Peak power was defined as the highest mechanical

power output elicited during the test. Mean power was defined as the average mechanical power during the 20 s test. The fatigue index was determined by dividing the highest power output by the lowest power output. A total of three 20-s buy Barasertib Wingate tests (one Wingate test per 10-min period) were performed during each trial and measures were averaged over the three sprints. Questionnaires Selleck Ro 61-8048 Prior to each bout of performance measures subjects were asked to complete a questionnaire containing four questions using a 5-point rating scale. Subjects were asked to rate their energy level, fatigue level, feelings of alertness and feelings of focus for task using the following verbal anchors: 1 = very low; 2 = low; 3 = average; 4 = high; 5 = very high. The same researcher performed

all test administrations and tests were conducted under controlled conditions (a quiet room). The average response of the three testing sessions was computed. Supplement On each visit subjects consumed 120 ml of the ready to drink supplement or placebo. The supplement used is marketed as Redline Extreme® (Vital Pharmaceuticals, Davie, FL) and contains caffeine anhydrous, beta-alanine, vitamin C, and the following see more herbal and botanical compounds; evodiamine, N-acetyl-L-tyrosine, hordenine, 5-hydroxytryptophan, potassium citrate, N-methyl tyramine, sulbutiamine, vinpocetine, yohimbine HCL, and St. John’s wort extract. The placebo was similar in appearance and taste to Redline Extreme®, but contained only an inert substance. Statistical analyses Statistical analysis of the data was accomplished using a repeated measures analysis of variance. In the event of a significant F-ratio, LSD post-hoc tests were used for pairwise comparisons. Comparisons of the average performance measures for the three testing periods were analyzed using paired student’s T-tests. A criterion alpha level of p ≤ 0.05 was used to

determine statistical significance. All data are reported as mean ± SD. Results The responses to the questionnaire can be seen in Table 1. The average energy level during the three testing periods was significantly higher for SUP than PL. In addition, focus for task was significantly greater at T3 for SUP than PL, and the Protein kinase N1 average focus for task for all three testing periods combined was significantly higher for SUP than PL. Average feelings of alertness tended to be higher (p < 0.06) for SUP than PL. No significant differences in perceived levels of fatigue were seen between the groups. Table 1 Response to performance questionnaire Question Group T1 T2 T3 AVG My energy level is: Sup 3.7 ± 0.7 3.5 ± 0.7 3.3 ± 0.6 3.5 ± 0.5 *   PL 3.2 ± 0.6 3.2 ± 0.6 2.8 ± 0.9 3.1 ± 0.5 My fatigue level is: Sup 2.3 ± 0.9 2.8 ± 0.8 3.1 ± 0.7 2.7 ± 0.6   PL 2.4 ± 0.7 3.1 ± 0.5 3.3 ± 0.9 2.9 ± 0.5 My feeling of alertness is: Sup 3.7 ± 0.7 3.6 ± 0.5 3.6 ± 0.7 3.6 ± 0.4   PL 3.3 ± 0.7 3.4 ± 0.7 3.1 ± 1.0 3.3 ± 0.

2006; Mortimer et al. 2006), causing a major part of work disability and long-term sick leave in Sweden (Borg et al. 2001). Musculoskeletal pain and long-term sick leave is higher among women than among men workers (Dellve C59 wnt molecular weight et al. 2006), and among human service organization workers (HSOs)

compared with other occupational groups. The high prevalence of long-lasting sick leave due to neck pain among female workers stresses the need for intervention methods that are easily applied and can increase work ability and return to work. The rehabilitation activity among HSO-workers has been low in Sweden. Among the largest group of HSOs, nursing aides and BIBF 1120 mw assistants, few (2%) received occupational rehabilitation and few (3–5%) returned to work from 2 weeks of sick leave within 30 days (Dellve et al. 2006). A number of studies

have reported difficulties in rehabilitation and return to work from long-term sick leave in general and due to neck pain in particular (Savikko et VX-680 molecular weight al. 2001; Nielsen et al. 2006; Ekbladh 2008). This point to the need for methods to better support return to work and regained work ability among female workers with musculoskeletal disorder, especially with neck pain. However, work ability is a broad concept comprising the physical, psychological, and social capability of a worker to perform and interact within their work, the individual’s specific work demands, health conditions, and mental triclocarban resources (Ilmarinen and Rantanen 1999; Ludvigsson and Alexandersson 2006). Thus, several dimensions of work ability need to be used to capture the effect of intervention on work

ability, e.g. general perception of work ability, muscular strength, vitality, and other dimensions of health (i.e., both self-rated and laboratory assessed). This randomized control study investigates whether 1 month’s intervention with myofeedback through an easy-to-wear electromyography (EMG) device, or a short intensive muscular strength training program both coached by an ergonomist at the participants’ homes, can increase work ability and decrease pain among female workers on long-term sick leave (exceeding 60 days). The theoretical framework is that muscle tension in the neck is related to insufficient rest, which is a risk factor for chronic pain (Veiersted and Westgaard 1993) and that an intervention that changes the muscle activation pattern will increase health by reducing pain and thereby increasing the work ability. One of the theories for the etiology of neck pain, which may have an association with the muscle activation pattern, is an overload of the low threshold motor units, i.e., the type 1 muscle fibers.

According to Equation 1, the calculated C s values of ZnO nanorods, pristine Gr sheets, and the graphene-ZnO hybrid electrode are 36, 112, and 156 F g−1, respectively, at a scan rate of 5 mV s−1. The specific capacitance of the graphene-ZnO hybrid electrode was much higher than that of the ZnO nanorods and pristine Gr sheets. Moreover, this value

is higher than that of previously reported. To obtain a more detailed information on the capacitance performance of the as-prepared graphene-ZnO hybrid nanostructure, the CV curves with various scan rates were studied. Figure 4b summed the C s of ZnO, pristine Gr, and graphene-ZnO hybrid electrodes at various scan rates. It can be seen that the Pitavastatin mw specific capacitance decreased with an increase in the scan rate from 5 to 500 mV s−1. The reason may be that insufficient time available for ion diffusion and adsorption inside the smallest pores within a large particle at high scan rates

[37]. Moreover, the C s of the graphene-ZnO hybrid electrode was much higher than that of a ZnO and pristine Gr electrodes for all the scan rates tested. Figure 4c shows galvanostatic charge–discharge measurements of the graphene-ZnO hybrid electrode at a constant current density of 2.0 mA cm−2. It can be seen that the curves were linear and exhibited a typical triangular shape even charging/discharging NADPH-cytochrome-c2 reductase for 12,000 s, which indicated good electrochemical capacitive characteristics. The enhanced electrochemical performance learn more of the graphene-ZnO hybrid

can be attributed to the sandwiched structure. Here, the graphene in the hybrid electrode provides better electronic conductivity and excellent interfacial contact between ZnO and graphene, which results in the fast transportation of electrons throughout the entire electrode matrix [38]. Moreover, it is evident that when the ZnO size is MM-102 datasheet reduced to nanometer dimensions, the surface area and electroactive sites increase, which effectively reduces the diffusion length of the Na+ ion in the electrode matrix [39, 40]. Figure 4 CV curves, specific capacitance, galvanostatic charge–discharge curve, and Nyquist plots of electrodes. (a) CV curves of the as-prepared ZnO, graphene and the graphene-ZnO hybrid electrode at a scan rate of 5 mV s−1 in 0.5 M Na2SO4 electrolyte solution. (b) Specific capacitance of ZnO, pristine graphene, and the graphene-ZnO hybrid electrode at different scan rates calculated from CV curves. (c) Galvanostatic charge–discharge curve of the graphene-ZnO hybrid electrode at a constant current density of 2.0 mA cm−2. (d) Nyquist plots for ZnO, pristine graphene, and the graphene-ZnO hybrid electrode.

Nanoparticles reveal completely new or improved properties based on specific characteristics such as size, distribution and morphology, Tipifarnib clinical trial if compared with larger particles of the bulk material they are made of [21]. Since the absorption of minerals by the plant is non-selective, some of the metal ions in conjunction with anions may cause toxicity if they exceed the tolerance limit of the plant. When the nanoparticles are absorbed, they are subsequently translocated and accumulated in different parts of the plants forming complex with carrier proteins. It is, however, not yet clear

as to how some plant species select certain nanoparticles and reject others. If they are larger than the pore of root, they get accumulated at the surface, and when they are smaller, they get absorbed and transported to other parts of the plants. It is the present requirement to produce more food crops from the extant resources. Genetically modified crops are a way to substantially produce better food grain, but it has some implications [22]. The production of food crop from engineered nanoparticle is another alternative. A wide range of metal oxide nanoparticles (ZnO, TiO2, Al2O3, FeO, Fe2O3, etc.), fullerenes, carbon nanotubes, quantum dots, etc. have an increasing range of applications (Figure 1) for different purposes [23] and make their way easily in the environment

[24, 25]. Their potential adverse effects on the environment and human health are being subjected to intense debate [26]. Although nanoparticles, whether natural or synthetic, are being used in every sphere LXH254 in vitro of life, their Nintedanib datasheet exploitation in agriculture is limited. Studies have been directed towards seed germination, root elongation, foliar growth and seed and crop development [27]. The use of nanoparticles without knowing the toxic effect on the plant may sometimes cause mutation, which may be very damaging to both plants and ecosystem. Nanoparticles

when sprayed or inoculated will penetrate and transported to various parts of the plant. Some nanoparticles are stored in extracellular space and some within the cell. Some plants reject the nanoparticles and some SB273005 mouse accept or store them (Figure 2). Inadvertent use of rare and precious metal nanoparticles generally does not show any positive effect on the plant except for their storage and blocking the passage of vessels [28–30]. The process of nanoparticle accumulation in plants may be used to clean up nanoparticle contamination and extraction of metal from such plants. The extraction of metal from such plants is called phytomining or phytoextraction [6, 31, 32]. An et al. [33] have reported an increase in ascorbate and chlorophyll contents in leaves of asparagus treated with silver nanoparticles. Likewise, soybean treated with nano-iron showed increased weight of beans [34].

Upon irradiation by a laser pulse, the system begins to oscillate between quantum energy levels. A full quantum mechanical description is beyond the scope of this article, but an analogy can be drawn to a collection of springs, set into motion by the external perturbation (the pulse). Imagine that each of the springs oscillates

with a slightly different frequency, analogous to inhomogeneous broadening wherein the electronic transition frequencies BMS345541 concentration of a collection of chromophores vary, described by (2) above for photosynthetic light-harvesting complexes. The result of this distribution of frequencies is that the “springs,” oscillating in phase immediately after interaction with the pulse, become gradually less synchronized over time. This is known as dephasing. Imagine then that at some later instant, the motion of the

springs is simultaneously reversed by another perturbing pulse. As long as each of the springs maintains its original oscillation frequency and changes only its direction, the overall dephasing is reversed also. When this reverse Protein Tyrosine Kinase inhibitor dephasing or rephasing process occurs not with springs but with a collection of chromophores interacting with laser pulses, the effect is for the sample to emit a light pulse “echoing” the input pulse at the instant when the oscillators are once more in phase. The key to the unique information contained in photon echo signals is that the appearance of a photon echo signal see more depends on each of the springs remembering its initial

oscillation frequency and phase. If, on the other hand, the frequencies are individually modified or the phases shifted (as can occur through coupling to vibrational motions Farnesyltransferase of the pigments or proteins), the collective motion of the springs devolves into random noise; the constructive interference—rephasing—is never realized, and a photon echo signal is not emitted. Thus, the signal is uniquely sensitive to the coupling between the electronic transitions on the pigments and the nuclear motions of the “bath” (motions of the pigments themselves and of the surrounding protein). Recent work, including some of the experiments summarized here, has shown that, in fact, the detailed pigment–protein interactions in photosynthesis play an important role in controlling energy flow through the complexes. Furthermore, photon echo signals track energy transfer between the electronic states of neighboring chromophores. Therefore, photon echo experiments are well suited to the study of photosynthetic light harvesting. The experimental pulse sequence for three-pulse photon echo experiments is shown in Fig. 1. The first input pulse instigates the initial dephasing process described above.

2. Fliermans CB, Cherry WB, Orrison LH, Smith SJ, Tison DL, Pope DH: Ecological distribution of Legionella pneumophila. Appl Environ Microbiol 1981,41(1):9–16.PubMed 3. Bartram J, Chartier Y, Lee JV, Pond K, Surman-Lee S, (editors): Legionella and prevention of legionellosis. World Health Organization 2007. 4. Joseph CA, Ricketts KD: Legionnaires disease in Europe 2007–2008. Euro

Surveill 2010.,15(8): 5. Ferre MR, Arias C, Oliva JM, Pedrol A, Garcia M, Pellicer T, Roura P, Dominguez A: A community outbreak of Legionnaires’ CRT0066101 disease associated with a cooling tower in Vic and Gurb, Catalonia (Spain) in 2005. Eur J Clin Microbiol Infect Dis 2009,28(2):153–159.PubMedCrossRef 6. selleck chemicals Borgen K, Aaberge L, Werner-Johansen O, Gjosund K, Storsrud B, Haugsten S, Nygard K, Krogh T, Høiby EA, Caugant DA, Kanestrøm A, Simonsen Ø, Blystad H: Cluster of Legionnaires selleck screening library disease linked to an industrial plant in southeast Norway, June – July 2008. Euro Surveill 2008.,13(38): 7. Castilla J, Barricarte A, Aldaz J, Garcia CM, Ferrer T, Pelaz C, Pineda S, Baladron B, Martin I, Goni B, Aratajo P, Chamorro J, Lameiro F, Torroba L, Dorronsoro L, Martinez-Artola V, Esparza MJ, Gastaminza MA, Fraile P, Aldaz P: A large

Legionnaires’ disease outbreak in Pamplona, Spain: early detection, rapid control and no case fatality. Epidemiol Infect 2008,136(6):823–832.PubMedCrossRef 8. Rota MC, Caporali MG, Massari M: European Guidelines for Control and Prevention of Travel Associated Legionnaires’ Disease: the Italian experience. Euro Surveill 2004.,9(2): 9. ISO 11731–2:2006 Dansk Standard Histone demethylase Water quality-Detection and enumeration of Legionella-Part 2: Direct membrane filtration method for waters with low bacterial counts 10. Krojgaard LH, Krogfelt KA, Albrechtsen HJ, Uldum SA: Cluster of Legionnaires disease in a newly built block of flats, Denmark, December 2. Euro Surveill 2011.,16(1): 11. Jensen JS, Borre MB, Dohn B: Detection of Mycoplasma genitalium by PCR amplification of the 16S rRNA gene. J Clin Microbiol

2003,41(1):261–266.PubMedCrossRef 12. Bonetta S, Bonetta S, Ferretti E, Balocco F, Carraro E: Evaluation of Legionella pneumophila contamination in Italian hotel water systems by quantitative real-time PCR and culture methods. J Appl Microbiol 2010,108(5):1576–1583.PubMedCrossRef 13. Wellinghausen N, Frost C, Marre R: Detection of legionellae in hospital water samples by quantitative real-time LightCycler PCR. Appl Environ Microbiol 2001,67(9):3985–3993.PubMedCrossRef 14. Joly P, falconnet P-A, André J, Weill N, Reyrolle M, Vandenesch F, Maurin M, Etienne J, Jarraud S: Quantitative Real-Time Legionella PCR for environmental water samples:Data interpretation. Appl Environ Microbiol 2006,72(4):2801–2808.PubMedCrossRef 15. Yanez MA, Carrasco.Serrano C, Barberá VM, Catalán V: Quantitative detection of Legionella pneumophila in water samples by immunomagnetioc purification and real-time PCR amplification of the dotA gene. Appl Environ Microbiol 2005,71(7):3433–3441.

Nearly identical

sets of peptides were detected in supernatants from strains D445, Bbr77 and RB50, and these included peptides corresponding to T3SS substrates previously identified using RB50 (Table 2). Bsp22, which polymerizes to form an elongated needle tip complex [30], BopB and BopD, which form the plasma membrane translocation apparatus [14, 29, 31], BopN, a homolog of Yersinia YscN which functions H 89 as a secreted regulator [32], and the BteA effector were present in supernatants from wild type strains, but absent in supernatants of ΔbscN derivatives. In the course of this analysis we discovered a novel T3SS substrate encoded from a conserved hypothetical ORF (BB1639), herein named BtrA, in supernatant fractions from RB50, D445 and Bbr77 but not from their ΔbscN derivatives. Importantly, examination of complex IV secretion substrates failed to identify unique polypeptides that were not expressed by Doramapimod RB50 or did not match the RB50 protein database. The relative amounts of T3SS substrates released into culture supernatants, as assessed by SDS-PAGE and western blot analysis, also failed to correlate with relative levels of cytotoxicity (Additional file 2 Figure S1). Although

these observations did not reveal obvious differences in the T3SS secretome that could account for the hypercytotoxic phenotypes of D445 and Bbr77, it is important to consider that the activity of the bsc T3SS and its substrate specificity are regulated at multiple levels, and results obtained using broth-grown cells provide only a crude approximation of T3SS activity during infection (see Discussion). Table 2 nLC-MSMS secretome analysis Protein name NCBI accession number Sequence coverage (%) RB50 RB50ΔbscN D445 D445ΔbscN Bbr77 Bbr77ΔbscN Bsp22 gi|33568201 41 – 59 – 60 – BopN gi|33568200 24 – 29 – 24 – BopB gi|33568205 5 – 5 – 18 – BopD gi|33568204 50 – 51 – 54 – BteA gi|33568834 7 – 6 – 28 – BtrA gi|33568223 26 – 18 – 26 – Summary of nLC-MSMS data indicated as peptide coverage for indicted T3SS substrate proteins in supernatant fractions

from B. bronchiseptica strains grown to mid-log phase in Stainer-Scholte medium. Virulence of complex IV strains during respiratory infections To determine if relative levels of cytotoxicity measured however in vitro correlate with virulence in vivo, we used a murine respiratory intranasal challenge model [24]. Groups of 4–6 week old female specific-pathogen-free C57BL/6NCr mice were intranasally see more infected with 5 x 105 CFU. At this dose, RB50 establishes nonlethal respiratory infections that generally peak around day 10 post-inoculation and are gradually cleared from the lower respiratory tract, while persisting in the nasal cavity [33].As shown in Figure 4A, complex IV strains segregated into two groups. The first caused lethal infections in some (D444, Bbr77) or all (D445) of the infected animals. The second group (D446, Bbr69) caused nonlethal infections similar to RB50. Figure 4 In vivo characterization of selected complex IV B.

EMSA Recombinant K. pneumoniae Fur protein was expressed in E. coli and purified as previously described [22]. DNA fragment of the putative promoter region of ryhB was respectively PCR amplified by using specific primer sets (Table 2). The purified His6-Fur was incubated with 10-ng DNA in a 15 μl solution containing 50 mM Tris–HCl (pH 7.5), 100 mM NaCl, 100 mM dithiothreitol, 200 μM MnCl2,

and 1 μg/μl BSA at room temperature for 20 min. The samples were then loaded onto a native gel of 5% nondenaturing polyacrylamide GSK2245840 containing 5% glycerol in 0.5× TB buffer (45 mM Tris–HCl, pH 8.0, 45 mM boric acid). Gels were electrophoresed with a 20-mA current at 4°C and then stained with SABR safe Gel stain (Invitrogen). FURTA FURTA was performed according to the method described by Stojiljkovic et al. [64]. DNA sequences containing a putative Fur box were PCR amplified with specific primer sets and then cloned into pT7-7. The resulting plasmids were introduced into the E. coli strain H1717, and the transformants were plated onto MacConkey-lactose plates containing 100 μg/ml ampicillin and 30 μM Fe(NH4)2(SO4)2. The indicator strain H1717 contained a chromosomal fhuF::lacZ fusion, and a low affinity Fur box Rabusertib solubility dmso has been demonstrated in the fhuF promoter.

The introduction of pT7-7 derived plasmids carrying Fur-binding sequences could thus cause the removal of Fur from the fhuF Fur box [60]. H1717 harboring pT7-7 was Cetuximab manufacturer used as a negative control. Colony phenotype was observed after incubation

at 37°C for 10 h. Red colony (Lac+) denoted a FURTA-positive phenotype and indicated the binding of Fur to the DNA sequence cloned into the pT7-7 plasmid. check details Extraction and quantification of CPS CPS was extracted and quantified as previously described [65]. The glucuronic acid content, represents the amount of K. pneumoniae K2 CPS, was determined from a standard curve of glucuronic acid (Sigma-Aldrich) and expressed as micrograms per 109 CFU [46]. qRT-PCR Total RNAs were isolated from early-exponential-phase grown bacteria cells by use of the RNeasy midi-column (QIAGEN) according to the manufacturer’s instructions. RNA was DNase-treated with RNase-free DNase I (MoBioPlus) to eliminate DNA contamination. RNA of 100 ng was reverse-transcribed with the Transcriptor First Strand cDNA Synthesis Kit (Roche) using random primers. qRT-PCR was performed in a Roche LightCycler® 1.5 Instrument using LightCycler TaqMan Master (Roche). Primers and probes were designed for selected target sequences using Universal ProbeLibrary Assay Design Center (Roche-applied science) and listed in Additional file 2: Table S1. Data were analyzed using the real time PCR software of Roche LightCycler® 1.5 Instrument. Relative gene expressions were quantified using the comparative threshold cycle 2-ΔΔCT method with 23S rRNA as the endogenous reference.

2). Maximum species richness was found at around 1000 m. The CYT387 highest overall richness with 14 rattan species was found in a plot at Moa (890 m). Commercially important rattan species were found only below 1250 m (Fig. 2a). The density of rattan palms along the elevational gradient also showed

a hump-shaped pattern, with highest overall densities (250–500 individuals per 0.1 ha) around 1000–1500 m (Fig. 2b). The plot with the highest overall density of rattan palms (almost 600 individuals) was located at Gunung Nokilalaki (1500 m). In the lowland forests, commercially important species made up almost all of the individuals. Fig. 2 a Species richness and b density of all rattan palms (circles, continuous lines) and commercially important rattan palms (triangles, dashed lines) in relation to elevation in Lore Lindu National INCB28060 ic50 Park. The commercially important rattan

palms include Calamus zollingeri, C. ornatus var. celebicus and Daemonorops macroptera. Trend lines are polynomial models of second order as presented in Table 2 Polynomial models of second order accounted for 59 and 85% of the variation of overall rattan species richness Semaxanib molecular weight and commercially important rattan species richness along the elevational gradient, respectively (Fig. 2a, Table 2). For overall and commercially important rattan species densities, polynomial models accounted for 32 and 54% of the elevational patterns, respectively (Fig. 2b, Table 2). On the other hand, no significant relationships were found between species richness or density and precipitation (Table 2). Table 2 Correlation between species richness and density with elevation and precipitation Factor R² All species Commercial species Richness Density Richness Density Elevation 0.59*** 0.32*** 0.85*** 0.54*** Precipitation

0.03 0.16* 0.01 0.06 The residua of the elevational models were tested against precipitation * P < 0.05, *** P < 0.001 Elevational ranges of rattan species The individual rattan species showed distinct elevational ranges (Fig. 3). Characteristic rattan palms of the forests below 1200–1300 m were Cobimetinib molecular weight mainly already described species: C. didymocarpus, C. kandariensis, C. leptostachys, C. minahassae, C. ornatus var. celebicus, C. symphysipus, C. zollingeri, D. macroptera and K. celebica. On the other hand, the montane forests were inhabited by mostly undescribed rattan species, although a few undescribed species were also recorded in the lowland forests. On average, elevational species ranges were 515 ± 323 (SD) m, ranging from 100 m (7 species) to more than 1000 m (3 species). The majority of species were found throughout their elevational ranges, but a few species showed gaps of 100-400 m where they were not recorded. Fig. 3 Elevational ranges of rattan species recorded in Lore Lindu National Park. Elevation is divided into elevational belts of 100 m (*missing elevational belts have no data).

In studies where no genotyping method was used, it was assumed that each isolate represented a strain. Results and discussion Comparative performance

of the five molecular methods The percentage of correctly identified strains obtained using the five identification methods, and the number of misidentified non-targeted species greatly depended upon the method used (Tables 1 and 2). The percentage of misidentified strains ranged from 16.8% to 67.4% (Table 2). The m-PCR method of Kabeya et al. [15] had the worst performance, and produced unreliable results for all three of its targeted species (Tables 1 Necrostatin-1 price and 2). Although all strains of A. cryaerophilus and A. skirrowii were correctly identified, a further eight and six non-targeted species, respectively, were mistakenly identified as one of these two species (Table 1). Furthermore, only 4.8% of the A. butzleri strains were correctly identified, with six non-targeted species being confused with this species (Tables 1 and 2). Globally, the Kabeya et https://www.selleckchem.com/products/VX-680(MK-0457).html al. m-PCR method correctly identified just 32.6% (31/95) of the studied strains. Although this method

was also designed to differentiate PRI-724 cost subgroups 1A and 1B of A. cryaerophilus, not all strains of these subgroups were correctly identified (Table 2). This correlates with the in silico observations of Douidah et al. [9] who reported that the primer used [15] were not specific enough to provide correct identification of A. cryaerophilus at the subgroup level. Further to this, Debruyne et al.[21] have suggested, that based on results of AFLP and hsp60 analyses, the subgroup nomenclatures 1A and 1B should be abandoned. The second least reliable method analysed was the m-PCR technique described by Houf et al.[14]. This correctly

identified 55.8% (53/95) of the strains (Table 2), including all those belonging PJ34 HCl to its targeted species (A. butzleri, A. cryaerophilus, and A. skirrowii; Table 1). This method was 100% reliable for the identification of A. butzleri, and there was no confusion with other species. However, nine of the fourteen non-targeted species generated the typical amplicon of A. cryaerophilus; two that of A. skirrowii; and two simultaneously generated both amplicons (Tables 1 and 2). Only A. cibarius produced no amplification when using this method (Table 2). These results agree with previous studies that showed the existence of misidentifications when using this method [1, 5–7]. A similar number of correctly identified strains (83.2%) were obtained when using the other three evaluated methods (Pentimalli et al.[16]; the combined method of Douidah et al. [9] and De Smet et al.[17]; and Figueras et al.[18]). However, the number of misidentified non-targeted species differed depending upon the method used (Tables 1 and 2). Most misidentification occurred when using the method of Pentimalli et al.[16]. In this case, four non-targeted species were confused with A. butzleri, one with A.