Agnès Galezowski

MD Julie Delyon

PhD Laurence Le Cleach

MD Sarah Guégan

MD, PhD Emilie Ducroux

MD Alexandre Alanio

MD, PhD Diane Lastennet

MD, PhD Philippe Moguelet

MD Ali Dadban

MD Marie Thérèse Leccia

PhD François Le Pelletier

MD Camille Francès

MD Céleste Lebbé

MD, PhD; Stéphane Barete
email: stephane.barete@aphp.fr

Deep cutaneous fungal infections in solid-organ transplant recipients

Keywords: ; deep cutaneous fungal infection, phaeohyphomycosis, solid organ transplant recipients, antifungal treatment 4

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

INTRODUCTION

Deep cutaneous fungal infections (DCFIs) are varied and more frequent in immunocompromised patients, particularly in adult solid-organ transplant recipients (s-OTRs). [START_REF] Miele | Primary cutaneous fungal infections in solid organ transplantation: a case series[END_REF][START_REF] Patterson | Epidemiology of fungal infections in solid organ transplant patients[END_REF] Few series have reported DCFIs in adult s-OTR patients [START_REF] Boyce | Phaeohyphomycosis due to Alternaria species in transplant recipients[END_REF][START_REF] Diernaes | Subcutaneous Phaeohyphomycosis due to Alternaria dennisii in an Immunocompromised Patient[END_REF][START_REF] Santiago | Diagnosis of deep cutaneous fungal infections: correlation between skin tissue culture and histopathology[END_REF] . Epidemiological and clinical data, fungal and transplantation parameters as well as therapeutic management are poorly described. Due to the increasing population of s-OTR that are at risk for fungal infections, it is important to better characterize these infections in order to reduce delays in diagnosis and initiate treatment to limit potential fungal dissemination, that would be associated with increased mortality. [START_REF] Santiago | Diagnosis of deep cutaneous fungal infections: correlation between skin tissue culture and histopathology[END_REF] This retrospective observational multicenter study was conducted in France to determine DCFIs diagnostic characteristics and outcome with treatments in s-OTRs.

PATIENTS AND METHODS

Epidemiological, clinical, fungal and transplantation parameters were retrospectively collected in solid-OTR diagnosed with DCFI in centers belonging to Skin and Organ Transplantation Group of the French Society of Dermatology (GPGO for Groupe Peau et Greffe d'Organe).

Solid-OTR adult cases were included if they had a proven diagnosis of invasive fungal infection as defined by EORTC-Mycoses Study Group. [START_REF] De Pauw | Revised definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) Consensus Group[END_REF] DCFI was determined as a dermal fungal infection, including systemic fungal infection with cutaneous involvement, in opposition to superficial fungal infection, excluded from the series. DCFI was considered primary, if any extracutaneous organ was involved at diagnosis. S-OTRs cases were selected if they had cutaneous lesions available for samples investigations (pathological and/or fungal analysis). Diagnosis was assessed by pathological criterion or direct cytopathological criteria (molds or hyphae) associated with either tissue infiltration or filamentous molds in culture.

Patients gave their informed consent in accordance with Helsinki ethical statement.

We collected data including epidemiological (sex, date of diagnosis, geographic origin), clinical (description of cutaneous lesions, number of lesions, trauma before DCFI, duration between transplantation and onset of the DCFI) and transplantation characteristics (age at transplantation, types of organ transplants, immunosuppressive (IS) drugs and their adverse events including malignancies and infections). DCFI diagnosis was based on pathological analysis (i.e. Hematoxylin Eosin Saffron (HES) and periodic acid-Schiff (PAS) and/or Gomori-Grocott methenamine silver stains) and fungal identification (culture and/or PCR methods). Cases with undetermined DCFI fungal identification were not included. Fungal species were identified with standard microbiological methods in each participating center. A minority of fungal species identification was assessed by PCR method (data not shown) as many samples appeared in a period without this diagnosis method. S-OTRs patients with DCFI were treated according to local practices depending on therapeutic options in each center. Specific treatments for DCFIs were classified as medical treatment, including systemic or local antifungal treatment, surgery, or combined treatments. Efficacy of treatments for DCFI was evaluated at each visit by a global assessment of clinical, microbiologic and radiographic responses (when available), and classified as complete (CR) or partial (PR) response, progression under treatment (Pro), resistance (R) or death (D), as previously published. [START_REF] Segal | Defining responses to therapy and study outcomes in clinical trials of invasive fungal diseases: Mycoses Study Group and European Organization for Research and Treatment of Cancer consensus criteria[END_REF] CR was the resolution of all attributable signs, symptoms, and radiographic abnormalities (if ever) present at baseline with fungal eradication; PR was their improvement greater than 50% (reduction of number or size for unique lesion); Pro was defined as increase of size or number of lesions. R was defined as the inefficacy of treatment to reduce cutaneous lesion.

According to the current taxonomy, homogeneous fungal categories relevant for clinicians and mycologists were defined. [START_REF] Hibbett | A higherlevel phylogenetic classification of the Fungi[END_REF][START_REF] Revankar | Melanized fungi in human disease[END_REF] Six fungal opportunistic categories were a posteriori settled with a centralized mycological analysis: phaeohyphomycoses (alternarioses, coelomycetes), hyalohyphomycoses, scytalidium infections, cryptococcoses-histoplasmoses, mycetomas and miscellaneous DCFIs. [START_REF] Gramaje | Phaeoacremonium: from esca disease to phaeohyphomycosis[END_REF][START_REF] Reblova | Phylogenetic Reconstruction of the Calosphaeriales and Togniniales Using Five Genes and Predicted RNA Secondary Structures of ITS, and Flabellascus tenuirostris gen. et sp. nov[END_REF][START_REF] Valenzuela-Lopez | DNA sequencing to clarify the taxonomical conundrum of the clinical coelomycetes[END_REF]

STATISTICAL METHODS

Categorical variables were summarized as counts and percentages and quantitative variables as medians and interquartile ranges where appropriate.

Comparisons on qualitative variables (type of transplant, type and number of immunosuppressive drugs, clinical distribution of lesions and treatment responses according to fungal pathogens) were performed with Fisher's Exact test.

RESULTS

Demographic and transplantation data

Forty-six patients diagnosed from 1998 to 2016 from 8 centers were included in the study.

The median age at diagnosis was 56.5 years (16-72 years) with a male to female sex ratio of 2.54. Forty-eight percent of patients originated from Europe. Characteristics of solid-OTR with DCFIs are reported in Table 1. Median and mean delays between transplantation and diagnosis of DCFI were respectively 13.

Clinical characteristics

Local trauma on exposed skin areas preceding infection occurred in 7 patients (15%).

DCFIs were primary and not spread from a systemic infection in 41 cases (89 %). The mean delay between occurrence of cutaneous lesions and DCFI diagnosis was 4 months. Cutaneous lesions predominated statistically on lower limbs (n=24, 52%) in most DCFI categories (P=0.006). 54% of patients had one or two lesions (Figure 1). Lesions were polymorphic: nodules and/or papules (n=26, 56%), abscess (n=7, 15%), cellulitis/ panniculitis or inflammatory plaques (n=6, 13%), ulcerated or necrotic lesions (n=4, 9%), or other (blisters (n=1), proliferative lesion (n=1), not specified (n=1)).

Histopathology findings

Pathological cutaneous analysis was available for 40 patients. Pathological features showed multinucleated giant cells granulomas (n=20, 50%), suppurative granulomas (n=13, 32.5%) or dermal fibrosis (n=4, 10%) (Figure 2A). The analysis was non-specific for 3 cases. Specific histochemical staining with either Periodic acid-Schiff or Grocott methenamine silver was positive in 24 cases (60%), and both were positive in 19 cases (47.5%). Fungal filaments were detected in 18 cases (45%) and hyphae and yeast in 4 cases.

Fungal species identification

Following the recent taxonomic literature, six different DCFI categories related to fungal species identifications were observed (Table 2). Phaeohyphomycoses (n=21, 46%) (e-Table 1) were the most frequently diagnosed DCFI including 16 cases of alternariosis (35%) (Figure 1B,1C,1D). Hyalohyphomycoses were diagnosed in 8 cases (17%) including 4 patients with aspergillosis. Six patients had cryptococcosis, 2 had histoplasmosis.

Scytalidioses were diagnosed in 4 cases (9%) (Figure 2E,2F). Only 2 cases of Madurella spp mycetomas, one case of candidiasis and one case of Pityrosporum spp were observed. Further analyses to investigate the proportion of DCFI categories according to the type of organ transplanted, type of IS drug regimens (CNI versus MMF), and number of IS drugs (≤2 versus ≥3) did not show significant differences between DCFI categories for these analyses (P=0.42, P=0.86, P= 0.60, respectively).

Therapeutic management

Treatments included systemic antifungal drugs alone (n=22; 48%), surgery alone (n=7; 15%), and surgery combined with systemic antifungal drugs (n=13; 28%). (e-Table 2). One patient died within two days of diagnosis without specific treatment. Most patients (n= 35; 76%) received systemic antifungal drugs as monotherapy: voriconazole (n=12; 26%), liposomalamphotericin B (n=7; 15%), itraconazole (n=1), posaconazole (n=3; 6.5%), flucytosine, (n=4; 9%), fluconazole (n=4; 9%), terbinafine hydrochloride (n=1), caspofungin (n=1). Median duration of treatment was 2.5 months (0.5 to 33 months).

Immunosuppressive drug regimens were modified for 28 patients (61%) (e-Table 2). In 9 patients, one (n=7) or two IS drugs (n=2) were withdrawn including 8 cases for MMF drug. In 19 patients, tapering of one (n=12) or two (n=7) IS drugs was performed. Three patients underwent switch of their IS drugs (2 switches from MMF to azathioprine, 1 switch from azathioprine to MMF).

Outcome and evolution

During a median follow-up period of 45 months, 29 patients had complete remission, 7 had progression, 3 were resistant to treatment, and 2 died before evaluation (7 and 11 days after DCFI diagnosis). In addition, 5 patients were lost to follow-up (e-Table 2).

Among patients with complete remission (n=29), 6 had relapses and required additional systemic antifungal treatments to reach complete remission.

Among patients with progression (n=7), 4 had subsequent complete remission after systemic antifungal treatment initiation, 2 died of DCFI-unrelated causes at 1.5 and 3 months after the diagnosis, and the DCFI was still present for 1 patient at the end of the study.

Among patients resistant to treatment (n=3), 1 required surgery and additional systemic antifungal treatment to reach complete remission.

During follow-up, 14 patients (30%) died of DCFI-unrelated causes. Among 8 patients, two had acute organ rejections (heart/ liver), two had pneumocystis pneumonia, one had bacterial septic shock, one had pulmonary embolism, one had HTLV-1 lymphoma progression, and another had advanced epidermoid carcinoma of the head. The remaining 6 patients died of undetermined causes.

DISCUSSION

This multicenter observational study has highlighted a large diversity of DCFIs in solid-OTR in France. Primary DCFI in s-OTRs occurred mostly in the first two years after transplantation, which is in line with a previous series of 22 s-OTRs patients in Italy, the single and last series reported in Europe. [START_REF] Tessari | Incidence and clinical predictors of primary opportunistic deep cutaneous mycoses in solid organ transplant recipients: a multicenter cohort study[END_REF] In this case-control multicentric cohort retrospective study, risk factors reported for DCFI were the first 2 years period after transplantation, being a renal transplant recipient, being transplanted after the age of 50 years.

In our observational study, without available s-OTRs controls, these risks were not determined. However, DCFIs appeared also in a majority of renal transplant patients (above 70%), at a mean age of transplantation of 53.5 years. Phaeohyphomycoses in our series were the most frequent DCFIs representing a prevalence of 46%. This high prevalence has been previously reported in a series of 30 solid-OTR patients in USA. [START_REF] Mccarty | Phaeohyphomycosis in transplant recipients: Results from the Transplant Associated Infection Surveillance Network (TRANSNET)[END_REF] Among these phaeohyphomycoses, a 76% rate of Alternaria spp was observed. This high proportion of this pigmented fungus in DCFIs might be linked to its common detection in the soil environment and its ability to penetrate skin by local trauma circumstance described in some immunocompromised patients. [START_REF] Boyce | Phaeohyphomycosis due to Alternaria species in transplant recipients[END_REF][START_REF] Diernaes | Subcutaneous Phaeohyphomycosis due to Alternaria dennisii in an Immunocompromised Patient[END_REF] Looking at proportions across the six categories of DCFI identified, there was no statistical association with the type of organ transplanted, type of IS drug regimens, number of IS drugs prescribed. The mean delay between onset of cutaneous lesions and DCFI diagnosis was 4 months. This quite long delay may be explained by the heterogeneous clinical presentations of DCFIs observed and the lack of expertise of general practitioners for dermatological issues for S-OTRs patients. However, solitary or multiple nodular lesions on limbs and particularly on lower limbs, on exposed body parts, associated with granulomatous pathological pattern are very suggestive of DCFIs, especially if the fungal specific staining is positive in skin sample after biopsy. [START_REF] Schieffelin | Phaeohyphomycosis fungal infections in solid organ transplant recipients: clinical presentation, pathology, and treatment[END_REF] Alternatively, DCFIs should be suspected in cases of resistance of cutaneous lesions to initial antibiotics prescription, and thus require multiple investigations for microbiological analyses including fungal culture from skin biopsy. However, some cases might exhibit these suggestive elements for DCFIs but lack despite investigations for a definite fungal identification. In these difficult cases, any consensus is reported but one might repeat fungal investigations and be prone to initiate treatment for DCFI after elimination of other non-infectious diagnosis.

One of the main goals of treatment of DCFIs in solid-OTR is pretending to prevent a lifethreatening fungal dissemination. In our study, no disseminated fungal infection after primary DCFI diagnosis nor direct DCFI-related death occurred. If this mortality has been reported for up to 4-10 % of patients [START_REF] Santiago | Diagnosis of deep cutaneous fungal infections: correlation between skin tissue culture and histopathology[END_REF] for localized DCFIs, we believe that new high potency systemic antifungal treatments have modified this outcome. Indeed, systemic antifungal treatment alone or combined with surgery was used in 76% of patients and led to complete response in 57% of cases. Among 89% of patients with follow-up, 63% had complete response with treatments with no significant differences according to DCFI categories. The reasons for failure of initial treatment are multiple and difficult to assess regarding for example insufficient tapering of IS drug regimen, inappropriate dose of specific systemic anti-fungal therapy, bad compliance of the patient. Moreover, 30% of patients in our cohort had died in the first 3 years after DCFI diagnosis. This may be explained in part by the high level of immunosuppression that the occurrence of DCFIs reveal and by other associated infections, organ transplant rejections and other vascular or cancer causes in these patients. Considering immunological treatment, modulation of immunosuppression was associated to specific antifungal treatment in 61% of patients. It was mostly based on IS drugs tapering or withdrawing of mycophenolate mofetil in addition with the necessity to adapt systemic antifungal interaction especially voriconazole and posaconazole with CNI as previously reported in phaeohyphomycoses. [START_REF] Ferrándiz-Pulido | Cutaneous infections by dematiaceous opportunistic fungi: Diagnosis and management in 11 solid organ transplant recipients[END_REF] This IS modulation when feasible is considered very important for the DCFI outcome by analogy to management of systemic invasive fungal infection. [START_REF] Welte | Invasive mould infections in solid organ transplant patients: modifiers and indicators of disease and treatment response[END_REF] To both validate IS modulation and optimize individualized antifungal treatments, a specific management in good collaboration with organ transplant specialists and the multidisciplinary network was required.

CONCLUSION

This observational study has highlighted the large diversity of DCFIs observed in organ transplant recipients with the highest prevalence of phaeohyphomycoses. Nodular lesions on lower limbs and granulomatous findings on histopathology were helpful diagnostic clues. An appropriate therapeutic management by a multidisciplinary team of dermatologists, transplant specialists, pathologists and mycologists, based on specific mostly systemic antifungal treatment, surgery and IS modulation, is required to obtain complete DCFI remission.

  4 and 28 months (2-144 months). Patients were transplanted at a mean age of 53.5 years (16-72 years). Most frequent transplanted organs were renal (n=31, 67%) and heart (n=5, 11%). IS drug regimens at diagnosis of DCFI were triple (n=30), double (n=13), quadruple (n=1), or single (n=1). IS drug regimens included prednisone (n=43), calcineurin inhibitors (CNI) (n=41, including 7 patients with tacrolimus), mycophenolate mofetil (MMF) (n=31), cyclosporine (CsA) (n=3), azathioprine (n=2), and everolimus (n=1).

Figures of legendsFigure 1 :Figure 2 :Figure 1 .Figure 2 .

 1212 Figures of legends

Table 2 : Fungal species identifications from DCFIs

 2

	Fungal species

DCFIs for deep cutaneous fungal infections

This article has no funding source. Disclosure AG, SB, JDCF, have no conflict of interest to declare CL received research grants or honoraria from Roche, BMS, MSD, GSK, Novartis, Amgen, outside the submitted work. This study was supported by Skin and Organ Transplantation Group (GPGO) of the French Society of Dermatology.

Capsule Summary

• A variety of deep cutaneous fungal infections (DCFIs) occur in solid-organ transplant recipients but phaeohyphomycoses are the most common.

• DCFIs should be managed by a multidisciplinary team and may require modulation of immunosuppression, systemic antifungal treatment, and/or surgery to obtain complete response.

ACKNOWLEDGMENT SECTION

We are indebted to all contributors of this study, organ transplant specialists and solid-OTR patients and donors.

Abbreviation and acronym list:

DCFI: deep cutaneous fungal infection; s-OTRs: solid organ transplant recipient; HES: Hematoxylin Eosin Saffron; PAS: periodic acid-Schiff; IS: immunosuppressive drugs; CNI: calcineurin inhibitor; MMF: mycophenolate mofetil; BCC: basal cell carcinoma; SCC: A,B,C,D

Alternariosis pathological and mycological analyses (


Charlotte Gaillard
email: charlotte.gaillard@inrae.fr

Ludovic Brossard

Jean-Yves Dourmad

Improvement of feed and nutrient efficiency in pig production through precision feeding

Keywords: farming conditions, feed conversion ratio, nutritional models, pig production, precision feeding

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

Introduction

Animal feed, human food, and bio-industries compete for crop resources, which places societal pressure on farming. Moreover, feed cost represents around two thirds of the production costs for fattening pigs [START_REF] Pomar | Applying precision feeding techniques in growing-finishing pig operations[END_REF], and 15-17% of the production costs for sows and their litters until weaning [START_REF] Solà-Oriol | Feeding strategies in pig production: Sows and their piglets[END_REF]. Consequently, nutrition is a major mechanism for improving the sustainability of pig production. Reducing the use of feed would reduce feed cost, and consequently nutrient excretion. It can also influence product quality: lean-to-fat ratio, fat quality, and the homogeneity of products. Currently, most of fattening pigs are group-housed and fed based on the average pig requirements of the room or the pen [START_REF] Whittemore | Optimisation of feed supply to growing pigs and breeding sows[END_REF]. Consequently, some pigs are overfed and others are underfed. Sows are usually fed two diets, one restrictively during gestation and the other nearly ad libitum during lactation [START_REF] Solà-Oriol | Feeding strategies in pig production: Sows and their piglets[END_REF]; both based on an average sow's requirements. The goal of precision feeding is to develop systems that estimate and deliver, at the right time, a ration with a quantity and composition adapted to the requirements of each animal. The challenges in these systems reside in estimating individual requirements and distributing different diets to animals in the same group. These systems would improve feed and nutrient efficiency, major issues for the sustainability of all pig production systems (conventional and alternative). The objectives of this review were 1) to define feed efficiency and present the factors that influence it, as well as challenges to and strategies for improving it; 2) to define precision feeding and the sources of variability in nutrient requirements and show the need for new technology to obtain real-time data; and 3) to present the current models and applications of precision feeding for fattening pigs and sows.

Feed and nutrient efficiency

Definition and measure of feed efficiency

Feed efficiency (FE) is the ratio of mean daily weight gain to mean daily feed consumption over a given period [START_REF] Gilbert | Sélection pour l'efficacité alimentaire chez le porc en croissance : opportunités et challenges[END_REF]. It can also be expressed as the ratio of growth to energy intake, which depends less on energy density in the diet. However, in practice, the inverse of FE is generally used for pigs, representing the efficiency of converting feed into weight gain [START_REF] Gilbert | Sélection pour l'efficacité alimentaire chez le porc en croissance : opportunités et challenges[END_REF]. This feed conversion ratio (FCR) is similar to an economic measure of feed cost, whereas FE is similar to biological efficiency. The animals with the lowest FCR tend to be the most efficient [START_REF] Bouquet | Vers une sélection d'animaux encore plus efficaces ![END_REF].

Genetic improvement has reduced the FCR for most of the conventional pig breeds [START_REF] Bouquet | Vers une sélection d'animaux encore plus efficaces ![END_REF]. Nevertheless, producers still seek to minimize FCR. Selecting animals for increased growth may lead to increased ingestion, whereas selecting for decreased ingestion may lead to decreased growth. More recently, to avoid this selection difficulty, residual feed intake (RFI) was used as another measure of efficiency. The RFI is calculated as an animal's daily feed consumption minus the quantity of feed required to meet its theoretical energy requirements [START_REF] Bouquet | Vers une sélection d'animaux encore plus efficaces ![END_REF]. Pigs with high RFI are less energy efficient because they produce more heat, mainly due to increased physical activity and basal metabolic rate [START_REF] Barea | Energy utilization in pigs selected for high and low residual feed intake[END_REF]. RFI has high variability, but genetic selection can decrease it. Since RFI is not correlated with growth but is positively correlated with feed intake and FCR, selecting animals for lower RFI should have no influence on growth but would reduce ingestion. However, recent studies at IFIP (French Pork and Pig Institute) showed that selecting for FCR results is still more economic than selecting for RFI [START_REF] Bouquet | Vers une sélection d'animaux encore plus efficaces ![END_REF].

For fattening pigs, FCR is usually calculated from 10 weeks of age (around 30 kg of body weight, BW) to slaughter (around 115 kg of BW). FCR can also be calculated from weaning to around 10 weeks of age, or from weaning to slaughter [START_REF] Gilbert | Sélection pour l'efficacité alimentaire chez le porc en croissance : opportunités et challenges[END_REF]. FCR for reproductive sows is more difficult to assess. It can be expressed, as for fattening pigs, as the amount of feed consumed over a given period divided by the BW gain of sows and piglets (total BW of piglets at weaning plus net increase in sow BW) over the same period. Another way to express FCR for sows would be as the amount of feed consumed per weaned piglet produced.

FCR can be expressed in different units; kg feed/kg gain is the most common unit, but MJ energy/kg gain is often used to consider variations in feed energy content. Cost of feed/kg gain is another way to express FCR that is more similar to the economic efficiency [START_REF] Gilbert | Sélection pour l'efficacité alimentaire chez le porc en croissance : opportunités et challenges[END_REF]. Ultimately, FCR is not determined by growth rate and feed intake, but by factors that influence them, such as genetics, feeding practices, environmental conditions and health status. For example, data obtained from a French pig farm survey (Table 1) indicate that FCR depends on the breeding system and production period: herd FCR is greatest for the breedersale-at-weaning system (5.45 kg) and lowest for the weaner-fattener system (2.64 kg/kg).

FCR is greater for the fattening period than for the post-weaning period, and these values vary among farms.

Factors that influence feed efficiency

Three equations can be used to express the FCR of growing pigs in different ways to identify the factors that influence FCR and how they do so. FCR depends on feed intake, feed spillage and animal growth (Eq. 1). It also depends on feed digestibility, digestive efficiency of pigs, the relative importance of maintenance and growth (Eq. 2 and 3), and the tissue (i.e. lean-tofat ratio, Eq. 2) or chemical (Eq. 3) composition of weight gain.

[Eq. 1] FCR = (feed intake + spillage) / pig growth [Eq. 2] FCR = (indigestible + maintenance + growth) / (lean + fat + bone + skins + organs) [Eq. 3] FCR = (indigestible + maintenance + growth) / (protein + water + lipids + minerals) Spillage. In Eq. 1, increased spillage increases the FCR. Spillage can be reduced by selecting feeder type. In the study of [START_REF] Pierozan | Factors affecting the daily feed intake and feed conversion ratio of pigs in grow-finishing units: the case of a company[END_REF], a linear dump feeder had less feed waste and lower FCR than other types of feeders, such as conical semiautomatic feeders (2.41 and 2.44, respectively, P = 0.04). Comparisons between feeder types are limited due to their diversity and the difficulty in developing trials to test the effect of feeder type on FCR, as it requires modifying the feeders [START_REF] Pierozan | Factors affecting the daily feed intake and feed conversion ratio of pigs in grow-finishing units: the case of a company[END_REF]. Feeder characteristics (individual shoulder protection, number of places per pig, and water supply) and localization in the pen are essential when selecting a feeder to reduce feed waste [START_REF] Averós | Meta-analysis on the effects of the physical environment, animal traits, feeder and feed characteristics on the feeding behaviour and performance of growingfinishing pigs[END_REF]. [START_REF] Averós | Meta-analysis on the effects of the physical environment, animal traits, feeder and feed characteristics on the feeding behaviour and performance of growingfinishing pigs[END_REF] reported that feeders with shoulder protection resulted in lower FCR than unprotected feeders due to a reduction of pig aggressions at the feeder. Feed restriction may also help reduce feed waste and improve FE [START_REF] Patience | A review of feed efficiency in swine: biology and application[END_REF]. Another way to reduce spillage is to fed pellets instead of mash diets. Indeed, the review of [START_REF] Vukmirović | Importance of feed structure (particle size) and feed form (mash vs. pellets) in pig nutrition -A review[END_REF] reported a general agreement on the fact that feeding pellets to pigs improved FCR, by improving DM digestibility and reducing feed wastage, compared to feeding mash diets. However, size particle is reduced during pelleting process, which can have negative effects on the gastrointestinal tract health.

Feed intake, growth and maintenance. Genotype determines growth rate because feed intake and growth rate differ among breeds. For example, Piétrain males have lower feed intake, growth rate, and fat deposition than Large White, Landrace and Duroc males [START_REF] Edwards | Evaluation of Duroc-vs. Pietrain-sired pigs for growth and composition[END_REF](Fig. 1). FCR is a function of BW, and as pigs grow toward market weight, they become less efficient at converting feed into BW gain [START_REF] Patience | A review of feed efficiency in swine: biology and application[END_REF]. This increase in FCR results from the increase in maintenance requirements, which depend on BW, and the decrease in the muscle lean-to-fat ratio. There is an increase in ingestion far above the protein deposition capacity; therefore, nutrients are deposited as fat, which decreases feed efficiency.

However, this increase in FCR with BW varies with sex. For entire males, FCR increases slightly as BW increases, whereas for female and castrated pigs, FCR increases more rapidly with BW due to differences in feed intake and growth rate between sexes (Noblet et al., 1994) (Fig. 1).

During lactation, parity affects sow's feed intake. Sows of parity 1 or 2 consume about 15% less feed than older sows [START_REF] Koketsu | Characterization of feed intake patterns during lactation in commercial swine herds[END_REF]. This gradual increase in feed intake with advancing parity is consistent with the increase in maintenance energy requirements associated with the age-related increase in BW [START_REF] O'grady | Voluntary feed intake by lactating sows[END_REF]. Sows' mean daily feed intake also increases as litter size increases from small litters of 3-6 piglets up to 11 piglets, whereas it remains relatively constant for more than 11 piglets. Sows with small litters (< 7 piglets) have a mean daily feed intake of 4 kg lower than that of sows with larger litters [START_REF] Koketsu | Characterization of feed intake patterns during lactation in commercial swine herds[END_REF]. This increase in feed intake can be related to the increase in milk production as litter size increases [START_REF] Auldist | The influence of litter size on milk production of sows[END_REF]Ngo et al., 2012).

Environmental factors such as space allowance, group size, number of feeders, flooring conditions, enrichment, temperature, ventilation rate, relative humidity, pathogens and stressors influence FCR [START_REF] Averós | Meta-analysis on the effects of the physical environment, animal traits, feeder and feed characteristics on the feeding behaviour and performance of growingfinishing pigs[END_REF][START_REF] Averós | A meta-analysis of the combined effect of housing and environmental enrichment characteristics on the behaviour and performance of pigs[END_REF] due to variation in feed intake.

Digestibility of feed and digestive efficiency of pigs. In Eq. 2 and 3, FCR increases as the indigestible portion of the feed increases. The digestibility of feed depends greatly on its composition [START_REF] Ponter | Tables of composition and nutritional value of feed materials[END_REF] and on animal digestive capacity, which has been shown to vary among pigs in interaction with feed composition (Noblet et al., 2013). For example, a greater proportion of fiber in the diet is a challenge for digestion and results in high variability in pig performance. Noblet et al. (2013) found that the digestibility of energy is influenced by sire, which suggests that digestibility depends on heritable genetic variability. Kyriazakis (2011) also reports that selecting pigs for digestive efficiency would improve nutrient efficiency. Further research is needed to understand underlying mechanisms.

Composition of weight gain (lean-to-fat ratio).

Eq. 2 and 3 indicate that the chemical and tissue compositions of BW gain influence FCR. The chemical composition of empty BW gain in minerals and protein (around 3% and 16-17% of empty BW, respectively) (Table 2) is similar among breeds and sexes, except for certain traditional breeds whose protein content is lower (Noblet et al., 1994). Conversely, the percentages of water and lipids are largely related to genotype and sex, which influence the energy content of gain. Adipose tissue yields around four times as much energy as muscle tissue (protein and lipid deposition cost 9.1 and 29.2 MJ of ME per kg, respectively) because it contains less water. This results in large differences in the energy content of BW gain among genotypes (e.g. 11.2 MJ/kg in Piétrain males vs. 21.1 MJ/kg in Meishan castrates) and between sexes (12.3, 13.8, and 15.6 MJ/kg for Large-White males, females, and castrated males, respectively) (Noblet et al., 1994). Over the past 40 years, most genetic improvement in FCR has been obtained by reducing the proportion of lipids in BW gain. Selection for fast growing and lean animals has increased the potential for protein deposition and reduced the amount of energy required to achieve this potential, usually with little influence on feed intake [START_REF] Gilbert | Sélection pour l'efficacité alimentaire chez le porc en croissance : opportunités et challenges[END_REF].

In the meta-analysis of [START_REF] Averós | Meta-analysis on the effects of the physical environment, animal traits, feeder and feed characteristics on the feeding behaviour and performance of growingfinishing pigs[END_REF], pigs fed ad libitum had higher FCR than pigs fed restrictively. The effect of feed restriction on FCR varied with the type of animal. For a lean animal, a restriction in energy supply reduced similarly the deposition of proteins and lipids while for a fat animal, fat deposition is reduced in priority without increasing protein deposition [START_REF] Gilbert | Sélection pour l'efficacité alimentaire chez le porc en croissance : opportunités et challenges[END_REF]. Therefore, in fat animals, FCR decreases, whereas in lean animals FCR changes little or even increases due to increased maintenance requirements [START_REF] Bikker | Performance and body composition of finishing gilts (45 to 85 kilograms) as affected by energy intake and nutrition in earlier life. 2. Protein and lipid accretion in body components[END_REF].

Nutrient utilization.

As previously mentioned, FCR is affected by the efficiency of energy utilization, which depends on the energy content of BW gain and the effect of maintenance requirements. Similarly, to the energy, the efficiency of utilizing nutrients, such as amino acids (AA) and minerals, is also affected by nutrient digestibility and maintenance requirements Above maintenance, the apparent efficiency of use of digestible minerals and AA for tissue deposition depends on their metabolic efficiency of retention, and on their possible oversupply. In case of nutrient undersupply, the marginal efficiency of retention is at highest, but due to insufficient supplies, growth rate decreases resulting in an increased FCR.

Conversely, when nutrients are supplied in excess to the requirement they contribute to increasing excretion, which results in reduced efficiency of retention. For instance, in growing pigs, metabolic efficiency of digestible lysine retention is about 72%, whereas on average in practice, over the fattening period, its apparent efficiency with conventional two-phase feeding programs is only about 45% [START_REF] Van Milgen | InraPorc: A model and decision support tool for the nutrition of growing pigs[END_REF]. Thus, improving efficiency of use of nutrients requires (i) to improve their digestibility and (ii) to provide them, over time, as close as possible to individual animals' requirements in order to limit their oversupply.

Up to 80% of P in feedstuff (cereals and seeds) is tightly bound in phytate, but pigs do not produce enough phytase enzyme to degrade phytate, which encapsulates P, protein, and AA in feedstuffs. Adding exogenous phytase is thus an effective way to improve the digestibility and efficiency of utilizing P and, to some extent, other nutrients [START_REF] Jondreville | Le phosphore dans la nutrition des porcs[END_REF].

Adequate supplies of minerals and AA are needed to respectively maximize growth and bone mineralization (Letourneau et al, 2016) and protein retention, and therefore minimize FCR.

The supply of an AA such as lysine influences FCR in different ways (Fig. 2). The lack of one or more AA limits protein deposition as protein synthesis decreases. This results in an increase in lipid deposition and consequently a decrease in growth rate and an increase in FCR (van Milgen, 2008). When one AA is limiting, the other AA in excess are catabolized and excreted as urea, reducing energy efficiency and increasing cost and waste. Similarly, an excessive supply of nutrients increases nutrient excretion, fat deposition and FCR. Nutrient requirements also depend on pig characteristics such as sex, age and genotype. For example, entire males require more lysine than females (Fig. 2) and castrated males. In growing pigs, the AA requirement relative to energy decreases as BW increases [START_REF] Van Milgen | InraPorc: A model and decision support tool for the nutrition of growing pigs[END_REF], Noblet et al., 2016). In sows, the AA requirement increases with the stage of pregnancy and is greatest during lactation [START_REF] Dourmad | InraPorc: A model and decision support tool for the nutrition of sows[END_REF]. Similar trends are observed for mineral requirements of growing pigs and sows. This indicates the need to optimize each individual's nutrient ingestion to maximize individual efficiency and reduce excretion. Taking into account individual characteristics (age, breed, sex, BW) can be used to better feed to animals up to their individual requirement. Nutrient ingestion can be optimized by using predictive models of nutrient requirements [START_REF] Brossard | Chapter 10 -Modelling nutrient requierments for pigs to optimize feed efficiency[END_REF] (see section 3).

1.3.Strategies to improve feed efficiency

Genetics and microbiota. Feed efficiency is one of the most important selection criteria in breeding programs, as it affects total cost of pig production and environmental footprint [START_REF] Kanis | Breeding for societally important traits in pigs[END_REF][START_REF] Reckmann | Comparative life cycle assessment (LCA) of pork using different protein sources in pig feed[END_REF]. Thus, FE and related traits are a major target for genomic selection, a growing and promising method. Until recently, FCR and RFI were the two main traits used to evaluate FE, as described in the first part of this review. With the development of new technology and automatic data recording, recent studies have been looking for new traits related to FE like, for example, feed intake and feeding behavior (daily occupation time, daily feeder visit, and daily feeding rate) recorded daily and individually.

Major quantitative trait loci for feed intake and for feeding behavior traits have been identified on different chromosomes as well as the positional and functional candidate genes [START_REF] Reyer | Exploring the genetics of feed efficiency and feeding behaviour traits in a pig line highly selected for performance characteristics[END_REF]. This is a first step toward the understanding of the genetic connection between distinct feeding behavior traits and FE that can be used to select the most efficient animals.

Genetic factors are also influencing the abundance of distinct bacterial species [START_REF] Benson | Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors[END_REF]. Several studies found a link between the porcine intestinal microbiome and FE (McCormack et al., 2019a;[START_REF] Tan | Metagenomic analysis of cecal microbiome identified microbiota and functional capacities associated with feed efficiency in Landrace finishing pigs[END_REF]. While the effect of intestinal microbiota in expressing FE has been confirmed, methods to phenotype the microbiota should be developed to use this information on farms. McCormack et al. (2019b) reported the effect of fecal microbiota transplantation (FMT), using fecal extracts from highly feed-efficient pigs, in pregnant sows on the offsprings performance. The FE of the offsprings from a mother under FMT was improved compared to the one of offsprings from a mother not under FMT (similar feed intake but growth differences). However, the negative effect of FMT on the offsprings growth limits the application of this procedure in commercial farms. Manipulations of bacterial populations can also be used to improve digestibility and FE (Le [START_REF] Sciellour | Effect of dietary fiber content on nutrient digestibility and fecal microbiota composition in growing-finishing pigs[END_REF] that would allow more flexibility regarding ration composition especially on the amount of fibers. Niu et al. (2015) reported that several bacteria were correlated with apparent crude fiber digestibility; of these, Clostridium is associated with dietary fiber metabolism.

They also found that the abundance and diversity of the gut microbiota in pigs increased and changed with increasing age. Intestinal microbiota facilitates digestion of fiber, but its effects on the variability in FE needs to be assessed and broken down into the fraction that depends on animal genetics, the fraction that depends on breeding conditions and the fraction that sows transmit to piglets at birth and during lactation.

Finally, robust indicators need to be developed to quantify the sensitivity of animals to variations in the environment. For example, it seems that feed intake and growth rate of pigs selected for lower RFI are less affected by the quality of the environment than those of pigs selected for higher RFI [START_REF] Gilbert | Review: divergent selection for residual feed intake in the growing pig[END_REF].

Removing external stressors. [START_REF] Black | A review. Factors limiting the performance of growing pigs in commercial environments[END_REF] reported that removing one or more stressors, or reducing their influence, improves pig performance because the stressors may have additive effects. Structural changes in buildings like the addition of cooling systems, floor type reduce climate stress [START_REF] Black | A review. Factors limiting the performance of growing pigs in commercial environments[END_REF]. During a short-term exposure to hot conditions (32°C as opposed to 21°C), pigs ate about 60 to 100 g less feed each day per °C of heat stress [START_REF] Heitman | The Effects of Air Temperature and Relative Humidity on the Physiological Well Being of Swine[END_REF][START_REF] Heitman | Ambient air temperature and weight gain in swine[END_REF]. In their meta-analysis, [START_REF] Renaudeau | A meta-analysis of the effects of high ambient temperature on growth performance of growing-finishing pigs[END_REF] reported that feed intake and average daily weight gain of growingfinishing pigs are decreasing with increasing temperature starting from 20°C. Moreover, these effects were more pronounced with increasing pig body weight. Ambient temperature clearly impact feed intake and consequently FCR. Structural changes in the building also help improving cleanliness. Reducing the microbial load by ensuring building hygiene has increased production and decreased disease incidence [START_REF] Floc'h | Importance of sanitary environment for growth performance and plasma nutrient homeostasis during the post-weaning period in piglets[END_REF]. In a dirty environment measured through air quality (amount of ammonia, CO2 and dust), pigs' feed intake has been found to decrease of 100 g/kg compared to a cleaner environment, especially for individually housed pigs [START_REF] Currie | Measurement of air quality and weaner pig performance in two different environments[END_REF][START_REF] Lee | Effect of group size and environment on weaner pig performance and plasma cortisol concentration[END_REF]. The type of feeder (design, access, location) also has an effect on FCR [START_REF] Ramaekers | Estimating individual body weights of group-housed growing-finishing pigs using a forelegs weighing system[END_REF].

Adequate nutrient supply. As previously indicated, feed and nutrient efficiency depend partly on nutrient utilization, which is based on adjusting nutrient supply to requirements.

Therefore, one way to improve FE is to refine this adjustment by estimating animal requirements more accurately. Two methods are generally used to estimate nutrient requirements for pigs: empirical and factorial. Briefly, requirements in the empirical method correspond to those of a population for a given performance target and time interval.

However, the estimated requirements cannot be extrapolated to other situations because they vary as a function of animal characteristics and environments [START_REF] Pomar | Modeling stochasticity: Dealing with populations rather than individual pigs[END_REF]. In contrast, nutrient requirements in the factorial method are estimated for an average animal at a given stage. However, pig performance depends on pig characteristics (genetic, age, weight, sex, social status and health), feed characteristics (feed allowance, nutrient composition and digestibility), and housing conditions (ambient temperature and space allowance) (Noblet and Quiniou, 1999). Models based on the factorial approach have been developed to simulate performance of a single animal and can predict nutrient requirements and appropriate feeding strategies (e.g., [START_REF] Van Milgen | InraPorc: A model and decision support tool for the nutrition of growing pigs[END_REF]. However, because they are based on an average animal, feeding strategies based on this approach means that many animals are inevitably underfed or overfed [START_REF] Pomar | Modeling stochasticity: Dealing with populations rather than individual pigs[END_REF]. Individual variability influences the efficiency of nutrient utilization [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]. In addition, the fact that nutrient requirements change over time needs to be considered to predict them more accurately. Precision feeding requires developing new feeding strategies to refine the adjustment of nutrient supply to requirements.

Individual variability, the key point of precision feeding

2.1.Precision feeding: definition, objectives, advantages

Precision feeding is one way to better consider individual variability in nutrient requirements within a group. It involves using technology to provide the right amount of feed, with the right composition and at the right time, to a group of animals or to individuals [START_REF] Pomar | Applying precision feeding techniques in growing-finishing pig operations[END_REF]. Precision feeding aims to improve characterization of individuals (feed intake, growth potential, body condition, physical activity, health, etc.) or small groups to better adapt the quantity, quality and timing of feed supplied to them. It also aims to improve efficiency by reducing farm costs, reducing excretion, and monitoring quality (Fig. 3). Applying precision feeding and doing so accurately requires assessing the nutritional potential of feed ingredients and nutrient requirements of each animal to formulate balanced diets accurately to minimize nutrient deficiency or excess [START_REF] Pomar | Applying precision feeding techniques in growing-finishing pig operations[END_REF]. The results of previous precision feeding assessments are promising [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], Pomar et al., 2014[START_REF] Andretta | Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs[END_REF]. For growing-finishing pigs, compared to a classic three-phase group-feeding strategy, adjusting feed composition daily based on the performance of an average animal in the group decreased N excretion by 12% without influencing growth [START_REF] Pomar | Effet d'une alimentation multiphase quotidienne sur les performances zootechniques, la composition corporelle et les rejets d'azote et de phosphore du porc charcutier[END_REF]. This continual adjustment also has an economic advantage because it can be based on a mixture of two feeds, one with a high nutrient content and one with a lower nutrient content. At the individual scale, precision feeding of growingfinishing pigs further reduces N and P excretions compared to a multiphase group-feeding strategy (respectively, 38 vs. 42 g/d for N, and 5 vs. 6 g/d for P) [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].

Simulations indicate that precision feeding could also be beneficial for sows: using a multiphase feeding strategy (a mixture of two feeds) during gestation reduced the quantity of lysine ingested (-17%), N excretion (-19%), and feed cost (-8%) [START_REF] Dourmad | Chapter 4 -Modelling nutrient utilization in sows: a way towards the optimization of nutritional supplies[END_REF].

These results for sows need to be confirmed with trials in experimental farms. "On-farm" application of precision feeding requires designing and developing measuring devices (for intake, BW), calculation methods and a feeding system that provides the required amount of feed with a composition that optimizes animal performance while minimizing the use of farm resources [START_REF] Pomar | Applying precision feeding techniques in growing-finishing pig operations[END_REF].

2.2.Variation in nutrient requirements

Two main sources of variation in nutrient requirements must be considered: variations over time and differences between animals. Nutrient requirements vary over time [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] and among growing pigs in a group receiving the same feed [START_REF] Pomar | Effet d'une alimentation multiphase quotidienne sur les performances zootechniques, la composition corporelle et les rejets d'azote et de phosphore du porc charcutier[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], due to sex (castrated or entire males, females), age (different nutritional requirements), weight and individual potential. Nutritional requirements of sows also vary with individual characteristics such as physiological status, age, weight and prolificacy [START_REF] Dourmad | Development of a decision support tool for precision feeding of pregnant sows[END_REF]. Sows of parity 1, 2, or 3 continue to grow while gestating, whereas sows of parity 4 or more have already reached their mature weight, which means that their requirements are limited to maintenance, gestation and rebuilding body reserves used up during gestation. These between-animal variations influence the population response, the efficiency of nutrient utilization, and consequently the optimal nutrient supply for the 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]. Therefore, stochasticity has been introduced into models to address variability, simulate responses of groups of pigs [START_REF] Pomar | Modeling stochasticity: Dealing with populations rather than individual pigs[END_REF] and define strategies to improve on-farm nutrient efficiency [START_REF] Brossard | Chapter 10 -Modelling nutrient requierments for pigs to optimize feed efficiency[END_REF].

These variations among animals and over time show the relevance of developing more individualized feeding strategies (see section 3).

In growing pigs, [START_REF] Cloutier | Evaluation of a method estimating real-time individual lysine requirements in two lines of growingfinishing pigs[END_REF] reported that the factorial method used to estimate individual daily lysine requirements was able to accommodate the small genetic differences in feed intake without a specific correction for genetic differences. This method can be used in precision feeding systems without adjustments for small genetic differences but should be studied further for larger genetic differences in feed intake and protein deposition patterns.

2.3.Feeding technologies and real-time data

Improving FE requires considering individual feeding requirements. Precision feeding uses feeding technologies to adjust animals' diets. Development of precision feeding systems requires automatic data collection, data processing, and system monitoring.

Technology and individual data collection. Farm animals can be identified by radiofrequency (RFID), which makes data collection reliable and simplifies management of individuals [START_REF] Cornou | Use of information from monitoring and decision support systems in pig production: Collection, applications and expected benefits[END_REF]. Using RFID and sensors automates farm equipment, which can transfer real-time data to a farmer or an automated decision support system that can make rapid management decisions (Fig. 3). [START_REF] Cornou | Use of information from monitoring and decision support systems in pig production: Collection, applications and expected benefits[END_REF] listed several sensors used in pig production and how their data can be used to support decisions. Automatic identification of an animal is the first step in monitoring production efficiency and is performed on pig farms usually by placing an ear-tag containing an RFID chip on each animal to recognize the animal, for example at the feeder. Electronic feeding stations can record the number, time and duration of the visits, and the quantity of feed ingested by each pig. In commercial herds, only sows are individually identified at electronic feeding stations. Individual identification is uncommon for fattening pigs due to its cost, although the technology is available for selection herds. Several technologies exist that automatically record BW: foreleg weighing systems [START_REF] Ramaekers | Estimating individual body weights of group-housed growing-finishing pigs using a forelegs weighing system[END_REF], image analysis [START_REF] Parsons | Real-time control of pig growth through an integrated management system[END_REF], the walk-through using machine vision [START_REF] Banhazi | Improved image analysis based system to reliably predict the live weight of pigs on farm: Preliminary results[END_REF] and photogrammetry to determine pigs' three-dimensional shapes [START_REF] Wouters | Image-analysis parameters as inputs for automatic environmental temperature control in piglet houses[END_REF]. Knowing the body composition might also be required to individualize the diet, especially in gestating sows. Body composition can be determined by analyzing images or videos or measuring backfat thickness with ultrasound; however, this last technique is performed manually and would need to be automated. Pig activity, which may also be of interest, can be automatically recorded using photocells, force sensors for sows housed in crates [START_REF] Oliviero | Using movement sensors to detect the onset of farrowing[END_REF], and accelerometers for sows in loose housing or crates [START_REF] Cornou | Modeling of sows diurnal activity pattern and detection of parturition using acceleration measurements[END_REF].

Finally, pig temperature influences FE and can be automatically recorded using an ear-based temperature sensor or estimated using an image-analysis procedure based on the pig's thermoregulatory behavior [START_REF] Wouters | Image-analysis parameters as inputs for automatic environmental temperature control in piglet houses[END_REF].

Data processing. The sensors described above provide large amounts of data on a daily basis. The biological characteristic of interest needs to be extracted from each measurement.

First, the data are cleaned by removing abnormal values, which requires defining thresholds.

Then, the characteristic of interest is generally extracted by smoothing the data; the amount of smoothing is based on the objective [START_REF] Friggens | Chapter 2 -Faire émerger les informations clés des données de l'élevage de précision[END_REF]. These data will then serve as inputs to models to predict animals' nutrient requirements.

Models and applications of precision feeding

Applying precision feeding requires developing models that predict nutrient requirements and using the models to test and select new feeding strategies. These models are of interest to compare alternative production systems to existing ones, from a time and economical point of view, and also to gain confidence in the success of a new strategy before testing it in real-life.

Until now, most of the production models were based on an average animal, but individual variability need to be considered to gain in precision [START_REF] Knap | Aspects of stochasticity: variation between animals[END_REF][START_REF] Kyriazakis | Book review -A quantitative biology of the pig[END_REF].

3.1.

Fattening 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] developed a model that predicts real-time individual AA requirements of growing-finishing pigs. The model consists of two components (Fig. 4). The empirical component uses individual pig information (intake and BW) in real time to estimate daily feed intake and daily gain for the current day. Based on these estimates, the mechanistic model uses factorial equations to predict net energy intake and AA requirements (expressed through standardized ileal digestible lysine, SID Lys). The optimal AA concentration needed to meet each pig's requirements is predicted daily. To do so, the model requires at least seven consecutive feed intake measurements and two BW measurements to begin predicting feed intake, BW and the nutrient requirements.

The [START_REF] Hauschild | Development of sustainable precision farming systems for swine: Estimating real-time individual amino acid requirements in growing-finishing pigs[END_REF] model was evaluated using data from a previous trial [START_REF] Pomar | Effet d'une alimentation multiphase quotidienne sur les performances zootechniques, la composition corporelle et les rejets d'azote et de phosphore du porc charcutier[END_REF] that tested the influence of a daily 3-phase or multiphase feeding strategy on pig efficiency. Daily feed intake and BW trajectories of an animal could be predicted 1 day or 7 days in advance, respectively, with an average mean absolute error of 12% and 1.8%, respectively. The mechanistic component of the Hauschild model has been used in two animal trials [START_REF] Zhang | L'alimentation de précision chez le porc charcutier: Estimation des niveaux dynamiques de lysine digestible nécessaires à la maximisation du gain de poids[END_REF][START_REF] Cloutier | Evaluation of a method estimating real-time individual lysine requirements in two lines of growingfinishing pigs[END_REF]. In the [START_REF] Zhang | L'alimentation de précision chez le porc charcutier: Estimation des niveaux dynamiques de lysine digestible nécessaires à la maximisation du gain de poids[END_REF] trial, the model accurately predicted SID Lys requirements of pigs of 25-55 kg BW, but underpredicted the requirements of heavier animals. In the [START_REF] Cloutier | Evaluation of a method estimating real-time individual lysine requirements in two lines of growingfinishing pigs[END_REF] trial, the model was used to predict individual daily SID Lys requirements and to consider the influence of small genetic differences on them. Three trials evaluated the overall approach of estimating real-time AA requirements and the effect of switching from conventional to precision feeding systems in growing-finishing pig operations on productive performance, nutrient utilization, body composition and environmental costs [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], Pomar et al., 2014[START_REF] Andretta | Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs[END_REF]. [START_REF] Pomar | The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed cost in growing-finishing pigs[END_REF] found that a daily phase-feeding strategy (mixing two feeds)

reduced N intake by 7.3%, P intake by 3.3%, N excretion by 11.7%, P excretion by 1.9% and feed cost by 1.3% compared to those of a 3-phase feeding strategy. [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] found that a multiphase individual feeding strategy reduced SID AA intake by 27%, P excretion by 27% and N excretion by 20% compared to those of a 3-phase feeding strategy. [START_REF] Andretta | Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs[END_REF] found that an individual feeding strategy (in which the mixing proportions of two feeds were updated daily to meet 100% of the lysine requirement) reduced SID Lys intake by 26%, N excretion by 30% and feeding cost by 10% compared to those of a group-feeding strategy. These three trials show that using precision feeding techniques to feed growing-finishing pigs with diets that are tailored daily is an effective approach to reduce nutrient excretion without compromising performance. It confirms that combining precision feeding with real-time modeling of requirements can improve the efficiency of use of feed and nutrient, and to some extend the economic result. However, this requires more sophisticated equipment (e.g. equipment for feed storage and distribution, smart feeders, weighing scale), with more supervision, inducing additional costs that were generally not considered in the economic evaluation. Predictive models require further improvements, such as including health factors (environmental stressors, pathogen levels), and prediction of technical, economic and environmental effects of precision feeding on commercial farms. For example, Monteiro et al. ( 2016) used the InraPorc decision support tool (https://inraporc.inra.fr/inraporc/index_en.html) to predict production data that they then used as input data for life cycle assessment to compare environmental effects of four pig feeding strategies. They predicted that an individual feeding strategy yielded the lowest life cycle effects for pig fattening in all situations (in France and Brazil). Finally, this technology needs to be implemented and validated on commercial farms. Current technology can feed pig groups based on their weight, but automatic feeders with a decision support tool are not yet commercially available.

Sows in gestation and lactation

Mechanistic models such as InraPorc [START_REF] Dourmad | InraPorc: A model and decision support tool for the nutrition of sows[END_REF] and the model of [START_REF] Hansen | Energy and nutrient deposition and excretion in the reproducing sow: Model development and evaluation[END_REF] were developed to simulate energy and nutrient partitioning of reproductive sows on a daily time step. These models represent sows as the sum of multiple compartments: body protein, body lipids, body minerals and the uterus [START_REF] Dourmad | InraPorc: A model and decision support tool for the nutrition of sows[END_REF] (Fig. 5). Equations describing nutrient utilization by sows were used to build InraPorc, which predicts daily nutrient and energy flows from feed to storage in the body and then excretion. InraPorc simulates daily utilization of key nutrient pools by a sow. InraPorc also predicts energy and AA requirements of sows based on production objectives, as well as changes in body composition due to a given feeding strategy or housing condition (Gaillard et al., 2019a). [START_REF] Dourmad | Chapter 4 -Modelling nutrient utilization in sows: a way towards the optimization of nutritional supplies[END_REF] used InraPorc to simulate and evaluate two-phase and multiphase feeding strategies during gestation. Simulations results indicate that compared to one-phase feeding, the two-phase and multiphase strategies could respectively reduce crude protein (CP) intake by 10% and 14%, SID Lys intake by 11% and 17%, P intake by 5% and 7 %, N excretion by 15% and 20%, and P excretion by 9% and 12%. [START_REF] Dourmad | Development of a decision support tool for precision feeding of pregnant sows[END_REF] and Gaillard et al. (2019a) developed a decision support tool for gestating sows based on InraPorc.

Optimal supply for a given sow was determined each day by a factorial approach that considered all the information available about the sows (genotype, parity, gestation stage, etc.). Energy supply was calculated for each sow to reach a target BW at farrowing. Precision feeding with the mixing of two feeds was then simulated and compared to conventional feeding (single feed). Simulations indicated that compared to conventional feeding, precision feeding could reduce total SID Lys supply by 27%, total CP supply by 28%, and the number of under-or over-fed sows (Gaillard et al., 2019b). Adapting the feeding strategy during gestation to capture changes in nutrient requirements more adequately appears a promising approach to reduce N and P excretion without increasing feed cost, but this remains to be validated on experimental farms. During gestation, sows are housed in groups, offering the potential to use automatic feeders and apply these new feeding plans that consider sow characteristics, such as parity, weight, and backfat thickness at the start of gestation.

However, although this approach is possible, the use of models and the potential to improve FE remains limited in practice, mainly due to insufficient data collection and the lack of decision support systems.

On most farms, sows are fed different diets for gestation and lactation instead of the same diet for both, reducing N and P excretion by 20-25% [START_REF] Dourmad | Nitrogen consumption, utilisation and losses in pig production in France, The Netherlands and Denmark[END_REF]. Currently, few studies have focused on improving feeding strategies for lactating sows, even though sow requirements vary greatly. A precision feeding strategy might be useful for lactating sows because nutrient requirements per kg of diet vary greatly as a function of milk production and feed intake. For example, sows of parity 1 have greater requirements due to their lower feed intake. Nutrient requirements also vary by season due to the influence of temperature on feed intake and milk production. [START_REF] Gauthier | Dynamic modeling of nutrient use and individual requirements of lactating sows[END_REF] developed a decision support system based on InraPorc that could be incorporated in automated feeding equipment. Simulations compared a conventional feeding strategy to a precision feeding strategy; the latter could reduce mean lysine intake by 6.8%, P intake, and the number of under-or over-fed lactating sows.

Modeling mineral requirements

Minerals are a major component of pig nutrition. Because P is necessary for bone development and the metabolism of growing pigs, it must be supplied in pig diets. Dietary P of plant origin has low digestibility for pigs, but addition of P and/or phytase increases feed cost. The oversupply and low digestibility of P also results in high P excretion, which affects the environment. Therefore, models that predict mineral requirements are required to optimize mineral supply and minimize excretion [START_REF] Brossard | Chapter 10 -Modelling nutrient requierments for pigs to optimize feed efficiency[END_REF]. Minerals have received little modeling attention because most models have focused on AA. [START_REF] Jondreville | Le phosphore dans la nutrition des porcs[END_REF] used a factorial approach to estimate P requirements for maintenance and production in different physiological stages, and it was later added to InraPorc for growing pigs [START_REF] Van Milgen | InraPorc: A model and decision support tool for the nutrition of growing pigs[END_REF] and sows [START_REF] Dourmad | InraPorc: A model and decision support tool for the nutrition of sows[END_REF]. This approach considers the influence of the type of diet (pellets or mash) and the addition of phytase on digestibility. The model allows dietary P supply to be adjusted to pig performance and physiological status and predicts the influence of performance level on apparent digestible P requirements. However, P requirements for growth are estimated from animal BW gain, which has certain limitations.

More mechanistic models have therefore been developed in which mineral content (P and calcium) can vary independently of protein and lipid mass [START_REF] Letourneau-Montminy | Modeling the metabolic fate of dietary phosphorus and calcium and the dynamics of body ash content in growing pigs[END_REF]. These deterministic and mechanistic research models can be used to improve decision support tools to develop feeding strategies that minimize P excretion. These models must also consider that mineral requirements change during each physiological stage (e.g. an increase in calcium requirements at the end of gestation).

For gestating sows, recent simulations of a precision feeding strategy based on lysine requirements still report an important excess in phosphorus (Gaillard et al., submitted). This is partly because the implemented strategy was based on lysine requirements with only two diets. Hence, in such condition it was not possible to deal with the different dynamic of lysine and phosphorus requirements over the whole gestation. To modulate lysine and phosphorus supplies independently, one solution would be to calculate the proportions of the two mixed diets (High Lysine and Low Lysine) based on lysine and phosphorus requirements simultaneously, and therefore propose 3 different diets to combine for precision feeding instead of two (High Lysine + High P, Low Lysine + High P, Low Lysine + Low P).

However, the strategy might be less efficient then for lysine and will need to be evaluated and compared with the present feeding strategy, based on lysine only, in terms of production, excretion, and costs.

Conclusion

Feed efficiency can be expressed as the ratio of mean daily weight gain to mean daily feed consumption during a given period. In practice, the inverse of this ratio is generally used for breeding animals and represents the efficiency in converting feed into weight gain (feed conversion ratio, FCR Noblet, J., Quiniou, N., 1999. Principaux facteurs de variation du besoin en acides aminés du porc en croissance. Techni-Porc 22, 9-16.

Niu, Q., Li, P., Hao, S., Zhang, Y., Kim, S.W., Li, H., Ma, X., Gao, S., He, L., Wu, W., Huang, X., Hua, J., Zhou, B., Huang, R., 2015. Dynamic distribution of the gut

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .Figure 3 .Figure 4 .Figure 5 .

 2345 Figure 2. Effect of the ratio of available lysine to MJ digestible energy on mean feed

  ). Several factors influence FCR, such as feed spillage, feed digestibility, composition of weight gain, feed intake and nutrient utilization. The FCR can be decreased by selecting the appropriate form of feed and nutrient density and supply, and by reducing negative effects of environmental factors. Precision feeding is based on managing individual variability within a group and uses feeding technologies (e.g. sensors, feeders) to provide the right amount of feed, with the right composition, and at the right time, to a group of animals or to individuals. Predictive models of nutrient requirements and excretion, such as InraPorc, have been developed to select optimized feeding strategies. For growing pigs, precision feeding is a promising solution to reduce nutrient excretion by daily adjusting the supply of nutrients to individuals. Recent simulations results indicate that this might also be an appropriate feeding strategy for sows. Decision support models could be enhanced by improving sensors or considering factors such as ambient temperature and animal physical activity, which also influence energy utilization and consequently the FCR. sows and dietary supplementation of inulin in offspring. Appl. Environ. Microbiol. 85, 1255-1219. doi: 10.1128/aem.01255-19** Monteiro, A., Garcia-Launay, F., Brossard, L., Wilfart, A., Dourmad, J.Y., 2016. Effect of feeding strategy on environmental impacts of pig fattening in different contexts of production: evaluation through life cycle assessment. J. Anim. Sci. 94, 4832-4847. doi: 10.2527/jas2016-0529 Mullan, B.P., Moore, K.L., Payne, H.G., Trezona-Murray, M., Pluske, J.R., Kim, J.C., 2011. Feed efficiency in growing pigs -what's possible? Rec. Adv. Anim. Nutr. -Aust.18, 17-22. Ngo, T.T., Quiniou, N., Heugebaert, S., Paboeuf, F., Dourmad, J.Y., 2012. Influence du rang de portée et du nombre de porcelets allaités sur la production laitière des truies. Journées Rech. Porcine 44, 195-196. Noblet, J., Dourmad, J.Y., van Milgen, J., 2016. Etat des lieux et perspectives sur la nutrition du porc. Journées Rech. Porcine 48, 85-96. Noblet, J., Gilbert, H., Jaguelin-Peyraud, Y., Lebrun, T., 2013. Evidence of genetic variability for digestive efficiency in the growing pig fed a fibrous diet. Anim. 7, 1259-1264. doi: 10.1017/S1751731113000463 Noblet, J., Karege, C., Dubois, S., 1994. Prise en compte de la variabilité de la composition corporelle pour la prévision du besoin énergétique et de l'efficacité alimentaire chez le porc en croissance. Journées Rech. Porcine 26, 267-276.

Acknowledgements

This work was supported by Feed-a-Gene, a project that has received funding from the European Union's Horizon 2020 research and innovation program [grant agreement No. 633531].


MD Gauthier Mouillet
email: gauthiermouillet@gmail.com

MD Romain Gallet

MD Julien Ternacle

MD Emmanuel Teiger

Second Percutaneous Mitral Valve Implantation to Treat Acute Hemolysis Complicating Mitral Valve in Ring Implantation

Keywords: acute hemolysis, Transcatheter mitral valve implantation, SAPIEN 3, failure of mitral valve repair Left ventricle outflow track LVOT Mitral Regurgitation

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

2

History of presentation:

A 85 years-old woman was referred to our institution for pulmonary oedema. She had previously undergone mitral valve repair (MVR) with a 26 mm Physio II annuloplasty ring (Edwards, Irvine, CA). Transthoracic echocardiography revealed a severe mitral regurgitation (MR) related to a failure of the mitral valve repair. Given the high surgical risk (Euroscore 2 17%) and the extreme frailty of the patient (BMI<16 kg/m2), the Heart Team concluded that the risk of a redo surgery was prohibitive. Because of a complete restriction of the posterior leaflet, Mitraclip was not an option and it was decided to perform a mitral VIR implantation.

After comprehensive cardiac computed tomography analysis, a 26 mm SAPIEN-3 (Edwards, Irvine, CA) prosthesis was chosen (figure1). The procedure was performed under general anaesthesia and with transoesophageal echocardiography (TEE) guidance as previously described. After valve implantation and catheter removal, only mild MR was observed and the final mean gradient was 3 mmHg (Video 1). Within 12 hours after the procedure, the patient presented an anuric Acute Kidney Injury (AKI) associated with acute anemia, while cardiac and hemodynamic conditions remained stable.

Past Medical History:

Here extracardiac pas medical history included chronic obstructive pulmonary disease, chronic kidney kisease (Creatinine 200µmol/l, eGFR< 10ml/min), cystocele and hysterocele cure and right ovariectomy. Cardiovascular risk factors were hypertension, dyslipidemia and type 2 diabetes.

Differential diagnosis:

Differential diagnosis included mechanical hemolysis, toxic hemolysis and acute hemorrhage.

Investigations:

A mechanical hemolysis was suspected and confirmed by the presence of circulating schizocytes and the increase in plasmatic LDH and unconjugated bilirubin. The initial conservative strategy became rapidly ineffective with the need of weekly blood transfusion and worsening of renal function (increase in creatinine up to 450 μmol/l) (figure2). In this setting, a new TEE was performed to address the mechanism of hemolysis. The SAPIEN-3 valve appeared to be positioned too proximal (i.e. "too atrial") and was responsible for a high velocity regurgitation through the frame with no increase in the severity of the residual leak nor bioprosthesis migration. The length of prosthesis present in the atrium was 10 mm, and therefore, neither the outer nor the inner skirt was effective in sealing the ring (Figure 2).

Circumferential cells created by the frames present between the Physio II annuloplasty ring

and the skirt of the Sapien-3 valve created multiple residual leaks that were very likely to be responsible for the hemolysis (Figure 2B).

Management:

Eventually, we decided, to implant a 2 nd 26 mm SAPIEN-3 prosthesis with a more anterior/ventricular position than the first prosthesis while remaining in the plane of the Physio-2 ring to prevent the risk of LVOT obstruction. We hypothesized that the outer skirt of this second prosthesis would seal the gap between the ring and the skirt of the first prosthesis and thus reduce RL. In addition, we thought that the leaflets of the first SAPIEN-3 prosthesis would participate in the sealing after they were locked in the open position by occluding the cells of the frame. Because of the atrial shortening of the Sapien-3 prosthesis during its expansion, we planned to position the 2 nd prosthesis between the landmarks 7mm and 9 mm (Figure 3A,3B).

The bailout procedure was performed under general anesthesia with TEE monitoring.

A second 26 mm SAPIEN-3 prosthesis was successfully implanted in the first one, at the desired position. All residual leaks regressed after valve implantation (Video 2). Consistently, mechanical hemolysis rapidly disappeared with a normalization of haptoglobin, bilirubin and LDH and the disappearance of circulating schyzocytes. Finally, renal function recovered to its baseline level (Creatinin level = 150 μmol/l) (table1).

Discussion:

Transcatheter mitral valve replacement for bioprosthetic valves degeneration and ring annuloplasty failure has been described as a safe technique with a high technical success [START_REF] Muller | Transcatheter Mitral Valve Replacement for Patients With Symptomatic Mitral Regurgitation: A Global Feasibility Trial[END_REF]. This case highlights a potential severe complication of TMVI, which has not been described to date (2) (3) (2) (4) . One may argue that a 2nd SAPIEN 3 implantation during the index procedure would have prevented this mechanical complication. Nevertheless, TEE monitoring during procedure showed only mild MR and a second implantation in order to prevent hemolysis had not been described before. Moreover, no bedside tests assessing the risk of hemolysis after valve implantation of paravalvular leak closure have yet been developed.

We hypothesize that the mechanical hemolysis was related to the mechanical traumatism suffered by the red blood cells during the passage through the stent of the Edwards prosthesis. This hypothesis is strengthened by the concomitant regression of the residual leaks and the hemolysis in this patient. Consistently, we never experienced hemolysis after VIV procedures in our institution, probably because in that case, the Sapien-3 frame is entirely covered by the surgical bioprosthesis, thus providing a better as compared to three-dimensional saddle shape of the Physio II annuloplasty ring. Therefore, hemolysis may be more frequent after VIR than after VIV technique.

Follow-up:

Three months follow-up confirmed the good result without significant MR and acceptable trans-mitral gradient (4 mmHg).

Conclusion:

VIR technique is a relatively new technique that may lead to several complications including mechanical hemolysis. "Valve in Sapien" implantation seems to be safe and effective for the treatment of "frame related" mechanical hemolysis. This case emphasizes the importance of meticulous positioning under TEE monitoring. It also highlights the importance of a perfect immediate result, as even a small RL can cause hemolysis.

Learning Objectives:

1. "Frame related" hemolysis is a potential complication of valve in ring implantation 2. Optimal positioning of the prosthesis is mandatory during valve in ring implantation

3 .Figure 1 :

 31 FIGURE TITLES/LEGENDS

Figure 2A :

 2A Figure 2A: Physio 2-26 mm saddle shaped circular ring: Average saddle height and inter-

Figure 2B :

 2B Figure 2B: Mechanism of hemolysis. Mechanical Hemolysis related to the regurgitation

Figure 3A :

 3A Figure 3A: Positioning of the Sapien-3 valve. Landing zone (the surgical annulus: purple

Figure 3B :VIDEO 1 :VIDEO 2 :

 3B12 Figure 3B: Positioning of the Sapien-3 valve. Ventricular length of the device remains

Table 1

 1 First Valve Implant follow-Up 2nd Valve Implant Follow-up

		Day 0	Day 1	Week 1	Month 2		2nd Valve Implant	Week 1	Month 3
	Platelet (/mm3)	114	108	110	136	Platelet (/mm3)	113	90	NA
	Hemoglobin (g/dl)	9,9	7,1	7,7	8,3	Hemoglobin (g/dl)	8,2	9	10,6
	Schizocytes	NA	+	+	++	Schizocytes	-	-	-
	Reticulocytes (%)	NA	4	7	NA	Reticulocytes (%)	NA	NA	NA
	Bilirubin total	NA	38	NA	80	Bilirubin total	81	19	19
	LDH	NA	230	2471	4962	LDH	4186	1214	293
	Haptoglobin	NA	NA	0	0	Haptoglobin	0	0	1,3
	Serum Creatinin (µmol/l)	170	194	387	395	Serum Creatinin (µmol/l)	350	319	173