A E L A R U E

P . S W I D E R * , P . D U R U * D Daviaud

M Q U I N T A R D

Yohan Davit
email: yohan.davit@imft.fr

A E Larue
email: anne.larue@imft.fr

Quantitative 3D comparison of biofilm imaged by X-ray micro-tomography and two-photon laser scanning microscopy

Keywords: Biofilms, contrast agent, porous media, two-photon microscopy, X-ray micro-tomography, 3D printing

matrix [START_REF] Hall-Stoodley | Bacterial biofilms: from the natural environment to infectious diseases[END_REF][START_REF] Lewandowski | Fundamentals of Biofilm Research[END_REF]. Such colonies, called biofilms, are ubiquitous in the environment [START_REF] Fierer | The diversity and biogeography of soil bacterial communities[END_REF][START_REF] Lewandowski | Fundamentals of Biofilm Research[END_REF] and provide the nested bacteria with a better resistance to various stresses including predators and biocides [START_REF] Stewart | Antibiotic resistance of bacteria in biofilms[END_REF][START_REF] Mah | Mechanisms of biofilm resistance to antimicrobial agents[END_REF]. When developing on a substrate, and specifically in porous media, biofilms can induce significant changes to the medium's transport properties (porosity, permeability) [START_REF] Cunningham | Influence of biofilm accumulation on porous media hydrodynamics[END_REF][START_REF] Rittmann | Microbial ecology of biofilms observation of thick biofilm ac-cumulation and structure in porous media and corresponding hydrodynamic and mass transfer effects[END_REF][START_REF] Thullner | Computational pore network modeling of the influence of biofilm permeability on bioclogging in porous media[END_REF] with many implications in engineering, for example bio-filters [START_REF] Ho | Microbial populations analysis and field application of biofilter for the removal of volatile-sulfur compounds from swine wastewater treatment system[END_REF], soil remediation [START_REF] Valls | Exploiting the genetic and biochemical capacities of bacteria for the remediation of heavy metal pollution[END_REF][START_REF] Singh | Biofilms: implications in bioremediation[END_REF], microbial enhanced oil recovery (MEOR) [START_REF] Armstrong | Microbial enhanced oil recovery in fractional-wet systems: a pore-scale investigation[END_REF], CO 2 storage [START_REF] Mitchell | Biofilm enhanced geologic sequestration of supercritical CO2[END_REF][START_REF] Morozova | Monitoring of the microbial community composition in deep subsurface saline aquifers during CO2 storage in Ketzin, Germany[END_REF] and medical applications like biofilms orthopaedic infections [START_REF] Stoodley | Orthopaedic biofilm infections[END_REF]. A variety of systems have been developed to understand the effect of biofilm accumulation in porous media hydrodynamics and mass transport [START_REF] Cunningham | Influence of biofilm accumulation on porous media hydrodynamics[END_REF][START_REF] Rittmann | Microbial ecology of biofilms observation of thick biofilm ac-cumulation and structure in porous media and corresponding hydrodynamic and mass transfer effects[END_REF][START_REF] Valiei | A web of streamers: biofilm formation in a porous microfluidic device[END_REF] but the challenge of accessing the structure and distribution of biofilms in a 3D porous structure, at the meso-scale, still remains. We argue that the lack of understanding about the fundamentals of biofilm development in porous media is mostly due to the limits of currently used imaging methods [START_REF] Vafai | Porous Media: Applications in Biological Systems and Biotechnology[END_REF].

Indeed, optical imaging techniques have been used on 2D porous structures and transparent 3D porous media [START_REF] Yarwood | Noninvasive quantitative measurement of bacterial growth in porous media under unsaturated-flow conditions[END_REF][START_REF] Leon-Morales | Interactions between laponite and microbial biofilms in porous media: implications for colloid transport and biofilm stability[END_REF][START_REF] Leis | Optically transparent porous medium for nondestructive studies of microbial biofilm architecture and transport dynamics[END_REF][START_REF] Kim | Biofilm morphology as related to the porous media clogging[END_REF], but these methods are hindered by the opacity of most 3D porous structures. Although magnetic resonance imaging techniques [START_REF] Seymour | Magnetic resonance microscopy of biofilm structure and impact on transport in a capillary bioreactor[END_REF] are advancing, such methods yield anisotropic images and do not currently provide sufficient spatial resolution. Approaches based on Xray computed micro-tomography (X-ray CMT) [START_REF] Wildenschild | X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems[END_REF] are also being explored as this technique enables, in theory, the imaging of large volumes of porous media with sub-micron resolution. However, X-ray absorption coefficients for the biofilm and the surrounding aqueous phase

Summary

Optical imaging techniques for biofilm observation, like laser scanning microscopy, are not applicable when investigating biofilm formation in opaque porous media. X-ray microtomography (X-ray CMT) might be an alternative but it finds limitations in similarity of X-ray absorption coefficients for the biofilm and aqueous phases. To overcome this difficulty, barium sulphate was used in [START_REF] Davit | Imaging biofilm in porous media using X-ray computed microtomography[END_REF] to enable high-resolution 3D imaging of biofilm via X-ray CMT. However, this approach lacks comparison with well-established imaging methods, which are known to capture the fine structures of biofilms, as well as uncertainty quantification. Here, we compare two-photon laser scanning microscopy (TPLSM) images of Pseudomonas Aeruginosa biofilm grown in glass capillaries against X-ray CMT using an improved protocol where barium sulphate is combined with low-gelling temperature agarose to avoid sedimentation. Calibrated phantoms consisting of mono-dispersed fluorescent and X-ray absorbent beads were used to evaluate the uncertainty associated with our protocol along with three different segmentation techniques, namely hysteresis, watershed and region growing, to determine the bias relative to image binarization. Metrics such as volume, 3D surface area and thickness were measured and comparison of both imaging modalities shows that X-ray CMT of biofilm using our protocol yields an accuracy that is comparable and even better in certain respects than TPLSM, even in a nonporous system that is largely favourable to TPLSM.

Introduction

Bacteria often develop in sessile colonies at the interface between two phases, mostly on solid surfaces, where the microorganisms are embedded in a complex self-secreted polymer are very similar and therefore contrast agents must be used to differentiate between phases. Contrast agents such as silvercoated micro-spheres [START_REF] Iltis | Imaging biofilm architecture within porous media using synchrotron-based X-ray computed microtomography[END_REF]), 1-chloronaphtalene (Rolland du Roscoat et al., 2014), iron sulphate [START_REF] Carrel | Biofilm imaging in porous media by laboratory X-ray tomography: combining a non-destructive contrast agent with propagation-based phase-contrast imaging tools[END_REF] and barium sulphate (BaSO4) [START_REF] Davit | Imaging biofilm in porous media using X-ray computed microtomography[END_REF] have been tested, but currently each of these has drawbacks restricting their application to specific experimental conditions.

Silver-coated micro-spheres map the biofilm surface only, making it cumbersome and inaccurate to perform volume segmentation. 1-chloronaphtalene is an oily solvent which may induce complex two-phase flows in a water-saturated porous medium with a reduced penetration in small pores where capillary pressure is too high. Further, as noted by [START_REF] Carrel | Biofilm imaging in porous media by laboratory X-ray tomography: combining a non-destructive contrast agent with propagation-based phase-contrast imaging tools[END_REF], it is a powerful biocide that may significantly modify the biofilm structure upon contact with the matrix. BaSO 4 has the advantages of being nontoxic and to provide a very good contrast on X-ray laboratory machines [START_REF] Carrel | Biofilm imaging in porous media by laboratory X-ray tomography: combining a non-destructive contrast agent with propagation-based phase-contrast imaging tools[END_REF]. Its main drawback is the high viscosity of the suspension, which can damage the biofilm structure upon introduction. The fact that a dilute, and thus less viscous, solution of BaSO 4 with water causes important sedimentation of the barium sulphate particles, reduces the applicability of this contrast agent. [START_REF] Carrel | Biofilm imaging in porous media by laboratory X-ray tomography: combining a non-destructive contrast agent with propagation-based phase-contrast imaging tools[END_REF] used iron sulphate FeSO 4 in dilute form that is progressively added to the biofilm during the growth phase. This innovative method for marking the biofilm phase only (the first one to do so) was achieved as the inorganic compound FeSO 4 renders the biofilm more visible when imaging with phase contrast, although with very little distinction and therefore the potential for uncertainty in the segmentation process. The need for continuous introduction of FeSO 4 during growth further limits imaging to laboratory grown biofilms and makes it impossible to visualize samples collected in the environment or from engineering systems. FeSO 4 can only be used in phase contrast which is a less accessible technique than absorption contrast.

Each of the elaborated solutions is adapted to specific experimental conditions, it is therefore quite difficult to compare and classify contrast agents among themselves. In fact, considering the variability of biofilms in porous media samples, whether in terms of physical properties (e.g. size and opacity), micro-organism types and investigation inquiries (e.g. scale of studied phenomenon), the search for a single ideal contrast agent may be vain. Instead, we believe that a way forward is to build a library of contrast agents containing specificities, domains of applicability and uncertainty evaluations for each approach. Developing such a library requires not only to find novel contrast agents and protocols, but also to better characterize the existing ones. In particular, we note that, although all the established approaches may yield significant uncertainty, none of the corresponding studies have attempted to quantify it or to compare the results with a well-known 3D imaging method for biofilm. [START_REF] Davit | Imaging biofilm in porous media using X-ray computed microtomography[END_REF] and [START_REF] Iltis | Imaging biofilm architecture within porous media using synchrotron-based X-ray computed microtomography[END_REF] compared qualitatively a radiograph with 2D images obtained by shadowscopy and light microscopy, respectively, but a 3D comparison is still required for a complete quantitative analysis. The limitations of commonly used optical imaging methods for 3D imaging of meso-scopic scale samples and for opaque porous media being the main obstacles of such 3D validation.

In this work, we use glass capillaries as a simplified porous medium model, which allows us to image 3D biofilm structures using both X-ray CMT and optical fluorescence imaging, which is by far the most widespread imaging method in the field. More specifically, two-photon laser scanning microscopy (TPLSM) images of biofilm are compared with X-ray CMT using an improved protocol where barium sulphate is combined with a low-gelling temperature agarose to avoid sedimentation and facilitate transport and manipulation by mechanical stabilization. Direct comparison between the two techniques is made possible by an innovative system involving additive manufacturing, which enables rotating the capillary manually for TPLSM acquisition to gather a maximum amount of information without stopping the flow. Uncertainty associated with image processing and segmentation is evaluated using calibrated phantoms consisting of mono-dispersed fluorescent and X-ray absorbent beads. Three different segmentation techniques, namely hysteresis, region growing and watershed were employed for binarization and measurements of biofilm volume, 3D surface area and maximum thickness were performed. Quantitative comparison of both imaging modalities along with the uncertainty yielded by our protocol is achieved.

Materials and methods

Contrast agent preparation for X-ray CMT

The contrast agent was prepared beforehand of each experiment. It consisted of a mixture of commercially available medical-grade barium sulphate suspension (Micropaque R , Guerbet, France) and Sigma-Aldrich R low-gelling temperature agarose. This low-gelling temperature agarose was specially chosen because it enabled the injection of the contrast agent in liquid form. Indeed, it was introduced in the capillary in fluid phase at 37 • C and its polymerization was then induced by cooling down the sample in the range 8-17 • C. Once polymerized, the hydro-gel remained solid due to a hysteresis effect for temperatures up to its melting point (∼50 • C), which allowed for transportation, facilitated manipulations and permitted imaging using X-ray CMT at room temperature without de-polymerization. The gel solution was prepared at a w/v concentration of 2% by mixing 0.2 g of agarose powder with 10 mL of bacterial culture medium in an Erlenmeyer flask. The mixture was then autoclaved at 121 • C to ensure complete dissolution of the gel powder and sterilization of the mixture. After autoclaving, barium sulphate suspension was added to the nonpolymerized gel to achieve a concentration of 50% w/w of the gel solution. Barium sulphate particles were approximately 1 μm in size and are insoluble in water. The stock product contained 1 g per mL of barium sulphate. The mixture was vortexed and filtered using a vacuum pump with 30 μm nylon filters to remove barium sulphate aggregates. The final concentration of barium sulphate in the contrast agent was approximately 30% by weight, slightly decreased by the filtration procedure.

The viscosity of the contrast agent and pure Micropaque R suspension was assessed using a ThermoFisher R (Massachusetts, USA) 2 • cone and plate rheometer at 37 • C being the temperature at which it was introduced in the sample. For an identical shear rate, the viscosity of the contrast agent (∼ 10 -1 Pa.s) was 53% smaller than the viscosity of pure barium sulphate suspension (∼ 2 × 10 -1 Pa.s).

Biofilm growth protocol

Pseudomonas Aeruginosa (ATCC 10145) expressing green fluorescent protein (GFP) was used as a model organism for this experiment. P. Aeruginosa is a gram-negative, facultatively anaerobe bacterium known for producing strong biofilms [START_REF] Ma | Assembly and development of the Pseudomonas aeruginosa biofilm matrix[END_REF][START_REF] Lieleg | Mechanical robustness of Pseudomonasaeruginosa biofilms[END_REF]. Cultures were reconstructed from frozen stock in 6 mL of sterile culture media (Nutrient Broth from Sigma-Aldrich R ) with 300 μL of Ampicillin solution (concentration of 6 mg/mL) and incubated for 48 h at 37 • C in sterile centrifuge tubes. Strict aseptic protocol was followed to ensure that no contamination of the original strain occurred. Cultures were then washed via centrifugation at 3000 rpm for 15 min and re-suspended in 1 mL aliquot of fresh culture medium for immediate inoculation in 5 cm-long squarecross sectional VitroCom R (New Jersey, USA) glass capillaries (inner dimensions 500 μm x 500 μm, wall thickness 100 μm). Inoculated capillaries stood overnight. Custom-designed 3D-printed caps filled with silicone were used to ensure watertightness at the capillary ends. Inoculation and flow were done by piercing the caps with 30G needles mounted on a syringe or luer fittings, respectively. On removal of the needle, the puncture was closed, without intervention, by the silicone, ensuring the sealing of the caps.

The fluidic circuit for biofilm growth was set up as shown in Figure 1. It consisted of a micro-fluidic pressure controller (OB1 0-2 bars Elveflow R , France) pressurizing a reservoir filled with ultra-pure water. The reservoir output flowed through a flow sensor (0.2-5 mL/min Elveflow R ) which was used to servo-control the flow rate via a computer and the micro-fluidic pressure controller. This enabled maintaining a fixed flow rate throughout the circuit. A Harvard R (Massachusetts, USA) PHD 2000 syringe pump was used to introduce a concentrated culture medium solution (30 times more than normal concentration) in the circuit. Ultra-pure water and nutrient solution met at the T-junction before the capillary to allow mixing and were separated to prevent biofilm from spreading across the fluidic circuit. Indeed, if the main reservoir was filled with nutrient solution at normal concentration, bacteria would move by chemotaxis and form biofilm upstream until reaching the reservoir, fouling the valves and sensors on its way. The nutrient solution in the syringe pump was prepared at a concentration inhibiting bacterial development which has been determined by preliminary experiments. The fluidic circuit was open and effluent flow from the capillaries was directed to a waste reservoir. We will refer to the capillaries as flow cells in the next paragraphs.

After inoculation, the flow cells were connected to the fluidic circuit and flow rate was set at 800 μL/min, flow rate at which biofilm was grown continually for 48 h at room temperature (23.5 ±1 • C thermostated room).

Imaging protocol

Two-photon microscopy. This imaging modality was chosen for comparison as it is a widespread technique for biofilm observation [START_REF] Vroom | Depth penetration and detection of pH gradients in biofilms by twophoton excitation microscopy[END_REF][START_REF] Choi | Interactions of nanosilver with Escherichia coli cells in planktonic and biofilm cultures[END_REF][START_REF] Neu | Innovative techniques, sensors, and approaches for imaging biofilms at different scales[END_REF]; it also enables imaging large volumes of hundreds of micrometers to a few millimeters. Approaches based on electron microscopy [START_REF] Sugimoto | Imaging of bacterial multicellular behaviour in biofilms in liquid by atmospheric scanning electron microscopy[END_REF], which are also often used for imaging biofilms, provide a far superior resolution but cannot be used in our case as they are limited in the volume of the sample.

The bacterial strain expressed GFP and a tetramethylrhodamine (TAMRA) conjugate of Concanavalin-A (C80 ThermoFisher R Invitrogen, California, USA) was used to stain polysaccharides (α-mannoprosyl and α-glucopyranosyl residues) from the extracelullar polymeric matrix. The solution was prepared in 0.1 M sodium bicarbonate solution at a concentration of 200 μg/mL of Concanavalin-A. The flow cell was disconnected from the fluidic circuit for the introduction (at approximately 40 μL/min) of the fluorophore, which was then left to stand for 15 min before imaging.

Fluorescence imaging was performed using a (7MP Zeiss R , Germany) two-photon microscope with a 20x immersion lens of numerical aperture 1. Excitation wavelength was 880 nm and GFP from the bacteria appeared in the green channel, whereas the TAMRA attached to the EPS matrix appeared in the red channel. A Z-stack with laser compensation was done over 600 μm of depth until no sufficient signal was perceived. Flow of culture medium was maintained inside the microscope 37 • C thermostated cubicle, using a syringe pump, at half the growth flow rate. The flow rate was decreased to prevent potential biofilm detachment which could be caused by the temperature difference from growth conditions.

Due to the refractive index of glass being significantly different from that of water, biofilm grown on the flow cell walls, in the laser direction, was difficult to observe. The custom designed caps and capillary holder, with integrated pool for immersion imaging, illustrated in Figure 2, enabled rotating the capillary at four different angles (0 • ,90 • , 180 • and 270 • ). This capacity was used for imaging at two different angles (0 • and 90 • ) to gather information on half of the flow cell's cross section. Only two angles were chosen to minimize laser impact on the biofilm structures. Spatial resolution was 1.186 μm/pixel for the focal plane and the Z step between two slices was 1.5 μm. Acquisition time was 20 min for each angle.

X-ray micro-tomography.

When fluorescence imaging was complete, the contrast agent for X-ray CMT was immediately introduced. We proceeded to an introduction at a flow rate 10 times less than the growth flow rate as no biofilm sloughing was observed at this flow rate during previous experiments.

Once effluent was white due to the barium sulphate, the needles were slowly removed to avoid potential pressure oscillations. The flow cell was refrigerated for 15 min for rapid polymerization of the gel which prevented sedimentation of the barium sulphate particles.

We then proceeded to X-ray CMT image acquisition using a Phoenix Nanotom R (General Electric measurements, France) with a Tungsten target at 90 kV and 80 μA. Low voltage and amperage were selected to prevent excessive heating of the sample and de-polymerizing of the agarose. In addition, lowest scanning time (500 ms per frame) and minimum averaging (3 frames) were selected to reduce total acquisition which was about 1 h and 30 min for images of 2000 × 2000 pixels of size 2 μm.

Phantom uncertainty measurement

In order to measure the errors generated during every step of image acquisition and processing, an imaging phantom consisting of several fluorescent mono-dispersed beads (Sigma Aldrich R ) of diameter 6 ± 0.18 μm was used. Imaging for both modalities was performed under the same parameter configuration as for the flow cell containing biofilm. For reference values, we used the dimension and confidence interval given by the supplier and bright-field microscopy images as second control.

For evaluation of TPLSM, 0.2 μL of the bead solution was mixed with 40 μL of agarose gel which has a similar refractive index as that of water. The mixture was then introduced in a glass capillary, which was sealed and refrigerated for polymerization.

For X-ray CMT, 0.5 μL of the bead solution was mixed with 40 μL contrast agent and introduced in a capillary. The capillary was then refrigerated for polymerization of the contrast agent.

Image preprocessing

Image processing was carried out using Avizo R (FEI, Oregon, USA) 9 Fire Edition software.

Two-photon stacks were deconvolved using experimental red (for TAMRA) and green (for GFP) point spread functions (PSFs) obtained from imaging of fluorescent sub-resolution particles. The 'Deconvolution' function used an iterative maximum like-hood algorithm where the number of iterations (here 40) must be specified. The phantom stack, consisting of a red channel only, was deconvolved accordingly. The 0 • and 90 • biofilm stacks were manually registered and fused using the 'Add image' function which logically sums the image intensities.

Due to the difference between the refractive indices of glass and water, biofilm grown on the flow cell walls, in the laser direction, was difficult to observe. The custom designed caps and capillary holder illustrated in Figure 2 The rotation of the capillary was seen to induce a shift of about 50 μm in the Y direction and about 100 μm in the Z direction for the corresponding image acquisitions. The 0 • and 90 • stacks thus had to be aligned before being merged. The stacks were then processed with a symmetric nearest neighbour filter which has the remarkable property of well preserving edges [START_REF] Hall | Smooth operator: smoothing seismic interpretations and attributes[END_REF]. This algorithm compares each voxel to its symmetrically opposite connected neighbours. The grey levels of these symmetric pairs of voxels, that are closest in intensity to the central one, are averaged. The centre voxel is then replaced by the mean of its current grey level and of this calculated average. enabled rotating the capillary at two different angles (0 • and 90 • ) to gather information on half of the flow cell's cross section. Figure 3 shows two-photon images of a P. Aeruginosa biofilm, where the position of the lens relative to the flow cell is shown to indicate the direction of acquisition. Figures 3(A X-ray tomographic stacks were cropped to select only the part that was imaged by TPLSM. The stacks were then filtered using a non-local means filter. This algorithm looks for similar neighbourhoods, in a given search window, of the region around the current voxel [START_REF] Buades | A non-local algorithm for image denoising[END_REF]. It then computes the mean of all voxels in these neighbourhoods, weighted by how similar they are to the targeted one. This weighing function determining the similarity value is Gaussian. Under the assumption that the noise present in the image is white noise, this filter will preserve most features, even if they are small and thin.

Image segmentation

One way to proceed with segmentation for quantitative measurements is to optimize noise filtering as well as the different steps of binarization to obtain the best possible segmentation of the different phases. The main concern with this approach is that it fails to evaluate the uncertainty due to the fact that part of the information, primarily because of noise and artefacts, cannot be recovered. One way to tackle this issue is to further study the uncertainty propagation throughout the different steps and choices of parameters. However, such an analysis is difficult to perform. Here, we adopt a middle-ground approach: we hypothesize that the uncertainty can be bounded by using three completely different, simple and commonly used segmentation techniques (namely hysteresis, watershed and region growing). The first segmentation technique, hysteresis binarization, creates a binary image by specifying a lower and an upper threshold (Schl ¨uter et al., 2014). The voxels above the upper threshold and those above the lower threshold and below the upper one, that are connected to a voxel from the upper region, are selected. Watershed segmentation consists of viewing the grey-level image as a topographic map, with the brightness of each point being representative of its height [START_REF] Wildenschild | X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems[END_REF]Schl ¨uter et al., 2014). The map is then 'flooded' using an automatic gradient magnitude algorithm, creating barriers and basins corresponding to the grey levels. In region growing segmentation, a seed voxel is chosen and a grey-level range is specified. Then, all connected voxels, sharing at least one face (six possibilities for a voxel) with the chosen one and having a grey level from the specified range, are automatically selected, rendering the largest possible connected area within the user-specified range (Schl ¨uter et al., 2010). For the three segmentation methods, a binary image was obtained as final image.

Each approach required, at some step, a user-defined threshold. This value was chosen from the image histogram where it could be observed which range of grey levels belonged to a specific material. The impact of the choice of this threshold grey level was evaluated by segmenting using a value slightly above and below (±10% of the grey-level range of the bead) the chosen one. The metrics obtained were then compared to the measurements obtained by the selected threshold.

Once all the images were segmented, automatic measurements were extracted from connected structures (using Avizo R 'Label analysis' function). For the beads, diameters in X, Y and Z direction obtained by the generated bounding box of each bead as well as surface area and volume (voxel count) were measured. From this volume, the diameter of an equivalent sphere was calculated and its discrepancy relative to the supplier's information and bright-field microscopy was determined. For the biofilm images, the maximum thickness was extracted along with volume and surface area. For thickness measurements, biofilm structures were isolated and the bounding box size in the dimension perpendicular to the flow cell walls was noted. The surface area was automatically estimated by the software from the boundary curve of the exposed outer voxel and in case of noncontinuous signal, chordal approximation was performed. The difference between X-ray CMT and TPLSM measurements was then determined and plotted.

Results

Bead phantom uncertainty analysis

Via TPLSM, the fluorescent micro-spheres were bright and uniformly distributed inside the capillary. The beads were highly radiopaque when imaged by X-ray CMT and appeared as small white spots. The sensitivity analysis for the choice of greylevel threshold rendered slightly larger (below threshold) or smaller (above threshold) beads, but the difference was inferior to the standard deviation obtained by the selected threshold. This indicated a very little impact of the operator's choice of grey level on the metrics. Figure 4 shows images of a microsphere obtained by TPLSM (right), at a spatial resolution of 1.186 μm/pixel and X-ray CMT (left), at 2 μm/pixel, where the background is greyish due to the contrast agent. Their respective grey-level histograms are illustrated on top and the result of three different binarization techniques below.

Measurements were performed for an average of 15 beads. Figure 5 shows the diameter measured in X, Y and Z directions using the segmentation techniques for both imaging modalities. The supplier's confidence interval (5.82 -6.18 μm) is shown as well as the bright-field microscopy measurements (6.7 ± 0.05 μm).

Most measurements were between 6 and 7 μm, except for the Z direction of TPLSM because of the PSF. Indeed, this stretching of the fluorescence in the laser direction was due to the anisotropy intrinsic to the imaging approach and to the mismatch of refractive indices (water and glass) within the sample. The mean absolute error (relative to and normalized by 6 μm, i.e. supplier information) calculated for X-ray CMT for equivalent spherical diameter was 17.97% (see details in Table 1). For TPLSM, the mean absolute error was 14.79%. X-ray CMT slightly over-evaluated the phantom diameters, but the size of the error bars was similar in all three Fig. 4. Top and middle: grey-level histograms of a bead imaged by X-ray CMT and TPLSM, respectively. The smoothing spline (black) illustrates the main peaks. For X-ray CMT, the first two left peaks belong to the greyish background, the last one represents the bead. For TPLSM, the left peak belongs to the black background and the small centre peak represents the bead. Bottom: grey-level images of a bead via X-ray CMT and TPLSM along with the results of the three segmentation techniques, namely hysteresis, region growing and watershed. TPLSM spatial resolution was 1.186 μm/pixel (5 pixels ∼ 6 μm) and X-ray CMT was 2 μm/pixel (3 pixels = 6 μm).

by the two modalities was greatly decreased when compared with bright-field microscopy.

The mean absolute errors normalized by supplier's information and bright-field microscopy, respectively, for surface area and volume of TPLSM and X-ray CMT are shown in Table 2. It can be noted that for surface area measurements, X-ray CMT proved more precise (11.62% compared to 28.9% for TPLSM). For volume, on contrary, TPLSM was more accurate. For both metrics, the error bars of both imaging modalities were of directions showing the isotropic property of this modality and indicating a smaller variability. This overestimation may be due to perhaps inexact supplier information, since bright-field microscopy measurements gave a relatively larger diameter (6.7 μm), thus reducing the error generated by X-ray CMT. In Table 1, the absolute error (relative to and normalized by 6 and 6.7 μm), on equivalent spherical diameter for the two imaging modalities and three image segmentation techniques, is illustrated. It can be indeed observed that the error generated Table 1. Absolute error, relative and normalized by 6 μm (supplier's information) and 6.7 μm (bright-field microscopy), for TPLSM and X-ray CMT and using the three segmentation techniques (region growing, hysteresis and watershed).

Control diameter

Imaging comparable order of magnitude. In any case, X-ray CMT imaging was slightly less precise than TPLSM for phantom imaging in this set up, except for surface area metrics and watershed segmentation for diameter measurements.

Comparison of TPLSM and X-ray CMT acquisitions of biofilm images

Visual comparison between TPLSM (red and green) images overlaid with X-ray CMT (grey) stacks (Fig. 6) shows biofilm structures on the flow cell wall. Two samples are illustrated (upper and lower images) and the orthogonal views [XZ for (A) and (D), XY for (B) and (E) and YZ for (C) and (F)] of each are shown. Some barium sulphate aggregates can be observed as very bright spots, immobilized by the polymerization of the gel and thus confirming the sedimentation issue resolution. The white dashed rectangles represent the portion of the flow cell where maximum two-photon signal was obtained (using two angles of acquisition) for quantitative analysis.

For TPLSM, biofilm structures being more than 50 μm in depth appeared hollow. This is because the fluorescence signal was blocked by the biofilm layer closest to the lens. This phenomenon also shielded successive biofilm formations in the laser direction as shown in Figure 3(D)-(F), where only one biofilm structure appeared when imaging at 0 • . The appearing void structures are pointed by the white arrows, where it can be seen that only the contour of the biofilm formation could be visualized. The fact that these structures came out as hollow leads to unreliable calculation of biofilm volume by the segmentation techniques (Fig. 7). A correction was achieved when the structure's contour was well-enough delineated, where the void part was filled manually. This corrected TPLSM stack was labelled with a star*. In some structures (see cyan arrows in Fig. 6D), it was not possible to properly reconstruct the whole contour as there was missing signal, only thickness and surface area measurements were performed then. The plots in Figure 7 illustrate the quantitative analysis for distinct samples (in different colours), each shape representing a segmentation technique. Four biofilm structures noted by S1S1, S1S2, S2S1 and S2S2 (two from each sample) are illustrated. They correspond to a region of interest in the vicinity of the biofilm structures pointed by arrows in Figure 6. Volume measurements using X-ray CMT captured more biofilm than TPLSM, even when the hollow structures were manually filled. This procedure for correcting the fluorescence shielding was minimal so as not to introduce unwanted excess signal and may explain this difference (especially for the S1S2 in cyan). In average, TPLSM found 0.71 times less biofilm than X-ray CMT and TPLSM* found 0.28 times less. Also, Xray CMT showed more variability among the segmentation techniques than TPLSM. For 3D surface area, TPLSM generally had larger values than X-ray CMT, possibly because of the higher resolved images, showing more tortuous details. There is an exception for S1S2 (cyan) as TPLSM showed less signal for the thin biofilm layer next to the main structure (see the difference between the means for each structure was 44.14 μm (pink), 22.71 μm (blue), 19.44 μm (magenta) and 2.31 μm (cyan). In average, TPLSM found 0.18 times thicker biofilms than X-ray CMT.

The correspondence between both imaging modalities seems to depend on the type of biofilm structure that is imaged. For simple, relatively flat structures, we observe that the two images have almost perfect correspondence (see Fig. 6A and C on the right wall). However, thicker structures appear hollow via TPLSM, whereas X-ray CMT renders them as slightly flattened (see Fig. 6C left wall and D middle structure). The thickness measurements confirm this observation as for big structures, the difference in the means is larger (22.71 and 44.14 μm, respectively), whereas it is 2.31 μm for a small structure. For S2S1 (magenta), which is the biofilm formation situated in the flow cell corner (Fig. 6D), there is little difference in thickness (TPLSM finds 0.1 times more than X-ray CMT) probably because this cornered-type structure had a larger adhesion surface increasing its stability and reducing its exposition to flow-induced shear stress.

Figure 8 illustrates the 3D images of both sample images by TPLSM (right) and X-ray CMT (left). The four analysed structures are pointed by the black arrows. The two image modalities show corresponding 3D biofilm formation with very good similarity. It can be observed that X-ray CMT yielded smoother images than TPLSM, probably due to its coarser spatial resolution, also, the 'flattening' effect of X-ray CMT on the structures from S1 can be assessed. Discrepancies in the presence of biofilm at certain locations can also be observed between the two image modalities (see Supplementary Fig. S1 for superimposed 3D figures of both imaging techniques). For example, in S1, there is thicker biofilm on the upper flow cell wall via X-ray CMT than TPLSM, and oppositely, less biofilm on the lower flow cell wall. The same applies to S2, phantom diameters, X-ray CMT provides isotropic resolution (voxel size of 2 μm here) and an error of about 17% compared to supplier information, whereas the error using TPLSM was about 14% but with anisotropic spatial resolution. The bright-field microscopy measurements indicate a possible erroneous supplier information for the phantom geometry, and thus greatly reducing the error generated by our protocol using X-ray CMT (6.61%). Other sources of uncertainty are the large micro-sphere versus fluid absorption contrast, potential biases from the segmentation techniques and artefacts intrinsic to X-ray CMT such as beam hardening. Such errors could be corrected by working with more resolved systems, using synchrotron radiation or improving the image processing. Using less absorbent micro-spheres could also improve this analysis.

The situation is a lot more complicated when comparing both methods for biofilm imaging. Qualitatively, a very good correspondence between X-ray CMT and TPLSM images is observed (see Fig. 6 and the 3D volumes in Fig. 8), with a good correlation showing contrast in CMT and fluorescence in TPLSM. The correspondence between both modalities also seems to depend on the type of biofilm structures that are being visualized. For relatively flat structures, the two images have almost perfect correspondence (Fig. 6A). However, for thicker where there seems to be more biofilm captured by TPLSM than X-ray CMT on the flow cell wall. In these TPLSM 3D images, hollow structures having a well-delineated contour have been manually filled. There is an exception for S2S1 where there was not sufficient signal and the structure thus still appears void.

Discussion

Although the setup was optimized for TPLSM in capillary tubes, X-ray CMT proved quite precise in characterizing the phantom geometry. TPLSM is very accurate in the focal plane (X and Y directions) but has poor accuracy in the Z direction (Fig. 5). This is due to the anisotropy intrinsic to the imaging approach and to the mismatch of refractive indices, which produces large PSFs, which stretch the fluorescence signal in the Z direction, even after deconvolution with experimental PSF acquired under biofilm imaging parameters. Other microscopy configurations could have been used for single phantom (micro-sphere) imaging, increasing the accuracy and easing the deconvolution. However, such parameters would have been irrelevant for biofilm imaging, highlighting the difficulty in accurately using TPLSM for imaging large volumes. Even though it slightly overestimates biofilm structures larger than about 50 μm, TPLSM images show hollow biofilm formations, this being a consequence of fluorescence shielding and a major issue of the method for imaging large biofilm structures.

For larger structures, X-ray CMT also provides slightly flattened structures (Fig. 6B). We hypothesize that this 'flattening' effect results from the multiple stresses the biofilm was subjected to. Part of it is most likely due to the first acquisitions by TPLSM. Indeed, the laser excitation is mostly infrared and water (being highly absorbent) inside the sample tends to heat. Even if flow was maintained during image acquisition to minimize this warming effect, such heating probably had an impact on the biofilm structures. Even if all precautions were taken to decrease the duration of image acquisition (less time on each slice and less averaging), in many samples it could be visually observed that the flow cell contained slightly less biofilm after the TPLSM imaging, just before introduction of the contrast agent for X-ray CMT. Another important factor involves mechanical interactions between the contrast agent and the biofilm, including abrasion and shear stress upon introduction and the polymerization of the gel (e.g. swelling) that could flatten the biofilm. The concentration of 2% (w/v) gel was carefully chosen to be minimal for a complete polymerization, but biofilm damage, especially for thick and weakened structures, cannot be fully excluded. Still, our protocol for injection has been meticulously devised and every detail is of relative importance for an optimized contrast agent introduction.

However, contrary to [START_REF] Carrel | Biofilm imaging in porous media by laboratory X-ray tomography: combining a non-destructive contrast agent with propagation-based phase-contrast imaging tools[END_REF], we did not observe, either with naked eye or by comparison of both modalities, that introducing the contrast agent leads to massive sloughing of biofilms. There are different reasons why this might be the case. First and foremost, [START_REF] Carrel | Biofilm imaging in porous media by laboratory X-ray tomography: combining a non-destructive contrast agent with propagation-based phase-contrast imaging tools[END_REF] mentioned the introduction of air bubbles in their column during injection of barium sulphate; we believe that this fourth phase could have interacted and damaged biofilm structures as we have observed air bubbles tearing biofilm in capillaries during early experiments. It is also important to note that their sample had already been irradiated once before the introduction of barium sulphate. X-rays can have a harmful impact [START_REF] Hitchcock | Soft X-ray spectromicroscopy of biological and synthetic polymer systems[END_REF][START_REF] Iltis | Imaging biofilm architecture within porous media using synchrotron-based X-ray computed microtomography[END_REF] on both the micro-organisms and the extracellular polymeric substances (EPSs), this could have weakened the biofilm structures and increased abrasion effects. Other reasons for these discrepancies can be attributed to the difference in the flow rate. The Darcy velocity in [START_REF] Carrel | Biofilm imaging in porous media by laboratory X-ray tomography: combining a non-destructive contrast agent with propagation-based phase-contrast imaging tools[END_REF] was 1.06 mm/s, whereas the velocity for our flow cell was 52 mm/s. This increase in Darcy velocity could have generated more resistant biofilm structures that can sustain greater shear stress. In addition, the bacterial strain we used, P. Aeruginosa, is known to produce strong biofilms [START_REF] Ma | Assembly and development of the Pseudomonas aeruginosa biofilm matrix[END_REF][START_REF] Lieleg | Mechanical robustness of Pseudomonasaeruginosa biofilms[END_REF], whereas their team grew biofilms from unknown possibly multiple micro-organisms. Another aspect is the decreased viscosity of our contrast agent along with the low injection flow rate which, we believe, induced less shear stress upon the biofilm structures.

Conclusion

Qualitative and quantitative 3D analysis showed that the proposed contrast agent enables a reliable observation of biofilm using X-ray CMT. We have quantified the uncertainty yielded by our protocol and showed the importance of such analysis if any metrics are to be extracted from the images. Also, for a fixed imaging modality and sample, the metrics obtained were nearly independent of the binarization process, confirming the quality of our imaging acquisitions. The poor correspondence for some measurements showed the potential damage imaging that techniques can have on delicate biofilm structures, especially TPLSM heating effect on water. Our protocol has also revealed advantages of X-ray CMT over TPLSM for 3D imaging, notably for large volumes, with a very good isotropic spatial resolution of 2 μm/pixel and the absence of optical shielding artefacts. Our method is robust, reproducible and reliable for strong biofilm structures of P. Aeruginosa grown at a constant flow rate. As the very first comparison with the most widely used technique in the field and uncertainty analysis made for imaging of biofilm using X-ray CMT, it is very encouraging and provides an efficient contrast agent for future studies in porous media. Experimenting other bacterial strains at different flow rates will evaluate the range of applicability of our protocol. Other perspectives are the investigation of the impact of other parameters such as flow rate, nutrient concentration and pore geometry on the morphology and the distribution of P. Aeruginosa biofilms in porous media of pore size larger than 100 μm.

Fig. 1 .

 1 Fig. 1. Fluidic circuit used to grow biofilm for 2 days at fixed flow rate.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Three-dimensional-printed flow cell (capillary) holder with silicone filled caps and integrated pool for immersion lens. The four notches enabled rotating the flow cell at four different angles. The picture was taken with the notches out, the cap well-fitted in the support which maintained the flow cell in place.

  ) and (D) illustrate the images acquired with the flow cell positioned at 0 • and Figures 3(B) and (E) show the images acquired with the flow cell rotated at 90 • where the complementary information on the biofilm grown on the top and bottom walls can be observed. Finally, Figures 3(C) and (F) are the merged stacks of (A) and (B) and (D) and (E), respectivel.

Fig. 5 .

 5 Fig.5. Top: mean phantom diameter determined in X, Y and Z directions and equivalent spherical diameter derived from 3D volumes, the error bars representing the standard deviation. Measurements were made by processing the same beads with different segmentation techniques, namely region growing, watershed and hysteresis. Bottom: mean surface area (left) and mean volume (right) are illustrated. For all plots, the control dimensions (supplier information and bright-field microscopy) are also illustrated.

Fig. 6 .

 6 Fig. 6. Visual comparison of TPLSM images superimposed on X-ray CMT stacks for two samples (S1 upper and S2 lower). (A) and (D) illustrate the cross section of the flow cell (XZ) for two different samples, (B) and (E) and (C) and (F) show the side views XY and YZ, respectively. White dashed rectangles represent the portion of the flow cell used for quantitative analysis, the arrows show TPLSM structures appearing hollow, in white when manual correction was possible and in cyan, when the contour of the structure was not well delineated enough. Fluorescence was enhanced for visual purposes.

Fig. 7 .

 7 Fig. 7. Quantitative analysis of biofilm metrics: comparison both imaging modalities where each colour represents a sample and the three used segmentation techniques are illustrated by different shapes. Volume measurements displayed in the upper plot where TPLSM* represents the corrected hollow structures. Three-dimensional surface areas are shown in middle plot and maximum thickness in lower plot.

Fig. 8 .

 8 Fig. 8. Three-dimensional visualizations of the analysed samples showing corresponding biofilm formation on the flow cell wall. The different structures pointed by arrows are recognizable.

Table 2 .

 2 Absolute error, relative and normalized by supplier's information and bright-field microscopy for 3D metrics (surface area and volume) of TPLSM and X-ray CMT imaged beads.

			Imaging	Mean
	Measurement	Control metrics	modality	error
	Surface area	Supplier's information	TPLSM	60.8%
		(113 μm 2 )	X-ray CMT	39.3%
		Bright-field microscopy	TPLSM	28.9%
		(141 μm 2 )	X-ray CMT	11.62%
	Volume	Supplier's information	TPLSM	57%
		(113 μm 3 )	X-ray CMT	75.8%
		Bright-field microscopy	TPLSM	13.3%
		(157 μm 3 )	X-ray CMT	26.6%

Acknowledgements

We thank Ruddy Soeparno and Maëlle Ogier from IMFT for their help and Sophie Allart, Magda Rodrigues and Astrid Canivet for their assistance at the imaging facility platform of the Centre de Physiopathologie de Toulouse Purpan (IN-SERM).

This project was funded by the grant BioPorousLab (Transversalité, 2014) from the IDEX UNITI.

The authors declare no conflict of interest.

Supporting Information

Additional supporting information may be found online in the Supporting Information section at the end of the article.


Zeynep Ozturk Yurt

Rik Eshuis
email: h.eshuis@tue.nl

Anna Wilbik
email: a.wilbik@maastrichtuniversity.nl

Irene Vanderfeesten
email: i.t.p.vanderfeesten@tue.nl

Context-Aware Process Modelling for Medicinal Product Development

Keywords: Context-Awareness, Enterprise Modelling, Business Process Management, Conceptual Modelling

Advanced Therapy Medicinal Products (ATMPs) are highly innovative medicinal products that are based on biomedical technology. ATMP development processes need to comply with complex regulatory frameworks. Currently, biomedical scientists that develop ATMPs manage the regulatory aspects of the ATMP development processes in an ad-hoc fashion, resulting in inefficiencies such as rework, or even withdrawal of ATMPs from the market if the regulatory requirements are not adequately addressed. This paper presents an explorative case study in which we investigate enterprise modelling and context-aware business processes to support ATMP scientists in managing the regulatory aspects of ATMP development processes more efficiently and effectively. The main objective of this case study is to offer regulatory-based guidance to scientists. We use enterprise models (domain, goal and process models) to describe the important concepts and views in ATMP development processes. By introducing context-awareness to the models, we enable regulatory-based guidance that supports ATMP scientists in performing relevant tasks to address the regulatory requirements efficiently and effectively.

Introduction

Advanced Therapy Medicinal products (ATMPs) are medicines for human use that are based on innovative biomedical technologies [START_REF]Advanced Therapy Medicinal Products: Overview[END_REF]. Being a medicinal product for human use, ATMPs need to comply with complex regulations about safety and efficacy. Therefore, the two most prominent views in ATMP development processes are scientific development and regulatory compliance. Currently, the ATMP scientists manage regulatory aspects of ATMP development processes in an ad-hoc fashion. Yet, ATMP development processes suffer from inefficiencies such as reworks and withdrawal of ATMPs from market, due to not being able to adequately demonstrate regulatory compliance [START_REF] Elsallab | Mitigating deficiencies in evidence during regulatory assessments of advanced therapies: A comparative study with other biologicals[END_REF][START_REF] Morrow | Addressing pressing needs in the development of advanced therapies[END_REF].

The cause of this is the complexity of ATMP regulatory framework and scientists' lack of regulatory knowledge and its impact on the scientific development process. ATMP regulations describe high level goals to be achieved in order to make sure that the ATMP being developed is safe and effective. This is done by, for instance, demonstrating physiological and biochemical properties of the product. Also, ATMP regulations are flexible, depending on development setting different regulatory requirements apply. Here, the development setting covers a set of factors related to the ATMP, defined by the scientist e.g., type of materials used, regulatory classification of materials etc. These two factors make the management of regulatory aspects of ATMP development challenging for scientists. Therefore, there is a need to support ATMP scientists in managing the regulatory aspects more efficiently and effectively.

To enable this support, we first need to investigate the ATMP development setting. Enterprise Modelling is an effective approach to capture, understand and relate the elements of a complex setting [START_REF] Sandkuhl | Enterprise Modeling: Tackling Business Challenges with the 4EM Method[END_REF]. Enterprise modelling can support many purposes, for example, strategy development [START_REF] Köhler | Enterprise modeling at the work system level: Evidence from four cases at dhl express europe[END_REF], change management [START_REF] Van Gils | Enterprise modelling in the age of digital transformation[END_REF] or process improvement [START_REF] Koç | Capability-driven digital service innovation: Implications from business model and service process perspectives[END_REF]. In this case study, we use enterprise modelling as the stepping stone to enable more efficient and effective management of ATMP development processes. Using enterprise models such as: domain, goal and process models, we capture, understand and relate the main elements in ATMP development processes in a structured way. Building upon these models, we focus more on process modelling, in order to investigate ways to provide regulatory-based guidance in the scientific development processes.

Context-aware BPM deals with identifying factors that drive flexibility and variability in business processes [START_REF] Rosemann | Contextualisation of business processes[END_REF]. Several authors investigated the notion of context for business processes with an aim to identify factors that affect the design and execution of a business process and make business processes contextaware by integrating these factors and their effect to the process models [START_REF] Song | Towards a comprehensive understanding of the context concepts in context-aware business processes[END_REF]. In this paper, we use the notion of context-awareness in BPM to guide scientists in working more efficiently and effectively towards regulatory compliance.

This paper presents an explorative case study in which context-aware ATMP development processes are modelled. We use enterprise models to describe scientific and regulatory views in ATMP development. To describe the main concepts and their relations in ATMP development, we use a domain model. We represent the scientific development process in a flexible process model and regulatory requirements in a goal model. By introducing context-awareness, we make the link between different regulatory contexts, regulatory requirements and the scientific development process explicit, and guide scientists in performing relevant tasks to address the regulatory requirements. Thereby, this paper presents an exemplary case study for guiding users in flexible and knowledge-intensive processes towards regulatory compliance.

The object of the case study presented in this paper is the biomaterial development process, which is a part of ATMP development processes, from the Horizon2020 iPSpine project 5 . In iPSpine, an ATMP for lower back pain is be-ing developed. As a part of this project, we develop a digital platform to enable efficient and effective management of ATMP development processes. Therefore, this case study is driven by the problems in iPSpine.

The remainder of this paper is organized as follows. Section 2 introduces ATMP development processes and the problem addressed in this case study, and presents the objectives of the case study. Section 3 discusses how the objectives of the case study are addressed. Section 4 presents the preliminary evaluation made with iPSpine stakeholders on the usefulness of models and ideas presented in this paper. Section 5 discusses the relevant work on Context-Aware BPM. Lastly, Section 6 concludes the paper.

ATMP Development: The Need for Guidance

In this section, we introduce the ATMP development processes and the problem we address in this case study.

ATMP Development Process

Development of ATMPs involves several stages and the overall aim in these stages is to develop a safe and effective medicinal product. This is accomplished by collaboration of many stakeholders, where scientists and regulatory consultants are the main ones. Figure 1, describes the main phases and stakeholders in ATMP development. Research shows that ATMP development processes are associated with many hurdles such as reworks and even withdrawal of the ATMP due to shortcomings in providing adequate evidence for regulatory compliance [START_REF] Elsallab | Mitigating deficiencies in evidence during regulatory assessments of advanced therapies: A comparative study with other biologicals[END_REF]. This contributes to increased development costs and time-to market. Lack of regulatory knowledge among scientists is an important factor for these hurdles [START_REF] Elsallab | Mitigating deficiencies in evidence during regulatory assessments of advanced therapies: A comparative study with other biologicals[END_REF]. Being an expert, a scientist requires minimal guidance about the scientific aspects of ATMP development. However, establishing and maintaining the link between the scientific development process and the complex regulatory framework of the ATMP development is challenging for a scientist. In other words, there is a need to bridge the gap between the scientific and regulatory views on ATMP development processes. This requires identification and description of important concepts in an ATMP development setting. To do so, we use the conceptual enterprise models. The following section presents these models.

Note that, for demonstration purposes, we use models from a biomaterial development process, which is a part of the ATMP development studies. The models we use in this paper are simplified for readability and space considerations.

Modelling ATMP Development

Enterprise modelling covers several models [START_REF] Köhler | Enterprise modeling at the work system level: Evidence from four cases at dhl express europe[END_REF][START_REF] Zachman | A framework for information systems architecture[END_REF]. Depending on the purpose of the enterprise modelling job, the models used and the level of detail included in the models should change [START_REF] Köhler | Enterprise modeling at the work system level: Evidence from four cases at dhl express europe[END_REF].

For this case study, the purpose of modelling is to represent and relate the two most prominent views, regulatory and scientific views, of ATMP development processes. The regulatory view covers the reason or motivation behind performing ATMP development processes, i.e. the aim is to develop a safe and effective(in other words, regulatory compliant) product. The scientific view covers the activities to develop the product. Therefore, goal and process models are essential elements for our purpose. To understand and relate different concepts in these different views, a domain model is also essential.

There are other models used in enterprise modelling. For instance, actor /resource models and business rule models [START_REF] Köhler | Enterprise modeling at the work system level: Evidence from four cases at dhl express europe[END_REF], organization and network models [START_REF] Zachman | A framework for information systems architecture[END_REF]. However, we haven't used such models since they do not provide considerable information for our modelling purpose. For example, modelling the different actors/resources and their relations do not provide any implications about the scientific and regulatory views in ATMP development, or modelling the business rules, e.g., some scientific procedures that constraint how experiments should be done, is not within the scope of our modelling purpose.

First, we built a domain model with domain experts, to structure the domain knowledge and understand complex concepts and the problems in the domain. Figure 2 shows the domain model we have created for this case study, using UML class diagrams.

ATMP regulations do not induce strict rules on how things should be done throughout the development process. Instead, they involve high-level goals that should be considered in order to demonstrate that the ATMP being developed is safe and effective. Therefore, we represent the regulatory requirements using goal models. Figure 3 shows an excerpt from the goal model of biomaterial development process in GRL notation [START_REF] Union | [END_REF].

Lastly, we model the scientific development process using flexible process models. ATMP development processes are knowledge-intensive processes. Traditional BPM focuses on managing routine and predictable work. Knowledgeintensive processes have different characteristics [START_REF] Di Ciccio | Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches[END_REF]. Traditional BPM is limited when it comes to supporting flexible and unpredictable knowledge-intensive processes [START_REF] Di Ciccio | Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches[END_REF]. Case Management is an approach that recently emerged to overcome these limitations [START_REF] Aalst | Case handling: A new paradigm for business process support[END_REF][START_REF] Swenson | Mastering the Unpredictable: How Adaptive Case Management Will Revolutionize the Way That Knowledge Workers Get Things Done[END_REF]. Therefore, we chose to support ATMP development processes with Case Management and, modelled the scientific development process A top-down analysis of regulatory goals results in a goal model where the leaf goals are satisfiable by means of sub-processes or tasks in the process model. This way, we build a link between the regulatory goals and the scientific development process. Each leaf goal in the goal model corresponds to a milestone of a single task or sub-process in the process model. The milestones corresponding to the leaf goals have the same labels as the goals.

The Need for Guidance

Looking at the goal model in Figure 3, we see that there is a set of sub-goals that are required to achieve Demonstrate Physiological and Biochemical Characterization goal.

Indeed, some factors related to the development process and the ATMP being developed determine which of the sub-goals (regulatory requirements) are required to achieve the Demonstrate Physiological and Biochemical Characterization goal. We refer to a set of such factors as the context of the ATMP development process. For ATMP development, the context is defined by several factors related to the ATMP. For instance, scientist's choice of regulatory classifications for the components of an ATMP or the type of starting material of an ATMP. An example decision tree followed by scientists for regulatory classification decisions is shown in Figure 5. For different contexts, e.g., different classification options Correspondingly, since the regulatory goals drive the scientific development process, i.e. the scientist performs experiments to address regulatory requirements, context also affects the scientific development process. The ATMP development process model on Figure 4 covers all possible tasks a scientist can perform throughout the development process. Yet depending on the context, since context defines which regulatory requirements are applicable, some tasks are required to address the regulatory goals whereas some are not. The scientist can still perform other tasks that are not required to address the regulatory goals of the current context, for instance, out of scientific interest or to explore alternative contexts (See Figure 5).

The scientist starts the process with an initial assumption on the context. However, the context is subject to changes throughout the development process. For example, different options (e.g., classifying the biomaterial as medical device or excipient) are investigated throughout the development. Depending on the results the scientist obtains throughout the process, she can decide to, for instance, classify the biomaterial as a medical device instead of as an excipient, following the decision tree in Figure 5. This would change the context, regulatory goals to be addressed and hence the tasks to be executed to address relevant regulatory requirements. To ensure that the scientists performs the relevant tasks that addresses the relevant regulatory requirements, it is important to make explicit on the process model which tasks are required under which conditions (context).

In this case study, we intend to address the need of regulatory guidance in ATMP development processes. As a result of an analysis of literature on managing ATMP development processes and our interviews with iPSPine stakeholders, we have identified the objectives in Table 1 for our case study.

Solution Design & Development

The need for regulatory guidance, as discussed in Section 2, motivated us to use the notion of context and context-awareness to guide ATMP scientists in working towards regulatory compliance. The following sections present how we address the objectives in Table 1.

Contextualizing the Domain Model

(Sub-Objective 1) Every business process has a specific domain. Correspondingly, everything that influences a process is related to this domain [START_REF] Mattos | A formal representation for context-aware business processes[END_REF]. Therefore, what constitutes context for a business process lies in the domain model. This motivates our choice of using domain models as a baseline to define context in ATMP development processes. For ATMP development, the experiments performed, results obtained, properties of the ATMP being developed or decisions taken throughout the process form the context of the development process. For instance, a decision, which is a part of the ATMP development process, about the regulatory classification of components of the ATMP is an important contextual element.

Below is an example (part of the) domain model and context definition. First, we created the domain model with experts. In the domain model, entities and their attributes are marked as contextual, shown in dashed boxes in Figure 6, if they determine the regulatory goals to be addressed by the development process. For example, the decision about classification of biomaterial shown in Figure 5, is represented as different roles that a biomaterial entity can take and marked as contextual element(See C1 and C2 in Figure 6).

Instantiation of each contextual element is a partial context (C5,C6,C7,C1,C2 on Figure 6). Also, combined instantiations of multiple contextual elements with different values is a partial context (C3, C4 on Figure 6). Contexts which share the same contextual elements but with different values are mutually exclusive(C5,C6,C7 or C1,C2 or C3,C4 on Figure 6). Contexts which include a combination of multiple contextual elements might imply contexts including less contextual elements (e.g., C4→C2, C3→C2, C4→C7 on Figure 6). So they are not exclusive.

A set of non-exclusive partial contexts form the overall context in an ATMP development process (See context in Figure 8).

Contextualizing the Goal Model

(Sub-Objective 2) Having defined contexts, we annotate the root and the parent goals in the goal model with context labels, indicating which goal is adoptable under which conditions. The semantics of context annotations are provided by Ali et al. [START_REF] Ali | A goal-based framework for contextual requirements modeling and analysis[END_REF]. If a root goal, G, is annotated with a context label C i , that means G is activated iff context C i holds. If there is a goal G i , that is decomposed into a sub-goal G j with and(or) decomposition links then the link is annotated with a context label. This means, goal G i , requires(can be achieved) via G j iff context C i hold.

These annotations enable us to derive the context for leaf goals. Figure 7 shows an example goal model for ATMP development processes where the context for leaf goals are derived using the contexts of goal model variants which includes these leaf goals (See Figure 8).

The idea of using contextual goal models is inspired from [START_REF] Ali | A goal-based framework for contextual requirements modeling and analysis[END_REF]. In [START_REF] Ali | A goal-based framework for contextual requirements modeling and analysis[END_REF], authors use contextual goal models to model contextual requirements for an information system. Here, we use contextual goal models as a means to contextualize process models. In the following section, we describe how contextual goal models are used to contextualize ATMP development processes.

Contextuzalizing the Process Model

(Sub-Objective 3) Our intention here is to contextualize the process model such that it guides scientists throughout the process execution. This is achieved by deriving the context of the leaf goals in the goal model. A leaf goal corresponds to a milestone of a single task or sub-process in the process model. Accordingly, once we derive the context of leaf goals, the corresponding task/sub-process is elements) affects whether a task is required or not. In the following paragraphs, we illustrate how context-aware process models support scientists on an example.

Looking at Figure 4, we see that once the Create Gel task is completed, a set of tasks are enabled. Being a knowledge-worker, the scientist has the flexibility to choose which tasks to perform and which not. Although this flexibility is an essential part of the process, it is important to support the scientist in making the these choices.

Consider the task Test swelling degree. Without any information on context, the scientist is free to skip this task. However, skipping this task would cause a problem if the biomaterial has a natural starting material and is classified as starting material in the drug substance (context C1 in Figure 6 holds). Skipping the task will result in not being able to Demonstrate compliance with Ph.Eur. 2.8.4 Swelling Index, (See Figure 7), and this will result in failing to get the authorizations for clinical trials. With the models in this paper, the scientist can choose a specific context and this way is able to see which goals and tasks are relevant. Thereby, the scientist ensures that the relevant regulatory requirements are addressed.

Additionally, it is important to explicitly show the factors (contextual elements) defining whether a task is relevant for the current context or not. For instance, knowing that the contextual elements related to Test swelling degree task are Starting material type and Role of biomaterial, the scientist sees how Role of the biomaterial affects the development process. This helps them define the right context, e.g., choosing the appropriate Role of the biomaterial in Figure 5, that require fewer tests, which is more time and cost-efficient.

Lastly, since the context can change throughout the development, so do the regulatory goals to be addressed and the set of tasks to be executed to address those regulatory goals. Context-aware process models support scientists by making explicit which tasks are required for which contexts. Thereby, context-aware process models implicitly supports scientists in working more efficiently by help-ing them in prioritizing the tasks that are relevant for more contexts rather than performing redundant tasks that are only valid for a specific context, which is unlikely to occur.

Preliminary Evaluation

Initial feedback on the models and ideas presented in this paper has been gathered from senior iPSpine and regulatory experts. The stakeholders indicated that they are positive about the usefulness of models and ideas in practice.

Next, the usefulness of the main-objective of this case study was discussed with three junior scientists who are working on the biomaterial development studies, which is the part of ATMP development processes we focus in this case study. The scientists mentioned that the idea of linking the process model and the goal model is "definitely useful" when the scientific development is at the stage where different regulatory frameworks (different contexts, domain model) are investigated. They mentioned that they can use these models to justify what they have done (process) and identify what they need to do to better comply with the chosen regulatory framework (goals).

Further evaluation will follow when the models are implemented on the process management platform developed within the scope of iPSpine project.This implementation is currently under development. First, we plan to conduct semistructured interviews with junior biomaterial development scientists who are actively using the platform. Then, further interviews will follow with senior scientists and other development cases will be implemented and tested in the platform.

Discussion

Our focus in this case study is to support scientists in working towards regulatory compliance. We do this by means of context-aware process models. In this regard, the notion of context in context-aware BPM is related to our purpose. Therefore, in this section, we discuss context-aware BPM papers that are highly relevant for our case study.

Context is not a new notion for BPM. Several authors investigated this notion with the aim of making the processes context-aware; responsive to the changes in its environment. Song et al. [START_REF] Song | Towards a comprehensive understanding of the context concepts in context-aware business processes[END_REF] present a comprehensive survey about various definitions of context in context-aware BPM. The authors conclude that there is still a lack of consensus in BPM on how context is defined, represented and integrated to the business processes [START_REF] Song | Towards a comprehensive understanding of the context concepts in context-aware business processes[END_REF].

A related work on contextualizing business processes is the paper by De La Vara et al. [START_REF] De | Business processes contextualisation via context analysis[END_REF][START_REF] De | Compro: A methodological approach for business process contextualisation[END_REF]. They use a context analysis method [START_REF] Ali | A goal-based framework for contextual requirements modeling and analysis[END_REF] to contextualize business processes. The context analysis method uses a set of expressions, socalled facts, to check if a particular context holds. A context analysis model defines alternative ways (or alternative combinations of facts) for checking a context, referred to as context variants. In our case, a context can not be verified by facts. Context is defined by different values taken by the contextual elements chosen by the scientist(user).

The importance of goals for investigating and integrating context for business processes is already discovered in context-aware BPM literature [START_REF] Heravizadeh | Making workflows context-aware: A way to support knowledge-intensive tasks[END_REF][START_REF] Mattos | A formal representation for context-aware business processes[END_REF][START_REF] Rosemann | Contextualisation of business processes[END_REF]. In these papers, goals are used to identify contextual elements, i.e., factors that have an impact on the achievement of business goals, and relate them to the business processes.

Similarly, in this case study, we use goals as a facilitator for identifying and relating context and contextual elements to business processes. However, different from existing approaches [START_REF] Heravizadeh | Making workflows context-aware: A way to support knowledge-intensive tasks[END_REF][START_REF] Mattos | A formal representation for context-aware business processes[END_REF][START_REF] Rosemann | Contextualisation of business processes[END_REF], we use contextual goal models for this purpose. In the existing approaches [START_REF] Mattos | A formal representation for context-aware business processes[END_REF][START_REF] Rosemann | Contextualisation of business processes[END_REF], analysis of process goals is only limited to identification of top level objectives and discovering factors(contextual elements) that have an effect on the achievement of those goals. Heravizadeh et al. [START_REF] Heravizadeh | Making workflows context-aware: A way to support knowledge-intensive tasks[END_REF], decomposes the process objectives into smaller objectives to discover contextual elements and to link them to the business process. However, in their case, context only affects how or how well the goals are achieved, but the goals are fixed. In our case, different contexts imply different goals.

Another related research area is guidance/recommendations for flexible, knowledge intensive processes. Supporting flexible and knowledge intensive processes is an emerging topic in BPM [START_REF] Di Ciccio | Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches[END_REF]. Providing guidance and recommendations for those processes has also drawn some attention [START_REF] Barba | User recommendations for the optimized execution of business processes[END_REF][START_REF] Huber | Next step recommendation and prediction based on process mining in adaptive case management[END_REF][START_REF] Voorberg | Decision support for declarative artifact-centric process models[END_REF][START_REF] Weber | Cbrflow: Enabling adaptive workflow management through conversational case-based reasoning[END_REF]. These approaches provide guidance using historical knowledge about previous cases. ATMP development is a new field with a huge variability between different projects. Also, no historical data from previous projects is available for use. For this reason, existing process guidance approaches are not suitable for our case study. In this regard, this paper presents an exemplary case study for guiding flexible and knowledge-intensive processes where no historical data is available.

Lastly, although business process variability modelling [START_REF] Ayora | VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems[END_REF][START_REF] Rosa | Business process variability modeling: A survey[END_REF] is a related research area, it is not the focus of this paper. The main problem we address in this paper was to bridge the gap between the regulatory and scientific views on an ATMP development process. So, our main focus was to identify and integrate the (regulatory related) factors that causes a variability in the scientific development process. Business process variability modelling approaches focus on the next step: deriving different process variants. They do not focus on identification and representation of the factors driving the variability. Also, deriving the process variant for a particular context is not intended in this case study. Alternative contexts are explored throughout an ATMP development study e.g., different classification decisions in Figure 5. So, the process model should cover not only a single process variant of a particular context, but multiple process variants corresponding to alternative contexts that are explored in a single development process.

Conclusion

In this paper, we have presented a case study on modelling context-aware processes. The immediate contribution of this research are the models presented in Section 3 created and implemented for the iPSpine project.These models are used to guide ATMP development scientists work towards regulatory compliance in an efficient and effective manner. Furthermore, this case study presents an exemplary approach for guiding flexible and knowledge-intensive processes through context-awareness.

Contextualization of the existing process model provides a solution for guiding the scientists towards regulatory compliance in this case study. However, ATMP development processes are diverse. The process models we provide in this case study cover only a single ATMP development study in the iPSpine project. As future work, we plan to investigate creating process models, that cover the tasks for a set of contexts explored throughout an ATMP development study, using goal models.

Fig. 1 .

 1 Fig. 1. ATMP Development Process & Stakeholders (Stakeholders and Scope of this study in bold)

Fig. 2 .

 2 Fig. 2. Domain Model of ATMP Development

Fig. 3 .

 3 Fig. 3. Goal Model of Biomaterial Development

  Figure 4 shows an excerpt from the biomaterial development process model.

Fig. 4 .

 4 Fig. 4. Process Model of Biomaterial Development

Fig. 5 .

 5 Fig. 5. Decision Tree

Fig. 6 .

 6 Fig. 6. Domain Model(Simplified) and Example Context Definitions

Fig. 7 .Fig. 8 .

 78 Fig. 7. Contextual Goal Model

Fig. 9 .

 9 Fig. 9. Contextualized Process Model

Table 1 .

 1 Objectives of the Case StudyMain Objective Guide the scientists performing the scientific development process towards relevant regulatory goals.

	Sub-Objective 1 Define and represent context for ATMP development pro-
	cesses.
	Sub-Objective 2 Represent the variability of regulatory goals, depending
	on context.
	Sub-Objective 3 Represent the effect of context on the scientific develop-
	ment process.

https://ipspine.eu/

Acknowledgements. The work presented in this paper is part of iPSpine project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 825925.