Spyros Gidaris
email: spyros.gidaris@enpc.fr

Praveer Singh
email: praveer.singh@enpc.fr

Nikos Komodakis
email: nikos.komodakis@enpc.fr

UNSUPERVISED REPRESENTATION LEARNING BY PRE-DICTING IMAGE ROTATIONS

Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training Con-vNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: https://github.com/gidariss/FeatureLearningRotNet.

INTRODUCTION

In recent years, the widespread adoption of deep convolutional neural networks [START_REF] Lecun | Gradient-based learning applied to document recognition[END_REF] (ConvNets) in computer vision, has lead to a tremendous progress in the field. Specifically, by training ConvNets on the object recognition [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF] or the scene classification (Zhou et al., 2014) tasks with a massive amount of manually labeled data, they manage to learn powerful visual representations suitable for image understanding tasks. For instance, the image features learned by ConvNets in this supervised manner have achieved excellent results when they are transferred to other vision tasks, such as object detection [START_REF] Girshick | Fast r-cnn[END_REF], semantic segmentation [START_REF] Long | Fully convolutional networks for semantic segmentation[END_REF], or image captioning [START_REF] Karpathy | Deep visual-semantic alignments for generating image descriptions[END_REF]. However, supervised feature learning has the main limitation of requiring intensive manual labeling effort, which is both expensive and infeasible to scale on the vast amount of visual data that are available today.

Due to that, there is lately an increased interest to learn high level ConvNet based representations in an unsupervised manner that avoids manual annotation of visual data. Among them, a prominent paradigm is the so-called self-supervised learning that defines an annotation free pretext task, using only the visual information present on the images or videos, in order to provide a surrogate supervision signal for feature learning. For example, in order to learn features, Zhang et al. (2016a) and [START_REF] Larsson | Learning representations for automatic colorization[END_REF] train ConvNets to colorize gray scale images, [START_REF] Doersch | Unsupervised visual representation learning by context prediction[END_REF] and [START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF] predict the relative position of image patches, and [START_REF] Agrawal | Learning to see by moving[END_REF] predict the egomotion (i.e., self-motion) of a moving vehicle between two consecutive frames. The rationale behind such self-supervised tasks is that solving them will force the ConvNet to learn semantic image features that can be useful for other vision tasks. In fact, image representations learned with the above self-supervised tasks, although they have not managed to match the performance of supervised-learned representations, they have proved to be good alternatives for transferring on other vision tasks, such as object recognition, object detection, and semantic segmentation (Zhang et al., 2016a;[START_REF] Larsson | Learning representations for automatic colorization[END_REF]Zhang et al., 2016b;[START_REF] Larsson | Colorization as a proxy task for visual understanding[END_REF][START_REF] Doersch | Unsupervised visual representation learning by context prediction[END_REF][START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF][START_REF] Noroozi | Representation learning by learning to count[END_REF]Pathak et al., 2016a;[START_REF] Doersch | Multi-task self-supervised visual learning[END_REF]. Other successful cases of unsupervised feature learning are clustering based methods [START_REF] Dosovitskiy | Discriminative unsupervised feature learning with convolutional neural networks[END_REF][START_REF] Liao | Learning deep parsimonious representations[END_REF][START_REF] Yang | Joint unsupervised learning of deep representations and image clusters[END_REF], reconstruction based methods [START_REF] Bengio | Greedy layer-wise training of deep networks[END_REF][START_REF] Fu | Unsupervised learning of invariant feature hierarchies with applications to object recognition[END_REF][START_REF] Masci | Stacked convolutional autoencoders for hierarchical feature extraction[END_REF], and methods that involve learning generative probabilistic models [START_REF] Goodfellow | Generative adversarial nets[END_REF]; [START_REF] Donahue | Adversarial feature learning[END_REF]; [START_REF] Radford | Unsupervised representation learning with deep convolutional generative adversarial networks[END_REF].

Our work follows the self-supervised paradigm and proposes to learn image representations by training ConvNets to recognize the geometric transformation that is applied to the image that it gets as input. More specifically, we first define a small set of discrete geometric transformations, then each of those geometric transformations are applied to each image on the dataset and the produced transformed images are fed to the ConvNet model that is trained to recognize the transformation of each image. In this formulation, it is the set of geometric transformations that actually defines the classification pretext task that the ConvNet model has to learn. Therefore, in order to achieve unsupervised semantic feature learning, it is of crucial importance to properly choose those geometric transformations (we further discuss this aspect of our methodology in section 2.2). What we propose is to define the geometric transformations as the image rotations by 0, 90, 180, and 270 degrees. Thus, the ConvNet model is trained on the 4-way image classification task of recognizing one of the four image rotations (see Figure 2). We argue that in order a ConvNet model to be able recognize the rotation transformation that was applied to an image it will require to understand the concept of the objects depicted in the image (see Figure 1), such as their location in the image, their type, and their pose. Throughout the paper we support that argument both qualitatively and quantitatively. Furthermore we demonstrate on the experimental section of the paper that despite the simplicity of our self-supervised approach, the task of predicting rotation transformations provides a powerful surrogate supervision signal for feature learning and leads to dramatic improvements on the relevant benchmarks.

Note that our self-supervised task is different from the work of [START_REF] Dosovitskiy | Discriminative unsupervised feature learning with convolutional neural networks[END_REF] and [START_REF] Agrawal | Learning to see by moving[END_REF] that also involves geometric transformations. [START_REF] Dosovitskiy | Discriminative unsupervised feature learning with convolutional neural networks[END_REF] train a ConvNet model to yield representations that are discriminative between images and at the same time invariant on geometric and chromatic transformations. In contrast, we train a ConvNet model to recognize the geometric transformation applied to an image. It is also fundamentally different from the egomotion method of [START_REF] Agrawal | Learning to see by moving[END_REF], which employs a ConvNet model with siamese like architecture that takes as input two consecutive video frames and is trained to predict (through regression) their camera transformation. Instead, in our approach, the ConvNet takes as input a single image to which we have applied a random geometric transformation (i.e., rotation) and is trained to recognize (through classification) this geometric transformation without having access to the initial image.

Our contributions are:

• We propose a new self-supervised task that is very simple and at the same time, as we demonstrate throughout the paper, offers a powerful supervisory signal for semantic feature learning.

• We exhaustively evaluate our self-supervised method under various settings (e.g. semisupervised or transfer learning settings) and in various vision tasks (i.e., CIFAR-10, Ima-geNet, Places, and PASCAL classification, detection, or segmentation tasks).

• In all of them, our novel self-supervised formulation demonstrates state-of-the-art results with dramatic improvements w.r.t. prior unsupervised approaches.

• As a consequence we show that for several important vision tasks, our self-supervised learning approach significantly narrows the gap between unsupervised and supervised feature learning.

In the following sections, we describe our self-supervised methodology in §2, we provide experimental results in §3, and finally we conclude in §4. The core intuition of our self-supervised feature learning approach is that if someone is not aware of the concepts of the objects depicted in the images, he cannot recognize the rotation that was applied to them.

METHODOLOGY

2.1 OVERVIEW

The goal of our work is to learn ConvNet based semantic features in an unsupervised manner. To achieve that goal we propose to train a ConvNet model F (.) to estimate the geometric transformation applied to an image that is given to it as input. Specifically, we define a set of K discrete geometric transformations G = {g(.|y)} K y=1 , where g(.|y) is the operator that applies to image X the geometric transformation with label y that yields the transformed image X y = g(X|y). The ConvNet model F (.) gets as input an image X y * (where the label y * is unknown to model F (.)) and yields as output a probability distribution over all possible geometric transformations:

F (X y * |θ) = {F y (X y * |θ)} K y=1 , (1)
where F y (X y * |θ) is the predicted probability for the geometric transformation with label y and θ are the learnable parameters of model F (.).

Therefore, given a set of N training images D = {X i } N i=0 , the self-supervised training objective that the ConvNet model must learn to solve is:

min θ 1 N N i=1 loss(X i , θ), (2)
where the loss function loss(.) is defined as:

loss(X i , θ) = - 1 K K y=1 log(F y (g(X i |y)|θ)). (3)
In the following subsection we describe the type of geometric transformations that we propose in our work.

CHOOSING GEOMETRIC TRANSFORMATIONS: IMAGE ROTATIONS

In the above formulation, the geometric transformations G must define a classification task that should force the ConvNet model to learn semantic features useful for visual perception tasks (e.g., object detection or image classification). In our work we propose to define the set of geometric transformations G as all the image rotations by multiples of 90 degrees, i.e., 2d image rotations by 0, 90, 180, and 270 degrees (see Figure 2). More formally, if Rot(X, φ) is an operator that rotates image X by φ degrees, then our set of geometric transformations consists of the K = 4 image rotations G = {g(X|y)} 4 y=1 , where g(X|y) = Rot(X, (y -1)90). Forcing the learning of semantic features: The core intuition behind using these image rotations as the set of geometric transformations relates to the simple fact that it is essentially impossible for a ConvNet model to effectively perform the above rotation recognition task unless it has first learnt to recognize and detect classes of objects as well as their semantic parts in images. More specifically,

Maximize prob. F 3 ( X 3 ) Predict 0 degrees rotation (y=0) Maximize prob. F 2 ( X 2 ) Maximize prob. F 1 ( X 1 ) Maximize prob. F 0 ( X 0 )
Predict 180 degrees rotation (y=2)

Predict 90 degrees rotation (y=1)

Objectives:

Figure 2: Illustration of the self-supervised task that we propose for semantic feature learning. Given four possible geometric transformations, the 0, 90, 180, and 270 degrees rotations, we train a ConvNet model F (.) to recognize the rotation that is applied to the image that it gets as input.

F y (X y * ) is the probability of rotation transformation y predicted by model F (.) when it gets as input an image that has been transformed by the rotation transformation y * .

to successfully predict the rotation of an image the ConvNet model must necessarily learn to localize salient objects in the image, recognize their orientation and object type, and then relate the object orientation with the dominant orientation that each type of object tends to be depicted within the available images. In Figure 3b we visualize some attention maps generated by a model trained on the rotation recognition task. These attention maps are computed based on the magnitude of activations at each spatial cell of a convolutional layer and essentially reflect where the network puts most of its focus in order to classify an input image. We observe, indeed, that in order for the model to accomplish the rotation prediction task it learns to focus on high level object parts in the image, such as eyes, nose, tails, and heads. By comparing them with the attention maps generated by a model trained on the object recognition task in a supervised way (see Figure 3a) we observe that both models seem to focus on roughly the same image regions. Furthermore, in Figure 4 we visualize the first layer filters that were learnt by an AlexNet model trained on the proposed rotation recognition task. As can be seen, they appear to have a big variety of edge filters on multiple orientations and multiple frequencies. Remarkably, these filters seem to have a greater amount of variety even than the filters learnt by the supervised object recognition task.

Absence of low-level visual artifacts: An additional important advantage of using image rotations by multiples of 90 degrees over other geometric transformations, is that they can be implemented by flip and transpose operations (as we will see below) that do not leave any easily detectable low-level visual artifacts that will lead the ConvNet to learn trivial features with no practical value for the vision perception tasks. In contrast, had we decided to use as geometric transformations, e.g., scale and aspect ratio image transformations, in order to implement them we would need to use image resizing routines that leave easily detectable image artifacts.

Well-posedness: Furthermore, human captured images tend to depict objects in an "up-standing" position, thus making the rotation recognition task well defined, i.e., given an image rotated by 0, 90, 180, or 270 degrees, there is usually no ambiguity of what is the rotation transformation (with the exception of images that only depict round objects). In contrast, that is not the case for the object scale that varies significantly on human captured images.

Implementing image rotations: In order to implement the image rotations by 90, 180, and 270 degrees (the 0 degrees case is the image itself), we use flip and transpose operations. Specifically, In order to generate the attention map of a conv. layer we first compute the feature maps of this layer, then we raise each feature activation on the power p, and finally we sum the activations at each location of the feature map. For the conv. layers 1, 2, and 3 we used the powers p = 1, p = 2, and p = 4 respectively. For visualization of our self-supervised model's attention maps for all the rotated versions of the images see Figure 6 in appendix A.

for 90 degrees rotation we first transpose the image and then flip it vertically (upside-down flip), for 180 degrees rotation we flip the image first vertically and then horizontally (left-right flip), and finally for 270 degrees rotation we first flip vertically the image and then we transpose it.

DISCUSSION

The simple formulation of our self-supervised task has several advantages. It has the same computational cost as supervised learning, similar training convergence speed (that is significantly faster than image reconstruction based approaches; our AlexNet model trains in around 2 days using a single Titan X GPU), and can trivially adopt the efficient parallelization schemes devised for supervised learning [START_REF] Goyal | Accurate, large minibatch sgd: Training imagenet in 1 hour[END_REF], making it an ideal candidate for unsupervised learning on internetscale data (i.e., billions of images). Furthermore, our approach does not require any special image pre-processing routine in order to avoid learning trivial features, as many other unsupervised or self-supervised approaches do. Despite the simplicity of our self-supervised formulation, as we will see in the experimental section of the paper, the features learned by our approach achieve dramatic improvements on the unsupervised feature learning benchmarks.

EXPERIMENTAL RESULTS

In this section we conduct an extensive evaluation of our approach on the most commonly used image datasets, such as CIFAR-10 ( [START_REF] Krizhevsky | Learning multiple layers of features from tiny images[END_REF], ImageNet [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF], PASCAL [START_REF] Everingham | The pascal visual object classes (voc) challenge[END_REF], and Places205 (Zhou et al., 2014), as well as on various vision tasks, such as object detection, object segmentation, and image classification. We also consider several learning scenarios, including transfer learning and semi-supervised learning. In all cases, we compare our approach with corresponding state-of-the-art methods.

CIFAR EXPERIMENTS

We start by evaluating on the object recognition task of CIFAR-10 the ConvNet based features learned by the proposed self-supervised task of rotation recognition. We will here after call a Con-vNet model that is trained on the self-supervised task of rotation recognition RotNet model.

Implementation details:

In our CIFAR-10 experiments we implement the RotNet models with Network-In-Network (NIN) architectures [START_REF] Lin | Network in network[END_REF]. In order to train them on the rotation prediction task, we use SGD with batch size 128, momentum 0.9, weight decay 5e -4 and lr of 0.1. We drop the learning rates by a factor of 5 after epochs 30, 60, and 80. We train in total for 100 epochs. In our preliminary experiments we found that we get significant improvement when during training we train the network by feeding it all the four rotated copies of an image simultaneously instead of each time randomly sampling a single rotation transformation. Therefore, at each training batch the network sees 4 times more images than the batch size.

Evaluation of the learned feature hierarchies: First, we explore how the quality of the learned features depends from their depth (i.e., the depth of the layer that they come from) as well as from the total depth of the RotNet model. For that purpose, we first train using the CIFAR-10 training images three RotNet models which have 3, 4, and 5 convolutional blocks respectively (note that each conv. block in the NIN architectures that implement our RotNet models have 3 conv. layers; therefore, the total number of conv. layers of the examined RotNet models is 9, 12, and 15 for 3, 4, and 5 conv. blocks respectively). Afterwards, we learn classifiers on top of the feature maps generated by each conv. block of each RotNet model. Those classifiers are trained in a supervised way on the object recognition task of CIFAR-10. They consist of 3 fully connected layers; the 2 hidden layers have 200 feature channels each and are followed by batch-norm and relu units. We report the accuracy results of CIFAR-10 test set in Table 1. We observe that in all cases the feature maps generated by the 2nd conv. block (that actually has depth 6 in terms of the total number of conv. layer till that point) achieve the highest accuracy, i.e., between 88.26% and 89.06%. The features of the conv. blocks that follow the 2nd one gradually degrade the object recognition accuracy, which we assume is because they start becoming more and more specific on the self-supervised task of rotation prediction. Also, we observe that increasing the total depth of the RotNet models leads to increased object recognition performance by the feature maps generated by earlier layers (and after the 1st conv. block). We assume that this is because increasing the depth of the model and thus the complexity of its head (i.e., top ConvNet layers) allows the features of earlier layers to be less specific to the rotation prediction task.

Exploring the quality of the learned features w.r.t. the number of recognized rotations: In Table 2 we explore how the quality of the self-supervised features depends on the number of discrete rotations used in the rotation prediction task. For that purpose we defined three extra rotation recognition tasks: image regions that do not include any of the empty image areas introduced by the rotation transformations (and which can easily indicate the image rotation). We observe that indeed for 4 discrete rotations (as we proposed) we achieve better object recognition performance than the 8 or 2 cases. We believe that this is because the 2 orientations case offers too few classes for recognition (i.e., less supervisory information is provided) while in the 8 orientations case the geometric transformations are not distinguishable enough and furthermore the 4 extra rotations introduced may lead to visual artifacts on the rotated images. Moreover, we observe that among the RotNet models trained with 2 discrete rotations, the RotNet model trained with 90 • and 270 • rotations achieves worse object recognition performance than the model trained with the 0 • and 180 • rotations, which is probably due to the fact that the former model does not "see" during the unsupervised phase the 0 • rotation that is typically used during the object recognition training phase.

Comparison against supervised and other unsupervised methods: In Table 3 we compare our unsupervised learned features against other unsupervised (or hand-crafted) features on CIFAR-10.

For our entries we use the feature maps generated by the 2nd conv. block of a RotNet model with 4 conv. blocks in total. On top of those RotNet features we train 2 different classifiers: (a) a nonlinear classifier with 3 fully connected layers as before (entry (Ours) RotNet + non-linear), and (b) three conv. layers plus a linear prediction layer (entry (Ours) RotNet +conv.; note that this entry is basically a 3 blocks NIN model with the first 2 blocks coming from a RotNet model and the 3rd being randomly initialized and trained on the recognition task). We observe that we improve over the prior unsupervised approaches and we achieve state-of-the-art results in CIFAR-10 (note that each of the prior approaches has a different ConvNet architecture thus the comparison with them is just indicative). More notably, the accuracy gap between the RotNet based model and the fully supervised NIN model is very small, only 1.64 percentage points (92.80% vs 91.16%). We provide per class breakdown of the classification accuracy of our unsupervised model as well as the supervised one in Table 9 (in appendix B). In Table 3 we also report the performance of the RotNet features when, instead of being kept frozen, they are fine-tuned during the object recognition training phase. We observe that fine-tuning the unsupervised learned features further improves the classification performance, thus reducing even more the gap with the supervised case.

Correlation between object classification task and rotation prediction task: In Figure 5a, we plot the object classification accuracy as a function of the training epochs used for solving the selfsupervised task of recognizing rotations, which learns the features used by the object classifier.

Table 4: Task Generalization: ImageNet top-1 classification with non-linear layers. We compare our unsupervised feature learning approach with other unsupervised approaches by training non-linear classifiers on top of the feature maps of each layer to perform the 1000-way ImageNet classification task, as proposed by [START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF]. For instance, for the conv5 feature map we train the layers that follow the conv5 layer in the AlexNet architecture (i.e., fc6, fc7, and fc8). Similarly for the conv4 feature maps. We implemented those non-linear classifiers with batch normalization units after each linear layer (fully connected or convolutional) and without employing drop out units. All approaches use AlexNet variants and were pre-trained on ImageNet without labels except the ImageNet labels and Random entries. During testing we use a single crop and do not perform flipping augmentation. We report top-1 classification accuracy.

Method Conv4 Conv5

ImageNet labels from [START_REF] Bojanowski | Unsupervised learning by predicting noise[END_REF] 59.7 59.7

Random from [START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF] 27.1 12.0

Tracking [START_REF] Wang | Unsupervised learning of visual representations using videos[END_REF] 38.8 29.8 Context [START_REF] Doersch | Unsupervised visual representation learning by context prediction[END_REF] 45.6 30.4 Colorization (Zhang et al., 2016a) 40.7 35.2 Jigsaw Puzzles [START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF] 45.3 34.6 BIGAN [START_REF] Donahue | Adversarial feature learning[END_REF] 41.9 32.2 NAT [START_REF] Bojanowski | Unsupervised learning by predicting noise[END_REF] -36.0 (Ours) RotNet 50.0 43.8

More specifically, in order to create the object recognition accuracy curve, in each training snapshot of RotNet (i.e., every 20 epochs), we pause its training procedure and we train from scratch (until convergence) a non-linear object classifier on top of the so far learnt RotNet features. Therefore, the object recognition accuracy curve depicts the accuracy of those non-linear object classifiers after the end of their training while the rotation prediction accuracy curve depicts the accuracy of the RotNet at those snapshots. We observe that, as the ability of the RotNet features for solving the rotation prediction task improves (i.e., as the rotation prediction accuracy increases), their ability to help solving the object recognition task improves as well (i.e., the object recognition accuracy also increases). Furthermore, we observe that the object recognition accuracy converges fast w.r.t. the number of training epochs used for solving the pretext task of rotation prediction.

Semi-supervised setting: Motivated by the very high performance of our unsupervised feature learning method, we also evaluate it on a semi-supervised setting. More specifically, we first train a 4 block RotNet model on the rotation prediction task using the entire image dataset of CIFAR-10 and then we train on top of its feature maps object classifiers using only a subset of the available images and their corresponding labels. As feature maps we use those generated by the 2nd conv. block of the RotNet model. As a classifier we use a set of convolutional layers that actually has the same architecture as the 3rd conv. block of a NIN model plus a linear classifier, all randomly initialized. For training the object classifier we use for each category 20, 100, 400, 1000, or 5000 image examples. Note that 5000 image examples is the extreme case of using the entire CIFAR-10 training dataset. Also, we compare our method with a supervised model that is trained only on the available examples each time. In Figure 5b we plot the accuracy of the examined models as a function of the available training examples. We observe that our unsupervised trained model exceeds in this semi-supervised setting the supervised model when the number of examples per category drops below 1000. Furthermore, as the number of examples decreases, the performance gap in favor of our method is increased. This empirical evidence demonstrates the usefulness of our method on semi-supervised settings.

EVALUATION OF SELF-SUPERVISED FEATURES TRAINED IN IMAGENET

Here we evaluate the performance of our self-supervised ConvNet models on the ImageNet, Places, and PASCAL VOC datasets. Specifically, we first train a RotNet model on the training images of the ImageNet dataset and then we evaluate the performance of the self-supervised features on the image Context [START_REF] Doersch | Unsupervised visual representation learning by context prediction[END_REF] 19.7 26.7 31.9 32.7 30.9 Context Encoders (Pathak et al., 2016b) 18.2 23.2 23.4 21.9 18.4 Colorization (Zhang et al., 2016a) 16.0 25.7 29.6 30.3 29.7 Jigsaw Puzzles [START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF] 23.0 31.9 35.0 34.2 29.3 BIGAN [START_REF] Donahue | Adversarial feature learning[END_REF] 22.0 28.7 31.8 31.3 29.7 Split-Brain (Zhang et al., 2016b) 21.3 30.7 34.0 34.1 32.5 Counting [START_REF] Noroozi | Representation learning by learning to count[END_REF] 23 Implementation details: For those experiments we implemented our RotNet model with an AlexNet architecture. Our implementation of the AlexNet model does not have local response normalization units, dropout units, or groups in the colvolutional layers while it includes batch normalization units after each linear layer (either convolutional or fully connected). In order to train the AlexNet based RotNet model, we use SGD with batch size 192, momentum 0.9, weight decay 5e -4 and lr of 0.01. We drop the learning rates by a factor of 10 after epochs 10, and 20 epochs. We train in total for 30 epochs. As in the CIFAR experiments, during training we feed the RotNet model all four rotated copies of an image simultaneously (in the same mini-batch).

their generalization on classes that were "unseen" during the unsupervised training phase. As can be seen, even in this case our method manages to either surpass or achieve comparable results w.r.t. prior state-of-the-art unsupervised learning approaches.

CONCLUSIONS

In our work we propose a novel formulation for self-supervised feature learning that trains a Con-vNet model to be able to recognize the image rotation that has been applied to its input images. Despite the simplicity of our self-supervised task, we demonstrate that it successfully forces the Con-vNet model trained on it to learn semantic features that are useful for a variety of visual perception tasks, such as object recognition, object detection, and object segmentation. We exhaustively evaluate our method in various unsupervised and semi-supervised benchmarks and we achieve in all of them state-of-the-art performance. Specifically, our self-supervised approach manages to drastically improve the state-of-the-art results on unsupervised feature learning for ImageNet classification, PASCAL classification, PASCAL detection, PASCAL segmentation, and CIFAR-10 classification, surpassing prior approaches by a significant margin and thus drastically reducing the gap between unsupervised and supervised feature learning.

Figure 1 :

 1 Figure1: Images rotated by random multiples of 90 degrees (e.g., 0, 90, 180, or 270 degrees). The core intuition of our self-supervised feature learning approach is that if someone is not aware of the concepts of the objects depicted in the images, he cannot recognize the rotation that was applied to them.

  Input images on the models Conv1 27 × 27 Conv3 13 × 13 Conv5 6 × 6 (a) Attention maps of supervised model Conv1 27 × 27 Conv3 13 × 13 Conv5 6 × 6 (b) Attention maps of our self-supervised model

Figure 3 :

 3 Figure 3: Attention maps generated by an AlexNet model trained (a) to recognize objects (supervised), and (b) to recognize image rotations (self-supervised).In order to generate the attention map of a conv. layer we first compute the feature maps of this layer, then we raise each feature activation on the power p, and finally we sum the activations at each location of the feature map. For the conv. layers 1, 2, and 3 we used the powers p = 1, p = 2, and p = 4 respectively. For visualization of our self-supervised model's attention maps for all the rotated versions of the images see Figure6in appendix A.

  Figure 4: First layer filters learned by a AlexNet model trained on (a) the supervised object recognition task and (b) the self-supervised task of recognizing rotated images. We observe that the filters learned by the self-supervised task are mostly oriented edge filters on various frequencies and, remarkably, they seem to have more variety than those learned on the supervised task.

Figure 5 :

 5 Figure 5: (a) Plot with the rotation prediction accuracy and object recognition accuracy as a function of the training epochs used for solving the rotation prediction task. The red curve is the object recognition accuracy of a fully supervised model (a NIN model), which is independent from the training epochs on the rotation prediction task. The yellow curve is the object recognition accuracy of an object classifier trained on top of feature maps learned by a RotNet model at different snapshots of the training procedure. (b) Accuracy as a function of the number of training examples per category in CIFAR-10. Ours semi-supervised is a NIN model that the first 2 conv. blocks are RotNet model that was trained in a self-supervised way on the entire training set of CIFAR-10 and the 3rd conv. block along with a prediction linear layer that was trained with the object recognition task only on the available set of labeled images.

Table 1 :

 1 Evaluation

	Model	ConvB1 ConvB2 ConvB3 ConvB4 ConvB5
	RotNet with 3 conv. blocks	85.45	88.26	62.09	-	-
	RotNet with 4 conv. blocks	85.07	89.06	86.21	61.73	-
	RotNet with 5 conv. blocks	85.04	89.76	86.82	74.50	50.37

of the unsupervised learned features by measuring the classification accuracy that they achieve when we train a non-linear object classifier on top of them. The reported results are from CIFAR-10. The size of the ConvB1 feature maps is 96 × 16 × 16 and the size of the rest feature maps is 192 × 8 × 8.

Table 2 :

 2 Exploring the quality of the self-supervised learned features w.r.t. the number of recognized rotations. For all the entries we trained a non-linear classifier with 3 fully connected layers (similar to Table1) on top of the feature maps generated by the 2nd conv. block of a RotNet model with 4 conv. blocks in total. The reported results are from CIFAR-10. , 45 • , 90 • , 135 • , 180 • , 225 • , 270 • , 315 •

	# Rotations Rotations	CIFAR-10 Classification Accuracy
	4	0 • , 90 • , 180 • , 270 •	89.06
	8	0 • 88.51
	2	0 • , 180 •	87.46
	2	90 • , 270 •	85.52

Table 3 :

 3 Evaluation of unsupervised feature learning methods on CIFAR-10. The Supervised NIN and the (Ours) RotNet + conv entries have exactly the same architecture but the first was trained fully supervised while on the second the first 2 conv. blocks were trained unsupervised with our rotation prediction task and the 3rd block only was trained in a supervised manner. In the Random Init. + conv entry a conv. classifier (similar to that of (Ours) RotNet + conv) is trained on top of two NIN conv. blocks that are randomly initialized and stay frozen. Note that each of the prior approaches has a different ConvNet architecture and thus the comparison with them is just indicative.

	Method	Accuracy
	Supervised NIN	92.80
	Random Init. + conv	72.50
	(Ours) RotNet + non-linear	89.06
	(Ours) RotNet + conv	91.16
	(Ours) RotNet + non-linear (fine-tuned)	91.73
	(Ours) RotNet + conv (fine-tuned)	92.17
	Roto-Scat + SVM Oyallon & Mallat (2015)	82.3
	ExemplarCNN Dosovitskiy et al. (2014)	84.3
	DCGAN Radford et al. (2015)	82.8
	Scattering Oyallon et al. (2017)	84.7

Table 5 :

 5 Task Generalization: ImageNet top-1 classification with linear layers. We compare our unsupervised feature learning approach with other unsupervised approaches by training logistic regression classifiers on top of the feature maps of each layer to perform the 1000-way ImageNet classification task, as proposed byZhang et al. (2016a). All weights are frozen and feature maps are spatially resized (with adaptive max pooling) so as to have around 9000 elements. All approaches use AlexNet variants and were pre-trained on ImageNet without labels except the ImageNet labels and Random entries.

	Method	Conv1 Conv2 Conv3 Conv4 Conv5
	ImageNet labels	19.3	36.3	44.2	48.3	50.5
	Random	11.6	17.1	16.9	16.3	14.1
	Random rescaled Krähenbühl et al. (2015)	17.5	23.0	24.5	23.2	20.6
	Context (Doersch et al., 2015)	16.2	23.3	30.2	31.7	29.6
	Context Encoders (Pathak et al., 2016b)	14.1	20.7	21.0	19.8	15.5
	Colorization (Zhang et al., 2016a)	12.5	24.5	30.4	31.5	30.3
	Jigsaw Puzzles (Noroozi & Favaro, 2016)	18.2	28.8	34.0	33.9	27.1
	BIGAN (Donahue et al., 2016)	17.7	24.5	31.0	29.9	28.0
	Split-Brain (Zhang et al., 2016b)	17.7	29.3	35.4	35.2	32.8
	Counting (Noroozi et al., 2017)	18.0	30.6	34.3	32.5	25.7
	(Ours) RotNet	18.8	31.7	38.7	38.2	36.5

Table 6 :

 6 Task & Dataset Generalization: Places top-1 classification with linear layers. We compare our unsupervised feature learning approach with other unsupervised approaches by training logistic regression classifiers on top of the feature maps of each layer to perform the 205-way Places classification task(Zhou et al., 2014). All unsupervised methods are pre-trained (in an unsupervised way) on ImageNet. All weights are frozen and feature maps are spatially resized (with adaptive max pooling) so as to have around 9000 elements. All approaches use AlexNet variants and were pretrained on ImageNet without labels except the Place labels, ImageNet labels, and Random entries.

	Method	Conv1 Conv2 Conv3 Conv4 Conv5
	Places labels Zhou et al. (2014)	22.1	35.1	40.2	43.3	44.6
	ImageNet labels	22.7	34.8	38.4	39.4	38.7
	Random	15.7	20.3	19.8	19.1	17.5
	Random rescaled Krähenbühl et al. (2015)	21.4	26.2	27.1	26.1	24.0

  classification tasks of ImageNet, Places, and PASCAL VOC datasets and on the object detection and object segmentation tasks of PASCAL VOC.

		.3	33.9	36.3	34.7	29.6
	(Ours) RotNet	21.5	31.0	35.1	34.6	33.7

ACKNOWLEDGEMENTS

This work was supported by the ANR SEMAPOLIS project, an INTEL gift, and hardware donation by NVIDIA.

Table 7: Task & Dataset Generalization: PASCAL VOC 2007 classification and detection results, and PASCAL VOC 2012 segmentation results. We used the publicly available testing frameworks of [START_REF] Krähenbühl | Data-dependent initializations of convolutional neural networks[END_REF] for classification, of [START_REF] Girshick | Fast r-cnn[END_REF] for detection, and of [START_REF] Long | Fully convolutional networks for semantic segmentation[END_REF] for segmentation. For classification, we either fix the features before conv5 (column fc6-8) or we fine-tune the whole model (column all). For detection we use multi-scale training and single scale testing. All approaches use AlexNet variants and were pre-trained on Ima-geNet without labels except the ImageNet labels and Random entries. After unsupervised training, we absorb the batch normalization units on the linear layers and we use the weight rescaling proposed by [START_REF] Krähenbühl | Data-dependent initializations of convolutional neural networks[END_REF] (which is common among the unsupervised methods). As customary, we report the mean average precision (mAP) on the classification and detection tasks, and the mean intersection over union (mIoU) on the segmentation task. Egomotion [START_REF] Agrawal | Learning to see by moving[END_REF] 31.0 54.2 43.9 Context Encoders (Pathak et al., 2016b) 34.6 56.5 44.5 29.7 Tracking [START_REF] Wang | Unsupervised learning of visual representations using videos[END_REF] 55.6 63.1 47.4 Context [START_REF] Doersch | Unsupervised visual representation learning by context prediction[END_REF] 55.1 65.3 51.1 Colorization (Zhang et al., 2016a) 61.5 65.6 46.9 35.6 BIGAN [START_REF] Donahue | Adversarial feature learning[END_REF] 52.3 60.1 46.9 34.9 Jigsaw Puzzles [START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF] -67.6 53.2 37.6 NAT [START_REF] Bojanowski | Unsupervised learning by predicting noise[END_REF] 56.7 65.3 49.4 Split-Brain (Zhang et al., 2016b) 63.0 67.1 46.7 36.0 ColorProxy [START_REF] Larsson | Colorization as a proxy task for visual understanding[END_REF] 65.9 38.4 Counting (Noroozi et al., ImageNet classification task: We evaluate the task generalization of our self-supervised learned features by training on top of them non-linear object classifiers for the ImageNet classification task (following the evaluation scheme of [START_REF] Noroozi | Unsupervised learning of visual representations by solving jigsaw puzzles[END_REF]). In Table 4 we report the classification performance of our self-supervised features and we compare it with the other unsupervised approaches. We observe that our approach surpasses all the other methods by a significant margin.

For the feature maps generated by the Conv4 layer, our improvement is more than 4 percentage points and for the feature maps generated by the Conv5 layer, our improvement is even bigger, around 8 percentage points. Furthermore, our approach significantly narrows the performance gap between unsupervised features and supervised features. In Table 5 we report similar results but for linear (logistic regression) classifiers (following the evaluation scheme of Zhang et al. (2016a)). Again, our unsupervised method demonstrates significant improvements over prior unsupervised methods.

Transfer learning evaluation on PASCAL VOC: In Table 7 we evaluate the task and dataset generalization of our unsupervised learned features by fine-tuning them on the PASCAL VOC classification, detection, and segmentation tasks. As with the ImageNet classification task, we outperform by significant margin all the competing unsupervised methods in all tested tasks, significantly narrowing the gap with the supervised case. Notably, the PASCAL VOC 2007 object detection performance that our self-supervised model achieves is 54.4% mAP, which is only 2.4 points lower than the supervised case. We provide the per class detection performance of our method in Table 8 (in appendix B).

Places classification task:

In Table 6 we evaluate the task and dataset generalization of our approach by training linear (logistic regression) classifiers on top of the learned features in order to perform the 205-way Places classification task. Note that in this case the learnt features are evaluated w.r.t.

APPENDIX A VISUALIZING ATTENTION MAPS OF ROTATED IMAGES

Here we visualize the attention maps generated by an AlexNet model trained on the self-supervised task of rotation recognition for all the rotated copies of a few images. We observe that the attention maps of all the rotated copies of an image are roughly the same, i.e., the attention maps are equivariant w.r.t. the image rotations. This practically means that in order to accomplish the rotation prediction task the network focuses on the same object parts regardless of the image rotation.

Attention maps of Conv3 feature maps (size: 13 × 13)

APPENDIX B PER CLASS BREAKDOWN OF DETECTION AND CLASSIFICATION

PERFORMANCE

In Tables 8 and9 we report the per class performance of our unsupervised learning method on the PASCAL detection and CIFAR-10 classification tasks respectively.


Christophe Regnault
email: christophe.regnault@gmail.com

Marie Usal
email: marie.usal@hotmail.fr

Sylvie Veyrenc
email: sylvie.veyrenc@univ-grenoble-alpes.fr

Karine Couturier
email: karine.couturier@univ-grenoble-alpes.fr

Cécile Batandier
email: cecile.batandier@univ-grenoble-alpes.fr

Anne-Laure Bulteau
email: anne-laure.bulteau@ens-lyon.fr

David Lejon
email: dlejon@rovaltainresearch.com

Alexandre Sapin
email: asapin@rovaltainresearch.com

Bruno Combourieu
email: bcombourieu@rovaltainresearch.com

Maud Chetiveaux
email: maud.chetiveaux@univ-nantes.fr

Le May Cédric
email: cedric.lemay@univ-nantes.fr

Lafond Thomas
email: thomas.lafond@univ-rennes1.fr

Muriel Raveton
email: muriel.raveton@univ-grenoble-alpes.fr

Stéphane Reynaud
email: stephane.reynaud@univ-grenoble-alpes.fr

Unexpected metabolic disorders induced by endocrine disruptors in Xenopus tropicalis provide new lead for understanding amphibian decline

Keywords: endocrine disruptors, metabolic syndrome, amphibian population decline, transgenerational, Xenopus tropicalis

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

Introduction

Dramatic declines in amphibian populations in wetlands have been described worldwide since the 1980s [START_REF] Lips | Riding the wave: reconciling the roles of disease and climate change in amphibian declines[END_REF], with current extinction rates reaching levels that are 211 times higher than background levels [START_REF] Roelants | Global patterns of diversification in the history of modern amphibians[END_REF]. It has been suggested that multiple pollution of wetlands by endocrine disruptors (ED) plays a key role, together with other well-known threats including habitat loss, the introduction of exotic species, increased UV radiation, water acidification, and emerging infectious diseases [START_REF] Hayes | The cause of global amphibian declines: a developmental endocrinologist's perspective[END_REF][START_REF] Carey | Amphibian declines: an immunological perspective[END_REF][START_REF] Kiesecker | Synergism between trematode infection and pesticide exposure: a link to amphibian limb deformities in nature[END_REF]. Some field studies using landscape-scale data have suggested that ED may correlate with population decline in several amphibian species. Therefore, it was suggested that contamination by ED is one of the key contributing factors responsible for altering amphibian population fitness [START_REF] Fedorenkova | Ranking ecological risks of multiple chemical stressors on amphibians[END_REF]. However, despite evidence suggesting that when the first symptoms of ED toxicity arise in an amphibian population it is often too late to stop the destructuring of the community and the population decline processes [START_REF] Hayes | The cause of global amphibian declines: a developmental endocrinologist's perspective[END_REF], the role of ED in this phenomenon has been called into question by a number of researchers who have suggested that amphibians are no more vulnerable to ED than other species [START_REF] Kaplan | Amphibians rarely give earliest warning of pollution[END_REF]. ED have been extensively studied in vertebrates, focusing on their capacity to interfere with reproductive development and sex differentiation. Exposure to ED at the larval stage can disrupt organogenesis and gonadal differentiation [START_REF] Helbing | The metamorphosis of amphibian toxicogenomics[END_REF][START_REF] Langlois | Fadrozole and finasteride exposures modulate sex steroid-and thyroid hormone-related gene expression in Silurana (Xenopus) tropicalis early larval development[END_REF][START_REF] Crump | Exposure to the herbicide acetochlor alters thyroid hormone-dependent gene expression and metamorphosis in Xenopus Laevis[END_REF][START_REF] Opitz | Description and initial evaluation of a Xenopus metamorphosis assay for detection of thyroid system-disrupting activities of environmental compounds[END_REF][START_REF] Hammond | Influence of temperature on thyroid hormone signaling and endocrine disruptor action in Rana (Lithobates) catesbeiana tadpoles[END_REF][START_REF] Hayes | Atrazine induces complete feminization and chemical castration in male African clawed frogs (Xenopus laevis)[END_REF][START_REF] Hayes | Herbicides: feminization of male frogs in the wild[END_REF] by acting on thyroid and reproductive axes crosstalk [START_REF] Duarte-Guterman | Mechanisms of crosstalk between endocrine systems: regulation of sex steroid hormone synthesis and action by thyroid hormones[END_REF]. Another sensitive phase is the breeding season during which exposure of adult frogs to ED at critical phases can lead to egg and sperm maturation defects [START_REF] Hayes | Demasculinization and feminization of male gonads by atrazine: consistent effects across vertebrate classes[END_REF][START_REF] Fan | Atrazine-induced aromatase expression is SF-1 dependent: implications for endocrine disruption in wildlife and reproductive cancers in humans[END_REF][START_REF] Fan | Herbicide atrazine activates SF-1 by direct affinity and concomitant co-activators recruitments to induce aromatase expression via promoter II[END_REF][START_REF] Hayes | Characterization of atrazine-induced gonadal malformations in African clawed frogs (Xenopus laevis) and comparisons with effects of an androgen antagonist (cyproterone acetate) and exogenous estrogen (17beta-estradiol): Support for the demasculinization/feminization hypothesis[END_REF][START_REF] Hayes | Hermaphroditic, demasculinized frogs after exposure to the herbicide atrazine at low ecologically relevant doses[END_REF][START_REF] Urbatzka | Endocrine disrupters with (anti)estrogenic and (anti)androgenic modes of action affecting reproductive biology of Xenopus laevis: I. Effects on sex steroid levels and biomarker expression. Comparative biochemistry and physiology[END_REF][START_REF] Cevasco | Endocrine disrupting chemicals (EDC) with (anti)estrogenic and (anti)androgenic modes of action affecting reproductive biology of Xenopus laevis: II. Effects on gonad histomorphology. Comparative biochemistry and physiology[END_REF][START_REF] Orton | Do hormone-modulating chemicals impact on reproduction and development of wild amphibians?[END_REF][START_REF] Safholm | Risks of hormonally active pharmaceuticals to amphibians: a growing concern regarding progestagens[END_REF]. In addition to these developmental and reproductive effects, there are also growing concerns that metabolic disorders might be linked with ED [START_REF] Casals-Casas | Endocrine disruptors: from endocrine to metabolic disruption[END_REF]. Data from epidemiological studies suggest that environmental ED might account for a substantial proportion of the incidence of metabolic diseases in humans [START_REF] Chevalier | Endocrine disruptors: new players in the pathophysiology of type 2 diabetes?[END_REF][START_REF] Chevalier | Bisphenol A: Targeting metabolic tissues[END_REF][START_REF] Chevalier | Endocrine disruptors: A missing link in the pandemy of type 2 diabetes and obesity?[END_REF]. Despite these epidemiological correlations and experimental evidence in mammals [START_REF] Jiang | Mitochondrial dysfunction in early life resulted from perinatal bisphenol A exposure contributes to hepatic steatosis in rat offspring[END_REF][START_REF] Alonso-Magdalena | Bisphenol A exposure during pregnancy disrupts glucose homeostasis in mothers and adult male offspring[END_REF][START_REF] Susiarjo | Bisphenol a exposure disrupts metabolic health across multiple generations in the mouse[END_REF], the potential involvement of ED in metabolic disorders as a possible cause of a reduction in fitness in amphibians has been largely neglected. Recent studies did demonstrate that acute exposure to benzo[a]pyrene (BaP) and triclosan (TCS) can induce marked metabolic disorders in female and male Xenopus tropicalis with an insulin resistance-like (IR) syndrome phenotype and hepatotoxicity due to impaired lipid metabolism [START_REF] Regnault | Metabolic and immune impairments induced by the endocrine disruptors benzo[a]pyrene and triclosan in Xenopus tropicalis[END_REF][START_REF] Regnault | Impaired liver function in Xenopus tropicalis exposed to benzo[a]pyrene: transcriptomic and metabolic evidence[END_REF]. However, the precise impact of ED, at concentrations typically found in aquatic ecosystems, on metabolism and the subsequent consequences on amphibian health and their decline is unknown.

Here we reveal the real impact of ED on amphibian metabolism and their consequences in terms of fitness by studying two widespread ED, BaP and TCS, at environmental concentrations [START_REF] Dortch | Modeling water and sediment contamination of Lake Pontchartrain following pump-out of Hurricane Katrina floodwater[END_REF][START_REF] Perez | Triclosan occurrence in freshwater systems in the United States (1999-2012): a meta-analysis[END_REF] in the range of safe drinking water [START_REF] Canada | Guidelines for Canadian Drinking Water Quality: Guideline Technical Document -Benzo[a]pyrene[END_REF][START_REF] Who | Polynuclear aromatic hydrocarbons in Drinking-water. Background document for development of WHO. in Guidelines for drinking-water quality[END_REF]. Exposing female Xenopus from tadpole to mature adult stage (12 month) to BaP and TCS at 50 ng.L -1 led to a hepatic transcriptomic signature and liver, muscle, and pancreatic physiological impairments typical of a pre-diabetes state. For BaP exposure, the observed metabolic syndrome was irreversible after one year of depuration in clean water. Thus, by testing the relationship between ED exposure and fitness we observed that exposed animals displayed a dramatic decrease in reproductive success and produced progeny (F1) with a decreased capacity to reproduce and produce viable progeny (F2). These data suggest that ED may have a direct causal relationship to amphibian population decline by disrupting energetic metabolism, and provide new insights into their transgenerational effects.

Results and Discussion

Development of ED-exposed animals (F0)

BaP and TCS-exposed animals showed a significant time delay to metamorphosis (21 and 31 days longer to reach 90% frog metamorphosis in the population (Fig. S1a) but no significant decrease in their size and weight at metamorphosis was observed (Fig. S1b-c). Moreover, regardless of the ED considered, no difference in the time delay to achieve sexual maturity was observed in exposed females (Fig. S1d).

ED induce a marked metabolic syndrome in Xenopus typical of a pre-diabetes state

No difference in basal serum glucose levels was found between the exposed and control animals. However, ED exposure led to a reduction in glucose tolerance with a 2.4 to 1.8 fold increase in blood glucose (Fig. 1a). Investigating the physiology of the liver showed that the EDexposed animals which had become glucose intolerant displayed a marked increase in their hepatosomatic index, correlated with marked hepatic steatosis (Fig. 1b-c). This deregulation of lipid metabolism in the liver was associated with marked hypertriglyceridemia (Fig. 1d).

Mitochondria orchestrate energy metabolism from lipids through substrate oxidation via βoxidation [START_REF] Koliaki | Adaptation of hepatic mitochondrial function in humans with nonalcoholic fatty liver is lost in steatohepatitis[END_REF] and mitochondrial dysfunction has been associated with chronic liver steatosis [START_REF] Jiang | Mitochondrial dysfunction in early life resulted from perinatal bisphenol A exposure contributes to hepatic steatosis in rat offspring[END_REF]. Here we showed a mitochondrial uncoupling in exposed animals highlighted by an increase in the State 2 respiratory rate and a decrease of respiratory control index (RCI) (Fig 1 e,f). Fatty liver and mitochondria uncoupling has been associated with oxidative stress [START_REF] Koliaki | Adaptation of hepatic mitochondrial function in humans with nonalcoholic fatty liver is lost in steatohepatitis[END_REF]. A general deficit of Fe-S cluster proteins, such as aconitase, a tricarboxylic acid cycle mitochondrial enzyme that contains a 4F-4S cluster as prosthetic group, is a phenotype commonly associated with oxidative stress in the mitochondria [START_REF] Bulteau | Redox-dependent modulation of aconitase activity in intact mitochondria[END_REF]. Our results suggest that ED exposure induces increased rates of free radical production by the mitochondria that result in aconitase inactivation (Fig. 1g). In addition, since citrate synthase activity remained stable regardless of the treatment considered (Fig. 1h), the changes in mitochondrial parameters described above were not associated with variations in mitochondrial density in the liver. Taken together, our results suggest that liver steatosis induced by ED is linked to mitochondrial dysfunction, and specifically to mitochondrial uncoupling associated with oxidative stress.

Liver lipid accumulation has been also widely associated with hepatoxicity [START_REF] Yamaguchi | Inhibiting triglyceride synthesis improves hepatic steatosis but exacerbates liver damage and fibrosis in obese mice with nonalcoholic steatohepatitis[END_REF]. ED-exposed Xenopus displayed a 2.8 to 3.2 increase in serum activity of alanine aminotransferase (ALAT; Fig. 1i) and liver necrosis associated with pronounced tissue disorganization. Under magnification, the liver tissue in exposed Xenopus showed hepatocytes with fewer cell contacts and irregular shapes together with large areas of necrosis (Fig. 1j-o). Proteasome dysregulation has been associated with endoplasmic reticulum (ER) stress and is the primary event of insulin resistance development [START_REF] Otoda | Proteasome dysfunction mediates obesity-induced endoplasmic reticulum stress and insulin resistance in the liver[END_REF]. Here we found that ED exposure induced a 2-fold decrease in liver proteasome activity (Fig. 1 p) suggesting that general insulin resistance syndromes occur regardless of the pollutant studied. This development of insulin resistance syndrome in EDexposed Xenopus is supported by the 2-fold increase in insulin production by the pancreatic beta cells (Fig. 1q). In type 2 prediabetes, an increased metabolic demand for insulin due to insulin resistance precedes the onset of hyperglycemia. There is therefore a period of normal glycaemia, during which the pancreatic β cells compensate for insulin resistance through the hypersecretion of insulin [START_REF] Kasuga | Insulin resistance and pancreatic beta cell failure[END_REF]. Nonalcoholic fatty liver disease (NAFLD) and its progression to nonalcoholic steatohepatitis (NASH) are hepatic signs of metabolic disorders including prediabetes and insulin resistance syndrome. Histological modifications associated with NAFLD/NASH include steatosis, inflammation, hepatocellular ballooning and fibrosis [START_REF] Takahashi | Histopathology of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis[END_REF]. Liver sections of ED-exposed animals presented severe steatosis, leucocyte infiltrates, and hepatocyte ballooning, especially in BaP-treated animals (Fig. 1k,n). TCS-exposed animals displayed swollen blood vessels typical of NAFLD (44) (Fig. 1o). The severity of the histopathological phenotypes observed suggests that TCS-exposed animals presented livers with modifications typical of NAFLD and that BaP-exposed Xenopus presented livers with modifications typical of progression to NASH. However, regardless of the liver sections considered, and despite the large areas of necrosis, no fibrosis was observed in BaP-exposed frogs (Fig. 1k). In addition to the liver, pancreatic and blood phenotypes, we also observed a marked decrease in muscle glycogen contents for ED-exposed animals (Fig. 1r) suggesting an insulin resistance syndrome in the skeletal muscle [START_REF] Petersen | The role of skeletal muscle insulin resistance in the pathogenesis of the metabolic syndrome[END_REF]. Taken together, our results suggest that ED exposure may lead to a prediabetes phenotype and more generally to the development of large metabolic syndrome in frogs exposed throughout their life cycle. Moreover, we observed a marked increase in the liver glycogen content in BaP-exposed animals (Fig. 1s). These results highlight that the metabolic syndrome observed with this compound is associated with a glycogenic hepatopathy which has been described in humans as a complication of uncontrolled diabetes [START_REF] Satyarengga | Glycogenic Hepatopathy: A Complication of Uncontrolled Diabetes[END_REF].

Liver transcriptomic signatures in ED-exposed animals confirm the metabolic syndrome

Liver transcriptomes of ED-exposed animals highlighted the differential transcription of 322 and 121 genes for BaP and TCS, of which 40 and 66 % were up regulated, respectively (Tables S1 andS2). Interestingly, despite having a similar prediabetes phenotype, the liver transcriptomic signatures varied for different treatments. KEGG pathway enrichment analyses performed on the differentially transcribed genes indicate a link between BaP treatment and "Metabolic pathways" (2.9-fold), "Biosynthesis of amino acids" (3.5-fold), "Steroid hormone biosynthesis" (3.7-fold); "Peroxisome" (3.7-fold), "Chemical carcinogenesis" (3.8-fold); "Carbon metabolism" (3.8-fold); "Metabolism of xenobiotics by cytochrome P450" (4.1-fold); "Glycine, serine and threonine metabolism" (7.8-fold); "Glyoxylate and dicarboxylate metabolism" (8-fold); "Protein export" (13.2-fold) and "Protein processing in endoplasmic reticulum" (5.4-fold) (Fig. 2a). For TCS exposure a significant enrichment was observed for "FoxO signaling pathway" (3.7-fold); "Protein processing in endoplasmic reticulum" (3.4-fold) "Metabolism of xenobiotics by cytochrome P450" (7.4-fold) and "Chemical carcinogenesis" (7.1-fold) (Fig. 2a). Interestingly, the "Metabolism of xenobiotic by cytochrome P450" and "Chemical carcinogenesis" pathway enrichment found for both ED do not correlate with the differential transcriptions of genes involved in BaP and TCS metabolism or bioactivation. This suggests that frogs' strategy for xenobiotic protection is not linked with the induction of biotransformation activities [START_REF] Regnault | Metabolic and immune impairments induced by the endocrine disruptors benzo[a]pyrene and triclosan in Xenopus tropicalis[END_REF][START_REF] Regnault | Impaired liver function in Xenopus tropicalis exposed to benzo[a]pyrene: transcriptomic and metabolic evidence[END_REF] S1 andS2).

(Fig 2b and Tables

Chronic ER stress due to the accumulation of unfolded proteins in the liver has been described in various models of NAFLD [START_REF] Pagliassotti | Endoplasmic reticulum stress in obesity and obesity-related disorders: An expanded view[END_REF]. Here we found a marked enrichment of the pathways linked to protein processing and export, highlighted by a decrease in ED-exposed animals in the transcription of genes coding for proteins involved in adaptive unfolded protein response (UPR) (hspa5, dnajc3, dnajc9, dnajb9, dnajb11, pdia4, hsp90b1, calr, Fig2.c, e and Tables S1 andS2).

Since ER stress has been linked to the inhibition proteasome activity (41) these results suggest that the adaptive UPR response was inhibited by ED exposure in frogs. This type of inhibition has been linked to cell death [START_REF] Bensellam | Hypoxia reduces ER-to-Golgi protein trafficking and increases cell death by inhibiting the adaptive unfolded protein response in mouse beta cells[END_REF] and explains the large areas of necrosis observed in the liver sections of BaP-exposed animals (Fig. 1k).

The other pathways found to be enriched must be considered as a whole and suggest that an IR phenotype occurs in X. tropicalis exposed to ED. These pathways mainly encompass genes linked to carbohydrate or lipid metabolism (Fig 2b,d). In addition, we found several other differentially transcribed genes for which a deregulation has also been associated with IR and NAFLD/NASH (Fig 2e). IR is characterized by an increase in the enzyme-coding transcripts involved in gluconeogenesis, such as glucose-6 phosphatase (g6pc) or glyoxylate reductase (grhpr.1) [START_REF] Bechmann | The interaction of hepatic lipid and glucose metabolism in liver diseases[END_REF][START_REF] Burri | The Role of PPARalpha Activation in Liver and Muscle[END_REF]. Indeed, we found a marked overtranscription of the g6pc (6.8-fold for BaP and 2.5-fold for TCS) and grh.1 (2.6-fold for BaP) genes. Moreover, the high overtranscription of the insulin-like growth factor 1 binding protein (igfbp-1) repressed by insulin in physiological conditions [START_REF] Brismar | Effect of insulin on the hepatic production of insulin-like growth factor-binding protein-1 (IGFBP-1), IGFBP-3, and IGF-I in insulin-dependent diabetes[END_REF] confirmed this phenotype for BaP-exposed Xenopus (Fig. 2b,e). The high activation of gluconeogenesis in BaP-exposed animals might be related to the high overtranscription of the nuclear receptors nr4a1 (6.7-fold) and nr4a2 (14-fold) which are key activators of glucose production in the liver (52) (Fig. 2 e). IR syndrome has also been associated with a deregulation of lipid metabolism, leading to the accumulation of triglycerides (steatosis) in the liver [START_REF] Naik | Genomic aspects of NAFLD pathogenesis[END_REF]. Here we found that the marked liver steatosis observed in ED-exposed animals was linked to the deregulation of several genes controlling lipid metabolism. Indeed, we found a marked under-transcription of sulfotransferase 2b1 (sult2b1) (0.21 for BaP and 0.35-fold for TCS), an enzyme known to protect the liver from steatosis and to inhibit lipogenesis [START_REF] Musso | Cholesterol metabolism and the pathogenesis of non-alcoholic steatohepatitis[END_REF]. Moreover, BaP-exposed animals presented a marked deregulation of the patatin-like phospholipase domain containing 3 (pnpla3, 0.26-fold) gene whose inhibition has been associated with lipid accumulation in the liver [START_REF] Mondul | PNPLA3 I148M Variant Influences Circulating Retinol in Adults with Nonalcoholic Fatty Liver Disease or Obesity[END_REF]. In TCS-exposed animals, several genes associated with liver triglyceride or cholesterol accumulation were found to be overtranscribed, including the 7-dehydrocholesterol reductase (dhcr7, 2.75-fold) and the Kruppel-like factor 2 (klf2, 2.52-fold) [START_REF] Regnault | Impaired liver function in Xenopus tropicalis exposed to benzo[a]pyrene: transcriptomic and metabolic evidence[END_REF][START_REF] Chen | Kruppel-like factor 2 promotes liver steatosis through upregulation of CD36[END_REF].

As described above, physiological observations indicated a gradation between liver manifestations of IR in ED-exposed animals. Liver symptoms varied from NAFLD for TCSexposed animals to a progression to NASH in BaP-exposed Xenopus (Fig1). Transcriptomic data confirmed these phenotypes with a higher number of genes highlighting the severity of liver dysfunction for BaP exposure. Indeed, we found a marked under-transcription of several genes involved in lipid metabolism (acss2.2, 0.24-fold; elovl2, 0.35-fold; hmgcs1, 0.31-fold; mgat2, 0.34-fold) for which a deregulation has been associated with the progression from NAFLD to NASH [START_REF] Nagaya | Down-regulation of SREBP-1c is associated with the development of burned-out NASH[END_REF]. NASH is also associated with inflammatory processes [START_REF] Takahashi | Histopathology of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis[END_REF]. Here we found that the leukocyte infiltrates observed in BaP-exposed animals were associated with the deregulation of key genes involved in inflammation including cd3e (3.15-fold), cd40 (3.34-fold), fas (2.11-fold), gadd45g (2.15-fold), susd1 (3.15-fold), vasp (0.48-fold), vegfc (2.75-fold).

BaP-induced metabolic syndrome and glucose intolerance are not reversible

In order to test the reversibility of the symptoms observed we performed a one-year depuration of the exposed animals. Interestingly, whereas the glucose intolerance phenotype appeared reversible in TCS-exposed Xenopus, individuals exposed to BaP remained glucose intolerant after one year of depuration in clean water (Fig. 3a). However, whichever ED was considered, the depurated animals still displayed a marked increase in their hepatosomatic index correlated with marked hepatic steatosis (Fig. 3b-c). These hepatic manifestations were accompanied by a marked insulin secretory defect regardless of the ED exposure considered (Fig. 3d). Since only the BaP-exposed animals remained glucose intolerant, our results suggest that the marked liver damage induced by this ED is not reversible.

ED-induced metabolism impairments are associated with decreased fitness and multigenerational effects.

Our data reveal that ED at environmental concentrations induce a marked metabolic impairment in female Xenopus, on which the resources found in the eggs are entirely dependent [START_REF] Croce | Independent responsiveness of frog liver low-density lipoprotein receptor and HMGCoA reductase to estrogen treatment[END_REF]. In order to test the consequences of parental exposure on their progeny, these females were mated with exposed males. Out of the five matings we performed for each exposure condition, only two were successful in TCS-exposed animals. Indeed, despite their appropriate sexual maturity, three out of five TCS-exposed females did not accept the male, suggesting that TCS impairs sexual behavior in amphibians, as has been previously described in fish [START_REF] Schultz | Effects of triclosan and triclocarban, two ubiquitous environmental contaminants, on anatomy, physiology, and behavior of the fathead minnow (Pimephales promelas)[END_REF].

Like their BaP-exposed genitor, the progeny not directly exposed to ED showed a significant time delay to metamorphosis (35 days more to reach 90% frog metamorphosis in the population (Fig. 4a) demonstrating the multigenerational impact of ED. Moreover, regardless of the ED considered, successful mating produced F1 individuals of smaller size and weight (Fig 4 a-b) demonstrating a multigenerational aggravation of the phenotype observed in their genitors.

These results may have a major impact on amphibian population decline. Indeed, delayed metamorphosis, along with decreased size at metamorphosis reduces adult recruitment and the likelihood of reproduction in amphibians [START_REF] Smith | Adult recruitment in chorus frogs: effects of size and date at metamorphosis[END_REF]. Smaller size at metamorphosis limits food availability for newly-metamorphosed frogs because of gape limitation and increases their vulnerability to predators which are also gape-limited [START_REF] Vito | Effects of snake predation on aggregation and metamorphosis of Pacific treefrog (Hyla regilla) larvae[END_REF]. In addition, decreased size at metamorphosis combined with subsequent decreased post-metamorphic growth decreases the amphibians' chances of surviving overwintering. This negative effect is especially true for females, for which size is directly proportional to fecundity [START_REF] Hayes | Pesticide mixtures, endocrine disruption, and amphibian declines: are we underestimating the impact?[END_REF]. Here we found that F1 females from BaP-exposed genitors showed a significant time delay to achieving sexual maturity (141 days more to reach 90% female mature in the population (Fig. 4 c). In order to evaluate the reproductive success of F1 progeny and to study the fitness of their exposed genitors, five matings were performed with F1 animals from control and BaP-exposed parents, and only one for TCS (corresponding to the only two adult Xenopus surviving from F0 matings under this condition). Interestingly, regardless of the ED considered, a disturbance in mating behavior was observed, characterized by a delayed time for amplexus formation. At three hours post stimulation; only two amplexus were successful for F1 from BaP parental exposure and that from TCS parental exposure was not successful (Fig. 4e). F1 mates performed for each parental exposure resulted in a highly variable number of hatched eggs. However, a strong tendency revealed a dramatic decrease in the number of eggs hatched by F1 individual from BaP and TCS-exposed parents (Fig. 4f) suggesting a decrease fitness induced by ED exposure.

Much emphasis has been placed on implicating ED in amphibian population decline. As suggested by epidemiological studies in humans, we found that ED can induce severe metabolic disorders in amphibians at concentrations in the range of those considered safe for higher vertebrates. Moreover, parental exposure to ED can alter the development and the reproduction of their progeny, which could result in decreased fitness. Taken as a whole, the findings from this study constitute a starting point for considering ED as a contributing cause of amphibian population decline through their disruption of energetic metabolism, and provide new insights into the transgenerational effects of ED, not only in amphibians, but also in higher vertebrates including humans

Methods

Animals

Pre-metamorphic Xenopus tropicalis aged seven days were purchased from the "Xenopus national husbandry" (CRB "Xenopes"), University of Rennes 1 (http://xenopus.univ-rennes1.fr/).

Xenopus exposure to benzo[a]pyrene (BaP) and triclosan (TCS)

Xenopus were exposed collectively at 25°C in tanks (150 tadpoles per condition). The medium was partially renewed every day (1/4 of the total volume) and completely renewed weekly with a solution of MMR containing either BaP (97% purity, Sigma-Aldrich, St-Louis, MO, USA) or TCS (97% purity, Sigma-Aldrich) at an initial concentration of 200 ng.L -1 . The control frogs were exposed to Dimethyl sulfoxide (DMSO, vehicle) at 4/1000 (v:v) concentration. Concentrations of pollutants in the media over time were controlled by GC-MS / MS. For this purpose, freshly prepared and contaminated media were placed in the same tanks and with the same light, temperature and Xenopus density conditions for populations exposed during the experiment.

The results (not shown) reveal that 85% of BaP and 87% of TCS were absorbed within 24 hours and that the periodicity of the medium change was equivalent to a weekly average concentration of 50 ng.L -1 . These concentrations were chosen to match the BaP or TCS concentrations usually found in polluted waters [START_REF] Dortch | Modeling water and sediment contamination of Lake Pontchartrain following pump-out of Hurricane Katrina floodwater[END_REF][START_REF] Perez | Triclosan occurrence in freshwater systems in the United States (1999-2012): a meta-analysis[END_REF] and are in the range of maximal safe drinking water concentrations for BaP [START_REF] Canada | Guidelines for Canadian Drinking Water Quality: Guideline Technical Document -Benzo[a]pyrene[END_REF][START_REF] Who | Polynuclear aromatic hydrocarbons in Drinking-water. Background document for development of WHO. in Guidelines for drinking-water quality[END_REF], no data for TCS available yet.

After 12 months of exposure, 4 females for each exposure condition were used to perform a glucose tolerance test and 8 others (divided in two pools of four to ensure sufficient material for performing all experiments) were rapidly sacrificed with a blow to the head and immediately dissected to sample the liver, the pancreas and the muscles. Blood was collected by means of an intra-cardiac puncture. For each exposure condition three females were transferred into clean medium and maintained for 12 months to mimic a depuration period. The organ sampling procedure was carried out as previously described.

Hepatosomatic index.

Each Xenopus and their liver were weighed to the nearest 0.1 mg and the hepatosomatic index (HSI) was calculated as follows: HSI = (liver weight [g]) × (total body weight [g]) -1 × 100

Histological observations

After dissection, the liver (same lobe/frog) and the pancreas were embedded in Tissue OCT (Labonord -VWR, Templemars, France), immediately flash frozen in liquid nitrogen, and conserved at -80°C. Transverse sections (7 μm) were produced using a cryostat (CM3050 S, Leica, Nussloch, Germany) with the temperature of the cryochamber and specimen set at -25°C.

Frozen sections were mounted on Super-Frost plus microscope slides (Labonord).

The frozen sections of the liver were stained with Oil Red O (ORO) to assess total lipid hepatic content as previously described [START_REF] Regnault | Impaired liver function in Xenopus tropicalis exposed to benzo[a]pyrene: transcriptomic and metabolic evidence[END_REF].

Hepatic glycogen content was estimated using Periodic Acid Schiff (PAS) staining. The slides were briefly rinsed in water and incubated with 0.5% periodic acid for 10 min before staining with Schiff reagent for 15 min and hematoxylin for 1 min. The sections were dehydrated by 1 min incubation in 70% ethanol, followed by 1 min incubation in 95% ethanol, followed by 1 min incubation in toluene. PAS scoring was performed by two people independently. PAS staining score (0 to 4) was assigned depending on the percentage of glycogen in cells. A score of 0 corresponds to one cell without glycogen and a score of 4 corresponds to one cell filled with glycogen.

Frozen pancreas sections were fixed for 10 min in 4% formaldehyde and washed in phosphate buffered saline (PBS) containing 0.5% bovine serum albumin (BSA). A saturation step was performed for 30 min in PBS containing 5% BSA. The sections were then incubated overnight at 4°C with guinea pig anti-human insulin serum (Y370; Yanaihara Institute, Fujinomiya, Japan) diluted 1:500 in PBS-BSA 0.5%. The sections were then washed again in PBS-BSA 0.5% and incubated with goat anti-guinea pig IgG (H+L) FITC-conjugated (SouthernBiotech) diluted 1:200 in PBS-BSA 0.5%, for 30 min at room temperature, in the dark. We used the Converter AP (Roche) on the frozen sections according to the manufacturer's instructions to convert the fluorescence signal into a colorimetric signal. Revelation was performed using Fast Red (Sigma-Aldrich, France) as substrate for 15 min. After labelling, the frozen sections were counterstained with Mayer's Haemalun (Merck, Germany) and mounted with aqueous mounting medium (aquatex®,Merck, Germany). Each slide (one per animal) was photographed under a Nikon Eclipse E600 microscope using an Olympus DP70 digital camera. Two to five pictures were taken, corresponding to the five hot spots of insulin labelling by section. Each picture was imported into the GNU Image Manipulation Program (GIMP v2.8.6) and the area of each labelling acini found in the pictures was quantified with the histogram dialog. Because the insulin appeared magenta on the pictures, the labelling intensity was estimated by the inverse of the green channel level, again using the histogram dialog. For each slide, this intensity was quantified as previously described [START_REF] Regnault | Impaired liver function in Xenopus tropicalis exposed to benzo[a]pyrene: transcriptomic and metabolic evidence[END_REF] and used to standardize acini intensity. Insulin production was obtained as a multiplication of the acinus area by the standardized labelling intensity for each acinus.

Hepatic mitochondrial respiration

The mitochondria were prepared as described in Garait et al. [START_REF] Garait | Fat intake reverses the beneficial effects of low caloric intake on skeletal muscle mitochondrial H(2)O(2) production[END_REF] (isolation medium: pH 7.4, 250 mM sucrose, 20 mM Tris, 1 mM EGTA). Mitochondrial protein concentration was determined with a Pierce modified Lowry protein assay, according to the manufacturer's instructions (ThermoFisher Scientific). The mitochondrial oxygen consumption rate was measured polarographically with a Clark oxygen electrode, in a closed and stirred glass cell. The respiration medium (pH 7.2, 125 mM KCl, 20 mM Tris, 5 mM Pi, 1 mM EGTA) was supplemented with 5 mM glutamate, 2.5 mM malate and 5 mM succinate for measuring complex I and complex II-mediated mitochondrial respiration (State 2). States 3 respiration was measured after the addition of 500 mM adenosine diphosphate (ADP) and the respiratory control index (RCI) was defined as the ratio state 3/state 2.

Liver proteasome activity

Peptidase activity of the proteasome was assayed using a fluorogenic peptide, succinyl-Leu-Leu-Val-Tyr-7-Amido-4-Methylcoumarin (LLVY-AMC) Sigma-Aldrich (Saint Quentin Falavier, France) as previously described [START_REF] Bulteau | Age-dependent declines in proteasome activity in the heart[END_REF].

Liver aconitase activity

Mitochondria were suspended in 25 mM phosphate buffer pH 7.25 supplemented with 0.05% Triton X-100 and aconitase activity was assayed spectrophotometrically at 340 nm, as previously described [START_REF] Bulteau | Redox-dependent modulation of aconitase activity in intact mitochondria[END_REF].

Liver citrate synthase activity

Citrate synthase activity was measured as the rate of appearance of thionitrobenzoic acid followed by the increase of absorbance at 412 nm during 3 min in the presence of 10 μg of isolated mitochondria proteins, 100 mM Tris-HCl pH8.0, 0.1%Triton X100, 300 mM acetylCoA, 100μM DTNB, and 500 μM oxaloacetate.

Muscle glycogen content

40 mg of muscle were lysed in 40% KOH at 100°C during 30 min before cooling and glycogen precipitation with 100% ethanol during one night at -20°C. After centrifugation at 10,000 x g for 15 min at 4°C, the glycogen pellet was hydrolyzed in glucose by incubation in 2N HCL at 100°C during 3h. After neutralization with 2N NaOH, the concentration of glucose was measured with the GAGO-20 kit (Sigma Aldrich, France) following the manufacturer's instructions.

Glucose tolerance test

Blood glucose was measured by digital sampling, 0, 1, 2, and 4 h after glucose injection in the dorsal lymph sac (1 mg.g -1 fresh mass) using a handheld plasma calibrated glucometer (Accu-Chek performa, Roche Diagnostics, Meylan, France). For statistical comparison, areas under curve after glucose challenge were used.

Alanine aminotransferase activity in the serum

Serum was obtained from fresh blood centrifugation (3,000 × g for 10 min at 4 °C) and stored at -80°C. Alanine aminotransferase (ALAT) activity was measured using 3 mL of pure Xenopus serum, according to the manufacturer's instructions (Alanine Aminotransferase Activity Assay Kit, Sigma-Aldrich).

Plasma Triglyceride measurements

Plasma total triglyceride concentrations were determined using a triglycerides GPO-PED kit (Sobioda, France).

Statistics

For F0 individual data are expressed as the mean ± SEM. Each value was derived from 4 individual experiments for frogs immediately sacrificed after 12 months' exposure and from 3 individual experiments for frogs sacrificed after 12 months of depuration post-exposure. Since the nature of the distribution of the results was unknown (Gaussian or not) for 4 or 3 replicates, the comparison of each treatment (either BaP or TCS) with a single control, was performed using a Wilcoxon test.

The distributions of F0 and F1 individual development times were compared between control populations and each parental ED-exposed population separately with a Kaplan-Meyer test to evaluate global curve differences.

The distribution of F0 and F1 female maturity time between control populations and each parental ED-exposed population was determined separately with a Kaplan-Meyer test to evaluate global curve differences.

For the comparison of the size and weight of F1 individuals, since the nature of the distribution of the results was not Gaussian for TCS parental exposure, the comparison of each parental treatment (either BaP or TCS) with a single control, was performed using a Wilcoxon test.

Liver RNA extraction and sequencing

For each biological replicate, total RNA was extracted from 15 mg liver using the RNAqueous®-4PCR Kit (Ambion, USA) according to manufacturer's instructions. Total RNA quality and quantity were controlled on an Agilent 2100 Bioanalyzer (Agilent, USA). RNA-seq libraries were prepared using the TruSeq Stranded mRNA sample Prep kit (Illumina) and sequenced on an Illumina GAIIx sequencer as 75 bp reads by Hybrigenics-Helixio (Clermont-Ferrand, France).

Mapping reads on the Xenopus genome

Sequenced reads were assigned to each sample (unplexing) and adaptors were removed. The quality of the reads for each sample was checked using FastQC. The reads were then filtered based on their length, pairing and quality using Trimmomatic (v0.32.1) [START_REF] Bolger | Trimmomatic: a flexible trimmer for Illumina sequence data[END_REF]. Cleaned reads were mapped to the Xenopus genome (JGI4.2 assembly) with Ensembl annotations (release 79) using Bowtie-0.12.7 / TopHat 2 (v0.6) software (66) (http://tophat.cbcb.umd.edu) (with a maximum intron size of 250,000 bp to discover novel junctions between exons).

Gene expression quantification and differential analysis

The Cufflinks (v0.0.7) tool was applied to TopHat alignments to enumerate for each library the number of short reads overlapping the Ensembl annotated genes [START_REF] Trapnell | Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks[END_REF]. The Cuffmerge (v0.0.6) script implanted in Cufflinks was used to merge multiple assemblies from each sample. Gene transcription levels were computed from alignments carried out using TopHat 2 and compared statistically using the Cuffdiff (v0.0.7) tool implemented in a Galaxy pipeline (http://galaxyproject.org). Genes were considered differentially transcribed when the transcription ratio TR (BaP or TCS treated/control) was > 1.8 in either direction with an adjusted p-value lower than 0.05 after multiple testing correction.

Functional annotation enrichment

Differentially expressed genes were subjected to annotation enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping using the online functional annotation tool DAVID (Database for Annotation, Visualisation and Integrated Discovery, http://david.abcc.ncifcrf.gov/) [START_REF] Huang Da | Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists[END_REF] with all the genes detected in our experiment as background (9,310 genes). Xenopus gene identifiers were transformed into their human orthologs to improve the richness of the output as previously described [START_REF] Regnault | Impaired liver function in Xenopus tropicalis exposed to benzo[a]pyrene: transcriptomic and metabolic evidence[END_REF]. Significance was calculated using a modified Fisher 's Exact test (p < 0.05). Heat maps of expression profiles for genes sharing enriched annotation pathways or other genes associated with these pathways were produced using TM4 Multi experiment Viewer (MeV) software [START_REF] Saeed | TM4: a free, open-source system for microarray data management and analysis[END_REF].

Xenopus mating and progeny development

At the end of the exposure period, five F0 females and ten F0 males per condition were naturally mated. Mating behavior was induced by two injections of human chorionic gonadotropin (hCG) in the dorsal lymph sac of each Xenopus as previously described [START_REF] Showell | Natural mating and tadpole husbandry in the western clawed frog Xenopus tropicalis[END_REF]. After hatching, tadpoles were bred has described above. F1 development was characterized by the time from hatching to metamorphosis together with the snout-vent length and the weight at metamorphosis. At adult stage, F1 animals were mated as described before.

  They were allowed to acclimatize for one week at 25°C with a photoperiod of 12:12 hours in Marc's Modified Ringer's (MMR) 0.1X medium (pH 7.4, NaCl = 10 mM, KCl = 2 × 10 -1 mM, MgSO 4 = 10 -1 mM, CaCl 2 = 2 × 10 -1 mM, HEPES = 5 × 10 -1 mM) prior to starting the experiments. The frogs were fed daily ad libitum with finely ground pellets of trout food (Aquatic 3, Special Diets Services) and the grinding level was adjusted to the size of their jaw throughout their growth. The density of young tadpoles aged two weeks was 50 individuals per 2 L of medium, corresponding to 2 tanks per exposure condition, conditions described below. To satisfy the volume needed over the growth period, frogs were moved to larger tanks prior to the metamorphosis stage (2 tanks of 30 individuals aged 30 days per 4 L of medium, for each condition). At the end of metamorphosis Xenopus were dispatched into 4 tanks (10 individuals aged 150 days per 4 L of medium, for each condition). All experiments were performed in an animal house accredited by the French Ministry of Animal Welfare (i.e. Rovaltain research company, n° A 26 004 1401) and in accordance to the recommendations of the ethics committee ComEth Grenoble -C2EA -12 (animal welfare agreement n° 02545.03).

Fig. 1 :

 1 Fig. 1: Endocrine disruptor exposure leads to metabolic impairments in Xenopus. a,

Fig. 2 :

 2 Fig. 2: Liver transcriptome following endocrine disruptor exposure indicates a marked metabolic impairment typical of IR syndrome. a, the genes showing a significant differential

Fig. 3 :

 3 Fig. 3: Persistence of metabolic impairments in Xenopus after one year of depuration. a,

Fig. 4 :

 4 Fig. 4: Parental exposure to endocrine disruptors leads to delayed metamorphosis time

Acknowledgements

This work was funded by the IRS-IDEX Grant of the ComUE Université Grenoble-Alpes.

Christophe Regnault was funded by Ministry of Higher Education and Research. Marie Usal was funded by the Région Auvergne-Rhône-Alpes.

Data access

RNA-seq sequence data have been deposited at EMBL-EBI (http://www.ebi.ac.uk/ena) under the accession number PRJEB18463.

Author Contributions:

CR and MU prepared the samples, performed mRNAseq and histological experiments, analyzed data, performed statistical analyses and helped to draft the manuscript; SV performed histological manipulations; KC and CB performed mitochondrial experimentations; ALB performed proteasome, aconitase and citrate synthase experiments; DL, AS and BC were in charge of animal rearing and exposure; MC and CLM performed plasma triglyceride measurements; MR designed the study, participated in sample preparation, participated in data analyses and participated in writing the manuscript; SR conceived and coordinated the study, participated in sample preparation, analyzed data and wrote the manuscript. All authors read and approved the final manuscript.

Supplementary figure legends