Małgorzata E Arlet
email: maarlet@yahoo.com

Colin A Chapman

Lynne A Isbell

Freerk Molleman

Raivo Mänd

Peeter Hõrak

James R Carey

Social and ecological correlates of parasitic infections in adult male gray-cheeked mangabeys (Lophocebus albigena)

Keywords: Nematodes, male rank, fecal glucocorticoids, fecal testosterone, immigrants, helminths

Intestinal parasites may constitute an important evolutionary and ecological force. Our aim was to identify social, physiological, and environmental factors that correlate with intestinal parasite infections in adult male gray-cheeked mangabeys (Lophocebus albigena). We analyzed 102 fecal samples collected over 19 months from 18 adult males for the incidence (proportions of samples with parasites) and intensity (total number of parasites per gram of feces) of infection of nematodes relative to social status, fecal glucocorticoid and testosterone metabolites, group size, and rainfall, all of which are factors that earlier studies suggested can be important mediators of parasite load. Parasite incidence was greater in immigrant males compared to low-and high-ranking males whereas parasite intensity was greater in immigrant males and low-ranking males compared to high-ranking males. Fecal samples with more parasites had higher concentrations of fecal glucocorticoid and testosterone metabolites than fecal samples with fewer parasites. As immigrant males had a greater incidence of parasites and higher concentrations of both metabolites than resident males, this profile appears to fit immigrant males best. We also found higher nematode intensities in mid-sized groups, and during periods with more rainfall. Our results suggest that it will be fruitful for future studies to explore the role of immigrant males as spreaders of intestinal parasites.

Introduction

Parasitic diseases are known to play an important role in animal behavior, ecology, and evolution [START_REF] Huffman | Primate parasite ecology: the dynamics and study of host-parasite relationships[END_REF][START_REF] Nunn | Infectious diseases in primates: behavior, ecology and evolution[END_REF]. Intestinal parasites in particular can be major mediators of animal overall body condition, as some are pathogenic and reduce fitness [START_REF] Huffman | Primate parasite ecology: the dynamics and study of host-parasite relationships[END_REF][START_REF] Nunn | Infectious diseases in primates: behavior, ecology and evolution[END_REF]. Within species or social groups, parasite loads often vary systematically. Studies of marsupials (Antechinus stuartii) [START_REF] Bradley | Stress and mortality in a small marsupial (Antechinus stuartii, Macleay)[END_REF]) and several other species of mammals (e.g., rodents: Jackson and Farmer 1970; fur seals (Arctocephalus forsteri): [START_REF] Negro | Correlation between male social status, testosterone levels, and parasitism in a dimorphic polygynous mammal[END_REF]; yellow baboons (Papio cynocephalus): [START_REF] Hausfater | Social and reproductive correlates of parasite ova emissions by baboons[END_REF][START_REF] Muller-Graf | Intestinal parasite burden in five troops of olive baboons (Papio cynocephalus anubis) in Gombe Stream National Park, Tanzania[END_REF]; mandrills (Mandrillus sphinx): [START_REF] Setchell | Parasite prevalence, abundance, and diversity in a semi-free-ranging colony of Mandrillus sphinx[END_REF][START_REF] Setchell | Is brightest best? Testing the Hamilton-Zuk hypothesis in mandrills[END_REF] have shown that susceptibility to intestinal parasites can correlate with the host's social and physiological condition. Parasitic infections may be correlated, for example, with host social status, homeostatic imbalance (stress), testosterone level, membership in particular groups, and group size.

The relationship between host social status and parasitic infection is complex because these factors can also be influenced directly by the number of intraspecific contacts and the effectiveness of immune defenses [START_REF] Altizer | Social organization and parasite risk in mammals: integrating theory and empirical studies[END_REF][START_REF] Muehlenbein | The application of endocrine measures in primate parasite ecology[END_REF][START_REF] Sapolsky | Neuroendocrinology of the stress-response[END_REF], which in turn may be influenced by diet and the nature of social contact. Usually, social status is viewed as rank position within a hierarchy based on the outcome of competition over desirable resources [START_REF] Harcourt | Dominance and fertility among female primates[END_REF][START_REF] Pusey | The ecology of relationships[END_REF][START_REF] Whitten | Diet and dominance among female vervet monkeys (Cercopithecus aethiops)[END_REF] but it can also reflect the quality and quantity of previous interactions with others in the group. Immigrants, for example, have a shorter history with their new groups and may be especially challenged as they attempt to fit into the existing hierarchy. Variation in parasitism is assumed to be strongly influenced by variation in social contact [START_REF] Griffin | Community structure and the spread of infectious disease in primate social networks[END_REF]. Thus, if higher-ranking individuals experience more social contacts they may be more exposed to parasites [START_REF] Altizer | Social organization and parasite risk in mammals: integrating theory and empirical studies[END_REF][START_REF] Møller | Parasites and the evolution of host social behavior[END_REF][START_REF] Nunn | Infectious diseases in primates: behavior, ecology and evolution[END_REF][START_REF] Stuart | Primates and parasites: A case for a multidisciplinary approach[END_REF]. Immigrants may also be more exposed to parasites as they move from one group to another [START_REF] Altizer | Social organization and parasite risk in mammals: integrating theory and empirical studies[END_REF][START_REF] Møller | Parasites and the evolution of host social behavior[END_REF]. Whether highranking individuals or immigrants are at greater risk of parasitic infections may also be influenced by differential access to foods in their habitat. Higher-ranking individuals are expected to have better access to high quality food that may help boost their immune systems and thus their ability to fight parasitic infections whereas lower-ranking and immigrant individuals may spend more time finding food, and may be more calorically deprived [START_REF] Sapolsky | Social status and health in humans and other animals[END_REF], with immune systems less able to fight parasitic infections.

Challenging social interactions can be associated with long-term exposure to stressors and coping mechanisms (McEwan and Stellar 1993;[START_REF] Sapolsky | The influence of social hierarchy on primate health[END_REF], thus affecting hormones that can also influence parasitic infections through their effects on the immune system. Cortisol and testosterone are generally considered to suppress immune function, making hosts less effective at controlling infections, including parasitic infections [START_REF] Bailey | Maternal separation disrupts the integrity of the intestinal microflora in infant rhesus monkeys[END_REF][START_REF] Braude | Stress, testosterone and the immunoredistribution hypothesis[END_REF][START_REF] Friedman | Environmental stress mediates changes in neuroimmunological interactions[END_REF][START_REF] Muehlenbein | Intestinal parasite infections and fecal steroid levels in wild chimpanzees[END_REF]). In the case of cortisol, this may occur because although the stress response can be adaptive for immediate threat, chronic stress is pathogenic [START_REF] Munck | Physiological functions of glucocorticoids during stress and their relation to pharmacological actions[END_REF]. Thus, male mandrills with higher glucocorticoids had a higher diversity of gastrointestinal parasites [START_REF] Setchell | Stress, social behaviour, and secondary sexual traits in a male primate[END_REF]).

In the case of testosterone, while high testosterone levels may facilitate reproductive effort by promoting competitive behavior and muscle anabolism (to aid in mate attraction and competition with conspecifics), it also exhausts resources and inhibits immune function, thus generating greater opportunities for parasitic infection and increasing morbidity and mortality [START_REF] Mougeot | Elevated spring testosterone increases parasite intensity in male red grouse[END_REF][START_REF] Muehlenbein | Testosterone-mediated immune functions and male life histories[END_REF][START_REF] Sheldon | Ecological immunology: costly parasite defenses and trade-offs in evolutionary ecology[END_REF][START_REF] Zuk | Disease, endocrine-immune interactions, and sexual selection[END_REF]. Chimpanzees with higher testosterone levels had greater parasite richness (number of species) [START_REF] Muehlenbein | Intestinal parasite infections and fecal steroid levels in wild chimpanzees[END_REF], and fur seals with high testosterone levels had greater numbers of parasite eggs per gram of feces [START_REF] Negro | Correlation between male social status, testosterone levels, and parasitism in a dimorphic polygynous mammal[END_REF]. In Grant's gazelles (Nanger granti), however, the relationship between testosterone and parasitism varied depending on parasite species [START_REF] Ezenwa | Unravelling complex associations between testosterone and parasite infection in the wild[END_REF].

Higher levels of testosterone and cortisol are often related to increased levels of aggression in high-rank males (e.g. Arlet et al. 2009a;[START_REF] Barrett | Endocrine correlates of rank, reproduction, and female-directed aggression in male Japanese macaques (Macaca fuscata)[END_REF][START_REF] Bercovitch | Dominance rank and reproductive maturation in rhesus macaques (Macaca mulatta)[END_REF][START_REF] Cavigelli | Mating season aggression and fecal testosterone levels in male ring-tailed lemurs (Lemur catta)[END_REF][START_REF] Kraus | Physiological suppression of sexual function of subordinate males: a subtle form of intrasexual competition among male sifakas (Propithecus verreauxi)?[END_REF]. A positive association between rank and testosterone level was found in male rhesus macaques (Macaca mulatta [START_REF] Bercovitch | Dominance rank and reproductive maturation in rhesus macaques (Macaca mulatta)[END_REF][START_REF] Rose | Consequences of social conflict on plasma testosterone levels in rhesus monkeys[END_REF], Japanese macaques (M. fuscata: [START_REF] Barrett | Endocrine correlates of rank, reproduction, and female-directed aggression in male Japanese macaques (Macaca fuscata)[END_REF], gray-cheeked mangabeys [START_REF] Arlet | Social factors increase fecal testosterone levels in wild male gray-cheeked mangabeys (Lophocebus albigena)[END_REF], and chimpanzees (Pan troglodytes: [START_REF] Muehlenbein | Dominance rank and fecal testosterone in adult male chimpanzees (Pan troglodytes scheinfurthii) at Ngogo, Kibale National Park, Uganda[END_REF]. Thus given the effect of testosterone on immune system suppression, one might expect higher-ranking males to have greater parasite loads than lower-ranking males. Overall, among male vertebrates, this seems to hold true with regard to gastrointestinal helminths [START_REF] Habig | Social status, immune response and parasitism in males: a meta-analysis[END_REF]. Among primates, chimpanzees at Ngogo, Uganda, are consistent with the vertebrate pattern: high-ranking males have higher parasite richness than low-ranking males [START_REF] Muehlenbein | Intestinal parasite infections and fecal steroid levels in wild chimpanzees[END_REF]. Yellow baboons (P. cynocephalus) are also consistent, but only during periods of dominance stability [START_REF] Hausfater | Social and reproductive correlates of parasite ova emissions by baboons[END_REF]. When the dominance hierarchy was unstable, high-ranking males had lower egg emissions than low-ranking males [START_REF] Hausfater | Social and reproductive correlates of parasite ova emissions by baboons[END_REF]. However, no association was found between rank and parasitic infection in a different population of chimpanzees [START_REF] Seraphin | The reproductive ecology and stress physiology of free-ranging male chimpanzees in Budongo Forest, Uganda[END_REF] or in populations of olive baboons [START_REF] Muller-Graf | Intestinal parasite burden in five troops of olive baboons (Papio cynocephalus anubis) in Gombe Stream National Park, Tanzania[END_REF], rhesus macaques [START_REF] Gordon | Seasonal rhythm in plasma testosterone levels in the rhesus monkey (Macaca mulatta): a three-year study[END_REF]) and bonobos (P. paniscus: Sannen el al. 2004). In orangutans (Pongo pygmaeus), mandrills, and red jungle fowl (Gallus gallus) dominant males exhibited high testosterone levels and low parasite loads [START_REF] Thompson | Low testosterone correlates with delayed development in male orang-utans[END_REF][START_REF] Setchell | Is brightest best? Testing the Hamilton-Zuk hypothesis in mandrills[END_REF][START_REF] Zuk | Disease, endocrine-immune interactions, and sexual selection[END_REF]. This may be because testosterone can increase immunocompetence for certain phenotypes [START_REF] Peters | Testosterone treatment is immunosuppressive in superb fairy-wrens, yet free-living males with high testosterone are more immunocompetent[END_REF].

To further complicate matters, while testosterone affects behaviors that can cause stress, stress can also influence testosterone levels [START_REF] Sapolsky | Social status and health in humans and other animals[END_REF]. In olive baboons the response of testosterone to stress differed by rank. Testosterone decreased in subordinate males during stressful situations but increased in dominant males [START_REF] Sapolsky | The influence of social hierarchy on primate health[END_REF]. Thus, there appear to be complex interrelationships between social status, cortisol, testosterone, and parasites, making it difficult to predict who is most vulnerable to parasitic infections.

Larger group size may also increase parasitic infections, the expectation being that larger groups increase the number of social contacts [START_REF] Griffin | Community structure and the spread of infectious disease in primate social networks[END_REF], but again, the direction does not appear to be uniform. Positive associations have been found between group size and 1) the number of intestinal protozoan species in gray-cheeked mangabeys (Lophocebus albigena) [START_REF] Freeland | Primate social groups as biological islands[END_REF], 2) nematode diversity in olive baboons [START_REF] Mcgrew | Intestinal parasites of sympatric Pan troglodytes and Papio spp. at two sites: Gombe (Tanzania) and Mt. Assirik (Senegal)[END_REF] and 3) parasitic infection rate in several Amazonian primates [START_REF] Davies | Malaria infection rate of Amazonian primates increases with body weight and group size[END_REF] but no associations between group size and various measures of parasitism have been found in other studies of primates [START_REF] Chapman | Can parasites be a selective force influencing primate group size?: A test with red colobus[END_REF]Nunn 2002aNunn , 2002b;;[START_REF] Nunn | Promiscuity and the primate immune system[END_REF][START_REF] Semple | Immune system evolution among anthropoid primates: parasites, injuries and predators[END_REF][START_REF] Snaith | Bigger groups have fewer parasites and similar cortisol levels: a multi-group analysis in red colobus monkeys[END_REF][START_REF] Vitone | Body size, diet and sociality influence the species richness of parasitic worms in anthropoid primates[END_REF], but see Rifkin et al. 2012).

A study of red colobus monkeys (Procolobus rufomitratus) even found a negative correlation between group size and parasite incidence [START_REF] Snaith | Bigger groups have fewer parasites and similar cortisol levels: a multi-group analysis in red colobus monkeys[END_REF].

Finally, a positive relationship between humidity and parasite infection rate has been suggested for chimpanzees [START_REF] Huffman | Current evidence for self-medication in primates: A multidisciplinary perspective[END_REF][START_REF] Mcgrew | Intestinal parasites of sympatric Pan troglodytes and Papio spp. at two sites: Gombe (Tanzania) and Mt. Assirik (Senegal)[END_REF], olive and Guinea baboons [START_REF] Mcgrew | Intestinal parasites of sympatric Pan troglodytes and Papio spp. at two sites: Gombe (Tanzania) and Mt. Assirik (Senegal)[END_REF], muriquis (Brachyteles arachnoides) [START_REF] Stuart | A coprological survey of parasites of wild muriquis Brachyteles arachnoides and brown howling monkeys Alouatta fusca[END_REF], mantled howler monkeys (Alouatta palliata) [START_REF] Stuart | Coprological survey of parasites of wild mantled howling monkeys, Alouatta palliata palliata[END_REF], and black howler monkeys (A. pigra) [START_REF] Eckert | Coprological surveys of Alouatta pigra at two sites in Belize[END_REF]. Moist conditions appear to promote reinfection and a higher incidence of infections, probably because eggs and larvae survive better in humid environments than in dry ones [START_REF] Hausfater | Alternation of sleeping groves by yellow baboons (Papio cynocephalus) as a strategy for parasite avoidance[END_REF]. However, some nematode species (e.g., Trichuris spp., Strongyloides spp.) have not shown seasonal variation in infections of primate populations [START_REF] Huffman | Current evidence for self-medication in primates: A multidisciplinary perspective[END_REF][START_REF] Mcgrew | Intestinal parasites of sympatric Pan troglodytes and Papio spp. at two sites: Gombe (Tanzania) and Mt. Assirik (Senegal)[END_REF]).

Gray-cheeked mangabeys are phylogenetically closely related to the more widely studied baboons [START_REF] Burrell | Mitochondrial evidence for the hybrid origin of the kipunji, Rungwecebus kipunji (Primates: Papionini)[END_REF][START_REF] Harris | Nuclear gene trees and the phylogenetic relationships of the mangabeys (Primaes: Papionini)[END_REF]. Like most baboons, home range overlap is extensive [START_REF] Janmaat | Longterm site fidelity and individual home range shifts in grey-cheeked mangabeys[END_REF]) and females reproduce aseasonally [START_REF] Arlet | Determinants of reproductive performance among female gray-cheeked mangabeys (Lophocebus albigena) in Kibale National Park, Uganda[END_REF]. However, they are more arboreal and live in smaller groups, making them valuable for comparative analyses. Adult female gray-cheeked mangabeys usually remain in their natal groups throughout life, whereas males disperse from their natal groups as subadults [START_REF] Olupot | Mangabey dispersal and conservation in Kibale Forest National Park, Uganda[END_REF][START_REF] Olupot | Mangabey dispersal and conservation in Kibale Forest National Park, Uganda[END_REF]Waser 2001a, 2005). Secondary (breeding) adult male dispersers compete with other males for receptive females and rank as they attempt to join other groups [START_REF] Olupot | Mangabey dispersal and conservation in Kibale Forest National Park, Uganda[END_REF]Waser 2001a, 2005). A study of fecal glucocorticoid metabolites (fGCM) showed that immigrant males initially had a mean of 1.38 times higher fGCM profiles than resident males, but over time their FGCM levels had declined and by the end of 6 months were similar to those of resident males (Arlet et al. 2009a).

Our aim was to identify the social, physiological, and environmental factors that are positively correlated with gastrointestinal nematode infections in adult male mangabeys. We focus on nematodes because they can be noninvasively sampled through collection of fecal samples and can have deleterious effects on health. Some more commonly observed nematodes in wild primates include species of the genera Physaloptera, Enterobius, Trichuris, and Strongylus [START_REF] Nunn | Infectious diseases in primates: behavior, ecology and evolution[END_REF]. Primates become infected with these worms by the fecal-oral route, ingesting feces or contaminated substrates (soil, vegetation) that contain third-stage larvae (Strongylidae spp.) or first-stage larvae in eggs (Enterobius, Trichuris), through skin contact with infective larvae (Strongylidae spp.), or through ingestion of infected arthropods (Physaloptera; [START_REF] Nunn | Infectious diseases in primates: behavior, ecology and evolution[END_REF]. Although parasites such as Trichuris are typically asymptomatic, heavy infections of Strongyloides are associated with mucosal inflammation, ulceration, dysentery, weight loss, and death [START_REF] Chapman | Do food availability, parasitism, and stress have synergistic effects on red colobus populations living in forest fragments?[END_REF]. Given that parasitic infections have variable associations with social status, physiological condition, group size, and climatic conditions in other primates, we did not establish predictions but conducted an exploratory study about the health of males under different social, physiological, and environmental conditions.

Methods

Study area and subjects

We conducted the study from December 2005 to July 2007 in Kibale National Park, Uganda (0°13' -0°41'N and 30°19' -30°32'E), near Makerere University Biological Field Station, at Kanyawara. Kibale (795 km 2 ) is a moist, evergreen, medium altitude forest with a mosaic of swamp, grassland, thicket, and colonizing forest [START_REF] Chapman | Habitat alteration and the conservation of African primates: a case study of Kibale National Park, Uganda[END_REF][START_REF] Struhsaker | The red colobus monkey[END_REF]). Rainfall has a bimodal pattern, with rainy seasons typically in March-May and August-November, and mean annual rainfall (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) of 1749 mm [START_REF] Chapman | Scale issues in the study of primate foraging: red colobus of Kibale National Park[END_REF][START_REF] Valtonen | Tropical phenology: bi-annual rhythms and interannual variation in an Afrotropical butterfly assemblage[END_REF]. We collected data on rainfall daily at the Makerere University Biological Field Station.

The mangabey population in Kibale has been studied since the 1970s and multiple groups are well habituated. We collected behavioral data and fecal samples from 18 adult males in five groups (BT1, CC, LC1, LC2, and MK). Group size ranged from 9 to 23 individuals, with 1-9 adult males per group (Arlet et al. 2009a).

We considered an individual male as an adult when he was able to give "whoopgobble" calls, a characteristic call produced only by adult males (Waser 1977a). We observed four adult males individually recognized by color collars from a previous study [START_REF] Olupot | Mangabey dispersal and conservation in Kibale Forest National Park, Uganda[END_REF]) and 14 animals identified by relative size and distinguishing features (e.g., tail shape, scars, and fractures; [START_REF] Arlet | Species and sex differences in type and frequencies of injuries and impairments among four arboreal primate species in Kibale National Park, Uganda[END_REF]. Estimating the age of adult males and thus the effect of age on parasite load was not possible because no visible physical markers of male age are available for this species and most males immigrated into our study groups from non-study groups. Nevertheless, we knew that all four collared males were at least 20 years old in 2015.

Male status

Earlier studies suggested that mangabey groups lack clear male dominance hierarchies [START_REF] Olupot | Patterns of male residency and inter-troop transfer in mangabeys (Lophocebus albigena)[END_REF]Waser 1977b), perhaps because reduced visibility between individuals, the complex three-dimensional structure of the forest canopy, and wide group spread reduce both the actual and observed frequency of interactions. However, we have collected sufficient behavioral data to demonstrate the existence of dominance hierarchies in this species (Arlet and Isbell 2009;[START_REF] Arlet | Social factors increase fecal testosterone levels in wild male gray-cheeked mangabeys (Lophocebus albigena)[END_REF]. Hierarchies are more obvious in some smaller groups, while in larger groups two or three males usually have a similar high dominance rank with a high number of reversals. Relative position in the male dominance hierarchy within each group was determined from the outcome of dyadic agonistic interactions, including chases, fights, and supplants during focal samples. This was done for two study periods (December 2005-July 2006, and January-July 2007). We observed 21-257 agonistic interactions per group. Dominance matrices were constructed for each group with rank-order determined by placing winners above losers while minimizing the number of reversals against the hierarchy (i.e., interactions below the diagonal) (Arlet and Isbell 2009;[START_REF] Arlet | Social factors increase fecal testosterone levels in wild male gray-cheeked mangabeys (Lophocebus albigena)[END_REF]. The percentage of reversals against the hierarchy was 18.2-25.9% for four groups where we classified two males as high-ranking (HR) and all other males as low-ranking (LR). In the fifth and largest group (LC1) 37.7-46.1% of agonistic interactions were reversals. In this group we assigned high rank to three males who won 20% more fights than other males and low rank to four males who lost more than 33% fights to these high-ranking males. We measured linearity in five groups using the Landau index h' (for 2006 and 2007, respectively): BT1 (h=1, P=0.11; h=0.7, P=0.37), CC (h=1, P=0.38; h=0.9, P=0.38), LC1 (h=0.77, P=0.003; h=0.75, P=0.24), LC2 (h=1, P=1.0; h=1, P=0.82), MK (h=1, P=0.38; h=1, P=1.0). Values of h that are closer to 1 indicate greater linearity whereas values closer to 0 indicate the opposite. Landau h values are high in these groups, but with such small group sizes it is not possible to confirm linearity of most dominance hierarchies in most years. Given the high percentage of reversals and the lack of statistically significant linearity, labelling males more precisely than high-or low-rank seems unwarranted.

Immigrant males

Four males who joined groups during the study were regarded as immigrants.

Three immigrants remained in the study groups for the duration of the study, and one male moved between two groups.

Collection of fecal samples for parasites and hormone analyses

We collected fecal samples from December 2005 to July 2007. We did not collect fecal samples on the first observation day of each period because we did not know what experiences males had during previous days when they were not under observation. After the first day, when a focal individual defecated, the time was noted, the sample was collected within minutes of defecation, placed in a sterile scintillation vial, and stored on ice (for hormone analysis). Approximately 5 grams were placed into 10% formalin for parasite analysis. At the end of the day, all samples designated for hormonal analyses were placed in a -20 °C freezer until removed for hormone solubilization [START_REF] Strier | Behavioral and endocrine characteristics of the reproductive cycle in wild muriqui monkeys, Brachyteles arachnoides[END_REF]Whitten et al. 1999).

We collected one sample per male per particular day, with time intervals of at least one day between samples from the same male. We aimed to collect samples from the same individuals during both dry and wet seasons. We chose 102 samples from 18 adult males for analysis (Table 1), considering the collection date (to compare the impact of rainfall) and balancing the number of samples from high-and low-rank and immigrant males (for rank and status factors). Two males, PL and MF, changed their status, thus their samples were analysed separately based on their status at the time of fecal collection. The number of fecal samples analyzed per individual adult male ranged from 2 to 10, with a median of 5.0.

Analysis of fecal samples for parasites

Samples (5 g of feces) were stored individually in 5.0 ml vials in a 10% formalin solution. Preserved samples were transported to McGill University (Montreal, Canada), where we examined 1 g of fecal material for helminth eggs and larvae using concentration by sodium nitrate flotation and fecal sedimentation, primarily sedimentation that involved examining the total sediment [START_REF] Sloss | Veterinary clinical parasitology[END_REF]C.A. Chapman et al. unpublished data).

Parasites were identified on the basis of eggs or larvae color, shape, contents, and size [START_REF] Jessee | Identification of intestinal helminth eggs in Old World primates[END_REF]. Our ability to identify parasite species from host fecal examination is limited. Consequently, we present our findings at the level of genus when possible and superfamily for the strongyle-type helminths. Measurements were made to the nearest 0.1 micron ± SD using an ocular micrometer fitted to a compound microscope, and representatives were photographed. Egg counts are dependent on a number of host factors, including age and diet (e.g., [START_REF] Stear | Different patterns of faecal egg output following infection of Scottish blackface lambs with Ostertagia circumcincta[END_REF], limiting the conclusions we can draw using this method. However, it is frequently used to describe infections (e.g., [START_REF] Chapman | Do food availability, parasitism, and stress have synergistic effects on red colobus populations living in forest fragments?[END_REF][START_REF] Ezenwa | Habitat overlap and gastrointestinal parasitism in sympatric African bovids[END_REF][START_REF] Gulland | The role of nematode parasites in Soay sheep (Ovis aries L.) mortality during a population crash[END_REF]). We used the term 'nematode incidence', to describe the proportion of individuals infected with Physaloptera, Trichuris, and Strongyloidea spp. We used the term 'nematode intensity' to describe the number of parasites (total numbers of eggs, larvae, and adult stage) per gram of feces per male. In six cases a parasite egg was damaged or we were unable to obtain a good image and these went unidentified.

No fecal samples appeared watery or had a discoloration that would suggest illness.

Like many previous studies, we assume that gastrointestinal helminth infection influences overall condition [START_REF] Chapman | Do food availability, parasitism, and stress have synergistic effects on red colobus populations living in forest fragments?[END_REF][START_REF] Gillespie | Gastrointestinal parasites of the guenons of western Uganda[END_REF][START_REF] Howells | Patterns of gastro-intestinal parasites and commensals as an index of population and ecosystem health: the case of sympatric western chimpanzees (Pan troglodytes verus) and Guinea baboons (Papio hamadryas papio) at Fongoli, Senegal[END_REF]). We conducted parasite analysis from the same fecal mass that was used for fGCM and fTM analyses.

Hormone extraction in the field

The dry weight of each sample was calculated using the percent water of a sample from the same homogenized fecal sample from which the hormones were extracted, by drying these samples to constant weight in the field [START_REF] Chapman | Do food availability, parasitism, and stress have synergistic effects on red colobus populations living in forest fragments?[END_REF]. Our previous study of fGCM (with the same sample set) showed that time of day did not have a significant effect on cortisol concentration in adult male mangabeys (Arlet et al. 2009a).

Therefore we analyzed samples both from the mornings and afternoons.

Hormones were solubilized at the field station using the protocol employed for red colobus [START_REF] Chapman | Do food availability, parasitism, and stress have synergistic effects on red colobus populations living in forest fragments?[END_REF]. A fecal sample was removed from the freezer, thawed, and homogenized using a spatula. Then, 0.50 g was solubilized using a 5.0 pH citrate buffer/95% ethanol solution (10 ml, 1:1) that was mixed for 21 -27 hrs. After mixing, samples were spun in a centrifuge for 30 min at 3200 rounds per minute to separate the supernatant containing the hormones from the fecal pellet, and then 2 ml of the supernatant was passed through a solid phase extraction cartridge (Alltech maxi-clean filter) for storage and transport (Strier and Ziegler 2005) to the University of Wisconsin.

Hormone extraction in the laboratory

The samples were sent to the Wisconsin National Primate Research Center's (WNRPC) Assay Services, where the method for measuring cortisol and testosterone concentration was validated and samples analyzed. At the WNPRC, cortisol and testosterone were extracted from the filters. The cartridges were washed with 1 ml of 20% methanol and the columns were eluted with 2 ml methanol. This methanol was dried, resuspended in 1 ml ethanol, and 50 μl was taken for the enzyme immunoassay. The WNPRC lab used the antibody R4866 for cortisol analysis which were developed by [START_REF] Munro | Development of cortisol enzyme immunoassay in plasma[END_REF] and are well characterized. The antibody crossreacts 60% with cortisone (a metabolite of cortisol), 2.5% with corticosterone and less than 1% with other steroids [START_REF] Ziegler | The relationship of cortisol levels to social environment and reproductive functioning in female cotton-top tamarins Saguinus oedipus[END_REF]. The WNPRC lab used the antibody R159 for testosterone [START_REF] Munro | Development of cortisol enzyme immunoassay in plasma[END_REF]. The antibody crossreacts 92.4% with dihydrotestosterone, 11.2% with 4-androsten-3beta, 17beta-diol, 5.4% with dehydroandrosterone, 3.4% with androstenediol, 2.1% with androstenedione and less than 1% with other steroids (Ziegler et al.1995).

An assay comparing serial dilutions of pooled mangabey samples to cortisol standards found no significant difference between the slopes (t=-1.62, df=25, p>0.05).

Accuracy was determined by superimposing values from pooled mangabey samples on a standard curve. The mean accuracy over eight pooled samples was 109% with standard deviation 2.9%. The inter-assay coefficients of variation were 13.6 and 12.8 for the low and high pools, respectively, and the intra-assay coefficients of variation were 3.9 and 2.1 for the low and high pools, respectively. An assay comparing serial dilutions of pooled mangabey samples to testosterone standards found no significant difference between the slopes (t= 1.903, df=26, p=0.07). The mean accuracy over eight pooled samples was 109.19% with standard deviation 2.17%. The inter-assay coefficients of variation were 10.6 and 11.7 for the low and high pools, respectively, and the intra-assay coefficients of variation were 3.5 and 1.9 for the low and high pools, respectively.

Statistical Analyses

The data represent a count variable that can be described as a Poisson distribution: a few samples had many parasites and 74% of the samples had no parasites at all. We structured the data by group (N = 5) and by individual/status (N = 20; 18 males plus 1 change of status for 2 males). Group LC1 with its three HR males was included in the analyses.

We performed all analyses using the statistical software R (R Development Core Team 2011). We used Generalized Linear Mixed Models (GLMM), run with the lme4 package (Bates et al. 2011) to test with binomial distribution (with logit function) to investigate nematode incidence (proportion of individuals infected) using social status (high rank/low rank/immigrant), fecal glucocorticoid (fGCM) concentration, fecal testosterone metabolite concentration (fTM), group size, and rainfall as predictor variables. We used GLMM with a Poisson distribution (with log function) to investigate nematode intensity (number of eggs, larvae, and adults per g fecal sample) using the same predictor variables with centered values for fGCM and fTM. We performed post-hoc analyses using ANOVA to investigate possible differences in parasite incidence and intensity among males of different status.

Ethical Note

All fecal samples were collected from habituated, wild animals without interfering with their activities. The research complied with with the laws of Uganda and the protocols were approved by the Uganda Wildlife Authority and Uganda National Council for Science and Technology at the time the field research was conducted.

Results

Correlates of nematode incidence

Overall, the model of nematode incidence was highly significant (likelihood ratio test comparing full and null models: χ 2 = 57.02, df = 1, P < 0.001). Nematode incidence was significantly associated with male status, concentrations of fGCM, fTM, and group size but not with rainfall (Table 2).

There was no significant difference in parasite incidence between low-and highranking males (ANOVA: F=1.52, df=1, P=0.22). However, a greater proportion of immigrant males had parasites than low-ranking (ANOVA: F=3.92, df=1.0, P=0.05) and high-ranking males (ANOVA: F=13.72, df=1.0, P<0.001; Figure 1). Samples with parasites had higher fGCM and fTM levels than those without parasites (Table 2, Figures 2,3).

Higher than mean (93.9 ng/g) concentrations of fGCM were observed for 63.9% of infected samples but only 25.7% of samples with no parasites. Similarly, higher than mean (62.3 ng/g) concentrations of fTM were observed for 54.4% of infected samples but only 36.9% of samples with no parasites. Half of the infected samples came from medium-sized groups (14-16 individuals), whereas the remaining came from larger (18-22 individuals) and smaller groups (fewer than 13 individuals; Figure 4).

Correlates of nematode intensity

Overall, the model of nematode intensity was significant (likelihood ratio test comparing full and null models: χ 2 = 453.2, df = 1, P < 0.001). Nematode intensity (total number of parasites at all life stages/g feces/male) was significantly related to male status, concentrations of fGCM and fTM, group size, and rainfall (Table 3).

Fecal samples from low-ranking and immigrant males were not significantly different in parasite intensity (ANOVA: F=3.73, df=1, P=0.06) but fecal samples from each had significantly greater nematode intensities than those from high-ranking males (ANOVA: immigrant males F=11.62, df=1.69, P=0.001; low-ranking males: F=3.85, df=1.71, P=0.05; Figure 5).

Nematode intensity was significantly related to concentrations of both fGCM and fTM and there was a significant interaction between these two hormone metabolites (Table 3): when fTM was higher, then the positive effect of fGCM on parasite intensity was not as strong as predicted by the two combined main effects. Qualitatively, higher fGCM was associated with higher parasite intensity both for samples with low fTM and for samples with high fTM levels (Figure 6). Furthermore, nematode intensity was greatest at intermediate group sizes of 14 to 18 individuals within the group (Table 3, Figure 7).

Finally, samples collected during rainy season months had greater nematode intensities than those from dry season months (Table 3, Figure 8).

Discussion

Our results show that low-ranking or immigrant status, physiological stress (as measured by fGCM), high concentration of testosterone (as measured by fTM), living in mid-sized groups, and the rainy season all contribute to high nematode infections in adult male gray-cheeked mangabeys. Prolonged stressful conditions can suppress the immune system [START_REF] Munck | Physiological functions of glucocorticoids during stress and their relation to pharmacological actions[END_REF], making it easier for parasites to establish themselves. In fact, psychological stress has been shown to increase vulnerability specifically to parasitic infection [START_REF] Bailey | Maternal separation disrupts the integrity of the intestinal microflora in infant rhesus monkeys[END_REF]. Negative associations between cortisol and immune measures have also been found in chimpanzees [START_REF] Muehlenbein | The costs of dominance: testosterone, cortisol and intestinal parasites in wild male chimpanzees[END_REF], yellow and olive baboons [START_REF] Alberts | Behavioral, endocrine, and immunological correlates of immigration by an aggressive male into a natural primate group[END_REF][START_REF] Sapolsky | Insulin-like growth factor I is suppressed in socially subordinate male baboons[END_REF], and red colobus [START_REF] Chapman | Do food availability, parasitism, and stress have synergistic effects on red colobus populations living in forest fragments?[END_REF]. Conditions for low-ranking males may indeed be stressful. They are, by definition, males who lose more often in competitive interactions with others. Under stable conditions, low-ranking males have higher cortisol levels than high-ranking males in yellow baboons (Sapolsky et al. 1997) and mandrills [START_REF] Setchell | Stress, social behaviour, and secondary sexual traits in a male primate[END_REF]). While we were able to construct dominance hierarchies we also note the high percentage of reversals against the hierarchy in large groups, suggesting that male dominance hierarchies in graycheeked mangabeys are relatively fluid or unstable over time. Such conditions have been shown to increase cortisol levels for those being challenged in, for example, olive baboons [START_REF] Sapolsky | Cortisol concentrations and the social significance of rank instability among wild baboons[END_REF]) and rhesus macaques [START_REF] Higham | The endocrinology of male rhesus macaque social and reproductive status: a test of the challenge and social stress hypotheses[END_REF]).

Elsewhere we have shown that immigrant male gray-cheeked mangabeys have higher cortisol levels than resident males (i.e., both high-and low-ranking males), and that this may last for up to six months after males have transferred into a new group (Arlet et al. 2009a). Stressful conditions may manifest early on in the dispersal process. Although dispersing males do not incur increased aggression before leaving their groups (Olupot and Waser 2001a), traveling apart from groups appears to increase males' risk of mortality, most likely from crowned eagle (Stephanoaetus coronatus) predation (Olupot and Waser 2001b). Males traveling alone or entering a new group may also lack social support, which is an important mechanism for coping with stressful conditions [START_REF] Crockford | Social stressors and coping mechanisms in wild female baboons (Papio hamadryas ursinus)[END_REF][START_REF] Wingfield | Reproduction and resistance to stress: when and how[END_REF]. Finally, the dispersal phase may expose males to more parasites as the males come into contact with unfamiliar conspecifics harboring different parasites [START_REF] Chapman | Protozoan parasites in group-living primates: Testing the biological island hypothesis[END_REF][START_REF] Freeland | Primate social groups as biological islands[END_REF]VanderWaal et al. 2014a).

We also found that greater nematode incidence and intensity were associated with higher concentrations of fTM. Similar results have been reported for other species (e.g., mandrills: [START_REF] Setchell | Is brightest best? Testing the Hamilton-Zuk hypothesis in mandrills[END_REF], chimpanzees: Muehlenbein 2006). Testosterone can have an immunosuppressive effect [START_REF] Prall | Testosterone and immune function in primates: A brief summary with methodological considerations[END_REF][START_REF] Salvador | The cost of producing a sexual signal: testosterone increases the susceptibility of male lizards to ectoparasite infestation[END_REF][START_REF] Weatherhead | Parasite faunas, testosterone and secondary sexual traits in male red-winged blackbirds[END_REF]). However, although dominant males had the highest concentrations of fTM, they had the lowest nematode intensities. It is unclear why, if dominant males have high testosterone, and higher testosterone is associated with higher parasite loads, dominant males did not have high parasite loads. It is possible that in dominant males testosterone has an immunocompetence effect [START_REF] Peters | Testosterone treatment is immunosuppressive in superb fairy-wrens, yet free-living males with high testosterone are more immunocompetent[END_REF]. Our data suggest that the testosterone signal in parasitic infections might be coming from immigrant males. They had higher mean values of fTM (51.6) and nematode intensity (23.8; Table 1) than low-ranking males (testosterone: 46.6; nematode intensity: 7.3; Table 1). The trend is similar if outlier immigrant male KK, with the highest nematode load, is removed.

We also found that both parasite incidence and intensity were related to group size, as reported in an earlier study of gray-cheeked mangabeys at Kibale [START_REF] Freeland | Primate social groups as biological islands[END_REF], however, the mid-sized groups of 14-16 individuals were most infected with nematodes during our study. We suggest that this pattern is also related to male movements and social interactions. Immigrant males are attracted to groups with more estrous females [START_REF] Arlet | Mating tactics in male grey-cheeked mangabeys (Lophocebus albigena)[END_REF]Olupot and Waser 2001a), and the chance of finding fertile females generally increases in larger groups. However, the largest groups may also include more competitors such that immigrant males may be deterred from transferring to them. Thus, mid-sized groups may be 'optimal' for male immigration. In giraffe (Giraffa camelopardalis), which have a fission-fusion social organization structured around social cliques (VanderWaal et al. 2014b), individuals that interact with others outside their social clique have higher parasite loads (VanderWaal et al. 2014a). Our study suggests the same for immigrant male gray-cheeked mangabeys. The high frequency of social interactions between immigrant males and estrous females may also make such males parasite spreaders, exposing members of their new groups to parasites obtained from their former groups.

Finally, we found that parasite intensity was positively correlated with rainfall. Our results are thus consistent with what is known about the effects of climatic conditions on parasites (e.g., [START_REF] Huffman | Current evidence for self-medication in primates: A multidisciplinary perspective[END_REF][START_REF] Mcgrew | Intestinal parasites of sympatric Pan troglodytes and Papio spp. at two sites: Gombe (Tanzania) and Mt. Assirik (Senegal)[END_REF][START_REF] Stuart | A coprological survey of parasites of wild muriquis Brachyteles arachnoides and brown howling monkeys Alouatta fusca[END_REF].

In summary, our results repeatedly point to immigrant males as important vessels for nematode infection in gray-cheeked mangabey populations. In the future it would be beneficial to investigate the social aspects of parasite transmission. Social networks analyses have recently been employed to document that gut bacteria can be socially transmitted in yellow baboons [START_REF] Tung | Social networks predict gut microbiome composition in wild baboons[END_REF] and to identify who is most likely to spread bacterial and helminth infections in giraffes (VanderWaal et al. 2014a). This approach may be especially fruitful in elucidating in greater detail the role of immigrant male gray-cheeked mangabeys specifically, and immigrants in general, in parasite transmission.

Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8

 12345678 Fig.1Nematode incidence (proportion of samples with parasites) in male gray-cheeked

Table 1 .

 1 Individual identity, social status (high-ranking male (HR), low-ranking male (LR), 807 or immigrant male (M)), group identity, group size, nematode incidence and intensity, and 808 fecal glucocorticoid (fGCM) and testosterone metabolite (fTM) concentrations among adult 809 male gray-cheeked mangabeys in Kibale National Park,Uganda, 2006Uganda, -2007. .

	810
	811
	812

Table 2 .

 2 Results of a GLMM with binomial distribution examining factors associated with 813 parasite incidence in adult male gray-cheeked mangabeys in Kibale National Park, 2006-

	814
	815	2007.
	816
	817

Group size Incidence (mean±SD) Physaloptera sp. Trichuris sp. Strongyloidea sp. Unknown sp. Intensity fGCM ng/g (mean±SD) fTM ng/g (mean±SD) Sample size

	PL	HR	BT1	18	0.0 ± 0.0	0	0	0	0	0	76.2 ± 28.4	63.0 ± 8.9	6
	LM	HR	BT1	18	0.4 ± 0.6	0	3	0	1	4	71.1 ± 16.1	78.8 ± 3.8	5
	PL	LR	BT1	18	1.0 ± 0.0	3	1	3	0	7	36.5 ± 4.7	60.2 ± 1.6	2
	BG2	LR	BT1	18	0.4 ± 0.5	7	0	0	0	7	53.8 ± 9.8	51.8 ±7.7	7
	KR	HR	CC	15	0.3 ± 0.6	0	0	0	1	1	67.8 ± 25.3	63.3 ± 3.1	3
	MF	HR	CC	18	0.3 ± 0.5	2	0	0	0	2	82.8 ± 13.5	74.8 ± 17.3	4
	KJ	LR	CC	16	0.6 ± 0.6	25	0	0	1	26	68.7 ± 69.4	47.2 ± 25.7	5
	KK	M	CC	16	0.7 ± 0.5	51	0	12	3	66	137.4 ± 57.2	50.8 ± 11.3	10
	MF	M	CC	16	0.7 ± 0.5	18	0	8	0	18	220.5 ± 107.6	59.1 ± 23.4	4
	MG	HR	LC1	22	0.0 ± 0.0	0	0	0	0	0	110.1 ± 15.9	93.0 ± 14.5	7
	R	HR	LC1	21	0.0 ±0.0	0	0	0	0	0	132.6 ± 29.7	101.5 ± 5.6	5
	YM	HR	LC1	21	0.5 ± 0.6	0	0	2	0	2	115.3 ± 47.8	88.7 ± 8.9	4
	MR	LR	LC1	22	0.0 ± 0.0	0	0	0	0	0	28.7 ± 15.2	43.5 ± 8.7	6
	NK	LR	LC1	22	0.4 ± 0.6	1	0	3	0	4	70.2 ± 37.7	45.7 ± 27.8	5
	BW	M	LC1	22	0.4 ± 0.5	2	1	4	0	7	98.6 ± 29.3	60.7 ± 26.0	8
	HL	M	LC1	21	0.6 ± 0.5	2	0	2	0	4	183.1 ± 29.3	47.6 ± 26.1	7
	IB	HR	LC2	12	0.2 ± 0.5	1	1	0	0	2	41.8 ± 4.7	77.9 ± 13.3	5
	NY	LR	LC2	12	0.0 ± 0.0	0	0	0	0	0	33.7 ± 13.1	19.8 ± 10.3	4
	KY	HR	MK	15	0.3 ± 0.6	0	0	1	0	1	71.0 ± 3.0	74.5 ± 9.3	3
	KC	LR	MK	14	0.5 ± 0.7	5	0	2	0	7	82.8 ± 38.7	65.9 ± 15.4	2
					Estimate	SE	P
			Intercept	-5.30	2.20	0.022
			Status		4.75	3.09	0.002
			fGCM	0.03	0.01	0.001
			fTM		0.09	0.02	0.001
			Group size	-0.43	0.12	0.001
			Rainfall	0.08	0.09	0.351

Table 3 .

 3 Results of a GLMM with Poisson distribution examining factors associated with parasite intensity in adult male gray-cheeked mangabeys in Kibale NationalPark, 2006Park, - 2007. .

		Estimate	SE	P
	Intercept	1.080	0.720	0.140
	Status	3.940	3.970	<0.001
	fGCM	0.010	0.001	<0.001
	fTM	0.060	0.009	<0.001
	Group size	-0.280	0.040	0.011
	Rainfall	0.130	0.030	0.003
	fGCM x fTM -0.003	<0.001	<0.001

Acknowledgments

We thank the Uganda Wildlife Authority, Uganda National Council for Science and Technology and personnel at the Makerere University Biological Field Station for permission to work in Kibale National Park. We thank field assistants Akora Charles, Katusebe Swaibu, Kaseregenyu Richard, Koojo John, and Sabiti Richard for their invaluable assistance in the field. We thank Chesley Walsh and Johanna Bleecker for parasite analyses and Ants Kaasik and Veronique Biquand for advice on the statistical analyses. Finally, we appreciate the comments and suggestions by the editor and two anonymous reviewers that greatly improved the manuscript.


Jessica M Oakes

Alison L Marsden

Celine Grandmont

Chantal Darquenne

Irene E Vignon-Clementel
email: irene.vignon-clementel@inria.fr

Distribution of Aerosolized Particles in Healthy and Emphysematous Rat Lungs: Comparison Between Experimental and Numerical Studies

Keywords: Airflow, Particle deposition, Multi-scale, Aerosol exposure experiments, Computational fluid dynamics

In silico models of airflow and particle deposition in the lungs are increasingly used to determine the therapeutic or toxic effects of inhaled aerosols. While computational methods have advanced significantly, relatively few studies have directly compared model predictions to experimental data. Furthermore, few prior studies have examined the influence of emphysema on particle deposition. In this work we performed airflow and particle simulations to compare numerical predictions to data from our previous aerosol exposure experiments. Employing an image-based 3D rat airway geometry, we first compared steady flow simulations to coupled 3D-0D unsteady simulations in the healthy rat lung. Then, in 3D-0D simulations, the influence of emphysema was investigated by matching disease location to the experimental study. In both the healthy unsteady and steady simulations, good agreement was found between numerical predictions of aerosol delivery and experimental deposition data. However, deposition patterns in the 3D geometry differed between the unsteady and steady cases. On the contrary, satisfactory agreement was not found between the numerical predictions and experimental data for the emphysematous lungs. This indicates that the deposition rate downstream of the 3D geometry is likely proportional to airflow delivery in the healthy lungs, but not in the emphysematous lungs. Including small airway collapse, variations in downstream airway size and tissue properties, and tracking particles throughout expiration may result in a more favorable agreement in future studies.

Introduction

Computational fluid and particle dynamics simulations provide detailed spatial and temporal distributions of airflow and particles in healthy and diseased lungs. However, to increase confidence in these models, results must be validated against in vivo experimental data. While several groups have shown good agreement between three-dimensional (3D) flow (6; 19) and particle-based (14; 15; 16; 35; 32) models with in vitro experiments, these comparisons are not sufficient for validation of in vivo conditions. While one-dimensional (1D) particle transport models have relatively well predicted in vivo data of total and regional deposition in the human (5; 2; 13) and rat lung, (27; 1) they do not provide detailed spatial information. Recently, Minard et al. [START_REF] Minard | Phase-contrast MRI and CFD modeling of apparent (3)He gas flow in rat pulmonary airways[END_REF] showed promising agreement between in silico predictions and in vivo magnetic resonance (MR) derived flow fields in rat lungs. While these previous studies have advanced the validity of computational models, none of them compared multi-scale simulations to regional particle deposition data in both healthy and diseased lungs.

Emphysema, a disease characterized by increased tissue compliance, expanded acinar volume, and decreased small airway diameter compared to normal, [START_REF] Thurlbeck | Emphysema: Definition, Imaging, and Quantification[END_REF] has been shown to impact particle deposition in the lungs (20; 30; 4). To study the influence of emphysema-like morphometric changes on particle deposition, we previously [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF] employed MR methods [START_REF] Oakes | Regional Distribution of Aerosol Deposition in Rat Lungs Using Magnetic Resonance Imaging[END_REF] to determine lobar deposition in elastase-treated and healthy rat lungs. Results showed, for rats ventilated with the same breathing parameters, that particle concentration was higher in the elastase-treated lungs, compared to the healthy lungs. However, the distribution of particles to the lobes was the same in the healthy and emphysematous rats [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF] despite the MR and histological measurements suggesting heterogeneous distribution of emphysema-like structures in several lobes of the emphysematous group.

Recently, we developed a 3D-0D coupled numerical model to study airflow and particle deposition in healthy and emphysematous rat lungs [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF] that was parameterized from the experimental data of Oakes et al. [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF]. The goal of the current study was to extend this previous 3D geometric model [START_REF] Oakes | Rat airway morhometry measured from in-situ MRI-based geometric models[END_REF] and to compare regional deposition predictions to experimental data [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF]. These simulations required matching the numerical model as closely as possible to the experimental conditions and comparing the predicted distribution of inhaled particles to the experimental lobar deposition. Using this in silico model, we explored the influence of flow conditions (unsteady versus steady) and initial particle spatial distribution on deposition and lobar delivery.

Methods

Multi-Scale Numerical Airflow Framework

The numerical framework has been previously described in Oakes et al. [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF]. The 3D geometry was created with a custom version of the open source software, SimVascular (simvascular.org) [START_REF] Schmidt | The Simbios National Center: Systems Biology in Motion[END_REF] and includes up to 16 airway generations, with 81 terminal airways (Figure 1) [START_REF] Oakes | Rat airway morhometry measured from in-situ MRI-based geometric models[END_REF]. Airflow was simulated with an open source stabilized finite element Navier-Stokes solver, assuming rigid walls and incompressible Newtonian flow (density = 1.2 x 10 -6 g mm 3 , viscosity = 1.81 x 10 -5 g mm-s ). A custom linear solver was employed, incorporating resistance-based preconditioning with a combination of GMRES and conjugate gradient methods [START_REF] Esmaily Moghadam | A new preconditioning technique for implicitly coupled multidomain simulations with applications to hemodynamics[END_REF]. Three respiratory cycles were simulated with six Newton iterations of the linear system within each time step of 10 -4 s. Anisotropic mesh adaptation based on the Hessian of the velocity field was employed to ensure mesh convergence of the solution (18) using meshsim (Simmetrix, Troy NY). Mesh convergence was confirmed for both the flow field and particle deposition.

A no-slip boundary condition was assigned at the airway walls, the experimentally measured time-varying pressure (P H (t) or P E (t), H: healthy, E: Emphysema) ( 22) was applied at the trachea face (Figure 1A) and 0D resistance (R i, j ) and compliance (C i, j ) models (3) were implemented at each distal face by implicitly coupling the 0D models to the 3D model [START_REF] Esmaily Moghadam | A modular numerical method for implicit 0D/3D coupling in cardiovascular finite element simulations[END_REF]. A representative healthy and emphysematous rat were chosen to match the rat-specific global parameters with its pressure curve. The 0D model is governed by the following equation:

R i, j dV(t) i, j dt + V(t) i, j C i, j = P(t) i, j -P peep , (1)
where V i, j is the inspired volume, P(t) i, j is the pressure at each distal face and the j and i indices identify the distal faces, with lobe j and assigned airway number i. P peep is the constant pressure of 1 cmH 2 O (20). Note that the distal pressure P D in Figure 1 does not appear in this equation as it describes the evolution of the inspired, not the total, volume [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF]. To prevent numerical instability a convective stabilization scheme [START_REF] Esmaily Moghadam | A comparison of outlet boundary treatments for prevention of backflow divergence with relevance to blood flow simulations[END_REF] was imposed at all faces with β = 0.1. The resulting resistance in the 3D geometry was calculated at the time of maximum inspiration (R 3D,insp ) and expiration (R 3D,ex ) by dividing the average pressure drop in the 3D geometry by the flow rate at the trachea.

In the next two paragraphs, the parameters R i, j and C i, j were derived using a combination of the experimental measures and a purely 0D model (e.g. Figure 1 C). With this formulation, it was assumed that the 3D resistance did not influence the average flow repartition in the distal branches of the 3D tree. Therefore, the driving pressures, P H (t) or P E (t) (Figure 1 A) remained the relevant P(t) i, j for these solely 0D models. This assumption was tested and its validity is confirmed in the discussion section.

Healthy 0D Parameters

The 0D model parameters for the healthy simulations were defined as:

C i, j = A i, j α j C global A T j (2)
R i, j = R global A T j α j A i, j , (3)
where α j is the volume of each lobe divided by the total volume of the lung derived from imaging data at total lung capacity [START_REF] Oakes | Rat airway morhometry measured from in-situ MRI-based geometric models[END_REF], A i, j is the cross-sectional area of each distal face and A T j = N airway i=1

A i, j . The global parameters were set to the values previously determined from the experimental data (R global = 0.098 [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF]. With these definitions R i, j and C i, j are functions of both the fractional cross-sectional area and experimentally-measured lobar volume fraction α j . Neglecting the 3D region as explained above, all the distal RC 0D models in parallel are mathematically equivalent to a single global RC 0D model. This is due to the fact that the products R i, j C i, j are all equal, i.e. their relaxation times are the same. Following our previous work [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF], the 0D resistance during expiration was set to 1.5 times the 0D resistance during inhalation (R i, j ex = 1.5R i, j ) [START_REF] Rubini | Flow and volume dependence of rat airway resistance during constant flow inflation and deflation[END_REF].

cmH 2 O -s -cm -3 , C global = 0.236 cm 3 -(cmH 2 O) -1 )
In addition to the unsteady simulations, two steady simulations were performed in which either a constant mean (7408 mm 3 s ) or maximum (12025 mm 3 s ) inhalation flow rate was set at the inflow trachea face and R i, j were applied as the outlet boundary conditions at the distal faces.

Emphysema 0D Parameters

Emphysematous regions were heterogeneously located in the lung to match the experiments [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF]. The lung was divided into normal (Zone 1) and diseased (Zone 2) regions (Figure 1B). Due to this heterogeneity, a new procedure to estimate the distal parameters was designed, based on a purely 0D model. In fact, several RC models in parallel are not mathematically equivalent to a single RC model when the characteristic times, that is, the products τ = RC are not the same. Within each homogeneous region (normal or diseased) these products are the same by construction. However between regions, this is no longer true, that is, a diseased RC and a normal RC in parallel are no longer formally equivalent to a global RC model. [Note that only the global compliance, and not the global resistance, changed for the emphysematous rats compared to the healthy rats [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF], resulting in different relaxation times between these two states.] As a result, the relationship between experimentally determined global R and C, and lobar or sublobar values is no longer simple. R i, j and C i, j cannot be described as they were for the healthy simulations, inversely, or directly, proportional to the volume and area fractions. However, they can be determined with a 0D model, in which each homogeneous region (normal or diseased) is represented by a single RC model (Figure 1C).

As the airways in Zone 1 are normal, they were assigned the same R i, j , R i, j ex and C i, j as in the unsteady healthy simulations. The inspired volumes, V j , of the two normal lobes were calculated by directly solving:

R norm j dV j dt + V j C norm j = P E (t) -P peep , (4)
where

R norm j = 1 N j i=1 1 R i, j
and C norm j = N j i=1 C i, j . N j is defined as the total number of airways within Zone 1 of each lobe j.

The total inspired volume V T dis of the three diseased lobes (diaphragmatic, cardiac, and left lobes) were found by subtracting the volume delivered to the apical and intermediate lobe from the total inhaled volume (2.2 mL) [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF]. The remaining volume was distributed amongst the three diseased lobes with V E j = V T dis α j 3 j=1 α j . For Zone 1 (i.e. the healthy part) of the diseased lobes, V j was calculated directly with Eqn. 4 for R norm j and C norm j of Zone 1 and was subtracted from V E j to find the total inspired volume of the diseased part of each diseased lobe, V dis,insp j . Then,

R dis j dV dis j dt + V dis j C dis j = P E (t) -P peep (5)
was inversely solved and unique R dis j and C dis j were found to satisfy V dis j (t = t insp ) = V dis,insp j . R i, j and C i, j of the terminal airways in Zone 2 were then distributed according to:

C i, j = A i, j C dis j A T dis j (6) R i, j = R dis j A T dis j A i, j , (7)
where A T dis, j is the total cross-sectional area of the terminal airways within Zone 2 of each lobe.

Particle Tracking and Comparison to Experimental Data

Following the airflow simulations, monodisperse inert particles (diameter = 0.95µm, density = 1.35gcm -3 ) were tracked throughout inspiration using Lagrangian methods to solve a reduced form of the Maxey-Riley equation (29; 22). The particles were released at the trachea face throughout inspiration, and the number of particles released was either assigned to be proportional to the local flow rate at the particle starting location (staggered release) or constant in both space and time (uniform release). The gravity vector direction was set to simulate a rat in the supine position. Particles were assumed to have either deposited or exited once they left the 3D geometry at either the airway walls or one of the distal faces, respectively. Particle deposition was checked to be independent of both the time step and number of particles released. The percentage of deposited particles was calculated by normalizing the number of particles deposited by the number of particles inspired. Normalized particle delivery to each lobe was calculated by

P Del j = N j i=1 N exit i, j + N wall j 5 j=1 ( N j i=1 N exit i, j + N wall j ) , (8)
where N exit i, j is the number of particles to exit each distal face and N wall j is the number of particles to deposit on the walls of the airways within lobe j of the 3D geometry. The volume normalized particle delivery, V P Del j , was calculated for each lobe by

V P Del j = P Del j α j . (9)
Both P Del j and V P Del j were compared to the experimental values of P Dep j and V P Dep j , respectively [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF]. Note that V P Dep j was found by normalizing P Dep j by each rat's α j found in the experimental study. (20)

Results

Resistance and Compliance Identification

Parameter results are presented in Table 1. R dis j was smaller and C dis j was larger in Zone 2 of the emphysematous lungs compared to Zone 2 of the healthy lungs (Table 1). The relaxation time constant, τ = RC, was the same for all lobes in the healthy simulations (Table 1). For the emphysematous simulations, Zone 1 had the same τ as the healthy simulations. However τ in Zone 2 was larger in the emphysematous lungs compared to the healthy lungs (Table 1).

Multi-scale CFD Simulations

Five mesh adaptions were performed and the fourth mesh (∼ 3 million elements) was used for all simulations. Particle deposition was sensitive and slow to converge with mesh refinement despite there being little difference in the flow field between mesh adaptions. Tuning of R i, j was not necessary, as the difference in inspired volume between the 0D model prediction and the 3D simulation result was only 2%. Additionally, the normalized lobar tidal volume was the same as α j for the healthy case but not for the emphysematous case (Table 2). The flow distribution was mainly driven by the compliance, as the pressure drop given by V i, j C i, j of Eqn. 1 was ∼10 times greater than the pressure drop given by R i, j dV i, j dt . For Zone 1, the percentage of total flow delivery was less in the emphysematous lungs compared to the healthy lungs (Table 1 and Figure 2). Conversely, the delivery of air to Zone 2 was higher in the emphysematous rats compared to the healthy rats (Table 1 and Figure 2). Zone 2 of the emphysematous simulations took longer to empty, as evident by their slower relaxation rates, τ (Table 1), compared to the same regions of the healthy lung (Figure 2). In the emphysematous simulations the diseased regions finished filling after the normal regions (0.384 s vs 0.360 s).

Flow Profiles in the 3D Geometry

The velocity profile at the trachea entrance was mainly parabolic throughout inspiration and became biased towards the right lung as the flow approached the main carina (Figure 3). The flow slows down as it travels deeper into the lung (Figure 3), since the total cross-sectional area of the 3D geometry increases with increasing generation. As seen previously ( 22), the flow patterns were highly dependent on the time-point in the breathing cycle (t=0.091 s and t=0.258 s, same flow rate, Figure 3B) as demonstrated in Figure 3C and E for the healthy case and Figure 3D andF for the emphysematous case.

The main bronchi's cross-sectional area increased along their length [START_REF] Oakes | Rat airway morhometry measured from in-situ MRI-based geometric models[END_REF], which caused an adverse pressure gradient along the main bronchioles (Figure 4). The opposite effect occurred for the terminal airways; the cross sectional area decreased along their length, which created an enhanced pressure drop (Figure 4) and increased flow velocity (Figure 3). The time-averaged resistances in the 3D geometry were larger than predicted by both Poiseuille's model and with Pedley et al.'s (25) correction (Table 3).

Steady vs. Unsteady: Contrary to the unsteady simulations, the normalized lobar flow distribution for the steady simulations did not match the normalized lobar volume, α j (Table 2). The largest deviation was for the intermediate lobe of the steady mean inflow case, where 25% more flow was delivered than expected based on the assumption that the flow distribution is proportional to the lobar volume. This resulted in higher velocity magnitudes in the airways leading to this lobe, compared to the unsteady simulations at the same time point (Figure 3A vs. Figure 3C andE). The pressure drop in the steady simulation at maximum inhalation was 26% greater than the unsteady simulation (Figure 4A 1: Total model parameters (R and C) and relaxation times (τ = RC), for each zone of the healthy and emphysematous lungs (Figure 1C), total cross-sectional area of the terminal airways within each zone and the total inhaled volume (tidal volume) to each zone found from the 0D-3D simulations are presented. Healthy vs. Emphysematous: In the emphysema case, the flow velocities were higher in Zone 2 and lower in Zone 1 compared to the same regions of the healthy simulations (Figure 3D vs C and Figure 3F vs. E). This difference is especially apparent during the deceleration phase of inhalation (Figure 3E andF), likely caused by the airflow phase shift between the two zones in the emphysematous lungs (Figure 2). The flow reverses direction and moves towards the trachea in all the airways at the start of expiration for the healthy simulation (Figure 3B and Figure 3G)). However, in the emphysematous simulation, the air is both moving away (Zone 2) and towards (Zone 1) the trachea at the start of expiration (Figure 3H), creating asynchrony between the two regions.

While the pressure drop in the 3D geometry is quite similar at maximum inhalation (Figure 4 A and D), the average pressure drop during peak exhalation in the emphysematous lungs was 36% of the pressure drop in the healthy lungs (Figure 4 F vs C). The maximum 3D resistance in the 3D geometry was smaller in the emphysematous simulations compared to the healthy simulation for both inspiration and expiration (Table 3). However, the time-averaged 3D resistance was nearly the same (Table 3) between the two simulations.

Particle Deposition in the 3D Geometry

There was negligible difference in total particle deposition between the healthy (2.82%) and emphysematous (2.98%) simulations. Similar to our previous study [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF], particles deposited mainly at the bifurcation zones and backside (in the direction of gravity) of the 3D geometry (Figure 5) for both the healthy and emphysematous simulations. The relative difference in the percentage of deposited particles in the uniform seeding was 1.5% of the staggered release. Deposition in the 3D geometry was 6.0% less and 6.6% greater than the unsteady simulations for the mean and maximum steady simulations, respectively. Additionally, while the particle deposition sites were spread out in the unsteady simulations, particles tended to pile up in the steady simulations, creating regions of higher particle concentration (Figure 5C andD).

The distribution of deposition in the 3D geometry was impacted by the disease (Figure 6A). The percentage of particles depositing on the airway walls was higher in Zone 2 and lower in Zone 1 in emphysema compared to the healthy simulations (Figure 6A). The concentration of deposited particles was highest in the apical lobe, compared to the other four lobes (Figure 6B) for both the healthy and emphysematous simulations. Particle concentration in Zone 2 was higher in the emphysematous than in the healthy simulations (Figure 6B). Figure 6C shows that the apical lobe had the greatest and the intermediate lobe the smallest number of particles depositing on the wall, when normalized by the flow rate percentage, for both the healthy and emphysematous simulations.

Particle Distribution: Comparison to Experimental Deposition

There was excellent agreement between the experimental deposition data, P Dep , and the simulated lobar delivery, P Del , for the steady and unsteady healthy simulations (Figure 7A). P Del was slightly lower and slightly higher than P Dep for the apical and cardiac lobes, respectively. While there was no statistical difference (t-test, p>0.5) between V P Dep and V P Del , V P Dep was greater than 1 for the apical and intermediate lobe.

The distribution of particle delivery to the 5 lobes was highly dependent on the seeding profile (Figure 7A); uniform seeding overestimated P Del to the left lobe and underestimated P Del to the diaphragmatic lobes compared to P Dep (Figure 7A).

For the three diseased lobes, good agreement was found between P Del and P Dep for the emphysematous simulations (Figure 7B). V P Del of particles in the emphysematous simulations (Figure 7D

Re2r

l , where Re is the Reynolds number) were done so by systematically summing up the resistance in each airway by moving from the most distal regions of the tree towards the trachea. diaphragmatic and cardiac lobes; volume normalized delivery was overestimated in the left lobe and underestimated in the apical and intermediate lobes.

Discussion

The goal of this current study was to compare simulated particle delivery in healthy and emphysematous lungs to in vivo experimental data. To do this, 3D-0D in silico models were created to represent the experimental conditions. Favorable agreement was found for the healthy simulations, however, as the peripheral deposition efficiency differs between diseased and healthy regions of the lung, similar agreement was not found for the emphysematous lungs.

Particle Deposition and Distribution

Steady vs Unsteady Simulations

Despite steady and unsteady simulations velocity differences (Figure 3), both reasonably predicted the healthy experimental particle lobar distribution (Figure 7A). This finding suggests that if only lobar particle delivery is needed and there is little influence of inertia on the particle transport, then steady simulations may be appropriate. However, if one aims to predict locations of high particle concentration, then steady simulations may not be sufficient (Figure 5).

Staggered vs. Uniform Release

There was good agreement between the experimental deposition and the particle delivery to the lobes for the staggered release, but not for uniform release (Figure 7A). In the latter, the number of released particles was uniform throughout inspiration, independent of the particle's starting location. Slow and fast streamlines carried the same number of particles, causing an unrealistically high (left lobe) or low (diaphragmatic lobe) delivery to areas of the lung which they fed. However, the 3D deposition number was relatively unaffected by this seeding density profile. The staggered release best represents the exposure experiments: a long tube led to the trachea [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF], causing particles to convect with the fully developed flow. In agreement with previous work [START_REF] Zhang | Effect of particle inlet distribution in a triple bifurcation lung airway model[END_REF], particle distribution in the lung is sensitive to the seeding method and therefore care should be taken when choosing how to initially distribute the particles.

Influence of Airway Geometry

While not statistically different than unity (20), V P Dep in the apical lobe tended to be larger than predicted by α j for the healthy case (Figure 7C). Since the simulated apical V P Del was close to unity (Figure 7C), lobar ventilation or geometric differences may explain this enhanced deposition. The apical ventilation may be greater than assumed by α j , resulting in increased particle delivery and thus deposition. Deposition efficiency could also be greater since the apical lobe exhibits dichotomous branching characteristics, while the other lobes branch monopodially [START_REF] Oakes | Rat airway morhometry measured from in-situ MRI-based geometric models[END_REF]. Indeed, the apical short branching structure and high surface area to volume characteristics caused enhanced deposition in this 3D region (Figure 6B). The apical lobe also has a shorter path length to the acinus than the other lobes [START_REF] Yeh | Anatomic models of the tracheobronchial and pulmonary regions of the rat[END_REF], which may result in a higher particle concentration delivery to the acinus and therefore enhanced deposition in this region. The opposite was found for the intermediate lobe, where the branching structure is highly monopodial.

As particles are transported by convection in the conducting airways fewer particles were delivered to lower flow areas. However, lower velocity also means more time to sediment and deposit on the wall. For example, in the diseased cardiac lobe, flow rate and particle delivery increased in emphysema, but particle deposition was higher in the healthy simulations (Figure 6C). The opposite effect was seen for the diaphragmatic lobe (Figure 6C).

Particle Deposition in Emphysema

Experimental total deposition was higher in emphysema. However, these results were not replicated computationally: total 3D deposition was not influenced by the heterogeneous disease. As this 3D geometry only consists of about 4% ( 23) of total lung volume, this enhanced deposition may occur downstream of the considered geometry, or during expiration. Furthermore, even though there was asynchrony in the filling and emptying of the lobes in the emphysematous simulations (Figure 3H), this only occurred for a short period of the respiration cycle. It is possible that this asynchrony becomes more significant as the disease progresses, causing air to be trapped. Particles suspended in this trapped air will likely have time to sediment and deposit. This failure to flush out potentially harmful particles could contribute to accelerated disease progression.

The numerical simulations were unable to fully reproduce the emphysema P Dep (Figure 7B andD). Particle delivery was not proportional to lobar volume in the emphysema case (Figure 7D). While measured lobar volume was similar between healthy and emphysematous rats [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF], computed airflow and hence particle delivery were enhanced in the diseased regions because of their higher compliance. As the inspired air volume distribution no longer followed the lobar volume fraction, lobar particle delivery was no longer proportional to α j in contrast to the healthy rat case (Figure 7D vs C).

Deposition efficiency in diseased and healthy regions are likely to be different with less deposition occurring in the large acinar airspaces of the diseased lung parenchyma (31; 21). However, reduced traction of the emphysematous tissues on the adjacent small airways decrease their size, potentially increasing small airway deposition. Additionally, small emphysema airways are prone to collapse, (33) likely causing local particle trapping, enhancing sedimentation. But, flow is slower in normal regions (Figure 3), potentially favoring sedimentation in these regions. These contradicting effects make it difficult to predict the behavior of particles downstream of the 3D model and should be examined in future simulations.

Limitations

Constant R i, j , R i, j ex and C i, j values were used for the 0D model parameters, even though they most likely change throughout inhalation and exhalation. However, it was not possible to define time varying R i, j , R i, j ex and C i, j , as only constant R global and C global could be found from the experimental data [START_REF] Oakes | Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments[END_REF]. As the particles exhibited little inertia, it is unlikely time varying R i, j and C i, j parameters would impact the particle distribution to the lobes.

While measured experimentally with MRI, [START_REF] Oakes | MRI-based measurements of aerosol deposition in the lung of healthy and elastase-treated rats[END_REF] there is some uncertainty in the uniformity and location of the disease in the emphysematous lungs. Simulations would benefit from more detailed knowledge of disease location, perhaps measured experimentally by ventilation (12; 8), tissue compliance or sub lobar volumes. While regional ventilation maps in elastase-treated rat lungs [START_REF] Emami | Early changes of lung function and structure in an elastase model of emphysema-a hyperpolarized 3He MRI study[END_REF] exist, it is difficult to apply them to numerical simulations, although (sub-)lobar information would be highly valuable in parameterizing multi-scale simulations.

Conclusions

This study was the first to compare 3D numerical particle deposition simulations to experimental data in both healthy and diseased conditions. While both the steady and unsteady simulations reasonably predicted the lobar distribution of particles in the healthy case, the deposition patterns in the 3D geometry was quite different between the two. This finding indicates that if only lobar distribution is needed, steady simulations may be appropriate for the flow and particle properties considered in this study. However, unless the flow delivery to each lung region is known beforehand, emphysema can only be modeled using the unsteady multi-scale techniques employed in the current study. Unlike for the healthy simulations, we were unable to match the emphysema experimental data. This was likely because the airways and pulmonary region downstream of the 3D geometry were lumped in the RC models. In the healthy simulations, it is possible that deposition efficiency downstream of the 3D geometry is relatively similar between lobes. However, in emphysematous lungs, where normal regions are neighboring diseased regions, the deposition efficiency is likely not the same between these different areas of the lung. Therefore factors such as enhanced deposition in the normal acinus regions compared to the diseased regions, smaller airway diameters in diseased regions compared to normal regions, airway collapse in diseased regions, and particle transport through expiration, may impact deposition efficiency downstream of the 3D geometry considered in this study.

Healthy Emphysematous

Figure 1 :

 1 Figure 1: 3D airway geometry (23) used for all the simulations. Panel A: Identification of the airways leading to the five lobes. Panel B and C: Definition of the zones used for the emphysematous simulations. Zone 2 (diseased region) was set to be at the bottom 1 /3 of the left, cardiac and diaphragmatic lobes. Zone 1 (normal region) was defined as the top 2 /3 of the left, cardiac and diaphragmatic lobes and the entire apical and intermediate lobes. Panel C shows the distribution of the normal (R norm and C norm ) and diseased (R dis and C dis ) parameters for each zone.

Figure 2 :

 2 Figure 2: Volume of air entering each zone of each lobe (zone definition is given in Figure 1B) and the total lung (bottom right panel) for the healthy and emphysematous simulations.

Figure 3 :

 3 Figure 3: Velocity magnitudes and streamlines ( cm s ) for the mean inflow steady simulation (A) and the unsteady healthy and emphysematous simulations at the time of mean inspiration before and after the time of maximum inhalation (Healthy: panels C and E, Emphysematous: panels D and F). For visualization purposes the top portion of the trachea is removed in the figure. The velocity magnitude of the bottom part of the left lung (for location see panel E) at the end of inhalation is shown for the healthy (G) and emphysematous (H) simulations. The same phenomenon occurs at the intersection of Zone 2 and Zone 1 in the cardiac and diaphragmatic lobes. Arrows show the general flow direction. The time points where the flow field was plotted for each panel is shown in panel B. 13

Figure 4 :

 4 Figure 4: Pressure drop in the 3D geometry (cmH 2 O) at maximum inhalation (panels A and D) and maximum exhalation (panels C and F) for the healthy and emphysematous simulations. The red arrow on Panel A shows one of the areas where there was an adverse pressure gradient caused by the increasing diameter of the bronchi. Panel B shows the pressure drop for the steady simulation when the maximum flow rate was prescribed at the trachea face.The pressure drop was calculated by subtracting the 3D pressure by the mean pressure at the trachea or most distal airway for the inspiration and expiration panels, respectively. Panel E shows the time points where the pressure plots were taken.

Figure 5 :Figure 6 :Figure 7 :

 567 Figure 5: Particle deposition locations for the unsteady healthy (panels A and B) and steady simulations (panels C and D). For the steady simulations, blue and red particles were used for the mean and maximum flow rate, respectively.

  vs B).

	Table	Left: Zone 1	Left: Zone 2	Cardiac: Zone 1	Cardiac: Zone 2	Diaphragmatic: Zone 1	Diaphragmatic: Zone 2	Intermediate: Zone 1	Apical: Zone 1
		0.420 0.420 0.0562 0.0562 0.0236 0.0236 26.3 26.3 23.8 17.4	0.826 0.575 0.0286 0.0727 0.0236 0.0418 13.4 13.4 12.1 21.8	1.26 1.26 0.0187 0.0187 0.0236 0.0236 6.28 6.28 7.91 5.78	2.70 2.05 0.0209 0.0204 0.0236 0.0418 2.93 2.93 3.68 6.25	0.509 0.509 0.0463 0.0463 0.0236 0.0236 18.8 18.8 19.61 14.3	1.193 0.762 0.0198 0.0548 0.0236 0.0418 8.03 8.03 8.37 16.41	0.739 0.739 0.0319 0.0319 0.0236 0.0236 11.5 11.5 13.52 9.90	0.907 0.907 0.0260 0.0260 0.0236 0.0236 12.8 12.8 11.01 8.06	Healthy Emphysema Healthy Emphysema Healthy Emphysema Healthy Emphysema Healthy Emphysema	cmH 2 O-s cm 3 s cm 3 cmH 2 O % %	Resistance (R) Compliance (C) τ = RC Cross Sectional Area Tidal Volume

Table 2 :

 2 Subtending lobe volume percentage (α j )[START_REF] Oakes | Rat airway morhometry measured from in-situ MRI-based geometric models[END_REF], inhaled percent lobar volume for the unsteady simulations, and percent lobar flow division for the two steady simulations.

		Subtending Lobe Healthy Unsteady Steady: Mean Steady: Maximum	Emphysema
		Volume, α j * 100 Tidal Volume, %	Flow, %	Flow, %	Tidal Volume, %
	Apical	11.0	11.0	11.9	11.1	8.06
	Intermediate	13.5	13.5	18.1	14.2	9.90
	Diaphragmatic	28.0	28.0	27.9	26.8	30.7
	Cardiac	11.6	11.6	10.4	11.4	12.0
	Left	35.9	35.9	31.7	36.4	39.2

Table 3 :

 3 ) matched V P Dep for only the Resistance ( cmH 2 O s cm 3 ) in the 3D geometry calculated from pressure drop found from the 0D-3D simulations and from empirical formulas. Maximum and time-averaged simulated resistances were calculated by taking the mean pressure drop from the trachea to each of the distal faces and by dividing it by the flow rate at the trachea. Resistances calculated with either Poiseuille (R p = 8µl πr 4 , where l and r are the length and radius of the airway, respectively) or Pedley et al.'s correction (25) (R ped =

			Inspiration		Expiration
		Maximum Time-Averaged Maximum Time-Averaged
	Healthy 0D-3D Simulation	0.087	0.020 ± 0.010	0.196	0.024 ± 0.014
	Emp. 0D-3D Simulation	0.056	0.021 ± 0.007	0.061	0.024 ± 0.009
	0D (22)	-	0.098	-	0.147
	Poiseuille	0.003	0.003	0.003	0.003
	Pedley's Correction (25)	-	0.007	-	-
	1.85R p √
	4	2

Acknowledgments

Chantal Darquenne was supported in part by grant 1R21HL087805-02 from the NHLBI (NIH), Alison L. Marsden was supported in part by an NSF CAREER award and the Burroughs Wellcome Fund Career Award at the Scientific Interface, Jessica M. Oakes was fully supported by the Whitaker International Program administered by the International Institute of Education, and Celine Grandmont was partially supported by the ANR-08-JCJC-0013 and ANR-11-TECS-0006 grants. An associated team INRIA grant covered visit costs for joint work of the co-authors. The authors would like to thank Shawn Shadden for sharing his expertise in particle tracking.

Conflict of interest

The authors have no conflict of interest related to the work presented in this manuscript.