. 1 were individually analyzed by multiple sequence alignment and search of conserved signatures/motifs.
High quality reads were obtained after removing raw SOLiD reads with median QV (quality value) below 13. Forty-five base pair (bp) long sequences were obtained from the termini of each exon read to construct the splice junction database. Fifty bp long RNA-seq reads were mapped to R. miehei assembled genome with the software Corona_Lite_Plus_4. 2. 1 (Applied Biosystems) at 5 mismatches while the unmapped reads were mapped to the splice junction database. In the order of first 45 bp (at 4 mismatches), 40 bp (at 4 mismatches), 35 bp (at 3 mismatches) and 30 bp (at 3 mismatches) sequences obtained from unmapped reads were iteratively mapped to genome and splice junction database. Reads with multiple alignments were discarded. Differentially expressed gene under two growth temperatures were identified using the DEGseq software  [65]  by the following criteria: P < 0.
Availability of supporting data The Rhizomucor miehei CAU432 whole genome sequence has been submitted to GenBank and deposited at DDBJ/ EMBL/GenBank under the accession No. AGBC00000000 (http://www. ncbi. nlm. nih. gov/nuccore/AGBC00000000). The version described in this paper is the first version, AGBC01000000. The RNA-seq data have been deposited at NCBI Short Read Archive under series accession No. SRP008125 (http://www. ncbi. nlm. nih. gov/sra/?term= SRP008125) for the samples of R. miehei CAU432 growing at 30°C and 50°C, respectively. The sequence and annotation data are also available at http://foodenzyme. cau. edu. cn/data_base/index. html. These include genome sequence, datasets for genes and proteins, a summary of the results from Pfam analyses and a Blast server.
Acknowledgements This research was supported by the  National Science Fund for Distinguished Young Scholars  (Project No. 31325021 ) and  National Natural Science Foundation of China  (Project No. 31371718 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We also thank  Guogua Xiao  for his technical assistance in gene family evolution and  Dongmei Tian  for her technical assistance in transcriptome analysis.
Moreover, experimental recombination has been obtained, with an asymmetric horizontal transfer similar to the ones occurring among bacteria  [10]. Lastly, it is widely accepted that some of the T. cruzi near-clades have a hybrid origin. Several scenarios of hybridization have been proposed  [16, [32] [33] [34] [35] , based on interpretation of phylogenetic trees. Recombination probably plays a crucial rule in the evolution of this pathogen on an evolutionary scale  [14] , and a clearer picture of the evolution of T. cruzi lineages may therefore be obtained using a network, best adapted to represent ''genealogies-like'' patterns, expected when both parental and daughter lineages are susceptible to be present in the same datasets  [32, 33, 34]. Apart from illustrating the reticulate evolution better than a phylogenetic tree, a network can be analysed using network tools specifically developed to better describe the clustering of some groups of lineages (near-clades) and their specific position in the evolution and diversification of nearclades (Figure  1 ).
18, p,10 23 ). This indicates the existence of a hierarchized structure in the dataset. Almost identical results were obtained when scanning the network of 81 stocks analyzed for the 66 MLGs discriminated using 19 microsatellite loci  [35] , with a similar (,CC. = 0. 88) and significant clustering (CCo = 0. 40, p,10 23 ) at a much higher percolation point (Dp = 0. 71; Figure  2b ). In both cases, the lowest threshold (thr) maintaining 'among near-clade' connection (thr = 0. 07 for MLEE, Figure  S1 , 0. 42 for microsatellites; Figure  2 ) corresponds to the persistence of links between TcV and VI when all other near-clades are disconnected. This confirms the genetic proximity between TcV and TcVI. The network can be scanned at decreasing thresholds starting from Dp down to this last point of 'among near-clade' disconnection.
80) in the near-clade containing TcVI stocks according to MLEE data. Neighbor-Net results also show numerous ''boxes'' built by parallel lines identifying the same split or branch and indicating clashing patterns of relationships. Such incompatible splits, numerous in this case both for MLEE and microsatellite data (Figure  S2 ), are expected under the hypothesis of an extensive number of hybridization events  (Huson & Bryant, 2006). Besides the illustration of clustering and indices of reticulate events, MLEE NeighborNet showed distances that also support the existence of a cluster TcII,III,V,VI, with TcII appearing more distantly related from the three other near-clades. However, this split appears less clearly on the basis of microsatellite data.
Clustering and identification of near-clades The discrimination of near-clade appears clearly at the percolation threshold on network (Figure  2 ), with the single exception of TcVI Brener that remains clustered with TcV instead of TcVI at the lowest threshold according to MLEE data. This is possibly due to a high variance level for low distances, and the assignment appears correct with the microsatellite dataset, for which less stocks are available. The distinction between TcV and TcVI is unclear with NeighborNet on the MLEE dataset (Figure  S2 ) as well as with Girvan Newman community analysis (Figure  S3 ). The relative divergence among near-clades however appears clearly when scanning the networks at increasing thresholds, which makes it possible with this method to distinguish between clustering and divergence patterns that result from the most recent events of diversification and emergence on one hand, and the most ancient events on the other hand.