Dr Quentin Le Hingrat
email: quentin.lehingrat@aphp.fr

Marine Perrier

Charlotte Charpentier

Alaric Jacquot

Nadhira Houhou-Fidouh

Diane Descamps

Benoit Visseaux

Was Zika introduced to Brazil by participants at the 2013 Beach Soccer World Cup held in Tahiti: A phylogeographical analysis

Keywords: Zika virus, Brazil, French Polynesia, Evolution, Phylogeny

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

Introduction

Zika virus (ZIKV) is an arthropod-borne virus (arbovirus) isolated for the first time in 1947 from a febrile rhesus monkey infected near the Zika forest in Uganda [START_REF] Dick | Zika virus I. Isolations and serological specificity[END_REF]. It is now classified into two genotypes: the old African lineage and the Asian lineage [START_REF] Lanciotti | Genetic and serologic properties of Zika virus associated with an epidemic, Yap State, Micronesia, 2007[END_REF]. ZIKV infections are mainly asymptomatic or mild with patients displaying fever, maculopapular rash, myalgia, arthralgia and conjunctivitis [START_REF] Musso | Zika Virus[END_REF]. ZIKV was initially responsible for a limited number of short epidemics throughout Africa and Asia, mainly documented retrospectively through serological surveys 1 .

Due to the important cross-reactivity of serological assays with antibodies directed against other Flavivirus, these results must be interpreted with caution but suggest that ZIKV was endemic in numerous sub-Saharan countries during the second half of the 20 th century and spread to several Asian countries where it was responsible of local outbreaks [START_REF] Musso | Zika Virus[END_REF][START_REF] Faye | Molecular Evolution of Zika Virus during Its Emergence in the 20th Century[END_REF].

In 2007, a first large outbreak of "dengue-like illness" occurred on Yap Island, in the Federated States of Micronesia [START_REF] Duffy | Zika virus outbreak on Yap Island, Federated States of Micronesia[END_REF]. Serological and molecular analyses identified ZIKV as the cause of this outbreak with an estimation of almost 75% of infected-inhabitants. In October 2013, the first cases of ZIKV infection were reported in French Polynesia. As in Yap Island, the epidemic flared, infecting up to 49% of the general population and 66% of schoolchildren [START_REF] Aubry | Zika Virus Seroprevalence, French Polynesia, 2014-2015[END_REF]. From French Polynesia, ZIKV has spread to other Pacific countries (e.g. New Caledonia, Solomon Islands, Fiji, Samoa) and also to Brazil where it was responsible for a massive and severe outbreak which fueled the current epidemic across all South and Central America [START_REF] Musso | Rapid spread of emerging Zika virus in the Pacific area[END_REF]. During these latest outbreaks, severe complications have been described, including severe thrombocytopenia sometimes leading to deaths [START_REF] Karimi | Thrombocytopenia and subcutaneous bleedings in a patient with Zika virus infection[END_REF][START_REF] Sarmiento-Ospina | Zika virus associated deaths in Colombia[END_REF], Guillain-Barré Syndroms (GBS) in French Polynesia [START_REF] Duffy | Zika virus outbreak on Yap Island, Federated States of Micronesia[END_REF] and microcephaly cases in South America [START_REF] Schuler-Faccini | Possible Association Between Zika Virus Infection and Microcephaly -Brazil, 2015[END_REF][START_REF] De | New trends of the microcephaly and Zika virus outbreak in Brazil, July 2016-December 2016[END_REF][START_REF] Rodriguez-Morales | Diagnosis and outcomes of pregnant women with Zika virus infection in two municipalities of Risaralda, Colombia: Second report of the ZIKERNCOL study[END_REF]. Since then, imported-cases have regularly been described in North America, Europe or Asia, underlying the threat of new epidemics in countries with competent vectors.

It is well established that ZIKV was introduced in Brazil from French Polynesia [START_REF] Musso | Virus Transmission from French Polynesia to Brazil[END_REF]. However, the circumstances of this introduction remain unknown [START_REF] Musso | Virus Transmission from French Polynesia to Brazil[END_REF][START_REF] Zanluca | First report of autochthonous transmission of Zika virus in Brazil[END_REF]. Two sport events, the Confederation Cup (June 2013) and the Va'a Championship (August 2014), were evoked but ruled out by phylogenetic analyses estimating the introduction time of ZIKV into Brazil between August 2013 and April 2014 [START_REF] Faria | Zika virus in the Americas: Early epidemiological and genetic findings[END_REF].

As additional sequence data is available since this study, we performed new phylogeographical analyses to investigate recent ZIKV evolution, refine the introduction date of ZIKV into Brazil and assess the potential role of all international sport events held in French Polynesia and Brazil.

Material and methods

Sequences database

All ZIKV full genome sequences available on the 1 st of March 2017 were retrieved from GenBank database (http://www.ncbi.nlm.nih.gov/genbank). We annotated all sequences with collection date and sampling location, using data directly available in GenBank or in related publications. Poor quality, clonal or iterative sequences were discarded resulting in a dataset of 209 full genomes. As we focused on the Asian lineage's evolution, African lineage sequences (n = 27) were filtered, resulting in a final dataset of 182 geographically-annotated ZIKV sequences.

Phylogenetic analyses

Sequences were aligned using Clustal omega 1.2.4 9 and Muscle [START_REF] Edgar | MUSCLE: multiple sequence alignment with high accuracy and high throughput[END_REF], then manually curated using AliView 1.18 [START_REF] Larsson | AliView: a fast and lightweight alignment viewer and editor for large datasets[END_REF]. The presence of recombinant forms was assessed using RDP4 software [START_REF] Martin | RDP4: Detection and analysis of recombination patterns in virus genomes[END_REF]. The best fitting nucleotide substitution model was identified, using jModelTest v2. 1.10 [20], as the general time reversible (GTR) model with a gamma distribution (n=4). A phylogenetic tree was inferred by approximate maximum of likelihood using FastTree 2.1 [START_REF] Price | FastTree 2--approximately maximum-likelihood trees for large alignments[END_REF]. Bayesian phylogenies were estimated using BEAST 1.8.4 [START_REF] Drummond | Bayesian Phylogenetics with BEAUti and the BEAST 1.7[END_REF], with different coalescent models (Gaussian Markov Random Field (GMRF) Bayesian skyride coalescent, Skyline and Skygrid) and under strict or relaxed molecular clock models. Those Bayesian MCMC analyses were run for 100 million generations sampling every 10,000 th , using the maximum likelihood phylogeny tree previously generated as starting tree. Model comparisons were performed based on the Bayes Factors and a model was retained as fitting the data best when the Bayes factor was above 5.

Effective Sampling Size (ESS) for all estimated parameters were explored in Tracer and were above 200, as recommended. After discarding 10% burn-in, log files were combined using LogCombiner. The maximum clade credibility tree (MCCT) generated with TreeAnnotator was then edited in FigTree v1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/).

Results

To explore ZIKV epidemiology, we performed a phylogeographical analysis on 182 available ZIKV sequences belonging to the Asian lineage. No recombinant viruses were detected. The bestfit model combination was found to be the Skyline demographic model with a relaxed molecular clock. Under this model, we found an estimated nucleotide substitution rate of 1.114 x 10 -3 substitutions/site/year (95% Higher Probability Densities (95HPD) = 9.93x10 -4 -1.254x10 -3 ). The Brazilian clade was monophyletic, as in previous studies and in favor of a single successful introduction of ZIKV in Brazil from French Polynesia (country probability = 1.0) [START_REF] Faria | Zika virus in the Americas: Early epidemiological and genetic findings[END_REF][START_REF] Faria | Establishment and cryptic transmission of Zika virus in Brazil and the Americas[END_REF] (Figure 1). During this phylogeographical analysis, we also observed that seven ZIKV strains isolated recently, since 2013, presented phylogenetic branches deeply connected with ZIKV strains circulating in South-East Asia prior to the French Polynesian outbreak (posterior probabilities > 0.9 for all). These strains were isolated from 2 Singaporean patients in late 2016 [START_REF] Ho | Outbreak of Zika virus infection in Singapore: an epidemiological, entomological, virological, and clinical analysis[END_REF], 3 Thai inhabitants between 2013 and 2016, and from 2 travelers returning from Thailand in 2013 and 2016 [START_REF] Ellison | Complete Genome Sequences of Zika Virus Strains Isolated from the Blood of Patients in Thailand in 2014 and the Philippines in 2012[END_REF][START_REF] Fonseca | Case Report: First Case of Zika Virus Infection in a Returning Canadian Traveler[END_REF]. Those recent strains were interspersed among older isolates from South East Asia and the median tMRCA estimates of the nodes encompassing those sequences ranged from 2007 to 2012.

To determine the introduction date of ZIKV in

Discussion

In this study, we provided an updated phylogenetic reconstruction of ZIKV spreading in Asia and South America, allowing a dating of the ZIKV introduction period into Brazil with narrower confidence intervals. In phylogenetics, the tMRCA of a clade represents the origin of a clade diversification and, for infectious diseases, it is a good estimate of the onset of a local outbreak, while the tMRCA of the node immediately preceding the node of the Brazilian clade (i.e. node A in this study) can be used to define the divergence time between French Polynesian and Brazilian sequences [START_REF] Bryant | Out of Africa: A Molecular Perspective on the Introduction of Yellow Fever Virus into the Americas[END_REF]. Thus, this latter tMRCA can be indicative of the initial introduction time before a delayed large diversification due to a massive outbreak, notably driven by seasonality of mosquito-borne diseases, as demonstrated by Faria et al. [START_REF] Faria | Establishment and cryptic transmission of Zika virus in Brazil and the Americas[END_REF]. In this study, Faria et al. demonstrated the existence of long periods of ZIKA pre-detection cryptic transmission in recipient regions within South America. In such scenario, the sole use of the tMRCA of the Brazilian clade may be slightly biased and indicate the time of such massive outbreak beginning instead of the proper introduction time. Thus, these two tMRCA should be combined to better define the most probable introduction date interval. In our work, using those two tMRCA, we defined a new and slightly larger interval for ZIKV introduction into Brazil, ranging from July 2013 to March 2014.

With this new interval, and as previously published [START_REF] Faria | Zika virus in the Americas: Early epidemiological and genetic findings[END_REF], the Football World Cup and the Va'a Championship were both out of the estimated time span of ZIKV introduction into Brazil. The Confederation Cup was hosted at a date slightly outside our confidence interval but cannot be entirely ruled out, due to the stochastic nature of a phylogeographical analysis. The previously uninvestigated Beach Soccer World Cup was held in Tahiti between the 18 th and the 28 th of September 2013, in a timeframe compatible with ZIKV introduction into Brazil and at a time showing the highest probability density for the divergence time between Polynesian and Brazilian ZIKV lineages (Figure 2). This event gathered more than 100,000 spectators and 16 national teams, including Brazil and French Polynesia. The two teams were present during all the competition and faced during the third-place play-off. To our knowledge, this event was the only mass-gathering event reuniting Brazilian and French Polynesian inhabitants at a date within the time span estimated in our phylogenetic reconstruction. During this competition, ZIKV was already circulating in French Polynesia and no prevention measures had been implemented yet as ZIKV epidemic remained undetected in French Polynesia until early October 2013 [START_REF] Pettersson | How Did Zika Virus Emerge in the Pacific Islands and Latin America?[END_REF][START_REF] Faria | Phylogeographical footprint of colonial history in the global dispersal of human immunodeficiency virus type 2 group A[END_REF]. Interestingly, we also observed that imported-cases reported in Canada [START_REF] Fonseca | Case Report: First Case of Zika Virus Infection in a Returning Canadian Traveler[END_REF] and China (Genbank accession number KY328290), as well as autochthonous cases in Singapore and Thailand [START_REF] Ho | Outbreak of Zika virus infection in Singapore: an epidemiological, entomological, virological, and clinical analysis[END_REF][START_REF] Buathong | Detection of Zika Virus Infection in Thailand, 2012-2014[END_REF], all post-2013, were phylogenetically deeply connected with different Asian strains and their most recent common ancestors. The time of their MRCA were estimated from 2007 up to 2012. This ruled out a hypothetic infection of these patients by strains originating from Pacific islands or South American countries. Furthermore, those sequences were interspersed among South East Asian isolates, independently of their sampling year, and did not cluster altogether, suggesting that ZIKV has been evolving separately in several locations across South East Asia. Very few studies focused on ZIKV transmission in Asia. Indeed, despite the hypothesis that ZIKV might have spread largely in Asia, its circulation has only been documented in Vietnam in 2016 [START_REF] Moi | Zika virus infection and microcephaly in Vietnam[END_REF] in Cambodia between 2007 and 2016 [START_REF] Duong | Low Circulation of Zika Virus, Cambodia, 2007-2016[END_REF], in Singapore in 2016 [START_REF] Ho | Outbreak of Zika virus infection in Singapore: an epidemiological, entomological, virological, and clinical analysis[END_REF][START_REF] Maurer-Stroh | South-east Asian Zika virus strain linked to cluster of cases in Singapore, August 2016[END_REF], and in Thailand from 2012 to 2014 [START_REF] Buathong | Detection of Zika Virus Infection in Thailand, 2012-2014[END_REF]. More recently, a study also suggested an undetected endemic circulation of ZIKV in Thailand since 2002 [START_REF] Ruchusatsawat | Long-term circulation of Zika virus in Thailand: an observational study[END_REF]. The endemic circulation of ZIKV in these Asian countries needs to be confirmed and evaluated to assess the health risk for travelers [START_REF] Dinh | Zika virus in Vietnam, Laos, and Cambodia: are there health risks for travelers?[END_REF] and the potential for transmission to neighboring countries with competent vectors [START_REF] Jia | Zika virus infection in travelers returning from countries with local transmission, Guangdong, China[END_REF].

Our study presents some limitations. Firstly, if this work highlights that the Beach Soccer World Cup is concomitant with the introduction of ZIKV in Brasil, we cannot rule out an introduction through routine human travels by a single traveler, as previously hypothesized [START_REF] Faria | Zika virus in the Americas: Early epidemiological and genetic findings[END_REF]. Indeed, it is often thought that large-scale patterns in human mobility can provide more testable hypotheses about viral introduction and emergence than ad hoc hypotheses focused on specific events. However, as large-scale events are prone to facilitate outbreaks of infectious diseases [START_REF] Abubakar | Global perspectives for prevention of infectious diseases associated with mass gatherings[END_REF] and as our estimated time of ZIKV introduction into Brazil corresponds sharply to the time of the Beach Soccer World Cup, we believe that the possibility that this event triggered ZIKV introduction into Brazil should be taken into account. It might be important to keep this potential risk in mind when deciding whether to implement prevention measures against arboviral infections in future massgathering events. Another limitation of our study is that the low number of available sequences from Asian regions may limit the accuracy of tMRCA for the deeper nodes of the tree. However, it does not alter our observation of distinct ZIKV strains in several Asian countries suggesting a largely unexplored and continuous circulation of ZIKV in Asia since 2007. This finding is consistent with the recent description of ZIKV endemic circulation in Thailand [START_REF] Ruchusatsawat | Long-term circulation of Zika virus in Thailand: an observational study[END_REF].

Conclusion

In this work, we suggest the Beach Soccer Worldcup held in Tahiti in September 2013 as a potential trigger of ZIKV introduction into Brazil. We also observed recent ZIKV cases phylogenetically connected to ancestors older than the South America epidemic and suggesting the endemic circulation of ZIKV in Asia. Identifying ZIKV-endemic Asian countries is of peculiar importance as most South-East Asian countries are already endemic for dengue virus. Due to the similarity of clinical symptoms, technical hurdles for diagnosis and, probably, low incidence in these countries, ZIKV circulation may remain undetected. Specific epidemiological efforts should be exerted to identify ZIKV-endemic countries, to better understand ZIKV circulation and to limit diffusion to neighboring regions with competent vectors.

  Brazil, we estimated the time of the most recent common ancestor (tMRCA) of two nodes, A and B. Node A encompasses all South American sequences and the two closest French Polynesian sequences (GenBank accession numbers: KX447518 and KX447510). The tMRCA of this node represents the divergence time of French Polynesian and Brazilian sequences and, thus, the earliest possible date of ZIKV introduction to Brazil. Node B includes only South American sequences and represents the beginning of ZIKV expansion in Brazil and, thus, the latest possible date of ZIKV introduction to Brazil. Those two clades were well-supported (posterior probability = 1.0 for both) (Figure 1). Median tMRCA of nodes A and B are the 13 rd of September 2013 (95HPD = 8 th of July 2013-4 th of November 2013) and the 1 st of January 2014 (95HPD = 12 th of October 2013-21 st of March 2014), respectively (Figure 2). Thus, the ZIKV transmission from French Polynesia to Brazil may have occurred at any time point between the 8 th of July 2013 and the 21 st of March 2014. Posterior probability densities of the tMRCA of those two nodes are shown in Figure 2, along with dates of sport events suspected to have led to the transmission of ZIKV to Brazil: Confederation Cup (15 th -30 th June 2013; I), Football World Cup (12 th June-13 th July 2014; II), Va'a Championship Sprint Canoe Race (14 th -18 th August 2014; III) and Beach Soccer World Cup (18 th -28 th September 2013; IV). The Football World Cup and the Va'a Championship Sprint Canoe Race were held months after the estimated introduction time interval, while the Confederation Cup was hosted a few days before the estimated 95HPD. Only the Beach Soccer World Cup, held in Tahiti between the 18 th and the 28 th of September 2013, fitted the time distribution of estimated 95HPD of nodes A and B.

  With an updated sequence dataset, we estimated ZIKV introduction in Brazil between July 2013 and March 2014. Our confidence interval differs slightly from previous evaluations done by Faria et al.[START_REF] Faria | Establishment and cryptic transmission of Zika virus in Brazil and the Americas[END_REF], Metsky et al.[START_REF] Metsky | Zika virus evolution and spread in the Americas[END_REF] and Pettersson et al.[START_REF] Pettersson | How Did Zika Virus Emerge in the Pacific Islands and Latin America?[END_REF] (October 2013 to April 2014, August 2013 to July 2014 and October 2013 to February 2014, respectively). Differences can be largely explained by the definitions retained in each study for inferring the introduction date. Indeed, they have all estimated the introduction date as the tMRCA of the Brazilian clade[START_REF] Faria | Establishment and cryptic transmission of Zika virus in Brazil and the Americas[END_REF][START_REF] Metsky | Zika virus evolution and spread in the Americas[END_REF][START_REF] Pettersson | How Did Zika Virus Emerge in the Pacific Islands and Latin America?[END_REF] and a similar value was obtained in our work for this particular tMRCA (October 2013 to March 2014).

Figure 1 :

 1 Figure 1: Phylogeography of Zika virus' Asian lineage

Figure 2 :

 2 Figure 2: Posterior probability densities of the estimated ages of the most recent common

Acknowledgements

No particular funding was received for this study. A grant of the Assistance Publique -Hôpitaux de Paris (AP-HP) supported Q.L.H.


Virginie Chasseigne

Ahmed Larbi

Jean Goupil

Iskander Bouassida

Marlène Buisson

Jean Paul Beregi

Julien Frandon
email: julien.frandon@chu-nimes.fr

Establishment of a cooperation program with radiologists and evaluation of complications

Keywords: Peripherally inserted central catheter (PICC), Venous access, Cooperation program, Safety, Complication

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

Introduction

Peripherally inserted central catheters (PICC) are central venous catheters (CVC) inserted via peripheral veins in the ante-cubital fossa or above, and terminate at the junction of the superior vena cava with the right atrium [START_REF] Hoshal | Total intravenous nutrition with peripherally inserted silicone elastomer central venous catheters[END_REF][START_REF] Mermel | Clinical practice guidelines for the diagnosis and management of intravascular catheter-related infection: 2009 update by the infectious diseases society of america[END_REF]. Over the past decade, the use of PICC has steadily grown. They allow durable and non-permanent venous access with several benefits over classic CVC including a less invasive alternative with fewer perioperative complications and greater cost-effectiveness [START_REF] Ng | Peripherally inserted central catheters in general medicine[END_REF][START_REF] Chopra | Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta-analysis[END_REF][START_REF] Viart | Comparison between implantation costs of peripherally inserted central catheter and implanted subcutaneous ports[END_REF].

However, PICCs are also associated with severe complications including catheter related bloodstream infection and deep venous thrombosis (DVT), and minor complications such as catheter occlusions, insertion site redness, discomfort or difficult removal. These complications can lead to extended hospital stay and increased costs [START_REF] Chopra | Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta-analysis[END_REF][START_REF] Chopra | Bloodstream infection, venous thrombosis, and peripherally inserted central catheters: reappraising the evidence[END_REF]. It has been proven that educating healthcare personnel "regarding the indications for intravascular catheter use and proper procedures for the insertion and maintenance of intravascular catheters could prevent the risk of complications" [START_REF] Grady | Guidelines for the prevention of intravascular catheter-related infections[END_REF].

In many countries, PICCs are inserted both by interventional radiologists or a dedicated nursing-based venous access team. This relatively new specialty allows nurses to place and manage venous access in hospitalized patients [START_REF] Meyer | Developing an alternative workflow model for peripherally inserted central catheter placement[END_REF][START_REF] Burns | Facility wide benefits of radiology vascular access teams[END_REF][START_REF] Burns | Facility wide benefits of radiology vascular access teams, part 2[END_REF]. A growing number of hospitals have introduced vascular nursing teams who are responsible for insertion of devices [START_REF] Krein | Use of designated nurse PICC teams and CLABSI prevention practices among U.S. hospitals: a survey-based study[END_REF].

In France, PICC placement was previously considered as a medical procedure that could not be delegated. However since 2015, radiology technicians are allowed to insert PICCs under radiologist supervision [START_REF] Has | AC/SEVAM du 28 janvier 2015 du collège de la haute autorité de santé relatif au protocole de coopération « Radiologues interventionnels et manipulateurs en electroradiologie médicale pour la pose des PICC-line en salle de radiologie interventionnelle[END_REF][START_REF] Dabadie | Development and initial evaluation of a training program for peripherally inserted central catheter (PICC) placement for radiology residents and technicians[END_REF]. In a context of a growing number of PICCs insertions in our hospital, a cooperation program started in April 2016.

The purpose of this study was to evaluate the cooperation program in order to compare incidence of complications after PICC placement between radiologists and technicians

Patients and Methods

Study population and cooperation program

The local institutional review board approved our protocol and all patients signed written informed consent. The cooperation program began in April 2016 and all patients who underwent PICC placement were recorded in a database (RedCap® software, 7.6.0 version).

When patients had more than one PICC placement during the study period, only the first placement was considered for analysis. Data including operator (radiologist or technician), patient demographic data, catheter type and size, site of insertion, date of PICC insertion and removal, were collected for all patients. Analysis ended in February 2017.

Patients who underwent PICC placement from January 1st to January 31st 2017, were followed-up until PICC removal. Complications and reasons for PICC removal were prospectively recorded.

The cohort was composed of adult patients only. Patients from intensive care unit (ICU) were not addressed to our department. The follow-up period ended upon PICC removal for each patient.

Patients were eligible for the cooperation-delegation program when they were able to understand the program, speak the country language, be without mental disease or immunodeficiency, have an insertion scheduled within normal working hours, and have a delegated radiology technician available. If these criteria were validated, they were proposed the cooperation program and included in the technician group. The others insertions were radiologist-led and patients were included in the radiologist group. The cooperation program was available from 8 AM to 6 PM from Monday to Friday. Patients were not randomized.

PICC placement technique

PICC line protocol was standardized between groups and consisted of chlorexidine skin preparation, hand washing prior PICC placement, use of sterile gloves, sterile gown, cap, mask for providers inserting and assisting with insertion, full-body sterile drape for patient.

All PICCs were placed under ultrasound guidance (selection of the appropriate vein so that the vein diameter in mm should be at least the catheter diameter in Fr) and fluoroscopic guidance (tip placement) in a dedicated fluoroscopy ward for vascular access placement either by a trained interventional radiologist (5 radiologists with more than 5 years of experiment in central line placements) or by a trained technician. Five technicians with extensive experience in PICC placement assistance and volunteered to participate, were trained following a specific procedure including: 20 hours of theoretical training, 20 PICC insertions as main assistant of radiologist, 10 PICC insertions as operator with a senior radiologist as assistant and a practical examination under a senior radiologist supervision at the end. Before the procedure, the agreement of the patient was always obtained for the insertion led by technicians. When PICC placement was performed by a technician, a senior radiologist was always available should he be needed. The PICC devices used were valved PowerPicc solo2® single-lumen (4F) or double-lumen (5F) (Bard Access Systems). PICCs were maintained in place with StatLock® adhesive dressings (StatLock, Bard).

Definitions of cooperation indicators and complications

The analysis included number of patients eligible to the program and adhesion rate; number of help requests from the radiology technician to the radiologist and justified alarm rate; insertion rate by radiologist after a failed insertion by the technician; delegated insertion rate with at least one declared adverse effect during the insertion; patient rate with less than two days between the demand and PICC placement; satisfaction level from patients, radiologist and technicians.

After each PICC placement led by a technician, the patient satisfaction was orally evaluated on a scale from 1 to 5: (1 very unsatisfied; 2: unsatisfied; 3: neutral; 4: satisfied; 5: very satisfied). For scores below 4, a written questionnaire was used to determine the patient feeling. The technician and the radiologist satisfaction were evaluated with the same scale in separate interviews with the head of department.

Central line-associated bloodstream infection (BSI) was defined as a bacteremia in a patient who had at least one positive blood culture result obtained from a peripheral vein, clinical manifestations of infection and no other apparent source for bloodstream infection except the catheter placement in the 48-hour period before the BSI [START_REF] Grady | Guidelines for the prevention of intravascular catheter-related infections[END_REF]. Catheter-related bloodstream infections were defined by the same organism recovered from peripheral blood culture and from quantitative culture of the catheter. DVT was defined as thrombosis involving the deep veins of the arm detected by Doppler ultrasound. Suspected DVT was defined as clinical signs without ultrasound confirmation. Catheter occlusion was defined as a total occlusion of the device (i.e., if unable to perform injection or sampling and/or no blood reflux). Catheter dwell time was calculated as the difference between the insertion and removal dates.

Statistical analysis

Data were analyzed on a per PICC placement basis. The incidence rates of PICC infections were estimated as incidence rate per 1000 catheter days. Quantitative variables were expressed as mean, SD and ranges; Qualitative variables were expressed as raw numbers, proportions and percentages. Comparisons of qualitative variables between patients who had PICC placement by radiologists and those who had PICC placement by technicians were performed using Chi 2 test or Fisher's exact test. Student test was used to compare quantitative variables. P values ≤ 0.05 were considered statistically significant. Data analysis was performed using SPSS® software (23.0 version).

Results

Population

From April 2016 to end of February 2017, a total of 722 patients had PICC placement. There were 382 men and 340 women with a mean age of 66.8 ±15.8 (SD) years (range: 18-94 years); of these, 433/722 patients (59.97%) had PICC placement by technicians and 289/722 (40.03%) by radiologists (Fig. 1).

A total of 442/722 patients (61.22%) were included in the cooperation program (Table 1).

Technicians needed radiologists' help in 23/442 patients (5.20%) because of puncture failure after 2 attempts (16/23, 70%) or failure of progression of the wire in the vein (7/23, 30%); 6 of these 23 insertions (6/23; 26%) were subsequently achieved by radiologists. One declared adverse event (1/442, 0.23%) occurred during insertion with abundant bleeding at the insertion puncture.

The satisfaction levels ≥ 4 (i. e., 4/5) from patients, radiologists and radiology technicians were respectively at 98.41% (n= 435/442), 100% (n= 5/5), 100% (n= 5/5). For the 7/442 patients (1.6%) with a satisfaction level < 4 (i. e., 1/2/3), 4 indicated pain (n=3) and delay between the demand and PICC insertion (n=1).

Subgroup results

From the 1st to 31st January 2017, 77 patients underwent PICC insertion (Fig. 2); of these three patients (3/77; 3.9%) were lost to follow-up. Trained technicians inserted 44/77 PICCs (57.1%) whereas radiologists inserted 33/77 PICCs (42.9%). For a total of 2713 catheter days, the median PICC dwell time was 18.9 days (mean 36.7 ± 42.8 [SD] days; range 1-182 days).

The mean age of this subgroup was 67.1 ± 16.5 (SD) years, with older patients in the radiologist group (P = 0.053), most of them were male (40//77, 52.0%) (Table 2).

The complication rate was 26.0% (7.4 per 1000 PICC-days, n = 20/77) without statistical difference between technicians and radiologists (P = 0.296) (Table 3). Complications included 8 PICC-related infections (8/77, 10.4%) for an incidence rate of 2.95 per 1000 catheter days;

3 DVT (3/77, 3.9%), and 9 catheter occlusions (9/77, 11.7%), without statistically significant difference between the groups.

Discussion

The study evaluated the feasibility of a new cooperation program for PICC insertions in our center. Patients, technicians, and radiologists showed great satisfaction with the program. No significant differences in terms of complications between radiologists and radiology technician operators were found. To the best of our knowledge no data exists concerning the PICC insertion delegation to the radiology technicians and the related complications.

Our 11-month results showed that 60.8% of PICC insertions were delegated. This proportion was also found during our one-month prospective period (57.1% of PICC insertions delegated). Baker et al. in their delegation program of PICC insertions to nurses achieved a 74 % delegation rate [START_REF] Barber | A nurse led peripherally inserted central catheter line insertion service is effective with radiological support[END_REF]. Our lower results could be explained by the technicians' availability and restrictive inclusion criteria for cooperation. Indeed, this study represented the first delegation program in our center and several months were needed to train technicians with a standardized program, thus the pool of trained technicians was small. We also had to maintain patient safety and be very strict with delegation criteria. Another reason was the nonavailability of the ward, some PICC insertions on eligible patients had to be done by radiologists at the end of the scheduled program, so outside "delegation hours". 76 % of PICC insertions were in less than 48 hours. With a dedicated ward (only for PICC insertions and no other interventional procedures) and less exclusive delegation criteria, the delegation ratio would have been higher and waiting time between the demand and the PICC insertions would have been reduced.

In our study, the low rate of insertions done by radiologists after a failed insertion by the technician (1.4%) and delegated insertion rate with at least one declared adverse effect (0.2%) showed that radiology technicians were able to independently manage most PICC insertions.

These results favorably compare with those from other delegation programs with nurses [START_REF] Barber | A nurse led peripherally inserted central catheter line insertion service is effective with radiological support[END_REF][START_REF] Gamulka | Evaluation of a unique, nurse-inserted, peripherally inserted central catheter program[END_REF]. This could be explained by the theorical and practical training, the PICC insertion protocol with adequate vein selection by ultrasound and fluoroscopic guidance and the selection of experimented technicians familiar with endovascular procedures. When they were not able to, the radiologists were always available to evaluate and help make decisions, and in each case the request for radiologist help was justified (100% rate). Similarly, the high satisfaction levels of patients, radiologists and radiology technicians showed that the cooperation program could be a satisfactory solution.

The comparison tests for the demographic profile and the device characteristics showed no significant differences between the 2 groups of patients. There was a trend for age (P = 0.053) with older patients in RG, which could be due to a higher rate of protocol exclusion and more disabled patients.

In this study, the confirmed PICC related infection rate (CRBSI) was 0.74 per 1000 catheter-days, which was lower than reported in the literature. This could be explained by the absence of ICU patients, the small size of our cohort compared to those found in the literature [START_REF] Bouzad | Peripherally inserted central catheter-related infections in a cohort of hospitalized adult patients[END_REF][START_REF] Ajenjo | Peripherally inserted central venous catheter-associated bloodstream infections in hospitalized adult patients[END_REF][START_REF] Chopra | PICC-associated bloodstream infections: prevalence, patterns, and predictors[END_REF] and by insertions in an interventional radiology ward in sterile conditions with a high level of microbiological control (ISO class 7 room). There were less CRBSI in RG group but without statistical significance (P = 0.202). If we consider the suspected infections (CLABSI), this rate is higher, closer to what is found in the literature and similar in the two groups.

Despite positive blood cultures, these infections were considered "suspected" only because a negative culture of the PICC tip was observed after the device removal. However, all of these patients were under prophylactic antibiotic therapy potentially explaining the negative culture of the device.

In our study, the DVT rate was 1.3%, which was lower than the results found in the literature [START_REF] Chopra | Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta-analysis[END_REF][START_REF] Parás-Bravo | Complications of peripherally inserted central venous catheters: a retrospective cohort study[END_REF][START_REF] Fallouh | Peripherally inserted central catheter-associated deep vein thrombosis: a narrative review[END_REF]. In their systematic review and meta-analysis Chopra et al. found a PICC-related DVT rate of 2-7% [START_REF] Chopra | Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta-analysis[END_REF] whereas Fallouh et al. reported rates varying between 2-75% according to study population, testing modality and threshold for diagnosis [START_REF] Fallouh | Peripherally inserted central catheter-associated deep vein thrombosis: a narrative review[END_REF]. The PICC diameter and the vein evaluation before placement may explain this low rate: 62.3% of PICCs inserted were 4F devices. In a retrospective cohort study of 966 single PICC placements, patients who received 5-and 6-French PICCs were more likely to develop PICC-DVT compared to 4-French PICCs (OR 3.56, [95 % CI: 1.31-9.66] vs. OR 2.21, 95% CI: 1.04-4.70, respectively) [START_REF] Chopra | Peripherally inserted central catheter-related deep vein thrombosis: contemporary patterns and predictors[END_REF].

In our study, the 11.7% PICC occlusion rate was greater than those reported in the literature and led to catheter removal. The incidence of lumen occlusion is between 2.4% and 8.7% among in-and outpatients [START_REF] Bertoglio | Peripherally inserted central catheters (PICCs) in cancer patients under chemotherapy: a prospective study on the incidence of complications and overall failures[END_REF][START_REF] Schneider | Evaluation of PICC complications in orthopedic inpatients with bone infection for longterm intravenous antibiotics therapy[END_REF][START_REF] Szeinbach | Evaluating catheter complications and outcomes in patients receiving home parenteral nutrition[END_REF][START_REF] Funk | Two-year trends of peripherally inserted central catheterline complications at a tertiary-care hospital: role of nursing expertise[END_REF]. Our higher rate can be explained by the lack of institutional nursing care for PICC use (systematic pulse flushing with saline before and after every use on every lumen) and the lack of institutional procedure to deal with occlusion at the time of the study. Although there is no evidence of the superiority of valved PICC to prevent PICC occlusion [START_REF] Johnston | The effect of peripherally inserted central catheter (PICC) valve technology on catheter occlusion rates -the "ELeCTRiC" study[END_REF][START_REF] Pittiruti | A prospective, randomized comparison of three different types of valved and non-valved peripherally inserted central catheters[END_REF], we chose to use proximal valved PICC line.

The low complication rate in this study compared with published rates can potentially be explained by the level of training required by the operators and the skills and competence achieved. In Australia, credentialing involved didactic learning with tutorials administered by senior ICU physicians with a written examination involving pre-insertion assessment, intraprocedural complication management and post-insertion assessment and management [START_REF] Alexandrou | Central venous catheter placement by advanced practice nurses demonstrates low procedural complication and infection rates--a report from 13 years of service[END_REF]. In our center, practical training included the technician observing an important number of catheter insertions prior to undertaking the skill. Procedural volume also played a role where technicians undertook 10 supervised PICC insertions. The interventional radiologists supervised the credentialing of the radiology technicians.

Robinson et al. found that introduction of a dedicated PICC team resulted in a 33% reduction in CVC placement as inappropriate patients were identified and CVCs not inserted [START_REF] Robinson | Improved care and reduced costs for patients requiring peripherally inserted central catheters: the role of bedside ultrasound and a dedicated team[END_REF]. In our study, we found that 76% of the PICC insertions led by radiology technicians were done in less than two days. Robinson et al. found similar results with an 80% decrease in average patient waiting time for PICC insertion, facilitating a more timely discharge from hospital.

The study concluded that a dedicated PICC service should become the standard of care for all hospitals with high-volume PICC use [START_REF] Robinson | Improved care and reduced costs for patients requiring peripherally inserted central catheters: the role of bedside ultrasound and a dedicated team[END_REF]. In addition, PICC insertion led by technicians could be cost effective: Walker et al. found that the radiologist group had a statistically significant increased cost (42%) over the nurse group [START_REF] Walker | nurse-led PICC insertion: is it cost effective?[END_REF]. For the same reason vascular access nurses seemed to increasingly represent a key partner in the care of hospitalized patients [START_REF] Chopra | Vascular nursing experience, practice knowledge, and beliefs: results from the Michigan PICC1 survey[END_REF], the radiology technicians had an important and a legitimate role in the PICC circuit at our hospital. Moreover, delegation programs with technicians facilitate logistic issues involved in coordinating the availability of operators in radiology departments which have no nursing team. This study has some limitations. The major limitation was that the study was conducted in a unique center with a relatively small subgroup of patients who were prospectively studied. No randomization was applied and the cooperation program was analyzed retrospectively. At the time of the study, PICC were placed in an interventional ward with fluoroscopic guidance in our center. More modern guidance systems are developing such as electrocardiogram guidance [START_REF] Yuan | Superior success rate of intracavitary electrocardiogram guidance for peripherally inserted central catheter placement in patients with cancer: a randomized open-label controlled multicenter study[END_REF]. With these new technologies, most of the PICC insertions don't require interventional radiology suite. Cooperation program with radiology technicians seemed not to be the more suitable. We could consider less cost effective solutions for radiology department such as bedside insertion delegated to nurses.

In conclusion, this study is the first delegation program in our country showing no significant difference in terms of complications between PICC insertions led by technicians or radiologists. Other studies on larger randomized cohorts are needed to confirm these results in other centers. It would be interesting to apply a medico-economic approach of these results to confirm whether PICC placements done by technicians will be cost effective.

Figure 1 .

 1 Figure 1. Flow chart of patients inclusion into the study.

Figure 2 .

 2 Figure 2. Diagram shows reasons for PICC removal a subgroup of 77 patients who were prospectively studied.

Table 1 .

 1 Cooperation indicators and 11-month follow-up results.

Table 2 .

 2 Patients and PICC devices characteristics in the prospectively studied subgroup of patients.

Table 3 .

 3 Complications in the prospectively studied subgroup of 77 patients. Note. Numbers are proportions. Numbers in parentheses are percentages * Rates are expressed as per 1000 catheter days

	Note. Numbers are raw numbers. Numbers in parentheses are proportions followed by
	percentages
	* On a Likert scale from 1 to 5

Acknowledgements

We would like to thank Sarah Kabani for English language assistance.

This study was not supported by any funding Conflicts of Interest: The authors declare that they have no conflict of interest Ethical approval : obtained.

Funding sources

This research did not receive any specific fundings

Conflicts of interest

The authors have no conflicts of interest to disclose in relation with this study.


Aladine Chetouani
email: aladine.chetouani@univ-orleans.fr

Sylvie Treuillet

Matthieu Exbrayat

Sébastien Jesset

Classification of Engraved Pottery Sherds Mixing Deep-Learning Features by Compact Bilinear Pooling

The ARCADIA project aims at using pattern recognition and machine learning to promote a systematic analysis of the large corpus of archaeological pottery fragments excavated in Saran (France). Dating from the High Middle Ages, these sherds have been engraved with repeated patterns using a carved wooden wheel. The study of these engraved patterns allows archaeologists to better understand the diffusion of ceramic productions. In this paper, we present a method that classifies patterns of ceramic sherds by combining deep learning-based features extracted from some pre-trained Convolutional Neural Network (CNN) models. A dataset composed of 888 digital patterns extracted from 3D scans of pottery sherds was used to evaluate our approach. The classification capacity of each CNN model was first assessed individually. Then, several combinations of common pooling methods using different classifiers were tested. The best result was obtained when features of the VGG19 and ResNet50 models were combined using Compact Bilinear Pooling (CBP) with a high classification rate of 95.23%.

Introduction

Pottery fragments are among the most abundant archaeological materials and provide archaeologists with dating information to understand trade flows and social interactions. A common process in Europe during Late Antiquity and the High Middle Ages was for each potter to personalize his production by applying a hand-engraved wood wheel in the still fresh clay producing a repetitive geometrical decoration in relief. From the 1970s, medieval ceramology began to build typological references of these decorations. However, the analysis of these ceramic sherds is still mainly based on a manual stamping or visual recognition and remains very laborious and time consuming. In view of the increasing size of the corpus, there is a need to automate the heavy tasks of inventorying and classifying ceramic artifacts by the use of pattern recognition and artificial intelligence. The recognition of the relief patterns stamped by the potters needs to be robust to the variability due to the curvature of the vase, the mode of application, the state of con-servation and so on. In this paper, we propose to mix deep learning-based features to classify the digital patterns extracted from 3D scans of engraved pottery sherds. We test some stateof-the-art Convolutional Neural Networks (CNNs) with a fine tuning on our dataset. Then, we compare different feature pooling strategies to show the relevance of combining CNN-based features using Compact Bilinear Pooling (CBP). The paper is organized as follows: related work is presented in Section 2. Section 3 describes the archaeological materials. Section 4 introduces the proposed pipeline and details strategies for deep learning, feature pooling and classification. Section 5 presents the experiments and the discussion. Section 6 concludes the paper.

Related Work

The new low-cost scanning technologies by laser scanner or photogrammetry promote the use of pattern recognition and artificial intelligence in particular to automate the heavy tasks of inventorying and classifying artifacts in the fields of cultural heritage [START_REF] Gomes | 3d reconstruction methods for digital preservation of cultural heritage: A survey[END_REF]. For archaeological pottery fragments, studies have proposed a classification based either on the shape and profile [START_REF] Willis | Accurately estimating sherd 3d surface geometry with application to pot reconstruction[END_REF][START_REF] Kampel | Rule based system for archaeological pottery classification[END_REF][START_REF] Son | Axially symmetric 3d pots configuration system using axis of symmetry and break curve[END_REF][START_REF] Zheng | Reassembling 3d thin fragments of unknown geometry in cultural heritage[END_REF][START_REF] Zvietcovich | A novel method for estimating the complete 3d shape of pottery with axial symmetry from single potsherds based on principal component analysis[END_REF], on the color and material characterization [START_REF] Kampel | Color classification of archaeological fragments[END_REF][START_REF] Stanco | Computer graphics solutions for pottery colors specification[END_REF][START_REF] Farjas | Deepening in the 3d modelling: Multisource analysis of a polychrome ceramic vessel through the integration of thermal and hyperspectral information[END_REF][START_REF] Smith | Classification of archaeological ceramic fragments using texture and color descriptors[END_REF][START_REF] Abadi | Gabor filter-based texture features to archaeological ceramic materials characterization[END_REF][START_REF] Makridis | Automatic classification of archaeological pottery sherds[END_REF] or on their decorations [START_REF] Guarnera | Classification of decorative patterns in kamares pottery[END_REF][START_REF] Zhou | Identifying designs from incomplete, fragmented cultural heritage objects by curvedpattern matching[END_REF][START_REF] Zhou | Design identification of curve patterns on cultural heritage objects: Combining template matching and cnn-based re-ranking[END_REF]. Shape analysis is generally based on an ideal model of surface of revolution to extract an axis and a profile, then used to match the sherds, sometimes going as far as an automatic assembly of a pot from broken sherds [START_REF] Willis | Accurately estimating sherd 3d surface geometry with application to pot reconstruction[END_REF]. However, axis detection remains a complex problem for small sherds because they are weakly curved and not perfectly axially symmetric objects and resemble a patch of a sphere [START_REF] Angelo | Axis estimation of thin-walled axially symmetric solids[END_REF]. Recently, CNN was applied for the content-based retrieval of complete or nearly complete three-dimensional vessel replicas [START_REF] Benhabiles | Convolutional neural network for pottery[END_REF] based on a shape vocabulary. Few studies have attempted to classify pottery based on their decoration. The decorative style of Kamarès (2000 BC) studied in [START_REF] Guarnera | Classification of decorative patterns in kamares pottery[END_REF] presents refined polychrome decorations that use a wide of figurative themes. Classification of the pottery is based on the identification of some elementary decorative fragments in a corpus using 2D shape matching by contour flexibility. However, the Kamarès style is very different from the engraved pottery excavated in Saran: our High Middle Age sherds are rough and engraved with repetitive geometrical patterns. A second study [START_REF] Zhou | Identifying designs from incomplete, fragmented cultural heritage objects by curvedpattern matching[END_REF] proposed computer-vision algorithms to study paddle-stamped pottery sherds found on archaeological sites of the Native southeastern north American communities of the woodland period. The stylistic design of these carved wooden paddles (from 500 BC up to 19th century) is characterized by a curvilinear pattern with multiple parallel lines. The authors address the problem of automatically identifying the underlying carved wooden paddles by matching the curve pattern fragment impressed on a sherd to a complete decoration corpus. Recently, they proposed a CNN-based approach for curvilinear pattern segmentation from a 3D scan of the sherd and a two-stage matching algorithm by training a dual-source CNN to re-rank the candidate identified by traditional template matching [START_REF] Zhou | Design identification of curve patterns on cultural heritage objects: Combining template matching and cnn-based re-ranking[END_REF]. Even if more research of pattern recognition in pottery decoration were available, the myriad forms of the decorations on ceramics mean that the resulting methods would not necessarily be the most appropriate for the domestic archaeological pottery excavated in Saran. In previous studies, we proposed a pipeline for the automatic classification of engraved sherds based on 3D scans. Binary patterns were automatically extracted from the 3D scans and the classification was done by training a SVM model with pyramid histograms of visual words (PHOW). The recognition rate was below 85% [START_REF] Debroutelle | Automatic classification of ceramic sherds with relief motifs[END_REF]. Then, we exploited well-known CNN models : AlexNet [START_REF] Krizhevsky | One weird trick for parallelizing convolutional neural networks[END_REF], VGG11 [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] and ResNet18 [START_REF] He | Deep residual learning for image recognition[END_REF], with a fine tuning on our dataset. By comparing different options for the classification stage (i.e. replacing the fully connected layers of the network with another classifier), the best results were obtained with the combination ResNet18+SVM with a classification rate of about 88 % on the same dataset [START_REF] Chetouani | Classification of ceramic sherds based on convolutional neural network[END_REF].

In continuation of these previous studies, the current work proposes to mix deep learning features by using hybrid network architectures with several pooling strategies. Based on the same set of sherds, greyscale relief maps are used instead of binary images, which leads to additional data. Consequently, deeper CNN networks (VGG16, VGG19, ResNet50) are learned to obtain a more detailed description. In doing so, the performances are greatly improved.

Archaeological Materials

Since 1994, several archaeological excavations have documented a major production site of domestic potteries in Saran (Loiret, France) dating from the High Middle Ages (sixtheleventh Centuries). Many of the wheel-stamped pottery fragments show a repetitive decoration about 1 millimeter deep and 1.5 to 3 cm wide, depending on the dimensions of the wooden cylinders used by the potters (see Fig. 1). The most common patterns notched by potters on wooden cylinders included sticks, squares, chevrons and diamonds in one or several lines. For digital pattern extraction, we used a NextEngine scanner to provide a 3D model of each sherd. Next, we created a 2D shaded view of the surface with the Meshlab software by controlling the orientation of the 3D mesh (with a default illumination model). This manual operation is very easy to do just after the scanning step even by a non-expert. This procedure provides a snapshot of a grey-level relief map of the sherd (see Fig. 2). Efficient image processing was then developed to automatically detect the salient region focused on the relief motif. The salient region is here assumed to be the highest textured region. The FAST detector [START_REF] Rosten | Machine learning for high-speed corner detection[END_REF] is applied to the variance image of the grayscale map and then the DBSCAN grouping method [START_REF] Ester | A density-based algorithm for discovering clusters in large spatial databases with noise[END_REF] is used to detect the cluster with the highest textured point density. The convex envelope of the cluster corresponds to the salient region. An illustration of the salient region detected is given in Fig. 2. For more details, the reader is referred to [START_REF] Debroutelle | Automatic classification of ceramic sherds with relief motifs[END_REF]. The dataset is composed of 888 grey-level images resized to 224x224 with a black background. Table 1 presents the number of samples for the four most representative classes and Fig. 4 shows a sample of each class.

Proposed Pipeline

The proposed pipeline is summarized in Fig. 3. After extracting the salient region as shown above, the proposed pipeline consists in three main steps: deep learning-based feature extraction, feature pooling and classification. These steps are described in the following subsections.

Feature Extraction

For feature extraction, we used CNNs similar to those in previous work [START_REF] Chetouani | Classification of ceramic sherds based on convolutional neural network[END_REF]. However, considering that the inputs used in this work are greyscale relief maps instead of binary images, we trained slightly deeper networks to take this additional information into account. In this study, four CNN models are compared and different combinations are evaluated.

• AlexNet: In 2012, AlexNet was one of the first networks to point out the efficiency of CNN for classification tasks [START_REF] Krizhevsky | One weird trick for parallelizing convolutional neural networks[END_REF]. Its architecture consists of 5 convolution layers and 3 fully-connected layers. The authors highlight three main points: the popular activation function ReLU (Rectified Linear Unit), the dropout which consists in removing some connections between neurons of the fully connected layers to prevent over-fitting, and the overlap during the pooling step.

• VGG: Developed in 2014 by the Oxford Visual Geometry Group, VGG models use only 3x3 filters instead of the 11x11 and 5x5 filters employed in AlexNet's first two layers [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF]. Several versions were proposed with 11, 13, 16 and 19 layers. In this paper, VGG16 and VGG19 were employed.

• ResNet: The Residual Neural Network model was introduced in 2015 with the core idea that letting the stacked layers fit a residual mapping is easier than letting them directly fit the desired underlying mapping [START_REF] He | Deep residual learning for image recognition[END_REF].

The authors introduce a so-called "identity shortcut connection" so that the output F(x) of each Conv-ReLU-Conv series is replaced by F(x)+x. Different versions were developed with 18, 34, 50 and 152 layers. The version we used in this paper is ResNet50.

Feature Combination

It is well-known that classification performances can take advantage of mixing different sources of knowledge. Feature pooling is used to achieve invariance to image transformations, more compact representations, and better robustness to noise [START_REF] Boureau | A theoretical analysis of feature pooling in visual recognition[END_REF]. Many of the popular methods for extracting visual features previously used already included pooling, such as variants based on local histograms (SIFT and HOG), bag-of-visual words, Fisher encoding, VLAD or bilinear models.

In this study, we combined deep learning-based features provided by the selected CNN into a 'bag of features' used as input for the classifier by testing several pooling strategies. Concatenation (Concat) is the first intuitive pooling strategy producing a resulting vector of size equal to the length of both vectors.

Instead of just concatenating vectors, the pooling operation can be an element-wise operation between vectors, typically a sum, an average, a max, a product, etc. Element-Wise Multiplication (EWM) and Element-Wise Sum (EWS) are the most widely applied operations. Both operations have also been used together, denoted here as (EWM+EWS).

Recently, [START_REF] Lin | Bilinear cnn models for finegrained visual recognition[END_REF] showed that by using Bilinear Pooling (BP), significant improvements in visual recognition accuracy can be achieved. It consists in forming a global vector by applying the outer product of two vectors. BP is discriminative but the output representation is very high dimensional. Noticing that bilinear features are closely related to polynomial kernels, [START_REF] Gao | Compact bilinear pooling[END_REF] proposed more compact representations that can be learned end-to-end by back propagation. The latter, so called Compact Bilinear Pooling (CBP), consists in projecting the extracted feature vectors into a lower dimensional space and then combining the projected features. The authors used the Count Sketch (CS) projection function [START_REF] Pham | Fast and scalable polynomial kernels via explicit feature maps[END_REF] because of a specific property that they demonstrated: the CS projection of the outer product of two vectors is the convolution of the CS projections of each individual vector (see Equ. 1). Then, the convolution can be easily rewritten into an element-wise product by applying Fourier transform (see Fig. 5). It is worth noting that CBP can be easily extended to Compact Multi-linear Pooling (CMP) to combine more than two vectors [START_REF] Algashaam | Multispectral periocular classification with multimodal compact multi-linear pooling[END_REF].

Ψ(F1 ⊗ F2) = Ψ(F1) Ψ(F2) (1)
where Ψ denotes the Count Sketch projection. ⊗ and are the outer product and the convolution, respectively.

Experiments

In this section, we evaluate the performance of the proposed pipeline. We first compare the performance of each of the CNNs considered by unit tests. Next, the efficiency of CBP and CMP provided by different combinations of deep learningbased feature vectors is considered. The best configuration is finally used to compare different classification and pooling strategies.

Evaluation Protocol

All the tests were done by splitting the dataset into trainingvalidation (66%) and test (33%) sets randomly without overlap. In order to ensure the representation of each class, the splitting was applied for each class. This procedure was repeated 20 times and the average classification rate was considered as the evaluation metric.

Unit Tests of Networks

The four selected networks (AlexNet, VGG16, VGG19, ResNet50) were tested with fine tuning on our dataset. We modified the FC layers of each of them and the Softmax layer was adapted to the number of classes (four). The Stochastic Gradient Descent (SGD) was used during the training with a batch size and a learning rate set respectively to 32 and 0.01. The momentum and the number of epochs were fixed respectively to 0.9 and 100. At each epoch, we shuffled the training data and we stored the model. We finally kept the model that provided the best performance. To expand the training dataset, we applied flipping and translations (data augmentation). It is obvious that rotation cannot be used because of the confusion it would introduce between square and diamond patterns. To ensure homogeneity, the size of the output vector was fixed at 128 for all the models.

Table 2 shows the average classification rate obtained for each CNN model. AlexNet gives a rather low classification rate (less than 70%), while the other three give much higher performance (more than 90%). The two models VGG19 and ResNet50 have similar and stable results with a slight lead for ResNet50 (93.05% against 92.95% for VGG19).

Compared to our previous work [START_REF] Chetouani | Classification of ceramic sherds based on convolutional neural network[END_REF], the performances are greatly improved by using greyscale relief maps instead of binary images with slightly deeper architectures. It clearly provides a better characterization of the pattern. The recognition rate increases by about 10% while the standard deviation drops: 81.5% for VGG11 in our previous work with binary images against 91.7% for VGG16 with greyscale relief maps herein. We compared different combinations of the deep learningbased features provided by the selected CNNs into a 'bag of features'. All pairwise vector combinations provided by two CNNs were compared using Compact Bilinear Pooling (CBP), as well as triplet combinations by Compact Multilinear Pooling (CMP). We kept the same parameters for the pooling tests as those that had been used during unit tests.

Table 3 shows the classification rates obtained. Since AlexNet is the least efficient network, it tends to decrease the performances when it is associated with other features. The most significant improvements are achieved for the pair VGG19+ResNet50 (6) and the triplets including this pair (9 and 10). It corresponds to the combination of the models that provided the best performances for unit tests (see Table 2). We can also notice a significant drop in standard deviations showing more stable behavior using feature pooling.

To determine whether if the differences in the obtained results are significant, a one-way analysis of variance (ANOVA) was applied to the six pooling strategies tested: Concat, EWM, EWS, EWM+EWS, BP and CBP with D=100 (see Fig. 6). For each box, the middle mark corresponds to the average value and the contours of the box are the 25th and 75th per-centiles. The min and max interval is defined by excluding values greater than three standard deviations from the mean. The outliers are pointed by the red crosses. As can be seen, the distribution of CBP has a lower standard deviation than the others, without outliers. A pairwise significance test of the difference between the CBP and the other five pooling strategies is also reported in Table 4, giving the p-values and the F-values. A p-value less than 0.05 indicates that the difference is significant, while 94.12 0.74 a F-value close to 1 indicates that the means and the standard deviations are similar. As can be seen, (EWM+EWS,CBP) obtained the highest p-value (0.2576) which means that the results of the two methods are not significantly different and its low F-value (1.32) means that their distributions are similar. However, as the number of parameters of (EWM+EWS) is 2.5 times (2.560K/1.000K) higher than the CBP and the accuracy of CBP is slightly higher (95.23), CBP with D=100 was retained as the best solution. Table 5 shows the confusion matrix for the best configuration (6). As expected, the diagonal contains high classification rates. A few confusions can be observed, in particular between class 2 (sticks) and class 3 (squares). The total confused cases between these two classes represent 3.86% and 5.88%. This is not surprising if we look at some samples, as shown in Fig. 7. Among Fig. 6. Results of the one-way ANOVA for the pooling strategies: 1) Concat, 2) EWM, 3) EWS, 4) EWM+EWS, 5) BP and 6) CBP with D=100.

the geometric shapes that make up the pattern, it is sometimes difficult to distinguish between sticks and squares, probably because of the approximate shapes carved by the potter on the wooden wheel, the slippage when applied in fresh clay and the Then, we evaluated the impact of the dimension (D) of the projection space when using the Count Sketch function (see Fig. 8). As can be seen, we achieved high classification rates even for small dimensions and the best performance was obtained for the D=100. Finally, we compared the classification performance with and without the extraction step of the salient region (see Table 6). A classification focused on the relief pattern improves the performance by 3.66% compared to the use of the 2D view of the whole sherd. The 'bag of features' given by pooling the features provided by several CNNs is here used as input for 2 Fully Connected (FC) layers of size 50, followed by a Softmax layer of size 4 (i.e. 4 classes). Each FC layer is followed by a (ReLU + dropout) layer. We compared different options for the classification step by replacing the previous FC layers with a Support Vector Machine (SVM) or a Linear Discriminant Analysis (LDA). For SVM, various kernel functions were tested and we kept the Gaussian function as the best result. For LDA, different distances were tested and we kept the Diaglinear function that integrates an estimation of the covariance matrix as the best performance. Fig. 9 summarizes all these comparisons. We employed here the pair of deep learning-based feature vectors that provided the best performance (VGG19 and ResNet50). Table 7 shows the results obtained for different pooling strategies and different classifiers. We compared three classifiers (SVM, LDA, FC). The 'bag of features' used as input for the classifiers was obtained by Concatenation (Concat), Element-Wise Multiplication (EWM), Element-Wise Sum (EWS), the two operations together (EWM+EWS), Bilinear Pooling (BP) and finally by the Compact Bilinear Pooling (CBP). For the latter, we show the performance obtained for 2 dimensions (D=50 and D=100). All pooling strategies improve the performance with an increase up to 2% compared to unit tests of each CNN (see Table 2). The best configuration is CBP(VGG19, ResNet50) + FC, with 95.23% closely followed by the configuration Concat+SVM and CBP+LDA. However, CBP implies a much more compact representation since the number of learnable parameters for CBP (100*50*50*4) is about 40% less than that of Concat ((128+128)*50*50*4).

Conclusion

In this paper, we proposed a method to classify patterns of ceramic sherds by combining feature vectors provided by CNN models. Four pre-trained CNN models were employed with fine-tuning. The deep learning-based features thus extracted were then combined through different pooling strategies (Concat, EWM, EWS, EWM+EWS, CB and CBP) and the performance of different classifiers (SVM, LDA and FC) was compared. The best performance was obtained with CBP(VGG19,ResNet50)+FC (95.23%). In future work, we will first increase the dataset by integrating other patterns from several sites. We will consider geometric aspects (lines, shapes, etc.) to better discriminate between patterns. To go further, we will also try to classify wheels for each pattern class which is still a challenging task.

Fig. 1 .

 1 Fig. 1. Excavated pottery sherds from the Saran archaeological site (Loiret, France) that present relief decorations impressed by carved wooden cylinders.

Fig. 3 .

 3 Fig. 3. Proposed pipeline

Fig. 5 .

 5 Fig. 5. Compact Bilinear Pooling strategy

Fig. 7 .

 7 Fig. 7. Confusing samples: Red blocks represent the wrongly predicted forms (squares and sticks on the left and right images, respectively), while the green blocks highlight the expected right forms (sticks and squares on the left and right images, respectively).

Fig. 8 .

 8 Fig. 8. Average classification rates according to the dimension of the projection space for CBP(VGG19,ResNet50).

Fig. 9 .

 9 Fig. 9. Framework of the Classifier comparison

Table 1 .

 1 Composition of our dataset

	Class	Type	Number of sherds
	1	Diamonds	211
	2	Sticks (2 lines)	259
	3	Squares (3 lines)	274
	4	Chevrons	144

Table 2 .

 2 Average percentage of classification of each CNN model

		Average percentage Standard
		of classification (%) deviation
		CNN features
	AlexNet	69.62	2.53
	VGG16	91.73	3.06
	VGG19	92.95	1.41
	ResNet50	93.05	1.31

5.3. Evaluation of Deep Learning-based Feature Pooling

Table 3 .

 3 Evaluation of deep learning-based feature combinations using CBP and CMP

		Average percentage of classification (%) Standard deviation
		Combination of 2 vectors
	1 CBP(AlexNet,VGG16)	91.37	0.83
	2 CBP(AlexNet,VGG19)	92.00	1.03
	3 CBP(AlexNet,ResNet50)	90.98	1.38
	4 CBP(VGG16,VGG19)	92.05	1.02
	5 CBP(VGG16,ResNet50)	92.98	1.32
	6 CBP(VGG19,ResNet50)	95.23	0.60
		Combination of 3 vectors
	7 CMP(AlexNet,VGG16,VGG19)	89.82	0.96
	8 CMP(AlexNet,VGG16,ResNet50)	89.43	1.28
	9 CMP(AlexNet,VGG19,ResNet50)	93.87	0.71
	10 CMP(VGG16,VGG19,ResNet50)

Table 4 .

 4 Pairwise significance test of the difference between CBP and the other pooling strategies.

		p-value F-value
	(Concat, CBP)	0.0256	5.4
	(EWM, CBP)	0.0259	5.37
	(EWS, CBP)	0.0589	3.79
	(EWM+EWS, CBP) 0.2576	1.32
	(BP, CBP)	0.0002	16.78

Table 5 .

 5 Confusion matrix (%) for CBP(VGG19,ResNet50)

			Predicted classes
		1	2	3	4
		1 97.75 0.56	1.13	0.56
	True Classes	2 0.83 94.09	3.86	1.21
		3 0.43	5.88 93.69	0
		4 0.97	1.81	0.14	97.08
	degradation over time.

Table 6 .

 6 Performance comparison with and without the extraction step of salient region using CBP(VGG19,ResNet50)+FC

		Average percentage Standard
		of classification (%) deviation
		Saliency extraction
	Without	91.57	1.38
	With	95.23	0.60
	5.4. Comparison of Classifiers and Pooling Strategies

Table 7 .

 7 Comparison of different classifiers and pooling strategies with the best deep learning-based features given by VGG19 and ResNet50.

		Average percentage	Feature size/
		of classification (%) Nbr. of param.
		with ± std
		SVM
	Concat	94.75 ± 0.58	256/-
	EWM	94.20 ± 1.13	128/-
	EWS	94.28 ± 0.99	128/-
	EWM+EWS	94.72 ± 0.80	256/-
	CBP	94.70 ± 0.74	100/-
		LDA
	Concat	94.10 ± 0.71	256/-
	EWM	94.53 ± 1.27	128/-
	EWS	94.47 ± 1.29	128/-
	EWM+EWS	94.15 ± 1.60	256/-
	CBP	94.65 ± 0.71	100/-
		FC (Used classifier)
	Concat	94.53 ± 1.21	256/2,560K
	EWM	94.65 ± 0.95	128/1,280K
	EWS	94.67 ± 1.16	128/1,280K
	EWM+EWS	94.95 ± 0.93	256/2,560K
	BP	94.30 ± 1.14	16384/163,840K
	CBP with D=50	94.52 ± 0.93	50/500K
	CBP with D=100	95.23 ± 0.60	100/1,000K

Acknowledgments

This work was part of the ARCADIA project funded by the Region Centre-Val de Loire. Thanks to Teddy Debroutelle for his contribution for digital pattern extraction from the pottery fragments corpus.