domain: sdv sde

text:
Laurent Augusto

David L Achat

Mark Ronald

Mark R Bakker

Frédéric Bernier

Didier Bert

Rim Khlifa

Céline Meredieu

Pierre Trichet

Frederic F Danjon
email: frederic.danjon@pierroton.inra.fr

Frederic Bernier

Rhim Khlifa

Mineralomass and simulated yield by stump removal in root systems of Pinus pinaster in south-west France

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


Francis Fleurat-Lessard

Jean-Michel Savoie

Florence Richard-Forget

Christian Barreau

Nadia Ponts

Vessela Atanasova-Penichon

Laetitia Pinson-Gadais

Véronique Pronier

Thierry Gibard

Marie-France Neveux

Corine Grimaldi

Christos Athanassiou

Maria Otilia Carvalho

Shlomo Navarro

George Opit

Catherine Regnault-Roger

Matthias Schöller

Vaclav Stejskal

Phyllis Weintraub

S Savoldelli

Ali A Isikber

H K Tofel

R Guruprasad

Cornel Adler

Matthias Schoeller

Subramanyam

A A Hameed

Canadas Pedraza

De Paredes

Weichel L Birner
email: erich.birner@basf.com

and by-products

Keywords: 20 Suppression of Plodia interpunctella moth population by mating disruption Cephalonomia tarsalis control Integrated pest management, food industry, monitoring, insecticides. Session 1: Recent advances in IPSP and current trends in post-harvest Metal bin, grain, insects, molds, remote monitoring, phosphine recycling, fumigation, liquid insecticides 3 -Carifend™: an alternative method remote monitoring, phosphine recycling, fumigation, liquid insecticides Stored tobacco, insect, IPM, Carifend™, tobacco quality, tobacco

and Food Safety" of the INRA Research Centre of Bordeaux-Aquitaine.

This scientific meeting, held every two years, under the auspices of the International Organization for Biological and integrated Control (IOBC), regularly brings together scientists, engineers, industry players, stakeholders and professionals in post-harvest agrofood chain sanitation quality and safety to report the state of progress in applied research in stored product integrated protection. The socio-economic reality of increasing safety and quality traceability concern by foodstuffs purchasers, processed food manufacturers, food supply chains, consumer demand, and the application of recent European Regulations of "Hygiene package" and "Pesticide package" in all food chains accelerate the need for greater production of applied research results. The real challenge of developing new approaches and the application of necessary changes in current programs of integrated pest management in food processing plants and in stored products is impeded by a global reduction of human and material resources available for research and development on pests and pathogens contamination of durable agricultural products and derived food products.

Regardless of this critical situation, the 9th IPSP 2013 Conference is meeting the challenge for the transition towards an efficient control of post-harvest pests and pathogens with both the limitation chemical use and the improvement of risk control of infestation or contamination of durable foods by insect pests, molds or their impurities and toxins. At IPSP 2013 Conference, the optimal solutions for tackling this difficult challenge will be presented through 90 communications comprising the scientific program. Their authors come from more than 25 countries, confirming the interest of the international scientific community for this IPSP Conference.

The Local Organizing Committee of IPSP 2013 wishes to thank all the contributors making this Conference intriguing and relevant. We are grateful to IOBC and INRA for their advice and support for the organisation. We really appreciate the financial support of our sponsors and organizations and wish to thank the Scientific Advisory Committee for their advice in building the scientific program of this Conference. Thank you also to all the authors of communications for making the work of the Organizing Committee possible.

Avant-propos

La 9 ème Conference OILB-SROP sur la Protection Intégrée des Produits stockés (PIPS) est organisée pour la 1 ère fois par l'U.R. INRA "Mycologie et Sécurité des Aliments" (MycSA) du Centre INRA de Bordeaux-Aquitaine.

Cette Conférence biennale, placée sous les auspices de l'Organisation Internationale de Lutte Biologique et Intégrée (OILB), regroupe des scientifiques, ingénieurs, acteurs économiques et professionnels du monde entier intéressés par la maîtrise de qualité et la sécurité sanitaire dans les chaînes alimentaires post-récolte et pour faire l'état d'avancement de la recherche appliquée en Protection Intégrée des Produits stockés. Dans le contexte socio-économique actuel, la sécurité sanitaire et la traçabilité sont prioritairement exigées par les traders de produits alimentaires, les entreprises de transformations, les distributeurs ou les consommateurs. L'application des récents règlements européens du "paquet hygiène" et du "paquet pesticide" dans toutes les chaînes alimentaires accélère les besoins en résultats de recherche. Le challenge à relever consiste à développer de nouvelles approches et applications pratiques de solutions innovantes en protection intégrée contre les ravageurs et les contaminations des chaînes alimentaires après récolte. Cet objectif est contrecarré par la diminution constante du nombre de chercheurs qui se consacrent à ce domaine de recherche dans le monde. Toutefois, la 9 ème Conférence PIPS 2013 contribue à relever ce challenge de la transition vers une maitrise des ravageurs, agents pathogènes ou contaminants des denrées aprèsrécolte en limitant l'utilisation des pesticides tout en améliorant la prévision et la maîtrise des risques d'infestation ou de contamination des produits alimentaires durables par leurs impuretés ou leurs toxines. Pour la Conférence PIPS 2013, 90 communications ont été présentées pour contribuer à relever ce challenge. Leurs auteurs sont issus de plus de 25 pays différents, ce qui confirme l'intérêt de la communauté scientifique internationale pour cette thématique de la protection intégrée.

Le Comité d'Organisation de la Conférence PIPS 2013 remercie tous ceux qui ont contribué au succès et à l'attractivité de cette Conférence et en particulier l'OILB et l'INRA. Le soutien des entreprises et institutions qui ont participé financièrement à cette entreprise a été très apprécié. Le travail du comité scientifique pour effectuer la sélection difficile des communications orales ainsi que les efforts de l'ensemble des auteurs des communications (orales ou affichées) sont également à remercier pour la qualité et la rigueur scientifique de leurs prestations.

Francis Fleurat-Lessard Abstract: Acoustic detection of stored grain insects is based on their ability to generate sound (by feeding, flying, moving, probing for oviposition etc). In recent decades, acoustic devices of various kinds have been marketed for commodity inspection. The efficacy of acoustic devices in detecting the insects hidden inside the grain mass depends on many factors such as the sensor type, substrate structure, the interface between sensor and substrate, the assessment duration, the size and behavior of the insect, the density of the infestation, and the distance between the insects and the sensors. During the present study, adults and larvae of the most common pests of stored wheat have been detected in varying population density (0.1, 0.5, 1, 2, 10 & 50 per kg of wheat). Acoustic emissions of adults of Sitophilus oryzae, Rhyzopertha dominica, Tribolium confusum, Oryzaephilus surinamensis, Trogoderma granarium, Cryptolestes ferruginneus and Lasioderma serricorne and larvae of T. confusum, T. granarium, Ephestia kuehniella and Plodia interpunctella were recorded. The acoustic detector system consisted of a piezoelectric sensor mounted on the end of a probe that was pushed into the grain and a portable acoustic emission amplifier (AED-2010L, Acoustic Emission Consulting, Inc.) connected with a computer. All sound recordings took place in the lab using a sound insulated box in order to dampen background sound. Each recording lasted 3 minutes and was replicated 10 times. The system was 100% precise in negative predictions (predicting the absence of a pest when none is present) and considerably successful in positive predictions (predicting the presence of the pest when at least one individual is present in the grain mass). More analytically, prediction for infestation was rated falsely negative in 50-70%, 30-60%, 0-20% and 0-10% when actual population density was 0.1, 0.5, 1 and 2 adults / kg, respectively, irrespective of pest species. Respective values for larvae were 60-70%, 30-60, 0-40% and 0-30%. The system was always successful in detecting infestation when population density was > 2 individuals / kg irrespective of pest species or developmental stage. Our results are discussed on the basis of enhancing the use of acoustic devices as detection tools in storage facilities.

Keywords: Acoustic detection, stored wheat, piezoelectric sensor, sound analysis.

-The use of bio-test in the disinfestation of food industries

Abstract type: Poster

Süss L. 1 , Cassani S. 2 & Gallizia F. 1
Abstract: The proper use of bio-test in food industries, in the case of treatments with use of sulphuryl fluoride or hot air, is essential for assessing the results obtained.

In 2012 were given to several Pest Control Companies n. 490 bio-tests. Two special containers were prepared: one sized cm 7 x 5 x 4 h for placement on floors or in the crevices and the other, of sized cm 6 x 6 x 7 h to be hanged at different heights in empty silos. The treatments were carried out in mills and food industries. The species employed in the bio-tests were Tribolium confusum (stage egg, larvae, pupa, adult or mix population), Ephestia kuehniella (eggs and larvae), Plodia interpunctella (eggs and larvae), Sitophilus oryzae (adults and a mix population of eggs, larvae and pupae in rice) and Lasioderma serricorne (adults and a mix population of eggs, larvae and pupae in biscuits). Detailed operational guidelines were provided. Three TNT bio-test for controlling the pre-treatment mortality were also prepared for each group of bio-test at any treatment; one of the three to be kept in the laboratory and two others to be transported together with the Bio-test submitted to treatments. All the insects of any stage placed in the TNT-Bio-test resulted fully alive. Overall complete mortality occurred in 368 of 387 total, while in 19 cases survival was recorded. Obviously, at some points of the work processes have not been reached the desired saturation of sulphuryl fluoride at the temperature of hot air required to achieve a complete mortality. In these few cases and few sites localized treatments with pesticides became indispensable. These mechanisms for survival under conditions of water stress have been studied in several species. Despite its importance in stored grains there has been no study on the water balance in S. zeamais. Thus, this work aims to assess the water balance and aggregation behavior in pyrethroid resistant (with and without associated fitness cost) and susceptible strains, seeking to understand their potential. Our objective was to compare female and male adults of susceptible and resistant strains observing: (1) the moisture necessary for the survival;

Keywords

(2) differences in longevity (this response may be due to a balance of water in particular) and if these results may be related to insecticide resistance (and associated fitness costs); and also (4) if the aggregation behavior of this species could regulate water loss through the formation of groups of insects. The results indicated that S. zeamais was extremely vulnerable to low relative humidities in the absence of food. The three strains showed loss of water of approximately 30% which is a relatively low value, but enough to significantly compromise their survival. It was still possible to observe differences between the individual and the group in the conservation of water, but there are no differences between group size. The group retains more water than the insect isolated.

Keywords

:

  Sitophilus zeamais, water balance, insecticide resistance

14:00 -15:50 Session 2: Biology and ecology of S-P pests and pathogens in IPSP Wednesday 03 July Room "Badiane" 08:40 -10:20 Session 5: Chemical control in stored product IPM programs 15:10 -15:40 Coffee break (Poster removal) 15:40 -16:50 Session 6: IPSP decision support tools and integrated approach Chairpersons: Bhadriraju Subramanyam & Francis Fleurat-Lessard Page

	15:40 -16:10	Aspects related to decision support tools and IPM in food chain	P. Trematerra	119
		Technical and economic analysis of
	16:10 -16:30	pest management practice in 14 grain	K. Crepon	120
		elevators
		Influence of storage practices and kind	M.-P. Leblanc &
	16:30 -16:50	of structure on insect presence in grain	F. Fleurat-
		bulks	Lessard

121 16:50 -17:15 Closing Ceremony

	C. Athanassiou & F.
	Fleurat-Lessard

List of posters Session 1: Recent advances in IPSP and Post-Harvest IPM current trends Chairpersons: Phyllis Weintraub & M. Otilia Carvalho No. Page

	Integration of Isaria fumosorosea with DE for R. dominica control	Waqas Wakil	7
	Combination of B. hebetor and γ-radiation on Ephestia control in dates	A.A. AL-Taweel	8
	Using the parasitoid T. evanescens with
	pheromone traps in date palm orchards and in	A.A. AL-Taweel	9
	date warehouses
	Session 2:

Biology and ecology of S-P pests and pathogens useful in IPSP Chairpersons: George Opit & Christos Athanassiou

	Occurrence and identification of insect pests in sunflower seeds and rape	S. Dauguet	18
	Population density estimation of stored grain pests based on their acoustic emissions	P.A. Eliopoulos	19
	Detection of insect infestation in stored wheat based on their acoustic emissions	P.A. Eliopoulos	19 2
	The influence of plant characteristics on the field infestation	M. A. Amro	20
	Susceptibility of different pulse grain to infestation of cowpea beetle	M.A. Amro	21
	Effect of different temperatures and host density of Ephestia on B. hebetor efficacy	A.A. AL-Taweel	22
	Mycotoxicological Analysis of stored groundnuts in Southwest of Algeria	H. Meliani & A. Moussaoui	23
	The use of bio-test in the disinfestation of food industries	L. Süss & al.	24
	Sitophilus zeamais: water balance and behavior	N.M. Pinho Guedes	25
	Session 3:

Phytochemicals and semio-chemicals use in stored-product IPM Chairpersons: Catherine Regnault-Roger & Ali A. Isikber

	Study and analysis of Insecticidal & antimicrobial activity of essential oils Effectiveness of Laurus Nobilis and Mentha pullegium on S. zeamais Identification of pure compounds in apricot and apple electrophysiologically A. F. Ndomo Insecticidal effect of ozone against Plodia, F. Mouhouche M.G. Barros Tribolium, Cryptolestes & Oryzaephilus spp. C. Athanassiou et al. Investigations on microsporidian pathogens of P. interpunctella in Turkey K.F. Acar Insecticidal properties of peas homologous proteins (Pisum sativum L.) A. Mebarkia Stored-Product insects natural enemies in Sicilian wheat P. Suma Testing insecticidal efficacy of pure and combined natural substances on S. granarius S. Trdan Effect of fluctuating temperatures on the development of V.canescens S. Andreadis First application in France of heat disinfestation of a large wheat mill B. Vacquer Potential of Anisopterus calandrae and Lariophagus distinguendus as biocontrol agents of Callosobruchus maculatus O. Benkhellat Biological and molecular analysis of an isolated granulovirus Tecia solanivora against potato tuber moths A. Arroyo Natural occurrence of entomopathogenic fungi isolated from stored grain insect species in Punjab W. Wakil Session 5:	38 39 60 61 62 63 64 65 66 67 68

Chemical control in Stored-Product IPM programs Chairpersons: Shlomo Navarro & Yann Ciesla No. Page

	A comparison of efficacy of current insecticides with bio-based and mineral active substances	F. Fleurat-Lessard et al.	82
	Feeding deterrent activity of new alpha-asarone congeners	B. Lozowicka	83
	Weevil x insecticide does 'personality' matter?	R.N.C. Guedes	84
	Phosphine resistance in Cryptolestes ferrugineus from stored wheat in Oklahoma	C. Konemann	85
	Insecticidal effect of chlorantraniliprole against stored cereal product insect pests	N. Kavallieratos	86
	Insecticidal efficacy of thiamethoxam against T. confusum J. du Val	V. Goundy	87
	Determination of hexamethylentetramine in foods by GC-MS	Meehye Kim	88

25 -Sitophilus zeamais: water balance and behavior

  : bio-test, hot air, sulphuryl fluoride, food industries Water availability and temperature are the two most important abiotic variables that influence the distribution and abundance of insects. Insects have physiological and behavioral mechanisms that enable their survival in the absence of water for a long period.

	Abstract type: Poster
	Guedes N.M.P. 1 , Braga L.S. 1 , Silva L.B. 2 , Guedes R.N.C. 1 1 Dep. of Entomology, Federal University of Vicosa (UFV), 36570-000, Vicosa, MG, Brasil 2 Federal University of Piaui, Campus Bom Jesus, PI, Brasil
	E-mail: nmpguedes@gmail.com
	Abstract:

Università degli Studi di Milano-DEFENS, Via Celoria

2, 20133 Milano, Italy 2 Entomologia applicata, Laboratorio di RicercaAgroblu srl, via San Bernardo 35, 20017 Rozzano (Milano), Italy E-mail: guglielmo.cassani@agroblu.com

This year, the 9th

 Bourlaux, CS 20032, F-33882Villenave d'Ornon, France E-mail: francis.fleurat-lessard@bordeaux.inra.fr Abstract: A laboratory test-bench was built up to simulate high-temperature-short-time (HTST) exposure of infested grain samples by hidden stages of Sitophilus granarius or S. oryzae in a fluidized bed in order to model temperature-exposure time relationship. The reduction of emergence of the two weevil species (S. granarius in bread making wheat and S. oryzae in durum wheat) was determined after different exposure times in the fluidized bed exposure chamber crossed by 2 m.s -1 airflow at inlet air temperatures of 60, 90, 120 and 150°C. The influence of a grain moisture content from 14 to 19% w.b. on lethal time 50% (LT 50 ) and lethal time 99% (probit 8 or LT 100 ) was also observed. The main microbiological and technological characteristics checked on grain samples after heat treatment were measured. Higher the moisture content longer the exposure time required for the same mortality rate to be achieved. The "temperature-exposure time products" assessed at LT 50 and LT 100 were modeled as asymptotic negative regression curves. LT 100 was observed in less than 10 s exposure time for temperature at air inlet of 120 and 150°C, whatever the moisture content level. The reduction of fungal colonies was more important with long exposure time at moderately high temperature (90°C) than with short exposure time at very high temperature (150°C) (giving similar levels of insect mortality rate). For bread-making wheat, it was not observed significant changes in alveogramme rheological test parameters processed with white flour extracted from heattreated wheat samples, provided that air temperature and initial grain moisture content are at moderately high level (less than 150°C and 15% m.c.). The HTST treatment of cereal grain is an efficient and rapid method of control of weevil internal feeder stages and may be developed at practical scale for organic cereals or high-added-value use for special healthy cereal food disinfestation.

Keywords: Sitophilus granrius, Sitophilus oryzae, high-temperature-short-time treatment, wheat grain sample, fluidized-bed, quality characteristics changes. Abstract: As a result of high infestation of Dates by Ephestia spp. during its storage period which was for about 3-6 months before manufacturing and marketing it , Iraq used to apply Methyl bromide as fumigant agent to stop its infestation with Ephestia spp ,but because this fumigant agent proved to be carcinogen and ozone depleting agent , a decision was taken to stop using it internationally and nationally. This decision made Iraqi scientist to search for another methods to control Ephestia spp. or to decrease infestation to the lowest percentage. Therefore, Iraqi scientist recently concentrated on using IPM specifically biological control agents (Egg parasitoid, Trichogramma evanescens and larval parasitoid, Bracon hebetor in addition to the pheromone traps for this purpose. The research carried out into two date warehouses, one in Karbala province, while the other in the Babylon province which belong to the Iraqi company for manufacturing and marketing Dates. In these two date warehouses, the biological agents mentioned above were released periodically in addition to hanging pheromone traps. The results revealed that the percentage of preserving date in these two date warehouses were 97.5 and 98.1 % in comparison with 90.5 and 92.5 % into the control dates warehouses in which biological agents and pheromone traps did not used. Furthermore, to prove that this method for controlling Ephestia spp. in date warehouse was succeeded, we examined date from another date warehouse containing about 3250 tons of date packed in polyethylene bags and treated with methyl bromide to protect it from Ephestia spp. infestation. Unfortunately the percentage of preserving dates was only 91.7%. Finally our results showed that the mean number of Ephestia spp. captured by pheromone traps in the control warehouse which treated with methyl bromide was 18 times more than that captured into warehouses treated with biological agents mentioned above and pheromone traps.

-Application of Biological Control Agents and Pheromone Traps For Controlling Ephestia spp. in Date Warehouses of Two Provinces in Iraq

Keywords: Ephestia spp. date warehouses, pheromones traps, capture limitation, biocontrol species, Bracon hebetor 87

-Stored-product insect natural enemies in wheat industry in Sicily

Abstract type: Poster Suma P., Bella S., La Pergola A. & Russo A.

Dept of Agri-food and Environmental Systems Management -Applied Entomology sec. University of Catania, via S. Sofia, 100, 95123 Catania, Italy E-mail: suma@unict.it Abstract: The cultivation of wheat in Sicily (Italy) has its origins in ancient times and today it turns out to be the one that invests the largest areas in the island. Connected to this cultivation, a flourishing industry of primary and secondary processing has been developed and is particularly competitive in the global market thanks to the qualitative features of sicilian durum wheat. In the last decade research activities were undertaken in order to implement quality by studying infesting arthropods and by developing integrated control methods. In this context, during the monitoring activities of the stored grain pests carried out over the past decade in sevral grain industries and warehouses in Sicily, the following natural enemies were collected: Withius piger (Simon) (Pseudoscorpionida; Whiitidae), Xylocoris flavipes (Reuter) (Hemiptera; Anthocoridae), Anisopteromalus calandrae (Howard) (Hymenoptera; Pteromalidae), Habrobracon hebetor Say (Hymenoptera; Braconidae), Theocolax elegans (Westwood) (Hymenoptera; Pteromalidae), Venturia canescens (Gravenhorst) (Hymenoptera; Ichneumonidae), Cephalonomia sp. (Hymenoptera; Bethylidae). More extensive researches are currently in progress for some of these species. Here we discuss their potential use in the framework of the IPM programs.

Keywords: stored grain, predators, parasitoids, Italy.

-Testing the insecticidal efficacy of individual and combined use of four different natural substances against granary weevil (Sitophilus granarius) under laboratory conditions

Abstract type: Poster

Trdan S.

University of Ljubljana, Biotechnical Faculty, Jamnikarjeva 101, Ljubljana, Slovenia. E-mail: stanislav.trdan@bf.uni-lj.si Abstract: Laboratory experiment was carried out to evaluate the impact of different natural substances on the mortality of the granary weevil (Sitophilus granarius) adults. We tested the insecticidal properties of diatomaceous earth (commercial formulation SilicoSec®), quartz sand of local origin, Neem leaf powder (offered in a local market as fertilizer and plant strengthener), common beech wood ash, combination of SilicoSec® and wood ash, combination of Neem leaf powder and wood ash, combination of quartz sand and wood ash, combination of all 4 substances tested. Natural substances in a form of powder were mixed with grain in different dose rates. Wheat grains were treated with 450 and 900 ppm of SilicoSec® and quartz sand as self-based formulation, wood ash (5 weight (w)% and 2.5 w%) as self-based formulation, Neem leaf dust (1.25 w% and 2.5 w%). Individual treatments were evaluated as mixtures of SilicoSec (450 ppm) and wood ash (2.5 w%), Neem leaf powder (2.5 w%) and wood ash (2.5 w%); quartz sand (450 ppm) and wood ash (2.5 w%); SilicoSec® (225 ppm), wood ash (2.5 w%), Neem leaf powder (0.625 w%) and quartz sand (225 ppm). Control treatment was presented as untreated grain. Specific treatment was composed from 500±0.01 g of wheat grain and concentration of natural substance and it was replicated three times. Mortality of adults was evaluated seventh, fourteenth and twenty-first day after exposure. Insecticidal efficacy was tested at three different temperatures (20, 25, and 30°C) and two different relative humidity levels (55 and 75%). Significantly the highest mortality of adults was evaluated in treatment treated with formulation consisting of wood ash and SilicoSec® (66.38±2.64%), meanwhile wood ash (2.5 w%) provoked 63.35±2.74 % mortality. In general, mortality after 21 days reached 48.52±0.01% and it was also higher at lower humidity level (46.02±1.66%). Interactions between different factors of experiment will be presented, and suggestions for the practical use of the most effective substance will be given.

Keywords: Granary weevil, physical control, diatomaceous earth, quartz sand, neem leaf powder, common beech ash, mortality, efficacy. 90 Abstract: Thermal disinfestations of flour mill structures and equipment were traditionally used before the 1960's, but they have been replaced by methyl bromide fumigation until the international ban of methyl bromide in 2005. During the last decade, heat disinfestation of flour mills was developed in Northern America and in some European countries from the 2000's workshops of heat disinfestation of wheat mills were carried out. In France, the first industrial application of this technology was attempted in 2010 for the disinfestation of a large wheat mill and was carried out by Agronet Company in collaboration with INRA Research Laboratory in Bordeaux. The heating of the flour mill was performed from electric heat generators of Thermonox® technology (Germany). The electric heaters are forced heat air ventilators enabling to reach the target temperature of 50 to 55°C in about 10-12 h in all parts of the mill. Then, temperature is maintained at this level during 24 to 36 h and optimised from the indications of temperature sensors in order to obtain complete mortality of insects, whatever their development stage. Encaged insects (Tribolium castaneum and Rhyzopertha dominica) placed in the mill during mill heating were killed at more than 97%. Very few survivals were only observed on the floor at the ground level of the mill. The reproductive capacity of individuals surviving thermal exposure was severely reduced. This first industrial application of heat disinfestation process to a large wheat mill confirmed the results of laboratory studies performed earlier at INRA Laboratory. The economic evaluation of the heat treatment in the conditions described above revealed that it is competitive compared to fumigation with similar requirements in the preparation of the building before treatment. This technology seems very promising for periodic eradication of insect colonies in cereal processing industries without use of pesticide.

-First application in France of heat disinfestation of a large wheat mill

Keywords: Wheat mill, electric heat generator, Thermonox® technology, heat disinfestation, industrial scale, stored-product insect, complete mortality.

Abstract: Technical audits were achieved in 14 grain elevators in order to record pest management practices and to evaluate the cost of insect control management practices. Stored grain elevators that were selected for this study were representative of the different ways of insect pest prevention and control system in France. The selection of storage sites was based on the results of a previous study led on 95 elevators that identified 6 keyfactors correlated with pest infestation: type of storage bin (vertical silo or 'flat' store), preventive sanitation of empty storage structures, grain cleaning, grain aeration, insecticide treatment on grain, grain temperature monitoring equipment. By combining these 6 factors, 7 modalities of pest management have been described. For each modality, two grain elevators that theoretically apply each modality have been sampled among the 95 elevators surveyed in Leblanc et al. surveys in 2010 and2011. The evaluation carried out after the visit of the selected storage facilities revealed that pest management was mainly based on chemical treatment whatever the kind of insect management system was applied. Although all the visited elevators were equipped with ambient-air aeration systems, the airflow rate and the cooling time were often found inadequate to efficiently cool all stored grain bins. Grain cleaning cannot be made systematically at reception because harvested grain lots needing cleaning are delivered at a much higher rate than the grain cleaner can receive. Pest management costs were assessed in the range 0.15 €/t to 1.40 €/t, according to the 7 modalities. The major variation factor was the type of pesticide used. The economic loss related to price penalties in the case of quality defect at the supply of domestic or export grain markets for cereals are not considered as a driving factor for insect pest management policy adaptation by grain-elevator managers. Bourlaux, CS 20032, 33882 Villenave d'Ornon, France E-mail: marie-pierre.leblanc@franceagrimer.fr Abstract: A survey of stored-wheat bulks in France was carried out during two years in spring season 2010 and 2011 to improve the knowledge about the relationships between grain storage practices, preventive sanitation, grain insecticidal treatment, stored-grain temperature management, and presence of insect species in grain samples. 95 wheat storage sites were surveyed each year (after 8-9 month storage). Besides the quantitative analysis of grain samples for insect presence and residues content, a questionnaire was submitted to grain-store managers to record their storage practices, the characteristics of their equipment for grain quality preservation, and the preventive and corrective means they use for insect control. The data from these two groups of variables were processed in a multidimensional statistical analysis to reveal significant correlations between grain management practices for sanitary quality maintenance and the level of presence of insects in wheat samples. It was observed a strong relation between the kind of storage structure and the frequency of insect presence in wheat samples. Flat storage of large grain bulks was showed the most risky storage structure vs. vertical storage in metal or concrete bins. A relation was observed between insect presence frequency in samples and stored grain temperature level and also, with the lack of temperature measurement equipment installed inside grain bins linked to cooling aeration systems. Preventive sanitation before grain harvest period, i.e. systematic cleaning and insecticide spraying of unfilled bins, was related to 50% reduction of insect presence frequency in corresponding grain samples. The preventive chemical treatment of harvested grain before long-term storage was not related to a limitation of insect presence frequency in samples taken after a 8-9-month storage period. The results of this representative survey give objective arguments for the implementation of good IPM practices and risk prevention for grain sanitary quality preservation during long-term storage periods.

Keywords

Keywords: Grain bulk sampling, grain storage site, national survey, insect presence frequency, insecticide residues content, storage practices, multidimensional data processing.

Authors of oral presentations (chronological order of presentation)

127 NOTES NOTES


† P<0

C Vs Exp

est destinée au dépôt

A dietary supplementation in antioxidants and leucine during and after immobilization allows an accelerated recovery of muscle mass in adult rats

Isabelle Savary-Auzeloux, Hugues Magne, Carole Migné, Marion Oberli, Denis Breuillé, Magali Faure, Karine Vidal, Marie Perrot, Lydie Combaret, Didier

Remond, et al.

To cite this version:

Isabelle

Introduction

Bed rest or inactivity result in muscle wasting (Phillips et al, 2009). Besides the alteration of physical performance associated with immobilization, the reduction of muscle mass leads also to an overall reduction of organism defenses. Muscle is indeed an important store of amino acids used to counteract the impact of deleterious environmental stresses.

A prevention of muscle loss during the immobilization period followed by a better recovery in then an important public health issue.

It is now well established that exercise is very efficient to prevent muscle mass loss following immobilization (Ferrando et al, 1997). However, exercise is not always relevant in some specific physio-pathological situations such as invalidating diseases or frailty for instance. Nutrition can be a surrogate anabolic strategy capable to preserve muscle mass during immobilization and to speed up the recovery period. The induction of an oxidative stress and a lowered response of protein synthesis to meal has been observed during immobilization. Nevertheless, these deleterious effects on muscle mass could be counteracted by a targeted nutritional strategy.

Protocol and experimental model Conclusion

  Savary-Auzeloux, Hugues Magne, Carole Migné, Marion Oberli, Denis Breuillé, et al.. A dietary supplementation in antioxidants and leucine during and after immobilization allows an accelerated recovery of muscle mass in adult rats. 1 st Montréal International Symposium of GRAPA : from Humans to Cells (MHC), May 2013, Montréal, Canada. , 2013. hal-02748300

  Aim of the study: To assess the beneficial effect of a combined nutritional strategy (leucine and antioxidants) to counteract muscle loss and sustain / stimulate muscle protein synthesis during immobilization and subsequent recovery in rats 265 adult rats (6 months) were subjected to unilateral hindlimb immobilization for 8 days (I8) and allowed to recover for 10 to 40 days (R10, R15, R20, R30, R40, n=18 per time). Animals were fed a control diet (C) or the same diet (EXP) supplemented with AOX (hesperitin-7-O-glucoside, vitamin E, green tea catechins, curcumin, rutin) (I0 to I8), AOX+leucine (AOX + LEU) (I8 to R15) and LEU (R15 to R40). Muscle mass and protein synthesis were measured in the post prandial (PP) and the post absorptive (PA) states in immobilized and non-immobilized gastrocnemius muscle. Data were analysed by ANOVA. The protein muscle mass was significantly (P<0.05) decreased by 39% both in the Control (C) and experimental diet (EXP) after the immobilization period (I8) relatively to I0 A significant (P<0.05) increased muscle protein mass was observed in the EXP group relatively to the C group during the recovery period : ie : 30 and 40 days after the cast removal. Absolute synthesis rate in the EXP group was above the C group during the recovery period (R40: +24% and +30% for EXP vs C in the PA and PP states respectively, P<0.05) The dietary supplementation in leucine and antioxidants : -Did not limit the muscle protein mass loss after immobilization -Accelerated muscle protein mass recovery via a stimulation of both the post prandial and post absorptive muscle protein synthesis (i.e. : throughout the entire day) which improved anabolism and shortened the duration of the recovery period. Hence, such a nutritional strategy can be a valuable means to promote recovery after disuse for adult patients unable to exercise during recovery. Absolute protein synthesis rate in immobilized gastrocnemius muscle A dietary supplementation in antioxidants and leucine during and after immobilization allows an accelerated recovery of muscle mass in adult rats


Marie-Christine Daunay

F Bletsos

J W Hennart

J P W Haanstra

G Van Der

Potentialities of wild relatives for eggplants and eggplants rootstocks breeding

Keywords: Eggplant, S. me/ongena, S. aethiopicum, S. macrocarpon, wild So/anum, Verticillium, Me/oidogyne incognita, resistance, grafting, genetic resources, breeding, rootstock

published or not. The documents may come

Breakthroughs in the Genetics and Breeding of Capsicum and Eggplant

Potentialities ofwild relatives for eggplants and eggplants rootstocks breeding.

Daunay M.C', Bletsos F,z, Hennart J.w. 3 , Haanstra J.P.W. 4 University (Nijmegen, The Netherlands) and at INRA (Montfavet, France). Part ofthese collections has been characterized for traits of agronomie interest during the so called EGGNET project (1999)(2000)(2001)(2002)(2003)(2004) within the frame of the European Union GENRES programme, project. Verticillium wilt and root knot nematode (Meloidogyne incognita) resistances have been screened for by artificial inoculation on young plantlets. Good levels of resistance have been identified in other So/anum species th an the weil known and partially resistant S. torvum and S. sisymbriifolium. Graft affinity experiences using the wild material as rootstock and S. melongena as scion have been carried out on young as weil as on adult plants and various levels of grafting affinity have been identified. The characterization results obtained are discussed in the light of the present understanding of interspecific cross compatibility between wild and cultivated eggplants. The results obtained clearly indicate the high potential of the wild diversity for eggplants and eggplants rootstocks breeding, as weil as the need to intensif)' the characterization ofthese collections for traits of agronomie interest.


est destinée au dépôt

Changes in tissue amino acid composition and protein metabolism in piglets due to a limiting supply of total sulphur amino acids

Jose Alberto Conde-Aguilera, Carlos Cobo Ortega, Nathalie Le Floc'H, Y.

Mercier, Jaap J. van Milgen

To cite this version:

Jose

Introduction & Objective

Materials & Methods

Piglets were divided in 2 groups and received a diet either deficient (TSAA-) or sufficient (TSAA+) in total sulphur amino acids for 10 d. Diets were formulated to meet the nutritional recommendations except for the Met and total sulphur amino acid supplies in TSAA-, which were 31 and 28% below requirements, respectively. Selected samples were collected for analysis.

Results & Discussion

Performance and muscle protein metabolism Amino acid composition of longissimus dorsi (LM), rhomboideus (RM) and semitendinous (SM) muscles, liver, proximal and distal jejunum, ileum, kidney and skin

Conclusion

The animal shows a certain plasticity of tissues to respond to a limiting total sulphur amino acid supply. This questions the use of a constant amino acid profile to establish amino acid requirements. The functional consequences of these changes remain to be studied. In pigs, the amino acid requirements are usually evaluated through body weight gain or N retention, which assume that the amino acid composition of the total body protein is constant. However, there are doubts on the validity of this hypothesis. A deficient total sulphur amino acids (Met+Cys) supply has been shown to modify the amino acid profile of tissue proteins (Conde-Aguilera et al., 2010), but mechanisms are still unknown.

The objective of this study was to evaluate the response of piglets to a deficient total sulphur amino acid supply in terms of protein synthesis, activities of proteolytic enzymes, and the amino acid composition of body tissues.

Changes in tissue amino

  Alberto Conde-Aguilera, Carlos Cobo Ortega, Nathalie Le Floc'H, Y. Mercier, Jaap J. van Milgen. Changes in tissue amino acid composition and protein metabolism in piglets due to a limiting supply of total sulphur amino acids. 4th International symposium on energy and protein metabolism and nutrition, Sep 2013, Sacramento, United States. Wageningen Academic Publishers, EAAP Publication, 134, 2013, EAAP Publication. hal-01210462

  weight; 1 g of protein synthesized per day per mg of RNA; 2 relative fluorescence units per min per mg of protein


Conclusion

The MAGIC population shows greater phenotypic diversity than the founders lines, including new phenotypes. This diversity can directly be introduced on breeding programs. When compared to bi-parental populations, MAGIC populations present around the 60% of increase in recombination events. Six different QTLs related with fruit weight were detected, showing the great power of MAGIC panels to map complex quantitative characteres. This is coupled with a great precission, QTLs co-localize with already cloned genes. This approach allowed us to estimate the different founder line alleles on the quantitative character. This differences allow the dessign of new combinations of alleles to tune the final phenotypes in breeding programs.

Bibliography

Introduction Materials and methods

Results : Analysis of Multi-allelic Advance Generation Inter-cross

Identifying the genes controlling the variation of quantitative traits is a key goal for breeders. Genetic variations underlying quantitative traits (QTL) have been mapped by traditional linkage mapping, however this is limited to the analysis of traits differing between two lines. Thanks to the increase in molecular markers, genome-wide association studies (GWAs) were then proposed, nevertheless, the results might be limited by linkage disequilibrium. In contrast, analysis of multi allelic advanced generation intercross (MAGIC) populations allow a wide range of variability to be analyzed and avoids dealing with population structure.

We have constructed a MAGIC population by crossing 8 tomato lines, representing a wide range of genetic diversity within the species. A set of 1536 SNPs markers was selected to genotype the MAGIC population and used to construct a linkage map. The power and precision to detect QTLs controlling quantitative characters was tested.

Increasing diversity

Increasing recombination

Power to map quantitative characters The 8 founders lines were re-sequenced and mapped against the tomato reference genome (TGC, 2012 Comparasion between the MAGIC genetic map, with bi-parental populations maps (Sim et al, 2012). Recombination increases 60%.

MAGIC EXPIM EXPEN

Mapping fruit weight control. Based on the QTL profiling with mpMap we detected 6 QTLs, for five of them our possition co-localized with already detected QTLs. For kown genes like fw2.2 we are less than 1Mb from the gene. GWAs gives associations at similar locations.

QTL profiling GWAS Manhattan Plot

Vilmorin & Cie

  INRA, UR1052, GAFL, BP94,F-84143 Montfavet, France (mathilde.causse@avignon.inra.fr) (2) Northwest A&F University, College Horticulture, Yang Ling, Shaanxin, 712100, P.R.China (3) INRA, EPGV, Centre de Génotypage, Evry, France A MAGIC population was developed from four Solanum lycopersicum lines (Levovil, Stupicke Polni Rane, LA0147, Ferum) and four cherry type tomato (S. l. var cerasiforme) lines (Cervil, Criollo, Plovdiv 24A, LA1420), cross scheme detailed down. The population, formed by 400 lines, was phenotyped at two different locations in the South of France. Plant, flower and fruit characteristics were scored. Fine metabolite and enzyme profilings were carried out for a subset of 130 lines.

We thank Emma Huang for helpful advices with statistical analysis. The project is funded by the ANR Genomics, project MAGICTom-SNP, in collaboration with Vilmorin & Cie.

EPGV Evry


Dragan Milenkovic

Aurelie Mauray

André Mazur

Catherine Felgines

Christine Morand

Bilberry anthocyanin-rich extract exerts atheroprotective property through complex molecular mechanism of action

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

Bilberry anthocyanin-rich extract exerts atheroprotective property through complex molecular mechanism of action

Anthocyanins are water-soluble plant pigments that belong to the large group of polyphenols and more specifically to the subclass of flavonoids and bilberry is one of the richest sources of anthocyanins. Dietary intake of anthocyaninrich foods has been associated with a reduced risk of coronary heart disease and reduction of atherosclerotic lesions development in mice. However, little is known about the molecular mechanisms underlying the cardiovascular protective effect of anthocyanins.

The aim of our studies was to evaluate the effects of a bilberry anthocyanin-rich extract, when provided by the diet at a nutritional dose, on the development of atherosclerosis in apolipoprotein E-deficient mice and explore the in vivo mechanisms of action using a global transcriptomic approach.

After a 16-week supplementation, a significant reduction of the size of atherosclerotic plaques was observed as compared to the control one (-15 %,). The plasma antioxidant capacity was not modified by the bilberry extracts, neither were levels of aortic F2isoprostanes nor hepatic TBARS. Microarray analyses revealed that the BEsupplemented diet affected the expression of 1261 genes in aortas. Bioinformatic analyses indicated that these genes are involved in the regulation of processes underlying atherosclerosis development, such as cell adhesion, migration, inflammation, or angiogenesis. The gene expression profile suggests lower monocyte infiltration into aorta and consequently lesser atherosclerosis development. In the liver, the anthocyanin-rich extract affected the expression of 2,289 genes; genes involved in pathways such as cholesterol metabolism, VLDL removal, reverse cholesterol transport but also inflammatory responses.

In conclusion, supplementation of the diet with bilberry anthocyanin-rich extract led to a significant inhibition of plaque development in apolipoprotein E deficient mice without an effect on oxidative stress parameters, suggesting the implication of other mechanisms of action. The use of holistic transcriptomic approach provided new and global integrative view of molecular mechanisms involved in the preventive action of bilberry anthocyanin-rich extract against atherosclerosis. Globally, bilberry extract induced changes in gene expression to an anti-inflammatory profile, which could be related to its anti-atherogenic properties.

  Dragan Milenkovic a , Aurelie Mauray a,b , Andrzej Mazur a , Catherine Felgines b , Christine Morand a

	a INRA, Human Nutrition Unit, UMR1019, INRA-Clermont Ferrand/Theix, 63122 Saint-Genès-
	Champanelle, France
	b Laboratoire de Pharmacognosie, Université Clermont 1, UFR Pharmacie, 63001 Clermont-
	Ferrand, France


France Morio

JF Martin

Session 7 -Optimizing phenotypic flexibility, physiological benefits and healthy ageing Differences in metabolic trajectories and flexibility revealed by metabolomics during overfeeding in normal and overweight subjects

Shifts in dietary pattern, food composition and processing as well as a modification in physical activity are linked to nutritional transitions. In the context of deleterious behavior, transition has led to an energy imbalance not only in the Western world but also in emerging countries with an increasing prevalence of overweight and obesity. Obesity is associated with increased risk of metabolic syndrome which is a complex disease involving dysregulation of several metabolic pathways. The purpose of the present work was to compare metabolic trajectories followed by free living non-obese overweight and lean subjects during a moderate weight gain induced by a lipid-enriched supplementation of the diet. Metabolomics allowed us determining early changes in metabolic signatures with indicators of metabolic alterations.

Thirty eight male adult subjects, 19 normal weight (BMI: 20-24.9 kg.m2, no family history of obesity; NW) and 19 overweight (BMI: 25-29.9 kg.m2, known family history of obesity; OW) were recruited and submitted for 56 days to an overfeeding protocol consisting in adding an excess of 3,300 kJ/d to their usual diet. Conventional anthropometric and metabolic parameters were measured over time. Plasma and urine samples were collected at different time points for an untargeted metabolomic approach. Deproteinized plasma and diluted urine samples were analyzed using UPLC-QToF-Micro mass spectrometer. Univariate (ANOVA) and multivariate (OSC-PLS) models were used to explore the kinetic differences between OW and NW subjects. Metabolite identifications were performed using public databases and complementary analyses on a LTQ Orbitrap Velos high resolution mass spectrometer. For most of the anthropometric, biochemical, metabolic parameters and for some plasma metabolomic data, the phenotypes were discriminated at any time of the kinetic. On the whole, plasma metabolite signatures evolve similarly in NW and OW subjects. In comparison with the NW, lower abundances of plasma highly unsaturated lysophospholipids were observed in OW while increased levels of saturated isomers occurred with the intervention in both groups. Urinary metabolomic profiles clearly evidenced different metabolic trajectories between groups, generally characterized in OW by an increase over time of short-and medium-chain acylcarnitines and bile acids. Changes in metabolic signatures induced by the overfeeding protocol occurred at different time points for the NW and OW volunteers. Therefore, the comprehensive metabolic profiling provided by metabolomics revealed a differential response to the nutritional intervention in urine metabolomes of NW and OW, indicating dissimilar metabolic flexibility. Changes with weight status of plasma and urine metabolites, mostly related to modifications in fatty acid β-oxidation and metabolism, and inflammation indicate a greater metabolic flexibility in NW subjects. In conclusion, this study demonstrates that untargeted metabolomics allows detecting subtle metabolic changes linked to differences in metabolic flexibility that were not evidenced by a classical approach. Keywords metabolic trajectory; metabolomics; overweight; human nutritional intervention


Keywords: apprentissage par renforcement, itération de la politique, réseau de neurones cascadés, fonctions de base

Expansion incrémentale des fonctions de base en apprentissage par renforcement utilisant un réseau de neurones cascadés Résumé : En apprentissage automatique, en plus des algorithmes eux-mêmes, la représentation des données est un point d'une très grande importance. Les efforts effectués au niveau du pré-traitement sont un ingrédient clé du succès. Sur une tâche donnée et un jeu de données fixé, un algorithme très peu performant peut produire d'excellents résultats si la représentation des données est changée, que ce soit en terme d'efficacité de l'algorithme, ou en termes de qualité de la solution. Malgré la littérature conséquente sur le sujet, le problème du choix de la représentation des données est toujours ouvert. Dans ce papier, nous proposons d'approcher le problème dans le contexte de l'apprentissage par renforcement. En particulier, nous nous intéressons à la découverte d'une "bonne" représentation des données. Nous mettons en oeuvre cette approche avec l'algorithme LSPI. Pour cela, nous utilisons un réseau de neurones cascadés (cascade-correlation network) qui génère automatiquement un ensemble de fonctions de base qui mène à une meilleure approximation de la fonction valeur, et, par voie de conséquence, à des politiques de meilleures qualité. Nous montrons que cette approche est praticable sur un ensemble de problèmes.

Introduction

Reinforcement learning (RL) is the problem faced by an agent that is situated in an environment and must learn a particular behavior through repeated trialand-error interactions with it [START_REF] Sutton | Reinforcement Learning: An Introduction[END_REF]; at each time step, the agent observes the state of the environment, chooses its action based on these observations and in return receives some kind of "reward", in other words a reinforcement signal, from the environment as feedback. The aim of the agent is to find a policy, a way of choosing actions, that maximizes its overall gain -a function of rewards, such as the (discounted) sum or average over a time period. RL has been and is being extensively studied under different settings (online / offline, discrete / continuous state-action spaces, discounted / average reward, perfect / imperfect state information; etc.) and various methods and algorithms (dynamic programming, Monte Carlo methods, temporal-difference learning, etc.) have been proposed. One point common to all such approaches is that, regardless of how they do it what is being produced as a solution is a mapping from inputs, observations, to outputs, actions. It is natural that different approaches may require or prefer the input data be in different forms, but still the same data can be represented in many different ways conforming to the specified form. This brings up the questions of what the best representation of input data is for a given method or algorithm, and how it can be found.

In particular, in this paper, we will focus on Least-Squares Policy Iteration (LSPI) algorithm [START_REF] Lagoudakis | Least-squares policy iteration[END_REF] which uses a linear combination of basis functions to approximate a state-action value function and learn a policy from a given set of experience samples. The basis functions map state-action tuples into real numbers, and each basis function performs a mapping that is different from the others. From the point of view of LSPI, the real input becomes the values of basis functions evaluated for given state-action pairs. Therefore, the set of basis functions that is being employed directly affects the quality of the solution, and the question transforms into "what is the set of best basis functions?". We seek to provide a possible answer to this question by proposing a method that incorporates a cascade correlation learning architecture into LSPI and iteratively adds new basis functions to a set of initial basis functions. In Section 2, we first introduce policy iteration and the LSPI algorithm. Sections 3 describes cascade correlation learning architecture followed by the details of the proposed method for basis function expansion in Section 4. Section 5 presents empirical evaluations on some benchmark problems, and a review of related work can be found in Section 6. Finally, Section 7 concludes.

Least-Squares Policy Iteration

A Markov decision process (MDP) is defined as tuple (S, A, P, R, γ) where S is a set of states, A is a set of actions, P(s, a, s ′ ) is the transition function which denotes the probability of making a transition from state s to state s ′ by taking action a, R(s, a) is the expected reward function, and γ is the discount factor that determines the importance of future rewards. A policy is a probability distribution over actions conditioned on the state; π(s, a) denotes the probability that policy π selects action a at state s. An optimal policy maximizes the expected total discounted reward from any initial state if followed. Policy iteration is a general framework for finding an optimal policy; starting from an initial policy, two basic steps, namely policy evaluation followed by policy improvement, are applied consecutively and iteratively until convergence to an optimal policy is achieved or certain stopping criteria is met. Let π i be the policy at iteration i. Policy evaluation step consists of finding the state value function,

V πi (s) = E at∼πi ∞ t=0 γ t r t |s 0 = s
or the state-action value function,

Q πi (s, a) = E at∼πi,t>0 ∞ t=0 γ t r t |s 0 = s, a 0 = a
of the current policy, where a t is the action chosen by π i and r t is the reward received at time t. In some restricted cases1 , this can be accomplished by solving numerically or analytically the system of the Bellman equations

Q πi (s, a) = R(s, a) + γ S P(s, a, s ′ )V πi (s ′ )ds ′ (1)
V πi (s) = A π i (s, a)Q πi (s, a)da (2)
The integrals over state and action spaces are replaced by finite sums in the discrete case. The right-hand side of the equations applied to a given state(action) value function for a policy π define the Bellman operators denoted by T π . Alternatively, one can employ Monte Carlo methods by sampling multiple trajectories (i.e. a sequence s 0 a 0 r 0 s 1 . . . of states, actions and rewards) and evaluate the expectations by taking the average value over such roll-outs, or use temporal difference learning in which the Bellman equations are considered as update rules and state(-action) value function estimate is successively updated based on the previous estimates. In the policy improvement step, the state(action) value function obtained in the policy evaluation step is used to derive a new policy π i+1 which would perform at least as good as π i , i.e. satisfies V πi+1 (s) ≥ V πi (s) for all s ∈ S. For a deterministic policy, this can be realized by defining π i+1 greedy with respect to π i as

π i+1 (s) = arg max a∈A Q πi (s, a) (3)
Policy iteration is guaranteed to converge to an optimal policy in a finite number of steps if both state and action spaces are finite, the value function and the policy are represented perfectly and the policy evaluation step is solved exactly. However, in most cases it may not be possible to fulfill these requirements (eg. in problems with large or infinite state and action spaces) and the value function and/or the policy need to be approximated, leading to the so-called approximate policy iteration approach. It has been shown that if the error in the approximations are bounded then approximate policy iteration generates policies such that their performance is also bounded with respect to the optimal policy, yet the performance bound can be arbitrarily large as the discount factor, γ, gets closer to 1 [START_REF] Bertsekas | Neuro-Dynamic Programming[END_REF][START_REF] Munos | Error bounds for approximate policy iteration[END_REF].

Least-Squares policy iteration (LSPI) is an off-line and off-policy approximate policy iteration algorithm proposed by [START_REF] Lagoudakis | Least-squares policy iteration[END_REF]. Rather than relying on continual interactions with the environment or a generative model, it works on a fixed set of samples collected arbitrarily. Each sample is of the form (s, a, r, s ′ ) indicating that executing action a at state s resulted in a transition to state s ′ with an immediate reward of r. The state-action value function is approximated by a linear form

Q π (s, a) = m-1 j=0 w j φ j (s, a)
where φ j (s, a) denote the basis functions and w j are the parameters. Basis functions, also called features, are arbitrary functions of state-action pairs, but are intended to capture the underlying structure of the target function and can be viewed as doing dimensionality reduction from a larger space to ℜ m . Typical examples of basis functions are polynomials of a given degree or radial basis functions, such as Gaussian and multiquadratics, each possibly associated with a different center and scale. Note that, in the case of discrete state-action spaces, this generic form also includes tabular representations as a special case, but in general the number of basis functions, m, is much smaller compared to |S| |A|.

Instead of representing the policy explicitly, LSPI opts to determine the action that is imposed by the current policy at a given state by directly evaluating Eq. 3 (hence the policy improvement step becomes inherent). This may not be a feasible operation when the number of actions is large or possibly infinite, except in certain cases where a closed form solution is achievable; however, in most problems this drawback may not pose a significant problem as the action space is generally less susceptible to discretization compared to the state space.

Given a policy π i+1 , greedy with respect to Q πi which is defined by the set of parameters w πi j , LSPI performs the policy evaluation step and determines Q πi+1 , in other words the corresponding set of new parameters w πi+1 j , by invoking an algorithm called LSTDQ. One can easily observe that by definition the stateaction value function Q π of a policy π is necessarily a fixed point of the Bellman operator, i.e. Q π = T π Q π (Eq. 1), which also holds for Q πi+1 . Due to the specific choice of linear function approximation, any Q π is confined to the subspace spanned by the basis functions {φ j }, and therefore, an approximation Q πi+1 to Q πi+1 which stays invariant under the Bellman operator may not exist. Instead, LSTDQ tries to find an approximation Q πi+1 which is equal to the orthogonal projection of its image under the Bellman operator. Such Q πi+1 also possesses the property that

Q πi+1 = arg min Q π T πi+1 Q πi+1 -Q π 2
A motivation for choosing this particular approximation is expressed as the expectation that the approximation should be close to the projection of Q πi+1 onto the subspace spanned by the basis functions if subsequent applications of the Bellman operator point in a similar direction. Without going into the details, given any N samples, LSTDQ finds Q πi+1 by solving the m × m system Aw πi+1 = b RR n 6505 where φ(s, a) = [φ 0 (s, a)φ 1 (s, a) . . . φ m-1 (s, a)] ⊤ is the row vector of basis functions evaluated at (s,a), and

A = 1 N N i=1 φ(s i , a i ) φ(s i , a i ) -γφ(s ′ i , π i+1 (s ′ i )) ⊤ , b = 1 N N i=1 φ(s i , a i )r i ,
both of which in the limit converge to the matrices of the least-squares fixedpoint approximation obtained by replacing Q πi+1 = Φw πi+1 in the system

Q πi+1 = Φ(Φ ⊤ Φ) -1 Φ ⊤ (T πi+1 Q πi+1 )
Here, Φ(Φ ⊤ Φ) -1 Φ ⊤ is the orthogonal projection and Φ denotes the matrix of the values of the basis functions evaluated for the state-action pairs. The details of the derivation can be found in the seminal paper [START_REF] Lagoudakis | Least-squares policy iteration[END_REF].

Algorithm 1 The LSPI algorithm.

input Set of samples, D, number of basis functions, m, basis functions, φ, discount factor, γ, stopping criterion, ǫ.

output w π Initialize weights w π . i ← 0 repeat i ← i + 1 w πi ← w π w π ← LSTDQ(D, m, φ, γ, w πi ) until w πi -w π < ǫ {weights have converged}
The LSPI algorithm presented in Algorithm 1 has been demonstrated to provide "good" policies within relatively small number of iterations. Furthermore, as it is possible to use a single and common sample set for all policy evaluations, LSPI makes efficient use of the available data; as such, it is quite suitable for problems in which data gathering process is time consuming and costly. However, the quality of the resulting policies depends on two important factors: the basis functions, and the distribution of the samples.

In an offline setting, one may not have any control on the set of samples and too much bias in the samples would inevitably reduce the performance of the learned policy. On the other hand, in an on-line setting, LSPI allows different sample sets to be employed at each iteration; thus, it is possible to fine tune the trade-off between exploration and exploitation by collecting new samples using the current policy.

Regarding the basis functions, the user is free to choose any set of functions as long as they are linearly independent (a restriction which can be relaxed in most cases by applying singular value decomposition). As shown in [START_REF] Lagoudakis | Least-squares policy iteration[END_REF], and in accordance with the generic performance bound on policy iteration, if the error between the approximate and the true state-action value functions at each iteration is bounded by a scalar ǫ, then in the limit the error between the optimal state-action value function and those corresponding to the policies generated by INRIA LSPI is also bounded by a constant multiple of ǫ. Therefore, selecting a "good" set of basis functions has a significant and direct effect on the success of the method.

In general, the set of basis functions is defined by the user based on domain knowledge, and usually in a trial and error fashion. They can either be fixed, or one can start from an initial subset of predefined basis functions and iteratively introduce remaining functions based on the performance of the current set, so-called feature iteration approach [START_REF] Bertsekas | Temporal differences-based policy iteration and applications in neuro-dynamic programming[END_REF]. However, as the complexity of the problem increases it also gets progressively more difficult to come up with a good set of basis functions. Generic approaches, such as regular grids or regular radial basis function networks, which are quite successful in small problems, become impractical due to the exponential growth of the state-action spaces with respect to their dimension. Therefore, given a problem, it is highly desirable to determine a compact set of such basis functions automatically. In the next section, we will first describe a particular class of function approximator called cascade-correlation networks, and then present how they can be utilized in the LSPI algorithm to iteratively expand the set of basis functions.

Cascade Correlation Networks

Cascade correlation is both an architecture and a supervised learning algorithm for artificial neural networks introduced by [START_REF] Fahlman | The cascade-correlation learning architecture[END_REF]. It aims to overcome step-size and moving target problems that negatively affect the performance of backpropagation learning algorithm. Similar to traditional neural networks, the neuron is the most basic unit in cascade correlation networks. However, instead of having a predefined topology with the weights of the fixed connections between neurons getting adjusted, a cascade correlation network starts with a minimal structure consisting only of an input and an output layer, without any hidden layer. All input neurons are directly connected to the output neurons (Figure 1a). Then, the following steps are taken:

1. All connections leading to output neurons are trained on a sample set and corresponding weights (i.e. only the input weights of output neurons) are determined by using an ordinary learning algorithm until the error of the network no longer decreases. This can be done by applying the regular "delta" rule, or using more efficient methods such as quick-prop or rprop. Note that, only the input weights of output neurons (or equivalently the output weights of input neurons) are being trained, therefore there is no back-propagation.

2. If the accuracy of the network is above a given threshold then the process terminates.

3. Otherwise, a set of candidate units is created. These units typically have non-linear activation functions, such as sigmoid or Gaussian. Every candidate unit is connected with all input neurons and with all existing hidden neurons (which is initially empty); the weights of these connections are initialized randomly. At this stage the candidate units are not connected to the output neurons, and therefore are not actually active in the network. Let s denote a training sample. The connections leading to a candidate unit are trained with the goal of maximizing the sum S over all output units o of the magnitude of the correlation between the candidate units value denoted by v s , and the residual error observed at output neuron o denoted by e s,o . S is defined as

S = o | s (v s -v)(e s,o -e o )|
where v and e o are the values of v s and e s,o averaged over all samples, respectively. As in step 1, learning takes place with an ordinary learning algorithm by performing gradient ascent with respect to each of the candidate units incoming weights:

∂S ∂w i = s,o σ o (e s,o -e o )f ′ s I i,s
where σ o is the sign of the correlation between the candidates value and output o, f ′ s is the derivative for sample s of the candidate units activation function with respect to the sum of its inputs, and I i,s is the input the candidate unit received from neuron i for sample s. Note that, since only the input weights of candidate units are being trained there is again no need for back-propagation. Besides, it is also possible to train candidate units in parallel since they are not connected to each other. By training multiple candidate units instead of a single one, different parts of the weight space can be explored simultaneously. This consequently increases the probability of finding neurons that are highly correlated with the residual error. The learning of candidate unit connections stops when the correlation scores no longer improve or after a certain number of passes over the training set. Now, the candidate unit with the maximum correlation is chosen, its incoming weights are frozen (i.e. they are not updated INRIA in the subsequent steps) and it is added permanently to the network by connecting it to all output neurons (Figure 1b andc). The initial weights of these connections are determined based on the value of correlation of the unit. All other candidate units are discarded. Until the desired accuracy is achieved at step 2, or the number of neurons reaches a given maximum limit, a cascade correlation network completely selforganizes itself and grows as necessary. One can easily observe that, by adding hidden neurons one at a time and freezing their input weights, training of both the input weights of output neurons (step 1) and the input weights of candidate units (step 3) reduce to one step learning problems. Since there is no error to back-propagate to previous layers the moving target problem is effectively eliminated. Also, by training candidate nodes with different activation functions and choosing the best among them, it is possible to build a more compact network that better fits the training data.

One observation here is that, unless any of the neurons has a stochastic activation function, the output of a neuron stays constant for a given sample input. This brings the possibility of storing the output values of neurons which in return reduces the number of calculations in the network and improve the efficiency drastically compared to traditional multi-layer back-propagation networks, especially for large data sets. But more importantly, each hidden neuron effectively becomes a permanent feature detector, or to put it another way, a basis function in the network; the successive addition of hidden neurons in a cascaded manner allows, and further, facilitates the creation of more complex feature detectors that helps to reduce the error and better represent the functional dependency between input and output values. We would like to point out that, this entire process does not require any user intervention and is wellmatched to our primary goal of determining a set of good basis functions for function approximation is RL, in particular within the scope of LSPI algorithm. We will now describe our approach for realizing this.

Using Cascade Correlation Learning Architecture in LSPI

As described in Section 2, in LSPI the state-action value function of a given policy is approximated by a linear combination of basis functions. Our aim here is to employ cascade correlation networks as function approximators and at the same time use them to find useful basis functions in LSPI. Given a reinforcement learning problem, suppose that we have a set of state-action basis functions Φ = {φ 1 (s, a), φ 2 (s, a), . . . , φ m (s, a)}. Using this basis functions and applying LSPI algorithm on a set of collected samples of the form (s, a, r, s ′ ), we can find a set of parameters w i together with an approximate state-action value function

Q(s, a) = m i=1 w i φ i (s, a)
and derive a policy π which is greedy with respect to Q. Let N be a cascade correlation network with m inputs and a single output having linear activation function (i.e. identity function). In this case, the output of the network is a linear combination of the activation values of input and hidden neurons of the network weighted by their connection weights. Initially, the network doesn't have any hidden neurons and all input neurons are directly connected to the output neuron. Therefore, by setting the activation function of the i th input neuron to φ i and the weight of its connection to the output neuron to w i , N becomes functionally equivalent to Q and outputs Q(s, a) when all input neurons receive the (s, a) tuple as their input. Now, the Bellman operator T π is known to be a contraction in L ∞ norm, that is for any state-action value function Q, T π Q is closer to Q π in the L ∞ norm, and in particular as mentioned in Section 2, Q π is a fixed point of T π . Ideally, a good approximation would be close to its image under the Bellman operator. As opposed to the Bellman residual minimizing approximation, least-squares fixedpoint approximation, which is at the core of the LSPI algorithm, ignores the distance between T π Q and Q but rather focuses on the direction of the change. Note that, if the true state-action value function Q π lies in the subspace spanned by the basis functions, that is the set of basis functions is "rich" enough, fixedpoint approximation would be solving the Bellman equation and the solution would also minimize the magnitude of the change. This hints that, within the scope of LSPI, one possible way to drive the search towards solutions that satisfy this property could be to expand the set of basis functions by adding new basis functions that are likely to reduce the distance between the found state-action value function Q and T π Q over the sample set.

For this purpose, given a sample (s, a, r, s ′ ), in the cascade correlation network we can set r + γ Q(s ′ , π(s ′ )) as the target value for (s, a) tuple, and train candidate units that are highly correlated with the residual output error, i.e. Q(s, a)r + γ Q(s ′ , π(s ′ )) . At the end of the training phase, the candidate unit having the maximum correlation is added to the network by transforming it into a hidden neuron, and becomes the new basis function φ m+1 ; φ m+1 (s, a) can be calculated by feeding (s, a) as input to the network and determining the activation value of the hidden neuron. Through another round of LSPI learning, one can obtain a new least-squares fixed-point approximation to the state-action value function Q ′ (s, a) = m+1 i=1 w ′ i φ i (s, a) which is more likely to be a better approximation also in the sense of Bellman residual minimization. The network is then updated by setting the weights of connections leading to the output neuron to w ′ i for each basis function. This process can be repeated, introducing a new basis function at each iteration, until the error falls below a certain threshold, or a policy with adequate performance is obtained. We can regard this as a hybrid learning system, in which the weights of connections leading to the output neuron of the cascade correlation network are being regulated by the LSPI algorithm. Note that, the values of all basis functions for a given (s, a) tuple can be found with a feed-forward run over the network, and as stated before can be cached for efficiency reasons if desired. The complete algorithm that incorporates the cascade correlation network and basis function expansion to LSPI is presented in Algorithm 2.

One possible problem that may emerge with the proposed method is that, especially when the sample set is small, with increasingly complex basis functions there may be over-fitting, and the on-line performance of the resulting policy may degrade. This can be avoided by increasing the amount of samples, or alternatively a cross-validation approach can be ensued. Suppose that for a particular reinforcement learning problem, we are given multiple sample sets. The intuition is that a set of "good" basis functions should give rise to "good" policies and similar value functions for all sample sets. By applying LSPI algorithm independently on each sample set but training a single set of candidate units over all sample sets, in other words having a common set of basis functions, one can obtain basis functions, and consequently policies, that are less biased to training data.

Experiments

We have evaluated the proposed method on three problems: chain walk [START_REF] Lagoudakis | Least-squares policy iteration[END_REF], pendulum swing-up and multi-segment swimmer. Chain walk is an MDP consisting of a chain of n states. There are two actions, lef t and right, which succeed with probability 0.9, or else fail, moving to the state in the opposite direction. The first and last states are dead-ends, i.e. going left at the first state, or right at the last state revert back to the same state. The reward is 1 if an action ends up in a predefined set of states, and 0 otherwise. The pendulum swing-up and multi-segment swimmer problems are dynamical systems where the state is defined by the position and velocity of the elements of the system, and actions (applied forces) define the next state. These are non-linear control tasks with continuous state spaces. In pendulum, the aim is to keep a simple pendulum actuated by a bounded torque in vertical upright position. Since the torque available is not sufficient, the controller has to swing the pendulum back and forth to reach the goal position. The state variables are the angle of the pendulum and its angular speed. We used two discrete actions, applying a torque of -5, and 5. The reward is defined as the cosine of the angle of the pole. In swimmer, a swimmer moving in a two dimensional pool is being simulated.

The swimmer is made of n (n ≥ 3) segments connected to each other with n -1 joints. The goal is to swim as fast as possible to the right by applying torques to these joints and using the friction of the water. There are 2n + 2 state variables consisting of (i) horizontal and vertical velocities of the center of mass of the swimmer, (ii) n angles of its segments with respect to vertical axis and (iii) their derivatives with respect to time; the actions are the n -1 torques applied at segment joints. The reward is equal to the horizontal velocity of the swimmer. The system dynamics and more detailed information about Swimmer problem can be found in [START_REF] Coulom | Reinforcement Learning Using Neural Networks with Applications to Motor Control[END_REF].

In all problems, we started from a set of basis functions consisting of the following:

1. a constant bias function (i.e. 1), 2. a basis function for each one of the state variables, which returns the normalized value of that variable, and 3. a basis function for each possible value of each control variable, which returns 1 if the corresponding control variable in the state-action tuple is equal to that value, and 0 otherwise.

Therefore, the number of the initial basis functions were 4 (1+1+2), 5 (1+2+2) and 3 + 4n for chain, pendulum and swimmer problems respectively, where n is the number of swimmer segments. In the LSPI algorithm, we set ǫ = 0.0001 and limit the number of iterations to 20. The samples for each problem are collected by running a random policy, which uniformly selects one of possible actions, for certain number of time steps (or episodes). In cascade correlation network, we trained an equal number of candidate units having Gaussian and sigmoid activation functions using RPROP method [START_REF] Riedmiller | A direct adaptive method for faster backpropagation learning: the rprop algorithm[END_REF]. In RPROP, instead of directly relying on the magnitude of the gradient for the updates (which may lead to slow convergence or oscillations depending on the learning rate), each parameter is updated in the direction of the corresponding partial derivative with an individual time-varying value. The update values are determined using an adaptive process that depends on the change in the sign of the partial derivatives. We allowed at most 100 passes over the sample set during the training of candidate units, and employed the following parameters: △ min = 0.0001, △ ini = 0.01, △ max = 0.5, η -= 0.5, η + = 1.2.

Figure 2 shows the results for the 50-state (numbered from 0 to 49) chain problem using 5000 samples from a single trajectory. Reward is given only in states 9 and 40, therefore the optimal policy is to go right in states 0-8 and 25-40, and left in states 9-24 and 41-49. The number of candidate units was 4. In [START_REF] Lagoudakis | Least-squares policy iteration[END_REF], using 10000 samples LSPI fails to converge to the optimal policy with polynomial basis function of degree 4 for each action, due to the limited representational capability, but succeeds with a radial basis function approximator having 22 basis functions. Using cascade-correlation basis expansion, after 10 basis functions near-optimal policies can be obtained and after 20 basis functions it converges to the optimal policy. The basis functions start from simpler ones and get more complex in order to better fit the target function. A set of basis functions generated in a sample run are presented in Figure 3.

The results for the pendulum problem is presented in Figure 4. For this problem, we again collected 5000 samples restarting from a random configuration every 20 time steps, and trained 10 candidate units. As new basis functions are added, the performance of the policies found by LSPI algorithm improves consistently attaining near-optimal levels. Also, the value function obtained after 40 iterations is very close to the true one and successfully captures the shape of the function including the sharp edges around the ridge (Figure 5). We observed a similar behavior on more complex 5-segment swimmer problem as presented in Figure 6. For this problem, we collected 100000 samples restarting from a random configuration every 50 time steps. The number of trained candidate units was 10 as in the pendulum problem, but we allowed RPROP to make more passes (a maximum of 200) over the sample set.

Related Work

Basis function, or feature, selection and generation is essentially an information transformation problem; the input data is converted into another form that "better" describes the underlying concept and relationships, and "easier" to process by the agent. As such, it can be applied as a preprocessing step to a wide range of problems and have been in the interest of the data-mining community, in particular for classification tasks. Following the positive results obtained using efficient methods that rely on basis functions (mostly, using linear approximation architectures) in various domains, it also recently attracted attention from the RL community.

In [START_REF] Menache | Basis function adaptation in temporal difference reinforcement learning[END_REF], Menache et al. examined adapting the parameters of a fixed set of basis functions (i.e, center and width of Gaussian radial basis functions) for estimating the value function of a fixed policy. In particular, for a given set of basis function parameters, they used LST D(λ) to determine the weights of basis functions that approximate the value function of a fixed control policy, and then applied either a local gradient based approach or global cross-entropy method to tune the parameters of basis functions in order to minimize the Bellman approximation error in a batch manner. The results of experiments on a grid world problem show that cross-entropy based method performs better compared to the gradient based approach.

In [START_REF] Philipp | Automatic basis function construction for approximate dynamic programming and reinforcement learning[END_REF], Keller et al. studied automatic basis function construction for value function approximation within the context of LSTD. Given a set of trajectories and starting from an initial approximation, they iteratively use neighborhood component analysis to find a mapping from the state space to a low-dimensional space based on the estimation of the Bellman error, and then by discretizing this space aggregate states and use the resulting aggregation matrix to derive additional basis functions. This tends to aggregate states that are close to each other with respect to the Bellman error, leading to a better approximation by incorporating the corresponding basis functions.

In [START_REF] Parr | Analyzing feature generation for value-function approximation[END_REF], Parr et al. showed that for linear fixed point methods, iteratively adding basis functions such that each new basis function is the Bellman error of the value function represented by the current set of basis functions forms an orthonormal basis with guaranteed improvement in the quality of the approximation. However, this requires that all computations are exact, in other words, are made with respect to the precise representation of the underlying MDP. They also provide conditions for the approximate case, where progress can be ensured for basis functions that are sufficiently close to the exact ones. Their application in the approximate case on LSPI is closely related to our work, but differs in the sense that a new basis function for each action is added at each policy-evaluation phase by directly using locally weighted regression to approximate the Bellman error of the current solution.

In contrast to these approaches that make use of the approximation of the Bellman error, including ours, the work by Mahadevan et al. aims to find policy and reward function independent basis functions that captures the intrinsic domain structure that can be used to represent any value function [START_REF] Sridhar Mahadevan | Representation policy iteration[END_REF][START_REF] Johns | Constructing basis functions from directed graphs for value function approximation[END_REF][START_REF] Mahadevan | Proto-value functions: A laplacian framework for learning representation and control in markov decision processes[END_REF]. Their approach originates from the idea of using manifolds to model the topology of the state space; a state space connectivity graph is built using the samples of state transitions, and then eigenvectors of the (directed) graph Laplacian with the smallest eigenvalues are used as basis functions. These eigenvectors possess the property of being the smoothest functions defined over the graph and also capture the nonlinearities in the domain, which makes them suitable for representing smooth value functions.

To the best of our knowledge, the use of cascade correlation networks in reinforcement learning has rarely been investigated before. One existing work that we would like to mention is by [START_REF] Rivest | Combining td-learning with cascadecorrelation networks[END_REF], in which a cascade correlation network together with a lookup-table is used to approximate the value function in an on-line temporal difference learning setting [START_REF] Rivest | Combining td-learning with cascadecorrelation networks[END_REF]. It differs from our way of utilizing the cascade correlation learning architecture to build basis functions in the sense that in their case, cascade correlation network purely functions as a cache and an approximator of the value function, trained periodically at a slower scale using the state-value tuples stored in the lookuptable.

Conclusion

In this paper, we explored a new method that combines cascade correlation learning architecture with least-squares policy iteration algorithm to find a set of basis function that would lead to a better approximation of the state-action value function, and consequently results in policies with better performance. The experimental results indicate that it is effective in discovering such functions. An important property of the proposed method is that the basis function generation process requires little intervention and tuning from the user.

In the proposed method, LSPI is run to completion at each iteration, and then a new basis function is generated using the cascade correlation training (Algorithm 2). This benefits from a better approximation for the current set of basis functions. An alternative approach would be to add new basis functions within the LSPI loop after the policy evaluation step. This may lead to better intermediate value functions and steadier progress towards the optimal solution, but the resulting basis functions may not be useful at later iterations. It is also possible to combine both approaches by temporarily adding new basis functions within the LSPI loop and then discarding them. We are currently investigating these possibilities.

Although, our focus was on LSPI algorithm in this paper, the approach is in fact more general and can be applied to other reinforcement learning algorithms that approximate the state(-action) value function with a linear architecture. We pursue future work in this direction and also apply the method to more complex domains.

INRIA

Figure 1 :

 1 Figure 1: (a) Initial configuration of a simple cascade-correlation network with two inputs and a single output (in gray). (b) and (c) show the change in the structure of the network as two new hidden nodes are subsequently added. Solid edges indicate input weights that stay fixed after the candidate training phase.

4 .

 4 Return back to step 1.

INRIA Algorithm 2

 2 The LSPI algorithm with basis function expansion using cascade correlation network. input Set of samples, D, number of basis functions, m, basis functions, φ, discount factor, γ, stopping criterion for LSPI, ǫ, number of candidate units, n. output w π Create a cascade correlation network N with m inputs and a single output, and set activation functions of input neurons to φ i . repeat w π ← LSP I(D, m, φ, γ, ǫ) Set the weight of connections in N leading to the output neuron to w π i . Calculate the residual output error, Q(s, a) -(r + γ Q(s ′ , π(s ′ ))) Train n candidate units on N . κ ← Candidate unit having the maximum correlation with the residual output error. Add κ to N . m ← m + 1 φ m ← κ {Function represented by κ} until termination condition is satisfied

Figure 2 :

 2 Figure 2: 50-state chain. (State-action value functions with 20 basis functions (top), and policy after every 2 basis functions (bottom).

Figure 3 :Figure 4 :

 34 Figure 3: Examples of basis functions for the 50-state chain problem.

Figure 5 :

 5 Figure 5: Q(s, π(s)) for the pendulum problem where π is the resulting policy.

Figure 6 :

 6 Figure 6: The results for the 5-segment swimmer.

RR n 6505

Such as problems with finite and small state-action spaces, or linear quadratic optimal control problems in which the underlying MDP model is known.INRIA


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It isn't all about Rugby: New Zealand and France unite to give Douglas-fir somatic embryogenesis protocols a workout

Cathy Hargreaves, Cathie B. Reeves, Claire Le Mette, Jean-François Trontin,

Marie-Anne Lelu-Walter

To cite this version: D ouglaS-Fir is the second most important exotic forest species in New Zealand next to radiata pine which is currently 89% of the planted estate. There is significant interest in genetically improving this species to provide diversification of the national planted forest estate away from radiata pine. Benefits include; the better matching of species to sites at higher altitude and cooler climates; minimising exposure to disease; timber would produce a wider range of forest products. Additionally it would give us a contingency species if radiata pine was to fail in some significant way. Breeding programs to improve and select the best Douglas-fir genes for New Zealand conditions are advanced and two seed orchards have been established in the South Island.

Propagation of this species is challenging, seed production is affected by climatic factors and production of control-pollinated seed is not straight forward. The combination of irregular seed crops, plagiotrophism and dormancy in cuttings as well as the limited life of stool beds (approximately 2 years as compared to 4 plus years, for radiata pine) make producing improved genetic planting stock problematic. Somatic embryogenesis could potentially address some of these issues via amplification of control-pollinated seed, production of non-plagiotrophic plants and arrest of maturation in clonal material while field testing takes place. There is potential to integrate somatic embryogenesis with nursery stoolbed production methods.

Somatic embryogenesis methods are established for this species overseas and part of the Weyerhaeuser forest strategy. We wanted to develop independent capability with this species using P. radiata protocols. Preliminary investigations were started in New Zealand to develop SE capability with Douglas-fir essentially adapting the P. radiata protocols we were most familiar with. We got to know our French collaborators and the importance of Douglas-fir in France (2.6 million m 3 harvested/year) via the two previous IUFRO Somatic Embryogenesis of Forest Trees conferences held in Whistler (Canada) and Suwon (Korea). INRA and FCBA have been developing breeding programs for growth rate, branching and other traits of interest for more than 40 years. Joint funding was obtained for two years from both the French and New Zealand governments via the Dumont d'Urville scheme to develop the collaboration. Progress with this species is a direct result of happy and vigorous discussions (and a bit of mutual rugby appreciation through 2011).

Using only slightly modified radiata pine protocols we were able to initiate cell lines, cryopreserve tissue, mature embryos and establish plants in the greenhouse. But, there were a few challenges. Culture rates using pine methods were satisfactory but the resultant tissue grew and many of the lines had a mixture of both embryogenic non embryogenic tissue. This was the most step with regard to developing capability with Douglas-fir at Scion. We are pleased to report that progress has been made with proliferation and this is the data we would like to concentrate on in this presentation. However, all the methods we are using will be discussed and data presented as appropriate on initiation, proliferation, cryopreservation, maturation and organogenesis.

Keywords: somatic embryogenesis, Douglas-fir, initiation, proliferation S1-10 somatic embryogenesis

01267976 It isn't all about Rugby: New Zealand and France unite to give Douglas-fir somatic embryogenesis protocols a workout

	C. HargreaveS 1 , C. reeves 1 , C. le MeTTé 2 , J.-F. TronTin 3
	& M.-a. lelu-walTer 2
	1 Scion, New Zealand, 2 Research Unit on Breeding, Genetic and Physiology of Forest Trees, France, 3 FCBA Technological In-
	stitute, Biotechnology and Advanced Forestry Department, Cestas, France
	cathy.hargreaves@scionresearch.com

Cathy Hargreaves, Cathie B. Reeves, Claire Le Mette, Jean-François Trontin, Marie-Anne Lelu-Walter. It isn't all about Rugby: New Zealand and France unite to give Douglas-fir somatic embryogenesis protocols a workout. 2. International Conference of the IUFRO Working Party 2.09.02, "Somatic Embryogenesis and Other Vegetative Propagation Technologies", Jun 2012, Brno, Czech Republic. 2012, Integrating vegetative propagation, biotechnologies and genetic improvement for tree production and sustainable forest management. Book of abstracts. hal-