Théo Martin

Pierre Gasselin

Nathalie Hostiou

Gilles Feron

L Laurens

F Purseigle

Halachmi, Ilan and Pnini, Tal and Izhaki, Ido Degani

Amir

Rebecca L Schewe

Diana Stuart

Robots and Transformations of Work in Farms -Protocol for a Systematic Review

whether they are published or not. The documents may come

Introduction

Since its introduction in our language by science fiction and C.Kapek in 1920 [START_REF] Capek | RUR : Rossum's Universal Robots (Edition de la Différence[END_REF], the word robot has always been the object of hope or fear. On one hand, robotization is expected to enhance our lives, release from hard works and even represent a hope for a job liberation [START_REF] Asimov | Runaround[END_REF]. On the other hand, robotization and automation threaten some jobs and even our social organization built around work [START_REF] Rifkin | The End of the Work[END_REF]. But social sciences show that it is more about a displacement and a deep transformation than a replacement [START_REF] Dujarier | Le travail du consommateur : de McDo à eBay, comment nous coproduisons ce que nous achetons[END_REF][START_REF] Autor | Why are there still jobs[END_REF][START_REF] Flichy | Les nouvelles frontières du travail à l'ère du numérique[END_REF][START_REF] Gaborieau | Quand l'ouvrier devient robot[END_REF][START_REF] Askenazy | AI and employment: An artificial threat[END_REF][START_REF] Casilli | En attendant les robots[END_REF]).

Agriculture has long been concerned with robotization. Since the late 1990s in Western and Northern Europe, many dairy farms have introduced automated milking system. Furthermore, nowadays new stakeholders arrive in the agricultural sector such as start-ups and venture capitalists (Rotz, Duncan, et al. 2019). They are following -or encouraging -a movement of digital revolution in agriculture [START_REF] Himesh | Digital revolution and Big Data: A new revolution in agriculture[END_REF]) which represents a promising market. New agricultural sectors are concerned by robotics such as aquaculture, horticulture, vine-growing or field crops [START_REF] Duckett | Agricultural Robotics: The Future of Robotic Agriculture[END_REF]. In developed countries, this robotization takes place in a context of transformation of agricultural work. Between 2007 and 2013, the agricultural employment in European Union dropped by 19% [START_REF] Hostiou | Employment and farmers' work in European ruminant livestock farms: A review[END_REF]) related to a significant labour productivity gain. In France, while the historical family farming model is still the majority, there has been unprecedented growth in the number of salaried workers (Centre d'Études et de Prospective, 2019). Job insecurity and part-time, outsourcing, diversification, rising of suicide or differentiation by skill levels are all transformations of agricultural work in developed countries. These two phenomena -robotization and work transformations -raise questions about their possible interactions. But agriculture is rarely the subject of such analyses linking robotization and global transformations of work [START_REF] Hostiou | Impact of precision livestock farming on work and human-animal interactions on dairy farms[END_REF][START_REF] Carolan | Automated agrifood futures: robotics, labor and the distributive politics of digital agriculture[END_REF].

The diversity of disciplines and scientific communities [START_REF] Malanski | Characteristics of "work in agriculture" scientific communities. A bibliometric review[END_REF] interested in work in agriculture encourages a systematic review to describe the full range of work transformations related to robotics. This present review is also a means of questioning robotization across all relevant agricultural sectors and countries. The purpose of this systematic review is to answer two questions about the links between work and robotization in agriculture. First, we will question what the transformations of work in agriculture are related to robotics. Secondly, we will identify the explanatory factors of robotization in relation to work in farms.

Several methods can provide answers to these questions. We have chosen a systematic review of literature (SRL) considering that literature already provides a diversity of answers that can be synthesized by the systematic approach. According to Petticrew & Robert "systematic reviews are literature reviews that adhere closely to a set of scientific methods that explicitly aim to limit systematic error (bias), mainly by attempting to identify, appraise and synthesize all relevant studies (of whatever design) in order to answer a particular question (or set of questions)" [START_REF] Petticrew | Systematic Reviews in the Social Sciences[END_REF]. A SRL is an original approach in agricultural social sciences and none has ever been done on work in agriculture. This review is also a methodological contribution and proposition to shed light on certain research questions within our scientific community.

Methods

Concepts and framework

 From key-concepts to the query The main difficulty of our review question lies in the concept of "work in agriculture". In fact, "work" is a polysemic word and rich in a variety of ways of seeing. Each discipline, each current of thought has its own definition of work: e.g. sociology of work, ergonomics, economics, zootechnics, agronomy. This disciplinary diversity is accompanied by a diversity of analytical frameworks and approaches, what we call dimensions of work. All dimensions of work are concerned by robotization, thus the robot invites to consider work from a multidisciplinary perspective. Several hypotheses about work transformations related to farm robotization are the motive for a systematic review. We have classified these hypotheses into four dimensions: farm structure, technical-economic performances, work organization and meaning of work. These dimensions reflect both the disciplinary diversity of the work in agriculture and the systemic transformations that the robot produces.

i.

Different hypotheses are relating to structural changes on the farm and in the labour market. Robotization would encourage an increase in farm size [START_REF] Rotz | Automatic Milking Systems, Farm Size, and Milk Production[END_REF][START_REF] Fleming | Is big data for big farming or for everyone? Perceptions in the Australian grains industry[END_REF], changes in social relation of production (e.g. use of off-farm work, wage labour development), changes in added value distribution, work segmentation and wage inequality as a result of high-skill/low-skilled bifurcated labour market [START_REF] Kristal | The causes of rising wage inequality: The race between institutions and technology[END_REF]Cohen 2017, Rotz, Gravely, et al. 2019). ii.

Other hypotheses concern technical-economic performances. We assume that robotization is generally accompanied by an improvement in technical work performances: yield increase, physical labour productivity (yield/worker) and economic labour productivity increase (NVA6 /worker). However, this is accompanied by an increase in production costs, a decrease in the economic productivity of the land (NVA/ha) and changes in agricultural products quality (e.g. milk quality in the case of AMS 7 ). iii.

The third sort of hypotheses concerns work organization. Our framework of work organization is composed of two dimensions: individual organization on-farm level and collective organization (on-farm and off-farm levels). On the one hand, at on-farm level, we assume that robotization changes task repartition between workers and time organization (farming system approach -N. [START_REF] Hostiou | A method for assessing work productivity and flexibility in livestock farms[END_REF]. We assume that robotization lead to a decrease in on-call work, the appearance of new tasks and the disappearance of some tasks, a new working time management. On the other hand, the collective dimension of work organization is also concerned. Our hypotheses are that robotization changes self-help groups and are created robot focus groups. We also suppose that the work collaboration of farmers changes. In particular, we assume the new role of advisers and new relations between farmers and advisers. Technicians of robotic companies could be new stakeholders into farm work. iv.

Last, the meaning of work brings together different aspects of the subjective dimension of work [START_REF] Fiorelli | Improving farm working conditions: a proposal to characterise the individual relationship to work. A case study based on French multi-job-holder sheep farmers[END_REF]. a. We assume that the robot allows a lower physical workload and arduousness. But it can also increase the mental workload by the permanence of the man-machine relationship via digital intrusion; b. New individual identities (e.g. job representation) and feeling of community could go along with robotization. This may also encourage a blurring of the boundaries between professional and personal life (links between spaces and identities); c. The relationship with animals and plants can be transformed by robotization (e.g. work relation with animals, plant representation from a living object to a data system); d. The construction of the rationality can be affected by the robotization with an increase of metric representations of reality and less resort to sensitive observations of living things.

These hypotheses depend both on the type of production and on the type of robot. The types of farms and forms of labour mobilization should also be determinant.

In elaborating the research query, we need to cover the disciplinary and conceptual diversity of work in agriculture while eliminating into results background noise as much as possible. First we have referenced all its thesaurus using AGROVOC [START_REF] Caracciolo | The AGROVOC linked dataset[END_REF]). Then we compared our thesaurus of "work" with other major reviews related to work in agriculture [START_REF] Hostiou | Impact of precision livestock farming on work and human-animal interactions on dairy farms[END_REF], 2020[START_REF] Malanski | Characteristics of "work in agriculture" scientific communities. A bibliometric review[END_REF]. "Work" is the term most commonly used in scientific communities interested in work in agriculture [START_REF] Hostiou | Employment and farmers' work in European ruminant livestock farms: A review[END_REF][START_REF] Hostiou | Impact of precision livestock farming on work and human-animal interactions on dairy farms[END_REF][START_REF] Malanski | Characteristics of "work in agriculture" scientific communities. A bibliometric review[END_REF]. "Labor" or "labour" are mainly used in sociology and economy to look at work as a social relationship to each other but also as a performance (labour productivity). "Job" often refers to the notion of identity and status. The "Task" is mainly an ergonomic vision of work. Last, "Employment" generally refers to a macroeconomic vision of work. Obviously, the semantic boundaries are more complex. But we have tried to restore this semantic diversity in order to analyse the transformations of work in all its dimensions.

 Robot definition and thesaurus

Thinking a systematic definition of the robot is difficult since its conception evolves in time and space.

Robot covers very different realities from the first robots in the beginning of the 20th seeking to copy animal behaviour to validate biological and psychological hypotheses [START_REF] Meyer | Dei ex Machinis : Volume III -De Jean-Baptiste Schwilgué au milieu du XXème siècle[END_REF]) and today's the "social robots" that integrate the social rules and behaviours of humans (Belpaeme et al. 2018). The forms and uses of robots are also diversified as Erico Guizzo from the IEEE8 points out:" « There are robots the size of a coin and robots bigger than a car. Some robots can make pancakes. Others can land on Mars» [START_REF] Guillot | Histoire de la robotique : des automates aux premiers robots[END_REF]). As a result, the scientific community does not share a clear definition of a robot.

The most commonly used definition of a robot comes from the Robotic Industries Association (RIA) which defines the robot as "a reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed functions for the performance of a variety of tasks". Its definition is complex and debated because it refers to the different conceptions of work and autonomy. Moreover, this definition does not seem to be adapted to the agricultural context. There are at least four elements that make agriculture a special sector and justify a specific definition for agricultural robot. First, farm activities are deeply settled in ecosystems and farmers work with living beings as animals and plants. Secondly, agriculture involves a wide variety of tasks: e.g. animal care, tillage, equipment maintenance, accounting. Thirdly, agriculture is subject to great variability, both due to natural conditions (climate, pests) and the volatility of food prices. Finally, it is important to consider that 90% of farms around the word are family farms9 (FAO10 ) with its particular work organizations, social relations and economic reasoning [START_REF] Tchayanov | L'Organisation de l'Economie Paysanne[END_REF][START_REF] Gasselin | L'agriculture en famille : travailler, réinventer, transmettre[END_REF][START_REF] Bosc | Diversity of Family Farming Around the World[END_REF]). These specificities make agriculture a different sector especially from industry -the first sector concerned by robotization -in which the division of labour, its organization and social relations are wholly singular.

Lowenberg-DeBoer et al. ( 2020) proposes a definition of a field crop robot: "a mobile, autonomous, decision-making, mechatronic device that accomplishes crop production tasks (e.g. soil preparation, seeding, transplanting, weeding, pest control and harvesting) under human supervision, but without direct human labour". Here, the mobile character is essential and adapted to the dispersion of the field crop plots. But it is not compatible with livestock robots as automated milking system. Because this definition is limited to field crops, we propose here a definition of the agricultural robot adapted to the different farming sectors. For this definition, we refer to the different concepts mobilized in the different existing definitions (cf. Annexe 2):

 robot's autonomy and no human intervention;  decision-making : adaptation to the environment through data collection and analysis;  multifunctional: performs different tasks;  re-programmable via a man-machine interface;

The concepts of robot's autonomy and a relative independence of human work are mobilized by Lowenberg-DeBoer et al. ( 2020) as usual into the different definitions of the robot (cf. Annexe 2). In agriculture, autonomy refers to the autonomy of farmers. This is why we prefer the concepts of adaptation to its environment. This adaptation is allowed by data collection (via sensors), data analysis (software) and tasks modulation (via actuators). This adaptation to the environment is what differentiates the robot from the automate [START_REF] Guillot | Histoire de la robotique : des automates aux premiers robots[END_REF]. The agricultural robot environment differs from an industrial robot environment by a relationship to the living beings (animals and plants). The robot does not require direct human intervention. However, it is not independent of human labour, whether for its manufacture, its maintenance or for the new tasks it imposes (e.g. programming, data interpretation). So we prefer to specify that robot performs without direct human intervention.

The concept of decision-making is also often used for the robot's definition. Because decision is an important concept for farm system and farmers' choice analysis, using it for the robot is ambiguous. A farmer's decision cannot be substituted by a robot especially because it enlists cognitive and affective dimensions (Carpentier-Roy 1992, Cerf and Sébillotte 1997, Dejours and Gernet 2016). So we have decided to rule it out. Multifunctionality and re-programmability are also usually used into robot definitions.

After all, we propose a definition of the agricultural robot (AR) that will be used for the selection of publications and compatible with different agricultural sectors (vine-growing, livestock, field crop, horticulture). This definition of the AR includes three parts: i. what does it do? ii. what it is? iii. what does it produce?

An agricultural robot is an re-programmable mechatronic 11 device that performs different farming tasks without direct human intervention and adapted to its environment through data collection and analysis. The agricultural robot is characterized by interactions with the biological and physical environment (plants, soil, animals, etc.). It is composed of a hardware artefact made up of one or more sensors (data collection) and actuators (tasks making) and a software artefact that allows data analysis (task modulation). The agricultural robot produces tasks (actuators), data (sensors) and services (data analysis).

This definition is a first proposition for publication selection and will certainly evolve during the final analysis of the papers. It is a starting point for understanding what is and what is not a robot in agriculture.

For the query research, we used two types of robot thesaurus. The first type refers to object and their characteristics: "autonomous" and "automated". The difference with "robot" is not explicit given the difficulties to define the robot. So, "autonomous systems" or "automated systems" are commonly used as a "robot" synonym. The second type of thesaurus refers to concepts. The robot can be included into broader concepts as precision agriculture, digital agriculture, smart farming or agriculture 4.0. Consequently, we considered these concepts in our query.

 Agriculture thesaurus

Work transformations after robotization can be studied on specific tasks and not on the whole farm organization. Below (Table 1), we have referenced the agricultural tasks that are subject to robotization. This list is not exhaustive given the diversity of robot definitions and the rapid development of the agricultural robotic sector. So using these tasks for the research query represents a risk of non-exhaustiveness. So we preferred to reference the main agricultural orientations concerned by robotization: livestock, dairy farming, field crops, horticulture and vine-growing. These terms are sometimes preferred to generic terms (farm, farming, agriculture).

 PICO framework

We use a PICO framework for the formulation of our systematic review question [START_REF] Methley | PICO, PICOS and SPIDER: A comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews[END_REF].

We present the different component of the PICO search tool in the table below:

Research strategy

 Information sources

We use two databases: Web of Science Core Collection (WoS) and Scopus. Some articles identified during the query test phase can be added to the corpus after validation by at least two reviewers. Grey literature publications may be added if they are cited in peer-reviewed articles into the corpus and thus recognized as useful by the scientific community (reports from collectives, research or consulting offices and written by scientists).

 The query

For this query, we will consider articles, books, book chapters and reviews. We exclude proceeding papers because they are part of a publication process but do not necessarily represent a finalized scientific work. There are no limits on disciplines, methods and countries. Indeed, the aim is also to show how the question of the transformation of work in robotic farms come up according to disciplines, periods and countries. We collect all this document published between 1955 and 2020. The 1990s correspond to the development of the first milking robots -automated milking system-on dairy farms. It is therefore from these years onwards that the number of publications increases. However, it will be pertinent to analyse how the robotization of work in agriculture issues has evolved over time. The robot question in agriculture has been asked earlier (Sistler 1987, Kawamura and[START_REF] Kawamura | Robots in agriculture[END_REF]. This SRL will enable to see whether older publications have addressed the issue. The search will only include articles in English.

After several tests to assess the relevance of the terms included into our query, we have retained the following equation. Below, we detail our query for the WoS database. We use the same query for Scopus -some differences are due to the syntax and Boolean operator's specificities.

Our three key concepts -robot, agriculture and work -are broad and imprecise concepts. A search associating these three concepts with AND in the field TOPICS gives too much background noise. That's why we have associated these terms two by two in the title and the remaining third in the TOPICS.  Selection process: the screening Each stage of article selection involves at least two participants (2 for screening and 3 for eligibility). Any conflict between participants regarding this selection will be arbitrated during a meeting to discuss and decide.

 WoS research

EndNote® will be used to remove duplicates after article extraction from the two databases. For the first studies selection stage -the screening -, we will use Rayyan®, a free web app, "that helps expedite the initial screening of abstracts and titles using a process of semi-automation" [START_REF] Ouzzani | Rayyan-a web and mobile app for systematic reviews[END_REF].

Two reviewers -T. Martin and P. Gasselin -will carry out this screening without the possibility to see the choice of the other.

 Selection process: eligibility criteria

Then, an assessment of eligibility will be conducted. As well, three reviewers will be in charge of this assessment for eligibility: P. Gasselin, N.Hostiou and T. Martin (Annexe 2). This selection follows preestablished criteria:

 Language: English;  The type of technology considered is well characterized: we will exclude publications that deal with broad concepts as precision farming, smart farming and agriculture 4.0 if the type of technology is not specified -our intervention concerns robots;  Work in agriculture is considered at farm level: we will not consider work transformations in food process firms -our population PICO contains farm, farm system, farmers and farm workers;  At least one of the dimensions of work in agriculture is addressed (PICO framework): farm structure, performances, organization and meaning;

Data analysis  Outcomes

Two different types of data will be extracted from articles. Qualitative data concerning work organization changes and meaning of work will be extracted. Some quantitative data will be considered related to work performances (e.g. labour productivity, yields per worker). But we will not carry out a meta-analysis and thus extract its data. We will limit our analysis to collecting the conclusions of quantitative analyses (e.g. increase or decrease in productivity, increase or decrease in working time).

If the variability of the conclusions and the number of data allow it, a meta-analysis can be carried out afterwards. Thus, this review does not include quantitative analysis.

 Risk of bias

The risk of bias can compromise the proof of evidence related to work transformation. The risk of bias analysis does not constitute a review or an assessment. First of all, we consider that in science every methodological approach can be improved. Secondly, this bias analysis does not constitute an absolute evaluation but an assessment relative to our question and our choice of analytical framework. So there's no judgement on the methodological choices made. The risk of bias depends on four criteria:

 No sample description (characterization of the farm diversity considered)  No robot description: the robot considered must be identified (brand and model) or described in detail to be able to assess his compatibility with our robot definition.  No worker characterization: who are we talking about? (e.g. a farmer, a family member, a seasonal worker, an outsource worker); What type of work, tasks? (e.g. milking, herd observation, harvesting)  Comparison robustness: more generally, the main risk of bias is to attribute work change that does not come from the robots but from structural changes on the farm or from the farm's socio-economic and regulatory context.

We classify the risk of bias at three levels -high, moderate and some concerns -according to the aggregation of pre-established criteria. The aggregation method, i.e. the weight of each criterion will be detailed later according to the types of studies included in qualitative analysis. We use this table for each publication in order to assess the risk of bias.

 Qualitative analysis

Our work analysis framework consists of three axes. The first corresponds to the performances of the work. No data extraction is foreseen but an analysis of the conclusion with regard to the other axes of analysis. The second axis is work organization. Here we consider the individual organization of work through the temporal arrangement of the different tasks of a worker. The notions of autonomy and constraints are central to our analysis. We will also consider the collective organization of work both on and off the farm (e.g. for France: CUMA, self-help groups). Thirdly, we will analyse work through its meaning, i.e. all the perceptions, values and other things that discourses can provide.

The work transformation analysis method is based on a comparative approach. First of all, this comparison will be made between robotized and non-robotized farms or before and after robot adoption. Then, we will compare: This research gives three results. None of these three reviews are systematic. This first review [START_REF] Aune | Precision farming for increased land and labour productivity in semi-arid West Africa. A review[END_REF]) looks at the effects of the types of seed used and the fertilization applied on yields. Moreover, it is specific to West Africa. This review illustrates the plasticity of certain concepts such as precision agriculture. Here the precision agriculture practises are seed types, mineral fertilization and manure [START_REF] Aune | Precision farming for increased land and labour productivity in semi-arid West Africa. A review[END_REF]. In other cases, precision agriculture is defined as "a whole-farm management approach using information technology, satellite positioning data, remote sensing, and data gathering" [START_REF] Koutsos | Benefits from the adoption of precision agriculture technologies[END_REF]. The second review [START_REF] Aravind | Task-based agricultural mobile robots in arable farming: A review[END_REF]) is limited to crop farming and does not include livestock. It is an overview of robots in open arable farming and robots are categorized by the type of operation (e.g. tilling, harvesting, crop scouting and control, seeding). There is no work transformation analysis. The third review is the closest to our research question. It focuses on the transformations of work in precision livestock with special attention to robots (milking and feeding). It provides a relevant example of work transformations analysis. However, it is limited to animal production and is not systematic. This research confirms the absence of systematic review related to robotics systems and work transformations in agriculture. It also indicates the presence of a scientific community interested in the work transformations and technologies in livestock [START_REF] Hostiou | Impact of precision livestock farming on work and human-animal interactions on dairy farms[END_REF][START_REF] Dedieu | Transversal views on work in agriculture[END_REF][START_REF] Malanski | Characteristics of "work in agriculture" scientific communities. A bibliometric review[END_REF][START_REF] Servière | Le travail en élevage et ses mutations[END_REF].

 Corpus of publications used for query testing

The following table includes essential articles with regards to work in agriculture. All these publications will have to be in the final list of publications. We use this list as a test list for query assessment. This test list has been produced through an alert system for new publications and a non-systematic review related to robots and work in agriculture.

Administrative information

Authors

Support

Annexes

Annexe 1: Authors contributions based on PRISMA diagram [START_REF] Moher | Preferred reporting items for systematic reviews and metaanalyses: The PRISMA statement[END_REF]

Figure 1 :

 1 Figure 1: Translate a research question into keywords and thesaurus for query construction

  * OR automat* OR autonom* OR digital* OR precision* OR smart* OR 4.0) Set 2: TS=(work* OR labor OR labour OR job* OR task* OR employment*) Set 3: TS=(farm* OR agricultur* OR livestock* OR crop* OR horticultur* OR vine* OR wine* OR viti* OR dair* OR milk* OR rural* ) Set 4: TI=(work* OR labor OR labour OR job* OR task* OR employment*) Set 5: TI=(farm* OR agricultur* OR livestock* OR crop* OR horticultur* OR vine* OR wine* OR viti* OR dair* OR milk* OR rural* ) Set 6: TS=(robot* OR automat* OR autonom* OR digital* OR precision* OR smart* OR 4Document types : Article OR Book OR Book Chapter OR Review Custom year range : 1955 to 2020 Web of Science Core Collection : SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI  Scopus research ((TITLE(robot* OR automat* OR autonom* OR digital* OR precision* OR smart* OR 4.0)) AND (TITLE(work* OR labor OR labour OR job* OR task* OR employment*)) AND (TITLE-ABS-KEY(farm* OR agricultur* OR livestock* OR crop* OR horticultur* OR vine* OR wine* OR viti* OR dair* OR milk* OR rural* ))) OR ((TITLE(robot* OR automat* OR autonom* OR digital* OR precision* OR smart* OR 4.0)) AND (TITLE(farm* OR agricultur* OR livestock* OR crop* OR horticultur* OR vine* OR wine* OR viti* OR dair* OR milk* OR rural* )) AND (TITLE-ABS-KEY(work* OR labor OR labour OR job* OR task* OR employment*))) OR ((TITLE(work* OR labor OR labour OR job* OR task* OR employment*)) AND (TITLE(farm* OR agricultur* OR livestock* OR crop* OR horticultur* OR vine* OR wine* OR viti* OR dair* OR milk* OR rural* )) AND (TITLE-ABS-KEY(robot* OR automat* OR autonom* OR digital* OR precision* OR smart* OR 4.0))) AND ( LIMIT-TO ( PUBSTAGE,"final" ) ) AND ( LIMIT-TO ( DOCTYPE,"ar" ) OR LIMIT-TO ( DOCTYPE,"re" ) OR LIMIT-TO ( DOCTYPE,"ch" ) OR LIMIT-TO ( DOCTYPE,"bk" ) ) AND ( LIMIT-TO ( LANGUAGE,"English" ) )

Table 1 .

 1 Agricultural tasks robotized at farm level

	CROPS FARMING	LIVESTOCK
	(Aravind et al. 2017)	(Allain 2019)
	Harvesting, picking	Milking
	Tilling (soil works)	Feeding
	Crop scouting and control	Cleaning
	Seeding and transplanting	Treatment
		Transport
	We choose not to include aquaculture, although it is sometimes associated with the agricultural sector.
	Indeed, work and robotics in aquaculture refer to very different technical, economic and social realities
	from those of agriculture.

Table 2 :

 2 Participants, Interventions, Comparators and Outcomes -PICO description

	PICO COMPONENTS
		Farm, farm system, farmers and farm workers in all agricultural
	Participants (population)	sectors (e.g. field crops, livestock, horticulture, vine-growing)
		(in consistency with our work analysis framework)
		Adoption of robots for farming
	Interventions (exposures)	(in consistency with our robot definition -page 5)
	Comparators (control)	 Farm without robots  Farm before robot adoption
		 Farm structure and labour market: farm size, social relationship of
		production (e.g. family work, wage labour), added value
		distribution, any variable related to production factors organization
		and interaction (land, labour and capital) and labour market;
		 Performances:
		i. Technical indicators (e.g. yield, efficiency, quality of milk,
		energy sobriety, input economy);
		ii. Economic indicators (e.g. productivity, incomes, surplus, job
		created);
		 Organization at individual and collective levels: For both
		organization dimensions, we will pay particular attention to the
		autonomy of work and workers:
	Outcomes	iii. On-farm level: any analysis related to the question: who do what and when?
		iv. Off-farm level: any analysis related to off-farm work as
		advisors, robot technicians and collective organizations (e.g.
		self-help groups, roles of robot technicians, new roles for farm
		advisors);
		 Meaning:
		i. Mental and physical workload (e.g. arduousness, digital
		intrusion)
		ii. Individual and collective identities (e.g. job representations,
		feeling of community)
		iii. Relation to the living (plants, animals, ecosystems)
		iv. Pleasure at work
		v. Rationality construction (e.g. metric, pragmatic, economic)

Table 3 :

 3 Risk of bias assessment

	Study	Sample description	Robot description	Worker characterization -Who ?	Worker characterization -What ?	Comparison robustness	Risk of bias
	1	Y/PY/PN/N/NI Y/PY/PN/N/NI	Y/PY/PN/N/NI	Y/PY/PN/N/NI	Y/PY/PN/N/NI  High
							 Moderate
							 Some concerns

Y: Yes, PY: Probably Yes, PN: Probably No, N: No, NI: No Indication

Table 4 :

 4 In order to verify the relevance of our systematic review project with regard to the state of the art, we carried out a search on Web Of Science (WoS). The goal is to check that no review has been performed on our research question. Initial research confirmed that there is no review concerning work and robot in agriculture. The query used for this preliminary research is the following: Web of Science Research TI=((work* OR labor OR labour OR job* OR task* OR employment* ) AND ( robot* OR automat* OR autonom* OR digital* OR precision* OR smart*) AND (farm* OR agricultur* OR livestock* OR crop* OR horticultur* OR harvest* OR milk* OR pick* OR rural OR feed* ) AND review) Review related to the work in agriculture -WoS research on 04/04/2020

	Indexes=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI Timespan=1955-2020
	TITLE	YEAR AUTHORS

 The different types of farms (e.g. size, type of work, crop and livestock diversity)  The different productive sectors (e.g. dairy, field crops, horticulture)  The different countries studied Bibliographic context  Preliminary research

farming for increased land labour productivity in semi-arid West Africa. A review

	2017 Aune, Jens B.; Coulibaly, Adama; Giller, Ken E.	Agronomy	for	Sutainable
		Developement
	doi: 10.1007/s13593-017-0424-z
	Task-

based agricultural mobile robots in arable farming: A review

	2017 Aravind, Krishnaswamy R.; Raja, Purushothaman; Perez-Ruiz,	Spanish Journal of Agricultural
	Manuel	Research
	doi:10.5424/sjar/2017151-9573

Impact of precision livestock farming on work and human- animal interactions on dairy farms. A review

		2017 Hostiou, Nathalie; Fagon, Jocelyn; Chauvat, Sophie ; Turlot,	Biotechnologie	Agronomie
		Amélie ; Kling-Eveillard, Florence; Boivin, Xavier; Allain,	Société et Environnement
	doi: 10.25518/1780-4507.13706	Clément

Table 6 :

 6 SRL participants

	AUTHORS	MAIL	POSITION	INSTITUTIONAL AFFILIATION
	Théo MARTIN	theo.martin@inrae.fr	PhD Student	UMR Innovation -INRAE -
		(corresponding author and		Montpellier
		guarantor)
	Pierre GASSELIN pierre.gasselin@inrae.fr	Researcher	UMR Innovation -INRAE -Montpellier
	Nathalie HOSTIOU	nathalie.hostiou@inrae.fr	Researcher	UMR Territoires -INRAE -Lempdes
	Gilles FERON	gilles.feron@inrae.fr	Researcher	CSGA -INRAE -Dijon
	Lucette LAURENS	lucette.laurens@supagro.fr	Professor	UMR Innovation -Paul Valéry University -Montpellier
	François PURSEIGLE	francois.purseigle@sciencespo.fr	Professor	UMR Agir -ENSAT INP -Toulouse

Net Value Added

Institute of Electrical and Electronics Engineers

90% of farm are run by an individual or a family who rely primarily on family labour

http://www.fao.org/zhc/detail-events/fr/c/272677/

This SRL is part of the PhD thesis of Théo Martin. This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004.The PhD thesis is also funded by the INRAE meta-program GloFoodS. So all the funds used for this SRL comes from the French public sector.

Authors contributions

This SRL is part of a PhD study led by Théo Martin (UMR Innovation -INRAE). He leads this review and therefore participates in all stages. Pierre Gasselin, co-director of the thesis participates in the review of the protocol, the selection of papers (screening and eligibility), the analysis and writing stages. Nathalie Hostiou, participates in the protocol review, paper selection by eligibility criteria, qualitative analysis and writing stages. Lucette Laurens, co-director and François Purseigle, supervisor of the thesis, contribute to the qualitative analysis phase and the paper review. Gilles Feron has contributed to the formulation of this protocol as part of its expertise in systematic review. The Annexe 2 details the contributions of each.


Valerie Deplano
email: valerie.deplano@univ-amu.fr

Mourad Boufi

Vlad Gariboldi

Anderson D Loundou

Xavier Benoit D'journo

Jennifer Cautela

Amina Djemli

Yves S Alimi

Valérie Deplano

Xavier Benoit

D ′ Journo

Mechanical characterisation of human ascending aorta dissection

Keywords: Human ascending aorta, Acute type A dissection, Biaxial tensile test, In vivo measurements, Histological analysis

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.

Introduction

An aortic dissection is a vascular pathology consisting in a tear of the aortic wall intima layer which can propagate downstream or/and upstream along the aorta, creating a false lumen through which blood flows. The Stanford classification divides aortic dissections into two types: type A when the initial tear is located along the ascending aorta and upstream of the left subclavian artery, and type B when the tear is located on the descending aorta.

Acute type A dissection is characterised by rapidly developing severe complications such as aortic rupture, whereas chronic dissection is not diagnosed initially, being asymptomatic; patients remain stable. Acute type A dissection has a short-and long-term spontaneous mortality of 70% [START_REF] Golledge | Acute aortic dissection[END_REF] and although its prognosis has been improved by surgical treat-ment, post-operative mortality remains high. The tear initiates when the aortic wall can no longer bear the wall stress exerted. While some biaxial tensile tests [START_REF] Matsumoto | Biaxial tensile properties of thoracic aortic aneurysm tissues[END_REF], [START_REF] Haskett | Microstructural and biomechanical alterations of the human aorta as a function of age and location[END_REF], [START_REF] Martin | Significant differences in the material properties between aged human and porcine aortic tissues[END_REF], [START_REF] Azadani | Comparison of Mechanical Properties of Human Ascending Aorta and Aortic Sinuses[END_REF], [START_REF] Kamenskiy | Biaxial mechanical properties of the human thoracic and abdominal aorta, common carotid, subclavian, renal and common iliac arteries[END_REF]), or pressurised tests [START_REF] Labrosse | Mechanical behaviour of human aortas: experiments material constants and 3-D finite element modelling including residual stress[END_REF]) have been performed on healthy samples of human ascending aorta, there are few existing studies on dissected samples from acute type A dissection. To the authors ′ knowledge, only [START_REF] Babu | Biomechanical properties of human ascending thoracic aortic dissections[END_REF] assessed mechanical properties of entire wall fragments from type A dissection. Studies using biaxial tensile or bulge inflation tests have generally focused on ascending thoracic aortic aneurysms (ATAA) [START_REF] Okamoto | Mechanical properties of dilated human ascending aorta[END_REF], [START_REF] Pham | Biomechanical characterization of ascending aortic aneurysm with concomitant bicuspid aortic valve and bovine aortic arch[END_REF], [START_REF] Duprey | Biaxial rupture properties of ascending thoracic aortic aneurysms[END_REF]) rather than on dissection. Moreover, there have been few in vivo investigations of mechanical behaviour in dissections on the ascending aorta. [START_REF] Koullias | Mechanical deterioration underlies malignant behavior of aneurysmal human ascending aorta[END_REF] and [START_REF] Vitarelli | Aortic wall mechanics in the Marfan syndrome assessed by transesophageal tissue doppler echocardiography[END_REF] used echocardiography measurements to assess structural characteristics of ATAA and Marfan patients respectively. [START_REF] Shingu | Augmentation index is elevated in aortic aneurysm and dissection[END_REF] were the only authors to use an echo-tracking system to obtain distensibility measurements, but on chronic dissection. Furthermore, the components of the extracellular matrix (ECM), their concentration and their organization are known to play an essential role in the mechanical behaviour of the human aorta [START_REF] Tsamis | Elastin and collagen fibre microstructure of the human aorta in ageing and disease: a review[END_REF]. Today, there is greater emphasis on quantifying the arterial microstructure and microarchitecture using, for example, microscopy image analysis [START_REF] Koch | A custom image-based analysis tool for quantifying elastin and collagen micro-architecture in the wall of the human aorta from multi-photon microscopy[END_REF] to corroborate macroscopic mechanical response and enrich constitutive laws and thus numerical modelling (Pasta et al. (2014), [START_REF] Thunes | Structural modelling reveals microstructure-strength relationship for human ascending thoracic aorta[END_REF]).

Here, we sought insights into the mechanical behaviour of the ascending aorta in acute type A dissection because of its high mortality risk. Three mechanical characterisations were therefore performed at different investigation levels. In vivo measurements were performed on both healthy and pathological patients to obtain results at structure level. These measurements were combined with appropriate in vitro biaxial tensile tests on both healthy and dissected aortic samples to assess information at tissular level. Finally, qualitative and quantitative histological analyses were carried out on both types of samples to link structural, tissular and microstructural level.

Methods

Tissue preparation

Healthy ascending aorta, Haa, tubular structures were harvested from unused aortic segments after lung transplantation. Dissected ascending aorta, Daa, fragments were collected from patients after surgical repair. Informed consent was obtained based on established research board protocol in each hospital. All samples were stored, refrigerated at 4 • C in 0.9% NaCl solution and tested less than 50h after tissue extraction.

The Haa tubular structures (figure 1a) were longitudinally cut along the curvature. Three types of samples were extracted from dissected fragments (figure 1b) : a region without dissected layers, W DaaL, n=1, (figure 1d), the adventitia layer alone, n=3, (figure 1e), and the media associated with the intima layer called intimomedial flap, n=3, (figure 1f). Square 20X20mm samples (a reproducible size) were subsequently obtained from healthy and dissected samples, using a dedicated cutting device. Seven healthy samples from 5 different donors of mean age 65.3±4.9 years, with a mean thickness of 2.26±0.19mm, were tested. The mean thickness and patient mean age of the flap and adventitia samples were 1.98±0.12mm; 63.7±9.2 years and 1.06±0.17mm; 59.7±8.3 years respectively. The thickness of W DaaL was 1.79mm and the patient was 57 years old. Thickness was measured via image processing of the undeformed samples [START_REF] Deplano | Biaxial tensile tests of the porcine ascending aorta[END_REF]). Table A.1 Appendix A reported demographic data and risk factors of all in vitro tested samples.

In vitro mechanical tests

The square samples were mounted using a home-made biaxial set-up described in [START_REF] Deplano | Biaxial tensile tests of the porcine ascending aorta[END_REF]. Briefly, the circumferential, θ, and longitudinal, L, directions of the sample were in line with the biaxial displacements.

A displacement-driven protocol was applied while the ratio, α, remained constant. α = λ θ λ L , noted α = λ θ : λ L , was the stretch ratio, λ θ and λ L being the stretch in the θ, and L directions respectively.

Forces resulting from the displacements were measured using two load cells (10N ±0.0015N, 31E10, Honeywell) located on each direction of displacement. 3D displacement measurements (Dantec Q-400 software) were performed using Stereoscopic Digital Image Correlation. Samples were submerged in aqueous 0.9% NaCl solution maintained at 37 • C during the experiments.

The maximum stretch, λ max , was set at 1.2 of the gripped undeformed sample area, X L × X θ , where X L = X θ = 18mm were measured before the protocol was applied. Twenty loading and unloading cycles of preconditioning were first performed at λ max and α = 1 : 1 following by k=7 consecutive cycles, each one at different stretch ratios : 0.75:1, 1:0.75, 0.5:1, 1:0.5, 0.25:1, 1:0.25, 1:1. This protocol was applied for a maximum stretch rate of λmax = 3.84 × 10 -2 s -1 . Initial zero stress was assessed by tuning the position of the suture lines by 0.1mm steps at a very low displacement rate (0.1mm/s) until the load cell signals detected were above their noise level (2.4mV). A hybrid displacement/force control was implemented during unloading to avoid bending the sample with respect to its original dimensions.

The first Piola Kirchhoff stress tensor, P , was determined using f θ and

f L , the recorded force, X k-1 θ , X k-1
L and E 0 , the undeformed gripped sample dimensions and thickness. For each cycle performed during one protocol,

X k-1 θ and X k-1
L correspond to the undeformed lengths of the sample at the end of the k-1 unloading path.

The components of P were expressed by

P k θθ = f θ X k-1 L E 0 and P k LL = f L X k-1 θ E 0 .
The components of the second Piola Kirchhoff stress tensor, S, were then obtained using S k θθ = P k θθ λ θ and S k LL = P k LL λ L , those of the Cauchy stress tensor, σ, using: σ k θθ = P k θθ λ θ and σ k LL = P k LL λ L . λ i were calculated from the SDIC displacement measurements. The superscript k will be omitted in the rest of the paper.

Histological characterisation

Healthy and dissected segments (n=3 and n=4 respectively) were first fixed in 10% formalin (Appendix A details their characteristics). For each sample, 2 contiguous samples were cut along the axial and transversal artery axis. They were dehydrated in ethanol and embedded in paraffin. Four con-tiguous sections 5 µm thick were cut and stained with orceid and Trichrome masson for the visualisation of elastin and collagen fibres respectively. Calopix software was then used for image processing and extraction of the relative density of the stained structures. Density was defined as the ratio of stained surface to total surface for a region of interest of the sample. For each sample, density was measured in 4 regions in media layers and in the entire wall, and averages were calculated.

In vivo measurements

To determine the mechanical behaviour of both healthy and dissected ascending aorta, in vivo transoesophageal echocardiography measurements were performed on two new groups of patients not used for the in vitro tests

Results

Constitutive modelling

Figures 2a) to d), which represent S -λ curves for both Haa and W DaaL samples, show nonlinear and rather anisotropic behaviour. These mechanical responses were therefore modelled by a Holzapfel-Gasser-Ogden (HGO) form of strain energy function, ψ, as per [START_REF] Babu | Biomechanical properties of human ascending thoracic aortic dissections[END_REF].

ψ = ψ iso +ψ aniso with ψ iso = C 2 (I 1 -3) and ψ aniso = k 1 k 2 (e (k 2 (I 4 -1) 2 ) -1) consid-
ering that collagen fibres are symmetrically oriented in the arterial wall. C

and k 1 are positive dimensional (Pa) coefficients and k 2 is dimensionless. I 1 is the first invariant of the strain tensor and

I 4 = λ 2 θ cos 2 γ + λ 2 L sin 2 γ, where
γ is the angle between the fibre direction and the circumferential direction.

, ψ = 1 2 (A θθ E 2 θθ + 2A θL E θθ E LL + A LL E 2 LL ). E θθ
and E LL are the components of the Green-Lagrangian strain tensor. The coefficients A θθ and A LL describe the tissue stiffness in the θ and L directions respectively and A θL is related to the interaction between the two directions.

Whatever the constitutive modelling, the components of the second Piola Kirchhoff stress tensor can be derived from ψ according to S = ∂ψ ∂E . The coefficients C, k 1 , k 2 and γ angle for the HGO model, and A θθ , A LL and A θL for the linear model were thus obtained using an optimization procedure (using fmincon function in Matlab). Part of the mechanical data from the biaxial protocol was simultaneously fitted to the constitutive model. For

Haa and W DaaL, mechanical data from α= 0.5:1; 1:0.5, and 1:1 were used.

For the Daa flap, mechanical data from α= 0.75:1; 1:0.75, 0.5:1, 1:0.5 were considered.

The method involved minimizing the sum of the error χ α (equation 1), e.g.

α χ α where α was the stretch ratio and m the number of experimental data recorded during loading (e.g m=60).

χ α = m k=1 (S exp θθ -S mod θθ ) 2 k + (S exp LL -S mod LL ) 2 k (1)
The superscript exp indicates the stress values from the experimental data, mod those predicted by the model. andb) underline the good fit between experimental data and constitutive modelling for flap mean data. This is further illustrated by figures 4a) and b), which show that the model ′ s prediction of S -E behaviour for α = 1 is very close to the experimental behaviour, even though this latter dataset was not used in the identification procedure.

Mechanical behavior of healthy and dissected ascending aorta

In vitro mechanical tests

None of the tensile tests performed on the adventitia Daa samples could be exploited, due to systematic tearing under stress.

To compare the mechanical behaviour of Haa with that of W DaaL, σ-λ curves for α=1 in both longitudinal and circumferential directions were plotted using an HGO model (figure 5). As mentioned, both samples exhibited

= 2 A LL -A θθ A LL +A θθ , is 0.175 (Table C.2 Appendix C). While
Haa and W DaaL show a stiffer response in the longitudinal direction, flap samples behave in the opposite way. 60±4.5 versus 54±4 respectively). Moreover, figures 7b) and a) clearly show areas with broken elastin fibres and dislocations, when flap media are compared with Haa media.

Histological characterisation

In vivo measurements

Comparison of E p and β parameters (Table 2) between the two groups of patients shows that those with dissection have a significantly higher stiffness index than control patients, with 20.7±24 versus 6.1±2.8, p=0.013. In addition, mean E p in the dissection group is 436±393 kPa, compared to 198±107 kPa in the control group (p=0.041).

The systolic and diastolic aortic diameters are significantly larger in the dissection group (p<0.001 for both) and no correlation is found between systolic ascending aorta diameter and stiffness index (p=0.87) and E p (p= 0.82).

Discussion and conclusion

The first question that merits discussion is whether the behaviour of the healthy aortic ascending wall is anisotropic or isotropic. This suggests the need to link analyses performed at different scales to enhance understanding.

Results in the literature differ depending on the donors ′ age. [START_REF] Azadani | Comparison of Mechanical Properties of Human Ascending Aorta and Aortic Sinuses[END_REF] and [START_REF] Martin | Significant differences in the material properties between aged human and porcine aortic tissues[END_REF] showed isotropic behaviour for both relatively young (mean age 47) and very old (aged from 81 to 98) donors. [START_REF] Labrosse | Mechanical behaviour of human aortas: experiments material constants and 3-D finite element modelling including residual stress[END_REF] and [START_REF] Haskett | Microstructural and biomechanical alterations of the human aorta as a function of age and location[END_REF] observed anisotropic behaviour with a stiffer mechanical response in the circumferential direction than in the longitudinal direction for patients aged from 31 to 71. However, Haskett et al.

(2010) also observed that the longitudinal direction tended to stiffen with age more than the circumferential direction, with donors over 61 showing lower longitudinal than circumferential peak strain. In addition, [START_REF] Kamenskiy | Biaxial mechanical properties of the human thoracic and abdominal aorta, common carotid, subclavian, renal and common iliac arteries[END_REF] reported that 7 patients out of 8, with a mean age of 54, had a more compliant mechanical response in the circumferential direction than longi-tudinally. The general mechanical behaviour of our donors, 58 to 69 years old, nonlinear and anisotropic, is consistent with behaviours observed in the literature for the same age (figure 8). In our case, the mechanical response is stiffer in the longitudinal direction. In parallel, the ratio of collagen to elastic areal density is greater for a sample face stretched in the longitudinal direction. As collagen fibres contribute to stiffening whereas elastic fibres increase distensibility, the histological analysis confirms the results obtained using biaxial tensile tests.

Several components of the extra-cellular matrix may be involved in dissection and the associated mechanisms are not yet fully understood. Dysfunction in the contractile apparatus within smooth muscle cells may place the thoracic aorta at increased risk (Emmott et al., 2016). [START_REF] Humphrey | Possible Mechanical Roles of Glycosaminoglycans in Thoracic Aortic Dissection and Associations with Dysregulated TGF-β[END_REF] or [START_REF] Roccabianca | Quantification of regional differences in aortic stiffness in the aging human[END_REF] have, meanwhile, revealed that pooled glysaminoglycans/proteoglycans can induce stress concentration and pressure swelling within the aortic wall, and thus contribute to dissection. Nevertheless, elastin and collagen fibres are obviously implicated in arterial wall elasticity, tensile stiffness and strength, and anisotropy with stiffer mechanical response in the longitudinal direction could also play a role in dissection.

Few mechanical characterisations have been performed on dissected ascending aortic walls; it is therefore important to compare healthy and dissected samples. Like Haa, W daaL presents a stiffer mechanical response in the longitudinal direction. Although the number of samples we tested is a limitation, this result is in agreement with the only published work using biaxial tests to characterise the mechanical behaviour of W DaaL: [START_REF] Babu | Biomechanical properties of human ascending thoracic aortic dissections[END_REF] observed the same anisotropic feature for patients over 50 years old with a dissection. Complementarity between in vitro and in vivo measurements, a strength of our method, enables us to further the analysis. By subjecting excised aorta samples to mechanical solicitation via in vitro biaxial tensile tests, anisotropy, nonlinearity and stress are assessed at different stretch values; Young ′ s modulus can also be inferred within a specific range of solicitations. In vivo measurements enable aorta radial strain and pressure to be assessed at systolic and diastolic instants, thereby revealing aorta distensibility (β or E p ). These two supplementary measurements allow arterial stiffness to be assessed from a different perspective. Here, W DaaL showed greater rigidity than Haa. Only one W daaL was tested, but this in vitro assessment is confirmed by in vivo measurements performed on 13 patients with dissection and 22 without. Patients with dissection had a significantly higher stiffness index and E p values than control. Moreover, as the in vivo measurements were performed on patients of comparable ages, the dissection stiffening cannot solely be associated with ageing. The relevance of our in vivo measurements is underlined by the fact that we found a mean E p value in the control group, consistent with the literature values based on normal populations similar to ours in age and vascular risk factors [START_REF] Hirata | The Marfan syndrome: abnormal aortic elastic properties[END_REF] and [START_REF] Stefanadis | Distensibility of the ascending aorta: comparison of invasive and non-invasive techniques in healthy men and in men with coronary artery disease[END_REF]).

Moreover, in vivo measurements can challenge assumptions in the clinical indexes used to evaluate dissection risk. The aortic diameter is often considered an important parameter to assess dissection evolution. Here, although both systolic and diastolic diameters were significantly larger in the dissection group, no correlation appears between systolic diameter and β or E p . As dissection is clearly associated with stiffening, diameter does not therefore appear to be relevant to predict the evolution of dissection.

More work should be focused on better evaluating dissection evolution risk.

Not only is there a lack of mechanical characterisations of dissected aorta samples, but there is also a pressing need to couple such studies with microstructural analysis, so as to link overall mechanical response to microstructural modification and focus on local changes in wall integrity. The present work has some limitations. A larger number of samples would be preferable, especially for biaxial tensile tests; we were mainly limited by the logistics of testing fresh tissue. The tensile test protocol was established in such a way as to be relevant to both healthy and dissected samples. In particular, a rather low value of λ max =1.2 was chosen to test the dissected adventitia layer because of its fragility. Unfortunately, we did not manage to test this specific sample, which would have permitted us to increase λ max for the other mechanical tests. Finally, the quantitative analysis of collagen results contained in the histomorphetric data should be considered with caution, because of the nonspecific nature of Trichrome Masson staining. Despite these limitations, which are currently the subject of ongoing studies, our combination of in vitro, in vivo and histological results provides new insights into the mechanical characteristics associated with ascending aorta dissection.

Acknowledgment

The authors thank the Labex MEC ANR-11-LABX-0092 for financial support. We thank Marjorie Sweetko for English language revision. 2010) respectively; extracted from [START_REF] Roccabianca | Quantification of regional differences in aortic stiffness in the aging human[END_REF]). Our curves are plotted using constitutive modelling and α=1. Solid and dashed lines are used for the L and θ direction respectively.

C(kPa) k 1 (kPa) k 2 γ R 2 θ R 2

  and histological characterisations. The control group (n=22, age 67±9 years, 19 males) was composed of patients without any ascending aorta pathology, while the second group (n=13, age 69±10 years, 9 males) was composed of patients admitted and treated for acute type A dissection. The mechanical characterisation of both groups entailed determining the Peterson modulus, E p , and stiffness index, β. E p = ∆P ǫ , β = ln Psys P dias ǫ , where ǫ = ∆D D dias , ∆P and ∆D were the difference between the diastolic and systolic pressure (P dias , P sys ) and diameter (D dias , D sys ) values respectively. Details of pressure and diameter acquisitions as well as inclusion criteria for each group are given in Appendix B.

Figures

  Figures 3 a) and b), which show S -E curves for the Daa intimomedial flap, highlight more or less linear and anisotropic behaviour. According to these results, the mechanical response was described by a 3-parameter polynomial strain energy function, ψ = 1 2(A θθ E 2 θθ + 2A θL E θθ E LL + A LL E 2 LL ). E θθ

  nonlinear and anisotropic behaviour. The mechanical response of W DaaL was stiffer than that of Haa and both their responses were stiffer in the longitudinal than in the circumferential direction. Although rather linear, the mechanical behaviour of Daa flap samples (figures 3a) and b) was also anisotropic ; however their A θθ coefficient was greater than A LL one, 277.653 versus 232.861 kPa, (TableC.2 Appendix C).To compare anisotropy characteristics of Haa and W DaaL, stress levels at 80mmHg and 120mmHg were first computed using Laplace law σ θ = P r h ;P and r being P dias and D dias 2 or P sys and Dsys 2 respectively (Table2); h being the wall thickness. Using the experimental σλ curves, values of longitudinal and circumferential stretches were then inferred for σ θ Laplace values at 80 and 120mmHg using interpolation. The assessment of anisotropy at diastole, Ani 80 , and systole, Ani 120 were then defined[START_REF] Kamenskiy | Biaxial mechanical properties of the human thoracic and abdominal aorta, common carotid, subclavian, renal and common iliac arteries[END_REF] as the difference in longitudinal and circumferential stretches divided by their average value. Whatever the stress level considered, the negative anisotropy values confirm that mechanical response is stiffer in the longitudinal direction for both healthy and W DaaL samples (TableC.1 Appendix C). Moreover, these high values show that anisotropy increases when there is dissection, Ani 80 =-0.017, Ani 120 =-0.03 for Haa versus -0.025 and -0.037 respectively for W DaaL. Finally, for Daa flap samples the mean anisotropic level, defined as Ani

Figures 6

 6 Figures 6 and 7 illustrate histological transversal and axial cuts of Haa and Daa flap samples respectively. A sketch of both cuts (figure 6e) shows that the transversal cut (TC) and axial cut (AC) generate faces that will be stretched in the longitudinal and circumferential direction respectively.Observations of the Haa TC reveal less elastin and more collagen fibres than for the AC (figures 6a), b) and c), d) respectively). The fibre areal densities (Table1) quantitatively confirm these qualitative data. The ratio of collagen

  Furthermore, fluid structure numerical modelling can add important information on the quantification of hemodynamic loads at the wall and which are thus transmitted/perceived by the arterial wall cells. To date, few numerical studies have used FSI modelling and appropriate arterial wall constitutive laws[START_REF] Alimohammadi | Development of a patient-specific multi-scale model to understand atherosclerosis and calcification locations : comparison with in vivo data in aortic dissection[END_REF]. The results obtained in this work on flap and dissected ascending aorta wall stiffness could be used to perform such FSI numerical simulations, thereby contributing to the understanding of hemodynamic stimuli. This would improve modelling of the aortic cell response to such hemodynamic solicitations.To conclude, this work used in vitro biaxial tensile tests, in vivo measurements via transoesophageal echocardiography and histological characterisation for ascending aorta mechanical characterisation, thereby combining tissular, structural and microstructural levels in an original approach. To our knowledge, this is the first study reporting biaxial tensile tests on the intimomedial flap, and our in vivo investigations of mechanical properties on acute type A dissection are the first in humans. Our major findings are : i)The mechanical response of the intimomedial flap shows linear behaviour up to stretch values of 1.2. Conversely, Haa and W DaaL behave in a nonlinear way. However, all show anisotropy. ii) The mechanical response of Haa in the longitudinal direction is stiffer than in the circumferential direction. iii) Dissection promotes ascending aorta stiffening.

Figure 1 :Figure 2 :Figure 3 Figure 4 :Figure 5 :

 12345 Figure 1: a) Representative healthy human ascending aorta, Haa. b) Representative fragment of dissected aorta, Daa. Side view of representative wall thickness of c) Haa, d) Daa without dissected layers, e) adventicia layer of Daa, f) intimomedial flap of Daa.

Figure 6 :

 6 Figure 6: Transversal (a and b)and axial (c and d) histological cuts of Haa. Elastin fibres are colored in black a) and c) (orceid staining). Collagen fibres are colored in blue b) and d) (Trichrome masson staining). e) Scheme of arterial cuts and their corresponding stretching directions.

Figure 7 :Figure 8 :

 78 Figure 7: Transversal and axial histological cuts of Daa flap. Elastin fibres are colored in black a) and c) (orceid staining). Collagen fibres are colored in blue b) and d) (Trichrome masson staining). Zoom of the media layer histological transversal cut stained with orceid. e) Haa, f) Daa flap.

Table C

 C

	components derived from the strain energy function fitted the experimental
	data with average determination coefficients R 2 L of 0.811 and 0.911 respec-
	tively and R 2 θ of 0.861 and 0.902 respectively (Table C.1 Appendix C). For
	mean data obtained from the flap samples, we found R 2 of 0.991 and 0.995
	in the longitudinal and circumferential direction respectively (Table C.2 Ap-
	pendix C). Figures 3a)

.1 Appendix C displays the C, k 1 , k 2 coefficients and γ angle for the mean data obtained from the healthy samples (n=7) and for the single W DaaL sample tested. Table

C

.2 Appendix C presents the A ij coefficients obtained through the optimization procedure for each Daa flap sample and the mean data obtained from all flap samples. For each constitutive modelling process, the coefficients obtained at the end of optimization procedures converged towards the same values, whatever the initial guesses tested.

For mean data obtained from Haa samples and W DaaL data, the stress

Table 1

 1

	) quantitatively confirm these qualitative data. The ratio of collagen
	to elastic areal density within the Haa TC total wall is greater than for AC,
	3.61 versus 1.68.
	Qualitative and quantitative comparison of histological cuts of Haa and
	dissected samples (figures 6, 7 and table 1) highlights lower elastin fibre den-
	sity and higher collagen fibre density for the dissected samples within the
	AC of the media layer compared to healthy samples (20±2 versus 30±6 and

Table 1 :

 1 Average values in % of the elastin and collagen fibres areal density for both healthy and dissected samples.

Table 2 :

 2 Systolic and diastolic diameter and pressure values from in vivo measurements. Resulting Peterson modulus and stiffness index values for control and dissection groups. Data were expressed as mean ± standard deviation. Comparisons were performed using the Mann-Whitney U test. The statistical significance was defined as p<0.05

		Control group Dissection group	p
		(n=22)	(n=13)
	Systolic diameter (mm)	34.8±4.9	47.7±5.5	<0.001
	Diastolic diameter (mm)	33.4±4.9	44.3±5.2	<0.001
	Systolic pressure (mmHg)	137±25	110±22	0.06
	Diastolic pressure (mmHg)	79±14	57±17	0.01
	E p (kPa)	198±107	436±393	0.041
	β	6.1±2.8	20.7±24	0.013

  TableC.1: Coefficients of the HGO constitutive modelling, R 2 values and anisotropy level obtained through the optimization procedure for Haa, mean data obtained from the n=7 healthy samples and for Daa wall without dissected layers.

			L	Ani 80 Ani 120
	Haa	29.828	10.369 20.93 0.922 0.861 0.811 -0.017 -0.03
	Daa wall 13.950	62.056 12.01 0.879 0.902 0.911 -0.025 -0.037

Table C .

 C 2 shows the A ij constitutive modelling coefficients of Daa flap samples, the R 2 values obtained during the identification procedure and the anisotropy level, Ani = 2 A LL -A θθ A LL +A θθ .TableC.2: A ij coefficients, anisotropy index and R 2 values obtained through the optimization procedure for each flap sample and for their mean data.

				R 2 θ	R 2 L	Ani
	F1S1	299.375	111.499	251.451 0.988 0.992 0.174
	F1S2	265.494	96.506	212.808 0.978 0.991 0.220
	F2S1	293.325	114.408	236.580 0.980 0.976 0.214
	Mean data	277.653	114.350	232.861 0.995 0.991 0.175

Flap(F)ID Sample(S)ID A θθ (kPa) A θL (kPa) A LL (kPa)

Conflict of interest statement

All authors declare that there are no conflict of interest. The number of healthy samples (n=3) histologically characterised, was a subset of those tested on the biaxial tensile device. Two out of four dissected samples, which have been histologically characterised, were also tested on the biaxial tensile device. The 2 dissected samples, that were not tested in vitro, came from patients with similar risk factors as those tested.

Appendix A. Table of subject characteristics

Appendix B. Methodology of in vivo measurements

To determine the ascending aorta Peterson modulus and stiffness index for healthy and pathological cases, a standardized protocol using available In the control group, patients were selected to match those in dissection group for age and vascular risk factors. They displayed no severe ascending aorta atherosclerosis, aortic-valve disease nor ascending aorta aneurysm.

Appendix C. Tables of results: coefficients of constitutive modelling, R 2 and anisotropy levels