dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting structure was identical to the one showed in Fig. 2a , albeit its resolution was slightly lower (15. 5 Å). Fitting of X-ray structures. Rigid-body fitting of the AP-domains of the reconstructed structures was performed by the computation of cross-correlation coefficients between the reconstructed cryo-EM maps and chain A of the B. subtilis Lon AP-domain (PDB ID 3M6A)  20  in the colores package  55  of the Situs program suite  56  , followed by interactive docking in Sculptor  57. Six AP-domain monomers could be fitted into both full-length cryo-EM S855A Lon maps without clashes (Fig. 2 ). The N-terminal domain of both full-length S855A Lon structures was fitted in UCSF Chimera with six copies of a fragment of the E. coli Lon N-terminal domain crystal structure (PDB ID: 3LJC)  21  containing residues 1-219.
The red line corresponds to the wild type, the green to the S855A mutant and blue to the Δ 270 hLon mutant. It can be seen that the Δ 270 mutant has a much lower ATPase activity than wt hLon, even lower than the S855A mutant. All three forms still show ATPase stimulation by β -casein. (d) Peptidase activity of wt hLon and its Δ 270 mutant. The triangles (▵ ) and circles (⚪)show the data for the basal and β -casein-stimulated peptidase activity, respectively. The red line corresponds to the wt and blue to the Δ 270 hLon mutant. It can be seen that the Δ 270 mutant basal activity is about one-third of the wild-type, while its stimulated activity is closer to one half. The S855A mutant has no detectable peptidase activity. (e) Protease activity of wild type hLon and its S855A and Δ 270 mutants.
Acknowledgements This work was supported by the grants  13-32339P  and  P302/12/G157  from the  Czech Science Foundation , the  Prvouk/1LF/1  and  UNCE204022  grants from  Charles University in Prague , the  OPVK CZ. 1. 07/2. 3. 00/30. 0030grant  from the  Ministry of Education, Youth and Sport of the Czech Republic , and by the grants  CZ. 2. 16/3. 1. 00/24010 ( OPPK program ) and  CZ. 1. 05/1. 1. 00/02. 0109 ( BIOCEV -Biomedicine Center of the Academy of Sciences and Charles University ) of the  European Regional Development Fund. V. P. , L. A. ,  J. Bauer ,  J. Bellová , G. O. , N. K. and E. K. were supported by the  Slovak Grant Agency  ( VEGA 2/0113/14 ) and by the  Slovak Research and Development Agency  ( APVV-0123-10 ). This work was further supported by Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI) , the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and by the  IT4Innovations Centre of Excellence project  ( CZ.
This was done by computationally integrating multiple publicly-available protein-protein interaction (PPI) databases. We focused on tumour DEGs versus EC genes up-regulated in the angiogenic phenotype and representing cell membrane, extracellular matrix and secreted molecules (resulting in 1004 proteins and 3240 PPIs) (Figure  6F ). This analysis indicates direct PPIs between two compartments, but also indirect connections via other proteins not encoded by the DEGs, and offers a systems-level characterization of the molecular cross-talk between these two cell types. It underscores that such an interplay is highly inter-connected and can be mediated by multiple functionally-related proteins. By further focussing on the direct connections between the two cell types, we identified numerous interactions within extracellular matrix and plasma membrane proteins associated with angiogenesis (Suppl.
Immunohistochemistry Coronal sections (8µm) from paraffin-embedded brains were stained with hematoxylin (Dako) and 1% eosin (Sigma). For immunostaining, sections were pretreated for 5min with Proteinase K (Dako) followed by 30min incubation at 95°C in retrieval solution (Dako). The Dako Envision+System-HRP was used following the manufacturer's instructions. Primary and secondary antibodies were incubated for 1h. Signal was developed with 3,3′-diaminobenzidine chromogen in 5-20 min. IHC of mouse endothelial cells was performed on isopentane flash-frozen tissues and cryostat sections (10µm) were fixed with acetone and chloroform. Nonspecific binding was blocked with 2% FBS in TBS and antibodies were incubated for 1 hr at RT. Pictures were acquired with a Leica DMI 6000B microscope. Vessel quantification was done using ImageJ software.
Genome-wide gene expression analysis Total RNA was extracted using the QIAGEN ® RNeasy Mini Kit (human-specific microarrays) or the Trizol ® LS extraction kit (Life Technologies) (mousespecific microarrays), according to the manufacturer's protocol. GeneChip ® Human Gene 1. 0ST Arrays and GeneChip ® Mouse Gene 2. 0 ST Arrays (Affymetrix) were used to determine the expression profiles. Total RNAs were processed using the Ambion WT expression kit (Life Technologies) and the Affymetrix WT Terminal & Labeling kit before being hybridized on Affymetrix arrays (protocol P/N 702808 Rev. 6). Upon hybridization, microarrays were washed, stained and scanned according to manufacturer's standard procedures. CEL files containing hybridization raw signal intensities were imported into the Partek GS software for statistical analysis.
First, probe intensities were summarized to gene expression signals using Partek default options (GCcontent adjustment, RMA background correction, quantile normalization, log2 transformation and summarization by means. R statistical environment was used for hierarchical clustering, principal component analysis and for empirical Bayesian statistics (LIMMA  [39] , R/Bioconductor). Lists of differentially expressed genes (DEG) were obtained with the eBayes/LIMMA. FDR was calculated with the Benjamini and Hochberg approach  [40]. Thresholds were set up for FDR < 0. 01 and absolute fold change (abs(FC)) ≥ 2 for human specific microarrays and FDR < 0. 01, any FC for mouse specific microarrays. The DAVID ® database  [41]  and the Ingenuity Pathway Analysis (IPA; Ingenuity Systems) were used for data mining.
REVIGO was used for summarization of altered gene ontology (GO) terms  [42]. Venn diagram analysis was performed with the SUMO software (http://angiogenesis. dkfz. de/oncoexpress/ software/). IPA was used for the identification of the DEGs with specific location: cell membrane, extracellular matrix. Microarray data are available in the ArrayExpress database (https://www. ebi. ac. uk/arrayexpress/) under accession numbers E-MTAB-3948 and E-MTAB-3949. Putative protein-protein interaction analysis was performed for proteins located at the plasma membrane and extracellular matrix encoded by the DEGs upregulated in angiogenic tumours (P13 vs. P8, FDR < 0. 01, FC > 2) and up-regulated in EC (P13 vs. NB ECs, FDR < 0. 01, FC > 1). Mouse genes were mapped to human homologues and unique and unambiguous gene symbols were considered for further analyses.
AcKNOWLEDGMENts We thank  Virginie Baus ,  Amandine Bernard  and  François Bernardin  for excellent technical assistance,  Dr. Peter Nazarov  and  Arnaud Muller  for support in microarray analysis. We are grateful to  Prof. Thomas Würdinger  from the  Free University, Medical Centre, Amsterdam, NL  for providing the HBMVEC cells.
Data analysis EMG was amplified ×1000. EMG, acceleration and positional data were sampled at 1000 Hz and digitized by an MC 6026 PCI card. Further analysis was carried out offline using custom MATLAB scripts (MathWorks Matlab 2011a, USA). EMG was band-pass filtered  Hz, fourth-order Butterworth zero-phase-lag filter) and rectified. Acceleration signals were high-pass filtered (0. 1 Hz, fourth-order Butterworth dual filter) to correct for artefactual modulation within the range of target frequencies. For one representative subject, the amplitude spectra of the finger acceleration and EMG were obtained by NEUROSPEC software (v. 2. 0, 2008) in order to display four 5 s snapshots at different phases of the 120 s target tracking (figure  2 ); static (second 5-10), maximum upward finger velocity (second 52.
Statistics We wanted to know whether, and at which time point during the transition between posture and movement, the frequency of acceleration and its neural drive would significantly change. Therefore, the 95% confidence interval (CI) was calculated for the peak frequency of acceleration and EMG over time. For both variables, the frequencies covered by the CI over the last 5 s of the two static periods (at the start and end of the movement) were designated the 'postural' frequency range (figures 3c and 4c). We then measured the time when the limits of the CI during the tracking movement would exceed this range, of which the first and last time points were used to signify a borderline finger movement that signifies a change in frequency. Wavelet data as well as finger position and finger velocity were averaged over trials and subjects and finally smoothed (moving average with time constant = 0.
Data accessibility. All raw data are available through the Dryad repository: http://dx. doi. org/10. 5061/dryad. r1j0s. Authors' contributions. C. V. , R. R. and M. L. contributed substantially to conception and design, or acquisition of data, or analysis and interpretation of data; C. V. , R. R. and M. L. drafted the article, or revised it critically for important intellectual content; and C. V. , R. R. and M. L. have read and approved the manuscript before submission.
Assessment of rho À or rho°production The mutant Δsod2 was grown in YPD at 1 mM PQ. Three rounds of 24 h culture were performed. Cells were then diluted and spread on YPD plates to obtain approx. 200 cells per plate. After three days growth, the colonies were replica-plated to YPG agar. The number of colonies able to grow on this respiratory medium was recorded. This was compared to data obtained with untreated cultures.
We first tested the effect of PQ on the respiratory growth of the wt strain CWWT. The strain was cultivated in YPEth medium with and without primaquine (PQ). Cultures were inoculated at an OD 600 nm of 0. 1 and incubated at 28 °C with vigorous aeration. In parallel, the effect on fermentative growth was monitored. The CWWT strain was grown in YP10 medium without aeration with or without 2 mM PQ, and a rho°mutant derived from CWWT was cultured in YPGal medium with vigorous aeration with and without 2 mM PQ. Cell densities were measured as OD 600 nm when the cultures reached the stationary phase. The data are presented as percentage of growth relative to control, i. e. untreated by PQ (Fig. 1A ). At 2 mM PQ, respiratory growth was severely inhibited but not fermentative growth.
6 (b) ) and production methods were optimized. A 5x5 laser matrix device was constructed at ASSE to verify the mirror shape. Moreover, RSE upgraded its optical simulation software (Zemax) to allow for a precise simulation and optimization of the optical performance of a mirror-based concentrator. Based on this, RSE designed a new mirror based concentrator, whose performance has been optimized including the angular acceptance of the multi-junction solar cell and the effective spectral reflectivity of the mirrors measured by RSE at different incidence angles. A sensitivity analysis of the optical components to mechanical misalignments and shaping errors was also performed. A new simulation technique was developed in order to evaluate the photocurrents generated in each of the subcells of the multi-junction device.
RNAseq Data Analysis Initial data cleaning and reads mapping was performed by BGI Tech (Shenzhen, China). The libraries were sequenced as 50bp single-end reads using Illumina Hiseq2000 according to the manufacturer's instructions. Clean reads have been mapped using SOAP2. 21 on reference genomes: Nipponbare for rice and 70-15 for Magnaporthe. Annotated rice transcripts from Nipponbare genome were provided by BGI Tech. M. oryzae annotation datasets were downloaded from the Magnaporthe comparative Sequencing Project at the Broad Institute (assembly release 8)  (Chiapello et al. , 2015). We chose 4 dpi for RNASeq analysis as in our experience, fungal RNA is too diluted in earlier time points for appropriate detection and statistical analysis  (Kawahara et al. , 2012). For one repetition and on average, 528 k reads were found with a unique perfect match on M.
We considered as differentially expressed all genes that were significant in at least one of the statistical test. Enrichment analyses of the different functions were tested against the whole genome annotation with a χ 2 test corrected by Bonferroni. For DEG analysis of rice genes, we considered as differentially expressed genes, one that were significant (FDR of 5%) in at least one of the conditions. Difference in log2 fold change ratio between drought-stressed and unstressed conditions were tested using agricolae R package with a Kruskal-Wallis test corrected by Bonferroni with a p-value of 1%. Bam files, reads counts and RPKM per gene are available on GEO database (GSE84800).
Annotation of M. oryzae The reference genome Mg8 was used for the analysis. This annotation is based on the genome sequence of the 70-15 isolate and we used a closely related isolate FR13  (Chiapello et al. , 2015). Gene coding for Carbohydrate-Active enZYmes (CAZY) were annotated based on CAZY. org database  (Lombard et al. , 2014). Small secreted proteins were annotated based on three criteria: (1) the presence of a signal peptide longer than 15 aa at the beginning of the gene, (2) the absence of a transmembrane (TM) domain or a TM domain in the first 10 aa, and (3) a protein size smaller than 250 aa. Signal peptide was detected using SignalP software  (Petersen et al. , 2011). Transmembrane domains were detected using TMHMM program  (Krogh et al. , 2001).
Drought Stress Affects M. oryzae Virulence Program RNA sequencing was used at 4 dpi to monitor fungal gene expression in drought-stressed and unstressed plants. The RNA sequencing depth (see Materials and Methods) allowed a good coverage of M. oryzae genes (in average 38% gene coverage). A total of 336 fungal genes were found as differentially expressed in at least one of the statistical analysis (Supplementary Table  S2 ). Based on our annotation analysis in this gene set, 52 genes are putative small secreted protein and 35 are cellwall degrading, CAZY enzymes (respectively, 15. 5 and 11. 3% of DEG genes). In the 12827 genes that, we annotated in our database, only 800 have been identified as putative small secreted protein (i. e. , 6%) and 482 as CAZY enzyme (i. e. , 3. 8%). Thus the DEG set is significantly enriched in both protein categories (Figure  2A ) that are major players in fungal virulence.
When, we then looked at the expression values, a clear picture was visible: genes coding for putative effectors were repressed while those for CAZY enzymes were induced in drought-stressed plants compared to unstressed ones (Figure  2A ). We further extended this observation by measuring with RT-qPCR fungal gene expression in two different M. oryzae strains at different time points along the infection process. These analyses showed that the expression of most tested fungal effectors (Avr-Pita, BAS1, BAS4, SLP1, SLP2; Figure  2B  and Supplementary Figure  S3 ) was reduced in drought-stressed plants compared to unstressed ones. For Guy11, a lesser reduction of expression was also accompanied with a delay in expression. Moreover, out of the 112 M. oryzae genes repressed, 38 are putative effectors (34%) while only 6% would have been expected from genomic data (Supplementary Table  S2 ).
Only the categories with more than 10 genes are shown. A χ 2 test (corrected by Benjamini) was used to evaluate significant differences between dataset and genome ( * P < 0. 05; * * * P < 0. 001). (B) Gene expression of three fungal effectors was measured by RT-qPCR during all fungal cycle in plants that experienced no stress (gray) or 3-day drought (black bars). Two different virulent isolates of M. oryzae were used (Guy11 and FR13). The expression was normalized with the constitutive fungal gene MG4. The data represent the mean and SD from four replicates. A t-test was used to compare gene expression in non-stressed and drought stressed plants ( * * P < 0. 01; * P < 0. 05). to relate to the fact that at the time of our analysis, drought has been stopped for more than 4 days. General stress-related plant genes had an expression enhanced by the combination of stresses (Supplementary Figure  S4C ) and the expression pattern of 503 genes could not be predicted from single stress situations (Supplementary Figure  S4D ).
Our finding that drought can affect some, but not all tested plant R genes extends the vulnerability of R genes to this abiotic stress (Figure  3 ). In the case of heat stress, it was shown that R protein re-localization is responsible for the loss of activity of these proteins  (Zhu et al. , 2010). Whether drought also affects R protein localization requires further analysis and several scenarios could explain the partial breakdown of resistance in our experiments: low expression of the effector gene and normal expression of the cognate R gene (e. g. , Avr-Pita/Pi-ta) or down-regulation of the R gene (e. g. , Pi-i). Our dataset did not allow following the global expression of R gene analogs during drought because, we measured the expression 4 days after the start of drought recovery. We used the RiceDB database  (Narsai et al.
A t-test was used to compare non-stressed and drought stressed plants and different letters indicate significant differences (P < 0. 01). (D) Expression of drought-inducible genes (DREB2A and NAC6) and disease-inducible (PR3, PAL, PBZ1, and POX22. 3) marker genes before fungal inoculation (Supplementary Table  S1 ). The expression was measured before re-watering (-8 hpi) and 2 h after re-watering (at the time of inoculation, 0 hpi). Gene expression was measured by RT-qPCR and normalized with the Actin gene (see Materials and Methods). (E) Cytological quantification of M. oryzae FR13 disease progression on Nipponbare seedlings. For simplification only advanced infection of at least two cells is presented as a percentage of all the spores observed. Each data point is an average and SD from three replicates.
In our dataset, we could not find any species with both high stroke frequency and large body size (Figure  4E ). Litterbugs    and G ) and for Gerris buenoi on water (E and H) with leg deployment (dashed line) and amplitude of movement (solid line). Results are showed in Table  S3. See also Figures  S3  and S4 , Table  S3 , and Movie S1. (Dipsocoromorpha; Figure  1A ;  [28] ) are characteristically small  [29] , and similarly the ancestral state of body size in the Gerromorpha has been undoubtedly small to very small  [5]. It is possible that because of their small body size, the ancestors of the Gerromorpha were free from the constraint imposed on larger bugs, such that stroke frequency increased and facilitated the transition to water surface life. Subsequently, the evolution of rowing combined with the significant increase in the length of the driving midlegs  [10, [30] [31] [32] [33] , and most likely changes in the associated innervation and musculature  [22, 34, 35] , may have contributed to reducing stroke frequency while maintaining high speeds.
Conclusions Understanding how the evolution of behavior and morphology can be associated with niche expansion and species diversification is a major challenge in evolutionary biology  [1, 3, 17, 18]. We have shown that lineages that remained in the ancestral habitat, composed of solid substrates, produce low maximum speeds (A) A significant positive correlation is observed between stroke frequency and speed in species employing the tripod gait using both Spearman correlation test on raw data (rho raw: 0. 78, adjusted p: 3. 8eÀ2) and with phylogenetic correlation (rho PIC: 0. 92, adjusted p: 3. 6eÀ3). (B) There is no correlation between stroke frequency and speed when we add species employing the rowing gait to the dataset using both Spearman correlation test on raw data (rho raw: À0. 30, adjusted p: 3.
Phylogenetic Reconstruction Phylogeny reconstruction was conducted using 14 molecular markers with the software Geneious version 7. 1. 9 using plugins MrBayes version 3. 2. 6  [37]  (one million generations; 25% burnin) and PhyML version 3. 0  [38] , using GTR model with 100 bootstraps. More details can be found in the Supplemental Experimental Procedures.
Inference of Energy Consumption We inferred the amount of energy spent per stroke based on the procedure from  [40]. Kinetic energy (E k in joules) used during a stroke is determined using the following expression: E k = 0. 5mv 2 , where m is the mass of the insect in grams, and v is the velocity generated during one stroke in meters per second. The analyses were performed using speed data extracted from the highspeed movies. Calculations and sample sizes are detailed in the Supplemental Experimental Procedures.
ACKNOWLEDGMENTS We thank  W. Salzburger ,  E. Abouheif ,  F. Bonneton ,  S. Viala ,  A. Decaras , and  W. Toubiana  for comments and discussion;  H. Labrique  and  J. C. Streito  for help with the Dipsocoromorpha; and  M. Burrows  for advice on inferring energy expenditure. This work was funded by  ERC-CoG  # 616346 ,  ATIP-Avenir , and  CNPq -PVE # 400751/2014-3  to A. K. and a postdoctoral fellowship by the  Swiss National Science Foundation  to  M. E. S. Specimens  from  Brazil  were collected under SISBIO permit # 43105-1.
Image analyses and base calling were performed using the Illumina HiSeq Control Software and Real-Time Analysis component. Demultiplexing was performed using Illumina's sequencing analysis software (CASAVA 1. 8. 2). Data quality was assessed using FastQC from the Babraham Institute and the Illumina software SAV (Sequencing Analysis Viewer). The concentration in the samples corresponding to long-term survival (9d postinoculation) were too low for sequencing and revealed contamination with genomic DNA, so they were not further analysed.
RNA-seq analysis Sequences were mapped with Bowtie  [36]. To detect non-coding RNA, mapped sequence files where pooled by phase (exponential or stationary). Then, each file was submitted to Detr'Prok  [37]  to detect positions of small RNA, antisens RNA and 5′ Untranslated regions. Bedtools  [38]  is used to extract corresponding sequences. Sequences where submitted to Rfam  [39]  to eliminate non characterized sequences. Finaly, non coding RNA validated by Rfam and CDS were counted with htseqcount  [40]  using the ABIMS Roscoff platform. A list of differentially expressed genes was generated using an R software package: SARtools  [41] , embedded Deseq2  [42]  and EdgeR  [43]  (modified t-test adjusted, Pvalues < 0. 05). A comparison of the results obtained with either Deseq2 or EdgeR produced a more exhaustive list using EdgeR.
Most of the differentially expressed genes from Deseq2 were present in the EdgeR results. For this reason, only the results obtained using EdgeR are discussed below. The protein sequences of the strain were subjected to a search against the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG) using Ghost-KOALA (http://www. kegg. jp/ghostkoala/) to retrieve KEGG orthology (KO) identifiers. 1143 out of 2304 genes were annotated automatically. Differentially expression data were associated with these KO identifiers and underwent gene set enrichment analysis on KEGG orthology pathways using the GAGE package  [44]  of R software to highlight pathways that were significantly differentially expressed (pval <0. 1).