Martin Reichard
email: reichard@ivb.cz

Kety Giannetti

Tania Ferreira
email: godinho.ferreira@unice.fr

Milan Vrtílek

Matej Polačik

Radim Blažek

Miguel Godinho Ferreira

Environment and sex control lifespan and telomere length in wild-derived African killifish

Keywords: killifish, sex differences, longevity, comparative analysis, telomere

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INTRODUCTION

Telomeres comprise the ends of all eukaryotic chromosomes and are composed of repetitive DNA sequences and specialized proteins (de Lange 2005). Telomeres protect chromosome ends from illegitimate DNA repair and, through the action of telomerase, from DNA replicationrelated chromosome shortening [START_REF] Olovnikov | A theory of marginotomy. The incomplete copying of template margin in enzymic synthesis of polynucleotides and biological significance of the phenomenon[END_REF][START_REF] Greider | A telomeric sequence in the RNA of Tetrahymena telomerase required for telomere repeat synthesis[END_REF][START_REF] Forsyth | Telomerase and differentiation in multicellular organisms: turn it off, turn it on, and turn it off again[END_REF]. Telomere attrition results in the loss of its protective function and cell proliferation arrest. Critically short telomeres trigger DNA damage responses and genomic instability, a hallmark of aging [START_REF] López-Otín | The hallmarks of aging[END_REF]. Apart from the "endreplication problem", several other factors have been linked to telomere attrition. These include exposure to oxidative damage [START_REF] Opresko | Oxidative damage in telomeric DNA disrupts recognition by TRF1 and TRF2[END_REF][START_REF] Glade | A glance at ... telomeres, oxidative stress, antioxidants, and biological aging[END_REF][START_REF] Ahmed | PRDX1 and MTH1 cooperate to prevent ROS-mediated inhibition of telomerase[END_REF], inflammation [START_REF] Zhang | Ageing and the telomere connection: An intimate relationship with inflammation[END_REF], and environmental stressors [START_REF] Epel | Accelerated telomere shortening in response to life stress[END_REF][START_REF] Chatelain | The association between stressors and telomeres in non-human vertebrates: a meta-analysis[END_REF]. Cells with short telomeres may undergo apoptosis and, if compensatory cell proliferation is no longer available, short telomeres result in cell senescence and inflammation, ultimately disrupting tissue homeostasis [START_REF] Roos | DNA damage-induced cell death by apoptosis[END_REF][START_REF] Armanios | Short telomeres are sufficient to cause the degenerative defects associated with aging[END_REF][START_REF] Carneiro | Short telomeres in key tissues initiate local and systemic aging in zebrafish[END_REF][START_REF] El Mai | Opposing p53 and mTOR/AKT promote an in vivo switch from apoptosis to senescence upon telomere shortening in zebrafish[END_REF]. Impaired tissue homeostasis is at the core of ageing-associated degeneration.

Although not completely understood, telomere shortening in animals, particularly in humans, is a strong promoter of aging-associated degenerative phenotypes [START_REF] Armanios | Short telomeres are sufficient to cause the degenerative defects associated with aging[END_REF][START_REF] Moskalev | The role of DNA damage and repair in aging through the prism of Koch-like criteria[END_REF]). Short telomeres are closely associated with age pathologies such as diabetes mellitus, cardiovascular disease, atherosclerosis, dementia and various cancers [START_REF] Kota | Reduced telomere length in neurodegenerative disorders may suggest shared biology[END_REF][START_REF] Sethi | Role of telomeres and associated maintenance genes in Type 2 Diabetes Mellitus: A review[END_REF][START_REF] Aviv | Mutations, Cancer and the Telomere Length Paradox[END_REF][START_REF] Martinez | Heart-Breaking Telomeres[END_REF]. Loss-of-function mutations in genes related to telomere maintenance are responsible for several human syndromes, such as dyskeratosis congenita, aplastic anemia and idiopathic pulmonary fibrosis [START_REF] Vulliamy | Disease anticipation is associated with progressive telomere shortening in families with dyskeratosis congenita due to mutations in TERC[END_REF][START_REF] Yamaguchi | Mutations in TERT, the gene for telomerase reverse transcriptase, in aplastic anemia[END_REF][START_REF] Tsakiri | Adult-onset pulmonary fibrosis caused by mutations in telomerase[END_REF]. Similarly, studies using animal models have revealed that mutations in telomerase give rise to age-associated deficiencies and reduction of lifespan [START_REF] Rudolph | Longevity, stress response, and cancer in aging telomerase-deficient mice[END_REF][START_REF] Henriques | Telomerase is required for zebrafish lifespan[END_REF][START_REF] Harel | The African Turquoise Killifish: A Model for Exploring Vertebrate Aging and Diseases in the Fast Lane[END_REF][START_REF] Lex | Telomere shortening produces an inflammatory environment that increases tumor incidence in zebrafish[END_REF].

Previous studies have highlighted an association between telomere length and lifespan [START_REF] Fick | Telomere length correlates with life span of dog breeds[END_REF][START_REF] Heidinger | Telomere length in early life predicts lifespan[END_REF][START_REF] Barrett | Telomere length and dynamics predict mortality in a wild longitudinal study[END_REF][START_REF] Ibanez-Alamo | Urban blackbirds have shorter telomeres[END_REF]. Correlative analyses suggest that, within the same species, individuals with shorter telomeres are prone to disease and die earlier [START_REF] Salomons | Telomere shortening and survival in free-living corvids[END_REF][START_REF] Fairlie | Lifelong leukocyte telomere dynamics and survival in a free-living mammal[END_REF][START_REF] Wilbourn | The relationship between telomere length and mortality risk in non-model vertebrate systems: a meta-analysis[END_REF]), but it remains unclear whether telomere length is an indicator of individual condition [START_REF] Glei | Predicting Survival from Telomere Length versus Conventional Predictors: A Multinational Population-Based Cohort Study[END_REF]. While the protective functions of telomeres are conserved across eukaryotes, telomere shortening with age is not observed in all species (Monaghan andOzanne 2018, Burraco, Comas et al. 2020).

Unlike humans, some animals do not repress telomerase in somatic cells and maintain long telomeres throughout their lives [START_REF] Sherr | Cellular senescence: mitotic clock or culture shock?[END_REF]DePinho 2000, Forsyth, Wright et al. 2002). Across species, long telomeres do not correlate with longer lifespans. In fact, longer-lived mammals possess shorter telomeres than short-lived mammals, such as mice and rats [START_REF] Gomes | Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination[END_REF]. Instead, the rate of telomere attrition, rather than telomere length per se, appears to correlate with species-specific lifespans [START_REF] Haussmann | Telomeres shorten more slowly in long-lived birds and mammals than in short-lived ones[END_REF][START_REF] Epel | The rate of leukocyte telomere shortening predicts mortality from cardiovascular disease in elderly men[END_REF][START_REF] Boonekamp | Nestling telomere shortening, but not telomere length, reflects developmental stress and predicts survival in wild birds[END_REF][START_REF] Dantzer | Telomeres shorten more slowly in slow-aging wild animals than in fast-aging ones[END_REF][START_REF] Whittemore | Telomere shortening rate predicts species life span[END_REF].

In humans, women typically have longer telomeres, experience lower rates of telomere attrition and live longer [START_REF] Gardner | Gender and telomere length: systematic review and meta-analysis[END_REF][START_REF] Lapham | Automated assay of telomere length measurement and informatics for 100,000 subjects in the genetic epidemiology research on adult health and aging (GERA) cohort[END_REF][START_REF] Aubert | Collapse of telomere homeostasis in hematopoietic cells caused by heterozygous mutations in telomerase genes[END_REF]. While not universal across species, sex differences in telomere length have been reported in most vertebrate taxa studied (reviewed in Barret and Richardson 2011). However, many of these studies report relatively weak effects that fail to be reproduced. It is also not consistent which sex possesses longer telomeres and it is even less clear whether sexspecific telomere length correlates with lifespan or individual condition [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF][START_REF] Wilbourn | The relationship between telomere length and mortality risk in non-model vertebrate systems: a meta-analysis[END_REF][START_REF] Bauch | Sex-specific telomere length and dynamics in relation to age and reproductive success in Cory's shearwaters[END_REF]. Hormonal profiles and reproductive stress have been suggested as the causes of differential telomere shortening, but the mechanisms underlying sex differences in telomere length remain largely unexplored [START_REF] Hartmann | Telomeres shorten while Tert expression increases during ageing of the short-lived fish Nothobranchius furzeri[END_REF][START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF], Graf, Hartmann et al. 2013[START_REF] Bauch | Sex-specific telomere length and dynamics in relation to age and reproductive success in Cory's shearwaters[END_REF].

We performed a comparative experimental study to examine the association between telomere length and lifespan in African annual killifishes from the genus Nothobranchius. This group of small and short-lived fishes, and the Turquoise killifish (Nothobranchius furzeri) in particular, has recently been established as a model for aging studies. These fish combine the advantages of available resources (reviewed in [START_REF] Cellerino | From the bush to the bench: the annual Nothobranchius fishes as a new model system in biology[END_REF][START_REF] Hu | The African turquoise killifish: A research organism to study vertebrate aging and diapause[END_REF], cheap and convenient husbandry (Polačik, Blažek et al. 2016) and, importantly, links between laboratory studies and natural populations (Reichard and Polačik 2019). We used a set of strains derived from natural populations, which have evolved along a cline of aridity in their natural habitat [START_REF] Tozzini | Parallel evolution of senescence in annual fishes in response to extrinsic mortality[END_REF], Blažek, Polačik et al. 2017), to test how variation in lifespan among strains relates to telomere length. As an extension of the association of longer telomeres with shorter-lived species [START_REF] Gomes | Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination[END_REF], we predicted that telomere length would be negatively associated with lifespan across strains.

Within populations, male killifish are shorter lived than females (Reichard, Polačik et al. 2014, Reichard, Blažek et al. 2020), leading to a prediction that females may possess longer telomeres than males. Telomerase expression can be detected in Nothobranchius throughout their lives [START_REF] Hartmann | Telomeres shorten while Tert expression increases during ageing of the short-lived fish Nothobranchius furzeri[END_REF]), but telomeres do shorten with age. [START_REF] Hartmann | Telomeres shorten while Tert expression increases during ageing of the short-lived fish Nothobranchius furzeri[END_REF] reported that in a longer-lived strain (MZM 0304), old adults (26 weeks old) had 8% and 27% shorter telomeres in muscle and skin tissues, respectively, compared to young adults (5 weeks old). In contrast, telomeres do not appear to shorten between the ages of 5 and 13 weeks in the short-lived GRZ strain [START_REF] Hartmann | Telomeres shorten while Tert expression increases during ageing of the short-lived fish Nothobranchius furzeri[END_REF].

We took advantage of the diversity of killifish strains derived from different wild populations and data on their natural habitats. We measured the lifespan of nine N. furzeri strains and five strains of the closely related N. kadleci [START_REF] Bartáková | Terrestrial fishes: rivers are barriers to gene flow in annual fishes from the African savanna[END_REF] in laboratory conditions. We estimated telomere length in 12 of these strains at the time of sexual maturity (4 weeks) and linked them to juvenile growth. Using Telomere Restriction Fragment (TRF) by Southern blot analysis, we tested the statistical distribution of telomere length within individual samples to understand how variability in telomere length, rather than its mean, is associated with juvenile growth and lifespan estimates. Finally, we report telomere lengths for another seven strains of five other Nothobranchius species with various lifespans.

RESULTS

Lifespan: males from dry-region strains have shorter lifespans

We used strictly standardized conditions to estimate the lifespan of 14 strains from the N. furzeri-N. kadleci species complex. Across strains, median lifespan varied almost twofold, from 151 days (inbred GRZ strain) to 276 days (Table 1). Overall, males were shorter lived than females (Mixed effects Cox model controlling for population identity nested within species identity: z = 5.39, p <0.001). This pattern was especially consistent in N. furzeri. Sex-specific lifespan estimates for each strain are detailed in Table 1.

The N. furzeri-kadleci complex is composed of locally constrained populations living in small ephemeral pools which are genetically distinct from adjacent populations [START_REF] Bartáková | Strong population genetic structuring in an annual fish, Nothobranchius furzeri, suggests multiple savannah refugia in southern Mozambique[END_REF]. All strains used in our analyses were derived from natural populations, for which we knew the exact location of their original pool. We characterized the environmental conditions in which these natural populations lived using long-term data on precipitation (www.worldclim.org). The wild populations are distributed along a clear cline, where increasing altitude and decreasing latitude are associated with lower precipitation (Figure 1).

Our comprehensive set of wild-derived strains confirmed previous conclusions using contrasts between pairs of strains [START_REF] Tozzini | Parallel evolution of senescence in annual fishes in response to extrinsic mortality[END_REF], Blažek, Polačik et al., 2017) that median lifespan correlates positively with local precipitation (t12 = 2.64, P = 0.021) for data on both sexes pooled). Interestingly, for sex-specific lifespan estimates (which were not tested in previous studies), this relationship holds in males (LM: t12 = 3.19, p = 0.008) but not females (t13 = 0.32, p = 0.752) (Figure 2). Our results indicate that males, but not females, from arid regions have shorter median lifespans than those from humid regions, suggesting that perhaps strains from arid regions would exhibit shorter telomeres.

Average telomere length is a good descriptor of telomere length distribution

Telomere length was estimated using Telomere Restriction Fragment (TRF) by Southern blot analysis on samples from 11 wild derived populations (10 fish per population, with equal sex ratio). The final sample size (n = 92, Table 1) was slightly reduced due to lower quality of genomic DNA. All fish examined were young adults of the same age (28 days old), except for a single population (p222; 69 days old). All tissue samples comprised small biopsies from the caudal fin.

We first explored the relationships between various descriptors of telomere length distributions. Using a biplot based on a Principal Component Analysis (PCA) of the data distribution parameters (Figure 3), we found that disparate measures of telomere length (mean, median, 5-95% quantiles) were all highly positively correlated (Supplementary Table 1). The measures of telomere length variability within individual fish (inter-quartile range and coefficient of variation) were not strongly related to the estimates of telomere length (Figure 3, Supplementary Table 1). Based on this information, we used the mean as the measure of telomere length and the coefficient of variation (CV) as a measure of intra-individual telomere length variability in further analyses. These two measures were not correlated (Pearson, r = -0.01, n = 92, p = 0.940).

Telomere length is shorter in males and in faster growing individuals

The exploratory analysis revealed a difference in mean telomere length, from 5.29 (s.e. 0.42) to 9.92 (s.e. 0.42) kbp across various strains (Figure 4, Supplementary Figure 1, Supplementary Table 2). When comparing telomere length between males and females, we accounted for interstrain variation using strain ID as a random effect.

We first tested how parameters of telomere length vary in relation to sex and juvenile growth (measured as individual body size at age of 28 days). Our set of candidate models contained the two explanatory variables and their interaction, along with a null model (Table 2). Candidate models were fitted to the data and compared using the Akaike Information Criterion corrected for small sample size (AICc). The best model contained only sex (Table 2a) and demonstrated that males had shorter telomeres than females (Linear Mixed Model with strain ID modelled as a random factor: F1,80.4 = 23.13, P < 0.001). Across strains, mean telomere length was 7.37 kbp (s.e. 0.45) in females and 6.18 (s.e. 0.24) in males. Strain identity explained a large part of variation in all models; the values of R 2 GLMM conditional were approximately 6 times larger than R 2 GLMM marginal (Table 2).

We note that the second best model (model M2 in Table 2a, DAIC = 1.99) indicated that rapid juvenile growth (i.e. large size at the age of 28 days) was associated with relatively shorter telomere length (F1,88.9 = 4.47, P = 0.037) but the trend became weaker and statistically nonsignificant after removal of the strain with older (and larger) fish (p222: 69 old vs. 28 days old fish) (F1,75.7 = 3.52, P = 0.064; Supplementary Table 3).

Intra-individual variation in telomere length was not related to sex, juvenile growth or their interaction (Table 2b).

Telomere length tends to be shorter in strains from more humid environments

We then tested whether total annual precipitation in the original habitat, which is associated with (male) lifespan, also correlated with telomere length. The best fitting model (Table 3a) included sex differences in telomere length (F1,80.5 = 23.08, P < 0.001) and a trend towards negative association with precipitation totals (Figure 5; F1,9.0 = 4.11, P = 0.072), suggesting that killifish strains from regions with higher precipitation tend to possess shorter telomeres.

Telomere length variability (CV) was neither related to precipitation nor to sex (Table 3b), although it varied considerably across strains (Figure 4b).

Telomere length is not associated with median lifespan

Given that the sexes differed in their lifespans, we used sex-specific lifespan estimates to test the association between telomere length and population-level longevity (Figure 6). The best model (Table 4a) confirmed sex differences in telomere length (F1,8071 = 18.57, P < 0.001), but did not demonstrate a link between mean telomere length and median lifespan estimate (F1,88.7 = 2.39, P = 0.126). Therefore, consistent with previous studies at the inter-specific level, telomere length does not appear to predict longevity among different killifish strains.

For telomere length variability (CV), none of the candidate models was better than a null model (Table 4b).

Telomere length in other Nothobranchius species

Finally, we expanded telomere length analysis to three other Nothobranchius species from Mozambique (N. orthonotus, N. pienaari, N. rachovii) that widely coexist with N. furzerikadleci species complex (Reichard, Janáč et al. 2017). We also included two species from Tanzania (N. rubripinnis, N. eggersi), where a more humid climate is linked to a longer duration of wet habitat phase [START_REF] Bartáková | Terrestrial fishes: rivers are barriers to gene flow in annual fishes from the African savanna[END_REF].

Telomere length among these species was comparable (range 3.6-7.3 kbp, Supplementary

Figure 2) but generally lower than the ). Finally, we also tested telomere length in a highly inbred GRZ strain of N. furzeri that was bred under different laboratory conditions from the other fish and, therefore, was not included in the overall analysis. Mean telomere length in the GRZ fish (7.33 (0.29), n=3) corresponded to the predicted values based on their origin in a relatively dry region.

DISCUSSION

Telomeres and telomerase prevent the continuous erosion of chromosome-ends caused by lifelong cell division. In several species, including humans, telomeres shorten with age and their decline is considered a hallmark of ageing (López-Otín, Blasco et al. 2013). Telomere structure and function is conserved across eukaryotes and their exhaustion leads to multiple effects both at the cellular and systemic level, causing disruption of organ homeostasis and disease [START_REF] Armanios | Short telomeres are sufficient to cause the degenerative defects associated with aging[END_REF][START_REF] Carneiro | Short telomeres in key tissues initiate local and systemic aging in zebrafish[END_REF]. Traditionally, telomere length has been considered indicative of "biological age" and a useful proxy to predict individual health status and longevity. However, evidence from several non-human vertebrates now contradicts this assumption as simplistic.

In our large sample of wild-derived annual killifish strains obtained from a gradient of environmental conditions and natural lifespans, we found that telomere length varied across strains and tended to be shorter in populations that came from more humid regions where Consistent with previous observations across species [START_REF] Gomes | Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination[END_REF], killifish from more humid regions (and hence with longer lifespans, both predicted and observed) had shorter telomeres than those from arid regions. This complements an analogous trend found across mammalian species [START_REF] Gomes | Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination[END_REF]) and demonstrates that telomere length differences among populations reflect patterns found among species.

Alternatively, telomere length differences across strains may simply have been fixed through genetic drift. Natural Nothobranchius populations are genetically distinct units [START_REF] Bartáková | Terrestrial fishes: rivers are barriers to gene flow in annual fishes from the African savanna[END_REF] but are more fragmented and have smaller effective population sizes in the dry part of their range (Willemsen, Cui et al. 2020). Killifish populations from dry regions experience increased mutation load, leading to a reduction in their lifespans [START_REF] Cui | Relaxed Selection Limits Lifespan by Increasing Mutation Load[END_REF], Willemsen, Cui et al. 2020). However, our data demonstrate that dry-region populations tend to have longer telomeres, including the highly inbred GRZ population with apparently the strongest mutation load. This trend is further strengthened by the telomere length of other three Nothobranchius species from Mozambique (N. orthonotus, N. pienaari, N. rachovii), widely coexisting with the N. furzeri-kadleci species complex and two species from a more humid region in coastal Tanzania (N. eggersi, N. rubripinnis).

Telomere length was comparable in all these longer-lived species (Blažek, Polačik et al. 2017, Wildekamp 2004) but generally shorter than in the N. furzeri-N. kadleci complex, suggesting that genetic drift is unlikely a driver of such consistent population-specific differences in telomere length.

Sex differences in telomere length were consistent across strains (Supplementary Table 2), with male telomeres 22% shorter on average. Remarkably, males also had consistently shorter lifespans than females in all nine N. furzeri (but only in 2 of 5 N. kadleci) populations.

All our experimental fish were housed in social tanks. This pattern of sex differences in lifespan corroborates conclusions from a separate study (Reichard, Blažek et al. 2020) where N. furzeri (but not N. kadleci) males in social tanks suffered higher baseline mortality than females.

Higher male mortality was not reflected in sex differences in any measure of functional aging (oxidative stress, kidney and liver histopathology, accumulation of lipofuscin) (Reichard, Blažek et al. 2020). Individual variation in telomere length distribution did not differ between the sexes, discounting the possibility that females possess mechanisms for countering cells with short telomeres. However, it is plausible that males are more susceptible to telomere erosion due to higher hormonal and environmental stress. Nothobranchius fish display remarkable sexual dimorphism and dichromatism. Males are larger and more brightly colored than females and continuously compete for mating opportunities [START_REF] Cellerino | From the bush to the bench: the annual Nothobranchius fishes as a new model system in biology[END_REF]Polačik 2019). Male reproductive strategy (higher growth, investment in bright coloration and aggressive interactions) therefore implicates a higher metabolic cost and more rapid growth than the female reproductive strategy (Bonduriansky, Maklakov et al. 2008). This may underlie the faster decline in telomere length in males, which we recorded as a shorter telomere length in young adult males.

Even within the sexes, rapid juvenile growth tended to be associated with relatively shorter telomere length. Our sampling at the age of 28 days encompassed the period of most rapid growth rate in Nothobranchius lifespan, with fish growing from an initial body length of 5 mm to 50 mm [START_REF] Bartáková | Strong population genetic structuring in an annual fish, Nothobranchius furzeri, suggests multiple savannah refugia in southern Mozambique[END_REF], Vrtílek, Žák et al. 2018a). This raises a plausible hypothesis that growth rate, primarily in early life, at least partly affects the kinetics of telomere erosion and overall telomere length. We now plan a follow-up study with repeated longitudinal sampling of the same individuals to test this hypothesis.

Studies on telomere length in the field of ecology and evolution typically use real-time PCR assays to determine Telomere-to-Single Copy Gene (T/S) ratio which provides an indirect estimate of mean telomere length [START_REF] Lai | Comparison of telomere length measurement methods[END_REF]). However, the detrimental effects of telomere shortening are expected to arise from a frequently dividing subpopulation of cells within a particular tissue, which may not be well represented in the T/S ratio. We estimated telomere length using the robust TRF method that yields a direct distribution of telomere fragments in the sample. Importantly, we confirmed that estimates of mean values are a useful proxy for telomere length distribution as they closely correlated with other parameters of telomere length distribution, including a set of 5-95% quantiles. Thus, mean telomere length represents a suitable estimate of telomere kinetics in adult Nothobranchius killifishes.

Additionally, the measure of telomere length variability within an individual, the coefficient of variation, was not associated with any pattern in our data (between sexes, among strains, or in juvenile growth). This suggests that the dispersion in telomere length distribution is a strainspecific character controlled by regulatory pathways other than those used in determining telomere length. As telomeres shorten, wider telomere distributions will give rise to more critically short telomeres than narrow ones, thus providing an independent parameter to control telomere decline with age.

Overall, our results demonstrate a clear variation in telomere length between sexes and environments among wild-derived strains of Nothobranchius fishes and highlight their suitability for understanding the adaptive value of telomere length across individuals, populations, and species.

MATERIALS AND METHODS

Fish origin and environmental data

Study strains were descendants of wild populations imported from Mozambique and the adjacent area of Zimbabwe. In this region, a clear gradient of aridity is generated by a decrease in total precipitation with increasing distance from the coast, increasing altitude and decreasing latitude associated with lower precipitation (Figure 1; Blažek, Polačik et al. 2017). Fish from wild populations were imported between 2009 and 2012 during dedicated expeditions and wildderived strains were maintained in the Institute of Vertebrate Biology (IVB) in Brno, Czech

Republic according to published protocol (Polačik, Blažek et al. 2016). One strain (GRZ) was imported in 1969 from Zimbabwe and another strain (MZM-0410, coded as pMZM in our dataset) was imported in 2004. The origin of each study strain is illustrated in Figure 1 and GPS coordinates are provided in Table 1. Other strains of Mozambican (N. orthonotus, N. pienaari, N. rachovii) and Tanzanian species (N. eggersi and N. rubripinnis) originate from our own imports between 2008 and 2017 (Supplementary Table 4). Housing conditions were the same for all species.

Data on precipitation (represented by annual rainfall totals) and aridity were downloaded from www.worldclim.org. Aridity was measured as aridity index (total precipitation/modelestimated evapotranspiration). Given that precipitation and aridity were highly collinear (Pearson r = 0.989, n = 12, P < 0.001), we only present analyses using total precipitation.

Fish housing and lifespan estimates

Fish were kept at the IVB facility under standard conditions and care was taken to minimize inbreeding (i.e. a large number of fish were sources of offspring for the next generation, though no specific breeding design was followed).

For the estimates of lifespan, eggs of 14 strains were hatched simultaneously. The eggs were kept in diapause at 19°C. Two weeks prior to hatching, the eggs were placed in 28°C to initiate release from Diapause II and development to prehatching stage. In August 2016, the incubation substrate (peat) was watered with dechlorinated tap water (conductivity 600microS/cm, temperature 16°C) in a 2L tank. Fish hatched within 1 day and were immediately fed on freshly hatched Artemia nauplii. After 5 days, fish were moved to larger tanks (40 L) and their density was standardized across strains. At 3 weeks, fish were weaned from Artemia nauplii to frozen chironomid larvae, a standard diet of captive Nothobranchius (Polačik, Blažek et al. 2016).

At 6 weeks, fish density was standardized to 50 fish per strain, though some strains had a slightly lower density (Table 1). All tanks were monitored daily and any dead fish was recorded and removed. Daily censuses continued until all fish were dead.

Median lifespan was calculated for each strain (and each sex within a strain) using the survfit function in the survival package. 95% confidence intervals for median lifespan estimates were calculated using a log-log function.

Telomere length estimates

Telomere length was estimated through Telomere Restriction Fragment (TRF) analysis followed by Southern blotting. Fish from 12 strains (all strains used in lifespan estimate except for GRZ, p108, and p109) were hatched on 6 May 2019, following the same protocol as for lifespan estimates. When all fish were sexually mature, at the age of 28 days, a biopsy from the caudal fin was taken from 5 male and 5 female fish from each population and flash frozen in liquid nitrogen. Samples of other species were handled in the same way, except they were stored in 100% ethanol instead of being flash frozen. Due to logistic reasons, one population (N.

furzeri, MZCS 222) was hatched earlier (26 March 2019) and was older (69 days) at the time of sampling, although it was raised following identical methods and kept in the same room. To account for the potential effect of age on telomere length, we re-ran statistical analyses with this population excluded from the dataset. This population had no effect on the qualitative interpretation and, in the main text, we present the final analysis with this population included.

The exception is the test of relationship between body length at sexual maturity (28 days) and telomere length, where we present both the full and truncated datasets as this is where the removal of MZCS 222 strain affected the strength of statistical association.

Genomic DNA was extracted using a lysis buffer (Thermo Scientific #K0512)

supplemented with 1 mg/ml Proteinase K (0.5 mg/ml final concentration, Sigma, MO, USA)

and RNase A (1:100 dilution, Sigma, MO, USA). Samples were incubated at 50°C for 18 h in a thermomixer. Purification of genomic DNA was carried out using equilibrated phenolchloroform (Sigma, MO, USA) and chloroform-isoamyl alcohol extraction (Sigma, MO, USA).

Following quantification and normalization, genomic DNA was digested with RsaI and HinfI enzymes (NEB, MA, USA) for 12 h at 37°C. Digested samples were electrophoresed on a 20 cm 0.6% agarose gel, in 0.5% TBE buffer, at 4°C for 17 h at 110 constant voltage. Southern blotting was performed as previously described [START_REF] Kimura | Measurement of telomere length by the Southern blot analysis of terminal restriction fragment lengths[END_REF] with minor changes; a telomere probe, (CCCTAA)4, labelled with [α-32P]-dCTP through the Prime-it II random primer labelling kit (Stratagene) was used. Raw data from Southern Blots are presented as Supplementary Figure 1.

Statistical analysis

We first extracted pre-selected parameters of the telomere distribution and analyzed them using Principal Component Analysis (PCA) to test their covariance. We then selected two parameters (mean telomere length and coefficient of variation) representing two uncorrelated descriptors of individual-fish telomere populations for further analyses.

To test the effects of predictors on telomere length and intra-individual variability, we used an information theoretic approach (Burnham and Anderson 2002). A set of biologically plausible models was constructed, with various level of complexity, and including a null (intercept-only) model. All models included strain identity as a random effect to control for non-independence of telomere length across individuals from the same population. We then compared the fit of the models using the Akaike Information Criterion corrected for small sample size (AICc). The best model minimizes AICc value. Model within DAICc between 2 and 5 are considered to have a comparable power.

Analyses were performed in lmer and MuMIn packages in the R 3.5.2 environment.

Before applying statistical models, data exploration was undertaken as recommended by [START_REF] Zuur | A protocol for conducting and presenting results of regression-type analyses[END_REF].

  populations naturally live longer. The natural habitats of annual killifish are seasonal pools in the African savannah. They are inundated during the rainy season, fish hatch, grow rapidly and attain sexual maturity within a few weeks(Vrtílek, Žák et al. 2018a). The pools desiccate during the dry season and killifish populations persist as dormant eggs in the pool sediment. The wet phase may vary from a few weeks to several months(Vrtílek, Žák et al. 2018b) depending on the amount of precipitation in the area(Blažek, Polačik et al. 2017). Habitat duration, which varies considerably across the precipitation gradient of killifish distribution, determines maximum lifespans. Male lifespans in our laboratory assays were strongly associated with annual precipitation levels in their area of their origin, though female lifespans were not.

FIGURESFig. 1 .

 1 FIGURES

Fig. 2 .

 2 Fig. 2. Relationship between median lifespan of males (blue) and females (red) and mean

Table 2 .

 2 List of candidate models to test the relationship of mean telomere length (a) and its intra-individual variation (b) with sex and juvenile growth. Model structure (fixed effects and their interaction, and random effect of strain ID), degrees of freedom (d.f.), Akaike Information Criterion corrected for small sample size (AICc), difference between the best fitting model and candidate model (DAICc) indicating strength of support for a particular model, amount of variability explained by fixed effects (R 2 marginal) and by combination of fixed and random effects (R 2 conditional).

	Model

Table 3 .

 3 List of candidate models to test the relationship of mean telomere length (a) and its intra-individual variation (b) with precipitation.Model structure (fixed effects and their interaction, and random effect of strain ID), degrees of freedom (d.f.), Akaike Information Criterion corrected for small sample size (AICc), difference between the best fitting model and candidate model (DAICc) indicating strength of support for a particular model, amount of variability explained by fixed effects (R 2 marginal) and by combination of fixed and random effects (R 2 conditional).

	Model

Table 4 .

 4 List of candidate models to test the relationship of mean telomere length (a) and its intra-individual variation (b) with median sexspecific strain-level lifespan estimates. Model structure (fixed effects and their interaction, and random effect of strain ID), degrees of freedom (d.f.), Akaike Information Criterion corrected for small sample size (AICc), difference between the best fitting model and candidate model (DAICc) indicating strength of support for a particular model, amount of variability explained by fixed effects (R 2 marginal) and by combination of fixed and random effects (R 2 conditional).

	Model

Acknowledgement Funding came from Czech Science Foundation (19-01781S) (to MR), Portuguese Fundação para a Ciência e Tecnologia (POCI-01-0145-FEDER-016390) (to MGF) and French Université Côte d'Azur Academie 4 Installation Grant (to MGF). All work was carried out in accordance with relevant guidelines and regulations. Collection of wild strains complied with legal regulations of Mozambique (collection licenses: DPPM/053/7.10/08, 175/154/ IIP/2009/DARPE, DPPM/083/7.10/10, DPPM/330/7.10/10, DPPM/069/7.10/11, DPPM/088/7.10/12). Experimental work was approved by the Ethical Committee of the Institute of Vertebrate Biology (No. 163-12) and by Ministry of Agriculture (CZ 62760203) in accordance with legal regulations of the Czech Republic. We thank R. Spence for valuable comments and English corrections. All primary data are available at Figshare repository (doi: 10.6084/m9.figshare.12888566).

Author contributions

MR and MGF conceived and designed the study. RB and MP collected data on lifespan. MV and MR raised fish for telomere length estimates. KG, TF performed genomic DNA extractions, TRF and Southern hybridizations. KG, TF and MGF analyzed and interpreted telomere length.

MR and MV conducted statistical analyses. MR, KG and MGF drafted the manuscript. All authors significantly contributed to the final text.

Table 1. List of all strains from the N. furzeri-N. kadleci species complex used in the study. Populations are ordered according to median lifespan. Precipitation represents mean annual precipitation total, aridity is the index of aridity calculated from precipitation-evaporation differentials (both derived from CGIAR Consortium for Spatial Information: www.cgiar-csi.org). Sample sizes for telomere length study (telomeres) and longevity estimates (N fish) are shown. Median lifespan was calculated using the surv function in the survival package.


Eslam Mohammed

Di Meng

Sergi Pujades

Morphology-based individual vertebrae classification

Keywords:

The human spine is composed, in non-pathological cases, of 24 vertebrae. Most vertebrae are morphologically distinct from the others, such as C1 (Atlas) or C2 (Axis), but some are morphologically closer, such as neighboring thoracic or lumbar vertebrae. In this work, we aim at quantifying to which extent the shape of a single vertebra is discriminating. We use a publicly available MICCAI VerSe 2019 Challenge dataset containing individually segmented vertebrae from CT images. We train several variants of a baseline 3D convolutional neural network (CNN) taking a binary volumetric representation of an isolated vertebra as input and regressing the vertebra class. We start by predicting the probability of the vertebrae to belong to each of the 24 classes. Then we study a second approach based on a two-stage aggregated classification which first identifies the anatomic group (cervical, thoracic or lumbar) then uses a group-specific network for the individual classification. Our results show that: i) the shape of an individual vertebra can be used to faithfully identify its group (cervical, thoracic or lumbar), ii) the shape of the cervical and lumbar seems to have enough information for a reliable individual identification, and iii) the thoracic vertebrae seem to have the highest similarity and are the ones where the network is confused the most. Future work will study if other representations (such as meshes or pointclouds) obtain similar results, i.e. does the representation have an impact in the prediction accuracy?

Introduction

The human spine is usually composed of 24 vertebrae. They are structured in three anatomic groups: seven cervical (C1-C7), twelve thoracic (T1-T12) and five lumbar (L1-L5). Each group shares morphological and functional characteristics motivating their anatomic group classification. The three groups are illustrated in Figure 1. Automatic identification of vertebrae in spinal imaging, such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), is crucial in the context of clinical diagnosis and surgical planning. While some vertebrae, such as the first cervical (C1) have a very distinctive shape, other vertebrae, such as neighboring thoracic or lumbar vertebrae share a visually similar morphological appearance. In this work, we aim at quantifying to which extent the shape of a single vertebra is discriminating. This knowledge aims at providing a source of relevant information to the global full spine identification methods. For example, distinctive vertebrae can be used as reliable anchor points in global methods, whereas the contribution of unreliable predictions can be diminished. As of today, the classification of all vertebrae in a CT volume still remains a major challenge for the community [START_REF] Sekuboyina | Verse: A vertebrae labelling and segmentation benchmark[END_REF].

In this work we propose to train a 3D convolutional neural network (CNN) architecture that, given the morphology of an isolated vertebra as input, can first predict the probability of the vertebrae to belong to each of the 24 classes. Then, motivated by the fact that the anatomic groups have distinctive features, as illustrated in Fig. 1, we study a second approach by first identifying the anatomic group (cervical, thoracic or lumbar), and then, its individual identification with a per-group specialized network. To perform our study, we use the publicly available MICCAI VerSe 2019 Challenge dataset [START_REF] Sekuboyina | Verse: A vertebrae labelling and segmentation benchmark[END_REF]. As the medical dataset contains a small number of annotated samples, we study the impact of several augmentation techniques (rotation, translation, noise addition) in the classification task. Preliminary results show that: i) the shape of an individual vertebra can be used to faithfully identify its group (cervical, thoracic or lumbar), ii) the shape of the cervical and lumbar seems to have enough information for a reliable individual identification, and iii) the thoracic vertebrae seem to have the highest similarity and are the ones where the network is confused the most.

Many computer-aided tasks in medical imaging were classically done via feature based methods [START_REF] Camlica | Medical image classification via svm using lbp features from saliency-based folded data[END_REF], [START_REF] Lo | Support vector machine for breast mr image classification[END_REF]. AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] was the first to obtain remarkable results in many of the visual challenges (e.g. classification, segmentation and detection). Henceforth, researchers in medical imaging have further studied the use and improvement of convolutional neural networks for a wide range of applications. The U-Net architecture [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] came up with an architecture for segmenting microscopic images that incorporates two main paths for capturing image context and other for precise localization. The 3D multi-task fully connected architecture [START_REF] Liao | Joint vertebrae identification and localization in spinal ct images by combining short-and long-range contextual information[END_REF] describes a complex yet comprehensive approach for vertebral segmentation and localization based on the contextual information of the surrounding organs. Indeed, this implies relying on an accurate methodology for vertebral identification. More recently, the work proposed by [START_REF] Lessmann | Iterative fully convolutional neural networks for automatic vertebra segmentation and identification[END_REF] segmenting the vertebrae in an iterative manner, states the importance of the individual identification of vertebrae. The network can decide, using the instance memory, whether to segment the next vertebra or retrieve it. In our work we re-implement a baseline convolutional neural network [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] in 3D, to learn and quantify how accurately vertebrae can be identified. Also, we quantify the effect of using different augmentation techniques in the prediction accuracy of the trained models.

Methodology

In our work, we start by extracting and pre-processing the vertebrae data, then we augment it with different strategies. With the processed data as input we train several networks to predict the individual vertebrae classification.

Data Extraction

The dataset used for our study is the VerSe dataset [START_REF] Sekuboyina | Verse: A vertebrae labelling and segmentation benchmark[END_REF], which is a spine dataset of 80 CT scans with voxel-level vertebral annotations. The challenge of the dataset is to achieve segmentation, identification and localization of the vertebrae in CT volumes. In our work we use the ground truth annotation masks of the volumes as input data to train the classifiers. We extract every individual vertebra from the CT volumes and we obtain binary volumes (masks) for each vertebra.

Connected Components. The annotation masks of the VerSe dataset contain noise in the form of small isolated groups of voxels. After extracting the individual vertebrae, we use a 3D connected component algorithm to extract the biggest connected component(e.g. our desired vertebra). Then, we use the bounding box of the biggest connected component to obtain the clean volume.

Padding. Volumetric networks take as input a predefined volume structure having the same size. However, the obtained volumes from the previous step have different dimensions. To decide on the network input size we use the largest volume dimension in the dataset plus a margin of approx. 20%, namely a cube of size 128x128x128 -where a power of two was preferred. All volumes are then centered and zero-padded to match this size.

Augmentation

A common useful technique to overcome the scarcity of data while training neural networks is data augmentation. Proposing variations of the same instance is essential to teach the network the desired invariance and robustness when there are few data samples for training. That is, adding more samples to leverage the training process by learning more complex features that consolidate the network's discriminative ability towards more robust features. We used four augmentation techniques: rotations, translations, scale and additive noise.

We considered the rotations around the 3 axes, hence introducing multiple orientations to the network. Because the acquired volumes have a coherent global orientation, we uniformly sampled angles θ in the range of [-20, 20] 3 degrees.

In order to teach the networks the translation invariance we feed as training samples translated inputs of the desired objects. So, instead of learning a centered object in the cube, samples are shifted by a δ ∈ R 3 offset. The translation is uniformly sampled from the interval [-20, 20] 3 mm.

Another transformation that we consider is the scaling property of an object. We applied a uniform scaling factor γ ∈ R, uniformly sampling from the interval [0.8, 1.2]

We also added additive noise to the input data. We used salt-and-pepper noise by sampling from a Poisson distribution with parameter λ = 0.05.

Our mechanism to generate an augmented dataset involves applying 10 random combinations of the aforementioned transformations to each bone. Hence, we have 10 random versions of each bone as training samples. All these transformations were conducted while preserving the input size cube to 128 × 128 × 128.

Network Architecture

Our CNN architecture described in Figure 2 is composed of 8 convolutional blocks followed by a fully connected layer 4096. Then a SoftMax layer is applied to get the probabilities of each class of the input volumes. Each convolutional block (except for the early fist 2 blocks) consists of 4 layers; a 3D downsampling pooling layer of filter size 2 × 2 × 2 that shrinks the input volume to half of its size, a 3D convolutional layer of kernel 3 × 3 × 3 with stride 1 and padding a Batch Normalization [START_REF] Ioffe | Batch normalization: Accelerating deep network training by reducing internal covariate shift[END_REF] layer of momentum 0.95 for computing the running mean and variance and a ReLU non-linearity.

The settings for the network were fixed during the experiments. We used the Pytorch [START_REF] Paszke | Pytorch: An imperative style, high-performance deep learning library[END_REF] framework for the training with batch size of 4 cubes per GPU and an initial learning rate 0.001 that decays by half every 20 epochs. The training was parallelized on 2 NVIDIA GPU Quadro RTX 5000. For the learning and weight optimization we use Adam optimizer [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF] since it implies an adaptive learning rate for each layer after each step of optimization hence a faster convergence. To ensure that overfitting is avoided, we regularize our loss function using L2 regularization with penalty value 0.0001. We use a weighted cross entropy loss with the weights compensating for the low-sample classes more than those of higher sampled classes.

To train our networks, we choose the optimal model in a network optimization scheme. We use a validation set and we manually retrieve the model with the least loss and the highest accuracy. An example of the evaluation of the loss and accuracy is shown in Fig. 3, where results were obtained after the model converges without over-fitting.

Experiments

We perform several experiments. In the first one we study the direct classification of a vertebra in its 24 possible classes. In a second experiment we study a twostage aggregated classification scheme. First, the anatomic group classification is performed (cervical, thoracic, lumbar), i.e. a 3-level classification problem. Then, three specialized networks are trained, one for each anatomic group.

24-level Classification

We first train our model to classify, individually, between all 24 vertebrae classes. We use all augmentation strategies presented in Sec. 2.2 and we use a random split of the data into train, validation and test. The evaluation of the loss and accuracy on the train and validation sets is presented in Fig. 3. The obtained classifications results have an accuracy of 80% on the test set. In order to assess the relevance of the different augmentations we performed the same experiment by only using one augmentation strategy. We present the results on Tab. 1, where Rot is only using the rotation augmentation, Trans is only using the translation transformation, Rot + Trans combines random transformations for each strategy on each sample, and Full includes all previous augmentations with scale and the noise augmentation. In all cases each sample was augmented with 10 random variations. The results on Tab. 1 show the benefit of the different augmentation strategies, with the Full technique systematically obtaining the best results. To further inspect the obtained results, in Fig. 4 we present the confusion matrices for the validation and test set. For the cervical and lumbar group, the only confusions arise with direct neighbouring vertebrae. However, the vertebrae in the middle of the thoracic segment (T5 to T9) present the highest confusions. Let us note that if we focus on the anatomic group predictions (cervical, thoracic and lumbar), only 1.5 % of the vertebrae were miss-classified, i.e. one anatomic group was predicted instead of the correct one. This high accuracy motivated us to explore a two-step classification scheme, where the anatomic group is first classified (cervical, thoracic or lumbar), and then a group-specific network is used.

3-level Classification

In the next experiment we train the same architecture network on the full augmented dataset but to only predict 3 labels, i.e. from a segmentation mask the network predicts an anatomic group: cervical, thoracic or lumbar.

We evaluate the full dataset using a 8-fold strategy and report the results on the aggregation of all test sets. The model achieves a 99.3% accuracy and the confusion matrix is presented in Fig. 5. These results show that the morphology of an individual vertebra contains relevant information to accurately distinguish the anatomic group of the vertebrae. Our next step is to study if a per-group model can better classify the individual vertebrae.

Individual-per-group Classification

In this experiment, we train 3 per-group specific models, namely a model for the cervical vertebrae (7-classes), one for thoracic vertebrae(12-classes) and another model for the lumbar vertebrae (5-classes).

For the cervical group we obtain an accuracy of 84.68%. Fig. 6 illustrates the confusion matrix of the cervical predictor. As expected, C1 and C2 have a very characteristic shape, making them easy for the network to be accurately identified. Then, we can observe that most confusions happen between C4, C5 and C6, while the shapes of C3 and C7 seem to be more accurately distinguishable with less confusions.

For the thoracic group we obtain an accuracy of 76.92% . Fig. 7 illustrates the confusion matrix of the thoracic predictor. Most confusions arise in the section between T5 and T9, where distant vertebrae up to two neighbours are wrongly predicted (T7 for T5, T8 for T6 or T7 for T9). These results indicate that the shape of the middle section of the thoracic vertebrae is most similar, making them less individually identifiable.

For the lumbar group we obtain an accuracy of 86.08% . Fig. 8 illustrates the confusion matrix of the lumbar predictor. It is worth noting that the failures are evenly distributed with the direct neighbouring vertebrae. While L1 and L2 have a slightly higher confusion rate, the shape of L4 and L5 seems to be more accurately distinguishable with less confusions.

Comparison: 24-level vs 3-level plus individual per-group

We studied two different approaches for the vertebrae classification: to directly predict one of the 24 vertebrae or to sequentially predict the anatomic group of a single vertebrae then use a specific per-group network.

The accuracy of the 24-level model is 71.07%, while the two steps aggregated accuracy is 85.47%.

A finer, per anatomic group analysis, shows that the 24-level model obtains an accuracy of 71.34% for the cervical vertebrae, 60.91% for the thoracic vertebrae and 75.14% for the lumbar vertebrae. For the two-stage method the results are 84.68% for the cervical vertebrae, 76.92% for the thoracic and 86.45% for the lumbar vertebrae.

This indicates that the two-stage approach is preferable in terms of classification accuracy, with a significant improvement in the classification of the cervical vertebrae. Interestingly, the trends in the confusion matrices in both approaches are consistent, with T5 to T9 presenting overall the greatest confusions.

Conclusion and Future Work

In this work we present a study on the classification of the vertebrae using its morphology. With the results of our current experiments we observe that the individual vertebrae classification is a non-trivial problem due to the morphological similarity of neighbouring vertebrae. However, our experiments confirm that classifying a vertebrae into its anatomic group is relatively straightforward for the networks. Moreover, our preliminary results point that: i) the shapes of the cervical and lumbar vertebrae seem to have enough information for a reliable individual identification, and ii) the thoracic vertebrae (T5 -T9) seem to have the highest similarity and are the ones where the network is most confused.

Our upcoming investigation will explore two leads: the use of neighbouring vertebrae to do the predictions and the exploration of other representations.

As most confusions arise with the network predicting the neighbouring vertebrae, we will consider an individual vertebrae with its surrounding neighbouring vertebrae as input. By considering the neighbouring vertebrae we hypothesize that the surrounding context will lead to a better classification. We plan to apply the same methodology: first we will refine the 3 anatomic classes into 5 classes(cervical only, cervical+thoracic, thoracic only, thoracic+lumbar, lumbar only). Then we will consider each individual class network as before: 7 for cervical vertebrae, 12 for thoracic vertebrae and 5 for lumbar vertebrae. Similarly we will compare the results of the two step method to the direct 24-level strategy.

A second lead of improvement will be to explore other representations for the input data. Instead of binary volumetric mask, we will consider distance fields [START_REF] Park | Deepsdf: Learning continuous signed distance functions for shape representation[END_REF] and point clouds [START_REF] Qi | Pointnet: Deep learning on point sets for 3d classification and segmentation[END_REF] [START_REF] Prokudin | Efficient learning on point clouds with basis point sets[END_REF]. By converting the data into different representations, we hypothesize that each representation may capture different features of the individual morphology.

While the VerSe 2019 Dataset contains some dysmorphic cases, the dataset does not have a medical label. Thus we did not assess the models performance on such anomalies. Future work will aim at quantifying the sensitivity of the trained models to pathological cases.

Acknowledgments

The work by Eslam Mohammed and Di Meng was funded by the SPINE PDCA project. The work by Sergi Pujades was funded by the ANR SEMBA project.

Fig. 1 .

 1 Fig. 1. Illustration of the shape of the vertebrae [10]. I -Representative shape of cervical C3-C7. II -Representative shape of the thoracic T1-T12. III -Representative shape of the lumbar L1-L5.

Fig. 2 .

 2 Fig. 2. The proposed 3D Convolutional Neural Network. Numbers under each block describe the size of the output kernels after each operation. Numbers on the z-axis of each block describe the size of the output cube in R 3 .

Fig. 3 .

 3 Fig. 3. Evaluation of the loss and accuracy of the training and validation sets for the 24 class classification optimization, w.r.t the number of epochs in training. First 8-fold.

Fig. 4 .

 4 Fig. 4. Confusion matrix for the 24-level classification task.

Fig. 5 .

 5 Fig. 5. Confusion matrix for the 3-level classification task.

Fig. 6 .

 6 Fig. 6. Confusion matrix for the cervical vertebrae network.

Fig. 7 .

 7 Fig. 7. Confusion matrix for the thoracic vertebrae network.

Fig. 8 .

 8 Fig. 8. Confusion matrix for the lumbar vertebrae network.

Table 1 .

 1 1, Ablation study on the impact of the augmentation strategies. Train, validation and test sets of one 8-fold are reported. The best value is consistently obtained for the full augmentation strategy, highlighted in bold.

		Rot Trans Rot+Trans Full
	Train Set Accuracy 99.5 99.8	99.8	100
	Validation Set Accuracy 73 70	75	81
	Test Set Accuracy	74 67	70	80