Hanna Pamula
email: pamulah@agh.edu.pl

Agnieszka Pocha

Maciej Klaczynski

DEEP LEARNING METHODS FOR ACOUSTIC MONITORING OF BIRDS MIGRATING AT NIGHT

The project aims to supplement nocturnal bird migration research with long-term audio recordings. We checked how different deep learning methodsnamely convolutional neural networks (CNN) and residual neural networks (ResNets)perform in the nocturnal flight calls detection task. Moreover, we used transfer learning from image classification models to determine if we can improve automatic detection performance. The best model obtained the AUC score over 95% (area under the receiver operating characteristic curve). Although higher bird detection accuracy is still needed because of the strong dataset imbalance, the results are very promising and suggest that deep learning models applied to audio data have great potential in supplementing bird migration studies.

INTRODUCTION

Billions of birds migrate between breeding and wintering sites every year. Traditionally, migration studies are concentrated on day migration. This is primarily determined by the fact that the most popular methodssuch as visual observations and bird ringingare most appropriate for daytime research. However, as most bird species migrate at night, much information about migration is lost, so novel techniques are used to fill the knowledge gaps [START_REF] Kg | A comparison of traffic estimates of nocturnal flying animals using radar, thermal imaging, and acoustic recording[END_REF]. One of these methods is acoustic monitoring.

As autonomous recording devices are getting cheaper and more accessible, the bioacoustics monitoring projects become the "big data" research area, generating terabytes of audio data [START_REF] Stowell | Bird detection in audio: a survey and a challenge[END_REF]. However, manual analysis of long-term recordings is a very time-consuming and inefficient process requiring expert knowledge. For example, the "scanning" of the spectrograms in search for impulsive nocturnal birds' calls can take up to 2-3 times more time than the duration of the recording. For such big datasets, manual annotation is not feasible, so effective automatic detection of birds' calls is crucial.

While there is plenty of research focused on bird songs, the nocturnal avian flight calls projects are not yet common, regardless of the recent interest in this topic [START_REF] Lostanlen | Birdvox-full-night: A dataset and benchmark for avian flight call detection[END_REF][START_REF] Bm | Nocturnal flightcalling behaviour predicts vulnerability to artificial light in migratory birds[END_REF]. In this article, we present the results of research on flight calls detection, based on the well-established deep learning methods -CNNs and ResNets.

MATERIALS AND METHODS

Field Recordings

In the 2016 -2019 seasons, field recordings at the site of increased migration of birds were performed. Long-term recordings of the night calls of bird migrants were conducted every season for 50-60 days, from the first half of September to the beginning of November. The recording sets were deployed on a narrow spit between Bukowo Lake and the Baltic Sea in Dabkowice near Darlowo (54°20'16 "N, 16°14'38" E). 4-6 microphones were set up in different places: on a dune, in the reeds, and in a more quiet placea clearing near the lake. The tests were performed with SM2+ recorders connected to directional SMX-NFC microphones, placed on a flat plastic plate (Wildlife Acoustics Inc., USA), and mounted on poles 3-5 m high. The recordings were performed every night, from sunset to sunrise. In total, over 3,000 hours of recordings were collected in each season. In addition, a weather station was deployed for the two last seasons.

Training and Testing Set

At first, two automatic voice detection programs were checked; unfortunately, they did not extract the voices from the recordings accurately [START_REF] Pamula | Monitoring akustyczny ptaków migrujących nocą -zagadnienia związane z automatyczną detekcją głosów[END_REF]. In search of effective bird calls detection methods, we created training and testing sets for deep learning models. We manually annotated over 50 hours of recordings from different nights and various weather conditions, for which we used the Audacity software1 . The annotations were performed only on a time scale, not on the frequency axis. Only passerines' calls were annotated, as we assumed that other bird groups might contain calls of local or resting individuals (owls, ducks, geese, etc.).

As avian flight calls are sporadic and short (10-300 ms), a balanced training set was created. It consisted of clips derived from 94 30-min long recordings. The clips were 500 ms long, overlapping by 150 ms. The training set comprised all positive clips and the same number of randomly picked negative clips. If no call was present in the recording, a small number of negative frames was added to a subset for background noise representation. As a result, the training set consisted of more than 15.9k clips with a 45% bird call presence. There were 20 annotated full recordings (30-min long) in the test set, prepared as in the training set. As the test set comprised full continuous recordings, it was strongly unbalanced, resulting in over 103k clips with only 1.2% bird call presence.

Deep Learning Detection Methods

Three different approaches were testedconvolutional neural networks (CNN), residual neural networks (ResNet-18 and ResNet-50 architecture [START_REF] Bm | Nocturnal flightcalling behaviour predicts vulnerability to artificial light in migratory birds[END_REF]), and transfer learning from pre-trained ResNet models.

Convolutional Neural Networks

To fully use the potential of CNNs, we used image-like representations of the sound: spectrogram, melspectrogram, and multitaper2 (with two tapers). We have chosen representation parameters in such a way, that each of them had a similar size, about 60x148 px. Each time 4.5-9kHz range was covered. The hyperparameters of the CNNs were optimized by grid-search over a parameter grid with 2-3 values for each parameter. ReLU activation was used for all but last layer, where we used sigmoid. Table 1 presents the chosen CNN hyperparameters, while Figure 1 illustrates the schematic network architecture.

Residual Neural Networks

To adapt ResNets for bird call detection, we changed the number of classes from 1000 to 2 (birdno bird) and the input size to 1 channel (mel-spectrogram), as opposed to 3-channel in the original model (colour images).

Transfer Learning

ResNet-18 and ResNet-50 were used, initialised with random and with weights pre-trained on ImageNet [START_REF] Deng | Imagenet: A large-scale hierarchical image database[END_REF]. Models were trained five times, and their predictions on the test set were averaged. For ResNet-18 the additional experiments were performed, with freezing the weights of the first 1 or 2 layers (all ResNet architectures are composed of four layers, sometimes called stages. Every layer is composed of several blocks). As an input, a melspectrogram was used in each channel, orfor chosen experimentsdifferent signal representations were put in each channel (spectrogram, mel-spectrogram, multitaper).

RESULTS AND DISCUSSION

For each experiment, the receiver operating characteristic curve (ROC) and the area under that curve (AUC) was calculated.

CNNs

Figure 2 shows the results for the CNN models with different input representations. Although the training process for spectrogram and mel-spectrogram architectures gave varying outcomessome models were not trained at all, resulting in AUC oscillating around 50%creating an ensemble of five independent runs led up to higher AUC score than for any of the individual models. ? ? ? ? ? ? ?

x N time 10.48465/fa.2020.0650

ResNets

For ResNet models, the best AUC score was obtained by ResNet-50 with different signal representations in each channel as input, initialised with pre-trained weights, and by ResNet-18 without pre-training (Figure 3). However, the differences between the models were negligible; only the ResNet-50 without using the weights pre-trained on ImageNet got lower result. Thus, using the pre-training weights seems beneficial for ResNet-50, but not for ResNet-18.

Comparing representations

The results obtained for mel-spectrogram representation in each channel were very similar to 3 representations (AUC = 95.1% for ResNet-18 IW, 95.0% for ResNet-50 IW, 95.2% for ResNet-18 NP, 93.8% for ResNet-50; compare with Figure 3). Because of these negligible differences, we do not present the separate chart. Hence, the three representations instead of one, did not significantly improve the performance, and the neural network did not profit from the additional data representations.

Transfer learning

In the last experiment, we checked how freezing some of the weights of the pre-trained ResNet-18 model influenced the performance. The obtained results clearly indicate that freezing the layers was degrading for the performance in all three variants, resulting in the AUC measure in the 75.9-93.3% range (Figure 4).

Comparison of the best performing models

Finally, two best models of each neural network type were compared. To put the obtained AUC measures in a wider context, the data reduction on the test set was calculated (Table 2). The data reduction was defined as reducing the time of the recordings, retaining high percent of the avian flight calls. Two recall values were chosen: retaining 80% and 90% of calls present in the test set. Depending on the goal and application of the bird call detection, stricter conditions may be selected. The test set can then be reduced from 4 hours to less than 3 hours while retaining 90% of calls. However, if we choose the recall of 80%, the set may be condensed to only 24 minutes, and around one out of every three samples will contain the bird call while the initial rate was 1:82. The ResNet model with such settings could greatly reduce the effort of bird detection and annotation in long-term audio recordings.

Architecture

SUMMARY

In this work we have shown that using deep learning methodsespecially ResNet architectureis beneficial for the presented research topic: detecting nocturnal avian flight calls. However, we could not find evidence that transfer learning from the models pre-trained on ImageNet has positive effects on the results. The outcomes are promising, and they suggest that deep learning methods applied to audio data have great potential to support nocturnal bird migration research.

Figure 2 .Table 1 . 1 .

 211 Figure 2. ROC curves for three CNN architectures. The dashed lines represent the result of individual five runs of the same algorithm. Each solid curve is a voting ensemble of five models.

Figure 3 .

 3 Figure 3. ROC curves for ResNet-18 and ResNet-50 models. IW-model initialised with pre-trained weights, NPno pre-training. 3 channel input: spectrogram, mel-spectrogram, multitaper. Each curve is a voting ensemble of five models. For the sake of clarity, the individual five runs were not shown.

Figure 4 .

 4 Figure 4. ROC curves for ResNet-18 models. IWmodel initialised with pre-trained weights, f_allfreezing all layers apart from the last one, f_2freezing two layers, f_1 freezing the first layer only. Input: melspectrogram. Each curve is a voting ensemble of five models. For the sake of clarity, the individual five runs were not shown.

Table 2 .

 2 The data reduction table for the best performing CNN and ResNet models. Results for retaining 80% and 90% of the calls from the test set. FP false positive, TPtrue positive.

		FP	TP Precision Data reduction
	Retaining 80% of calls (recall 80%)
	CNN spectro	6911	984	12.5%	~1h 6min/14h
	ResNet18 NP	1873	984	34.4%	~24 min/14h
	Retaining 90% of calls (recall 90%)
	CNN spectro 33635 1107	3.3%	~4h 50min/14h
	ResNet18 NP 12580 1107	8.1%	~2h 54min/14h

Audacity®. Version

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e-Forum Acusticum, December 7-11, 2020

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10.48465/fa.2020.0650


PhD Guillaume Mabilleau
email: guillaume.mabilleau@univ-angers.fr

Aleksandra Mieczkowska

Nigel Irwin

Peter-R R Flatt

Daniel Chappard

Optimal bone mechanical and material properties require a functional 1 GLP-1 receptor 2 3

Keywords: Bone quality, Glucagon like peptide-1, bone strength, bone matrix, bone mineral. 18

come

Optimal bone mechanical and material properties require a functional glucagon-like peptide-1 receptor.

INTRODUCTION 37

Bone is a living mineralized material, highly complex and constantly remodeled in mass and 38 architecture to adapt and repair the damage induced by growth, ageing and mechanical stress. In 39 order to maintain a constant bone mass, bone remodeling necessitates a spatio-temporal coupling 40 between osteoclasts, the bone-resorbing cells, and osteoblasts, the bone-forming cells. Bone 41 remodeling is a complex process regulated by various stimuli that may affect osteoclast and osteoblast 42 physiologies. A role for the gastro-intestinal tract in bone remodeling has been suggested with 43 evidence that modulation of serum markers of bone resorption is mirrored with the profile of gut 44 hormone release after a meal [START_REF] Elnenaei | Postprandial bone turnover 402 is independent of calories above 250 kcal[END_REF][START_REF] Henriksen | Role of gastrointestinal hormones in postprandial reduction 418 of bone resorption[END_REF].

calcified tissues was determined as reported by Bassett et al. [START_REF] Bassett | Quantitative X-ray imaging of rodent 370 bone by Faxitron[END_REF] with the following 115 modifications. Briefly, a 1.5-mm thick steel plate, a 1.5-mm pure aluminum wire and a 1.5-mm thick 116 polyester plate were used on each microradiograph and served as standards. Before converting the 117 16-bit DICOM images into 8-bit tiff images, the histogram was stretched from the polyester (gray level 118 0) to the steel (gray level 255) standards using ImageJ 1.45s. Increasing gradations of mineralization 119 density were represented in 16 equal intervals using the 16-colours lookup table in ImageJ 1.45s. The 120 frequency of occurrence of an i grey level (Fi) was calculated as follows:

136

Society for Bone and Mineral Research [START_REF] Bouxsein | Guidelines for 379 assessment of bone microstructure in rodents using micro-computed tomography[END_REF]

140

Quantitative backscattered electron imaging was employed to determine the bone mineral density 141 distribution (BMDD) as previously reported [START_REF] Gaudin-Audrain | Glucose-dependent insulinotropic polypeptide receptor deficiency leads to modifications of 411 trabecular bone mass and quality in mice[END_REF][START_REF] Roschger | Validation of quantitative backscattered 451 electron imaging for the measurement of mineral density distribution in human bone biopsies[END_REF].

142

Rapidly after 3-point bending, lower portions of the right femur diaphysis were embedded -1 of the phosphate band [START_REF] Paschalis | FTIR 445 microspectroscopic analysis of human osteonal bone[END_REF];

(3) carbonate-to-phosphate ratio 197 was expressed by the ratio of integrated areas of the υ2 CO 3 2-region (850-890cm-1) to the υ1, υ3

198 phosphate band (900-1200cm -1 ) [START_REF] Boskey | Comparison of mineral quality and 376 quantity in iliac crest biopsies from high-and low-turnover osteoporosis: an FT-IR microspectroscopic 377 investigation[END_REF]) and ( 4) collagen maturity, determined as the 199 relative ratio of subbands located at 1660 cm -1 and 1690 cm

251

Nanomechanical properties of the bone matrix are reduced in GLP-1R KO mice 252

As compared with WT animals, GLP-1R-deficient animals exhibited a significant 19% decrease in 253 maximum load, as well as a significant reduction in bone matrix hardness (-19%) (Table 2). The

254

indentation modulus was unchanged between the two groups of animals but the energy dissipated 255 was significantly reduced by 15% in GLP-1R deficient mice.

256

Collagen maturation but not mineral maturation is altered in GLP-1R KO mice 257

The bone mineral density distribution in the bone matrix was investigated by qBEI in WT and GLP-1R

258

KO mice (Table 3). Interestingly, no differences were observed between the two groups of animals in 259 any of the studied parameters, indicating that the profile of mineralization of the bone matrix was 260 similar in both cases. Similarly, the mineral-to-matrix ratio determined from FTIR measurements was 261 not significantly different between WT and GLP-1R -/-animals (Figure 3A). The mineral maturity and

267

However, no significant correlations were found between maximum load or hardness with the 268 1660/1690 cm -1 ratio (R²=0.33, p=0.067 and R²=0. 13, p=0.268, respectively).

269

GLP-1R is not expressed in bone cells

270

As expected, a strong expression of GLP-1R was found in lung and pancreatic islets and to a lower 271 extent in heart (Figure 5). On the other hand, Glp-1r transcripts were not found in either osteoblasts or

281

material properties [START_REF] Turner | Basic biomechanical measurements of bone: a tutorial[END_REF]. Indeed, material properties calculated from stress-strain 282 curves indicated that work to fracture and yield strength were significantly reduced in deficient animals

283

but not ultimate strength or strain or the elastic modulus. Taken together these data indicate that 284 cortical bone morphology and material properties may be altered. In the C57BL/6 mouse strain, at the 285 midshaft femur, bone tissue is composed almost exclusively of cortical bone [START_REF] Bouxsein | Ovariectomy-382 induced bone loss varies among inbred strains of mice[END_REF]

295

Indeed, the GLP-1R is expressed in paraventricular, arcuate and dorsomedial nuclei of the 296 hypothalamus [START_REF] Merchenthaler | Distribution of pre-pro-glucagon and glucagon-like 431 peptide-1 receptor messenger RNAs in the rat central nervous system[END_REF]). Some of these regions have been implicated in the central 297 control of bone remodeling by targeting osteoblasts and as such, alterations of bone quality could also 298 result from activation/inactivation of specific central relays. Furthermore, these mice present with a 299 mild glucose intolerance represented by fasting hyperglycemia [START_REF] Ayala | Glucagon-like 363 peptide-1 receptor knockout mice are protected from high-fat diet-induced insulin resistance[END_REF]. Regarding the 300 beneficial role of insulin in bone growth and or quality, dysregulation of insulin action in these mice 301 may also represent another mechanism leading to the observed phenotype. Furthermore, alterations

302

of IGF-1R expression have been noted in these mice [START_REF] Cornu | Glucagon-like peptide-1 391 protects beta-cells against apoptosis by increasing the activity of an IGF-2/IGF-1 receptor autocrine 392 loop[END_REF]) and could also participate to

303

the reduction in bone diameter and hence bone quality. The GLP-1R is also expressed in C cells of 304 the thyroid gland and exerts, when activated by GLP-1 or its stable analogues, a stimulating effect on 10 calcitonin secretion in rodents (Bjerre [START_REF] Knudsen | Glucagon-like Peptide-1 receptor agonists activate 373 rodent thyroid C-cells causing calcitonin release and C-cell proliferation[END_REF]. Calcitonin is one of the most efficient 306 inhibitors of bone resorption in vitro (Moonga, et al. 1992;[START_REF] Zaidi | Evidence that the action of calcitonin on rat 460 osteoclasts is mediated by two G proteins acting via separate post-receptor pathways[END_REF]). Evidence from previous 307 studies suggests that basally, male GLP-1R KO mice present with a 2.5 fold increase in circulating 308 plasma calcitonin [START_REF] Madsen | GLP-1 receptor agonists and the thyroid: C-cell effects in mice are 428 mediated via the GLP-1 receptor and not associated with RET activation[END_REF]. As regard to the inhibitory action of calcitonin on bone 309 resorption, it would be legitimate to hypothesize that bone resorption should be the primary target of 310 calcitonin increase. However, recent evidence made with genetic ablation of either the calcitonin or 311 calcitonin receptor genes highlighted an unexpected phenotype of high bone mass with no or little 312 apparent effect on bone resorption [START_REF] Dacquin | Amylin inhibits bone resorption while the calcitonin receptor controls 395 bone formation in vivo[END_REF][START_REF] Davey | Calcitonin receptor plays a physiological role to protect against hypercalcemia in 398 mice[END_REF][START_REF] Hoff | Increased bone mass is an unexpected phenotype associated with 421 deletion of the calcitonin gene[END_REF]. This

317

Another factor that strongly affects bone strength is the quality of the bone matrix (Ammann et al.

318

2007). GLP-1R KO mice presented with a decrease in maximum load (-17%), hardness (-19%) and 319 dissipated energy (-15%). Owing to the nanocomposite composition of the bone matrix, any of the 320 collagen or mineral might be involved in the reduction in material properties. Investigation of the bone 321 mineral, by qBEI and FTIR, revealed that neither the degree of mineralization nor the maturity of the 322 mineral was affected by the GLP-1R deletion. However, it is important to bear in mind that due to 323 instrument limitations, we were not capable of investigating mineral cristallinity as proposed by Farlay

324

et al [START_REF] Farlay | Mineral maturity and crystallinity index are 408 distinct characteristics of bone mineral[END_REF]). On the other hand, the maturity of the collagen matrix, determined as the 325 1660/1690 cm -1 ratio, was significantly reduced. Some controversies exist in the interpretation of this 326 ratio as some groups reported that this ratio represent the trivalent over divalent cross-links ratio but

327

recently an elegant study of Farlay and collaborators demonstrated that variation in this parameter 328 could be more related to secondary structure of the collagen matrix after mineralization than a ratio of 329 trivalent over divalent collagen crosslinks [START_REF] Farlay | The ratio 1660/1690 cm(-1) measured by infrared 405 microspectroscopy is not specific of enzymatic collagen cross-links in bone tissue[END_REF]). Nevertheless, it is an indicator of 330 collagen maturity. The reduction of collagen maturity could itself explain the reduction in material

331

properties, as we found a significant positive association between collagen maturity and dissipated 332 energy. Furthermore, alteration of the collagen matrix often alters the post-yield properties of the bone 333 matrix [START_REF] Bailey | Age-related changes 367 in the biochemical properties of human cancellous bone collagen: relationship to bone strength[END_REF][START_REF] Burr | The contribution of the organic matrix to bone's material properties[END_REF][START_REF] Zioupos | The role of collagen in the declining mechanical properties of 463 aging human cortical bone[END_REF]. In our study, the observed reduction in 334 mature trivalent crosslinks could also account for the decreased post-yield energy determined by 3-335 point bending.

336

In conclusion, mice lacking the GLP-1R appear to have a decrease in bone strength observed at the 337 anatomical level with a decrease in three-point bending resistance and decreased cortical thickness.

338

Bone strength was also reduced at the tissue level and was associated with reductions of collagen

Where

  Ni represents the number of pixels with the i grey level and Nt the total number of pixels. The 123 frequency distribution as a function of grey level was plotted and the mean grey level (GL mean ) of each 124 bone was deduced from this distribution using the following formula: 125 126 where GLi represents the value of the i grey level. 127 128 X-ray microcomputed tomography (MicroCT) 129 MicroCT analysis was performed in the proximal left femur with a Skyscan 1172 microtomograph 130 (Bruker MicroCT, Kontich, Belgium) equipped with an X-ray tube working at 69 kV/100 µA. The 131 pixel size was fixed at 3.75 µm, the rotation step at 0.25° and exposure was done with a 0.5-mm 132 aluminum filter. The region of interest (VOI) was located 6-mm below the growth plate. External bone 133 diameter (B.Dm in mm), marrow diameter (Ma.Dm in mm), cortical thickness (Ct.Th in µm), and cross-134 sectional moment of inertia (CSMI in mm 4 ) were measured with a lab-based routine made with ImageJ 135 1.45s (NIH, Bethesda, MD) according to guidelines and nomenclature proposed by the American

  with an average of 32 scans in transmission mode. Background spectral images were collected 180 under identical conditions from the same BaF 2 windows at the beginning and end of each experiment 181 to ensure instrument stability. For FTIR analysis, 10 spectra were acquired approximately 6-mm below 182 the growth plate on cortical bone, corresponding to the fracture zone observed by 3 point bending, and 183 analyzed with the Opus Software (release 5.5, Bruker). The contribution of the embedding 184 polymethylmethacrylate (pMMA) and water vapor were corrected for each spectrum prior to baseline 185 correction. Then individual spectra have been subjected to curve fitting using a commercial available 186 software package (Grams/AI 8.0, Thermofisher scientific, Villebon sur Yvette, France). Briefly, using 187 the Levenberg-Maquardt algorithm every absorption band is characterized by its area. The second 188 derivative spectrum was used to determine the number and the position of the bands constituting 189 every spectral interval. All bands were positioned at maximal intensities with a Gaussian shape. 190 Position, height, width at half intensity and area were obtained. Peaks corresponding to amide I and II 191 (1350-1725 cm -1 ) and v1v3 phosphate (900-1200cm -1 ) were considered for further analysis. The 192 evaluated IR spectral parameters were (1) mineral-to-matrix ratio which reflects the degree of 193 7 mineralization of the bone matrix, calculated from the ratio of integrated areas of the phosphate υ1, υ3 194 band at 900-1200 cm -1 to the amide I band at 1585-1725 cm -1 ; (2) mineral maturity, which reflects the 195 ratio of apatic vs. nonapatic domains, calculated as the ratio of the relative intensity of subbands at 196 1020 and 1030cm

262

  Figure 3D). As shown in Figure4, a positive correlation (R²=0.62, p<0.004) was found between

  study, we investigated bone strength in a mouse model of GLP-1R deficiency to 276 understand how the GLP-1/GLP-1R pathway may affect bone strength. Three-point bending 277 experiments, performed on the midshaft femur, revealed a significant decrease in the total absorbed 278 energy as well as reduction in ultimate load, stiffness, yield load, post-yield energy, work to fracture 279 and yield strength. These results suggest reduced bone strength in these animals. However, several 280 factors might influence the outcome of three-point bending including bone microarchitecture and bone

313

  unexpected phenotype indicated a possible inhibitory action (direct or indirect) of calcitonin on bone 314 formation without affecting bone resorption. Therefore, the contributing effect of increased circulating 315 calcitonin to bone formation in our GLP-1R KO model remains a possible scenario.

339Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :

 12345 FIGURE LEGENDS

  Figure1

Bone Mineral Density Distribution measured by backscattered electron imaging.

). Three-D models represent a region of 137 interest of 2 mm centered 6 mm below the growth plate. 138 139

Fourier Transformed InfraRed spectroscopy (FTIR) 173

  cortical bone area was imaged at a 200X nominal magnification, corresponding to a pixel size of 0.5 154 µm per pixel. The grey levels distribution of each image was analyzed with a lab-made routine using 155 Image J. Three variables were obtained from the bone mineral density distribution: Ca peak is the most 156 frequently observed calcium concentration, Ca mean is the average calcium concentration and Ca width is

	157
	158	the width of the histogram at half maximum of the peak.
	159
	160	Nanomechanical testing
	161	Nanoindentation tests evaluated the mechanical properties of the bone matrix. As nanoindentation
	162	assesses volume of material at a length scale less than that of individual microstructural features in
	163	bone, this technique avoids confounding factors such as bone microarchitecture and porosity that
	164	affect tissue properties at larger length scales such as 3-point bending. Tests were performed on the
	165	same sample used for qBEI measurements after rapid polishing. Briefly, femurs were rehydrated
	166	overnight in saline prior to nanoindentation testing. Eight indents were positioned in cortical bone 6-
	167	mm below the growth plate with a NHT-TTX system (CSM, Peseux, Switzerland) equipped with a
	168	Berkowitch diamond probe. The indents were done up to a depth of 900 nm with a loading/unloading
	169	rate of 40mN/min. At maximum load, a holding period of 15 seconds was applied to avoid creeping of
	170	the bone material. Maximum load, indentation modulus, hardness and dissipated energy were
	171	determined according to Oliver and Pharr (Oliver and Pharr 1992).
	172
	174	After 3 point bending, upper portions of the femur diaphysis were embedded in pMMA at 4°C.
	175	Sections of 4µm thickness were cut dry on a heavy duty microtome equipped with tungsten carbide
	176	knives (Leica Polycut S) and sandwiched between BaF 2 optical windows. Spectral analysis were
	177	obtained on a Bruker Vertex 70 spectrometer (Bruker optics, Ettlingen, Germany) interfaced with a
	178	Bruker Hyperion 3000 infrared microscope equipped with a standard single element Mercury
		Cadmium Telluride (MCT) detector (750-4000cm	-1 ). Infrared spectra were recorded at a resolution of
	143
	144	undecalcified in methylmethacrylate at 4°C. Polymethylmethacrylate blocks were then polished to a 1-
	145	µm finish with diamond particles, carbon-coated and observed with a scanning electron microscope
	146	(EVO LS10, Carl Zeiss Ltd, Nanterre, France) equipped with a five quadrant semi-conductor
	147	backscattered electron detector. The microscope was operated at 20 keV with a probe current of 120
	148	pA and a working distance of 8.5 mm. The backscattered signal was calibrated using pure carbon
	149	(Z=6, mean grey level = 25), pure aluminum (Z=13, mean grey level =225) and pure silicium (Z=14,

mean grey level =253) standards (Micro-analysis Consultants Ltd, St Ives, UK). For these 150 contrast/brightness settings, the BSE grey level histogram was converted into weight percentage of 151 calcium. Eventual changes in brightness and contrast due to instrument instabilities were checked by 152 monitoring the current probe and imaging the reference material (C, Al and Si) every 15 min. The 6

is required for optimum bone strength 234

  Bone strength of the femur was assessed by three-point bending and the results are presented Table2351. In GLP-1R KO mice, mechanical parameters such as ultimate load, yield load, stiffness, total

	233	RESULTS
	GLP-1R 236
	237	absorbed and post-yield energies were reduced significantly by 17 %, 21%, 25 %, 34% and 26%
	200 238	-1 of the amide I peak. Although multiple respectively as compared with WT animals. No differences in ultimate displacement were observed
	201 239	theories between these two groups of animals. Material properties such as work to fracture and yield strength exists about the significance of this ratio (pyridinium trivalent to
	202 240	dehydrodihydroxylysinonorleucine divalent collagen cross-links, modifications of secondary structure were significantly reduced in GLP-1R-deficient mice by 20%, 20% and 7% respectively as compared
	203 241	of collagen molecules), this ratio indicates the maturity of the collagen of the bone matrix (Farlay, et al. with WT mice. No significant differences were observed for ultimate strength, breaking strength,
	204 242	2011; Paschalis, et al. 2001). ultimate strain and elastic modulus.
	205	GLP-1R-
	206	Gene expression
	219
	220	transcript	was	amplified	by	quantitative	PCR	using	primer	pairs	5'-
	221	GGGTCTCTGGCTACATAAGGACAAC-3' and 5'-AAGGATGGCTGAAGCGATGAC-3'. The expression
	222	level of each sample was normalized against gapdh mRNA expression using primer pairs 5'-
	223	AATGGATTTGGACGCATTGGT-3' and 5'-TTTGCACTGGTACGTGTTGAT-3'.
	224
	225	Statistical analysis
	226	Results were expressed as mean ± standard error of the mean (SEM). Non-parametric Mann-Whitney
	227	U-test was used to compare the differences between the groups using the Systat statistical software
	228	release 13.0 (Systat software Inc., San Jose, CA). Correlation between material properties and
	229	collagen cross-link ratio was assessed by a least squares linear regression analysis. Differences at
	230	p<0.05 were considered significant.
	231
	232
											8

Bone marrow cells were isolated from the long bones of 16 weeks-old WT animals by flushing tibias 207 and femurs with alpha-MEM as previously reported

[START_REF] Mieczkowska | Thiazolidinediones induce osteocyte 433 apoptosis by a G protein-coupled receptor 40-dependent mechanism[END_REF]

. For osteoblast 208 differentiation, bone marrow cells were plated into a 25 cm 2 culture flask until confluency (~3 days).

209

For osteoclast culture, bone marrow cells were cultured for 24hrs into a 25 cm 2 flask in alpha-MEM 210 supplemented with 10% fetal calf serum, 2 mM L-glutamine, 100 U/ml penicillin and 100 µg/ml 211 streptomycin to allow stromal cells attachment. Non adherent cells were then collected and plated in 212 24 well-plate in alpha-MEM supplemented with 10% fetal calf serum, 2 mM L-glutamine, 100 U/ml 213 penicillin, 100 µg/ml streptomycin, 25 ng/ml macrophage-colony stimulating factor (M-CSF-R&D 214 systems, Abingdon, UK) and 50 ng/ml soluble RANKL (peprotech Ltd, London, UK). After seven days 215 of culture, multinucleated osteoclasts were evidenced in culture as previously described (Mabilleau, et 216 al. 2011). Total RNA were extracted from osteoblast or osteoclast ex vivo cultures, lung, pancreatic 217 islets and heart using TriZol, reversed transcribed using iScript cDNA synthesis kit (Bio-Rad) and 218 amplified by real-time PCR using SYBR GREEN PCR master mix (Bio-Rad). The mouse Glp1r mRNA

deficiency results in altered femoral mineral density and cortical geometry 243

  As represented Figure2A, the bone mineral content seemed lower in GLP-1R KO animals and indeed,244the frequency of occurrence of grey level, representing the bone mineral content, was switched toward 245 the left in GLP-1R KO mice indicating a lower bone mineral content. Furthermore, the mean grey level 246 was significantly reduced by 5.2% in GLP-1R KO mice as compared with WT controls (p=0.021).

247

Histomorphometric analysis of cortical bone (Figure

2B

) revealed that B.Dm was reduced significantly 248 by 6% in GLP-1R KO animals as compared with WT controls (p=0.034) whilst Ma.Dm was unchanged 249 (p=0.289). Consequently, Ct.Th and CSMI were lowered significantly by 13% and 25% respectively in 250 deficient animals as compared with WT (p=0.034 and p=0.031 respectively).

Table 1 :

 1 Three-point bending parameters in WT and GLP-1R KO mice

	491
					WT (n=12)	GLP-1R KO (n=8)	p value
		Ultimate load (N)		14.47 ± 0.35	11.95 ± 0.27 *	0.034
		Ultimate displacement (mm)	0.31 ± 0.006	0.30 ± 0.033	1.000
		Stiffness (N.mm	-1 )		42.68 ± 0.85	31.92 ± 5.45 *	0.05
		Total absorbed energy (N.mm)	2.33 ± 0.09	1.54 ± 0.24 *	0.021
		Yield load (N)			11.83 ± 0.45	9.37 ± 0.42 *	0.021
		Post-yield energy (N.mm)	1.52 ± 0.05	1.12 ± 0.12 *	0.048
		Ultimate strength (MPa)	84.2 ± 2.0	82.9 ± 2.0	0.513
		Breaking strength (MPa)	80.9 ± 3.4	79.2 ± 7.3	0.783
		Ultimate strain			0.013 ± 0.001	0.011 ± 0.002	0.714
		Elastic modulus (GPa)		3.0 ± 0.2	3.0 ± 0.5	0.513
		Work to fracture (KJ.m	-2 )	0.211 ± 0.007	0.168 ± 0.017*	0.047
		Yield strength (MPa)		69.3 ± 1.7	64.2 ± 0.5 *	0.05
	492	*: p<0.05 vs. WT animals
	493
	494

Table 2 :

 2 Nanomechanical properties of the bone matrix

	495
			WT	GLP-1R KO	p value
			(n=12)	(n=8)
		Maximum load (mN)	11.9 ± 0.6	9.9 ± 0.4 *	0.037
		Hardness (MPa)	627.8 ± 34.4	510.7 ± 28.9 *	0.028
		Indentation modulus (GPa)	12.9 ± 0.6	12.1 ± 0.4	0.203
		Dissipated energy (mN.nm)	3189.7 ± 176.7	2708.6 ± 130.9 *	0.028
	496	*: p<0.05 vs. WT animals
	497
	498

Table 3 :

 3 Bone mineral density distribution in WT and GLP-1R deficient mice

	499
		WT	GLP-1R KO	p value
		(n=12)	(n=8)
	Ca peak (%)	26.9 ± 0.7	26.8 ± 0.7	1.00
	Ca mean (%)	26.3 ± 0.6	26.3 ± 0.7	0.936
	Ca width (%)	2.7 ± 0.1	2.9 ± 0.2	0.337
	500
	501

plate (mid-shaft of the femur) and represented a height of 2 mm. The relative mineral content of

Moonga BS, Alam AS, Bevis PJ, Avaldi F, Soncini R, Huang CL & Zaidi M 1992 Regulation of

cytosolic free calcium in isolated rat osteoclasts by calcitonin. J Endocrinol 132 241-249.

Nuche-Berenguer B, Moreno P, Esbrit P, Dapia S, Caeiro JR, Cancelas J, Haro-Mora JJ & Villanueva-

insulin-resistant states. Calcif Tissue Int 84 453-461.

Nuche-Berenguer B, Moreno P, Portal-Nunez S, Dapia S, Esbrit P & Villanueva-Penacarrillo ML 2010

DECLARATION OF INTEREST 345

The authors declared no conflict of interest that could be perceived as prejudicing the impartiality of 346 the research reported.