dataesr/scientific-paragraphs-categorization huggingface dataset:
About genome organization in E. coli, the DNA is condensed into a compact structure called the nucleoid and is organized into macrodomains  [25]. However the fine spatial organization of the DNA inside a macrodomain is still largely debated. This illustration is an original photograph of David Goodsell's painting. It differs from the version published in  [26] [27]. With kind permission of David S. Goodsell. doi:10. 1371/journal. pone. 0053609. g001 can effectively be generated in NAB over a cubic Bezier curve but the control points must be specified numerically since there is no interactive graphical user interface. 3DNA is also not interactive. However, the web-based interface to the program (Web 3DNA)  [10]  greatly helps the user model a DNA/protein complex. The chosen approach is to choose a PDB file representing the structure of the protein of interest, then to enter the sites onto the protein where the DNA binds and to launch a calculation yielding a DNA 3D structure wrapping the protein.
Information like protein-protein or DNA-protein crosslinks  [11] [12]  or tomograms might provide further proofs of the model. Powerful for studying shapes, GraphiteLifeExplorer is not designed for detailed local structure studies. Single/double nicks, mismatchs, kinks or interbase stretches cannot be created by using the tool exclusively. Moreover a few structural deficiencies can subsist such as O3' -P bond abnormal stretches between adjacent residues of the same chain. Clashes between atoms can also occur at the apex for severe curvatures. The user needs to be aware that cyclized DNA can be created (figure  3 ) but closing of DNA whose length is not a multiple of 10. 4 requires the pitch to slightly differ from 10. 4 base pairs per turn. Refinement can be done by exporting the model in the Protein Data Bank (PDB) file format (see 1 Export capability) and by performing an Amber basedmolecular dynamics  [13].
Export Capability GraphiteLifeExplorer relies on its export capabilities coupled with well-established tools for physics-based animation or minimization computations. A scene can be exported either as a set of full atom representations in the Protein Data Bank (PDB) file format or as a set of geometric shapes. PDB export. Each component of a scene (DNA and proteins) can individually be saved in the PDB format in which new atomic coordinates account for the spatial transformation that occurred during the composition of the scene. Figure  10  shows an example of a scene initially made with GraphiteLifeExplorer and visualized in Maya and Blender. Exporting in PDB thus allows the user to perform any task requiring a full-atom description of the components in an external tool. Geometry export. As shown Figure  10 , tubular shaped linkers and molecular surfaces can be exported as geometric objects (surfaces) in a common 3D format called Virtual Reality Markup Language (VRML).
g. , the Frenet frame  [15] ). But ideally, we would like the frames in the sequence to  The CTD is moved manually relatively to the NTD. (c) A surface appearance is given where residues 331 of the NTD and 432 of the CTD are colored in blue to help the modeling of two 40 nm long amino-acid linkers connecting the two domains. Although their amino-acid sequence is known, the linkers are disordered and therefore missing in the attempts to experimentally solve the 3D structure of the whole MutL. Here, the linkers have been built as a simple tube of fixed length and in agreement with AFM images  [29]. Some residues at the surface of the N-ter domain have been colored in red to show where another repair protein called MutH interacts with MutL. PDB code N-ter: 1B63, PDB code C-ter: 1x9z (both subunits have been reconformed as suggested in  [30] ).
We build a binary hierarchy over the sequence of frames F u. The root node consists of the entire sequence. The subsequence at one node is split in two sub-subsequences of equal length to form the two child nodes. At each node, a sphere bounding all the sampling points in the subsequence of the node is computed. We stop splitting a node when it contains about ten base pairs. [31]. In Blender, a script written by L. Autin (Scripps) superimposes an inverse kinematic armature to the linkers. This mechanical articulation greatly helps and facilitates the animation of the proteins from a first position (right figure) to a second position (left figure): thanks to the IK chain, moving the pink and green domains results in a reconformation of the linkers, pushed or pulled like a chain, in the deformation of the geometry (the tubes representing the linkers) attached to the linkers, and in the displacement of the CTD domain (green-blue/yellow).
Conclusions 3D modeling has proved its use beyond education and illustration as it helps to build our understanding  [4]  by formulating hypotheses and new avenues of research  [20]. With the constant flow of new information regarding the spatiotemporal organization of living systems, the development of integrative tools  [21]  linking architecture, genetic information and metabolism is a key step to understand how processes are performed inside the spatially structured cells. At the current stage, GraphiteLifeExplorer enables to build spatial arrangements of proteins and nucleic acids representing cellular processing, while looking for a complete bacterium in the future. To our knowledge, GraphiteLifeExplorer is the first interactive tool with the capability to freely model the shape of DNA strands, and is far easier to use than previous approaches using generic 3D modeling software.
Acknowledgments The authors thank  Ludovic Autin  ( The Scripps Research Institute ) and  Julie Bernauer  ( Inria ) for revising this manuscript and the numerous and fruitful discussions. The authors are also grateful to  Dmitry Sokolov  ( Inria ) for his help in the programming effort, to  Adam Gardner  ( The Scripps Research Institute ) for his participation in the creation of figure 10, and to  Ivan Junier  ( Center for Genomic Regulation ) for providing DNA simulation data.
This work was partially funded by the  European Research Council  ( GOOD-SHAPE FP7-ERC-StG-205693 )(http://erc. europa. eu/starting-grants/). This work was partially funded by the  Fourmentin-Guilbert Scientific Foundation  which had a role in software design, software test, decision to publish, and preparation of the manuscript (http://www. fourmentinguilbert. org/?set_language = en).
Availability GraphiteLifeExplorer supports Windows Seven, Linux and Mac OS X (Lion). The pre-compiled program is freely available at http://www. loria. fr/ ˜shornus/FFG/gle. html. The source code can be downloaded from http://gforge. inria. fr/frs/?group_id = 1465. Tutorials are available from http://www. lifeexplorer. eu/3dmodels/tutorials.
ZNFs bind to DNA by inserting the a-helix into the major groove and use three or four exposed residues of the helix to make specific contacts with three or four DNA bases  [13, 14]. To recognise their target DNA in a cellular context, ZNFs are usually present in tandem repeats separated by a short linker. Each ZNF repeat binds specifically to DNA using the a-helix and the repeats wrap around the DNA. Some ZNFs like SW15, ADR1 or GAGA, however, are present in only one to three copies and use extensions of the ZNF motif to further contact DNA  [15] [16] [17]. Hyperthermophilic archaea that thrive in hot springs (. 80uC) are infected by viruses that show unique morphological and genomic properties that distinguish them from bacteriophages and eukaryal viruses  [18]. The majority of the proteins of these viruses does not have detectable homologues in the databases, however, a relatively high proportion is predicted to carry transcription-factor associated folds (up to ,10% of proteins encoded in genomes with about 50 putative genes)  [19].
NMR Experiments were performed on a Varian NMR System 600 spectrometer (Agilent Technologies, Santa Clara) with a proton resonating frequency of 599. 4 MHz. The spectrometer was equipped with a cryogenic probe. Spectra were recorded at 25uC and referenced to sodium 4,4-dimethyl-4-silapentane-1sulphonate following IUPAC recommendations. Data were collected using VnmrJ 2. 3A (Agilent Technologies), processed with NMRPipe  [24]  and analysed with NMRView 5. 2. 2  [25]. Standard two-and three-dimensional experiments were recorded to assign chemical shifts to the protein 1 H, 13 C and 15 N nuclei: 13 C or 15 N HSQC (heteronuclear single quantum coherence)  [26] ), HNCO, HNCACB, CBCA(CO)NH  [27] , H(CC-TOC-SY)NNH, C(CC-TOCSY)NNH  [28, 29] , (HB)CB(CGCD)HD and (HB)CB(CGCDCE)HE  [30]. AFV1p06 backbone dynamics analysis was based on 15 N relaxation experiments  [31]  used to calculate the longitudinal (R 1 ) and transverse 15 N (R 2 ) relaxation rates.
Phylogenetic Analysis To gather the amino acid sequences for phylogenetic analysis, we searched the non-redundant protein sequence database (nr) at NCBI for homologues of AFV1 virus p06-ORF59a (GI: 82056192) using the PSI-BLAST algorithm 2. 2. 26+  [45]  in ten iterative steps with default parameters. Whenever the algorithm ran out of new proteins to include in the iteration, the protein with the best E-value and with a conserved C2H2 motif was manually picked. Sequences were aligned using the CLC Sequence Viewer software (CLC Bio, Denmark) with default parameters. The tree was calculated using the Neighbour-Joining method and a 100 replicate bootstrap analysis. The protein knowledgebase UniProtKB database was questioned using the query ''zinc AND finger AND C2H2'' to obtain the sequences of proteins with predicted ZNF motifs in the three domains of life.
AFV1p06 contains two Cys residues (C13 and C16) and a single His residue (H29) that are part of the ZNF motif and that could be involved in zinc coordination. Nevertheless, the protein lacks the fourth zinc ligand, which could be either a water molecule or the side chain of residue E34 that in the sequence alignments with ZNF proteins is positioned close to the fourth zinc ligand (H or C). We performed a NMR chemical shift analysis to verify if residues C13, C16 and H29 could be involved in metal chelation. On the one hand, the Cb and Ca chemical shifts of residues C13 and C16 were in agreement with those of metalloproteases  [46] , indicating that both cysteine Sc atoms bind zinc; on the other hand, the comparison of the aromatic ring carbon chemical shifts (Cd2 and Ce1) of H29 with that of histidine residues (deposited in the BMRB database) that bind or do not bind zinc, indicated that H29 binds zinc and that it ligates zinc through its Ne2 atom.
Structure of AFV1p06 The structure ensemble of AFV1p06 shows a compact and convergent region between residues 8-50 and disordered N (1-7) and C (51-59) termini (Figure  1 , Table  1 ). The structure consists of a three-stranded antiparallel b-sheet (residues 8-13, 19-20 and 45-50) packed against an a-helix  (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) , as well as of a short 3 10 helix (41-43) located at the end of a long loop between the a-helix and the third strand of the b-sheet. As expected, the region with the ZNF sequence motif (residues 9-35) shows a typical zing finger fold with an antiparallel b-hairpin packed against an a-helix and with the zinc ion sandwiched between the latter structural elements. Indeed, a search for structural homologues in the DALI database (http://www.
The electrostatic potential of AFV1p06 on the a-helix face is positive (Figure  4 ) and seems thus favourable for interacting with the negatively charged DNA poly-anion. Moreover, residues that occupy the DNA-contacting positions in AFV1p06 [T  (21) , K (+2), Q (+3) and L (+6)] have been observed in ZNF-DNA complexes and could in principle establish specific contacts with DNA bases  [13]. Manual docking of AFV1p06 into ZNF-DNA complex structures [PDB codes 2JPA and 2GLI,  [59, 60] ] suggests that AFV1p06 may also interact with DNA using the a-helix: the superimposition of the structure of AFV1p06 with that of ZNFs in complex with DNA indicates that residues at key positions of the helix could indeed make contacts with DNA and that the basic residues R8, R21  (22)  and K23 (+1), would be close to the DNA phosphate groups.
Despite its small size, in the case of the N-terminal GATA-1 ZNF and its FOG ZNF partner, it has been observed that the ZNF fold can cope with simultaneous specific protein-DNA and protein-protein interactions or that two different ZNFs can bind to form heterodimers that bind DNA specifically  [61] [62] [63]. Also, fungal GATA proteins involved in gene regulation display only one ZNF motif, bind to specific DNA sequences and can mediate protein-protein interactions that are important to regulate gene expression  [64]. It should be underlined that proteins bearing the C2H2 zinc finger motif are essentially known and characterised in eukaryotes. In this domain of life, ZNFs are predicted to be coded by more than 1% of the genes compared to 0. 07% for the archaea (278 examples in the Uniprot database) and only 0.
This work was supported by the  Institut Pasteur  and the  Centre National de la Recherche Scientifique  ( CNRS UMR 3528 ). FG, CD and CJ were supported by the  French «Ministere de l'Enseignement Superieur et de la Recherche». The 600 MHz spectrometer was funded by the  Region Ile de France  and the  Institut Pasteur. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Control Population 2,349 controls (1,953 Caucasian; 160 Asian; 236 African based on self-reports and confirmed by genotyping) were genotyped on the same platform as the patients. Subjects were primarily recruited from the Philadelphia region through the Hospital's Health Care Network and included disease-free children in the age range of 0-18 yrs of age who had high quality, genome-wide genotyping data from blood samples, and no serious underlying medical disorders, including but not limited to neurodevelopmental disorders, cancer, chromosomal abnormalities, and known metabolic or genetic disorders.
PCR conditions were 95uC denaturation for 10 minutes; 40 cycles each of 95uC for 15 seconds and 60uC for 1 minute; a dissociation curve at 95uC for 15 seconds, 60uC for 15 seconds, and again 95uC for 15 seconds. The passive internal reference dye (ROX) was used to adjust for any differences in the final reaction volumes across multiple wells. Data analysis was carried out using the DDCT method to obtain the relative quantity (RQ) values after normalizing with at least two of four reference genes (ALB, RPP14, HEM3, and GPR15). Thus, for each sample at least 8 different RQ values were obtained (2 different primers62 different normal control individuals62 different reference genes). If the 95% confidence interval of the mean RQ was between 0. 7 and 1. 3 then the copy state was considered to be normal.
Whole Exome Sequencing We performed exome sequencing on DNA from patient LP09-141a1 (ACC-PMG) with a paternally inherited deletion on 16p13. 11. We used the Agilent SureSelect 50 Mb All Exon kit for target capture, and sequenced 100 base pair paired end reads on Illumina Hiseq. Exome data was aligned to hg19 with Burroughs-Wheeler aligner (http://bio-bwa. sourceforge. net/bwa. shtml). Duplicate reads were marked using Picard (http://picard. sourceforge. net/) and excluded from further analysis. Single nucleotide variants (SNVs) and short insertions and deletions (indels) were called using samtools and exome coverage was determined with Genome Analysis Toolkit (http://www. broadinstitute. org/gsa/wiki/index. php/ The_Genome_Analysis_Toolkit). Variants were annotated using SeattleSeq (http://snp.
The exercise consists of a progressive submaximal individualized cycling exertion using an adapted cycle ergometer, during the three weekly dialysis sessions. Biological markers of nutrition (albumin, prealbumin) will be followed monthly and all patients will be assessed for body composition, walk function, muscle strength, postural stability and quality of life at baseline and during the eighth week (t +2 ), the sixteenth week (t +4 ) and the twenty-fourth week (t +6 ) of the 6-month adapted rehabilitation program. Discussion: The successful completion of this current trial may give precious clues in understanding PEW and encourage nephrologists to extend prescription of exercise programs as well as therapeutic and as preventive interventions in this high-risk population. Trial registration: The protocol for this study was registered with the France Clinical Trials Registry NCT01813851.
Participants' recruitment All of the 210 patients treated by hemodialysis or onlinehemodiafiltration in the participating centers will be screened for the presence of criteria for PEW. The principal investigators will review the existing patients' database for serum albumin, serum prealbumin, C-reactive protein (CRP) levels, body mass index (BMI) and the presence of weight loss according to the PEW criteria. Potential subjects will have additional tests (measure of lean body mass index by bio-impedance and a 3-day dietary record by a qualified dietician in order to evaluate their protein and energy intake). Patients fulfilling inclusion/exclusion criteria and having signed an informed consent for the study participation will undergo randomization generated by a computer program. We expect to recruit approximately fifty HD patients for this study.
For all data collection of postural sway, we will use a Kistler force plateform (model 9286BA) with subject weight normalization. Data will be sampled at 100 Hz and recorded online. The body sway will be quantified by displacement of the center of foot pressure (COP) in the anterior-posterior (AP) and in the medial-lateral direction (ML). For measuring all of the COP parameters, raw data will be filtered using a fourth-order Butterworth, zero-phase low-pass at 10 Hz. The COP parameters will then be the mean and standard deviation (SD) of COP position in AP and ML directions, SD of velocity in AP and ML directions, mean velocity and area (90% confidence ellipse) (see  [44]  for details of formulas).
The 3D model is projected along the line of sight, assuming spherical symmetry and convolved with the Planck PSF to produce a projected model function f (r). Finally we fit f (r) to the data using a χ 2 minimisation of its distance from the radial profiles y(r) + dy(r) derived from the MILCA map (y(r)) and 1000 realization of its additive noise model (dy(r)). The χ 2 is calculated in the principal component basis of these noise realisations. This procedure uses an orthog-onal transformation to diagonalise the noise covariance matrix which, thus, decorrelates the additive noise fluctuation. It is important to say that, as the parameters of the fitting functions are highly degenerate, we adopt two techniques to quantify the uncertainties, i) for each individaul parameter, and ii) for the overall model (that is, the global model envelope).
XMM-Newton data analysis The XMM-Newton results presented in this paper have been derived from analysis of the mosaic obtained by combining 27 XMM-Newton pointings of the Coma cluster available in the archive. The XMM-Newton data have been prepared and analysed using the procedure described in detail in  Bourdin et al. (2011), and Bourdin & Mazzotta (2008). We estimated the Y X = M gas × T parameter of Coma iteratively using the Y X -M 500 scaling relation calibrated from hydrostatic mass estimates in a nearby cluster sample observed with XMM-Newton  (Arnaud et al. 2010) ; we find R 500 ≈ (47 ± 1)arcmin ≈ (1. 31 ± 0. 03) Mpc and we use this value throughout the paper. To study the surface brightness and temperature radial profiles we use the forward approach described in  Bourdin et al. (2011)  taking care to project the temperature profile using the formula appropriate for spectroscopy; i.
In the left and right panels of Fig. 7  we show with a grey shadow the corresponding 3D pressure jump models with their errors for the west and south-east sectors, respectively. For convenience in Fig. 6  we overlay the data points with the best-fit projected y models after and before the convolution with the Planck PSF. As shaded region, we report the envelope derived from the 68. 4% of models with the lowest χ 2. In the lower pan-els we show the ratio between the data and the best-fit model of the projected y profile in units of the relative error. This figure clearly shows that the pressure jump model provides a good fit to the observed profiles for both the west and south-east sectors. Furthermore the comparison of the projected model before and after the convolution with the PSF clearly shows that, for the Coma cluster, the effect of the Planck PSF smoothing is secondary with respect to projection effects.
After filtering, we convolve the the diffuse radio emission to 10 arcmin resolution to match the Planck y map. We then extract the radio and y signals from the r < 50 arcmin region of the cluster and plot the results in Fig. 9. This is the first quantitative surface-brightness comparison of radio and SZ brightnesses 2. We fit the data in the log-log plane using the Bayesian linear regression algorithm proposed by  Kelly (2007) , which accounts for errors in both abscissa and ordinate. The radio errors of 50 mJy/10 ′ beam are estimated from the off-source scatter, which is dominated by emission over several degree scales which is incompletely sampled by the interferometer. We find a quasi-linear relation between the radio emission and the y signal: y 10 -5 = 10 (0. 86±0. 02) F (0. 92±0. 04) R , (7) where F R is the radio brightness in Jy beam -1 (10 arcmin beam FWHM).
Discussion So far In this paper we have presented the data analysis of the Coma cluster observed in its SZ effect by the Planck satellite. In Sect. 5 and Sect. 6 we showed that, thanks to its great sensitivity, Planck is capable of detecting significant SZ emission above the zero level of the y map up to at least 4 Mpc which corresponds to R ≈ 3 × R 500. This allows, for the first time, the study of the ICM pressure distribution in the outermost cluster regions. Furthermore, we performed a comparison with radio synchrotron emission. Here we discuss our results in more detail.
2010). Fig. 5  shows that this latter model indeed reproduces the data well at r < R 500. Nevertheless, as for Model A, it still underestimates the observed y signal at larger radii. The observed profile clearly requires a shallower pressure profile in the cluster outskirts, as evident in Models C and D. This is important, as the external pressure slopes of both Model A and B are tuned to reproduce the mean slope predicted by the hydrodynamic simulations of  Borgani et al. (2004) ,  Nagai et al. (2007) , and  Piffaretti & Valdarnini (2008, from now on, B04+N07+P08). The Planck observation shows that the pressure slope for Coma is flatter than this value. This is also illustrated in Fig. 11  where we report the pressure slope as a function of the radius in our models: we find that while at R = 3 × R 500 the mean predicted pressure slope is > 4.
13 : Comparison between the Planck and XMM-Newton derived deprojected total pressure profiles. Upper panel: Blue line and light blue shaded region are the deprojected pressure profile, with its 68. 4% confidence level errors, obtained from the X-ray analysis of the XMM-Newton data (see text). The black line and grey shaded regions are the best-fit and 68. 4% confidence level errors from the Model C pressure profile resulting from the fit shown in Fig. 5. Lower panel: Ratio between the XMM-Newton and Planck derived pressure profiles. The black line and the grey shading indicate the best-fit and the 68. 4% confidence level errors, respectively. one, which implies that this kind of contamination is negligible in the Coma region. Thus, if there is SZ contamination it is probably related to the filamentary structures surrounding the cluster.