Olivier Besson
email: olivier.besson@isae-supaero.fr

François Vincent
email: francois.vincent@isae-supaero.fr

Sub-pixel detection in hyperspectral imaging with elliptically contoure d t -distribute d background

Keywords:

Detection of a target with known spectral signature when this target may occupy only a fraction of the pixel is an important issue in hyperspectral imaging. We recently derived the generalized likelihood ratio test (GLRT) for such sub-pixel targets, either for the so-called replacement model where the presence of a target induces a decrease of the background power, due to the sum of abundances equal to one, or for a mixed model which alleviates some of the limitations of the replacement model. In both cases, the background was assumed to be Gaussian distributed. The aim of this short communication is to extend these detectors to the broader class of elliptically contoured distributions, more precisely matrix-variate t -distributions with unknown mean and covariance matrix. We show that the generalized likelihood ratio tests in the t -distributed case coincide with their Gaussian counterparts, which confers the latter an increased generality for application. The performance as well as the robustness of these detectors are evaluated through numerical simulations.

Problem statement

Hyperspectral imaging has become an increasingly popular tool for remote sensing and scene information retrieval, whether for civil or military needs and in a large number of applications, including analysis of the spectral content of soils, vegetation or minerals, detection of man-made materials or vehicles, to name a few [START_REF] Eismann | Hyperspectral remote sensing[END_REF][START_REF] Manolakis | Hyperspectral Imaging Remote Sensing[END_REF] . One of the challenges of hyperspectral imaging is to detect a target -whose spectral signature is assumed to be known-within a background whose statistical properties are not fully known [START_REF] Manolakis | Detection algorithms for hyperspectral imaging applications[END_REF][START_REF] Manolakis | Detection algorithms in hyperspectral imaging systems: an overview of practical algorithms[END_REF][START_REF] Nasrabadi | Hyperspectral target detection : an overview of current and future challenges[END_REF] . Depending on the spatial resolution of hyperspectral sensors and the size of the target, the latter may occupy the totality or only a fraction of the pixel under test (PUT), in which case one speaks of sub-pixel targets. In the latter case, the target replaces part of the background in the PUT, leading to the so-called replacement model [START_REF] Manolakis | Hyperspectral subpixel detection using the linear mixing model[END_REF][START_REF] Manolakis | Hyperspectral signal models and implications to material detection algorithms[END_REF] .

Whatever the case, full-pixel or sub-pixel targets, the problem can be formulated as a conventional composite hypothesis problem [START_REF] Manolakis | Detection algorithms for hyperspectral imaging applications[END_REF][START_REF] Manolakis | Detection algorithms in hyperspectral imaging systems: an overview of practical algorithms[END_REF][START_REF] Nasrabadi | Hyperspectral target detection : an overview of current and future challenges[END_REF][START_REF] Manolakis | Hyperspectral subpixel detection using the linear mixing model[END_REF][START_REF] Manolakis | Hyperspectral signal models and implications to material detection algorithms[END_REF][START_REF] Dipietro | Performance evaluation of hyperspectral detection algorithms for sub-pixel objects[END_REF][START_REF] Frontera-Pons | Adaptive nonzero-mean Gaussian detection[END_REF] : given a vector y ∈ R p -where p denotes the number of spectral bands used-which represents the reflectance in the PUT, is there a component along t -the signature of interest (SoI)-in addition to the background? Since the background statistics depend on unknown parameters (for instance mean and covariance matrix) a set of training samples Z ∈ R p×n , hopefully free of the SoI t , is observed whose statistics are assumed to match those of the background in the PUT. These training samples are gathered in the vicinity of the PUT (local detection) or along the whole image (global detection).

Recently in [START_REF] Vincent | Generalized likelihood ratio test for subpixel target detection in hyperspectral imaging[END_REF] we addressed sub-pixel detection using the replacement model under a Gaussian background, and we derived the plain generalized likelihood ratio test (GLRT) by maximizing the joint distribution of ( y, Z ) with respect to all unknown parameters. Moreover, motivated by some limitations of the replacement model, especially the fact that the filling factor of a sub-pixel target may not be in practice as large as expected, we also derived the GLRT for a mixed model where presence of a target induces a partial replacement of the background [START_REF] Vincent | Generalized likelihood ratio test for modified replacement model in hyperspectral imaging detection[END_REF] . These two detectors assume a Gaussian background. However, evidence of non-Gaussianity of hyperspectral data has been brought [START_REF] Manolakis | Hyperspectral image processing for automatic target detection applications[END_REF][START_REF] Matteoli | A tutorial overview of anomaly detection in hyperspectral images[END_REF] and therefore it is of interest to extend detectors originally devised for Gaussian background to the broader class of elliptically contoured (EC) distributions [START_REF] Anderson | Theory and Applications of Elliptically Contoured and Related Distributions[END_REF][START_REF] Kai-Tai | Generalized Multivariate Analysis[END_REF] . The aim of this communication is thus to extend our recent GLRTs from the Gaussian case to the matrix variate t -distributed case. We will show that the GLRTs coincide with their Gaussian counterparts. The paper is organized as follows. In Section 2 , we consider each of the three models and derive the corresponding GLRTs. The latter are evaluated in Section 3 on simulated data but where the target spectral signature, the mean and covariance matrix of the background are obtained from real hyperspectral images. https://doi.org/10.1016/j.sigpro.2020.107662

GLRT for matrix variate t -distributed background

As stated before, let us assume that we wish to decide whether a given vector y contains a signature of interest t in the presence of disturbance z whose mean value μ and covariance matrix are unknown, and let us assume that a set of training samples z i , i = 1 , . . . , n are available which share the same distribution as z . These samples can be collected around the PUT or along the whole image. We simply assume here that n > p . Therefore, we would like to solve the following problem:

H 0 : y = z ; z i d = z , i = 1 , . . . , n H 1 : y = αt + βz ; z i d = z , i = 1 , . . . , n (1)
where d = means "has the same distribution as". In (1) , t corresponds to the assumed spectral signature of the target and α denotes its unknown amplitude. When β = 1 one obtains the conventional additive model. When β = 1 -α the replacement model is recovered, and the mixed model corresponds to an arbitrary β.

In order to derive the GLRT, we need to specify the joint distribution of y and Z where Z = z 1 z 2 . . . z n . As said in the introduction, we assume that y Z follows a matrix-variate tdistribution with ν degrees of freedom so that we need to solve the following composite hypothesis testing problem:

H 0 : y Z d = T p,n +1 ( ν, M 0 , (ν -2) , I n +1 ) H 1 : y Z d = T p,n +1 ν, M 1 , (ν -2) , β 2 0 T 0 I n ( 2
)
where

M 0 = μ μ1 T n , M 1 = αt + βμ μ1 T n , 1 n is a n × 1
vector with all elements equal to one, μ stands for the mean value of the background while denotes its covariance matrix. In (2) , T () stands for the matrix variate t -distribution [START_REF] Gupta | Matrix Variate Distributions[END_REF][START_REF] Kotz | Multivariate T Distributions and their Applications[END_REF] so that the probability density function (p.d.f.) of the observations under each hypothesis is given by

p 0 (y , Z ) = C | | -n +1 2 I p + -1 ν -2 y -μ Z -μ1 T n × y -μ Z -μ1 T n T -ν+ n + p 2 p 1 (y , Z ) = Cβ -p | | -n +1 2 I p + -1 ν -2 ˜ y -μ Z -μ1 T n × ˜ y -μ Z -μ1 T n T -ν+ n + p 2 (3)
with ˜

y = β -1 (y -αt ) and C = p ((ν+ n + p) / 2) π p(n +1) / 2 p ((ν+ p-1) / 2)
. It should be observed that the columns of y Z are only uncorrelated but not independent, as p(y , z 1 , . . . , z n ) cannot be factored as p(y ) n i =1 p(z i ) .

We now derive the GLRT for the problem in [START_REF] Manolakis | Hyperspectral Imaging Remote Sensing[END_REF] . Let us start by considering the following function f ( ) where S is some positive definite matrix:

f ( ) = | | -n +1 2 I p + (ν -2) -1 -1 S -ν+ n + p 2 = | | ν+ p-1 2 + (ν -2) -1 S -ν+ n + p 2 (4) Differentiation of log f ( ) yields ∂ log f ( ) ∂ = ν + p -1 2 -1 - ν + n + p 2 ( + (ν -2) -1 S ) -1 . ( 5
)
Setting this derivative of to zero, we can see that f ( ) achieves its maximum at * =

(ν + p -1) S (ν -2)(n + 1) = γ S (6) It follows that max p 0 (y , Z ) = C y -μ Z -μ1 T n (y -μ) T (Z -μ1 T n ) T -n +1 2 max p 1 (y , Z ) = C β -p ˜ y -μ Z -μ1 T n ( ˜ y -μ) T (Z -μ1 T n ) T -n +1 2 (7) with C = Cγ -p(n +1) / 2 [1 + (ν -2) -1 γ -1 ] -p(ν+ n + p) / 2 .
Since C is the same under H 0 and H 1 it will cancel out in the GLR and therefore the GLR does not depend on ν. This means that ν does not need to be known since its estimation is not actually required to obtain the GLR. Now, for any vector x ,

M ( μ) = x -μ Z -μ1 T n x -μ Z -μ1 T n T = (x -μ)(x -μ) T + (Z -μ1 T n )(Z -μ1 T n ) T = xx T -μx T -x μ T + μμ T + ZZ T -μ1 T n Z T -Z1 n μ T + n μμ T = (n + 1) μμ T -μ(x + Z1 n ) T -(x + Z1 n ) μ T + xx T + ZZ T = (n + 1) μ - x + Z1 n n + 1 μ - x + Z1 n n + 1 T + xx T + ZZ T - (x + Z1 n )(x + Z1 n ) T n + 1 = (n + 1) μ - x + Z1 n n + 1 μ - x + Z1 n n + 1 T + x Z I n +1 - 1 n +1 1 T n +1 n + 1 x Z T ( 8
)
Consequently, min

μ | M ( μ) | = x Z P ⊥ n +1 x Z T ( 9
)
with P ⊥ n +1 the orthogonal projector on the null space of 1 n +1 . Hence, we arrive at max μ,

p 0 (y , Z ) = C y Z P ⊥ n +1 y Z T -n +1 2 max μ, p 1 (y , Z ) = C β -p ˜ y Z P ⊥ n +1 ˜ y Z T -n +1 2 (10)
Next, for any vector x and matrix Q (not necessarily

P ⊥ n +1 ), x Z Q x Z T = x Z Q 11 Q 12 Q 21 Q 22 x Z T = Q 11 xx T + ZQ 21 x T + xQ 12 Z T + ZQ 22 Z T = Q 11 x + Q -1 11 ZQ 21 x + Q -1 11 ZQ 21 T + ZQ 2 . 1 Z T ( 11
)
where

Q 2 . 1 = Q 22 -Q 21 Q -1 11 Q 12 . Therefore, x Z Q x Z T = ZQ 2 . 1 Z T × 1 + Q 11 x + Q -1 11 ZQ 21 T ZQ 2 . 1 Z T -1 x + Q -1 11 ZQ 21 ( 12
)
Coming back to the case

Q = P ⊥ n +1 = I n +1 -(n + 1) -1 1 n +1 1 T n +1 , we have Q = 1 0 T 0 I n -(n + 1) -1 1 1 T n 1 n 1 n 1 T n ( 13
)
so that (15) A few important observations can be made regarding this result. First, for all three models, the GLRs in [START_REF] Kai-Tai | Generalized Multivariate Analysis[END_REF] coincide with their Gaussian counterparts . For the additive model a proof is given in Appendix A . A more intuitive way to figure out this equivalence is to realize that the expression of the GLR in [START_REF] Kai-Tai | Generalized Multivariate Analysis[END_REF] does not depend on ν and that, letting ν grow to infinity, one should recover the GLR for Gaussian distributed data. As for the replacement and the mixed models, the expression in ( 15) is exactly that of the ACUTE and SPADE detectors of [START_REF] Vincent | Generalized likelihood ratio test for subpixel target detection in hyperspectral imaging[END_REF] and [START_REF] Vincent | Generalized likelihood ratio test for modified replacement model in hyperspectral imaging detection[END_REF] respectively, where the GLRTs for the replacement model and the mixed model are derived under the Gaussian assumption. Therefore, the latter are still GLRTs for a much broader class of distributions than initially expected.

Q 11 = 1 -(n + 1) -1 = n (n + 1) -1 Q 21 = -(n + 1) -1 1 n Q 22 = I n -(n + 1) -1 1 n 1 T n Q 2 . 1 = I n -n -1 1 n 1 T n = P ⊥ n (14) It follows that Q -1 11 ZQ 21 = -n -1 Z1 n = -z and ZQ 2 . 1 Z T = ZP ⊥ n Z T = ZZ T -n z z T = S . Hence, the GLR is given by GLR = 1 + Q 11 (y -z ) T S -1 (y -z ) (n +1) / 2 min α,β β p [ 1 + Q 11 ( ˜ y -z ) T S -1 ( ˜ y -z ) ] (n +1) / 2 = [1 + n n +1 (y -z ) T S -1 (y -z )] (n +1) / 2 min α,β β p [1 + n n +1 ( y -αt β -z ) T S -1 ( y -αt β -z )] (n +1) / 2
Let us also briefly comment on the implementation of the GLRT.

For the additive model β = 1 , and the minimization problem in ( 15) is a simple linear least-squares problem for which a closedform solution can be obtained. This yields GLR 2 / (n +1)

AM = 1 + n n +1 (y -z ) T S -1 (y -z ) min α 1 + n n +1 (y -z -αt ) T S -1 (y -z -αt ) = 1 + n n +1 (y -z ) T S -1 (y -z ) 1 + n n +1 (y -z ) T S -1 (y -z ) n n [(y -z ) T S -1 t ] 2 t T S -1 t ≡ n n +1 [(y -z ) T S -1 t ] 2 [1 + n n +1 (y -z ) T S -1 (y -z )][ t T S -1 t ] ( 16
)
The GLR in ( 16) generalizes Kelly's detector to the case of a noncentered Student distributed background. Note that ( 16) differs from Kelly's detector by the n n +1 factor.

As for the replacement model, β = 1 -α and the minimization should be conducted with respect to α only, i.e., GLR RM =

1 + n n +1 S -1 / 2 (y -z ) 2 (n +1) / 2 min α (1 -α) p 1 + n n +1 S -1 / 2 (y -αt -(1 -α) z ) 2 (1 -α) 2 (n +1) / 2 ( 17
)
As shown in [START_REF] Vincent | Generalized likelihood ratio test for subpixel target detection in hyperspectral imaging[END_REF] , this simply amounts to finding the (unique) positive root of a 2nd-order polynomial. Finally, for the mixed model where β is arbitrary, one has a 2-D minimization problem. However, minimization over α can be done analytically, leaving only a minimization over β:

GLR MM = 1 + n n +1 S -1 / 2 (y -z ) 2 (n +1) / 2 min β β p 1 + n n +1 P ⊥ S -1 / 2 t S -1 / 2 (y -β z ) 2 β 2 (n +1) / 2 ( 18
)
Again the solution is obtained as the unique positive root of a second-order polynomial equation [START_REF] Vincent | Generalized likelihood ratio test for modified replacement model in hyperspectral imaging detection[END_REF] . Therefore for both the replacement model and the mixed model, all unknown parameters can be obtained in closed-form.

Numerical simulations

In the present section, we will compare the detectors developed above. The additive model GLR AM in [START_REF] Gupta | Matrix Variate Distributions[END_REF] will be referred to as mKELLY in the sequel as it consists in a slight modification of Kelly's original GLRT [START_REF] Kelly | An adaptive detection algorithm[END_REF] . The replacement model GLR RM in (17) and the modified model GLR MM in [START_REF] Kelly | An adaptive detection algorithm[END_REF] will be referred as to ACUTE [START_REF] Vincent | Generalized likelihood ratio test for subpixel target detection in hyperspectral imaging[END_REF] and SPADE [START_REF] Vincent | Generalized likelihood ratio test for modified replacement model in hyperspectral imaging detection[END_REF] . These last two detectors have already been assessed against real data drawn from the RIT [START_REF] Snyder | Development of a web-based application to evaluate target finding algorithms[END_REF] and Viareggio [START_REF] Acito | Hyperspectral airborne "Viareggio 2013 trial" data collection for detection algorithm assessment[END_REF] experiments. Herein, we evaluate their performance as well as their robustness on simulated yet realistic data. More precisely we consider an image acquired in Viareggio (Italy) in May 2013 from an aircraft flying at 1200 meters. The original data is a [450 × 375] pixels map in the Visible Near InfraRed (VINR) band (400 -1000 nm ) with a spatial resolution of about 0.6 meters. The scene is composed of parking lots, roads, buildings, sport fields and pine woods. Different kinds of vehicles as well as colored panels served as known targets. For each of these targets, a spectral signature obtained from ground spectroradiometer measurements is available. Herein, we consider t to be the signature of the V5 target, μ and are respectively the sample mean and sample covariance matrix obtained from the whole Viareggio image. It has to be noticed that these raw radiance data have first been converted to reflectance measurements using an ELM method [START_REF] Ferrier | Evaluation of apparent surface reflectance estimation methodologies[END_REF][START_REF] Smith | The use of the empirical line method to calibrate remotely sensed data to reflectance[END_REF] . The number of spectral bands used is p = 32 and the number of training samples is n = 60 . The background is simulated using a t distribution with ν degrees of freedom. As could be anticipated, ACUTE exhibits the best performance since it corresponds to the GLRT for the specific case β = 1 -α. However, SPADE is shown to incur a small degradation compared to this optimal detector. On the contrary, mKELLY exhibits a significant performance loss, mostly because E { z } = E { z k } , a fact that is not accounted for in the additive model, contrary to the other two detectors. We now assess the robustness of ACUTE and SPADE. More precisely, we study their performance when β varies between β = 1 -α (replacement model) and β = 1 (additive model). In Fig. 2 , we display the probability of false alarm ( P fa ) gain of ACUTE and SPADE with respect to mKELLY, i.e., 10 log 10 P fa ( GLR AM ) P fa ( GLR RM/MM ) . This P fa gain allows to measure the improvement (if positive) or the loss (when negative) of both ACUTE and SPADE with respect to mKELLY.

In this figure α is fixed to 0.05 and the probability of detection is P d = 0 . 5 . Fig. 2 confirms that ACUTE is slightly better than SPADE when the actual value of β is close to 1 -α. However, as soon as β departs from 1 -α SPADE shows better performance than ACUTE. Moreover, SPADE performs better than mKELLY for most values of β unless β comes very close to 1, but in this case the loss of SPADE with respect to mKELLY is marginal. Therefore, SPADE provides the best robustness, with small performance loss compared to the optimal solution whatever the value of β.

Concluding remarks

In this communication, we considered the detection of a subpixel target in hyperspectral imaging when the background is no longer Gaussian but t -distributed. The GLRTs for a general mixed model, including the standard additive case and the replacement one, were derived, generalizing the Gaussian versions previously derived. For the three specific values of β considered in the literature, it was shown that the GLRTs remain the same and hence the detectors initially proposed under a Gaussian framework have more generality than expected. Moreover, they do not depend on the unknown degree of freedom of the t -distribution. Numerical simulations showed that SPADE provides a very good trade-off as it is always close to or better than Kelly and ACUTE which are optimal only for specific values of β.

Fig. 1 .Fig. 2 .

 12 Fig. 1. ROC for the replacement model β = (1 -α) .

Fig. 1

 1 Fig. 1 plots the Receiver Operation Curve (ROC) obtained for the replacement model [ β = 1 -α] with α = 0 . 05 , for different values of ν ranging from ν = 5 (heavy-tailed distribution) to ν = 10 , 0 0 0 (nearly Gaussian distributed background). As could be anticipated, ACUTE exhibits the best performance since it corresponds to the

Declaration of Competing Interest

The authors acknowledge that there is no conflict of interest regarding the paper entitled "Sub-pixel detection in hyperspectral imaging with elliptically contoured t-distributed back-ground".

Appendix A. GLR for the additive model and Gaussian distributed background

In this appendix, we derive the GLRT for Gaussian distributed background and for the additive model. We thus consider the following detection problem

The p.d.f. of ( y, Z ) is in this case

It follows that max p 0 ( y, Z ) and max p 1 ( y, Z ) are proportional to [START_REF] Manolakis | Hyperspectral signal models and implications to material detection algorithms[END_REF] which holds for Student distributions. From there, everything follows and the GLRs for Student or Gaussian distributions are the same and are given by [START_REF] Kai-Tai | Generalized Multivariate Analysis[END_REF] .

CRediT authorship contribution statement


Patrice Baa-Puyoulet

Mathieu Labernardiere

Nicolas Parisot

Jean-Pierre Gauthier

Gérard Febvay

Federica Calevro

Yvan Rahbé

Hubert Charles

Jean-Christophe Simon

Stefano Colella

From SymbAphidBase to SymbAphidCyc: two companion databases to study and compare aphid symbionts from genomes to metabolic pathways

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

From SymbAphidBase to SymbAphidCyc: two companion databases to study and compare aphid symbionts from genomes to metabolic pathways

  RésuméL'évolution des techniques de séquençage donne des opportunités sans précédent pour l'étude des bactéries symbiotiques et comprendre leurs contributions à la physiologie de leur hôte grâce à la génomique comparative et l'analyse des réseaux métaboliques. Les pucerons sont un modèle établi, ils abritent un endosymbiote obligatoire primaire, Buchnera aphidicola, et plusieurs symbiotes secondaires facultatifs contribuant à la physiologie et à l'adaptation de l'insecte. Récemment, nous avons développé SymbAphidBase (http://symbaphidbase.cycadsys.org/) : une base de données ad hoc pour stocker et analyser les séquences génomiques des symbiotes de pucerons. Actuellement, SymbAphidBase comprend les génomes des 17 souches de B. aphidicola de 8 espèces de pucerons disponibles dans GenBank (voir le poster ECCB 2014 http://f1000.com/posters/browse/summary/1096898). Dans une deuxième phase, nous avons utilisé CycADS (http://www.cycadsys.org/), un système automatisé de gestion d'annotations que nous avons développé pour générer une annotation fonctionnelle enrichie orientée vers la reconstruction métabolique (pour faciliter son utilisation, le pipeline CycADS a récemment été intégré dans la plate-forme web Galaxy) : Les données génomiques ainsi que les annotations fonctionnelles obtenues en utilisant différentes méthodes (telles que KAAS, PRIAM, Blast2GO, InterProScan) ont été collectées pour créer SymbAphidCyc (http://symbaphidcyc.cycadsys.org/), la collection de bases de données métaboliques dédiée aux symbiotes du puceron. En conclusion, les 2 bases de données complémentaires, SymbAphidBase et SymbAphidCyc, facilitent le stockage et la comparaison des données génomiques ainsi que l'analyse du métabolisme pour une meilleure compréhension de la symbiose chez les pucerons.

	Patrice BAA-PUYOULET 1 , Mathieu LABERNARDIERE 1 , Nicolas PARISOT 1 , Jean-Pierre GAUTHIER 2 ,
	Gérard FEBVAY 1 , Federica CALEVRO 1 , Yvan RAHBE 1 , Hubert CHARLES 1 , Jean-Christophe SIMON 2
	and Stefano COLELLA 1
	1 UMR203 BIOLOGIE FONCTIONNELLE INSECTES ET INTERACTIONS (BF2I), INSA LYON, 20 ave Albert
	Einstein, bât Pasteur, 69621, Villeurbanne, Cedex, France
	2 UMR 1349 INSTITUTE OF GENETICS ENVIRONMENT AND PLANT PROTECTION (IGEPP), INRA, Domaine
	de la Motte, BP 35327, 35653, Le Rheu, Cedex, France
	Auteur à contacter : Patrice.Baa-Puyoulet@lyon.inra.fr

Summary

The next-generation sequencing methods evolution opened the way to unprecedented opportunities to study symbiotic bacteria through genomic comparisons and metabolic networks analyses to understand their contributions to their host biology. Aphids are an established model: they harbour an obligate primary endosymbiont, Buchnera aphidicola, and several facultative secondary symbionts contributing to the insect's physiology and adaptation. Recently we developed SymbAphidBase (http://symbaphidbase.cycadsys.org/): an ad hoc genome database to store and analyse aphid symbionts' genome sequences. At present SymbAphidBase includes the genomes of 17 strains of B. aphidicola from 8 different aphid species available in GenBank (see poster ECCB 2014 http://f1000.com/posters/browse/summary/1096898). In a second phase, we used CycADS (http://www.cycadsys.org/), an automated annotation management system that we developed during the pea aphid genome project, to generate an enriched functional annotation geared towards metabolic reconstruction. It is worth noting that the CycADS pipeline has been recently integrated into the Galaxy web-based platform to ease its use. The genomic data as well as the functional annotations obtained using different methods (such as KAAS, PRIAM, Blast2GO, InterProScan), were collected to easily build SymbAphidCyc (http://symbaphidcyc.cycadsys.org/), the database collection for the aphid symbionts. In conclusion, SymbAphidBase and SymbAphidCyc companion databases facilitate genome data storage and comparisons, as well as metabolism analysis for the better understanding of symbiosis physiology in aphids.


The Model : Discrete Time Spatial SEHDR Model

S r E r H r D r R r Initial Population T E T H T D T R p(S 0 ) p(E 0 ) p(H 0 ) λ r p(S → E |r ) I r Transitions SE t+1 i ∼ Binom(S t i , 1 -e -λ it ) lag( EH) ∼ Gamma(k lat , θ lat ) HD t+1 i ∼ Binom(H t i , ρ it ) lag( DR) ∼ Geom Tr (1 -p R , lag max )

Force of Infection

λ itr κ = β N 0 i -R itr i ∈N i a i tr m i iκ I i tr Aim : Approximate f (m, β, ρ, θ lat , p R |Y ) ∝ f (Y |m, β, ρ, θ lat , p R )f (m)f (β)f (ρ)f (θ lat )f (p R )
The Model : Dispersal Kernels (1)

Mixtures of Exponential Powered Kernels The Model : Dispersal Kernels (2)

f (d , c θ |h, w, k) = n l=1 w l e -(d/h l ) k l Z l {n = 1, k = 2} ⇔ Gaussian {n = 1, k = 1} ⇔ Exponential {n = 1, k < 1} ⇔ "Long-tailed" {n > 1, k = 1} ⇔
The key advantage of mixture exponential kernels is...

m AB (h, w) = x∈A y ∈B n l=1 w l e -||x-y ||/h l /Z l d xd y x∈A xd x = n l=1 w l m AB (h l , w l ).
..we only calculate m AB once ! ! Basis connectivity matrices defined using

h l = 1 2 E [d |h l , k = 1] = 3 2 × 1.08 l (1) l ∈ {1, 2, . . . , 100} (2)
Shape Constrained Weights

w l = CDF beta l n |α 1 , α 2 -CDF beta l -1 n |α 1 , α 2 =⇒ Beta-
T E T H T D T R p(S0) p(E0) p(H0) λr p(S → E |r ) Ir Transitions SE t+1 i ∼ Binom(S t i , 1 -e -λ it ) lag( EH) ∼ Gamma(k lat , θ lat ) HD t+1 i ∼ Binom(H t i , ρ it ) lag( DR) ∼ Geom Tr (1 -p R , lag max ) Force of Infection λ itr κ = β N 0 i -R itr i ∈N i a i tr m i iκ I i tr Aim : Approximate f (α, β, ρ, θ lat , p R |Y ) ∝ f (Y |α, β, ρ, θ lat , p R )f (α)f (β)f (ρ)f (θ lat )f (p R ) Approach : Markov chain Monte Carlo
Simulation Study : Outline

Aim : Test accuracy / precission of estimated parameters.

3 Kernel Scenarios

α 1 = (12727, 29264) ⇐= n = 1, {E [d l |h l ]} = 25 α 2 = (9.3, 18.1) ⇐= n = 2, {E [d l |h l ]} = {25, 100}, ω = 2 3 , 1 3
α 3 = (5.5, 8.4) ⇐= n = 3, {E [d l |h l ]} = {25, 100, 300}, ω = { 3 6 , 2
f (α, β, p H0 , Z |Y ) f (α, β, p H0 , Z , ρ, |Y ) f (α, β, p H0 , Z , θ lat |Y ) f (α, β, p H0 , Z , ρ,
1 1 1 1 1 1 1 1 2 2 1 1 1 1972 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1974 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1975 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1978 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1979 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1980 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1981 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1982 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1983 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1984 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1985 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1986 0 0 0 0 0 0 0 1 0 0 0 3 0 2 1 3 1987 0 1 1 1 2 2 2 2 2 3 4 1 3 2 6 3 1988 0 0 1 1 1 1 1 1 2 2 1 2 2 1 2 1 1989 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1990 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1991 0 0 0 0 0 0 1 1 1 2 2 1 2 3 2 3 1992 0 0 0 0 1994 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1995 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1996 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1997 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1998 2000 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2001 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2002 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 2003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

  Mixture of Exponentials Connectivity m ABκ = x∈A y ∈B f κ (||xy ||)d xd y x∈A xd x Numerical Integration CaliFlopp software. Bouvier et al (2009) Environmental Modelling & Software.

  θ lat |Y ) 10 epidemics / kernel x introduction scenario = 210 simulated epidemics Simulation Study : Markov Chain Monte Carlo 3 kernel scenarios 7 introduction scenarios ⇒ 210 simulated epidemics 4 estimation scenarios 10 Markov chains per estimation scenarios x simulation ⇒ 8400 Markov chains Gibbs sampler Continuous parameters | Classification (Adaptive Metropolis-Hastings) Classification | Continuous parameters Automated burn-in detection 25000 post burn-in iterations Thinning = 25 Convergence diagnostics of R package coda Simulation Study : Source Locations / Intro Scenario Simulation Study : Nine Example Simulations Simulation Study : True vs. Fitted Kernels Five-figure statistics for the 3 kernel scenarios (marginal over introduction scenario, estimation scenario 4) Includes Kullback-Leibler Distance. Simulation Study Results : Kullback-Leibler Distance Accuracy Mean Posterior KLD Precission Span of 95%CI of Posterior KLD Simulation Study : Summary Source connectivity =⇒ Statistical Power Force of Infection & Kernel Size inversely related Multi-modality least problematic when K small and kernel large Gibbs updates of Z quite awkward Precission & accuracy affected by kernel and source scenario Precission & accuracy NOT affected by estimation scenario PPV-M in Gard : Case Study K unidentifiable using Bayesian approach =⇒ fix K ∈ {10, 25} 30 × Gibbs samplers | K =⇒ 480 Markov chains Continuous parameters | Everything else (Adaptive Metropolis-Hastings) Classification | Everything else Missing Inspection Dates | Everything else Missing Plantation Dates | Everything else MCMC control Automated burn-in detection 100000 post burn-in iterations Thinning = 25 Convergence diagnostics of R package coda PPV-M in Gard : estimated timing of introductions given K -orchard level

  Weighted Mixture of Exponentials (BWME) Kernels.

	The Model : With BWME Kernels
		Initial
		Population
	Sr	Er	Hr	Dr	Rr

Regnaut & Goudet (2005) Detecting the number of clusters of individuals using the software structure : a simulation study. Molecular Ecology.

Acknowledgements

The Sharka Team Gael Thebaud, Samuel Souyberand, Joel Chadoeff, Sylvie Dalot, Sylain Grizard, Emmanuel Jaquot, Gerard Labonne Financial Support INRA's Santé, Plante et Environment department.

PPV-M data

Fédération Départementale de Défense Contre les Organismes Nuisibles of Gard Department.

Discrete time steps

The Model : Priors


Alessandro Carmona Martinez

Eric Trably

Nicolas Bernet

Nicolas Bernet Direct

Direct microbial electrosynthesis or hydrogen mediated microbial synthesis of acetate ?

Alessandro Carmona, Eric Trably and Nicolas Bernet INRA, UR0050, Laboratoire de Biotechnologie de l'Environnement, Avenue des Etangs, Narbonne, F-11100, France alessandro.carmona@supagro.inra.fr, eric.trably@supagro.inra.fr and nicolas.bernet@supagro.inra.fr Microbial electrosynthesis (MES) is considered as a promising technology for the production of value added products by bacteria. Most of the studies reporting acetate production by MES have used experimental conditions that favor the production of H 2 by water electrolysis: applied potentials (E app. ) more negative than -650 mV vs. SCE. Thus, efforts are required to determine whether MES proceeds via i) direct electron transfer (DET) through for example cytochromes in contact with the electrode ("true" MES at E app. ≥ -650 mV) or ii) a microbial synthesis process based on H 2 -mediated electron transfer (MET) via water electrolysis (E app. ≤ -650 mV).

Pure cultures of representative homoacetogens (HA) were grown at -900 mV during chronoamperometry (CA): Acetobacterium woodii, Sporomusa sphaeroides and Sporomusa silvacetica). Multiple tests were carried out at different E app. values (Fig. 1A). NaHCO 3 and the electrode were the only available sources of carbon and electrons, respectively. Scanning electron microscopy (SEM) was used to inspect the electrode surface (Fig. 1B). Between each E app. cyclic voltammetry (CV) was used to elucidate the possible ET carried out by each strain (Fig. 1C).

Product rate per electrode surface area (P ESA ) depended on E app . At -200 mV during CA no acetate was produced. When comparing P ESA at E app . values that favored the production of acetate via MET and values that favored DET, the effect of using H 2 as an electron carrier was clearly shown (P ESA ~30 versus 15 g m -2 d -1 for S. sphaeroides). SEM allowed us to observe cells with different shapes attached to the electrode (Fig. 1B). The CV analysis showed the appearance of a reduction peak centered at around -500 mV which might indicate the potential at which electron transfer occurred. By better understanding of the microbial electron transfer mechanisms, new strategies to improve the production of molecules by MES can now be formulated.

Fig. 1 .

 1 Fig. 1. A) P ESA vs. E app . during CA calculated according to Patil et al., 2015 (Biotech. Adv.). B) SEM picture of an electrode fiber cover by S. silvacetica cells. C) CVs at different E app . during CA.

Acknowledgements: A.C. was financially supported by the French National Research Agency (Project: ANR-10-BTBR-02).


Sylvie Muratorio

Matthieu Lingrand

Elodie Magnanou

Joseph Guarigue

S Oddou-Muratorio

J Garrigue #

J A Magdalou #

Impacts of climate-related dieback on regeneration in a drought-prone European beech population

come

Interactions between biotic and abiotic environment and tree functional traits determine carbohydrate and water fluxes at tree-level, which in turn affect demographic rates and fitness at tree-and population-level. The total number of reproductive trees (Nobs) and the amongtrees variations of seed production determine the effective population size (Ne).

Climate-induced tree dieback may result in a reduced quantity and/or quality of seed output at population level, even without or before tree mortality. We investigate the relationship between individual tree dieback and fecundity in the forest tree Fagus sylvatica (the European beech) in

Southern France (La Massane, Pyrénées Orientales). Fagus sylvatica is known to be sensitive to summer drought, but shows so far limited mortality; for instance at the study site the mortality rate is 1. 75%/year (between 2003 and 2005) while the rate of tree dieback (measured through foliar mortality) is 10.5 %.

Methods

1-Long-term survey of Fagus sylvatica dieback in La Massane integral reserve

Distribution of female and male fecundities on each plot

Results: relationship between dieback and fecundity  Significant increasing female and male fecundity with increasing DBH (Plot1: effect=0.016; Plot 2 : effect=0.027; pval<0.001).  Significant decreasing female (but not male) fecundity with increasing cumulated leaf mortality (DEFCUM) when correcting for DBH effects (Plot 2 only; effect=-0.067; pval<0.001).  However, intermediate leaf mortality (LeafM=5) associated with variable (and high) female fecundity.  No significant relationship between O. mucida (OUDEM) occurrence and male or female fecundity, but few records of O. mucida presence.

 Significant correlation between male and female fecundities  Trend for a correlation between foliar mortality (DEFCUM) and O. mucida (OUDEM) occurrence.

Conclusions  Individual tree dieback (through foliar mortality) is associated to lower relative female fecundity, consistently with costly nut-seeds in Fagus sylvatica .



General view of La Massane beech forest

                         N j N k k j i jk p j i p j j i ij s i s i F , M | G T Φ m s + BAF , M | G T m + M , M | G sT Ψ m + BAF | G T m = ) P(G 1 1      N l l il s j ij s ij F ) (d p

  trees genotypes & positions THE DATA THE SPATIALLY EXPLICIT MATING MODEL (SEMM-Oddou-Muratorio & Klein 2008, Klein et al. 2008) Oudemansiella mucida (OUDEM) frequently occurs on beech trunk in situation of water stress Water-stress induced leaf mortality (LeafM) can reduce functioning costs, but also lead to loss of vitality and ultimately tree mortality. Female fecundity F ♀ j leaf mortality (LeafM) and O. mucida presence (OUDEM) -Selfed (s) Local mother (1-m s ) Each mother j ( ij ) Immigrant seed flow (m s ) -Pollinated by a local male k ( jk ) Pollen and seed dispersal kernel were modeled with exponential power distribution with parameters ( P, b P ) and ( S, b S ).  Pollen is dispersed on average at  P =62 m, with non negligible events of long distance dispersal (b P =0.46)  Seeds are dispersed on average at  S =12 m, with fewer long distance dispersal events than for pollen (b P =0.83)  13% of the seedlings have their mother-tree out of the plot, and 67% of the offspring with local mother have their fathertree out of the plot.

  Non negligible female fecundity can however be achieved for intermediate leaf mortality, suggesting adaptive plastic response aiming at reducing functioning costs.  Perspective : investigating whether there is a change in allocation to reproduction versus growth for trees showing evidence of dieback.

  pollen or seed dispersal kernel for all adult trees. -Each tree can contribute as a male or female to seedlings rain. Tot 337 1 5 2 3 9 17 36 8 14 432 Number of trees per category of leaf mortality (LeafM) and O. mucida (Klein, E.K., Desassis, N., & Oddou-Muratorio, S. (2008) Pollen flow in the wildservice tree, Sorbus torminalis (L.) Crantz. IV. Whole inter-individual variance of male fecundity estimated jointly with the dispersal kernel. Molecular Ecology, 17, 3323-3336. -Oddou-Muratorio, S. & Klein, E.K. (2008) Comparing direct vs. indirect estimates of gene flow within a population of a scattered tree species. Molecular Ecology, 17, 2743-2754.


domain: math info

text:
Dr Beatrice Laroche
email: beatrice.laroche@jouy.inra.fr

Dr Sophie Schbath
email: sophie.schbath@jouy.inra.fr

Dr Julie Aubert
email: julie.aubert@agroparistech.fr

Pr Thomas Curtis
email: tom.curtis@ncl.ac.uk

Dr Mahendra Mariadassou
email: mahendra.mariadassou@jouy.inra.fr

Pr William Sloan

Modeling Microbial ecosystem using Meta-Omic data Organisers

Session Description:

Technologies make it now possible to obtain a large number of data concerning microbial ecosystems, such as a set of genes (genomics), RNA (transcriptomics), proteins (proteomics), metabolites (metabolomics), etc. "Meta"-omic approaches are used to understand microbial communities as a whole. These communities can be composed of a small number of microorganisms or a huge crowd, depending on the type of ecosystem considered, but in all cases the complexity of these data is a challenge to achieving their integration, analysis and modelling. This session therefore focuses on modelling and model-based applications in microbial ecology using various modelling techniques ranging from statistical to mechanistic models, with meta-omic data to support the modelling and model analysis research presented. A secondary goal of the session is to provide a broad cross section of the area, rather than focusing on specific application areas, leading to fruitful comparison and discussions.

The proposed presentations illustrate the use of meta-genomic and metatranscriptomic data for the modelling of  Environmental Microbial Ecosystem and Microbial Bioprocesses  Animal or human microbiome  Food Microbial Ecosystem.

Schedule:

6x (25 min + 5 min for questions and discussion) = 3h min

List of presentations:

Dominant Species are Specialists. (Dr. Mahendra Mariadassou) A survey of very heterogeneous microbiotes reveals that abundant species are more likely to be specialist than expected under a null model of neutral microbiote assembly. We first present the null model of microbiote assembly and then discuss some mechanisms that could lead to the specificity -abundance relationship.

Predicting microbial community assembly. (Pr. William Sloan)

The functioning of complex and diverse communities of microorganisms is critical in agriculture, engineering and medicine. Yet the coming together, or assembly, of these communities is poorly understood. When the microbial communities fail to function as we hoped they might it is often attributed to the 'wrong' community microbes having assembled. But we currently have no means of ensuring that the 'right' community assembles. Thus many economically important bio technologies are engineered by a process of trial and error. The ability to predict and hence manage the process of community assembly would thus have wide set applications. We use high resolution time series pyrosequenced amplicons from multiple bioreactors to show that a simple birth-death-immigration process can capture much of the variance in community assembly and that by controlling immigration the probability that key species form part of the community can potentially be controlled.

Energy and Rates of Evolution in Bacteria. (Pr. Thomas Curtis) The talk will present data to suggest that the rates of evolution are shaped by energetic constraints. It will show how these concepts are very relevant to contemporary challenges in microbial ecology in general and Engineering Biology in particular, with a special nod to the challenges of the calibration of multi-scale models of microbial systems.

Metagenomics data analysis using a latent block model: application to plant-microbial communities interactions in the rhizosphere. (Dr Julie Aubert and Sophie Schbath) Metagenomics is the study of microbial communities directly from environmental samples. We will propose a model and the tools of inference associated for a simultaneous clustering: the one on the populations of bacteria constituting the metagenome, and the other one on the samples. We will apply this model on the plant-microbial communities interactions in the rhizosphere.

Metagenomic data analysis and integration in a functional population model of fiber degradation by the human intestinal microbiota. (Dr Beatrice Laroche)

The human intestinal microbiota is a complex microbial ecosystem that plays a crucial role in several aspects of human health. It is particularly involved in the metabolism of residual fibers, through anaerobic digestion, thus providing significant energy (Short Chain Fatty Acids, simple sugars) and vitamins to the host. Whole Genome Sequencing (WGS) data from metagenomic analyses give an insight of the content in terms of genes of an entire microbial community, even if the organisms that compose it cannot be cultivated. In addition to the potential of conventional molecular inventory techniques (such as targeting ADNr16s), which allows an analysis of diversity, WGS approaches provide an access to the functions. We show how these data can be used to analyse the structure of carbohydrates degradation-related functions. We show how the information obtained can be integrated in a high level mechanistic in silico model of the microbiota carbohydrate trophic chain.

Study of the stability and functional redundancy of a food microbial ecosystem using physico-chemical and transcriptomics data. (Dr Julie Aubert)

The resilience is the capacity of an ecosystem to return to a steady or cyclic state following a perturbation. To study this phenomenon, we have developed an RNA-seq based approach on a model cheese reduced ecosystem composed of nine species. We propose a method to integrate, analyse and link diverse data in order to understand the microbial ecosystem resilience.


Marie Josephe Amiot-Carlin

systems for good health and nutrition

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.

• Lifestyle and diet (overmal-and under-nutrition, obesity, cancers…) • Food safety (pesticides, mycotoxins…)

• Food security, inequalities

• Respect of culture & traditions
• Efficient for all actors (producers, consumers…)

• Innovation

• Employement

Evidence linking plant food intake and human health

Diversity of plant products

A large diversity of nutrients

  Agoecology & sustainable food systems for good health & nutrition M.J. Amiot-Carlin SESSION 4 -SUSTAINABILITY OF FRUIT & VEGETABLE PRODUCTION Nutrition, Obesity and Thrombotic Risk (NORT) UMR INRA 1260, INSERM 1062, Aix-Marseille Université Campus Santé La Timone, Marseille, France Healthy diet, healthy environment within a fruitful economy: the role of fruit and vegetables Milan, June 3rd-5th 2015

  Nutrients and diversity of plant sourcesLoss of traditional food culture in the current dominant "agro-industrial" food system Dietary patterns associated with nutrient deficiencies and excess energy consumption Shift to an increased accessibility of inexpensive agricultural supplies and an erosion of agro-biodiversity.Decrease of micronutrient content under intensified croppingThomas, Nutr Health 2003 Quantity and diversity are effective to improve the global nutritional quality of diet Fruit and vegetable recommendation > 5 a day (400 g) Different colours Australian Dietary Guidelines (National Health And Medical Research Council, 2013) Enjoy a wide variety of nutritious foods from these five groups every day Plenty of vegetables, including different types and colours, and legumes/beans Data French survey ( INCA2) -n=1918 adults(> 18 y) Food consumption recorded during a week Constitution of a food database (nutrients, toxicants, prices, greenhouse gas emissions …) Nutritional scores MAR Indicator of good nutritional quality Mean % of daily recommended intakes for 20 key nutrients (namely proteins, fibres, retinol eq., thiamine, riboflavin, niacin, vitamin B6, folates, vitamin B12, ascorbic acid, vitamin E, vitamin D, calcium, potassium, iron, magnesium, zinc, copper, iodine, and selenium)MER Indicator of bad nutritional quality.Mean % of maximum of daily recommended values for 3 harmful nutrients (namely saturated fatty acids, sodium, and free sugars)Quantity and diversity have been on nutritional quality of diet quantity 400 g or 550 g F&V Model 2: quantity and diversity 400 g or 550 g including 50% fruit et 50% vegetables Model 3: quantity and high diversity 400 g or 550 g F&V including:1 serve of green vegetable, 2 fruits, 2 raw FV, 80 mL juice (maximum), 10 g nuts and 20 g dried fruits Quantity and diversity of F&V have been tested on nutritional quality of diet: 3 models were buildEffect of quantity and diversity of F&V on the "good" nutritional quality of diet high diversityEffect of quantity and diversity on the "bad" nutritional quality of diet Plant derived foods and drinks are rich in numerous bioactive substances Phytochemicals or non-essential micronutrients : polyphenols sulphur compounds non-provitaminic A carotenoids (lycopene and lutein)All these non-nutritive substances have beneficial effects phytochemicals vary greatly within a species and according to the agro-environmental conditionsThe biosynthesis of phenolics is increased under environmental stress providing biotic protection of crops Shelf life of plant-derived products is enhanced "Natural" pest control Additional nutritional qualities and human health benefits. Wild plants Organic-crops Conclusions Diversity of agricultural resources is essential for healthy dietary patterns Combination of different plants is a strategy to fit with both nutritional requirements and eco-agriculture Agro-ecology and sustainable dietary patterns for good nutrition and health Journées Francophones de Nutrition 2015 For more informations www.lesjfn.fr + Symposium on "Nutritional Transition in the Mediterranean Basin" on the 12th December

Scientific publications/year New agricultural model reconciling economic, social and environnemental challenges Key words biodiversity, agricultural, landscape, forest, plant, conservation, ecosystem, species


P Duchamp-Viret

Marie-Christine Lacroix

Nicola Kuszewski

Christine Baly

Part Two -Perception of Flavor Compounds

published or not. The documents may come   L'archive ouverte pluridisciplinaire


Claude Genot

come

Involvement of the interfacial layer and protein partition on oxidation of oil-in-water emulsions

-En cas de mauvaise foi de votre part : par la nullité du contrat -Si votre mauvaise foi n'est pas établie : par une réduction de l'indemnité en proportion de la prime payée par rapport à la prime qui aurait été due si le risque avait été complétement et exactement déclaré.

Date:

Signature de l'assuré


S De Smet

D Corpet

M Meurillon

G Caderni

S Rohrmann

S Sasazuki

P Weijenberg

M Cantwell

P Vineis

F A Beland

E Cho

D M Klurfeld

L Le Marchand

R Sinha

M Stern

Coffee and some other hot beverages IARC Monograph Working Group Members B W Stewart (Australia)-meeting chair

Observers

D D Alexander, for the EpidStat Institute, USA; B L Booren, for the North American Meat Institute, USA; J Carretier, for the Léon Bérard Centre, France;

Carcinogenicity of consumption of red and processed meat

In October, 2015, 22 scientists from ten countries met at the International Agency for Research on Cancer (IARC) in Lyon, France, to evaluate the carcinogenicity of the consumption of red meat and processed meat. These assessments will be published in volume 114 of the IARC Monographs. 1 Red meat refers to unprocessed mammalian muscle meat-for example, beef, veal, pork, lamb, mutton, horse, or goat meat-including minced or frozen meat; it is usually consumed cooked. Processed meat refers to meat that has been transformed through salting, curing, fermentation, smoking, or other processes to enhance fl avour or improve preservation. Most processed meats contain pork or beef, but might also contain other red meats, poultry, off al (eg, liver), or meat byproducts such as blood.

Red meat contains high biologicalvalue proteins and important micronutrients such as B vitamins, iron (both free iron and haem iron), and zinc. The fat content of red meat varies depending on animal species, age, sex, breed, and feed, and the cut of the meat. Meat processing, such as curing and smoking, can result in formation of carcinogenic chemicals, including N-nitroso-compounds (NOC) and polycyclic aromatic hydrocarbons (PAH). Cooking improves the digestibility and palatability of meat, but can also produce known or suspected carcinogens, including heterocyclic aromatic amines (HAA) and PAH. High-temperature cooking by panfrying, grilling, or barbecuing generally produces the highest amounts of these chemicals. 2,3 Depending on the country, the proportion of the population that consumes red meat varies worldwide from less than 5% to up to 100%, and from less than 2% to 65% for processed meat. The mean intake of red meat by those who consume it is about 50-100 g per person per day, with high consumption equalling more than 200 g per person per day. 4 Less information is available on the consumption of processed meat.

The Working Group assessed more than 800 epidemiological studies that investigated the association of cancer with consumption of red meat or processed meat in many countries, from several continents, with diverse ethnicities and diets. For the evaluation, the greatest weight was given to prospective cohort studies done in the general population. High quality population-based case-control studies provided additional evidence. For both designs, the studies judged to be most informative were those that considered red meat and processed meat separately, had quantitative dietary data obtained from validated questionnaires, a large sample size, and controlled for the major potential confounders for the cancer sites concerned.

The largest body of epidemiological data concerned colorectal cancer. Data on the association of red meat consumption with colorectal cancer were available from 14 cohort studies. Positive associations were seen with high versus low consumption of red meat in half of those studies, including a cohort from ten European countries spanning a wide range of meat consumption and other large cohorts in Sweden and Australia. [5][START_REF] Larsson | Red meat consumption and risk of cancers of the proximal colon, distal colon and rectum: the Swedish Mammography Cohort[END_REF][START_REF] English | Red meat, chicken, and fi sh consumption and risk of colorectal cancer[END_REF] Of the 15 informative case-control studies considered, seven reported positive associations of colorectal cancer with high versus low consumption of red meat. Positive associations of colorectal cancer with consumption of processed meat were reported in 12 of the 18 cohort studies that provided relevant data, including studies in Europe, Japan, and the USA. 5,8-11 Supporting evidence came from six of nine informative case-control studies. A meta-analysis of colorectal cancer in ten cohort studies reported a statistically signifi cant dose-response relationship, with a 17% increased risk (95% CI 1•05-1•31) per 100 g per day of red meat and an 18% increase (95% CI 1•10-1•28) per 50 g per day of processed meat. [START_REF] Chan | Red and processed meat and colorectal cancer incidence: meta-analysis of prospective studies[END_REF] Data were also available for more than 15 other types of cancer. Positive associations were seen in cohort studies and population-based casecontrol studies between consumption of red meat and cancers of the pancreas and the prostate (mainly advanced prostate cancer), and between consumption of processed meat and cancer of the stomach.

On the basis of the large amount of data and the consistent associations of colorectal cancer with consumption of processed meat across studies in different populations, which make chance, bias, and confounding unlikely as explanations, the majority of the Working Group concluded that there is sufficient evidence in human beings for the carcinogenicity of the consumption of processed meat. Chance, bias, and confounding could not be ruled out with the same degree of confi dence for the data on red meat consumption, since no clear association was seen in several of the high quality studies and residual confounding from other diet and lifestyle risk is difficult to exclude. The Working Group concluded that there is limited evidence in human beings for the carcinogenicity of the consumption of red meat.

There is inadequate evidence in experimental animals for the carcinogenicity of consumption of red meat and of processed meat. In rats treated with colon cancer initiators and promoted with low calcium diets containing either red meat or processed meat, an increase in the occurrence of colonic preneoplastic lesions was reported in three and four studies, respectively. [START_REF] Pierre | Beef meat and blood sausage promote the formation of azoxymethaneinduced mucin-depleted foci and aberrant crypt foci in rat colons[END_REF][START_REF] Pierre | Beef meat promotion of dimethylhydrazine-induced colorectal carcinogenesis biomarkers is suppressed by dietary calcium[END_REF][START_REF] Santarelli | Meat processing and colon carcinogenesis: cooked, nitrite-treated, and oxidized high-heme cured meat promotes mucin-depleted foci in rats[END_REF] The mechanistic evidence for carcinogenicity was assessed as strong for red meat and moderate for processed meat. Mechanistic evidence is mainly available for the digestive tract. A meta-analysis published in 2013 reported a modest but statistically significant association between consumption of red or processed meat and adenomas (preneoplastic lesions) of the colorectum that was consistent across studies. [START_REF] Aune | Red and processed meat intake and risk of colorectal adenomas: a systematic review and meta-analysis of epidemiological studies[END_REF] For genotoxicity and oxidative stress, evidence was moderate for the consumption of red or processed meat. In human beings, observational data showed slight but statistically significant associations with APC gene mutation or promoter methylation that were identifi ed in 75 (43%) and 41 (23%) of 185 archival colorectal cancer samples, respectively. [START_REF] Gay | Dietary, lifestyle and clinicopathological factors associated with APC mutations and promoter methylation in colorectal cancers from the EPIC-Norfolk study[END_REF] Consuming well done cooked red meat increases the bacterial mutagenicity of human urine. In three intervention studies in human beings, changes in oxidative stress markers (in urine, faeces, or blood) were associated with consumption of red meat or processed meat. [START_REF] Pierre | Calcium and alpha-tocopherol suppress cured-meat promotion of chemically induced colon carcinogenesis in rats and reduce associated biomarkers in human volunteers[END_REF] Red and processed meat intake increased lipid oxidation products in rodent faeces. [START_REF] Pierre | Beef meat and blood sausage promote the formation of azoxymethaneinduced mucin-depleted foci and aberrant crypt foci in rat colons[END_REF] Substantial supporting mechanistic evidence was available for multiple meat components (NOC, haem iron, and HAA). Consumption of red meat and processed meat by man induces NOC formation in the digestive tract. High red meat consumption (300 or 420 g/day) increased levels of DNA adducts putatively derived from NOC in exfoliated colonocytes or rectal biopsies in two intervention studies. [START_REF] Leu | Butyrylated starch intake can prevent red meat-induced O6-methyl-2-deoxyguanosine adducts in human rectal tissue: a randomised clinical trial[END_REF][START_REF] Lewin | Red meat enhances the colonic formation of the DNA adduct O6-carboxymethyl guanine: implications for colorectal cancer risk[END_REF] Few human data, especially from intervention studies, were available for processed meat. Haem iron mediates formation of NOC, and of lipid oxidation products in the digestive tract of human beings and rodents. Haem iron effects can be experimentally suppressed by calcium, supporting its contribution to carcinogenic mechanisms. Meat heated at a high temperature contains HAA. HAA are genotoxic, and the extent of conversion of HAA to genotoxic metabolites is greater in man than in rodents. Meat smoked or Overall, the Working Group classifi ed consumption of processed meat as "carcinogenic to humans" (Group 1) on the basis of sufficient evidence for colorectal cancer. Additionally, a positive association with the consumption of processed meat was found for stomach cancer.

The Working Group classified consumption of red meat as "probably carcinogenic to humans" (Group 2A). In making this evaluation, the Working Group took into consideration all the relevant data, including the substantial epidemiological data showing a positive association between consumption of red meat and colorectal cancer and the strong mechanistic evidence. Consumption of red meat was also positively associated with pancreatic and with prostate cancer.

We declare no competing interests.

  Food and Agriculture Organization of the United Nations Statistics Division. Food balance. 2015. http://faostat3.fao.org/browse/ FB/*/E (accessed July 9, 2015).

	Véronique Bouvard, Dana Loomis,
	Kathryn Z Guyton, Yann Grosse,
	Fatiha El Ghissassi,
	Lamia Benbrahim-Tallaa, Neela Guha,
	Heidi Mattock, Kurt Straif, on behalf of
	the International Agency for Research
	on Cancer Monograph Working Group
	International Agency for Research on Cancer,
	Lyon, France
	1	International Agency for Research on Cancer.
		Volume 114: Consumption of red meat and
		processed meat. IARC Working Group. Lyon;
		6-13 September, 2015.
		IARC Monogr Eval Carcinog Risks Hum (in press).
	2	Alaejos MS, Afonso AM. Factors that aff ect the
		content of heterocyclic aromatic amines in
		foods. Comp Rev Food Sci Food Safe 2011;
		10: 52-108.
	3	Alomirah H, Al-Zenki S, Al-Hooti S, et al.
		Concentrations and dietary exposure to
		polycyclic aromatic hydrocarbons (PAHs) from
		grilled and smoked foods. Food Control 2011;
		22: 2028-35.
	4

5 Norat T, Bingham S, Ferrari P, et al. Meat, fi sh, and colorectal cancer risk: the European Prospective Investigation into cancer and nutrition. J Natl Cancer Inst 2005; 97: 906-16.

www.thelancet.com/oncology Vol 16 December 2015

www.thelancet.com/oncology Vol 16 December 2015

a research project funded by the World Cancer Research Fund, a registered charity. All other working group members declare no competing interests. Invited Specialists None Representatives A Christodoulidou, for the European Food Safety Authority (EFSA), Italy; I Margaritis, for the French Agency for Food, Environment and Occupational Health and Safety (ANSES), France; Y Totsuka, for the National Cancer Center Research Institute,


The challenge of health evaluation in pigs and poultry: what is expected from biomarkers?

Nathalie Le Floc'h, INRA UMR1348 PEGASE (Physiology, Environment, and Genetics for the Animal and Livestock Systems) 35590 Saint Gilles, France https://www6.rennes.inra.fr/pegase_eng/ Nathalie.lefloch@rennes.inra.fr Generally speaking, the evaluation of health in pigs and poultry refers to the concept of disease and disease detection. In veterinary medicine, disease detection is not really challenging when it concerns sick animals expressing clinical signs of disease, particularly when the clinical signs can be undoubtedly attributed to a specific infectious disease attested by the presence of the pathogen or any trace of its presence (antibody for instance).

Assessment of preclinical or subclinical diseases inducing no clear clinical sign and multifactorial diseases, for which management is clearly involved (so called "production diseases"), is much more challenging. More recently, there is a growing interest for developing tools and indicators to assess the global health status of an animal. In this context, the health status is defined as a complete state of physiological, physical and psychological well-being associated to efficient and high level of production. This new approach differs from the first one in that the disease and its prevention are not the main goal anymore.

Clearly, the aim is to develop and promote high standard practices, management strategies and production system that preserve both the health status and the animal productivity. For all these situations, which are the most frequently encountered in pig and poultry farms, the use of biomarkers is particularly relevant to assess the health status of an animal or a herd.

In human, health assessment is defined as "an evaluation of the health status of an individual by performing a physical examination after obtaining a health history. Various laboratory tests may also be ordered to confirm a clinical impression or to screen for dysfunction." This definition partly applies to farm animals except that the health history and the individual responses can be very difficult to assess or at least submitted to the interpretation of the farmer. To get over this difficulty, many efforts have been put on the research and validation of biomarkers usable on farm animals. A biomarker is a characteristic of the individual and its biology that can be objectively measurable and that is evaluated as an indicator of normal biological or pathogenic processes. More generally a biomarker is anything that can be used as an indicator of a disease/healthy state or some other physiological state of an individual.

What is expected from biomarkers is to be sensitive and predictive of a "disease" in order, for instance, to adapt the treatment (medication) when necessary or to correct the management.

Otherwise a biomarker has to discriminate between health and disease status.

Different categories of biomarkers exist and are generally related to the immune, metabolic and stress (psychological or physiological) statuses. These categories of biomarkers are usually nonspecific of a disease and have the advantage to really focus on the animal response to different kind of "challenges", including animal response to the environment and management. Other biomarkers have to be more specific, as for instance biomarkers required for early detection of leg disorders (osteochondrosis). A biomarker can be detected in the different fluids of the body (blood, saliva, urine), feces or tissues. Serum acute phase proteins are probably the most common biomarkers used in both pigs and poultry. Since a decade, biomarkers related to oxidative stress are generating the interest of research, veterinarians and even feed companies. Indeed, these molecules are connected to the animal metabolic status and thus are probably involved in many biological processes associated to productive functions (growth, reproduction, feed efficiency). There is a real need to develop biomarkers easy to measure in routine, not too expensive, quantitative and predictive. The role of research is to determine whether and why a molecule can become relevant as biomarker and to clearly point out the limits of these biomarkers.


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 Important to quantify Staphylococcus at species level to determine if they are really useful ripening bacteria  Undesirable Staphylococcus in milk, not dominant on teat surface juice Staphylococcus diversity in « teat surface juice » and milk

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Cows with somatic cell count >400 000 during lactation period (2 year experiments)  Number of cows with CSS>400 000 similar in the three groups : maximum 4/group , less in group without treatment  Trends to lower number of mastitis in group without treatment

Sub-clinical mastitis 6 4 1

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Evolution of teat's microbial count during one lactation period Comparison of « teat surface juice » microbial count between the 3 treatment's group  Higher G+C+ bacteria ( dominant population ) count on teat without treatment than with iodinated product or glycerol treatment ( not due to increase during treatment )  Other microbial groups at lower level than G+C+ bacteria and similar whatever the treatment

D<1Log

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Evolution of milk microbial count during one lactation period  More variation according to the date of sampling than to treatment :  higher count of +C+ bacteria at sampling1  Variation not associated with that of teat count

Month after treatment Month after treatment

Month after treatment

Iodine treatment

Glycerol treatment NO treatment

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.014 You created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com)

Iodinated

.017 Comparison of « teat surface juice » and milk Gram+ catalase+ after 8 months of treatment (%isolats)

 Bacillus, Jeotgalicoccus and other genera found on teat with glycerol tretment not found in the corresponding milk  Microbacterium, Rhotia, Kocuria dominant in milk not found in the dominant population on teat

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.024 More Gram negative genera diversity in milk than in teat surface juice Some genera found in milk may have other origin than teats For teat surface juice, there was no difference according to treatment * Found from literature data

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.025 You created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com)

.026 Frequency of lactic acid bacteria (%isolats)

 On teat whatever the treatment dominance of Aerococcus and Enterococcus  In milk: dominance of Lactobacillus, Lactococcus, Enterococcus

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Questions for further studies  Is it important to act on the microbiota of the teat skin to increase microbial diversity in milk or cheese? o Teat reservoir of Gram + catalase + bacteria (including ripening bacteria) but need to better know how micro-organisms transfer to milk or cheese environment =Need to track at strain level o but some bacterial species dominant on teat not dominant in milk. Will they then express cheese and become dominant?

 Is it the best strategy to give an advantage to certain species at milk production in regard with the reduction of diversity in the core and rind of cheese?

 Is Claude Bernard right "The microbe is nothing, the "terrain " (surrounding) is everything" o Are sensorial cheese qualities mainly govern by cheese making and ripening process ?

*

  two lactation yearsYou created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com) Sampling once a month throughout the whole lactationFor each lot, for milk and « teat surface juice » : mix at equal volume the individual samplesYou created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com) Selectivity of this medium studied in Floracq project

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  Approach of microbial diversity by identification of isolat on different mediaOne lactation periodYou created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com).016Diversity in Gram + catalase + bacteria in » teat surface juice » after 6 or 8 months of post dipping treatment Dominant Gram + Catalase+ genera were not the same at month 6 and 8 of the experiment (one lactation)

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  in « teat surface juice » after 8 months of treatment (%isolats)  Whatever the treatement Staphylococcus, Bacillus, Arthrobacter were the dominant Gram + catalase+ genera  Higher diversity in « teat surface juice » with glycerol treatment  Jeotgaliococcus one of the dominant genera with Iodinated products You created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com) More stability in the dominant population in milk than on « teat juice »  Whatever the treatment Staphylococcus, Microbacterium, Kocuria (at month 8), Brachybacterium, Corynebacterium were in the dominant population in milk You created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com) Whatever the treatment Staphylococcus, Microbacteirum, Kocuria , Rothia were the dominant Gram + Catalase+ genera  Trend to the dominance of Staphylococcus without treatment and less Kocuria  Nearly same number of genus for the 3 treatments Gram+ catalase+ bacteria milk after 8 months of treatment (%isolats)You created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com)

  Whatever the treatment, dominance of Chryseobacterium Occurrence of Gram negative species varied more according to sampling period than to treatmentMonth 6 Month 8

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  Teat sampling with wipe : o Is it the same effciency to recover micro-organisms than the milking machine? Experimental design in experimental farm with limited number of cow o Need to check the results at a large scale but experiment difficult to settle  Great diversity of Gram positive catalase positive bacteria in teat juice and change according to the sampling date o Difficult to compare the G+C+ bacteria profil according to treatment  Stability of G+C+ genera profil in milk but variability in Gram negatif bacteria  Microbial profil dependant on microbial analysis performed o Need to combine counting on media and identification of isolates o Analysis of teat and milk microbiota by highthroughput sequencing is in progress You created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com) .028 Preliminary inputs In the herd studied, no dramatic effect of post dipping treatment on SSC, mastitis occurrence, count in different microbial group on teat and especially in milk, no awful bacterial balance without treatment No spectacular positive effect of post dipping treatment to increase level of bacteria having a technological interest ( lactic acid bacteria, ripening bacteria)and to modify significantly the bacterial balance in milk o Glycerol treatment may be interesting to have more diversity in G+C+ bacteria o No post dipping treatment may favor Staphylococcus Staphylococcus quantification at species level (desirable and undesirable ones) should be better considered

/mL) Month after treatment Gram+ catalase+ bacteria

		7,00	7,00
		6,50	6,50
	Log (	1 Log (ufc4,00 2 3 4 5 6 7 8 9 4,50 5,00 5,50 6,00	3,00 3,50 4,00 4,50 5,00 5,50 6,00	1	2	3	4	5	6	7	8	9

ufc/mL) Month after treatment Bacteria on PCA medium

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Log (ufc/mL) Month after treatment Gram negative bateria

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Log (ufc/mL) Month after treatment Lactic acid bacteria

		Iodine treatment	Glycerol treatment	NO treatment
		Iodinated Glycerol Nothing	S
	(cfu/ml juice)
	Microflora on PCA	5.89	5.96	6.27 ***
	Gram+ catalase+ bacteria	5.34	5.49	5.84	**
	Gram negative bacteria	3.44	3.27	3.32	ns
	Lactic acid bacteria	3.70	3.91	3.83	ns
	Enterococcus	1.30	1.45	1.34	ns
	Lactobacillus	1.97	1.96	1.95	ns
	Moulds	1.96	2.08	2.09	ns
	Yeasts	1.91	1.74	2.03	ns
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 Greater variation in G+C+ bacteria level and bacteria on PCA medium with iodine treatment  Variation in level of Gram negative bacteria and LAB according to sampling month

dominant microbial group : same proportion of G+C+ bacteria, LAB and Gram-  For each microbial group, the level was similar for the 3 treatments

	Comparison of milk microbial count
	between the 3 treatment's group
			Glycerol	Nothing	S
	Microflora flora	4.75	4.84	4.84	ns
	Gram positive + catalase+ bacteria	3.47	3.53	3.53	ns
	Gram negative bacteria	3.22	3.32	3.18	ns
	Lactic acid bacteria	3.45	3.55	3.52	ns
	Enterococcus	0.71	1.02	0.75	ns
	Lactobacillus	2.95	3.05	2.96	ns
	Moulds	2.83	2.89	2.80	ns
	Yeasts	2.88	2.97	2.92	ns

 No

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Dominant Gram +catalase +bacteria on « teat surface juice » and milk whatever the treatment and sampling date

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Are genera detected on teat present in milk? ? ?

* According to literature data ( cf review Montel et al, 2014) You created this PDF from an application that is not licensed to print to novaPDF printer (http://www.novapdf.com)

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S. vitulinus

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Thank you

Happy cow!!!

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