dataesr/scientific-paragraphs-categorization huggingface dataset:
Crystals appeared after six months at ambient temperature. The crystals were cryoprotected with 20% ethylene glycol. The crystals belonged to space group C121, with lattice parameters a = 136. 56, b = 94. 13, c = 89. 11 A ˚, = 90, = 125. 49, = 90 and five molecules in the asymmetric unit. Diffraction data were collected using an ADSC Q315 detector on the MX2 beamline at the Australian Synchrotron. The data were indexed, integrated and reduced with XDS. The SIMBAD MoRDa DB search led to successful structure solution with the 156-residue cyanase from S. proteamaculans (PDB entry 4y42). After refinement it became clear that this was the protein that had crystallized instead of the cytokine complex. The structure of the cyanase was refined with phenix. refine (Adams et al. , 2010)  to 1. 91 A ˚resolution, yielding R and R free values of 16.
0% and 20. 2%, respectively. Manual model inspection and modifications were performed with Coot. In the crystal, ten molecules of the protein form a dimeric pentagonal ring (Fig. 5 ). The coordinates and structure factors have been deposited in the Protein Data Bank as entry 6b6m. Following refinement, the cyanase crystallized was found to have the same sequence as PDB entry 4y42 in spite of the fact that the cytokine was produced in an E. coli cell line and the receptors were produced in insect cells. This suggests that one of the expression organisms had became contaminated with S. proteamaculans, which in turn led to the contaminant crystals. Both the SIMBAD contaminant search and the ContaMiner contaminant search allow users to limit the search to common contaminants from a specific host organism.
Normally, this is a logical step that saves computing time; however, this case demonstrates the value of making no assumptions where contaminant origin is concerned. This case also highlighted a limitation in the iteration of the SIMBAD lattice-parameter search used. PDB entry 4y42 had been identified as a search model by the lattice-parameter search. However, subsequent MR/refinement failed to provide a solution. Analysis of why PDB entry 4y42 failed to provide a solution at the lattice-parameter stage revealed an oversight in how structures were being input as search models. At the time that this case was run, all models identified by the latticeparameter search were input into MR with no modifications following download from the PDB. This method had proved sufficient to solve structures which had been crystallized in identical forms to structures already present in the PDB.
However, this would break in scenarios where structures were crystallized in symmetry-related space groups. In this instance, our search model (PDB entry 4y42) was crystallized in space group P1 with ten molecules in the asymmetric unit, whereas our crystals had crystallized in space group C121 with only five molecules in the asymmetric unit. Using PDB entry 4y42 as a search model without modification led to the failure of MR as the MR search was trying to place too many monomers. SIMBAD has subsequently been modified as a result of this case to use a Matthews coefficient to check whether a search model can fit into the asymmetric unit prior to MR. If the full PDB entry is too large to be used as a search model, only the first chain is used. This alteration allowed a solution to be found at the lattice-parameter search instead of the MoRDa DB search in subsequent testing.
0, 20%(w/v) PEG 6000, 1. 0 M lithium chloride] in the reservoir. Single crystals reached a length of approximately 60 mm and were flash-cooled in liquid nitrogen. X-ray diffraction data were collected on beamline I04 at Diamond Light Source, UK employing radiation of wavelength 0. 97946 A ˚. The diffraction data were processed using XDS and scaled with AIMLESS  (Evans & Murshudov, 2013)  from the CCP4 program suite. The crystals grew in space group P1, with unit-cell parameters a = 69. 34, b = 90. 14, c = 114. 76 A ˚, = 107. 10, = 105. 60, = 95. 98  , and diffracted X-rays to 2. 93 A ˚resolution. Initial phasing attempts failed using molecular replacement (MR) with models displaying 20-30% sequence identity to our target (the best hits available in the PDB) as search probes. Ab initio/MR phasing strategies such as ARCIMBOLDO also proved to be unsuccessful, likely owing to limited resolution.
We decided to optimize the crystallization conditions with the aim of obtaining crystals that diffracted X-rays to higher resolution, as well as to apply experimental phasing methods. Similar crystals did grow in the optimization plates after threemonth incubations from 2 ml hanging drops with 1 ml reservoir solution in the reservoir, indicating that crystallogenesis was reproducible, even though new protein batches were used. However, a contaminant search performed at this point with SIMBAD readily identified PDB entry 3vu3  (Yonekura et al. , 2013)  as a successful MR search model. Four copies of the 84 kDa product of the E. coli katE gene were found in the P1 unit cell, revealing the known homotetrameric assembly of catalase HPII (Fig. 6b ). The structure was refined by iterative cycles of manual model building with Coot and refinement with BUSTER  (Bricogne et al.
The different elements of the SIMBAD pipeline have very different computational demands. The fastest step in the pipeline is the lattice-parameter search. The experimental lattice parameters are compared with the lattice parameters stored in the Niggli cell database (129 947 at the time of writing) in less than 10 s. Subsequent MR can take as little as 30 s when the top-scoring search model results in a solution and typically less than 15 min for more difficult cases. The next fastest step, the contaminant search, typically runs in about 15 min using four cores (3. 2 GHz, Intel i5-6500) when run against the full contaminant database (349 structures and 443 associated MoRDa domains at the time of writing). Users can reduce the number of search models to try by specifying the expression organism using the UniProt mnemonic (The UniProt Consortium, 2017); for example, E.
Future developments There are several areas that will be explored in the future to determine whether they improve the effectiveness of SIMBAD. A key area will be expanding the database used by the MoRDa DB search to also include truncated variants and oligomeric forms of proteins. As the MoRDa DB is a reduced database, the top model identified by the SIMBAD MoRDa DB search will not necessarily be the closest available match in the PDB. Therefore, another area to explore is whether homologues of the best search model which were removed when the redundancy was reduced in the construction of the MoRDa DB can provide a better MR solution. To date, it has been difficult to build an accurate picture of common contaminants, as these structures often go either unsolved or unpublished. As SIMBAD becomes used more regularly we foresee the possibility of gathering significantly more data on common contaminants and therefore improving our contaminant database.
We are also developing SIMBAD to use ContaBase (provided by ContaMiner) as a source to update our contaminant database. Therefore, in the event that a user identifies a novel contaminant, we suggest submitting the contaminant to ContaBase, where it will benefit both future SIMBAD and ContaMiner searches. Another avenue to explore is whether alternative scoring systems increase the effectiveness of SIMBAD, as might alternative MR programs for the rotation search in place of the current Patterson-based AMoRe search. In particular, we plan to explore the maximum-likelihood-based rotation search in Phaser using its convenient capacity to process a batch of search models in a single job. Of key interest will be how other MR programs affect the sensitivity of the pipeline and its speed. Currently, the lattice-parameter search and contaminant search are available in CCP4i and CCP4i2 on *nix-based architectures, with plans to bring SIMBAD to CCP4 online services.
2. 3. 2. MoRDa DB search. The MoRDa DB step of SIMBAD screens the MoRDa DB for potential MR templates. MoRDa includes its own edited version of the PDB which contains a nonredundant domain database of $90 000 domains (at the time of this study). The SIMBAD pipeline processes the entire set using the fast AMoRe rotation search. The models are used as they are defined in the MoRDa database with no additional modifications. Each is then ranked by Z-score and the top 200 solutions are passed on to MOLREP followed by REFMAC5 to perform full MR and refinement. Based on preliminary testing, this figure of 200.
Acknowledgements The development of SIMBAD was inspired by a collaboration with  Jon Cooper  of  UCL. We would also like to thank  Martyn Winn  for his useful suggestions on the development of the SIMBAD pipeline. We acknowledge the part of the work performed on the DPS protein at the CCP4/APS school in 2016. RW thanks  Gregory A. Petsko  from  Weill Cornell Medicine  for support of the research.
Plasma samples Plasma samples were collected at baseline, before bevacizumab administration (D1), and before the second bevacizumab administration (D21). Clinical data and neuro-imaging characteristics (when available) were recorded and the bevacizumab trough concentration was determined before the second administration. Plasmas from 10 healthy controls were also collected and processed in an identical way to the patients' samples (blood drawn in the hospital, plasma stored in the same biobank) before being analyzed. Plasma concentration of bevacizumab was quantified by enzyme linked immunosorbent assay (Elisa, Theradiag, Croissy Beaubourg, France)  [21].
Sample analysis by DSC DSC thermograms were collected using a highsensitivity differential scanning VP-DSC microcalorimeter (MicroCal, Northampton, MA, now part of Malvern Instruments Ltd) according to the manufacturer's instructions. 580 μL of sample were used to fill the DSC chamber. Scans were recorded from 10°C to 110°C with a ramp heating rate of 1°C/min using the midfeedback mode and a filtering period of 2s. Thermograms were treated using software Origin 7 (OriginLab Corporation, Northampton, MA) as previously described  [22]. After the first run was completed, a second run of the same sample was performed without emptying the cell and subtracted from the raw data obtained in the first scan. Thermograms were plotted as the excess partial molar heat capacity (cal K -1 mol -1 ) versus temperature (°C).
Correlations were analyzed using the Spearman test. All reported p-values were two-sided, and p<0. 05 was considered statistically significant. Minimal and maximal curve values, associated temperatures, and areas under the curve were then extracted from the denaturation profiles to be compared directly between patients and controls. All statistical analyses were performed by PASW Statistics 22 from IBM Corporation. To explore the DSC profiles by principal component and clustering analysis, we used either 10 or the 5 following profile parameters: heat capacity (cal K -1 mol -1 ) and temperature (°C) of the negative peak; the minimal and the maximal values of the heat capacity as well as the temperature where the heat capacity is minimal. Standardization (z-score) and cluster analysis were performed in Mathematica 10 software from Wolfram Research using the following methods: "Agglomerate" which finds clustering hierarchically, "Optimize" which finds clustering by local optimization, "KMeans" which uses k-means clustering algorithm, "KMedoids" which partitions around medoids and "Spectral" which uses spectral clustering algorithm, with Manhattan or Euclidian distances.
Data analysis. Video recordings were analyzed using Ethovision XT 8. 5 software (Noldus Information Technology, Wageningen, The Netherlands). For each replication, the total duration, the total distance and the average speed in each side of the arena were calculated. The data between the two sides of the arena being paired, comparisons were performed using the Student's t test for paired data when normality and homoscedasticity were present (respectively evaluated by the Shapiro's test and the Fisher's exact test), or using the Wilcoxon test when these conditions were not fulfilled. All analyses were performed with the R studio software (Version 0. 98. 1103), with a significance level set at α = 0. 05. Two other parameters (the number of experiments in which the larva started to move in a side and the curvature of the larval path) were also calculated and compared between sides but, as the results were not significant (see Supplementary Figs S3 and S4), they were not shown in the present manuscript.
Discovery and characterization of MITEs in viruses To scan viral genomes for MITEs, we characterized MITEs do novo in the most recent publicly available database of viral genomes (5170 viruses) from viruSITE  [20]  as of May 2016 using MITE-hunter  [21]. Ten candidate MITEs were retrieved and were classified into four superfamilies of DNA transposons (Additional file 1: Table  S1 ). These excluded twelve elements whose TSD could not be determined (Additional file 2: Table  S2 ). Boundaries of all the MITE candidates could be well-defined through alignment of multiple full-length copies along with their flanking sequences (Additional file 3: Figure  S1a ). These repeats shared all features reported for MITEs (3), including a small size (116-422-bp), TIRs, 2-8-bp TSDs, stable secondary structure, lack of coding capacity and high size homogeneity (Additional file 1: Table  S1  and Additional file 3: Figure  S1a, d ).
Putative autonomous partners for MITE transposition MITEs have no capacity of transposing and need to borrow the transpositional machinery from their master elements  (2). Generally, the sequence similarity of MITEs and DNA transposon responsible for their transposition is restricted to TIRs. Therefore, we investigated potential autonomous elements of these MITEs in both viral and cellular host genomes (e. g. Acanthamoeba castellanii for pandoraviruses) using homology analysis. Autonomous DNA transposons potentially involved in the spread of eight MITEs were discovered in cellular hosts (or species related to their hosts) (Fig. 3  and Additional file 1: Table  S1 ). We found that these autonomous elements had all the hallmarks of the corresponding superfamilies. For example, both hAT-5 and hATm-6 were flanked by 8-bp TSD and encoded an intact transposase with a conserved dimerization domain (PF05699) at the C-terminus, characteristic features of the hAT superfamily  [24].
We also noticed similarity of termini (13/15 nucleotides) between hATm-NA6_CcBV and two autonomous elements from Cotesia vestalis and Rhodnius prolixus (Fig. 3c ). Interestingly, autonomous DNA transposons sharing the same terminal sequences (5′-TAGGGTG and CACCCTA-3′) with that of hATm-NA6_CcBV were also present in the genomes of Cotesia congregata bracovirus (CcBV) (GenBank accession number MF689003) as well as in its insect host, C. congregata (Additional file 2: Table  S2  and Additional file 5: Figure  S3c ). Analysis of its transposase showed that both autonomous elements had encoded a relatively recent function protein which suggests transposition activity occurred in the wasp genome impacting the virus. Sun et al. [6]  reported the presence of an autonomous partner of Submariner-NA_PS in the pandoravirus amoebal host A.
First, MITEs computationally identified in the viral genomes were confirmed using PCR amplification and sequencing of PCR products (GenBank accession numbers MF576508-MF576521). Second, we plotted the coverage of six MITEs plus 1000 bp before and after their genomic positions on the genomes of P. salinus and P. dulcis and found no significant drop in coverage (Additional file 10: Figure  S6 ). Next, we investigated the relationship between MITEs found in viral and host genomes. A phylogenetic tree of full-length copies of Submariner-NA elements indicated that pandoravirus elements formed a monophyletic clade (Fig. 5a ), indicating that it was a single ancestral transfer, more likely from host to virus, followed by spread of the MITEs within viral genomes. We should also notice that this result can also be due to multiple host-to-virus HT from one or more hosts that have not been sequenced yet.
Identification and characterization of candidate MITE-related sequences and putative autonomous partners in their potential host genomes Based on similar TIRs and TSD with those of known DNA transposons  [3] , ten of candidate MITEs identified in viruses could be classified into the superfamilies. We noticed that these ten MITEs resided in viruses, which infect insects or amoebas. Therefore, we looked for these MITE-related sequences in the viral hosts. We used the consensus sequences or representatives of these ten MITEs as queries to BLASTn against the publicly available genome sequences of 227 insects and 15 amoebas as well as the National Center for Biotechnology Information (NCBI) Nucleotide collection database, and all obtained hits were manually checked. If MITEs found in one species were present in less than five full-length copies, which make us difficult to reconstruct reliable consensus sequences, the best match of the queries was used to calculate its copy number and construct multiple alignments together with MITEs from other species.
To test for HTs, MITEs were considered to be present in one viral host if one transposon showed ≥ 60% similarity to the consensus sequences at the nucleotide level and over ≥ 60% of their length. To identify their potential autonomous partners, all significant hits ranging from 1000 bp to 10-kb bearing some similar sequences with those of eight MITEs were retrieved from viral and their host genomes. These longer elements were characterized using the following four methods: (1) TIRs were predicted using fastPCR  [50] ;  (2)  Open reading frame was analyzed using FGENESH  [51]  or GENSCAN (http://genes. mit. edu/GENSCAN. html); (3)  The NCBI Conserved Domain Database (CDD) was employed to identify their putative functional domains; (4) Their nucleotide or protein sequences were used as queries to search against Repbase  [22].
Sequence analysis To identify hATm-like elements in other species, we carried out BLASTp and tBLASTn searches using the predicted hATm transposase sequence from the blood sucking bug R. prolixus as a query against the NCBI Non-redundant protein and Nucleotide collection database, respectively. All significant hits with an identity ≥ 30% and coverage ≥ 60% with an e-value ≤ 5 × 10 -20 were obtained  [25]. The detail information is shown in Additional file 12: Table  S5  and Additional file 13: Table  S6. Copies of all retrieved sequences were extracted with 5000 bp flanking sequences from their host genomes to determine the boundary as well as TSD. Sequence logos were created by WebLogo  [52]  using 30 nt (15 nt upstream and 15 nt downstream) of their flanking sequences including TSD. Transposase amino acid sequences from 102 hAT elements have been aligned using T-Coffee  [53] , with expresso mode (structural alignment).
Acknowledgments We thank  Cedric Feschotte  and  Clement Gilbert  for his valuable suggestions to improve our manuscript. We thank  Karine Musset  for performing amplification and sequencing of bracovirus sequences. We also thank  Gaelen R Burke  for demonstrating that CMC-NA_1 identified in Microplitis mediator bracovirus is false-positive using PCR amplification and sequencing of PCR products.
Leveraging modern imaging and engineering, magnetic resonance elastography (MRE) provides an alternative approach to tissue stiffness characterization that is quantitative and non-invasive  ( Glaser et al. , 2012 ). MRE uses phase contrast imaging to record a full three dimensional field of displacements in tissue induced by a transducer (a common approach is harmonic MRE which employs a single-frequency vibration)  ( Parker et al. , 2011 ). The recorded data represents movement of mechanical waves and, by employing a reconstruction algorithm, the underlying tissue stiffness can be quantified. MRE has been applied to disease areas such as liver fibrosis  ( Bonekamp et al. , 2009; Huwart et al. , 2008; Rouvière et al. , 2006; Venkatesh et al. , 2013; Yin et al. , 2007 )  and breast cancer  ( McKnight et al.
Image data In MRE, 3D displacement data corresponding to waves induced by a single frequency transducer are recorded by MR imaging at several time points along the wave cycle. These are then transformed into a complex valued field based on the time harmonic assumption. As is typical of MR images, the harmonic displacement data are represented on a uniform 3D grid of voxels, which may also be referred to as a stack of images or slices. So, the true continuous displacement field, u , is approximated by the discrete set of MRI obtained values, ˆ u , and these are related by ˆ u ( i, j, k ) = u ( h 1 i, h 2 j, h 3 k ) + ( h 1 i, h 2 j, h 3 k ) , (6) where represents the error due to discretization, noise, voxel averaging, and other factors, ( i, j, k ) counts over the voxels in the ( x, y, z ) directions, respectively, and h 1 , h 2 , and h 3 are the voxel dimensions.
Results The reconstruction methods described in Section 2 were assessed by applying them to a range of MRE data sets. Verification of the methods, by reconstructing phantom data with known stiffness distributions, is described first. Next, several tests for investigating the methods' robustness to noise are introduced and the results of these tests are presented. Multiple anatomical data sets were considered in order to show the methods' robustness to complex data and the resulting correlation of stiffness to anatomy. Table  1  contains a summary of the phantom and anatomical data sets used in this work. The sequence used to acquire these data sets is described in  ( Garteiser et al. , 2013 ). For the global method, the regularization parameters were optimized in each data set by varying the parameters over many reconstructions and comparing the results with the corresponding magnitude image.
Robustness to noise Several tests were developed and run to determine the robustness of each reconstruction method to noise in the displacement data. Effects of both the true noise, inherent in the image, and artificially added noise were tested. These tests were applied to both the CIRS phantom and breast phantom data sets, with results summarized in Figs. 6  and 7  , respectively. A total of six tests were considered: • %Er -Percent error between the mean reconstructed G value and the reported or measured value, • SD G , SD G -The standard deviation of reconstructed G , G , • %Dec -The percent decrease in the mean reconstructed G when 6% noise is added, • MSD G , MSD G -Mean standard deviation of G , G over 50 cases with 6% random noise added to each. The %Er, SD, and %Dec tests consider stiffness values from region 5 in the CIRS phantom and region G in the breast phantom.
The two MSD tests consider values from the entire data set. For all tests, data from the middle five slices of the CIRS phantom and the middle three slices of the breast phantom were used. While the %Er metric could be presented as a test of accuracy it is used here to measure robustness to noise. It is presumed that, for direct methods, the SNR and the ratio of wavelength to voxel size define a stiffness threshold where higher stiffness values will be underestimated and lower values will be overestimated  ( Arunachalam et al. , 2017; Connesson et al. , 2015; Honarvar et al. , 2016; Papazoglou et al. , 2008 ). This can be seen in Figs. 3  and 5  as well as in phantom results presented in other work  ( Baghani et al. , 2011; Honarvar et al. , 2012; 2013 ). For typical MRE data quality, this ideal wavelength to voxel size ratio has usually been found to lie approximately between 10 and 20  ( Arunachalam et al.