Glendie Marcelin

Mariette Ducatez

Thomas Fabrizio

Ph.D Richard Webby
email: richard.webby@stjude.org

S K Majumdar

F J Brenner

J E Huffman

R G Mclean

A I Panah

P J Pietrobon

S P Keeler

S Shive

Influenza Pandemic: An Overview

Introduction

Influenza A, B, and C viruses are negative sense single stranded RNA viruses belonging to the Orthomyxoviridae family. Influenza B viruses predominately infect humans, but have been isolated from seals [START_REF] Osterhaus | Influenza B virus in seals[END_REF] and have been shown able to experimentally infect ferrets [START_REF] Jakeman | Efficacy of 2'-deoxy-2'-fluororibosides against influenza A and B viruses in ferrets[END_REF] and mice [START_REF] Chen | Protection against influenza B virus infection by immunization with DNA vaccines[END_REF]. Influenza C viruses have a range of described hosts including humans, dogs, and pigs. In comparison to influenza B and C viruses, influenza A viruses have a broader range of avian and mammalian hosts and they are further subdivided into subtypes based upon the antigenicity of their neuraminidase (NA) or hemagglutinin (HA) surface glycoproteins. Currently there are 9 NA and 16 HA subtypes indentified, but only N1 and N2 and H1, H2, and H3 have been documented as forming stable lineages in humans. Because of the fact that it is only influenza A viruses that have shown the capacity to emerge as pandemic viruses in humans, the remaining sections of this review will concentrate solely on these viruses.

Influenza A virus particles consist of 8 segments of RNA encoding up to 11 proteins: HA, NA, nucleoprotein (NP), matrix 1 and 2 (M1 and M2), polymerase acidic (PA), polymerase basic 1, 1-F2, and 2 (PB1, PB1-F2, PB2), non structural 1 and 2 (NS1 and NS2also known as nuclear export protein or NEP). The virus particle is approximately 80-120 nanometers in diameter and is mainly spherical but can sometimes exist in a filamentous form [START_REF] Lamb | The gene structure and replication of influenza virus[END_REF]. The particle is made up of a M1 protein-lined hostderived virus envelope in which the HA, NA, and M2 proteins are embedded and which encloses the ribonucleoprotein containing core.

In influenza terms, a pandemic is a disease caused by a novel virus that rapidly spreads through humans populations across large geographical areas. In contrast, an epidemic is a disease outbreak that affects more people than normal but is localized to one region. In humans, seasonal influenza typically reaches epidemic proportions during most winters in temperate climates while pandemics which are caused by interspecies transmission of animal viruses to humans are less frequent but of greater concern. The World Health Organization (WHO) has created a stage categorization system (stages 1 to 6 followed by "post-peak" and "post pandemic" stages) that defines epidemiologic features of an emerging outbreak. Stage 6 is characterized by global spread and marks the official declaration of a pandemic.

Previous influenza pandemics

The first reliable account of an influenza pandemic was in the summer 1580, where influenza-like illness spread from Asia to Africa and on to Europe [START_REF] Potter | A history of influenza[END_REF]. The second suspected influenza pandemic began in Russia during the spring of 1729, spread around the globe in about 3 years and became more severe in subsequent waves of infection [START_REF] Patterson | Pandemic Influenza 1700 to 1900: A Study in Historical Epidemiology[END_REF][START_REF] Potter | A history of influenza[END_REF]. The last influenza pandemic of the 18 century started in China in the fall of 1781 and quickly spread throughout Europe. Following the 1781 pandemic, there were three influenza pandemics in 1830-1831, 1833-1834, and a more severe Russian Flu from 1889 to 1890. The Russian flu originated in Central Asia in 1889 and rapidly spread along the rail networks into Europe [START_REF] Tognotti | Influenza pandemics: a historical retrospect[END_REF]. By December of 1889, it had already spread to North America and extended on to Asia, New Zealand, and Australia by March of 1890 [START_REF] Cunha | Influenza: historical aspects of epidemics and pandemics[END_REF]. Although the Russian flu was severe, historical records indicate the mortality was around 1% with an estimated 1 million deaths [START_REF] Cunha | Influenza: historical aspects of epidemics and pandemics[END_REF].

In the past 100 years, there have been a total of four influenza pandemics (Figure 1). The first and most severe of these, the "Spanish flu", occurred from 1918-1919 with an estimated 2.5% mortality worldwide [START_REF] Tognotti | Influenza pandemics: a historical retrospect[END_REF]. Although disputed, the Spanish flu likely started in North America in March of 1918 [START_REF] Potter | A history of influenza[END_REF] and rapidly spread across the U.S. among military troops and later into Europe during World War I. With the large number of movement of troops across Europe the causative H1N1 virus rapidly spread across Europe and had reached China and the Philippines by July of 1918 [START_REF] Potter | A history of influenza[END_REF]. The Spanish flu spread in three waves of increasing virulence.

Two pandemics occurred in the 1950's and 1960's; the "Asian" and "Hong Kong" influenza pandemics. The Asian influenza pandemic appeared in February of 1957 in China and was caused by a virus of the H2N2 subtype. This virus transmitted readily among the human population that was immunologically naive to this new subtype and it displaced the circulating H1N1 virus, although the latter reemerged in 1977 [START_REF] Potter | A history of influenza[END_REF]. Within 6 months the "Asian" flu had circulated worldwide and by 1958 an estimated 1-2 million people had died [START_REF] Tognotti | Influenza pandemics: a historical retrospect[END_REF]. The next pandemic began in 1968 in Hong Kong and was caused by a virus of the H3N2 subtype. As in 1957, the new virus rapidly dis- placed the circulating 1957 pandemic virus. By mid 1969 the new pandemic virus had spread to Africa, North and South America [START_REF] Tognotti | Influenza pandemics: a historical retrospect[END_REF].

The current influenza pandemic

The pandemic of the new millennium was detected in March of 2009, when nasopharyngeal swabs were collected from two epidemiologically unrelated children in California who were experiencing respiratory infections. The swabs tested positive for influenza but could not be subtyped. By mid April, a novel H1N1 virus (pH1N1) was identified by the U.S. CDC [START_REF] Dawood | Emergence of a novel swine-origin influenza A (H1N1) virus in humans[END_REF]. Retrospectively it was shown the virus had already been circulating for some time in Mexico. By May 6th 2009, the virus had spread beyond North America to South America and Europe with 1882 laboratory confirmed cases (Centers for Disease Control and Prevention, 2009a).

In a little more than a month, June 11th 2009, the number of laboratory confirmed cases had jumped to 28,774 as reported to the WHO by 74 countries with 144 deaths giving a conservative mortality rate of about 0.5% which is slightly higher than seasonal influenza (World Health Organization, 2009). At this time, the WHO raised the pandemic alert to phase 6 indicating sustained human-to-human transmissions in multiple regions with no possibility of geographic containment. It took previous pandemics strains several months to spread across the globe while pH1N1 virus took a little more than a month. This can likely be attributed to more accessible modes of transportation and increased affordability of international.

Although pH1N1 continued to circulate widely and spread to additional countries, the number of new cases detected began to decline in July suggesting the initial wave of the pandemic was on the decline (Figure 2). By late September and early October, both the CDC and WHO reported increasing flu activity in temperate climates signaling the start of the second wave of the pandemic which peaked by late October and early November of 2009 in the Northern Hemisphere and was at near baseline levels by March 2010 (Figure 2).

A remarkable response to the 2009 pandemic was the near real-time characterization of the virus and the dissemination of this information. The pandemic virus originates from a reassortment event between a "triple reassortant" North American swine virus, and an Eurasian swine strain [START_REF] Dawood | Emergence of a novel swine-origin influenza A (H1N1) virus in humans[END_REF][START_REF] Garten | Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans[END_REF]. The North American triple reassortant lineage established itself in the US swine population in 1998 [START_REF] Zhou | Genetic reassortment of avian, swine, and human influenza A viruses in American pigs[END_REF] and has been identified in humans in the US from 2005 to 2009 [START_REF] Shinde | Triple-reas-sortant swine influenza A (H1) in humans in the United States, 2005-2009[END_REF]. The Eurasian swine viruses transmitted from birds to swine in Europe in the late 1970's. Antigenically, and as suggested by the genetic data, the pandemic virus shares more antigenic sites with North American swine viruses than with the seasonal human H1N1 strains [START_REF] Garten | Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans[END_REF].

Smith et al. have used evolutionary reconstructions to confirm that each gene segment of pH1N1 had swine ancestry, although the pandemic virus was not detected in swine before its emergence in humans (Smith et al., 2009a). These investigators also hypothesized that pH1N1 had infected humans several months before the first human case was identified and that this was also likely the case with the three pandemic viruses of the 20th century (Smith et al., 2009a;Smith et al., 2009b).

Since its detection in humans, pH1N1 viruses have shown a very low genetic diversity suggesting a unique introduction into the human population as well as a relatively naïve population limiting the selection pressure thus far [START_REF] Garten | Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans[END_REF].

Properties of the current pandemic influenza virus

pH1N1 viruses have been shown to be slightly more pathogenic than seasonal H1N1 viruses in mice and ferrets models [START_REF] Itoh | In vitro and in vivo characterization of new swine-origin H1N1 influenza viruses[END_REF][START_REF] Maines | Transmission and pathogenesis of swine-origin 2009 A(H1N1) influenza viruses in ferrets and mice[END_REF][START_REF] Munster | Pathogenesis and transmission of swine-origin 2009 A(H1N1) influenza virus in ferrets[END_REF]. Unlike seasonal H1N1 strains, pH1N1 viruses replicate in the intestinal tract [START_REF] Maines | Transmission and pathogenesis of swine-origin 2009 A(H1N1) influenza viruses in ferrets and mice[END_REF] as well as in the trachea, bronchi, and bronchioles of ferrets [START_REF] Munster | Pathogenesis and transmission of swine-origin 2009 A(H1N1) influenza virus in ferrets[END_REF].

In pigs, where pH1N1 infections have been acquired from humans, the resulting disease is similar to that caused by endemic swine influenza viruses with similar clinical symptoms as well as viral tropism [START_REF] Brookes | Replication, Pathogenesis and Transmission of Pandemic (H1N1) 2009 Virus in Non-Immune Pigs[END_REF][START_REF] Itoh | In vitro and in vivo characterization of new swine-origin H1N1 influenza viruses[END_REF]. In guinea pigs, pH1N1 viruses replicate with similar efficiency to that of a seasonal H3N2 influenza virus [START_REF] Steel | Transmission of pandemic H1N1 influenza virus and impact of prior exposure to seasonal strains or interferon treatment[END_REF] and they have also been shown able to replicate in non-human primates [START_REF] Itoh | In vitro and in vivo characterization of new swine-origin H1N1 influenza viruses[END_REF]. In adult humans, pH1N1 induced symptoms are generally similar to those of seasonal influenza with clinical manifestations such as fever, cough and sore throat [START_REF] Shimada | Epidemiology of influenza A(H1N1)v virus infection in Japan[END_REF]. In the young, however, disease progression can be somewhat more severe with the pandemic strain and it is able to replicate lower in the respiratory tract. The transmission of pH1N1viruses has been studied in several animal models. In ferrets, the gold-standard for modeling transmission in humans, the virus was efficiently transmitted to contact animals [START_REF] Itoh | In vitro and in vivo characterization of new swine-origin H1N1 influenza viruses[END_REF][START_REF] Maines | Transmission and pathogenesis of swine-origin 2009 A(H1N1) influenza viruses in ferrets and mice[END_REF][START_REF] Munster | Pathogenesis and transmission of swine-origin 2009 A(H1N1) influenza virus in ferrets[END_REF]. Comparing transmission of pH1N1 and seasonal H1N1 viruses in ferret was, however, not trivial: while Munster et al. observed equally efficient aerosol or respiratory droplets transmissions for the two viruses [START_REF] Munster | Pathogenesis and transmission of swine-origin 2009 A(H1N1) influenza virus in ferrets[END_REF], pH1N1 had less efficient respiratory droplet transmission in a study by [START_REF] Maines | Transmission and pathogenesis of swine-origin 2009 A(H1N1) influenza viruses in ferrets and mice[END_REF] highlighting the importance of experimental variations. pH1N1 viruses are also transmitted well in guinea pigs [START_REF] Steel | Transmission of pandemic H1N1 influenza virus and impact of prior exposure to seasonal strains or interferon treatment[END_REF] and pigs [START_REF] Brookes | Replication, Pathogenesis and Transmission of Pandemic (H1N1) 2009 Virus in Non-Immune Pigs[END_REF][START_REF] Itoh | In vitro and in vivo characterization of new swine-origin H1N1 influenza viruses[END_REF][START_REF] Lange | Pathogenesis and transmission of the novel swine-origin influenza virus A/H1N1 after experimental infection of pigs[END_REF], although chickens in contact with infected pigs did not contract the virus [START_REF] Lange | Pathogenesis and transmission of the novel swine-origin influenza virus A/H1N1 after experimental infection of pigs[END_REF].

The ferret model has widely been used to evaluate co-infections and viral fitness between seasonal (H1N1 or H3N2) and pH1N1 viruses. In a recent study [START_REF] Perez | Fitness of pandemic H1N1 and seasonal influenza A viruses during co-infection: evidence of competitive advantage of pandemic H1N1 influenza versus seasonal influenza[END_REF], all ferrets in contact with pandemic and seasonal virus infected ferrets contracted only the pandemic strain suggesting a biological advantage of pH1N1 over its seasonal counterparts.

In the influenza season of 2009, seasonal and pandemic H1N1 strains co-circulated in the Southern hemisphere raising the possibility of natural co-infections. In New Zealand, seasonal H1N1 was the dominant strain circulating during the early months and co-infections with pH1N1 were therefore of particular concern due to the risk of emergence of a pandemic virus containing a contemporary seasonal H1N1 neuraminidase gene; a gene which imparts oseltamivir resistance. The two viruses circulated in the country with varying ratios throughout the winter: in the week of June 8th, 14% of the influenza positive samples were confirmed as pH1N1, this number rising to 80% in the week of June 29th (Centers for Disease Control and Prevention, 2009b). Natural co-infection events involving pH1N1 and seasonal H1N1 viruses have been described but so far no natural reassortant have been identified [START_REF] Scalera | The first pandemic of the 21st century: a review of the 2009 pandemic variant influenza A (H1N1) virus[END_REF]. Another fear is that pH1N1 might reassort with highly pathogenic H5N1 strains eventually leading to a much higher case fatality rate.

Risk factors for infection with pandemic influenza viruses

Several factors are associated with increased susceptibility to pandemic influenza viruses. Underlying medical conditions such as hypertention, pulmonary diseases and immunocompromised status all contribute to increased susceptibility [START_REF] Campbell | Risk of severe outcomes among patients admitted to hospital with pandemic (H1N1) influenza[END_REF]. The mechanisms related to an increased risk for infection with zoonotic viruses such as H5N1

are not yet fully elucidated, however, reports suggest that exposure to wild birds [START_REF] Bouscambert-Duchamp | Avian influenza in children][END_REF] or variations in host genes [START_REF] Boon | Host genetic variation affects resistance to infection with a highly pathogenic H5N1 influenza A virus in mice[END_REF][START_REF] Ocana-Macchi | Hemagglutinin-dependent tropism of H5N1 avian influenza virus for human endothelial cells[END_REF][START_REF] Srivastava | Host genetic background strongly influences the response to influenza a virus infections[END_REF] are important. Pre-existing diseases also place individuals at higher risk to severe outcome with pH1N1 infection but it should be noted that factors such as age, obesity, secondary bacterial infections, and pregnancy have been the focus of research. With mechanisms not yet understood, young people make up the majority of pH1N1 cases which is in direct contrast to what is observed with seasonal influenza. Age stratified studies show that the elderly are at reduced risk of infection or developing complications [START_REF] Dawood | Emergence of a novel swine-origin influenza A (H1N1) virus in humans[END_REF][START_REF] Hancock | Cross-reactive antibody responses to the 2009 pandemic H1N1 influenza virus[END_REF], strongly suggesting age-related susceptibility. Likely, the presence of pre-existing antibodies that cross react with pH1N1 are associated with lowered risk in the elderly. Obesity is a more recent-ly identified risk factor. In one report, obesity was among the most frequently identified underlying conditions in fatal pH1N1 cases [START_REF] Vaillant | Epidemiology of fatal cases associated with pandemic H1N1 influenza 2009[END_REF]. Secondary bacterial infections are common in children and adults previously infected with influenza and it has been demonstrated that bacterial pneumonia commonly co-exists with pH1N1 (Centers for Disease Control and Prevention, 2009b) and is associated with death or hospitalization in the U.S. [START_REF] Louie | Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California[END_REF]. Lastly, for reasons unknown, pregnant or post partum women (up to 2 weeks) have more severe disease with pH1N1 [START_REF] Jamieson | H1N1 2009 influenza virus infection during pregnancy in the USA[END_REF][START_REF] Louie | Severe 2009 H1N1 influenza in pregnant and postpartum women in California[END_REF][START_REF] Pratt | Pandemic A (H1N1) 2009 influenza-an enhanced hazard during pregnancy[END_REF]. Animal studies are required to elucidate the mechanisms that occur during or immediately after pregnancy that increases disease severity to pH1N1.

Immunity to zoonotic and pandemic influenza viruses

Infection with all influenza viruses triggers both innate and adaptive immunological responses. Effector mechanisms include, but are not limited to, cytokines, macrophage and natural killer (NK) cells, all of which provide the first line of defense to influenza by limiting replication and spread of the virus in the respiratory tract. When viruses are not controlled by innate immune mechanisms, the host then relies upon adaptive responses which include T and B lymphocytes. Antibodies raised against hemagglutinin (HA) are especially important in the response to influenza virus because they neutralize virus particles and limit infection [START_REF] Possee | Studies on the mechanism of neutralization of influenza virus by antibody: evidence that neutralizing antibody (anti-haemagglutinin) inactivates influenza virus in vivo by inhibiting virion transcriptase activity[END_REF][START_REF] Tamura | Neutralization and infection-enhancement epitopes of influenza A virus hemagglutinin[END_REF][START_REF] Bizebard | Structure of influenza virus haemagglutinin complexed with a neutralizing antibody[END_REF]. Taken together, innate and adaptive immune mechanisms cooperatively protect against influenza infection and disease. Although these factors play an important role in the defense against common influenza subtypes H1N1, H3N2 in humans and animals [START_REF] Sladkova | The role of cytokines in the immune response to influenza A virus infection[END_REF][START_REF] Svitek | Severe seasonal influenza in ferrets correlates with reduced interferon and increased IL-6 induction[END_REF], little is known of their relevance with regards to putative zoonotic or pandemic strains, H5N1 and H1N1 respectively.

Despite limited knowledge of immune mechanisms associated with zoonotic and pandemic strains, emerging evidence point to the role of antibody and effector cells. In mice, neutrophils and macrophages are important [START_REF] Tumpey | Pathogenicity of influenza viruses with genes from the 1918 pandemic virus: functional roles of alveolar macrophages and neutrophils in limiting virus replication and mortality in mice[END_REF]. In contrast, elderly survivors of the 1918 H1N1 pandemic have sustained circulating B lymphocytes that secrete antibodies able to neutralize virus [START_REF] Yu | Preparation of armored RNA as a control for multiplex real-time reverse transcription-PCR detection of influenza virus and severe acute respiratory syndrome coronavirus[END_REF], demonstrating that neutralizing antibodies may have played a role in immunity to 1918 H1N1. Little is known about later pandemics although reports revealed that a proportion of humans have cross reactive antibodies to pandemic H2N2 or H3N2 viruses after exposure to a different subtype of virus [START_REF] Pyhala | Antibody status to influenza A/Singapore/1/57(H2N2) in Finland during a period of outbreaks caused by H3N2 and H1N1 subtype viruses[END_REF][START_REF] Viboud | Multinational impact of the 1968 Hong Kong influenza pandemic: evidence for a smoldering pandemic[END_REF]. Much of what is known about immune responses to avian H5N1 strains involves studies in animal models. In ferrets, molecules important for T and B lymphocyte signaling are down-regulated and the chemically-induced inhibition of chemokine CXCR3 signaling results in decreased disease severity of H5N1, thus pointing to important roles for innate and adaptive immune responses. In in vitro studies, H5N1 viruses attenuate type I interferon responses in human bronchial epithelial cultures seemingly as a means to maintain a high level of virulence [START_REF] Zeng | Highly pathogenic avian influenza H5N1 viruses elicit an attenuated type i interferon response in polarized human bronchial epithelial cells[END_REF]. Healthy naïve human subjects possess strong CD4 + T lymphocyte responses to HA, NA, M and NP proteins of H5N1 presumably because of the genetic conservation between these proteins and those of recent circulating H1N1, H2N2, H3N2 strains [START_REF] Roti | Healthy human subjects have CD4+ T cells directed against H5N1 influenza virus[END_REF].

Studies that show reactivity in humans to ancient influenza strains show that [i] a great amount of antibodies to zoonotic or pandemic strains are naturally present in naïve populations and [ii] older humans possess a large degree of immunological memory. These observations suggest a degree of conservation between epitopes of ancient and current pandemic viruses as exemplified in the elderly, where a large proportion of this age group have pre-existing serum antibodies with reactivity to pH1N1 [START_REF] Hancock | Cross-reactive antibody responses to the 2009 pandemic H1N1 influenza virus[END_REF]. The strength of such preexisiting immunity at the population level is likely a key factor to whether a zoonotic influenza virus has the capacity to spread widely in humans.

Reports suggest that the degree of preexisiting humoral immunity to pH1N1 viruses associates positively with age [START_REF] Hancock | Cross-reactive antibody responses to the 2009 pandemic H1N1 influenza virus[END_REF][START_REF] Itoh | In vitro and in vivo characterization of new swine-origin H1N1 influenza viruses[END_REF]. Children (6 months-9 years) and adults (18-64 years) had a low level of pre-existing serum antibodies to pH1N1, whereas, older adults (≥ 60 years) had a much greater level. It is believed that previous exposure to older strains of H1N1 (such as the 1918 pandemic) induced cross reactive antibody responses to antigenically similar pH1N1 in mainly older individuals. Although we are gaining some insights, we are yet to fully understand the factors required for immune responses to zoonotic and pandemic strains of influenza despite their importance.

Pandemic preparedness

In 2005, the WHO released an updated set of recommendations for preparation and response to future influenza pandemics. These recommendations included proper planning, surveillance, intervention, reduction and evaluation during the different pandemic phases (World Health Organization, 2009). During the first three phases of a potential pandemic, the recommendations are intended to increase communication between local and national health authorities as well as the WHO to minimize improper response plans. In pandemic phase 4, the focus shifts to containing the new influenza virus. This includes actions at the household, local, national and multi-national levels. The national and international responsibilities include continued surveillance and reporting to the WHO as well as the sharing of specimens and reagents with other countries for further virus characterization. National agencies are also recommended to begin releasing antivirals and vaccines, if available. These responsibilities continue through the remaining phases of the pandemic, but for phases 5 and 6 national agencies are recommended to increase vaccine and antiviral availability as well as help to provide resources to nations with limited resources as well as to countries which may have a heavier burden of disease. As the pandemic begins to subside, the WHO recommends countries remain vigilant for future waves of disease by continuing to monitor activity and to begin rebuilding inventories of therapeutics and vaccine. Assessing the effectiveness of measures taken during the present pandemic will help for future pandemic preparedness. Because these are merely suggested guidelines, many countries take different steps in an effort to prevent and minimize the effect of the pandemic, however, the steps taken during the current pandemic across many nations were very similar.

In Mexico, once a "sanitary emergency" was declared, local and national agencies established weekly meetings. Various forms of media, including news broadcasts, posters and brochures were disseminated instructing the general population to use masks, wash hands frequently and utilize alcohol gel sanitizers, as well as to avoid crowded events and to remain home as a form of self quarantine [START_REF] Cordova-Villalobos | The influenza A(H1N1) epidemic in Mexico. Lessons learned[END_REF]: very similar non pharmaceutical interventions as recommended by WHO or the U.S. CDC [START_REF] Jain | Novel influenza A(H1N1): clinical presentation, diagnosis, and management[END_REF].

From late April to mid May, Australia attempted to prevent entry of pH1N1 by increasing surveillance of incoming travelers. Pilots were instructed to monitor the health of passengers [START_REF] Appuhamy | The changing phases of pandemic (H1N1) 2009 in Queensland: an overview of public health actions and epidemiology[END_REF]. Thermal scanners were also installed in airports to identify incoming passengers with fevers (Department of Health and Ageing, 2009). Passengers identified as sick were treated by border nurses and referred to health units [START_REF] Appuhamy | The changing phases of pandemic (H1N1) 2009 in Queensland: an overview of public health actions and epidemiology[END_REF]. Surveillance of the general population was also greatly increased by establishing temporary flu clinics to respond to clusters of disease [START_REF] Appuhamy | The changing phases of pandemic (H1N1) 2009 in Queensland: an overview of public health actions and epidemiology[END_REF]. These clinics helped to prevent overcrowding of hospitals. Once the virus had entered, Australian health officials attempted to contain the viral spread by closing schools in which a student was diagnosed with the novel virus strain [START_REF] Waterer | Public health management of pandemic (H1N1) 2009 infection in Australia: A failure![END_REF]. School closures were also employed in many other countries, such as the U.S. and Mexico, to curb the spread of the pandemic [START_REF] Balinska | Behavioural responses to influenza pandemics: what do we know?[END_REF]. After the virus could no longer be contained, the focus shifted to providing resources to those most at risk of severe complications [START_REF] Appuhamy | The changing phases of pandemic (H1N1) 2009 in Queensland: an overview of public health actions and epidemiology[END_REF], as did most other countries. Similar responses were seen worldwide with various successes. The ultimate goal is to modify current pandemic preparedness responses in order create more effective plans.

Conclusion

Despite the huge public health consequence, even with its comparatively low mortality the World Bank estimated the cost burden of pH1N1 at 0.7 to 4.8% of the global gross domestic product, we know remarkably little about the factors modulating the emergence of pandemic influenza in humans. The latest pandemic is a great example of how much we do not know. The pandemic virus likely came from swine, not directly from birds; the virus emerged in the Americans, not Asia; the virus did not contain any of the genetic markers associated with viral pathogenicity; the virus was of a subtype already in humans; the hardest hit were the young, not the elderly; the disease severity was not unlike epidemic influenza in most age groups. Although in hindsight we can reconcile some of these observations, many we cannot. Despite some of the perceived deficiencies in the planning for and response to the latest pandemic, one thing is for sure, we have been offered our best opportunity to follow the emergence and spread of a pandemic in real time. The consequence of this is that whereas the past pandemics provided only minimal guidance, the data collected during the next few years will hopefully place us in a stronger position to deal with the next

Figure 1 :

 1 Figure 1: Timeline of modern influenza pandemics. † As of 19 March 2010, the H1N1 pandemic status remains at phase 6. * As of 19 March 2010 there have been 16,713 reported deaths attributed to the 2009 H1N1 pandemic. This value is an underestimate because many deaths are not confirmed cases of influenza and may be the result of other conditions.

Figure 2 :

 2 Figure 2: Influenza activity in the United States (adapted from CDC data). † The percent positive includes only subtyped laboratory confirmed cases divided by the total number of samples tested.* Seasonal Influenza values include both H1N1 and H3N2 subtyped viruses.


Abdeslam Kadrani
email: abdeslam.kadrani@avignon.inra.fr

Bénédicte Quilot-Turion

Mohamed Mahmoud Ould Sidi

Nadine Hilgert

Michel Génard

Françoise Lescourret

Sujet du projet

A tool to conveive sustainable production systems_case of the peach-brown rot couple

Abdeslam Kadrani

I. Contenu scientifique du projet

 Contexte et objectifs du projet

Les systèmes de production doivent s'adapter, dans le contexte de changement climatique, pour faire face à la croissance de la demande sociale en termes de qualités organoleptiques nutritionnelles et environnementale des produits alimentaires. En effet, l'amélioration de la qualité demeure toujours une question d'importance pour la production de fruits mais le contrôle des maladies végétales est désormais une priorité pour la santé des végétaux, la sécurité alimentaire et les caractéristiques environnementales. Par conséquent, la question cruciale pour l'avenir est de savoir comment concevoir les meilleures combinaisons des ressources génétiques et des pratiques culturelles adaptées à un environnement spécifique afin de répondre à l'exigence des consommateurs en termes de qualité. Dans le cadre de ce projet, nous avons étudié le cas du système « pêcher-monilioses ». Les monilioses sont les principales maladies fongiques aériennes dommageables pour les arbres fruitiers à noyaux, et notamment le pêcher. Elles occasionnent des traitements chimiques à l'approche de la maturité des fruits ce qui est un problème de point de vue environnement et santé. Aucune autre alternative (lutte biologique, résistance génétique, …) à la lutte chimique n'est disponible, d'où l'application nécessaire des fongicides jusqu'à la pré-récolte. De plus, l'utilisation massive des fongicides ne permet pas toujours de contrôler la maladie et parfois elle pourrait favoriser l'apparition de souches résistantes. Dans ce contexte, on se propose d'élaborer un outil de conception de systèmes de production durables associant idéotypes de pêcher et opérations culturales innovantes pour réduire la sensibilité à la pourriture brune (la moniliose) en verger et conserver ou améliorer la qualité des fruits.

Le projet PRIMo (Peach-Rot Ideotypes Modelling) repose sur une démarche alliant l'expérimentation et la modélisation. L'expérimentation mène à quantifier l'inoculum aérien et suivre son évolution temporelle en fonction de facteurs climatiques pour permettre d'évaluer les risques épidémiques aux différents stades de développement du fruit. Le second volet de l'expérimentation consiste en une étude de la variabilité génétique d'un ensemble de variétés de pêcher contrastées pour leur résistance aux monilioses. Les informations collectées de l'expérimentation sont utilisées pour modifier et améliorer un modèle existant de 'Fruit Virtuel' simulant la croissance et la qualité du fruit en fonction de pratiques culturales et du climat (Lescourret et Génard, 2005). Enfin, le modèle développé couplé avec les méthodes d'optimisation permettent d'identifier les stratégies de production à la fois plus durable et économiquement viables combinant des idéotypes et des pratiques novatrices. Au début du projet j'ai d'abord réalisé une étude bibliographique approfondie sur le sujet et j'ai assisté à plusieurs réunions de travail avec l'équipe de projet. Ce qui m'a permis de mieux comprendre les composantes du modèle de 'Fruit Virtuel' ainsi que son fonctionnement. Ce modèle décrit l'élaboration de la croissance et la qualité des fruits. Ses entrées sont le climat (température, humidité, rayonnement), la charge fruitière (éclaircissage), stress hydrique (irrigation) et des paramètres génétiques (génotypiques). Après l'analyse de la version originale du modèle, j'ai entrepris des modifications sur la base des données génotypiques disponibles afin d'améliorer certaines de ces composantes (équations de croissance).

 Programme du travail et Méthodologie

Ensuite, une analyse de sensibilité du modèle est réalisé afin d'identifier les paramètres importants, c'est-à-dire ceux qui affectent significativement les différentes sorties du modèle (probabilité d'infection, qualité, rendement). Six principaux paramètres intervenant dans la croissance, le métabolisme du sucre et la sensibilité aux cracks (pourritures) ont servi pour définir les génotypes. L'étape suivante était consacrée à la formulation du problème de la conception des idéotypes de pêche satisfaisant à l'exigence de qualité des fruits et la sensibilité à la pourriture brune en un problème d'optimisation. Les variables de décision (ou de sélection) de ce problème sont les paramètres génétiques précédemment identifiés et les critères considérés pour l'optimisation sont : le poids frais du fruit, sa teneur en sucre (qualité gustative), et la densité des cracks sur la peau (portes d'entrée pour les monilioses). Les deux premiers critères sont à maximiser, tandis que le dernier est à minimiser. Plusieurs contraintes ont été aussi prises en compte sur les critères et les paramètres.

Les problèmes de décision multicritère, leurs contextes d'application et les principales approches de résolution existantes ont été aussi abordés et étudiés en profondeur. Les méthodes exactes de résolution explorent la totalité de l'espace de recherche et elles peuvent produire une solution optimale, lorsque cet espace est de petite taille et aucune contrainte de temps n'est imposée. Cependant, le temps de calcul nécessaire pour atteindre une solution optimale peut être important. Dans ce cas, les méthodes approchées représentent une bonne alternative indispensable et complémentaire. Ces méthodes ne cherchent pas à obtenir la solution optimale du problème traité, mais elles peuvent donner une solution proche de l'optimum, en un temps de calcul relativement court. Cet avantage vient du fait que ces méthodes, contrairement aux méthodes exactes, examinent seulement une partie de l'espace de recherche et, qu'elles sont souvent guidées par des heuristiques dans le choix des zones visitées. Nous avons proposé deux approches pour résoudre le problème multi-objectifs construit : l'une est de type Pareto dominance (NSGA-II : Nondominated Sorting Genetic Algorithm II) et l'autre est une approche agrégative et résolue par la méthode d'optimisation par essaims particulaires. Afin de valider les approches et les méthodes proposées, nous avons couplé le modèle de 'Fruit Virtuel' avec les méthodes d'optimisation (les algorithmes évolutionnaires et les algorithmes de l'optimisation par essaim particulaires) et nous avons réalisé une série de tests qui ont prouvé la qualité de nos approches. Les scenarii que nous avons simulés sont variés pour différents climats, différents niveaux de charge en fruits et différents pratiques culturelles.

 Résultats

Nous avons utilisé le modèle de 'Fruit Virtuel' pour simuler la qualité du fruit de génotypes virtuels, dans deux sites climatiques (Avignon et Bordeaux), pour des niveaux de charge en fruits et le stress hydrique contrasté. Les traits simulés par le modèle sont la masse de fruit, la teneur en sucre, et la sensibilité à la pourriture brune (densité de la peau de fissures). Les simulations ont permis de montrer des interactions entre les génotypes, les pratiques et le climat. Des antagonismes forts entre les caractères d'intérêt ont été observés. Les simulations ont également montré un comportement émergent du modèle illustrant comment des comportements inattendus des systèmes complexes peuvent être prédits et analysés par des modèles de simulation basés sur

Rapport d'activités 2010/2012

Projet 0902-011 des processus physiologiques. A partir de ces résultats, les meilleures combinaisons de génotypes, de pratiques culturales, et de climats optimisant les traits de la qualité du fruit ont été identifiés. Ce travail a permis de démontrer l'utilité de la conception assistée par des modèles pour proposer des systèmes de production combinant des pratiques culturelles et des idéotypes variétaux innovantes. Il a donné des résultats intéressants qui ont fait l'objet des publications dans des revues internationales (EJA, IJSIR) et des conférences nationales et internationales avec comité de lecture (ESA AGRO'2010, ROADEF'2011, ICSI'2011, ISMFROM'2011, META'2012).

 Mots clés : Modèle écophysiologique, idéotypes, pêches, Optimisation multi-objectifs, NSGA-II, Optimisation par essaim de particules, Algorithme génétique, Performances de comparaison.

II. Articles

• Articles publiés :

1. Kadrani A., Ould Sidi M. M., Quilot-Turion B., Génard M., Lescourret F. (2012) L'algorithme NSGA-II a fourni une grande diversité de solutions (paramètres génétiques) parmi lesquelles le décideur peut choisir le compromis le mieux adapté entre les critères suivant un objectif particulier. La plupart des solutions ont été distribuées le long de front de Pareto assurant une bonne convergence de l'algorithme. Par ailleurs, l'algorithme a fourni d'autres solutions dans des zones non encombrées qui constituent des alternatives originales pour le décideur. Les résultats ont confirmé le fort antagonisme entre les critères considérés. Les gros fruits possèdent une faible teneur en sucre et une hausse densité de fissures et pour une masse donnée, les fruits qui ont amélioré leur taux de sucre ont une densité de fissures plus élevée. Dans un schéma de sélection actuel, la masse de fruit serait le seul critère considéré, mais d'autres schémas alternatifs pourraient être envisagés pour l'avenir, en favorisant la qualité organoleptique ou les pratiques respectueuses de l'environnement.

Mots clés: Optimisation, modèle écophysiologique, idéotypes, pêches.

• Article en cours de traitement : Mots clés: Optimisation multi-objectifs, NSGA-II, performances de comparaison, Modèle de 'Fruit Virtuel'.

• Article en cours de rédaction :

Résumé:

Dans le souci d'améliorer la qualité des solutions pour les systèmes de productions durables de fruits de pêches, nous nous sommes intéressés à étudier une autre famille d'algorithmes inspirés également de la biologie, il s'agit de l'optimisation par essaims particulaires. Cette étude est motivée par l'application réussie de cette méthode dans le cas d'un problème avec un seul objectif (Kadrani & al. 2012).

Les algorithmes évolutionnaires sont connus comme des méthodes robustes et efficaces pour résoudre les problèmes multi-objectifs mais ils demandent un effort considérable de calcul pour effectuer des mécanismes permettant de préserver la diversité des solutions au sein de la population et nécessitent aussi beaucoup de paramètres. Les algorithmes de l'optimisation par essaim de particules sont basés sur la métaphore biologique. Ils sont inspirés d'imiter le mouvement des oiseaux, des poissons ou d'autres organismes dans leur recherche de nourriture. Chaque particule (oiseaux, poissons ...) est guidée par sa propre expérience ainsi que par le partage social de l'information dans l'essaim. Ces algorithmes ont deux objectifs principaux: améliorer la convergence vers le vrai front de Pareto et produire un ensemble bien réparti de solutions non-dominées. Grand nombre de versions de l'algorithme de l'optimisation par essaim de particules dans le cas multi-objectifs (MOPSO) ont été proposés dans la littérature. La version proposée, MOPSO-CD, par Raquel et Naval (2005) est l'une des meilleurs algorithmes existants de MOPSO permettant de faire face à des problèmes multi-objectifs. L'algorithme MOPSO-CD intègre certaines mécanismes des algorithmes évolutionnaires pour maintenir la diversité des solutions tels que l'élitisme, les opérateurs de la diversité, les opérateurs de mutation et la distance d'encombrement.

4.

  Kadrani A., Ould Sidi M. M., Quilot-Turion B., Génard M., Lescourret F. "Comparison of evolutionary and particle swarm optimization in improving fruit quality".



  Réunions régulières sur Avignon et Montpellier avec l'équipe du travail.  Etude bibliographique: Modèle de 'Fruit Virtuel', modélisation et aide à la décision.  Analyse du modèle de 'Fruit Virtuel'.  Identification des paramètres et des entrées du modèle qui influencent ses sorties.  Choix de l'approche de résolution.  Développement et implémentation des algorithmes. 

Tests de validation Rapport d'activités 2010/2012 Projet 0902-011

. "Particle swarm optimization to design ideotypes for sustainable fruit production systems".

  La méthode a été couplée avec le modèle de 'fruit virtuel' pour faire des simulations pour différents scénarios liés au climat, le niveau de charge en fruits et le stress hydrique. Les résultats obtenus par l'algorithme montrent clairement l'antagonisme entre la masse de fruit et sa teneur en sucre pour les différents cas de priorité d'importance considérés pour les deux traits au cours de la croissance finale du fruit. En effet, une augmentation excessive de la masse du fruit entraîne une teneur faible en sucre ce qui peut être expliqué en partie par l'accumulation de l'eau et une grande dilution de sucres dans le fruit. Une croissance excessive de fruits se traduit également par une augmentation de la densité des cracks. Turion B., Ould Sidi M. M., Kadrani A.,Hilgert N., Génard M., Lescourret F. (2011). "

	Rapport d'activités 2010/2012	Projet 0902-011
	2. Quilot-

International Journal of Swarm Intelligence Research (IJSIR) 3 (2), 1-19.

Résumé:

Dans le travail

(Quilot et al., 2011)

nous avons formulé le problème de la conception des idéotypes de pêche répondant à l'exigence de qualité des fruits et d'une faible sensibilité à la pourriture brune dans un environnement donné sous forme d'un problème d'optimisation multiobjectifs. L'approche de résolution choisie pour résoudre ce problème est une méthode de Pareto. Malheureusement, cette approche se concentre sur la génération du front Pareto et mais elle ignore l'étape de la prise de décision puisque tous les critères sont considérés de même importance

(Coello, Veldhuizen, & Lamont, 2007)

. Dans ce travail nous avons tenu en considération le fait que le décideur varie ses préférences sur les traits du fruit suivant les marchés. Ainsi, le poids de chaque critère est déterminé en fonction de l'importance de chaque objectif et la solution finale dépend de la perspective du décideur. C'est pour cette raison que nous avons adopté l'approche d'agrégation pondérée pour transformer le problème multi-objectif de la conception des idéotypes de pêche à un problème avec un seul objectif. Nous avons par la suite utilisé la méthode d'optimisation par essaim de particules (Particle Swarm Optimization (PSO)) pour résoudre le problème obtenu. C'est une méthode d'optimisation stochastique basée sur la reproduction d'un comportement social et développée par

Kennedy et Eberhart (1995)

. Les résultats trouvés sont considérés satisfaisants pour les systèmes de productions durables de fruits de pêches. Enfin, une petite comparaison est faite entre l'algorithme d'essaim particulaires et l'algorithme génétique (AG) et a montré que la méthode PSO permet d'obtenir des meilleures performances que l'AG.

Mots clés: Optimisation par essaim de particules, algorithme génétique, idéotypes, pêches.

Optimization of parameters of the 'Virtual Fruit' model to design peach genotype for sustainable fruit production systems".

  European Journal of Agronomy (EJA) 42 (2012), 34-48. L'objectif de ce travail est d'illustrer comment un modèle de simulation basé sur un processus écophysiologique pourrait être éventuellement utilisé pour concevoir des génotypes et de proposer des systèmes de production durables en appliquant une méthodologie d'optimisation. Dans ce cadre, nous avons pris le système pêche-monilioses comme cas d'étude et nous avons utilisé le modèle du 'Fruit Virtuel' pour simuler la qualité du fruit de génotypes virtuels pour un ensemble donné de pratiques culturelles et un environnement cible. Le challenge est de concevoir des idéotypes de pêche permettant d'optimiser le compromis entre trois critères antagonistes pour quatre différents scénarios culturels. Il s'agit donc, de maximiser deux critères liés à la qualité gustative à savoir le poids frais du fruit et sa teneur en sucre. Un troisième critère à minimiser lié à la sensibilité à la pourriture jaune, il s'agit de réduire la densité des cracks sur la peau pour limiter la contamination aux monilioses. Le problème ainsi reformulé est un problème d'optimisation multi objectifs où les objectifs sont les trois critères (le calibre de fruit, sa teneur en sucre et la densité des cracks) et les variables d'optimisation sont les six paramètres de 'fuit virtuel', sélectionnés à la base d'une analyse de sensibilité. Le modèle de 'fruit virtuel' est par la suite couplé à un algorithme évolutionnaire multi objectifs, à savoir NSGA-II, pour résoudre le problème multi objectifs obtenu.

	Résumé:

Compromising NSGA-II performances and stopping criteria: case of virtual peach design". Résumé:

  Les algorithmes évolutionnaires de l'optimisation multicritères permettent d'explorer l'espace de recherche dans un temps de calcul raisonnable. Ces méthodes fournissent au décideur un ensemble de solutions diversifiées. Le décideur doit donc faire son choix final de meilleur compromis entre les critères. Cependant, une difficulté lors de l'utilisation de ces algorithmes est le temps convenable pour arrêter le calcul. Généralement, on utilise comme critère d'arrêt de l'algorithme un nombre maximum prédéfini de générations. Malheureusement, cela peut conduire à de nombreuses générations inutiles et par conséquent diminuer le taux de la qualité de la solution par rapport au temps de calcul. Les auteurs de(Roudenko & Schoenauer, 2004.) ont discuté cet aspect, et l'ont illustré par l'algorithme NSGA-II(Deb et al. 2002). Ils ont introduit un nouveau critère d'arrêt qui peut en partie remédier à cet inconvénient. Ce critère

3. Ould Sidi M. M., Kadrani A., Quilot-Turion B., Génard M., Lescourret F. "

Rapport d'activités 2010/2012 Projet 0902-011 consiste

  à arrêter le processus lorsque l'algorithme a effectué un nombre donné d'itérations sans améliorer la solution. Il est basé sur l'écart type de la distance de l'encombrement, une mesure de contrôle de la diversité de NSGA-II, pour un nombre donné d'itérations. Les auteurs ont testé ce critère dans le cas des problèmes bi objectifs sans contraintes et ont affirmé qu'il est difficile de l'utiliser avec NSGA-II pour des problèmes d'optimisation sous contraintes avec plus que deux critères. La question que nous abordons ici est la possibilité d'utiliser ce critère d'arrêt afin d'améliorer le temps de calcul de NSGA-II dans le cas d'un problème d'optimisation à trois objectifs avec contraintes. Ainsi, nous comparons les résultats obtenus avec la version originale de NSGA-II à ceux obtenus lors de la modification de la version originale en incluant ce critère d'arrêt. Dans ce travail, nous avons pris comme d'étude le problème de la conception des stratégies de gestion innovantes qui optimisent les interactions génotype x environnement x pratiques pour limiter la contamination des fruits de pêchers par la pourriture brune tout en conservant ou en améliorant la qualité des fruits. On a donc six paramètres génétiques à combiner pour créer des génotypes et trois traits de la qualité des fruits et de la sensibilité à la pourriture brune à optimiser. Ces traits constituent les critères de du problème d'optimisation multi-objectifs à résoudre et les paramètres génétiques représentent ses variables de décision et on a six contraintes sur ces variables. Le modèle de 'Fruit Virtuel' sera couplé à ses algorithmes pour faire les simulations.

Rapport d'activités 2010/2012 Projet 0902-011

Nous avons choisi cette version de MOPSO pour comparer ses performances avec celles de NSGA-II, considéré comme une référence par la communauté des algorithmes évolutionnaires, dans le cadre du projet des systèmes de productions durables de fruits de pêches Mots clés: Optimisation multi-objectifs, Optimisation par essaim de particules, MOPSO, MOPSO-CD, NSGA-II, pêches. . Evolut. Computat. 6, 182-197.  Eberhart, R., & Kennedy, J., 1995. A new optimizer using particle swarm theory. Paper presented at the 6th international symposium on micro machine and human science, IEEE service center, Piscataway, NJ, Nagoya, Japan.  Genard, M., Bertin, N., Borel, C., Bussieres, P., Gautier, H., Habib, R., et al., 2007. Towards a virtual fruit focusing on quality: modelling features and potential uses. Journal of Experimental Botany, 58( 5), 917-928.  Hammer, G. L., Kropff, M. J., Sinclair, T. R., & Porter, J. R., 2002. Future contributions of crop modelling -from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. European Journal of Agronomy, 18(1-2), 15-31.  Kadrani A., Ould Sidi M. M., Quilot-Turion B., Génard M., Lescourret F. (2012) and Goal Programming, April 14-16, 2004, Hammamet, Tunisia.

III. Communications et Colloques


domain: math info

text:
Sébastien Massoni
email: sebastien.massoni@gmail.com

Madalina Olteanu
email: madalina.olteanu@univ-paris1.fr

Nathalie Villa-Vialaneix
email: nathalie.villa@toulouse.inra.fr

Which dissimilarity is to be used when extracting typologies in sequence analysis? A comparative study

Originally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. The choice of an appropriate dissimilarity measure is a major issue when dealing with sequence analysis for life sequences. Several dissimilarities are available in the literature, but neither of them succeeds to become indisputable. In this paper, instead of deciding upon one dissimilarity measure, we propose to use an optimal convex combination of different dissimilarities. The optimality is automatically determined by the clustering procedure and is defined with respect to the within-class variance.

Introduction

Originally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. However, measuring dissimilarities or similarities for categorical sequences has always been a challenge in practice. This challenge becomes even harder in social sciences where these measures need some theoretical foundations. Choosing the appropriate dissimilarity or dissimilarity for life-sequence analysis is a key issue which relates to the resulting typologies. The literature on this topic is very rich and still very debated. Each method has its own advantages and drawbacks [START_REF] Elzinga | Sequence similarity: a nonaligning technique[END_REF][START_REF] Robette | Explorer et décrire les parcours de vie: les typologies de trajectoires. CEPED[END_REF]. In this paper, we introduce a different approach. Instead of deciding upon one specific dissimilarity, we propose to use several ones, optimally combined. We consider three main categories of dissimilarities : χ 2 -metric [START_REF] Fénelon | The sequence of steps in the analysis of youth trajectories[END_REF], optimal matching [START_REF] Robette | Explorer et décrire les parcours de vie: les typologies de trajectoires. CEPED[END_REF] and non-alignement techniques [START_REF] Elzinga | Sequence similarity: a nonaligning technique[END_REF]. Since our final goal is to extract typologies for life sequences, we are looking for the best convex combination of the different dissimilarities which provides the best clusters in terms of homogeneity. The algorithm used for clustering is a self-organizing map (SOM). We use a modified version of the online relational SOM introduced in [START_REF] Olteanu | On-line relational som for dissimilarity data[END_REF]. In the algorithm proposed here, an additional step is added to each iteration. During this step, the coefficients of the convex combination of dissimilarities are updated according to a gradient-descent principle which aims at minimizing the extended within-class variance. The rest of the manuscript is organized as follows : Section 2 reviews the different dissimilarities usually used to handle categorical time series. Section 3 describes the online relational SOM for multiple dissimilarities. Section 4 presents a detailed application for sequences related to schoolto-work transitions.

Dissimilarities for Life Sequences

Three main categories of dissimilarities were addressed in our study. Each of them is briefly described below. χ 2 -distance. Historically, factor analysis was used first to extract typologies from life sequences, [START_REF] Fénelon | The sequence of steps in the analysis of youth trajectories[END_REF]. The sequences, which are categorical data, were transformed by running a multiple correspondence analysis (MCA) on the complete disjunctive table. Then, clustering methods adapted to continuous data were applied and the main typologies were extracted. Performing MCA and then computing the Euclidean distance on the resulting vectors is equivalent to computing the χ 2 -distance on the rows of the complete disjunctive table. The χ 2 -distance is weighting each variable by the inverse of the associated frequency. Hence, the less frequent situations have a larger weight in the distance and the rare events become more important. Also, the χ 2 -distance emphasizes the contemporary identical situations, whether these identical moments are contiguous or not. However, the transitions between statuses are not taken into account and input vectors are close only if they share contemporary statuses throughout time. Optimal-matching dissimilarities. Optimal matching, also known as "edit distance" or "Levenshtein distance", was first introduced in biology by [START_REF] Needleman | A general method applicable to the search for similarities in the amino acid sequence of two proteins[END_REF] and used for aligning and comparing sequences. In social sciences, the first applications are due to [START_REF] Abbott | Optimal matching methods for historical sequences[END_REF]. The underlying idea of optimal matching is to transform the sequence i into the sequence i ′ using three possible operations: insertion, deletion and substitution. A cost is associated to each of the three operations. The dissimilarity between i and i ′ is computed as the cost associated to the smallest number of operations which allows to transform i into i ′ . The method seems simple and relatively intuitive, but the choice of the costs is a delicate operation in social sciences. This topic is subject to lively debates in the literature [START_REF] Abbott | Sequence analysis and optimal matching methods in sociology. Review and prospect[END_REF][START_REF] Wu | Some comments on "sequence analysis and optimal matching methods in sociology, review and prospect[END_REF] mostly because of the difficulties to establish an explicit and sound theoretical frame. Among optimal-matching dissimilarities, we selected three dissimilarities: the OM with substitution costs computed from the transition matrix between statuses as proposed in [START_REF] Müller | Extracting knowledge from life courses: Clustering and visualization[END_REF], the Hamming dissimilarity (HAM, no insertion or deletion costs and a substitution cost equal to 1) and the Dynamic Hamming dissimilarity (DHD as described in [START_REF] Lesnard | Setting cost in optimal matching to uncover contemporaneous socio-temporal patterns[END_REF]). Obviously, other choices are equally possible and the costs may be adapted, depending whether the user wants to highlight the contemporaneity of situations or the existence of common, possibly not contemporary, sub-sequences. Non-alignment techniques. Since the definition of costs represents an important drawback for optimal-matching dissimilarities, several alternatives were proposed in the literature. Here, we considered three different dissimilarities introduced by C. Elzinga [START_REF] Elzinga | Sequence similarity: a nonaligning technique[END_REF][START_REF] Elzinga | Sequence analysis: metric representations of categorical time series[END_REF]: the longest common prefix (LCP), the longest common suffix or reversed LCP (RLCP) and the longest common subsequence (LCS). Dissimilarities based on common subsequences are adapted to handle transitions between statuses while they take into account the order in the sequence. They are also able to handle sequences of different lengths.

Relational SOM

Extracting typologies from life sequences requires clustering algorithms based on dissimilarity matrices. Generally, hierarchical clustering or Kmeans are used in the literature, [START_REF] Robette | Explorer et décrire les parcours de vie: les typologies de trajectoires. CEPED[END_REF]. In this paper, we focus on a different approach, based on a Self-Organizing Map (SOM) algorithm [START_REF] Kohonen | Self-Organizing Maps, 3rd Edition[END_REF]. The interest of using a SOM algorithm adapted to dissimilarity matrices was shown in [START_REF] Olteanu | Career-path analysis using optimal matching and self-organizing maps[END_REF]. Self-organizing maps possess the nice property of projecting the input vectors in a two-dimensional space, while clustering them. In [START_REF] Olteanu | Career-path analysis using optimal matching and self-organizing maps[END_REF], the authors used dissimilarity SOM (DSOM) introduced by [START_REF] Miikkulainen | Fast algorithm and implementation of dissimilarity self-organizing maps[END_REF]. OM with substitution cost defined from the transition matrix was used to measure the dissimilarity between sequences. While DSOM improves clustering by additionally providing a mapping of the typologies, it still has a major drawback: prototypes have to be chosen among the input vectors. Thus, the clustering doesn't allow for empty clusters, which may be quite restrictive in some cases. Moreover, this property of DSOM makes it very sensitive to the initialization. The computation time is also very important, since the research of the prototype is done exhaustively among all input vectors and the algorithm is of batch type. Online relational SOM. Inspired by the online kernel version of SOM [START_REF] Donald | The kernel self organising map[END_REF], [START_REF] Olteanu | On-line relational som for dissimilarity data[END_REF] recently proposed an online version of SOM for dissimilarity matrices, called online relational SOM. Online relational SOM is based on the assumption that prototypes may be written as convex combinations of the input vectors, as previously proposed in [START_REF] Hammer | Topographic processing of relational data[END_REF]. This assumption gives more flexibility to the algorithm, which now allows for empty clusters. Moreover, since the algorithm is online, the dependency on the initialization lessens and the computation time also decreases. In the online relational SOM, n input data, x1, . . . , xn, taking values in an arbitrary input space G, are described by a dissimilarity matrix ∆ = (δij )i,j=1,...,n such that ∆ is non negative (δij ≥ 0), symmetric (δij = δji) and null on the diagonal (δii = 0). The algorithm maps the data into a low dimensional grid composed of U units which are linked together by a neighborhood relationship H(u, u ′ ). A prototype pu is associated with each unit u ∈ {1, . . . , U } in the grid. To allow computation of dissimilarities between the prototypes (pu)u and the data (xi)i, the prototypes are symbolically represented by a convex combination of the original data pu ∼ i βuixi with βui ∈ [0, 1] and i βui = 1. Online multiple relational SOM. As explained in the introduction, the choice of a dissimilarity measure in social sciences is a complex issue. When the purpose is to extract typologies, the results of the clustering algorithms are highly dependent on the criterion used for measuring the dissemblance between two sequences of events. A different approach is to bypass the choice of the metric: instead of having to choose one dissimilarity measure among the existing ones, use a combination of them. However, this alternative solution requires an adapted clustering algorithm. Similarly to the multiple kernel SOM introduced in [START_REF] Olteanu | Multiple kernel self-organizing maps[END_REF], we propose the multiple relational SOM (MR-SOM). Here, D dissimilarity matrices measured on the input data, ∆ 1 , . . . , ∆ D , are supposed to be available. These matrices are combined into a single one, defined as a convex combination:

∆ α = d α d ∆ d where α d ≥ 0 and D d=1 α d = 1.
If the (α d ) are given, relational SOM based on the dissimilarity ∆ α aims at minimizing over (βui)ui and (α d ) d the following energy function :

E ((βui)ui, (α d ) d ) = n i=1 U u=1 H (f (xi), u) δ α (xi, pu(βu)) , where f (xi) is the neuron where xi is classified 4 , δ α (xi, pu(βu)) is defined by δ α (xi, pu(βu)) ≡ ∆ α i βu -1 2 β T u ∆ α βu and ∆ α i is the i-th row of the matrix ∆ α .
When there is no a-priori on the (α d ) d , we propose to include the optimization of the convex combination into an online algorithm that trains the map. Following an idea similar to that of [START_REF] Rakotomamonjy | [END_REF], the SOM is trained by performing, alternatively, the standard steps of the SOM algorithm (i.e., affectation and representation steps) and a gradient descent step for the (αi)i. To perform the stochastic gradient descent step on the (α d ), the computation of the derivative of E |x i = M u=1 H (f (xi), u) δ α (xi, pu(βu)) (the contribution of the randomly chosen observation (xi)i to the energy) with respect to α is needed. But,

D id = ∂E|x i ∂α d = M u=1 H (f (xi), u) ∆ d i βu -1 2 β T u ∆ d βu
, which leads to the algorithm described in Algorithm 1.

Application for the analysis of Life Sequences

Data. For illustrating the proposed methodology and its relevance for categorical time series analysis, we used the data in the survey "Generation 98" from CEREQ, France (http://www.cereq.fr/). According to the French National Institute of Statistics, 22,7% of young people under 25 were unemployed at the end of the first semester 2012. 5 Hence, Algorithm 1 Online multiple dissimilarity SOM 1: ∀ u and i initialize β 0 ui randomly in R and ∀ d, set α d . 2: for t=1,. . . ,T do 3:

Randomly choose an input xi 4:

Assignment step: find the unit of the closest prototype

f t (xi) ← arg min u=1,...,M
δ α,t (xi, pu(βu))

5:

Representation step: update all the prototypes: ∀ u,

β t ul ← β t-1 ul + µ(t)H(f t (xi), u) δ il -β t-1 ul 6:
Gradient descent step: update the dissimilarity:

∀ d = 1, . . . , D, α t d ← α t-1 d + ν(t)D t d and δ α,t ← d α t d δ d .
7: end for the question of how is achieved the transition from school to employment or unemployment is crucial in the current economic context. The dataset contains information on 16 040 young people having graduated in 1998 and monitored during 94 months after having left school. The labormarket statuses have nine categories, labeled as follows: permanent-labor contract, fixed-term contract, apprenticeship contract, public temporarylabor contract, on-call contract, unemployed, inactive, military service, education. The following stylized facts are highlighted by a first descriptive analysis of the data as shown in Figure 1: -permanent-labor contracts represent more than 20% of all statuses after one year and their ratio continues to increase until 50% after three years and almost 75% after seven years; -the ratio of fixed-terms contracts is more than 20% after one year on the labor market, but it is decreasing to 15% after three years and then seems to converge to 8%; -almost 30% of the young graduates are unemployed after one year.

This ratio is decreasing and becomes constant, 10%, after the fourth year. In this dataset, all career paths have the same length, the status of the graduate students being observed during 94 months. Hence, we suppose that there are no insertions or deletions and that only the substitution costs have to be defined for OM metrics. This is equivalent to supposing low substitution costs with respect to the insertion-deletion costs. This choice may be considered restrictive, since in this case the OM metrics will only highlight the contemporaneity of situations. However, Elzinga metrics such as the LCS used in the manuscript are built starting from common, although not contemporary, subsequences and are very simdistance) and the graphical illustrations were carried out using the TraMineR package [START_REF] Gabadinho | Analyzing and visualizing state sequences in r with traminer[END_REF]. The online multiple dissimilarity SOM was implemented by the authors. Seven different dissimilarities were considered: the χ 2 -distance, the Hamming dissimilarity (HAM), OM with substitution-cost matrix computed from the transition matrix as shown in Section 2, the dynamic Hamming dissimilarity (DHD) as defined in [START_REF] Lesnard | Setting cost in optimal matching to uncover contemporaneous socio-temporal patterns[END_REF], the longest common prefix (LCP), the longest common suffix or reversed LCP (RLCP), the longest common substring (LCS).

Preliminary study

Since the original data contain more than 16 000 input sequences and since the relational SOM algorithms are based on dissimilarity matrices, the computation time becomes rapidly very important. Training the map on the entire data set requires several hours or days of computing time. Hence, in order to identify the role of the different dissimilarities in extracting typologies, we considered several samples drawn at random from the data. For each of the experiments below, 50 samples containing 1 000 input sequences each were considered. For each sample, the seven dissimilarity matrices listed above were computed and normalized according to the max norm. In order to assess the quality of the maps, three indexes were computed : the quantization error and the dispersion between prototypes for quantifying the quality of the clustering and the topographic error for quantifying the quality of the mapping, [START_REF] Pölzlbauer | Survey and comparison of quality measures for selforganizing maps[END_REF]. These quality criterai all depend on the dissimilarities used to train the map but the results are made comparable by using normalized dissimilarities.

Optimal-matching metrics. The first experiment was concerned with the three optimal-matching metrics. The results are listed in Table 1. According to the mean values of the α's, the three dissimilarities contributed to extracting typologies. The Hamming and the dynamical Hamming dissimilarities have similar weights, while the OM with cost-matrix defined from the transition matrix has the largest weight. The mean quantization error computed on the maps trained with the three dissimilarities optimally combined is larger than the quantization error computed on the map trained with the OM metric only. On the other hand, the topographic error is improved in the mixed case. In this case, the joint use of the three dissimilarities provides a trade-off between the quality of the clustering and the quality of the mapping. The results in Table 1 confirm the difficulty to define adequate costs in OM and the fact that the metric has to be chosen according to the aim of the study : building typologies (clustering) or visualizing data (mapping). 1. Preliminary results for three OM metrics Elzinga metrics. When MR-SOM clustering is performed using the three Elzinga metrics only, the results in Table 2 are clearly in favor of the LCS. This result is less intuitive. For example, the LCP metric has been widely used in social sciences and more particularly for studying school-to-work transitions. Indeed, it is obvious that all sequences start with the same status, being in school. Hence, the longer two sequences will be identical, the less different they should be. However, according to our results, it appears that if the purpose of the study is to build homogeneous clusters and identify the main typologies, LCS should be used instead. Thus we can assume that a trajectory is not defined by the first or the final job but rather by the proximity of the transitions during the career-path. As in the previous example, the quality indexes in Table 2 show that the use of an optimally-tuned combination of dissimilarities provides a nice trade-off between clustering (the quantization error) and mapping (the topographic error). OM, LCS and χ 2 metrics. Finally, the MR-SOM was run with the three OM metrics, the best Elzinga dissimilarity, LCS, and the χ 2distance. According to the results in Table 3, the χ 2 -distance has the most important weight and it contributes the most to the resulting clustering. The weights of the other dissimilarities are generally below 5%. The clustering and the resulting typologies are then defined by the contemporaneity of their identical situations, rather then by the transitions or the common subsequences. Hence, it appears that the timing and not the duration or the order is important for the clustering procedure. This confirms the importance of the history on the identification of a trajectory. Some temporal events are crucial on the labor market and a common behavior during these periods is determinant to define a common typology. However, let us remark two things. On the one hand, the quantization error is significantly improved, hence the clustering properties of the mixture of the five dissimilarities are better than for the previous examples. On the other hand, the topographic error becomes very large, hence the mapping properties are degraded. The combination of the five dissimilarities is then particularly adapted for extracting typologies, but is less interesting for visualization purposes. 3. Preliminary results for the five best dissimilarities

Results on the Whole Data Set

In addition to the statistical indexes computed in the previous section, we can compare different dissimilarities by inspecting the resulting selforganizing maps. Three maps were trained on the whole data set : the first is based on the χ 2 -distance, the second on the best performing Elzinga metric in the above section, the length of the longest subsequence (LCS), while the third was obtained by running online multiplerelational SOM on the three optimal-matching dissimilarities (OM, Hamming, DHD). We can note that the three maps provide some common paths: a fast access to permanent contracts (clear blue), a transition through fixed-term contracts before obtaining stable ones (dark and then clear blue), a holding on precarious jobs (dark blue), a public temporary contract (dark green) or an on-call (pink) contract ending at the end by a stable one, a long period of inactivity (yellow) or unemployment (red) with a gradual return to employment. The maps obtained by LCS and OM dissimilarities are quite similar. A drawback of the OM map is its difficulty to integrate paths characterized by a long return in the educative system (purple). This path is better integrated in the LCS map. The visual interpretation of the two maps gives support to the OM map due to a progressive transition on the map between trajectories of exclusion on the west and quick integration on the east. This reading is less clear on the LCS map. The χ 2 map is a little bit different: we observe more different trajectories (by example a start by apprenticeship contract (clear green) ending with a fixed-term or a permanent-term contract). The reading of the map is easy without any outliers paths and a clear distinction of the trajectories between north (exclusion -education in west, unemployment in east), middle (specific short-term contracts -public, apprenticeship and on-call from west to east) and south (integrationlong term contracts in east, short term ones in east). Overall its diversity and its ease to read give support to the χ 2 map against the LCS and OM ones. This confirms that the overweighting of the χ 2 -distance on the five dissimilarities could be attributed to a better fit of this dissimilarity on our dataset.

Conclusion and future work

A modified version of online relational SOM, capable of handling several dissimilarity matrices while automatically optimizing a convex combination of them, was introduced. The algorithm was used for analyzing life sequences for which the question of selecting an appropriate metric is largely debated. Instead of one dissimilarity, we used several categories that were automatically mixed in an optimal combination. As explained in the previous section, the main drawback of the proposed relational SOM algorithm is related to the computation time. We are currently investigating a sparse version which will allow us to handle very large datasets.

Fig. 1 .

 1 Fig. 1. Labor market structure

  0.06612 0.03515 0.03529 0.03602 0.82739 α-Std 0.04632 0.02619 0.02630 0.03150 0.07362 b) Quality criteria for the SOM-clustering Metric Optimally

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Final map obtained with the χ 2 -distance

Table 2 .

 2 Preliminary results for three Elzinga metrics

		a) Optimally-tuned α
		Metric LCP RLCP LCS
		α-Mean 0.02739 0.00228 0.97032
		α-Std 0.02763 0.00585 0.02753
	b) Quality criteria for the SOM-clustering
	Metric	LCP	RLCP	LCS Optimally-tuned α
	Quantization error	379.77573 239.63652 93.50893	107.1007
	Topographic error	0.07788	0.04344	0.07660	0.0495
	Prototype dispersion 2693.47676 2593.21763 2094.27678	2080.8514

Usually, it is simply the neuron whose prototype is the closest to xi: see Algorithm 1.

All computations were performed with the free statistical software environment R (http://cran.r-project.org/,[START_REF]R Development Core Team: R: A Language and Environment for Statistical Computing[END_REF]). The dissimilarity matrices (except for the χ 2 -


Jacques Baudry

Stéphanie Aviron

Françoise Burel

Laurence Hubert-Moy

Jean Nabucet

Clémence Vannier

Chloé Vasseur

Monitoring agricultural landscapes: the interplay between concepts and methods the case of hedgerow network landscapes Small scale landscapes in Western Europe

Monitoring, surveying: protocols are based on a conceptual framework (how we represent the landscape), we do not just go out to see what's there.

The conceptual framework is built from research questions, theories Landscape ecology drive a shift to monitor landscapes instead of "independent" habitats Because of the advances in science and new societal demand, research questions change Because of the advances in science and problems with empirical data, theories evolve New protocols …. •From the patch matrix to the mosaic representation of landscapes, we gain insights in their functioning.

•We just start to explore the added value of sensors at multiple scales.

•The time dimension (short, medium and long terms dynamics) appears more and more as being important as a control of biodiversity (distribution and abundance of species).

• Ecosystem services emphasize the role of abundant species with "little conservation value" Hopefully this may lead to more functional maps & landscape metrics obrigado pela sua atenção

  Lessons from the Pleine-Fougères long term study site1993: questionChoice of a study site How do biodiversity patterns change when hedgerows are removed and how much the diversity of farming systems explains differences in landscape patterns? and a right polygon (9) a polygon is linked to a farmConclusion: from structural to functional landscape representationsFahrig, L., J.Baudry, et al. (2011). "Functional landscape heterogeneity and animal biodiversity in agricultural landscapes." Ecology Letters 14(2): 101-112.

Chloé Vasseur, Alexandre Joannon, Stephanie Aviron, Françoise Burel, Jean-Marc Meynard, Jacques Baudry (submitted) The cropping systems mosaic: How does the hidden heterogeneity of agricultural landscapes drive populations?It drives populations (e.g. carabids, aphids) from one field to the next


The principles of the DIVA program

Like all ministry of environment research programs, DIVA aims at providing scientific evidences to define policies and management strategies on topics relevant to the ministry

The questions:

• What are the relationships between farming systems and practices and various component of biodiversity?

• How are policies designed?

• How do local actors as individual or groups deal with this issue of biodiversity and the implementation of policies? The major importance of beta diversity is overlooked by policy makers and planners

The landscape dimension of biodiversity

Vineyard landscape (Saumur Champigny)

The importance of beta diversity

  coordinator that insures: a) A continuous animation of the research teams with yearly seminars b) Work on selected topics involving all teams 10 Every grassland, pond, field, vineyard etc. has only a small part of the species in the area 46 species of aquatic plants (from 0 to 22 in a pond)

  But policies (e.g. agri-environmental policies) target fields

The public policies to combat the loss of biodiversity

The principles of the DIVA (Public Action Agriculture Biodiversity) program

The landscape dimensions of the interactions between biodiversity and agriculture

The actors involved in policy design and landscape management

Some perspective for research & action

•The implementation of EU, national policies is designed at fine regional scale by institutional actors (Natural Regional Parks, regional administration that can adapt national measures)

•Zoning is also used (EU Natura 2000)

•Agricultural biodiversity also depends of regulation for food production (i.e. date of grazing to enhance milk quality for cheese production etc.)

•A new national policy implement ecological networks at regional scale by Regional and State regional representatives