Jose F Rodrigues-Jr

Jean-Louis Pepin
email: jpepin@chu-grenoble.fr

Lorraine Goeuriot
email: lorraine.goeuriot@imag.frsihem.amer-yahia@imag.fr

Sihem Amer-Yahia

An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening

Keywords: obstructive sleep apnea screening, machine learning, decision trees, naive Bayes

The identification of Obstructive Sleep Apnea (OSA) relies on laborious and expensive polysomnography (PSG) exams. However, it is known that other factors, easier to measure, can be good indicators of OSA and its severity. In this work, we extensively investigate the use of Machine Learning techniques in the task of determining which factors are more revealing with respect to OSA. We ran extensive experiments over 1,042 patients from the Centre Hospitalier Universitaire of the city of Grenoble, France. The data included ordinary clinical information, and PSG results as a baseline. We employed data preparation techniques including cleaning of outliers, imputation of missing values, and synthetic data generation. Following, we performed an exhaustive attribute selection scheme to find the most representative features. We found that the prediction of OSA depends largely on variables related to age, body mass, and sleep habits more than the ones related to alcoholism, smoking, and depression. Next, we tested 60 regression/classification algorithms to predict the Apnea-Hypopnea Index (AHI), and the AHI-based severity of OSA. We achieved performances significantly superior to the state of the art both for AHI regression and classification. Our results can benefit the development of tools for the automatic screening of patients who should go through polysomnography and further treatments of OSA. Our methodology enables experimental reproducibility on similar OSA-detection problems, and more generally, on other problems with similar data models.

INTRODUCTION

Obstructive Sleep Apnea (OSA) is the predominant type of sleep breathing disorder; it is characterized by repeated episodes of complete or partial obstructions of the upper airway while sleeping, usually associated with a reduction of the blood oxygen saturation. OSA has the potential of reducing the quality of life and increasing the risks of cardio-vascular comorbidities [START_REF] Gilat | Obstructive sleep apnea and cardiovascular comorbidities: a large epidemiologic study[END_REF]. Yet, a great part of the patients that suffer from OSA is not detected [START_REF] Jennum | Epidemiology of sleep apnoea/hypopnoea syndrome and sleep-disordered breathing[END_REF]. The detection of OSA is based on the results of a polysomnography exam, which records brain waves, blood oxygen level, heart rate and breathing, eye and leg movements during hours of monitored sleep. For OSA diagnosis, the Apnea-Hypopnea Index (AHI) measured by polysomnography breathing monitoring is the gold standard. However, polysomnography is a laborious and expensive exam carried out on a limited basis, when OSA is already suspected in a patient.

Rather than polysomnography, simpler tests in the form of direct observations, i.e. questionnaires, are used for screening OSA patients. However, according to AlGhanim et al. [START_REF] Alghanim | The economic impact of obstructive sleep apnea[END_REF], even the bestvalidated test, the "STOP-BANG", is limited when used for screening based on only eight questions [START_REF] Network | The official stop-bang questionnaire website[END_REF]. Another test, the Epworth Sleepiness Scale has minimal OSA-detection capabilities [START_REF] Chervin | The epworth sleepiness scale may not reflect objective measures of sleepiness or sleep apnea[END_REF]. And the Berlim Questionaire heavily varies in terms of sensitivity and specificity, revealing its impreciseness in several clinical scenarios [START_REF] Chung | Validation of the berlin questionnaire and american society of anesthesiologists checklist as screening tools for obstructive sleep apnea in surgical patients[END_REF]. By inspecting such questionnaires, one can argue that the pieces of evidence that they collect are fail-proof as, for example, "Has anyone Observed you Stop Breathing or Choking/Gasping during your sleep?" But, still, they fail in a significant number of situations denoting that the identification of OSA still lacks the "proper questions" [START_REF] Alghanim | The economic impact of obstructive sleep apnea[END_REF] [START_REF] Chervin | The epworth sleepiness scale may not reflect objective measures of sleepiness or sleep apnea[END_REF].

In this work, we approach the problem with the use of stateof-the-art ML techniques. We demonstrate that good performance in screening sleep apnea is possible in an automated manner departing from simple measures collected in the every-day hospital practice. We introduce a methodology based on an elaborate data pre-processing technique, followed by an exhaustive attribute selection, and a comprehensive experimentation over 60 regression and classification algorithms. The attribute selection allowed us to discuss the most important attributes for OSA diagnosis, confirming known facts and suggesting future investigations. We use regression to predict the continuous value of the AHI based on simple clinical measures, like the body mass index and sleepiness of the patient; and, similarly, we use classification to predict the severity of OSA as given by four possible classes: an AHI < 5 events per hour is considered normal (class 0); mild (class 1) for 5 ≤ AHI < 15; moderate (class 2) for 15 ≤ AHI < 30; and severe (class 3) for AHI ≥ 30. As we review in Section 2, the performance of OSA diagnosis ranges in between 70% and 80%; that is, it is not a problem easy to solve.

Our results can benefit the development of tools for the automatic screening of patients who go through polysomnography and further treatments of OSA. Our thorough methodology enables experimental reproducibility on similar OSA-detection problems, and more generally, on other problems with similar datasets. We review related works in the next section; in Section 3, we detail our dataset, an important factor in this kind of research. Figure 1 illustrates our methodology, which comprises three stages: (i) data preparation and attribute selection, presented in Sections 4 and 5, respectively; (ii) algorithm selection, presented in Section 6; and (iii) training and evaluation, presented in Section 7 along with a comparison to related works. We present further discussion in Section 8, and Section 9 concludes the paper.

RELATED WORK

We consider state-of-the-art approaches that investigate the task of predicting the presence or the severity of Obstructive Sleep Apnea based on factors other than the polysomnography-based Apnea-Hypopnea Index (AHI).

The work of Ustun et al. [START_REF] Ustun | Clinical prediction models for sleep apnea: the importance of medical history over symptoms[END_REF] discusses OSA prediction based on clinical indicators before employing their method named Supersparse Linear Integer Model (SLIM). This method aims to find a simple arithmetic scoring questionnaire to be carried on paper; the score is used as an indicator of a clinical condition of interest. Although the results are not comparably sound to other more elaborated works, the authors elegantly demonstrated that OSA is to be diagnosed by means of easier-to-acquire factors.

In the work of Wu et al. [START_REF] Wu | A new method for self-estimation of the severity of obstructive sleep apnea using easily available measurements and neural fuzzy evaluation system[END_REF], the authors use a neural fuzzy evaluation system [START_REF] Zhao | A new gradient descent approach for local learning of fuzzy neural models[END_REF] over 17 patient variables to predict the occurrence of OSA, as well as the value of indicator AHI. Their findings, by means of a stepwise regression [START_REF] Armitage | Modelling continuous data[END_REF], indicate that variables Body Mass Index (BMI), difference of systolic blood pressure before going to sleep and early in the morning, and the Epworth Sleepiness Scale (ESS) [START_REF] Abrishami | A systematic review of screening questionnaires for obstructive sleep apnea[END_REF], were the most important factors in predicting OSA. Their work was based on 150 patients. Our work uses one order of magnitude more patients.

Mencar et al. [15] perform an extensive investigation of the applicability of ML methods to detect OSA. They use 8 classification strategies and 6 regressions over 19 variables; they found that Support Vector Machine [START_REF] Steinwart | Support vector machines[END_REF] and Random Forest models [START_REF] Breiman | Random forests[END_REF] work best for classification, while Support Vector Machine and Linear Regression [START_REF] Seber | Linear regression analysis[END_REF] are most effective on predicting the AHI value. They also demonstrated that a limited number of variables is enough for such tasks. Their work was based on 313 patients. Our work uses one order of magnitude more patients. Moreover, they do not investigate the ample set of ML algorithms that we use.

More recently, Huang et al. [12] conducted a robust investigation of over 6,875 individuals using Support Vector Machines. Their results are convincing and widely discussed with abundant evidence. However, the applicability of their work is limited. The authors used the AHI in data modeling and preparation. To achieve high performance, they partitioned data according to gender, age, and AHI, which resulted in a different model for each partition; each model relying on a different set of variables. Despite the fact that this methodology can reveal important facts, it is not usable in a real scenario because, initially, one does not have the AHI value.

It interesting to note that each work considers a different set of variables; they vary in number and type. This is possibly due to the specific medical routine that originated each work. As we discuss in Section 8, this fact and the use of proprietary datasets do not permit an absolute comparison among different works. We address this issue by detailing and comparing the steps of our methodology.

THE MARS DATASET

We use a dataset named MARS, provided by the Centre Hospitalier Universitaire of the city of Grenoble, France. The dataset comprises of 1,042 patients, each one depicting 20 features including age, gender, smoking, (yes/no), number of cigarette packets per year, alcohol use (yes/no), physical exercise frequency (weekly), nocturia frequency (urination at night), Medical Research Council scale for the difficulty in breathing, New York Heart Association for heart failure, Epworth Sleepiness Scale, asthenia (physical strength loss) score, depression score, restless legs syndrome score, body mass index, neck circumference (cervical perimeter), waistline (abdominal perimeter), hip measurement, systolic blood pressure, diastolic blood pressure, and cardiac frequency. All of them are measurable without complex exams in comparison to the expensive and timeconsuming polysomnography. 1: Set of attributes in the dataset MARS along with their short version used throughout the text.

For this investigation, we consider polysomnography results in an attended setting (sleep laboratory) as the "gold standard" for the diagnosis of OSA. Among the measures acquired by a polysomnography session, the most important is the Apnea-Hypopnea Index (AHI). Hence, all of the records of our dataset include a continuous AHI value that ranges from 0 to 141. The AHI is defined as the average sum of apneas and hypopneas per hour of sleep; apnea is defined as the absence of airflow for more than 10 seconds; and hypopnea corresponds to a reduction in the respiratory effort with more than 4% oxygen desaturation. The value of the AHI in adults points to four classes of OSA: an AHI < 5 events per hour is considered normal (class 0); mild (class 1) for 5 ≤ AHI < 15; moderate (class 2) for 15 ≤ AHI < 30; and severe (class 3) for AHI ≥ 30. This referential allows the application of two ML tasks: (i) prediction of the AHI continuous value for a given patient; (ii) classification of a patient into one of the four classes of OSA. For patients positively diagnosed (AHI > 5), continuous positive airway pressure (CPAP) therapy is the recommended treatment.

From Figure 2(a), one can see that MARS consists mostly of OSA-positive cases (AHI ≥ 5), notably of severe cases (AHI ≥ 30). The data distribution indicates that the patients who look for OSA screening already have a diagnosis suspicion. This reveals that the dataset is severely imbalanced -see Figure 2(b), showing that only 6.5% of patients have a normal status (non-OSA).

DATA PREPARATION

MARS is a real-world generated dataset used in a production setting. As such, it includes inconsistent values, missing values, and imbalance. These problems directly impact the use of MARS and had to be treated beforehand.

Inconsistent values

For treating inconsistent values, we drew boxplots [START_REF] Wickham | years of boxplots[END_REF] considering all fields, from which we identified patients having attributes whose values are far from the interquartile range (IQR) and its lower and upper 1.5 whiskers. We also found patients with negative age values and null polysomnography exams. Accordingly, our first data preparation was to exclude all these records because they could not be fixed and had the potential to jeopardize the learning of our algorithms.

Missing values

We learned that the majority of records had at least one missing value. This occurred for as many as 20 attributes. This was possibly because the very patient did not know the value or because the annotation was faulty. While null values are not necessarily a threat to learning data patterns, many ML algorithms simply cannot operate in such circumstances due to numeric or algorithmic reasons.

We solved this problem by substituting the missing values of each field by its median value. The reason is that: (i) none of the fields data distribution was clearly Gaussian, hence we could not use the mean value; (ii) the algorithms we use are based on Naive Bayes and Decision Trees, which are barely affected by the median value as it provides little or no information for a given patient with respect to the upper and lower 50% of the data.

Imbalance

As we discussed in Section 3, the data is heavily skewed to cases with higher severity. The impact of this imbalance is that the ML algorithms are not able to correctly learn which regions of the space refer to which classes. As we can see in Figure 3(a), although the samples of class 3 (severe) patients are concentrated in a specific central region, there are samples of class 3 scattered all over. Many samples of class 3 can be considered as outliers (located away from the denser cluster) that occupy odd regions of the space, yet, they outnumber other classes due to the strong imbalance. As a result, during our initial experiments, our algorithms reported that all the test samples belonged to class 3. To alleviate this problem, we applied oversampling technique ADASYN [START_REF] He | Adasyn: Adaptive synthetic sampling approach for imbalanced learning[END_REF], an improved version of method SMOTE. This method works by identifying the hard-to-classify samples of the minority class by means of a k-NN neighborhood inspection (we use k=5 and Euclidean distance); the samples of interest are the ones with the highest ratio of 𝑘 nearest neighbors belonging to a different class. It then generates synthetic points in the proximity of these samples by linearly interpolating data; the interpolation considers a sample of interest and its closest neighbors of the same class, creating synthetic data in the space between them. Given two samples 𝑥 𝑖 and 𝑥 𝑧𝑖 , the new synthetic data point is given by:

𝑠 𝑖 = 𝑥 𝑖 + (𝑥 𝑧𝑖 -𝑥 𝑖 ) * 𝜆 (1)
where 3: Effects of oversampling given by precision and recall of the standard Naive-Bayes algorithm -original versus oversampled data, using 75% for train and 25% for testing over 100-rounds stratified sampling.

𝜆 ∈ [0,
Table 3 depicts the effects of oversampling using the standard Naive-Bayes algorithm (refer to Section 5). Before oversampling ("Original"), normal-class patients could not be detected during testing. This is because their space was populated with outliers from the other classes; as a result, that space was interpreted as characterizing classes 2 (moderate) and 3 (severe). After oversampling ("Oversampled"), the misinterpreted regions of the space characterized the minority classes (normal and mild) -this new setting obviously reduced the recall of the majority class (severe), but now we can detect normal patients, and more mild patients. Nevertheless, the Precision regarding the majority classes was barely impacted, which indicates that the learning algorithm was able to absorb the new information without "forgetting" the original setting, see Table 3, 1st column, rows 5 and 9.

ATTRIBUTE SELECTION

In Section 3, we saw that the original data comprises 20 variables. We observed that most of them are of little use in the ML process. This is because some of them are correlated, noisy, ineffective for decision making, or simply non-relevant for the problem at hand. Besides, this large number of variables demands excessive processing time. To find which variables were the most relevant to our problem, we used a Naive-Bayes classifier [START_REF] Ren | Naive bayes classification of uncertain data[END_REF] to experiment with all possible attribute combinations. That is, we trained and validated the classifier over all the 20 𝑘=1 20! 𝑘! * (20-𝑘)! =1,048,575 possible attribute combinations. For each round, we used a 5-fold cross-validation, averaging the one-versus-the-rest Area Under the Receiver-Operating Characteristic (AUC-ROC) performance results with respect to the predicted class of the patients.

We used the classifier provided by the Scikit-Learn library 1 . It implements the standard algorithm which draws the distributions of each variable and computes the conditional probability 𝑃 (𝑦 𝑖 |𝑥 𝑖 ). In our case, for each sample validation, the algorithm computes 𝑃 (𝑦 𝑖 = 𝑐𝑙𝑎𝑠𝑠 |𝑥 𝑖 = {𝑠𝑒𝑡 𝑜 𝑓 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠 𝑣𝑎𝑙𝑢𝑒𝑠}). Once the probability is computed, we calculate the ROC curve by varying the classification threshold for each class. We opted for Naive Bayes because it depends on data distributions, it is interpretable, it is known for providing accurate results [START_REF] Saritas | Performance analysis of ann and naive bayes classification algorithm for data classification[END_REF], and because it has low computing cost.

We performed the processing twice, which produced slightly different results due to the data shuffling. The final set of attributes corresponds to the set intersection of the results of the two machines: age, smoking, nocturia frequency, depression score, body mass index, neck circumference, hip measurement, and diastolic blood pressure. Table 4 presents the results for Naive-Bayes. The results show improvements in both sensitivity and specificity when using the selected attributes. 4: Detection of classes using Naive-Bayes in terms of sensitivity and specificity (one versus the rest) for all the attributes, and for the selected attributes.

All attributes

Discussion on the selected attributes

We selected the attributes based on strong experimental evidence. Yet, gender, which is a variable pointed out as important in other works, was not selected in our procedure -see Table 5. For instance, gender appears as important in the works of Mencar et al. [15] and Ustun et al. [START_REF] Ustun | Clinical prediction models for sleep apnea: the importance of medical history over symptoms[END_REF], but not in our setting. Possibly, this is because females are under-represented in our work, and in the works of Wu et al. [START_REF] Wu | A new method for self-estimation of the severity of obstructive sleep apnea using easily available measurements and neural fuzzy evaluation system[END_REF] and Huang et al. [12]; in our dataset, only 26% of the patients are women.

Features related to the patient's age and weight (body mass index, waistline, neck circumference, and/or hip measurement) are attributes of choice in all the related works -definitely, obesity is 1 https://scikit-learn.org/stable/modules/naive_bayes.html relevant to the detection of OSA [START_REF] Vgontzas | Sleep apnea and sleep disruption in obese patients[END_REF]. This fact is revealing and, at the same time, it demonstrates the complexity of the problem; this is because not every patient with OSA is obese. One question that might be worth pursuing in a future work is: which factors cause patients with low bmi to develop OSA? Still looking at Table 5, the presence of blood-pressure-related features is noticeable, but less straightforward; these features appear in three of the five works (including ours). It is easy to find work [START_REF] Jiang | Obesity and hypertension[END_REF] that states a strong correlation between obesity and hypertension (also diabetes), and between OSA and hypertension [START_REF] Dopp | Obstructive sleep apnea and hypertension: Mechanisms, evaluation, and management[END_REF]. Therefore, while obesity might explain OSA and hypertension, OSA can worsen hypertension issues even further, a relevant discussion, but that is outside the scope of this work -please refer to the work of Wolk et al. [START_REF] Wolk | Obesity, sleep apnea, and hypertension[END_REF]. Another relevant feature refers to the Epworth Sleepiness Scale, which points out that daily sleepiness is quite relevant -the limitation here is that sleepiness is subjective in nature, and might be caused by other factors other than OSA, reducing the discriminability of this feature [START_REF] Chervin | The epworth sleepiness scale may not reflect objective measures of sleepiness or sleep apnea[END_REF]. Other non-unanimous features, considering our review, include smoking and nocturia frequency, which seem to have an impact on OSA detection, but that are not fundamental.

Work

Selected features Wu et al. [START_REF] Wu | A new method for self-estimation of the severity of obstructive sleep apnea using easily available measurements and neural fuzzy evaluation system[END_REF] age; body mass index; Epworth Sleepiness Scale; waistline; neck circumference; and difference of blood pressure before going to sleep and early in the morning Mencar et al. [15]

CHOOSING AN ALGORITHM

The automatic detection of Obstructive Sleep Apnea defines two types of problems that depart from the clinical signals (see Table 1) of a given patient: regression or classification. Regression corresponds to determining the value of the Apnea-Hypopnea Index. Classification corresponds to determining the status of a patient, a 4-classes problem (normal, mild, moderate, or severe). For the two tasks, we benefit from the advanced and matured frameworks available in the Scikit-Learn ecosystem (https://scikit-learn.org/); accordingly, we experimented with 60 regression and classification algorithms to find the most effective solutions -see Table 6

Regression problem

For regression, we experiment with 28 different algorithms as detailed in Table 6. Figure 4 presents the 𝑅 2 -score (or coefficient of determination) computed with each of the available regressors over the MARS dataset, using default parameters and the selected attributes presented in Section 5. We compute the performance over a 33%-split validation set, iterating up to 300 times depending on the algorithm. The regressors are ordered according to their score, the best ones with a higher score to the right. One can see that regressors HistGradientBoosting, and ExtraTrees had the best performances.

Classification problem

For classification, we experiment with 32 different algorithms as detailed in the Appendix. Figure 5 presents the accuracy score computed with each of the available classifiers over MARS, using default parameters and the selected attributes in Section 5. Similarly to the experiments with regressors, we compute the performance

Algorithm of choice

Considering the performance for both regression and classification, after experimenting with 60 algorithmic configurations, we proceed using algorithm ExtraTrees (Extremely Randomized Trees [START_REF] Geurts | Extremely randomized trees[END_REF]). Extremely Randomized Trees refer to an ensemble algorithm quite similar to the popular Random Forests [START_REF] Breiman | Random forests[END_REF]. The principle is to use a bagging technique, that is, to pick multiple sets of random samples from the data (with replacement), each one using a subset of the original features; then, a decision tree is built for each set; the final classification is given by majority voting. Extremely Randomized Trees differ from Random Forests in two aspects: the algorithm does not use bagging but a single dataset; each tree builds on random splits rather than on using the "best split" according to a metric such as the Gini Impurity [START_REF] Breiman | Random forests[END_REF]. Both algorithms build hundreds of trees; we experimented with 150, after empirical testing.

The use of Extremely Randomized Trees is known to work better in the presence of noisy attributes, that is, attributes that do not add

COMPARISON TO RELATED WORKS

Table 7 summarizes our comparison to the related work. Unfortunately, we are not able to experiment on the same datasets as they are proprietary and inaccessible. For this reason, besides the metrics usually used for performance in regression and classification, we use other dimensions related to the whole process, in particular to data preparation. This contributes to an improved methodology overall and a more comprehensive evaluation.

In Table 7, one can see that our results are superior or comparable to all of the former works with respect to regression and classification metrics using the attributes selected in Section 5 over Extremely Randomized Trees, as explained in Section 6. We computed the metrics using 100 rounds of random stratified-sampling crossvalidation, each round with 66% of the data for training and 33% for validation (oversampled dataset). With respect to the steps of the methodology, our work is more extensive; this is especially true for the number of methods used to find the best models for regression and classification. As reported in Section 6, we experimented with 28 regression algorithms, and 32 classification algorithms.

DISCUSSION ON OUR CONTRIBUTION

Concerning the data preparation, although our pre-processing steps are not new, we believe we put together a comprehensive set of techniques: distribution analysis, box-plot-based outlier detection and elimination, imputation of missing values, synthetic data generation, and principal component visual analysis. The attribute selection process, in turn, was performed over a procedure with results potentially more precise than those of other works because it was carried exhaustively to find the best set of attributes. Nevertheless, we warn that a larger and more diversified dataset is likely to yield more universal conclusions. This is an open issue for OSA-detection in general.

Despite our good results presented in Section 7, the absence of a benchmark does not permit experimentation in the same conditions, preventing the extrapolation of our conclusions. Hence, as we report in Table 7 and demonstrate in Section 6, instead of advocating for a specific method, we experimented with the largest set of possible approaches. We also provide numbers on the efficacy of each possible solution both for regression and classification. This amplitude is the strongest point of our work; with this thorough experimentation, we provide a versatile methodology on how to solve OSA risk-prediction problems, including the source code for this project, available at https://github.com/jfrjunio/OSAML. Overall, our data analysis and ML processes can guide not only the OSA-detection problem, but also tasks that rely on datasets with a similar model and goal.

As a last remark, we reflect on a critical aspect common to all the reviewed works, including ours: the limited data size. The largest one presented in [12] reports a study involving 6,875 patients. None of the existing works explicitly includes attributes such as ethnic diversity and gender under-representation, which have long been considered as a determinant in medicine [START_REF] Cruickshank | Ethnic factors in health and disease[END_REF]. This fact is to be taken into account not only with respect to our work, but to all the works reviewed so far.

CONCLUSIONS

We described the process of using ML in the task of automated Obstructive Sleep Apnea (OSA). We used the MARS dataset from the Centre Hospitalier Universitaire of the city of Grenoble, France, which comprises 1,042 patients diagnosed with all the four levels of OSA In our analysis, we found that the dataset was challenging due to its complexity, missing values, extreme class imbalance, large number of attributes, and due to the fact that patients with normal diagnostic (non-OSA) were not categorically different from those with severe OSA problems. The solution was two-fold, including an extensive dataset pre-processing, and a robust algorithm selection. We tuned the dataset using oversampling, reducing the detection of positive OSA patients, but increasing the detection of normal non-OSA patients as measured by sensitivity, specificity, precision, recall, f1-measure, and AUC-ROC. For attribute selection, we experimented with 60 algorithms (28 regressors and 32 classifiers). The verdict was to use Extremely Randomized Trees, which are tailored to noisy data. As for our contribution, we follow our methodology for data preparation and algorithm selection since each dataset presents its peculiarities even for the same problem.

We also investigated the most important attributes for OSA detection, which included recurrent factors of age, weight, blood pressure, and sleepiness-related measures. We compared them to the selected features indicated in the related work. For our dataset in particular, the features of nocturia and depression played a relevant role. However, we warn the reader that each dataset has its characteristics and a corresponding set of features. Further investigation is required on such diversity. Finally, we suggest the need for a more robust and diversified dataset because the ones used in this and other works are rather small in the number of patients, not systematically covering ethnic diversity and gender representativeness.

Figure 1 :

 1 Figure 1: Overview of our methodology, which comprises three stages: (i) data preparation and attribute selection, (ii) algorithm selection, and (iii) training and evaluation.

Figure 2 :

 2 Figure 2: AHI distributions in the MARS dataset. (a) Distribution of the AHI continuous values. (b) Distribution of the OSA classes based on the AHI value.

Figure 3 :

 3 Figure 3: Principal Component Analysis 2D scatter plot visualization. (a) Original data. (b) Data oversampled with technique ADASYN [11].

Figure 3 (

 3 Figure 3(b) presents the Principal Component Analysis scatter plot visualization after oversampling with ADASYN. The visualization shows that many regions populated by class 0 and class 1 samples became more densely populated with samples of these classes. This is particularly true in the periphery of the space. In the central part of the space, one can see that classes 0 and 1 are still hard to separate, which constitutes a challenge for the algorithms.

Figure 4 :

 4 Figure 4: Comparative 𝑅 2 -score plot of all the 41 regressors using default parameters over dataset MARS.

Figure 5 :

 5 Figure 5: Comparative accuracy plot of all the 32 classifiers using default parameters over dataset MARS.

Table 1

 1 summarizes the features and their short names used throughout the paper.

	Textual	Short name
	age	age
	gender	gender
	tabagism (yes/no)	tabagism
	number of cigarette packets per	cigarette_packets
	year
	alcohol use (yes/no)	alcohol
	physical exercise frequency	exercise_times_per_week
	nocturia frequency	nocturia_times_per_night
	Medical Research Council for diffi-	mrc
	culty in breathing
	New York Heart Association for	nyha
	heart failure
	Epworth Sleepiness Scale	epworth_score
	asthenia (physical strength loss)	asthenia_score
	score
	depression score	depression_score
	restless legs syndrome score	rls_score
	body mass index	bmi
	neck circumference	neck_circumference
	waistline	waistline
	hip measurement	hip_measurement
	systolic blood pressure	syst_bp
	diastolic blood pressure	diast_bp
	Table

Table 2 :

 2 1] is a random number. As its authors point out, ADASYN adaptively shifts the classification decision boundary toward the difficult examples. In Table2, we present the original cardinality of the data and the oversampled cardinality after ADASYN. Data cardinality, before and after oversampling.

	Class Original Oversampled
	0	68	561
	1	115	565
	2	293	566
	3	566	570
	Total	1,042	2,262

Table 5 :

 5 Selected features after preprocessing in the state-ofthe-art works.

		body mass index; gender; and Epworth
		Sleepiness Scale
	Huang et al. [12] age; waistline; neck circumference; snoring;
		sleep onset latency; and witnessed apnea
	Ustun et al. [21] age; body mass index; gender; diabetes; hy-
		pertension; and tabagism
	This work	age; nocturia frequency; body mass index;
		depression score; neck circumference; hip
		measurement; diastolic blood pressure; and
		tabagism

Table 6 :

 6 . Lists of the regression and classification algorithms experimented over dataset MARS.

	Regression	Classification
	Bayesian ARD	Ada Boost
	Bagging	Bagging
	Bayesian Ridge	Bernoulli Naive Bayes
	Elastic Net	Calibrated CV
	Elastic Net CV	Decision Tree
	Extra Trees	Extra Tree
	Gradient Boosting	Extra Trees
	Hist Gradient Boosting	Gaussian Naive Bayes
	K-Neighbors	Gaussian Process
	Lars	Gradient Boosting
	Lars CV	Hist Gradient Boosting
	Lasso	K-Neighbors
	Lasso CV	Label Propagation
	Lasso Lars	Label Spreading
	Lasso Lars CV	Linear Discriminant Analysis
	Lasso Lars IC	Linear Support Vector
	Linear	Logistic Regression
	Linear Support Vector	Logistic Regression CV
	Multi-layer Perceptron	Multi-layer Perceptron
	Nu Support Vector	Multinomial Naive Bayes
	Partial Least Squares	Nearest Centroid
	Random Forest	Nu Support Vector
	Ridge	Passive Aggressive
	Ridge CV	Perceptron
	Stochastic Gradient Descent Quadratic Discriminant Analysis
	Support Vector Machine	Random Forest
	Theil-Sen	Ridge
	Transformed Target	Ridge CV
	XGBoost	Stacking
		Stochastic Gradient Descent
		Support Vector Machine
		XGBoost

This work

Huang et al.

ACKNOWLEDGMENTS

This research was financed by French agency Multidisciplinary Institute in Artificial Intelligence (Grenoble Alpes, ANR-19-P3IA-0003); and by Brazilian agencies Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (2018/17620-5, and 2016/17078-0); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (406550/2018-2, and 305580/2017-5); and Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES, Finance Code 001). We thank NVidia for donating the GPUs that supported this work.

value to the algorithmic decision. Hence, having our experiments recommend the use of Extremely Randomized Trees is a signal that our dataset is significantly noisy; however, this recommendation is not necessarily ideal for other OSA risk-prediction datasets. Instead, our recommendation is to perform the same algorithmic massive evaluation to find which regressor and classifier work better -accordingly, we make the source code that performs this evaluation available at https://github.com/jfrjunio/OSAML.

Evaluation metrics

Each kind of problem demands specific evaluation metrics. In the case of regression, we use root mean squared error (RMSE), mean absolute error (MAE), and median absolute error (MAE). For classification, we use precision, sensitivity/recall, specificity, F1-score, and AUC-ROC. In the case of classification, the binary metrics are computed for each class versus the others, after which we average the results for each class. With these sets of metrics, it is possible to compare our work to all the works presented in Section 2.


José Ricardo Samaca Martinez

Evelyne Toussaint

Xavier Balandraud

Daniel Berghezan

Jean-Benoit Le Cam

Jean-Benoît Le Cam

Microcalorimetry at the crack tip of rubber

Keywords: Notched rubber specimen, crack tip, heat sources, full-field measurement, motion compensation technique

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

INTRODUCTION

Full-field measurement techniques have recently spread in the experimental mechanics community. For materials undergoing large deformations, such as elastomers, one problem consists in tracking points at their surface that undergo large displacements. Digital Image Correlation (DIC) is probably the most suitable technique [START_REF] Le | A review of the challenges and limitations of full-field measurements applied to large heterogeneous deformations of rubbers[END_REF] that can be used to tackle this issue. When thermo-mechanical couplings occur, temperature variation fields can be measured at the surface of specimens subjected to mechanical loads using infrared thermography. However, the temperature fields are influenced by conduction as well as heat exchanges with ambient air and grips. This is the reason why we use the heat sources, which are deduced from the temperature variation fields using a numerical strategy based on the heat equation. When large deformations occur, the calculation of heat sources is complicated. Recent work [START_REF] Toussaint | Combining displacement, strain, temperature and heat source fields to investigate the thermomechanical response of an elastomeric specimen subjected to large deformations[END_REF] has been performed in case of large deformations using both displacement and temperature fields that were measured simultaneously during the test. They were combined to obtain heat source maps. In the present paper, the procedure to combine displacement and temperature fields to calculate heat sources is first presented. This procedure is applied to the calculation of heat sources in the crack tip vicinity of a notched rubber specimen.

METHODOLOGY

A motion compensation technique is required to calculate heat sources. This compensation enables us to associate a displacement and a temperature at any point of the specimen surface in the reference configuration, which is chosen as the undeformed state. For this purpose, four steps are necessary:

Step 1: two back-to-back cameras are employed. Displacement field is calculated using DIC technique. The number of measurement points is equal to the number of ZOIs (zones of interest), which is different from that of the IR camera. For displacement fields, Eulerian or Lagrangian configuration can be used, whereas thermal fields are measured in the current configuration. It can be noted that the sensor of each camera does not capture exactly the same zone and the dimension of the pixels of both cameras is not the same.

Step 2: the displacement field is interpolated in such a way that this quantity becomes known over the same grid as that of the infrared camera.

Step 3 : the temperature field is plotted in the reference coordinate. The infrared camera sensor remains fixed whereas the physical points of the surface (where the temperature measurement is performed) move during the test. Since both the temperature and displacement fields are known exactly on the same grid (thanks to the preceding step), a new map is obtained by combining both fields.

Step 4: the same procedure as previously is applied to the reference temperature field. The temperature variations field θ is therefore known in the undeformed configuration by merely substracting the initial temperature field to the current one.

THERMOMECHANICAL FRAMEWORK

Temperature fields measured at the flat surface of a specimen by an IR camera are 2D, i.e. bidimensional. At a given point on the surface, the temperature is nearly homogeneous through the thickness. In fact, a small temperature gradient exists close to the specimen faces due to the heat exchange by convection with the air, but the surface temperature can be considered as very close to the mean temperature in the thickness. Then, by integrating the 3D heat diffusion equation over the specimen thickness [START_REF] Chrysochoos | An infrared image processing to analyze the calorific effects accompanying strain localization[END_REF] and defining the mean thermal disequilibrium through the thickness between the specimen and its surroundings by (x; y), the following bidimensional formulation of the heat diffusion equation is obtained: [START_REF] Le | A review of the challenges and limitations of full-field measurements applied to large heterogeneous deformations of rubbers[END_REF] where  is a time constant characterizing the heat exchanges by convection with ambient air along the direction perpendicular to the specimen plane, D is the thermal diffusivity, is the "heat source" expressed here in °C/s.

EXPERIMENTAL SETUP

The material considered here is a filled styrene-butadiene rubber. The sample geometry is a typical pure shear shaped sample, with a 90 mm width, 13 mm height and 2 mm thick. It is notched on one side using a razorblade. The test was performed using an Instron 5543 uniaxial testing machine with a capacity of 500 N under imposed displacement and triangular signal. The displacement rate of the moving grip was set at ±200 mm/min. A set of 10 cycles with a maximum global stretch ratio of 1.5 is performed. Figure 1 presents the experimental set-up. Two cameras were used during the test: -a cooled 16-bit dynamic Pco-Edge sCMOS camera with 5.5 million pixels. DIC was performed with a resolution of 0.05 pixels and a spatial resolution of 10 pixels corresponding to the distance between two independent points. The acquisition frequency was set at 50 Hz. -a Cedip Jade infrared camera. The detector size is 320 x240 (76800) pixels. The integration time and the acquisition frequency were set at 1500 μs and 50 Hz, respectively. The thermal resolution, namely the noise-equivalent temperature difference, is equal to 20 mK for a temperature range of 5 -40 °C.

RESULTS

The evolution of displacement field and the corresponding temperature variation fields were measured during the two first cycles of the test. The displacement fields that occur during the test are needed to establish the correspondence between the location of the material points of the specimen at any stage of the loading (in the current configuration) and at the beginning of the test (in the reference configuration). Logically, the maximum temperature variation is located in the vicinity of the crack tip. The zone of influence of the crack undergoes highly large strains.

CONCLUSION

Measuring simultaneously the displacement and the temperature variations and calculating the corresponding heat source fields on the surface of elastomeric specimens subjected to mechanical loading provides information of importance on the complex thermo-mechanical behavior. Specific practical problems have been solved in this work for the determination of these fields. One of them concerns the mapping of all quantities in the reference configuration. A motion compensation technique using both kinematic and thermal fields measurement has been developed. The procedure was then used to determine heat source field, in the crack tip vicinity of notched rubber specimens.

Fig. 1 :

 1 Fig. 1: experimental setup


Lisa Genochio

Arnaud Mazurier

Jean Dumoncel

Charlotte E G Theye

Clément Zanolli
email: clement.zanolli@gmail.com

Inner structural organization of the mandibular corpus in the late Early Pleistocene human

Keywords: mandibular cortical bone thickness, Homo, late Early Pleistocene, Tighenif épaisseur de l'os cortical mandibulaire, Homo, Pléistocène inférieur final, Tighenif

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

Inner structural organization of the mandibular corpus in the late Early Pleistocene human specimens Tighenif 1 and Tighenif 2

Lisa Genochio, Arnaud Mazurier, Jean Dumoncel, Charlotte E.G. Theye,

Clément Zanolli

To cite this version:

Lisa Genochio, Arnaud Mazurier, Jean Dumoncel, Charlotte E.G. Theye, Clément Zanolli. Inner structural organization of the mandibular corpus in the late Early Pleistocene human specimens Tighenif 1 and Tighenif 2. Comptes Rendus. Palevol, 2019, 10.1016/j.crpv.2019.09.002. hal-02379305

Introduction

The locality of Tighenif (formerly Ternifine) is situated in Northwestern Algeria, in the province of Mascara. The site, a sand quarry near the village of Palikao, was discovered in 1870. Between 1954-1956[START_REF] Arambourg | L'hominine fossile de Ternifine (Algérie)[END_REF][START_REF] Arambourg | A recent discovery in human paleontology: Atlanthropus of Ternifine (Algeria)[END_REF][START_REF] Arambourg | Récentes découvertes de paléontologie humaine réalisées en Afrique du Nord française (L'Atlanthropus de Ternifine -L'Hominien de Casablanca)[END_REF][START_REF] Arambourg | Découverte en Afrique du Nord des restes humains du Paléolithique inférieur[END_REF] carried out large scale excavations that led to the discovery of human fossil remains associated with a lithic assemblage of Acheulean tools. The human fossil record consists of nine isolated teeth, two nearly complete adult mandibles (Tighenif 1 and 3), one adult hemi-mandible (Tighenif 2), and a parietal fragment (Tighenif 4). As a whole, the dental record suggests a minimum of five individuals, including four adults and one juvenile [START_REF] Arambourg | Le gisement de Ternifine[END_REF][START_REF] Schwartz | The human fossil record. Craniodental morphology of genus Homo (Africa and Asia)[END_REF][START_REF] Tillier | Les dents d'enfant de Ternifine (Pléistocène moyen d'Algérie)[END_REF]. Based on biostratigraphy and paleomagnetic dating, the site was originally estimated to be c. 700 ka old [START_REF] Geraads | The Pleistocene hominid site of Ternifine Algeria: new results on the environment, age, and human industries[END_REF].

However, a recent revision of the faunal list compared with those from East African sites suggests a late Early Pleistocene age, close to 1 Ma as more likely [START_REF] Geraads | Pleistocene Carnivora (Mammalia) from Tighenif, Algeria[END_REF][START_REF] Sahnouni | The Oldowan in North Africa within a biochronological framework. The cutting edge: new approaches to the archeology of human origins[END_REF]. Accordingly, the fossil human assemblage from Tighenif would currently represent the earliest recovered so far from Northwest Africa, even if the region was certainly inhabited by humans since at least c. 2.4-1.9 Ma [START_REF] Sahnouni | 1.9-million-and 2.4-million-year-old artifacts and stone tool-cutmarked bones from Ain Boucherit, Algeria[END_REF].

The Tighenif human assemblage was originally attributed to Atlanthropus mauritanicus [START_REF] Arambourg | L'hominine fossile de Ternifine (Algérie)[END_REF][START_REF] Arambourg | A recent discovery in human paleontology: Atlanthropus of Ternifine (Algeria)[END_REF]. However, because of some similarities with the Chinese dentognathic material from Zhoukoudian [START_REF] Antón | Natural history of Homo erectus[END_REF][START_REF] Antón | Defining Homo erectus: size considered[END_REF][START_REF] Howell | European and Northwest African Middle Pleistocene hominids[END_REF][START_REF] Schwartz | The human fossil record. Craniodental morphology of genus Homo (Africa and Asia)[END_REF][START_REF] Tillier | Les dents d'enfant de Ternifine (Pléistocène moyen d'Algérie)[END_REF], the assemblage was later integrated into the Homo erectus hypodigm. Based on archaeological and paleontological evidence, some authors have considered the possibility of relationships between the North African human groups represented at Tighenif and those peopling southern Europe at some points during the Early-Middle

Pleistocene [START_REF] Arribas | On the ecological connection between sabre-tooths and hominids: faunal dispersal events in the Lower Pleistocene and a review of the evidence for the first human arrival in Europe[END_REF][START_REF] Finlayson | The role of climate in the spatio-temporal pattern of human colonization and extinction in the Pleistocene with specific reference to the Mediterranean Region[END_REF]. However, while direct comparisons of the fossils from Tighenif with three late Early Pleistocene mandibles from the site of Gran Dolina-TD6, Spain, revealed a number of similarities, they have been interpreted

as plesiomorphic for the genus Homo (Bermúdez de [START_REF] Bermúdez De Castro | A Hominid from the Lower Pleistocene of Atapuerca, Spain: Possible ancestor to Neandertals and modern humans[END_REF][START_REF] Bermúdez De Castro | Comparative analysis of the Gran Dolina-TD6 (Spain) and Tighennif (Algeria) hominin mandibles[END_REF][START_REF] Bermúdez De Castro | Continuity versus discontinuity of the human settlement of Europe between the late Early Pleistocene and the early Middle Pleistocene. The mandibular evidence[END_REF]. Given the particularly robust morphology of the Tighenif mandibles, Bermúdez de Castro and collaborators (2007) have suggested to consider these specimens as representatives of a Homo ergaster subspecies: Homo ergaster mauritanicus. More recently, the mandibles from Tighenif were compared with the Mauer specimen and found to be compatible with the H. heidelbergensis hypodigm, the latter being considered as an Afro-European species [START_REF] Mounier | Définition du taxon Homo heidelbergensis Schoetensack, 1908: analyse phénétique du massif facial supérieur des fossiles du genre Homo du Pléistocène moyen[END_REF][START_REF] Mounier | Is Homo heidelbergensis a distinct species? New insight on the Mauer mandible[END_REF]. However, the anterior symphyseal morphology of the Tighenif specimens as well as some Neanderthal-like features present in these specimens continue to raise some questions about their relationship with the anatomically modern humans and Neanderthal lineages [START_REF] Mounier | Is Homo heidelbergensis a distinct species? New insight on the Mauer mandible[END_REF].

Additionally, the microtomographic-based analysis of the inner structural organization of some deciduous and permanent teeth from the Algerian assemblage highlighted a complex pattern with a combination of unique, primitive, Neanderthal-like and modern human-like features [START_REF] Macchiarelli | From outer to inner structural morphology in dental anthropology. The integration of the third dimension in the visualization and quantitative analysis of fossil remains[END_REF][START_REF] Zanolli | Tissue proportions and enamel thickness distribution in the early Middle Pleistocene human deciduous molars from Tighenif, Algeria[END_REF][START_REF] Zanolli | Endostructural characterization of the H. heidelbergensis dental remains from the early Middle Pleistocene site of Tighenif, Algeria[END_REF]. Thus, partly due to the robustness of the mandibles and to the rare comparative evidence available so far for human fossils sampling this chrono-geographical range, the taxonomic affinities of Tighenif specimens are still unclear.

Combined with the signal from the external morphology, the internal structure of the mandible potentially holds a blend of functional and taxonomic information useful in the study of the human fossil record [START_REF] Daegling | Functional significance of cortical bone distribution in anthropoid mandibles: An in vitro assessment of bone strain under combined loads[END_REF][START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF][START_REF] Grine | Functional morphology, biomechanics and the retrodiction of early hominin diets[END_REF][START_REF] Zanolli | Structural organization and tooth development in a Homo aff. erectus juvenile mandible from the Early Pleistocene site of Garba IV at Melka Kunture, Ethiopian highlands[END_REF]. A common assumption in biomechanical studies on craniofacial morphology is that there is a functional link between the mandibular structure and the forces triggered by mastication and other feeding behaviors [START_REF] Daegling | Mandibular biomechanics and the paleontological evidence for the evolution of human diet. Evolution of the human diet: The know, the unknown, and the unknowable[END_REF][START_REF] Godinho | The bitting performance of Homo sapiens and Homo heidelbergensis[END_REF][START_REF] Holmes | Dietary effects on development of the human mandibular corpus[END_REF][START_REF] Hylander | Functional anatomy and biomechanics of the masticatory apparatus[END_REF][START_REF] Sella-Tunis | Human mandibular shape is associated with masticatory muscle force[END_REF][START_REF] Toro-Ibacache | The relationship between skull morphology, masticatory muscle force and cranial skeletal deformation during biting[END_REF]. In fact, it is widely accepted that, under strain variations, the mandibular bone is remodeled in areas where it faces higher loads, i.e, where strain magnitude is above a particular threshold [START_REF] Frost | Bone "mass" and the "mechanostat": a proposal[END_REF][START_REF] Frost | Bone's mechanostat: a 2003 update[END_REF][START_REF] Martinez-Maza | Bone remodelling in Neanderthal mandibles from the El Sidrón site (Asturias, Spain)[END_REF][START_REF] Pearson | The aging of Wolff's "law": Ontogeny and responses to mechanical loading in cortical bone[END_REF][START_REF] Ruff | Who's afraid of the big bad Wolff?: "Wolff's law" and bone functional adaptation[END_REF][START_REF] Schwartz-Dabney | Variations in cortical material properties throughout the human dentate mandible[END_REF]. Thus, local variation in bone organization is, at least partly, linked with differences in functionally-related strain magnitude, and strain analysis can be employed to investigate whether cortical asymmetry is related to biomechanical demands [START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF][START_REF] Gröning | Comparing the distribution of strains with the distribution of bone tissue in a human mandible: A finite element study[END_REF]; but see [START_REF] Ichim | Functional significance of strain distribution in the human mandible under masticatory load: numerical predictions[END_REF].

In the mandible, an asymmetric pattern of post-canine cortical bone thickness (CBT) distribution is documented in all anthropoids, where the lingual aspect of the corpus exhibits thinner cortical bone compared to the lateral aspect [START_REF] Daegling | Mandibular morphology and diet in the genus Cebus[END_REF][START_REF] Daegling | Compact bone distribution and biomechanics of early hominid mandibles[END_REF][START_REF] Demes | Stress-strength relationships in the mandibles of hominoids[END_REF]. This asymmetric pattern, which seems to transcend phylogenetic, allometric or ecological aspects, has been explained as resulting from the combined effects of vertical occlusal forces and torsions of the corpus during mastication [START_REF] Daegling | Functional significance of cortical bone distribution in anthropoid mandibles: An in vitro assessment of bone strain under combined loads[END_REF][START_REF] Dechow | Elastic properties and masticatory bone stress in the macaque mandible[END_REF][START_REF] Demes | Stress-strength relationships in the mandibles of hominoids[END_REF]. This interpretation is also supported by studies based on Finite Element Analysis (FEA) showing that high strain areas fit well with regions bearing higher CBT, thus highlighting a thicker buccal CBT in the posterior mandibular region [START_REF] Gröning | Comparing the distribution of strains with the distribution of bone tissue in a human mandible: A finite element study[END_REF]. However, the role and interpretation of this asymmetric mandibular CBT distribution is still debated. [START_REF] Ichim | Functional significance of strain distribution in the human mandible under masticatory load: numerical predictions[END_REF], for example, have suggested that the asymmetric pattern in modern human is not directly related to masticatory strains, i.e., strictly functionally-related, but rather the genetically-controlled result of a long-term modeling/remodeling balance process achieved during evolution. Asymmetry in cortical bone topography across the mandible could also reflect handedness, as it has been suggested for the Neanderthal specimen from Regourdou [START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF][START_REF] Volpato | Hand to mouth in a Neandertal: right-handedness in Regourdou 1[END_REF]. With this respect, also the Algerian fossil specimen Tighenif 1 exhibits an asymmetric occlusal wear pattern of the post-canine dentition [START_REF] Arambourg | Le gisement de Ternifine[END_REF][START_REF] Zanolli | Endostructural characterization of the H. heidelbergensis dental remains from the early Middle Pleistocene site of Tighenif, Algeria[END_REF].

In the present study, we quantitatively assess the still unreported internal structural organization of the post-canine corpus of Tighenif 1 and Tighenif 2. Given the especially robust morphology of the two mandibles and their macrodont dentition [START_REF] Arambourg | Récentes découvertes de paléontologie humaine réalisées en Afrique du Nord française (L'Atlanthropus de Ternifine -L'Hominien de Casablanca)[END_REF][START_REF] Bermúdez De Castro | Comparative analysis of the Gran Dolina-TD6 (Spain) and Tighennif (Algeria) hominin mandibles[END_REF][START_REF] Zanolli | Endostructural characterization of the H. heidelbergensis dental remains from the early Middle Pleistocene site of Tighenif, Algeria[END_REF], we predict that these fossils will have an absolutely thicker cortical bone compared to extent humans.. In addition, we also evaluate whether, and to what extent, buccal vs. lingual relative asymmetry in CBT distribution in the two fossils is affected by their robust morphology, i.e., if a larger size of the mandibular corpus tends to lower, or magnify, the pattern commonly measured in recent humans [START_REF] Daegling | Functional significance of cortical bone distribution in anthropoid mandibles: An in vitro assessment of bone strain under combined loads[END_REF][START_REF] Demes | Stress-strength relationships in the mandibles of hominoids[END_REF].

Materials and methods

Materials

In the analyses, we uniquely considered the two specimens Tighenif 1 and Tighenif 2 because of the relatively poor preservation conditions of the partial mandible Tighenif 3, whose corpus is affected by numerous post-mortem breaks. To assess cortical bone post-canine distribution, we used X-ray microtomographic (µCT) records of the two Algerian fossil specimens performed in 2010 at the University of Poitiers by a X8050-16 Viscom AG equipment (microfocus directional X-ray source with an imaging system comprising a 9 in.

triple field intensifier coupled to a 12 bits, 1004x1004-pixel CCD) according to the following parameters: 120 and 130 kV voltage; 0.60 and 0.50 mA current; a projection each 0.30° and 0.25°, respectively for Tighenif 1 and Tighenif 2. The final volumes were reconstructed using Feldkam algorithm with DigiCT v.2.3.3 (Digisens SA, France) in 8-bit format, with a final isotropic voxel size of 115.3 µm and 98.5 µm, respectively. The virtual reconstructions of these two specimens are represented in Figure 1A-B.

For comparison, we also used the mandibles of 10 ( 5

Methods

A semi-automatic threshold-based segmentation of the virtual slices of all investigated specimens was preliminarily carried out using Avizo 8.1 (Visualization Science Group Inc) and ImageJ 1.51k [START_REF] Schneider | NIH Image to ImageJ: 25 years of image analysis[END_REF] following the half-maximum height methods (HMM; [START_REF] Spoor | Linear measurements of cortical bone and dental enamel by computed tomography: applications and problems[END_REF] and the region of interest thresholding methods (ROI-Tb protocol, [START_REF] Fajardo | Assessing the accuracy of high-resolution Xray computed tomography of primate trabecular bone by comparisons with histological sections[END_REF]. In processing the record of the two fossil specimens Tighenif 1 and Tighenif 2, some manual corrections were locally necessary to virtually fill the cracks and reliably separate bone from matrix infill and air. On the Tighenif 1 mandible, major breaks are found on the left side especially at the P3 level at the inferior aspect of the mandible (Fig. 1C). This Algerian fossil was consolidated and reconstructed with plaster; however, the cortical and trabecular bone is still preserved and well-distinguished. The Tighenif 2 specimen presents local bone micro-breaks but is better preserved than Tighenif 1 (Fig. 1D). In fact, considering the estimated chronology of this material around 1 Ma, besides some cracks and larger fractures affecting the bone locally, the internal bony structure is well preserved. However, because of the variable positions of the fractures and local taphonomic alterations, a bi-dimensional analytical strategy was preferred to extract the maximum of information directly comparable between the two specimens.

We developed a specific protocol to estimate the CBT between the two premolars (P3/P4) and between the first and second molars (M1/M2) for both sides on each specimen. Firstly, a reference plane was defined as the plane fitting a set of semilandmarks along the most inferior line of the mandible corresponding to the inferior maximum of curvature of the (right or left) mandibular corpus (Fig. 2A). The posteriormost landmarks were placed distally to the M3.

Then, CBT was assessed across four coronal slices manually oriented perpendicularly in the XY and XZ plans to the reference plane and to the alveolar arcade axis, respectively located between the P3 and P4 and between the M1 and M2 (Fig. 2B). We used tpsDig32 2.30 [START_REF] Rohlf | tpsDig: digitize landmarks and outlines[END_REF] to place semilandmarks on the four extracted slices of each specimen (two left and two right slices if available). We then defined a lingual and a buccal compartment on each slice: a line tangential to the alveolar margin has been drawn as upper limit and the lingual and buccal compartments were created by drawing a second perpendicular line passing by the middle of the corpus until the most inferior point. Thereafter, a first curve was drawn following the external margins of the cortical bone, while a second one was placed along the periosteal cortical bone margins (Fig. 2C-E). An identical number of points for both curves was placed internally for each specimen in order to measure the cortical thickness as the shortest distance between the corresponding external and internal points. These curve points have been transformed into landmark coordinates by multiplying them by the voxel size. Then, a distance matrix was created for each specimen calculating the distance between each pair of corresponding landmarks. The cortical area (CA, in mm²) has also been measured on the buccal and lingual compartments with the Avizo 8.1 software.

Cortical bone thickness variation was analyzed on the sections taken along the mandible corpus in the statistical environment R 3.5.3 (R Development Core Team, 2019). Data were visualized using the package ggplot2 2.2.1 [START_REF] Wickman | ggplot2: Elegant Graphics for Data Analysis[END_REF]. In order to allow direct comparisons between the fossil specimens and the extant human sample, we calculated a scale- ACT*100/TBA 1/2 ), where 2D ACT is the ratio between cortical area (CA, in mm²) and the endosteal contour length (ecl, in mm) (2D ACT=CA/ecl, in mm). The 2D RCT calculation was distinctly performed for each lingual and buccal side. The percentages of asymmetry between the P3/P4 and M1/M2 sections, and between the lingual and buccal aspects were calculated on each corpus section with the formula found in [START_REF] Corruccini | Correspondence between enamel hypoplasia and ondontometric bilateral asymmetry in Australian twins[END_REF] buccallingual)/(buccal+lingual)/2)). For all variables, a number of intra-and inter-observer tests for accuracy run by two independent observers revealed differences less or equal to 5% (e.g., [START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF].

Results

Comparative cortical bone distribution on each bucco-lingual virtual section (respectively at the P3/P4 and M1/M2 levels on right and left parts the mandible corpus; see Fig. 2) was assessed in the two Tighenif specimens (only on the preserved left corpus for Tighenif 2) and extant human sample (Fig. 3A-D).

At the P3/P4 position (Fig. 3A-B), Tighenif 1, Tighenif 2 and extant humans exhibit a similar pattern on the left and right sides. More specifically, the two Algerian specimens mostly fall into the modern human (MH) range which presents a high variability (meaning that some of MH specimens approximate the Tighenif cortical bone topography at this position).

However, there are some exceptions, indeed, Tighenif 1 exceeds the MH range at the inferolingual aspect on both side and Tighenif 2 displays higher cortical bone than Tighenif 1 and MH at the inferior aspect. By looking at the cortical area (CA, Table 1), the Tighenif specimens present higher CA than MH. While at the P3/P4 buccal level, Tighenif 2 has substantially higher values than Tighenif 1, the opposite condition is observed lingually. When considering the scale-free bi-dimensional Relative Cortical Thickness index (2D RCT; Table 2) at each P3/P4 section, the two Tighenif specimens display higher values than the average modern human condition. Tighenif 1 2D RCT exceeds that of Tighenif 2, showing its maximal value on the right side. With the CA and 2D RCT measurements, we can also observe different bucco-lingual asymmetry patterns between our specimens (Table 1 andTable 3). The Tighenif specimens show a thicker buccal cortical bone except at the left P3/P4 section for Tighenif 1. Modern humans exhibit the opposite pattern at this position with a cortical bone thicker lingually than buccally for both right and left side of the mandible.

At the M1/M2 position, the Tighenif specimens present a similar cortical bone distribution with a substantially thicker cortical bone than MH (reaching up to nearly three times the average modern human values), except for Tighenif 2 left buccal aspect which fall within the modern human range (Fig. 3C-D). Moreover, at the right M1/M2, the Tighenif 1 specimen presents a symmetrical bucco-lingual distribution that differs from the right side. The Algerian specimens also exhibit a higher CA compared to the MH range. Tighenif 1 systematically displays substantially higher values than Tighenif 2 for the left side (Table 1). When standardizing through the 2D RCT index (Table 2), the fossils from Tighenif still have a relatively thicker cortical bone than MH and Tighenif 2 shows thicker cortical bone than Tighenif 1. The Tighenif specimens display absolutely and relatively thicker buccal compartment than the lingual one at the M1/M2 level (Table 1 andTable 3). Our limited modern human sample shows more variation, with on average thicker buccal cortical bone on the left side of the mandible, but equivalent values between the buccal and lingual compartments on the right side.

When comparing the P3/P4 and M1/M2 sections, the two fossil specimens exhibit different conditions. Tighenif 1 shows higher CA at the left molar level while the contrary is observed for Tighenif 2. In addition, Tighenif 1 right side also shows the opposite condition, with higher CA at the premolar level (Table 1). In modern humans, there is more variation, especially on the buccal aspect, but the lingual CA is on average higher at the premolar level than posteriorly.

However, the Tighenif 1 mandible presents thicker cortical bone at the left M1/M2 section than at the left P3/P4 section and the opposite pattern on the right side whereas Tighenif 2 and MH exhibit equivalent 2D RCT between the two positions (Table 2). The two fossil specimens show higher asymmetry at the left premolar level than posteriorly. Tighenif 1 right side displays similar levels of slight buccal asymmetry at both premolar and molar levels. In modern humans, the left and right P3/P4 sections show similarly high magnitudes of asymmetry, comparable to those of the left side of both specimens from Tighenif, but lower degree of asymmetry at the M1/M2 level.

Discussion and conclusions

In this study, we aimed to characterize the post-canine cortical bone thickness (CBT) in the Tighenif 1 and Tighenif 2 mandibular remains. Our results showed that both specimens have comparable absolute and relative CBT and distribution patterns at the P3/P4 and M1/M2 levels.

In Tighenif 1, in which both sides of the mandible are preserved, there is a relationship between the relative thick cortical bone at the right P3/P4 level and the important dental occlusal wear found on this side [START_REF] Arambourg | Le gisement de Ternifine[END_REF][START_REF] Zanolli | Endostructural characterization of the H. heidelbergensis dental remains from the early Middle Pleistocene site of Tighenif, Algeria[END_REF]. This may suggest that the cortical bone topography of this specimen could be biomechanically related to masticatory and/or paramasticatory differential loadings [START_REF] Daegling | Functional significance of cortical bone distribution in anthropoid mandibles: An in vitro assessment of bone strain under combined loads[END_REF][START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF][START_REF] Gröning | Comparing the distribution of strains with the distribution of bone tissue in a human mandible: A finite element study[END_REF]. Recently, [START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF] have also found a concordance between wear asymmetry and thicker cortical bone distribution in the Neanderthal specimen Regourdou 1, likely in relation to the right-handedness and paramasticatory activities previously reported for this individual [START_REF] Volpato | Hand to mouth in a Neandertal: right-handedness in Regourdou 1[END_REF].

There is also a noticeable difference in maximal CBT and distribution pattern on the left buccal side of Tighenif 1 compared with that of Tighenif 2. This localized difference could be due to inter-individual variation (including sexual dimorphism), possibly linked to some subtle expression of the mandibular torus [START_REF] Arambourg | Le gisement de Ternifine[END_REF]. In addition, it is noteworthy that Tighenif 2 has a CBT mostly equivalent to that of Tighenif 1, but shows less advanced dental occlusal wear [START_REF] Arambourg | Le gisement de Ternifine[END_REF][START_REF] Zanolli | Endostructural characterization of the H. heidelbergensis dental remains from the early Middle Pleistocene site of Tighenif, Algeria[END_REF].

Thus, it would be too simple to consider that the differences in CBT distributions and the very thick cortical bone in the Tighenif specimens were only explained by a functional response during the masticatory process. It is also important to note that the smaller and less robust specimen Tighenif 2 has been suggested to represent a younger individual than Tighenif 1 and/or could also be a female [START_REF] Arambourg | Le gisement de Ternifine[END_REF].

We also highlighted major differences along the post-canine corpus between the two Tighenif specimens and modern humans. Tighenif CBT absolute values and topographic distribution approximate the modern condition at the P3/P4 level (especially on the left side)

while they substantially deviate from the modern human pattern posteriorly. In terms of relative values, the 2D RCT index supports the proportionally more robust state of the Tighenif specimens compared with modern humans.

Biomechanical studies of extant human mandibles also suggested that the torsional moments related to masticatory constraints are greatly reduced in the anterior corpus [START_REF] Hylander | Stress and strain in the mandibular symphysis of primates: a test of competing hypotheses[END_REF], while lateral transverse bending or "wishboning" stresses increase posteriorly particularly along the lingual margins [START_REF] Daegling | Functional significance of cortical bone distribution in anthropoid mandibles: An in vitro assessment of bone strain under combined loads[END_REF][START_REF] Dechow | Elastic properties and masticatory bone stress in the macaque mandible[END_REF][START_REF] Demes | Stress-strength relationships in the mandibles of hominoids[END_REF]. The Tighenif mandibles show differences in buccal vs. lingual CBT. [START_REF] Ichim | Functional significance of strain distribution in the human mandible under masticatory load: numerical predictions[END_REF] have suggested that in modern humans this asymmetric pattern between buccal and lingual aspects is not related to masticatory strains. Instead, it could result from a retained evolutionary process possibly linked with the reduction of the face and tooth size in H. sapiens. In any case, the role and interpretation of this asymmetric mandibular CBT distribution is debated, and as shown in our limited comparative sample, there seems to be some variation that should be investigated further. The two Tighenif specimens exhibit thicker buccal than lingual cortical bone at the post-canine level (except for a single spot at the left P3/P4 section where the bone is locally fractured and thus may affect the CBT assessment). This condition resembles what has been described for the modern human condition [START_REF] Daegling | Functional significance of cortical bone distribution in anthropoid mandibles: An in vitro assessment of bone strain under combined loads[END_REF][START_REF] Dechow | Elastic properties and masticatory bone stress in the macaque mandible[END_REF][START_REF] Demes | Stress-strength relationships in the mandibles of hominoids[END_REF]. Martinez-Maza and colleagues (2011) have also suggested that the buccal side of mandibular ramus in Neanderthals is affected by higher remodeling activities resulting from mechanical force. This was also evidenced in the Neanderthal specimen Regourdou 1 where the cortical bone under the P3 exhibits an asymmetric pattern in CBT distribution with the left buccal aspect being thicker [START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF].

Substantial size and morphological differences between the fossil specimens from Tighenif and modern human mandibles represent taxonomic features likely reflecting adaptations to withstand biomechanical stresses in relation to the facial architecture [START_REF] Ledogar | The biomechanics of bony facial "buttresses" in south african austalopiths: An experimental study using finite element analysis[END_REF][START_REF] Strait | The structural rigidity of the cranium of Australopithecus africanus: Implications for diet, dietary adaptations, and the allometry of feeding biomechanics[END_REF]. Such differences could be explained by dissimilarities in physiology. Indeed, numerous hormones are implicated in the systemic control of bone modeling (reshaping of bone through modeling drifts that occur primarily during growth), and remodeling (replacement of old or damaged bone tissue through a process of coupled resorption and formation) [START_REF] Hublin | Le torus occipital transverse et les structures associées: évolution dans le genre Homo[END_REF][START_REF] Hublin | Some comments on the diagnostic features of Homo erectus[END_REF][START_REF] Kini | Physiology of bone formation, remodeling and metabolism[END_REF]Raggatt and Patridge, 2010;) have argued that many morphological features of H. erectus, including cranial superstructures, thick cranial bones, and thick post-cranial cortical bone, could be the result of a single physiological process as a variation in endocrine production. While this may result from an allometric and differential bone growth related to the increase in size and robusticity, it is still unclear whether it could be of adaptive value.

The robusticity of the two Tighenif specimens is associated with the strong development of superstructures such as the sulcus intertoralis, lateral prominence, and the wide extramolar sulcus. In addition, the lateral prominence is positioned between the P3/P4 for Tighenif 1, between the M1/M2 for Tighenif 2 and the mental foramen is found between the P3/P4 for these two African specimens. Moreover, all the premolars from the Tighenif 1 and Tighenif 2 specimens exhibit complex 2R Tomes' root morphologies (Prado-Simón et al., 2012a, 2012b;[START_REF] Zanolli | Endostructural characterization of the H. heidelbergensis dental remains from the early Middle Pleistocene site of Tighenif, Algeria[END_REF]. Altogether, these morphological differences may have an impact on the CBT assessment but are also part of the variation characteristic of a taxon. To this date, only a few studies have characterized the internal structure of the mandibular corpus in fossil hominins [START_REF] Daegling | Compact bone distribution and biomechanics of early hominid mandibles[END_REF][START_REF] Fiorenza | Dental macrowear and cortical bone distribution of the Neanderthal mandible from Regourdou (Dordogne, Southwestern France)[END_REF][START_REF] Zanolli | Structural organization and tooth development in a Homo aff. erectus juvenile mandible from the Early Pleistocene site of Garba IV at Melka Kunture, Ethiopian highlands[END_REF], while the extent of variation of the modern human condition remains to be investigated. Pending future comparisons of the internal bony signature of the Tighenif mandibles with other fossil specimens and larger modern human samples, considering the already proposed mix of features, including the expression of Neanderthal-like features on the external bone morphology and teeth [START_REF] Mounier | Is Homo heidelbergensis a distinct species? New insight on the Mauer mandible[END_REF][START_REF] Zanolli | Tissue proportions and enamel thickness distribution in the early Middle Pleistocene human deciduous molars from Tighenif, Algeria[END_REF][START_REF] Zanolli | Tissue proportions and enamel thickness distribution in the early Middle Pleistocene human deciduous molars from Tighenif, Algeria[END_REF][START_REF] Zanolli | Endostructural characterization of the H. heidelbergensis dental remains from the early Middle Pleistocene site of Tighenif, Algeria[END_REF],

Figure captions

  females and 5 males) adult modern humans (MH) selected from the Pretoria Bone Collection housed in the Department of Anatomy of the University of Pretoria, South Africa[START_REF] L'abbé | The Pretoria bone collection: A modern South African skeletal sample[END_REF]. These materials were scanned at the microfocus X-ray tomography facility (MIXRAD) of the South African Nuclear Energy Corporation SOC Ltd (Necsa), Pelindaba, South Africa, using a Nikon XTH 225 ST (Metris) equipment according to the following parameters: 100 kV voltage; 0.10 mA current; a projection each 0.36°. Depending on their size, the final volumes of the 10 comparative specimens were reconstructed with an isotropic voxel size ranging from 66.5 µm to 79.6 µm.Ethical clearance for using this comparative sample was obtained from the Faculty of Health Sciences Research Ethics committee of the University ofPretoria (Ref. no. 340/2015).

  free bi-dimensional Relative Cortical Thickness (2D RCT) index following the same analytical protocol used in[START_REF] Cazenave | Inner structural organization of the distal humerus in Paranthropus and Homo[END_REF] for the distal humeral diaphysis. This parameter assesses the amount of cortical bone with respect to the volume occupied by the trabecular bone. The RCT index is calculated as the Average Cortical Thickness (2D ACT) multiplied by 100 and divided by the square root of the trabecular area (TBA, in mm²) (2D RCT=2D

Fig. 1 Fig. 1

 11 Fig. 1 Virtual renderings of the mandible Tighenif 1 (A) and of the hemi-mandible Tighenif 2 (B) in superior and lateral views. The longitudinal virtual cross-sections along the corpus of Tighenif 1 (C) and Tighenif 2 (D) illustrate the degree of preservation of the internal bone structure.Fig. 1 Rendus virtuels de la mandibule Tighenif 1 (A) et de l'hémi-mandibule Tighenif 2 (B) en vues supérieure et latérales. Les coupes virtuelles longitudinales passant au niveau du corpus des spécimens Tighenif 1 (C) et Tighenif 2 (D) permettent d'apprécier le degré de préservation de la structure osseuse interne.

Fig. 2

 2 Fig. 2 Position of the semilandmarks on the inferior aspect of the mandible corpus used to fit a reference plane (A), and position of the slices perpendicular to the reference plane and to the mandibular corpus main axis (B), located between the P3 and P4 and M1 and M2. Virtual crosssections set at the M1/M2 level in Tighenif 1 (C), Tighenif 2 (D) and a modern human (E)showing how the buccal (b) and lingual (l) aspects were delimited for their separate analyses, as well as the position of the semilandmarks placed along the external mandibular surface and on the internal cortical bone margin to assess the cortical bone thickness (CBT).

Fig. 2

 2 Fig. 2 Emplacement des points repères sur la partie inférieure de la mandibule utilisés pour positionner le plan de référence (A) et positions des deux coupes perpendiculaires au plan de référence et à l'axe principal du corps mandibulaire, placées entre la P3 et la P4 ainsi qu'entre la M1 et la M2. Coupes virtuelles au niveau M1/M2 chez Tighenif 1 (C), Tighenif 2 (D) et un humain moderne (E) montrant comment l'aspect buccal (b) et l'aspect lingual (l) ont été délimités pour leur analyse séparée, ainsi que les points repères placés au niveau de la marge externe et interne de l'os cortical utilisés pour mesurer l'épaisseur de l'os cortical (CBT).

Fig. 3

 3 Fig. 3 Variations of CBT (in μm) at P3/P4 and M1/M2 from lingual (l) toward the inferior aspect (i) and to the buccal side (b) in the two Tighenif specimens and in ten modern humans (MH). The average value (dark line) and the variation expressed by plus or minus one standard deviation (grey area) are illustrated for the modern humans. A: between the P3/P4 on the left side; B: between the P3/P4 on the right side; C: between the M1/M2 on the left side; D: between the M1/M2 on the right side.

Fig. 3

 3 Fig. 3 Variations de CBT (en µm) entre P3/P4 et M1/M2 le long de l'aspect lingual (l) jusqu'à la partie inférieure (i) et en remontant vers l'aspect buccal (b) chez les deux fossiles de Tighenif et chez dix humains modernes (MH). La moyenne (ligne noire) ainsi que les variations (aire grise) représentées par plus ou moins un écart-type, sont illustrées pour notre échantillon de référence. A: entre P3/P4 du côté gauche, B: entre P3/P4 du côté droit, C: entre M1/M2 du côté gauche, D: entre M1/M2 du côté droit.

Acknowledgements

The microCT record of Tighenif 1 and Tighenif 2 has been realized and made available by R. Macchiarelli and A.M. thanks to the collaboration provided at the MNHN Paris by C. Argot and H. Lelièvre. For access to the modern human comparative material and for scanning, we acknowledge F. de Beer, J. Hoffman, G.C. Kruger, A.C. Oettlé. For scientific discussion and methodological support, we are grateful to S. Cobb, P. Cox, L. Fitton, R. Macchiarelli and P. O'Higgins. Research supported by the French INEE-CNRS and the AMI grant of the University of Toulouse. C.E.G.T. was supported by the University of Pretoria Postgraduate Research Support Bursary.

we hypothesize that the CBT condition in the Tighenif mandible specimens may approximate the Neanderthal pattern, notably at the molar level.


G Restoux

E Verrier

T Heams

P Calvel

& X Rognon

Online training for a diversified audience: some achievements and some lessons learned

Keywords:

In this paper, we present the realizations of our team in the field of online training. Two categories of realizations may be considered: (i) series of online materials which constitute a consistent whole on a large topic (packages) and (ii) punctual materials on more focused topics. Some lessons are drawn from these experiences and we conclude that online training is a valuable tool which comes in addition to the work of teachers but does not replace it.

Introduction

Our institution, namely AgroParisTech, provides with training at different levels of higher education: (i) curriculum for "ingénieurs" (from 3 rd year of Bachelor to 2 nd year of Master), (ii) Masters, (iii) PhD and, (iv) continuing education for people employed in various sectors (industry, extension services, academia, etc.). In addition, online training is under development in different fields covered by our institution.

In the field of animal breeding and genetics, our team contributes to all the above levels. Among other contributions, we coordinate 3 courses or programs: a 2nd year of MSc. course: Predictive and integrative animal biology (PRIAM) [START_REF]PRIAM[END_REF]; an International PhD program: European Graduate School in Animal Breeding and Genetics (EGS-ABG) [2]; a national continuing education course: Higher course in animal breeding (CSAGAD) [3].The purpose of this paper is twofold: (i) to present our realizations and projects of online training, and (ii) to draw some lessons from these experiences.

Online realizations in the field of animal breeding and genetics

Contribution to an international French speaking network for online training

In 2007, a network of French speaking higher education institutions was created to develop an international online training platform, called GÉNET [START_REF] Génet | [END_REF]. Currently, this network is coordinated by the University of Tours and comprises 11 higher education institutions from 5 different countries: France (7 institutions, including AgroParisTech), Benin, Burkina Faso, Switzerland, and Togo (1 institution each).

The field covered is genetics, i.e. all theoretical and applied branches of genetics. This field includes population, quantitative and molecular genetics, and animal breeding as well. Ethical and societal issues are also addressed. The main target is represented by students from the first year of Bachelor to the last year of Master. Indeed, by nature, the platform is available to any people and, then, can also be recommended to PhD candidates, trainees in continuing education, etc. Since the aim of such a resource is to reach the larger audience as possible all the contents are delivered in open access.

The purpose of the network is to provide the students with any material that can help them to assimilate the concepts, tools and methods of genetics. The purpose is not to develop a full online course that can lead to award students with a diploma. Then, here are the materials available on the website: online training modules; tutorials; exercises with their solution; short videos; full texts (pdf); slides; glossary; frequently asked questions; etc.

Our own realizations

N.B. In the text below, the level targeted is indicated by an initial (B = Bachelor, M = Master, PhD = doctoral course) plus, for Bachelor and Master levels, the year concerned.

Packages

Here, "package" means a series of materials which constitute a consistent whole.

Our online course (B3/M1) in quantitative genetics [START_REF][END_REF] is divided in 6 chapters. For each chapter, students may find (i) a full text that can be downloaded, (ii) the answers to the exercises included in this text and, (iii) slides showing additional illustrations.

Our online basic course (B3) in animal breeding [START_REF]Animal breeding[END_REF] is divided in 16 short chapters. For each chapter, a video plus slides are available. The video shows a teacher giving a (short) lecture and the slides are the support of the lecture. For three groups of chapters, a quiz allows students to benchmark themselves.

An online advanced course (PhD) in environmental genetics [START_REF][END_REF] is provided with the same tools than above (videos + slides), but without quiz.

Slides from a training course (B3), firstly intended to people working in extension services in Vietnam, are available [START_REF]Introduction to animal breeding[END_REF].

Other realizations

Other materials are available on various topics, as online modules, slides or samples of papers to be downloaded. However, these materials do not constitute a whole.

1) Kinship and inbreeding (B3/M1): concepts, computation methods, consequences for populations [START_REF]Kinship and inbreeding[END_REF]. 2) The halothane gene in pig breeding (B3) [START_REF]The halothane gene[END_REF].

3) A simple model for selection against a recessive allele (B3/M1) [START_REF]Selection against a recessive allele[END_REF]. 4) Implementing genomic selection (continuing education) [START_REF]Genomic selection[END_REF]. 5) Estimating genetic gain (M1) [START_REF]Genetic gain[END_REF].

6) The use of crossbreeding (B3) [START_REF]Crossbreeding[END_REF]. 7) Ethical and societal issues dealing with animal breeding and genetics (B3/M2) [START_REF]Gén'éthique[END_REF].

Lessons drawn from our experiences

Our first webpages for online training were created in 1998. From this time, we developed online tools but at irregular intervals. Our experiences allow us to draw some lessons.

Before developing online training, the purpose and the target must be defined accurately. Indeed, there is a gap in efforts between simply providing pedagogical material and offering courses and evaluation tools allowing to award with a diploma. Moreover the size of the targeted audience must be taken into account when designing new online contents, in particular when they are interactive and thus need computing resources in terms of web server storage capacity. Developing online training is highly time consuming. Our largest steps were performed when we were able to enrol people full time working on our projects. New development kits with simplified handling are regularly available but are quite expensive and not cross compatible. Thus a common solution must be chosen by all the teachers of a group that meets all their expectations.

There is a need to propose some up-to-date contents in particular in the fast evolving field of genetics in the era of genomics. Scientific knowledge is thus not enough to develop online modules that can be time consuming. Thus scientific survey must be coupled with a reactive team of web technicians, having a strong command of web pages design and development, in order to rapidly propose an appropriate content.

A feedback from users is required. It is hardly obtained in the absence of compulsory evaluation modules. Thus, even if the online content is proposed with an open access, it is feasible to ask for a mandatory registration to get a direct monitoring of the use of the platform and a feedback about it.

Finally and as a brief conclusion, we are convinced that social interactions and face to face exchanges between students and teachers are still required, and we believe that this need will remain for a long time. In particular practicals are necessary and need direct and live exchange with the teachers. Then we consider that online training is not a goal per se but is an additional element in the toolbox of teachers that will allow to get deeper in some topics and make courses more attractive.