T Herard

Y Delzescaux

Laurent Remion

Lucas

J Sarton

N Courilleau

A-S Hérard

T Delzescaux

Y Remion

L Lucas

VIRTUAL REVIEW OF LARGE SCALE IMAGE STACK ON 3D DISPLAY

Keywords: Virtual microscopy, 3D display, Out-of-core hierarchical visualization, GPU rendering

HAL is

INTRODUCTION

Current image-generating technologies are essential tools within modern biology despite the large amounts of data they generate [START_REF] Amunts | BigBrain: An ultrahigh-resolution 3d human brain model[END_REF]. Interactive visualization of these volumetric data is essential partly because i) our screens are only able to display a fraction of the resolutions of large images and ii) they help to understand the threedimensional structures of images and determine spatial relationships between regions of interest.

In practice, and thanks to the advances in computer technology, the development of VM [START_REF] Catalyurek | The virtual microscope[END_REF][START_REF] Wang | Vir-tualMicroscopy: Ultra-fast interactive microscopy of gigapixel/terapixel images over internet[END_REF][START_REF] Molin | Scale Stain: Multi-resolution feature enhancement in pathology visualization[END_REF] has partially addressed this issue. As a reminder, a VM could be defined as a system that simulates the observation of microscopic samples on computer, by mimicking a conventional microscope enabling to observe, navigate [START_REF]Deep zoom[END_REF][START_REF]Openseadragon[END_REF], and annotate virtual slides [START_REF] Corredor | Flexible architecture for streaming and visualization of large virtual microscopy images[END_REF]. Nevertheless, Hortsch [START_REF] Hortsch | From microscopes to virtual reality -How our teaching of histology is changing[END_REF] notes a drawback in such system used as a virtual microscope to study virtual slides. Indeed, he reports that users of such equivalent tools were frustrated to have only a single plane focus, and to lose threedimensional perception. To compensate these disadvantages, we propose to extend visualization to 3D by using autostereoscopic display [START_REF] Holliman | Three-dimensional displays: A review and applications analysis[END_REF] in order to improve the quality of the user experience. In addition, this extension allows the user to freely navigate -zooming in or out -in the whole volume rather than in a single slide.

In order to reach this quality of navigation, we draw our inspiration from out-of-core methods management of large volume data on GPU used in the context of volume visualization. Usually, in these approaches, one creates a pyramidal level of resolutions, subdivided into blocks of data (called bricks). The Gigavoxel approach [START_REF] Crassin | Gigavoxels: Ray-guided streaming for efficient and detailed voxel rendering[END_REF], stores the bricks in a tree structure. We rather turned to the proposed method of Hadwiger et al. [START_REF] Hadwiger | Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach[END_REF] who provide a virtual memory approach with a multi-level, multi-resolution page table mechanism for ray casting rendering. This approach has already been validated by a concrete application with interactive exploration of petascale volumes [START_REF] Beyer | Exploring the Connectome: Petascale volume visualization of microscopy data streams[END_REF].

Based on this out-of-core method, we extend it to propose a visualization-driven pipeline which generates stereoscopic multiview frames on GPU at interactive time from data bigger than GPU memory.

The remainder of this paper is organized as follows. In Section 2, a brief overview of the system is provided. In Section 3, our visualization-driven pipeline is detailed. Issues on dataset representation are specifically studied. Experimental results are then presented and discussed in Section 4. Finally, conclusion is given in Section 5.

MATERIALS AND METHODS

The data used in this article was obtained from a C57BL/6J mouse brain processed to improve block-face photograph resolution compared to a previous research realized on Alzheimer's Disease [START_REF] Dubois | Detection by voxelwise statistical analysis of significant changes in regional cerebral glucose uptake in an APP/PS1 transgenic mouse model of Alzheimer's disease[END_REF]. The photographic volume produced was composed of block-face images of the surface of the frozen brain taken before the cutting process (slice thickness of 20 µm to derive histological sections not treated in this work). Every image was recorded with a digital camera (Oscar, Allied Vision). These photographs were taken at the end of the cryostat wheel crank course, ensuring that the brain was in the same position for all sections (x and y-axes) and at the same distance from the camera (z-axis). A laptop was used to control the camera, making it possible to take images remotely; the images were stored directly on this laptop. For the processed brain, we obtained a series of 800 blockface photographs with an in-plane resolution of 2.16 × 2.16 µm per pixel. Image processing steps was performed using BrainVISA software (http://brainvisa.info) to reconstruct the volume. Such 3D photographic information was proposed in many papers to perform efficient histological 3D reconstructions based on series of sections.

This dataset comprise a preliminary result with a 3D high resolution volume (far superior to MRI images) even if the precision does not reach the one given by 3D histological data. In addition, it provides a good overview and interesting anatomical information (white matter -gray matter). (2) An address translation system with a multi-level multi-resolution page table hierarchy and a caching system on the GPU. (3) A bricked multi-resolution pyramidal representation stored on a larger space storage.

System Overview

The system we propose aims to response to the following needs by providing i) a way to navigate interactively through a large multiresolution image stack, ii) a mechanism to manage very large volumes of data, and iii) a visualization on 3D multi-view displays.

Based on a GPU architecture (Fig. 1), it includes a specific approach for handling datasets larger than GPU memory capabilities in addition to ensure an interactive time navigation for the users. Remember that GPUs do not have a lot of memory, which implies the use of an appropriate data structure. To overcome this limitation, a distinct out-of-core algorithm was designed to manage datasets and to reduce I/O bottleneck. In a preprocessing step, a bricked multiresolution pyramidal data representation of the volume is created (Fig. 1.3). In addition, in order to address the entire volume, the system uses a multi-level, multi-resolution page table hierarchy (Fig. 1.2) and a caching system to store and manage the bricks.

The user can navigate through the volume by performing one of the following actions: i) a zoom in / out on a slide, which implies a change of precision level in the pyramid; ii) a move on the current slide, which means a [x, y] pan; or iii) a move in the depth of volume which is a change of slices. As shown in figure 1, the system was built on a visualization driven pipeline. In other words, if one of these actions is observed, the system runs automatically the following sequence.

First step, according to the new view position, the system calculates all bricks needed to build the final multi-view image. Second step, the system tries to get the access to the bricks in the GPU memory (Fig. 1.2). In the case the system does not find them (cache misses), it automatically sends asynchronous request to the CPU to fetch them. They are either, in a simple cache in the CPU memory or in the storage device, where the bricked multi-level volume representation is stored (Fig. 1.3). Third step, the system creates the final multi-view image and displays it on a 3D multi-view screen (Fig. 1.1.c).

Data representation

In order to reach an interactive time navigation (≥ 15 FPS), a welldesigned data architecture is required. The architecture used to handle huge volume of data is based on the work of Hadwiger et al. [START_REF] Hadwiger | Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach[END_REF] who proposed a bricked pyramidal data representation also called 3D mipmap (Fig. 1.3). Rather than trying to use the whole volume, this data representation makes possible the use of small independent bricks (e.g., 16 3 or 32 3 voxels). However, the approach proposed in [START_REF] Hadwiger | Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach[END_REF] was based on a complete interactive-time pipeline, from data acquisition to visualization, which is not the case here. Indeed, the volume acquisition was already performed, and the construction of the pyramid can be done outside the stream. In addition, to obtain better performances, the data stored on the hard drive could be compressed; however, since the system aims to offer a interactive-time navigation, it implies to have an extraction algorithm with a throughput satisfying this constraint.

The study proposed by Fogal et al. [START_REF] Fogal | An analysis of scalable GPU-based ray-guided volume rendering[END_REF] gives a good overview to choose correctly the optimal brick size. They highlight two important points: i) it is, more efficient to use a large brick size to transfer and store the data (e.g., 128 3 or 256 3 ); ii) for rendering, it is more interesting to use small brick size (e.g., 32 3 ) (Sec. 4).

As a bonus, with the use of this data structure, it is possible to apply different down-sampling ratios on each axes of the volume for each level in the pyramidal representation. It is important to note that acquiring biomedical data may introduce an anisotropic representation, and this data structure can easily deal with this by applying appropriate ratios.

VISUALIZATION-DRIVEN PIPELINE

Volume data construction

The navigation is performed in a virtual volume (Fig. 1.1.a) that represents the whole stack images at the highest resolution. In this virtual area, a 3D position representing the upper-left corner of the section to visualize is determined. Moreover, the appropriate level of resolution in the pyramid is chosen according to the pixel size projection of the image in the screen space. In addition to the screen definition, it is possible to deduce which bricks will be required to compose the N images (N = number of view of the display) needed to compute the multi-view image that will be displayed afterwards (Sec. 3.3). Then, a brick request is sent to the GPU cache manager for each of these required bricks.

In order to address the whole large volume, a virtual memory scheme is used with an address translation mechanism. This is ensured directly on the GPU by a multi-level, multi-resolution page table hierarchy. In the case where the volume is too massive, the same concept of virtualization can be applied on the page table itself. To handle this data structure, two pools are created on the GPU memory (global or texture memory), one for the page table hierar- chy, the other one for the data bricks (Fig. 1.2). The cache updates are managed with an LRU policy.

... ... ... ... H W H W W H [ x ref +n ∆ x y ref z ref +n ∆ z ] [ x ref -n ∆ x y ref z ref -n ∆ z ] k a k b [x ref , y ref , z ref ]
Strategies can be applied when the navigation is only done through the pyramid of resolutions (meaning a fixed camera position) to handle a progressive loading, and preserve an interactive rate. The available bricks of lower resolution could be used while those from the requested level are not loaded in the GPU memory.

Views generation

To produce the image that will be displayed on the N -view autostereoscopic screen, it is necessary to generate the N views and to compose them (Sec. 3.3). These views are created according to a reference area, which is the one the user is interested in, the one corresponding to the 3D position used during the virtual volume navigation. This process has a strong dependency on the display hardware (definition and view number N ).

Let The images around the reference image are selected by applying an offset on the x-axis (noted ∆x), encoding the horizontal disparity [START_REF] Jones | Controlling perceived depth in stereoscopic images[END_REF][START_REF] Lucas | 3D Video: From Capture to Diffusion[END_REF] and a ∆z for the slice position. In the volume, k b slices before and ka slices after the reference slice (z ref ) are selected. The value ka may be different from k b , depending on the number of views of the display (e.g., if the display requires an even number of views). In this way, the positions for the images before, with n ∈ When all the previously described information are known, the brick requests can be sent to the cache, in order to construct the N images. Different strategies can be considered to handle cache misses. As soon as all the required bricks of an image are present in the cache, this image can be built while the cache is fetching missing bricks of the other images.

This case could occur when the position of the camera changes on the x or z-axis. Unfortunately, because all images required to produce the multi-view frame are sharing the same y ref , cache misses may happen when the camera moves on this axis. Then, there is no other choice than waiting for all missing bricks from the cache. Rendering quality and user interaction are improved by adding an alpha transparency information during the pixel compositing. A transfer function (TF) is used to determine this transparency coefficient (and eventually the color) for each intensity values. Then, to integrate this transparency through the thickness of the N slices, an alpha-blending computation is operated on the images.

It is possible to consider that a fully transparent brick, according to the TF, will not be loaded into the GPU cache. This TF can be easily updated by the user with a visualization impact in interactive time. Indeed, a TF update will trigger the pipeline only from the alpha-blending step since all the required bricks are already in the cache.

3D display

In order to obtain a valuable image, each of the N pre-build views V i c (x, y) must be combined conforming to the autostereoscopic display device according to the expression:

V f inal c (x, y) = i V i c (x, y) F i c (x, y) with c ∈ {R, G, B}
As shown in figure 2.b, this combination is achieved by selecting each color component of the final image in the view locally indicated by dedicated filtering masks F i : {0, 1} 3 [Z 2 ] (specific to each 3D display). This process can be easily implemented with a simple GPU kernel; it consists of N [H × W ] matrix product and a global sum. The multiplexing filters are of the size of the display definition, and they are always the same; so they can be loaded once on the GPU during an initialization step.

RESULTS AND DISCUSSION

Memory usage

According to the following settings: a display with a H × W screen size, a volume with p-bytes encoded pixels and a brick size of b 3 pixels, in the worst case scenario, the working-set size can be determined with:

H b + 1 × W b + 1 × 2 × (b 3 × p) (1)
With an HD 1920 × 1080 display and a volume with RGBA32 pixels, this working-set size will take ~560 MB with b = 32 and ~270 MB with b = 16. We can notice than modern GPUs memory can easily handle this, meaning there is no need to consider strategies for the GPU memory overflow.

Table 1 shows that bigger are the bricks better is the memory bandwidth which is in line with the study of Fogal et al. [START_REF] Fogal | An analysis of scalable GPU-based ray-guided volume rendering[END_REF] about the brick size for the data transfers.

Their study also points out that it is interesting, in the case of GPU volume ray-casting, to use small bricks for the renderer. In the same way for our multi-stereoscopic frame renderer, using small bricks provides a finer granularity on data that will be required to build a frame and avoid the loading of unnecessary data. Thus, the memory space used on the GPU can be optimized with smaller bricks, as shown with equation [START_REF] Amunts | BigBrain: An ultrahigh-resolution 3d human brain model[END_REF]. In addition, it could be interesting to take in consideration the L1 cache size of the GPUs. Hence, in order to optimize the number of cycles, it could be interesting to use bricks that can entirely fit in L1 cache to avoid to load / unload brick chunks. In modern GPUs architecture the L1 cache has a size of 24 -48 kB, it induces to take in consideration the use of 16 3 bricks (16 kB).

To conclude, it would be better to use a rebricking strategy in order to handle large bricks for data transfers and small bricks in the GPU cache for the renderer. An other idea could be to use non-cubic bricks and to link the brick size to the sizes and views the display requires (e.g., [x, y, z] = [256, 128, 32]). 1. Per-brick load and transfer time analysis according to the brick dimensions. The samples were taken using a computer with the following specs: Intel i7 6700HQ @ 2.6GHz, Geforce GTX960M and an SSD in PCIe

Time analysis

Table 1 illustrates the time needed to fetch a brick from the disk to GPU. As expected, loading a brick from the disk is significantly more time consuming than loading it from the CPU memory. Fortunately, this time is taken in consideration only if a requested brick is not present in our GPU cache and not present in the CPU cache. Furthermore, the transfer time does not count during the frame creation time. When a brick is missing, the system raises an asynchronous request to fetch it and try to compute the frame by using bricks from lower resolutions. The time to build one multi-view frame is ~30ms, it considers the selection and the composition of the views required for the multi-view frame, alpha blending and multiplexing. This processing time depends on the size of the output 3D screen and the number of views that compose it. It is not affected by the size of the bricks or the selected zooming level. These times validate the navigation and visualization at an interactive rate (~30 FPS) on volumes that exceeds GPU memory. We can talk about output-sensitive algorithms since render time is directly linked to the size of the output (the screen) and not to the size of the input (the image stack).

3D visualization perception

The figure 3 shows two multi-view frames computed for an HD (16:10) autosteroscopic 8-views display, with a volume of 5520 × 7000 × 800 RGBA32 pixels and [∆x, ∆z] = [START_REF] Molin | Scale Stain: Multi-resolution feature enhancement in pathology visualization[END_REF][START_REF] Amunts | BigBrain: An ultrahigh-resolution 3d human brain model[END_REF].

The choices for the values of ∆x and ∆z were made in order to maximize the perceived depth. The tests highlight the fact that the physical distance between two slices needs to be considered in the choice of the value ∆z. In one case, with a large distance between slices, whatever the value of ∆z, the visualization is not smooth. In the other case, a very short gap combined to a too small ∆z do not provide the feeling to move in the depth of the volume. In the same way, using a high value for ∆x cause a major visual discomfort in addition to a non-desired blur effect.

Thus, the objective to merge the concept of Deep Zoom applied on a 3D volume displayed on a multiscopic display was conclusive and a real depth effect can be observed. However, the perception was only visually assessed by the authors and cannot be considered as sufficient. A rigorous statistical study on a large sample of users is required. In order to increase the quality of the system, the study should focus on the feedback of a pool of users, on their perceptions on multiple samples with different settings.

CONCLUSION

In this article we introduced a proof of concept to virtualize a microscope on autostereoscopic displays. To achieve this, we propose a visualization driven pipeline based on a GPU rendering using a multilevel, multiresolution data structure in order to create a multiview frame. Thanks to this architecture, the user is able to perform an interactive navigation through multiresolution images of massive datasets.

Fig. 1 .

 1 Fig. 1. Visualization-driven system overview: The navigation in a virtual image stack and in a given slice (1.a) induces a streaming and a construction stage to compose a multi-view image (1.b) that can be visualized on a multiscopic 3D display (1.c). (2) An address translation system with a multi-level multi-resolution page table hierarchy and a caching system on the GPU. (3) A bricked multi-resolution pyramidal representation stored on a larger space storage.

Fig. 2 .

 2 Fig. 2. (a) Computing area positions in the selected slices. (b) Multiplexing filters application on views.[START_REF] Nocent | 3d displays and tracking devices for your browser: A plugin-free approach relying on web standards[END_REF]

  W and H be the width and the height of the display definition respectively, and p = [x ref , y ref , z ref ], the position of the upperleft corner for the reference image. All the bricks covering the area from the position [x ref , y ref ] to the position [x ref + W, y ref + H], in the slice z ref , are required to create this reference image.

[ 1 ;

 1 k b ], are pn = [x ref -n∆x, y ref , z ref -n∆z] and for the images after, with n ∈ [1; ka], are pn = [x ref + n∆x, y ref , z ref + n∆z] (Fig. 2.a). In the same way as the reference image, all the bricks from positions p to [p.x + W, p.y + H] in the corresponding p.z slice are needed.

Fig. 3 .

 3 Fig. 3. Results: Mouse brain block-face volume (~86 GB) render in ~30 ms. (a) A full large-scale slide inside an images stack. (b) The reference images used, rebuilt from the bricks. (c) The results of 8-views frames composition with transfer function and alpha-blending.

  Table

	Brick Size	Disk → CPU	CPU → GPU	Bandwidth
	(RGBA32)	(µs)	(µs)	CPU → GPU
	16 3	944	204	78.4 MB/s
	32 3	1 488	246	532.5 MB/s
	64 3	6 945	376	2.6 GB/s
	128 3	31 970	867	9.2 GB/s

This work is supported by the French national funds (PIA2'program) under contract No. P112331-3422142 (3DNS project). We would like to thank the three clusters (Cap Digital and Systematic for ICT and Medicen for Health) that support this project.


J Sarton

N Courilleau

Y Remion

L Lucas

TOWARDS AN INTERACTIVE NAVIGATION IN LARGE VIRTUAL MICROSCOPY IMAGES ON 3D DISPLAYS

Keywords: Virtual high-resolution microscopy, 3D display, Out-of-core hierarchical visualization, GPU rendering

Acquiring biomedical images -multichannel, 3D, and/or time-lapse -using modern techniques such as slide scanners, confocal, or electron microscopy generates today huge data streams. This trend raises a large number of issues related to the management of these massive datasets. Efficient solutions for data storage and processing must be developed in order to deliver increasingly reliable and faster analyses. In addition, the improvement of workflows also requires the reinforcement of visualization capabilities. In this article, we introduce a new approach for high capacity screening of slices based on a tool combining pyramidal images representation and 3D visualization on multi-stereo display, in order to simulate virtual microscopy (VM). The preliminary results suggest that the proposed solution facilitates the reading and the understanding of data essentially because they are spatialized.

INTRODUCTION

Current biomedical acquisition devices can generate ultra-high resolution image data. Electron microscopy like digital histology allows, for instance, to deliver volumes of brain tissue with a resolution that may contain terabytes of raw data [START_REF] Amunts | BigBrain: An ultrahigh-resolution 3d human brain model[END_REF]. Interactive visualization of these volumetric data is essential partly because; i) our screens are only able to display a fraction of the resolutions of large images; and ii) they help to mentally understand the three-dimensional structures of images -how cells are organized to form tissues and organs or how a pathology changes the nature of the tissues.

In practice and thanks to the advances in computer technology, the development of VM [START_REF] Catalyurek | The virtual microscope[END_REF][START_REF] Wang | Vir-tualMicroscopy: Ultra-fast interactive microscopy of gigapixel/terapixel images over internet[END_REF][START_REF] Molin | Scale Stain: Multi-resolution feature enhancement in pathology visualization[END_REF] has partially addressed this issue. As a reminder, a VM could be defined as a system that simulates the observation of microscopic samples This work is supported by the French national funds (PIA2'program) under contract No. P112331-3422142 (3DNS project). We would like to thank the three clusters (Cap Digital and Systematic for ICT and Medicen for Health) that support this project. on computer mimicking a conventional microscope allowing to observe, navigate, and annotate virtual slides.

Our solution is based on this latter concept of VM. While many of these issues were addressed on the web, no formal study has ever been conducted in order to assess the contribution of 3D vision in this context. Our system, based on a multiresolution pyramidal representation of stacked images, provides a multiview display to improve the quality of the user experience.

Like DeepZoom [START_REF]Deep zoom[END_REF] or other equivalent methods [START_REF] Corredor | Flexible architecture for streaming and visualization of large virtual microscopy images[END_REF][START_REF] Openseadragon | [END_REF], the basic idea of our tool consists in streaming visualization of large multiresolution datasets. Nevertheless, Hortsch [START_REF] Hortsch | From microscopes to virtual reality -How our teaching of histology is changing[END_REF] notes a drawback in such system used as a virtual microscope to study virtual slides. He reports that users of such equivalent tools were frustrated to have only a single plane focus and to lose three-dimensional perception. To compensate these disadvantages, we propose a rendering of each sight on an autostereoscopic screen [START_REF] Holliman | Three-dimensional displays: A review and applications analysis[END_REF]. In addition, this extension allows the user to freely navigate -zooming in or out like Google Earth -in the whole volume rather than in a single slide.

In order to reach this quality of navigation, we draw our inspiration from out-of-core methods management of large volume data on GPU used in the context of volume visualization. Usually, in these approaches, a pyramidal level of resolutions is created from the datasets. Each level of the pyramid is subdivided into blocks of data (called bricks) of the same size regardless the level. The Gigavoxel approach by Crassin et al. [START_REF] Crassin | Gigavoxels: Ray-guided streaming for efficient and detailed voxel rendering[END_REF] stores the bricks in a tree structure. We rather turned to the proposed method of Hadwiger et al. [START_REF] Hadwiger | Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach[END_REF] who provides a virtual memory approach with a multi-level, multi-resolution page table mechanism. This approach has already been validated by a concrete application with interactive exploration of petascale volumes [START_REF] Beyer | Exploring the Connectome: Petascale volume visualization of microscopy data streams[END_REF].

The remainder of this paper is organized as follows. In Section 2, a brief overview of the system is provided. In Section 3, our visualization-driven pipeline is detailed. Experimental results are then presented and discussed in Section 4. Finally, conclusions are given in Section 5.

978-1-5090-5743-6/16/$31.00 c 2016 IEEE 2) An addresse translation system with a multi-level multi-resolution page table hierarchy and a caching system on the GPU. (3) A bricked multi-resolution pyramidal representation stored on a larger space storage.

METHODOLOGY

System Overview

An overview of our system architecture is shown in Fig. 1. Our method proposes a threefold concept:

• an interactive on-demand streaming system to navigate in large multi-resolution images;

• an out-of-core data management with a GPU cache mechanism to manipulate very large volumes;

• a visualization on 3D multi-view displays.

The treatments to apply this rendering method on 3D display (Fig. 1.1.c) are mainly matrix computations (Fig. 1.1.b). They are completely adapted to the massively parallel architecture of GPUs. Furthermore, the use of GPUs is recommended to reach an interactive navigation. However, the restricted memory space on GPUs implies the use of an appropriate data representation and data structure to manipulate huge volumes in an out-of-core way. That is why a bricked multi-resolution data representation is proposed, computed in a preprocessing step (Fig. 1.3). In addition, we propose to use a multi-level, multi-resolution page table hierarchy as data structure to address the entire volume in a virtual way (Fig. 1.2) and, a GPU cache mechanism to store and manage bricks. This allows us to propose an interactive on-demand, bricks streaming system (Fig. 1.

2).

The entire pipeline is driven by the visualization. When the view changes, it triggers the following steps. First, the system determines which bricks are needed to the final multi-view frame construction. Second, using the GPU data structure, it accesses these bricks. For bricks that are not present on the GPU memory (cache miss), it send a request to the CPU to asynchronously get them back. They are either, in the CPU memory or on the storage device, where the bricked multi-level volume representation is stored. Third, when all the needed bricks are present in the GPU cache, it builds the final multi-view frame and visualize it on the 3D multi-view display.

The change of the view position can result to one of the following actions: i) a zoom in/out on a given slide, which induces a pyramid navigation; ii) a [x, y] pan in a given slide in the same pyramid level; or iii) a slice change (volume depth) in the same pyramid level.

Data representation

Before being able to use the data in the real-time pipeline for the navigation, the volume needs to be transformed. This step consists in the creation of a bricked pyramidal data representation (Fig. 1.3). The bricked representation allows to manipulate small (e.g., 16 3 or 32 3 pixels) independent bricks rather than the whole large volume. In contrast to Hadwiger et al. [START_REF] Hadwiger | Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach[END_REF], the acquisition of the whole volume is considered to be already completed and it does not need to handle tile streams from a microscope. Thus, the brick creation step can be done once on the whole volume in a preprocessing step. This data representation is then fully stored on a large storage space like a hard drive disk. It is possible to stock these data in a compressed format in order to manipulate smaller files. The chosen compression algorithm should be able to compute the extraction step in real-time before providing the brick to the GPU memory.

Fogal et al. [START_REF] Fogal | An analysis of scalable GPU-based ray-guided volume rendering[END_REF] propose a study on the optimal size for the bricks. It is shown to be more interesting to have a brick size of 128 3 or 256 3 for the storage or for the data transfer but it is preferable, for the renderer, to manipulate smaller bricks, such as 32 3 . However, this study was made for the visualization via volume ray-casting. In our case, it could be more interesting to use even smaller brick size in our renderer. To perform this, the use of the rebricking system presented by Fogal et al. [START_REF] Fogal | An analysis of scalable GPU-based ray-guided volume rendering[END_REF] could considered.

It can note that acquisitions of biomedical data may provide an anisotropic representation. The data representation shown is able to deal with this, by applying different downsampling ratios for each axes of the volume and for each level in the pyramidal representation.

VISUALIZATION-DRIVEN PIPELINE

Volume data construction

To compute the final multi-view image that will be displayed on the multi-view screen (Sec. 3.2), it needs to create the N images (N = number of view of the display) required to its composition. The navigation is performed in a virtual volume (Fig. 1.1.a) that represents the size of the whole volume to the highest resolution. In this virtual volume, a 3D position and a level of resolution in the pyramid are determined. The 3D position could make reference to upper-left corner of the reference area to visualize. The level of resolution is chosen by the user. From the previously described information and the definition of the screen, the system deduce which bricks will be required to compose the N images. Finally, for each required bricks, brick requests are sent to the cache manager.

The data structure used is a multi-level multi-resolution page table hierarchy [START_REF] Hadwiger | Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach[END_REF]. It allows the addressing of the whole volume with the concept of virtual memory. The volume is virtualized and we reach it with the translation of a virtual address to a physical address thanks to the page table hierarchy. In the case where the volume is too massive, we can apply the same concept of virtualization on the page table itself. This is why we talk about multi-level table. According to Hadwiger et al. [START_REF] Hadwiger | Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach[END_REF], such hierarchy with two levels allows the addressing of several peta-bytes of data and is more appropriate than a tree structure. To handle this data structure we use the memory of the GPUs. Two pools are created, one for the page table hierarchy and the other one for the data (bricks, Fig. 1.2). The cache updates are managed with a Least Recently Used (LRU) cache.

When the navigation is only done through the pyramid of resolution (meaning a fixed [x, y] camera position), it could be interesting to use a strategy to handle a progressive loading of levels of resolution. When this case occurs, the bricks of lower resolution could be used while the ones from the requested resolution are not loaded in the GPU memory.

3D display

Our system uses an N -view autostereoscopic display. We now describe our proposal to create a viewable frame on such screens. This process has a strong dependency on the display hardware (definition and view number N ).

To create a viewable frame, it is required to generate the N images that compose it. These images are created according to a reference image. The reference image is the one the user is interested in; it is the one corresponding to the 3D position used during the virtual volume navigation. We select, in the volume, k b slices before and k a slices after the reference slice position (z ref ). The value k a may be different from k b , depending on the number of views of the display (e.g., if the display requires an even number of views) and where the camera is positioned in the volume (e.g., if the camera position is at the edge -front or back -of the volume).

... ... ... ... H W H W W H [ x ref +n ∆ x y ref z ref +n ∆ z ] [ x ref -n ∆ x y ref z ref -n ∆ z ] k a k b [x ref , y ref , z ref ]
To pick up the images around the reference one, we apply an offset on the x-axis (noted ∆ x ), encoding the horizontal disparity [START_REF] Jones | Controlling perceived depth in stereoscopic images[END_REF][START_REF] Lucas | 3D Video: From Capture to Diffusion[END_REF] and a ∆ z for the slice position. In this way and according to the previous notations, the positions for the images before, with n ∈ 2). In the same way as the reference image, we need all the bricks from positions p to p.x+W and p.y +H in the corresponding p.z slice.

[1; k b ], are p n = [x ref - n∆ x , y ref , z ref -n∆ z ] and for the images after, with n ∈ [1; k a ], are p n = [x ref + n∆ x , y ref , z ref + n∆ z ] (Fig.
All the required information are now known and the brick requests can be sent to the cache, in order to construct the N images. However, cache misses may appear if the camera position was changed from the previous rendering pass. In this case, we could consider different strategies. While the cache is fetching missing bricks, we can start building images as soon as all their required bricks are available. This case could occur when the position of the camera changes on the x or z-axis. Sadly, because all images required to produce the multi-view frame are sharing the same y ref , cache misses could happened when the camera moves on this axis, there is no other choice than waiting for all missing bricks from the cache.

In order to obtain a valuable image, each of the N prebuild views V i c (x, y) must be combined conforming to the autostereoscopic display device according to the expression

V f inal c (x, y) = i V i c (x, y) F i c (x, y) with c ∈ {R, G, B}.
As shown in Figure 4, this combination is achieved by selecting each color component of the final image in the view locally indicated by dedicated filtering masks F i : {0, 1}

3 [Z 2 ] (specific to each 3D display). This process can be easily implemented with a simple GPU kernel. Regarding the filters, they are provided as inputs. So they can be loaded once on the GPU during an initialization step.

Finally, the final frame is ready to be displayed on the 3D display once the sum of the N resulting matrix is done.

RESULTS AND DISCUSSION

Given the following configuration: an high definition screen (1920 × 1080), a brick size of 32 3 , ∆ x = 4 and ∆ z = 1. The number of bricks required to compose the final multi-view image will be, in the worst case, 60×34×2 = 4080 bricks. For a volume with 8-bit pixel encoding, it will take around 130M B.

Modern GPU memory can easily handle this, meaning there is no need to consider strategies for the GPU memory overflow. It allows us more freedom on the number of bricks loaded in the cache. This given 32 3 brick size allows the user to nav-igate freely and decently in the volume before getting cache misses.

In order to perform the tests of our system we were using an HD autostereoscopic display using a 16:10 aspect ratio. This display is an 8-view autostereoscopic display with a definition of 1920 × 1200. The sample we used was representation of a flower (from [3]) in a discretized volume 1024 3 of unsigned 8-bit where the voxels are uniformly spaced by 60 µm. In order to get a better rendering, we decided to do a preliminary processing on the volume to transform it to a 2048 3 volume, using a bicubic interpolation. We applied this transformation in order to have one voxel per pixel for a high definition display. In addition, we applied a LUT in order to have a volume encoded on 3 channels in colours. The result is shown in Figure 3.

We chose the delta values [∆ x , ∆ z ] = [START_REF] Amunts | BigBrain: An ultrahigh-resolution 3d human brain model[END_REF][START_REF]Deep zoom[END_REF]. It appears that the value of ∆ z is directly limited by the physical distance between two slices. With a distance too large between slices, even if ∆ z = 1, the visualization will not be smooth. However, if the distance between two slices is very short and ∆ z is too small, the user will not have the feeling to move in the depth of volume. In the same way, the problem appears on the x-axis where a high ∆ x gives an unpleasant feeling when the user changes of perspective; furthermore, it introduces an undesirable blur effect. Thus, the objective to merge the concept of Deep Zoom applied on a 3D volume displayed on a multiscopic display was conclusive and a real depth effect can be observed. However, perception was only visually assessed by the authors of this paper, and a statistical study on a larger sample of users is required. In order to increase the quality of the system, the study should be focused on the feedback of a pool of persons on their perceptions on multiple samples with different settings. Moreover, the strategies used to create the images may be improved when cache misses on y-axis appear.

CONCLUSION

We have proposed in this article a 3D vision extension to VM. This approach allows users to interactively navigate through large multiresolution datasets while preserving the depth information. To achieve this we suggest a GPU visualizationdriven pipeline based on an adapted data structure. The preliminary results suggest that the proposed solution facilitates the reading and the understanding of data essentially because they are spatialized.

Fig. 1 .

 1 Fig. 1. System overview: The navigation in a virtual image stack and in a given slice (1.a) induce a streaming and a construction stage to compose a multi-view image (1.b) that can be visualized on a multiscopic 3D display (1.c). (2) An addresse translation system with a multi-level multi-resolution page table hierarchy and a caching system on the GPU. (3) A bricked multi-resolution pyramidal representation stored on a larger space storage.

Fig. 2 .

 2 Fig. 2. Computing area positions in the selected slices.

Fig. 3 .

 3 Fig. 3. Results: The left image is the reference image used, rebuilt from the bricks. The right image shows the result of the 8-views frames composition.

Fig. 4 .

 4 Fig.4. Filters application on views.[START_REF] Nocent | 3d displays and tracking devices for your browser: A plugin-free approach relying on web standards[END_REF]


Amine Khendek

Maud Alix

Sandrine Viot

Yannick Ledoré

Camille Rousseau

Robert Mandiki

Patrick Kestemont

Tomas Policar

Pascal Fontaine

Sylvain Milla
email: sylvain.milla@univ-lorraine.fr

How does a domestication process modulate oogenesis and reproduction performance in Eurasian perch?

Keywords: Perca fluviatilis, Recirculating Aquatic System, GnRH, Gonadotropins, Sexual steroids

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

 [START_REF] Price | Behavioral aspects of animal domestication[END_REF]defined the domestication as "the process whereby a population of animals becomes adapted to humans and captivity conditions through one or several combinations of genetic changes throughout the generations". [START_REF] Digard | La domestication animale revisitée par l'anthropologie[END_REF] proposed to define domestication as "the action that men exercise over animals they own, even by raising them". Thus the list of domesticated fish species goes from the range of 25-30 in the first definition to more than 200 according to the definition of [START_REF] Digard | La domestication animale revisitée par l'anthropologie[END_REF]. This shows that the concept of domestication still divides opinions within the scientific community. However, everyone agrees that fish populations may undergo, through the domestication process, changes in their genome due to adaptation to environmental stressors, genetic selections (deliberate or not) or inbreeding phenomena [START_REF] Balon | About the oldest domesticates among fishes[END_REF][START_REF] Price | Behavioral aspects of animal domestication[END_REF]. It induces changes at anatomical, physiological and molecular levels thereby affecting a variety of biological functions (e.g. Brummet et al. 2004). There is abundant literature on the domestication effects on growth [START_REF] Withler | Genetic variation in body weight and flesh colour of coho salmon (Oncorhynchus kisutch) in British Columbia[END_REF][START_REF] Hassin | Domestication of the white grouper Epinephlus aeneus, 1[END_REF]Tymchuck et al. 2006) and stress response in teleosts (Pickering andPottinger 1989, 1997;[START_REF] Vandeputte | Génétique et adaptation chez les poissons : domestication, résistance au stress et adaptation aux conditions de milieu[END_REF][START_REF] Douxfils | Physiological and proteomic evidences that domestication process differentially modulates the immune status of juvenile Eurasian perch (Perca fluviatilis) under chronic confinement stress[END_REF]). On the one side, it has been shown that globally domestication reduces fish stress response, by rendering them less fearful as showed in salmonids (Pickering andPottinger 1989,1997;[START_REF] Vandeputte | Génétique et adaptation chez les poissons : domestication, résistance au stress et adaptation aux conditions de milieu[END_REF], or less sensitive to acute handling stressors in Eurasian perch [START_REF] Douxfils | Physiological and proteomic evidences that domestication process differentially modulates the immune status of juvenile Eurasian perch (Perca fluviatilis) under chronic confinement stress[END_REF]. But in European sea bass (Dicentrarchus labrax) it has been shown that heritability of cortisol response to stress was low [START_REF] Volckaert | Heritability of cortisol response to confinement stress in European sea bass dicentrarchus labrax[END_REF]). On the other side, the domestication seems to boost the growth performance [START_REF] Withler | Genetic variation in body weight and flesh colour of coho salmon (Oncorhynchus kisutch) in British Columbia[END_REF][START_REF] Hassin | Domestication of the white grouper Epinephlus aeneus, 1[END_REF]Tymchuck et al. 2006).

Contrary to growth and stress response, the effects of domestication process on reproduction have received limited attention. Moreover, it appears that this field of research has conducted to conflicting results. In the ide (Leuciscus idus), [START_REF] Krejszeff | Domestication affects spawning of the ide (Leuciscus idus) preliminary study[END_REF] showed that domestication positively influenced the ovulatory responses following the Ovopel TM hormonal treatment. In contrast, they showed that domestication exhibited a negative effect on embryo survival. More recently, a study on the same species showed different results by pointing out a lower ovulatory response to Ovopel TM in the pond-cultured group relatively to wild fish [START_REF] Ciesla | Changes in ide (Leuciscus idus) female's reproductive parameters after stimulation with carp pituitary homogenate (CPH) and Ovopel: the effect of domestication?[END_REF]) and no domestication effect was observed on the embryo survival. Moreover, the effect of domestication can be different from one species to another.

In Atlantic salmon (Salmo salar) domestication led to an 83% decrease of the hatching rates [START_REF] Jonsson | Cultured Atlantic salmon in nature: a review of their ecology and interaction with wild fish[END_REF]. In cod (Gadus morhua), domestication negatively affected fertilization rates, hatching and larvae survival (Salze et al. 2006). In brown trout (Salmo trutta) domestication negatively affected sperm quality [START_REF] Dannewitz | Reproductive success ofhatcheryproduces and wild-born brown trout in an experimental stream[END_REF]) but had a positive effect on spawning and egg weight (Randak et al. 2006). Consequently, notable differences also exist between the fish families, the fish species and the fish populations.

These differences led us to investigate the domestication effects on reproductive ability of Eurasian perch (Perca fluviatilis), a species whose domestication history is still recent, and belonging to a fish family poorly investigated.

Eurasian perch is a freshwater fish species highly valuable in Europe [START_REF] Fontaine | Development of European inland fish culture and domestication of new species[END_REF]. The perch market is local and the current production of market-size perch in Europe is thus unable to satisfy the demand, which is mainly centered in the Alpine region [START_REF] Setälä | Spatial integration of freshwater fish markets in the Northern Baltic Sea area[END_REF][START_REF] Watson | The European market for perch (Perca fluviatilis)[END_REF]. Consequently to a relative decline of wild stocks due to intensive fisheries, and to an increasing demand for small sized perch especially in Switzerland, intensive perch culture has been developed in the past two decades, and Eurasian perch has become a relevant species in European aquaculture [START_REF] Fontaine | Eurasian perch culture, a way of diversification for freshwater aquaculture[END_REF][START_REF] Watson | The European market for perch (Perca fluviatilis)[END_REF]. Up to now, only one study has examined the effect of domestication on Eurasian perch reproduction [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF].

Results showed a positive effect of domestication on fertilization rates, but a negative effect on ovulation and hatching. Actually, there is no detailed study aiming at understanding the influence of domestication on both the mating performance and the reproductive mechanisms underlying these effects. Our hypothesis is that disruption in the level of the endocrine actors that control the reproductive cycle explains the changes of the reproductive performance along the domestication process.

The aim of this study was to compare two perch populations (F1 and "domesticated"), exposed to a special photothermal program allowing the control of gonadogenesis advancement, in terms of morphological parameters (gonado-somatic index, hepato-somatic index), levels of GnRH and gonadotropins gene expression, sexual steroids and vitellogenin, as well as the oocyte developmental stages and reproductive performance.

Materials and methods

Fish

Two populations of F1 and "domesticated" perch were studied in this experiment, both originally came from the Geneva Lake (French-Swiss border). F1 breeders came from wild broodstock (F0) collected as eggs directly taken from Geneva Lake. According to the local producer (Lucas Perches, Hampont, France), "domesticated" breeders are generated from seven generations fish raised in captivity, even if the introduction of wild individuals during reproduction seasons cannot be totally excluded. The domestication history of these fish is thus a "black box". The F1 and domesticated breeders were issued from an artificial reproduction completed on November 1st, 2011 and were then raised on the producer facilities, before coming to the URAFPA-MAN facilities located in Nancy (France) on February 6 th , 2012, where they were placed in two 6000 L. tanks for growth. At their arrival to our facilities, fish of both populations were 3-months-old. From their hatching, the two studied fish populations (F1 and "domesticated") have thus been raised in equal and standardized husbandry conditions, at the Lucas perches facilities, fish were raised at a temperature range of 18°C-20°C and a photoperiod of 13L/11D with a blue light or a red light. At the URAFPA-MAN facilities fish were kept at 20°C and a photoperiod of 16L/8D during the growth period. Fish were daily fed to apparent satiation during the growth period and during the experiment (Le Gouessant pellets n°5, proteins 47%, lipids 13%, ash 6.90% and fiber 2.2%).Fish were handled in accordance with national and international guidelines for protection of animal welfare.

Experimentation and water quality management

Experiment started on May 17 th , 2013, fish were 18 months old and never spawned before.

Breeders of both populations were transferred to 500 L. independent experimental tanks (3 tanks per population with 48 fish per each tank), each tank had his own filtration system and 25% of the water was changed every month. At the beginning of the experiment, fish weight ranged between 67-137.8g for the "domesticated" fish and in the range of 70.4-129.8g for the F1 ones. Both populations were exposed to a photothermal program enable to induce and drive the gonadogenesis [START_REF] Fontaine | Broodstock management and control of the reproductive cycle[END_REF]) (Fig. 1).

In each tank, water quality was measured three times per week. pH was maintained between 7.0 and 7.5 by NaCO 3 additions. The dissolved oxygen was maintained over of 6 mg/L. The total ammonia (0,36 ± 0,53 mg/L) and nitrite (0,08 ± 0,12 mg/L) nitrogen were measured using a CARY I spectrophotometer and remained always below 1 mg/L.

Fish were sampled at days T0, T35 (decrease of temperature), T157 (vernalization period) and females per tank were caught at each sampling period. The first sampling occurred just before the initial photoperiod decrease. At each sampling date, fish were anesthetized into a Tricaine methanesulfonate (MS-222) bath (120 mg/L, Sigma). Blood was sampled from the caudal vein using a syringe, then stored on ice in heparinized microtubes until centrifuged at 3000g for 10 min (Centrifuge Jouan C-412). Aliquots of plasma were stored at -80°C until ELISA analysis was performed. Then, each fish was euthanatized by overanesthetizing in Tricaine methanesulfonate (MS-222) bath (240 mg/L), weighted and dissected for gonad and liver weighting and calculation of the gonadosomatic index (GSI=100× gonad weight/total fish weight) and hepatosomatic index (HSI= 100× liver weight/total fish weight). For gene expression analysis, the whole brain and pituitary were snap frozen (separately) in liquid nitrogen and stored at -80°C.

Gonadal histology

Samples of ovaries were stored in a Bouin-Holland solution for 1 week, washed once with water, twice with 70 % ethanol and stored in absolute ethanol [START_REF] Abdulfatah | Effects of photothermal kinetics and amplitude of photoperiod decrease on the induction of the reproduction cycle in female Eurasian perch (Perca fluviatilis)[END_REF]. Then, fragments of gonads were cut into 6-mm-thick slices, dehydrated with OTTIX solution (DIAPATH SpA, Italy/MM France) and embedded in a paraffin substitute dubbed Diawax (DIAPATH SpA, Italy/MM France) (adapted from [START_REF] Langeron | Précis de Microscopie: Technique-Expérimentation-Diagnostic[END_REF]. Sections of 6 µm were cut from the Diawax block and stained with a Masson's trichrome-haematoxylin Gill III (Merck, Darmstadt, Germany), 0.5 % phloxine B (VWR, California, USA) and 0.5 % light green (Sigma, Saint Quentin Fallavier, France) according to [START_REF] Rinchard | Comparative study of reproductive biology in single and multiple spawner cyprinid fish: I. Morphological and histological features[END_REF].

The stage of oocyte development was determined according to [START_REF] Wallace | Cellular dynamic aspects of oocyte growth in teleosts[END_REF].

Observations have been completed with a light upright optical microscope (Nikon Eclipse Ni-U), Nikon BR software (Nikon France, Champigny sur Marne, France).

Total RNA extraction and reverse transcription

Total RNA was extracted from the brain and pituitary using TRIzol reagent (VWR, CTRIZ02) according to the manufacturer's protocol. Concentration of total RNA was measured by UV absorption spectrometry using a Jasco V-630 Spectrophotometer. DNase treatment (DNase I Thermo Fischer Scientific, EN0521) was performed according to manufacturer's recommendations. Reverse transcription was performed with M-MLV reverse transcriptase (Sigma, M1302) according to the manufacturer's recommendations. For each sample 1µg of total RNA was used. Reaction mixes have been done following manufacturer's recommendations. Each reverse transcription mix was incubated at 37°C for 50 min and then heated at 90°C for 10 min, c-DNA samples were finally kept at -80°C.

Gene expression analysis

Primers were designed based on a multiple alignment of sequences available in the NCBI data base, targeting conserved regions of the FSHb and LHb-subunits, the GnRH 3 as well as the β-Actin as housekeeping gene.

In order to isolate and to sequence P. fluviatilis FSHb, LHb-subunits and the GnRH 3 cDNAs from the brain, a PCR reaction was performed. PCR conditions were as follows: denaturing cycle of 95°C for 2 min, followed by 35 cycles of 95°C for 1 min, 58°C for 1 min, 72°C for 1 min, followed by a final elongation step of 72°C for 5 min. PCR products were checked on 1% agarose gel electrophoresis. The single obtained band corresponding to the expected product size was excised from the gel and the amplicon was purified with E.Z.N.A. Pure Kit (VWR, D6493-01). Purified products were cloned employing the InsTAclone PCR Cloning KIT (Thermo Fischer Scientific, K1214), and plasmids of 2 days cultures of bacterial colonies (Escherichia coli). Plasmids were analyzed by PCR using M7 universal primers and agarose gel to confirm the size of the inserted cDNA. Automated sequencing was performed (ABI3730XL DNA sequencer, Macrogen, Korea). Upon gene identification, primers for quantification were designed on two different exons with the FastPCR software V6.5. (Table 1).

Real-time PCR was carried out with a StepOne Plus q-PCR Cycler (Applied Technologies) using QUANTA SYBR green reagent (VWR, 733-1382) in a 20 µL reaction volume (5 µL diluted cDNA, 2.5 µL (600 nM) of each primer, 10 µL of 2-fold diluted SYBR-Green solution) under the following conditions: 95°C initial denaturation for 10 min, followed by 40 cycles of 95°C denaturation for 30s, 60°C primer annealing and extension for 1 min. All PCRs were run in duplicate. Expression was determined by the comparative CT method (DDCT) considering a calibrator sample (pooled brain or pituitary cDNA).

A melting curve analysis was carried out to ensure assay specificity. The absence of amplified genomic DNA was validated by comparing the CT obtained in samples with and without DNase treatments and by designing the primers on two different exons. Samples were normalized to β-Actin mRNA.

Hormone assays

17<beta>-Estradiol (E2, ng/mL) was assayed on 50 µL of plasma using the DIAsource E2-ELISA kit (DIAsource, KAP0621). When necessary, dilution from 1/20 to 1/70 of the plasma samples was performed. Sensitivity was 5 pg/ml. CV intra-assay varied between 3.1 and 2.6% and CV inter-assays varied between 4.7 and 2.4% for low and high levels, respectively. Testosterone (T, ng/mL) was assayed on 25 µL of plasma using the DIAsource Testosterone ELISA Kit (DIAsource, KAPD1559). When necessary, a dilution 1/2 of the plasma samples was performed. Sensitivity was 83 pg/mL, CV intra-assay varied between 1.5 and 9.5 % and CV inter-assays varied between 7.6 and 8.7% for low and high levels, respectively. The androgen 11-ketotestosterone (11KT, ng/mL) was assayed on 50 µL of plasma diluted 1/50 in ELISA buffer using the Cayman 11-keto Testosterone EIA Kit (Cayman, 582751) and sensitivity was 1.3 pg/mL. CV intra-assay varied between 2.6 and 8.4 % and CV inter-assays varied between 9.4 and 12.1% for low and high levels, respectively. Plasma 17<alpha>,20<beta>-dihydroxy-4-pregnen-3-one (DHP, ng/mL) was assayed using radioimmunoassay according to [START_REF] Fostier | Stereoidogenesis in rainbow trout (Salmo gairdneri) at various preovulatory stages: changes in plasma hormone level and in vivo responses of the ovary to salmon gonadotropin[END_REF] adapted by [START_REF] Mandiki | Effects of sex steroids and their inhibitors on endocrine parameters and gender growth differences in Eurasian perch (Perca fluviatilis) juveniles[END_REF], following two extractions with cyclohexane/ethyl acetate (v/v). The DHP antibody was provided by Dr A. Fostier (INRA, Rennes, France).

Cortisol was assayed on 20 µL of plasma using the DIAsource Cortisol ELISA Kit (DIAsource, KAPDB 270). The intra-assay coefficient of variation was 5.6%, the inter-assay coefficient of variation was 6.9%. The detection limit was 2.5 ng/mL.

Vitellogenin assay

Vitellogenin was indirectly estimated with Alkali-labile phosphate levels (ALP, µg/mL). ALP levels were assayed on 80 µL of plasma. Plasma samples were first centrifuged for precipitation of high molecular weight proteins with trichloroacetic acid (20%). The pellet was then washed with absolute ethanol, chloroform solution -di-ethyl ether -absolute ethanol (1: 2: 2), acetone and finally the di-ethyl ether. The dried pellet was then dissolved in a NaOH solution (2N) brough to 100°C for 15 min, and then the phosphate release was neutralized by adding cold HCl (2N). Phosphates are then assayed by the method of Stanton (1968) using a phosphorus standard solution for calibration, addition of molybdic acid and reducer solutions and finally by reading the absorbance at 660 nm with a spectrophotometer.

Aromatase assay

Aromatase activity was measured as the specific release of tritiated water produced by conversion of [1-H 3 ]-androstenedione to estrone, as previously described by Noakson et al. (2001) with some modifications [START_REF] Mandiki | Effects of sex steroids and their inhibitors on endocrine parameters and gender growth differences in Eurasian perch (Perca fluviatilis) juveniles[END_REF]. Fifteen milligrams of frozen tissue (gonads) were homogenized in a buffer solution (50 mM potassium phosphate, 250 mM sucrose and 10 mM dithiothreitol pH 7,4; 1:10). A tenth of the homogenate were added of a NADPH-substrate system consisting of 1 mM NADPH, 25 nM Androst-4-ene-3, 17-dione, [1β-3 H(N)] 275 nM Androst-4-ene-3, 17-dione in the buffer solution and incubated with gentle shaking for 60 min at 30°C. The reaction was stopped by adding trichloroacetic acid.

Samples were then centrifuged and the supernatant extracted with chloroform, the bottom contained the protein. The aqueous phase was washed with dextran (5%)-activated charcoal (0.5%), centrifuged, and an aliquot of the supernatant was added to a liquid scintillating to determine the radioactivity due to the presence of [ 3 H] H 2 O. For this purpose a liquid scintillation counter was used. The Lowry method was used to determine protein concentration after denaturing the protein with NaOH. The aromatase activity was expressed in fmol min -1 mg -1 protein.

Spawning season and reproductive performance

Spawning season started on March 25 th , 2014 and finished on April 3 th , 2014. 77% of the females spawned during the spawning season in the F1 population and 78% of the females spawned in the "domesticated" one. Spawning was induced by temperature increase at the end of the photothermal programme and no hormonal treatment was used during the experiment.

Males and females were kept together in the same tanks. Females were stripped every day during the spawning season at 5:00 AM. When a female was ovulating, ribbons were collected in a dry basin and fertilized with 1mL of sperm originated from three different males, in order to optimize the fertilization success. Ribbons were then transferred into incubators and maintained at constant temperature (13 ± 0,5°C). After 24h, 48h, 72h and 120h post fertilization, samples of approximately 200 eggs from each ribbon were observed to calculate survival rates. Hatching started at 8 days post fertilization. Larvae were measured and analyzed for malformations (sclerosis, lordosis, yolk bag malformations) with a binocular microscope, on fixed embryos (n=6 spawns).

Statistical analysis

Data are expressed as mean ± standard deviation with individual fish as the experimental unit.

Normality of data and homogeneity of variances were tested using Shapiro-Wilk and Levene test, and data were either log-transformed or arcsin square-root transformed (for data expressed in percentage) when needed. When data respected the assumptions of normality, a two-way analysis of variance (ANOVA) with two independent factors (population and time) was performed followed by LSD post hoc tests to identify significant differences. The level of significance used in all tests was p<0.05. When data were not normally distributed, a Kruskal-Wallis test was performed followed by the nonparametric Mann-Whitney test. All statistical analyses were performed using the STATISTICA software (StatSoft, Tulsa, USA).

Results

GSI and gonadal development stages

During the first month of the experiment (T0 to T35), females of both populations had a low GSI (below 1%) (Fig. 2). Significant increase of the GSI was observed after five months (T157) (P<0.05), no significant difference was observed between the two populations until T157. At T295, the "domesticated" females showed a significantly higher GSI (p<0.05) in comparison with the F1 ones (Fig. 2, P<0.05). Histological analysis revealed that during the first five weeks of the photothermal program, both female populations showed immature gonads with more than 90% of protoplasmic oocytes. Since T157 both populations showed more advanced gonad maturation and no significant difference between the two populations was pointed out (Fig. 3). However, the protoplasmic oocyte diameter was significantly more elevated for the "domesticated" females (109±19µm) than for the F1 ones (104±11µm) at T35 (p<0.05). The same observation was noticed at T295 with (152±13µm) for "domesticated" females and (138±12µm) for the F1 ones (p<0.05). At T295, "domesticated" females showed a more advanced gonadal development than the F1ones with respectively 54% and 39% of late vitellogenesis oocytes (Fig. 3), even if there was no significant difference in terms of the oocyte diameter for vitellogenic oocytes.

Sexual steroids, vitellogenin and aromatase

Values for plasma 17β-estradiol (E2) were the lowest levels at T0 and T35 for both female populations (less than 0.5 ng/mL) (Fig. 4A), with a significantly higher level (p<0.05) for domesticated females at T35 in comparison with F1 ones. At T157, plasma E2 levels significantly increased for both populations in comparison with T35 levels. At T295, a drop of E2 levels was noticed in the F1 population, so E2 values were significantly higher (p<0.05) in domesticated females than in F1 ones.

Plasma Testosterone (T) levels started to increase since T157 until the end of the experiment for both populations (Fig. 4B). Significant difference was observed at T157 when plasma T levels were significantly higher for F1 females in comparison with the "domesticated" fish (p<0.05). Plasma vitellogenin levels followed a similar pattern of change as those observed for T (Fig 4C ), with a significant difference (p<0.05) at T157 in favor of F1 females. 11-Ketotestosterone (11-KT) levels remained low for both female populations until T295, when 11-KT levels significantly increased but no significant difference between the two groups was observed (Fig. 4D).

Plasma DHP levels showed an increasing pattern along the experimental time-course for both populations (Fig. 5A) and no significant difference was observed between the two groups. In the same way, the aromatase showed an increasing activity, but no significant difference was registered between the two groups (Fig. 5B).

GnRH and GtHs gene expressions

GnRH mRNA levels did not show any significant difference between the two populations (Fig. 6A,B), but showed a two-fold increase at T35 and remained at this level until T157.

βFSH mRNA levels showed a quick increase between T0 and T35 for both populations (Fig. 6C, D), and were significantly higher in the F1 perch brains in comparison with their "domesticated" counterparts at T157 and decreased at the last sampling time. In both brain and pituitary tissues (Fig. 6E,F), βLH mRNA levels increased coincidently with advancing ovarian development. Significant differences in favor of the F1 population were observed at T35 and T295 in the brain (p<0.05) (Fig. 6E) and pituitary (p<0.005) (Fig. 6F).

Plasma cortisol

Plasma cortisol levels showed a significant difference between the two groups with higher values found in the F1 population (p<0.05) at T35 than the "domesticated" fish (Fig. 7). But, the difference between the two groups was not significant at the other sampling times.

Reproductive performance

The development rates at 24h, 48h, 72h and 120h post-fertilization were evaluated for the two populations. The F1 population showed a better survival rate than the "domesticated" one at the four observation times (Table 4) (p<0.05). The F1 population showed a higher eyed stage larvae percentage and a higher hatching rate than the "domesticated" population. F1 population larvae were of better quality than their "domesticated" counterparts in regard of percentage of deformities (p<0.05).

Discussion

In the present study, changes in gonadosomatic index in the population of "domesticated" fish corresponds to the profile described by [START_REF] Migaud | Induction of out-of-season spawning in Eurasian perch Perca fluviatilis: effects of rates of cooling and cooling durations on female gametogenesis and spawning[END_REF], and the maximum level of GSI reached at the last sampling time (18.5±3.2%), is consistent with the level obtained in Eurasian perch females by [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF]. These GSI values were significantly higher than those registered in F1 fish (15.8±3.2%), being themselves lower than those recorded by [START_REF] Migaud | Synthesis of sex steroids in final oocyte maturation and induced ovulation in female Eurasian perch, (Perca fluviatilis)[END_REF] and [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] in wild Eurasian perch populations (F0). In agreement with the above GSI results, "domesticated" females showed a slightly more advanced gonadal development than the F1 ones with respectively 54% and 39% of late vitellogenesis oocytes at the end of the photothermal program, and a more elevated protoplasmic oocyte diameter than the F1 ones. This further supports the more advanced gonadogenesis in the "domesticated" fish by comparison to their F1 counterparts. These results do not corroborate previous observations made by [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] in Eurasian perch females. Differences between farmed and wild perch have been suggested to be caused mainly by unsuitable feed for the farmed fish [START_REF] Bell | Effect of broodstock dietary lipid on fatty acid compositions of eggs from sea bass (Dicentrarchus labrax)[END_REF][START_REF] Kestemont | Cathepsin L in eggs and larvae of perch Perca fluviatilis: variations with developmental stage and spawning period[END_REF][START_REF] Izquierdo | Effect of broodstock nutrition on reproductive performance of fish[END_REF][START_REF] Kestemont | Feeding and nutrition of European percid broodstock and early life stages[END_REF][START_REF] Henrotte | Egg and larval quality, and egg fatty acid composition of Eurasian perch breeders (Perca fluviatilis) fed different dietary DHA/EPA/AA ratios[END_REF]. These authors stated that, to address lower reproduction in farmed perch, it is necessary to improve the diet by adding prey fish and a larger proportion of natural ingredients to the feed. In the experiment of [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] wild fish were fed with prey fish and farmed ones with both industrial food and prey fish, while in our study, both populations were fed only by industrial food. This could be one element explaining the discrepancy between the two studies.

Moreover, we cannot rule out the implication of other external factors (e.g. type of photothermal program) and internal factors (e.g fish age) in the monitoring of divergent data.

Our wild population is a F1 generation issued from F0 broodstock caught as eggs from the wild (Geneva Lake) and reared in artificial conditions during their whole life. The wild population studied by [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] is a wild population reared in ponds and transferred in artificial conditions for the experiment, which implies acclimation phenomena due to the transfer to artificial conditions, which may in turns alter gonad maturation and reproduction.

In addition, we cannot exclude the influence of internal factors like fish age that has been shown to influence reproductive capacity in teleosts (Abi-Ayad, 1998). Our fish were eighteen months old for both populations, whereas fish studied by [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] were three years old. The geographical origins, and by consequence, potentially the genetic patrimony, is another internal factor that can impact fish reproduction and justify divergent results.

While the gonadogenesis was more advanced in the "domesticated" population, the final reproductive outcomes were markedly better in the F1 fish. The fertilization rate monitored at 24h was almost 3-fold higher in the F1 fish (78±35%) in comparison with the "domesticated" group (28±29%). The fecundity rates observed by [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] were more elevated (90%) in both populations. [START_REF] Kouril | Artificial of perch propagation of European perch (Perca fluviatilis) by means of a GnRH analogue[END_REF] reported 60-95% fertilization of eggs in wild perch after artificial stripping which is in agreement with our values for the F1 population. This difference of fertilization rates is an additional indication that the egg quality in Eurasian perch is highly variable between the studies and also within the same population.

The highest contrast between farmed and F1 perch in the parameters of egg quality was recorded after the evaluation of hatching rates. A really poor percentage of hatching rate was obtained for the farmed fish (2±2%), slightly lower that the data provided by [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] in other farmed population. This poor ova quality could be explained by the fact that "domesticated" fish exhibited a faster gametogenesis than the F1 ones and they did not accumulate enough nutrients for embryo development. This better egg quality in F1 fish was also confirmed by comparing the percentages of malformations between both populations. Anyway these results and those of [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] suggest that "domesticated" broodstock in Eurasian perch exhibit lower reproductive performance than fish at an earlier stage of domestication.

In the present study, the better reproductive performance in F1 are a priori surprising regarding the gonadogenesis and oocyte development results. To understand the physiological mechanisms that are behind these differences, we investigated some physiological actors driving the control of oogenesis control. The estrogen 17-β estradiol (E2) levels were higher in the "domesticated" fish than in the F1 fish, which is in agreement with the GSI results. Contrarily, testosterone levels were higher in F1 females than in "domesticated" ones. We tried to understand these opposite results by measuring aromatase activity (enzyme which converts testosterone to 17-beta estradiol). No significant difference in aromatase activity was found between the two populations while we expected a decline in aromatase activity for the F1 population. Our observation cannot explain this difference in sexual steroids levels between the two populations. However, E2 being the principal mediator of vitellogenin production and incorporation in the oocytes [START_REF] Lubzens | Oogenesis in teleost fish: how fish eggs are formed[END_REF]), these more elevated E2 levels in domesticated fish should have been accompanied with higher levels of vitellogenin in the plasma. However opposite results were found. These conflicting observations might be explained by three simultaneous phenomena: firstly Eurasian perch displays strongly elevated testosterone levels by comparison to other fish species and these testosterone levels are 20-50fold higher than the E2 level (Milla et al. 2009a). Secondly, like E2, testosterone is a stimulator of vitellogenin production in fish [START_REF] Ramezani-Fard | Endocrine control of oogenesis in teleosts[END_REF]. Thirdly, more elevated testosterone levels were measured in the F1 perch than in the domesticated ones.

All together, we speculate that the higher vitellogenin level measured in F1 fish would be more explained by the differential testosterone level between both groups rather than the classically described E2 involvement. Concerning 11 keto-testosterone, given the low level observed in comparison with the testosterone level and the absence of contrasted concentrations between both groups, we consider that this sexual steroid was not a major actor explaining this divergence of oogenesis progress between F1 and domesticated fish.

Moreover, to explain the divergence in oogenesis and reproductive performance between the two populations, we investigated the plasma 17α,20β-dihydroxy-4-pregnen-3-one (DHP), which is the maturation inducing steroid (MIS) in percids [START_REF] Nagahama | Endocrine control of oocyte maturation[END_REF]. Our hypothesis was that, a difference in kinetics of the final oocyte maturation between the two populations would be at the origin of the reproductive performance divergence. But plasma DHP levels which shows a classical kinetics of change did not display different levels between the two female groups. However it has been reported that another MIS is associated with the final oocyte maturation control in some perciform species: it is the 17α, 20β,21trihydroxy-4-pregnen-3-one (20β-S) [START_REF] Thomas | Changes in the plasma levels of maturationinducing steroids in several perciform fishes during induced ovulation[END_REF][START_REF] Asahina | Synthesis of 17a,21-dihydroxy-4-pregnen-3,20-dione, 17a,2003-dihydroxy-4-pregnen-3-one, and 17a,200,21-trihydroxy-4-pregnen-3-one in the ovaries of tobi-numeri dragonet, Repomucenus beniteguri, Callionymidae Teleostei[END_REF][START_REF] Modesto | The sex steroids of toadfish, (Halobatrachus didactylus)[END_REF][START_REF] King | Hormonal regulation of final maturation of striped bass oocytes in vitro[END_REF], we can imagine that in perch, final oocyte maturation could be controlled by the combined action of DHP and 20β-S, but we cannot exclude the hypothesis that the DHP could be the principal MIS in perch but it has to be sampled more frequently during the final oocyte maturation phase. All combined, our hypothesis is that this domestication process displayed distinct effects according to the time-course of oogenesis. Indeed, farmed fish exhibited an earlier gonadogenesis and vitellogenesis than F1 ones, then turning to depreciative effects on final oogenesis, oocyte quality, finally traduced by poor reproductive performance.

Several studies have reported the effect of domestication on reproduction efficiency in many species [START_REF] Dannewitz | Reproductive success ofhatcheryproduces and wild-born brown trout in an experimental stream[END_REF][START_REF] Salze | Egg quality determinants in cod (Gadus morhua L.): egg performance and lipids in eggs from farmed and wild broodstock[END_REF]Randak et al. 2006;[START_REF] Jonsson | Cultured Atlantic salmon in nature: a review of their ecology and interaction with wild fish[END_REF][START_REF] Krejszeff | Domestication affects spawning of the ide (Leuciscus idus) preliminary study[END_REF][START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF][START_REF] Ciesla | Changes in ide (Leuciscus idus) female's reproductive parameters after stimulation with carp pituitary homogenate (CPH) and Ovopel: the effect of domestication?[END_REF].These data suggest that physiological changes occur during the domestication process due to farming conditions. However, these studies do not provide information about the endocrine mechanisms in broodstock explaining these changes in reproductive capacity. As the level of sex steroids is modified in our study, we hypothesized that a disruption of the gonadotropic axis occurred during the domestication process. Significant differences between the two populations were monitored in the level of gonadotropin gene expressions in the brain and pituitary, while GnRH gene expression was not affected. However, we only investigated the GnRH3 form in our experiment, but there is another form involved during the reproduction in fish which is the GnRH1 [START_REF] Yaron | Regulation of fish gonadotropins[END_REF]) and which could be affected as well during the domestication process. These results support the hypothesis of a gonadotropic axis disruption, starting at the gonadotropic level affecting in turns the secretion of sexual steroids in the gonad. As the level of FSH and LH transcripts were higher in F1 fish, in particular at the two last sampling times, we think that the more markedly rise in testosterone level in this population is due to LH rise, then leading to a better reproductive performance in F1 fish. Indeed, blood plasma testosterone is regulated after exposure to the LH agonist hCG in perch females at the end of the reproductive cycle (Milla et al. 2009a). However, we cannot rule out the fact that the measures of FSH and LH transcript levels might not reflect the circulating levels of GTHs. Moreover, the hormonal differences observed between the two populations might reflect a differential threshold of action of these hormones between the two populations. [START_REF] Kristan | Comparison of reproduction characteristics and broodstock mortality in farmed and wild Eurasian Perch (Perca fluviatilis L.) females during spawning season under controlled conditions[END_REF] proposed that the stress state of fish would explain the differences in the reproductive performance between the two populations. Even if there was not any problem in terms of water quality, infection or mortality, the level of cortisol were not low during this experiment and the level was higher in F1 fish before the alveoli cortical stage of the cycle.

Consequently, in accordance with the proposal of Kristan, we cannot rule out the fact that the differential cortisol levels or sensitivity to stressful conditions led to differential advancement of early gonadogenesis between the two groups of fish, stress and cortisol being major modulators of fish reproduction [START_REF] Barton | Stress in fishes: a diversity of responses with particular reference to changes in circulating corticosteroids[END_REF]Milla et al. 2009b;[START_REF] Schreck | Stress and fish reproduction: The roles of allostasis and hormesis[END_REF]. But, this precocious difference alone would not be sufficient to explain the major differences in the reproductive performance, as fish were probably in the same level of stress state since the vitellogenesis stage.

In conclusion, this study showed that this domestication route negatively affected the reproductive performance. However, some conflicting results were observed about the reproductive physiological parameters: on the one hand, this domestication process advanced the start of gonadogenesis until the start of the vitellogenesis period, probably caused by more precocious E2 rise. Then, the lower level of gonadotropins transcripts and testosterone potentially led to a drop in vitellogenin in comparison to F1 fish, further affecting the reproductive success. This study showed that this domestication process negatively affected reproduction, so more attention should be paid by the farmers to the choice of renewing broodstock in particular when they maintain fish coming from several generations of domestication in their facilities,

Legends

Fig. 1 .

 1 Fig. 1. The photothermal program (adapted from Fontaine et al. 2015). The photothermal program was used to induce perch reproduction. The dark curve corresponds to the photoperiod variations. The light curve corresponds to the temperature variations. T0, T35, T157, T295 are the four sampling times which represent days 0, 35, 157 and 295 after the start of the experiment.

Fig. 2 .

 2 Fig. 2. GSI changes (%) in F1 and domesticated females. (9 females per sampling period + Standard deviation). Sampling times T0, T35, T157 and T295 correspond respectively to days 0, 35, 157 and 295 after the beginning of the photothermal program. The asterisk represents a significant difference between the 2 fish populations (p<0.05).

Fig. 3 .

 3 Fig. 3. Different developmental stages observed in domesticated and F1 perch female ovaries. (9 females per sampling time + Standard deviation). D and F1 refer to Domesticated and F1 respectively. Sampling periods T0, T35, T157 and T295 correspond respectively to days 0, 35, 157 and 295 after the beginning of the photothermal program.

Fig. 4 .

 4 Fig. 4. Plasma sexual steroids and vitellogenin levels variation. (A) E2 levels variation (ng/mL) in F1 and domesticated females. (B) Testosterone levels variation (ng/mL) in F1 and domesticated females. (C) Vitellogenin levels variation (µg ALP/mL) in F1 and domesticated females. (D) 11KT variations (pg/mL) in F1 and domesticated females. (9 females per time per population + standard deviation). Sampling periods T0, T35, T157 and T295 correspond respectively to days 0, 35, 157 and 295 after the beginning of the photothermal program. The asterisk (*) represents a significant difference (p<0.05) and (**) (p<0.001).

Fig. 5 .

 5 Fig.5. Plasma DHP and aromatase activity. (A) Plasma DHP levels (ng/mL) variation in domesticated and F1 perch females. (B) Aromatase activity variation (fmol/min.mg) in domesticated and F1 perch females. (9 females per time per population + standard deviation). Sampling periods T0, T35, T157 and T295 correspond respectively to days 0, 35, 157 and 295 after the beginning of the photothermal program.

Fig. 6 .

 6 Fig. 6. GnRH 3 and gonadotropins gene expression quantification in the brain and pituitary. (A) GnRH 3 mRNA levels in F1 and domesticated female brains. (B) GnRH 3 mRNA levels in F1 and domesticated female pituitaries. (C) βFSH mRNA levels in F1 and domesticated female brains. (D) βFSH mRNA levels in F1 and domesticated female pituitaries. (E) βLH mRNA levels in F1 and domesticated female brains.. (F) βLH mRNA levels in F1 and domesticated female pituitaries. (9 females per time per population + standard deviation). Sampling periods T0, T35, T157 and T295 correspond respectively to days 0, 35, 157 and 295 after the beginning of the photothermal program. mRNA levels have been quantified by q-PCR. The asterisk (*) represents a significant difference (p<0.05) and (**) (p<0.001).

Fig. 7 .

 7 Fig. 7. Cortisol levels variation (ng/mL) in F1 and domesticated females. (9 females per time per population + standard deviation). Sampling times T0, T35, T157 and T295 correspond respectively to days 0, 35, 157 and 295 after the beginning of the photothermal program. The asterisk (*) represents a significant difference (p<0.05).

Funding. This study was supported by the FranceAgrimer establishment (French establishment of national agricultural and marine products) contract number: 2013-1566, and partly supported by projects CENAKVA (No. CZ.1.05/2.1.00/01.0024) and CENAKVA II (No. LO1205 under the NPU I program).

Authors contributions. Amine Khendek was responsible for the carrying out data analyses and wrote the main part of the manuscript, and performed the gene expression analysis.

Sylvain