Biodivers Conserv. doi:10.​1007/​s10531-013-0446-z Zachos FE, Hartl GB, Suchentrunk F (2007) Fluctuating asymmetry and genetic variability in the roe deer (Capreolus capreolus): a test of the developmental stability hypothesis in mammals using neutral molecular markers. Heredity 98:392–400PubMed Zelnik I, Čarni BI 10773 cost A (2013) Plant species diversity and composition

of wet grasslands in relation to environmental factors. Biodivers Conserv. doi:10.​1007/​s10531-013-0448-x”
“Introduction Tropical forests contain much of the world’s terrestrial biodiversity and significant carbon stocks (Bunker et al. 2005). Particular interest centres on assessing the biodiversity value of modified and disturbed forest ecosystems and the ability of such systems to buffer biodiversity losses expected with the degradation selleck screening library or conversion of more pristine habitats (Wright and Muller-Landau 2006; Chazdon et al. 2009). A complete inventory of organisms is not feasible (Lawton et al. 1998), but conservation management can benefit from the identification of any surrogate that broadly predicts overall biodiversity

by reflecting the major determinants of taxonomic variety and species richness (Meijaard and Sheil 2012). One approach is to find and use easily assessed indicators (partial measures or estimator surrogates, sensu Sarkar and Margules 2002). However, selection of such indicators remains predominantly intuitive rather than evidence-based (Howard et al. 1997; Lawton et al. 1998; Watt 1998; Noss 1999; Dudley et al. 2005; Kessler et al. 2011; Le et al. 2012) and there remains the challenge of distinguishing change that can be attributed to external anthropogenic factors from underlying natural processes (Magurran et al. 2010). Candidate indicators such as landscape metrics, remotely-sensed variables, multi-species indices these and formulated measures of ecosystem complexity or genetic diversity have found wide application but are of limited

practicality in forests (UNEP-CBD 1996; Kapos et al. 2001; Delbaere 2002; European Academies’ Science Advisory Council (ESAC) 2004; Gregory et al. 2005; Duraiappah and Naeem 2005). Thus forest biodiversity surveys still maintain a taxonomic focus even though the costs of obtaining sufficient sampling can be high and the utility of any one species, or another single taxon, as a predictor of others remains uncertain (Lawton et al. 1998; Watt et al. 1998; Dufrêne and Legendre 1997; UNEP/CBD 2003; Gregory et al. 2005, but see also Blasticidin S Schulze et al. 2004). Further, at large spatial scales where within-region diversity is large, higher level taxa (up to family level) must often be used (Villaseñor et al. 2005), but even this is only justifiable where extensive species data are already available (Sarkar et al. 2005).

J Clin

Microbiol 2009,47(9):2751–2758.PubMedCrossRef 33. American Public Health Association: Addressing the use of fluoroquinolone antibiotics in agriculture. Am J Public Health 2001,91(3):518–519. 34. Poppe C: Salmonella enteritidis BAY 1895344 in Canada. Int J Food Microbiol 1994,21(1–2):1–5.PubMedCrossRef 35. Rankin SC, Benson CE, Platt DJ: The distribution of serotype-specific plasmids among different subgroups of strains of Salmonella enterica serotype Enteritidis: characterization of molecular variants by restriction enzyme fragmentation patterns. Epidemiol Infect 1995,114(1):25–40.PubMedCrossRef 36. Boonmar S, Bangtrakulnonth A, Pornrunangwong S, Terajima J, Watanabe H, Kaneko K, Ogawa M: Epidemiological analysis of Salmonella enteritidis isolates from humans and broiler chickens in Thailand by phage typing and pulsed-field gel electrophoresis. J Clin Microbiol 1998,36(4):971–974.PubMed

37. Boxrud D, Pederson-Gulrud K, Wotton J, Medus C, learn more Lyszkowicz E, Besser J, Bartkus JM: Comparison of multiple-locus variable-number tandem repeat analysis, pulsed-field gel electrophoresis, and phage typing for subtype analysis of Salmonella enterica serotype Enteritidis. J Clin Microbiol 2007,45(2):536–543.PubMedCrossRef Authors’ contributions CP, SP, PC identified and serotyped all isolates as well as provided CX-4945 cell line epidemiological data. RA carried out the phagetyping. CAS carried out the pulsed field gel electrophoresis. ES participated in the design of the study and performed the statistical analysis. EHT carried out the MLVA, the analysis, and helped to draft the manuscript. MM participated in design, the analysis, and helped to draft the manuscript. RSH conceived of the study, participated in its Progesterone design, coordination, and draft the manuscript. All authors read and approved the final manuscript.”
“Background The human gastrointestinal tract (GIT) comprises an extremely dense and diverse microbiota. The GIT of an adult may harbour even 2 kg of bacterial

biomass representing over 1000 bacterial species, of which majority can not be cultivated [1]. This microbiota in the large intestine is mainly composed of Firmicutes and Bacteroidetes phyla making up respectively over 75% and 16% of total microbes in the GIT [1]. The human intestinal microbiota has recently been shown to cluster into three distinct enterotypes [2] and of these enterotypes, Bacteroides and Prevotella dominated microbial communities have been reported to be associated with long-term diets [3]. Previously, twin studies have suggested a role for the host genotype in determining the microbiota composition [4], but the genetic host factors potentially affecting the gastrointestinal microbiota composition are unknown to a large extent.

Nat Rev Microbiol 2006, 4:577–587.PubMedCrossRef 2. Longo D, Hasty J: Dynamics of single-cell gene expression. EVP4593 datasheet Mol Syst Biol 2006, 2:64.PubMedCrossRef 3. Losick R, Desplan C: Stochasticity and cell fate. Science 2008, 320:65–68.PubMedCrossRef 4. Rao CV, Wolf DM, Arkin AP: Control, exploitation and tolerance of intracellular noise. Nature 2002, 420:231–237.PubMedCrossRef 5. Raser JM, O’Shea EK: Noise in gene expression: origins, consequences, and control. Science 2005, 309:2010–2013.PubMedCrossRef 6. Davidson CJ, Surette MG: Individuality in bacteria. Annu Rev Genet 2008, 42:253–268.PubMedCrossRef 7. Fraser D, Kaern M: A chance at survival: gene expression noise and phenotypic diversification strategies. Mol Microbiol 2009, 71:1333–1340.PubMedCrossRef

8. McAdams HH, Arkin A: It’s a noisy business! Genetic regulation at the nanomolar scale. Trends Genet 1999,

15:65–69.PubMedCrossRef 9. Veening JW, Smits WK, Kuipers OP: Bistability, epigenetics, and bet-hedging in bacteria. Annu Rev Microbiol 2008, 62:193–210.PubMedCrossRef 10. Amir A, Kobiler O, Rokney A, Oppenheim AB, Stavans J: Noise in timing and precision of gene activities in a genetic cascade. Mol Syst Biol 2007, 3:71.PubMedCrossRef 11. Arkin A, Ross J, McAdams HH: Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 1998, 149:1633–1648.PubMed 12. Pearl S, Gabay C, Kishony R, Oppenheim A, Balaban NQ: Nongenetic individuality in the host-phage interaction. PLoS Biol 2008, 6:e120.PubMedCrossRef 13. St-Pierre F, Endy D: Determination of cell fate selection during phage lambda infection. Proc Natl Acad Sci USA 2008, 105:20705–20710.PubMedCrossRef PRI-724 clinical trial 14. Cai L, Friedman N, Xie XS: Stochastic protein expression in individual cells at the single molecule level.

Nature 2006, 440:358–362.PubMedCrossRef 15. Elowitz MB, Levine AJ, Siggia ED, Swain PS: Stochastic gene expression in a single cell. Science 2002, 297:1183–1186.PubMedCrossRef 16. Ito Y, Toyota H, Kaneko K, Yomo T: How selection affects phenotypic fluctuation. Mol Syst Biol 2009, 5:264.PubMedCrossRef 17. Ozbudak EM, Thattai PtdIns(3,4)P2 M, Kurtser I, Grossman AD, van Oudenaarden A: Regulation of noise in the expression of a single gene. Nat Genet 2002, 31:69–73.PubMedCrossRef 18. Maamar H, Raj A, Dubnau D: Noise in gene expression determines cell fate in Bacillus subtilis . Science 2007, 317:526–529.PubMedCrossRef 19. Bar-Even A, Paulsson J, Maheshri N, Carmi M, O’Shea E, SRT1720 molecular weight Pilpel Y, Barkai N: Noise in protein expression scales with natural protein abundance. Nat Genet 2006, 38:636–643.PubMedCrossRef 20. Blake WJ, M KA, Cantor CR, Collins JJ: Noise in eukaryotic gene expression. Nature 2003, 422:633–637.PubMedCrossRef 21. Fraser HB, Hirsh AE, Giaever G, Kumm J, Eisen MB: Noise minimization in eukaryotic gene expression. PLoS Biol 2004, 2:e137.PubMedCrossRef 22. Acar M, Mettetal JT, van Oudenaarden A: Stochastic switching as a survival strategy in fluctuating environments.

2). YcjU has been annotated in sequence data bases as a putative β-phosphoglucomutase that belongs to the superfamily of haloacid dehalogenase (HAD)-like hydrolases. In vitro, YcjU hydrolyzes small phosphodonors [36], which suggest that the protein is likely to have other physiological roles. The yibA GS-9973 in vivo mutant was among the most sensitive to UV irradiation and H2O2 (Fig. 2). YibA is a predicted lyase containing a HEAT-repeat, which forms a rod-like helical structure in proteins. Transcription profiling experiments suggested that yibA may belong to the σ32 regulon [37], whose genes are expressed in E. coli in response to heat shock. Thus, the role of YibA in antimicrobial

susceptibility may be exerted through alternative sigma factor-regulated stress responses. However, the yibA mutant was not particularly sensitive

to high temperature. A third mutant, in yfbQ, was the most sensitive to mitomycin C. The only information available refers to the gene product as a potential aminotransferase. Reactive oxygen species-mediated response to lethal antimicrobials Although no MK0683 mouse clear metabolic connection exists among the genes we identified, some guidance can be gained from the recent proposal that lethal antimicrobials share a common cell death pathway involving a reactive oxygen cascade [6, 7]. The lethal activity of a variety of antimicrobials, including the fluoroquinolone norfloxacin, is accompanied by an increase in hydroxyl radical, and lethal activity is greatly reduced by treating E. coli cells with agents that block the accumulation of hydroxyl radical [6]. The idea emerged that lethal antimicrobials act in part by generating a selleck inhibitor signal that causes an accumulation of superoxide, which reacts with iron-sulfur clusters Elongation factor 2 kinase to release peroxide

and iron. Peroxide and iron then form highly toxic hydroxyl radicals through the Fenton reaction. Superoxide can also be converted to peroxide by superoxide dismutase and by spontaneous dismutation. The resulting increase in peroxide would contribute to the formation of hydroxyl radical. In support of this idea, we found that deletion of both superoxide dismutase genes reduced the lethality of norfloxacin [38]. As expected, a deficiency of catalase, which converts peroxide to water, led to an increase in the lethality of norfloxacin [38]. Mutations in genes that normally protect from the accumulation of reactive oxygen species would be recovered by our screen for hyperlethality to nalidixic acid. Such mutants are expected to also be more readily killed by other DNA damaging agents, such as mitomycin C, peroxide, and UV irradiation, as seen for 9 of the 14 of the genes we identified. Complementation of hyperlethality by cloned genes To determine whether the hyperlethal phenotype of the mutants was caused by deficiency of the mutant genes rather than polar effects due to Tn5 insertion, we selected several mutants for complementation using wild-type genes cloned into plasmids.

“
“Background Burkholderia pseudomallei is a Gram-negative bacterium that is the causative agent for melioidosis, a disease endemic in Southeast Asia and Northern Australia with significant morbidity and mortality [1, 2]. The bacterium exhibits broad host range and has been shown to cause disease in cattle, pigs, goats, horses, dolphins, koalas, kangaroos, deers, cats, dogs and gorillas [3]. Acquisition of the bacterium could be through inhalation of aerosol, ingestion of contaminated water and inoculation through

open skin [4]. In humans, the disease could present with varied manifestations ranging from asymptomatic infection, localized disease such as pneumonia or organ abscesses to systemic disease with septicemia [5]. The disease could www.selleckchem.com/products/lazertinib-yh25448-gns-1480.html be acute or chronic, and relapse from latency is possible [6]. The versatility of B. pseudomallei as a pathogen is check details reflected in its huge 7.24 Mb genome organized into two chromosomes click here [7]. One of the most important virulence factors that has been partially characterized in B. pseudomallei is its Type Three Secretion Systems (T3SS), of which it has three [8, 9]. Each T3SS typically consists of a cluster of about 20

genes encoding structural components, chaperones and effectors which assemble into an apparatus resembling

a molecular syringe that is inserted into host cell membrane for the delivery of bacterial effectors into host cell cytosol. One of the B. pseudomallei Oxymatrine T3SS known as Bsa or T3SS3 resembles the inv/mxi/spa T3SS of Salmonella and Shigella, and has been shown to be important for disease in animal models [10]. The other two T3SS (T3SS1 and 2) resemble the T3SS of plant pathogen Ralstonia solanacearum [11] and do not contribute to virulence in mammalian models of infection [12]. Being a soil saprophyte and having the plant pathogen-like T3SS raise the possibility that B. pseudomallei could also be a plant pathogen. As B. pseudomallei is a risk group 3 agent with specific requirements for containment, we first test this hypothesis using the closely related species B. thailandensis as a surrogate model especially in experiments where risk of aerosolization is high, before we verify key experiments with B. pseudomallei. B. thailandensis is considered largely avirulent in mammalian hosts unless given in very high doses [13, 14]. We infected both tomato as well as rice plants with B.

6c). PTH resulted in significantly higher osteoclast

surface after the VC treatment but not after the ALN/DEX treatment. Likewise, significantly higher osteoblast surface by PTH was noted after the VC treatment but not after the ALN/DEX treatment (Fig. 6d). The ALN/DEX treatment had no apparent effect on osteoblast LY3039478 cost surface. The numbers of empty Thiazovivin cell line osteocyte lacunae and necrotic bone were significantly higher in the ALN/DEX-VC group versus control (Fig. 6e, f). PTH significantly reduced the numbers of empty osteocyte lacunae and presence of necrotic bone. Similarly, PTH significantly reduced the numbers of empty lacunae and necrotic bone when the VC-VC and VC-PTH groups were compared, suggesting that PTH promoted osteocyte survival. PMN infiltration was also significantly higher in the ALN/DEX-VC group and PTH significantly reduced PMNs in the ALN/DEX-PTH group (Fig. 6g). Connective tissue maturation as measured by collagen apposition was lower in the ALN/DEX-VC group vs. control (Fig. 6h); however, PTH significantly enhanced the collagen apposition regardless of presence or absence of the ALN/DEX treatment. There were no differences noted RG7112 in the numbers of blood vessels between groups (Fig. 6i). Fig. 6 Histomorphometric assessments of extraction wound healing. (a) Representative images of

frontal-sections of the extraction wounds. Six rats developed necrotic lesions in the ALN/DEX-VC group and only one in the ALN/DEX-PTH group. Both the ALN/DEX and PTH treatments resulted in significantly higher bone area vs. control (b). The ALN/DEX treatment significantly suppressed, and PTH after VC, significantly increased osteoclast surface (c). PTH significantly increased osteoblast surface after VC but not after ALN/DEX (d). Significantly higher numbers of empty

osteocyte lacunae and necrotic bone area were noted Fossariinae in the ALN/DEX-VC group vs. control. PTH suppressed the numbers of empty lacunae and necrotic bone area significantly after ALN/DEX and after VC (e, f). PMN infiltration was significantly higher in the ALN/DEX-VC group versus control. PTH dramatically suppressed PMN infiltration when given after ALN/DEX (g). Significantly lower collagen apposition was noted in the ALN/DEX-VC group vs. control. PTH increased collagen apposition significantly after ALN/DEX and after VC (h). No treatment regimen altered blood vessel numbers (i). *p < 0.05; **p < 0.01; ***p < 0.001 versus control (VC-VC); †p < 0.05; ††p < 0.01 versus the ALN/DEX-VC group Discussion The ALN/DEX treatment resulted in high bone mass in both the tibia and jaw as anticipated [26]. However, its effect on osseous wound healing was distinct; the ALN/DEX treatment enhanced early osseous healing in the tibial wounds by increasing bone fill, while it impaired tooth extraction wound healing with exposed bone.

At selected locations a visual inspection of available sequence traces

was performed to Selleck Captisol identify lower confidence SNPs (Additional file 1: Table S6). To identify “ancestral” or genetically stable SNPs we selected SNPs that were present in more than three strains. To pick out SNPs linked to disease the SNPs were grouped according whether the sequenced genome was first isolated from patients with asymptomatic or symptomatic disease. The list of weighted selection criteria included whether the SNPs enriched asymptomatic or symptomatic isolates, if the SNP was present in repeat regions or large E. histolytica protein families, whether it was contained in genes with any potential role in virulence, or if orthologous sequences were present in the non-pathogenic but closely related species E. dispar [37]. The selected SNPs are shown in Additional file 1: Table S6. Preliminary amplicon sequencing and see more validation PCR amplifications were performed on a C1000 Thermal Cycler (Bio-Rad) using the High Fidelity Phusion DNA polymerase Master Mix (Finnzymes). Sample DNA (0.5 μl) was added to a 25 μl reaction mix containing 125 pm of the designated primers (5 nM). After an initial denaturation step of 98°C, denaturation at 98°C for 10 sec, annealing of primers at 50°C for 30 sec and elongation at 72°C for 30 sec was performed for 34 cycles. This was followed by a final extension

at 72°C for 10 min. BTK inhibitor research buy The amplified products were separated on a 2% agarose gel and the DNA fragments of the correct size were gel purified and sequenced by Sanger sequencing (GENEWIZ, Inc). PCR amplification of SNP markers and preparation ofmuliplexed sequencing libraries For clinical samples and low copy number culture material, amplicons were generated by nested PCR (see Additional file 1: Table S2 and S3). PCR amplifications were carried out Tau-protein kinase using Phusion High Fidelity DNA polymerase Master Mix (Finnzymes). 1 μl of first round amplified DNA was used as template for the second round of amplification, using the same

conditions as for the first round PCR with the exception that the annealing temperature was increased to 60°C and the nested PCR primers were used with tails that contained the unique “barcode” sequences and adaptors necessary for Illumina paired-end sequencing, as described by Meyer and Kircher (Additional file 1: Table S4) [59]. DNA from cultured parasites was used directly as template for the second round PCR amplification only, as its more abundant template made nested PCR unnecessary. After this step, the different PCR products amplified from original samples were pooled in groups of 5 or 6 and one μl was amplified using 200 nM of the IS4 primer and an indexing primer (Additional file 1: Tables S2 and S4) for an initial denaturation step of 98°C, denaturation at 98°C for 10 sec, annealing of primers at 60°C for 20 sec and elongation at 72°C for 20 sec was performed for 34 cycles. This was followed by a final extension at 72°C for 10 min.

Many of the same genes or classes of genes which were ranked highly by MHS are also identified by GCS. RNA polymerase RpoB/C, topoisomerase, gyrase, and several tRNA synthetases all rank highly by both methods. However, several interesting

genes not identified by MHS are placed at the top of the GCS ranking. For example, pyruvate phosphate dikinase, PPDK, has previously been identified by pathway analysis as a potential drug target [39]. By MHS, PPDK was ranked at position 309; GCS ranking placed it at position 3. Table 4 Top 20 wBm genes ranked by GCS. Annotations taken from the Refseq release of the wBm proteome. Rank GCS GI Annotation 1 101 58584652 2-oxoglutarate dehydrogenase complex, E1 component 2 101 58584298 Topoisomerase IA: TopA 3 101 58584469 Pyruvate phosphate dikinase 4 101 58584904 DNA-directed RNA polymerase: RpoB/RpoC 5 101 58584952 Ribonucleotide-diphosphate Fludarabine manufacturer reductase alpha subunit 6 101 58584808 ATP-dependent Lon protease 7 101 58584662 DNA gyrase subunit A 8 101 58584705 Succinate dehydrogenase 9 101 58584602 Translation elongation factor, GT-Pase: FusA 10 101 58584729 Threonyl-tRNA synthetase 11 101 58584633 NADH dehydrogenase gamma sub-unit 12 101 58584752 Molecular chaperone: DnaK 13 101 58584862

Leucyl-tRNA synthetase 14 101 58584524 Translocase 15 100.994 58585021 DNA gyrase, topoisomerase II, B sub-unit: GyrB 16 100.989 58584924 GTP-binding protein: LepA 17 100.987 58584410 ATP-dependent Zn protease: HflB 18 100.986 58584731 NADH:ubiquinone oxidoreductase, NADH-binding, chain LY3039478 cell line F 19 100.974 58584620 Isoleucyl-tRNA synthetase Idoxuridine 20 100.974 58584756 DNA polymerase III alpha subunit Plotting MHS versus GCS demonstrates the

identification of complementary sets of essential genes The two methods of essential gene prediction used in this study identified complementary RG7112 partially overlapping sets of wBm genes. Identification of a gene by both methods bolsters confidence in a prediction of essentiality. Genes uniquely identified by an individual method may represent, for MHS, genes essential to general bacterial processes; and for GCS, genes specifically important to the Rickettsiales order. To assess the distribution of essentiality prediction by both methods, the MHS and GCS for each wBm gene was graphed as a scatter plot (Figure 5). Lines indicating the empirically determined thresholds for the prediction of essentiality by each method produce four quadrants showing the classes of predicted essential genes. The upper-right quadrant contains 245 genes predicted essential by both methods. The upper-left quadrant contains 299 genes which are not similar to essential genes in more distantly related bacteria, but are still highly conserved across Rickettsiales. These genes represent a promising class of drug targets which are likely to be more specific to wBm.

Effect of low concentrations

of dissolved oxygen on zoospore survival As in the dissolved oxygen elevation assays, the greatest colony counts in the control bottles occurred at 10-min exposure for P. megasperma and at 2- or 4-h exposure for the other three species (Table 3). Table 3 Linear regression analyses of colony counts (y) and levels (x) of dissolved oxygen reduction from that in the control Hoagland’s solution by Phytophthora species and exposure time z Species Exposure (h) Intercept ( a ) Slope ( b ) P P. megasperma 0 (10 min) 18.2 -1.0 0.0936   2 11.3 -0.2 0.6267   4 9.9 -0.8 0.0104   8 7.4 -0.3 0.2903   24 8.4 -0.7 0.0292   48 7.6 -0.9 0.0015   72 4.5 -0.3 0.0724 P. nicotianae 0 7.8 0.8 0.1067   2 25.0 -1.2 0.0548   4 28.5 -2.6 0.0008   8 12.3 -0.4 0.4421   24 5.1 -0.2 0.4100   48 3.6 0.0 0.8670

  72 2.2 0.1 0.3973 P. pini HSP inhibitor 0 9.1 0.4 0.2462   2 32.6 -0.3 0.6893   4 37.2 -2.1 0.0002   8 20.8 -1.3 < 0.0001 Selonsertib research buy   24 14.4 -0.8 0.0034   48 7.4 -0.3 0.2382   72 8.3 -0.5 0.0313 P. Tucidinostat mouse tropicalis 0 27.8 -1.8 0.0156   2 31.4 -1.3 0.0749   4 29.7 -0.3 0.6712   8 22.5 -0.1 0.8042   24 7.8 -0.3 0.1730   48 0.7 0.4 0.0008   72 0.4 0.2 0.0079 zLinear model: y = a + bx, in which x = 5.3 - meter readings of dissolved oxygen in the Hoagland’s solutions after being bubbled with pure nitrogen, so 0 ≤ x ≤ 5.3 mg L-1. Zoospore survival of the four species assessed in this study also was negatively impacted by low concentrations of dissolved oxygen in two distinct patterns (Table 3). One pattern is represented by P. megasperma and P. pini. The impact on these two species generally occurred at 4-h or longer exposures at which their colony counts decreased with increasing level of dissolved oxygen reduction from the normal concentration of 5.3 mg L-1 in the control Hoagland’s solution. The greatest rate of decrease in colony counts

occurred at 48-h exposure for P. megasperma at 0.9 colony per unit of dissolved oxygen reduction (P = 0.0015) and at 4-h exposure for P. pini at 2.1 (P = 0.0002). Phytophthora Cyclin-dependent kinase 3 nicotianae and P. tropicalis showed an exactly opposite pattern. The colony counts decreased with increasing level of reduction in dissolved oxygen concentration at both 2- and 4-h exposures for P. nicotianae, 10-min and 2-h exposures for P. tropicalis. These results indicate that P. nicotianae and P. tropicalis are more prone than P. megasperma and P. pini to hypoxia stress in aquatic environments. They help understand the more consistent and greater recoveries of P. megasperma and P. pini than other major plant pathogens including P.

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