Stéphanie Ducrot

Marianne ´jover

Jérémy Danna

Stéphanie Maziero

Marie Vernet

Mélanie Jucla

Frédérique Audic

Yves Chaix

An Investigation of Eye Movement Dysfunctions in Children with Neurofibromatosis type 1

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An Investigation of Eye Movement Dysfunctions in Children with Neurofibromatosis type 1

S Ducrot 1 , M Jover 2 , J Danna 3 , S Maziero 4 , M Vernet 1 , M Jucla 5 , F Audic 6 , Y Chaix 4, 7

Today's estimates indicate that 30-60% of Neurofibromatosis type 1 (NF1) children suffer from learning disorder, including reading disabilities, secondary to language and/or visuospatial deficits (Chaix et al., 2017) with a profound impact on their academic achievement (Krab et al., 2008). Some studies suggest a delay in the maturation of low-level vision processes in children with NF1 (e.g. saccadic system, Lasker et al., 2003;magnocellular processing, Ribeiro et al., 2012). The findings from typically developing readers suggest a strong relationship between reading ability and visual processing (Leibnitz, et al., 2017). The main goal of this study was to investigate the occurrence of perceptual, visuo-attentional and oculomotor deficits in NF1 children and their potential to explain reading behavior and reading problems in this population. Forty-two children with NF1 (9,8 ± 1,5 years, 26 girls) and forty-two control children (TD, 10 ± 1,1 years, 20 girls) participated in the study. Parents and children gave their informed consent prior to the experiment, approved by the local ethics committee. Reading and visual processing skills were respectively evaluated with the Alouette test (Lefavrais, 2005) and the DEM test (Garzia et al., 1990). We also recorded eye movements while a subgroup of 17 NF1 children (9;8 ± 1;4 years, 10 girls) and 21 control children (9;4 ± 0;9 years, 10 girls) performed an oculomotor lateralized bisection task on words, strings of hash marks and solid lines.

Method

DEM test (especially the HT) can be used clinically to distinguish NF1 with RD from NF1 without RD. Analysis of eye movement patterns represents a potential way to identify differences in the cognitive processing and visuo-attentional mechanisms underlying reading in NF1 children before the occurrence of school failure. Both language and visual aspects of reading should be targeted in intervention programs. NF1-RD displayed poor results in visual attention and generation of saccadic processing (e.g., less accurate, more variability, no sensibility to the discreteness of the stimuli).

-No PVL for NF1-RD who were less accurate, and more variable in saccadic programming -More flattened curve for NF1-nonRD -> TD children locate their 1 st fixation mostly around P2 whereas the landing positions cover a more expanded zone, P2-P3 with NF1-nonRD Oculomotor bisection task : participants were asked to move their eyes as quickly as possible to a position they thought to be the middle of the stimulus and to validate this position by pressing a button DEM test : type of oculomotor behavior

  Chaix, Y., Lauwers-Cancesc, V.,Faure Marie, N. et al.(2017). Deficit in phonological processes: A characteristic of the neuropsychological profile of children with NF1. Child Neuropsychology, 24, 1-17.Garzia, R. P., Richman, J. E., Nicholson, S. B., & Gaines, C. S.(1990). A new visual-verbal saccade test: the development eye movement test (DEM). J Am Optom Assoc, 61, 124-35. Krab, L. C., de Goede-Bolder, A., Aarsen, F. K., Pluijm, S. M., Bouman, M. J., van der Geest, J.N., et al. (2008). Effect of simvastatin on cognitive functioning in children with neurofibromatosis type 1: a randomized controlled trial. JAMA, 300, 287-294.Lasker, A. G., Denckla, M. B., Zee, D. S. (2003). Ocular motor behavior of children with neurofibromatosis 1. Journal of Child Neurology, 18, 348-55. Lefavrais, P. (2005). Test de l'Alouette, version révisée ECPA, Paris. Leibnitz, L., Grainger, J., Muneaux, M., & Ducrot, S. (2017). Visuo-attentional processes and reading: Assessment and prospects. Topics in Cognitive Psychology, 116, 1-26. Ribeiro, M., Violante, I., Bernardina, I., Ramos, F., Saraiva, J., Reviriego, P….CasteloBranco, M. (2012). Abnormal achromatic and chromatic contrast sensitivity in neurofibromatosis. Investigative Opthalmology and Visual Science, 53, 287-293. No significant correlations were found between DEM parameters and the density of landing positions (r=-0.368 and r=-0.011 , for VT/density in P2-P3 and HT/density in P2-P3) Children with NF1 presented lower reading skills than TD children (**p<.001). -> 18 NF1 presented a Reading Delay (NF1-RD) and 24 didn't (reading age and performance in both tasks : DEM test (r=.55, p<.05) and ocular bisection task (r=.59, p<.05) was evidenced in NF1 children. &3 -Preferred Viewing Location (PVL) for TD and NF1-nonRD between the beginning and the middle of the word This work has benefited from support from the French Government, managed by the French National Agency for Research (ANR), under the project title DYSTAC-MAP (ANR-13-APPR-0010) and from the Association Neurofibromatoses & Recklinghausen in France. Mobile infrared head mounted eye tracker (EYELINK II, SR Research Ltd).

2018 Joint Global Neurofibromatosis Conference Paris, november 2-6 2018 Background Results Conclusions DEM test : digit reading tasks contact : stephanie.ducrot@univ-amu.fr

	1 Aix Marseille Univ, CNRS, LPL, Marseille, France	2 Aix Marseille Univ, PSYCLE, Aix-en-Provence, France
	3 Aix Marseille Univ, CNRS, LNC, Aix-en-Provence, France	4 ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
	5 URI Octogone-Lordat, Université de Toulouse, France	6 Service de Neurologie Pédiatrique, CHU Timone-Enfants, Marseille, France
	7 Centre Hospitalier Universitaire de Toulouse-Purpan, Toulouse, France


Ngoc Yen

Phuong Cao

Benoît Celse

Denis Guillaume

Isabelle Guibard

Joris W Thybaut

Benoit Celse
email: benoit.celse@ifp.fr

Assessment of the dominant factors during hydroprocessing stabilization

Keywords: hydrocarbon, hydrotreating, kinetic modelling, reactor, stabilization, transient data

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Introduction

Hydrotreating is a catalytic conversion process in petroleum refining, among others for removing impurities such as nitrogen and sulphur compounds from hydrocarbon streams. A kinetic model is a significant asset in, not to say essential for the adequate design and simulation of such a process [START_REF] Becker | Hydrotreatment modeling for a variety of VGO feedstocks: A continuous lumping approach[END_REF]. It is usually developed based on experimental data acquired at steady-state conditions. One of the main challenges of hydrotreating process is that establishing this steady state typically requires several days, leading to long experimentation times in order to obtain sufficient data for kinetic modelling. However, during the transient phase towards the steady state, effluent analyses are already carried out at regular time intervals to detect the steady state and to ensure that the reaction is under control. These available transient data are currently not used for kinetic modelling because the stabilization behaviour is not well understood.

The aim of this work is, first, to get a better understanding of the stabilization behaviour during these transient conditions and secondly, to use these transient data to predict the steady-state reaction performance. If this value is far from a target, the operators can change the operating condition without waiting for stabilization and without the use of a complex model.

Materials & Methods

The experimental data are acquired using the IFPEN Hydrotreatment (HDT) pilot plant operating in a continuous manner. The total catalyst volume in the reactor amounts to 50 cm 3 . Operating conditions are adjusted after having reached the steady state corresponding with the previous operating conditions. The acquired data cover 11 Vacuum Gas Oil (VGO) feeds over two catalysts. Operating conditions were chosen to cover a wide range for the VGO HDT process: Liquid Hourly Space Velocities (LHSV) from 0.5 to 4 h -1 , temperature from 350 to 410 °C and total pressure between 50 and 140 bar. The provided data are the 'liquid product nitrogen content' (N) with time on stream (TOS), totalling 920 measurements, see Figure 1. A series of points corresponding to one experimental run is called one 'episode'. Figure 1 shows 7 episodes corresponding to 42 data points during around 45 days. A hydrodynamics study on the pilot plant has been carried out using a tracer technique to estimate the stabilization time purely due to hydrodynamic. The objective is to identify whether the stabilization time is determined by hydrodynamic or kinetic phenomena.

Second episode

First episode

Exploratory data analysis [START_REF] Tukey | Exploratory Data Analysis[END_REF] is applied to our available data to assess the stabilization evolution. It resulted in a first-order transfer function as shown in Equation (1).

                       init init final init TOS TOS N N N N exp 1 . (1)
Where N is the liquid product nitrogen at a specific time on stream (ppm); N init is the liquid product nitrogen corresponding to the first experimental point of episode (ppm); N final is the last experimental point of episode (ppm); TOS is the time on stream (h); TOS init is the time on stream corresponding to the first experimental point of episode (h) and τ is the characteristic time of the episode (h).

The characteristic time τ of each episode presented in the equation is estimated via nonlinear least-squares (solid line in Figure 1).

A multiple linear regression with interaction technique is then used for τ prediction to evaluate the phenomena underlying the transient data. The general linear model with interaction is illustrated in Equation ( 2

): b x x a x a x a      ... 2 1 12 2 2 1 1  (2)
Where x 1 , x 2 are the input variables, which can be LHSV, temperature, pressure or feed properties such as organic nitrogen content, organic sulphur content, resin content in feed, etc.; x 1 x 2 is the interaction term between x 1 and x 2 ; a 1 , a 2 , a 12 ,… and b are the coefficients.

The most influential input variables of the model are determined via variable selection technique called 'leaps' [START_REF] Furnival | Jr, Regressions by Leaps and Bounds[END_REF]. Fitting linear model and nonlinear model was done by using respectively 'lm' and 'nls' function in 'stats' package in R software. Variable selection was carried out in R by using 'leaps' package obtained from CRAN repository [START_REF] Lumley | based on Fortran code by A. Miller[END_REF].

The model is then tested against new data with other feedstocks. For each episode, τ is predicted by the model. Knowing τ and the first two points of the episode, it is possible to predict the evolution of the episode as well as the Nitrogen content at steady state calculated via Equation (3) which is developed from Equation (1). The leftover data of the episode are used to validate the model. If the predicted steady-state value is far from the target, the operating condition can be adjusted without waiting for stabilization.

  init init init final N TOS TOS N N N              exp 1 (3)

Results

The hydrodynamic response to a tracer step reached steady state significantly faster than the stabilization of the HydroDeNitrogenation (HDN) behaviour. Hence, chemical phenomena are involved in the latter.

Stabilization was found to follow a first-order model, which is similar to the observation of [START_REF] Sau | Effects of organic nitrogen compounds on hydrotreating and hydrocracking reactions[END_REF] in the study of effect of organic nitrogen compounds on hydroprocessing reaction. The characteristic time τ reflects the time required to reach this steady state. First episodes take more time to stabilize than other episodes. The model was applied on two similar HDT catalysts (Catalyst A and B). Two linear models for τ were built; one for first episodes (model M1) and another for other episodes (model M2).

Model M1 was obtained with a R 2 of 0.83, as shown in the left side of Figure 2. It consists of three variables (LHSV, pressure, resin of feed) and one interaction term LHSV*resin, which are selected by the leaps algorithm. An inverse relationship between LHSV and τ was found. Temperature is not a dominant factor. These two results are coherent with [START_REF] Elizalde | Dynamic modeling and simulation of a bench-scale reactor for the hydrocracking of heavy oil by using the continuous kinetic lumping approach[END_REF] who studied the dynamic behaviour of hydrocracking using the continuous kinetic lumping approach. A direct relationship between pressure and τ was observed. The interaction term shows that the impact of LHSV on τ depends on the value of feed resin, i.e., the polar components with high molecular weight. The resin in VGO can contain nitrogen. where LHSV exp is the experimental LHSV of the episode; LHSV pre and T pre are respectively LHSV and temperature of the previous episode; resin and S feed are respectively resin content and organic sulphur content in the feed. As can be seen, the model relies by not only on the feed and operating conditions but also on the operating conditions of the previous episode. It shows that the transient behaviour of spent catalyst is more difficult to predict than for the fresh catalyst.

Model M1 Model M2

Model was tested by using new data with other feedstocks. τ is predicted by the model and the steady-state value is estimated via Equation (3). Model M2 (for other episodes) is evaluated and is shown in Figure 4. Model predicts well the evolution of episode 2, 3, 5, 7 and 8. Regarding episode 4 and 6, it seems that the transient behaviour did not follow a first-order model, which could be explained by the measurement error/experiment problem or the complex behaviour of spent catalyst. The experimentation duration is longer for these both episodes than for the others. It is possible that there was an experiment problem. If the first three points of episode 4 and 6 are discarded, the prediction is more reliable, see Figure 5. This might be investigated in another study.

Conclusions

Stabilization behaviour is a critical point for hydroprocessing experiments. In this work, transient data of hydrotreating process were used to assess this stabilization. It is shown that the stabilization follows the first-order model. A characteristic time for stabilization τ was defined. Two models (one for the first episodes, another for the others) were built to calculate the stabilization time and identify the most influential parameters (LHSV, resin of feed, pressure). Good prediction results were obtained, particularly for the first episodes. The stabilization of other episodes is more complicated to predict.

The model can predict the stabilization time and steady-state value from two initial measures. If the predicted steady-state value would be far from the intended target, operators can change the operating condition without waiting for stabilization. This model will be linked with an Advanced Process Control Algorithm. This model will be also linked to design of experiment algorithm in order to be used to fit kinetic parameters [START_REF] Celse | Experimental Design in Nonlinear Case Applied to Hydrocracking Model: How Many Points Do We Need and Which Ones?[END_REF].

Figure 1 .

 1 Figure 1. Data representation (P = 140 bar, point: experimental data, solid line: model fitting Equation (1))

Figure 2 .

 2 Figure 2. Parity plot with 95% confidence interval for M1 (left, R 2 = 0.83) and M2 (right, R 2 = 0.66) However, the stabilization of other episodes seems more complex. The parity plot for model M2 is shown in the right side of Figure 2. The latter includes seven input variables (LHSV exp , LHSV pre , T pre ,

  Figure 3 shows two examples of such prediction for first episodes (model M1). The two first points of episode which are used to estimate the steady-state value are shown in circle shape. The remaining data in the episode are represented by triangles. The Nitrogen predicted values are very close to the experimental values. The model can predict quite well the transient behaviour and the steady-state value. This algorithm may be linked to an Advanced Process Control method to reach as fast as possible some Nitrogen targets.

Figure 3 .

 3 Figure 3. Two examples of the prediction of model M1 (points: experimental data, solid line: model prediction; TOS: time on stream)