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CCAC, Ottawa, ON; 1993. 38. Ng L,

Martin KI, Alfa M, Mulvey M: Multiplex PCR for the detection of tetracycline resistant genes. Mol Cell Probes 2001, 15: 209–215.PubMedCrossRef 39. Lanz R, Kuhnert P, Boerlin P: Antimicrobial selleckchem resistance and resistance gene determinants in clinical Escherichia coli from different animal species in Switzerland. Vet Microbiol 2003, 91: 73–84.PubMedCrossRef 40. Nadkarni MA, Martin FE, Jaques NA, Hunter N: Determination of bacterial load by real-time PCR using a broad range (universal) probe and primer set. Microbiol 2002, 148: 257–266. 41. Huws SA, Edwards JE, Kim EJ, Scollan ND: Specificity and sensitivity of Eubacterial primers utilized for molecular profiling of bacteria within complex microbial ecosystems. J Microbiol Meth 2007, 70: 565–569.CrossRef 42. SAS Institute Inc: SAS/STAT User’s Guide. SAS Institute Inc., Cary, NC, USA; 2001. Authors’ contributions TWA participated in study design and coordination, data analysis and drafted the manuscript. LJY participated in study design and sample collection. TR consulted on PCR analysis. RRR provided information on the https://www.selleckchem.com/products/Flavopiridol.html relevance of the findings to human health. ET consulted INCB018424 cost on environmental implications of transmission of resistance genes. LBS assisted with study coordination. TAM was the overall project leader and participated in design and coordination of project

and contributed Palmatine to the final copy of the manuscript. All authors have read and approve the final manuscript.”
“Background

Staphylococcus aureus is a major cause of both nosocomial and community-acquired infections worldwide. Because staphylococci can adapt rapidly to varying environmental conditions they are quick to develop resistance to virtually all antibiotics and multiple-drug resistance, especially in methicillin-resistant S. aureus (MRSA), severely restricts antibiotic therapy options. One of the major targets for antimicrobial agents is the bacterial cell envelope, which is a complex, multi-macromolecular structure that undergoes highly ordered cycles of synthesis and hydrolysis, in order to facilitate cell division while maintaining a protective barrier against environmental stresses. There are several different classes of antibiotics that target specific cell envelope structures or enzymatic steps of cell wall synthesis (Figure 1). Figure 1 Schematic representation of the enzymatic steps involved in S. aureus cell wall synthesis and the targets of cell wall active antibiotics. Fosfomycin inhibits the enzyme MurA (UDP- N -acetylglucosamine-3-enolpyruvyl transferase) that catalyses the addition of phosphoenolpyruvate (PEP) to UDP- N -acetyl-glucosamine (GlcNAc) to form UDP-N-acetyl-muramic acid (UDP-MurNAc) [34]. D-cycloserine prevents the addition of D-alanine to the peptidoglycan precursor by inhibiting D-alanine:D-alanine ligase A and alanine racemase [35].

11 (1.90) 91.46 (1.81) 91.67 (3.00) 91.90 (4.39) MCH (pg) 30.13 (1.00) 30.50 (0.81) 30.80 (1.29) 30.91 (1.56) MCHC (g/dl) 33.10 (1.15) 33.37 (1.03) 33.61 (0.59) 33.62 (0.29) Lymphocytes (K/μl) 2.07 (0.26)

1.86 (0.43) 1.89 (0.44) 1.54 (0.34) Monocytes (K/μl) 0.46 (0.15) 0.45 (0.21) 0.27 (0.21) 0.48 (0.24) Neutrophils (K/μl) EX 527 manufacturer 3.34 (1.11) 3.19 (1.15) 2.67 (0.90) 3.02 (2.10) Eosinophils (K/μl) 0.22 (0.18) 0.23 (0.17) 0.15 (0.11) 0.24 (0.14) Basophils (K/μl) 0.06 (0.05) 0.06 (0.02) 0.07 (0.04) 0.07 (0.04) Data are presented as means and standard deviations. No significant differences were observed with resistance training or between groups throughout the 28-day study for whole blood clinical chemistry variables (p > 0.05). Discussion The results of the present study support our hypothesis, indicating that NO-Shotgun® supplementation in conjunction with a 28 days of heavy resistance training, is effective at increasing fat-free mass, muscle strength and mass, myofibrillar protein content, and markers

of check details satellite cell activation, while having no effect on whole blood and serum clinical safety markers in untrained males. Our results agree with previously reported studies that resistance training, when performed in conjunction with creatine [24, 25], whey protein and leucine [36], and HMB [37, 38] is effective at improving body composition, muscle strength and www.selleckchem.com/products/cftrinh-172.html mass and markers of satellite cell activation. We observed both NO and PL to significantly increase total body mass (P = 0.001). Additionally, fat-free mass was increased in both groups, and the 4.75% increase

in NO was significantly greater than the 1.69% increase in PL. These findings are similar to results observed after 12 wk of heavy resistance training and creatine supplementation, where fat-free mass was increased 9.44% in the creatine group and 1.84% in the carbohydrate placebo group [24]. In addition, 10 wk of heavy resistance training and whey protein and amino acid supplementation resulted in increases in fat-free mass of 5.62% compared to increases of 2.70% for carbohydrate placebo Isotretinoin [34]. Relative to muscle strength, we observed NO to increase in bench press and leg press strength by 8.82% and 18.40%, respectively, compared to the respective increases in bench press and leg press strength of 0.74% and 10.30% for PL. However, only bench press was significantly greater for NO compared to PL (p = 0.003). Our observed increases in muscle strength are supported by previous studies which demonstrated heavy resistance training, when combined with creatine [24, 27], protein and amino acids (34), and whey protein and leucine [24] to improve strength levels when compared to placebo. However, it should be noted that NO-Shotgun® contains beta-alanine, which has been shown to possibly potentiate the effects of creatine.

AvrPtoB is annotated to several child terms of “”GO:0052031 modulation by symbiont of host defense response”" including: “”GO:0034054 negative Fludarabine datasheet regulation by symbiont of host defense-related

programmed cell death [PCD]“”, “”GO:0034055 positive regulation by symbiont of host defense-related programmed cell death”", “”GO:0033660 negative regulation by symbiont of host resistance gene-dependent defense response”", “”GO:0075132 negative regulation by symbiont of host protein kinase-mediated signal transduction”", and “”GO:0052034 negative regulation by symbiont of pathogen-associated molecular pattern-induced host innate immunity”". At first glance, these annotations may appear contradictory – after all, how can the same gene product be

annotated to both “”GO:0034055 positive regulation by symbiont of host defense-related PCD”" and “”GO:0034054 negative regulation check details by symbiont of host defense-related PCD”"? In this case, the answer lies in the secondary or dual taxon field incorporated into the GO database as part of the PAMGO project. This field functions to indicate the identities of the organisms between which the interaction is occurring. Thus, closer examination learn more reveals that “”GO:0034055 positive regulation by symbiont of host defense-related PCD”" applies to AvrPtoB in the Pto DC3000 interaction with S. lycopersicum (tomato) while “”GO:0034054 negative regulation by symbiont of host defense-related PCD”" is used to annotate the interaction between Pto DC3000 and Nicotiana benthamiana (tobacco). In fact, annotation to “”GO:0034054 negative regulation by symbiont of host defense-related PCD”" is shown in triplicate to reflect interactions of Pto DC3000 in three separate hosts – Nicotiana benthamiana, Nicotiana tabacum cv. Xanthi, and Arabidopsis thaliana. Where additional clarification of strains and genotypes of interacting organisms is required, users can refer to the associated publications found in the reference field of the GO annotation. In

addition to annotations in the Biological Process ontology, annotations to the Cellular Component and Molecular Function Dehydratase ontologies are also shown. As one of the most thoroughly characterized of the Pto DC3000 effectors, AvrPtoB has several Molecular Function annotations that provide insight on the specific enzymatic and binding capabilities by which AvrPtoB accomplishes the processes described above. Molecular Function annotations include: “”GO:0019901 protein kinase binding”", “”GO:0004842 ubiquitin-protein ligase activity”", and “”GO:0031624 ubiquitin conjugating enzyme binding”". Just as documenting the taxa of interacting organisms is critical to the usefulness of biological process terms, so documentation of interacting proteins significantly enhances the value of Molecular Function terms.

g., H2O2 or AgNO3) to form the nanostructures. Nanoparticles or thin films of noble metals (e.g., Au, Ag, or Pt) are used to catalyze the etching. Two-level nanoscaled porous Si nanowires were even synthesized with highly doped Si using MaCE, and Ag nanoparticles acted as catalyst [15–17]. Zigzag Si nanowires were Trichostatin A research buy fabricated with (111)-oriented Si by MaCE (with Ag nanoparticles as catalyst) [18]. These zigzag Si nanowires were even fabricated

with (100)-oriented Si by a two-step MaCE (with Au film as catalyst) [19]. In general, the structural properties and morphologies of the nanostructured Si produced by MaCE are affected by three main factors: (1) the properties of the deposited noble metals, including the type and form of the metal, and its deposition method; (2) the properties of the Si wafer, including the doping type and level and the crystallographic this website orientation; and (3) the properties of the etchant, including etchant composition, concentration,

and temperature. By combining MaCE with nanolithography, many ordered nanostructures were fabricated. For example, ordered arrays of Si nanowires and nanopillars were fabricated using a combination of laser interference lithography or nanosphere lithography and MaCE [20–22]. An Au/Ag bi-layer metal mesh with an array of holes, prepared from an see more anodic aluminum oxide membrane, was used to fabricate Si nanowires by MaCE [23]. In this paper, the fabrication Montelukast Sodium of ordered arrays of nanoporous Si nanopillars, ordered arrays of nanoporous Si nanopillars with nanoporous base, and Si nanopillars with nanoporous shells using a combination of substrate conformal imprint lithography (SCIL) and MaCE (with Au film as catalyst) is presented. The mechanisms of MaCE are systematically investigated, and the fabricated nanoporous pillars should have the potential

for applications in sensors and optoelectronics. Methods The fabrication process is schematically represented in Figure 1a. As shown in Figure 1b, an array of elliptical pillars with hexagonal symmetry was defined using SCIL on two types of (100)-oriented p-Si wafers: one is highly doped (B-doped, ρ < 0.005 Ω cm), and the other is lightly doped (B-doped, ρ = (6.0−10.5) Ω cm). The periodicity (the distance between two adjacent pillars) is 1.0 μm, and the major and minor diameters of the ellipses are 613 and 385 nm, respectively. SCIL was developed by Philips Research and SÜSS MicroTec as a new technique of nanoimprint lithography, and this new technique possesses the advantages of both UV nanoimprint lithography techniques with a rigid stamp for best resolution and with a soft stamp for large-area patterning [24]. Two steps of reactive ion etching (RIE) were performed to transfer the structure into the Si substrate: the residual layer of the resist was removed using inductively coupled plasma RIE, and then the structure was transferred into the Si using RIE.

JAMA 2008, 299: 425–436.CrossRefPubMed 8. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri Etomoxir mouse M, Campiglio M, Menard S, Palazzo JP,

Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM: MicroRNA gene Selisistat expression deregulation in human breast cancer. Cancer Res 2005, 65: 7065–7070.CrossRefPubMed 9. Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9: 189–198.CrossRefPubMed 10. He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, Calin GA, Liu CG, Franssila K, Suster S, Kloos RT, Croce CM, de la Chapelle A: The role of microRNA genes in papillary thyroid carcinoma. PNAS 2005, 102: 19075–19080.CrossRefPubMed 11. Ciafre SA, Galardi S, Mangiola A, Ferracin M, Liu CG, Sabatino G: Extensive modulation of a set of microRNAs in primary glioblastoma. Biochem Biophys Res Commun 2005, 334:

1351–1358.CrossRefPubMed 12. Porkka KP, Pfeiffer DMXAA order MJ, Waltering KK, Vessella RL, Tammela TL, Visakorpi T: MicroRNA Expression Profiling in Prostate Cancer. Cancer Res 2007, 67: 6130–6135.CrossRefPubMed 13. Metzler M, Wilda M, Busch K, Viehmann S, Borkhardt A: High expression of precursor microRNA-155/BIC RNA in children with Burkitt lymphoma. Genes Chromosomes Cancer 2004, 39: 167–169.CrossRefPubMed 14. Murakami Y, Yasuda T, Saigo K, Urashima T, Toyoda H, Okanoue T: Comprehensive analysis of microRNA expression patterns in hepatocellular carcinoma and non-tumorous tissues. Oncogene 2006, 25: 2537–2545.CrossRefPubMed 15. Nam EJ, Yoon H, Kim SW, Kim H, Kim YT, Kim JH: MicroRNA

Expression Profiles in Serous Ovarian Carcinoma. Clin Cancer Res 2008, 14: 2690–2695.CrossRefPubMed 16. Zhang L, Huang J, Yang N, Greshock J, Megraw MS, Giannakakis A, Liang Florfenicol S, Naylor TL, Barchetti A, Ward MR, Yao G, Medina A, O’brien-Jenkins A, Katsaros D, Hatzigeorgiou A, Gimotty PA, Weber BL, Coukos G: MicroRNAs exhibit high frequency genomic alterations in human cancer. PNAS 2006, 103: 9136–9141.CrossRefPubMed 17. Bloomston M, Frankel WL, Petrocca F, Volinia S, Alder H, Hagan JP: MicroRNA Expression Patterns to Differentiate Pancreatic Adenocarcinoma From Normal Pancreas and Chronic Pancreatitis. JAMA 2007, 297: 1901–1908.CrossRefPubMed 18. Ferlay J, Bray F, Pisani P, Parkin DM: GLOBOCAN 2002 Cancer Incidence, Mortality and Prevalence Worldwide IARC CancerBase No.5, version 2.0. Lyon, France: IARC Press 2004. 19. Carvalho AL, Ikeda MK, Magrin J, Kowalski LP: Trends of oral and oropharyngeal cancer survival over five decades in 3267 patients treated in a single institution. Oral Oncol 2004, 40: 71–76.CrossRefPubMed 20.

2 Relationships of transpiration efficiency (TE) and leaf carbon isotope composition (δ13C) among 96 natural accessions of Arabidopsis thaliana. Symbols represent best linear unbiased predictors (BLUPs) associated with breeding values for each accession (see text). Open and filled symbols represent spring and winter accession means, respectively. Lines represent linear regression; r 2 and P values are given Variation in components of WUE The CP673451 nmr 18 natural accessions of Arabidopsis in experiment 2 were selected to represent a wide range of intrinsic WUE as indicated by δ13C (Table 1). Whole-plant gas exchange measurements

in a custom cuvette (Fig. 1) OICR-9429 mouse showed that these lines also exhibit considerable variation in whole rosette A and g s in a common environment (Fig. 3). Accession mean whole rosette A ranged between 10 and 16 μmol m−2 s−1, but the heritability was not significantly different from zero (P = 0.137). g s showed significant genetic variation, ranging between 0.17 and 0.45 mol m−2 s−1 with a heritability of H 2 = 0.33 (accession P value = 0.002). In addition, g s was a better predictor of variation in δ13C than A. We found a significant negative correlation between δ13C and g s among accessions (r 2 = 0.40, P = 0.0027), and a weaker correlation between δ13C and A (r 2 = 0.25,

P = 0.036). In general, the high conductance check details lines had low intrinsic WUE, as indicated by δ13C, but there was a wide range of δ13C in the MG132 low conductance lines, suggesting additional sources of variation. The expected negative correlation between δ13C and g s was largely caused by the spring accessions. The winter accessions tended to show the opposite pattern (not significant), with the exception of Tamm-2, an accession from Finland that had the highest g s

of all. Fig. 3 Relationships between assimilation (A), stomatal conductance (g s), and leaf carbon isotope composition (δ13C) at 350 μmol photons m−2 s−1 from whole-shoot gas exchange of 18 accessions of Arabidopsis selected from the larger panel of accessions to represent extremes in δ13C. Open and filled symbols represent spring and winter accession means, respectively. Lines represent linear regression; r 2 and P values are given Despite the lack of heritability of A and the weak correlation of A with δ13C, we did find a significant positive correlation between g s and A among accessions (r 2 = 0.78, P = 0.00001). This is consistent with the optimization of stomatal regulation to maximize carbon gain while minimizing the water loss (Katul et al. 2010). Accessions that have high conductance should be under selection for increased biochemical capacity (Bloom et al. 1985). Although, it is not formally stated, such optimality approaches interpret consistent patterns of correlation in physiological traits (Reich et al.

Specific activities were determined by a modified Miller method [41]. Briefly, cells were www.selleckchem.com/products/sgc-cbp30.html harvested during different growth stages and resuspended in Z-Buffer to an OD600 of 0.5-0.7. Samples were prepared in triplicates by adding 100 μL of cell suspension to 900 μL Z-buffer with 0.27% (v/v) β -mercaptoethanol, 50 μL chloroform and 100 μL 0.1% SDS and vortexing for 10 seconds. After equilibration at 28°C for 10 minutes, the reaction was started by addition of 0.2 mL o-nitrophenyl-D-galactoside (ONPG) [4 mg * mL-1 ] and incubating the samples at 28°C. The reactions Thiazovivin were stopped with 0.5 mL Na2 CO3 [1M] when samples developed a yellowish color. Samples were centrifuged for 5 minutes at 13,000 rpm and OD420 was

recorded. Specific activities were expressed as Miller Units and calculated as follows: 1 Miller Unit = 1000 *

(OD420 )/(t * V * OD600 ), where t = time V= volume OD= optical density Biofilm cultivation Biofilms were grown at 30°C in three-channel flow cells as decribed previously [12]. Briefly, LB overnight cultures of the relevant S. oneidensis MR-1 strains were diluted 1/100 in LB and grown to early stationary phase. Then the optical density at 600 nm was adjusted to 0.01 in 4M MM or LM without carbon source. 1 mL of the OD600 = 0.01 cell suspension was injected into each flow channel while the medium flow was stopped. The flow Belinostat clinical trial cells were inverted (glass slide facing bottom) and incubated for 40 min at 30°C. After incubation flow cells were reverted and medium was pumped through the flow cell at a constant velocity of 0.3 mm/s per channel by a Watson-Marlow Bredel (Cornwall, United Kingdom) 205S peristaltic pump. Biofilm studies were carried out in triplicate in at least two independent experiments. Biofilm image acquisition and processing Microscopic visualization of biofilms was performed using an upright Leica TCS SP2 AOBS confocal laser scanning microscope (CLSM; Leica Microsystems, Methane monooxygenase Wetzlar, Germany) using the following objectives: HCX PL APO 63X/1.2 W CORR CS and HC PL FLUORTAR 20X/0.5. For three-dimensional reconstruction

of biofilm images, CLSM images were processed with the IMARIS software package (Bitplane AG, Zuerich, Switzerland) and Adobe Photoshop. Flow cytometry 24 h old LM grown biofilm of S. oneidensis MR-1 wild type and mutant cells carrying a P mxd ::gfp reporter construct were harvested from the flow chamber, passed 50 times through a 25 gauge needle to suspend any cell aggregates and fixed in 2% paraformaldehyde. Flow cytometry data were obtained using a BD FACSCalibur flow cytometer (BD Biosciences, San Jose, CA). Samples were analysed using the 488 nm excitation from an argon-ion LASER at 15 mW. Detector voltages were set at defined values [800 V for the fluorescence channel (FL1) and both the FL1 and forward scatter channel amp gain were set to logarithmic scale] prior to the experimental analysis in which samples were run in succession on the same day.

In addition, another limitation of this analytical method includes the magnetic field applied for ZFC measurements which must be small compared to the anisotropy field of the MNPs [30], and it also neglects particle-particle dipolar interactions which increase the apparent blocking temperature [31]. This technique, however, could give a very reliable magnetic size of the nanoparticle analyzed. Dark-field microscopy relies on direct visual inspection of the optical signal emitted from the MNP while it undergoes

Brownian motion. After the trajectories of each MNP over time t are recorded, the two-dimensional mean-squared displacement 2 > = 4Dt is used to calculate IWR-1 the diffusion coefficient D for each particle. Later on, the hydrodynamic diameters can be estimated via the selleck inhibitor Stokes-Einstein equation for buy Sepantronium the diffusion coefficients calculated for individual particles, averaging over multiple time steps [18]. Successful implementation of this technique depends on the ability to trace the particle optically by coating the MNP with a noble metal that exhibits surface Plasmon resonance within a visible wavelength. This extra synthesis step has significantly restricted the use of this technique as a standard route for sizing MNPs. The

size of an MNP obtained through dark-field microscopy is normally larger than the TEM and DLS results [17]. It should be noted that dark-field microscopy can also be employed for direct visualization of a particle flocculation event [32]. As for AFM, besides the usual topographic analysis, magnetic imaging of

a submicron-sized MNP grown on GaAs substrate has been performed with magnetic force microscopy equipment [33]. Despite all the recent breakthroughs, sample preparation and artifact observation are still the limiting aspect for the wider use of this technology for sizing MNPs [34]. The particle size and size distribution can also be measured with an acoustic spectrometer which utilizes the sound pulses transmitted through a particle suspension to extract the size-related information [29]. Based on the combined effect Resveratrol of absorption and scattering of acoustic energy, an acoustic sensor measures attenuation frequency spectra in the sample. This attenuation spectrum is used to calculate the particle size distribution. This technique has advantages over the light scattering method in studying samples with high polydispersity as the raw data for calculating particle size depend on only the third power of the particle size. This scenario makes contribution of the small (nano) and larger particles more even and the method potentially more sensitive to the nanoparticle content even in the very broad size distributions [35]. DLS, also known as photon correlation spectroscopy, is one of the most popular methods used to determine the size of MNPs.

The historic 027 isolate CD196 exhibits a similar level of tolerance to strain 630 [18]. This increase in tolerance to p-cresol in the modern hypervirulent 027 isolates may be linked to increased virulence. In addition, the hypervirulent PCR-ribotype 027 strain has a higher capacity to convert tyrosine to p-HPA resulting in a higher overall yield of p-cresol. Analysis of the decarboxylase mutants revealed that although

C. difficile can tolerate p-cresol, high www.selleckchem.com/products/VX-765.html levels have a deleterious effect on the growth rate of C. difficile, as the mutants grow better in-vitro than their respective parent strains. Although it is evident that the 027 ribotype R20291 is more tolerant to p-cresol and produces significantly more p-cresol

than other strains, the mechanism of tolerance to p-cresol does not appear to be linked to its production. These results indicate that there is an intricate balance between optimal p-cresol production AZD6244 and deleterious effects on growth. Conclusions The hypervirulent R20291 strain produces high levels of p-cresol, and has an elevated tolerance, which may contribute to the colonisation and dissemination of the 027 clonal lineage by providing a selective advantage. There is a delicate interplay between relative p-cresol production and growth rate, whereby R20291 may have reached an advantageous compromise. Materials and methods Bacterial strains and culture C. difficile strains used in this study were 630, 630Δerm and R20291. Strain 630, PCR-ribotype 012, was originally isolated from a patient with severe PMC in Zurich, Switzerland in 1982. 630Δerm is an erythromycin sensitive strain that was isolated after passage of the original sequenced strain 630 [19]. Erythromycin sensitivity is required Rucaparib for the construction of C. difficile

gene inactivation mutants. R20291, a hypervirulent PCR-ribotype 027 strain was isolated from an outbreak at Stoke Mandeville hospital in 2006 and was provided by Jon Brazier (Anaerobe reference laboratory, Cardiff, UK). Strains were stored at -80°C and were cultured on BHI Agar (Oxoid), supplemented with 0.05% L-cysteine and cycloserine/cefoxitin antibiotic supplement (Fluka) at the recommended concentrations for 1 to 2 days under anaerobic conditions, in a Modular Atmosphere Control System 500 (Don Stattic purchase Whitney Scientific) at 37°C. Liquid cultures were grown in BHI broth (Oxiod) supplemented with 0.05% L-cysteine and cycloserine/cefoxitin antibiotic supplement (Fluka) with and without 0.1% p-HPA (Sigma), or in yeast peptone (YP) broth, 16 gL-1 peptone (Sigma), 5 gL-1 yeast (Sigma), and 5 gL-1 NaCl2 (Sigma). E. coli strain CA434, the conjugation donor, was grown in Luria-Bertani (LB) broth or agar supplemented with 12.5 μg/ml chloramphenicol. Para-cresol tolerance assays Primary cultures were inoculated with three single colonies into pre-equilibrated media, shaking at 50 rpm on an orbital shaker. At an OD600 nm of 0.3-0.