domain: sdv sde sdv.bv

text:
Marion Bardy
email: marion.bardy@inra.fr

Ariane Gaunand

Nicolas Saby

Dominique Arrouays

Laurence Colinet

Nathalie Munier-Jolain

An assessment of the societal impacts of French soil databases

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

Soil inventories

Context



  et al. 2008, Soil Use and Management SOC evolution (g/kg sol) Villaneau et al. 2013, Environ.Chem.Lett Identification of potential impacts:  Direct relationship with end-user  Direct relationship with intermediary organisations (provision of data + regular surveys)  « By chance »: spontaneous feedback  Description of confirmed impacts:  Series of interviews… sometimes many intermediaries before reaching THE end-user  Impact confirmed (and described)… or not assessment of societal impacts of French soil databases  More than the impacts themselves:  Better knowledge of data end-users  Enhanced recognition of the usefulness of French soil databases  Basis for setting up a new communication strategy regarding soil databases: very successful national conference with testimonies of end-users  Perspectives :  Add new impact descriptions  Gather testimonies of end-users in a booklet or a documentary

For any question regarding :  This case study: marion.bardy@inra.fr  The Asirpa methodology: ariane.gaunand@inra.fr

Thanks for your attention!

Marion BARDY / Global Soil Security 05/12/2016


M Al-Jammas

J Agabriel

J Vernet

I Ortigues-Marty

QUANTITATIVE RELATIONSHIPS BETWEEN THE TISSUE COMPOSITION OF BOVINE CARCASS AND EASILY OBTAINABLE INDICATORS

Keywords: adipose tissue, carcass composition, cattle

The most accurate determination of beef carcass quality involves dissection of cut or entire carcass. This, however, is very costly and cumbersome. An alternative is to determine easily obtainable indicators. The objective of this study was to derive quantitative relationships between indirect carcass indicators and measured carcass tissue composition. A meta-analysis was applied on 25 published trials with cattle. The selected indicators were USDA yield grade, fat thickness, marbling, ribeye area and carcass conformation and fatness scores. The USDA yield grade was the most highly related to changes in carcass adipose tissue and muscle weights. Fat thickness was also related to changes in both adipose tissue and muscle weights. Other indicators were less correlated with changes in carcass tissue composition. The relationships obtained in this study depend on measurement accuracy and different deposition kinetics for adipose tissue.

INTRODUCTION

Carcass quality depends on the tissue composition and the muscularity of slaughtered animals. Information about carcass composition helps to determine nutritional requirement, animal performance and the cost of production of beef cattle. Carcass quality in cattle is evaluated by a variety of methods, each of which may involve different techniques. Direct methods determine the composition (tissue or chemical) of the carcass from the complete dissection of the half or the entire carcass. The most accurate method is the chemical analysis of the carcass [START_REF] Powell | An evaluation of quantitative estimates of beef carcass composition[END_REF]. However, the physical separation of the carcass into muscle, adipose tissue and bone is a useful indicator of retail composition [START_REF] Lunt | Techniques for Predicting Beef Carcass Composition[END_REF] or tissue development [START_REF] Robelin | Composition corporelle des bovins: évolution au cours du développement et differences entre races[END_REF]. These measures are difficult and expensive to implement in practice particularly with cattle.

Indirect methods are commonly used. In Europe the conformation (ConS) and fatness (FS) scores are used. In other countries as USA, the yield grade (YG), subcutaneous fat thickness (SFT), marbling (Mar) and ribeye area (longissimus dorsi area, LMA) are used. Therefore, the comparison of published quantitative results on carcass quality is difficult because of this diversity of approaches. We recently showed [START_REF] Al-Jammas | Comparaison de Methodes d'évaluation de la composition Chimique de la carcasse chez les bovins par Méta-analyse[END_REF] that variations in yield grade and fat thickness may properly explain the variations of chemical composition of carcass. The objective of this study was to determine, from published results, the quantitative relationships between variations of tissue composition of carcass and the variation of easily obtainable indicators.

II. MATERIALS AND METHODS

Publications describing cattle carcass and published in international scientific journals were searched using the web of science without restriction on the years. ). For all models, the normality of residuals was verified, and influent data were identified from analysis of residuals, high leverage, Cook's distance and DEFIT [START_REF] Sauvant | Meta-analyses of experimental data in animal nutrition[END_REF]. The contribution of each explanatory variable to the total variance of the model was calculated. The models were compared in terms of adjusted R 2 , residual means square errors of the model (RMSE), and the mean square error of prediction (MSPE) [START_REF] Kobayashi | Comparing Simulated and Measured Values Using Mean Squared Deviation and its Components[END_REF]: 2 , where x are predicted values; y are observed values.

MSPE = 1/n Ʃ i=1 (X i -y i )

III. RESULTS AND DISCUSSION

A total of 25 scientific papers were identified, they included 28 experiments for a total of 117 treatments. The animals differed in gender (4% female, 43% male, 49% castrated males and 4% implanted castrated males), and/or breeds (45% beef, 29% dual purpose and 26% dairy). The descriptive statistics for variables retrieved from the publications showed wide ranges in all live animal and carcass characteristics (Table 1). The majority of data (88%) were associated with the continental breeds or their cross breeds with the lowest adipose tissue weights and the highest HCW and muscle weights. The remaining 12 % of data came from British breeds. Overall HCW varied between 138 -487 kg, with 67% of data between 300-380 kg. Adipose tissues varied between 11-137 kg, with 81% of data between 30-80 kg, representing 16-24% of HCW. The SFT and YG were the most highly related to changes of carcass adipose tissue weight (Model 1, 2) with the highest adjusted R 2 and among the lowest RMSE and MSEP. The proportions of variance explained by the study effect were the lowest, indicating that there were no major additional variation factors besides the 2 covariates. These two indicators were already shown to be well related to the proportion of lipids in the carcass [START_REF] Al-Jammas | Comparaison de Methodes d'évaluation de la composition Chimique de la carcasse chez les bovins par Méta-analyse[END_REF]. Besides, SFT is considered in the calculation of yield grade; it expresses the quantity of subcutaneous adipose tissue which is related positively to total adipose tissue between birth and adult age [START_REF] Robelin | Différenciation, croissance et développement du tissu adipeux[END_REF]. Marbling, despite its relative subjectivity [START_REF] O'mara | Prediction of Slaughter Cow Composition Using Live Animal and Carcass Traits[END_REF] was also related to carcass adipose tissue, even though the quality of the relationship was lower than for SFT and YG because of a greater proportion of the variance explained by the study effect. The proportion of model variance explained by LMA (Model 4) was very small (5%) confirming results by Powell et al. [START_REF] Powell | Prediction chemical composition of beef carcasses from easily obtainable carcass variables[END_REF] who showed that LMA accounted for less than 6% of the variation in chemical carcass composition (protein, fat) when animals of widely varying weights were included. There was no significant relationship between ConS and adipose tissue weight contrary to FS (Model 6) but the proportion of variance explained by FS was 17% which is very low compared with other indicators (YG, SFT, Mar). These results were similar to those of Indurain et al. [START_REF] Indurain | The relationship of carcass measurements to carcass composition and intramuscular fat in Spanish beef[END_REF] who showed that fatness score was more efficient than conformation for predicting carcass composition. The first driver of changes in carcass muscle weight was HCW (Model 7), explaining a high proportion of the variance (from 64 to 89 %) in all models (Models 8 to 13), while indicators contributed for only 0 to 21%. In general, the study effect was lower in muscle relationships (Models 7 to 13) than in adipose tissue relationships (Models 1 to 6) indicating that carcass muscle weight was less affected by other variation factors (such as feeding conditions) than adipose tissue [START_REF] Robelin | Composition corporelle des bovins: évolution au cours du développement et differences entre races[END_REF]. Again, the YG and SFT were the most highly related to changes in carcass muscle weight (Models 8, 9), with the highest adjusted R 2 and among the lowest RMSE and MSEP. By contrast, Mar and LMA were less related to changes of muscle weight (Models 10, 11). There was a significant relationship between ConS and muscle weight (Model 12) but it was associated with a high error of prediction (29% of muscle weight). The relationships between muscle and FS (Model 13) was better than that with ConS. but these two scores (ConS and FS) were poorly related to changes in carcass muscle weights, as already noted for adipose tissues.

IV. CONCLUSION

Our results adjusted on finished cattle data showed that indirect measurements on carcass particularly fat thickness and yield grade can be good indicators of changes in tissue composition of the carcass. The exploration of these relationships should be completed by taking into account other factors of variation (breed, sex...).

Table 1 :

 1 Mean values, standard deviations (SD), minimum (Min) and maximum (Max) of carcass weight, tissue composition and indicators.

		Number 1
	Variable			Mean SD	Min	Max
		n e	n m
	Hot carcass weight, kg	116 1 313.8 63.6 138.3 487.5
	Cold carcass weight, kg	116 1 307.5 62.4 135.5 477.8
	Adipose tissue, kg	116 1	51.6 27.2 11.1 137.2
	Muscle, kg	116 1 205.3 54.9 93.1 364.1
	Bone, kg	116 1	51.7 12.8 24.6	76.5
	Fat thickness, mm	60 57	8.8	3.9	3.2	20.2
	Marbling /10	43 74	3.8	0.9	2.3	6.3
	Yield grade	22 95	2.7	0.9	1.1	4.7
	Ribeye area, cm 2	79 38 68.3 13.6 38.9 106.0
	Conformation score /5	67 50	2.9	0.8	0.7	4.8
	Fatness score /5	72 45	2.9	0.8	1.4	4.8
	1: number of data (treatment): n e : number of existing data,
	n m : number of missing data.
	Relationships		between	carcass	tissue
	composition and indicators are presented in
	Table 2. Hot carcass weight was more highly
	correlated with the muscle weight than with
	adipose tissue weight (Model 7 vs. 1) explaining
	a greater proportion of the variance (74 vs. 3%).
	Relationships were generally improved when
	indicators were included as additional covariate.
	Most indicators (YG, SFT, Mar, FS,) were
	positively correlated with adipose tissue weight
	(model 2, 3, 4, 6) and negatively correlated with
	muscle weight (Models 8, 9, 10, 13), contrary to
	LMA and ConS (Model 5, 11). These results can
	be explained by the negative correlations
	between adipose tissue and muscle weights at
	similar carcass weight.

Table 2 .

 2 Intra-study relationships between carcass tissue weights (adipose tissue and Muscle,kg) and easily obtainable indicators (yield grade, subcutaneous fat thickness, marbling score, longissimus dorsi area, and score of conformation and fatness of carcass) in finishing cattle.

	N° Y	Number 1	Equation 2	RMSE	Adj-R 2 MSEP		% variance explained 3
			N sy	N tr					In HCW	Sy
	1		28	117 24.41±9.26 *** + 0.10±0.03 *** HCW	9.05	0.89	61.5	-	3	88
	2 3 4 5	Adipose tissue, kg	7 14 11 16	22 57 43 76	-10.71±11.8 NS + 0.12±0.04 *** HCW+20.00±2.99 *** YG 2.08±8.98 NS + 0.12±0.03 *** HCW +3.51±0.35 *** SFT 3.52± 10.03 NS +0.09±0.04 * HCW +12.28±1.37 *** Mar 58.44±11.05 *** +0.22±0.04 *** HCW -0.81±0.20 *** LMA	5.74 4.65 4.91 8.99	0.98 0.98 0.97 0.91	19.5 15.6 16.9 61.8	62 74 24 5	26 2 35 15	10 22 38 73
	6		15	72	-1.77±8.28 NS +0.04±0.02 * HCW +12.41±1.68 *** FS/5	5.55	0.90	23.5	17	0.1	75
	7		28	117 -38.54±9.27 *** + 0.76±0.02 *** HCW	9.06	0.97	61.5	-	74	24
	8		7	22	1.04±7.07 NS + 0.71±0.03 *** HCW -18.04±1.79 *** YG	3.44	0.99	7.0	0	89	10
	9		14	57	-11.61±6.20 NS +0.70±0.02 *** HCW -2.92±0.24 *** SFT	3.21	0.99	7.4	5	82	13
	10 11	Muscle, kg	11 16	43 76	-9.62±9.43 NS +0.70±0.04 *** HCW -9.17±1.29 *** Mar -73.69±10.83 *** +0.62±0.04 *** HCW +0.94±0.20 *** LMA	4.62 8.82	0.99 0.97	14.9 59.3	0 1	70 69	1 27
	12		14	67	-54.88±10.87 *** +0.79±0.03 *** HCW +5.55±2.87 * ConS	8.18	0.97	50.9	21	64	13
	13		15	72	-15.11±8.62 * +0.83±0.02 *** HCW -12.93±1.74 *** FS	5.78	0.98	25.5	13	72	13
	1: number of data: Nsy: number of studies, Ntr: number of treatments.

2: YG: Yield grade, HCW: Hot carcass weight, SFT: subcutaneous fat thickness, Mar: Marbling, LMA: Longissimus area, ConS: conformation score, FS: fatness score.. 3: In: indicator (YG, SFT, Mar, LMA, ConS and FcS), Sy: Study. NS: not significant P>0.10, *: P<0.05, ** :P<0.01, ***:P<0.001.

st International Congress of Meat Science and Technology, 23-28 th August 2015, Clermont-Ferrand, France


Proteome data mining using ProteINSIDE online tool

Nicolas KASPRIC 1,2 , Matthieu REICHSTADT 1,2 , Brigitte PICARD 1,2 , Jérémy TOURNAYRE 1,2 and Muriel BONNET 1,2 1 INRA, UMR1213 Herbivores, F-63122 Saint-Genès-Champanelle, France 2 Clermont Université, VetAgro Sup, UMR1213 Herbivores, F-63000, Clermont-Ferrand, France

Corresponding authors: nicolas.kaspric@clermont.inra.fr and muriel.bonnet@clermont.inra.fr

Summary

Genomic and proteomic experiments are widely acknowledged to produce a huge amount of data to be analyzed. The challenge for scientist working on ruminant species is to extract meaningful biological context for proteins or genes to generate new hypothesis. This challenge is currently difficult because of the lack of an integrative workflow that hinders the efficiency and the robustness of data mining, thus scientists must use several tools often not designed for ruminant studies. We designed the online tool ProteINSIDE [1] that provides an overview of the biological information stored in public databases (NCBI and UniProt) or provided by annotations according to the Gene Ontology. It also predicts proteins that are secreted to search for proteins that mediate signalization between cells or tissues, and analyses protein-protein interactions to identify proteins contributing to a process or to visualize functional pathways. Using lists of proteins or genes as a unique input, ProteINSIDE is an original all-in-one tool to mine genomic and proteomic data from cattle, sheep, goat, human, rat, and mouse. A user-friendly web interface provides access to create analyzes and view results that can be sorted online or directly downloaded as several file formats (image, pdf, tabular, spreadsheet, fasta…). ProteINSIDE's database collects and stores the biological information required to the efficient functioning of ProteINSIDE and is monthly updated to gather the last available knowledge. ProteINSIDE was successfully bench tested with 1000 protein identifiers from each species by comparison with DAVID, BioMyn, and AgBase designed for information retrieval and annotation, as well as with PrediSi, and Phobius that predict secreted proteins. ProteINSIDE is freely available using a simple internet browser at www.proteinside.org (an example of results is provided on the home page).

Résumé

Les expérimentations de génomique et de protéomique produisent de grandes quantités de données à analyser. Le défi pour les scientifiques travaillant sur les ruminants est d'extraire de ces expérimentations une information biologique pertinente afin de générer de nouvelles hypothèses de recherche. Actuellement, l'absence d'outils automatisés dédiés aux ruminants entrave l'exploration efficace et robuste de données disponibles. Les scientifiques utilisent donc différents outils qui ne sont pas forcément conçus pour l'analyse des données issues des ruminants. Nous avons conçu l'outil en ligne ProteINSIDE [1] qui synthétise l'information biologique stockée dans les bases de données publiques (NCBI et UniProt) ou fournie par les annotations fonctionnelles issues de l'ontologie des gènes. Il prédit aussi les protéines qui sont sécrétées et qui interviennent dans la signalisation entre les cellules ou tissus, et identifie les interactions entre protéines pour identifier et visualiser celles qui contribuent à un même processus biologique. ProteINSIDE est un outil tout-en-un pour la fouille des données de génomique et de protéomique chez le bovin, l'ovin, le caprin, l'homme, le rat et la souris. L'interface web permet la création d'analyses et la visualisation des résultats en temps réel ou leurs téléchargement dans de nombreux formats (images, pdf, tableaux, données brutes, fasta...). La base de données de ProteINSIDE recueille et stocke l'information biologique nécessaire pour un fonctionnement efficace. Elle est mise à jour mensuellement afin de recueillir les dernières connaissances. ProteINSIDE a été testé avec des jeux de données de 1000 protéines par espèce et a été comparé avec succès à DAVID, BioMyn et AgBase conçus pour la recherche d'information et l'annotation, ainsi qu'à PrediSi et Phobius qui prédisent les protéines sécrétées. ProteINSIDE est disponible gratuitement en utilisant un simple navigateur internet à l'adresse www.proteinside.org (un exemple de résultats est visible sur la page d'accueil).

  1.Kaspric N, Picard B, Reichstadt M, Tournayre J, Bonnet M: ProteINSIDE to Easily Investigate Proteomics Data from Ruminants: Application to Mine Proteome of Adipose and Muscle Tissues in Bovine Foetuses. PLoS One 2015, 10:e0128086.


domain: shs sdv info

text:
Jean Vernet

Matthieu Matthieu Reichstadt

Fr Reichstadt

Marwa Al-Jammas

Isabelle Ortigues

Isabelle Or- Tigues

teaching and research institutions in France or abroad, or from public or private research centers.