Adrian-Gabriel Chifu

Sébastien Fournier
email: sebastien.fournier@lsis.org

SegChain: Towards a generic automatic video segmentation framework, based on lexical chains of audio transcriptions

Keywords: CCS Concepts, Information systems → Recommender systems, Video search, Web searching and information discovery, Video retrieval, Story segmentation, Lexical chains, Transcriptions

With the advances in multimedia broadcasting through a rich variety of channels and with the vulgarization of video production, it becomes essential to be able to provide reliable means of retrieving information within videos, not only the videos themselves. Research in this area has been widely focused on the context of TV news broadcasts, for which the structure itself provides clues for story segmentation. The systematic employment of these clues would lead to thematically driven systems that would not be easily adaptable in the case of videos of other types. The systems are therefore dependent on the type of videos for which they have been designed. In this paper we aim at introducing SegChain, a generic unsupervised framework for story segmentation, based on lexical chains from transcriptions. SegChain takes into account the topic changes by perceiving the fluctuations of the most frequent terms throughout the video.

INTRODUCTION

With the multimedia broadcasting development, via increasingly diversified channels and with the democratization of video production, like the case of textual content production, it becomes essential to provide effective means for retrieving the information contained within videos and not only the videos themselves. Furthermore, the very use of audiovisual content is not linear anymore, but becoming an "on demand" service. Users themselves choose the content Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

WIMS'16 June 13-15, 2016, Nîmes, France they wish to watch. However, the large amount of existing videos requires assistance, so that users may pertinently choose what they desire to watch and eventually offer them not a video but rather a series of videos, or going even further, propose a sequence of video segments. To achieve this result, it is essential to have a description of the video and, if we want to go further, a description of the segments or semantically homogeneous chapters within the videos. This is the case of the huge collection of Internet hosted videos that challenge both video content providers and users when it comes to video information retrieval. Thus, one of the challenges is to automatically build such segments and to provide data regarding these segments with the purpose of facilitating their research. In order to automatically build these video segments, according to various studies, one of the most effective solutions is to use the inherent video multimodality. To better exploit multi-modality, while still remaining as generic as possible, it is particularly necessary to understand the interlacing of these various modalities. Indeed, the majority of research is essentially focused so far on the construction and discovery of semantically homogeneous segments within videos coming from the field of television news. Yet, it is a relatively small sub-type and it has a well defined structure (with invariants easy to operate, like the presenter, or some repeating key phrases), facilitating the discovery of these semantically homogeneous segments. Therefore, to cover a wider range of videos, our work is part of a more generic framework. SegChain, the framework that we want to develop, will use all the existing terms within a video, while trying to extract specific elements present in such systems. Therefore, we want at first to study the contribution of each of these terms on border detection between two chapters or segments. Our work in this article focuses on the use of video transcripts to identify the semantically homogeneous segments. For this, we propose in this paper a method based on lexical chains [START_REF] Sitbon | Topic segmentation using weighted lexical links (wll)[END_REF], coupled with similarity measures in order to follow the topic variations reflected in the perception of the term fluctuation throughout the video.

The educational video (MOOC) has lately known a popularity boost. Many educational and online course platforms have been developed, thus the large amount of video content should be treated such that the information could be optimally retrieved and exploited. We therefore consider, in addition to TV news broadcasts, MOOC videos in our evaluation (see Section 5), since MOOC platforms could benefit from our framework. SegChain could be integrated in multimodal treatment of MOOC videos, being useful for story segmentation, keyword identification, content recommendation, linkage with semantically homogeneous segments from other videos and so on.

The novelty of SegChain consists in the way the lexical chains for the most frequent terms are considered. We introduce a chain compactness measure to filter out the terms that are too dispersed along the transcription. Another distinctive trait is the two-step boundary detection when SegChain seeks first the similarity variation between short segments of transcription text in order to suggest and then checks once again the similarity between the suggestions, in order to aggregate them in larger zones that are semantically homogeneous. SegChain is also language independent, requiring only a stop-word list for the considered language.

This article is organized as follows. The second section presents the related work both on story segmentation and on lexical chains. The third section presents our video segmentation lexical chains-based method that exploits changes in topics through the perception of the fluctuation of these terms throughout the video. The fourth section presents an example of application on a video of a French television newscast, followed by a preliminary evaluation in the fifth section. Finally, the article is concluded and the future work is presented.

RELATED WORK

Story segmentation

Story unit (chapter) detection for videos has been studied with focus on TV news videos and more particularly during TRECVID competitions in 2003 and 2004. In order to achieve such a task, as mentioned in [START_REF] Chua | Story boundary detection in large broadcast news video archives[END_REF] and [START_REF] Hsu | COLUMBIA-IBM NEWS VIDEO STORY SEGMENTATION IN TRECVID[END_REF], multimodal use of available resources (transcript, video and audio dialogue) yielded the best results.

Thus, Dumont et al. [START_REF] Dumont | Automatic Story Segmentation for TV News Video Using Multiple Modalities[END_REF], through machine learning techniques (random forest), combine features such as silence, speaker detection and descriptors obtained from image processing such as visual activity or logo detection to discover the transitions between chapters. In [START_REF] Poulisse | News story segmentation in multiple modalities[END_REF], several modalities are used, such as the text with keywords or named entities, the video with transitions between shots, the audio with the use of silences and the internal structure of TV news.

However, a key element to detect chapters in TV news is the main presenter (anchor) detection and its consequently employment as the starting point of chapters. Thus, O'hare et al. [START_REF] Smeaton | A generic news story segmentation system and its evaluation[END_REF] use the presence of the presenter coupled with techniques based on shot classification. From work which is also based on the presence of a presenter, we can also mention the research of [START_REF] Colace | A Probabilistic Framework for TV-News Stories Detection and Classification[END_REF], [START_REF] Misra | TV news story segmentation based on semantic coherence and content similarity[END_REF] and [START_REF] Zlitni | Automatic topics segmentation for TV news video using prior knowledge[END_REF].

Colace [START_REF] Colace | A Probabilistic Framework for TV-News Stories Detection and Classification[END_REF] use Bayesian networks to identify the presenter and get begining of chapters. Misra [START_REF] Misra | TV news story segmentation based on semantic coherence and content similarity[END_REF] also rely on the presence of main presenter to detect chapters, employing afterwards the color similarity of adjacent shots. In [START_REF] Zlitni | Automatic topics segmentation for TV news video using prior knowledge[END_REF] anchor identification is also employed while building a true signature using the points of interest identification in the image, also including colorimetry. To build this signature, the authors also use the internal structure of the program.

Other work focuses on audio transcript analysis and detects transitions chapters by extracting key terms like "welcome" or "no transition".

Approaches from the literature achieve excellent results, sometimes exceeding 90% or 95% of precision and recall as in the work of [START_REF] Zlitni | Automatic topics segmentation for TV news video using prior knowledge[END_REF]. However, even if newscasts have slightly different structures, they are still often created by following simple rules that provide important clues for automatic content analysis [START_REF] Goyal | Split and Merge Based Story Segmentation in News Videos[END_REF]. Moreover, the systematic taking into account of these clues leads to a strong specialization of systems making them difficult to adapt to other types of videos. These systems are highly dependent on the video program for which they have been designed.

Lexical chains

Various approaches have been applied to speech or text based topic segmentation. One of such approach is lexical chains. For instance, in [START_REF] Kan | Linear Segmentation and Segment Significance[END_REF] the authors rely on text representation as weighted lexical chains. The research from [START_REF] Hearst | TextTiling: segmenting text into multi-paragraph subtopic passages[END_REF], [START_REF] Reynar | Topic Segmentation: Algorithms and Applications[END_REF] and [START_REF] Choi | Advances in domain independent linear text segmentation[END_REF] yields close approaches, using similarities but use different way to represent the text. Another approach, presented by [START_REF] Utiyama | A statistical model for domain-independent text segmentation[END_REF], uses hidden Markov models.

In [START_REF] Ferret | Improving Text Segmentation by Combining Endogenous and Exogenous Methods[END_REF] and in [START_REF] Guinaudeau | Enhancing lexical cohesion measure with confidence measures, semantic relations and language model interpolation for multimedia spoken content topic segmentation[END_REF], the authors extended previous research and try to address some of their drawback by integrating semantically related terms to the segmentation model in order to extend the description of the possible segments.

More recently, [START_REF] Claveau | Topic segmentation of TV-streams by watershed transform and vectorization[END_REF] used an approach that can be seen as an improved variant of text-tiling using new segmentation technique inspired from the image analysis field and relying on a new way to compute similarities between candidate segments called vectorization.

STORY SEGMENTATION BASED ON LEX-ICAL CHAINS

To have an independent and generic approach, we apply the lexical chains using a different text representation and we define additional pretreatment operations on these chains. The proposed method is based on the following hypothesis: within a video segment, there is a homogeneous distribution of the most frequent terms. Obviously, the topic shift involves changes in this distribution. The detection of these changes establishes the borders between two homogeneous segments.

We work in this context with the textual information contained in video transcripts, which is represented here by a subtitle file. This file is composed by multiple subtitle units. A subtitle unit is the text chunk uniquely identified in time by the start and end timestamps. One story unit lasts for 3-4 seconds on average.

Let S be the set of subtitle units such that:

S = {sti}
Let T be the set of the most frequent terms such that:

T = {ti} Definition 1. (Apparition function)
The apparition function determines if a term ti occurs in a subtitle unit stj. The apparition function (denoted app) is defined as follows:

app (ti, stj) = 1, if ti ∈ stj, 0, otherwise , i ∈ [0, |T |] et j ∈ [0, |S|) (1)
Definition 2. (The set of positions of initial terms) In our method, since we have sparse vectors, for each term ti, we shall store only the positions of subtitle units stj for which the function app has a value different than zero. This set is called n0app and it is defined as follows:

n0app (ti) = {j | app (ti, stj) = 0} , i ∈ [0, |T |) and j ∈ [0, |S|) (2)
As shown by Sitbon and Bellot [START_REF] Sitbon | Topic segmentation using weighted lexical links (wll)[END_REF], there are multiple ways to compute the value of a hiatus. We consider the following definition. Definition 3. (Hiatus) A hiatus represents the average of distances between two occurrences divided by the total length of this term's repartition along the entire subtitle set. Thus, the hiatus is defined as follows:

hiat (ti) = |n0app(t i )|-2 k=0 n0app (ti) k+1 -n0app (ti) k |n0app (ti)| -1 , i ∈ [0, N ) (3)
Definition 4. (Lexical chains) For two consecutive occurrences k and k +1 of a term ti, a chain is created between k and k + 1 if the distance between these occurrence positions is inferior or equal to the hiatus value computed for the term ti. For i ∈ [0, |T |) et j ∈ [0, |S|), the belonging of a term position to a chain is computed by:

appC (ti, stj) =          1, if j ∈ n0app (ti) 1, if n0app (ti) k < j < n0app (ti) k+1 and n0app (ti) k+1 -n0app (ti) k < hiat (ti) 0, otherwise (4)
Thereby, a lexical chain is the maximal set of consecutive not null positions in appC, as in equation 5.

chain(ti) = (d, f ∀k, d ≤ k ≤ f, appC (ti, st k ) = 1 and d , f , ∀k , d ≤ k ≤ f , appC ti, st k = 1
(5) The lexical chain length represents the difference between the first and the last position in the chain, as follows:

lenCh (ti) = {f -d | (d, f ) ∈ chain (ti)} (6)
Definition 5. (Chain compactness) The chain compactness defines a chain's discriminant ability. More a chain is compact, more it will be discriminating in relation to the topic change in the video. The chain compactness measure takes into account the first and the last occurrence of a term, the total number of subtitle units, the total number of a term's occurrences, the number of chains in which the term occurs and the maximal length of all the chains of all the terms. It is defined as follows:

comp (ti) = n0app (ti) |n0app(t i )|-1 -n0app (ti) 0 |S| × |n0app (ti)| × |chain (ti) | max j∈[0,N ] (max (lenCh (tj))) (7)
Next, the terms are ordered descending descending with respect to their comp score. These terms are filtered afterwards by their score computed in equation 7. Terms with comp value inferior to a threshold are discarded. The threshold is chosen such that a percentage of the terms are filtered out (from 25% to 40% in our experiments). The new set of terms T is defined as follows:

T = T -{ti | comp (ti) < α} (8)
Definition 6. (Apparition by subtitles) Each subtitle unit is represented by the set of appC for each. We therefore obtain a term apparition set, denoted cSt, with respect to each subtitle unit and defined as follows:

cSt (sti) = appC (tj, sti) | j ∈ 0, |T | (9)
Definition 7. (Subtitle unit similarity) The similarity between two subtitle units corresponds to the cosine similarity between two consecutive subtitle units represented by cSt (equation 10). The similarity is defined as follows:

sim (k) = cos (cSt (st k ) , cSt (st k+1 )) , k ∈ 0, |T | -1 (10)
With the purpose of identifying topic shifts, we first identify the subtitle units that are candidates for borders between two topics. These border positions are the local minima of the similarity values computed by equation 10.

minima = {i | sim (i) ≤ sim (x) , ∀x ∈ [i, i + x]} (11)
Definition 8. (Subtitle unit segment) A subtitle unit segment represents the sum of cSt values between two consecutive local minima positions.

segment = i+1 k=i cSt (st k ) i ∈ minima (12)
Definition 9. (Semantically homogeneous segment border) In order to compute the definitive border positions that separate every two consecutive and semantically homogeneous segments, we compute the similarity between the subtitle unit segments from the segment set. This similarity measure is computed by the function called sim2 which also employs the cosine similarity measure. The similarity value is then compared to a threshold (0.6 in our experiments).

sim2 (k) = cos (segment (k) , segment (k + 1)) , k ∈ [0, |segment| -1) (13)
The border positions for the semantically homogeneous segments are thus the positions that verify the following equation:

f ront = {k | sim2 (k) ≥ β} (14)
To obtain the final results, that is to say the segmentation in semantically homogeneous areas within the video and also to enable the capability of information retrieval inside the video, we propose the algorithm (Algorithm 1) that brings together the elements composing SegChain.

EXAMPLE

We have ran SegChain on a video in order to test it. This video has a length of approximately 40 minutes and it is a TV news broadcast from France TV. In Figure 1 we display the terms considered after the removal of the less discriminant terms, judged by equation 7. In this figure, the terms are ordered by their compactness values of their lexical chain. For instance, the terms "dette" and "pense" are considered as the terms that have the most compact lexical chains. The horizontal lines represent the lexical chains identified for each term. This figure also shows the candidate positions for borders, marked with red vertical lines, as well as the definitive borders (black vertical lines) that split the semantically homogeneous segments. The horizontal axis represents the subtitle units, while the vertical axis represents the terms.

The sim similarity values are displayed in Figure 2 to show the term similarity variation between neighbor subtitle units. From the local minima of this curve the candidate positions for segmentation are selected.

PRELIMINARY EVALUATION

To conduct a preliminary evaluation we considered several aspects: employment of effectiveness measures, evaluation on TV news, evaluation on MOOC videos, comparison with state-of-the-art.

Regarding the evaluation measures, three measures have been employed: P recision, Recall and F measure. These measures are well known in information retrieval and there are widely used in story segmentation evaluation [START_REF] Lin | for Lecture Videos : A Linguistics-Based Approach[END_REF], [START_REF] Misra | TV news story segmentation based on semantic coherence and content similarity[END_REF].

Definition 10. (Precision)

The segmentation precision measures how many good frontiers have been detected among all the detections and it is defined as follows:

P recision = |good f ront| |all detected f ront| , (15)
where |good f ront| represents the number of correctly detected frontiers, with an established degree of confidence, while the total number of detected frontiers is represented by |all detected f ront|. The degree of confidence's value (conf ) represents the number of subtitle units, at left and at right from the detected frontier (detected f ront), for which the following condition is verified:

true f ront ∈ [detected f ront-conf, detected f ront+conf ],
where true f ront represents a real frontier, from the ground truth.

If the condition is true, the detected f ront will be considered as good f ront.

We need to mention that our notion of good f ront is very restrictive, since we set the degree of confidence conf with the value 1. This means that, a detected f ront is a good f ront only if it is itself a true f ront, or at least one of it's left or right neighbors are a true f ront. Definition 11. (Recall) The segmentation recall measures how many good frontiers have been detected among all the true frontiers and it is defined as follows:

Recall = |good f ront| |all true f ront| , (16)
where |all true f ront| represents the number of all true frontiers, from the ground truth.

Definition 12. (F measure) The F measure is the harmonic mean of P recision and Recall values:

F measure = 2 × P recision × Recall P recision + Recall (17)
We evaluated SegChain over the TV news video in French (40 minutes) from Section 4, which has been manually annotated with true frontiers. In order to test the generality of the framework, we also tested the performance on a MOOC video in English (41 minutes). The method is language independent, the only requirement being a stop-word list for the respective language. The ground truth in the MOOC case is automatically generated as follows: several small MOOC videos are gathered together in a single larger video and the ground truth frontiers represent the limits of each small video. We also compare our method's effectiveness with the effectiveness of TextTiling [START_REF] Hearst | TextTiling: segmenting text into multi-paragraph subtopic passages[END_REF], a state-of-the-art method.

The performances of the two methods are displayed in Table 1 and Table 2, in a comparative manner. One can notice that the TextTiling has a better recall in the case of the TV news video, while SegChain yields a better precision and a better F measure value. Having a better precision is a good advantage since our objective is to segment as precisely as possible. On the other hand, on the MOOC video we obtain the same recall value (which is also a good one), and a much better precision, also implying a better F measure value. This is encouraging, having in mind the difficulties of MOOC video segmentation, mentioned below.

The MOOC videos represent a challenge compared to TV news videos, since the structure is different. In most cases, within a MOOC the vocabulary remains the same. On the other hand, in TV news videos the topics are very well segregated, there are hints such as the anchor talking and various keywords. Moreover, from the multi-modal perspective of video image treatment, the shot boundaries from MOOC videos are more difficult to be correctly extracted. For example, in the case of TV news videos the shot changes are more clear (going from studio to field broadcast), while in the case of MOOC videos, the changes are more subtle (the slide background colors remain basically the same when slides are changing). Therefore, the success of a textual segmentation approach is crucial when multi-modal treatment fails.

We also list here several limitations of TextTiling. It requires a context window that impacts severely on the number of segments. For instance, for a window of 30 words (default value), 57 segments have been found, while for a window of 100 words 14 segments have been found. In addition, TextTiling is designed to work with paragraphs and well formatted sentences, and it is not the case of automatically generated audio transcriptions. Moreover, TextTiling outputs segments that basically have the same length, which is unrealistic since the topics can vary in time length in an important manner (from less than a minute up to a few dozens of minutes). The generic framework proposed in this paper does not have these limitations.

CONCLUSION

The research from this article introduced SegChain, an approach that aims at defining a generic framework for video segmentation. In order to achieve this objective, our work is firstly based on audio transcription processing to identify semantically homogeneous segments. For this, we proposed in this article a lexical chain-based method [START_REF] Sitbon | Topic segmentation using weighted lexical links (wll)[END_REF], combined with cosine-based similarity measures in order to detect topic variations through the perception of term fluctuation within chains, all along the video. We provided several definitions that are employed in a semantically homogeneous segment detection algorithm. We have presented an application example over a 40 minute long video.

We have conducted a preliminary evaluation of SegChain on the French TV news broadcast taken as example and on an English MOOC video. The performance of SegChain was compared to the performance of the TextTiling method [START_REF] Hearst | TextTiling: segmenting text into multi-paragraph subtopic passages[END_REF] through three effectiveness measures: P recision, Recall and F measure. SegChain has obtained better performance in terms of P recision and F measure in both cases, but a lower Recall. In the case of the English MOOC the Recall values are the same as TextTiling.

On a short term perspective, we wish to validate our method on the TRECVID 2003-4 that are most commonly employed for this task in order to be able to compare our results with the state of the art.

However, we want to insist on assessing the SegChain's robustness on other video types than news TV, such as various types of MOOCs (of different length, subject, etc.) or videos that have a structure less clearer than news TV, since we believe we would obtain better results on videos with a more "free" structure than methods specifically built for news TV.

Figure 1 :

 1 Figure 1: Segment borders for the lexical chains of the most frequent terms

Figure 2 :

 2 Figure 2: Cosine similarity between neighbor subtitle units

  Algorithm 1 SegChain: The story segmentation method Requires: A subtitle file in TRS or SRT format. Ensures: Subtitle unit IDs corresponding to the segment borders, as well as the most frequent terms for each seg-

		ment.
	1: if TRS file format then
	2:	convert file into SRT format
	3: extract the raw text from the subtitle file
	4: remove punctuation
	5: tokenize text into words
	6: remove stop words
	7: compute term frequency
	8: T ← the most frequent N terms
	9: build S
	10: for all ti ∈ T do
	11:	compute the app (ti, stj) vector
	12:	compute hiat (ti)
	13:	compute chain (ti)
	14:	compute comp (ti)
	15: compute T
	16: for all Stj ∈ S do
	17:	compute sim (st)
	18: compute minima
	19: for all Stj ∈ S do
	20:	compute segment
	21: compute f ront

Table 1 :

 1 Performance measures for TextTiling and SegChain, on the TV news video

	TV news video TextTiling SegChain
	P recision	0.1071	0.3333
	Recall	0.4285	0.2143
	F measure	0.1714	0.2609

Table 2 :

 2 Performance measures for TextTiling and

	SegChain, on the MOOC video
	MOOC video TextTiling SegChain
	P recision	0.0408	0.1429
	Recall	0.6667	0.6667
	F measure	0.0769	0.2353

ACKNOWLEDGMENTS

This work has been carried out thanks to the support of the A*MIDEX project (n • ANR-11-IDEX-0001-02) funded by the "Investissements d'Avenir" French Government program, managed by the French National Research Agency (ANR).


Michaël R Laurent

Stéphane Blouin

Laetitia Davidovic
email: davidovic@ipmc.cnrs.fr

Antoine Leboucher

Patricia Bermudez-Martin

Xavier Mouska

Ez-Zoubir Amri

Didier F Pisani

Fmr1-Deficiency Impacts Body Composition, Skeleton, and Bone Microstructure in a Mouse Model of Fragile X Syndrome Q14 Q1 Q3

Keywords: fragile X syndrome, bone microstructure, skeleton, tomography, trabecula, muscle, physical activity

Fragile X syndrome (FXS) is a neurodevelopmental disorder associated with intellectual disability, hyperactivity, and autism. FXS is due to the silencing of the X-linked FMR1 gene. Murine models of FXS, knock-out (KO) for the murine homolog Fmr1, have been generated, exhibiting CNS-related behavioral, and neuronal anomalies reminiscent of the human phenotypes. As a reflection of the ubiquitous expression of the FMR1 gene, FXS is also accompanied by physical abnormalities. This suggests that the FMR1-deficiency could impact skeletal ontogenesis. In the present study, we highlight that Fmr1-KO mice display changes in body composition with an increase in body weight, likely due to both increase of skeleton length and muscular mass along with reduced visceral adiposity. We also show that, while Fmr1-deficiency has no overt impact on cortical bone mineral density (BMD), cortical thickness was increased, and cortical eccentricity was decreased in the femurs from Fmr1-KO mice as compared to controls. Also, trabecular pore volume was reduced and trabecular thickness distribution was shifted toward higher ranges in Fmr1-KO femurs. Finally, we show that Fmr1-KO mice display increased physical activity. Although the precise molecular signaling mechanism that produces these skeletal and bone microstructure changes remains to be determined, our study warrants further investigation on the impact of FMR1-deficiency on whole-body composition, as well as skeletal and bone architecture.

INTRODUCTION

Fragile X syndrome (FXS) is a neurodevelopmental disorder associated with intellectual disability, Q6 attention deficits, and hyperactivity disorders, and autism spectrum disorders [START_REF] Chelly | Genetics and pathophysiology of mental retardation[END_REF][START_REF] Gecz | The genetic landscape of intellectual disability arising from chromosome X[END_REF]. FXS affects 1/4,000 males and 1/7,000 females worldwide and is caused by the silencing of the X-linked Fragile X Mental Retardation 1 (FMR1) gene located in a fragile chromosomal site in 27q3 [START_REF] Hagerman | The fragile X prevalence paradox[END_REF][START_REF] Oberle | Instability of a 550-base pair DNA segment and abnormal methylation in fragile X syndrome[END_REF]. In FXS patients, abnormal expansions of CGG trinucleotide repeats in the 5 ′ untranslated region of the FMR1 gene lead to an hypermethylation of the upstream CpG island in the gene promotor and to FMR1 silencing [START_REF] Hagerman | The fragile X prevalence paradox[END_REF][START_REF] Oberle | Instability of a 550-base pair DNA segment and abnormal methylation in fragile X syndrome[END_REF][START_REF] Penagarikano | The pathophysiology of fragile x syndrome[END_REF]. Murine Fmr1-knock-out (KO) models of FXS have been generated, exhibiting CNS-related behavioral, and neuronal anomalies reminiscent of the human phenotypes [START_REF] Consortium | Fmr1 knockout mice: a model to study fragile X mental retardation[END_REF][START_REF] Mientjes | The generation of a conditional Fmr1 knock out mouse model to study Fmrp function in vivo[END_REF]. The FMR1 gene is not only expressed in the central nervous system (CNS) but also in a variety of peripheral tissues, including adipose tissue, and liver [START_REF] Devys | The FMR-1 protein is cytoplasmic, most abundant in neurons and appears normal in carriers of a fragile X premutation[END_REF][START_REF] Khandjian | A heterogeneous set of FMR1 proteins is widely distributed in mouse tissues and is modulated in cell culture[END_REF][START_REF] Davidovic | The fragile X syndrome: one protein missing and 1001 disoriented mRNAs[END_REF][START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF]. Adult skeletal and cardiac muscle are the only peripheral tissues tested so far not expressing FMRP, while both white adipose tissue (WAT) and brown adipose tissue (BAT) express FMRP [START_REF] Devys | The FMR-1 protein is cytoplasmic, most abundant in neurons and appears normal in carriers of a fragile X premutation[END_REF][START_REF] Khandjian | A heterogeneous set of FMR1 proteins is widely distributed in mouse tissues and is modulated in cell culture[END_REF][START_REF] Davidovic | The fragile X syndrome: one protein missing and 1001 disoriented mRNAs[END_REF][START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF][START_REF] Davidovic | Alteration of expression of muscle specific isoforms of the fragile X related protein 1. (FXR1P) in facioscapulohumeral muscular dystrophy patients[END_REF]. This suggests that longterm pathological consequences of FMR1-deficiency are not solely confined to the central nervous system disorders and more likely extend to physiological dysfunctions in peripheral systems. We have previously demonstrated in FXS mouse model how Fmr1-deficiency increased systemic utilization of lipids, reduced adiposity and provoked significant changes in metabolic homeostasis, some of which were also translatable to FXS patients [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF]. Besides presenting metabolic phenotypes, FXS is also accompanied by physical abnormalities [START_REF] Kidd | Fragile X syndrome: a review of associated medical problems[END_REF]. In subsets of FXS patients, morphometric studies have highlighted increased stature and height [START_REF] Butler | Anthropometric and craniofacial patterns in mentally retarded males with emphasis on the fragile X syndrome[END_REF] and a general overgrowth in prepubertal boys affected by FXS [START_REF] Kidd | Fragile X syndrome: a review of associated medical problems[END_REF][START_REF] De Vries | General overgrowth in the fragile X syndrome: variability in the phenotypic expression of the FMR1 gene mutation[END_REF]. FXS patients present connective tissue dysplasia [START_REF] Hagerman | Consideration of connective tissue dysfunction in the fragile X syndrome[END_REF], dental and mandibular anomalies [START_REF] Sabbagh-Haddad | Fragile X syndrome: panoramic radiographic evaluation of dental anomalies, dental mineralization stage, and mandibular angle[END_REF], orthopedic anomalies such as scoliosis [START_REF] Davids | Orthopaedic aspects of fragile-X syndrome[END_REF], and abnormal metacarpophalangeal pattern profile [START_REF] Butler | Metacarpophalangeal pattern profile analysis in fragile X syndrome[END_REF]. In addition, FXS patients display specific craniofacial abnormalities with reduced facial depth, hypoplasticity of the nasal bone-cartilage interface and narrow mid-facial width exaggerating ear prominence [START_REF] Heulens | Craniofacial characteristics of fragile X syndrome in mouse and man[END_REF]. This suggests that Fmr1-deficiency could have widespread peripheral effects, including on skeletal ontogenesis. Besides subtle craniofacial anomalies with morphometric changes in the mandible and skull [START_REF] Heulens | Craniofacial characteristics of fragile X syndrome in mouse and man[END_REF], skeletal particularities, and bone microstructure have not yet been studied in FXS mouse model. In this study, we examined in details body composition and bone microstructure of adult Fmr1-KO male mice.

MATERIALS AND METHODS

Animals Procedures

Fmr1-KO2 mice on the C57Bl/6J background used in this study were originally described in Mientjes et al. [START_REF] Mientjes | The generation of a conditional Fmr1 knock out mouse model to study Fmrp function in vivo[END_REF] and were named Fmr1-KO all throughout the manuscript. Fmr1-heterozygous females and Fmr1-KO male founders were obtained from Rob Willemsem (Erasmus University, Rotterdam, Netherlands) and backcrossed for at least 10 generations on a C57Bl6/J WT background (Janvier Labs, France). All Fmr1-WT and Fmr1-KO male used in this study were littermates born to Fmr1 heterozygous females mated with Fmr1-KO males. Pups were weaned at 28 days of age, identified by ear tags, and genotyped by PCR as described [START_REF] Mientjes | The generation of a conditional Fmr1 knock out mouse model to study Fmrp function in vivo[END_REF]. Pups from various litters were then randomly grouped according to their genotype in cages and had ad libitum access to water and standard chow (reference 4RF25, Mucedola) composed of cereals (53.7%), animal proteins (4.7%), vegetal proteins (30.5%), lipids (soy oil, 1.4%), vitamins and minerals (4.1%), amino acids (0.1%). Animals were housed in a temperature (22-24 • C) and hygrometry (70-80%)-controlled room with a 12 h light-dark cycle (lights on at 08:00). Animals were housed in medium-size (up to 5 animals/cage) or large cages (up to 10 animals/cage) filled with wooden bedding, one plastic house and nesting material for enrichment. All the described experiments were performed on male animals at exactly 4months (16 weeks) of age. After cervical dislocation, muscle Tibialis anterior, visceral, and subcutaneous WAT, as well as Q10 interscapular BAT were dissected by trained personnel and weighted with a precision balance.

Osteocalcin Measure

Animals were anesthetized and blood was withdrawn by cardiac puncture, incubated for at least 30 min at room temperature (RT) then centrifuged at 2,000 rpm, 10 min, RT. Serum was collected and immediately snapped-frozen in liquid nitrogen prior storage at -80 • C until use. Osteocalcin was measured in serum using a dedicated ELISA kits (Takara), according strictly to the manufacturer's instructions.

Microcomputerised Tomography Analysis: Skeleton Morphology

For the analysis of whole-body bone morphology (Figure 1), anesthetised animals were introduced in a SkyScan µCT -1178 X-ray tomograph (Bruker) and analyzed as previously described [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF][START_REF] Beranger | Oxytocin reverses ovariectomy-induced osteopenia and body fat gain[END_REF]. Mice were scanned using the following parameters: 104 µm pixel size, 49 kV, 0.5 mm thick aluminum filter and a rotation step of 0.9 • . 3D reconstructions and analysis of skeleton, vertebra and femur lengths as well as skeleton volume were performed using NRecon and CTAn software (Skyscan). Skeleton volume was calculated using CTan software by extracting from µCT scans all voxels exceeding a single global CT value, ensuring that differences between study groups are due to experimental effects rather than image-processing effects. The representative skeleton reconstruction from one Fmr1-WT animal (Figure 1D) was obtained by further processing of the 3D reconstructed sections using the ImageJ plugin 3D viewer and the BoneJ plugin [START_REF] Doube | BoneJ: free and extensible bone image analysis in ImageJ[END_REF]. Bone and skeleton lengths were calculated using the 3D coordinates of the structure extremities specified in Figure 1D.

For skeleton length we used the anterior coordinate of the nasal bone and the posterior coordinate of the first caudal vertebra. For femur length, we used the extreme coordinate of the femoral epiphysis and the extreme coordinate of the proximal epiphysis (measured on both femurs and averaged). For L3-L6 vertebra length we used the anterior extreme coordinate of L3 vertebra and the posterior extreme coordinate of L6 vertebra.

Microcomputerised Tomography Analysis: Bone Microstructure

For measurements of bone mineral density (BMD) and microstructure, we proceeded according to the guidelines for Assessment of Bone Microstructure in Rodents Using Micro-Computed Tomography [START_REF] Bouxsein | Guidelines for assessment of bone microstructure in rodents using micro-computed tomography[END_REF]. After necropsy, femora were individually scanned with a SkyScan µCT-1173 X-ray tomograph (Bruker) set with the following parameters: 7.76 µm pixel size, 45 kV, 1 mm thick aluminum filter, 0.5 • of rotation step, as previously described [START_REF] Beranger | Oxytocin reverses ovariectomy-induced osteopenia and body fat gain[END_REF]. 3D reconstructions and bone microstructure analysis were performed using NRecon and CTAn software (Skyscan). Bone microstructure was reconstructed using CTan software by extracting from µCT scans all voxels exceeding a single global CT value.

The region of interest (ROI) for calculations of femur cortical bone microstructure parameters was defined as a mid-shaft segment of 400 µm starting at 55% of the total femur length from the proximal epiphysis extremity (corresponding on average to a distance of 8.68 mm for WT animals and 8.77 mm for KO animals) and extending up to 400 µm distally. The ROI for calculations of femur trabecular bone microstructure parameters was defined as a 900 µm-segment of the distal metaphysis, located on average 450 µm away from the epiphyseal line and excluding cortical bone. The segment started at a distance equivalent to 13.5% of the total femur length measured from the distal extremity of the femur, corresponding on average to a distance of 2.13 mm in the WT and 2.15 mm in the KO.

For volumetric cortical BMD calculation, a BMD phantom rod pair supplied by the manufacturer and suitable for mouse bone analysis (2 mm of diameter) with different known densities (0.25 and 0.75 g.cm -3 calcium hydroxyapatite) was scanned along with the samples, using scan settings identical to those for acquisitions on femurs. Individual cortical (total cross-sectional object area, object area per slice, cortical area fraction, cortical thickness, mean polar moment of inertia (MMI), eccentricity) and trabecular (bone volume fraction, trabecular number, trabecular thickness, trabecular separation, total volume of trabecular pores) bone microstructure parameters were extracted on each section of the corresponding 3D ROI, using the CTAn software and then averaged as recommended in Bouxsein et al. [START_REF] Bouxsein | Guidelines for assessment of bone microstructure in rodents using micro-computed tomography[END_REF]. Their means and standard deviations (SD) for each group are reported in Tables 1,2. Representative cortical and trabecular ROI from one Fmr1-WT and one Fmr1-KO animal were obtained by further processing of the 3D reconstructed segments using the ImageJ plugin 3D viewer and the BoneJ plugin [START_REF] Doube | BoneJ: free and extensible bone image analysis in ImageJ[END_REF].

Locomotor Activity Recordings

Gross locomotor activity was determined in an open-field arena. Animals were placed in a brightly illuminated arena (200 Lux) and their movements were recorded for 10 min. An automated tracking system (Anymaze) was used to determine total distance moved, rotations number and number of center entries. The measurement of individual spontaneous activity was made using actimetry chambers (Imetronic) consisting of cages equipped with infrared beams able to detect in real time horizontal and vertical movements (rearings). Animals were individually placed in actimetry chambers under a 12-h light/dark cycle, with free access to food and drinking water. To avoid biases in measurements due to stress possibly inducing hyperlocomotion in a novel environment, an habituation period (Day 0: 11:00 p.m. to Day 1: 8:00 a.m.) preceded the 24 h-recording of horizontal and vertical activity (Day 1: 8:00 a.m. to Day 2: 8:00 a.m.).

Statistics

Normality of data was assessed using Kolmogorov-Smirnov's test and outliers removed using the robust regression and outlier removal (ROUT) method [START_REF] Motulsky | Detecting outliers when fitting data with nonlinear regression -a new method based on robust nonlinear regression and the false discovery rate[END_REF]

RESULTS

Fmr1-Deficiency Impacts Skeleton and Body Mass Distribution

We assessed several basic parameters in 4-months old Fmr1-KO and WT male littermates (Figure 1). Fmr1-KO mice display a significant increase in total body weight as compared to WT (Figure 1A). Furthermore, we weighed Tibialis anterior muscle and epididymal fat depots and showed that muscle weight was significantly increased in Fmr1-KO mice, to the expense of visceral adipose tissue that was reduced (Figures 1B,C). In contrast, subcutaneous or BAT weights were similar in Fmr1-WT and KO animals (Supplementary Figure 1). This agreed with the reduction of visceral adipose tissue volume and lack of variation in subcutaneous adipose tissue volume observed in a previous study in Fmr1-KO animals [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF].

To precisely study the skeleton, we measured a number of skeletal parameters on 3D reconstructions of skeleton from micro-computerized X-ray tomography data (Figure 1D). We found that Fmr1-deficiency is accompanied by a significant increase in total mouse length, as well as in skeletal volume ( Figures 1E,F). Femur maximal length and L3-L6 vertebral length, which are reliable indicators of general skeleton length, were also both significantly increased in Fmr1-deficient animals (Figures 1G,H).

Finally, the ratios: muscle weight/body weight, femur length/body length, L4-L6 length/body length were not significantly different between Fmr1-KO and WT animals (Supplementary Table 1), indicating that Fmr1-KO animals exhibited similar proportions to WT animals.

Fmr1-Deficiency Determines Changes in Bone Microstructure

To further resolve the microstructure of bone in Fmr1-WT and KO mice, we analyzed BMD and structure of femurs at the level of the mid-shaft cortical bone, using high resolution micro-computerized X-ray tomography (Figure 2A, Table 1). Volumetric BMD measured in the mid-shaft was not significantly affected by Fmr1-deficiency (Table 1, Figure 2B). We did not observe changes in the mean total crossectional object area, the mean object area per slice or the mean cortical area fraction (Table 1).

Qualitative analysis of transversal sections of mid-shaft cortical bone showed that Fmr1-deficiency impact the cortical pattern and skews toward higher ranges the cortical thickness distribution (Figure 2C). Quantitative analysis over a 400 µm mid-shaft block revealed that Fmr1-KO mice exhibit a significant increase in average cortical thickness as compared to WT (Table 1, Figure 2D). Furthermore, the mean polar MMI, an estimator of bone strength was not significantly impacted in Fmr1-KO mice (Table 1).

We also assessed cortical eccentricity, a measure of bone cross-sectional geometry, which is known to influence biomechanical response of the bone during habitual loading. Cortical eccentricity appeared significantly shifted toward lower values in Fmr1-KO femurs (Table 1, Figure 2E). This is indicative of a rounder shape of the bone at the mid-shaft level in Fmr1-KO animals as compared to WT.

We then studied bone trabecular microstructure by analyzing transversal sections of a 900 µm segment located in the distal femoral metaphysis (Figure 3A). Fmr1-KO mice displayed a significant increase in the bone volume fraction as compared to WT littermates (Table 2, Figure 3B). The average trabecular numbers, average trabecular separation, and average trabecular thickness were not significantly impacted by Fmr1-deficiency (Table 2). However, when looking in closer details at the trabecular thickness distributions over the 900 µm segment, we observed that Fmr1-deficiency significantly shifted to higher ranges the trabecular thickness (Figure 3C), and this shift was significant (Figure 3D). Furthermore, the total volume of trabecular pores was significantly decreased in Fmr1-KO animals (Table 2), in line with the increase in bone volume fraction.

Finally, we measured circulating osteocalcin, a bone-hormone exclusively secreted by osteoblasts with roles in bone formation, metabolism, and adaptation to exercise [START_REF] Moser | Osteocalcin-A versatile bone-derived hormone[END_REF]. In Fmr1-KO animals, serum osteocalcin levels were similar to Fmr1-WT levels (Supplementary Figure 2).

Fmr1-Deficiency Leads to Increased Physical Activity in Mouse

Physical activity is a strong contributor to adaptations in bone architecture [START_REF] Thompson | Physical activity and exercise in the regulation of human adipose tissue physiology[END_REF] and the changes we observed in Fmr1-KO mouse in terms of body composition and bone microstructure recapitulate the ones observed in mice subjected to exercise [START_REF] Dominguez | Increased nitric oxide-mediated vasodilation of bone resistance arteries is associated with increased trabecular bone volume after endurance training in rats[END_REF]. We therefore monitored the impact of Fmr1-deficiency on spontaneous locomotor activity. In the open-field test, Fmr1-KO mice were significantly more active than WT littermates in total distance traveled (Figure 4A), number of rotations (Figure 4B) and showed a higher number of center entries (Figure 4C). As open-field recordings are performed over a short period of time (10 min.), we then monitored continuous locomotor activity in Fmr1-KO and WT littermates in actimetry chambers over 24 h in a standard dark-light cycle. Independently of genotypes, horizontal (Figures 4D,E) and vertical activities (rearings, Figures 4F,G) display a characteristic nychthemeral rhythm, with two nocturnal peaks and reduced movements during the light phase. There was a significant effect of genotype over time on horizontal activity (Figure 4D). Cumulative horizontal activity was significantly increased in the nocturnal phase for Fmr1-KO animals as compared to their WT littermates (Figure 4E). Similarly, there was a significant effect of genotype over time on vertical activity (Figure 4F). Cumulative vertical activity was significantly increased in the nocturnal phase for Fmr1-KO animals as compared to their WT littermates (Figure 4G). These results indicate that Fmr1-deficient mice display a sustained hyperactivity as compared to wildtype mice.

DISCUSSION

In the present study, we show that Fmr1-deficiency in mice, as a model of FXS pathology, translates into increased body weight, skeleton length, and volume. Some studies report a general overgrowth in boys affected by FXS [START_REF] Kidd | Fragile X syndrome: a review of associated medical problems[END_REF][START_REF] De Vries | General overgrowth in the fragile X syndrome: variability in the phenotypic expression of the FMR1 gene mutation[END_REF] and increased stature and height [START_REF] Butler | Anthropometric and craniofacial patterns in mentally retarded males with emphasis on the fragile X syndrome[END_REF], which is in line with our observations in Fmr1-KO mice. In Fmr1-KO mice, overweight, and overgrowth is accompanied by an increase in muscular mass and a reduction of intra-abdominal adiposity. The later result corroborates our previous observations of reduced WAT, decreased circulating leptin and increased lipid catabolism in Fmr1-KO mice [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF] and previous findings of reduced lipid storage in FXS Drosophila model [START_REF] Weisz | Loss of Drosophila FMRP leads to alterations in energy metabolism and mitochondrial function[END_REF]. This suggests that Fmr1-deficiency provokes fat mass redistribution impacting visceral WAT and sparing subcutaneous WAT and BAT. Whole-body energy expenditure, that is critically regulated by BAT, was also not impacted by Fmr1-deficiency, as we have previously shown using indirect calorimetry [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF]. Also, body temperature of Fmr1-KO mice was similar to WT (Leboucher & Davidovic, unpublished observations). All these data confirm the lack of overt phenotype related to BAT in Fmr1-KO animals and suggest that this tissue is unlikely involved in the described phenotypes. No epidemiological study on the FXS population is available to-date regarding the adiposity in typical FXS patients. The reduced adiposity we observed in Fmr1-KO mice could at first sight be contradictory with the reports of hyperphagia and obesity in a rare subset of Fragile X patients displaying Prader Willi-like (PWL) phenotypes [START_REF] Nowicki | The prader-Willi phenotype of fragile X syndrome[END_REF]. However, these traits are not observed in typical FXS patients [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF][START_REF] Kidd | Fragile X syndrome: a review of associated medical problems[END_REF][START_REF] Hagerman | Fragile X syndrome[END_REF]. Our results warrant further clinical studies to characterize body composition in FXS patients.

When looking closer at bone microstructure parameters, we observed that BMD was not significantly affected by Fmr1deficiency, indicating that the bone mineralization process is unlikely affected in Fmr1-KO animals. However, bone architecture of Fmr1-KO mice is impacted, with decreased volume of trabecular pore, increased trabecular bone volume fraction and a shift toward higher trabecular thickness ranges, accompanied by increased cortical thickness, and eccentricity. The changes in bone microstructure which we highlight in Fmr1-KO animals resemble the ones observed in humans [START_REF] Best | Trabecular bone in the calcaneus of runners[END_REF] or animals [START_REF] Dominguez | Increased nitric oxide-mediated vasodilation of bone resistance arteries is associated with increased trabecular bone volume after endurance training in rats[END_REF] subjected to long-term physical exercise. Indeed, physical activity is not only a strong contributor to adaptations in bone architecture but it also provokes body mass redistribution, notably a decrease in fat mass and an increase in the lean mass [START_REF] Thompson | Physical activity and exercise in the regulation of human adipose tissue physiology[END_REF], which is what we observe in Fmr1-KO mice as compared to control mice. Also, the changes in shape of the proximal femur with an increased eccentricity could reflect the changing loading pattern during growth due to increased physical activity. Indeed, changes in bone morphology and strength can be enhanced through functional loading as shown in human studies [START_REF] Sugiyama | Functional adaptation to mechanical loading in both cortical and cancellous bone is controlled locally and is confined to the loaded bones[END_REF][START_REF] Travison | Lean mass and not fat mass is associated with male proximal femur strength[END_REF]. As bone architecture reflects the history load originating both from muscle contractions as well as from the effect of gravity, the increased cortical thickness and the shift toward higher trabecular thickness in Fmr1-KO animals could correspond to a functional adaptation to the mechanical stress induced by increased physical activity and body weight. We therefore hypothesize that the increased physical activity observed in Fmr1-KO mice could indirectly participate to changes in body composition. Importantly, physical activity behavior is mostly centrally regulated and thus could be a possible consequence of Fmr1-deficiency in the CNS. Increased daily locomotor activity alterations had been previously welldocumented in Drosophila FXS model [START_REF] Mcbride | Behavior in a drosophila model of fragile X[END_REF] and in younger 8 weeks-old male Fmr1-KO animals [START_REF] Gaudissard | Behavioral abnormalities in the Fmr1-KO2 mouse model of fragile X syndrome: the relevance of early life phases[END_REF]. Our results indicate that 4-months old Fmr1-deficient mice display a sustained increase in physical activity as compared to wildtype mice, suggesting that hyperactivity-related phenotypes are not transient and persist in adulthood. This agrees with attention deficits and hyperactivity disorder (ADHD) being a frequent comorbidity diagnosed in FXS patients [START_REF] Kidd | Fragile X syndrome: a review of associated medical problems[END_REF]. Furthermore, physical activity is known to trigger beneficial metabolic changes, notably reduction in circulating triglycerides and cholesterol and better response to insulin, which are phenotypes we have recently described in the Fmr1-KO mice [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF]. All these data point toward important roles for FMR1 gene at multiple levels in the organism: both central and peripheral. FMRP, the protein encoded by the FMR1 gene is a RNAbinding protein which controls mRNA translation. FMRP is widely expressed in the organism, including in adipose tissue, however, FMRP is absent from the adult muscle [START_REF] Devys | The FMR-1 protein is cytoplasmic, most abundant in neurons and appears normal in carriers of a fragile X premutation[END_REF][START_REF] Khandjian | A heterogeneous set of FMR1 proteins is widely distributed in mouse tissues and is modulated in cell culture[END_REF][START_REF] Davidovic | The fragile X syndrome: one protein missing and 1001 disoriented mRNAs[END_REF][START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF][START_REF] Davidovic | Alteration of expression of muscle specific isoforms of the fragile X related protein 1. (FXR1P) in facioscapulohumeral muscular dystrophy patients[END_REF]. It is therefore unlikely that FMRP will directly participate to muscle remodeling. In addition to increased physical activity, dysregulated translation regulation in bone tissue in the absence of FMRP could also participate to the skeletal and bone microstructure changes we observed here. Also, craniofacial abnormalities have been previously identified in both FXS patients and FXS mouse model [START_REF] Heulens | Craniofacial characteristics of fragile X syndrome in mouse and man[END_REF], and this occurs independently of physical activity, suggesting that FMRP could have a direct role on bone formation/resorption processes. Further molecular studies would be required to identify FMRP mRNA targets in bone and its contribution to the shaping of bone microstructure.

Our study has some limitations. First, we have assessed bone phenotypes at a single age (4-month-old) and only in male animals. The stability of these phenotypes over time (during growth and aging) and the impact of sex should be further investigated. Circulating bone hormones, including osteocalcin levels, could also be assessed during growth. Second, detailed histo-morphometric studies of bone coupled to in vitro studies in osteoblasts and osteoclasts derived from Fmr1-KO and WT mice could contribute to identify the mechanism by which Fmr1-deficiency regulates bone morphology. Third, to estimate whether the changes in body composition are accompanied with improved physical capacities, Fmr1-KO mice could be subjected to forced exercise training in a treadmill combined with indirect calorimetry. Finally, further studies in FXS patients are warranted to assess the translationality of our findings in FXS mouse model. In this context, the use of Dual Energy X-Ray Absorptiometry (DEXA) technology, easily amenable both to mice, and humans to study visceral fat mass and lean body mass would enable an in-depth characterization of body mass distribution in FXS.

To our knowledge, this study is the first to document changes in body mass composition, skeleton, and bone architecture induced by Fmr1-deficiency in mice. These changes may result in part from adaptations to the hyperactivity and increased body weight that we highlight in Fmr1-KO mice or from the loss of the translational regulator FMRP in bone or adipose tissue. We have previously shown that Fmr1-KO mice present a variety of metabolic changes, notably increased glucose, and insulin tolerance as well as a metabolic shift toward increased lipid catabolism, this being likely mediated by the loss of FMRP-mediated hepatic translation repression [START_REF] Leboucher | The translational regulator FMRP controls lipid and glucose metabolism in mice and humans[END_REF]. To which extend these metabolic abnormalities could also be linked to increased physical activity remains to be investigated. Given that recent studies have shown that a dynamic crosstalk between peripheral organs is required for the optimal control of metabolic homeostasis [START_REF] Wei | Bone-specific insulin resistance disrupts whole-body glucose homeostasis via decreased osteocalcin activation[END_REF][START_REF] Karsenty | Molecular bases of the crosstalk between bone and muscle[END_REF], our study further paves the way for further investigation of skeletal and metabolic changes in FXS patients.

FIGURE 1 |

 1 FIGURE 1 | Fmr1-deficiency modifies body mass distribution. Body weight (A), Tibialis anterior muscle (B), as well as visceral white adipose tissue (WAT) (C) weights

FIGURE 2 |

 2 FIGURE 2 | Fmr1-deficiency does not impact bone mineral density (BMD) but modifies cortical bone architecture. (A) Location of ROI used for mid-shaft cortical bone 3D reconstruction of a 400 µm diaphysis section. meta., Metaphysis; epi., epiphysis. (B) Bone mineral density. (C) Representative 3D reconstructions of cortical thickness in Fmr1-KO and WT femurs. Average cortical thickness (D) and cortical bone eccentricity (E) in Fmr1-KO and WT 4 months-old male animals. For (B,D,E), data are presented as dot-plots featuring means ± SEM in red. Statistical significance of differences was measured using 2-tailed Student's T-test (D) or Mann & Whitney U-test (E). *p < 0.05. n = 12-15 animals/group.

FIGURE 3 |

 3 FIGURE 3 | Fmr1-deficiency lowers trabecular bone volume and shifts the trabecular thickness distribution to higher values. (A) Location of ROI used for trabecular bone 3D reconstruction over a 900 µm bone segment located 450 µm distal to the epiphyseal line. meta., Metaphysis; epi., epiphysis. (B) Average bone volume fraction in Fmr1-KO and WT femurs. (C) Representative 3D reconstructions of trabecular thickness in Fmr1-KO and WT femurs. (D) Trabecular thickness distribution in reconstructed Fmr1-KO and WT ROI. Data are presented as means ± SEM. For (B), statistical significance of differences was measured using 2-tailed Student's T-test: *p < 0.05). For (D) 2-way ANOVA: p(Genotype) = 0.3103, p(Range) < 0.0001, p(Interaction) = 0.0037; Fisher's LSD post-hoc tests for genotype-wise comparisons. *p < 0.05. n = 12-15 animals/group.

FIGURE 4 |

 4 FIGURE 4 | Fmr1-deficiency increases locomotor activity. Fmr1-KO and WT adult male littermates were monitored for 10 min in an open-field arena and total distance traveled (A), number of rotations (B), and center entries (C) were calculated. Horizontal activity measured in actimetry chambers over 24 h (D) and corresponding histograms of cumulative activity in dark/light phases (E). Vertical activity measured in actimetry chambers over 24 h (F) and corresponding histograms of cumulative activity in dark/light phases (G). n = 12 animals/group. For (A-C), data are presented as dot-plots featuring means ± SEM. Statistical significance of differences was measured using 2-tailed Student's T-tests. *p < 0.05. For (D-G), data are presented as means ± SEM. For (D) 2-way ANOVA: p(Genotype) = 0.0053, p(Time) < 0.0001, p(Interaction) = 0.0002; Holm-Šidák's post-hoc tests for genotype-wise comparisons. **p < 0.01; ***p < 0.001. For (E) 2-way ANOVA: p(Genotype) = 0.0024, p(Phase) < 0.0001, p(Interaction) = 0.0599; Holm-Šidák's post-hoc tests for genotype-wise comparisons. **p < 0.01; ***p < 0.0001. For (F) 2-way ANOVA: p(Genotype) = 0.0394, p(Phase) < 0.0001, p(Interaction) = 0.0006; Holm-Šidák's post-hoc tests for genotype-wise comparisons. *p < 0.05, **p < 0.01; ***p < 0.001. For (G) 2-way ANOVA: p(Genotype) = 0.0348, p(Phase) < 0.0001, p(Interaction) = 0.042; Holm-Šidák's post-hoc tests for genotype-wise comparisons. *p < 0.05, **p < 0.01.

  . Bone Phenotypes in a Mouse Model of Fragile X Syndrome

TABLE 1 |

 1 Comparison analysis of variables for cortical bone morphology in Fmr1-KO and WT femurs.Mean and standard deviations (SD) to the mean are reported for Fmr1-WT and KO animals, as well as p-value for the Student's T-test ( a ) or the Mann & Whitney U-test ( b ). Significant p-values (p < 0.05) are bolded. n = 13-15 animals/group.

	Leboucher et al.
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TABLE 2 |

 2 Comparison analysis of variables for trabecular bone microarchitecture in Fmr1-KO and WT femurs.

	Abbreviation	Variable description	Unit	Mean WT	SD WT	Mean KO	SD KO	p-value
	BV/TV	Bone volume fraction	%	18.6300	5.1660	22.3700	5.5000	0.0469 b
	Tb.N	Trabecular number	nb/mm	1.1110	0.5512	1.1160	0.9092	0.5164 b
	Tb.Th	Trabecular thickness	mm	0.06106	0.007229	0.06624	0.00632	0.0619 a
	Tb.Sp	Trabecular separation	mm	0.2140	0.0257	0.2005	0.0292	0.2295 a
	Tb.Po	Total volume of trabecular pores	mm 3	1.7320	0.1008	1.5510	0.1443	0.0007

a Mean and standard deviations (SD) are reported for Fmr1-WT and KO animals, as well as p-value for the Student's T-test ( a ) or the Mann & Whitney U-test ( b ). Significant p-values (p < 0.05) are bolded. n = 12-15 animals/group.

Frontiers in Endocrinology | www.frontiersin.org

September 2019 | Volume 10 | Article 678

ACKNOWLEDGMENTS

We thank the IPMC animal facility for expert animal care, in particular N. Guy, V. Thieffin, and B. Donay. We are greatly indebted to MD Dr. Sattonet (Polyclinique Saint-Jean, Cagnessur-Mer) for providing access to the high energy µCT Skyscan and for helpful advices for its use.

DATA AVAILABILITY STATEMENT

Q12

The datasets generated for this study are available on request to the corresponding author.

ETHICS STATEMENT

All animal studies were conducted in facilities accredited by French legal authorities (Direction Départementale de Protection des Populations des Alpes-Maritimes, accreditation #C-06-152-Q8 5). Procedures involving animal experimentation were approved by the local animal experimentation ethics committee and the French Ministère de l'Enseignement Supérieur et de la Recherche (agreement #00788.01 and #05224.01).

AUTHOR CONTRIBUTIONS

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo. 2019.00678/full#supplementary-material

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bruno Martin

Another Co*cryption Method

Keywords: Cryptography, compression, pseudo-random sequences, security

We consider the enciphering of a data stream while being compressed by a LZ algorithm. This has to be compared to the classical encryption after compression methods used in security protocols. Actually, most cryptanalysis techniques exploit patterns found in the plaintext to crack the cipher; compression techniques reduce these attacks. Our scheme is based on a LZ compression in which a Vernam cipher has been added. We make some security remarks by trying to measure its randomness with statistical tests. Such a scheme could be employed to increase the speed of security protocols and to decrease the computing power for mobile devices.

Introduction

Information security is currently one of the main challenges in computer networks. In the emergent communication paradigm where * Bruno.Martin@unice.fr wireless and wired networks are interoperating, security issues become crucial. Traditional technologies are every day more inadequate and existing standards should be improved for use in resource restricted environments. We aim to develop a secure algorithm for confidentiality, but cheaper in terms of size and computing power.

In many security protocols, a compression algorithm is run prior encrypting the data to increase the security and the bandwidth. These algorithms are run on the original stream. They all stem from research by J. Ziv and A. Lempel who have designed two compression algorithms: LZ77 and LZ78 [START_REF] Salomon | Data compression, the complete reference[END_REF]. After compression, if the speed of computation is taken into account, the compressed data is enciphered with the use of a stream cipher like RC4 (let us recall that RC4 is 15 times quicker than a 3DES and is used in protocols like WEP and SSL [START_REF] Rescorla | SSL and TLS: Designing and Building Secure Systems[END_REF]).

In the present paper, we propose to scramble (encipher) a data stream while it is being compressed. We assume the reader familiar with classical compression algorithms and with secret key cryptography for which a good introduction is [START_REF] Stallings | Cryptography and Network Security[END_REF]. The paper is orga-nized as follows: section 1 presents the basis of our idea, while section 2 recalls the related results. In section 3 we illustrate our idea with a "toy" implementation which uses a compression algorithm from the Lempel-Ziv family. Some statistical tests have been made and are presented in section 4.

The idea

Our idea is to encipher the data stream while it is being compressed by a lossless dictionary algorithm. The basic idea which motivates this proposition is that a compressed stream is already almost random and a good candidate to be scrambled by a simple Vernam cipher. This comes from the notion of incompressibility introduced with Kolmogorov complexity. A.N. Kolmogorov [START_REF] Kolmogorov | Three approaches to the quantitative definition of information[END_REF] has proposed a complexity which speaks about objects rather than the usual classes of languages addressed by classical complexity. Informally, Kolmogorov complexity corresponds to the size of the smallest program p which can print out on its standard output the object x. If ♯p < ♯x, we say that x is compressible, otherwise incompressible. It provides a modern notion of randomness dealing with the quantity of information in individual objects which says that an object x is random if it cannot be represented by a shorter program p whose output is x or, in other words, if x is incompressible [START_REF] Salomon | Data compression, the complete reference[END_REF]. From this point of view, the output of any compression algorithm is an approximation of a random sequence, although highly reversible. Our idea is to scramble the output of a com-pression algorithm by a Vernam cipher and to do this while the data stream is being compressed in order to avoid to pass the compressed data stream to another encryption process. From the above discussion, the output of our scheme should be almost random.

Related work

Actually, there are two methods sharing the same idea but in a slightly different way. The first one is called concryption and has been patented by Security Dynamics (US Patent #5479512). It is a method for the integrated compression and encryption (concryption) of clear data. For concryption, the clear data and an encryption key are obtained, at least one compression step is performed and at least one encryption step is performed utilizing the encryption key. The encryption step is preferably performed on the final or intermediate results of a compression step, with compression being a multistep operation. The second method is called compryption [START_REF] Crandall | Comcryption[END_REF] and is due to R. E. Crandall when he was Apple's Chief Cryptographer. Roughly, his idea is to index a great number of entropy compression algorithms by a secret key. He then gets a holistic (one-pass) compress/encrypt algorithm. This method is currently used for enciphering the passwords in the keychain application starting with Mac OS 9 and still used in Mac OS X from Apple. It is recorded under US Patent #6154542, "Method and apparatus for simultaneously encrypting and compressing data".

The proposed scheme

We use the mode of operation of LZ 78 which uses a growing dictionary [START_REF] Salomon | Data compression, the complete reference[END_REF]. It starts with 2 9 = 512 entries (with the first 256 entries already filled up, eventually after an initial permutation). While this dictionary is in use, 9 bit pointers are written onto the output stream after encryption by a Vernam cipher. When the original dictionary is filled up, its size is doubled to 1024 entries and 10 bit pointers are then used (and encrypted as well) until the pointer size reaches a maximum value set by the user. When the large dictionary is filled up, the program continues without changes to the dictionary but with monitoring the compression ratio. If this ratio falls down a predefined threshold, the dictionary is deleted and a new 512 entries dictionary is started. The algorithm below presents the scheme. In the sequel, we denote by PRBS a pseudo-random Boolean sequence. The implementation was just made as a proof-of-concept in C++ and using the LEDA library1 which provides a sizable collection of data types and algorithms in a form which allows them to be used by non-experts.

The difference with concryption is that we use a single pass compression algorithm while they require the compression to be a multistep operation, and it is not based on an entropy compression algorithm used in the comcryption method, although an entropy compression algorithm could be added to shorten the mostly used pointers which are returned by the algorithm.

Analysis

Though a plot of the output (see figure 1) is rather encouraging, we were deceived while testing outputs with a χ 2 test for which the results were a little bit too weak. This may be explained by the rather bad choice of a "toy" linear feedback shift register for generating the PRBS. We expect the result to be improved with the use of good pseudo-random generators like RC4, or even with so-called "perfect" PRBS generators.

Further testing should be made according to [3]

Discussion

Although not truly pseudo-random (but this is also not a pseudo-random generator), the output of our compression and encryption scheme is encouraging if we look at the typical output depicted by figure 1. Further study should be made with the help of a good pseudo-random generator with classical tests and a fine tuning of all the parameters.

2 Avaible from http://diehard.darwinports.com.

The use of compression and encryption mixed together should increase the bandwidth, decrease the latency as well as it also might decrease the energy consumption required for the same purpose when using encryption after compression for mobile devices or RFID.

Index = 256 ;

 256 Length = 9; Word = null; Limit = 12; Initialise 256 inputs in Dictionary //(eventually after a permutation) //(a+b stands for concatenation) REPEAT read S //(Read a symbol from the stream) IF Word+S is in Dictionary THEN Word ; Emit = true ENDIF UNTIL no data found IF Emit = false THEN Output the (index of Word) XOR (PRBS) // Vernam cipher ENDIF

Figure 1 :

 1 Figure 1: Probabilities of the output.

  which requires a PRG to pass a number of statistical tests or the Marsaglia tests, a set of 23 very strong tests of randomness implemented in the Diehard program 2 .

Avaible from http://www.mpi-sb.mpg.de/LEDA.