dataesr/scientific-paragraphs-categorization huggingface dataset:
All optimization simulations were started with 70 mM of cellobiose, 70 mM of phosphate, 1 mM of NADP. Each simulation run represented 9000 min (150 h) of the cell-free system operation. The optimization process was performed with the optimization module of Copasi using genetic algorithm as solver. Copasi have a set of default values for genetic algorithm that are adapted at the optimization of biochemical kinetics: 200 generations, 20 population size. Random number generator  [11]  takes care of the introduction of genetic variation by mutation and cross-over. This set-up allows an optimization process with acceptable solution in 5-10 min. In theory, genetic algorithm could provide the best solution to a problem but requires a lot of computing time, an infinity period in extreme case. In respectable time delay, genetic algorithm doesn't give the perfect solution but it covers a set of solution near the optimal solution.
Artificial neural network modeling The model above used for optimization was built on prior knowledge of the kinetic laws and properties associated with each enzyme in the system and the definition of ordinary differential equations. However, sometimes this knowledge is incomplete. In this case, the construction of a robust ODE model is very difficult and it is at stake to find other modeling approaches. One such method is modeling using artificial neural networks (ANN). ANN models require only an initial dataset of input and output vectors. ANN models are very dependent of the only 32 simulations that test 32 different starting conditions. For each of them, only one input among the sixteen inputs was modified between the two possible values used for "Base zero" (hence a total of 2 × 16=32 starting conditions) while the other inputs were set to the following default initial values: 10 U/mL for enzyme concentration, 1 mM for NADP and 70 mM for both cellobiose and phosphate.
"Base B" was built using only 12 different simulation settings. Values for the starting conditions were set as for "Base A" but only E3, E4, E13, NADP + , cellobiose and phosphate initial values were toggled. Hence, "Base B" is a subset of "Base A". E3, E4, E13 were modified to create this "Base B" dataset because they were determined as the enzymes which influence most the output of the system. We also wanted to investigate whether a dataset built by changing only the concentration of these enzymes could be sufficient to identify an adequate model. Henceforth, we would test how efficient is ANN able to learn from few data. The last dataset, hereafter called "Base V", was generated for the purpose of validating ANN models trained with the small datasets "Base A" and "Base B". This dataset constituted of 625 simulations settings generated after combining different starting concentrations for E3, E4, E13 and NADP.
All possible combinations of five initial concentrations for E3, E4, E13 (5, 8. 75, 12. 5, 16. 25, 20 U/mL) and 5 initial concentrations for NADP (0. 5, 0. 875, 1. 25, 1. 625, 2 mM) were tested thus accounting a total of 625 starting conditions. All four datasets were obtained through simulations of 8000 min. The data points for inputs and outputs used in our ANN modeling were all normalized between -1 and 1 with the following equation: 2 2 max min i norm,i max min X + X X X = X X - - where, X i is unnormalized data point i for an input or an output, X min is the minima among all the data points of an input or output, X max is the maxima among all the data points of an input or output and X norm,i is the normalized value (between -1 and 1) for the data point i. Normalization is a recommended pre-treatment of the datasets in neural network modeling.
The dataset is randomly splitted in 10 parts. Nine parts were used as training dataset and the remaining part was used as the test dataset. An auto-prediction operation is performed too with the identified model for evaluation. During the validation phase, the model is tested on the validation dataset "Base V". The RMSE (root-mean-square error) and the R 2 (coefficient of determination) were the selected metric in order to evaluate the model performance during the learning phase and the validation: 1 n i i i= o p RMSE = n - ∑ and ( )( ) ( ) ( ) 2 1 2 2 1 1 n i m i m i= n n i m i m i= i= o o p p R2 = o o p p   - -     - - ∑ ∑ ∑ where, o i is the measured activity of the i th data point, o m is the mean of the measured activity, p i is the predicted activity of the i th data point, p m is the mean of the predicted activity, n is the number of data points, RMSE was calculated from normalized data between -1, 1.
It is also possible to download the whole  simulation results in the form of a flat text file containing comma separated values of concentration for all metabolites as a function of time. This simulator for example can be useful to test very simply how the improvement of an enzyme would impact the H 2 production. Using artificial neural networks to model H 2 productivity of the cell-free system, two ANN models were built (see Methods section), one for predicting the rate of H 2 production and another for predicting the final H 2 concentration. Results for these two models built after the "Base zero" dataset which is derived from 65,536 simulations of H 2 production are given in Table  4. These results show that, both ANN models which had a 5-neurons hidden layer were able to predict accurately the kinetics and the yield of the synthetic system.
This shows that, provided a database of H 2 production trajectories is available, it is possible to model the output of this H 2 producing synthetic system with the prior knowledge of the starting conditions, i. e the inputs. In real life situations, it is impossible to generate experimentally such a large database because of cost and time constraints (for 65,536 runs of the system each of 9,000 minutes, 409,600 days would be required and total would cost more than 10 million USD). In fact, we wanted to approach real life situations by building smaller training datasets following experimental design principles and check whether these are sufficient to generate good models using ANN. We hence investigated the ability of ANN to build good models with two much smaller training datasets. The first dataset, "Base A", was built from 32 simulations and the second dataset, "Base B", was derived from 12 simulations.
After training with these two datasets, we evaluated how the ANN models accurately predicted the rate of production and the final concentration of H 2. Detailed results given in Table  5  show indeed that, with as few as 12 and 32 inputs/outputs in the training set, the ANN models can remarkably predict with good accuracy both the kinetics and yield of the H 2 producing system with RMSE < 0. 36 and R 2 >0. 86. Figure  5  shows the corresponding scatter plots for the "Base V" validation dataset which comprises 625 data points. The data were all normalized between 1, -1 prior to the learning phase (see Method). Interestingly, the models predicted values superior to 1 and inferior to -1. The validation dataset contained data points that were indeed beyond these boundaries but the models were less accurate in these areas.
Overall, rate of H 2 production was predicted with lower RMSE than for final H 2 concentration (Table  5 ) and as illustrated by the lesser dispersal of the data points away from the bisector line (Figure  5 ). Interestingly, "Base A" and "Base B" generated ANN models with very similar RMSE and R 2 values on the validation set and with overall performance close to the more accurate models built with the much larger "Base zero" dataset. This indicates that ANN is quite robust to the size of learning datasets provided that the learning datasets are appropriately designed. It also indicates that restraining the ANN inputs to only the 3 rate limiting enzymes and to the starting substrates (as for "Base B") is indeed an interesting design strategy. Also, it can be concluded that small learning datasets which are easier to construct experimentally can provide very satisfactory models for predicting kinetics and yield of such a synthetic system.
We studied double stage optimization combining E 0 with K m or λ. The advantage of these double stages optimizations is that they can be easily applied experimentally. As it is difficult to find or engineer enzymes with improved k cat , this double stage optimization involving E 0 in first stage and K m or λ optimization in second stage is a very good alternative. In the same line, we evaluated the most pertinent set of enzymes for optimization so as to reduce the combinatorial complexity of the experimental design. Three enzymes, E3 (glucose-6-phosphate dehydrogenase), E4 (6-phosphogluconic dehydrogenase) and E13 Results for autoprediction and k-fold cross validation were evaluated on the learning dataset itself. Results for validation were evaluated on the independent "Base V" dataset (see Method for details).
These information are not always fully available and may remain unknown for long. We explored a modeling approach by ANN to have a global overview of the performance of a synthetic system through the sole knowledge of few initial enzyme and substrate concentrations and the corresponding outputs. This approach requires hence only empirical data. We used the cell-free system of Ye et al. [1]  as object of modeling and our knowledge-based ODE model as generator of empirical data. We showed that ANN model with practical dataset sizes allowed accurate prediction of the kinetics and yields of the system. The knowledge of the limiting enzymes was interesting since it allows simplification of the experimental design for constituting the learning database. Our ANN model works better to predict the initial rate of H 2 production than the final concentration of hydrogen.
Acknowledgement NF is supported by a PhD grant from  Conseil Régional de La Réunion and European Union (Fonds Social Européen)  under file number 20112717-tiers 157173. BGP and FC have been supported by  Conseil Régionale de La Réunion. BO acknowledges support from  Université de Nantes. The authors thank  Peaccel SAS  for providing infrastructural and technical support for hosting the bioH2 simulator.
Introduction In this section the contribution of CPO to seismic anisotropy in the deep Earth with cases of olivine and the role of melt is illustrated. The CPO in rocks of upper-mantle origin is now well established (e. g. ,  Mercier, 1985; Nicolas and Christensen, 1987; Mainprice et al. , 2000)  as direct samples are readily available from the first 50 km or so, and xenoliths provide further sampling down to depths of about 220 km. Ben Ismaı ¨l and Mainprice (1998) created a database of olivine CPO patterns from a variety of the upper-mantle geodynamic environments (ophiolites, subduction zones, and kimberlites) with a range of microstructures. However, for the deeper mantle (e. g. ,  Wenk et al. , 2004)  and inner core (e. g. ,  Merkel et al. , 2005)  we had to rely traditionally on highpressure and -temperature experiments to characterize the CPO at extreme conditions.
g. ,  Hirth and Kohlstedt, 1996). The melting will occur in upwelling 'wet' peridotites at a well-defined depth when the solidus is exceeded and the volume fraction Figure  21  The classification of olivine CPO proposed by  Jung and Karato (2001)  as a function of stress and water content. The water content scale in ppm H/Si is that originally used by Jung and Karato. The water content scale in ppm wt is more recent calibration used by  Bolfan-Casanova (2005). The numbers in brackets are the percentage of samples with the fabric types found in the database of Ben Ismaı ¨l and Mainprice (1998). Mishibayashi (per. com. , 2006). X marks the lineation, the horizontal line is the foliation plane. Contours given in times are uniform. of melt produced is controlled by the amount of water present.
, 2005b) , which shows that the energy barrier for [100] direction slip increases with hydrostatic pressure, whereas for [001] it is constant, which could explain the transition form [100] to [001] with pressure. It remains a possibility that [001] slip occurs at high pressure in dry samples, but experiments with better control of water content are needed to confirm or deny this possibility. What are the seismic consequences of the recent discovery of C-type CPO in experiments in hydrous conditions? The classic view of mantle flow dominated by [100]{0kl} slip is not challenged by this new discovery as most of the upper mantle is will be dry and at low stress. The CPO associated with [100] slip, that is, 90. 8% of the Ben Ismaı ¨l and Mainprice database (1998), produces seismic azimuthal anisotropy for horizontal flow with maximum V p , polarization of the fastest S-wave parallel to the flow direction and V SH > V SV.
In this work we considered two forms of the intrinsic luminosity function for this population, i. e. , the Schechter fit and the broken power law dependence. In the latter case, lensing with intermediate magnifications may cause significant distortions of the shape of the luminosity function, and thus the effect of lensing can be easily corrected for. The former case is qualitatively very similar to that of Lyman-break galaxies, with the sole difference that there is no reduction in the lensing probability due to the large surface brightness of an extended lens. This is because the foreground lenses, which are drawn from common galaxies, are not expected to be bright at sub-millimeter wavelengths. The discrepancy in the Schechter parameters introduced by lensing by virialized halos with intermediate magnifications (µ ≤ 2) is ∼ 23% in the normalization and ∼ 15% in the critical luminosity when the best fit to current data is considered at flux values 3 -200 mJy.
This work was supported in part by was supported by the  LabEx ENS-ICFP :  ANR-10-LABX-0010/ANR-10-IDEX-0001-02 PSL  (for A. F. ) and  NSF  grant  AST-1312034  (for A. L. ). We thank  S. Bussmann  and  I. Dvorkin  for useful discussions.
2 model, mass transfer limited regime). The AC model (II. 3) is also, obviously, included. The kinetic parameters and mass and heat transfer coefficients are mentioned in detail in the original Ref. [71]. The authors identified and studied the influence of different operating conditions, and they presented a detailed discussion of their theoretical predictions. Comparison with experimental data indicates that up to 60 % conversion, numerical results fit satisfactorily experimental values. Finally, the paper discusses the influence of anisotropy, peripheral fragmentation and evolution of the reactive surface. It is important to remark here that the authors state and prove that the usual UC or HSC particle models do not represent satisfactorily the transient charcoal transformation. Hastaoglu and Hassam  [72]  implemented a general gas-solid reaction model to study the fast reaction of flash pyrolysis of wood for producing char, tar and gases.
Although the formulation was not applied to combustion, it can be easily adapted to consider various heterogeneous reactions; so, this model can also describe char combustion, as remarked by the authors  [72]. They also carried out a set of experimental measurements that were used to fit kinetic parameters. But the main contribution of this article is the fact that a set of experimental data concerning the particle shrinkage was obtained as a function of time (conversion) and used in the model equation. Phenomena for the used wood particles was obtained experimentally as a function of time or solid conversion, and included in the model formulation. Particle porosity effects were also considered and included in conservation equations. The numerical described by Hastaoglu and Berruti  [73]  was used for solving the set of equations for a single wood particle.
In the kinetically controlled regime, the ''homogeneous or uniform-conversion'' (UC) model is the simplest model, as it is the ''heterogeneous shrinking-core'' (HSC) model if a severe diffusional control occurs  [17]. Bhat et al. [97]  used both UC and HSC models to study coal-derived and biomass-derived chars. They concluded that, up to 1073 K, the HSC model fits their experimental data better. However, diffusional resistances appear to influence the gasification rate at temperatures higher than 1123 K. The HSC model is better suited for high temperature predictions because of the higher reaction rates. Yasyerli et al. [98]  considered that the uniform model with a constant specific reaction rate is inadequate to describe char gasification. Gomez-Barea  [99]  and Gomez-Barea et al. [100]  implemented a deactivation model for predicting gasification rates of several lignite particles, and they further emphasized the incapacity of both UC and HSC models to acceptably explain their experiments.
Acknowledgments This study was developed in the  CONICET (MINCyT)-CNRS Argentine-French collaboration  agreement ( SYNSOLGAS PROJECT ). G. D. Mazza  and  J. M. Soria  are  Research Members of CONICET (Argentina). It was supported by the  SOLSTICE Laboratory of Excellence of the French ''Investments  for the future'' programme managed by the  National Agency for Research  under contract  ANR-10-LABX-22-01.
Data and Methods A series of nine outcrops from natural cliffs, active and disused quarries, and with orientations both parallel and perpendicular to palaeoflow were studied in both the East England Shelf and in the East Irish Sea Basin on-shore in West Cumbria (Fig. 1 ) to characterize the internal sedimentary architecture of fluvial deposits of the Sherwood Sandstone Group. Thirteen lithofacies are recognized on the basis of lithological characteristics recognizable in outcrop: colour, grain-size, sorting, composition of matrix. Lithofacies have been assigned to two facies associations representative of channelized and non-confined (extra-channel) fluvial deposition. Four representative sedimentary log sections have been measured in the St Bees area (West Cumbria) and three in South Yorkshire.
Red silty mudstones (Fm) are commonly associated with amalgamated channel-fill elements (F4) and form thin, 0. 05 to 0. 1 m-thick beds that drape the surface of crossbedded sandstone bodies and which lack evidence of an erosional contact (Figs. 9 and 11B). Major bar forms are characterized by a low-spread of cross bed-foresets azimuths. Foreset azimuths from cross-bedded sandstone beds (Fx, Fxs, Fxt) measured in the North Head and South Head members show an average palaeo-bedform migration direction towards the NNW (mean vector = 333°; mean vect or length = 0,90; n=96). Palaeocurrent data (Fig. 5B ) recorded from barform structures of the North Head Member indicate a vector mean of 318°, whereas cross-beds of stacked barforms of the South Head Member indicate a vector mean of 338° (Fig. 5B ).
g. ,  Bonadonna et al 2013; Engwell et al. 2013; Klawon et al. 2014). In addition, no study has yet attempted to correlate all derivable textural parameters with the full range of multidisciplinary data available. To partially resolve these issues, a working group funded by the European Science Foundation, through the MeMoVolc program (http://www. memovolc. fr/), was set up. The group was composed of experts actively working on integration of textural, deposit and geophysical data, equally balanced between four theme areas: (i) particle-texture studies, (ii) deposit analysis, (iii) chemistry and (iv) geophysics. The priorities of the meeting were discussion and definition of:  Improved standards, precision and measurement protocols needed by the particletexture studies.  Best practices for particle-texture studies in order to have comparable datasets from different types of eruptions.
2014; Leduc et al. 2015). (ii) Bulk measurements of particle characteristics The fastest and most straightforward textural measurement of individual pyroclasts is density (vesicularity), which provides basic information on processes related to gas exsolution and escape  (Houghton and Wilson 1989). The densities of lapilli and small bombs can be determined by comparing their weights in water and air following the Archimedes principle. Clasts can be made impermeable with silicone waterproofing spray, by immersion in cellulose acetate, or by using Parafilm TM wax. This technique is fairly rapid and yields large arrays of data with a reproducibility within 10-30 kg m -3 and accuracy within 30 kg m -3 (Barker et al.
We should aim to collate all data and measurements that can be provided by each discipline at some central host site and evaluate whether we need more from each field; 4. Quantification and statement of the precision of the measurements must always be made, and a set of standards must be produced to allow data quality control has to be followed; 5. The community needs to explore and discuss the best means to improve the quality of the measurements and the amount of data available; 6. Guidelines should be agreed on regarding essential key parameters that need to be extracted, versus those that are less important. Common standards need to be established that allow these key parameters to be shared by all groups; 7. Central to this is creation of an open access data bank to support essential geophysical, deposit, textural and geochemical data integration and sharing.
DynVolc: an integrated database Inspired by this effort, a database -DynVolc (Dynamics of Volcanoes) -is now operative at http://wwwobs. univ-bpclermont. fr/SO/televolc/dynvolc/index. php. This database is part of an observation system within the services provided by Observatoire de Physique du Globe de Clermont-Ferrand (OPGC). It is an attempt to provide an integrated and accessible library for all multi-disciplinary data sets for explosive eruptive events. This database is an integrated collection of data from physical and geophysical observations of dynamic volcanic processes. DynVolc database spans the full range of explosive and effusive activity. Its intent is to provide a library of standards for eruptive styles, for each of which the data base provides:  field data (i. e. , results of field mapping, outcrop and sample descriptions)  key deposit features (thickness, areal dispersion, sedimentary structure, grain size) .
Acknowledgements L Gurioli  thanks the  MeMoVolc ,  European Science Foundation  grant (ref. n. 4253). G Valentine D Swanson  and the editor,  J White  are thanked for their constructive reviews. This research was financed by the  French Government Laboratory of Excellence initiative  no  ANR-10-LABX-0006 , the  Région Auvergne , and the  European Regional Development Fund. This is Laboratory of Excellence ClerVolc contribution number XXX.
coli O145 strains was deduced from the O145wzy (O-antigen polymerase) gene (Table  2 ). Realtime PCR reactions were set up as singleplex assays. TaqMan PCR probes and primers used in this work were designed with the software Primer Express V3. 0 (Applied Biosystems) and are described in Table  2. The position of primers and gene probes for specific detection of O145.
Nucleotide sequencing The nucleotide sequence of the PCR products were determined by Sanger sequencing  [34]  and analyzed by the use of the Accelrys DS Gene software package (Accelrys Inc. , USA). The nucleotide sequences of the respective products for fliC homologs were determined and have been submitted to European Nucleotide Archive (ENA). Accession numbers are listed in Table  1. Detection of EHEC O145:H25 and EHEC O145:H28 from spiked readyto eat-salad samples by real-time PCR A lot of ready-to-eat, pre-cut salad was purchased at wholesale and spiked with high (100-1000 cfu/ gram) and low (1-10 cfu/ gram) quantities of EHEC O145:H25 (CB12641) and EHEC O145:H28 (CB12513) strains (Table  1 ). Salad samples spiked with non-O145 EHEC (CB14655, O121:H19, stx 2a , eae), STEC (CB15589, O104:H7, stx 1c ) and EPEC (CB12054, O157:H45, eae) strains as well a native, unspiked salad sample served as negative controls.