C Pomar

J Van Milgen

A Remus

Precision livestock feeding, principle and practice 1

Keywords: Precision livestock farming, farm management, automatization, modern livestock production, nutrition

Precision livestock farming (PLF) is proposed to the livestock industry as an essential tool to enhance sustainability and competitiveness

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Precision livestock feeding is part of PLF and can have a great impact in livestock profitability due the ability of feeding pigs with diets tailored daily to their nutrient requirements.

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Precision livestock feeding can decrease livestock environmental impacts by optimizing the use of dietary nutrients and animal nutrient utilization efficiency which results in less nutrient excretion.

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Mathematical models developed for precision livestock feeding must be designed to operate in real-time using system measurements. These models are structurally different from traditional nutrition models.

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The success of PLF is dependent on the precision livestock feeding integration into the system, as well, the adaptability and training of the farmers to use PLF systems.

Introduction

Precision livestock farming (PLF) is an innovative production system approach that can be defined as the management of livestock using the principles and technologies of process engineering [START_REF] Wathes | Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?[END_REF]. The intensive and integrated use of advances in animal science and in the technology of information and communication are the basis for the development of PLF. One of the objectives for developing PLF systems is the on-line continuous and automatic monitoring of animals to support farmers in the management of animal production such as feeding strategies, control of the growth rate, and health management [START_REF] Berckmans | Automatic on-line monitoring of animals by precision livestock farming[END_REF]. The main purpose of PLF is, however, to enhance farm profitability, efficiency, and sustainability (Banhazi et al., 2012a). Precision animal nutrition or precision livestock feeding is considered in this document as part of the PLF approach and involves the use of feeding techniques that allow the proper amount of feed with the suitable composition to be supplied in a timely manner to a group of animals [START_REF] Parsons | Real-time Control of Pig Growth through an Integrated Management System[END_REF][START_REF] Cangar | Effects of different target trajectories on the broiler performance in growth control[END_REF][START_REF] Niemi | The value of precision feeding technologies for grow-finish swine[END_REF] or to individual animals in a group [START_REF] Pomar | Applying precision feeding techniques in growing-finishing pig operations[END_REF][START_REF] Andretta | The impact of feeding growing-finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition[END_REF]. The on-farm application of precision livestock feeding requires the design and development of measuring devices (e.g., to determine the animal's feed intake and weight), computational methods (e.g., estimating in a timely manner nutrient requirements based on the actual animal's growth), and feeding systems capable of providing the required amount and composition of feeds that will generate the desired production trajectory.

The practical application of precision livestock feeding can have great impact in livestock profitability. Feed is the most important cost component in commercial growing-finishing pig production systems and represents between 60 and 70% of the overall production costs. Similar figures hold for broilers and other livestock. Given that nutrients that are not retained by the animal or in animal products are excreted via the urine and faeces or as heat, and that the efficiency by which domestic animals transform dietary nutrients into animal products are generally low, improving the nutrient efficiency can largely contribute to reducing production costs and improve the sustainability of livestock production systems. In fact, nitrogen and phosphorous, which are among the most costly nutrients in livestock feeds, are retained with efficiency rarely greater than 35% [START_REF] Dourmad | Nitrogen consumption, utilisation and losses in pig production in France, The Netherlands and Denmark[END_REF][START_REF] Poulsen | Phosphorus consumption, utilisation and losses in pig production in France, The Netherlands and Denmark[END_REF]. The inefficiency of nitrogen and phosphorous use has different causes. First, part of these ingested nutrients are used for basal metabolic processes involving degradation (catabolism) and synthesis (anabolism), or are lost in the digestive tract through desquamation and endogenous secretions. These losses are generally referred to as maintenance losses. Nutrients are also lost during the production of animal products (e.g., body protein and lipid, milk, and eggs). In growing animals, the losses associated with the utilization of the first-limiting amino acid for body protein deposition can largely be attributed to the inevitable catabolism [START_REF] Heger | Efficiency of utilization of essential amino acids in growing rats at different levels of intake[END_REF][START_REF] Mohn | Influence of dietary lysine and energy intakes on body protein deposition and lysine utilization in the growing pig[END_REF]. These inevitable amino acid losses should be differentiated from other metabolic losses related to the preferential amino acid catabolism, which results from the catabolism of amino acids given in excess, from the excretion of chemically unavailable absorbed amino acids (e.g., heat damaged proteins) [START_REF] Batterham | A comparison of the availability and ileal digestibility of lysine in cottonseed and soya-bean meals for grower/finisher pigs[END_REF], and from the use of amino acids for the synthesis of non-protein body compounds [START_REF] Moughan | Simulation of the daily partitioning of lysine in the 50 kg liveweight pig -A factorial approach to estimating amino acid requirements for the growth and maintenence[END_REF]. In growing animals fed cereal-based diets, the sum of the undigested nitrogen and the losses associated with digestion, maintenance functions, and body protein deposition may represent more than 40% of the total ingested nitrogen.

Pigs, broilers, and other livestock animals are typically raised and fed in groups, usually with the same feed that is given to all animals in the group during a given period of time. However, nutrient requirements vary largely among animals in a population [START_REF] Pomar | Modeling stochasticity: Dealing with populations rather than individual pigs[END_REF][START_REF] Brossard | Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy[END_REF]) and these requirements evolve over time following individual patterns [START_REF] Hauschild | Development of sustainable precision farming systems for swine: Estimating real-time individual amino acid requirements in growing-finishing pigs[END_REF][START_REF] Andretta | The impact of feeding growing-finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition[END_REF]. When growth maximization is the objective of a commercial production system, nutrients have to be provided at a level that will allow the most nutrient demanding animals in the group to express their growth potential [START_REF] Hauschild | Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs[END_REF]. In this situation, almost all animals receive more nutrients than they need. Providing animals with high levels of nutrients to maximize herd performance is common practice in commercial livestock operations even though maximum growth does not ensure maximum economic efficiency [START_REF] Hauschild | Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs[END_REF][START_REF] Niemi | The value of precision feeding technologies for grow-finish swine[END_REF]. Besides the estimated 40% nitrogen loss associated with digestion, maintenance, and production inefficiencies, an additional 30% loss results from protein given in excess to optimize the production response of the group. To account for the variability among animals but also among feed ingredients and other uncontrolled factors (e.g., environment, health) nutritionists include safety margins when formulating diets to ensure the maximum population responses. The need of these safety margins can be seen as an admission of our inability to precisely estimate the nutrient requirements of groups of animals [START_REF] Patience | Precision in swine feeding programs: An integrated approach[END_REF].Precision nutrition will play an important role in future animal production systems because innovative monitoring approaches simplify the determination of nutrient requirements which, when estimated in real-time, allow for the possibility of feeding animals, individually or as a group, according to specific production objectives. These objectives include the maximization or the controlling of growth rate, or to minimize the excess supply of nutrients and reducing environmental impacts. Safety margins are not required in precision livestock feeding. Compared to a 3-phase feeding program for growing pigs, precision livestock feeding can reduce protein intake by 25% and reduce nitrogen excretion by almost 40% while feed cost can be reduced more than 10% [START_REF] Pomar | Precision feeding can significantly reduce feeding cost and nutrient excretion in growing animals[END_REF]. Because animals and feed distribution are monitored and controlled automatically, precision livestock feeding will reduce the time that nutritionists and farm staff will spend on animal observation, decision-making, and applying production strategies, enabling them to work on other aspects of farm management. The objective of this chapter is to describe the basic concepts of precision livestock feeding, its essential elements and illustrate practical applications of precision livestock feeding for growing and finishing pigs.

The basic concepts of precision livestock feeding

Precision animal nutrition or precision feeding concerns the use of feeding techniques that provide animals with diets tailored according to the production objectives (i.e., maximum or controlled production rates), including environmental and animal welfare issues. Precision livestock feeding is presented in this document as the practice of feeding individual animals or groups of animals while accounting for the changes in nutrient requirements that occur over time and for the variation in nutrient requirements that exists among animals. As defined in this document, the accurate determination of available nutrients in feeds and feed ingredients, precise diet formulation, and the determination of the nutrient requirements of individual animals or groups of animals should be included in the development of precision livestock feeding [START_REF] Sifri | Precision Nutrition for Poultry[END_REF][START_REF] Van Kempen | Near-Infrared Reflectance Spectroscopy in Precision Feed Formulation[END_REF][START_REF] Pomar | Applying precision feeding techniques in growing-finishing pig operations[END_REF]. The operation of precision livestock feeding in commercial farms requires the integration of three types of activities: 1) automatic collection of data, 2) data processing, and 3) actions concerning the control of the system [START_REF] Aerts | Dynamic Data-based Modelling of Heat Production and Growth of Broiler Chickens: Development of an Integrated Management System[END_REF][START_REF] Berckmans | Automatic on-line monitoring of animals by precision livestock farming[END_REF]Banhazi et al., 2012b). Application of precision livestock feeding at the individual level is only possible where measurements, data processing, and control actions can be applied to the individual animal [START_REF] Wathes | Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?[END_REF].

Automatic data collection

Measurements on the animal, the feeds and the environment are essential in precision livestock feeding and these have to be measured directly and frequently (if possible, continuously). Measurements that can be made at the animal level include feed intake (e.g., quantity eaten, feed intake behaviour), its physical state (e.g., body weight, body composition), and indicators of its behavioural and health status (e.g., physical activity, interactions among animals). The availability and the rapid development of new devices and emerging sensor technologies to PLF and precision livestock feeding, offer a great potential for animal monitoring. Available technologies and sensors have been described by [START_REF] Wathes | Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?[END_REF] and include low-cost cameras which, in combination with image analysis, can be used to quantify animal behaviour and estimate body weight. Realtime sound analysis and audio-visual observations have been proposed to monitor health status and welfare in pigs [START_REF] Vranken | Precision livestock farming for pigs[END_REF] and behaviour in laying hens [START_REF] Berckmans | Automatic on-line monitoring of animals by precision livestock farming[END_REF][START_REF] Vranken | Precision livestock farming for pigs[END_REF].

Besides the availability of technologies allowing the measurement of animal traits, some guiding principles have to be used for choosing the appropriate and relevant devices and sensors to be used in precision livestock feeding. [START_REF] Black | More beef from pastures: current knowledge, adoption and research opportunities[END_REF] used the Hazard Analysis Critical Control Point (HACCP) in the Australian "More Beef from Pastures" program. The HACCP was proposed to ensure that the most important processes determining productivity and profitability in an animal enterprise were identified and could be controlled and manipulated with the least chance of failure [START_REF] Black | Memorial Lecture: Role of research in advancing animal agriculture in the 21st century[END_REF] including the development of PLF applications (Banhazi et al., 2012b). In the context of automatic data collection for precision livestock feeding, the HACCP principles are a) to identify the factors that have quantitatively a major impact in the determination of the response of the animal or of the population to the nutrient supply, and b) for each one of these factors, determine the measurements that have to be taken at the farm or animal level to ensure the application of precision livestock feeding. At this point, precision livestock feeding developers have to avoid the temptation of looking for practical applications of currently available sensors but rather concentrate on identifying the most important physiological factors and measurements needed to establish optimal feeding strategies. These measurements have to be related to the precise evaluation of the nutritional value of the diet, the real-time determination of nutrient requirements [START_REF] Pomar | Applying precision feeding techniques in growing-finishing pig operations[END_REF], and the responses of the animal to the nutrient supply. The application of HACCP principles to identify production hazards is not addressed further in this paper and the reader is referred to [START_REF] Black | Memorial Lecture: Role of research in advancing animal agriculture in the 21st century[END_REF] for more information on this issue.

Data processing

Collected data has to be processed to allow for control activity in precision livestock feeding. Mathematical modelling is a methodology used to understand and to quantify complex biological phenomena involved in animal production and can be the basis for data processing in precision livestock feeding control systems. A mathematical model is an equation or a set of equations representing the behaviour of the system [START_REF] Thornley | Mathematical Models in Agriculture[END_REF]. Computer simulation, in its broadest sense, is described as the process of defining a mathematical-logical model for the real system and experimenting with this model on a computer [START_REF] Pritsker | Introduction to simulation and SLAM II[END_REF][START_REF] Thornley | Mathematical Models in Agriculture[END_REF]. By definition, models are a simplification of the system they represent, but the most relevant factors implicated in the animal responses that are to be controlled in precision livestock feeding need to be represented into the model. Mathematical models developed for precision livestock feeding, however, have to be designed to operate in real-time using real-time system measurements and, therefore, they are structurally different from traditional nutrition models, which are developed to work in a retrospective manner to simulate and understand known production situations. The basic principles for model development have to be reviewed because not all the models are adequate for precision livestock feeding and a model structure has to be chosen according to the available information and the desired control design of the system.

Mathematical models can take many different forms depending on model objectives and structure. Indeed, models can be empirical or mechanistic, deterministic or stochastic, static or dynamic, and real-time or prospective. Information about the development of mathematical models in animal science can be found elsewhere [START_REF] Thornley | Mathematical Models in Agriculture[END_REF]. Different approaches have been used to predict animal growth [START_REF] Van Milgen | Precision pork production: Predicting the impact of nutritional strategies on carcass quality[END_REF]. The earliest and still very common approach is empirical in which growth is described by a single or few mathematical equations. Empirical models use a black-box approach and are developed to describe the responses of a system without a description of the system itself and unconstrained by biological principles [START_REF] Thornley | Mathematical Models in Agriculture[END_REF]. The empirical approach can provide effective prediction in a narrow range of situations related to experimental conditions under which the data were collected. However, the empirical approach fails to extrapolate results in situations beyond those used in the original experimental conditions. Because model parameters and structure do not have a biological meaning, these models need to be fitted with appropriate data to simulate each situation. Therefore, to ensure flexibility and to allow effective prediction in a wide range of situations, models with mechanistic (deductive) components are preferred [START_REF] Baldwin | Principles of modelling animal systems[END_REF][START_REF] Whittemore | An approach to pig growth modeling[END_REF]. Mechanistic models provide some degree of understanding of the biological phenomena implicated in the response of the system [START_REF] Thornley | Mathematical Models in Agriculture[END_REF]. Mechanistic mathematical models have been the preferred approach in animal sciences since the 70s when protein and lipid deposition (and the resulting body weight gain) was modelled from the nutrient supply [START_REF] Whittemore | Theoretical aspects of a flexible model to stimulate protein and lipid growth in pigs[END_REF]. This and other early models have inspired the development of other nutritional models simulating growth in pigs [START_REF] Black | Simulation of energy and amino acid utilisation in the pig[END_REF][START_REF] Moughan | Description and validation of a model simulating growth in the pig (20-90 kg liveweight)[END_REF][START_REF] Pomar | Computer-simulation model of swine production systems. 1. Modeling the growth of young-pigs[END_REF][START_REF] Birkett | A computational framework for a nutrient flow representation of energy utilization by growing monogastric animals[END_REF][START_REF] Green | Architecture of a harmonized model of the growing pig for the determination of dietary net energy and protein requirements and of excretions into the environment (IMS Pig)[END_REF][START_REF] Halas | Modelling of nutrient partitioning in growing pigs to predict their anatomical body composition. 1. Model description[END_REF][START_REF] Van Milgen | InraPorc: A model and decision support tool for the nutrition of growing pigs[END_REF], poultry [START_REF] Emmans | A model of the growth and feed intake of ad libitum fed animals, particularely poultry[END_REF][START_REF] Emmans | Genetic components of potential and actual growth[END_REF][START_REF] Hancock | The evaluation of the growth parameters of six strains of commercial broiler chickens[END_REF][START_REF] Gous | Evaluation of parameters needed to describe the overall growth, the chemical growth and the growth of feathers and breast muscles in broilers[END_REF], turkeys [START_REF] Hurwitz | Characterization of Growth and Development of Male British United Turkeys[END_REF][START_REF] Rivera-Torres | Mechanistic modeling of turkey growth response to genotype and nutrition[END_REF], conceptus growth and milk production in sows [START_REF] Dourmad | InraPorc: a model and decision support tool for the nutrition of sows. (Special Issue: Mathematical models that predict the effects of feed characteristics on animal performance[END_REF]NRC, 2012) and egg production in hens [START_REF] Fisher | A model for the description and prediction of the response of laying hens to amino acid intake[END_REF].

Mathematical models can be deterministic or stochastic. Deterministic models make a unique prediction for each specific set of input variables without any associated probability distribution. Stochastic models contain random elements in the model, so that, in addition to predicting the expected value of a performance trait, they also predict its dispersion [START_REF] Thornley | Mathematical Models in Agriculture[END_REF]. Variation is essential and inherent to living systems and variation among the animals significantly contributes to the efficiency with which nutrients can be used [START_REF] Curnow | A smooth population response curve based on an abrupt threshold and plateau model for individuals[END_REF] independently of genetic variation [START_REF] Knap | Stochastic simulation of growth in pigs: relations between body composition and maintenance requirements as mediated through protein turn-over and thermoregulation[END_REF][START_REF] Knap | Animal-intrinsic variation in the partitioning of body protein and lipid in growing pigs[END_REF][START_REF] Pomar | Modeling stochasticity: Dealing with populations rather than individual pigs[END_REF][START_REF] Brossard | Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy[END_REF][START_REF] Vautier | Accounting for variability among individual pigs in deterministic growth models[END_REF], and environmental or animal management aspects [START_REF] Wellock | Modeling the effects of stressors on the performance of populations of pigs[END_REF]. This variation is essential for the understanding of the biological mechanisms implicated in the response of populations to the nutrient intake, given that the response of a population to treatments differs in magnitude and shape from that of an individual animal [START_REF] Pomar | Modeling stochasticity: Dealing with populations rather than individual pigs[END_REF]. Mathematical models designed to estimate nutrient requirements and responses in a population of animals need to account for individual variation. Static models do not contain time as a driving variable and do not make time-dependent predictions. Dynamic models are developed to quantify and to study the evolution of a system over time [START_REF] Thornley | Mathematical Models in Agriculture[END_REF]. Essential elements of dynamic models are the differential equations in which time is an independent variable driving the rate of change of the state variables of the system. Most models in animal science and specifically in swine nutrition are dynamic because of the animal responses and requirements change over time.

The utilization of mechanistic models in precision livestock feeding systems has been criticized because these models are overly complex and the information required by the model to simulate practical conditions is not always available [START_REF] Aerts | Dynamic Data-based Modelling of Heat Production and Growth of Broiler Chickens: Development of an Integrated Management System[END_REF][START_REF] Wathes | Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?[END_REF]. On the other hand, the simplicity of empirical models is counteracted by the difficulty to represent interactions between nutrients and animals. Despite the fundamental structural differences between empirical and mechanistic models in the way they predict the response of the animal to the nutrient supply, both types of models have to be calibrated a priori using data collected from reference populations [START_REF] Pomar | Estimating real-time individual amino acid requirements in growing-finishing pigs : towards a new definition of nutrient requirements?[END_REF] in which the phenotypic performance potential of the animal is quantified. Indeed, mechanistic growth models for pigs use intrinsic characteristics of a reference population either to describe the potential (phenotypic) protein deposition and feed intake patterns [START_REF] Dourmad | InraPorc: a model and decision support tool for the nutrition of sows. (Special Issue: Mathematical models that predict the effects of feed characteristics on animal performance[END_REF][START_REF] Van Milgen | InraPorc: A model and decision support tool for the nutrition of growing pigs[END_REF]NRC, 2012) or potential body protein and lipid deposition [START_REF] Emmans | A model of the growth and feed intake of ad libitum fed animals, particularely poultry[END_REF][START_REF] Black | Simulation of energy and amino acid utilisation in the pig[END_REF] while empirical models have the animal responses embedded into the model. To be used in precision livestock feeding, empirical and mechanistic models are, therefore, challenged by the difficulty of identifying the right reference population for its calibration, the fact that actual populations and individual animals may follow feed intake and growth patterns different than the ones observed in the reference population [START_REF] Pomar | Estimating real-time individual amino acid requirements in growing-finishing pigs : towards a new definition of nutrient requirements?[END_REF].

The computational power and reliability of modern information technologies empower the utilization of advanced recursive technologies in the development of PLF and precision livestock feeding applications [START_REF] Wathes | Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?[END_REF]. These modelling techniques (e.g., artificial neural networks) estimate unknown model parameters of an abstract mathematical model, based on online input and output measurements. Model parameters are estimated on-line during the process, resulting in a model that continuously adapts its response to on-line process inputs and outputs. There are few examples in which these models have been used in PLF or precision livestock feeding applications [START_REF] Korthals | Evaluation of Neural Networks as a Tool for Management of Swine Environments[END_REF][START_REF] Bridges | Techniques for development of swine performance response surfaces[END_REF][START_REF] Aerts | Modelling the static and dynamic responses of total heat production of broiler chickens to step changes in air temperature and light intensity[END_REF][START_REF] Thomson | Feasibility of using neural networks for real-time prediction of poultry deep body temperature responses to stressful changes in ambient temperature[END_REF]. The limitation of using the recursive approach in precision livestock feeding is related to the fact that model parameters and model structure do not provide biological insight in the causal mechanisms implicated in animal responses, that animal response and input parameters may have unsymmetrical variation, and that the animal responses to input variation does not evolve in the same timeframe. For example, when animal processes are modelled for which there is a significant time lag between the effects of varying input parameters (e.g., dietary lysine intake) and the response (e.g., body weight gain and composition), the autocalibration capability of these recursive models is limited and they will generate irregular control signals [START_REF] Cangar | Effects of different target trajectories on the broiler performance in growth control[END_REF]. Rapid animal responses such as a behavioural response to inputs such as temperature and light intensity may be easily controlled by recursive models in PLF applications [START_REF] Aerts | Modelling the static and dynamic responses of total heat production of broiler chickens to step changes in air temperature and light intensity[END_REF].

The disadvantages associated with black-box models can be overcome by using an intermediate approach of grey-box models in which recursive technologies and mechanistic models are combined. This approach was suggested by [START_REF] Bridges | Techniques for development of swine performance response surfaces[END_REF] who used a mechanistic swine growth model to generate physiological response data and this response data were then used to train and validate three backward propagation neural network models describing the effect of the environment on average daily gain, feed intake, heat production, and physiological status of the animal. The authors concluded that neural network models can be used to simplify data extraction from complex models and be used in instances where the use of the full model is difficult or impossible. Another grey-box model application was proposed by [START_REF] Hauschild | Development of sustainable precision farming systems for swine: Estimating real-time individual amino acid requirements in growing-finishing pigs[END_REF], who combined black-box (i.e., empirical) and "knowledge-based" (i.e., mechanistic) model components to estimate daily amino acid requirements in individual growing-finishing pigs. The empirical component of this model estimated daily feed intake, body weight, and daily gain based on individual information collected in real time. Based on these daily estimations, the mechanistic model component predicted the concentration of amino acids required to meet the daily growth needs. The principles behind this model approach have been described (Hauschild et al., 2010;[START_REF] Pomar | Estimating real-time individual amino acid requirements in growing-finishing pigs : towards a new definition of nutrient requirements?[END_REF] and validated [START_REF] Andretta | The impact of feeding growing-finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition[END_REF]Andretta et al., 2016b).

Control of the system

The main objective of precision livestock feeding is to monitor, manage, and control animal feeding and nutrition continuously and automatically. Data collection and monitoring devices provide the farmer with detailed information about the animal's actual conditions and performance, the utilization of farm resources, while data processing helps with system surveillance (e.g., disease detection) and the estimation of optimal production strategies (e.g., optimal slaughter and production strategies). This information can also be used by an automatic controller to make decisions, which in the context of precision livestock feeding, will typically be the amount and the composition of the feed to be given to an individual or to a group of animals. Depending on the production objectives, the controller can be programmed to maximize growth rate, to minimize feed cost, to minimize nutrient excretion, or to meet another objective.

The determination of nutrient requirements and the control of the nutrient intake through feed composition and intake are two essential elements of precision livestock feeding. For a given animal and at a given time during his life, daily nutrient requirements can be estimated by the sum of the requirements for maintenance and production. These requirements are estimated for each nutrient taking into account the efficiency with which each nutrient is used. For a given animal, maintenance and production requirements change over time and so do nutrient requirements (NRC, 2012). Farm animals are often raised and fed in groups although, within a group, animals differ in feed intake and growth potential. Consequently, nutrient requirements vary among animals [START_REF] Pomar | Modeling stochasticity: Dealing with populations rather than individual pigs[END_REF][START_REF] Wellock | Modeling the effects of stressors on the performance of populations of pigs[END_REF][START_REF] Brossard | Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy[END_REF]. The dynamic and the between-animal variation are the two main sources of variation in nutrient requirements that can be controlled in precision livestock feeding systems. Production systems in which animals are fed individually can be used to control both sources of variation while in group-fed systems only the time-dependent variation can be controlled.

Therefore, several potential control strategies are available for the application of precision livestock feeding in commercial conditions. In feeding systems where animals are offered with feed ad libitum, the only way to control the nutrient intake is by varying the composition of the distributed feed. In ad libitum group-fed systems, animals can be fed for maximal production by providing nutrients following the time-dependent nutrient requirements of the group or for a given production strategy (i.e., body composition, population uniformity), by controlling the composition of the served feed. When animals are individually fed and offered feed ad libitum, both the between animal and the time-dependent nutrient requirements variation can be controlled. In feeding systems where animals are offered feed restrictively, the amount and the composition of the feed can be controlled. Maximum growth rate will not be attained in this situation although feeding strategies can be established to account for between animal and the time-dependent variation in nutrient requirements. For example, feed restriction in pregnant sows allows controlling body weight gain and fatness while maintaining conceptus growth. Examples of these control approaches for growing animals will be given in the following section.

The implementation of precision livestock feeding principles in growing and finishing pig production systems

Conventional growing and finishing pig feeding programs are designed to maximize population body weight gain, optimize carcass fatness, etc., and they provide a single feed to all the pigs in the pen or herd within each feeding phase. One to four-phase feeding systems are nowadays popular in commercial growing-finishing pig operations [START_REF] Niemi | The value of precision feeding technologies for grow-finish swine[END_REF]NRC, 2012) but it is acknowledged that increasing the number of feeding phases reduces feed costs, improves feed efficiency, and decreases nutrient excretion [START_REF] Letourneau Montminy | Impact de la méthode de formulation et du nombre de phases d'alimentation sur le coût d'alimentation et les rejets d'azote et de phosphore chez le porc charcutier[END_REF][START_REF] Brossard | Définir des stratégies alimentaires alliant performance économique et impact environnemental grâce à la modélisation du groupe de porcs en croissance[END_REF]. Multi-phase group-feeding systems allow the adjustment of the feed composition over time to better match the population nutrient requirements. Moving from conventional feeding systems to precision livestock feeding systems requires not only to increase the number of feeding phases, but also using the information concerning the actual status and evolution of the animal (e.g., feed intake, body weight, body composition) to control feed supply.

Accurate and automatic measurement of the amount of feed consumed daily by individuals or groups of pigs is an essential information element required for the implementation of precision livestock feeding in growing and finishing pig operations. Although liquid feeding systems provide predetermined amounts of feeds to pens, they are of limited use to provide information on the feed intake because feed is provided at restricted levels with these systems. The availability of commercial devices for measuring dry feed intake is still limited and seldom used for the implementation of precision livestock feeding in commercial piggeries. An individual feed intake recording system has been developed in the UK for the real-time control of growth [START_REF] Parsons | Real-time Control of Pig Growth through an Integrated Management System[END_REF]. The system is able to weigh the feed delivered to each pig at each visit. Similar precision livestock feeding system has been developed by [START_REF] Pomar | The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed costs in growing-finishing pigs[END_REF] using an automated recording system (IVOG system, Insentec B.V., Marknesse, the Netherlands). Another example of dry feeders measuring the consumed feed has been developed in Australia [START_REF] Banhazi | Field Evaluation of a Prototype Sensor for Measuring Feed Disappearance in Livestock Buildings[END_REF]Banhazi et al., 2012a). This device can accurately measure the amount of feed supplied through the feed line (by an innovative feed sensor), estimating the amount of feed delivered to each feeder. Finally, an automatic and intelligent precision feeder (AIPF) developed for precision feeding of growing-finishing pigs has been developed [START_REF] Pomar | Agent-based simulation framework for virtual prototyping of advanced livestock precision feeding systems[END_REF], which is able to provide a specific quantity and composition of feed to individual pigs at each feeder visit. The functioning of these AIPF feeders has been described elsewhere [START_REF] Pomar | Agent-based simulation framework for virtual prototyping of advanced livestock precision feeding systems[END_REF][START_REF] Pomar | Estimating real-time individual amino acid requirements in growing-finishing pigs : towards a new definition of nutrient requirements?[END_REF] and the feeders have been used in several research projects [START_REF] Andretta | The impact of feeding growing-finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition[END_REF][START_REF] Cloutier | Evaluation of a method estimating real-time individual lysine requirements in two lines of growing-finishing pigs[END_REF]Andretta et al., 2016a).

Accurate and regular body weight measurements performed without causing stress and requiring labour input is a great asset for the implementation of precision livestock feeding in growing-finishing pig facilities. Available technologies for automatic animal weighing include conventional load cell platforms [START_REF] Turner | Automatic weight monitoring of pigs-Part 1: Trials of prototype weigh platforms National Institute of Agricultural Engineering Divisional Note[END_REF] and the combination of video cameras and image analysis. The possibility of estimating the weight of a pig from specific areas and dimensions through digital image analysis has been developed [START_REF] Schofield | Evaluation of image analysis as a means of estimating the weight of pigs[END_REF][START_REF] Brandl | Determination of live weight of pigs from dimensions measured using image analysis[END_REF][START_REF] Whittemore | A case for size and shape scaling for understanding nutrient use in breeding sows and growing pigs[END_REF][START_REF] Doeschl-Wilson | The relationship between the body shape of living pigs and their carcass morphology and composition[END_REF]White et al., 2004a) and used in several experiments. Lean growth is the major determinant of amino acid requirements in growing animals. Modern pigs are capable of maintaining high levels of lean deposition at heavier live weights. Measuring backfat and muscle thickness can be precious information to estimate body fat and protein.

Although different technologies are available, ultrasound is without doubt the most widely used because of its cost, reliability, and portability [START_REF] Moeller | Evolution and use of ultrasonic technology in the swine industry[END_REF]. However, it is still a manual operation and although automatic measurement methods of backfat thickness have been proposed [START_REF] Tillett | AP-Animal Production Technology: Predicting Sensor Placement Targets on Pigs using Image Analysis[END_REF][START_REF] Frost | A vision guided robot for tracking a live, loosely constrained pig[END_REF]), these technologies have not been developed further since then.

After measuring the essential information concerning the feeds and animals, precision livestock feeding requires to determine optimal nutrient concentration of feeds to automatically provide animals with the amount and composition of the feed according to the established production objectives. Precision nutrition can be used in pig growing facilities to either allow pigs perform at their maximal growth potential or drive growth rate and body composition by restricting feed or nutrient intake. Actual pig growth models (e.g., [START_REF] Van Milgen | InraPorc: A model and decision support tool for the nutrition of growing pigs[END_REF]NRC, 2012) have been developed to operate in a retrospective manner and are calibrated after all growth data have been collected to simulate the production situation. These models are designed to predict, under specific situations, the consequences of feed and nutrient intake in terms of animal responses (e.g., protein and lipid deposition, body weight growth). These models are used to evaluate the nutrient utilization by the animal and to test nutritional strategies. Mathematical models developed to be used in precision livestock feeding systems need to operate in real-time using appropriate realtime animal information (e.g., body weight and composition), behaviour (e.g., feed intake), environment (e.g., ambient temperature), health (e.g., body temperature, sounds to detect health status), and other parameters. When these models are conceived to achieve the animals' full growth potential, they can be devoid of feedback control elements and provide predictions based on actual and recent animal information. The objective of this real-time model-control approach is not to manipulate the animal response (i.e., body weight gain or composition) but to deliver the controlled production factors (e.g., feed composition) at the levels required for maximum growth.

Automatic blenders, feeders and feed, and animal management devices are required to apply the controller decisions. The development of feeding systems that allow blend-feeding and the automatic distribution of two or more feeds that, when combined in variable ratios, can meet the requirements of pigs throughout their growing period [START_REF] Feddes | A system for providing protein for pigs in intermediately sized grower/finisher barns[END_REF][START_REF] Pomar | The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed costs in growing-finishing pigs[END_REF] makes the phase-feeding technique cost-effective. The feeds can be complete diets formulated to satisfy the requirements of pigs at the beginning and at the end of their growing period or to contain complementary amounts of nutrients in such a way that when blended, the feeds become complete diets [START_REF] Joannopoulos | The diet problem[END_REF]. Blending two feeds may be seen as a promising option for feed companies, since it means that there are just two feeds to prepare, with only the proportions changing between the feeding phases and between farms. These group and individual feeding precision livestock feeding systems will benefit from using accurate and individual feed intake and body weight measurements to drive the amount and composition of the feeds to be served to the pigs. To further develop precision livestock feeding systems, it is necessary to improve our actual understanding of several animal metabolic processes. Precision livestock feeding is still based on models and nutritional concepts of average population responses. When feeding individual pigs with daily tailored diets, these traditional nutritional concepts seem insufficient [START_REF] Remus | Modelos para estimar exigências nutricionais de aminoácidos e resposta à ingestão de metionina: sistema tradicional por fases x nutrição de precisão[END_REF][START_REF] Ghimire | Variation in protein content and efficiency of lysine utilisation in growing-finishing pigs[END_REF][START_REF] Remus | Pigs receiving daily tailored diets have different amino acid requirements than pigs raised in conventional phase feeding systems[END_REF]. It is necessary to distinguish the nutritional requirements of a population from those of an individual. Individual pigs are able to modulate growth and the composition of growth according to the level of available amino acids [START_REF] Remus | Modelos para estimar exigências nutricionais de aminoácidos e resposta à ingestão de metionina: sistema tradicional por fases x nutrição de precisão[END_REF]. Also, pigs can respond differently to the same amount of ingested amino acid, due to differences in the efficiency of amino acid utilization. These aspects are not considered in current nutritional models, which assume that the efficiency by which animals use the available amino acids is constant. Similarly, the amino acid composition of whole body protein is assumed to be constant as well, while it has been shown that it can vary. Similar results have been found for the efficiency of calcium and phosphorus utilization [START_REF] Gonzalo | Consequences of dietary calcium and phosphorus depletion and repletion feeding sequences on growth performance and body composition of growing pigs[END_REF]. Understanding the metabolic processes responsible for the observed variation between individual animals in their ability to use dietary nutrients is challenging nutritionists and modellers but is required to further improve the efficiency of livestock production. Advances in precision livestock feeding rely on the development of sound nutritional concepts and comprehensive biological models developed to more precisely estimate individual nutrient requirements in real-time.

PLF and precision livestock feeding systems used in practice

The real-time modelling-control approach was used by [START_REF] Pomar | The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed costs in growing-finishing pigs[END_REF] to control the timedependent variation of group-housed pigs offered feed ad libitum. Feed intake was measured daily with an automatic device and animals were weighed manually every two weeks. The desired diet composition was obtained by blending two feeds with a high and a low nutrient concentration. Nutrient requirements of the group were estimated each day based on body protein and growth rates observed in animals of similar genetic background. Comparing the traditional three-phase feeding system to the daily-phase feeding system, the authors concluded that protein intake could be reduced by 7% while nitrogen excretion was reduced by 12%.

Controlling the time-dependent and the among-animal variation can further help reducing nutrient intake and excretion. This modelling approach was used to estimate nutrient requirements in real-time in individual pigs [START_REF] Hauschild | Development of sustainable precision farming systems for swine: Estimating real-time individual amino acid requirements in growing-finishing pigs[END_REF][START_REF] Pomar | Estimating real-time individual amino acid requirements in growing-finishing pigs : towards a new definition of nutrient requirements?[END_REF] and applied to feed pigs individually with daily tailored diets [START_REF] Andretta | The impact of feeding growing-finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition[END_REF]. The latter authors showed that daily adjustment of the diet resulted in a 27% reduction in total lysine intake, without affecting growth. This additional 20% reduction in lysine intake in relation to group-fed pigs could be obtained by feeding the animals individually and thus controlling simultaneously the time-dependent and the between-animal variation. Although reducing feed cost depends to a great extent on feed prices, it is expected that feed cost can be reduced by 1-3% when only controlling the time-dependent variation while a 8-10% reduction can be obtained when controlling also the among-animal variation.

Restricting feed or nutrient intake has been proposed in several precision livestock feeding systems with the objective to minimize feed cost, ammonia emissions, or to maximize the return per pig space. [START_REF] Demmers | Simultaneous Monitoring and Control of Pig Growth and Ammonia Emissions Ninth International Livestock Environment Symposium[END_REF] used an automated feeding system to provide the desired amount of feed of fixed composition to each pen. Daily body weight was estimated using a commercial visual image analysis system. The controller was based on a recursive neural network of growth and ammonia emission models, which were calibrated from previous experiments. The system was used to control the amount of feed delivered to pens and the ambient temperature to optimize growth and reduce ammonia emissions. A precision livestock feeding system was also used by [START_REF] Niemi | The value of precision feeding technologies for grow-finish swine[END_REF] to study multi-phase and two phases feeding systems and growth patterns in terms of economic return per pig space. In this multi-phase feeding system, the amount of feed, the protein concentration in the diet, and the time to reach slaughter weight were optimized on a daily basis. The controller included a stochastic dynamic model that estimated nutrient requirements as a function of body weight and evaluated the different scenarios to maximize the return on capital investment. The authors concluded that producers would benefit from adjusting diet composition on a daily basis but that the optimal production strategy and the return on investment are affected by the variation among pigs and the variation in feed and carcass prices.

A real-time system for the integrated control of population pig growth and pollutant emissions was also proposed [START_REF] Whittemore | Nutrition management of growing pigs[END_REF][START_REF] Parsons | Real-time Control of Pig Growth through an Integrated Management System[END_REF] using an automatic daily feed intake recording device and a visual image analysis system to estimate daily body weight [START_REF] Schofield | Evaluation of image analysis as a means of estimating the weight of pigs[END_REF]White et al., 2004b). Pigs were fed ad libitum in this precision livestock feeding system with diets varying in crude protein concentration. A high and a low-protein diets were manually blended to obtain the desired level of protein in the final mix to be served. The authors concluded that weight gain in pigs can be controlled through the proposed ad libitum feeding precision livestock feeding system and that some control of body fatness may also be possible.

Factors that can influence the successful application of precision livestock feeding systems on farms

Precision livestock feeding can be considered as a component of a PLF system and the successful on-farm application of precision livestock feeding systems will face similar challenges as other PLF systems. [START_REF] Wathes | Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?[END_REF] considered PLF as an "embryonic technology with great promise" but they also acknowledged that few PLF have been implemented successfully so far. In addition, there may be a long time path between development and application. For example, the milking robot was developed in the 80s and has been commercialized since the early 90s but, despite 25 years of availability, it has yet to revolutionize the dairy industry. In an article with the provocative title "Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?", [START_REF] Wathes | Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall?[END_REF] discussed the development and adoption of PLF systems. Others [START_REF] Groot Koerkamp | Precision Livestock farming: creating order beyond control[END_REF]Banhazi et al., 2012a;Banhazi et al., 2012b) have expanded on these ideas and the main issues in the development and successful adoption of PLF (and thus precision livestock feeding) can be summarized as follows:

• There is a strong need for coordination and to involve different experts and stakeholders in the development and implementation of PLF (i.e., researchers, engineers, technology suppliers, economists, farmers, consumers, and citizens). • With the rapid development and available of sensors, more focus should be paid to data interpretation and control mechanisms. • Not all processes need to be automated; it is about assisting farmers, not about automatic farms. Groot [START_REF] Groot Koerkamp | Precision Livestock farming: creating order beyond control[END_REF] argued that there is not necessarily an intrinsic connection between (better) measurements and (better) control and that the allocation of controlling power is an important factor to consider. Who is in control: a machine, the farmer, the animal? Groot [START_REF] Groot Koerkamp | Precision Livestock farming: creating order beyond control[END_REF] suggested that recursive control by animals may be an alternative means to create order in complex biological systems, which, to some extent, can be interpreted as the consideration of agro-ecological principles in PLF.

• The benefits of PLF systems should be verified on the farm.

• Appropriate deployment of PLF systems and training, service and support for farmers should be assured. The latter may imply the development of a new service industry. As indicated by Banhazi et al. (2012b), farmers are biologists by nature and only technologists occasionally.

Although they do invest in technology, it is typical machinery that they look forward to buying as opposed to software, sensors or services. • Awareness and education for consumers and citizens. Citizens may perceive PLF as a further industrialization of livestock production. Education should help appreciating controlled, animal-centric livestock production, while looking for attributes that make modern production better for the animal and more sustainable. Food production should be made more transparent.

For example, the EU-funded BrightAnimal project suggested using social networks, FarmCams, and a "Be a farmer for a day" initiative to improve awareness.

Some of these issues addressed above have been considered in projects funded by EU-funded projects such as EU-PLF (www.eu-plf.eu), ALL-SMART-PIGS (www.all-smart-pigs.com/), and Feed -a-Gene (www.feed-a-gene.eu).

An increasing concern is the adaptability and training required by farmers to use PLF systems. Some authors [START_REF] Van Hertem | Appropriate data visualisation is key to Precision Livestock Farming acceptance[END_REF] believe that use of appropriate data visualization tools can facilitate the farmer acceptance and adoption of PLF applications. These authors tested and evaluated PLF systems on ten fattening pig farms and five broiler farms. Data of production, climate and behaviour was continuously measured, analyzed daily and made available on a webbased tool. Nearly 50% of the farmers took the training, but only 28% of the trained farmers actively used the tool. According to the authors, the success of the training seemed to be dependent on the complexity of the system installed on the farm (e.g., environmental sensors) and the training/education of the end user. They conclude that training is fundamental for the adoption of such systems.

Future perspectives

As indicated above, different technologies are now available for real-time and individual phenotyping and the availability of feeder equipment allows the distribution of specific diets to individual animals. An important issue that needs to be addressed further in the future is the control of the system, and how precision livestock feeding can and should interact with other components of PLF and with livestock production in general. For example, precision livestock feeding allows having large groups of pigs in a single pen, but this raises questions on how the group size affects animal behaviour and health, on pen design, and on management of animals in the pen. In recent years, some growing-finishing pig facilities are moving to larger groups of up to 1,000 pigs/pen. These facilities are generally equipped with auto-sorting systems that weigh individual animals before entering the feed court, identifying pigs that reach market weight, and sort them into a loading pen [START_REF] Street | Effects of housing finishing pigs in two group sizes and at two floor space allocations on production, health, behavior, and physiological variables[END_REF]. The development of nutritional concepts and models specially designed for precision livestock feeding and the system integration, to provide early alerts about changes in the system (e.g. health status based on a reduction in the feed intake) seem to be of great importance. These issues need to be addressed in the future if precision livestock feeding is to go beyond being an alternative feeding technique based on the optimization of the nutrient supply to the animal. Precision livestock feeding has the potential to be an important element of innovative livestock production systems, which may involve changes in several processes and elements within the system [START_REF] Groot Koerkamp | Precision Livestock farming: creating order beyond control[END_REF].

This project was funded by Swine Innovation Porc within the Swine Cluster

2: Driving Results through Innovation research program which founds were provided by Agriculture and Agri-Food Canada through the AgriInnovation Program as well as by provincial producer organizations and industry partners. Funding was also provided by the European Union's Horizon 2020 research and innovation program under grant No. 633531.


.012 01/15/2018 OpenSILEX  Open source software set

• Methods, tools, components to implement information system for experimental data in agriculture and environment

• Organized system for the collection, structuration, storage, exchange and treatment of information

A

  Designed for data management in phenotyping platforms • Management of huge, complex and heterogeneous data (millions of images, sensor data, etc)  Implement good practices of data management • Make FAIR data • Foster collaborations (Open and Flexible) • Ability to understand and reproduce data processing • Ability to enforce Open Data through DMP Alice BOIZET / Data Integration for High-Scientific objects (plant, plant organ, plot, etc.) are identified by URI standardized, unambiguous, shared, Specific platform application Ontology (OWL) formalizes Objects and Events (RDF) and allows to link reference ontologies (SKOS) Major assets: • Allows management of huge and complex data • International identification (URI and DOI) • Semantic (ontologies, standardized vocabularies) • Portal interoperability and Open technologies • Provenance and reproducibility data processing • Flexible design  Beside the prototype, a production version: • 5 instances from various installations (field and greenhouse) • One instance → Over 300 Tb of data over 10 plant species • Set of several instances to manage Petabytes  Collaborative support and development: • Done by INRA-MISTEA team • New contributors to come

How to contribute ?

01/15/2018  Prototype demonstration of OpenSILEX-PHIS:

• http://phis.inra.fr/


domain: math info sdv sde

text:
EMPHASIS-PREP: Preparation of European Infrastructure for Multi-Scale Plant Phenotyping And Simulation for Food Security in a Changing Climate

Semantics and Plant Phenotyping Data Structuration for Data Analytics

• Highly heterogeneous data (e.g. images, growth curves)

• Multi-spatial and temporal scale data (leaf to canopy level)

• Multi-source data (field, platform)

• Environmental data

• Enrich datasets with knowledge and metadata (enable reuse of data and meta-analyses)

• Interoperate and integrate data into external resources (e.g. modelling platforms or external databases)

• Provide FAIR data (functionnaly linked data)

We need an Information system capable to • Enrichment of data with knowledge and metadata via annotation

• User-friendly and interactive web interface

• Flexible Web Services for the integration of a diverse and multi-source data

PHIS approaches

Object Identification

URIs (Uniform Resource Identifiers)

 standardized, unique, unambiguous identification of objects

How do we link all these objects?

Application Ontologies

OEPO (Ontology for Experimental Phenotypic Objects)

=> assignes types to objects involved in phenotyping experiments (e.g. sensors, plants, experimental context) and defines a specialization hierarchy between them according to the specificities of the installations and experiments.

OEEv (Ontology of Experimental Events (OEEv)

=> characterises events that occur during an experiment, (e.g. dates of sowing, application of a given treatment, harvesting), experimental management or any category of technical problem.

Whereas OEPO and OEEv refer to objects and events that can be specific to either field or controlled conditions, a number of the terms and definitions follow recommendations adopted by EMPHASIS and reuses reference ontologies including:

• Crop Ontology

Environmental sensors

Each object is related to different objects, properties and data

'

  Plots lodged after the storm'Events follow the Web Annotation Data Model that allows assigning motivation and purpose attributes to annotations (e.g. oa:describing, oa:replying, etc.). Dublin Core properties such as dcterms:created or dcterms:creator are also used.Event AnnotationOn the web interace, clicking on a data point automatically displays the images associated to this point together with the associated annotations Web Service API • RESTFul (Representational state transfer) • Services available using URIs. • Developed in Java with Jersey implementation of JAX-RS (Java API for RESTful Web Services) standard • Implements the Breeding API : new version (v1.2) to be implemented • Swagger interactive documentation (data-interchange format JSON) API documentation (Swagger) PHIS architecture

, organise and manage:

	But also and very important…
	• Plant accidents
	• Human mistakes … Structured data • platform failures • > 44000 raw and segmented images • ca. 200000 phenotypic observations • ↑ number of non exploitable / misleading data
	• >10000s points of environmental data
	… Non structured data
	• Visual scores
	• Lab book notes
	• Events
	• Measurements in companion platforms or
	compartments

Use case: PhenoArch generates daily...

• Store

Data visualisation

• Display of images, and time courses of phenotypic and environmental variables are automatically adapted to the particular experiments and variables • Raw images, segmented images and metadata can be displayed in both field and greenhouse experiments

Variable management and interoperability

Variable = Trait + Method + Unit PHIS includes extensible scientific computing modules based on R packages for calculating elaborated variables and generating experimental summaries and reports.

Data analysis

In short PHIS:  Allows management of huge and complex data thanks to a flexible design


Louise Huot

Audrey Bigourdan

Pierre-Alain Girard

Sylvie Pagès

Nicolas Nègre

Duvic Bernard

Dgimi

Spodoptera frugiperda immune response to the nematobacterial complex Steinernema carpocapsae-Xenorhabdus nematophila

In both tissues, the immune responses take place between 0 and 10 h post-infestation. Despite little variations, they then remain induced until the end of the infectious process.

Post-infestation temporal evolution of representative immune genes' induction levels in the hemocytes and in the fat body

Relative RT-qPCR quantifications were performed on triplicate samples with the Rpl32 housekeeping gene as reference. Differential expression was assessed with Student t tests and black dots indicate genes with insignificant variations (p-value > 0.05). Clustering analyses are based on Pearson correlation coefficients.

The S. carpocapsae-X. nematophila nematobacterial complex (NBC) is a natural symbiotic association between a nematode and a bacterium that is pathogenic for insects. The nematode infests its hosts via the intestinal tract and releases its bacterial symbiont in their hemocoel. The bacterium then grows extracellularly and participates in host killing by secreting virulence factors. Due to its originality and use in biocontrol, this NBC's interactions with insects have been extensively studied. However, still little is known about the insects' immune responses to the dual infection. Here, we provide a topologic and transcriptional analysis of the immune response of a lepidopteran model, Spodoptera frugiperda, to the NBC. Our main goals are (i) to describe the structure of the immune response that is induced after infestation and (ii) to identify candidate genes that are likely to interact with each NBC partner.

Parasitism

Symbiosis

S. carpocapsae

Nematode

X. nematophila

Gram negative bacterium

Death in

~ 24 h

Temporal RT-qPCR analysis of the immune responses 2 Despite only weak and variable immune responses are induced in the midgut (not shown), all the main components of the hemocytes' and fat body's immunities are strongly induced at 15 hpi. The hemocytes' response is particularly diversified at the humoral as well as at the cellular level whereas the fat body's response is more targeted against Gram negative bacteria.

1 RNAseq on immunocompetent tissues Discovery of candidate new immune genes 4

The GBO cluster is predicted to encode secreted proteins with homologs in lepidopterans and bacteria. We hypothesize they were acquired by HGT from bacteria and highjacked to perform antibacterial immune functions.

The Unk cluster is predicted to encode secreted peptides and small proteins that we hypothesize to be anti-nematode immune factors only found in the genus Spodoptera.

v

In the two tissues, X. nematophila is the main inducer of most of the selected genes, and especially of the well known antibacterial ones. However, S. carpocasae is the main inducer of some melanization and encapsulation-related genes as well as of most lectins, which are candidate immune receptors. Overall, these results suggest that the hemocytes and the fat body both respond by adapted ways to each NBC partner.

3 RT-qPCR dissection of the immune responses

Conclusion & Outlook Introduction Methodology & Results

Here, we show that the hemocytes' and fat body's immunities are strongly induced after infestation by the NBC. The immune responses are diversified and correspond to mixes of bacterium-and nematode-induced ones. They involve many genes that could take part in the response to each partner, including the Unk and GBO ones, which had never been reported in other interaction models. At this stage, our main objectives are (i) to characterize the interactions of these genes with the NBC and (ii) to improve our analysis of the midgut immune response.

Log2 fold change

Induction levels of representative immune genes after independent injections of the whole nematobacterial complex and of each partner

The tissues were collected 13 h after injection with either 10 nematobacterial complexes (NBC), 10 axenic S. carpocapsae (S.c.) or 200 X. nematophila symbionts (X.n.). RT-qPCR relative quantifications were performed on triplicate samples with the Rpl32 housekeeping gene as reference. Statistical differences between treatments were assessed by one-way ANOVA followed by Tukey tests. Top panels : antimicrobial immunity-related genes . Bottom panel s: melanization-related , cellular immunity-related (Hemocytes only) and « undeterminated » genes. In the longer term, our goal is to get an accurate picture of the dialogue that takes place between the three members of the interaction in order to identify the precise causes of the immune system's failure.

Hemocytes

  variations in the hemocytes and in the fat body at a middle time point of 15 h post infestation Triplicate RNAseq data were analyzed with Kallisto/Sleuth using tresholds of 0.01 for p-values, -1 and +1 for log2 fold changes and 5 for pseudocount means. The immune genes were identified by homology and classified as (A) antimicrobial immunity-related, (B) melanization-related, (C) cellular immunity-related and (D) « undeterminated ». Black dots indicate genes with insignificant variation. HC : Hemocytes, FB : Fat body.


domain: sdv sde

text:
Kim Milferstedt

Esmee Joosten

Hicham Ouazaite

Elie Le Quemener

Jérôme Hamelin

Oxygenic

Elie Desmond-Le Quéméner

Oxygenic photogranules: from ecology to wastewater treatment

Keywords: filamentous cyanobacteria, wastewater treatment, microbial ecology, modeling, biotechnology

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.

The syntrophic interaction of heterotrophic and phototrophic bacteria with an exchange of CO2 and O2 have been known for a long time. There have been ample suggestions in the literature of using this interplay for biotechnological application where organic matter is oxidized, e.g., in wastewater treatment. However, severe bottlenecks, for example the separation of bacteria and microalgae from the treated wastewater, have made the application challenging.

The recently discovered oxygenic photogranules, with filamentous cyanobacteria as phototrophic microorganisms, overcome this problem. These roughly spherical, dense microbial aggregates with a diameter of several millimeters are easily separated from the treated water by settling. In addition, compared to the conventionally used activated sludge system for wastewater treatment, oxygenic photogranules combine two major advantages: (1) oxygen for the oxidation of wastewater constituents is produced in-situ. Costly external aeration is not necessary, however, artificial light may be necessary setting off the gain. (2) CO2 and nitrogen species remain fixed as they are to a great extend incorporated into the growing phototrophic biomass.

The formation of photogranules is not well understood from a microbial ecological point of view even though it is crucial for the ecological engineering of photogranules, i.e., the design, management and prediction of ecosystem behavior using ecological theory. In our laboratory, we combine fundamental knowledge derived from experiments on the ecology of photogranules (e.g., community requirements for photogranulation; the relationship between light availability and microbial activity) with mathematical models describing mass transfer of metabolites in the photogranule matrix and biophysical properties of community members (e.g., morphology and motility of cyanobacteria). At the same time, we operate laboratory scale reactor systems to validate the fundamental principles and to suggest reactor conditions that optimize the biotechnological potential of photogranules. This potential includes the treatment of wastewater, the ecological engineering of the photogranule community to degrade specific compounds and the production of compounds with added value and nutrient recycling.

Oxygenic photogranules have been shown to treat domestic wastewater meeting typical effluent regulations. It is now time to decipher their formation to unlock additional benefits of this novel kind of microbial consortium in biotechnological applications like wastewater treatment.


domain: sdv info

text:
Jean-Ste ´phane Joly

J.-S Joly
email: joly@inaf.cnrs-gif.fr

Aquatic Model Organisms in Neurosciences: The Genome-Editing Revolution

The use of aquatic model organisms has been greatly diversified in laboratories. Zebrafish is the most advanced aquatic species for the use of Crispr-Cas9 in laboratories. Because of the simplicity and broad applicability of this later system, knock-out is now efficiently performed at medium scale. Forward genetics in zebrafish can now be performed by CRISPR-based F0 screening using high speed and high content phenotyping for example by confocal imaging. As zebrafish, marine model organisms have the prominent advantage to be transparent, all the more at young stages (embryos and larvae) or when fixed samples are cleared by novel methods. The Cripsr-Cas9 system is routinely used in the ascidian Ciona intestinalis. It also starts to be used in many other marine models, such as the medusa Clythia hemispherica. We provide at the end of this review a list of aquatic model species and some examples of questions on the origin of our nervous system that can be coped with these models, where the possibility to perform genome editing would constitute a major advance.

Introduction

With the expansion of biochemistry and molecular biology during the twentieth century, researchers focused more and more on a few model organisms such as nematodes, fruit flies, or mice. These models were amenable to many experimental approaches in molecular biology and biochemistry. Recently, a novel species, the zebrafish, has emerged as a major laboratory model. Initially selected because its transparent embryo is an excellent system in which to study development, it has now become the second most used animal in laboratories worldwide. Thus, the current applications of zebrafish studies are now highly diversified in neurobiology, immunology, adult physiology, oncology, and regenerative medicine, exploiting its advantages for in vivo approaches and imaging.

In parallel, due to the explosion of sequencing methods, there has been a clear trend towards the diversification of model organisms, especially those used in neurosciences. In this paper, we will focus on applications of such new models in evolution of development or biomedical research. There are several reasons to use an increasing number of model organisms: first, the classical models are not representative of most branches of the tree of life, and second, many questions now need to be studied in vivo and established models are not always well adapted to many of those biological or medical questions.

We will here elaborate why the use of genome editing in these models offers revolutionary perspectives. Future genome-editing experiments should indeed allow us to unveil the function of critical genes in almost any species; this approach was previously restricted to a few model species, or even restricted to mouse for most precise modifications by homologous recombination. With genome editing, it will become possible with functional data to study the evolutionary origin of highly diversified cell types such as neurons and, in addition, to interrogate how extremely complex cellular organizations such as those found in the brain were built in the course of evolution.

Zebrafish: With the CRiSPR-Cas9 System, Forward Genetic Screens Are Back Again Zebrafish is a vertebrate, so it has a body plan fundamentally similar to ours [START_REF] Onai | The evolutionary origin of the vertebrate body plan: the problem of head segmentation[END_REF]): like humans, zebrafish have a notochord that is a central pile of turgescent cells that confers rigidity to embryo and larvae and serves as the support axis for the development of the spine. Muscles are located on both sides of the notochord. The nervous system is found dorsally and intestine ventrally. Zebrafish embryos, during the so-called "phylotypic stage" [START_REF] Slack | The zootype and the phylotypic stage[END_REF], use the same developmental pattern as human embryos, involving the colinear activation with time and space of Hox gene expressions to build axial structures.

Many organs, including brain, also have similar general organisations in zebrafish and humans. Hence, various aspects of neurobiology can be studied in this species. Zebrafish has a tripartite brain. Brains, as other organs in fish such as fins or hearts, regenerate after a mechanical injury. Some complex behaviours like fear [START_REF] Amo | The habenulo-raphe serotonergic circuit encodes an aversive expectation value essential for adaptive active avoidance of danger[END_REF], social behaviour [START_REF] Chou | Social conflict resolution regulated by two dorsal habenular subregions in zebrafish[END_REF]) and memory can be studied in adults. Additionally, more basic behaviours can be studied in the 5-day larvae, at a stage most amenable for imaging and for which no authorization for animal experimentation is required [START_REF] Naumann | From wholebrain data to functional circuit models: the zebrafish optomotor response[END_REF]. This makes the model easy to use for applications such as neurotoxicology performed by academic labs or cosmetology companies.

At the end of the twentieth century, the zebrafish was suggested to be a promising model for genetic screens relevant to human diseases [START_REF] Mullins | Large-scale mutagenesis in the zebrafish: in search of genes controlling development in a vertebrate[END_REF]. However, during the evolution of vertebrates, an additional genome duplication occurred in the teleost fish lineage, leading to the presence of many duplicated genes in fish genomes that greatly complicate the analysis of screen data. Another pitfall was that the zebrafish has maternal factors carried by the egg. So, when a gene is mutated, the effect of the mutation is in most cases not visible at early stages because maternal factors are present to insure correct development. Therefore, a disappointingly low number of mutations were identified following large-scale screens by random mutagenesis in embryos. Because many zebrafish screens were performed during early larval stages, most identified mutants failed to exhibit phenotypes similar to human rare diseases, which often appear much later in human life.

After 2000, zebrafish became a very useful model for reverse genetics when phenotypical analyses of mutants were performed with spectacular time-lapse imaging in live fish, for example, during early development [START_REF] Olivier | Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy[END_REF], neurogenesis [START_REF] Barbosa | Live imaging of adult neural stem cell behavior in the intact and injured zebrafish brain[END_REF], hematopoeisis [START_REF] Renaud | Studying cell behavior in whole zebrafish embryos by confocal live imaging: application to hematopoietic stem cells[END_REF] or immune response [START_REF] Levraud | Through the looking glass: witnessing hostvirus interplay in zebrafish[END_REF].

Nowadays, the zebrafish is the most advanced aquatic species for the use of Cripsr-Cas9 in laboratories [START_REF] Shah | Approaching perfection: new developments in zebrafish genome engineering[END_REF]. In this species, however, a challenge remains: efficient insertions of point mutations (for example, to mimic human missense mutations) are still generated at low rates [START_REF] Renaud | Improved genome editing efficiency and flexibility using modified oligonucleotides with TALEN and CRISPR-Cas9 nucleases[END_REF]. For these applications, the very fast early development of the zebrafish is a drawback, as it probably makes the repair events following DNA cutting by the CRISPR protein highly mosaic and hard to detect in the progeny. Improvements to target the repair construct to the nucleus of the one-cell stage embryo will have to be developed. Modified oligonucleotides could improve KI rates. Alternatively, plasmidic constructs with long homology arms have been used in a recently published method [START_REF] Hoshijima | Precise genome editing by homologous recombination[END_REF] to perform KI at large scale in zebrafish; unfortunately, this method remains labor intensive.

Knock-out is now efficiently performed in zebrafish at medium scale [START_REF] Shah | Rapid reverse genetic screening using CRISPR in zebrafish[END_REF]. Hence so-called forward genetics in zebrafish again seems to have a bright future. To study the molecular basis of a given phenotype in a particular cell type, large gene families can be targeted for mutations, and, importantly, mutations of duplicated genes and of their close paralogs can be performed jointly, due to the possibility of injecting arrays of CRISPR guide RNAs.

For large-scale forward screens, methods of large-scale phenotyping, at first hand by 3D imaging, still need to be optimized. Thus, a current priority for zebrafish researchers is to improve rapid imaging technologies at large scale and at later stages, to make zebrafish a better model. Such a model would provide a perfect context for analyzing large collections of mutants. Tissue-clearing methods [START_REF] Seo | Clearing and labeling techniques for large-scale biological tissues[END_REF]) and high-speed imaging methods, such as those using highly sensitive video cameras, have recently emerged and will certainly be crucial for these approaches.

Optimizing the Cripsr-Cas9 System in Transparent Marine Animals

Most marine model organisms have the obvious advantages, as zebrafish, to have transparent embryos and larvae, a feature selected in water throughout evolution to escape predators. Transparency is crucial for the microscopic analysis of development in these models. Eggs can generally be obtained in huge numbers. Although they are sometimes quite big because of the presence of vitelline reserves, embryos are composed of only a few cells and the lineage analysis is thus easy in these simple and compact embryos. In ascidian embryos, for example, the notochord only has 64 cells. Significant progress in understanding human cardiac developmental gene network was made in ascidian models. This unique insight provided direction for the reprograming of cell lineages in human cell cultures: following the observation that Ci-es1/2 and Ci-mesp generated cardiac progenitors in ascidians, researchers transdifferentiated human dermal fibroblasts into cardiac progenitors [START_REF] Islas | Transcription factors ETS2 and MESP1 transdifferentiate human dermal fibroblasts into cardiac progenitors[END_REF].

The Cripsr-Cas9 system was used in a study using the ascidian Ciona intestinalis [START_REF] Stolfi | Tissue-specific genome editing in Ciona embryos by CRISPR/Cas9[END_REF]). This study, from Lionel Christiaen's group, reported the success of tissue-specific genome editing in this species. Optimization of plasmid constructs was performed, in which specific ubiquitous U6 promoters were used to drive guide RNA expression, and tissue-specific promoters were designed to drive the expression of the Cas9 protein. Introducing the CRISPR-Cas9 components in ascidians was quite easy because a large number of eggs could be electroporated with plasmid DNA, producing both the CRISPR protein and the guide RNAs. Nevertheless, while breeding of this species in inland laboratories has been performed [START_REF] Joly | Culture of Ciona intestinalis in closed systems[END_REF]), it remains difficult. Improvements are still required to reliably obtain the culture of stable lines of transgenic animals.

The Cripsr-Cas9 system is beginning to be used in many other aquatic model organisms (for a fascinating example in lampreys, [START_REF] Square | CRISPR/ Cas9-mediated mutagenesis in the sea lamprey Petromyzon marinus: a powerful tool for understanding ancestral gene functions in vertebrates[END_REF]. For example, fascinating experiments (unpublished) have been performed in the medusa Clytia hemisphaerica (Tsuyioshi Momose, CNRS Villefranche-sur-Mer, personal communication). This model recently emerged as a remarkable cnidarian species useful for evo-devo studies [START_REF] Houliston | Clytia hemisphaerica: a jellyfish cousin joins the laboratory[END_REF]. Experiments were in part supported by the French network named "Etude Fonctionnelle sur les Organismes Mode `les (EFOR, www.efor.net)", promoting research-including genome editing and imaging-in metazoan model organisms.

Success in Clythia is due to obtaining full life cycles in laboratory aquaria, generating quasi-immortal, vegetatively growing colonies. Moreover, adult medusae spawn daily, generating transparent and easy to inject eggs. Rates of Cripsr-Cas9 knock-outs are strikingly high: over 700 embryos, all with potential knock-outs as seen by the loss of fluorescent protein activity, can be generated in a single injection experiment.

Injection of the NLS Cas9 protein/sgRNA into unfertilized eggs can be performed as soon as 1 hour after ovulation and before subsequent fertilization. In this condition, the Cas9 protein probably has time to be targeted to its cutting site before the first division of the embryo occurs. One first exciting application of this method was the deletion of green fluorescent proteins, making newly generated transgenic lines suitable for imaging applications. Indeed, in these species, endogenous fluorescent activity hinders potential observation of GFP in newly generated transgenic animals. The availability of mutants in such species will offer novel routes for fundamental research in evolution and development [START_REF] Galliot | Origins of neurogenesis, a cnidarian view[END_REF]). In such non-marine species, a challenge remains to keep animals alive in captivity long term. Significant investments will also be needed to obtain colonies of inbred lines, which should become reference laboratory lines for the corresponding communities of worldwide researchers.

More and More Aquatic Model Organisms for Diversified Uses

Examples of emerging aquatic models of increasing evolutionary distances from vertebrates are described below. These models are located at several key nodes of the metazoan branches of the tree of life. Alternative fish models to zebrafish [START_REF] Schartl | Beyond the zebrafish: diverse fish species for modeling human disease[END_REF]) allow the study of particular evolutionary processes, such as adaptation to cave life in the Astyanax mexicanus. To study the emergence of synapomorphic (specific and shared) vertebrate features in evolution, lampreys (including the sea lamprey, Petromyzon marinus, an agnathan), lancelets (the cephalochordate amphioxus, Branchiostoma lanceolatum) or tunicates (such as the urochordate Ciona intestinalis) are very relevant models. Other more distantly related bilaterians such as the polychaete annelid Platynereis dumerilii provide insight into which features were already present in the common ancestor of all bilaterian organisms, the so-called "urbilateria." Even more distant metazoans, with no bilateral symmetry but rather radial symmetry, are also used in laboratories. Thus, Ctenophores (sea gooseberries) and Cnidarians (corals, jellyfish, sea anemones) can be used to study ancient features of nerve cell types. Also, sponges and placozoans constitute fascinating basal Metazoans, with no nervous system.

In Biomedical Research, Why and How Should We Use Aquatic Models to Study Diseases of the Nervous System?

A first obvious use of model organisms is to generate so-called "models of diseases." In most cases, these models are mutants or transgenic animals that reproduce at best pathological conditions observed in humans, such as neural degeneration. With the advent of precise genome-editing methods, the capacity to generate point mutations in any model by introducing a repair construct bearing the mutations will constitute a true revolution. Indeed, mutations at orthologous positions to variations found in human diseases can be generated in these model animals if genomes can be aligned in the region surrounding the mutation. Then the phenotypical effects of the abnormal protein function can be described, for example, using live imaging to observe abnormal cell behaviors, such as proliferation or migration. Therein resides the great advantage of these aquatic models, with a diversity of developmental and genomic contexts and transparency allowing easy imaging.

Moreover, in an applied perspective in regenerative medicine, understanding how cell types emerged during evolution helps us identify crucial pathways that are, for example, active in stem cells in normal and pathological conditions. The Crispr-Cas9 system applied to aquatic model organisms will offer an unprecedented opportunity to characterize the key genes and pathways that are active following injury or degeneration. They promote regeneration responses in animals with regenerative capacities and could be useful in regenerative medicine, if (re)activated in humans [START_REF] Karra | Redirecting cardiac growth mechanisms for therapeutic regeneration[END_REF].

A Short Natural History of the Nervous System: Several Questions on Its Origin

This chapter provides examples of what marine model organisms bring us in the domain of neuroscience. Many essential questions can indeed be examined with these models, providing us more knowledge about how the human brain was shaped through evolution, and this should help us better understand pathologies and their pleiotropic effects.

Evolution indeed sifts through the noise and allows us to focus on key genes and pathways that have remained crucial for specific cell types throughout evolution. Looking at extant species, and describing common features that are likely to be ancestral and shared, is also a way to "reconstruct" the nervous system of the putative last common ancestor between the two compared species. In this domain, the absence of fossils of nervous system and brains has impeded researchers.

According to Detlev Arendt [START_REF] Arendt | The evolution of cell types in animals: emerging principles from molecular studies[END_REF], homology hypotheses are based on the comparison of genes, cytological features and ontological location in the body of the embryo. Functional experiments with genome editing in model organisms will add extremely important cues to this domain.

The Ctenophores are colorful planctonic animals that have a sophisticated nervous net allowing them to swim and to emit beautiful waves of fluorescent flashes. A long-standing debate is whether these animals have neuron-like cells, which would have appeared independently of the neurons of our nervous system during evolution. In this respect, examining the phylogenetic position of these animals is primordial: are they closely related to bilaterians or rahter do they form an out-group of metazoans? If they are more distantly related to us than sponges, this would indeed mean that the nervous system was invented twice in evolution, because it is very unlikely that sponges, which would be closer relatives to bilaterians, secondarily lost neural cells.

Until this controversy is resolved, it will not be possible to know whether there were two independent origins of the nervous system in animals, which would, of course, be a very exciting possibility. However, as argued in a recent review [START_REF] Jager | Ctenophores: an evolutionary-developmental perspective[END_REF], many lines of evidence now suggest that ctenophores are closely related to bilaterians and that neurons appeared only once in evolution. In favour of a single nervous system type is the presence of the well-known neurogenesis SoxB gene and the presence of acetylcholine and numerous common GPCRs. In any case, ctenophores should provide key insights into deeply conserved features of animal neural cells.

Marine organisms also allow us to examine how the nervous system became condensed and centralized [START_REF] Arendt | From nerve net to nerve ring, nerve cord and brain evolution of the nervous system[END_REF] while becoming much more complex in the course of evolution to terrestrial life [START_REF] Nomaksteinsky | Centralization of the deuterostome nervous system predates chordates[END_REF]. Starting from a diffuse nerve net in a swimming larva, brains became huge, formed from the so-called embryonic neural plate in vertebrates later undergoing neurulation to form the neural tube.

Another question about the origin of the nervous system is how the vertebrate tripartite nervous system arose in the course of evolution. The vertebrate central nervous system is composed of anterior brain, posterior brain and spinal cord. Such organisation occurred with the emergence of borders between brain domains, such as the midbrain/hindbrain boundary. Recently, following studies in an annelid worm, Arendt and colleagues proposed that the nervous system of the common bilaterian ancestor was probably composed of two independent domains (corresponding to the vertebrate forebrain/midbrain and hindbrain) that later fused during evolution [START_REF] Tosches | The bilaterian forebrain: an evolutionary chimaera[END_REF].

Very recently, and in line with Arendt's hypothesis, Chris Lowe's group proposed that the adult body plan of an indirect developing hemichordate develops by adding a Hox pattern trunk to an anterior larval territory, confirming the hypothesis that marine larvae are "swimming heads" [START_REF] Gonzalez | The adult body plan of indirect developing hemichordates develops by adding a Hox-patterned trunk to an anterior larval territory[END_REF].

Concerning the origin of vertebrate synapomorphic characters, ascidians provide evidence of the precraniate or prevertebrate origins of the neural crest. Neural crest cells are stem cells with migratory behaviors and the capacity to differentiate into incredibly diversified tissues and locations. The evolutionary origin of neural crest is obscure. They were first thought to be a vertebrate sinapomorphy. Bill Jeffery identified migratory pigment cells in ascidians [START_REF] Jeffery | Migratory neural crest-like cells form body pigmentation in a urochordate embryo[END_REF][START_REF] Jeffery | Ascidian neural crest-like cells: phylogenetic distribution, relationship to larval complexity, and pigment cell fate[END_REF]) and, more recently, Lionel Christiaen's group identified migrating neuron precursors in the ascidian Ciona intestinalis. Interestingly, these precursors were shown to arise from the border of the neural plate, a hallmark of the neural crest in vertebrates [START_REF] Stolfi | Migratory neuronal progenitors arise from the neural plate borders in tunicates[END_REF].

Conclusion

Cripsr-Cas9 system and other genome-editing techniques can now be used in various aquatic model organisms for extremely diversified applications, which will lead to new models of brain diseases for biomedical research. Also, these models will provide strategies to characterize human genetic variations linked to diseases at large scale and suggest new avenues for regenerative medicine, because of the exceptional abilities of most aquatic models to regenerate. Basic biological knowledge will, of course, benefit from this revolution, and one might expect many more revolutionary discoveries from the exploration of the genomes of multiple animal aquatic species using Cripsr-Cas9 approaches.

J.-S. Joly

Aquatic Model Organisms in Neurosciences: The Genome-Editing Revolution

Acknowledgments Marine Joly is warmly acknowledged for help and discussion on the talk and manuscript. Pierre Boudinot contributed a lot to the improvement of earlier versions of this manuscript. I wish to thank T. Momose for sharing unpublished data and M. Manuel for discussion.

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