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Population dynamicsThe abundance of A. omorii peaked in April 2016, March 2017, and April–May 2018, ranging from 2.99 × 104 to 6.73 × 104 individuals m−3 (Fig. 3, Table 1). Anakubo and Murano28 reported that the abundance of A. omorii, including individuals from the nauplius to adult stage, peaked at 5.34 × 104 m−3 in April 1981 in Tokyo Bay. Itoh and Aoki18 surveyed A. omorii in Tokyo Bay from March 1990 to November 1992 and found that abundance peaked at 2.13 × 104 individuals m−3 in March. Tachibana et al.40 reported that abundance peaked at 2.60 × 104 individuals m−3 in April in 2007. The peak abundance in Tokyo Bay apparently occurs between March and May; this pattern was also observed in the Seto Inland Sea (Table 1). According to Kasahara et al.15, the peak period of abundance of adults and later-stage copepodites in the Seto Inland Sea occurred in mid-May at  > 3.5 × 104 individuals m−3. The abundance of adults and later-stage copepodites significantly decreased in July. The planktonic population disappeared from the water column in mid-August and recovered in November, when the water temperature was  50 g wet weight m−3). Hence summer jellyfish bloom probably has a strong negative impact on the abundance of A. omorii. Unfortunately, we do not have enough information on jellyfish abundance in Onagawa Bay, effect of jellyfish should be considered for the better understanding of A. omorii dynamics in future.A. omorii abundance became zero only from September to November 2016 in the 3 years of the present study. However, in other years, A. omorii appeared at low abundance in summer, as reported in previous studies conducted in Tokyo Bay 16,18,28,40. The period of complete disappearance was short, and low densities were recorded throughout the year in most studies (Table 1), indicating that the population is maintained in the planktonic stages even under unfavorable conditions in Tokyo Bay.EPR and egg type dynamicsSEPR peaked in winter (January or February) and May in both years at both stations (Fig. 8a). The second peaks were caused by increase in unhatched egg production, except for station CB in 2017 (Figs. 7b,c and 8b,c). Increase in unhatched egg production occurred when the surface water temperature exceeded 18 °C (Fig. 2a) and day length increased to  > 14 h, similar to that reported by Uye5. From January to May 1982, 80% of eggs that were newly spawned by A. omorii females collected in Fukuyama Harbor of the Seto Inland Sea were subitaneous; resting eggs appeared in June when the surface water temperature exceeded 17.5 °C. In the present study, on June 9, 32% of the total eggs produced were resting and did not hatch within 14 days but hatched after being reincubated at 15 °C for 2 weeks. Uye5 also demonstrated the effect of photoperiod on egg production by A. omorii via laboratory experiments under two temperature conditions (15 °C and 20 °C ± 1 °C). Approximately half were resting eggs under l4L–10D photoperiodic conditions at both water temperatures, indicating that photoperiod is important for shift to resting eggs. Many copepods have dormancy strategies at the thermal limits of species distribution46. In Tokyo Bay, the photoperiod exceeds 14 h in mid-May, when the water temperature usually exceeds 18 °C (Fig. 2a). Therefore, higher water temperatures and increased day length periods are synchronized in early summer in Tokyo Bay and may serve as cues for diapause egg production.Unhatched PEPR exceeded subitaneous PEPR from May to June (Fig. 9), when abundance drastically decreased to  45%) (Fig. 6) in the late period of A. omorii appearance (in May at station CB and in early June at station F3). However, in early June at station CB and late June at station F3, when the abundance sharply decreased, the proportion of females producing subitaneous eggs increased, whereas those producing quiescent eggs decreased from the previous month (Fig. 11). Uye5 reported a similar result; the proportion of diapause eggs to total eggs produced by A. omorii in the Seto Inland Sea peaked on June 9 and then reduced by half by June 30. Quiescent eggs have been defined as subitaneous eggs with arrested development that remain in a quiescent stage in unsuitable environmental conditions49. Uye5 defined diapause eggs as eggs that did not hatch during 2 weeks at in situ water temperatures but hatched within 2 weeks when the incubation temperature decreased to 15 °C. These eggs may be classified as quiescent eggs in the present study. The results of Uye5 and the present study suggest that not all produced eggs of this species shift from subitaneous to quiescent eggs at higher water temperatures.As mentioned in the previous subsection, a planktonic population of A. omorii has been found in mid-summer at low abundance in Tokyo Bay16,18,28,40 (Fig. 3). Similar results were reported in Maizuru Bay50. Itoh et al.45 investigated the vertical distribution of copepods at 1-m depth intervals at station F3 in Tokyo Bay in mid-summer, when hypoxia develops near the bottom, and showed that A. omorii population had a sharp peak, with densities exceeding 1.5 × 103 individuals m−3, at 8 m at a water temperature of 18 °C and just above the hypoxic zone45. This suggests that A. omorii maintains a planktonic stage even at low density in mid-summer, whereas most of the population estivates by forming resting eggs in bottom sediments. These mid-summer populations are presumably hatched from subitaneous eggs spawned in mid-July (Figs. 7, 10). Uye5 also reported that more than half of the eggs were still subitaneous in late July in the Seto Inland Sea. Therefore, we tentatively think that this phenomenon is a bet-hedging strategy of A. omorii in an unfavorable and uncertain environment. In contrast, Ueda50 stated that the increase in subitaneous EPR in summer was due to immature development of this species. Thus, these remaining populations may not contribute to the autumnal development of the population. To understand how A. omorii survive in mid-summer, more detailed field investigations are warranted, including egg and nauplii dynamics in the water column, egg hatching process from sediments, and differences in endogenous factors in individual females producing subitaneous and diapause eggs in summer.Information on A. omorii’s delayed-hatching eggs is strictly limited. Delayed-hatching eggs are eggs hatching over a wide time span regardless of environmental conditions4,11. Takayama and Toda4 defined the unhatched eggs of A. japonica hatching during 72 h–50 days as “delayed-hatching eggs.” Thus, delayed-hatching eggs may have been included in the quiescent and diapause eggs defined in this study. Our results showed that no eggs hatched between 48–96 h and 7 days in the experiment at in situ water temperature; many quiescent eggs hatched within a few days after reincubation at lower water temperatures (Figs. 10, 11). Therefore, delayed-hatching eggs may not have been produced in the present study.Effects of water temperature on the production of subitaneous and diapause eggsMultiple regression analysis revealed that subitaneous SEPR negatively correlated with bottom water temperature, inconsistent with the results of Uye3. EPR and copepod growth generally increase with increased water temperature51. Uye3 reported that EPR of A. omorii also increased with water temperature; they developed a simple model equation describing the fecundity of A. omorii in Onagawa Bay via a laboratory experiment:$${text{F}} = 0.000{331 }left( {{text{T}} + {12}.0} right)^{{{3}.{25}}} {text{SW}}_{{text{f}}} /left( {0.{47}0 + {text{S}}} right),$$
where F is daily fecundity (eggs female−1 day−1), T is water temperature (°C), S is chlorophyll a concentration (µg L−1), and Wf is female carbon content (µg). The fecundity predicted by the above described model was similar to the observed EPR of this species in Onagawa Bay3.Many studies have used Uye’s equation to estimate A. omorii egg production. Kang et al.52 reported A. omorii’s EPR in Ilkwang Bay to be 22–57 eggs female−1 day−1, which was higher than that in the present study (1.6–18.7 eggs female−1 day−1) (Fig. 7a). Liang and Uye17 estimated A. omorii’s EPR in the Seto Inland Sea by two methods: the above described model (estimated incubation fecundity)3 and based on the number of eggs remaining in the water column and the adult female population (egg-ratio fecundity)53. In the Seto Inland Sea, the estimated incubation fecundity was 26–60 eggs female−1 day−1 and the egg-ratio fecundity was 0.5–25 eggs female−1 day−1; the estimated incubation fecundity was always greater than the egg-ratio fecundity17.Suspension-feeding copepods may ingest their own eggs and nauplii. In the Seto Inland Sea, possible egg predators were the dominant copepods Centropages abdominalis and A. omorii54. Based on their abundance (0.2–39 predators L−1) and assuming a predator clearance rate of 50 mL d−1, C. abdominalis and A. omorii could remove 1–86% of eggs in the water column per day. Liang and Uye17 noted that their predators were abundant when the abundance of surviving eggs in the water column was low; therefore, they tentatively concluded that the difference between the two estimates was due to egg loss by predation, including cannibalism. However, it is unlikely that fecundity reached its highest value ( > 50 eggs female−1 day−1) in mid-July when the population disappeared from the water column17. At that time, the water temperature was 25 °C, which also does not support the increase in fecundity observed by Liang and Uye17.The model equation of Uye3 was derived from Onagawa Bay, where the average water temperature is 7.7–21.9°C55. In laboratory experiments using A. omorii from Onagawa Bay, EPR decreased when the water temperature exceeded 22.5°C3. In Uye’s equation3, the decrease in egg production above 22.5 °C was not foreseen, whereas water temperature exceeded 22.5 °C in the Seto Inland Sea17, Ikkwang Bay52, and Tokyo Bay (Fig. 2). Thus, Uye’s model equation3 is not applicable to these warm environments.Based on the temperature regime, seasonal population dynamics and egg types produced are divided into two types: no resting egg production in the colder Onagawa Bay and resting egg production in the warmer Tokyo Bay and Seto Inland Sea. As mentioned above, A. omorii in Onagawa Bay exists throughout the year, even in summer14 and hardly produces diapause eggs5,7. However, the population almost disappears in late summer in Tokyo Bay16,18,28,40 and the Seto Inland Sea15,17. Furthermore, in these warm coastal waters, A. omorii produced diapause eggs just before copepodite disappearance from the water column. Therefore, a separate equation for estimating egg production should be developed, depending on the temperature regime of the habitat.Recent climate change, particularly global warming, may affect A. omorii’s egg production. In Tokyo Bay, between 1955 and 2015, the water temperature increased by 1.0 °C and 0.94 °C at the surface and bottom layers, respectively, in winter and autumn56,57. In summer, the water temperature at both the surface and bottom layers decreased, probably due to strengthened estuary circulation56,57. Considering the response of A. omorii to water temperature, the increase in winter temperature might reduce subitaneous egg production, resulting in delayed population increase. In contrast, the decrease in summer temperature might lead to reduced diapause egg production per amount of body carbon. The long-term trends of water temperature might have different effects on each egg type production and alter the dynamics of A. omorii egg production.Effects of phytoplankton composition on the production of subitaneous and diapause eggsThe EPR of A. omorii may be saturated at low (1–2 µg L−1) chlorophyll a concentrations3,19. However, multiple regression analysis revealed that small diatoms stimulate subitaneous SEPR (Figs. 8, 12). The EPR at station CB was quite high ( > 14 eggs female−1 day−1) in January and February 2018, when the diatoms comprised Skeletonema and Chaetoceros. In contrast, at station F3, EPR drastically decreased from 18.7 ± 6.3 eggs female−1 day−1 in January to 8.4 ± 4.6 eggs female−1 day−1 in February 2018. The EPR at station F3 in February was significantly lower than that at station CB (Tukey’s post hoc test, p  95%) at station CB in January and February 2018, suggesting that small diatoms ingestion enhances A. omorii’s egg production.It is also likely that small nanoflagellates have an adverse effect on egg production. At station F3, the proportion of nanoflagellates to total phytoplankton carbon biomass was high ( > 93%) in February and March (Fig. 12), when EPR was quite low ( More

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Combined culture-dependent and culture-independent approach identifies the prevalent and viable bacterial community members of the human lung post-transplantTo characterize the bacterial community composition of the lung microbiota post-transplant, we performed 16S rRNA gene amplicon sequencing of 234 longitudinal BALF samples from 64 lung transplant recipients collected over a 49-month period (Fig. 1a, Supplementary Table 1). A total of 7164 operational taxonomic units (OTUs) were identified, excluding OTUs contributing to reads in 11 negative control samples32 (see “Methods”, Supplementary Fig. 1a, Supplementary Data 1 and 2). In accordance with previous studies on BALF samples from healthy non-transplant individuals4,5,6,26, we found that Bacteroidetes and Firmicutes followed by Proteobacteria and Actinobacteria are the most abundant phyla in the post-transplant lung (Fig. 1b). Prevalence analysis across all BALF samples showed that the community composition is highly variable with only 22 OTUs shared by ≥50% of the samples (Supplementary Fig. 1b, Supplementary Data 3). However, these 22 OTUs constituted 42% of the total number of rarefied reads, indicating that they are predominant members of the post-transplant lung microbiota (Fig. 1c, Supplementary Fig. 1c, Supplementary Table 2, Supplementary Data 3). They belonged to the genera Prevotella 7, Streptococcus, Veillonella, Neisseria, Alloprevotella, Pseudomonas, Gemella, Granulicatella, Campylobacter, Porphyromonas and Rothia, the majority of which are also prevailing community members in the healthy human lung3,5,7,26, suggesting a considerable overlap in the overall composition of the lung microbiota between the healthy and the transplanted lung.Fig. 1: Combining BALF amplicon sequencing and bacterial culturing to deduce the microbial ecology of deep lung microbiota.a Schematic of the sampling of Bronchoalveolar lavage fluid (BALF) from lung transplant recipients over time (months post-transplant). b Relative abundances (%) of most abundant phyla across BALF samples. Box plots show median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers) as well as outliers (single points). c Prevalence (% samples) vs contribution to total reads across samples for most abundant phyla. Dot color shows different genera and size show total rarefied reads. Gray dashed horizontal line shows prevalence ≥50%. d Scatter plot shows correlation between number of observed OTUs and bacterial counts per BALF sample obtained by quantifying 16S rRNA gene copies with qPCR. Linear regression is shown by the blue line with gray shaded area showing 95% confidence interval (n = 234, two-sided, F(1, 232) = 91.04, P = 2.2 × 10−16), Coefficient of correlation; R2 = 0.28. e Bar chart shows lung taxa (genera; OTU IDs) that contributed ≥75% of total bacterial biomass across samples (n = 234). Venn diagram inset shows overlap (yellow) between the most prevalent (≥50% incidence, light blue) and the most abundant (≥75% total count, red) taxa in the transplanted lung. Bar colors also show the same.Full size imageDifferences in bacterial loads between samples can skew community analyses when based on relative abundance profiling alone. Therefore, we used qPCR to determine the total copies of the 16S rRNA gene as an estimate for bacterial counts, and normalized the abundances of each OTU across the 234 samples (absolute abundance). We found that the bacterial counts vastly differed between samples, ranging between 101 and 106 gene copies per ml of BALF (Supplementary Fig. 1d). The number of observed OTUs increased with decreasing counts (Fig. 1d) suggesting that a large fraction of the OTUs were detected in samples of low bacterial biomass and hence represent either transient or extremely low-abundant community members, or sequencing artefacts and contaminations. In turn, 19 of the 7164 OTUs constituted >75% of the total bacterial biomass detected across the 234 BALF samples (Fig. 1e). This included 11 of the 22 most prevalent OTUs (see above) plus eight OTUs that were detected in only a few samples but at very high abundance (Staphylococcus; OTU_2, Corynebacterium 1; OTU_16 and OTU_24, Anaerococcus; OTU_49 and OTU_234, Haemophilus; OTU_78, Streptococcus; OTU_6768, Peptoniphilus; OTU_63, Supplementary Table 2). It is important to differentiate these opportunistic colonizers from other community members with low incidence, as they reached very high bacterial counts in some samples with potential implications for lung health.To demonstrate the viability of prevalent lung microbiota members and to establish a reference catalogue of bacterial isolates from the human lung for experimental studies, we complemented the amplicon sequencing with a bacterial culturing approach (Supplementary Fig. 2). We cultivated 21 random BALF samples from 18 individuals, on 15 different semi-solid media (both general and selective) in combination with 3 oxygen concentrations; aerobic, 5% CO2, and anaerobic (See “Methods” and Supplementary Table 3), representing 26 different conditions. We cultured fresh BALF immediately upon extraction (within 2 h), as we observed loss in bacterial diversity upon cultivating frozen samples. This resulted in a total of 300 bacterial isolates, representing 5 phyla, 7 classes, 13 orders, and 17 families from which we built an open-access biobank called the Lung Microbiota culture Collection (LuMiCol, Supplementary Data 4, https://github.com/sudu87/Microbial-ecology-of-the-transplanted-human-lung).To examine the extent of overlap between bacteria in LuMiCol and the diversity obtained by amplicon sequencing, we included 16S rRNA gene sequences from 215 isolates that passed our quality filter into the community analysis, which allowed for the retrieval of OTU-isolate matching pairs32 (Methods). We found that 213 isolates matched to 47 OTUs (Fig. 2a, c, Supplementary Data 5), including 17 of the most prevalent and abundant bacteria (Fig. 1e, Supplementary Table 2). As expected, bacteria with high abundance in the amplicon sequencing-based community analysis were isolated more frequently, with Firmicutes revealing the highest isolate diversity (Fig. 2a–c, Supplementary Data 4, 5) and being recovered under the most diverse culturing conditions.Fig. 2: A lung microbiota culture collection (LuMiCol) reveals extended diversity and phenotypic characteristics of the lower airway bacterial community.a Phylogenetic tree of the 47 OTU-isolate matching pairs inferred with FastTree. Branch bootstrap support values (size of dark gray circles) ≥80% are displayed. b Growth characteristics of each OTU-isolate matching pair in three different oxygen conditions (Anaerobic – light brown, 5% CO2-yellow, aerobic-light blue, n = 3). Column with pie charts shows growth on semi-solid agar. Heatmap shows median change in Optical Density (OD) at 600 nm growth in three different liquid media (THY, RPMI, RPMI without glucose) over 3 days. c Cumulative counts of each OTU-isolate matching pair across all BALF samples (gray). d Number of isolates in Lumicol (black) per OTU-isolate matching pair. Taxa are labeled as genus; OTU ID, with an indication of whether they are prevalent (gray rectangle) or opportunistic (magenta rectangle) in the lower airway community. The names of the closest hit in databases: eHOMD and SILVA are used as species descriptor.Full size imageIn summary, our results from the combined culture-dependent and culture-independent approach show that the lung microbiota post-transplant is highly variable in terms of both bacterial load and community composition with many transient and low-abundant bacterial taxa. However, a few community members display relatively high prevalence and/or abundance suggesting that they represent important colonizers of the human lung.LuMiCol informs on the diversity and metabolic preferences of culturable human lung bacteriaWe characterized the culturable community members of the lower respiratory tract contained in LuMiCol by testing a wide range of growth conditions and phenotypic properties (see “Methods”). The majority of the cultured isolates could taxonomically be assigned at the species level based on genotyping of the 16S rRNA gene V1-V5 region. However, the limited taxonomic resolution offered by this method does not allow to discriminate between closely related strains, which can include both pathogenic and non-pathogenic bacteria. Hence for Streptococcus, we additionally tested for type of hemolysis (alpha, beta, or gamma) and resistance to optochin, which differentiates the pathogenic pneumococcus and the non-pathogenic viridans groups (Fig. 2a, Supplementary Fig. 2b, c). This demonstrated that the 16 Streptococcus OTU-isolate pairs belong to the viridans group of streptococci (VS)33. Interestingly, these isolates exhibited the highest genotypic and phenotypic diversity throughout our collection and belonged to five OTUs among the 22 most prevalent community members, with Streptococcus mitis (OTU_11) present in 93.6% of all samples.BALF from healthy individuals contains amino acids, citrate, urate, fatty acids, and antioxidants such as glutathione but no detectable glucose34, which is associated with increased bacterial load and infection35,36,37. To get insights into basic bacterial metabolism, we assessed the growth of all 47 isolates matching an OTU under different oxygen concentrations. We used undefined rich media (Todd-Hewitt Yeast extract) and defined low-complexity liquid media (RPMI 1640), including a glucose-free version to mimic the deep lung environment (see “Methods”). Despite the presence of oxygen in the human lung, the majority of the isolates were either obligate or facultative anaerobes (Fig. 2a), including some of the most prevalent members (Prevotella melaninogenica (OTU_3), Streptococcus mitis (OTU_11), Veillonella atypica (OTU_6) and Granulicatella adiacens (OTU_17). A similar trend was also observed in liquid media under anaerobic conditions, with the exception of the genera Prevotella, Veillonella and Granulicatella. Most streptococci from the human lung grew best in complex liquid media containing glucose under anaerobic conditions, including the most prevalent species in our cohort, S. mitis (OTU_11) (Fig. 2b). However, noticeable exceptions were S. vestibularis (OTU_34), S. oralis (OTU_3427 and OTU_1567), and S. gordonii (OTU_10031), which grew equally well in the presence of oxygen and in low-complexity liquid medium (Fig. 2b). Most Actinobacteria grew best on rich medium in the presence of 5% CO2, with an exception of Actinomyces odontolyticus (OTU_39), which required anaerobic conditions. Some Actinobacteria grew equally well in anaerobic conditions as in the presence of 5% CO2, i.e., Corynebacterium durum (OTU_501), Actinobacteria sp. oral taxon (OTU_328 and OTU_228).The two most predominant opportunistic pathogens in our lung cohort, P. aeruginosa (OTU_1) and S. aureus (OTU_2), grew best in rich liquid medium in the presence of oxygen (Fig. 2c), although these also grew to lower degree under anaerobic conditions. These results indicate that changes in the physicochemical conditions in the lung may favor the growth of these two opportunistic pathogens. In summary, our observations from the bacterial culture collection provide first insights into the phenotypic properties of human lung bacteria and will serve as a basis for future experimental work.Identification of four compositionally distinct pneumotypes post-transplant using machine learning based on ecological metricsTo detect and characterize differences in bacterial community composition between BALF samples from transplant patients, we clustered the samples using an unsupervised machine learning algorithm based on pairwise Bray–Curtis dissimilarity32 (beta diversity, See “Methods”, Supplementary Data 6). This segregated the samples into four partitions around medoids (PAMs) at both phylum and OTU level (Fig. 3a, b, Supplementary Fig. 3a, b). We refer to these clusters as “pneumotypes” PAM1, PAM2, PAM3, and PAM4 (Supplementary Table 4). PAM1 formed the largest cluster consisting of the majority of samples (n = 115) followed by PAM3 (n = 76), PAM2 (n = 19), and PAM4 (n = 24) (Supplementary Data 7). Examination of various diversity measures (Species occurrence, OTU diversity, OTU richness, Fig. 3c–e), distribution of the dominant community members (Fig. 3f), and bacterial counts (16S rRNA gene copies, Fig. 3g) revealed distinctive characteristics between the four pneumotypes.Fig. 3: Bacterial communities of the lung post-transplant fall into four ‘pneumotypes’ with distinct community characteristics.a, b Principal component analysis shows Partition around medoids (PAMs) at phylum and OTU level respectively generated by k-medoid-based unsupervised machine learning using Bray–Curtis dissimilarity (occurrence and abundance). Pneumotypes are color coded: Balanced (red, n = 115), Staphylococcus (green, n = 19), Microbiota-depleted (MD, blue, n = 76), and Pseudomonas (orange, n = 24). c–g Violin plots show distributions of pairwise species occurrence (Sorenson’s index, PERMANOVA, two-sided, F(3, 229) = 8.49, P = 9.9 × 10−5), OTU diversity (Kruskal–Wallis test, χ2 = 89.2, df = 3, two-sided, P = 2.2 × 10−16), OTU richness (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), proportion of most dominant OTUs (Kruskal–Wallis test, χ2 = 94.45, df = 3, two-sided, P = 2.2 × 10−16), and total bacterial counts (ANOVA, F(3, 229) = 43.9, two-sided, P = 2.2 × 10−16), respectively, across the four pneumotypes. h, i Enrichment analysis of prevalence (green dotted line ≥50%) and absolute abundance across all samples of the 30 most dominant taxa (i.e., OTUs) in PneumotypeBalanced and PneumotypeMD respectively, when each was compared to the other three combined pneumotypes (gray boxes). Differential abundances after enrichment analysis was calculated between each PAM and the other three PAMs combined, using ART-ANOVA. j Heatmap shows relative percentage of taxa (right colored panel) cultured from paired samples of Bronchial aspiration (BA) and Bronchoalveolar lavage fluid (BALF) from each pneumotype (left colored panel). Oropharyngeal flora mainly corresponds to PneumotypeBalanced (i.e., Streptococcus, Prevotella, Veillonella). All box plots including insets show median (middle line), 25th, 75th percentile (box) and 5th and 95th percentile (whiskers) as well as outliers (single points). Multiple comparison of beta diversity indices was done by pairwise PERMANOVA (adonis) with False Discovery rate (FDR). Post hoc analyses (95% Confidence Interval) were done by using Tukey’s test (ANOVA) or Dunn’s test (Kruskal test) with False Discovery Rate (FDR) or least-squares means (ART-ANOVA) with False Discovery Rate (FDR). * P  More

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