SEARCHES FOR NEW PHYSICS IN PHOTON AND JET FINAL STATES

Recent results from searches of physics beyond the standard model in pp collisions are reported, in particular, reactions involving high transverse momentum photons or jets in their final state. Data analyzed by the CDF and D0 experiments at the Run II of the Tevatron correspond to integrated luminosities between 1 and 2f b -1 depending of the analyses.

Introduction

At an energy frontier collider, the usual way to search for indices of physics beyond the standard model (SM) is to look for the collisions with the highest momentum-transfer particles. Typically, one chooses a particular model, and the event selection is optimized to enhance its contribution against the SM expectation. The absence of any deviation in data provides a limit on the production cross-section times the branching ratio for the channel under study, which is then translated into exclusion limits in the parameter space of this model. However, by nature, a new phenomena is unknown, and it exists a lot of models at disposal. This is the reason which motivates the "signature-based" search strategy which casts a wider look for deviations to the SM.

Both strategies will be reported here for final states with photons and jets.

Randall-Sundrum (RS) graviton

Many models with extra spatial dimensions have been proposed to solve the hierarchy problem.

In the RS model 1 , the SM brane and the Planck brane are separated by an extra dimension with a warped geometry. Only the graviton is allowed to propagate in this extra dimension. It appears as Kaluza-Klein (KK) towers in the SM brane. This model has only 2 parameters: M 1 , the mass of the lowest KK excited mode, and k/M P l , a dimensionless coupling constant whose value should lie between 0.01 and 0.1. KK towers couple to any boson or fermion pairs. CDF 2 looks separately at γγ and ee final states, whereas D0 3 looks at both final states at once as they look similar in the electromagnetic calorimeter. Because of the spin 2 of the graviton, the ratio of the branching ratios to γγ and ee final states is 2. Both experiments have analyzed about the same amount of data (∼ 1 f b -1 ), and found no excess of events over the SM predictions ( Drell-Yan and QCD where jets are misidentified as photons) excluding graviton masses below 900 GeV /c 2 for k/M P l = 0.1. Fig. 1 shows the excluded contour in the 2D parameter space as measured by D0. 1.1 fb ∅ D NLO cross-section observed limit expected limit σ 1 ± expected limit σ 2 ± expected limit 3 Gauge mediated SUSY breaking(GMSB) SUSY 4 is a broken symmetry. Experimental signatures are determined through the manner and scale of the SUSY breaking. In the GMSB scenario, the lightest supersymmetric particle (LSP) is the gravitino, a very light and weakly interacting particle. The next to lightest supersymmetric particle (NLSP) is assumed in this analysis to be the neutralino which decays into the LSP and a photon. Assuming R-parity conservation 5 , SUSY particles are pair produced and the experimental signature will be 2 photons and missing energy from the 2 gravitinos. To get a quantitative result, the "Snowmass Slope SPS 8" model 6 is considered. All the GMSB parameters a are fixed as a function of the effective energy scale Λ of SUSY breaking.

In this event topology, the SM background is the Zγγ production where the Z boson decays into neutrinos. There is also important instrumental background from events with real E / T (W boson production) and fake E / T ( QCD where jets are misidentified as photons). Fig. 2 (left) shows the E / T distribution. The observed distribution agrees well with the SM prediction; the entire spectrum is then used to set limits on the GMSB production cross section. Fig. 2 (right) shows the 95% C.L. cross section limit as a function of the effective scale Λ obtained by D0 7 . The observed limit on the signal cross section is below the prediction of the Snow-mass Slope model for Λ < 91.5 T eV , or for gaugino masses m χ0

1 < 125 GeV /c 2 and m χ± 1 < 229 GeV /c 2 .

Large Extra Dimensions

The hierarchy problem can also be solved by postulating the existence of n new large extra dimensions as proposed first by Arkani-Hamed, Dimopoulos and Dvali 8 (ADD); the extra volume serves to dilute gravity so that it appears weak in our 3D world as the graviton is the only particle allowed to propagate in the extra space. If the extra dimensions are compactified in a torus of radius R, according to the Gauss law, one can relate the fundamental Planck mass scale M D , R, the Planck mass and the number of extra dimensions by the relation M 2 P lanck = 8πM n+2 D R n , allowing M D to be compatible with the electroweak scale.

In this model, the graviton can be produced directly in the reaction q q → Gγ; G will remain undetected leaving a signature with a single photon and E / T .

The only SM background is the Zγ production where the Z boson decays into a neutrino pair. In addition to the usual instrumental background coming from misidentification of electrons or jets into photons, the event topology is rather sensitive to a contribution from beam halos and a The messenger mass Mm = 2Λ, the number of messengers N5 = 1, tan(β) = 15, µ > 0.

cosmics where muons produced photons by bremsstrahlung.

To fight the latter background, both experiments had to develop specific tools in addition to the usual ones based on the EM shower profile. Special hit finders in the tracker starting from the EM cluster increase the track veto efficiency. In addition, D0 uses a EM pointing tool thanks to its preshower detector, and CDF the timing system built within its EM calorimeter.

The results of CDF which has analysed about 2f b -1 of data, twice as much as D0 9 , are displayed in Fig. 3. The left plot shows a good agreement for the photon transverse between data and the sum of the various backgrounds. This allows to set limits on the fundamental scale M D (right plot) as a function of the number of extra dimensions. For n > 4 the limits are comparable with the limits obtained in the monojet search, and better than the LEP combined result 10 . CDF Run II Preliminary, 2.0 fb 5 Squarks and gluinos, stops

ν ν → , Z γ Z γ → τ / µ e/ → W γ , lost γ γ Non-Collision τ / µ , lost e/ γ W/Z ADD n=4 m=0.8 TeV

Number of Extra Dimensions

Squarks and gluinos

Squarks and gluinos can be copiously produced at the Tevatron if they are sufficiently light. The analysis is performed within the mSUGRA model [START_REF] Nilles | [END_REF] . The final state is composed of jets with a large E / T due to the two escaping neutralinos, assumed to be the LSP. According to the relative mass of squarks and gluinos different event topologies are to be expected. If squarks are lighter than gluinos, a "dijet" topology is favored. On the contrary if squarks are heavier than gluinos, the final state contains at least 4 jets. Finally, the jet multiplicity is at least 3 if squarks and gluinos have similar masses. After a common event preselection, the three topologies have been studied and optimised separately. The left plot on Fig. 4 shows the D0 E / T distribution obtained in the "dijet" search, the right one is obtained by CDF in the "3-jet" search. D0 has analyzed 2.1 f b -1 of data without finding any excess over the SM predictions. It allows to extend the exclusion domain in the squark gluino plane (Fig. 5). Using the most conservative hypothesis, D0 12 (CDF 13 ) excludes a gluino lighter than 308(290) GeV /c 2 .

Stop

Due to the large Yukawa coupling, there could be a large mixing in the 3rd generation of squarks. The lighest of the 2 stops could be the lightest squark and even the NLSP. Furthermore if its mass is less than the sum of the masses of the b quark, the W boson, and the neutralino, the dominant decay mode is t → c χ0 1 , a flavor changing loop decay, assumed to be 100% in the analysis. The final state will then be 2 acoplanar charm jets and E / T . The analysis proceeds with 2 jets detected in the central part of the detector with a loose heavy quark tag for one of them. No excess of events has been observed 14 in about 1 f b -1 of data, which provides a lower limit for the stop mass at 149 GeV /c 2 for a neutralino mass of 63 GeV /c 2 (Fig. 6). 6 Signature-based searches

Gluino Mass (GeV

Search for anomalous production of di photon events

In its quest for "signature-based" excess, CDF has searched for anomalous production of events in the γγ + E / T topology. In this analysis, use is made of the E / T resolution model. The aim of this model is to discriminate events with large mismeasured E / T from events with real E / T . It has been shown to provide a better background rejection power than a simple E / T cut. This model is based on the assumption that individual particle's energy resolution has Gaussian shape proportional to particle's √ E T . Only two sources of fake E / T are considered : soft unclustered energy ( from underlying event and multiple interactions ), and jets. The latter is responsible for most of the E / T as it is collimated energy in contrast to the former which is spread out all over the calorimeter. According to this model, each event is given a E / T significance value. Most of the QCD background is eliminated by requiring a significance above 5, leaving only the expected number of SM events with real E / T (Fig. 7), and not much room for an extra signal. of data and measured the dijet differential cross section (Fig. 8). The spectrum is fitted by the smooth parametrization :

dσ dm = p 0 (1 -x) p 1 /x p 2 +p 3 log(x) , x = m/ (s). ( 1
)
This parametrization is found to fit well the dijet spectra from PYTHIA and HERWIG MC events as well as from NLO pQCD. As no evidence for existence of a new massive particle is observed, limits on new particle production cross sections can be derived as a function of the dijet mass. These limits are then translated into mass exclusion limits, see Table 1.

Conclusions

No hints of physics beyond the SM have been found so far. As the Tevatron is continuing to provide experiments with more data to analyze, the quest for indices will be pursued by CDF

Figure 1 :

 1 Figure 1:95% C. L. upper limit on k/M P l versus graviton mass compared with the RS expected limit.

Figure 2 :

 2 Figure 2: The E / T distribution in γγ data with the various background contributions (left). Predicted cross section for the Snowmass Slope model versus Λ. The observed and expected 95% C.L. limits are also shown (right).

  Photon E 40 60 80 100 120 140 160 180 200

Figure 3 :

 3 Figure 3: The left figure shows the E / T distribution in the CDF monophoton search. The signal expected from the ADD model (n=4, m=0.8TeV) is added on top of the SM backgrounds. The right figure shows the exclusion limits for the ADD model obtained in this analysis, in comparison with the CDF jet and E / T result and LEP combined result.

Figure 4 :

 4 Figure4: Distributions of E / T after applying all analysis criteria except the one on E / T for the "dijet" (D0 left) and "3-jets" (CDF right) squark-gluino analyses; data (points with error bars) and the cumulated contributions from SM background, QCD background and signal MC are shown.

Figure 5 :

 5 Figure 5: Excluded region in the squark and gluino mass plane; newly excluded domain by D0 is shown in dark shading. The region where no mSUGRA solution can be found is shown hatched.

Figure 6 :

 6 Figure 6: Region in the t-χ0 1 mass plane excluded at the 95% C.L. by the D0 search. The yellow band represents the theoretical uncertainty on the scalar top quark pair production cross section due to PDF and renormalization and factorization scale choice.

Figure 7 :

 7 Figure 7: Distribution of missing transverse energy significance for diphoton candidates.

Figure 8 :

 8 Figure 8: The measured dijetmass spectrum and results of the fit to the parametrization form 1.

Table 1 :

 1 Mass exclusion ranges for several models. Some analyses presented in this talk have already been published, for the others, further details can be found at:CDF http://www-cdf.fnal.gov/physics/exotic/exotic.html D0 http://www-d0.fnal.gov/Run2Physics/WWW/results/np.htm

	Model description	Observed mass exclusion range
		(GeV /c 2 )
	Excited quark (f = f ′ = fs = 1)	260-870
	Color octet technirho
	(top-color-assisted-technicolor couplings)	260-1110
	Axigluon and flavor universal coloron
	(mixing of 2 SU(3)'s cot(theta)=1)	260-1250
	E6 diquark	290-630
	W' (SM couplings)	280-840
	Z' (SM couplings)	320-740
	and D0.

Acknowledgments

The author would like to thank the CDF and D0 working groups for providing the material for this talk, and the organizers of the Rencontres for a very enjoyable conference and the excellent organization.


domain: math info

text:
Hervé Monod

Annie Bouvier
email: <annie.bouvier@jouy.inra.fr>

André Kobilinsky

.R listofkeyrings.R planor.R basep.R randomize.R designkey.R listofdesignkeys.R planordesign.R bigm.R makedesignkey R Lazyload

Title Generation of regular factorial designs

designs, including fractional designs, orthogonal block designs,row-column designs and split-plots.

planor-package Generation of regular factorial designs

Description

A package dedicated to the automatic generation of regular factorial designs, including fractional designs, orthogonal block designs, row-column designs and split-plots.

Details

The user describes the factors to be controlled in the experiment and the anova model to be used when the results will be analysed. He or she also specifies the size of the design, that is, the number of available experimental units. Then planor looks for a design satisfying these specifications and possibly randomizes it. The core of the algorithm is the search for the key matrix, an integer matrix which determines the aliasing in the resulting factorial design.

The user may use the function regular.design where all these steps are integrated, and transparent by default. Alternatively, the steps can be decomposed by using successively the functions planor.factors, planor.model, planor.designkey and planor.design. For the expert user, the function planor.designkey can give several key matrix solutions. Alias and summary methods allow to study and compare these solutions, in order to select the most appropriate one for the final design.

Note

• The bigmemory package should have been installed previously to the planor installation ; if installed in a non standard library, planor must be installed in the same directory

• Option planor.max.print can be set to limit the amount of the matrices that are printed, to planor.max.print rows and columns. Default: 20.

Author(s)

Hervé Monod, Annie Bouvier, André Kobilinsky nlevels=rep(3,5), model=~block+(A+B+C+D)^2, estimate=~A+B+C+D, nunits=3^3, randomize=~block/UNITS) print(mydesign) # DUMMY ANALYSIS # Here we omit two-factor interactions from the model, so they are # confounded with the residuals (but not with ABCD main effects) set.seed(123) mydesigndata=mydesign@design mydesigndata$Y <-runif( 27) mydesign.aov <-aov(Y ~block + A + B + C + D, data=mydesigndata) summary(mydesign.aov) # 2. STEP-BY-STEP GENERATION, USING planor.designkey F0 <-planor.factors(factors=c( "block", LETTERS[1:4]), nlevels=rep(3,5), block=~block) M0 <-planor.model(model=~block+(A+B+C+D)^2, estimate=~A+B+C+D) K0 <-planor.designkey(factors=F0, model=M0, nunits=3^3, max.sol=2) summary(K0) mydesign.S4 <-planor.design(key=K0, select=2)

alias-methods

alias-methods

Methods for function alias in package planor: summarize the design properties

Description

Summarize the design properties of an object containing key matrices. Display the design keys matrix(ces) and the factorial effects confounded with the mean.

Usage

Details

• When object is a keymatrix, "alias" displays the key matrix and the factorial effects confounded with the mean. It prints the unaliased treatment effects, then the groups of aliased treatment effects, then the treatments effects confounded with block effects and finally the unaliased block effects, when considering all the factorial terms that are represented in the model argument, which is set if missing to the identity matrix (main effects only).

Value

• When object is a keymatrix, a vector with (i) the number of unaliased treatment effecs; (ii) the number of mutually aliased treatment effects; (iii) the number of treatment effects aliased with block effects.

• When object is a designkey, an invisible NULL.

as.data.frame.planordesign

• When object is a listofkeyrings, the factors, the model and the number of solutions for each prime in a list indexed by the primes p of the object. Each element is a 3-column matrix with one row per solution for prime p. The columns give (i) the number of unaliased treatment effecs; (ii) the number of mutually aliased treatment effects; (iii) the number of treatment effects aliased with block effects.

• The method is NOT YET IMPLEMENTED on objects of class listofdesignkeys.

See Also

Classes where this method applies: designkey, keymatrix, listofkeyrings.

Examples

Value

A data frame with attributes "factors", "model", "designkey", "nunits", "recursive".

bind-methods

Examples

### Creation of a planordesign object K0 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c(3,2,2,3,2,2), model=~R*C + (A+B1+B2)^2, estimate=~A:B1+A:B2, nunits=12, base=~R+C+U, max.sol=2) P0 <-planor.design(key=K0, select=1) # Convert into a data frame D0=as.data.frame(P0)

bind-methods

Methods for function bind in package planor: bind two objects

Description

Bind two objects of the same class.

Usage

## S4 method for signature designfactors,designfactors bind(x, y)

Arguments

x an object of the first class in the signature.

y an object of the second class in the signature.

Value

An object of the same class as x and y containing their joint content.

Note

Factors of the same name are repeated with distinct names and advertised with a warning.

See Also

Class

Description

An S4 class to represent the design factors in the planor package and to store their characteristics.

Objects from the Class

Objects from this class can be created explicitly by calls to planor.factors or implicitly by functions such as planor.designkey.

Slots

fact.info: a dataframe with one row per factor and with columns progressively storing information on the factors, in particular their numbers of levels (nlev).

pseudo.info: a dataframe with one row per pseudofactor and with columns progressively storing information on the pseudofactors. levels: a list of numeric or character vectors, with each vector containing the levels of one factor.

Methods

[ extract a subset of factors and update all the slots.

bind bind two objects of the class and update all the slots. See bind.

length return the number of factors.

names return the names of the factors.

Details

Depending on the context and on the construction stage, fact.info may contain logical columns that identify the block factors (block), the ordered factors (ordered), the basic factors (basic) and so on. It may also include columns that store the information on the hierarchy relationships between factors, if any.

In planor, factors are systematically decomposed into pseudofactors which all have a prime number of levels and which play a key role in the design generation. The information on the pseudofactors is stored in the pseudo.info slot. In addition to the columns of fact.info, it contains a column (called parent) to give the factor that each pseudofactor decomposes.

Author(s)

Object of the class

Value

A data frame which contains the design.

See Also

Classes where this method applies: planordesign.

Examples

### Creation of a planordesign object K0 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c(3,2,2,3,2,2), model=~R*C + (A+B1+B2)^2, estimate=~A:B1+A:B2, nunits=12, base=~R+C+U, max.

keyring-class

Class keyring and methods of the class

Description

An S4 class to represent a list of design-key matrices which are associated with the same prime and which represent alternative solutions to the same design specifications.

Objects from the Class

Each component of the structure returned by planor.designkey is a keyring when the case is not recursive.

Slots

.Data: a list of keymatrix objects.

p: a prime number.

LIB: a list containing a vector of row names and a vector of column names. The names are the same for all key matrices.

pseudo.info: a dataframe containing information on the pseudofactors associated with the key matrices. See the description of the class designfactors.

Extends

Class "list", from data part. Class "vector", by class "list", distance 2.

Methods

show display the object. See show-method.

summary summarize the design properties. See summary

Note

Each key matrix in a keyring object is a possible solution to the same factors, model and estimate specifications, with respect to the same prime number. An object of class listofkeyrings is a list of keyring objects associated with the different primes involved in a given factorial design problem.

Author(s)

H. Monod, and A. Bouvier nunits: the number of units in the design.

Extends

Class "list", from data part. Class "vector", by class "list", distance 2.

Methods alias FUNCTION NOT YET IMPLEMENTED

[ extract one design key in the list.

pick extract one design key in the list. See pick.

planor.design build a design from one design key in the list. See planor.design.

show display the object. See show.

summary summarize the design properties. See summary

Author(s)

H. Monod, and A. Bouvier

listofkeyrings-class

See Also

Creator function: planor.designkey

Examples

showClass("listofdesignkeys") ### Creation of a "listofdesignkeys" object K0 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c(3,2,2,3,2,2), model=~R*C + (A+B1+B2)^2, estimate=~A:B1+A:B2, nunits=12, base=~R+C+U, max.sol=2) # Show the object show(K0) ## Method length length(K0) ## Extraction: the following two commands are equivalent K <-K0[2] K <-pick(K0,2) listofkeyrings-class Class listofkeyrings and methods of the class

Description

An S4 class to store design key solutions when there is only one prime involved or when the solutions are independent between primes.

Objects from the Class

Objects are created by planor.designkey, when the case is not recursive.

Slots

.Data: a list of objects of class keyring associated with different primes.

factors: an object of class designfactors which contains the factors' specifications. model: a "list" which contains the model and estimate specifications.

nunits: the number of units of the design.

Extends

Class "list", from data part. Class "vector", by class "list", distance 2. makedesignkey Methods alias give the aliasing for each key-matrix. See alias.

[ extract one design key by taking one key matrix per prime.

pick extract one design key by taking one key matrix per prime. See pick.

planor.design build a design using one key matrix per prime. See planor.design.

show display the object. See show.

summary summarize the design properties from object. See summary.

H. Monod

See Also

Class designkey

Examples when keys is a listofkeyrings object, the index vector to select the key matrix for each prime.

mat1 <-cbind(diag(3),1) colnames(mat1) <-c("A","B","C","D") mat2 <-cbind(diag(2),c(1,2)) colnames(mat2) <-c("E","F","G") mat.dk <-

planor.design-methods

Value

An object of class designkey, which contains the selected design Note K <-pick(K0,1) can be simply written K <-K0[1]

See Also

Classes where this method applies: listofdesignkeys, listofkeyrings.

Examples

# Creation of an object of class "listofdesignkeys" K2 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c Arguments key an object of the first class in the signature, or a vector of integers. randomize an optional formula to specify the block structure for design randomization. selection

when key is a listofdesignkeys object, an integer scalar.

when key is a listofkeyrings object, should be an index vector to select the key matrix for each prime. ... additional arguments, in particular those related to randomization (see planor.randomize). start an integer from where to start the series of symbols.

Details

• When key is numeric, it should be a vector of integers of length s. Then, the function generates a full factorial n 1 xn 2 x...xn s design with columns considered as factors. It returns an integer matrix with prod(n) rows and s columns giving all combinations along the rows, in lexicographic order. • When key is a listofdesignkeys object, build one design from a selected solution.

Value

An object of class planordesign, which contains the design built from the input. This function is restricted to giving a single design. When key is numeric, see Details

See Also

Classes where this method applies: designkey, listofdesignkeys, listofkeyrings.

Examples

### Creation of a listofdesignkeys object K0 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c(3,2,2,3,2,2), model=~R*C + (A+B1+B2)^2, estimate=~A:B1+A:B2, nunits=12, base=~R+C+U, max.sol=2) ## Method planor.design applied on the listofdesignkeys object P0 <-planor.design(key=K0, select=1) ## Method planor.design applied on a designkey object P0 <-planor.design(K0[1]) ### Creation of a listofkeyrings object K0 <-planor.designkey(factors=c(LETTERS[1:4], "block"), nlevels=rep(3,5), model=~block+(A+B+C+D)^2, estimate=~A+B+C+D, nunits=3^3, base=~A+B+C, max.sol=2, verbose=TRUE) ## Method planor.design applied on a designkey object P0 <-planor.design(K0[1]) P0.R <-planor.design(K0[1], randomize=~A+B+C+D) ## randomize the final design planor.designkey

Search for a design key or a collection of design keys

Description

Search for a design key or a collection of design keys that satisfy the design properties specified by the arguments. This function calls the core algorithms implemented in planor.

Usage

Details

The methods implemented in planor rely on a decomposition of the design search according to prime numbers. The prime numbers involved are those that decompose the numbers of levels of the factors. For example, if all factors have 2, 4, or 8 levels, then the number of units must be a power of 2 and the only prime number involved is 2. This is called the symmetric case. But if at least one factor has 6 levels, or if factor A has 2 levels and factor B has 3 levels, then the number of units must be the product of a power of 2 by a power of 3. In this case the search is automatically decomposed into one for prime 2 and one for prime 3. This is called the asymmetric case.

In the symmetric case with prime p, a regular factorial design requires a single key matrix of integers modulo p. In the asymmetric case, it requires one key matrix per prime. In planor, key matrices are stored in objects of class keymatrix. The lists made of one key matrix per prime are called design keys. They are stored in objects of class designkey.

The function planor.designkey essentially searches for design keys that satisfy the user specifications. For technical reasons, however, its output can take two different forms: either an object of class listofkeyrings or an object of class listofdesignkeys. The function planor.designkey detects automatically which case applies. In the first case (independent case), the key matrix solutions can be searched independently between primes and they are stored in objects of class listofkeyrings. The second case (recursive case) occurs exceptionnally. In that case the search cannot be independent between primes and so the different solutions are directly stored in a list of class listofdesignkeys.

Value

an object of class listofkeyrings in most cases. Otherwise, i.e in recursive cases, an object of class listofdesignkeys.

Note

The base formula must be an additive formula involving a subset of factors, called the basic factors.

Using the base argument ensures that the design solutions will include the full factorial design for the basic factors. This option can speed up the search because it restricts the domain to be explored by the search algorithm.

Note

The basic usage is to specify the names of the factors by a character vector of length n in argument factors and their numbers of levels by a numeric vector of length n in argument nlevels. Alternatively, the factors argument can be an integer n, in which case the first n capital letters of the alphabet are used as factor names. If nlevels is a scalar s, it is considered that all factors have s levels. There are two more possibilities which allow for alphanumeric factor levels. If factors is a dataframe, the factors in this dataframe are extracted together with their levels. Finally factors can be a named list of n vectors, with each vector containing the levels of one factor. Note that nlevels is ignored in these latter two cases. See the examples. The argument block allows to specify the block or nuisance factors. This information is used by the alias and summary functions but it has no effect on the design generation and randomization which depend on other arguments.

Author(s)

H. Monod, and A. Bouvier

See Also

Class designfactors

Examples

planor.factors(c("A","B","C","P"),c(2,3,6,3)) planor.factors(LETTERS[1:12],2) planor.factors(12,2) planor.factors( c("A","B","Block"), 3, block=~Block ) zz <-planor.factors( c("A","B","Block"), c(2,3,5)) zz@levels$A <-c("plus","moins") planor.factors(factors=list(A=c("plus","moins"), B=1:3, Block=1:5)) AB <-data.frame( A=c(rep(c("a","b"),3)), B=rep(c("z","zz","zzz"),rep(2,3) model (TRUE for the factors present in at least one model formula),

basic (TRUE for the basic factors).

Note

This function is called at the start of the design search. It is essentially a check that the factors in all three arguments are coherent, even though it performs some additional tasks. The function stops if it detects a model or basic factor that is absent from factors. This is because the number of levels of such a factor is unknown and so the design search cannot proceed. Besides, the function eliminates the factors that do appear neither in model nor in base and it reorders the factors by putting first the basic ones.

Author(s)

H

Note

The user can specify: 1/ either, model or listofmodels or both, 2/ or, resolution and factors, and possibly listofmodels.

When model and resolution are both set, model is ignored.

The second case, -when resolution and factors are set -, causes the automatic generation of the main c(model, estimate) pair. Assuming S denotes the additive formula including all factors, -if resolution is odd, the model formula is ~(S)^(resolution-1)/2, -if resolution is even, the model formula is ~(S)^(resolution/2) and the estimate formula is ~(S)^(resolution/2)-1.

Author(s)

H. Monod, and A. Bouvier out.order a list of data factors that will be used to order the rows of the randomized design; if missing, the factors of the block formula are used.

keep.initial if TRUE, the initial row order of the design is stored in column InitialUNITS of the returned dataframe.

Value the input data frame after randomization.

Note

Each name in blockformula must correspond to a factor of the dataframe data.

Examples

showClass("planordesign") ### Creation of a listofdesignkeys object K0 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c(3,2,2,3,2,2), model=~R*C + (A+B1+B2)^2, estimate=~A:B1+A:B2, nunits=12, base=~R+C+U, max.sol=2) ## Creation of a planordesign object from K0 P0 <-planor.design(key=K0, select=1) show(P0)

summary-methods

Value

NULL

Note

-The number of rows and columns of the matrices that are printed are limited by the option planor.max.print -Objects of the class are automatically displayed by invocation of 'show' (see examples).

See Also

Classes where this method applies: designkey, keymatrix, keyring, listofdesignkeys, listofkeyrings

Examples

# Creation of a "listofdesignkeys" object K0 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c(3,2,2,3,2,2), model=~R*C + (A+B1+B2)^2, estimate=~A:B1+A:B2, nunits=12, base=~R+C+U, max.sol=2) ## Method show applied on a "keymatrix" object show

(K0[[1]][[1]]) ## Method

Usage

## S4 method for signature designkey summary(object, show="dtbw", save="k", ...) ## S4 method for signature keymatrix summary(object, fact, block, show="dtbw", save="k", ...)

## S4 method for signature keyring summary(object, show="tbw",save ="kw", ...)

## S4 method for signature listofdesignkeys summary(object, show="tbw", save="kw", ...)

## S4 method for signature listofkeyrings summary(object, show="tbw", save="kw", ...)

Arguments object an object of the class fact a character or numeric vector of parent factor names for the columns of the object block a logical vector to identify the columns of the object associated with a block factor show an optional string to identify the type of information to display. The recognized letters are: 'd' for the design keys matrices, 't' for the treatment effects confounded with the mean, 'b' for the block-and-treatment effects confounded with the mean, 'w' for the weight profiles save an optional string to identify the type of information to return. The recognized letters are: 'k' for the kernel matrices, 'w' for the weight profiles of the treatment effects confounded with the mean. ... ignored

Details

The amount of display depends on the value of the argument show, and the type of returned information depends on the value of the argument save.

• When object is of class keymatrix, "summary" prints the key matrix, the factorial effects confounded with the mean, and the weight profiles of the effects confounded with the mean, according to the value of the argument show.

The keymatrix argument being denoted by key, -The rows of key are associated with units factors (or pseudofactors) while its columns are associated with treatment or block factors (or pseudofactors).

-The vectors in the arguments fact and block give information on the treatment and block factors, so that their length is expected to be equal to the number of columns of key.

-If missing, fact attributes a distinct parent factor to each column of key and block is set to TRUE for all columns. "summary" returns a list with the components required by the argument save.

summary-methods

• When object is of class designkey, "summary" prints the summary of each of the key matrices. It returns a list with as many components as key matrices, each one with the components required by the argument save.

• When object is of class listofdesignkeys, "summary" prints the summary of each key matrix in each design key. It returns a list with as many components as design keys, each one is a list of the key matrices summaries.

• When object is of class listofkeyrings, "summary" prints the summary of each key matrix in each keyring. It returns a list with as many components as keyrings, each one is a list of the key matrices summaries.

• When object is of class keyring, "summary" prints the summary of each of its key matrices.

It returns a list with as many components as key matrices.

Value

A list. See Details Information returned for each key matrix depends on the argument save.

• When save includes the character 'k', the returned list has a component named 'k'.It is a matrix, the columns of which are kernel generators of the key matrices.

• When save includes the character 'w', the returned list has a component named 'w', which contains the weight profiles of the effects confounded with the mean.

Note

The number of rows and columns of the matrices that are printed are limited by the option planor.max.print.

See Also

Classes where this method applies: designkey, keymatrix, keyring, listofdesignkeys, listofkeyrings

Examples

### Creation of a "listofdesignkeys" object K0 <-planor.designkey(factors=c("R","C","U","A","B1","B2"), nlevels=c(3,2,2,3,2,2), model=~R*C + (A+B1+B2)^2, estimate=~A:B1+A:B2, nunits=12, base=~R+C+U, max.sol=2) ## Method summary applied on a "keymatrix" object r <-summary(K0

[[1]][[1]])
## Method summary applied on a "designkey" object summary(K0[1], save=NULL) # Method summary applied on the "listofdesignkeys" object r <-summary(K0, show="dt") ### Creation of a "listofkeyrings" object K0 <-planor.designkey(factors=c(LETTERS[1:4], "block"), nlevels=rep(3,5), model=~block+(A+B+C+D)^2, estimate=~A+B+C+D, nunits=3^3, base=~A+B+C, max.sol=2) # Method summary applied on the "keymatrix" object r <-summary(K0[[1]][[1]]) # Method summary applied on the "keyring" object r <-summary(K0[[1]]) # Method summary applied on the "listofkeyrings" object r <-summary(K0, show="dtb", save ="k") print(r) 6, 8, 11-13, 18-23, 25-27 designfactors-class, 7 designkey, 4, 5, 10, 12, 15-17, 19, 25, 28, 30 designkey-class, 8 getDesign, 25 getDesign (getDesign-methods), 9 getDesign,planordesign-method (getDesign-methods), 9 getDesign-method (getDesign-methods), 9 getDesign-methods, 9 keymatrix, 4, 5, 8, 11, 19, 27-30 keymatrix-class, 10 keyring, 10, 11, 13, 28, 30 keyring-class, 11 length,designfactors-method (designfactors-class), 7 list, 8, 11-13 listofdesignkeys, 5, 8, 15-17, 19, 25, 27, 28, 30 listofdesignkeys-class, 12 listofkeyrings, 5, 8, 11, 15-17, 19, 25, 27, 28 2, 3, 7, 11-14, 18, 19, 26 planor.factors, 2, 7, 18, 19, 20, 21, 23, 26, 27 planor.harmonize, 21 planor.model, 2, 18, 19, 21, 22, 26, 27 planor.randomize, 17, 23 planordesign, 5, 9, 17, 26 planordesign-class, 25 regular.design, 2, 25, 26 show, 8, 12, 14 show,designkey-method (show-methods), 27 show,keymatrix-method (show-methods), 27 show,keyring-method (show-methods), 27 show,listofdesignkeys-method (show-methods), 27 show,listofkeyrings-method (show-methods), 27 show-method, 10, 11 show-method (show-methods), 27 show-methods, 27 structure, 10 summary, 8, 10-12, 14 summary,designkey-method (summary-methods), 28 summary,keymatrix-method (summary-methods), 28 summary,keyring-method (summary-methods), 28 summary,listofdesignkeys-method (summary-methods), 28 summary,listofkeyrings-method (summary-methods), 28 summary-methods, 28 summary.designkey (summary-methods), 28 summary.keymatrix (summary-methods), 28 summary.keyring (summary-methods), 28

R

  topics documented: planor-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 alias-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 as.data.frame.planordesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 bind-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 designfactors-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 designkey-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 getDesign-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 keymatrix-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 keyring-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 listofdesignkeys-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 listofkeyrings-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 makedesignkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 pick-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 planor.design-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 planor.designkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 planor.factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 planor.harmonize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 planor.model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 planor.randomize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 planordesign-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 regular.design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 show-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 summary-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Index 32

  (Applied Mathematics and Informatics Unit, INRA UR 341 -Jouy-en-Josas, France. URL: http://www.jouy.inra.fr/mia_eng/) References see citation(planor) Examples # DESIGN SPECIFICATIONS # Treatments: four 3-level factors A, B, C, D # Units: 27 in 3 blocks of size 9 # Non-negligible factorial terms: # block + A + B + C + D + A:B + A:C + A:D + B:C + B:D + C:D # Factorial terms to estimate: # A + B + C + D # 1. DIRECT GENERATION, USING regular.design mydesign <-regular.design(factors=c("block", LETTERS[1:4]),

  Class keymatrix and methods of the class Description An S4 class to represent an elementary key matrix in package planor Objects from the Class Objects from this class are usually components of an object of class keyring or designkey Slots .Data: a matrix of integers modulo p p: a prime number Extends Class matrix, from data part. Class array, by class "matrix", distance 2. Class structure, by class "matrix", distance 3. Class vector, by class "matrix", distance 4, with explicit coerce. Methods alias gives the aliasing relationships of the key matrix. See alias.show display the object. See show-method summary summarize the design properties. See summary

  , method pick in class listofkeyrings, method summary in class keymatrix and the class keyring listofdesignkeys-class Examples showClass("keyring") ### Creation of a listofkeyrings object K0 <-planor.designkey(factors=c("block", LETTERS[1:4]), nlevels=rep(3,5), model=~block+(A+B+C+D)^2, estimate=~A+B+C+D, nunits=3^3, base=~A+B+C, max.sol=2) ## Method show applied on a keyring component of K0 show(K0[[1]]) listofdesignkeys-class Class listofdesignkeys and methods of the class Description An S4 class to represent a list of design key solutionsObjects from the ClassObjects are created by planor.designkey, when the search is recursive.Slots.Data: a list of objects of class designkey.factors: an object of class designfactors which contains the factors' specifications.model: a "list" which contains the model and estimate specifications.

  Author(s) H. Monod, and A. Bouvier See Also Creator function: planor.designkey Examples showClass("listofkeyrings") ### Creation of a listofkeyrings objct K0 <-planor.designkey(factors=c(LETTERS[1:4], "block"), nlevels=rep(3,5), model=~block+(A+B+C+D)^2, estimate=~A+B+C+D, nunits=3^3, base=~A+B+C, max.sol=2) show(K0) makedesignkey A function to turn integer matrices into an object of class designkey Description Creates an object of class designkey directly from a list of integer matrices Usage makedesignkey(keys, primes) Arguments keys a list of n integer matrices with column names primes a vector of n prime numbers Details the names of the factors are extracted from the matrix column names pick-methods Value an object of class designkey Author(s)

  , planor.model, and the classes designfactors, listofkeyrings, listofdesignkeys Examples K0 <-planor.designkey(factors=c("block", LETTERS[1:4]), nlevels=rep(3,5), model=~block+(A+B+C+D)^2, estimate=~A+B+C+D, nunits=3^3, base=~A+B+C, max.sol=2) ## With automatic model generation Km <-planor.designkey(factors=c("block", LETTERS[1:4]), nlevels=rep(2,5), resolution=3, nunits=2^4) planor.factors planor.factors Create an object of class 'designfactors' Description A function to create an object of class designfactors, either by giving the factor names and their numbers of levels, or by giving a named list of factor levels. Both ways can be used in the same call. Additional information can be provided that will be used during the design search or in the summary functions. Usage planor.factors(factors = NULL, nlevels = NULL, block = NULL, ordered = NULL, hierarchy = NULL, dummy = FALSE) Arguments factors a character vector of factor names, or possibly a scalar, a dataframe or a list (see DETAILS) nlevels a vector of level numbers for each factor name (see DETAILS) block an additive model formula to indicate the block factors ordered an additive model formula to indicate the quantitative factors (not used at all in the present version) hierarchy a formula or a list of formulae to indicate hierarchy relationships between factors (see the planor vignette for details) dummy a logical to identify dummy factors created and deleted by PLANOR functions for technical reasons Value An object of class designfactors

Examples#

  Basic example planor.model(model=~block + (A+B+C)^2, estimate=~(A+B+C)^2) # Resolution: both calls to planor.model below are equivalent planor.model(model=~(A+B+C+D)^2, estimate=~A+B+C+D) myfactors <-planor.factors(factors=c(LETTERS[1:4]), nlevels=rep(2,4)) planor.model(resolution=4, factors=myfactors) # Complicated examples planor.model(~A+B+C+D+A:B, ~A+B+C+D, listofmodels=list(c(~E+F,~E))) planor.model(~A+B+C+D+A:B,~A+B+C+D, listofmodels= list(c(~E+F,~E), ~G, ~H, c(~M+N,~N)))planor.randomize A function to randomize a factorial design according to an orthogonal block structureDescriptionA function to randomize a factorial design according to a specified block structure formula Usage planor.randomize(blockformula, data, out.order, keep.initial=FALSE)

  show applied on a "designkey" object show(K0[1]) ## Method show applied on the "listofdesignkeys" object show(K0) K0 # the same ### Creation of a "listofkeyrings" object K0 <-planor.designkey(factors=c(LETTERS[1:4], "block"), nlevels=rep(3,5), model=~block+(A+B+C+D)^2, estimate=~A+B+C+D, nunits=3^3, base=~A+B+C, max.sol=2) ## Method show applied on a "keyring" object show(K0[[1]]) print(K0[[1]]) # the same K0[[1]] # the same ## Method show applied on the "listofkeyrings" object show(K0) summary-methods Methods for function summary in package planor Description Summarize the design properties of an object, by printing the summary of each key matrix

  where this method applies: designfactors

	designfactors-class	Class designfactors and methods of the class
	Examples
	F1 <-planor.factors(factors=c("block",LETTERS[1:4]), nlevels=c(6,6,4,2,6))
	F2 <-planor.factors(factors=c("block",LETTERS[11:12]), nlevels=c(6,6,4))
	### Method bind on designfactors objects
	F3 <-bind(F1,F2)
	names(F3)

  Otherwise the arguments factors, nlevels, ordered, hierarchy follow the syntax of planor.factors. nlevels See planor.factors. Ignored if factors is of class designfactors. ordered See planor.factors. Ignored if factors is of class designfactors. hierarchy See planor.factors. Ignored if factors is an object of class designfactors. model a list of model-estimate pairs of formulae, typically an output from planor.model. Otherwise the arguments model, estimate and listofmodels follow the syntax of planor.model. estimate See planor.model. Ignored if model is a list. listofmodels See planor.model. Ignored if model is a list. base an optional formula to specify the basic factors. These factors must belong to the factors argument planor.model Value An object of class designfactors very similar to factors, but with two additional logical columns in slots fact.info and pseudo.info:

		), C=1:6 )
	planor.factors(factors=AB)
	planor.harmonize	Harmonize the factors

Description Harmonize the factors originating from a list of factors, a list of models, and a list of basic factors (this function is essentially for internal use) Usage planor.harmonize(factors, nlevels, ordered, hierarchy, model, estimate, listofmodels, base) Arguments factors an object of class designfactors, typically an output from planor.factors).

  . Monod, and A. Bouvier Description A function to declare the factorial terms that must be considered as non-negligible and the factorial terms that must be estimable when the experiment will be analysed. It contains all the non-negligible factorial terms. estimate optional formula specifying the factorial terms to estimate. If missing, it is considered that all factorial terms in model have to be estimated. listofmodels list of c(model, estimate) pairs, where model and estimate are formulae; using several pairs allows more flexibility in the design constraints (see Kobilinsky, 2005, or the split-plot example in the vignette); estimate is optional. resolution an integer larger than or equal to 3, to specify the design resolution. When set, the model and estimate arguments are ignored. See Note. factors a designfactors object, typically an output from planor.factors. It must be set only when the resolution argument is used.

	Examples
	F2 <-planor.factors(factors=c("block",LETTERS[1:4]), nlevels=c(6,6,6,4,2))
	M2 <-planor.model( model=~block+(A+B+C)^2, estimate=~A+B+C )
	F2.h <-planor.harmonize(factors=F2, model=M2, base=~A+B)
	names(F2)
	names(F2.h)
	planor.model	Model and estimate specifications for a design search
	Usage
	planor.model(model, estimate, listofmodels, resolution, factors)
	Arguments
	model	main model formula.

Value

A list of c(model, estimate) pairs, where model and estimate are formulae

  The only exception is UNITS. If UNITS is used in blockformula but absent from data, a factor is added to data, with one level per row. See the examples below for the usage of UNITS in blockformula.Objects can be created by calls to method planor.design applied on an object of class designkey or on an object of class listofkeyrings, and by calls to regular.design when argument output is equal to 'planordesign' Slots design: a dataframe containing the final design factors: an object of class designfactors which contains the factors' specifications model: a list containing the model and estimate specifications designkey: a list which contains the designkey matrices used to create the object nunits: the number of units of the design recursive: a "logical" equal to TRUE if the design has been constructed recursively

	planordesign-class	Class planordesign and methods of the class
	Description
	An S4 class to represent a final design
	Objects from the Class
	Author(s) Methods
	H. Monod, and A. Bouvier getDesign extract a design data frame. See getDesign
	as.data.frame turn into a data frame. See as.data.frame.planordesign
	Author(s)
	H. Monod, and A. Bouvier
	See Also
	Creators: method planor.design applied on an object of class designkey or class listofkeyrings
	or class listofdesignkeys
	See also class designfactors.

  27 summary-methods, 28 * Topic package planor-package, 2 [,designfactors,ANY,ANY,ANY-method (designfactors-class), 7 [,designfactors-method (pick-methods), 15 [,listofdesignkeys,ANY,ANY,ANY-method (listofdesignkeys-class), 12 [,listofdesignkeys-method (pick-methods), 15 [,listofkeyrings,ANY,ANY,ANY-method (listofkeyrings-class), 13 [,listofkeyrings-method (pick-methods), 15 [,planordesign,ANY,ANY,ANY-method (planordesign-class), 25 [,planordesign-method (pick-methods), 15

	bind,designfactors,designfactors-method
	(bind-methods), 6
	bind-method (bind-methods), 6
	bind-methods, 6
	Index designfactors,
	* Topic classes
	designfactors-class, 7
	designkey-class, 8
	keymatrix-class, 10
	keyring-class, 11
	listofdesignkeys-class, 12
	listofkeyrings-class, 13
	planordesign-class, 25
	* Topic design
	alias-methods, 4
	bind-methods, 6
	designfactors-class, 7
	designkey-class, 8
	getDesign-methods, 9
	keymatrix-class, 10
	keyring-class, 11
	listofdesignkeys-class, 12
	listofkeyrings-class, 13 pick-methods, 15 planor-package, 2 planor.design-methods, 16 planor.designkey, 18 planor.factors, 20 planor.harmonize, 21 planor.model, 22 planor.randomize, 23 planordesign-class, 25 regular.design, 26 show-methods, 27 * Topic methods alias-methods, 4 as.data.frame.planordesign, 5 bind-methods, 6 getDesign-methods, 9 pick-methods, 15 planor.design-methods, 16 show-methods, alias, 8, 10, 14, 20 alias,designkey-method (alias-methods), 4 alias,keymatrix-method (alias-methods), 4 alias,listofdesignkeys-method (alias-methods), 4 alias,listofkeyrings-method (alias-methods), 4 alias-methods, 4 alias.designkey (alias-methods), 4 alias.keymatrix (alias-methods), 4 alias.listofdesignkeys (alias-methods), 4 alias.listofkeyrings (alias-methods), 4 array, 10 as.data.frame,planordesign-method (as.data.frame.planordesign), 5 as.data.frame.planordesign, 5, 25
	bind, 7
	bind (bind-methods), 6

  planor.design, 2, 8, 12, 14, 25 planor.design (planor.design-methods), 16 planor.design,designkey-method (planor.design-methods), 16 planor.design,listofdesignkeys-method (planor.design-methods), 16 planor.design,listofkeyrings-method (planor.design-methods), 16 planor.design,numeric-method (planor.design-methods), 16 planor.design-methods, 16 planor.designkey,

	, 30
	listofkeyrings-class, 13
	makedesignkey, 14
	matrix, 10
	names,designfactors-method
	(designfactors-class), 7
	pick, 12, 14
	pick (pick-methods), 15
	pick,listofdesignkeys-method
	(pick-methods), 15
	pick,listofkeyrings-method
	(pick-methods), 15
	pick-method (pick-methods), 15
	pick-methods, 15
	planor (planor-package), 2
	planor-package, 2


Lucie Marandel
email: lucie.marandel@st-pee.inra.fr

Inge Geurden

Stéphane Panserat

Nutritional programming and epigenetics: the teleost challenge

come


domain: sdv shs

text:
Clementine Calba

Flavie Goutard

Daniel Cornelis

François Charrier

Anne-Lise Tran

Véronique Chevalier

Stephanie Desvaux

Pascal Hendrikx

François Roger

Surveillance in Corsica Island : a Mediterranean Lab

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

Surveillance in Corsica Island a Mediterranean Lab

For centuries, agricultural production in Corsica was located in the central part of the island (mountainous area). During the twentieth century, a massive rural exodus led to the gradual abandonment of agricultural activities and provided conditions conducive to the development of an extensive type of farming on abandoned land.

The national park of Corsica is covering almost half of the surface of the island and is home to two endangered subspecies of hoofed mammals, the mouflon (Ovis aries musimon) and Corsican red deer (Cervus elaphus corsicanus). There is as well a large population of wild boar all over the territory.

Breeding areas are located in the mountainous areas and in the western part of the island. The livestock population is estimated at 74,000 cattle, 152,585 sheep and 46,940 goats (source: INSEE 2013).

The total pig population is estimated at 58,855 heads.

Their husbandry practices are specific to Corsica, with feral and freerange systems and 90% of farmers producing their own meat.

These unique conditions could make Corsican environment suitable for the emergence of new pathogens. -Further work based on a "One Health" approach should be conducted à estimate the risk of introduction, the potential consequences for public health and possible risk mitigation strategies (including surveillance).

New approaches in risk communication and risk management (based on social sciences methods ):

à Involvement of farmers, hunters associations, official authorities and medical sector in the process of decision-making and to achieve multisectorial management.

Development of participatory approaches:

à Understand farmers' motivations to report or not health events. à Understand local preference in risk reduction measures.

Health surveillance in

  emergence of BT virus (BVT-2), transmitted by Culicoides imicola, probably due to global warming. -2003: "rapid risk assessment" resulted in the use of sentinel goats to detect new BT serotypes (BTV-4) à first models for BT prediction on Mainland France (and Europe) built up and validated with initial data from Corsica. -2004: emergence of BTV-16 -2013: emergence of BTV-1 -Corsica still at risk for the introduction of new BTV serotypes African Swine Fever (ASF) -Possible introduction and maintenance from neighbouring Sardinia, where the disease is endemic. -Development of a risk assessment framework taking into account (i) livestock and wildlife population density, (ii) spatiotemporal patterns of contacts between domestic and wild compartments and (iii) socio-cultural practices à inform riskbased surveillance on swine diseases. Rift Valley Fever (RVF) -Considered as a threat for Europe (and Corsica) due to animal movements and existing competent vectors.

Figure 1 :

 1 Figure 1: SchemaFc representaFon of the reporFng system of passive wildlife surveillance in Corsica


William Dudefoi

Hélène Terrisse

Mireille Richard-Plouet

Bernard Humbert

Marie-Hélène Ropers

Comparative study of E171 food grade and P25 Ti02 nanoparticles

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

COMPARATIVE STUDY OF E171 FOOD GRADE AND P25 TiO 2 NANOPARTICLES

W. Dudefoi 1 , H. Terrisse 2 , M. Richard-Plouet 2 , B. Humbert 2 , E. Gautron 2 , M-H. Ropers Titanium dioxide (TiO 2 ) is a white metal oxide commonly used as a white pigment in various applications such as paints, cosmetic and food products. TiO 2 food additive is referred to as E171 in Europe and INS171 in North America, and is mainly used in sugar confectionary, constituting the coating of sweets and chewing-gum. Unfortunately, it was recently discovered that food grade TiO 2 exhibits a nano-sized fraction, representing up to 36% of the particles [1]. Due to the classification of TiO 2 particles as potentially harmful for humans by inhalation [2], the toxicity of ingested TiO 2 nanoparticles, in general, needs to be evaluated and more specifically, with food grade particles. However, the reference particles are mostly the very well-known Degussa P25 particles, a kind of TiO 2 particles widely used for photocatalytic applications [3]. In this study, we evaluated whether P25 and food grade particles are similar. For this purpose, several food grade TiO 2 samples and P25 particles were investigated for their physicochemical characteristics (size distribution, shape, crystallinity, reactivity, surface properties). Several methods relevant from physical chemistry were applied: transmission electron microscopy TEM, X-ray diffraction XRD, laser scattering particle size distribution analyzer, X-ray photoelectron spectrometry XPS, zeta potential measurements, Raman and IR spectroscopies, Inductively coupled plasma atomic emission spectroscopy ICP-AES, Diffuse Reflectance Infrared Fourier Transform Spectroscopy DRIFTS and specific surface area analysis. The results show that E171 and P25 have different compositions and surface chemistry. TEM analysis confirmed the presence of nano-sized particles in food grade TiO 2 E171, with a mean diameter of 131 nm and 26% of nanoparticles, whereas P25 particles have a mean diameter of 23 nm with 100% of nanoparticles. The E171 isoelectric points lie between pH 2 and pH 4.2, whereas P25 isoelectric point is pH 6.2. In accordance with zeta potential measurements, laser scattering particle size distribution analysis shows that E171 tend to form larger agglomerates at pH < 5 whereas P25 forms larger agglomerates at pH > 5. Moreover, XRD, XPS, FT-RAMAN, DRIFTS, surface specific area analysis and ICP-AES analysis confirmed that P25 is a mixture of 75% rutile and 25% anatase with a low degree of hydrolysed surface oxygens, whereas the crystalline variety of E171 is anatase only, coated with hydroxide in a larger extend, some organic species in variable proportions, and sometimes Silicon and Aluminum, depending on the suppliers. This work confirms the recent paper of Yang et al., 2014 [4] and provides additional data. We also found a certain heterogeneity among food grade TiO 2 samples, leading us to strongly recommend researchers to take into account the different surface chemistry of E171 particles for their studies and properly characterize their samples before each of their toxicological studies. In any case, using Degussa P25 particles does not appear to be a reliable model to study the fate of food grade TiO2 in the gastro-intestinal tract.

Fig. 1 .

 1 Fig. 1. A: Food grade TiO 2 / B: TEM image of food grade TiO 2 / C: Zeta potential of food grade and P25 TiO2

  1,*

	1 INRA, UR1268 Biopolymères Interactions Assemblages, 44300 Nantes, France
	2 Institut des Matériaux Jean Rouxel IMN, Université de Nantes, CNRS, 44322 Nantes
	*e-mail: marie-helene.ropers@nantes.inra.fr

Acknowledgements

This work has been carried out in the framework of the Labex Serenade (ANR-11-LABX-0064) and of the A*MIDEX project (ANR-11-IDEX-0001-02), funded by the «Investissements d'Avenir» French Government program managed by the French National Research Agency (ANR).