Research Article
18 April 2019

The Indoor-Air Microbiota of Pig Farms Drives the Composition of the Pig Farmers’ Nasal Microbiota in a Season-Dependent and Farm-Specific Manner

ABSTRACT

Prior studies have demonstrated an influence of the built environment on the human nasal microbiota. However, very little is known about the influences of working on a pig farm on the human nasal microbiota. We longitudinally collected samples from 30 pig farms (air and nasal swabs from humans and pigs) in Switzerland from 2014 to 2015. As controls, nasal swabs from cow farmers and individuals with no contact with farm animals were included. An analysis of the microbiota for all samples (n = 609) was performed based on 16S rRNA gene sequencing (MiSeq) and included the investigations of source-sink dynamics. The numbers of indoor airborne particles and bacterial loads in pig farms were also investigated and were highest in winter. Similarly, the microbiota analyses revealed that the alpha diversity values of the nares of pig farmers were increased in winter in contrast to those of samples from the nonexposed controls, which displayed low alpha diversity values throughout the seasons. Source-sink analyses revealed that bacteria from the noses of pigs are more commonly coidentified within the pig farmers' microbiota in winter but to a less extent in summer. In addition, in winter, there was a stronger intrasimilarity for samples that originated from the same farm than for samples from different farms, and this farm specificity was partially or completely lost in spring, summer, and fall. In conclusion, in contrast to nonexposed controls, a pig farmer’s nasal microbiota is dynamic, as the indoor-air microbiota of pig farms drives the composition of the pig farmer’s nasal microbiota in a season-dependent manner.
IMPORTANCE The airborne microbiota of pig farms poses a potential health hazard and impacts both livestock and humans working in this environment. Therefore, a more thorough understanding of the microbiota composition and dynamics in this setting is needed. This study was of a prospective design (12 months) and used samples from different sites. This means that the microbiota of air, animals (pigs), and humans was simultaneously investigated. Our findings highlight that the potential health hazard might be particularly high in winter compared to that in summer.

INTRODUCTION

Pig farmers are exposed to a lot of potentially hazardous substances during their daily routine, including high concentrations of bacteria which are present inside pig confinement buildings (13). Many studies have monitored the overall air quality inside pig farms (49) and the possible transmission of zoonotic bacteria (1014). The majority of studies have focused on sampling during a very short time period or even only one point in time and therefore do not address possible seasonal variations in pig farms. In Switzerland, there is a mean temperature variation of 18°C throughout the year, with low temperatures in winter, high temperatures in summer, and intermediate temperatures in spring and fall (15). These temperature shifts lead to different behaviors and farm management habits in the pig farming industry. In winter, openings such as doors and windows are closed, and a lower ventilation rate is taken up to minimalize heat loss. There are studies that have investigated seasonal variabilities of bacteria, archaea, and fungi in the air of pig buildings, and they have found a seasonal effect (1619). However, none of these studies have investigated the influence of this variability on the human nasal microbiota. Recent studies have found evidence that there is substantial exchange of microbial communities among humans and the animals with which they are in close contact (14, 20, 21). In a previous study, we also showed the presence of a specific microbiota pattern for each investigated pig farm in winter by a cross-sectional approach (14). The present study used a longitudinal approach to address the seasonal variability of the nasal microbiota of pig farmers as well as the nasal microbiota of pigs and the air inside the pig confinement building. We utilized Illumina MiSeq sequencing in combination with the recently developed DADA2 pipeline (22). The present study was performed to address the following questions. (i) What changes occur in the human nasal microbiota throughout the seasons in individuals with and without close contact with farm animals? (ii) Are these potential changes driven by the airborne microbiota and are they farm specific?

RESULTS

Overview of the sampling, measuring particle, and bacterial load.

The numbers of samples collected per season and per sample type are shown in Fig. S1 in the supplemental material. We first assessed the inhalable particle fraction (the particles that enter the respiratory system) of the air samples inside the pig confinement buildings for all four seasons (Fig. 1A). The lowest mass of particles was measured in summer, and there was a significant difference between summer and the other three seasons (winter, P = 0.01; spring, P = 0.001; fall, P = 0.013). We next quantified the amount of bacteria inside pig confinement buildings by performing 16S rRNA quantitative PCR (qPCR) with air samples from 30 pig farms. As for the particles, a similar relationship was also observed for bacterial concentrations in the air; gene copy numbers were significantly lower in summer than in winter (P < 0.001), spring (P = 0.004), and fall (P = 0.03) (Fig. 1B).
FIG 1
FIG 1 Particle and bacterial loads inside pig confinement buildings throughout the year. The mass of particles in the inhalable fraction (log transformed) (A) and 16S rRNA gene copy numbers (log transformed) (B) are indicated. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Analyzing the microbiota within pig farms.

After excluding samples for reasons of quality control (Fig. 2), microbiota analyses were conducted for overall 609 samples from pigs, air, pig farmers, cow farmers, and nonexposed individuals (for details, see Tables S1 and S2). Following 16S rRNA PCR and subsequent MiSeq sequencing, 20,940,045 sequences were received (mean number of reads per sample, 34,384; standard deviation [SD], 18,453) and clustered into 26,203 distinct sequence variants (SVs). The numbers of reads per sample ranged from 3,364 to 127,393. The 26,203 SVs were classified into 48 phyla, of which 4 dominant phyla made up >97% of the overall abundance (Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes) (Fig. 3). The relative abundances of phyla and families were similarly distributed across seasons for each sample type (Fig. 3A and B). We tested for significant differences of families and phyla between subsequent seasons for all sample types using an analysis of variance (ANOVA)-like differential expression (ALDEx) analysis. There were significant differences between fall and winter in families and phyla from pig farmers, differences in the phyla Bacteroidetes and Cyanobacteria in air samples, and significant differences in both phyla and families in pigs for all comparisons (see Table S3). In pig farmers and air, the fraction of significant phyla and families was less than 10%, and in pigs, the significant fraction was higher with up to 25% (see Fig. S2).
FIG 2
FIG 2 Flow chart of sample processing, including information on excluded samples. *, two cow farmers and nine nonexposed individuals were fully excluded from the study, as there was only one high-quality sample per individual left after sample processing.
FIG 3
FIG 3 Longitudinal taxonomy plots. Cumulative bar charts comparing relative abundances of samples from pigs, air, pig farmers, cow farmers, and nonexposed individuals throughout the seasons at the phylum level (A) and family level (B) are illustrated. Clostridiales here represents all families of this order except Clostridiaceae.

Variable alpha diversity values throughout the year within pig farmers but not within nonexposed controls.

We next estimated alpha diversity by calculating richness and Shannon diversity indices (SDIs). Regarding pig farmers, there were significant differences between winter and the remaining three seasons (P = 0.01 [spring], P < 0.001 [summer], and P = 0.03 [fall] for SDI; P < 0.001 for all comparisons for richness) (Fig. 4A and B). The nonexposed group had consistent and low richness and SDI values with no differences between seasons (P > 0.05 for richness; P > 0.05 for SDI). In the remaining sample types, the highest values were in winter and lower values in spring and summer. Furthermore, there was a significant increase in fall compared to that in summer in pigs (P = 0.02 for richness; P = 0.003 for SDI).
FIG 4
FIG 4 Alpha diversity analyses of samples from pigs, air, pig farmers, cow farmers, and nonexposed individuals throughout the seasons. This is illustrated by the differences of richness (observed sequence variants [SVs]) (A) and Shannon diversity indices (B) based on season for each sample type. The lines represent the means and the shaded areas the 95% confidence intervals. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Variable seasonal contributions of pigs’ nasal microbiomes on the pig farmers’ microbial assemblages.

The unweighted Jaccard and weighted Ružička dissimilarity indices were used to investigate differences in microbial compositions between different sample types (pig, air, pig farmer, cow farmer, and nonexposed), between different seasons (winter, spring, summer, and fall), and between different farms (location number) (see Tables S1 and S2). Unsurprisingly, our data indicate that most of the microbial variation (57%) could be explained by the sample type (permutational multivariate analysis of variance [PERMANOVA], P = 0.001) (see Table S4). The location effect of the various farms was stronger for pig (24%), air (37.4%), and pig farmers (29.3%) and weaker for cow farmers (9.7%) compared to the overall contribution. However, we also detected influences of season and number of animals kept on the farm on the pig farmers’ nasal microbiomes. We therefore next assessed the contribution of pigs’ nasal microbiomes as potential sources of a pig farmer’s microbial assemblage in each season by using a Bayesian source tracker analysis. Although there was no epidemiological link or contact, the nasal microbiome of nonexposed individuals was included as a “control” source in this analysis, as we aimed to clarify the extent of microbiota similarity between the pig farmers and the nonexposed volunteers.
The proportions of each pig farmer’s microbiome coming from pigs ranged from a mean value of 35% (SD, 28%) in summer to 52% (SD, 27%) in winter (Fig. 5A). In contrast, using the nonexposed individuals as potential sources revealed lower proportions in winter than in summer. The numbers for unknown sources remained constant throughout the year (Fig. 5A). Similar results were obtained if using the airborne microbiota as the source (Fig. 5B).
FIG 5
FIG 5 Contribution of pigs’ nasal microbiomes as potential sources of a pig farmer’s microbial assemblage in each season using Bayesian source tracker analysis. (A) The proportions of each pig farmer’s microbiome coming from pigs, nonexposed individuals, and unknown sources are indicated. (B) A separate analysis revealing the proportions of each pig farmer’s microbiome coming from the air and unknown sources is also shown. **, P < 0.01.

Differences within and between farms are most pronounced in winter.

As the outcomes of particle/bacterial load measurements from the air are probably pig farm specific, we next tested whether in winter, there was a weaker intradissimilarity for samples that originated from the same farm than for samples from different farms. We therefore compared unweighted and weighted pairwise distances between samples from the same farm (within farm) with those from samples from different farms (between farms) for each season (Fig. 6). We indeed revealed an overall strong farm-specific effect in winter, which was not observable for the other seasons. The largest and most significant differences for all comparisons were for samples obtained during winter (pig versus pig, pig farmer versus pig farmer, pig versus air, air versus pig farmer, and pig versus pig farmer) for unweighted and weighted analyses (P < 0.001 for all comparisons) (Fig. 6A and B). The findings therefore indicated that a pig farm-specific composition of the microbiota can specifically be seen in winter.
FIG 6
FIG 6 Within and between pig farms dissimilarity measurements throughout the seasons. Shown are unweighted Jaccard (A) and weighted Ružička (B) distances in microbiota compositions within farms (pairwise distances between sample types originated from the same farm) and dissimilarities between farms (pairwise distances between samples originating from different farms). The lines represent the means and the shaded areas the 95% confidence intervals. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

DISCUSSION

The understanding of the formation and function of microbial communities in built environments and how this impacts human health is important. In this study, we investigated the influence of seasons on the composition of microbial communities from air samples taken inside the pig barn and from nasal samples from pigs, pig farmers, cow farmers, and individuals who were not exposed to farm animals or the farming environment. We detected changes between seasons in both alpha and beta diversity analyses. Furthermore, we observed that the microbial composition was influenced by sampling location for samples from pig farms and from cow farmers.
Firmicutes made up the majority of the microbiota in air samples from inside the pig building during all seasons. A high Firmicutes abundance in swine confinement buildings was also found in a previous study (23). In another study, the outdoor bioaerosols in urban and rural environments were investigated, and the taxonomic profiles were shown to be quite similar throughout the year. However, the phylum Firmicutes was not as abundant as we have observed inside pig farms (24). This might indicate that the composition of the microbiota of the indoor air of pig farms is not substantially influenced by the outdoor airborne microbial communities, though we did not have outdoor samples to further explore this hypothesis in our study.
Firmicutes, Proteobacteria, and Actinobacteria were the most abundant phyla in the nasal samples from humans for all seasons in our study. This is consistent with other studies that investigated the microbiota of the nasal vestibule, the nasopharynx, and the trachea in a longitudinal manner (2527). However, there was a significantly lower alpha diversity in summer in nasal samples from pig and cow farmers than in winter in our study. This change in alpha diversity was not seen in nonexposed controls who probably spent most of their time in indoor environments (offices and homes). Importantly, air samples and nasal samples from pigs showed the same trend as the nasal swabs from pig farmers, with increased alpha diversity values in winter. Previous studies also found higher diversity in winter than in summer for both bacteria and fungi in the air in pig confinement buildings (16, 17).
We previously revealed strong clustering based on sample type (14), and the longitudinal data presented here demonstrate that this applies regardless of season. Nevertheless, a significant effect of season on the microbiota was observed in the human-derived sample types from pig and cow farmers but not nonexposed individuals. In young children and adults, it has also been shown that season acts as a major factor in driving the dynamics of the nasal microbiota (25, 28). In contrast to our findings, De Boeck et al. reported no correlation between season and microbial composition in healthy humans (29). However, they only collected one sample per study participant, and it is likely that the interindividual variation was too strong to detect seasonal correlations.
In winter, samples from the same farm share more of their microbiota than do samples from different farms, indicating the presence of farm-specific microbiomes. Depending on the sample type comparison, this strong specificity is partially or fully lost in the following seasons. These differences can also be explained by the aforementioned hypothesis. Due to the low temperatures in winter, windows and especially doors are opened less frequently, and the airborne bacteria from the indoor air may be less likely to be diluted with outdoor air. This would increase the impacts of humans, pigs, and possibly various other sources in the pig building on the microbial composition and would ultimately promote the emergence of a farm-specific microbiota. A higher frequency of opening doors in spring, summer, and fall would lead to a decrease of this farm-specific effect.
A main strength of this study is its longitudinal nature, as this made it possible to account for the impact of the individual, such as from lifestyle choices or host genetics, on the nasal microbiota. We were able to correct for clustering on the level of the individual to accurately investigate the impact of season and location for pig farmers. By collecting different sample types from the same farm throughout the year, it was possible to show that the farm-specific effect is strongest in winter and partially or even completely lost in the subsequent seasons. A further strength of the study is that the recruitment of cow farmers and individuals without contact with farm animals as control groups made it evident that the nasal microbiota of people with a farming lifestyle are more heavily affected by seasonal changes than those of individuals who spend the majority of their lives in an indoor environment.
Finally, there are also limitations. A typical disadvantage in longitudinal studies is the difficulty of collecting follow-up samples (30); some individuals were excluded, as they either dropped out of the study after one season of sampling or were found to be unsuitable in the downstream analysis. We were also unable to assess an effect of season for identical pigs, since they were difficult or impossible to follow-up after they were dispatched to other farms or slaughtered.
In conclusion, we showed that season has a significant effect on the human nasal microbiota and that this effect is much more distinct in individuals who work on farms with livestock. Future research should be conducted to investigate which factors are responsible for this strong impact of season.

MATERIALS AND METHODS

Study design.

Ethical clearance for this study was obtained from the Human Research Ethics Committee of the Canton Vaud (243/14 and P_2017-00265) and the Veterinary Ethics Committee of the Canton Vaud (VD2903). The sampling locations were visited four times during the sample period: in winter (October to March), spring (April to May), summer (June to August), and fall (September). The periods of the seasons were chosen on the basis of previously observed temperature patterns in Switzerland based on meteorological data from the last ten years (15). Winter and summer were characterized with cold (mean, 8°C) and warm (mean, 25°C) temperatures, respectively, while spring and fall were represented by moderate temperatures (mean, 18°C). Among the overall 30 visited farms, 10 were naturally and 20 were mechanically ventilated. With the exception of very few, windows were always closed in winter and also during the other seasons. However, all the doors were systematically opened during the hot days (always in summer and sometimes also in spring and fall).

Sampling of nasal swabs and air.

All pig farms (n = 30) were visited, and on each farm, nasal samples from suckling or weaning pigs and pig farmers as well as air samples from inside the pig confinement building were collected as previously described (14). As controls, two groups were sampled from the same geographic area and the same socioeconomic level. First, samples were collected from cow farmers who have a farming lifestyle and contact with cows but no contact with pigs. For the second control group, samples were taken from nonexposed individuals who work in offices with no farming lifestyle and no contact with any type of farm animal. All human participants provided a nasal sample taken from the anterior cavity; the participants took the samples themselves using a sterile cotton swab under the supervision of the study personnel.
Questionnaires were administered individually to each participant to determine health status. The same human individuals but different pigs were sampled for each season. Several pig farmer and pig samples originated from the same location, and several cow farmers also shared a location. Each nonexposed sample originated from a unique location, as all of these nonexposed participants lived and worked at independent locations. All samples were immediately transferred to 4°C after collection and then were stored at −20°C within 4 h.

Particle sampling of the air and 16S rRNA quantitative PCR.

A direct-reading portable optical counter, model Grimm 1.109 (Grimm Aerosol Technik GmbH & Co., Ainring, Germany), was used to measure particles inside pig confinement buildings. We measured mass fraction of inhalable particles (PM10). Units were expressed in microgram per cubic meter, and the log-transformed values were visualized. Sampling was performed in the central pen at a height of approximately 1 m for 10 min (one read every 10 s). The average PM10 concentrations were calculated for each farm, and seasonal differences were determined with a linear model (lmer function). Simultaneously and at the same location, airborne bacteria were sampled with a Coriolis μ air sampler (Bertin Technologies, Montigny-le-Bretonneux, France) at a flow rate of 300 liters/min for 10 min. Airborne particles were collected into a sterile cone containing 15 ml 0.005% Triton X-100 solution. Repeated sampling was not performed on the same day or during the same week to assess variations. However, in the international ISO norm (ISO16000-18) concerning the sampling of cultivable airborne fungi, it is mentioned that either 4 or 5 replicates representing a total volume of 300 liters are sufficient to assess daily exposure and variability, which is 10 times less than in our study (3,000 liters in total).
The bacterial load inside the pig confinement buildings was ascertained by 16S quantitative PCR (qPCR). In brief, DNA was extracted from the Coriolis air samples by using a Qiagen DNA Minikit (Qiagen, Hilden, Germany) and was subsequently used to quantify the copy numbers of total bacteria by using universal primers for the V9 region of the 16S rRNA gene (forward, 3′-CGGTGAATACGTTCYCGG-5′; reverse, 3′-GGWTACCTTGTTACGACTT-5′) (31). The 25-μl qPCR mixture contained 12.5 μl of 2× SYBR green PCR master mix (Applied Biosystems, Foster City, CA, USA), 2.5 μl each of the 10 μM forward and reverse primers, 5 μl DNA template, and 2.5 μl of sterile DNA-free water. The experiments were carried out on the QuantStudio 7 Flex real-time PCR system using 0.1 ml MicroAmp Fast 96-well reaction plates (Applied Biosystems, Foster City, CA, USA). In each run, a standard curve was included, using serial 10-fold dilutions of Escherichia coli DNA to determine the gene copy numbers. A negative control was included in every run. The following cycling conditions were used: 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. The results were collected using the QuantStudio 6 and 7 Flex software. Each sample was measured in triplicates. Samples with high threshold cycle (CT) values (>35) were excluded from analysis (17/105 [16.2%]). The 16S rRNA gene copy numbers were logarithmically transformed for analysis, and seasonal differences were determined using a linear model (lmer).

Sample processing and 16S rRNA amplification for microbiota analyses.

Sample processing for 16S rRNA microbiota analyses from nasal swabs and air samples was performed during the first 8 weeks of storage at −20°C. DNA extraction, amplification, and Illumina MiSeq sequencing were conducted as previously described (14). Briefly, DNA was extracted using the Qiagen DNA Minikit (Qiagen, Hilden, Germany), and the V4 region of the 16S rRNA gene was amplified using forward (5′-GTGCCAGCMGCCGCGGTAA-3′) and reverse (5′-GGACTACHVGGGTWTCTAAT-3′) primers previously described (32). Amplicons were submitted to the Next Generation Sequencing Platform at the University of Bern for indexing and pair-end sequencing (2 × 250 bp) on the Illumina MiSeq platform (San Diego, CA, USA). Samples with the following characteristics were excluded from the study: samples taken from individuals with antibiotics intake during sampling, samples from pig farmers working with pigs for less than 6 months, samples with a DNA concentration below 1 ng/µl after PCR and purification, and samples with less than 3,300 high-quality sequence reads after sequencing. In addition, samples from 11 individuals (2 cow farmers and 9 nonexposed) were excluded, because only one of the four samples remained after sample processing.
Several quality-control samples were included during our sampling and processing procedures. A clean nasal swab tip which was exposed for several seconds during sampling was used to check for contamination. In addition, an extraction control (200 µl phosphate-buffered saline [PBS]) was included for every batch of 60 samples, and a PCR control (10 µl sterile water) was included for each amplification batch to confirm that there was no reagent-based contamination.
Reads were analyzed using the DADA2 package version 1.5.0 and workflow (22) in R version 3.1.2 (http://www.R-project.org) as previously described (14). The sequences were not rarefied for downstream analyses, since the DADA2 algorithm drastically reduces the issues of having different sequencing depths for the samples being compared, which is the main reason for rarefying (14).

Diversity and source tracker analyses.

All calculations were performed in R utilizing the vegan package, and all graphs were created using the ggplot2 package.
Significant differences in phyla and families were assessed via ANOVA-like differential expression (ALDEx) analysis in R using the aldex2 package. Instances of the centered log-ratio transformation values were generated (aldex.clr function), and significant differences were assessed. Overall significant differences were investigated via an omnibus test (generalized linear model and Kruskal Wallis tests for one-way ANOVA with Benjamini-Hochberg [BH] correction [33]; aldex.glm function), and significant differences between seasons were assessed using Wilcoxon rank tests with BH correction (33) (aldex.ttest). Alpha diversity was assessed with the functions estimate (richness) and diversity (Shannon diversity indices [SDIs]). Significant differences between seasons were assessed with linear regression models with random effects to correct for clustering on location level, as several samples originated from the same location, and to correct for clustering on the individual identification (ID) level, as there were multiple samples from the same human individual (one sample per season). The regression model was calculated with the lmer function from the lmeTest package.
Beta diversity was assessed by unweighted Jaccard index (presence-absence based) and weighted Ružička index (abundance-based) of dissimilarity, and the distances were calculated using the vegdist function. Significant clustering between seasons was determined by permutational multivariate analysis of variance using 1,000 Monte Carlo permutation tests (PERMANOVA; adonis function) followed by Benjamini-Hochberg (BH) correction for multiple testing (33). The post hoc tests were performed with stratification for clustering on season level for pig and air samples, as the pig samples came from a different animal each season and the air sample was not taken at the exact same spot each time. Therefore, it was not possible to calculate the influence of season on variance for these sample types.
Significant differences between the seasons in the within and between farms dissimilarity plot and differences between sample types in the between seasons plot were assessed by Kruskal-Wallis rank sum tests (kruskal.test function) and post hoc Mann-Whitney test (wilcox.test function) with Benjamini-Hochberg (BH) correction for multiple comparisons (33). The SourceTracker software package was used to determine the potential sources of bacteria in pigs and air and their importance in the pig farmers’ noses sampled (34).

Data availability.

The BioProject study accession number is PRJEB26637.

ACKNOWLEDGMENTS

We thank all the participants in this study and Lauren Bradford for proofreading the manuscript.
This work was supported by Swiss National Science Foundation (SNF) grant 310030_152880 to Anne Oppliger and Markus Hilty.

Supplemental Material

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Published In

cover image Applied and Environmental Microbiology
Applied and Environmental Microbiology
Volume 85Number 91 May 2019
eLocator: e03038-18
Editor: Johanna Björkroth, University of Helsinki
PubMed: 30824439

History

Received: 18 December 2018
Accepted: 19 February 2019
Published online: 18 April 2019

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KEYWORDS

  1. airborne microbiota
  2. nasal microbiota
  3. occupational exposure
  4. pig farmers
  5. seasonal variation

Contributors

Authors

Julia G. Kraemer
Institute for Infectious Diseases, University of Bern, Bern, Switzerland
Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
Susanne Aebi
Institute for Infectious Diseases, University of Bern, Bern, Switzerland
Anne Oppliger
Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
Institute for Infectious Diseases, University of Bern, Bern, Switzerland

Editor

Johanna Björkroth
Editor
University of Helsinki

Notes

Address correspondence to Markus Hilty, [email protected].

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