dataesr/scientific-paragraphs-categorization huggingface dataset:
They also show preferred chemical pairing different from those measured for βfibers  [98]. However this result is only based on comparison with the literature and it could be due to differences in the datasets and/or the algorithms. The geometry of the side chains has been so far neglected when it appears in our study as a key parameter. Using Steiner Minimal Tree approach (SMT), MacGregor Smith et al proposed an elegant geometrical representation of the amino acids that was successfully applied to the problem of protein folding  [112]. It will be interesting to extend this approach onto protein interfaces to see if the specificity of protein interfaces may be provided by the geometry of the amino acids rather than their sole chemistry. Similar double layer of interactions, has been observed at the interfaces between colicins and their cognate immunity proteins  [113].
We have observed in our 1056 β-strand interface dataset that the side chain pairs also have specific network features. The side chain hot spot sub-networks have nodes with more contacts than the backbone or the PIRA (Parallel In Register Alignment) networks, used to predict fiber sequences  [98]. Yet the β-strand interfaces have no hubs and maintain a low interconnectedness (little communication between residues of the interface), probably a mechanism to resist the effect of mutation by secluding the nodes of the networks (Fig. 7 ). Simultaneously, the side chain residues make less than three contacts avoiding stringency on the choice of amino acids capable of making an interface and providing the β-strand interface high sequence plasticity. Robustness and plasticity of networks are well explored by graph theory and there are several very inspiring papers on that topic  [83, [118] [119] [120].
The paper is devoted to the presentation of validation cases carried out for the models describing the corium behaviour in the "lower plenum" of the reactor vessel implemented in the V2. 0 version of the ASTEC integral code, jointly developed by IRSN (France) and GRS (Germany). In the ASTEC architecture, these models are grouped within the single ICARE module and they are all activated in typical accident scenarios. Therefore, it is important to check the validity of each individual model, as long as experiments are available for which a single physical process is involved. The results of ASTEC applications against the following experiments are presented: FARO (corium jet fragmentation), LIVE (heat transfer between a molten pool and the vessel), MASCA (separation and stratification of corium non miscible phases) and OLHF (mechanical failure of the vessel).
Introduction ASTEC is an integral code jointly developed by IRSN (France) and GRS (Germany) to simulate the whole sequence of a severe accident in nuclear power plants, from the initiating event up to the fission products releases and their behaviour in the containment, and finally to appreciate radioactive releases out of the containment. The "lower plenum" models implemented in ASTEC deal with the corium behaviour in the reactor vessel from its slump from the central part of the core, its possible fragmentation and solidification in water present in the lower plenum, up to the possible failure of the vessel lower head, taking into account the corium stratification (separation of chemical phases) and heat transfers with the vessel lower head wall. The adequate representation of all of these phenomena should in principle allow an accurate determination of the time of vessel rupture and the characteristics (temperature, composition.
ASTEC modelling of the corium behaviour in the vessel lower plenum In case of reactor calculations performed with the ASTEC code, when the molten corium relocates from the core down to the lower plenum, the melt jets interact with water and may be totally or partially fragmented depending on the level of water inside the vessel. The code evaluates the jet break-up length and deduces the fragmentation rate and the associated water vaporization. Accurate evaluation of this steam production is important in order to be able to predict the associated pressurization. If the melt jet is totally fragmented, a debris bed is created at the bottom of the lower plenum, but if it is only partially fragmented, a corium layer is formed at the bottom, covered by the debris bed (Fig. 1 ). Then ASTEC evaluates the separation of non-miscible liquid oxide and metal phases and the stratification of those phases up to 3 corium layers.
Comparison of ASTEC V1. 3 and V2. 0 results The L14 and L28 experiments have already been simulated with the V1. 3 version of ASTEC  (Bandini et al. , 2010). Thus the same modelling has been considered for the V2. 0 calculations. It uses the ICARE module for the vessel phenomena, coupled with the CESAR module for the circuit thermal-hydraulics (in particular for annular space simulation in L28 test). With the V1. 3 version the best results were obtained considering a diameter of 3. 5 mm for the debris formed by fragmentation for the L14 test and 3 mm for the L28 test assuming spherical particles. These values are in accordance with the range of particle diameters observed experimentally (see  Magallon, 2006). The same parameters have been chosen for comparison with the V2. 0 version. The results are presented in Figs.
e. the debris bed surface taken into account by the model, and S cake the surface of the non fragmented part of the corium measured after the experiment. The results of this calculation are presented in Fig. 6. These modifications have no significant impact on the water level in the inner vessel and in the annular space, which remain in good agreement with the experimental results. As a conclusion, the ASTEC V2. 0 code predictions for corium fragmentation are reasonably good and follow the trends observed in the experiments. However, some limitations in the modelling have been identified and are summarized in the next section.
7b ). Two correlations are also available for the evaluation of the debris diameter in ASTEC. The most elaborated correlation  (Namiech et al. , 2004)  allows diameter variation during the fragmentation process. In particular, the calculated diameter depends on the jet velocity, the surrounding pressure and temperature, etc. However, the diameter obtained with this correlation is bigger than the one leading to the code best-estimate results on FARO. Thus pressure in the vessel is underestimated, as well as the water temperature and the swollen water level in the annular space. This discrepancy could be explained by the fact that, in Namiech's correlation, secondary fragmentation of the debris is not taken into account. This phenomenon leads to reduce the size of the debris initially formed and therefore to increase the pressure and the water temperature.
ASTEC validation vs. LIVE experiments The LIVE-L1 and LIVE-L6 experiments, performed at KIT (Germany), were designed to study the late phase of core degradation, the formation and stability of crusts and melt stabilization in the reactor pressure vessel by external cooling. In the L6 experiment the configuration with a two-layer melt pool was investigated, with a heated lower layer and an unheated upper layer of molten material. More information about L1 and L6 For the modelling of the experiments, the molten pool was treated as one homogeneous layer in the L1 experiment and as two layers for the L6 one (for the heated and the unheated parts of the melt). The objective of the validation was to investigate the heat transfer between the corium and the vessel wall. The ASTEC V2 simulations were performed using only the ICARE module in stand-alone.
L6 experiment No study was performed with the V1. 3 version for this experiment. With the V2. 0 code version, the calculated melt temperature is globally 15 K higher than the measured temperatures. A calculation performed with reduced heating power, to take into account the heat losses due to experimental devices estimated from experimental results (see Fig. 11 ), leads to a lower discrepancy for the melt temperature between 5 K and 10 K (Fig. 10 ). The power distribution between both layers is in agreement with the experimental one (around 20% of the heating power is transmitted from the lower layer to the upper one, see Fig. 11 ). The calculated crust thickness at the end of the experiment and during each steady-state phase is in good agreement with the experimental values (Fig. 12 ), taking into account the crust conductivity equal to 0.
MASCA experiments The MASCA series of experiments, performed by NRC-KI (Russia), were designed to study the phase separation of mixtures of UO 2 , ZrO 2 , Zr and Fe/steel. Some of the experiments also involved fission products simulants, namely Mo, Ru, Sr, Ba, Ce, La, in order to evaluate their re-distribution between the non-miscible phases. For more information, readers may refer to  Asmolov et al. (2004 Asmolov et al. ( , 2007)) ,  Asmolov and Tsurikov, 2004, and Bechta et al. (2008). The V2. 0 version of ASTEC was used to simulate the 21 experiments of the MASCA programme: 17 with 0. 5 kg of mixture (STFM series) and 4 with 2 kg (MA series). The metal-oxide corium stratification model implemented in ASTEC V2. 0 code comes from the one developed for ICARE/CATHARE V2. 0 IRSN code  (Salay and Fichot, 2005).
For all the experiments, the ASTEC V2. 0 predictions for corium separation in the presence of structural materials (Fe/steel) and for fission products distributions are reasonably good and follow the trends observed. The relative errors in metallic mass predictions are on average of 21% for the STFM series (36% maximum) and of 12% for the MA series (21% maximum). Fig. 13  depicts the U, Zr and steel concentrations in the metal phase as a function of relative metal addition predicted for all experiments involving the corium composed with zirconium being initially oxidized at 32%. The experimental data trends are well reproduced by the code calculations. The limitation of this validation task is the transient behaviour of the separation (the mass transport between the layers is calculated by modelling migration of drops) which could not be assessed due to lack of experimental data.
It may also be noted that the current heat transfer model does not take into account the vessel thinning and deformation due to creep. The elongation value of the vessel as a function of time is presented in Fig. 15. The calculated elongation at the end of the curve is close to the experimental one, even if the failure is obtained earlier in the code. The different vessel rupture models available in ASTEC (COMBESCURE, OEUF and LOHEY) have also been tested to evaluate the sensitivity on the time and position of the vessel failure. The results obtained are summarized in Table  3. For the 3 different models, the failure localisations evaluated by the code for the first failure criteria reached (default parameter) are comparable to the experimental one (60 • -75 • in the calculations vs. 75 • in the experiment) but the rupture time is underestimated from 3% to 13%.
That is why default values fixed in the code for these criteria cannot be reached before criteria 1 and 6. We should note that, both experimentally and numerically, the failure mode is not clearly identified for the OLHF1 experiment. This difficulty was already underlined in the benchmark performed on this experiment with different calculation codes  (Nicolas et al. , 2003). However, in that case, the rupture times evaluated by ASTEC for the different failure modes are sufficiently close for the simulation of accident scenarios in a reactor. More generally, depending on the accidental scenario, thermal loadings on the lower head vessel could be different and lead to a specific failure mode. Thus, the improvement of thermal loadings evaluation for various transients is an important issue to increase the accuracy of failure time evaluations.
Conclusion The ASTEC V2. 0 code was assessed by comparison with four series of experiments, each of them dealing with one important phenomenon affecting the corium behaviour in the lower plenum. The FARO experiments were selected to assess the model of the fragmentation of corium falling into a water pool. Overall, the model may be considered satisfactory for the prediction of the increase of pressure resulting from the interaction with the water and of the fraction of fragmented corium, even if, for L28 test, the mixing of debris and non fragmented corium makes the comparison difficult. As a first step of improvement, it seems necessary to simulate other experiments in order to confirm that the default value used for the debris diameter does not lead to larger uncertainty than the one obtained from the two selected FARO tests.
DISCUSSION Results presented here extend the known functional repertoire of the mTERF protein family by demonstrating a role for Zm-mTERF4 in group II intron splicing. We show that Zm-mTERF4 associates in vivo with many chloroplast introns and splicing factors, and that it is required for the efficient splicing of several of the   S2. A replicate dataset using a different array design and independent stromal preparation is shown in Supplementary Figure  S2. introns with which it associates. This role in plastid RNA splicing impacts the biogenesis of the plastid translation machinery, which in turn affects photosynthesis and plant viability. Molecular functions for only a handful of mTERF proteins have been established. The mTERF proteins in metazoa affect mitochondrial transcription, DNA (continued) replication and translation [reviewed in  (11, 40) ], and several of these functions are understood in considerable mechanistic detail.
ASSESSMENT OF TRADE-OFFS To understand which adaptive policies would work best for WAIT, an assessment of its performance trade-offs was done. It involved comparing the throughput (TP) and the performance bugs identified by WAIT when using different configurations. These metrics were collected through RPT and the WAIT report, respectively. All tests were done in an environment composed of eight VMs: Five application nodes, one WAIT server, one load balancer and one load tester (using RPT 8. 5). All VMs had 2 virtual CPUs, 3GB of RAM, and 50GB of HD; ran Linux Ubuntu 12. 04L, and Oracle Hotspot JVM 7 with a maximal heap of 2GB. The application nodes ran Apache Tomcat 6. The full DaCapo 6  benchmark 9. 12 was chosen as application set because it offers a wide range of application behaviours to test.
Second, the number of application nodes was increased (to 10 nodes) to test our framework in a bigger test environment. This experiment also involved modifying the source code of JPetStore to manually inject five common performance issues (two lock contentions, two deadlocks and one I/O latency bug). Two types of runs were performed: The first type involved a tester using WAIT manually (M-WAIT). A second type of run involved using WAIT through our automation framework (A-WAIT). In both cases, the tester did not know the number or characteristics of the injected bugs. The results are summarized in Table  1. After comparing the runs, two time savings were documented when using A-WAIT: First, the effort to identify bugs was decreased 68%. This was the result of simplifying the analysis of the WAIT reports: Instead of having multiple reports to be manually correlated, the tester using A-WAIT only needed to monitor a single report which incrementally evolved.
Using AHC with an automatic setting of the dissimilarity threshold (XLSTAT software), the 263 recorded neurons were separated into three classes of heterogeneous sizes (Figure  8A ). The observation of the recordings corresponding to the central objects of each class reveals that bursting, irregular and regular tonic neurons were clustered in separate classes (class 1, 2 and 3, respectively; Figure  8B ). Unsurprisingly, CV ISI was one of the parameters presenting the most significant differences between the central objects (Figure  8B ) or the centroids (Figure  8C ) of each class. Nonetheless, other parameters such as AP half-width, AP rise slope, AP decay slope, and AHP amplitude also presented notable differences between the three classes (Figure  8B, C ). Although age was not included as one of the variables in the AHC analysis, the age of the animals corresponding to each recorded neuron was known such that we were able to determine the age of each observation (Figure  8A ), of the central objects (see Figure  8B ) but also the mean age for each class (Figure  8C ).
Dimensional stacking entails visualizing a high-dimensional data set in a two-dimensional space and has been recently applied to analyze the electrical behavior of databases of complex realistic neuron models  (Taylor et al. , 2006). In the current study, we performed ANOVA on 16 electrophysiological parameters across 14 developmental stages, thus, to compress the results into two visible dimensions, we (i) designed a two entrytable comprising 105 cells corresponding to all stage-to-stage comparisons (P2-P3 vs P5, P2-P3 vs P6, etc) (ii) divided each of these 105 cells into 16 sub-cells corresponding to the 16 electrophysiological parameters analyzed at each developmental stage, and (iii) color-coded the sub-cells as a function of the statistical significance of the ANOVA test performed (non significant, p < 0.
05, p < 0. 01, p < 0. 001). As a consequence, although Figure  3  strictly represents the results of separate univariate analyses, it also provides a general view of the changes in the electrical phenotype of dopaminergic neurons, since each cell summarizes the differences in 16 electrophysiological parameters between two specific developmental stages: while the evolution of 1 electrophysiological parameter can be tracked through development by looking at one specific sub-cell, looking at the mosaic pattern of the large cells gives an indication of the general change in electrophysiological phenotype. This type of stacked representation is likely to be useful to anyone analyzing changes in a large number of parameters across a large number of states, and is increasingly used when analyzing the behavior of databases of models  (Taylor et al.
Electrophysiology data acquisition and analysis Data were acquired with a HEKA EPC 10/USB patch-clamp amplifier (HEKA Elektronik, Germany) and patchmaster software (HEKA Elektronik). Analysis was conducted using FitMaster v2x30 (HEKA Elektronik) and Igor Pro (version 6. 0, WaveMetrics). Statistical analysis (performed according to data distribution) included: linear regression, unpaired t test, Mann Whitney, paired t test, one-way ANOVA and Fisher exact test for proportions, with a p value <0. 05 being considered statistically significant. Statistics were performed utilizing SigmaPlot 10. 0 (Jandel Scientific, UK) and Prism 6 (GraphPad Software, Inc. , La Jolla, CA). Unless otherwise stated, data are presented as box and whisker plots, with the box representing the median value, the 25th and 75th percentiles, and the whiskers representing the 10th and 90th percentiles.
In box and whisker plots, all outliers are represented. Agglomerative Hierarchical Clustering Analysis (AHC) and Principal Component Analysis (PCA) were performed using XLSTAT 2011 (Addinsoft, France). AHC dissimilarity level was calculated based on euclidian distance, and agglomeration was performed using Ward's method. The dissimilarity threshold used for defining the number of classes of objects was automatically set by the XLSTAT software. PCA was based on the covariance matrix (n) of the observations/variables table. Only the PC1 and PC2 (accounting for 93. 76% of the total variance) were kept, and the corresponding factor loadings (F1 and F2) for each individual recording were analyzed and plotted. Experiments where the effects of TTX, apamin and leak negative conductance injection on firing were tested (Figure  11 ) were added as supplementary data in the PCA but were not included in the calculation of the principal components.
Immunohistochemistry and analysis Acute slices containing Neurobiotin tracer-filled cells were fixed 30 min in 4% paraformaldehyde at 4°C and immunolabelled with anti-tyrosine hydroxylase (Millipore, 1:9000 or Abcam, 1:2000) and Streptavidin Alexa Fluor 594 (Invitrogen; 1:12,000; 1. 66 ng/ml) and donkey anti-sheep Alexa Fluor 488 (Invitrogen; 1:1000 2 µg/ml) as previously described  (Wolfart et al. , 2001; Amendola et al. , 2012). All immunolabelling was viewed on a Leica TCS-SP2 confocal microscope (Leica Microsystems, Wetzlar, Germany), and images were captured using Leica LAS-AF software. Figures were prepared using.
Acknowledgements This work was funded by  Inserm  ( Avenir grant  to  J-MG , post-doctoral fellowship to JA), the  Fyssen Foundation  (J-MG),  Conseil Général des Bouches du Rhône   CG13  ( J-MG ) and the  National Health and Medical Research Council of Australia  (post-doctoral overseas training fellowship 544940 to AW), the  Fondation pour la Recherche Medicale  (post-doctoral fellowship to AW), the  French Ministry of Research  ( doctoral fellowship  to MAD), the  Fondation France Parkinson  (doctoral fellowship to MAD), and the  French National Research Agency  ( ANR JCJC grant ROBUSTEX  to  J-MG ). We would like to thank  C Pauchet-Lopez  and  M Baudoux  for technical assistance, and  Dr F Tell ,  Dr D Debanne  and  Dr S Temporal  for helpful discussions on the manuscript.
Determination of cell viability Cell viability was monitored using Uptiblue reagent (Interchim) as previously described  [22]. Cells were seeded in 96-well plates at a density of 5x10 3 cells/well for MPM cells in culture medium for 24h. Then compounds were added for an additional 72h and Uptiblue reagent (5%, v/v) was then added to the culture medium for 2h at 37°C. Fluorescence was measured at 605nm after a green epi illumination excitation using a ChemiDoc™ MP imaging system (Biorad). Quantification was performed using ImageJ 1. 41o software. For kinetic experiments, culture medium containing Uptiblue was replaced by medium with or without drug for 72h and the procedure for cell viability measurement was repeated twice. Results were expressed as percentage of untreated cells.
Detection of Reactive oxygen species Cells were seeded at a density of 1x10 6 cells/well in 12 well plates. 24h after seeding, cells were washed once with PBS and incubated for 30min at 37°C with the CM-H2DCFA probe (Life Technologies), resuspended in PBS at a final concentration of 5µM, washed once with PBS and treated with Cisplatin or PEITC alone or in combination. 24h after treatment, cells and their supernatant were harvested and analyzed by flow cytometry (FACSCalibur; Becton Dickinson). Ten thousand events were collected and analyzed with the FACS Flowjo Software (Tree Star Inc).
Detection of apoptosis Cells were seeded at a density of 1x10 6 cells/well in 6-well plates and treated with indicated concentrations. After 24h, floating and adherent cells were combined, labeled using Annexin V-allophycocyanin (APC) (Becton Dickinson) following the manufacturer's instructions, and analyzed by flow cytometry (FACSCalibur; Becton Dickinson). Ten thousand events were collected and analyzed with the FACS Flowjo Software (Tree Star Inc).
24h after seeding, cells were treated with cisplatin at 0. 8mg/l in combination with PEITC at 4µM or 6µM for 72h. Cell viability was then measured using Uptiblue counting reagent as described above. Fluorescence was measured at 605nm after a green epi illumination excitation using a ChemiDoc™ MP imaging system (Biorad). Quantification was performed using ImageJ 1. 41o software.
ACKNOWLEDGEMENTS This work was supported by  INSERM ,  CNRS , the  Ligue National Contre la Cancer  for  ID grant , the  Ligue inter-regionale Contre le Cancer: committees of Morbihan ,  Sarthe, Vendée, Loire-Atlantique and Poitou-Charentes ,  ARSMESO44. The authors thank MicroPICell and Cytocell core facilities for the microscopic and the flow cytometry experiments, respectively.