URFM Eric Rigolot

Prospect on prescribed burning development in France

 Analysis of quantitative indicators:

• total area burnt,

• number of PB operations,

• distribution among PB objectives,

• mean area per operation,

• response rate to PB demand.  Identification of alternative qualitative indicators  Survey among 23 PB teams to identify the constraints and possible levers for PB development.

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Eric RIGOLOT / PB prospect in France A practice well below its potential

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Eric



  PB has been developed in France for more than 25 years,  Under the umbrella of the PB National Working Group,  Institutional recognition through dedicated legislation and regulations,  A specific training system. 5 years rolling average of annual area prescribed burnt in France ha … but a stagnation of PB area To identify the constraints to the development of the practice,  possible levers for further extend of PB activities Data base (2001-2016) on PB practice in France of the PB national working group.



  Short PB windows during fall  Lack of frost  Drought conditions during some winters  Unavailabilty of the staff  Diversity of the duties PB bosses  PB is not a priority (other fuel reduction technics)  Decrease of permanent staff in the Forest Service  Lake of PB bosses in some areas (Pyrenees) and crews in other (Lake of flexibility when team is composed of several agencies  Change of team leader  Rigidity when civil protection teams are supporting local PB team  Cumbersome procedures of operation planning  Environmental constraints  Smaller PB size  Limitations in periods and locations  Air quality with prohibited periods (despite PB derogation)  Sociological constraints (more than economical)  Institutionnal constraints  Lake of communication (WUI)  Stop after PB escape (Aveyron) Response is not unique and often case specific  More flexibility in the organisation to better fit to demand  More dedicated PB bosses in some regions  Standardization of cost assessment methods to better underline the benefits and constraints of PB towards other fuel treatment methods  Need to distinguish responses for PB development in rangeland and forest areas

Thursday, February 2nd 2017 18 active PB teams 7 sleeping PB teams Results: French PB landscape 2016 situation: .07 Eric RIGOLOT / PB prospect in France Thursday, February 2nd 2017 Results: quantitative indicators Low response rate to annual PB demand

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	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2016


Lucie Aulus Giacosa

Matthias Vignon

Mathieu Buoro

Philippe Gaudin

Francois Gueraud

Jean-Christophe Aymes

Growth models and estimation of migratory reaction norm for invasive brown trout (Salmo trutta L.) in Kerguelen Islands

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

Call for presentations

October, 2 nd -4 th 2017 Westotel Nantes Atlantique -34 rue de la Vrière 44240 La Chapelle sur Erdre

Growth models and estimation of migratory reaction norm for invasive brown trout (Salmo Trutta L.) in Kerguelen Islands.

SUMMARY

In a context of global warming, generalized polar ice retreat provides new habitats where native or nonnative fish species may establish. Because of its dispersal and adaptive capacities, Brown trout (Salmo trutta L.), a cold water facultative anadromous salmonid, is a viable candidate to colonize those kinds of ecosystem. The case of introduced brown trout to Sub Antarctic Kerguelen Islands in the 1950's provides a good opportunity to investigate local adaptation through growth patterns and evolution of age at first marine migration. Based on scales and otoliths sampled from three populations that differ in their colonization date (from 1968 to 2000) and environmental conditions, we determined age at capture and age at first marine migration of each fish. We then measured scale radius at each age and back calculate the length at all ages.

To do so, we compared two approaches (GAM models versus more classical parametric models) while taking into account various sources of variance (methodological, populational …). Based on these results, we explored probabilistic reaction norms of age and size at migration, and compare how these norms differ between our populations with respect to both colonization date and environment.

KEYWORDS

Growth, invasion, life history traits, migration, reaction norm.

Session in which your presentation proposal fits*:

	Lucie Aulus, Matthias Vignon, Mathieu Buoro, Philippe Gaudin, François Guéraud,
	Jean-Christophe Aymes
	UMR ECOBIOP 1224 INRA/UPPA, Aquapôle INRA, Quartier Ibarron, 64310 Saint-Pée sur Nivelle,
	FRANCE.

 Decision making, management and adaptive governance  Social-ecological system (SES) research, or "Putting the "S" in LTSER  Global initiatives  Biogeochemical cycles  Biodiversity and ecosystem services  Environmental risks  Poster session: Sensors and analytical tools  Free poster session


• A set of discrete variables X, each with a domain D

• We define a joint function on all variables f : D X → S

• By decomposing the joint function to a set C of functions of small arity (factors)

• Concise way of describing complicated functions

Function Aggregation -WCSP

S ≡ R + ∪ {0, ∞} f (x) = c∈C c(x)
• f represents a cost or energy or potential

• Each c is a cost function Function Aggregation -MRF S ≡ R + ∪ {0} f (x) = c∈C c(x)
• Each c is a probability table

P(x) = f (x) Z Z = 1 x c∈C c(x )
WCSP/MRF Equivalence

• Given MRF P, a WCSP P has

c (x) = -log c(x)
Then exp(-f (x)) ∝ P(x) Z = 1

x c∈C exp(-c(x ))

• So we deal with costs only

MAP

• Maximum a posteriori estimation • Compute assignment with maximum probability in MRF

• By equivalence to WCSP, same problem as cost minimization

• Optimization of an NP-hard set, hence FP NP

• Generalizes Boolean satisfiability, constraint satisfaction

Partition Function

• Compute Z, the normalization constant (probability mass of the function)

• P PP -complete

• By Toda's theorem, this is Beyond PH

• Partition X into variable sets A, B

• Compute assignment x A that maximizes probability mass of f | x A

• NP PP Aside: WCSP as COP

• WCSP combines crisp CSP with arbitrary polynomial objective

• Clever dual bounds

• Small arity is not necessary • Can use the machinery developed in CSP for more expressiveness

• Higher level language • Propagators

• Global Cost Functions an underexplored area

• New scenarios

∀x c∈S c(x) = c∈S c (x)
• Dates back to at least the Held-Karp lower bound for TSP

WCSP reparameterization

Move(c 1 , c 2 , x, α)

• Shifts α units of cost between c 1 and c 2 on the common assignment x

• Shift direction: sign of α.

• α constrained: no negative costs!

• Commonly restricted to scope(c 1 ) ⊂ scope(c 2 ) and in particular |scope(c 1 )| = 1:

Project({i}, {i, j}, a, α) Example Example Project({1, 2}, {2}, a, 1) → Example Project({1, 2}, {2}, a, 1) → ← Project({1, 2}, {2}, a, -1)

Conclusions

• Reparameterization is a universal tool

• Maintains cost/probability of all assignments, so always applicable • Non-trivial improvement of trivial bounds

• Precise connection to linear programming in cost minimization

• Hierarchies of strengthening reparameterizations which change network

• Linear programming cuts Q?

•

  Prune subtree as soon as upper bound for Z (f x A ) is lower than incumbent 1 Start with root node, corresponding to initial problem 2 Pick an open node 3 Compute Z (f | x A ) upper bound u 1 If u < εU, close node; else 2 If all A variables have been assigned, compute Z (f x A ), replacing incumbent if needed; else 3 Make a binary choice on variables in A, replace by two new nodes

4 Go to step 2

Min Sum Diffusion

1 Choose overlapping factors c 1 , c 2 2 For every x in the intersection, choose α so that c 1 (x) = c 2 (x)

3 Repeat until convergence

• Averages factors

• Will converge as number of iterations goes to infinity, as long as each pair of factors is chosen infinitely often Upper bound for Partition Function

• Product of mass of all factors

• Proof: by distributing the product over the sum Approximate Z

• Branch and bound


Keywords: Gael Thébaud, Identification and optimization of sharka management parameters through sensitivity analysis, 16

published or not. The documents may come

Identification and optimization of sharka management parameters through sensitivity analysis


Luke G Barrett

Jeremy J Burdon

Peter H Thrall

Loup Rimbaud
email: loup.rimbaud@csiro.au

Julien Papaix

Assessing the performance of landscape-based strategies to deploy major-gene resistance

Assessing the performance of landscape-based strategies to deploy major-gene resistance

Loup Rimbaud, Julien Papaix,

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs very quickly after resistance is deployed, resulting in significant crop losses and a continuing need to breed new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolutionary potential of pathogen populations and thus increase the durability of resistance deployment. These strategies mainly rely on using different combinations of resistance sources in time, space, or both. In time, such combination consists of crop rotations. In space, resistance sources can be deployed in the same cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental assessment of the efficiency (i.e. ability to reduce disease impact) and the durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of different deployment strategies presents a major challenge.

Therefore, we developed a spatially-explicit stochastic model to assess the epidemiological and evolutionary outcomes of the major deployment options described above when one or two major genes for resistance are present. In addition, we analysed the impact of landscape organisation (as defined by the proportion of fields cultivated with a resistant cultivar, and their spatial aggregation) and epidemiological or evolutionary parameters (e.g. dispersal abilities, mutation rate, cost of infectivity) through sensitivity analyses and polynomial regression.

The model has been parameterised for wheat qualitative resistance to rusts, caused by fungi of the genus Puccinia, but can be applied to many other pathosystems. Early results suggest that strategies offering the best epidemiological control of the disease are not necessarily the most durable. Key words: deployment strategy, durable resistance, gene-for-gene resistance, plant disease, rust of wheat, spatially-explicit modelling


Loup Rimbaud
email: loup.rimbaud@csiro.au

Julien Papaix

Luke Barrett

Jeremy Burdon

Pete Thrall

Assessing the performance of landscape-based strategies to deploy major-gene resistance

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs very quickly after resistance is deployed, resulting in significant crop losses and a continuing need to breed new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolutionary potential of pathogen populations and thus increase the durability of resistance deployment. These strategies mainly rely on using different combinations of resistance sources in time, space, or both. In time, such combination consists of crop rotations. In space, resistance sources can be deployed in the same cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental assessment of the efficiency (i.e. ability to reduce disease impact) and the durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of different deployment strategies presents a major challenge.

Therefore, we developed a spatially-explicit stochastic model to assess the epidemiological and evolutionary outcomes of the major deployment options described above when one or two major genes for resistance are present. In addition, we analysed the impact of landscape organisation (as defined by the proportion of fields cultivated with a resistant cultivar, and their spatial aggregation) and epidemiological or evolutionary parameters (e.g. dispersal abilities, mutation rate, cost of infectivity) through sensitivity analyses and polynomial regression.

The model has been parameterised for wheat qualitative resistance to rusts, caused by fungi of the genus Puccinia, but can be applied to many other pathosystems. Early results suggest that strategies offering the best epidemiological control of the disease are not necessarily the most durable. Key words: deployment strategy, durable resistance, gene-for-gene resistance, plant disease, rust of wheat, spatially-explicit modelling


Iadine Chades
email: <iadine.chades@csiro.au>

Guillaume Chapron
email: <gchapron@carnivoreconservation.org>

Marie-Josee Cros

Regis Frederick Garcia

Sabbadin Maintainer

Marie-Josée Cros

Package 'MDPtoolbox'

Keywords:

The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.

MDPtoolbox-package Markov Decision Processes Toolbox

Description

The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.

Details

Package: MDPtoolbox Type: Package Version: 4.0.3 Date:

2017-03-02 License: BSD (4.4)

Examples

# Generates a random MDP problem set.seed(0) mdp_example_rand (2,2) mdp_example_rand (2,2,FALSE) mdp_example_rand (2,2,TRUE) mdp_example_rand (2,2,FALSE,matrix(c (1,0,1,1),2,2)) # Generates a MDP for a simple forest management problem MDP <-mdp_example_forest() # Find an optimal policy results <-mdp_policy_iteration(MDP$P, MDP$R, 0.9)

#

Value

V new value fonction. V is a vector of length S. policy policy is a vector of length S. Each element is an integer corresponding to an action.

mdp_check

Examples

# With a non-sparse matrix P <-array(0, c(2,2,2)) P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE) P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE) R <-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) mdp_bellman_operator(P, R, 0.9, c(0,0)) # With a sparse matrix P <-list() P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE) P[ [2]] <-Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE) mdp_bellman_operator(P, R, 0.9, c(0,0))

Description

Checks whether a matrix is square and stochastic Usage mdp_check_square_stochastic(X)

Arguments

X a matrix

Details mdp_check_square_stochastic checks if the matrix (X) is square and stochastic (sums of rows equal to 1). If it is the case, the function returns an empty error message. In the opposite case, the function returns an error message describing the problem.

Value

Returns a character string which is empty if the matrix is square and stochastic. In the opposite case, the variable contains problem information.

policy

a policy. policy is a S length vector. Each element is an integer corresponding to an action.

V0

(optional) starting point. V0 is a S length vector representing an inital guess of the value function. By default, V0 is only composed of 0 elements. epsilon (optional) search for an epsilon-optimal policy. epsilon is a real greater than 0. By default, epsilon = 0.01. max_iter (optional) maximum number of iterations. max_iter is an integer greater than 0. If the value given in argument is greater than a computed bound, a warning informs that the computed bound will be used instead. By default, max_iter = 1000.

Details

mdp_eval_policy_iterative evaluates the value fonction associated to a policy applying iteratively the Bellman operator.

Value

Vpolicy value fonction. Vpolicy is a S length vector.

Examples

# With a non-sparse matrix P <-array(0, c(2,2,2)) P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE) P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE) R <-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) policy <-c(2,1) mdp_eval_policy_iterative(P, R, 0.8, policy)

# With a sparse matrix P <-list() P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE) P[ [2]] <-Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE) mdp_eval_policy_iterative(P, R, 0.8, policy) mdp_eval_policy_matrix Evaluates a policy using matrix inversion and product

Description

Evaluates a policy using matrix operation

Vpolicy

value function of the optimal policy. Vpolicy is a S length vector.

Details

For some states, the evaluation of the value function may give close results for different actions. It is interesting to identify those states for which several actions have a value function very close the optimal one (i.e. less than 0.01 different). We called this the search for near optimal actions in each state.

Value

Details

mdp_example_forest generates a transition probability (SxSxA) array P and a reward (SxA) matrix R that model the following problem. A forest is managed by two actions: Wait and Cut. An action is decided each year with first the objective to maintain an old forest for wildlife and second to make money selling cut wood. Each year there is a probability p that a fire burns the forest.

Here is the modelisation of this problem. Let 1, ... S be the states of the forest. the Sth state being the oldest. Let Wait be action 1 and Cut action 2. After a fire, the forest is in the youngest state, that is state 1.

The transition matrix P of the problem can then be defined as follows: The reward matrix R is defined as follows:

P (, , 1) =            p 1 -p 0 . . . . . .
R(, 1) =            0 . . . . . . . . . 0 r1            R(, 2) =           0 1 . . . . . . 1 r2         

Details

mdp_finite_horizon applies backwards induction algorithm for finite-horizon MDP. The optimality equations allow to recursively evaluate function values starting from the terminal stage. This function uses verbose and silent modes. In verbose mode, the function displays the current stage and the corresponding optimal policy.

Value V value fonction. V is a [S,(N+1)] matrix. Each column n is the optimal value fonction at stage n, with n = 1, ... N. V[,N+1] is the terminal reward. policy optimal policy. policy is a [S,N] matrix. Each element is an integer corresponding to an action and each column n is the optimal policy at stage n.

cpu_time CPU time used to run the program

Examples

# With a non-sparse matrix P <-array(0, c(2,2,2)) P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE) P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE) R <-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) mdp_finite_horizon(P, R, 0.9, 3) mdp_LP # With a sparse matrix P <-list() P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE) P[ [2]] <-Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE) mdp_finite_horizon(P, R, 0.9, 3)

Value

V optimal value fonction. V is a S length vector policy optimal policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function

policy0

(optional) starting policy. policy0 is a S length vector. By default, policy0 is the policy which maximizes the expected immediate reward. max_iter (optional) maximum number of iterations to be done. max_iter is an integer greater than 0. By default, max_iter is set to 1000. eval_type (optional) define function used to evaluate a policy. eval_type is 0 for mdp_eval_policy_matrix use, mdp_eval_policy_iterative is used in all other cases. By default, eval_type is set to 0.

Details mdp_policy_iteration applies the policy iteration algorithm to solve discounted MDP. The algorithm consists in improving the policy iteratively, using the evaluation of the current policy. Iterating is stopped when two successive policies are identical or when a specified number (max_iter) of iterations have been performed.

Value

V optimal value fonction. V is a S length vector policy optimal policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function epsilon (optional) search for an epsilon-optimal policy. epsilon is a real in ]0; 1]. By default, epsilon = 0.01. max_iter (optional) maximum number of iterations to be done. max_iter is an integer greater than 0. By default, max_iter = 1000.

Details

mdp_policy_iteration_modified applies the modified policy iteration algorithm to solve discounted MDP. The algorithm consists, like policy iteration one, in improving the policy iteratively but in policy evaluation few iterations (max_iter) of value function updates done. Iterating is stopped when an epsilon-optimal policy is found.

Value

V optimal value fonction. V is a S length vector policy optimal policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function.

iter number of iterations cpu_time CPU time used to run the program

Examples

# With a non-sparse matrix P <-array(0, c(2,2,2)) P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE) P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE) R<-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) mdp_policy_iteration_modified(P, R, 0.9) # With a sparse matrix P <-list() P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE) P[ [2]] <-Matrix(c(0, 1, 0.1, 0.9), (optional) : number of iterations to perform. N is an integer that must be greater than the default value. By default, N is set to 10000.

Details

mdp_Q_learning computes the Q matrix, the mean discrepancy and gives the optimal value function and the optimal policy when allocated enough iterations. It uses an iterative method.

Value Q an action-value function that gives the expected utility of taking a given action in a given state and following an optimal policy thereafter. Q is a [S,A] matrix. mean_discrepancy discrepancy means over 100 iterations. mean_discrepancy is a vector of V discrepancy mean over 100 iterations. Then the length of the vector for the default value of N is 100.

V value function. V is a S length vector.

policy policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function

Examples

# With a non-sparse matrix P <-array(0, c(2,2,2)) P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE) P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE) R <-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) # Not run # mdp_Q_learning(P, R, 0.9) # With a sparse matrix P <-list() P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE) P[ [2]] <-Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE) # Not run # mdp_Q_learning(P, R, 0.9) Iterating is stopped when an epsilon-optimal policy is found or after a specified number (max_iter) of iterations is done.

Value policy optimal policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function.

average_reward average reward of the optimal policy. average_reward is a real. The algorithm consists, like value iteration, in solving Bellman's equation iteratively Vn+1(s) calculation is modified. The algorithm uses Vn+1(s) instead of Vn(s) whenever this value has been calculated. In this way, convergence speed is improved. Iterating is stopped when an epsilonoptimal policy is found or after a specified number (max_iter) of iterations.

Value policy epsilon-optimal policy. policy is a S length vector. Each element is an integer corresponding to an action which maximizes the value function.

iter number of done iterations.

epsilon

(optional) : search for an epsilon-optimal policy epsilon is a real in ]0; 1]. By default, epsilon is set to 0.01.

V0

(optional) : starting value function. V0 is a S length vector. By default, V0 is only composed of 0 elements.

Details

mdp_value_iteration_bound_iter computes a bound on the number of iterations for the value iteration algorithm to find an epsilon-optimal policy with use of span for the stopping criterion.

Value max_iter maximum number of iterations to be done. max_iter is an integer greater than 0.

Examples

# With a non-sparse matrix P <-array(0, c(2,2,2)) P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE) P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE) R <-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) mdp_value_iteration_bound_iter(P, R, 0.9) # With a sparse matrix P <-list() P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.

R

  topics documented: MDPtoolbox-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_bellman_operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_check_square_stochastic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_computePpolicyPRpolicy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_computePR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_eval_policy_iterative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_eval_policy_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_eval_policy_optimality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_eval_policy_TD_0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_example_forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_example_rand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_finite_horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mdp_LP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 MDPtoolbox-package mdp_policy_iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 mdp_policy_iteration_modified . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 mdp_Q_learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 mdp_relative_value_iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 mdp_span . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 mdp_value_iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 mdp_value_iterationGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 mdp_value_iteration_bound_iter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Index 27

  Visualise the policy results$policy mdp_bellman_operator Applies the Bellman operator Description Applies the Bellman operator to a value function Vprev and returns a new value function and a Vprev-improving policy. Usage mdp_bellman_operator(P, PR, discount, Vprev) Arguments P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. PR reward array. PR can be a 2 dimension array [S,A] possibly sparse. discount discount factor. discount is a real number belonging to ]0; 1]. Vprev value fonction. Vprev is a vector of length S. Details mdp_bellman_operator applies the Bellman operator: PR + discount*P*Vprev to the value function Vprev. Returns a new value function and a Vprev-improving policy.

  . P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. discount discount factor. discount is a real number which belongs to [0; 1[. policy a policy. policy is a S length vector. Each element is an integer corresponding to an action. matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) mdp_eval_policy_matrix(P, R, 0.9, c(1,2)) of 'near optimal' actions for each state Description Determines sets of 'near optimal' actions for all states Usage mdp_eval_policy_optimality(P, R, discount, Vpolicy) Arguments P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. discount discount factor. discount is a real number which belongs to [0; 1[.

mdp_policy_iteration

  Solves discounted MDP using policy iteration algorithm Description Solves discounted MDP with policy iteration algorithm Usage mdp_policy_iteration(P, R, discount, policy0, max_iter, eval_type) Arguments P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. discount discount factor. discount is a real which belongs to ]0; 1[.

  using modified policy iteration algorithm Description Solves discounted MDP using modified policy iteration algorithm Usage mdp_policy_iteration_modified(P, R, discount, epsilon, max_iter) Arguments P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. discount discount factor. discount is a real number which belongs to [0; 1[. For discount equals to 1, a warning recalls to check conditions of convergence.

  , R, 0.9) mdp_Q_learning Solves discounted MDP using the Q-learning algorithm (Reinforcement Learning) Description Solves discounted MDP with the Q-learning algorithm (Reinforcement learning) Usage mdp_Q_learning(P, R, discount, N) Arguments P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. discount discount factor. discount is a real which belongs to ]0; 1[ N

  Detailsmdp_span computes the span of the W vector: max W(s) -min W(s)Value the value of the span of the W vector.

  for the number of iterations for the value iteration algorithm Description Computes a bound on the number of iterations for the value iteration algorithm Usage mdp_value_iteration_bound_iter(P, R, discount, epsilon, V0) Arguments P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. discount discount factor. discount is a real which belongs to ]0; 1[.

cpu_time

  CPU time used to run the program.

  Detailsmdp_check checks whether the MDP defined by the transition probability array (P) and the reward matrix (R) is valid. If P and R are correct, the function returns an empty error message. In the opposite case, the function returns an error message describing the problem.

	Examples
	# With a non-sparse matrix
	P <-array(0, c(2,2,2))
	P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE)
	P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE)
	R <-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE)
	mdp_check(P, R)
	# With a sparse matrix
	P <-list()
	P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE)
	P[[2]] <-Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE)
	mdp_check(P, R)
	Value
	Returns a character string which is empty if the MDP is valid. In the opposite case, the variable
	contains problem information

mdp_check Checks the validity of a MDP Description Checks the validity of a MDP Usage mdp_check(P, R) Arguments P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. mdp_check_square_stochastic Checks if a matrix is square and stochastic

  

	mdp_example_rand mdp_finite_horizon	Generates a random MDP problem Solves finite-horizon MDP using backwards induction algorithm
	Description
	Description
	Solves finite-horizon MDP with backwards induction algorithm
	Generates a random MDP problem
	Usage
	Usage mdp_finite_horizon(P, R, discount, N, h)
	mdp_example_rand(S, A, is_sparse, mask)
	Arguments
	P Arguments	transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S].
	S	number of states. S is an integer greater than 0
	A	number of actions. A is an integer greater than 0
	R	reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element
		containing a sparse matrix [S,S]. Elements of R are in ]-1; 1[
	Value
	P Examples	transition probability array. P is a [S,S,A] array.
	R mdp_example_rand(2, 2) reward matrix. R is a [S,A] matrix
	mdp_example_rand(2, 2, FALSE)
	mdp_example_rand(2, 2, TRUE)
	Examples mdp_example_rand(2, 2, FALSE, matrix(c(1,0,1,1),2,2))
	mdp_example_forest()

is_sparse

(optional) used to generate sparse matrices. is_sparse is a boolean. If it is set to true, sparse matrices are generated. By default, it is set to false. mask (optional) indicates the possible transitions between states. mask is a [S,S] matrix composed of 0 and 1 elements (0 indicates a transition probability always equal to zero). By default, mask is only composed of 1.

Details

mdp_example_rand generates a transition probability matrix (P) and a reward matrix (R). Optional arguments allow to define sparse matrices and pairs of states with impossible transitions.

Value

P transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. R reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. discount discount factor. discount is a real number which belongs to [0; 1[. N number of stages. N is an integer greater than 0. h (optional) terminal reward. h is a S length vector. By default, h = numeric(S).

  Detailsmdp_relative_value_iteration applies the relative value iteration algorithm to solve MDP with average reward. The algorithm consists in solving optimality equations iteratively.

	Arguments
	P	transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]],
		each element containing a sparse matrix [S,S].
	R	reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element
		containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse.
	epsilon	(optional) : search for an epsilon-optimal policy. epsilon is a real in [0; 1]. By
		default, epsilon is set to 0.01
	max_iter	(optional) : maximum number of iterations. max_iter is an integer greater than
		0. By default, max_iter is set to 1000.
	mdp_relative_value_iteration
		Solves MDP with average reward using relative value iteration algo-
		rithm
	Description
	Solves MDP with average reward using relative value iteration algorithm
	Usage
	mdp_relative_value_iteration(P, R, epsilon, max_iter)

  (optional) : maximum number of iterations. max_iter is an integer greater than 0. If the value given in argument is greater than a computed bound, a warning informs that the computed bound will be considered. By default, if discount is not egal to 1, a bound for max_iter is computed, if not max_iter = 1000.Detailsmdp_value_iteration applies the value iteration algorithm to solve discounted MDP. The algorithm consists in solving Bellman's equation iteratively. Iterating is stopped when an epsilon-optimal policy is found or after a specified number (max_iter) of iterations.

	Arguments
	P	transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]],
		each element containing a sparse matrix [S,S].
	V0 R	reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element (optional) : starting value function. V0 is a (Sx1) vector. By default, V0 is only composed of 0 elements. containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse.
	discount	discount factor. discount is a real which belongs to ]0; 1]. For discount equals
		to 1, a warning recalls to check conditions of convergence.
	epsilon	(optional) : search of an epsilon-optimal policy. epsilon is a real in ]0; 1]. By
		default, epsilon is set to 0.01.
	max_iter	(optional) : maximum number of iterations to be done. max_iter is an integer
		greater than 0. If the value given in argument is greater than a computed bound,
	Value	a warning informs that the computed bound will be considered. By default, if discount is not equal to 1, a bound for max_iter is computed, if not max_iter is
	policy	optimal policy. policy is a S length vector. Each element is an integer corre-set to 1000.
	V0	sponding to an action which maximizes the value function. (optional) : starting value function. V0 is a S length vector. By default, V0 is
	iter	number of done iterations. only composed of 0 elements.
	cpu_time	CPU time used to run the program.
	Examples
	# With a non-sparse matrix
	P <-array(0, c(2,2,2))
	P[,,1] <-matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE)
	P[,,2] <-matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE)
	mdp_value_iteration R<-matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE) Solves discounted MDP using value iteration algorithm
	mdp_value_iteration(P, R, 0.9)
	# With a sparse matrix
	Description P <-list()
	P[[1]] <-Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE) Solves discounted MDP with value iteration algorithm P[[2]] <-Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE)
	mdp_value_iteration(P, R, 0.9)
	Usage
	mdp_value_iteration(P, R, discount, epsilon, max_iter, V0)
	Arguments mdp_value_iterationGS Solves discounted MDP using Gauss-Seidel's value iteration algo-
		rithm
	P	transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]],
		each element containing a sparse matrix [S,S].
	R Description	reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element
		containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse.
	discount Solves discounted MDP with Gauss-Seidel's value iteration algorithm. discount factor. discount is a real number which belongs to [0; 1[. For discount
	Usage	equals to 1, a warning recalls to check conditions of convergence.
	mdp_value_iterationGS(P, R, discount, epsilon, max_iter, V0)

epsilon

(optional) : search for an epsilon-optimal policy. epsilon is a real in ]0; 1]. By default, epsilon = 0.01.

max_iter Details mdp_value_iterationGS applies Gauss-Seidel's value iteration algorithm to solve discounted MDP.

  2),2,2, byrow=TRUE, sparse=TRUE)

	Index
	mdp_bellman_operator, 3
	mdp_check, 4
	mdp_check_square_stochastic, 5
	mdp_computePpolicyPRpolicy, 6
	mdp_computePR, 7
	mdp_eval_policy_iterative, 8
	mdp_eval_policy_matrix, 9
	mdp_eval_policy_optimality, 10
	mdp_eval_policy_TD_0, 11
	mdp_example_forest, 12
	mdp_example_rand, 14
	mdp_finite_horizon, 15
	mdp_LP, 16
	mdp_policy_iteration, 17
	mdp_policy_iteration_modified, 18
	mdp_Q_learning, 19
	mdp_relative_value_iteration, 20
	mdp_span, 22
	mdp_value_iteration, 22
	mdp_value_iteration_bound_iter, 25
	mdp_value_iterationGS, 23
	MDPtoolbox (MDPtoolbox-package), 2
	MDPtoolbox-package, 2


The technological pathway use to select the bacteria

Examples of selected bacteria

Let's make probiotic cheese!!!

.016

A reverse-engineered cheese

  Lactobacillus delbrueckii subsp. Bulgaricus Description: Gram+ bacillus, non-pathogenic, food-grade Address: mammary gland, raw milk Playground : yogurt, cheese, with his buddy S. thermophilus Profession : acidification, coagulation, proteolysis, aroma Lactobacillus delbrueckii Propionibacterium freudenreichii

  activated receptors (PPARs) regulate T-cell cytokine production. The zona occludens proteins (ZO-1, ZO-2 and ZO-3) : tight junction proteins that function as cross-linkers Cheese protects at the cell level

  Key proteins are expressed in the cheese

					Microfiltr. Milk
	Fig.
	1				Powder 100 g/L	Fig. 2	Peptide
								identification
					Cream 150 g/L
		Isolation of bacterial fraction from cheese		Sterilisation Trypsin Shaving	Analyses of resulting peptides UHT Milk by NanoLC-ESI-MS/MS
	Surface layer protein B Surface protein with SLH domain Cell wall Cell wall Surface layer protein A Cell wall Internaline A Miscellaneous	slpB slpE slpA inlA	Culture P. freudenreichii -569,10 81 77,3 1 1.1 -271,21 40 21,5 1 1.2 -7,48 3 0,6 1 1.3 30°C, 72 h -838,69 103 26,9 2 2.1	Culture L. delbrueckii 42°C, 4 h
	Heat shock protein 20 2 60 kDa chaperonin 2	Protein folding Protein folding	hsp20 2 -69,41 groL2 -275,04	10 36	Pre-cheese 19,9 3 3.1 17,0 4 4.1	Fermented Milk
	60 kDa chaperonin 1	Protein folding	groL1 -371,42	49	14,8 4	4.2
		Adaptation to atypical
	Cold shock-like protein CspA methyltransferase Methylmalonyl-CoA mutase large subunit conditions Miscellaneous Specific carbohydrate metabolic pathway	cspA mutB -331,83 -28,17 -66,29	3 7 43	15,5 5 15,0 6 14,1 7	5.1 Clotting, stirring, cooking, 6.1 moulding, pressing, wrapping, ripening 7.1
	Elongation factor Tu	Translation elongation tuf	-177,14	23	13,6 8	8.1
	50S ribosomal protein L7/L12	Ribosomal proteins	rplL	-79,13	10	13,2 9	9.1
	Lsr2-like protein	Miscellaneous		-24,65	3	13,0 10	10.1
	30S ribosomal protein S9	Ribosomal proteins	rspI	-74,75	12	12,4 11	11.1
	30S ribosomal protein S7	Ribosomal proteins	rpsG	-77,54	14	11,5 12	12.1
		Protein of unknown
	Hypothetical protein	function		-183,92	23	10,8 13	13.1
	30S ribosomal protein S1	Ribosomal proteins	rpsA	-214,68	27	10,7 14	14.1
	ErfK/YbiS/YcfS/YnhG precursor	Cell wall		-120,71	20	10,4 15	15.1
		Metabolism of
		coenzymes and
	NAD(P)(+) transhydrogenase	prosthetic groups	pntA	-146,56	19	10,4 16	16.1
	50S ribosomal protein L35	Ribosomal proteins	rpmI	-17,33	4	10,0 17	17.1

Description

Function Gene log(E value) Peptides PAI Group Sub-group

 Highly strain-dependent

Promising immunomodulators ?

Selected probiotics IL-10 +


Thierry Astruc

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

About the book

This title reviews developments related to beef quality, starting with the way breeding and growth affect meat quality. The book then discusses cattle nutrition and management before concluding with an assessment of factors determining individual quality traits such as colour and flavour.

About the editor

  Controversies surrounding the impact of the fat content of beef on human health: Jennifer Fleming and Penny Kris-Etherton, Penn State University, USA Ensuring safety and quality in the production of beef Volume 2: Qualtity Edited by: Emeritus Professor Michael E. Dikeman, Kansas State University, USA

	BURLEIGH DODDS SERIES IN AGRICULTURAL SCIENCE
		NOW AVAILABLE
	Part 1 Breeding and growth Ensuring safety 1. Biological types of cattle: carcass and meat quality: M. A. Price,	10. Beef carcass grading and classification: Michael E. Dikeman, Kansas State University, USA
	University of Alberta, Canada and quality in the 2. Traditional animal breeding of cattle to improve carcass composition production of beef and meat quality: Matt Spangler, University of Nebraska, USA	11. Branded beef programmes: B. N. Harsh and D. D. Boler, University of Illinois, USA 12. Ageing, physical and chemical methods for improving tenderness
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			and palatability: J. W. S. Yancey, University of Arkansas, USA
	Dr Michael Dikeman is Emeritus Professor of Meat Science at Kansas State University, USA. He is a past President of the American Meat Science Association and 15. Measuring and assessing beef quality and sensory traits for retailers Pages 442 Pub. Date April 2017 Price £190/$240/€230/C$325 Series No AS12 and consumers: Derek A. Griffing and Christy L. Bratcher, Auburn University, USA 6. Effects of metabolic modifiers on beef carcass composition and meat Dikeman, Kansas State University, USA 16. Role of beef in human nutrition and health: Chunbao Li, Nanjing quality: John M. Gonzalez, Sara M. Ebarb, Kelsey J. Phelps and Michael E. Dikeman, Kansas State University, USA Agricultural University, China Order via our online bookshop at www.bdspublishing.com, your usual book 7. Understanding the effects of handling, transportation, lairage and Part 4 Emerging trends
	of the Federation of American Societies of Food Animal slaughter on cattle welfare and beef quality: Michael S. Cockram, 17. The future of DNA technologies for improving beef quality: marbling, supplier, or pass to your librarian. University of Prince Edward Island, Canada fatty acid composition and tenderness: Elly Ana Navajas, Instituto Enquiries to info@bdspublishing.com 8. The effects of carcass chilling and electrical stimulation on visual beef Nacional de Investigación Agropecuaria, Uruguay Sciences (FASFAS -now FASS). quality and palatability: Phillip E. Strydom, Agricultural Research 18. The sustainability and 'carbon footprints' of conventional and
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	Oklahoma State University, USA and Richard A. Mancini, University
	of Connecticut, USA
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Ensuring safety and quality in the production of beef Vol 2: Quality Available in print and online formats: ISBN -print 978-1-78676-060-9 Volume 2: Quality Edited by Emeritus Professor Michael E. Dikeman Kansas State University, USA BURLEIGH DODDS SERIES IN AGRICULTURAL SCIENCE Delivering knowledge for the global scientific community Climate-smart publishing in agricultural science 3. Muscle fibre types and beef quality: Thierry Astruc and Annie Vénien, INRA, France 4. Factors affecting fat content and distribution of fat in cattle and carcasses: Stephen B. Smith, Texas A&M University, USA Part 2 Management of cattle 5. Beef cattle nutrition and its effects on beef quality: Christopher J. Richards, Oklahoma State University, USA and and Michael E.


Richard Moot
email: richard.moot@labri.fr

Lambek Grammars, Tree Adjoining Grammars and Hyperedge Replacement Grammars

Two recent extension of the nonassociative Lambek calculus, the Lambek-Grishin calculus and the multimodal Lambek calculus, are shown to generate class of languages as tree adjoining grammars, using (tree generating) hyperedge replacement grammars as an intermediate step. As a consequence both extensions are mildly context-sensitive formalisms and benefit from polynomial parsing algorithms.

1 Introduction [START_REF] Joshi | The convergence of mildly context-sensitive grammar formalisms[END_REF] have shown that many independently proposed mildly context-sensitive grammar formalisms -combinatory categorial grammars, head grammars, linear indexed grammars and tree adjoining grammars (TAGs) -generate the same class of string languages.

For the Lambek calculus L [START_REF] Lambek | The mathematics of sentence structure[END_REF], [START_REF] Pentus | Lambek grammars are context free[END_REF] has shown that L grammars generate only context-free languages. Two recent incarnations of Lambek grammars have sought to extend the generative capacity the Lambek calculus: the multimodal Lambek calculus NL3 R [START_REF] Moortgat | Categorial type logics[END_REF] and the Lambek-Grishin calculus LG [START_REF] Moortgat | Symmetries in natural language syntax and semantics: the Lambek-Grishin calculus[END_REF]. Both of these systems use the non-associative Lambek calculus NL [START_REF] Lambek | On the calculus of syntactic types[END_REF], for which polynomial algorithms exist [START_REF] De Groote | The non-associative Lambek calculus with product in polynomial time[END_REF][START_REF] Capelletti | Parsing with Structure-Preserving Categorial Grammars[END_REF], as their base, but add interaction principles to augment the descriptive power. While both systems have been shown to handle linguistic phenomena for which no satisfactory Lambek calculus analysis exists, little is

T ↓ α T β T β α
Figure 1: Substitution known about the exact class of languages generated by either system or about the complexity cost of adding these interaction principles.

In the current paper I shown that both NL3 R and LG generate the same class of languages as TAGs, using hyperedge replacement grammars as an intermediate step.

Tree Adjoining Grammars and Hyperedge Replacement Grammars

Tree Adjoining Grammars [START_REF] Joshi | Treeadjoining grammars[END_REF] combine trees using the operations of substitution (shown in Figure 1) which replaces a nonterminal leaf T ↓ by a tree with root T and adjunction (shown in Figure 2) which replaces and internal node A by a tree with root node A and foot node A * . Formally, TAGs are defined as follows.

Definition 1 An TAG is a tuple Σ, N S , N A , I, A such that • I is a finite set of initial trees,

•
• A is a finite set of auxiliary trees.

The trees in I ∪ A are called the elementary trees.

Trees are subject to the following conditions:

• the root nodes of all initial trees are members of N S ,

• the root nodes of all auxiliary trees are members of N A ,

• every auxiliary tree has exactly one leaf which is a member of N A . This leaf is called the foot node,

• all other leaves of elementary trees are members of N S ∪ Σ.

A TAG satisfying the additional condition that all elementary trees have exactly one terminal leaf is called a lexicalized tree adjoining grammar (LTAG). This leaf is called the lexical anchor,

In addition, a TAG is allowed to specify constraints on adjunction. Let A ∈ N A and let t be the set of auxiliary trees with root node A and foot node A * . A node A in a elementary tree α is said to have selective adjunction in case it specifies a subset t t of trees which are allowed to adjoin at this node. The special case where t = ∅ is called null adjunction. Finally, a node can specify obligatory adjunction where an auxiliary tree has to be adjoined at the node.

The only difference with the standard definition of tree adjoining grammars [START_REF] Joshi | Treeadjoining grammars[END_REF] is the use of separate alphabets for auxiliary and substitution nonterminals. In addition to making the substitution marker T ↓ and the foot node marker A * technically superfluous, this will make the different embedding results which follow slightly easier to prove.

Definition 2 An LTAG' grammar G is an LTAG satisfying the following additional conditions.

• all internal nodes of elementary trees have exactly two daughters,

• every adjunction node either specifies the null adjunction or the obligatory adjunction constraint without any selectional restrictions,

• every adjunction node is on the path from the lexical anchor to the root of the tree.

The definition of LTAG' is very close to the definition of normal or spinal form LTAGs used by [START_REF] Joshi | The convergence of mildly context-sensitive grammar formalisms[END_REF] and by [START_REF] Vijay-Shanker | The equivalence of four extensions of context free grammars[END_REF] to show correspondence between LTAGs and combinatory categorial grammars, so it should be no surprise it will serve as a way to shown inclusion of tree adjoining languages in multimodal and Lambek-Grishin languages. The only difference is that the adjunction nodes are required to be on the path from the root to the lexical anchor instead of the foot node.

Lemma 3 For every LTAG grammar G there is a weakly equivalent LTAG' grammar G .

Proof (sketch)

The proof is analogous to the proof of [START_REF] Vijay-Shanker | The equivalence of four extensions of context free grammars[END_REF].

2

A hypergraph is a set of hyperedges, portrayed as an edge label in a rectangular box, which can be incident to any number of vertices. These connections are portrayed by lines (called 'tentacles') labelled 0, . . . , n (the selectors) for a hyperedge of arity n + 1. A hyperedge replacement grammar [START_REF] Engelfriet | Context-free graph grammars[END_REF][START_REF] Drewes | Hyperedge replacement graph grammars[END_REF]) replaces a hyperedge with a nonterminal symbol by a hypergraph.

The rank of a terminal or nonterminal symbol is the number of its tentacles. The rank k of a HR grammar is the maximum number of tentacles of a nonterminal symbol. We will be particularly interested in HR grammars of rank two (HR 2 ) even α

• (0) • (1) • • A 1 0 β β α β β • • Figure 3: Hyperedge Replacement
though we do use terminal nodes with multiple edges. Figure 3 shows an example of hyperedge replacement for a nonterminal A of rank two. It is attached to two vertices, represented by fat dots, with selectors 0 and 1 respectively. If A → α is a rule in the HR grammar, we can replace the hyperedge A by first deleting it, then identifying the external node (0) of α with the node which was connected to tentacle 0 and external node (1) of α with the node which was connected to tentacle 1. The similarity with Figure 2 should be striking. Note however, that since the grammatical objects of hyperedge replacement grammars are hypergraphs, β and β need not be disjoint. In fact, even the two tentacles are allowed to reach the same vertex. However, when we restrict the right hand sides of rules to be trees1 then, as we will see in Lemma 5, hyperedge replacement and adjunction will correspond exactly.

There are several ways to represent trees in HR grammars, but the following will turn out to be convenient for our applications. A node with label A and n daughters 2 is represented as a hyperedge A with n + 1 tentacles, with tentacle 0 pointing towards the parent node and tentacles 1, . . . , n selecting its daughters from left to right.

Definition 4 A hypergraph H is a (hyper-)tree iff every node in H is incident to two hyperedges, once by a selector 0 and once by a selector > 0, except the root node, which is incident to a single hyperedge by selector 0.

Lemma 5 HR 2 grammars generating trees and TAG grammars are strongly equivalent.

Proof (sketch) From TAG to HR 2 , we start with a TAG G and categorise the different adjunction nodes, introducing new symbols whenever two nodes labelled by the same symbol of N A either select a different set of trees or differ with respect to obligatory adjunction to obtain a TAG G which is equivalent to G up to a relabelling of the members of N A . Now let t be an initial tree with root node T in G , we transform it into a hypertree t corresponding to Definition 4, with each adjunction point replaced by a unary branch with the nonterminal A corresponding to the adjunctions possible at the node and each leaf U marked for substitution replaced by a nonterminal leaf U and add rule T → t to the HR grammar.

Each of the members of A ∈ N A in G has a set of auxiliary trees t assigned to it as well as an indication of whether or not adjunction is obligatory. For each α ∈ t we add a rule A → α to the grammar. In addition, if adjunction is not obligatory we add a rule A → •, eliminating the nonterminal hyperedge. Now every adjunction corresponds to a hyperedge replacement as shown in Figure 3 and every substitution to the same figure, but with both the 1 tentacle and the β subtree removed.

From HR 2 to TAG we use the fact that we generate a tree and that all nonterminals are of rank ≤ 2.

Suppose A is a nonterminal of rank two and A → α is a rule in the HR 2 grammar G. In case α is a single node • we mark all adjunctions of a nonterminal A as optional in the grammar. If not, we add the auxiliary tree α which we obtain from α by labelling the external node (0) by A and the external node (1) by A * to the TAG.

Suppose T is a nonterminal of rank one and T → t is a rule in G. By Definition 4, in order for this rule to be productive the single external node has to be the root. We label the root by T and add the resulting tree as initial tree to the TAG. Again, it is easy to see that every hyperedge replacement of a nonterminal of rank 1 corresponds to a substitution and every hyperedge replacement of rank 2

• • g 1 0 • s + 0 • np - 0 • Jo 0 • np + 0 • ran 0 • • • • 1 2 0 • s - 0 Figure 4: AB lexicon
corresponds to an adjunction in the generated TAG grammar. 2

Lambek Grammars

Before talking about Lambek grammars, seen from a hypergraph perspective, it is useful to first show a lexicon for AB grammars. Figure 4 shows a trivial AB lexicon for the phrase 'Jo ran'. As usual in categorial grammars, we distinguish between positive and negative occurrences of atomic formulas. 3 The tree on the left indicates that the goal formula g of this grammar is a positive s atomic formula. The lexical entry for 'ran' indicates it is looking for an np to its left to produce an s, whereas the entry for 'Jo' simply provides an np.

Note that because of the extra unary hyperedge at the root nodes, the two lexical trees are not trees according to Definition 4. I will refer to them as typed trees.

The axiom rule (Figure 5) shows how positive and negative formulas of the same type cancel each-other out by identifying the nodes selected by the two unary hyperedges. The resemblance with the substitution operation in Figure 1 should be clear, though we do not require α and β to be disjoint: instead we require that an AB derivation -a set of applications of the axiom rule -ends in a (non-typed) tree where all leaves are labelled by terminal symbols.

With respect to Lambek grammars, we are primarily interested in two recent extensions of it, the multimodal Lambek calculus with two modes in-3 Negative formulas correspond to resources we have and positive formulas correspond to resources we need. Think of A + as being similar to A ↓ . teracting by means of the mixed associativity and mixed commutativity structural rules [START_REF] Moortgat | Categorial type logics[END_REF] and the Lambek-Grishin calculus with the Grishin class IV interactions [START_REF] Moortgat | Symmetries in natural language syntax and semantics: the Lambek-Grishin calculus[END_REF].

Extending the AB hypergraph calculus in this way involves adding new constructors and graph contractions which eliminate them. This moves the hypergraph calculus (or at least the intermediate structures in the derivations) further away from trees, but we will continue to require that the result of connecting and contracting the graph will be a (non-typed) tree. Contractions will correspond to the logical rules [R/ i ], [L• i ] and [R\ i ] in the NL3 R case and to the logical rules [L ], [R ] and [L ] in the LG case. The contraction for [L•] is shown in the middle and on the right of Figure 6.

An additional constraint on the trees will be that in only contains mode 0 in the NL3 R case and that it doesn't contain the 'inverse' Grishin structural connective ';' in the LG case. This has as a consequence that in any proof, the Grishin connectives and the connectives for mode 1 can only occur in pairs.

The calculus sketched here is just a hypergraph interpretation of the proof nets for the multimodal Lambek calculus of [START_REF] Moot | Proof nets for the multimodal Lambek calculus[END_REF] and their extension to LG of [START_REF] Moot | Proof nets for display logic[END_REF], who show that it is sound and complete with respect to the sequent calculus.

As we have seen, it is trivial to model the substitution operation: in this respect substitution is modelled in a way which is equivalent up to notational choices to the work on partial proof trees [START_REF] Joshi | Partial proof trees as building blocks for a categorial grammar[END_REF].

Figure 7 shows how to model the adjunction operation, with the solution for NL3 R on the left and Figure 6 shows how this allows us to adjoin a hypergraph corresponding to an auxiliary tree to this adjunction point. After two axiom rules, we will have the structure shown on the left. The mixed associativity and commutativity structural rules allow us to move the • 1 hyperedge down the tree until we have the structure shown in the middle. Finally, we apply the contraction, deleting the • 1 and L• 1 edges and identifying the two nodes marked by selector 0 to obtain the structure on the right. Remark how these steps together perform an adjunction operation. For the sake of efficiency we will usually not apply the structural rules explicitly. Instead, we will use a generalised contraction, which moves directly from the left of the figure to the structure on the right.

• • • • 1 1 2 0 • • • L• 1 1 2 0 α β β • • • • 1 1 2 0 • • • L• 1 1 2 0 β β α β β α → →
Lemma 6 If G is an LTAG' grammar, then there exists a strongly equivalent NL3 R grammar G and a strongly equivalent LG grammar G .

Proof (sketch)

For each lexical tree t of G we construct a lexical tree t in G and a lexical tree t in G , translating every adjunction point by the left hand side of Figure 7 for G and by its right hand side for G . Now let d be a LTAG' derivation using grammar G. We translate this derivation into an NL3 R derivation d and an LG derivation d as follows: • Whenever we adjoin a tree with root A and foot A * we perform the A axiom for the root node, the B axiom for the foot node followed by the generalised contraction shown in Figure 6.

• • • • 1 1 2 0 • • • L• 1 1 2 0 • B - 0 • A + 0 • • • R 1 0 2 • • • ; 1 0 2 • B - 0 • A + 0
In order to show we generate only the LTAG' derivations we have to show that no other combination of axioms will produce a proof net. Given the separation of non-terminals into N S and N A as well as the contraction requirement this is trivial. 2

To complete the proofs, we show that there is an HR grammar G generating the hypergraphs corresponding to the proofs of an LG or NL3 R grammar G, that is to say the grammar G generates sequences of lexical graphs which, using axiom connections and generalised contractions contract to a tree.

Lemma 7 If G is a Lambek Grammar, then there exists a strongly equivalent HR 2 grammar G .

Proof (sketch)

Let G be a NL3 R or an LG grammar. We generate a hyperedge replacement grammar H of rank two which generate all proof nets, that is to say all lexical graphs which by means of axiom connections and generalised contractions convert to a tree. Conceptually, we can think of this HR grammar as operating in three phases:

• • V 1 0 → • • • L• 1 1 2 0 • • V 1 0 • • T 1 0 • • • • 1 1 2 0 (1) (0)
Figure 8: Lambek grammar as HR grammaradjunction 1. Expand T (tree) nonterminals to generate all binary branching trees with V (vertex) nonterminals between all branches.

2. Expand V nonterminals to perform generalised expansions, that is to say inverse contractions, as shown in Figure 8.

3. Erase the V nonterminals which correspond to 'flow' formulas and disconnect the V nonterminals which correspond to axioms and as shown in Figure 9. Now, given a sequence of lexical graphs, a total matching of the positive and negative axiomatic formulas and a sequence s of generalised contractions contracting this proof net to a tree we can generate an HR derivation d by induction on the length of s. The induction hypothesis is that during steps 1 and 2 we always have a hypergraph corresponding to the proof net P which has a V edge for every vertex in P.

In case s is 0, there are no expansions and we already a binary tree with a V edge for every vertex in it. Since the T rules allow us to generate any binary branching tree, G can generate this tree as well. For every V edge in the tree which is the result of an axiom rule, we apply its inverse, shown in Figure 9 on the right, and we erase all other V edges as shown on the left of the same figure.

In case s > 0, we know by induction hypothesis that the hypergraph representation of the proof net we are constructing has a V edge for each of the vertices in the proof net. Because each subtree of the hypergraph has started as a T edge, this

• • V 01 1 0 → (0) • (1) • • V 11 1 0 → • ax - a 1 (1) • ax + a 1 (0)
Figure 9: Lambek grammar as HR grammarflow/axiom is true of the α subtree as well. We rearrange the HR derivation in such a way that all expansions of the T edge into α occur at the end, then insert the expansion shown in Figure 8 just before this sequence. The result is a valid HR derivation of a hypergraph which contracts to the same tree as the proof net.

2

Polynomial Parsing

The strong correspondence between Lambek grammars and hyperedge replacement grammars does not immediately give us polynomial parsing for Lambek grammars: as shown in [START_REF] Drewes | Hyperedge replacement graph grammars[END_REF] for example, even hyperedge replacement grammars of rank 2 can generate NP complete graph languages, such as the Hamiltonian path problem. [START_REF] Lautemann | The complexity of graph languages generated by hyperedge replacement[END_REF] presents a (very abstract) version of the well-known Cocke, Kasami and Younger algorithm for context-free string grammars [START_REF] Hopcroft | Introduction to Automata Theory, Languages and Computation[END_REF] for hyperedge replacement grammars and presents two ways of obtaining polynomial complexity, the first of which will interest us here. It uses the Proof Lautemann discusses HR grammars generating graph languages and not hypergraph languages. However, this is not a real restriction given that we can interpret a hypergraph as a bipartite graph with the vertices corresponding to the vertices in the hypergraph as one partition and the hyperedges as the second partition. Figure 11 shows this interpretation of the leftmost graph in the middle of the figure. On the right, we can see how deleting the central vertex, corresponding to the hyperedge in the original graph on the left, results in (at most) three distinct components. The rank of the hyperedge replacement grammar for proof nets is two. Finally, for a sentence with w words, we start with w disjoint lexical graphs. Therefore, deleting two vertices from w disjoint graphs produces at most w + 6 disjoint graphs.

• • • L• 1 1 2 0 • • • • 1 2 0 • • •
2

It is slightly unsatisfactory that this complexity result uses the number of words in the sentence w as a constant, though it seems possible to eliminate the constant by using a slightly more specific algorithm. [START_REF] Pentus | Lambek calculus is NP-complete[END_REF] has shown that the associative Lambek calculus L is NP complete. The reason deleting a vertex from an NL3 R or LG hypergraph gives at most three different hypergraphs is because we work with a non-associative system. Although [START_REF] Moot | Proof nets for the multimodal Lambek calculus[END_REF] show that associativity is easily accommodated in the proof net calculus, it results in a system without upper bound on the number of daughters which a node can have. Therefore, deleting a vertex from an L hypergraph can result in an unbounded number of connected components.

Discussion and Future Work

When looking at the proof of Lemma 5, it is clear that HR 1 grammars generate substitution only TAGs, whereas HR 2 grammars generate TAGs. The tree generating power of HR grammars increases with the maximum rank of the grammar. For example, it is easy to generate the non-TAG language a n b n c n d n e n f n using nonterminals of rank 3. In general, [START_REF] Engelfriet | Tree languages generated by context-free graph grammars[END_REF] show that TR(HR tr ), the set of tree languages generated by hyperedge replacement grammar such that the right hand side of all rules is a (hyper-)tree is equal to CF T sp , the context-free tree grammars which are simple in the parameters, ie. without copying or deletion of trees, which is a different way of stipulating the linear and non-erasing constraint on linear context-free rewrite systems (LCFRS) [START_REF] Vijay-Shanker | Characterizing structural descriptions produced by various grammatical formalisms[END_REF]. [START_REF] Weir | Linear context-free rewriting systems and deterministic tree-walking transducers[END_REF] shows that string generating hyperedge replacement grammars generate the same languages as LCFRS and multi-component tree adjoining grammars (MCTAGs). All this suggests a possible extension of the current results relating tree generating HR grammars of rank > 2 to MC-TAGs and LCFRS.

With respect to NL3 R , it seems possible to extend the current results, increasing the number of modes to generate richer classes of languages, possibly the same classes of languages as those generated by LCFRS and MCTAGs. For LG, such extensions seem less evident. Indeed, an appealing property of LG is that we do not need different modes, but if we are willing to add different modes to LG then extensions of the classes of languages generated seem possible.

An interesting consequence of the translations proposed here is that they open the way for new parsing algorithms of Lambek grammars. In addition, compared to earlier work like that of Moortgat and Oehrle (1994), they give radically new ways of implementing phenomena like Dutch verb clusters in NL3 R .

Conclusions

NL3 R and LG are mildly context-sensitive formalisms and therefore benefit from the pleasant properties this entails, such as polynomial parsability. TAGs and HR grammars, because of the simplicity of their basic operations, have played a central role in establishing this correspondence.

  Figure 2: Adjunction

Figure 5 :

 5 Figure 5: The axiom rule

Figure 6 :

 6 Figure 6: A contraction and structural rules for simulating adjunction

Figure 7 :

 7 Figure 7: Lambek hypergraphs for adjunction

FigureFigure 10 :

 10 Figure10summarises the different inclusions with their corresponding lemma's.

Figure 11 :

 11 Figure 11: s-separability of the bipartite graph corresponding to a proof net

This is TR(HRtr) from(Engelfriet and Maneth,

2000).2 We assume here that all occurrences of A have the same number of daughters, which we can accomplish by a simple renaming, if necessary.

LOGCFL is the complexity class of of problems which are log-space reducible to the decision problems for contextfree grammars. Vijay-Shanker et al.,(1987) show that linear context-free rewrite systems and multicomponent TAGs are also in this complexity class.


USA John E Morley

Germany Stefan D Anker

William J Evans -Durham

David J Glass

USA Aminah Jatoi

Kamyar Kalantar

USA Jennifer Le-Rademacher

USA Jeffrey Crawford

Ken Fearon

Luigi Ferrucci

USA James Carson

H Alexander Ebhardt

Italy Maurizio Muscaritoli

UK Zudin Puthucheary

Linda Denehy

USA Andrea Bonetto

Stephan Von Haehling

William Germany

USA Evans

USA Peggy Cawthon

USA Eric Orwoll

USA Daniel Marks

Roberto Bernabei

Annemie Schols

The Netherlands

Germany Stefan Herzig

UK Andrew Coats

USA Joanne Elena

Vickie Baracos

Switzerland Florian Strasser

Switzerland Ct

Sami Antoun

USA Robert Mak

Italy Giovanni Mantovani

Teresa Zimmers

Alessandro Laviano

Jerome Feige

Switzerland

USA Roger Fielding

Germany Jürgen Bauer

Jann Arends

Germany

Tommy Cederholm

Josep Argilés

Bill Evans

USA Egidio Del Fabbro

Anne Letsch

U K Palliative

USA Erin Talbert

Italy Andrea Graziani

Mitja Lainscak

USA M Models of William Evans

Andrew Judge

Taiwan Sue-Joan Chang

USA Lisa Freeman

Italy Paolo Bossi

Ashok Kumar

Camila Almeida

Brazil Diabetes

Rodríguez Mañas

Spain

Paul Gregorevic

Australia

UK Giuseppe Rosano

Stefan Andreas

Josep Argiles

Canada Steven Heymsfield

Usa Body

Brazil M Christina Gonzalez

UK Thomas Maden-Wilkinson

Thiago Gonzalez

Germany Tobias Winkler

USA Andrew Hendifar

Moran Grinberg

Eva Topinkova

Validating Sarc-F

Jean Woo

Hong Kong

Sweden

Samuel Breit

45 Special sessions: pre-registrations only Statistical Seminar

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

The Society on Sarcopenia, Cachexia and Wasting Disorders (SCWD) is pleased to invite you to attend its 10 th International Conference on Cachexia, Sarcopenia and Muscle Wasting that will be held on December 8-10, 2017, in Rome, Italy.

As an international and multidisciplinary organization, the SCWD seeks to further research on cachexia, sarcopenia and muscle wasting in order to bring practical solutions to healthcare teams worldwide.

Since the first cachexia conference that took place in Berlin in 2000, over 2000 physicians from over 25 countries have attended our educational sessions.

We expect this SCWD conference in 2017 to be a unique forum for all medical professionals dealing with muscle wasting in their disciplines and we look forward to meeting you in Rome.

We have already more than 180 abstracts and that is very encouraging.

Looking forward to meeting you in Rome.

.

Follow

E Clinical Track Biological versus numerical aging

		D	Basic track	Mechanisms of muscle wasting
		CHAIRS: Denis Guttridge, USA
			Paola Costelli, Italy
	Skeletal muscle ammonia metabolism in non-hepatological	Autophagy dysregulation in muscle stem cells during aging
	disease Srinivasan Dasarathy, USA		Marco Sandri, Italy
	Kidney dysfunction as a contribution to muscle wasting in chronic illness Giacomo Garibotto, Italy	Mechanisms of pathological replacement of skeletal muscle tissue with adipose tissue Renata Dmitrieva, Russia
	Separating inflammation and deconditioning as contributors to muscle wasting in COPD Paul Greenhaff, UK	Alterations in beta adrenoreceptor activity and muscle vs. tissue wasting Jochen Springer, Germany
	Molecular signaling homology across muscle wasting	ER stress and UPR in skeletal muscle health and disease
	conditions		Ashok Kumar, USA
	Dario Coletti, Italy
	Sarcopenia in 2017 -what is new? Francesco Landi, Italy	Role of JAK/STAT signalling in muscle wasting Teresa Zimmers, USA
		Mitochondrial impairment in skeletal muscle w
			Antonio	, Zorzano	Spain
	Yours sincerely,
	John E. Morley
	Stefan D. Anker
	CHAIRS: Jann Arends, Germany
	Roger Fielding, USA
	DNA methylation and the biological clock
	Simone Ecker, UK
	Frailty scoring: biological aging phenotypes in the critically
	ill survivors
	Zudin Puthucheary, UK
	Relationship between aging and physiological function
	Stephen Harridge, UK
	Nutritional status and satiety and the interaction with
	biological ageing
	Alessandro Laviano, Italy
	Panel discussion

asting F Basic track Tumor-secreted factors, cytokines and concepts of cachexia development

	Advances	in	automatic	CT	image	segmentation	-	CHAIRS: Didier Attaix, France
	application	for	body	composition	analysis	?	Giovanni Mantovani, Italy
	CT	imaging Karteek	phase Popuri, in	trials Canada 2/3	-	what	the	regulators	expect	Pankaj Singh, USA Metabolic regulators of cachexia in cancer
	to	see?									New insights on cancer cachexia
											Denis Guttridge, USA
											The muscle gp130 receptor's role for cancer-induced anabolic
											resistance: therapeutic implications for improving cancer
											survival
											James Carson, USA
	J

Muscle loss in liver diseases

	CHAIRS: Srinivasan Dasarathy, USA
	Pankaj Singh, USA
	Epidemiology and clinical significance of sarcopenia in liver
	cirrhosis
	Mathias Plauth, Germany
	Significance of sarcopenia and sarcopenic obesity in fatty liver
	Manuela Merli, Italy
	Molecular mechanisms of sarcopenia in liver cirrhosis
	Michela Giusto, Italy

Role of HiF in muscle wasting

Steven Olde Damink, The Netherlands

Treatment approaches for muscle wasting in liver cirrhosis

Srinivasan Dasarathy, USA

EDUCATIONAL OBJECTIVES:

Following this activity, the audience will be able to:

1. Review the latest information about the pathophysiology of muscle wasting and its epidemiology. 2. Screen for and diagnose cachexia, sarcopenia, frailty and dysmobility resulting from the following diseases or chronic conditions: cancer, cardiovascular disease, COPD, CKD, dementia/Alzheimer' s disease, diabetes, infectious diseases, stroke and aging. 3. Develop appropriate management programs for patients across the continuum of care.

TARGET AUDIENCE:

Internists, oncologists, cardiologists, gerontologists, palliative care specialists, primary care physicians and nurses.


Context

In vitro digestion

The rennet gel forms a compact coagulum in the stomach due to acid gastric fluids  the gastric emptying of caseins is slowed down

Food structure affects nutritional properties and possible health effects of nutrients

The structure of dairy products regulates the kinetics of protein digestion and the release of amino acids in the bloodstream.

Yogurt acidtype gel pH 4

Curd-type milk gel using rennet pH 6.6

Results

Rennet gel is digested much slower than acid gel

The size of the particle is decreasing over time, but the particle is still present after 2 hours of enzymatic digestion

Rennet gel Addition of pepsin (t 30  t 120 min)

Results

Rennet

  of food particle breakdown in the stomach is critical to control the structure that a food will adopt in gastric conditions. 19-21 March 2019 Do they degrade the protein substrates at the surface of the matrix, which thus gradually erodes, releasing products from enzymatic reaction in the stomach? Or do they diffuse within the protein gel particles to reach their substrates? How do gastric acid secretion and pepsin reach their substrates in the stomach? Rapid and total hydrolysis of caseins No visible change of -lactoglobulin



  frictions caused by the polymer chains slow down the solute Obstruction-scaling model (Amsden,1998) process of food proteins can lead to the formation of particles with specific structural properties that change the kinetic of digestion

(Kong & Singh, 2009a, 2009b, 2011) Discussion


Christophe Jaeger
email: christophe.jaeger@inra.fr

Joël Aubin

Ecological intensification of multi trophic aquaculture ponds: experimental approach

Introduction

Attempts increase considerably toward aquaculture, which needs to be simultaneously more productive and more environmentally friendly. On the basis of a participatory process, an ecological intensive pond system has been designed. Different hypotheses were taken into account (Aubin et al., 2014): the combination of intensive and extensive areas provides more services than solely intensive or extensive areas; the coupling of intensive ponds and planted pond permits to decrease environmental impacts while maintaining or increasing the fish productivity; The use of feed in polyculture benefits to all reared species. To test these assumptions, the system studied was a specific Integrated Multi Trophic Aquaculture (IMTA) composed by a polyculture of common carp (Cyprinus carpio) associated with secondary species: roach (Rutilus rutilus,) and Perch (Perca fluviatilis), fed by formulated feed and coupled with a planted pond for water depuration and biodiversity support.

Materials and methods

The experiment lasted from March 1 st to December 8 th 2016. The experimental design was composed of three treatments (Fig. 1) with 2 replicates by treatment, in 500 m² ponds. The first treatment consists in 2 "extensive" fishponds in which nutrients were only provided by the watershed river water, at filling up and evaporation compensation. The second treatment consists in "semi-intensive" fishponds, where fish were fed with commercial pellets. The third treatment consists in 2 "coupled" systems composed of two connected ponds: one similar to "semi-intensive" fishponds and the second, only planted of different species of indigenous macrophytes.

At stocking and harvesting, fish in each pond were sized and weighed. Survival rate, condition factor (K), Specific Growth Rate (SGR) and Feed Conversion Rate (FCR) were calculated. Water was weekly monitored for pH, temperature, dissolved oxygen saturation, conductivity, and turbidity. Water was monthly analyzed for phosphorus (P) and nitrogen (N) compound concentrations. In each pond, total phytoplankton concentration was monthly assessed with a fluorometer phytoplankton analyzer. In order to determine the emissions of each treatment to waterbodies, mass balance for N and P compounds was established. Rates of nutrients (N and P from water and feed) recovery in fish biomass were also calculated.

Results

Production intensity was higher in "semi-intensive" treatment with an average value of 2 256kg/ha whereas average value for "coupled" fishponds was 1 926kg/ha. Including the planted pond area, average production yield in "coupled" treatment was 963kg/ha compared to 507kg/ha for "extensive" treatment. At harvest, fish biomass gain corresponded to an average multiplication factor of 6.3 in "extensive" fishponds, 15.5 in "intensive fishponds and 13.6 in "coupled" fishponds. FCR values for "semi-intensive" fishponds were globally 16% better than "coupled" fishponds. Total chlorophyll concentration ranged between 2 and 101µg/l in "extensive" ponds, between 24 and 293µg/l in "semi-intensive" ponds and between 4 and 106µg/l in "coupled" ponds. All along the experiment, Total N (TN) concentrations ranged 0.18 to 2.55mg/l in "extensive" ponds, 0.91 to 4.55mg/l in "intensive" ponds and 0.70 to 4.80mg/l in "coupled" ponds. Total P (TP) concentrations ranged 0.05 to 0.25mg/l in "extensive" ponds, 0.05 to 0.93mg/l in "intensive" ponds and 0.04 to 0.55mg/l in "coupled" ponds. Mass balance calculation indicated that in all treatments, draining water released less TN from ponds than input water whereas it was the opposite for TP. In "semiintensive" treatment, more than 75% of the TN inputs (in feed, input water and young fish) was recovered in fish biomass produced whereas it was about 50% in "coupled" treatment and 25% in "extensive" treatment. Regardless of treatment, TP inputs were lower than the ones exported with fish and water.

Discussion-Conclusion

Feed clearly enhanced fish production. It seems that in planted ponds a competition exists between primary producers for nutrients to the benefit of macrophytes. In parallel, system with planted pond enhanced water quality in decreasing average TN and TP in water, and avoided phytoplankton bloom, especially Cyanophyceae development. It seems that planted ponds buffered TN and TP variations in time but failed to reduce P released in downstream water bodies at draining and limit fish growth potential. Mass balance calculation shows a clear use of TN and TP from the pond mud stock, may be due to fish activity and water circulation. In an ecological intensification perspective, this work partially showed the ability of "coupled" ponds system to improve fish production compared to extensive practices and to enhance water quality compared to intensive practices. Planted ponds present also potential for supporting biodiversity and producing plants of market value, which needs to be evaluated in the future. Further investigations will be carried out in the IMTA Effect project framework.

Reference

Aubin J., Rey-Valette H., Mathé S., Wilfart A., Legendre M., Slembrouck J., et al. 2014. Guide for implementing ecological intensification of aquaculture systems, INRA, Rennes, France, 131 p..

Figure 1 :

 1 Figure 1: schematic representation of the experimental design