dataesr/scientific-paragraphs-categorization huggingface dataset:
Improvements of the database The NUCLEA database has benefited from all aspects of the data production not only within SARNET2 but also from other data published in the scientific literature which are taken into account in the improvement of the database. The advantage of such Networks is that the proposed work is that considered most important by the whole severe accident community. This is not only the data required but also the ability to use experimental data sets for validation of the modelled results. Below are some specific examples from the individual experiments. 3. 6. 1. Modelling of the UO 2 -ZrO 2 -FeO system In Fig. 18  the variation of the experimental liquidus and solidus temperatures with composition of the FeO-equimolar (UO 2 -ZrO 2 ) system as determined at ITU is shown in comparison the NUCLEA database.
Modelling of the UO 2 -CaO phase diagram The data collected from the VPA-IMCC, thermal Analysis and Galakhov l-furnace tests in the PRECOS project were used to update the UO 2 -CaO pseudo-binary diagram. This is seen in Fig. 19. This work has not only confirmed the high melting point of the CaO, but also determined the eutectic composition as well as other important characteristics and the difficulties of working with hydroscopic materials. As previously mentioned, the eutectic composition and temperature have been found to correspond to 32. 55 ± 1. 75 mol% UO 2 and 1870 ± 25 °C to be compared to the previous Holc's data, 37 mol% and 1850 ± 20°C. The actual modelling of NUCLEA which takes into account the Roche's data  (Roche et al. , 1993)  who determined the eutectic composition and temperature at 37 mol% UO 2 and 1945 °C respectively could be reconsidered with these new results.
AREVA GmbH The compositions of the coexisting metallic and oxidic liquid in the EPICOR tests were calculated using the GEMINI2 code  (Cheynet, 2007)  and the NUCLEA-10 thermodynamic database. Thermodynamic calculations show that boron and carbon almost completely concentrate in the metallic liquid, and with residual interstitial solid solution in the metallic solid, their presence in the oxidic liquid was negligible. The experimentally evaluated values of the U, Zr and Fe (Cr, Ni. ) contents in the oxidic and metallic melts were compared with GEMINI-2 predictions using the NUC-LEA-10 database. It was found that the calculations give a rather good coincidence both for the U and Zr contents in the oxidic melt, and the Fe (Cr, Ni. ) contents in the metallic melt. However, the calculations considerably overestimate (by up to 12 wt%) the Ucontent in the metallic liquid in comparison with the experimental findings.
Conclusions The research under work package 6 concerning the molten corium concrete interaction (MCCI) for the SARNET2 project and associated ISTC projects was reviewed. It demonstrates significant progress in understanding physical chemistry of high temperature corium melts and in quality improvement of data used for thermodynamic modelling of severe accident phenomena. This includes knowledge obtained in the large scale testing of the VULCANO facility in CEA Cadarache, in the cold crucible testing of UO 2 -containing systems carried out at NITI, Sosnovy Bor and by AREVA GmbH, as well as in UJV, Rez. Small scale testing of UO 2 -containing systems was also carried out at ITU in support of phase diagram determination for these refractory systems. IVTAN in collaboration with NITI also performed melting point determinations using laser heating techniques.
The data produced by this study has been introduced into the data banks and used to validate the thermodynamic models and improve the phase diagram construction and hence severe accident modelling predictions. The improvement of a key in-vessel system (U-Zr-Fe-O) and in a difficult system (high CaO content in UO 2 -CaO) has been given as direct examples. The understanding of complex coria systems is advancing as the secondary interactions from ex-vessel light elements (e. g. Ca, Si) on heavy element components (U,Zr) is researched. The large facility testing has demonstrated that anisotropy in the MCCI erosion needs to be more clearly defined and how variable the local conditions can be that yield this corrosion/erosion profile uncertainty. Such effects (e. g. crust stability or structure, gravity effects) need to be understood before full 3-dimensional modelling can be reliable.
In the growing field of systems biology, the knowledge of protein concentrations is highly required to truly understand metabolic and adaptational networks within the cells. Therefore we established a workflow relying on long chromatographic separation and mass spectrometric analysis by data independent, parallel fragmentation of all precursor ions at the same time (LC/MS E ). By prevention of discrimination of co-eluting low and high abundant peptides a high average sequence coverage of 40% could be achieved, resulting in identification of almost half of the predicted cytosolic proteome of the Gram-positive model organism Bacillus subtilis (>1,050 proteins). Absolute quantification was achieved by correlation of average MS signal intensities of the three most intense peptides of a protein to the signal intensity of a spiked standard protein digest.
In order to quantify the highest possible number of expressed proteins in an absolute manner mass spectrometry based methods seem to be the method of choice. Within the field of proteomics MS is often coupled with liquid chromatography to reduce sample complexity prior to MS analysis (LC/MS). Commonly applied data dependent acquisition (DDA) methods for peptide identification suffer from some limitations. Often low abundant peptides with a low MS signal intensity are discriminated and their isobaric precursor ions cannot be isolated leading to low scores in database search and wrong assignments  (12, 13). These obstacles lead to lower protein sequence coverage in general and higher numbers of protein identifications based on a single peptide only. In contrast, with data independent acquisition (DIA) methods like LC/MS E (14) all available precursor ions are fragmented in parallel without any selection by switching between low and high collision energy scans in high frequency.
Additionally four proteins were targeted that showed significant differences between the growth conditions (Ald, GapA, RocD, PckA). Briefly, three with heavy isotope labeled peptides per protein were spiked into the protein digests and analyzed by LC-SRM. Peptides were separated via reverse phase nanoHPLC (Ettan MDLC, GE Healthcare) by a 90 min gradient. SRM measurements were perfomed by 4000 QTRAP (ABSciex). Data analysis was performed in MultiQuant 1. 2 (ABSciex). Detailed MS-parameters can be found in supplemental material and Material of reference  (6). LC-SRM for the four additional proteins was carried out with a reverse phase nanoHPLC (easy-nLCII, Thermo Fisher) using a 80 min gradient. SRM measurements were performed by a TSQ Vantage (Thermo Fisher). Data analysis was performed with Pinpoint 1.
8426 Da/e, lock mass window: 0. 25 Da, low energy threshold: 250 cts, elevated energy threshold: 30 cts, retention time window: automatic, intensity threshold of precursor/fragment ion cluster: 1000 cts. Database search was carried out by the ion accounting algorithm (22) against randomized B. subtilis 168 database  (23)  with added common laboratory contaminants and yeast ADH1 sequence (8,438 entries) using following parameters: peptide tolerance: automatic, protein tolerance: automatic, min fragment ions matches per peptide: 1, min fragment ions matches per protein: 5, min peptide matches per protein: 2, primary digest reagent: trypsin, missed cleavages: 2, fixed modification: carbamidomethylation C (ϩ57. 0215), variable modifications: deamidation N, Q (ϩ0. 9840), oxidation M (ϩ15. 9949), pyrrolidonecarboxylacid N-TERM (-27.
9949), false discovery rate (FDR): 5%, calibration protein: yeast ADH1. All identification and quantitation data are summarized in supplemental Table  S8  and are available at the PRIDE database. For additional filtering of database search results, all proteins identified in only one of the nine replicates of one sample (three technical replicates of each of the three biological replicates) were discarded leading to a FDR between 3 and 4% on protein level. These FDR thresholds were applied for the global analysis of the data. For comparison of the dataset to other published data set, all proteins had to be identified in three out of the nine replicates, which led to a FDR below 1%. Replicate measurements were utilized for calculation for confidence intervals and statistical significance analysis.
RESULTS AND DISCUSSION Protein Identification-For global absolute protein quantification, B. subtilis cells were grown either in glucose and ammonia salts based minimal medium (condition S) or in amino acid based defined medium (condition CH). Tryptically digested extracts of these cells were analyzed by a dataindependent-acquisition workflow (LC/MS E ) in three technical replicates for each of the three biological replicates. After database search using the ion-accounting algorithm  (24)  an additional replicate filter was applied to exclude false positive identifications randomly occurring across replicate measurements. Each protein had to be identified for acceptance at least in three out of nine replicate measurements. The replicate filter criterion rendered a FDR of 0. 8% for condition S and of 0.
1  for condition S. Technical replicates were utilized to calculate the coefficient of variance (CV). The average CV of technical replicates was below 30% and even below 20% for more than half of the quantified proteins (summarized results of absolute and relative quantification in supplemental Table  S1 ). We first investigated the precision of the Hi3 method on LC/MS E -data by comparing our results with previously published copy numbers per cell determined by Western blot quantification. Although the growth conditions in the chosen publications were not the same like in the presented work, the quantifications are still within a twofold range (Table  I ). For further verification of absolute quantification results, the same samples were independently analyzed via an additional MS-based highly precise absolute quantification method (AQUA approach (  5 )).
Therefore, this approach can be seen as gold standard for absolute protein quantification in small scale experiments and as a good orthogonal method to verify the results from the Hi3 method. Quantification results of AQUA and Hi3 approach were in good agreement (Table  II ). The only big discrepancy in the quantification results was the quantification of RocD in condition CH (AQUA: 1184 m/c and Hi3 113,714 m/c), which might be explained by a wrong peptide assignment during the database search of the LC/MS E data. However, the two datasets didn't show a significant difference in a Wilcoxon-Rank-Sum test (p ϭ 0. 44 for condition S and p ϭ 0. 46 for condition CH) and an average error of about 1. 4-fold. Given this degree of consistence the Hi3 approach presented here can accomplish a similar accuracy as other current methods for estimating absolute protein abundance on a proteomewide scale  (6, 11).
Genome-scale absolute abundance in general results in a higher variance in the protein abundance compared with the single peptide/protein quantification by AQUA. A way to improve the precision of the Hi3 approach is to increase the number of technical/biological replicates (in our case nine replicates per condition were analyzed). Statistical Analysis: Reproducibility and Quality of LC/MS E -Hi3 Method-The dataset, composed of nine replicates, has been deeply analyzed in both conditions to evaluate (a) the reproducibility of LC/MS E method, (b) the bias in protein quantification that should arise from protein abundance, localization or the nature of protein and (c) the relative error in protein quantification. For both conditions, the correlation between biological and technical replicates was computed.
As expected, the variance of protein increases with the mean value. Therefore, we applied a variance stabilizing transformation (Log2 transformation) on the original dataset and then computed the empirical distribution of the residual error. Both distributions for condition S and CH are close to normal distribution with longer left and right tails than standard normal distribution (supplemental Fig. S3  for condition S and CH). In fine, 94. 8% of all residual errors in condition S (and 95. 02% in condition CH) belong to a Ϯ 1. 96 band around protein average (supplemental Figs. S4 and S5) where is the standard deviation of the error residues. We computed the 95% confidence intervals of log data by bootstrapping the set of error residues. The converse transformation was finally applied on the mean and on each bound of the confidence intervals to deduce the final interval values.
Consistency of the Data Set With the Known Regulatory Network-Data analysis was mainly performed on the central metabolism because most of the regulations active in different growth conditions modulate expression of proteins involved in these metabolic pathways. Despite the limitations of the protein quantification method mentioned above, an almost complete coverage of metabolic pathways could be obtained (supplemental Fig. S8 , definition of pathways in supplemental Table  S5 ). We then performed a detailed comparison between dataset predictions of altered protein abundances based on the model and the method provided by Goelzer and coworkers  (19)  (supplemental Table S1, S6). We used functional categories of the enzymes that are very similar to categories of SubtiWiki  (34)  but not identical in order to facilitate the description of the cellular processes for our analytical tools.
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  (50)  with the data set identifier PXD000154. Reviewers are suggested to use the reviewer account with PRIDE Inspector (http://code. google. com/p/pride-toolsuite/wiki/PRIDEInspector# Getting_PRIDE_Inspector) in order to get access to all of the files belonging to our submission. To get access PRIDE Inspector needs to be opened. The download can be started using option Private Download/ProteomeXchange/PX reviewer account details. PX reviewer account details are as follows: ProteomeXchange accession: PXD000154.
Results and discussion High pressure density for the dimethyl carbonate + p-xylene; diethyl carbonate + p-xylene; n-octane + p-xylene and n-decane + p-xylene binary mixtures have been measured in this work and they are presented in tables 4-7. Table  8  presents the coefficients for the modified Tait equation (equation (  1 )) beside the average absolute deviation (AAD) and average percentual deviation (APD) for each composition in all range of temperature and pressure studied. The parameters of dimethyl carbonate, diethyl carbonate, n-octane and n-decane pure component have been reported in a previous work  [10]. Figure  1  shows the pure p-xylene density at (288. 15, 298. 15, 308. 15) K and the relative density deviations between the data reported in this work and those comparable experimental data available in the literature at T = 298.
This work investigates the mechanism of the uranyl interaction with birnessite, one of the most common layertype MnO 2 mineral at the Earth's surface, by coupling macroscopic (surface complexation experiments) and microscopic (EXAFS measurements) approaches. The sorption of uranyl on synthetic hexagonal birnessite, the low-pH birnessite form, was studied under various conditions of pH (3-6), electrolyte backgrounds (0. 1 M NaClO 4 , NaNO 3 and Na 2 CO 3 ), and solid/liquid ratios (from 0. 27 to 4. 5 g/L). Sorption isotherms exhibit a complex form indicative of at least two types of sorption sites. EXAFS data reveal the presence of two equatorial O shells at ca. 2. 32 Å and 2. 46 Å for all the samples, and a Mn shell at ca. 3. 38 Å in the low-pH (≤5) samples only. No U-U pair was detected, despite the presence of polynuclear dissolved species in some of the samples.
Data acquisition and reduction EXAFS measurements were performed on the BM20 ROBL beamline at ESRF (Grenoble, France), using a Si(111) double-crystal monochromator  (Reich et al. , 2000). Uranium L III -edge X-ray absorption spectra were recorded at room temperature in fluorescence-yield detection mode using a multi-element Ge solid-state detector. For the preparation of the samples ultra-pure reagents were used in order to avoid an interfering fluorescence signal of the Sr Kα line at 14. 2 keV. A vanadium metal foil was used to attenuate the Mn Kα fluorescence from the substrate. Owing to the low U/Mn ratios in the samples (Table  1 ), five to fifteen EXAFS scans were collected from each sample and averaged to improve the signal-to-noise ratio. XAFS data reduction was made using the Athena software  (Ravel and Newville, 2005).
U VI adsorption experiments The aqueous speciation of U VI in our experimental solutions was calculated using PHREEQC 2. 18. 3  (Parkhurst and Appelo, 1999). The wateq4f database was implemented with the UO 2 (OH) 2 species and the NEA-TBD recommended U VI aqueous equilibrium constants  (Guillaumont et al. , 2003). The solubility constants for schoepite and β-UO 2 (OH) 2 were taken equal to 5. 39 (Critical Stability Constants of Metal Complexes Database, NIST Standard Reference Database 46 version 7) and 5. 61 (Chemical Thermodynamics Database, NIST Standard Reference Database 2, version 1. 1) respectively. These calculations demonstrate that uranyl-hydroxy species were predominant for the experiments at pH 5 and the highest U VI concentration only, whereas at pH 3 and 4 the predominant dissolved species were uranyl ions.
Among the most recent published models, only  Tonkin et al. (2004)  and  Appelo and Postma (1999)  proposed complexation constants for Mn ions, and merely this latter work includes Na and K complexation constants. Models and complexation constants from  Tonkin et al. (2004)  or  Appelo and Postma (1999)  were implemented in the program PHREEQC  (Parkhurst and Appelo, 1999)  in order to reproduce the net surface charge from our titration experiment conditions (I = 0. 05 M; S/L ratio = 2. 2 g/L), but none of the calculated surface charge accurately fits our data (Fig. 2a , models [1] and  [2] ). No improvement of the fitwas achieved by adjusting the birnessite specific surface area or surface site density. Specifically, these models were unable to reproduce the Fig. 6. Sketch of hypothetical U VI sorption sites on hexagonal birnessite.
Acknowledgments This research was funded by the  ACI "Eau et Environnement" CNRS program. We acknowledge the  European Synchrotron Radiation Facility (Grenoble)  for provision of synchrotron radiation facilities and we would like to thank the  ROBL  staff for assistance. We are especially grateful to  Alain Manceau  ( ISTerre, France ) for its initiation to the "birnessite world", and his improvement of the manuscript. The authors acknowledge  D. Millon , for the atomic absorption analyses,  J. Samuel  and  R. Boutin  for ICP analyses,  F. Hubert  and  J. L. Cezard  for XRD acquisition and  G. Morvan  for SEM imaging, as well as  M. Lanson  ( ISTerre, Grenoble ) for its introduction to the birnessite synthesis. We would like to thank the editor,  Jeremy Fein , and two anonymous reviewers for their comments which helped to improve this manuscript.
Validation The validity of the model to predict the evolution of temperature and drying rate throughout the granular bed during convective superheated steam drying is confirmed by using sets of data available for the drying of porous particles  [28]. The authors investigated experimentally superheated steam drying of porous alumina and ceramic particles packed bed with a diameter of 9. 5 and 10 mm, respectively. A high-resolution load cell continuously registered the weight of the entire test section, and the steam inlet and outlet temperatures were monitored as well as the centre temperature of a varying number of spheres. The internal bed vessel is a cylinder with a diameter of 0. 3 m. The bed height was varied from 100 to 200 mm. All the experiments were performed at atmospheric pressure.
Results for NVOC and VOC Yields The coded and decoded values of independent variables and the responses obtained in the multivariate study for each experiment are shown in Table  1. Time and microwave power are evaluated, with NVOC and VOC Yields as responses. In this second part of the study, the effect microwave power and time of extraction on the yields of soluble material in the extract and on the VOC yield separated from the product were evaluated by response surface methodology. ANOVA for NVOC yield gave a coefficient of determination (R 2 ) of 83. 2%, which indicates a close agreement between experimental and predictive values. It can be explained by the very close results between all the experiments. ANOVA data summarized in a Pareto chart in Figure  2  for NVOC yield represents the significant effects of all variables (linear and quadratic) and their interactions.
2% as coefficient of determination (R 2 ) which indicates a good agreement between experimental and predictive values. The effect of time is majorly influencing the results followed by its quadratic effect, showing that a plateau was reached for the extraction of VOC. Both the linear and quadratic effects of microwaves power lie below the level of significant influence. This means that only time is significant in the separation process of the VOC and the quadratic effect of time suggest a final time of extraction exists. The experimental data obtained from the CCF allowed us to determine an empirical relationship linking response studied (VOC Yield) and key variables involved in the model (in coded units). Thus, a 2nd order polynomial equation was obtained Equation (  2 ): = 23. 09 + 0. 0092 × -0.
Subsequent to the development, TLC plates were dried in a current of air from the ADC2. The densitometric analysis was performed on CAMAG TLC Scanner 3 (Camag; Muttenz, Switzerland). The slit dimension was kept 6. 00 × 0. 10 mm and 10 mm/s Scanning speed was employed. The scanning was performed at 307 nm in reflectance/absorbance mode. The source of radiation used was Deuterium/Tungsten lamp emitting a continuous UV spectrum between 190 and 400 nm. Each track is scanned and baseline correction is used. All operations were monitored by WinCATS software (V 1. 4. 7. 2018, Camag; Muttenz, Switzerland). Standard of boldine were prepared by solubilizing 10 mg of boldine (Extrasynthèse, Genay, France) in 500 mL of chloroform. Five spots of the standard of increasing volume were made to obtain concentration rage of 50-300 ng/spot.
A total of 12 different combinations, including 2 2 full factorial design (±1) with four axial points (±α) and four replicates of center point (coded 0), chosen in random order according to a CCF configuration for two factors, was employed for response surface modeling. The selected optimization responses were the yield of Non Volatile Organic Componds and Volatile Organic Compounds yields. The experimental designs used were constructed and the experimental results were processed by using the software STATGRAPHICS PLUS (Version 15. 01. 02, Statistical Graphics Corporation, Rockville, MD, USA, 2000). An analysis of variance (ANOVA) with 95% confidence level was then carried out for each response variable in order to test the model significance and suitability. The F-value in ANOVA is the ratio of mean square error to the pure error obtained from the replicates at design center and the p-value defines the significance of the different variables.