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To further understand the effect of sequencing errors on PCA, we performed procrustes analysis with the original datasets vs. datasets with simulated base error rates of 1% (Additional file 1: Figure S4). All pair-wise comparisons show that sequencing errors did not greatly affect the

PCA based on the Jaccard distance, in support of our conclusions detailed above. Microbial composition and biomarker determination The two datasets showed significantly Selleck LY2109761 different community structures (Figure 3a). Although the gut flora of all subjects consisted primarily of Firmicutes, Bacteroidetes and Proteobacteria, the relative abundance of these microbes MK-4827 mw varied significantly. Compared to the V6F-V6R dataset, the V4F-V6R dataset identified higher levels of Bacteroidetes and lower levels of Firmicutes (Figure 3c). Interestingly, the categories of genera identified by the two primer sets were similar to each

other, while the relative abundance of the genera differed (Figure 3b). We suggest that both the primer bias and sequencing errors contributed to these differences, but the former may have contributed more because sequencing errors usually occur buy CUDC-907 at a very low frequency and do little to change the overall relative abundance. Several studies have compared microbial community structures using different primer sets [11, 21]. These studies usually found significant primer biases in the evaluation of microbial ecology. However, here we demonstrated for the first time that PCA using the Jaccard distance was minimally affected by primer bias and differences in sequencing quality, suggesting the feasibility of performing meta-analysis for sequences obtained from different sources. Figure 3 Microbial structure at phylum

and genus level. (a) Microbial structures new of each individual determined at the phylum level by the two primer sets. (b) Microbial structures of each individual determined at the genus level by the two primer sets. (c) Relative abundance of Firmicutes and Bacteroidetes determined by the two primer sets. We used LEfSe for the quantitative analysis of biomarkers within different groups (Figure 4 and Additional file 1: Figure S2). This method was designed to analyze data in which the number of species is much higher than the number of samples and to provide biological class explanations to establish statistical significance, biological consistency, and effect-size estimation of predicted biomarkers [16]. To simulate a simple meta-analysis, we compared the microbiomes of four individuals two at a time (e.g., A vs. C and B vs. D). The results demonstrated that when the data from the two individuals came from the same dataset, their biomarkers were generally similar.

J Strength Cond Res 2011, 25:3461–3471.PubMedCrossRef 13. Tyrrell VJ, Richards G, Hofman P, Gillies GF, Robinson E, Cutfield WS: Foot-to-foot bioelectrical impedance analysis: a valuable tool for the measurement of body composition in children. Int J Obes 2001, 25:273–278.CrossRef 14. Utter AC, Nieman DC, Ward AN, Butterworth DE: Use of the leg-to-leg bioelectrical impedance method in assessing body composition MEK inhibitor change in obese women. Am J Clin Nutr 1999, 69:603–607.PubMed 15. Swartz AM, Evan MJ, King GA, Thompson DL: Evaluation of a foot-to-foot bioelectrical impedance analyser in highly active, moderately

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field and laboratory methods for measurement of body composition in boys. Obes Res 2003, 11:852–858.PubMedCrossRef 17. du Vigneaud V, Simmonds S, Chandler Fosbretabulin in vivo JP, Cohn M: A further investigation of the role of betaine in transmethylation reactions in vivo. J Biol Chem 1946, 165:639–648.PubMed 18. Storch KJ, Wagner DA, Young VR: Methionine kinetics in adult men: effects of dietary betaine on L-[2H3-methyl-1–13C]methionine. Am J Clin Nutr 1991, 54:386–394.PubMed 19. Wise CK, Cooney CA, Ali SF, Poirier LA: Measuring S-adenosylmethionine in whole blood, red blood cells and cultured cells using a fast preparation method and high-performance liquid chromatography. J Chromatogr B Biomed Sci Appl 1997, 696:145–152.PubMedCrossRef 20. Branch JD: Effect of creatine LGX818 clinical trial supplementation on body composition and performance: a meta-analysis.

Int J Sport Nutr Exerc Metab 2003, 13:198–226.PubMed 21. Del Favero S, Roschel H, Artioli G, Ugrinowitsch C, Tricoli V, Costa A, Barroso R, Negrelli Megestrol Acetate AL, Otaduy MC, da Costa Leite C, Lancha-Junior AH, Gualano B: Creatine but not betaine supplementation increases muscle phosphorylcreatine content and strength performance. Amino Acids 2011. doi: 10.1007/s00726–011–0972–5 22. Kumar R: Role of naturally occurring osmolytes in protein folding and stability. Arch Biochem Biophys 2009, 491:1–6.PubMedCrossRef 23. Bounedjah O, Hamon L, Savarin P, Desdorges B, Curmi PA, Pastre D: Macromolecular crowding regulates the assembly of mRNA stress granules after osmotic stress: a new role for compatible osmolytes. J Biol Chem 2011. doi: 10.1074/jbc.M111.292748 jbc.M111.292748 24. Ueland PM: Choline and betaine in health and disease. J Inherit Metab Dis 2011, 34:3–15.PubMedCrossRef 25. Kraemer WJ, Bailey BL, Clark JE, Apicella J, Lee EC, Comstock BE, Dunn-Lewis C, Volek J, Kupchak B, Anderson JM, Craig SAS, Mares CM: The influence of betaine supplementation on work performance and endocrine function in men [abstract]. J Strength Cond Res 2011, 25:s100-s101. 26.

meningtidis (Mc) recombinant Fpg protein. (A) 1 ng of purified Mc Fpg or 0.032 Units of E. coli Fpg was incubated with 10–50 fmol of a 24 bp duplex oligodeoxyribonucleotide containing a single 8oxoG residue opposite A, T C or G. Base Combretastatin A4 datasheet excision and strand cleavage were analysed by 20% PAGE and phosphorimaging. The arrow indicates the cleaved DNA substrate. * denotes 32P-labelled strand. selleck S; substrate. (B) Quantification of strand cleavage activity by Mc Fpg. The results represent the average of three independent experiments and

error bars indicate the standard deviation of the mean. Table 3 DNA glycosylase activity of N. meningitidis (Mc) recombinant Fpg protein. Substrate Released bases (fmol)   Average (St. dev.)c N. meningitidis Fpga 75 (± 30) E. coli Fpgb 64 (± 44) No enzyme 12 (± 4) a 500 ng of protein was employed in each reaction b 160 Units of protein was employed in each reaction c standard deviation

of the mean Removal of formamidopyrimidine (faPy) from [3H]-methyl-faPy-poly(dG·dC) DNA by recombinant Mc and E. coli Fpg. The results MRT67307 supplier are given as the average of five independent measurements. Mc is a bacterium that seemingly spontaneously produces a plethora of variants upon which selection can act, instead of sensing the environment and changing accordingly [37]. One of the major processes governing genetic changes in Neisseria sp. is phase variation. Phase variation is mediated by unstable polynucleotide tracts allowing the gene expression to be switched on or off [37]. Recently, several genome maintenance genes have been shown to modulate phase variation frequencies, including the mismatch repair components mutS and mutL, the nucleotide excision repair

gene uvrD and the translesion DNA polymerase dinB [38–41]. Since Mc Fpg is able to remove oxidized ADP ribosylation factor guanines, although in an error-free manner, we wanted to investigate a potential contribution of Mc fpg on phase variation of polyG tracts. Mc strains NmZ1099_UROS (Control), NmZ1099_UROSΔfpg (Δfpg) and NmZ1099_UROSΔmutS (ΔmutS) were constructed and examined by S12 ribosomal gene switching in a spectinomycin-selection assay (Figure 3). Phase variation was, as previously reported [38–41], significantly increased in the ΔmutS (30-fold) background compared to the wild-type level (***p < 0.001). However, the Mc fpg mutant exhibited only moderate increase (2-fold) compared to the wild-type level (***p < 0.001), and thus MutS exerts a more profound effect on the stability of Mc polyG tracts than Fpg. Likewise, the Mc fpg mutant was recently shown to generate only a weak mutator phenotype when assessed for its spontaneous mutation frequency in a rifampicin assay [9]. In conclusion, Fpg is not a major player in modulating Mc mutation frequencies. Figure 3 Assessment of meningococcal (Mc) phase variation. Phase variation frequency for Mc strains NmZ1099_UROS (Control), NmZ1099_UROSΔfpg (Δfpg) and NmZ1099_UROSΔmutS (ΔmutS) as examined by a spectinomycin assay.

These findings in the IPCC AR4 WG3 have received a lot of attention in recent years during the international negotiation process. However, the background information of Table SPM. 5 (Hanaoka et al. 2006) and original literature of Box 13.7 (Den Elzen and Meinshausen 2006) did not provide detailed information on the feasibility of achieving such GHG mitigation targets and their mitigation costs in the

mid-term (around 2020–2030). Since the IPCC AR4 was published, several modeling comparison studies have been done or are ongoing, such as the Energy Modeling Forum (EMF) 22 (Clarke Selleck BV-6 et al. 2009), Adaptation and Mitigation Strategies (ADAM) (Edenhofer et al. 2010), Asia Modeling Exercise (AME), EMF 24 and so on. However, these modeling comparison studies focused mainly on long-term (up to 2100) climate stabilization scenarios. In light of that, this comparison study focuses on an

in-depth analysis of the mid-term (2020–2030) transition scenarios analyzed using a global multi-region and multi-sector model. Mitigation potentials in major GHG emitting countries by multi-regional analysis The IPCC AR4 WG3 also pointed out that mitigation GANT61 efforts over the next two to three decades will have a large impact on opportunities to achieve lower stabilization levels and

that energy efficiency plays a key role in many scenarios for most regions and timescales (see pp 15–16 of the SPM in the IPCC AR4 WG3). BIX 1294 ic50 Improved energy efficiency is one of society’s most important instruments for combating climate change in the short- to mid-term. In order to reinforce these key messages, the role of energy intensity improvement in the GHG stabilization scenarios for six different categories on Table SPM. 5 in the IPCC AR4 WG3 were analyzed in detail for the short- to mid-term by Hanaoka et al. (2009). However, most of results were aggregated on a global scale due to a lack of data availability on a national scale and only one analysis has been done on multi-regional CYTH4 scales in Category IV on Table SPM. 5. Box 13.7 in the IPCC AR4 WG3, while its original literature (Den Elzen and Meinshausen 2006) also gives information on emission levels in Annex I groups in 2020 but does not indicate any key messages on a national scale. Therefore, this comparison study focuses on more detailed regional aggregations that cover the major GHG emitting countries and regions such as USA, EU27, Russia, China, India, Japan, the whole of Asia and Annex I, by using a global model with multi-regions.

Table 1 Characteristics of studied groups including anthropometric traits, dental status, and bone mineral density (BMD)   Tooth wear patients (n = 50) Controls (n = 20) P values Age (years) 47.5 ± 5 46.5 ± 6 NS Female/male ratio 16/34 8/12   Number of teeth (mean; range) 23 (14–28) 27 (26–28) NS Tooth Wear Index (TWI) 2.3 ± 0.5 0.8 ± 0.4 <0.001 Height (cm) 173.5 ± 7.2 175.0 ± 11.1 NS Wright (kg) 79.2 ± 9.8 80.4 ± 11.8 NS Body mass index P505-15 order (BMI) 26.8 ± 3.9 26.2 ± 2.7 NS Women   BMD femur [g/cm2] 0.93 ± 0.12 0.97 ± 0.13 NS   T-score for BMD femur −0.45 ± 0.96 −0.17 ± 1.21 NS   Z-score for BMD femur 0.04 ± 1.13 0.22 ± 1.01 NS   BMD spine [g/cm2]

1.08 ± 0.16 1.23 ± 0.22 0.02   T-score for BMD spine −0.93 ± 1.33 0.24 ± 1.97 0.02   Z-score for BMD spine −0.60 ± 1.59 0.42 ± 1.73 <0.001 Men   BMD femur [g/cm2] 1.00 ± 0.12 1.02 ± 0.16 NS   T-score for BMD femur −0.52 ± 0.89 −0.35 ± 1.24 NS   Z-score for BMD femur −0.15 ± 0.82 −0.04 ± 1.18 NS   BMD spine [g/cm2] 1.12 ± 0.11 1.21 ± 0.14 0.02   T-score for BMD spine −0.92 ± 0.96 −0.08 ± 1.08 0.02 GF120918 order   Z-score for BMD spine −1.08 ± 0.96 −0.27 ± 1.01 <0.001 Mean ± SD are

shown NS not statistically significant Table 2 Dietary intakes of calcium, zinc, copper, phosphates, and vitamin D in studied subjects   Tooth wear patients (n = 50) Controls (n = 20) P values Daily amount % of RDI Daily amount % of RDI Calcium (mg) 762.9 ± 279.9 94 730.8 ± 269.2 91 NS Zinc (mg) 14.03 ± 4.9 111 11.4 ± 2.8 91 0.05 Copper (mg) 1.57 ± 0.4 69 1.4 ± 0.3 60 NS Phosphorus (mg) 1,585 ± 521 250 1,368 ± 240 210 NS Vitamin D (μg) 4.78 ± 4.5   3.21 ± 1.8   NS Mean values ± SD and % of recommended many daily intakes (RDIs) are shown NS denote not statistically significant

differences The analysis of biopsies showed difference in copper amount in the enamel between the groups. No correlation between enamel copper and the degree of tooth wear was observed, however, significant difference was found in Cu content in the enamel between first and second levels of wear (p = 0.04). Tooth wear patients had significantly decreased copper content in comparison to controls despite normal salivary and serum concentrations of this element in the two groups (Table 3). Salivary concentrations of calcium, zinc, and copper were similar in patients and controls. There were no differences in serum 25-hydroxyvitamin D, PTH activity, or bone formation marker (osteocalcin) between the two groups. Table 3 Comparison of calcium, zinc, and copper contents in enamel bioptates, saliva; serum concentrations of the https://www.selleckchem.com/products/pci-32765.html elements, and serum levels of hydroxyvitamin D, PTH, and bone formation marker (mean values ± SD are given)   Tooth wear patients (n = 50) Controls (n = 20) P values Enamel   Ca [mg/L] 1.884 ± 1.382 1.853 ± 1.241 NS   Zn [mg/L] 0.142 ± 0.041 0.084 ± 0.022 0.05   Cu [μg/L] 19.861 ± 13.171 36.673 ± 22.

J Bone Miner Res 15(7):1384–1392CrossRefPubMed 5. Ray NF, Chan JK, Thamer M, Melton LJ 3rd (1997) Medical expenditures for the treatment of osteoporotic fractures in the United States in 1995: report from the National Osteoporosis Foundation. J Bone Miner Res 12(1):24–35CrossRefPubMed 6. McAdam-Marx C, Lafleur J, Kirkness C, Asche C (2007) Postmenopausal osteoporosis current and future treatment options. P&T 32(7):392–402 7. Gehlbach SH, Fournier M, Bigelow C (2002) Recognition of osteoporosis by primary selleckchem care physicians. Am J Public Health 92(2):271–273CrossRefPubMed 8. WHO (2003) Prevention and management

of osteoporosis. Geneva 9. Brennan RM, Wactawski-Wende J, Crespo CJ, Dmochowski J (2004) Factors

associated with treatment initiation after osteoporosis screening. Am J Epidemiol 160(5):475–483CrossRefPubMed TH-302 10. Cole RP, Palushock S, Haboubi A (1999) Osteoporosis management: physicians’ recommendations and womens’ compliance following osteoporosis testing. Women Health 29(1):101–115CrossRefPubMed 11. Cranney A, Tsang JF (2008) Leslie WD (2008) Factors predicting osteoporosis treatment initiation in a regionally based cohort. Osteoporos Int 20(9):1621–1625CrossRefPubMed 12. Kirk JK, Spangler JG, Celestino FS (2000) Prevalence of osteoporosis risk factors and treatment among women aged 50 years and older. Pharmacotherapy 20(4):405–409CrossRefPubMed 13. Marci CD, Anderson WB, Viechnicki MB, Greenspan SL (2000) Bone mineral densitometry substantially influences health-related behaviors of postmenopausal women. Calcif Tissue Int 66(2):113–118CrossRefPubMed 14. Phillipov G, Mos E, Scinto S, Phillips PJ (1997) Initiation of hormone 4��8C replacement therapy

after diagnosis of osteoporosis by bone densitometry. Osteoporos Int 7(2):162–164CrossRefPubMed 15. Riggs BL, Melton LJ 3rd (1995) The worldwide problem of osteoporosis: insights afforded by epidemiology. Bone 17(5 Suppl):505S–511SCrossRefPubMed 16. Rubin SM, Cummings SR (1992) Results of bone densitometry affect women’s decisions about taking measures to prevent fractures. Ann Temsirolimus mw Intern Med 116(12 Pt 1):990–995PubMed 17. Siris ES, Miller PD, Barrett-Connor E et al (2001) Identification and fracture outcomes of undiagnosed low bone mineral density in postmenopausal women: results from the National Osteoporosis Risk Assessment. JAMA 286(22):2815–2822CrossRefPubMed 18. Solomon DH, Brookhart MA, Gandhi TK et al (2004) Adherence with osteoporosis practice guidelines: a multilevel analysis of patient, physician, and practice setting characteristics. Am J Med 117(12):919–924CrossRefPubMed 19. Torgerson DJ, Thomas RE, Campbell MK, Reid DM (1997) Randomized trial of osteoporosis screening. Use of hormone replacement therapy and quality-of-life results. Arch Intern Med 157(18):2121–2125CrossRefPubMed 20.

All mice were trained by treadmill running 5 times per week for 2 weeks.

SP was dissolved in distilled water and 800-mg/kg body weight daily doses and administered orally intraperitoneally before the running exercise to the SP group for 2 weeks [13–15]. The CON group was treated with vehicle only (distilled water 5 mL/kg body weight). was measured before and after the 2 weeks training period. We also evaluated energy LY3039478 cost metabolism during exercise for 1 h after the 2 weeks training period. Mice were fasted 3 h before the 1 h exercise. We obtained blood, liver glycogen, and gastrocnemius-white and red muscle samples at three time points: rest, immediately after exercise and 1 h post-exercise. The buy Thiazovivin mice were fed ad libitum with a standard diet (5 L79; Orient Bio, Inc.) containing the RG7112 following nutrients (g/kg diet): crude protein, 180; crude fat, 52; crude fiber, 52; minerals, 57; and carbohydrates, 368. The calorically based protein:fat:carbohydrate ratio (%) was 21:14:65, and the gross and metabolizable caloric contents of the diet were 4.04 and 3.21 Kcal/g, respectively. The body weights and food intake were monitored daily throughout

the experiment. All mice were housed in standard plastic cages under controlled humidity (50%) and temperature (23°C ± 1°C) conditions and with alternating 12-h light/dark cycles. All experimental procedures were performed at the Animal Experiment Research Center of Konkuk University. This study was conducted in accordance with the ethical guidelines of the Konkuk University Institutional Animal Care and Use Committee. Silk peptides SP were obtained from Worldway Co. Ltd. (Jeoneui, Korea). The SP primarily comprised amino acids in the following order of concentration: Ala (34.36%) > Gly (27.23%) > Iso (15.51%) > Ser

(9.58%) > minor amino acids. Composition Fossariinae details are shown in Table 1. The SP composition according to molecular weight was as follows: an approximate range of 150–350 D and an average molecular weight of approximately 250 D. Table 1 Amino acid compositions (%) of SP Amino acid SP (silk peptide) Ala 34.36 Gly 27.23 Iso 15.51 Ser 9.58 Val 3.49 Thr 2.00 Asp 1.68 Glu 1.28 Ile 1.25 Leu 1.24 Phe 0.87 Pro 0.44 Tyr 0.41 His 0.21 Arg 0.17 Met 0.10 Lys 0.10 Cys 0.05 Trp 0.05 Sum 100.00 Training method Running mice were adapted to treadmill training (treadmill from Daejong Systems, Korea) at a fixed intensity (15 m/min, 8° slope) for 3 days. All mice were then tested for a certain period at a frequency of 5 times per week for a total of 2 weeks. The following protocols were used: 20 m/min, 8° slope, 50 min/day for the first week and 25 m/min, 8° slope, 50 min/day (about 75% of maximum ) for the second week [16].

This publication summarises the discussions of a meeting organised by ESCEO at that congress, with the selleck inhibitor topic; Generics versus branded medication in osteoporosis: The Alliance has had no editorial control over this publication. Competing interests JA Kanis receives consulting fees, paid advisory boards, lecture fees and/or grant support from the majority of companies concerned with skeletal metabolism. J-Y Reginster receives consulting fees, paid advisory boards, lecture fees and/or grant support from Ebewee Pharma,

Zodiac, Analis, Theramex, Nycomed, Novo-Nordisk, Bristol Myers Squibb, Merck Sharp & Dohme, IBSA, Genevrier, Novartis, Servier, Roche, GlaxoSmithKline, Teijin, Teva, Merckle, Negma, NPS, Amgen, UCB, Wyeth, Lilly and Rottapharm. J-M Kaufman receives consulting fees, paid advisory boards, lecture fees and/or grant support from Amgen, Eli Lilly, GlaxoSmithKline, Merck, Novartis, Procter & Gamble, Roche, Sanofi-Aventis, Servier and Warner Chilcott. JD Ringe gives advice to and lectures for different pharmaceutical companies in the field of osteoporosis. JD Adachi receives consulting fees, paid advisory boards, lecture fees or grant

support from the following: Amgen, Astra Zeneca, Eli Lilly, GlaxoSmithKline, Merck, Novartis, Nycomed, Pfizer, Procter & Gamble, Roche, Sanofi-Aventis, Servier, BMS and Wyeth. M Hiligsmann receives lecture fees and/or grant support from Amgen, Servier and Novartis. R Rizzoli receives consulting fees, paid advisory boards and/or lecture Momelotinib order fees from most companies concerned with bone disease. C Cooper receives consulting fees and paid advisory boards for Alliance for Better Bone Health, GlaxoSmithKline, Roche, Merck Sharp and Dohme, Lilly, Amgen, Wyeth, Novartis, Servier and Nycomed. References 1. Kanis JA, Johnell O (2005) Requirements for DXA for the management of osteoporosis in Europe. Osteoporos Int

16:229–238PubMedCrossRef 2. Delmas PD (2002) Treatment of postmenopausal osteoporosis. Lancet 359:2018–2026PubMedCrossRef 3. Compston J, Cooper A, Cooper C Cell press et al (2009) Guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men from the age of 50 years in the UK. Maturitas 62:105–108PubMedCrossRef 4. Van Staa TP (2006) The pathogenesis, epidemiology and management of glucocorticoid-induced osteoporosis. Calcif Tissue Int 79:129–137PubMedCrossRef 5. Compston JE (2007) Emerging consensus on prevention and treatment of glucocorticoid-induced osteoporosis. Curr Rheumatol Rep 9:78–84PubMedCrossRef 6. Papaioannou A, Morin S, Cheung A et al (2010) 2010 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ 182:1864–see more 1873PubMedCrossRef 7. McClung MR, Geusens P, Miller PD et al (2001) Effect of risedronate on the risk of hip fracture in elderly women. Hip Intervention Program Study Group. N Engl J Med 344:333–340PubMedCrossRef 8.

6%), the ST-45 CC (10.8%), the ST-48 CC (4.9%) and the ST-677 CC (2.9%). Of the 50 STs observed among the isolates, 23 (46%) were novel. Thirty-two isolates (31.4%) had a unique ST, and the

most common STs among the isolates were ST-53 (12.7%), followed by ST-61 (7.8%) and ST-883 (6.9%). CC ST aspA glnA gltA glyA pgm tkt #AZD6244 molecular weight randurls[1|1|,|CHEM1|]# uncA ST-21 CC 21 (3) 2 1 1 3 2 1 5   43 2 1 5 3 4 1 5   50 (4) 2 1 12 3 2 1 5   53 (13) 2 1 21 3 2 1 5   141 2 1 10 3 2 1 5   262 (2) 2 1 1 3 2 1 3   333 (2) 2 1 21 2 2 1 5   451 (4) 2 1 2 3 2 3 5   561 2 1 21 4 2 1 5   761 2 1 1 4 2 1 5   883 (7) 2 17 2 3 2 1 5   1459 2 1 1 2 2 1 5   1823 2 1 177 3 2 1 5   1952 2 1 12 3 1 1 5   2956 2 17 2 2 2 1 5   2957 (2) 2 1 1 3 393 318 5   2958 2 1 12 3 2 20 5   2959 2 1 2 137 2 3 5   2996 (2) 2 1 2 4 2 3 5   3352 2 1 2 2 2 3 5   3788 4 1 6 3 2 1 5   3810 14 4 1 3 19 1 5 ST-22 CC 3892 1 3 6 3 3 3 3 ST-42 CC 42 1 2 3 4 5 9 3 ST-45 CC 45 (3) 4 7 10 4 1 7 1   97 4 7 10 4 1 1 1   230 4 7 41 4 42 7 1   242 (2) 4 7 10 2 1 7 1   1701

4 7 10 4 1 51 1   2663 (2) 4 7 10 3 1 7 1   3357 4 7 10 3 42 51 1 ST-48 CC 475 (3) 2 4 1 4 19 62 5   2955 2 4 1 2 19 62 5   3893 2 4 2 2 7 51 5 ST-61 CC 61 (8) 1 4 2 2 6 3 17   618 (3) 1 4 2 2 6 3 5   820 1 4 2 4 6 3 17   2974 1 4 2 3 2 3 234   3351 (3) 1 4 2 3 6 3 17   3509 1 4 2 4 6 3 38   3894 10 4 2 3 6 3 17 ST-206 CC 3360 2 17 5 4 2 1 5 ST-658 CC 3000 2 4 2 4 19 1 8 ST-677 CC 677 (3) 10 81 50 99 Fosbretabulin in vitro Protein kinase N1 120 76 52 Unassigned 58 19 24 23 20 26 16 15   586 (4) 1 2 42 4 98 58 34   2961 1 17 2 4 2 3 5   2999 2 2 107 4 120 76 1   3354 2 2 42 4 98 58 5   3787 1 4 1 4 19 62 5 Numbers in parentheses after each ST denote the number of isolates.