mutans (Figure 7). Control cells of BI 2536 cost wildtype and ΔmleR were grown in neutral THBY before being transferred to pH 3.1 without L-malate. Both strains showed no difference in the survival under these conditions (Figure 7). To determine the influence of malate and the mleR regulator on the response of S. mutans to a rapid pH shift, both the wildtype and the mleR mutant were grown in neutral THBY and then subjected to pH 3.1 in the presence of 25 mM malate. In both strains the number of surviving cells after

20 minutes was similar to the selleck compound control (Figure 7). However, after 40 minutes the number of viable cells increased significantly compared to the control in the wildtype. Thus, the genes for MLF were induced within this time period selleck inhibitor and the conversion of malate contributed to the aciduricity. Without a functional copy of mleR, the number of viable cells also

increased after 40 minutes but to a much lesser extend compared to the wildtype. This again shows that a shift to an acidic pH is satisfactory to induce the MLF genes in the absence of mleR. When the mle genes were induced by low pH and L-malate in a preincubation step before transferring the cells to pH 3.1, an immediately increased viability was already seen 20 minutes after acid shock. Again, the wildtype exhibited a significantly enhanced survival compared to the mleR knockout mutant. The data show that the MLF genes are induced during the acid adaptation response but a functional copy of mleR in conjunction with its co-inducer L-malate is needed to achieve maximal expression. Figure 7 Acid tolerance assay. Role of malate for the survival of S. mutans wildtype (A) and ΔmleR mutant (B) after acid stress. Diamond, control, cells were incubated in neutral THBY without

malate and subjected to pH 3.1 without malate; Circle, very cells were incubated in neutral THBY without malate and subjected to pH 3.1 with malate; Triangle, cells were incubated in acidified THBY with malate and subjected to pH 3.1 with malate. Quantitative real time PCR showed an up-regulation of the adjacent gluthatione reductase upon the addition of 25 mM free malic acid (Figure 5). Therefore, we tested the capability of S. mutans to survive exposure to 0.2 (v/v) hydrogen peroxide after incubation of cells in acidified THBY and malate to induce this gene. However, no difference between wildtype and ΔmleR mutant was observed (data not shown). Discussion The aciduric capacity of S. mutans is one of the key elements of its virulence. Contributing mechanisms are increased activity of the F1F0-ATPase, changes in the membrane protein and fatty acid composition, the induction of stress proteins and the production of alkaline metabolites [10, 20–22]. Extrusion of protons via the F1F0-ATPase consumes energy in the form of ATP. Hence, the yield of glycolytic activity and ATP production is diminished at low pH, S.

Disease-free periods and overall survival time in these groups were examined using Kaplan-Meier graphs and selleck inhibitor log-rank tests (SPSS for Windows version 14.0, Chicago, IL, USA). The Proteasome inhibitor degree of linear relationship between pairs of variables measured on a continuous scale was summarized using correlation (r) and a test for zero slope in a corresponding linear regression model. Kruskal-Wallis’ test was used to test the null hypothesis of equal cisplatin sensitivity for the cell lines. For comparison of 18F-FDG uptake between the cell lines, the following multiple linear regression model was used:FDG = c1 + b1V + c2I2 + b2I2V + c3I3 + b3I3V + c4I4 + b4I4V + c5I5 + b5I5V + c6I6 + b6I6V

where the dependent variable 18F-FDG is 18F-FDG uptake and the independent variables are: V = Number of viable cells five dummy variables contrasting cell lines 2–6 to cell line 1: Ij = 1 if cell line = j, j = 2–6 Ij = 0 otherwise and five interaction parameters (products): IjV = V if cell line = j, j = 2–6 IjV = 0 otherwise This linear model has 12 parameters with the following interpretation: c1: Intercept for the reference cell line

(1) b1: Slope for the reference cell line (1) cj: Intercept difference between cell line j and the reference cell line, j = 2–6 bj: Slope difference between cell line j and the reference cell line, j = 2–6 In this modelling framework, an F-test was used to test the null hypothesis of equal 18F-FDG uptake for the cell lines at a fixed number Non-specific serine/threonine protein kinase of viable this website cells. The packages SPSS 14.0 (Chicago, IL, USA) and Stata 10.0 (StataCorp 2007, College Station, TX, USA) were used for statistical analysis. Results Patients: primary tumour characteristics and clinical course Six new permanent squamous cell carcinoma lines in vitro

were established from 18 HNSCCs, which constitutes an overall success rate of 33%. The overall survival of the patients as a function of the propensity of their tumours to grow in vitro, calculated from date of diagnosis, is shown in Figure 1. The outcome for the patients from whom cell lines could be established was worse than for the other patients; the median overall survival being 8 vs. 78 months (p = 0.009;logrank test), and the fraction of 5-year survival 0 vs. 67%. The mean disease-free survival time was 13 months for the patients whose tumours grew as cell lines, compared with 80 months for those whose cancers did not grow in vitro (p = 0.056). No differences were observed in the two groups regarding tumour site, TNM status, stage, grade, ploidity or DNA indices (data not shown). Figure 1 Overall survival of the patients stratified by propensity of their tumours to grow in vitro. Survival times were calculated from date of diagnosis. Four patients were still alive (survival >100 months) when this analysis was carried out.

The standardised index of association ( ) is a commonly used measure of intergenic recombination. Another measure of recombination over more than just one locus is the r/m ratio. This is the ratio of EPZ004777 chemical structure probabilities that a base change occurs by recombination or mutation. The results for these two tests (Table  1) are in agreement for each of the four species apart from N. meningitidis where the value of is anomalous being higher than that for S. pneumoniae. There has been the suggestion that sample bias may cause dramatic effects on the value for giving a distorted value. This effect may be diminished by including just a single example of each sequence type but the removal GSK1838705A mw of many

isolates can reduce the ability to estimate the extent of recombination from linkage disequilibrium [19]. Our analysis included just one example of each ST, but the value

for N. meningitidis is MI-503 still higher than would be expected. As noted by others [20, 21] a high value does not necessarily infer clonality since linkage disequilibrium can still be observed in species that are highly recombining due to population structuring as observed in Helicobacter pylori for example [22]. Therefore the high value of for N. meningitidis may indicate a highly structured population such that the epidemic epidemiology leads to a superficially clonal population [20]. Based on these results overall L. pneumophila has intermediate levels of recombination between those of S. aureus and N. meningitidis. The value of indicates a population

that tends towards being clonal, although again this may be due to a very structured population. Table 1 Values of the standardised index of association G protein-coupled receptor kinase and recombination to mutation ratio   Standardised Index of Association ( ) Recombination to mutation ratio (r/m) Staphylococcus aureus (Clonal) 0.193 1.6 Streptococcus pneumoniae (Intermediate) 0.044 9.3 Neisseria menigitidis (Panmictic) 0.116 32.5 Legionella pneumophila 0.153 16.8 Based on the sequences from SBT a reticulate network tree was drawn using the Neighbor-net algorithm of SplitsTree. Reticulate networks attempt to provide a more ‘explicit’ representation of evolutionary history than traditional phylogenetic trees such as phylograms. They are often depicted as a phylogenetic tree with additional edges. The internal nodes in this network represent ancestral species, and nodes with more than two parents correspond to ‘reticulate’ events such as recombination: the more splits in the branches seen in the resulting tree the more recombination or HGT is likely to have taken place. The SplitsTree computed from the L. pneumophila data (Figure  1) gives strong evidence for significant recombination between a subset of the lineages present within the tree and yields a highly significant phi test (p = 0.0).

maltophilia by electrospray ionization mass spectrometry (ESI-MS) and gas chromatography and mass spectrometry (GC-MS analysis) [7]. Functional analysis of rpfF or rpfC mutants in different bacterial species suggests that the general www.selleckchem.com/products/oicr-9429.html role of the DSF-signaling system in the modulation of virulence seems to be conserved, but the regulatory mechanisms and DSF-dependent traits may differ among taxa [8, 15–17]. Xanthomonas oryzae pv. oryzae (Xoo) is a causal

agent of bacterial blight disease of rice [18]. Xoo enters either through wounds or hydathodes, multiplies in the epitheme and moves to the xylem vessels where active multiplication results in blight disease symptoms on rice leaves. Similar to Xcc, Xoo also produces a range of virulence factors, including EPS, AZD2281 molecular weight extracellular enzyme, iron-chelating siderophores, and the Akt inhibitor type III-secretion dependent effectors, which are collectively essential for virulence [19–23]. Null mutation of rpfC in Xoo wild type strain T3000 substantially affects the EPS synthesis and virulence [24]. The rpfF mutants of an Indian Xoo wild type isolate BXO43 are attenuated in virulence and defective in

growth under low iron conditions [15]. More recently, a report showed that mutations in the core rpf genes rpfB, rpfF, rpfC and rpfG reduced the EPS levels, xylanase activity, motility, and virulence of Xoo strain KACC10331 [25]. These findings suggest that DSF signalling

system in Xoo Methane monooxygenase is involved in the regulation of virulence factor production. However, little is known about the chemical structure of the DSF-family signals in Xoo and the factors influencing the signal production. In this study, the comparative genomics analysis revealed that Xoo genome shares the key components of DSF biosynthesis and signalling with Xcc. The DSF production assay of rpfF, rpfC, rpfG mutants showed that Xoo uses a similar autoregulation mechanism as Xcc to control DSF biosynthesis. We further found that Xoo produces three DSF-family signals: DSF, BDSF and a novel signal with two double bonds, which was designated as CDSF. All the three DSF-family signals induce the EPS production and extracellular xylanase activity in the rpfF mutant of Xoo with variable efficiencies. Moreover, we found that the production and the ratio of the DSF-family signals are affected by the culture medium composition. Results Xoo uses the similar mechanism of Xcc in autoregulation of DSF biosynthesis In Xcc, the rpf cluster is involved in DSF biosynthesis, signal sensing and response. RpfF, a putative enoyl-CoA hydratase, is a key enzyme involved in DSF biosynthesis and mutation of rpfF abolishes DSF production [4]. RpfC negatively controls DSF biosynthesis by binding to RpfF at low cell density [10], and disruption of rpfC results in a 16-fold higher DSF accumulation than the wild-type Xcc [5, 11].

A number of additional interesting suggestions on the potential origin of the key features are reviewed by Williamson et al. (2010 and references therein). Puzzling on chloroplast ancestry from an initial endosymbiotic event It is widely accepted that chloroplasts are derived from a single one-time event where a cyanobacterium was taken up into a BVD-523 order eukaryotic single-celled organism 3-deazaneplanocin A mw (Delwiche 1999) which formed the base for all eukaryotic photosynthetic organisms (Green 2010; Ryes-Prieto et al. 2008; Yoon et al. 2004). This idea has become a paradigm that is widely illustrated in text books and continues to have

considerable support from phylogenomic analyses (Hackett et al. 2007; Keeling 2010). Phylogenetic analyses indeed can be constructed to show that extant cyanobacteria fall into a monophyletic line and suggest that the heterocyst formers diverged when atmospheric O2 concentrations increased (Tomitani et al. 2006) around the time

of the great oxidation event. The reductive reasoning of a one-time uptake of a cyanobacterium, into one eukaryotic host, followed Bafilomycin A1 chemical structure by linear descent of photosynthetic eukaryotes, although logically appealing appears to be countered by widely observed biological diversity. One critical assumption is that the eukaryotic host cell for the cyanobacterium already contained a mitochondrion derived from an α-proteobacterial ancestor (Gray et al. 2001). This raises the question of whether, and if, the mitochondrial progenitor and its eukaryotic host were already tolerant of the toxic effects (Aple and Hirt 2004) from O2 which would have been generated by the cyanobacterial endosymbiont’s photosynthesis. Thus, it has to be assumed that (1) the mitochondrial-bacterial-progenitor had evolved in an oxygenic environment

or that (2) a rapid tolerance to oxygenic damaging effects developed after entry of the oxygen producing cyanobacterial endosymbiont with extant characteristics. A scenario of gradual adaptation toward oxygen production in transition forms, Phosphoprotein phosphatase and the subsequent acquisition of a proteobacterial-like mitochondrial ancestor would be more biologically logical. Best estimates suggest that the concentration of O2 was still rather low (Fig. 1, Payne et al. 2010; Frei et al. 2009) at the time when the proposed cyanobacterial-to-chloroplast uptake occurred in the early Proterozoic Eon. A potential eukaryotic host could have come from the base of the animal ancestral lineage, possibly related to opisthokonts (Yoon et al. 2004). According to timeline calculations by Yoon et al. (2004), the cyanobacterial endosymbiotic event of the cyanobacterial-to-chloroplast transition would have been somewhat prior to ca. 1.

As shown in Figure 2B, after FMNPs were conjugated with HAI-178 antibody, the as-prepared nanoprobes’ photoluminescence (PL) intensity was lower than that of FMNPs, exhibiting a left shift of 40 nm, which was due to the decrease in the polarization rate of the surrounding molecules, resulting in the decrease of stokes displacement and finally resulting in

a blue shift in the emission spectra. Figure 2C showed that prepared FMNPs exhibited green color. Figure 2D showed that the magnesium intensity of as-prepared FMNPs and find more magnetic nanoparticles was 3.21 emu/g. Figure 2 Characterization of FMNPs and HAI-178-FMNPs. (A) HR-TEM of FMNPs. (B) PL spectra of FMNPs and HAI-178-FMNPs.

(C) Fluorescent image of prepared FMNPs. (D) Magnesium of FMNPs and Selleckchem RG7112 magnetic nanoparticles In the course of preparing HAI-178 antibody-FMNPs nanoprobes, we found that the surface functionalization of FMNPs was very the key to conjugate HAI-178 antibody with FMNPs via covalent bond. We observed that carboxyl groups on the surface of FMNPs conjugated with HAI-178 antibody easier than amino groups on the surface of FMNPs. In our experiment, the average coupling rate of HAI-178 antibody with FMNPs-COOH was 80.29%. Nanoprobes for targeting in vitro gastric cancer cells The targeting ability of as-prepared nanoprobes in vitro was observed by fluorescence microscope. As shown in Figure 3A, HAI-178-conjugated FMNPs existed around MGC803 cellular membrane. HAI-178 antibody-FMNPs nanoprobes could enter into the cytoplasm of MGC803 cells after 4 h incubation with MGC803 cells, Mannose-binding protein-associated serine protease but not inside the nucleus, which highly suggests that HAI-178 antibody-conjugated FMNPs can target MGC803 cells specifically. Figure 3 Fluorescent

microscope observation of HAI-178-FMNPs bound to surface of MGC803 cells. (A) HAI-178-FMNPs combined to the surface of MGC803 cell membrane (×10); inset is the magnified image (×100). (B) HAI-178-FMNPs bound to the membrane of MGC803 cells, blue nucleus (DAPI staining) (×10). Nanoprobes for fluorescent imaging of in vivo gastric cancer cells To evaluate the tumor-targeting properties of HAI-178 antibody-conjugated FMNPs nanoprobes, MGC803 cells-bearing nude mice models were prepared and monitored under a Cilengitide non-invasive manner for 12 h by using IVIS fluorescence imaging system. Figure 4A showed the nude mouse loaded with MGC803 gastric cancer cells. Figure 4B showed the strong fluorescent signal in the tumor site of gastric cancer-bearing nude mouse at 12 h post-injection. Figure 4C showed that strong fluorescent signals only existed in the tumor site of gastric cancer-bearing nude mouse.

The slow growth of the particles’ energy with the decrease in QD radius

in the case of Kane’s dispersion law is caused exactly by this fact. The situation is similar for excited states of both cases; however, the energy difference is considerably strong. Thus, at , the energy difference of ground states of parabolic and Kane’s dispersion cases selleck is ΔE ground ≃ 2.6E g , whereas for excited states it is ΔE excited ≃ 15.24E g . Figure 2 Dependences of ground- and first excited-state energies of electron-positron pair. They are in a spherical QD on a QD radius in strong SQ regime. The dependence of the energy of electron-positron coupled pair – a positronium – on a QD radius in a spherical QD in the weak SQ regime is illustrated in the Figure 3. As it is seen from the figure, in the weak SQ regime, when the Coulomb interaction energy of particles significantly prevails over the SQ energy

of QD walls, the Ps energy curve behaviors in parabolic selleck chemical and Kane’s dispersion cases differ radically. With the decrease in radius, the energy of the Ps changes the sign and PKC inhibitor becomes positive in the parabolic case (see (28)). This is a consequence of SQ and Coulomb quantization competition. The situation is opposite in the case of the two-band Kane’s model approximation. In this case, the decrease in the radius changes the Coulomb quantization due to band interaction. In

other words, in the case of nonparabolic dispersion law, the Coulomb interaction is stronger (see e.g., [42]). With the increase in radius, both curves tend to the limit of see more free Ps atoms of the corresponding cases (these values are given in dashed lines). The sharp increase in Coulomb interaction in the case of nonparabolicity accounting in the particles’ dispersion law becomes more apparent from the comparison of dashed lines. Figure 3 Dependence of Ps energy on a QD radius in a spherical QD in weak SQ regime. Figure 4 illustrates the dependence of Ps binding energy in a spherical QD on the QD radius for both dispersion laws. As it is seen in the figure, with the increase in QD radius, the binding energy decreases in both cases of dispersion law. However, in the case of Kane’s dispersion law implementation, energy decrease is slower, and at the limit R 0 → ∞, the binding energy of nonparabolic case remains significantly greater than in parabolic case. Thus, at in Kane’s dispersion case, the binding energy is , in the parabolic case, it is , and at value , they are and , respectively. Note the similar behavior as for the curves of the particle energies and the binding energies in the case of a 2D circular QD. Figure 4 The dependence of the Ps binding energy in a spherical QD on a QD radius.

Louis, MO, USA). Real-time primer pairs were designed using ABI software to amplify a sequence that contains two or more exons whenever possible. The amplification selleck screening library efficiencies of the primers used were above 90%. The specific sequences for each pair of primers are listed in Table 1. β-actin was amplified as an internal control. The real-time qPCR reaction conditions were set at 95°C for 10 min followed by 40 cycles at 95°C for 15 s and 60°C for 60 s. The results were analyzed using the comparative cycle threshold

(Ct) method as previously described [35]. The expression level of each gene was normalized to a β-actin (ΔCt) and the fold changes for each gene were calculated by comparing the test and control samples from the ΔΔCt values. Table 1 Nucleotide sequence of real-time qPCR primers Primers Sequence (5′-3′) MMP1-F ATG CTG AAA CCC TGA AGG TG MMP1-R CTG CTT GAC CCT CAG AGA CC MMP2-F AGG GCA CAT CCT ATG ACA GC MMP2-R ATT TGT TGC CCA GGA AAG TG MMP3-F GCA GTT TGC TCA GCC TAT CC MMP3-R GAG TGT CGG AGT CCA GCT TTC TIMP1-F CTG TTG TTG CTG TGG CTG AT TIMP1-R TCC GTC CAC AAG CAA TGA

GT β-actin -F TTG GCA ATG AGC GGT T β-actin -R AGTTGAAGGTAGTTTCGTGGAT Total protein extraction and detection of MMP-3 by ELISA Total proteins were extracted from homogenized HGFs using CellLyticTM MT-mammalian cell lysis extraction reagent (Sigma, USA). Protein concentrations in both of the cell-bound fraction and culture supernatant were see more measured respectively by BCA protein assay kit (Pierce, Thermo Scientific, USA) according to the manufacturer’s instructions. Enzyme-linked immunosorbent assay (ELISA) was performed to confirm the expression of MMP-3 proteins (BioRad Laboratories, Hercules, CA, USA). The protein expression in both cell lysate and culture supernatants were measured following manufacturer’s instruction with the minimal detectable concentration of 0.009 ng/ml. No cross reactivity or no interference was observed with recombinant MMP-3. The absorbance values were determined by a micro-plate reader (Victor,

Vienna, VA, very USA) at optical absorbance of 450 nm and the final concentration was determined with reference to a standard curve. Experiments were repeated two times with three biological replicates. Western blot analysis for MMP-2, -3 and TIMP-1 proteins Total cell lysates were prepared and 40 μg of cellular extracts were separated by 10% SDS-PAGE gel and subsequently transferred onto a polyvinylidene difluoride membrane (PVDF). The proteins were then blocked against the protein-free blocking buffer (Pierce, Thermo Scientific) for 1 h. Afterwards, membranes were Torin 1 purchase incubated overnight at 4°C with primary antibodies against polyclonal rabbit anti-human IgG; MMP-2 (1:1000; Cell signaling), MMP-3 (1:1000; BioVendor) and TIMP-1 (1:1000; Cell signaling), and incubated with horseradish peroxidase (HRP) conjugated anti-rabbit secondary antibodies (1:10000).

Likewise, studies performed in other phytopathogenic bacteria have focused on specific topics regarding low temperature function [4]. Global knowledge about the strategies used by these phytopathogens, in terms of temperature change which influences virulence stage and disease development, is very scarce and most of these studies have Autophagy inhibitor focused on animal pathogens where high temperature caused this effect [13, 14]. Therefore, this study was undertaken with the objective to understand how phytopathogenic bacteria, in particular the bacterial pathogen P. syringae pv. phaseolicola NPS3121, respond to temperature changes

related to the development of the most of plant diseases. Results and discussion Low temperature (18°C) negatively affects the growth rate of P. syringae pv. phaseolicola NPS3121 To obtain a global view regarding the strategies used by P. syringae pv. phaseolicola NPS3121 in response

to physiologically relevant temperature changes, we used DNA microarray technology. We compared gene expression profiles in OICR-9429 solubility dmso the P. syringae pv. phaseolicola NPS3121 wild-type (wt) strain grown at 18°C and 28°C in M9 minimal media. These temperatures represent conditions that either favor the development of the disease (18°C) or do not (28°C) [8]. Initially, to evaluate the effect of temperature and establish the growth stage for this study, we performed bacterial growth curves of the P. syringae pv. phaseolicola NPS3121 strain grown under the conditions mentioned above. The results showed that at low temperature (18°C), the growth rate of the bacteria decreases

approximately 0.5-fold relative to 28°C (Figure 1A). This behavior was reproducible in all performed kinetics. The effect of low temperature on the growth rate of several Pseudomonas syringae strains, including pv. phaseolicola, had been previously observed with similar results to this study [15]. Because previous results from our group indicated that during the transition phase, low temperature-induced differential expression in the phaseolotoxin synthesis genes (Pht cluster) occurs [12], we performed this study with Oxymatrine cells LY2603618 chemical structure harvested during this growth stage, which allowed us to use this cluster as a control for the microarray and ensure the virulent stage of the bacterium. Thus, parallel cultures of P. syringae pv. phaseolicola NPS3121 grown at 28°C and 18°C were harvested at the transition phase and RNA was extracted. The results presented in this work reflect the adapted state and significant genes, whose expression is differentially maintained over long-term growth at a given temperature. Figure 1 Low temperature decreases the bacterial growth rate and favors phaseolotoxin production. Panel A shows the bacterial growth curves of P. syringae pv. phaseolicola NPS3121 grown at 18°C and 28°C.

During the irradiation, the base pressure of chamber was maintained at approximately 10−7 mbar. The ion beam current density

was kept constant at 15 μA/cm2. The beam was scanned uniformly over an area of 10 mm × 10 mm by electromagnetic beam scanner. After irradiation, the samples were analyzed by Nano Scope IIIa atomic force microscope (AFM; Bruker AXS Inc, Madison, WI, USA) under ambient conditions in tapping mode. Cross-sectional transmission electron microscopy (XTEM) was carried using a Tecnai-G2-20 TEM (FEI, Hillsboro, OR, USA) facility operating at 200 kV. The cross-sectional specimens for TEM study were prepared by Ar ion beam milling at 4 kV/20 μA and at an angle of 4° with respect to the sample surface. Figure 1 Schematic view

of 50 keV Ar + ion beam irradiation. For first stage (to prepare two deferent depth locations of a/c interface) at an angle of (a) 60° and (b) 0°, Protein Tyrosine Kinase inhibitor with respect to surface normal; second stage NCT-501 in vivo irradiation (for fabrication of ripples) at an angle of 60° named as (c) set A and (d) set B. Testing the hypothesis AFM characterization was carried out on all samples after each irradiation step. After first irradiation, the average RMS roughness for both sets of the samples was nearly similar Selleckchem AR-13324 (0.5 ± 0.1 and 0.6 ± 0.1 nm). In the second stage, all samples were irradiated by a stable 50 keV Ar+ at same angle of incidence (60°) for all fluences. Figure 2a,b,c,d, and e,f,g,h shows the AFM images for set A and set B samples after the second stage irradiation at the fluences of 3 × 1017, 5 × 1017, 7 × 1017, and 9 × 1017 ions per square centimeter, respectively. It was found that for set A, the wavelength and amplitude were increasing with increase in irradiation fluence (as shown in Figure 3). For set B samples, the average wavelengths of ripples were nearly same tuclazepam as that of set A samples at corresponding fluences. However, the observed average amplitudes of ripples are about one order less in magnitude for set B as compared to those for set A since the only difference between two sets of samples was

the depth location of a/c interfaces. If the evolution ripples were based on curvature-dependent sputtering and surface diffusion, we should have got ripples of identical dimensions for corresponding equal fluence in both sets of samples. Despite similar initial surface morphology of both sets of samples after first stage of irradiation, the observation of similar wavelength and lower amplitude of ripples in set B samples as compared to set A samples casts doubt on the validity of Bradley-Harper and its extended theories. It can be emphasized here that we repeated complete set of experiment with two different ion beams and at different energies (Ar at 50 keV and Kr at 60 keV). And in all cases, the observed trend was similar. To the best of the authors’ knowledge, there is no existing model which could physically explain this anomaly.