dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The research in Mons is supported by the  Science Policy Office of the Belgian Federal Government  ( PAI 7/5 ), the  European Commission / Walloon Region (FEDER -Smartfilm   RF  project), and by the  Belgian National Fund for Scientific Research (FNRS). Research in Strasbourg was supported by the  European Commission  through the  Graphene Flagship  ( GA-604391 ), the  Agence Nationale de la Recherche  through the  LabEx CSC  ( ANR-10-LABX-0026_CSC ) and the  International Center for Frontier Research in Chemistry (icFRC). B. W. , J. M. R. , and J. C. are FNRS research fellows.
1 pg/ml). The IL-17A concentration with ESAT-6 stimulation was a null value for all kits, so SI could not be calculated. The positive threshold for the SI was set at 3 (black line) Fig. 5  Intra-assay variability of internal control. %CV concentration for each of the 13 plates calculated using internal control duplicates for the selected 15-plex Millipore magnetic kit (see Material and Methods). Mean %CVs are represented by red lines. IL-23 and IL-1β %CVs were not calculated due to concentration values < LLOQ and > ULOQ, respectively content measured by Luminex  [20]. However, we observed that having cell viability above 80 % on the day of stimulation (D0) was not sufficient to ensure low cell mortality during the culture. Indeed, a dose of 100 ng/ml SEB (10-fold lower than that used in the study by Defawe and colleagues) killed near 50 % of PBMC after 5 days of culture (data not shown).
Statistical analysis The concentration of each analyte was obtained by interpolating FI to a dilution standard curve over at least 7 dilution points supplied with the kit and calculated using a 5PL curve by the Bio-Plex Manager 5. 0 software (Bio-Rad). ICCs were used to assess reproducibility. The ICC is a measure of reliability, assessing the proportion of overall variability that is explained by the variability due to differences between samples  [31]. Generally, reliability is considered to be good if the point estimate of the intra-class correlation coefficients is at least >0. 7, and ideally > 0. 8. ICCs were derived from linear mixed effects models with a random effect for the intercept. The ICC was defined as the ratio of variance between supernatants to total variance. The assumption of Gaussian distributions of the residuals was verified.
CVs (standard deviation over mean) were used to assess precision. A SI (calculated as a ratio between stimulated-cell supernatant concentration values and background concentration values) was used to reveal differences between the kits. All graphs were prepared using Microsoft Excel and GraphPad Prism. SAS (version 9. 2, SAS Institute, Cary, North Carolina, USA) and GraphPad Prism (version 6. 0, GraphPad Software, La Jolla, California, USA) were used for statistical analyses.
Funding This work was supported by the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-10-LABX-77 , from the  Agence Nationale de Recherche contre le Sida et les hépatites (Inserm-ANRS ) and from  FP7-Health-2009 on African-European Research Initiative in Co-infections of Poverty Related  and  Neglected Infectious Diseases. L. Richert  received a PhD grant financed by  Sidaction  (http://www. sidaction. org/).
Identification procedure To be easier, the identification procedure has to be based on experimental loading conditions identical to those under which is studied the material behavior, that is to say small oscillations around a static preload. Thus, the prediction of both models has first to be accurate in the case of static loading conditions. However, the best way to identify parameters of models is to decompose the identification with several loading conditions, each of them corresponding to a particular set of parameters. For example, for the Zener model, in order to isolate the parameters of the top spring a, static loading tests have to be performed. So, the parameters of the top spring can be identified with the preload data, and the parameters of bottom branch (spring b and dashpot viscosity) are identified with data collected during oscillating loading conditions.
( 12 ) Where ε 1 is a given penalty parameter. Finally, if we denote the time discretization parameter by t n = nδt, the velocity and the pressure at time t n by (u n , p n ), the velocity of a particle at time t n by W n and the position of its center by X n , we can write our algorithm as : W n = 1 V olume(B) B u n dV (13) X n+1 = X n + δtW n (14) (u n+1 , p n+1 ) solves : 2η 0 V τ (u n+1 ) : τ (v) dV + 2 ε B τ (u n+1 ) : τ (v) dV - V p n+1 ∇ • v dV = 0, ( 15 ) V q∇ • u n+1 dV = 0, (16) u n+1 = u ∞ on ∂V. ( 17 ) This algorithm is valid for any dimension. Here, it has been implemented in 2D using a user-friendly FEM software Freefem++  [46]  (http://www. freefem. org/ff++).
V. ACKNOWLEDGEMENTS. This work has been supported by the  LabEx Tec 21  ( Investissements d Avenir -grant  agreement  ANR-11-LABX-0030 ) and the  ANR  project  Vivabrain -grant  agreement  ANR-12-MONU-0010. Most of the computations presented in this paper were performed using the Froggy platform of the CIMENT infrastructure https://ciment. ujf-grenoble. fr, which is supported by the  Rhône-Alpes region  ( GRANT CPER07 13 CIRA ) and the  EquipMeso  project ( ANR-10-EQPX-29-01 ). A. F. acknowledges financial support from  CNES  ( Centre National d Etudes Spatiales)  and  ESA  ( European Space Agency).
OECD GD 29 describes how the dissolution rate of metals and metal compounds in aqueous media can be measured. However, there are no nanospecific guidelines for such tests and also no proposed cut-off values to distinguish soluble from non-soluble nanomaterials are proposed  (Tantra et al. , 2015). Therefore, a comparison of the dissolution rate of the nanomaterial to that of the chemical components of which it is composed, might give an indication on the possibility to use the data of the non-nanomaterial (read-across). If a nanomaterial has a very fast dissolution rate (i. e. close to instantly dissolved), the nanomaterial can be evaluated using the information on the chemical composition(s) of the non-nanomaterial (follow the green arrow in Fig. 1  towards the box "classical (nonnanomaterial) approach").
Acknowledgements The research leading to these results has been partially funded by the  European Union Seventh Framework Programme  ( FP7/ 2007e2013 ) under the project  NANoREG  (A common European approach to the regulatory testing of nanomaterials), grant agreement  310584. We would like to acknowledge the other  T5. 7 partners  NIA, KI ,  CNR , and  VSB  for their valuable input during the discussions and the process of development of the risk assessment approach presented here. In addition, NANoREG partners from other tasks within WP5 as well as partners form other work packages are kindly acknowledged for their contribution to the discussion at the consortium meetings and other meetings and for the input to the manuscript. RIVM colleagues  Cornelle Noorlander ,  Jaco Westra  and  Cindy Bekker  are thanked for their contribution to the work and  Fleur van Broekhuizen ,  Marja Pronk ,  Dick Sijm ,  Theo Vermeire  and  Jan Roels  are acknowledged for feedback, critical reading and reviewing of the manuscript.
Acc e p ted P r e p r i nt This article is protected by copyright. All rights reserved A b s t r a c t For engineers, it is interesting to gain insight in the effect of control strategies on microbial communities, on their turn influencing the process behaviour and its stability. This contribution assesses the influence of process dynamics on the microbial community in a biofilm reactor for nitrogen removal, which was controlled according to several strategies aiming at nitrite accumulation. The process dataset, combining conventional chemical and physical data with molecular information, was analysed through a correlation analysis and in a simulation study. During nitrate formation, an increased nitrogen loading rate (NLR) resulted in a drop of the bulk liquid oxygen concentration without resulting in nitrite accumulation.
2015; Michelland et al. 2009 ). The Simpson diversity index (D SSCP ) was calculated for each fingerprinting profile as D SSCP = -ln∑(peak areas) 2  (Loisel et al. 2008). This diversity index reflects the underlying diversity from the SSCP profile independently of sample size  (Rosenzweig 1995) : a low and high D SSCP depicts a low and high diversity, respectively. C o r r e l a t i o n a n a l y s i s To analyse the large amount of data on the process dynamics and the effect on the microbial dynamics, a correlation analysis was performed in IBM SPSS statistics 22 (Armonk, New York, U. S. ). The correlation was expressed using the Pearson product-moment correlation coefficient r; r = 1 indicates total positive correlation, r = 0 no correlation, and r = -1 total negative correlation. Only correlations with a p-value smaller than 0.
M o d e l l i n g t h e d y n a m i c r e a c t o r b e h a v i o u r Reactor model A 1-dimensional biofilm model, including biomass variations perpendicular to the carrier on which the considered microorganisms grow, was set up to describe the experimental data of  Bougard et al. (2006b)  and was implemented in the Aquasim software  (Reichert 1994). Growth and decay of AOB, NOB and heterotrophs were considered to describe two-step nitrification besides COD removal, based on the model of  Mozumder et al. (2014). In contrast to the latter model, anammox was not included in this study and a single state variable was used to describe heterotrophic biomass, while  Mozumder et al. (2014).
Also FA and FNA inhibition of AOB and NOB had to be implemented in the model and the decay rate and inhibition constants for FA and FNA of both the AOB and the NOB guild had to be calibrated. The values for decay rate, maximum growth rate, the affinity constants and inhibition constants of both the AOB and NOB were estimated by minimizing the sum of the squares of the weighted deviations (χ 2 ) between the measurements and the simulation results of the bulk liquid ammonium, nitrite and nitrate concentrations, using the Aquasim software  (Reichert et al. 1995). The sum (χ 2 ) extends over all the data points of all variables specified as fit targets (TNH, TNO 2 and NO 3 -), which were given equal weights (χ 2 tot = χ 2 TNH + χ 2 TNO2 + χ 2 NO3 ). The sum was minimized numerically using the secant algorithm  (Ralston and Jennrich 1978)  and the maximum number of interactions was set at 200, in order to keep the computational time reasonable.
). Although the Nash Sutcliffe criterion was negative for total ammonium (E TNH = -0. 11), total nitrite (E TNO2 = -0. 91) and nitrate (E NO3 = -0. 43) in Period A and for total ammonium (E TNH = -1. 61) in Period B, the model efficiencies obtained were generally larger than for Approach1. The model efficiencies for total nitrite (E TNO2 = 0. 37) and nitrate in Period B (E NO3 = 0. 18) were positive even though quite low, due to the large variability of the dataset. Visual inspection (Figures3B and 3C) confirmed that, following Approach 2, the model reflected the overall reactor behaviour better It was concluded that, to reflect the experimental observations, (at least) two periods had to be distinguished for model calibration: Period A (0-100 days) and Period B (100-592 days). These periods were based on the AOB population shift observed byBougard et al.
o w l e d g e m e n t s Thomas Vannecke was supported by the Research Foundation-Flanders (FWO) through a Ph. D. fellowship. The collaboration between the Biosystems Engineering Department (UGent, Belgium) and the LBE-INRA (France) was supported by the Tournesol Project T2013. 03). Mari Winkler was funded by a Marie Curie Intra-European Fellowship (PIEF-GA-2012-329328). Comment citer ce document : Vannecke, T. P. , Bernet, N. , Winkler, M. K. , Santa-Catalina, G. , Steyer, J. -P. , Volcke, E. I. (Auteur de correspondance) (2016). Influence of process dynamics on the microbial diversity in a nitrifying biofilm reactor: Correlation analysis and simulation study. Biotechnology and Bioengineering, 113 (9), 1962-1974. , DOI : 10. 1002/bit. 25952 Comment citer ce document : Vannecke, T. P. , Bernet, N.
Approach 2: Conventional model (1 AOB and 1 NOB) distinguishing two periods As it was not possible to simulate the whole experimental period with a single AOB and a single NOB population, the experimental dataset was split up into the two periods i. e. Period A (0 -100 days) and Period B (100 -592 days)dominated by two different AOB species: Nitrosomonas halophila and Nitrosomonas europaea, respectively. The model was then calibrated individually for these periods and the relative changes of the parameter values over these two periods were verified.
A c k n o w l e d g e m e n t s Thomas Vannecke was supported by the Research Foundation-Flanders (FWO) through a Ph. D. fellowship. The collaboration between the Biosystems Engineering Department (UGent, Belgium) and the LBE-INRA (France) was supported by the Tournesol Project T2013. 03). Mari Winkler was funded by a Marie Curie Intra-European Fellowship (PIEF-GA-2012-329328). Comment citer ce document : Vannecke, T. P. , Bernet, N. , Winkler, M. K. , Santa-Catalina, G. , Steyer, J. -P. , Volcke, E. I. (Auteur de correspondance) (2016). Influence of process dynamics on the microbial diversity in a nitrifying biofilm reactor: Correlation analysis and simulation study. Biotechnology and Bioengineering, 113 (9), 1962-1974. , DOI : 10. 1002/bit. 25952 Comment citer ce document : Vannecke, T. P.
Approach 2: Conventional model (1 AOB and 1 NOB) distinguishing two periods As it was not possible to simulate the whole experimental period with a single AOB and a single NOB population, the experimental dataset was split up into the two periods i. e. Period A (0 -100 days) and Period B (100 -592 days)dominated by two different AOB species: Nitrosomonas halophila and Nitrosomonas europaea, respectively. The model was then calibrated individually for these periods and the relative changes of the parameter values over these two periods were verified. The process performance (χ² tot = 1334) was better reflected when the model was calibrated for Period A (χ² tot_A : 254; χ² TNH : 63; χ² TNO2 : 109; χ² NO3 : 82) and Period B (χ² tot_B : 1080; χ² TNH : 693; χ² TNO2 : 168; χ² NO3 : 219) separately (Approach 2, Figures 3B and 3C Comment citer ce document : Vannecke, T.
PLA is one of the most promising bio-compostable and bio-degradable thermoplastic polymers made from renewable sources. PLA is generally produced by ring opening polymerization (ROP) of lactide using the metallic/bimetallic catalyst (Sn, Zn, and Al) or other organic catalysts in a suitable solvent. In this work, reactive extrusion experiments using stannous octoate Sn(Oct) 2 and tri-phenyl phosphine (PPh) 3 were considered to perform ROP of lactide. Ultrasound energy source was used for activating and/or boosting the polymerization as an alternative energy (AE) source. Ludovic ® software, designed for simulation of the extrusion process, had to be modified in order to simulate the reactive extrusion of lactide and for the application of an AE source in an extruder. A mathematical model for the ROP of lactide reaction was developed to estimate the kinetics of the polymerization process.
The isothermal curves generated through this model were then used by Ludovic software to simulate the "reactive" extrusion process of ROP of lactide. Results from the experiments and simulations were compared to validate the simulation methodology. It was observed that the application of an AE source boosts the polymerization of lactide monomers. However, it was also observed that the predicted residence time was shorter than the experimental one. There is potentially a case for reducing the residence time distribution (RTD) in Ludovic ® due to the 'liquid' monomer flow in the extruder. Although this change in parameters resulted in validation of the simulation, it was concluded that further research is needed to validate this assumption.
This research led to the investigation of an ultrasound as an AE source for the ROP process, as it can provide considerable activation energy to initiate polymerization reaction  [19, 26, 28]. In this work, the impact of AE on ROP of PLA using a metal catalyst has been investigated through extrusion experiments and validated using Ludovic ® simulations. For this, the work has been divided into three different stages Figure  1:  (1) Extrusion-to obtain experimental data of ROP of PLA using Sn(Oct) 2 as catalyst and the application of ultrasound; (2) Simulation batch process-the model developed in previous work  [29]  was used to obtain isothermal curves; and (3) implementation of the isothermal curves (output of Step 2) in Ludovic ® software  [30]  to simulate reactive extrusion of ROP of PLA.
The premise for using an AE source in the metal catalyst process was to reduce the role of the metal catalyst by supporting the AE source by enhancing the efficiency of the process. Experimental data for reactive extrusion of ROP of Lactide was obtained at the laboratory level. The experiments were conducted using stannous octoate as a catalyst. In addition, ultrasound source was applied to facilitate the mixing of monomer and catalyst. On the other hand, the mathematical model to describe the polymerization of PLA in a batch process was presented in a previous work  [29]. The reaction mechanism includes three basic stages in ROP reactions (initiation, propagation and termination) and other side reactions (trans-esterification, chain transfer, non-radical chain scission, etc. ). This model was implemented in Ludovic ®.
This was conducted using isothermal curves, with particular reference to the variation of number average molecular weight ( ) and conversion (X) with time. Ludovic ® uses the isothermal curves (provided as an input table) to estimate the stage and/or progress of the polymerization during the reactive extrusion. To the best of the authors' knowledge, this is a significant step forward in the state-of-the-art in this area of research. In addition, the effect of AE has also examined in Ludovic ®. Consideration of the AE source in large scale simulations through Ludovic ® software is another innovation in this area. Finally, analysis and comparison of experimental data and simulated results are presented in this work. The results obtained through the reactive extrusion experiment were used to validate the output from the Ludovic ® simulation.
In addition, the effect of AE has also examined in Ludovic ®. Consideration of the AE source in large scale simulations through Ludovic ® software is another innovation in this area. Finally, analysis and comparison of experimental data and simulated results are presented in this work. The results obtained through the reactive extrusion experiment were used to validate the output from the Ludovic ® simulation. This work also attempts to explain the discrepancies in the output from the software vis-à-vis reactive extrusion.
This process was divided into two parts: firstly, the development of a mathematical model describing the ROP in a batch process and, secondly, integrating this model into Ludovic ® software (Saint-Etienne, France). From the ROP model for the batch process, isothermal curves for the evolution of number average molecular weight (M n ) and conversion (X) with time were obtained. These curves were discretized and integrated into Ludovic ®. This way, Ludovic ® was able to estimate a degree of conversion and growth of the polymer chains during the simulation of the reactive extrusion process. Reaction mechanisms can be defined for any ROP reaction (mechanisms will tend to be similar to the one we have defined). However, depending on the monomer, initiator, and catalyst and, indeed, the ratios of the monomer to catalyst and catalyst to the initiator, the reaction parameters will change, and as such they will have to always be validated against experiments.
Modelling of ROP Reaction Mechanism in Reactive Extrusion Ludovic ® is one of the most widely used software packages for the simulation of extrusion processes  [30, 32, 33]. At present, commercial versions of this software are not available with an option to simulate reactive extrusion, i. e. , when a reaction happens during the extrusion process (within the extruder barrel). In order to adapt the software for accurate simulation of ROP of PLA, it was necessary to introduce the effect of the reaction happening within the extruder barrel to the properties of the system, i. e. , variation of the concentrations of initiator, monomer, polymer and their varying percentages as the reaction progresses. Ludovic ® software today has the capacity to simulate the extrusion process (considering all thermal and mechanical degradation processes, etc.
). It is available commercially and is used by industries to fine-tune their throughputs. Ludovic ® software works by breaking down the extrusion process (along the barrel) into small subsections (nodes) and using the (input) isothermal curves (molecular weight vs. time, viscosity vs. time, shear rate vs. time, etc. ) to predict the outcome of the process of extrusion over each of the subsections. These results during each subsection are integrated over the extrusion barrel. As the reactive extrusion processes (such as that of PLA) take hold in the industry, Ludovic ® software will have to be updated to take into account the effect of the continuous reactive extrusion. This means the software will have to be upgraded to take into account the effect of the chemical reaction (batch process modelling) as well as the effect of the extrusion process.
Hence, isothermal curves (obtained from the mathematical modelling of the batch process) depicting the ring opening polymerization of PLA process were developed (as in ref  [29] ) and the Ludovic ® software was upgraded to accept these as inputs. Unlike traditional extrusion, the molecular weight undergoes drastic changes (monomer to polymer) during this process and, hence, such attempts at inputting a reactive extrusion model into the Ludovic ® software will have to be validated through experiments. To implement this, isothermal curves for the variation of M n and X with time were generated with the mathematical model based on a batch process reaction mechanism. Some of the properties of the system can be calculated using the degree of polymerization, given by the conversion and average molecular weight, as they represent the percentage of monomer and length of polymer chain at a particular time of reaction.
Alternative Energy Implementation in Ludovic ® Software To the best of the authors' knowledge, application of AE source within the Ludovic simulator has not been reported in the literature. The AE source can be implemented as a source of heat or thermal impact on the reaction mechanism. Due to its unique application, the effect of the AE source on reaction is different to those that occur during conventional heating (Heat Furnace). The AE source device was attached to the barrel (Grey Zone) in Ludovic ® Figure  3. The two specific requirements are To determine the extent of the temperature effect, Ludovic ® provides the temperature at each C-chamber zone. This temperature is the result of mechanical, conduction and alternative energy applied within the extruder. The geometry of the extruder, rotation speed of the screw, throughput and melt influence are factors that affect the mechanical energy from shearing; and this impacts the thermal response of the material.