dataesr/scientific-paragraphs-categorization huggingface dataset:
4 , where we have plotted the hodograph of the mean velocity vector for both the current model and the NCAR LES dataset. Clearly, the wind direction near the surface (making an angle of ≈36 • with the x-direction) resulting from the model corresponds very well to the LES data, and also the wind turning in the rest of the boundary layer is well predicted. In the context of this stably stratified test case, it is relevant to investigate the issue of the critical Richardson number discussed in Sect. 2. 3. In Fig. 5a  we show the current model results for the velocity variances plotted against the gradient Richardson number. From Fig. 5a  it is clear that all velocity fluctuations decay at a specific critical Richardson number. In this case, the critical Ri value in the current model appears to be close to 0.
Also the boundary conditions are based on rather general relations for the logarithmic layer. We were able to make a thorough comparison of model simulations with LES data in the GABLS1 case  (Beare et al. 2006). The results in Figs. 1  and 2  show very good agreement with available LES data (especially the dataset from NCAR) and the agreement appears significantly better than with a standard TKE scheme. The good results of the model for this stably stratified case are consistent with those presented in  Lazeroms et al. (2013)  for turbulent channel flow. A very interesting result in Figs. 1  and 2  is the close agreement of the current model and the EFB closure as implemented in the ARPEGE model, even though they are based on somewhat different theoretical considerations. Most notably, the EFB closure as presented in  Zilitinkevich et al.
Acknowledgments The authors wish to thank  Dr Frederik Schenk  for fruitful discussions and feedback on the manuscript. The first author is grateful for the opportunity to carry out part of the work presented in this paper at the  GMAP group of Météo-France, Toulouse , and the hospitable atmosphere there. Funding by the  Swedish Research Council  (VR) through Grant No. 621-2013-5784  is gratefully acknowledged.
For the 20-50% centrality class, the generated p T distribution was defined based on FONLL perturbative QCD calculations  [56, 57]  multiplied by the nuclear modification factor predicted by the BAMPS partonic transport model  [58] , which reproduces the measured nonstrange D-meson R AA in semi-central collisions within uncertainties  [16]. The generated particles were transported through the ALICE detector using the GEANT3  [59]  particle transport package together with a detailed description of the geometry of the apparatus and of the detector response. The simulation was tuned to reproduce the position and width of the interaction vertex distribution, the number of active electronic channels and the accuracy of the detector calibration, and their time evolution within the Pb-Pb data taking period.
The efficiencies were evaluated in centrality classes corresponding to those used in the analysis of the data in terms of charged-particle multiplicity, hence of detector occupancy. In the left-hand panel of Fig. 2 , the (Acc × ε) values for prompt and feed-down D + s mesons with rapidity |y| < y fid are shown for the 0-10% centrality class. The same figure shows also the (Acc × ε) values for the case without the PID selections, demonstrating that this selection is about 85% efficient for the signal. The magnitude of (Acc × ε) increases with increasing p T , from 0. 4% in the lowest p T interval up to 2% in 8 < p T < 12 GeV/c. The (Acc × ε) values for D + s from beauty-hadron decays are larger than those for prompt D + s by a factor of approximately 2. 5-3. 5 depending on p T , because the decay vertices of the feed-down D + s mesons are more displaced from the primary vertex and they are, therefore, more efficiently selected by the analysis cuts.
The efficiency of the selections used in the centrality interval 20-50% is higher by a factor of about two with respect to that in the most central events, because the smaller combinatorial background in semi-peripheral collisions allowed the usage of looser selections on the D + s candidates. The ratio of prompt to inclusive contributions in the D + s -meson raw yield, f prompt , was evaluated using a procedure similar to the one adopted for the pp measurement  [45]. The contribution of feed-down from B decays in the raw yield depends on p T and on the applied geometrical selection criteria. The feed-down contribution was estimated using the beauty-hadron production cross section from FONLL perturbative QCD calculations for pp collisions at √ s = 2. 76 TeV scaled by the average nuclear overlap function T AA in each centrality class, the B→D+X decay kinematics from the EvtGen package  [60]  and the Monte Carlo efficiencies for feed-down D + s mesons.
] [10]  Adverse drug reactions such as arterial hypertension, pleural effusion and vascular events were unexpected and reported after approval of the TKI. For imatinib, the first TKI employed in CML, an association has been established between treatment and side effects that impair QoL especially in younger patients. 7, 11, 12 For newer TKIs, data on QoL are scarce. 13 mproved survival rates in CML lead to increasing prevalence of the disease. With more than 400 000 CML patients expected in Europe by the year 2050, CML is set to become an important chronic non-communicable disease. The cost of treatment per patient is currently estimated at ~30 000-40 000€ per year in most European countries. In view of recommended lifelong treatment,  6  this represents a significant burden on health care systems,  14  even though costs are expected to decline when generic imatinib becomes available in most countries.
TFR was estimated ~37% at 36 months when the STIM criteria were applied, that is, close to STIM or TWISTER results. The study was also safe as all patients were sensitive to re-treatment. The main conclusion drawn from the study was to use 'loss of MMR' as the more appropriate trigger for restarting treatment after TKI discontinuation than loss of deep molecular response or CMR. Most recently, data of the ISAV (Imatinib Suspension and Validation) study were presented. 31 One hundred and twelve patients with at least 2-year imatinib treatment and at least 18 months undetectable transcripts in qRT-PCR were followed for a median of 21. 6 months. Cumulative incidence of relapses was 52% after 36 months. Relapse was defined as loss of MMR (two consecutive positive PCRs with one result at least above 0.
Line of treatment Patients on first-line TKI therapy and those on second line due to intolerance to first line may be considered for a cessation attempt. Duration of TKI therapy before stop Preliminary data suggest a favorable impact of a longer duration of therapy, but exact numbers are pending. Ongoing studies like EURO-SKI will determine optimal duration of therapy before a stop attempt may be considered. Level of MR before stop Exact level still unknown. Data suggest a minimum of MR  4. Ongoing studies like EURO-SKI will determine level and duration of MR before a stop attempt may be considered. Duration of deep MR before stop Longer duration seems to be favorable, but exact numbers are pending. Ongoing studies like EURO-SKI will determine level and duration of MR before a stop attempt may be considered.
Patient information was obtained by telephone interviews conducted by trained interviewers. All statistical analyses used SAS software, V. 8. 02 (SAS, Cary, North Carolina, USA). Quantitative variables were described by frequency, mean, SD, median and range. Qualitative variables were described by the frequency and percentage of each modality. For qualitative variables, Fisher's exact and χ 2 tests were used to compare groups. Univariate analyses were performed. Analysis of variance (ANOVA) (time×age in 2 classes) was performed, with the significance threshold set at <0. 05.
RESULTS Between June 2007 and June 2008, 1517 patients were enrolled in the study, of whom 1358 (89. 5%) satisfied the inclusion criteria. A total of 1354 patients had data (including age) available on D0, and were included in the analysis (figure  1 ). At D0, the mean age of patients was 67. 7 years (range: 50-95): 55% 70-and 45% 70+, including 37. 1%≥80 years and 14. 6%≥85 years (figure  1 ). Most patients were women in both groups: 61. 2% of 70-and 62. 2% of 70+ patients. At M3, M6 and M12, respectively, PHN was present in 14. 3%, 10. 4% and 7. 7% of 70+ patients who answered the questionnaire item (n=627, 608 and 544, respectively) and in 9. 7%, 7. 2% and 4. 8% of 70-patients who answered the questionnaire item (n=462, 442 and 393, respectively). As compared with the other patients, 70+ patients more frequently reported at least one comorbidity at diagnosis (77.
Methods Coarse-grained model of IgGs. The coarse-grained model of antibodies was built with the shape-based algorithm  35, 36  implemented in the VMD package  37  , by choosing N = 96 spheres. This number is a good trade-off between the constraints of keeping the structure light enough (fewer spheres) and reproducing faithfully enough the three-dimensional shape of the domains (more spheres). The diameter of the beads composing the IgG was set equal to the smallest center-to-center distance found in the CG assembly, multiplied by 0. 95 (to prevent tangency of contiguous beads). Based on this criterion, we obtain R α = 0. 44 nm. The resulting assembly of spheres was then bound into a scaffolded structure by requiring all spheres within a cutoff radius R c = 22 Å within each other to be joined by very stiff springs (stiffness κ = 50 kcal/mol/Å 2 ).
A set of different configurations was then obtained by sampling a constant-energy trajectory with initial condition given by the crystallographic structure (PDB code: 1IGT). In order to reconstruct the trajectory, we integrated Newton's equation numerically. = -∇ ( ) m V r 6 i i through a position-extended Forest-Ruth like (PEFRL) symplectic algorithm  38  with a time step dt = 2. 8 fsec. The mass m of the effective beads was fixed at = /.  m M 96 1 67 IgG kDa. The trajectory was sampled every 0. 56 psec to obtain the coordinates of different conformers. The Fab-Fab and Fab-Fc angles were obtained for each conformer by first constructing the inertial ellipsoids of the three domains. The three angles (ψ and the equivalent φ 1 , φ 2 ) were then computed from the scalar products of the vectors describing the major axes of the three ellipsoids (pointing outward from the hinge).
Introduction Interactions between proteins or proteins and DNA are a generic process underlying many biological processes. Thanks to the growing number of complexes available in the Protein Data Bank (PDB  [1] ), generic principles of protein-protein interactions have been discovered  [2] [3] [4] [5] [6] [7] [8]. Protein-protein binding sites are relatively large and flat; their organization shows as a core of hydrophobic residues surrounded by a rim of polar residues that occlude the solvent. In addition, only a small set of residues contribute most to the free energy of binding. Hetero-and homo-complexes have similar interface properties  [7, 9] , but interfaces from transient complexes tend to be smaller and less hydrophobic than interfaces from permanent complexes  [10, 11]. In this anatomic description of protein binding sites, nothing has been said concerning the terminal residues of protein chains, which are often hybridized in biochemical techniques aimed at detecting protein-protein interactions.
Results We took into account the presence and integrity of terminal residues in structures, and computed their frequency at protein-protein interfaces and specific regions of interfaces, in a large dataset of 17,658 binary complexes, non-redundant at the interface level, termed DIMER70, and a filtered dataset of 5,203 proteins, non-redundant at the monomer level, termed MONO-MER25. We analyzed their conformational rearrangement upon complexation using the Docking Benchmark  [23] , the correlation between the presence of both terminals at an interface and the potential link with the experimental techniques for detecting protein-protein interactions. We also analyzed three datasets of protein-DNA structures for the frequency of terminal residues in interfaces and preference for different interface regions.
N-and C-terminal residues differ, with less genuine N-terminal residues (32%) than C-terminal (45%). This difference is mainly due to the prevalence of modification of N-terminal residues (15%) compared to C-terminal residues (5%). N-terminal residues are also slightly more often missing (53%) than C-terminal residues (50%). The same trend is observed on the smaller MONOMER25 dataset (9746 terminal residues analyzed from 6235 chains), see Figure A in S1 File. The classification of all the terminal residues in the DIMER70 dataset is available in S2 File. In order to study terminal residues in their native structural context, we restrict the rest of our analyses to the genuine class (25,078 terminal residues from the DIMER70 dataset).
Terminal Residues Are Mostly Located at the Surface of the Proteins We computed the relative accessible surface area of terminal residues in the context of isolated monomers. Using a 25% of relative accessible area cutoff as in  [9] , we found that 94% of the genuine terminal residues from the DIMER70 dataset are located at the surface of the proteins, without asymmetry between N-terminal and C-terminal residues. The same proportion is observed for the MONOMER25 dataset. When looking at raw accessibility values N-terminal residues were found marginally more exposed than C-terminal residues (Figure B in S1 File).
One Quarter of Terminal Residues are Involved in Protein-Protein Interfaces We classified residues into different regions (surface, interior, core, rim, support) according to their exposed surface area, in isolated monomers and in complexes, as described in the Materials and Methods section. The amino-acid compositions in different regions are shown in Figure  C  in S1 File. As previously observed  [7] , protein surfaces are enriched in polar residues compared to the global composition and interface cores are richer in hydrophobic residues than the rim. On average, 23% of the genuine terminal residues are involved in the protein-protein interfaces in the DIMER70 dataset (21% for N-terminal and 24% for C-terminal). Similar results are obtained for the MONOMER25 data set (see Tables A and B in S1 File).
Terminal Residues Are Not Significantly Preferred or Avoided in the Interfaces In this section, we assess whether the rate of terminal residues in interfaces is different from what is expected by chance. For this, we simulate 1000 data sets with a random model based on hypergeometric laws for each protein, as explained in Materials and Methods. This model takes into account the size of each protein, and the fact that different categories of complexes display different size distributions (see Figure E in S1 File). The simulated data sets allow us to draw empirical distributions for the fraction of residues involved in interfaces, and assess how our observed fractions differ from the random expectation. As shown in Figures F and G in S1 File, the observed fractions of residues in interfaces fall within the simulated ranges for both DIMER70 and MONOMER25 dataset, meaning that there is no over-or under-representation of terminal residues at the protein-protein interfaces.
This preference is confirmed on the MONOMER25 dataset (Figure I in S1 File). It is known that the rim of interfaces is enriched in polar and charged residues  [26]. It is thus expected to find an enrichment of terminal residues, since they are charged. We performed the same analysis on charged residues. The majority of them, when involved in interfaces, are located in the rim, although to a lesser extent than terminal residues: 76% for lysine, 62% for arginine, 66% for aspartate and 69% for glutamate residues. Simulations show that charged residues are, as expected, over-represented in the rim regions (Figure J in S1 File).
Co-occurrence of Terminal Residues in Interfaces In this section, we ask the following question: do terminal residues co-occur at protein-protein interfaces? We counted the co-occurrence of N-terminal and C-terminal residues at proteinprotein interfaces, in the DIMER70 data set, and computed a Chi-squared test to assess the independence, see Table  1. We found that the co-occurrence of terminal residues from a same chain is favored (pvalue = 5. 5e-14), although this preference is modest (ratio of observed versus expected = 1. 34). This confirms a previous observation that tails tend to be close to each other  [17]. When found a significant co-occurence of terminal residues from different chains in homo-dimeric A: complex between the caspase-recruitment domain of APAF-1 (Apoptotic Protease Activating Factor 1) and the pro-domain of human procaspase-9 (PDB code 3YGS  [27] ).
In this complex, all the terminal residues are genuine, and none of them is involved at the protein-protein interface. B: complex between two subunits of the mRNA capping enzyme of the vaccinia virus (PDB code 2VDW  [28] ). The genuine N-terminal residue of the small subunit is involved in the rim of the interface. C: complex between a human carboxypeptidase and an inhibitor from leech (PDB code 1DTD  [29] ). In this complex, the C-terminal residue of the inhibitor is buried at the core of the interface. The close-up view reveals that the terminal carboxylate group of the backbone is interacting with a Zinc ion, which is also coordinated by three side-chains of the enzyme. Interfaces are colored in yellow (core) and cyan (rim). Terminal residues are represented as opaque spheres and highlighted by arrows.
Images are generated using UCSF Chimera  [30]. doi:10. 1371/journal. pone. 0162143. g004 interfaces: terminal residues co-occur in interfaces more often than expected. This is linked to the previous observations: since terminal residues from the same chain tend to co-occur at interfaces (due to their proximity), by symmetry it will also favor the co-occurrence across different chains. By contrast, there is no significant co-occurrence in the case of terminal residues from partner chains across the interface for heterodimers. Data were insufficient to derive meaningful statistics from the MONOMER25 dataset.
Does Terminal Residue Modification Impair Interaction Detection? We retrieved, for each complex in the DIMER70 data set, the experimental methods used to detect the interaction, from the IntAct database. We separated the interactions between those detected  using methods that require hybridization of tag peptides (such as TAP-tag purification) or fusion domains (such as two-hybrid), and those detected using other methods. We thus had to map from InterEvol chains, through PDB chains, to Uniprot chains. We then searched IntAct for the experimental methods used to detect the interactions. In order to get meaningful results, we restricted our analysis to biological interfaces and genuine residues. Out of the 7079 complexes considered, IntAct provides information for 849 complexes (12%), with a better coverage for hetero-complexes (615 out of 1823, i.
Terminal Residues at Protein-DNA Interfaces We analyzed the location of terminal residues in two datasets prepared by us: 156 transcription factors complexed with DNA and 265 DNA-binding proteins with other functions. We found that 16% of the genuine N-terminal and 4% of the genuine C-terminal residues were involved in protein-DNA interfaces in transcription factors, versus 6% of genuine N-terminal and 3% of the genuine C-terminal in the other DNA-binding proteins. Thus, N-terminal residues seem more frequent in protein-DNA interfaces than C-terminal ones, both in transcription factors and DNA-binding proteins with other functions. In these data sets, no filtering was carried out to reduce the redundancy between protein sequences. In order to get statistics on non-redundant data, we analyzed the location of terminal residues in a dataset prepared by  [32]  composed of 303 protein-DNA complexes, irrespective of their function, non-redundant at the 70% level (the sparsity of the PDB does not allow to reduce further the redundancy).
Data set Protein-protein interfaces. An initial list of 17,658 binary interfaces was retrieved from the InterEvol resource  [39]  (http://biodev. cea. fr/interevol/downloads. html). This list is nonredundant at the dimer level, at the 70% threshold. We termed this list DIMER70. In this list, interfaces are already annotated in terms of: • heterodimer or homodimer complexes, • obligate or non-obligate complexes (predicted by NOXclass  [40] ), • biological interfaces or crystal contacts (predicted by NOXclass). We further made the distinction between interfaces extracted from complexes with two chains (dimers) and those extracted from higher order complexes (K-mers), from information available in the PDB. We filtered the DIMER70 list in order to reduce the redundancy between monomers to 25%, using PISCES (http://dunbrack.
Protein-DNA complexes. Protein-DNA complex structures solved by X-ray crystallography at a resolution better than 2. 60 Å and a maximum R free value of 0. 4. were obtained from the PDB. The complexes were defined as any structures containing at least one protein chain, and a double-stranded DNA longer than six base pairs. All entries containing significantly   [35] ). In this complex, protein terminal residues are genuine and none of them is involved in the DNA-protein interface. Orange spheres are calcium ions. B: complex between transcription factor PU. 1 and DNA (PDB code 1PUE  [36] ). The N-terminal residue of the transcription factor (depicted in cartoon representation) is involved at the rim of the interface with DNA (pink surface). C: complex between the DNA binding domain of the C.
elegans Tc3 transposase and DNA (PDB code 1TC3  [37] ). The N-terminal residue of the protein is buried at the core of the interface and the C-terminal residue is involved at the rim of the interface. Protein interface is colored in yellow (core region) and cyan (rim regions). Terminal residues are represented as opaque spheres and highlighted by arrows. Images are generated using UCSF Chimera  [30]. Another list of protein-DNA complexes with various functions were retrieved from  [32] , and filtered using PISCES using the following parameters: sequence percentage identity <70%, resolution <3Å, R-factor <0. 3, sequence length between 40 and 10,000, include non-X-ray structures, exclude Cα-only structures, cull PDB by chain. This filtered list contained 304 protein chains, from 278 PDB entries.
IntAct. We retrieved interaction data from the IntAct resource  [42]. The xml files contained in the pmidMIF25. zip archive were parsed to retrieve interactions related to the PDB structures of the DIMER70 dataset. The following experimental methods were considered implying tail modification: adenylate cyclase complementation, affinity technology, anti-bait coimmunoprecipitation, anti-tag coimmunoprecipitation, bimolecular fluorescence complementation, dihydrofolate reductase reconstruction, pull down, two-hybrid, two-hybrid array, two-hybrid fragment pooling approach, two-hybrid pooling approach, ubiquitin reconstruction. The correspondence between PDB chains and Uniprot IDs was performed using the PDB/Uniprot Mapping resource (http://bioinf. org. uk/pdbsws/) [43].