\)   Nested models are compared using the likelihood ratio (LR) test. Under the null hypothesis that the models do not differ the likelihood test statistic approximately follows a χ2 distribution with m degrees of freedom where m is the number of additionally included covariates. The LR-test statistic is computed as two times the difference between the log likelihoods (LL): LR = 2 [LL(present model) – LL(reference model)]. The use of likelihood ratio tests is limited to nested models. In order to compare non-nested models we used the graphical methods described by Blossfeld and Rohwer (2002). We performed a non-parametric

estimation of a survivor function using the PND-1186 datasheet selleck screening library product limit estimation (Kaplan and Meier 1958). Then, given a parametric assumption, the survivor function is transformed so that the results become a linear function that can be plotted. If the model is appropriate, the resulting plot should be linear and the accuracy of the fit can be evaluated with the R 2 measure. The graphical check, however, is not possible for the Gompertz–Makeham model (unless a = 0 or c = 0). Pseudoresiduals were also computed to check the statistical fit of the parametric models (Cox and Snell 1968). If the model is appropriate, the pseudoresiduals should follow approximately a standard exponential distribution. Napabucasin concentration A plot of the logarithm of

the survivor function against the residuals should be a straight line that passes through the origin (Blossfeld and Rohwer 2002). Ethical approval Ethical approval was sought from the Medical Ethics Committee of the University Medical Center Groningen, who advised that according to Dutch law ethical clearance why was not required for this secondary study on sickness absence data. Results Between 1998 and 2001, 16,433 employees (30%) had a total of 22,159 long-term sickness absence episodes. The majority of workers (73%; 11,923) who were long-term absent had one episode; 21% (N = 3,495) had two episodes and 6% (N = 1,015) had three or more long-term

absence episodes. Onset of long-term sickness absence From the generalized gamma distributions with k = 1 it can be seen that the exponential model and the Weibull model give the best fit (see Table 1). The Weibull model does not have a better fit than the exponential model (LR(1) = 2, p = 0.157). The Gompertz–Makeham model does have a better fit than the exponential model: LR(2) = 10 (p = 0.007). The negative C-parameter of the Gompertz–Makeham model indicates a declining rate of long-term absence with increasing duration. In Fig. 2 the graphical checks are plotted. The plots of the exponential and the Gompertz–Makeham models show a straight line suggesting good fits. However, the exponential model is the simplest of the parametric alternatives, and seems a good choice because of that simplicity.

However, further work is needed to investigate the possibility of a functional core Autophagy activity inhibition saliva microbiome. To extend these results to more groups and additional

ape species, we also analyzed the saliva microbiomes of apes from the Leipzig Zoo. The zoo apes exhibit extraordinary diversity in their saliva microbiome that is not evident in the sanctuary apes, with over 180 bacterial genera identified in just 17 zoo apes, compared to 101 bacterial genera identified in 73 apes and human workers at the sanctuaries. Moreover, there is no consistent distinction among the saliva microbiomes of zoo bonobos, chimpanzees, gorillas, or orangutans. The results are in stark contrast to the results obtained from the sanctuary apes. Furthermore, we detect a significantly higher amount of shared OTUs among zoo apes than among the apes and human workers from the OICR-9429 supplier same sanctuary. It therefore appears as if the zoo environment is indeed Temsirolimus datasheet having a significant impact on the saliva microbiome of zoo apes, which seems to contradict the conclusions based on the comparison of sancturary apes and human workers. The artificial nature of the zoo environment (in particular, the closer

proximity of the zoo apes to both other apes and other species) may be responsible for this difference, but further investigation and comparisons of zoo animals with their wild counterparts are needed. One of the most striking Cytidine deaminase differences between the wild and zoo ape microbiomes was the entire absence of Enterobacteriaceae in zoo apes, with a correspondingly higher representation of Neisseria and Kingella instead. Apparently the zoo environment prevents Enterobacteraceae from steadily colonizing the oral cavity. This in turn suggests that Enterobacteriaceae – when not constantly introduced from the environment – are replaced by the related but truly endogenous

(or highly host-associated) genera from the Pasteurellaceae and Neisseriaceae families. Hence, environment may play an important role in terms of the opportunities for particular bacteria to colonize the oral cavity. Another striking difference between the zoo and wild ape microbiomes is the very high number of low-abundance bacterial taxa in zoo apes. It is plausible to assume that those organisms are introduced by the food provided in the zoo. As such they might represent only transient species, given that the indigenous microflora is usually able to defend its ecological niches successful against foreign bacteria [33]. This barrier against foreign bacteria is based on interactions between the indigenous microflora and the immune system, which in turn is the result of long-term coevolution in animals [34]. However, the interplay between the immune system and indigenous microflora might work best in the natural habitat, where it evolved.

Cancer Res 1998, 58: 1521–3.PubMed 42. Takeuchi H, Kuo C, Morton DL, Wang HJ, Hoon DS: Expression of differentiation melanoma-associated antigen genes is associated with favorable disease outcome in advanced-stage melanomas. Cancer Res 2003, 63: 441–8.PubMed 43. DiMaio D, Mattoon D: Selleckchem Adriamycin Mechanisms of cell transformation by papillomavirus E5 proteins. Oncogene 2001, 20: 7866–73.CrossRefPubMed 44. Ashby AD, Meagher L, Campo MS, Finbow ME: E5 transforming proteins of papillomaviruses do not disturb the activity of the vacuolar H(+)-ATPase. J Gen Virol 2001, 82: 2353–62.PubMed 45. Bravo IG, Crusius K, Alonso A: The E5 protein of the human papillomavirus type 16 modulates

composition and dynamics of membrane lipids in keratinocytes. Arch Virol 2005, 150: 231–46.CrossRefPubMed 46. Suprynowicz FA, Disbrow

GL, Krawczyk E, Simic V, Lantzky K, Schlegel R: selleck inhibitor HPV-16 E5 oncoprotein upregulates lipid raft components caveolin-1 and ganglioside GM1 at the plasma membrane of cervical cells. Oncogene 2008, 27: 1071–1078.CrossRefPubMed 47. Kivi N, Greco D, Auvinen P, Auvinen E: Genes involved in cell adhesion, cell motility and mitogenic signaling are altered due to HPV 16 E5 protein expression. Oncogene 2008, 27: 2532–41.CrossRefPubMed 48. Watabe H, Valencia JC, Yasumoto K, Kushimoto T, Ando H, Muller J, Vieira WD, Mizoguchi M, Appella E, Hearing VJ: Regulation of tyrosinase processing and trafficking by organellar pH and by proteasome activity. J Biol Chem 2004, Selleckchem Staurosporine 279: 7971–81.CrossRefPubMed 49. Lewis C, Baro MF, Marques M, PIK-5 Grüner M, Alonso A, Bravo IG: The first hydrophobic region of the HPV16 E5 protein determines protein cellular location and facilitates anchorage-independent

growth. Virol J 2008, 5: 30.CrossRefPubMed Competing interests The authors declare that they have no competing interests. Authors’ contributions FDD prepared the viral strains and conduced the molecular analysis and helped in coordinating the work. CF participated in data analysis and interpretation and in manuscript preparation. CB and MP have been involved in western blot analysis, enzymatic assays and data interpretation. FP and SM participated in cell culture and cellular work and helped with viral strain preparation. CC participated in study design and critical revision of the manuscript. RC participated in the study design and coordination and helped to revise the manuscript. FDM conceived of the study, participated in its design and coordination, has been involved in data analysis and interpretation and helped to draft the manuscript. All authors read and approved the final manuscript.”
“Background Bladder cancer is the second most common urologic malignancy and accounts for approximately 90% of cancers of the urinary tract. Is the fourth most incident cancer in male and ninth in females [1].

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KA, Benson KF, Carter SG, Mitzner MA, Reeves S, Robinson L: Antioxidant bioavailability and rapid immune-modulating effects after consumption of a single acute MG-132 mouse dose of a high-metabolite yeast immunogen: results of a placebo-controlled double-blinded crossover pilot study. J Med Food 2011, 14:1002–1010.PubMedCentralPubMedCrossRef 27. Moyad

MA, Robinson LE, Kittelsrud JM, Reeves SG, Weaver SE, Guzman AI, Bubak ME: Immunogenic yeast-based fermentation product reduces allergic OSI-027 in vivo rhinitis-induced nasal congestion: a randomized, double-blind, placebo-controlled trial. Adv Ther 2009, 26:795–804.PubMedCrossRef 28. Moyad MA, Robinson LE, Zawada ET, Kittelsrud J, Chen DG, Reeves SG, Weaver S: Immunogenic yeast-based fermentate for cold/Flu-like symptoms in nonvaccinated individuals. J Altern Complement Med 2010, 16:213–218.PubMedCrossRef 29. Possemiers S, Verhelst A, Maignien L, van den Abbeele P, Reeves SG, Robinson LE, Raas T, Pluvinage P, Schneider Y, van de Wiele T, Marzorati M: A dried yeast fermentate selectively modulates both the luminal and mucosal gut microbiota, enhances butyrate production and protects against inflammation, as studied in an intergrated in vitro approach. 2013, Agric. Food Chem 2013, 61:9380–9392.CrossRef 30. Nickerson CA, Ott CM, Wilson JW, Ramamurthy R, LeBlanc CL, Höner zu Bentrup K, Hammond T, Pierson DL: Low-shear modeled microgravity: a global environmental regulatory signal affecting bacterial gene expression, physiology, and pathogenesis. J Microbiol Methods 2003, 54:1–11.PubMedCrossRef 31.

00 1.00   1.00 1.00    Oral glucocorticoid use 0.88 (0.52–1.47) 1.50 (1.02–2.20) 0.217 0.75 (0.38–1.50) 1.86 (1.23–2.83) 0.065  No antidepressant use 1.00 1.00   1.00 1.00    Antidepressant use 2.15 (1.22–3.79) 1.50 (1.15–1.96) 0.608 3.27 (1.63–6.55) 1.63 (1.18–2.27) 0.260  No anxiolytic use 1.00 1.00   1.00 1.00    Anxiolytic use 1.80 (0.97–3.34) 1.14 (0.82–1.59) 0.101 2.18 (1.04–4.57) 1.17 (0.79–1.73)

0.044  No anticonvulsant Baf-A1 mouse use 1.00 1.00   1.00 1.00    Anticonvulsant use 5.36 (2.76–10.39) 0.96 (0.53–1.76) 0.000 6.88 (2.91–16.27) 1.19 (0.61–2.33) 0.002 aAdjusted for the same confounders as described below Table 2 for any and osteoporotic fracture, but the confounder is not added to the model if it is similar to

the drug being investigated bThe interaction term (MG × drug use in the previous 6 months) was investigated within the cohort of MG patients and controls Conversely, within the group of incident MG patients risk of fracture was twofold VX-680 datasheet higher in those with a recent use of antidepressants (AHR 2.15 [95 % CI 1.22–3.79]), twofold higher for anxiolytics (AHR 1.80 [95 % CI 0.97–3.34]) and fivefold increased with recent use of anticonvulsants (AHR 5.36 [95 % CI 2.76–10.39]). Typical osteoporotic fracture risk was threefold higher within incident MG patients with recent use of antidepressants SBE-��-CD datasheet (AHR 3.27 [95 % CI 1.63–6.55]), twofold higher with recent use of anxiolytics (AHR 2.18 [95 % CI 1.04–4.57]) and sevenfold higher with recent use of anticonvulsants (AHR 6.88 [95 % CI 2.91–16.27]). None of the remaining risk factors for fracture, which are described in the “Methods section”, showed a significant increased or decreased risk for any fracture or for fractures at osteoporotic sites. Finally, within the complete cohort with both incident MG patients and control patients, the interaction medroxyprogesterone term between MG and anxiolytics showed statistical significance for osteoporotic fracture (p value < 0.05). The interaction term between MG and anticonvulsants showed statistical significance for both osteoporotic and any fracture (p value < 0.05). To further investigate whether a true association between MG and fracture

risk had been averaged out by a fluctuating hazard function, we showed that MG duration was not related to fracture risk: 1-year risk of any fracture yielded an AHR of 1.15 (95 % CI 0.88–1.52) in patients with MG versus population-based controls, while 5-year risk (AHRs of 0.97 [95 % CI 0.74–1.28]) and 10-year risk (AHR 0.94 [95 % CI 0.71–1.23]) were not different. The Kaplan–Meier curve as presented in Fig. 1 showed similar results with a non-significant log-rank test (p value > 0.05) when MG patients were compared with control patients.

2008b). The study revealed that regardless of learn more whether the spin–orbit coupling (SOC) part of the ZFS was estimated with the Pederson–Khanna or the quasi-restricted

KU55933 in vitro orbitals approach, accounting for the spin–spin (SS) interaction always improves the results. The physical necessity of accounting for the SS interaction is shown from its 30% contribution to the axial D parameters. In general, the calculations were found to overestimate systematically the experimental D values by 60%. The authors call attention to the fact that the signs of calculated axial ZFS parameters are unreliable once E/D > 0.2. Calculated D and E/D values were found to be highly sensitive to small structural changes; disconcertingly, the use of optimized geometries was found to lead to a significant deterioration of theoretical predictions relative to experimental XRD geometries. A subsequent study (Zein and Neese 2008) showed that using the coupled-perturbed spin–orbit coupling (CP-SOC) approach (Neese 2007) together with hybrid DFT functionals

this website leads to a slope of the correlation line between experimental and calculated D values that is essentially unity, provided that the direct SS interaction is properly included in the treatment. For the case of the hyperfine coupling to the metal, DFT performance is not entirely satisfactory (Munzarova and Kaupp 1999; Munzarova et al. 2000). Since this property

involves three contributions (Fermi contact, spin–dipolar, and spin–orbit coupling) Bcl-w which feature different physical mechanisms, it is difficult to calculate all of them simultaneously with quantitative accuracy. Ligand HFCs are easier to compute but, again, results are less accurate than for organic radicals, and errors of 30% must be tolerated (Neese 2001b). Kossmann et al. (2007) investigated the performance of modern DFT functionals for the prediction of molecular hyperfine couplings in extended test calculations for a series of small radicals and transition metal complexes. It was shown that for the prediction of metal and ligand HFCs, TPSS is better than BP86, but more importantly, that the hybrid variant TPSSh is significantly superior to TPSS and probably even better than the “de facto standard” B3LYP functional. The double-hybrid B2PLYP functional also affords accurate predictions, particularly for HFCs of metal nuclei, but the existence of outliers suggests that this method may lack stability. The reliable performance of the TPSSh functional has since received additional confirmation in our recent study (Pantazis et al. 2009) aimed at the analysis of hyperfine coupling parameters in tetramanganese models of the OEC.

First, the LSPR λ max of bare Au nanoshells was measured to be 830 nm. The LSPR λ max after incubation to the BSA solution

was measured to be 885 nm, corresponding to an Pitavastatin datasheet additional 55-nm red shift, which was a wavelength shift two times larger than that of the reported nanohole substrate as a femtomole-level LSPR sensor [18]. Also, we confirmed that this peak position was not shifted after immersion in water. Furthermore, since the BSA molecule has no selective adsorption, this peak shift was attributed to the LSPR response to the changing of the local refractive index with the adsorption of BSA, which physically adsorbed to the gold surface of nanoshells and the substrate at the gap of nanoshells. It is indicated that we can improve the detection Ruboxistaurin in vivo efficiency by localizing MRT67307 the adsorption area of the target molecule without gold film directly laminated on the glass substrate. After immersion in water for 24 h, it is found that the λ max of nanoshell arrays returned to 834 nm. It is revealed that the

red shift of peak position was due to the physical adsorption of BSA proteins. Additionally, it is indicated that the LSPR peak did not return to its initial position because of the incomplete removal of BSA only with immersion in water. For application to bio/chemical detection devices, it should be noted that the signal transduction Exoribonuclease mechanism in this nanosensor is a reliably measured wavelength shift in the NIR region. Figure 4 LSPR spectra of nanoshells before/after BSA attachment in (a) Au and (b) Cu nanoshell arrays. All spectra were collected in the air. Figure 4b shows the change

of LSPR properties taken from Cu nanoshell arrays before/after incubation to the BSA solution. In the air, the LSPR λ max of the bare Cu nanoshell arrays was measured to be 914 nm. Exposure to the BSA solution resulted in LSPR λ max = 944 nm, corresponding to an additional 30-nm red shift. In the case of Cu nanoshells, they exhibited a not so low sensitivity to the adsorption of molecule relative to Au. While Cu nanoshell arrays have problems to solve about their oxide layer and chemical stability, it is possible for inexpensive Cu to substitute for Au because of its sensitivity to the adsorption of biomolecule. We could evaluate the difference in LSPR sensing performance by changing the metal materials in the experiment. Conclusion In summary, we successfully fabricated uniform metal nanoshell arrays in a large area (30 × 60 mm2) on glass substrates and characterized the geometry and the optical properties based on the LSPR of the Au, Ag, and Cu nanoshell arrays. The LSPR λ max of Au and Cu were at longer wavelengths than that of Ag nanoshell arrays of similar structural parameters. This result indicates that Au and Cu are superior to Ag as materials for NIR light-responsive plasmonic sensors.

We could prove binding of 2 ST4 gbb orthologs, BGA66 and BGA71, to human FHL-1, whereas BGA66 also bound CFH. Moreover, both these and other orthologs from the gbb54 family were also able to bind CFH from various animal species. Results Serum susceptibility testing of borrelial strains To assess and to compare serum susceptibility of B. garinii PBi and VSBP as well as B. burgdorferi ss B31, spirochetes were incubated for 3 h with either 50% NHS or 50% HI NHS. As shown in Fig 1, >75% of the cells of B. garinii ST4 PBi and B. burgdorferi ss B31 survived in serum, indicating that both strains resist complement-mediated killing.

In contrast, B. garinii non-ST4 strain VSBP was highly sensitive to complement as 99% of the cells were immobilized and showed blebs after 3 hours. Incubation of strains PBi, VSBP, and B31 with HI NHS resulted in no or very little immobilisation. Summarising B. garinii ST4 PBi and B. www.selleckchem.com/products/azd8186.html burgdorferi ss B31 are resistant to human serum when incubated with active human complement, while B. garinii non-ST4 VSBP is not human serum resistant. GANT61 chemical structure Figure 1 In vitro serum susceptibility of B. garinii ST4 PBi, B. garinii non-ST4 VSBP, and B. burgdorferi ss B31. Resistance to complement was determined by counting motile spirochetes by dark-field microscopy and values obtained were represented as percentages

of survival. All strains were tested in triplicate with 50% NHS and HiNHS. VSBP is rapidly killed by complement, while >75%of B. burgdorferi

ss B31 and B. garinii ST4 PBi are alive after 3 hours of incubation. The detection of the membrane attack complex deposited on borrelial cells after complement activation To test whether membrane attack complex (MAC) was formed on the surface of different strains after complement activation, spirochetes were incubated with 25% serum and deposition of the MAC was Selleckchem Dibutyryl-cAMP detected by immuno-fluorescence microscopy (IF) (Fig 2). The majority of the cells of B. garinii ST4 PBi and B. burgdorferi ss B31 stained negative for the MAC while all B. garinii non-ST4 VSBP were fully covered with MAC. This finding indicates that B. garinii ST4 PBi and B. burgdorferi ss B31 allow formation of the MAC on their bacterial Casein kinase 1 surface only to a limited extent in comparison to B. garinii non-ST4 strain VSBP. Figure 2 Detection of deposited C5b-9 complex on the surface of Borrelia by Immunofluorescence microscopy. B. garinii PBi and VSBP and B. burgdorferi ss B31 were incubated with 25% NHS and deposition of C5b-C9 was detected by a MAb. Few cells of B. garinii ST4 PBi stained positive for C5b-C9, while almost all spirochetes were covered with C5b-C9 using B. garinii non-ST4 VSBP. The absence of deposition of C5b-C9 onto B. burgdorferi ss B31 is comparable to B. garinii ST4 PBi. Detection of bound complement regulators to different borrelial strains In order to elucidate the capability of serum resistant B.

Adherence assays showed that strain Cf205 displayed a mannose-resistant AA phenotype (Figure 1A) indistinguishable to that developed by EAEC prototype strain 042 (Figure 1C). As with the prototype EAEC strain,

Cf205 strain displayed the characteristic stacked-brick pattern on the periphery of the cells and autoagglutination on the glass coverslip. Therefore, this strain was termed aggregative C. freundii (EACF). By contrast, Adriamycin ic50 control strain Cf047 developed diffuse adherence (Figure 1B). Figure 1 Adhesion to HeLa cells and ultrastructural analyses of aggregative C. freundii. Micrographs A and B show the adherence pattern displayed by aggregative C. freundii 205 (EACF 205) and diffusely adherent C. freundii 047, respectively. For comparison,

AA pattern displayed by prototype EAEC strain 042 is shown in the micrograph C. Electronic micrographs of EACF 205 are shown in the frames D and E. Both planktonic and surface-associated EACF cells did not displayed fimbrial structures; however, an extracellular matrix was detected surrounding the bacterial cells (arrows in frames D and E). Given the occurrence of aggregative Trichostatin A clinical trial adherence in C. freundii, the presence of EAEC adhesion related fimbrial genes together with 7 additional EAEC molecular Selleck Ku-0059436 markers were tested (Table 1). None of the EAEC-specific genetic markers were detected in the EACF strain and in the diffusely adherent strain as well. Additionally, eleven virulence markers associated with four other E. coli pathogenic categories were also tested and included markers for toxins and adhesins (Table 1). None of these tested markers were detected in the examined C. freundii strains. C. freundii strains were also tested negative for gene sequences of the self-recognizing adhesin Ag43.

Table 1 Primers used for detection of E. coli molecular markers Gene Locus description Primer sequence (5′-3′) Amplicon length (bp) Annealing temperature (°C) Reference Enteroaggregative Phospholipase D1 E. coli markers aat AA probe (CVD432) CTGGCGAAAGACTGTATCAT 630 55-60 [9]     CCATGTATAGAAATCCGCTGTT       aggR Transcriptional activator CTAATTGTACAATCGATGTA 324 50 This study     CTGAAGTAATTCTTGAAT       aggA Aggregative fimbria I (AAF I) GCTAACGCTGCGTTAGAAAGACC 421 55-60 [9]     GGAGTATCATTCTATATTCGCC       aafA AAF/II GACAACCGCAACGCTGCGCTG 233 50 [9]     GATAGCCGGTGTAATTGAGCC       agg3A AAF/III GTATCATTGCGAGTCTGGTATTCAG 462 60 [5]     GGGCTGTTATAGAGTAACTTCCAG       pilS Type IV pilus ATGAGCGTCATAACCTGTTC 532 58 [14]     CTGTTGGTTTCCAGTTTGAT       pic Mucinase TTCAGCGGAAAGACGAA 500 55-60 [9]     TCTGCGCATTCATACCA       pet Plasmid-encoded toxin CCGCAAATGGAGCTGCAAC 1,133 55-60 [9]     CGAGTTTTCCGCCGTTTTC       astA EAEC heat-stable toxin CCATCAACACAGTATATCCGA 111 55-60 [9]     GGTCGCGAGTGACGGCTTTGT       Enteropathogenic E.

The largest increases in capacitance occurred for samples with a Selleckchem BI 10773 moderate initial copper content combined with a small amount of copper removal, resulting in numerous small pits in the post-dealloy topography. The

largest capacitance ratio observed for these samples implies a factor of 3 increase in surface area after dealloying. Hydrogen evolution reaction measurements To characterize the catalytic behavior of the samples, HER measurements were made both before and after dealloying. Example Tafel plots of the data are shown in Figure 6. In general for these samples, the HER current density is larger after dealloying for low overpotentials, but smaller after dealloying for larger overpotentials. That is, the dealloyed samples are more reactive at lower overpotentials but less reactive at higher overpotentials for HER measurements. In addition, the Inhibitor Library data show a range of Tafel slopes for the

overpotential range measured. This effect is more significant for the as-deposited samples. Figure 6 HER measurements of two samples both before and after the dealloying process. Current densities were calculated Belnacasan order with respect to the geometric area of the sample. The initial copper content in the films are (a) 12.6±0.6% and (b) 21.4±1.1%. The copper content in the dealloyed films are (a) 11.4±0.6% and (b) 13.9±0.7%. For each set of measurements, the high overpotential data (between -350 and -200 mV) were fit to the Tafel equation, J = J 0 e −B η , where J is the current density and η is the overpotential. The Tafel slope, , and exchange current density, J 0, were determined from the fit parameters. The results are shown in Figure 7 as a function of the Cu composition initially in the sample. Consistent with the data in Figure 6, the samples tend to have both higher Tafel slope and higher exchange current density after dealloying compared to their as-deposited counterparts. This combination causes the crossing of the HER curves in Figure 6, where the dealloyed samples are more reactive at lower overpotentials and less reactive

at higher overpotentials. Figure 7 Tafel slope and current density Selleckchem Temsirolimus extracted from HER measurements. (a) Tafel slope and (b) exchange current density from HER measurements of the as-deposited and dealloyed NiCu thin films as a function of Cu content in the film before dealloying. For the as-deposited samples, the Tafel slopes tend to be around 100 to 125 mV/dec. In contrast, the Tafel slopes for the dealloyed samples are generally higher, most above 175 mV/dec. One possible reason for these larger Tafel slopes is a decrease in effective area available for reaction at higher overpotentials due to larger gas evolution rates. This effect may be increased by the more porous nature of the dealloyed samples, allowing gas bubbles to be trapped more easily.