dataesr/scientific-paragraphs-categorization huggingface dataset:
96 Â R mean of data ). Each pair of measurements can therefore be easily classified as interchangeable or noninterchangeable. Interchangeability rate can be easily calculated by the ratio between number of interchangeable pair of measurements and the total number of pair of measurements. Moreover, as the status of each pair of measurements is known, cutoff values can be used to determine the range of values over which the 2 devices can be considered to be interchangeable (95% confidence interval [CI]). 2. 1. 3. Calculation of a cutoff to define the interchangeability range. Before determining the cutoff, the average of paired measurements was calculated and these values were sorted in ascending order. The average inclusion rate for the whole dataset was then calculated. Data points with the lowest average value were then deleted and the average inclusion rate was calculated.
Original data of de Wilde comparing different CO devices with an RM. The study design has been previously published. [10] Briefly, 24 consecutive patients were included prior to cardiac surgery at Leiden University Medical Center (The Netherlands) from February 1992 to June 1996. CO measurements by 5 different arterial pulse contour techniques using an arterial catheter (Wesseling's method, LiDCO, PiCCO, Hemac method, and Modelflow), routinely performed in cardiac surgery patients, were simultaneously recorded during pulmonary artery thermodilution (bolus injection of 10 ml iced dextrose 5% solution at 4-7°C) as RM. Measurements were performed at different predefined time-points: 3 minutes after induction of anesthesia, immediately after sternotomy, after opening the pericardium, just before and just after cardiopulmonary bypass, after sternal fixation, after completion of surgery, and after drug dose changes.
A 2-sided P-value <. 05 was considered for statistical significance. Statistical analyses were performed using Excel version 14. 4. 8 (Microsoft Corporation, Redmond, WA), and R software version 3. 0. 3 (www. r-project. org). To facilitate the use of our method we provide all data, methods, formulae and R scripts that are available in Appendices 1, http://links. lww. com/MD/C130 2, http://links. lww. com/MD/ C131 and 3, http://links. lww. com/MD/C132. Researchers can easily copy and paste their own values (reference and TM), put the RC in a specified excel box and collect the interchangeable status of every pair of measurement.
Reanalysis of the original data from Robert B. P. de Wilde Original data based on comparison of CO measurements provided by 5 different devices and Swan Ganz catheter are presented in Figure  3. [10]  A total of 199 hemodynamic time points were recorded. The repeatability of the RM was 10% with 3 cold thermodilution boli, as previously described. [16] A cutoff value of 3. 8±0. 7 L min À1 can be determined for Modelflow. As shown in Figure  4 , Modelflow shows good interchangeability, while the other pulse contour devices have a poor interchangeability (Hemac or Wesseling's cZ) or are not clinically relevant (LiDCO or PiCCO). Modelflow has a higher interchangeability rate than Hemac ( * P =. 022) and the other devices ( * * * P <. 0001) (Fig. 4 ). All data and calculations are available in Appendix 3, http://links.
2 ). We later combined both datasets in a single de novo assembly, yielding 16,959 scaffolds (N50 length 268,823 bp). Genome-size estimates based on k-mer coverage are 1. 19 Gbp for S. goreaui and 1. 07 Gbp for S. kawagutii (Supplementary Table  3 ), comparable to those for other sequenced Symbiodinium genomes. We also recovered sequences putatively derived from their plastid genomes (Supplementary Tables 4, 5 and 6), including their distinct core conserved regions (Supplementary Table  7 ), and from their mitochondrial genomes; see Supplementary Note 1 for details. The repeat content of the assembled genomes ranged from 16. 0% (S. kawagutii) to 27. 9% (Symbiodinium microadriaticum); a large peak in transposable element (TE) abundance observed at high divergence (Kimura distance  22   15-25) in all genomes (Supplementary Fig.
All annotated genes from S. goreaui and S. kawagutii genomes are listed in Supplementary Data 3 and 4, respectively. Of the 35,913 proteins predicted in S. goreaui,  Protein kinase (Pfam PF00069), reverse transcriptase (PF07727), ion transport protein (PF00520) and ankyrin repeats (PF12796) are among the most-abundant domains in Symbiodinium, appearing among the ten most abundant for each of the four genomes (Supplementary Table  12 ). Ankyrin repeat motifs are important in the recognition of surface proteins, and more generally in protein-protein interactions and have been implicated in mediating host-symbiont interactions across a variety of endosymbiotic associations  25. Thus, proteins potentially involved in host-symbiont interaction (with phosphorylation, ion transport and protein recognition/interaction domains) are well represented in the predicted Symbiodinium proteomes.
Using this 31-taxon dataset as background, we assessed the over-representation or under-representation of protein domains within our various groups of Symbiodinium proteins. We found 270 domains (Supplementary Data 5) to be significantly overrepresented (hypergeometric test, Benjamini-Hochberg 27 adjusted p ≤ 0. 05) in Symbiodinium. Interestingly, many domains e. g. C-5 cytosine-specific DNA methylase (PF00145), planctomycete cytochrome c (PF07635) and sigma-70 region 2 (PF04542) of RNA polymerase are also overrepresented in the four Symbiodinium genomes in a similar comparison against 880,909 proteins in a 15-taxon set that includes ten other dinoflagellates and the immediate outgroup Perkinsus marinus (Supplementary Data 6). Therefore, compared to related eukaryotes and to other dinoflagellates, Symbiodinium is enriched in functions involved in methylation of cytosine, (photosynthetic) energy production and RNA polymerisation.
Hydroxymethylation of uracil is common (12-70%) in dinoflagellate genomes  5  ; while cytosine methylation has been described in Symbiodinium  28  , our findings suggest that cytosine methylation is more prominent in Symbiodinium than in these other dinoflagellates. Activation of some retrotransposons is part of the stressresponse mechanism in diatoms, plants and other eukaryotes  29. The reverse transcriptase domain (PF07727) is enriched in Symbiodinium compared to both the 31-taxon and 15-taxon sets, suggesting that retrotransposition could be a prominent mechanism of stress response in Symbiodinium and dinoflagellates. Although we set a stringent threshold for removing putative bacterial or viral sequences (see Methods), 40 (~0. 1%) of the final 41,289 genome scaffolds of S. goreaui have significant hits (BLASTN E ≤ 10 -20 ) to the virus genomes  30  isolated from the same S.
microadriaticum, 47 in S. minutum and in S. goreaui, and 46 in S. kawagutii (Fig. 4a ; Supplementary Data 9). Eight of the 11 meiosis-specific proteins were detected in all four Symbiodinium. REC114, SAD1 and XRS2 found weaker matches (E ≥ 10 -14 ) in one to two genomes, although confirmatory UniProt domains were usually present (Supplementary Data 9). RAD17 is the Schizosaccharomyces pombe homolog of S. cerevisiae RAD24  46  , for which we find highly significant matches (E ≤ 10 -127 ) in all four Symbiodinium. Moreover, 15 of the 51 genes show evidence of positive selection in Symbiodinium against other dinoflagellates (Supplementary Data 9). Our data imply that these four Symbiodinium are, or until recently have been, capable of meiosis. Response to light stress. Mycosporine-like amino acids (MAAs) are ultraviolet (UV)-protective compounds that, in corals and other marine organisms, also act as antioxidants scavenging reactive oxygen species.
The final genome assemblies, predicted gene models and proteins are available at http://refuge2020. reefgenomics. org/. We adopted multiple approaches to assess genome completeness. Established methods including CEGMA  67  and BUSCO  79  are based on conserved genes in a limited number of eukaryote model organisms that are distantly related to dinoflagellates. The use of these methods resulted in relatively low recovery of conserved eukaryote genes in Symbiodinium (e. g. 33-42% of BUSCO genes; Supplementary Fig. 7B ) when run at default setting. We further assessed completeness using BLAST based on predicted proteins from the gene models and the assembled genome scaffolds. For each genome, we searched (BLASTP, E ≤ 10 -5 )against the predicted proteins using the 458 CEGMA proteins  67. We also searched against the CEGMA proteins using the genome scaffolds (BLASTX E ≤ 10 -5 ), against genome scaffolds using the 458 CEGMA proteins (TBLASTN, E ≤ 10 -5 ), and against genome scaffolds using the 458 CEGMA transcripts (TBLASTX, E ≤ 10 -5 ) (Supplementary Data 1 and Supplementary Fig.
Analysis of plastid genomes. Plastid genomes of dinoflagellates occur as minicircles. Here we focused on our ALLPATHS-LG genome assemblies. To identify putative plastid genome fragments in our genome data, we used plastid gene sequences identified in Symbiodinium type C3  81  , Symbiodinium minutum  82  and Heterocapsa triquetra 7 as queries in BLASTN searches against our genome assemblies. To identify the conserved core regions in the putative plastid genome sequences, we set a high mismatch penalty (match score = 1, mismatch scores = -4, gap opening cost = 5, and gap extension cost = 2, E ≤ 10) in reciprocal BLASTN searches. The identified core region was then used to identify other genome scaffolds that were not previously identified by alignment with known plastid-encoded genes. These scaffolds were searched against the NCBI's nonredundant nucleotide database (BLASTN at default parameters) to assess if they align to any known genes.
All scaffolds identified as being of plastid origin, both those encoding known plastid genes and those encoding only core regions, were checked for circularisation using pairwise BLASTN (E ≤ 10 -10 ). Artemis  83  and Artemis Comparison Tool (ACT)  84  were used to annotate the isolated scaffolds. The putative plastid genome sequences and their annotation are available at http:// refuge2020. reefgenomics. org/. Analysis of mitochondrial genomes. Mitochondrial genes from the dinoflagellates Alexandrium catenella and Karlodinium micrum were used as queries to identify putative mitochondrial genome fragments within our ALLPATHS-LG assemblies using BLASTN (E ≤ 10 -10 ). Nucleotide sequences of the cox1, cox3 (cytochrome oxidase subunits 1 and 3 of complex IV) and cob (cytochrome b of complex III) genes and fragments of the large subunit rRNA (LSU rRNA) and the small subunit rRNA (SSU rRNA) were retrieved from the NCBI non-redundant nucleotide database.
Scaffolds with cox1, cox3 and cob hits were considered putative mitochondrial genome fragments, and were assessed for evidence of circularisation using pairwise BLASTN. The putative mitochondrial genome sequences and their annotation are available at http://refuge2020. reefgenomics. org/. Functional annotation of gene models. We adopted a similar approach to Aranda et al. 12  to annotate gene models based on sequence similarity searches against known protein sequences. Protein sequences predicted using the standard genetic code were used as query (BLASTP, E ≤ 10 -5 ) first against Swiss-Prot, and those with no Swiss-Prot hits subsequently against TrEMBL (both databases from Uni-Prot release 2016_10). GO (http://geneontology. org/) terms associated with Swiss-Prot and TrEMBL hits were obtained using the UniProt-GOA mapping (release 2016_10).
For this analysis, we focus on homologous protein sets from the 15-taxon dataset. For the 15-taxon set we sorted the 310,617 protein sets into 1656 single-copy (ortholog) and 16,301 multicopy sets. Multiple sequence alignments were carried out using MAFFT 86 v7. 245 at -linsi mode; questionably aligned columns and rows were removed from these alignments using trimAl  87  with the -automated1 option. Branch-site models (BSMs; see below) require a reference topology. We follow Price and Bhattacharya  31  to generate a maximum-likelihood (ML) reference species tree using single-copy protein sets. The trimmed single-copy protein alignments were concatenated prior to ML inference of the species phylogeny using IQTREE  88  ; each alignment represents a partition for which the best evolutionary model was determined independently.
For these, we used MACSE  90  to derive the codon alignments. We applied the BSM implemented in the codeml program in PAML 4. 9  91  to detect positive selection signal unique to the Symbiodinium lineage. BSMs allow the dN/dS ratio (ω) to vary among both sites and branches, making it possible to infer selection at both. We computed two models: a null model with fixed ω = 1, and an alternative model that estimates ω in our defined foreground branches (here, the node that leads to all Symbiodinium lineages). We then compared the likelihoods of these two models to determine the better fit. To reduce false positives we applied q-value estimation for false discovery rate control, as implemented in R package qvalue to adjust p values. Instances with an adjusted p ≤ 0. 05 were considered significant.
goreaui), and ERS1940392, ERS1940393, ERS1940394, ERS1940395 and ERS1940396 (for S. kawagutii). Assembled genomes, predicted gene models and proteins are available at http://refuge2020. reefgenomics. org/. Code availability. The customised scripts for AUGUSTUS and PASA used in this study were previously published in Aranda et al. 12  ; they are available at http://smic. reefgenomics. org/download/.
Acknowledgements We thank  Todd LaJeunesse  for information on the original isolation of S. kawagutii. H. L. was supported by an  Australian Research Council  grant ( DP150101875 ) awarded to M. A. R. and C. X. C. T. G. S. is supported by an  Australian   Government Research Training Program Scholarship. R. A. G. -P. is supported by an  International Postgraduate Research Scholarship  and a  University of Queensland Centenary Scholarship. This project was supported by the computational resources of the  Australian National Computational Infrastructure (NCI) National Facility  systems through the  NCI Merit Allocation Scheme  (Project  d85 ) awarded to M. A. R. and C. X. C. The data used in this project were funded by the  Great Barrier Reef Foundation 's  Resilient Coral Reefs Successfully Adapting to Climate Change research and development program  in collaboration with the  Australian Government, Bioplatforms Australia  through the  National Collaborative Research Infrastructure Strategy (NCRIS) ,  Rio Tinto  and a family foundation.
Single crystal diffraction studies X-ray data for PdL2 complex was collected at room temperature with a Bruker APEX II CCD area detector diffractometer using Mo Kα radiation (k = 0. 71073 Å). Data collections, cell refinements, data reductions and absorption corrections were performed using multiscan methods with Bruker software [  [37] ,  [38] ,  [39] ]. The structure was solved by direct methods using SIR2004  [40]. The non-hydrogen atoms were refined anisotropically by the full matrix least squares method on F 2 using SHELXL  [41]. All hydrogen atoms were added at ideal positions and constrained to ride on their parent atoms. Molecular graphics were prepared with the Olex 2 program  [42]. Crystallographic data for complex are listed in Table  S1. Selected bond distances and angles are summarized in Table  S2.
Circular dichroism spectroscopy The circular dichroism (CD) spectra were recorded at room temperature using 0. 1 cm quartz cell in the near-UV region (190-260 nm) for HSA. The CD spectrum of HSA solution was recorded before and after addition of the nano-scale PdL2 complex with molar ratio 1:1. The analysis CD of HSA spectra were carried out by CDNN software. The content of secondary conformation of HSA in each step was calculated from this technique.
Viscosity measurement Viscosity experiments were carried out using a rotational viscometer and the measurements were performed at 200 rpm at room temperature. The viscosity of CT-DNA solution was measured in the presence of various amounts of the bulk and nano-scale PdL2. The obtained data are presented as (η/η0) 1/3 versus [PdL2]/[CT-DNA], where η and η0 are viscosity of CT-DNA in the absence and presence of PdL2, respectively.
Molecular docking calculations Docking study was carried out to indicate the HSA and DNA-binding site for PdL2 complex. The 3D structure of PdL2 was obtained using the. cif file of its X-ray crystal structure. The. cif file was converted to the. pdb format using the Mercury software (http://www. ccdc. cam. ac. uk/). The crystal structures of HSA (PDB ID: 1AO6) and DNA (PDB ID: 423D) with sequence d(ACCGACGTCGGT)2 were taken from the Brookhaven Protein Data Bank (http://www. rcsb. org/pdb). The resolution of these files was 2. 5 and 1. 6 Å for HSA and DNA, respectively. Water molecules of the. pdb files were deleted, missing hydrogen atoms and Gasteiger charges were added. Flexible ligand docking was carried out by AutoDock 4. 2. 5. 1 molecular docking program using the implemented empirical free energy function and the Lamarckian Genetic Algorithm  [48].
Molecular dynamics (MD) simulation MD simulation method was used to compare the structural changes of HSA and DNA in the absence and presence of PdL2 complex. The PdL2-HSA and PdL2-DNA complexes with the most negative free binding energy were considered as the initial conformations for the MD studies. GROMACS 5. 1. 2 package  [49]  and Amber99 force field  [50, 51]  were used to carry out all MD studies. The partial atomic charges of PdL2 were calculated using Gaussian 03  [52]  at the level of B3LYP/LanL2dz  [53] , while the other intramolecular force-field parameters were generated with ACPYPE  [54]  and VFFDT  [55]  for the organic moiety and the metal center, respectively. Free biomacromolecules and PdL2-biomacromolecule complexes were located in the cubic box with the periodic boundary conditions in the three directions.
Circular dichroism study The CD spectra of HSA in free and bonded to nano-scale PdL2 complex states are shown in Fig. 4. The CD spectrum of HSA indicates two negative peaks at 208 and 222 nm which are characteristic absorption peaks of α-helical structure of protein attributed to the n-π* transfer for the bonds of the α-helix  [52, 53]. According to Fig. 4 , the intensity of HSA peak has been altered slightly due to the binding of nano-complex. This observation was confirmed by CDNN software analysis for protein secondary structure contents (Fig. 4 ).
Acknowledgements The financial supports of the research council of  University of Isfahan  are gratefully acknowledged. Supplementary data CCDC 1554815 contain the supplementary crystallographic data for PdL2. These data can be obtained free of charge via http://www. ccdc. cam. ac. uk/conts/retrieving. html, or from the  Cambridge Crystallographic Data Centre , 12  Union Road , Cambridge  CB2 1EZ , UK. Fax:  +44 1223 336 033 ; or e-mail:deposit@ccdc. cam. ac. uk. Supplementary data to this article can be found online at doi: https://doi. org/10. 1016/j. molliq. 2018. 05. 077.
To summarise the results over the replicates, we summed up the mean square errors (comparing observed and predicted recovery) for each of the replicates. We highlight the parameter combinations that have the lowest global error rate, i. e. whose deviation from the observed recovery is on average, over the whole recovery curve, within 1% of the best model. This is equivalent to the maximum likelihood estimate of the parameters assuming normal errors. The code for the FRAP parameter estimation is available on github: https://github. com/rstojnic/suh_frap.
, 2013; Olivo-Marin, 2002). Different diffusive states, their dwell times and transition probabilities were computed using variational Bayesian treatment of Hidden Markov models based on  (Persson et al. , 2013). The analysis was performed on tracks longer than three time points. To benchmark the vbSPT analysis software in the context of our biological data, we simulated trajectories of random walk with four diffusion states, in a discretised cell geometry (with 15 nm resolution) as discussed in  (Persson et al. , 2013). Given the simulated single molecule positions we generated images by assuming a 2D Gaussian point spread function (s = 120 nm). Poisson and Gaussian noise (s = 1. 1 e-, matching the readout noise of the camera used in our experiments) was subsequently added to the images. A representative experimental and simulated image is shown in Figure  S3.
Spot detection, tracking and analysis were performed using the procedure discussed above and results from ten data sets are shown in Figure  S3. The vbSPT software successfully differentiated between different diffusion states for all the data sets, albeit yielding very slight variations in the diffusion coefficients due to the added noise (for D1-D3) and the confinement induced by cell boundaries (D4). For a more detailed discussion on the performance of vbSPT under different conditions see  (Persson et al. , 2013). We used fixed samples to estimate the localization errors under the experimental conditions. The covariance of the computed localisations of the same fixed molecule over time gives a measure of the precision for our imaging system of approximately 55 nm (with the Spot Tracking plug-in of icy) and 33nm (with a custom written localisation software), within the range estimated in other SMT studies (e.
ACKNOWLEDGMENTS We thank members of the  Bray Lab and of the Notch community  for helpful discussions and  Bill Harris  for comments on the manuscript. This work was supported by grants from the  Medical Research Council  to  S. J. B. ( MR/L007177/1 ) and to K. H. ( MR/K015850/1 ), from  Wellcome Trust  (L. M. ;  099130/Z/12/Z ) to CAIC, from  NIH  to  R. K. ( CA178974 ), from  ANR (ChronoNet)  and  CNRS  to F. P. and P. V. , and by studentships from the  Wellcome Trust  for J. F. -S. and M. J. ( 099744/Z/12/Z ), from  EPSRC for S. A. R. ( EP/L015455/1 ), and from  BBSRC  for Z. P. ( BB/J014540/1 ). S. B. was an  Amgen Scholar.
Functional Categorization of Drought-Responsive Proteins in Diverse Cultivars of Cauliflower Proteins from spots collected from the master gel were identified by the tandem mass spectrometry coupled with liquid chromatography (LC-MS/MS). Obtained data were used for searching Mascot against the Viridiplantae section of the NCBInr database (version 20160525 containing 88005140 protein sequences). In addition, Gelmap tool (Available online: https://gelmap. de/projects-arabidopsis/) was applied to compare cauliflower and Arabidopsis proteomic maps, and to validate MS identifications. Because mitochondrial proteome from the non-green apical part of cauliflower curds was investigated, the use of a 2D PAGE reference map of Arabidopsis cell culture mitochondrial proteome was particularly advisable.
Detected tryptic peptides were highly similar to early response dehydrins (ERD14 and ERD14-like) from various Brassica species, particularly B. oleracea var. oleracea and B. rapa (GenBank accession. version identifiers XP_013592580. 1 and XP_009128158. 1, respectively; Table  S6 ). However, analyzed protein spots also contained highly abundant mitochondrial proteins (listed in Table  S6 ). Finally, we focused on the comparison of selected proteomic and transcriptomic responses important for cauliflower mitochondrial biogenesis in drought, including abundance profiling of mRNA coding for the identified dehydrins.